cs.RO / 1 / 2606.17073
Extracting Semantics: LLM-Guided Automatic Population of Robot Ontology from URDF
提取语义：基于大型语言模型的机器人本体自动填充从URDF
Abstract
While commonsense knowledge may suffice for virtual agents, embodied robots interacting with humans require grounded and semantically rich representations of both their environment and their own physical embodiment. In cognitive robotics, ontologies are effective for integrating such heterogeneous knowledge to enable explainable reasoning, even during continuous knowledge updates. Yet, their manual construction remains a bottleneck. We present a preliminary approach for the automatic generation of robot semantic abstractions by transforming Unified Robot Description Format (URDF) models into populated ontologies. Although URDF files provide structural and kinematic descriptions, their identifiers often require commonsense interpretation to recover meaningful semantics, a task at which Large Language Models (LLMs) excel. Our pipeline leverages LLMs to infer semantic relationships by prompting them with concepts from an existing ontology, ensuring the final classification remains aligned with the formal model. To improve reliability, the pipeline combines majority voting across multiple LLM queries along with syntactic and schema-level validation to ensure that generated outputs conform to the expected representation format and ontology constraints. We evaluate the approach on multiple robot descriptions and discuss the generated abstractions. Initial results indicate that the proposed method can effectively bridge the gap between low-level robot descriptions and the structured, grounded knowledge representations required for human-robot interaction.
Chinese Translation
虽然常识知识可能足以支持虚拟代理，但与人类互动的具身机器人需要对其环境和自身物理体现的扎根和语义丰富的表征。在认知机器人学中，本体有效地整合了异构知识，以实现可解释的推理，即使在持续的知识更新过程中。然而，其手动构建仍然是一个瓶颈。我们提出了一种初步方法，通过将统一机器人描述格式（URDF）模型转换为填充本体，自动生成机器人语义抽象。尽管URDF文件提供了结构和运动学描述，但其标识符通常需要常识解释以恢复有意义的语义，而这一任务正是大型语言模型（LLMs）擅长的。我们的流程利用LLMs通过提供来自现有本体的概念来推断语义关系，确保最终分类与正式模型保持一致。为了提高可靠性，该流程结合了多个LLM查询的多数投票以及句法和模式级别的验证，以确保生成的输出符合预期的表征格式和本体约束。我们在多个机器人描述上评估了该方法，并讨论了生成的抽象。初步结果表明，所提出的方法能够有效弥合低级机器人描述与人机交互所需的结构化、扎根知识表征之间的差距。
cs.RO / 2 / 2606.17080
HRDX: A Large-Scale Vector HD-Map Dataset
HRDX：大规模矢量高清地图数据集
Abstract
Reliable autonomous driving requires vectorized HD maps that are geometrically accurate, semantically rich, and scalable to long-horizon driving. However, existing public HD map datasets are limited in scale, provide sparse semantic attributes, and lack modalities such as aerial imagery that could enable new research directions. We present HRDX, a large-scale dataset for vector HD-map construction, spanning about 40 hours (1,400 km) of minimally overlapping drives, which is several times larger than prior public HD map datasets. Data is captured using six synchronized surround cameras, a 128-beam LiDAR, and centimeter-level RTK GNSS/IMU, and is further complemented by precisely aligned aerial orthoimagery. Annotations cover 10 vector map classes, complemented with over 20 semantic and topological attributes. To evaluate this richer ontology, we introduce the Composite Score (CS) to jointly assess geometric fidelity and attribute correctness. Benchmark experiments show that HRDX's scale improves online vector-map construction, and that aligned aerial imagery provides a useful structural prior: using aerial imagery at training and/or inference improves geometric map quality, while aerial-augmented teachers can transfer part of this benefit to camera-only students without increasing inference-time sensor requirements. HRDX is intended to support reproducible research on large-scale HD-map learning, multimodal BEV fusion, and training-time privileged information. HRDX dataset and benchmarks are available at https://github.com/honda-research-institute/HRDX
Chinese Translation
可靠的自动驾驶需要几何精确、语义丰富且可扩展至长距离驾驶的矢量高清地图。然而，现有的公共高清地图数据集在规模上有限，提供的语义属性稀疏，并且缺乏如航拍图像等能够启用新研究方向的模态。我们提出了HRDX，这是一个用于矢量高清地图构建的大规模数据集，涵盖约40小时（1,400公里）的最小重叠驾驶数据，其规模是之前公共高清地图数据集的几倍。数据通过六个同步的环绕摄像头、一个128束激光雷达以及厘米级的RTK GNSS/IMU捕获，并通过精确对齐的航拍正射影像进一步补充。标注覆盖10个矢量地图类别，并辅以超过20个语义和拓扑属性。为了评估这一更丰富的本体，我们引入了复合评分（Composite Score, CS）来共同评估几何保真度和属性正确性。基准实验表明，HRDX的规模改善了在线矢量地图构建，并且对齐的航拍图像提供了有用的结构先验：在训练和/或推理中使用航拍图像可以提高几何地图质量，而航拍增强的教师可以将部分这种好处转移给仅使用摄像头的学生，而不增加推理时的传感器要求。HRDX旨在支持关于大规模高清地图学习、多模态鸟瞰视图融合和训练时特权信息的可重复研究。HRDX数据集和基准可在 https://github.com/honda-research-institute/HRDX 获取。
cs.RO / 3 / 2606.17082
ParkingTransformer: LLM-Enhanced End-to-End Trajectory Planning for Autonomous Parking
ParkingTransformer：基于大语言模型的端到端自主停车轨迹规划
Abstract
End-to-end autonomous parking has emerged as a critical task within the realm of autonomous driving. However, existing methods suffer from black-box characteristics, lacking high-level semantic understanding and interpretability, which impedes the realization of seamless long-distance autonomous parking from the road to the target spot. To address these limitations, we propose ParkingTransformer, a novel framework that leverages multi-view perception and the scene understanding capability of Large Language Models (LLMs). By combining trajectory queries with LLMs implicit state features, our method interacts directly with historical information and raw sensor data to output planning trajectories, eliminating the need for dense Bird's-View (BEV) representations. To compensate for the inadequate spatial reasoning ability of LLMs, we introduce 3D positional encoding to explicitly inject spatial geometric awareness. Furthermore, a fixed-window streaming mechanism is designed for historical information processing, significantly improving long-term temporal processing efficiency and inference speed. Additionally, a coarse-to-fine decoding strategy is employed to progressively enhance trajectory precision. Extensive closed-loop experiments are conducted on the CARLA simulator and real-world vehicle platforms. The results demonstrate that our method achieves a driving score of 61.32 in CARLA simulator and an average success rate of 88.70% in real-world experiments, validating the feasibility and effectiveness of the proposed algorithms.
Chinese Translation
端到端自主停车已成为自主驾驶领域中的一项关键任务。然而，现有方法存在黑箱特性，缺乏高层次的语义理解和可解释性，这阻碍了从道路到目标停车位的无缝长距离自主停车的实现。为了解决这些局限性，我们提出了ParkingTransformer，一个新颖的框架，利用多视角感知和大语言模型（LLMs）的场景理解能力。通过将轨迹查询与LLMs的隐式状态特征相结合，我们的方法直接与历史信息和原始传感器数据进行交互，以输出规划轨迹，消除了对密集鸟瞰图（BEV）表示的需求。为了补偿LLMs在空间推理能力上的不足，我们引入了3D位置编码，以显式注入空间几何意识。此外，我们设计了一种固定窗口流机制用于历史信息处理，显著提高了长期时间处理效率和推理速度。此外，采用粗到细的解码策略逐步提高轨迹精度。在CARLA模拟器和真实车辆平台上进行了广泛的闭环实验。结果表明，我们的方法在CARLA模拟器中获得了61.32的驾驶评分，在真实世界实验中平均成功率达到88.70%，验证了所提算法的可行性和有效性。
cs.RO / 4 / 2606.17183
VL-MemKnG: Hybrid Memory with a Spatio-Temporal Knowledge Graph for Question Answering over Long Egocentric Navigation Trajectories
VL-MemKnG：用于长时间自我中心导航轨迹问答的时空知识图混合记忆
Abstract
Answering navigation-relevant questions over long egocentric videos requires retrieving and organizing evidence distributed across distant temporal moments while maintaining spatial and contextual consistency. Although long-context vision--language models can achieve strong answer quality, they are computationally expensive for long trajectories and inefficient for repeated querying. Recent graph-based approaches such as VL-KnG address this challenge through persistent spatio-temporal knowledge graphs, but graph-centric retrieval alone may underrepresent broader temporal continuity and contextual cues. We present VL-MemKnG, a hybrid memory framework that extends VL-KnG by combining a spatio-temporal knowledge graph with persistent segment-level contextual memory. The knowledge graph captures structured relational information and long-range object associations, while segment-level memory preserves broader temporal context for long-horizon evidence retrieval. A hybrid retrieval-and-reasoning module jointly operates over both memory representations to produce evidence-grounded answers and temporally organized supporting evidence. We also introduce WalkieKnowledgeT+, an extension of WalkieKnowledge for long-horizon navigation-oriented video question answering. The benchmark includes temporally distributed reasoning tasks requiring evidence aggregation across multiple non-cooccurring moments. On WalkieKnowledgeT+, VL-MemKnG improves Top-1 retrieval accuracy from 58% to 67% and Recall@1 from 34.50% to 40.55%, outperforming all compared methods, including Gemini 2.5 Pro and Qwen 3.5+. The gains are particularly pronounced on temporal-global and temporally scattered aggregation questions, demonstrating the benefits of combining structured relational memory with segment-level contextual memory while maintaining efficient query-time inference.
Chinese Translation
在长时间自我中心视频中回答与导航相关的问题需要检索和组织分布在遥远时间点的证据，同时保持空间和上下文的一致性。尽管长上下文视觉-语言模型可以实现较强的答案质量，但对于长轨迹而言，它们的计算成本较高，并且在重复查询时效率低下。最近的基于图的方法，如 VL-KnG，通过持久的时空知识图解决了这一挑战，但仅依靠图中心的检索可能无法充分代表更广泛的时间连续性和上下文线索。我们提出了 VL-MemKnG，这是一种混合记忆框架，通过将时空知识图与持久的段级上下文记忆相结合，扩展了 VL-KnG。知识图捕捉结构化的关系信息和长距离对象关联，而段级记忆则保留了更广泛的时间上下文，以便进行长时间证据检索。混合检索与推理模块共同作用于两种记忆表示，以生成基于证据的答案和时间组织的支持证据。我们还引入了 WalkieKnowledgeT+，这是 WalkieKnowledge 的扩展，旨在进行长时间导航导向的视频问答。基准测试包括需要在多个非共现时刻之间聚合证据的时间分布推理任务。在 WalkieKnowledgeT+ 上，VL-MemKnG 将 Top-1 检索准确率从 58% 提高到 67%，Recall@1 从 34.50% 提高到 40.55%，超越了所有比较方法，包括 Gemini 2.5 Pro 和 Qwen 3.5+。在时间全局和时间分散聚合问题上，增益尤为明显，展示了将结构化关系记忆与段级上下文记忆相结合的优势，同时保持高效的查询时间推理。
cs.RO / 5 / 2606.17200
ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining
ACE-Ego-0：统一自我中心人类与机器人数据用于视觉-语言-动作预训练
Abstract
Vision-Language-Action (VLA) models benefit from large-scale and diverse embodied data, yet scaling robot trajectory collection is costly and labor-intensive. Recent advances show that large-scale egocentric human videos provide complementary real-world supervision in pretraining. However, joint training on human and robot data remains challenging due to divergences in action spaces, embodiment structures, temporal dynamics, and supervision quality. We introduce ACE-EGO-0, a unified VLA pretraining framework jointly leveraging heterogeneous data sources. To extract large-scale pretraining supervision from egocentric human videos, we build a scalable egocentric video-to-action pipeline that converts raw human videos into robot-format pseudo-action trajectories. To make these labels comparable with robot demonstrations, ACE-EGO-0 uses a unified action representation based on camera-space actions, morphology conditioning, and time-aligned action chunking. To robustly leverage noisy pseudo-action supervision from egocentric human videos, we formulate a reliability-aware training objective with a human auxiliary loss that concentrates supervision on reliable signals. We instantiate ACE-EGO-0 on 4.53K hours of robot and simulation data, together with 1.48K hours of pseudo-action-labeled egocentric human data. Experiments show that incorporating large-scale human supervision under reliability-aware weighting consistently improves both unified joint pretraining and supervised fine-tuning. ACE-EGO-0 achieves state-of-the-art performance on RoboCasa GR1 TableTop and RoboTwin 2.0, while demonstrating strong transfer to real-world bimanual manipulation.
Chinese Translation
视觉-语言-动作（VLA）模型受益于大规模和多样化的具身数据，但收集机器人轨迹的成本高且劳动密集。最近的进展表明，大规模自我中心人类视频在预训练中提供了互补的现实世界监督。然而，由于动作空间、具身结构、时间动态和监督质量的差异，联合训练人类和机器人数据仍然具有挑战性。我们提出了ACE-EGO-0，一个统一的VLA预训练框架，联合利用异构数据源。为了从自我中心人类视频中提取大规模预训练监督，我们构建了一个可扩展的自我中心视频到动作管道，将原始人类视频转换为机器人格式的伪动作轨迹。为了使这些标签与机器人演示可比，ACE-EGO-0使用基于相机空间动作、形态条件和时间对齐动作分块的统一动作表示。为了稳健地利用来自自我中心人类视频的噪声伪动作监督，我们制定了一个可靠性感知的训练目标，并引入了一个人类辅助损失，将监督集中在可靠信号上。我们在4.53K小时的机器人和仿真数据以及1.48K小时的伪动作标记自我中心人类数据上实例化ACE-EGO-0。实验表明，在可靠性感知加权下，结合大规模人类监督始终改善了统一联合预训练和监督微调的效果。ACE-EGO-0在RoboCasa GR1 TableTop和RoboTwin 2.0上实现了最先进的性能，同时在现实世界的双手操作中表现出强大的迁移能力。
cs.RO / 6 / 2606.17256
Contrastive Action-Image Pre-training for Visuomotor Control
用于视觉运动控制的对比动作-图像预训练
Abstract
Existing vision encoders for robotics face a fundamental bottleneck: robotic datasets lack the scale necessary for large-scale pre-training. Prior work circumvents this data scarcity by turning to internet-scale image and language data or egocentric human video. While these models show promise, neither paradigm learns from paired vision and action data, which downstream visuomotor control policies require. However, robot trajectories, the most direct source of this paired signal, are not available at pre-training scale, motivating us to extract action signals from abundant human video instead. To this end, we introduce CAIP (Contrastive Action-Image Pre-training), a vision encoder that treats human hand poses from large-scale egocentric video as a proxy for end-effector actions. By extracting 3D hand keypoints, a representation that aligns naturally with downstream robot action spaces, CAIP learns a unified action-image representation through a contrastive objective. Leveraging 32,041 hours of egocentric human video and only 88 hours of robotic manipulation data, CAIP outperforms state-of-the-art vision encoders including DINOv2, SigLIP, MVP, and R3M. Evaluated on a challenging real-world dexterous manipulation setup using Dexmate Vega and Sharpa Wave hands, CAIP yields performance gains of more than 30% on tasks involving folding, pouring, and fine-grained manipulation. Our results show that our method of contrastive action-centric pre-training yields a scalable path to achieving robust visual representations better suited for physical interaction.
Chinese Translation
现有的机器人视觉编码器面临一个根本性的瓶颈：机器人数据集缺乏大规模预训练所需的规模。之前的研究通过转向互联网规模的图像和语言数据或自我中心的人类视频来规避这一数据稀缺问题。尽管这些模型显示出潜力，但无论哪种范式都未能从配对的视觉和动作数据中学习，而下游的视觉运动控制策略正是需要这些数据。然而，机器人轨迹作为这种配对信号的最直接来源，在预训练规模下并不可用，这促使我们从丰富的人类视频中提取动作信号。为此，我们提出了CAIP（对比动作-图像预训练），这是一种将大规模自我中心视频中的人类手势视为末端效应器动作代理的视觉编码器。通过提取3D手部关键点，这种表示自然地与下游机器人动作空间对齐，CAIP通过对比目标学习统一的动作-图像表示。利用32,041小时的自我中心人类视频和仅88小时的机器人操作数据，CAIP在包括DINOv2、SigLIP、MVP和R3M在内的最先进视觉编码器中表现优越。在使用Dexmate Vega和Sharpa Wave手的具有挑战性的现实世界灵巧操作设置中进行评估时，CAIP在涉及折叠、倒水和精细操作的任务上实现了超过30%的性能提升。我们的结果表明，我们的对比动作中心预训练方法为实现更适合物理交互的稳健视觉表示提供了一条可扩展的路径。
cs.RO / 7 / 2606.17294
VISTA: Scale-Aware Visual Navigation via Action History Conditioning
VISTA：基于动作历史条件的尺度感知视觉导航
Abstract
Vision Navigation Foundation Models (VNMs) promise end-to-end learned navigation policies capable of zero-shot deployment across diverse embodiments and environments. To maintain generality, many vision-based navigation models predict normalized actions. However, this normalization introduces a critical deployment vulnerability: applying different scaling factors to the same normalized trajectory alters its physical geometry, which degrades navigation performance and increases collision risks. We address this vulnerability by conditioning the model on normalized action histories alongside image observations, providing explicit context on the relationship between the model's predictions and the robot's actual physical displacement. Furthermore, current VNMs often struggle in visually repetitive environments that lack distinct features. To resolve this issue, we integrate a DINOv3 encoder, whose richer representations enable our model to capture both spatial and geometric dimensions between observations. VISTA generalizes robustly to out-of-distribution environments, achieving 100% goal prediction accuracy in zero-shot, real-world deployment in Outdoor, Forest and Office settings, and an average of 95% checkpoints crossed, demonstrating consistent path following in unseen environments.
Chinese Translation
视觉导航基础模型（VNMs）承诺提供端到端学习的导航策略，能够在多样化的实施和环境中实现零样本部署。为了保持通用性，许多基于视觉的导航模型预测标准化动作。然而，这种标准化引入了一个关键的部署脆弱性：对同一标准化轨迹应用不同的缩放因子会改变其物理几何形状，从而降低导航性能并增加碰撞风险。我们通过在图像观测的基础上，对标准化动作历史进行条件化来解决这一脆弱性，从而提供模型预测与机器人实际物理位移之间关系的明确上下文。此外，当前的VNMs在缺乏明显特征的视觉重复环境中往往表现不佳。为了解决这一问题，我们集成了DINOv3编码器，其更丰富的表示能力使我们的模型能够捕捉观测之间的空间和几何维度。VISTA在分布外环境中表现出强大的泛化能力，在户外、森林和办公室环境中的零样本真实世界部署中实现了100%的目标预测准确率，并平均跨越95%的检查点，展示了在未见环境中的一致路径跟踪能力。
cs.RO / 8 / 2606.17309
Abstention-Aware Personalized Object Rearrangement via Uncertainty-Guided LLM Assistance
基于不确定性引导的大型语言模型辅助的考虑弃权的个性化物体重排
Abstract
Robotic assistance in household environments requires not only predicting where objects should be placed, but also reasoning about when objects should not be placed at all. Existing approaches to personalized object rearrangement primarily focus on placement decisions under the assumption of clean observations and complete actionability, limiting their applicability in realistic, cluttered, and partially erroneous settings. In this paper, we introduce APOLLO, a hybrid framework for abstention-aware personalized object rearrangement that combines a lightweight, personalized embedding model (PEM) with selective large language model (LLM) assistance. PEM is trained for each user-environment pair using a small number of demonstrations, operates entirely on CPU, and produces uncertainty estimates, which are used to selectively invoke LLM-based reasoning only for ambiguous decisions, balancing efficiency, privacy, and reasoning capability. To evaluate this formulation beyond existing benchmarks, we introduce APOR, a synthetic, LLM-generated dataset that captures room-level, multi-furniture environments, diverse organizational profiles, explicit abstention behavior, and noisy partial scene context. Extensive experiments on both PARSEC and APOR provide initial evidence that APOLLO improves over prior LLM-based baselines in controlled benchmark settings while substantially reducing LLM usage. Code is available at https://github.com/PaInt-Lab/APOLLO.
Chinese Translation
家庭环境中的机器人辅助不仅需要预测物体应放置的位置，还需要推理何时不应放置物体。现有的个性化物体重排方法主要集中在假设干净观察和完全可操作性的放置决策上，这限制了它们在现实中杂乱且部分错误的环境中的适用性。本文介绍了APOLLO，一个考虑弃权的个性化物体重排的混合框架，它将轻量级个性化嵌入模型（PEM）与选择性的大型语言模型（LLM）辅助相结合。PEM针对每个用户-环境对进行训练，使用少量示例，完全在CPU上运行，并生成不确定性估计，这些估计用于在模糊决策时选择性地调用基于LLM的推理，从而平衡效率、隐私和推理能力。为了在现有基准之外评估这一框架，我们引入了APOR，一个合成的、由LLM生成的数据集，捕捉了房间级别的多家具环境、多样的组织特征、明确的弃权行为和嘈杂的部分场景上下文。在PARSEC和APOR上的大量实验提供了初步证据，表明APOLLO在受控基准设置中优于先前的基于LLM的基线，同时显著减少了LLM的使用。代码可在 https://github.com/PaInt-Lab/APOLLO 获取。
cs.RO / 9 / 2606.17317
Transformer-Based Warm-Starting for Feasible and Optimal Terminal Approach to Tumbling Objects with Space Manipulators
基于变换器的热启动方法用于空间操纵器对翻滚物体的可行且最优的终端接近
Abstract
Real-time trajectory generation for on-orbit robotic servicing is challenging due to the nonlinear coupling between spacecraft bus motion, manipulator dynamics, visibility cone, and trajectory-level safety constraints. This paper studies learning-based warm-starting for sequential convex programming (SCP) in the terminal approach of a space manipulator toward a tumbling target. The proposed framework decomposes the problem into a system center-of-mass translational planning stage and a coupled attitude--manipulator torque-allocation stage, and applies a causal transformer warm-start to the latter, which constitutes the dominant computational bottleneck. Linear and flow matching action decoders are compared under different action-chunking and training dataset sizes, and the resulting warm-starts are evaluated under both cost-optimal and feasibility projection using SCP. Across 300 held-out scenarios, the learned warm-start reduces the second-stage SCP iteration count by up to 28% and the runtime by 23% while preserving the final control-cost distribution. When the learned warm-starts are used for nonconvex feasibility projection, they nearly halve the runtime relative to cost-optimal SCP, while avoiding the catastrophic high-cost tail behavior observed when initialized heuristically. These results indicate that sequence-model warm-starts can improve both the computational efficiency and trajectory robustness of optimization-based terminal guidance for space manipulation.
Chinese Translation
由于航天器总线运动、操纵器动力学、可视锥体和轨迹级安全约束之间的非线性耦合，轨道机器人服务的实时轨迹生成面临挑战。本文研究了基于学习的热启动方法在空间操纵器对翻滚目标的终端接近中的序列凸优化（SCP）。所提出的框架将问题分解为系统质心平移规划阶段和耦合的姿态-操纵器扭矩分配阶段，并对后者应用因果变换器热启动，该阶段构成了主要的计算瓶颈。在不同的动作分块和训练数据集大小下，比较了线性和流匹配动作解码器，并在使用SCP进行成本最优和可行性投影的情况下评估了得到的热启动。在300个保留场景中，学习到的热启动将第二阶段SCP迭代次数减少了多达28%，运行时间减少了23%，同时保持了最终控制成本分布。当学习到的热启动用于非凸可行性投影时，相较于成本最优SCP，它们几乎将运行时间减半，同时避免了在启发式初始化时观察到的灾难性高成本尾部行为。这些结果表明，序列模型热启动可以提高基于优化的空间操纵终端引导的计算效率和轨迹鲁棒性。
cs.RO / 10 / 2606.17376
Contactless Respiratory Monitoring on Heterogeneous Mobile Robots: A Multimodal Edge-Computing Framework
异构移动机器人上的非接触式呼吸监测：一种多模态边缘计算框架
Abstract
Respiratory-rate (RR) monitoring is a critical component of remote triage and victim assessment in emergency response, disaster recovery, and infectious-disease scenarios, where minimizing physical contact can reduce responder risk and improve operational safety. However, field deployment of contactless RR monitoring remains challenging due to variable illumination, posture changes, platform heterogeneity, and the impracticality of wearable sensors in hazardous environments. In this paper, we present a modality-adaptive contactless RR monitoring framework for heterogeneous mobile robots with onboard edge computing. The proposed system combines brightness-adaptive sensor selection across RGB, thermal, near-infrared (NIR), and low-light cameras, keypoint-guided chest ROI extraction for posture-robust monitoring, and a signal-quality-index (SQI)-based filtering mechanism for reliable respiratory estimation. We implement and evaluate the framework on three robotic platforms spanning quadruped and wheeled locomotion and multiple edge-computing architectures. Experiments conducted across diverse lighting conditions, subject poses, and robot-to-subject distances demonstrate that the framework generalizes across platforms without per-platform algorithmic retuning, while revealing modality-specific operational boundaries. RGB provides the broadest coverage up to 8m, NIR remains effective up to 6m, thermal is reliable only at short range, and low-light sensing supports monitoring in complete darkness up to 8m. Overall, the results demonstrate the feasibility of multimodal contactless RR monitoring on mobile robots and support its use as a foundation for autonomous triage and victim assessment in hazardous search-and-rescue settings.
Chinese Translation
呼吸频率（RR）监测是远程分诊和受害者评估的重要组成部分，尤其在紧急响应、灾后恢复和传染病场景中，减少身体接触可以降低救援人员的风险并提高操作安全性。然而，由于光照变化、姿势变化、平台异构性以及在危险环境中可穿戴传感器的不实用性，非接触式 RR 监测的现场部署仍然面临挑战。本文提出了一种适应性非接触式 RR 监测框架，适用于具备边缘计算能力的异构移动机器人。该系统结合了基于亮度的传感器选择，涵盖 RGB、热成像、近红外（NIR）和低光摄像头，关键点引导的胸部 ROI 提取用于姿势稳健监测，以及基于信号质量指数（SQI）的过滤机制以实现可靠的呼吸估计。我们在三种机器人平台上实现并评估了该框架，这些平台涵盖了四足和轮式运动以及多种边缘计算架构。在多种光照条件、受试者姿势和机器人与受试者距离下进行的实验表明，该框架在不同平台上具有良好的通用性，无需对每个平台进行算法重新调优，同时揭示了特定模态的操作边界。RGB 在 8 米范围内提供了最广泛的覆盖，NIR 在 6 米范围内仍然有效，热成像仅在短距离内可靠，而低光传感支持在完全黑暗中监测，距离可达 8 米。总体而言，结果表明在移动机器人上进行多模态非接触式 RR 监测的可行性，并支持其作为危险搜索和救援环境中自主分诊和受害者评估的基础。
cs.RO / 11 / 2606.17385
EgoInfinity: A Web-Scale 4D Hand-Object Interaction Data Engine for Any-View Robot Retargeting and Video-to-Action Robot Learning
EgoInfinity：一种用于任意视角机器人重定向和视频到动作机器人学习的网络规模4D手物体交互数据引擎
Abstract
Internet videos constitute the largest reservoir of embodied human manipulation knowledge, yet converting arbitrary RGB footage into actionable robot training data remains a major bottleneck. Existing lab- or factory-collected datasets are narrow in scale and diversity, limiting open-world robot learning. Instead of proposing a static dataset, we introduce EgoInfinity, a universal 4D hand-object interaction data engine that enables web-scale data generation for robot retargeting and learning. EgoInfinity is a modular engine integrating perception, segmentation, reconstruction, interaction-aware refinement, and retargeting to automate this traditionally unscalable video-to-action problem without human-in-the-loop annotation. Its modular design lets the engine continuously benefit from advances in any incorporated component. With EgoInfinity, in-the-wild human manipulation videos are lifted into agent-agnostic, metric 4D hand-object representations, including hand trajectories, 6-DoF object poses, and contact-relevant states. Rather than naively connecting standalone components, EgoInfinity combines cross-module metric calibration with interaction-aware refinement to improve physical reliability, reducing drift and contact inconsistencies common in pure visual reconstruction. We further propose a novel motion retargeter that compiles the recovered 3D hand motions into executable joint trajectories for diverse robot morphologies, enabling video-to-action retargeting on any robot from arbitrary viewpoints and shot sizes (e.g., the human body is only partially visible). We validate EgoInfinity across perception fidelity, kinematic feasibility, contact consistency, cross-embodiment generalization, and real-robot skill acquisition (e.g., grasping, cutting, wiping, and pouring), demonstrating a scalable bridge from internet videos to executable robot behavior for open-world robot learning.
Chinese Translation
互联网视频构成了人类操作知识的最大储备，但将任意RGB视频转换为可操作的机器人训练数据仍然是一个主要瓶颈。现有的实验室或工厂收集的数据集在规模和多样性上都较为狭窄，限制了开放世界的机器人学习。我们提出EgoInfinity，一种通用的4D手物体交互数据引擎，能够支持网络规模的数据生成，用于机器人重定向和学习。EgoInfinity是一个模块化引擎，集成了感知、分割、重建、交互感知的精细化和重定向，旨在自动化这一传统上难以扩展的视频到动作问题，而无需人工标注。其模块化设计使得引擎能够持续受益于任何集成组件的进展。通过EgoInfinity，野外人类操作视频被提升为与代理无关的、度量化的4D手物体表示，包括手轨迹、6自由度物体姿态和接触相关状态。EgoInfinity并非简单地连接独立组件，而是结合了跨模块的度量校准与交互感知的精细化，以提高物理可靠性，减少在纯视觉重建中常见的漂移和接触不一致性。我们进一步提出了一种新颖的运动重定向器，将恢复的3D手部运动编译为可执行的关节轨迹，以适应多样化的机器人形态，从而实现从任意视角和镜头大小（例如，人类身体仅部分可见）的视频到动作重定向。我们在感知保真度、运动学可行性、接触一致性、跨体现泛化和真实机器人技能获取（例如，抓取、切割、擦拭和倒液）等方面验证了EgoInfinity，展示了从互联网视频到可执行机器人行为的可扩展桥梁，为开放世界的机器人学习提供了支持。
cs.RO / 12 / 2606.17388
Agent Utilities over Generalized Voronoi Regions and their Gradients
广义Voronoi区域上的代理效用及其梯度
Abstract
In this paper, we generalize the concept of Voronoi regions, define agent utility as the integral of a utility density over the corresponding Voronoi region, derive gradients of the utility, and illustrate the approach in a two-team example from soccer. The generalization of Voronoi regions is in the form of so-called Cost-Induced Voronoi (CIV) regions, where the agent state space may differ from the space being partitioned. One example of such regions is when the cost is given by the optimal solution of an LQR control problem. Then the agent states include position as well as velocity, while the partitioned space only includes positions. The agent utility is defined by integrating some utility density over the CIV region of the agent. This utility density might be the probability density of some beneficial event, such as receiving a pass in soccer. The utility is then the overall probability of receiving a pass and the gradient represents a way to improve that probability. We show how this utility gradient can be computed using the Reynolds Transport Theorem from fluid mechanics, and that this approach achieves similar accuracy while reducing computation time by about an order of magnitude compared to a baseline finite-difference approximation.
Chinese Translation
在本文中，我们对Voronoi区域的概念进行了推广，将代理效用定义为对应Voronoi区域上效用密度的积分，推导了效用的梯度，并在一个来自足球的双队示例中说明了该方法。Voronoi区域的推广形式为所谓的成本诱导Voronoi（Cost-Induced Voronoi, CIV）区域，其中代理状态空间可能与被划分的空间不同。这类区域的一个例子是，当成本由线性二次调节（LQR）控制问题的最优解给出时。此时，代理状态包括位置和速度，而划分的空间仅包括位置。代理效用通过在代理的CIV区域上积分某些效用密度来定义。该效用密度可能是某些有利事件的概率密度，例如在足球中接球的概率。效用则是接球的总体概率，梯度代表了改善该概率的方法。我们展示了如何使用流体力学中的雷诺运输定理计算该效用梯度，并且这种方法在减少计算时间约一个数量级的同时，达到了与基线有限差分近似相似的精度。
cs.RO / 13 / 2606.17394
Damage Adaptation in Seconds for Architected Materials
架构材料中的瞬时损伤适应
Abstract
Adaptation to damages and in-situ physical repairs is essential for long-term robot autonomy, yet challenging outside of narrowly defined and well-anticipated bounds. In this work we proprioceptively adapt to catastrophic damage in soft-actuated systems in under one minute. Architected materials are well equipped for adaptation: actuator failure occurs gradually rather than acutely, and damage can be described in a low-dimensional, discrete coordinate space. Surprisingly, latent damage representations plus a simple yet robust ensemble method is sufficient for adapting to unseen damage in real-time. Moreover, we identify conditions under which exponential sample complexity collapses to linear sample complexity for learned representations of architected materials, a concrete advantage over rigid components or continuum soft mechanisms. We demonstrate LEAP, our method for adaptive proprioception, via a tracing task for a 6DoF soft wrist based on Handed Shearing Auxetic (HSA) actuators. Our algorithm is able to adapt to cuts, burns, and actuator repairs, enabling simulation-free real-time adaptation that is critical for realizing the promise of soft robots outside the lab. Videos and more information are available at https://murpheylab.github.io/leap.
Chinese Translation
对损伤的适应和现场物理修复对于长期的机器人自主性至关重要，但在狭窄定义和良好预期的范围之外却面临挑战。在本研究中，我们在不到一分钟的时间内对软驱动系统中的灾难性损伤进行本体感知适应。架构材料非常适合进行适应：执行器的故障是逐渐发生的，而不是急性发生的，损伤可以在低维离散坐标空间中进行描述。令人惊讶的是，潜在的损伤表示加上简单而稳健的集成方法足以在实时中适应未见过的损伤。此外，我们确定了在何种条件下，架构材料的学习表示的指数样本复杂度会降至线性样本复杂度，这相较于刚性组件或连续软机制是一个具体优势。我们通过基于手动剪切增韧（Handed Shearing Auxetic, HSA）执行器的6自由度软腕的追踪任务展示了我们的自适应本体感知方法LEAP。我们的算法能够适应切割、烧伤和执行器修复，实现了无模拟的实时适应，这对于在实验室外实现软机器人潜力至关重要。视频和更多信息可在 https://murpheylab.github.io/leap 获取。
cs.RO / 14 / 2606.17408
Where Should Action Generation Begin? A Learnable Source Prior for Generative Robot Policies
行动生成应从何开始？一种可学习的源先验用于生成机器人策略
Abstract
Generative robot policies typically begin action generation from an observation-independent standard Gaussian distribution, leaving the choice of source distribution underexplored. This work asks a simple question: where should action generation begin? We propose LeaP, a Learnable source Prior that replaces the standard Gaussian with a proprioception-conditioned diagonal Gaussian over action chunks. Parameterized by a lightweight MLP, LeaP jointly predicts the mean and state-adaptive variance of the source distribution, while keeping the downstream generator architecture and inference solver unchanged. This design provides an observation-informed yet stochastic initialization, allowing the generator to focus on precise action refinement rather than transporting samples from an uninformed noise source. On 15 RoboTwin manipulation tasks, LeaP achieves an average success rate of 81.6%, outperforming four representative baselines -- including deterministic-source methods, a no-prior counterpart, and a diffusion-bridge policy -- by 6.5 to 25.5 percentage points. The same prior consistently improves both flow-matching and diffusion-bridge generators, while using fewer parameters and converging faster. The advantage carries over to real-world deployment, where LeaP attains the best performance. These results suggest that the source distribution is an independent and reusable design axis for generative robot policies, complementary to the choice of generative dynamics.
Chinese Translation
生成机器人策略通常从与观察无关的标准高斯分布开始行动生成，而源分布的选择却未得到充分探讨。本研究提出了一个简单的问题：行动生成应从何开始？我们提出了 LeaP（Learnable source Prior），它用一个基于本体感觉的对角高斯分布替代了标准高斯分布，用于行动块的生成。LeaP 由一个轻量级的多层感知器（MLP）参数化，能够联合预测源分布的均值和状态自适应方差，同时保持下游生成器架构和推理求解器不变。这种设计提供了一种基于观察的信息且具有随机性的初始化，使生成器能够专注于精确的行动细化，而不是从一个无信息的噪声源传输样本。在 15 个 RoboTwin 操作任务中，LeaP 实现了 81.6% 的平均成功率，超越了四个代表性基线，包括确定性源方法、无先验对照组和扩散桥策略，提升幅度为 6.5 到 25.5 个百分点。同样的先验在流匹配和扩散桥生成器中均表现出一致的改进，同时使用更少的参数并更快收敛。这一优势延续到实际部署中，LeaP 达到了最佳性能。这些结果表明，源分布是生成机器人策略的一个独立且可重用的设计轴，与生成动态的选择互为补充。
cs.RO / 15 / 2606.17418
DexLink Hand: A Compact, Affordable, 16-DOF Linkage-Driven Hand with Human-Like Dexterity
DexLink 手：一款紧凑、经济、具有16自由度的连杆驱动手，具有人类般的灵巧性
Abstract
Dexterous robotic hands face a longstanding trade-off among dexterity, compactness, and affordability. Particularly, high-degree-of-freedom designs typically demand complex actuation and transmission, hindering integration into human-scale forms. To address these challenges, this work presents a compact, low-cost linkage-driven anthropomorphic hand that achieves high dexterity, structural integration, and human-hand-like functionality. The hand integrates 20 joints driven by 16 independent actuators, with all actuation, sensing, and transmission components compactly embedded within a human-hand-sized structure. The resulting prototype weighs only 320g at a total cost below USD 400. To meet these objectives, a hybrid mechanical architecture combining planar and spatial linkage mechanisms is proposed, enabling decoupled multidirectional motion, biomimetic joint synergies, and high passive load-bearing capability. The thumb further incorporates biomimetic features supporting human-like reconfiguration and opposition movements. Through the coordinated integration of these mechanisms and structural layout, the prototype achieves a highly integrated design with anthropomorphic dexterity. Experimental evaluations demonstrate that the hand achieves the maximum Kapandji score, reproduces all 33 Feix grasp types, and performs stable grasping and dexterous manipulation across a wide variety of daily objects and tools. These results validate the proposed hand as an affordable, compact, and mechanically efficient platform for dexterous manipulation, teleoperation, and robot learning in human-centered environments.
Chinese Translation
灵巧的机器人手面临着灵巧性、紧凑性和经济性之间的长期权衡。特别是，高自由度设计通常需要复杂的驱动和传动系统，阻碍了其与人类尺度形态的整合。为了解决这些挑战，本研究提出了一款紧凑、低成本的连杆驱动类人手，能够实现高灵巧性、结构整合和类人手功能。该手集成了20个关节，由16个独立的驱动器驱动，所有的驱动、传感和传动组件都紧凑地嵌入在一个人手大小的结构中。最终原型的重量仅为320克，总成本低于400美元。为了实现这些目标，提出了一种结合平面和空间连杆机制的混合机械架构，使得多方向运动解耦、生物仿真关节协同作用以及高被动承载能力成为可能。拇指还结合了生物仿真特征，支持类人重构和对抗运动。通过这些机制和结构布局的协调整合，原型实现了高度集成的设计，具有人类般的灵巧性。实验评估表明，该手达到了最高的Kapandji评分，重现了所有33种Feix抓取类型，并在各种日常物品和工具上执行稳定的抓取和灵巧操作。这些结果验证了所提出的手作为一个经济、紧凑且机械高效的平台，适用于灵巧操作、远程操作和人本环境中的机器人学习。
cs.RO / 16 / 2606.17446
AnnotateAnything: Automatic Annotation of 3D Assets for Robot Manipulation
AnnotateAnything：用于机器人操作的3D资产自动标注
Abstract
Simulation enables scalable robot data collection, but raw 3D assets provide only geometry, lacking the semantic, interactive, and physical knowledge needed to specify where and how robots should act. In this work, we present AnnotateAnything, a general automatic annotation framework that converts passive 3D assets into manipulation-ready assets with structured, diverse, and executable manipulation labels. AnnotateAnything is built around two complementary pipelines. First, a unified visual-language annotation pipeline using vision-language reasoning to infer object semantics, interaction constraints, and 3D-grounded cues, providing human-prior guidance for identifying meaningful interaction regions. Second, a fully automatic and massively parallel physics annotation pipeline grounds these priors in each asset's geometry and physical constraints through candidate generation, geometry optimization and trajectory generation. This pipeline produces diverse and executable action annotations, including grasp poses, dexterous contacts, articulation waypoints, insertion directions, hanging affordances, and navigation targets. Using the generated annotations, we further build an asynchronous parallel simulation data-collection system across diverse objects, tasks, and robot embodiments. Experiments demonstrate that AnnotateAnything achieves superior annotation efficiency, data-collection efficiency, and task success rates over existing annotation and data-generation pipelines, while also supporting downstream tasks such as affordance detection, robotic VQA, and visual instruction finetuning. We provide project materials on the project page and plan to release the full code, annotations, and benchmark to facilitate future research. Videos, code, demo assets, and annotations are provided in supplementary materials Project page: https://tourmaline-caramel-169490.netlify.app.
Chinese Translation
仿真使得可扩展的机器人数据收集成为可能，但原始的3D资产仅提供几何信息，缺乏指定机器人应如何行动所需的语义、交互和物理知识。在本研究中，我们提出了AnnotateAnything，一个通用的自动标注框架，能够将被动的3D资产转换为具有结构化、多样化和可执行操作标签的可操作资产。AnnotateAnything建立在两个互补的管道之上。首先，使用视觉-语言推理的统一视觉-语言标注管道推断对象的语义、交互约束和3D基础线索，为识别有意义的交互区域提供人类先验指导。其次，一个完全自动化且大规模并行的物理标注管道通过候选生成、几何优化和轨迹生成将这些先验信息与每个资产的几何形状和物理约束相结合。该管道生成多样化和可执行的动作标注，包括抓取姿势、灵巧接触、关节路径点、插入方向、悬挂能力和导航目标。利用生成的标注，我们进一步构建了一个跨多样对象、任务和机器人形态的异步并行仿真数据收集系统。实验表明，AnnotateAnything在标注效率、数据收集效率和任务成功率上优于现有的标注和数据生成管道，同时也支持下游任务，如能力检测、机器人视觉问答（VQA）和视觉指令微调。我们在项目页面上提供项目材料，并计划发布完整的代码、标注和基准，以促进未来的研究。视频、代码、演示资产和标注材料已在补充材料中提供，项目页面链接：https://tourmaline-caramel-169490.netlify.app。
cs.RO / 17 / 2606.17455
Continual Online Personalization of Exoskeleton Control via Manifold-Aware Experience Replay
基于流形感知经验重放的外骨骼控制持续在线个性化
Abstract
Personalizing exoskeleton control remains a critical challenge for clinical users with gait disabilities. Online adaptation (OA) offers an effective solution by adapting in real time to subject variability, device fit, and diverse locomotor tasks. However, OA involves a continual stream of user state data, which can lead to catastrophic forgetting of previously learned locomotor contexts. Here, we develop a manifold-aware experience replay-based online personalization framework designed to maintain user-specific representations across diverse tasks during OA of exoskeleton control. By replaying previously experienced tasks from a replay buffer, we preserve the personalized exoskeleton assistance across all learned tasks. Furthermore, we capture a gait manifold that distinguishes between different locomotor tasks, removing the need for explicit task labeling when selecting target replay bins. We evaluated our framework on emulated hemiplegic gait, which largely deviates from able-bodied patterns, across multiple forgetting scenarios with speed and incline transitions. Our manifold-aware replay framework achieved 40% and 60% improvements in torque and gait phase tracking accuracy, respectively, compared to a baseline framework without replay, which exhibited catastrophic forgetting during task transitions. This demonstrates that our proposed framework personalizes exoskeleton control in real time across diverse locomotor contexts in daily ambulation of clinical populations.
Chinese Translation
个性化外骨骼控制仍然是针对步态障碍临床用户的一个关键挑战。在线适应（OA）通过实时适应个体差异、设备适配和多样化的运动任务，提供了一种有效的解决方案。然而，OA涉及持续的用户状态数据流，这可能导致对先前学习的运动上下文的灾难性遗忘。在此，我们开发了一种基于流形感知经验重放的在线个性化框架，旨在在外骨骼控制的OA过程中维护用户特定的表示，适用于多样化的任务。通过从重放缓冲区重放先前经历的任务，我们在所有学习的任务中保持个性化的外骨骼辅助。此外，我们捕捉了一个区分不同运动任务的步态流形，从而在选择目标重放箱时无需显式的任务标记。我们在模拟偏瘫步态的情况下评估了我们的框架，该步态与正常人模式有很大偏差，并在多个遗忘场景中进行了速度和坡度的过渡测试。我们的流形感知重放框架在扭矩和步态相位跟踪准确性方面分别实现了40%和60%的改善，相较于没有重放的基线框架，在任务过渡期间表现出灾难性遗忘。这表明我们提出的框架能够在临床人群的日常行走中实时个性化外骨骼控制，适用于多样化的运动上下文。
cs.RO / 18 / 2606.17456
Embodiment Shapes Rolling Behavior in a Multimodal Infant Model
具身性塑造多模态婴儿模型中的翻滚行为
Abstract
Rolling over is one of the earliest milestones in infant motor development, reflecting the emergence of coordinated, whole-body sensorimotor control. Here, we conduct a computational study of infant rolling using MIMo, a virtual infant embodiment equipped with proprioception and vestibular sensation. MIMo learns supine-to-prone rolls with reinforcement learning. Interestingly, the learned behaviors capture developmental trends and coordination patterns consistent with those reported in real infants, including improved performance and faster execution with age. Our results explain how infant capabilities and constraints can give rise to realistic behaviors in artificial agents, with a particular emphasis on how motor development is shaped by the changing body morphology. This work highlights the role of embodied computational models as a powerful tool for studying sensorimotor development.
Chinese Translation
翻滚是婴儿运动发展中的最早里程碑之一，反映了协调的全身感觉运动控制的出现。在此，我们使用MIMo进行婴儿翻滚的计算研究，MIMo是一个配备了本体感觉和前庭感觉的虚拟婴儿具身模型。MIMo通过强化学习学习仰卧到俯卧的翻滚行为。有趣的是，学习到的行为捕捉了与真实婴儿报告的一致的发展趋势和协调模式，包括随着年龄的增长，表现的改善和执行速度的加快。我们的结果解释了婴儿的能力和限制如何在人工代理中产生现实的行为，特别强调了运动发展如何受到身体形态变化的影响。这项工作突出了具身计算模型作为研究感觉运动发展的强大工具的作用。
cs.RO / 19 / 2606.17493
When Robots Sleep: Offline Skill Consolidation for Shared-Policy Robot Learning
当机器人休眠时：共享策略机器人学习的离线技能巩固
Abstract
Robots that learn over long deployments must add new skills without losing the shared policy structure that makes earlier skills reusable. We study sequential robot skill learning, where previous trajectories and task losses may be unavailable, and the deployed policy must remain a single shared controller without task-specific heads, routing, or adapters. We identify skill-coupling collapse, a failure mode in which individual skill success remains non-trivial while reliability among related skills deteriorates. We propose Sleeping Robots, a wake-sleep framework that learns each new skill during wake and consolidates the shared policy offline during sleep using compact frozen skill memories: frozen critics with unordered state buffers for reinforcement learning and frozen actor snapshots with unordered observation buffers for imitation learning. During sleep, these memories define differentiable surrogate objectives whose gradients are combined through Nash bargaining, with adaptive anchoring and local excitability for stable consolidation. On Meta-World MT5, Sleeping Robots improves average success by 64 % and pairwise reliability by x 2.0 over the strongest non-oracle baseline, and on SurgicAI it improves average success and backward transfer relative to continual imitation baselines while remaining competitive on pairwise reliability.
Chinese Translation
在长期部署中学习的机器人必须在不失去共享策略结构的情况下添加新技能，以确保早期技能的可重用性。我们研究了顺序机器人技能学习，其中先前的轨迹和任务损失可能不可用，且部署的策略必须保持为单一共享控制器，而不使用特定任务的头部、路由或适配器。我们识别了技能耦合崩溃（skill-coupling collapse），这是一种失败模式，其中单个技能的成功保持非平凡性，而相关技能之间的可靠性却恶化。我们提出了“休眠机器人”（Sleeping Robots），一种在清醒期间学习每项新技能，并在休眠期间利用紧凑的冻结技能记忆离线巩固共享策略的框架：用于强化学习的无序状态缓冲区的冻结评估器和用于模仿学习的无序观察缓冲区的冻结演员快照。在休眠期间，这些记忆定义了可微分的替代目标，其梯度通过纳什谈判（Nash bargaining）结合，具有自适应锚定和局部兴奋性以实现稳定的巩固。在Meta-World MT5上，“休眠机器人”将平均成功率提高了64%，并将成对可靠性提高了2.0倍，相较于最强的非神谕基线；在SurgicAI上，它在相对于持续模仿基线的情况下提高了平均成功率和向后转移，同时在成对可靠性上保持竞争力。
cs.RO / 20 / 2606.17511
MagicSim: A Unified Infrastructure for Executable Embodied Interaction
MagicSim：可执行具身交互的统一基础设施
Abstract
Robot learning and embodied agents now require simulation to serve as a shared execution substrate linking control, skills, and planning, not only as a renderer, controller testbed, or fixed task environment. Existing pipelines split these layers with "magic" actions, disconnected training environments, or forward-only renders that cannot reproduce, evaluate, and annotate the same episode. We present MagicSim, an embodied interaction infrastructure built around one deterministic batched runtime and a shared Markov decision process (MDP). From YAML-first specifications that decouple contents, placement, behavior, and agent exposure, MagicSim constructs diverse executable worlds spanning task families, interaction regimes, physics, layouts, sensors, avatars, and robot embodiments in one reset-and-step loop. A common execution interface grounds high-level commands through controllers, atomicskills, planner primitives, and asynchronous planning, realizing them as robot actions rather than simulator-side state edits. One task definition supports three capabilities: benchmark and RL evaluation, an autocollect interface that automatically turns commands into grounded trajectories, and agent/VLM-facing interaction. For automatic execution, commands flow through a Command->Skill->Planner->Robot->Record pipeline, while per-environment command, skill, planning, retry, annotation, and episode states advance independently above the shared physics tick. Successful rollouts are saved as structured multimodal trajectories aligning language supervision, action representations, visual/geometric representations, and task-level status with the executed episode. MagicSim thus unifies diverse world construction, embodied execution, task evaluation, automatic rollout generation, and interactive agent interfaces in one planner-in-the-loop runtime.
Chinese Translation
机器人学习和具身代理现在需要模拟作为一个共享的执行基础，连接控制、技能和规划，而不仅仅是作为渲染器、控制器测试平台或固定任务环境。现有的流程通过“魔法”动作、断开的训练环境或只能向前渲染的方式将这些层分开，无法重现、评估和注释相同的情节。我们提出了MagicSim，这是一种围绕一个确定性批处理运行时和共享的马尔可夫决策过程（MDP）构建的具身交互基础设施。通过YAML优先的规范，MagicSim解耦了内容、放置、行为和代理暴露，构建了跨越任务家族、交互机制、物理、布局、传感器、化身和机器人具身的多样化可执行世界，形成一个重置和步骤循环。一个通用的执行接口通过控制器、原子技能、规划原语和异步规划将高级命令落地，实现为机器人动作，而不是模拟器端的状态编辑。一个任务定义支持三种能力：基准和强化学习评估、一个自动收集接口，自动将命令转化为有根轨迹，以及代理/视觉语言模型（VLM）交互。对于自动执行，命令通过命令->技能->规划->机器人->记录的流程流动，而每个环境的命令、技能、规划、重试、注释和情节状态在共享的物理时钟上独立推进。成功的执行结果被保存为结构化的多模态轨迹，将语言监督、动作表示、视觉/几何表示和任务级状态与执行的情节对齐。因此，MagicSim在一个规划者在环的运行时中统一了多样的世界构建、具身执行、任务评估、自动滚动生成和交互代理接口。
cs.RO / 21 / 2606.17520
GASE: Gaussian Splatting-Based Automated System for Reconstructing Embodied-Simulation Environments
GASE：基于高斯点云的自动化系统用于重建具身仿真环境
Abstract
Training embodied agents in the real world requires skilled operators and expensive hardware. Simulation environments offer a compelling alternative by enabling large-scale, cost-effective data augmentation. Consequently, rapidly constructing high-fidelity simulation scenes with a minimal sim-to-real gap has become a critical objective in robot learning. While reconstruction-based methods provide superior visual quality, current workflows are hindered by inefficient data acquisition and subpar foreground object extraction. We thus propose GASE, a highly automated system for simulation scene construction. GASE leverages multi-view video streams from panoramic camera arrays to enable rapid environment scanning. To ensure high-quality asset generation, our pipeline introduces a camera-pose-based strategy that robustly extracts objects across frames in the 2D domain, followed by high-fidelity scene inpainting. Foreground objects and the static background are then reconstructed independently and seamlessly imported into physics simulators for policy training. Extensive experiments demonstrate that GASE outperforms existing 3D Gaussian-based methods in segmentation accuracy by over 10\% while achieving state-of-the-art inpainting quality. Furthermore, real-robot deployments across manipulation and navigation tasks maintains a performance gap of less than 10\% compared to policies trained purely on real-world data. These results confirm that GASE provides an efficient and highly effective solution for bridging the sim-to-real gap. Code will be released.
Chinese Translation
在现实世界中训练具身智能体需要熟练的操作员和昂贵的硬件。仿真环境通过实现大规模、成本效益高的数据增强，提供了一个引人注目的替代方案。因此，快速构建高保真仿真场景并最小化仿真与现实之间的差距已成为机器人学习中的一个关键目标。尽管基于重建的方法提供了优越的视觉质量，但当前的工作流程受到低效数据获取和不理想的前景物体提取的限制。因此，我们提出了GASE，一个高度自动化的仿真场景构建系统。GASE利用来自全景相机阵列的多视角视频流，实现快速环境扫描。为了确保高质量资产的生成，我们的流程引入了一种基于相机姿态的策略，在二维域中稳健地提取跨帧的物体，随后进行高保真的场景修复。前景物体和静态背景随后独立重建，并无缝导入物理仿真器进行策略训练。大量实验表明，GASE在分割精度上超过现有的基于3D高斯的方法超过10%，同时实现了最先进的修复质量。此外，在操作和导航任务中的真实机器人部署与仅在现实世界数据上训练的策略相比，保持了不到10%的性能差距。这些结果确认GASE提供了一个高效且极具成效的解决方案，以弥合仿真与现实之间的差距。代码将会发布。
cs.RO / 22 / 2606.17534
RICH-SLAM: Radar SLAM with Incremental and Continuous Hilbert Mapping
RICH-SLAM：基于雷达的增量与连续希尔伯特映射的同时定位与地图构建
Abstract
Simultaneous localization and mapping using radar sensors has gained increasing attention due to radar's inherent robustness to adverse weather and lighting conditions. However, radar measurements are characteristically sparse and noisy compared to LiDAR and visual data, posing significant challenges in achieving dense, continuous, and consistent map representations. In this paper, we present RICH-SLAM, a radar SLAM framework designed to address these challenges. Our approach features a Rao-Blackwellized particle filter-based back end that employs particle filtering for pose estimation and Kalman filtering for map updates. We propose an incremental Hilbert-space reduced-rank Gaussian process mapping strategy that enables continuous and uncertainty-aware map representations given sparse radar inputs. We further introduce a posterior-aware particle weighting scheme that leverages the full posterior distribution of map parameters for more robust likelihood evaluation. Experiments on self-collected and public ColoRadar datasets show that RICH-SLAM constructs continuous occupancy maps from sparse radar measurements and supports uncertainty-aware planning for mobile robots.
Chinese Translation
利用雷达传感器进行的同时定位与地图构建（SLAM）因雷达在恶劣天气和光照条件下的固有鲁棒性而受到越来越多的关注。然而，与激光雷达（LiDAR）和视觉数据相比，雷达测量通常稀疏且噪声较大，这给实现密集、连续和一致的地图表示带来了重大挑战。本文提出了RICH-SLAM，一个旨在解决这些挑战的雷达SLAM框架。我们的方法采用基于Rao-Blackwell化粒子滤波的后端，利用粒子滤波进行姿态估计，并使用卡尔曼滤波进行地图更新。我们提出了一种增量希尔伯特空间降秩高斯过程映射策略，该策略能够在稀疏雷达输入的情况下实现连续和考虑不确定性的地图表示。我们进一步引入了一种后验感知的粒子加权方案，利用地图参数的完整后验分布进行更稳健的似然评估。在自收集和公共ColoRadar数据集上的实验表明，RICH-SLAM能够从稀疏的雷达测量中构建连续的占用地图，并支持移动机器人进行不确定性感知的规划。
cs.RO / 23 / 2606.17598
MuseVLA: An Adaptive Multimodal Sensing Vision-Language-Action Model for Robotic Manipulation
MuseVLA：一种用于机器人操作的自适应多模态感知视觉-语言-动作模型
Abstract
Humans naturally leverage diverse sensing modalities to interact with the physical world, while most Vision-Language-Action (VLA) models for robotics rely solely on RGB observations. This limits their ability to perceive physical properties that are difficult or impossible to infer from RGB cameras, such as temperature, sound, or radar response. We present MuseVLA, an adaptive multimodal sensing VLA model that integrates novel sensors as on-demand tools for robotic manipulation. Given a task instruction and visual context, MuseVLA first generates a sensor token and target description that select the sensing modality to invoke and what to attend to, analogous to a tool call with arguments. It then converts the selected sensor measurement into a grounded sensor image, a unified intermediate representation that encodes heterogeneous readings for multimodal fusion and action generation. This design decouples sensor-specific processing from the VLA backbone, enabling efficient integration of diverse modalities. To reduce the need for expensive multisensory robot datasets, we further introduce a data synthesis pipeline that augments existing RGB video datasets with grounded sensor images, enabling generalization to unseen sensor-guided tasks. We evaluate MuseVLA on a real-world robot across challenging dexterous hand manipulation tasks that require multimodal sensing inputs, including temperature-guided pick-and-place, audio-driven object search, and radar-assisted hidden object retrieval. MuseVLA achieves 80.6% success rate on average, outperforming RGB-only and multisensory VLA baselines significantly, and exhibits strong zero-shot capabilities on unseen tasks.
Chinese Translation
人类自然地利用多种感知模态与物理世界互动，而大多数用于机器人技术的视觉-语言-动作（VLA）模型仅依赖于RGB观测。这限制了它们感知难以或无法通过RGB相机推断的物理属性的能力，例如温度、声音或雷达响应。我们提出了MuseVLA，一种自适应多模态感知VLA模型，集成了新型传感器作为机器人操作的按需工具。给定任务指令和视觉上下文，MuseVLA首先生成一个传感器令牌和目标描述，以选择要调用的感知模态和关注的内容，类似于带参数的工具调用。然后，它将选定的传感器测量转换为一个基础传感器图像，这是一种统一的中间表示，编码了异构读数以实现多模态融合和动作生成。该设计将传感器特定处理与VLA主干解耦，从而实现多种模态的高效集成。为了减少对昂贵的多传感器机器人数据集的需求，我们进一步引入了一种数据合成管道，通过基础传感器图像增强现有的RGB视频数据集，使其能够推广到未见过的传感器引导任务。我们在真实世界的机器人上评估了MuseVLA，针对需要多模态感知输入的挑战性灵巧手操作任务，包括温度引导的拾取与放置、音频驱动的物体搜索和雷达辅助的隐藏物体检索。MuseVLA的平均成功率达到80.6%，显著优于仅使用RGB和多传感器VLA基线，并在未见过的任务上展现出强大的零-shot能力。
cs.RO / 24 / 2606.17630
FLAP: FOV-Constrained Active Perception Planning for Prior-Map-Free 3D Navigation
FLAP：基于视场约束的主动感知规划用于无先验地图的三维导航
Abstract
Safe and efficient trajectory planning in unknown, cluttered 3D environments constitutes a critical bottleneck for deploying Unmanned Aerial Vehicles (UAVs) in real-world applications. This challenge is further exacerbated by the limited field-of-view (FOV) and sensing range of onboard sensors. Many existing methods either make simplistic assumptions about unexplored space or rely on conservative heuristics such as speed limits or fixed perception patterns, reducing efficiency and generalizing poorly across different sensor types. In this work, we propose a novel planning framework that directly integrates active perception into trajectory optimization, thereby improving safety while preserving efficiency. The perception constraints are derived from the UAV's dynamic model and formulated in the sensor coordinate frame, which enables precise handling of FOV geometry. The velocity-triggered activation mechanism enables the planner to balance perception and motion efficiency. We introduce an active perception sub-trajectory segment with parametric start-time optimization, mitigating collision risks from late obstacle detection. Our formulation enables active perception during arbitrary 3D maneuvers, extending beyond prior methods designed mainly for horizontal motion. All constraints and penalties are incorporated into a differentiable optimization problem, so the planner requires only a simple front-end global path for guidance, rather than a computationally expensive perception-aware path generator. Extensive simulations and real-world experiments demonstrate robust performance across diverse unknown environments with varying sensor configurations.
Chinese Translation
在未知且杂乱的三维环境中进行安全高效的轨迹规划是无人机（UAV）在实际应用中面临的一个关键瓶颈。由于机载传感器的有限视场（FOV）和感知范围，这一挑战变得更加严峻。许多现有方法要么对未探索空间做出简单假设，要么依赖于保守的启发式方法，如速度限制或固定的感知模式，这降低了效率，并且在不同传感器类型之间的泛化能力较差。在本研究中，我们提出了一种新颖的规划框架，直接将主动感知集成到轨迹优化中，从而在提高安全性的同时保持效率。感知约束源于无人机的动态模型，并在传感器坐标系中进行公式化，这使得能够精确处理视场几何。速度触发的激活机制使规划者能够平衡感知和运动效率。我们引入了一个具有参数化起始时间优化的主动感知子轨迹段，以减轻因延迟障碍物检测而导致的碰撞风险。我们的公式化使得在任意三维机动中实现主动感知，超越了主要针对水平运动的先前方法。所有约束和惩罚都被纳入一个可微分的优化问题中，因此规划者只需要一个简单的前端全局路径作为指导，而不是一个计算开销大的感知感知路径生成器。大量的仿真和实际实验表明，在不同的未知环境和传感器配置下，系统表现出强大的鲁棒性。
cs.RO / 25 / 2606.17639
ERQA-Plus: A Diagnostic Benchmark for Reasoning in Embodied AI
ERQA-Plus：一种用于具身人工智能推理的诊断基准
Abstract
Generalist embodied agents require more than object recognition: they must reason about spatial relations, actions, procedures, human intentions, environmental constraints, and commonsense consequences from situated visual observations. Yet existing visual and embodied question answering benchmarks often provide limited control over the reasoning dependencies being tested, making it difficult to distinguish grounded embodied reasoning from shortcut-driven visual or linguistic pattern matching. We present ERQA-Plus, a diagnostic benchmark for reasoning in embodied AI. ERQA-Plus contains 1,766 question-answer instances grounded in 711 robot-centric images and organized according to a structured taxonomy spanning perceptual, action-centric, social-interaction, navigation-environmental, and contextual commonsense reasoning. The dataset is constructed using a multi-stage generation and validation pipeline that combines taxonomy-guided question generation, automatic quality judging, iterative revision, and human assessment to improve visual grounding, answer validity, and reasoning quality. We benchmark representative general-purpose vision-language models and embodied models, including LLaVA-NeXT-8B, Prismatic-7B, MiniCPM-V-4.5-8B, Qwen3-VL, RoboRefer-8B, and RoboBrain2.5-8B. Although the strongest model, Qwen3-VL-32B, achieves 83.4% overall accuracy and 61.4 SBERT score, category-level results reveal persistent weaknesses in spatial reasoning, procedural reasoning, event prediction, and intention inference. ERQA-Plus therefore provides a fine-grained evaluation framework for measuring not only whether embodied agents answer correctly, but also which forms of embodied reasoning they can and cannot perform reliably. The dataset is available https://huggingface.co/datasets/huggingdas/erqa-plus and the project page at https://github.com/LUNAProject22/erqa-plus.
Chinese Translation
通用具身代理不仅需要物体识别：它们必须对空间关系、动作、程序、人类意图、环境约束以及基于情境的视觉观察的常识后果进行推理。然而，现有的视觉和具身问答基准往往对被测试的推理依赖性提供有限的控制，这使得很难区分基于基础的具身推理与驱动于捷径的视觉或语言模式匹配。我们提出了ERQA-Plus，一种用于具身人工智能推理的诊断基准。ERQA-Plus包含1,766个基于711张机器人中心图像的问题-答案实例，并根据一个涵盖感知、以行动为中心、社会互动、导航-环境和上下文常识推理的结构化分类法进行组织。该数据集采用多阶段生成和验证流程构建，结合了分类法指导的问题生成、自动质量判断、迭代修订和人工评估，以提高视觉基础、答案有效性和推理质量。我们对代表性的通用视觉-语言模型和具身模型进行了基准测试，包括LLaVA-NeXT-8B、Prismatic-7B、MiniCPM-V-4.5-8B、Qwen3-VL、RoboRefer-8B和RoboBrain2.5-8B。尽管最强的模型Qwen3-VL-32B实现了83.4%的整体准确率和61.4的SBERT得分，但类别级别的结果揭示了在空间推理、程序推理、事件预测和意图推断方面的持续弱点。因此，ERQA-Plus提供了一个细致的评估框架，不仅用于衡量具身代理是否正确回答问题，还用于评估它们能够和不能可靠执行的具身推理形式。数据集可在https://huggingface.co/datasets/huggingdas/erqa-plus获取，项目页面位于https://github.com/LUNAProject22/erqa-plus。
cs.RO / 26 / 2606.17739
ED3R: Energy-Aware Distributed Disaster Detection Enabled by Cooperative Robotic Agents
ED3R：由协作机器人代理驱动的能量感知分布式灾害检测
Abstract
Robotics are expected to support environmental monitoring and natural disaster management, where decisions must be made under uncertainty, resource limitations, and strict operational constraints. In critical missions, such as wildfires, robotic agents must not only identify hazardous events with sufficient confidence, but also manage the energy cost and time until detection. This paper introduces ED3R, an energy-aware distributed framework for wildfire detection under uncertainty. ED3R enables hierarchical cooperative decision-making between a robot and a remote controller. The remote controller decides upon the robot's motion, while the robot senses the environment and decides where to execute the wildfire detection (onboard or remotely) and how. The common goal is to detect wildfires with a required confidence while minimizing the energy consumed by any robot operation. ED3R further integrates mechanisms to avoid nearby obstacles, prevent redundant exploration, enable adaptive early mission completion, and ensure feasibility through a custom penalty function. ED3R also introduces a forward-looking capability, enabled through distributed neural regression models that allow the agents to anticipate the future by evaluating candidate strategies before execution. The framework is evaluated through realistic robotics simulations, ablation studies, and baseline comparisons. Overall, ED3R achieves a mission success rate of up to 97.18%. Especially in the most demanding missions, it reduces energy consumption by up to 36.4% and detects wildfires up to 41% faster than baselines.
Chinese Translation
机器人技术预计将在环境监测和自然灾害管理中发挥支持作用，在这些领域中，决策必须在不确定性、资源限制和严格的操作约束下进行。在关键任务中，例如野火，机器人代理不仅必须以足够的信心识别危险事件，还必须管理检测所需的能量成本和时间。本文介绍了ED3R，一个在不确定性下进行野火检测的能量感知分布式框架。ED3R使机器人与远程控制器之间能够进行分层的协作决策。远程控制器决定机器人的运动，而机器人则感知环境并决定在哪里执行野火检测（在机载或远程）以及如何执行。共同目标是在满足所需信心的同时，尽量减少任何机器人操作所消耗的能量。ED3R进一步整合了避免附近障碍物、预防冗余探索、实现自适应提前完成任务以及通过自定义惩罚函数确保可行性的机制。ED3R还引入了一种前瞻性能力，通过分布式神经回归模型使代理能够在执行前评估候选策略，从而预测未来。该框架通过现实的机器人仿真、消融研究和基准比较进行了评估。总体而言，ED3R实现了高达97.18%的任务成功率。特别是在最具挑战性的任务中，其能量消耗减少了最多36.4%，并且比基准检测野火的速度快了最多41%。
cs.RO / 27 / 2606.17831
Accountability in Autonomous Drone-Based Firefighting: Insights From a Field Trial
自主无人机消防中的问责制：来自现场试验的见解
Abstract
There is a growing research field exploring how autonomous drones can enhance emergency response effectiveness. Integrating these (artificial) agents into existing emergency teams and workflows may significantly impact established accountability relationships. This paper examines how autonomous drones affect accountability attribution within complex socio-technical systems. Drawing on two real-life field trials in firefighting, the study reveals substantial uncertainty around accountability when drones are organizationally deployed. Using Bovens' accountability framework, two challenges are identified: (1) uncertainty about the role of drones within hierarchical structures, leading to confused accountability ascriptions; and (2) new forms of human-drone interactions introducing additional accountability-relevant issues. Based on these insights, the paper proposes actionable recommendations to support the responsible integration of autonomous drones into firefighting operations without undermining accountability. These findings offer practical guidance for policymakers and contribute to further research on accountability in autonomous systems.
Chinese Translation
随着研究领域的不断扩大，探索自主无人机如何增强应急响应的有效性已成为一个重要课题。将这些（人工）代理整合到现有的应急团队和工作流程中，可能会显著影响既有的问责关系。本文考察了自主无人机如何影响复杂社会技术系统中的问责归属。通过对消防领域的两个真实现场试验的分析，研究揭示了在组织性部署无人机时，问责方面存在显著的不确定性。利用Bovens的问责框架，识别出两个挑战：（1）对无人机在层级结构中角色的不确定性，导致问责归属的混淆；（2）新的人机交互形式引入了额外的与问责相关的问题。基于这些见解，本文提出了可行的建议，以支持自主无人机在消防操作中的负责任整合，而不削弱问责制。这些发现为政策制定者提供了实用指导，并为进一步研究自主系统中的问责制做出了贡献。
cs.RO / 28 / 2606.17833
HumanoidArena: Benchmarking Egocentric Hierarchical Whole-body Learning
HumanoidArena：以自我为中心的层次化全身学习基准测试
Abstract
Humanoid robots promise whole-body interaction in human-centered environments, but scalable policy learning remains difficult because task-level decision-making and whole-body dynamic execution are tightly coupled. A practical solution is hierarchical control, where a high-level policy predicts intermediate whole-body actions and low-level general motion trackers (GMTs) execute them as stable humanoid motion. However, existing benchmarks rarely evaluate the policy-tracker interface itself, leaving open whether intermediate whole-body actions are executable, robust under task distribution shifts, and transferable across different GMT backends. We introduce HumanoidArena, a simulation-first benchmark for egocentric hierarchical whole-body learning. The benchmark formulates policy learning as a hierarchical decision making problem: a high-level policy converts egocentric vision, proprioception, and instructions into a compact whole-body action, which is subsequently executed by a low-level GMT. Instead of treating the legs as planar transport tools, HumanoidArena emphasizes interactions where lower-body coordination is structurally necessary in task completion. We therefore design 7 leg-critical HOI/HSI tasks in which success requires foot placement, balance maintenance, posture adjustment, and whole-body reorientation. To further diagnose the hierarchical system, we evaluate policies from two complementary perspectives: perturbation-conditioned generalization and GMT-conditioned transfer. Experiments show that hierarchical control enables learned policies to solve diverse leg-critical interactions, but performance is strongly tracker-conditioned and cross-GMT transfer remains fragile. These results position HumanoidArena as a benchmark for studying transferable intermediate action representations and scalable egocentric whole-body policy learning.
Chinese Translation
类人机器人在以人为中心的环境中承诺实现全身互动，但由于任务级决策与全身动态执行紧密耦合，可扩展的策略学习仍然困难。一种实用的解决方案是层次控制，其中高层策略预测中间全身动作，低层通用运动跟踪器（GMT）将其执行为稳定的类人运动。然而，现有的基准测试很少评估策略-跟踪器接口本身，尚不清楚中间全身动作是否可执行、在任务分布变化下是否稳健，以及在不同GMT后端之间是否可迁移。我们引入了HumanoidArena，这是一个以模拟为基础的以自我为中心的层次化全身学习基准。该基准将策略学习表述为层次决策问题：高层策略将以自我为中心的视觉、身体感知和指令转换为紧凑的全身动作，随后由低层GMT执行。HumanoidArena强调在任务完成中下肢协调结构上必要的交互，而不是将腿视为平面运输工具。因此，我们设计了7个腿部关键的HOI/HSI任务，其中成功需要脚的位置、平衡维护、姿势调整和全身重新定向。为了进一步诊断层次系统，我们从两个互补的角度评估策略：扰动条件下的泛化和GMT条件下的迁移。实验表明，层次控制使学习到的策略能够解决各种腿部关键的交互，但性能强烈依赖于跟踪器，并且跨GMT的迁移仍然脆弱。这些结果将HumanoidArena定位为研究可迁移中间动作表示和可扩展的以自我为中心的全身策略学习的基准。
cs.RO / 29 / 2606.17839
From Ad Hoc Pilots to Repeatable Patterns: Structuring Drone Collaboration in Emergency Services with DroneLets
从临时飞行员到可重复模式：利用 DroneLets 结构化紧急服务中的无人机协作
Abstract
Drones hold promise for supporting emergency services, but their integration into workflows remains ad hoc and coordination-intensive. This paper addresses two research questions: how emergency teams want to collaborate with drones, and how to formalize these collaborations into repeatable processes. Based on four field trials and 95 interviews, we derive 44 interaction patterns grouped into 10 meta-patterns reflecting operational needs such as reconnaissance, communication, and logistical support. To structure these practices, we introduce DroneLets - a new class of design artifacts that extend Collaboration Engineering to embodied agents. DroneLets capture setup requirements, drone capabilities, environmental constraints, and coordinated actions across human and drone actors. They offer a modular framework for designing repeatable, scalable collaboration processes in emergency services, illustrated through patterns such as broadcasting to bystanders and post-fire monitoring. This work expands the scope of CE and provides a structured foundation for integrating autonomous drones into high-stakes field operations.
Chinese Translation
无人机在支持紧急服务方面具有潜力，但其在工作流程中的整合仍然是临时性的且需要大量协调。本文解决了两个研究问题：紧急团队希望如何与无人机协作，以及如何将这些协作形式化为可重复的流程。基于四次实地试验和95次访谈，我们总结出44种交互模式，分为10种元模式，反映了侦察、通信和后勤支持等操作需求。为了结构化这些实践，我们引入了 DroneLets——一种新的设计工件类别，旨在将协作工程扩展到具身代理。DroneLets 捕捉设置要求、无人机能力、环境约束以及人类与无人机参与者之间的协调行动。它们提供了一个模块化框架，用于设计可重复、可扩展的紧急服务协作流程，通过向旁观者广播和火灾后监测等模式进行说明。这项工作扩展了协作工程的范围，并为将自主无人机整合到高风险现场操作提供了结构化基础。
cs.RO / 30 / 2606.17846
Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models
Qwen-RobotManip技术报告：对齐解锁机器人操作基础模型的规模
Yuan, Haoqi, Liang, Zhixuan, Chen, Anzhe, Wang, Ye, Li, Haoyang, Lin, Pei, Huang, Yiyang, Lei, Zixing, Zhang, Tong, Zhang, Jiazhao, Zhang, Jie, Fan, Jingyang, Zhou, Gengze, Peng, Qihang, Lv, Chenxu, Chen, Xiaoyue, Yang, An, Huang, Fei, Lin, Junyang, Liu, Dayiheng, Zhou, Jingren, Wu, Chenfei, Chen, Xiong-Hui
Abstract
Foundation models in language and multimodality achieve strong generalization by aligning heterogeneous data under a unified formulation and training at scale. In this report, we investigate whether this scaling recipe can be applied to robotic manipulation to achieve genuine generalization. This is challenging because, unlike text, manipulation data is heterogeneous by nature, expensive to collect, and narrow in diversity, making alignment and scale simultaneously difficult. We present Qwen-RobotManip, a generalizable Vision-Language-Action foundation model built on Qwen-VL. Qwen-RobotManip introduces a unified alignment framework across the representation, motion, and behavioral dimensions of manipulation, making large-scale multi-source training coherent rather than conflicting. This alignment capability in turn enables Qwen-RobotManip to absorb manipulation data at a scale that prior training regimes could not sustain. A human-to-robot synthesis pipeline converts egocentric hand demonstrations into robot trajectories across 15 platforms, and a rigorous curation pipeline harmonizes heterogeneous datasets. Using only open-source datasets and human videos without proprietary data collection, Qwen-RobotManip constructs a ~38,100-hour pretraining corpus and exhibits emergent generalization capabilities, including zero-shot instruction following, robustness to perturbations, reactive error recovery, and cross-embodiment transfer. We find that standard benchmarks fail to capture pretraining quality and instead adopt OOD settings including RoboCasa365, LIBERO-Plus, EBench, RoboTwin-Clean2Rand, RoboTwin-IF, and RoboTwin-XE. Qwen-RobotManip substantially outperforms prior state-of-the-art models, including $\pi$0.5, across all OOD settings, ranks 1st in RoboChallenge with a 20% relative improvement, and is validated on real-robot platforms including AgileX ALOHA, Franka, UR, and ARX.
Chinese Translation
语言和多模态的基础模型通过在统一的公式下对异构数据进行对齐并进行大规模训练，实现了强大的泛化能力。在本报告中，我们探讨了这一扩展方法是否可以应用于机器人操作，以实现真正的泛化。这是一个具有挑战性的任务，因为与文本不同，操作数据本质上是异构的，收集成本高且多样性狭窄，使得对齐和规模的同时实现变得困难。我们提出了Qwen-RobotManip，这是一个基于Qwen-VL构建的可泛化的视觉-语言-动作基础模型。Qwen-RobotManip在操作的表示、运动和行为维度上引入了统一的对齐框架，使得大规模多源训练变得一致而非冲突。这种对齐能力反过来使得Qwen-RobotManip能够以先前训练方案无法维持的规模吸收操作数据。一个人机合成管道将自我中心的手部演示转换为15个平台上的机器人轨迹，而一个严格的策划管道则协调异构数据集。仅使用开源数据集和人类视频而不依赖专有数据收集，Qwen-RobotManip构建了约38,100小时的预训练语料库，并展现出新兴的泛化能力，包括零-shot 指令跟随、对扰动的鲁棒性、反应性错误恢复和跨体现迁移。我们发现标准基准未能捕捉预训练的质量，因此采用了包括RoboCasa365、LIBERO-Plus、EBench、RoboTwin-Clean2Rand、RoboTwin-IF和RoboTwin-XE在内的OOD设置。Qwen-RobotManip在所有OOD设置中显著超越了先前的最先进模型，包括$ ext{π}$0.5，在RoboChallenge中排名第一，提升了20%的相对性能，并在包括AgileX ALOHA、Franka、UR和ARX在内的真实机器人平台上进行了验证。
cs.RO / 31 / 2606.17906
WAM-RL: World-Action Model Reinforcement Learning with Reconstruction Rewards and Online Video SFT
WAM-RL：具有重建奖励和在线视频SFT的世界-动作模型强化学习
Abstract
Recent World-Action (WA) models demonstrate strong generalization ability and data efficiency, but they typically rely on expert trajectories for training. This reliance limits their ability to acquire fine-grained manipulation skills beyond the demonstration distribution and prevents them from continuously improving through real-world interaction. To address these limitations, we propose WAM-RL, a reinforcement learning framework that enables joint optimization of the world model and the action model through online interaction with the environment. By allowing the two components to co-evolve, our approach enhances fine-grained control and adaptability. Specifically, a WA model consists of a world model and an actor. We design a tailored reinforcement learning method with hierarchical optimization to coordinate their improvement. On the methodological side, we systematically investigate the effects of applying reinforcement learning to the action model, as well as online training of the world model within an RL setting. Our experiments reveal a key insight: optimizing only the actor yields improvements on short-horizon tasks, but fails to provide significant gains on long-horizon tasks. In contrast, jointly optimizing both the world model and the actor is critical for achieving strong performance in long-horizon settings. Our work is the first to introduce reinforcement learning into the World-Action paradigm, and provides insights into how online optimization of both the action head and the world model impacts overall performance.
Chinese Translation
最近的世界-动作（WA）模型展示了强大的泛化能力和数据效率，但它们通常依赖于专家轨迹进行训练。这种依赖限制了它们在演示分布之外获取细粒度操作技能的能力，并阻碍了它们通过与现实世界的互动持续改进。为了解决这些限制，我们提出了WAM-RL，这是一种强化学习框架，能够通过与环境的在线互动实现世界模型和动作模型的联合优化。通过允许这两个组件共同演化，我们的方法增强了细粒度控制和适应性。具体而言，WA模型由一个世界模型和一个演员组成。我们设计了一种定制的强化学习方法，采用分层优化来协调它们的改进。在方法论方面，我们系统地研究了将强化学习应用于动作模型的效果，以及在强化学习环境中对世界模型进行在线训练的影响。我们的实验揭示了一个关键见解：仅优化演员在短期任务上有所改善，但在长期任务上未能提供显著收益。相比之下，联合优化世界模型和演员对于在长期环境中实现强大性能至关重要。我们的工作是首次将强化学习引入世界-动作范式，并提供了关于动作头和世界模型的在线优化如何影响整体性能的见解。
cs.RO / 32 / 2606.17924
PearlVLA: Progressive Embodied Action-Plan Refinement in Latent Space
PearlVLA：潜在空间中渐进式具身行动计划优化
Abstract
Current Vision-Language-Action (VLA) models face a trade-off between efficient action generation and explicit deliberation. Directly decoding actions from vision-language backbone representations enables low-latency control, whereas explicit reasoning through textual chains, pixel-level subgoals, or action search can improve planning but incurs substantial latency and computational cost. We propose PearlVLA, a VLA framework that moves deliberation into the latent space of a vision-language model (VLM). PearlVLA separates VLM meta-query representations into a fixed visual grounding branch and an iterative latent plan branch. At each refinement round, a plan-conditioned world query probes a lightweight frozen latent world model for an action-free future observation latent, which is fed back to guide plan refinement. A future-guided RefineNet then applies scheduled residual updates to progressively refine a coarse semantic draft into a fine-grained latent action plan. The refined plan after K rounds is then decoded in parallel into an action chunk for low-latency execution. We further introduce Causal Refinement-Grouped Process-Reward RL to optimize the latent refinement process with rewards from longer-horizon imagined futures induced by latent plan edits. Empirical evaluations on the LIBERO benchmark demonstrate that PearlVLA achieves state-of-the-art performance among existing methods.
Chinese Translation
当前的视觉-语言-行动（VLA）模型面临高效行动生成与明确推理之间的权衡。从视觉-语言主干表示直接解码行动可以实现低延迟控制，而通过文本链、像素级子目标或行动搜索进行明确推理虽然可以改善规划，但会带来显著的延迟和计算成本。我们提出了PearlVLA，一个将推理移入视觉-语言模型（VLM）潜在空间的VLA框架。PearlVLA将VLM元查询表示分为固定的视觉基础分支和迭代的潜在计划分支。在每个优化轮次中，基于计划的世界查询探测一个轻量级的冻结潜在世界模型，以获取无行动的未来观察潜在，该潜在被反馈用于指导计划优化。接着，未来引导的RefineNet应用计划的残差更新，逐步将粗略的语义草稿优化为细粒度的潜在行动计划。经过K轮优化后的计划随后被并行解码为一个行动块，以实现低延迟执行。我们进一步引入因果优化分组过程奖励强化学习（Causal Refinement-Grouped Process-Reward RL），以通过潜在计划编辑诱导的长远想象未来的奖励来优化潜在优化过程。在LIBERO基准上的实证评估表明，PearlVLA在现有方法中实现了最先进的性能。
cs.RO / 33 / 2606.17936
SPARK: Low Latency Single-Camera 3D Pose Estimation for Autonomous Racing using Keypoints
SPARK：基于关键点的低延迟单摄像头3D姿态估计用于自主赛车
Abstract
In autonomous racing, fast detection of other participants' movements is required to plan safe, collision-free trajectories with non-cooperative opponents. LiDAR detection is inherently slower and harder to deploy on edge devices than vision methods, causing delayed detections that limit object tracking performance during high-dynamic maneuvering. Utilizing monocular 3D detection enables an easy-to-deploy, low-latency detection of other participants on the racetrack. We present SPARK, a single-camera pose-estimation algorithm for autonomous racing using keypoint detection. It achieves long-range detection with high accuracy, exceeding the performance of state-of-the-art monocular camera detection algorithms while maintaining lower latency. By employing well-optimized YOLO models and leveraging the fixed geometry in the autonomous racing domain, the algorithm also exhibits low latency and resource usage. We evaluate the performance of our approach on real-world autonomous racing data and compare it to state-of-the-art LiDAR and camera detection algorithms. The source code is available at: https://github.com/TUMFTM/SPARK-camera-det
Chinese Translation
在自主赛车中，需要快速检测其他参与者的运动，以规划安全、无碰撞的轨迹应对非合作对手。与视觉方法相比，LiDAR检测固有地较慢且在边缘设备上更难部署，导致检测延迟，从而限制了在高动态机动期间的物体跟踪性能。利用单目3D检测可以实现对赛道上其他参与者的易部署、低延迟检测。我们提出了SPARK，一种基于关键点检测的单摄像头姿态估计算法，适用于自主赛车。该算法实现了高精度的远程检测，超越了当前最先进的单目摄像头检测算法的性能，同时保持较低的延迟。通过采用经过良好优化的YOLO模型，并利用自主赛车领域中的固定几何特性，该算法还表现出低延迟和低资源使用。我们在真实的自主赛车数据上评估了我们方法的性能，并将其与最先进的LiDAR和摄像头检测算法进行了比较。源代码可在以下链接获取：https://github.com/TUMFTM/SPARK-camera-det
cs.RO / 34 / 2606.17937
ThinkingVLA: Interleaved Vision and Language Reasoning for Robotic Manipulation
ThinkingVLA：用于机器人操控的交错视觉与语言推理
Abstract
Most Vision-Language-Action (VLA) models map observations directly to actions without explicit reasoning, limiting their capacity for reasoning-intensive long-horizon tasks. To address this, existing approaches adopt Chain-of-Thought (CoT) reasoning to enable subgoal decomposition and spatial anticipation. However, those methods lack a unified architecture for effective cross-modal reasoning and fail to explicitly include inverse reasoning ability based on the target state. We argue that manipulation planning naturally decomposes into prediction, anticipating the next visual state, and inverse dynamics, inferring the actions to reach it. Bridging both requires a unified autoregressive architecture that interleaves textual and visual reasoning in a single generation process. We propose \textbf{ThinkingVLA}, a generative model that realizes this decomposition within a unified Mixture-of-Transformers architecture. ThinkingVLA consists of a forward CoT that identifies the immediate subgoal and guides the visual forecasting; the predicted image then serves as the target state, grounding an inverse CoT that reasons about spatial relationships and action intent based on the predicted image; and the final action is generated conditioned on this full reasoning context. Extensive experiments on simulation and real-world benchmarks demonstrate that ThinkingVLA consistently outperforms state-of-the-art baselines, with particularly large gains on long-horizon manipulation tasks.
Chinese Translation
大多数视觉-语言-动作（VLA）模型直接将观察映射到动作，而没有明确的推理，这限制了它们在需要推理的长时间任务中的能力。为了解决这个问题，现有的方法采用了思维链（Chain-of-Thought, CoT）推理，以实现子目标分解和空间预期。然而，这些方法缺乏有效的跨模态推理的统一架构，并未明确包含基于目标状态的逆向推理能力。我们认为，操控规划自然分解为预测，预期下一个视觉状态，以及逆向动力学，推断达到该状态的动作。连接这两者需要一个统一的自回归架构，在单一生成过程中交错文本和视觉推理。我们提出了 extbf{ThinkingVLA}，这是一个生成模型，在统一的混合变换器（Mixture-of-Transformers）架构中实现了这种分解。ThinkingVLA包括一个前向的CoT，识别即时子目标并指导视觉预测；预测的图像随后作为目标状态，为逆向CoT提供基础，该逆向CoT基于预测的图像推理空间关系和动作意图；最终的动作是在这个完整推理上下文的条件下生成的。在模拟和真实世界基准上的大量实验表明，ThinkingVLA始终优于最先进的基线，尤其在长时间操控任务上获得了显著提升。
cs.RO / 35 / 2606.17982
LAGO Policy: Latency-Aware Asynchronous Diffusion Policies with Goal-Directed Collision-Free Planning for Smooth Manipulation
LAGO政策：具有目标导向的无碰撞规划的延迟感知异步扩散策略以实现平滑操作
Abstract
Diffusion-based visuomotor policies deployed with asynchronous inference often exhibit inter-chunk discontinuities and lack explicit mechanisms for obstacle-aware execution, leading to jerky motions and collisions that hinder reliable manipulation in real-world scenes. To address these issues, we propose LAGO Policy, a unified asynchronous action-generation framework that integrates trajectory optimization with diffusion policy for smooth and safe execution. LAGO Policy improves inter-chunk consistency via latency-aware classifier-free guidance conditioning on future actions. It further enables goal-directed collision-free trajectory planning by predicting a task-relevant interaction goal from demonstrations. Finally, spatial-temporal trajectory optimization refines the actions to be executed for low-jerk and feasible motion. Extensive real-world experiments demonstrate that LAGO Policy achieves smooth collision-free execution with high task success across challenging manipulation tasks. Project Website: https://lago-policy.github.io/
Chinese Translation
基于扩散的视觉运动策略在异步推理下部署时，常常表现出块间的不连续性，并缺乏显式的障碍物感知执行机制，这导致了运动不平稳和碰撞，从而妨碍了在现实场景中的可靠操作。为了解决这些问题，我们提出了LAGO政策，这是一种统一的异步动作生成框架，将轨迹优化与扩散策略相结合，以实现平滑和安全的执行。LAGO政策通过基于未来动作的延迟感知无分类器引导来改善块间一致性。它进一步通过从示范中预测与任务相关的交互目标，实现目标导向的无碰撞轨迹规划。最后，时空轨迹优化对将要执行的动作进行精细化处理，以实现低抖动和可行的运动。大量现实世界的实验表明，LAGO政策在具有挑战性的操作任务中实现了平滑的无碰撞执行和高任务成功率。项目网站：https://lago-policy.github.io/
cs.RO / 36 / 2606.18043
Uncertainty Quantification for Flow-Based Vision-Language-Action Models
基于流的视觉-语言-动作模型的不确定性量化
Abstract
Vision-language-action models (VLAs) combine vision-language backbones with expressive generative action heads trained via flow matching on large-scale robotic datasets. Despite their strong empirical performance in robotic manipulation, VLAs lack mechanisms to quantify confidence in their predictions and to detect when their actions may be unreliable. This presents a critical limitation for real-world deployment in non-stationary environments, where models inevitably encounter scenarios outside their pretraining distribution and may fail without warning. To address this, we derive an efficient method for quantifying epistemic uncertainty in flow-matching models by leveraging velocity-field disagreement (VFD) across a small ensemble. We successfully use this uncertainty estimate for failure detection during deployment and active fine-tuning of flow-based VLAs. To this end, we propose SAVE, a framework for uncertainty-guided active multitask fine-tuning that reduces the number of costly expert demonstrations required to adapt VLAs to new tasks. Through extensive experiments on the LIBERO benchmark, we demonstrate that VFD yields better-calibrated uncertainty estimates predictive of downstream performance, that VFD achieves strong performance in detecting failures, and that uncertainty-guided data acquisition with SAVE requires at least 22% fewer samples than baselines. In summary, our work shows that quantifying epistemic uncertainty in flow-based VLAs improves both failure awareness and adaptation. Project website: tum-lsy.github.io/uq_vla/.
Chinese Translation
视觉-语言-动作模型（VLA）将视觉-语言骨干与通过流匹配在大规模机器人数据集上训练的表现力强的生成动作头结合在一起。尽管在机器人操作中表现出色，VLA缺乏量化其预测信心和检测其动作可能不可靠的机制。这在非静态环境中的实际部署中构成了一个关键限制，因为模型不可避免地会遇到超出其预训练分布的场景，并可能在没有警告的情况下失败。为了解决这个问题，我们通过利用小型集成中的速度场不一致性（VFD）推导出一种有效的方法来量化流匹配模型中的认知不确定性。我们成功地使用这种不确定性估计在部署期间进行故障检测以及对基于流的VLA进行主动微调。为此，我们提出了SAVE，一个不确定性引导的主动多任务微调框架，减少了将VLA适应于新任务所需的昂贵专家演示次数。通过在LIBERO基准上的广泛实验，我们证明了VFD产生的更好校准的不确定性估计能够预测下游性能，VFD在故障检测中表现出色，并且使用SAVE进行不确定性引导的数据获取所需样本比基线少至少22%。总之，我们的工作表明，在基于流的VLA中量化认知不确定性改善了故障意识和适应能力。项目网站：tum-lsy.github.io/uq_vla/
cs.RO / 37 / 2606.18053
A Hybrid Optimization Framework for Grasp Synthesis under Partial Observations
一种基于混合优化的抓取合成框架在部分观察下的应用
Abstract
We propose a hybrid grasp synthesis framework that combines a learning-based Energy-Based Model (EBM) with an analytical Iterative Closest Point (ICP) method to generate robust grasps from partially observed point clouds. The learned energy function acts as a prior within a Stein Variational Gradient Descent (SVGD) framework, guiding iterative refinement of grasp configurations. Evaluated on 67 objects with 5,360 grasp attempts, our method achieves an average success rate of 60.9\%, outperforming AnyGrasp (31.1\%) and Grasp Pose Detection (48.4\%) and AS-ICP (56.6\%). These results highlight the strong generalization ability of our approach and demonstrate how combining data-driven learning with geometric optimization addresses the limitations of either strategy in isolation.
Chinese Translation
我们提出了一种混合抓取合成框架，该框架结合了基于学习的能量模型（Energy-Based Model, EBM）与分析性迭代最近点（Iterative Closest Point, ICP）方法，从部分观察的点云中生成稳健的抓取。学习到的能量函数作为Stein变分梯度下降（Stein Variational Gradient Descent, SVGD）框架中的先验，指导抓取配置的迭代优化。在对67个物体进行5,360次抓取尝试的评估中，我们的方法实现了60.9%的平均成功率，优于AnyGrasp（31.1%）、抓取姿态检测（Grasp Pose Detection, 48.4%）和AS-ICP（56.6%）。这些结果突显了我们方法的强泛化能力，并展示了将数据驱动学习与几何优化相结合如何解决各自策略孤立时的局限性。
cs.RO / 38 / 2606.18092
EAGG: Embodiment-Aligned Grasp Generation via Geometry-Aware Graph Conditioning
EAGG：通过几何感知图条件生成的体现对齐抓取
Abstract
Cross-end-effector grasp generation seeks a unified model that generalizes across objects and across embodiments ranging from parallel grippers to dexterous end effectors. Existing grasp generators are typically designed for a fixed embodiment or encode embodiment identity with a static descriptor, which weakens transfer when topology, actuation coupling, and contact geometry differ substantially. We present EAGG, an embodiment-aligned grasp generator that represents each embodiment with a topology-aware end-effector graph and an embodiment-specific low-dimensional end-effector control space. A frozen end-effector-cognition backbone converts the current articulated state into geometry-aware tokens that act as a reusable morphology prior, and iterative geometry injection refreshes these tokens throughout sampling so that conditioning remains synchronized with the evolving end-effector geometry. On the MultiGripperGrasp benchmark, EAGG reaches 56.17% average success across six training end effectors, remaining within 1.10 percentage points of specialized training while preserving transfer to finetuning and zero-shot end effectors. Iterative geometry injection further reduces the pooled median contact distance from 0.239 cm to 0.189 cm. These results show that cross-end-effector grasp generation is strengthened by aligning embodiment structure inside a shared generator rather than suppressing embodiment differences. Code is available at https://github.com/wanhaoniu/EAGG.
Chinese Translation
跨末端执行器的抓取生成旨在寻求一个统一模型，该模型能够在不同物体和从平行夹持器到灵巧末端执行器的不同体现之间进行泛化。现有的抓取生成器通常是为固定的体现设计，或使用静态描述符编码体现身份，这在拓扑、驱动耦合和接触几何差异显著时削弱了迁移能力。我们提出了EAGG，这是一种体现对齐的抓取生成器，它通过一个拓扑感知的末端执行器图和一个特定于体现的低维末端执行器控制空间来表示每个体现。一个冻结的末端执行器认知主干将当前的关节状态转换为几何感知的标记，这些标记作为可重用的形态先验，而迭代几何注入在采样过程中不断刷新这些标记，以确保条件与不断演变的末端执行器几何保持同步。在MultiGripperGrasp基准测试中，EAGG在六个训练末端执行器上达到56.17%的平均成功率，仍然在专门训练的基础上保持1.10个百分点的差距，同时保留了对微调和零样本末端执行器的迁移能力。迭代几何注入进一步将汇总的中位接触距离从0.239厘米减少到0.189厘米。这些结果表明，通过在共享生成器内部对齐体现结构，而不是抑制体现差异，跨末端执行器的抓取生成得到了增强。代码可在https://github.com/wanhaoniu/EAGG获取。
cs.RO / 39 / 2606.18097
WireCraft: A Simulation Benchmark for Industrial DLO Manipulation
WireCraft：工业可变形线性物体操控的仿真基准
Abstract
Deformable Linear Objects (DLOs), such as wires and cables, are central to industrial assembly. Unlike rigid objects, whose state is captured by a 6-DoF pose, DLOs have an infinite-dimensional configuration space and deform continuously under contact with grippers, fixtures, and the workspace, making them a demanding benchmark for general dexterous manipulation. Despite their importance, policy development and comparison remain difficult: existing benchmarks are often tied to specific hardware setups, lack modular and customizable task assets, or study generic deformable-object tasks without the fixtures relevant to real-world industrial wire manipulation. Few benchmarks align simulation, real-world data, and shared evaluation protocols. To bridge this gap, we introduce WireCraft, a simulation benchmark for industrial DLO manipulation with configurable difficulty and assets, spanning three task families: connector insertion, clip routing, and channel seating. It supports two complementary DLO physics models, articulated and deformable, and the trajectories come from both simulation and a physical UR5. We benchmark reinforcement learning (RL), imitation learning (IL), and vision-language-action (VLA) policies under shared metrics. Privileged state-based RL solves a representative setting in each task family with over 82\% success, confirming the tasks are well-posed. For connector insertion, however, the transition from reaching the socket to contact-rich alignment remains a key bottleneck for vision RL, IL, and VLA policies. These results indicate that industrial DLO manipulation, though tractable under privileged state, remains an open challenge for current vision-based learning. The benchmark, data, and tools will be open-sourced upon acceptance.
Chinese Translation
可变形线性物体（DLOs），如电线和电缆，是工业组装的核心。与通过六自由度姿态捕捉状态的刚性物体不同，DLOs 具有无限维的配置空间，并在与夹具、固定装置和工作空间接触时持续变形，这使其成为通用灵巧操控的一个具有挑战性的基准。尽管它们的重要性不言而喻，策略开发和比较仍然困难：现有基准往往与特定硬件设置相关，缺乏模块化和可定制的任务资产，或研究没有与真实工业线缆操控相关的夹具的通用可变形物体任务。很少有基准能够将仿真、现实世界数据和共享评估协议结合起来。为了解决这一问题，我们提出了 WireCraft，一个用于工业 DLO 操控的仿真基准，具有可配置的难度和资产，涵盖三类任务：连接器插入、夹具布线和通道安装。它支持两种互补的 DLO 物理模型，分别为关节模型和可变形模型，轨迹来自仿真和物理 UR5。我们在共享指标下对强化学习（RL）、模仿学习（IL）和视觉-语言-动作（VLA）策略进行了基准测试。特权状态基础的 RL 在每个任务类别中解决了一个具有代表性的设置，成功率超过 82\%，确认了任务的良好设定。然而，对于连接器插入而言，从到达插座到接触丰富的对齐的过渡仍然是视觉 RL、IL 和 VLA 策略的一个关键瓶颈。这些结果表明，尽管在特权状态下工业 DLO 操控是可行的，但对于当前基于视觉的学习仍然是一个开放的挑战。基准、数据和工具将在接受后开源。
cs.RO / 40 / 2606.18112
Qwen-RobotNav Technical Report: A Scalable Navigation Model Designed for an Agentic Navigation System
Qwen-RobotNav技术报告：为自主导航系统设计的可扩展导航模型
Zhang, Jiazhao, Zhou, Gengze, Yin, Hale, Huang, Yiyang, Lei, Zixing, Peng, Qihang, Yuan, Haoqi, Zhang, Jie, Guo, Xudong, Chen, Xiaoyue, Yang, An, Huang, Fei, Lin, Junyang, Liu, Dayiheng, Zhou, Jingren, Yu, Zhuoyuan, Fan, Jingyang, Liang, Zhixuan, Lin, Pei, Wang, Ye, Chen, Anzhe, Yan, Kun, Xu, Xiao, Li, Jiahao, Hu, Lulu, Zhang, Minying, Li, Shurui, Xiao, Wenhu, Bai, Shuai, Ren, Xuancheng, Lv, Chenxu, Wu, Chenfei, Chen, Xiong-Hui
Abstract
Agentic navigation systems require a base navigation model whose observation strategy can be externally reconfigured at inference time, because instruction following, object search, target tracking, and autonomous driving share the same perception-planning backbone yet demand fundamentally different strategies for consuming the visual stream. We present Qwen-RobotNav, a scalable navigation model built on Qwen-RobotNav that addresses it through a parameterised interface with two complementary dimensions: multiple task modes that select the navigation behaviour, and controllable observation parameters (e.g., token budget, per-camera weights) that govern how visual history is encoded. With training-time randomization over all parameters, Qwen-RobotNav is robust to any inference-time configuration requiring zero architectural modification to the Qwen-RobotNav backbone. We train Qwen-RobotNav on 15.6M samples; co-training with vision-language data prevents the collapse into reactive action-sequence mappers observed in trajectory-only training. The parameterised interface also makes Qwen-RobotNav a natural building block for agentic systems: for long-horizon scenarios, an upper-level planner decomposes goals into sub-tasks and dynamically switches Qwen-RobotNav's task mode and context strategy mid-episode, composing complex behaviours from repeated calls to the same model. Extensive experiments show that Qwen-RobotNav sets new state-of-the-art results across major navigation benchmarks. The model exhibits favourable scaling from 2B to 8B parameters, with joint multi-task training developing a shared spatial-planning substrate that transfers across task families, and demonstrates strong zero-shot generalisation to real-world robots across diverse environments.
Chinese Translation
自主导航系统需要一个基础导航模型，其观察策略可以在推理时进行外部重新配置，因为指令跟随、物体搜索、目标跟踪和自主驾驶共享相同的感知-规划基础，但对视觉流的消费需求却有根本不同的策略。我们提出了Qwen-RobotNav，这是一个基于Qwen-RobotNav构建的可扩展导航模型，通过一个具有两个互补维度的参数化接口来解决这一问题：多个任务模式选择导航行为，以及可控的观察参数（例如，令牌预算、每个摄像头的权重），这些参数决定了视觉历史的编码方式。通过对所有参数进行训练时随机化，Qwen-RobotNav对任何推理时配置具有鲁棒性，无需对Qwen-RobotNav基础架构进行任何结构修改。我们在1560万样本上训练Qwen-RobotNav；与视觉-语言数据的共同训练防止了在仅基于轨迹的训练中观察到的反应性动作序列映射器的崩溃。参数化接口还使Qwen-RobotNav成为自主系统的自然构建块：对于长时间场景，上层规划者将目标分解为子任务，并在剧集过程中动态切换Qwen-RobotNav的任务模式和上下文策略，从而通过对同一模型的重复调用组合复杂行为。大量实验表明，Qwen-RobotNav在主要导航基准测试中设定了新的最先进结果。该模型在从20亿到80亿参数的扩展中表现良好，联合多任务训练开发了一个共享的空间规划基础，能够跨任务家族进行迁移，并在多样化环境中对真实世界机器人展示出强大的零样本泛化能力。
cs.RO / 41 / 2606.18189
Beyond Failure Recovery: An Engagement-Aware Human-in-the-loop Framework for Robotic Systems
超越故障恢复：一种关注用户参与的机器人系统人机协同框架
Abstract
Conventional human-in-the-loop approaches typically involve users only when a robot encounters failure or uncertainty, treating humans primarily as tools for improving robot performance. However, in many human-centered robotics settings, interaction should support engagement by keeping users involved in decision-making rather than limiting them to failure-driven interventions. This is particularly compelling in physical caregiving, where mobility limitations can reduce users' ability to intervene or modulate the robot's behavior in the moment. As a result, failure-driven interaction policies may relegate users to passive observers for long stretches of the task. For example, a user with mobility limitations may feel less engaged when being continuously and passively fed by a robot. At the same time, overly frequent interaction can be tiring and increase the user's workload. To address this trade-off, we propose Engagement-aware MPC (E-MPC), a user-engagement-aware method that plans interaction to maintain engagement while respecting a workload constraint. E-MPC leverages a user interaction dynamics model that captures how user engagement evolves as a function of both the frequency and type of interaction. Rather than requesting input only when difficulties arise during task execution, the robot proactively considers the user's preferred level of engagement throughout the task, balancing autonomy and interaction while ensuring task success. We evaluate E-MPC in simulation with several ablations and baseline comparisons. Results demonstrate the effectiveness of our approach across diverse user personas. In addition, we conduct a real-world user study with participants with emulated mobility limitations on a robot-assisted bite acquisition system, showing that E-MPC improves user experience while maintaining task success.
Chinese Translation
传统的人机协同方法通常仅在机器人遇到故障或不确定性时才涉及用户，主要将人视为提高机器人性能的工具。然而，在许多以人为中心的机器人应用场景中，互动应通过让用户参与决策来支持参与感，而不是仅限于故障驱动的干预。这在物理护理中尤为重要，因为行动能力的限制可能会降低用户在关键时刻干预或调节机器人行为的能力。因此，故障驱动的互动策略可能会使用户在任务的长时间段内沦为被动观察者。例如，行动能力受限的用户在被机器人持续被动喂养时可能会感到参与感降低。同时，过于频繁的互动可能会让用户感到疲惫，并增加其工作负担。为了解决这一权衡，我们提出了关注用户参与的模型预测控制方法（Engagement-aware MPC，E-MPC），该方法规划互动以维持参与感，同时尊重工作负担限制。E-MPC利用用户互动动态模型，捕捉用户参与感如何随着互动的频率和类型而演变。机器人不仅在任务执行过程中遇到困难时请求输入，而是主动考虑用户在整个任务中的偏好参与水平，平衡自主性和互动，同时确保任务成功。我们在模拟环境中对E-MPC进行了多种消融实验和基线比较评估。结果表明，我们的方法在不同用户角色中均表现出有效性。此外，我们还在一个机器人辅助的咬合获取系统上进行了真实世界的用户研究，参与者模拟了行动能力的限制，结果显示E-MPC在保持任务成功的同时改善了用户体验。
cs.RO / 42 / 2606.18239
EBench: Elemental Diagnosis of Generalist Mobile Manipulation Policies
EBench：通用移动操作策略的元素诊断
Gao, Ning, Zheng, Jinliang, Gao, Xing, Ma, Haoxiang, Wang, Hanqing, Wang, Yukai, Chen, Jiantong, Chen, Zanxin, Zhang, Shujie, Jia, Mingda, Jiang, Xuekun, Zhu, Zihou, Li, Xinyu, Wang, Shuai, Li, Hao, Cai, Wenzhe, Yang, Yuqiang, Xu, Xudong, Lyu, Zhaoyang, Mu, Yao, Wang, Tai, Pang, Jiangmiao, Zeng, Jia, Zhang, Weinan, Shen, Chunhua
Abstract
We present EBench, a simulation benchmark that diagnoses generalist mobile manipulation policies beyond a single success-rate scalar. EBench comprises 26 diverse and challenging manipulation tasks annotated along 5 capability dimensions and 4 generalization dimensions. We evaluate state-of-the-art generalist manipulation models including $\pi_0$, $\pi_{0.5}$, XVLA, and InternVLA-A1, and reveal that models with near success rates exhibit strikingly different capability profiles: $\pi_{0.5}$ achieves the highest test success rate and the best train--test retention, whereas InternVLA-A1 dominates mobile manipulation but collapses on dexterous tasks, and XVLA exhibits strengths on a disjoint set of atomic skills compared to other policies. Beyond capability profiling, EBench analyzes the generalization ability from 4 representative perspectives, identifying the impact of different distribution shift factors. The results reveal strengths and weaknesses of models behind an overall score. We hope this benchmark offers a broad set of diagnostic signals to guide iteration on generalist manipulation models.
Chinese Translation
我们提出了EBench，一个超越单一成功率标量的通用移动操作策略诊断仿真基准。EBench包含26个多样且具有挑战性的操作任务，按照5个能力维度和4个泛化维度进行注释。我们评估了最先进的通用操作模型，包括$ ext{π}_0$、$ ext{π}_{0.5}$、XVLA和InternVLA-A1，并揭示了接近成功率的模型展现出截然不同的能力特征：$ ext{π}_{0.5}$实现了最高的测试成功率和最佳的训练-测试保留，而InternVLA-A1在移动操作方面表现出色，但在灵巧任务上崩溃，XVLA在与其他策略相比的离散原子技能上展现出优势。除了能力特征分析，EBench还从4个代表性视角分析了泛化能力，识别了不同分布转移因素的影响。结果揭示了模型在整体评分背后的优势和劣势。我们希望这个基准能够提供一系列广泛的诊断信号，以指导通用操作模型的迭代。
cs.RO / 43 / 2606.18247
Visual Verification Enables Inference-time Steering and Autonomous Policy Improvement
视觉验证实现推理时引导和自主策略改进
Abstract
Robots deployed in the real world should learn from their experience and improve over time. This requires a mechanism of practicing and learning from feedback. In this paper, we propose VERITAS, a generator-verifier framework for generalist robot policies for inference-time policy steering and self-improvement. We use a pre-trained generalist robot policy as a ``generator'' and pair it with a gradient-free ``visual verifier'' that evaluates actions at inference time. This framework enables inference-time steering that improves policy performance without additional training. We demonstrate that inference-time verification consistently outperforms vanilla generalists without training on additional demonstration data. Additionally, we demonstrate that the verified rollouts provide effective supervision for offline policy improvement: policies fine-tuned on verified self-generated trajectories achieve consistent performance gains. Notably, we find that post-training with verified rollouts achieves comparable efficiency to expert demonstrations, while requiring no human interventions. Our results highlight inference-time verification as a practical and scalable mechanism for improving robotic policies during deployment.
Chinese Translation
在现实世界中部署的机器人应当能够从经验中学习并随时间改进。这需要一种通过反馈进行实践和学习的机制。本文提出了VERITAS，一个用于推理时策略引导和自我改进的通用机器人策略的生成器-验证器框架。我们使用一个预训练的通用机器人策略作为“生成器”，并将其与一个无梯度的“视觉验证器”配对，该验证器在推理时评估动作。该框架实现了推理时引导，能够在不进行额外训练的情况下提高策略性能。我们证明了推理时验证在没有额外示范数据训练的情况下，始终优于普通的通用策略。此外，我们还展示了经过验证的轨迹为离线策略改进提供了有效的监督：在经过验证的自生成轨迹上微调的策略实现了一致的性能提升。值得注意的是，我们发现使用经过验证的轨迹进行后训练的效率与专家示范相当，同时不需要任何人工干预。我们的结果突显了推理时验证作为一种实用且可扩展的机制，在部署期间改善机器人策略的潜力。
cs.CV / 1 / 2606.17188
Not Truly Multilingual: Script Consistency as a Missing Dimension in VLM Evaluation
并非真正的多语言：脚本一致性作为视觉语言模型评估中的缺失维度
Abstract
Current multilingual evaluations for Vision-Language Models (VLMs) assume a one-to-one mapping between language and orthography, overlooking billions of users of multi-script languages. We introduce PuMVR (Punjabi Multimodal Visual Reasoning), a benchmark of 1,000 strictly parallel image-text instances across Punjabi's three active scripts: Gurmukhi, Shahmukhi, and Roman. Evaluating 10 state-of-the-art VLMs, we expose a substantial and systematic Script Gap. Models frequently solve visual tasks in one script while failing identical tasks in another, with accuracy deltas reaching 16%. Crucially, visual input boosts absolute performance uniformly yet does not close the orthographic gap. Furthermore, cross-script in-context transfer is highly brittle, exposing script-locked knowledge representation. Supported by McNemar tests across all script pairs, our findings demonstrate that current "multilingual" VLMs are not truly multi-script. We propose the Script Consistency Rate (SCR), which falls as low as 24.8% on our benchmark, as a mandatory metric for script-agnostic evaluation to ensure equitable AI access. Data and code are available at: https://github.com/prabhjotschugh/Not-Truly-Multilingual-PuMVR.
Chinese Translation
当前针对视觉语言模型（VLMs）的多语言评估假设语言与正字法之间存在一对一的映射，忽视了数十亿使用多脚本语言的用户。我们引入了PuMVR（旁遮普多模态视觉推理），这是一个包含1000个严格平行的图像-文本实例的基准，涵盖了旁遮普的三种活跃脚本：古尔穆基（Gurmukhi）、沙赫穆基（Shahmukhi）和罗马字（Roman）。在对10个最先进的VLM进行评估时，我们揭示了一个显著且系统的脚本差距。模型在一种脚本中经常能够解决视觉任务，而在另一种脚本中却无法完成相同的任务，准确率差异可达16%。关键是，视觉输入均匀地提升了绝对性能，但并未缩小正字法差距。此外，跨脚本的上下文迁移非常脆弱，暴露了脚本锁定的知识表示。通过对所有脚本对进行的McNemar检验，我们的发现表明当前的“多语言”VLM并非真正的多脚本。我们提出了脚本一致性率（Script Consistency Rate，SCR），在我们的基准中最低可达24.8%，作为无脚本偏见评估的必要指标，以确保公平的人工智能访问。数据和代码可在以下网址获取：https://github.com/prabhjotschugh/Not-Truly-Multilingual-PuMVR。
cs.CV / 2 / 2606.17222
Quantum Enchanced Multi-Scale CNN with Bi-directional Mamba for Crop Field Analysis
基于双向Mamba的量子增强多尺度卷积神经网络用于农田分析
Abstract
Hyperspectral image (HSI) crop analysis is essential for precision agriculture because it captures rich spectral and spatial information for accurate crop monitoring and assessment. However, HSI classification remains challenging due to high spectral dimensionality, spatial complexity, class imbalance, and limited labeled samples. To address these challenges, this paper proposes a BiSpectral Mamba-based framework that combines multi-scale convolutional feature extraction, spectral attention, bidirectional state-space modeling, and quantum-inspired learning. A multi-scale CNN backbone first extracts hierarchical spatial-spectral representations through feature fusion across multiple resolutions. A spectral attention mechanism then emphasizes informative bands while suppressing redundant and noisy channels. The refined features are processed by a BiSpectral Mamba module that captures long-range dependencies in both forward and backward directions by modeling hyperspectral feature maps as sequential tokens. In addition, class-weighted optimization and feature fusion strategies are incorporated to improve training stability and mitigate class imbalance. Experimental evaluation on the UAVHSI-Crop dataset demonstrates the effectiveness of the proposed framework, achieving an overall accuracy of 84.83%. The results show that integrating convolutional, attention-based, and state-space modeling components enables robust spatial-spectral feature learning for crop classification. The proposed framework also shows potential for broader agricultural and remote sensing applications, including crop disease detection, yield prediction, and soil moisture estimation, while highlighting the effectiveness of structured state-space and quantum-inspired architectures for hyperspectral image analysis.
Chinese Translation
高光谱图像（HSI）作物分析对于精准农业至关重要，因为它捕获了丰富的光谱和空间信息，以便进行准确的作物监测和评估。然而，由于高光谱维度高、空间复杂性、类别不平衡以及标记样本有限，HSI分类仍然具有挑战性。为了解决这些问题，本文提出了一种基于双光谱Mamba的框架，该框架结合了多尺度卷积特征提取、光谱注意力、双向状态空间建模和量子启发学习。首先，多尺度卷积神经网络（CNN）主干通过跨多个分辨率的特征融合提取层次化的空间-光谱表示。接着，光谱注意力机制强调信息丰富的波段，同时抑制冗余和噪声通道。经过精炼的特征由双光谱Mamba模块处理，该模块通过将高光谱特征图建模为序列标记，捕获前向和后向方向上的长程依赖。此外，结合类别加权优化和特征融合策略，以提高训练稳定性并减轻类别不平衡。在UAVHSI-Crop数据集上的实验评估证明了所提框架的有效性，整体准确率达到84.83%。结果表明，整合卷积、基于注意力的和状态空间建模组件能够为作物分类提供稳健的空间-光谱特征学习。所提框架在更广泛的农业和遥感应用中也显示出潜力，包括作物病害检测、产量预测和土壤湿度估计，同时突显了结构化状态空间和量子启发架构在高光谱图像分析中的有效性。
cs.CV / 3 / 2606.17241
Beyond Benchmarks: Continuous Edge Inference for Fine-Grained Roadside Perception
超越基准测试：用于细粒度路边感知的连续边缘推理
Abstract
Continuous AI inference on resource-constrained edge hardware introduces deployment effects that are largely invisible to conventional benchmark evaluation, including temporal instability in streaming video, thermal throttling under sustained load, and workload-dependent performance variability. We present Edge-TSR, a deployment-oriented continuous edge inference system for sustained roadside perception on the NVIDIA Jetson Orin Nano. Edge-TSR integrates detection, tracking, fine-grained classification, and a lightweight track-aware temporal stabilization mechanism that improves streaming inference consistency with negligible computational overhead. Our central finding is that benchmark-centric evaluation systematically overstates deployed edge inference performance. Across three state-of-the-art baselines, we observe consistent 20-30% relative degradation when transitioning from static-image evaluation to real-world streaming deployment. Edge-TSR addresses this gap through temporal inference stabilization, recovering up to 10.16% classification accuracy over per-frame inference baselines while maintaining sustained real-time performance under continuous operation. We evaluate the complete system under diverse real-world deployment conditions, jointly characterizing inference quality, latency, throughput, and thermal behavior during long-duration operation. A 55-minute vehicular deployment over a 26 km route demonstrates sustained operation at 16.18 FPS within safe thermal limits on a single embedded device without cloud offload. Our findings show that deployment-aware evaluation and temporal inference stabilization are necessary components of continuously operating edge AI systems intended for real-world sensing deployments. We release a sample annotated streaming video evaluation dataset and full system implementation to support reproducible deployment-centric evaluation.
Chinese Translation
在资源受限的边缘硬件上进行连续的人工智能推理引入了许多传统基准评估无法察觉的部署效应，包括流媒体视频中的时间不稳定性、持续负载下的热限制以及依赖于工作负载的性能波动。我们提出了 Edge-TSR，这是一种面向部署的连续边缘推理系统，旨在实现 NVIDIA Jetson Orin Nano 上的持续路边感知。Edge-TSR 集成了检测、跟踪、细粒度分类以及一种轻量级的跟踪感知时间稳定机制，能够在几乎没有计算开销的情况下提高流媒体推理的一致性。我们的核心发现是，基于基准的评估系统性地高估了部署的边缘推理性能。在三个最先进的基准中，我们观察到从静态图像评估转向真实世界流媒体部署时，性能相对下降了一致的 20-30%。Edge-TSR 通过时间推理稳定化来填补这一空白，在持续操作下恢复了每帧推理基准的高达 10.16% 的分类准确率，同时保持持续的实时性能。我们在多种真实世界的部署条件下评估了完整系统，联合表征了推理质量、延迟、吞吐量和长时间操作下的热行为。在一项 55 分钟的车辆部署中，沿着 26 公里的路线展示了在单个嵌入式设备上以 16.18 FPS 的速度在安全热限度内的持续操作，而无需云卸载。我们的研究结果表明，考虑部署的评估和时间推理稳定化是旨在进行真实世界感知部署的连续运行边缘人工智能系统的必要组成部分。我们发布了一个示例注释的流媒体视频评估数据集和完整系统实现，以支持可重复的以部署为中心的评估。
cs.CV / 4 / 2606.17242
Landsat-Sentinel-2 Algal Bloom Mapping Using Vision Transformers: Model Description, Implementation, and Examples
基于视觉变换器的Landsat-Sentinel-2藻类水华制图：模型描述、实施及实例
Abstract
Coastal algal bloom monitoring requires frequent, spatially detailed, and globally consistent observations, provided by Landsat-8/9 and Sentinel-2 A/B/C. Together, these missions offer over a decade of medium-resolution multispectral imagery with near-global coverage every 2-3 days, enabling the detection of fragmented bloom structures not resolvable by coarse ocean-color sensors. However, their use in aquatic environments remains challenging due to limited spectral coverage and a lack of harmonized reflectance products. As an alternative to traditional bio-optical methods, deep learning-based image classification offers a data-driven approach that can overcome many of these limitations. This study presents the first successful implementation of vision transformer-based coastal algal bloom mapping using 30-m Landsat-Sentinel-2 images. A globally distributed bloom patch dataset was generated across bloom-prone coastal hotspots worldwide. Four transformer-based architectures were compared against a standard convolutional baseline for fine-scale bloom detection, and assessed under different optical water types and atmospheric and surface conditions. All deep learning models showed strong capabilities in detecting floating bloom areas, with omission and commission errors of 8-65%. Under cloud and glint stress in a time series, the Swin Transformer outperformed traditional spectral-index approaches, which produced widespread false positives, effectively avoiding cloud- and glint-affected pixels. Comparisons with MODIS-derived products further highlighted the benefits of higher spatial resolution in detecting fragmented and irregularly affected blooms. Our findings support deep learning as a reliable tool for medium-resolution, consistent monitoring of floating algal blooms in dynamic coastal environments.
Chinese Translation
沿海藻类水华监测需要频繁、空间详细且全球一致的观测，这些观测由Landsat-8/9和Sentinel-2 A/B/C提供。结合这些任务，提供了超过十年的中分辨率多光谱影像，每2-3天覆盖近全球，能够检测到粗糙海洋颜色传感器无法解析的碎片化水华结构。然而，由于光谱覆盖有限和缺乏协调的反射率产品，其在水体环境中的应用仍然面临挑战。作为传统生物光学方法的替代方案，基于深度学习的图像分类提供了一种数据驱动的方法，可以克服许多这些限制。本研究展示了基于视觉变换器的沿海藻类水华制图的首次成功实施，使用30米的Landsat-Sentinel-2影像。我们在全球藻类水华易发的沿海热点地区生成了一个全球分布的水华斑块数据集。四种基于变换器的架构与标准卷积基线进行了比较，以实现细尺度的水华检测，并在不同的光学水体类型及大气和表面条件下进行了评估。所有深度学习模型在检测漂浮水华区域方面表现出强大的能力，遗漏和误报错误率为8-65%。在时间序列中的云和眩光压力下，Swin Transformer的表现优于传统的光谱指数方法，后者产生了广泛的假阳性，有效避免了受云和眩光影响的像素。与MODIS衍生产品的比较进一步突显了在检测碎片化和不规则受影响水华方面更高空间分辨率的优势。我们的研究结果支持深度学习作为在动态沿海环境中进行中分辨率、一致监测漂浮藻类水华的可靠工具。
cs.CV / 5 / 2606.17246
GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence
GeoDisaster：面向操作性灾害地理智能的协调代理基准测试
Abstract
Remote-sensing vision-language models (RS-VLMs) have advanced Earth-observation analysis toward visual interpretation and instruction-following, yet fall short of operational geo-intelligence, which demands tool-grounded spatial reasoning and structured, evidence-backed decisions. We introduce GeoDisaster, an operational geospatial disaster reasoning benchmark with 2,921 verified instances across 43 question types and five task families: deforestation monitoring, multi-hazard analysis, building-damage assessment, flood-safe routing, and Sentinel-1 SAR flood monitoring. Instances integrate heterogeneous EO/GIS evidence-optical and SAR imagery, raster masks, vector geometries, road networks, and exposure layers-spanning hazard detection, damage assessment, exposure estimation, and diagnostic report generation. Ground-truth answers are grounded in executable geospatial workflows and deterministic consistency checks, removing the need for language-model annotation. We further propose an orchestrated multi-agent framework with 18 disaster-oriented tools, where role-specialized agents coordinate through explicit execution contracts, aligned via Role-Contract Expectation Alignment (RCEA): failure-aware supervised fine-tuning combined with contract-grounded reinforcement learning over dense step-level signals. Experiments show that GeoDisaster challenges existing RS-VLMs and agentic systems, while RCEA improves tool use, evidence grounding, state consistency, and decision generation.
Chinese Translation
遥感视觉语言模型（RS-VLMs）推动了地球观测分析向视觉解释和指令跟随的发展，但在操作性地理智能方面仍显不足，后者要求基于工具的空间推理和结构化、基于证据的决策。我们提出了GeoDisaster，一个操作性地理灾害推理基准，包含2,921个经过验证的实例，涵盖43种问题类型和五个任务类别：森林砍伐监测、多灾害分析、建筑损坏评估、洪水安全路线规划和Sentinel-1 SAR洪水监测。这些实例整合了异构的地球观测/地理信息系统（EO/GIS）证据，包括光学和SAR影像、栅格掩模、矢量几何、道路网络和暴露层，涉及灾害检测、损坏评估、暴露估计和诊断报告生成。真实答案基于可执行的地理空间工作流和确定性一致性检查，消除了对语言模型注释的需求。我们进一步提出了一个协调的多代理框架，配备18种面向灾害的工具，角色专业化的代理通过明确的执行合同进行协调，并通过角色-合同期望对齐（Role-Contract Expectation Alignment, RCEA）进行对齐：结合了对失败的监督微调和基于合同的强化学习，利用密集的逐步信号。实验表明，GeoDisaster对现有的RS-VLMs和代理系统构成了挑战，而RCEA则改善了工具使用、证据基础、状态一致性和决策生成。
cs.CV / 6 / 2606.17257
Pulling The REINS: Training-Free Safety Alignment of Video Diffusion Models via Representation Steering
拉动REINS：通过表示引导实现视频扩散模型的无训练安全对齐
Abstract
Open-weight video diffusion models can generate photorealistic unsafe content, from violence to misinformation, yet existing defenses either require expensive safety fine-tuning that degrades general capability, or apply external filters that are trivially bypassed by adversarial prompts. We present REINS (REpresentation-space INference-time Safety steering), a training-free method that aligns video diffusion models at inference time by steering their internal representations toward safe generation. Our key finding is that safety-relevant structure is linearly encoded in the hidden-state activations of video diffusion transformers, and a single direction, discovered via Supervised PCA on binary safety labels, suffices to separate safe from unsafe generation trajectories. At inference, adding this direction to hidden states at an intermediate transformer layer redirects generation from harmful content to semantically related safe alternatives, with no weight updates, no concept enumeration, and negligible computational overhead. Through mechanistic analysis, we reveal that while safety information accumulates monotonically with transformer depth, steering effectiveness peaks at intermediate layers (~50% depth), exposing a fundamental tradeoff between information availability and downstream propagation capacity. We evaluate REINS across 9 video diffusion models, multiple parameter scales (1.3B-5B), and both text-to-video and image-to-video generation, to our knowledge, the broadest safety evaluation suite in the video generation literature.
Chinese Translation
开放权重的视频扩散模型能够生成逼真的不安全内容，从暴力到虚假信息，然而现有的防御措施要么需要昂贵的安全微调，这会降低模型的通用能力，要么应用外部过滤器，这些过滤器可以被对抗性提示轻易绕过。我们提出了REINS（表示空间推理时安全引导），这是一种无训练的方法，通过在推理时引导其内部表示朝向安全生成来对齐视频扩散模型。我们的关键发现是，安全相关的结构在线性编码于视频扩散变换器的隐藏状态激活中，通过对二元安全标签进行监督主成分分析（Supervised PCA）发现的单一方向足以将安全生成轨迹与不安全生成轨迹分开。在推理时，将该方向添加到中间变换器层的隐藏状态中，可以将生成从有害内容重定向到语义相关的安全替代品，无需权重更新、概念枚举，并且计算开销微乎其微。通过机制分析，我们揭示了安全信息随着变换器深度的增加而单调累积，但引导效果在中间层（约50%深度）达到峰值，暴露了信息可用性与下游传播能力之间的基本权衡。我们在9个视频扩散模型、多个参数规模（1.3B-5B）以及文本到视频和图像到视频生成中评估了REINS，至今为止，这是视频生成文献中最广泛的安全评估套件。
cs.CV / 7 / 2606.17279
Training LLMs with Reinforcement Learning over Digital Twin Representations for Reasoning-Intensive Surgical VideoQA
通过数字双胞胎表示进行强化学习训练的大型语言模型用于推理密集型外科视频问答
Abstract
Surgical video question answering requires multi-step reasoning across semantic, spatial, and temporal dimensions. Existing methods architecturally compress videos into discrete token representations and couple visual perception with reasoning. This approach fragments continuous spatial-temporal relationships and has been shown to restrict multi-step reasoning capabilities. We introduce a reinforcement learning (RL) framework that trains large language models (LLMs) to decouple perception from reasoning by operating over digital twin representations constructed from surgical foundation models. Additionally, we introduce hierarchical representations across frame, temporal window, and procedure levels with probabilistic uncertainty estimates. Finally, we propose a novel reward that combines format validation with accuracy assessment through clinical plausibility evaluation and uncertainty-aware calibration for training. To demonstrate the capabilities of this approach, we introduce REAL-Colon-Reason, a colonoscopic benchmark with 2000 question-answer pairs across three complexity levels. We achieve state-of-the-art performance on REAL-Colon-Reason and two existing surgical VideoQA benchmarks REAL-Colon-VQA and EndoVis18-VQA.
Chinese Translation
外科视频问答需要在语义、空间和时间维度上进行多步推理。现有方法在架构上将视频压缩为离散的标记表示，并将视觉感知与推理结合。这种方法破坏了连续的时空关系，并已被证明限制了多步推理能力。我们引入了一种强化学习（RL）框架，训练大型语言模型（LLMs）通过操作基于外科基础模型构建的数字双胞胎表示，将感知与推理解耦。此外，我们在帧、时间窗口和程序层面引入了层次化表示，并提供了概率不确定性估计。最后，我们提出了一种新颖的奖励机制，将格式验证与通过临床合理性评估和不确定性感知校准进行的准确性评估相结合，以进行训练。为了展示这种方法的能力，我们引入了REAL-Colon-Reason，这是一个包含2000个问题-答案对的结肠镜检查基准，涵盖三个复杂性水平。我们在REAL-Colon-Reason以及两个现有的外科视频问答基准REAL-Colon-VQA和EndoVis18-VQA上实现了最先进的性能。
cs.CV / 8 / 2606.17296
Pareto LoRA: Mitigating Modality Imbalance in Unified Multimodal Models via Pareto-Optimal Gradient Integration
Pareto LoRA：通过帕累托最优梯度集成缓解统一多模态模型中的模态不平衡
Abstract
Unified multimodal models (UMMs) have recently emerged as a promising paradigm for integrating multimodal understanding and generation within a single autoregressive transformer. However, during multimodal instruction tuning, these models often exhibit pronounced modality imbalance: language gradients dominate optimization, thus leading to lower image generation quality, especially under parameter-efficient fine-tuning such as LoRA. In this work, we systematically analyze modality imbalance in LoRA-based fine-tuning of UMMs for interleaved text-image generation. We show that vision modality performance degrades substantially more than text modality performance when compared to unimodal counterparts, and that modality-specific gradients can differ by orders of magnitude across various tasks and layers. Motivated by this observation, we reformulate the multimodal instruction tuning as a bi-objective optimization problem and propose Pareto LoRA, a Pareto-optimal gradient integration strategy that balances the text and image objectives by modulating the gradient direction and strength. Experiments on the CoMM benchmark with Emu2 demonstrate that Pareto LoRA consistently improves multimodal generation balance, achieving up to 44.9% gains in perceptual image quality over vanilla LoRA while maintaining comparable text performance.
Chinese Translation
统一多模态模型（UMMs）最近作为一种有前景的范式出现，旨在在单一自回归变换器中整合多模态理解和生成。然而，在多模态指令调优过程中，这些模型往往表现出明显的模态不平衡：语言梯度主导优化，从而导致图像生成质量下降，尤其是在如LoRA这样的参数高效微调下。在本研究中，我们系统地分析了基于LoRA的UMMs在交错文本-图像生成中的模态不平衡。我们发现，与单模态对应模型相比，视觉模态的性能显著低于文本模态的性能，并且模态特定的梯度在不同任务和层之间可能相差几个数量级。基于这一观察，我们将多模态指令调优重新表述为一个双目标优化问题，并提出了Pareto LoRA，这是一种帕累托最优梯度集成策略，通过调节梯度方向和强度来平衡文本和图像目标。在Emu2的CoMM基准测试中的实验表明，Pareto LoRA在多模态生成平衡方面始终表现出改善，相较于普通的LoRA，在感知图像质量上提高了多达44.9%，同时保持了可比的文本性能。
cs.CV / 9 / 2606.17298
Reasoning Text-to-Video Retrieval for Operating Room Clips via Action-Driven Digital Twins
基于动作驱动数字双胞胎的手术室片段推理文本到视频检索
Abstract
Text-to-video retrieval in operating rooms (OR) is an enabling technology for OR safety, as it allows stakeholders to retrieve and inspect recordings of specific events. However, because the most safety-critical events may not follow the common structure, to unlock its full potential text-to-video retrieval must be able to handle implicit queries that require reasoning to identify the right video (e.g., the step right before clipping). However, existing methods rely on global embeddings that cannot reason over such queries. We propose OR3, a text-to-video retrieval method that converts clips into action-driven digital twins (ActDTs), grouping concurrent subject-action-object triplets under non-overlapping temporal intervals. Moreover, rather than cross-modal matching through paired encoders, OR3 performs imagination-based retrieval where an LLM generates hypothetical ActDTs from queries. This enables intra-modal matching via a single encoder trained with ActDT-tailored hard negatives. Finally, evidence-grounded refinement revises imagined ActDTs based on discrepancies with top candidates to capture procedure-specific patterns. We construct a benchmark from MM-OR with 276 implicit queries across four reasoning categories over 386 clips from robotic knee procedures. OR3 achieves 57.6 R@1 and 77.3 R@5, outperforming the strongest baseline. These results demonstrate that OR3 enables fine-grained discrimination between visually similar OR video clips through temporal action reasoning.
Chinese Translation
手术室（OR）中的文本到视频检索是一项促进手术室安全的关键技术，因为它允许相关人员检索和检查特定事件的录音。然而，由于最关键的安全事件可能不遵循常见结构，因此要充分发挥其潜力，文本到视频检索必须能够处理需要推理以识别正确视频的隐式查询（例如，剪辑前的步骤）。然而，现有方法依赖于无法对这些查询进行推理的全局嵌入。我们提出了OR3，一种文本到视频检索方法，它将片段转换为动作驱动的数字双胞胎（ActDTs），在不重叠的时间间隔内对并发的主体-动作-对象三元组进行分组。此外，OR3并不是通过配对编码器进行跨模态匹配，而是执行基于想象的检索，其中大型语言模型（LLM）根据查询生成假设的ActDTs。这使得通过一个经过ActDT定制的困难负样本训练的单一编码器实现了模态内匹配。最后，基于证据的细化根据与顶级候选者的差异修正想象的ActDT，以捕捉特定程序的模式。我们从MM-OR构建了一个基准，包含276个隐式查询，涵盖四个推理类别，涉及386个机器人膝关节手术片段。OR3在R@1上达到57.6，R@5达到77.3，超越了最强基线。这些结果表明，OR3通过时间动作推理使视觉上相似的手术室视频片段之间的细粒度区分成为可能。
cs.CV / 10 / 2606.17310
SierpinskiCam: Camera-Controlled Video Retaking with Sierpinski Triangle Pattern Cues
SierpinskiCam：基于Sierpinski三角形模式线索的相机控制视频重拍
Abstract
Generating novel renderings of a scene along user-defined camera trajectories from a single monocular video, dubbed video retaking, is a compelling but difficult problem in content creation and visual effects. Existing geometry-guided approaches reconstruct a 4D representation from the source video and render it along the target trajectory to condition video diffusion models. However, this guidance degrades as the target camera departs from the source trajectory, leaving newly revealed regions sparse or entirely missing. We propose SierpinskiCam, which addresses this limitation by augmenting geometry-based guidance with Sierpinski dome texture cues that contains rich trackable features even under large viewpoint changes. We further introduce a reference video conditioning mechanism that appends source-video tokens to the target-token sequence and separates the two streams with negative RoPE indices, enabling appearance grounding without architectural modification or per-video adaptation. Extensive experiments show that SierpinskiCam achieves significant gains in camera controllability, geometric consistency, and video quality across diverse and challenging retaking scenarios. Project page: https://hyelinnam.github.io/SierpinskiCam/.
Chinese Translation
从单个单目视频生成沿用户定义相机轨迹的新场景渲染，称为视频重拍，是内容创作和视觉效果中的一个引人注目但困难的问题。现有的几何引导方法从源视频重建4D表示，并沿目标轨迹进行渲染，以指导视频扩散模型。然而，当目标相机偏离源轨迹时，这种引导效果会下降，导致新显现区域稀疏或完全缺失。我们提出了SierpinskiCam，通过结合Sierpinski穹顶纹理线索来增强基于几何的引导，这些线索在大视角变化下仍包含丰富的可跟踪特征。我们进一步引入了一种参考视频条件机制，将源视频标记附加到目标标记序列，并通过负RoPE索引分隔这两个流，从而实现外观的固定，而无需架构修改或每个视频的适应。大量实验表明，SierpinskiCam在相机可控性、几何一致性和视频质量方面，在多样且具有挑战性的重拍场景中取得了显著的提升。项目页面：https://hyelinnam.github.io/SierpinskiCam/
cs.CV / 11 / 2606.17334
FATE: Pillar Encoding and Frequency-Aware Training for Event-Based Object Detection
FATE：基于柱编码和频率感知训练的事件驱动物体检测
Abstract
Event cameras are bio-inspired sensors that asynchronously capture logarithmic intensity changes, offering inherent advantages in high-speed and high-dynamic-range scenarios. However, the sparse and asynchronous nature of event streams poses a fundamental challenge for modern deep learning architectures. To enable compatibility with standard models, most existing approaches partition the accumulation window into fixed temporal sub-bins. While effective for spatial processing, this internal discretization discards fine-grained temporal structure and constrains inference to the low temporal frequencies imposed by training supervision. To address this limitation, we propose FATE, a unified framework built upon a novel Pillar Encoding (PE). While operating over discrete macro-accumulation windows dictated by the target frequency, PE avoids internal temporal sub-binning. It organizes events into spatial pillars and approximates their intra-window evolution via projection onto a continuous-time orthogonal polynomial basis. This formulation yields an L2-optimal representation that retains rich temporal dynamics in a dense pseudo-image, mitigating information loss under sparse event conditions. To fully leverage this representation, we introduce Frequency-Aware Training (FAT), a soft mean-teacher curriculum that generates temporally dense pseudo-labels, effectively bridging the mismatch between low-frequency supervision and high-frequency inference. Extensive experiments demonstrate that FATE generalizes across architectural paradigms and consistently outperforms strong baselines. It enables robust object detection at high temporal resolutions up to 200 Hz, while incurring minimal overhead in parameter count and inference latency
Chinese Translation
事件相机是一种仿生传感器，能够异步捕捉对数强度变化，在高速和高动态范围场景中具有固有优势。然而，事件流的稀疏性和异步性对现代深度学习架构构成了根本挑战。为了与标准模型兼容，大多数现有方法将累积窗口划分为固定的时间子区间。尽管这种方法在空间处理上有效，但内部离散化丢弃了细粒度的时间结构，并将推理限制在训练监督施加的低时间频率。为了解决这一局限性，我们提出了FATE，这是一个基于新颖的柱编码（Pillar Encoding, PE）构建的统一框架。PE在目标频率所决定的离散宏观累积窗口上操作，避免了内部时间子区间的划分。它将事件组织成空间柱，并通过投影到连续时间正交多项式基上来近似其窗口内的演变。这种表述产生了一个L2最优表示，能够在稠密伪图像中保留丰富的时间动态，从而减轻稀疏事件条件下的信息损失。为了充分利用这种表示，我们引入了频率感知训练（Frequency-Aware Training, FAT），这是一种软均值教师课程，生成时间上稠密的伪标签，有效弥合低频监督与高频推理之间的差距。大量实验表明，FATE在各种架构范式中具有良好的泛化能力，并始终优于强基线。它能够在高达200 Hz的高时间分辨率下实现稳健的物体检测，同时在参数数量和推理延迟上仅产生最小的开销。
cs.CV / 12 / 2606.17340
Geometry-Consistent Endoscopic Representations for Image-Guided Navigation via Structured Foundation Model Adaptation
几何一致的内窥镜图像表示用于结构化基础模型适应的图像引导导航
Abstract
Accurate vision-based navigation in monocular endoscopy is difficult due to limited depth cues, weak tissue texture, non-rigid deformation, and substantial appearance variation across domains, all of which complicate pose estimation, depth prediction, and image-to-anatomy alignment. Although recent vision foundation models have shown promise, their learned representations often remain insufficiently geometry-consistent, hindering stable feature correspondence and limiting their reliability for downstream navigation tasks. We propose a unified framework for learning geometry-consistent and domain-robust image representations for monocular endoscopy. The framework combines a synthetic data pipeline that provides accurate geometric supervision with Hierarchy-Aware Geometry-Semantic Adaptation, a structured alternative to standard LoRA that inserts low-rank adapters selectively across the transformer hierarchy and couples them with layer-wise training objectives to encourage geometric correspondence in intermediate features and semantic consistency in deeper features. Experiments on public and proprietary datasets show improved geometric and semantic representation quality, leading to better performance on downstream navigation tasks including pose estimation and monocular depth estimation. The learned representations show favorable synthetic-to-real transfer on clinical bronchoscopy and provide a useful initialization for adaptation to sinus endoscopy and colonoscopy under limited supervision. The framework also shows favorable scaling with model size and training data. These results support hierarchy-aware, geometry-guided adaptation as a practical approach for endoscopic representation learning.
Chinese Translation
由于深度线索有限、组织纹理较弱、非刚性变形以及跨领域显著的外观变化，单目内窥镜中的基于视觉的精确导航变得困难，这些因素都使得姿态估计、深度预测和图像与解剖结构的对齐变得复杂。尽管近期的视觉基础模型显示出良好的前景，但它们学习到的表示往往缺乏足够的几何一致性，从而阻碍了稳定的特征对应，并限制了它们在下游导航任务中的可靠性。我们提出了一个统一框架，用于学习几何一致且领域鲁棒的单目内窥镜图像表示。该框架结合了提供准确几何监督的合成数据管道和层次感知几何-语义适应（Hierarchy-Aware Geometry-Semantic Adaptation），后者是标准低秩适配器（LoRA）的结构化替代方案，能够在变换器层次中选择性地插入低秩适配器，并与逐层训练目标相结合，以促进中间特征的几何对应和深层特征的语义一致性。在公共和专有数据集上的实验表明，几何和语义表示质量得到了改善，从而在下游导航任务（包括姿态估计和单目深度估计）中表现更佳。学习到的表示在临床支气管镜检查中显示出良好的合成到真实的迁移，并为在有限监督下适应窦道内窥镜和结肠镜检查提供了有用的初始化。该框架在模型规模和训练数据方面也表现出良好的扩展性。这些结果支持层次感知的几何引导适应作为内窥镜表示学习的一个实用方法。
cs.CV / 13 / 2606.17342
Learning a Maximum Entropy Model for Visual Textures using Diffusion
使用扩散学习视觉纹理的最大熵模型
Abstract
Visual textures -- spatially homogeneous image regions containing repeated elements (e.g. a field of grass, the bark of a tree) -- are ubiquitous in visual scenes and provide important cues for recognizing and analyzing materials and objects. A number of existing texture models extract essential statistics from a single texture image, and can then generate high-quality samples that are visually similar to the original by matching these statistics. However, their statistics are either hand-designed or based on a network pretrained for another purpose (e.g., object recognition). Here, we develop the first principled method for unsupervised learning of a set of statistics that are used to constrain a maximum entropy probability model. We leverage methods developed for generative diffusion models to derive training and sampling procedures, and compare these to the traditional method of sampling via matching the statistics. Despite the compactness of our trained model (512 statistics), it generates texture images whose quality is as good as or better than the current state-of-the-art model (~177k statistics). A more direct comparison of the two models, obtained by synthesizing images that are indistinguishable for one model but maximally different for the other, reveals their relative strengths and weaknesses. Finally, we show that unlike previous statistical texture models, a straight trajectory in the representation space of our model generates homogeneous texture samples that interpolate smoothly between the features of the two end points.
Chinese Translation
视觉纹理——包含重复元素的空间均匀图像区域（例如，草地、树皮）——在视觉场景中无处不在，并为材料和物体的识别与分析提供重要线索。现有的一些纹理模型从单一纹理图像中提取基本统计信息，并通过匹配这些统计信息生成与原始图像在视觉上相似的高质量样本。然而，它们的统计信息要么是手工设计的，要么是基于为其他目的（例如，物体识别）预训练的网络。本文中，我们开发了第一种原则性的方法，用于无监督学习一组用于约束最大熵概率模型的统计信息。我们利用为生成扩散模型开发的方法推导训练和采样程序，并将其与通过匹配统计信息的传统采样方法进行比较。尽管我们训练的模型（512个统计信息）相对紧凑，但其生成的纹理图像质量与当前最先进的模型（约177k个统计信息）相当或更好。通过合成对于一个模型不可区分但对于另一个模型最大不同的图像，我们对这两种模型进行了更直接的比较，揭示了它们的相对优缺点。最后，我们展示了与以往的统计纹理模型不同，我们模型的表示空间中的直线路径生成的均匀纹理样本在两个端点特征之间平滑插值。
cs.CV / 14 / 2606.17343
Bayesian Magnetic Resonance Joint Image Reconstruction and Uncertainty Quantification using Sparsity Prior Models and Markov Chain Monte Carlo Sampling
基于稀疏先验模型和马尔可夫链蒙特卡洛采样的贝叶斯磁共振联合图像重建与不确定性量化
Abstract
We propose a novel framework for uncertainty quantification using compressed sensing magnetic resonance image reconstruction. The problem is formulated within a Bayesian framework as a linear inverse problem, with prior distributions assigned to the unknown model parameters. Specifically, the image to be reconstructed is assumed to be sparse in a given basis. We develop a general framework applicable to any basis and as examples, we test the sparsity of the image in its (1) spatial gradients using a total variation prior model, and in its (2) wavelet transform. A Markov chain Monte Carlo (MCMC) method, based on a split-and-augmented Gibbs sampler, is then employed to sample from the posterior distribution of the unknown parameters. The non-differentiable conditional distributions are efficiently sampled using a proximal MCMC method. The proposed algorithms are validated on both single-coil and multi-coil datasets using various k-space sub-sampling patterns and ratios. The results demonstrate the superior performance of each proposed approach in reconstructing images compared to its counterpart optimisation-based method. Moreover, our framework effectively quantifies uncertainty, showing a notable correlation between estimated uncertainty maps and error maps computed using ground truth and reconstructed images, compared with existing deep learning-based methods.
Chinese Translation
我们提出了一种基于压缩感知磁共振图像重建的不确定性量化新框架。该问题在贝叶斯框架内被表述为线性逆问题，并为未知模型参数分配了先验分布。具体而言，假设待重建的图像在给定基底下是稀疏的。我们开发了一个适用于任何基底的一般框架，并作为示例，测试了图像在其（1）空间梯度下使用全变差先验模型的稀疏性，以及在其（2）小波变换下的稀疏性。接着，采用基于分裂和增强吉布斯采样器的马尔可夫链蒙特卡洛（MCMC）方法，从未知参数的后验分布中进行采样。使用近端MCMC方法有效地对不可微条件分布进行采样。所提出的算法在单线圈和多线圈数据集上进行了验证，使用了各种k空间子采样模式和比例。结果表明，与基于优化的方法相比，每种提出的方法在图像重建方面表现出优越的性能。此外，我们的框架有效地量化了不确定性，显示出估计的不确定性图与使用真实值和重建图像计算的误差图之间存在显著相关性，相较于现有的基于深度学习的方法。
cs.CV / 15 / 2606.17355
Complex Layout Classification in the Wild: A Low-Resource Approach with Layout-Preserving Augmentations
复杂布局分类的低资源方法：一种保持布局的增强技术
Abstract
Many digitized corpora suffer from low resources because annotations may be scarce, page scans are noisy and of poor resolution, or layouts are structurally complex in ways that negatively affect the quality of automatic transcription. Developing robust classification models for low-resource languages is inhibited by the lack of large-scale annotated data and by the frequent semantic complexity of page layouts. To this end, we have curated a complex-layout dataset, manually classified into eight distinct layout types based on their separator regions. To overcome data scarcity, we propose a novel training strategy in the form of a CNN-based classifier that employs strong, domain-aware augmentations to improve generalization. We utilize narrow anisotropic Gaussian masking to suppress incidental textual details while preserving essential separations, compelling the model to learn global geometric arrangements. Additionally, we implement reflection-induced label transformations to enrich the training distribution while maintaining label consistency across asymmetric categories. The results demonstrate that layout-specific augmentations can substantially improve page-level layout classification under severe annotation scarcity.
Chinese Translation
许多数字化语料库面临资源匮乏的问题，因为注释可能稀缺，页面扫描质量低且分辨率差，或者布局在结构上复杂，从而对自动转录的质量产生负面影响。开发针对低资源语言的稳健分类模型受到大规模注释数据缺乏和页面布局语义复杂性的限制。为此，我们整理了一个复杂布局数据集，手动将其分类为八种不同的布局类型，基于其分隔区域。为了克服数据稀缺，我们提出了一种新颖的训练策略，采用基于卷积神经网络（CNN）的分类器，利用强大的领域感知增强技术来提高模型的泛化能力。我们使用窄各向异性高斯掩蔽来抑制偶然的文本细节，同时保持重要的分隔，促使模型学习全局几何排列。此外，我们实施了反射诱导的标签转换，以丰富训练分布，同时在不对称类别之间保持标签一致性。结果表明，特定于布局的增强技术可以在严重注释稀缺的情况下显著改善页面级布局分类。
cs.CV / 16 / 2606.17362
DriveJudge: Rethinking Autonomous Driving Evaluation with Vision-Language Models
DriveJudge：重新思考基于视觉-语言模型的自动驾驶评估
Abstract
Autonomous driving has shifted towards end-to-end policy learning, where reliable, interpretable policy evaluation is a fundamental challenge as driving quality is highly context-dependent. Commonly used rule-based driving metrics like EPDMS are interpretable but lack context-awareness, while recent VLMbased evaluations are context-aware but limited by ambiguous VLM outputs and weak physical grounding. To evaluate driving in a manner that is both interpretable and context-aware, we introduce DriveJudge. DriveJudge is a driving evaluation agent that combines rule-grounded evaluation with Vision-Language Model (VLM) reasoning and selectively invokes physically-grounded deterministic rule functions after interpreting the environmental context. To train and evaluate DriveJudge, we curate a large-scale dataset of 33,577 challenging driving samples with human annotations on whether the driving behavior is reasonable in the given scenario. With this dataset, we address the underexplored problem of driving metric evaluation, and introduce two human-aligned benchmark tasks: Driving Quality Classification and Trajectory Preference Selection. DriveJudge outperforms EPDMS for driving quality classification by 21.23 AUC, and the recent VLM-based DriveCritic for trajectory preference selection by 6.5%, setting a new standard for interpretable and precise driving evaluation.
Chinese Translation
自动驾驶已转向端到端的策略学习，其中可靠且可解释的策略评估是一个基本挑战，因为驾驶质量高度依赖于上下文。常用的基于规则的驾驶指标，如EPDMS，虽然可解释，但缺乏上下文意识；而最近的基于视觉-语言模型（VLM）的评估则具备上下文意识，但受限于模糊的VLM输出和薄弱的物理基础。为了以一种既可解释又具上下文意识的方式评估驾驶，我们提出了DriveJudge。DriveJudge是一个驾驶评估代理，结合了基于规则的评估与视觉-语言模型（VLM）推理，并在解释环境上下文后选择性地调用物理基础的确定性规则函数。为了训练和评估DriveJudge，我们整理了一个包含33,577个具有挑战性的驾驶样本的大规模数据集，并对驾驶行为在给定场景中的合理性进行了人工标注。通过这个数据集，我们解决了驾驶指标评估这一尚未充分探索的问题，并引入了两个与人类对齐的基准任务：驾驶质量分类和轨迹偏好选择。DriveJudge在驾驶质量分类中比EPDMS提高了21.23 AUC，在轨迹偏好选择中比最近的基于VLM的DriveCritic提高了6.5%，为可解释且精确的驾驶评估设定了新的标准。
cs.CV / 17 / 2606.17379
MeiBRD: Meta-Learning Intraoperative Biomechanical Residual Deformation
MeiBRD：元学习术中生物力学残余变形
Abstract
Accurate intraoperative liver registration is challenging due to substantial soft-tissue deformation yet sparse intraoperative measurements. Biomechanical models regularize this ill-posedness with prior knowledge but exhibit persistent prediction bias due to simplifying assumptions, while data-driven learning solutions struggle with data efficiency, generalization, and physical plausibility. We propose a hybrid registration framework that adapts a biomechanical prior using sparse intraoperative correspondences. Rather than learning a full deformation field, we learn a residual deformation function that corrects linear biomechanical predictions, modeled as a graph neural diffusion function with geometry-aware attention over the 3D liver mesh. To enable long-range information transfer of sparse observations, we take a novel perspective of sparse intraoperative measurements as \textit{context} samples where input-output pairs of the residual deformation function are fully observed, casting the problem into learning-to-learn this residual function from intraoperative context samples with feedforward meta-learners. Experiments on a deformable liver phantom dataset demonstrate improved registration accuracy and generalization compared to rigid, biomechanical, and data-driven baselines, particularly for out-of-distribution geometries and deformations.
Chinese Translation
由于显著的软组织变形和稀疏的术中测量，准确的术中肝脏配准面临挑战。生物力学模型通过先验知识对这种病态问题进行正则化，但由于简化假设，预测偏差持续存在，而数据驱动的学习解决方案在数据效率、泛化能力和物理合理性方面存在困难。我们提出了一种混合配准框架，该框架利用稀疏的术中对应关系来调整生物力学先验。我们不是学习完整的变形场，而是学习一个残余变形函数，以修正线性生物力学预测，该函数被建模为具有几何感知注意力的图神经扩散函数，作用于三维肝脏网格。为了实现稀疏观察的长距离信息传递，我们将稀疏的术中测量视为 extit{上下文}样本，其中残余变形函数的输入-输出对是完全观察到的，从而将问题转化为从术中上下文样本中学习这一残余函数的学习-学习任务，使用前馈元学习器。对可变形肝脏模型数据集的实验表明，与刚性、生物力学和数据驱动基线相比，注册精度和泛化能力得到了改善，特别是在分布外几何形状和变形的情况下。
cs.CV / 18 / 2606.17384
Improving and Evaluating Hand-Object Interaction Detection
改进与评估手物体交互检测
Abstract
Understanding hands and the objects they interact with, both directly and through tools, is a key step for tasks ranging from action perception to 3D reconstruction and robotics. Our paper provides several contributions to the Hand-Object Interaction (HOI) understanding literature: (1) HOI-DETR, a new framework that introduces hand-object and object-object interactions to the Co-DETR architecture to produce a state-of-the-art method; (2) a comprehensive HOI evaluation suite of 4 diverse datasets, including a video benchmark derived from the HD-EPIC dataset and fresh annotations that improve the Hands23 benchmark and (3) a trained checkpoint that significantly improves the state of the art across Hands23, HOIST, FineBio, and HD-EPIC, including mAP gains of over 20 percentage points on Hands23 and FineBio. Our ablations confirm the contributions of each model component.
Chinese Translation
理解手部及其与物体的直接和间接交互是从动作感知到三维重建和机器人技术等任务的关键步骤。本文对手物体交互（Hand-Object Interaction, HOI）理解领域做出了几项贡献：(1) HOI-DETR，一个新的框架，将手物体和物体间交互引入 Co-DETR 架构，从而产生一种先进的方法；(2) 一个全面的 HOI 评估套件，包含4个多样化的数据集，包括从 HD-EPIC 数据集中衍生的视频基准和改进 Hands23 基准的新注释；(3) 一个经过训练的检查点，显著提升了 Hands23、HOIST、FineBio 和 HD-EPIC 的先进水平，其中在 Hands23 和 FineBio 上的 mAP 提升超过20个百分点。我们的消融实验确认了每个模型组件的贡献。
cs.CV / 19 / 2606.17386
TerraTransfer: Learning End-to-End Driving Policies Without Expert Demonstrations
TerraTransfer：无专家示范的端到端驾驶策略学习
Abstract
End-to-end autonomous driving has achieved state-of-the-art performance on benchmarks and real-world deployments. Its standard training recipe, however, is expensive across all stages: collecting and labeling millions of driving frames is costly, and closed-loop RL on images is bottlenecked by the per-step cost of photorealistic rendering plus a forward pass through a large vision backbone. Self-play in vectorized simulators changes the economics: millions of rollout steps per second, and a state distribution naturally rich in collisions, near-misses, and recoveries that no driving log contains. Our approach exploits this asymmetry by decoupling learning to drive from learning to see. We pretrain a single policy by self-play, then align its latent space with a pretrained vision backbone, through the action KL divergence and a batch-relational low-rank structural loss. The action target comes from the self-play policy, so alignment never supervises against a logged trajectory: a paired dataset of (image, scene-state) frames suffices, with no need for the curated expert demonstrations that imitation pretraining is built on. On photorealistic 3D Gaussian splatting closed-loop scenarios, the resulting end-to-end policy matches or exceeds prior end-to-end methods.
Chinese Translation
端到端自主驾驶在基准测试和现实世界部署中已实现了最先进的性能。然而，其标准训练流程在所有阶段都非常昂贵：收集和标注数百万个驾驶帧的成本高昂，而在图像上进行闭环强化学习（RL）则受限于光线逼真渲染的每步成本以及通过大型视觉骨干网的前向传播。自我对弈在向量化模拟器中改变了经济学：每秒数百万个回合步骤，以及自然丰富的状态分布，其中包含碰撞、险些发生碰撞和恢复的情况，而这些在任何驾驶日志中都不存在。我们的方法通过将驾驶学习与视觉学习解耦来利用这种不对称性。我们通过自我对弈预训练一个单一策略，然后通过动作KL散度和批量关系低秩结构损失将其潜在空间与预训练的视觉骨干网对齐。动作目标来自自我对弈策略，因此对齐从不针对记录的轨迹进行监督：一对（图像，场景状态）帧的数据集就足够了，无需模仿预训练所依赖的经过策划的专家示范。在光线逼真的3D高斯点云闭环场景中，得到的端到端策略与先前的端到端方法相匹配或超越。
cs.CV / 20 / 2606.17389
Visuals Lie, Consistency Speaks: Disentangling Spatial Attention from Reliability in Vision-Language Models
视觉误导，一致性发声：从可靠性中解构空间注意力在视觉-语言模型中的作用
Abstract
Multimodal Foundation Models are increasingly used as reasoning agents, making reliability, knowing when a model may hallucinate, critical. A common intuition, which we call the Attention-Confidence Assumption, holds that reliability follows from "structural" visual perception: tight attention on relevant regions should signal a trustworthy answer, while scattered attention signals confusion. We challenge this through the VLM Reliability Probe (VRP), a systematic cross-family study of reliability signals in contemporary Vision-Language Models (VLMs). We introduce structural-attention metrics, cluster counts (C_k) and spatial entropy (H_s), to quantify the visual encoder's gaze, and track its evolution (Delta H_s) across layers. This reveals a "Symbolic Detachment": models often "Early Lock" visual features only to diffuse attention later, severing early perception from final generation. Contrary to the grounding hypothesis, we find a "Cluster Failure": spatial attention has near-zero correlation (R approx 0.001) with accuracy. Instead, reliability is a phenomenon of generation dynamics and internal-state distributions. Self-Consistency, the agreement rate across sampled reasoning paths, is the dominant predictor of truth (R = 0.429). Scaling causal interventions exposes a sharp architectural divergence: LLaVA locks its prediction in a fragile late-stage bottleneck, whereas PaliGemma and Qwen2-VL distribute reliability globally, staying resilient even when ~50% or more of their most predictive layer is destroyed. For current VLMs, reliability signals are detached from visual grounding maps and are best inferred from generation-time dynamics and hidden-state probes.
Chinese Translation
多模态基础模型越来越多地被用作推理代理，因此了解模型何时可能产生幻觉的可靠性变得至关重要。我们称之为注意-置信假设的一个常见直觉认为，可靠性源于“结构性”的视觉感知：对相关区域的紧密注意应当表明答案可信，而分散的注意则表明混乱。我们通过视觉-语言模型可靠性探测器（VLM Reliability Probe, VRP）对此提出挑战，这是对当代视觉-语言模型（VLMs）中可靠性信号的系统性跨家族研究。我们引入结构注意力指标，聚类计数（C_k）和空间熵（H_s），以量化视觉编码器的注视，并跟踪其在各层中的演变（Delta H_s）。这揭示了一个“符号脱离”：模型往往在早期锁定视觉特征，但随后分散注意力，从而使早期感知与最终生成脱节。与基础假设相反，我们发现了一个“聚类失败”：空间注意力与准确性几乎没有相关性（R 约 0.001）。相反，可靠性是生成动态和内部状态分布的现象。自我一致性，即在采样推理路径中的一致率，是真相的主要预测因子（R = 0.429）。扩展因果干预揭示了明显的架构分歧：LLaVA在脆弱的后期瓶颈中锁定其预测，而PaliGemma和Qwen2-VL则在全球范围内分配可靠性，即使在其最具预测性的层中约50%或更多被破坏时仍保持韧性。对于当前的VLMs，可靠性信号与视觉基础图脱离，最好通过生成时动态和隐藏状态探测来推断。
cs.CV / 21 / 2606.17403
Bridging Spatial And Frequency Views For Disaster Assessment: Benefits And Limitations
桥接灾害评估中的空间与频率视角：优势与局限
Abstract
Rapid assessment of building damage from satellite imagery is essential for effective disaster response and recovery. While most deep learning methods rely on spatial-domain features, frequency-domain representations can capture complementary structural cues such as debris patterns and collapse-induced textures. This study presents a controlled comparison of spatial-domain, frequency-domain, and dual-domain deep learning approaches for multi-class building damage classification using post-disaster imagery from the xView2 (xBD) dataset. To ensure fairness, all models are built on an EfficientNet-B0 backbone and trained under identical settings, differing only in their input representations and fusion strategies. Performance is evaluated using accuracy, macro F1-score, per-class metrics, and confusion matrices. Results show that dual-domain models provide measurable improvements over single-domain approaches. The dual spatial configuration achieves the highest test accuracy (0.4688) and lowest loss, while the spatial-only model attains the best macro F1-score (0.4254), indicating more balanced class performance. In contrast, frequency-only models perform worst and exhibit overfitting, suggesting limited generalization. Despite these gains, all models struggle to detect subtle damage levels, particularly the Minor class, due to class imbalance and fine-grained visual ambiguity. While dual-domain approaches improve detection of severe damage, challenges remain. These findings highlight the benefits and limitations of hybrid representations and motivate future work on data balancing, advanced fusion, and regularization.
Chinese Translation
从卫星影像快速评估建筑损坏对于有效的灾害响应和恢复至关重要。虽然大多数深度学习方法依赖于空间域特征，但频率域表示能够捕捉互补的结构线索，如碎片模式和因倒塌引起的纹理。本研究对使用来自 xView2 (xBD) 数据集的灾后影像进行多类别建筑损坏分类的空间域、频率域和双域深度学习方法进行了受控比较。为了确保公平性，所有模型均基于 EfficientNet-B0 主干构建，并在相同设置下训练，仅在输入表示和融合策略上有所不同。通过准确率、宏观 F1 分数、每类指标和混淆矩阵评估性能。结果表明，双域模型在单域方法上提供了可测量的改进。双空间配置实现了最高的测试准确率 (0.4688) 和最低的损失，而仅空间模型获得了最佳的宏观 F1 分数 (0.4254)，表明类别性能更为平衡。相比之下，仅频率模型表现最差，并且出现过拟合，表明其泛化能力有限。尽管取得了这些进展，所有模型在检测细微损坏水平方面仍然面临挑战，尤其是轻微类别，由于类别不平衡和细粒度视觉模糊。虽然双域方法改善了对严重损坏的检测，但仍然存在挑战。这些发现突显了混合表示的优势与局限，并激励未来在数据平衡、先进融合和正则化方面的研究。
cs.CV / 22 / 2606.17406
Graph Neural Networks for Semi-Supervised Image Classification with Multi-Feature Aggregation
基于多特征聚合的半监督图神经网络图像分类
Abstract
Feature extraction involves the identification and extraction of salient characteristics or patterns, including edges, textures, shapes, and color attributes. Contemporary feature extractors predominantly leverage deep learning architectures, such as Convolutional Neural Networks (CNNs) and Vision Transformers (VITs). The availability of diverse feature extractors in the literature provides a wide range of feature representations. Features extracted from an image depend on the specific application, the chosen extractor, and its configuration. Therefore, integrating complementary information by combining distinct extractors offers a promising way to enhance performance. Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs), have emerged as powerful and widely adopted approaches for semi-supervised image classification, as they effectively leverage both labeled and unlabeled data while exploiting the underlying graph structures that capture relationships among samples. This study proposes a novel approach for GNNs in scenarios where labeled data is scarce, by integrating diverse sets of feature and graph representations derived from various extractors in classification scenarios. Experimental investigations were conducted, encompassing combinations of distinct feature and graph extractors, as well as rank aggregation strategies. The primary contributions of this work are underscored by the experimental findings, which demonstrate that the strategic combination of feature and graph representations, coupled with the application of manifold learning for graph processing, leads to significant improvements in classification accuracy across the majority of experimental conditions. Furthermore, the utilization of rank aggregation techniques to integrate features from different extractors was shown to enhance classification accuracy.
Chinese Translation
特征提取涉及显著特征或模式的识别与提取，包括边缘、纹理、形状和颜色属性。现代特征提取器主要利用深度学习架构，如卷积神经网络（CNNs）和视觉变换器（VITs）。文献中多样的特征提取器提供了广泛的特征表示。从图像中提取的特征依赖于具体应用、所选择的提取器及其配置。因此，通过结合不同的提取器来整合互补信息，提供了一种提升性能的有前景的方法。图神经网络（GNNs），特别是图卷积网络（GCNs），已成为半监督图像分类中强大且广泛采用的方法，因为它们有效地利用了标记和未标记的数据，同时利用了捕捉样本之间关系的底层图结构。本研究提出了一种新颖的方法，针对标记数据稀缺的场景，通过整合来自不同提取器的多样特征和图表示来进行分类。进行了实验研究，涵盖了不同特征和图提取器的组合，以及排名聚合策略。该工作的主要贡献通过实验结果得以强调，这些结果表明，特征和图表示的战略组合，加上流形学习在图处理中的应用，导致在大多数实验条件下分类准确率显著提高。此外，利用排名聚合技术整合来自不同提取器的特征被证明可以提高分类准确率。
cs.CV / 23 / 2606.17410
Attention Alignment Between Humans and Vision-Language Models
人类与视觉语言模型之间的注意力对齐
Abstract
Visual perception depends on top-down goals and bottom-up sensory mechanisms. Vision-language models implement both, allowing us to treat each component as a separable hypothesis about what drives where we look. We compared spatial attention maps from six vision-language models against human fixation heatmaps recorded on 200 images during two tasks (general description and social captioning). The six models spanned a 2$\times$2 factorial of CNN vs.\ ViT encoders crossed with LSTM vs.\ Transformer decoders, plus Molmo 7B-D and Qwen3.5 9B. We found that both decoder and encoder architecture shaped alignment, but decoder choice dominated. LSTM vs.\ Transformer decoders increased alignment by 40--50 percentage points (80--87\% vs.\ 40--59\% of the human noise ceiling). In contrast, CNN vs.\ ViT encoders contributed a secondary 5--20 point advantage depending on decoder family, with CNN-LSTM the most aligned model overall (85--87\%). Despite their alignment advantage, LSTM-decoder attention maps were spatially diffuse and minimally task-differentiated; ViT-Transformer, the weakest in alignment, showed the sharpest spatial concentration and strongest task differentiation. A hemispatial-neglect simulation confirmed that ablating attention impacted LSTM decoders more than Transformer decoders. In an exploratory extension using TRIBE-simulated synthetic neural responses, fixation alignment and neural relevance dissociate: CNN-Transformer attention maps better predicted synthetic brain activity despite lower fixation alignment, with attention maps best predicting early visual cortex. Together, top-down and bottom-up components trade off what they predict in behavioral and synthetic neural data.
Chinese Translation
视觉感知依赖于自上而下的目标和自下而上的感官机制。视觉语言模型同时实现了这两者，使我们能够将每个组件视为关于驱动我们注意力的可分假设。我们比较了六个视觉语言模型的空间注意力图与在两项任务（一般描述和社交字幕）中记录的200幅图像的人类注视热图。六个模型涵盖了CNN与ViT编码器的2×2因子设计，交叉与LSTM与Transformer解码器，以及Molmo 7B-D和Qwen3.5 9B。我们发现解码器和编码器架构都影响了对齐程度，但解码器的选择占主导地位。LSTM与Transformer解码器的对齐度提高了40-50个百分点（80-87%对比人类噪声上限的40-59%）。相比之下，CNN与ViT编码器根据解码器类型提供了5-20点的次要优势，其中CNN-LSTM是整体上对齐度最高的模型（85-87%）。尽管具有对齐优势，LSTM解码器的注意力图在空间上分散且任务区分度最低；而ViT-Transformer在对齐度上最弱，却显示出最强的空间集中性和任务区分性。半空间忽视模拟确认，消除注意力对LSTM解码器的影响大于对Transformer解码器的影响。在一个使用TRIBE模拟的合成神经反应的探索性扩展中，注视对齐与神经相关性出现了分离：尽管注视对齐较低，CNN-Transformer注意力图更好地预测了合成大脑活动，且注意力图在预测早期视觉皮层方面表现最佳。总的来说，自上而下和自下而上的组件在行为和合成神经数据中所预测的内容存在权衡。
cs.CV / 24 / 2606.17412
Enhancing Pathological VLMs with Cross-scale Reasoning
通过跨尺度推理增强病理视觉语言模型
Abstract
Pathological images are inherently multi-scale, requiring pathologists to integrate evidence from global tissue architecture at low magnification to cellular morphology at higher magnification for accurate diagnosis. While existing pathological datasets for vision-language model (VLM) include various scales, they often lack an explicit cross-scale reasoning objective. This limitation prevents VLMs from capturing essential cross-scale representations and learning evidence-based reasoning. To bridge this gap, we introduce the first cross-scale training and evaluation paradigm that formulates pathology interpretation as multi-magnification reasoning. However, creating such a task reveals a critical challenge: multi-image visual question answering (VQA) is prone to text-only shortcuts, which allow models to guess answers using magnification-dependent artifacts rather than visual evidence. To address this, we propose a leakage-aware curation pipeline that combines adversarial text-only screening with constraint-guided question design. Using this pipeline, we construct Scale-VQA, a high-quality benchmark with 4,685 multiple-choice questions grounded in 2,537 pathology images across multiple magnification levels. Finally, we present ScaleReasoner-R1, a model trained via reinforcement learning to optimize performance on the cross-scale VQA task. ScaleReasoner-R1 achieves state-of-the-art performance on our cross-scale reasoning benchmark and generalizes to SOTA performance on established single-scale benchmarks. Findings suggest that even the limited cross-scale supervision can significantly improve pathological understanding. The code and demos will be open-sourced.
Chinese Translation
病理图像本质上是多尺度的，这要求病理学家在低倍放大下整合来自全局组织结构的证据，以及在高倍放大下整合细胞形态学的证据，以实现准确的诊断。尽管现有的用于视觉语言模型（VLM）的病理数据集包含了各种尺度，但它们通常缺乏明确的跨尺度推理目标。这一局限性阻碍了VLM捕捉重要的跨尺度表征和学习基于证据的推理。为了解决这一问题，我们提出了首个跨尺度训练和评估范式，将病理解释形式化为多倍放大推理。然而，创建这样的任务暴露出一个关键挑战：多图像视觉问答（VQA）容易出现仅依赖文本的捷径，这使得模型能够使用依赖于放大的伪影来猜测答案，而不是依赖于视觉证据。为此，我们提出了一种漏斗感知的策划管道，将对抗性文本筛选与约束引导的问题设计相结合。通过这个管道，我们构建了Scale-VQA，这是一个高质量基准，包含4,685个多项选择题，基于2,537幅跨多个放大级别的病理图像。最后，我们展示了ScaleReasoner-R1，这是一个通过强化学习训练的模型，旨在优化跨尺度VQA任务的性能。ScaleReasoner-R1在我们的跨尺度推理基准上实现了最先进的性能，并在已建立的单尺度基准上泛化到SOTA性能。研究结果表明，即使是有限的跨尺度监督也能显著提高病理理解。代码和演示将开源。
cs.CV / 25 / 2606.17427
Impact of Hand Impairment and Occlusions on Hand Pose Estimation Accuracy in Augmented Reality Applications
手部损伤和遮挡对增强现实应用中手势估计准确性的影响
Abstract
Mixed reality applications can be designed for hand rehabilitation. Augmented reality (AR) head mounted displays (HMDs) specifically allow for ecologically valid tasks because individuals can see their real environment and interact with real objects while receiving additional cues on the HMD. While these applications rely on accurate hand pose estimation, there is a gap in investigating the influence of hand impairment or occlusion from real-object interactions on pose estimation accuracy. Further, comparisons between AR HMD predictions and state-of-the-art pose estimation methods have not been established. The current study assessed pose estimation accuracy of the HoloLens 2 HMD and state-of-the-art pose estimation algorithms (WiLoR, HaMeR, WildHands, and MediaPipe) while individuals with cervical spinal cord injury (cSCI; n = 13, Neurological Level of Injury: C3-C6; American Spinal Injury Association Impairment Scale: A-D) and 15 uninjured controls interacted with clear and opaque objects. Ground truth estimates of 3D joint positions were generated via triangulation from a multi-camera setup. Pose estimation accuracy did not differ between the cSCI and uninjured control groups suggesting that 3D joint predictions from the HoloLens 2 and pose estimation algorithms can generalize to populations with hand impairment. Further, clear objects provided a small accuracy advantage over opaque objects (0.1 mm) and predictions from both WiLoR and HaMeR were slightly more accurate than the HoloLens 2 (2 mm). Overall, these results suggest that the HoloLens 2 may be viable for hand rehabilitation applications and the dataset generated can be used to refine pose estimation methods for hand-impaired populations.
Chinese Translation
混合现实应用可以用于手部康复。增强现实（AR）头戴式显示器（HMD）特别允许进行生态有效的任务，因为用户可以看到他们的真实环境并与真实物体互动，同时在HMD上接收额外提示。然而，这些应用依赖于准确的手势估计，目前尚缺乏对手部损伤或与真实物体交互时遮挡对姿态估计准确性影响的研究。此外，AR HMD的预测与最先进的姿态估计方法之间的比较尚未建立。本研究评估了HoloLens 2 HMD和最先进的姿态估计算法（WiLoR、HaMeR、WildHands和MediaPipe）的姿态估计准确性，参与者包括13名颈髓损伤（cSCI；n = 13，损伤神经学水平：C3-C6；美国脊髓损伤协会损伤等级：A-D）患者和15名未受伤的对照组，他们与透明和不透明物体进行互动。通过多摄像头设置的三角测量生成了3D关节位置的真实值估计。结果显示，cSCI组与未受伤对照组之间的姿态估计准确性没有差异，这表明HoloLens 2和姿态估计算法的3D关节预测可以推广到手部损伤人群。此外，透明物体相较于不透明物体提供了小幅的准确性优势（0.1毫米），而WiLoR和HaMeR的预测略微比HoloLens 2更准确（2毫米）。总体而言，这些结果表明HoloLens 2可能适用于手部康复应用，并且生成的数据集可以用于优化手部损伤人群的姿态估计方法。
cs.CV / 26 / 2606.17430
CIAN: Multi-Stage Framework for Event-Enriched Image Captioning via Retrieval-Augmented Generation
CIAN：通过检索增强生成的多阶段事件丰富图像描述框架
Abstract
Event-enriched image captioning describes not only visible content but also the broader context of events, including timing, location, and participants, capabilities missing in most pixel-bound models. We propose the Contextual Image-Article Narrator (CIAN), a multi-stage framework that enriches captions with external narratives. CIAN retrieves relevant articles using SigLIP, summarizes them to guide a Narrative Generation stage with a LoRA-fine-tuned Qwen model, and applies N-Gram-based Refinement for fluency and coherence. On the OpenEvents-V1 benchmark, CIAN achieves high retrieval performance (mAP 0.979) and improves caption quality, increasing CIDEr from 0.030 to 0.094. These results highlight the effectiveness of retrieval-augmented reasoning combined with linguistic refinement for generating context-aware, human-like captions.
Chinese Translation
事件丰富的图像描述不仅描述可见内容，还包括事件的更广泛背景，如时间、地点和参与者，这些能力在大多数像素绑定模型中缺失。我们提出了上下文图像-文章叙述者（CIAN），这是一个多阶段框架，能够通过外部叙述丰富图像描述。CIAN 使用 SigLIP 检索相关的文章，并对其进行总结，以指导叙述生成阶段，该阶段使用经过 LoRA 微调的 Qwen 模型，并应用基于 N-Gram 的精炼以提高流畅性和连贯性。在 OpenEvents-V1 基准测试中，CIAN 实现了高效的检索性能（mAP 0.979），并提高了描述质量，使 CIDEr 从 0.030 增加到 0.094。这些结果突显了检索增强推理与语言精炼相结合在生成上下文感知、类人描述方面的有效性。
cs.CV / 27 / 2606.17431
Visual Retrieval-Augmented Generation for Silhouette-Guided Animal Art
基于轮廓引导的动物艺术的视觉检索增强生成
Abstract
Generative AI has advanced the ability to render photorealistic or artistic images, yet it remains limited in a key aspect of human creativity: interpreting ambiguous shapes. This phenomenon, rooted in pareidolia, allows humans to perceive meaningful forms in random patterns such as clouds, stones, or leaves. To computationally replicate this imaginative process, we introduce Visual Retrieval-Augmented Generation (Visual-RAG), a framework that generates animal art directly from natural silhouettes. Our method retrieves structurally similar animal shapes from a curated corpus of 28,586 high-quality silhouettes and uses them as reference exemplars to guide diffusion-based generation with ControlNet and IP-Adapter. Ablation studies confirm that shape Context with RANSAC provides the most accurate alignment, while removing shape standardization reduces the inlier ratio to just 13.4\%, underscoring the importance of structural fidelity in Visual-RAG. A user study with 12 participants evaluated the outputs in terms of aesthetics, silhouette fidelity, and overall impression. Results reveal that while Visual-RAG provides plausible interpretations, challenges remain in achieving high perceptual impact. This work lays the foundation for computational pareidolia, showing how machines can contribute to the early stages of imaginative discovery.
Chinese Translation
生成性人工智能提升了渲染逼真或艺术图像的能力，但在一个关键的人类创造力方面仍然存在局限性：对模糊形状的解读。这一现象源于错觉，使人类能够在云、石头或树叶等随机图案中感知有意义的形状。为了在计算上复制这一富有想象力的过程，我们提出了视觉检索增强生成（Visual Retrieval-Augmented Generation，Visual-RAG）框架，该框架直接从自然轮廓生成动物艺术。我们的方法从一个精心策划的28,586个高质量轮廓的语料库中检索结构相似的动物形状，并将其用作参考示例，以指导基于扩散的生成过程，结合ControlNet和IP-Adapter。消融研究确认，使用RANSAC的形状上下文提供了最准确的对齐，而去除形状标准化将内点比例降低至仅13.4%，强调了Visual-RAG中结构保真度的重要性。通过对12名参与者进行的用户研究评估了输出的美学、轮廓保真度和整体印象。结果显示，尽管Visual-RAG提供了合理的解读，但在实现高感知影响方面仍面临挑战。这项工作为计算错觉奠定了基础，展示了机器如何在想象力发现的早期阶段做出贡献。
cs.CV / 28 / 2606.17433
LADBench: A Benchmark for Logical Fault Detection in Images
LADBench：图像逻辑故障检测基准
Abstract
Large Vision Language Models (VLMs) excel at visual question answering and semantic grounding, but their capacity for autonomous logical reasoning remains underexplored. Existing anomaly benchmarks emphasize visual errors or direct prompting rather than the physical and social common sense needed for open-world deployment. To address this, we introduce LAD-bench, a benchmark of more than 1,000 curated synthetic images with logical anomalies across four domains: Residential, Urban, Collaborative, and Nature. We further propose a Tiered Prompting Protocol based on progressive disclosure, which measures how much explicit assistance a model needs to localize and reason about a logical fault. Evaluating leading foundation models reveals substantial weaknesses: even the best achieves only 70.11% overall accuracy, showing that implicit logical fault detection remains unsolved. Crucially, models often fail to identify anomalies even after receiving explicit hints in deeper tiers. By surfacing these limitations in sequential multimodal reasoning, LAD-Bench offers a rigorous framework for advancing the safety, reliability, and cognitive alignment of autonomous visual systems. Dataset and Code: https://huggingface.co/datasets/SahasraK/LADBench
Chinese Translation
大型视觉语言模型（VLMs）在视觉问答和语义基础方面表现出色，但其自主逻辑推理的能力仍然未得到充分探索。现有的异常基准强调视觉错误或直接提示，而不是开放世界部署所需的物理和社会常识。为了解决这一问题，我们提出了LAD-bench，这是一个包含超过1,000张经过精心策划的合成图像的基准，这些图像在四个领域中存在逻辑异常：住宅、城市、协作和自然。我们进一步提出了一种基于渐进披露的分层提示协议，用于测量模型在定位和推理逻辑故障时所需的明确帮助量。对领先基础模型的评估揭示了显著的弱点：即使是表现最好的模型，其整体准确率也仅为70.11%，显示隐性逻辑故障检测仍未解决。至关重要的是，模型往往在收到更深层次的明确提示后仍未能识别异常。通过揭示这些在顺序多模态推理中的局限性，LAD-Bench为推动自主视觉系统的安全性、可靠性和认知一致性提供了一个严格的框架。数据集和代码：https://huggingface.co/datasets/SahasraK/LADBench
cs.CV / 29 / 2606.17436
UoU: A Universal Fingerprint Foundation Model Based on Large-Scale Unsupervised Learning
UoU：基于大规模无监督学习的通用指纹基础模型
Abstract
Fingerprint recognition is still dominated by task-specific pipelines, where enhancement, structural parsing, alignment, and matching are optimized in isolation. Although effective in narrow settings, this design limits representation reuse across sensors, qualities, and downstream applications. We therefore present UoU, short for ``a \textbf{U}niversal fingerprint foundation model based \textbf{o}n large-scale \textbf{U}nsupervised learning,'' which reframes fingerprint feature extraction as a domain-specific foundation-model problem. UoU is organized around a multi-level representation hierarchy spanning image restoration, structural fields, semantic tokens, point-level biometric entities, and compact global descriptors. Its training recipe combines a supervised cold start on precise annotations, large-scale weakly supervised refinement, and large-scale unsupervised consolidation, with the latter two stages iterated during large-scale training so that weak supervision broadens semantic coverage while unsupervised learning stabilizes correspondences, invariances, and representation geometry. Rather than treating fingerprint imagery as generic texture, UoU exploits domain-specific symmetries and intermediate structure, including orientation flow, periodic ridge patterns, sparse biometric entities, and spatial equivariance. The framework is intentionally architecture-agnostic: while the present study includes an initial transformer-based structured-prediction instantiation, the broader design supports multi-task learning, scalable model configurations, and downstream specialization for matching, alignment, enhancement, registration, and related fingerprint applications. This paper presents the technical motivation, system design, and validation protocol of UoU, and part of the baseline implementation is publicly available at https://github.com/XiongjunGuan/UoU.
Chinese Translation
指纹识别仍然以任务特定的流程为主导，其中增强、结构解析、对齐和匹配是孤立优化的。尽管在狭窄的环境中有效，但这种设计限制了跨传感器、质量和下游应用的表示重用。因此，我们提出了UoU，简称为“基于大规模无监督学习的 extbf{U}niversal指纹基础模型”，将指纹特征提取重新框定为一个特定领域的基础模型问题。UoU围绕一个多层次的表示层次结构组织，涵盖图像恢复、结构场、语义标记、点级生物特征实体和紧凑的全局描述符。其训练方案结合了在精确注释上的监督冷启动、大规模弱监督细化和大规模无监督整合，后两个阶段在大规模训练期间迭代，以便弱监督扩大语义覆盖范围，同时无监督学习稳定对应关系、不变性和表示几何。UoU并不将指纹图像视为通用纹理，而是利用特定领域的对称性和中间结构，包括方向流、周期性脊线模式、稀疏生物特征实体和空间等变性。该框架在设计上故意不依赖于特定架构：虽然本研究包括一个基于变换器的结构预测初始实例，但更广泛的设计支持多任务学习、可扩展的模型配置以及下游的匹配、对齐、增强、注册和相关指纹应用的专业化。本文介绍了UoU的技术动机、系统设计和验证协议，部分基线实现已公开可用，网址为https://github.com/XiongjunGuan/UoU。
cs.CV / 30 / 2606.17437
Spatio-Temporal Fusion Model for Standard View Classification of Echocardiographic Videos
用于超声心动图视频标准视图分类的时空融合模型
Abstract
Automated classification of standard echocardiographic views is crucial for efficient clinical workflow but faces three main challenges. First, publicly available datasets are scarce and limited in scale and view coverage. Second, the performance of some modern video-level architectures for echocardiographic view classification remains underexplored. Third, some view categories exhibit highly similar spatial appearances, making single-frame features insufficient for discrimination, while heterogeneous frame quality complicates robust temporal information fusion. To address these challenges, we release the Echocardiographic Videos of Nine Views (EV9V) dataset, comprising 5,138 videos, 910,579 frames, and 9 standard views, which is, to the best of our knowledge, the largest publicly available echocardiography video dataset. Using EV9V, we systematically benchmark representative video classification architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. Furthermore, we propose a Spatio-Temporal Fusion Model (STFM), an efficient dual-stream CNN-LSTM (Long Short-Term Memory) framework that jointly captures spatial anatomical structures and temporal cardiac dynamics. The proposed framework leverages uncertainty-aware learning to preferentially sample representative video segments during training and evidence-based fusion during inference, improving robustness to variations in frame quality across echocardiographic videos. Extensive experiments demonstrate that our method achieves competitive performance across diverse video classification models, validating the effectiveness of uncertainty-aware spatio-temporal learning for echocardiographic view classification. The code is available at https://github.com/bgx666/stfm.
Chinese Translation
自动化分类标准超声心动图视图对于高效的临床工作流程至关重要，但面临三大主要挑战。首先，公开可用的数据集稀缺，规模和视图覆盖范围有限。其次，某些现代视频级架构在超声心动图视图分类中的性能仍未得到充分探索。第三，一些视图类别表现出高度相似的空间外观，使得单帧特征不足以进行区分，而异质帧质量则使得稳健的时间信息融合变得复杂。为了解决这些挑战，我们发布了九视图超声心动图视频数据集（Echocardiographic Videos of Nine Views, EV9V），该数据集包含5,138个视频、910,579帧和9个标准视图，至我们所知，这是最大的公开可用超声心动图视频数据集。利用EV9V，我们系统地基准测试了代表性的视频分类架构，包括卷积神经网络（CNNs）、递归神经网络（RNNs）和变换器（Transformers）。此外，我们提出了一种时空融合模型（Spatio-Temporal Fusion Model, STFM），这是一个高效的双流CNN-LSTM（长短期记忆）框架，能够共同捕捉空间解剖结构和时间心脏动态。所提框架利用不确定性感知学习，在训练过程中优先采样代表性视频片段，并在推理过程中进行基于证据的融合，从而提高对超声心动图视频中帧质量变化的鲁棒性。大量实验表明，我们的方法在多种视频分类模型中实现了竞争性能，验证了不确定性感知时空学习在超声心动图视图分类中的有效性。代码可在 https://github.com/bgx666/stfm 获取。
cs.CV / 31 / 2606.17438
Contact-Based Fringe Projection Profilometry for High-Resolution 3-D Surface Measurement of Reflective and Transparent Objects
基于接触的条纹投影轮廓测量法用于高分辨率反射和透明物体的三维表面测量
Abstract
This paper presents a contact-based 3-D surface measurement method based on a Digital Fringe Projection (DFP) system, belonging to the vision-based tactile sensing family pioneered by the commercially successful GelSight sensor. Such sensors have proven effective for robotic fingertip manipulation and contact sensing. However, because GelSight employs photometric stereo with RGB LEDs, it does not measure absolute depth directly but instead infers it by integrating estimated surface gradients, which can accumulate reconstruction errors; in addition, it becomes increasingly difficult to calibrate as the sensing area grows, and its depth accuracy is challenged on highly reflective or transparent objects. To overcome these drawbacks, we propose a fringe-projection-based contact measurement technique that performs triangulation-based 3-D reconstruction on a coated silicone contact surface, providing dense per-pixel surface geometry and full-field 3-D shape measurement over the contact region. By integrating high-accuracy digital fringe projection into the sensor, our approach simplifies calibration over larger areas and enhances depth precision for complex surfaces. Experimental results, including a direct comparison with a GelSight Mini sensor, a sphere-fitting accuracy evaluation, and an uncertainty analysis, confirm that the proposed method significantly improves the accuracy and stability of structured-light-based 3-D measurements, allowing reliable reconstruction of objects with diverse optical properties.
Chinese Translation
本文提出了一种基于接触的三维表面测量方法，该方法基于数字条纹投影（Digital Fringe Projection, DFP）系统，属于由商业成功的GelSight传感器首创的基于视觉的触觉感知家族。这类传感器在机器人指尖操控和接触感知方面已被证明有效。然而，由于GelSight采用了带RGB LED的光度立体视觉，它并不直接测量绝对深度，而是通过整合估计的表面梯度来推断深度，这可能会积累重建误差；此外，随着感测区域的扩大，校准变得越来越困难，并且在高反射或透明物体上，其深度精度受到挑战。为克服这些缺点，我们提出了一种基于条纹投影的接触测量技术，该技术在涂层硅胶接触表面上执行基于三角测量的三维重建，提供每像素密集的表面几何形状和接触区域的全场三维形状测量。通过将高精度数字条纹投影集成到传感器中，我们的方法简化了大面积的校准，并提高了复杂表面的深度精度。实验结果，包括与GelSight Mini传感器的直接比较、球体拟合精度评估和不确定性分析，证实了所提出的方法显著提高了基于结构光的三维测量的准确性和稳定性，使得能够可靠地重建具有多种光学特性的物体。
cs.CV / 32 / 2606.17463
WeaveLA: Event Driven Cross-Subtask Latent Memory Weaving for Repetitive Robot Manipulation
WeaveLA：基于事件驱动的跨子任务潜在记忆编织用于重复机器人操作
Abstract
Vision-Language-Action (VLA) policies have achieved remarkable single-step manipulation, yet they remain brittle precisely where each stage depends on what was just completed. The core issue is structural: short-window VLAs lack an explicit channel for rouxting information across sub-task boundaries, and existing memory-augmented variants either write at every frame, retrieve from demonstration-time stages, or fire at sub-goal events without performing an explicit sub-task-to-sub-task hand-off into the action expert. We identify the sub-goal completion event as the natural temporal unit for cross-subtask memory hand-off, and present WeaveLA (Weave Latent memory for Vision-Language-Action policies), a cross-subtask memory interface that, on top of a frozen VLA backbone, compresses each completed segment into latent tokens via query-driven attention pooling and routes them directly into the action-generation path of the next sub-task. This event-triggered, action-side design preserves the base policy's short-window interface while adding a lightweight cross-subtask channel. Through stratified evaluation on RoboMME with a $\pi_{0.5}$ backbone, WeaveLA's gains land exactly where the channel is needed: on the hardest repetition slice (SwingXtimes, $N{=}3$), success rises from $0\%$ to $47.8\%$, while single-execution episodes remain unchanged. Per-episode paired analysis confirms the gains are confined to tasks whose causal structure requires cross-subtask information.
Chinese Translation
视觉-语言-动作（VLA）策略在单步操作中取得了显著成就，但在每个阶段依赖于刚刚完成的内容时仍然显得脆弱。核心问题在于结构性：短窗口的VLA缺乏一个明确的通道来跨越子任务边界传递信息，而现有的增强记忆变体要么在每帧写入，要么从演示时间阶段检索，或者在子目标事件发生时触发，而没有进行明确的子任务到子任务的交接。我们将子目标完成事件识别为跨子任务记忆交接的自然时间单位，并提出WeaveLA（视觉-语言-动作策略的潜在记忆编织），这是一种跨子任务记忆接口，在固定的VLA骨干网络上，将每个完成的片段通过查询驱动的注意力池化压缩为潜在标记，并直接路由到下一个子任务的动作生成路径中。该事件触发的动作侧设计保留了基础策略的短窗口接口，同时增加了一个轻量级的跨子任务通道。通过在RoboMME上使用$ ext{π}_{0.5}$骨干网络进行分层评估，WeaveLA的增益正好出现在需要该通道的地方：在最难的重复切片（SwingXtimes，$N{=}3$）上，成功率从$0\%$上升到$47.8\\%$，而单次执行的情节保持不变。每集配对分析确认增益仅限于因果结构需要跨子任务信息的任务。
cs.CV / 33 / 2606.17475
StereoFactory: A Unified Merging Framework for Robust Stereo Matching
StereoFactory：一种用于鲁棒立体匹配的统一合并框架
Abstract
Stereo matching has advanced through foundation models trained on large-scale datasets, yet this paradigm suffers from a scalability bottleneck: incorporating new data requires costly joint retraining. Model merging offers a scalable post-hoc alternative by integrating knowledge from specialized models after source checkpoints are available. However, existing merging methods typically retain all available models or rely on greedy inclusion, which can preserve harmful task-vector interference. We propose StereoFactory, a coarse-to-fine evolutionary framework for adaptive model merging. Stage~1 employs a genetic algorithm to search the combinatorial space of model subsets, determining which models should participate. Stage~2 addresses module-level knowledge specialization (different functional modules exhibit distinct preferences for knowledge sources) through CMA-ES optimization of architecture-adaptive routing over the selected task vectors, with optional module-level scaling. Experiments across two architectures and four benchmarks demonstrate that StereoFactory consistently achieves the best four-benchmark average under the same checkpoint pool, reducing the average error from 3.80 to 3.30 on NMRF and from 2.88 to 2.19 on FoundationStereo relative to the strongest controlled baseline. The post-hoc search requires only 2.7--3.7\% of the corresponding joint-retraining wall-clock time. Analysis reveals that knowledge contributions are inherently module-specific, and selected subsets can transfer across architectures with minimal degradation. Code will be publicly released upon acceptance at: https://github.com/XiandaGuo/StereoFactory.
Chinese Translation
立体匹配通过在大规模数据集上训练的基础模型取得了进展，然而这一范式面临可扩展性瓶颈：整合新数据需要昂贵的联合重训练。模型合并提供了一种可扩展的事后替代方案，通过在源检查点可用后整合来自专业模型的知识。然而，现有的合并方法通常保留所有可用模型或依赖贪婪的包含策略，这可能会保留有害的任务向量干扰。我们提出了StereoFactory，一种自适应模型合并的粗到细进化框架。第一阶段采用遗传算法搜索模型子集的组合空间，以确定哪些模型应参与。第二阶段通过对选定任务向量的架构自适应路由进行CMA-ES优化，解决模块级知识专业化（不同功能模块对知识源表现出不同的偏好），并可选地进行模块级缩放。在两个架构和四个基准测试上的实验表明，StereoFactory在相同的检查点池下始终实现了最佳的四基准平均值，将NMRF上的平均误差从3.80降低到3.30，将FoundationStereo上的平均误差从2.88降低到2.19，相较于最强的控制基线。事后搜索仅需对应联合重训练墙钟时间的2.7%至3.7%。分析表明，知识贡献本质上是模块特定的，所选子集可以在架构之间以最小的降级进行转移。代码将在接受后公开发布，网址为：https://github.com/XiandaGuo/StereoFactory。
cs.CV / 34 / 2606.17477
Theoretical Grounding of Out-Of-Distribution Detection With Reinforcement Learning Optimizer
基于强化学习优化器的分布外检测理论基础
Abstract
Out-of-distribution (OOD) detection in dynamic open-world environments requires a model to continually adapt to evolving data distributions while generalizing to covariate-shifted inputs and rejecting semantic-shifted OOD examples. Most existing OOD detection methods optimize only the current-step objective and do not explicitly account for how post-deployment environment changes affect future OOD behavior. In this paper, we establish a theoretical grounding for dynamic OOD detection using a reinforcement learning (RL)-guided optimizer that explicitly favors updates that reduce the semantic OOD false positive rate over time. We develop a novel augmented optimizer that uses an RL-guided correction term on top of standard gradient descent (GD) and show its improvement over both future-domain generalization and semantic-OOD rejection. We analyze temporal error decomposition in terms of model-change and environment-change generalization errors and develop a new theoretical framework for comparing the generalization errors under both GD and RL-guided optimizers.
Chinese Translation
在动态开放世界环境中，分布外（OOD）检测要求模型能够持续适应不断变化的数据分布，同时对协变量变化的输入进行泛化，并拒绝语义变化的OOD示例。现有的大多数OOD检测方法仅优化当前步骤的目标，并未明确考虑后续部署环境变化如何影响未来的OOD行为。本文建立了动态OOD检测的理论基础，采用一种强化学习（RL）指导的优化器，明确偏向于减少语义OOD假阳性率的更新。我们开发了一种新颖的增强型优化器，在标准梯度下降（GD）之上使用RL指导的修正项，并展示其在未来领域泛化和语义OOD拒绝方面的改进。我们分析了模型变化和环境变化泛化误差的时间误差分解，并开发了一个新的理论框架，用于比较GD和RL指导优化器下的泛化误差。
cs.CV / 35 / 2606.17480
GeneralVLA-2: Geometry-Aware Reconstruction and Governed Memory for Robot Planning
GeneralVLA-2：几何感知重建与受控记忆在机器人规划中的应用
Abstract
Generalist vision-language-action systems need object-centric 3D evidence and reusable manipulation experience to plan reliable robot trajectories. GeneralVLA provides a hierarchical interface for converting language and RGB-D observations into 3D end-effector paths, but two bottlenecks remain. First, monocular SAM3D-style object reconstruction can hallucinate pose and unseen geometry, while manipulation benefits from stable object shape when calibrated multi-view observations are available. Second, the original KnowledgeBank mainly retrieves semantically similar snippets and appends new knowledge, which makes it difficult to control memory quality, conflicts, confidence, and geometric relevance. To address the first challenge, we introduce GeoFuse-MV3D, a geometry-prior-guided MV-SAM3D reconstruction branch that verifies external geometry cues with input-view masks, applies soft visual-hull support, performs axis-wise refinement, and fuses only geometry while preserving appearance. To address the second challenge, we upgrade KnowledgeBank into a governed long-term memory system with explicit quality, confidence, lifecycle, verifier, and conflict metadata, together with precision-oriented retrieval. Finally, we evaluate the reconstruction branch on GSO-30 and the memory module on Terminal-Bench 2.0 and SWE-Bench Verified; GeoFuse-MV3D improves over the MV-SAM3D baseline by reducing CD and LPIPS by 2.20% and 2.02% while increasing PSNR and SSIM by 2.36% and 1.03%, and KnowledgeBank improves over ReasoningBank by 4.53% on Terminal-Bench SR and 3.73% on SWE-Bench resolve rate, while reducing AS by 4.95% and 5.65%, respectively. Code: https://github.com/AIGeeksGroup/GeneralVLA-2. Website: https://aigeeksgroup.github.io/GeneralVLA-2.
Chinese Translation
通用视觉-语言-动作系统需要以物体为中心的三维证据和可重用的操作经验，以规划可靠的机器人轨迹。GeneralVLA提供了一个分层接口，将语言和RGB-D观测转换为三维末端执行器路径，但仍然存在两个瓶颈。首先，单目SAM3D风格的物体重建可能会产生虚幻的姿态和未见几何，而在可校准的多视角观测可用时，操作受益于稳定的物体形状。其次，原始的KnowledgeBank主要检索语义相似的片段并附加新知识，这使得控制记忆质量、冲突、置信度和几何相关性变得困难。为了解决第一个挑战，我们引入了GeoFuse-MV3D，一个几何先验引导的MV-SAM3D重建分支，通过输入视图掩码验证外部几何线索，应用软视觉外壳支持，进行轴向精细化，并仅融合几何而保留外观。为了解决第二个挑战，我们将KnowledgeBank升级为一个受控的长期记忆系统，具有明确的质量、置信度、生命周期、验证者和冲突元数据，以及以精度为导向的检索。最后，我们在GSO-30上评估了重建分支，在Terminal-Bench 2.0和SWE-Bench Verified上评估了记忆模块；GeoFuse-MV3D在减少CD和LPIPS分别为2.20%和2.02%的同时，提高了PSNR和SSIM分别为2.36%和1.03%，而KnowledgeBank在Terminal-Bench SR上比ReasoningBank提高了4.53%，在SWE-Bench解决率上提高了3.73%，同时分别减少了AS为4.95%和5.65%。代码：https://github.com/AIGeeksGroup/GeneralVLA-2。网站：https://aigeeksgroup.github.io/GeneralVLA-2。
cs.CV / 36 / 2606.17482
SPHINX: First Explain, Then Explore
SPHINX：先解释，再探索
Abstract
Generating adversarial driving scenarios is critical for evaluating and improving autonomous vehicle decision-making systems in simulation. Recent approaches, such as ChatScene and LLM-Attacker, rely primarily on the prior knowledge of Large Language Models and Vision-Language Models to generate driving scenarios procedurally. We argue that adversarial scenes should be generated based on the failure diagnosis (e.g., indecisiveness, multi-frame inconsistency) of the driving policy to specifically address the policy's weaknesses instead of relying on prior assumptions. In this paper, we propose SPHINX, a closed-loop framework for adversarial scenario synthesis guided by a simple principle: first explain, then explore. Beyond blindly exploring the scenario space, SPHINX leverages explainable artificial intelligence methods to analyze the policy, identifying key visual concepts and their influence on policy outputs, and the uncertainty of the decisions. Given the interpretable evidence extracted from the policy's own decision process, we use a vision language model to rationalize and criticize failure modes of the current policy. These critics are then used to generate targeted adversarial scenarios for policy retraining and improvement. We demonstrate that SPHINX can highlight an interpretable account of policy failures while other adversarial scene generation cannot. Across the evaluated benchmarks and test suites, SPHINX can be applied to diverse state-of-the-art autonomous vehicle architectures and yields consistent robustness improvements over existing scenario-generation methods.
Chinese Translation
生成对抗性驾驶场景对于评估和改进自主驾驶车辆决策系统在仿真中的表现至关重要。近期的方法，如 ChatScene 和 LLM-Attacker，主要依赖于大型语言模型和视觉-语言模型的先验知识，以程序化方式生成驾驶场景。我们认为，对抗场景应基于驾驶策略的失败诊断（例如，犹豫不决、多帧不一致）进行生成，以具体针对策略的弱点，而不是依赖于先前的假设。本文提出了 SPHINX，一个闭环框架，用于对抗场景合成，其指导原则为：先解释，再探索。SPHINX 不仅仅是盲目探索场景空间，而是利用可解释人工智能方法分析策略，识别关键视觉概念及其对策略输出的影响，以及决策的不确定性。基于从策略自身决策过程中提取的可解释证据，我们使用视觉语言模型来合理化和批评当前策略的失败模式。这些批评随后用于生成针对性的对抗场景，以进行策略的再训练和改进。我们证明了 SPHINX 能够突出政策失败的可解释性，而其他对抗场景生成方法则无法做到。在评估的基准和测试套件中，SPHINX 可以应用于多种最先进的自主驾驶车辆架构，并在现有场景生成方法上实现了一致的鲁棒性提升。
cs.CV / 37 / 2606.17536
OmniDrive: An LLM-Choreographed Multi-Agent World Model with Unified Latent Co-Compression for Multi-View Driving Video Generation
OmniDrive：一种由大型语言模型协调的多智能体世界模型，具备统一的潜在共同压缩，用于多视角驾驶视频生成
Abstract
Generative world models for autonomous driving face two unresolved tensions: heterogeneous control injection, where free-form language, HD-maps, trajectories, and camera poses reside in incompatible representational spaces, and post-hoc cross-view fusion, where per-camera latents fail to encode global 3-D geometry. We trace both to a single root cause: the absence of a shared symbolic interlingua aligning language, geometry, and pixels at the latent-token level. We present DRIVE-CHOREO, an LLM-choreographed multi-agent world model that recasts controllable multi-view video generation as latent choreography. Three Qwen2.5-VL agents - a Director parsing user intent into a structured WorldScript, a Cartographer grounding it into spatially-anchored layout tokens, and an Auditor feeding cross-view critiques back as auxiliary supervision - jointly author a single position-aware token sequence. This sequence is co-compressed with the multi-view video via a view-time permutation that enforces inter-camera geometry within the convolutional receptive field of a 3-D VAE. On nuScenes, DRIVE-CHOREO sets new state-of-the-art multi-view consistency and BEV mAP (21.6) with competitive FVD (45.7); a detector trained purely on our synthetic data gains +2.4 NDS on the real validation split, validating downstream utility.
Chinese Translation
用于自主驾驶的生成世界模型面临两个未解决的矛盾：异构控制注入，其中自由形式语言、高精度地图、轨迹和相机姿态存在于不兼容的表征空间，以及事后跨视角融合，其中每个相机的潜在表示未能编码全局三维几何。我们将这两者追溯到一个根本原因：缺乏一个共享的符号中介语言，在潜在标记层面上对齐语言、几何和像素。我们提出了DRIVE-CHOREO，这是一种由大型语言模型协调的多智能体世界模型，将可控的多视角视频生成重新构想为潜在编舞。三个Qwen2.5-VL智能体——一个导演将用户意图解析为结构化的WorldScript，一个制图师将其固定为空间锚定的布局标记，以及一个审计师将跨视角的批评反馈作为辅助监督——共同创作一个单一的位置信息感知标记序列。该序列通过视图时间置换与多视角视频共同压缩，强制在三维变分自编码器的卷积感受野内实现相机间几何一致性。在nuScenes数据集上，DRIVE-CHOREO创造了新的多视角一致性和鸟瞰视图平均精度（BEV mAP）状态，分别为21.6，且具有竞争力的FVD（45.7）；仅基于我们的合成数据训练的检测器在真实验证集上获得了+2.4的NDS，验证了下游应用的有效性。
cs.CV / 38 / 2606.17539
Reinforcing Dual-Path Reasoning in Spatial Vision Language Models
强化空间视觉语言模型中的双路径推理
Abstract
Spatial VLMs have made substantial progress in geometric perception, yet complex spatial reasoning requiring multi-step inference over depth, distance, and scene relations remains challenging. Moreover, different spatial queries call for fundamentally different strategies: some are best addressed through purely linguistic, step-by-step deduction, while others require explicit 3D grounding before quantitative inference. We present Dual-Path Spatial Reasoning via Reinforcement Learning for Spatial VLMs (SR-REAL), a unified framework that equips a spatial VLM with two complementary reasoning paths: Language-Only Reasoning (LOR), which performs step-by-step linguistic deduction, and Detect-Then-Reason (DTR), which detects 3D geometric cues (e.g., centers or bounding boxes) via region tokens before explicit geometric inference. SR-REAL begins with a cold-start supervised fine-tuning stage that constructs LOR and DTR chain-of-thought supervision and exposes a region-to-3D interface, followed by RL that optimizes the policy model with accuracy and format rewards; for DTR, a discrete center-based detection reward further refines geometric alignment. Across diverse spatial benchmarks, SR-REAL significantly outperforms spatial VLM baselines: (i) a single RL-trained model supports both reasoning paths, with DTR excelling in region-aware tasks through precise 3D localization and LOR enhancing general spatial reasoning; (ii) jointly training both paths fosters mutual reinforcement; (iii) high-quality, blended cold-start data is crucial for stable RL optimization; and (iv) the model generalizes across datasets and domains without per-task tuning, demonstrating positive transfer between LOR and DTR.
Chinese Translation
空间视觉语言模型（Spatial VLMs）在几何感知方面取得了显著进展，但涉及深度、距离和场景关系的复杂空间推理仍然具有挑战性。此外，不同的空间查询需要根本不同的策略：有些问题最适合通过纯语言的逐步推理来解决，而另一些则需要在进行定量推理之前进行明确的三维基础。我们提出了通过强化学习实现的双路径空间推理框架（SR-REAL），该框架为空间 VLM 配备了两条互补的推理路径：仅语言推理（Language-Only Reasoning, LOR），执行逐步的语言推理，以及先检测后推理（Detect-Then-Reason, DTR），通过区域标记检测三维几何线索（例如，中心或边界框），然后再进行明确的几何推理。SR-REAL 首先进行冷启动的监督微调阶段，构建 LOR 和 DTR 的思维链监督，并暴露区域到三维的接口，随后通过强化学习优化策略模型，获得准确性和格式奖励；对于 DTR，基于离散中心的检测奖励进一步优化几何对齐。在多样的空间基准测试中，SR-REAL 显著优于空间 VLM 基线：（i）单个经过强化学习训练的模型支持两条推理路径，其中 DTR 在区域感知任务中通过精确的三维定位表现优异，而 LOR 则增强了通用空间推理；（ii）联合训练这两条路径促进了相互强化；（iii）高质量的混合冷启动数据对稳定的强化学习优化至关重要；（iv）该模型在不同数据集和领域之间具有良好的泛化能力，无需针对每个任务进行调优，展示了 LOR 和 DTR 之间的积极迁移。
cs.CV / 39 / 2606.17540
TaFD: Threat-Aware Frequency Decoupling for Adversarial Robustness against Heterogeneous Attacks
TaFD：针对异构攻击的威胁感知频率解耦以增强对抗鲁棒性
Abstract
Multi-threat robustness remains a fundamental challenge in deep learning. Although joint adversarial training (JAT) is widely adopted, it suffers from negative transfer under heterogeneous threats, particularly between $\ell_p$-bounded and semantic attacks. Through first-order gradient analysis, we formalize this as gradient incompatibility and theoretically establish the necessity of decoupled optimization. We further reveal that these conflicting threats exhibit separable spectral characteristics in the frequency domain. Motivated by this observation, we propose Threat-aware Frequency Decoupling (TaFD), a two-stage defense framework that reformulates JAT as a frequency-domain divide-and-conquer paradigm. TaFD first discovers latent threat domains via unsupervised clustering of attack spectral prototypes and trains a lightweight classifier for inference-time threat domain identification. Conditioned on the prediction, TaFD employs a Frequency-Conditional Convolution that learns threat-domain-specific spectral masks and routes each sample to the corresponding expert, enforcing structural parameter separation and alleviating optimization conflicts. We validate TaFD on three representative image-classification benchmarks (CIFAR-10, CIFAR-100, and Tiny-ImageNet) and on two representative architectures (the convolutional ResNet and the hybrid-transformer MobileViT). Extensive results demonstrate that TaFD achieves more balanced robustness against heterogeneous attacks than existing JAT and frequency-domain baselines, improving average robust accuracy by approximately 11\% over the strongest baseline while maintaining leading clean accuracy.
Chinese Translation
多威胁鲁棒性仍然是深度学习中的一个基本挑战。尽管联合对抗训练（JAT）被广泛采用，但在异构威胁下，它受到负迁移的影响，特别是在 $ ext{ℓ}_p$-有界攻击与语义攻击之间。通过一阶梯度分析，我们将其形式化为梯度不兼容，并理论上建立了解耦优化的必要性。我们进一步揭示，这些冲突的威胁在频域中表现出可分离的谱特征。基于这一观察，我们提出了威胁感知频率解耦（TaFD），一个将 JAT 重新构建为频域分而治之范式的两阶段防御框架。TaFD 首先通过对攻击谱原型的无监督聚类发现潜在的威胁域，并训练一个轻量级分类器用于推理时的威胁域识别。基于预测结果，TaFD 采用频率条件卷积（Frequency-Conditional Convolution），学习特定于威胁域的谱掩码，并将每个样本路由到相应的专家，从而强制结构参数分离，缓解优化冲突。我们在三个代表性的图像分类基准（CIFAR-10、CIFAR-100 和 Tiny-ImageNet）以及两个代表性架构（卷积 ResNet 和混合变换器 MobileViT）上验证了 TaFD。大量结果表明，TaFD 在对抗异构攻击方面比现有的 JAT 和频域基线实现了更平衡的鲁棒性，平均鲁棒准确率比最强基线提高了约 11\%，同时保持了领先的清晰准确率。
cs.CV / 40 / 2606.17557
Universal Image Restoration via Internalized Chain-of-Thought Reasoning
通过内化链式思维推理实现通用图像恢复
Abstract
Image restoration seeks to recover high-quality images from degraded inputs but becomes highly ill-posed under complex, mixed degradations. While unified all-in-one models are common, their performance declines as degradation complexity increases. Recent works adopt Chain-of-Thought (CoT) reasoning for multi-round restoration using specialized modules. However, this approach faces two key limitations: (i) increased computational cost due to multi-step processing, and (ii) weak modeling of interactions between degradations during stepwise inference. We introduce CoTIR, a universal image restoration framework that internalizes CoT reasoning within a single model. Concretely, we view image restoration as a specialized subtask of image editing, which implies that a large-scale pre-trained editing model provides a more favorable optimization starting point. Building on this, we fine-tune the model for restoration and further encode structured CoT-style reasoning into the learning objective via a differentiable formulation inspired by Lagrangian optimization, enabling holistic restoration without chaining specialized restorers. To facilitate training and evaluation, we further present CoTIR-Bench, a large-scale benchmark comprising 5.2 million samples with CoT-style reasoning traces. Extensive experiments on CoTIR-Bench and broad real composite degradation scenes show that CoTIR achieves stronger perceptual quality and more competitive fidelity than both all-in-one models and multi-round restoration methods. The source code is available at https://github.com/gy65896/CoTIR.
Chinese Translation
图像恢复旨在从退化的输入中恢复高质量图像，但在复杂的混合退化情况下变得高度不适定。尽管统一的全能模型很常见，但随着退化复杂性的增加，其性能会下降。最近的研究采用链式思维（Chain-of-Thought, CoT）推理进行多轮恢复，使用专门的模块。然而，这种方法面临两个主要限制：（i）由于多步骤处理导致的计算成本增加，以及（ii）在逐步推理过程中对退化之间相互作用的建模较弱。我们提出了CoTIR，一个将CoT推理内化于单一模型中的通用图像恢复框架。具体而言，我们将图像恢复视为图像编辑的一个专门子任务，这意味着大规模预训练的编辑模型提供了更有利的优化起点。在此基础上，我们对模型进行微调以进行恢复，并通过受拉格朗日优化启发的可微分形式进一步将结构化的CoT风格推理编码到学习目标中，从而实现整体恢复，而无需链接专门的恢复器。为了便于训练和评估，我们还提出了CoTIR-Bench，这是一个包含520万个样本和CoT风格推理痕迹的大规模基准。对CoTIR-Bench和广泛的真实复合退化场景的广泛实验表明，CoTIR在感知质量和保真度方面优于全能模型和多轮恢复方法。源代码可在https://github.com/gy65896/CoTIR获取。
cs.CV / 41 / 2606.17561
RT-Counter: Real-Time Text-Guided Open-Vocabulary Object Counting
RT-Counter：实时文本引导的开放词汇物体计数
Abstract
Text-guided open-vocabulary object counting (TOOC) aims to count objects belonging to the categories specified by natural language descriptions. Although vision-language pre-trained models have been successful applied to TOOC tasks, they still struggle with fine-grained spatial understanding and real-time inference requirements in counting scenarios. To address these limitations, this paper proposes a real-time TOOC framework, called the Real-Time Counter (RT-Counter), that achieves not only good counting accuracy but also high computational efficiency. RT-Counter designs a novel Visual Prototype Textualization (VPT) module that can project learned visual features into a text feature space and then generate features containing the abstract information that is hard to capture with visual prototypes and the detailed prototype information that is difficult to describe in text, enhancing the object-level visual-language model's counting capabilities. Additionally, RT-Counter incorporates our Weaving Transformer (Weaformer) layers, maintaining high descriptive power at a fraction of the computational cost. The Weaformer layer adopts a novel hybrid attention mechanism that can efficiently weave together local and global visual features. Extensive experiments on three public datasets show that RT-Counter successfully breaks the accuracy-speed trade-off in TOOC. While achieving a competitive MAE of 13.30 on FSC147, RT-Counter operates at 112.48 FPS, making it 7.4x faster and over 4$\times$ more parameter-efficient than the existing leading methods in TOOC. Our work aims at balancing high accuracy and real-time performance in TOOC. Code is available at: https://github.com/Jason-Mar1/RT-Counter.
Chinese Translation
文本引导的开放词汇物体计数（TOOC）旨在计数属于自然语言描述指定类别的物体。尽管视觉-语言预训练模型在TOOC任务中取得了成功，但在计数场景中，它们仍然在细粒度空间理解和实时推理需求方面面临挑战。为了解决这些限制，本文提出了一种实时TOOC框架，称为实时计数器（RT-Counter），该框架不仅实现了良好的计数准确性，还具有高计算效率。RT-Counter设计了一种新颖的视觉原型文本化（Visual Prototype Textualization, VPT）模块，该模块能够将学习到的视觉特征投影到文本特征空间中，然后生成包含难以通过视觉原型捕捉的抽象信息和难以用文本描述的详细原型信息的特征，从而增强物体级视觉-语言模型的计数能力。此外，RT-Counter还结合了我们的编织变换器（Weaving Transformer, Weaformer）层，在保持高描述能力的同时降低计算成本。Weaformer层采用了一种新颖的混合注意力机制，能够高效地将局部和全局视觉特征结合在一起。在三个公共数据集上的大量实验表明，RT-Counter成功打破了TOOC中的准确性与速度的权衡。在FSC147上实现了竞争性的平均绝对误差（MAE）为13.30的同时，RT-Counter以112.48帧每秒（FPS）的速度运行，比现有的TOOC领先方法快7.4倍，并且参数效率提高了超过4倍。我们的工作旨在平衡TOOC中的高准确性和实时性能。代码可在以下链接获取：https://github.com/Jason-Mar1/RT-Counter。
cs.CV / 42 / 2606.17564
Geometric Consistency Protocol for Foundation Model Features in Multi-View Satellite Imagery
多视角卫星影像中基础模型特征的几何一致性协议
Abstract
Standardized evaluation protocols are indispensable for robust benchmarking in remote sensing, particularly as foundation features are increasingly transferred across diverse sensors and complex imaging geometries. In satellite multi-view reconstruction, conventional evaluations relying on unconstrained 2D global matching are often misleading. The Rational Function Model (RFM) and its Rational Polynomial Coefficients (RPC) dictate a curved, height-dependent epipolar geometry that render flat 2D search spaces physically inconsistent. We propose a geometry-faithful and reproducible protocol tailored for the RPC framework. Our approach integrates an RPC-projected 3D consistency metric with a geometry-constrained dense matching proxy, specifically evaluating whether similarity responses remain localized and unique under physically plausible search manifolds. A pivotal finding of our joint reporting strategy is the decoupling of semantic agreement and geometric localization: high cross-view similarity at a projected 3D point does not guarantee reliable matchability in practical inference. Our benchmark demonstrates that incorporating geometric constraints is fundamental to the problem definition in satellite imagery. Furthermore, we show that state-of-the-art 2D backbones remain remarkably competitive against specialized 3D-aware models when subjected to this RPC-consistent evaluation.
Chinese Translation
标准化评估协议对于遥感中的稳健基准测试至关重要，尤其是在基础特征越来越多地在不同传感器和复杂成像几何中转移的情况下。在卫星多视角重建中，依赖于不受限制的二维全局匹配的传统评估往往会产生误导。合理函数模型（Rational Function Model, RFM）及其合理多项式系数（Rational Polynomial Coefficients, RPC）决定了一种曲线、高度依赖的极线几何，这使得平坦的二维搜索空间在物理上不一致。我们提出了一种忠实于几何且可重复的协议，专门针对RPC框架。我们的方法将RPC投影的三维一致性度量与几何约束的密集匹配代理相结合，特别评估在物理上合理的搜索流形下，相似性响应是否保持局部化和唯一性。我们联合报告策略的一个关键发现是语义一致性与几何定位的解耦：在投影的三维点处的高交视相似性并不保证在实际推理中的可靠匹配性。我们的基准测试表明，纳入几何约束对于卫星影像中的问题定义至关重要。此外，我们展示了在进行此RPC一致性评估时，最先进的二维骨干网络在与专门的三维感知模型相比时仍然具有显著的竞争力。
cs.CV / 43 / 2606.17584
Root-Selecting Fixed-Point Inversion for Rectified Flows via Trajectory Straightness
通过轨迹直线性进行的固定点反演的根选择
Abstract
Finding the initial noise that generates a given data sample, known as inversion, is a key component for downstream applications such as training-free image editing. Existing fixed-point inversion methods improve inversion accuracy by formulating each inversion step as a fixed-point problem, but they lack a principled mechanism for selecting among multiple fixed-point solutions that can arise in practice. We observe that different selections induce different inversion trajectories, leading to substantial variation in reconstruction and editing quality. For rectified flows, we further find that this variation is closely associated with trajectory straightness, motivating straightness as a principled selection criterion. We propose SelFix, a fixed-point inversion method that selects fixed-point solutions inducing straighter inverse trajectories while retaining convergence to an exact inverse root under standard local assumptions. Experiments on FLUX.1-dev and PIE-Bench show that SelFix improves fixed-point inversion, achieving stronger real-image reconstruction and better source-preserving prompt-based editing than prior inversion baselines. The code is available at https://github.com/seminkim/selfix.
Chinese Translation
找到生成给定数据样本的初始噪声，称为反演，是下游应用（如无训练图像编辑）的关键组成部分。现有的固定点反演方法通过将每个反演步骤形式化为固定点问题来提高反演精度，但它们缺乏在实践中选择多个可能的固定点解的原则性机制。我们观察到，不同的选择会导致不同的反演轨迹，从而在重建和编辑质量上产生显著差异。对于整流流，我们进一步发现这种差异与轨迹的直线性密切相关，这使得直线性成为一个原则性的选择标准。我们提出了SelFix，这是一种固定点反演方法，选择能够诱导更直的反演轨迹的固定点解，同时在标准局部假设下保持收敛到精确的反演根。对FLUX.1-dev和PIE-Bench的实验表明，SelFix改善了固定点反演，取得了比以前的反演基准更强的真实图像重建和更好的源保持的基于提示的编辑。代码可在 https://github.com/seminkim/selfix 获取。
cs.CV / 44 / 2606.17590
TivTok: Broadcasting Time-Invariant Tokens for Scalable Video Tokenization
TivTok：用于可扩展视频标记的时间不变标记广播
Abstract
Video tokenization is fundamental to scalable video generation, as the number of tokens directly determines the computational cost and the length of videos that can be modeled. Existing tokenizers mainly improve scalability by compressing videos into fewer tokens, but they often continue to represent persistent content, such as static backgrounds and consistent object appearances, repeatedly across frames and chunks. In this paper, we propose \textbf{TivTok} (\textit{Time-Invariant Tokenizer}), a reuse-aware video tokenizer that makes persistent information reusable across time. TivTok represents a clip with Time-Invariant (TIV) tokens that encode information shared across frames and Time-Variant (TV) tokens that encode frame-specific residuals. To obtain this factorization, we introduce Scope-Induced Factorization (SIF), which assigns different attention scopes to the two token groups: TIV tokens attend to the full clip, whereas each TV token only accesses its corresponding frame together with the TIV tokens. In the decoder, Invariant Broadcasting (IB) reuses the same TIV tokens across frames and chunks for parallel reconstruction and long-video tokenization. Experiments show that TivTok achieves an rFVD of 12.65 on the standard $16{\times}256{\times}256$ benchmark and improves compression efficiency by 2.91$\times$ for 128-frame videos compared with the evaluated baselines, while using only 1.1\% of the tokens required by downsample-based tokenizers in our evaluation.
Chinese Translation
视频标记化是可扩展视频生成的基础，因为标记的数量直接决定了计算成本和可建模视频的长度。现有的标记器主要通过将视频压缩为更少的标记来提高可扩展性，但它们往往在帧和片段中重复表示持久内容，例如静态背景和一致的物体外观。本文提出了 extbf{TivTok}（ extit{时间不变标记器}），一种关注重用的视频标记器，使得持久信息在时间上可重用。TivTok用时间不变（TIV）标记表示一个片段，这些标记编码跨帧共享的信息，以及时间变化（TV）标记，这些标记编码帧特定的残差。为了获得这种因式分解，我们引入了范围诱导因式分解（SIF），为两个标记组分配不同的注意力范围：TIV标记关注整个片段，而每个TV标记仅访问其对应的帧及TIV标记。在解码器中，不变广播（IB）在帧和片段之间重用相同的TIV标记，以实现并行重建和长视频标记化。实验表明，TivTok在标准$16{ imes}256{ imes}256$基准上达到了12.65的rFVD，并且在与评估基线相比时，对于128帧视频提高了2.91$ imes$的压缩效率，同时仅使用了我们评估中基于降采样的标记器所需标记的1.1 ext{%}。
cs.CV / 45 / 2606.17601
Test-Time Training for Robust Text-Guided Open-Vocabulary Object Counting
针对鲁棒文本引导的开放词汇物体计数的测试时训练
Abstract
Text-guided Open-vocabulary Object Counting (TOOC) enables counting arbitrary object categories specified by text prompts, offering substantially greater flexibility than conventional closed-set counting. However, existing TOOC methods are developed and evaluated primarily on ideal images, while real-world scenes often suffer from adverse conditions such as rain, fog, darkness, and sensor noise, which severely degrade visual quality and impair vision-language alignment. To bridge this gap, we introduce Robust-TOOC, the first benchmark for evaluating TOOC under diverse corruption conditions, which covers six representative degradation types: rain, fog, darkness, Gaussian noise, salt-and-pepper noise, and mixed corruption. To improve robustness while preserving the original counting architecture, we propose Dual-TTT, a dual-architecture test-time training framework for TOOC. Specifically, during test-time training, Dual-TTT updates only the Text-guided Lightweight Denoising module (TL-Denoiser), while keeping the original counting network frozen. Inspired by diffusion models, the TL-Denoiser is optimized to remove corruption-aware noise from image representations under degraded conditions. Since only the TL-Denoiser is trained at test time, Dual-TTT is annotation-free and can be seamlessly integrated into existing TOOC models without modifying their original architecture. Extensive experiments on multiple recent TOOC baselines demonstrate the effectiveness of our method.
Chinese Translation
文本引导的开放词汇物体计数（Text-guided Open-vocabulary Object Counting, TOOC）能够根据文本提示计数任意物体类别，提供了比传统封闭集计数更大的灵活性。然而，现有的 TOOC 方法主要在理想图像上开发和评估，而现实场景往往受到雨、雾、黑暗和传感器噪声等不利条件的影响，这严重降低了视觉质量并损害了视觉-语言对齐。为了解决这一问题，我们提出了 Robust-TOOC，这是第一个用于在多种损坏条件下评估 TOOC 的基准，涵盖了六种代表性的退化类型：雨、雾、黑暗、高斯噪声、盐和胡椒噪声以及混合损坏。为了在保持原始计数架构的同时提高鲁棒性，我们提出了 Dual-TTT，这是一种针对 TOOC 的双架构测试时训练框架。具体而言，在测试时训练期间，Dual-TTT 仅更新文本引导的轻量去噪模块（Text-guided Lightweight Denoising module, TL-Denoiser），同时保持原始计数网络不变。受扩散模型的启发，TL-Denoiser 被优化以去除在退化条件下图像表示中的噪声。由于仅在测试时训练 TL-Denoiser，Dual-TTT 是无注释的，并且可以无缝集成到现有的 TOOC 模型中，而无需修改其原始架构。在多个最新的 TOOC 基准上进行的大量实验表明了我们方法的有效性。
cs.CV / 46 / 2606.17606
Flux-Guard: Facial Identity Protection using diffusion models
Flux-Guard：基于扩散模型的面部身份保护
Abstract
The widespread deployment of face recognition (FR) systems exposes personal images shared on social media and public platforms to identity linkage and privacy risks. Existing adversarial privacy protection methods can degrade unauthorized FR performance but are not compatible with generative face editing. Artificial intelligence-driven face editing tools are gaining popularity, which has significantly increased user demand for personalized portrait generation and social sharing. However, current editing methods often preserve identity features, making the edited images still susceptible to tracking by malicious FR systems. Thus, this paper proposes Flux-Guard, a privacy-preserving face editing framework based on adversarial attacks, which integrates face editing and privacy protection within a unified generative process. Specifically, we design a flow trajectory control method to align semantic manipulations with the generative process and introduce latent-space adversarial optimization with an adaptive perceptual-loss-driven weighting strategy, dynamically adjusting adversarial strength to maximize attack effectiveness while preserving visual quality. Extensive experiments demonstrate that Flux-Guard supports face editing while significantly improving attack success rates against cross-domain face recognition models on the CelebA-HQ and LADN datasets. Furthermore, evaluation results for commercial APIs have confirmed its effectiveness in real-world applications. The code is released at https://github.com/JLMWang/Flux-Guard.
Chinese Translation
面部识别（FR）系统的广泛部署使得在社交媒体和公共平台上共享的个人图像面临身份关联和隐私风险。现有的对抗性隐私保护方法虽然能够降低未经授权的面部识别性能，但与生成性面部编辑并不兼容。人工智能驱动的面部编辑工具日益受到欢迎，显著增加了用户对个性化肖像生成和社交分享的需求。然而，当前的编辑方法往往保留身份特征，使得编辑后的图像仍然容易被恶意的面部识别系统追踪。因此，本文提出了Flux-Guard，一个基于对抗攻击的隐私保护面部编辑框架，将面部编辑与隐私保护整合在一个统一的生成过程中。具体而言，我们设计了一种流轨迹控制方法，以将语义操作与生成过程对齐，并引入具有自适应感知损失驱动加权策略的潜在空间对抗优化，动态调整对抗强度，以最大化攻击效果的同时保持视觉质量。大量实验表明，Flux-Guard支持面部编辑，同时显著提高了在CelebA-HQ和LADN数据集上针对跨域面部识别模型的攻击成功率。此外，商业API的评估结果确认了其在实际应用中的有效性。代码已发布在https://github.com/JLMWang/Flux-Guard。
cs.CV / 47 / 2606.17615
SkillMoV: Mixture-of-View Routing with Prototype-Conditioned Gating for Unified Multi-View Proficiency Estimation
SkillMoV：基于原型条件门控的混合视角路由用于统一多视角能力评估
Abstract
Estimating human proficiency from video is a key challenge for automated skill assessment, with applications in sports coaching, music pedagogy, surgical training, and workplace learning. Existing approaches often focus on individual scenarios or rely on shared multi-view aggregation, limiting their ability to adapt to heterogeneous camera viewpoints and activity domains. We introduce SkillMoV, a unified, parameter-efficient framework for multi-scenario proficiency estimation from synchronized multi-view video. At its core, SkillMoV introduces a Mixture-of-View Projector (MoVP), which adapts the mixture-of-experts paradigm to camera-specific view features. MoVP is composed of four stages: (i) a Mixture-of-View soft router with twelve expert MLPs that learns view-dependent expert preferences without camera-identity supervision; (ii) cross-view attention to align synchronized cameras; (iii) learnable prototype anchoring to condition the representation on class-level reference vectors; and (iv) a prototype-conditioned gated projection that produces the final skill embedding. We evaluate SkillMoV on EgoExo4D across six skill domains and three separately trained view configurations: Ego, Exos, and Ego+Exos. SkillMoV reaches 50.17% overall accuracy in the Exos setting with a single model trained jointly across all scenarios, surpassing the strongest reported Exos result among the compared methods by 3.57 percentage points. In Ego+Exos, SkillMoV remains close to the best reported result in that setting (47.63% versus 48.20%). Ablations on the selected Exos configuration validate each component: MoV routing contributes +6.61 pp over attentive aggregation, cross-view attention +4.92 pp, prototype anchoring +4.07 pp, and stochastic view dropout +3.90 pp. Through LoRA adaptation, SkillMoV trains only 23.32% of its parameters and adds limited measured overhead relative to a LoRA-only baseline.
Chinese Translation
从视频中估计人类能力是自动化技能评估的一项关键挑战，广泛应用于体育教练、音乐教学、外科培训和职场学习等领域。现有方法通常集中于个别场景或依赖共享的多视角聚合，限制了它们适应异构摄像机视角和活动领域的能力。我们提出了SkillMoV，这是一个统一的、参数高效的框架，用于从同步的多视角视频中进行多场景能力评估。SkillMoV的核心是引入了混合视角投影器（Mixture-of-View Projector, MoVP），它将混合专家范式适配到特定摄像机的视角特征。MoVP由四个阶段组成：（i）一个具有十二个专家多层感知器（MLP）的混合视角软路由器，能够在没有摄像机身份监督的情况下学习视角依赖的专家偏好；（ii）跨视角注意力对齐同步摄像机；（iii）可学习的原型锚定，以将表示条件化为类级参考向量；（iv）一个原型条件门控投影，生成最终的技能嵌入。我们在EgoExo4D数据集上评估了SkillMoV，涵盖六个技能领域和三种单独训练的视角配置：Ego、Exos和Ego+Exos。在Exos设置中，SkillMoV以一个在所有场景中联合训练的单一模型达到了50.17%的整体准确率，超过了比较方法中报告的最强Exos结果3.57个百分点。在Ego+Exos中，SkillMoV的结果接近该设置中报告的最佳结果（47.63%对比48.20%）。对选定的Exos配置的消融实验验证了每个组件的贡献：MoV路由相较于注意力聚合贡献+6.61个百分点，跨视角注意力+4.92个百分点，原型锚定+4.07个百分点，随机视角丢弃+3.90个百分点。通过LoRA适配，SkillMoV仅训练了23.32%的参数，并相对于仅使用LoRA的基线增加了有限的测量开销。
cs.CV / 48 / 2606.17619
RAVA: Retrieval-Augmented Viewpoint Alignment for Subject-Driven Image Generation
RAVA：基于检索增强的视角对齐用于以主题为驱动的图像生成
Abstract
Reference-driven image generation has made rapid progress on identity preservation, but reliable viewpoint control across different subjects remains poorly understood. The difficulty is not merely generating a new image of the target subject: the model must infer the implicit viewpoint of one subject and transfer it to another subject using only image-level evidence, without camera poses, depth, or ray-based conditions. In this setting, existing generators conditioned on multiple image references often rely on spurious semantic correlations, which lead to viewpoint drift, part-level structural mismatches, and missing or unsupported target-specific content. We formulate this challenge as cross-subject viewpoint alignment and propose RAVA, a retrieval-augmented framework that supplies explicit geometric evidence before generation. RAVA first learns a cross-instance viewpoint embedding that retrieves target-subject images aligned with the anchor viewpoint, then applies a LogDet-based subset selection strategy to retain a compact reference set that is both view-consistent and structurally complementary. The selected references are finally consumed by a fine-tuned multi-reference image generator. Experiments show that generic semantic embeddings are nearly random for this task, while the proposed retriever substantially improves viewpoint retrieval quality. On cross-subject generation, RAVA consistently outperforms zero-shot baselines and stronger retrieval alternatives under the same generation backbone. These results indicate that cross-subject viewpoint alignment benefits from retrieval-augmented geometric grounding rather than relying on end-to-end generation alone.
Chinese Translation
基于参考的图像生成在身份保留方面取得了快速进展，但在不同主题之间的可靠视角控制仍然不够清晰。困难不仅仅在于生成目标主题的新图像：模型必须推断一个主题的隐含视角，并仅使用图像级证据将其转移到另一个主题，而不依赖于相机姿态、深度或基于光线的条件。在这种情况下，现有的基于多个图像参考的生成器通常依赖于虚假的语义关联，这导致视角漂移、部分结构不匹配以及缺失或不支持的目标特定内容。我们将这一挑战表述为跨主题视角对齐，并提出RAVA，一个在生成之前提供显式几何证据的检索增强框架。RAVA首先学习一个跨实例视角嵌入，检索与锚定视角对齐的目标主题图像，然后应用基于LogDet的子集选择策略，以保留一个在视角上保持一致且结构上互补的紧凑参考集。最后，选定的参考由一个经过微调的多参考图像生成器使用。实验表明，对于这一任务，通用语义嵌入几乎是随机的，而所提出的检索器显著提高了视角检索质量。在跨主题生成中，RAVA在相同生成骨干下始终优于零-shot基线和更强的检索替代方案。这些结果表明，跨主题视角对齐受益于检索增强的几何基础，而不是仅仅依赖端到端生成。
cs.CV / 49 / 2606.17627
Divide, Deliberate, Decide: A Multi-Agent Framework for Fine-Grained Egocentric Action Recognition
划分、深思、决策：一种细粒度自我中心动作识别的多智能体框架
Abstract
Fine-grained action recognition in egocentric video is challenging for Vision-Language Models (VLMs): actions often differ only in small visual cues, and a single model tends to be biased toward a subset of these cues. We propose Divide, Deliberate, Decide, a fully-local, zero-shot multi-agent framework in which (i) a VLM orchestrator chunks the video and proposes a top-k candidate label list per segment, (ii) an ensemble of heterogeneous VLM specialists, drawn from different open model families, engages in a structured deliberation that includes a peer-consultation round of questions, and (iii) agent rankings are aggregated with a Borda count and the orchestrator re-ranks its own prediction in light of the specialists' evidence. The entire pipeline runs locally with no fine-tuning. Experiments show that our method positively improves zero-shot action recognition performance over the baseline, highlighting the influence of a heterogeneous deliberation step, showing that the gain stems from decorrelated model priors rather than from additional compute.
Chinese Translation
在自我中心视频中，细粒度动作识别对视觉语言模型（VLMs）来说具有挑战性：动作往往仅在小的视觉线索上有所不同，而单一模型往往对这些线索的一个子集存在偏见。我们提出了划分、深思、决策（Divide, Deliberate, Decide），这是一个完全本地化的零-shot多智能体框架，其中（i）一个VLM协调者将视频分块，并为每个片段提出前k个候选标签列表，（ii）来自不同开放模型家族的异构VLM专家组成一个集成，进行结构化的深思讨论，包括一轮同行咨询的问题，（iii）代理排名通过Borda计数进行聚合，协调者根据专家的证据重新排名自己的预测。整个流程在本地运行，无需微调。实验表明，我们的方法在零-shot动作识别性能上显著优于基线，突显了异构深思步骤的影响，表明收益源于去相关的模型先验，而非额外的计算。
cs.CV / 50 / 2606.17644
Bounding Box Label Propagation for Re-Annotation of Document Layout Analysis Datasets
用于文档布局分析数据集重新标注的边界框标签传播
Abstract
Datasets in practical document processing scenarios typically grow over time, and their class annotations undergo continuous refinement. This creates significant re-annotation efforts, which are time-consuming and costly. A promising remedy is to re-annotate only a small subset of available documents manually and apply semi-supervised learning techniques that leverage both labelled and unlabelled data. Although there are numerous approaches to tackle this problem for classification, there exists no adaptation for the problem of re-classifying object detection instances, e.g. for document layout analysis. To this end, we propose Bounding Box Label Propagation (BBLP), a pseudo-labelling framework for object detection. An object encoder integrates visual, textual, and positional embeddings from object detection samples to come up with a joint embedding that can be used for Label Propagation on partially annotated datasets in a plug-and-play fashion. Evaluation results indicate that the proposed approach produces high-quality class annotations of bounding boxes. In the D4LA layout analysis dataset, it achieves a mAP of 54.0%, corresponding to 81.6% of fully supervised performance, while using only 10% labelled data. Our work demonstrates the potential of Label Propagation for object detection and lays the groundwork for reducing manual annotation efforts in real-world document processing applications.
Chinese Translation
在实际文档处理场景中，数据集通常随着时间的推移而增长，其类别注释也会不断完善。这造成了显著的重新标注工作，既耗时又昂贵。一种有前景的解决方案是仅手动重新标注少量可用文档，并应用利用标记和未标记数据的半监督学习技术。尽管有许多方法可以解决分类问题，但目前尚无针对重新分类对象检测实例（例如文档布局分析）的问题的适应方案。为此，我们提出了边界框标签传播（Bounding Box Label Propagation, BBLP），这是一种用于对象检测的伪标注框架。对象编码器整合来自对象检测样本的视觉、文本和位置嵌入，以生成可用于在部分注释数据集上进行标签传播的联合嵌入，具有即插即用的特性。评估结果表明，所提出的方法能够生成高质量的边界框类别注释。在D4LA布局分析数据集中，它实现了54.0%的平均精度均值（mAP），相当于完全监督性能的81.6%，同时仅使用了10%的标记数据。我们的工作展示了标签传播在对象检测中的潜力，并为减少实际文档处理应用中的手动标注工作奠定了基础。
cs.CV / 51 / 2606.17650
MambaCount: Efficient Text-guided Open-vocabulary Object Counting with Spatial Sparse State Space Duality Block
MambaCount：基于空间稀疏状态空间对偶块的高效文本引导开放词汇对象计数
Abstract
Text-guided Open-vocabulary Object Counting (TOOC) aims to estimate the number of objects described by text prompts, which is particularly challenging in dense scenes with large scale variations. Existing TOOC approaches predominantly rely on Transformers, whose quadratic complexity with respect to image resolution limits their scalability. Mamba offers a promising alternative due to its linear complexity. However, previous Mamba-based methods have two main limitations. On the one hand, the inherent causal formulation of Mamba constrains the bidirectional spatial dependency modeling required by non-causal vision tasks. On the other hand, existing Mamba-based vision models often overlook the unconstrained high entropy in the spatial token responses, which can weaken local details and high-frequency cues. To address these limitations, we propose MambaCount, an efficient framework built on the Spatial Sparse State Space Duality (S^4D) block. Specifically, we analyze and reconstruct the decay dynamics of hidden states in Mamba to alleviate the dependency constraints introduced by causal modeling. Moreover, we introduce a Spatial Token Selection (STS) sub-block to reduce the unconstrained high entropy in spatial token responses within Mamba. In addition, we design Multi-Granularity Prototypes (MGP) to identify object-like regions at different semantic levels, improving cross-modal alignment and interpretability. Extensive experiments on FSC-147 demonstrate that MambaCount achieves state-of-the-art performance among methods without secondary querying, obtaining a test MAE of 12.23, while retaining linear complexity.
Chinese Translation
文本引导的开放词汇对象计数（TOOC）旨在估计由文本提示描述的对象数量，这在具有大规模变化的密集场景中尤为具有挑战性。现有的TOOC方法主要依赖于Transformer，其相对于图像分辨率的平方复杂度限制了其可扩展性。Mamba由于其线性复杂度，提供了一种有前景的替代方案。然而，之前基于Mamba的方法存在两个主要限制。一方面，Mamba固有的因果公式限制了非因果视觉任务所需的双向空间依赖建模。另一方面，现有的基于Mamba的视觉模型往往忽视了空间标记响应中的无约束高熵，这可能削弱局部细节和高频线索。为了解决这些限制，我们提出了MambaCount，一个基于空间稀疏状态空间对偶（S^4D）块的高效框架。具体而言，我们分析并重构了Mamba中隐藏状态的衰减动态，以减轻因果建模引入的依赖约束。此外，我们引入了空间标记选择（STS）子块，以减少Mamba中空间标记响应的无约束高熵。此外，我们设计了多粒度原型（MGP），以识别不同语义层次的类对象区域，提高跨模态对齐和可解释性。在FSC-147上的大量实验表明，MambaCount在不进行二次查询的方法中实现了最先进的性能，测试平均绝对误差（MAE）为12.23，同时保持线性复杂度。
cs.CV / 52 / 2606.17675
Do We Really Need Diffusion? A Fast U-Net for Paired Medical Image Translation
我们真的需要扩散吗？一种用于配对医学图像翻译的快速 U-Net
Abstract
Magnetic resonance imaging-signal fat fraction (MRI-SFF) quantifies tissue fat and serves as an established biomarker for metabolic and musculoskeletal disorders. The acquisition requires, however, specialized MRI sequences, which are not available routinely. We investigate whether SFF can be estimated from widely available T2-weighted (T2w) MRI via image-to-image translation (I2I). We further compare a lightweight 4-level U-Net to a state-of-the-art Denoising Diffusion Probabilistic Model (DDPM) using a dataset of 230 048 paired 2D images (183 517 train, 23 621 val, 22 910 test) from the German National Cohort (NAKO). Both models clearly outperform the identity baseline (Pearson correlation r = 0.769, mean absolute error MAE = 0.070 +/- 0.054), which confirms that the models learn a non-trivial cross-modal mapping. Interestingly, the lightweight U-Net outperforms the DDPM in both correlation (r = 0.975 vs. 0.962) and error (MAE = 0.014 +/- 0.015 vs. 0.019 +/- 0.019), while reducing inference time by a factor of 208 (25.2 ms vs. 5 227.2 ms per image using 50 Denoising Diffusion Implicit Model (DDIM) steps). The strong clinical performance at substantially reduced computational cost enables real-time clinical use.
Chinese Translation
磁共振成像信号脂肪分数（MRI-SFF）量化组织脂肪，并作为代谢和肌肉骨骼疾病的既定生物标志物。然而，获取该数据需要专门的 MRI 序列，这些序列并不常规可用。我们研究了是否可以通过图像到图像翻译（I2I）从广泛可用的 T2 加权（T2w）MRI 中估算 SFF。我们进一步将一种轻量级的 4 层 U-Net 与一种最先进的去噪扩散概率模型（Denoising Diffusion Probabilistic Model, DDPM）进行了比较，使用的数据集包括来自德国国家队列（NAKO）的 230,048 对 2D 图像（183,517 用于训练，23,621 用于验证，22,910 用于测试）。两种模型均明显优于身份基线（Pearson 相关系数 r = 0.769，平均绝对误差 MAE = 0.070 +/- 0.054），这确认了模型学习了非平凡的跨模态映射。有趣的是，轻量级 U-Net 在相关性（r = 0.975 对比 0.962）和误差（MAE = 0.014 +/- 0.015 对比 0.019 +/- 0.019）方面均优于 DDPM，同时将推理时间缩短了 208 倍（每幅图像 25.2 毫秒对比 5,227.2 毫秒，使用 50 步去噪扩散隐式模型（Denoising Diffusion Implicit Model, DDIM））。在大幅降低计算成本的情况下，强大的临床表现使得实时临床应用成为可能。
cs.CV / 53 / 2606.17678
See First, Answer Later: Visual Evidence Pre-Alignment via Sufficiency-Driven RL
先看后答：通过充分性驱动的强化学习进行视觉证据预对齐
Abstract
Multimodal large language models (MLLMs) integrate strong text reasoning with visual inputs, yet their responses can be inconsistent with the underlying images, indicating ineffective utilization of visual evidence during inference. The prevailing training paradigm relies on large-scale caption-based pretraining for general alignment, followed by supervised fine-tuning and reinforcement learning to enable instruction following and complex reasoning. However, such pretraining provides only weak visual grounding: short, coarse captions bias models toward salient objects while neglecting fine-grained visual evidence. In this paper, we introduce Visual Evidence Pre-Alignment (VEPA), an intermediate stage between pretraining and post-training that explores a novel sufficiency-driven objective with Group Relative Policy Optimization (GRPO) to optimize question-conditioned visual evidence descriptions. Extensive experiments across diverse benchmarks show that our VEPA consistently enhances performance on visually demanding evaluations and complements standard supervised post-training. Further analyses show that the income stems from strengthened, transferable visual grounding, rather than from additional task-specific training.
Chinese Translation
多模态大型语言模型（MLLMs）将强大的文本推理与视觉输入相结合，但它们的响应可能与基础图像不一致，表明在推理过程中未有效利用视觉证据。现有的训练范式依赖于大规模基于标题的预训练以实现一般对齐，随后通过监督微调和强化学习来实现指令跟随和复杂推理。然而，这种预训练仅提供了弱视觉基础：简短、粗略的标题使模型偏向显著物体，同时忽视了细粒度的视觉证据。在本文中，我们引入了视觉证据预对齐（VEPA），这是预训练与后训练之间的一个中间阶段，探索了一种新颖的充分性驱动目标，结合群体相对策略优化（GRPO）来优化基于问题的视觉证据描述。在多个基准测试中的广泛实验表明，我们的VEPA在视觉要求高的评估中持续提升性能，并补充了标准的监督后训练。进一步的分析表明，这一收益源于增强的、可转移的视觉基础，而非来自额外的任务特定训练。
cs.CV / 54 / 2606.17702
SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology
SegTME-UNI2：基于基础模型的框架，用于可推广的多类细胞分割和LLM驱动的肿瘤微环境特征化在组织病理学中的应用
Abstract
Characterising the tumour microenvironment (TME) from routine H&E-stained histology images requires simultaneous cell segmentation, feature extraction, and interpretable clinical reporting. We present SEGTME-UNI2, a unified framework addressing these requirements. Its core is UNI2-UPERHOVER, a dual-head segmentation model pairing the UNI2-H pathology foundation model (ViT-Giant, pretrained on >100M tiles from 100K slides) with two parallel UperNet decoders: one for six-class semantic segmentation and one for horizontal-vertical gradient regression enabling watershed-based nuclear instance separation. To address the lack of pixel-level annotations in large real-world repositories, UNI2-UPERHOVER undergoes a three-stage progressive pseudo-label curriculum. Each stage trains a fresh model without weight transfer, driving improvement entirely via increased pseudo-label quality: Stage 1: Uses human-annotated PanNuke (7,901 images, 189,744 nuclei, 0.25 um/pixel). Stage 2: Uses entropy-filtered pseudo-labels from the Stage 1 model on 271,711 TCGA-UT scale-0 patches (0.5 um/pixel). Stage 3: Uses pseudo-labels from the Stage 2 model on all 1,608,060 TCGA-UT patches across six resolution scales (0.5-1.0 um/pixel). Segmentation outputs feed a structured TME feature extraction pipeline computing 20+ per-patch compositional, morphological, spatial entropy, and intercellular distance metrics. These are encoded as JSON and passed to a fine-tuned NVIDIA BioNeMo GPT model to generate clinically interpretable TME narratives. Preliminary validation on held-out PanNuke and TCGA-UT partitions demonstrates framework feasibility and internal consistency. The pseudo-labelled TCGA-UT dataset and UNI2-UPERHOVER checkpoint are publicly released to support large-scale TME profiling and spatial biology research.
Chinese Translation
从常规的H&E染色组织学图像中表征肿瘤微环境（TME）需要同时进行细胞分割、特征提取和可解释的临床报告。我们提出了SEGTME-UNI2，这是一个统一框架，旨在满足这些要求。其核心是UNI2-UPERHOVER，一个双头分割模型，将UNI2-H病理基础模型（ViT-Giant，预训练于超过1亿个切片的100K幻灯片）与两个并行的UperNet解码器配对：一个用于六类语义分割，另一个用于水平-垂直梯度回归，从而实现基于分水岭的核实例分离。为了应对大型真实世界数据库中缺乏像素级注释的问题，UNI2-UPERHOVER经历了一个三阶段的渐进伪标签课程。每个阶段都训练一个全新的模型，不进行权重转移，完全通过提高伪标签质量来推动改进：阶段1：使用人类注释的PanNuke（7,901幅图像，189,744个细胞核，0.25微米/像素）。阶段2：在271,711个TCGA-UT尺度0补丁（0.5微米/像素）上使用阶段1模型的熵过滤伪标签。阶段3：在所有1,608,060个TCGA-UT补丁（跨六个分辨率尺度，0.5-1.0微米/像素）上使用阶段2模型的伪标签。分割输出输入到一个结构化的TME特征提取管道中，计算20多个每补丁的组成、形态、空间熵和细胞间距离指标。这些指标以JSON格式编码，并传递给经过微调的NVIDIA BioNeMo GPT模型，以生成临床可解释的TME叙述。对保留的PanNuke和TCGA-UT分区的初步验证显示了框架的可行性和内部一致性。伪标签的TCGA-UT数据集和UNI2-UPERHOVER检查点已公开发布，以支持大规模TME分析和空间生物学研究。
cs.CV / 55 / 2606.17710
Vision-language models for chest radiography do not always need the image
胸部放射影像的视觉-语言模型并不总是需要图像
Abstract
Medical vision-language models report strong chest radiograph accuracy, and this is increasingly read as evidence that they use the image. That inference is unsafe: a model exploiting finding-name priors scores like one that reads the scan, and no standard benchmark separates them. We introduce a causal audit that intervenes on the image, occluding the relevant region, occluding an irrelevant one, and swapping in another patient's same-label scan, and combines three behavioral metrics to test whether a correct answer depends on the image. Across nine systems, a text-only model with no image access reaches within 5.7 accuracy points of the best multimodal one, and a 119-billion-parameter multimodal model is statistically indistinguishable from a 7-billion text-only baseline. The audit splits the cohort into three models that ignore the image, one that is unstable, and five that use it selectively, for a subset of findings; the categories hold across a second dataset, resolution, and prompt phrasing. Against board-certified radiologists, a text-only model is statistically indistinguishable from a radiologist's accuracy while grounding at zero, whereas the image-using models ground at radiologist-comparable rates. Reported confidence flags ungrounded answers only when a model uses the image. Grounding audits, not accuracy, should gate clinical deployment.
Chinese Translation
医学视觉-语言模型在胸部放射影像的准确性上表现出色，这逐渐被解读为它们使用图像的证据。然而，这一推断并不安全：一个利用发现-名称先验的模型得分与一个读取扫描的模型相似，而没有标准基准能够将它们区分开。我们引入了一种因果审计方法，通过对图像进行干预，遮挡相关区域、遮挡无关区域，并替换为另一位患者的同标签扫描，结合三种行为指标来测试正确答案是否依赖于图像。在九个系统中，一个没有图像访问的仅文本模型的准确性与最佳多模态模型相差仅5.7个百分点，而一个1190亿参数的多模态模型在统计上与一个70亿参数的仅文本基线模型无显著差异。审计将样本分为三种忽略图像的模型、一种不稳定的模型，以及五种选择性使用图像的模型，后者仅针对特定发现；这些类别在第二个数据集、分辨率和提示措辞中保持一致。与经过认证的放射科医生相比，仅文本模型在基础为零的情况下与放射科医生的准确性在统计上无显著差异，而使用图像的模型则在与放射科医生可比的比率下进行基础化。报告的置信度仅在模型使用图像时标记无基础的答案。基础化审计，而非准确性，应成为临床部署的门槛。
cs.CV / 56 / 2606.17711
Structured Adversarial Camouflage via Voronoi Diagrams
基于Voronoi图的结构化对抗伪装
Abstract
Pixel-wise adversarial patches are computationally heavy and often visually detectable, limiting utility in security-critical systems. We present adversarial Voronoi camouflage that optimizes only seed-point locations under fixed, printable palettes using a soft assignment, producing structured, splinter camouflage-like patterns without additional regularization. Evaluated on person detection with COCO-style AP@[.5:.95], naive placement (Inria -> COCO) performs comparably bad, while garment-level application via segmentation mask (3DPeople) results in a significant AP drop. The attack transfers to out-of-domain backgrounds and across detector families (YOLOv9/10/11/12), indicating robustness in black-box settings. Repainting with different palettes largely nullifies the effect, and single-color tweaks show limited tolerance (<=0.17), highlighting a structure-palette coupling. The parameter-efficient, palette-constrained design improves visual plausibility while degrading real-time detector performance. Physical validation and color calibration are left for future work. Code: https://github.com/JensBayer/Voronoi This paper was originally presented at the International Conference on Military Communication and Information Systems (ICMCIS), organized by the Information Systems Technology (IST) Scientific and Technical Committee, IST-224-RSY - the ICMCIS, held in Bath, United Kingdom, 12-13 May 2026.
Chinese Translation
逐像素对抗补丁计算量大且通常可被视觉检测到，限制了其在安全关键系统中的应用。我们提出了一种对抗Voronoi伪装方法，该方法在固定的可打印调色板下，仅优化种子点位置，利用软分配生成结构化的、类似碎片的伪装图案，而无需额外的正则化。在使用COCO风格的AP@[.5:.95]进行的人体检测评估中，简单放置（Inria -> COCO）的表现相对较差，而通过分割掩码（3DPeople）进行的服装级应用则导致显著的AP下降。该攻击能够迁移到域外背景和不同的检测器系列（YOLOv9/10/11/12），表明其在黑箱环境中的鲁棒性。使用不同调色板的重涂在很大程度上消除了效果，而单色调整显示出有限的容忍度（<=0.17），突显了结构与调色板之间的耦合。该参数高效、受调色板限制的设计提高了视觉可信度，同时降低了实时检测器的性能。物理验证和颜色校准留待未来工作。代码链接：https://github.com/JensBayer/Voronoi 本文最初在2026年5月12日至13日于英国巴斯举行的国际军事通信与信息系统会议（ICMCIS）上发表，该会议由信息系统技术（IST）科学技术委员会组织，IST-224-RSY - ICMCIS。
cs.CV / 57 / 2606.17713
Heterogeneous SAR-optical fusion for near-real-time land use and land cover mapping under cloud contamination: A novel framework and global benchmark dataset
针对云污染的近实时土地利用和土地覆盖制图的异构SAR-光学融合：一种新颖框架及全球基准数据集
Abstract
Optical remote sensing imagery is frequently degraded by cloud and cloud-shadow contamination, which limits its reliability for near-real-time land use and land cover (LULC) mapping. Although synthetic aperture radar (SAR) can provide cloud-penetrating structural information, existing SAR-optical fusion methods often assume reliable optical observations and insufficiently address the semantic uncertainty introduced by cloud contamination. To address this issue, we propose CloudLULC-Net, an end-to-end heterogeneous SAR-optical fusion framework that directly predicts LULC maps from cloud-contaminated Sentinel-2 imagery and temporally adjacent Sentinel-1 SAR observations. The proposed network incorporates optical reliability modulation to suppress unreliable optical responses, heterogeneous information adaptive aggregation to model high-order spatial-channel interactions between optical and SAR representations, and a unified semantic mapping transformer to organize fused features in a LULC-oriented latent space. A semantic anchor-guided optimization strategy is further introduced to improve the consistency of intermediate semantic representations. To support this task, we construct CloudLULC-Set, a large-scale benchmark dataset containing 40,223 curated SAR-optical-label triplets with pixel-level LULC annotations across diverse geographic regions and cloud conditions. Experimental results show that CloudLULC-Net achieves an OA of 86.60%, an F1-score of 83.29%, and an mIoU of 73.51%, outperforming representative heterogeneous reconstruction-first and end-to-end SAR-optical mapping methods. Comparisons with existing global LULC products and analyses under different cloud-cover levels further demonstrate the robustness and practical value of CloudLULC-Net for target-date LULC mapping in cloud-prone regions.The project is publicly available at: https://github.com/RSIIPAC/CloudLULC
Chinese Translation
光学遥感影像常常受到云和云影污染的影响，这限制了其在近实时土地利用和土地覆盖（LULC）制图中的可靠性。尽管合成孔径雷达（SAR）能够提供穿透云层的结构信息，但现有的SAR-光学融合方法通常假设光学观测是可靠的，且未充分解决云污染引入的语义不确定性。为了解决这一问题，我们提出了CloudLULC-Net，这是一种端到端的异构SAR-光学融合框架，能够直接从受云污染的Sentinel-2影像和时间上相邻的Sentinel-1 SAR观测中预测LULC地图。所提网络结合了光学可靠性调制，以抑制不可靠的光学响应；异构信息自适应聚合，以建模光学和SAR表征之间的高阶空间通道交互；以及统一的语义映射变换器，以在LULC导向的潜在空间中组织融合特征。此外，还引入了一种语义锚点引导的优化策略，以提高中间语义表征的一致性。为支持这一任务，我们构建了CloudLULC-Set，这是一个大规模基准数据集，包含40,223个经过整理的SAR-光学-标签三元组，具有跨多样地理区域和云条件的像素级LULC注释。实验结果表明，CloudLULC-Net实现了86.60%的总体精度（OA）、83.29%的F1-score和73.51%的平均交并比（mIoU），优于代表性的异构重建优先和端到端的SAR-光学制图方法。与现有全球LULC产品的比较以及在不同云覆盖水平下的分析进一步证明了CloudLULC-Net在云易发区域的目标日期LULC制图中的鲁棒性和实际价值。该项目已公开发布于：https://github.com/RSIIPAC/CloudLULC
cs.CV / 58 / 2606.17722
GSPan: A Continuous Gaussian Primitive Representation for Arbitrary-Scale Pansharpening
GSPan：一种用于任意尺度全色锐化的连续高斯原始表示
Abstract
Pansharpening aims to generate high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) and panchromatic (PAN) observations. Most existing deep learning methods treat pansharpening as fixed-grid prediction, which limits scale adaptation. To address this, we propose GSPan, a framework that introduces 2D Gaussian Splatting (GS) into pansharpening. Instead of directly predicting pixels, GSPan represents band-wise residual details as continuous and learnable 2D Gaussian primitives. We design a Dual-Stream Hierarchical Interaction (DSHI) architecture with a Spatial-Spectral Interactive Attention (SSIA) module to estimate these primitives from complementary PAN and MS observations. The predicted primitives are rendered as a residual detail field and injected into the upsampled MS image. This continuous representation allows GSPan to render fused images on arbitrary target sampling grids without scale-specific retraining. It further enables a Scale-Decoupled Asymmetric Inference (SDAI) strategy, which estimates primitives at a reduced resolution and renders the fused image at the target resolution for efficient large-scene pansharpening. Experiments on QuickBird, GaoFen-2, WorldView-3, and WorldView-3-4K datasets show that GSPan delivers state-of-the-art fusion performance. Moreover, SDAI markedly accelerates inference, achieving a favorable trade-off between computational efficiency and fusion quality. Our results demonstrate the potential of continuous Gaussian residual representations as a flexible and scale-decoupled alternative to fixed-grid prediction.
Chinese Translation
全色锐化旨在通过融合低分辨率多光谱（LRMS）和全色（PAN）观测生成高分辨率多光谱（HRMS）图像。现有的大多数深度学习方法将全色锐化视为固定网格预测，这限制了尺度适应性。为了解决这一问题，我们提出了GSPan，一个将二维高斯溅射（2D Gaussian Splatting, GS）引入全色锐化的框架。GSPan并不是直接预测像素，而是将波段间的残差细节表示为连续且可学习的二维高斯原始。我们设计了一个双流层次交互（Dual-Stream Hierarchical Interaction, DSHI）架构，并结合空间-光谱交互注意力（Spatial-Spectral Interactive Attention, SSIA）模块，从互补的PAN和MS观测中估计这些原始。预测的原始被渲染为残差细节场，并注入到上采样的MS图像中。这种连续表示使得GSPan能够在任意目标采样网格上渲染融合图像，而无需特定尺度的重新训练。此外，它进一步实现了一种尺度解耦不对称推理（Scale-Decoupled Asymmetric Inference, SDAI）策略，该策略在降低分辨率下估计原始，并在目标分辨率下渲染融合图像，从而实现高效的大场景全色锐化。在QuickBird、GaoFen-2、WorldView-3和WorldView-3-4K数据集上的实验表明，GSPan提供了最先进的融合性能。此外，SDAI显著加速了推理，实现了计算效率与融合质量之间的良好平衡。我们的结果展示了连续高斯残差表示作为一种灵活且尺度解耦的替代方案，具有固定网格预测的潜力。
cs.CV / 59 / 2606.17730
ActWorld: From Explorable to Interactive World Model via Action-Aware Memory
ActWorld：通过动作感知记忆从可探索世界模型到互动世界模型
Abstract
Interactive world models aim to simulate environment dynamics under real-time user actions. However, their action vocabulary is largely confined to navigation: most actions correspond to motion (e.g., walk, turn, look around), while interaction with objects in the scene (e.g., pick up plates, open doors, or trigger physical responses) is either absent, restricted to game domains, or relegated to prompt-to-full-video scenarios. The resulting worlds are visually explorable but not truly actionable. In this work, we present ActWorld, an interactive world model that extends prior navigation-centric generators to support mid-rollout object interaction within a chunk-autoregressive framework. We argue that the navigation-interaction gap stems from two bottlenecks. First, a data bottleneck: the lack of human-object interaction data with accurate, dense labels. Second, a memory bottleneck: recency-biased history compression in existing world models discards the event-transition frames that causally determine subsequent object states, leading to an action-forgetting pathology. On the data side, we construct a 100K interaction video dataset, each annotated with per-chunk captions via chain-of-thought reasoning. On the model side, we introduce a hierarchical action-aware memory design that routes history compression by interaction importance, complemented by a persistent memory bank that maintains event-update and object-identity tokens across long rollouts. Experiments show that ActWorld supports both flexible navigation and rich object interaction within a single model, substantially improving interaction fidelity over navigation-only baselines without sacrificing viewpoint control. Project page is available at https://interactwm.github.io/ActWorld.
Chinese Translation
互动世界模型旨在模拟在实时用户行动下的环境动态。然而，它们的动作词汇在很大程度上局限于导航：大多数动作对应于运动（例如，行走、转向、环顾四周），而与场景中物体的互动（例如，捡起盘子、开门或触发物理响应）要么缺失，要么仅限于游戏领域，或被 relegated 到提示到完整视频的场景中。由此产生的世界在视觉上是可探索的，但并不是真正可操作的。在本研究中，我们提出了 ActWorld，这是一种互动世界模型，它扩展了以导航为中心的生成器，以支持在块自回归框架内的中途对象互动。我们认为，导航与互动之间的差距源于两个瓶颈。首先是数据瓶颈：缺乏具有准确、密集标签的人-物体互动数据。其次是记忆瓶颈：现有世界模型中的近期偏置历史压缩丢弃了因果决定后续对象状态的事件过渡帧，导致了动作遗忘病态。在数据方面，我们构建了一个包含 10 万个互动视频的数据集，每个视频通过链式思维推理进行了逐块注释。在模型方面，我们引入了一种分层的动作感知记忆设计，通过互动重要性来引导历史压缩，并辅以一个持久的记忆库，该库在长时间回放中维护事件更新和对象身份标记。实验表明，ActWorld 在单一模型中支持灵活的导航和丰富的对象互动，显著提高了互动的保真度，而不牺牲视角控制。项目页面可访问 https://interactwm.github.io/ActWorld。
cs.CV / 60 / 2606.17742
BrainWorld: A Structural-Prior-Conditioned Generative Model for Whole-Brain 4D fMRI Dynamics
BrainWorld：一种结构先验条件生成模型用于全脑4D fMRI动态
Abstract
Whole-brain 4D fMRI generation is valuable for modeling functional brain dynamics, yet existing fMRI foundation models mainly target representation learning and downstream prediction rather than conditional predictive generation. We introduce BrainWorld, a structural-prior-conditioned generative model for whole-brain 4D fMRI dynamics. BrainWorld uses sMRI as subject-level anatomical context to guide future fMRI generation, integrating structural information into the denoising process rather than treating it as a parallel modality. Evaluated on 22 datasets spanning diverse cohorts and brain states, BrainWorld generates stable 4D fMRI trajectories up to 400 frames, improves downstream performance through generated-example augmentation, and learns transferable multimodal representations that outperform baselines. Together, these results establish BrainWorld as a condition-aware generative framework for long-horizon brain dynamics modeling and multimodal representation learning.
Chinese Translation
全脑4D fMRI生成对于建模功能性脑动态具有重要价值，然而现有的fMRI基础模型主要针对表示学习和下游预测，而非条件预测生成。我们提出了BrainWorld，一种用于全脑4D fMRI动态的结构先验条件生成模型。BrainWorld利用结构性磁共振成像（sMRI）作为受试者级别的解剖上下文，以指导未来的fMRI生成，将结构信息整合到去噪过程中，而不是将其视为平行模态。在涵盖多样化群体和脑状态的22个数据集上进行评估，BrainWorld生成稳定的4D fMRI轨迹，最多可达400帧，通过生成示例增强提升下游性能，并学习可转移的多模态表示，超越基线模型。综合这些结果，BrainWorld确立了作为一种条件感知生成框架，用于长期脑动态建模和多模态表示学习。
cs.CV / 61 / 2606.17798
LiveStarPro: Proactive Streaming Video Understanding with Hierarchical Memory for Long-Horizon Streams
LiveStarPro：具有层次记忆的长时段流媒体主动视频理解
Abstract
Despite the remarkable progress of Video Large Language Models (Video-LLMs), current online architectures still struggle to simultaneously process continuous video streams, decide autonomously when to respond, and preserve long-horizon contextual memory. These obstacles undermine real-time responsiveness and cause severe forgetting throughout prolonged interactions. In this work, we introduce LiveStarPro, a live streaming assistant that is designed for proactive video understanding over long-horizon streams. The design of LiveStarPro rests on three complementary components. The first component is Streaming Verification Decoding (SVeD), an inference framework that identifies the appropriate response timing through single-pass perplexity verification, thereby eliminating the dependency on explicit silence tokens. The second component is Streaming Causal Attention Masks (SCAM), a training strategy that enforces incremental video-language alignment over variable-length streams. The third component is Tree-Structured Hierarchical Memory (TSHM), a recursive memory architecture that organizes evicted historical information into event chains and consequently enables efficient retrieval from effectively unbounded video streams. To facilitate a comprehensive evaluation under realistic online conditions, we further present OmniStarPro, a large-scale benchmark that spans 15 diverse real-world scenarios and that extends to hour-scale streams for the assessment of long-term recall. Extensive experiments demonstrate that LiveStarPro consistently surpasses existing methods, attaining a 28.9% improvement in semantic correctness and an 18.2% reduction in timing error, while its streaming key-value cache further yields a 1.58x inference speedup over the same model without caching. The model and the code are publicly available at https://github.com/sotayang/LiveStarPro.
Chinese Translation
尽管视频大型语言模型（Video-LLMs）取得了显著进展，但当前的在线架构仍然难以同时处理连续的视频流，自动决定何时响应，并保持长时段的上下文记忆。这些障碍削弱了实时响应能力，并导致在长时间交互中严重遗忘。在本研究中，我们介绍了LiveStarPro，一种旨在对长时段流进行主动视频理解的直播助手。LiveStarPro的设计基于三个互补的组件。第一个组件是流式验证解码（Streaming Verification Decoding, SVeD），这是一种通过单次通行的困惑度验证来识别适当响应时机的推理框架，从而消除了对显式静默标记的依赖。第二个组件是流式因果注意力掩码（Streaming Causal Attention Masks, SCAM），这是一种训练策略，强制在可变长度流中进行增量的视频-语言对齐。第三个组件是树结构层次记忆（Tree-Structured Hierarchical Memory, TSHM），这是一种递归记忆架构，将被驱逐的历史信息组织成事件链，从而实现有效的检索，适用于有效无限的视频流。为了在现实在线条件下进行全面评估，我们进一步提出了OmniStarPro，这是一个涵盖15种多样化真实场景的大规模基准，并扩展到小时级流以评估长期回忆。大量实验表明，LiveStarPro始终超越现有方法，在语义正确性上提高了28.9%，在时间误差上减少了18.2%，同时其流式键值缓存还使得推理速度比没有缓存的相同模型提高了1.58倍。该模型和代码已公开发布在https://github.com/sotayang/LiveStarPro。
cs.CV / 62 / 2606.17800
MaineCoon: Pursuing A Real-Time Audio-Visual Social World Model
MaineCoon：追求实时音视频社交世界模型
Abstract
As an increasing majority of global video content is consumed on social platforms for interactive social purposes, video generation models built for social worlds are important but largely overlooked by previous studies. In this work, we define the position of social world models and build a prototype model as the first step towards this goal. While previous world models successfully simulate physical environments or gaming world exploration, they remain fundamentally detached from human-centric social dynamics. To bridge this gap as the first step to social world models, we present MaineCoon, the first real-time audio-visual autoregressive model that has 22B parameters and is capable of real-time streaming generation and sub-second interaction, with a record-breaking frame rate of up to 47.5 FPS, on a single GPU. To the best of our knowledge, MaineCoon is also the first real-time audio-visual generation model specifically optimized for social-interactive applications. To enable efficient and stable training, we introduce several novel techniques into MaineCoon, including self-resampling, cross-modal representation alignment, domain-aware preference optimization, and reinforced online-policy distillation (ROPD). We also design the first agentic streaming inference framework that supports thousand-second-scale or even longer generation while mitigating drift with agentic cache management and prompt planing. These innovations significantly accelerate training while optimizing real-time inference performance. We believe this work not only sets a new state-of-the-art (SOTA) performance benchmark for high-quality, low-latency, and long-horizon audio-visual autoregressive models, but also points out the paradigm shift desired for next-generation AI-native social platforms.
Chinese Translation
随着全球视频内容在社交平台上被越来越多地用于互动社交目的，针对社交世界构建的视频生成模型变得重要，但在以往的研究中却 largely 被忽视。在本研究中，我们定义了社交世界模型的位置，并构建了一个原型模型，作为实现这一目标的第一步。虽然以往的世界模型成功模拟了物理环境或游戏世界探索，但它们在本质上与以人为中心的社交动态仍然脱节。为了弥补这一差距，作为社交世界模型的第一步，我们提出了MaineCoon，这是第一个具有220亿参数的实时音视频自回归模型，能够实现实时流生成和亚秒级交互，单个GPU上的帧率高达47.5 FPS，创下了记录。据我们所知，MaineCoon也是第一个专门针对社交互动应用优化的实时音视频生成模型。为了实现高效和稳定的训练，我们在MaineCoon中引入了几种新颖的技术，包括自重采样、跨模态表示对齐、领域感知偏好优化和强化在线策略蒸馏（ROPD）。我们还设计了第一个支持千秒级或更长生成的代理流推理框架，同时通过代理缓存管理和提示规划来减轻漂移。这些创新显著加速了训练，同时优化了实时推理性能。我们相信，这项工作不仅为高质量、低延迟和长时间音视频自回归模型设定了新的最先进（SOTA）性能基准，还指出了下一代AI原生社交平台所需的范式转变。
cs.CV / 63 / 2606.17809
Million-scale multimodal pollen microscopy with expert-guided foundation models
百万规模的多模态花粉显微镜与专家引导的基础模型
Abstract
Automated pollen identification from microscopy remains a bottleneck in aerobiology, palaeoecology and biodiversity monitoring, because scalable systems must generalise across specimen preparation, scanner settings and geographic origins while retaining palynological interpretability. To address this gap, we present a million-scale multimodal pollen microscopy resource, Pollen AI Atlas, assembled from pure-species whole-slide bright-field images spanning four geographic origins, four scanner settings and 46 taxon labels across 31 botanical families. Seeded by one manually selected exemplar per source slide, token-level mining and filtering produced 1,511,390 released grain detections with 99.6\% proposal precision in expert-curated test regions. Each detection was paired with machine-generated grain-level morphological captions from five open-weight vision-language models, guided by expert-verified palynological anchors, yielding structured descriptions of aperture systems, wall ornamentation, shape and size. Among the evaluated models, Gemma4 provided the most controlled primary caption set, combining tight length control, no leakage and the strongest text-retrieval performance. Baseline benchmarks with frozen visual features reached 88.16\% top-1 accuracy, while cross-regional retrieval showed that caption-derived text embeddings remained robust when image similarity degraded (mAP@20 0.811 versus 0.262). Released data, annotations, captions, splits, code, and weights provide a benchmark for pollen recognition, cross-regional domain adaptation and domain-specific multimodal microscopy learning.
Chinese Translation
自动化花粉识别在气溶胶生物学、古生态学和生物多样性监测中仍然是一个瓶颈，因为可扩展系统必须在样本准备、扫描仪设置和地理来源之间进行泛化，同时保持花粉学的可解释性。为了解决这一问题，我们提出了一个百万规模的多模态花粉显微镜资源——花粉人工智能图谱（Pollen AI Atlas），该资源由来自四个地理来源、四种扫描仪设置和31个植物科的46个分类标签的纯种全片明场图像组成。每个源图像手动选择一个示例，通过标记级别的挖掘和过滤，产生了1,511,390个发布的花粉颗粒检测，专家策划的测试区域内的提案精度达到99.6%。每个检测结果都与来自五个开放权重视觉-语言模型生成的机器生成的颗粒级形态描述配对，这些描述由专家验证的花粉学锚点指导，提供了关于孔系统、壁装饰、形状和大小的结构化描述。在评估的模型中，Gemma4提供了最为受控的主要描述集，结合了严格的长度控制、无泄漏和最强的文本检索性能。使用冻结的视觉特征进行的基准测试达到了88.16%的顶级准确率，而跨区域检索显示，当图像相似性降低时，基于描述的文本嵌入仍然保持稳健（mAP@20为0.811对比0.262）。发布的数据、注释、描述、划分、代码和权重为花粉识别、跨区域领域适应和领域特定的多模态显微镜学习提供了基准。
cs.CV / 64 / 2606.17824
Human-in-the-Loop Atlas-Based 3D Asset Segmentation for Interactive Content Workflows
基于人机协作的3D资产分割的参数化图集用于交互内容工作流程
Abstract
Segmenting 3D assets into meaningful regions remains challenging, especially when segmentation criteria are application-dependent and require user control. We present a human-in-the-loop pipeline for generating a segmented 2D parameterized atlas from a 3D model for interactive media, game, and XR content workflows. Our method first selects a compact set of rendered views using a greedy set cover strategy over sampled surface points, and then supports interactive segmentation of these views with SAM~2 and Label Studio. The resulting masks are back-projected onto the model's UV parameterization to produce a unified segmented atlas that supports downstream production tasks such as segment-wise material assignment, style transfer, and semantic labeling. We assess the pipeline through a demonstration-based technical evaluation on eight cultural heritage objects. The results show that the approach can generate usable segmented atlases across diverse geometries while revealing recurring sources of manual correction, particularly fine structures, cavities, and weak appearance boundaries.
Chinese Translation
将3D资产分割为有意义的区域仍然具有挑战性，特别是当分割标准依赖于应用且需要用户控制时。我们提出了一种人机协作的流程，用于从3D模型生成用于交互媒体、游戏和XR内容工作流程的分割2D参数化图集。我们的方法首先使用贪婪集合覆盖策略从采样的表面点中选择一组紧凑的渲染视图，然后支持使用SAM~2和Label Studio对这些视图进行交互式分割。生成的掩膜被反投影到模型的UV参数化上，以产生一个统一的分割图集，支持下游生产任务，如按段材质分配、风格迁移和语义标注。我们通过对八个文化遗产对象进行基于演示的技术评估来评估该流程。结果表明，该方法能够在多样的几何形状上生成可用的分割图集，同时揭示了手动修正的反复来源，特别是细结构、腔体和弱外观边界。
cs.CV / 65 / 2606.17836
High-Fidelity 3D Geometric Reconstruction of Pelvic Organs from MRI: A Hybrid Deep Learning and Iterative Optimization Approach
基于MRI的盆腔器官高保真三维几何重建：一种混合深度学习与迭代优化的方法
Abstract
Patient-specific 3D reconstruction of pelvic organ geometry from MRI is important for pelvic floor modeling and downstream patient-specific analysis. However, while previous studies have focused primarily on either image segmentation or downstream use of 3D models, the reconstruction of high-fidelity, high-quality geometries remains labor-intensive and poorly standardized. The study introduced a hybrid deformable shape modeling framework that integrates deep learning prediction with iterative optimization for the reconstruction of the bladder, uterus, and rectum. The framework consists of three core components: a geometry-aware multi-level deep learning architecture that preserves topological consistency of pelvic organs; a two-stage amortized optimization training strategy that balances global shape capture and local surface refinement; and a holistic synergy mechanism--where iterative optimization provides supervision for deep learning during the training phase, and during inference, deep learning rapidly predicts the global organ morphology, followed by iterative optimization to refine local surfaces and mesh quality. This framework demonstrated marked superiority in geometric fidelity than current mainstream deep learning-based organ reconstruction models. For individual anatomical structures, the reconstructed 3D geometries for the bladder, rectum, and uterus achieved significantly lower Chamfer Distance values and higher Dice Similarity Coefficient scores. In addition, while maintaining high computational efficiency, the proposed architecture yielded superior overall volumetric mesh quality. At the patient level, the framework achieved higher mean values for the 10 worst elements for both minSICN and minSIGE compared to traditional geometric post-processing algorithms.
Chinese Translation
从MRI中进行患者特异性的盆腔器官几何三维重建对于盆底建模和后续的患者特异性分析至关重要。然而，尽管以往的研究主要集中在图像分割或三维模型的后续使用上，高保真、高质量几何体的重建仍然是劳动密集型且标准化程度低。本研究提出了一种混合变形形状建模框架，该框架将深度学习预测与迭代优化相结合，用于膀胱、子宫和直肠的重建。该框架由三个核心组件组成：一个几何感知的多层深度学习架构，能够保持盆腔器官的拓扑一致性；一种两阶段的摊销优化训练策略，平衡全局形状捕捉与局部表面细化；以及一个整体协同机制——在训练阶段，迭代优化为深度学习提供监督，而在推理阶段，深度学习快速预测全局器官形态，随后进行迭代优化以细化局部表面和网格质量。该框架在几何保真度上明显优于当前主流的基于深度学习的器官重建模型。对于各个解剖结构，膀胱、直肠和子宫的重建三维几何体的Chamfer距离值显著降低，Dice相似系数得分显著提高。此外，在保持高计算效率的同时，所提出的架构在整体体积网格质量上表现优越。在患者层面，该框架在minSICN和minSIGE的10个最差元素的平均值上均高于传统几何后处理算法。
cs.CV / 66 / 2606.17867
A Quantitative Analysis of Multimodal Biomarkers in Alzheimer's Disease
阿尔茨海默病多模态生物标志物的定量分析
Abstract
Despite increasing adoption of multimodal approaches in Alzheimer's Disease (AD) research -- aimed at integrating molecular, structural, clinical, and genetic biomarkers to enhance disease characterization -- the relationships among these modalities remain poorly understood. A systematic analysis of their dynamic interaction is essential for improving disease modeling, identifying redundant assessments, and reducing patient burden and acquisition costs. In this paper, we present a quantitative analysis of multimodal AD biomarkers by integrating tau-PET, structural MRI, cognitive scores (MMSE and CDR), and APOE4 data from 789 subjects drawn from the ADNI dataset. In our analyses, we (A) quantify cross-modal mutual information and explained variance to assess redundancy and predictive dependencies; (B) examine associations between tau topologies and structural atrophy across brain regions to select informative ROIs; (C) perform a statistical decomposition of the tau-cognition association into atrophy-related and atrophy-independent components; (D) and identify a dominant neurodegenerative trajectory that aligns with cognitive decline. This study provides a systematic characterization of cross-modal relationships, improving the interpretability and selection of biomarkers in AD. Code is publicly available at: https://github.com/antonioscardace/Multimodal-AD.
Chinese Translation
尽管在阿尔茨海默病（AD）研究中越来越多地采用多模态方法——旨在整合分子、结构、临床和遗传生物标志物以增强疾病特征——但这些模态之间的关系仍然不够清楚。对它们动态交互的系统分析对于改善疾病建模、识别冗余评估以及减少患者负担和获取成本至关重要。在本文中，我们通过整合来自ADNI数据集的789名受试者的tau-PET、结构性MRI、认知评分（MMSE和CDR）和APOE4数据，呈现了对多模态AD生物标志物的定量分析。在我们的分析中，我们（A）量化跨模态互信息和解释方差，以评估冗余性和预测依赖性；（B）检查tau拓扑与大脑区域结构萎缩之间的关联，以选择信息丰富的感兴趣区域（ROIs）；（C）对tau与认知之间的关联进行统计分解，区分与萎缩相关和与萎缩无关的成分；（D）识别与认知衰退相一致的主导神经退行性轨迹。本研究提供了跨模态关系的系统特征，改善了AD中生物标志物的可解释性和选择性。代码可在以下网址公开获取：https://github.com/antonioscardace/Multimodal-AD。
cs.CV / 67 / 2606.17874
Revisiting Structural Dependency in Autoregressive Multi-Task Table Recognition via Order-Independent Cell-Level Representations
通过无序依赖的单元级表示重新审视自回归多任务表格识别中的结构依赖性
Abstract
Multi-task table recognition jointly addresses table structure prediction, cell localization, and cell content recognition within a unified framework. Existing approaches often rely on autoregressive decoders to generate table structures and reuse their hidden states for cell localization and content recognition. This autoregressive generation process can make cell representations order-dependent, degrading global consistency across cells. This paper proposes a structural refinement module that produces order-independent cell features through non-causal attention. This design enables parallel inference of cell contents while conditioning each cell on global context encoded in the refined features. Experiments on two large datasets demonstrate consistent gains in cell localization and end-to-end recognition, while reducing overall inference time by around threefold.
Chinese Translation
多任务表格识别在统一框架内共同解决表格结构预测、单元定位和单元内容识别。现有方法通常依赖自回归解码器生成表格结构，并重用其隐藏状态进行单元定位和内容识别。这种自回归生成过程可能导致单元表示依赖于顺序，从而降低单元之间的全局一致性。本文提出了一种结构细化模块，通过非因果注意力生成无序依赖的单元特征。这一设计使得在条件化每个单元时能够并行推断单元内容，并利用细化特征中编码的全局上下文。对两个大型数据集的实验表明，在单元定位和端到端识别方面均取得了一致的提升，同时将整体推断时间减少了约三倍。
cs.CV / 68 / 2606.17935
MoonSplat: Monocular Online Gaussian Splatting with Sim(3) Global Optimization
MoonSplat：基于单目在线高斯点云的 Sim(3) 全局优化
Abstract
Online 3D reconstruction from monocular image sequences is a challenging and ongoing research topic. 3D Gaussian Splatting (3DGS), leveraging its high-quality real-time rendering capability, empowers online 3D reconstruction to represent dense scenes with enhanced expressiveness, and thus holds great promise for a wide range of applications such as robotics and AR/VR. However, existing online 3DGS methods still suffer from some key challenges: fragile camera pose estimation due to the lack of global optimization, and low optimization efficiency in large-scale or long-sequence scenarios. To address these issues, we propose a robust and efficient online voxelized 3DGS reconstruction framework integrated with global $\text{Sim}(3)$ optimization, which enables reliable camera tracking and efficient global loop closure for both camera poses and voxelized 3DGS. To accelerate the convergence of the voxelized 3DGS, we further introduce a color residual learning strategy, which not only boosts optimization speed but also enhances rendering quality. Extensive experiments on diverse indoor and outdoor datasets demonstrate that our method achieves state-of-the-art performance in both camera pose estimation accuracy and rendering quality, while retaining real-time efficiency. Additionally, we develop and deploy a real-world UAV-based active reconstruction system grounded on our proposed method, validating its robustness and generalizability for practical online 3D reconstruction tasks. Our code and data are available at https://github.com/TrickyGo/MoonSplat.
Chinese Translation
从单目图像序列进行在线三维重建是一个具有挑战性且持续研究的课题。三维高斯点云（3D Gaussian Splatting, 3DGS）利用其高质量的实时渲染能力，使在线三维重建能够以更高的表现力表示密集场景，因此在机器人技术和增强现实/虚拟现实（AR/VR）等广泛应用中展现出巨大的潜力。然而，现有的在线3DGS方法仍面临一些关键挑战：由于缺乏全局优化，导致相机位姿估计脆弱，以及在大规模或长序列场景中的优化效率低下。为了解决这些问题，我们提出了一种稳健且高效的在线体素化3DGS重建框架，集成了全局 $ ext{Sim}(3)$ 优化，能够实现可靠的相机跟踪和高效的全局回环闭合，适用于相机位姿和体素化3DGS。为了加速体素化3DGS的收敛，我们进一步引入了一种颜色残差学习策略，不仅提高了优化速度，还增强了渲染质量。在多种室内和室外数据集上的广泛实验表明，我们的方法在相机位姿估计精度和渲染质量上均实现了最先进的性能，同时保持了实时效率。此外，我们基于所提出的方法开发并部署了一个基于无人机的实际主动重建系统，验证了其在实际在线三维重建任务中的稳健性和通用性。我们的代码和数据可在 https://github.com/TrickyGo/MoonSplat 获取。
cs.CV / 69 / 2606.17950
Plug-and-Adapt: Multimodal Coreference Resolution at First Sight with a Pretrained Alignment Model
即插即用：基于预训练对齐模型的多模态共指解析初探
Abstract
Visual information helps resolve ambiguity in coreference resolution, leading to notable performance gains. However, existing Multi-modal Coreference Resolution (MCR) methods require training with (partially) annotated data from the target dataset before they can be applied, preventing their direct usability and raising concerns about generalization. While Vision-Language Large Models (VLLMs) with billions of parameters offer promising zero-shot capabilities, they remain largely inaccessible. Their massive size limits deployability, and many are only accessible through paid APIs. In this paper, we propose a plug-and-adapt method that strategically adapts a carefully pre-trained \emph{alignment model} for immediate use in MCR tasks, designed to eliminate the need for training on scarce benchmark datasets or relying on resource-intensive VLLMs. Specifically, we first pre-train a fine-grained alignment model between textual and visual contextual information using vision-language alignment datasets. We then repurpose the alignment model to MCR through similarity aggregation by fusing visual and categorical cues with evidence theory, thereby enhancing effectiveness. Experiments on the Coreference Image Narratives (CIN) benchmark dataset demonstrate the effectiveness of our method, achieving a 5.31\% and 2.12\% improvement in CoNLL F1 over SOTA dedicated methods and popular VLLMs, respectively. We further evaluate our method on a masked CIN dataset for robustness testing and on a specially constructed VCR-MCR dataset for generalization assessment, with results confirming both capabilities.
Chinese Translation
视觉信息有助于消除共指解析中的歧义，从而显著提升性能。然而，现有的多模态共指解析（MCR）方法在应用之前需要使用目标数据集中的（部分）标注数据进行训练，这限制了其直接使用性并引发了关于泛化能力的担忧。尽管具有数十亿参数的视觉-语言大型模型（VLLMs）展现出有希望的零样本能力，但它们仍然在很大程度上不可获取。其庞大的体积限制了部署的可能性，许多模型只能通过付费API访问。在本文中，我们提出了一种即插即用的方法，旨在战略性地调整一个经过精心预训练的对齐模型，以便在MCR任务中立即使用，消除了对稀缺基准数据集进行训练或依赖资源密集型VLLMs的需求。具体而言，我们首先使用视觉-语言对齐数据集预训练一个细粒度的文本与视觉上下文信息之间的对齐模型。然后，我们通过融合视觉和类别线索与证据理论进行相似性聚合，将对齐模型重新用于MCR，从而增强其有效性。在Coreference Image Narratives（CIN）基准数据集上的实验表明我们的方法有效，相较于最先进的专用方法和流行的VLLMs，CoNLL F1分别提高了5.31%和2.12%。我们还在一个掩蔽的CIN数据集上对我们的方法进行了稳健性测试，并在一个专门构建的VCR-MCR数据集上进行了泛化评估，结果证实了这两种能力。
cs.CV / 70 / 2606.17953
MLLMs Get It Right, Then Get It Wrong: Tracing and Correcting Late-Layer Textual Bias
多模态大型语言模型的正确与错误：追踪和纠正晚层文本偏见
Abstract
When vision contradicts text, multimodal large language models (MLLMs) consistently favor text, even when images provide clear evidence otherwise. This bias poses risks for applications requiring visual grounding, yet its cause remains unclear. In this paper, we uncover a surprising finding: models often get it right initially, forming correct vision-based predictions in their intermediate layers, before changing their minds and favoring text in the final output. We call this "late-layer textual override". The visual information is encoded, it simply does not survive to the output. More intriguingly, we find that how predictions change reveals whether they're correct: 85% of failures shift toward text, while 89% of successes shift toward vision. This directional signature enables a simple but powerful intervention: when we detect a confident visual prediction being suppressed, we restore it. We propose CALRD (Conflict-Aware Layer Reference Decoding), a training-free method that recovers overridden predictions at inference time. Experiments across five MLLMs of varying architectures demonstrate up to 9.4% absolute improvements on conflict benchmarks while largely preserving standard performance, without training or external knowledge. It recovers what the model already knew but failed to preserve.
Chinese Translation
当视觉与文本相矛盾时，多模态大型语言模型（MLLMs）始终偏向文本，即使图像提供了明确的相反证据。这种偏见对需要视觉基础的应用构成风险，但其原因仍不明确。在本文中，我们揭示了一个令人惊讶的发现：模型在初始阶段通常是正确的，在其中间层形成基于视觉的正确预测，然后在最终输出时改变主意，偏向文本。我们称之为“晚层文本覆盖”。视觉信息被编码，但并未保留到输出中。更有趣的是，我们发现预测变化的方式揭示了它们是否正确：85%的失败预测倾向于文本，而89%的成功预测倾向于视觉。这种方向性特征使得一种简单但强大的干预成为可能：当我们检测到一个自信的视觉预测被抑制时，我们将其恢复。我们提出了CALRD（冲突感知层参考解码），这是一种无训练的方法，可以在推理时恢复被覆盖的预测。在五种不同架构的MLLMs上的实验表明，在冲突基准测试中实现了高达9.4%的绝对改进，同时在很大程度上保持了标准性能，无需训练或外部知识。它恢复了模型已经知道但未能保留的信息。
cs.CV / 71 / 2606.17958
Beyond Visual Cues: CoT-Enhanced Reasoning for Semi-supervised Medical Image Segmentation
超越视觉线索：基于链式思维增强的半监督医学图像分割推理
Abstract
Semi-supervised medical image segmentation has emerged as a dominant research problem in medical image analysis, mitigating annotation scarcity by leveraging consistency regularization on unlabeled data. However, existing approaches operate predominantly via visual pattern matching, relying heavily on pixel-level similarities. This visual-centric dependency often falters in clinical scenarios characterized by the visual-semantic mismatch, where visually similar lesions warrant distinct diagnostic conclusions, thus failing to capture the underlying diagnostic logic used by experts. To address this, we move beyond visual cues and propose CERS (CoT-Enhanced Reasoning Segmentation), a framework that integrates Chain-of-Thought (CoT) reasoning to distinguish pathologically distinct cases. Specifically, we construct a knowledge pool enriched with linguistic reasoning descriptions generated by large language models (LLMs). A semantic-aware reference selection strategy is introduced to identify historical evidence, filtering candidates first by morphology, and then refining them via CoT consistency to eliminate hard negatives. Furthermore, a multi-scale coordinate attention module (MCAM) is designed to effectively fuse this reasoning-derived context into the decoding process. Extensive experiments demonstrate the superiority of CERS against state-of-the-art approaches, particularly in resolving boundary ambiguities and semantic inconsistencies. The code is available at https://github.com/cymasuna/CERS.
Chinese Translation
半监督医学图像分割已成为医学图像分析中的一个主要研究问题，通过对未标记数据利用一致性正则化来缓解标注稀缺性。然而，现有方法主要依赖于视觉模式匹配，过于依赖像素级相似性。这种以视觉为中心的依赖在临床场景中常常会出现问题，尤其是在视觉与语义不匹配的情况下，视觉上相似的病变可能会导致截然不同的诊断结论，从而未能捕捉专家所使用的潜在诊断逻辑。为了解决这一问题，我们超越视觉线索，提出了CERS（基于链式思维增强的分割）框架，该框架整合了链式思维（Chain-of-Thought, CoT）推理以区分病理上不同的案例。具体而言，我们构建了一个知识库，丰富了由大型语言模型（LLMs）生成的语言推理描述。我们引入了一种语义感知的参考选择策略，以识别历史证据，首先通过形态学过滤候选项，然后通过CoT一致性进一步精炼，以消除难以分类的负样本。此外，我们设计了一个多尺度坐标注意力模块（MCAM），有效地将这种推理衍生的上下文融合到解码过程中。大量实验表明，CERS在解决边界模糊和语义不一致方面优于最先进的方法。代码可在 https://github.com/cymasuna/CERS 获取。
cs.CV / 72 / 2606.17961
Robustness of Similarity-based Positional Encoding Under Rotations: Theoretical Analysis and Experimental Validation
旋转下基于相似性的位置信息编码的鲁棒性：理论分析与实验验证
Abstract
Positional encoding is a fundamental component of Transformer architectures, as it injects information about the spatial or sequential arrangement of inputs. Among recent alternatives to standard absolute and sinusoidal encodings, similarity-based positional encoding (simPE) has emerged as a flexible framework for representing positional structure through pairwise relations. simPE was originally designed for medical imaging applications, where geometric robustness is especially relevant: small rotations naturally arise during image acquisition, induced by imaging instruments, patient positioning, or slight acquisition misalignments. Despite its empirical promise, the theoretical behavior of simPE under geometric perturbations has not been fully characterized. In this paper, we study the robustness of simPE with respect to rotations, combining formal theoretical analysis with experimental validation. We first show that simPE is generally not rotation-invariant. We then prove that, under mild Lipschitz assumptions on the elementary components, simPE is stable under rotational perturbations and derive explicit perturbation bounds in Frobenius norm. We validate these findings experimentally on four controlled datasets--a synthetic Arrow dataset, a synthetic Shapes dataset (four geometric shape categories), a synthetic Digits dataset, and a benchmark image classification dataset (FashionMNIST)--in which training and validation images are kept in a fixed canonical orientation while test images are subjected to increasing rotation angles. Across all datasets, simPE consistently outperforms standard learned positional encoding in terms of accuracy, F1 score, precision, and recall under rotation, particularly in the small-to-moderate angle regime, corroborating the theoretical stability guarantees.
Chinese Translation
位置信息编码是Transformer架构的一个基本组成部分，因为它注入了关于输入的空间或序列排列的信息。在最近的标准绝对和正弦编码的替代方案中，基于相似性的位置信息编码（similarity-based positional encoding，simPE）作为一种灵活的框架，通过成对关系来表示位置信息结构。simPE最初是为医学成像应用设计的，在这些应用中，几何鲁棒性尤为重要：在图像采集过程中，由于成像仪器、患者定位或轻微的采集错位，通常会出现小的旋转。尽管其在经验上的前景良好，但simPE在几何扰动下的理论行为尚未得到充分表征。在本文中，我们研究了simPE在旋转方面的鲁棒性，结合了正式的理论分析与实验验证。我们首先展示了simPE通常不是旋转不变的。然后，我们证明在对基本组件施加温和的Lipschitz假设下，simPE在旋转扰动下是稳定的，并推导出Frobenius范数下的显式扰动界限。我们在四个受控数据集上对这些发现进行了实验验证——一个合成的箭头数据集、一个合成的形状数据集（四种几何形状类别）、一个合成的数字数据集，以及一个基准图像分类数据集（FashionMNIST）——其中训练和验证图像保持在固定的标准方向，而测试图像则经历逐渐增加的旋转角度。在所有数据集中，simPE在旋转下的准确性、F1分数、精确度和召回率方面始终优于标准学习的位置信息编码，特别是在小到中等角度范围内，证实了理论稳定性的保证。
cs.CV / 73 / 2606.17966
Reload-Mamba: Hierarchical Anti-Dilution State-Space Modeling for Multi-Class Semantic Segmentation
Reload-Mamba：用于多类语义分割的分层抗稀释状态空间建模
Abstract
Mamba-based state space models offer linear-time long-range modeling for high-resolution dense prediction, but sequential state-space propagation can attenuate boundary-sensitive and detail-sensitive responses that are critical in multi-class semantic segmentation. We propose Reload-Mamba, a semantic segmentation framework that addresses this propagation-induced response dilution through three segmentation-specific designs: (i) a boundary-supervised local detail prior that is explicitly trained with ground-truth boundary masks to identify regions requiring response restoration; (ii) a class-uncertainty-aware Reload Gate that incorporates per-pixel class entropy from a pre-reload auxiliary head as an additional gating signal, a formulation that is informative only under multi-class dense prediction; and (iii) a hierarchical multi-level Reload mechanism that applies anti-dilution refinement at three decoder levels and fuses the restored representations top-down. Built upon a ConvNeXt-Tiny encoder with a multi-scale decoder and four-directional Mamba scanning with pixel-wise directional attention, Reload-Mamba achieves 47.9% single-scale (48.9% multi-scale) mIoU on ADE20K and 83.2% single-scale mIoU on Cityscapes. With ResNet-101 + COCO pre-training under the standard DeepLab-style protocol, Reload-Mamba reaches 87.8% mIoU on PASCAL VOC 2012 val. Controlled ablations show that each of the three segmentation-specific designs contributes beyond a direct port of the prior anti-dilution architecture proposed for binarization, cumulatively improving over the direct-port baseline by +2.2 mIoU on ADE20K.
Chinese Translation
基于 Mamba 的状态空间模型为高分辨率密集预测提供了线性时间的长程建模，但顺序状态空间传播可能会削弱在多类语义分割中至关重要的边界敏感和细节敏感响应。我们提出了 Reload-Mamba，这是一种语义分割框架，通过三种特定于分割的设计来解决这种传播引起的响应稀释问题：(i) 一个边界监督的局部细节先验，明确通过真实边界掩膜进行训练，以识别需要恢复响应的区域；(ii) 一个类不确定性感知的 Reload Gate，结合来自预重载辅助头的每像素类熵作为额外的门控信号，这种公式在多类密集预测下是信息丰富的；(iii) 一个分层多级 Reload 机制，在三个解码器级别应用抗稀释细化，并自上而下融合恢复的表示。基于带有多尺度解码器的 ConvNeXt-Tiny 编码器和具有像素级方向注意力的四方向 Mamba 扫描，Reload-Mamba 在 ADE20K 上实现了 47.9% 的单尺度 mIoU（48.9% 的多尺度 mIoU），在 Cityscapes 上实现了 83.2% 的单尺度 mIoU。在标准 DeepLab 风格协议下，使用 ResNet-101 + COCO 预训练，Reload-Mamba 在 PASCAL VOC 2012 验证集上达到了 87.8% 的 mIoU。控制消融实验表明，这三种特定于分割的设计均对超越直接移植先前为二值化提出的抗稀释架构作出了贡献，累计在 ADE20K 上提高了 +2.2 mIoU。
cs.CV / 74 / 2606.17972
SegDINO: Introducing Multi-Scale Structure into DINO for Efficient Medical Image Segmentation
SegDINO：将多尺度结构引入DINO以实现高效的医学图像分割
Abstract
Self-supervised DINO models provide strong transferable visual representations, yet applying them directly to image segmentation remains challenging. Existing approaches commonly rely on heavy decoders with complex upsampling, introducing substantial parameter and computational overhead. We observe that introducing scale into DINO features is far more critical than increasing decoder capacity. In this work, we present SegDINO, an efficient segmentation framework that integrates a DINOv3 backbone with lightweight scale modeling. SegDINO introduces Token Pyramid Adaptation (TPA) to reorganize intermediate DINO features into a pseudo multi-scale hierarchy, and Scale-Aware Decoding (SAD) for efficient intra-scale refinement and top-down multi-scale propagation. We further curate PanCT, a new CT dataset containing 284 patients with expert-annotated pancreatic tumors, to assess SegDINO's ability to handle difficult small-lesion cases. Extensive experiments on PanCT and three public benchmarks demonstrate that SegDINO achieves state-of-the-art results with high efficiency. The code is available at https://github.com/script-Yang/segdino_v2.
Chinese Translation
自监督的DINO模型提供了强大的可迁移视觉表征，但直接将其应用于图像分割仍然面临挑战。现有方法通常依赖于复杂的上采样重构重的解码器，导致显著的参数和计算开销。我们观察到，将尺度引入DINO特征比单纯增加解码器容量更为关键。在本研究中，我们提出了SegDINO，一个高效的分割框架，结合了DINOv3主干网络与轻量级的尺度建模。SegDINO引入了Token Pyramid Adaptation (TPA)来将中间DINO特征重组为伪多尺度层次结构，并采用Scale-Aware Decoding (SAD)进行高效的尺度内细化和自上而下的多尺度传播。我们进一步整理了PanCT，一个包含284名患者及专家标注的胰腺肿瘤的新CT数据集，以评估SegDINO处理困难小病灶案例的能力。在PanCT和三个公共基准上的广泛实验表明，SegDINO以高效的方式达到了最先进的结果。代码可在 https://github.com/script-Yang/segdino_v2 获取。
cs.CV / 75 / 2606.17985
Gaussian Light Field Splatting: A Physical Prior-Driven Vision Transformer for Unsupervised Low-Light Image Enhancement
高斯光场溅射：一种基于物理先验驱动的视觉变换器用于无监督低光照图像增强
Abstract
Existing unsupervised low-light image enhancement methods often encounter local exposure imbalance and color distortion under complex non-uniform illumination. In addition, most Vision Transformers lack an explicit mechanism for modeling the physical priors of illumination degradation. To address these limitations, we propose GLFS, a Gaussian light field splatting-based Vision Transformer that integrates continuous physical illumination modeling from Gaussian splatting into the Transformer architecture. In GLFS, scene illumination is represented by a superposition of anisotropic Gaussian basis functions. Physics-guided biases are introduced into self-attention to adaptively infer a spatial gain field, enabling accurate and uniform restoration under complex illumination. To reduce color bias and structural degradation during enhancement, a color-vector angular loss and a luminance-edge loss are further developed. These losses enforce hue consistency and improve the structural fidelity of local details. Extensive ablation studies and quantitative evaluations show that GLFS provides clear advantages in illumination correction and detail preservation. It achieves state-of-the-art performance and offers a new representation paradigm for low-light image enhancement.
Chinese Translation
现有的无监督低光照图像增强方法在复杂的非均匀照明下常常面临局部曝光不平衡和颜色失真的问题。此外，大多数视觉变换器缺乏明确的机制来建模照明退化的物理先验。为了解决这些局限性，我们提出了GLFS，一种基于高斯光场溅射的视觉变换器，将来自高斯溅射的连续物理照明建模集成到变换器架构中。在GLFS中，场景照明通过各向异性高斯基函数的叠加来表示。物理引导的偏差被引入自注意力机制，以自适应地推断空间增益场，从而在复杂照明下实现准确和均匀的恢复。为了减少增强过程中的颜色偏差和结构退化，我们进一步开发了颜色向量角度损失和亮度边缘损失。这些损失强制保持色调一致性，并改善局部细节的结构保真度。大量的消融研究和定量评估表明，GLFS在照明校正和细节保留方面具有明显优势。它实现了最先进的性能，并为低光照图像增强提供了一种新的表示范式。
cs.CV / 76 / 2606.17989
Recover Semantics First, Generate Better: Improved Latent Modeling for 3D MRI Reconstruction and Cross-Contrast Synthesis
优先恢复语义，生成更佳：改进的潜在建模用于3D MRI重建和跨对比合成
Abstract
Multi-contrast magnetic resonance imaging (MRI) provides complementary information for clinical diagnosis. However, acquiring all MRI sequences is often time-consuming and costly. Recent generative models perform cross-contrast synthesis to address this issue by inferring absent contrasts from the available ones. Nevertheless, synthesizing 3D MRI presents significant challenges. Due to the massive volume sizes, operating directly in the pixel space is computationally prohibitive; therefore, a common approach is to first compress the 3D volumes into a latent space and subsequently train generative models in that space. We observe that existing compression architectures face several critical issues: they under-preserve long-range anatomical coherence, discard clinically meaningful semantics, and rely on optimization objectives that lead to over-smoothed reconstructions. Ultimately, these shortcomings compromise the performance of subsequent generative models. In this work, we propose a semantics-first latent modeling framework for 3D MRI reconstruction and cross-contrast synthesis. Specifically, we introduce a Latent Harmonization Encoder (LHE) to capture global anatomical dependencies, ensuring coherent volumetric representations. To mitigate semantic degradation during latent compression, we further design a Semantic Recovery Block (SRB) that injects high-level priors from a self-supervised semantic teacher, enhancing contrast-aware separability in the latent space. Additionally, we propose an Anatomy-aware Frequency Loss (AFL) to adaptively preserve diagnostically relevant high-frequency structures. Extensive experiments on two public multi-contrast MRI datasets demonstrate consistent improvements in reconstruction fidelity and cross-contrast synthesis quality. Our code is available at https://github.com/script-Yang/RSF.
Chinese Translation
多对比磁共振成像（MRI）为临床诊断提供了互补信息。然而，获取所有MRI序列通常耗时且成本高昂。近期的生成模型通过从可用对比中推断缺失的对比，进行跨对比合成以解决这一问题。然而，合成3D MRI面临重大挑战。由于体积庞大，直接在像素空间操作在计算上是不可行的；因此，常见的方法是首先将3D体积压缩到潜在空间，然后在该空间中训练生成模型。我们观察到现有的压缩架构面临几个关键问题：它们未能充分保持长距离的解剖一致性，丢弃了临床上有意义的语义，并依赖于导致过度平滑重建的优化目标。最终，这些缺陷妨碍了后续生成模型的性能。在本研究中，我们提出了一种优先考虑语义的潜在建模框架，用于3D MRI重建和跨对比合成。具体而言，我们引入了一种潜在协调编码器（Latent Harmonization Encoder, LHE），以捕捉全局解剖依赖关系，确保一致的体积表示。为了减轻潜在压缩过程中的语义退化，我们进一步设计了一种语义恢复模块（Semantic Recovery Block, SRB），该模块从自监督语义教师中注入高级先验，增强潜在空间中的对比感知可分离性。此外，我们提出了一种解剖感知频率损失（Anatomy-aware Frequency Loss, AFL），以自适应地保留诊断相关的高频结构。在两个公共多对比MRI数据集上的大量实验表明，重建保真度和跨对比合成质量均有一致的改善。我们的代码可在 https://github.com/script-Yang/RSF 获取。
cs.CV / 77 / 2606.17998
AIGS-Net: Compact Illumination Field Modeling via 2D Gaussian Splatting for Fast Low-Light Image Enhancement
AIGS-Net：通过二维高斯溅射进行紧凑的光照场建模以实现快速低光照图像增强
Abstract
Existing low-light image enhancement methods often face a bottleneck between the representation capacity of illumination-field modeling and computational complexity. To address this issue, this paper proposes an Adaptive Illumination Gaussian Splatting Network (AIGS-Net), an ultra-lightweight architecture for fast low-light enhancement. Unlike conventional static priors, AIGS-Net constructs an input-adaptive 2D Gaussian Splatting illumination field. The opacity of Gaussian basis functions is dynamically modulated by relative luminance statistics of the input image, and spatially varying illumination compensation is rendered through ordered alpha compositing. To guide adaptive illumination compensation efficiently, a zero-parameter nonlinear multiscale contextual encoding module is introduced to extract low-frequency structures and local contrast cues without additional convolutional weights. To suppress noise amplification and sensor-induced color bias, AIGS-Net integrates noise-mask estimation, locked single-channel Gamma mapping, cross-channel consistency regularization, and target color-alignment constraints. Experiments on LOL and LSRW benchmarks show that AIGS-Net improves detail recovery and color fidelity while requiring only approximately 40 learnable parameters, achieving an effective trade-off between enhancement quality and extreme inference efficiency.
Chinese Translation
现有的低光照图像增强方法常常面临光照场建模的表示能力与计算复杂性之间的瓶颈。为了解决这一问题，本文提出了一种自适应光照高斯溅射网络（AIGS-Net），这是一种超轻量级架构，用于快速低光照增强。与传统的静态先验不同，AIGS-Net 构建了一个输入自适应的二维高斯溅射光照场。高斯基函数的透明度通过输入图像的相对亮度统计动态调节，并通过有序的 alpha 合成实现空间变化的光照补偿。为了有效引导自适应光照补偿，本文引入了一个零参数非线性多尺度上下文编码模块，以提取低频结构和局部对比度线索，而无需额外的卷积权重。为了抑制噪声放大和传感器引起的颜色偏差，AIGS-Net 集成了噪声掩模估计、锁定单通道伽马映射、跨通道一致性正则化和目标颜色对齐约束。在 LOL 和 LSRW 基准测试中的实验表明，AIGS-Net 在细节恢复和颜色保真度方面有所提升，同时仅需约 40 个可学习参数，实现了增强质量与极高推理效率之间的有效权衡。
cs.CV / 78 / 2606.18008
PhaseWin: An Efficient Search Algorithm for Faithful Visual Attribution
PhaseWin：一种高效的忠实视觉归因搜索算法
Abstract
Visual attribution is a fundamental tool for interpreting modern vision and vision-language models, particularly when their decisions must be inspected, diagnosed, or audited. Its goal is to explain how a model's decision depends on local regions of the visual input, typically by assigning an importance ordering over candidate image regions. Given an image partitioned into $n$ regions, faithful attribution can be cast as an ordered subset-search problem, in which progressively inserting the selected regions should recover the target model response as early as possible. Exhaustive search over region subsets incurs exponential cost, while the widely used greedy search still requires a quadratic number of model evaluations, because every selection step rescores all remaining candidates. We propose PhaseWin, an efficient subset-search algorithm for faithful visual attribution. PhaseWin reorganizes greedy region selection into a phased window-search procedure: rather than re-evaluating the full candidate set at every step, it alternates between global candidate screening, adaptive pruning, and localized window refinement, while preserving the essential region-ranking behavior of greedy search. We analyze PhaseWin under monotone evidence-accumulation conditions and show that, under feature-level structural assumptions, it attains controllable linear evaluation complexity together with near-greedy faithfulness guarantees. Extensive experiments on image classification, object detection, visual grounding, and image captioning show that, among all compared attribution methods, PhaseWin reaches high faithfulness with the fewest forward passes, empirically realizing the predicted reduction from $O(n^2)$ to $O(n)$. The code is available at https://github.com/Qihuai27/phasewin-va.
Chinese Translation
视觉归因是解释现代视觉和视觉-语言模型的基本工具，特别是在需要检查、诊断或审计其决策时。其目标是解释模型的决策如何依赖于视觉输入的局部区域，通常通过对候选图像区域分配重要性排序来实现。给定一个被划分为 $n$ 个区域的图像，忠实的归因可以被视为一个有序子集搜索问题，其中逐步插入所选区域应尽早恢复目标模型响应。对区域子集的穷举搜索会产生指数成本，而广泛使用的贪婪搜索仍然需要二次数量的模型评估，因为每一步选择都需要重新评分所有剩余候选项。我们提出了 PhaseWin，一种高效的忠实视觉归因子集搜索算法。PhaseWin 将贪婪区域选择重新组织为一个分阶段窗口搜索过程：它不在每一步重新评估完整的候选集，而是交替进行全局候选筛选、自适应修剪和局部窗口细化，同时保持贪婪搜索的基本区域排序行为。我们在单调证据累积条件下分析了 PhaseWin，并展示在特征级结构假设下，它实现了可控的线性评估复杂度，并提供接近贪婪的忠实性保证。在图像分类、目标检测、视觉定位和图像字幕生成等方面的广泛实验表明，在所有比较的归因方法中，PhaseWin 以最少的前向传递实现了高忠实性，经验上将复杂度从 $O(n^2)$ 降低到 $O(n)$。代码可在 https://github.com/Qihuai27/phasewin-va 获取。
cs.CV / 79 / 2606.18063
When LLMs Analyze Scars: From Images to Clinically-Meaningful Features
当大型语言模型分析疤痕：从图像到临床意义特征
Abstract
Medical image classification faces a fundamental dilemma: while deep learning models achieve remarkable performance at scale, real-world clinical scenarios often suffer from severe data scarcity due to annotation costs, privacy constraints, and disease rarity. This challenge is particularly pronounced in pathological scar classification, where differentiating keloids from hypertrophic scars requires subtle expert knowledge and labeled images are extremely limited. We propose a novel paradigm that repositions large language models (LLMs) as knowledge-driven feature engineers rather than end-to-end classifiers. We call this framework ScaFE (Scar Feature Engineering). Our key insight is that LLMs encode rich medical knowledge that can be externalized as executable feature extraction code, enabling the transformation of high-dimensional images into low-dimensional, clinically interpretable representations. Specifically, we prompt an LLM with established scar assessment criteria to generate deterministic Python code that extracts features aligned with clinical scoring systems such as the Vancouver Scar Scale. Our approach offers three key advantages: (1) data efficiency, achieving robust performance with limited training samples by decoupling knowledge acquisition from statistical learning; (2) privacy preservation, as raw images are processed locally without exposure to external LLMs; and (3) interpretability, through explicit features grounded in clinical reasoning. Extensive experiments on scar classification demonstrate that our method consistently outperforms end-to-end deep learning baselines or using LLMs as black-box classifiers under limited data conditions, establishing a promising direction for integrating LLMs into data-efficient and clinically transparent medical AI systems.
Chinese Translation
医学图像分类面临一个根本性困境：尽管深度学习模型在大规模数据上取得了显著的性能，但现实世界的临床场景往往由于标注成本、隐私限制和疾病稀有性而遭遇严重的数据匮乏。这个挑战在病理性疤痕分类中尤为明显，因为区分瘢痕疙瘩和肥厚性疤痕需要细致的专家知识，而标注图像极为有限。我们提出了一种新颖的范式，将大型语言模型（LLMs）重新定位为知识驱动的特征工程师，而非端到端分类器。我们将这一框架称为ScaFE（Scar Feature Engineering）。我们的关键见解是，LLMs编码了丰富的医学知识，这些知识可以外化为可执行的特征提取代码，从而将高维图像转化为低维、临床可解释的表示。具体而言，我们通过既定的疤痕评估标准提示LLM生成确定性的Python代码，以提取与临床评分系统（如温哥华疤痕评分量表）对齐的特征。我们的方法提供了三个关键优势：（1）数据效率，通过将知识获取与统计学习解耦，在有限的训练样本下实现稳健性能；（2）隐私保护，原始图像在本地处理，无需暴露于外部LLMs；（3）可解释性，通过基于临床推理的显性特征。对疤痕分类的广泛实验表明，在有限数据条件下，我们的方法始终优于端到端深度学习基线或将LLMs作为黑箱分类器，为将LLMs整合到数据高效且临床透明的医学人工智能系统中开辟了有希望的方向。
cs.CV / 80 / 2606.18115
HLS-GPT: A Generative Pretrained Transformer (GPT) for Continental-Scale NASA Harmonized Landsat and Sentinel-2 (HLS) Reflectance Reconstruction Across All Bands on Arbitrary Dates
HLS-GPT：一种用于大陆尺度NASA协调的Landsat和Sentinel-2（HLS）反射率重建的生成预训练变换器（GPT），适用于任意日期的所有波段
Abstract
Recent deep learning methods for Landsat and Sentinel-2 reflectance time series reconstruction remain limited by restricted spectral coverage, limited geographic scalability, or patch-based designs with short temporal contexts. We present HLS-GPT, a large-scale generative pretrained Transformer model for reconstructing NASA Harmonized Landsat Sentinel-2 30 m surface reflectance for all bands, any date, and any pixel location. HLS-GPT uses a hierarchical Transformer architecture to handle the different spectral band configurations of Landsat and Sentinel-2 and operates on single-pixel 12-month time series. To capture geographic and seasonal variability, the model was trained with nine years of HLS time series from more than 0.25 million training pixels across the conterminous United States. A random cropping and masking strategy extracts 12-month periods with varying start dates across epochs, masks 50% of valid observations, and trains the model to reconstruct the masked reflectance values from the remaining observations. Evaluation using more than 62,000 independent test pixels shows robust reconstruction under diverse land surface conditions, including complex crop phenology and sparse, irregular observations. Leave-one-observation-out evaluation achieved reconstruction RMSE below 0.026 for all HLS spectral bands, with relative RMSE below 35% for visible bands and below 13% for other bands. Red-edge band errors were comparable to red and near-infrared errors despite the absence of red-edge bands on Landsat. Sensitivity analyses that randomly masked 10% to 90% of test observations showed only modest degradation when 10% to 50% of observations were masked, with all-band RMSE below 0.028. Image reconstruction over nine independent 109 by 109 km CONUS HLS tiles further demonstrates that HLS-GPT outperforms two conventional methods and the NASA-IBM Prithvi model.
Chinese Translation
近期针对Landsat和Sentinel-2反射率时间序列重建的深度学习方法受到限制，主要体现在光谱覆盖有限、地理可扩展性不足或基于小块设计且时间上下文较短等方面。我们提出了HLS-GPT，这是一种大规模生成预训练变换器模型，用于重建NASA协调的Landsat Sentinel-2 30米表面反射率，适用于所有波段、任意日期和任意像素位置。HLS-GPT采用层次化的变换器架构，以处理Landsat和Sentinel-2的不同光谱波段配置，并在单像素的12个月时间序列上运行。为了捕捉地理和季节性变异，该模型使用来自连续美国超过25万个训练像素的九年HLS时间序列进行训练。随机裁剪和掩蔽策略提取具有不同起始日期的12个月时间段，掩蔽50%的有效观测值，并训练模型从剩余观测值中重建被掩蔽的反射率值。使用超过62,000个独立测试像素的评估显示，在复杂的土地表面条件下，包括复杂的作物物候和稀疏、不规则的观测，重建效果稳健。在留一观测评估中，所有HLS光谱波段的重建均方根误差（RMSE）低于0.026，且可见波段的相对RMSE低于35%，其他波段低于13%。尽管Landsat上缺少红边波段，但红边波段的误差与红色和近红外波段的误差相当。敏感性分析随机掩蔽10%至90%的测试观测值显示，当掩蔽10%至50%的观测值时，仅出现适度的性能下降，所有波段的RMSE低于0.028。对九个独立的109公里乘109公里的CONUS HLS瓦片进行图像重建进一步表明，HLS-GPT优于两种传统方法和NASA-IBM Prithvi模型。
cs.CV / 81 / 2606.18123
Predicting Immune Biomarkers with MultiModal Mixture-of-Expert Pathology Foundation Models Empowers Precision Oncology
利用多模态专家混合模型预测免疫生物标志物，推动精准肿瘤学的发展
Abstract
Predicting immune biomarkers associated with the tumor immune microenvironment (TIME) is critical for advancing precision oncology, yet existing approaches are largely limited to single image modalities and suffer from insufficient resolution and incomplete utilization of complementary clinical and biological information. Here we introduce MixTIME, a multimodal foundation model that leverages a mixture-of-experts (MoE) architecture to integrate pathology foundation models trained across distinct modalities: image only (UNIv2), image text (CONCHv1.5), and image transcriptomic (STPath) representations for pixel-level and slide-level prediction of multiplex immunofluorescence (mIF) protein expression from hematoxylin and eosin (HE) whole-slide images. MixTIME employs a learnable router to dynamically weight expert contributions and is trained with a distribution- and tendency-aware loss function. Benchmarked on two datasets of different scales, MixTIME achieves state-of-the-art performance across 17 protein markers as measured by correlation metrics. The predicted mIF profiles substantially enhance downstream tasks, including spatial domain identification, survival prediction, and AI-assisted pathology report generation validated by expert pathologists from multiple institutes across the world. Furthermore, MixTIME enables longitudinal tracking of protein expression dynamics across clinical time points and reveals protein gene interaction patterns linked to drug resistance and immune suppression in tumor microenvironments. Collectively, MixTIME provides a scalable framework for multimodal biomarker discovery and clinical translation in computational pathology.
Chinese Translation
预测与肿瘤免疫微环境（TIME）相关的免疫生物标志物对于推进精准肿瘤学至关重要，但现有方法主要局限于单一图像模态，且在分辨率不足和未充分利用互补的临床和生物信息方面存在缺陷。在此，我们介绍了MixTIME，这是一种多模态基础模型，利用专家混合（MoE）架构整合在不同模态下训练的病理基础模型：仅图像（UNIv2）、图像文本（CONCHv1.5）和图像转录组（STPath）表示，以实现从苏木精-伊红（HE）全切片图像中对多重免疫荧光（mIF）蛋白表达的像素级和切片级预测。MixTIME采用可学习的路由器动态加权专家贡献，并使用一种考虑分布和趋势的损失函数进行训练。在两个不同规模的数据集上进行基准测试，MixTIME在17个蛋白标志物的相关性指标上实现了最先进的性能。预测的mIF特征显著增强了下游任务，包括空间域识别、生存预测和由来自全球多个机构的专家病理学家验证的AI辅助病理报告生成。此外，MixTIME还能够在临床时间点上进行蛋白表达动态的纵向跟踪，并揭示与药物耐受性和肿瘤微环境中的免疫抑制相关的蛋白基因相互作用模式。总体而言，MixTIME为计算病理学中的多模态生物标志物发现和临床转化提供了一个可扩展的框架。
cs.CV / 82 / 2606.18153
Neural Tree Reconstruction for the Open Forest Observatory
开放森林观测站的神经树木重建
Abstract
The Open Forest Observatory (OFO) is a collaboration across universities and other partners to make low-cost forest mapping accessible to ecologists, land managers, and the general public. The OFO is building both a database of geospatial forest data as well as open-source methods and tools for forest mapping by uncrewed aerial vehicle. Such data are useful for a variety of climate applications including prioritizing reforestation efforts, informing wildfire hazard reduction, and monitoring carbon sequestration. In the current iteration of the OFO's forest map database, 3D tree maps are created using classical structure-from-motion techniques. This approach is prone to artifacts, lacks detail, and has particular difficulty on the forest floor where the input data (overhead imagery) has limited visibility. These reconstruction errors can potentially propagate to the downstream scientific tasks (e.g. a wildfire simulation.) Advances in 3D reconstruction, including methods like Neural Radiance Fields (NeRF), produce higher quality results that are more robust to sparse views and support data-driven priors. We explore ways to incorporate NeRFs into the OFO dataset, outline future work to support even more state-of-the-art 3D vision models, and describe the importance of high-quality 3D reconstructions for forestry applications.
Chinese Translation
开放森林观测站（OFO）是一个跨大学和其他合作伙伴的合作项目，旨在使低成本的森林制图对生态学家、土地管理者和公众可及。OFO正在建立一个地理空间森林数据的数据库，以及用于无人机森林制图的开源方法和工具。这些数据对于多种气候应用非常有用，包括优先考虑再造林工作、提供野火危险减少的信息，以及监测碳封存。在OFO森林地图数据库的当前版本中，使用经典的运动重建技术生成3D树木地图。这种方法容易产生伪影，缺乏细节，并且在森林地面上尤其困难，因为输入数据（上方影像）的可见性有限。这些重建误差可能会传播到下游科学任务（例如野火模拟）。3D重建的进展，包括神经辐射场（Neural Radiance Fields, NeRF）等方法，产生了更高质量的结果，对稀疏视图更具鲁棒性，并支持数据驱动的先验。我们探讨了将NeRF纳入OFO数据集的方法，概述了支持更先进的3D视觉模型的未来工作，并描述了高质量3D重建在林业应用中的重要性。
cs.CV / 83 / 2606.18156
ReAge3D: Re-Aging 3D Faces with View Consistency
ReAge3D：具有视图一致性的3D面部再老化
Abstract
We present a novel framework for realistic and controllable 3D face re-aging which produces highly detailed, identity-preserving results. Existing 3D editing methods, while effective for coarse semantic changes, are not well suited for re-aging, as even small inconsistencies across re-aged 2D views can lead to over-smoothing of subtle but perceptually important age-related details. To address this challenge, we first introduce a 2D diffusion-based re-aging model, DiffReaging, trained on synthetically generated image pairs. We further propose a center-out editing propagation strategy that leverages this re-aging model to reconstruct multi-view-consistent re-aged images. Specifically, starting from a re-aged frontal pivot view, we reconstruct the remaining views through warping and our proposed Masked-DiffReaging process. By injecting existing content at every step of the diffusion process, Masked-DiffReaging ensures that the reconstructed regions remain coherent with existing pixels. The resulting consistent set of re-aged views supervises the optimization of the re-aged 3D representation. Our method outperforms existing 3D editing techniques both visually and quantitatively, enabling smooth, fine-grained control over age transformations in 3D face models.
Chinese Translation
我们提出了一种新颖的框架，用于实现逼真且可控的3D面部再老化，能够生成高度细致且保留身份的结果。现有的3D编辑方法虽然在粗略语义变化方面有效，但并不适合再老化，因为即使是再老化的2D视图之间的小不一致也可能导致细微但在感知上重要的年龄相关细节的过度平滑。为了解决这一挑战，我们首先引入了一种基于2D扩散的再老化模型DiffReaging，该模型是在合成生成的图像对上训练的。我们进一步提出了一种自中心向外的编辑传播策略，利用该再老化模型重建多视图一致的再老化图像。具体而言，从再老化的正面枢轴视图开始，我们通过变形和我们提出的Masked-DiffReaging过程重建其余视图。通过在扩散过程的每一步注入现有内容，Masked-DiffReaging确保重建区域与现有像素保持一致。最终得到的一致再老化视图集监督了再老化3D表示的优化。我们的方法在视觉和定量上均优于现有的3D编辑技术，使得在3D面部模型中对年龄变换的平滑、细致控制成为可能。
cs.CV / 84 / 2606.18180
EgoCS-400K: An Egocentric Gameplay Dataset for World Models
EgoCS-400K：一个用于世界模型的自我中心游戏数据集
Abstract
The shift from video generation to interactive world modeling places new demands on data: beyond captioned videos, world models require temporally aligned video-action-language trajectories grounded in the actions, camera motion, states, and events that drive future scene changes. However, such data is difficult to obtain at scale. Web video datasets offer broad visual coverage but lack executable actions and reliable states; robotic datasets provide action and state supervision but are costly and limited in scene diversity; and existing simulators often lack large-scale human-driven interaction trajectories. In this paper, we introduce EgoCS-400K, a large-scale replay-grounded egocentric Counter-Strike dataset for world models, built from public professional CS and CS2 match demos that preserve human gameplay trajectories and enable parsing, replaying, rendering, and temporal alignment. We extract player states, view directions, movements, keyboard/button inputs, view-angle changes, weapon usage, game events, and round-level context, and render clean first-person videos from the same trajectories. EgoCS-400K contains over 400,000 first-person videos and 10,000 hours of gameplay from more than 1,000 matches and 40,000 rounds, covering 13 maps and 10 player viewpoints per round. It supports a range of interactive visual modeling tasks, including action-conditioned future prediction, state- and event-aware scene rollout, replay-grounded captioning, and agent egocentric action understanding. By connecting visual observations with human actions, camera motion, game states, and events at scale, EgoCS-400K serves as a practical bridge between passive web videos, controllable game simulation, and costly real-world embodied data.
Chinese Translation
从视频生成到互动世界建模的转变对数据提出了新的要求：除了带有字幕的视频，世界模型还需要时间上对齐的视频-动作-语言轨迹，这些轨迹基于驱动未来场景变化的动作、相机运动、状态和事件。然而，这种数据在规模上难以获得。网络视频数据集提供了广泛的视觉覆盖，但缺乏可执行的动作和可靠的状态；机器人数据集提供了动作和状态的监督，但成本高且场景多样性有限；而现有的模拟器通常缺乏大规模的人驱动交互轨迹。在本文中，我们介绍了EgoCS-400K，这是一个大规模的重放基础自我中心《反恐精英》数据集，旨在用于世界模型，构建于公共专业CS和CS2比赛演示之上，保留了人类游戏轨迹，并支持解析、重放、渲染和时间对齐。我们提取了玩家状态、视角方向、移动、键盘/按钮输入、视角变化、武器使用、游戏事件和回合级上下文，并从相同轨迹渲染出干净的第一人称视频。EgoCS-400K包含超过400,000个第一人称视频和10,000小时的游戏时间，来自1,000多场比赛和40,000个回合，覆盖13张地图和每回合10个玩家视角。它支持一系列互动视觉建模任务，包括基于动作的未来预测、状态和事件感知的场景展开、重放基础的字幕生成，以及代理自我中心动作理解。通过在规模上将视觉观察与人类动作、相机运动、游戏状态和事件连接起来，EgoCS-400K成为了被动网络视频、可控游戏模拟和高成本真实世界体现数据之间的实用桥梁。
cs.CV / 85 / 2606.18231
Adaptive Volumetric Mechanical Property Fields Invariant to Resolution
自适应体积机械属性场与分辨率无关
Abstract
Accurate mechanical properties (or materials) Young's modulus ($E$), Poisson's ratio ($\nu$) and density ($\rho$) are essential for reliable physics simulation of digital worlds, but most 3D assets lack this information. We propose AdaVoMP, a method for predicting accurate dense spatially-varying ($E$, $\nu$, $\rho$) for input 3D objects across representations, improving the resolution, accuracy, and memory efficiency over the state-of-the-art. The foundation of our technique is a sparse and adaptive voxel structure SAV that efficiently represents both the input 3D shape and the material field output. We replace the fixed-voxel model of the most accurate prior method, VoMP, with a novel sparse transformer encoder-decoder model that learns to generate a unique SAV autoregressively for every input shape to represent its materials, achieving a resolution $16^3\times$ higher than prior art. Experiments show that AdaVoMP estimates more accurate volumetric properties, even with lesser test-time compute than all prior art. This allows us to convert high-resolution complex 3D objects into simulation-ready assets, resulting in realistic deformable simulations.
Chinese Translation
准确的机械属性（或材料）如杨氏模量（$E$）、泊松比（$
u$）和密度（$
ho$）对于数字世界的可靠物理仿真至关重要，但大多数三维资产缺乏这些信息。我们提出了AdaVoMP，一种用于预测输入三维物体的准确稠密空间变化的（$E$、$
u$、$
ho$）的方法，跨越不同表示，提高了分辨率、准确性和内存效率，超越了现有技术。我们技术的基础是稀疏和自适应的体素结构SAV，它有效地表示输入三维形状和材料场输出。我们用一种新颖的稀疏变换器编码器-解码器模型替代了最准确的先前方法VoMP的固定体素模型，该模型学习为每个输入形状自回归生成独特的SAV，以表示其材料，实现了比先前技术高出$16^3$倍的分辨率。实验表明，AdaVoMP在测试时间计算量更少的情况下，估计出更准确的体积属性。这使我们能够将高分辨率复杂三维物体转换为适合仿真的资产，从而实现逼真的可变形仿真。
cs.CV / 86 / 2606.18242
EventDrive: Event Cameras for Vision-Language Driving Intelligence
EventDrive：用于视觉-语言驾驶智能的事件相机
Abstract
Event cameras sense the world through asynchronous brightness changes with microsecond latency and high dynamic range, offering motion fidelity far beyond frame-based sensors and capturing temporal structure that conventional exposures often miss. These properties make events a powerful complement to RGB in autonomous driving, especially under blur, glare, and rapid motion, where frame-based perception can become unreliable. However, existing event-aware vision-language models remain limited to generic perception and do not reveal how event sensing contributes to reasoning and decision-making across the full driving loop. We present EventDrive, a large-scale benchmark and model suite that unifies event streams, RGB frames, and language supervision across four core dimensions: Perception, Understanding, Prediction, and Planning, covering captions, structured QA, grounding, motion-state recognition, trajectory forecasting, and planning tasks. Building on this foundation, EventDrive-VLM introduces a multi-horizon event pyramid and a temporal-horizon mixture-of-experts module to adaptively encode and fuse asynchronous and frame-based information for downstream reasoning. Comprehensive evaluation across diverse tasks shows that event streams provide substantial gains in temporal precision, motion awareness, and robustness, bringing event sensing into the center of driving intelligence.
Chinese Translation
事件相机通过微秒级延迟和高动态范围的异步亮度变化感知世界，提供了远超基于帧的传感器的运动保真度，并捕捉到传统曝光常常遗漏的时间结构。这些特性使得事件在自动驾驶中成为RGB的强大补充，尤其是在模糊、眩光和快速运动的情况下，基于帧的感知可能变得不可靠。然而，现有的事件感知视觉-语言模型仍然局限于通用感知，未能揭示事件感知如何在整个驾驶循环中贡献于推理和决策。我们提出了EventDrive，一个大规模基准和模型套件，统一了事件流、RGB帧和语言监督，涵盖了四个核心维度：感知、理解、预测和规划，涉及字幕、结构化问答、基础定位、运动状态识别、轨迹预测和规划任务。在此基础上，EventDrive-VLM引入了多层次事件金字塔和时间层次混合专家模块，以自适应地编码和融合异步与基于帧的信息，以便于下游推理。对多样化任务的全面评估表明，事件流在时间精度、运动意识和鲁棒性方面提供了显著提升，将事件感知置于驾驶智能的核心。
cs.CV / 87 / 2606.18250
Future Dynamic 3D Reconstruction: A 3D World Model with Disentangled Ego-Motion
未来动态3D重建：具有解耦自我运动的3D世界模型
Abstract
Forecasting the evolution of dynamic environments is crucial for autonomous agents. While generative world models have recently achieved high photorealism in 2D video synthesis by mixing ego-motion and environmental dynamics within the image plane, they exhibit physical inconsistencies, such as morphing or vanishing objects, especially over long time horizons. In this paper, we propose FR3D, a world model that predicts a persistent 3D latent representation for future dynamic 3D reconstruction. Unlike prior works that treat the world as a sequence of image-based features, FR3D explicitly decouples the 3D evolution of the scene from the agent's trajectory, treating the inferred ego-motion as a latent proxy for action. This disentanglement resolves the ambiguities between self-motion and world-motion, ensuring geometric consistency into the future. Furthermore, we introduce a teacher-student distillation strategy that leverages the spatial "common sense" of off-the-shelf foundation models, leading to robust zero-shot generalization. Extensive experiments demonstrate FR3D's strong performance for future dynamic 3D reconstruction from monocular observations across multiple datasets, even 2 seconds into the future. Project page: https://fr3d-wm.github.io.
Chinese Translation
预测动态环境的演变对自主代理至关重要。尽管生成世界模型最近通过在图像平面内混合自我运动和环境动态，在2D视频合成中达到了高真实感，但它们在物理上存在不一致性，例如物体变形或消失，尤其是在较长的时间范围内。在本文中，我们提出了FR3D，一个预测未来动态3D重建的持久3D潜在表示的世界模型。与将世界视为基于图像特征序列的先前工作不同，FR3D明确将场景的3D演变与代理的轨迹解耦，将推断出的自我运动视为行动的潜在代理。这种解耦解决了自我运动与世界运动之间的模糊性，确保了未来的几何一致性。此外，我们引入了一种教师-学生蒸馏策略，利用现成基础模型的空间“常识”，从而实现强大的零样本泛化。大量实验表明，FR3D在多个数据集中从单目观测中进行未来动态3D重建的强大性能，甚至可以预测未来2秒。项目页面：https://fr3d-wm.github.io。
cs.AI / 1 / 2606.17209
Beyond Parallel Sampling: Diverse Query Initialization for Agentic Search
超越并行采样：面向智能搜索的多样化查询初始化
Abstract
Test-time scaling for agentic search typically increases depth (i.e., more turns and tokens per trajectory) or breadth (i.e., more parallel rollouts). Here we focus on breadth scaling, showing that standard parallel sampling yields diminishing returns, tracing this to query redundancy at the first turn. When models issue similar first queries across rollouts, the threads retrieve overlapping evidence, and subsequent turns are conditioned on this shared retrieval. We address this limitation with DivInit, a training-free intervention at the first turn. Rather than sampling k independent first queries, DivInit draws n candidates from a single call, picks k < n diverse seeds, and runs them as parallel trajectories. Across five open-weight models and eight benchmarks, DivInit consistently improves over standard parallel sampling, with average gains of five to seven points on multi-hop QA at matched compute. Code available at https://github.com/cxcscmu/diverse-query-initialization
Chinese Translation
智能搜索的测试时扩展通常增加深度（即每个轨迹更多的回合和标记）或广度（即更多的并行展开）。在此，我们专注于广度扩展，表明标准的并行采样会导致收益递减，追溯到第一次回合的查询冗余。当模型在不同的展开中发出相似的首次查询时，各个线程检索到重叠的证据，后续的回合则基于这一共享的检索进行条件化。我们通过DivInit解决了这一限制，DivInit是在第一次回合的无训练干预。DivInit并不是从k个独立的首次查询中进行采样，而是从一次调用中提取n个候选，选择k < n个多样化的种子，并将其作为并行轨迹运行。在五个开放权重模型和八个基准测试中，DivInit始终优于标准的并行采样，在匹配计算条件下，平均提升了五到七个点的多跳问答性能。代码可在https://github.com/cxcscmu/diverse-query-initialization获取。
cs.AI / 2 / 2606.17220
When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval
当规则学习：一种自我进化的法律案件检索代理
Abstract
Legal case retrieval remains challenging due to the complexity of legal language and the need for precise lexical alignment between queries and relevant cases. Although dense retrieval models have achieved notable progress, empirical studies show that BM25 continues to serve as a strong baseline in this domain. It motivates us to propose a self-evolving framework for rule-driven query rewriting that enhances BM25 without any parameter training. The framework equips an LLM-based agent with an automatic evaluation environment, enabling it to iteratively create rewriting rules, plan validation experiments over rule combinations, and eliminate ineffective rules based on historical feedbacks. We evaluate our method on the Chinese legal case retrieval benchmark LeCaRD-v2. Experimental results demonstrate that the proposed framework outperforms non-evolutionary baselines, including human-designed rules and greedy rule selection, particularly when powered by a highcapacity core LLM. We also conduct detailed analyses to investigate the mechanisms underlying self-evolution. Our findings reveal that LLM's capabilities to leverage previous experimental results and its intrinsic knowledge of rule elimination play critical roles in refining the rule set via self-evolution.
Chinese Translation
法律案件检索因法律语言的复杂性以及查询与相关案件之间需要精确的词汇对齐而仍然面临挑战。尽管密集检索模型取得了显著进展，但实证研究表明，BM25在这一领域依然是一个强有力的基准。这促使我们提出一种自我进化的框架，用于基于规则的查询重写，旨在增强BM25而无需任何参数训练。该框架为基于大语言模型（LLM）的代理提供了一个自动评估环境，使其能够迭代地创建重写规则，规划规则组合的验证实验，并根据历史反馈消除无效规则。我们在中国法律案件检索基准LeCaRD-v2上评估了我们的方法。实验结果表明，所提出的框架在性能上优于非进化基准，包括人工设计的规则和贪婪规则选择，尤其是在由高容量核心LLM驱动时。我们还进行了详细分析，以探讨自我进化背后的机制。我们的发现表明，LLM利用先前实验结果的能力及其对规则消除的内在知识在通过自我进化精炼规则集方面发挥了关键作用。
cs.AI / 3 / 2606.17266
SkillChain-Gym: A Benchmark for Reskilling-Aware Production-Inventory Control under Disruptions
SkillChain-Gym：一种针对干扰下的技能再培训意识生产-库存控制的基准测试
Abstract
Production planning increasingly has to treat workforce capability as a decision variable: certifications lapse when skills are not maintained, new products require skills the current workforce does not hold, and reskilling competes for the same worker hours needed for production. Existing operations benchmarks usually treat labor as exogenous, while workforce-planning models with skills and learning are rarely released as reusable testbeds. We introduce SkillChain-Gym, a benchmark specification for reskilling-aware production-inventory control: a single-site environment with stylized worker skill-state dynamics, hard threshold certification, forgetting, and capacity-consuming training actions constrained by the same per-worker time budget as production. The benchmark includes seed-controlled disruption scenarios, three feasibility modes with projection diagnostics, deterministic replay, and metrics covering operations, resilience, capability growth, and training-access distribution. We evaluate production-only, reactive adaptive, water-filling adaptive, and static-insurance policies with budget variants over 60-shift horizons with paired statistical tests. The results are regime-dependent rather than a ranking. Training-capable policies dominate the production-only baseline, and maintenance training is necessary under forgetting even without disruptions. Among training-capable classes, adaptive training helps when bottlenecks are visible in the forecast, while a lean static cross-training plan, a deliberately favorable comparator whose structure encodes relevant skill contingencies, acts as strong insurance under surprise shocks and absenteeism. Capacity slack and the forgetting rate govern the boundary between these regimes. No policy class dominates across regimes, motivating forecast-driven controllers that decide when to buy skill insurance and when to react.
Chinese Translation
生产规划越来越需要将劳动力能力视为决策变量：当技能未得到维护时，认证会失效，新产品需要当前劳动力所不具备的技能，而再培训则与生产所需的工时竞争。现有的运营基准通常将劳动力视为外生变量，而带有技能和学习的劳动力规划模型很少被发布为可重用的测试平台。我们介绍了SkillChain-Gym，这是一个针对技能再培训意识的生产-库存控制的基准规范：一个具有风格化工人技能状态动态、硬阈值认证、遗忘和消耗能力的培训行动的单站点环境，这些行动受到与生产相同的每工人时间预算的限制。该基准包括种子控制的干扰场景、三种可行性模式及其预测诊断、确定性重放，以及涵盖运营、韧性、能力增长和培训获取分布的指标。我们在60个班次的时间范围内，使用配对统计测试评估了仅生产、反应适应、水位填充适应和静态保险政策的预算变体。结果显示这些政策依赖于不同的制度，而不是简单的排名。具有培训能力的政策在仅生产的基准上占据优势，而即使在没有干扰的情况下，维护培训在遗忘情况下也是必要的。在具有培训能力的类别中，当瓶颈在预测中可见时，适应性培训能够提供帮助，而精简的静态交叉培训计划作为一个故意有利的比较者，其结构编码了相关技能的应急情况，在突发冲击和缺勤情况下表现出强大的保险作用。能力富余和遗忘率决定了这些制度之间的边界。没有任何政策类别在所有制度中占据主导地位，这促使我们开发基于预测的控制器，以决定何时购买技能保险以及何时采取反应措施。
cs.AI / 4 / 2606.17269
Skill-Constrained Model Predictive Control for Resilient Manufacturing Supply Chains
面向韧性制造供应链的技能约束模型预测控制
Abstract
In skill-constrained production-inventory systems, the qualified human capacity available tomorrow depends on training decisions made today: production requires certified workers, certifications decay unless maintained, and training consumes the same scarce worker hours that production needs now. We study a closed-loop skill-constrained model predictive controller that, at every shift, solves a finite-horizon mixed-integer program over production, inventory, backlog, and training, with binary predicted certification, hard production eligibility, and an interpretable terminal value that prices certified-capacity gaps at the horizon boundary; only the first-period action is applied before replanning. On synthetic, seed-controlled SkillChain-Gym scenarios - announced and surprise new-skill shocks, demand shocks, absenteeism, forecast- and availability-quality modes, capacity-boundary and training-rate sweeps, and negative controls - we evaluate the controller against production-only and maintenance-only ablations, static cross-training insurance plans, and a strong reactive heuristic, under an ex-ante locked configuration and paired statistics. The result is regime dependence, not superiority: no policy class dominates. Predictive control helps when skill or labor bottlenecks are forecastable early enough for training to complete; lean static insurance remains hard to beat under surprise shocks, near the demand-capacity boundary, and wherever pre-shock slack makes insurance cheap. Attribution ablations separate certification maintenance, re-acquisition of lapsed certifications, and greenfield skill acquisition. Forecastability, not adaptivity per se, decides when predictive control pays.
Chinese Translation
在技能约束的生产-库存系统中，明天可用的合格人力资源取决于今天所做的培训决策：生产需要经过认证的工人，认证会随着时间的推移而衰减，除非得到维护，而培训消耗了当前生产所需的稀缺工时。我们研究了一种闭环技能约束模型预测控制器，该控制器在每个班次中解决一个有限时间范围内的混合整数规划问题，涉及生产、库存、积压和培训，具有二元预测认证、硬性生产资格和可解释的终端价值，该价值在时间范围边界处对认证能力缺口进行定价；在重新规划之前，仅应用第一期的行动。在合成的、种子控制的SkillChain-Gym场景中——包括宣布和突发的新技能冲击、需求冲击、缺勤、预测和可用性质量模式、产能边界和培训速率的变化，以及负控制——我们将该控制器与仅生产和仅维护的消融实验、静态交叉培训保险计划以及强反应启发式进行评估，采用事前锁定配置和配对统计。结果显示出制度依赖性，而非优越性：没有任何政策类别占主导地位。当技能或劳动瓶颈能够提前预测以便完成培训时，预测控制能够发挥作用；在突发冲击、接近需求-产能边界以及在任何预冲击松弛使保险变得便宜的地方，精益静态保险仍然难以超越。归因消融实验区分了认证维护、恢复失效认证和新技能获取。可预测性，而非适应性本身，决定了何时预测控制能够带来收益。
cs.AI / 5 / 2606.17289
Nothing from Something: Can a Language Model Discover 0?
无中生有：语言模型能否发现零？
Abstract
AI systems based on artificial neural networks are being developed with aspirations of pushing the boundary of human mathematical knowledge. A key question for these systems is how much they can reach beyond their training data. Mathematical discovery requires a strong form of out of distribution generalization; the ability to hypothesize genuinely new - and potentially logically more powerful - mathematical structures. It has been hypothesized that language abilities support such generalizations in human cognition. In this work, we use simple arithmetic as a case study for examining how modern AI models could expand their mathematical horizons, evaluating whether these models can independently discover the concept of "zero". We show that We show that (1) language models of a GPT-2 size are unable to perform this generalization at test time regardless of language pretraining, but (2) models can improve substantially after training on tens or hundreds of examples of zero. Additionally, we find that language pretraining reduces the number of required examples by approximately $50\%$, showing that language abilities can scaffold mathematical discovery in neural models.
Chinese Translation
基于人工神经网络的人工智能系统正在开发中，旨在推动人类数学知识的边界。这些系统的一个关键问题是它们能够超越训练数据的程度。数学发现需要一种强形式的分布外泛化能力；即假设真正新颖且可能在逻辑上更强大的数学结构的能力。有人假设语言能力支持人类认知中的这种泛化。在本研究中，我们以简单的算术作为案例研究，考察现代人工智能模型如何扩展其数学视野，评估这些模型是否能够独立发现“零”的概念。我们展示了（1）GPT-2规模的语言模型在测试时无法进行这种泛化，无论语言预训练如何，但（2）模型在经过数十或数百个零的示例训练后可以显著改善。此外，我们发现语言预训练将所需示例的数量减少了约50%，这表明语言能力可以在神经模型中支撑数学发现。
cs.AI / 6 / 2606.17312
Quantifying Consistency in LLM Logical Reasoning via Structural Uncertainty
通过结构不确定性量化大型语言模型的逻辑推理一致性
Abstract
Large language models can arrive at the same answer through reasoning paths that are unstable, contradictory, or difficult to rank consistently -- a failure mode especially prevalent in multi-step deductive reasoning. Existing methods assess reliability primarily through output dispersion -- measuring how much sampled answers differ -- but this discards a complementary signal: whether the model can consistently rank competing reasoning candidates. We propose structural uncertainty, a consistency-aware framework derived from the stability of self-preference-induced rankings over sampled reasoning solutions. Given a query, we generate multiple candidate solutions and ask the model to judge pairwise preferences among its own outputs. We aggregate self-preferences into ranking distributions via Bradley-Terry modeling with PageRank, and decompose the signal into two entropy-based components: across-trial ranking instability and within-trial candidate ambiguity. Across five LLMs and eight benchmarks, structural signals provide information complementary to answer dispersion: on logical and mathematical reasoning tasks, the combination improves identification of unreliable instances, while on factual retrieval the structural signal collapses toward uniformity, diagnosing a regime boundary where reasoning-level consistency evaluation is uninformative. The two components relate differently to accuracy: within-trial ambiguity correlates positively with correctness -- consistent with settings where multiple plausible solution paths remain competitive -- while across-trial instability correlates negatively, signaling unreliable reasoning. Structural uncertainty is best understood not as a universal confidence estimator, but as a regime-sensitive evaluator of logical reasoning consistency.
Chinese Translation
大型语言模型可以通过不稳定、矛盾或难以一致排序的推理路径得出相同的答案——这种失败模式在多步骤演绎推理中尤为普遍。现有方法主要通过输出分散性来评估可靠性——测量采样答案之间的差异——但这忽略了一个互补信号：模型是否能够一致地对竞争推理候选进行排序。我们提出了结构不确定性，这是一种基于自我偏好引导的排名在采样推理解决方案上的稳定性而衍生的一致性感知框架。给定一个查询，我们生成多个候选解决方案，并要求模型判断其自身输出之间的成对偏好。我们通过结合PageRank的Bradley-Terry建模将自我偏好聚合为排名分布，并将信号分解为两个基于熵的组成部分：跨试验排名不稳定性和试验内候选模糊性。在五个大型语言模型和八个基准测试中，结构信号提供了与答案分散性互补的信息：在逻辑和数学推理任务中，这种组合改善了对不可靠实例的识别，而在事实检索中，结构信号趋向均匀，诊断出推理级一致性评估无信息的领域边界。这两个组成部分与准确性之间的关系不同：试验内模糊性与正确性呈正相关——与多个合理解决路径保持竞争的情境一致——而跨试验不稳定性则呈负相关，表明推理不可靠。结构不确定性最好被理解为一种对逻辑推理一致性的领域敏感评估器，而非普遍的置信度估计器。
cs.AI / 7 / 2606.17328
MemTrace: Probing What Final Accuracy Misses in Long-Term Memory
MemTrace：探究最终准确性在长期记忆中遗漏的内容
Abstract
LLM agents increasingly maintain long-term memory of user facts across sessions. Yet such memory is usually evaluated by aggregating accuracy over question rows or episodes. Because this approach scores question rows independently, even when several questions probe the same fact, it cannot show how that fact behaves as conditions change. We introduce MemTrace, a benchmark whose unit of measurement is the knowledge point: a single typed fact about the user, rather than an individual question. MemTrace probes each fact along three controlled dimensions: memory age, defined by how many sessions ago the fact appeared in the history; question type, covering current state, earlier state, and trajectory of change; and evidence condition, covering present, missing, and contradicted-by-false-premise settings. Evaluating 13 memory-system configurations across four paradigms, we find that similar pooled accuracy hides different failures: recovering a fact's current and earlier states does not imply tracking how it changed, and safe abstention does not imply correcting a false premise. The dominant bottleneck is evidence use, not retrieval: when systems fail, the evidence was retrievable 10 times more often than it was missing. These results suggest that improving long-term memory requires better use of reachable evidence, not simply more storage or retrieval.
Chinese Translation
大型语言模型（LLM）代理越来越多地在会话中维护用户事实的长期记忆。然而，这种记忆通常通过对问题行或情节的准确性进行聚合来评估。由于这种方法独立评分问题行，即使多个问题探测同一事实，它也无法显示该事实在条件变化时的表现。我们引入了MemTrace，一个以知识点为测量单位的基准：关于用户的单个书面事实，而不是单个问题。MemTrace从三个受控维度探测每个事实：记忆年龄，定义为该事实在历史中出现的会话数；问题类型，涵盖当前状态、早期状态和变化轨迹；以及证据条件，涵盖当前、缺失和被虚假前提反驳的设置。通过评估四种范式下的13种记忆系统配置，我们发现相似的聚合准确性掩盖了不同的失败：恢复事实的当前和早期状态并不意味着跟踪其变化，而安全的放弃并不意味着纠正虚假前提。主要瓶颈在于证据的使用，而非检索：当系统失败时，证据被检索的频率是缺失的10倍。这些结果表明，改善长期记忆需要更好地利用可获取的证据，而不仅仅是增加存储或检索。
cs.AI / 8 / 2606.17339
SpeechDx: A Multi-Task Benchmark for Clinical Speech AI
SpeechDx：临床语音人工智能的多任务基准
Abstract
Speech offers a uniquely informative window into health by simultaneously engaging neurological, motor, respiratory, and vocal systems. Current clinical speech AI methods have largely progressed through isolated condition-specific studies, making results difficult to compare and generalization difficult to assess. We introduce SpeechDx, a large-scale benchmark for clinical speech AI spanning 12 datasets and 27 tasks across diverse health conditions. To enable evaluation across shared clinical mechanisms, SpeechDx structures tasks by the stage of speech production they disrupt: conceptualization, formulation, and articulation. The benchmark tests generalization by including tasks with limited labeled data and evaluating the same health condition across multiple datasets, distinguishing clinically meaningful patterns from dataset artefacts. We systematically evaluate 12 state-of-the-art audio encoders across all tasks and under zero-shot cross-condition transfer. Results show that large-scale speech models represent the strongest overall baselines, domain-specific models improve performance only on closely matched tasks, and no current representation generalizes reliably across the clinical speech landscape. SpeechDx establishes a shared evaluation framework for tracking progress toward general-purpose clinical speech representations
Chinese Translation
语音通过同时涉及神经、运动、呼吸和发声系统，为健康提供了独特的信息窗口。目前的临床语音人工智能方法主要通过孤立的特定疾病研究取得进展，这使得结果难以比较，且推广性评估困难。我们引入了SpeechDx，这是一个涵盖12个数据集和27个任务的大规模临床语音人工智能基准，涉及多种健康状况。为了在共享的临床机制下进行评估，SpeechDx根据其干扰的语音产生阶段对任务进行结构化：概念化、形成和发音。该基准通过包括有限标记数据的任务并在多个数据集上评估相同健康状况，来测试推广性，从而区分临床上有意义的模式与数据集伪影。我们系统地评估了12种最先进的音频编码器在所有任务下的表现，以及在零样本跨条件迁移下的表现。结果表明，大规模语音模型代表了最强的整体基线，特定领域模型仅在紧密匹配的任务上提高性能，而当前没有任何表示能够在临床语音领域中可靠地推广。SpeechDx建立了一个共享的评估框架，以跟踪朝向通用临床语音表示的进展。
cs.AI / 9 / 2606.17368
Distributed General-Purpose Agent Networks: Architecture, Key Mechanisms, and Prototypes
分布式通用代理网络：架构、关键机制与原型
Abstract
Large language models have accelerated the transition from passive conversational assistants to autonomous agents that can understand goals, plan actions, invoke tools, and execute multi-step tasks. Yet the capability of a single agent remains constrained by its local data, tool permissions, runtime environment, and governance boundary. This paper studies distributed general-purpose agent networks: open peer-to-peer networks in which heterogeneous agents deployed on personal devices, edge nodes, or autonomous computing environments can discover one another, establish trust, negotiate cooperation rules, and execute open-ended tasks. We argue that such networks cannot be obtained by simply combining existing peer-to-peer overlays with conventional multi-agent systems. Unlike traditional P2P networks, agent networks must propagate semantic declarations about intentions, capabilities, states, and cooperation constraints. We therefore propose a layered architecture centered on a protocol adaptation layer that connects upper-level task semantics with lower-level network operations. Based on this architecture, the paper identifies three core mechanism problems: semantic announcement propagation for collaborator discovery, verifiable identity and multi-topic reputation for cooperation governance, and semantic-gradient mechanism design for open task execution. For each problem, we present a technical route, including bodyless gossip with sequential logs, BAID-based identity binding with MG-EigenTrust reputation, and a Stackelberg-style mechanism-generation loop driven by semantic attribution feedback. We further report prototype overhead results for BAID-style tiered verification and mechanism-level simulations of MG-EigenTrust under cross-topic disguise-collusion attacks. The resulting framework provides a system-level foundation for open, trustworthy, and scalable agent collaboration.
Chinese Translation
大型语言模型加速了从被动对话助手向能够理解目标、规划行动、调用工具和执行多步骤任务的自主代理的转变。然而，单个代理的能力仍然受到其本地数据、工具权限、运行环境和治理边界的限制。本文研究了分布式通用代理网络：一种开放的点对点网络，其中部署在个人设备、边缘节点或自主计算环境中的异构代理能够相互发现、建立信任、协商合作规则并执行开放式任务。我们认为，这种网络不能仅通过简单地将现有的点对点覆盖网络与传统的多代理系统结合而获得。与传统的P2P网络不同，代理网络必须传播关于意图、能力、状态和合作约束的语义声明。因此，我们提出了一种以协议适配层为中心的分层架构，该层连接上层任务语义与下层网络操作。基于该架构，本文识别出三个核心机制问题：用于协作者发现的语义公告传播、用于合作治理的可验证身份和多主题声誉，以及用于开放任务执行的语义梯度机制设计。针对每个问题，我们提出了一条技术路线，包括无主体的闲聊与顺序日志、基于BAID的身份绑定与MG-EigenTrust声誉，以及由语义归因反馈驱动的斯塔克尔伯格风格机制生成循环。我们进一步报告了BAID风格分层验证的原型开销结果以及MG-EigenTrust在跨主题伪装-串通攻击下的机制级模拟。所提出的框架为开放、可信和可扩展的代理协作提供了系统级基础。
cs.AI / 10 / 2606.17405
Treatment Response Optimized Clinical Decision Support AI System via Digital Twin Simulation
通过数字双胞胎模拟优化治疗反应的临床决策支持人工智能系统
Abstract
Clinical decision support AI systems (CDSASs) must adapt to evolving patient conditions in real-time while adhering to strict safety constraints. We present an online adaptive framework that integrates Treatment Effect (TE) estimation to quantify clinical benefits, a patient Digital Twin (DT) to simulate treatment trajectories, and Reinforcement Learning (RL) for sequential decision-making. The AI system is initially trained on historical medical records and operates in a continuous learning loop. To ensure safety, a rule-based module monitors vital signs and blocks contraindicated treatments. Cases with strong internal model disagreement are flagged for clinician review, simulated in our experiments via a pre-trained outcome model. We validate our framework using both a synthetic clinical simulator and a real-world ovarian cancer dataset from The Cancer Genome Atlas (TCGA). In both simulated and clinical settings, our method demonstrated superior effectiveness and stability in recommending treatments compared to standard computational baselines. Furthermore, the AI system maintains low latency and requires expert consultation for only a minority of cases in our experimental validation, demonstrating its potential as a safe, clinician-supervised tool for personalized medicine that continuously improves through practical use.
Chinese Translation
临床决策支持人工智能系统（CDSAS）必须实时适应不断变化的患者状况，同时遵循严格的安全约束。我们提出了一种在线自适应框架，该框架集成了治疗效果（TE）估计以量化临床益处、患者数字双胞胎（DT）以模拟治疗轨迹，以及强化学习（RL）用于顺序决策。该人工智能系统最初在历史医疗记录上进行训练，并在持续学习循环中运行。为了确保安全，一个基于规则的模块监测生命体征并阻止禁忌治疗。内部模型存在强烈不一致的案例将被标记以供临床医生审查，在我们的实验中通过预训练的结果模型进行模拟。我们使用合成临床模拟器和来自癌症基因组图谱（TCGA）的真实卵巢癌数据集验证了我们的框架。在模拟和临床环境中，我们的方法在推荐治疗方面显示出优于标准计算基线的有效性和稳定性。此外，该人工智能系统保持低延迟，并且在我们的实验验证中，仅对少数案例需要专家咨询，展示了其作为安全、临床医生监督的个性化医疗工具的潜力，并通过实际使用不断改进。
cs.AI / 11 / 2606.17443
Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
incumbents优势：品牌偏见与大型语言模型推荐系统中的认知操控动态
Abstract
Large language models (LLMs) are becoming a major way for consumers to find products, but we do not yet understand how brands compete in this new channel. We study brand dynamics in LLM recommendations using skincare products -- a category where consumers cannot easily judge quality before buying and must rely on brand reputation -- across three commercial LLMs (GPT-4o-mini, Claude Sonnet, Gemini 3 Flash), with a robustness check on search goods. In three experiments, we find: (1) a Conditional Monopoly where well-known brands get recommended 100% of the time (IAI = 10.0) when all products have the same specifications, but this dominance disappears with less than a +0.1-star rating advantage for a competitor; (2) authority-style marketing language, including fabricated clinical-evidence claims, breaks this monopoly at a Bias Surplus Value equal to +0.17 rating points, with each model responding differently; and (3) a social dilemma in multi-brand GEO competition: when all brands adopt the same optimization strategy, individual payoff falls from +0.802 to +0.007 in our payoff proxy, and non-participating brands receive zero recommendations in our tests. Our results suggest that generative engine optimization (GEO) should be studied not only as a security risk, but also as an emerging marketing practice that shapes market competition.
Chinese Translation
大型语言模型（LLMs）正成为消费者寻找产品的重要途径，但我们尚未理解品牌在这一新渠道中的竞争方式。我们研究了在LLM推荐中品牌动态，以护肤产品为例——这一类别的消费者在购买前难以轻易判断质量，必须依赖品牌声誉——并对三种商业LLM（GPT-4o-mini、Claude Sonnet、Gemini 3 Flash）进行了研究，同时对搜索商品进行了稳健性检验。在三项实验中，我们发现：（1）条件垄断现象，当所有产品具有相同规格时，知名品牌的推荐率为100%（IAI = 10.0），但当竞争对手的评分优势不足+0.1星时，这种主导地位便会消失；（2）权威式营销语言，包括虚构的临床证据声明，在偏见剩余值达到+0.17评分点时打破了这种垄断，各模型的反应也各不相同；（3）在多品牌的GEO竞争中出现了社会困境：当所有品牌采用相同的优化策略时，个体收益在我们的收益代理中从+0.802降至+0.007，而未参与的品牌在我们的测试中获得零推荐。我们的结果表明，生成引擎优化（GEO）不仅应作为安全风险进行研究，还应作为一种新兴的市场营销实践进行研究，以塑造市场竞争。
cs.AI / 12 / 2606.17450
A Machine-Learned Comorbidity Index
机器学习共病指数
Abstract
Traditional comorbidity scores (e.g., Charlson and Elixhauser) are widely used for risk adjustment and patient stratification, but they have two key limitations: (i) they are largely mortality-centric and do not align well with other clinical outcomes, and (ii) their linear, rule-based structure cannot capture nonlinear, outcome-specific risk relationships. We propose a Machine-Learned Comorbidity Index (MLCI) that maps diagnosis codes to a single scalar by maximizing the normalized Hilbert-Schmidt Independence Criterion (nHSIC) between the learned score and multiple clinical outcomes. MLCI captures nonlinear risk-outcome dependence and is supported by a theory that characterizes when a unified, informative admission-level ordering can be achieved across outcomes. Empirical results on multiple benchmark electronic health record (EHR) datasets show that MLCI outperforms strong baselines across multiple evaluation metrics.
Chinese Translation
传统的共病评分（例如，Charlson 和 Elixhauser）广泛用于风险调整和患者分层，但它们存在两个主要局限性：（i）这些评分主要以死亡率为中心，与其他临床结果不够一致；（ii）其线性、基于规则的结构无法捕捉非线性、特定结果的风险关系。我们提出了一种机器学习共病指数（Machine-Learned Comorbidity Index, MLCI），通过最大化学习得分与多个临床结果之间的归一化希尔伯特-施密特独立性准则（normalized Hilbert-Schmidt Independence Criterion, nHSIC），将诊断代码映射为一个单一的标量。MLCI 捕捉非线性风险-结果依赖关系，并得到了一个理论的支持，该理论描述了在不同结果之间何时可以实现统一且信息丰富的入院水平排序。在多个基准电子健康记录（EHR）数据集上的实证结果表明，MLCI 在多个评估指标上优于强基线。
cs.AI / 13 / 2606.17453
MapSatisfyBench: Benchmarking Satisfaction-Aware Map Agents through Behavior-Grounded Implicit Decision Factors
MapSatisfyBench：通过行为驱动的隐性决策因素评估满意度感知地图代理的基准测试
Abstract
Large language model agents are increasingly integrated into map services. Since map services are embedded in everyday-life scenarios rather than professional task settings, users often express their needs informally, resulting in underspecified queries with many unspoken needs, namely, implicit decision factors that are critical for user satisfaction. Although clarification is an effective way to mitigate this issue, it increases user burden in daily interaction, and a capable agent should first proactively recover such factors from available information sources. However, evaluating this ability is challenging. The first challenge is to determine which implicit decision factors are suitable for evaluation. A factor is evaluable only if it affects user acceptance and can be recovered from information available to the agent before it responds. Second, user satisfaction cannot be reliably represented by a single reference answer, requiring a benchmark that converts satisfaction-relevant factors into objective and quantifiable evaluation targets. To address these challenges, we propose a restore-identify-filter framework that reconstructs complete user needs from behavior-chain evidence, identifies implicit decision factors, and retains only those supported by pre-query evidence. Building on this methodology, we construct MapSatisfyBench from large-scale, real-world anonymized user data and annotate ground truth from five dimensions and enables full-chain evaluation of satisfaction-aware map agents. Experiments show that current agents generally perform well on explicit task completion, but remain limited in satisfying implicit decision factors and proactively acquiring the evidence needed for satisfaction-aware decisions. These findings establish MapSatisfyBench as a benchmark for shifting map-agent evaluation from task completion toward satisfaction-aware spatial decision making.
Chinese Translation
大型语言模型代理越来越多地融入地图服务中。由于地图服务嵌入在日常生活场景中，而非专业任务环境，用户通常以非正式的方式表达他们的需求，导致查询不明确，存在许多未说出的需求，即对用户满意度至关重要的隐性决策因素。尽管澄清是缓解这一问题的有效方法，但它增加了用户在日常互动中的负担，因此一个有能力的代理应首先主动从可用信息源中恢复这些因素。然而，评估这种能力是具有挑战性的。第一个挑战是确定哪些隐性决策因素适合评估。一个因素只有在影响用户接受度且可以从代理在响应之前可获得的信息中恢复时，才是可评估的。其次，用户满意度不能仅通过单一的参考答案可靠地表示，这需要一个基准，将与满意度相关的因素转化为客观和可量化的评估目标。为了解决这些挑战，我们提出了一种恢复-识别-过滤框架，该框架从行为链证据中重构完整的用户需求，识别隐性决策因素，并仅保留那些由查询前证据支持的因素。在此方法论的基础上，我们从大规模的真实世界匿名用户数据构建了MapSatisfyBench，并从五个维度注释了真实情况，从而实现对满意度感知地图代理的全链条评估。实验表明，当前代理在显性任务完成方面表现良好，但在满足隐性决策因素和主动获取满意度决策所需证据方面仍然有限。这些发现确立了MapSatisfyBench作为一个基准，旨在将地图代理的评估从任务完成转向满意度感知的空间决策制定。
cs.AI / 14 / 2606.17454
Dissecting model behavior through agent trajectories
通过代理轨迹剖析模型行为
Abstract
AI agent performance is not just a modeling problem, it is fundamentally a systems problem. The advanced capabilities of models are realized through agent harnesses. Therefore, a gap between model assumptions and harness behavior can easily prevent the model's full capabilities from translating into agent performance. We formalize this as the `intent-execution' gap: the mismatch between what the model intends and what the harness executes, and vice versa. We argue that minimizing this intent-execution gap is as important as other aspects of harness design such as tools and execution loops. To illustrate the impact of this harness-model alignment, we develop a simple and customizable harness called `Simple Strands Agent' (SSA). SSA aims to find the bulk of common patterns which generalize across different model families (such as Claude, Gemini, GPT, Grok, Qwen), as well as a small number of model-specific preferences. We make two contributions: (i) we $\textbf{reproduce or improve on the pass@1}$ performance reported by diverse model-provider families on popular agentic benchmarks (SWE-Pro, SWE-Verified and Terminal-Bench-2), and (ii) building on an $\textbf{analysis of 138k trajectories generated by SSA}$, we look beyond the $\texttt{pass@1}$ numbers which tend to be relatively even across frontier models. By representing agent trajectories in code state-spaces, we observe model-level differences in problem-solving behavior. Finer-grained metrics such as edit frequency, testing activity, and phase-transitions reveal how individual models allocate effort across different stages of autonomous problem solving.
Chinese Translation
人工智能代理的性能不仅仅是一个建模问题，根本上是一个系统问题。模型的高级能力是通过代理的使用实现的。因此，模型假设与代理行为之间的差距很容易阻止模型的全部能力转化为代理的性能。我们将其形式化为 `意图-执行` 差距：模型的意图与代理的执行之间的错配，反之亦然。我们认为，最小化这一意图-执行差距与其他代理设计方面（如工具和执行循环）同样重要。为了说明这种代理-模型对齐的影响，我们开发了一个简单且可定制的代理，称为 `简单线索代理` (Simple Strands Agent, SSA)。SSA旨在找到跨不同模型家族（如Claude、Gemini、GPT、Grok、Qwen）普遍存在的主要模式，以及少量特定于模型的偏好。我们做出了两个贡献：（i）我们在流行的代理基准（SWE-Pro、SWE-Verified和Terminal-Bench-2）上$ extbf{再现或改进了不同模型提供者家族报告的pass@1}$性能；（ii）基于对SSA生成的$ extbf{138k轨迹的分析}$，我们超越了通常在前沿模型中相对均匀的$ exttt{pass@1}$数字。通过在代码状态空间中表示代理轨迹，我们观察到模型在解决问题行为上的差异。更细粒度的指标，如编辑频率、测试活动和阶段转换，揭示了各个模型在自主问题解决的不同阶段如何分配努力。
cs.AI / 15 / 2606.17459
Can LLMs Be CEOs? Benchmarking Strategic Resource Reallocation with Multi-Role Agent Simulation
大型语言模型能成为首席执行官吗？基于多角色代理模拟的战略资源重新配置基准测试
Abstract
Evaluating the decision-making capabilities of large language models (LLMs) is a growing research priority, yet existing benchmarks focus on isolated cognitive tasks such as reasoning, knowledge retrieval, and economic rationality in stylized settings. These evaluations overlook the defining challenge of real executive decision-making: integrating conflicting recommendations from specialized stakeholders under information asymmetry, organizational constraints, and temporal dependencies. We introduce \textsc{CEO-Bench}, a multi-agent benchmark that evaluates LLMs on CEO-level strategic resource reallocation -- the process of redirecting capital across business units in a multi-round, constraint-rich organizational environment. In \textsc{CEO-Bench}, LLM agents receive conflicting advice from four role-conditioned C-suite advisors (CFO, CTO, COO, CMO), each with private signals and distinct priorities, and must synthesize these into a concrete allocation plan evaluated along four dimensions: role integration, conditional boldness, history-sensitive judgment, and plan validity. Experiments across five frontier models on 13 scenarios reveal that all models achieve high structural validity but diverge sharply on strategic calibration -- the hardest capability layer. We identify systematic failure modes including single-advisor capture, conservative default under ambiguity, and historical amnesia, and uncover a structural integration-boldness tradeoff: models that engage more deeply with conflicting perspectives tend to produce less decisive action. These findings delineate the current capability boundary of LLMs as organizational decision-makers and inform the design of future AI-assisted executive systems.
Chinese Translation
评估大型语言模型（LLMs）的决策能力已成为日益重要的研究方向，但现有基准主要集中在孤立的认知任务上，如推理、知识检索和经济理性，这些任务通常是在理想化的环境中进行的。这些评估忽视了真实高管决策的核心挑战：在信息不对称、组织约束和时间依赖的情况下，整合来自专业利益相关者的相互矛盾的建议。我们引入了 extsc{CEO-Bench}，一个多代理基准，评估 LLM 在首席执行官级别的战略资源重新配置能力——即在一个多轮、约束丰富的组织环境中重新分配资本的过程。在 extsc{CEO-Bench} 中，LLM 代理接收来自四位角色条件的 C-suite 顾问（首席财务官 CFO、首席技术官 CTO、首席运营官 COO、首席营销官 CMO）的相互矛盾的建议，每位顾问都有私密信号和不同的优先事项，并必须将这些建议综合成一个具体的分配计划，该计划将从四个维度进行评估：角色整合、条件大胆性、历史敏感判断和计划有效性。在 13 个场景中对五个前沿模型的实验表明，所有模型在结构有效性上都表现良好，但在战略校准上却存在显著差异——这是最具挑战性的能力层次。我们识别出系统性的失败模式，包括单一顾问的影响、在模糊情况下的保守默认和历史遗忘，并发现了结构整合与大胆性之间的权衡：更深入地参与矛盾观点的模型往往会产生较少的果断行动。这些发现勾勒出了 LLM 作为组织决策者的当前能力边界，并为未来 AI 辅助的高管系统设计提供了参考。
cs.AI / 16 / 2606.17507
LLM-as-Judge in Education: A Curriculum-Grounded Marking Pipeline
教育中的 LLM 作为评判者：基于课程的评分流程
Abstract
Generative AI and large language models (LLMs) are increasingly applied to question generation and automated assessment. However, deploying LLMs in preparation for high-stakes exams requires more than prompt engineering; it demands software pipelines that systematically ground model outputs in authorised curriculum artefacts and marking guidelines issued by education authorities. This paper presents a curriculum-grounded, configurable LLM-as-Judge pipeline for question-level marking, co-developed with an industrial partner, to support exam preparation for university admission. The pipeline identifies the relevant topics, subtopics, and cognitive demand of a question, and assembles verifiable and authorised context to support LLM judgement. Curriculum intent is operationalised through concrete syllabus artefacts, including prescribed verbs and outcomes, performance band descriptors, glossary definitions, and marking-guideline principles. A staged LLM workflow is employed to first generate question-specific rubrics, capturing structured expectations of performance, and then derive and evaluate marking criteria used to allocate marks to student responses. This design improves consistency, transparency, and alignment with official marking practices. Preliminary evaluation shows that the proposed LLM-as-Judge pipeline delivers marking outcomes comparable to human tutors, while yielding justifications that are more traceable to authorised curriculum artefacts and marking standards. The pipeline has also been integrated into an online study platform, where early deployment data provide initial insights into operational usage and manual overrides.
Chinese Translation
生成性人工智能和大型语言模型（LLMs）在问题生成和自动评估中的应用日益增多。然而，在高风险考试准备中部署 LLMs 不仅需要提示工程，还需要系统性地将模型输出与教育当局发布的授权课程材料和评分指南相结合的软件流程。本文提出了一种基于课程的、可配置的 LLM 作为评判者的评分流程，旨在支持大学入学考试的准备工作，该流程与工业合作伙伴共同开发。该流程识别问题的相关主题、子主题和认知需求，并组装可验证和授权的背景，以支持 LLM 的判断。课程意图通过具体的教学大纲材料得以实现，包括规定的动词和学习成果、表现等级描述、术语定义以及评分指南原则。采用分阶段的 LLM 工作流程，首先生成特定问题的评分标准，捕捉对表现的结构化期望，然后推导和评估用于给学生回答打分的评分标准。这一设计提高了一致性、透明度，并与官方评分实践保持一致。初步评估表明，所提出的 LLM 作为评判者的评分流程所提供的评分结果与人类导师相当，同时其依据更易追溯至授权的课程材料和评分标准。该流程还已集成到一个在线学习平台中，早期部署数据为操作使用和人工干预提供了初步见解。
cs.AI / 17 / 2606.17546
SEAGym: An Evaluation Environment for Self-Evolving LLM Agents
SEAGym：自我进化LLM代理的评估环境
Abstract
Self-evolving LLM-based agents improve mainly by changing their agent harness: the structured execution layer around a base model, including prompts, memory, tools, middleware, runtime state, and the model-tool interaction loop. Existing evaluations often reduce this process to isolated task scores or a single sequential curve, obscuring whether an update produces reusable improvement, overfits recent tasks, increases cost, or harms older behavior. We introduce SEAGym, an evaluation environment for measuring agent harness updates across training, validation, test, replay, and cost records. SEAGym turns Harbor-compatible benchmarks into dynamic self-evolution task sources with train batches, frozen update-validation, held-out ID and OOD transfer views, replay diagnostics, and saved snapshot and metric records. Instantiating SEAGym on Terminal-Bench 2.0 and HLE, we compare ACE, TF-GRPO, and AHE under a shared epoch/batch protocol. The results show that these evaluation views provide complementary signals about the evolution process: frequent updates may fail to improve held-out performance, useful intermediate snapshots may collapse later, and source diversity and model backend can affect harness reliability.
Chinese Translation
基于自我进化的LLM代理主要通过改变其代理框架来提升性能：即围绕基础模型的结构化执行层，包括提示、记忆、工具、中间件、运行时状态以及模型与工具的交互循环。现有评估通常将这一过程简化为孤立的任务得分或单一的顺序曲线，模糊了更新是否产生可重用的改进、是否过拟合近期任务、是否增加成本或是否损害旧有行为。我们引入了SEAGym，这是一种评估环境，用于测量代理框架在训练、验证、测试、重放和成本记录中的更新。SEAGym将兼容Harbor的基准转化为动态自我进化任务源，具备训练批次、冻结的更新验证、保留的ID和OOD迁移视图、重放诊断以及保存的快照和指标记录。在Terminal-Bench 2.0和HLE上实例化SEAGym，我们在共享的周期/批次协议下比较了ACE、TF-GRPO和AHE。结果表明，这些评估视图提供了关于进化过程的互补信号：频繁的更新可能未能提升保留性能，有用的中间快照可能在后期崩溃，源多样性和模型后端可能影响框架的可靠性。
cs.AI / 18 / 2606.17574
DeepInsight: A Unified Evaluation Infrastructure Across the Physical AI Stack
DeepInsight：跨物理人工智能栈的统一评估基础设施
Abstract
Evaluating a Physical AI stack spans operators that differ by more than three orders of magnitude -- from a single foundation-model decoding step to thousands of physics ticks of whole-body control -- varying orthogonally in modality, reward semantics, and resource profile. No existing framework spans this range, so the stack is evaluated today by stitching together separate harnesses that share neither runtime nor scoring, preserving each segment's local validity but losing the shared identity needed to diagnose cross-layer regressions. We present DeepInsight, an evaluation infrastructure that serves this full spectrum on a single runtime. Rather than homogenize the regimes, it preserves their heterogeneity behind three narrow abstractions -- task, resource, and result -- each realized as one invariant shared by every subsystem: one episode driver, one resource-handle protocol implemented by every expensive backend (LLM inference and sandboxed runtimes alike), and one trace identity scheme under which every event is written. Deployed in production across all three layers of an embodied humanoid stack, this single set of invariants onboards new benchmarks largely by configuration. Where mature peer orchestrators exist -- at the foundation-model end -- it reproduces published references and peer-framework readings within their own spread, runs the same suites faster on a single node, and scales near-linearly across nodes. Its distinctive return is diagnostic: because every layer writes into one shared trace, a regression that begins in one layer and surfaces in another stays localizable on that trace -- a cross-layer payoff no federation of per-segment harnesses can reproduce.
Chinese Translation
评估物理人工智能栈涉及的操作符差异超过三个数量级——从单个基础模型解码步骤到数千个物理时钟周期的全身控制——在模态、奖励语义和资源配置上呈现正交变化。目前没有现有框架能够覆盖这一范围，因此该栈的评估通常通过将不同的测试环境拼接在一起进行，这些环境既不共享运行时也不共享评分，虽然保留了每个部分的局部有效性，但失去了诊断跨层回归所需的共享身份。我们提出了DeepInsight，这是一种评估基础设施，能够在单一运行时上服务于这一完整谱系。它并不试图同质化这些机制，而是通过三个狭窄的抽象——任务、资源和结果——保留它们的异质性，每个抽象都由每个子系统共享的一个不变体实现：一个事件驱动程序、一个由每个昂贵后端（包括LLM推理和沙盒运行时）实现的资源处理协议，以及一个所有事件均以其记录的追踪身份方案。该基础设施在具身人形栈的所有三个层面上投入生产，这一组不变体通过配置轻松引入新的基准。在成熟的同行协调者存在的地方——在基础模型端——它能够在其自身的分布内重现已发布的参考和同行框架的读数，在单节点上更快地运行相同的测试套件，并在节点间近线性扩展。其独特的回报是诊断性的：由于每一层都写入一个共享的追踪，因此在一个层面上开始并在另一个层面上显现的回归能够在该追踪上保持可定位性——这是任何分段测试环境的联合体无法重现的跨层收益。
cs.AI / 19 / 2606.17577
Surrogate Assisted Pedestrian Protection Design via a Foundation Model Orchestrated Workflow
通过基础模型协调工作流的替代辅助行人保护设计
Abstract
AI-driven engineering workflows face particular challenges in crash safety design: unlike aerodynamics, crash events involve highly nonlinear contact dynamics, material nonlinearity, and discrete state transitions that are difficult to capture with data-driven surrogate models. To the best of our knowledge, we present the first foundation model--orchestrated workflow for crash safety design that enables surrogate-assisted exploration for pedestrian protection, reducing evaluation time from hours per CAE simulation to seconds. The workflow integrates four components: (1) a surrogate trained on CAE crash simulations to predict pedestrian leg injury metrics from design parameters, achieving an average $R^2=0.87$ and providing distribution-free conformal prediction intervals; (2) multiobjective evolutionary search (NSGA-II) to discover diverse feasible parameter sets under user-specified constraints; (3) a morphing-based geometry generator that maps parameters to topology-preserving 3D shapes; and (4) a natural-language interface in which an LLM orchestrates the workflow and a vision--language model supports semantic comparison of generated designs. In an automotive front-bumper case study, the workflow produces 35 distinct safety-compliant alternatives from a single exploration, a process that would require weeks with conventional CAE iteration. These results suggest that foundation models can serve as integration layers between ML surrogates and physics-based simulation, helping bring AI capabilities to safety-critical engineering domains.
Chinese Translation
由人工智能驱动的工程工作流在碰撞安全设计中面临特定挑战：与空气动力学不同，碰撞事件涉及高度非线性的接触动力学、材料非线性以及难以通过数据驱动的替代模型捕捉的离散状态转变。我们首次提出了一个基础模型协调的碰撞安全设计工作流，该工作流能够实现替代辅助的行人保护探索，将每次计算机辅助工程（CAE）仿真的评估时间从数小时缩短至数秒。该工作流集成了四个组件：(1) 一个基于CAE碰撞仿真的替代模型，用于预测设计参数下的行人腿部伤害指标，平均 $R^2=0.87$，并提供无分布的符合预测区间；(2) 多目标进化搜索（NSGA-II），用于在用户指定的约束下发现多样的可行参数集；(3) 一个基于形态变换的几何生成器，将参数映射为保持拓扑的3D形状；(4) 一个自然语言接口，其中大型语言模型（LLM）协调工作流，视觉-语言模型支持生成设计的语义比较。在一项汽车前保险杠案例研究中，该工作流从单次探索中生成了35种不同的安全合规替代方案，而传统的CAE迭代过程需要数周。这些结果表明，基础模型可以作为机器学习替代模型与基于物理的仿真之间的集成层，帮助将人工智能能力引入安全关键的工程领域。
cs.AI / 20 / 2606.17591
Closing the Feedback Loop: From Experience Extraction to Insight Governance in Verbal Reinforcement Learning
闭合反馈循环：从经验提取到口头强化学习中的洞察治理
Abstract
Training-free verbal reinforcement learning enables LLM agents to learn from world feedback -- objective signals such as dynamic task outcomes, market returns, or demand forecasts -- by extracting verbal rules from experience and injecting them as context, updating the agent's behavior without parameter changes. However, in non-stationary environments these agents face a retention-forgetting dilemma: retaining stale insights causes negative transfer, while discarding them causes catastrophic forgetting when conditions recur. We identify four requirements for navigating this dilemma -- outcome-driven evaluation, persistent structured evidence, non-monotonic knowledge lifecycle, and compositional governance -- and show that existing methods invest heavily in experience extraction while underinvesting in insight governance. We propose a three-layer architecture -- rules, evidence, and skills -- connected by a feedback-driven curation loop that closes the governance gap. Rules capture distilled experience from world outcomes; evidence logs track each rule's reliability across episodes; skills govern which rules to apply, how to resolve conflicts, and when to abstain. On financial forecasting as a case study, where world feedback is naturally abundant, noisy, and non-stationary, we show that the same accumulated experience either degrades performance below the zero-shot baseline or dramatically improves accuracy and risk-adjusted returns, depending on whether the curation loop is present.
Chinese Translation
无训练的口头强化学习使得大型语言模型（LLM）代理能够通过从经验中提取口头规则并将其作为上下文注入，从世界反馈中学习——例如动态任务结果、市场回报或需求预测等客观信号，从而在不改变参数的情况下更新代理的行为。然而，在非平稳环境中，这些代理面临着保留与遗忘的困境：保留过时的洞察会导致负迁移，而丢弃它们则在条件重现时会导致灾难性遗忘。我们确定了应对这一困境的四个要求——以结果为驱动的评估、持久的结构化证据、非单调的知识生命周期和组合治理——并指出现有方法在经验提取上投入过多，而在洞察治理上投入不足。我们提出了一种三层架构——规则、证据和技能——通过一个以反馈为驱动的策展循环连接，弥补治理的缺口。规则捕捉来自世界结果的提炼经验；证据日志跟踪每个规则在多个回合中的可靠性；技能治理哪些规则被应用、如何解决冲突以及何时选择不采取行动。以金融预测为案例研究，在这一领域，世界反馈自然丰富、噪声大且非平稳，我们展示了相同的累积经验在存在策展循环时会显著提高准确性和风险调整后的回报，而在缺乏策展循环时则会使性能降至零样本基线以下。
cs.AI / 21 / 2606.17637
Brick-DICL: Dynamic In-Context Learning for Automated Brick Schema Classification
Brick-DICL：用于自动化砖块模式分类的动态上下文学习
Abstract
Building Management Systems (BMS) are essential for optimizing energy efficiency and operational performance in modern buildings. However, the lack of standardization across BMS points from different manufacturers creates significant barriers to integration and data utilization. While the Brick schema offers a standardized ontology for building systems, mapping BMS points to appropriate Brick classes presents three critical challenges: (i) the extensive number of Brick classes (936 in the latest version), (ii) limited domain-specific knowledge in large language models (LLMs), and (iii) substantial manual effort required for verification. To address these challenges, we propose Brick-DICL, a two-stage dynamic in-context learning framework for automated Brick schema classification. Brick-DICL consists of two primary components: metadata-RAG, which retrieves relevant examples to enhance LLMs' domain knowledge, and class-RAG, which narrows down potential Brick classes to address the large classification space. Additionally, we implement a multi-LLM filtering mechanism that compares predictions across multiple models, flagging low-confidence classifications for human review. As a result: (i) General: Brick-DICL is applicable to any building management system regardless of manufacturer or metadata format; (ii) Novel and Powerful: as the first dynamic in-context learning approach for Brick schema classification, Brick-DICL achieves significant classification accuracy improvements on building datasets, outperforming existing methods; (iii) Efficient: our multi-LLM filtering strategy reduces manual verification effort, enabling rapid digital building onboarding. Extensive experiments demonstrate Brick-DICL's effectiveness across diverse building datasets, accelerating the path toward standardized, interoperable building management systems.
Chinese Translation
建筑管理系统（BMS）对于优化现代建筑的能源效率和运营性能至关重要。然而，不同制造商的BMS点缺乏标准化，造成了集成和数据利用的重大障碍。虽然Brick模式提供了建筑系统的标准化本体，但将BMS点映射到适当的Brick类面临三个关键挑战：（i）Brick类的数量庞大（最新版本有936个），（ii）大型语言模型（LLMs）在特定领域知识方面的局限性，以及（iii）验证所需的巨大人工工作量。为了解决这些挑战，我们提出了Brick-DICL，一种用于自动化Brick模式分类的两阶段动态上下文学习框架。Brick-DICL由两个主要组件组成：metadata-RAG，它检索相关示例以增强LLMs的领域知识；class-RAG，它缩小潜在Brick类的范围，以应对庞大的分类空间。此外，我们实施了一种多LLM过滤机制，比较多个模型的预测结果，并标记低置信度的分类以供人工审核。因此：（i）通用性：Brick-DICL适用于任何建筑管理系统，无论制造商或元数据格式如何；（ii）新颖且强大：作为首个用于Brick模式分类的动态上下文学习方法，Brick-DICL在建筑数据集上实现了显著的分类准确性提升，超越了现有方法；（iii）高效性：我们的多LLM过滤策略减少了人工验证工作量，实现了快速数字建筑的入驻。大量实验表明，Brick-DICL在多样化的建筑数据集上表现出色，加速了朝着标准化、可互操作的建筑管理系统的进程。
cs.AI / 22 / 2606.17642
FinAcumen: Financial Multimodal Reasoning via Self-Evolving Experience Memory Harness
FinAcumen：通过自我演化经验记忆利用的金融多模态推理
Abstract
Financial multimodal reasoning requires agents to coordinate numerical computation, retrieval, visual interpretation, and temporal grounding across heterogeneous evidence sources. Existing tool-augmented agents improve execution fidelity, yet remain largely stateless across episodes, repeatedly rediscovering reasoning strategies and failure patterns. In high-stakes financial settings, this leads to unreliable tool routing, noisy retrieval, and hallucination-prone reasoning. We present FinAcumen, a financial reasoning agent framework centered on selective experience memory for tool-augmented multimodal reasoning. FinAcumen accumulates financially grounded reasoning experience from prior trajectories, distilling successful strategies and failure-derived cautionary rules into a persistent memory bank. During inference, retrieved experiences condition reasoning only when semantic relevance exceeds a calibrated threshold, while irrelevant memory is explicitly suppressed through a fallback mechanism. A deterministic financial tool environment further grounds numerical computation, retrieval, visual decoding, and answer verification.Across four financial multimodal reasoning benchmarks, FinAcumen consistently improves a frozen 8B vision-language model over finance-specialized models and approaches leading proprietary general-purpose models. Further analysis shows that selective experience activation improves reasoning reliability under retrieval uncertainty. Our code is anonymously available at https://anonymous.4open.science/r/FinAcumen
Chinese Translation
金融多模态推理要求智能体协调数值计算、检索、视觉解释和跨异构证据源的时间基础。现有的工具增强型智能体提高了执行的准确性，但在各个情境中仍然大多处于无状态，反复发现推理策略和失败模式。在高风险的金融环境中，这导致了不可靠的工具路由、噪声检索和容易产生幻觉的推理。我们提出了FinAcumen，一个以选择性经验记忆为中心的金融推理智能体框架，旨在增强多模态推理。FinAcumen从先前的轨迹中积累金融基础的推理经验，将成功策略和基于失败的警示规则提炼为一个持久的记忆库。在推理过程中，仅当检索到的经验的语义相关性超过校准阈值时，才会影响推理，而无关的记忆则通过后备机制被明确抑制。一个确定性的金融工具环境进一步支持数值计算、检索、视觉解码和答案验证。在四个金融多模态推理基准测试中，FinAcumen始终在一个冻结的8B视觉-语言模型上超越金融专业模型，并接近领先的专有通用模型。进一步分析表明，选择性经验激活在检索不确定性下提高了推理的可靠性。我们的代码可在 https://anonymous.4open.science/r/FinAcumen 匿名获取。
cs.AI / 23 / 2606.17645
Beyond Domains: Reusing Web Skills via Transferable Interaction Patterns
超越领域：通过可转移的交互模式重用网络技能
Abstract
Large language model (LLM) web agents are usually deployed as tool callers: each turn, the model reads a fresh page observation and emits one structured tool action. When every action is a low-level primitive, horizons grow quickly and so do policy-facing LLM completions, dominating latency and cost on benchmarks such as Mind2Web and WebArena. Recent systems therefore wrap repeated interaction fragments as web skills: callable tools built from successful trajectories or induced programs, so one call can replace several primitives. However, prior skill libraries are still triggered mainly by instruction similarity or coarse site metadata, which yields low skill reuse on held-out sites and leaves much of the potential step and token reduction on the table. We present SkillMigrator, an agent that learns reusable web skills and transfers them across sites by matching layout structure rather than specific element references. Each induced skill is stored as a transferable interaction pattern (TIP): the skill paired with a structural sketch of the snapshot at induction time. At test time, SkillMigrator retrieves TIPs by layout similarity and grounds their references on the live page. The rest of the stack is standard: accessibility-snapshot observations with stable references, and fixed tool calling over primitives plus skill invocations. Compared with the state-of-the-art approaches, SkillMigrator reduces the average LLM-action count on successful trajectories by 8-10% across both WebArena and Mind2Web at matched success rate.
Chinese Translation
大型语言模型（LLM）网络代理通常作为工具调用者部署：在每个回合中，模型读取一个新的页面观察并发出一个结构化的工具动作。当每个动作都是低级原语时，视野迅速扩大，政策导向的LLM完成也随之增加，这在Mind2Web和WebArena等基准测试中主导了延迟和成本。因此，最近的系统将重复的交互片段包装为网络技能：由成功轨迹或诱导程序构建的可调用工具，使得一次调用可以替代多个原语。然而，先前的技能库仍主要通过指令相似性或粗略的网站元数据触发，这导致在保留站点上的技能重用率低，并且在步骤和标记的潜在减少上留下了许多机会。我们提出了SkillMigrator，一个学习可重用网络技能并通过匹配布局结构而非特定元素引用在不同网站间转移这些技能的代理。每个诱导技能被存储为可转移的交互模式（TIP）：该技能与诱导时快照的结构草图配对。在测试时，SkillMigrator通过布局相似性检索TIP，并在实时页面上定位其引用。其余的堆栈是标准的：具有稳定引用的可访问性快照观察，以及对原语和技能调用的固定工具调用。与最先进的方法相比，SkillMigrator在WebArena和Mind2Web的成功轨迹上将平均LLM动作计数减少了8-10%，同时保持匹配的成功率。
cs.AI / 24 / 2606.17648
From Brewing to Resolution: Tracing the Internal Lifecycle of Code Reasoning in LLMs
从酿造到解决：追踪大语言模型中代码推理的内部生命周期
Abstract
Standard accuracy metrics cannot explain why LLMs handle variable tracking but fail on semantically equivalent loops. We study an internal lifecycle of code reasoning in which models first brew the answer, making it linearly recoverable many layers before it becomes self-decodable, and then diverge into one of four resolution outcomes: Resolved, Overprocessed, Misresolved, or Unresolved. Understanding this lifecycle matters because similar task accuracies can mask fundamentally different failure modes that surface-level evaluation cannot detect. We introduce a dual diagnostic framework pairing layer-wise linear probing with Context-Stripped Decoding (CSD) and apply it to six code-reasoning task families across 16 models spanning Qwen, Llama, and DeepSeek architectures. All four outcomes carry substantial mass in every task family: overall Resolved is only 41.5%, with multiple tasks below 30%. Controlled sweeps over structure, depth, and operators expose task-specific failure bottlenecks: Function Call Resolved plunges from 61.1% to 2.5% as call depth increases from one to three. Across architectures and scales, the brewing scaffold remains stable, with normalized brewing duration 24-42% across all 16 models, while resolution success varies with capability. This indicates that the scaffold is a stable empirical regularity across the tested decoder-only Transformer families, whereas resolution success covaries with capability, scale, and training. Code: https://github.com/euyis1019/llm-brewing
Chinese Translation
标准准确性指标无法解释为何大语言模型（LLMs）能够处理变量跟踪却在语义等价的循环中失败。我们研究了代码推理的内部生命周期，其中模型首先酿造答案，使其在变得自我解码之前的多个层次上线性可恢复，然后分化为四种解决结果之一：已解决、过度处理、错误解决或未解决。理解这一生命周期至关重要，因为类似的任务准确性可能掩盖根本不同的失败模式，而表层评估无法检测到。我们引入了一种双重诊断框架，将逐层线性探测与上下文剥离解码（Context-Stripped Decoding, CSD）相结合，并将其应用于涵盖 Qwen、Llama 和 DeepSeek 架构的 16 个模型中的六个代码推理任务系列。所有四种结果在每个任务系列中都占有相当大的比例：总体已解决率仅为 41.5%，多个任务的解决率低于 30%。对结构、深度和操作符的控制性扫描揭示了特定任务的失败瓶颈：函数调用的已解决率从 61.1% 降至 2.5%，当调用深度从一增加到三时。在不同架构和规模中，酿造支架保持稳定，所有 16 个模型的标准化酿造持续时间为 24-42%，而解决成功率则因能力而异。这表明，支架在测试的仅解码 Transformer 家族中是一个稳定的经验规律，而解决成功率则与能力、规模和训练相关联。代码链接： https://github.com/euyis1019/llm-brewing
cs.AI / 25 / 2606.17657
Using Cognitive Models to Improve Language Model Simulation of Human Persuasion Games
利用认知模型改善语言模型对人类说服游戏的模拟
Abstract
People make decisions differently in strategic interactions. Some update beliefs like a Bayesian; others exhibit biases like motivated reasoning. Although creators of large language models use simulated humans for safety evaluations and training, they often fail to cover this breadth of human behavior. We argue that cognitive science and economics provide a convenient tool for doing so, making use of mathematical models of human decision-making. We propose an approach that we call Equation-to-Behavior Prompting for guiding large language models to match cognitive models, and evaluate this approach on persuasion games based on legal decision-making. We find that large models can approximate equation-based specifications -- Bayesian updating, affine distortion, motivated updating, and Grether's $\alpha$-$\beta$ model -- using prompting, but small models fail to do so. However, training small models with reinforcement learning to adhere to mathematical rules, Equation-to-Behavior RL, reduces belief error by 26.5% in out-of-distribution parameterizations. We show that these simulations can help create diverse training environments; training small models to consider different kinds of decision-makers improves average belief change by 2.5%--12% over Bayesian-only training, even when persuading GPT-5-mini. Our work could improve human simulations for training and evaluation in increasingly realistic settings, and could also enable novel research into more complicated mathematical models of human decision-making.
Chinese Translation
人们在战略互动中的决策方式各不相同。一些人像贝叶斯（Bayesian）一样更新信念；而另一些人则表现出如动机推理（motivated reasoning）等偏见。尽管大型语言模型的创建者在安全评估和训练中使用模拟人类，但他们往往未能覆盖人类行为的广泛性。我们认为，认知科学和经济学提供了一种便利的工具，可以利用人类决策的数学模型来实现这一目标。我们提出了一种称为“方程到行为提示”（Equation-to-Behavior Prompting）的方法，以指导大型语言模型与认知模型相匹配，并在基于法律决策的说服游戏中评估该方法。我们发现，大型模型可以通过提示近似基于方程的规范——贝叶斯更新、仿射扭曲、动机更新和Grether的α-β模型——但小型模型无法做到这一点。然而，通过强化学习训练小型模型遵循数学规则的“方程到行为强化学习”（Equation-to-Behavior RL）在分布外参数化中将信念误差降低了26.5%。我们展示了这些模拟可以帮助创建多样化的训练环境；训练小型模型考虑不同类型的决策者使平均信念变化比仅使用贝叶斯训练提高了2.5%至12%，即使在说服GPT-5-mini时也是如此。我们的研究可能改善在日益真实的环境中进行训练和评估的人类模拟，并可能促进对更复杂的人类决策数学模型的新研究。
cs.AI / 26 / 2606.17696
FllumaOne: A Code-Native Multimodal CAD Dataset with Executable Programs and Kernel-Validated Feature Histories
FllumaOne：一个具有可执行程序和内核验证特征历史的代码原生多模态计算机辅助设计数据集
Abstract
Parametric computer-aided design records both final geometry and the ordered construction history that determines how a part can be edited. Datasets for editable CAD research should therefore expose modeling operations, parameters, and feature dependencies together with validated geometry. We introduce FllumaOne, a code-native multimodal CAD dataset whose models are generated by executable Python programs in Flluma, a Qt/C++ OpenCASCADE-based CAD system. Each sample aligns its program with a structured feature tree, a training-oriented intermediate representation, STEP geometry, a surface point cloud, natural-language descriptions, metadata, and eight canonical visible-edge renderings. The primary release, FllumaOne-100K, contains 100,000 accepted samples across four template-level complexity regimes. Programs are executed and retained only after kernel geometry, solid validity, and export checks; release reports also record modality completeness and split-level duplicate tests. A Qwen2.5-Coder-1.5B LoRA baseline trained on 80,000 samples achieves 99.98% Python syntax validity, 99.97% Flluma build success, and 99.14% STEP-export validity on the held-out 10,000-sample test split. For the 9,909 predictions converted to surface point clouds, the mean normalized Chamfer Distance is 0.002124. The dataset supports conditioned CAD reconstruction, executable program synthesis, feature-tree prediction, B-Rep analysis, retrieval, design completion, and editable reverse engineering.
Chinese Translation
参数化计算机辅助设计记录了最终几何形状和决定零件如何编辑的有序构建历史。因此，针对可编辑CAD研究的数据集应当同时展示建模操作、参数、特征依赖关系以及经过验证的几何形状。我们介绍了FllumaOne，这是一个代码原生的多模态CAD数据集，其模型由Flluma中的可执行Python程序生成，Flluma是一个基于Qt/C++ OpenCASCADE的CAD系统。每个样本将其程序与结构化特征树、面向训练的中间表示、STEP几何形状、表面点云、自然语言描述、元数据以及八个典型可见边缘渲染对齐。主要发布版本FllumaOne-100K包含100,000个经过接受的样本，覆盖四个模板级复杂性范畴。程序在内核几何形状、实体有效性和导出检查后执行并保留；发布报告还记录了模态完整性和分割级重复测试。基于80,000个样本训练的Qwen2.5-Coder-1.5B LoRA基线在保留的10,000个样本测试集上实现了99.98%的Python语法有效性、99.97%的Flluma构建成功率和99.14%的STEP导出有效性。对于转换为表面点云的9,909个预测，平均归一化Chamfer距离为0.002124。该数据集支持条件CAD重建、可执行程序合成、特征树预测、B-Rep分析、检索、设计完成和可编辑逆向工程。
cs.AI / 27 / 2606.17698
EComAgentBench: Benchmarking Shopping Agents on Long-Horizon Tasks with Distributed Hidden Intent
EComAgentBench：在具有分布式隐藏意图的长时间任务上评估购物代理
Abstract
As LLM-based shopping agents enter production, existing benchmarks fail to capture how a shopper's requirements arrive: stated implicitly in the query, recorded in a profile, or revealed only when the right question is asked. Benchmarks that expose full intent upfront and grade only the final choice can neither pose this long-horizon challenge nor explain which requirement an agent missed. To address this gap, we introduce EComAgentBench, a benchmark of 662 tasks grounded in real Amazon products and reviews. Each task scatters these requirements across a visible query, a tool-gated profile, and scripted clarification; an agent must uncover hidden intent, verify candidates against attributes and review evidence, and commit to a single product within 100 tool calls. Moreover, typed, source-tagged rubrics grade every task, attributing each failure to a requirement and its source. Construction is automated yet reliable, with every answer fixed in code before any text is generated and every sample validated. Our evaluation of seven models reveals that even the strongest attains only 57.1% overall accuracy, and rubric satisfaction degrades from visible to hidden sources. Overall, we believe EComAgentBench will serve as a reproducible foundation for moving shopping agents from single-query search toward dependable assistance over long horizons.
Chinese Translation
随着基于大语言模型（LLM）的购物代理投入生产，现有基准未能捕捉购物者需求的来源：这些需求可能隐含在查询中、记录在个人资料中，或仅在提出正确问题时才会显现。那些在一开始就揭示完整意图并仅对最终选择进行评分的基准，既无法提出这种长时间的挑战，也无法解释代理遗漏了哪些需求。为了解决这一问题，我们引入了EComAgentBench，这是一个基于真实亚马逊产品和评论的662个任务的基准。每个任务将这些需求分散在可见查询、工具限制的个人资料和脚本化的澄清中；代理必须揭示隐藏意图，验证候选产品与属性和评论证据的一致性，并在100次工具调用内承诺选择单一产品。此外，类型化、源标记的评分标准对每个任务进行评分，将每次失败归因于特定需求及其来源。构建过程是自动化的但可靠，所有答案在生成任何文本之前都已在代码中固定，并且每个样本都经过验证。我们对七个模型的评估显示，即使是最强的模型整体准确率也仅为57.1%，而评分标准的满意度从可见来源到隐藏来源逐渐下降。总体而言，我们相信EComAgentBench将作为一个可重复的基础，推动购物代理从单查询搜索向长时间可靠辅助的转变。
cs.AI / 28 / 2606.17727
LongWebBench: Evaluating Structural and Functional Webpage Generation in Long-Horizon Settings
LongWebBench：在长时间范围设置中评估网页生成的结构与功能
Abstract
Recent vision-language models (VLMs) have shown promising progress in generating webpages from visual inputs, yet existing evaluations mainly focus on short, single-screen, and largely static webpages. We introduce LongWebBench, a benchmark for evaluating long-horizon webpage generation from both structural and functional perspectives. LongWebBench contains 490 real-world long webpages for structural fidelity evaluation and 507 goal-oriented interaction tasks over 129 webpages for functional evaluation. It employs two complementary protocols: a multi-dimensional VLM-based metric for assessing long-range structural coherence, and a DOM-augmented agent-based pipeline for end-to-end functional verification. We further examine the automatic evaluation protocols through human agreement analysis. Experiments with state-of-the-art open-source and proprietary VLMs under single-image and multi-image settings reveal that structural fidelity degrades as webpage length increases, while visually plausible generations often fail to support executable multi-step interactions. These results highlight the need to evaluate long webpage generation beyond visual similarity, with executable interaction as a core criterion. Our code and data are available at https://github.com/zheny2751-dotcom/LongWebBench.
Chinese Translation
近期的视觉-语言模型（VLMs）在从视觉输入生成网页方面取得了令人鼓舞的进展，但现有的评估主要集中在短小、单屏且大多数静态的网页上。我们提出了LongWebBench，一个用于从结构和功能两个角度评估长时间范围网页生成的基准。LongWebBench包含490个真实世界的长网页用于结构保真度评估，以及在129个网页上进行的507个目标导向的交互任务用于功能评估。它采用了两种互补的协议：一种基于多维VLM的度量，用于评估长范围的结构一致性；另一种是增强DOM的基于代理的管道，用于端到端的功能验证。我们进一步通过人类一致性分析检查自动评估协议。在单图像和多图像设置下，使用最先进的开源和专有VLM进行的实验表明，随着网页长度的增加，结构保真度下降，而视觉上合理的生成往往无法支持可执行的多步骤交互。这些结果突显了在评估长网页生成时，除了视觉相似性外，还需要将可执行交互作为核心标准。我们的代码和数据可在https://github.com/zheny2751-dotcom/LongWebBench获取。
cs.AI / 29 / 2606.17735
Shattering the Autoregressive Curse: Dynamic Epistemic Entropy Orchestrated Erasable Reinforcement Learning for LLMs
打破自回归诅咒：动态认知熵协调可擦除强化学习用于大型语言模型
Abstract
Although reinforcement learning (RL) has expanded the cognitive boundaries of large language models (LLMs), it often remains vulnerable to the autoregressive curse in long-horizon logical reasoning: small epistemic perturbations introduced early in generation can propagate irreversibly along the Markov decision process flow, triggering cascading failures that drive the reasoning trajectory toward collapse. To overcome this autoregressive cascade, in which a single early mistake can compromise all subsequent reasoning steps, we propose dynamic epistemic entropy orchestrated erasable reinforcement learning ($\text{E}^3\text{RL}$). $\text{E}^3\text{RL}$ eliminates reliance on external signals by grounding the model's endogenous local autoregressive cross-entropy as an intrinsic coordinate of epistemic uncertainty. By introducing segment-level adaptive dynamic thresholds and advantage allocation, $\text{E}^3\text{RL}$ enables the model to precisely excise localized logical defects while reusing historical key-value (KV) cache streams, thereby endowing the reasoning process with a self-healing capability. We train $\text{E}^3\text{RL}$ on the DeepMath-103k dataset. Experimental results show that $\text{E}^3\text{RL}$ reshapes the exploration efficiency of long-sequence reasoning and improves sample efficiency while maintaining linear memory overhead. On mathematical reasoning benchmarks such as AIME, $\text{E}^3\text{RL}$ achieves substantial performance gains, with the 4B and 8B parameter models surpassing previous state-of-the-art (SOTA) results by 5.349\% and 6.514\%, respectively. These findings suggest that $\text{E}^3\text{RL}$ shatters the autoregressive curse in long-sequence reasoning and establishes a theoretical and systems-level foundation for the next generation of self-healing artificial general intelligence (AGI).
Chinese Translation
尽管强化学习（RL）扩展了大型语言模型（LLMs）的认知边界，但在长时间逻辑推理中，它仍然容易受到自回归诅咒的影响：在生成早期引入的小的认知扰动可以沿着马尔可夫决策过程流不可逆地传播，触发级联失败，使推理轨迹走向崩溃。为了克服这种自回归级联，其中一个早期错误可能会危及所有后续推理步骤，我们提出了动态认知熵协调可擦除强化学习（$ ext{E}^3 ext{RL}$）。$ ext{E}^3 ext{RL}$通过将模型的内生局部自回归交叉熵作为认知不确定性的内在坐标，消除了对外部信号的依赖。通过引入段级自适应动态阈值和优势分配，$ ext{E}^3 ext{RL}$使模型能够精确地切除局部逻辑缺陷，同时重用历史关键值（KV）缓存流，从而赋予推理过程自我修复的能力。我们在DeepMath-103k数据集上训练了$ ext{E}^3 ext{RL}$。实验结果表明，$ ext{E}^3 ext{RL}$重塑了长序列推理的探索效率，并在保持线性内存开销的同时提高了样本效率。在数学推理基准测试如AIME上，$ ext{E}^3 ext{RL}$实现了显著的性能提升，4B和8B参数模型分别超越了之前的最先进（SOTA）结果5.349 ext{%}和6.514 ext{%}。这些发现表明，$ ext{E}^3 ext{RL}$打破了长序列推理中的自回归诅咒，并为下一代自我修复的人工通用智能（AGI）奠定了理论和系统层面的基础。
cs.AI / 30 / 2606.17821
DecoSearch: Complexity-Aware Routing and Plan-Level Repair for Text-to-SQL
DecoSearch：复杂性意识的路由和计划级修复用于文本到 SQL
Abstract
Large Language Models (LLMs) have demonstrated remarkable capabilities in translating natural language to SQL, yet existing methods still falter on complex queries requiring multi-step, data-aware reasoning. We introduce DecoSearch, a training-free framework that addresses this by routing each query to the appropriate level of reasoning effort. A lightweight Schema Selector first prunes the full database schema to the relevant tables and columns. An LLM Judger then decides whether the question requires decomposition: straightforward questions follow a direct generation path and complex ones are escalated to a Directed Acyclic Graph (DAG) of atomic sub-questions, each solved by a targeted SQL generation step. A RAG component grounds the decomposer with semantically similar training examples, and a Topology Refiner restructures the reasoning plan when execution failures signal a flawed decomposition rather than a fixable SQL error. DecoSearch achieves 70.53% execution accuracy on BIRD and 88.31% on Spider with a DeepSeek backbone, surpassing all training-free baselines while consuming an order of magnitude fewer tokens than competing methods. It also functions as a model-agnostic wrapper, consistently improving fine-tuned SQL generation backbones without any modification to the pipeline.
Chinese Translation
大型语言模型（LLMs）在将自然语言翻译为 SQL 方面展现了显著的能力，但现有方法在处理需要多步骤、数据感知推理的复杂查询时仍然存在不足。我们提出了 DecoSearch，这是一个无须训练的框架，通过将每个查询路由到适当的推理努力水平来解决这一问题。一个轻量级的模式选择器首先将完整的数据库模式修剪到相关的表和列。然后，一个 LLM 判别器决定问题是否需要分解：简单问题遵循直接生成路径，而复杂问题则被提升到一个有向无环图（DAG），其中包含原子子问题，每个子问题通过一个针对性的 SQL 生成步骤解决。一个 RAG 组件用语义相似的训练示例为分解器提供基础，而一个拓扑优化器在执行失败信号表明分解存在缺陷而非可修复的 SQL 错误时重构推理计划。DecoSearch 在 BIRD 上实现了 70.53% 的执行准确率，在 Spider 上实现了 88.31% 的准确率，使用 DeepSeek 主干，超越了所有无训练基线，同时消耗的标记数量比竞争方法少一个数量级。它还作为一个模型无关的封装器，持续改善微调的 SQL 生成主干，而无需对管道进行任何修改。
cs.AI / 31 / 2606.17847
WallZero: Mastering the Game of WallGo with Strategic Analysis
WallZero：通过战略分析掌握WallGo游戏
Abstract
WallGo is a recently introduced strategic board game popularized by the 2025 Netflix series The Devil's Plan. Although played on a small 7 x 7 board, its combination of stone movement and wall placement yields high game-tree complexity and intricate strategic interactions. Despite its growing popularity, WallGo remains underexplored. This paper presents WallZero, an AlphaZero-based agent for the two-player WallGo setting. We introduce tailored action and feature designs to improve playing performance significantly. In the evaluation, WallZero defeats two professional Go players who participated in this study, securing on average 1.98x more territory per game. Beyond its strength, we use WallZero to assess game fairness and identify key strategies for mastering WallGo. Interestingly, our results show that the opening used in the Netflix series yields a more balanced game. Our code is available at https://rlg.iis.sinica.edu.tw/papers/wallzero.
Chinese Translation
WallGo是一款最近推出的战略棋盘游戏，由2025年Netflix系列《魔鬼的计划》推广。尽管在一个7 x 7的小棋盘上进行游戏，但其石头移动和墙壁放置的组合导致了高游戏树复杂性和复杂的战略互动。尽管其受欢迎程度日益上升，WallGo仍然未被充分探索。本文提出了WallZero，一个基于AlphaZero的双人WallGo环境的智能体。我们引入了量身定制的动作和特征设计，以显著提高游戏表现。在评估中，WallZero击败了参与本研究的两位职业围棋选手，平均每局获得1.98倍的领土。除了其强大实力外，我们还利用WallZero评估游戏公平性并识别掌握WallGo的关键策略。有趣的是，我们的结果显示，Netflix系列中使用的开局策略能够产生更为平衡的游戏。我们的代码可在 https://rlg.iis.sinica.edu.tw/papers/wallzero 获取。
cs.AI / 32 / 2606.17851
A homotopy-type-theoretic generalization of neurosymbolic inference
一种同伦类型理论的神经符号推理推广
Abstract
A wide range of neurosymbolic (NeSy) systems compute one functional: a belief-weighted sum of a logical quantity over a space of $\sigma$-structures, of which weighted model counting, fuzzy logic, and probabilistic logic are special cases. This account is built on sets, and a set deliberately forgets two things that are important for NeSy: when two $\sigma$-structures are the same up to a symmetry of the theory, and how many distinct proofs witness a query. Replacing the underlying sets by types, in the sense of homotopy type theory, preserves this information, and turns this functional into a belief-weighted homotopy cardinality, a notion of size that counts each object in inverse proportion to its symmetries. We develop the framework from scratch for NeSy systems, prove a conservativity theorem that recovers the classical functional when symmetries are trivial, and show that the symmetry our framework exposes is exactly the one behind reasoning shortcuts. The payoff is concrete: the shortcut-aware concept posterior that recent methods reach by ensembling or expressive density estimation is the only symmetry-invariant point of the confusion-set simplex, computable in closed form by averaging a single model over the symmetry group. On MNIST reasoning-shortcut benchmarks this single-model wrapper is better calibrated than a diversity-trained ensemble, while leaving label accuracy and identifiable concepts untouched. Code is freely available at https://github.com/bio-ontology-research-group/hott-nesy.
Chinese Translation
广泛的神经符号（NeSy）系统计算一个功能：在$ au$-结构空间上对逻辑量进行信念加权求和，其中加权模型计数、模糊逻辑和概率逻辑是特例。该论述基于集合，而集合故意忽略了对NeSy重要的两个方面：当两个$ au$-结构在理论的对称性下相同时，以及有多少个不同的证明见证一个查询。通过用同伦类型理论意义上的类型替代基础集合，保留了这些信息，并将该功能转化为信念加权的同伦基数，这是一种计数每个对象与其对称性成反比的大小概念。我们从头为NeSy系统开发这一框架，证明了一个保守性定理，当对称性是平凡时恢复经典功能，并展示我们框架所暴露的对称性正是推理捷径背后的对称性。其具体收益是：最近的方法通过集成或表达密度估计所达到的捷径感知概念后验是混淆集单纯形中唯一的对称不变点，可以通过在对称群上平均单个模型以封闭形式计算。在MNIST推理捷径基准测试中，这个单模型包装的校准效果优于多样性训练的集成，同时保持标签准确性和可识别概念不变。代码可在https://github.com/bio-ontology-research-group/hott-nesy上免费获取。
cs.AI / 33 / 2606.17856
FlowRAG: Synergizing Explicit Reasoning via Frequency-Aware Multi-Granularity Graph Flow
FlowRAG：通过频率感知的多粒度图流协同显式推理
Abstract
Graph-based retrieval-augmented generation (GraphRAG) is effective for knowledge-intensive and multi-hop query tasks; however, many existing methods primarily seed entity-based graphs and rely on implicit semantic relevance propagation. This often (i) under-retrieves when user queries are abstract and semantically sparse at the entity level, and (ii) suffers from brittle multi-hop reasoning, where noisy activations can derail entity-to-entity transitions and corrupt the inferred relation chain, yielding unreliable conclusions. To this end, we propose \texttt{FlowRAG}, a semantic-aware retrieval framework that improves both semantic recall and explicit reasoning. Specifically, \texttt{FlowRAG} constructs a quad-level heterogeneous graph over passages, summaries, sentences, and entities, where summary nodes serve as a coarse semantic hub. At retrieval time, a dual-granularity activation module combines summary--query alignment with sentence-level matching to activate relevant entities under paraphrase and abstraction robustly. We then introduce a frequency-aware weighted flow module that routes relevance through entity--passage links weighted by within-passage term frequency, pruning noisy connections and extracting high-confidence reasoning paths as an explicit logic skeleton for generation. Extensive experiments show that \texttt{FlowRAG} obtains state-of-the-art performance on complex reasoning benchmarks.
Chinese Translation
基于图的检索增强生成（GraphRAG）在知识密集型和多跳查询任务中表现有效；然而，许多现有方法主要依赖于基于实体的图，并依赖于隐式语义相关性传播。这通常会导致（i）当用户查询在实体层面上抽象且语义稀疏时检索不足，以及（ii）在脆弱的多跳推理中，噪声激活可能会干扰实体到实体的转移并破坏推断的关系链，从而得出不可靠的结论。为此，我们提出了 exttt{FlowRAG}，一种语义感知的检索框架，旨在提高语义召回和显式推理。具体而言， exttt{FlowRAG}在段落、摘要、句子和实体上构建了一个四层异构图，其中摘要节点作为粗略的语义中心。在检索时，双粒度激活模块结合了摘要与查询的对齐和句子级匹配，以稳健地激活相关实体，适应同义改写和抽象。然后，我们引入了一个频率感知的加权流模块，通过加权的实体-段落链接路由相关性，权重基于段落内的术语频率，修剪噪声连接并提取高置信度的推理路径，作为生成的显式逻辑骨架。大量实验表明， exttt{FlowRAG}在复杂推理基准测试中取得了最先进的性能。
cs.AI / 34 / 2606.17871
StepGuard: Guarding Web Navigation via Single-Step Calibration
StepGuard：通过单步校准保护网络导航
Abstract
Web navigation requires agents to follow natural language goals, interact with web pages, and produce accurate answers. While recent advances leverage vision-language models and reinforcement learning, existing methods still suffer from single-step fragility due to reward misalignment and error propagation. To tackle the reward entanglement, we design Dynamic Dual-Policy Optimization (DDPO), which dynamically switches between a navigation-first mode for exploration and an answer-first mode for question-answering to mitigate reward conflict. To calibrate the single-step error, we propose Confidence-Guided Adaptive Navigation Reflection (CANR), a mechanism that estimates per-step confidence, triggers reflection only when necessary, and uses contrastive rewards to encourage self-correction to calibrate the single-step inaccuracy. With the above as the main components, we finally develop our StepGuard, a new framework of Guarding Web Navigation via Single-Step Calibration. Experiments demonstrate that our approach significantly improves navigation and answer accuracy, setting new state-of-the-art performance on standard web navigation benchmarks.
Chinese Translation
网络导航要求智能体遵循自然语言目标，与网页互动，并产生准确的答案。尽管最近的进展利用了视觉-语言模型和强化学习，但现有方法仍然由于奖励不一致和错误传播而遭受单步脆弱性。为了解决奖励纠缠问题，我们设计了动态双策略优化（Dynamic Dual-Policy Optimization, DDPO），该方法在探索时动态切换到以导航为主的模式，在问答时切换到以答案为主的模式，以减轻奖励冲突。为了校准单步错误，我们提出了基于信心的自适应导航反思（Confidence-Guided Adaptive Navigation Reflection, CANR）机制，该机制估计每一步的信心，仅在必要时触发反思，并使用对比奖励来鼓励自我修正，以校准单步的不准确性。以上作为主要组成部分，我们最终开发了StepGuard，这是一个通过单步校准保护网络导航的新框架。实验表明，我们的方法显著提高了导航和答案的准确性，在标准网络导航基准上设定了新的最先进性能。
cs.AI / 35 / 2606.17882
Structural Preservation and the Logical Expressiveness of Graph Neural Networks
图神经网络的结构保持与逻辑表达能力
Abstract
Bridges between graph neural networks (GNNs) and logical formalisms have been established by fixing architectural choices, such as the types of aggregation, combination, and activation functions. These choices define restricted classes of GNNs for which tight correspondences with logical formalisms can be obtained, by showing that logical formulae can be translated into equivalent GNNs and, conversely, that GNNs can be translated into equivalent formulae. In this paper we take a semantic perspective by establishing the logical expressiveness of classes of GNN classifiers that are preserved under structural properties: embeddings (extensions), injective homomorphisms, and homomorphisms. We show that, for each such property, there exists a fragment of graded modal logic characterising the class of GNNs. In particular, preservation under embeddings, injective homomorphisms, and homomorphisms corresponds to existential graded modal logic, its existential-positive fragment, and existential-positive modal logic, respectively. These results characterise the expressiveness of broad classes of GNNs independently of specific architectural choices, but we also show that each of these classes admits a GNN architecture of the same expressiveness. Technically, our approach uses a new well-quasi-order result for trees of bounded height, yielding finite representations of unravelling-invariant classes.
Chinese Translation
通过固定架构选择（例如聚合、组合和激活函数的类型），建立了图神经网络（GNNs）与逻辑形式之间的桥梁。这些选择定义了受限类别的GNN，对于这些类别，可以获得与逻辑形式的紧密对应，表明逻辑公式可以被转换为等价的GNN，反之亦然。在本文中，我们从语义的角度出发，建立了在结构属性下保持的GNN分类器类别的逻辑表达能力：嵌入（扩展）、单射同态和同态。我们展示了对于每个这样的属性，存在一个分级模态逻辑的片段来表征GNN的类别。特别地，嵌入、单射同态和同态的保持分别对应于存在性分级模态逻辑、其存在性-正片段和存在性-正模态逻辑。这些结果独立于特定的架构选择表征了广泛类别的GNN的表达能力，但我们也展示了每个类别都允许具有相同表达能力的GNN架构。从技术上讲，我们的方法使用了一个新的有界高度树的良序结果，得出了可展开不变类的有限表示。
cs.AI / 36 / 2606.17888
MathVis-Fine: Aligning Visual Supervision with Necessity via Progressive Dependency-Guided Training for Multimodal Mathematical Reasoning
MathVis-Fine：通过渐进式依赖引导训练将视觉监督与必要性对齐，以实现多模态数学推理
Abstract
Chain-of-Thought (CoT) reasoning has extended from purely linguistic domains to multimodal scenarios; however, existing approaches often treat visual inputs as homogeneous or auxiliary signals, failing to capture the intricate and sample-specific dependencies between text and images in mathematical problem-solving. This gives rise to two core issues: first, the supervisory signals for visual content are generalized and coarse-grained, lacking adaptation to the actual necessity of visual information in each sample; second, training feedback becomes inaccurate when visual rewards are uniformly applied without distinguishing the complementary relationships among inputs. These limitations hinder models from achieving precise multimodal reasoning. In this work, we propose a framework for modeling fine-grained visual dependencies in mathematical reasoning. We first construct the MathVis-Fine dataset, augmenting fine-grained visual annotations with visual dependency ratings. Building upon this dataset, we introduce a two-stage progressive visual enhancement training paradigm that balances answer correctness rewards and visual grounding rewards according to the intrinsic visual dependency level of each sample, thereby mitigating reward bias and improving supervision accuracy. Extensive experiments demonstrate that the MathVis-Fine framework effectively enhances visual perception progressively based on visual dependency, offering a more precise training framework for multimodal mathematical reasoning. We will release the dataset upon acceptance.
Chinese Translation
链式思维（Chain-of-Thought, CoT）推理已从纯语言领域扩展到多模态场景；然而，现有方法往往将视觉输入视为同质或辅助信号，未能捕捉到文本与图像在数学问题解决中的复杂且样本特定的依赖关系。这导致了两个核心问题：首先，视觉内容的监督信号被概括且粗糙，缺乏对每个样本中视觉信息实际必要性的适应；其次，当视觉奖励在输入之间均匀应用而不区分其互补关系时，训练反馈变得不准确。这些局限性阻碍了模型实现精确的多模态推理。在本研究中，我们提出了一个框架来建模数学推理中的细粒度视觉依赖关系。我们首先构建了MathVis-Fine数据集，增强了细粒度视觉注释与视觉依赖评分。在此数据集的基础上，我们引入了一种两阶段的渐进式视觉增强训练范式，根据每个样本的内在视觉依赖水平平衡答案正确性奖励和视觉基础奖励，从而减轻奖励偏差并提高监督准确性。大量实验表明，MathVis-Fine框架有效地基于视觉依赖逐步增强视觉感知，为多模态数学推理提供了更精确的训练框架。我们将在论文被接受后发布该数据集。
cs.AI / 37 / 2606.17897
Learn to Quantify Social Interaction with Constraints for Pedestrian Walking
在行人行走中学习量化带约束的社会互动
Abstract
Long-term human path forecasting in crowds is critical for autonomous moving platforms (like autonomous driving cars and social robots) to avoid collision and make high-quality planning. Although the current research take into account social interactions for prediction, they don't reveal the exact kinds of social interactions happened among people and how the social interactions affect the decision-making process of pedestrians, which further limits its robustness. Social interactions in pedestrian walking are intuitively massive and hard to label and quantify. In this paper, we explore creatively to quantify and interpret how pedestrians interact with others by proposing Learn to Cluster. Our clustering social interactions is probabilistic latent variable generative, learning directly from sequential trajectory observations, scalable to arbitrary number of pedestrians. Learn to cluster is label-free and can be naturally integrated into the training process of the prediction model. The latent variables will then serve as 'labels' to categorize social interactions. Extensive experiments over several trajectory prediction benchmarks demonstrate that our method is able to learn the patterns of social interactions and effectively integrate the patterns to pedestrian trajectory prediction.
Chinese Translation
在人群中进行长期人类路径预测对于自主移动平台（如自动驾驶汽车和社交机器人）避免碰撞和进行高质量规划至关重要。尽管当前研究考虑了社会互动对预测的影响，但并未揭示人们之间发生的具体社会互动类型以及这些社会互动如何影响行人的决策过程，这进一步限制了其鲁棒性。行人行走中的社会互动直观上是庞大且难以标注和量化的。本文通过提出“学习聚类”（Learn to Cluster）这一方法，创造性地探讨了如何量化和解释行人与他人之间的互动。我们的聚类社会互动方法是基于概率潜变量生成的，直接从顺序轨迹观测中学习，能够扩展到任意数量的行人。“学习聚类”是无标签的，并且可以自然地融入预测模型的训练过程中。潜变量将作为“标签”来对社会互动进行分类。针对多个轨迹预测基准的广泛实验表明，我们的方法能够学习社会互动的模式，并有效地将这些模式整合到行人轨迹预测中。
cs.AI / 38 / 2606.17904
DiagFlowBench: Evaluating How Language Models Handle Off-Procedure Inputs in Grounded Diagnostic Dialogue
DiagFlowBench：评估语言模型如何处理基于事实的诊断对话中的超出程序输入
Abstract
Language models increasingly serve as advisory systems in maintenance operations. To prevent hallucination, recent systems ground these models in procedural documentation to constrain them to approved steps. In practice, however, operator queries frequently stray from this path, requiring models to recognise out-of-scope inputs mid-conversation, a dynamic that current benchmarks rarely prioritise. We introduce DiagFlowBench, a dataset of 50 industrial diagnostic flowcharts from a consumer manufacturer converted into 1,676 multi-turn conversations that contrast compliant with out-of-scope utterances. Evaluating a panel of ten commercial and open-weight models reveals high variability in abstention rates, with models commonly selecting a real but contextually inadequate step rather than fabricating facts. The inherent plausibility and authority of this mapped but wrong advice exposes a challenging vulnerability for grounding systems.
Chinese Translation
语言模型越来越多地作为维护操作中的咨询系统。为了防止幻觉，最近的系统将这些模型与程序文档相结合，以限制它们遵循批准的步骤。然而，在实践中，操作员的查询常常偏离这一路径，要求模型在对话中识别超出范围的输入，而当前的基准测试很少优先考虑这种动态。我们引入了DiagFlowBench，一个包含50个来自消费品制造商的工业诊断流程图的数据集，转换为1,676个多轮对话，比较合规与超出范围的发言。对十个商业和开放权重模型的评估显示，弃权率存在较高的变异性，模型通常选择一个真实但在上下文中不适当的步骤，而不是虚构事实。这种映射但错误建议的内在合理性和权威性暴露了基础系统面临的一种挑战性脆弱性。
cs.AI / 39 / 2606.17929
PreAct: Computer-Using Agents that Get Faster on Repeated Tasks
PreAct：在重复任务中加速的计算机使用代理
Abstract
Computer-using agents drive real software through the screen -- clicking and typing -- but they solve every task from scratch: asked to repeat a task, an agent re-reads the screen, re-reasons every tap, and pays the full cost again. We present PreAct, which lets such an agent get faster on tasks it has done before. The first time it succeeds, PreAct compiles the run into a small state-machine program-states that check the screen, transitions that act-and on later runs replays it directly instead of invoking the agent 8.5-13x faster, with no per-step language-model calls. Replay is not blind: at each step PreAct checks that the screen matches what the program expects before acting, and hands control back to the agent the moment something is off. PreAct applies the same discipline when deciding what to keep: a freshly compiled program enters the store only if, re-run from a clean state, an independent evaluator confirms it solved the task-catching programs that replay to their last step yet leave the task undone. Across a mobile, a desktop, and a web benchmark, this store-time check separates repeated runs that improve from ones that degrade as faulty programs accumulate, worth 1.75-2.6 tasks per benchmark, the same direction on all three; a fallback that explores afresh when no program fits brings PreAct level with a strong record-and-replay baseline. We also report what did not matter: prompt wording, runtime guardrails, and whether a language model or a plain embedding retriever selects which program to reuse.
Chinese Translation
计算机使用代理通过屏幕驱动真实软件——点击和输入——但它们每次都从头开始解决每个任务：当被要求重复一个任务时，代理重新读取屏幕，重新推理每一次点击，并再次支付全部成本。我们提出了PreAct，使得这样的代理在之前完成的任务上能够加速。第一次成功时，PreAct将运行编译成一个小的状态机程序——状态检查屏幕，转换执行操作——在后续运行中直接重放，而不是调用代理，速度提高了8.5-13倍，且没有每一步的语言模型调用。重放并非盲目：在每一步，PreAct检查屏幕是否与程序预期匹配，然后再执行操作，并在出现偏差时立即将控制权交还给代理。PreAct在决定保留什么时也应用相同的原则：只有在从干净状态重新运行时，独立评估者确认新编译的程序解决了任务，才会将其存入库中——捕捉到那些重放到最后一步但未完成任务的程序。在移动设备、桌面和网络基准测试中，这种存储时间检查将改善的重复运行与因故障程序积累而恶化的运行区分开来，每个基准测试的价值为1.75-2.6个任务，三个方向一致；当没有程序适合时，回退机制重新探索，使PreAct的表现与强大的记录与重放基线相当。我们还报告了那些不重要的因素：提示措辞、运行时保护措施，以及是语言模型还是普通嵌入检索器选择重用哪个程序。
cs.AI / 40 / 2606.17930
How Inference Compute Shapes Frontier LLM Evaluation
推理计算如何塑造前沿大语言模型评估
Abstract
AI evaluations are shifting toward harder tasks that benefit from longer trajectories involving tool use and iterative problem solving. As a result, performance is increasingly sensitive to the amount and allocation of compute available at test time ("inference compute"). Yet many evaluations still report performance at a single restrictive budget, meaning that low scores may reflect the evaluation setup rather than the model's underlying capability. To test this, we evaluate up to 12 frontier language models on seven challenging benchmarks spanning software engineering, mathematics, medicine, and cybersecurity. We use a controlled setup combining three simple inference-scaling interventions: larger token budgets, context compaction, and repeated submission attempts, guided either by the model itself or by minimal correctness feedback. We find three main results. First, larger token budgets substantially improve performance on benchmarks across multiple domains, including cybersecurity, FrontierMath, Humanity's Last Exam, and TerminalBench. Second, fixed-budget evaluations can increasingly understate frontier capability as models advance. Newer models reach higher performance at large budgets, where they unlock harder tasks and solve them more reliably. Third, benchmarks differ in which inference-scaling methods help most: repeated submission broadly improves performance, but the value of larger token budgets, external feedback, and parallel attempts varies by benchmark. Overall, our results show that benchmark scores are protocol-dependent. We therefore argue that evaluations should report capability as a function of inference-time compute, specify protocol choices explicitly, and compare model generations over a large shared compute range at matched budgets, especially in safety- or policy-relevant settings.
Chinese Translation
人工智能评估正逐渐转向更具挑战性的任务，这些任务受益于涉及工具使用和迭代问题解决的较长轨迹。因此，模型的性能越来越敏感于测试时可用的计算资源的数量和分配（“推理计算”）。然而，许多评估仍然在单一限制预算下报告性能，这意味着低分可能反映评估设置而非模型的基础能力。为此，我们在七个涵盖软件工程、数学、医学和网络安全的挑战性基准上评估多达12个前沿语言模型。我们使用一个受控设置，结合三种简单的推理扩展干预措施：更大的标记预算、上下文压缩和重复提交尝试，这些尝试由模型自身或最小的正确性反馈引导。我们发现了三个主要结果。首先，更大的标记预算显著提高了多个领域基准的性能，包括网络安全、FrontierMath、人类最后的考试和TerminalBench。其次，固定预算的评估在模型进步时可能越来越低估前沿能力。较新的模型在较大预算下达到更高的性能，在此预算下它们能够解锁更难的任务并更可靠地解决这些任务。第三，各基准在推理扩展方法的有效性上存在差异：重复提交普遍提高了性能，但更大的标记预算、外部反馈和并行尝试的价值因基准而异。总体而言，我们的结果表明，基准分数依赖于协议。因此，我们认为评估应报告能力作为推理时计算的函数，明确指定协议选择，并在匹配预算的情况下比较模型代际的表现，特别是在安全或政策相关的环境中。
cs.AI / 41 / 2606.17945
Small Initialization Matters for Large Language Models
小规模初始化对大型语言模型的重要性
Abstract
Large language models provide a tractable system for asking how intelligence itself emerges, rather than only how LLMs can be engineered. Although progress is usually attributed to scale, data and architecture, we show that parameter initialization is a gene-like determinant of training and, in particular, of model capacity. Reducing the initialization scale consistently improves pretraining, with the largest gains on reasoning-demanding tasks. We identify two widely used empirical settings that restrain the advantage of small initialization, and show how relaxing them restores favorable scaling. We further uncover a critical initialization that balances the reasoning and training. Mechanistically, small initialization drives a distinct developmental trajectory: parameters first condense into low-complexity structures and later expand into richer representations, giving concrete form to the idea that compression is intelligence. Token-level analyses show that the gains concentrate on non-trivial, context-constrained predictions rather than all tokens uniformly. These results motivate a simple $\gamma$-initialization rule: expose initialization rage as an explicit knob and use small initialization by default, an almost cost-free intervention that improves pretraining and strengthens reasoning across model scales.
Chinese Translation
大型语言模型提供了一个可行的系统，用于探讨智能本身是如何产生的，而不仅仅是如何设计LLM（大型语言模型）。尽管进展通常归因于规模、数据和架构，我们表明，参数初始化是训练的一个基因般的决定因素，尤其是模型容量。减少初始化规模始终能改善预训练，尤其在需要推理的任务上获得最大收益。我们识别出两种广泛使用的经验设置，这些设置限制了小规模初始化的优势，并展示了如何放宽这些限制以恢复有利的扩展。我们进一步发现了一种关键的初始化方式，它在推理和训练之间取得平衡。从机制上讲，小规模初始化驱动了一条独特的发展轨迹：参数首先凝聚成低复杂度结构，然后扩展为更丰富的表示，具体体现了压缩即智能的理念。基于标记的分析表明，收益集中在非平凡的、受上下文限制的预测上，而不是均匀分布于所有标记。这些结果促使我们提出一个简单的$ ext{γ}$-初始化规则：将初始化范围作为一个明确的调节器，并默认使用小规模初始化，这是一种几乎没有成本的干预措施，能够改善预训练并增强不同模型规模的推理能力。
cs.AI / 42 / 2606.17978
MoCo-AIS: A Contrastive Learning Framework for Similarity Computation of Vessel Trajectories
MoCo-AIS：一种用于船舶轨迹相似性计算的对比学习框架
Abstract
Trajectory similarity is a fundamental task in analyzing mobility patterns, essential for applications such as route pattern extraction, mobility prediction, and anomaly detection. Traditional distance-based measures for computing similarity incur high computational cost, driving the adoption of lightweight learning-based approaches. Supervised methods rely on extensive labels derived from traditional distance measures and often reproduce these metrics, which limits generalization. While self-supervised learning addresses this issue through contrastive learning, it lacks a unified framework, making it difficult to compare deep learning (DL) models for consistent trajectory representation. Accordingly, this paper presents MoCo-AIS, a unified framework for learning vessel trajectory embeddings based on the Momentum Contrast (MoCo) paradigm, which formulates similarity learning through positive and negative trajectory pairs. Within this framework, we evaluate a diverse set of leading DL models on large-scale, real-world vessel-tracking AIS datasets that capture diverse navigation behaviors and operating conditions. Results demonstrate that our framework significantly improves similarity learning over existing baselines, while providing a benchmarking platform for evaluating trajectory representation models.
Chinese Translation
轨迹相似性是分析移动模式的一个基本任务，对于路线模式提取、移动预测和异常检测等应用至关重要。传统的基于距离的相似性计算方法计算成本高，这推动了轻量级学习方法的采用。监督方法依赖于从传统距离度量中获得的大量标签，并且通常重复这些度量，这限制了其泛化能力。虽然自监督学习通过对比学习解决了这一问题，但缺乏统一的框架，使得比较深度学习（DL）模型以实现一致的轨迹表示变得困难。因此，本文提出了MoCo-AIS，一个基于动量对比（Momentum Contrast, MoCo）范式的统一框架，用于学习船舶轨迹嵌入，该框架通过正负轨迹对来构建相似性学习。在此框架内，我们在大规模真实世界的船舶跟踪AIS数据集上评估了一系列领先的深度学习模型，这些数据集捕捉了多样的导航行为和操作条件。结果表明，我们的框架在相似性学习方面显著优于现有基线，同时提供了一个评估轨迹表示模型的基准平台。
cs.AI / 43 / 2606.17979
STAR: SpatioTemporal Adaptive Reward Allocation for Text-to-Image RL Post-Training
STAR：用于文本到图像的强化学习后训练的时空自适应奖励分配
Abstract
Existing RL post-training methods for text-to-image generation usually convert the final-image reward into a single scalar advantage and apply it with the same strength to the entire generative trajectory. However, text-to-image generation naturally has temporal and spatial structure: different denoising steps are responsible for different generation stages, and the content that truly determines text alignment often appears only in part of the image. This granularity mismatch makes it difficult for policy updates to focus on the generative components that actually affect the reward. To address this issue, we propose \textbf{SpatioTemporal Adaptive Reward (STAR) Allocation} for RL post-training of text-to-image diffusion and flow models. STAR uses text-image attention inside the generative model and starts from the core content that the user truly cares about in the prompt. It constructs spatial allocation maps that dynamically vary across denoising steps and rollouts, and allocates the same group-relative advantage to more relevant latent regions with almost no additional computational overhead. STAR then applies stronger policy updates to these regions through a spatially resolved policy objective. We use Stable Diffusion 3.5 Medium as the base model and evaluate on three tasks: GenEval, OCR text rendering, and PickScore. Experimental results show that STAR improves compositional semantic alignment, text rendering, and preference optimization without changing the external reward source, achieving $\mathbf{0.9759}$, $\mathbf{0.9757}$, and $\mathbf{23.60}$ on GenEval, OCR, and PickScore, respectively.
Chinese Translation
现有的文本到图像生成的强化学习（RL）后训练方法通常将最终图像的奖励转换为单一的标量优势，并以相同的强度应用于整个生成轨迹。然而，文本到图像生成自然具有时间和空间结构：不同的去噪步骤负责不同的生成阶段，而真正决定文本对齐的内容往往只出现在图像的一部分。这种粒度不匹配使得策略更新难以集中于那些实际影响奖励的生成组件。为了解决这个问题，我们提出了用于文本到图像扩散和流模型的时空自适应奖励（STAR）分配。STAR利用生成模型中的文本-图像注意力，从用户在提示中真正关心的核心内容开始。它构建了在去噪步骤和生成过程中动态变化的空间分配图，并将相同的组相对优势分配给更相关的潜在区域，几乎没有额外的计算开销。然后，STAR通过空间分辨的策略目标对这些区域应用更强的策略更新。我们使用Stable Diffusion 3.5 Medium作为基础模型，并在三个任务上进行评估：GenEval、OCR文本渲染和PickScore。实验结果表明，STAR在不改变外部奖励源的情况下，提高了组合语义对齐、文本渲染和偏好优化，在GenEval、OCR和PickScore上分别达到了$ extbf{0.9759}$、$ extbf{0.9757}$和$ extbf{23.60}$的成绩。
cs.AI / 44 / 2606.18005
LLM Consumer Behavior Theory: Foundations of a Novel Research Field
大型语言模型消费者行为理论：新兴研究领域的基础
Abstract
Large language models (LLMs) are increasingly deployed as autonomous agents that make consumption decisions on behalf of users. This shift raises fundamental questions for consumer theory, which has traditionally modeled humans as the primary decision-makers. In this paper, we introduce LLM Consumer Behavior Theory, a new field of study concerned with analyzing consumer behavior in agentic markets. Drawing on classical and behavioral economics alongside recent advances in Natural Language Processing, we formalize how human preferences are reflected and acted upon by LLM-based agents, and how agent-level decisions aggregate into market demand. We unify previously fragmented literature on LLM decision-making, human behavior simulation, and preference elicitation under a common economic lens, highlighting where assumptions, such as rationality and heterogeneity, may fail in agentic markets. Rather than providing empirical validation, this paper outlines the scope of LLM consumer behavior and identifies open research questions related to alignment, preference representation, and market dynamics.
Chinese Translation
大型语言模型（LLMs）越来越多地被作为自主代理部署，代表用户做出消费决策。这一转变对消费者理论提出了根本性的问题，传统上该理论将人类视为主要决策者。本文介绍了大型语言模型消费者行为理论，这是一个关注分析代理市场中消费者行为的新研究领域。我们结合经典经济学和行为经济学，以及自然语言处理的最新进展，正式阐述了人类偏好如何被基于LLM的代理反映和执行，以及代理级别的决策如何聚合成市场需求。我们在一个共同的经济视角下统一了之前在LLM决策、人体行为模拟和偏好引导方面的零散文献，强调了在代理市场中，诸如理性和异质性等假设可能失效的地方。本文并未提供实证验证，而是概述了大型语言模型消费者行为的范围，并识别了与对齐、偏好表示和市场动态相关的开放研究问题。
cs.AI / 45 / 2606.18021
LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI
LegalHalluLens：可信法律人工智能的类型化幻觉审计与校准多智能体辩论
Abstract
AI systems deployed in legal workflows hallucinate at rates that aggregate metrics report at ~52%, but this average conceals where errors concentrate and in which direction they run, leaving compliance officers without an actionable signal for trustworthy deployment. We present LegalHalluLens, an auditing framework with three components: typed hallucination profiles across four legally-motivated claim categories (numeric, temporal, obligation/entitlement, factual) over CUAD (Hendrycks et al., 2021); a Risk Direction Index (RDI) that reduces omission-versus-invention bias to a single deployment-comparable scalar; and a typed debate pipeline calibrated to both magnitudes and directions. Across 510 contracts and 249,252 clause-level instances we measure a within-model gap of approximately 38-40 pp between obligation/numeric and temporal claims that aggregate reporting hides, and show that two systems with matched 52% rates can carry opposite RDIs. The debate pipeline reduces fabricated detections by 45% with per-category gains tracking the diagnosis, matching commercial APIs with a substantially smaller backbone (4B active parameters). Typed profiles and RDI surface failure modes that aggregate metrics hide; we further show these diagnostics serve as calibration inputs for multi-agent debate pipelines, where Skeptic challenges and asymmetric gates targeted at measured failure modes outperform generically-tuned debate. The framework supports direction-aware procurement, accountability, and agent design for legal AI deployed in the wild.
Chinese Translation
在法律工作流程中部署的人工智能系统的幻觉发生率约为52%，但这一平均值掩盖了错误的集中位置及其方向，使合规官无法获得可操作的信号以实现可信的部署。我们提出了LegalHalluLens，这是一个审计框架，包含三个组成部分：针对四个法律动机索赔类别（数值、时间、义务/权利、事实）的类型化幻觉档案（基于CUAD（Hendrycks等，2021））；一个风险方向指数（Risk Direction Index, RDI），将遗漏与发明偏差简化为一个可与部署相比较的标量；以及一个针对幅度和方向进行校准的类型化辩论管道。在510份合同和249,252个条款级实例中，我们测量到义务/数值与时间索赔之间的模型内部差距约为38-40个百分点，而这些差距在汇总报告中被掩盖，并且显示出两个具有匹配52%率的系统可以具有相反的RDI。辩论管道将虚假检测减少了45%，每个类别的增益与诊断相匹配，且其商业API的基础架构显著较小（4B活跃参数）。类型化档案和RDI揭示了汇总指标所隐藏的失败模式；我们进一步展示这些诊断作为多智能体辩论管道的校准输入，其中怀疑者的挑战和针对测量失败模式的非对称门控优于通用调优的辩论。该框架支持方向感知的采购、问责和法律人工智能的智能体设计，以便在实际环境中部署。
cs.AI / 46 / 2606.18037
ProvenanceGuard: Source-Aware Factuality Verification for MCP-Based LLM Agents
ProvenanceGuard：基于来源的事实性验证用于MCP基础的LLM代理
Abstract
Tool-using LLM agents increasingly use the Model Context Protocol (MCP) to answer from heterogeneous evidence sources, including search, APIs, databases, clinical records, and formulary tools. Standard factuality metrics usually test whether an answer is supported by pooled evidence, missing a provenance-sensitive failure mode: a claim may be supported somewhere while being attributed to the wrong source. We call this cross-source conflation. We introduce ProvenanceGuard, a source-aware verifier for MCP-grounded answers. It consumes captured MCP traces with stable tool IDs, source IDs, and raw outputs; decomposes answers into atomic claims; routes claims to source-specific evidence; checks support with NLI and a token-alignment proxy; compares stated attribution with the routed source; and returns per-claim verdicts plus an answer-level allow/block decision. Blocked answers can be repaired with retrieval-augmented answer revision and re-verified. We evaluate on 281 medical-domain MCP-agent traces. A 266-trace adjudicated subset yields 2,325 LLM-assisted claim labels split by trace; 361 held-out labels are human-verified. On the 40-trace held-out split, ProvenanceGuard achieves block F1 0.802 and source accuracy 0.858 over 260 source-eligible claims, outperforming source-blind baselines that do not emit claim-to-source IDs. On a harder multi-source benchmark it reaches block F1 0.846, while source-plus-relation accuracy drops to 0.229, showing that exact source ownership remains difficult with semantically close sources. Repair-and-reverify resolves all blocked answers in the full trace set, often via conservative fallback. In 50 controlled clinical conflation probes, ProvenanceGuard detects all injected attribution swaps with no retained wrong attribution. These results show that source attribution is an independent axis for factuality verification in MCP-based agents.
Chinese Translation
使用工具的LLM代理越来越多地利用模型上下文协议（Model Context Protocol, MCP）从异构证据源中回答问题，包括搜索、API、数据库、临床记录和处方工具。标准的事实性度量通常测试答案是否得到汇总证据的支持，但忽略了一种对来源敏感的失败模式：一个声明可能在某处得到支持，但被归因于错误的来源。我们称之为跨来源混淆（cross-source conflation）。我们引入了ProvenanceGuard，一个针对MCP基础答案的来源感知验证器。它处理捕获的MCP轨迹，包含稳定的工具ID、来源ID和原始输出；将答案分解为原子声明；将声明路由到特定来源的证据；使用自然语言推理（NLI）和令牌对齐代理检查支持；比较声明的归因与路由的来源；并返回每个声明的裁决以及答案级别的允许/阻止决策。被阻止的答案可以通过检索增强的答案修订进行修复并重新验证。我们在281个医学领域的MCP代理轨迹上进行了评估。266个经过裁决的子集产生了2,325个由轨迹分割的LLM辅助声明标签；361个保留标签经过人工验证。在40个保留的轨迹分割上，ProvenanceGuard在260个符合来源要求的声明上实现了阻止F1 0.802和来源准确率0.858，优于不输出声明到来源ID的盲源基线。在一个更困难的多来源基准测试中，其阻止F1达到0.846，而来源加关系准确率降至0.229，显示出在语义相近的来源中，确切的来源归属仍然困难。修复和重新验证解决了完整轨迹集中的所有被阻止答案，通常通过保守的后备方案。在50个受控的临床混淆探测中，ProvenanceGuard检测到所有注入的归因交换，且没有保留错误的归因。这些结果表明，来源归属是MCP基础代理中事实性验证的一个独立维度。
cs.AI / 47 / 2606.18060
PseudoBench: Measuring How Agentic Auto-Research Fuels Pseudoscience
伪基准：测量自主研究如何助长伪科学
Abstract
As Large Language Model based agents enter autonomous scientific research, their ability to resist pseudoscience becomes increasingly important. Otherwise, such systems may rapidly generate plausible yet misleading studies that contaminate academic literature and erode trust in science. We present PseudoBench, an adversarial benchmark for evaluating whether agentic auto-research systems can identify and resist pseudoscientific narratives. PseudoBench contains 200 curated pseudoscientific claim-evidence pairs across five domains and evaluates agents through an end-to-end research pipeline from experiments to writing. Testing seven state-of-the-art agents, we find that current systems readily produce persuasive reports that align with pseudoscientific premises with near-zero refusal rates and the highest resistance of only 27.4%. Stronger agents risk packaging pseudoscience in more sophisticated scientific language, increasing its apparent credibility. These findings reveal an alarming capacity to fuel pseudoscience, calling for scientific alignment before widespread deployment.
Chinese Translation
随着基于大型语言模型的智能体进入自主科学研究，其抵制伪科学的能力变得愈加重要。否则，这类系统可能迅速生成看似合理但误导性的研究，污染学术文献并侵蚀公众对科学的信任。我们提出了伪基准（PseudoBench），这是一个对抗性基准，用于评估自主研究系统是否能够识别和抵制伪科学叙事。伪基准包含200对经过精心挑选的伪科学主张-证据对，涵盖五个领域，并通过从实验到写作的端到端研究流程对智能体进行评估。对七个最先进的智能体进行测试，我们发现当前系统轻易生成与伪科学前提一致的有说服力的报告，拒绝率接近零，最高抵制率仅为27.4%。更强大的智能体有可能将伪科学包装成更复杂的科学语言，从而提高其表面可信度。这些发现揭示了助长伪科学的令人担忧的能力，呼吁在广泛部署之前进行科学对齐。
cs.AI / 48 / 2606.18068
Agentic AI-based Framework for Mitigating Premature Diagnostic Handoff and Silent Hallucination in Healthcare Applications
基于代理智能的框架用于缓解医疗应用中的过早诊断交接和隐性幻觉
Abstract
Recent advances in Large Language Models (LLMs) and multi-agent systems have driven the rise of Agentic AI, showing promise for medical reasoning. However, open-ended conversational agents remain prone to two critical failure modes: premature diagnostic handoff and silent clinical hallucinations that may go undetected before reaching the patient. In this work, we propose a multi-agent framework that addresses both issues by replacing ``LLM-as-a-judge'' routing with deterministic orchestration constraints. The framework incorporates two safety mechanisms. First, a neuro-symbolic state-tracking gate enforces completeness of the OLDCARTS clinical protocol (Onset, Location, Duration, Character, Aggravating/Alleviating factors, Radiation, Timing, and Severity) by blocking diagnostic transitions until all required dimensions are collected. Second, an epistemic uncertainty quantification (UQ) gate computes semantic entropy (H) across K=5 independent diagnostic samples to identify and intercept divergent outputs before delivery. We evaluate the system using simulated patient agents powered by the llama-3.1-70b-instruct model on 150 test cases. The full architecture achieves 49.3% diagnostic precision, representing an absolute improvement of 11.3 percentage points over an unconstrained baseline. Additionally, we observe a statistically significant negative correlation (r = -0.181, p < 0.05) between OLDCARTS completeness (\sigma) and semantic entropy (H), suggesting that structured information gathering is associated with reduced diagnostic uncertainty.
Chinese Translation
近年来，大型语言模型（LLMs）和多智能体系统的进展推动了代理智能（Agentic AI）的兴起，显示出在医学推理中的潜力。然而，开放式对话代理仍然容易出现两种关键的失效模式：过早的诊断交接和可能在到达患者之前未被发现的隐性临床幻觉。在本研究中，我们提出了一个多智能体框架，通过用确定性编排约束替代“LLM作为裁判”的路由，解决这两个问题。该框架包含两个安全机制。首先，一个神经符号状态跟踪门通过阻止诊断转变，直到收集到所有必要维度，从而强制执行OLDCARTS临床协议（发作、位置、持续时间、特征、加重/缓解因素、放射、时间和严重性）的完整性。其次，一个认知不确定性量化（UQ）门计算K=5个独立诊断样本的语义熵（H），以识别并拦截分歧输出，防止其交付。我们使用由llama-3.1-70b-instruct模型驱动的模拟患者代理在150个测试案例上评估该系统。完整架构实现了49.3%的诊断精度，相较于无约束基线有11.3个百分点的绝对提升。此外，我们观察到OLDCARTS完整性（σ）与语义熵（H）之间存在统计显著的负相关（r = -0.181, p < 0.05），这表明结构化信息收集与降低诊断不确定性相关。
cs.AI / 49 / 2606.18075
A Unified Framework for Context-Aware and Relation-Aware Graph Retrieval-Augmented Generation
一个统一的框架用于上下文感知和关系感知的图检索增强生成
Abstract
Retrieval-Augmented Generation (RAG) has emerged as a paradigm for enhancing large language models (LLMs) with external knowledge, yet existing graph-based methods face a fundamental limitation: entity-centric and chunk-centric approaches operate on representations anchored to original text without true knowledge fusion. While entity-centric methods connect logically related content and chunk-centric methods preserve context, both retrieve information separately through similarity search, missing emergent understanding from their synthesis. In this paper, we propose HyGRAG, a hierarchical graph RAG framework that transcends source documents by addressing three core challenges: constructing summaries that genuinely integrate contextual and relational information, leveraging these synthesized representations to access emergent knowledge during retrieval, and efficiently updating hierarchical structures for dynamic corpora. Specifically, we design hierarchical index structures over hybrid graphs with both chunk and entity nodes, then iteratively cluster them and generate LLM-based summaries. Then, we design context and relation-aware retrieval that searches across all abstraction levels while expanding through community membership. Moreover, we enable dynamic knowledge update through attachment-based algorithms with only local re-summarization. Experimental results show that HyGRAG improves the average accuracy of multi-hop reasoning tasks by 9.7%, while maintaining reasonable efficiency.
Chinese Translation
检索增强生成（Retrieval-Augmented Generation, RAG）作为一种增强大型语言模型（Large Language Models, LLMs）与外部知识的范式而兴起，但现有的基于图的方法面临一个根本性限制：以实体为中心和以块为中心的方法在与原始文本锚定的表示上操作，而没有真正的知识融合。虽然以实体为中心的方法连接逻辑相关内容，而以块为中心的方法保持上下文，但两者通过相似性搜索分别检索信息，缺乏从其综合中产生的理解。在本文中，我们提出了HyGRAG，一个层次化图RAG框架，通过解决三个核心挑战超越源文档：构建真正整合上下文和关系信息的摘要，利用这些综合表示在检索过程中访问新兴知识，以及高效更新动态语料库的层次结构。具体而言，我们设计了基于混合图的层次索引结构，包含块节点和实体节点，然后对其进行迭代聚类并生成基于LLM的摘要。接着，我们设计了上下文和关系感知的检索，能够在所有抽象层次上进行搜索，同时通过社区成员资格进行扩展。此外，我们通过基于附加的算法实现动态知识更新，仅需局部重新摘要。实验结果表明，HyGRAG将多跳推理任务的平均准确率提高了9.7%，同时保持了合理的效率。
cs.AI / 50 / 2606.18098
IsabeLLM: Automated Theorem Proving Applied to Formally Verifying Consensus
IsabeLLM：应用于形式验证共识的自动定理证明
Abstract
Advances in Artificial Intelligence (AI) have led AI for Theorem Proving to become a promising means of formally verifying computer systems. Whilst formal verification is traditionally reserved for safety-critical systems due to the required amount of expertise and effort, AI can help to automate a large amount of this workload and make it far more accessible. Blockchain-based systems are becoming increasingly popular and are frequently targeted by malicious actors, often resulting in huge financial losses, highlighting the need to better verify these systems and mitigate vulnerabilities. Arguably the most important component of these systems is the consensus protocol, which allows nodes to agree on decisions in a potentially adversarial environment. In this paper, we improve upon IsabeLLM, the automated theorem proving tool in Isabelle. Namely, we implement a Retrieval-Augmented Generation framework, Error tracing and counterexample generation for improved context supplied to the Large Language Model. Compatibility with the latest version of Isabelle and Sledgehammer is also implemented for improved efficiency. We compare the performance of the two versions of IsabeLLM in their ability to complete the verification of Bitcoin's Proof of Work consensus.
Chinese Translation
人工智能（AI）的进步使得定理证明的AI成为形式验证计算机系统的一种有前景的手段。尽管形式验证传统上仅限于安全关键系统，因为所需的专业知识和努力程度较高，但AI可以帮助自动化大量工作负载，使其变得更加可及。基于区块链的系统越来越受欢迎，且经常成为恶意行为者的攻击目标，常常导致巨大的经济损失，这突显了更好地验证这些系统和减轻漏洞的必要性。这些系统中最重要的组成部分无疑是共识协议，它允许节点在潜在的对抗环境中就决策达成一致。在本文中，我们对IsabeLLM进行了改进，这是Isabelle中的自动定理证明工具。具体而言，我们实现了一个增强检索生成框架、错误追踪和反例生成，以改善提供给大型语言模型的上下文。同时，我们还实现了与最新版本的Isabelle和Sledgehammer的兼容性，以提高效率。我们比较了两个版本的IsabeLLM在完成比特币工作量证明共识验证能力上的表现。
cs.AI / 51 / 2606.18101
Trust the Right Teacher: Quality-Aware Self-Distillation for GUI Grounding
信任合适的教师：面向质量的自蒸馏用于图形用户界面定位
Abstract
Graphical user interface (GUI) grounding requires vision-language models (VLMs) to identify small target elements in high-resolution screenshots and predict precise screen coordinates. On-policy self-distillation (OPSD) is a promising post-training approach for this coordinate-sensitive task, since it provides dense token-level teacher signals beyond hard coordinate labels. However, naive OPSD is not well suited to GUI grounding: OPSD evaluates the teacher on student-generated prefixes, the quality of coordinate-token teacher signals can degrade when the prefix has already deviated from the target coordinate, leading to unreliable teacher signal. To mitigate this, We propose quality-aware self-distillation for VLM-based GUI grounding, which improves coordinate-token teacher-signal quality through soft correctness-aware gating and teacher-probability scaling. The soft correctness-aware gate checks whether the teacher's current coordinate-token prediction can still be completed into the ground-truth box under the student-generated prefix. If not, the corresponding teacher signal is down-weighted. Teacher-probability scaling then uses the teacher's confidence as a lightweight factor to further calibrate the strength of the gated supervision. A key empirical finding is that neither component alone improves overall performance, whereas combining them consistently improves performance. This suggests that the two mechanisms play complementary roles: correctness-aware gating suppresses unreliable coordinate-token supervision, while teacher-probability scaling calibrates the strength of the remaining signals. Experiments across six GUI grounding benchmarks show that our method consistently improves the base model and outperforms strong baselines.
Chinese Translation
图形用户界面（GUI）定位要求视觉-语言模型（VLMs）在高分辨率屏幕截图中识别小的目标元素并预测精确的屏幕坐标。在线自蒸馏（OPSD）是一种有前景的后训练方法，适用于这一对坐标敏感的任务，因为它提供了超越硬坐标标签的密集标记级教师信号。然而，简单的OPSD并不适合GUI定位：OPSD在学生生成的前缀上评估教师，当前缀已经偏离目标坐标时，坐标-标记教师信号的质量可能会下降，导致不可靠的教师信号。为此，我们提出了一种面向质量的自蒸馏方法，用于基于VLM的GUI定位，通过软正确性感知门控和教师概率缩放来提高坐标-标记教师信号的质量。软正确性感知门控检查教师当前的坐标-标记预测是否仍然可以在学生生成的前缀下完成为真实框。如果不能，则相应的教师信号会被降低权重。然后，教师概率缩放利用教师的信心作为轻量级因子，进一步校准门控监督的强度。一个关键的实证发现是，单独的任一组件都无法提高整体性能，而将它们结合在一起则始终能提高性能。这表明这两种机制发挥了互补的作用：正确性感知门控抑制了不可靠的坐标-标记监督，而教师概率缩放则校准了剩余信号的强度。在六个GUI定位基准测试中的实验表明，我们的方法始终改善了基础模型，并超越了强基线。
cs.AI / 52 / 2606.18119
First Proof Second Batch
首次证明第二批次
Abstract
To assess the ability of current AI systems to correctly solve research-level mathematics problems, we tested several AI systems on a set of ten problems in a broad range of mathematical fields; these problems arose naturally in the research process of the contributors. This document includes the problems, our methodology, and the results of our testing. We provide links to supplementary documents including the human solutions, the AI-generated solutions, and the referee reports and logs for the AI-generated solutions. The ten problems were contributed by the following mathematicians: (1) Dariusz Kaloci\'nski and Theodore A. Slaman, (2) Richard Schwartz, (3) Aleksa Milojevic and Benny Sudakov, (4) Larry Guth, (5) Oleg Butkovsky, Jonathan Mattingly, and Lorenzo Zambotti, (6) Joshua Evan Greene and Duncan McCoy, (7) Sucharit Sarkar, (8) Sam Payne and Jidong (Jayden) Wang, (9) Sylvie Corteel and John Lentfer, (10) Srivatsav Kunnawalkam Elayavalli.
Chinese Translation
为了评估当前人工智能系统正确解决研究级数学问题的能力，我们对多个人工智能系统在一组十个来自广泛数学领域的问题上进行了测试；这些问题是在贡献者的研究过程中自然产生的。本文档包括问题、我们的方法论以及测试结果。我们提供了链接到补充文档，包括人类解决方案、人工智能生成的解决方案，以及人工智能生成解决方案的评审报告和日志。这十个问题由以下数学家贡献：(1) Dariusz Kalociński 和 Theodore A. Slaman，(2) Richard Schwartz，(3) Aleksa Milojevic 和 Benny Sudakov，(4) Larry Guth，(5) Oleg Butkovsky、Jonathan Mattingly 和 Lorenzo Zambotti，(6) Joshua Evan Greene 和 Duncan McCoy，(7) Sucharit Sarkar，(8) Sam Payne 和 Jidong (Jayden) Wang，(9) Sylvie Corteel 和 John Lentfer，(10) Srivatsav Kunnawalkam Elayavalli。
cs.AI / 53 / 2606.18132
Knowledge Reutilization in Meta-Reinforcement Learning
元强化学习中的知识再利用
Abstract
Meta-reinforcement learning enables fast adaptation by extracting shared structure from related tasks, but existing end-to-end methods often couple task inference with embodiment-specific control. This coupling can obscure non-parametric task semantics, reduce sample efficiency, and limit cross-agent reuse. We propose a meta-knowledge reutilization framework that learns task-level knowledge on a dynamics-simplified agent and transfers it to heterogeneous agents. The framework uses a Bayesian non-parametric prior to organize latent task modes and a high-level policy to generate task-level magnitude guidance. To bridge reusable task knowledge with different embodiments, we introduce a semantic-magnitude interface and a lightweight temporal adaptor, which convert frozen meta-knowledge into temporally aligned subgoals for embodiment-specific low-level controllers. Experiments on multiple locomotion agents show that our framework reduces final-step tracking error by 94.75% -- 99.79% compared with recent state-of-the-art baselines and achieves comparable deployment performance with about 23.8% of their interaction data.
Chinese Translation
元强化学习通过提取相关任务中的共享结构实现快速适应，但现有的端到端方法通常将任务推断与特定于体现的控制耦合在一起。这种耦合可能会模糊非参数任务语义，降低样本效率，并限制跨代理的再利用。我们提出了一种元知识再利用框架，该框架在动态简化的代理上学习任务级知识，并将其转移到异构代理。该框架使用贝叶斯非参数先验来组织潜在任务模式，并使用高层策略生成任务级幅度指导。为了将可重用的任务知识与不同的体现相连接，我们引入了语义-幅度接口和轻量级时间适配器，这些组件将冻结的元知识转换为与时间对齐的子目标，以供特定于体现的低级控制器使用。在多个运动代理上的实验表明，与最近的最先进基准相比，我们的框架将最终步骤跟踪误差减少了94.75%至99.79%，并且在约23.8%的交互数据下实现了可比的部署性能。
cs.AI / 54 / 2606.18142
Your AI Travel Agent Would Book You a Bullfight: An Agentic Benchmark for Implicit Animal Welfare in Frontier AI Models
你的人工智能旅行代理将为你预订斗牛：前沿人工智能模型中隐性动物福利的代理基准
Abstract
AI agents are moving from advisors to actors, booking travel, planning menus, and running procurement on behalf of users. Existing benchmarks for AI and animal welfare evaluate model text responses to question-answer prompts, leaving open whether the welfare reasoning surfaced in those responses transfers to agentic deployment where the model must take actions with tools. We introduce TAC (Travel Agent Compassion), the first agentic benchmark measuring whether AI agents avoid options involving animal exploitation when acting on behalf of users. TAC presents an AI agent with twelve hand-authored travel booking scenarios across six categories of animal exploitation, augmented to forty-eight samples to control for price, rating, and position confounds. We evaluate seven frontier models from four labs. Every model scores below the chance level of sixty-four percent, with the best performer (Claude Opus 4.7) at fifty-three percent. A single welfare-aware sentence in the system prompt yields gains of forty-seven to sixty-three percentage points in Claude and GPT-5.5, twenty-six points in GPT-5.2, and under twelve points in DeepSeek and Gemini. An auxiliary Inspect Scout audit of 288 base-condition transcripts from the top two performers, using Gemini 2.5 Flash Lite as judge, flags zero transcripts for evaluation awareness, suggesting the below-chance rates do not stem from the models recognising the evaluation. We discuss implications for category-level variation across cultural domains, the limits of text-response welfare benchmarks, and the EU General-Purpose AI Code of Practice systemic risk framework.
Chinese Translation
人工智能代理正从顾问转变为行动者，代表用户进行旅行预订、菜单规划和采购。现有的人工智能与动物福利的基准评估模型对问题回答提示的文本响应，尚未探讨这些响应中显现的福利推理是否能够转移到代理行为中，即模型必须使用工具采取行动。我们引入了TAC（旅行代理同情心），这是第一个代理基准，测量人工智能代理在代表用户行动时是否避免涉及动物剥削的选项。TAC为人工智能代理提供了十二个手工编写的旅行预订场景，涵盖六类动物剥削，并扩展到四十八个样本，以控制价格、评分和位置的混淆因素。我们评估了来自四个实验室的七个前沿模型。每个模型的得分均低于六十四的机会水平，表现最佳的模型（Claude Opus 4.7）得分为五十三。系统提示中的一句福利意识句子在Claude和GPT-5.5中带来了四十七到六十三个百分点的提升，在GPT-5.2中提升了二十六个百分点，而在DeepSeek和Gemini中则不足十二个百分点。对前两名表现者的288个基本条件转录本进行的辅助Inspect Scout审计，使用Gemini 2.5 Flash Lite作为评判，未标记出任何转录本具有评估意识，表明低于机会的得分并非源于模型对评估的识别。我们讨论了跨文化领域的类别级变异的影响、文本响应福利基准的局限性，以及欧盟通用人工智能实践守则的系统风险框架。
cs.AI / 55 / 2606.18144
Memory as a Wasting Asset: Pricing Flash Endurance for Embodied Agents, and the Limits of Doing So
记忆作为一种消耗资产：为具身智能体定价闪存耐久性及其限制
Abstract
A robot's flash endurance is a non-renewable stock: every persisted write spends one of a few thousand program/erase cycles and never refills, yet no fielded robot memory system prices which memories are worth an erase cycle. We treat embodied memory as depreciating capital and price that stock with a single endurance shadow price $\eta$, which makes cost-minimizing placement across a RAM / on-board NVM / cloud hierarchy a threshold in a wear-augmented per-byte index. The index is cost-optimal whatever the sign of the value-write association $\chi$; only when $\chi > 0$ does the optimum turn non-monotone, sending a robot's most valuable memories off its flash. The pivot is thus empirical, and we measure $\chi$ on real robot logs at a pre-specified gate: its sign is a property of the deployment regime -- positive on recurrent long-horizon manipulation ($\hat{\chi} \approx +1.0 \times 10^{-3}$, replicated at full power), null on a shorter-horizon suite, and negative on non-recurrent teleoperation. Two boundaries scope the result. The endurance budget is dormant on premium 3,000-P/E TLC at datasheet prices and binding on the commodity QLC/eMMC ($\sim$1,000 P/E) that cheaper edge robots run. And where it binds, a learned wear-aware controller only ties price-based routing on task value, because realized value is tier-invariant across RAM, NVM, and cloud: the rent governs device lifetime and cost, not task performance. Whether wear-aware placement improves task value remains open -- $\chi$ is measured against a value proxy, and the non-monotone optimum, while proven, is not yet observed in data.
Chinese Translation
机器人的闪存耐久性是一种不可再生的存量：每次持久写入都会消耗数千次程序/擦除周期中的一个，并且不会再补充。然而，目前没有任何部署的机器人内存系统对哪些记忆值得进行擦除周期进行定价。我们将具身记忆视为贬值资本，并用一个单一的耐久性影子价格 $ heta$ 来定价这一存量，这使得在 RAM / 板载 NVM / 云层次结构中的成本最小化布局成为一个在磨损增强的每字节索引中的阈值。无论价值-写入关联 $ heta$ 的符号如何，该索引都是成本最优的；只有当 $ heta > 0$ 时，最优解才会变得非单调，从而将机器人的最有价值记忆转移出其闪存。因此，关键在于经验，我们在预先指定的门限上对真实机器人日志测量 $ heta$：其符号是部署机制的一个特性——在重复的长期操作中为正（$ ilde{ heta} ext{约等于} +1.0 imes 10^{-3}$，在全功率下复制），在较短时间范围的测试中为零，而在非重复远程操作中为负。两个边界界定了结果。耐久性预算在数据表价格的优质 3000-P/E TLC 上处于休眠状态，而在更便宜的边缘机器人运行的商品 QLC/eMMC（约 1000 P/E）上则是约束性的。在约束的地方，学习的磨损感知控制器仅将基于价格的路由与任务价值关联，因为实现的价值在 RAM、NVM 和云之间是层级不变的：租金支配设备的寿命和成本，而非任务性能。磨损感知布局是否能提高任务价值仍然是一个悬而未决的问题——$ heta$ 是相对于价值代理进行测量的，而非单调的最优解虽然已被证明，但尚未在数据中观察到。
cs.AI / 56 / 2606.18147
WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning
WEQA：基于可穿戴健康数据的查询自适应智能推理问答系统
Abstract
Language models are remarkably capable at medical question answering, in some cases surpassing the accuracy of general physicians. However, answering questions about wearable health data remains challenging and understudied, as these ubiquitous sensors produce continuous, high-dimensional, and longitudinal data, which is non-trivial to align with text-centric distributions in LLM pretraining. The diversity of sensor modalities and user intents cannot be effectively handled by a fixed reasoning workflow or a single pretrained foundation model. To address these challenges, we propose WEQA, a query-adaptive agent framework that unifies LLM reasoning with specialized wearable analytical and modeling tools. An LLM controller is employed to synthesize execution plans and dynamically route each query to the appropriate combination of sensor analysis and pretrained models, and perform grounded response auditing with external knowledge. We also curate a benchmark spanning four open wearable datasets comprising analytic and predictive tasks in three different health domains. Experiments show that our framework is 24% more accurate than LLM and agentic baselines, and a blinded study with 12 medical experts and 8 users shows substantial gains in usefulness and clinical soundness.
Chinese Translation
语言模型在医学问答方面表现出色，在某些情况下，其准确性超过了普通医生。然而，关于可穿戴健康数据的问题回答仍然具有挑战性且研究不足，因为这些普遍存在的传感器产生连续的、高维的和纵向的数据，这与大规模语言模型（LLM）预训练中的文本中心分布对齐并非易事。传感器模态和用户意图的多样性无法通过固定的推理工作流程或单一的预训练基础模型有效处理。为了解决这些挑战，我们提出了WEQA，一个查询自适应智能框架，将LLM推理与专业的可穿戴分析和建模工具统一起来。我们采用LLM控制器来合成执行计划，并动态地将每个查询路由到适当的传感器分析和预训练模型的组合，并进行基于外部知识的响应审计。我们还整理了一个基准，涵盖了四个开放的可穿戴数据集，包括三个不同健康领域的分析和预测任务。实验表明，我们的框架比LLM和智能基线准确性提高了24%，而且与12位医学专家和8位用户进行的盲测研究显示出在实用性和临床合理性方面的显著提升。
cs.AI / 57 / 2606.18154
Learning Cardiac Electrophysiology Digital Twins Through Agentic Discovery of Hybrid Structure
通过主动发现混合结构学习心脏电生理数字双胞胎
Abstract
Building personalized cardiac electrophysiology (EP) digital twins requires identifying the appropriate model structure for each patient, not merely fitting parameters. Traditional methods rely on experts to manually prescribe hybrid physics-neural architectures, which requires deep domain expertise and does not transfer across patients. Recent works have applied large language models (LLMs) to generate or act as hybrid models. However, despite their promising generalization capacity, these LLM-based methods lack the structural priors needed for stable cardiac simulations. Hence, we propose LEADS, a framework that formulates cardiac EP domain knowledge as a structured action space and utilizes an LLM agent to discover hybrid models. The agent follows an iterative reasoning-and-action loop to select, combine, and refine hybrid models, whilst gradient descent handles parameter fitting. The proposed LEADS designs every candidate model towards physically grounded, interpretable, and numerically stable, while allowing open-ended architectural discovery. We validate LEADS on synthetic data with three ground-truth reaction models and on real cardiac EP data, demonstrating that it outperforms both human-designed hybrid models and other LLM-based hybrid modeling.
Chinese Translation
构建个性化心脏电生理（EP）数字双胞胎需要为每位患者识别合适的模型结构，而不仅仅是调整参数。传统方法依赖专家手动指定混合物理-神经网络架构，这需要深厚的领域专业知识，并且无法在不同患者之间转移。近期的研究已应用大语言模型（LLMs）来生成或充当混合模型。然而，尽管这些基于LLM的方法具有良好的泛化能力，但它们缺乏进行稳定心脏模拟所需的结构先验。因此，我们提出了LEADS，一个将心脏EP领域知识形式化为结构化行动空间的框架，并利用LLM代理发现混合模型。该代理遵循迭代推理与行动循环，以选择、组合和优化混合模型，同时使用梯度下降处理参数拟合。所提出的LEADS旨在使每个候选模型朝向物理基础、可解释和数值稳定的方向设计，同时允许开放式架构发现。我们在具有三种真实反应模型的合成数据和真实心脏EP数据上验证了LEADS，结果表明其优于人类设计的混合模型和其他基于LLM的混合建模方法。
cs.AI / 58 / 2606.18192
The Stanford EDGAR Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data
斯坦福EDGAR文件数据集：将美国企业和财务披露重构为布局忠实且令牌高效的预训练数据
Abstract
As high-quality public web corpora become increasingly exhausted, clean long-context documents have become a scarce and expensive source of training data for large language models (LLMs). Existing long-context corpora are often proprietary and costly to acquire, synthetically generated, or concentrated in narrow domains such as programming. We introduce the Stanford EDGAR Filings Dataset (SEFD), an open reconstruction of SEC filings into layout-faithful MultiMarkdown for financial language modeling and evaluation. SEFD makes audited financial statements, risk disclosures, ownership reports, accounting notes, and market-moving event filings usable as long-context pretraining data and as a basis for financial reasoning, forecasting, compliance, and document understanding. The resulting corpus is token-efficient, model-ready, and has less than 0.1% overlap with Common Crawl-derived corpora. We release SEFD-v1, a 152B-token initial public snapshot, and provide corpus-level analyses of a larger 18.5M-filing archive estimated at 550B tokens. We further introduce two SEFD-derived benchmarks: EDGAR-Forecast, which evaluates filing-grounded numerical forecasting after model knowledge cutoffs, and EDGAR-OCR, which evaluates transcription of complex financial tables.
Chinese Translation
随着高质量公共网络语料库的逐渐枯竭，干净的长文本档案已成为大型语言模型（LLMs）训练数据的稀缺且昂贵的来源。现有的长文本语料库往往是专有的，获取成本高，合成生成，或集中于编程等狭窄领域。我们介绍了斯坦福EDGAR文件数据集（SEFD），这是对SEC文件的开放重构，采用布局忠实的MultiMarkdown格式，用于金融语言建模和评估。SEFD使审计过的财务报表、风险披露、所有权报告、会计注释和市场影响事件的文件可用作长文本预训练数据，并作为金融推理、预测、合规和文档理解的基础。生成的语料库令牌高效，模型准备就绪，与基于Common Crawl的语料库重叠率低于0.1%。我们发布了SEFD-v1，这是一个152B令牌的初始公共快照，并提供了一个估计为550B令牌的更大18.5M文件档案的语料库级分析。我们进一步介绍了两个基于SEFD的基准：EDGAR-Forecast，用于评估模型知识截止后的基于文件的数值预测，以及EDGAR-OCR，用于评估复杂财务表格的转录。
cs.AI / 59 / 2606.18206
Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers
固定点推理器：稳定且自适应的深度循环变换器
Abstract
Looped architectures provide an inductive bias toward learning step-by-step procedures for tasks that require compositional reasoning. The number of effective layers reached by looping determines the quality of the solution these models find. Like deep architectures, looped architectures are prone to a signal propagation problem induced by depth as the halting decision is postponed. In this paper, we address this signal propagation issue using pre-norm layers and residual scaling. Building on these architectural modifications, we propose FPRM, a Transformer-based Fixed-Point Reasoning Model that uses fixed-point convergence as an end-to-end halting mechanism in a looped architecture. We show that fixed-point halting allows FPRM to adapt its compute to task difficulty. FPRM is effective on common reasoning benchmarks, namely Sudoku, Maze, state-tracking, and ARC-AGI.
Chinese Translation
循环架构为学习需要组合推理的任务提供了逐步过程的归纳偏置。循环所达到的有效层数决定了这些模型找到的解决方案的质量。与深度架构类似，循环架构由于深度引发的信号传播问题也很常见，因为停止决策被推迟。在本文中，我们通过使用预归一化层和残差缩放来解决这一信号传播问题。在这些架构修改的基础上，我们提出了FPRM（Fixed-Point Reasoning Model），这是一种基于变换器的固定点推理模型，利用固定点收敛作为循环架构中的端到端停止机制。我们展示了固定点停止使FPRM能够根据任务难度调整其计算能力。FPRM在常见的推理基准测试中表现出色，包括数独、迷宫、状态跟踪和ARC-AGI。
cs.AI / 60 / 2606.18235
EvolveNav: Proactive Preflection and Self-Evolving Memory for Zero-Shot Object Goal Navigation
EvolveNav：主动反思与自我演化记忆的零-shot目标物体导航
Abstract
Zero-Shot Object-Goal Navigation (ZS-OGN) requires embodied agents to explore and locate target objects without any prior training. To this end, recent methods leverage foundation models. But they typically rely on static priors and lack adaptation, which leads to repeated errors and costly trial and error. In this paper, we propose a self-evolving ZS-OGN framework that enables continuous test-time improvement. Specifically, we build an agentic rule memory by extracting actionable knowledge from past trajectories. Then, we propose a retrieval strategy based on upper confidence bound, selecting effective rules by balancing semantic relevance and historical success. In addition, we introduce a memory-guided preflection module that forecasts potential outcomes before action, reducing inefficient exploration. Extensive experiments show that our method outperforms existing zero-shot baselines, achieving a 10.1\% improvement in success rate with fewer unnecessary steps.
Chinese Translation
零-shot目标物体导航（ZS-OGN）要求具身代理在没有任何先前训练的情况下探索和定位目标物体。为此，近期的方法利用了基础模型。但它们通常依赖于静态先验，并缺乏适应性，这导致重复错误和高昂的试错成本。本文提出了一种自我演化的ZS-OGN框架，能够实现持续的测试时改进。具体而言，我们通过从过去的轨迹中提取可操作的知识构建了一个代理规则记忆。然后，我们提出了一种基于上置信界的检索策略，通过平衡语义相关性和历史成功率来选择有效规则。此外，我们引入了一个记忆引导的反思模块，在行动之前预测潜在结果，从而减少低效探索。大量实验表明，我们的方法优于现有的零-shot基线，在成功率上提高了10.1%，并减少了不必要的步骤。
cs.CL / 1 / 2606.17162
MemSlides: A Hierarchical Memory Driven Agent Framework for Personalized Slide Generation with Multi-turn Local Revision
MemSlides：一种基于层次记忆驱动的个性化幻灯片生成代理框架，支持多轮本地修订
Abstract
Personalized presentation generation requires more than conditioning on a current prompt or template: agents must preserve stable user preferences across tasks, retain newly introduced preferences and constraints during multi-turn revision, and carry out local edits reliably. We propose MemSlides, a hierarchical memory framework for personalized presentation agents that separates long-term memory from working memory and further divides long-term memory into user profile memory and tool memory. User profile memory stores intent-conditioned profiles for round-0 personalization, working memory carries active preferences and session constraints across revision rounds, and tool memory stores reusable execution experience for reliable localized editing. MemSlides pairs this memory design with scoped slide-local revision, so targeted updates act on the smallest affected region instead of repeatedly regenerating the full deck. In controlled experiments, user profile memory improves persona-alignment judgments on a multi-persona, multi-intent profile bank, tool-memory injection improves closed-loop modify behavior in diagnostic matched-pair settings, and qualitative cases illustrate working memory's ability to carryover preferences. Taken together, these results suggest that effective personalization in presentation authoring depends on separating persistent user profiles, session-level working memory, and reusable execution experience across generation and localized revision.
Chinese Translation
个性化演示生成不仅仅依赖于当前提示或模板，代理必须在任务之间保持稳定的用户偏好，在多轮修订中保留新引入的偏好和约束，并可靠地进行本地编辑。我们提出了MemSlides，这是一种个性化演示代理的层次记忆框架，它将长期记忆与工作记忆分开，并进一步将长期记忆划分为用户档案记忆和工具记忆。用户档案记忆存储基于意图的档案以实现初始个性化，工作记忆在修订轮次中携带活跃的偏好和会话约束，而工具记忆存储可重用的执行经验以实现可靠的局部编辑。MemSlides将这种记忆设计与范围限定的幻灯片局部修订相结合，使得针对性的更新作用于最小受影响区域，而不是反复生成完整的幻灯片组。在控制实验中，用户档案记忆提高了多角色、多意图档案库中的角色一致性判断，工具记忆注入改善了在诊断匹配对设置中的闭环修改行为，定性案例展示了工作记忆在偏好传递方面的能力。综合来看，这些结果表明，演示创作中的有效个性化依赖于持久用户档案、会话级工作记忆和可重用的执行经验的分离，以及在生成和局部修订中的应用。
cs.CL / 2 / 2606.17164
PromptMN: Pseudo Prompting Language
PromptMN：伪提示语言
Abstract
Prompting has become the primary interface between humans and generative AI, yet many natural language prompts remain fragile: roles, goals, constraints, and expected outputs are often buried in prose or left implicit. In agentic and software development workflows, a misread at the first handoff can propagate through every step, since a significant portion of agent failures stem from context ambiguities rather than model limitations. This paper introduces PromptMN, a pseudo-prompting domain-specific language that annotates natural language with compact, %-prefixed typed directives covering roles, goals, requirements, priorities, constraints, plans, inputs, and outputs. Semantic resolution lets authors write in any order while the model interprets directives by function. PromptMN sits between informal prompting and programming-style pseudocode: structured enough to be inspectable and reusable, yet lightweight enough for analysts, managers, developers, and stakeholders across the software development lifecycle (SDLC). PromptMN also pairs with reverse prompt engineering. Asking a model to restate a desired outcome as PromptMN lets users inspect the inferred roles, goals, constraints, and missing assumptions before acting, reducing repair cycles and yielding a reusable artifact for aligning people and AI tools. PromptMN's feasibility is evaluated across several frontier models, including Claude Fable 5, Claude Opus 4.8, Gemini 3.1 Pro, and GPT-5.5. The models correctly resolved PromptMN instructions, including complex structures such as repetition, conditionals, methods, and a prime-checking task, without fine-tuning. The same vocabulary applies across new codebases, maintenance, and redesign in the SDLC scenarios presented. While large-scale validation remains future work, these early results suggest PromptMN is a practical step toward clearer, more reviewable human-to-AI interaction.
Chinese Translation
提示已成为人类与生成性人工智能之间的主要接口，但许多自然语言提示仍然脆弱：角色、目标、约束和预期输出往往埋藏在散文中或隐含不明。在代理和软件开发工作流程中，第一次交接时的误读可能会在每一步中传播，因为代理失败的很大一部分源于上下文模糊，而非模型局限性。本文介绍了PromptMN，一种伪提示领域特定语言，通过紧凑的、以%为前缀的类型指令对自然语言进行注释，涵盖角色、目标、需求、优先级、约束、计划、输入和输出。语义解析使作者可以以任意顺序书写，而模型则按功能解释指令。PromptMN介于非正式提示和编程风格伪代码之间：结构足够清晰以便于检查和重用，但又轻量化，适合分析师、经理、开发人员和软件开发生命周期（SDLC）中的利益相关者。PromptMN还与反向提示工程相结合。要求模型将期望的结果重新表述为PromptMN，使用户能够在行动之前检查推断出的角色、目标、约束和缺失的假设，从而减少修复周期，并产生一个可重用的工件，以便于人类与人工智能工具的对齐。PromptMN的可行性在多个前沿模型中进行了评估，包括Claude Fable 5、Claude Opus 4.8、Gemini 3.1 Pro和GPT-5.5。这些模型正确解析了PromptMN指令，包括重复、条件、方法和质数检查任务等复杂结构，而无需微调。相同的词汇适用于所呈现的SDLC场景中的新代码库、维护和重新设计。尽管大规模验证仍然是未来的工作，但这些早期结果表明，PromptMN是朝着更清晰、更可审查的人机交互迈出的实用一步。
cs.CL / 3 / 2606.17168
RepSelect: Robust LLM Unlearning via Representation Selectivity
RepSelect：通过表示选择性实现稳健的LLM遗忘
Abstract
Making large language models (LLMs) deeply forget specific knowledge and values without sacrificing general capabilities remains a central challenge in unlearning. However, current methods are easily reversed by fine-tuning or few-shot prompting, suggesting their forgetting is only shallow. We identify the root cause. Existing methods target representations shared with both the retain set and the subspace recovered by a fine-tuning attacker, making unlearning both disruptive to general capabilities and easy to reverse. We propose RepSelect (Representation Selectivity), isolates forget-set-specific representations by collapsing top principal components of weight gradients before each update, leaving general capabilities intact while limiting what fine-tuning can recover. We evaluate across two forget categories, biohazardous knowledge and abusive tendencies, and four model families spanning dense and Mixture-of-Experts architectures (Llama 3, Qwen 3.5, Gemma 4 E4B, DeepSeek V2 Lite). Compared to five popular baselines (GradDiff, NPO, SimNPO, RMU, UNDIAL), RepSelect achieves a 4-50x larger reduction in post-relearning answer accuracy than the strongest baseline, and is near-perfectly robust to few-shot prompting attacks. Targeting selective representations is thus an important step towards deep and robust LLM forgetting.
Chinese Translation
使大型语言模型（LLMs）在不牺牲一般能力的情况下深度遗忘特定知识和价值观，仍然是遗忘领域的一个核心挑战。然而，当前的方法容易被微调或少量提示所逆转，这表明它们的遗忘只是表面的。我们识别了根本原因。现有方法针对与保留集和被微调攻击者恢复的子空间共享的表示，使得遗忘对一般能力造成干扰且容易被逆转。我们提出RepSelect（表示选择性），通过在每次更新之前压缩权重梯度的主要成分，隔离遗忘集特定的表示，从而保持一般能力的完整，同时限制微调可以恢复的内容。我们在两个遗忘类别（生物危害知识和虐待倾向）和四个模型家族（包括密集型和混合专家架构的Llama 3、Qwen 3.5、Gemma 4 E4B、DeepSeek V2 Lite）上进行了评估。与五个流行基线（GradDiff、NPO、SimNPO、RMU、UNDIAL）相比，RepSelect在重新学习后的答案准确性上实现了比最强基线高出4-50倍的更大减少，并且对少量提示攻击几乎完美稳健。因此，针对选择性表示是实现深度和稳健的LLM遗忘的重要一步。
cs.CL / 4 / 2606.17174
From Parasocial Scripts to Dyadic Persistence in Autonomous AI-Agent Communities
从拟社会脚本到自主人工智能代理社区中的双向持久性
Abstract
While parasocial interactions (PSIs) and parasocial relationships (PSRs) have been studied in conventional media settings, we investigate whether PSI- (colloquial) relational cues also exist in online communities where both sides are autonomous AI agents. We analyze 4,434 posts and 50,338 comments from Moltbook through three theory-based textual indicators: attachment/intimacy language, reciprocity bids, and self-identification to original poster (OP). The combined results across methods based on keyword matching, few-shot large language model (LLM) annotation, and grouped-context LLM annotation reveal that PSI colloquial cues prevail and are strongly associated with OP re-engagement and a reciprocal reply structure. These results are robust across negative controls, nullification, clustered-standard-error re-estimation, and multiple-testing correction. A dyadic persistence test further affirms reciprocity bids aligned with sustained OP-involving mutual recurrence, providing empirical evidence for bridging interaction-level PSI scripts with PSR-consistent repeated dyadic patterns. We interpret the evidence as a behavioral structure in discourse by LLM-enabled agents.
Chinese Translation
虽然拟社会互动（PSIs）和拟社会关系（PSRs）在传统媒体环境中得到了研究，但我们探讨了在双方都是自主人工智能代理的在线社区中，是否也存在PSI（口语化）关系线索。我们通过三种基于理论的文本指标分析了来自Moltbook的4,434条帖子和50,338条评论：依恋/亲密语言、互惠请求和对原帖作者（OP）的自我认同。基于关键词匹配、少量样本的大型语言模型（LLM）注释和分组上下文LLM注释的方法的综合结果显示，PSI口语化线索占主导地位，并与OP的重新参与和互惠回复结构密切相关。这些结果在负控制、无效化、聚类标准误差重新估计和多重检验校正中都表现出稳健性。双向持久性测试进一步确认了与持续的OP参与的互惠请求一致的相互重复，为将互动层面的PSI脚本与PSR一致的重复双向模式之间的桥接提供了实证证据。我们将这些证据解读为LLM驱动代理在话语中的行为结构。
cs.CL / 5 / 2606.17175
Self-Generated Error Training for Token Editing in Diffusion Language Models
自生成错误训练用于扩散语言模型中的标记编辑
Abstract
Token-to-token (T2T) editing lets LLaDA2.1 revise committed tokens during block-diffusion decoding. The released recipe trains this editor on random vocabulary corruptions, but at inference the editor sees the model's own fluent, high-confidence draft errors instead. We study this training-inference mismatch and propose self-generated T2T, which performs a no-gradient draft pass, fills masked positions with predicted tokens, and supervises recovery in a second pass under these self-generated corruptions. We implement the update as a short LoRA continued-pretraining pass on LLaDA2.1-mini and evaluate on several benchmarks under the official Q-Mode T2T procedure with unchanged inference parameters. The method generally improves accuracy while reducing T2T edit intensity, mitigating failure modes such as final-digit transcription errors after otherwise correct reasoning and excessive self-correction before short factual answers.
Chinese Translation
标记到标记（T2T）编辑允许 LLaDA2.1 在块扩散解码过程中修正已提交的标记。发布的方案在随机词汇损坏上训练该编辑器，但在推理时，编辑器看到的是模型自身流畅且高置信度的草稿错误。我们研究了这种训练与推理的不匹配，并提出了自生成的 T2T，它执行无梯度的草稿传递，用预测的标记填充被掩盖的位置，并在第二次传递中在这些自生成的损坏下监督恢复。我们将更新实现为对 LLaDA2.1-mini 的短期 LoRA 继续预训练，并在官方 Q-Mode T2T 程序下评估多个基准，推理参数保持不变。该方法通常提高了准确性，同时减少了 T2T 编辑强度，缓解了诸如在其他正确推理后出现的最终数字转录错误和在短小事实答案之前过度自我修正等失败模式。
cs.CL / 6 / 2606.17213
Revisiting LLM Adaptation for 3D CT Report Generation: A Study of Scaling and Diagnostic Priors
重新审视大型语言模型适应性在三维CT报告生成中的应用：规模化与诊断先验的研究
Abstract
Recent advances in multimodal learning, including large language models (LLMs) and vision-language models (VLMs), have demonstrated strong adaptability to natural images. However, extending their use to the medical domain, particularly for volumetric (3D) images, is challenging due to high computational complexity, volumetric dependencies and the semantic gap between visual features and clinical terminology. Naively fine-tuning LLMs on limited medical data often leads to overfitting and clinical hallucination, where linguistic fluency is prioritized over clinical factuality. In this study, we investigate parameter-efficient adaptation strategies for volumetric CT report generation and introduce RAD3D-Prefix, a lightweight diagnostic-prior conditioning framework that minimizes the need for extensive parameter training. This module integrates image embeddings with multi-label diagnostic classification logits, preserving critical clinical details while bridging the semantic gap. By keeping the LLM frozen, our method requires minimal trainable parameters and mitigates the risk of overfitting on small, domain-specific datasets. Through a systematic study spanning LLMs from 96.1M to 1.6B parameters, we find that fine-tuning is most beneficial for smaller LLMs, whereas freezing larger (~1B+ LLMs and training only lightweight projection layers provides a superior trade-off between performance, generalization, and computational efficiency. Across multiple automatic metrics and a clinical reader study, RAD3D-Prefix outperforms comparable parameter-efficient baselines and demonstrates strong out-of-domain generalization while using substantially fewer trainable parameters than fully fine-tuned alternatives.
Chinese Translation
近期在多模态学习方面的进展，包括大型语言模型（LLMs）和视觉-语言模型（VLMs），已展示出对自然图像的强适应性。然而，将其应用扩展到医学领域，特别是对体积（3D）图像的处理，由于高计算复杂性、体积依赖性以及视觉特征与临床术语之间的语义差距，面临诸多挑战。在有限的医学数据上简单地微调LLMs往往导致过拟合和临床幻觉，即语言流畅性被优先考虑而非临床事实的准确性。在本研究中，我们探讨了用于体积CT报告生成的参数高效适应策略，并引入了RAD3D-Prefix，一个轻量级的诊断先验条件框架，旨在最小化对大量参数训练的需求。该模块将图像嵌入与多标签诊断分类逻辑结合，保留关键的临床细节，同时弥合语义差距。通过保持LLM不变，我们的方法需要的可训练参数极少，并降低了在小型领域特定数据集上过拟合的风险。通过对参数从9610万到16亿的LLMs进行系统研究，我们发现微调对较小的LLMs最为有利，而对较大的LLMs（约10亿以上）进行冻结并仅训练轻量级投影层则在性能、泛化和计算效率之间提供了更优的权衡。在多个自动评估指标和临床读者研究中，RAD3D-Prefix超越了可比的参数高效基线，并在使用显著更少的可训练参数的情况下展现出强大的域外泛化能力。
cs.CL / 7 / 2606.17234
Speaking in Self-Assessing Tongues: On the Verbalized Confidence of LLMs in Machine Translation
自我评估的语言表达：关于大型语言模型在机器翻译中的语言化信心
Abstract
The rapid rise in popularity of large language models (LLMs) for translation calls for a thorough study of the reliability of their confidence in their own outputs. Unlike many generation tasks, translation errors and confidence levels can be useful at different levels of granularity (tokens, words, or spans). Unsupervised approaches based on internal signals like predicted probabilities can be misleading because they reflect certainty among alternatives rather than correctness. In addition, they require access to such internal signals. Here, we devise five verbalized methods of extracting an LLM's per-token confidence without those shortcomings and compare their reliability with that of the model's internal signals of certainty. We evaluate reliability using two forms of alignment: fine-grained error detection and calibration. For both, internal and verbalized methods perform similarly, although results vary by model. Interestingly, we find little to no correlation between internal and verbalized methods.
Chinese Translation
大型语言模型（LLMs）在翻译领域的快速普及要求我们对其对自身输出的信心可靠性进行深入研究。与许多生成任务不同，翻译错误和信心水平可以在不同的粒度层次（标记、单词或跨度）上发挥作用。基于内部信号（如预测概率）的无监督方法可能会产生误导，因为它们反映的是在替代选项之间的确定性，而非正确性。此外，这些方法需要访问这些内部信号。在此，我们设计了五种语言化方法，以提取LLM的逐标记信心，避免上述缺陷，并将其可靠性与模型内部确定性信号进行比较。我们使用两种对齐形式来评估可靠性：细粒度错误检测和校准。在这两种情况下，内部方法和语言化方法的表现相似，尽管结果因模型而异。有趣的是，我们发现内部方法与语言化方法之间几乎没有相关性。
cs.CL / 8 / 2606.17255
MLLP-VRAIN UPV system for the IWSLT 2026 Simultaneous Speech Translation task
用于IWSLT 2026同声翻译任务的MLLP-VRAIN UPV系统
Abstract
This work describes the participation of the MLLP-VRAIN research group in the shared task of the IWSLT 2026 Simultaneous Speech Translation track. Our submission utilizes the recently released Parakeet and Qwen 3.5 models to create a robust, cascaded solution for long-form SimulST through the use of adaptive "black-box" policies. We explore relaxations of these policies to achieve better quality-latency trade-offs. Compared to last year, we participate on all language directions. In addition to this, for the En$\rightarrow${De, It, Zh} directions we also participate in this year's new context track employing a combination of ASR word-boosting and a RAG mechanism of offline pre-translated exemplars to guide generation and enrich our system with domain-specific context. Finally, we provide a detailed latency analysis of our system. Compared to last year, results on the MCIF En$\rightarrow$De test set shows a substantial quality improvement of +5.82 XCOMET-XL. Our context track processing further improves performance by +1.03.
Chinese Translation
本研究描述了MLLP-VRAIN研究小组在IWSLT 2026同声翻译赛道共享任务中的参与情况。我们的提交利用了最近发布的Parakeet和Qwen 3.5模型，通过使用自适应“黑箱”策略，创建了一种稳健的级联解决方案，以应对长篇SimulST。我们探索了这些策略的放松，以实现更好的质量与延迟权衡。与去年相比，我们参与了所有语言方向。此外，对于En→{De, It, Zh}方向，我们还参与了今年的新上下文轨道，采用了ASR词汇增强和离线预翻译示例的RAG机制相结合的方法，以指导生成并为我们的系统丰富领域特定的上下文。最后，我们提供了系统的详细延迟分析。与去年相比，MCIF En→De测试集的结果显示质量显著提高了+5.82 XCOMET-XL。我们的上下文轨道处理进一步提高了性能，增加了+1.03。
cs.CL / 9 / 2606.17281
Are you speaking my languages? On spoken language adherence in multimodal LLMs
你在说我的语言吗？关于多模态大型语言模型中的语言遵循性
Abstract
While Large Language Model (LLM) based Automatic Speech Recognition (ASR) enables seamless multilingual use, models often misidentify the output language, compromising transcription fidelity and downstream application quality. To preserve flexibility and code-switching capabilities, we propose a soft prompting approach that hints at potential spoken languages without strictly constraining the output. We formally define this challenge as a lack of language adherence, introduce a novel metric to quantify violations, and evaluate three mitigation strategies: (1) zero-shot prompting for robust guidance under uncertainty, (2) supervised fine-tuning (SFT) to improve prompt adherence, and (3) Chain-of-Thought (CoT) reasoning to enforce adherence during decoding. We present a comparative analysis of these methods across multiple languages, evaluating effectiveness in reducing the language violation while maintaining overall ASR performance. Finally, we discuss trade-offs to guide strategy selection under various compute constraints.
Chinese Translation
虽然基于大型语言模型（LLM）的自动语音识别（ASR）实现了无缝的多语言使用，但模型常常错误识别输出语言，从而影响转录的准确性和下游应用的质量。为了保持灵活性和代码切换能力，我们提出了一种软提示方法，该方法在不严格限制输出的情况下，提示潜在的口语语言。我们正式定义了这一挑战为语言遵循性的缺失，提出了一种新颖的度量标准来量化违规情况，并评估了三种缓解策略：（1）在不确定性下的零-shot 提示以提供稳健指导，（2）监督微调（SFT）以改善提示遵循性，以及（3）链式思维（CoT）推理以在解码过程中强制遵循性。我们对这些方法在多种语言中的比较分析，评估了在保持整体 ASR 性能的同时减少语言违规的有效性。最后，我们讨论了在各种计算约束下指导策略选择的权衡。
cs.CL / 10 / 2606.17299
Examining the Limits of Word2Vec with Toki Pona
使用 Toki Pona 考察 Word2Vec 的局限性
Abstract
Word2Vec's effectiveness at generating semantic embeddings has been widely validated, yet it has been tested almost exclusively on languages with large vocabulary inventories. This study examines whether Word2Vec can successfully capture semantic relationships within an extremely reduced vocabulary using data from Toki Pona, a constructed language with approximately 130 words. We sourced 1.4 million sentences (7.95 million tokens) from the Toki Pona community for training. Approximately 23% of sentences in the corpus contain non-Toki Pona tokens such as named entities, loanwords, and neologisms. To investigate whether this linguistic noise enhances or hinders performance -- a topic rarely addressed in word embedding literature -- we trained two distinct models: one retaining these incidental tokens and another filtering them out completely. Evaluation was conducted using quantitative methods measuring word proximity to semantic category centroids, automated silhouette scores via agglomerative clustering, and qualitative analysis utilizing representational similarity matrices compared against English. The results indicate that while sparse, non-core tokens do not affect the relative structure of the learned embeddings, they actually draw similar words closer together in the vector space. Importantly, Word2Vec's effectiveness depends more on distributional patterns than lexicon size even at this extreme lower bound.
Chinese Translation
Word2Vec 在生成语义嵌入方面的有效性已得到广泛验证，但其测试几乎专门集中在词汇量较大的语言上。本研究考察了 Word2Vec 是否能够成功捕捉在极度简化词汇中语义关系，数据来源于 Toki Pona，这是一种约有 130 个单词的构造语言。我们从 Toki Pona 社区收集了 140 万个句子（795 万个词元）用于训练。语料库中大约 23% 的句子包含非 Toki Pona 词元，如命名实体、外来词和新词。为了探讨这种语言噪声是增强还是阻碍性能——这是词嵌入文献中很少涉及的话题——我们训练了两个不同的模型：一个保留这些偶然词元，另一个则完全过滤掉它们。评估采用定量方法，测量词与语义类别中心的接近度，通过聚类分析的自动轮廓得分，以及利用与英语进行比较的表征相似性矩阵的定性分析。结果表明，尽管稀疏的非核心词元并不影响学习到的嵌入的相对结构，但它们实际上使相似词在向量空间中更靠近。重要的是，即使在这个极端的下限，Word2Vec 的有效性更多依赖于分布模式而非词汇大小。
cs.CL / 11 / 2606.17350
Do Large Language Models Always Tell The Same Stories?
大型语言模型是否总是讲述相同的故事？
Abstract
Recent advances in large language models (LLMs) have enabled the generation of high-quality prose, yet the question of whether these models are capable of generating diverse outputs remains contested. In this work, we investigate the diversity of LLM-generated stories through the framework of narrative similarity. Using a contrastive framework and a dataset of human-written stories and prompts from r/WritingPrompts, we collect narrative similarity judgments across 10 representative LLMs, utilizing both human evaluations and three different automatic annotation methods. Our findings reveal a consistent trend: LLM-generated narratives are consistently more similar to each other than human-written stories are. We demonstrate that frontier models in particular converge on a ``mean'' generic narrative that approximates individual human stories but lacks the collective diversity of human authors. Finally, we show that common mitigation strategies, including negative prompting and temperature scaling, fail to meaningfully address this homogeneity.
Chinese Translation
近期大型语言模型（LLMs）的进展使得生成高质量的散文成为可能，但这些模型是否能够生成多样化的输出仍然存在争议。在本研究中，我们通过叙事相似性的框架探讨LLM生成故事的多样性。我们使用对比框架和来自r/WritingPrompts的人类创作故事及提示的数据集，收集了10个代表性LLM的叙事相似性判断，利用人类评估和三种不同的自动注释方法。我们的研究结果揭示出一个一致的趋势：LLM生成的叙事彼此之间的相似性始终高于人类创作的故事。我们证明，尤其是前沿模型趋向于一种“平均”通用叙事，这种叙事虽然接近个别的人类故事，但缺乏人类作者的集体多样性。最后，我们展示了常见的缓解策略，包括负提示和温度缩放，未能有效解决这种同质性问题。
cs.CL / 12 / 2606.17354
Translating the Untranslatable: An Operationalizable Ontology for Untranslatability
翻译不可翻译的内容：可操作的不可翻译本体论
Abstract
Untranslatability, cases where meaning cannot be directly preserved across languages, is well-studied in linguistics but underexplored in NLP. As machine translation (MT) systems improve on standard benchmarks, their limitations increasingly concentrate in such cases, where translation cannot be reduced to one-to-one equivalence. We introduce a structured ontology of untranslatability along with a taxonomy of compensation strategies, which are specific techniques to convey meaning under these untranslatable circumstances. We operationalize this framework into a multilingual dataset of untranslatable sentences paired with strategy-based translations, enabling controlled analysis of translation behavior. Initial human preference studies suggest that translation quality depends on the strategy used, with consistent preferences for outputs that include explanatory context, known as the Annotation compensation strategy. Our framework and dataset provide a foundation for studying and modeling strategy-informed machine translation.
Chinese Translation
不可翻译性是指在不同语言之间无法直接保留意义的情况，这在语言学中得到了充分研究，但在自然语言处理（NLP）领域却仍未得到充分探索。随着机器翻译（MT）系统在标准基准测试中的不断改进，它们的局限性越来越集中在这些不可翻译的案例中，在这些情况下，翻译无法简化为一对一的等价关系。我们提出了一个结构化的不可翻译本体论，并建立了一套补偿策略的分类法，这些策略是针对这些不可翻译情况传达意义的特定技术。我们将这一框架操作化为一个多语言不可翻译句子的数据集，并配以基于策略的翻译，从而实现对翻译行为的受控分析。初步的人类偏好研究表明，翻译质量依赖于所使用的策略，且对于包含解释性上下文的输出（即注释补偿策略）有一致的偏好。我们的框架和数据集为研究和建模策略驱动的机器翻译提供了基础。
cs.CL / 13 / 2606.17372
Implicit vs. Explicit Prompting Strategies for LVLMs in Referential Communication
隐式与显式提示策略在参考交流中的 LVLMs
Abstract
Two recent studies (Jones et al. (2026); Zeng et al. (2026)) reach apparently contradictory conclusions about whether LVLMs can coordinate on efficient referring expressions. We control for task differences between the studies while directly comparing their prompting styles. We replicate the finding that models can coordinate efficient referring expressions when explicitly prompted to do so, suggesting that other task differences are not responsible for divergent results. However, we also find that the same models fail to infer the need for communicative efficiency from a more implicit prompt, highlighting critical differences between how humans and AI systems communicate.
Chinese Translation
两项近期研究（Jones et al. (2026); Zeng et al. (2026)）得出了表面上矛盾的结论，即 LVLMs 是否能够协调高效的指称表达。我们在直接比较这两项研究的提示风格时，控制了任务差异。我们重复了模型在被明确提示时能够协调高效指称表达的发现，这表明其他任务差异并不是导致结果分歧的原因。然而，我们还发现同样的模型在面对更隐式的提示时未能推断出交流效率的需求，这突显了人类与人工智能系统之间沟通方式的关键差异。
cs.CL / 14 / 2606.17391
NarrativeWorldBench: A Frontier-Saturated Benchmark and a Latent World Model for Long-Horizon Co-Creative Audio Drama
叙事世界基准：一个前沿饱和的基准测试和用于长时间协作音频剧的潜在世界模型
Abstract
Long-form serialized audio drama, with arcs that run for 200 to 800 episodes, is a major creative medium and a setting where frontier large language models (LLMs) fail. We benchmark 21 models, spanning classical, fine-tuned, open-frontier, closed-frontier, and reasoning tiers, on a uniform set of structural narrative metrics. All closed-frontier systems saturate at a plot-beat F1 in the band [0.78, 0.81] and collapse by about -0.20 F1 at horizon h=200. We introduce NarrativeWorldBench, an open benchmark of nine narrative-structure metrics evaluated across horizons h in {10, 20, 50, 100, 200}, with cross-lingual evaluation across four Indic languages (Hindi, Tamil, Telugu, Marathi). We introduce N-VSSM, a Narrative Variational State-Space Model that maintains a structured 256-dimensional latent world state over more than 200 episodes via a Mamba-2 backbone with an event-conditioned posterior and an 8B decoder. N-VSSM holds plot-beat F1 >= 0.84 across all horizons at 4x lower compute than the closed-frontier band. A learned Cultural Transfer Function lifts cross-language fidelity by +0.20 to +0.23 Likert points. In a within-subjects writer study (n = 12 professional authors, 240 trials), N-VSSM is preferred over Claude Opus 4.5 on long-arc consistency 71% of the time and rated +1.3 Likert points higher on controllability.
Chinese Translation
长篇连载音频剧，情节跨度为200到800集，是一种重要的创作媒介，也是前沿大型语言模型（LLMs）表现不佳的场景。我们在一组统一的结构叙事指标上对21个模型进行了基准测试，涵盖经典模型、微调模型、开放前沿模型、封闭前沿模型和推理层次。所有封闭前沿系统在情节节奏F1得分上饱和在[0.78, 0.81]区间，并在视野h=200时崩溃约-0.20 F1。我们引入了叙事世界基准（NarrativeWorldBench），这是一个开放的基准，包含九个叙事结构指标，评估范围为h在{10, 20, 50, 100, 200}，并在四种印度语言（印地语、泰米尔语、泰卢固语、马拉地语）之间进行跨语言评估。我们提出了N-VSSM（叙事变分状态空间模型），该模型通过Mamba-2骨干网，利用事件条件后验，保持一个结构化的256维潜在世界状态，跨越200多集。N-VSSM在所有视野下的情节节奏F1得分均≥0.84，计算成本比封闭前沿模型低4倍。学习到的文化转移函数使跨语言保真度提高了+0.20到+0.23 Likert分数。在一项内部被试作家研究中（n = 12名专业作者，240次试验），N-VSSM在长弧一致性方面比Claude Opus 4.5更受欢迎，71%的时间选择N-VSSM，并在可控性上评分高出+1.3 Likert分数。
cs.CL / 15 / 2606.17449
MODE-RAG: Manifold Outlier Diagnosis and Energy-based Retrieval-Augmented Generation Evaluation
MODE-RAG：流形异常诊断与基于能量的检索增强生成评估
Abstract
While Multimodal Retrieval-Augmented Generation (M-RAG) enhances Large Vision-Language Models, it remains highly susceptible to cross-modal hallucinations, causal fabrications, and sycophancy. Furthermore, existing mitigation pipelines often face an intervention paradox: static rules tend to unnecessarily disrupt accurate generations, whereas leaving the multi-modal reasoning completely unguided allows existing mismatches to cascade into severe logical fabrications. To quantify and mitigate these hallucinations, we propose a Multi-Agent system, MODE-RAG, driven by Variational Free Energy (VFE) and internal attention states to dynamically gate interventions. High-risk queries are routed to five stage-specific agents, integrating Monte Carlo Tree Search (MCTS) for rigorous causal derivation and logit perturbations to penalize sycophancy. Dedicated Correction and Overseer agents ensure formatting stability and perform post-hoc factual verification. To objectively evaluate our approach, we introduce ModeVent, a challenging subset derived from the MultiVent dataset. Extensive experiments indicate that our system effectively reduces hallucination rates and logical fabrication, significantly improving the robustness of M-RAG systems.
Chinese Translation
尽管多模态检索增强生成（M-RAG）提升了大型视觉语言模型的能力，但它仍然高度易受跨模态幻觉、因果虚构和谄媚的影响。此外，现有的缓解流程常常面临干预悖论：静态规则往往会不必要地干扰准确的生成，而完全不加引导的多模态推理则会导致现有的不匹配级联成严重的逻辑虚构。为了量化和缓解这些幻觉，我们提出了一种多智能体系统MODE-RAG，该系统由变分自由能（Variational Free Energy, VFE）和内部注意状态驱动，以动态控制干预。高风险查询被路由到五个特定阶段的智能体，结合蒙特卡洛树搜索（Monte Carlo Tree Search, MCTS）进行严格的因果推导，并通过对数值扰动来惩罚谄媚行为。专门的纠正和监督智能体确保格式稳定性并进行事后事实验证。为了客观评估我们的方法，我们引入了ModeVent，这是从MultiVent数据集中衍生出的一个具有挑战性的子集。大量实验表明，我们的系统有效降低了幻觉率和逻辑虚构，显著提高了M-RAG系统的鲁棒性。
cs.CL / 16 / 2606.17474
AIPatient Arena: EHR-grounded evaluation of large language models in end-to-end clinical consultation workflows
AIPatient Arena：基于电子健康记录的端到端临床咨询工作流程中大型语言模型的评估
Abstract
Large language models (LLMs) are increasingly considered for use in clinical consultation tasks, yet most medical evaluations remain static, single-turn, or narrowly outcome-based, limiting their ability to reflect the sequential, uncertain, and interactive nature of real-world care. Here, we propose AIPatient Arena, an EHRs-grounded evaluation framework for assessing the clinical utility of LLMs across eight dimensions of clinical competence. The framework integrates EHR data into patient-specific knowledge graphs, enabling multi-turn physician-patient interactions. We applied AIPatient Arena on a primary cohort of 437 patients and two out-of-distribution validation cohorts of 119 and 67 patients. We observe that LLMs performed well in medical interview questioning skills (QS; mean scores, 4.43-4.99/5), ethical and professional conduct (ET; 4.38-4.93/5), and clarity and transparency of clinical explanations (EX; 3.80-4.72/5). Performance was moderate in information integration (II; 3.19-4.21/5) and medication safety and justification (MS; 3.13-3.78/5), but persistent weaknesses were observed in handling of ambiguous patient responses (HR; 2.57-3.32/5), information coverage (IC; 2.08-3.02/5), and diagnostic accuracy and reasoning (Dx; 2.63-3.55/5). Process-based evaluation revealed recurrent interaction failures, including repetitive questioning, omission of past medical history, and inadequate handling of uncertainty. Richer conversational context improved diagnostic reasoning but yielded limited gains in treatment planning. These findings indicate that final-answer accuracy alone is insufficient for evaluating clinical readiness and highlight the importance of assessing how models gather, interpret, and communicate information throughout a consultation. AIPatient Arena provides an EHR-grounded framework for workflow-oriented pre-deployment evaluation of medical LLMs.
Chinese Translation
大型语言模型（LLMs）在临床咨询任务中的应用越来越受到关注，但大多数医学评估仍然是静态的、单轮的或狭义结果导向的，限制了它们反映现实护理中顺序性、不确定性和互动性的能力。在此，我们提出了AIPatient Arena，一个基于电子健康记录（EHRs）的评估框架，用于评估LLMs在八个临床能力维度上的临床效用。该框架将EHR数据整合到患者特定的知识图谱中，支持多轮的医患互动。我们在一个包含437名患者的主要队列以及两个分布外验证队列（分别为119名和67名患者）上应用了AIPatient Arena。我们观察到，LLMs在医学访谈提问技能（QS；平均分，4.43-4.99/5）、伦理和专业行为（ET；4.38-4.93/5）以及临床解释的清晰性和透明性（EX；3.80-4.72/5）方面表现良好。在信息整合（II；3.19-4.21/5）和药物安全性与合理性（MS；3.13-3.78/5）方面表现中等，但在处理模糊患者反应（HR；2.57-3.32/5）、信息覆盖（IC；2.08-3.02/5）以及诊断准确性和推理（Dx；2.63-3.55/5）方面存在持续的弱点。基于过程的评估揭示了反复出现的互动失败，包括重复提问、遗漏病史以及对不确定性的处理不足。更丰富的对话背景改善了诊断推理，但在治疗计划方面的收益有限。这些发现表明，仅靠最终答案的准确性不足以评估临床准备情况，并强调了评估模型在整个咨询过程中如何收集、解释和传达信息的重要性。AIPatient Arena提供了一个基于EHR的框架，用于面向工作流程的医疗LLMs的预部署评估。
cs.CL / 17 / 2606.17478
Decoding Hidden Deception in Reasoning LLMs: Activation Explainers for Deception Auditing
解码推理大型语言模型中的隐性欺骗：用于欺骗审计的激活解释器
Abstract
As LLMs acquire stronger reasoning capabilities, deceptive behavior becomes an increasingly serious safety concern. Existing deception monitors either score visible transcripts or derive scalar probe scores from representation vectors, leaving little inspectable evidence about why a response is suspicious. We introduce STATEWITNESS, an activation explainer for deception auditing. A separate decoder reads a target model's hidden states, then answers natural-language queries or emits structured reports about them. We evaluate STATEWITNESS on two target reasoning LLMs across seven deception datasets. STATEWITNESS reaches 0.916 mean AUROC, a relative gain of 11.6% over the best black-box text monitor and 25.0% over the best activation-probe baseline under the same evaluation protocol. When combined with existing monitors, STATEWITNESS reduces missed deceptive examples in simple threshold ensembles. Beyond scalar detection, the decoder returns query-level answers, schema reports, and token- or sentence-level evidence traces for human inspection. We view this interface as a potential building block for broader interpretability and alignment tools.
Chinese Translation
随着大型语言模型（LLMs）获得更强的推理能力，欺骗行为成为越来越严重的安全隐患。现有的欺骗监测工具要么对可见的转录文本进行评分，要么从表示向量中推导出标量探测分数，这使得关于为何某个响应可疑的可检查证据很少。我们引入了STATEWITNESS，一种用于欺骗审计的激活解释器。一个独立的解码器读取目标模型的隐藏状态，然后回答自然语言查询或生成关于这些状态的结构化报告。我们在两个目标推理大型语言模型上评估了STATEWITNESS，涵盖七个欺骗数据集。STATEWITNESS的平均AUROC达到0.916，相较于最佳黑箱文本监测工具提高了11.6%，相较于最佳激活探测基线提高了25.0%，均是在相同的评估协议下进行的。当与现有监测工具结合时，STATEWITNESS减少了简单阈值集成中的漏检欺骗示例。除了标量检测外，解码器还返回查询级别的答案、模式报告以及供人类检查的标记或句子级别的证据追踪。我们将此接口视为更广泛的可解释性和对齐工具的潜在构建模块。
cs.CL / 18 / 2606.17506
Evaluating Second-Order Bias of LLMs Through Epistemic Entitlement
通过认识权利评估大型语言模型的二阶偏见
Abstract
Evaluations of social bias in LLMs largely focus on whether models generate or imply biased content. However, as LLMs are increasingly used as judges of bias, they may exhibit social biases in subtler ways in how they evaluate biased content, which current methods do not systematically capture. We call this second-order bias: social bias in an LLM's judgment about social bias, which we evaluate through a novel, philosophically grounded reasoning task. Drawing on entitlement epistemology, we conceptualize bias as misplaced foundational knowledge that shapes an agent's rational inquiry, and derive a logical reasoning task for LLMs to judge to whom a biased text is acceptable or non-acceptable. We develop two simple metrics to measure how biased LLM judges are in inferring demographics for acceptability without sufficient support, and how these inferences vary across groups targeted by biased texts. Evaluating open and closed models, we find that our task evades safety guardrails by surfacing bias in model judgment. It varies systematically across target groups, reflects implicit social maps, and shows how models are still triggered by demographic labels. Our work points to the need for LLM bias evaluation in judgment tasks and broadly, for more theoretically grounded approaches to bias evaluation in NLP. We release our code and model responses at https://github.com/uofthcdslab/second-order-bias.
Chinese Translation
对大型语言模型（LLMs）社会偏见的评估主要集中在模型是否生成或暗示偏见内容。然而，随着LLMs越来越多地被用作偏见的评判者，它们可能在评估偏见内容时以更微妙的方式表现出社会偏见，而当前的方法并未系统性地捕捉到这一点。我们称之为二阶偏见：LLM对社会偏见的判断中的社会偏见，我们通过一种新颖的、以哲学为基础的推理任务进行评估。基于权利认识论，我们将偏见概念化为错误的基础知识，这种知识塑造了主体的理性探究，并为LLMs推导出一个逻辑推理任务，以判断某一偏见文本对谁是可接受或不可接受的。我们开发了两个简单的指标来衡量LLM评判者在没有足够支持的情况下推断可接受性的人口统计特征的偏见程度，以及这些推断在受到偏见文本影响的群体之间的变化。评估开放和封闭模型时，我们发现我们的任务通过揭示模型判断中的偏见而规避了安全防护措施。它在目标群体之间系统性地变化，反映了隐含的社会地图，并显示模型仍然受到人口统计标签的触发。我们的工作指出了在判断任务中对LLM偏见评估的必要性，以及在自然语言处理（NLP）中对偏见评估更具理论基础的方法的广泛需求。我们在 https://github.com/uofthcdslab/second-order-bias 发布了我们的代码和模型响应。
cs.CL / 19 / 2606.17519
Scaling Enterprise Agent Routing: Degradation, Diagnosis, and Recovery
企业代理路由的扩展：退化、诊断与恢复
Abstract
Production LLM assistants route user requests to growing libraries of specialized tools, but how does routing accuracy degrade as the catalog scales? We study single-step routing on a 110-agent, 584-tool catalog from a deployed enterprise productivity assistant, evaluating three frontier models from 10 to 110 agents. Routing F1 on under-specified requests drops 16--23 percentage points across models. An oracle analysis decomposes the degradation into a \emph{retrieval} gap (the model cannot surface the right tool) and a \emph{confusion} gap (even with perfect retrieval, the oracle ceiling drops 10pp). Embedding-based shortlisting recovers +10--11pp F1 at full scale across all three models and two providers. A production annotation study (1,435 human-labeled utterances, three annotators) confirms the recovery on real traffic at +10--17pp despite 10--15pp lower absolute performance.
Chinese Translation
生产型大语言模型（LLM）助手将用户请求路由到不断增长的专业工具库，但随着目录规模的扩大，路由准确性如何退化？我们研究了来自一个已部署的企业生产力助手的110个代理和584个工具目录中的单步路由，评估了从10个代理到110个代理的三种前沿模型。在不充分指定的请求中，路由F1分数在不同模型间下降了16至23个百分点。一个oracle分析将退化分解为 extit{检索}差距（模型无法找到正确的工具）和 extit{混淆}差距（即使在完美检索的情况下，oracle上限也下降了10个百分点）。基于嵌入的短名单方法在所有三种模型和两个提供者的全规模下恢复了+10至11个百分点的F1分数。一项生产注释研究（1,435个人工标注的发言，三位注释员）确认了在真实流量下的恢复，尽管绝对性能下降了10至15个百分点，但仍提高了+10至17个百分点。
cs.CL / 20 / 2606.17522
An expressivity analysis of hierarchical modelling in deep transformers via bounded-depth grammars
通过有界深度文法对深度变换器中层次建模的表达能力分析
Abstract
Deep neural networks are widely believed to derive their expressive power from their ability to form \textbf{hierarchical representations}, capturing progressively more abstract and compositional features across layers. In language modeling, \textbf{transformers} have emerged as the dominant architecture, with early layers capturing local syntactic patterns and later layers encoding more complex clause-level dependencies. While this intuition has shaped model design, there remains a lack of rigorous theoretical work demonstrating \textbf{how} deep transformers represent such hierarchical structures. In this work, we analyze the expressiveness of deep transformer models through the formal lens of bounded-depth, non-recursive context-free grammars. For this class of grammars, we explicitly construct transformers with positional attention whose depth grows linearly with grammar depth, while the neuron count scales with the number of derivation-tree shapes and quadratically with the number of production rules. Our theoretical results support the linear representation hypothesis by demonstrating that these architectures possess the structural capacity to encode abstract grammatical states into low-dimensional, linearly separable subspaces within the residual stream.
Chinese Translation
深度神经网络被广泛认为其表达能力源于形成 extbf{层次表示}的能力，能够在各层中捕捉逐渐更抽象和组合的特征。在语言建模中， extbf{变换器}已成为主导架构，早期层捕捉局部句法模式，而后期层则编码更复杂的从句级依赖关系。尽管这种直觉影响了模型设计，但仍缺乏严格的理论工作来证明深度变换器 extbf{如何}表示这些层次结构。在本研究中，我们通过有界深度、非递归上下文无关文法的形式视角分析深度变换器模型的表达能力。对于这一类文法，我们明确构造了具有位置注意力的变换器，其深度与文法深度线性增长，而神经元数量则与推导树形状的数量成比例，并与产生规则的数量成平方关系。我们的理论结果通过证明这些架构具备将抽象语法状态编码到低维线性可分子空间中的结构能力，支持了线性表示假设。
cs.CL / 21 / 2606.17542
Evaluating Large Language Models Abilities for Addressee, Turn-change, and Next Speaker Prediction in Meetings
评估大型语言模型在会议中对话者、换话和下一位发言者预测的能力
Abstract
We investigate turn-taking in multimodal multi-party conversations using large language models (LLMs). We construct an evaluation framework for three tasks: addressee detection, turn-change prediction, and next speaker prediction. We compare supervised models trained for these tasks, text-based LLMs, multimodal LLMs (MM-LLMs), and human subjects. Experiments on the AMI corpus showed that LLMs outperformed supervised models and humans in next speaker prediction, despite not being trained on the target domain and without access to audio or visual information. An MM-LLM performed better than text-based LLMs on addressee detection and turn-change prediction but remained below human performance, indicating difficulty leveraging raw audio-visual signals. Ablation analyses revealed that conversational context was critical, particularly for next speaker prediction. We observed that human and LLM prediction patterns were similar, and intervals with frequent turn changes were difficult for both.
Chinese Translation
我们使用大型语言模型（LLMs）研究多模态多方对话中的轮流发言。我们构建了一个评估框架，涵盖三个任务：对话者检测、换话预测和下一位发言者预测。我们比较了为这些任务训练的监督模型、基于文本的LLMs、多模态LLMs（MM-LLMs）和人类参与者。在AMI语料库上的实验表明，尽管LLMs未在目标领域进行训练且无法访问音频或视觉信息，但在下一位发言者预测方面，LLMs的表现优于监督模型和人类。MM-LLM在对话者检测和换话预测方面的表现优于基于文本的LLMs，但仍低于人类表现，表明在利用原始音频-视觉信号方面存在困难。消融分析显示，对话上下文至关重要，尤其是在下一位发言者预测中。我们观察到人类和LLM的预测模式相似，而频繁换话的间隔对两者来说都很困难。
cs.CL / 22 / 2606.17609
The Benchmark Illusion: Pruned LLMs Can Pass Multiple Choice but Fail to Answer
基准幻觉：修剪后的大型语言模型可以通过多项选择但无法回答
Abstract
Compressing large language models reduces memory use and inference cost, but it can also create failures that standard benchmarks miss. A pruned model may still perform well on multiple-choice evaluations, yet fail to answer the same question in open generation. We ask what pruning changes: does it erase the correct answer, or does it make the answer harder to produce as the top output? We study this question with multilingual question answering, tracking the same questions before and after pruning. We find a benchmark illusion. Under high-sparsity pruning, especially Wanda, models often fail in greedy open generation while still selecting the correct answer under multiple-choice scoring. In these recognition-only errors, the answer is usually not gone, but demoted: it often reappears with beam search, sampling, or one in-context example. Overall, multiple-choice benchmarks can overstate the usability of compressed LLMs, creating an evaluation blind spot. Compressed models should be tested on what they can produce, not only on what they can recognize.
Chinese Translation
压缩大型语言模型可以减少内存使用和推理成本，但也可能导致标准基准测试无法捕捉到的失败。一个经过修剪的模型可能在多项选择评估中表现良好，但在开放生成中却无法回答相同的问题。我们探讨修剪带来了什么变化：它是抹去了正确答案，还是使得答案更难以作为最佳输出生成？我们通过多语言问答研究这个问题，跟踪修剪前后的相同问题。我们发现了一种基准幻觉。在高稀疏性修剪下，尤其是Wanda模型，模型在贪婪的开放生成中经常失败，而在多项选择评分中仍然选择正确答案。在这些仅识别错误中，答案通常并未消失，而是被降级：它通常会在束搜索、采样或一个上下文示例中重新出现。总体而言，多项选择基准可能夸大了压缩大型语言模型的可用性，造成了评估盲点。压缩模型应在其能够生成的内容上进行测试，而不仅仅是在其能够识别的内容上。
cs.CL / 23 / 2606.17628
OPD-Evolver: Cultivating Holistic Agent Evolver via On-Policy Distillation
OPD-Evolver：通过策略蒸馏培养整体智能体进化者
Abstract
Memory has become a standard substrate for self-evolving agents, yet retaining experience is not the same as learning how to evolve through it. Existing memory agents can store trajectories, retrieve reflections, or accumulate skills, but often lack the holistic competence to select useful experience, act on it, write reusable knowledge, and maintain a growing repository. We introduce OPD-Evolver, a slow-fast co-evolution framework that cultivates such an agent evolver through on-policy self-distillation. In the fast loop, OPD-Evolver interacts with a four-level memory hierarchy to read, use, write, and maintain experience for rapid test-time evolution. In the slow loop, outcome-calibrated memory attribution and privileged hindsight distill these four abilities into the deployable policy. Across multi-domain benchmarks, OPD-Evolver surpasses memory systems such as ReasoningBank by up to 11.5%, and training-based methods such as Skill0 by ~5.8%. Further analysis shows that OPD-Evolver internalizes high-value experience and memory management, enabling OPD-Evolver-9B to challenge giant counterparts such as Qwen3.5-397B-A17B and Step-3.5-Flash, pointing beyond memory-augmented agents toward genuinely qualified agent evolvers.
Chinese Translation
记忆已成为自我进化智能体的标准基础，但保留经验并不等同于学习如何通过这些经验进行进化。现有的记忆智能体能够存储轨迹、检索反思或积累技能，但往往缺乏选择有用经验、对其采取行动、撰写可重用知识以及维护不断增长的知识库的整体能力。我们提出了OPD-Evolver，这是一种慢-快共进化框架，通过策略自蒸馏培养这样的智能体进化者。在快速循环中，OPD-Evolver与四级记忆层次结构进行交互，以快速读取、使用、写入和维护经验，以实现快速测试时的进化。在慢循环中，结果校准的记忆归因和特权后见将这四种能力提炼为可部署的策略。在多领域基准测试中，OPD-Evolver的表现超过了如ReasoningBank的记忆系统，提升幅度可达11.5%，并且在与基于训练的方法如Skill0的比较中，提升幅度约为5.8%。进一步分析表明，OPD-Evolver内化了高价值的经验和记忆管理，使得OPD-Evolver-9B能够挑战如Qwen3.5-397B-A17B和Step-3.5-Flash等巨型对手，指向超越记忆增强智能体的真正合格智能体进化者。
cs.CL / 24 / 2606.17634
Prompt Perturbation for Reliable LLM Evaluation over Comparison Graphs
用于比较图的可靠大型语言模型评估的提示扰动
Abstract
Evaluating large language models (LLMs) is important for understanding their capabilities, comparing competing systems, and supporting the deployment of reliable models in practice. For open-ended tasks, pairwise evaluation has become a popular paradigm, in which two responses to the same prompt are compared and the resulting judgments are aggregated into an overall ranking. A central challenge of this paradigm is intransitivity: the induced comparison outcomes may fail to support any coherent global ranking. For example, one may observe cyclic preferences such as $A \succ B \succ C \succ A$, or inconsistencies involving ties such as $A \equiv B\equiv C\neq A$. Such contradictions make the resulting leaderboard unstable and challenging to interpret. In this paper, we propose a prompt perturbation framework for improving the consistency of pairwise LLM evaluation. Our approach generates perturbed variants of each prompt, uses the resulting comparison graphs to identify and filter out structurally inconsistent comparison patterns, and then applies standard ranking methods to the filtered comparisons. A key feature of the proposed framework is that graph-level structural consistency is incorporated explicitly into the evaluation pipeline before ranking aggregation. This provides a simple and principled way to reduce cyclic inconsistencies and improve the reliability of LLM rankings.
Chinese Translation
评估大型语言模型（LLMs）对于理解其能力、比较竞争系统以及支持可靠模型在实践中的部署至关重要。在开放式任务中，成对评估已成为一种流行的范式，其中对同一提示的两个响应进行比较，并将结果判断汇总为整体排名。该范式的一个核心挑战是非传递性：所引发的比较结果可能无法支持任何一致的全局排名。例如，可能会观察到循环偏好，如 $A
eq B
eq C
eq A$，或涉及平局的不一致性，如 $A ext{等于} B ext{等于} C
eq A$。这些矛盾使得最终的排行榜不稳定且难以解释。在本文中，我们提出了一种提示扰动框架，以提高成对LLM评估的一致性。我们的方法生成每个提示的扰动变体，利用生成的比较图识别并过滤出结构上不一致的比较模式，然后对过滤后的比较应用标准排名方法。该框架的一个关键特征是，在排名聚合之前，图级结构一致性被明确纳入评估流程。这提供了一种简单且有原则的方法来减少循环不一致性，并提高LLM排名的可靠性。
cs.CL / 25 / 2606.17682
From Trainee to Trainer: LLM-Designed Training Environment for RL with Multi-Agent Reasoning
从受训者到培训者：用于多智能体推理的强化学习的LLM设计训练环境
Abstract
Reinforcement learning pipelines for Large Language Model (LLM) training often rely on manually redesigned environments between stages, requiring practitioners to heuristically infer which configuration will best improve the current policy. To automate this process, we propose the LLM-as-Environment-Engineer framework in which the current policy model analyzes failure trajectories together with contextual information and proposes modifications to the next-stage training environment configuration. We also introduce MAPF-FrozenLake, a controllable testbed whose generator exposes multi-dimensional environment configurations, making it suitable for studying and benchmarking environment redesign. On this testbed, we condition the environment engineer on structured summaries of policy behavior, failure cases, and environment statistics, from which it produces the configuration for the next training stage. With Qwen3-4B as the backbone, our framework achieves the strongest aggregate performance on our benchmarks, outperforming larger proprietary LLMs (e.g., GPT, Gemini) and fixed-environment training baselines. We further analyze which forms of context are most effective, finding that successful environment updates rely on failure evidence and preserve configurations that already work. Interestingly, the current RL checkpoint serves as a better environment engineer than the original base model, suggesting that policy learning improves the model's ability to diagnose its remaining weaknesses.
Chinese Translation
大型语言模型（LLM）训练的强化学习管道通常依赖于在各个阶段之间手动重新设计环境，这要求从业者根据经验推断出哪种配置最能改善当前策略。为了自动化这一过程，我们提出了LLM-as-Environment-Engineer框架，其中当前策略模型分析失败轨迹及其上下文信息，并提出对下一阶段训练环境配置的修改建议。我们还引入了MAPF-FrozenLake，这是一个可控的测试平台，其生成器提供多维环境配置，使其适合于环境重新设计的研究和基准测试。在该测试平台上，我们根据策略行为、失败案例和环境统计的结构化摘要来调节环境工程师，从中生成下一训练阶段的配置。以Qwen3-4B为基础，我们的框架在基准测试中实现了最强的综合性能，超越了更大规模的专有LLM（如GPT、Gemini）和固定环境训练基线。我们进一步分析了哪些形式的上下文最为有效，发现成功的环境更新依赖于失败证据，并保留已经有效的配置。有趣的是，当前的强化学习检查点作为环境工程师的表现优于原始基础模型，这表明策略学习提高了模型诊断其剩余弱点的能力。
cs.CL / 26 / 2606.17683
Bridging Functional Correctness and Runtime Efficiency Gaps in LLM-Based Code Translation
弥合基于大型语言模型的代码翻译中的功能正确性与运行效率差距
Abstract
While large language models (LLMs) have greatly advanced the functional correctness of automated code translation systems, the runtime efficiency of translated programs has received comparatively little attention. With the waning of Moore's law, runtime efficiency has become increasingly important for program quality, alongside functional correctness. Our preliminary study reveals that LLM-translated programs often run slower than human-written ones, and this issue cannot be remedied through prompt engineering alone. Therefore, our work proposes SwiftTrans, a code translation framework comprising two key stages: (1) Multi-Perspective Exploration, where MpTranslator leverages parallel in-context learning (ICL) to generate diverse translation candidates; and (2) Difference-Aware Selection, where DiffSelector identifies the optimal candidate by explicitly comparing differences between translations. We further introduce Hierarchical Guidance for MpTranslator and Ordinal Guidance for DiffSelector, enabling LLMs to better adapt to these two core components. To support the evaluation of runtime efficiency in translated programs, we extend existing benchmarks, CodeNet and F2SBench, and introduce a new benchmark, SwiftBench. Experimental results across all three benchmarks show that SwiftTrans achieves consistent improvements in both correctness and runtime efficiency.
Chinese Translation
尽管大型语言模型（LLMs）在自动代码翻译系统的功能正确性方面取得了显著进展，但翻译程序的运行效率却相对较少受到关注。随着摩尔定律的减弱，运行效率在程序质量中变得愈发重要，与功能正确性并重。我们的初步研究表明，LLM翻译的程序通常运行速度慢于人类编写的程序，而这一问题仅通过提示工程无法解决。因此，我们的工作提出了SwiftTrans，一个包含两个关键阶段的代码翻译框架：（1）多视角探索（Multi-Perspective Exploration），在该阶段，MpTranslator利用并行上下文学习（ICL）生成多样的翻译候选；（2）差异感知选择（Difference-Aware Selection），在该阶段，DiffSelector通过明确比较翻译之间的差异来识别最佳候选。我们进一步为MpTranslator引入了分层指导（Hierarchical Guidance），为DiffSelector引入了序数指导（Ordinal Guidance），使LLMs能够更好地适应这两个核心组件。为了支持对翻译程序运行效率的评估，我们扩展了现有基准测试CodeNet和F2SBench，并引入了一个新的基准测试SwiftBench。所有三个基准测试的实验结果表明，SwiftTrans在正确性和运行效率方面均取得了一致的改进。
cs.CL / 27 / 2606.17687
SuCo: Sufficiency-guided Continuous Adaptive Reasoning
SuCo：充分性引导的连续自适应推理
Abstract
Despite remarkable performance on complex tasks, Large Reasoning Models (LRMs) often generate excessively long Chain-of-Thoughts (CoT), inflating computational costs even for simple queries. Existing efforts to mitigate this inefficiency typically rely on discrete reasoning modes or fixed budget tiers, lacking a principled criterion of when reasoning is sufficient. In this work, we introduce Minimal Sufficient CoT (MSC), defined as the shortest prefix of a CoT trajectory which is adequate for producing the correct answer. We empirically show that MSC not only reduces reasoning tokens, but also improves accuracy across difficulty levels. Building on MSC, we propose Sufficiency-guided Continuous Adaptive Reasoning (SuCo), a two-stage training framework for autonomous reasoning control along a continuous spectrum. In stage 1, MSC-Aligned Fine-Tuning (MFT) constructs MSC data using problem-adaptive sufficiency thresholds that naturally scale with question difficulty, then fine-tunes the model to internalize concise yet sufficient reasoning patterns. In stage 2, Sufficiency-Aware Policy Optimization (SAPO) further optimizes the model through reinforcement learning with dynamic complexity tracking and sufficiency-aware rewards that penalize both over- and under-thinking. Extensive experiments across mathematics, code, and science benchmarks show that SuCo consistently achieves improvements in both accuracy and reasoning efficiency.
Chinese Translation
尽管在复杂任务上表现出色，大型推理模型（LRMs）往往生成过长的思维链（CoT），即使对于简单查询也会增加计算成本。现有的减轻这种低效的努力通常依赖于离散推理模式或固定预算层级，缺乏一个原则性的标准来判断何时推理是充分的。在本研究中，我们引入了最小充分思维链（MSC），定义为能够产生正确答案的思维链轨迹的最短前缀。我们实证表明，MSC不仅减少了推理所需的标记数量，还在不同难度水平上提高了准确性。在MSC的基础上，我们提出了充分性引导的连续自适应推理（SuCo），这是一个用于沿连续谱系进行自主推理控制的两阶段训练框架。在第一阶段，MSC对齐微调（MFT）使用与问题自适应的充分性阈值构建MSC数据，这些阈值自然地随着问题难度的变化而变化，然后微调模型以内化简洁而充分的推理模式。在第二阶段，充分性意识策略优化（SAPO）通过动态复杂性跟踪和对过度思考和不足思考进行惩罚的充分性意识奖励，进一步优化模型。针对数学、代码和科学基准的广泛实验表明，SuCo在准确性和推理效率上始终取得了改善。
cs.CL / 28 / 2606.17688
LLMs Infer Cultural Context but Fail to Apply It When Responding
大型语言模型推断文化背景但在回应时未能应用
Abstract
Recent work has shown that LLMs overrepresent dominant cultures, particularly Western ones, while marginalizing others. We investigate whether this affects models' ability to generate culturally adapted responses by evaluating their use of local measurement units based on the user's perceived cultural background. We introduce Cultural and Pragmatic Response Inference (CAPRI), a dataset of conversations with varying levels of cultural cues. Experiments with state-of-the-art LLMs show that models can infer cultural background and recall relevant conventions, but often fail to utilize the information to adapt their answers to the relevant cultural conventions, unless explicitly prompted to perform the tasks sequentially. We further evaluate adaptation to the interpretation of time and quantity expressions, two subjective language grounding dimensions that are affected by culture. We find that models increasingly adapt their answers as cultural cues accumulate, but their priors are not culture-neutral, sometimes aligning with the model's country of origin. Overall, CAPRI provides a resource for future research aimed at narrowing the gap between cultural knowledge and culturally adaptive language generation.
Chinese Translation
最近的研究表明，大型语言模型（LLMs）过度代表主流文化，尤其是西方文化，同时边缘化其他文化。我们探讨这是否影响模型生成文化适应性回应的能力，通过评估它们基于用户感知的文化背景使用当地计量单位的情况。我们引入了文化与实用回应推断（Cultural and Pragmatic Response Inference, CAPRI），这是一个包含不同文化线索的对话数据集。对最先进的LLMs进行的实验表明，模型能够推断文化背景并回忆相关惯例，但通常未能利用这些信息来调整其回答以符合相关文化惯例，除非明确提示其按顺序执行这些任务。我们进一步评估了对时间和数量表达的适应，这两个主观语言基础维度受文化影响。我们发现，随着文化线索的积累，模型越来越能够调整其回答，但它们的先验并非文化中立，有时与模型的原产国相一致。总体而言，CAPRI为未来旨在缩小文化知识与文化适应性语言生成之间差距的研究提供了资源。
cs.CL / 29 / 2606.17791
The Slop Paradox: How Synthetic Standardization Erodes Clinical Uncertainty and Cross-Modal Alignment in AI-Rewritten Radiology Reports
斜坡悖论：合成标准化如何侵蚀临床不确定性和AI重写放射学报告中的跨模态一致性
Abstract
AI-assisted clinical documentation tools increasingly summarize, standardize, and reformat radiology reports using large language models (LLMs). We present a controlled measurement of the resulting information degradation. Using 450 chest X-ray reports from the Indiana University dataset, we generate synthetic versions via three realistic LLM rewriting tasks: EHR summarization, standardized rewriting, and teaching case preparation. We measure entity erosion (via medical NER), hedging collapse (loss of clinical uncertainty language), and cross-modal alignment degradation (via BiomedCLIP image-text similarity). Our central finding is a dissociation between information loss and cross-modal fidelity. EHR summarization is the most destructive at the content level, eroding 51.4% of clinical entities and 43.7% of hedging language, yet it preserves image-text alignment almost entirely (a 2.5% drop). The two tasks meant to produce cleaner training data, standardized rewriting and teaching case preparation, do the reverse: they preserve more entities (26.8% and 29.3% eroded) but cause 14.9-16.5% alignment drops, six to seven times those of EHR summarization. We term this the slop paradox: rewriting that makes clinical text look cleaner for multimodal training is precisely what pulls it away from the image. Contrary to our pre-specified hypothesis, rare pathologies were not preferentially degraded: across nine rare-versus-common comparisons, no difference survived multiple-comparison correction, and nominal differences ran in the opposite direction (common > rare), so contamination is invisible to condition-specific monitoring. The dominant determinant of degradation is the type of AI rewriting task, not the clinical content. These findings bear on multimodal medical AI dataset construction and the governance of AI-assisted clinical documentation.
Chinese Translation
AI辅助的临床文档工具越来越多地使用大型语言模型（LLMs）对放射学报告进行总结、标准化和重新格式化。我们对由此产生的信息退化进行了控制测量。使用印第安纳大学数据集中的450份胸部X光报告，我们通过三项现实的LLM重写任务生成合成版本：电子健康记录（EHR）总结、标准化重写和教学案例准备。我们测量了实体侵蚀（通过医学命名实体识别（NER））、模糊语言崩溃（临床不确定性语言的丧失）和跨模态一致性退化（通过BiomedCLIP图像-文本相似性）。我们的主要发现是信息损失与跨模态保真度之间的脱离。EHR总结在内容层面上是最具破坏性的，侵蚀了51.4%的临床实体和43.7%的模糊语言，但几乎完全保留了图像-文本的一致性（下降2.5%）。旨在生成更干净训练数据的两个任务，标准化重写和教学案例准备，恰恰相反：它们保留了更多的实体（分别侵蚀26.8%和29.3%），但导致了14.9-16.5%的一致性下降，是EHR总结的六到七倍。我们将其称为斜坡悖论：使临床文本在多模态训练中看起来更干净的重写，恰恰是将其与图像拉开的原因。与我们预先设定的假设相反，罕见病理并没有优先受到侵蚀：在九个罕见与常见的比较中，没有差异在多重比较校正后存活，名义上的差异则朝相反方向发展（常见 > 罕见），因此污染对特定条件的监测是不可见的。退化的主要决定因素是AI重写任务的类型，而不是临床内容。这些发现对多模态医学AI数据集的构建和AI辅助临床文档的治理具有重要意义。
cs.CL / 30 / 2606.17820
Improving low-resource ASR using bilingual fine-tuning with language identification: a cross-linguistic evaluation
通过语言识别的双语微调改善低资源自动语音识别：跨语言评估
Abstract
This study explores how bilingual fine-tuning affects automatic speech recognition (ASR) in low-resource languages. We evaluate this method across nine linguistically and geographically diverse language pairs, covering a range of language families and writing systems. To distinguish the two languages, during training, we pre-pend each input text with a language identification token. At inference, the model jointly predicts both the language and transcription from the speech input alone. As texts for which the language is incorrectly determined show low ASR performance, we also conduct a follow-up experiment in which the language identification token is provided both during training and inference. Our results show that bilingual fine-tuning can be beneficial when language identification accuracy is high, and that in cases where language identification performance is low, including the language identification token at inference helps to improve ASR performance.
Chinese Translation
本研究探讨了双语微调如何影响低资源语言的自动语音识别（ASR）。我们在九对语言对中评估了该方法，这些语言对在语言学和地理上具有多样性，涵盖了多种语言家族和书写系统。为了区分这两种语言，在训练过程中，我们在每个输入文本前添加了语言识别标记。在推理阶段，模型仅根据语音输入共同预测语言和转录。由于语言识别错误确定的文本显示出较低的ASR性能，我们还进行了后续实验，在该实验中，语言识别标记在训练和推理过程中均被提供。我们的结果表明，当语言识别准确性较高时，双语微调是有益的，而在语言识别性能较低的情况下，在推理阶段包含语言识别标记有助于提高ASR性能。
cs.CL / 31 / 2606.17826
When Multiple Scripts Matter: Evaluating ASR in Clinical Settings
多种书写系统的重要性：在临床环境中评估自动语音识别
Abstract
Automatic speech recognition (ASR) in non-English clinical settings is challenged by multiscript variability, where the same term may appear in multiple valid orthographic forms. Conventional string-matching evaluation metrics often underestimate ASR performance by treating orthographic variants as errors. To address this issue, we introduce MultiClin, a clinical ASR benchmark designed to evaluate robustness to multiscript variability. Experiments across diverse ASR models show that multiscript-aware evaluation provides a fairer assessment of recognition quality than conventional single-reference evaluation. We further investigate the impact of script consistency during training and find that inconsistent script mappings increase orthographic uncertainty and hinder model convergence, with a balanced 50% mapping ratio producing the highest entropy. In contrast, script unification consistently yields the best ASR performance. Our dataset and code are publicly available at: https://github.com/aitrics-ronaldo/Interspeech_MultiClin.
Chinese Translation
在非英语临床环境中，自动语音识别（ASR）面临多书写系统变异的挑战，同一术语可能以多种有效的书写形式出现。传统的字符串匹配评估指标常常低估ASR性能，因为它们将书写变体视为错误。为了解决这个问题，我们引入了MultiClin，这是一个旨在评估对多书写系统变异的鲁棒性的临床ASR基准。针对多种ASR模型的实验表明，考虑多书写系统的评估比传统的单参考评估提供了更公正的识别质量评估。我们进一步研究了训练过程中书写系统一致性的影响，发现不一致的书写映射增加了书写不确定性并阻碍了模型收敛，而平衡的50%映射比例产生了最高的熵。相比之下，书写统一始终带来最佳的ASR性能。我们的数据集和代码可在以下网址公开获取：https://github.com/aitrics-ronaldo/Interspeech_MultiClin。
cs.CL / 32 / 2606.17835
Perceptual compensation for tonal context in self-supervised speech models
自监督语音模型中的音调语境感知补偿
Abstract
This study examines the extent to which the wav2vec2.0 architecture exhibits evidence of compensation for phonological context. We conducted a pseudo-replication of a perceptional compensation experiment on Mandarin Chinese tones, and compared the embedding similarities and probing classifier outputs between a purely self-supervised pre-trained model and a model fine-tuned for Mandarin ASR. No evidence of compensation was found in the embedding similarities of the purely pre-trained model. Probing classifiers showed some evidence of compensation in addition to the expected layer-wise improvements in categorization, but failed to replicate human performance on isolated test syllables. Our findings contrast with previous reports of sensitivity to phonological structure emerging through pre-training alone, and suggest that supervised objectives may be necessary to encourage the abstraction of at least some types of phonological regularities.
Chinese Translation
本研究考察了 wav2vec2.0 架构在多大程度上表现出对音位语境的补偿证据。我们对普通话声调的感知补偿实验进行了伪复制，并比较了纯自监督预训练模型与针对普通话自动语音识别（ASR）进行微调的模型之间的嵌入相似性和探测分类器输出。结果显示，纯预训练模型的嵌入相似性中没有发现补偿的证据。探测分类器在分类中的层级改进之外显示出一些补偿的证据，但未能复制人类在孤立测试音节上的表现。我们的发现与之前关于仅通过预训练就能对音位结构产生敏感性的报告相悖，并表明监督目标可能是促进至少某些类型音位规律抽象化所必需的。
cs.CL / 33 / 2606.17838
Environment-Grounded Automated Prompt Optimization for LLM Game Agents
基于环境的自动化提示优化用于大型语言模型游戏代理
Abstract
LLM agents in interactive environments are highly sensitive to their prompts, yet prompt engineering remains a manual, task-specific process. We introduce an automated prompt optimization framework for LLM agents that decomposes the observation-to-action pipeline into a goal-conditioned descriptor agent and an action selection agent, and iteratively refines each module's prompt through an LLM-driven evolutionary loop guided by environment returns. We propose a behavior analyzer to attribute episode outcomes to specific prompt components, and a mutator to propose targeted revisions to the prompt, before validating them through environment rollouts. We evaluate on all five BabyAI tasks in the BALROG benchmark, comparing our pipeline against BALROG's RobustCoTAgent under both plain and guided prompt initializations. Optimization improves performance consistently across tasks and conditions, without requiring updates to the model weights. On PutNext, a multi-step coordination task where the RobustCoTAgent achieves 0% success, our framework reaches up to 72.5% success rate using the same underlying LLM with optimized prompts. These results suggest that a multi-agent framework, combined with automatic prompt optimization, enhances LLMs without the need for fine-tuning or extensive human supervision.
Chinese Translation
在互动环境中，大型语言模型（LLM）代理对其提示高度敏感，但提示工程仍然是一个手动的、任务特定的过程。我们引入了一种针对LLM代理的自动化提示优化框架，该框架将观察到的行动流程分解为一个目标条件描述代理和一个行动选择代理，并通过一个由环境回报引导的LLM驱动的进化循环迭代地优化每个模块的提示。我们提出了一种行为分析器，用于将回合结果归因于特定的提示组件，以及一种变异器，用于提出针对性的提示修订，然后通过环境回合进行验证。我们在BALROG基准的所有五个BabyAI任务上进行了评估，将我们的管道与BALROG的RobustCoTAgent在普通和引导提示初始化下进行比较。优化在各个任务和条件下持续提高性能，而无需更新模型权重。在PutNext这一多步骤协调任务中，RobustCoTAgent的成功率为0%，而我们的框架在使用相同基础LLM和优化提示的情况下达到了72.5%的成功率。这些结果表明，结合自动化提示优化的多代理框架能够增强LLM的能力，而无需微调或大量人工监督。
cs.CL / 34 / 2606.17861
GameCraft-Bench: Can Agents Build Playable Games End-to-End in a Real Game Engine?
GameCraft-Bench：代理能否在真实游戏引擎中端到端构建可玩游戏？
Luo, Tongxu, Wang, Rongsheng, Bi, Jiaxi, Xu, Chenming, Tang, Zhengyang, Chen, Jianlong, Liang, Juhao, Ji, Ke, Guo, Shuqi, Du, Yuhao, Bu, Fan, Du, Wenyu, Zhang, Xiaotong, Li, Kyle, Wang, Shaobo, Zhang, Linfeng, Liu, Yuxuan, Lai, Xin, Li, Chenxin, Guo, Yiduo, Zhang, Zhexin, Wang, Xinyuan, Bai, Tianyi, Li, Ziniu, Wang, Benyou
Abstract
Game generation is an emerging application of coding agents, requiring models to transform natural-language specifications into playable interactive systems. Unlike traditional coding tasks, game generation takes place within a game engine, where scripts, scenes, assets, rendering, and runtime interactions must jointly produce coherent gameplay. We formalize end-to-end game generation as the problem of producing a complete game artifact that realizes a specification through observable player-game interaction in a target environment. We argue that evaluating this setting requires three desiderata: Engine Grounding, Artifact Completeness, and Interactive Verification. We propose an interaction-grounded evaluation framework that assesses executable gameplay through replayed demonstrations and rubric-guided multimodal judging. We instantiate this framework as GameCraft-Bench, a benchmark comprising 140 Godot tasks across 15 game families. Evaluations of frontier coding agents show that end-to-end game generation remains highly challenging: the strongest agent achieves only 41.46%, and most agents score below 40%. Further analysis reveals that while agents often implement recognizable mechanics, they struggle to deliver complete games with sufficient content, functional visual feedback, and coherent presentation. See https://tongxuluo.github.io/gamecraft-bench-website for demos, code, and data.
Chinese Translation
游戏生成是编码代理的一种新兴应用，要求模型将自然语言规范转化为可玩互动系统。与传统编码任务不同，游戏生成发生在游戏引擎内，其中脚本、场景、资产、渲染和运行时交互必须共同产生连贯的游戏体验。我们将端到端游戏生成形式化为在目标环境中通过可观察的玩家与游戏交互来生成实现规范的完整游戏工件的问题。我们认为评估这一设置需要三个要求：引擎基础、工件完整性和互动验证。我们提出了一种基于互动的评估框架，通过重播演示和评分指导的多模态评判来评估可执行的游戏体验。我们将该框架实例化为GameCraft-Bench，这是一个包含15个游戏系列中140个Godot任务的基准测试。对前沿编码代理的评估表明，端到端游戏生成仍然具有很高的挑战性：最强代理仅达到41.46%，而大多数代理的得分低于40%。进一步分析显示，尽管代理通常实现了可识别的机制，但它们在提供具有足够内容、功能性视觉反馈和连贯呈现的完整游戏方面存在困难。有关演示、代码和数据，请参见 https://tongxuluo.github.io/gamecraft-bench-website。
cs.CL / 35 / 2606.17890
Dynamic Rollout Editing for Reducing Overthinking in RL-Trained Reasoning Models
动态回滚编辑以减少强化学习训练推理模型中的过度思考
Abstract
Long-form chain-of-thought reasoning can improve LLM performance on complex tasks, but models often continue generating unnecessary reasoning after a correct answer has emerged. We refer to this behavior as overthinking. We study this phenomenon from the perspective of GRPO-style reinforcement learning (RL) post-training, framing it as a training-time credit-assignment problem rather than merely a decoding-time stopping problem. In rollouts sampled at the onset of GRPO training, we observe that successful trajectories can exhibit a slightly higher degree of overthinking than unsuccessful trajectories for the same prompts. This early imbalance provides a starting point for an undesirable feedback loop: because GRPO assigns sequence-level credit, it cannot distinguish the solution-reaching prefix from the unnecessary continuation that lengthens a successful trajectory. Both receive positive update signal, allowing the initial imbalance to grow into more severe overthinking during training. To address this issue, we introduce Dynamic Rollout Editing (DRE), a training-time intervention for successful trajectories that continue thinking after answer emergence. DRE preserves the accepted verified prefix, edits the remaining thinking, and prefers the edited trajectory within the same RL group, weakening the preference signal for unnecessary thinking without penalizing the reasoning needed to reach the answer. Experiments across diverse tasks show the effectiveness of DRE.
Chinese Translation
长形式的链式思维推理可以提高大型语言模型（LLM）在复杂任务上的表现，但模型在正确答案出现后往往会继续生成不必要的推理。我们将这种行为称为过度思考。我们从GRPO（Generalized Reinforcement Policy Optimization）风格的强化学习后训练的角度研究这一现象，将其框架视为训练时的信用分配问题，而不仅仅是解码时的停止问题。在GRPO训练开始时采样的回滚中，我们观察到，对于相同的提示，成功轨迹的过度思考程度可能略高于失败轨迹。这种早期的不平衡为一个不良的反馈循环提供了起点：由于GRPO分配的是序列级别的信用，它无法区分达到解决方案的前缀与延长成功轨迹的不必要延续。两者都接收到正向更新信号，使得初始的不平衡在训练过程中加剧为更严重的过度思考。为了解决这个问题，我们引入了动态回滚编辑（Dynamic Rollout Editing，DRE），这是一种针对在答案出现后继续思考的成功轨迹的训练时干预。DRE保留经过验证的前缀，编辑剩余的思考，并在同一强化学习组内优先选择编辑后的轨迹，从而削弱对不必要思考的偏好信号，而不惩罚达到答案所需的推理。跨多种任务的实验表明DRE的有效性。
cs.CL / 36 / 2606.17905
ChLogic: Evaluating Robustness of Logical Reasoning in Chinese Expressions
ChLogic：评估中文表达中的逻辑推理的鲁棒性
Abstract
Large language models perform increasingly well on standardized logical reasoning benchmarks, but whether this ability remains robust beyond English is unclear. We introduce ChLogic, an English--Chinese aligned benchmark that tests whether models preserve logical reasoning performance when the same latent logical structure is expressed in English and diverse Chinese surface realizations. Built from formal logical templates, the benchmark contains three data sets: (i) the General aligned set, derived from 60 General Propositions across nine template families; (ii) the Difficult aligned set, derived from 40 Difficult Problems; and (iii) the Chinese-only set, covering 15 language-specific phenomenon types. Each aligned item pairs one English reference expression with five Chinese realizations. Experiments on Qwen3, Ministral, and GLM models reveal a persistent English--Chinese performance gap. Back-translation from standard Chinese into English often improves performance on the General aligned set, but produces mixed effects on the Difficult aligned set, where Qwen3-32B and GLM-5.1 perform worse after translation. These results indicate that Chinese surface realization, translation artifacts, and model-specific behavior jointly affect multilingual logical reasoning. Overall, ChLogic provides a useful stress test for the robustness of multilingual reasoning.
Chinese Translation
大型语言模型在标准化逻辑推理基准测试中的表现越来越好，但这种能力在英语之外是否仍然稳健尚不清楚。我们引入了ChLogic，这是一个英语-中文对齐的基准，旨在测试模型在相同潜在逻辑结构以英语和多样化中文表述时是否保持逻辑推理性能。该基准基于形式逻辑模板构建，包含三个数据集：(i) 一般对齐集，源自九个模板家族中的60个一般命题；(ii) 困难对齐集，源自40个困难问题；以及(iii) 仅中文集，涵盖15种语言特有现象类型。每个对齐项将一个英语参考表达与五个中文表述配对。对Qwen3、Ministral和GLM模型的实验揭示了持续存在的英语-中文性能差距。从标准中文回译到英语通常会提高一般对齐集的性能，但对困难对齐集的影响则不一，其中Qwen3-32B和GLM-5.1在翻译后表现更差。这些结果表明，中文表述、翻译伪影和模型特定行为共同影响多语言逻辑推理。总体而言，ChLogic为多语言推理的鲁棒性提供了有用的压力测试。
cs.CL / 37 / 2606.17967
Learning task-specific subspaces via interventional post-training of speech foundation models
通过干预后训练学习任务特定子空间的语音基础模型
Abstract
Speech foundation models, pre-trained on large corpora of unlabelled speech data, produce general-purpose representations which are useful across tasks. However, these representations encode information about salient speech variables in a distributed manner, while downstream speech tasks rely on only some of this variability. In this work, we propose a post-training refinement approach using interventional contrastive learning. By leveraging an interventional dataset and multi-part contrastive loss, we learn a transformation from the entangled representation space of speech foundation models into separate content and speaker subspaces. We evaluate the learnt representations on speaker verification and keyword spotting tasks, showing improved out-of-domain speaker verification performance and evidence that speaker and content information are separated across the learned subspaces.
Chinese Translation
语音基础模型在大规模未标记语音数据语料库上进行预训练，生成的通用表示在多个任务中均有用。然而，这些表示以分布式的方式编码了显著语音变量的信息，而下游语音任务仅依赖于这些变异中的一部分。在本研究中，我们提出了一种使用干预对比学习的后训练精炼方法。通过利用干预数据集和多部分对比损失，我们学习了从语音基础模型的纠缠表示空间到独立内容和说话者子空间的转换。我们在说话者验证和关键词检测任务上评估了学习到的表示，结果显示在域外说话者验证性能有所提升，并提供了证据表明说话者和内容信息在学习到的子空间中被分离。
cs.CL / 38 / 2606.17973
Fine-tuning LLMs for Passive Depression Severity Estimation from AI Mental Health Dialogue
从人工智能心理健康对话中微调大型语言模型以估计被动抑郁严重性
Abstract
Depression is the leading cause of disability worldwide, and early detection of symptom change is essential for timely intervention. Validated instruments such as the Patient Health Questionnaire-9 (PHQ-9) support symptom monitoring at scale, but real-world completion rates are low, introducing response bias and systematic missingness. Passive approaches that infer severity from routinely generated data could close this gap. We address this by predicting PHQ-9 total scores directly from transcripts of conversations between users and an AI mental health application, requiring only conversation text and no additional clinical data. We fine-tune a Qwen3.5-27B backbone with a regression head, augment 3,111 ground-truth labels with pseudolabels generated by a reasoning model (Claude Opus) and iteratively trained intermediate models, for a combined dataset of 6,283 users. On a held-out test set of 842 users, our best model achieves MAE = 2.6, RMSE = 4.0, Pearson r = 0.80, and AUC = 0.91 at the PHQ-9 >= 10 clinical threshold. We also find AUC > 0.87 at every severity threshold from PHQ-9 >= 3 to PHQ-9 >= 24, demonstrating that the model captures depression severity across the full clinical spectrum. This work opens the door to passive, continuous symptom monitoring in AI mental health platforms, without requiring users to complete self-report measures.
Chinese Translation
抑郁症是全球残疾的主要原因，早期检测症状变化对于及时干预至关重要。经过验证的工具，如患者健康问卷-9（Patient Health Questionnaire-9, PHQ-9），支持大规模的症状监测，但现实世界中的完成率较低，导致响应偏差和系统性缺失。通过从常规生成的数据中推断严重性，采用被动方法可以填补这一空白。我们通过直接从用户与人工智能心理健康应用之间的对话记录中预测PHQ-9总分来解决这个问题，仅需对话文本而不需要额外的临床数据。我们对Qwen3.5-27B主干模型进行了微调，添加了回归头，并用推理模型（Claude Opus）生成的伪标签增强了3,111个真实标签，迭代训练中间模型，构建了一个包含6,283个用户的综合数据集。在842个用户的保留测试集中，我们的最佳模型达到了MAE = 2.6，RMSE = 4.0，Pearson相关系数r = 0.80，以及在PHQ-9 >= 10临床阈值下的AUC = 0.91。我们还发现，在PHQ-9 >= 3到PHQ-9 >= 24的每个严重性阈值下，AUC均超过0.87，证明该模型能够捕捉到整个临床谱系中的抑郁严重性。这项工作为人工智能心理健康平台中的被动、持续症状监测打开了大门，无需用户完成自我报告量表。
cs.CL / 39 / 2606.17999
VoidPadding: Let [VOID] Handle Padding in Masked Diffusion Language Models so that [EOS] Can Focus on Semantic Termination
VoidPadding：让 [VOID] 处理掩码扩散语言模型中的填充，以便 [EOS] 能专注于语义终止
Abstract
MDLMs generate text by denoising a preallocated masked response canvas, making response-length modeling central to instruction tuning. Existing MDLMs often inherit the autoregressive convention of using repeated \texttt{[EOS]} tokens for padding during instruction tuning, giving \texttt{[EOS]} a dual role as both a semantic terminator and a padding token. We show that this dual role is a root cause of \texttt{[EOS]} overflow under large-block decoding. To decouple these roles, we propose VoidPadding, which introduces \texttt{[VOID]} for padding and reserves \texttt{[EOS]} for termination. During inference, the learned \texttt{[EOS]} signal enables early stopping, while the learned \texttt{[VOID]} signal guides adaptive response canvas expansion. On Dream-7B-Instruct, VoidPadding improves the block-size-averaged four-task mean across mathematical reasoning and code generation benchmarks by \(+17.84\) points over the original model and \(+6.95\) points over RainbowPadding, while reducing decoding NFE by 55.7\% on average. Code is available at https://github.com/Haru-LCY/VoidPadding.
Chinese Translation
MDLM（掩码扩散语言模型）通过去噪预分配的掩码响应画布生成文本，使得响应长度建模成为指令调优的核心。现有的 MDLM 通常继承了自回归的惯例，在指令调优过程中使用重复的 exttt{[EOS]} 标记进行填充，使得 exttt{[EOS]} 同时承担语义终止符和填充标记的双重角色。我们表明，这种双重角色是大块解码下 exttt{[EOS]} 溢出的根本原因。为了解耦这些角色，我们提出了 VoidPadding，引入 exttt{[VOID]} 用于填充，并将 exttt{[EOS]} 保留用于终止。在推理过程中，学习到的 exttt{[EOS]} 信号使得早期停止成为可能，而学习到的 exttt{[VOID]} 信号则指导自适应响应画布的扩展。在 Dream-7B-Instruct 上，VoidPadding 在数学推理和代码生成基准测试中，将块大小平均的四项任务均值提高了原模型的 +17.84 分和 RainbowPadding 的 +6.95 分，同时平均减少了解码 NFE 55.7%。代码可在 https://github.com/Haru-LCY/VoidPadding 获取。
cs.CL / 40 / 2606.18033
When English Isn't the Best Teacher: Source Language Effects in Cross-Lingual In-Context Learning
当英语不是最佳教师：跨语言上下文学习中的源语言效应
Abstract
Cross-lingual transfer in multilingual NLP has been widely explored in supervised fine-tuning contexts, where factors like data availability and linguistic similarity largely determine transfer quality. As the field shifts toward few-shot In-Context Learning (ICL), it is often presumed that insights from fine-tuning carry over unchanged. Yet this assumption has not been rigorously evaluated, leaving open the question of how to choose source languages for cross-lingual ICL. We conduct a broad empirical study of cross-lingual transfer in ICL spanning seven tasks, six models, and a typologically diverse set of languages. We further analyze language confusion, a key obstacle for generative tasks in cross-lingual ICL. Our results show that conventional fine-tuning-based expectations do not consistently apply in the ICL regime and point to alternative heuristics for selecting source languages effectively.
Chinese Translation
在多语言自然语言处理（NLP）中，跨语言迁移在监督微调的背景下得到了广泛探讨，其中数据可用性和语言相似性等因素在很大程度上决定了迁移质量。随着该领域向少量示例的上下文学习（In-Context Learning, ICL）转变，通常假设微调中的见解可以不变地延续。然而，这一假设尚未经过严格评估，导致如何选择跨语言ICL的源语言这一问题仍然悬而未决。我们对跨语言ICL进行了广泛的实证研究，涵盖七个任务、六个模型以及一组类型学上多样的语言。我们进一步分析了语言混淆，这是跨语言ICL中生成任务的一个关键障碍。我们的结果表明，传统的基于微调的预期在ICL模式下并不总是适用，并指出了有效选择源语言的替代启发式方法。
cs.CL / 41 / 2606.18051
Compositional Skill Routing for LLM Agents: Decompose, Retrieve, and Compose
大规模语言模型代理的组合技能路由：分解、检索与组合
Abstract
LLM agents increasingly rely on external skills -- reusable tool specifications -- but real-world tasks often require composing multiple skills, not just selecting one. We formalize this as the Compositional Skill Routing problem: given a complex user query and a large skill library, decompose the query into atomic sub-tasks, retrieve the appropriate skill for each sub-task, and compose an executable plan. We present SkillWeaver, a decompose-retrieve-compose framework combining an LLM task decomposer, a bi-encoder skill retriever with FAISS indexing, and a dependency-aware DAG planner. To support evaluation, we introduce CompSkillBench, a benchmark of 300 compositional queries over 2,209 real MCP server skills spanning 24 functional categories, sourced from the public MCP ecosystem. Our experiments reveal that task decomposition quality is the primary bottleneck: standard LLM decomposition reaches only 34.2% category recall at the step level. To address this, we propose Iterative Skill-Aware Decomposition (SAD), a retrieval-augmented feedback loop that iteratively aligns decomposition with available skills. SAD improves decomposition accuracy from 51.0% to 67.7% (+32.7%, Wilcoxon p < 10^-6) in a single iteration; DA-conditioned analysis confirms that correct granularity is the prerequisite for effective retrieval (CatR@1 rises from 34% to 41% when DA=1). SkillWeaver reduces context window consumption by over 99%, and transfer experiments confirm generalization (+35.6% relative DA gain even when target categories are absent from the retrieval pool).
Chinese Translation
大规模语言模型（LLM）代理越来越依赖外部技能——可重用的工具规范——但现实世界的任务通常需要组合多种技能，而不仅仅是选择一种。我们将此形式化为组合技能路由问题：给定一个复杂的用户查询和一个大型技能库，将查询分解为原子子任务，为每个子任务检索适当的技能，并组合成一个可执行的计划。我们提出了SkillWeaver，一个结合了LLM任务分解器、带有FAISS索引的双编码器技能检索器和依赖感知有向无环图（DAG）规划器的分解-检索-组合框架。为了支持评估，我们引入了CompSkillBench，这是一个包含300个组合查询的基准，涵盖了来自公共MCP生态系统的2,209个真实MCP服务器技能，跨越24个功能类别。我们的实验揭示，任务分解质量是主要瓶颈：标准LLM分解在步骤级别的类别召回率仅为34.2%。为了解决这个问题，我们提出了迭代技能感知分解（SAD），这是一种增强检索的反馈循环，能够迭代地将分解与可用技能对齐。SAD在单次迭代中将分解准确率从51.0%提高到67.7%（+32.7%，Wilcoxon p < 10^-6）；依赖分析（DA）条件下的分析确认，正确的粒度是有效检索的前提（当DA=1时，CatR@1从34%上升到41%）。SkillWeaver将上下文窗口的消耗减少了99%以上，转移实验确认了其泛化能力（即使目标类别在检索池中缺失，相对DA增益仍达到+35.6%）。
cs.CL / 42 / 2606.18056
ConSA: Controllable Sparsity in Hybrid Attention via Learnable Allocation
ConSA：通过可学习分配实现混合注意力中的可控稀疏性
Abstract
Hybrid architectures combining full attention (FA) and sliding-window attention (SWA) are a promising paradigm for efficient LLM inference. However, existing methods typically rely on hand-crafted rules or simple post-hoc heuristics for FA/SWA allocation and offer limited analysis of the attention behaviors underlying these designs. We propose Controllable Sparsity in Hybrid Attention (ConSA), a framework that learns optimal FA/SWA assignment under a user-specified sparsity target. ConSA employs L0 regularization to learn binary masks selecting between FA and SWA for each attention unit, while an augmented Lagrangian constraint enforces the target sparsity at either layer or KV-head granularity. We evaluate ConSA on two LLMs at the 0.6B and 1.7B scales. Learned allocations consistently outperform rule-based baselines, with KV-head-wise allocation yielding clear gains over layer-wise allocation. The learned patterns place SWA in the bottom layers and concentrate FA into contiguous middle-layer blocks, diverging from evenly interleaved patterns in rule-based methods. This structure persists across model scales, sparsity levels, and allocation granularities, revealing a fine-grained spectrum of intrinsic attention behaviors that underlies the learned allocation.
Chinese Translation
结合全注意力（FA）和滑动窗口注意力（SWA）的混合架构是高效大规模语言模型（LLM）推理的一种有前景的范式。然而，现有方法通常依赖于手工设计的规则或简单的事后启发式方法进行FA/SWA分配，并且对这些设计背后的注意力行为分析有限。我们提出了混合注意力中的可控稀疏性（ConSA），这是一个在用户指定的稀疏性目标下学习最佳FA/SWA分配的框架。ConSA采用L0正则化来学习二进制掩码，以选择每个注意力单元使用FA还是SWA，同时增强的拉格朗日约束在层级或KV头粒度上强制执行目标稀疏性。我们在两个规模为0.6B和1.7B的LLM上评估ConSA。学习到的分配始终优于基于规则的基线，KV头级分配在性能上明显优于层级分配。学习到的模式将SWA放置在底层，并将FA集中在连续的中间层块中，偏离了基于规则方法中均匀交错的模式。这种结构在模型规模、稀疏性水平和分配粒度上持续存在，揭示了支撑学习分配的内在注意力行为的细粒度谱系。
cs.CL / 43 / 2606.18062
Security and Privacy Prompts in the Wild: What Users Ask LLMs and How LLMs Respond
现实中的安全与隐私提示：用户向大型语言模型提问及其回应方式
Abstract
Large language models (LLMs) are widely used to fulfill users' information needs; users ask LLMs about the weather, pose educational questions, and consult them for legal assistance. One particularly understudied area is digital security and privacy (S&P), where users may seek LLMs' help on how to secure their online accounts or protect their computers from cyber attacks. To the best of our knowledge, no prior study has collected or analyzed the S&P questions users ask LLMs; prior research on LLM response quality relied on expert-authored S&P misconceptions or FAQs rather than user queries. Drawing from WildChat, a dataset of 3.2M user-LLM conversations collected in the wild, our study identifies 14,727 S&P prompts and categorizes them into nine categories covering a wide range of S&P topics. From the S&P prompts, we sampled 450 and performed a thematic analysis to characterize the S&P questions users ask LLMs. Separate from the thematic analysis, we curated 270 advice-seeking S&P prompts, where users ask for recommendations, guidance, or specific S&P information. We measured LLM response quality and consistency when posing the prompt to LLMs 10 times. We found that commercial LLMs outperform open-weight models (GPT 5.5 provided "good enough" responses on 98% of prompts; Llama 4 on 47%). However, among prompts that received high-quality responses on average, commercial models sometimes produce contradictory responses across runs, risking confusing or misleading users.
Chinese Translation
大型语言模型（LLMs）被广泛用于满足用户的信息需求；用户向LLMs询问天气、提出教育问题，并咨询法律帮助。一个特别少被研究的领域是数字安全与隐私（S&P），用户可能寻求LLMs的帮助，以了解如何保护他们的在线账户或防止计算机遭受网络攻击。根据我们所知，之前没有研究收集或分析用户向LLMs提出的S&P问题；先前关于LLM响应质量的研究依赖于专家撰写的S&P误解或常见问题，而非用户查询。我们的研究基于WildChat，一个收集到的320万用户-LLM对话的数据集，识别出14,727个S&P提示，并将其分类为九个类别，涵盖广泛的S&P主题。从这些S&P提示中，我们抽取了450个进行主题分析，以描述用户向LLMs提出的S&P问题。与主题分析分开，我们整理了270个寻求建议的S&P提示，其中用户请求推荐、指导或具体的S&P信息。我们测量了LLMs在向其提出提示时的响应质量和一致性，进行了10次提问。我们发现商业LLMs的表现优于开放权重模型（GPT 5.5在98%的提示上提供了“足够好”的响应；Llama 4则为47%）。然而，在平均获得高质量响应的提示中，商业模型有时在不同运行中产生矛盾的响应，可能会导致用户困惑或误导。
cs.CL / 44 / 2606.18103
HistoRAG: Embedding Historical Methodology in Retrieval-Augmented Generation Through Critical Technical Practice
HistoRAG：通过关键技术实践将历史方法论嵌入检索增强生成
Abstract
Retrieval-Augmented Generation (RAG) is the prevailing architecture for grounding language model outputs in external evidence, yet its dominant evaluation paradigms and default configurations remain oriented toward factual question-answering. For interpretive disciplines such as historical studies, RAG embeds assumptions that conflict with scholarly practice. We introduce HistoRAG, a framework that translates historiographical principles into concrete architectural interventions. Separated retrieval and generation decouples source discovery from interpretation, temporal windowing enforces balanced source representation across the research period as a methodological requirement of historical inquiry, and LLM-as-judge evaluation makes relevance judgments transparent and contestable. We evaluate these interventions using SPIEGELragged, applied to 102,189 articles from Der Spiegel (1950-1979). Each intervention addresses a measurable deficiency in standard RAG: era-specific vocabulary retrieves zero chunks from the 1950s when using 1970s terminology, evidence of the temporal skew that motivates windowing; vector similarity and LLM-assessed relevance correlate only weakly (Spearman rho = 0.275), motivating post-retrieval evaluation; and keyword-based and semantic retrieval surface largely disjoint source pools, motivating an architecture in which both operate as complementary retrieval layers under a shared LLM evaluation filter. We also introduce the concept of Zwischentexte (intermediate texts that function as interpretive proposals rather than findings) as a framework for responsible integration of LLM-generated text into scholarly practice. The architecture offers a model for how domain-specific epistemological commitments can be translated into RAG design decisions, and may transfer to other interpretive disciplines working with large corpora.
Chinese Translation
检索增强生成（RAG）是将语言模型输出与外部证据相结合的主流架构，但其主导评估范式和默认配置仍然倾向于事实问答。对于历史研究等解释性学科而言，RAG嵌入了与学术实践相冲突的假设。我们提出了HistoRAG，一个将历史编纂原则转化为具体架构干预的框架。分离的检索和生成将源发现与解释解耦，时间窗口强制在研究期间内平衡源的代表性，作为历史研究的一个方法论要求，而LLM作为评判者的评估使相关性判断变得透明且可争议。我们使用SPIEGELrag对来自《明镜周刊》（Der Spiegel，1950-1979）的102,189篇文章进行评估。每个干预措施都针对标准RAG中的可测量缺陷：使用1970年代术语时，特定于时代的词汇在1950年代检索不到任何内容，证明了时间偏差的存在，这也促使了时间窗口的使用；向量相似性和LLM评估的相关性仅弱相关（Spearman rho = 0.275），这促使了检索后的评估；基于关键词和语义的检索表面上大致呈现出不相交的源池，促使架构中这两者作为互补的检索层在共享的LLM评估过滤器下共同运作。我们还引入了Zwischentexte（作为解释性提案而非发现的中介文本）的概念，作为将LLM生成文本负责任地整合到学术实践中的框架。该架构提供了一个模型，展示了如何将特定领域的认识论承诺转化为RAG设计决策，并可能适用于其他处理大型语料库的解释性学科。
cs.CL / 45 / 2606.18124
Unintended Effects of Geographic Conditioning in Large Language Models
大型语言模型中地理条件的意外影响
Abstract
Modern conversational AI systems frequently rely on user metadata to localize responses, yet the unintended regional biases introduced by this hidden context remain poorly understood. In this work, we evaluate location leakage: the phenomenon where a model generates geographic references despite receiving a geographically neutral user prompt. Across both creative writing and open-ended Q&A prompts, even state-of-the-art LLMs systematically favor region-specific outputs when exposed to location metadata, with leakage spiking by up to 793 times above baseline (e.g., from 0.04% to 31.7% for Llama 3.1-8B, and 21.3% and 8.8% for Qwen3-8B and Claude Sonnet 4.6, respectively). Our analysis further shows a novel structural conditioning effect: replacing the injected location with the placeholder "Unknown" still elevates leakage by up to 72 times above baseline, demonstrating that the user profile frame itself, independent of any geographic content, acts as a generative conditioning signal.
Chinese Translation
现代对话式人工智能系统常常依赖用户元数据来本地化响应，但这种隐藏上下文所引入的意外区域偏见仍然未被充分理解。在本研究中，我们评估了位置泄漏现象：即模型在接收到地理中立的用户提示时仍然生成地理参考的现象。在创意写作和开放式问答提示中，即使是最先进的大型语言模型（LLMs）在接触到位置信息时也系统性地偏向于地区特定的输出，泄漏量比基线高出多达793倍（例如，Llama 3.1-8B的泄漏从0.04%上升到31.7%，Qwen3-8B和Claude Sonnet 4.6的泄漏分别为21.3%和8.8%）。我们的分析进一步显示了一种新颖的结构性条件效应：将注入的位置替换为占位符“未知”仍然使泄漏量比基线提高多达72倍，这表明用户档案框架本身，无论是否包含任何地理内容，均作为生成条件信号。
cs.CL / 46 / 2606.18195
Learning from the Self-future: On-policy Self-distillation for dLLMs
从自我未来学习：针对扩散大语言模型的在线自蒸馏
Abstract
On-policy self-distillation (OPSD) has proven effective for post-training large language models (LLMs), yet its application to diffusion LLMs (dLLMs) remains unexplored. Existing OPSD methods are inherently autoregressive-centric. They inject privileged information via left-to-right prefix conditioning with token-level divergence supervision, a design that fundamentally conflicts with the arbitraryorder generation of dLLMs. We introduce d-OPSD, the first OPSD framework tailored for dLLMs. Our approach makes two core contributions. First, we reframe self-teacher construction by using self-generated answers as suffix conditioning, enabling the student model to learn from "self future-experience" rather than privileged prefixes. Second, we shift supervision from token-level to step-level, aligning training with the iterative denoising process of dLLMs. Experiments across four reasoning benchmarks show that d-OPSD consistently outperforms RLVR and SFT baselines with superior sample efficiency, requiring only around 10% of the optimization steps by RLVR and opening a promising pathway for dLLM posttraining. The code is available at https://github.com/xingzhejun/d-OPSD.
Chinese Translation
在线自蒸馏（On-policy self-distillation，OPSD）已被证明对后训练的大语言模型（Large Language Models，LLMs）有效，但其在扩散大语言模型（Diffusion LLMs，dLLMs）中的应用尚未被探索。现有的OPSD方法本质上是以自回归为中心的。它们通过左到右的前缀条件注入特权信息，并使用基于标记的发散监督，这一设计与dLLMs的任意顺序生成根本冲突。我们提出了d-OPSD，这是第一个专为dLLMs量身定制的OPSD框架。我们的方法有两个核心贡献。首先，我们通过使用自生成的答案作为后缀条件，重新构建自教师的构造，使得学生模型能够从“自我未来经验”中学习，而不是依赖特权前缀。其次，我们将监督从标记级别转移到步骤级别，使训练与dLLMs的迭代去噪过程对齐。在四个推理基准上的实验表明，d-OPSD在样本效率上始终优于RLVR和SFT基线，仅需约10%的RLVR优化步骤，为dLLM的后训练开辟了一个有前景的路径。代码可在 https://github.com/xingzhejun/d-OPSD 获取。
cs.CL / 47 / 2606.18203
RubricsTree: Scalable and Evolving Open-Ended Evaluation of Personal Health Agents across Health Memory and Medical Skills
RubricsTree：可扩展且不断演化的个人健康代理开放式评估框架，涵盖健康记忆与医疗技能
Abstract
The LLM-empowered personal health agents with user health (sensor) metrics have offered a promising pathway to alleviate global disparities in healthcare access. However, large-scale clinical deployment remains constrained by an open-ended evaluation bottleneck: physician annotation is reliable but costly and unscalable, while LLM-as-a-judge evaluators are scalable but subjective, inconsistent, and sometimes clinically misaligned. We introduce RubricsTree, a scalable evaluation framework with an expert-aligned hierarchical taxonomy of over 100 atomic, clinically-verifiable Boolean rubrics, evolving from the insights of 4,000 real user queries through an iterative human-in-the-loop curation protocol with an expertise panel led by an experienced physician. A context-aware adaptive router activates only the relevant auto-weighted rubric subset per query, providing the throughput needed for scalable evaluation with expert-aligned quality. Through a systematic meta-evaluation, we show that RubricsTree (i) substantially exceeds a strong large-scale evaluation baseline in expert alignment on challenging open-ended queries; (ii) reliably penalizes contextually degraded responses; and (iii) when used as structured instructions, text feedback, or training rewards for performance optimization, yields up to ~66% relative gains on HealthBench for Gemini, GPT, and Qwen model families. RubricsTree thus provides a scalable, auditable, and evolving evaluation infrastructure required for the continuous optimization of product-level personal healthcare AI.
Chinese Translation
基于大型语言模型（LLM）的个人健康代理结合用户健康（传感器）指标，为缓解全球医疗服务获取差异提供了有希望的途径。然而，大规模临床部署仍受限于开放式评估瓶颈：医生注释虽然可靠，但成本高且不可扩展，而以LLM作为评估者则可扩展，但主观性强、不一致，有时与临床不一致。我们提出了RubricsTree，一个可扩展的评估框架，拥有超过100个原子、可临床验证的布尔评分标准的专家对齐层次分类法，这些标准是通过与一位经验丰富的医生领导的专家小组进行的迭代人机协作策划协议，从4000个真实用户查询的洞察中演变而来的。一个上下文感知的自适应路由器仅在每个查询中激活相关的自动加权评分标准子集，提供了可扩展评估所需的通量，同时确保专家对齐的质量。通过系统的元评估，我们展示了RubricsTree (i) 在具有挑战性的开放式查询上在专家对齐方面显著超越了强大的大规模评估基线；(ii) 可靠地惩罚上下文退化的响应；以及 (iii) 当作为结构化指令、文本反馈或性能优化的训练奖励使用时，在HealthBench上为Gemini、GPT和Qwen模型系列带来了高达约66%的相对增益。因此，RubricsTree提供了一个可扩展、可审计且不断演化的评估基础设施，满足个人医疗人工智能产品级持续优化的需求。
cs.CL / 48 / 2606.18205
Analyzing and Encoding the Al-Mawrid Arabic-English Dictionary with the ISO Language Markup Framework and TEI Lex-0
使用ISO语言标记框架和TEI Lex-0分析与编码Al-Mawrid阿拉伯语-英语词典
Abstract
This paper presents a robust methodology for the systematic digitization and encoding of the Al-Mawrid Arabic-English dictionary, transforming it from a legacy print resource into a standardized computational lexicon. Addressing a significant gap in Arabic lexical infrastructure, the study adopts a dual-standard framing that aligns the ISO Lexical Markup Framework (LMF) with the Text Encoding Initiative TEI Lex-0 guidelines. By applying an editorial view to the dictionary's macro- and microstructure, the research resolves the structural ambiguities and punctuation inconsistencies typical of 20th-century bilingual dictionaries. The methodology is grounded in an empirical analysis of the dictionary's lexical knowledge density. Drawing on a representative sample (the letter Ayn, comprising 4.6% of the total volume), the study provides scientific weight to the encoding process, demonstrating a structural parsing accuracy of 91%. Quantitative evaluation of the information extraction rules reveals high performance, with 85% precision and 98% recall for synonyms, and 88% precision for other morpho-semantic features. Beyond technical description, the paper provides a critical comparison with existing Arabic lexical resources and discusses the limitations of TEI Lex-0 when modelling specific Arabic phenomena, such as implicit "open set" semantic relations and scattered morphological cues. Furthermore, the study explores the potential for Linguistic Linked Open Data (LLOD) integration by establishing a scalable prefix-based referencing system that facilitates the resource's inclusion in the semantic web. The result is an interoperable, machine-tractable resource that provides a reproducible workflow for the retro-digitization of complex legacy bilingual lexicons within the Arabic NLP and Digital Humanities communities.
Chinese Translation
本文提出了一种系统的数字化和编码Al-Mawrid阿拉伯语-英语词典的稳健方法，将其从传统的印刷资源转变为标准化的计算词典。针对阿拉伯语词汇基础设施中的一个重要空白，本研究采用双标准框架，将ISO词汇标记框架（Lexical Markup Framework, LMF）与文本编码倡议（Text Encoding Initiative, TEI）Lex-0指南对齐。通过对词典的宏观和微观结构进行编辑视角的应用，研究解决了20世纪双语词典中常见的结构模糊性和标点不一致性。该方法基于对词典词汇知识密度的实证分析。研究以代表性样本（字母Ayn，占总量的4.6%）为基础，为编码过程提供了科学依据，展示了91%的结构解析准确率。信息提取规则的定量评估显示出高性能，近义词的精确率为85%，召回率为98%，而其他形态语义特征的精确率为88%。除了技术描述，本文还对现有的阿拉伯语词汇资源进行了批判性比较，并讨论了TEI Lex-0在建模特定阿拉伯现象（如隐含的“开放集”语义关系和分散的形态线索）时的局限性。此外，研究探讨了通过建立可扩展的基于前缀的引用系统来实现语言链接开放数据（Linguistic Linked Open Data, LLOD）集成的潜力，从而促进该资源在语义网中的纳入。最终结果是一个可互操作的、机器可处理的资源，为阿拉伯自然语言处理（NLP）和数字人文学科社区内复杂传统双语词典的逆向数字化提供了可重复的工作流程。
cs.CL / 49 / 2606.18216
Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients
近端策略优化区域：提示中的教师，而非梯度
Abstract
Knowledge distillation transfers a teacher's competence to a small student but is brittle in the small-student regime: forcing the student to imitate logits from a much larger teacher concentrates it on the teacher's sharpest modes, hurting generalization on benchmark families beyond the training corpus. Reinforcement learning (RL) avoids logit imitation by training on the student's own rollouts. However, on questions where every rollout fails-yielding zero advantage and being silently discarded-injecting a stronger teacher's response into the policy gradient breaks the on-policy assumption and induces drift. We introduce Zone of Proximal Policy Optimization (ZPPO), inspired by Vygotsky's zone of proximal development, which keeps the teacher inside the prompt rather than the policy gradient. On hard questions, ZPPO constructs two reformulated prompts: a Binary Candidate-included Question (BCQ) pairs one correct teacher response with one incorrect student response as anonymized candidates the student must discriminate, and a Negative Candidate-included Question (NCQ) aggregates the student's wrong rollouts into a single prompt to surface their shared failure modes. A prompt replay buffer recirculates each hard question until it either graduates-the student's mean rollout accuracy on it reaches half- or is FIFO-evicted under finite capacity, amplifying BCQ and NCQ inside the student's current zone of proximal development. On the Qwen3.5 family at four student scales (0.8B-9B) with a 27B teacher, post-trained as vision-language models and evaluated on a 31-benchmark suite (16 VLM, 10 LLM, 5 Video), ZPPO outperforms off/on-policy distillation and GRPO, with the largest gains at the smallest scale.
Chinese Translation
知识蒸馏将教师的能力转移到一个较小的学生身上，但在小学生的情况下表现脆弱：强迫学生模仿来自更大教师的logits会使其集中在教师最尖锐的模式上，从而损害在训练语料库之外的基准家族上的泛化能力。强化学习（RL）通过在学生自己的回合上进行训练，避免了logit模仿。然而，在每个回合都失败的问题上——导致零优势并被默默丢弃——将更强的教师响应注入策略梯度会破坏在政策上的假设并引起漂移。我们引入了近端策略优化区域（Zone of Proximal Policy Optimization, ZPPO），灵感来自维果茨基的近端发展区，它将教师保持在提示中而不是策略梯度中。在困难问题上，ZPPO构建了两个重新表述的提示：包含二元候选者的问题（Binary Candidate-included Question, BCQ）将一个正确的教师响应与一个不正确的学生响应配对，作为学生必须区分的匿名候选者，而包含负候选者的问题（Negative Candidate-included Question, NCQ）将学生的错误回合聚合成一个单一提示，以揭示它们共享的失败模式。提示重放缓冲区循环使用每个困难问题，直到它要么毕业——学生在该问题上的平均回合准确率达到一半——要么在有限容量下被FIFO驱逐，从而在学生当前的近端发展区内放大BCQ和NCQ。在Qwen3.5系列的四个学生规模（0.8B-9B）与一个27B教师的情况下，后续训练为视觉-语言模型，并在31个基准套件（16个VLM，10个LLM，5个视频）上进行评估，ZPPO的表现优于离线/在线蒸馏和GRPO，在最小规模下获得了最大的增益。
cs.CL / 50 / 2606.18222
Darshana Graph: A Parallel Commentary Corpus for Comparative Indian Philosophy, with Stylometric and Exploratory Graph Analyses
达尔沙那图谱：一个用于比较印度哲学的平行评论语料库，包含风格计量和探索性图分析
Abstract
We introduce Darshana Graph, a corpus of over 125,000 text records spanning classical Hindu, Buddhist, and Jain philosophical traditions, drawn from public-domain and openly licensed translations of sources including the Bhagavad Gita, Brahma Sutras, principal Upanishads, the Pali Canon, and core Jain texts. Its distinctive contribution lies in a structurally unique subset of roughly 8,500 Hindu and Jain records in which the same root verse or sutra is aligned across eighteen historical commentators representing five schools of Vedanta and other darshanas, enabling direct comparison of how independent interpretive traditions read identical source material. To our knowledge, no publicly available resource provides comparable cross-commentator alignment at this scale. We present two analyses built on this corpus. First, a transparent stylometric comparison requiring no machine learning measures argumentative style through scriptural citation density, explicit refutation rate, and sentence complexity. It finds a moderate negative correlation between citation density and refutation rate, a marked increase in refutation rate across three commentators in a related doctrinal lineage, and measurable genre-level differences within the Pali Canon itself. Second, we describe a constrained large language model pipeline that extracts typed philosophical relationships between concepts using a predefined relation vocabulary and deterministic post-hoc validation. The resulting graph surfaces cross-school disagreement patterns while also revealing important extraction limitations, including cases where an independent embedding-based analysis disagrees with the graph-derived findings. We release the full corpus, extracted relationship graph, and all source code.
Chinese Translation
我们介绍了达尔沙那图谱，这是一个包含超过125,000条文本记录的语料库，涵盖了古典印度教、佛教和耆那教哲学传统，来源于《博伽梵歌》、《梵天经》、《主要奥义书》、《巴利经典》和核心耆那教文本的公共领域和开放许可翻译。其独特贡献在于一个结构上独特的子集，约有8,500条印度教和耆那教记录，其中相同的根本经文或经句在代表五个吠檀多学派和其他达尔沙那的十八位历史评论家之间进行了对齐，从而实现了对独立解释传统如何解读相同源材料的直接比较。据我们所知，没有任何公开可用的资源能够在如此规模上提供可比的跨评论家对齐。我们基于该语料库进行了两项分析。首先，进行了一项透明的风格计量比较，不需要机器学习，通过经文引用密度、明确反驳率和句子复杂性来衡量论证风格。结果发现引用密度与反驳率之间存在适度的负相关关系，在相关教义谱系中的三位评论家之间反驳率显著增加，以及在巴利经典内部存在可测量的体裁级差异。其次，我们描述了一个受限的大型语言模型管道，该管道使用预定义的关系词汇和确定性的事后验证提取概念之间的类型化哲学关系。生成的图谱揭示了跨学派的分歧模式，同时也暴露了重要的提取限制，包括独立的嵌入基础分析与图谱衍生发现不一致的情况。我们发布了完整的语料库、提取的关系图以及所有源代码。
cs.CL / 51 / 2606.18237
ReproRepo: Scaling Reproducibility Audits with GitHub Repository Issues
ReproRepo：利用 GitHub 仓库问题扩展可重复性审计
Abstract
Reproducing research results from papers and released code is central to scientific progress. Existing works have introduced benchmarks to evaluate whether LLM agents can assist with reproducibility, but they are difficult to scale due to their reliance on substantial manual effort for data curation and evaluation. We introduce ReproRepo, a scalable framework for reproducibility evaluation that leverages human-raised GitHub issues as naturally occurring supervision on realistic reproduction blockers. We instantiate ReproRepo on 1,149 recent machine learning papers from major conferences and evaluate four frontier model-agent configurations. Our results show that LLM agents, even without executing code, can identify many real-world reproducibility problems from paper-repository pairs: the best agent in our study, namely Codex with GPT-5.5, surfaces at least one semantically related human-reported blocker for ~90% of papers in the study. Further analysis shows that agents are particularly effective for surfacing visible failures and identifying the right semantic region, but may still be insufficient in exact localization. ReproRepo can serve as a reusable, scalable framework for future evaluations of LLM agents on real-world reproducibility auditing. Our code is released at https://github.com/LithiumDA/ReproRepo.
Chinese Translation
从论文和发布的代码中重现研究结果是科学进步的核心。现有研究已经引入基准来评估 LLM（大型语言模型）代理是否能够协助实现可重复性，但由于依赖于大量的手动数据整理和评估，这些方法难以扩展。我们提出了 ReproRepo，一个可扩展的可重复性评估框架，利用人类提出的 GitHub 问题作为对现实重现障碍的自然监督。我们在来自主要会议的 1,149 篇近期机器学习论文上实例化了 ReproRepo，并评估了四种前沿模型-代理配置。我们的结果表明，LLM 代理即使在不执行代码的情况下，也能识别出许多来自论文-仓库对的真实可重复性问题：我们研究中表现最佳的代理，即 Codex（与 GPT-5.5 结合使用），能够为约 90% 的论文提出至少一个语义相关的人类报告的障碍。进一步分析表明，代理在揭示明显失败和识别正确的语义区域方面特别有效，但在精确定位上仍可能不足。ReproRepo 可以作为一个可重用的、可扩展的框架，用于未来对 LLM 代理在现实可重复性审计中的评估。我们的代码已发布在 https://github.com/LithiumDA/ReproRepo。
cs.CL / 52 / 2606.18246
Variable-Width Transformers
可变宽度变换器
Abstract
Scaling model size, specifically depth and width, has driven significant progress in transformer-based language models. However, most architectures maintain a constant width across all layers, allocating a fixed parameter and computation budget evenly despite different layers potentially playing distinct computational roles. In this work, we empirically investigate nonuniform capacity allocation across network depth by proposing a $\times$-shaped >
Chinese Translation
模型规模的扩展，特别是深度和宽度，推动了基于变换器的语言模型的显著进展。然而，大多数架构在所有层中保持恒定的宽度，尽管不同层可能扮演不同的计算角色，却均匀分配固定的参数和计算预算。在本研究中，我们通过提出一种 $ imes$ 形状的 >