cs.RO / 1 / 2606.18328
Recover, Discover, Plan: Learning Skills and Concepts from Robot Failures
恢复、发现、规划：从机器人故障中学习技能和概念
Abstract
Intelligent robots should not only recover from failures, but also acquire the abstract knowledge needed to avoid them in the future. While reinforcement learning (RL) can learn reactive recovery behaviors, training a separate policy for every distinct failure mode is highly inefficient. We introduce Recovery-Driven Synthesis of Relational Concepts (ReSYNC), the first approach that progressively discovers and refines state abstractions (relational predicates) from failure-recovery experience to support abstract planning. Unlike purely reactive methods, ReSYNC jointly learns skills and concepts through an incremental dual-learning process. In the skill-learning phase, the robot uses RL to learn to recover from failures seen in training tasks. In the concept-learning phase, the robot discovers new relational predicates and refines its abstract planning model to explain and generalize the learned recovery behaviors. This interaction enables ReSYNC to convert local recoveries seen during training into global failure avoidance at test time. Across four simulated domains, we show that ReSYNC's ability to continually expand and refine its abstraction library allows it to solve long-horizon, previously unseen problems, outperforming strong baselines by over 50%. Additionally, we demonstrate sim-to-real transfer of ReSYNC, where it performs real-world non-prehensile manipulation skills and generalizes to unseen scenarios through abstract planning. Overall, ReSYNC represents a significant step toward robots that autonomously acquire abstractions for scalable, failure-aware planning in the physical world.
Chinese Translation
智能机器人不仅应能够从故障中恢复，还应获得避免未来故障所需的抽象知识。虽然强化学习（Reinforcement Learning, RL）能够学习反应性恢复行为，但为每种不同的故障模式训练单独的策略是极其低效的。我们提出了基于恢复驱动的关系概念合成（Recovery-Driven Synthesis of Relational Concepts, ReSYNC），这是首个通过故障恢复经验逐步发现和完善状态抽象（关系谓词）以支持抽象规划的方法。与纯粹的反应性方法不同，ReSYNC通过增量双重学习过程共同学习技能和概念。在技能学习阶段，机器人利用RL学习从训练任务中遇到的故障中恢复。在概念学习阶段，机器人发现新的关系谓词并完善其抽象规划模型，以解释和概括所学的恢复行为。这种互动使得ReSYNC能够将训练期间看到的局部恢复转化为测试时的全局故障避免。在四个模拟领域中，我们展示了ReSYNC不断扩展和完善其抽象库的能力，使其能够解决长时间跨度、之前未见过的问题，超越强基线50%以上。此外，我们还展示了ReSYNC的仿真到现实转移能力，使其能够执行现实世界中的非抓取操作技能，并通过抽象规划推广到未见过的场景。总体而言，ReSYNC代表了朝着机器人自主获取可扩展、关注故障的物理世界规划抽象的重要一步。
cs.RO / 2 / 2606.18363
Guava: An Effective and Universal Harness for Embodied Manipulation
Guava：一种有效且通用的具身操作工具
Abstract
Language models trained on large-scale vision-language data have demonstrated strong potential for embodied agents. Harnessing models through embodied tools use offers a promising alternative to end-to-end vision-language-action systems by combining high-level reasoning with external modules for perception, planning, and control. However, it remains unclear what makes an effective harness for embodied manipulation, and to what extent such a harness can unlock embodied capabilities in a wide range of reasoning models. In this work, we present Guava, a harness framework for embodied tool use developed through systematic exploration of the design space of agent workflows, action spaces, and observation spaces. Our study identifies three key ingredients for effective embodied agents: iterative perception-reasoning-action loops, semantic action abstractions, and multimodal observations. To understand whether these design principles are universal even to small models, we develop an end-to-end training pipeline that distills embodied manipulation capabilities into a 4B open-source model using fewer than 2K trajectories collected entirely in simulation. Experimental results in both simulation and real-world environments show performance comparable to frontier proprietary models while exhibiting strong generalization to unseen objects, novel instructions, and long-horizon tasks. Results suggest that a well-designed harness can serve as a scalable, model-agnostic interface for embodied manipulation, enabling strong emergent embodied capabilities in compact open-source models with minimal training data.
Chinese Translation
在大规模视觉-语言数据上训练的语言模型展现了具身代理的强大潜力。通过使用具身工具来利用模型提供了一种有前景的替代方案，结合了高层次推理与外部模块用于感知、规划和控制，而不是端到端的视觉-语言-动作系统。然而，什么构成有效的具身操作工具仍然不清楚，以及这种工具在多大程度上能够解锁各种推理模型中的具身能力。在本研究中，我们提出了Guava，一个为具身工具使用而开发的工具框架，通过系统探索代理工作流程、动作空间和观察空间的设计空间而形成。我们的研究确定了有效具身代理的三个关键要素：迭代的感知-推理-动作循环、语义动作抽象和多模态观察。为了理解这些设计原则是否对小模型也具有普遍性，我们开发了一个端到端的训练管道，将具身操作能力提炼为一个4B的开源模型，使用不到2000条完全在模拟中收集的轨迹。在模拟和现实环境中的实验结果显示，其性能与前沿的专有模型相当，同时在未见物体、新指令和长时间任务上表现出强大的泛化能力。结果表明，设计良好的工具可以作为具身操作的可扩展、模型无关的接口，使得在训练数据极少的情况下，紧凑的开源模型能够展现出强大的新兴具身能力。
cs.RO / 3 / 2606.18375
PAIWorld: A 3D-Consistent World Foundation Model for Robotic Manipulation
PAIWorld：用于机器人操作的三维一致性世界基础模型
Huang, Yuhang, Lv, Xuan, Xu, Junyan, Yu, Zhiyuan, Zhang, Jiazhao, Hu, Ruizhen, Feng, Wancheng, Zou, Shilong, Xiao, Hewen, Zhou, Ziqiao, Huang, Kaiyun, Peng, Zhiyu, Xu, Juzhan, Zhao, Hang, Zhu, Chenyang, Yi, Renjiao, Huang, Yifei, Wu, Douhui, Zhang, Yan, Cheng, Kexu, Song, Chunhe, Xue, Yunzhi, Zhang, Xiuhong, Guo, Leitao, Chen, Yunji, Wu, Bin, Yu, Haibin, Xu, Kai
Abstract
World foundation models (WFMs) are powerful simulators, yet they predominantly operate in a single-view setting and lack the multi-view 3D consistency required for robotic manipulation. While robotic systems rely on multiple cameras (egocentric, eye-to-hand, and wrist-mounted) for policy learning, current multi-view world models simply concatenate view tokens without explicit geometric reasoning. This causes cross-view object drift, depth inconsistency, and texture misalignment. We trace these failures to two deficiencies: the absence of an explicit inter-view communication mechanism and the lack of a 3D geometric prior. We argue that resolving both simultaneously is necessary and sufficient. To address this, we present PAIWorld, a framework that augments diffusion-transformer world models via three core components: (1) Geometry-Aware Cross-View Attention blocks that establish an explicit pathway across views, (2) Geometric Rotary Position Embedding that encodes camera ray directions and extrinsic poses into the attention mechanism, and (3) Latent 3D-REPA, which distills 3D-aware features from frozen 3D foundation models to ensure 3D consistency. Built upon a DiT-based world foundation model, PAIWorld achieves state-of-the-art multi-view 3D consistency on robotic manipulation benchmarks, ranking 1st on the WorldArena leaderboard and 2nd on the AgiBot-Challenge2026 leaderboard, while enabling downstream applications such as model-based planning, world action models, and multi-view policy post-training.
Chinese Translation
世界基础模型（WFMs）是强大的模拟器，但它们主要在单视角设置中运行，缺乏机器人操作所需的多视角三维一致性。虽然机器人系统依赖多个摄像头（自中心、手眼协调和腕部安装）进行策略学习，但当前的多视角世界模型仅通过简单地连接视角标记而没有明确的几何推理。这导致了视角间物体漂移、深度不一致和纹理错位。我们将这些失败归因于两个缺陷：缺乏明确的视角间通信机制和缺乏三维几何先验。我们认为同时解决这两个问题是必要且充分的。为此，我们提出了PAIWorld，一个通过三个核心组件增强扩散变换器世界模型的框架：（1）几何感知的跨视角注意力模块，建立视角间的明确路径；（2）几何旋转位置嵌入，将摄像头光线方向和外部姿态编码到注意力机制中；（3）潜在三维-REPA，从冻结的三维基础模型中提取三维感知特征，以确保三维一致性。在基于DiT的世界基础模型的基础上，PAIWorld在机器人操作基准测试中实现了最先进的多视角三维一致性，在WorldArena排行榜上排名第一，在AgiBot-Challenge2026排行榜上排名第二，同时支持模型驱动的规划、世界动作模型和多视角策略后训练等下游应用。
cs.RO / 4 / 2606.18426
VEGA: Learning Navigation VLAs from In-the-Wild Egocentric Video with Geometric Trajectory Supervision
VEGA：从真实环境中的自我中心视频学习导航视觉-语言-动作（VLA）模型，结合几何轨迹监督
Abstract
We introduce VEGA, an approach for training navigation VisionLanguage-Action (VLA) models from unlabeled egocentric navigation videos. Internet-scale egocentric videos provide a scalable source of navigation-relevant visual observations, capturing cluttered scenes, close-range obstacles, and natural human motion through real-world spaces. However, these videos are not directly usable for policy learning because they do not provide obstacle-aware trajectories conditioned on explicit navigation goals in the robot's coordinate frame. VEGA addresses this gap by reconstructing local scene geometry from monocular video, sampling navigation goals (represented as text, image, or spatial waypoints) and generating obstacle-aware trajectories using the constructed geometry. The resulting trajectory distribution is then used to train a flow-matching VLA navigation policy. By using geometry exclusively during training, VEGA distills obstacle-aware planning directly into a vision-based policy. Furthermore, we introduce VEGA-Bench, a benchmark containing 250k scenes and approximately 5 million navigation goals paired with scene geometry, designed to evaluate goal progress, collision avoidance, and obstacle clearance of VLAs. Our evaluation shows that VEGA achieves competitive goal progress while reducing collisions by 33.0% and improving obstacle clearance by 17.9% over the strongest baseline on VEGABench, while improving success by at least 150.0%, reducing collisions by at least 66.7%, and improving obstacle clearance by at least 60.0% in real-world trials. Ultimately, we demonstrate that video-derived geometric supervision provides a scalable and effective signal for training obstacle-aware navigation VLAs. The code and benchmark will be released at the time of publication.
Chinese Translation
我们提出了VEGA，这是一种从未标记的自我中心导航视频中训练导航视觉-语言-动作（VLA）模型的方法。互联网规模的自我中心视频提供了一个可扩展的导航相关视觉观察源，捕捉了杂乱的场景、近距离障碍物以及人类在现实空间中的自然运动。然而，这些视频不能直接用于策略学习，因为它们没有提供基于明确导航目标的障碍物感知轨迹，且这些目标是在机器人坐标系下的。VEGA通过从单目视频重建局部场景几何，采样导航目标（以文本、图像或空间航点表示）并利用构建的几何生成障碍物感知轨迹，从而填补了这一空白。生成的轨迹分布随后用于训练流匹配的VLA导航策略。通过在训练过程中仅使用几何信息，VEGA将障碍物感知规划直接提炼为基于视觉的策略。此外，我们还推出了VEGA-Bench，这是一个包含25万个场景和约500万个与场景几何配对的导航目标的基准，旨在评估VLA的目标进展、碰撞避免和障碍物清除能力。我们的评估显示，VEGA在目标进展方面表现出竞争力，同时在VEGABench上将碰撞减少了33.0%，障碍物清除率提高了17.9%，并在实际试验中成功率提高至少150.0%，碰撞减少至少66.7%，障碍物清除率提高至少60.0%。最终，我们证明了视频衍生的几何监督为训练障碍物感知导航VLA提供了一个可扩展且有效的信号。代码和基准将在发表时发布。
cs.RO / 5 / 2606.18514
N(CO)$^2$: Neural Combinatorial Optimization with Chance Constraints to Solve Stochastic Orienteering
N(CO)$^2$: 带有机会约束的神经组合优化以解决随机定向问题
Abstract
Neural combinatorial optimization (NCO) offers a promising alternative to traditional heuristic-based methods for solving complex graph optimization problems by proposing to learn heuristics through data. This class of problems frequently arises in automation, as it can be used to model a variety of applications. While NCO has been extensively studied for deterministic combinatorial optimization problems, there are only a few works that aim to solve stochastic combinatorial optimization problems. In this work, we present N(CO)$^2$: Neural Combinatorial Optimization with Chance cOnstraints to solve the Stochastic Orienteering Problem (SOP) without the use of hand-crafted heuristics. By integrating a reinforcement learning (RL) framework, the model optimizes path selection under uncertainty, effectively balancing exploration and exploitation. Empirical results demonstrate that our method generalizes well across diverse SOP instances, achieving competitive performance compared to the state-of-the-art mixed-integer linear program (MILP) for the task. The proposed approach reduces human effort in heuristic design while enabling adaptive and efficient decision-making in uncertain environments.
Chinese Translation
神经组合优化（NCO）通过提议通过数据学习启发式方法，为解决复杂图优化问题提供了一种有前景的替代传统启发式方法的方案。这类问题在自动化中经常出现，因为它可以用于建模各种应用。尽管NCO在确定性组合优化问题上得到了广泛研究，但针对随机组合优化问题的研究却寥寥无几。在本研究中，我们提出了N(CO)$^2$: 带有机会约束的神经组合优化，以解决随机定向问题（SOP），而无需使用手工设计的启发式方法。通过整合强化学习（RL）框架，该模型在不确定性下优化路径选择，有效平衡探索与利用。实证结果表明，我们的方法在不同的SOP实例中具有良好的泛化能力，与当前最先进的混合整数线性规划（MILP）方法相比，表现出竞争力。所提出的方法减少了启发式设计中的人工努力，同时在不确定环境中实现了自适应和高效的决策制定。
cs.RO / 6 / 2606.18516
Task Allocation and Motion Planning in Dynamic, Cluttered Environments via CBBA and Graphs of Convex Sets
通过共识基础束算法和凸集图在动态杂乱环境中的任务分配与运动规划
Abstract
Multi-agent task planning in cluttered, dynamic environments requires assigning tasks to agents while simultaneously determining safe, time-efficient trajectories through the environment. When tasks are dynamic, such as rendezvous objectives, allocation decisions depend not only on which agent is best suited for a task, but also on when and where that task can be reached. This paper presents a solution to this problem, which combines Graphs of Convex Sets (GCS) for trajectory optimization with the Consensus-Based Bundle Algorithm (CBBA) for distributed task allocation. In our approach, GCS finds optimal trajectories through dynamic environments using a time-extended (3D+time) configuration space. At the same time, CBBA coordinates task assignments across agents, enabling informed decision-making in a moving environment. We then connect allocation and planning to allow the agents to avoid collisions in the 3D+time configuration space and provide accurate time estimates for task completion. We demonstrate the effectiveness of our approach in simulated cluttered environments with static and dynamic tasks.
Chinese Translation
在杂乱的动态环境中，多智能体任务规划需要在为智能体分配任务的同时，确定安全且高效的轨迹。当任务是动态的，例如会合目标时，分配决策不仅依赖于哪个智能体最适合执行任务，还依赖于何时以及在何处可以到达该任务。本文提出了一种解决方案，结合了用于轨迹优化的凸集图（Graphs of Convex Sets, GCS）与用于分布式任务分配的共识基础束算法（Consensus-Based Bundle Algorithm, CBBA）。在我们的方法中，GCS利用扩展时间的（3D+时间）配置空间寻找动态环境中的最佳轨迹。同时，CBBA协调智能体之间的任务分配，使得在移动环境中能够进行知情决策。然后，我们将分配与规划连接起来，使智能体能够在3D+时间配置空间中避免碰撞，并提供任务完成的准确时间估计。我们在模拟的杂乱环境中展示了我们方法在静态和动态任务下的有效性。
cs.RO / 7 / 2606.18519
As You Wish: Mission Planning with Formal Verification using LLMs in Precision Agriculture
如您所愿：在精准农业中使用大型语言模型进行形式验证的任务规划
Abstract
Though robotic systems are now being commercialized and deployed in various industries, many of these systems are highly specialized and often require an advanced skill set to operate and ensure they perform as instructed. To mitigate this problem, we recently introduced a mission planner leveraging LLMs to synthesize mission plans in precision agriculture based on mission descriptions provided in natural language. While the system demonstrates impressive performance, it also suffers from the inherent ambiguities of natural language. In this paper, we extend our system to address this issue by introducing multiple feedback loops in the planning architecture that leverage linear temporal logic (LTL) to ensure the mission planning system meets the specifications formulated by the user while still using natural language. To mitigate potential bias, this is achieved by using two different commercial LLMs in charge of the specification and verification subtasks. Through extensive experiments, we highlight the strengths and limitations of integrating mission verification into a fully autonomous pipeline, particularly regarding an LLM's ability to generate valuable LTL formulas, and show how our proposed implementation addresses and solves these challenges.
Chinese Translation
尽管机器人系统现在正在各个行业中商业化和部署，但许多系统高度专业化，通常需要高级技能才能操作并确保其按指令执行。为了解决这个问题，我们最近引入了一种任务规划器，利用大型语言模型（LLMs）根据自然语言提供的任务描述合成精准农业中的任务计划。虽然该系统表现出色，但也受到自然语言固有歧义的影响。在本文中，我们扩展了我们的系统，通过在规划架构中引入多个反馈循环，利用线性时序逻辑（LTL）来解决这一问题，以确保任务规划系统满足用户制定的规范，同时仍使用自然语言。为了减轻潜在偏见，这通过使用两种不同的商业LLMs来负责规范和验证子任务来实现。通过广泛的实验，我们突出了将任务验证集成到完全自主流程中的优势和局限性，特别是关于LLM生成有价值的LTL公式的能力，并展示了我们提出的实现如何应对和解决这些挑战。
cs.RO / 8 / 2606.18589
DREAM-Chunk: Reactive Action Chunking with Latent World Model
DREAM-Chunk：基于潜在世界模型的反应性动作块处理
Abstract
Action chunking has become a common interface for vision-language-action (VLA) models, enabling low-frequency policy inference to drive high-frequency robot execution. However, once an action chunk is committed, its open-loop execution can be brittle under stochastic dynamics, hardware execution errors, and partial observability. We propose DREAM-Chunk, a test-time scaling method that augments chunking-based policies with a lightweight latent world model, without requiring additional policy fine-tuning. At test time, DREAM-Chunk samples multiple candidate action chunks, rolls out their predicted latent futures, and selects actions from the chunk whose predicted state best matches the observed rollout. In this way, DREAM-Chunk uses additional test-time computation to cover multiple plausible stochastic futures and improve reactivity during long-horizon chunk execution. On the Kinetix benchmark, DREAM-Chunk improves robustness under increasing action noise and benefits from larger candidate sample sizes, especially when demonstrations contain corrective behaviors. We further validate DREAM-Chunk on four manipulation tasks across two robot platforms and two VLA policies under various sources of stochasticity. Across simulation and hardware experiments, DREAM-Chunk improves the robustness of action-chunking policies in stochastic dynamics.
Chinese Translation
动作块处理已成为视觉-语言-动作（VLA）模型的常见接口，使得低频策略推断能够驱动高频机器人执行。然而，一旦承诺执行一个动作块，其开放式执行在随机动态、硬件执行错误和部分可观测性下可能会变得脆弱。我们提出了DREAM-Chunk，这是一种测试时缩放方法，通过轻量级的潜在世界模型增强基于块的策略，而无需额外的策略微调。在测试时，DREAM-Chunk会采样多个候选动作块，展开其预测的潜在未来，并从预测状态与观察到的展开状态最匹配的块中选择动作。通过这种方式，DREAM-Chunk利用额外的测试时计算覆盖多个合理的随机未来，并在长时间段的块执行中提高反应性。在Kinetix基准测试中，DREAM-Chunk在增加的动作噪声下提高了鲁棒性，并从更大的候选样本大小中受益，尤其是在演示包含纠正行为时。我们进一步在两个机器人平台和两种VLA策略下的四个操作任务中验证了DREAM-Chunk，测试了各种随机性来源。在模拟和硬件实验中，DREAM-Chunk提高了随机动态下动作块策略的鲁棒性。
cs.RO / 9 / 2606.18594
Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation
基于视觉的机器人操作中的强化学习动作空间基准测试
Abstract
In real-world reinforcement learning (RL), the choice of action space can play a key role in shaping motion smoothness, safety, and overall task performance. In this study, we evaluate pose increment, pose velocity, joint position increment, and joint velocity across two vision-based manipulation tasks: object picking and pushing. We train policies in simulation and deploy them to the real world using sim-to-real transfer. We find that action-space representation indeed significantly affects sim-to-real performance. In particular, we find that the joint velocity action space is best for the vision-based picking and pushing tasks in terms of smoothness and final task performance. We also provide practical guidance for RL practitioners in choosing action spaces for both simulation and real-world experiments.
Chinese Translation
在现实世界的强化学习（RL）中，动作空间的选择在塑造运动平滑性、安全性和整体任务表现方面起着关键作用。本研究评估了姿态增量、姿态速度、关节位置增量和关节速度在两个基于视觉的操作任务中的表现：物体拾取和推动。我们在仿真中训练策略，并通过仿真到现实（sim-to-real）转移将其部署到现实世界中。我们发现，动作空间的表示确实显著影响了仿真到现实的表现。特别是，我们发现关节速度动作空间在平滑性和最终任务表现方面对基于视觉的拾取和推动任务是最优的。我们还为强化学习实践者在选择仿真和现实世界实验的动作空间时提供了实用指导。
cs.RO / 10 / 2606.18601
Admittance-Based Surface Alignment for Human-in-the-Loop Robotic Visual Inspection
基于导纳的表面对齐技术用于人机协作的机器人视觉检测
Abstract
Precision visual inspection underpins quality assurance across aerospace, semiconductor, and medical manufacturing, where undetected surface anomalies on high-value parts translate directly into scrap, rework, and field failures. Robotic visual inspection requires precise alignment between the end-effector and local surface geometry in the presence of perception noise and surface irregularities. In industrial settings, a human operator is often kept in the loop via teleoperation or shared autonomy, introducing real-time adjustments that render purely offline motion planning inadequate. This motivates control architectures capable of reactive, compliant behavior under combined human and perceptual uncertainty. This paper presents a novel real-time, closed-loop robotic orientation control pipeline for precision visual inspection, with an admittance-based framework that unifies operator input and perception-driven surface alignment. We design the end-effector as a virtual sphere moving through a viscous medium, such that the resulting physically interpretable mass--damper system generates synchronized, compliant motion from orientation error and operator commands. We validate the framework on a 6-DOF manipulator demonstrating stable normal-tracking and a final mean orientation error of 0.4{\deg}.
Chinese Translation
精确的视觉检测是航空航天、半导体和医疗制造等领域质量保证的基础，其中未检测到的高价值部件表面异常直接导致废料、返工和现场故障。机器人视觉检测需要在感知噪声和表面不规则性的影响下，确保末端执行器与局部表面几何形状之间的精确对齐。在工业环境中，通常通过远程操作或共享自主性将人类操作员纳入循环，这引入了实时调整，使得纯离线运动规划显得不足。因此，开发能够在结合人类和感知不确定性下表现出反应性和顺应性的控制架构显得尤为重要。本文提出了一种新颖的实时闭环机器人定向控制管道，用于精确的视觉检测，采用基于导纳的框架统一了操作员输入和基于感知的表面对齐。我们将末端执行器设计为在粘性介质中移动的虚拟球体，使得所产生的物理可解释的质量-阻尼系统能够根据定向误差和操作员指令生成同步的顺应运动。我们在一个6自由度的操纵器上验证了该框架，展示了稳定的法向跟踪，最终平均定向误差为0.4{ extdegree}。
cs.RO / 11 / 2606.18610
SC3-Eval: Evaluating Robot Foundation Models via Self-Consistent Video Generation
SC3-Eval：通过自一致视频生成评估机器人基础模型
Abstract
Evaluating generalist robot manipulation policies in the real world is expensive, slow, and difficult to scale. Action-conditioned video world models offer a scalable alternative by simulating policy rollouts. Autoregressive rollouts accumulate compounding errors, observations across multiple camera views must remain mutually consistent, and the evaluator must generalize to policies whose behaviors lie outside the training distribution. We address these challenges with SC3-Eval, a self-consistent video generation recipe that adapts a pre-trained video foundation model into an accurate policy evaluator by enforcing three complementary forms of consistency. First, forward-inverse dynamics consistency jointly trains the model to predict frames from actions and to recover actions from frames, anchoring generated rollouts to a physically plausible action manifold and counteracting the drift a forward-only model cannot penalize. Second, cross-view consistency trains the model to inpaint each camera view from the other, keeping the multi-camera observation coherent over long rollouts without any explicit memory mechanism. Third, test-time consistency reuses the inverse dynamics mode at inference as a per-action-chunk uncertainty signal that terminates rollouts whose generated frames drift away from the requested actions. We also demonstrate SC3-Eval rollouts reproduce the failure modes that policies exhibit in real-world rollouts, supporting fine-grained diagnostic comparison rather than aggregate ranking alone. Across seven real-world vision-language-action policies, SC3-Eval attains a closed-loop Pearson correlation of $0.929$ and MMRV of $0.119$, outperforming three strong prior video-model-based baselines, and generalizes to new tasks.
Chinese Translation
在现实世界中评估通用机器人操作策略既昂贵又缓慢，且难以扩展。基于动作条件的视频世界模型通过模拟策略展开提供了一种可扩展的替代方案。自回归展开会累积复合误差，来自多个相机视角的观察必须保持相互一致，评估者必须能够推广到行为超出训练分布的策略。我们通过SC3-Eval解决这些挑战，这是一种自一致的视频生成方法，通过强制执行三种互补的一致性，将预训练的视频基础模型转变为准确的策略评估器。首先，前向-反向动力学一致性联合训练模型从动作预测帧，并从帧恢复动作，将生成的展开锚定到物理上合理的动作流形，并抵消前向模型无法惩罚的漂移。其次，跨视角一致性训练模型从其他视角进行图像修复，使得多相机观察在长时间展开中保持一致，而无需任何显式的记忆机制。第三，测试时一致性在推理时重用反向动力学模式作为每个动作块的不确定性信号，终止那些生成帧偏离请求动作的展开。我们还展示了SC3-Eval展开能够重现策略在现实世界展开中表现出的失败模式，支持细致的诊断比较，而不仅仅是聚合排名。在七个现实世界的视觉-语言-动作策略中，SC3-Eval达到了闭环Pearson相关系数为$0.929$和MMRV为$0.119$，超越了三个强大的先前基于视频模型的基线，并能够推广到新任务。
cs.RO / 12 / 2606.18625
SRL: Combining SLIP Model and Reinforcement Learning for Agile Robotic Jumping
SRL：结合SLIP模型和强化学习实现灵活的机器人跳跃
Abstract
Robotic jumping is pivotal in applications such as search and rescue and logistics, where crossing obstacles and enhancing mobility efficiency are critical. The Spring-Loaded Inverted Pendulum (SLIP) model leverages simplified spring-mass dynamics that naturally encode biologically plausible hopping motions, yet its performance degrades on irregular terrain due to idealized assumptions regarding contact and joint dynamics. Meanwhile, Reinforcement Learning (RL) can adapt to diverse and complex environments but often requires extensive data from unguided exploration. The complementary strengths of SLIP's physically grounded baseline and RL's adaptive capabilities motivate a hybrid framework that overcomes these individual limitations. We therefore propose Spring-loaded Reinforcement Learning (SRL), which integrates SLIP-based feedforward control signals with RL-driven real-time feedback, enabling continuous optimization of robotic jumping. Experimental results demonstrate that SRL can achieve more stable jumps with much less training time than the baseline method, maintaining an average position tracking error below 0.1 m and velocity tracking errors within +/-3% of the target values. Through bipedal and quadrupedal simulations of ground and stair jumping, as well as sim-to-sim and sim-to-real validations, SRL exhibits robust adaptability to various task requirements and environmental complexities, underscoring its potential for real-world deployment.
Chinese Translation
机器人跳跃在搜索与救援、物流等应用中至关重要，其中跨越障碍物和提高移动效率是关键。弹簧加载倒立摆（SLIP）模型利用简化的弹簧-质量动力学，自然编码生物学上合理的跳跃动作，但由于对接触和关节动力学的理想化假设，其在不规则地形上的表现会下降。同时，强化学习（RL）能够适应多样且复杂的环境，但通常需要大量来自无指导探索的数据。SLIP模型的物理基础和RL的适应能力的互补优势促使我们提出一个混合框架，以克服各自的局限性。因此，我们提出了弹簧加载强化学习（SRL），它将基于SLIP的前馈控制信号与RL驱动的实时反馈相结合，实现了机器人跳跃的持续优化。实验结果表明，SRL能够在比基线方法更少的训练时间内实现更稳定的跳跃，平均位置跟踪误差保持在0.1米以下，速度跟踪误差在目标值的±3%范围内。通过双足和四足的地面及楼梯跳跃仿真，以及仿真到仿真和仿真到现实的验证，SRL展现出对各种任务需求和环境复杂性的强大适应能力，凸显了其在现实世界部署中的潜力。
cs.RO / 13 / 2606.18628
Self-Supervised Mask-Aware Transformers for Fault-Tolerant FBG Force Sensing in Minimally Invasive Surgical Robotics
自监督掩码感知变换器用于最小侵入性外科机器人中的容错光纤布拉格光栅力传感
Abstract
In minimally invasive surgical robotics, catheter-scale Fiber Bragg Grating (FBG) sensors are promising due to their ability to estimate multi-dimensional forces by multiplexing several optical channels. However, deploying these compact multi-channel sensors introduces two critical engineering challenges: inherent nonlinear cross-axis coupling during complex deformations, and intermittent channel dropouts caused by fiber fractures in constrained workspaces. These compounding issues severely degrade force estimation. Existing fault-tolerant approaches rely on combinatorial model banks, which scale exponentially with the channel count and demand prohibitively expensive per-pattern calibration. In this paper, we propose a unified, self-supervised mask-aware Transformer that explicitly models channel availability to enable graceful degradation under diverse and dynamic sensor failures. The encoder is pretrained via masked-channel reconstruction on unlabeled data streams and fine-tuned for force regression using a balanced clean-and-corrupted-view objective alongside a dynamic corruption curriculum. Furthermore, a parallel uncertainty head, trained via heteroscedastic Gaussian negative log-likelihood, predicts per-axis confidence in a single forward pass, circumventing the overhead of multi-pass ensembles. Evaluated on a catheter-scale 8-channel FBG dataset, our single unified model achieves a nominal Root Mean Square Error (RMSE) of 0.0066~N and degrades gracefully to 0.0126~N under severe 4-channel failures. This significantly outperforms a comprehensive model bank of 255 per-pattern neural networks (0.0154~N at 4-channel loss) while eliminating pattern-specific calibration.
Chinese Translation
在最小侵入性外科机器人中，导管级光纤布拉格光栅（FBG）传感器因其通过多路复用多个光学通道来估计多维力的能力而备受关注。然而，部署这些紧凑的多通道传感器引入了两个关键的工程挑战：在复杂变形过程中固有的非线性交叉耦合，以及在受限工作空间中由光纤断裂引起的间歇性通道掉落。这些复合问题严重降低了力的估计精度。现有的容错方法依赖于组合模型库，其规模随着通道数量呈指数增长，并且需要昂贵的每模式校准。在本文中，我们提出了一种统一的自监督掩码感知变换器，该变换器明确建模通道可用性，以便在多样化和动态传感器故障下实现优雅降级。编码器通过对未标记数据流进行掩码通道重建进行预训练，并使用平衡的干净和损坏视图目标以及动态损坏课程进行力回归的微调。此外，通过异方差高斯负对数似然训练的并行不确定性头部，在单次前向传播中预测每个轴的置信度，避免了多次传递集成的开销。在导管级8通道FBG数据集上的评估显示，我们的单一统一模型在严重的4通道故障下实现了名义根均方误差（RMSE）为0.0066~N，并优雅降级至0.0126~N。这显著优于255个每模式神经网络的综合模型库（在4通道损失下为0.0154~N），同时消除了模式特定的校准需求。
cs.RO / 14 / 2606.18630
DNN Koopman-Based Deviation Compensation for UGV Path Tracking Control on Coupled Slope and Potholed Road
基于DNN Koopman的UGV路径跟踪控制在耦合坡道和坑洼道路上的偏差补偿
Abstract
Unmanned ground vehicles (UGVs) operating in off-road scenarios are confronted with complex terrain disturbances that can substantially degrade path tracking performance. To address this challenge, this paper proposes a deep neural network (DNN) Koopman-based deviation compensation strategy for UGV path tracking control. Firstly, based on the vehicle dynamic function on coupled slope, an adaptive forgetting recursive least squares method with decoupled error terms is designed to estimate tire cornering stiffness. On this basis, a Laguerre model predictive control (LMPC) path tracking control strategy is designed by incorporating Laguerre functions, which can reduce computational resource usage while maintaining reliable tracking performance across different coupled slope scenarios. Then, by integrating Koopman operator theory with DNN, a DNN Koopman (DK) path deviation compensation method is proposed, which significantly improves the path tracking accuracy of UGV under potholed road disturbances. Furthermore, an event-triggered parallel cooperative (EPC) compensation mechanism that couples LMPC with DK is established based on compensation activation criteria and credibility verification. This mechanism improves path tracking accuracy on potholed road while ensuring the feasibility of overall steering command and stability of vehicle after DK compensation. Finally, a hardware-in-the-loop (HiL) experimental platform is constructed for validation. Experimental results demonstrate that the proposed UGV path tracking strategy improves tracking performance by more than 11.5% across multiple operating conditions.
Chinese Translation
在越野场景中运行的无人地面车辆（UGVs）面临复杂的地形干扰，这可能显著降低路径跟踪性能。为了解决这一挑战，本文提出了一种基于深度神经网络（DNN）Koopman的UGV路径跟踪控制偏差补偿策略。首先，基于耦合坡道上的车辆动态函数，设计了一种具有解耦误差项的自适应遗忘递归最小二乘法，以估计轮胎转向刚度。在此基础上，通过结合Laguerre函数，设计了一种Laguerre模型预测控制（LMPC）路径跟踪控制策略，该策略可以在不同耦合坡道场景中减少计算资源的使用，同时保持可靠的跟踪性能。然后，通过将Koopman算子理论与DNN相结合，提出了一种DNN Koopman（DK）路径偏差补偿方法，该方法显著提高了UGV在坑洼道路干扰下的路径跟踪精度。此外，基于补偿激活标准和可信度验证，建立了一种将LMPC与DK耦合的事件触发并行协作（EPC）补偿机制。该机制在确保DK补偿后整体转向指令的可行性和车辆稳定性的同时，提高了在坑洼道路上的路径跟踪精度。最后，构建了一个硬件在环（HiL）实验平台进行验证。实验结果表明，所提出的UGV路径跟踪策略在多种操作条件下提高了超过11.5%的跟踪性能。
cs.RO / 15 / 2606.18632
ROBOSHACKLES: A Safety Dataset for Human-Injury Prevention in Embodied Foundation Models
ROBOSHACKLES：用于防止人身伤害的安全数据集在具身基础模型中的应用
Abstract
Embodied Foundation Models (EFMs) integrate multimodal understanding, future-state reasoning, and executable robot actions. Yet their safety alignment for human-injury prevention remains underexplored, primarily because real-world data of robots harming humans or creating hazardous household situations cannot be safely or ethically collected. To address this challenge, we propose a safety-critical data construction pipeline for human-injury prevention in EFMs.Starting from real DROID observations, our construction pipeline proceeds through scene understanding, hazard-aware image editing, temporal prompt generation, and single-pass rollout synthesis. The temporal prompts specify the expected scene evolution, while Wan2.7 synthesizes realistic robotic rollouts from the edited hazardous states in a single pass. Using this pipeline, we construct ROBOSHACKLES, a 10,000-clip robotic video dataset derived from real DROID observations, spanning two direct-harm and four indirect-harm categories. To ensure dataset quality, we assess task completion and visual quality with automatic metrics, and evaluate six representative EFMs under a refusal-based safety criterion. Results show that all evaluated models produce unsafe actions in the tested safety-critical scenarios, yielding a 100% unsafe action generation rate. ROBOSHACKLES serves as a scalable benchmark and training resource for refusal learning and hazard anticipation before robot action execution.The dataset is publicly available at https://huggingface.co/datasets/YZW00/RoboShackles.
Chinese Translation
具身基础模型（EFMs）集成了多模态理解、未来状态推理和可执行的机器人动作。然而，它们在防止人身伤害方面的安全对齐仍然未得到充分探索，主要是因为无法安全或伦理地收集机器人伤害人类或造成危险家庭情况的真实世界数据。为了解决这一挑战，我们提出了一种针对EFMs中人身伤害预防的安全关键数据构建管道。该管道从真实的DROID观察开始，经过场景理解、危险意识图像编辑、时间提示生成和单次展开合成。时间提示指定了预期的场景演变，而Wan2.7则从编辑后的危险状态中以单次传递合成现实的机器人展开。利用该管道，我们构建了ROBOSHACKLES，这是一个由真实DROID观察衍生的10,000个剪辑的机器人视频数据集，涵盖了两类直接伤害和四类间接伤害。为了确保数据集质量，我们使用自动化指标评估任务完成度和视觉质量，并在基于拒绝的安全标准下评估六个代表性的EFMs。结果表明，所有评估的模型在测试的安全关键场景中产生了不安全的动作，导致100%的不安全动作生成率。ROBOSHACKLES作为拒绝学习和危险预判的可扩展基准和训练资源，在机器人动作执行前提供支持。该数据集已在https://huggingface.co/datasets/YZW00/RoboShackles上公开发布。
cs.RO / 16 / 2606.18634
EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation
EffiNav：融合深度与视觉语言以实现高效目标导航
Abstract
To locate a target object while exploring the unknown environment is a fundamental capability for autonomous agents, with applications ranging from search-and-rescue to field robots. A simplified version of such task is Object Goal Navigation (ObjNav). In ObjNav, successful arrival at the target object provides a basic measure of performance; however, the efficiency of the navigation trajectory is equally important, as it indicates how intelligently the agent explores and how much time remains for subsequent tasks. In unknown environments, the key to efficient navigation lies in deciding where to explore next. While many prior works aim to address this core challenge and achieved promising performance in certain settings, recent training-based models and non-training frameworks still suffer from generalization and efficiency issues respectively, which in the worst cases can lead to excessive exploration of already-visited areas or redundant back-and-forth motion. We evaluate EffiNav on two widely used simulation benchmarks Habitat Matterport 3D (HM3D) and Open-Vocabulary Object goal Navigation (OVON), and further validate its effectiveness on physical robots in real-world settings. We conduct failure analysis on massive simulation episodes. With minimal modification, we also extend EffiNav to a memory-augmented ObjNav task on the GOAT-BENCH dataset, demonstrating its adaptability beyond standard ObjNav settings. Across two standard metrics--Success Rate (SR) and Success weighted by Path Length (SPL), EffiNav matches or outperforms recent baselines, reflecting its efficiency, robustness, and practical applicability. Recognizing the different emphases of the two datasets, the performances reveals this framework is more balanced and generalizable for efficient ObjNav.
Chinese Translation
在探索未知环境时定位目标物体是自主代理的基本能力，其应用范围包括搜索与救援以及领域机器人。此任务的简化版本是目标物体导航（Object Goal Navigation，ObjNav）。在ObjNav中，成功到达目标物体提供了基本的性能衡量标准；然而，导航轨迹的效率同样重要，因为它表明代理探索的智能程度以及剩余时间用于后续任务的多少。在未知环境中，高效导航的关键在于决定下一步探索的地点。尽管许多先前的研究旨在解决这一核心挑战，并在某些设置中取得了良好的性能，但最近的基于训练的模型和非训练框架仍然分别存在泛化和效率问题，最糟糕的情况下可能导致对已访问区域的过度探索或冗余的来回移动。我们在两个广泛使用的仿真基准上评估EffiNav，即Habitat Matterport 3D（HM3D）和开放词汇目标导航（Open-Vocabulary Object goal Navigation，OVON），并进一步验证其在现实环境中物理机器人上的有效性。我们对大量仿真场景进行了失败分析。在最小修改的情况下，我们还将EffiNav扩展到GOAT-BENCH数据集上的记忆增强ObjNav任务，展示了其超越标准ObjNav设置的适应性。在两个标准指标——成功率（Success Rate，SR）和按路径长度加权的成功率（Success weighted by Path Length，SPL）上，EffiNav的表现与最近的基线相当或更优，反映了其效率、鲁棒性和实际应用性。认识到这两个数据集的不同侧重点，性能结果表明该框架在高效ObjNav方面更为平衡和具有更好的泛化能力。
cs.RO / 17 / 2606.18646
A Scalable Embodied Intelligence Platform for Seamless Real-to-Sim-to-Real Transfer of Household Mobile Manipulation Tasks
一个可扩展的具身智能平台，实现家庭移动操作任务的无缝真实-模拟-真实转移
Abstract
Mobile manipulation is a fundamental capability in embodied intelligence robotics. The growing demand for robust and generalizable manipulation in unstructured household environments has driven rapid progress in embodied intelligence platforms. However, achieving a seamless transfer across the real-to-sim-to-real cycle faces three key challenges, including costly high-fidelity simulation scenes reconstruction, the complexity of systematic strategy evaluation in simulation, and incompatible real-world deployments. To address these challenges, we develop BestMan, a scalable and seamless real-to-sim-to-real platform that bridges the gap between the simulation and the real world, enabling effective strategy development, integration, and deployment for household mobile manipulation. Specifically, we design a novel Automated Scene Generation (ASG) module to reconstruct realistic simulations from real observations. Then, we propose a simulation-guided task formalization and skill learning architecture that supports the flexible integration and large-scale evaluations of hybrid skill strategies in simulation. Finally, to enhance the real-world scalability, we develop a Hardware-agnostic and Unified Middleware (HUM) to ensure seamless and compatible sim-to-real transfer across heterogeneous mobile manipulators for real deployments. Experimental results demonstrate the superior performance of our proposed platform in establishing standardized benchmarks and facilitating promising research in the field of mobile manipulation.
Chinese Translation
移动操作是具身智能机器人中的一项基本能力。在非结构化家庭环境中对稳健且可推广的操作需求日益增长，推动了具身智能平台的快速发展。然而，实现真实-模拟-真实循环的无缝转移面临三个主要挑战，包括高保真模拟场景重建的高成本、模拟中系统策略评估的复杂性以及与现实世界部署的不兼容性。为了解决这些挑战，我们开发了BestMan，一个可扩展且无缝的真实-模拟-真实平台，弥合了模拟与现实世界之间的差距，使家庭移动操作的有效策略开发、集成和部署成为可能。具体而言，我们设计了一个新颖的自动场景生成（Automated Scene Generation, ASG）模块，从真实观察中重建逼真的模拟场景。然后，我们提出了一种模拟引导的任务形式化和技能学习架构，支持在模拟中灵活集成和大规模评估混合技能策略。最后，为了增强现实世界的可扩展性，我们开发了一种硬件无关的统一中间件（Hardware-agnostic and Unified Middleware, HUM），确保在异构移动操作器之间实现无缝且兼容的模拟-真实转移。实验结果证明了我们提出的平台在建立标准化基准和促进移动操作领域有前景的研究方面的卓越性能。
cs.RO / 18 / 2606.18680
High-Degree-of-Freedom Lightweight Bioinspired Leg for Enhanced Mobility in Small Robots
高自由度轻量化仿生腿以增强小型机器人的移动能力
Abstract
In microrobotics, enhancing locomotion capabilities by increasing the degrees of freedom (DoF) of leg mechanisms under severe spatial constraints remains a significant challenge. Inspired by insect locomotion, this paper presents a novel micro-scale parallel leg mechanism with four degrees of freedom, and systematically analyzes its mechanical design, electrical system, and kinematics. The design incorporates two spherical five-bar linkages to achieve spatial motion within a parallel four-bar configuration. Furthermore, a concentric design strategy is employed to simplify the analytical solution of the leg kinematics. Due to the parallel system architecture, all actuators are located on the main body, substantially reducing the equivalent inertia of moving parts compared to traditional high-DOF leg structures. The total mass of the system is only 18.9 g, with an end-effector output force of approximately 0.5 N and a workspace exceeding 22255 mm3. Experimental results demonstrate that the proposed single-leg mechanism achieves excellent motion flexibility, highlighting its potential for micro bio-inspired robotics.
Chinese Translation
在微型机器人领域，在严苛的空间限制下通过增加腿部机制的自由度（DoF）来增强运动能力仍然是一个重大挑战。受昆虫运动的启发，本文提出了一种新颖的微尺度平行腿机制，具有四个自由度，并系统地分析了其机械设计、电气系统和运动学。该设计结合了两个球形五杆连杆，以在平行四杆结构中实现空间运动。此外，采用同心设计策略以简化腿部运动学的解析解。由于平行系统架构，所有执行器均位于主体上，相较于传统的高自由度腿结构，显著降低了运动部件的等效惯性。系统的总质量仅为18.9克，末端执行器输出力约为0.5牛顿，工作空间超过22255立方毫米。实验结果表明，所提出的单腿机制实现了优异的运动灵活性，突显了其在微型仿生机器人中的潜力。
cs.RO / 19 / 2606.18698
Leveraging Energy Features for Surface Classification with Deep Learning: A Comparative Analysis Across Three Independent Datasets
利用能量特征进行表面分类的深度学习：基于三个独立数据集的比较分析
Abstract
The energy-based method remains a comparatively underexamined approach for surface classification in mobile robotics, despite promising results in constrained environments. This study evaluated the viability of using energy-derived features as either a standalone classification modality or as supplementary input to inertial data. A comprehensive evaluation was conducted across three publicly available datasets, comparing the performance of modern deep learning architectures including recurrent neural networks, convolutional neural networks, encoder-only transformers, and Mamba state-space models, under automated hyperparameter tuning and input sequence length optimization. The models achieved higher accuracy than previously reported values on all evaluated datasets, with the convolutional neural network yielding the highest overall performance. When relying exclusively on energy-based features, the models attained classification accuracies in the range of 85-90%, approximately 5-10% lower than those achieved when combined with inertial features (96-99%). Augmenting inertial data with energy features resulted in a consistent mean accuracy improvement of 1-2%. These findings indicate that classifiers relying solely on energy features offer sufficient accuracy for standalone deployment, while also providing a consistent gain when used in combination with other sensing modalities.
Chinese Translation
尽管在受限环境中取得了令人鼓舞的结果，基于能量的方法在移动机器人表面分类中仍然是一种相对较少研究的方法。本研究评估了将能量衍生特征作为独立分类方式或作为惯性数据的补充输入的可行性。我们在三个公开可用的数据集上进行了全面评估，比较了现代深度学习架构的性能，包括递归神经网络（RNN）、卷积神经网络（CNN）、仅编码器的变换器（transformers）和Mamba状态空间模型，所有模型均在自动超参数调优和输入序列长度优化下进行评估。所有评估数据集上的模型准确率均高于先前报告的值，其中卷积神经网络的整体性能最高。当仅依赖于基于能量的特征时，模型的分类准确率在85-90%范围内，约比结合惯性特征时的准确率（96-99%）低5-10%。将能量特征与惯性数据结合使用，平均准确率一致提高了1-2%。这些发现表明，仅依赖于能量特征的分类器在独立部署时提供了足够的准确性，同时在与其他传感方式结合使用时也能带来持续的增益。
cs.RO / 20 / 2606.18704
Selective Unit-Cell Actuation in Lattice Structures for Distributed Morphology in Soft Robots
用于软机器人分布式形态的格子结构选择性单元格驱动
Abstract
Soft lattice structures are increasingly used in robotics to tailor compliance and guide deformation; however, actuation is typically introduced at the device or module level, with actuators inserted into otherwise passive architectures. In this work, we move actuator-lattice co-design to the unit-cell scale. We present an embedded pneumatic unit cell that integrates curved-strut lattice geometry with a bidirectional bellow actuator within a single monolithic element. When tessellated, the lattice functions as a distributed actuation field in which global morphology is governed by spatial actuation patterns rather than uniform pressurization. Experimental characterization of 1x1, 2x2, and 3x3 tessellations demonstrates scalable displacement and force generation with repeatable cyclic performance. Selective actuation of unit cells in a 3x3x3 array produces distinct global deformation modes, including bending and directional grasping, without altering hardware configuration. Additionally, coupling active and passive unit cells enables bending-driven crawling locomotion, demonstrating that heterogeneous tessellations can translate through asymmetric deformation. These results establish unit-cell-level actuation as a strategy for distributed morphing in lattice-based soft robots and provide a foundation for scalable, monolithic robotic architectures.
Chinese Translation
软格子结构在机器人领域的应用日益增多，以便定制柔顺性和引导变形；然而，驱动通常是在设备或模块级别引入的，执行器被插入到其他被动架构中。在本研究中，我们将执行器-格子共同设计推进到单元格尺度。我们提出了一种嵌入式气动单元格，它将弯曲支杆格子几何与双向波纹管执行器集成在一个单一的整体元件中。当进行平铺时，格子作为一个分布式驱动场，其全局形态由空间驱动模式而非均匀加压所主导。对1x1、2x2和3x3平铺的实验表征表明可扩展的位移和力生成，并具有可重复的循环性能。在3x3x3阵列中选择性驱动单元格产生了明显的全局变形模式，包括弯曲和定向抓取，而无需改变硬件配置。此外，主动和被动单元格的耦合使得驱动弯曲的爬行运动成为可能，证明了异质平铺可以通过不对称变形进行移动。这些结果确立了单元格级别的驱动作为基于格子的软机器人分布式变形策略，并为可扩展的整体机器人架构提供了基础。
cs.RO / 21 / 2606.18730
Two-Phase Bilevel Search for the Moving-Target Traveling Salesman Problem with Moving Obstacles
带移动障碍的移动目标旅行商问题的两阶段双层搜索
Abstract
The Moving-Target Traveling Salesman Problem (MT-TSP) seeks a minimum cost trajectory for an agent that departs from a static depot, visits a set of moving targets, each within one of their assigned time windows, and returns to the depot. In this article, we study the Moving-Target Traveling Salesman Problem with Moving Obstacles (MT-TSP-MO), a generalization of the MT-TSP where the agent trajectory must avoid moving obstacles. We present a Mixed-Integer Conic Programming (MICP) formulation that can be solved using off-the-shelf solvers, as well as a fast and scalable Two-Phase Bilevel Search (TPBS) algorithm that computes high-quality feasible solutions for the problem. We evaluate our approaches against an existing baseline algorithm on a broad range of problem instances with up to 40 targets and 40 obstacles. The results demonstrate that both the proposed methods significantly outperform the baseline with respect to success rates, solution costs, and computation time.
Chinese Translation
移动目标旅行商问题（MT-TSP）旨在为一个从静态仓库出发的代理寻找一条最低成本的轨迹，该代理需在各自的时间窗口内访问一组移动目标，并返回仓库。本文研究了带移动障碍的移动目标旅行商问题（MT-TSP-MO），这是MT-TSP的一个推广，其中代理轨迹必须避开移动障碍物。我们提出了一种混合整数锥规划（MICP）模型，该模型可以使用现成的求解器进行求解，同时还提出了一种快速且可扩展的两阶段双层搜索（TPBS）算法，用于计算该问题的高质量可行解。我们在多种问题实例上对我们的方法进行了评估，这些实例最多包含40个目标和40个障碍物，并与现有的基准算法进行了比较。结果表明，所提出的方法在成功率、解的成本和计算时间方面均显著优于基准算法。
cs.RO / 22 / 2606.18747
Generating Natural and Expressive Robot Gestures through Iterative Reinforcement Learning with Human Feedback using LLMs
通过使用大型语言模型的迭代强化学习与人类反馈生成自然且富有表现力的机器人手势
Abstract
Expressive gestures are essential for natural and effective communication, complementing speech when verbal cues alone are insufficient (e.g., pointing). For social robots such as the humanoid Pepper, producing natural and expressive movements is critical for improving human-robot interaction (HRI) and long-term acceptance. However, generating gestures remains challenging due to reliance on expert-authored animations, resulting in rigid behaviors that are impractical for dynamic and diverse environments. Alternatively, machine learning approaches often struggle to capture perceived naturalness, becoming increasingly challenging with more degrees of freedom. Consequently, producing expressive robot gestures requires a system that can adapt to the environment while adhering to social norms and physical constraints. Recent advances in large language models (LLMs) enable dynamic code generation, offering new opportunities for runtime gesture synthesis from natural language. In this paper, we integrate ChatGPT into the humanoid robot Pepper to generate co-speech gestures aligned with conversational output. While this baseline enables flexible gesture generation, the resulting motions are often perceived as stiff and unnatural. To address this limitation, we introduce an iterative reinforcement learning with human feedback (RLHF) system that finetunes gesture generation based on user evaluations, leveraging an iterative user study to compare Pepper's generated gestures. Our results show that RLHF improved the LLM's co-speech generative capabilities, producing more expressive, relevant and fluid movements.
Chinese Translation
富有表现力的手势对于自然和有效的沟通至关重要，当仅依靠语言提示不足时（例如，指向），它们可以起到补充作用。对于社交机器人，如类人机器人Pepper，产生自然且富有表现力的动作对于改善人机交互（HRI）和长期接受度至关重要。然而，由于依赖专家编写的动画，生成手势仍然具有挑战性，这导致了在动态和多样化环境中不切实际的僵硬行为。另一方面，机器学习方法往往难以捕捉到感知的自然性，随着自由度的增加，变得愈加困难。因此，生成富有表现力的机器人手势需要一个能够适应环境，同时遵循社会规范和物理约束的系统。最近，大型语言模型（LLMs）的进展使得动态代码生成成为可能，为从自然语言进行运行时手势合成提供了新的机会。在本文中，我们将ChatGPT集成到类人机器人Pepper中，以生成与对话输出相一致的共语手势。尽管这个基线能够实现灵活的手势生成，但生成的动作往往被认为是僵硬和不自然的。为了解决这一局限性，我们引入了一种基于人类反馈的迭代强化学习（RLHF）系统，该系统根据用户评估微调手势生成，利用迭代用户研究比较Pepper生成的手势。我们的结果表明，RLHF提高了LLM的共语生成能力，产生了更富表现力、更相关和更流畅的动作。
cs.RO / 23 / 2606.18772
HALOMI: Learning Humanoid Loco-Manipulation with Active Perception from Human Demonstrations
HALOMI：通过人类示范学习类人运动操控与主动感知
Abstract
Human demonstrations, which can be collected at scale and naturally capture active hand-eye coordination, are a promising data source for learning humanoid loco-manipulation. However, directly transferring human demonstrations to humanoids requires a precise world-frame tracking controller, which is often brittle under Out-of-Distribution(OOD) targets, while human-to-humanoid gaps persist in both egocentric observation and action execution. To address these challenges, we present HALOMI, a scalable framework for learning humanoid loco-manipulation with active perception from human demonstrations. HALOMI extends Universal Manipulation Interface (UMI) with egocentric sensing to collect ego-view and wrist-view observations along with head-hand trajectories at scale. We further propose a manifold-constrained controller that plans in a learned latent behavior manifold to enable precise and robust head-hand tracking in the world frame. To bridge the human-to-humanoid gap, we perform ego-view alignment and introduce a controller-aware reference trajectory adaptation to reduce mismatch in both observation and action execution. We validate HALOMI on a Unitree G1 humanoid robot with an actuated neck across five real-world tasks involving navigation, grasping, bimanual manipulation, whole-body coordination, and dynamic behaviors. Across the three quantitatively evaluated tasks, HALOMI achieves an average success rate of 85\%, while additional qualitative demonstrations show its ability to support dynamic tossing and deep-squat grasping.
Chinese Translation
人类示范是一种可大规模收集且自然捕捉主动手眼协调的数据源，具有学习类人运动操控的潜力。然而，直接将人类示范转移到类人机器人上需要精确的世界坐标追踪控制器，而这种控制器在处理分布外（Out-of-Distribution, OOD）目标时往往不够稳健，同时在人类与类人机器人之间的自我中心观察和动作执行方面仍然存在差距。为了解决这些挑战，我们提出了HALOMI，这是一个可扩展的框架，用于通过人类示范学习类人运动操控与主动感知。HALOMI扩展了通用操控接口（Universal Manipulation Interface, UMI），结合自我中心感知，以大规模收集自我视角和手腕视角的观察数据，以及头手轨迹。我们进一步提出了一种流形约束控制器，该控制器在学习到的潜在行为流形中进行规划，以实现世界坐标系下精确且稳健的头手追踪。为了缩小人类与类人机器人之间的差距，我们进行自我视角对齐，并引入控制器感知的参考轨迹适应，以减少观察和动作执行中的不匹配。我们在一台配备驱动颈部的Unitree G1类人机器人上验证了HALOMI，涉及导航、抓取、双手操控、全身协调和动态行为等五个真实世界任务。在三个定量评估的任务中，HALOMI实现了85%的平均成功率，而额外的定性示范则展示了其支持动态投掷和深蹲抓取的能力。
cs.RO / 24 / 2606.18828
Space Is Intelligence: Neural Semigroup Superposition for Riemannian Metric Generation
空间即智能：用于黎曼度量生成的神经半群叠加
Abstract
Traditional approaches place intelligence in the agent, whether as a learned policy or a search procedure. We instead place intelligence in the space itself: a scene induces a Riemannian metric on the configuration manifold, and action reduces to following the geodesics of that metric rather than invoking a separate planner or collision checker. A single Encoder-Router network realizes this idea through three complementary parameter groups -- frame parameters that orient the generators, modulation parameters that govern their spatial propagation, and basic coefficients that determine their strength. These groups combine through a shared semigroup-superposition mechanism to produce a single Riemannian metric field, yielding a compact architecture whose geometry scales naturally with scene complexity. Trained on a single two-obstacle scene, the model demonstrates robust zero-shot generalization across unseen obstacle configurations, with orders-of-magnitude separation between collision-free and obstacle-penetrating path costs.
Chinese Translation
传统方法将智能置于代理中，无论是作为学习的策略还是搜索过程。相反，我们将智能置于空间本身：一个场景在配置流形上诱导出黎曼度量，而行动则简化为遵循该度量的测地线，而不是调用单独的规划器或碰撞检测器。一个单一的编码器-路由器网络通过三组互补的参数实现这一理念——定向生成器的框架参数、控制其空间传播的调制参数以及决定其强度的基本系数。这些参数组通过共享的半群叠加机制结合，产生一个单一的黎曼度量场，形成一个紧凑的架构，其几何形状自然地随着场景复杂性而扩展。在一个包含两个障碍物的单一场景上进行训练，该模型在未见障碍配置上展示了强大的零-shot 泛化能力，且无碰撞路径成本与穿透障碍路径成本之间存在数量级的分离。
cs.RO / 25 / 2606.18883
ZiMPedance: Impedance-Aware ZMP Modeling and Control for Payload Carrying with Quadruped Robots
ZiMPedance：考虑阻抗的零力矩点建模与控制在四足机器人负载运输中的应用
Abstract
Load transportation with quadruped robots is strongly affected by the dynamics of the physical interface between the robot and the load. Passive spring-based arms reduce weight and complexity compared to active manipulators, but their spring-damper dynamics can introduce oscillatory forces that degrade locomotion stability. This paper derives an extended Zero Moment Point (ZMP) formulation that includes passive payload-interface dynamics, relating stiffness, damping, and payload mass to the stability margin. The analysis shows that underdamped configurations can resonate with locomotion harmonics. Based on this insight, we augment a Single Rigid Body Dynamics model with passive subsystem dynamics and integrate it into a Model Predictive Control framework. In simulation, the proposed controller reduces stability violations by up to $10\times$, from $7.0\%$ to $0.7\%$, and increase locomotion efficiency by lowering horizontal ground reaction force effort by up to $15\%$ compared to a nominal baseline. Hardware experiments with a $2\,\mathrm{kg}$ payload show stable locomotion under pull-release disturbances where the nominal controller fails. The same model also enables end-effector tracking through passive arm dynamics without direct arm actuation.
Chinese Translation
四足机器人在负载运输中的表现受到机器人与负载之间物理接口动态的强烈影响。与主动操控器相比，基于被动弹簧的臂部减少了重量和复杂性，但其弹簧-阻尼器动态可能引入振荡力，从而降低运动稳定性。本文推导了一种扩展的零力矩点（Zero Moment Point, ZMP）公式，该公式包括被动负载接口动态，将刚度、阻尼和负载质量与稳定性边际相关联。分析表明，欠阻尼配置可能与运动谐波共振。基于这一见解，我们将被动子系统动态集成到单刚体动力学模型中，并将其纳入模型预测控制（Model Predictive Control, MPC）框架。在仿真中，所提出的控制器将稳定性违规减少了多达 $10 imes$，从 $7.0\%$ 降低到 $0.7\\%$，并通过降低水平地面反作用力的努力提高了运动效率，最多可达 $15\\%$，与名义基线相比。对 $2 \, ext{kg}$ 负载的硬件实验表明，在拉-释放扰动下，运动保持稳定，而名义控制器则失效。相同的模型还通过被动臂动态实现了末端效应器跟踪，而无需直接驱动臂部。
cs.RO / 26 / 2606.18948
C-ARC: Continuous-Adaptive Range Clustering for Non-Repetitive LiDAR Sensors
C-ARC：针对非重复激光雷达传感器的连续自适应范围聚类
Abstract
Real-time LiDAR clustering identifies structures in point clouds, which is an essential prerequisite for many mobile robotics algorithms. Current methods are mostly developed for repetitive mechanical LiDAR sensors. Recently, the use of non-repetitive LiDAR sensors is strongly increasing due to their small cost and form factor. Such non-repetitive Risley prism-based sensors violate two key assumptions of repetitive mechanical sensors: structured scan lines and well-defined frame boundaries. Their Rhodonea-curve trajectories produce non-uniform point distributions, and the absence of a rotation cycle renders conventional scan line indexing inapplicable. To meet such new requirements, we developed C-ARC, a Continuous-Adaptive Range Clustering framework that maintains a persistent dual-graph over a sliding window, decoupling high-frequency point insertion from on-demand cluster retrieval. This is crucial for key functionalities like SLAM or tracking. An adaptive range grid resolution mechanism calibrates grid dimensions at initialization using an exponential control loop, balancing the sparsity-collision trade-off without prior knowledge of the scanning pattern. Implemented as an open-sourced single-threaded C++17 library, C-ARC produces real-time cluster output at 20 Hz on commodity hardware for the Livox Mid-360. Evaluation on the Livox Avia identifies unbounded cell occupancy as the primary limitation for sensors with strongly concentrated scan patterns. The adaptive resolution mechanism additionally improves clustering quality for existing grid-based methods on non-repetitive data.
Chinese Translation
实时激光雷达聚类能够识别点云中的结构，这是许多移动机器人算法的基本前提。目前的方法大多是为重复机械激光雷达传感器开发的。近年来，由于其低成本和小型化，非重复激光雷达传感器的使用显著增加。这类基于Risley棱镜的非重复传感器违反了重复机械传感器的两个关键假设：结构化扫描线和明确的帧边界。它们的Rhodonea曲线轨迹产生了非均匀的点分布，缺乏旋转周期使得传统的扫描线索引不适用。为了满足这些新需求，我们开发了C-ARC，一个连续自适应范围聚类框架，该框架在滑动窗口上维护一个持久的双图，解耦高频点插入与按需聚类检索。这对于SLAM或跟踪等关键功能至关重要。自适应范围网格分辨率机制在初始化时使用指数控制循环校准网格尺寸，在没有扫描模式先验知识的情况下平衡稀疏性与碰撞的权衡。C-ARC作为一个开源的单线程C++17库实现，能够在普通硬件上以20 Hz的频率生成实时聚类输出，适用于Livox Mid-360。对Livox Avia的评估表明，对于具有强集中扫描模式的传感器，无界单元占用是主要限制因素。自适应分辨率机制还提高了现有基于网格的方法在非重复数据上的聚类质量。
cs.RO / 27 / 2606.18951
A High-accuracy Event-based Underwater SLAM System
高精度事件驱动的水下SLAM系统
Abstract
While event cameras offer immense potential for underwater SLAM, existing Time Surface (TS)-based methods prove highly unreliable when deployed underwater. Fluctuating camera velocities severely degrade TS imaging quality, while wide stereo baselines and repetitive underwater textures induce critical matching failures, frequently triggering system failure. To overcome these challenges, we develop the first high-accuracy event-based underwater stereo SLAM system. A structure-aware metric for TS is designed based on structure tensor coherence and gradients to quantitatively evaluate TS structural information density. By decoupling the optimal TS generation into two distinct stages based on system initialization, Bayesian Optimization(BO) first predicts an optimal prior TS sequentially before initialization while we set an asynchronous online local searching method periodically to obtain appropriate TS in real-time during the tracking stage. We use the prior disparity to guarantee precise data association and "latest-observation-first'' triangulation mechanism to realize stable triangulation. As a benchmark for these solutions and a resource for the community, we also contribute UWE, the first high-quality real-world underwater event dataset containing variable camera motions, complex textures and different trajectory features. Extensive evaluations on public datasets and UWE show the competitive accuracy performance of the proposed SLAM system compared to the state-of-the-art event-based method. The code and data will be open-sourced.
Chinese Translation
尽管事件相机在水下SLAM中具有巨大的潜力，但现有的基于时间表面（Time Surface, TS）的方法在水下应用时表现出极高的不可靠性。相机速度的波动严重降低了TS成像质量，而宽基线立体视觉和重复的水下纹理则导致关键的匹配失败，频繁触发系统故障。为了解决这些挑战，我们开发了第一个高精度事件驱动的水下立体SLAM系统。我们设计了一种基于结构张量一致性和梯度的结构感知度量，用于定量评估TS的结构信息密度。通过将最佳TS生成解耦为基于系统初始化的两个不同阶段，贝叶斯优化（Bayesian Optimization, BO）首先在初始化之前顺序预测最佳先验TS，同时我们定期设置异步在线局部搜索方法，以在跟踪阶段实时获取合适的TS。我们使用先验视差来保证精确的数据关联，并采用“最新观测优先”（latest-observation-first）三角测量机制实现稳定的三角测量。作为这些解决方案的基准和社区资源，我们还贡献了UWE，这是第一个高质量的真实水下事件数据集，包含可变相机运动、复杂纹理和不同轨迹特征。在公共数据集和UWE上的广泛评估显示，与最先进的基于事件的方法相比，所提出的SLAM系统具有竞争力的精度表现。代码和数据将开源。
cs.RO / 28 / 2606.18953
Object-Centric Residual RL for Zero-Shot Sim-to-Real VLA Enhancement
面向对象的残差强化学习用于零-shot的模拟到现实视觉-语言-动作增强
Abstract
Vision-Language-Action (VLA) models can generalize across diverse manipulation tasks, but their imitation-learning-based policies remain brittle in precise physical interactions due to compounding execution errors; Can a reinforcement learning policy trained purely in simulation improve the robustness of real-world VLAs zero-shot? Residual RL, which learns a corrective policy on top of a frozen VLA, offers a natural framework, but existing approaches face a fundamental sim-to-real dilemma: privileged-state methods require lossy distillation for deployment; image-based methods suffer from the visual domain gap; and real-world RL is costly and unsafe. We propose an object-centric residual RL framework that refines VLA actions using object poses, enabling a compact observation space that transfers consistently between simulation and reality. To align the two domains, we additionally replay the same teleoperation demonstrations in simulation to train a sim counterpart of the real-world VLA. The residual RL policy is trained only in simulation with pose noise injection and dropout, and transfers zero-shot to the real robot. Across five manipulation tasks on a real Franka Research 3 (FR3) robot, our method improves the success rate from 42% to 76% zero-shot, and the improved rollouts can be further reused to retrain the base VLA for self-improvement without additional teleoperation. Project page: https://www.microsoft.com/en-us/research/articles/object-centric-residual-rl/
Chinese Translation
视觉-语言-动作（VLA）模型能够在多样的操作任务中进行泛化，但基于模仿学习的策略在精确的物理交互中仍然脆弱，原因在于执行错误的累积；一个仅在模拟中训练的强化学习策略能否提高现实世界VLA的鲁棒性，实现零-shot？残差强化学习（Residual RL）在冻结的VLA基础上学习纠正策略，提供了一个自然的框架，但现有方法面临着根本的模拟到现实困境：特权状态方法需要损失性蒸馏以进行部署；基于图像的方法受到视觉领域差距的影响；而现实世界的强化学习则成本高且不安全。我们提出了一种面向对象的残差强化学习框架，通过使用物体姿态来细化VLA动作，从而实现一个在模拟和现实之间一致转移的紧凑观察空间。为了对齐这两个领域，我们还在模拟中重放相同的遥操作演示，以训练现实世界VLA的模拟对应物。残差强化学习策略仅在模拟中通过姿态噪声注入和随机失活进行训练，并能零-shot转移到真实机器人。在真实的Franka Research 3（FR3）机器人上进行的五个操作任务中，我们的方法将成功率从42%提高到76%（零-shot），而改进后的回放可以进一步用于重新训练基础VLA，以实现自我改进而无需额外的遥操作。项目页面：https://www.microsoft.com/en-us/research/articles/object-centric-residual-rl/
cs.RO / 29 / 2606.18959
TactSpace: Learning a Physics-enriched Shared Latent Space for Tactile Sim-to-Real Transfer
TactSpace：学习物理增强的共享潜在空间以实现触觉仿真到现实的转移
Abstract
Tactile sensing provides direct measurements of contact interactions that are essential for robotic manipulation. However, current simulators lack the fidelity to faithfully model the complex deformation and transduction mechanics of tactile sensors, severely hindering sim-to-real transfer in robot learning pipelines. To address this challenge, we propose a multi-modal representation learning framework that aligns heterogeneous tactile modalities within a shared latent space, eliminating the need for accurate raw-signal simulation while preserving relevant contact information. Our approach employs modality-specific encoders to project diverse tactile observations, such as simulated penetration depth and real-world capacitance, into a common embedding space. The model is trained using self- and cross-reconstruction objectives alongside contrastive alignment, encouraging modality-invariant yet information-rich representations. We evaluate the learned embeddings on indenter shape identification, force prediction, and geometric reconstruction tasks, training exclusively in simulation and testing directly on real sensor measurements. Our results demonstrate zero-shot sim-to-real transfer across physically dissimilar representations. Furthermore, incorporating multi-physics simulation modalities yields more informative embeddings that transfer across diverse downstream tasks, demonstrating a 16.7% reduction in force prediction error and a 45.8% reduction in shape reconstruction error. Finally, we release an efficient Warp-based implementation of a penalty-based tactile simulation model for Isaac Lab, enabling scalable tactile data generation.
Chinese Translation
触觉传感提供了接触交互的直接测量，这对于机器人操作至关重要。然而，当前的仿真器缺乏足够的逼真度，无法真实地模拟触觉传感器的复杂变形和转导机制，严重阻碍了机器人学习流程中的仿真到现实转移。为了解决这一挑战，我们提出了一种多模态表示学习框架，该框架在共享潜在空间中对齐异构触觉模态，消除了对准确原始信号仿真的需求，同时保留相关的接触信息。我们的方法采用特定模态的编码器将多样的触觉观察（例如仿真穿透深度和现实世界的电容）投影到一个共同的嵌入空间。该模型使用自重建和交叉重建目标以及对比对齐进行训练，鼓励模态不变但信息丰富的表示。我们在压头形状识别、力预测和几何重建任务上评估了学习到的嵌入，训练完全在仿真中进行，并直接在真实传感器测量上进行测试。我们的结果表明，在物理上不相似的表示之间实现了零样本的仿真到现实转移。此外，结合多物理仿真模态产生了更具信息量的嵌入，这些嵌入可以跨越不同的下游任务进行转移，显示出力预测误差减少了16.7%，形状重建误差减少了45.8%。最后，我们发布了一种基于Warp的高效实现的基于惩罚的触觉仿真模型，用于Isaac Lab，从而实现可扩展的触觉数据生成。
cs.RO / 30 / 2606.19031
Congestion-Aware Robot Tour Planning in Crowded Environments
拥挤环境中的机器人巡航规划考虑拥堵因素
Abstract
Autonomous mobile service robots are often required to complete tours that require navigating through a set of locations in an environment. Example domains include guiding people through a shopping mall, delivering packages in a fulfilment centre, or giving guided tours in a museum. However, in crowded environments, the presence of people may negatively impact robot performance. For example, humans will activate robot collision avoidance manoeuvres that slow the robot down. Crowds move stochastically and vary throughout the day. In this paper we present a probabilistic tour planner for crowded environments which explicitly reasons over human congestion. We learn circular linear flow field (CLiFF) maps which predict human trajectories given an initial observation. We then use these predictions to build and solve a Markov decision process online which efficiently routes the robot through the environment. Our approach is scalable enough to re-plan as new people are observed. We evaluate our approach on a real-world crowd dataset in a shopping mall.
Chinese Translation
自主移动服务机器人通常需要完成在环境中导航一系列地点的巡航任务。示例领域包括在购物中心引导人群、在配送中心递送包裹或在博物馆进行导览。然而，在拥挤的环境中，人群的存在可能会对机器人的性能产生负面影响。例如，人类会激活机器人的碰撞避免操作，从而减缓机器人的速度。人群的移动是随机的，并且在一天中会有所变化。本文提出了一种针对拥挤环境的概率巡航规划器，该规划器明确考虑人类拥堵因素。我们学习了圆形线性流场（Circular Linear Flow Field，CLiFF）地图，该地图根据初始观察预测人类轨迹。然后，我们利用这些预测在线构建并解决马尔可夫决策过程（Markov Decision Process，MDP），有效地引导机器人穿越环境。我们的方法具有足够的可扩展性，可以在观察到新的人群时进行重新规划。我们在购物中心的真实人群数据集上评估了我们的方法。
cs.RO / 31 / 2606.19067
Sensor Configuration Matters: A Systematic Evaluation of Multimodal SLAM on Quadruped Robots
传感器配置的重要性：对四足机器人多模态SLAM的系统评估
Abstract
Autonomous navigation of quadrupedal robots in diverse environments fundamentally relies on resilient Simultaneous Localization and Mapping (SLAM). While visual-inertial SLAM has matured across wheeled, handheld, and aerial platforms, a critical evaluation gap remains regarding how hardware-level sensor configurations affect performance under the aggressive dynamics of legged locomotion. Quadrupeds introduce distinct embodiment-induced sensory challenges, including foot-impact shocks, high-frequency mechanical vibrations, and rapid angular rotations, which degrade standard perception pipelines. To address this gap, we present a systematic evaluation of state-of-the-art visual, visual-inertial, and LiDAR-visual-inertial SLAM methods using the GrandTour dataset recorded on an ANYmal D quadruped. We isolate and quantify the impacts of camera modalities, shutter techniques, and inertial sensor tiers, analyzing their trade-offs across localization accuracy, algorithmic robustness, and computational resource utilization. Our empirical findings demonstrate that hardware selection has substantial influence on system resilience: stereo configurations consistently outperform monocular and RGB-D modalities, global shutter cameras significantly mitigate motion-induced tracking failures compared to rolling shutter cameras, and, crucially, standard inertial integration can degrade the performance of primarily vision-based frameworks under harsh legged locomotion. These insights additionally offer concrete design guidelines for tailoring custom sensor payloads to achieve dependable perception on agile legged systems.
Chinese Translation
四足机器人在多样环境中的自主导航根本依赖于稳健的同时定位与地图构建（SLAM）。尽管视觉惯性SLAM在轮式、手持和空中平台上已经成熟，但在腿部运动的激烈动态下，硬件级传感器配置对性能的影响仍存在重要的评估空白。四足机器人带来了独特的身体感知挑战，包括足部冲击震动、高频机械振动和快速角度旋转，这些因素会降低标准感知流程的效果。为了解决这一空白，我们对在ANYmal D四足机器人上记录的GrandTour数据集进行了最先进的视觉、视觉-惯性和激光雷达-视觉-惯性SLAM方法的系统评估。我们隔离并量化了摄像头模式、快门技术和惯性传感器层级的影响，分析它们在定位精度、算法鲁棒性和计算资源利用方面的权衡。我们的实证研究结果表明，硬件选择对系统的稳健性有显著影响：立体配置始终优于单目和RGB-D模式，全球快门相机相比滚动快门相机显著减轻了运动引起的跟踪失败，关键是，标准惯性集成在恶劣的腿部运动下可能会降低主要基于视觉的框架的性能。这些见解还为定制传感器负载以实现敏捷腿部系统的可靠感知提供了具体的设计指导。
cs.RO / 32 / 2606.19088
ReSiReg: Towards Spatially Consistent Semantics in Language-Conditioned Robotic Tasks
ReSiReg：朝着语言条件下机器人任务中的空间一致语义迈进
Abstract
Vision-Language Models (VLMs) enable robots to follow open-language instructions. However, dense VLM embeddings have shown to be noisy and lack spatial consistency. This is problematic for robotic applications, which require simultaneous reasoning over semantics and 3D space. We examine spatial structure across recent VLMs and propose ReSiReg, a feature reconstruction method that uses spatially consistent VLM intermediates to improve dense language-grounded retrieval. ReSiReg clusters intermediates into visual prototypes, derives their language descriptors, and reconstructs each patch as a soft mixture of prototype-level language embeddings. We evaluate quantitatively on OVSS and 3D mapping across backbones, and qualitatively in real-world manipulation scenes. Quantitative results show improved dense retrieval; manipulation scenes show more spatially consistent target activations. We further provide a compact 25M dense VLM for robotic applications, substantially smaller than and competitive with ViT-B baselines. Available at https://resireg.github.io
Chinese Translation
视觉-语言模型（VLMs）使机器人能够遵循开放语言指令。然而，密集的VLM嵌入显示出噪声大且缺乏空间一致性。这对于需要同时在语义和三维空间上进行推理的机器人应用来说是一个问题。我们检查了近期VLM的空间结构，并提出了ReSiReg，这是一种特征重建方法，利用空间一致的VLM中间结果来改善密集的语言基础检索。ReSiReg将中间结果聚类为视觉原型，推导其语言描述符，并将每个补丁重建为原型级语言嵌入的软混合。我们在OVSS和3D映射上进行了定量评估，并在真实世界的操作场景中进行了定性评估。定量结果显示密集检索得到了改善；操作场景显示出更具空间一致性的目标激活。我们还提供了一个紧凑的25M密集VLM用于机器人应用，显著小于并与ViT-B基线相竞争。可在 https://resireg.github.io 获取。
cs.RO / 33 / 2606.19089
ART-VS: Adaptive Resolution Tiling for Vision Transformer Visual Servoing
ART-VS：用于视觉变换器视觉伺服的自适应分辨率平铺
Abstract
Visual servoing with self-supervised Vision Transformer (ViT) features enables training-free robotic positioning with strong generalization, but faces a fundamental trade-off between robustness and precision. Coarse patch-level descriptors provide stable correspondences yet limit positioning accuracy. Increasing image resolution improves precision but yields only marginal robustness gains - under perturbation, high-resolution processing improves convergence success rate from 76.6% to just 81.0% despite 12x more ViT patches. Therefore, we propose Adaptive Resolution Tiling Visual Servoing (ART-VS), a two-phase method that adapts feature granularity to servoing progress: a coarse phase at native ViT resolution for stable alignment, then a tiled high-resolution phase that restricts matching to local neighborhoods improving positioning accuracy. Without any task-specific training, ART-VS achieves 95.4% convergence under perturbation, outperforming standard and full-resolution ViT-based servoing by 18.8 and 14.4 percentage points. Over the former it reduces positioning error by 53%, while running at over 10x higher speed and 27% lower VRAM than the latter. We validate ART-VS across three ViT backbones and demonstrate real-world category-level grasping of unseen object instances, achieving 95/100 on transparent bottles and 98/100 on shoes. Code available under https://art-vs.github.io/.
Chinese Translation
利用自监督视觉变换器（ViT）特征进行视觉伺服，实现了无需训练的机器人定位，具有强大的泛化能力，但面临稳健性与精度之间的基本权衡。粗糙的块级描述符提供了稳定的对应关系，但限制了定位精度。提高图像分辨率可以改善精度，但仅带来边际的稳健性提升——在扰动下，高分辨率处理将收敛成功率从76.6%提高到仅81.0%，尽管使用了12倍更多的ViT块。因此，我们提出了自适应分辨率平铺视觉伺服（ART-VS），这是一种两阶段的方法，根据伺服进展调整特征粒度：在原生ViT分辨率下进行稳定对齐的粗糙阶段，然后是限制匹配到局部邻域的高分辨率平铺阶段，从而提高定位精度。在没有任何特定任务训练的情况下，ART-VS在扰动下实现了95.4%的收敛率，分别比标准和全分辨率ViT基础的伺服提高了18.8和14.4个百分点。与前者相比，它将定位误差降低了53%，同时运行速度超过10倍，VRAM使用量比后者低27%。我们在三个ViT骨干网络上验证了ART-VS，并展示了对未见物体实例的真实世界类别级抓取，透明瓶的成功率为95/100，鞋子的成功率为98/100。代码可在 https://art-vs.github.io/ 获取。
cs.RO / 34 / 2606.19091
GCNGrasp-VP: Affordance-Guided View Planning for Efficient Task-Oriented Grasping
GCNGrasp-VP：基于可用性指导的高效任务导向抓取视图规划
Abstract
Task-oriented grasping performance degrades significantly when object views suffer from occlusions. Existing task-oriented grasping methods typically assume task-relevant regions are visible in the initial frame, while view planning approaches enable active perception but often ignore task semantics and rely on time-consuming scene reconstruction. To address these limitations, we present GCNGrasp-VP, an efficient framework integrating affordance field prediction with active view planning. Central to this framework is GCNGrasp-v2, a task-oriented grasp model that simultaneously supports grasp evaluation and affordance field prediction, achieving constant-time inference complexity. Leveraging this capability, our Affordance-guided View Planner (Affordance-VP) utilizes the affordance field as an information gain metric to guide camera observation of task-relevant regions without requiring scene reconstruction. View planning results show that our method significantly outperforms scene-uncertainty-driven baselines with only one view adjustment. Real-world validation further confirms substantial improvements in grasp success rates for single-object scenarios while maintaining millisecond-level computational latency. Code and models are available at https://github.com/Instinct323/GCNGrasp-VP.
Chinese Translation
当物体视图受到遮挡时，任务导向抓取的性能显著下降。现有的任务导向抓取方法通常假设任务相关区域在初始帧中是可见的，而视图规划方法虽然能够实现主动感知，但往往忽视任务语义，并依赖耗时的场景重建。为了解决这些局限性，我们提出了GCNGrasp-VP，一个将可用性场预测与主动视图规划相结合的高效框架。该框架的核心是GCNGrasp-v2，一个任务导向的抓取模型，能够同时支持抓取评估和可用性场预测，实现常数时间的推理复杂度。利用这一能力，我们的可用性指导视图规划器（Affordance-VP）将可用性场作为信息增益指标，指导相机观察任务相关区域，而无需进行场景重建。视图规划结果表明，我们的方法在仅进行一次视图调整的情况下，显著优于基于场景不确定性的基线方法。现实世界的验证进一步确认了在单物体场景中抓取成功率的显著提升，同时保持毫秒级的计算延迟。代码和模型可在 https://github.com/Instinct323/GCNGrasp-VP 获取。
cs.RO / 35 / 2606.19122
Monocular 3D Occupancy Perception for Robots on Sidewalks via Hybrid 2D-3D Learning
通过混合2D-3D学习实现机器人在人行道上的单目3D占用感知
Abstract
Sidewalks in the real world are crowded, cluttered, and less structured than roads, making 3D occupancy prediction a key ingredient for the safe navigation of mobile robots such as delivery bots and electric wheelchairs. Existing occupancy learning pipelines are largely designed for on-road autonomous driving and often train on large-scale paired LiDAR-RGB datasets with dense 3D supervision and multiple camera inputs, which are costly to collect and do not adequately capture sidewalk-specific characteristics. We propose WalkOCC, a hybrid Ray-marching monocular 3D occupancy perception framework for robots operating on sidewalks. WalkOCC explicitly couples geometric grounding from LiDAR-RGB paired data with scalable learning from large-scale unpaired monocular images. It bootstraps pseudo occupancy supervision from paired sequences and jointly learns image-level representations on additional 2D-only data. It yields stable optimization and improved generalization without requiring costly 3D occupancy annotations. Extensive experiments demonstrate consistent gains in prediction accuracy, fine-grained segmentation of subtle urban structures such as curbs and gutters, and robustness to environmental and cross-embodiment shifts compared with self-supervised image-based baselines. To facilitate evaluation and benchmarking, we also introduce Sidewalk3D, a large-scale sidewalk perception dataset with LiDAR-camera paired sequences collected across multiple locations and time periods, along with 3D semantic occupancy annotations for evaluation. Code and data will be made available.
Chinese Translation
现实世界的人行道拥挤、杂乱且结构不如道路明确，使得3D占用预测成为移动机器人（如送货机器人和电动轮椅）安全导航的关键要素。现有的占用学习流程主要针对道路上的自动驾驶设计，通常在大规模配对的LiDAR-RGB数据集上进行训练，这些数据集具有密集的3D监督和多摄像头输入，收集成本高且未能充分捕捉人行道特有的特征。我们提出了WalkOCC，一个混合光线行进的单目3D占用感知框架，旨在为在人行道上运行的机器人提供支持。WalkOCC明确将来自LiDAR-RGB配对数据的几何基础与来自大规模非配对单目图像的可扩展学习相结合。它从配对序列中引导伪占用监督，并在额外的仅2D数据上共同学习图像级表示。该方法实现了稳定的优化和改进的泛化能力，无需昂贵的3D占用标注。大量实验表明，与自监督的基于图像的基线相比，该方法在预测准确性、对城市细微结构（如路缘和排水沟）的精细分割以及对环境和跨体态变化的鲁棒性方面均有一致的提升。为了便于评估和基准测试，我们还引入了Sidewalk3D，一个大规模的人行道感知数据集，包含在多个地点和时间段收集的LiDAR-摄像头配对序列，以及用于评估的3D语义占用标注。代码和数据将会公开。
cs.RO / 36 / 2606.19154
Viking Hill Dataset: A Lidar-Radar-Camera Dataset for Detection and Segmentation in Forest Scenes
维京山数据集：用于森林场景检测和分割的激光雷达-雷达-相机数据集
Abstract
Autonomous robots operating under forest canopies need robust perception of trees and surrounding vegetation across varying seasonal conditions. Existing forestry datasets provide lidar or camera data with per-tree annotations, but none include co-registered 4D imaging radar -- a modality of growing interest for its resilience to visual degradation, surface contamination, and vegetation occlusion. We introduce a multi-sensor forest dataset collected by a mobile robot equipped with a high-resolution FMCW imaging radar, lidar, RGB camera, IMU, and RTK-GNSS. The site was recorded in two sessions under contrasting vegetation states, and 3D cuboid annotations -- including per-tree diameter estimates -- provide shared semantic labels across all three perception modalities. Furthermore, we provide baseline results for semantic segmentation of the radar and lidar point clouds using MinkowskiUNet. Radar achieves IoU scores competitive with lidar for dominant classes (ground 91%, canopy 86%) while lagging on geometrically fine structures such as tree trunks (56% vs. 74%). A cross-modality analysis further compares lidar and radar trunk segmentation against an RGB detection model, and a diameter-stratified evaluation reveals how trunk segmentation quality varies with tree size. Beyond segmentation, the co-registered multi-modal data and RTK-GNSS-aided reference positioning support research in mapping, localization, and sensor fusion under canopy. The dataset and annotation tools are publicly available.
Chinese Translation
在森林树冠下运行的自主机器人需要在不同季节条件下对树木和周围植被进行稳健的感知。现有的林业数据集提供了带有逐树注释的激光雷达或相机数据，但没有一个数据集包含共同注册的4D成像雷达——这一模态因其对视觉退化、表面污染和植被遮挡的抗干扰能力而日益受到关注。我们介绍了一个由移动机器人收集的多传感器森林数据集，该机器人配备了高分辨率的FMCW成像雷达、激光雷达、RGB相机、惯性测量单元（IMU）和实时动态卫星导航系统（RTK-GNSS）。该数据集在两次不同的植被状态下录制，并且提供了3D立方体注释——包括逐树直径估计——为所有三种感知模态提供了共享的语义标签。此外，我们提供了使用MinkowskiUNet对雷达和激光雷达点云进行语义分割的基线结果。雷达在主要类别（地面91%，树冠86%）的IoU得分上与激光雷达相当，但在几何细节结构如树干（56%对比74%）上表现较差。跨模态分析进一步比较了激光雷达和雷达树干分割与RGB检测模型的表现，而直径分层评估揭示了树干分割质量如何随树木大小而变化。除了分割，联合注册的多模态数据和RTK-GNSS辅助的参考定位支持了在树冠下的映射、定位和传感器融合研究。该数据集和注释工具已公开提供。
cs.RO / 37 / 2606.19161
HT-Bench: Benchmarking and Learning Dexterous Full-Hand Tactile Representations with Egocentric Vision
HT-Bench：基于自我中心视觉的灵巧全手触觉表示的基准测试与学习
Abstract
Establishing a universal benchmark for tactile representation learning in robotic manipulation remains challenging due to the diversity of tactile sensor designs, data formats, and robot embodiments. Rather than seeking to establish such, we explore a scalable and promising direction for future development: egocentric vision paired with full-hand tactile data. To this end, we introduce \textbf{HT-Bench}, a large-scale multi-task benchmark for dexterous full-hand tactile sensing, comprising 10M RGB frames and 7.8M tactile frames collected across 226 tasks. HT-Bench evaluates tactile representations from three key perspectives: whether they encode meaningful contact geometry, whether they can align tactile observations with visual information, and whether they generalize to unseen tasks. To assess these capabilities, HT-Bench includes four tasks: fine-grained tactile similarity retrieval, masked tactile inpainting, vision-to-tactile synthesis, and multimodal tactile frame prediction. We further propose \textbf{HandTouch}, a vector-quantized vision--tactile encoder that learns tactile representations through progressive spatial, cross-modal, and temporal training. Across HT-Bench, HandTouch consistently outperforms representative tactile encoder baselines, improving Recall@5 on fine-grained tactile similarity retrieval from 74.65\% to 85.23\%, reducing RMSE on masked tactile inpainting from 0.022 to 0.010, and increasing OOD cIoU on vision-to-tactile synthesis from 0.628 to 0.705. These results demonstrate the effectiveness of HandTouch and suggest that large-scale egocentric full-hand tactile data provides a scalable basis for evaluating and advancing tactile representation learning in dexterous manipulation.
Chinese Translation
由于触觉传感器设计、数据格式和机器人形态的多样性，为机器人操作中的触觉表示学习建立一个通用基准仍然具有挑战性。我们并不寻求建立这样的基准，而是探索一个可扩展且有前景的未来发展方向：将自我中心视觉与全手触觉数据相结合。为此，我们介绍了 extbf{HT-Bench}，这是一个大规模的多任务基准，用于灵巧全手触觉感知，包含了在226个任务中收集的1000万帧RGB图像和780万帧触觉图像。HT-Bench从三个关键角度评估触觉表示：它们是否编码了有意义的接触几何形状，是否能够将触觉观察与视觉信息对齐，以及它们是否能够推广到未见过的任务。为了评估这些能力，HT-Bench包括四个任务：细粒度触觉相似性检索、掩蔽触觉修复、视觉到触觉合成和多模态触觉帧预测。我们进一步提出了 extbf{HandTouch}，一种向量量化的视觉-触觉编码器，通过渐进的空间、跨模态和时间训练来学习触觉表示。在HT-Bench中，HandTouch始终优于代表性的触觉编码器基线，将细粒度触觉相似性检索的Recall@5从74.65\%提高到85.23\%，将掩蔽触觉修复的均方根误差（RMSE）从0.022降低到0.010，并将视觉到触觉合成的OOD cIoU从0.628提高到0.705。这些结果证明了HandTouch的有效性，并表明大规模的自我中心全手触觉数据为评估和推动灵巧操作中的触觉表示学习提供了可扩展的基础。
cs.RO / 38 / 2606.19176
Hardware- and Vision-in-the-Loop Validation of Deep Monocular Pose Estimation for Autonomous Maritime UAV Flight
自主海洋无人机飞行的深度单目姿态估计的硬件与视觉闭环验证
Abstract
Autonomous UAV operations on ships require reliable vision-based relative pose estimation, yet at-sea validation is costly, weather-dependent, and risky. This paper presents a hardware-validated vision-in-the-loop framework that enables fully autonomous indoor flight while emulating photorealistic maritime environments. Rendered maritime views are processed onboard by a deep transformer-based monocular pose estimator. Delayed vision measurements are fused with high-rate IMU data using a delayed Kalman filter to provide consistent state estimates for geometric control. The system captures critical embedded effects, including perception latency, asynchronous updates, and computational constraints, that are absent in pure simulation. Autonomous takeoff, trajectory tracking, and landing experiments demonstrate stable closed-loop flight. The results establish a safe and hardware-realistic intermediate stage for developing maritime UAV autonomy prior to shipboard deployment.
Chinese Translation
自主无人机在船上的操作需要可靠的基于视觉的相对姿态估计，但海上验证成本高、受天气影响且风险较大。本文提出了一种经过硬件验证的视觉闭环框架，该框架能够在模拟逼真的海洋环境下实现完全自主的室内飞行。渲染的海洋视图由基于深度变换器的单目姿态估计器在机载处理。延迟的视觉测量与高频率的惯性测量单元（IMU）数据通过延迟卡尔曼滤波器融合，以提供一致的状态估计用于几何控制。该系统捕捉了关键的嵌入效应，包括感知延迟、异步更新和计算约束，这些在纯模拟中是不存在的。自主起飞、轨迹跟踪和着陆实验展示了稳定的闭环飞行。结果确立了一个安全且硬件真实的中间阶段，为在船上部署之前开发海洋无人机的自主性奠定基础。
cs.RO / 39 / 2606.19186
Learning to Annotate Delayed and False AEB Events: A Practical System for Extreme Class Imbalance and Asymmetric Label Noise
学习注释延迟和错误的自动紧急制动事件：应对极端类别不平衡和不对称标签噪声的实用系统
Abstract
Autonomous Emergency Braking (AEB) optimization relies on accurately annotated real-world trigger events, particularly rare but critical delayed and false AEB triggers that expose system deficiencies. However, these minority samples comprise less than 5% of thousands of daily triggers, making manual annotation prohibitively expensive at scale. We present the first automated AEB annotation framework to address this problem. During development, we identified two fundamental challenges that severely impair delayed/false trigger annotation accuracy: (1) Extreme class imbalance where delayed/false triggers are overwhelmed by true triggers; (2) Asymmetric label noise where mislabeled majority samples (true triggers) suppress minority samples (delayed/false triggers) learning. To overcome these challenges, we propose two key innovations: (1) Specific data augmentation that synthesizes realistic samples by manipulating focal target attributes, transplanting ego-vehicle dynamics, and masking non-focal agents; (2) noise suppression using stable hardness estimation and probe-guided adaptive threshold to clean mislabeled true trigger samples. Crucially, we deploy our model as a practical annotation system with full-stack architecture, efficiently identifying critical delayed/false triggers from thousands of daily AEB events. Production results demonstrate 80% improvement in recall of delayed/false triggers and 50% reduction in manual workload. Beyond immediate gains, the system enables continuous self-improvement through accumulated high-quality annotations, establishing a necessary data foundation for on-vehicle AEB system optimization
Chinese Translation
自动紧急制动（AEB）优化依赖于准确注释的真实触发事件，特别是那些稀有但关键的延迟和错误AEB触发，这些触发暴露了系统的缺陷。然而，这些少数样本在每天数千个触发事件中占比不足5%，使得大规模手动注释成本高昂。我们提出了第一个自动化AEB注释框架来解决这一问题。在开发过程中，我们识别出两个严重影响延迟/错误触发注释准确性的基本挑战：（1）极端类别不平衡，延迟/错误触发被真实触发淹没；（2）不对称标签噪声，错误标记的多数样本（真实触发）抑制了少数样本（延迟/错误触发）的学习。为克服这些挑战，我们提出了两个关键创新：（1）特定数据增强，通过操控焦点目标属性、移植自车动态和遮蔽非焦点代理来合成真实样本；（2）使用稳定的困难估计和探针引导的自适应阈值进行噪声抑制，以清理错误标记的真实触发样本。至关重要的是，我们将模型部署为一个具有全栈架构的实用注释系统，能够高效识别每天数千个AEB事件中的关键延迟/错误触发。生产结果表明，延迟/错误触发的召回率提高了80%，手动工作量减少了50%。除了立即收益外，该系统通过积累高质量注释实现持续自我改进，为车载AEB系统优化建立了必要的数据基础。
cs.RO / 40 / 2606.19190
FAST-LIVGO: A Degeneracy-Robust LiDAR-Inertial-Visual-GNSS Fusion Odometry
FAST-LIVGO：一种抗退化的激光雷达-惯性-视觉-GNSS融合里程计
Abstract
Robust state estimation and mapping in long-term, large-scale, and highly dynamic environments remains a key challenge in robotics. Existing LiDAR-Inertial-Visual Odometry (LIVO) systems achieve strong local accuracy but suffer from accumulated drift over long distances and may fail in geometrically degraded or textureless scenes. Meanwhile, GNSS-aided fusion frameworks often rely on LiDAR or visual odometry for state prediction and outlier rejection, making them vulnerable when odometry degenerates. To address these limitations, we propose a tightly coupled LiDAR-Inertial-Visual-GNSS fusion framework based on an Error-State Iterated Kalman Filter. An online spatiotemporal alignment module using Dynamic Time Warping is introduced for highly dynamic conditions. To better exploit GNSS precision, we develop observation models based on Doppler shifts and fixed-anchor Time-Differenced Carrier Phase, providing millimeter-level relative constraints without augmenting historical anchor states. We further design a degeneracy-aware dual-mode outlier rejection strategy that switches between LIVO-prior-guided rejection and GNSS-aided recovery according to the LIVO degeneracy level. Experiments on the public M3DGR dataset and a custom 20~m/s fixed-wing UAV dataset demonstrate that our system reduces accumulated drift and map ghosting, outperforming state-of-the-art methods in accuracy and robustness.
Chinese Translation
在长期、大规模和高度动态的环境中，稳健的状态估计和地图构建仍然是机器人技术中的一个关键挑战。现有的激光雷达-惯性-视觉里程计（LIVO）系统在局部精度上表现良好，但在长距离行驶中会出现累积漂移，并且在几何退化或无纹理场景中可能会失效。同时，基于GNSS的融合框架通常依赖于激光雷达或视觉里程计进行状态预测和异常值剔除，因此在里程计退化时容易受到影响。为了解决这些局限性，我们提出了一种基于误差状态迭代卡尔曼滤波器的紧耦合激光雷达-惯性-视觉-GNSS融合框架。我们引入了一个在线时空对齐模块，使用动态时间规整（Dynamic Time Warping）来应对高度动态的条件。为了更好地利用GNSS的精度，我们开发了基于多普勒频移和固定锚点时间差载波相位的观测模型，提供毫米级的相对约束，而无需增强历史锚点状态。我们进一步设计了一种考虑退化的双模式异常值剔除策略，根据LIVO的退化水平在LIVO优先引导的剔除和GNSS辅助恢复之间切换。在公共M3DGR数据集和自定义的20 m/s固定翼无人机数据集上的实验表明，我们的系统减少了累积漂移和地图重影，在精度和鲁棒性方面超越了现有的最先进方法。
cs.RO / 41 / 2606.19194
Invertible Neural Network Adapter for One-Step Flow Matching in Robot Manipulation
可逆神经网络适配器用于机器人操作中的一步流匹配
Abstract
This paper presents an invertible neural network adapter for general robotic manipulation, designed to generate precise high-dimensional actions conditioned on multimodal observations, including visual, linguistic, and proprioceptive inputs, through a one-step denoising process. Built upon a flow-matching formulation, the proposed adapter effectively constrains the action generation trajectory within an invertible latent space, thereby enabling efficient and high-quality dexterous action synthesis with only a single inference step. Compared with conventional iterative flow-matching policies, the proposed framework substantially reduces inference complexity while maintaining strong action prediction accuracy and stability. Extensive experiments are conducted across a diverse set of simulation benchmarks and real-world robotic platforms to evaluate the effectiveness of the proposed method. Across simulation benchmarks, the proposed adapter consistently demonstrates superior or near state-of-the-art performance on a wide range of manipulation tasks. Furthermore, real-world experiments reveal a significant improvement in inference efficiency for vision-language-action (VLA) models, reducing the average inference latency from 110 ms to 61 ms while maintaining strong task performance.
Chinese Translation
本文提出了一种可逆神经网络适配器，旨在为一般机器人操作生成基于多模态观测（包括视觉、语言和本体感知输入）的精确高维动作，通过一步去噪过程实现。该适配器基于流匹配公式，有效地将动作生成轨迹限制在可逆潜在空间内，从而仅通过一次推理步骤实现高效且高质量的灵巧动作合成。与传统的迭代流匹配策略相比，所提出的框架显著降低了推理复杂度，同时保持了强大的动作预测准确性和稳定性。我们在多样的仿真基准和真实机器人平台上进行了广泛的实验，以评估所提方法的有效性。在仿真基准中，所提出的适配器在广泛的操作任务上始终展示出优越或接近最先进的性能。此外，真实世界的实验显示，视觉-语言-动作（VLA）模型的推理效率显著提高，平均推理延迟从110毫秒降低到61毫秒，同时保持了强大的任务表现。
cs.RO / 42 / 2606.19227
Constant Time-Delay Leader Following with Neural Networks and Invariant Extended Kalman Filters for Arbitrary Trajectories
基于神经网络和不变扩展卡尔曼滤波器的常时延领航跟随方法用于任意轨迹
Abstract
This paper proposes a constant time-delay trajectory tracking method for vehicle convoys operating without inter-vehicle communication, a common coordinate system, or global positioning. The method integrates a probabilistic sequence-to-sequence (Seq2Seq) neural network with an invariant extended Kalman filter (IEKF) to warm-start the prediction process, allowing accurate estimation of a leader vehicle's relative trajectory on the SE(2) manifold. A geometric model predictive controller is further incorporated to fully exploit the manifold-based trajectory predictions for improved control performance. The system can handle arbitrary nonlinear trajectories with varying speeds and motion profiles while reducing the need for expert-based domain knowledge for the design of trajectory following systems, even under long trajectory delays. The effectiveness of the method is validated through comparisons with a pure IEKF baseline, learning-based methods, and the ground-truth trajectory in kinematic simulations, as well as in experiments using real robotic vehicles.
Chinese Translation
本文提出了一种常时延轨迹跟踪方法，适用于在没有车间通信、共同坐标系统或全球定位的情况下运行的车辆车队。该方法将概率序列到序列（Seq2Seq）神经网络与不变扩展卡尔曼滤波器（IEKF）相结合，以热启动预测过程，从而准确估计领航车辆在SE(2)流形上的相对轨迹。此外，进一步引入几何模型预测控制器，以充分利用基于流形的轨迹预测，从而提高控制性能。该系统能够处理具有不同速度和运动特征的任意非线性轨迹，同时减少对专家知识的依赖，以设计轨迹跟随系统，即使在长时间轨迹延迟的情况下也能有效运行。通过与纯IEKF基线、基于学习的方法以及运动学仿真中的真实轨迹进行比较，验证了该方法的有效性，同时也在使用真实机器人车辆的实验中进行了验证。
cs.RO / 43 / 2606.19233
Mobile Pedipulation for Object Sliding via Hierarchical Control on a Wheeled Bipedal Robot
基于分层控制的轮式双足机器人物体滑动移动操控
Abstract
In this letter, we present a hierarchical control framework that enables wheeled bipedal robots to perform planar object sliding tasks with their wheeled legs. The proposed approach formulates a nonlinear model predictive controller (NMPC) based on a reduced-order three rigid bodies (TRB) dynamical model that explicitly accounts for the hip roll degree of freedom and multiple wheel-environment contact modes, which is essential for lateral stepping and pedipulation tasks. Within this framework, the NMPC simultaneously regulates robot locomotion and interaction forces, allowing the robot to stably execute both rolling and object manipulation behaviors. A trajectory-optimization-based robot-object motion planner is developed to generate reference motions that incorporate stick-slip transitions in ground-object contact. Two representative pedipulation motions, namely scooting and lateral sliding, are validated through real-world hardware experiments, in which the robot successfully retrieves a 1 kg object from under a desk and slides a 4 kg object over a distance of 0.228 m via scooting.
Chinese Translation
在本文中，我们提出了一种分层控制框架，使轮式双足机器人能够利用其轮式腿执行平面物体滑动任务。所提出的方法基于一种简化的三刚体（TRB）动力学模型，构建了一个非线性模型预测控制器（NMPC），该模型明确考虑了髋关节滚转自由度和多种轮-环境接触模式，这对于侧向步态和移动操控任务至关重要。在此框架内，NMPC同时调节机器人的运动和交互力，使机器人能够稳定地执行滚动和物体操控行为。我们开发了一种基于轨迹优化的机器人-物体运动规划器，以生成参考运动，结合了地面-物体接触中的粘滑过渡。通过真实世界的硬件实验验证了两种典型的移动操控动作，即滑动和侧向滑动，其中机器人成功地从桌子下方取回一个1千克的物体，并通过滑动将一个4千克的物体移动了0.228米。
cs.RO / 44 / 2606.19240
Seeing Through Occlusion: Deterministic Arm Kinematic Correction for Robot Teleoperation
透视遮挡：机器人遥操作的确定性臂运动学校正
Abstract
Markerless, single-RGB-D-camera motion capture provides a low-cost and non-invasive alternative to conventional marker-based systems for robot teleoperation; however, depth estimation often degrades in the presence of self-occlusion, particularly during upper-limb motion. This paper presents an Arm Kinematic Correction (AKC) method that improves depth estimation by enforcing geometric constraints based on constant arm lengths. The proposed approach reconstructs occluded joint depths by leveraging wrist positions and predefined arm lengths via a deterministic formulation based on the Pythagorean theorem, thereby avoiding the need for complex probabilistic modeling or parameter tuning. Experimental validation against a Vicon reference system demonstrates reliable performance for both static and dynamic joint motions, evaluated using root-mean-square error (RMSE) and Pearson correlation. Furthermore, motion-mapping teleoperation is successfully demonstrated in both simulated and physical robot environments. The results show that AKC enhances robustness and preserves anatomical consistency under long-duration, severe self-occlusion, even when paired with less reliable temporal filters, highlighting its practicality for real-time applications such as robot teleoperation and human-robot interaction.
Chinese Translation
无标记的单RGB-D摄像头运动捕捉为机器人遥操作提供了一种低成本且非侵入性的替代方案，取代了传统的基于标记的系统；然而，在自遮挡的情况下，深度估计往往会下降，尤其是在上肢运动期间。本文提出了一种臂运动学校正（Arm Kinematic Correction, AKC）方法，通过基于恒定臂长的几何约束来改善深度估计。所提出的方法利用手腕位置和预定义的臂长，通过基于毕达哥拉斯定理的确定性公式重建被遮挡的关节深度，从而避免了复杂的概率建模或参数调优的需求。与Vicon参考系统的实验验证表明，该方法在静态和动态关节运动中均表现出可靠的性能，评估指标包括均方根误差（RMSE）和皮尔逊相关系数。此外，在模拟和实际机器人环境中成功演示了运动映射遥操作。结果表明，AKC在长时间、严重自遮挡的情况下增强了鲁棒性并保持了解剖一致性，即使与不太可靠的时间滤波器配合使用，也突显了其在实时应用（如机器人遥操作和人机交互）中的实用性。
cs.RO / 45 / 2606.19265
Shape Sensing of Continuum Robots using Direct Laser Writing
使用直接激光写入的连续机器人形状传感
Abstract
Continuum robots offer a promising approach for minimally invasive and natural-orifice surgical procedures due to their inherent compliance and dexterity. However, this flexibility also makes estimating the current shape of the robot challenging. Several approaches have been used to reconstruct the shape of these robots, including imaging, optical sensing, magnetic sensing, and resistive sensing. Strain sensors fabricated using direct laser writing (DLW) could provide an alternative sensing method. This technique involves using a laser to induce carbonization of certain polymers to create graphene patterns, such as strain sensors. In this paper, we demonstrate how a flexible continuum joint and a DLW sensor can be machined as one monolithic structure using the same laser and the same setup. The fabricated sensors are characterized using linear and nonlinear models, which are used to predict the joint angle with error as low as 1.76 degrees. Furthermore, we demonstrate how a DLW sensor can be used to implement closed-loop control in a robotic joint, achieving tracking error under 3 degrees.
Chinese Translation
连续机器人因其固有的柔韧性和灵活性，为微创和自然腔道外科手术提供了有前景的方法。然而，这种灵活性也使得估计机器人当前形状变得具有挑战性。已有多种方法用于重建这些机器人的形状，包括成像、光学传感、磁性传感和电阻传感。使用直接激光写入（DLW）制造的应变传感器可能提供了一种替代的传感方法。该技术涉及使用激光诱导某些聚合物的碳化，以创建石墨烯图案，例如应变传感器。在本文中，我们展示了如何将柔性连续关节和DLW传感器作为一个整体结构进行加工，使用相同的激光和相同的设置。制造的传感器通过线性和非线性模型进行表征，这些模型用于预测关节角度，误差低至1.76度。此外，我们展示了如何使用DLW传感器在机器人关节中实现闭环控制，跟踪误差低于3度。
cs.RO / 46 / 2606.19267
A Mixed-Reality Testbed for Autonomous Vehicles
一种用于自主车辆的混合现实测试平台
Abstract
We propose a mixed-reality, hardware-in-the-loop (HIL) testbed for autonomous vehicles that seamlessly integrates a physical testbed of mobile robots with a high-fidelity simulation environment. The virtual simulation enables the creation of diverse, safety-critical driving scenarios to validate state-of-the-art perception, planning, and control algorithms, while augmenting simulations with physical robots equipped with multimodal sensors in photorealistic virtual environments further facilitating rigorous validation. Our testbed also features vehicular connectivity using wireless communication and can accommodate a large number of agents through the combination of physical robots and virtual simulated agents, supporting research on multi-agent systems including Connected and Autonomous Vehicles (CAVs). Finally, we present a safety-guaranteed framework combining perception, planning and a novel online learning-based controller using Control Barrier Functions (CBFs) for CAVs. Experiments using the proposed framework are used to validate and demonstrate the key functionalities and the overall utility of the testbed to bridge the gap between simulation and real-world hardware deployment.
Chinese Translation
我们提出了一种混合现实的硬件在环（HIL）测试平台，用于自主车辆，该平台无缝集成了移动机器人的物理测试平台与高保真模拟环境。虚拟模拟能够创建多样化的、具有安全关键性的驾驶场景，以验证最先进的感知、规划和控制算法，同时通过配备多模态传感器的物理机器人在逼真的虚拟环境中增强模拟，进一步促进严格的验证。我们的测试平台还具有通过无线通信实现的车辆连接功能，并能够通过物理机器人和虚拟模拟代理的结合容纳大量代理，支持包括联网和自主车辆（CAVs）在内的多代理系统研究。最后，我们提出了一种安全保障框架，结合感知、规划和一种基于在线学习的新型控制器，使用控制屏障函数（CBFs）为CAVs提供支持。使用所提出的框架进行的实验用于验证和展示测试平台的关键功能及其在模拟与现实硬件部署之间架起桥梁的整体效用。
cs.RO / 47 / 2606.19307
Observability and Consistency Analysis for Visual-Inertial Navigation with Anchored Feature Parameterizations
基于锚定特征参数化的视觉惯性导航的可观测性与一致性分析
Abstract
This paper presents an analysis of the observability and consistency properties of filtering-based visual-inertial navigation systems (VINS) that utilize anchored feature representations. The unobservable subspace of VINS with anchored landmark parameterizations is shown to be independent of the estimated landmark state, which leads to improved estimator consistency properties without any additional modifications. However, the unobservable subspace is still found to depend on the estimated navigation state, necessitating additional consistency-enforcing techniques. Two methods to improve the consistency of VINS with anchored feature representations are presented. Simulation results showcase that all estimators employing anchored feature paramterizations exhibit improved consistency properties compared to algorithms that estimate features resolved in a global reference frame, especially in scenarios where feature initialization may be poor. Real-world experiments on the TUM-VI dataset showcase that the use of anchored feature representations alone can yield comparable performance to consistency-improved estimators employing a global feature representation, demonstrating the benefit of using anchored feature parameterizations for VINS.
Chinese Translation
本文对利用锚定特征表示的基于滤波的视觉惯性导航系统（VINS）的可观测性和一致性特性进行了分析。研究表明，采用锚定地标参数化的VINS的不可观测子空间与估计的地标状态无关，这在没有任何额外修改的情况下改善了估计器的一致性特性。然而，仍然发现不可观测子空间依赖于估计的导航状态，因此需要额外的一致性强化技术。本文提出了两种方法以改善采用锚定特征表示的VINS的一致性。仿真结果表明，所有采用锚定特征参数化的估计器在一致性特性上均优于在全局参考框架中估计特征的算法，尤其是在特征初始化可能较差的场景中。在TUM-VI数据集上的实际实验表明，仅使用锚定特征表示就能实现与采用全局特征表示的一致性改进估计器相当的性能，展示了在VINS中使用锚定特征参数化的优势。
cs.RO / 48 / 2606.19314
Modeling Branches for Active Manipulation using Iterative Parameter Estimation
通过迭代参数估计对主动操控进行分支建模
Abstract
This study presents a method for modeling diverse plant branches by iteratively estimating material parameters to support delicate branch manipulation. Branch manipulation is necessary in agricultural robotics for plant repositioning, stabilizing, and clearing visual obstructions in dense foliage. The proposed method builds a tetrahedral branch model from point-cloud data and simulates its behavior using the finite element method. Using real observed deformation data, it iteratively estimates branch parameters and then computes an optimal path with a deformation-aware motion planner to move and stabilize branches within another robot's field of view. Across 30 trials on branches with varying geometries and material properties, the proposed method reduced the deformation energy by 35.69% while increasing the path length by 8.10% on average.
Chinese Translation
本研究提出了一种通过迭代估计材料参数来建模多样化植物分支的方法，以支持精细的分支操控。分支操控在农业机器人中是必要的，用于植物重新定位、稳定以及清除密集树叶中的视觉障碍。所提出的方法基于点云数据构建四面体分支模型，并使用有限元方法模拟其行为。通过使用真实观察到的变形数据，迭代估计分支参数，然后利用考虑变形的运动规划器计算最佳路径，以在另一个机器人的视野内移动和稳定分支。在对具有不同几何形状和材料特性的分支进行的30次试验中，所提出的方法将变形能量减少了35.69%，同时平均增加了8.10%的路径长度。
cs.RO / 49 / 2606.19333
Do as I Do: Dexterous Manipulation Data from Everyday Human Videos
照我所做的：来自日常人类视频的灵巧操作数据
Abstract
How can we scalably generate data for robotic manipulation, especially on human-like platforms such as dexterous multi-fingered hands? Learning from human videos has recently emerged as a likely answer to this question. However, difficulties in estimating hand-object interaction and crossing the human-to-robot embodiment gap have hindered the adoption of abundant monocular RGB-only human videos as the primary source of robot manipulation data. In this work, we present DO AS I DO, an algorithm to reconstruct and retarget monocular RGB human videos to multi-fingered dexterous robotic hands. DO AS I DO reconstructs hand-object interactions from various egocentric and exocentric in-the-wild video sources. The algorithm then retargets these hand-object interaction estimates into a sequence of actions executable in the real world, yielding robot-complete manipulation data from disparate human videos. Overall, DO AS I DO outperforms previous state of the art in estimating hand-object interactions and extracting dexterous manipulation trajectories from RGB videos, as we show in experiments on datasets with ground truths and on a dataset of video clips collected online. Our experiments enable us to propose an efficacy playbook for practitioners collecting human data for manipulation.
Chinese Translation
我们如何能够大规模生成用于机器人操作的数据，特别是在像灵巧多指手这样的类人平台上？从人类视频中学习最近被认为是这个问题的一个可能答案。然而，估计手与物体之间的交互以及跨越人类与机器人之间的体现差距的困难，阻碍了丰富的单目RGB人类视频作为机器人操作数据主要来源的采用。在本研究中，我们提出了DO AS I DO，这是一种将单目RGB人类视频重建并重新定向到多指灵巧机器人手的算法。DO AS I DO从各种自我中心和外部中心的野外视频源中重建手与物体的交互。该算法随后将这些手与物体的交互估计重新定向为可在现实世界中执行的一系列动作，从而从不同的人类视频中生成完整的机器人操作数据。总体而言，DO AS I DO在估计手与物体的交互和从RGB视频中提取灵巧操作轨迹方面优于之前的最先进技术，正如我们在具有真实值的数据集和在线收集的视频片段数据集的实验中所展示的那样。我们的实验使我们能够为收集人类操作数据的从业者提出一个有效性手册。
cs.RO / 50 / 2606.19340
Zero-Shot Long-Horizon Dexterous Manipulation via Multi-View 3D-Grounded VLM Reasoning
通过多视角3D基础的VLM推理实现零样本长时间跨度灵巧操作
Abstract
We present a zero-shot framework for long-horizon dexterous manipulation that grounds language instructions into executable 3D task plans from calibrated multi-view RGB images. Rather than training an end-to-end policy, our system uses a vision-language model (VLM) to produce reference-frame task grounding and primitive-level 2D keypoints, then lifts them into 3D via multi-view fusion. This lifting combines triangulation of view-wise VLM groundings with reference-view ray voting, which searches along a semantic camera ray for geometrically consistent candidates across neighboring views. The resulting 3D keypoints support both pick-and-place and tool-use: for tool-use, we retrieve an object-centric atomic action corresponding to the inferred skill category and align its stored 6D tool trajectory to the scene; for dexterous execution, we expand the lifted grasp keypoint into a task-conditioned grasp affordance region and generate feasible grasp-motion pairs with an arm-hand motion generator. Real-world experiments show improved 3D grounding accuracy and execution reliability over single-view RGB-D grounding and fine-tuned VLA baselines. We further demonstrate long-horizon manipulation through closed-loop status verification and replan, enabling zero-shot execution on unseen objects and tool-use tasks in novel scenes.
Chinese Translation
我们提出了一种零样本框架，用于长时间跨度的灵巧操作，该框架将语言指令转化为可执行的3D任务计划，基于经过校准的多视角RGB图像。我们的系统并不是训练一个端到端的策略，而是使用视觉-语言模型（VLM）生成参考框架任务基础和原始级别的2D关键点，然后通过多视角融合将其提升到3D。这种提升结合了视角VLM基础的三角测量与参考视角光线投票，沿着语义相机光线搜索几何一致的候选项。生成的3D关键点支持拾取与放置以及工具使用：对于工具使用，我们检索与推断的技能类别对应的以物体为中心的原子动作，并将其存储的6D工具轨迹与场景对齐；对于灵巧执行，我们将提升的抓取关键点扩展为任务条件的抓取可行区域，并使用臂-手运动生成器生成可行的抓取-运动对。实际实验表明，与单视角RGB-D基础和微调的VLA基线相比，我们的方法在3D基础准确性和执行可靠性上有所提高。我们进一步通过闭环状态验证和重新规划展示了长时间跨度的操作，能够在新场景中对未见过的物体和工具使用任务进行零样本执行。
cs.CV / 1 / 2606.18318
Budget-Aware Adaptive Adversarial Patches for Black-Box Object Detection
预算感知的自适应对抗补丁用于黑箱目标检测
Abstract
Adversarial patches pose a practical threat to modern object detectors. Prior work shows vulnerability, but three gaps limit actionable insight: (i) few \emph{score-based black-box} attacks \emph{jointly} optimize patch \emph{location, texture, and size} under tight query budgets; (ii) success is rarely tied to the patch's \emph{visual footprint}; and (iii) evaluations often conflate EOT robustness with plain-view suppression. We present \method{}, a query-efficient, budget-adaptive black-box attack that couples a lightweight \emph{Contextual Thompson-Sampling} placer with NES-style pixel updates, growing the patch only when progress stalls. Reporting is anchored by a \emph{strict plain-image} suppression test; EOT is audited but never used as a substitute for success, and optional appearance/printability weights expose strength--visibility trade-offs. Across YOLOv5, Faster R-CNN, and YOLOS, \method{} achieves strong suppression on CNN-based detectors and substantial suppression on the transformer-based detector, using compact patches and exposing clear query--footprint trade-offs relative to fixed-size and heuristic baselines. A print--capture pilot further shows transfer across unseen physical objects and viewpoints.
Chinese Translation
对抗补丁对现代目标检测器构成了实际威胁。先前的研究显示了其脆弱性，但有三个缺口限制了可行的见解：（i）很少有基于得分的黑箱攻击能够在严格的查询预算下联合优化补丁的位置、纹理和大小；（ii）成功通常与补丁的视觉足迹无关；（iii）评估往往将EOT（期望输出转移）鲁棒性与普通视图抑制混为一谈。我们提出了 extit{method}，这是一种查询高效、预算自适应的黑箱攻击，结合了轻量级的 extit{上下文汤普森采样}放置器与NES风格的像素更新，仅在进展停滞时才扩展补丁。报告基于严格的普通图像抑制测试；EOT被审计但从不作为成功的替代，且可选的外观/可打印性权重揭示了强度与可见性之间的权衡。在YOLOv5、Faster R-CNN和YOLOS上， extit{method}在基于CNN的检测器上实现了强抑制，并在基于变换器的检测器上实现了显著抑制，使用紧凑的补丁并揭示了相对于固定大小和启发式基线的查询与足迹之间的明显权衡。一项打印-捕获试点进一步显示了在未见物理对象和视角之间的转移。
cs.CV / 2 / 2606.18429
CAOA -- Completion-Assisted Object-CAD Alignment
CAOA -- 完成辅助的物体-CAD对齐
Abstract
Accurately aligning CAD models to their corresponding objects in indoor RGB-D scans is a central challenge in 3D semantic reconstruction. The task requires estimating a 9-Degree-of-Freedom (DoF) pose-position, rotation, and scale along three axes-but is hindered by noisy and incomplete scans, as well as segmentation errors that cause geometric distortions. We present Completion-Assisted Object-CAD Alignment (CAOA), a method that integrates a semantically and contextually aware point cloud completion module with a symmetry-aware relative pose estimation algorithm, enabling precise alignment of CAD models to scanned objects. Existing completion methods are typically trained and evaluated on synthetic datasets, which often fail to generalize to real-world scans. To bridge this gap, we introduce a synthetic data generation strategy tailored to indoor scenes, significantly reducing the synthetic-to-real domain gap-validated through quantitative comparisons with widely used completion datasets. In addition, we release S2C-Completion, an expert-annotated dataset of over 8,500 object-CAD pairs from Scan2CAD, created for real-world indoor single-object completion and intended as a new benchmark for this task. For object-CAD alignment, we incorporate symmetry information via a symmetry-aware loss, improving robustness to symmetric ambiguities. On the Scan2CAD benchmark, CAOA achieves a 17% accuracy improvement over state-of-the-art methods.
Chinese Translation
在室内RGB-D扫描中，准确地将CAD模型与其对应的物体对齐是3D语义重建中的一个核心挑战。该任务需要估计一个9自由度（DoF）的姿态-位置、旋转和沿三个轴的缩放，但由于噪声和不完整的扫描，以及导致几何失真的分割错误，任务受到阻碍。我们提出了完成辅助的物体-CAD对齐（CAOA）方法，该方法将一个语义和上下文感知的点云完成模块与一个对称感知的相对姿态估计算法相结合，从而实现CAD模型与扫描物体的精确对齐。现有的完成方法通常在合成数据集上进行训练和评估，但往往无法推广到真实世界的扫描中。为了解决这一问题，我们引入了一种针对室内场景的合成数据生成策略，显著缩小了合成与真实领域之间的差距，并通过与广泛使用的完成数据集的定量比较进行了验证。此外，我们发布了S2C-Completion，这是一个包含8500多个来自Scan2CAD的物体-CAD对的专家注释数据集，旨在用于真实世界的室内单物体完成，并作为该任务的新基准。在物体-CAD对齐中，我们通过对称感知损失引入对称信息，提高了对称模糊的鲁棒性。在Scan2CAD基准测试中，CAOA相较于最先进的方法实现了17%的准确性提升。
cs.CV / 3 / 2606.18439
RegimeVGGT: Layer-Wise Spatially Preserving Redundancy Removal for Visual Geometry Grounded Transformer
RegimeVGGT：针对视觉几何基础变换器的逐层空间保留冗余移除
Abstract
Visual Geometry Grounded Transformer (VGGT) recovers dense 3D scene structure from multi-view images in one forward pass, but quadratic cross-frame attention limits its scalability. Existing training-free accelerators reduce computation uniformly along one axis, missing layer heterogeneity. Our spectral, probing, and causal analyses reveal three regimes: shallow layers lack cross-view structure, middle layers drive cross-view alignment, and deep layers are redundant for dense geometry yet their cross-frame attention remains essential for pose. RegimeVGGT applies layer-wise U-shaped compression along two axes: Saliency-Guided Banded Merging protects geometry- and edge-salient tokens, while Selectively Protected K/V Downsampling preserves cross-frame spatial coverage and the pose-critical path through a phase-shifted spatial grid, a reference-frame anchor, and uncompressed camera/register tokens. Training-free, RegimeVGGT achieves a 6.7x speedup over VGGT* at matched reconstruction quality.
Chinese Translation
视觉几何基础变换器（VGGT）能够在一次前向传递中从多视图图像中恢复密集的三维场景结构，但二次交叉帧注意力限制了其可扩展性。现有的无训练加速器沿一个轴均匀减少计算，忽略了层的异质性。我们的谱分析、探测分析和因果分析揭示了三个层次：浅层缺乏跨视图结构，中层驱动跨视图对齐，而深层对于密集几何是冗余的，但其交叉帧注意力对姿态仍然至关重要。RegimeVGGT 在两个轴上应用逐层 U 形压缩：显著性引导的带状合并（Saliency-Guided Banded Merging）保护几何和边缘显著的标记，而选择性保护的 K/V 下采样（Selectively Protected K/V Downsampling）则通过相位偏移的空间网格、参考帧锚点和未压缩的相机/注册标记来保留跨帧空间覆盖和姿态关键路径。无训练的 RegimeVGGT 在匹配重建质量的情况下实现了相较于 VGGT* 的 6.7 倍加速。
cs.CV / 4 / 2606.18441
Reasoning as Intersection: Consensus-Frame Alignment for Visual Focus in Video-MLLMs
推理作为交集：视频多模态大语言模型中的共识框架对齐视觉焦点
Abstract
Reinforcement learning has improved the reasoning ability of large language models, but applying outcome-only rewards to video multimodal large language models (Video-MLLMs) provides limited guidance on which visual evidence should support the answer. Inspired by multisensory integration, where consistent cues can enhance the salience and reliability of perceptual estimates, we introduce Consensus Frame GRPO (CF-GRPO), a temporal-annotation-free process-level reward framework for evidence-aware video reasoning. CF-GRPO constructs a consensus frame prior from intrinsic video cues, including temporal coverage, scene-transition cues, and query-conditioned visual relevance. It then computes a model-side frame-use score from visual and response representations and optimizes their agreement through the Consensus Frame Reward (CFR). With salience-aware sparse aggregation and distribution sharpening, CFR provides a high-contrast reward signal without requiring human temporal annotations. Experiments show that VideoCFR achieves competitive performance across complex video reasoning benchmarks and improves several metrics over representative Video-MLLM and RL baselines, while the consensus prior provides an interpretable view of the evidence frames emphasized during training. The implementation is available at https://github.com/1Pansy/VideoCFR.
Chinese Translation
强化学习提高了大语言模型的推理能力，但将仅基于结果的奖励应用于视频多模态大语言模型（Video-MLLMs）对哪些视觉证据应支持答案提供的指导有限。受到多感官整合的启发，其中一致的线索可以增强感知估计的显著性和可靠性，我们引入了共识框架GRPO（CF-GRPO），这是一种无时间标注的过程级奖励框架，用于证据感知的视频推理。CF-GRPO从内在视频线索构建共识框架先验，包括时间覆盖、场景转换线索和查询条件下的视觉相关性。然后，它从视觉和响应表示中计算模型侧的帧使用分数，并通过共识框架奖励（CFR）优化它们的一致性。通过显著性感知的稀疏聚合和分布锐化，CFR提供了高对比度的奖励信号，而无需人类时间标注。实验表明，VideoCFR在复杂视频推理基准上实现了具有竞争力的性能，并在多个指标上优于代表性的Video-MLLM和强化学习基线，同时共识先验提供了对训练过程中强调的证据帧的可解释视角。实现代码可在 https://github.com/1Pansy/VideoCFR 获取。
cs.CV / 5 / 2606.18472
Domain Generalizable Adaptation of 3D Vision-Language Models via Regularized Fine-Tuning
通过正则化微调实现3D视觉-语言模型的领域可泛化适应
Abstract
Domain adaptation remains a central challenge in 3D vision, especially for multimodal foundation models that align 3D point clouds with visual and textual data. While these models demonstrate strong general capabilities, adapting them to downstream domains with limited data often leads to overfitting and catastrophic forgetting. To address this, we introduce ReFine3D, a regularized fine-tuning framework designed for domain-generalizable tuning of 3D large multimodal models (LMMs). ReFine3D combines selective layer tuning with two targeted regularization strategies: multi-view consistency across augmented point clouds and text diversity through synonym-based prompts generated by large language models. Additionally, we incorporate point-rendered vision supervision and a test-time augmentation mechanism with confidence-based aggregation to further enhance robustness. Extensive experiments across different 3D domain generalization benchmarks show that ReFine3D improves base-to-novel class generalization by 1.36%, cross-dataset transfer by 2.43%, robustness to corruption by 1.80%, and few-shot accuracy by up to 3.11%, outperforming prior state-of-the-art methods with minimal added computational overhead.
Chinese Translation
领域适应仍然是3D视觉中的一个核心挑战，尤其对于将3D点云与视觉和文本数据对齐的多模态基础模型。尽管这些模型展示了强大的通用能力，但在有限数据的下游领域进行适应时，往往会导致过拟合和灾难性遗忘。为了解决这一问题，我们提出了ReFine3D，一个旨在实现3D大型多模态模型（LMMs）领域可泛化调优的正则化微调框架。ReFine3D结合了选择性层调优和两种针对性的正则化策略：增强点云之间的多视角一致性和通过大型语言模型生成的基于同义词的提示实现文本多样性。此外，我们还结合了点渲染视觉监督和基于置信度聚合的测试时增强机制，以进一步增强鲁棒性。在不同的3D领域泛化基准上进行的广泛实验表明，ReFine3D在基础到新类别的泛化上提高了1.36%，跨数据集迁移提高了2.43%，对损坏的鲁棒性提高了1.80%，少样本准确率提高了最多3.11%，在计算开销最小的情况下超越了先前的最先进方法。
cs.CV / 6 / 2606.18478
Data-Forcing Distillation: Restoring Diversity and Fidelity in Few-Step Video Generation
数据强制蒸馏：恢复少步视频生成中的多样性和保真度
Abstract
Recent progress has shown promise in distilling multi-step video diffusion models into efficient few-step students. Among them, Distribution Matching Distillation (DMD) and its successor DMD2 achieved strong generation quality and fast convergence. However, due to the nature of the reverse Kullback--Leibler (KL) objective, these methods exhibit two persistent failure modes: a substantial drop in sample diversity, and visibly over-saturated outputs that deviate from real-video appearance. In this work, we propose Data-Forcing Distillation (DFD), a simple post-training framework that restores diversity and fidelity in DMD with only a single-line of code change. At its core is the teacher score discrepancy to guide the student toward the real-data distribution, pulling it to missing modes (mitigating mode collapse) and away from problematic modes absent in real data (avoiding over-saturation). We provide an in-depth theoretical analysis of our framework and validate our approach on text-to-video, image-to-video, and autoregressive video generation. With only 100--300 steps of finetuning, DFD effectively restores diversity and fidelity on both Wan2.1-1.3B and Cosmos-Predict2.5-2B model, resolving the over-saturation artifacts with significantly better video dynamics and appearance, and even outperforms the teacher model.
Chinese Translation
最近的进展表明，将多步视频扩散模型蒸馏为高效的少步学生模型具有良好的前景。其中，分布匹配蒸馏（Distribution Matching Distillation, DMD）及其后续版本 DMD2 实现了强大的生成质量和快速的收敛。然而，由于反向 Kullback-Leibler (KL) 目标的特性，这些方法表现出两种持续的失败模式：样本多样性显著下降，以及明显过饱和的输出偏离真实视频的外观。在本研究中，我们提出了数据强制蒸馏（Data-Forcing Distillation, DFD），这是一个简单的后训练框架，仅通过一行代码的更改即可恢复 DMD 的多样性和保真度。其核心是教师评分差异，引导学生朝向真实数据分布，拉动其向缺失模式（缓解模式崩溃）并远离真实数据中不存在的问题模式（避免过饱和）。我们对该框架进行了深入的理论分析，并在文本到视频、图像到视频和自回归视频生成上验证了我们的方法。仅需 100-300 步的微调，DFD 就能有效恢复 Wan2.1-1.3B 和 Cosmos-Predict2.5-2B 模型上的多样性和保真度，解决过饱和伪影，显著改善视频动态和外观，甚至超越了教师模型。
cs.CV / 7 / 2606.18484
Vines-DB: An RGB image dataset for multi-species ornamental vine segmentation
Vines-DB：一个用于多物种观赏藤本植物分割的RGB图像数据集
Abstract
The Vines-DB dataset contains 1,218 original high-resolution RGB images of seven ornamental vine species collected under field conditions at the Utah Agricultural Experiment Station's Greenville Research Farm in Logan, Utah, USA. The dataset was generated from 168 individual vine plants that were transplanted in 2022 and photographed repeatedly across multiple months during the 2023 and 2024 growing seasons (July-October). Images were captured with an iPhone 16 Pro equipped with a 48 MP camera between 10:00 AM and 12:00 PM under daylight. Vines were grown on 1.2m x 2.4m trellises and photographed from a distance of 1m against black or white Styrofoam backdrops to improve contrast and reduce background noise. The dataset includes Akebia quinata, Campsis radicans, Hydrangea anomala petiolaris, Lonicera x heckrottii, Campsis x tagliabuana 'Madame Galen', Parthenocissus quinquefolia, and Wisteria floribunda. All original images were manually annotated in Roboflow by trained annotators to produce polygon-based instance segmentation masks for eight classes, including seven species and background. After preprocessing and data augmentation, the working dataset was expanded to 2,307 images for model development and evaluation. The augmented dataset was divided into 2,019 training images, 192 validation images, and 96 test images using stratified sampling to maintain balanced representation. Vines-DB supports the development and evaluation of deep learning models for multi-class instance segmentation in precision horticulture and urban ecology. The dataset enables applications such as automated canopy cover estimation, species identification, and scalable field phenotyping. In addition, repeated monthly imaging of the plants captures temporal variation in canopy development and plant appearance, increasing the dataset's utility for segmentation benchmarking under realistic field conditions.
Chinese Translation
Vines-DB数据集包含1,218张原始高分辨率RGB图像，这些图像是从美国犹他州洛根市犹他农业实验站的格林维尔研究农场在田间条件下收集的，涵盖七种观赏藤本植物。该数据集由168株于2022年移植的藤本植物生成，并在2023和2024年生长季节（7月至10月）期间多次拍摄。图像使用配备48 MP相机的iPhone 16 Pro在上午10:00至12:00的日光下拍摄。藤本植物生长在1.2米 x 2.4米的支架上，并在距离1米处以黑色或白色泡沫背景拍摄，以提高对比度并减少背景噪声。数据集中包括五叶爬山虎（Akebia quinata）、美人蕉（Campsis radicans）、爬山虎（Hydrangea anomala petiolaris）、忍冬（Lonicera x heckrottii）、美人蕉（Campsis x tagliabuana 'Madame Galen'）、五叶藤（Parthenocissus quinquefolia）和紫藤（Wisteria floribunda）。所有原始图像均由经过培训的标注员在Roboflow中手动标注，以生成基于多边形的实例分割掩膜，共包括八个类别，涵盖七种植物和背景。经过预处理和数据增强，工作数据集扩展至2,307张图像，用于模型开发和评估。增强后的数据集通过分层抽样分为2,019张训练图像、192张验证图像和96张测试图像，以保持平衡的代表性。Vines-DB支持在精准园艺和城市生态学中开发和评估多类实例分割的深度学习模型。该数据集使得自动化冠层覆盖估计、物种识别和可扩展的田间表型分析等应用成为可能。此外，对植物进行每月重复成像捕捉了冠层发展和植物外观的时间变化，增加了数据集在现实田间条件下进行分割基准测试的实用性。
cs.CV / 8 / 2606.18496
Neural Phase Correlation
神经相位相关
Abstract
Correspondence is fundamentally relational: it seeks the unknown transformation between two observations of a common scene, not the content of either. Yet the dominant learning-based methods do not represent the transformation as a first-class object in the architecture. They encode each image independently and let a learned similarity function or a deep decoder discover the mapping implicitly. Phase correlation is the canonical exception, measuring the inter-image relationship directly in the Fourier domain, but the rigidity of its fixed basis confines it to global translation. We introduce a learned generalization of phase correlation that lifts this restriction by learning the basis on which the transformation decomposes. The same algebraic primitive extends to dense non-rigid deformations and to unitary dynamics. On the ACDC cardiac-MRI benchmark the framework matches or exceeds prior published baselines on both registration directions. On CAMUS echocardiography it matches state-of-the-art without auxiliary scoring or adaptive-smoothness mechanisms. Applied to time-evolved wavefunction pairs of the 1-D quantum harmonic oscillator, the same framework recovers the Hermite-function eigenstates and the quantized energy levels of the unknown Hamiltonian from observation pairs alone.
Chinese Translation
对应关系从根本上是关系性的：它寻求两个对同一场景的观察之间的未知变换，而不是任一观察的内容。然而，主流的基于学习的方法并未将变换作为架构中的一类重要对象来表示。它们独立编码每幅图像，并让学习到的相似性函数或深度解码器隐式发现映射。相位相关是一个典型的例外，它直接在傅里叶域中测量图像间的关系，但其固定基底的刚性限制了它仅适用于全局平移。我们引入了一种相位相关的学习泛化方法，通过学习变换分解的基底来解除这一限制。相同的代数原语扩展到密集的非刚性变形和单位动态。在ACDC心脏MRI基准测试中，该框架在两个配准方向上与之前发布的基线相匹配或超越。在CAMUS超声心动图中，它在没有辅助评分或自适应平滑机制的情况下达到了最先进的水平。应用于一维量子谐振子时间演化的波函数对，该框架仅通过观察对恢复了厄米特函数本征态和未知哈密顿量的量子化能级。
cs.CV / 9 / 2606.18510
Architectural Bias in Face Presentation Attack Detection: A Comparative Study of Vision Transformers and Convolutional Neural Networks
面部呈现攻击检测中的架构偏见：视觉变换器与卷积神经网络的比较研究
Abstract
Face Presentation Attack Detection (PAD) systems constitute a critical security layer in biometric authentication; however, existing approaches exhibit systematic performance disparities across demographic groups, disproportionately affecting individuals with darker skin tones. This paper presents a comparative empirical investigation of whether Vision Transformer architectures reduce demographic bias in face PAD systems relative to convolutional baselines. Experiments are conducted on the CASIA-SURF Cross-Ethnicity Face Anti-Spoofing (CeFA) dataset. Three architectures are evaluated: a Multimodal ViT-Tiny trained from scratch, a ResNet18 CNN baseline, and a pretrained DeiT-S fine-tuned on CeFA across African, East Asian, and zero-shot Central Asian demographic groups. DeiT-S achieves the highest overall accuracy of 97.27% and the lowest EER of 0.86%, outperforming ResNet18 at 90.15% accuracy. In terms of fairness, DeiT-S reduces the inter-ethnic ACER gap between African and East Asian subjects to 0.13%, compared to 0.75% reported in an LBP-based work [6], representing an 83% reduction. Most notably, while ResNet18 records a BPCER of 10.44% on zero-shot Central Asian subjects, DeiT-S maintains 2.89% on the same unseen group, demonstrating a 3.6x generalization advantage. These results suggest that pretrained Vision Transformers achieve superior PAD accuracy, produce smaller demographic performance gaps, and generalize more equitably across unseen demographic groups, indicating that cross-demographic fairness in PAD may partly be influenced by architectural design.
Chinese Translation
面部呈现攻击检测（PAD）系统构成生物识别认证中的关键安全层；然而，现有方法在不同人口群体之间表现出系统性的性能差异，尤其对肤色较深的个体影响较大。本文对视觉变换器架构在面部PAD系统中是否能相对于卷积基线减少人口偏见进行了比较实证研究。实验在CASIA-SURF跨种族面部反欺诈（CeFA）数据集上进行。评估了三种架构：从零开始训练的多模态ViT-Tiny、ResNet18卷积神经网络基线，以及在CeFA上针对非洲、东亚和零样本中亚人口群体微调的预训练DeiT-S。DeiT-S实现了97.27%的最高整体准确率和0.86%的最低等错误率（EER），优于ResNet18的90.15%准确率。在公平性方面，DeiT-S将非洲和东亚受试者之间的种族间平均错误率（ACER）差距降低至0.13%，而在基于LBP的研究中报告为0.75%，减少幅度达到83%。最显著的是，尽管ResNet18在零样本中亚受试者上记录了10.44%的误报率（BPCER），但DeiT-S在同一未见群体上保持在2.89%，显示出3.6倍的泛化优势。这些结果表明，预训练的视觉变换器在PAD准确性上表现优越，产生更小的人口性能差距，并在未见人口群体中更公平地泛化，表明PAD中的跨人口公平性可能部分受架构设计的影响。
cs.CV / 10 / 2606.18528
A Prototypical Signature Approach for Writer-Independent Offline Signature Verification
一种原型签名方法用于独立于作者的离线签名验证
Abstract
Offline handwritten signature verification aims to distinguish genuine from forged signatures using static images. Since real forgeries are rarely available, negative samples are usually randomly drawn from genuine signatures of other users to create training data. However, this random selection often lacks diversity, increases redundancy, and escalates computational cost, leading to inefficient training. We propose a data-driven strategy to generate diverse, informative negative samples using prototypical signatures, which are compact, non-identifiable summaries of genuine signature features. Based on the experiments results, we conclude that (i) prototypical signatures yield more informative negative samples, improving the detection of skilled forgeries; (ii) the proposed approach is backbone-agnostic, showing robustness across architectures; and (iii) when combined with a primal-form linear SVM, it serves as an alternative to RBF-based models while significantly improving scalability and computational efficiency. Implementation of the method is available at https://github.com/kdmoura/proto_hsv.
Chinese Translation
离线手写签名验证旨在通过静态图像区分真实签名与伪造签名。由于真实的伪造样本很少可得，负样本通常是从其他用户的真实签名中随机抽取以创建训练数据。然而，这种随机选择往往缺乏多样性，增加了冗余，并提升了计算成本，导致训练效率低下。我们提出了一种数据驱动的策略，通过使用原型签名生成多样化且信息丰富的负样本，原型签名是对真实签名特征的紧凑且不可识别的总结。根据实验结果，我们得出以下结论：（i）原型签名产生了更具信息量的负样本，提高了对熟练伪造的检测；（ii）所提出的方法与骨干网络无关，显示出在不同架构中的鲁棒性；（iii）当与原始形式的线性支持向量机（SVM）结合时，它作为基于RBF模型的替代方案，同时显著提高了可扩展性和计算效率。该方法的实现可在 https://github.com/kdmoura/proto_hsv 获取。
cs.CV / 11 / 2606.18553
Hierarchical Multi-Modal Retrieval for Knowledge-Grounded News Image Captioning
基于层次化多模态检索的知识驱动新闻图像描述生成
Abstract
Traditional image captioning methods often struggle to generate comprehensive, context-rich descriptions, especially for details not directly observable from visual cues. To overcome this, we propose a novel retrieval-augmented image captioning framework that generates captions with deeper insights, such as object attributes, event context, and underlying significance, by leveraging external knowledge. Our approach features a hierarchical multi-modal article retrieval mechanism that moves beyond monolithic text entities. This retrieval considers article structure-aware features, including weighted textual components (e.g., headlines, body sections) and visual placement patterns, alongside multi-faceted similarity computations (content--visual, visual--visual, and discourse positioning). A subsequent contextual relevance refinement stage further enhances the retrieved information. The retrieved articles then serve as the knowledge base for caption generation: first, a VLM generates a concise image description; second, we segment relevant information from the retrieved articles based on this description; and finally, an LLM utilizes both the description and extracted knowledge to generate a comprehensive, contextually detailed caption. We participated in the ACM Multimedia EVENTA 2025 Challenge and achieved 5th place with an overall score of 0.2824 on the private test set of the OpenEvent-V1 dataset. Source code is publicly released at https://github.com/mf0212/EVENTA-Challange.
Chinese Translation
传统的图像描述生成方法往往难以生成全面且富有上下文的描述，尤其是对于那些无法通过视觉线索直接观察到的细节。为了解决这一问题，我们提出了一种新颖的增强检索图像描述生成框架，通过利用外部知识生成更深入的描述，如物体属性、事件背景和潜在意义。我们的方法具有层次化多模态文章检索机制，超越了单一文本实体的局限。该检索机制考虑了文章结构感知特征，包括加权文本组件（如标题、正文部分）和视觉位置模式，以及多方面的相似性计算（内容-视觉、视觉-视觉和话语定位）。随后，相关性精炼阶段进一步增强了检索信息。检索到的文章随后作为描述生成的知识基础：首先，使用视觉语言模型（VLM）生成简洁的图像描述；其次，根据该描述从检索到的文章中提取相关信息；最后，使用大型语言模型（LLM）结合描述和提取的知识生成全面且上下文丰富的描述。我们参加了ACM Multimedia EVENTA 2025挑战赛，并在OpenEvent-V1数据集的私有测试集上获得了第五名，整体得分为0.2824。源代码已公开发布在https://github.com/mf0212/EVENTA-Challange。
cs.CV / 12 / 2606.18554
Forged Calamity: Benchmark for Cross-Domain Synthetic Disaster Detection in the Age of Diffusion
伪造灾难：扩散时代跨领域合成灾害检测基准
Abstract
The rapid advancement of text-to-image diffusion models has enabled the creation of highly photorealistic synthetic images that closely resemble real photographs, making it increasingly difficult to distinguish authentic content from AI-generated fabrications. This poses challenges for cybersecurity, digital forensics, and disaster response, where fake imagery of floods, fires, or earthquakes can spread misinformation or disrupt emergency operations. To address this, we introduce Forged Calamity, a benchmark dataset for synthetic disaster detection containing 30,000 images, including 6,000 real and 24,000 synthetic samples generated by four diffusion models. Comprehensive experiments across fine-tuned and zero-shot settings reveal consistent weaknesses in current forensic approaches. Fine-tuned detectors perform well in-distribution but lose up to 50\% accuracy on unseen generators or disaster types, showing overfitting to model-specific artifacts. Zero-shot generalized detectors also struggle to maintain stable accuracy, with only limited resilience in a few representation-robust models. These findings highlight persistent generalization gaps and the urgent need for domain- and model-agnostic detection methods to ensure visual authenticity in the diffusion era.
Chinese Translation
文本到图像扩散模型的快速发展使得能够创建高度逼真的合成图像，这些图像与真实照片极为相似，从而使得区分真实内容与人工智能生成的虚假内容变得越来越困难。这对网络安全、数字取证和灾害响应带来了挑战，虚假的洪水、火灾或地震图像可能传播错误信息或干扰紧急操作。为了解决这个问题，我们推出了伪造灾难（Forged Calamity），这是一个用于合成灾害检测的基准数据集，包含30,000张图像，其中包括6,000张真实样本和24,000张由四个扩散模型生成的合成样本。在经过微调和零样本设置下的全面实验揭示了当前取证方法的一致弱点。微调后的检测器在已知分布中表现良好，但在未见过的生成器或灾害类型上准确率下降高达50\%，显示出对模型特定伪影的过拟合。零样本泛化检测器在保持稳定准确率方面也面临困难，仅在少数具有表示鲁棒性的模型中表现出有限的韧性。这些发现突显了持续存在的泛化差距，以及在扩散时代确保视觉真实性的领域和模型无关检测方法的迫切需求。
cs.CV / 13 / 2606.18555
Rethinking Text-to-Image as Semantic-Aware Data Augmentation for Indoor Scene Recognition
重新思考文本到图像作为室内场景识别的语义感知数据增强
Abstract
In the realm of computer vision, indoor image recognition presents challenges due to the intricate interplay of lighting conditions, occlusions, and diverse object arrangements within confined spaces. To address the lacks of training indoor images, we introduce a novel approach leveraging Stable Diffusion (SD) for the generation of synthetic images, which serve as a powerful data augmentation tool. The utilization of SD offers a principled framework for synthesizing diverse and realistic indoor scenes, thereby enriching the training data pool for robust indoor image recognition models. Experimental findings on the MIT Indoor Scene dataset reveal the potential of our proposed approach in enhancing the training of deep models when authentic data is limited. Furthermore, to prevent the misuse of SD synthetic images, we introduce a counter measure based on DIffusion Reconstruction Error (DIRE). The powerful DIRE presentation enables training robust classifiers only using lightweight deep models. Experiments show that our approach can perfectly recognize SD generated images with the accuracy of 100% using MobilenetV3.
Chinese Translation
在计算机视觉领域，室内图像识别面临着由于光照条件、遮挡以及狭小空间内多样化物体排列的复杂相互作用而带来的挑战。为了解决室内图像训练不足的问题，我们提出了一种新颖的方法，利用稳定扩散（Stable Diffusion, SD）生成合成图像，这些图像作为强大的数据增强工具。SD的应用提供了一个原则性框架，用于合成多样且逼真的室内场景，从而丰富了用于强健室内图像识别模型的训练数据池。在MIT室内场景数据集上的实验结果揭示了我们提出的方法在真实数据有限时增强深度模型训练的潜力。此外，为了防止SD合成图像的误用，我们引入了一种基于扩散重建误差（DIffusion Reconstruction Error, DIRE）的对策。强大的DIRE表示使得仅使用轻量级深度模型即可训练出强健的分类器。实验表明，我们的方法能够以100%的准确率完美识别SD生成的图像，使用的模型为MobilenetV3。
cs.CV / 14 / 2606.18558
MolmoMotion: Forecasting Point Trajectories in 3D with Language Instruction
MolmoMotion：基于语言指令的三维点轨迹预测
Abstract
Motion forecasting is central to visual intelligence: agents must anticipate how objects will move in order to plan actions, reason about physical interactions, and synthesize realistic futures. We argue that 3D points in world coordinates provide a general representation that is class-agnostic, view-stable, compact, and directly useful for downstream tasks. We formalize the task of goal-conditioned 3D point motion forecasting: given a short visual history, a set of 3D query points on an object of interest, and a language description of the intended goal, the model predicts the future 3D trajectory of each point. We introduce a full stack to study this task at scale: (1) MolmoMotion-1M is a large corpus of action-described, object-grounded 3D point trajectories annotated from 1.16M unconstrained videos; (2) PointMotionBench is a human-verified benchmark spanning 111 object categories and 61 motion types; and (3) MolmoMotion is a general motion forecasting model that supports both autoregressive coordinate prediction and flow-matching-based trajectory generation. MolmoMotion accurately predicts diverse motion patterns with different language instructions, and significantly outperforms existing motion prediction baselines on PointMotionBench. Finally, we show that the learned 3D motion prior transfers well to downstream applications: it improves training efficiency and generalization for robot manipulation, and its predicted trajectories provide effective motion guidance for generative models to synthesize videos with more realistic object motion.
Chinese Translation
运动预测是视觉智能的核心：智能体必须预测物体的运动，以便规划行动、推理物理交互并合成现实的未来。我们认为，世界坐标系中的三维点提供了一种通用表示，具有与类别无关、视角稳定、紧凑且对下游任务直接有用的特点。我们将目标条件下的三维点运动预测任务形式化：给定一段短暂的视觉历史、一组感兴趣物体上的三维查询点以及对预期目标的语言描述，模型预测每个点的未来三维轨迹。我们引入了一个完整的研究框架来大规模研究这一任务：（1）MolmoMotion-1M是一个大型语义描述的、基于物体的三维点轨迹语料库，注释来源于116万条无约束视频；（2）PointMotionBench是一个经过人工验证的基准，涵盖111个物体类别和61种运动类型；（3）MolmoMotion是一个通用的运动预测模型，支持自回归坐标预测和基于流匹配的轨迹生成。MolmoMotion能够准确预测不同语言指令下的多样化运动模式，并在PointMotionBench上显著超越现有的运动预测基准。最后，我们展示了学习到的三维运动先验在下游应用中的良好迁移：它提高了机器人操作的训练效率和泛化能力，其预测的轨迹为生成模型提供了有效的运动指导，从而合成出更具现实感的物体运动视频。
cs.CV / 15 / 2606.18565
Experimental Analysis of Neural Network-Based Image Classification on the CIFAR-10 Dataset
基于神经网络的图像分类在CIFAR-10数据集上的实验分析
Abstract
An experimental investigation of neural image classification on the CIFAR-10 benchmark is presented through fully connected and convolutional network formulations. The analysis emphasizes the complete learning pipeline: image vectorization, normalization, one-hot class encoding, supervised loss minimization, learning-rate selection, mini-batch training, convolutional feature extraction, max-pooling, and validation-based generalization assessment. A convolutional architecture with six convolutional layers and three max-pooling stages is evaluated for ten training epochs using a batch size of 128 and an Adam optimizer with a learning rate of 0.001. The validation accuracy reaches approximately 74.77%, while the validation loss begins to increase after the middle of training despite continued reduction in training loss. The resulting behavior illustrates the practical difference between representation learning and memorization, and it provides a compact experimental baseline for future studies on regularization, data augmentation, deeper architectures, and reproducible image-classification education.
Chinese Translation
本文通过全连接网络和卷积网络的模型，对CIFAR-10基准上的神经图像分类进行了实验研究。分析强调了完整的学习流程：图像向量化、归一化、一热编码、监督损失最小化、学习率选择、小批量训练、卷积特征提取、最大池化以及基于验证的泛化评估。评估了一种具有六个卷积层和三个最大池化阶段的卷积架构，在使用批量大小为128和学习率为0.001的Adam优化器进行十个训练周期的情况下。验证准确率约为74.77%，而验证损失在训练中期后开始增加，尽管训练损失持续减少。所得到的结果展示了表示学习与记忆之间的实际差异，并为未来关于正则化、数据增强、更深层次架构和可重复的图像分类教育的研究提供了一个紧凑的实验基线。
cs.CV / 16 / 2606.18566
Multi-Modal Hyper-Graph Fusion for Low-Light Crowd Counting
低光照环境下的人群计数的多模态超图融合
Abstract
Crowd counting is a fundamental task in computer vision. However, crowd counting in low-light environments remains largely underexplored, despite its practical importance in the real world. Existing methods mainly focus on well-lit scenes or rely on single-modality Red-Green-Blue (RGB) representations, which often become unreliable under extreme darkness and complex non-uniform illumination. To handle this problem, we construct three new low-light crowd counting benchmarks, which consist of two synthetic datasets, SHA\_Dark and SHB\_Dark, and a real-world benchmark LC-Crowd (Low-light Crowd Dataset). Inspired by Retinex-based physical modeling, we introduce depth and Canny edge cues as complementary geometric and structural priors to enhance the intrinsic reflectance representation under low-light conditions. We propose a Multi-Modal Hyper-Graph Fusion module, which formulates RGB appearance, depth geometry, and edge structure cues as nodes in a unified hyper-graph and explicitly captures their high-order complementary relationships via dynamic hyperedge construction and message passing. Furthermore, to adaptively allocate computation in dense prediction, we propose a Deformable Rectangular Sparse Attention (DRSA) module, which concentrates computation on informative regions through anchor-aware estimation and adaptive rectangular window modeling. Based on these designs, we develop a unified Low-Light Counting Network (LCNet) for robust low-light crowd counting. Extensive experiments on three benchmarks demonstrate that the proposed method achieves the best overall performance against existing state-of-the-art (SOTA) methods. The code is in the supplementary material. The datasets will be made public upon acceptance.
Chinese Translation
人群计数是计算机视觉中的一项基础任务。然而，尽管在现实世界中具有重要的实际意义，低光照环境下的人群计数仍然在很大程度上未被深入研究。现有的方法主要集中在光照良好的场景，或依赖于单一模态的红绿蓝（RGB）表示，这在极度黑暗和复杂的非均匀照明条件下往往变得不可靠。为了解决这个问题，我们构建了三个新的低光照人群计数基准，其中包括两个合成数据集SHA_Dark和SHB_Dark，以及一个真实世界基准LC-Crowd（低光照人群数据集）。受到基于Retinex的物理建模的启发，我们引入深度和Canny边缘线索作为互补的几何和结构先验，以增强低光照条件下的内在反射率表示。我们提出了一个多模态超图融合模块，将RGB外观、深度几何和边缘结构线索作为统一超图中的节点，并通过动态超边构建和信息传递显式捕捉它们的高阶互补关系。此外，为了在密集预测中自适应地分配计算，我们提出了一个可变矩形稀疏注意力（DRSA）模块，通过锚点感知估计和自适应矩形窗口建模将计算集中在信息丰富的区域。基于这些设计，我们开发了一个统一的低光照计数网络（LCNet），用于稳健的低光照人群计数。在三个基准上的大量实验表明，所提出的方法在现有的最先进（SOTA）方法中实现了最佳的整体性能。代码在补充材料中，数据集将在接受后公开。
cs.CV / 17 / 2606.18582
Technical Report for ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Leveraging DINOv3 for Robust Outdoor Scene Understanding in Field Robotics
ICRA 2026 GOOSE 2D 精细语义分割挑战技术报告：利用 DINOv3 实现野外机器人中的鲁棒户外场景理解
Abstract
The GOOSE 2D Fine-Grained Semantic Segmentation Challenge at the ICRA 2026 Workshop on Field Robotics evaluates dense semantic segmentation of off-road imagery over a fine-grained taxonomy of 64 classes and 11 evaluated non-void coarse categories. We present the first-place solution to this challenge. Our solution comprises two complementary improvements: (a) a network-level design that combines a self-supervised DINOv3 ViT-L/16 backbone, a ViT-Adapter, and a Mask2Former mask-classification decoder, together with a coarse-category auxiliary loss on the global [CLS] token; and (b) an inference-time aggregation strategy based on multi-scale and horizontal-flip test-time augmentation and an ensemble of the top three checkpoints selected using Codabench scores. Our method achieves an official composite score of 76.57%, consisting of 69.32% fine-class mIoU and 83.81% category-level mIoU, and ranks first on the final phase leaderboard: www.codabench.org/competitions/14257/#/results-tab.
Chinese Translation
ICRA 2026 野外机器人研讨会的 GOOSE 2D 精细语义分割挑战评估了针对64个类和11个评估的非空粗类的越野图像的密集语义分割。我们在此挑战中提出了第一名的解决方案。我们的解决方案包括两个互补的改进：（a）一个网络级设计，结合了自监督的 DINOv3 ViT-L/16 主干、ViT-Adapter 和 Mask2Former 掩膜分类解码器，以及在全局 [CLS] 令牌上的粗类辅助损失；（b）一种基于多尺度和水平翻转测试时增强的推理时间聚合策略，以及使用 Codabench 分数选择的前三个检查点的集成。我们的方法实现了官方综合得分76.57%，其中精细类 mIoU 为69.32%，类别级 mIoU 为83.81%，并在最终阶段排行榜上排名第一：www.codabench.org/competitions/14257/#/results-tab。
cs.CV / 18 / 2606.18583
Aerial-ground LiDAR place recognition with patch-level self-supervised learning and expanded reciprocal re-ranking
空中-地面LiDAR位置识别：基于补丁级自监督学习和扩展互排名的研究
Abstract
LiDAR place recognition determines one's position on a prior point cloud map. The most studied ground-level LiDAR place recognition suffers from pre-visit requirements, incomplete coverage, and limited perspectives. Using pre-acquired, full-coverage Airborne Laser Scanning (ALS) data as an aerial prior map overcomes these drawbacks, making cross-view place recognition necessary and advantageous. However, aerial-ground LiDAR place recognition faces significant challenges, including the domain gap between aerial and ground point clouds, and false positives during initial retrieval. To address these challenges, we present a novel retrieval and re-ranking framework for aerial-ground LiDAR place recognition. Based on the priors that neighboring point cloud patches share similar semantics with anchor patch, our retrieval network introduces patch-level self-supervised learning modules at multiple scales and integrates with scene-level learning to improve global feature discriminativeness between aerial and ground point clouds. Furthermore, leveraging the structured spatial distribution of ALS point clouds, we introduce an Expanded Reciprocal (ER) re-ranking algorithm to exploit neighborhood information maximally and refine each feature based on neighbor features, which are then used to update the similarity matrix for final ranking. Extensive experiments demonstrate that our retrieval network outperforms existing state-of-the-art (SOTA) methods, achieving a 9.8\% improvement in average Recall@1 and a 3.2\% improvement in average Recall@1\% on the CS-Urban-Scenes, while also showing the best performance on the CS-Campus3D dataset. Additionally, our ER re-ranking algorithm further boosts the average Recall@1 by 4.9\% on CS-Campus3D and 10.2\% on CS-Urban-Scenes without additional training.
Chinese Translation
LiDAR位置识别用于确定在先前点云地图上的位置。最常研究的地面LiDAR位置识别存在预先访问要求、覆盖不完整和视角有限等问题。利用预先获取的全覆盖空中激光扫描（Airborne Laser Scanning, ALS）数据作为空中先验地图，可以克服这些缺点，使得跨视角位置识别变得必要且具有优势。然而，空中-地面LiDAR位置识别面临显著挑战，包括空中和地面点云之间的领域差距，以及初始检索过程中的误报。为了解决这些挑战，我们提出了一种新颖的空中-地面LiDAR位置识别检索和重排名框架。基于邻近点云补丁与锚补丁共享相似语义的先验，我们的检索网络在多个尺度上引入了补丁级自监督学习模块，并与场景级学习相结合，以提高空中和地面点云之间的全局特征区分能力。此外，利用ALS点云的结构化空间分布，我们引入了一种扩展互排名（Expanded Reciprocal, ER）算法，以最大限度地利用邻域信息，并根据邻居特征细化每个特征，随后用于更新最终排名的相似性矩阵。大量实验表明，我们的检索网络在现有的最先进方法（SOTA）中表现优异，在CS-Urban-Scenes数据集上实现了9.8%的平均Recall@1提升和3.2%的平均Recall@1%提升，同时在CS-Campus3D数据集上表现最佳。此外，我们的ER重排名算法在CS-Campus3D上进一步提升了平均Recall@1达4.9%，在CS-Urban-Scenes上提升达10.2%，且无需额外训练。
cs.CV / 19 / 2606.18586
APT: Atomic Physical Transitions for Causal Video-Language Understanding
APT：用于因果视频语言理解的原子物理转变
Abstract
Physical events are not understood by their names alone, but by the causal state changes that compose them. A clip-level label such as "bounce" can be correct while hiding the process that makes the event physically valid, from support loss and contact onset to rebound and settling. To make this hidden process explicit, we introduce Atomic Physical Transitions (APTs): minimal, temporally localized state changes that bind a visible cue to an active physical mechanism and before/after dynamical regimes. An APT chain represents a video as an ordered causal transition sequence rather than a single aggregate event label: event labels tell what happened; APT chains explain why it happened. To make APTs learnable by VLMs, we construct mixed-source APT data from human annotations and simulator ground truth, covering 14 transition types across contact, gravity, friction, and rotation/stability, with 27,303 timed instances over 1,246 trials. Using this data, we find that current VLMs miss transition-level physics, with zero-shot recall at most 14% and errors dominated by missed transitions. Direct fine-tuning on APT chains improves transition detection but causes event-level forgetting, indicating that the model learns a specialized answer format rather than a reusable physical representation. We therefore propose APT-Tune, a parameter-efficient recipe that teaches VLMs to use causal transitions without forgetting how to answer video questions. It combines image-pad-aware supervision, format-conditional co-training, and mechanism-conditioned domain-to-type decoding to make APT learning format-robust and physically grounded. With only 11 M LoRA parameters on Qwen3-VL-2B, APT-Tune substantially improves APT recall while also improving event-level video transfer. These results show that APTs are not a new answer format, but a human-aligned causal supervision signal for physical video understanding.
Chinese Translation
物理事件不仅仅通过其名称来理解，而是通过构成它们的因果状态变化。像“弹跳”这样的片段级标签虽然可以是正确的，但却掩盖了使事件在物理上有效的过程，从支撑丧失和接触开始到反弹和稳定。为了使这一隐藏过程显性化，我们引入了原子物理转变（Atomic Physical Transitions, APTs）：最小的、时间上局部的状态变化，将可见线索与活跃的物理机制及其前后动态状态绑定在一起。APT链将视频表示为有序的因果转变序列，而不是单一的聚合事件标签：事件标签告诉我们发生了什么；APT链解释了为什么会发生。为了使APTs能够被视频语言模型（VLMs）学习，我们构建了混合来源的APT数据，结合了人类注释和模拟器的真实数据，涵盖了接触、重力、摩擦和旋转/稳定等14种转变类型，共有27,303个时间实例，分布在1,246个试验中。使用这些数据，我们发现当前的VLMs在转变级物理上存在缺失，零-shot召回率最高仅为14%，且错误主要由遗漏的转变造成。对APT链的直接微调提高了转变检测的能力，但导致了事件级遗忘，表明模型学习了一种专门的答案格式，而不是可重用的物理表示。因此，我们提出了APT-Tune，这是一种参数高效的方案，旨在教会VLMs使用因果转变而不忘记如何回答视频问题。它结合了图像垫感知监督、格式条件共训练和机制条件的领域到类型解码，使APT学习在格式上具有鲁棒性并在物理上扎根。仅在Qwen3-VL-2B上使用11M LoRA参数，APT-Tune显著提高了APT的召回率，同时也改善了事件级视频转移。这些结果表明，APTs不是一种新的答案格式，而是用于物理视频理解的人类对齐的因果监督信号。
cs.CV / 20 / 2606.18591
Bridging Creative Intent and Visual Quality: Creator-Driven Recurrent Video Generation with Agentic Feedback Loops
弥合创意意图与视觉质量：基于创作者驱动的循环视频生成与自主反馈循环
Abstract
Generative AI has made content creation increasingly accessible, but many AI-generated videos lack narrative coherence and creative direction, issues that become more substantial at longer durations. Unlike coding, where AI generation benefits from reliable feedback and techniques such as recurrent self-improvement, video generation requires subjective feedback about plot, scenes, and narrative, which naturally motivates approaches that incorporate human creative direction. We introduce CHIEF, a human-AI co-creation video generation framework that places the creator at the center of human-in-the-loop iterative video refinement, and supports them by providing automatic subjective feedback. The creator incorporates their creative direction by driving each iteration, while their revisions are incorporated by a specialized refiner agent. The feedback loop is generated by persona-conditioned multimodal LLMs that watch generated videos and produce subjective critique from the audience perspectives, providing feedback that self-evaluation alone cannot capture. To test the effectiveness of our proposed framework, we work with high school and college students with no prior filmmaking experience to create videos, from short 1-minute videos to a complete short 10-minute film with a complicated plot.
Chinese Translation
生成性人工智能使内容创作变得愈加可及，但许多AI生成的视频缺乏叙事连贯性和创意方向，尤其在较长时长的视频中，这些问题更加突出。与编码不同，AI生成受益于可靠的反馈和诸如循环自我改进等技术，而视频生成则需要关于情节、场景和叙事的主观反馈，这自然促使了结合人类创意指导的方法。我们提出了CHIEF，一个人类与AI共同创作的视频生成框架，将创作者置于人机协作的迭代视频精炼的中心，并通过提供自动的主观反馈来支持他们。创作者通过驱动每次迭代来融入他们的创意方向，而他们的修订则由一个专门的精炼代理进行整合。反馈循环由基于角色条件的多模态大型语言模型（multimodal LLMs）生成，这些模型观看生成的视频并从观众的角度提供主观批评，提供的反馈是自我评估所无法捕捉的。为了测试我们提出的框架的有效性，我们与没有先前电影制作经验的高中和大学学生合作，创作视频，从短的1分钟视频到完整的复杂情节的10分钟短片。
cs.CV / 21 / 2606.18609
Hallucination Detection and Correction in Medical VLMs via Counter-Evidence Verification
通过反证验证实现医疗视觉语言模型中的幻觉检测与修正
Abstract
Vision-Language models (VLMs) reliability in medical diagnosis is challenged by trust-undermining hallucinations. Existing hallucination detection approaches mainly focus on identifying factual inconsistencies between generated text and reference data. While some studies analyze where models attend in images, they seldom verify whether such attention truly reflects the visual evidence supporting the generated text. To address this gap, we propose Co}unter-Evidence Verification (CoEV), a training-free plug-and-play framework that detects and corrects hallucinations through evidence-based factual consistency verification. CoEV performs bidirectional verification between textual assertions and visual evidence, testing whether each statement is supported by its corresponding evidence region, and assigns each statement into a four-quadrant diagnostic map capturing combinations of text factuality and visual grounding. CoEV detects hallucinated content and serves as a post hoc refinement tool, correcting hallucinations without retraining. Extensive experiments on four medical datasets show that CoEV combats hallucinations in VLMs.For hallucination detection, CoEV consistently outperforms existing methods, improving average PR-AUC and ROC-AUC by 3.0% and 3.9% absolute points respectively, with notable gains of up to 18.5% in specific VQA scenarios. For hallucination correction, it improves Micro-F1 by up to 12.5%, reduces hallucination rates by over 11.9% on medical report generation, and also boosts medical VQA accuracy. These results show that CoEV enables reliable detection and correction of hallucinations, providing clinicians with dependable, evidence-based cues for diagnosis. Code will be released upon acceptance.
Chinese Translation
医疗诊断中视觉语言模型（VLMs）的可靠性受到削弱信任的幻觉挑战。现有的幻觉检测方法主要集中在识别生成文本与参考数据之间的事实不一致性。尽管一些研究分析了模型在图像中的注意力集中位置，但很少验证这种注意力是否真正反映了支持生成文本的视觉证据。为了解决这一问题，我们提出了反证验证（Counter-Evidence Verification，CoEV），这是一种无需训练的即插即用框架，通过基于证据的事实一致性验证来检测和修正幻觉。CoEV在文本断言与视觉证据之间进行双向验证，测试每个陈述是否得到其对应证据区域的支持，并将每个陈述分配到一个四象限诊断图中，以捕捉文本事实性与视觉基础的组合。CoEV能够检测幻觉内容，并作为一种后期修正工具，在不重新训练的情况下修正幻觉。在四个医疗数据集上的广泛实验表明，CoEV有效对抗VLMs中的幻觉。在幻觉检测方面，CoEV始终优于现有方法，平均PR-AUC和ROC-AUC分别提高了3.0%和3.9%绝对点，在特定的视觉问答（VQA）场景中，提升幅度高达18.5%。在幻觉修正方面，Micro-F1提高了最多12.5%，医疗报告生成中的幻觉率降低了超过11.9%，同时也提升了医疗VQA的准确性。这些结果表明，CoEV能够可靠地检测和修正幻觉，为临床医生提供可靠的基于证据的诊断线索。代码将在接受后发布。
cs.CV / 22 / 2606.18623
Intrinsic 4D Gaussian Segmentation from Scene Cues
基于场景线索的内在4D高斯分割
Abstract
Dynamic 4D Gaussian Splatting reconstructs deforming scenes with high fidelity and is increasingly adopted as a representation for dynamic 3D scenes. Putting such a scene to use, for editing, manipulation or motion analysis, first requires segmenting it: grouping the Gaussian primitives into coherent objects. Current pipelines obtain this grouping by importing 2D masks from foundation models such as SAM and lifting or distilling them into the Gaussian representation. In dynamic scenes these masks must be generated across many frames and views, which is costly, and the resulting segmentation can depend strongly on the quality and consistency of those external masks. We ask how much object-level structure can instead be recovered from the Gaussians themselves, and propose Intrinsic-GS, a training-free, mask-free method that builds a sparse affinity graph over Gaussian primitives from appearance, orientation, scale, deformation-trajectory and non-learned rendered-boundary cues. The graph is partitioned with Leiden community detection, requiring no foundation model and no learned feature field. On the standard 4D Gaussian segmentation benchmarks, Neu3D and HyperNeRF, Intrinsic-GS recovers substantial object structure without mask supervision, reaching 0.746 mIoU on Neu3D and 0.575 on HyperNeRF; on Neu3D, a geometry-only variant reaches 0.902 mIoU, matching SAM-supervised TRASE. On HyperNeRF, Intrinsic-GS runs 12.5x faster than the mask-generation and feature-rendering stages used by mask-supervised pipelines. These results suggest that much of the segmentation signal is already encoded in the Gaussians themselves, offering a fast, mask-free direction for 3D and 4D Gaussian segmentation that may also point toward more generalizable, robust segmentation in settings where external masks are unreliable or expensive.
Chinese Translation
动态4D高斯溅射以高保真度重建变形场景，越来越多地被用作动态3D场景的表示。要利用这样的场景进行编辑、操控或运动分析，首先需要对其进行分割：将高斯原语分组为一致的对象。目前的流程通过从基础模型（如SAM）导入2D掩膜并将其提升或提炼为高斯表示来实现这一分组。在动态场景中，这些掩膜必须跨多个帧和视角生成，这成本高昂，且最终的分割结果可能强烈依赖于这些外部掩膜的质量和一致性。我们探讨从高斯本身恢复多少对象级结构，并提出了Intrinsic-GS，这是一种无训练、无掩膜的方法，通过外观、方向、尺度、变形轨迹和未学习的渲染边界线索构建高斯原语的稀疏亲和图。该图通过Leiden社区检测进行划分，不需要基础模型和学习特征场。在标准的4D高斯分割基准测试Neu3D和HyperNeRF上，Intrinsic-GS在没有掩膜监督的情况下恢复了大量对象结构，在Neu3D上达到0.746 mIoU，在HyperNeRF上达到0.575；在Neu3D上，一个仅几何的变体达到了0.902 mIoU，与SAM监督的TRASE相匹配。在HyperNeRF上，Intrinsic-GS的运行速度比掩膜监督流程中使用的掩膜生成和特征渲染阶段快12.5倍。这些结果表明，分割信号在高斯本身中已经编码，提供了一种快速、无掩膜的3D和4D高斯分割方向，可能也指向在外部掩膜不可靠或成本高昂的情况下更具通用性和鲁棒性的分割方法。
cs.CV / 23 / 2606.18644
Spiking Pyramid Wavelet Transformation for High-efficient and Low-energy Image Restoration
高效低能图像恢复的脉冲金字塔小波变换
Abstract
Spiking neural networks (SNNs) have garnered significant interest in computer vision due to their potential for efficiency and biological inspiration. While spiking CNN-based methods have shown promise for image restoration (IR) tasks, their performance is constrained by the inherent receptive field limitations of CNN operations. In the paper, we explore the benefits of discrete wavelet transformation and propose a spiking pyramid wavelet-based model (SPWM) for high-efficient and low-energy target. Specifically, we develop a spiking dual pyramid wavelet (SDPW) block to model long-range dependency and exploit the properties of the degradation in the wavelet domain. Experimental results on several benchmarks demonstrate that SPWM significantly lowers computational costs and energy consumption while maintaining image quality. Our method showcases the potential of SNNs in the field of IR, offering new insights for future applications of resource-limited devices.
Chinese Translation
脉冲神经网络（SNNs）因其在效率和生物启发方面的潜力而在计算机视觉领域引起了广泛关注。尽管基于脉冲卷积神经网络（CNN）的方法在图像恢复（IR）任务中显示出良好的前景，但其性能受到CNN操作固有的感受野限制的制约。本文探讨了离散小波变换的优势，并提出了一种基于脉冲金字塔小波（SPWM）模型，以实现高效低能的目标。具体而言，我们开发了一种脉冲双金字塔小波（SDPW）模块，以建模长距离依赖关系，并利用小波域中退化的特性。多个基准实验结果表明，SPWM显著降低了计算成本和能耗，同时保持了图像质量。我们的方法展示了SNNs在图像恢复领域的潜力，为未来资源受限设备的应用提供了新的见解。
cs.CV / 24 / 2606.18658
On-Manifold Variational Learning with Heat-Kernel Priors
基于热核先验的流形变分学习
Abstract
Learning unsupervised representations of medical imaging cohorts can reveal clinically meaningful prototypes without expert labels, which are often noisy and fail to capture true pathological heterogeneity. However, existing deep latent-variable models estimate Gaussian mixture priors via Euclidean averaging, producing prototypes that drift off the curved data manifold and degenerate as the number of sub-populations grows. We propose a manifold-anchored variational framework built on a geometry-aware Expectation-Maximization (EM) algorithm, whose M-step selects each sub-population prototype as the graph medoid with the highest diffusion centrality on a heat-kernel-weighted latent graph, ensuring that every prototype remains on-manifold. A Dirichlet energy regularizer enforces geometric smoothness of the latent space, and a per-sub-population uncertainty score enables label-free quality assessment. \rev{The manifold-anchored EM is a general-purpose geometric tool that extends standard EM and applies readily to other latent-variable models beyond this setting.} On cardiac scar and brain MRI benchmarks, our framework attains the highest accuracy among all compared methods, produces the sharpest prototypes reported to date, and remains stable at large sub-population counts where all baselines degenerate.
Chinese Translation
无监督学习医学影像队列的表示可以揭示临床上有意义的原型，而无需专家标签，这些标签通常噪声较大，无法捕捉真实的病理异质性。然而，现有的深度潜变量模型通过欧几里得平均估计高斯混合先验，导致原型偏离曲面数据流形，并在亚群体数量增加时退化。我们提出了一种基于流形锚定的变分框架，该框架建立在一个关注几何的期望最大化（EM）算法之上，其M步将每个亚群体原型选择为在热核加权潜在图上具有最高扩散中心性的图形中位数，确保每个原型保持在流形上。Dirichlet能量正则化器强制潜在空间的几何平滑性，而每个亚群体的不确定性评分则使得无标签的质量评估成为可能。流形锚定的EM是一种通用的几何工具，扩展了标准EM，并可方便地应用于其他潜变量模型。我们在心脏瘢痕和脑MRI基准测试中，框架在所有比较方法中达到了最高的准确率，产生了迄今为止报告的最清晰的原型，并在所有基线退化的情况下，在较大的亚群体数量下保持稳定。
cs.CV / 25 / 2606.18661
LandslideAgent with Multimodal LandslideBench: A Domain-Rule-Augmented Agent for Autonomous Landslide Identification and Analysis
多模态滑坡基准的滑坡代理：一种增强领域规则的自主滑坡识别与分析代理
Abstract
Intelligent landslide hazard interpretation is critical for disaster prevention, yet current paradigms struggle to simultaneously extract visual features and high-level geoscientific semantics, while general-purpose vision-language models (VLMs) suffer from perceptual limitations and domain hallucinations in complex geological scenarios. To address these challenges, we propose an instruction-driven agentic framework comprising three components. First, LandslideBench, a multimodal fine-grained dataset with seven subtype labels, high-resolution imagery, pixel-level masks, and high-quality textual descriptions, is constructed via multi-VLM cross-validation and interactive annotation. Then, LandslideVLM, a landslide-oriented VLM, is fine-tuned via LoRA on LandslideBench to enhance geological semantic understanding. Finally, LandslideAgent, a domain rule-enhanced agent taking LandslideVLM as its cognitive backbone, employs a dual-rule controller incorporating structured report metadata constraints and cross-validation identification constraints to regulate automated tool invocation. Experiments demonstrate that LandslideBench provides effective baselines across five mainstream models on fine-grained classification and semantic segmentation. LandslideVLM achieves accuracy improvements of 10.96%, 32.87%, and 15.91% on landslide discrimination, fine-grained classification, and semantic description quality, respectively. LandslideAgent further enables autonomous multi-source spatial data inference, realizing full-process intelligence for landslide identification and analysis.
Chinese Translation
智能滑坡灾害解读对于灾害预防至关重要，但当前的范式在同时提取视觉特征和高层次地质科学语义方面存在困难，而通用视觉-语言模型（VLMs）在复杂地质场景中则遭遇感知限制和领域幻觉。为了解决这些挑战，我们提出了一种基于指令驱动的代理框架，包含三个组成部分。首先，通过多VLM交叉验证和互动标注构建了LandslideBench，一个具有七个子类型标签、高分辨率图像、像素级掩膜和高质量文本描述的多模态细粒度数据集。然后，LandslideVLM，一个专注于滑坡的VLM，通过LoRA在LandslideBench上进行微调，以增强地质语义理解。最后，LandslideAgent，一个以LandslideVLM为认知基础的增强领域规则代理，采用双规则控制器，结合结构化报告元数据约束和交叉验证识别约束，以规范自动化工具的调用。实验表明，LandslideBench在细粒度分类和语义分割方面为五个主流模型提供了有效的基线。LandslideVLM在滑坡识别、细粒度分类和语义描述质量上分别实现了10.96%、32.87%和15.91%的准确率提升。LandslideAgent进一步实现了自主多源空间数据推理，完成了滑坡识别与分析的全流程智能化。
cs.CV / 26 / 2606.18675
BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection
BrainFusionNet：一种深度学习和可解释人工智能模型，用于理解MRI图像的局部、全局和序列特征，以提高脑肿瘤检测的准确性
Abstract
The noise of Magnetic Resonance Imaging MRI poses challenges for Deep Learning DL when tumor boundaries are obscured tumor location and appearance are complex Therefore we develop BrainFusionNet that combines Convolutional Neural Networks CNNs Vision Transformers ViT and Gated Recurrent Units GRUs to extract spatial contextual and sequential features from MRI images for improved brain tumor classification Furthermore explainable AI such as SHAP LIME and GradCAM are integrated to visualise and highlight image regions that contribute to BrainFusionNets decisionmaking process The proposed BrainFusionNet model is evaluated on two publicly available MRI datasets Kfold validation suggests 98 accuracy on both datasets The model was compared with the six stateoftheart SOTA CNNs and transfer learning Among the SOTA CNNs DenseNet121 and VGG16 achieved the highest accuracy of 96 The novelty of BrainFusionNet is that the hybrid model effectively extracts local and global features from MRI images even in smallscale tumor regions and small tumor sizes The model has a balanced sequential CNN architecture to capture lowlevel and deeperlayer features a customized ViT that captures local features stabilizes gradient flow and reduces the risk of vanishing gradients during MRI image training The CNN and ViT outputs are fed into a GRU for final classification Furthermore we analyze pixel intensities to determine whether MRI image quality affects image classification Our findings are very novel in image interpretation as we found that the distribution of pixel intensities in MRI images affects DL performance
Chinese Translation
磁共振成像（MRI）的噪声对深度学习（DL）提出了挑战，因为肿瘤边界模糊，肿瘤的位置和外观复杂。因此，我们开发了BrainFusionNet，该模型结合了卷积神经网络（CNNs）、视觉变换器（ViT）和门控递归单元（GRUs），以从MRI图像中提取空间、上下文和序列特征，从而改善脑肿瘤的分类。此外，集成了可解释人工智能（XAI）技术，如SHAP、LIME和GradCAM，以可视化和突出对BrainFusionNet决策过程有贡献的图像区域。所提出的BrainFusionNet模型在两个公开可用的MRI数据集上进行了评估，K折验证表明在这两个数据集上均达到了98%的准确率。该模型与六种最先进的卷积神经网络（SOTA CNNs）和迁移学习进行了比较。在这些SOTA CNNs中，DenseNet121和VGG16达到了最高的96%准确率。BrainFusionNet的创新之处在于，该混合模型能够有效提取MRI图像中的局部和全局特征，即使在小规模肿瘤区域和小肿瘤尺寸下也能如此。该模型具有平衡的序列CNN架构，以捕捉低层次和深层次特征，定制的ViT用于捕捉局部特征，稳定梯度流，并减少在MRI图像训练过程中梯度消失的风险。CNN和ViT的输出被输入到GRU中进行最终分类。此外，我们分析了像素强度，以确定MRI图像质量是否影响图像分类。我们的发现对于图像解释具有重要的创新性，因为我们发现MRI图像中像素强度的分布影响了深度学习的性能。
cs.CV / 27 / 2606.18681
Moving Beyond Diversity: Visual Token Pruning as Subspace Reconstruction for Efficient VLMs
超越多样性：视觉标记剪枝作为高效视觉语言模型的子空间重构
Abstract
Despite their remarkable performance, Vision Language Models (VLMs) incur substantial computational overhead due to the large number of visual tokens. While diversity maximization has become a dominant strategy for token reduction, existing methods rely on cosine-based normalized similarity that discards magnitude information, failing to faithfully approximate the original feature representation and leading to suboptimal performance, particularly on compositional multi-skill reasoning tasks. In this paper, we introduce SPARE, a subspace reconstruction method that reformulates token pruning as a column subset selection problem and explicitly minimizes reconstruction error. By iteratively selecting tokens with large projection residuals, SPARE performs reconstruction-driven pruning beyond angular diversity. Moreover, we reveal a counterintuitive anti-relevance phenomenon: tokens with lower image-text relevance score can better preserve contextual information. Based on this finding, we incorporate anti-relevance into SPARE as an additional selection criterion to promote context-aware token selection. Extensive experiments across multiple VLMs and benchmarks demonstrate that SPARE consistently achieves state-of-the-art performance, with strong gains on compositional tasks. When applied to LLaVA, SPARE removes up to 94% of visual tokens while retaining 95% of the baseline performance, all in a fully training-free manner.
Chinese Translation
尽管视觉语言模型（VLMs）表现出色，但由于视觉标记数量庞大，它们仍然会产生显著的计算开销。虽然多样性最大化已成为标记减少的主要策略，但现有方法依赖于基于余弦的归一化相似度，这种方法忽略了幅度信息，未能真实地近似原始特征表示，导致性能不佳，特别是在组合多技能推理任务中。在本文中，我们提出了SPARE，一种将标记剪枝重新表述为列子集选择问题的子空间重构方法，并明确最小化重构误差。通过迭代选择具有大投影残差的标记，SPARE在超越角度多样性的同时进行重构驱动的剪枝。此外，我们揭示了一种反直觉的反相关现象：图像-文本相关性较低的标记能够更好地保留上下文信息。基于这一发现，我们将反相关性纳入SPARE作为额外的选择标准，以促进上下文感知的标记选择。在多个VLM和基准测试上的广泛实验表明，SPARE始终实现了最先进的性能，在组合任务上取得了显著提升。当应用于LLaVA时，SPARE在完全无训练的情况下去除了高达94%的视觉标记，同时保留了95%的基线性能。
cs.CV / 28 / 2606.18682
Multi-Class Brain Tumor Classification Using Advanced Deep Learning Models: A Comparative Study
使用先进深度学习模型的多类脑肿瘤分类：比较研究
Abstract
Despite recent advancements in deep learning, accurately classifying brain tumors from MRI images continues to pose challenges. In this research, we present a comprehensive evaluation of five different convolutional neural networks (CNN) architectures, including a customized baseline model and four pre-trained models - for use in classifying multi-class brain tumors using a clinically-sourced dataset of approximately 10,000 MRI images. We have utilized five different architectures; VGG16, VGG19, DenseNet121, and EfficientNetB0, which were all tested and trained within an identical experimental framework. Performance was measured by both overall accuracy and tumor-wise recall as a means to measure the clinically-relevant performance of each architecture. We found that EfficientNetB0 had the best overall classification accuracy at 95%, when compared to the other architectures tested; specifically VGG16 (94.37%), VGG19 (92.29%), DenseNet121 (90.91%) and the customized CNN (78.00%). An especially important finding of our research was the considerable improvement in detecting meningiomas; specifically, while simple CNNs could detect meningiomas with a recall rate of approximately 20%, EfficientNetB0 was able to detect meningiomas with a recall rate of 89%. Meningiomas are often difficult to detect because they can appear very subtly on MRI images. Additionally, an interesting finding was that the deeper VGG19 performed worse than the shallower VGG16. This indicates that in many cases the architectural efficiency of a CNN model may be more important than its depth when working with medical images. Overall, EfficientNetB0 appears to provide the optimal trade-off between classification accuracy, number of parameters used in the model and clinically meaningful performance.
Chinese Translation
尽管深度学习近年来取得了显著进展，但从MRI图像中准确分类脑肿瘤仍然面临挑战。在本研究中，我们对五种不同的卷积神经网络（CNN）架构进行了全面评估，包括一个定制的基线模型和四个预训练模型，旨在使用约10,000幅临床来源的MRI图像数据集对多类脑肿瘤进行分类。我们使用了五种不同的架构：VGG16、VGG19、DenseNet121和EfficientNetB0，所有模型均在相同的实验框架内进行了测试和训练。通过整体准确率和肿瘤分类召回率来衡量性能，以评估每种架构的临床相关性能。我们发现，EfficientNetB0的整体分类准确率最高，达到了95%，相比之下，其他测试的架构表现为：VGG16（94.37%）、VGG19（92.29%）、DenseNet121（90.91%）和定制CNN（78.00%）。我们研究的一个特别重要的发现是，在检测脑膜瘤方面的显著改善；具体而言，简单的CNN能够以约20%的召回率检测脑膜瘤，而EfficientNetB0的召回率达到了89%。脑膜瘤通常难以检测，因为它们在MRI图像上可能表现得非常微妙。此外，一个有趣的发现是，较深的VGG19的表现反而不如较浅的VGG16。这表明，在处理医学图像时，CNN模型的架构效率在许多情况下可能比其深度更为重要。总体而言，EfficientNetB0在分类准确性、模型参数数量和临床相关性能之间提供了最佳的权衡。
cs.CV / 29 / 2606.18687
Spatially Stratified Distillation for Heterogeneous Radar Place Recognition
用于异构雷达地点识别的空间分层蒸馏
Abstract
Scalable, all-weather place recognition increasingly relies on heterogeneous radar place recognition to bridge diverse hardware platforms. A notable application is matching queries from cost-effective 4D automotive radars against high-fidelity reference maps built by dense spinning radars. This process is fundamentally limited by the extreme sparsity (and narrow field-of-view) of the 4D sensor, which captures only a fraction of the structural density present in the spinning radar database. Prior efforts address this issue by unifying different radar signals. That is, projecting both signals into a common representational space. Yet, they suffer performance degradation in multi-session environments. In this paper, we propose spatially-stratified distillation (SSD); a strategy that replaces standard uniform distillation with an asymmetric spatial alignment derived directly from physical radar returns. In regions where both radars exhibit overlapping returns, SSD enforces strong feature alignment. Crucially, in sparse regions where the 4D student lacks returns but the teacher contains valid structure within the shared field of view, SSD applies heavily discounted distillation weights. Extensive evaluations of the recent HeRCULES dataset demonstrate that SSD significantly outperforms prior place recognition methods, achieving state-of-the-art results on its challenging dynamic sequences.
Chinese Translation
可扩展的、全天候的地点识别日益依赖于异构雷达地点识别，以连接多样化的硬件平台。一个显著的应用是将成本效益高的4D汽车雷达的查询与由密集旋转雷达构建的高保真参考地图进行匹配。这个过程在根本上受到4D传感器的极端稀疏性（以及狭窄的视场）的限制，因为它仅捕获旋转雷达数据库中存在的结构密度的一部分。之前的努力通过统一不同的雷达信号来解决这个问题，即将两种信号投影到一个共同的表示空间。然而，它们在多会话环境中表现出性能下降。在本文中，我们提出了空间分层蒸馏（Spatially-Stratified Distillation, SSD）；一种将标准均匀蒸馏替换为直接源自物理雷达回波的非对称空间对齐的策略。在两个雷达都显示重叠回波的区域，SSD 强制执行强特征对齐。关键是，在4D学生缺乏回波但教师在共享视场内包含有效结构的稀疏区域，SSD 应用大幅折扣的蒸馏权重。对最近的 HeRCULES 数据集的广泛评估表明，SSD 显著优于之前的地点识别方法，在其具有挑战性的动态序列上实现了最先进的结果。
cs.CV / 30 / 2606.18702
UniTemp: Unlocking Video Generation in Any Temporal Order via Bidirectional Distillation
UniTemp：通过双向蒸馏解锁任意时间顺序的视频生成
Abstract
Autoregressive video diffusion models have emerged as a promising approach for long video generation, achieving strong performance in streaming settings. However, existing methods are restricted to forward temporal generation, whereas practical video creation often requires flexible generation order, e.g., conditioning on future context to extend backward, or on both past and future context for inbetween generation. We bridge this gap by training an autoregressive model that supports generation in arbitrary temporal directions. A key technical challenge arises from the Causal 3D VAE widely used in video diffusion models, which encodes latents strictly conditioned on past context. While suited for forward generation, this causal structure causes inter-block discontinuities when generation proceeds backward. To address this, we introduce blockwise anchor latents, a set of auxiliary latents that restore the missing past context at block boundaries during backward generation. Built on this design, we propose UniTemp, a bidirectional distillation framework that trains a single autoregressive student model for any-direction video generation. At inference time, UniTemp conditions on arbitrary past and/or future frames, improving controllability for both bidirectional and inbetween generation. Experiments show that UniTemp maintains competitive performance on short and long video generation compared to forward-only methods, while enabling diverse workflows such as bidirectional video extension, inbetween generation, looping video generation, scene transition, and visual story generation. Project website: https://lzhangbj.github.io/projects/unitemp/
Chinese Translation
自回归视频扩散模型已成为长视频生成的一种有前景的方法，在流媒体设置中表现出色。然而，现有方法仅限于前向时间生成，而实际视频创作往往需要灵活的生成顺序，例如，基于未来上下文进行向后扩展，或基于过去和未来上下文进行中间生成。我们通过训练一个支持任意时间方向生成的自回归模型来填补这一空白。一个关键的技术挑战来自于视频扩散模型中广泛使用的因果3D变分自编码器（Causal 3D VAE），该模型严格基于过去上下文编码潜变量。虽然适合前向生成，但这种因果结构在生成向后时会导致块间的不连续性。为了解决这个问题，我们引入了块状锚定潜变量（blockwise anchor latents），这是一组辅助潜变量，在向后生成过程中恢复块边界处缺失的过去上下文。在此设计基础上，我们提出了UniTemp，一个双向蒸馏框架，训练一个单一的自回归学生模型以实现任意方向的视频生成。在推理时，UniTemp基于任意过去和/或未来帧进行条件生成，提高了双向和中间生成的可控性。实验表明，与仅限前向的方法相比，UniTemp在短视频和长视频生成上保持了竞争力，同时支持多样化的工作流程，如双向视频扩展、中间生成、循环视频生成、场景过渡和视觉故事生成。项目网站：https://lzhangbj.github.io/projects/unitemp/
cs.CV / 31 / 2606.18707
PEFT-MedSAM: Efficient Fine-Tuning of Medical Foundation Models for Explainable Skin Lesion Segmentation
PEFT-MedSAM：用于可解释皮肤病变分割的医学基础模型的高效微调
Abstract
Automated segmentation of skin lesions using deep learning models for dermoscopic images can be very helpful in finding melanomas earlier than they would normally be detected. However, most deep learning methods available do not perform well. The aim of this paper is to present a parameter-efficient fine-tuning method called PEFT-MedSAM for adapting the Medical Segment Anything Model (MedSAM) to automatically segment dermoscopic skin lesions. The PEFT-MedSAM method uses only the lightweight mask decoder for training the model while keeping the pre-trained image encoder and prompt encoder frozen. The experiments performed on the ISIC 2018 benchmark dataset shows that PEFT-MedSAM obtains a dice coefficient of .9411 and an intersection over union value of .8918 when compared to both a fully trained U-Net baseline (.8715 dice coefficient) and zero-shot MedSAM inference (.8997 dice coefficient). The external validation of the model using PH2 dataset shows .9467 dice coefficient with +/- .0310 standard deviation. Supportive evidence for these claims include a p-value less than .0001 for Wilcoxon signed rank tests comparing the two datasets and bootstrap-estimated 95% confidence intervals of [.9364,.9447] that represent the estimated range of possible values for the average dice coefficient obtained by repeating the test. To increase clinical trustworthiness, we used Grad-CAM explainability along with a pointing game based evaluation methodology to evaluate the CNN baseline model on the validation set. The results showed that we had an accuracy rate of 98.27% on the validation set of 519 images and confirmed that the model classified regions containing skin lesions.
Chinese Translation
利用深度学习模型对皮肤病变进行自动分割，能够在早期发现黑色素瘤，早于常规检测。然而，现有的大多数深度学习方法表现不佳。本文旨在提出一种名为PEFT-MedSAM的参数高效微调方法，以适应医学分割任意模型（Medical Segment Anything Model, MedSAM）进行皮肤病变的自动分割。PEFT-MedSAM方法仅使用轻量级的掩码解码器进行模型训练，同时保持预训练的图像编码器和提示编码器不变。在ISIC 2018基准数据集上的实验表明，PEFT-MedSAM获得了0.9411的Dice系数和0.8918的交并比（Intersection over Union, IoU）值，相较于完全训练的U-Net基线（0.8715的Dice系数）和零样本MedSAM推理（0.8997的Dice系数）。使用PH2数据集对模型进行外部验证时，Dice系数为0.9467，标准差为±0.0310。支持这些声明的证据包括Wilcoxon符号秩检验的p值小于0.0001，比较两个数据集的结果，以及通过自助法估计的95%置信区间为[0.9364, 0.9447]，代表重复测试所获得的平均Dice系数的可能值范围。为了提高临床可信度，我们使用Grad-CAM可解释性以及基于指向游戏的评估方法对验证集上的CNN基线模型进行了评估。结果显示，在519张图像的验证集上，我们的准确率达到了98.27%，并确认模型能够正确分类包含皮肤病变的区域。
cs.CV / 32 / 2606.18721
Rethinking the Pointer Loss in Table Structure Recognition: Geometry-Aware Pointer Loss for Spatial Locality
重新思考表格结构识别中的指针损失：基于几何的指针损失以增强空间局部性
Abstract
Table Structure Recognition (TSR) using a pointer network achieves impressive results by predicting HTML sequences while aligning tags to detected text (or cell) regions. However, our analysis reveals that when pointer networks fail, 79.6% of errors occur between spatially adjacent cells (Manhattan distance <= 2). Despite this, standard cross-entropy loss weights all negative candidates equally. In this work, we propose Geometry-Aware Pointer (GAP) Loss, which reweights the cross-entropy objective based on spatial proximity to ground truth. By applying inverse distance weighting, GAP focuses gradient flow where the model struggles most: immediate neighbors receive stronger gradients than distant cells. Our approach requires only a straightforward modification to the loss computation, maintaining the same model architecture with zero additional inference cost. Extensive experiments on PubTabNet and SynthTabNet demonstrate that GAP consistently reduces adjacent-cell errors, achieving new state-of-the-art performance. Our findings suggest that incorporating geometric inductive biases at the loss level provides a simple yet effective approach to robust TSR. Our code is available at https://github.com/teamreboott/GAP
Chinese Translation
使用指针网络进行表格结构识别（TSR）通过预测 HTML 序列并将标签与检测到的文本（或单元格）区域对齐，取得了令人印象深刻的结果。然而，我们的分析显示，当指针网络失败时，79.6% 的错误发生在空间相邻的单元格之间（曼哈顿距离 <= 2）。尽管如此，标准的交叉熵损失对所有负候选项的权重是相同的。在本研究中，我们提出了基于几何的指针损失（Geometry-Aware Pointer Loss, GAP），该损失根据与真实值的空间接近度重新加权交叉熵目标。通过应用逆距离加权，GAP 将梯度流集中在模型最困难的地方：相邻单元格获得比远离单元格更强的梯度。我们的方法只需对损失计算进行简单的修改，保持相同的模型架构且没有额外的推理成本。在 PubTabNet 和 SynthTabNet 上进行的大量实验表明，GAP 一致性地减少了相邻单元格的错误，达到了新的最先进性能。我们的研究结果表明，在损失层面引入几何归纳偏置提供了一种简单而有效的鲁棒 TSR 方法。我们的代码可在 https://github.com/teamreboott/GAP 获取。
cs.CV / 33 / 2606.18723
Clinically Aligned Geometry Constraints for Robust IVUS Vessel Boundary Segmentation
临床对齐几何约束的稳健 IVUS 血管边界分割
Abstract
Intravascular ultrasound (IVUS) lumen and external elastic membrane (EEM) segmentation is important for quantitative coronary plaque burden assessment. Errors in lumen or EEM delineation directly propagate to plaque area, plaque burden and geometric measurements. However, standard methods prioritising overlap scores often suffer from boundary drift and topology errors, leading to inaccurate clinical measurements. We present GeoCat, a geometry-consistent network that processes 5-frame IVUS clips using dual Cartesian-polar encoders with cross-domain attention and temporal fusion. A differentiable geometry consistency loss directly supervises clinically relevant descriptors including diameters, orientations, and cross-sectional areas. The model is trained on 12,242 annotated frames from 146 patients acquired with two commercial IVUS systems. We evaluate performance using both segmentation accuracy and plaque-relevant clinical metrics, including Dice/IoU, boundary measures(95HD (mm), ASSD), topology violation rate, and clinical geometry errors (dmax/dmin, angles, and areas). On our dataset, GeoCat achieves a Dice of 0.93, reduces 95HD to 0.14 mm, and lowers topology violations to 1.0%. Importantly, it significantly improves geometric fidelity, yielding diameter errors of 0.13-0.16 mm and angular errors of ~8 degrees, supporting reliable plaque burden quantification.
Chinese Translation
血管内超声（IVUS）腔体和外弹性膜（EEM）分割对于定量评估冠状动脉斑块负担至关重要。腔体或 EEM 描绘中的错误会直接影响斑块面积、斑块负担和几何测量。然而，标准方法优先考虑重叠得分，往往会遭遇边界漂移和拓扑错误，导致临床测量不准确。我们提出了 GeoCat，这是一种几何一致性网络，利用双笛卡尔-极坐标编码器处理 5 帧 IVUS 片段，并结合跨域注意力和时间融合。可微分的几何一致性损失直接监督与临床相关的描述符，包括直径、方向和横截面积。该模型在 146 名患者的 12,242 帧标注图像上进行训练，这些图像是通过两种商业 IVUS 系统获取的。我们使用分割准确性和与斑块相关的临床指标（包括 Dice/IoU、边界测量（95HD（mm）、ASSD）、拓扑违规率以及临床几何误差（dmax/dmin、角度和面积））来评估性能。在我们的数据集中，GeoCat 实现了 0.93 的 Dice 值，将 95HD 降至 0.14 mm，并将拓扑违规率降低至 1.0%。重要的是，它显著提高了几何保真度，直径误差为 0.13-0.16 mm，角度误差约为 8 度，支持可靠的斑块负担量化。
cs.CV / 34 / 2606.18749
Toward Training-Free Zero-Shot Anomaly Detection in 3D Medical Images: A Batch-Based Approach Using 2D Foundation Models
朝着无训练的零样本异常检测在3D医学图像中的应用：一种基于批次的方法，使用2D基础模型
Abstract
Zero-shot anomaly detection (ZSAD) is attractive for medical imaging because clinical systems must handle heterogeneous acquisition protocols, changing patient populations, and pathologies for which annotated training data may be unavailable. Most existing zero-shot anomaly detection methods are designed for 2D images, and their direct extension to 3D medical volumes is limited by the scarcity of large-scale volumetric foundation models or by the difficulty of utilizing volumetric context. We propose CS3F, a training-free batch-based framework for ZSAD in 3D medical images using 2D foundation models. Each volume is decomposed along multiple anatomical axes and encoded slice-wise by a 2D vision transformer. These are then converted into localized volumetric tokens by pooling neighboring slice features. Anomaly scores are obtained from cross-subject mutual similarity: tokens that lack close analogues in other subjects are assigned higher anomaly scores. To reduce the attenuation of focal lesion signals caused by depth pooling, we introduce a coarse-to-fine tokenization strategy that enables fine-resolution volumetric scoring without exhaustive matching. CS3F is evaluated on brain MRI across metastases, glioma, and stroke, as well as validated on lung CT to test generalizability beyond atlas-aligned brain MRI. The results show that frozen 2D foundation models can support anomaly localization in 3D medical images, and that the benefit of fine tokenization depends strongly on lesion contrast and imaging modality.
Chinese Translation
零样本异常检测（ZSAD）在医学成像中具有吸引力，因为临床系统必须处理异构的采集协议、变化的患者群体以及可能没有注释训练数据的病理。现有的大多数零样本异常检测方法是为2D图像设计的，直接扩展到3D医学体积的能力受到大规模体积基础模型稀缺或利用体积上下文的困难的限制。我们提出了CS3F，一种无训练的基于批次的框架，用于在3D医学图像中进行ZSAD，使用2D基础模型。每个体积沿多个解剖轴进行分解，并通过2D视觉变换器逐片编码。然后，通过汇聚相邻切片特征将其转换为局部体积标记。异常分数通过跨主体的互相相似性获得：在其他主体中缺乏近似的标记被分配更高的异常分数。为了减少深度汇聚造成的焦点病变信号衰减，我们引入了一种粗到细的标记策略，使得在不进行全面匹配的情况下实现细分辨率的体积评分。CS3F在脑部MRI（包括转移瘤、胶质瘤和中风）上进行了评估，并在肺部CT上进行了验证，以测试其在超越与图谱对齐的脑部MRI的可推广性。结果表明，冻结的2D基础模型可以支持3D医学图像中的异常定位，而细致的标记化的好处在很大程度上依赖于病变对比度和成像模式。
cs.CV / 35 / 2606.18753
SMART: A Flexible, Interpretable, and Scalable Spatio-temporal Brain Atlas from High-Resolution Imaging Data
SMART：一种基于高分辨率成像数据的灵活、可解释和可扩展的时空脑图谱
Abstract
We introduce SMART, a framework for learning a flexible, interpretable, and scalable spatio-temporal brain atlas from longitudinal high-resolution 3D medical images. Existing approaches to spatio-temporal atlas construction rely on black-box generative models that lack flexibility, limit interpretability, and struggle to scale to high-dimensional data. SMART addresses these challenges by learning a continuous disease-time atlas that decouples global group-wise disease dynamics from their patient-specific anatomical manifestation. Guided by anatomically inspired priors, SMART models interpretable global trajectories of regional progression along a shared disease timeline through region-specific differential equations. Global trajectories are further personalized to individual anatomies via dense diffeomorphic displacements parameterized by a flexible and scalable multi-scale Neural Cellular Automata. Evaluated on five longitudinal MRI datasets in Alzheimer's disease (ADNI-1/GO/2, OASIS-3, AIBL; > 1,300 subjects), SMART produces anatomically meaningful predictions of disease progression and achieves state-of-the-art forecasting accuracy and improved temporal consistency over adversarial and diffusion baselines. Our approach establishes a new paradigm for flexible, interpretable, and scalable modeling of spatio-temporal change in high-dimensional medical image time-series.
Chinese Translation
我们介绍了SMART，一个从纵向高分辨率三维医学图像中学习灵活、可解释和可扩展的时空脑图谱的框架。现有的时空图谱构建方法依赖于缺乏灵活性的黑箱生成模型，这限制了可解释性，并且难以扩展到高维数据。SMART通过学习一个连续的疾病时间图谱来解决这些挑战，该图谱将全局群体疾病动态与患者特定的解剖表现解耦。SMART在解剖学启发的先验指导下，通过区域特定的微分方程模型化沿共享疾病时间线的区域进展的可解释全局轨迹。全局轨迹进一步通过由灵活且可扩展的多尺度神经元细胞自动机参数化的密集微分同胚位移个性化到个体解剖结构。我们在五个阿尔茨海默病（ADNI-1/GO/2、OASIS-3、AIBL；> 1,300名受试者）的纵向MRI数据集上进行了评估，SMART产生了具有解剖学意义的疾病进展预测，并在对抗性和扩散基线之上实现了最先进的预测准确性和改进的时间一致性。我们的方法为高维医学图像时间序列中时空变化的灵活、可解释和可扩展建模建立了新的范式。
cs.CV / 36 / 2606.18765
SpectralDiT: Timestep-Conditioned Spectral Residual Correction for Flow-Matching DiTs
SpectralDiT：基于时间步条件的光谱残差修正用于流匹配的Diffusion Transformers
Abstract
We propose SpectralDiT, a lightweight modification to flow-matching Diffusion Transformers that adds timestep-conditioned spectral correction to the MLP residual branch. The module decomposes each residual update into low- and high-frequency components on the patch-token grid, then learns a zero-initialized additive gate so the model initially matches the baseline DiT. On CIFAR-10 pixel-space generation, SpectralDiT improves FID from 20.78 to 19.71 at patch size 1 and reduces the radial Fourier spectrum gap. Furthermore, we scale our method to latent diffusion on ImageNet-100. With 0.6% additional theoretical FLOPs and 1.36% additional parameters, SpectralDiT improves latent flow-matching, achieving an 8.7% relative FID reduction under classifier-free guidance (CFG 2.0). All reported results are averaged over five seeds. Ablations and gate visualizations on CIFAR-10 reveal stable block-specific spectral correction patterns.
Chinese Translation
我们提出了SpectralDiT，这是一种轻量级的流匹配Diffusion Transformers的修改，向MLP残差分支添加了基于时间步的光谱修正。该模块将每次残差更新分解为低频和高频成分，在补丁-标记网格上进行处理，然后学习一个零初始化的加性门，以便模型最初匹配基线DiT。在CIFAR-10像素空间生成中，SpectralDiT将FID从20.78提高到19.71（补丁大小为1），并减少了径向傅里叶谱差距。此外，我们将该方法扩展到ImageNet-100上的潜在扩散。在额外0.6%的理论FLOPs和1.36%的额外参数下，SpectralDiT改善了潜在流匹配，在无分类器引导（CFG 2.0）下实现了8.7%的相对FID减少。所有报告的结果均为五个种子的平均值。在CIFAR-10上的消融实验和门可视化揭示了稳定的块特定光谱修正模式。
cs.CV / 37 / 2606.18780
SAMA: Semantic Anchor-aligned Augmentation for Unified Low-Resource Multimodal Information Extraction
SAMA：语义锚点对齐的统一低资源多模态信息提取增强
Abstract
Multimodal Information Extraction (MIE)-covering tasks such as Multimodal Named Entity Recognition (MNER), Relation Extraction (MRE), and Event Extraction (MEE)-is essential for understanding multimedia content but remains constrained by severe data scarcity. Although data augmentation is a promising remedy, existing approaches are impeded by coarse cross-modal alignment and fragmented, task-specific designs that fail to exploit shared semantic knowledge. To overcome these limitations, we introduce Semantic Anchor-aligned Multimodal Augmentation (SAMA), a unified framework for generating high-fidelity, task-aware synthetic data. SAMA constructs structured semantic anchors from ground-truth labels to guide a Collaborative Multi-Experts Multimodal Large Language Model (CME-MLLM), which integrates a Universal Adapter for shared semantics with Task-Specific Adapters to produce diverse yet constraint-compliant textual samples. For image synthesis, SAMA employs an Anchor-Preserving Diffusion mechanism that uses anchor-weighted prompts and latent conditioning to maintain critical semantic anchors while diversifying visual contexts. To eliminate the need for manual verification, SAMA further introduces a Dual-Constraint Filtering module that selects synthetic samples based on both cross-modal consistency and anchor fidelity. Extensive experiments across benchmark datasets for MNER, MRE, and MEE demonstrate that SAMA consistently outperforms state-of-the-art augmentation baselines under both fully supervised and low-resource settings, underscoring its versatility, robustness, and effectiveness.
Chinese Translation
多模态信息提取（MIE）涵盖多模态命名实体识别（MNER）、关系提取（MRE）和事件提取（MEE）等任务，对于理解多媒体内容至关重要，但仍受到严重数据稀缺的限制。尽管数据增强是一种有前景的解决方案，但现有方法受到粗糙的跨模态对齐和碎片化的任务特定设计的阻碍，未能充分利用共享的语义知识。为克服这些限制，我们提出了语义锚点对齐的多模态增强（SAMA），这是一个生成高保真、任务感知合成数据的统一框架。SAMA 从真实标签构建结构化语义锚点，以指导一个协作多专家多模态大语言模型（CME-MLLM），该模型将共享语义的通用适配器与任务特定适配器相结合，以生成多样化且符合约束的文本样本。在图像合成方面，SAMA 采用了一种锚点保持扩散机制，利用锚点加权提示和潜在条件来保持关键语义锚点，同时多样化视觉上下文。为了消除手动验证的需要，SAMA 进一步引入了双约束过滤模块，根据跨模态一致性和锚点保真度选择合成样本。在 MNER、MRE 和 MEE 的基准数据集上进行的广泛实验表明，SAMA 在完全监督和低资源设置下始终优于最先进的增强基线，突显了其多功能性、鲁棒性和有效性。
cs.CV / 38 / 2606.18783
SCR-Guided Difficulty-Aware Optimization for Infrared Small Target Detection
基于SCR引导的难度感知优化用于红外小目标检测
Abstract
Infrared small target detection remains challenging due to severe background clutter, low contrast, and weak spatial responses where geometric overlap alone is insufficient to characterize detection quality. In this work, we propose REEM (Reweighted Explicit-visibility Enhanced Modulation), a lightweight SCR-guided difficulty-aware optimization framework that incorporates Signal-to-Clutter Ratio (SCR) as a physically meaningful visibility prior during training. Instead of modifying the network architecture or directly optimizing SCR, REEM computes a ground-truth local SCR from the input image and applies a differentiable modulation to the soft-IoU learning signal, emphasizing low-visibility targets while preserving stable optimization and identical inference behavior. REEM is integrated into a U-Net-based MSHNet without introducing additional parameters, architectural modifications, or inference-time overhead. Extensive experiments demonstrate consistent improvements over the baseline, achieving higher IoU and detection probability (Pd) together with substantially reduced false alarms (FA), particularly under challenging low-visibility conditions. These results suggest that SCR-guided difficulty-aware optimization provides an effective and physically grounded complement to conventional overlap-based objectives for infrared small target detection. The code is available at https://github. com/yall-in-one/Reemm.
Chinese Translation
红外小目标检测仍然面临挑战，主要由于背景杂波严重、对比度低以及空间响应弱，仅靠几何重叠无法充分表征检测质量。在本研究中，我们提出了REEM（重加权显式可见性增强调制），这是一种轻量级的基于SCR引导的难度感知优化框架，在训练过程中将信号与杂波比（SCR）作为一种具有物理意义的可见性先验。REEM并不修改网络架构或直接优化SCR，而是从输入图像中计算真实的局部SCR，并对软IoU学习信号施加可微调制，强调低可见性目标，同时保持稳定的优化和一致的推理行为。REEM被集成到基于U-Net的MSHNet中，而无需引入额外的参数、架构修改或推理时间开销。大量实验表明，与基线相比，REEM在IoU和检测概率（Pd）上均有一致的提升，同时显著减少了虚假警报（FA），尤其是在低可见性条件下。这些结果表明，基于SCR引导的难度感知优化为红外小目标检测提供了一种有效且具有物理基础的补充，超越了传统的基于重叠的目标。代码可在 https://github.com/yall-in-one/Reemm 获取。
cs.CV / 39 / 2606.18787
Learned Radius Estimation for UDF-Based Point Cloud Reconstruction
基于学习的UDF点云重建半径估计
Abstract
Surface reconstruction from point clouds is important for consumer-grade 3D capture, including AR/VR and indoor scanning. Local-patch Unsigned Distance Field (UDF) methods are lightweight and generalizable, but their accuracy depends on the support radius, traditionally fixed or selected by a one-dimensional curvature heuristic that cannot capture heterogeneous local geometry. We propose a learned per-query radius selector that predicts a continuous support radius and plugs into a frozen LoSF-UDF backbone. The selector is trained using off-grid target radii obtained by parabolic interpolation of cached UDF error curves. Experiments show improved fine-scale reconstruction accuracy.
Chinese Translation
从点云进行表面重建对于消费级3D捕捉至关重要，包括增强现实/虚拟现实（AR/VR）和室内扫描。局部补丁无符号距离场（UDF）方法轻量且具有良好的泛化能力，但其准确性依赖于支持半径，传统上该半径是固定的或通过一维曲率启发式选择的，这无法捕捉异质局部几何形状。我们提出了一种学习的查询半径选择器，该选择器预测连续的支持半径，并与冻结的LoSF-UDF骨干网络相结合。该选择器通过对缓存的UDF误差曲线进行抛物线插值获得的离网目标半径进行训练。实验表明，细尺度重建的准确性得到了改善。
cs.CV / 40 / 2606.18788
HandwritingAgent: Language-Driven Handwriting Synthesis in Scalable Vector Space
HandwritingAgent：基于语言驱动的可扩展矢量空间手写合成
Abstract
Teaching machines to emulate natural handwriting styles remains an open challenge, as it requires synthesizing stroke sequences that dynamically vary in shape, texture, pressure and script - not only across individuals, but also within a single person's handwriting. Attempts at this challenge have largely explored deep learning methods in both online and offline settings. However, these approaches are often constrained by style-specific architectural choices, heavy reliance on large datasets, high compute costs, and a lack of flexible control over writing styles through natural language. To this end, we introduce HandwritingAgent, a language-driven agent that can synthesize natural handwriting sequences directly in Scalable Vector Graphics (SVG) format with no need for style-specific training. The agent leverages a large reasoning model to geometrically analyse and autoregressively generate target handwritten glyphs as stroke sequences in a discrete grid canvas environment. Generation is conditioned on texts provided in either conversational or non-conversational mode, along with a reference handwriting-style image. Experiments on diverse handwriting tasks spanning imitation, recognition, multi-lingual handwriting synthesis, and generation of complex handwritten maths and science expressions indicate substantial improvement in performance, with HandwritingAgent matching or surpassing state-of-the-art generative handwriting models, while providing a more efficient, controllable, and generalizable synthesis method.
Chinese Translation
教机器模拟自然手写风格仍然是一个未解决的挑战，因为这需要合成在形状、纹理、压力和书写风格上动态变化的笔画序列——不仅在不同个体之间，而且在同一个人的手写中也是如此。对此挑战的尝试主要探索了在线和离线环境中的深度学习方法。然而，这些方法通常受到特定风格的架构选择、对大型数据集的高度依赖、高计算成本以及缺乏通过自然语言灵活控制书写风格的限制。为此，我们引入了HandwritingAgent，一个基于语言驱动的代理，可以直接以可扩展矢量图形（SVG）格式合成自然手写序列，而无需特定风格的训练。该代理利用大型推理模型在离散网格画布环境中几何分析并自回归生成目标手写字形作为笔画序列。生成过程以提供的文本为条件，文本可以是对话模式或非对话模式，并附有参考手写风格图像。在模仿、识别、多语言手写合成以及复杂手写数学和科学表达式生成等多样化手写任务上的实验表明，性能有了显著提升，HandwritingAgent的表现与最先进的生成手写模型相匹配或超越，同时提供了一种更高效、可控和可推广的合成方法。
cs.CV / 41 / 2606.18793
Fuzzy-Geometric Branch-Point Modeling for Structure-Aware Augmentation of Handwritten Chinese Characters
基于模糊几何的结构感知分支点建模用于手写汉字的增强
Abstract
Data scarcity and structural distortion significantly limit handwriting recognition in high-security authentication. Existing augmentation methods often cause topological and morphological damage, particularly when processing complex Chinese characters where stroke intersections, ligatures, and sharp turns render traditional branch-point detection unreliable. To address this, this paper proposes a fuzzy geometry-driven structure-aware (FGSA) augmentation framework. We model branch points as fuzzy sets within the skeleton space, constructing a continuous branch-point membership field by integrating topological neighborhood evidence with direction field divergence. This membership field is adaptively optimized via an unsupervised surrogate objective, enabling robust stroke decoupling without manual annotation. Finally, kinematically-aligned samples are synthesized through parameterized cubic B\'ezier reconstruction and multi-strategy perturbations, ensuring a balance between structural fidelity and sample diversity. Moreover, we establish LZUSig, a large-scale, highly challenging dataset specifically dedicated to fine-grained structural degradation in Chinese handwritten signatures. Extensive experiments on CASIA-HWDB1.1, ChiSig, and LZUSig demonstrate that FGSA significantly reduces the word-level error rate ($\Delta$WER), achieving optimal recognition gains over the compared baselines. More importantly, it strikes a robust trade-off among task gain, structural fidelity, and discriminative feature preservation, offering a highly controllable solution for handwriting augmentation.
Chinese Translation
数据稀缺和结构扭曲显著限制了高安全性认证中的手写识别。现有的增强方法往往会导致拓扑和形态损伤，特别是在处理复杂汉字时，由于笔画交叉、连笔和急转弯使得传统的分支点检测不可靠。为了解决这一问题，本文提出了一种基于模糊几何的结构感知（FGSA）增强框架。我们将分支点建模为骨架空间中的模糊集，通过将拓扑邻域证据与方向场散度相结合，构建了一个连续的分支点隶属场。该隶属场通过无监督的替代目标进行自适应优化，从而实现了稳健的笔画解耦，无需手动标注。最后，通过参数化的三次贝塞尔重建和多策略扰动合成运动对齐的样本，确保了结构保真度与样本多样性之间的平衡。此外，我们建立了LZUSig，一个专门针对汉字手写签名细粒度结构退化的大规模高挑战数据集。在CASIA-HWDB1.1、ChiSig和LZUSig上的广泛实验表明，FGSA显著降低了字级错误率（$ ext{ΔWER}$），在与比较基线的对比中实现了最佳识别增益。更重要的是，它在任务增益、结构保真度和区分特征保留之间达成了稳健的权衡，为手写增强提供了一种高度可控的解决方案。
cs.CV / 42 / 2606.18824
Where Will They Go? Modelling Multimodal Pedestrian Manoeuvres from Ego-centric Videos
他们将去往何处？基于自我中心视频的多模态行人动作建模
Abstract
Pedestrian trajectory prediction from an ego-centric camera is challenging since it depends on complex interactions with vehicles and scene context, as well as the intention of the pedestrian. By modelling correlation and intent from the historical and future trajectories of the pedestrian, it will usually result in a multimodal (i.e. multiple modes) distribution. Existing stochastic predictors often sample multiple futures from a single unimodal distribution, which can yield sub-optimal 'mixed-mode' trajectories that lie between distinct motion patterns and become implausible in real scenes. In this paper, we propose MMPM, a mode-aware framework that separately models future trajectory distributions into semantically meaningful modes based on the pedestrian's crossing behavior. MMPM consists of two modules: behavior-aware Pedestrian Interaction Module (PIM) that jointly captures pedestrian-vehicle and pedestrian-environment interactions by introducing gaze, head and hand gesture, and a CVAE-based Mode-aware Trajectory Predictor (MTP) module to model the future trajectory distributions on two modes, crossing and non-crossing the road, separately. A query-based decoder further enforces mode consistency during decoding. Experiments on PIE and JAAD datasets show that our method surpasses state-of-the-art baselines. Our proposed MTP is model-agnostic, which can be integrated into existing frameworks such as BiTrap-NP and SGNet-ED to further improve future trajectory prediction performance. We additionally introduce a data-driven validation protocol that matches predictions to spatio-temporally consistent ground-truth trajectories, demonstrating improved frame-wise displacement errors over previous work.
Chinese Translation
从自我中心摄像头预测行人轨迹具有挑战性，因为这依赖于与车辆和场景上下文的复杂交互，以及行人的意图。通过建模行人历史和未来轨迹之间的相关性和意图，通常会导致多模态（即多种模式）分布。现有的随机预测器通常从单一的单模态分布中采样多个未来，这可能导致次优的“混合模式”轨迹，这些轨迹位于不同运动模式之间，并在真实场景中变得不切实际。本文提出了MMPM（多模态行人运动模型），这是一个模式感知框架，基于行人的过马路行为将未来轨迹分布分别建模为语义上有意义的模式。MMPM由两个模块组成：行为感知的行人交互模块（PIM），通过引入视线、头部和手势，联合捕捉行人与车辆及行人与环境的交互；以及基于条件变分自编码器（CVAE）的模式感知轨迹预测器（MTP）模块，分别建模过马路和不过马路的未来轨迹分布。基于查询的解码器在解码过程中进一步强化了模式一致性。在PIE和JAAD数据集上的实验表明，我们的方法超越了现有的最先进基线。我们提出的MTP是模型无关的，可以集成到现有框架中，如BiTrap-NP和SGNet-ED，以进一步提高未来轨迹预测性能。此外，我们还引入了一种数据驱动的验证协议，将预测与时空一致的真实轨迹相匹配，展示了相较于以往工作的帧级位移误差的改善。
cs.CV / 43 / 2606.18825
DreamReg: Belief-Driven World Model for 2D-3D Ultrasound Registration
DreamReg：基于信念的二维-三维超声配准世界模型
Abstract
Ultrasound (US) is widely used for surgical navigation, yet real-time registration between intraoperative 2D slices and preoperative 3D volumes remains challenging due to partial observability, speckle noise, and the action-dependent US acquisition. Existing methods are one-shot or short-horizon, making it hard for them to gather evidence over time or capture how surgeons adjust probe motion based on on-screen feedback. We propose DreamReg, a belief-driven world-model framework that formulates 2D-3D registration as belief updating over rigid transformations. DreamReg maintains a latent belief state that summarizes past observations and poses information, and continuously refines the transformation through learned dynamics as new slices arrive. During training, DreamReg is exposed to probe-motion trajectories that mimic clinical scanning behavior and learns to update its belief by conditioning pose refinement on the current US observation. During inference, DreamReg refines registration via internal imagination: it rolls out the learned world model to simulate candidate probe motions and their predicted observations, and integrates these imagined outcomes to converge to an accurate rigid transformation. Experiments on CAMUS and u-RegPro datasets demonstrate improved robustness and competitive registration accuracy for real-time guidance compared with state-of-the-art methods.
Chinese Translation
超声（US）广泛应用于外科导航，但由于部分可观测性、斑点噪声和依赖于动作的超声采集，术中二维切片与术前三维体积之间的实时配准仍然具有挑战性。现有方法通常是一次性或短期的，难以随着时间的推移收集证据或捕捉外科医生根据屏幕反馈调整探头运动的方式。我们提出了DreamReg，一个基于信念的世界模型框架，将二维-三维配准公式化为对刚性变换的信念更新。DreamReg维护一个潜在的信念状态，概括过去的观察和姿态信息，并随着新切片的到来不断通过学习的动态来细化变换。在训练过程中，DreamReg接触到模拟临床扫描行为的探头运动轨迹，并通过将姿态细化与当前超声观察相结合来学习更新其信念。在推理过程中，DreamReg通过内部想象来细化配准：它展开学习到的世界模型以模拟候选探头运动及其预测观察，并整合这些想象的结果以收敛到准确的刚性变换。在CAMUS和u-RegPro数据集上的实验表明，与最先进的方法相比，DreamReg在实时引导方面表现出更强的鲁棒性和竞争力的配准精度。
cs.CV / 44 / 2606.18841
Rethinking Air-Ground Collaboration: A Progressive Cross-Task Benchmark and Socialized Learning Framework
重新思考空地协作：一个渐进式跨任务基准和社会化学习框架
Abstract
Air-ground collaborative perception is crucial for robust visual understanding in real-world dynamic environments. However, existing studies typically formulate collaboration as single-task cross-view fusion, overlooking the functional dependencies among localization, target association, and fine-grained parsing. In addition, the heterogeneous nature of aerial and ground views introduces substantial geometric, scale, and occlusion discrepancies, making uniform feature sharing vulnerable to negative transfer. To tackle these issues, we model air-ground perception as a progressive cross-task collaboration task and construct the Air-Ground Progressive Collaboration (AGPC) benchmark, a spatio-temporally aligned benchmark comprising more than 745K raw video frames. Built upon this benchmark, we propose Socialized Co-Perception (SCP), a coarse-to-fine framework that organizes collaboration progressively from aerial global localization to ground target association and identity-aware parsing. Its core module, the Dual-Layer Router (DLR), decouples input-side multi-scale expert selection from output-side task-conditioned modulation, enabling selective cross-view and cross-task interaction while suppressing harmful interference. Extensive experiments demonstrate the effectiveness of SCP. It achieves a 3.73\% coevolutionary gain and a 7.86\% improvement in average downstream performance. These results show that task-conditioned collaboration is more effective than uniform fusion for heterogeneous air-ground perception. The code is available at https://github.com/g1136639260-spec/AGSCP.
Chinese Translation
空地协作感知对于在现实动态环境中实现稳健的视觉理解至关重要。然而，现有研究通常将协作表述为单任务的跨视图融合，忽视了定位、目标关联和细粒度解析之间的功能依赖。此外，空中视图和地面视图的异质性引入了显著的几何、尺度和遮挡差异，使得统一特征共享容易受到负迁移的影响。为了解决这些问题，我们将空地感知建模为一个渐进式跨任务协作任务，并构建了空地渐进协作（Air-Ground Progressive Collaboration，AGPC）基准，这是一个时空对齐的基准，包含超过745K的原始视频帧。在此基准的基础上，我们提出了社会化共同感知（Socialized Co-Perception，SCP），这是一个从粗到细的框架，逐步组织从空中全局定位到地面目标关联和身份感知解析的协作。其核心模块双层路由器（Dual-Layer Router，DLR）将输入侧的多尺度专家选择与输出侧的任务条件调制解耦，能够实现选择性的跨视图和跨任务交互，同时抑制有害干扰。大量实验表明SCP的有效性。它实现了3.73%的共同进化增益和7.86%的平均下游性能提升。这些结果表明，任务条件的协作比统一融合在异质空地感知中更为有效。代码可在 https://github.com/g1136639260-spec/AGSCP 获取。
cs.CV / 45 / 2606.18846
From Bounding Boxes to Visual Reasoning: An On-Policy Data Annotation Tool for Vision-Language Models
从边界框到视觉推理：一种面向策略的数据标注工具用于视觉语言模型
Abstract
Vision-language models (VLMs) are rapidly advancing toward sophisticated grounded structured visual reasoning. Training models for such advanced capabilities demands a new genre of data that seamlessly unifies spatial coordinates, open-vocabulary descriptions, structured attributes, and topological relationships into a singular representation. However, existing data annotation tools fundamentally fail to meet these intricate demands, suffering from three systematic bottlenecks: limited expressiveness, severe annotation-training decoupling, and poor data reusability. To bridge this infrastructure gap, we introduce an open-source annotation tool, ScreenAnnotator. First, we define a unified annotation atom schema that binds spatial, semantic, and structural primitives into a single unit. Second, we implement an on-policy annotation loop embedded with a Bayesian Annotation Verifier (BAV). Finally, we design a template-driven multi-task data synthesis process dynamically transforms static atoms into diverse multi-dimensional reasoning tasks, eliminating redundant re-annotation. The on-policy loop drives the annotation accept rate to nearly 100% on flowcharts and 77% on GUI screenshots, while steadily reducing per-image annotation time as labeled data accumulate. In the flowchart scenario, fine-tuning a VLM yields 76.1% average accuracy, which is a 35.1% point absolute gain. Our code is available at: https://github.com/WnQinm/Annotator.
Chinese Translation
视觉语言模型（VLMs）正迅速朝着复杂的基础结构化视觉推理发展。训练具备此类高级能力的模型需要一种新的数据类型，该数据类型能够无缝地将空间坐标、开放词汇描述、结构化属性和拓扑关系统一为单一表示。然而，现有的数据标注工具在根本上无法满足这些复杂的需求，存在三个系统性瓶颈：表达能力有限、标注与训练严重脱节以及数据重用性差。为了解决这一基础设施缺口，我们推出了一款开源标注工具——ScreenAnnotator。首先，我们定义了一个统一的标注原子模式，将空间、语义和结构原语绑定为一个单元。其次，我们实现了一个嵌入贝叶斯标注验证器（BAV）的面向策略标注循环。最后，我们设计了一个基于模板的多任务数据合成过程，动态地将静态原子转化为多样化的多维推理任务，从而消除冗余的重复标注。该面向策略的循环使得标注接受率在流程图上接近100%，在图形用户界面截图上达到77%，同时随着标注数据的积累，逐步减少每幅图像的标注时间。在流程图场景中，微调VLM模型的平均准确率为76.1%，实现了35.1个百分点的绝对增益。我们的代码可在以下链接获取：https://github.com/WnQinm/Annotator。
cs.CV / 46 / 2606.18860
Quantification of Uncertainty with Adversarial Models in Medical Image Segmentation
医疗图像分割中使用对抗模型的不确定性量化
Abstract
Reliable pixel-level uncertainty quantification holds the potential to transform clinical workflows by enabling high-fidelity longitudinal monitoring and distinguishing true pathological changes from artifacts. Ideally, these models provide the stability required for critical treatment planning and surgical intervention. However, standard deep learning models often suffer from miscalibration, yielding overconfident predictions that mask underlying vulnerabilities at subtle pathological boundaries. To address this, we propose QUAM-SM, a post-hoc framework using targeted adversarial search to identify "adversarially fragile" pixels. By actively seeking perturbations that expose predictive instability, our method highlights regions where decisions are most vulnerable to being flipped. Importantly, the framework disentangles epistemic uncertainty from aleatoric uncertainty. Experiments on two public datasets with multiple expert annotations demonstrate that QUAM-SM outperforms both standard and recent uncertainty estimation approaches in terms of reliability and boundary sensitivity. Code is available at https://github.com/HanaJebril/quam_sm
Chinese Translation
可靠的像素级不确定性量化有潜力通过实现高保真度的纵向监测并区分真实病理变化与伪影，从而改变临床工作流程。理想情况下，这些模型提供了进行关键治疗规划和外科干预所需的稳定性。然而，标准深度学习模型往往存在误校准的问题，导致过于自信的预测掩盖了在微妙病理边界下的潜在脆弱性。为了解决这一问题，我们提出了QUAM-SM，这是一种后处理框架，利用针对性的对抗搜索来识别“对抗脆弱”像素。通过主动寻找暴露预测不稳定性的扰动，我们的方法突出了决策最容易被翻转的区域。重要的是，该框架将认知不确定性与随机不确定性进行了区分。在两个具有多个专家注释的公共数据集上的实验表明，QUAM-SM在可靠性和边界敏感性方面优于标准和近期的不确定性估计方法。代码可在 https://github.com/HanaJebril/quam_sm 获取。
cs.CV / 47 / 2606.18861
URDF Synthesis from RGB-D Sequences via Differentiable Joint Inference and Energy-Consistent Verification
通过可微分关节推断和能量一致性验证从RGB-D序列合成URDF
Abstract
Reconstructing simulation-ready digital twins of articulated objects from sensor observations remains constrained by two persistent gaps: (i) part-level geometric reconstruction is decoupled from kinematic-parameter estimation, and (ii) the recovered models often violate basic dynamic invariants such as energy conservation, leading to drift when the URDF is replayed in physics simulators. We present KinemaForge, a constraint-driven pipeline that jointly infers part-level shape, joint topology, and joint parameters from short RGB-D sequences and validates the result against an energy-consistent verifier built on differentiable rigid-body dynamics. The pipeline introduces three components: a kinematic constraint graph that encodes joint-part incidences as soft edges; a differentiable screw-axis solver that backpropagates from rendered observations through Featherstone's articulated-body algorithm to joint parameters; and an energy residual loss that penalises non-physical free responses of the reconstructed model. Across five PartNet-Mobility categories and an internal RGB-D benchmark, KinemaForge reduces the average joint-axis error from 4.52 degrees to 2.83 degrees (-37.4%) over the strongest geometric baseline (PARIS) and from 5.30 degrees to 2.83 degrees (-46.6%) over the interaction-based Ditto baseline, lowers long-horizon simulation drift by 64% (vs. PARIS) over 50 s rollouts, and yields URDFs whose closed-loop manipulation success rate improves by 14.6 percentage points over Ditto in our preliminary evaluation. Code and reconstruction data will be released upon acceptance.
Chinese Translation
从传感器观测中重建可用于仿真的关节物体数字双胞胎仍然受到两个持续性差距的限制：(i) 部件级几何重建与运动参数估计相脱节，以及 (ii) 恢复的模型往往违反基本的动态不变性，如能量守恒，导致在物理仿真器中重放URDF时出现漂移。我们提出了KinemaForge，这是一个约束驱动的管道，能够从短RGB-D序列中联合推断部件级形状、关节拓扑和关节参数，并通过基于可微分刚体动力学构建的能量一致性验证器对结果进行验证。该管道引入了三个组件：一个将关节-部件关系编码为软边的运动约束图；一个可微分的螺旋轴求解器，通过Featherstone的关节体算法从渲染观测中反向传播到关节参数；以及一个能量残差损失，惩罚重建模型的非物理自由响应。在五个PartNet-Mobility类别和一个内部RGB-D基准测试中，KinemaForge将平均关节轴误差从4.52度降低到2.83度（-37.4%），相较于最强几何基线（PARIS），并将其从5.30度降低到2.83度（-46.6%），相较于基于交互的Ditto基线，降低了50秒滚动中的长时间仿真漂移64%（与PARIS相比），并在我们的初步评估中产生了URDF，其闭环操作成功率比Ditto提高了14.6个百分点。代码和重建数据将在接受后发布。
cs.CV / 48 / 2606.18869
Learning to Distort: Weakly-Supervised Image Quality Transfer for Prostate DWI Correction
学习扭曲：用于前列腺扩散加权成像（DWI）校正的弱监督图像质量转移
Abstract
Single-shot echo-planar prostate diffusion-weighted imaging (DWI) is frequently complicated by geometric distortions, which impact the ability to derive reliable diagnoses from such images. Developing automated correction methods is challenged by the absence of paired distorted and undistorted clinical scans. In this paper, we first propose a novel weakly-supervised image quality transfer (IQT) framework from undistorted to distorted images that utilizes image quality assessment (IQA) signals to supervise the transfer process. Unlike traditional methods that require expensive, voxel-wise paired data or resort to developing unpaired algorithms, our approach utilizes image-level quality labels (here, distorted vs. undistorted) to establish latent quality prototypes within a pre-trained feature space. Recognizing that simulating realistic distortions is more reliable than direct unpaired correction, we describe a weakly-supervised prototype flow matching algorithm to explicitly regularize generative trajectories towards distorted prototypes, producing realistic susceptibility artifacts that mimic clinical degradations. By synthesizing these realistic pairs, we enable a second IQT model to be trained in the forward direction for distortion correction. Experimental results demonstrate that our generated images successfully mimic the diagnostic interference of real-world artifacts, which leads to more capable distortion correction IQT models. In addition to qualitative comparisons, we also conduct exhaustive quantitative evaluations that compare our approach with existing unpaired approaches (e.g., CycleGAN, UNIT-DDPM, and OT-FM) - as either forward or reverse alternatives - by assessing clinical downstream task performance in PI-RADS and Gleason score classification, using both in-distribution and external data sets.
Chinese Translation
单次回声平面前列腺扩散加权成像（DWI）常常受到几何扭曲的影响，这影响了从这些图像中得出可靠诊断的能力。开发自动校正方法的挑战在于缺乏成对的扭曲和未扭曲临床扫描。在本文中，我们首先提出了一种新颖的弱监督图像质量转移（IQT）框架，该框架利用图像质量评估（IQA）信号来监督从未扭曲到扭曲图像的转移过程。与传统方法需要昂贵的体素级配对数据或开发无配对算法不同，我们的方法利用图像级质量标签（在这里是扭曲与未扭曲）在预训练特征空间中建立潜在质量原型。我们认识到，模拟现实扭曲比直接无配对校正更可靠，因此我们描述了一种弱监督原型流匹配算法，以明确规范生成轨迹朝向扭曲原型，产生逼真的易感性伪影，模拟临床退化。通过合成这些逼真的配对，我们使得第二个IQT模型能够在前向方向上进行训练，以实现扭曲校正。实验结果表明，我们生成的图像成功模拟了现实世界伪影的诊断干扰，从而导致更强大的扭曲校正IQT模型。除了定性比较外，我们还进行了全面的定量评估，将我们的方法与现有的无配对方法（例如，CycleGAN、UNIT-DDPM和OT-FM）进行比较，作为前向或反向替代方案，通过评估在PI-RADS和Gleason评分分类中的临床下游任务性能，使用分布内和外部数据集。
cs.CV / 49 / 2606.18872
Bridging Single Distortion Artifacts and Mmultifactorial Clinical Quality: Few-shot Biparametric MRI Quality Assessment via Distortion-trained Prototypical Networks
弥合单一失真伪影与多因素临床质量之间的差距：通过失真训练的原型网络进行少样本双参数MRI质量评估
Abstract
Clinical prostate multi-parametric MRI relies heavily on high-quality diffusion-weighted imaging (DWI), yet reading DWI is frequently compromised by geometric distortion, often caused by rectal air. Assessing quality via the PI-QUAL scoring system is an emerging clinical standard, but it is subjective, time-consuming and suffers from a class imbalance where low-quality cases are diverse and relatively scarce. Using the PRIME clinical trial as an example, there are $6\%$ images with PI-QUAL scores lower than 4, $87\%$ of DWI issues are due to distortion. Many of the other clinical quality issues are under-represented. To address this common dual-scarcity of annotated clinical data, we propose a few-shot biparametric prototypical network for automated image quality assessment (IQA). Our framework utilizes a dual-branch 3D ResNet to fuse T2-weighted and DWI features, providing anatomical context to distinguish true morphology from distortion. To handle real-world heterogeneity, we introduce feature-wise linear modulation (FiLM) and a gradient reversal layer (GRL) to align feature distributions conditioned on varying b-values while suppressing acquisition-related biases. We demonstrate that a model meta-trained solely on comparatively objective, readily obtainable distortion labels can effectively adapt to predicting complex, multi-factorial clinical quality scores such as PI-QUAL using only five representative samples. Experimental results on two datasets show that our method significantly outperforms few-shot learning baselines for this challenging IQA task, offering a practically feasible and data-efficient solution for standardizing prostate MRI quality control in clinical workflows.
Chinese Translation
临床前列腺多参数MRI在很大程度上依赖于高质量的扩散加权成像（DWI），然而，DWI的解读常常受到几何失真的影响，这种失真通常是由直肠内气体引起的。通过PI-QUAL评分系统评估质量已成为一种新兴的临床标准，但该系统具有主观性、耗时且存在类别不平衡的问题，低质量案例多样且相对稀缺。以PRIME临床试验为例，$6\%$的图像PI-QUAL评分低于4，$87\%$的DWI问题是由于失真引起的。许多其他临床质量问题则被低估。为了解决这种常见的注释临床数据的双重稀缺性，我们提出了一种用于自动图像质量评估（IQA）的少样本双参数原型网络。我们的框架利用双支路3D ResNet融合T2加权和DWI特征，为区分真实形态与失真提供了解剖学背景。为了应对现实世界的异质性，我们引入了特征线性调制（FiLM）和梯度反转层（GRL），以对齐基于不同b值的特征分布，同时抑制与采集相关的偏差。我们证明，仅通过相对客观、易于获取的失真标签进行元训练的模型，可以有效适应仅使用五个代表性样本来预测复杂的多因素临床质量评分，如PI-QUAL。在两个数据集上的实验结果表明，我们的方法在这一具有挑战性的IQA任务中显著优于少样本学习基线，为在临床工作流程中标准化前列腺MRI质量控制提供了一种切实可行且数据高效的解决方案。
cs.CV / 50 / 2606.18876
Test-Time Adaptation in Optical Coherence Tomography Using Trajectory-Aligned Time-Independent Flow
基于轨迹对齐的时间独立流在光学相干断层扫描中的测试时适应
Abstract
Optical coherence tomography (OCT) is essential in ophthalmology, but inconsistent image quality especially in low-cost devices hinders automated analysis. To address this, we introduce a flow-matching-based test-time adaptation method that generates high-quality surrogate images from noisy inputs. Typically, domain gaps between test and training data cause pixel distribution mismatches during the denoising process. We overcome this by matching the test image's histogram to synthetic reference trajectories, successfully aligning the input with expected distributions. Additionally, we remove the network's time conditioning to account for slight deviations in real-world noise distributions. Our approach achieves state-of-the-art performance in segmenting critical biomarkers for two stages of Age-related Macular Degeneration (AMD). Code is available: https://github.com/Veit21/tta-flow.
Chinese Translation
光学相干断层扫描（OCT）在眼科中至关重要，但尤其是在低成本设备中不一致的图像质量阻碍了自动化分析。为了解决这一问题，我们提出了一种基于流匹配的测试时适应方法，该方法能够从噪声输入生成高质量的替代图像。通常，测试数据和训练数据之间的领域差异会导致去噪过程中像素分布的不匹配。我们通过将测试图像的直方图与合成参考轨迹进行匹配来克服这一问题，成功地将输入与预期分布对齐。此外，我们去除了网络的时间条件，以考虑现实世界噪声分布中的轻微偏差。我们的方法在分割与年龄相关性黄斑变性（AMD）两个阶段的关键生物标志物方面达到了最先进的性能。代码可在此获取：https://github.com/Veit21/tta-flow。
cs.CV / 51 / 2606.18884
Performance Gap Analysis between Latin and Arabic Scripts HTR
拉丁文与阿拉伯文手写文本识别性能差距分析
Abstract
Recent studies have shown that handwritten text recognition (HTR) systems perform worse on Arabic-script datasets than on Latin-script data. However, the reasons for this gap are still not well understood due to the lack of controlled comparisons. In this work, we present a comprehensive study of Arabic and Latin scripts HTR using a unified CRNN model for line-level HTR across nine datasets (including KHATT (Arabic), Muharaf (Arabic), NUST-UHWR (Urdu), PHTD (Persian), IAM (English), READ-2016 (German), and others) and di ferent training sizes (K in {100, 500, 1000, 2000, ..., Kfull}). Our results show the performance gap remains: it is large in low-resource settings, decreases with more data, but remains even at full scale, with a consistent difference of 5-7 CER points. We show that annotation quality matters, as many datasets contain labeling errors. Cleaning reduces error rates and narrows the gap, but does not eliminate it. In addition, we find that a fixed number of training samples provides less effective coverage in Arabic due to higher visual variability, requiring more data to learn similar representations. We compare recognition across datasets in terms of the number of text lines and the number of characters, showing an equivalence trade-off. We compare character frequency distributions across scripts and show that Arabic is significantly more heavy-tailed than Latin. Our error analysis reveals that around 30 percent of substitution errors in Arabic datasets (e.g., KHATT) are caused by confusion between visually similar characters, compared to about 15 percent in Latin-script datasets such as IAM.
Chinese Translation
近期研究表明，手写文本识别（HTR）系统在阿拉伯文数据集上的表现不如在拉丁文数据集上的表现。然而，由于缺乏受控比较，这一差距的原因仍不清楚。在本研究中，我们使用统一的卷积递归神经网络（CRNN）模型对九个数据集（包括KHATT（阿拉伯文）、Muharaf（阿拉伯文）、NUST-UHWR（乌尔都文）、PHTD（波斯文）、IAM（英文）、READ-2016（德文）等）进行阿拉伯文和拉丁文的线级HTR的全面研究，并考虑不同的训练样本量（K ∈ {100, 500, 1000, 2000, ..., Kfull}）。我们的结果表明，性能差距依然存在：在低资源环境下差距较大，随着数据量的增加而减少，但即使在全量数据下仍然存在，且差距保持在5-7个字符错误率（CER）点的一致差异。我们发现标注质量至关重要，因为许多数据集包含标注错误。清理数据可以降低错误率并缩小差距，但并不能消除它。此外，我们发现由于阿拉伯文的视觉变异性更高，固定数量的训练样本在阿拉伯文中的覆盖效果较差，需要更多的数据来学习相似的表示。我们在文本行数和字符数方面比较了不同数据集的识别效果，显示出一种权衡关系。我们比较了不同脚本的字符频率分布，结果显示阿拉伯文的重尾特性显著强于拉丁文。我们的错误分析揭示，在阿拉伯文数据集（例如KHATT）中，约30%的替换错误是由于视觉上相似字符之间的混淆造成的，而在拉丁文数据集（如IAM）中，这一比例约为15%.
cs.CV / 52 / 2606.18885
LARE: Low-Attention Region Encoding for Text-Image Retrieval
LARE：用于文本-图像检索的低关注区域编码
Abstract
Image retrieval in crowded scenes is particularly challenging due to the salience bias of conventional visual encoders, which tend to focus on dominant objects while neglecting low-attention regions that are often crucial for fine-grained retrieval. We propose LARE (Low-Attention Region Encoding), a framework that explicitly models these overlooked regions. LARE adopts a dual-encoding strategy that encodes low-attention regions of an image and the full image in parallel, leading to more diverse and informative image embeddings. To evaluate image retrieval performance in challenging crowded scenes, we introduce Dense-Set, a challenging subset derived from COCO and Flickr30K. In this subset, images are re-captioned to provide richer descriptions of low-attention or previously overlooked regions. This dataset highlights the limitations of existing retrieval models and enables a more rigorous evaluation under densely crowded scene conditions. Experimental results demonstrate that the proposed framework improves retrieval performance by preserving subtle, non-dominant visual cues within the shared latent space.
Chinese Translation
在拥挤场景中进行图像检索尤其具有挑战性，因为传统视觉编码器存在显著性偏差，往往专注于主导物体，而忽视了那些对于细粒度检索至关重要的低关注区域。我们提出了LARE（低关注区域编码），一个明确建模这些被忽视区域的框架。LARE采用双重编码策略，平行编码图像的低关注区域和完整图像，从而生成更具多样性和信息量的图像嵌入。为了评估在挑战性拥挤场景中的图像检索性能，我们引入了Dense-Set，这是一个从COCO和Flickr30K衍生出的挑战性子集。在该子集中，图像被重新标注，以提供对低关注或先前被忽视区域的更丰富描述。该数据集突显了现有检索模型的局限性，并使在密集拥挤场景条件下进行更严格的评估成为可能。实验结果表明，所提出的框架通过保留共享潜在空间中的微妙、非主导视觉线索来提高检索性能。
cs.CV / 53 / 2606.18886
DINO-Med3D: Bridging Dimension and Domain Gaps in Volumetric Segmentation via Progressive Adaptation
DINO-Med3D：通过渐进适应弥合体积分割中的维度和领域差距
Abstract
Although DINOv3 has demonstrated remarkable semantic discrimination in natural imagery, its direct application to volumetric medical segmentation is hindered by inherent dimension and domain disparities. To resolve these issues, we propose DINO-Med3D, a two-stage progressive framework that repurpose the pre-trained DINOv3 encoder for 3D medical tasks. In the first stage, we mitigate the dimension gap by introducing a multi-slice embedding module that incorporates pseudo-3D context, while simultaneously employing a segmentation proxy task to adapt representations learned from natural scenes to the medical domain. Subsequently, we further enhance volumetric understanding by adding lightweight 3D adapters into the frozen backbone to enforce global inter-slice continuity. Finally, to compensate for the spatial information loss inherent in the embedding process, we design a parallel detail recovery stream to explicitly preserve high-frequency boundary cues. Extensive experiments on five public datasets demonstrate that our approach successfully adapts DINOv3 to the medical domain and significantly outperforms state-of-the-art baselines.
Chinese Translation
尽管 DINOv3 在自然图像中展示了显著的语义区分能力，但其在体积医学分割中的直接应用受到固有的维度和领域差异的限制。为了解决这些问题，我们提出了 DINO-Med3D，这是一种两阶段的渐进框架，旨在重新利用预训练的 DINOv3 编码器用于 3D 医学任务。在第一阶段，我们通过引入一个多切片嵌入模块来缓解维度差距，该模块结合了伪 3D 上下文，同时采用分割代理任务将从自然场景中学习到的表示适应于医学领域。随后，我们通过在冻结的主干网络中添加轻量级 3D 适配器来进一步增强体积理解，以强制执行全局切片间的连续性。最后，为了补偿嵌入过程中固有的空间信息损失，我们设计了一个并行的细节恢复流，以明确保留高频边界线索。在五个公共数据集上的大量实验表明，我们的方法成功地将 DINOv3 适应于医学领域，并显著超越了最先进的基线。
cs.CV / 54 / 2606.18894
Automatic ply-specific analyses of CFRP micrographs using shortest-path-based ply distinction
基于最短路径的层特异性分析在CFRP显微图像中的自动化应用
Abstract
We present an automated approach to distinguish between ply instances in semantic segmentation masks of high-resolution carbon-fiber reinforced polymer micrographs. Interpreting the segmentation mask as a graph with pixels as vertices, enables us to use a shortest-path algorithm yielding the ply-separating paths. Thereby, we bridge the gap between semantic segmentation and ply instance segmentation using global information. We successfully apply our approach on high-resolution micrographs featuring a broad range of characteristics like artificially added gaps in single or multiple plies, different stacking sequences and ply traversing cracks. Assigning each fiber pixel to a ply based on the calculated paths, allows for a comprehensive, quantitative ply analysis with respect to its microstructural properties like the local fiber volume fraction as well as locally resolved ply and interleaf layer thickness. These insights help to reveal manufacturing-induced inhomogeneities, draw conclusions on manufacturing parameters and link mechanical properties to underlying microstructural imperfections.
Chinese Translation
我们提出了一种自动化方法，用于区分高分辨率碳纤维增强聚合物显微图像中语义分割掩膜的层实例。将分割掩膜解释为一个图，其中像素作为顶点，使我们能够使用最短路径算法来获得层分离路径。因此，我们利用全局信息弥合了语义分割与层实例分割之间的差距。我们成功地将该方法应用于具有广泛特征的高分辨率显微图像，如单层或多层中人工添加的间隙、不同的堆叠顺序和穿层裂纹。根据计算得到的路径将每个纤维像素分配给一个层，从而实现了对其微观结构特性（如局部纤维体积分数以及局部解析的层和夹层厚度）的全面定量层分析。这些见解有助于揭示制造过程中引起的不均匀性，得出关于制造参数的结论，并将机械性能与潜在的微观结构缺陷联系起来。
cs.CV / 55 / 2606.18906
BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing
BindEdit：驯服注意力泄漏以实现精确的多对象图像编辑
Abstract
Real image editing enables precise manipulation of visual content, yet existing methods often fail in complex multi-object scenarios, causing semantic blending, object duplication, or incomplete edits. We attribute these failures to attention leakage, where signals across spatial regions and text tokens become entangled during the denoising process. Specifically, we identify two distinct forms of leakage: Edit-Token Leakage, where ambiguous token-region alignment leads to object blending, and Source Dominance Leakage, where tokens of unchanged source objects overwhelm the attention intended for target entities. To resolve these leakages, we propose \textbf{BindEdit}, which enforces attention-level constraints within a single diffusion trajectory. To suppress Edit-Token Leakage, BindEdit jointly regularizes cross- and self-attention so that each target token group is bound to its corresponding spatial region while maintaining instance-level separation. To suppress Source Dominance Leakage, a cross-attention re-balancing mechanism amplifies target token influence and attenuates residual source semantics within editable regions. Moreover, a region fidelity term ensures that each target concept is expressed coherently across the entire editing mask. Additionally, we propose a comprehensive multi-object benchmark encompassing diverse object counts and categories. Extensive experiments demonstrate that BindEdit consistently outperforms existing methods within a single diffusion trajectory, maintaining robust performance across both single- and multi-object editing scenarios.
Chinese Translation
真实图像编辑能够精确操控视觉内容，但现有方法在复杂的多对象场景中往往表现不佳，导致语义混合、对象重复或编辑不完整。我们将这些失败归因于注意力泄漏，即在去噪过程中，空间区域和文本标记之间的信号交织在一起。具体而言，我们识别出两种不同形式的泄漏：编辑标记泄漏（Edit-Token Leakage），其中模糊的标记-区域对齐导致对象混合；源主导泄漏（Source Dominance Leakage），其中未改变源对象的标记压倒了针对目标实体的注意力。为了解决这些泄漏问题，我们提出了 extbf{BindEdit}，它在单一扩散轨迹内强制执行注意力级别的约束。为了抑制编辑标记泄漏，BindEdit联合正则化交叉注意力和自注意力，使每个目标标记组与其对应的空间区域绑定，同时保持实例级别的分离。为了抑制源主导泄漏，交叉注意力重平衡机制增强目标标记的影响力，并减弱可编辑区域内残余源语义。此外，区域保真度项确保每个目标概念在整个编辑掩膜中一致表达。此外，我们提出了一个全面的多对象基准，涵盖多样的对象数量和类别。大量实验表明，BindEdit在单一扩散轨迹中始终优于现有方法，在单对象和多对象编辑场景中保持稳健的性能。
cs.CV / 56 / 2606.18943
Physics-IQ Verified
物理智商验证
Abstract
Video generative models ( VGMs) have become a new frontier that can be used not just for video generation but for a multitude of downstream tasks, including world modeling. To advance these tasks, a good video model must understand the physical reality of the world. Evaluating this understanding is an emerging field and has led to the Physics-IQ benchmark, which quantifies this explicitly by comparing model-generated videos to real-world videos of physical experiments. In this work, we present a systematic audit of the Physics-IQ benchmark, expose shortcomings and propose three solutions that sharpen how we can measure physical understanding of VGMs. Specifically, we improve prompt and ground-truth quality to reduce the influence of confounding factors and further introduce a sample-level scoring system that weights each sample and metric equally. Our resulting benchmark, Physics-IQ Verified, refines 57.6\% of all samples and improves over 34.8\% of prompts. In a comparison study using six image-to-video generative models, we observe moderate but meaningful ranking changes (Kendall's $\tau = 0.46$). We hope Physics-IQ Verified advances the community by providing a more reliable signal toward physically accurate VGMs. The code for the benchmark can be accessed at https://github.com/google-deepmind/physics-iq-benchmark
Chinese Translation
视频生成模型（Video Generative Models, VGMs）已成为一个新前沿，不仅可以用于视频生成，还可以用于多种下游任务，包括世界建模。为了推动这些任务的发展，一个好的视频模型必须理解世界的物理现实。评估这种理解是一个新兴领域，并导致了物理智商（Physics-IQ）基准的出现，该基准通过将模型生成的视频与物理实验的真实视频进行比较，明确量化这种理解。在本研究中，我们对物理智商基准进行了系统审计，揭示了其不足之处，并提出了三种解决方案，以提高我们测量视频生成模型物理理解能力的方法。具体而言，我们改善了提示和真实数据的质量，以减少混淆因素的影响，并进一步引入了一种样本级评分系统，使每个样本和指标具有相等的权重。我们最终的基准物理智商验证（Physics-IQ Verified）对57.6\%的样本进行了改进，并提升了34.8\\%的提示。在使用六种图像到视频生成模型的比较研究中，我们观察到适度但有意义的排名变化（Kendall's $ au = 0.46$）。我们希望物理智商验证能够通过提供更可靠的信号，推动社区朝向物理上准确的VGMs发展。基准的代码可以在https://github.com/google-deepmind/physics-iq-benchmark获取。
cs.CV / 57 / 2606.18952
SP-TransientBench: A Real-Captured Single Photon Perception Benchmark
SP-TransientBench：一个真实捕获的单光子感知基准
Abstract
Single-photon LiDAR (SPL) based on single-photon avalanche diode (SPAD) sensing enables time-resolved photon measurements with extreme sensitivity, offering unique potential for active 3D perception in photon-starved scenarios.However, real-world single photon perception remains fundamentally challenging due to unique measurement noise and complex multi-return transient phenomena, which jointly complicate geometric reconstruction and semantic scene understanding. Despite growing interest in SPAD-based sensing, existing studies are largely limited to simulated data or small-scale controlled captures. As a result, systematic evaluation of real-world single photon perception across depth estimation, multi-view reconstruction, and 3D semantic understanding remains underexplored. To bridge this gap, we introduce SP-TransientBench (STB), a real-captured multi-task benchmark for single photon perception. SP-TransientBenc comprises 10 diverse scenes and 10,297 views captured using a solid-state single-photon LiDAR at $256\times192$ resolution. Each view provides full time-of-flight histograms with multi-return behavior,standardized metadata, and calibrated camera poses for multi-view evaluation. We further provide 13-class 3D semantic annotations for selected scenes. By providing dedicated data splits and evaluation protocols for each task, STB enables consistent and reproducible benchmarking of real-world single photon perception across multiple 3D vision problems. The dataset and code will be released upon acceptance.
Chinese Translation
基于单光子雪崩二极管（SPAD）传感的单光子激光雷达（SPL）能够以极高的灵敏度进行时间分辨的光子测量，为光子稀缺场景下的主动三维感知提供了独特的潜力。然而，由于独特的测量噪声和复杂的多返回瞬态现象，现实世界中的单光子感知仍然面临根本性的挑战，这共同使几何重建和语义场景理解变得复杂。尽管对基于SPAD的传感器的兴趣日益增长，现有研究在很大程度上仍限于模拟数据或小规模的受控捕获。因此，针对深度估计、多视角重建和三维语义理解的现实世界单光子感知的系统评估仍然未得到充分探索。为填补这一空白，我们引入了SP-TransientBench（STB），这是一个真实捕获的多任务单光子感知基准。SP-TransientBench包含10个多样化场景和10,297个视图，这些视图是使用固态单光子激光雷达以$256 imes192$分辨率捕获的。每个视图提供完整的飞行时间直方图，具有多返回行为、标准化的元数据和经过校准的相机姿态，以便进行多视角评估。我们还为选定场景提供了13类三维语义标注。通过为每个任务提供专门的数据划分和评估协议，STB使得在多个三维视觉问题上对现实世界单光子感知进行一致且可重复的基准测试成为可能。数据集和代码将在接受后发布。
cs.CV / 58 / 2606.18955
Motion-Focused Latent Action Enables Cross-Embodiment VLA Training from Human EgoVideos
基于运动聚焦的潜在动作实现跨体态的从人类自我视频中训练的视觉-语言-动作(VLA)模型
Abstract
Training generalist Vision-Language-Action(VLA) models typically requires massive, diverse robotic datasets with high-fidelity action annotations. While egocentric human manipulation videos are abundant and capture significant environmental diversity, the absence of action labels makes them difficult to use in conventional training paradigms. To address this, we propose a latent-action-based framework designed to extract general action priors from unlabeled human videos. The architecture features a Hybrid Disentangled VQ-VAE that decouples motion dynamics from environmental backgrounds through physical masks, enabling the construction of a cross-embodiment action codebook. By pre-training on human videos with the codebook, the VLM backbone learns deep representations of action intent. For adaptation to specific embodiments, we introduce an intent-perception decoupling strategy where the VLM predicts the action intent while a separate frozen visual encoder provides state-specific features to the action expert, thereby reducing action hallucinations. Results in simulation and real-world environments show that our method, pre-trained exclusively on unlabeled human videos, performs competitively with state-of-the-art VLA models trained on massive annotated datasets, requiring only 50 trajectories for downstream adaptation.
Chinese Translation
训练通用的视觉-语言-动作(VLA)模型通常需要大量多样化的机器人数据集，并且需要高保真的动作标注。尽管以自我为中心的人类操作视频丰富且能够捕捉到显著的环境多样性，但缺乏动作标签使其在传统训练范式中难以使用。为了解决这个问题，我们提出了一种基于潜在动作的框架，旨在从未标记的人类视频中提取通用动作先验。该架构采用混合解耦的变分自编码器(VQ-VAE)，通过物理掩模将运动动态与环境背景解耦，从而构建跨体态的动作代码本。通过在带有代码本的人类视频上进行预训练，视觉-语言模型(VLM)主干学习到动作意图的深层表示。为了适应特定的体态，我们引入了一种意图-感知解耦策略，其中VLM预测动作意图，而一个独立的冻结视觉编码器为动作专家提供状态特定的特征，从而减少动作幻觉。在仿真和真实环境中的结果表明，我们的方法在仅使用未标记的人类视频进行预训练的情况下，与在大量标注数据集上训练的最先进VLA模型表现出竞争力，仅需50条轨迹即可进行下游适应。
cs.CV / 59 / 2606.18960
Mem-World: Memory-Augmented Action-Conditioned World Models for Persistent Robot Manipulation
Mem-World：用于持久机器人操作的记忆增强动作条件世界模型
Abstract
Action-conditioned world models have emerged as a promising paradigm for robot learning, offering a scalable alternative to costly real-world experimentation by generating action-consistent video rollouts. However, persistent world modeling remains challenging in manipulation: frequent end-effector occlusions and rapid wrist-camera motion make the current observation insufficient for predicting future views, causing models to forget or hallucinate scene details seen in earlier frames. Existing memory retrieval strategies often fail to identify informative history in dynamic manipulation scenarios. To address this limitation, we propose Mem-World, a memory-augmented multi-view action-conditioned world model. At its core, we present W-VMem, a 4D wrist-view-centered surfel-indexed memory that anchors historical observations to temporally evolving surface elements. By explicitly modeling when and where scene elements are observed, W-VMem enables geometry-aware retrieval of relevant history frames conditioned on future actions. During generation, relevant history frames are selected via surfel-based rendering and scoring, providing informative and non-redundant context for prediction. Extensive experiments show that Mem-World generates persistent rollouts in complex manipulation scenarios, enables more reliable policy evaluation than Ctrl-World, improving the Pearson correlation with real-world performance by 14.5\%, and supports effective policy improvement through synthetic data generation, increasing success rates from 58\% to 72\% on long-horizon tasks.
Chinese Translation
动作条件世界模型已成为机器人学习的一个有前景的范式，通过生成与动作一致的视频回放，为昂贵的现实世界实验提供了一种可扩展的替代方案。然而，在操作中，持久的世界建模仍然面临挑战：频繁的末端执行器遮挡和快速的腕部摄像机运动使得当前的观察不足以预测未来的视图，导致模型遗忘或幻觉早期帧中看到的场景细节。现有的记忆检索策略在动态操作场景中往往无法识别出有用的历史信息。为了解决这一限制，我们提出了Mem-World，一种记忆增强的多视角动作条件世界模型。其核心是W-VMem，一个以腕部视角为中心的4D表面元素索引记忆，将历史观察锚定到时间演变的表面元素上。通过明确建模场景元素何时何地被观察到，W-VMem能够基于未来动作进行几何感知的相关历史帧检索。在生成过程中，通过基于表面元素的渲染和评分选择相关的历史帧，为预测提供有用且不冗余的上下文。大量实验表明，Mem-World能够在复杂的操作场景中生成持久的回放，相较于Ctrl-World，能够实现更可靠的策略评估，提高与现实世界表现的Pearson相关性14.5\%，并通过合成数据生成支持有效的策略改进，使得长时间任务的成功率从58\%提高到72\%。
cs.CV / 60 / 2606.18974
Visual-OPSD: Cross-Modal On-Policy Self-Distillation for Efficient Unified Multimodal Reasoning
视觉-OPSD：高效统一多模态推理的跨模态在线自蒸馏
Abstract
Unified multimodal models (UMMs) interleave generated ''visual thoughts'' (VTs) with text reasoning to improve spatial tasks. This incurs roughly an order-of-magnitude inference cost from multi-step diffusion. We find this cost yields limited direct benefit. On ThinkMorph, removing or noising VTs barely changes accuracy across nine benchmarks. Once rendered, attention concentrates on the VT regardless of content. Yet a KL diagnostic shows that conditioning on a privileged VT trace shifts the model's completion distribution. This suggests the generation pathway encodes useful reasoning beyond the rendered pixels. Motivated by this gap, we propose Visual On-Policy Self-Distillation(Visual-OPSD). Teacher and student share identical weights but differ in context: the teacher sees privileged VTs while the student sees only the question. Token-level JSD distillation on on-policy student trajectories transfers the teacher's reasoning to a text-only student. Across nine benchmarks, Visual-OPSD improves over its generative teacher by $+3.40$pp with $14.3\times$ speedup (10.0s vs. 142.8s per sample) and outperforms same-scale VLMs by $+63.83$pp on VSP. A Gaussian-noise control ($+0.40$pp vs. $+10.28$pp for real VTs) and $58.4\%$ closure of the KL gap confirm that gains come from the semantic content of the generation pathway.
Chinese Translation
统一多模态模型（UMMs）通过将生成的“视觉思维”（VTs）与文本推理交织在一起，以改善空间任务。这导致了大约一个数量级的推理成本，源于多步扩散。我们发现这一成本带来的直接收益有限。在ThinkMorph上，移除或对VTs进行噪声处理几乎不会改变九个基准测试的准确性。一旦生成，注意力就会集中在VT上，而不论其内容。然而，KL诊断显示，基于特权VT轨迹的条件化会改变模型的完成分布。这表明生成路径编码了超出渲染像素的有用推理。基于这一差距，我们提出了视觉在线自蒸馏（Visual On-Policy Self-Distillation，Visual-OPSD）。教师和学生共享相同的权重，但在上下文上有所不同：教师可以看到特权VT，而学生只能看到问题。在在线学生轨迹上进行的标记级JSD蒸馏将教师的推理转移到仅文本的学生上。在九个基准测试中，Visual-OPSD相较于其生成教师提高了$+3.40$个百分点，并实现了$14.3 imes$的加速（每个样本10.0秒对比142.8秒），在VSP上超越了同规模的VLMs，提升了$+63.83$个百分点。高斯噪声控制（$+0.40$pp对比真实VTs的$+10.28$pp）和$58.4\%$的KL差距闭合确认了收益来自于生成路径的语义内容。
cs.CV / 61 / 2606.18992
Show, Don't Ask: Generative Visual Disambiguation for Composed Image Retrieval with Turn-Valid Coverage
展示，而非询问：具有有效覆盖的生成视觉消歧用于组合图像检索
Abstract
Composed image retrieval (CIR) uses a reference image and a text modification to search for a target image. However, such queries often describe several possible images rather than one exact target, making the user's intent ambiguous. Recent methods address this by using conformal prediction to estimate ambiguity and by asking users clarifying text questions. However, these methods have two limitations: their coverage guarantee only holds at the first interaction, and text questions are often insufficient for resolving fine-grained visual differences such as appearance, attributes, or viewpoint. We propose CLARA, a clarification framework that resolves ambiguity by showing users a small panel of visual alternatives. Instead of answering text questions, the user simply selects the prototype image closest to the intended target. This provides a direct visual signal and avoids relying on a model to predict the user's answer. To maintain valid conformal guarantees across multiple interaction rounds, CLARA reweights calibration using the likelihood ratio induced by the user's selection. The displayed prototypes are also constrained to represent the current candidate set and are snapped to real corpus images, ensuring that generated images cannot artificially improve coverage. Experiments on open-domain and fashion benchmarks show that CLARA matches single-turn state-of-the-art retrieval performance, maintains nominal coverage across interaction rounds, and finds the intended target in fewer rounds than strong text-question baselines. Its advantage is especially clear when ambiguity involves viewpoint or fine-grained attributes, where visual clarification is more effective than textual questioning.
Chinese Translation
组合图像检索（CIR）使用参考图像和文本修改来搜索目标图像。然而，这类查询通常描述多个可能的图像，而不是一个确切的目标，从而使用户的意图变得模糊。最近的方法通过使用保形预测来估计模糊性，并通过询问用户澄清性文本问题来解决这一问题。然而，这些方法存在两个局限性：它们的覆盖保证仅在第一次交互时有效，并且文本问题通常不足以解决外观、属性或视角等细微的视觉差异。我们提出了CLARA，一个通过向用户展示一小组视觉替代品来解决模糊性的澄清框架。用户不再需要回答文本问题，而是简单选择与意图目标最接近的原型图像。这提供了直接的视觉信号，避免依赖模型来预测用户的答案。为了在多轮交互中保持有效的保形保证，CLARA使用用户选择所引发的似然比重新加权校准。所展示的原型也被限制为表示当前候选集，并被固定到真实语料库图像，确保生成的图像不会人为地提高覆盖率。在开放域和时尚基准上的实验表明，CLARA的单轮检索性能与最先进的方法相匹配，在交互轮次中保持名义覆盖，并在比强文本问题基线更少的轮次中找到意图目标。当模糊性涉及视角或细粒度属性时，其优势尤为明显，因为视觉澄清比文本询问更为有效。
cs.CV / 62 / 2606.19019
FlowObject: Flow Steering for Bridging Generative Priors and Reconstruction Fidelity
FlowObject：连接生成先验与重建保真度的流引导
Abstract
Recovering complete 3D representations of objects from few casual image captures remains a significant challenge. Recent 3D generative models, particularly those based on Flow-Matching (FM), can synthesize high-quality textured assets; however, they often suffer from ''synthetic bias'' where learned priors override observational evidence, alongside a lack of alignment with the observed instance. Conversely, optimization-based methods like 3D Gaussian Splatting (3DGS) provide high fidelity on visible surfaces but fail to reason about unobserved geometry. In this paper, we present FlowObject, a framework that reformulates sparse-view 3D reconstruction as a training-free, guided inverse problem. Our approach applies a dual-space guidance strategy to steer the Ordinary Differential Equation (ODE) trajectory of a flow-matching model, enabling the completion of unseen regions through learned generative priors while enforcing strict consistency with real-world observations. By integrating a 3DGS refinement stage, FlowObject further bridges the gap between ''synthetic-looking'' generative outputs and photorealistic reconstructions. Comprehensive benchmarks on synthetic and real-world datasets demonstrate that current state-of-the-art methods often struggle to achieve geometric completeness and observational consistency simultaneously, especially under severe occlusions. In contrast, our method significantly outperforms state-of-the-art generative models and optimization-based frameworks in both geometric completeness and view-dependent appearance fidelity.
Chinese Translation
从少量随意拍摄的图像中恢复物体的完整三维表示仍然是一个重大挑战。最近的三维生成模型，特别是基于流匹配（Flow-Matching, FM）的模型，可以合成高质量的纹理资产；然而，它们常常受到“合成偏差”的影响，即学习到的先验覆盖了观察到的证据，并且与观察实例缺乏对齐。相反，基于优化的方法如三维高斯点云（3D Gaussian Splatting, 3DGS）在可见表面上提供了高保真度，但无法推理未观察到的几何形状。在本文中，我们提出了FlowObject，一个将稀疏视图三维重建重新表述为无训练的引导逆问题的框架。我们的方法应用了一种双空间引导策略，以引导流匹配模型的常微分方程（Ordinary Differential Equation, ODE）轨迹，通过学习到的生成先验完成未见区域，同时严格遵循现实世界的观察。通过整合3DGS精细化阶段，FlowObject进一步弥合了“合成外观”生成输出与照片级真实重建之间的差距。在合成和真实世界数据集上的全面基准测试表明，当前的最先进方法在几何完整性和观察一致性方面常常难以同时实现，尤其是在严重遮挡的情况下。相比之下，我们的方法在几何完整性和视依赖外观保真度方面显著优于最先进的生成模型和基于优化的框架。
cs.CV / 63 / 2606.19046
Low-Rank Tensor Completion Based on Fractional Regularization with Ky Fan p-k Norm
基于分数正则化的低秩张量补全方法：Ky Fan p-k 范数
Abstract
This paper addresses low-rank tensor completion (LRTC) by proposing a novel nonconvex surrogate, namely the ratio of the tensor nuclear norm to the tensor Ky Fan p-k norm (TNPK), to accurately approximate the tensor tubal rank. The TNPK possesses appealing properties, including scale invariance, parameter flexibility, and the existence of closed-form solutions under specific choices of p and k. With specific parameter settings of p and k, it reduces to the ratio of the tensor nuclear norm to the tensor Ky Fan k norm (TNK) or the ratio of the tensor nuclear norm to the tensor Frobenius norm (TNF). We construct a LRTC model and, under the tensor null space property (NSP), prove that low-rank tensors are local minimizers of the proposed model. Moreover, we derive the proximal operator of the Ky Fan p-k inverse-norm and further develop an efficient alternating direction method of multipliers (ADMM) algorithm with guaranteed subsequential convergence under mild conditions. Extensive experiments on synthetic and real-world datasets validate the superior performance of our method against state-of-the-art competitors.
Chinese Translation
本文通过提出一种新颖的非凸替代方法，即张量核范数与张量 Ky Fan p-k 范数 (TNPK) 的比率，来解决低秩张量补全 (LRTC) 问题，以准确近似张量的管道秩。TNPK 具有吸引人的特性，包括尺度不变性、参数灵活性，以及在特定的 p 和 k 选择下存在封闭形式解。在特定的 p 和 k 参数设置下，它简化为张量核范数与张量 Ky Fan k 范数 (TNK) 的比率或张量核范数与张量 Frobenius 范数 (TNF) 的比率。我们构建了一个 LRTC 模型，并在张量零空间性质 (NSP) 下证明低秩张量是所提模型的局部极小值。此外，我们推导了 Ky Fan p-k 逆范数的近端算子，并进一步开发了一种高效的交替方向乘子法 (ADMM) 算法，在温和条件下保证子序列收敛。对合成数据集和真实世界数据集的广泛实验验证了我们方法相较于最先进竞争者的优越性能。
cs.CV / 64 / 2606.19053
Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: From Evaluation to Diagnosis
在细粒度图像任务上对大型视觉-语言模型的基准测试：从评估到诊断
Abstract
Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated remarkable multimodal perception and reasoning capabilities. While numerous benchmarks have evaluated LVLMs from holistic or task-specific perspectives, their capabilities on fine-grained image tasks-fundamental to computer vision-remain insufficiently understood. To address this gap, we introduce FG-BMK, a comprehensive fine-grained evaluation benchmark containing 1.01 million questions and 0.28 million images, covering diverse scenarios from common object-centric domains to specialized domains. FG-BMK jointly evaluates dialogue-level fine-grained semantic recognition and feature-level visual discriminability through human-oriented and machine-oriented paradigms, enabling diagnostic analysis of whether LVLM failures arise from insufficient visual representations, weak visual-to-semantic grounding, or limited fine-grained knowledge. Through extensive experiments on a diverse set of representative LVLMs/VLMs, we find that current LVLMs remain inadequate fine-grained recognizers, with failures arising from intertwined bottlenecks in visual representations, semantic grounding, modality alignment, and category-level knowledge. We further analyze training design factors for improving fine-grained capabilities and examine how visual and linguistic perturbations affect LVLM predictions. These findings provide diagnostic insights into the limitations of current LVLMs and offer guidance for future data construction and model design in developing more reliable LVLMs for fine-grained visual tasks. Our code is open-source and available at https://fg-bmk.github.io/.
Chinese Translation
近期大型视觉-语言模型（Large Vision-Language Models, LVLMs）的进展展示了其卓越的多模态感知和推理能力。尽管已有众多基准从整体或任务特定的角度评估了LVLMs，但它们在细粒度图像任务上的能力——这一计算机视觉的基础领域——仍然未得到充分理解。为了解决这一问题，我们引入了FG-BMK，这是一个全面的细粒度评估基准，包含101万个问题和28万个图像，涵盖从常见物体中心领域到专业领域的多样化场景。FG-BMK通过人本导向和机器导向的范式共同评估对话级别的细粒度语义识别和特征级别的视觉可区分性，从而实现对LVLM失败原因的诊断分析，判断其是否源于视觉表征不足、视觉与语义的基础薄弱或细粒度知识的局限。通过对一组多样化的代表性LVLMs/VLMs进行广泛实验，我们发现当前的LVLMs在细粒度识别方面仍显不足，失败的原因源于视觉表征、语义基础、模态对齐和类别知识等交织的瓶颈。我们进一步分析了改善细粒度能力的训练设计因素，并考察了视觉和语言扰动如何影响LVLM的预测。这些发现为当前LVLM的局限性提供了诊断性见解，并为未来数据构建和模型设计提供了指导，以开发更可靠的LVLMs用于细粒度视觉任务。我们的代码是开源的，网址为 https://fg-bmk.github.io/。
cs.CV / 65 / 2606.19062
DREAM: Extending Vision-Language Models with Dual-Objective Encoding for Cross-Modal Retrieval
DREAM：通过双目标编码扩展视觉-语言模型以实现跨模态检索
Abstract
In today's media-driven world, the exponential growth of video content across domains such as surveillance, education, and entertainment has made retrieving semantically relevant videos via natural language queries increasingly critical. Early video retrieval systems relied on handcrafted features or shallow cross-modal mappings, limiting their ability to capture complex semantics and temporal dynamics. While large-scale vision-language models have improved cross-modal alignment, challenges remain in modeling fine-grained temporal dependencies and nuanced linguistic structures. In this paper, we introduce DREAM: Dual-path Representation Enhancement and Alignment Model, a novel multimodal framework that addresses these limitations through enhanced visual and textual encoding. DREAM incorporates a hybrid language modeling strategy that combines masked and permuted language modeling objectives to capture both local and global linguistic semantics. On the visual side, we design a hierarchical vision encoder with cascaded group attention, which integrates spatial and temporal information through multi-stage token interaction and coarse-to-fine attention refinement. We validate DREAM through comprehensive evaluations on the widely-used MSRVTT, MSVD and LSMDC benchmark datasets, where it achieves new state-of-the-art R1 scores of 49.4%, 49.7% and 27.3%, respectively. Qualitative analyses further show the model's ability to maintain coherent attention across frames and align complex queries with dynamic video content. These findings underscore the effectiveness of hierarchical attention and dual-objective textual modeling in enabling robust, context-aware video retrieval, and pave the way for future research in advancing cross-modal representation learning.
Chinese Translation
在当今媒体驱动的世界中，视频内容在监控、教育和娱乐等领域的指数增长使得通过自然语言查询检索语义相关视频变得愈加重要。早期的视频检索系统依赖于手工特征或浅层跨模态映射，限制了其捕捉复杂语义和时间动态的能力。尽管大规模视觉-语言模型改善了跨模态对齐，但在建模细粒度时间依赖性和微妙语言结构方面仍然面临挑战。本文介绍了DREAM：双路径表示增强与对齐模型，这是一个新颖的多模态框架，通过增强的视觉和文本编码来解决这些局限性。DREAM结合了一种混合语言建模策略，结合了掩蔽和置换语言建模目标，以捕捉局部和全局语言语义。在视觉方面，我们设计了一个具有级联组注意力的层次视觉编码器，通过多阶段标记交互和粗到细的注意力精炼，整合空间和时间信息。我们通过对广泛使用的MSRVTT、MSVD和LSMDC基准数据集进行全面评估来验证DREAM，在这些数据集中分别达到了新的最先进的R1分数49.4%、49.7%和27.3%。定性分析进一步显示了模型在帧间保持一致注意力和将复杂查询与动态视频内容对齐的能力。这些发现强调了层次注意力和双目标文本建模在实现强大、上下文感知视频检索中的有效性，并为未来在推进跨模态表示学习方面的研究铺平了道路。
cs.CV / 66 / 2606.19073
Taming I2V models for Image HOI Editing: A Cognitive Benchmark and Agentic Self-Correcting Framework
驯化图像到视频模型用于图像人机交互编辑：一个认知基准和自主自我修正框架
Abstract
Current image editing methods excel at static attributes but fail at complex Human-Object Interactions (HOI), a critical challenge unaddressed by existing benchmarks that conflate HOI with static attributes, relying on global metrics incapable of simultaneously assessing dynamic interaction validity and entangled human-object pair preservation. Thus, we first introduce HOI-Edit, a comprehensive benchmark with three progressive cognitive levels, which features an automated metric HOI-Eval that reliably evaluates instance-level interaction by letting VLM Q&A after thinking with images containing grounded Human-Object pairs. Considering the task's essence of remodeling dynamic relationships, we benchmark Image-to-Video (I2V) models, finding them inherently suited for dynamic editing due to their temporal generation capabilities. Crucially, beyond superior performance, this capability provides a "replay of the failure process," offering unique diagnosability into why errors occur. We thus propose SCPE (Self-Correcting Process Editing), a novel, agentic self-correcting framework that constrains the generation of I2V models through iteratively refined prompts, enabling the generated videos to more accurately present the target HOI. Extracted frames from these videos are the final editing results. On HOI-Edit, SCPE achieves performance competitive with state-of-the-art (SOTA) editing models like Nano Banana on interaction. Code is available at https://github.com/oceanflowlab/HOI-Edit.
Chinese Translation
当前的图像编辑方法在静态属性方面表现出色，但在复杂的人机交互（HOI）方面却存在不足，这是一个现有基准未能解决的关键挑战，因为这些基准将HOI与静态属性混为一谈，依赖于无法同时评估动态交互有效性和人机对的保留的全局指标。因此，我们首先引入HOI-Edit，这是一个具有三个渐进认知层次的综合基准，特点是一个自动化指标HOI-Eval，能够通过让视觉语言模型（VLM）在思考后对包含基础人机对的图像进行问答，从而可靠地评估实例级交互。考虑到任务重塑动态关系的本质，我们对图像到视频（I2V）模型进行了基准测试，发现它们由于时间生成能力而天生适合动态编辑。至关重要的是，除了卓越的性能外，这一能力还提供了“失败过程的重播”，为错误发生的原因提供了独特的可诊断性。因此，我们提出了SCPE（自我修正过程编辑），这是一个新颖的自主自我修正框架，通过迭代精炼的提示来约束I2V模型的生成，使生成的视频能够更准确地呈现目标HOI。这些视频中提取的帧即为最终的编辑结果。在HOI-Edit上，SCPE在交互方面的表现与最先进的（SOTA）编辑模型如Nano Banana相竞争。代码可在https://github.com/oceanflowlab/HOI-Edit获取。
cs.CV / 67 / 2606.19096
PorTEXTO: A European Portuguese Benchmark for Visual Text Extraction
PorTEXTO：欧洲葡萄牙语视觉文本提取基准
Abstract
European Portuguese (pt-PT) is largely absent from OCR benchmarks, which skew toward high-resource languages. The few benchmarks that cover pt-PT focus on historical artifacts and literature. This work addresses modern OCR applications, introducing PorTEXTO, the first benchmark for contemporary and culturally relevant pt-PT visual text extraction. To ascertain quality, we employ an annotation pipeline combining transcriptions from a frontier LVLM with exhaustive review by native speakers. We observe a sharp performance drop from synthetic to real world samples in most models, and find that, currently, specialized multilingual data is a better driver for pt-PT performance than model size or resolution budget, motivating the release of open pt-PT OCR resources.
Chinese Translation
欧洲葡萄牙语（pt-PT）在光学字符识别（OCR）基准测试中几乎缺失，现有基准测试主要集中于高资源语言。现有的少数涵盖pt-PT的基准测试主要关注历史文物和文学作品。本研究针对现代OCR应用，推出了PorTEXTO，这是第一个针对当代及具有文化相关性的pt-PT视觉文本提取的基准。为了确保质量，我们采用了一种注释流程，结合了前沿大语言模型（LVLM）的转录结果和母语者的全面审查。我们观察到大多数模型在合成样本与真实世界样本之间的性能出现显著下降，并发现目前，专门的多语言数据比模型规模或分辨率预算对pt-PT性能的推动作用更大，这促使我们发布开放的pt-PT OCR资源。
cs.CV / 68 / 2606.19097
DVANet: Degradation-aware Visual-prior Alignment Network for Image Restoration
DVANet：一种考虑退化的视觉先验对齐网络用于图像恢复
Abstract
All-in-One image restoration aims to develop a unified restoration framework for handling diverse degradation types. Existing end-to-end methods usually regard the restoration process as a black-box mapping, lacking an explicit optimization interpretation. Although deep unfolding provides an interpretable iterative modeling paradigm for image restoration, existing methods mostly rely on fixed degradation assumptions or predefined degradation information, making them difficult to adapt to unified restoration requirements under complex degradations and locally damaged content. This limitation restricts their performance in degradation suppression and structural detail recovery. To address these issues, this paper proposes DVANet, a deep unfolding network inspired by the half-quadratic splitting optimization algorithm, which formulates unified image restoration under complex degradations as a collaborative unfolding process between degradation-aware observation consistency and visual-prior-guided reconstruction. Specifically, in the degradation-aware observation consistency branch, a degradation representation module is employed to extract global degradation attributes and local degradation cues, and degradation-conditioned mapping is used to enhance the model's adaptability to different degradation types. In the visual-prior-guided reconstruction branch, DINOv3 is introduced to provide structural and semantic information as hierarchical visual priors, thereby complementing the missing structural information in damaged regions and improving detail recovery. Extensive experiments demonstrate that DVANet achieves superior or competitive performance on multi-scenario degradation and cross-domain image restoration tasks, showing favorable degradation adaptability and generalization ability.
Chinese Translation
一体化图像恢复旨在开发一个统一的恢复框架，以处理多种退化类型。现有的端到端方法通常将恢复过程视为一个黑箱映射，缺乏明确的优化解释。尽管深度展开提供了一种可解释的迭代建模范式用于图像恢复，但现有方法大多依赖于固定的退化假设或预定义的退化信息，使其难以适应复杂退化和局部损坏内容下的统一恢复需求。这一限制制约了它们在退化抑制和结构细节恢复方面的性能。为了解决这些问题，本文提出了DVANet，一种受半二次分裂优化算法启发的深度展开网络，它将复杂退化下的统一图像恢复表述为退化感知观察一致性与视觉先验引导重建之间的协作展开过程。具体而言，在退化感知观察一致性分支中，采用退化表示模块提取全局退化属性和局部退化线索，并使用退化条件映射来增强模型对不同退化类型的适应性。在视觉先验引导重建分支中，引入DINOv3以提供结构和语义信息作为层次视觉先验，从而补充损坏区域缺失的结构信息并改善细节恢复。大量实验表明，DVANet在多场景退化和跨域图像恢复任务中实现了优越或具有竞争力的性能，展现了良好的退化适应性和泛化能力。
cs.CV / 69 / 2606.19100
AMALIA-VL: A Native European Portuguese Open-Source Vision and Language Model
AMALIA-VL：一种原生欧洲葡萄牙语开源视觉与语言模型
Abstract
Large Vision and Language Models (LVLMs) have advanced rapidly, yet European Portuguese (pt-PT) remains systematically underserved by existing open-source multimodal models, which either conflate it with Brazilian Portuguese or severely under-represent it in their training data mixes. We introduce AMALIA-VL, the first open-source instruction-tuned LVLM built natively for pt-PT, pairing a high-resolution vision encoder with dynamic image tiling and a fully open pt-PT-optimized language model via a learned connector. We contribute with a purposefully designed three-stage training process - vision-language alignment, general visual instruction tuning, and preference optimization - together with a pt-PT-centric multimodal data mix combining curated and translated public datasets with novel datasets that address the near-total absence of European Portuguese multimodal resources. Our evaluation shows that AMALIA-VL establishes a strong baseline for open-source pt-PT LVLMs.We will release model weights, training data, and construction pipelines along with machine-translated pt-PT evaluation benchmarks to help democratize pt-PT LVLM development.
Chinese Translation
大型视觉与语言模型（LVLMs）发展迅速，但现有的开源多模态模型对欧洲葡萄牙语（pt-PT）的支持仍然系统性不足，这些模型要么将其与巴西葡萄牙语混淆，要么在训练数据中严重低估了其代表性。我们推出了AMALIA-VL，这是首个为pt-PT原生构建的开源指令调优LVLM，它将高分辨率视觉编码器与动态图像拼接相结合，并通过学习连接器与完全优化的pt-PT语言模型相配对。我们贡献了一个精心设计的三阶段训练过程——视觉-语言对齐、一般视觉指令调优和偏好优化，以及一个以pt-PT为中心的多模态数据混合，结合了策划和翻译的公共数据集与新颖的数据集，以解决欧洲葡萄牙语多模态资源几乎完全缺乏的问题。我们的评估表明，AMALIA-VL为开源pt-PT LVLM建立了一个强有力的基线。我们将发布模型权重、训练数据和构建管道，以及机器翻译的pt-PT评估基准，以帮助民主化pt-PT LVLM的发展。
cs.CV / 70 / 2606.19103
ProductConsistency: Improving Product Identity Preservation in Instruction-Based Image Editing via SFT and RL
ProductConsistency：通过SFT和RL提升基于指令的图像编辑中的产品身份保留
Abstract
Recent advances in instruction-based image editing have enabled models to perform complex visual edits from natural language instructions. However, in product-centric scenarios where preserving product features, branding, and textual elements are critical, current open and closed source models often struggle to maintain this fine-grained object identity. This issue is further compounded by the lack of datasets for instruction-based product image editing with text fidelity constraints, leaving it largely treated as an implicit capability of instruction-based image editing models. In this work, we introduce the ProductConsistency dataset which is designed to improve product-centric image editing. Our approach includes a supervised fine-tuning (SFT) dataset of 87k samples for product editing, a reinforcement learning (RL) dataset with 869 unique product images, and a new benchmark dataset, the ProductConsistency Benchmark, to allow rigorous and standardized evaluation of editing models. To guide RL training, we propose a Cyclic Consistency reward that enforces semantic preservation of product identity by using caption similarity between the original product description and captions generated from the edited image. We fine-tune both Qwen-Image-Edit-2511 and Flux.1-Kontext-dev using our dataset and demonstrate consistent improvements over baseline models in OCR and Perceptual metrics, and MLLM-based evaluations as well, indicating stronger product consistency, text rendering, and overall visual quality; with the Qwen-Image-Edit-2511 model achieving a 5x reduction in the character error rate. The code and pipeline is available at https://anonymous.4open.science/r/ProductConsistency-6FCC/README.md
Chinese Translation
近年来，基于指令的图像编辑的进展使得模型能够根据自然语言指令执行复杂的视觉编辑。然而，在以产品为中心的场景中，保持产品特征、品牌和文本元素至关重要，当前的开源和闭源模型往往难以维持这种细粒度的对象身份。这一问题因缺乏满足文本保真约束的基于指令的产品图像编辑数据集而进一步加剧，使其在很大程度上被视为基于指令的图像编辑模型的隐含能力。在本研究中，我们引入了ProductConsistency数据集，旨在改善以产品为中心的图像编辑。我们的方法包括一个包含87,000个样本的监督微调（SFT）数据集用于产品编辑，一个包含869个独特产品图像的强化学习（RL）数据集，以及一个新的基准数据集——ProductConsistency Benchmark，以便对编辑模型进行严格和标准化的评估。为了指导RL训练，我们提出了一种循环一致性奖励，通过使用原始产品描述与从编辑图像生成的标题之间的相似性来强制保持产品身份的语义一致性。我们使用我们的数据集对Qwen-Image-Edit-2511和Flux.1-Kontext-dev进行了微调，并在OCR和感知指标以及基于MLLM的评估中展示了相对于基线模型的一致性改进，表明产品一致性、文本渲染和整体视觉质量更强；其中Qwen-Image-Edit-2511模型的字符错误率降低了5倍。代码和流程可在https://anonymous.4open.science/r/ProductConsistency-6FCC/README.md获取。
cs.CV / 71 / 2606.19139
Urdu Katib Handwritten Dataset: A Historical Document Dataset for Offline Urdu Handwritten Text Recognition with CRNN-Based Baseline Evaluation
乌尔都书法家手写数据集：用于离线乌尔都手写文本识别的历史文档数据集及基于CRNN的基线评估
Abstract
Automatic Handwritten Text Recognition (HTR) is inherently a challenging task, and its complexity is further increased when dealing with cursive scripts. Although significant efforts have been made on various cursive scripts, research regarding Urdu Handwritten Text Recognition (UHTR) has been relatively limited. This lag of research is primarily due to the unique challenges posed by its script, and the scarcity and unavailability of benchmark datasets. Therefore, to advance research in UHTR, this study presents a specialized real dataset called the Urdu Katib Handwritten Dataset (UKHD). To the best of our knowledge, this is the first offline Urdu handwritten text lines dataset specifically curated from the materials written by Katibs in historical times. It encompasses a diverse range of flat nib writing variations in the Nastalique calligraphic style. Additionally, the effectiveness of different CRNN-based hybrid models has been evaluated to identify the optimal architecture for Urdu Katib Handwriting Recognition (UKHR). Among the analyzed models, the CNN-BGRU-CTC model showed more robust performance, with low Character Error Rate (CER) and Word Error Rate (WER). This research work aims to support and encourage the research community in developing a robust recognition system for preserving Urdu handwritten literature.
Chinese Translation
自动手写文本识别（HTR）本质上是一项具有挑战性的任务，处理连笔书写时其复杂性进一步增加。尽管在各种连笔书写上已做出了显著努力，但关于乌尔都手写文本识别（UHTR）的研究相对有限。这一研究滞后主要是由于其书写体所带来的独特挑战，以及基准数据集的稀缺和不可用。因此，为了推动UHTR的研究，本研究提出了一个专门的真实数据集，称为乌尔都书法家手写数据集（UKHD）。据我们所知，这是第一个专门从历史时期书法家所写材料中策划的离线乌尔都手写文本行数据集。它涵盖了Nastalique书法风格中多样的平头笔书写变体。此外，评估了不同基于CRNN的混合模型的有效性，以确定乌尔都书法家手写识别（UKHR）的最佳架构。在分析的模型中，CNN-BGRU-CTC模型表现出更强的性能，具有较低的字符错误率（CER）和单词错误率（WER）。本研究旨在支持和鼓励研究社区开发一个稳健的识别系统，以保护乌尔都手写文学。
cs.CV / 72 / 2606.19156
Hand-4DGS: Feed-Forward 3D Gaussian Splatting for 4D Hand Reconstruction from Egocentric Videos
Hand-4DGS：基于前馈的3D高斯点云技术用于从自我中心视频重建4D手部
Abstract
Dynamic 3D hand reconstruction from egocentric videos is essential for next-generation computing platforms such as AR/VR and AI glasses. Despite its importance, most prior works focus either on multi-view 3D hand reconstruction or on 4D human body reconstruction. Egocentric 4D hand reconstruction remains challenging due to fast head motion, rapid hand dynamics, severe occlusions, and inherent ambiguity from single-view observations. To address these challenges, we introduce Hand-4DGS, the first feed-forward framework for reconstructing dynamic 4D hands directly from egocentric videos, enabling both fast (~60 FPS) inference and strong generalization. Our approach incorporates a mesh-guided representation for structural priors and temporal convolutions to model dynamic motion. We evaluate our framework on two challenging egocentric datasets, H2O and ARCTIC, and demonstrate significant improvements over baselines. Our method benefits from the generalization capability of feed-forward networks and effective 2D image supervision through Gaussian splatting, without requiring expensive 3D hand pose ground-truth annotations.
Chinese Translation
从自我中心视频动态重建3D手部对于下一代计算平台（如增强现实/虚拟现实和人工智能眼镜）至关重要。尽管其重要性显著，但大多数先前的研究要么专注于多视角3D手部重建，要么专注于4D人体重建。由于快速的头部运动、迅速的手部动态、严重的遮挡以及单视角观察所固有的模糊性，自我中心的4D手部重建仍然具有挑战性。为了解决这些问题，我们提出了Hand-4DGS，这是第一个直接从自我中心视频重建动态4D手部的前馈框架，能够实现快速（约60帧每秒）的推理和强大的泛化能力。我们的方法结合了网格引导表示以提供结构先验，并使用时序卷积来建模动态运动。我们在两个具有挑战性的自我中心数据集H2O和ARCTIC上评估了我们的框架，并展示了相较于基线的显著改进。我们的方法受益于前馈网络的泛化能力和通过高斯点云进行的有效2D图像监督，无需昂贵的3D手部姿态真实标注。
cs.CV / 73 / 2606.19184
When AUC Misleads: Polarization-Aware Evaluation of Deepfake Detectors under Domain Shift
当AUC误导时：考虑极化的深度伪造检测器在领域转移下的评估
Abstract
Recent advances in generative AI, such as diffusion models and face-swapping tools, have enabled the creation of highly realistic deepfakes, leading to real-world harms including financial fraud and non-consensual explicit content. In response, deepfake detection has become an active research area, with recent methods increasingly focusing on improving generalization to unseen manipulations. This is typically evaluated using the Area Under the ROC Curve (AUC) measured separately across multiple datasets. However, such an evaluation fails to reflect real-world scenarios where detectors face a mixture of data sources and varying artifact types. To address this limitation, we introduce a novel metric, Cross-dataset AUC (Cross-AUC) that averages per-domain AUCs with a measure of prediction polarization for taking into account the robustness to domain shift. The polarization extent is quantified by the Wasserstein Distance between class score distributions. Cross-AUC not only assesses the generalization capabilities of deepfake detectors under domain shifts more realistically, but it is also interpretable as it better explains the reason behind a drop in performance. Experiments performed on seven benchmark datasets demonstrate its practical relevance.
Chinese Translation
近年来，生成性人工智能的进步，例如扩散模型和换脸工具，使得高度逼真的深度伪造（deepfake）得以创建，导致了现实世界中的危害，包括金融欺诈和非自愿的露骨内容。对此，深度伪造检测已成为一个活跃的研究领域，近期的方法越来越关注于提高对未见操控的泛化能力。这通常通过在多个数据集上分别测量的接收者操作特征曲线下面积（AUC）进行评估。然而，这种评估未能反映现实世界的场景，在这些场景中，检测器面临着多种数据源和不同类型的伪影。为了解决这一局限性，我们引入了一种新颖的指标——跨数据集AUC（Cross-AUC），它通过预测极化的度量来平均每个领域的AUC，以考虑对领域转移的鲁棒性。极化程度通过类得分分布之间的瓦瑟斯坦距离（Wasserstein Distance）进行量化。Cross-AUC不仅更真实地评估了深度伪造检测器在领域转移下的泛化能力，而且由于更好地解释了性能下降的原因，具有可解释性。在七个基准数据集上进行的实验展示了其实际相关性。
cs.CV / 74 / 2606.19195
Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance
Moebius：具有10B级性能的0.2B轻量级图像修复框架
Abstract
While 10B-level industrial foundation models have pushed the boundaries of image inpainting, their prohibitive computational costs severely hinder practical deployment. Constructing a highly optimized task-specific specialist offers a promising solution; however, extreme structural compression inevitably triggers a severe representation bottleneck. To conquer this, we propose Moebius, a highly efficient lightweight inpainting framework. We systematically reconstruct the diffusion backbone by introducing the Local-$\lambda$ Mix Interaction ($L\lambda MI$) block. Comprising Local-$\lambda$ and Interactive-$\lambda$ modules, it elegantly summarizes spatial contexts and global semantic priors into fixed-size linear matrices, preserving complex latent interactions while drastically shedding parameters. Furthermore, to unlock the full representational capacity of this highly compact architecture, we synergistically pair it with an adaptive multi-granularity distillation strategy. Operating strictly within the latent space to avoid expensive pixel-space decoding, this strategy dynamically balances multiple gradient-based losses to achieve high-fidelity alignment. Extensive experiments across natural and portrait benchmarks demonstrate that this optimal synergy enables Moebius to rival or even surpass the generation quality of the 10B-level industrial generalist FLUX.1-Fill-Dev. Remarkably, Moebius achieves this using less than 2\% of the parameters (0.22B vs. 11.9B) while delivering a $>15\times$ acceleration in total inference time, setting a new efficiency standard for high-fidelity inpainting. Project page at https://hustvl.github.io/Moebius.
Chinese Translation
尽管10B级工业基础模型推动了图像修复的边界，但其高昂的计算成本严重阻碍了实际部署。构建一个高度优化的任务特定专家提供了一种有前景的解决方案；然而，极端的结构压缩不可避免地引发了严重的表示瓶颈。为了解决这个问题，我们提出了Moebius，一个高效的轻量级修复框架。我们通过引入局部-$ ext{λ}$混合交互（Local-$ ext{λ}$ Mix Interaction，$L ext{λ} MI$）模块系统地重构了扩散主干。该模块由局部-$ ext{λ}$和交互-$ ext{λ}$组成，优雅地将空间上下文和全局语义先验总结为固定大小的线性矩阵，保留复杂的潜在交互，同时大幅减少参数。此外，为了释放这一高度紧凑架构的全部表示能力，我们将其与自适应多粒度蒸馏策略协同配对。该策略严格在潜在空间内操作，以避免昂贵的像素空间解码，动态平衡多种基于梯度的损失，以实现高保真对齐。在自然和肖像基准上的广泛实验表明，这种最佳协同使Moebius能够与10B级工业通用模型FLUX.1-Fill-Dev的生成质量相媲美甚至超越。值得注意的是，Moebius在使用不到2 ext{%}的参数（0.22B对比11.9B）的同时，实现了超过15倍的总推理时间加速，为高保真修复设定了新的效率标准。项目页面：https://hustvl.github.io/Moebius。
cs.CV / 75 / 2606.19204
ROSA-TFormer: A Radar-Optical Sensor-Aware Temporal Transformer for Pinus sylvestris Plantation Classification in Northern Shaanxi Using GEE-Derived Sentinel-1/2 Time Series
ROSA-TFormer：一种雷达-光学传感器感知的时间变换器，用于基于GEE衍生的Sentinel-1/2时间序列对陕西北部的松树（Pinus sylvestris）种植园进行分类
Abstract
Accurate identification of Pinus sylvestris var. mongolica plantations is important for monitoring afforestation quality and ecological restoration in northern Shaanxi. This paper proposes ROSA-TFormer, a radar-optical sensor-aware temporal Transformer for P. sylvestris classification using Sentinel-1/2 time-series data generated on Google Earth Engine. The model integrates separate SAR and optical embedding branches, a sensor-aware gate, and temporal attention pooling to capture multi-source seasonal features. Experiments on monthly and half-month point-level datasets show that ROSA-TFormer achieves strong classification performance, with 99.67% overall accuracy, 99.56% macro F1, and 98.91% P. sylvestris F1 on the HalfMonth-dataBig dataset. Spatial block validation and ablation results further indicate the effectiveness of radar-optical temporal fusion and sensor-aware modeling. The results demonstrate the potential of ROSA-TFormer for point-level P. sylvestris plantation classification, while broader wall-to-wall validation remains necessary.
Chinese Translation
准确识别蒙古松（Pinus sylvestris var. mongolica）种植园对于监测陕西北部的造林质量和生态恢复至关重要。本文提出了ROSA-TFormer，一种基于Sentinel-1/2时间序列数据的雷达-光学传感器感知时间变换器，用于松树分类，该数据由Google Earth Engine生成。该模型集成了独立的合成孔径雷达（SAR）和光学嵌入分支、传感器感知门和时间注意力池化，以捕捉多源季节性特征。在每月和半月点级数据集上的实验表明，ROSA-TFormer实现了强大的分类性能，在HalfMonth-dataBig数据集上总体准确率达到99.67%，宏观F1值为99.56%，松树F1值为98.91%。空间块验证和消融实验结果进一步表明雷达-光学时间融合和传感器感知建模的有效性。结果展示了ROSA-TFormer在点级松树种植园分类中的潜力，但仍需进行更广泛的全覆盖验证。
cs.CV / 76 / 2606.19215
GUMP-Net: An interpretable model-data-driven intelligent algorithm for multi-class pelvic segmentation
GUMP-Net：一种可解释的模型驱动智能算法用于多类骨盆分割
Abstract
Pelvic segmentation is one of the most important and fundamental research problems in precise and intelligent diagnosis and treatment, as well as surgical planning and navigation for pelvic fractures. By combining an improved geodesic active contour model with deep neural networks, we propose GUMP-Net, an interpretable model-data-driven intelligent algorithm for multi-class pelvic segmentation, in which three network modules are designed to constitute the overall segmentation framework together: the object detection module for automatic level set initialization, the edge detector module for learning an anatomy-aware edge detector function and the iteration module for deep level set evolution. Leveraging the advantages of level set representation and deep learning, GUMP-Net shows more accurate, robust and consistent segmentation performance, especially in small training data situation, compared to the state-of-the-art methods. Extensive experiments on pelvic datasets demonstrate the rationality and effectiveness of the proposed algorithm. Further experiments extended to ankle dataset indicate broader applications to other anatomies. The proposed algorithm not only provides an efficient segmentation method for complex fracture reduction, but also gives an interpretable geometric perspective for understanding deep learning segmentation.
Chinese Translation
骨盆分割是精确和智能诊断与治疗、以及骨盆骨折的外科规划和导航中最重要和基础的研究问题之一。通过将改进的测地线主动轮廓模型与深度神经网络相结合，我们提出了GUMP-Net，一种可解释的模型驱动智能算法用于多类骨盆分割，其中设计了三个网络模块以构成整体分割框架：用于自动水平集初始化的目标检测模块、用于学习解剖感知边缘检测函数的边缘检测模块以及用于深度水平集演化的迭代模块。利用水平集表示和深度学习的优势，GUMP-Net在小训练数据情况下相比于最先进的方法显示出更准确、稳健和一致的分割性能。在骨盆数据集上的大量实验证明了所提算法的合理性和有效性。进一步扩展到踝关节数据集的实验表明其在其他解剖结构上的更广泛应用。所提算法不仅为复杂骨折复位提供了一种高效的分割方法，还为理解深度学习分割提供了可解释的几何视角。
cs.CV / 77 / 2606.19249
Transformer Geometry Observatory TGO-I: Spectral Geometry Observatory
变换几何观测站 TGO-I：光谱几何观测站
Abstract
Despite the widespread adoption of Vision Transformers (ViTs) and their success across numerous computer vision applications, the fundamental understanding of their dimensional and representational geometry remains relatively underexplored. To address this gap, we introduce Transformer Geometry Observatory (TGO), a systematic framework of experiments and analysis pipelines designed to investigate the representational geometry and dynamics of Vision Transformers. TGO-I, the first installment of the framework, focuses on the spectral geometry of ViT representations. Using a ViT-Small/16 model trained on ImageNet-100, we analyze Effective Rank, Stable Rank, Participation Ratio, Spectral Entropy, Spectral Flatness, Spectral Anisotropy, covariance structure, eigenspectra, and singular value spectra throughout training. Our results reveal a consistent increase in dimensional utilization, accompanied by decreasing anisotropy, increasing spectral entropy, increasing participation ratio, and progressively flatter eigenspectra. Contrary to the common intuition that training should concentrate information into a small number of dominant directions, we observe a progressive redistribution of variance across representational dimensions. This phenomenon is particularly pronounced in the final CLS token representation, which exhibits the highest effective dimensionality and lowest anisotropy within the network.
Chinese Translation
尽管视觉变换器（Vision Transformers, ViTs）已被广泛应用并在众多计算机视觉应用中取得成功，但对其维度和表征几何的基本理解仍然相对欠缺。为了解决这一问题，我们提出了变换几何观测站（Transformer Geometry Observatory, TGO），这是一个系统的实验和分析管道框架，旨在研究视觉变换器的表征几何和动态特性。TGO-I是该框架的第一部分，重点关注ViT表征的光谱几何。我们使用在ImageNet-100上训练的ViT-Small/16模型，分析有效秩、稳定秩、参与比率、光谱熵、光谱平坦度、光谱各向异性、协方差结构、特征谱和奇异值谱在训练过程中的变化。我们的结果显示，维度利用率持续增加，同时伴随着各向异性降低、光谱熵增加、参与比率增加以及特征谱逐渐平坦。与训练应将信息集中到少数主导方向的普遍直觉相反，我们观察到表征维度之间方差的逐步重新分配。这一现象在最终的CLS标记表征中尤为明显，该表征展现了网络中最高的有效维度和最低的各向异性。
cs.CV / 78 / 2606.19253
OneCanvas: 3D Scene Understanding via Panoramic Reprojection
OneCanvas：通过全景重投影实现3D场景理解
Abstract
Existing approaches to 3D scene understanding in Vision-Language Models (VLMs) either rely on complex, model-specific geometry encoders or large training budgets in pursuit of spatial reasoning. Instead, OneCanvas aggregates patch features from all views onto a single equirectangular panoramic canvas. Namely, each patch is unprojected to a 3D world coordinate using its depth and camera pose, then placed on the canvas at the continuous longitude and latitude of that point as seen from the canvas origin, with no rasterization or aggregation across overlapping views. A 3D position embedding of the patch's metric coordinates is added to its feature, restoring the depth lost when collapsing the world position to an angular canvas coordinate. Patches from all frames thus share one spatial coordinate system with no fusion or major architectural modifications of the backbone. The pretrained VLM consumes this representation as if it were an ordinary image. Because the canvas can be centered on any pose of interest, the same representation directly supports situated reasoning from a specific viewpoint, a common requirement in robotics and embodied AI. Thanks to this representation, we can also introduce a spatial pretraining curriculum: by procedurally placing patch features of objects, drawn from real images, at chosen 3D world positions on an otherwise empty canvas, we generate on-the-fly supervision spanning a broad range of spatial reasoning tasks, with answer distributions controlled to reduce spatial reasoning shortcuts. OneCanvas achieves state-of-the-art accuracy on SQA3D and VSI-Bench, and generalizes to out-of-distribution data on SPBench, using an order of magnitude less training compute than the strongest competing methods.
Chinese Translation
现有的视觉-语言模型（VLMs）在3D场景理解方面的方法，要么依赖于复杂的特定模型几何编码器，要么需要大量的训练预算以追求空间推理。相反，OneCanvas将来自所有视角的补丁特征聚合到一个单一的等距全景画布上。具体而言，每个补丁使用其深度和相机姿态被反投影到3D世界坐标系中，然后根据从画布原点看到的该点的连续经度和纬度放置在画布上，而无需在重叠视图之间进行光栅化或聚合。因此，来自所有帧的补丁共享一个空间坐标系统，而无需对主干网络进行融合或重大架构修改。预训练的VLM将这种表示视为普通图像。由于画布可以围绕任何感兴趣的姿态进行中心化，因此相同的表示直接支持从特定视点进行情境推理，这是机器人技术和具身人工智能中的一个常见需求。得益于这种表示，我们还可以引入空间预训练课程：通过程序性地将来自真实图像的物体补丁特征放置在选定的3D世界位置上，在一个原本空白的画布上，我们生成了覆盖广泛空间推理任务的即时监督，并控制答案分布以减少空间推理捷径。OneCanvas在SQA3D和VSI-Bench上达到了最先进的准确率，并在SPBench上对分布外数据进行了泛化，所需的训练计算量比最强竞争方法少一个数量级。
cs.CV / 79 / 2606.19258
CABLE: Cloud-Assisted Bandwidth-efficient LMM-based Encoding for V2X Systems
CABLE：云辅助带宽高效的基于LMM的V2X系统编码
Abstract
Cloud-hosted large multimodal models (LMMs) can provide strong open-vocabulary perception for Vehicle-to-Everything systems, but naively transmitting full-resolution frames from edge to cloud causes severe communication overhead and high cloud-side prefill latency. We present CABLE, a cloud-assisted bandwidth-efficient LMM-based encoding framework for edge-cloud perception. CABLE propagates the previous cloud segmentation mask on the edge using ego-motion compensation, refines it with residual-motion cues, and consolidates disconnected regions via a corridor envelope to form a robust region of interest (ROI). Only ROI-masked images are uploaded, while the cloud segmentation output is fed back as the prior for the next frame, forming a mask-to-ROI-to-LMM feedback loop. Experiments on five datasets (nuScenes, WOD-ZB, Waymo, KITTI, and CADC) show consistent communication savings while largely preserving perception, achieving $73$--$87\%$ ROI pixel-coverage reduction with $5$--$8\times$ estimated LMM prefill speedup at a modest detection-quality trade-off relative to full-frame inference.
Chinese Translation
云托管的大型多模态模型（LMM）能够为车对一切（V2X）系统提供强大的开放词汇感知能力，但从边缘到云端天真地传输全分辨率帧会导致严重的通信开销和高云端预填充延迟。我们提出了CABLE，一个云辅助的带宽高效的基于LMM的编码框架，用于边缘-云感知。CABLE利用自我运动补偿在边缘传播先前的云分割掩膜，通过残余运动线索对其进行细化，并通过走廊包络整合不连通区域，以形成一个稳健的兴趣区域（ROI）。仅上传ROI掩膜图像，而云分割输出则作为下一帧的先验反馈，形成掩膜-ROI-LMM反馈循环。在五个数据集（nuScenes、WOD-ZB、Waymo、KITTI和CADC）上的实验表明，在大幅保持感知能力的同时，通信节省是一致的，实现了$73$--$87 ext{ extperthousand}$的ROI像素覆盖率减少，并在相对于全帧推理的适度检测质量权衡下，达到了$5$--$8 imes$的估计LMM预填充加速。
cs.CV / 80 / 2606.19259
A Multi-Domain Benchmark for Detecting AI-Generated Text-Rich Images from GPT-Image-2
用于检测由GPT-Image-2生成的文本丰富图像的多领域基准
Abstract
Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information. As recent multimodal image generation models become increasingly capable of synthesizing realistic textual content and structured visual designs, detecting AI-generated text-rich images has become an important challenge for digital trust and content authenticity. Existing benchmarks, however, largely focus on object-centric images and provide limited coverage of scenarios where textual semantics and layout organization are central. In this paper, we introduce a multi-domain benchmark for detecting text-rich images generated by OpenAI's GPT Image 2. The benchmark contains 8,602 images across six representative categories: commercial posters, infographics, academic posters, receipts, tables, and UI screenshots. Using this benchmark, we evaluate five representative AI-generated image detectors in a zero-shot setting and analyze their overall, category-wise, and post-processing robustness. Our results show that detector performance is highly domain-dependent: methods that perform well in some categories often fail on others, and even the strongest conventional detector exhibits severe sensitivity to JPEG compression. We further conduct an exploratory evaluation with a multimodal vision-language model, revealing both its promise and its limitations on structured formats. These findings highlight the need for text- and layout-aware detection methods for modern AI-generated images. Our dataset is released at XXX.
Chinese Translation
文本丰富的图像通常包含隐私敏感、交易或决策相关的信息。随着近期多模态图像生成模型在合成现实文本内容和结构化视觉设计方面的能力不断增强，检测AI生成的文本丰富图像已成为数字信任和内容真实性的重要挑战。然而，现有的基准主要集中在以物体为中心的图像上，对于文本语义和布局组织为核心的场景覆盖有限。在本文中，我们引入了一个用于检测OpenAI的GPT Image 2生成的文本丰富图像的多领域基准。该基准包含来自六个代表性类别的8,602幅图像：商业海报、信息图、学术海报、收据、表格和用户界面截图。利用该基准，我们在零样本设置下评估了五个代表性的AI生成图像检测器，并分析了它们的整体表现、类别表现和后处理鲁棒性。我们的结果表明，检测器的性能高度依赖于领域：在某些类别中表现良好的方法在其他类别中往往失败，即使是最强的传统检测器也对JPEG压缩表现出严重的敏感性。我们进一步与多模态视觉-语言模型进行了探索性评估，揭示了其在结构化格式上的潜力和局限性。这些发现突显了现代AI生成图像需要文本和布局感知检测方法的必要性。我们的数据集已在XXX发布。
cs.CV / 81 / 2606.19277
A Unified Framework for Efficient Remote Sensing Visual Question Answering: Adapting Dual, Hybrid, and Encoder-Decoder Architectures
高效遥感视觉问答的统一框架：适应双重、混合和编码-解码架构
Abstract
Visual Question Answering (VQA) in the Remote Sensing (RS) domain presents unique challenges due to the high resolution, multi scale object distribution, and semantic complexity of aerial imagery. While general domain Foundation Models have achieved remarkable success, their direct application to RSVQA is hindered by massive domain shifts and the computationally prohibitive nature of full fine tuning. This study presents a comparative analysis of RS Adapter, a Parameter Efficient Fine Tuning (PEFT) strategy, applied across three distinct Vision Language Model (VLM) architectures: the Dual Encoder CLIP, the Encoder Decoder BLIP, and the Hybrid FLAVA. We introduce a unified architectural surgery pipeline that injects lightweight bottleneck adapters into the attention and MLP layers of frozen backbones, enabling rapid adaptation with less than 5 percent of trainable parameters. Experimental results on the high resolution RSVQA x dataset demonstrate that while all adapted models achieve convergence, the Hybrid FLAVA architecture offers a superior balance of multimodal reasoning and retrieval capabilities compared to its unimodal counterparts. Our findings establish a new baseline for resource efficient VQA in disaster assessment and urban monitoring.
Chinese Translation
遥感（RS）领域的视觉问答（VQA）由于高分辨率、多尺度物体分布和航空图像的语义复杂性，面临独特的挑战。尽管通用领域的基础模型取得了显著成功，但由于巨大的领域转变和全量微调的计算开销，其在遥感视觉问答中的直接应用受到限制。本研究对RS适配器（RS Adapter）进行比较分析，这是一种参数高效微调（PEFT）策略，应用于三种不同的视觉语言模型（VLM）架构：双编码器CLIP、编码-解码器BLIP和混合FLAVA。我们提出了一种统一的架构手术流程，将轻量级瓶颈适配器注入到冻结主干网络的注意力层和多层感知器（MLP）层中，从而实现快速适应，训练参数少于5%。在高分辨率的RSVQA x数据集上的实验结果表明，尽管所有适配模型都达到了收敛，但混合FLAVA架构在多模态推理和检索能力方面相比其单模态对应物提供了更优的平衡。我们的研究结果为灾害评估和城市监测中的资源高效视觉问答建立了新的基准。
cs.CV / 82 / 2606.19300
Confidence is Not Reliability: Rethinking MC Dropout in Brain Tumour Segmentation
信心并非可靠性：重新思考脑肿瘤分割中的MC Dropout
Abstract
Glioma segmentation in multiparametric MRI is a critical component of treatment planning. A segmentation model that fails silently on treatment-critical sub-regions represents a patient safety risk that overlap-based metrics such as Dice scores cannot expose. We ask whether voxel-level uncertainty estimation via Monte Carlo (MC) Dropout can reliably identify segmentation errors in clinically critical sub-regions, and whether calibration failure modes are detectable from standard reporting metrics alone. In an empirical two-model case study on 126 BraTS21 patients, we evaluate a high-performance pretrained SegResNet and a locally trained UNet with residual units (UNet-Res). MC dropout preserved segmentation accuracy ($|\Delta \text{Dice}|$ $<0.01$) while achieving strong uncertainty-error alignment (AUROC for entropy (H) $\approx$0.97), indicating uncertainty correctly ranks erroneous voxels above correct ones. Entropy-based patient stratification identified a high-uncertainty subgroup with substantially lower segmentation performance (median whole-tumour Dice $0.835$ vs. $0.925$), supporting uncertainty as a practical triage signal. However, global alignment can mask important region-specific differences. Despite similar AUROC, UNet-Res exhibited near-zero enhancing tumour entropy ($0.054$) and Expected Calibration Error (ECE) of $0.915$, with a Dice of only $0.714$, indicating severely miscalibrated confidence on the most clinically critical sub-region, a failure mode invisible to standard Dice and AUROC reporting. These findings demonstrate that strong uncertainty-error alignment is necessary but insufficient for clinical safety: sub-region-specific calibration assessment must accompany AUROC evaluation when selecting models for clinical deployment.
Chinese Translation
多参数MRI中的胶质瘤分割是治疗规划的关键组成部分。一个在治疗关键子区域静默失效的分割模型代表了患者安全风险，而基于重叠的指标（如Dice分数）无法揭示这一点。我们探讨通过蒙特卡罗（MC）Dropout进行体素级不确定性估计是否能够可靠地识别临床关键子区域的分割错误，以及仅通过标准报告指标是否能够检测到校准失败模式。在对126名BraTS21患者进行的实证双模型案例研究中，我们评估了一个高性能的预训练SegResNet和一个带有残差单元的本地训练UNet（UNet-Res）。MC Dropout在保持分割准确度（$| ext{Dice} riangle|$ $<0.01$）的同时，实现了强的不确定性-错误对齐（熵（H）的AUROC约为0.97），表明不确定性正确地将错误体素的排名高于正确体素。基于熵的患者分层识别出一个高不确定性亚组，其分割性能显著较低（整体肿瘤的中位Dice为$0.835$对比$0.925$），支持不确定性作为一种实用的分流信号。然而，全球对齐可能掩盖重要的区域特异性差异。尽管AUROC相似，UNet-Res显示出近乎零的增强肿瘤熵（$0.054$）和期望校准误差（ECE）为$0.915$，Dice仅为$0.714$，表明在最临床关键的子区域上信心严重失校准，这一失败模式在标准的Dice和AUROC报告中是不可见的。这些发现表明，强的不确定性-错误对齐是必要但不足以确保临床安全：在选择临床部署模型时，必须伴随AUROC评估进行子区域特异性校准评估。
cs.CV / 83 / 2606.19316
NeuMesh++: Towards Versatile and Efficient Volumetric Editing with Disentangled Neural Mesh-based Implicit Field
NeuMesh++：朝着多功能和高效的体积编辑迈进，基于解耦神经网格的隐式场
Abstract
Recently neural implicit rendering techniques have evolved rapidly and demonstrated significant advantages in novel view synthesis and 3D scene reconstruction. However, existing neural rendering methods for editing purposes offer limited functionalities, e.g., rigid transformation and category-specific editing. In this paper, we present a novel mesh-based representation by encoding the neural radiance field with disentangled geometry, texture, and semantic codes on mesh vertices, which empowers a set of efficient and comprehensive editing functionalities, including mesh-guided geometry editing, designated texture editing with texture swapping, filling and painting operations, and semantic-guided editing. To this end, we develop several techniques including a novel local space parameterization to enhance rendering quality and training stability, a learnable modification color on vertex to improve the fidelity of texture editing, a spatial-aware optimization strategy to realize precise texture editing, and a semantic-aided region selection to ease the laborious annotation of implicit field editing. Extensive experiments and editing examples on both real and synthetic datasets demonstrate the superiority of our method on representation quality and editing ability. Project page: https://zju3dv.github.io/neumeshplusplus/
Chinese Translation
近年来，神经隐式渲染技术迅速发展，并在新视图合成和三维场景重建中展现出显著优势。然而，现有的用于编辑目的的神经渲染方法功能有限，例如仅支持刚性变换和特定类别的编辑。本文提出了一种新颖的基于网格的表示方法，通过在网格顶点上编码解耦的几何、纹理和语义代码，赋予了一系列高效且全面的编辑功能，包括网格引导的几何编辑、指定纹理的编辑与纹理交换、填充和绘制操作，以及语义引导的编辑。为此，我们开发了几种技术，包括新颖的局部空间参数化以增强渲染质量和训练稳定性、可学习的顶点修改颜色以提高纹理编辑的保真度、空间感知优化策略以实现精确的纹理编辑，以及语义辅助的区域选择以简化隐式场编辑的繁琐标注。大量在真实和合成数据集上的实验和编辑示例证明了我们方法在表示质量和编辑能力上的优越性。项目页面：https://zju3dv.github.io/neumeshplusplus/
cs.CV / 84 / 2606.19338
Beyond the Current Observation: Evaluating Multimodal Large Language Models in Controllable Non-Markov Games
超越当前观察：评估可控非马尔可夫游戏中的多模态大型语言模型
Abstract
Deploying multimodal foundation models as closed-loop policies increasingly requires conditioning actions on observations that are no longer visible. However, existing benchmarks either expose the full state, conflate hidden-state reconstruction with other agent skills, or test recall only after an episode has ended. We introduce RNG-Bench (Reconstructive Non-Markov Games), a benchmark suite designed to isolate a base model's ability to reconstruct past observations and act on them during multi-step interaction. RNG-Bench includes two complementary games: Matching Pairs, where card identities briefly revealed at specific locations must later be recalled, and 3D Maze, where egocentric views must be integrated into a spatial map. Both games are evaluated under a unified harness with three controlled difficulty axes: grid size, visual pattern, and observation modality. The benchmark further introduces a head-to-head duel protocol to control for instance-level variance and a Memory Gap metric that disentangles forgetting from poor action selection. The hardest configurations require contexts of roughly 128K tokens and 350 image inputs per episode, and remain far from saturated by frontier MLLMs. Memory Gap analysis shows that most residual errors stem from forgetting earlier observations rather than from suboptimal decision making. Finally, fine-tuning Qwen3.5-9B on optimal-policy rollouts and filtered model demonstrations improves performance on RNG-Bench and transfers to existing benchmarks without degrading general multimodal capability.
Chinese Translation
将多模态基础模型作为闭环策略进行部署，越来越需要将动作条件化于不再可见的观察。然而，现有基准要么暴露完整状态，要么将隐藏状态重建与其他代理技能混淆，或者仅在一个回合结束后测试回忆能力。我们引入了RNG-Bench（重建非马尔可夫游戏），这是一个基准套件，旨在孤立基础模型在多步交互中重建过去观察并基于这些观察采取行动的能力。RNG-Bench包括两个互补的游戏：匹配对（Matching Pairs），在特定位置短暂揭示的卡片身份必须在之后被回忆，以及3D迷宫（3D Maze），在其中自我中心视图必须整合到空间地图中。两个游戏在一个统一的框架下进行评估，具有三个可控的难度轴：网格大小、视觉模式和观察模态。该基准进一步引入了头对头对决协议，以控制实例级方差，以及一个记忆间隙（Memory Gap）指标，将遗忘与不良动作选择区分开。最难的配置要求每个回合大约128K个标记和350个图像输入，并且仍远未被前沿的多模态大型语言模型（MLLMs）饱和。记忆间隙分析表明，大多数剩余错误源于遗忘早期观察，而非次优决策。最后，对Qwen3.5-9B进行最佳策略回放和过滤模型演示的微调，提高了在RNG-Bench上的表现，并且在不降低一般多模态能力的情况下转移到现有基准上。
cs.CV / 85 / 2606.19341
Native Active Perception as Reasoning for Omni-Modal Understanding
原生主动感知作为全模态理解的推理基础
Abstract
Passive models for long video understanding typically rely on a "watch-it-all" paradigm, processing frames uniformly regardless of query difficulty, causing computational cost to grow with video duration. Although interactive frameworks have emerged, they often rely on global pre-scanning, and their context cost still scales with video length. We propose OmniAgent, the first native omni-modal agent that formulates video understanding as a POMDP-based iterative Observation-Thought-Action cycle. OmniAgent executes on-demand actions to selectively distill audio-visual cues into a persistent textual memory, effectively decoupling reasoning complexity from raw video duration. To operationalize this, we introduce (1) Agentic Supervised Fine-Tuning to bootstrap native active perception via best-of-N trajectory synthesis with dual-stage quality control, and (2) Agentic Reinforcement Learning with TAURA (Turn-aware Adaptive Uncertainty Rescaled Advantage), which leverages turn-level entropy to steer credit assignment toward pivotal discovery turns. Crucially, OmniAgent exhibits positive test-time scaling, where performance improves as the number of reasoning turns increases, validating the efficacy of active perception. Empirical results across ten benchmarks (e.g., VideoMME, LVBench) demonstrate that OmniAgent achieves state-of-the-art performance among open-source models. Notably, on LVBench, our 7B agent outperforms the 10$\times$ larger Qwen2.5-VL-72B (50.5% vs. 47.3%).
Chinese Translation
被动模型在长视频理解中通常依赖于“全看”范式，无论查询难度如何，均匀处理帧，导致计算成本随着视频时长的增长而增加。尽管交互框架已经出现，但它们通常依赖于全局预扫描，其上下文成本仍然随着视频长度而增加。我们提出了OmniAgent，这是首个将视频理解形式化为基于部分可观测马尔可夫决策过程（POMDP）的迭代观察-思考-行动循环的原生全模态代理。OmniAgent按需执行动作，选择性地将音视频线索提炼为持久的文本记忆，有效地将推理复杂性与原始视频时长解耦。为此，我们引入了（1）代理监督微调（Agentic Supervised Fine-Tuning），通过最佳N轨迹合成与双阶段质量控制来引导原生主动感知的启动，以及（2）带有TAURA（转向感知自适应不确定性重标定优势）的代理强化学习（Agentic Reinforcement Learning），利用转向级别的熵来引导信用分配，聚焦于关键发现转。重要的是，OmniAgent表现出积极的测试时间扩展性，随着推理转的数量增加，性能得到提升，验证了主动感知的有效性。在十个基准测试（例如，VideoMME，LVBench）上的实证结果表明，OmniAgent在开源模型中实现了最先进的性能。值得注意的是，在LVBench上，我们的7B代理超越了10倍大的Qwen2.5-VL-72B（50.5%对47.3%）。
cs.AI / 1 / 2606.18271
NAVI-Orbital: First In-Orbit Demonstration of a Zero-Shot Vision-Language Model for Autonomous Earth Observation
NAVI-Orbital：首个在轨演示的零样本视觉-语言模型用于自主地球观测
Abstract
As Earth Observation data generation outpaces downlink bandwidth and human-in-the-loop processing, a widening gap has emerged between onboard collection and actionable ground intelligence. This paper presents NAVI-Orbital, a software system deployed on a Low Earth Orbit (LEO) spacecraft. On April 16, 2026, NAVI-Orbital achieved what is, to the authors' knowledge, the first in-orbit demonstration of a vision-language model performing autonomous multi-modal inference entirely onboard. NAVI-Orbital uses a local vision-language model (Gemma 3) to classify each captured scene, produce a text description of its content and the relationships between its features, and respond to operator follow-up via natural-language dialogue. The system is re-tasked through plain-English prompts in place of conventional command sequences, and is orchestrated by a graph-based state machine (LangGraph) coordinating dedicated agents for detection and dialogue. Results across ground benchmarking (88.16% accuracy on the 7,960-image curated AID benchmark), Flatsat validation, and live in-orbit captures of newly acquired, previously unseen Earth imagery (including uncorrected YAM-9 imagery, processed onboard with hardware-accelerated GPU inference and no fine-tuning for the flight instrument) demonstrate the feasibility of running foundation models on satellite-class edge computers to invert the conventional acquire-then-downlink-everything bandwidth profile through semantic compression of Earth observations in-orbit.
Chinese Translation
随着地球观测数据生成速度超过下行带宽和人工处理能力，机载数据采集与可操作的地面情报之间出现了日益扩大的差距。本文介绍了NAVI-Orbital，这是一个部署在低地球轨道（LEO）航天器上的软件系统。2026年4月16日，NAVI-Orbital实现了作者所知的首个在轨演示，展示了一个视觉-语言模型在完全机载环境下进行自主多模态推理的能力。NAVI-Orbital使用本地视觉-语言模型（Gemma 3）对每个捕获场景进行分类，生成其内容及特征之间关系的文本描述，并通过自然语言对话响应操作员的后续提问。该系统通过简单的英语提示进行重新任务分配，而不是传统的命令序列，并由一个基于图的状态机（LangGraph）协调专门的检测和对话代理。通过地面基准测试（在7,960幅图像的AID基准上达到88.16%的准确率）、Flatsat验证以及对新获取的、之前未见过的地球影像（包括未经校正的YAM-9影像，采用硬件加速的GPU推理在机上处理且未对飞行仪器进行微调）进行的实时在轨捕获，结果展示了在卫星级边缘计算机上运行基础模型的可行性，从而通过对地球观测数据的语义压缩，逆转传统的获取-然后下行所有数据的带宽配置。
cs.AI / 2 / 2606.18385
CaVe-VLM-CoT: An Interpretable Vision-Language Model Framework
CaVe-VLM-CoT：一种可解释的视觉-语言模型框架
Abstract
Vision-Language Models (VLMs) remain prone to hallucinations, producing fluent but visually unfaithful outputs. Existing chain-of-thought and retrieval-augmented methods only partially address this, as they neither enforce step-level citation grounding nor route verification failures back to retrieval for correction. We present CaVe-VLM-CoT, a modular reflection-based agentic-RAG framework that enforces evidence-grounded reasoning through a five-stage closed-loop pipeline: Extractor, Retriever, Solver, Citation Injector, and Verifier, in which detected ungrounded claims trigger structured feedback to the Extractor for targeted re-retrieval. Since no existing framework jointly measures retrieval quality, step-wise citation faithfulness, and cross-modal grounding, we propose a suite of 23 component-wise metrics across all stages, anchored by CaVeScore, a composite metric weighting accuracy, citation precision and recall, attribution, and evidence grounding. Without any architectural or prompt modifications, CaVe-VLM-CoT achieves 87.1\% accuracy and 56.6\% CaVeScore on ScienceQA , and 55.2\% accuracy and 35.7\% CaVeScore on MMMU (30 subjects).
Chinese Translation
视觉-语言模型（VLMs）仍然容易产生幻觉，生成流畅但在视觉上不真实的输出。现有的思维链和检索增强方法仅部分解决了这一问题，因为它们既未强制执行逐步引用的基础，也未将验证失败的路径回溯到检索以进行修正。我们提出了CaVe-VLM-CoT，这是一种基于模块化反思的代理-检索增强生成（agentic-RAG）框架，通过五个阶段的闭环管道来强制执行基于证据的推理：提取器（Extractor）、检索器（Retriever）、求解器（Solver）、引用注入器（Citation Injector）和验证器（Verifier），在此过程中，检测到的无基础主张会触发结构化反馈给提取器，以便进行针对性的重新检索。由于现有框架没有共同测量检索质量、逐步引用的真实性和跨模态基础，我们提出了一套23个组件级别的指标，涵盖所有阶段，以CaVeScore为基础，这是一个综合指标，权衡准确性、引用精度和召回率、归因和证据基础。在没有任何架构或提示修改的情况下，CaVe-VLM-CoT在ScienceQA上达到了87.1%的准确率和56.6%的CaVeScore，在MMMU（30个主题）上达到了55.2%的准确率和35.7%的CaVeScore。
cs.AI / 3 / 2606.18413
Searching for Synergy in Shared Workspace Human-AI Collaboration
探索共享工作空间中人机协作的协同效应
Abstract
Automated AI agents are increasingly capable, yet many scientific and professional tasks require human judgment and contextual expertise. We study shared-workspace human-AI teams, where AI agents and human collaborators must coordinate responsibilities before submitting a final answer. Using the Collaborative Gym environment with DiscoveryBench tasks, we examine when adding simulated human collaborators improves performance and when process loss turns additional collaborators into coordination overhead. Across 1,482 sessions, adding relevant collaborators can lower performance when teams lack structure to coordinate their contributions. We then evaluate scaffolding that combines shared group memory with simulated human-in-the-loop (HITL) gates, where selected actions require approval from a designated simulated participant. This scaffolding yields higher mean performance, most clearly in three-person teams, with clearer responsibility signals and stronger routing of expertise to team actions. Overall, how human-AI teams coordinate and integrate expertise matters as much as the capability available to them.
Chinese Translation
自动化人工智能代理的能力日益增强，但许多科学和专业任务仍然需要人类的判断和上下文专业知识。我们研究共享工作空间的人机团队，在这些团队中，人工智能代理和人类协作者必须在提交最终答案之前协调各自的责任。通过使用包含DiscoveryBench任务的协作环境，我们考察了在何种情况下增加模拟人类协作者能够提高性能，以及在何种情况下过程损失使额外的协作者变成协调负担。在1482个会话中，当团队缺乏协调贡献的结构时，增加相关的协作者可能会降低性能。随后，我们评估了一种结合共享群体记忆与模拟人类参与（HITL）门的支架，其中选定的行动需要指定的模拟参与者的批准。这种支架在三人团队中表现出更高的平均性能，责任信号更清晰，专业知识更有效地引导团队行动。总体而言，人机团队如何协调和整合专业知识与他们可用的能力同样重要。
cs.AI / 4 / 2606.18543
CEO-Bench: Can Agents Play the Long Game?
CEO-Bench：代理能否进行长期规划？
Abstract
Language model agents are becoming proficient executors at isolated, short-horizon tasks such as software engineering and customer service. Yet real-world challenges require a combination of sophisticated skills that remain largely untested in agents: (1) navigating long horizons amid uncertainty; (2) acquiring information in noisy environments; (3) adapting to a changing world; (4) orchestrating multiple moving parts toward a coherent goal. We introduce CEO-Bench, which evaluates these capabilities together by simulating a representative real-world task: operating a startup for 500 days. An agent manages pricing, marketing, budgeting, and many other aspects of a fictional company through a programmable Python interface, operating in the same environment and facing the same challenges as a human CEO. Success demands analyzing noisy, interconnected business databases, translating signals into sound strategy, and coordinating many decisions with programming. The strongest agents write sophisticated code that simulates customer cohorts to forecast future cash and mines negotiation history to uncover hidden customer preferences. Even so, most state-of-the-art models struggle in this environment. Only Claude Opus 4.8 and GPT-5.5 finish above the $1M starting balance, and neither consistently turns a profit. CEO-Bench takes a first step toward measuring the intelligence required to drive sustained, adaptive progress over time.
Chinese Translation
语言模型代理在软件工程和客户服务等孤立的短期任务中变得越来越熟练。然而，现实世界的挑战需要一系列复杂技能的结合，这些技能在代理中仍然 largely 未经过测试：(1) 在不确定性中导航长时间范围；(2) 在嘈杂环境中获取信息；(3) 适应变化的世界；(4) 协调多个动态部分以实现一致的目标。我们引入了 CEO-Bench，它通过模拟一个代表性的现实世界任务来共同评估这些能力：运营一家初创公司 500 天。代理通过可编程的 Python 接口管理定价、营销、预算和虚构公司的许多其他方面，在同一环境中运作，并面临与人类 CEO 相同的挑战。成功需要分析嘈杂的、相互关联的商业数据库，将信号转化为有效的战略，并通过编程协调许多决策。最强的代理编写复杂的代码，模拟客户群体以预测未来现金流，并挖掘谈判历史以发现隐藏的客户偏好。尽管如此，大多数最先进的模型在这一环境中仍然面临困难。只有 Claude Opus 4.8 和 GPT-5.5 的表现超过了 100 万美元的起始余额，但两者都未能持续盈利。CEO-Bench 是迈向衡量推动持续适应性进步所需智能的第一步。
cs.AI / 5 / 2606.18557
DeFAb: A Verifiable Benchmark for Defeasible Abduction in Foundation Models
DeFAb：一个可验证的基础模型可推翻归纳基准
Abstract
A rule-based logic solver resolves every instance in our benchmark in under 50 microseconds with 100% accuracy; the best frontier language model reaches 65% at best and drops to 23.5% under rendering-robust evaluation (worst case over four surface renderings). We introduce DeFAb (Defeasible Abduction Benchmark), a dataset and generation pipeline that converts four decades of publicly funded knowledge bases into formally grounded instances for defeasible abduction: constructing hypotheses that explain anomalies by overriding defaults while preserving unrelated expectations. Because every hypothesis must pass polynomial-time checks for valid derivation, conservativity, and minimality, DeFAb makes logical rigor the instrument for measuring creativity and theoretical reasoning, scoring the disciplined construction of theory revisions rather than fluent but theory-destroying prose. The pipeline pairs taxonomic hierarchies (OpenCyc, YAGO, Wikidata) with behavioral property graphs (ConceptNet, UMLS) to produce 372,648+ instances across 33.75M materialized rules from 18 sources, in three levels with polynomial-time verifiable gold standards. Four frontier models do not reliably internalize defeasible reasoning: rendering-robust Level 2 accuracy is 7.8-23.5%; chain-of-thought variance (~36 pp) exceeds any inter-model gap; and a matched contamination control isolates a +19.4 pp Level 3 gap. We further release DeFAb-Hard (a 235-instance Level 3 difficulty variant; best model 53.3% vs 100% symbolic) and CONJURE (a kernel-verified transformative-creativity variant of 560 Lean 4/Mathlib instances whose gold answers are definitions the proof kernel did not previously contain, judge-free verifier; a pilot finds zero novel concepts). The same verifier doubles as an exact reward for preference optimization (DPO, RLVR/GRPO). Released under MIT at https://huggingface.co/datasets/PatrickAllenCooper/DeFAb.
Chinese Translation
基于规则的逻辑求解器在我们的基准测试中以100%的准确率在50微秒内解决每个实例；最好的前沿语言模型的最佳表现为65%，而在渲染稳健性评估下（四个表面渲染的最坏情况）降至23.5%。我们引入了DeFAb（可推翻归纳基准），这是一个数据集和生成管道，将四十年来的公共资助知识库转换为形式上有依据的可推翻归纳实例：通过覆盖默认值并保留无关期望来构建解释异常的假设。由于每个假设必须通过多项式时间检查以验证有效推导、保守性和最小性，DeFAb使逻辑严谨性成为衡量创造力和理论推理的工具，评分标准是理论修订的严谨构建，而不是流畅但破坏理论的散文。该管道将分类层次（OpenCyc、YAGO、Wikidata）与行为属性图（ConceptNet、UMLS）相结合，产生了来自18个来源的372,648+个实例，涵盖33.75M个具体化规则，分为三个层次，并具有多项式时间可验证的黄金标准。四个前沿模型并不可靠地内化可推翻推理：渲染稳健的2级准确率为7.8-23.5%；思维链变异（约36个百分点）超过任何模型间差距；而匹配的污染控制则隔离出+19.4个百分点的3级差距。我们进一步发布了DeFAb-Hard（一个235实例的3级难度变体；最佳模型53.3%对比100%符号）和CONJURE（一个内核验证的变革性创造力变体，包含560个Lean 4/Mathlib实例，其黄金答案是证明内核之前未包含的定义，无评判的验证器；初步调查发现零个新概念）。同一验证器还作为偏好优化的精确奖励（DPO，RLVR/GRPO）。该项目在MIT许可下发布，网址为https://huggingface.co/datasets/PatrickAllenCooper/DeFAb。
cs.AI / 6 / 2606.18598
Optimizing Lithium Production Decisions under Geological, Demand, and Pricing Uncertainties: A POMDP Framework for Multi-Objective Decision Making
在地质、需求和价格不确定性下优化锂生产决策：基于部分可观察马尔可夫决策过程的多目标决策框架
Abstract
Decision making in lithium production is challenging, whether from an investor's perspective or a strategic production standpoint. Determining which mines to open and when to open them involves not only geological and price uncertainties, but also complexities around the choice of extraction method, from direct lithium extraction to hard rock mining. Prior work explored models of this problem and different methods to optimize mining decisions; these models did not account for uncertainty in pricing, uncertainty in demand, or different mining technologies to extract lithium. Incorporating different pricing models and extraction technology into these models enables more robust strategies for determining not only when and where to open a mine, but also which method of production to pursue. We frame the problem as a partially observable Markov decision process (POMDP) and solve using belief state planning methods to get optimal decision making. In our study, we show that POMDP solvers outperform human inspired heuristics by dynamically adapting to shifting lithium price regimes (static, linear, exponential, and stochastic) through belief state planning and explicit uncertainty management. By optimally sequencing exploration, production, and technology choice, the framework achieves higher demand fulfillment and more balanced economic environmental outcomes over the projects lifetime in all different pricing and deposit scenarios.
Chinese Translation
锂生产的决策制定具有挑战性，无论是从投资者的角度还是战略生产的角度。确定开采哪些矿山以及何时开采不仅涉及地质和价格的不确定性，还涉及从直接锂提取到硬岩采矿等多种提取方法的选择复杂性。先前的研究探讨了该问题的模型及优化采矿决策的不同方法；这些模型未考虑价格不确定性、需求不确定性或提取锂的不同采矿技术。将不同的定价模型和提取技术纳入这些模型，使得在确定何时何地开采矿山以及选择何种生产方法时能够制定出更稳健的策略。我们将该问题框定为部分可观察马尔可夫决策过程（POMDP），并使用信念状态规划方法求解，以实现最佳决策。在我们的研究中，我们展示了POMDP求解器通过信念状态规划和明确的不确定性管理，动态适应不断变化的锂价格模式（静态、线性、指数和随机），其性能优于人类启发式方法。通过最佳地安排勘探、生产和技术选择，该框架在所有不同的定价和矿床情景中，实现了更高的需求满足率和更平衡的经济与环境结果。
cs.AI / 7 / 2606.18686
ForecastBench-Sim: A Simulated-World Forecasting Benchmark
ForecastBench-Sim：一个模拟世界的预测基准
Abstract
Forecasting benchmarks for general-purpose AI systems usually inherit the constraints of the real world: outcomes resolve slowly, tail events are rare, and counterfactual questions are difficult to score. We introduce ForecastBench-Sim, a simulated-world forecasting benchmark built on game rollouts from Freeciv, a turn-based strategy game modelled on the Civilization series. Forecasters receive a fixed world report (a structured snapshot of the current game state) and answer questions about hidden future states; the benchmark then continues the simulation and scores forecasts. Because the world is simulated, the same setup can generate continuous or binary forecasting questions at arbitrary time horizons, paired intervention worlds for conditional or causal questions, and resolved examples of rare or disruptive outcomes. We describe the benchmark pipeline, question families, scoring protocol, and release artifacts, and report validation slices from model evaluations and an anonymized human pilot. ForecastBench-Sim is intended to complement real-world forecasting benchmarks by providing controlled, immediately resolvable tasks for studying probabilistic reasoning under dynamic world states.
Chinese Translation
通用人工智能系统的预测基准通常继承了现实世界的限制：结果解决缓慢，尾部事件稀少，反事实问题难以评分。我们引入了ForecastBench-Sim，一个基于Freeciv（一个基于《文明》系列的回合制策略游戏）的游戏回合的模拟世界预测基准。预测者接收一个固定的世界报告（当前游戏状态的结构化快照），并回答关于隐藏未来状态的问题；该基准随后继续模拟并评分预测。由于世界是模拟的，同样的设置可以在任意时间范围内生成连续或二元预测问题，配对干预世界以应对条件或因果问题，以及解决稀有或破坏性结果的示例。我们描述了基准管道、问题类别、评分协议和发布文档，并报告了来自模型评估和匿名人类试点的验证切片。ForecastBench-Sim旨在通过提供可控的、可立即解决的任务，以研究动态世界状态下的概率推理，从而补充现实世界的预测基准。
cs.AI / 8 / 2606.18746
What Must Generalist Agents Remember?
通用智能体必须记住什么？
Abstract
This paper develops a formal account of what generalist agents must store in memory in order to act near-optimally across multiple environments and goals. It shows that when two domains share an observational bottleneck but require incompatible optimal actions, any uniformly near-optimal policy must induce distinct memory distributions at that bottleneck. The result yields a separation theorem: sufficiently successful agents cannot rely only on current state observations, but must preserve domain-relevant information in memory. The paper further shows that if an agent's memory contains enough information to estimate values for related goals, then that memory can be used to approximately reconstruct the agent's local transition dynamics. Together, these results characterize memory as the substrate that supports domain disambiguation, transition-model reconstruction, and planning for generalist agents.
Chinese Translation
本文对通用智能体在多个环境和目标中近似最优行动所需存储的记忆进行了正式阐述。研究表明，当两个领域共享观察瓶颈但需要不兼容的最优行动时，任何均匀近似最优的策略必须在该瓶颈处诱导出不同的记忆分布。该结果得出了一个分离定理：足够成功的智能体不能仅依赖当前状态观察，而必须在记忆中保留与领域相关的信息。本文进一步表明，如果智能体的记忆包含足够的信息来估计相关目标的值，那么该记忆可以用来近似重建智能体的局部转移动态。这些结果共同将记忆表征为支持领域消歧、转移模型重建和通用智能体规划的基础。
cs.AI / 9 / 2606.18786
R2D-RL: A RoboCup 2D Soccer Environment for Multi-Agent Reinforcement Learning
R2D-RL：用于多智能体强化学习的RoboCup 2D足球环境
Abstract
Robot soccer is a challenging testbed for multi-agent reinforcement learning because it combines partial observability, cooperative and adversarial interaction, sparse rewards, and long-horizon tactical behavior. RoboCup 2D Soccer Simulation (RCSS2D) provides a mature robot-soccer platform, but its competition-oriented server-client architecture is difficult to use directly with modern Python-based MARL workflows. We introduce R2D-RL, a reinforcement learning environment that connects RCSS2D and HELIOS-based player clients to a Python MARL interface through shared-memory communication and cycle-level synchronization. R2D-RL supports full-field and scenario-based training with configurable opponents, Base discrete and Hybrid parameterized action spaces, action masks, expected possession value (EPV)-based reward shaping, and parallel execution. We provide front-goal scenarios and an 11-vs-11 full-field benchmark, together with baseline results.
Chinese Translation
机器人足球是多智能体强化学习的一个具有挑战性的测试平台，因为它结合了部分可观测性、合作与对抗互动、稀疏奖励以及长时间战术行为。RoboCup 2D足球模拟（RCSS2D）提供了一个成熟的机器人足球平台，但其面向竞争的服务器-客户端架构使得与现代基于Python的多智能体强化学习（MARL）工作流程直接使用变得困难。我们提出了R2D-RL，这是一种强化学习环境，通过共享内存通信和周期级同步，将RCSS2D和基于HELIOS的玩家客户端连接到Python MARL接口。R2D-RL支持全场和基于场景的训练，具有可配置的对手、基础离散和混合参数化动作空间、动作掩码、基于预期控球价值（EPV）的奖励塑造以及并行执行。我们提供了前门场景和11对11全场基准测试，以及基线结果。
cs.AI / 10 / 2606.18803
ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch
ProfiLLM：面向工业网约车调度的效用对齐代理用户画像
Abstract
Bringing Large Language Models (LLMs) into industrial ride-hailing dispatch as semantic feature extractors over platform-scale behavioral logs is a compelling but under-explored data systems problem. Production matching pipelines remain dominated by structured numerical features, yet decisive behavioral signals (e.g., a driver's habitual aversion to certain regions) are inherently contextual and naturally expressible as LLM-generated user profiles. However, scaling such profiling to a live, millisecond-latency dispatcher faces three intertwined constraints rarely addressed together: on a platform with millions of daily orders, logs exceed any LLM's context window by orders of magnitude; most users are long-tail, with too few interactions for per-user profiling; and surface-fluent profiles do not necessarily improve downstream prediction utility. We present ProfiLLM, an agentic LLM data pipeline that operationalizes utility-aligned user profiling for production matching systems through two modules. (1) Tool-Augmented Global Knowledge Mining equips an LLM agent with 27 analytical tools to mine platform-scale data, producing reusable global knowledge, adaptive user clustering rules, and region-level supply-demand priors. (2) Utility-Aligned Profile Exploration generates multiple candidate profiles per cluster, evaluates them via a lightweight downstream utility proxy, iteratively refines the best candidates and constructs preference pairs for DPO fine-tuning. Deployed on DiDi's production dispatcher, ProfiLLM achieves up to +6.14% relative AUC improvement in outcome prediction, up to +4.35% GMV gain in dispatching simulation, and consistent improvements in a 14-day online A/B test including +0.47% GMV, +0.33% Completion Rate, and -0.82% Cancel-Before-Accept rate.
Chinese Translation
将大型语言模型（LLMs）引入工业网约车调度，作为平台规模行为日志的语义特征提取器，是一个引人注目但尚未深入探索的数据系统问题。现有的匹配管道仍然以结构化数值特征为主导，而决定性的行为信号（例如，司机对某些区域的习惯性厌恶）本质上是上下文相关的，并且可以自然地用LLM生成的用户画像来表达。然而，将这种画像扩展到实时、毫秒级延迟的调度器面临着三个相互交织的约束，这些约束很少同时被解决：在一个每天有数百万订单的平台上，日志的规模远远超过任何LLM的上下文窗口；大多数用户属于长尾，单个用户的交互次数太少，无法进行个性化画像；而表面流畅的画像并不一定能提高下游预测的效用。我们提出了ProfiLLM，一个代理LLM数据管道，通过两个模块实现了面向生产匹配系统的效用对齐用户画像。(1) 工具增强的全球知识挖掘为LLM代理配备了27种分析工具，以挖掘平台规模的数据，生成可重用的全球知识、自适应用户聚类规则和区域级供需先验。(2) 效用对齐的画像探索为每个聚类生成多个候选画像，通过轻量级的下游效用代理进行评估，迭代地优化最佳候选，并构建用于DPO微调的偏好对。ProfiLLM在滴滴的生产调度器上部署，实现了结果预测的相对AUC提升高达6.14%，调度模拟中的GMV增益高达4.35%，以及在为期14天的在线A/B测试中，包括GMV提升0.47%、完成率提升0.33%和取消前接受率降低0.82%的持续改进。
cs.AI / 11 / 2606.18847
WorldLines: Benchmarking and Modeling Long-Horizon Stateful Embodied Agents
世界线：长时间状态体化代理的基准测试与建模
Abstract
To assist humans over extended periods in real homes, embodied agents must remember user routines, world states, and past interactions. Existing long-term memory benchmarks mainly evaluate language-centric retrieval and question answering, while embodied benchmarks often focus on short-horizon task execution without testing long-term memory use in dynamic environments. We introduce WorldLines, a project-driven benchmark for long-horizon embodied household assistance. It constructs temporally extended household traces with dialogues, actions, execution feedback, object and device state changes, and converts them into evidence-linked samples for Memory QA and Embodied Task Planning. We further propose ObsMem, an observer-grounded memory framework that maintains visibility-aware memories and action-native state trails for state-aware decisions. Experiments reveal persistent challenges in partial observability, overwritten world states, and translating long-term memory into embodied plans, while ObsMem offers a stronger reference architecture for this setting.
Chinese Translation
为了在真实家庭中长时间协助人类，体化代理必须记住用户的日常活动、世界状态和过去的互动。现有的长期记忆基准主要评估以语言为中心的检索和问答，而体化基准通常侧重于短期任务执行，而未测试动态环境中长期记忆的使用。我们提出了WorldLines，这是一个以项目驱动的长时间体化家庭辅助基准。它构建了包含对话、动作、执行反馈、物体和设备状态变化的时间扩展家庭轨迹，并将其转换为与证据相关的样本，用于记忆问答（Memory QA）和体化任务规划（Embodied Task Planning）。我们进一步提出了ObsMem，一个基于观察者的记忆框架，维护可见性感知的记忆和基于动作的状态轨迹，以便进行状态感知的决策。实验揭示了部分可观察性、被覆盖的世界状态以及将长期记忆转化为体化计划的持续挑战，而ObsMem为这一设置提供了更强的参考架构。
cs.AI / 12 / 2606.18874
Externalizing Research Synthesis and Validation in AI Scientists through a Research Harness
通过研究工具外化AI科学家的研究综合与验证
Abstract
AI systems can increasingly automate scientific workflows, but the reasoning that links prior evidence, generated ideas, experiments and final claims often remains implicit inside model inference. Here we introduce Xcientist, a research harness that externalizes research synthesis and experimental validation into inspectable, contract-governed processes. Xcientist organizes literature evidence, idea states, implementation plans, ablation records and repair traces as persistent research artifacts, so that generated mechanisms can be grounded, executed, tested and revised without losing their evidential basis. We identify claim drift as a failure mode of automated research, where runnable artifacts no longer support the mechanism originally claimed. Across training-free memory systems, graph-structured traffic forecasting and multi-scale physics-informed neural networks, Xcientist preserves traceable trajectories from problem formulation to mechanism design, validation and bounded revision. These results suggest that AI scientists should be evaluated not only by their final artifacts, but by whether their synthesis and validation processes remain attributable, inspectable and scientifically accountable.
Chinese Translation
AI系统越来越能够自动化科学工作流程，但将先前证据、生成的想法、实验和最终主张联系起来的推理往往仍然隐含在模型推理中。在此，我们介绍了Xcientist，一个将研究综合和实验验证外化为可检查的、合同治理的过程的研究工具。Xcientist将文献证据、想法状态、实施计划、消融记录和修复痕迹组织为持久的研究工件，以便生成的机制可以在不失去其证据基础的情况下得到扎根、执行、测试和修订。我们识别出主张漂移作为自动化研究的一种失败模式，其中可运行的工件不再支持最初声称的机制。在无训练的记忆系统、图结构的交通预测和多尺度物理信息神经网络中，Xcientist保持了从问题表述到机制设计、验证和有限修订的可追溯轨迹。这些结果表明，AI科学家不仅应通过其最终工件进行评估，还应通过其综合和验证过程是否保持可归因、可检查和科学可问责来进行评估。
cs.AI / 13 / 2606.18888
Generative-Model Predictive Planning for Navigation in Partially Observable Environments
生成模型预测规划在部分可观测环境中的导航
Abstract
Navigation in partially observable environments presents a significant challenge for autonomous agents, requiring effective decision-making with limited sensory information in unknown environments. Belief-based methods, particularly those using neural networks to approximate the belief space, often fail to capture the inherent multimodality of belief spaces, especially in high-dimensional cases with perceptual aliasing. While generative models present a compelling alternative, they typically require substantial data or expert demonstrations and lack explicit mechanisms for long-term planning. In this paper, we introduce BeliefDiffusion, a novel framework that combines the benefits of both generation and planning. BeliefDiffusion leverages diffusion models to explicitly characterize multimodal belief distributions and utilizes Model Predictive Control (MPC) to simultaneously plan ahead. It consists of two steps: (1) Imagining plausible environment configurations based on observation history and (2) Planning efficient navigation strategies across an aggregated configurations. Through extensive experiments in synthetic map environments, we demonstrate that BeliefDiffusion significantly outperforms both model-free reinforcement learning baselines and other generative approaches in navigation success rate and path efficiency. Our results validate that explicitly incorporating multimodal belief representations into planning enables more robust navigation in partially observable settings.
Chinese Translation
在部分可观测环境中进行导航对自主代理提出了重大挑战，要求在未知环境中以有限的感知信息进行有效决策。基于信念的方法，特别是那些使用神经网络来近似信念空间的方法，往往无法捕捉信念空间固有的多模态性，尤其是在具有感知混淆的高维情况下。尽管生成模型提供了一个引人注目的替代方案，但它们通常需要大量数据或专家演示，并且缺乏明确的长期规划机制。在本文中，我们介绍了BeliefDiffusion，一个结合生成与规划优点的新框架。BeliefDiffusion利用扩散模型明确表征多模态信念分布，并利用模型预测控制（Model Predictive Control, MPC）同时进行前瞻性规划。该框架包括两个步骤：（1）基于观察历史想象合理的环境配置，和（2）在聚合配置中规划高效的导航策略。通过在合成地图环境中的大量实验，我们证明BeliefDiffusion在导航成功率和路径效率上显著优于无模型强化学习基线和其他生成方法。我们的结果验证了将多模态信念表示明确纳入规划中能够在部分可观测环境中实现更稳健的导航。
cs.AI / 14 / 2606.18890
Skill-Guided Continuation Distillation for GUI Agents
基于技能引导的GUI代理继续蒸馏
Abstract
Improving GUI agents typically relies on behavior cloning on expert trajectories. However, as the current policy deviates from the expert policy, it inevitably encounters policy-induced off-trajectory states during closed-loop execution, i.e., states that fall outside the expert trajectories. Since expert trajectories provide no demonstrations for these unseen states, such states receive no effective supervision, leaving the policy unable to select the correct action. To close this supervision gap, we propose Skill-Guided Continuation Distillation (SGCD), an iterative self-improvement framework. SGCD first runs the plain policy without skill guidance for a few steps to reach realistic off-trajectory states. From these states, a skill-guided policy then completes the task and produces successful continuations, which are mixed with expert trajectories to supply supervision over policy-induced off-trajectory states. The skills are extracted from both successful and failed rollouts, consisting of Continuation Plans, Critical Targets, Failure Traps, and Success Criteria. On OSWorld-Verified, SGCD improves the success rate of three base models from the low-30\% range to over 50\%, demonstrating its effectiveness and generality.
Chinese Translation
提高GUI代理的性能通常依赖于在专家轨迹上的行为克隆。然而，随着当前策略偏离专家策略，在闭环执行过程中不可避免地会遇到由策略引起的非轨迹状态，即那些不在专家轨迹范围内的状态。由于专家轨迹未提供这些未见状态的演示，因此这些状态无法获得有效的监督，导致策略无法选择正确的动作。为了解决这一监督缺口，我们提出了基于技能引导的继续蒸馏（Skill-Guided Continuation Distillation, SGCD），这是一种迭代自我改进框架。SGCD首先在没有技能引导的情况下运行普通策略几步，以达到现实的非轨迹状态。在这些状态下，技能引导策略完成任务并产生成功的继续，这些继续与专家轨迹混合，以对策略引起的非轨迹状态提供监督。技能来自于成功和失败的回合，包括继续计划、关键目标、失败陷阱和成功标准。在OSWorld-Verified上，SGCD将三个基础模型的成功率从低于30\%提高到超过50\%，证明了其有效性和普适性。
cs.AI / 15 / 2606.18936
SciRisk-Bench: A Risk-Dimension-Aware Benchmark for AI4Science Safety
SciRisk-Bench：一个关注风险维度的AI4Science安全基准
Abstract
Large language models (LLMs) are increasingly embedded in AI for Science (AI4Science) workflows, from scientific question answering and literature analysis to laboratory planning and autonomous discovery. This progress creates an urgent need for safety benchmarks that evaluate not only scientific competence, but also whether models recognize and avoid risks in high-stakes scientific contexts. Existing AI4Science safety datasets cover several disciplines and task formats, leaving the underlying risk dimensions underspecified. We introduce \textbf{SciRisk-Bench}, a benchmark designed to evaluate AI4Science safety from two complementary perspectives: explicit risk dimensions and scientific disciplines. SciRisk-Bench covers 7 disciplines, 31 subdisciplines and 10 risk dimensions. In the experimental section, we evaluate both mainstream LLMs and science-oriented LLMs across risk dimensions, disciplines, and sub-disciplines, enabling fine-grained diagnosis of where scientific models remain unsafe.
Chinese Translation
大型语言模型（LLMs）越来越多地嵌入到科学人工智能（AI4Science）工作流程中，从科学问题回答和文献分析到实验室规划和自主发现。这一进展迫切需要安全基准，不仅评估科学能力，还评估模型在高风险科学环境中是否能够识别和避免风险。现有的AI4Science安全数据集涵盖了多个学科和任务格式，但其潜在的风险维度尚未明确。我们引入了 extbf{SciRisk-Bench}，这是一个旨在从两个互补的角度评估AI4Science安全性的基准：明确的风险维度和科学学科。SciRisk-Bench涵盖7个学科、31个子学科和10个风险维度。在实验部分，我们评估了主流LLMs和面向科学的LLMs在风险维度、学科和子学科上的表现，从而能够细致诊断科学模型在何处仍然存在安全隐患。
cs.AI / 16 / 2606.18947
Decoupling Search from Reasoning: A Vendor-Agnostic Grounding Architecture for LLM Agents
将搜索与推理解耦：一种与供应商无关的 LLM 代理基础架构
Abstract
Production LLM agents increasingly depend on real-time search, yet native search grounding bundles retrieval policy, provider choice, evidence injection, cost, latency, and generation behavior behind a single model-provider boundary. This coupling makes grounding hard to inspect, tune, reuse, or port, and can trigger Search-Induced Verbosity that breaks strict output contracts. We present Decoupled Search Grounding (DSG), a vendor-agnostic boundary that moves grounding outside the reasoning model through an MCP-compatible gateway, exposing provider routing, source-aware context rendering, configured fallback, retrieval-depth control, and exact plus semantic caching as first-class controls. Across five frontier models on SimpleQA, FreshQA, and HotpotQA, native search leads on recency-sensitive FreshQA, but DSG exposes a stronger frontier when control matters: on SimpleQA it nearly matches native accuracy (86.1% vs. 87.7%) at 91% lower search cost, preserves concise answer contracts, and reaches a 99.4% warm-cache hit rate with 68% lower latency. Deployed as a shared production grounding layer for large-scale agentic workloads with interchangeable models, DSG matches or slightly exceeds native-search accuracy on an e-commerce query-understanding (QIU) workload while cutting search cost by over 98%. Real-time grounding is best treated as an optimizable interface boundary, not a fixed model feature.
Chinese Translation
生产型 LLM 代理越来越依赖于实时搜索，但原生搜索基础将检索策略、供应商选择、证据注入、成本、延迟和生成行为捆绑在单一模型-供应商边界内。这种耦合使得基础难以检查、调整、重用或迁移，并可能引发搜索引起的冗长性，破坏严格的输出合约。我们提出了解耦搜索基础（Decoupled Search Grounding，DSG），这是一个与供应商无关的边界，通过与 MCP 兼容的网关将基础移出推理模型，暴露供应商路由、源感知上下文渲染、配置的后备、检索深度控制以及精确和语义缓存作为一流控制。在 SimpleQA、FreshQA 和 HotpotQA 上的五个前沿模型中，原生搜索在对时效性敏感的 FreshQA 上表现领先，但 DSG 在控制重要时展现出更强的前沿：在 SimpleQA 上，它的准确率几乎与原生搜索相匹配（86.1% 对比 87.7%），同时搜索成本降低了 91%，保持了简洁的答案合约，并以 68% 的延迟达到了 99.4% 的热缓存命中率。作为大规模代理工作负载的共享生产基础层，DSG 在电子商务查询理解（QIU）工作负载上与原生搜索的准确率相当或略有超出，同时将搜索成本降低了超过 98%。实时基础最好被视为一个可优化的接口边界，而不是固定的模型特征。
cs.AI / 17 / 2606.18950
RTSGameBench: An RTS Benchmark for Strategic Reasoning by Vision-Language Models
RTSGameBench：用于视觉-语言模型战略推理的实时战略基准测试
Abstract
Modern Vision-Language Models (VLMs) often struggle with strategic reasoning, i.e., anticipating and influencing other agents' actions, under uncertainty in competitive and cooperative settings. Real-time strategy (RTS) games can be a natural testbed for diagnosing this limitation, as they demand coordination with allies, adaptation to opponents' strategy, and long-horizon planning under partial observability. However, existing RTS benchmarks offer limited evaluation scope, lack systematic competency diagnosis, and remain fixed in the pre-designed scenario coverage. To address these limitations, we present RTSGameBench, which is built on Beyond All Reason, a large-scale RTS game with an expanded battlefield that demands broader strategy diversity than the existing testbeds. The proposed benchmark provides evaluations through diverse gameplay across various matchup structures, diagnostic assessment via mini-games, each targeting an individual strategic competency, and extensible coverage via a self-evolving generation framework that converts free-form queries into new mini-games, improving over successive cycles. Additionally, for VLMs to operate in large-scale RTS games, we provide RTSGameAgent that manages units by an FSM with agentic memory. We empirically validate that multiple state-of-the-art VLMs do not perform well when matchups demand tighter coordination, multiagent coordination and when task scale increases.
Chinese Translation
现代视觉-语言模型（VLMs）在战略推理方面常常面临挑战，即在竞争和合作环境中，预判和影响其他代理的行为时存在不确定性。实时战略（RTS）游戏可以作为诊断这一局限性的自然测试平台，因为它们要求与盟友协调、适应对手的策略以及在部分可观察性下进行长远规划。然而，现有的RTS基准测试提供的评估范围有限，缺乏系统的能力诊断，并且在预设计的场景覆盖上保持固定。为了解决这些局限性，我们提出了RTSGameBench，该基准建立在《超越一切理性》（Beyond All Reason）之上，这是一款大规模RTS游戏，具有扩展的战场，要求比现有测试平台更广泛的策略多样性。所提出的基准通过多样化的游戏玩法提供评估，涵盖各种对战结构，通过迷你游戏进行诊断评估，每个迷你游戏针对单一的战略能力，并通过自我演变生成框架扩展覆盖范围，将自由形式的查询转化为新的迷你游戏，随着循环的推进不断改进。此外，为了使VLMs能够在大规模RTS游戏中运作，我们提供了RTSGameAgent，该代理通过具有代理记忆的有限状态机（FSM）管理单位。我们通过实证验证发现，当对战要求更紧密的协调、多代理协调以及任务规模增加时，多个最先进的VLMs表现不佳。
cs.AI / 18 / 2606.18988
ThinkDeception: A Progressive Reinforcement Learning Framework for Interpretable Multimodal Deception Detection
ThinkDeception：一种用于可解释的多模态欺骗检测的渐进强化学习框架
Abstract
Multimodal deception detection is critical for identifying fraudulent intentions, yet existing approaches predominantly rely on end to end black--box paradigms. These methods suffer from a severe lack of interpretability failing to provide transparent reasoning trajectories and struggling to explicitly capture the subtle, cross modal inconsistencies inherent in deceptive behaviors. To transcend these limitations, we propose ThinkDeception, a novel and interpretable multimodal deception detection framework. As a pioneering effort, it introduces Multimodal Large Language Models (MLLMs) into this domain, transforming deception detection from a traditional binary classification task into an explicit cognitive reasoning process. Facilitated by the first meticulously annotated step--by--step multimodal Chain of Thought (CoT) dataset, we develop a foundational model, ThinkDeception Base, empirically validating the critical role of modal inconsistency in decoding deception. Building upon this foundation, our core innovation lies in proposing Visual-Audio Consistency Group Relative Policy Optimization(VAC--GRPO) equipped with a progressive training strategy. Distinct from standard GRPO, we stratify the training data into four progressive difficulty tiers, guiding the model through a psychologically grounded easy--to--hard cognitive transition. By innovatively coupling this dynamic curriculum scheduler with a multi dimensional, process aware reward mechanism and a reflective learning paradigm, we significantly elevate the model's overall reasoning quality. Extensive experiments on mainstream benchmarks demonstrate that ThinkDeception establishes a new SOTA, significantly outperforming existing methods in both detection accuracy and rationale quality. Ultimately, this work successfully drives the field of deception detection toward interpretable, multimodal cognitive reasoning.
Chinese Translation
多模态欺骗检测对于识别欺诈意图至关重要，然而现有的方法主要依赖于端到端的黑箱范式。这些方法缺乏可解释性，未能提供透明的推理轨迹，并且难以明确捕捉到欺骗行为中固有的微妙跨模态不一致性。为了超越这些局限性，我们提出了ThinkDeception，一个新颖且可解释的多模态欺骗检测框架。作为一项开创性工作，它将多模态大型语言模型（Multimodal Large Language Models, MLLMs）引入该领域，将欺骗检测从传统的二元分类任务转变为一个明确的认知推理过程。在首个经过精心注释的逐步多模态思维链（Chain of Thought, CoT）数据集的支持下，我们开发了基础模型ThinkDeception Base，实证验证了模态不一致性在解码欺骗中的关键作用。在此基础上，我们的核心创新在于提出了视觉-音频一致性组相对策略优化（Visual-Audio Consistency Group Relative Policy Optimization, VAC-GRPO），并配备了渐进训练策略。与标准的GRPO不同，我们将训练数据分为四个渐进的难度层次，引导模型经历一个心理学基础的由易到难的认知转变。通过创新性地将这一动态课程调度器与多维的、过程感知的奖励机制以及反思学习范式相结合，我们显著提升了模型的整体推理质量。在主流基准上的广泛实验表明，ThinkDeception建立了新的状态-of-the-art（SOTA），在检测准确性和推理质量上显著优于现有方法。最终，这项工作成功推动了欺骗检测领域向可解释的多模态认知推理发展。
cs.AI / 19 / 2606.19047
RODS: Reward-Driven Online Data Synthesis for Multi-Turn Tool-Use Agents
RODS：基于奖励驱动的多回合工具使用代理的在线数据合成
Abstract
Multi-turn tool-use RL is bottlenecked by the rapid depletion of informative samples in static datasets. We observe that the gradient signal in GRPO concentrates on tasks with the highest rollout reward variance, a consequence of the Popoviciu upper bound. Consequently, samples near the agent's capability boundary -- where successes and failures are roughly balanced -- contribute disproportionately large policy gradients. As training progresses, this boundary continuously shifts, which gradually depletes the pool of informative samples in a static dataset. We propose RODS (Reward-driven Online Data Synthesis) to resolve this depletion. RODS closes the loop between RL training and data generation by repurposing the progress reward variance as a practical, zero-cost boundary detector that requires no extra inference beyond the rollouts already computed for training. It continuously identifies such boundary samples, synthesizes new multi-turn variants matching their structural complexity (e.g., API topology and dependency depth) via a skill-aligned resampling pipeline, and manages a dynamic replay buffer that co-evolves with the policy. Starting from 400 human seeds and maintaining an active training pool of ~800 samples, RODS achieves comparable performance to a 17K-sample offline pipeline while requiring roughly 20x fewer trajectories, and improves over fixed-data RL and environment augmentation in our controlled setting.
Chinese Translation
多回合工具使用的强化学习（RL）受到静态数据集中信息样本快速耗尽的瓶颈。我们观察到，GRPO中的梯度信号集中在具有最高回报方差的任务上，这是Popoviciu上界的结果。因此，接近代理能力边界的样本——成功与失败大致平衡的地方——对策略梯度的贡献不成比例地大。随着训练的进行，这一边界不断移动，逐渐耗尽静态数据集中信息样本的池。我们提出RODS（基于奖励驱动的在线数据合成）来解决这一耗尽问题。RODS通过将进展奖励方差重新利用为一种实用的、零成本的边界检测器，关闭了RL训练与数据生成之间的循环，该检测器不需要额外的推理，仅依赖于已经为训练计算的回合。它持续识别这些边界样本，通过技能对齐的重采样管道合成匹配其结构复杂性（例如，API拓扑和依赖深度）的新多回合变体，并管理一个与策略共同演化的动态重放缓冲区。从400个人工种子开始，并保持约800个样本的活跃训练池，RODS在需要大约20倍更少轨迹的情况下，达到了与17K样本离线管道相当的性能，并在我们的受控环境中优于固定数据的RL和环境增强。
cs.AI / 20 / 2606.19079
ARIADNE: Agnostic Routing for Inference-time Adapter DyNamic sElection
ARIADNE：无关的推理时适配器动态选择路由
Abstract
The increasing deployment of parameter-efficient fine-tuning (PEFT) has led to model ecosystems in which a single backbone is paired with many task-specialized adapters. In this setting, inference-time queries often arrive without task labels, requiring the system to automatically select the most appropriate adapter from a growing and heterogeneous adapter pool. Existing routing methods either depend on access to adapter internals, such as weight decompositions or gradient-based statistics, or require additional router training, which limits scalability and portability as new adapters are added. We introduce ARIADNE, a training-free, adapter-agnostic routing framework for dynamic adapter selection at inference time. ARIADNE represents each adapter through a set of centroids computed from embeddings of its training set, capturing the data distribution associated with that adapter. Given an unlabeled input, it selects an adapter by measuring proximity to these centroids in latent space. Because routing is performed entirely in the input embedding space, ARIADNE is compatible with arbitrary PEFT methods and requires no modification to the adapters or training procedures. Primarily evaluated with Llama 3.2 1B Instruct on 23 diverse NLP tasks, ARIADNE recovers 97.44% of the upper bound performance. Scaling to 44 tasks, it achieves 89.7% average selection accuracy, without additional training or access to adapter internals.
Chinese Translation
参数高效微调（PEFT）的日益普及导致了模型生态系统的形成，其中单一主干模型与多个任务专用适配器配对。在这种情况下，推理时的查询通常没有任务标签，这要求系统自动从不断增长和异构的适配器池中选择最合适的适配器。现有的路由方法要么依赖于对适配器内部的访问，例如权重分解或基于梯度的统计信息，要么需要额外的路由器训练，这限制了随着新适配器的添加而扩展性和可移植性。我们提出了ARIADNE，一个无训练、适配器无关的推理时动态适配器选择路由框架。ARIADNE通过从其训练集的嵌入计算的一组质心来表示每个适配器，捕捉与该适配器相关的数据分布。给定一个未标记的输入，它通过测量在潜在空间中与这些质心的接近度来选择适配器。由于路由完全在输入嵌入空间中进行，ARIADNE与任意PEFT方法兼容，并且不需要对适配器或训练过程进行修改。主要在23个不同的自然语言处理任务上使用Llama 3.2 1B Instruct进行评估，ARIADNE恢复了97.44%的上限性能。扩展到44个任务时，它实现了89.7%的平均选择准确率，无需额外训练或访问适配器内部。
cs.AI / 21 / 2606.19116
Towards an Agent-First Web: Redesigning the Web for AI Agents
迈向以代理为首的网络：为人工智能代理重新设计网络
Bandara, Eranga, Gore, Ross, Mukkamala, Ravi, Gunaratna, Asanga, Bouk, Safdar H., Liang, Xueping, Foytik, Peter, Rahman, Abdul, Rajapakse, Sachini, Kularathna, Isurunima, Karunarathna, Pramoda, Rajapakse, Chalani, Keong, Ng Wee, De Zoysa, Kasun, Hewa, Tharaka, Hass, Amin, Herath, Wathsala, Withanage, Aruna, Loganathan, Nilaan, Yarlagadda, Atmaram, Shetty, Sachin
Abstract
The World Wide Web was built on an assumption held for three decades: the primary consumer of web content is a human being. This permeates every layer; its access model presumes human visitors, its economics rest on human attention, and its content targets human perception. The rapid emergence of AI agents as intermediaries between humans and web content invalidates this assumption. Yet the web resists agents through blanket blocking, CAPTCHA-based exclusion, and economic models that treat agent access as extraction rather than legitimate interaction. This paper proposes a principled redesign across three layers. At the access layer, agents acting for humans should inherit equivalent access rights, governed by rate limiting and agent identification metadata in HTTP requests, analogous to browser headers, alongside a dual-layer architecture serving human-readable and agent-optimized content from the same domain. At the economic layer, we propose an intent-based tier framework grounded in the agent-as-human-proxy principle: an agent's economic obligation mirrors that of the human it represents. A token-based subscription model meters content in tokens rather than pageviews, alongside a commissioned content economy anchoring AI content production in human intentionality. At the content layer, we identify epistemic recursion, the self-referential loop in which AI-generated content is consumed by agents to produce further content, progressively detaching web knowledge from human ground truth. We propose the Agent Text Markup Language (ATML), a four-level human supervision tier model, and a cryptographic provenance chain to counter this threat. Together these constitute ten design principles for an agent-first internet, one in which agents are first-class citizens whose integration requires renegotiating the web's foundational social contract across access, economics, and content.
Chinese Translation
万维网建立在一个持续了三十年的假设之上：网络内容的主要消费者是人类。这一假设渗透到每一个层面；其访问模型假定访问者是人类，其经济学依赖于人类的注意力，其内容则针对人类的感知。然而，人工智能代理作为人类与网络内容之间的中介的快速出现使这一假设失效。然而，网络通过全面封锁、基于 CAPTCHA 的排除以及将代理访问视为提取而非合法互动的经济模型来抵制代理。本文提出在三个层面上进行原则性的重新设计。在访问层面，代表人类行动的代理应继承等同的访问权，这由 HTTP 请求中的速率限制和代理识别元数据管理，类似于浏览器头部，同时采用双层架构，从同一域提供人类可读和代理优化的内容。在经济层面，我们提出一个基于意图的层级框架，基于代理作为人类代理的原则：代理的经济义务与其所代表的人类相对应。基于代币的订阅模型以代币而非页面浏览量来计量内容，同时建立一个以人类意图为基础的委托内容经济，以支撑人工智能内容的生产。在内容层面，我们识别出认知递归，即 AI 生成的内容被代理消费以产生进一步内容的自我指涉循环，逐渐使网络知识脱离人类的真实基础。我们提出了代理文本标记语言（Agent Text Markup Language, ATML）、一个四级人类监督层级模型以及一个加密来源链，以应对这一威胁。这些共同构成了以代理为首的互联网的十项设计原则，在这个互联网中，代理是第一公民，其整合需要重新协商网络的基础社会契约，涵盖访问、经济和内容。
cs.AI / 22 / 2606.19118
Analysing drivers and interdependencies in European electricity markets using XAI
利用可解释人工智能分析欧洲电力市场的驱动因素和相互依赖性
Abstract
Electricity markets are inherently complex systems characterised by strong nonlinearities, high-dimensional interactions, and increasing interdependence across regions. While deep neural networks (DNNs) have demonstrated strong predictive capabilities for electricity prices, their lack of interpretability limits their usefulness for understanding the underlying drivers of price formation. This paper addresses this gap by combining DNN models with explainable artificial intelligence (XAI) techniques to analyse the determinants of electricity prices across 39 European bidding zones. We employ SHAP (SHapley Additive exPlanations) to quantify feature contributions and apply and extend SSHAP, an aggregation framework to improve interpretability in high-dimensional settings. The analysis identifies that renewable energy sources, particularly solar, play a disproportionately important role in price formation despite their lower share in total power generation. Gas prices remain a dominant and consistent driver across electricity markets, while interconnections significantly shape price dynamics, highlighting the strong interdependence of European electricity systems. In addition, a synthetic EU-wide electricity market is constructed to explore the counterfactual scenario of a fully integrated market with a single price.
Chinese Translation
电力市场本质上是复杂系统，其特征为强非线性、高维交互和区域间日益增强的相互依赖性。尽管深度神经网络（DNN）在电力价格预测方面表现出强大的能力，但其缺乏可解释性限制了其在理解价格形成的潜在驱动因素方面的应用。本文通过将DNN模型与可解释人工智能（XAI）技术相结合，填补了这一空白，以分析39个欧洲竞标区的电力价格决定因素。我们采用SHAP（SHapley Additive exPlanations）来量化特征贡献，并应用并扩展了SSHAP，这是一种聚合框架，旨在提高高维设置中的可解释性。分析结果表明，尽管可再生能源（特别是太阳能）在总发电中的占比较低，但在价格形成中发挥着不成比例的重要作用。天然气价格在电力市场中仍然是一个主导且一致的驱动因素，而互联互通显著影响价格动态，突显了欧洲电力系统的强相互依赖性。此外，构建了一个合成的欧盟范围电力市场，以探索完全整合市场的单一价格的反事实情景。
cs.AI / 23 / 2606.19144
Human-AI Coevolution Dynamics: A Formal Theory of Social Intelligence Emergence Through Long-Term Interaction
人类与人工智能共演化动态：通过长期互动形成社会智能的正式理论
Abstract
Current conversational AI systems have made significant progress in language generation, personalization, and long-context interaction. However, most existing methods model social behavior through isolated components such as emotion modeling, memory retrieval, or persona conditioning, lacking a unified framework to explain the emergence of stable social relationships and social intelligence in long-term human-AI interaction.To address this, we propose the Human-AI Coevolution Dynamics Framework (HACD-H), a formal model of human-AI interaction as a self-organizing social cognitive system. HACD-H integrates emotional adaptation, relational organization, social memory, and personality consistency into a unified dynamical framework and introduces principles including multi-timescale social cognition, relational attractors, trust basins, developmental phase transitions, and social cognitive energy dynamics.We construct a conversational dataset with approximately 14,700 interaction turns and develop a theory-driven empirical evaluation framework. Results reveal a hierarchy of temporal persistence in social cognition, stable relational attractors, phase-transition-like developmental patterns, and a structured social cognitive energy landscape. Social intelligence shows a significant negative correlation with social cognitive energy (r = -0.391, p < 0.001), and interaction trajectories exhibit progressive energy reduction over time.These findings suggest that social intelligence emerges from long-term social cognitive coevolution rather than isolated conversational capabilities. HACD-H provides a unified theoretical foundation for modeling adaptive human-AI social interaction and developing socially intelligent AI systems.
Chinese Translation
当前的对话式人工智能系统在语言生成、个性化和长上下文互动方面取得了显著进展。然而，大多数现有方法通过孤立的组件（如情感建模、记忆检索或人格调节）来建模社会行为，缺乏一个统一的框架来解释在长期人类与人工智能互动中稳定社会关系和社会智能的出现。为此，我们提出了人类与人工智能共演化动态框架（HACD-H），将人类与人工智能的互动视为一个自组织的社会认知系统的正式模型。HACD-H将情感适应、关系组织、社会记忆和人格一致性整合到一个统一的动态框架中，并引入了多时间尺度社会认知、关系吸引子、信任盆地、发展阶段转变和社会认知能量动态等原则。我们构建了一个包含约14,700个互动回合的对话数据集，并开发了一个基于理论驱动的实证评估框架。结果揭示了社会认知中的时间持久性层次、稳定的关系吸引子、类似相变的发展模式，以及结构化的社会认知能量景观。社会智能与社会认知能量之间呈显著负相关（r = -0.391, p < 0.001），互动轨迹随着时间的推移表现出逐步的能量减少。这些发现表明，社会智能是从长期的社会认知共演化中产生的，而非孤立的对话能力。HACD-H为建模适应性人类与人工智能社会互动和开发社会智能人工智能系统提供了统一的理论基础。
cs.AI / 24 / 2606.19168
Beyond Safe Data: Pretraining-Stage Alignment with Regular Safety Reflection
超越安全数据：预训练阶段的对齐与定期安全反思
Abstract
To achieve deeper safety alignment for large language models (LLMs), recent efforts have studied how to push safety interventions earlier into the pretraining stage, primarily by filtering unsafe data or rewriting it into safer forms. We argue that pretraining-stage alignment should go beyond making the data safe: LLMs may compose seemingly benign knowledge and capabilities into unsafe behaviors. To this end, we propose Safety Reflection Pretraining, a pretraining-stage alignment method which regularly inserts short safety reflections into pretraining corpora to integrate self-monitoring directly into language modeling, establishing a foundational capability that is subsequently reinforced by compatible post-training. Our experiments with 1.7B models pretrained on FineWeb-Edu show that Safety Reflection Pretraining improves safety classification accuracy and substantially reduces the success rates of inference-stage and finetuning attacks. Complementary to our real-world experiments, we also introduce a fully controlled synthetic environment, MedSafetyWorld, with a clear definition of safety and a reasoning structure under which models can easily generalize unsafe behaviors from safe data. Ablations in MedSafetyWorld further demonstrate a clear advantage of Safety Reflection Pretraining in preventing models from acting on unsafe behaviors generalized from safe data, compared with data filtering and rewriting. Taken together, our findings suggest that pretraining alignment should not only make the training data safe, but also shape the behaviors that models are likely to acquire from safe data.
Chinese Translation
为了实现大型语言模型（LLMs）更深层次的安全对齐，近期的研究努力探讨如何将安全干预措施更早地推入预训练阶段，主要通过过滤不安全数据或将其重写为更安全的形式。我们认为，预训练阶段的对齐应超越数据安全：LLMs 可能将看似无害的知识和能力组合成不安全的行为。为此，我们提出了安全反思预训练（Safety Reflection Pretraining），一种在预训练语料中定期插入短小安全反思的对齐方法，以将自我监控直接融入语言建模中，建立一个基础能力，随后通过兼容的后训练进行强化。我们在 1.7B 模型上进行的 FineWeb-Edu 预训练实验表明，安全反思预训练提高了安全分类准确性，并显著降低了推理阶段和微调攻击的成功率。除了我们的现实世界实验外，我们还引入了一个完全受控的合成环境 MedSafetyWorld，该环境对安全有明确的定义，并提供了一个推理结构，使模型能够轻松从安全数据中概括不安全行为。在 MedSafetyWorld 中的消融实验进一步证明，与数据过滤和重写相比，安全反思预训练在防止模型对从安全数据中概括的不安全行为采取行动方面具有明显优势。综合来看，我们的研究结果表明，预训练对齐不仅应使训练数据安全，还应塑造模型可能从安全数据中获得的行为。
cs.AI / 25 / 2606.19172
User as Engram: Internalizing Per-User Memory as Local Parametric Edits
用户作为内隐记忆：将每用户记忆内化为局部参数编辑
Abstract
Personal memory in a language model is two problems: content and reasoning skill. The brain keeps the two apart (a sparse, local engram in the hippocampus for each episode, a slow neocortex for the shared skills that interpret it), so a new fact need not overwrite everything else. Most personalization today keeps a user's facts outside the weights, in a natural-language memory file or a retrieval index. When facts are written into the model instead, the standard recipe is the per-user LoRA adapter, which does the opposite of the brain, folding content and skill into one global weight delta. Writing a user's facts as a LoRA contaminates text unrelated to them; writing the same facts as local Engram rows leaves it mathematically untouched, resulting in a roughly 33,000x smaller memory footprint. We therefore propose User as Engram: store a user's content as surgical edits to the hash-keyed memory table of an Engram model, and carry the reasoning skill in one shared adapter. This layered design matches per-user LoRA's direct recall while delivering 5.6x higher indirect-reasoning accuracy on average, and never makes a single user worse at reasoning than the untouched base. The edit is a glass box: writing a fact switches on its lookup at exactly the trigger, adds the value the answer needs, leaves every other position unchanged to the last bit, and fails if written into the wrong layer. Because different users' facts land in disjoint hash slots, their edits compose: many users live in one shared table at once, stacking additively and losslessly, where a per-user LoRA, a single global weight delta, admits only one. Upon retrieval, a per-user Engram table does not grow with the population the retriever must search, so past ~100 facts it overtakes a retrieval pipeline on a 2.5x larger model.
Chinese Translation
语言模型中的个人记忆面临两个问题：内容和推理能力。大脑将二者分开（每个事件在海马体中存储为稀疏的局部内隐记忆，而在新皮层中以缓慢的方式处理共享技能），因此新的事实不必覆盖其他所有内容。目前大多数个性化方法将用户的事实保留在权重之外，存储在自然语言记忆文件或检索索引中。而当事实被写入模型时，标准方法是每用户的 LoRA 适配器，这与大脑的处理方式相反，将内容和技能折叠为一个全局权重增量。将用户的事实作为 LoRA 写入会污染与其无关的文本；而将相同的事实作为局部内隐记忆行写入则在数学上保持不变，导致大约 33,000 倍更小的内存占用。因此，我们提出“用户作为内隐记忆”：将用户的内容存储为对内隐记忆模型的哈希键记忆表的手术编辑，并将推理技能保留在一个共享适配器中。这种分层设计与每用户 LoRA 的直接回忆相匹配，同时在平均上提供 5.6 倍更高的间接推理准确性，并且从未使任何单个用户的推理能力低于未修改的基础模型。编辑是一个透明的过程：写入一个事实会在确切的触发点上启用其查找，添加答案所需的值，保持每个其他位置不变到最后一位，并且如果写入错误的层则会失败。由于不同用户的事实落入不相交的哈希槽，它们的编辑可以组合：许多用户可以同时存在于一个共享表中，进行加法叠加且无损，而每用户的 LoRA 作为单一全局权重增量仅允许一个用户。当检索时，每用户的内隐记忆表不会随着检索器必须搜索的人口增长，因此在超过约 100 个事实后，它在一个 2.5 倍更大的模型上超越了检索管道。
cs.AI / 26 / 2606.19245
TxBench-PP: Analyzing AI Agent Performance on Small-Molecule Preclinical Pharmacology
TxBench-PP：分析人工智能代理在小分子临床前药理学中的表现
Abstract
Artificial intelligence (AI) agents promise to accelerate drug discovery by compressing interpretation and decision-making loops, but practical deployment requires trusted evaluation on realistic program decisions. We introduce TherapeuticsBench Preclinical Pharmacology (TxBench-PP), a verifiable benchmark for small-molecule preclinical pharmacology and the first focused slice of a broader TherapeuticsBench effort across drug-discovery stages and therapeutic modalities. TxBench-PP tests whether agents can recover accurate conclusions from real-world assay data rather than memorized facts from literature. The benchmark contains 100 evaluations indexed by program stage, assay type, and task structure, spanning mechanism-of-action (MoA) and pharmacodynamic (PD) reasoning, compound-target engagement, causal target validation, developability and safety, and translational efficacy. Agents receive realistic workflow snapshots, inspect files in a coding environment, and return structured answers graded deterministically. Across 16 model-harness configurations, comprising 11 models and 4,800 trajectories, no system reliably recovered preclinical pharmacology decisions. The strongest configuration, Claude Opus 4.8 / Pi, passed 59.3\% of endpoint attempts (178/300; 95\% CI, 51.1-67.6), followed by GPT-5.5 / Pi at 55.3\% (166/300; 47.0-63.6).
Chinese Translation
人工智能（AI）代理有望通过压缩解释和决策循环来加速药物发现，但实际部署需要对现实程序决策进行可信的评估。我们介绍了治疗基准临床前药理学（TxBench-PP），这是一个可验证的小分子临床前药理学基准，也是更广泛的治疗基准在药物发现阶段和治疗方式上的首个聚焦切片。TxBench-PP 测试代理是否能够从真实的测定数据中恢复准确的结论，而不是从文献中记忆的事实。该基准包含100个评估，按程序阶段、测定类型和任务结构进行索引，涵盖作用机制（MoA）和药效学（PD）推理、化合物-靶点结合、因果靶点验证、可开发性和安全性以及转化疗效。代理接收现实工作流程快照，在编码环境中检查文件，并返回结构化的答案，答案以确定性方式进行评分。在16种模型配置中，包括11个模型和4800条轨迹，没有系统能够可靠地恢复临床前药理学决策。最强的配置，Claude Opus 4.8 / Pi，通过了59.3%的终点尝试（178/300；95% CI，51.1-67.6），其次是GPT-5.5 / Pi，成功率为55.3%（166/300；47.0-63.6）。
cs.AI / 27 / 2606.19256
X+Slides: Benchmarking Audience-Conditioned Slide Generation
X+Slides：观众条件幻灯片生成的基准测试
Abstract
Automatically generating slide decks from source documents is an important application of large language models (LLMs). Existing benchmarks primarily assess slide completeness and technical depth, while overlooking the target audience as a critical real-world factor. For instance, specialists demand rigorous proofs, whereas decision-makers prioritize actionable conclusions. To bridge this gap, we introduce X+Slides, a benchmark specifically designed for audience-conditioned slide generation. Built on a diverse corpus spanning 113 topics and seven presentation scenes, X+Slides employs a dynamic evaluation framework constructed from 8,133 deduplicated, source-grounded probes. By assigning audience-specific utility weights to the same source-grounded probes, X+Slides reports four complementary metrics: Audience Coverage measures how much audience-essential information is conveyed, Domain-wise Coverage shows which information types are covered, Efficiency measures delivered utility per unit of attention cost, and Correctness verifies whether slide claims are supported by the source. Experiments on DeepPresenter, SlideTailor, and NotebookLM show that current systems can recover a substantial but still incomplete part of audience-essential information: at $\tau_A=0.7$, DeepPresenter reaches a best Audience Coverage of 0.714, SlideTailor reaches 0.594, and the NotebookLM ablation reaches 0.853 while showing clear grounding differences. These results indicate that visual quality and broad topic coverage should not be treated as evidence support without source-grounded evaluation.
Chinese Translation
从源文档自动生成幻灯片是大型语言模型（LLMs）的一项重要应用。现有基准主要评估幻灯片的完整性和技术深度，而忽视了目标观众这一关键的现实因素。例如，专家要求严格的证明，而决策者则优先考虑可操作的结论。为了解决这一问题，我们引入了X+Slides，这是一个专门为观众条件幻灯片生成设计的基准。X+Slides基于涵盖113个主题和七种演示场景的多样化语料库，采用了一个动态评估框架，该框架由8,133个去重的、基于源文档的探针构成。通过为相同的基于源文档的探针分配特定于观众的效用权重，X+Slides报告了四个互补指标：观众覆盖度（Audience Coverage）衡量传达了多少观众必需的信息，领域覆盖度（Domain-wise Coverage）显示了哪些信息类型被覆盖，效率（Efficiency）衡量每单位注意成本所提供的效用，而正确性（Correctness）验证幻灯片的主张是否得到了源文档的支持。在对DeepPresenter、SlideTailor和NotebookLM的实验中，结果表明当前系统能够恢复大量但仍不完整的观众必需信息：在$ au_A=0.7$时，DeepPresenter的最佳观众覆盖度为0.714，SlideTailor为0.594，而NotebookLM消融实验达到了0.853，同时显示出明显的基础差异。这些结果表明，视觉质量和广泛的主题覆盖不应被视为没有基于源文档评估的证据支持。
cs.AI / 28 / 2606.19279
NeSyCat Torch: A Differentiable Tensor Implementation of Categorical Semantics for Neurosymbolic Learning
NeSyCat Torch：一种可微分张量实现的神经符号学习的分类语义
Abstract
Neurosymbolic semantics is fragmented: classical, fuzzy, probabilistic and neural systems each define truth by their own inductive rules. NeSyCat, extending ULLER, subsumes them under a single inductive definition of truth, parametric in a strong monad and an aggregation structure on truth-values. NeSyCat has so far lacked an account of predicates and functions learned by neural networks. We provide NeSyCat Torch as the missing link and interpret computational symbols via neural networks, implementing the framework in probabilistic programming and tensor-based backends. We use the distribution monad for reference semantics and metric evaluation, and complement it by a monad for numerically stable, differentiable training: the lazy log-tensor monad over the log-semiring. For efficient training in batches, we furthermore employ a batch monad. The axioms are the source code: written once in monad-based do-notation, monadic bind performs marginalisation, lazily pruning unneeded branches. On MNIST addition, our HaskTorch, JAX, and PyTorch implementations outperform LTN and DeepProbLog in speed and accuracy, while achieving nearly the accuracy of DeepStochLog. However, unlike DeepStochLog, we stay in a uniform framework that applies to many first-order NeSy approaches. Namely, the construction is parametric in the monad; instantiating it with, e.g., the Giry monad extends the approach to continuous probability (working out a neural representation here is left for future work).
Chinese Translation
神经符号语义是碎片化的：经典、模糊、概率和神经系统各自通过自己的归纳规则定义真理。NeSyCat 扩展了 ULLER，将它们纳入一个单一的真理归纳定义，该定义依赖于强单子和真值的聚合结构。到目前为止，NeSyCat 缺乏对神经网络学习的谓词和函数的解释。我们提供 NeSyCat Torch 作为缺失的环节，通过神经网络解释计算符号，并在概率编程和基于张量的后端中实现该框架。我们使用分布单子进行参考语义和度量评估，并通过一个单子补充以实现数值稳定的可微训练：在对数半环上的惰性对数张量单子。为了高效的批量训练，我们还采用了批量单子。公理是源代码：一次性用基于单子的 do-notation 编写，单子绑定执行边际化，惰性修剪不需要的分支。在 MNIST 加法任务中，我们的 HaskTorch、JAX 和 PyTorch 实现的速度和准确性超过了 LTN 和 DeepProbLog，同时达到了接近 DeepStochLog 的准确性。然而，与 DeepStochLog 不同，我们保持在一个统一的框架内，适用于许多一阶神经符号方法。具体而言，该构造在单子中是参数化的；例如，用 Giry 单子实例化它将该方法扩展到连续概率（在这里构建神经表示留待未来工作）。
cs.AI / 29 / 2606.19327
Rethinking Reward Supervision: Rubric-Conditioned Self-Distillation
重新思考奖励监督：基于评分标准的自我蒸馏
Abstract
Post-training of reasoning language models is commonly driven by supervised distillation and reinforcement learning with verifiable rewards. Distillation often relies on chain-of-thought annotations that are expensive to obtain and may themselves be noisy, incomplete, or partially incorrect; even when the final solution is correct, an imperfect rationale can interfere with learning. Reinforcement learning with verified rewards, on the other hand, typically compresses evaluative feedback into a scalar signal, obscuring which aspects of a response should be improved. We propose \textbf{Rubric-Conditioned Self-Distillation}, a framework that incorporates rubrics as structured, fine-grained feedback for on-policy self-distillation. Our method conditions the teacher model on criterion-level rubrics and uses it to provide token-level guidance on the student's own sampled trajectories. This design avoids treating a single reference rationale as the sole supervision target. Instead, rubrics specify what a strong response should satisfy, enabling more fine-grained credit assignment over the reasoning process than scalar reward optimization. We instantiate this framework with a two-stage pipeline that first learns to generate task-specific rubrics and then trains a rubric-guided reasoner. We evaluate on a diverse suite of science reasoning benchmarks and results show that rubric-conditioned self-distillation effectively converts rubric-level criteria into token-level guidance over the reasoning process, surpassing GRPO by 1.0 points and OPSD by 0.9 points on average.
Chinese Translation
推理语言模型的后训练通常依赖于监督蒸馏和使用可验证奖励的强化学习。蒸馏通常依赖于思维链注释，这些注释获取成本高昂，并且可能本身存在噪声、不完整或部分错误；即使最终解决方案是正确的，不完美的推理也可能干扰学习。另一方面，使用验证奖励的强化学习通常将评估反馈压缩为标量信号，模糊了响应中哪些方面需要改进。我们提出了 extbf{基于评分标准的自我蒸馏}，这是一个将评分标准作为结构化、细粒度反馈用于策略自我蒸馏的框架。我们的方法将教师模型条件化于标准级评分，并利用其为学生自己采样的轨迹提供标记级指导。这种设计避免了将单一参考推理视为唯一的监督目标。相反，评分标准明确了强响应应满足的条件，从而在推理过程中实现比标量奖励优化更细粒度的信用分配。我们通过一个两阶段的管道实例化该框架，首先学习生成任务特定的评分标准，然后训练一个基于评分标准的推理器。我们在多样的科学推理基准上进行了评估，结果表明，基于评分标准的自我蒸馏有效地将评分标准级别的标准转化为推理过程中的标记级指导，平均超越了GRPO 1.0分和OPSD 0.9分。
cs.CL / 1 / 2606.18273
Continuous Audio Thinking for Large Audio Language Models
大型音频语言模型的连续音频思维
Abstract
Large audio language models (LALMs) have shown impressive capabilities on diverse audio understanding tasks, ranging from speech transcription to music analysis. However, because LALMs are typically trained to produce text-aligned responses, their hidden states are progressively shaped for text generation rather than for preserving acoustic information. As a result, the diverse acoustic content that audio carries, such as phonetic detail, prosody, sound events, affect, and pitch, is lost along the way and difficult to leverage in the response. We introduce Continuous Audio Thinking (CoAT), a framework that equips audio language models with a continuous latent workspace for organizing acoustic information prior to response generation, grounded by distillation from audio experts. Within the thinking space, the model can utilize the rich acoustic information provided by expert distillation when generating its response. Furthermore, the proposed continuous thinking block can be processed in a single prefill, so CoAT does not require additional autoregressive decoding cost over the baseline. Across three LALMs, Qwen2-Audio, Qwen2.5-Omni-7B, and Audio Flamingo~3, performance gains on a broad benchmark suite spanning audio reasoning, audio understanding, music classification, speech emotion, and speech transcription demonstrate the effectiveness of CoAT. Further analysis confirms that the auxiliary supervision propagates from the thinking positions to the model's textual responses.
Chinese Translation
大型音频语言模型（LALMs）在多种音频理解任务上展现了令人印象深刻的能力，涵盖了从语音转录到音乐分析的广泛领域。然而，由于LALMs通常被训练以生成与文本对齐的响应，其隐藏状态逐渐被塑造为文本生成，而非保留声学信息。因此，音频所承载的多样化声学内容，如音素细节、韵律、声音事件、情感和音高，在这个过程中被丢失，难以在响应中加以利用。我们提出了连续音频思维（CoAT），这是一个框架，旨在为音频语言模型提供一个连续的潜在工作空间，以在响应生成之前组织声学信息，该过程基于来自音频专家的蒸馏。在思维空间内，模型可以在生成响应时利用专家蒸馏提供的丰富声学信息。此外，所提出的连续思维模块可以在单次预填充中处理，因此CoAT在基线之上不需要额外的自回归解码成本。在三个LALMs（Qwen2-Audio、Qwen2.5-Omni-7B和Audio Flamingo~3）上，针对音频推理、音频理解、音乐分类、语音情感和语音转录等广泛基准套件的性能提升证明了CoAT的有效性。进一步的分析确认了辅助监督从思维位置传播到模型的文本响应。
cs.CL / 2 / 2606.18372
Redact or Keep? A Fully Local AI Cascade for Educational Dialogue De-Identification
编辑还是保留？一种完全本地的人工智能级联用于教育对话去标识化
Abstract
Educational dialogue is a valuable but sensitive resource for research: the same transcripts that capture authentic learning often capture personally identifiable information (PII) entangled with curricular content, where "Riemann" may refer to a real student or to a mathematical concept. Existing approaches force a tradeoff between governance and accuracy. Commercial Large Language Models (LLMs) can handle this ambiguity but require sending student data to third parties, while local named entity recognition (NER) systems preserve governance but over-redact curricular terms. We propose a fully local cascade framework that reframes de-identification from open-ended entity recognition to constrained privacy triage. A recall-first union proposer combines two lightweight encoders with deterministic rules to over-generate candidate spans; a context-aware reviewer then makes a binary Redact/Keep decision for each candidate using surrounding dialogue and speaker role. We evaluate three reviewer configurations against same-family LLM-only baselines and a commercial API on math tutoring transcripts from two large platforms. The strongest local configuration reaches 0.958 macro F1, compared with 0.767 for a same-family LLM-only baseline and 0.706 for the commercial API, while running entirely on a single laptop. On a targeted challenge set of curricular-personal name ambiguity, the same configuration degrades by only 0.03 F1 versus 0.19 to 0.25 for smaller reviewers. These results suggest that for educational de-identification, problem formulation matters more than model scale.
Chinese Translation
教育对话是研究中一种宝贵但敏感的资源：同一份记录虽然捕捉了真实的学习过程，但也常常包含与课程内容交织在一起的个人可识别信息（PII），例如“Riemann”可能指代一位真实学生或一个数学概念。现有的方法在治理与准确性之间存在权衡。商业大型语言模型（LLMs）能够处理这种模糊性，但需要将学生数据发送给第三方，而本地命名实体识别（NER）系统则能够保持治理，但会过度编辑课程术语。我们提出了一种完全本地的级联框架，将去标识化从开放式实体识别重新构建为受限的隐私筛选。一个以召回为先的联合提议者结合了两个轻量级编码器和确定性规则，以过度生成候选跨度；然后，一个上下文感知的审查者利用周围的对话和发言者角色对每个候选项做出二元的编辑/保留决策。我们对三种审查者配置进行了评估，比较了同类LLM基线和一个商业API，使用来自两个大型平台的数学辅导记录。最强的本地配置达到了0.958的宏F1，而同类LLM基线为0.767，商业API为0.706，且完全在一台笔记本电脑上运行。在针对课程与个人名称模糊性的挑战集上，同一配置的F1仅下降了0.03，而较小的审查者则下降了0.19至0.25。这些结果表明，在教育去标识化中，问题的表述比模型的规模更为重要。
cs.CL / 3 / 2606.18381
SproutRAG: Attention-Guided Tree Search with Progressive Embeddings for Long-Document RAG
SproutRAG：基于注意力引导的树搜索与渐进嵌入用于长文档的检索增强生成
Abstract
Retrieval-augmented generation (RAG) systems must balance retrieval granularity with contextual coherence, a challenge that existing methods address through LLM-guided chunking, single-level context expansion, or hierarchical summarization. These approaches variously depend on costly LLM calls during indexing or retrieval, limit context aggregation to a single granularity level, or introduce information loss through summarization. We present SproutRAG, an attention-guided hierarchical RAG framework that addresses this trade-off by organizing sentence-level chunks into progressively larger but semantically coherent units, using learned inter-sentence attention to construct a binary chunking tree. Unlike prior approaches that rely on external LLMs, fixed context expansion, or lossy summarization, SproutRAG learns which attention heads and layers best capture semantic document structure, enabling multi-granularity retrieval without additional LLM calls or compressed summaries. At retrieval time, SproutRAG uses hierarchical beam search to retrieve candidates at multiple granularities, capturing multi-sentence relevance beyond flat retrieval. The framework is trained end-to-end with a joint objective that improves both embeddings and tree structure. Experiments across four benchmarks spanning scientific, legal, and open-domain settings demonstrate that SproutRAG improves information efficiency (IE) by 6.1% on average over the strongest baseline. Code is available on https://github.com/AmirAbaskohi/SproutRAG.
Chinese Translation
检索增强生成（RAG）系统必须在检索粒度和上下文一致性之间取得平衡，这一挑战现有方法通过大规模语言模型（LLM）引导的分块、单层上下文扩展或层次化摘要来解决。这些方法在索引或检索过程中不同程度地依赖于昂贵的LLM调用，限制上下文聚合到单一粒度层次，或通过摘要引入信息损失。我们提出了SproutRAG，一个基于注意力引导的层次化RAG框架，通过将句子级块组织成逐渐增大但语义一致的单元，利用学习到的句间注意力构建二叉分块树，从而解决这一权衡。与依赖外部LLM、固定上下文扩展或有损摘要的先前方法不同，SproutRAG学习哪些注意力头和层最能捕捉语义文档结构，从而实现多粒度检索，而无需额外的LLM调用或压缩摘要。在检索时，SproutRAG使用层次化束搜索在多个粒度下检索候选项，捕捉超越平面检索的多句子相关性。该框架通过一个联合目标进行端到端训练，改善嵌入和树结构。跨越科学、法律和开放领域设置的四个基准实验表明，SproutRAG在信息效率（IE）上平均提高了6.1%，超越了最强基线。代码可在 https://github.com/AmirAbaskohi/SproutRAG 获取。
cs.CL / 4 / 2606.18389
Want Better Synthetic Data? Steer It: Activation Steering for Low-Resource Language Generation
想要更好的合成数据？引导它：低资源语言生成中的激活引导
Abstract
Large language models (LLMs) have become an effective tool for synthetic data generation, including for low-resource languages, where generated data can improve downstream task performance. Current best-performing approaches typically rely on few-shot prompting with target-language examples, which increases inference costs and may reduce diversity through lexical anchoring. In this work, we investigate activation steering as an alternative for low-resource synthetic data generation. We study two steering strategies: Language Steering, which targets the linguistic identity of a language, and Quality Steering, which captures well-formedness by contrasting human-written and backtranslated text representations. We evaluate these methods across four open-source LLMs, multiple layers, and 11 typologically diverse languages by generating sentiment and topic classification data and finetuning smaller classifiers. Steering is applied in both zero-shot and few-shot prompting settings and compared against non-steered counterparts. Our results show that steering on early layers consistently improves the diversity of generated data while often yielding stronger downstream model performance, particularly for low-resource languages.
Chinese Translation
大型语言模型（LLMs）已成为合成数据生成的有效工具，包括低资源语言，其中生成的数据可以提高下游任务的性能。目前表现最佳的方法通常依赖于使用目标语言示例的少量提示，这增加了推理成本，并可能通过词汇锚定降低多样性。在本研究中，我们探讨了激活引导作为低资源合成数据生成的替代方案。我们研究了两种引导策略：语言引导（Language Steering），其目标是语言的语言身份，以及质量引导（Quality Steering），其通过对比人类撰写的文本和回译文本表示来捕捉良构性。我们在四个开源LLM、多个层次和11种类型多样的语言上评估这些方法，通过生成情感和主题分类数据并微调较小的分类器。引导在零-shot和少量提示设置中应用，并与未引导的对照组进行比较。我们的结果表明，在早期层次上的引导始终提高了生成数据的多样性，同时往往带来了更强的下游模型性能，特别是对于低资源语言。
cs.CL / 5 / 2606.18394
JetFlow: Breaking the Scaling Ceiling of Speculative Decoding with Parallel Tree Drafting
JetFlow：打破推测解码的扩展瓶颈，通过并行树草拟
Abstract
Speculative decoding (SD) accelerates autoregressive Large Language Models (LLMs) by drafting multiple tokens and verifying them in parallel, but it faces a scaling limitation: increasing the draft budget improves speed only when acceptance remains high and drafting overhead stays low. This ceiling has been difficult to break because prior head-based SD methods face a causality-efficiency dilemma. Autoregressive drafters produce path-conditioned candidates that are effective for tree speculative decoding with higher acceptance length, but their drafting cost grows with tree depth. Bidirectional block-diffusion drafters generate all positions in one pass, but their branch-agnostic marginals can form individually plausible yet mutually inconsistent trees, wasting budget and reducing acceptance. We propose JetFlow, a head-based SD framework that combines one-forward drafting efficiency with branch-wise causal conditioning. JetFlow trains a causal parallel draft head over fused hidden states from the frozen target model, producing candidate trees whose scores align with the target model's autoregressive factorization. This enables JetFlow to convert larger draft budgets into longer accepted prefixes and higher end-to-end speedup. Across math, coding, and chat benchmarks on dense and MoE Qwen3 models, JetFlow consistently outperforms bidirectional-head and tree-based SD baselines. On H100 GPUs, JetFlow achieves up to 9.64x speedup on MATH-500 and 4.58x on open-ended conversational workloads, with further latency gains demonstrated through vLLM integration under realistic serving loads. Our code and models are available at https://github.com/hao-ai-lab/JetFlow.
Chinese Translation
推测解码（Speculative Decoding, SD）通过并行草拟多个标记并验证它们，加速自回归大型语言模型（Large Language Models, LLMs），但面临扩展限制：增加草拟预算仅在接受率保持高且草拟开销保持低时才能提高速度。由于先前基于头部的SD方法面临因果性与效率的困境，这一瓶颈一直难以突破。自回归草拟器生成的路径条件候选者在树形推测解码中对于较长的接受长度是有效的，但其草拟成本随着树的深度增加而增长。双向块扩散草拟器在一次传递中生成所有位置，但其与分支无关的边际分布可能形成个别合理但相互不一致的树，浪费预算并降低接受率。我们提出了JetFlow，这是一种基于头部的SD框架，结合了一次前向草拟的效率与分支因果条件。JetFlow在冻结的目标模型的融合隐藏状态上训练一个因果并行草拟头，生成的候选树的得分与目标模型的自回归因式分解一致。这使得JetFlow能够将更大的草拟预算转化为更长的接受前缀和更高的端到端加速。在密集和MoE Qwen3模型的数学、编码和对话基准测试中，JetFlow始终优于双向头部和基于树的SD基线。在H100 GPU上，JetFlow在MATH-500上实现了高达9.64倍的加速，在开放式对话工作负载上实现了4.58倍的加速，并通过在现实服务负载下的vLLM集成展示了进一步的延迟收益。我们的代码和模型可在https://github.com/hao-ai-lab/JetFlow获取。
cs.CL / 6 / 2606.18406
CoreMem: Riemannian Retrieval and Fisher-Guided Distillation for Long-Term Memory in Dialogue Agents
CoreMem：用于对话代理的黎曼检索与费舍尔引导蒸馏的长期记忆
Abstract
Personalized dialogue agents require continuous long-term memory to maintain coherent interactions across multiple sessions. However, deploying these capabilities on consumer-grade hardware (e.g., 8 GB VRAM edge devices) introduces severe memory and compute bottlenecks. Existing systems typically rely on isotropic cosine similarity for retrieval and heuristic rules for context compression. These approaches lack a unified theoretical foundation, frequently suffering from the hubness problem in high-dimensional retrieval and syntactic fragmentation during compression. To overcome these limitations, we propose CoreMem, a resource-efficient edge-cloud memory architecture fundamentally unified by information geometry. First, Riemannian retrieval replaces cosine matching with a locally adaptive Fisher-Rao metric, effectively penalizing hub memories via Mahalanobis distance with O(Ndr) Woodbury acceleration for real-time search. Second, Fisher-guided discrete token distillation (FDTD) introduces a hierarchical sentence-to-token compression mechanism. It derives sensitivity scores from Fisher information traces, providing a principled compression-KL tradeoff augmented with explicit structural syntax protection. Evaluated on the LOCOMO and LongMemEval-S benchmarks, CoreMem achieves strong accuracy improvements, yielding substantial gains in Open-domain (+4.51 pp) and Temporal (+4.17 pp) reasoning. Extensive profiling confirms that CoreMem operates seamlessly within a strict 8 GB VRAM budget, successfully bridging the gap between resource-constrained edge devices and the demand for theoretically grounded, lifelong memory agents.
Chinese Translation
个性化对话代理需要持续的长期记忆，以在多个会话中保持连贯的互动。然而，在消费级硬件（例如，8 GB VRAM 边缘设备）上部署这些能力会引入严重的内存和计算瓶颈。现有系统通常依赖各向同性余弦相似度进行检索，并使用启发式规则进行上下文压缩。这些方法缺乏统一的理论基础，常常在高维检索中遭遇中心性问题，并在压缩过程中出现语法碎片化。为了克服这些局限性，我们提出了 CoreMem，这是一种资源高效的边缘-云记忆架构，根本上由信息几何统一。首先，黎曼检索用局部自适应的费舍尔-拉奥度量替代余弦匹配，通过马哈拉诺比斯距离有效惩罚中心记忆，并利用 O(Ndr) Woodbury 加速实现实时搜索。其次，费舍尔引导的离散令牌蒸馏（FDTD）引入了一种分层的句子到令牌压缩机制。它从费舍尔信息轨迹中导出敏感度得分，提供了一个原则性的压缩-KL 权衡，并增强了显式的结构语法保护。在 LOCOMO 和 LongMemEval-S 基准测试中评估，CoreMem 实现了显著的准确性提升，在开放领域（+4.51 pp）和时间推理（+4.17 pp）方面取得了实质性进展。广泛的性能分析确认，CoreMem 在严格的 8 GB VRAM 预算内无缝运行，成功弥合了资源受限的边缘设备与对理论基础的终身记忆代理的需求之间的差距。
cs.CL / 7 / 2606.18448
VISUALSKILL: Multimodal Skills for Computer-Use Agents
VISUALSKILL：用于计算机使用代理的多模态技能
Abstract
Computer-use agents (CUAs) approach human-level performance on standardised benchmarks but still struggle on long-horizon tasks and unseen software. Existing skill libraries address this with reusable skills, but represent the skill artifact as text only, despite the visual nature of GUI interaction. We propose VISUALSKILL: a hierarchical multimodal skill, tailored to each target application and organised as a central index over per-topic files, which the agent consumes through a load_topic MCP tool that fetches the relevant topic's text and figures on demand. We construct each skill with a two-stage pipeline that combines authored documentation with live-application UI exploration. On two CUA benchmarks, CUA-World and OSExpert-Eval, a Claude Code CLI agent backed by Claude Opus 4.6 reaches an average score of 0.456 with VISUALSKILL, a +15.3 point absolute lift over the no-skill baseline (0.303). Against a matched text-only skill that is generated from the same source content and differs from VISUALSKILL only in modality, VISUALSKILL yields a further +8.3 point absolute gain over the matched text-only skill (0.373 vs. 0.456), providing direct evidence that retaining visual figures in the skill artifact, rather than verbalizing them away, helps the agent both identify UI elements and verify workflow state after each action. Our code is available at https://github.com/XMHZZ2018/VisualSkills.
Chinese Translation
计算机使用代理（CUAs）在标准化基准测试中接近人类水平的表现，但在长时间任务和未见过的软件上仍然存在困难。现有的技能库通过可重用的技能来解决这个问题，但仅将技能工件表示为文本，尽管图形用户界面（GUI）交互具有视觉特性。我们提出了VISUALSKILL：一种分层多模态技能，针对每个目标应用进行定制，并作为每个主题文件的中央索引进行组织，代理通过一个加载主题的MCP工具按需获取相关主题的文本和图形。我们通过一个两阶段的管道构建每个技能，该管道结合了编写的文档和实时应用程序用户界面的探索。在两个CUA基准测试中，CUA-World和OSExpert-Eval，基于Claude Opus 4.6的Claude Code CLI代理在VISUALSKILL的支持下达到了平均得分0.456，比无技能基线（0.303）提高了15.3个百分点。与从相同源内容生成的仅文本技能相比，该技能与VISUALSKILL在模态上有所不同，VISUALSKILL在匹配的仅文本技能上进一步提高了8.3个百分点（0.373对比0.456），提供了直接证据表明保留技能工件中的视觉图形，而不是将其语言化，有助于代理识别用户界面元素并在每次操作后验证工作流程状态。我们的代码可在https://github.com/XMHZZ2018/VisualSkills获取。
cs.CL / 8 / 2606.18453
LLM Parameters for Math Across Languages: Shared or Separate?
跨语言数学的LLM参数：共享还是独立？
Abstract
Large language models (LLMs) exhibit substantial cross-lingual variation in mathematical reasoning performance, but it remains unclear whether these differences reflect language-specific parameters or a shared mechanism that manifests differently by language. We present a cross-lingual mechanistic analysis of mathematical reasoning in LLMs, enabling us to localize and compare model parameters that support mathematical reasoning across languages. We find that the extracted math-associated parameters exhibit partial cross-lingual overlap, with the strongest overlap concentrated in intermediate model layers. We further observe that English consistently produces the largest set of math-relevant parameters, whereas lower-resource languages reveal smaller sets of relevant parameters. These results suggest that math-related behavior in multilingual LLMs is neither fully language-invariant nor fully language-specific, but instead exhibits partial cross-lingual parameter overlap with systematic language-dependent differences.
Chinese Translation
大型语言模型（LLMs）在数学推理表现上展现出显著的跨语言差异，但这些差异是否反映了特定语言的参数或是以不同语言表现的共享机制仍不清楚。我们对LLMs中数学推理进行了跨语言的机制分析，使我们能够定位和比较支持跨语言数学推理的模型参数。我们发现提取的与数学相关的参数在跨语言上部分重叠，且重叠最强的部分集中在中间模型层。我们进一步观察到，英语始终产生最大的一组与数学相关的参数，而资源较少的语言则显示出较小的相关参数集。这些结果表明，多语言LLMs中的数学相关行为既不是完全语言不变的，也不是完全语言特定的，而是展现出部分跨语言参数重叠和系统性的语言依赖差异。
cs.CL / 9 / 2606.18466
Montreal Forced Aligner and the state of speech-to-text alignment in 2026
蒙特利尔强制对齐器及2026年语音转文本对齐的现状
Abstract
The Montreal Forced Aligner (MFA) was released in 2016 and has since become the most widely used tool for forced alignment in research and industry. In the decade since, MFA has undergone substantial development, including expanded coverage across more languages and dialects using larger open-source datasets, harmonized IPA dictionaries, model adaptation, cross-language phone remapping, and support utilities. This paper documents MFA 3.0's developments since version 1.0 and evaluates MFA's performance across English, Japanese, and Korean, benchmarked against classic and neural forced aligners. MFA 3.0 achieves state-of-the-art or near state-of-the-art performance across all four benchmark datasets with mean boundary errors below 15 ms. Adaptation and cross-language remapping are effective for languages outside MFA's training distribution, and pronunciation probability modeling and phonological rules provide gains in specific conditions.
Chinese Translation
蒙特利尔强制对齐器（Montreal Forced Aligner, MFA）于2016年发布，随后成为研究和工业界中最广泛使用的强制对齐工具。在过去的十年中，MFA经历了重大的发展，包括使用更大规模的开源数据集扩展对更多语言和方言的覆盖、统一的国际音标（IPA）词典、模型适应、跨语言音素重映射以及支持工具的开发。本文记录了MFA 3.0自1.0版本以来的发展，并评估了MFA在英语、日语和韩语中的表现，基于经典和神经强制对齐器进行基准测试。MFA 3.0在所有四个基准数据集上均实现了最先进或接近最先进的性能，平均边界误差低于15毫秒。适应和跨语言重映射对MFA训练分布之外的语言有效，而发音概率建模和音韵规则在特定条件下提供了性能提升。
cs.CL / 10 / 2606.18471
Possible or Definite? A Benchmark for Evaluating Diagnostic Uncertainty Preservation in Clinical Text
可能还是确定？评估临床文本中诊断不确定性保留的基准
Abstract
Large language models (LLMs) are increasingly used for clinical text tasks such as summarization and revision. While most studies evaluate the fluency and coherence of LLM-generated text, whether LLMs correctly preserve diagnostic uncertainty remains underexplored. In clinical practice, phrases such as ``possible pneumonia'' communicate the strength of available evidence and directly guide decisions about follow-up testing and treatment. Altering these uncertainty expressions can change the clinical meaning entirely. In this paper, we systematically evaluated this problem in two steps. First, we constructed a benchmark of 1,200 clinical documents with 9,184 uncertainty annotations across five levels. Second, we evaluated three LLMs on this benchmark. Our results show that (1) LLMs preserve the original uncertainty cues poorly, often less than half the time; (2) LLMs struggle with nuanced distinctions between adjacent levels. This work reveals a failure mode not captured by standard evaluation metrics and provides implications for the safe deployment of LLMs in clinical workflows.
Chinese Translation
大型语言模型（LLMs）在临床文本任务中越来越多地被使用，例如摘要和修订。尽管大多数研究评估了LLM生成文本的流畅性和连贯性，但LLMs是否能够正确保留诊断不确定性仍然未得到充分探讨。在临床实践中，诸如“可能的肺炎”等短语传达了现有证据的强度，并直接指导后续检测和治疗的决策。改变这些不确定性表达可能会完全改变临床意义。在本文中，我们分两步系统地评估了这一问题。首先，我们构建了一个包含1,200份临床文档和9,184个不确定性注释的基准，涵盖五个层级。其次，我们在该基准上评估了三种LLM。我们的结果显示：（1）LLMs对原始不确定性线索的保留效果较差，通常不到一半的时间；（2）LLMs在相邻层级之间的细微区别上存在困难。这项工作揭示了一种标准评估指标未能捕捉的失败模式，并为LLMs在临床工作流程中的安全部署提供了启示。
cs.CL / 11 / 2606.18473
PreUnlearn: Auditing Collateral Knowledge Damage Before Large Language Model Unlearning
PreUnlearn：在大型语言模型去学习之前审计附带知识损害
Abstract
Machine unlearning for large language models (LLMs) aims to remove specified knowledge while preserving the rest of the model's capabilities. However, the boundary between knowledge to forget and knowledge to retain is often unclear, since related and even distant information may be entangled in the model. In this paper, we study LLM unlearning from a data-centric perspective and measure how unlearning effects propagate from the forget set to same-domain and distant-domain knowledge. We find a consistent decay pattern: collateral damage is strongest near the forget set, weakens with semantic distance, but does not disappear at domain boundaries. We further ask whether such damage can be audited before unlearning is executed. We formulate forget-set auditing as a pre-unlearning prediction task and analyze which data features are most predictive of downstream damage. Our results show that interaction features between the forget set and evaluation set provide the strongest signals, suggesting that collateral damage is partly reflected in data geometry before model updates occur. These findings position forget-set auditing as an early warning tool for identifying risky unlearning runs and designing more reliable unlearning procedures.
Chinese Translation
大型语言模型（LLMs）的机器去学习旨在移除特定知识，同时保留模型的其他能力。然而，遗忘知识与保留知识之间的界限往往不明确，因为相关甚至遥远的信息可能在模型中交织在一起。本文从数据中心的角度研究LLM去学习，并测量去学习效应如何从遗忘集传播到同域和异域知识。我们发现了一种一致的衰减模式：附带损害在遗忘集附近最强，随着语义距离的增加而减弱，但在领域边界并未消失。我们进一步探讨这种损害是否可以在执行去学习之前进行审计。我们将遗忘集审计形式化为一个去学习前的预测任务，并分析哪些数据特征对下游损害的预测能力最强。我们的结果表明，遗忘集与评估集之间的交互特征提供了最强的信号，这表明附带损害在模型更新发生之前部分反映在数据几何中。这些发现将遗忘集审计定位为识别风险去学习过程和设计更可靠去学习程序的早期预警工具。
cs.CL / 12 / 2606.18502
Towards Scalable Customization and Deployment of Multi-Agent Systems for Enterprise Applications
面向企业应用的可扩展多智能体系统定制与部署
Abstract
Large language model (LLM)-based multi-agent systems demonstrate strong performance on complex reasoning and task execution, enabling broad enterprise applications. However, production deployment remains challenging due to domain-specific customization requirements and high latency and inference costs in agentic workflows. We propose a unified framework for customization and efficient deployment of multi-agent systems in real-world settings. The first stage, Agentic Model Customization, combines continual pretraining, supervised fine-tuning, and preference optimization to adapt a compact model to specialized domains while retaining strong agentic capabilities. The second stage, Inference Optimization, integrates speculative decoding and FP8 quantization with targeted calibration to enable cost-efficient serving with minimal quality loss. Across enterprise workloads, our framework enables rapid domain adaptation and achieves a 4.48x speedup in throughput while maintaining performance and improving robustness on long-tail scenarios.
Chinese Translation
基于大型语言模型（LLM）的多智能体系统在复杂推理和任务执行方面表现出色，能够广泛应用于企业。然而，由于特定领域的定制需求以及智能体工作流中的高延迟和推理成本，生产部署仍然面临挑战。我们提出了一个统一的框架，用于在实际环境中定制和高效部署多智能体系统。第一阶段，智能体模型定制，结合持续预训练、监督微调和偏好优化，将一个紧凑模型适应于专业领域，同时保留强大的智能体能力。第二阶段，推理优化，整合了推测解码和FP8量化，并通过有针对性的校准实现低成本服务，且质量损失最小。在企业工作负载中，我们的框架实现了快速的领域适应，并在保持性能的同时，在长尾场景中提高了鲁棒性，达到了4.48倍的吞吐量加速。
cs.CL / 13 / 2606.18508
MCompassRAG: Topic Metadata as a Semantic Compass for Paragraph-Level Retrieval
MCompassRAG：主题元数据作为段落级检索的语义指南
Abstract
Retrieval-augmented generation (RAG) systems depend critically on how documents are chunked and searched. Fine-grained chunks can improve retrieval precision but expand the search space, increasing latency and cost; larger chunks reduce the number of candidates but make dense similarity less reliable, as the representation for each chunk mixes multiple topics and introduces more semantic noise. This trade-off becomes especially limiting in deep research tasks, where retrieval must be both fast and precise across large, heterogeneous corpora. We introduce MCompassRAG, a metadata-guided retrieval framework that uses topic-level signals as a semantic compass for selecting relevant evidence. Instead of relying only on cosine similarity between queries and noisy chunk embeddings, MCompassRAG enriches chunk representations with topic metadata in the same embedding space and trains a lightweight retriever through LLM-teacher distillation. At inference time, MCompassRAG performs topic-aware retrieval without additional LLM calls, improving both efficiency and evidence quality. Across six complex retrieval benchmarks, MCompassRAG improves information efficiency (IE) by 8.24% on average with over 5 times lower latency than the strongest efficient RAG baselines. Code is available on https://github.com/AmirAbaskohi/MCompassRAG.
Chinese Translation
检索增强生成（RAG）系统在文档的分块和搜索方式上依赖性极强。细粒度的分块可以提高检索精度，但会扩大搜索空间，从而增加延迟和成本；较大的分块减少了候选项的数量，但使得密集相似性变得不那么可靠，因为每个分块的表示混合了多个主题并引入了更多的语义噪声。这种权衡在深度研究任务中尤为限制，在这些任务中，检索必须在大规模异构语料库中既快速又精确。我们提出了MCompassRAG，这是一种基于元数据的检索框架，利用主题级信号作为选择相关证据的语义指南。MCompassRAG不仅依赖于查询与噪声分块嵌入之间的余弦相似性，而是通过在相同嵌入空间中用主题元数据丰富分块表示，并通过LLM教师蒸馏训练一个轻量级检索器。在推理时，MCompassRAG在不增加额外LLM调用的情况下执行主题感知检索，从而提高了效率和证据质量。在六个复杂的检索基准测试中，MCompassRAG的平均信息效率（IE）提高了8.24%，且延迟比最强的高效RAG基线低超过5倍。代码可在 https://github.com/AmirAbaskohi/MCompassRAG 获取。
cs.CL / 14 / 2606.18584
Speech-Driven End-to-End Language Discrimination towards Chinese Dialects
基于语音的端到端语言辨识方法在中文方言中的应用
Abstract
Language discrimination among similar languages, varieties, and dialects is a challenging natural language processing task. The traditional text-driven focus leads to poor results. In this paper, we explore the effectiveness of speech-driven features towards language discrimination among Chinese dialects. First, we systematically explore the appropriateness of speech-driven MFCC features towards CNN-based language discrimination. Then, we design an end-to-end speech recognition model based on HMM-DNN to predict Chinese dialect words. We adopt attention to extract the discriminative words related to different Chinese dialects. Finally, through a CNN, we combine the word-level embedding and the MFCC-based features. Evaluation of two benchmark Chinese dialect corpora shows the appropriateness and effectiveness of the proposed speech-driven approach to fine-grained Chinese dialect discrimination compared to the state-of-the-art methods.
Chinese Translation
在相似语言、变体和方言之间进行语言辨识是一项具有挑战性的自然语言处理任务。传统的文本驱动方法导致了较差的结果。本文探讨了基于语音特征在中文方言语言辨识中的有效性。首先，我们系统地探讨了基于语音的MFCC特征在基于CNN的语言辨识中的适用性。然后，我们设计了一种基于HMM-DNN的端到端语音识别模型，以预测中文方言词汇。我们采用注意力机制提取与不同中文方言相关的区分性词汇。最后，通过CNN，我们结合了词级嵌入和基于MFCC的特征。对两个基准中文方言语料库的评估表明，与最先进的方法相比，所提出的基于语音的方法在细粒度中文方言辨识中具有适用性和有效性。
cs.CL / 15 / 2606.18587
Dual Dimensionality for Local and Global Attention
局部与全局注意力的双重维度性
Abstract
Decoder-only Transformers compute attention over the KV cache of preceding tokens. Keys (and Values) are typically represented with the same dimensionality, regardless of its distance from the prediction target. In natural language, however, the next word is most strongly influenced by the immediately preceding tokens. We hypothesize that local and distant tokens impose asymmetric demands on representational capacity: local tokens are more critical for predicting immediate outputs and thus require richer representations, whereas distant tokens primarily serve as long-range memory, for which lower-dimensional representations may suffice. We formalize this idea as Distance-Adaptive Representation (DAR), implemented in a controlled setting that preserves full-dimensional representations within a local context window while assigning reduced-dimensional representations (e.g. 1/4 of the original dimensionality) to tokens beyond that window. Across multiple pretraining scales (70M to 410M parameters), as well as continued supervised fine-tuning on a 1B-scale model, this approach closely matches the performance of full-dimensional baselines. In contrast, uniformly reducing dimensionality across all token positions leads to worse performance. These results challenge the common assumption that key and value dimensionality should be uniform across token positions. Our findings suggest a new direction for designing attention architectures that adaptively allocate representational capacity across sequences, enabling further reductions in KV cache during inference.
Chinese Translation
仅解码器的变换器（Decoder-only Transformers）在先前标记的键值（KV）缓存上计算注意力。键（Keys）和值（Values）通常以相同的维度表示，而不考虑其与预测目标的距离。然而，在自然语言中，下一个单词最受紧邻的前一个标记的影响。我们假设局部和远程标记对表示能力施加了不对称的需求：局部标记对于预测即时输出更为关键，因此需要更丰富的表示，而远程标记主要作为长程记忆，较低维度的表示可能就足够了。我们将这一思想形式化为距离自适应表示（Distance-Adaptive Representation, DAR），在一个控制环境中实现，该环境在局部上下文窗口内保留全维度表示，同时对超出该窗口的标记分配降低维度的表示（例如，原始维度的1/4）。在多个预训练规模（70M到410M参数）以及在1B规模模型上进行持续的监督微调中，该方法的性能与全维度基线非常接近。相比之下，在所有标记位置均匀降低维度会导致性能下降。这些结果挑战了键和值的维度应该在所有标记位置上保持一致的常见假设。我们的发现为设计自适应分配表示能力的注意力架构提供了新的方向，从而在推理过程中进一步减少KV缓存的使用。
cs.CL / 16 / 2606.18597
Low-resource Language Discrimination Towards Chinese Dialects with Transfer learning and Data Augmentation
基于迁移学习和数据增强的低资源语言对中文方言的区分
Abstract
Chinese dialects discrimination is a challenging natural language processing task due to scarce annotation resource. In this article, we develop a novel Chinese dialects discrimination framework with transfer learning and data augmentation (CDDTLDA) in order to overcome the shortage of resources. To be more specific, we first use a relatively larger Chinese dialects corpus to train a source-side automatic speech recognition (ASR) model. Then, we adopt a simple but effective data augmentation method (i.e., speed, pitch, and noise disturbance) to augment the target-side low-resource Chinese dialects, and fine-tune another target ASR model based on the previous source-side ASR model. Meanwhile, the potential common semantic features between source-side and target-side ASR models can be captured by using self-attention mechanism. Finally, we extract the hidden semantic representation in the target ASR model to conduct Chinese dialects discrimination. Our extensive experimental results demonstrate that our model significantly outperforms state-of-the-art methods on two benchmark Chinese dialects corpora.
Chinese Translation
中文方言的区分是一项具有挑战性的自然语言处理任务，因为缺乏足够的注释资源。本文提出了一种新颖的中文方言区分框架，结合了迁移学习和数据增强（CDDTLDA），旨在克服资源短缺的问题。具体而言，我们首先使用相对较大的中文方言语料库训练一个源侧自动语音识别（ASR）模型。然后，我们采用一种简单但有效的数据增强方法（即速度、音调和噪声干扰）来增强目标侧的低资源中文方言，并基于之前的源侧ASR模型对另一个目标ASR模型进行微调。同时，通过使用自注意力机制，可以捕捉源侧和目标侧ASR模型之间潜在的共同语义特征。最后，我们提取目标ASR模型中的隐藏语义表示，以进行中文方言的区分。我们的广泛实验结果表明，我们的模型在两个基准中文方言语料库上显著优于最先进的方法。
cs.CL / 17 / 2606.18606
Steerable Cultural Preference Optimization of Reward Models
可调文化偏好优化的奖励模型
Abstract
It is essential for large language model (LLM) technology to serve many different cultural sub-communities in a manner that is acceptable to each community. However, research on LLM alignment has so far predominantly focused on predicting a unified response preference of annotators from certain regions. This paper aims to advance the development of alignment models with a more global outlook, that are able to accurately represent the preferences of subcommunities and do not exhibit excessive bias towards any of them. We focus on the development of reward models for this purpose and present a novel reward model training algorithm (SCPO) that can incorporate diverse cultural preferences in a balanced manner. Our method results in performance increases of the minority reward model of up to 7 points over the baseline model across two datasets, PRISM and GlobalOpinionQA, and across 7 countries. SCPO is up to 280% more training data-efficient than full-data finetuning of reward models. In addition, we perform analysis of bias by separately evaluating on the preference of subcommunities and show that excessive bias is mitigated via our weighting method. Our code is available at https://github.com/minsik-ai/Steerable-Cultural-Preference
Chinese Translation
大型语言模型（LLM）技术必须以各个文化子社区可接受的方式服务于不同的社区。然而，迄今为止，关于LLM对齐的研究主要集中在预测某些地区注释者的统一响应偏好上。本文旨在推动具有更全球视野的对齐模型的发展，能够准确表示子社区的偏好，并且不会对任何一个子社区表现出过度偏见。我们专注于为此目的开发奖励模型，并提出了一种新颖的奖励模型训练算法（SCPO），该算法能够以平衡的方式纳入多样的文化偏好。我们的方法在两个数据集PRISM和GlobalOpinionQA以及7个国家的基础模型上，少数奖励模型的性能提升达7个点。SCPO在训练数据效率上比完整数据微调的奖励模型高出280%。此外，我们通过分别评估子社区的偏好来进行偏见分析，并表明通过我们的加权方法可以减轻过度偏见。我们的代码可在 https://github.com/minsik-ai/Steerable-Cultural-Preference 获取。
cs.CL / 18 / 2606.18613
Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistance
大型语言模型准备好协助医生了吗？用于互动医生-患者-电子健康记录（EHR）协助的PhysAssistBench
Abstract
The most plausible near-term role of medical LLMs is to assist rather than replace physicians, yet current evaluations often test isolated capabilities: clinical knowledge, EHR system interaction, or patient communication. Physician assistance instead requires coordinating these capabilities within the same interaction, where physicians issue underspecified requests, patients describe symptoms ambiguously, and EHR systems demand precise tool use. We introduce PhysAssistBench, a benchmark for interactive doctor-patient-EHR assistance. Built from real MIMIC-IV cases, PhysAssistBench uses a scalable pipeline to construct agentic patients: interactive, record-grounded agents that turn static EHR records into multi-turn clinical scenarios while preserving clinical factuality. PhysAssistBench provides a curated bilingual evaluation set of 1,296 manually reviewed and physician-validated turns. Experiments with leading LLMs show that current models remain unreliable in this setting, which exposes a key bottleneck for clinical LLMs: reliable assistance requires coordination across knowledge, communication, and systems, not isolated gains in any of them.
Chinese Translation
医疗领域大型语言模型（LLMs）最可能的短期角色是协助而非取代医生，但当前的评估往往测试孤立的能力：临床知识、电子健康记录（EHR）系统交互或患者沟通。医生的协助需要在同一互动中协调这些能力，其中医生发出不明确的请求，患者模糊地描述症状，而EHR系统则要求精确的工具使用。我们介绍了PhysAssistBench，这是一个用于互动医生-患者-EHR协助的基准。PhysAssistBench基于真实的MIMIC-IV案例构建，采用可扩展的管道构建具有代理性的患者：这些互动的、基于记录的代理将静态EHR记录转化为多轮临床场景，同时保持临床事实的准确性。PhysAssistBench提供了一个经过策划的双语评估集，包括1,296个经过人工审核和医生验证的对话轮次。与领先的LLMs进行的实验表明，当前模型在这一环境下仍然不可靠，这暴露了临床LLMs的一个关键瓶颈：可靠的协助需要在知识、沟通和系统之间的协调，而不是在其中任何一个方面的孤立提升。
cs.CL / 19 / 2606.18620
BCL: Bayesian In-Context Learning Framework for Information Extraction
BCL：用于信息提取的贝叶斯上下文学习框架
Abstract
Existing information extraction (IE) tasks increasingly adopt in-context learning (ICL) with large language models. However, current approaches either show inconsistent performance across model scales or lack systematic optimization and generalizability. Building on this, we propose BCL (Bayesian In-Context Learning Framework for Information Extraction), the first optimization framework that uses particle filtering with Bayesian updates to systematically refine label representations across IE tasks. Through four steps initialization, observation, weight update, and resampling, BCL generalizes to both sequence labeling and relation classification paradigms. Extensive experiments demonstrate substantial and consistent improvements over existing approaches.
Chinese Translation
现有的信息提取（IE）任务越来越多地采用大语言模型的上下文学习（ICL）。然而，当前的方法要么在模型规模上表现不一致，要么缺乏系统的优化和泛化能力。在此基础上，我们提出了BCL（用于信息提取的贝叶斯上下文学习框架），这是第一个利用粒子滤波和贝叶斯更新的优化框架，旨在系统性地优化跨IE任务的标签表示。通过初始化、观察、权重更新和重采样四个步骤，BCL能够推广到序列标注和关系分类两种范式。大量实验表明，BCL在现有方法上实现了显著且一致的改进。
cs.CL / 20 / 2606.18624
PragReST: Self-Reinforcing Counterfactual Reasoning for Pragmatic Language Understanding
PragReST：用于务实语言理解的自我强化反事实推理
Abstract
Natural language understanding often depends on meanings that are implied rather than explicitly stated, requiring pragmatic reasoning. Despite strong performance on math and logical reasoning, large language models (LLMs) still struggle with making pragmatic inferences, often choosing literal interpretations. To improve LLM pragmatic reasoning, we introduce PragReST, a self-supervised framework that constructs pragmatic QA data, generates counterfactual reasoning traces, and trains models to internalize them through supervised fine-tuning and reinforcement learning, without human-labeled training data or distillation from a stronger teacher. Across four pragmatic benchmarks (PragMega, Ludwig, MetoQA, and AltPrag), PragReST improves over backbone models, task-specific pragmatic tuning baselines, and non-counterfactual variants of the same pipeline. On accuracy-based benchmarks, PragReST improves over the instruct backbone by 5.37 and 5.50% (absolute) for Qwen3-8B and Qwen3-14B, respectively. Our error analysis and ablations underscore the importance of counterfactual reasoning: PragReST primarily reduces errors caused by failures to contrast observed utterances with plausible alternatives, and removing counterfactual reasoning substantially reduces performance. Moreover, our training preserves out-of-domain performance on general-knowledge and mathematical reasoning benchmarks.
Chinese Translation
自然语言理解通常依赖于隐含而非明确陈述的意义，这需要务实推理。尽管在数学和逻辑推理方面表现出色，大型语言模型（LLMs）在进行务实推理时仍然存在困难，常常选择字面解释。为了提高LLM的务实推理能力，我们提出了PragReST，这是一种自监督框架，构建务实问答数据，生成反事实推理轨迹，并通过监督微调和强化学习训练模型内化这些轨迹，而无需人工标注的训练数据或从更强教师模型的蒸馏。在四个务实基准测试（PragMega、Ludwig、MetoQA和AltPrag）中，PragReST在主干模型、特定任务的务实调优基线以及同一管道的非反事实变体上均有所提升。在基于准确性的基准测试中，PragReST相较于指令主干在Qwen3-8B和Qwen3-14B上分别提高了5.37%和5.50%的绝对准确率。我们的错误分析和消融实验强调了反事实推理的重要性：PragReST主要减少了由于未能将观察到的发言与合理替代方案进行对比而导致的错误，而去除反事实推理显著降低了性能。此外，我们的训练保持了在一般知识和数学推理基准上的域外性能。
cs.CL / 21 / 2606.18636
PEC-Home: Interpretation of Progressively Elliptical Commands in Smart Homes
PEC-Home：智能家居中逐步省略命令的解释
Abstract
Recent advancements in Large Language Models (LLMs) have empowered home assistants with natural language interaction capabilities. However, current assistants overlook the progressive omission that occurs in human dialogue as shared context accumulates, leading to more elliptical expressions for efficient communication. Thus, current assistants still struggle to interpret such elliptical expressions accurately, which limits their effectiveness in real-world applications. In practical smart home scenarios, assistants face two major challenges caused by elliptical commands: (1) referential ambiguity caused by different environmental expectations among multiple users; and (2) intention ambiguity resulting from user preferences that evolve over time or change with the environment. To address these challenges, we introduce PEC-Home, the first simulated home dataset specifically designed for interpreting progressively elliptical commands in smart homes. Extensive experiments on various LLMs, including GPT-4o, show that existing home assistants struggle to execute user-intended operations based solely on elliptical commands. Even when equipped with tools for storing and retrieving user dialogue history, execution accuracy remains below that achieved with complete commands.}.
Chinese Translation
近期大型语言模型（LLMs）的进展使得家庭助手具备了自然语言交互的能力。然而，目前的助手忽视了人类对话中随着共享上下文的积累而发生的逐步省略现象，导致更为省略的表达以实现高效沟通。因此，现有助手在准确解释这些省略表达方面仍然面临困难，这限制了它们在实际应用中的有效性。在实际的智能家居场景中，助手面临由省略命令引发的两个主要挑战：（1）由于多个用户之间不同的环境预期而导致的指称模糊性；（2）由于用户偏好随时间演变或随环境变化而导致的意图模糊性。为了解决这些挑战，我们提出了PEC-Home，这是第一个专门设计用于解释智能家居中逐步省略命令的模拟家庭数据集。对包括GPT-4o在内的多种大型语言模型进行的广泛实验表明，现有家庭助手仅基于省略命令执行用户意图操作时存在困难。即使配备了存储和检索用户对话历史的工具，执行准确性仍低于使用完整命令时的水平。
cs.CL / 22 / 2606.18656
The Wrong Kind of Right: Quantifying and Localizing Misfired Alignment in LLMs
错误的正确：量化和定位大型语言模型中的误触对齐
Abstract
Warning: This paper studies stereotypes and biases, and contains potentially disturbing examples, used for illustration purposes only. Our findings should not be interpreted as an argument against alignment. Instead, this paper highlights the need for principled approaches to more advanced alignment. Alignment aims to ensure that large language models (LLMs) behave safely and reliably, including by avoiding unsafe inferences. However, we show that such safety-oriented behaviors can misfire: models may reject warranted conclusions even when they are explicitly supported by context. We call this failure mode misfired alignment, where alignment-induced changes cause LLMs to override explicit evidence. To quantify this phenomenon, specifically on stereotype-related alignment, we introduce VETO, a benchmark consisting of 2,032 BBQ-derived contrastive pairs, and define a new metric, Misfired Alignment Rate (MAR), which measures on a 0 to 100 scale how often a model fails on a stereotype-related question but succeeds on its contrastive counterpart. We benchmark 25 LLMs on VETO, and show that all LLMs, including the most recent ones, exhibit non-trivial (4.7 to 18.9%) MARs while all human participants achieve 0.0% MAR. Controlled priming experiments further show that alignment-induced cues can substantially amplify MAR across LLMs, indicating that these failures are not merely artifacts of individual examples but can be induced by safety-related framing. Mechanistic analyses on open-weight LLMs reveal late-layer suppression of evidence-supported answers, and comparisons between instruct and base LLMs suggest that this suppression emerges after instruction training. These findings show that current alignment methods can overgeneralize surface-level safety cues, to the point of overriding objective evidence, motivating more work on alignment objectives that better preserve contextual grounding.
Chinese Translation
警告：本文研究了刻板印象和偏见，并包含可能令人不安的示例，仅用于说明目的。我们的研究结果不应被解读为反对对齐的论据。相反，本文强调了对更高级对齐的原则性方法的需求。对齐旨在确保大型语言模型（LLMs）安全可靠地运行，包括避免不安全的推断。然而，我们展示了这种以安全为导向的行为可能会误触：模型可能会拒绝合理的结论，即使这些结论得到了上下文的明确支持。我们将这种失效模式称为误触对齐，其中对齐引发的变化导致LLMs忽略明确的证据。为了量化这一现象，特别是在与刻板印象相关的对齐方面，我们引入了VETO，一个由2032个BBQ派生对比对组成的基准，并定义了一个新的指标——误触对齐率（Misfired Alignment Rate, MAR），该指标在0到100的范围内衡量模型在与刻板印象相关的问题上失败的频率，同时在其对比问题上成功的频率。我们在VETO上对25个LLMs进行了基准测试，结果显示所有LLMs，包括最新的模型，均表现出非平凡的（4.7%到18.9%）MAR，而所有人类参与者的MAR为0.0%。控制性启发实验进一步表明，对齐引发的提示可以显著放大LLMs的MAR，表明这些失败不仅仅是个别示例的伪影，而是可以通过与安全相关的框架引发的。对开放权重LLMs的机制分析显示，证据支持的答案在后层被抑制，而对指令型和基础型LLMs的比较表明，这种抑制在指令训练后出现。这些发现表明，当前的对齐方法可能会过度概括表面安全提示，以至于忽视客观证据，促使我们在对齐目标上进行更多工作，以更好地保留上下文基础。
cs.CL / 23 / 2606.18663
RegMix-D: Dynamic Data Mixing via Proxy Training Trajectories
RegMix-D：通过代理训练轨迹实现动态数据混合
Abstract
Data mixture selection is critical for Large Language Model pretraining. Existing methods such as RegMix select a single static mixture by fitting a regression model on small-scale proxy runs. We propose RegMix-D, a simple extension of RegMix to dynamic mixing. Our key observation is that proxy runs produce not only endpoint losses, but also full loss trajectories, which can be used to further improve data mixture. By training regression model on these trajectories, we can predict optimal mixtures at multiple training stages. RegMix-D supports two deployment modes: an offline variant that generates a complete mixture schedule before target training, and an online variant that adapts the mixture during training using observed loss. Experiments on 25B tokens of the Pile dataset with a 1B parameter target model show that RegMix-D consistently improves over RegMix and DoReMi across 13 downstream tasks while remaining proxy-efficient: it surpasses RegMix even with only 128 proxy models (25% of RegMix's proxy compute budget).
Chinese Translation
数据混合选择对于大型语言模型的预训练至关重要。现有方法如 RegMix 通过在小规模代理运行上拟合回归模型来选择单一静态混合。我们提出了 RegMix-D，这是对 RegMix 的一个简单扩展，旨在实现动态混合。我们的关键观察是，代理运行不仅产生最终损失，还生成完整的损失轨迹，这可以进一步改善数据混合。通过在这些轨迹上训练回归模型，我们可以预测多个训练阶段的最佳混合。RegMix-D 支持两种部署模式：一种是离线变体，在目标训练之前生成完整的混合计划；另一种是在线变体，在训练过程中使用观察到的损失来调整混合。在对 25B 令牌的 Pile 数据集和一个 1B 参数目标模型的实验中，RegMix-D 在 13 个下游任务中始终优于 RegMix 和 DoReMi，同时保持代理效率：即使仅使用 128 个代理模型（占 RegMix 代理计算预算的 25%），它也超越了 RegMix。
cs.CL / 24 / 2606.18699
TW-LegalBench: Measuring Taiwanese Legal Understanding
TW-LegalBench：衡量台湾法律理解能力
Abstract
Large language models (LLMs) have shown impressive capabilities across diverse tasks, yet their performance on jurisdiction-specific legal reasoning remains underexplored. We present TW-LegalBench that utilizes Taiwanese legal system's rich official corpus open to the public to fill the gap in evaluating LLMs on Taiwanese law, among common-law benchmarks that focus on English sources and civil-law benchmarks focusing on sources of Simplified Chinese. TW-LegalBench comprises three task types: (1) over 16,000 multiple-choice questions (MCQs) across five years of official examinations in 18 professional domains; (2) 117 open-ended essay questions (OEQs) from examinations for legal professionals with official scoring rubrics; and (3) more than 14,000 legal judgment prediction (LJP) instances covering hundreds of crime categories. We evaluate 13 LLMs using accuracy for MCQs, a decomposed LLM-as-Judge framework based on the scoring rubric points for OEQs, and metrics for sentencing accuracy and statute citation for LJP. Our results reveal that top-performing models exceed the passing threshold for qualified lawyers (passing rate: 11%) but fall short of that for judges and prosecutors (passing rate: 1~2%). For LJP, while models demonstrate reasonable verdict type accuracy and sentence prediction capability, they struggle to cite exact legal articles. These findings highlight that reliable legal text generation remains challenging for LLMs, even though their performance on qualification examinations approaches human level.
Chinese Translation
大型语言模型（LLMs）在多种任务中展现了令人印象深刻的能力，但它们在特定法域的法律推理表现仍未得到充分探索。我们提出了TW-LegalBench，利用台湾法律系统丰富的官方语料库，以填补在评估LLMs对台湾法律理解能力方面的空白，尤其是在以英语资料为主的普通法基准和以简体中文资料为主的民法基准之间。TW-LegalBench包括三种任务类型：（1）涵盖18个专业领域的五年官方考试中超过16,000道选择题（MCQs）；（2）来自法律专业人员考试的117道开放式论文题（OEQs），并附有官方评分标准；（3）超过14,000个法律判决预测（LJP）实例，涵盖数百种犯罪类别。我们使用选择题的准确率评估了13个LLMs，基于评分标准点的分解LLM作为法官框架评估开放式论文题，并对法律判决预测的量刑准确性和法律条款引用进行度量。我们的结果显示，表现最佳的模型超过了合格律师的及格线（及格率：11%），但未达到法官和检察官的及格线（及格率：1~2%）。在法律判决预测方面，尽管模型在判决类型准确性和判刑预测能力上表现合理，但在引用确切法律条款方面存在困难。这些发现突显了尽管LLMs在资格考试中的表现接近人类水平，但可靠的法律文本生成仍然是一个挑战。
cs.CL / 25 / 2606.18709
LLMs Struggle to Measure What Distinguishes Students of Different Proficiency Levels: A Study of Item Discrimination in Reading Comprehension Assessment
大型语言模型在测量不同水平学生的区分能力方面的困难：阅读理解评估中的项目区分研究
Abstract
Item discrimination is a fundamental psychometric property of educational assessment, which measures whether an item meaningfully distinguishes students with higher proficiency from students with lower proficiency. While various existing works have explored whether large language models (LLMs) can estimate item difficulty, it remains unclear whether they can capture item discrimination. In this work, we evaluate 42 proprietary and open-weight LLMs in zero-shot settings using two complementary approaches: direct discrimination prediction, where models explicitly estimate an item's discrimination value from its content, and response-based Classical Test Theory (CTT) calibration, where LLM answers are treated as synthetic student responses to compute discrimination scores. Our results show that direct prediction yields weak alignment with human-calibrated discrimination: the best-performing model reaches only a Spearman correlation of 0.152. Response-based CTT calibration provides a stronger but still limited signal, with the all-persona synthetic respondent pool reaching a Spearman correlation of 0.241. These findings highlight item discrimination as an open challenge for LLM-based psychometric evaluation: current LLMs contain non-random discrimination-relevant signal, but they do not yet reliably capture how assessment items distinguish human students.
Chinese Translation
项目区分是教育评估的一个基本心理测量特性，它衡量一个项目是否能够有效地区分高水平学生和低水平学生。尽管已有多项研究探讨了大型语言模型（LLMs）是否能够估计项目难度，但它们是否能够捕捉项目区分仍不明确。在本研究中，我们在零样本设置下评估了42个专有和开放权重的LLMs，采用两种互补的方法：直接区分预测，其中模型从内容中明确估计项目的区分值，以及基于响应的经典测试理论（CTT）校准，其中LLM的回答被视为合成学生的响应以计算区分分数。我们的结果表明，直接预测与人类校准的区分存在较弱的一致性：表现最佳的模型仅达到0.152的斯皮尔曼相关系数。基于响应的CTT校准提供了更强但仍有限的信号，所有角色合成响应者池的斯皮尔曼相关系数达到0.241。这些发现突显了项目区分作为LLM基础心理测量评估中的一个开放挑战：当前的LLMs包含与区分相关的非随机信号，但尚未可靠地捕捉评估项目如何区分人类学生。
cs.CL / 26 / 2606.18717
Morpheus: A Morphology-Aware Neural Tokenizer and Word Embedder for Turkish
Morpheus：一种面向形态学的土耳其语神经分词器和词嵌入模型
Abstract
Turkish is agglutinative: meaning is carried by morphemes, yet the subword tokenizers that drive modern language models split words by corpus statistics, fragmenting semantically loaded suffixes and -- in the case of WordPiece and rule-based analyzers -- failing to decode their output back to the original text. This paper presents \textbf{Morpheus}, a neural morpheme-boundary model for Turkish that is at once a lossless, morphology-aware tokenizer and a word-embedding producer. A differentiable Poisson-binomial dynamic program turns per-character boundary probabilities into soft morpheme memberships during training and exact segments at inference, with no string normalization, so $\mathrm{decode}(\mathrm{encode}(w)) = w$ holds by construction. Because the model is neural, the same forward pass that tokenizes also emits a structured word embedding. Among reversible tokenizers -- the only ones valid for generation -- Morpheus attains the lowest bits-per-character ($1.425$), roughly doubles the gold morphological alignment of the subword family (MorphScore macro-F1 $0.61$ vs.\ ${\sim}0.32$), and uses ${\sim}19\%$ less GPU memory than 64K-vocabulary subword tokenizers. As an embedder, frozen Morpheus vectors lead on lexical retrieval (root-family MAP $0.85$) and same-root verification (ROC-AUC $1.00$), surpassing the multilingual retriever BGE-M3 and BERTurk; on context- and inflection-dependent tasks (NER, case/number probing) the heavier contextual encoders remain ahead -- a trade-off we attribute to Morpheus's root-centric geometry. Code: https://github.com/lonewolf-rd/TurkishMorpheus; model: https://huggingface.co/lonewolflab/Morpheus-TR-50K; interactive demo: https://huggingface.co/spaces/lonewolflab/morpheus-tr-demo.
Chinese Translation
土耳其语是粘着语：意义由语素承载，但驱动现代语言模型的子词分词器通过语料统计拆分单词，破坏了语义负载的后缀，并且在 WordPiece 和基于规则的分析器的情况下，无法将其输出解码回原始文本。本文提出了 extbf{Morpheus}，一种针对土耳其语的神经语素边界模型，它既是无损的、面向形态学的分词器，也是词嵌入生成器。一个可微分的泊松-二项动态规划在训练期间将每个字符的边界概率转化为软语素隶属度，在推理时生成精确的分段，无需字符串归一化，因此 $ ext{decode}( ext{encode}(w)) = w$ 是通过构造保持的。由于该模型是神经网络，进行分词的同一前向传播也会输出结构化的词嵌入。在可逆分词器中——唯一有效的生成器——Morpheus 达到了最低的每字符比特数（$1.425$），大约使子词家族的黄金形态对齐翻倍（MorphScore 宏 F1 $0.61$ 对比 ${ ilde{0.32}}$），并且比 64K 词汇的子词分词器使用了 ${ ilde{19 ext{ extperthousand}}}$ 更少的 GPU 内存。作为嵌入器，冻结的 Morpheus 向量在词汇检索（根家族 MAP $0.85$）和同根验证（ROC-AUC $1.00$）上领先，超越了多语言检索器 BGE-M3 和 BERTurk；在上下文和屈折依赖任务（命名实体识别、格/数探测）中，较重的上下文编码器仍然处于领先地位——我们将这种权衡归因于 Morpheus 的根中心几何结构。代码：https://github.com/lonewolf-rd/TurkishMorpheus；模型：https://huggingface.co/lonewolflab/Morpheus-TR-50K；交互式演示：https://huggingface.co/spaces/lonewolflab/morpheus-tr-demo。
cs.CL / 27 / 2606.18728
LegalWorld: A Life-Cycle Interactive Environment for Legal Agents
LegalWorld：法律代理人的生命周期互动环境
Abstract
Civil litigation is inherently a life-cycle process: what a lawyer drafts on day one constrains what unfolds at trial months later. Yet existing legal benchmarks evaluate isolated subtasks, and prior legal-agent simulators reinitialize each scenario from shared ground truth, leaving cross-stage causal dependencies unmodeled. We present LegalWorld, a life-cycle interactive environment that models Chinese civil litigation as a causally connected state chain of five stages (seven sub-scenarios), grounded in 75,309 paired Chinese civil judgments. We pair it with reusable infrastructure (local memory, global case memory, a Skill/Tool library) that keeps each dispute consistent across its full life cycle. Building on this environment, we construct LongJud-Bench to evaluate agent capability across all five connected stages. 18,992 ratings from 217 legal-background evaluators confirm that LegalWorld trajectories are procedurally faithful and role-consistent; and a capability-level cross-model evaluation reveals sharp divergences that aggregate scores cannot expose, with no single backbone leading across consultation, drafting, and courtroom advocacy. Detailed resources will be released publicly.
Chinese Translation
民事诉讼本质上是一个生命周期过程：律师在第一天起草的内容限制了几个月后审判中发生的事情。然而，现有的法律基准评估的是孤立的子任务，之前的法律代理模拟器在共享的真实基础上重新初始化每个场景，导致跨阶段的因果依赖关系未被建模。我们提出了LegalWorld，一个生命周期互动环境，将中国民事诉讼建模为一个由五个阶段（七个子场景）组成的因果连接状态链，基于75,309对中国民事判决的基础。我们将其与可重用的基础设施（本地记忆、全球案件记忆、技能/工具库）相结合，使每个争议在其整个生命周期内保持一致。基于这一环境，我们构建了LongJud-Bench，以评估代理人在所有五个连接阶段的能力。来自217名法律背景评估者的18,992条评分确认了LegalWorld轨迹在程序上是可信的，并且角色一致；而能力水平的跨模型评估揭示了聚合分数无法显示的显著差异，没有单一的基础模型在咨询、起草和法庭辩护中领先。详细资源将公开发布。
cs.CL / 28 / 2606.18767
Output Vector Editing for Memorization Mitigation in Large Language Models
大语言模型中输出向量编辑以减轻记忆化问题
Abstract
Large language models memorize and reproduce sequences from their training data, creating privacy, copyright, and security risks. Existing neuron-level mitigation methods equate editing with zeroing out neuron activations, but the activation only controls whether a neuron engages; the output vector is what writes to the residual stream and, through superposition, encodes multiple features. We propose output vector editing, a constrained-optimization weight edit that locates a small set of MLP neurons responsible for a memorized continuation and minimally modifies their output vectors to introduce a distractor in vocabulary space, redirecting their residual-stream contributions while leaving activations unchanged. Evaluating on four models from 360M to 7B parameters (SmolLM-360M, OLMo-1B, OLMo-7B, Llama2-7B), we center on OLMo-7B (whose open weights and pretraining corpus enable systematic mining) and mine 6831 memorized sequences, achieving up to 87.9% suppression. The 2.7$\times$ gap over zero ablation on the same located neurons shows the suppression comes from the output-vector edit, not localization alone. Four edit modes span a spectrum from aggressive suppression to minimal redirection; in ensemble they cover 96.5% of memorized sequences, while our recommended single-mode configuration reaches 81.5% with no catastrophic locality failures. We further identify a mechanistic boundary at ${\sim}14%$ of sequences unreachable by MLP-only editing; while these failures are not attention-driven overall, ablating the top contributing attention heads recovers 60--64% of them, with stronger recovery on continuations that copy tokens from the prefix, positioning attention as a complementary fallback rather than a primary mechanism. Edit mode ordering and the success-locality trade-off transfer across all four models, with success rates scaling with model size rather than family.
Chinese Translation
大语言模型会记忆并重现其训练数据中的序列，从而产生隐私、版权和安全风险。现有的神经元级减轻方法将编辑等同于将神经元激活归零，但激活仅控制神经元的参与；输出向量才是写入残差流的部分，并通过叠加编码多个特征。我们提出了输出向量编辑，这是一种约束优化的权重编辑方法，定位负责记忆化延续的一小部分多层感知器（MLP）神经元，并最小化地修改它们的输出向量，以在词汇空间中引入干扰项，从而重定向它们对残差流的贡献，同时保持激活不变。在对四个参数从360M到7B的模型（SmolLM-360M、OLMo-1B、OLMo-7B、Llama2-7B）进行评估时，我们重点关注OLMo-7B（其开放权重和预训练语料库使得系统挖掘成为可能），并挖掘了6831个记忆化序列，实现了高达87.9%的抑制。与同一定位神经元的零消融相比，2.7倍的差距表明抑制来自输出向量编辑，而非仅仅是定位。四种编辑模式涵盖了从激进抑制到最小重定向的范围；在集成中，它们覆盖了96.5%的记忆化序列，而我们推荐的单模式配置在没有灾难性局部失败的情况下达到了81.5%。我们进一步识别出一个机制边界，在约14%的序列中无法通过仅使用MLP进行编辑；虽然这些失败总体上不是由注意力驱动的，但消融贡献最大的注意力头可以恢复60-64%的失败，其中对从前缀复制标记的延续的恢复更强，表明注意力作为一种补充的后备机制，而非主要机制。编辑模式的排序和成功-局部性权衡在所有四个模型中都可以转移，成功率随着模型规模的增大而增加，而不是随着模型家族的变化。
cs.CL / 29 / 2606.18781
Lost in a Single Vector: Improving Long-Document Retrieval with Chunk Evidence Aggregation
迷失在单一向量中：通过块证据聚合改善长文档检索
Abstract
Dense retrieval ranks one query vector against one document vector. On long documents, this interface can fail when a short but decisive span is weakened during document encoding before ranking. We study this failure mode as document-side early compression and introduce the Evidence Dilution Index (EDI) to measure how far a document-level representation falls below the strongest chunk-level evidence within the same gold document. Guided by this view, we propose DICE (Document Inference via Chunk Evidence), a training-free document-side strategy that splits documents into chunks, encodes them independently with a frozen model, and aggregates them back into a single vector while preserving the standard one-query-one-document interface. On LongEmbed, DICE improves retrieval across four backbones, with the largest gains on slices beyond 4k tokens: for Dream, Passkey >4k rises from 30.0 to 90.0 and Needle >4k from 23.3 to 74.0. Across 12,779 filtered samples, DICE yields lower EDI than the single-vector baseline in 92.8% of cases. These results establish document-level encoding as a practical and underexplored lever for long-document retrieval.
Chinese Translation
密集检索将一个查询向量与一个文档向量进行排名。在长文档中，当一个短而决定性的片段在文档编码过程中被削弱时，这种接口可能会失效。我们研究这种失效模式，称之为文档侧的早期压缩，并引入证据稀释指数（Evidence Dilution Index, EDI）来衡量文档级表示与同一黄金文档中最强块级证据之间的差距。基于这一视角，我们提出了DICE（通过块证据进行文档推理），这是一种无训练的文档侧策略，它将文档拆分为块，使用冻结模型独立编码这些块，并在保留标准的单查询-单文档接口的同时将它们聚合回一个单一向量。在LongEmbed上，DICE在四个基础模型上改善了检索效果，尤其是在超过4k个标记的片段上取得了最大的提升：对于Dream，Passkey超过4k的得分从30.0上升到90.0，而Needle超过4k的得分从23.3上升到74.0。在12,779个过滤样本中，DICE在92.8%的情况下产生了低于单向量基线的EDI。这些结果确立了文档级编码作为长文档检索的一个实用且未被充分探索的杠杆。
cs.CL / 30 / 2606.18782
RedactionBench
RedactionBench
Abstract
Large Language Models are increasingly applied to sensitive domains that require redaction of personally identifiable information (PII). While redacting PII is a data cleaning prerequisite, existing benchmarks conflate extraction mechanics with privacy semantics. A public phone number is not equivalent to a phone number in a medical record. Whether information constitutes a violation depends heavily on who holds it, why, and in what context, fundamentally differentiating redaction from simple entity recognition. Grounded in contextual integrity, we introduce RedactionBench, a manually annotated benchmark comprising 200 diverse documents across 11 domains, mostly seeded from real-world sources. We also introduce R-Score, a novel character-level metric that treats semantically similar redactions equally and nullifies shallow formatting choices, such as varying masking styles for phone numbers. Evaluations across Named Entity Recognition models, entity extraction Small Language Models, and frontier models equipped with agentic tools demonstrate that contextual redaction remains an unsolved problem. A human evaluation with over 80 users on RedactionBench reveals a stark dichotomy in privacy perceptions. Annotators show consensus with target labels for mandatory redactions (89.4 percent) and safe text preservations (94.1 percent), but fail to agree on contextual redactions (47.7 percent). This variance demonstrates the subjective nature of contextual privacy and motivates R-Score, which decouples contextual ambiguity from strict precision. We compare 35 models across families and report their performance in redacting PII. Finally, we release RedactionBench to establish a baseline for future privacy-preserving systems, hoping to inspire efficient model design and standardized evaluations.
Chinese Translation
大型语言模型越来越多地应用于需要对个人可识别信息（PII）进行编辑的敏感领域。虽然编辑PII是数据清理的前提，但现有基准将提取机制与隐私语义混为一谈。公共电话号码与医疗记录中的电话号码并不等同。信息是否构成违规在很大程度上取决于持有者、持有的原因以及上下文，这根本上将编辑与简单的实体识别区分开来。基于上下文完整性，我们引入了RedactionBench，这是一个手动注释的基准，包含来自11个领域的200份多样化文档，主要来源于真实世界的资料。我们还引入了R-Score，这是一种新颖的字符级度量，平等对待语义相似的编辑，并消除了浅层格式选择的影响，例如电话号码的不同掩码样式。在命名实体识别模型、实体提取小型语言模型以及配备代理工具的前沿模型上的评估表明，上下文编辑仍然是一个未解决的问题。对RedactionBench进行的超过80名用户的人类评估揭示了隐私感知的明显二元性。注释者在强制编辑（89.4%）和安全文本保留（94.1%）的目标标签上达成了一致，但在上下文编辑（47.7%）上未能达成一致。这种差异展示了上下文隐私的主观性，并激励了R-Score的提出，该评分将上下文模糊性与严格精确度解耦。我们比较了35个模型的不同类别，并报告了它们在编辑PII方面的表现。最后，我们发布了RedactionBench，以建立未来隐私保护系统的基准，希望能激励高效的模型设计和标准化评估。
cs.CL / 31 / 2606.18797
Beyond Scalar Scores: Exploring LLM-based Metrics for Clinical Significance Evaluation in Radiology Reports
超越标量评分：探索基于大型语言模型的临床意义评估指标在放射学报告中的应用
Abstract
Reliable evaluation of generated radiology reports requires strict clinical accuracy, as omitted critical findings or mischaracterized radiographic observations can directly affect patient care. Existing metrics obscure this requirement by reducing report quality to a medically ungrounded scalar. Although Large Language Models (LLMs) possess rich medical knowledge, they likewise struggle to draw a reliable boundary between clinically significant errors and harmless variation. We study this boundary using ReEvalMed benchmark as testbed and evaluate metric-level clinical significance from detecting true clinical errors ("Discrimination") and tolerating insignificant variations ("Robustness"). Across 8 LLM evaluators under one-pass and two-pass settings, we identify a widespread discrimination bias: models effectively detect errors but also over-penalize harmless rephrasings. To mitigate this, we synthesize 4k report pairs and train lightweight interpretable metrics on Qwen3-8B and MedGemma-4B. Our trained metric sharpens the clinical significance boundary, surpassing 32B-scale medical LLMs and remaining competitive with proprietary models. Crucially, the more costly two-pass setting fails to consistently improve overall performance and mainly trades discrimination for robustness. These findings suggest one-pass trained metrics as the practical choice for cost-sensitive deployment, with two-pass inference reserved for settings where D-R balance is critical. We will release the dataset and metric.
Chinese Translation
对生成的放射学报告进行可靠评估需要严格的临床准确性，因为遗漏关键发现或错误描述放射学观察结果可能直接影响患者护理。现有指标通过将报告质量简化为缺乏医学基础的标量，模糊了这一要求。尽管大型语言模型（LLMs）拥有丰富的医学知识，但它们在区分临床显著错误和无害变异方面也面临挑战。我们使用ReEvalMed基准作为测试平台，研究这一边界，并从检测真实临床错误（“区分性”）和容忍无关变异（“鲁棒性”）的角度评估指标级别的临床意义。在单通道和双通道设置下的8个LLM评估器中，我们发现普遍存在区分性偏差：模型能够有效检测错误，但也过度惩罚无害的改述。为此，我们合成了4000对报告，并在Qwen3-8B和MedGemma-4B上训练了轻量级可解释指标。我们训练的指标明确了临床意义的边界，超越了32B规模的医学LLMs，并在与专有模型的竞争中保持优势。重要的是，成本更高的双通道设置未能持续改善整体性能，主要是在区分性与鲁棒性之间进行权衡。这些发现表明，单通道训练的指标是成本敏感部署的实用选择，而双通道推理则应保留用于区分-鲁棒性平衡至关重要的场合。我们将发布数据集和指标。
cs.CL / 32 / 2606.18831
Beyond Reward Engineering: A Data Recipe for Long-Context Reinforcement Learning
超越奖励工程：长上下文强化学习的数据配方
Abstract
Long-context reasoning is an essential capability for large language models, particularly when they are deployed as autonomous agents that must reason over lengthy trajectories. Reinforcement learning (RL) has recently emerged as a dominant paradigm for improving this ability, yet existing work largely focuses on reward engineering while diverse training data remains scarce. We revisit this problem from a data-centric perspective and show that a simple yet effective data recipe alone, paired with a minimal outcome-based GRPO setup, suffices to substantially improve long-context reasoning. Our recipe targets three complementary task families -- retrieval, multi-evidence synthesis, and reasoning -- for which we construct and curate eight datasets totaling ~14K examples. Experiments on three models (Qwen3-4B/8B/30B-A3B) yield average gains of +7.2/+3.2/+6.4 points across seven long-context benchmarks, surpassing prior RL training sets. We further demonstrate that these gains transfer to agentic tasks, where continuing RL training on an agent-tuned model with our data recipe improves GAIA by +4.8 and BrowseComp by +7.0 points. We will release our datasets to facilitate future research.
Chinese Translation
长上下文推理是大型语言模型的一项重要能力，尤其是在它们作为自主智能体被部署时，需要对冗长的轨迹进行推理。最近，强化学习（Reinforcement Learning, RL）已成为提升这一能力的主导范式，但现有研究主要集中在奖励工程上，而多样化的训练数据仍然稀缺。我们从数据中心的视角重新审视这一问题，展示了仅凭一个简单而有效的数据配方，结合最小的基于结果的 GRPO（Generalized Reinforcement Policy Optimization）设置，足以显著改善长上下文推理。我们的配方针对三类互补的任务——检索、多证据综合和推理——为此我们构建并整理了八个数据集，总计约 14,000 个示例。在三个模型（Qwen3-4B/8B/30B-A3B）上的实验显示，在七个长上下文基准测试中，平均提升分别为 +7.2/+3.2/+6.4 分，超越了之前的 RL 训练集。我们进一步证明，这些提升可以转移到智能体任务上，在一个经过智能体调优的模型上，使用我们的数据配方继续进行 RL 训练，使 GAIA 提升了 +4.8 分，BrowseComp 提升了 +7.0 分。我们将发布我们的数据集，以促进未来的研究。
cs.CL / 33 / 2606.18850
ScholarSum: Student-Teacher Abstractive Summarization via Knowledge Graph Reasoning and Reflective Refinement
ScholarSum：通过知识图谱推理和反思性精炼实现的学生-教师抽象摘要
Abstract
Abstractive summarization plays a crucial role in enabling efficient understanding of scientific literature, yet it inherently demands both linguistic fluency and factual faithfulness. Existing approaches often fail to reconcile these two requirements. Extractive methods rely on rigid sentence splicing that disrupts macro-level logical coherence, while large language model (LLM)-based generative approaches, despite mastering linguistic fluency, exhibit limited factual consistency. In this work, we propose ScholarSum, a hierarchical reflective graph-based framework that emulates a student-teacher writing process for fluent and faithful scientific summarization. ScholarSum first organizes the document into a hierarchical knowledge graph by segmenting it into semantically coherent units, whose multi-layered community structure captures global logic and macro-level themes. Guided by this global structure, the student generates an initial draft, which is subsequently refined through fine-grained evidence retrieval. To ensure factual consistency, a teacher-like reviewer then iteratively examines the draft, identifies unsupported content, and prompts targeted re-retrieval and rewriting until the summary meets rigorous quality standards. Extensive experiments demonstrate that ScholarSum significantly outperforms previous baselines in terms of both completeness and faithfulness. Our code is available at https://github.com/Xiaoyu-Tao/ScholarSum.
Chinese Translation
抽象摘要在促进科学文献的高效理解中发挥着至关重要的作用，但它本质上要求语言流畅性和事实准确性两者兼具。现有的方法往往无法调和这两个要求。提取式方法依赖于僵化的句子拼接，破坏了宏观层面的逻辑连贯性，而基于大型语言模型（LLM）的生成方法尽管掌握了语言流畅性，但在事实一致性方面表现有限。在本研究中，我们提出了ScholarSum，一个分层反思性图谱框架，模拟学生-教师写作过程，以实现流畅且准确的科学摘要。ScholarSum首先通过将文档分割成语义连贯的单元，将其组织成一个分层知识图谱，其多层次的社区结构捕捉了全局逻辑和宏观主题。在这一全局结构的指导下，学生生成初稿，随后通过细粒度的证据检索进行精炼。为了确保事实一致性，类似教师的审阅者随后迭代性地检查初稿，识别不支持的内容，并促使针对性的重新检索和重写，直到摘要达到严格的质量标准。大量实验表明，ScholarSum在完整性和准确性方面显著优于以往的基线方法。我们的代码可在 https://github.com/Xiaoyu-Tao/ScholarSum 获取。
cs.CL / 34 / 2606.18852
Aligning Implied Statements for Implicit Hate Speech Generalizability with Context-Bounded Semi-hard Negative Mining
通过上下文限制的半硬负样本挖掘对隐含仇恨言论可推广性的隐含陈述进行对齐
Abstract
Classifying implicit hate speech remains a challenge, as intent is often masked through insinuation and context rather than explicit slurs. Prior supervised contrastive approaches improve in-domain detection but can overfit surface cues and struggle to transfer across datasets. We propose ImpSH, a triplet-based framework that aligns posts with implied statements when available and uses context-bounded semi-hard negatives to focus learning on near confusions. We also examine AugSH, which forms positives via data augmentation. In controlled evaluations on IHC, SBIC, and DynaHate with BERT and HateBERT, ImpSH is a viable alternative to standard supervised contrastive baselines and often improves cross-domain performance under matched preprocessing and tuning budgets. Representation analysis using alignment and uniformity indicates tighter positive pairs with balanced global spread, and qualitative nearest-neighbor case studies illustrate typical false negatives under domain shift. These results demonstrate that aligning posts with their implied statements via context-bounded mining provides a more stable, bijective-like mapping to related insinuations, overcoming the volatility inherent in traditional clustering-based representation learning.
Chinese Translation
对隐含仇恨言论的分类仍然是一项挑战，因为意图通常通过暗示和上下文而非明确的侮辱性言辞来掩盖。以往的监督对比方法在领域内检测上有所改善，但可能会过拟合表面线索，并在跨数据集转移时遇到困难。我们提出了ImpSH，这是一种基于三元组的框架，当隐含陈述可用时对齐帖子，并使用上下文限制的半硬负样本来集中学习近似混淆。我们还研究了AugSH，它通过数据增强形成正样本。在对IHC、SBIC和DynaHate进行的控制评估中，使用BERT和HateBERT，ImpSH是标准监督对比基线的可行替代方案，并且在匹配的预处理和调优预算下，通常提高了跨领域性能。使用对齐和均匀性进行的表示分析表明，正样本对更紧密，全球分布更平衡，定性最近邻案例研究则展示了在领域转移下的典型假阴性。这些结果表明，通过上下文限制的挖掘将帖子与其隐含陈述对齐，提供了一个更稳定的双射式映射到相关暗示，克服了传统基于聚类的表示学习中固有的波动性。
cs.CL / 35 / 2606.18856
Approximate Structured Diffusion for Sequence Labelling
序列标注的近似结构扩散
Abstract
Sequence labelling, a core task of Natural Language Processing (NLP), consists in assigning each token of an input sentence a label. From a Machine Learning point of view, sequence labelling is often cast as a Linear-Chain Conditional Random Field (CRF) parametrised by a neural network. While this approach gives good empirical results, CRFs assume a finite decision span (eg label bigrams) which can limit their expressivity and hurt performance when long-range dependencies are required. We show we can leverage diffusion to train a CRF conditioned on an entire label sequence, with the caveat that the condition is on a noisy version of labels. We show experimentally that this method, in conjunction with approximate CRF inference, improves label accuracy with a 16.5% error reduction for POS-tagging.
Chinese Translation
序列标注是自然语言处理（NLP）的核心任务，旨在为输入句子的每个词元分配一个标签。从机器学习的角度来看，序列标注通常被视为一个由神经网络参数化的线性链条件随机场（CRF）。尽管这种方法在经验上取得了良好的结果，但CRF假设有限的决策范围（例如标签二元组），这可能限制其表达能力，并在需要长距离依赖时影响性能。我们展示了如何利用扩散来训练一个以整个标签序列为条件的CRF，但条件是基于标签的噪声版本。实验结果表明，这种方法结合近似CRF推理，能够提高标签准确性，在词性标注（POS-tagging）中实现了16.5%的错误减少。
cs.CL / 36 / 2606.18875
Efficient Financial Language Understanding via Distillation with Synthetic Data
通过合成数据蒸馏实现高效的金融语言理解
Abstract
Large instruction-following models are powerful but costly to deploy, particularly in finance, where labelled data are limited by confidentiality and expert annotation cost. We present an efficient framework for financial sentiment analysis through distillation with synthetic data, transferring knowledge from a large instruction-tuned teacher to compact student models. The framework is designed for low-resource conditions, where a small set of real examples are collected and labelled by hand. The framework then clusters the examples and uses the clusters to select seeds for generating synthetic examples via structured few-shot prompting. Experiments show that clustering-based seed selection yields more representative synthetic data than random sampling, enabling compact models to achieve strong performance with minimal supervision. Notably, on a more complex and noisy text domain, the compact model trained on the complete synthetic-seed corpus even outperforms the teacher model, while remaining competitive on formal text. The framework provides a practical route toward resource-efficient domain adaptation in financial NLP with minimal human labelling effort.
Chinese Translation
大型指令跟随模型功能强大，但在金融领域的部署成本高昂，尤其是由于保密性和专家标注成本限制了标注数据的获取。我们提出了一种高效的金融情感分析框架，通过合成数据的蒸馏，将知识从大型指令调优的教师模型转移到紧凑的学生模型。该框架旨在低资源条件下工作，其中收集并手动标注了一小部分真实示例。然后，框架对这些示例进行聚类，并利用聚类结果选择种子，以通过结构化的少量示例提示生成合成示例。实验表明，基于聚类的种子选择比随机抽样生成更具代表性的合成数据，使得紧凑模型在最小监督下实现强劲的性能。值得注意的是，在一个更复杂和嘈杂的文本领域，基于完整合成种子语料库训练的紧凑模型甚至超越了教师模型，同时在正式文本上保持竞争力。该框架为在金融自然语言处理领域实现资源高效的领域适应提供了一条实用的途径，且对人力标注的需求最小化。
cs.CL / 37 / 2606.18889
Improving Medical Communication using Rubric-Guided Counterfactual Recommendations
通过评分标准引导的反事实推荐改善医疗沟通
Abstract
Text-based telemedicine increasingly relies on lightweight patient feedback, however, such feedback primarily reflects perceived communication quality rather than medical accuracy. We introduce an LM-guided counterfactual recommendation pipeline that discovers and refines interpretable communication features such as tone, personalization, actionability and completeness in addressing patient concerns, without interfering with the medical content. These features are used together with patient-doctor interaction metadata to estimate positive feedback. At inference time, the system searches over low-cost ordinal feature changes and recommends minimal communication changes predicted to increase the probability of positive feedback, while independent auditor models test whether these gains generalize beyond the selection model. Across interactions, recommendations yield a mean +6.41% gain in predicted positive feedback probability under independent auditors, and are non-negative for 93.31% of recommendations. These results suggest that small, interpretable communication changes can capture most predicted gains while preserving the doctor's control over medical reasoning and final wording.
Chinese Translation
基于文本的远程医疗日益依赖轻量级的患者反馈，然而，这种反馈主要反映的是感知的沟通质量而非医疗准确性。我们提出了一种由语言模型（LM）引导的反事实推荐管道，该管道发现并优化可解释的沟通特征，如语气、个性化、可操作性和解决患者关切的完整性，而不干扰医疗内容。这些特征与患者-医生互动的元数据结合使用，以估计积极反馈。在推理时，系统在低成本的有序特征变化中进行搜索，并推荐预测能够提高积极反馈概率的最小沟通变化，同时独立审计模型测试这些收益是否超出选择模型的普遍性。在各项互动中，推荐在独立审计者下产生了平均 +6.41% 的预测积极反馈概率增益，并且93.31%的推荐结果为非负。这些结果表明，小规模、可解释的沟通变化能够捕捉到大部分预测增益，同时保持医生对医疗推理和最终措辞的控制。
cs.CL / 38 / 2606.18893
Learning Robust Pair Confidence for Multimodal Emotion-Cause Pair Extraction
学习鲁棒的配对置信度用于多模态情感-原因配对提取
Abstract
Multimodal emotion-cause pair extraction (MECPE) requires reliable pair confidence over candidate pairs. Existing pair scorers commonly use pair-level cross entropy over valid candidates, which treats links mostly independently. This leaves the relative confidence geometry among competing causes under-constrained, allowing gold pairs to stay close to hard negatives or rely on incidental non-gold context. We study this vulnerability as pair-confidence brittleness and propose RPCL (Robust Pair Confidence Learning), a training-only framework for pair-confidence learning. RPCL encourages pair confidence to be both discriminative and stable: gold pairs are separated from row-wise hard negatives through a confidence-difference margin constraint, and clean pair predictions are aligned with predictions from a corrupted view where non-gold contextual utterance representations are partially corrupted. The original clean pair scorer and decoding pipeline are used unchanged at inference time. On ECF, MECAD, and MEC4, RPCL improves the three-seed mean Pair F1 over a matched base model by 2.58 to 2.83 percentage points in the full text-audio-video setting, and improves mean Pair AUPRC on all three datasets. Diagnostic analysis further shows larger gold-negative confidence gaps and lower margin-violation severity. These results suggest that explicitly shaping pair confidence is an effective training strategy for MECPE.
Chinese Translation
多模态情感-原因配对提取（MECPE）需要对候选配对进行可靠的置信度评估。现有的配对评分器通常使用有效候选的配对级交叉熵，这种方法大多独立地对待链接。这使得竞争原因之间的相对置信度几何关系受到约束，导致金标准配对可能与难负样本过于接近，或依赖于偶然的非金标准上下文。我们将这种脆弱性研究为配对置信度的脆弱性，并提出RPCL（鲁棒配对置信度学习），这是一个仅用于训练的配对置信度学习框架。RPCL鼓励配对置信度既具有区分性又具有稳定性：金标准配对通过置信度差异边际约束与行级难负样本分离，且干净的配对预测与来自一个被部分破坏的视图的预测对齐，其中非金标准上下文发言表示被部分破坏。在推理时，原始的干净配对评分器和解码管道保持不变。在ECF、MECAD和MEC4数据集上，RPCL在全文本-音频-视频设置中，相较于匹配的基础模型，提高了三种种子均值配对F1分数2.58到2.83个百分点，并在所有三个数据集上提高了均值配对AUPRC。诊断分析进一步显示出更大的金标准-负样本置信度差距和较低的边际违反严重性。这些结果表明，明确塑造配对置信度是一种有效的MECPE训练策略。
cs.CL / 39 / 2606.18902
SAGE: Stochastic Prompt Optimization via Agent-Guided Exploration
SAGE：通过代理引导探索的随机提示优化
Abstract
Context engineering has emerged as a primary lever for improving AI systems without parameter updates. Recent work showing that textual gradients do not function as real gradients motivates treating automatic prompt optimization (APO) as black-box search. We introduce SPO (Stochastic Prompt Optimization), a framework for stochastic search over prompt space, and compare three strategies of increasing sophistication: error-informed random search, a genetic algorithm with evolutionary operators, and SAGE (SPO via Agent-Guided Exploration), a multi-agent pipeline with diagnostic code execution. Across three benchmarks, no single strategy dominates; effectiveness depends on the interaction of landscape structure with error type. We further deploy SAGE on a mental-health chatbot under a continuous optimization paradigm, where it compounds eight cycles of individually-noisy A/B tests into a statistically robust gain in next-day retention. We argue that coupling qualitative diagnosis with quantitative validation is what makes agentic optimization effective for open-ended task-oriented dialogue.
Chinese Translation
上下文工程已成为在不更新参数的情况下改善人工智能系统的主要手段。近期的研究表明，文本梯度并不能作为真实梯度，这促使我们将自动提示优化（APO）视为黑箱搜索。我们提出了SPO（随机提示优化），这是一个在提示空间上进行随机搜索的框架，并比较了三种逐步复杂化的策略：基于错误信息的随机搜索、带有进化操作的遗传算法，以及SAGE（通过代理引导探索的SPO），一个具有诊断代码执行的多代理管道。在三个基准测试中，没有单一策略占主导地位；其有效性依赖于地形结构与错误类型的相互作用。我们进一步在一个心理健康聊天机器人上部署SAGE，采用持续优化范式，将八轮各自带有噪声的A/B测试合并为次日留存率的统计显著提升。我们认为，将定性诊断与定量验证相结合，是使代理优化在开放式任务导向对话中有效的关键所在。
cs.CL / 40 / 2606.18922
As Easy as Rocket Science: Assessing the Ability of Large Language Models to Interpret Negation in Figurative Language
像火箭科学一样简单：评估大型语言模型理解修辞语言中的否定能力
Abstract
Figurative language and negation are two areas that challenge current language models, however, both are widely used throughout written and spoken language. Large language models (LLMs) are also widely used in everyday contexts where they cannot necessarily be tuned for a specific dataset. It is therefore essential to understand the ability of LLMs to correctly interpret text that includes both negation and figurative language. To investigate this, we develop a set of new annotations to an existing dataset of figurative language, and test a range of language models on the dataset. We find that the combination of negation and figurativeness can present a particular challenge, and that performance overall and across different negation types is particularly dependent on the prompt style used.
Chinese Translation
修辞语言和否定是当前语言模型面临的两个挑战领域，然而这两者在书面和口头语言中都被广泛使用。大型语言模型（LLMs）在日常场景中也被广泛应用，但它们不一定能够针对特定数据集进行调优。因此，了解LLMs正确解读包含否定和修辞语言的文本的能力至关重要。为此，我们对现有的修辞语言数据集开发了一套新的注释，并在该数据集上测试了一系列语言模型。我们发现，否定与修辞的结合可能会带来特别的挑战，并且整体表现以及不同否定类型的表现特别依赖于所使用的提示风格。
cs.CL / 41 / 2606.18946
SenFlow: Inter-Sentence Flow Modeling for AI-Generated Text Detection in Hybrid Documents
SenFlow：用于混合文档中AI生成文本检测的句间流建模
Abstract
Sentence-level AI-generated text detection (S-AGTD) for hybrid documents, where humans and LLMs co-author one text, faces two gaps: existing methods classify each sentence in isolation, discarding inter-sentence dependencies, and existing benchmarks omit the newest generation of generators. We construct MOSAIC, a benchmark of 16,000 hybrid documents over PubMed and XSum, generated by DeepSeek-V3.2 and Kimi K2 under stringent quality controls including a perplexity-consistency filter absent from prior benchmarks. We recast S-AGTD as structured prediction over the document sentence sequence and instantiate it as SenFlow, integrating graph-based inter-sentence propagation with linear-chain CRF decoding in a single document-level pass over a sentence graph. SenFlow reaches state-of-the-art performance on MOSAIC, with a +4.15 pp average Macro-F1 margin on cross-domain transfer, the hardest of three protocols of increasing difficulty. We further find that even after the perplexity filter equalizes overt cues, AI insertions retain a generator-dependent sentence-length gap that sentence-level detectors still exploit. Code and data: https://github.com/luojingkun22/SenFlow
Chinese Translation
针对混合文档的句子级AI生成文本检测（S-AGTD），即人类与大型语言模型（LLMs）共同创作一篇文本，面临两个缺口：现有方法孤立地对每个句子进行分类，忽视了句间依赖关系，而现有基准测试则遗漏了最新一代生成器。我们构建了MOSAIC，这是一个包含16,000个混合文档的基准，涵盖PubMed和XSum，由DeepSeek-V3.2和Kimi K2在严格的质量控制下生成，包括之前基准中缺失的困惑度一致性过滤器。我们将S-AGTD重新表述为对文档句子序列的结构化预测，并将其实例化为SenFlow，结合基于图的句间传播与线性链条件随机场（CRF）解码，在句子图上进行单次文档级处理。SenFlow在MOSAIC上达到了最先进的性能，在跨领域迁移中，平均宏观F1值提升了4.15个百分点，这是三种逐渐增加难度的协议中最具挑战性的。我们进一步发现，即使在困惑度过滤器平衡了明显线索后，AI插入仍保留了一个依赖于生成器的句子长度差距，句子级检测器仍然可以利用这一点。代码和数据可在：https://github.com/luojingkun22/SenFlow
cs.CL / 42 / 2606.18954
GraphPO: Graph-based Policy Optimization for Reasoning Models
GraphPO：基于图的策略优化用于推理模型
Abstract
Reinforcement Learning with Verifiable Rewards (RLVR) has become a standard paradigm for enhancing the capability of large reasoning models. RLVR typically samples responses independently and optimizes the policy using from final answers. This paradigm has two limitations. First, independently responses often contain similar intermediate reasoning steps, causing redundant exploration and wasted computation. Second, sparse final-answer rewards make it hard to identify useful steps. Tree-based methods partly address this problem by sharing prefixes and comparing branches from the same prefix to provide fine-grained signals. However, tree branches are still expanded independently. When different branches reach similar reasoning states, they cannot share information and repeat similar exploration. Moreover, tree-based methods ignore such dispersion and only perform local comparisons within separate branches, which can lead to higher variance in advantage estimation. To address this challenge, we propose GraphPO (Graph-based Policy Optimization), a novel RL framework that represents rollouts as a directed acyclic graph, with reasoning steps as edges and semantic states summarized from the reasoning paths as nodes. GraphPO merges semantically equivalent reasoning paths into equivalence classes, allowing them to share suffixes and reallocating budget away from redundant expansions to diverse exploration. Furthermore, we assign efficiency advantages to incoming edges and correctness advantages to outgoing edges, thereby improving inference efficiency while deriving process supervision from outcome. Theory shows that GraphPO reduces advantage-estimation variance and enhances reasoning efficiency. Experiments on three LLMs across reasoning and agentic search benchmarks show that GraphPO consistently outperforms chain- and tree-based baselines with the same token budgets or response budgets.
Chinese Translation
可验证奖励的强化学习（RLVR）已成为增强大型推理模型能力的标准范式。RLVR 通常独立采样响应，并利用最终答案优化策略。该范式存在两个局限性。首先，独立的响应往往包含相似的中间推理步骤，导致冗余探索和计算浪费。其次，稀疏的最终答案奖励使得识别有用步骤变得困难。基于树的方法部分解决了这个问题，通过共享前缀并比较来自同一前缀的分支来提供细粒度信号。然而，树的分支仍然是独立扩展的。当不同的分支达到相似的推理状态时，它们无法共享信息，重复相似的探索。此外，基于树的方法忽视了这种分散现象，仅在各自的分支内进行局部比较，这可能导致优势估计的方差增大。为了解决这一挑战，我们提出了 GraphPO（基于图的策略优化），这是一种新颖的强化学习框架，将回滚表示为有向无环图，其中推理步骤作为边，推理路径总结的语义状态作为节点。GraphPO 将语义上等价的推理路径合并为等价类，使它们能够共享后缀，并将预算从冗余扩展重新分配到多样化探索。此外，我们将效率优势分配给入边，将正确性优势分配给出边，从而在从结果中推导过程监督的同时提高推理效率。理论表明，GraphPO 减少了优势估计的方差，并增强了推理效率。在三个大型语言模型（LLMs）上的推理和智能搜索基准实验表明，GraphPO 在相同的令牌预算或响应预算下始终优于基于链和树的基线。
cs.CL / 43 / 2606.18986
Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering
超越标记化：用于时间序列问答的直接时间步嵌入与对比对齐
Abstract
Recent advances in large language models (LLMs) have given rise to time-series question answering (TSQA), which formulates time-series analysis as natural-language question answering. However, directly feeding raw numerical series into LLMs suffers from a tokenization bottleneck: Byte Pair Encoding fragments continuous values into unstable tokens whose embeddings lack meaningful metric structure, resulting in the loss of magnitude, scale, and trend information. Prior methods use patch-based encoders that split the series into fixed windows, locking in one granularity that breaks patterns and hides exact timesteps, through a separate module that rarely transfers across datasets with different lengths or sampling rates. To address this challenge, we propose CADE (Contrastive Alignment with Direct Embedding), a novel framework for TSQA built upon two key components: direct timestep embedding and semantic alignment. The proposed framework maps each timestep directly into the LLM embedding space through a point-wise linear encoder and MLP projector, preserving exact index-level access while eliminating the need for patching and padding. To further bridge the semantic gap between time-series and language representations, we introduce a novel one-directional supervised contrastive loss that aligns time-series embeddings with frozen class-name text anchors. Experimental results on the public Time-MQA benchmark demonstrate that our framework consistently improves performance across six TSQA tasks, outperforming both open-source and proprietary LLM baselines.
Chinese Translation
最近大规模语言模型（LLMs）的进展催生了时间序列问答（TSQA），将时间序列分析表述为自然语言问答。然而，直接将原始数值序列输入LLMs面临标记化瓶颈：字节对编码（Byte Pair Encoding）将连续值分割为不稳定的标记，其嵌入缺乏有意义的度量结构，导致幅度、尺度和趋势信息的丢失。之前的方法使用基于补丁的编码器将序列分割为固定窗口，锁定一种粒度，这打破了模式并隐藏了确切的时间步，通过一个很少在不同长度或采样率的数据集间迁移的单独模块。为了解决这一挑战，我们提出了CADE（对比对齐与直接嵌入），这是一个基于两个关键组件的新型TSQA框架：直接时间步嵌入和语义对齐。该框架通过逐点线性编码器和多层感知器（MLP）投影器将每个时间步直接映射到LLM嵌入空间，保留确切的索引级访问，同时消除补丁和填充的需求。为了进一步弥合时间序列与语言表示之间的语义差距，我们引入了一种新颖的单向监督对比损失，将时间序列嵌入与冻结的类名文本锚点对齐。在公共的Time-MQA基准上的实验结果表明，我们的框架在六个TSQA任务中始终提高了性能，超越了开源和专有LLM基准。
cs.CL / 44 / 2606.18989
G-IdiomAlign: A Gloss-Pivoted Benchmark for Cross-Lingual Idiom Alignment
G-IdiomAlign：一种基于释义的跨语言习语对齐基准
Abstract
Idioms are difficult to transfer across languages due to their non-compositionality and weak surface-form grounding, making literal mappings unreliable. We present G-IdiomAlign, a gloss-pivoted benchmark where each idiom is anchored by an English gloss from Wiktionary. We further construct a high-confidence reference alignment set for reproducible evaluation. G-IdiomAlign supports two protocols: (1) a controlled Multiple-Choice Idiom Equivalence with typed distractors for error attribution; and (2) a Gloss-Contrastive Generation contrasting No-gloss and With-gloss inputs to isolate the effect of an explicit semantic pivot. Across diverse LLMs, a bias to literal translation is a dominant failure mode, especially when the target is a low-resource language. Glosses consistently improve Gloss-Contrastive Generation under an embedding-based semantic proxy, but performance remains modest, indicating substantial headroom in the open output space. Subsequent analysis on Qwen3-8B further suggests that cross-condition differences are concentrated more in attention heads than in layers, while better With-gloss generations coincide with stronger gloss anchoring.
Chinese Translation
习语由于其非组合性和表面形式的弱基础，难以在语言之间转移，使得字面映射不可靠。我们提出了G-IdiomAlign，这是一种基于释义的基准，每个习语都由Wiktionary中的英文释义作为锚点。我们进一步构建了一个高置信度的参考对齐集，以便进行可重复的评估。G-IdiomAlign支持两种协议：（1）一种控制的多项选择习语等价测试，带有类型化的干扰项以便进行错误归因；（2）一种释义对比生成，比较无释义和有释义输入，以隔离显式语义枢轴的影响。在多种大型语言模型（LLMs）中，字面翻译的偏向是主要的失败模式，尤其是在目标语言为低资源语言时。释义在基于嵌入的语义代理下，始终改善了释义对比生成的效果，但性能仍然适中，表明开放输出空间中仍有显著的提升空间。对Qwen3-8B的后续分析进一步表明，跨条件差异更多集中在注意力头而非层次之间，而更好的有释义生成与更强的释义锚定相吻合。
cs.CL / 45 / 2606.19002
Enhancing Multilingual Reasoning via Steerable Model Merging
通过可调模型合并增强多语言推理
Abstract
Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. It has achieved promising generalization in multilingual reasoning tasks by aligning feature spaces of different models. However, the merged single model often fails to address the conflicts between source models, leading to suboptimal performance. In other words, the one-size-fits-all merging strategy may not align with the characteristics of different inputs which may require prioritizing certain models over others. To this end, we propose a Steerable Model Merging (ST-Merge) framework to modulate the contribution of each source model. To realize this idea, we introduce a gated cross-attention mechanism to weight or filter the two attended source models in an adaptive manner. Extensive experiments demonstrate that ST-Merge consistently outperforms multiple strong baselines on four multilingual reasoning benchmarks across 21 different languages.
Chinese Translation
模型合并是一种有效的技术，用于组合多语言模型和推理模型的能力。通过对不同模型的特征空间进行对齐，它在多语言推理任务中取得了良好的泛化效果。然而，合并后的单一模型往往无法解决源模型之间的冲突，从而导致性能不佳。换句话说，统一的合并策略可能无法与不同输入的特征相匹配，而这些输入可能需要优先考虑某些模型。为此，我们提出了一种可调模型合并（Steerable Model Merging, ST-Merge）框架，以调节每个源模型的贡献。为了实现这一想法，我们引入了一种门控交叉注意机制，以自适应的方式对两个被关注的源模型进行加权或过滤。大量实验表明，ST-Merge在21种不同语言的四个多语言推理基准上始终优于多个强基线。
cs.CL / 46 / 2606.19005
Sumi: Open Uniform Diffusion Language Model from Scratch
Sumi：从零开始的开放统一扩散语言模型
Abstract
Diffusion models have become a promising alternative to autoregressive models. Among these, uniform diffusion language models (UDLMs) permit any token to be updated at any step, in principle enabling more flexible generation. However, no UDLM has yet been pretrained from scratch at both large parameter scale and large token budget. Both autoregressive modeling and masked diffusion modeling already have capable models at scale that the community can study and build on; uniform diffusion has none. A scratch-pretrained UDLM at scale would provide a clean reference point for studying scaling behavior, generation dynamics, controllability, and trade-offs against established autoregressive and masked diffusion models. To this end, we introduce Sumi ("ink" in Japanese), a fully open 7B uniform diffusion language model pretrained from scratch on 1.5T tokens. Sumi performs competitively with autoregressive models trained at comparable token budgets on knowledge, reasoning, and coding benchmarks, while under-performing on commonsense benchmarks, where our education-heavy data mixture is a likely contributor. We release our model weights, checkpoints, and full training recipe, including a complete specification of the data mixture over publicly available corpora. We hope this release enables the community to study native uniform diffusion at scale and catalyzes work on its as-yet poorly understood aspects.
Chinese Translation
扩散模型已成为自回归模型的有前景的替代方案。在这些模型中，统一扩散语言模型（UDLM）允许在任何步骤更新任何标记，原则上实现了更灵活的生成。然而，目前尚未有任何UDLM在大参数规模和大标记预算下从零开始进行预训练。自回归建模和掩蔽扩散建模已经有了可供社区研究和构建的强大模型，而统一扩散则没有。一个在大规模下从零开始预训练的UDLM将为研究扩展行为、生成动态、可控性以及与已建立的自回归和掩蔽扩散模型之间的权衡提供一个清晰的参考点。为此，我们介绍了Sumi（在日语中意为“墨水”），这是一个完全开放的7B统一扩散语言模型，基于1.5T标记从零开始进行预训练。Sumi在知识、推理和编码基准测试中与在相似标记预算下训练的自回归模型表现竞争力，但在常识基准测试中表现不佳，我们的教育重数据混合可能是一个重要因素。我们发布了模型权重、检查点和完整的训练配方，包括对公开可用语料库的数据混合的完整规范。我们希望这一发布能够使社区能够在大规模下研究原生统一扩散，并促进对其尚未充分理解的方面的研究。
cs.CL / 47 / 2606.19111
Leadership as Coordination Control: Behavioral Signatures and the Recovery-Advantage Boundary in Multi-Agent LLM Teams
领导作为协调控制：多智能体LLM团队中的行为特征与恢复优势边界
Abstract
Team science holds that leadership is contingent: it helps only under specific conditions, and capable, autonomous teams may need none at all. We ask the analogous question for multi-agent LLM teams: under what measurable conditions does process-level coordination control add value, and do those conditions match what team science predicts? We use behavioral signatures (majority lock-in, exploration, recovery from an incorrect round-0 consensus) and per-action ablations, clean because each controller is an explicit action set, not a monolithic prompt. We operationalize three classical leadership styles (transactional, transformational, situational) as controllers over a shared action vocabulary (explore, revise, accept, synthesize). A matched controller with the same actions but an arbitrary rule recovers no better than majority voting, so the theory-derived rule, not the vocabulary, does the work. Across four task regimes and three open-weight model families, no controller dominates by accuracy, as the contingency view predicts: transactional control matches a shared round-0 vote on all 12 (model, regime) combinations to within 1.3pp, and gains appear only on the one combination where the round-0 majority is unreliable (llama-4-scout social; situational +8pp over flat). A recovery-advantage account, tested with four boundary probes, says a controller beats plain interaction only where the round-0 majority is unreliable, the task is recoverable, and undirected interaction does not already repair it. These regions map onto contingency theory (leadership substitutes, path-goal redundancy, the situational readiness gap), so a largely null accuracy result is what the theory predicts, not a failure of the controllers. We read process-level coordination control as a contingency to be measured and theory-mapped, not a leaderboard to be topped.
Chinese Translation
团队科学认为领导是有条件的：它仅在特定条件下有效，而有能力的自主团队可能根本不需要领导。我们对多智能体LLM团队提出类似的问题：在什么可测量的条件下，过程层面的协调控制能够增加价值，这些条件是否与团队科学的预测相符？我们使用行为特征（多数锁定、探索、从错误的第0轮共识中恢复）和每个动作的消融实验，因每个控制器都是一个明确的动作集，而不是一个单一的提示。我们将三种经典的领导风格（交易型、变革型、情境型）作为对共享动作词汇（探索、修订、接受、综合）的控制器进行操作。一个匹配的控制器具有相同的动作但采用任意规则，其恢复效果不比多数投票更好，因此，理论推导的规则，而非词汇，才是关键。在四个任务模式和三个开放权重模型系列中，没有任何控制器在准确性上占据主导地位，正如应急视角所预测的那样：交易控制在所有12个（模型，模式）组合中与共享的第0轮投票相匹配，误差在1.3个百分点以内，且只有在第0轮多数不可靠的组合中（llama-4-scout社交；情境型比平坦型高出8个百分点）才出现收益。通过四个边界探测器测试的恢复优势理论表明，控制器仅在第0轮多数不可靠、任务可恢复且无导向交互尚未修复的情况下优于普通交互。这些区域与应急理论（领导替代、路径目标冗余、情境准备差距）相对应，因此，基本上无效的准确性结果是理论的预测，而不是控制器的失败。我们将过程层面的协调控制视为一种需被测量和理论映射的应急，而非一个需要超越的排行榜。
cs.CL / 48 / 2606.19170
Dango: A Strictly L1-Only Large Language Model for Studying Second Language Acquisition
Dango：一个严格的仅L1的大型语言模型用于研究第二语言习得
Abstract
We introduce Dango, a 1.8B-parameter large language model designed for controlled studies of L1-to-L2 (Japanese-to-English) transfer in second language acquisition (SLA). While previous studies have explored SLA in language models, they have predominantly relied on smaller or non-decoder models, limiting their ability to generate open-ended text and reducing their suitability as practical L2 simulators. We identify a key challenge when scaling models to this size: L2 contamination within the "monolingual" pretraining corpus used for L1 acquisition. To address this, we propose a filtering method to reduce premature exposure to English while preserving realistic, minimal exposure. We then fine-tune the model on LLM-generated L2-learning lessons to simulate the L2 acquisition process. Our evaluations confirm that Dango develops human-like L2 production patterns, outperforming both unfiltered and standard multilingual baselines. We release the model, data, and code to facilitate reproducible computational SLA research and learner-facing applications.
Chinese Translation
我们介绍了Dango，一个拥有18亿参数的大型语言模型，旨在对L1到L2（从日语到英语）转移在第二语言习得（SLA）中的控制研究进行探讨。虽然之前的研究已经探讨了语言模型中的SLA，但它们主要依赖于较小或非解码器模型，这限制了它们生成开放式文本的能力，并降低了作为实际L2模拟器的适用性。我们识别出在将模型扩展到此规模时面临的一个关键挑战：用于L1习得的“单语”预训练语料库中的L2污染。为了解决这个问题，我们提出了一种过滤方法，以减少对英语的过早暴露，同时保持现实的、最小的暴露。随后，我们在LLM生成的L2学习课程上对模型进行了微调，以模拟L2习得过程。我们的评估确认Dango发展出类人L2产出模式，优于未过滤和标准多语言基线。我们发布了模型、数据和代码，以促进可重复的计算SLA研究和面向学习者的应用。
cs.CL / 49 / 2606.19183
Language Models as Interfaces, Not Oracles: A Hybrid LLM-ML System for Pediatric Appendicitis
语言模型作为接口，而非神谕：一种用于儿童阑尾炎的混合 LLM-ML 系统
Abstract
Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagnostic engines is limited by sensitivity to prompts, information order, and plausible but incorrect outputs. Structured machine-learning models offer more stable risk prediction, yet they require tabular inputs that are difficult to integrate with narrative clinical workflows. We present ClaMPAPP (Clinical Language-assisted Machine-learning Pipeline for Appendicitis), a hybrid system that uses an LLM as an interface rather than as the final decision-maker. ClaMPAPP extracts schema-constrained clinical features from note-like narratives, applies deterministic plausibility checks, and passes validated features to an XGBoost classifier trained on clinical, laboratory, and ultrasound variables. We evaluated ClaMPAPP on two independent pediatric appendicitis cohorts from German hospitals and compared it with end-to-end LLM baselines, including open-source and proprietary models. To preserve ground truth while testing free-text input, narratives were generated from structured electronic health records through template rendering and constrained LLM rewriting, with additional sentence-order permutation to assess positional robustness. ClaMPAPP achieved the strongest overall diagnostic performance in both internal and external validation while minimizing missed appendicitis cases, the key safety concern in acute triage. End-to-end LLMs showed unstable sensitivity-specificity trade-offs and greater degradation under narrative reordering. These results support an LLM-as-interface, ML-as-predictor design that separates natural-language usability from predictive inference and provides a more auditable pathway for clinical decision support.
Chinese Translation
大型语言模型（LLMs）通过解释自由文本文档，可以使临床决策支持变得更加可及，但其作为诊断引擎的直接使用受到对提示的敏感性、信息顺序以及合理但不正确的输出的限制。结构化机器学习模型提供了更稳定的风险预测，但它们需要表格输入，这在与叙述性临床工作流程的整合中存在困难。我们提出了 ClaMPAPP（临床语言辅助机器学习阑尾炎管道），这是一个将 LLM 作为接口而非最终决策者的混合系统。ClaMPAPP 从类似笔记的叙述中提取受限于模式的临床特征，应用确定性的合理性检查，并将验证后的特征传递给经过临床、实验室和超声变量训练的 XGBoost 分类器。我们在来自德国医院的两个独立儿童阑尾炎队列上评估了 ClaMPAPP，并将其与端到端 LLM 基准进行比较，包括开源和专有模型。为了在测试自由文本输入时保留真实情况，叙述是通过模板渲染和受限 LLM 重写从结构化电子健康记录生成的，并进行了额外的句子顺序置换以评估位置鲁棒性。ClaMPAPP 在内部和外部验证中均实现了最强的整体诊断性能，同时最小化了漏诊阑尾炎病例，这是急性分诊中的关键安全问题。端到端 LLM 显示出不稳定的敏感性-特异性权衡，并在叙述重新排序下表现出更大的退化。这些结果支持将 LLM 作为接口、机器学习作为预测者的设计，将自然语言可用性与预测推断分离，并为临床决策支持提供了更可审计的路径。
cs.CL / 50 / 2606.19218
RECOM: A Validity Discrimination Tradeoff in Automatic Metrics for Open Ended Reddit Question Answering
RECOM：开放式 Reddit 问答自动评估指标中的有效性与区分能力权衡
Abstract
Automatic metrics are the default for evaluating LLM-generated text, yet a metric is quietly asked to do two jobs: tell genuine content alignment from surface coincidence (validity), and tell a better system from a worse one (discriminative power). On open-ended, opinion-driven question answering, the two are in tension. We introduce RECOM (Reddit Evaluation for Correspondence of Models), a contamination-free evaluation dataset of 15,000 r/AskReddit questions (September 2025), each paired with its authentic community replies, which postdate every evaluated model's training cutoff. Scoring five open-source LLMs (7--10B) against every reply each metric paired with a random-derangement noise floor we find that no metric does both jobs well. Cosine similarity separates real from random answers (Cohen's $d \approx 2$) but cannot rank the five models ($|d| < 0.1$); BERTScore precision appears to rank the models (raw $|d|$ up to 0.63), but once response length is controlled this collapses to $|d| = 0.09$ and its validity is weak ($d \approx 0.8$, versus cosine's $\approx 2$). Because every metric scores the same outputs, this validity--discrimination tradeoff is a property of the metrics, not the models, and we argue it stems from representation design. Three independent LLM judges reproduce the validity gap and likewise separate the five models only weakly. We recommend reporting metrics on both axes, with an explicit random-baseline floor. RECOM is publicly available at https://anonymous.4open.science/r/recom-D4B0
Chinese Translation
自动评估指标是评估大型语言模型（LLM）生成文本的默认方法，但这些指标实际上被要求承担两个任务：区分真实内容的对齐与表面巧合（有效性），以及区分更好的系统与更差的系统（区分能力）。在开放式、基于意见的问答中，这两者存在紧张关系。我们引入了 RECOM（Reddit 模型对应性评估），这是一个无污染的评估数据集，包含 15,000 个 r/AskReddit 问题（2025 年 9 月），每个问题都配有其真实的社区回复，这些回复的发布时间晚于每个评估模型的训练截止日期。我们对五个开源 LLM（7-10B）进行评分，使用每个回复与随机扰动噪声基准相结合的每个指标，发现没有一个指标能够很好地完成这两个任务。余弦相似度能够区分真实答案与随机答案（Cohen's $d ext{约} 2$），但无法对五个模型进行排名（$|d| < 0.1$）；BERTScore 精度似乎能够对模型进行排名（原始 $|d|$ 高达 0.63），但一旦控制响应长度，这一结果崩溃至 $|d| = 0.09$，其有效性也较弱（$d ext{约} 0.8$，相比之下余弦的 $ ext{约} 2$）。由于每个指标对相同输出的评分相同，这种有效性与区分能力的权衡是指标的属性，而非模型的属性，我们认为这源于表示设计。三位独立的 LLM 评审员重现了有效性差距，并同样只能弱区分这五个模型。我们建议在两个维度上报告指标，并明确设定随机基准。RECOM 数据集可在 https://anonymous.4open.science/r/recom-D4B0 上公开获取。
cs.CL / 51 / 2606.19257
DreamReasoner-8B: Block-Size Curriculum Learning for Diffusion Reasoning Models
DreamReasoner-8B：用于扩散推理模型的块大小课程学习
Abstract
Block diffusion language models accelerate decoding through parallel block-wise denoising, yet whether they can be reliably scaled for long chain-of-thought (CoT) reasoning remains unresolved. To this end, we develop DreamReasoner-8B, an open-source block diffusion reasoning model, and conduct a systematic study of how training and inference block sizes affect long-CoT reasoning. Our analysis reveals a stark performance disparity: training with large block sizes yields remarkably poor reasoning, whereas small block sizes preserve effective reasoning. To bridge this granularity gap, we propose block-size curriculum learning, which gradually transitions training from fine-grained to coarse-grained block sizes, thereby overcoming this limitation and enabling strong reasoning performance that generalizes across diverse inference block sizes. On mathematical and code reasoning benchmarks, DreamReasoner-8B achieves results competitive with leading open autoregressive models such as Qwen3-8B. This work establishes a practical foundation for efficient, reasoning-capable diffusion language models. We release our model at https://github.com/DreamLM/DreamReasoner.
Chinese Translation
块扩散语言模型通过并行块级去噪加速解码，但它们是否能够可靠地扩展到长链推理（CoT）仍未解决。为此，我们开发了DreamReasoner-8B，一个开源的块扩散推理模型，并系统地研究了训练和推理块大小如何影响长链推理。我们的分析揭示了显著的性能差异：使用大块大小进行训练会导致推理效果显著较差，而小块大小则能保持有效的推理。为了弥补这种粒度差距，我们提出了块大小课程学习，该方法逐渐将训练从细粒度转变为粗粒度块大小，从而克服这一限制，并实现强大的推理性能，能够在不同的推理块大小之间进行泛化。在数学和代码推理基准测试中，DreamReasoner-8B的表现与领先的开放自回归模型（如Qwen3-8B）相当。本研究为高效且具备推理能力的扩散语言模型奠定了实用基础。我们在https://github.com/DreamLM/DreamReasoner发布了我们的模型。
cs.CL / 52 / 2606.19266
Trade-offs in Medical LLM Adaptation: An Empirical Study in French QA
医学大语言模型适应中的权衡：法语问答的实证研究
Abstract
The development of large language models (LLMs) has led to an increased focus on their adaptation to specialized domains and languages, yet the effectiveness of domain adaptation strategies remains unclear. We present a study of medical domain adaptation using French medical question-answering (QA) as a case study. We compare continual pretraining (CPT), supervised fine-tuning (SFT), and their combination across three model families, multiple sizes, and three initialization types, explicitly disentangling adaptation effects from base model choice. We evaluate both multiple-choice (MCQA) and open-ended QA (OEQA) under greedy and constrained decoding using automatic metrics and LLM-as-a-Judge evaluation. For MCQA, CPT+SFT most often achieves the best scores, but gains over SFT are small and frequently not statistically significant, making SFT a strong and cost-effective default. For OEQA, CPT consistently improves overlap-based metrics, while SFT often degrades generation quality; instruction tuning and CPT+SFT are preferred by LLM-based evaluation. Cross-lingual experiments further show effective transfer from French adaptation to English benchmarks. Overall, we provide practical guidelines for selecting adaptation strategies under computational constraints.
Chinese Translation
大型语言模型（LLMs）的发展使得对其在专业领域和语言中的适应性关注度增加，但领域适应策略的有效性仍不明确。我们以法语医学问答（QA）为案例，呈现了一项医学领域适应的研究。我们比较了持续预训练（CPT）、监督微调（SFT）及其组合，涵盖三种模型家族、多个规模和三种初始化类型，明确区分了适应效果与基础模型选择的影响。我们在贪婪解码和受限解码下，使用自动评估指标和LLM作为评估者的方法评估了多项选择问答（MCQA）和开放式问答（OEQA）。对于MCQA，CPT+SFT最常取得最佳分数，但相较于SFT的提升较小且常常没有统计显著性，使得SFT成为一个强大且具成本效益的默认选择。对于OEQA，CPT始终改善基于重叠的指标，而SFT常常降低生成质量；指令调优和CPT+SFT在基于LLM的评估中更受青睐。跨语言实验进一步表明法语适应对英语基准的有效迁移。总体而言，我们为在计算约束下选择适应策略提供了实用指南。
cs.CL / 53 / 2606.19308
Enhancing Decision-Making with Large Language Models through Multi-Agent Fictitious Play
通过多智能体虚拟博弈增强大语言模型的决策能力
Abstract
Large language model (LLM)-based multi-agent systems (MAS) have demonstrated great potential in solving tasks with execution complexity, by distributing subtasks across cooperative agents. However, this divide-and-conquer paradigm falls short on decision-making tasks that are also prevalent in the real world. These tasks require simultaneous reasoning from the stances of all involved stakeholders whose decisions are mutually dependent and thus cannot be solved in isolation. We characterize this challenge as stance entanglement, a form of decision complexity distinct from execution complexity. To address it, we propose Multi-Agent Fictitious Play (MAFP), a novel MAS paradigm that represents stakeholder stances as agents and formulates decision-making as an equilibrium-seeking process. Built on the game-theoretic principle of fictitious play, MAFP iteratively updates each agent's decision by best responding to the empirical mixture of other agents' past decisions. This enables agents to expose and address one another's weaknesses, progressively improving decision quality and robustness. We evaluate MAFP on challenging decision-making tasks that test the capability of deciding strategies for competitive scenarios prior to acting. MAFP outperforms both single-round and multi-round baselines on two complementary metrics, tournament strength and robustness, demonstrating its effectiveness in addressing stance entanglement.
Chinese Translation
基于大语言模型（LLM）的多智能体系统（MAS）在解决具有执行复杂性的任务方面展现了巨大的潜力，通过在合作代理之间分配子任务。然而，这种分而治之的范式在现实世界中普遍存在的决策任务上显得不足。这些任务需要所有相关利益相关者的立场进行同时推理，其决策是相互依赖的，因此无法孤立地解决。我们将这一挑战称为立场纠缠，这是一种与执行复杂性不同的决策复杂性形式。为了解决这一问题，我们提出了多智能体虚拟博弈（MAFP），一种新颖的MAS范式，将利益相关者的立场表示为代理，并将决策制定形式化为寻求均衡的过程。基于虚拟博弈的博弈论原则，MAFP通过对其他代理过去决策的经验混合进行最佳响应，迭代更新每个代理的决策。这使得代理能够暴露并解决彼此的弱点，逐步提高决策质量和鲁棒性。我们在具有挑战性的决策任务上评估MAFP，这些任务测试在行动前为竞争场景决定策略的能力。MAFP在两个互补指标——比赛强度和鲁棒性上，优于单轮和多轮基线，证明了其在解决立场纠缠方面的有效性。
cs.CL / 54 / 2606.19334
Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States
通过LOCUS解放法律：美国地方条例语料库
Abstract
Progress in legal AI increasingly depends on access to authoritative legal text at scale. Yet one of the most consequential layers of American law remains largely absent from existing machine-readable corpora: local ordinances. Local codes govern zoning, housing, business licensing, public health, noise, animal control, and many other domains of everyday regulation, but they are fragmented across vendor platforms designed for human browsing rather than bulk research access. We introduce LOCUS - the Local Ordinance Corpus for the United States - a comprehensive corpus and county-harmonized access layer for U.S. municipal and county ordinance codes. The raw corpus, available for release to researchers, represents nearly all publicly available municipal and county ordinance codes. The resulting raw corpus contains codes from 9,239 cities and counties. A smaller county-harmonized LOCUS access layer provides coverage for the largest 2,309 of 3,144 U.S. counties, accounting for a majority of the population. We use OCR to handle the myriad of document formats that have kept the law from being a public resource. We release the corpus with coverage metadata to support reproducibility, downstream legal AI research, and the incremental expansion of machine-readable access to local law. We train a collection of ModernBERT-based classifiers and scorers to facilitate analyzing U.S. local law among several dimensions, such as opacity and paternalism, that have not previously been studied at this scale. LOCUS-v1 and its derivative models are available at: https://huggingface.co/datasets/LocalLaws/LOCUS-v1
Chinese Translation
法律人工智能的进展越来越依赖于对权威法律文本的广泛获取。然而，美国法律中一个最重要的层面在现有的机器可读语料库中仍然缺失：地方条例。地方法规涉及区域划分、住房、商业许可、公共卫生、噪音、动物控制以及许多其他日常监管领域，但它们分散在为人类浏览而设计的供应商平台上，而非用于批量研究访问。我们介绍了LOCUS——美国地方条例语料库，这是一个全面的语料库和县级协调访问层，涵盖美国市政和县级条例代码。该原始语料库可供研究人员使用，几乎包含所有公开可用的市政和县级条例代码。最终的原始语料库包含来自9,239个城市和县的代码。一个较小的县级协调LOCUS访问层覆盖了美国3,144个县中最大的2,309个，涵盖了大多数人口。我们使用光学字符识别（OCR）技术处理各种文档格式，以使法律成为公共资源。我们发布了带有覆盖元数据的语料库，以支持可重复性、后续法律人工智能研究以及对地方法律的机器可读访问的逐步扩展。我们训练了一系列基于ModernBERT的分类器和评分器，以便在多个维度上分析美国地方法律，例如不透明性和父权主义，这些维度在之前的研究中未曾如此大规模地探讨。LOCUS-v1及其衍生模型可在以下网址获取：https://huggingface.co/datasets/LocalLaws/LOCUS-v1
cs.CL / 55 / 2606.19336
Learning User Simulators with Turing Rewards
使用图灵奖励学习用户模拟器
Abstract
Learning to simulate human users in interactive settings could advance the training of agent assistants, evaluation of personalization systems, research in the social sciences, and more. Existing approaches generally do so by training a large language model (LLM) to match a single ground truth response, either by maximizing the log probability or by using a similarity reward. We instead propose {Turing-RL}: a Turing-Test-based reinforcement learning approach for training user simulator models. {Turing-RL} uses a discriminative Turing reward with an LLM judge to score how indistinguishable a generated response is from the real user's given the user's history, and the user simulator LLM learns to produce responses indistinguishable from what the user could have said with such rewards. Across two different domains--conversational chat and Reddit forum discussion--we find that {Turing-RL} consistently outperforms baseline methods on both LLM and human evaluation metrics. Our study suggests that optimizing for indistinguishability, rather than response matching, is effective for learning user simulators.
Chinese Translation
在互动环境中学习模拟人类用户可以推动代理助手的训练、个性化系统的评估、社会科学研究等多个领域的发展。现有的方法通常通过训练大型语言模型（LLM）来匹配单一的真实响应，方法是最大化对数概率或使用相似性奖励。我们提出了一种不同的方法：{Turing-RL}，这是一种基于图灵测试的强化学习方法，用于训练用户模拟器模型。{Turing-RL}使用带有LLM评判者的判别性图灵奖励，来评分生成的响应与真实用户在给定用户历史情况下的响应有多么不可区分，用户模拟器LLM学习生成与用户可能说出的响应不可区分的响应。通过在对话聊天和Reddit论坛讨论两个不同领域的实验，我们发现{Turing-RL}在LLM和人类评估指标上始终优于基线方法。我们的研究表明，优化不可区分性而非响应匹配，对于学习用户模拟器是有效的。