cs.RO / 1 / 2606.04072
CADET: A Modular Platform for Evaluating Distributed Cooperative Autonomy in Connected Autonomous Vehicles
CADET：用于评估连接自主车辆中分布式协同自治的模块化平台
Abstract
Deep learning models are increasingly central to autonomous vehicle (AV) pipelines, yet their integration has traditionally followed a monolithic design where perception, planning, and control execute on a single onboard computer. This design overlooks the emerging paradigm of cooperative autonomy, where vehicles interact with roadside units (RSUs), edge servers, and cloud-hosted intelligence through vehicle-to-everything (V2X) connectivity. Cooperative perception and control improve safety and efficiency, but also introduce systems-level challenges: network latency, compute heterogeneity, and multi-tenant contention, all critically affect real-time decision-making. These challenges are further amplified by the increasing reliance on large foundation models, whose scale necessitates cloud deployment. We present CADET (Cooperative Autonomy through Distributed Experimentation Toolkit), a modular platform for systematic and reproducible evaluation of distributed cooperative autonomy systems under realistic deployment conditions. CADET decouples the AV stack into composable modules that can be flexibly deployed across vehicles, infrastructure, and edge/cloud tiers. The framework integrates state-of-the-art models, incorporates trace-driven network and workload emulation, and provides synchronized model-, system-, and task-level instrumentation. Through V2V and V2I experiments, we show that distributed deployment choices fundamentally shape safety, with V2V intent packets outperforming cloud-based perception and RSU-assisted perception sustaining safety until overloaded by concurrent requests. Although designed for AV pipelines, CADET also supports dataset-driven experimentation, enabling systems and ML researchers to benchmark distributed inference workloads independently of full vehicle simulation. CADET is open source, with code and demo available at https://nesl.github.io/cadet-web.
Chinese Translation
深度学习模型在自主车辆（AV）流程中日益重要，但其集成传统上遵循单体设计，即感知、规划和控制均在单个车载计算机上执行。这种设计忽视了协同自治的新兴范式，在该范式中，车辆通过车到一切（V2X）连接与路边单元（RSU）、边缘服务器和云托管智能进行交互。协同感知和控制提高了安全性和效率，但也带来了系统级挑战：网络延迟、计算异构性和多租户竞争，这些都会对实时决策产生重要影响。这些挑战在日益依赖大规模基础模型的背景下进一步加剧，因为其规模要求云部署。我们提出了CADET（通过分布式实验工具包实现协同自治），这是一个模块化平台，用于在现实部署条件下对分布式协同自治系统进行系统和可重复的评估。CADET将自主车辆堆栈解耦为可组合模块，能够灵活地部署在车辆、基础设施和边缘/云层中。该框架整合了最先进的模型，结合了基于跟踪的网络和工作负载仿真，并提供同步的模型、系统和任务级别的仪器。通过车对车（V2V）和车对基础设施（V2I）实验，我们展示了分布式部署选择对安全性的根本性影响，发现V2V意图数据包在性能上优于基于云的感知，而RSU辅助感知在并发请求过载之前保持安全性。虽然CADET是为自主车辆流程设计的，但它也支持数据集驱动的实验，使系统和机器学习研究人员能够独立于完整车辆仿真基准测试分布式推理工作负载。CADET是开源的，代码和演示可在 https://nesl.github.io/cadet-web 获取。
cs.RO / 2 / 2606.04111
AgenticDiffusion: Agentic Diffusion-based Path Planning for Vision-Based UAV Navigation
AgenticDiffusion：基于Agentic Diffusion的视觉导航无人机路径规划
Abstract
Indoor UAV navigation requires efficient exploration, scene understanding, and reliable trajectory execution under limited field-of-view observations. Existing vision-based navigation frameworks typically rely on single-view observations, limiting their ability to reason about occlusions, target visibility, and global scene structure. In this work, we propose AgenticDiffusion, a multi-view UAV navigation framework that coordinates language-guided reasoning, open-vocabulary target grounding, vision-based diffusion planning, and NMPC within a unified aerial navigation pipeline. Given a natural language instruction and synchronized first-person-view (FPV) and top-view observations, the framework determines the most informative viewpoint for navigation and generates a mission plan prior to trajectory execution. The targets are localized using an open-vocabulary grounding model, after which viewpoint-specific diffusion planners generate navigation trajectories for UAV execution. Using complementary viewpoints, the proposed framework reduces repeated target exploration and improves navigation efficiency in cluttered indoor environments. The framework was validated in four real-world UAV navigation scenarios involving adaptive viewpoint selection, multi-stage mission execution, long-horizon navigation, and safe landing-site selection. The experimental results demonstrated an overall mission success rate of 80% in 40 real-world trials, while the diffusion planners achieved a trajectory generation success rate of 100%.
Chinese Translation
室内无人机导航需要在有限的视场观察下进行高效的探索、场景理解和可靠的轨迹执行。现有的基于视觉的导航框架通常依赖单一视角的观察，这限制了它们对遮挡、目标可见性以及全局场景结构的推理能力。本文提出了AgenticDiffusion，这是一种多视角的无人机导航框架，协调语言引导推理、开放词汇目标定位、基于视觉的扩散规划和非线性模型预测控制（NMPC）在统一的空中导航流程中。给定自然语言指令以及同步的第一人称视角（FPV）和顶视图观察，框架确定最具信息量的导航视角，并在轨迹执行之前生成任务计划。目标使用开放词汇定位模型进行定位，随后特定视角的扩散规划器生成用于无人机执行的导航轨迹。利用互补的视角，所提出的框架减少了重复目标探索，提升了复杂室内环境中的导航效率。该框架在四个真实世界的无人机导航场景中得到了验证，这些场景涉及自适应视角选择、多阶段任务执行、长时间导航以及安全着陆点选择。实验结果表明，在40次真实世界试验中，整体任务成功率达到80%，而扩散规划器的轨迹生成成功率达到了100%。
cs.RO / 3 / 2606.04130
CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization
CLAW：通过对抗性潜变量正则化学习连续潜变量动作世界模型
Abstract
We introduce CLAW, a fully end-to-end self-supervised framework for learning a world model jointly with continuous latent action representations directly from action-free videos. Our approach leverages adversarial latent regularization and diffusion-based video generation to capture structured and semantically meaningful action representations while modeling rich, predictive environment dynamics, without relying on any action labels or annotations. By simultaneously training the Latent Action Model and world model, CLAW learns to reason about how inferred actions induce environment transitions from visual observations alone. We show that the resulting latent action world model supports both imitation learning from observation and goal-directed planning. In imitation learning, latent actions extracted from raw videos enable behavior cloning. For planning, CLAW generates sequences of latent actions and maps them to executable actions to reach desired goals. Extensive experiments across diverse tasks and embodiments demonstrate that CLAW produces semantically meaningful latent action representations, supports effective action transfer, and enables planning and imitation from observation, outperforming existing methods.
Chinese Translation
我们提出了CLAW，一个完整的端对端自监督框架，用于从无动作视频中直接学习一个世界模型并结合连续潜变量动作表示。我们的方法利用对抗性潜变量正则化和基于扩散的视频生成技术来捕捉结构化和语义丰富的动作表示，同时建模丰富的预测环境动态，而无需任何动作标签或注释。通过同时训练潜变量动作模型和世界模型，CLAW学习如何根据仅凭视觉观察推断出的动作引发环境转变。我们证明了生成的潜变量动作世界模型支持观察模仿学习和目标导向规划。在模仿学习中，从原始视频中提取的潜变量动作实现了行为克隆。对于规划，CLAW生成潜变量动作序列，并将其映射到可执行动作以达到预期目标。在多种任务和模型下的广泛实验表明，CLAW生成了语义意义丰富的潜变量动作表示，支持有效的动作转移，并能够从观察中进行规划和模仿，优于现有方法。
cs.RO / 4 / 2606.04149
CoPark: Learning Reactive Parking via Self-Play
CoPark：通过自我对弈学习反应式停车
Abstract
Learning a single policy that reaches a goal with high geometric precision while interacting safely with nearby agents poses conflicting objectives. Precision favors commitment to a fixed geometric plan, whereas interaction requires immediate deviation when another agent intrudes, causing policies optimized for one objective to often fail at the other. We study this problem in the context of reactive autonomous parking, where multiple vehicles must reach assigned slots with sub-meter terminal accuracy while remaining responsive to neighboring vehicles throughout the maneuver. We propose CoPark, a multi-agent self-play RL approach built on a residual-policy architecture. A precomputed offline plan provides a fixed action prior, while a residual head learns the reactive corrections. The residual policy learns behaviors under self-play, where data and scripting fall short, while the fixed prior holds the slot-frame geometry that pure policies struggle to reach reliably. The key design is a partner-threat-modulated, channel-asymmetric release of the prior. A continuous threat signal shifts authority of the longitudinal channel to the residual head to enable yielding, while the lateral channel remains anchored to the precomputed reference to preserve sub-meter slot alignment. A closed-loop refinement layer corrects residual terminal error from action-grid discretization. We train our policy on six parking lots and evaluate zero-shot on our new reactive-parking benchmark spanning Dragon Lake Parking (DLP) and DeepScenario Open 3D (DSC3D). CoPark achieves ~70-85% success with only 3-6% collision rate, substantially outperforming classical, imitation-learning, and large-scale RL baselines. Importantly, the results demonstrate emergent interaction behaviors such as reverse-yielding, mid-maneuver yielding, tight-corridor passing, and queuing.
Chinese Translation
同时满足高几何精度地达到目标和与邻近代理安全互动的单一策略学习面临冲突的目标。精度倾向于承诺固定的几何计划，而互动则需要在其他代理干扰时立即偏离，这导致针对一个目标优化的策略往往在另一个目标上失败。我们在反应式自动停车的背景下研究该问题，在此情境中，多辆车辆必须在整个操作过程中以亚米级的终端精度到达指定车位，同时对邻近车辆保持响应。我们提出了CoPark，一种基于残差策略架构的多代理自我对弈强化学习方法。预计算的离线计划提供了固定的动作先验，而残差头学习反应式修正。残差策略在自我对弈的环境中学习行为，在这些环境中，数据和脚本的表现有限，而固定先验则保持了纯策略难以可靠到达的车位框架几何。关键设计是伙伴威胁调制的、通道不对称的先验释放。持续的威胁信号将纵向通道的权限转移给残差头，以实现让步，而横向通道则固定在预计算参考上，以保持亚米级的车位对齐。闭环精化层纠正由于动作网格离散化引起的残差终端误差。我们在六个停车场上训练我们的策略，并在新的反应式停车基准上进行零-shot评估，该基准涵盖了龙湖停车场（Dragon Lake Parking, DLP）和深场景开放3D（DeepScenario Open 3D, DSC3D）。CoPark在成功率上达到了约70-85%，而碰撞率仅为3-6%，大幅超越了经典方法、模仿学习和大规模强化学习基准。重要的是，这些结果展示了新兴的互动行为，例如倒车让步、机动中的让步、紧凑走廊通行和排队。
cs.RO / 5 / 2606.04157
Selecting haptic guidance models in teleoperation: guidelines from a comparative user study
在远程操作中选择触觉引导模型：来自比较用户研究的指导原则
Abstract
Haptic guidance in teleoperation enhances operator performance through force feedback. This paper presents guidelines to select the most appropriate model considering the task, the environment and the operator. We define a unified formulation expressing most common models (spring-damper, potential field, and guiding tube) as variations of a stiffness-damping system with model-specific guiding functions. We conducted a user study comparing the three classical models across six scenarios with varying environmental conditions in a vertical farming task. Results show no universally superior model: spring-damper excels in cluttered environments, potential field in free spaces (but it shows risks near obstacles), and guiding tube offers a balanced compromise. We propose novel objective metrics to evaluate the interaction, and show that guiding force magnitude correlates with comfort and trust scores. These findings provide practical model selection guidelines through environmental characteristics and real-time evaluation metrics.
Chinese Translation
触觉引导在远程操作中通过力反馈提升操作员的表现。本文提出了选择最适合模型的指导原则，考虑任务、环境和操作员。我们定义了一个统一的表述，将大多数常见模型（弹簧-阻尼器、势场和引导管）视为具有模型特定引导函数的刚度-阻尼系统的变体。我们进行了一项用户研究，比较了三种经典模型在六种不同环境条件下的表现，应用于垂直农业任务。结果表明没有普遍优越的模型：在拥挤环境中，弹簧-阻尼器表现优异；而在开放空间中，势场表现较佳（但在障碍物附近存在风险）；引导管则提供了一个平衡的折衷方案。我们提出了新的客观指标来评估交互，且表明引导力的大小与舒适度和信任评分存在相关性。这些发现通过环境特征和实时评估指标提供了实用的模型选择指导。
cs.RO / 6 / 2606.04158
Multi-Agent Next-Best-View Optimization for Risk-Averse Planning
针对风险规避规划的多智能体下一最佳视图优化
Abstract
Multi-agent Next-Best-View (NBV) selection for safe path planning in uncertain and unknown environments requires informative, safety-aware, and efficient coordination. Centralized approaches rely on sharing raw sensor data or significant communication overhead, resulting in limited scalability. We propose a distributed, risk-aware multi-agent NBV framework in which each robot maintains a private local 3D Gaussian Splatting map and the team jointly maximizes expected information gain (EIG) restricted to masked zones along planned trajectories. The resulting distributed objective is solved by Consensus ADMM (C-ADMM) over a communication graph, with each robot exchanging only candidate viewpoints, planned trajectory descriptors, and scalar EIG contributions. Collision risk along each trajectory is modeled via Average Value-at-Risk (AV@R) over the local 3DGS map and used both to shape the masking radius and to score planned paths. Experiments in Gibson environments at multiple team sizes show that the distributed formulation approaches the centralized baseline in mapping quality and trajectory safety while reducing communication by orders of magnitude.
Chinese Translation
在不确定和未知环境中进行安全路径规划的多智能体下一最佳视图（NBV）选择需要信息丰富、安全意识强且高效的协调。集中式方法依赖于共享原始传感器数据或显著的通信开销，导致可扩展性受到限制。我们提出了一种分布式的、风险感知的多智能体NBV框架，其中每个机器人维护一个私人本地3D高斯点云（Gaussian Splatting）地图，团队共同最大化限制在沿规划轨迹的掩蔽区域内的期望信息增益（EIG）。通过在通信图上的共识自适应动量方法（Consensus ADMM, C-ADMM）解决所得到的分布式目标，每个机器人仅交换候选视点、规划轨迹描述符和标量EIG贡献。沿每条轨迹的碰撞风险通过对本地3DGS地图的平均风险价值（Average Value-at-Risk, AV@R）进行建模，同时用于塑造掩蔽半径和评分规划路径。在Gibson环境中进行的多种规模团队的实验表明，分布式公式在映射质量和轨迹安全性方面接近集中式基线，同时将通信减少了多个数量级。
cs.RO / 7 / 2606.04172
Affordance2Action: Task-Conditioned Scene-level Affordance Grounding for Real-Time Manipulation
Affordance2Action：用于实时操控的任务条件场景级可供性定位
Abstract
Task-conditioned manipulation requires grounding instructions to task-relevant functional parts rather than object categories. This setting is scene-dependent and often one-to-many in cluttered scenes: the same object may afford different interactions across tasks, while a single task may correspond to either one functional region or multiple valid functional regions, depending on the scene layout. Existing affordance datasets and benchmarks remain misaligned with this setting, as they typically focus on grasping or object-level affordances, rely on synthetic scenes, or assume a single instruction-region correspondence. We present Affordance2Action (A2A), a benchmark-centered learning framework for scene-level, task-conditioned part affordance grounding. At its core is A2A-Bench, a manipulation-oriented benchmark that covers both single-region and multi-region instruction correspondences in everyday scenes, with the latter highlighting the ambiguity and diversity of affordance grounding in realistic multi-object environments. To construct it at scale, we build A2A-AffordGen, an agent-assisted annotation pipeline that combines language-model filtering, interactive part segmentation, instance-level mask-out refinement, task-reasoning instruction generation, and human verification. A2A-Bench's supervision further supports diverse downstream applications, with real-time affordance grounding and affordance-conditioned manipulation policies as two representative examples. Experiments show that A2A exposes substantial gaps in generic segmentation, VLM-based grounding, and affordance distillation baselines, while improving task-level localization and providing useful spatial priors for downstream manipulation. All datasets and code will be publicly released to promote open research.
Chinese Translation
任务条件的操控需要将指令与与任务相关的功能部分进行定位，而非对象类别。这个设定依赖于场景，且在杂乱的场景中常常是多对一的：同一物体可能在不同任务中提供不同的交互方式，而单一任务可能对应于一个功能区域或多个有效功能区域，这取决于场景布局。现有的可供性数据集和基准测试仍未与这一设定对齐，因为它们通常专注于抓取或对象级可供性，依赖于合成场景，或者假设单一的指令-区域对应关系。我们提出了Affordance2Action (A2A)，一个以基准为中心的学习框架，针对场景级的、任务条件的部件可供性定位。其核心是A2A-Bench，一个面向操控的基准，涵盖了日常场景中单区域和多区域的指令对应关系，后者突显了在现实多物体环境中可供性定位的模糊性和多样性。为了大规模构建该基准，我们建立了A2A-AffordGen，一个结合了语言模型过滤、交互式部件分割、实例级掩膜细化、任务推理指令生成和人工验证的代理辅助标注管道。A2A-Bench的监督进一步支持多样化的下游应用，实时可供性定位和基于可供性的操控策略就是两个典型示例。实验表明，A2A揭示了在通用分割、基于VLM的定位和可供性蒸馏基线中的显著差距，同时改善了任务级定位，并为下游操控提供了有用的空间先验。所有数据集和代码将公开发布，以促进开放研究。
cs.RO / 8 / 2606.04185
Distribution-Free Risk-Aware Planning and Control Under Uncertainty Using Conformal Spectral Risk Control
基于无分布风险感知规划与控制的符合光谱风险控制方法在不确定性下的应用
Abstract
Safe navigation in dynamic and uncertain environments often relies on accurate estimation of, or assumptions about, the true underlying uncertainty. However, accurately characterizing the true uncertainty distribution is often difficult due to limited data or imperfect information. An incorrect understanding of the uncertainty and its associated risk may lead to dangerous decisions even under high levels of risk aversion. To address this issue, we propose a risk-aware model predictive control (RA-MPC) framework that incorporates prediction sets to guarantee risk control below a user-specified threshold without requiring assumptions about the underlying uncertainty distribution. To generate the prediction sets, we develop a distribution-free risk quantification framework that extends conformal risk control (CRC) to general spectral risk measures. We then show that incorporating the prediction sets into the MPC framework provides statistical safety guarantees in terms of spectral risk constraint satisfaction even under uncertainty misspecification. We validate the proposed framework in simulated vehicle obstacle avoidance scenarios, demonstrating improved safety and reduced solve time compared to a baseline RA-MPC framework.
Chinese Translation
在动态和不确定的环境中实现安全导航通常依赖于对真实潜在不确定性的准确估计或假设。然而，由于数据有限或信息不完善，准确表征真实的不确定性分布往往很困难。对不确定性及其相关风险的错误理解可能导致即使在高风险厌恶水平下也会做出危险的决策。为了解决这个问题，本文提出了一种风险感知模型预测控制框架（Risk-Aware Model Predictive Control，RA-MPC），该框架结合了预测集，确保风险控制低于用户指定的阈值，而无需对潜在不确定性分布做出假设。为了生成预测集，我们开发了一个无分布风险量化框架，该框架将符合风险控制（Conformal Risk Control，CRC）扩展到一般光谱风险度量。接着，我们展示了将预测集纳入MPC框架可以在不确定性误特征化的情况下提供光谱风险约束满足的统计安全性保证。我们在模拟的车辆障碍物规避场景中验证了所提框架，与基准RA-MPC框架相比，展示了更高的安全性和更低的求解时间。
cs.RO / 9 / 2606.04206
DLO-Lab: Benchmarking Deformable Linear Object Manipulations with Differentiable Physics
DLO-Lab：利用可微分物理学基准测试可变形线性物体操控
Abstract
We address the challenge of enabling robots to manipulate deformable linear objects (DLOs), such as ropes, cables, and rubber bands. Prior work has primarily focused on narrow, task-specific problems, often relying on real-world demonstrations or handcrafted heuristics. Such approaches, however, struggle to scale to the wide variety of materials and tasks encountered in practice, and collecting sufficiently diverse real-world data is often impractical. Additionally, existing simulation environments offer limited support for the broad spectrum of material behaviors necessary for generalizable DLO manipulation. To overcome these limitations, we introduce a differentiable simulator explicitly designed for versatile DLO manipulation. Our simulator models a wide range of material properties-including (in)extensibility, elasticity, bending plasticity, and complex interactions with other objects-providing a robust foundation for learning and evaluating manipulation skills. Building on this simulator, we propose a benchmark suite of representative tasks that highlight the unique challenges of DLO manipulation. The successful execution of these tasks is often hindered by the topological complexity and grasp sensitivity inherent to DLOs. Therefore, we introduce a specialized DLO agent that explicitly manages these challenges by proposing strategic grasping points and decomposing long-horizon tasks to maximize control authority. Finally, we evaluate various policy-learning algorithms using our framework, alongside sim-to-real transfer experiments, demonstrating our platform's potential to advance DLO manipulation.
Chinese Translation
我们解决了使机器人能够操控可变形线性物体（DLOs），如绳索、电缆和橡皮筋的挑战。之前的研究主要集中在狭窄的、特定任务的问题上，通常依赖于现实世界的示范或手工制定的启发式方法。然而，这些方法在应对实践中遇到的多种材料和任务方面往往难以扩展，收集足够多样化的现实世界数据也是不切实际的。此外，现有的模拟环境对一般化DLO操控所需的广泛材料行为的支持有限。为了克服这些局限性，我们提出了一种专门设计的可微分模拟器，旨在实现多功能的DLO操控。我们的模拟器建模了多种材料特性，包括（非）可伸缩性、弹性、弯曲塑性及与其他物体的复杂交互，为学习和评估操控技能提供了坚实的基础。在此模拟器之上，我们提出了一套基准测试任务，突显DLO操控的独特挑战。这些任务的成功执行常常受到DLO固有的拓扑复杂性和抓取敏感性的阻碍。因此，我们引入了一种专门的DLO代理，通过提出战略抓取点和将长时间范围任务分解，明确地管理这些挑战，以最大化控制权。最后，我们利用我们的框架评估了多种策略学习算法，并进行了从模拟到现实的迁移实验，展示了我们平台在推动DLO操控方面的潜力。
cs.RO / 10 / 2606.04222
Towards Estimating Normal and Shear Interface Pressures in Prosthetic Sockets via Least Squares and Mechanics Modeling
通过最小二乘法和力学建模估计假肢套筒中的法向和剪切界面压力
Abstract
Prosthetic socket fitting remains largely manual and iterative, and objective fit metrics are still limited. Part of the challenge is the lack of long-term real-life pressure data at the residual limb--socket interface. Traditional pressure sensors are prone to drift over time, and capture only normal pressures at sparse locations within the socket, missing a critical component for biomechanical analysis: shear. Although some sensors can report both normal and shear interface stresses, these components are often difficult to decouple because of measurement crosstalk. One potential path forward is to develop models that can augment available measurements. This work introduces a testbed to evaluate model performance under sparse pressure sensing using two complementary validation signals: (i) the global wrench (\ie, total forces and moments expressed in an orthonormal frame) transmitted through the socket, by an artificial residual-limb, and (ii) local interface loads (\ie, decoupled normal and shear pressure components in a right-hand-rule orthogonal frame that lives in each instrumented location) measured by sparse sensing clusters, each composed of four capacitance-sensing channels. Rather than presenting full-field pressure estimates, the focus is on an analysis sequence that quantifies how well candidate mechanical models explain both global and local measurements under controlled conditions. A quasi-static spring--mass contact model is evaluated, and its parameters are identified via a two-stage convex least-squares problem. Validation under static loading shows that estimating constant bias terms reduces steady offsets in the wrench channels and improves agreement with local measurements. A Pareto-front sensitivity analysis further illustrates how the trade-off between global and local objectives changes when bias terms are included.
Chinese Translation
假肢套筒的适配仍然在很大程度上依赖于人工和迭代的方法，且客观的适配标准仍然有限。部分挑战在于缺乏长期实际的残肢与套筒界面压力数据。传统的压力传感器随着时间的推移容易出现漂移，并且只能在套筒内稀疏的位置捕捉法向压力，错过了生物力学分析中的一个关键组成部分：剪切力。尽管一些传感器可以报告法向和剪切界面的应力，但由于测量交叉干扰，这些分量往往难以解耦。一个潜在的前进方向是开发能够增强可用测量数据的模型。本研究引入了一个测试平台，旨在评估在稀疏压力传感下模型的表现，使用两个互补的验证信号：(i) 通过假肢传递的全局力矩（即以正交规范框架表达的总力和力矩），以及(ii) 通过稀疏传感集群测量的局部界面负载（即在每个仪器位置测量的正交框架中的解耦法向和剪切压力分量），每个集群由四个电容传感通道组成。研究的重点不在于呈现全场压力估计，而是分析序列，量化候选力学模型在受控条件下对全局和局部测量的解释能力。评估了一种准静态弹簧-质量接触模型，并通过二阶段凸最小二乘问题识别其参数。在静态加载下的验证表明，估计常数偏差项可以减少力矩通道中的稳态偏移，从而改善与局部测量的匹配。Pareto前沿敏感性分析进一步显示了包含偏差项时全局和局部目标之间的权衡如何变化。
cs.RO / 11 / 2606.04226
PerceptTwin: Semantic Scene Reconstruction for Iterative LLM Planning and Verification
PerceptTwin：用于迭代 LLM 规划与验证的语义场景重建
Abstract
Simulation environments are useful for both robot policy learning and planning verification and validation. Traditionally, the process of creating a simulation was onerous. Creating a bespoke simulation environment for each individual environment that a robot would operate in was simply infeasible. In this work, we introduce PerceptTwin, a fully automatic pipeline that constructs interactive simulations directly from semantic scene representations produced by a robot's perception stack. PerceptTwin combines open-vocabulary object maps with 3D asset generation, affordance prediction, and commonsense condition checking. These interactive simulations can be used to validate and refine plans before they are executed on the robot hardware. Borrowing from the AI alignment literature, we also introduce an LLM judge that verifies plan correctness and alignment with human preferences. Experiments show that PerceptTwin feedback allows LLM planners to refine plans, enhance safety, and resist harmful black-box prompting attacks. In our suite of tasks, PerceptTwin improves plan success by an average of approximately 39% for GPT5, GPT5Mini, and GPT5Nano planners. Additionally, PerceptTwin also improves human plan verification by up to 18% on average for plans that fail due to unfilled skill preconditions. Our results demonstrate the potential of open-vocabulary scene simulation from robot perception as a foundation for safer, more reliable robot planning.
Chinese Translation
仿真环境对机器人策略学习以及规划验证和确认都非常有用。传统上，创建仿真的过程十分繁琐。为机器人将在其上操作的每一个环境创建一个定制的仿真环境是不可行的。在本研究中，我们介绍了 PerceptTwin，这是一个完全自动化的流程，能够直接从机器人感知堆栈生成的语义场景表示构建交互式仿真。PerceptTwin 将开放词汇的物体地图与 3D 资产生成、可使用性预测和常识条件检查相结合。这些交互式仿真可以在计划在机器人硬件上执行之前用于验证和完善计划。借鉴人工智能对齐文献，我们还引入了 LLM 判别器，来验证计划的正确性及其与人类偏好的对齐。实验表明，PerceptTwin 的反馈使 LLM 规划器能够完善计划、增强安全性，并抵御有害的黑箱提示攻击。在我们的任务集合中，PerceptTwin 将 GPT5、GPT5Mini 和 GPT5Nano 规划器的计划成功率平均提高了约 39%。此外，PerceptTwin 还提高了对于因技能前置条件未满足而失败的计划的人类验证成功率，平均提升幅度达到 18%。我们的结果展示了基于机器人感知的开放词汇场景仿真作为更安全、更可靠的机器人规划基础的潜力。
cs.RO / 12 / 2606.04233
What Are We Actually Benchmarking in Robot Manipulation?
我们在机器人操控中到底在评估什么？
Abstract
A robotics benchmark score measures success under one fixed evaluation setup, yet is routinely treated as evidence of general manipulation capability. We identify four failure modes, each of which weakens or invalidates a benchmark's role as a valid proxy for that capability: shortcut solvability, lack of statistical significance, creeping overfitting, and data-source dependence. We propose one diagnostic per failure mode. We audit LIBERO, CALVIN, SimplerEnv, RoboCasa, and RoboTwin 2.0 under these diagnostics. LIBERO and CALVIN fail multiple diagnostics. RoboCasa and RoboTwin 2.0 fail fewer, despite appearing far less often in recent progress claims. On LIBERO, a 0.09B probe with no language encoder scores at or near reported SOTA, and most reported gains are not provably statistically significant. On CALVIN, randomizing block poses within the training range drops performance for every tested policy. We release the four diagnostics with reference implementations for authors and reviewers to apply before treating a benchmark score as evidence of progress. Code and artifacts are available at https://ripl.github.io/manipulation_benchmark_audit/.
Chinese Translation
机器人基准评分在固定的评估设置下测量成功，但通常被视为一般操控能力的证据。我们识别出四种失效模式，每一种都削弱或使基准作为该能力有效代理的角色无效：捷径可解性、缺乏统计显著性、逐步过拟合以及数据源依赖性。我们为每种失效模式提出了一种诊断方法。我们对LIBERO、CALVIN、SimplerEnv、RoboCasa和RoboTwin 2.0进行了这些诊断的审计。LIBERO和CALVIN在多个诊断中失败。尽管RoboCasa和RoboTwin 2.0在最近的进展声称中出现频率较低，但它们失败的诊断较少。在LIBERO上，一种不带语言编码器的0.09B探针得分达到或接近报告的SOTA，且大多数报告的增益在统计上无法证明显著。在CALVIN上，在训练范围内随机化块姿势导致每种被测策略的性能下降。我们发布了四个诊断方法及其参考实现，供作者和审核者在将基准评分视为进展证据之前使用。代码和相关资料可在 https://ripl.github.io/manipulation_benchmark_audit/ 获取。
cs.RO / 13 / 2606.04248
RSC: Decentralized Rigid Formation Flocking for Large-Scale Swarms via Hybrid Predictive Control and Online Reconfiguration
RSC：通过混合预测控制和在线重构实现大型群体的去中心化刚性编队集群
Abstract
Decentralized rigid formation flocking requires a swarm of autonomous agents to maintain a predetermined geometric configuration while moving, relying solely on local sensing and communication. However, existing decentralized control methods struggle to maintain strict inter-agent distance constraints in cluttered environments, often suffering from local minima deadlocks, high frequency control oscillations, or limited flexibility during obstacle navigation, resulting in low success rate. To address these limitations, we propose Rigid Swarm Control (RSC), a decentralized control framework for large-scale rigid formation flocking. To escape local minima via robust long-term planning while ensuring short-term safety, RSC integrates finite-horizon trajectory predictions with a reactive artificial potential field (APF) safety controller within a hybrid architecture. Furthermore, to accelerate formation reassembly after obstacle traversal without interrupting task execution, RSC introduces an online leader-follower reconfiguration mechanism based on stable role exchange. Extensive evaluations in challenging cluttered environments with 25 UAVs demonstrate that RSC reliably unifies rigid formation maintenance, obstacle avoidance, and target tracking. Under strict success criteria - collision-free operation with a maximum relative edge-length error below 10%, RSC achieves an 83% success rate, significantly outperforming existing heuristic and learning-based baselines that fall below 5%.
Chinese Translation
去中心化的刚性编队集群要求一群自主代理在移动过程中维持预定的几何配置，仅依赖本地感知和通信。然而，现有的去中心化控制方法在复杂环境中难以维持严格的代理间距离约束，常常遭遇局部最小值死锁、高频控制振荡或在障碍物导航过程中灵活性不足的问题，导致成功率低。为了解决这些局限性，我们提出了刚性群体控制（Rigid Swarm Control, RSC），这是一个用于大规模刚性编队集群的去中心化控制框架。RSC结合有限时域的轨迹预测与反应式人工势场（Artificial Potential Field, APF）安全控制器，在混合架构下确保短期安全的同时，通过稳健的长期规划来逃避局部最小值。此外，为了在不打断任务执行的情况下加速障碍物通过后的编队重组，RSC引入了一种基于稳定角色交换的在线领导者-追随者重构机制。在具有挑战性的复杂环境中对25架无人机进行的大规模评估表明，RSC能够可靠地统一刚性编队维护、障碍物避免和目标跟踪。在严格的成功标准下——即在最大相对边长误差低于10%的情况下无碰撞操作，RSC达到了83%的成功率，显著超越了现有的启发式和基于学习的基准方法（其成功率低于5%）。
cs.RO / 14 / 2606.04269
Instant-Fold: In-Context Imitation Learning for Deformable Object Manipulation
即时折叠：基于上下文的可变形物体操控模仿学习
Abstract
Deformable object manipulation (DOM) is challenging due to high-dimensional, partially observable states that evolve through long-horizon, topology-changing interactions with multiple valid manipulation modes. We introduce Instant-Fold, an in-context imitation learning framework for DOM. Given a single human demonstration, our policy infers and executes diverse manipulation modes directly from the demonstration, including variations in spatial execution and ordering, without requiring gradient updates. Our approach first learns deformation-aware visual representations via temporal contrastive pretraining, after which a flow-matching transformer policy conditioned on the demonstration predicts actions to execute the intended manipulation mode. Trained entirely in simulation, Instant-Fold generalizes across diverse folding modes and transfers zero-shot to real-world settings without additional data collection or finetuning. Videos are available at https://instant-fold.github.io.
Chinese Translation
可变形物体操控（DOM）由于高维、部分可观察的状态以及通过长时间段、多拓扑变化的交互，涉及多种有效操控模式，具有挑战性。我们提出了即时折叠（Instant-Fold），这是一个针对可变形物体操控的基于上下文的模仿学习框架。给定单个的人类示范，我们的策略能够直接从示范中推断并执行多样化的操控模式，包括空间执行和顺序的变化，而无需梯度更新。我们的方法首先通过时间对比预训练学习关注变形的视觉表征，然后，基于示范的流匹配 Transformer 策略预测执行预期操控模式的动作。即时折叠完全在模拟环境中训练，能够在多样的折叠模式间泛化，并且无需额外的数据收集或微调即可零-shot 转移到现实世界场景。相关视频可在 https://instant-fold.github.io 查看。
cs.RO / 15 / 2606.04355
Think Fast and Far: Long-Horizon Online POMDP Planning via Rapid State Sampling
快速思考与远见：通过快速状态采样的长时间在线POMDP规划
Abstract
Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalability of POMDP solvers, long-horizon POMDPs remain difficult to solve. To alleviate the difficulty, this paper proposes a new approximate online POMDP solver, called Reference-Based Online POMDP Planning via Rapid State Space Sampling (ROP-RAS3). ROP-RAS3 uses novel extremely fast sampling-based motion planning techniques to sample the state space and generate a diverse set of macro actions online, which are then used to bias belief-space sampling and infer high-quality policies without requiring exhaustive enumeration of the action space -- a fundamental constraint for modern online POMDP solvers. ROP-RAS3 converges to a near-optimal reference-based solution at a rate that depends on the number of sampled actions, rather than the size of the action space. ROP-RAS3 is evaluated on various long-horizon POMDPs with up to 3000 lookahead steps and 35-dimensional state spaces, where the state, action and observation spaces can be continuous, discrete, or a hybrid of discrete and continuous. Although the reference-based optimal solution may not be the same as the optimal POMDP solution, empirical results indicate that in all of these problems, in terms of success rate, ROP-RAS3 outperforms other state-of-the-art methods by up to multiple folds. We also demonstrate the capability of our approach on a physical robot demonstration. This work extends the theory and empirical results of our ISRR24 paper. Code can be found at \texttt{https://github.com/RDLLab/ROPRAS3}.
Chinese Translation
部分可观测的马尔可夫决策过程（POMDP）是一个在不确定性下进行运动规划的通用而规范的框架。尽管POMDP求解器的可扩展性已取得巨大改善，长时间POMDP仍然难以解决。为了解决这个难题，本文提出了一种新的近似在线POMDP求解器，称为基于参考的在线POMDP规划通过快速状态空间采样（ROP-RAS3）。ROP-RAS3采用新颖的超快速采样基础的运动规划技术对状态空间进行采样，并在线生成多样化的宏动作，然后用于偏向信念空间采样，从而推导出高质量的策略，而不需要对动作空间进行穷尽枚举——这是现代在线POMDP求解器的一个基本限制。ROP-RAS3以一种取决于采样动作数量而非动作空间大小的速率收敛到近似最优的基于参考的解决方案。ROP-RAS3在多个长时间POMDP上进行了评估，具有多达3000步的前瞻性和35维的状态空间，其中状态、动作和观察空间可以是连续的、离散的或离散与连续的混合。尽管基于参考的最优解决方案可能与最优POMDP解决方案不同，但实证结果表明，在所有这些问题中，ROP-RAS3的成功率比其他最先进的方法高出多倍。我们还展示了我们的方法在物理机器人演示中的能力。本工作扩展了我们ISRR24论文的理论和实证结果。代码可以在 exttt{https://github.com/RDLLab/ROPRAS3} 找到。
cs.RO / 16 / 2606.04463
OSCAR: Omni-Embodiment Skeleton-Conditioned World Action Model for Robotics
OSCAR: 全能化骨架条件的世界动作模型用于机器人技术
Abstract
We present OSCAR, a precise action-conditioned video world model that generalizes across different robot embodiments and enables robot policy evaluation. Existing video world models face three main challenges for real-world robot evaluation: limited scenario diversity in current robot training datasets, imprecise action following, and poor generalization across embodiments for broad adoption. We tackle these challenges from two perspectives. At its core is a large-scale standardized data pipeline that curates, filters, and deduplicates broad robotics and egocentric human datasets, yielding a clean joint-training dataset that spans diverse tasks, scenarios, actions, and robot embodiments. To condition the video model, we adopt 2D kinematic skeleton rendering as a unified conditioning representation that generalizes across different robot arms or even human hands. We finetune the Cosmos-Predict2.5-2B model on a single GH200 GPU. Our model achieves significant improvement on action following, appearance quality, and motion consistency, compared to existing baselines, which either have a much larger model size or require more GPUs. We further deploy OSCAR to evaluate robot policies from RoboArena. Extensive experiments demonstrate the significant correlation between our virtual policy evaluation in OSCAR and real-world evaluation, paving the way for the future where robot policies can be purely evaluated in virtual generated worlds.
Chinese Translation
我们提出了OSCAR，这是一种精确的动作条件视频世界模型，能够在不同的机器人体现之间进行泛化，并支持机器人的策略评估。现有的视频世界模型在现实世界机器人评估中面临三个主要挑战：当前机器人训练数据集中场景多样性有限、动作跟随不准确，以及在不同体现之间的泛化能力较差，限制了其广泛应用。我们从两个方面解决这些挑战。其核心是一个大规模标准化的数据管道，该管道策划、过滤及去重了广泛的机器人和以自我为中心的人类数据集，生成了一个覆盖多样任务、场景、动作和机器人体现的干净联合训练数据集。为了对视频模型进行条件化，我们采用2D运动骨架渲染作为统一的条件表示，能够在不同的机器人手臂甚至人手之间进行泛化。我们在单个GH200 GPU上微调了Cosmos-Predict2.5-2B模型。与现有的基线模型相比，我们的模型在动作跟随、外观质量和运动一致性方面取得了显著改善，这些基线模型要么模型尺寸更大，要么需要更多的GPU。我们进一步部署OSCAR来评估RoboArena的机器人策略。广泛的实验表明，我们在OSCAR中的虚拟策略评估与现实世界评估之间存在显著相关性，为未来机器人策略能够仅在虚拟生成的世界中进行评估铺平了道路。
cs.RO / 17 / 2606.04477
TransTac: Visuo-Tactile Modality Transition via Ultraviolet-Encoded Transparent Elastomers
TransTac：通过紫外线编码透明弹性体实现视觉-触觉模态转换
Abstract
Vision-based tactile sensors (VBTS) recover high-resolution contact geometry but typically rely on opaque elastomer layers that prevent visual transparency, while RGB-D cameras provide global depth perception yet degrade significantly at close range. To address this limitation, we present TransTac, a transparent ultraviolet (UV)-encoded binocular VBTS that integrates visual observation and marker-based tactile reconstruction within a single compact device. The system employs a transparent elastomer embedded with UV-reflective markers and a prior-guided Delaunay stereo matching algorithm for robust sparse triangulation. To reliably detect densely distributed semitransparent markers, we develop a lightweight detector that enables stable localization under contact and deformation. The proposed prior-guided Delaunay matching improves correspondence robustness by approximately 21% compared with global assignment baselines while maintaining high reconstruction accuracy. In semantic evaluation, TransTac achieves up to 83.3% zero-shot recognition accuracy on tactile images, exceeding opaque tactile baselines by approximately 50 percentage points. Embedding analysis further reveals substantially stronger cross-modal alignment with natural images, with class-center similarity increasing from around 0.2 to over 0.77. Controlled near-distance experiments quantify the degradation of RGB-D depth reliability and demonstrate extended geometric coverage enabled by visuo-tactile integration. Finally, a compact prototype is implemented with an approximate hardware cost of $70.
Chinese Translation
基于视觉的触觉传感器（VBTS）能够恢复高分辨率的接触几何形状，但通常依赖于不透明的弹性体层，这阻碍了视觉透明性，而RGB-D相机提供全球深度感知却在近距离内显著下降。为了解决这一限制，我们提出了TransTac，一个透明的紫外线（UV）编码双目VBTS，能够将视觉观察与基于标记的触觉重建集成在一个紧凑的设备中。该系统采用嵌入UV反射标记的透明弹性体和先验引导的Delaunay立体匹配算法，实现稳健的稀疏三维重建。为了可靠地检测密集分布的半透明标记，我们开发了一种轻量级检测器，能够在接触和变形下实现稳定定位。所提出的先验引导Delaunay匹配比全局分配基线提高了约21%的对应鲁棒性，同时保持高重建精度。在语义评估中，TransTac在触觉图像上的零-shot识别准确率高达83.3%，比不透明触觉基线提高了约50个百分点。嵌入分析进一步显示与自然图像之间具有显著更强的跨模态对齐，类中心相似性从约0.2提高至超过0.77。控制的近距离实验量化了RGB-D深度可靠性的下降，并展示了通过视觉-触觉集成实现的更广泛几何覆盖。最后，实现了一个近似硬件成本为70美元的紧凑型原型。
cs.RO / 18 / 2606.04518
Cooperative Circumnavigation for Multiple Unmanned Surface Vehicles Without External Localization
无需外部定位的多无人水面车辆协同环绕目标
Abstract
This paper proposes a cooperative target circumnavigation framework for multiple unmanned surface vehicles (USVs) operating without external localization. The objective is to maintain a uniform circular formation of a specified radius around a target using only limited onboard sensing. The framework adopts a heterogeneous perception strategy that distinguishes between the asymmetric sensing relationships with the target and among the USVs. Specifically, the USVs obtain relative range and displacement measurements through active perception and inter-vehicle communication, while bearing measurements to a non-cooperative target are acquired via passive sensors. To estimate relative positions--both among USVs and between each USV and the target--we employ a Maximum Correntropy Kalman Filter and a Pseudo-Linear Kalman Filter, respectively. A coupled oscillator-based formation controller is designed to ensure system observability while achieving circumnavigation. Theoretical analysis demonstrates that the controller ensures the relative motions between the USVs, as well as that between each USV and the target, satisfy the persistent excitation condition, thereby guaranteeing observability of the Kalman-based filters. The effectiveness of the proposed approach is validated through numerical simulations.
Chinese Translation
本文提出了一种协同目标环绕框架，用于在无外部定位情况下操作的多无人水面车辆（USVs）。其目标是在一个特定半径的范围内，利用有限的车载传感器保持对目标的均匀圆形编队。该框架采用了一种异构感知策略，区分了与目标和USVs之间的非对称感知关系。具体而言，USVs通过主动感知和车间通信获取相对距离和位移测量，而与非合作目标的方位测量则通过被动传感器获得。为了估计USVs之间及每个USV与目标之间的相对位置，我们分别使用最大互信息卡尔曼滤波器（Maximum Correntropy Kalman Filter）和伪线性卡尔曼滤波器（Pseudo-Linear Kalman Filter）。设计了一种基于耦合振荡器的编队控制器，以确保系统的可观测性，同时实现环绕目标。理论分析表明，该控制器确保USVs之间，以及每个USV与目标之间的相对运动满足持久激励条件，从而保证卡尔曼滤波器的可观测性。通过数值仿真验证了所提方法的有效性。
cs.RO / 19 / 2606.04534
MAD: Mapping-Aware World Models for Agile Quadrotor Flight
MAD：面向敏捷四旋翼飞行的映射感知世界模型
Abstract
Agile quadrotor flight in cluttered scenes requires more than a reactive mapping from a depth image to a control command: the vehicle must remember which regions have been observed, infer nearby occupied space, and act under partial visibility and tight latency. In this paper, we present Mapping-Aware Dreamer (MAD), a geometry-aware world model for vision-based quadrotor flight. Instead of using raw-image reconstruction as the main self-supervised objective, MAD learns recurrent latent dynamics that reconstruct robocentric occupancy and visibility grid maps together with proprioceptive states. This design forces the latent state to encode local geometry, visibility history, and ego-motion in a form that is directly relevant to collision avoidance. MAD is trained in DiffAero using a GPU-parallel map-construction module that provides high-throughput supervision for occupancy and visibility. The learned representation is used in three policy-learning modes: imagination-based MAD-Dreamer and feature-extractor variants based on PPO and SHAC. Across visual navigation and racing tasks, MAD-based agents achieve higher success rates, faster flight, and better cross-task transfer than corresponding vision-only baselines. The model also produces interpretable map predictions and accurate ego-motion estimates from depth observations. We further deploy the learned policy on a physical quadrotor with an Intel RealSense D435i and demonstrate safe indoor and outdoor flight under limited sensing, reaching 9.66 m/s in simulation and 5.05 m/s in real-world forest experiments. These results show that mapping-aware world models provide a practical middle ground between modular aerial navigation and end-to-end learning.
Chinese Translation
在杂乱场景中的敏捷四旋翼飞行需要的不仅仅是从深度图像到控制命令的反应式映射：飞行器必须记住哪些区域已被观察、推断附近被占用的空间，并在部分可见性和严格延迟的情况下进行操作。本文提出了映射感知梦想者（Mapping-Aware Dreamer，MAD），这是一种对几何感知的世界模型，专为基于视觉的四旋翼飞行设计。与将原始图像重建作为主要自监督目标不同，MAD学习重现潜在动态，结合自我感知状态重构以机器人为中心的占用和可见性网格地图。这种设计迫使潜在状态以与碰撞避免直接相关的形式编码局部几何、可见性历史和自我运动。MAD在DiffAero中训练，利用一个GPU并行映射构建模块，为占用和可见性提供高吞吐量的监督。该学习到的表示在三种策略学习模式中使用：基于想象的MAD-Dreamer以及基于PPO和SHAC的特征提取变种。在视觉导航和赛车任务中，基于MAD的代理比相应的仅基于视觉的基线实现了更高的成功率、更快的飞行速度以及更好的跨任务迁移。该模型还从深度观察中生成可解释的地图预测和准确的自我运动估计。我们进一步在配备Intel RealSense D435i的实际四旋翼上部署学习到的策略，展示了在有限传感下的安全室内和室外飞行，在模拟中达到9.66米/秒，在现实世界森林实验中达到5.05米/秒。这些结果表明，映射感知世界模型在模块化空中导航和端到端学习之间提供了一个实用的折衷方案。
cs.RO / 20 / 2606.04569
MineXplore: An Open-Source Reinforcement Learning Exploration Benchmark for GNSS-Denied Underground Environment
MineXplore：一个针对无GNSS干扰地下环境的开源强化学习探索基准
Abstract
Underground mines present extreme conditions for autonomous robot navigation: GPS is denied, lighting is degraded, and tunnel topology is loop-rich and non-convex. Simulation benchmarks grounded in real production-mine geometry and compatible with GPU-accelerated learning pipelines do not yet exist in the open-source ecosystem. We present MineXplore, an open-source MuJoCo-based navigation benchmark derived from the Leung et al. 2017 Chilean underground copper mine dataset. The environment reconstructs a 104,423 sq.m tunnel network through an six-stage contour-to-MJCF pipeline incorporating octagonal wall cross-sections, LiDAR-sourced jagged wall geometry, three terrain friction zones, a global 5 degree incline, and periodic spot lighting. Geometric fidelity is validated at an Intersection over Union (IoU) of 0.9538 against the source survey map, and surface texture similarity scores 79.4% across six structural dimensions. A single-agent PPO baseline trained via RLlib across five independent random seeds achieves a best rolling coverage of 88.89% (3 of 5 seeds reaching the 90% coverage target), confirming that MineXplore supports stable and reproducible policy learning under realistic underground sensing and topology.
Chinese Translation
地下矿井为自主机器人导航提供了极端条件：GPS不可用，照明条件恶劣，隧道拓扑结构丰富且非凸。目前还不存在在开源生态系统中基于真实生产矿井几何结构的、以及与GPU加速学习流程兼容的模拟基准。我们推出了MineXplore，这是一个基于MuJoCo的开源导航基准，源自Leung等人2017年关于智利地下铜矿的数据集。该环境通过一个六阶段的轮廓到MJCF（MuJoCo类文件）流程重建了104,423平方米的隧道网络，包含八角形墙壁截面、LiDAR（激光雷达）源的锯齿形墙壁几何、三个地形摩擦区、全球5度的倾斜和周期性点光源。几何精度在与源测量图的交并比（IoU）为0.9538的对比中得到了验证，表面纹理相似度在六个结构维度上达到了79.4%。通过RLlib在五个独立随机种子上训练的单代理PPO（Proximal Policy Optimization）基准实现了最佳滚动覆盖率为88.89%（5个种子中有3个达到90%的覆盖目标），确认MineXplore能够支持在现实的地下传感和拓扑情况下稳定且可重现的策略学习。
cs.RO / 21 / 2606.04618
BPDA-GMM: Bayesian Probabilistic Data Association via Gaussian Mixture Models for Semantic SLAM
BPDA-GMM：通过高斯混合模型的贝叶斯概率数据关联用于语义SLAM
Abstract
Probabilistic data association (PDA) improves semantic SLAM in perceptually aliased scenes, but existing methods often assume a fixed landmark set, recompute association weights as the map grows, or rely on hand-tuned null-hypothesis weights. To address these limitations, we propose \textbf{BPDA-GMM}, an online Bayesian PDA framework for semantic SLAM with a growing object-level map. BPDA-GMM uses a Dirichlet-process prior to induce a Chinese Restaurant Process (CRP) association model, where accumulated evidence favors existing landmarks, and the concentration parameter assigns probability mass to new landmarks. For each semantic detection, plausible candidates are selected by a joint semantic-geometric gate, CRP-weighted association probabilities are computed, and object landmarks are updated as semantic Gaussians in closed form. The resulting landmark set forms a Gaussian mixture model, and its dominant component is passed to the back-end as a max-mixture semantic factor. When association weights are inconclusive, an ambiguity-triggered $\alpha$-divergence tempering step improves discrimination. Finally, a decoupled back-end zeroes the pose Jacobian of semantic factors, allowing noisy detections to refine landmarks without directly perturbing the trajectory. Experiments in simulation and on a real indoor dataset demonstrate improved trajectory accuracy, semantic mapping quality, and robustness to perceptual aliasing and classifier errors over state-of-the-art baselines. Code and video are publicly available at https://github.com/thanhnguyencanh/BPDA-SLAM.
Chinese Translation
概率数据关联（PDA）在感知别名场景中提升了语义SLAM的性能，但现有方法通常假设一个固定的地标集，随着地图的增长重新计算关联权重，或依赖于人工调优的虚无假设权重。为了解决这些限制，我们提出了 extbf{BPDA-GMM}，这是一个用于语义SLAM的在线贝叶斯PDA框架，支持增长的对象级地图。BPDA-GMM使用Dirichlet过程先验来诱导一个中国餐厅过程（CRP）关联模型，其中累积证据倾向于现有地标，浓度参数则为新地标分配概率质量。对于每个语义检测，通过联结语义-几何门选择合理的候选者，计算加权CRP的关联概率，并将对象地标更新为封闭形式的语义高斯分布。得到的地标集形成一个高斯混合模型，其主成分作为最大混合语义因子传递给后端。当关联权重不明确时，触发模糊的$ extalpha$-散度调温步骤可改善判别能力。最后，解耦的后端将语义因子的位姿雅可比矩阵归零，使得噪声检测能够在不直接扰动轨迹的情况下精确地优化地标。仿真实验和真实室内数据集的实验表明，与最先进的基线相比，在轨迹精度、语义映射质量以及对感知别名和分类器错误的鲁棒性方面显著提升。代码和视频公开可用，网址为 https://github.com/thanhnguyencanh/BPDA-SLAM。
cs.RO / 22 / 2606.04708
VISTA: Vision-Grounded and Physics-Validated Adaptation of UMI data for VLA Training
VISTA：基于视觉和物理验证的UMI数据适应VLA训练
Abstract
Universal Manipulation Interface (UMI) enables scalable real-world robot data collection without hardware-specific teleoperation, yet leveraging UMI data to train large-scale Vision-Language-Action (VLA) models remains fundamentally challenging. We identify two critical mismatches: wrist-mounted fisheye views, with severe radial distortion and local gripper-centric perspectives, are out-of-distribution for pretrained VLMs; and human-collected trajectories frequently violate kinematic limits, incur collisions, or exceed controller bandwidth, teaching VLA policies physically infeasible actions. To address the challenges, we present VISTA, a framework that bridges this dual gap through three synergistic components. (i)~UMI-VQA, the first large-scale VQA dataset tailored to wrist-mounted fisheye observations, aligns VLM representations to the distorted visual regime via auxiliary vision-language supervision. (ii)~A systematic physical-validation pipeline performs a data-completeness pre-check and scores each valid trajectory for trajectory continuity, self-collision risk, and execution fidelity before it enters training. (iii)~A two-stage co-training recipe jointly learns vision-language grounding on UMI-VQA and action prediction on validated trajectories. Our experiments empirically show that incorporating UMI-VQA consistently improves downstream policy performance, and that physical-validation scores are strongly predictive of deployment success. On diverse simulation and real-world manipulation tasks, VISTA significantly outperforms strong baselines including $\pi_{0.5}$, LingBot-VLA, and Wall-X. We release the physical-validation pipeline, UMI-VQA, validated trajectory data, and the pre-trained model for the community.
Chinese Translation
通用操控接口（Universal Manipulation Interface, UMI）使得无需特定硬件的远程操作便能进行可扩展的真实世界机器人数据收集，但利用UMI数据训练大规模视觉-语言-动作（Vision-Language-Action, VLA）模型仍然面临根本性的挑战。我们识别出两个关键的不匹配：腕部安装的鱼眼视图由于严重的径向失真和局部夹具中心视角，超出了预训练视觉-语言模型（VLMs）的分布范围；而人类收集的轨迹常常违反运动学限制，发生碰撞，或超出控制器带宽，从而教会VLA策略执行物理上不可行的动作。为了解决这些挑战，我们提出了VISTA，一个通过三个协同组成部分弥合这一双重差距的框架。(i) UMI-VQA，第一个专门为腕部安装的鱼眼观察量身定制的大规模视觉问答（Visual Question Answering, VQA）数据集，通过辅助的视觉-语言监督，将VLM的表示与扭曲的视觉范畴对齐。(ii) 一个系统的物理验证管道在数据完整性预检查中进行，给每个有效轨迹评分，评估轨迹连续性、自碰撞风险和执行保真度，以确保其在训练前的有效性。(iii) 一个双阶段的协同训练方案共同学习UMI-VQA上的视觉-语言对接和经过验证的轨迹上的动作预测。我们的实验实证表明，纳入UMI-VQA consistently 提高下游策略的性能，物理验证评分也能强有力地预测部署成功。在各种模拟和真实世界的操控任务中，VISTA显著超越了包括$ ext{π}_{0.5}$、LingBot-VLA和Wall-X等强基准。我们为社区发布了物理验证管道、UMI-VQA、有效轨迹数据和预训练模型。
cs.RO / 23 / 2606.04718
CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation
CoRe-MoE：适应多种地形的对比重加权专家混合模型用于类人机器人行走
Abstract
Humans primarily rely on walking and running to traverse complex terrains, without resorting to unnecessarily complex motion patterns. Similarly, humanoid robots should achieve smooth transitions between walking and running while maintaining natural and stable locomotion. However, unifying gait transition and multi-terrain adaptation within a single policy remains challenging due to gradient interference and the distribution shift induced by terrain-dependent visual and dynamic variations. Although Mixture-of-Experts (MoE) architectures can alleviate multi-skill interference, naive joint training often fails to yield clear expert specialization, limiting their effectiveness. To address these challenges, we propose CoRe-MoE, a two-stage reinforcement learning framework that decouples gait generation from terrain adaptation. In the first stage, a stable locomotion policy is learned to produce natural walking and running behaviors with smooth transitions. In the second stage, a terrain-aware MoE branch is introduced and trained with a contrastive objective to shape the gating network, enabling it to capture structured terrain representations and promote expert specialization. The final action is obtained via weighted fusion of the base gait policy and the terrain-aware branch, allowing the policy to preserve stable locomotion patterns while adapting to complex terrains. Extensive simulation results demonstrate that the proposed method outperforms baseline approaches in terms of success rate, locomotion stability, and multi-terrain adaptability. Furthermore, zero-shot deployment on a Unitree G1 humanoid robot validates the effectiveness of our framework, achieving robust walking and running across stairs, slopes, steps, obstacles, and unstructured outdoor terrains, while maintaining accurate foothold placement and dynamic stability under external disturbances.
Chinese Translation
人类主要依靠步行和奔跑来穿越复杂地形，而不采用不必要复杂的运动模式。同样，类人机器人应在保持自然和稳定的运动状态的同时，实现步行与奔跑之间的平滑过渡。然而，由于梯度干扰和地形相关的视觉与动态变化引起的分布转移，将步态转换与多地形适应统一到单一策略中仍然具有挑战性。尽管专家混合模型（Mixture-of-Experts, MoE）架构可以缓解多技能干扰，但简单的联合训练往往无法实现明确的专家专业化，从而限制了其有效性。为了解决这些挑战，我们提出了CoRe-MoE，一个两阶段的强化学习框架，解耦步态生成与地形适应。在第一阶段，学习稳定的运动策略，以生成自然的步行和奔跑行为，并实现平滑过渡。在第二阶段，引入一个地形感知的MoE分支，并通过对比目标进行训练，以塑造门控网络，使其能够捕捉结构化的地形表示并促进专家专业化。最终的动作通过基础步态策略与地形感知分支的加权融合获得，使该策略能够在适应复杂地形的同时保持稳定的运动模式。广泛的仿真结果表明，所提方法在成功率、运动稳定性和多地形适应能力方面优于基线方法。此外，在Unitree G1类人机器人上的零样本部署验证了我们框架的有效性，实现了在阶梯、坡道、台阶、障碍物以及非结构化户外地形上稳健的步行和奔跑，同时在外部干扰下保持准确的落脚位置和动态稳定性。
cs.RO / 24 / 2606.04746
CADENCE: Predicting Realized MAPF Execution Time Beyond Sum of Costs
CADENCE：超越成本总和的实际多智能体路径规划执行时间预测
Abstract
Multi-Agent Path Finding (MAPF) algorithms are increasingly used to plan motion for robot teams in industrial warehouses and robotic shared workspaces, but standard MAPF algorithm evaluation metrics, such as Sum of Costs (SoC), makespan, and planner runtime, can obscure how planner choices translate into realistic execution performance. We present CADENCE (Coordination and Action-Driven Estimation for Networked Continuous Execution), a hardware study of this evaluation gap on a fixed 7 by 7 workcell with seven differential drive robots, asking which features available before execution can best predict final wall-clock completion time. We compare SoC, total planned travel cost, primitive motion burden (how much basic motion the plan requires, such as makespan, turns, consecutive moves, and start-stop transitions), and interaction aware coordination structure (how much inter-robot coordination the plan induces, such as dependency links, interacting robot pairs, dependency depth, and crowding exposure). To test this, we generate 120 plans across 15 scenarios -- 5 Empty, 5 Medium Random, and 5 Bottleneck and execute each plan four times, yielding a 480 trial hardware corpus. Using both a scenario-held -- out ridge model and a trial-level mixed-effects model, we find that SoC alone is informative but incomplete, while primitive motion burden gives the strongest improvement, reducing held out error by about 48.6%-59.8% in MAE and 44.2%-61.4% in RMSE relative to SoC-only models. Interaction-aware coordination features add smaller, less uniform gains, most clearly in the mixed-effects analysis. Across both models and uncertainty checks, primitive motion burden is the most reliable additional signal beyond SoC, suggesting that much of the execution time gap is already visible in the offline plan before any robot starts moving.
Chinese Translation
多智能体路径规划（MAPF）算法在工业仓库和机器人共享工作空间中被越来越广泛地用于机器人团队的运动规划，但标准的MAPF算法评估指标，如成本总和（SoC）、完工时间和规划者运行时间，可能掩盖规划者选择如何转化为实际执行性能。我们提出了CADENCE（协调和基于动作的网络连续执行估算），在一个固定的7x7工作单元内使用七个差动驱动机器人对这一评估差距进行硬件研究，探讨在执行前哪些特征能够最好地预测最终的实际完成时间。我们比较了SoC、总计划旅行成本、原始运动负担（计划所需的基本运动量，如完工时间、转弯、连续移动和起止转换）以及考虑交互的协调结构（计划所诱发的机器人间协调程度，如依赖链接、交互机器人对、依赖深度和拥挤暴露）。为此，我们在15个场景中生成120个计划——5个空场景、5个中等随机场景和5个瓶颈场景，并对每个计划执行四次，得到480次试验的硬件数据集。通过一个场景保持的外推模型和一个试验级混合效应模型，我们发现SoC单独提供的信息是不完整的，而原始运动负担提供了最强的改进，相较于仅使用SoC模型，MAE的外推误差降低约48.6%-59.8%，RMSE降低约44.2%-61.4%。考虑交互的协调特征则提供了较小且不均匀的改善，尤其在混合效应分析中表现明显。在这两种模型及不确定性检查中，原始运动负担是超越SoC的最可靠额外信号，暗示在任何机器人开始移动之前，执行时间的差距已在离线计划中清晰可见。
cs.RO / 25 / 2606.04749
COP-Q: Safety-First Reinforcement Learning for Robot Control via Cholesky-Ordered Projection
COP-Q：通过Cholesky有序投影进行机器人控制的安全优先强化学习
Abstract
Safe robot control requires maximizing return while satisfying safety constraints. In off-policy safe reinforcement learning, reward and safety Q-values are commonly learned by separate critic ensembles, with uncertainty handled independently for each objective. This objective-wise treatment neglects inter-objective correlation and can lead to overly conservative value estimates, thereby reducing sample efficiency. To address this issue, we propose Cholesky-Ordered Projection Q-learning (COP-Q), a safety-first method that incorporates inter-objective covariance into vector-valued Q-value estimation. COP-Q constructs a generalized confidence bound in the joint Q-value space and uses Cholesky factorization to encode objective priority in a sequential form. This preserves conservatism on safety while adaptively reducing excessive conservatism on the reward objective. The resulting estimate is used in both temporal-difference target computation and actor optimization. COP-Q incurs minimal computational overhead and is readily compatible with most existing deep Q-learning frameworks. Experiments on robot locomotion in Brax and safe navigation in Safety-Gymnasium, covering both hard- and soft-safety settings, demonstrate that COP-Q achieves strong safety performance together with competitive or improved sample efficiency relative to representative baselines.
Chinese Translation
安全的机器人控制需要在满足安全约束的同时最大化回报。在离策略安全强化学习中，奖励和安全Q值通常由独立的批评者集合分别学习，对每个目标的不确定性也独立处理。这种目标导向的处理方式忽视了目标间的相关性，可能导致过于保守的价值估计，从而降低样本效率。为了解决这一问题，我们提出了Cholesky有序投影Q学习（COP-Q），这是一种安全优先的方法，将目标间的协方差融入向量值Q值估计中。COP-Q在联合Q值空间中构建了一种广义置信界，并使用Cholesky分解以序列形式编码目标优先级。这种方法在确保安全保守性的同时，适应性地减少了对奖励目标的过度保守。所得的估计结果用于时间差分目标计算和演员优化。COP-Q带来的计算开销最小，并且与大多数现有的深度Q学习框架兼容。在Brax中的机器人运动和Safety-Gymnasium中的安全导航实验中，涵盖了硬安全和软安全设置，结果表明，COP-Q在强安全性能的同时，相对于代表性基线实现了具有竞争力或改进的样本效率。
cs.RO / 26 / 2606.04776
SoftPINCH: EMG-Driven Soft Exoskeleton Assistance for Finger Flexion and Grasping
SoftPINCH：基于肌电图驱动的软性外骨骼对手指屈曲和抓握的辅助
Abstract
Surface electromyography (sEMG) provides a non-invasive interface for detecting hand-movement intention and controlling wearable assistive devices. However, reliable EMG-driven hand assistance remains challenging because EMG signals are affected by noise, motion artifacts, electrode placement, muscle fatigue, and inter-subject variability. At the same time, many hand exoskeletons remain mechanically restrictive or bulky, limiting comfort and natural hand motion. This work presents SoftPINCH, an EMG-driven soft wearable exoskeleton for thumb-index finger flexion and pinch grasp assistance. The system combines a tendon-driven soft exoskeleton, fingertip magnetic contact sensing, and neural EMG decoding for intention-based assistance. Surface EMG was recorded from forearm muscles during index and thumb movements, and three subject-independent decoding architectures were evaluated: LSTM, CNN+LSTM, and CNN+LSTM with attention. The CNN+LSTM and CNN+LSTM-attention models both achieved 99.4% LOSO test accuracy, outperforming the standalone LSTM, which reached 97.8%. However, the attention mechanism did not provide a significant improvement over CNN+LSTM, indicating that CNN-based feature extraction was sufficient for robust EMG representation. The CNN+LSTM model was therefore selected for real-time deployment due to its high accuracy and lower architectural complexity. Functional evaluation showed that active exoskeleton assistance reduced muscular effort during isolated finger flexion and object grasping. During weighted grasping, assistance reduced muscular effort across all tested loads, with a 92.6% reduction at the highest load. These results demonstrate the potential of SoftPINCH for intuitive, low-effort pinch assistance using real-time EMG-driven soft robotic control.
Chinese Translation
表面肌电图（sEMG）提供了一种非侵入性的接口，用于检测手部运动意图和控制可穿戴辅助设备。然而，由于肌电信号受到噪声、运动伪影、电极布置、肌肉疲劳和个体间变异性的影响，可靠的基于肌电图驱动的手部辅助依然具有挑战性。同时，许多手部外骨骼仍然在机械上具有限制性或笨重，从而限制了舒适性和自然手部运动。本研究提出了SoftPINCH，一种基于肌电图驱动的软性可穿戴外骨骼，旨在辅助拇指和食指的屈曲及夹持。该系统结合了腱驱动的软性外骨骼、指尖磁性接触传感和基于意图的神经肌电解码。在拇指和食指运动过程中，从前臂肌肉记录了表面肌电，并评估了三种不依赖于受试者的解码架构：长短期记忆网络（LSTM）、卷积神经网络加长短期记忆网络（CNN+LSTM）和带注意机制的卷积神经网络加长短期记忆网络（CNN+LSTM-attention）。CNN+LSTM和CNN+LSTM-attention模型均实现了99.4%的LOSO测试准确率，优于单一的LSTM（达到97.8%）。然而，注意机制并未对CNN+LSTM提供显著的改善，表明基于CNN的特征提取对于稳健的肌电图表示是足够的。因此，CNN+LSTM模型因其高准确度和较低的结构复杂性被选用于实时部署。功能评估显示，主动外骨骼辅助在独立手指屈曲和物体抓握过程中减少了肌肉负担。在加重抓握过程中，辅助在所有测试负荷下均降低了肌肉努力，在最高负荷下减少了92.6%。这些结果表明，SoftPINCH在使用实时肌电图驱动的软体机器人控制提供直观、低努力的夹持辅助方面具有潜力。
cs.RO / 27 / 2606.04818
Real-World Deployment of a 5G-Connected Edge-Controlled Aerial Robot in Industrial Subterranean Mines
工业地下矿井中5G连接的边缘控制无人机的现实世界部署
Abstract
This article presents the first real-world autonomous flight of a 5G-connected aerial robot controlled by an edge-offloaded controller, and aims to bridge the gap between controlled and factual setups. The robot operates within an active industrial subterranean mine, while the high-level controller is deployed in a nearby Kubernetes-based edge cluster. Communication between the robot and the edge is enabled via a 5G New Radio (NR) Standalone (SA) network. The chosen controller is a Model Predictive Controller (MPC), which generates control actions to allow the robot to navigate seamlessly through the mining environment. A human operator selects waypoints for the aerial robot, and the MPC generates smooth, collision-free paths for autonomous executions. The proposed 5G edge-based closed-loop system is evaluated in a real industrial setting and demonstrates the potential of edge-controlled robotic systems toward time-critical, safe and efficient future deployments.
Chinese Translation
本文呈现了首个由边缘卸载控制器控制的5G连接无人机在现实世界中的自主飞行，旨在填补控制设置与实际设置之间的差距。该机器人在一个活跃的工业地下矿井内工作，而高层控制器部署在附近的基于Kubernetes的边缘集群中。机器人与边缘之间的通信通过5G新无线电（New Radio, NR）独立（Standalone, SA）网络实现。所选控制器为模型预测控制器（Model Predictive Controller, MPC），其生成控制动作，使机器人能够在矿井环境中无缝导航。人工操作员为无人机选择航点，MPC生成平滑、无碰撞的路径以进行自主执行。所提议的基于5G的边缘闭环系统在真实的工业环境中进行了评估，展示了边缘控制机器人系统在未来时间关键、安全和高效部署方面的潜力。
cs.RO / 28 / 2606.04825
HapTile: A Haptic-Informed Vision-Tactile-Language-Action Dataset for Contact-Rich Imitation Learning
HapTile：一个触觉驱动的视觉-触觉-语言-动作数据集，用于接触丰富的模仿学习
Abstract
Despite the importance of tactile sensing for reliable manipulation, most existing Vision-Language-Action (VLA) datasets remain vision-only, and those that do incorporate tactile information typically lack the joint combination of task diversity, language conditioning, and action trajectories. Furthermore, existing teleoperation pipelines rarely provide haptic feedback to the operator, despite its established role in demonstration quality and manipulation stability. In this work, we present HapTile, a contact-grounded visuotactile manipulation dataset that advances beyond vision-only trajectory datasets by embedding physical interaction sensing at two levels: fingertip tactile feedback at the robot end-effector, and haptic-informed demonstrations at the teleoperator side. The data collection platform integrates haptic feedback directly into the teleoperation controller, enabling the operator to perceive contact interactions in real time. It is built around a standard and reproducible robotic system equipped with custom-designed fingertip tactile sensors. The dataset comprises everyday manipulation tasks spanning a broad range of contact-rich skills, including pick-and-place, folding, pressing, stacking, and other routine activities. Each task is paired with language instructions that condition the policy on the manipulation objective, together with synchronized visuotactile observations and action trajectories. In addition, we provide a benchmarking study on contact-rich policy learning using two baseline models to evaluate the effectiveness of the proposed contact-grounded dataset. The dataset and additional details are available on our website: haptile-dataset.github.io.
Chinese Translation
尽管触觉感知对可靠操作的重要性不言而喻，现有的大多数视觉-语言-动作（VLA）数据集仍然仅限于视觉，且那些包含触觉信息的数据集通常缺乏任务多样性、语言条件和动作轨迹的联合组合。此外，尽管触觉反馈在演示质量和操作稳定性中发挥着重要作用，现有的远程操作流程却很少向操作者提供触觉反馈。在本研究中，我们提出了HapTile，一个基于接触的视觉-触觉操控数据集，超越了仅有视觉轨迹的数据集，通过在两个层面上嵌入物理交互感知：机器人末端执行器的指尖触觉反馈，以及远程操作侧的触觉驱动演示。数据收集平台将触觉反馈直接集成到远程操作控制器中，使操作者能够实时感知接触交互。该平台建立在一个标准且可重复的机器人系统之上，配备定制设计的指尖触觉传感器。数据集包含日常操控任务，涵盖范围广泛的接触丰富技能，包括拿取和放置、折叠、按压、堆叠及其他常规活动。每个任务都配有语言指令，以对操控目标进行策略条件设定，同时提供同步的视觉-触觉观测和动作轨迹。此外，我们提供了一项关于接触丰富策略学习的基准研究，使用两个基线模型来评估所提议的基于接触的数据集的有效性。数据集和附加细节可在我们的网站上找到：haptile-dataset.github.io。
cs.RO / 29 / 2606.04829
M3imic: Learning a Versatile Whole-Body Controller for Multimodal Motion Mimicking
M3imic：学习一种多功能全身控制器以实现多模态运动模仿
Abstract
Building a general-purpose whole-body controller is essential for enabling diverse motion capabilities in humanoid robots across a wide range of downstream tasks, including locomotion and loco-manipulation. Different tasks rely on distinct motion reference modalities: locomotion primarily depends on coordinated robot joint trajectories, whereas manipulation requires precise end-effector trajectory tracking. Existing methods often overlook the representational mismatch between dense robot joint angles and sparse end-effector poses. To address this, we propose Multi-Modal Mimic (M3imic), a versatile multi-modal whole-body control framework that unifies heterogeneous motion reference modalities, including robot joint angles, human pose trajectories, and end-effector poses, using modality-specific encoders to map them into a shared latent space. Leveraging large-scale reinforcement learning in the simulator, we train a single policy that achieves sim-to-real transfer across multiple motion reference modalities without modality-specific retraining. Extensive simulation and real-world experiments on the Unitree G1 robot are conducted to evaluate the proposed framework. In simulation, the policy achieves a peak success rate of 98.42\% on an unseen test dataset, demonstrating its exceptional generalization capability. The code is available at https://github.com/Renforce-Dynamics/MultiModalWBC
Chinese Translation
构建一个通用的全身控制器对于使类人机器人在包括移动和运动操作在内的各种下游任务中具备多样的运动能力至关重要。不同任务依赖于不同的运动参考模态：运动主要依赖于协调的机器人关节轨迹，而操作则需要精确的末端执行器轨迹跟踪。现有方法常常忽视了密集的机器人关节角度与稀疏的末端执行器位姿之间的表示不匹配。为了解决这一问题，我们提出了多模态模仿（Multi-Modal Mimic，M3imic），这是一种多功能的多模态全身控制框架，将包括机器人关节角、人体姿态轨迹和末端执行器姿态在内的异构运动参考模态统一，通过模态特定的编码器将它们映射到共享的潜在空间。利用大规模强化学习在模拟器中，我们训练了一个单一策略，实现了在多个运动参考模态之间的仿真到现实转移，而无需特定模态的再训练。在Unitree G1机器人的广泛模拟和真实世界实验中对所提框架进行了评估。在模拟中，该策略在一个未见测试数据集上达到了98.42%的峰值成功率，展现了其卓越的泛化能力。代码可在 https://github.com/Renforce-Dynamics/MultiModalWBC 获取。
cs.RO / 30 / 2606.04853
Teaching Robots to Say 'I Don't Know' : SENTINEL for Uncertainty-Aware SLAM
教会机器人说 '我不知道'：用于不确定性感知的SLAM的SENTINEL
Abstract
Low-cost 2D LiDARs lack the intensity channel that higher-end sensors use to diagnose measurement failures, yet they are widely used on educational and budget robotics platforms. We present SENTINEL, a training - free, label - free reliability estimation framework that gives range - only LiDAR an effective diagnostic signal. SENTINEL combines geometry-based scan statistics with cross - modal depth consistency between LiDAR and an RGB - D camera to compute a per - scan reliability score between 0 and 1. When the score falls below a threshold, corrupted scans are rejected and the robot falls back to calibrated wheel odometry, preventing silent SLAM corruption. We evaluate SENTINEL on a GEFIER R1 four - wheel skid-steer robot equipped with an RPLidar A2M12 and an Intel RealSense D435i in a 185 cm by 245 cm arena containing controlled transparent and reflective failure elements on a central obstacle. Spatial reliability maps across five surface conditions, including glass, mirror, shiny paper, and a mixed mirror and shiny-paper condition, show clear separation between clean and failure cases, allowing affected regions to be identified as reject or noise. Because these failure modes are absent in simulation, validation is performed entirely on real hardware.
Chinese Translation
低成本的2D激光雷达缺乏高端传感器用于诊断测量失败的强度通道，但它们在教育和预算机器人平台上广泛使用。我们提出了SENTINEL，这是一种无需训练和标签的可靠性估计框架，为仅具测距功能的激光雷达提供有效的诊断信号。SENTINEL结合了基于几何的扫描统计与激光雷达和RGB-D相机之间的跨模态深度一致性，计算每次扫描的可靠性评分，范围在0到1之间。当评分低于阈值时，受损的扫描被拒绝，机器人则退回到经过校准的轮式里程计，从而防止了无声的SLAM失效。我们在一个配置有RPLidar A2M12和Intel RealSense D435i的GEFIER R1四轮滑移转向机器人上评估了SENTINEL，实验场地为185厘米×245厘米，包含在中央障碍物上的控制透明和反射故障元素。跨越包括玻璃、镜子、光面纸，以及混合镜子与光面纸条件的五种表面状态的空间可靠性地图显示出了清晰的干净和故障案例的分离，使受影响区域能够被识别为拒绝或噪声。由于这些故障模式在仿真中不存在，验证工作完全在真实硬件上进行。
cs.RO / 31 / 2606.04884
D$^3$-MoE:Dual Disentangled Diffusion Mixture-of-Experts for Style-Controllable End-to-End Autonomous Driving
D$^3$-MoE：用于风格可控的端到端自主驾驶的双去耦扩散专家混合模型
Abstract
Traditional end-to-end autonomous driving frameworks frequently suffer from the "style-averaging" dilemma when trained on high-variance human demonstrations, yielding homogenized, style-uncontrollable, and even kinematically unsafe policies. To overcome this limitation, we present D$^3$-MoE (Dual Disentangled Diffusion Mixture-of-Experts), which disentangles trajectory modeling along two complementary axes. On the behavioral axis, generation is decoupled from selection: a style-conditioned diffusion process synthesizes multi-style candidate trajectories in parallel within a single scene, allowing a downstream module to select the optimal trajectory based on user preference or an evaluation score. On the physical axis, decoupled longitudinal and lateral routers activate their respective experts during inference time, trained without manual labels using self-supervised targets from orthogonal ground-truth kinematics. These activated experts, architected as Diffusion Transformers (DiT) and equipped with style-conditioned AdaLN and asymmetric lateral-fusion cross-attention, independently predict their corresponding physical state before being reassembled into a unified, kinematically coherent trajectory. Extensive evaluations on the challenging NAVSIM benchmark demonstrate that D$^3$-MoE achieves state-of-the-art planning performance, reaching 88.2 PDMS and 84.3 EPDMS by default. Moreover, our Best-of-Three ensemble strategy effectively broadens the multi-modal solution space, raising performance to 91.3 PDMS and 87.5 EPDMS. Both quantitative and qualitative analyses jointly confirm the framework's advantages in planning quality and style controllability.
Chinese Translation
传统的端到端自主驾驶框架在基于高方差人类示例进行训练时常常面临 "风格均值化" 的困境，导致生成同质化、不可控风格，甚至在运动学上不安全的策略。为了解决这一局限性，我们提出了D$^3$-MoE（双去耦扩散专家混合模型），该模型沿两个互补轴解耦轨迹建模。在行为轴上，生成与选择相解耦：通过风格条件的扩散过程在单个场景中并行合成多种风格的候选轨迹，使下游模块能够根据用户偏好或评估得分选择最佳轨迹。在物理轴上，去耦的纵向和横向路由器在推理时激活各自的专家，这些专家无需手动标签，利用来自正交真实运动学的自监督目标进行训练。这些被激活的专家构建为扩散变压器（Diffusion Transformers, DiT），并配备风格条件的自适应层归一化（AdaLN）和不对称横向融合交叉注意力，独立预测各自的物理状态，然后重新组装成一个统一的、运动学一致的轨迹。在具有挑战性的NAVSIM基准上的大量评估表明，D$^3$-MoE实现了最先进的规划性能，默认情况下达到88.2 PDMS和84.3 EPDMS。此外，我们的三者最佳集成策略有效拓宽了多模态解空间，将性能提升至91.3 PDMS和87.5 EPDMS。定量和定性分析共同确认了该框架在规划质量和风格可控性方面的优势。
cs.RO / 32 / 2606.04907
WAM-Nav: Asymmetric Latent World-Action Modeling for Unified Visual Navigation
WAM-Nav：用于统一视觉导航的非对称潜在世界-行动建模
Abstract
Visual navigation requires generating smooth and collision-free trajectories under complex geometric and physical constraints. Existing reactive policies that directly map observations to actions lack anticipatory reasoning, limiting their ability to proactively avoid obstacles. While visual imagination offers predictive foresight, conventional modular approaches separate scene prediction from policy learning, often leading to error accumulation and inefficient inference. To address these limitations, we propose WAM-Nav, a Latent World-Action Model for embodied visual navigation that jointly learns action generation and latent visual foresight, enabling more robust and foresighted navigation decisions without compromising inference efficiency. Specifically, WAM-Nav utilizes a shared Diffusion Transformer for asymmetric joint diffusion to concurrently generate long-horizon actions and short-horizon visual foresight, reducing the inference latency and visual error accumulation inherent in multi-step autoregressive rollouts. To further encourage smooth and consistent trajectory generation, we introduce a dual-stream contextual conditioning mechanism that integrates episode-level ego-motion history with sequential visual observations. Combined with a unified goal alignment module that preserves balanced representations across goal types, WAM-Nav naturally supports Image-Goal, Point-Goal, and No-Goal exploration within a single policy. Extensive experiments on the challenging ClutterScenes and InternScenes benchmarks demonstrate strong generalization of WAM-Nav, particularly on Image-Goal and Point-Goal navigation, where it improves success rates by 15.7% and 3.3%, respectively. Real-world deployment further validates effective zero-shot sim-to-real transfer, achieving an average 85% task success rate across diverse indoor and outdoor environments.
Chinese Translation
视觉导航需要在复杂的几何和物理约束下生成平滑且无碰撞的轨迹。现有的反应性策略直接将观察映射到行动，缺乏预判推理，限制了其主动避障的能力。尽管视觉想象提供了预测前瞻，传统的模块化方法将场景预测与策略学习分开，往往导致错误累积和推理效率低下。为了解决这些局限性，我们提出了WAM-Nav，一个用于体现式视觉导航的潜在世界-行动模型，它共同学习行动生成和潜在视觉前瞻，使得导航决策更加稳健且具前瞻性而不妥协推理效率。具体而言，WAM-Nav利用共享的扩散变换器进行非对称联合扩散，同时生成长时域行动和短时域视觉前瞻，减少了多步自回归展开中固有的推理延迟和视觉错误累积。为了进一步鼓励平滑且一致的轨迹生成，我们引入了一种双流上下文条件机制，将情节级自我运动历史与序列视觉观察相结合。结合一个统一的目标对齐模块，保持不同目标类型之间的平衡表示，WAM-Nav自然而然地在单一策略中支持图像目标（Image-Goal）、点目标（Point-Goal）和无目标（No-Goal）探索。在挑战性的ClutterScenes和InternScenes基准上的大量实验表明，WAM-Nav具有强大的泛化能力，特别是在图像目标和点目标导航中，成功率分别提高了15.7%和3.3%。实际部署进一步验证了有效的零次模拟到现实转移，在多样化的室内和室外环境中实现了平均85%的任务成功率。
cs.RO / 33 / 2606.04968
Potential-Guided Flow Matching for Vision-Language-Action Policy Improvement
基于潜在引导的流匹配用于视觉-语言-动作策略改进
Abstract
Large vision-language-action (VLA) policies are increasingly trained as conditional generative models over action chunks. Yet deployment produces mixed-quality experience-successful demonstrations, partial completions, recoverable mistakes, and failures-that is difficult to use with standard imitation. Full behavior cloning (BC) imitates failures, filtered BC discards useful sub-trajectories, and offline reinforcement learning adds a large critic. We introduce ForesightFlow, a self-guided flow-matching policy that augments each generated action chunk with a learned success-potential trajectory. The same flow proposes and scores candidate actions, enabling best-of-$K$ inference without an external critic. The key issue is that policy improvement and value calibration require different supervision: advantage weighting should emphasize high-quality actions, but applying the same weights to potential coordinates suppresses failure gradients and creates overconfident scores. We address this with decoupled advantage-weighted flow matching, applying exponentiated advantage weights only to action velocities while training potential velocities uniformly. We further derive a one-step boundary estimator for conditional flow matching, allowing advantage computation with a single stop-gradient forward pass. Across five BEHAVIOR-1K simulation tasks and five real-world bimanual tasks, ForesightFlow improves over imitation baselines, matches the strongest separate-critic baseline in simulation success, improves real-world success, and reduces training compute by $38\%$. Ablations show that decoupling prevents value hallucination, the one-step estimator preserves candidate-ranking fidelity, and self-guided sampling improves long-horizon execution.
Chinese Translation
大型视觉-语言-动作（VLA）策略越来越多地作为动作块的条件生成模型进行训练。然而，部署过程中产生了混合质量的经验——成功示范、部分完成、可恢复的错误和失败——这些都难以与标准模仿相结合。完整的行为克隆（BC）模仿失败，而过滤后的 BC 则舍弃有用的子轨迹，离线强化学习则增加了一个大型评论者。我们引入了 ForesightFlow，一种自我引导的流匹配策略，该策略通过学习的成功潜力轨迹来增强每个生成的动作块。相同的流会提出并评分候选动作，从而实现无外部评论者的最佳-K推断。关键问题是策略改进和价值校准需要不同的监督：优势加权应强调高质量的动作，但将相同的权重应用于潜在坐标则会抑制失败梯度并导致过于自信的评分。我们通过解耦的优势加权流匹配来解决这一问题，仅将指数化的优势权重应用于动作速度，同时均匀训练潜在速度。我们进一步推导出一个条件流匹配的一步边界估计器，允许通过单次停止梯度向前传播计算优势。在五个 BEHAVIOR-1K 模拟任务和五个真实双手任务中，ForesightFlow 超过了模仿基准，在模拟成功中匹配最强的单独评论者基准，改善了真实世界的成功率，并减少了训练计算量达38%。消融实验表明，解耦可以防止价值幻觉，一步估计器保持候选排名的保真度，自我引导采样改善了长时间执行的效果。
cs.RO / 34 / 2606.05015
Generalization of World Models under Environmental Variability for Vision-based Quadrotor Navigation
环境变异下世界模型的推广用于基于视觉的四旋翼导航
Abstract
World models, learned generative models that predict how an environment evolves, have become a promising tool for sample-efficient robot learning. Yet how robust they are to environmental variability remains poorly understood. To address this, we conduct a systematic study using vision-based quadrotor navigation as a testbed problem, training DreamerV3-based world models under varying levels of environmental randomness and evaluating them across all levels through cross-environment validation, spanning both Self-Supervised Learning (SSL) pretraining and Reinforcement Learning (RL) fine-tuning. We then deploy all world models and associated navigation policies on a real quadrotor in unseen environments, including an open-loop run where the model receives just 2.5s of real sensory input before all sensors are cut off, leaving the system to navigate entirely in imagination over a 12m traverse. Our results show that world model robustness during SSL pretraining is a strong predictor of sim-to-real transfer: every model that generalized well in cross-environment SSL validation deployed successfully in the real world, passing through gaps as narrow as 0.67m, whereas the model that dominated simulation policy evaluation failed on the real platform. We further identify (a) the discrete latent size and (b) the training-sequence length as the dominant factors governing world model quality.
Chinese Translation
世界模型是学习的生成模型，能够预测环境的演变，已成为一种有前景的样本高效机器人学习工具。然而，它们对环境变异的鲁棒性仍然缺乏深入了解。为了解决这一问题，我们使用基于视觉的四旋翼导航作为测试问题进行系统研究，基于DreamerV3训练世界模型，测试不同级别的环境随机性，并通过跨环境验证评估这些模型在自监督学习（Self-Supervised Learning, SSL）预训练和强化学习（Reinforcement Learning, RL）微调中的表现。然后，我们将所有世界模型和相关的导航策略部署到真实的四旋翼无人机上，测试未见过的环境，包括一个开放循环的实验，模型仅接收2.5秒的真实传感器输入后所有传感器被切断，系统在12米的行程中完全依赖想象进行导航。我们的结果表明，世界模型在SSL预训练过程中的鲁棒性是对模拟到现实转移的强预测指标：在跨环境SSL验证中表现良好的每个模型都成功部署到现实世界中，能够通过宽度仅为0.67米的狭缝，而在模拟策略评估中占主导地位的模型在真实平台上却失败。我们进一步识别出（a）离散潜变量大小和（b）训练序列长度是影响世界模型质量的主要因素。
cs.RO / 35 / 2606.05143
HORIZON: Recoverability-Governed Curriculum for Physical-Domain Scaling
HORIZON：以可恢复性为主导的物理领域扩展课程
Abstract
Scaling robust robot policies requires more than broader randomization, because physical-domain experience must remain organized and learnable throughout training. We study when a policy can benefit from harder physics and identify recoverability as a central constraint in on-policy physical-domain scaling. In on-policy training, new dynamics are useful only insofar as they remain close enough to the current policy to generate corrective on-policy data, rather than collapsing rollouts into unrecoverable failures. Using quadruped locomotion as a physically demanding benchmark for embodied generalization, we introduce HORIZON, a checkpointed frontier curriculum that expands physical domains only within the current policy's recoverable boundary. HORIZON uses rollback and boundary refinement to govern each expansion step, turning fixed randomization into a continual process of physical-domain growth. Experiments reveal three regularities of physical-domain expansion. First, direct domain widening is uneven across physical axes and often unlearnable without staged ordering. Second, domain composition is non-monotonic, and adding more domains beyond a compact core can dilute recoverable joint samples and reduce overall robustness. Third, offline distillation of isolated experts cannot substitute for the joint interaction generated by on-policy curriculum. Together, these results frame physical-domain generalization as a continual growth problem for embodied control, with recoverability as the organizing principle for on-policy expansion.
Chinese Translation
扩展稳健的机器人策略不仅需要更广泛的随机化，因为物理领域的经验在训练过程中必须保持有序和可学习。我们研究了一个策略何时可以从更复杂的物理中受益，并将可恢复性确定为在线政策物理领域扩展中的一个核心约束。在在线训练中，新动态的有效性仅限于它们与当前策略保持足够的接近，以生成可纠正的在线数据，而不是将回放崩溃为不可恢复的失败。以四足步态作为具身泛化的物理挑战基准，我们引入了HORIZON，这是一个检查点的前沿课程，仅在当前策略的可恢复边界内扩展物理领域。HORIZON利用回滚和边界优化来指导每一步扩展，将固定随机化转变为物理领域增长的持续过程。实验揭示了物理领域扩展的三个规律。首先，直接的领域拓宽在物理轴上是不均匀的，且往往在没有分阶段排序的情况下不可学习。其次，领域组合是非单调的，超出紧凑核心的更多领域可能会稀释可恢复的联合样本并减少整体稳健性。第三，将孤立专家的离线蒸馏替代不了在线课程产生的联合交互。综合这些结果，将物理领域泛化框架看作具身控制的持续增长问题，以可恢复性作为在线扩展的组织原则。
cs.RO / 36 / 2606.05159
X4Val: Learning Neural Surrogates for Variance-Reduced Policy Evaluation
X4Val：用于方差减少的策略评估的神经代理学习
Abstract
Rigorous evaluation of learning-based robotic systems is an essential prerequisite for deployment. However, real-world test data is expensive to gather; moreover, in a typical iterative development context, data gathered from the latest policy is necessarily limited in scale. This motivates evaluation methodologies that make use of heterogeneous data sources, including simulation, historical policy logs, and data collected from related platforms or environments. While such auxiliary data are abundant and inexpensive, they are generally not directly representative of real-world outcomes -- for example, performance in simulation may differ substantially from performance in the real world -- making their principled use for high-confidence performance estimation challenging. In this paper, we introduce X4Val, a general framework for variance-reduced real-world metric estimation in the presence of non-paired, multi-domain data. X4Val embeds samples from real and auxiliary domains into a shared representation space and learns a transferable predictor of real-world metrics; this learned predictor is then incorporated into a control-variates estimator, enabling variance reduction even when paired samples are unavailable. We provide theoretical analysis and empirical evaluations on autonomous driving and real-world robot manipulation tasks, domains across which X4Val achieves up to 38.4% variance reduction and demonstrates consistent improvements over strong baselines. These results show that non-paired, heterogeneous data can be leveraged to substantially improve the sample efficiency of rigorous robotic system validation.
Chinese Translation
对基于学习的机器人系统进行严格评估是部署的必要前提。然而，收集真实世界的测试数据代价昂贵；此外，在典型的迭代开发环境中，从最新策略收集的数据在规模上必然有限。这就激励了利用异构数据源的评估方法，包括模拟、历史策略日志以及从相关平台或环境收集的数据。尽管这类辅助数据丰富且成本低廉，但它们通常并不直接代表真实世界的结果——例如，模拟中的表现可能与真实世界中的表现大相径庭——这使得以原则性的方法针对高可靠性表现估计变得具有挑战性。在本文中，我们介绍了 X4Val，一个用于在存在非配对的多域数据情况下进行方差减少的真实世界指标估计的通用框架。X4Val 将来自真实和辅助域的样本嵌入到共享表示空间中，并学习一个可迁移的真实世界指标预测器；然后将此学习到的预测器纳入控制变量估计器，即使在没有配对样本的情况下也能够实现方差减少。我们提供了理论分析和在自主驾驶以及真实世界机器人操作任务上的经验评估，结果表明 X4Val 在多个领域中实现了高达 38.4% 的方差减少，并在强基线之上显示出一致的改进。这些结果表明，非配对的异构数据可以被利用，以显著提高严格的机器人系统验证的样本效率。
cs.RO / 37 / 2606.05160
GRAIL: Generating Humanoid Loco-Manipulation from 3D Assets and Video Priors
GRAIL：从3D资产和视频先验生成类人运动-操作
Abstract
Scaling humanoid loco-manipulation requires robot-compatible demonstrations across diverse objects, whole-body motions, and scene geometries, but teleoperation and motion capture are difficult to scale because each collection depends on physical setups, instrumented actors, and robot operation. We present GRAIL, a digital generation pipeline that remains fully virtual until deployment: it composes 3D assets, simulator-ready scenes, and priors from video foundation models (VFMs) to synthesize interactions without rebuilding physical environments or teleoperating the robot. Rather than reconstructing unconstrained in-the-wild videos, GRAIL starts from fully specified 3D configurations in which object geometry, camera parameters, metric scale, environment depth, and a robot-proportioned character are known before video generation and reused during reconstruction. This privileged setup better conditions 4D recovery, allowing model-based object tracking, human motion estimation, and interaction-aware optimization to reconstruct metric 4D human-object interaction (HOI) trajectories with reduced depth ambiguity and morphology mismatch. We retarget the recovered motions to a humanoid robot and train complementary task-general trackers: an object-aware latent adaptor for manipulation and a scene-aware tracker for terrain traversal. GRAIL produces over 20,000 sequences spanning pick-up, object manipulation, sitting, and terrain traversal. Using only GRAIL-generated data, we train egocentric visual policies through a sim-to-real pipeline and deploy them on a Unitree G1 humanoid, achieving 84\% real-world success on diverse object pick-up and 90\% success on stair-climbing.
Chinese Translation
类人运动-操作的扩展需要在各种物体、全身运动和场景几何体之间进行与机器人兼容的演示，但由于每个集合依赖于物理设置、配备传感器的执行者和机器人的操作，远程操作和运动捕捉难以扩展。我们提出GRAIL，这是一个在部署之前保持完全虚拟的数字生成管道：它组合3D资产、模拟器就绪场景和来自视频基础模型（VFM）的先验，以合成交互，而无需重建物理环境或远程操作机器人。GRAIL不是重建不受约束的野外视频，而是从完全指定的3D配置开始，在这些配置中，物体几何、相机参数、度量比例、环境深度和与机器人比例匹配的角色在视频生成之前已知，并在重建过程中重新使用。这种特权设置更好地确定了4D恢复，允许基于模型的物体跟踪、人类运动估计和交互感知优化，以重建度量4D人机交互（HOI）轨迹，从而减少深度歧义和形态不匹配。我们将恢复的运动重新定向到类人机器人，并训练补充的任务通用跟踪器：用于操作的物体感知潜在适配器和用于地形穿越的场景感知跟踪器。GRAIL生成了超过20,000个序列，涵盖撬动、物体操作、坐下和地形穿越。仅使用GRAIL生成的数据，我们通过模拟到真实的管道训练自我中心的视觉策略，并将其部署在Unitree G1类人机器人上，在多样化的物体拾取任务中实现了84%的现实世界成功率，在爬楼梯任务中实现了90%的成功率。
cs.CV / 1 / 2606.04046
Dive into the Scene: Breaking the Perceptual Bottleneck in Vision-Language Decision Making via Focus Plan Generation
深入场景：通过生成集中计划突破视觉-语言决策中的感知瓶颈
Abstract
In embodied vision-language decision making tasks such as robotic manipulation and navigation, Vision-Language and Vision-Language-Action Models (VLMs & VLAs) are powerful tools with different benefits: VLMs are better at long-term planning, while VLAs are better at reactive control. However, their performance is limited by the same perceptual bottleneck: visual hallucinations arise due to the models' inability to distinguish task-relevant objects from distractors. In principle, accurate identification and focus on critical objects while filtering out irrelevant ones is the key to break this limitation. A straightforward solution is one-step focus: directly attending to essential objects. However, this approach proves ineffective because effective focus inherently requires deep scene understanding. To this end, we propose SceneDiver, a coarse-to-fine focus plan generation method for VLMs leveraging their long-term planning abilities, that first constructs a holistic scene graph to establish initial comprehension, then progressively decomposes the task into simpler sub-problems through an iterative cycle of recognition, understanding, and analysis. To enable reactive control, we also design a lightweight adapter for distilling the deliberate focus ability into VLAs. Evaluations on standard embodied AI benchmarks confirm that our method substantially reduces visual hallucinations for both VLMs and VLAs, while preserving computational efficiency in tasks requiring fast execution. Our code and data are released at: https://future-item.github.io/SceneDiver.
Chinese Translation
在机器人操作和导航等具体的视觉-语言决策任务中，视觉-语言模型（VLMs）和视觉-语言-行动模型（VLAs）是具有不同优势的强大工具：VLMs在长期规划方面表现更佳，而VLAs在反应控制方面更为出色。然而，它们的性能受到相同感知瓶颈的限制：由于模型无法区分与任务相关的物体与干扰物，导致出现视觉幻觉。原则上，准确识别并专注于关键物体，同时过滤掉无关物体，是突破这一限制的关键。一个简单的解决方案是一步聚焦：直接关注必要物体。然而，这一方法被证明无效，因为有效的专注本质上需要深入的场景理解。为此，我们提出了SceneDiver，一种针对VLMs的粗到细的聚焦计划生成方法，利用其长期规划能力，首先构建整体场景图以建立初步理解，然后通过识别、理解和分析的迭代循环逐步将任务分解为更简单的子问题。为了实现反应控制，我们还设计了一个轻量级适配器，以将深思熟虑的聚焦能力提炼到VLAs中。在标准的具体人工智能基准测试中的评估结果确认，我们的方法在不牺牲计算效率的前提下，显著减少了VLMs和VLAs的视觉幻觉，同时保持快速执行任务所需的计算效率。我们的代码和数据已发布于：https://future-item.github.io/SceneDiver。
cs.CV / 2 / 2606.04060
Weakly Supervised Incremental Segmentation via Semantic Anchors and Spatial Arbitration
通过语义锚点和空间仲裁实现弱监督增量分割
Abstract
Weakly Incremental Learning for Semantic Segmentation (WILSS) suffers from the continuous introduction of noisy supervision, which progressively corrupts class-level representations, leading to severe feature drift and semantic corruption, thereby causing newly learned classes to overwrite old ones. To address these issues, we propose a drift-resilient WILSS approach, named SASA, designed to stabilize semantic learning via Semantic Anchors and Spatial Arbitration. Specifically, at the representation level, we introduce semantic anchors of learnable tokens as rigid class-level references to preserve long-term semantic identity. Complementary to this, an elastic residual adaptation facilitates controlled, instance-specific refinement, ensuring a stable yet flexible learning trajectory. At the supervision level, we develop a Spatial Label Arbitration mechanism that performs geometry-aware decisions to directly filter unreliable signals and enforce a strict "one object, one class" constraint. By synergistically stabilizing representations and improving supervision reliability, SASA effectively mitigates feature drift under weak supervision. Extensive experiments on standard benchmarks demonstrate that our approach consistently outperforms existing state-of-the-art methods, particularly in challenging multi-step incremental settings. The code is available at https://github.com/ZhonggaiWang/SASA.
Chinese Translation
弱监督增量学习在语义分割（WILSS）中受到持续引入的噪声监督的影响，这逐渐破坏了类别级的表征，导致严重的特征漂移和语义损坏，从而造成新学习的类别覆盖旧类别。为了解决这些问题，我们提出了一种抗漂移的WILSS方法，命名为SASA，旨在通过语义锚点和空间仲裁来稳定语义学习。具体而言，在表征层面，我们引入可学习的语义锚点作为刚性的类别级参考，以保持长期的语义身份。与此相辅相成的是，一种弹性残差适应机制促进了受控的实例特定优化，确保稳定而灵活的学习轨迹。在监督层面，我们开发了一种空间标签仲裁机制，进行几何感知决策，以直接过滤不可靠信号并强制执行严格的“一个物体，一个类别”约束。通过协同稳定表征和提高监督的可靠性，SASA有效地缓解了在弱监督下的特征漂移。在标准基准上的大量实验表明，我们的方法始终优于现有的最先进方法，特别是在挑战性的多步骤增量设置中。代码可在 https://github.com/ZhonggaiWang/SASA 查阅。
cs.CV / 3 / 2606.04061
Intra-Modal Neighbors Never Lie: Rectifying Inter-Modal Noisy Correspondence via Graph-Based Intra-Modal Reasoning
同模邻域从不撒谎：通过基于图的同模推理纠正跨模态噪声对应关系
Abstract
Large-scale web-harvested datasets have fueled the progress of cross-modal retrieval but inevitably suffer from noisy correspondence, which severely degrades model generalization. Existing methods primarily address this by filtering out noise or seeking a substitute label, yet they predominantly remain bound by a "Discrete Selection" paradigm. We argue that relying on a single discrete proxy induces Single-Point Fragility and Discretization Error. To overcome these limitations, we propose a novel framework, Intra-modal Neighbor-aware Noise Rectification (IN2R), which shifts the paradigm from searching for a substitute to synthesizing a reliable supervision target. Leveraging the intrinsic geometric stability of intra-modal data, IN2R employs a Graph Refiner to perform relational reasoning over neighbors retrieved from a dynamic Cross-Model Memory. Instead of propagating discrete labels, our method synthesizes a continuous, soft prototype that reflects the consensus of the local semantic neighborhood, effectively rectifying inter-modal misalignment. Extensive experiments on Flickr30K, MS-COCO, and CC152K demonstrate that IN2R significantly outperforms state-of-the-art methods. Our code and pre-trained models are publicly available at https://github.com/liuyyy111/IN2R.
Chinese Translation
大规模网络采集的数据集促进了跨模态检索的发展，但不可避免地遭遇噪声对应问题，这严重损害了模型的泛化能力。现有的方法主要通过过滤噪声或寻找替代标签来解决这一问题，但它们依然主要受到“离散选择”范式的限制。我们认为，依赖单一离散代理会导致单点脆弱性和离散化误差。为克服这些局限，我们提出了一种新颖的框架——同模邻域警觉噪声纠正（Intra-modal Neighbor-aware Noise Rectification, IN2R），该框架将范式从寻找替代品转变为综合生成可靠的监督目标。利用同模数据的内在几何稳定性，IN2R采用图细化器对来自动态跨模型记忆的邻居进行关系推理。我们的方法合成一个连续的软原型，而不是传播离散标签，从而反映局部语义邻域的一致性，有效纠正跨模态错位。在Flickr30K、MS-COCO和CC152K数据集上的大量实验表明，IN2R显著优于最先进的方法。我们的代码和预训练模型已公开发布，网址为https://github.com/liuyyy111/IN2R。
cs.CV / 4 / 2606.04092
Optimal Transport Flow Matching by Design
通过设计实现最优传输流匹配
Abstract
Flow matching models learn to transport samples from a simple prior distribution to a complex data distribution. When prior-data pairs are coupled via optimal transport (OT), the learned trajectories are straight and non-crossing, enabling fast, even single-step, generation. However, computing the OT coupling in high dimensions is intractable, and existing methods attempt to solve the OT problem, at the cost of persistent bias or significant overhead. Rather than solving for the OT coupling, we reformulate the problem. Once the prior is treated as a design choice rather than a fixed input, the OT coupling between prior and data is no longer unique. Many priors admit an OT-optimal identity coupling to the data, leaving us free to choose one that is also tractable to sample. We identify low-frequency projection of natural images as such a choice. The identity coupling between data and its low-frequency representation is empirically OT-optimal, the prior is structured enough to be sampled by a lightweight model at inference, and the remaining flow-matching task reduces to synthesizing high-frequency detail. Interpolating the prior with Gaussian noise further improves generation quality while preserving the OT coupling. The approach requires no modifications to the flow model itself, and integrates naturally with latent-space models, classifier-free guidance, and one-step generation frameworks. Across all benchmarks, our method reduces trajectory curvature by more than $2\times$ compared to existing flow matching methods, yielding better generation quality in the few-step regime.
Chinese Translation
流匹配模型学习将样本从简单先验分布传输到复杂数据分布。当先验-数据对通过最优传输（Optimal Transport，OT）耦合时，学习到的轨迹是直线且不交叉的，从而实现快速甚至一步生成。然而，在高维中计算OT耦合是不可行的，现有方法试图解决OT问题，却会导致持久的偏差或显著的开销。我们并不是去解决OT耦合，而是对问题进行了重新表述。一旦将先验视为设计选择而非固定输入，先验与数据之间的OT耦合就不再是唯一的。许多先验允许与数据存在OT最优的恒等耦合，这使我们可以选择一个同时易于采样的耦合。我们识别出自然图像的低频投影作为这样一个选择。数据与其低频表示之间的恒等耦合在经验上是OT最优的，先验的结构足够，可以通过轻量模型在推理时进行采样，剩余的流匹配任务简化为合成高频细节。用高斯噪声插值先验进一步提高了生成质量，同时保持了OT耦合。该方法不需要对流模型本身进行修改，并与潜在空间模型、无分类器引导和一步生成框架自然结合。在所有基准测试中，我们的方法相比于现有流匹配方法将轨迹曲率减少了超过$2 imes$，在少步生成的情况下取得了更好的生成质量。
cs.CV / 5 / 2606.04098
When Seeing Is Not Believing -- A Benchmark for Search-Grounded Video Misinformation Detection
当视觉不再真实——搜索驱动的视频虚假信息检测基准
Abstract
Video misinformation increasingly operates at the semantic and evidential level: authentic footage may be selectively edited, temporally reordered, spliced across sources, or augmented with AI-generated content to construct false narratives. Such evidence-dependent manipulations cannot be reliably verified from the input video alone, because the missing, reordered, replaced, or recontextualized evidence lies outside the video itself. We introduce \textbf{EVID-Bench}, a benchmark for search-grounded video misinformation detection, where a system must search the open web for related videos and identify what information is false through cross-video comparison. EVID-Bench comprises 222 videos spanning 9 manipulation types across 3 categories: AI generation, single-source editing, and multi-source editing. All samples are verified to be undetectable by frontier models through visual inspection alone. We evaluate nine frontier multimodal models using a retrieval-augmented verification baseline. The best system achieves only 61.43\% point-level accuracy and 43.24\% video-level accuracy, while AI-generated manipulations remain especially challenging. Error analysis reveals recurring challenges: models fixate on irrelevant anchors, misattribute synthetic content to editorial splicing, and terminate search prematurely before fully explaining the manipulation.
Chinese Translation
视频虚假信息日益在语义和证据层面上运作：真实的画面可能被选择性编辑、时间顺序重新排列、跨来源拼接，或与人工智能生成的内容结合，以构建虚假叙事。此类依赖证据的操控不能仅通过输入视频可靠验证，因为缺失、重排、替代或重新语境化的证据超出了视频本身。我们推出了 extbf{EVID-Bench}，一个用于搜索驱动的视频虚假信息检测的基准，其中系统必须在开放网络中搜索相关视频，并通过跨视频比较识别虚假信息。EVID-Bench包含222个视频，涵盖3个类别的9种操控类型：人工智能生成、单一来源编辑和多来源编辑。所有样本经过验证，确认仅通过视觉检查无法被前沿模型检测。我们使用检索增强验证基线评估了九个前沿多模态模型。最佳系统仅达到61.43%的逐点准确率和43.24%的视频级准确率，而人工智能生成的操控仍然特别具有挑战性。错误分析揭示了反复出现的挑战：模型集中于无关锚点，错误地将合成内容归因于编辑拼接，并在未充分解释操控前过早终止搜索。
cs.CV / 6 / 2606.04107
Reflection Separation from a Single Image via Joint Latent Diffusion
通过联合潜在扩散从单幅图像中分离反射
Abstract
Single-image reflection separation is highly challenging under extreme conditions like glare or weak reflections. Existing methods often struggle to recover both layers in glare or weak-reflection scenarios because of insufficient information. This paper presents a diffusion model explicitly fine-tuned for this task, leveraging generative diffusion priors for robust separation. Our method simultaneously generates transmission and reflection layers through a unified diffusion model, incorporating a novel cross-layer self-attention mechanism for better feature disentanglement. We further introduce a disjoint sampling strategy to iteratively reduce interference between the layers during diffusion and a latent optimization step with a learned composition function for improved results in complex real-world scenarios. Extensive experiments demonstrate that our approach surpasses state-of-the-art methods on multiple real-world benchmarks. Project page: https://brian90709.github.io/diff-reflection-separation/
Chinese Translation
在强光反射或弱反射等极端条件下，单幅图像的反射分离任务非常具有挑战性。现有的方法在强光或弱反射场景中往往难以恢复两个层次，因为信息不足。本文提出了一种专门为此任务精准调优的扩散模型，利用生成扩散先验实现稳健的分离。我们的方法通过统一的扩散模型同时生成透射层和反射层，并引入了一种新颖的跨层自注意力机制，以改善特征的解耦。我们进一步引入了一种不相交采样策略，以在扩散过程中迭代减少层间干扰，并增加一个带有学习组成函数的潜在优化步骤，以在复杂的现实场景中获得更好的结果。大量实验表明，我们的方法在多个现实基准测试中超越了现有的最先进方法。项目页面： https://brian90709.github.io/diff-reflection-separation/
cs.CV / 7 / 2606.04133
Pinpoint: Grounded Worldwide Image Geolocation via Cross-Source Retrieval and Reranking
Pinpoint：通过跨源检索和重排序实现全球图像地理定位
Abstract
Image geolocation aims to estimate where a photograph was taken from its visual content. At worldwide scale, this remains challenging because visual evidence is often ambiguous, diverse, and unevenly distributed. Prior work has typically treated geolocation of ordinary internet photos and street-view imagery as separate tasks, despite their complementary strengths: internet photos better match the appearance distribution of user-captured queries, while street-view imagery provides denser, geographically grounded coverage. We present Pinpoint, a retrieve-and-rerank architecture that combines both sources in a coarse-to-fine pipeline. A contrastive image-GPS embedder is trained on both user-uploaded Flickr photos and street-view imagery, learning a shared image-GPS embedding space that is used to retrieve candidate locations. An attention-based reranker then rescores retrieved candidates by combining candidate-level visual and GPS features with cross-source evidence from nearby locations to ground the prediction. Unlike recent prior work, Pinpoint does not rely on multimodal large-language models, making inference faster and more reproducible. Pinpoint achieves state-of-the-art results across all metrics on standard benchmarks for internet photos (IM2GPS3k and YFCC4k) and street-view imagery (OSV-5M).
Chinese Translation
图像地理定位旨在通过图像的视觉内容估计照片的拍摄位置。在全球范围内，这一任务依然具有挑战性，因为视觉证据通常模糊、多样且分布不均。此前的研究通常将普通互联网照片和街景图像的地理定位视为两个独立的任务，尽管它们在互补优势上表现突出：互联网照片更能够匹配用户捕捉查询的外观分布，而街景图像则提供了更密集、更具地理基础的覆盖。我们提出了 Pinpoint，一种在粗定位到精细定位的流程中结合两种数据源的检索-重排序架构。一个对比图像-全球定位系统（GPS）嵌入模型在用户上传的 Flickr 照片和街景图像上进行训练，学习共享的图像-GPS 嵌入空间，用于检索候选位置。然后，基于注意力机制的重排序器通过结合候选位置的视觉特征和 GPS 特征，以及来自附近位置的跨源证据，重新计算检索候选项的得分，以支持预测的基础。与近期的相关研究不同，Pinpoint 不依赖多模态的大型语言模型，使得推理过程更快且更具可重现性。Pinpoint 在互联网照片（IM2GPS3k 和 YFCC4k）和街景图像（OSV-5M）的标准基准测试中，在所有指标上都达到了最先进的结果。
cs.CV / 8 / 2606.04166
End-to-End Text Line Detection and Ordering
端到端文本行检测与排序
Abstract
Practical text-recognition pipelines for historical documents typically decompose layout analysis into line detection followed by a separate reading-order step, with the latter most often handled by a hand-coded geometric heuristic that struggles with marginalia, multiple columns, tables, and source-specific editorial conventions. This article introduces Orli (Ordered Regression of Lines), an end-to-end model that casts both sub-tasks as a single image-to-sequence problem: from a page image, Orli autoregressively generates text-line baselines directly in reading order. Baselines are represented in a chord-frame parameterization that anchors a line's position, orientation, and extent while encoding local geometry through perpendicular offsets; an iterative refinement head and a local visual refiner produce the final curve. Trained on a heterogeneous corpus of 196,691 pages spanning ten writing systems, Orli marginally exceeds the previously reported state of the art for cBAD line detection without dataset-specific training, reaches near perfect coverage and ordering on multiple reading-order benchmarks zero-shot, and adapts to more specialized out-of-domain layouts with limited fine-tuning. The method's source code and model weights are available under an open license at https://github.com/mittagessen/orli.
Chinese Translation
用于历史文献的实际文本识别流程通常将布局分析分解为行检测和随后单独的阅读顺序步骤，后者通常由手工编码的几何启发式方法处理，但在处理附注、多个列、表格和特定来源的编辑规范时常常面临困难。本文介绍了Orli（Ordered Regression of Lines），一个端到端模型，将这两个子任务视为一个单一的图像到序列问题：从页面图像出发，Orli 自回归地直接生成按阅读顺序排列的文本行基线。基线采用和弦框架参数化表示，锚定了行的位置、方向和范围，同时通过垂直偏移编码局部几何；一个迭代细化头和一个局部视觉细化器生成最终曲线。在一个涵盖十种书写系统的196,691页异构语料库上训练，Orli 的表现略高于先前报告的 cBAD 行检测领域的最先进技术，且在多个阅读顺序基准上实现了近乎完美的覆盖和排序，无需特定于数据集的训练，并且能够通过有限的微调适应更专业的领域外布局。该方法的源代码和模型权重在https://github.com/mittagessen/orli 以开源许可方式提供。
cs.CV / 9 / 2606.04184
GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs
GroupToM-Bench：大规模语言模型中群体心智理论与非线性社会涌现的基准评估
Abstract
True general intelligence requires not only a model of the physical world but also a social world model: the capacity to infer how individual mental states interact and crystallize into group-level outcomes. Despite notable progress in individual-level Theory of Mind (ToM) reasoning, existing multimodal large language models fail at this broader task. Collective behavior emerges non-linearly from social tensions, conformity dynamics, and structural constraints, meaning it cannot be recovered by merely summing individual intentions. We present GroupToM-Bench, the first multimodal benchmark for group-level ToM, built around a causal chain spanning micro-level BDI states (belief, desire, intention), meso-level group tension and structural constraints, and macro-level outcome prediction and mechanistic attribution. To probe this full arc, we develop a seven-level cognitive audit framework. Experiments reveal a gap between current models and human baselines, highlighting a failure to process social structures and non-linear collective dynamics.
Chinese Translation
真正的通用智能不仅需要对物理世界的模型，还需要对社会世界的模型：推断个体心理状态如何相互作用并结晶为群体级结果的能力。尽管在个体层面心智理论（Theory of Mind, ToM）推理方面取得了显著进展，现有的多模态大型语言模型在这一更广泛的任务上表现不佳。集体行为是非线性地从社会紧张、从众动态和结构约束中涌现而出的，这意味着仅通过相加个体意图无法恢复这种行为。我们提出了GroupToM-Bench，这是第一个针对群体级ToM的多模态基准评估，它建立在一个因果链上，该因果链跨越微观层面的BDI状态（信念、愿望、意图）、中观层面的群体紧张和结构约束，以及宏观层面的结果预测和机制归因。为了探查这一完整过程，我们开发了一个七级认知审计框架。实验表明当前模型与人类基准之间存在差距，突显出处理社会结构和非线性集体动态的能力的缺失。
cs.CV / 10 / 2606.04198
Spatial Artifact Coherence Determines Codec Robustness in Patch-Based rPPG
空间伪影一致性决定基于补丁的远程光电容积描记（rPPG）中的编解码器鲁棒性
Abstract
Remote photoplethysmography (rPPG) achieves low heart-rate error on uncompressed benchmarks yet is deployed over compressed video channels in telehealth, neonatal ICU, and driver fatigue applications. No prior work identifies the physical quantity determining when spatial decomposition outperforms global-projection methods under codec compression. We propose Spatial Artifact Coherence (SAC), defined as the ratio of off-diagonal to diagonal energy in the 4x4 inter-patch Green-channel covariance matrix (bandpass 0.75-2.5 Hz), and the PatchPCA algorithm family (four codec-aware rPPG algorithms). We evaluate 280 subjects across three public datasets, 11 codec degradation variants (MPEG-4, H.265, H.264, JPEG, chroma subsampling), and 13 algorithms via Wilcoxon tests (BH-FDR, q < 0.05, 904 tests). SAC explains 93.8% of between-variant variance in PCA advantage (r = +0.969), with zero overlap between codec families: non-MPEG-4 variants cluster at SAC 0.10-0.18 with 84-90% PCA win rates, while MPEG-4 variants cluster at SAC 0.48-0.59 with 61% win rate and a 5.8x reduction in mean improvement. Within subjects, 78% confirm the expected pattern (p < 10^-22, dz = 0.73). Within-variant subject-level SAC correlation is r = +0.099, confirming SAC classifies codec families rather than predicting individual outcomes. MPEG-4's effect is structural (macroblock DCT geometry, not noise amplitude), governed by source codec state, not resolution. P-Hybrid is identified as the most deployment-robust algorithm. Two necessary operating conditions for PatchPCA advantage are established: SAC < 0.30 and low-to-moderate motion, directly ruling out raw-to-MPEG-4 transcoding pipelines. SAC provides a physically grounded metric for codec-aware rPPG algorithm selection in clinical remote monitoring systems.
Chinese Translation
远程光电容积描记（rPPG）在未压缩基准中实现了较低的心率误差，但在远程医疗、新生儿重症监护室和驾驶疲劳应用中却通过压缩视频通道进行部署。此前没有研究确定在编解码器压缩下，何种物理量会导致空间分解法优于全局投影法。我们提出了空间伪影一致性（Spatial Artifact Coherence，SAC），其定义为4x4互补件绿色通道协方差矩阵（带通0.75-2.5 Hz）中非对角能量与对角能量的比率，以及PatchPCA算法家族（四个编解码器感知的rPPG算法）。我们在三个公共数据集中对280个受试者进行评估，比较了11种编解码器降级变体（MPEG-4、H.265、H.264、JPEG、色度子采样）和13种算法，通过Wilcoxon检验（BH-FDR，q < 0.05，904次检验）。SAC解释了PCA优势的93.8%的变异性（r = +0.969），且不同编解码器家族之间没有重叠：非MPEG-4变体集聚在SAC 0.10-0.18区间，PCA胜率为84-90%，而MPEG-4变体集聚在SAC 0.48-0.59区间，胜率为61%，平均改善幅度减少了5.8倍。在个体受试者中，78%证实了预期模式（p < 10^-22，dz = 0.73）。同一变体的受试者级别SAC相关性为r = +0.099，确认SAC用于分类编解码器家族而非预测个体结果。MPEG-4的影响是结构性的（宏块DCT几何形状，而非噪声幅度），由源编解码器状态而非分辨率支配。P-Hybrid被确定为最具部署鲁棒性的算法。建立了PatchPCA优势的两个必要操作条件：SAC < 0.30，且存在低至中等的运动，这直接排除了原始到MPEG-4的转码管道。SAC为临床远程监测系统中编解码器感知的rPPG算法选择提供了一个物理基础的度量标准。
cs.CV / 11 / 2606.04240
Overview of the EReL@MIR 2025 Multimodal Document Retrieval Challenge (Track 1)
EReL@MIR 2025多模态文档检索挑战概述（第一赛道）
Abstract
Retrieval over visually-rich documents, pages that interleave text with figures, tables, and charts, is essential for multimodal retrieval-augmented generation, yet most retrievers still discard the visual channel. The \emph{Multimodal Document Retrieval Challenge}, Track~1 of the MIR Challenge at the first EReL@MIR workshop, co-located with The Web Conference 2025, asks participants to build a \emph{single} retrieval system that handles two complementary regimes: closed-set document page retrieval within long documents from a text query (MMDocIR), and open-domain retrieval of Wikipedia-style passages from an image or image-plus-text query (M2KR). Systems are ranked by the macro-average of mean Recall@$\{1,3,5\}$ over the two tasks. The challenge drew 455 entrants and 586 submissions across 22 teams. This report describes the challenge design, datasets, and evaluation protocol; reports the final standings; and analyses the three winning teams' systems. All three build on decoder-based Multimodal-LLM embedders from the Qwen2-VL family rather than on CLIP-style encoders, and differ chiefly in whether they reach the top through fine-tuned ensembles, training-free multi-route fusion with a strong vision-language re-ranker, or zero-shot late interaction. The training-free system finished within $0.1$ point of the fine-tuned winner.
Chinese Translation
在富含视觉信息的文档（页内交替包含文本、图形、表格和图表）中进行检索对于多模态检索增强生成至关重要，但大多数检索系统仍然忽视视觉通道。在2025年网络会议期间举办的首届EReL@MIR研讨会中， extit{多模态文档检索挑战}的第一赛道要求参与者构建一个 extit{单一}检索系统，以处理两种互补的模式：从文本查询中对长文档进行闭集文档页面检索（MMDocIR），以及从图像或图像加文本查询中检索维基百科风格的开放域段落（M2KR）。系统的排名基于两个任务的平均Recall@$ ext{1,3,5}$的宏平均值。此次挑战吸引了22支队伍的455名参赛者提交了586份作品。本报告描述了挑战的设计、数据集和评估协议；报告了最终排名；并分析了三支获胜团队的系统。这三支团队均基于Qwen2-VL系列的解码器构建多模态大语言模型嵌入器，而非CLIP风格的编码器，主要区别在于它们是通过微调的集成、无训练的多路融合配合强大的视觉-语言重排序器，还是零-shot晚期交互而获胜。该无训练系统的成绩在微调获胜者之上仅相差$0.1$分。
cs.CV / 12 / 2606.04249
Prospective Dynamic 3D MRI Reconstruction via Latent-Space Motion Tracking from Single Measurement
基于潜在空间运动跟踪的前瞻性动态三维MRI重建
Abstract
Prospective reconstruction is crucial in many clinical applications such as MRI-guided radiotherapy, which demands accurate image reconstruction and fast motion estimation from currently acquired measurements. However, prospective reconstruction remains challenging due to ultra-sparse sampling and stringent latency requirements. In this work, we propose PDMR, a Prospective Dynamic 3D MRI Reconstruction framework with latent-space motion tracking. Our core idea is to learn an efficient and generalizable latent manifold of motion fields offline, enabling rapid online adaptation for prospective reconstruction. Specifically, we parameterize the deformation vector fields (DVFs) on a low-dimensional manifold, effectively reducing the search space for fast online adaptation, and employ a tri-plane representation to achieve geometry-aware and memory-efficient encoding of 3D motion. Experiments on both XCAT digital phantoms and in-house abdominal MRI datasets demonstrate that PDMR achieves high-fidelity and temporally consistent reconstruction across multiple prospective scenarios (Immediate and After-2min), outperforming state-of-the-art retrospective and online methods. Our results suggest a promising pathway toward ultra-fast, motion-aware prospective MRI reconstruction in clinical practice.
Chinese Translation
前瞻性重建在诸多临床应用中至关重要，例如MRI引导的放射治疗，这要求从当前获取的测量数据中实现准确的图像重建和快速的运动估计。然而，前瞻性重建由于超稀疏采样和严格的延迟要求依然面临挑战。在本工作中，我们提出了PDMR，一个具有潜在空间运动跟踪的前瞻性动态三维MRI重建框架。我们的核心思想是离线学习一个高效且可推广的运动场潜在流形，从而实现前瞻性重建的快速在线适应。具体而言，我们在低维流形上参数化变形向量场（DVFs），有效降低快速在线适应的搜索空间，并采用三平面表示来实现对三维运动的几何感知和内存高效编码。我们在XCAT数字虚拟模体和内部腹部MRI数据集上的实验表明，PDMR在多个前瞻性场景（立即和2分钟后）中实现了高保真和时间一致的重建，优于最先进的回顾性和在线方法。我们的结果表明了在临床实践中实现超快速、运动感知的前瞻性MRI重建的良好前景。
cs.CV / 13 / 2606.04251
SBP-Net: Learning Thin Structure Reconstruction with Sliding-Box Projections
SBP-Net: 基于滑动框投影的细结构重建学习
Abstract
Reconstructing thin 3D structures is challenging due to their sparsity, scale variation, and complex geometry. Such structures arise in a wide range of domains, including medical imaging of vascular systems and industrial pipe systems. While recent neural methods perform well on dense surfaces, they often fail to recover fine thin geometries. We propose a reconstruction approach based on local depth projections, which provide an efficient and informative 2D representation of thin structures. Specifically, we traverse the 3D model with a sliding box to generate local orthographic depth projections, which are processed by a neural network to reconstruct missing thin structures in 2D. The local reconstructions are subsequently fused back into the 3D model to produce a coherent and detailed shape. Experiments on pulmonary artery reconstruction from CT volumes and industrial pipeline recovery from synthetic and real scans demonstrate improved preservation of fine structural details over existing methods.
Chinese Translation
重建细微的三维结构具有挑战性，原因在于其稀疏性、尺度变化和复杂几何形状。这类结构在多个领域中均有出现，包括血管系统的医学成像和工业管道系统。尽管最近的神经网络方法在密集表面上表现良好，但通常难以恢复细微的薄几何。我们提出了一种基于局部深度投影的重建方法，该方法提供了细结构的高效且信息丰富的二维表示。具体而言，我们通过滑动框遍历三维模型，生成局部正交深度投影，并由神经网络处理以重建二维中缺失的细结构。这些局部重建随后被融合回三维模型中，以产生一个连贯且详细的形状。在从CT体积重建肺动脉以及从合成和真实扫描中恢复工业管道的实验中，显示出相较于现有方法更好地保留了细微的结构细节。
cs.CV / 14 / 2606.04264
UniCanvas: A Diffusion-base Unified Model for Text-in-Image Joint Generation
UniCanvas：一种基于扩散的文本-图像联合生成统一模型
Abstract
Recent years have seen remarkable progress in unified vision-language models handling both multimodal understanding and generation within a single architecture. While autoregressive VLMs can reason across modalities, they fail to generate high-quality images. In contrast, diffusion models produce photorealistic visuals yet struggle to generate coherent text, making it challenging to develop a single unified model that can seamlessly handle both visual and text generation. Recent advances suggest that language can be effectively embedded within visual representations, allowing models to reason about textual semantics directly from images. To this end, we propose UniCanvas, a first attempt that unifies diffusion models to generate interleaved multimodal contents through text-in-image generation. Diffusion models naturally capture transformations on a shared pixel canvas, which can be viewed as world models of visual change. Instead of producing discrete text tokens, the model learns to represent language as visual patterns inside images, leveraging its inherent multimodal embedding space. This design allows the model to "draw" text naturally within a single pixel canvas during image synthesis, achieving seamless multimodal generation. Experiments demonstrate that UniCanvas improves performance over previous unified models, positioning text-in-image generation with diffusion models as a promising unified multimodal generation paradigm.
Chinese Translation
近年来，统一的视觉-语言模型在单一架构内处理多模态理解和生成方面取得了显著进展。虽然自回归的视觉-语言模型（VLMs）可以跨模态推理，但它们在生成高质量图像方面存在不足。相比之下，扩散模型能够生成逼真的视觉图像，但在生成连贯文本方面表现不佳，因此开发一个能够无缝处理视觉和文本生成的统一模型十分具有挑战性。近期的研究表明，语言可以有效嵌入到视觉表征中，使得模型能够直接从图像中推理文本语义。为此，我们提出了UniCanvas，这是首次将扩散模型统一用于通过文本-图像生成生成交错的多模态内容。扩散模型自然捕捉共享像素画布上的变换，这可以视为视觉变化的世界模型。该模型不是生成离散的文本标记，而是学习将语言表示为图像内部的视觉模式，利用其固有的多模态嵌入空间。这个设计使得模型能够在图像合成过程中自然地“绘制”文本于单一像素画布上，实现无缝的多模态生成。实验表明，UniCanvas在性能上优于之前的统一模型，将基于扩散模型的文本-图像生成视为一种有前景的统一多模态生成范式。
cs.CV / 15 / 2606.04271
StandardE2E: A Unified Framework for End-to-End Autonomous Driving Datasets
StandardE2E：一种统一的端到端自主驾驶数据集框架
Abstract
Autonomous driving has shifted from modular perception-prediction-planning stacks toward end-to-end (E2E) models that map sensor inputs directly to vehicle control, often regularized by auxiliary tasks such as 3D detection, motion forecasting, and HD-map perception. Progress is driven by a fast-growing ecosystem of sensor-rich driving datasets, yet each ships its own file formats, APIs, coordinate conventions, and modality coverage, leaving cross-dataset experimentation and even basic per-dataset preprocessing to be re-implemented per project. We present StandardE2E, a framework that provides a single unified interface over E2E driving datasets. StandardE2E (i) standardizes per-dataset preprocessing under one shared data schema; (ii) combines multiple datasets in a single PyTorch DataLoader for cross-dataset pretraining, auxiliary-task supervision, and scenario-level filtering; and (iii) reduces adding a new dataset to a single per-dataset mapping from raw frames to the canonical schema, leaving the entire downstream pipeline unchanged. The framework supports six datasets out of the box: Waymo End-to-End, Waymo Perception, Argoverse 2 Sensor, Argoverse 2 LiDAR, NAVSIM (OpenScene-v1.1), and WayveScenes101, and is released as the open-source standard-e2e Python package, available at https://github.com/stepankonev/StandardE2E.
Chinese Translation
自主驾驶已经从模块化的感知-预测-计划堆栈转向端到端（E2E）模型，这些模型直接将传感器输入映射到车辆控制，并常常通过辅助任务如3D检测、运动预测和高精度地图感知进行规范化。进展得益于快速增长的传感器丰富的驾驶数据集生态系统，但每个数据集都采用其特有的文件格式、API、坐标规范和模态覆盖，导致跨数据集实验及基本的每个数据集预处理需要在每个项目中重新实现。我们提出了StandardE2E，一个提供对E2E驾驶数据集的单一统一接口的框架。StandardE2E (i) 在一个共享的数据模式下标准化每个数据集的预处理；(ii) 在一个单一的PyTorch DataLoader中结合多个数据集以进行跨数据集的预训练、辅助任务监督和场景级过滤；(iii) 将添加新数据集的过程简化为从原始帧到规范模式的单一每个数据集映射，保持整个下游管道不变。该框架开箱即用地支持六个数据集：Waymo End-to-End、Waymo Perception、Argoverse 2 Sensor、Argoverse 2 LiDAR、NAVSIM (OpenScene-v1.1) 和 WayveScenes101，并作为开源的standard-e2e Python包发布，地址为 https://github.com/stepankonev/StandardE2E.
cs.CV / 16 / 2606.04282
FindIt: A Format-Informed Visual Detection Benchmark for Generalist Multimodal LLMs
FindIt：一个基于格式的通用多模态大型语言模型的视觉检测基准
Abstract
Multimodal large language models (MLLMs) are predominantly evaluated on free-form vision-language tasks such as visual question answering, captioning, and summarization. However, their practical use is rapidly expanding to more structured computer vision settings, where users prompt models to perform localization-centric tasks such as object detection, often within larger agentic or decision-making systems. Despite this shift, there is currently no standardized benchmark that systematically evaluates these capabilities at scale. In this work, we introduce the first comprehensive benchmark specifically designed to assess the promptable localization abilities of generalist MLLMs. Our benchmark spans four core task categories: object detection, referring expression detection, instance-level detection, and video-based detection. To enable consistent and fair evaluation, we develop a unified framework that standardizes inputs, enforces parsable bounding box outputs, and defines transparent evaluation protocols across tasks. Using this suite, we evaluate a diverse set of open-source and proprietary MLLMs, providing an in-depth analysis of their performance and limitations. Beyond accuracy, we examine models' ability to adhere to output format specifications, showing that current systems are highly sensitive to formatting constraints and often fail to generalize even to minor variations. Our results highlight both the strengths and shortcomings of state-of-the-art MLLMs in localization settings, and point toward important directions for improving multimodal model design and evaluation.
Chinese Translation
多模态大型语言模型（MLLMs）主要在自由形式的视觉语言任务中进行评估，如视觉问答、图像描述和摘要。然而，它们的实际应用正迅速扩展到更结构化的计算机视觉环境，在这些环境中，用户通过提示模型执行以定位为中心的任务，例如目标检测，而这些任务通常嵌入于更大的自主或决策系统中。尽管这种转变正在发生，目前尚无标准化基准可以系统地大规模评估这些能力。在本研究中，我们介绍了第一个专门设计的综合基准，用于评估通用多模态大型语言模型的可提示定位能力。我们的基准涵盖了四个核心任务类别：目标检测、指代表达检测、实例级检测和基于视频的检测。为了实现一致和公平的评估，我们开发了一个统一框架，标准化输入、强制可解析的边界框输出，并定义跨任务的透明评估协议。通过这一套工具，我们评估了一组多样的开源和专有的MLLMs，对它们的性能和局限性进行了深入分析。除了准确性之外，我们还考察了模型遵循输出格式规范的能力，结果表明当前系统对格式约束高度敏感，往往难以适应甚至微小的变化。我们的结果凸显了当前最先进的MLLMs在定位环境中的优缺点，并指明了改善多模态模型设计和评估的重要方向。
cs.CV / 17 / 2606.04291
A Cookbook of 3D Vision: Data, Learning Paradigms, and Application
三维视觉手册：数据、学习范式与应用
Abstract
3D vision has rapidly evolved, driven by increasingly diverse data representations, learning paradigms, and modeling strategies. Yet the field remains fragmented across representations and benchmarks, making it difficult to develop unified perspectives on efficiency, fidelity, and scalability. This work provides a data-centric taxonomy of 3D vision that connects geometric representations, datasets, learning frameworks, and applications within a single conceptual map. We begin by analysing the principal structural representations of 3D data--point clouds, meshes, voxels, and 3D Gaussians--along with their acquisition pipelines. We then examine how dataset design, benchmark construction, and supervision regimes shape recent advances, spanning 2D-supervised 3D learning, implicit neural representations, and 4D world modeling. Through this integrative lens, we clarify the relationships among representations, learning paradigms, and downstream tasks in reconstruction, generation, and video modeling, offering a consolidated view of emerging trends toward balancing efficiency and fidelity and toward multimodal geometric grounding.
Chinese Translation
三维视觉快速演进，受益于日益多样化的数据表示、学习范式和建模策略。然而，该领域在表示和基准测试方面仍然存在碎片化现象，这使得在效率、保真度和可扩展性方面形成统一的观点变得困难。本文提供了一种以数据为中心的三维视觉分类法，将几何表示、数据集、学习框架和应用在一个单一的概念图中联系起来。我们首先分析了三维数据的主要结构表示——点云、网格、体素和三维高斯——以及它们的获取流程。接着，我们考察了数据集设计、基准构建和监督机制如何塑造近期的进展，涵盖了二维监督的三维学习、隐式神经表示和四维世界建模。通过这一综合视角，我们澄清了表示、学习范式和重建、生成及视频建模的下游任务之间的关系，提供了一个整合的视图，以平衡效率与保真度，并促使多模态几何根植的发展趋势。
cs.CV / 18 / 2606.04299
Efficient and Training-Free Single-Image Diffusion Models
高效且无需训练的单图像扩散模型
Abstract
We consider the problem of generating images whose internal structure -- defined by the distribution of patches across multiple scales -- matches that of a single reference image. Recent approaches address this problem by training a diffusion model on a single image. But even in this setting, training is computationally expensive and requires hours of optimization. Instead, we model the image using a dataset of its patches at different scales. As this dataset is finite and the dimensionality of its patches is small, the score function for a noisy patch can be computed tractably using an optimal, closed-form denoiser, eliminating the need for neural network training. We integrate this patch-based denoiser into an efficient, training-free image diffusion model, and we describe how our method connects to classical patch-based image restoration techniques. Our approach achieves state-of-the-art generation quality and diversity compared to trained single-image diffusion models, and we demonstrate applications, including unconditional image generation, text-guided stylization, image symmetrization, and retargeting. Further, we show that our approach is compatible with latent space diffusion, and we show multiple additional acceleration techniques to achieve megapixel single-image generation in one second, and gigapixel generation in minutes.
Chinese Translation
我们考虑生成图像的问题，使其内部结构——由多个尺度上补丁的分布定义——与单一参考图像相匹配。最近的方法通过在单一图像上训练扩散模型来解决这个问题。但即使在这种情况下，训练过程计算成本高，并且需要数小时的优化。相反，我们通过构建不同尺度下的补丁数据集来建模图像。由于该数据集是有限的且其补丁的维度较小，因此可以使用最优的闭式去噪器以可处理的方式计算噪声补丁的得分函数，从而消除了对神经网络训练的需求。我们将这种基于补丁的去噪器集成进一个高效的、无需训练的图像扩散模型，并描述了我们的方法与经典的基于补丁的图像恢复技术的联系。与经过训练的单图像扩散模型相比，我们的方法实现了最先进的生成质量和多样性，并展示了应用，包括无条件图像生成、文本引导的风格化、图像对称化和重新定位。此外，我们还展示了我们方法与潜在空间扩散的兼容性，并展示了多种额外加速技术，实现了在一秒钟内生成百万像素的单图像，以及在数分钟内生成千万像素的图像。
cs.CV / 19 / 2606.04301
XSSR: Cross-Domain Self-Supervised Representative Selection for Efficient Annotation in Medical Image Segmentation
XSSR：用于医疗图像分割高效标注的跨域自监督代表选择
Abstract
Acquiring labeled medical image data is resource-intensive and a challenge further exacerbated in cross-domain scenarios where source and target datasets differ in imaging equipment, population, or clinical site. This study introduces XSSR (Cross-Domain Self-Supervised Representative Selection), a framework designed to minimize annotation effort in the target domain while maintaining robust segmentation performance. XSSR comprises three stages: first, a Masked Autoencoder (MAE) is trained on unlabeled source data to establish a shared embedding space without requiring target labels; second, a greedy selection algorithm scores unlabeled target samples based on a composite density, novelty, and diversity criterion; and third, a U-Net segmentation model is trained exclusively on the selected subset. The novelty-diversity trade-off parameter, alpha, is automatically calibrated by minimizing embedding-space coverage, eliminating manual tuning. We evaluate XSSR on three public benchmarks: Chest X-ray, RIGA+ retinal fundus imaging, and multi-site Prostate MRI, each under a fixed 5% annotation budget. XSSR achieves 99.3% of full-data performance on Chest X-ray using only 22 labeled samples, surpasses random selection by up to 2.5 Dice points on Prostate MRI, and consistently outperforms the CoreSet baseline by 0.4 to 1.2 Dice points across all datasets. Ablation studies indicate that diversity is the most influential scoring component, and per-site analysis shows that performance correlates with scanner similarity to the source domain.
Chinese Translation
获取标注的医疗图像数据资源密集，并且在源数据集与目标数据集在成像设备、人口或临床场所等方面存在差异的跨域场景中，这一挑战尤为突出。本研究提出了XSSR（跨域自监督代表选择），该框架旨在最小化目标域的标注工作量，同时保持稳健的分割性能。XSSR包括三个阶段：首先，在未标注的源数据上训练一个掩码自编码器（Masked Autoencoder，MAE），以建立一个共享的嵌入空间，而无需目标标签；其次，基于复合密度、新颖性和多样性标准，贪婪选择算法对未标注的目标样本进行评分；最后，基于选定的子集训练U-Net分割模型。新颖性-多样性权衡参数alpha通过最小化嵌入空间的覆盖率自动校准，消除手动调优的需求。我们在三个公共基准上评估XSSR：胸部X光、RIGA+视网膜眼底成像和多中心前列腺MRI，每个基准均在固定的5%标注预算下进行。XSSR在使用仅22个标定样本的情况下，在胸部X光上达到了全数据性能的99.3%，在前列腺MRI上超越随机选择达2.5个Dice点，并且在所有数据集中都持续优于CoreSet基线0.4至1.2个Dice点。消融研究表明，多样性是最具影响力的评分组成部分，而每个场所的分析显示，性能与扫描仪与源域的相似性存在相关性。
cs.CV / 20 / 2606.04323
Answer Self-Consistency with Margin-Triggered Question Re-Arbitration for the CVPR 2026 VidLLMs Challenge
基于边界触发问题重审的答案自一致性方法——CVPR 2026 VidLLMs 挑战的解决方案
Abstract
In this report, we present our solution for Track 2 of the CVPR 2026 VidLLMs Challenge. This track evaluates visual relational reasoning in videos, where models must infer relations that are not always explicitly visible. We propose Answer Self-Consistency with Margin-Triggered Question Re-Arbitration (ASC-MQRA), a training-free test-time reasoning framework built on a multimodal reasoning model. The core ASC component performs multiple stochastic video question-answering runs and aggregates their answer choices through answer-level self-consistency. This substantially improves over single-pass inference and forms our final test submission. We further study MQRA, a conditional re-arbitration module for low-margin examples where the first-stage vote distribution indicates uncertainty. Our vote-margin analysis shows that low-margin examples often retain the ground-truth answer among the top candidates, motivating MQRA to narrow the candidate set and re-watch the video only over the retained candidates. On validation, MQRA further improves over ASC, indicating that low-margin vote distributions can provide a useful uncertainty signal. On test, however, MQRA slightly degrades performance relative to ASC, suggesting that re-arbitration is sensitive to the size and category distribution of the triggered subset. Our final test submission therefore uses ASC without re-arbitration, achieving 72.73 average accuracy and 78.34 category-wise macro average accuracy on validation, and 81.16 average accuracy and 80.91 category-wise macro average accuracy on test. This report details our prompting strategy, implementation setup, ablation studies, and diagnostic analyses. The code is available at https://github.com/data-analytics-labo/ASC-MQRA
Chinese Translation
在本报告中，我们提出了 CVPR 2026 VidLLMs 挑战 Track 2 的解决方案。本 track 评估视频中的视觉关系推理，模型必须推断并不总是显性可见的关系。我们提出了答案自一致性与边界触发问题重审（ASC-MQRA），这是一个基于多模态推理模型的无训练、测试时推理框架。核心的 ASC 组件执行多次随机视频问答，并通过答案级自一致性整合它们的答案选择。这大大提高了单次推理的效果，并形成了我们的最终测试提交。我们进一步研究了 MQRA，这是一个针对低边际样本的条件重审模块，低边际样本的第一阶段投票分布表明不确定性。我们的投票边际分析显示，低边际样本通常在候选答案中保留真实答案，这促使 MQRA 缩小候选集并仅对保留的候选者重新观看视频。在验证阶段，MQRA 在 ASC 上进一步提高了性能，表明低边际投票分布可以提供有用的不确定性信号。然而，在测试阶段，MQRA 的表现相较于 ASC 略有下降，这表明重审对触发子集的大小和类别分布敏感。因此，我们的最终测试提交使用 ASC 而不进行重审，在验证集上实现了 72.73 的平均准确率和 78.34 的类别宏平均准确率，在测试集上实现了 81.16 的平均准确率和 80.91 的类别宏平均准确率。本报告详细介绍了我们的提示策略、实施设置、消融研究和诊断分析。代码可在 https://github.com/data-analytics-labo/ASC-MQRA 获取。
cs.CV / 21 / 2606.04343
Robust Multi-view Clustering against Imperfect Information
应对不完美信息的强健多视角聚类
Abstract
Real-world multi-view data always suffer from imperfect information problem, where the view-specific observations are absent (i.e., Incomplete Views, IV) and cross-view correspondences are mismatched (i.e., Noisy Correspondences, NC) for certain instances. As a remedy, numerous IV- and NC-oriented multi-view clustering (MvC) methods have been proposed, which however require either reliable correspondences or sufficiently complete instances, thus stopping short of addressing the imperfect information problem. In contrast, we observe that both IV and NC challenges originate from the same issue of imperfect cross-view counterpart information, where the counterpart of an anchor instance in another view might be either unavailable or unreliable. Based on the observation, we propose a novel robust MvC framework, termed Posterior-guided Latent Counterpart Inference (PLCI), which could handle both IV and NC in a unified manner. Specifically, PLCI formulates the desired cross-view counterpart of each anchor instance as a latent variable, and integrates both instance-level reliability and prototype-level semantic transport to infer the posterior distribution of the latent counterpart. Extensive experiments on six widely-used multi-view datasets against 10 state-of-the-art MvC methods demonstrate the effectiveness of PLCI for tackling the imperfect information problem. The code will be released upon acceptance.
Chinese Translation
现实世界中的多视角数据常常面临不完美信息问题，其中视角特定的观察数据缺失（即不完整视角，Incomplete Views, IV），以及跨视角对应关系不匹配（即噪声对应关系，Noisy Correspondences, NC）在某些实例中普遍存在。为此，许多针对IV和NC的多视角聚类（MvC）方法相继被提出，然而这些方法往往要求可靠的对应关系或足够完整的实例，因此仍无法有效解决不完美信息问题。相对而言，我们注意到IV和NC挑战源于同一问题，即不完美的跨视角对应信息，其中在其他视角中，锚实例的对应关系可能不可用或不可靠。基于这一观察，我们提出了一种新的强健MvC框架，称为后验引导的潜在对应推断（Posterior-guided Latent Counterpart Inference, PLCI），该框架能够以统一的方式处理IV和NC问题。具体而言，PLCI将每个锚实例的期望跨视角对应关系表示为一个潜在变量，并结合实例级的可靠性和原型级的语义传输，以推断潜在对应关系的后验分布。在六个广泛使用的多视角数据集上进行的广泛实验对比10种最先进的MvC方法，验证了PLCI在解决不完美信息问题上的有效性。代码将在论文被接收后发布。
cs.CV / 22 / 2606.04345
HYolo: An Intelligent IoT-Based Object Detection System Using Hypergraph Learning
HYolo：一种基于智能物联网的物体检测系统，采用超图学习
Abstract
This paper presents HYolo, an intelligent IoT-based object detection framework that integrates hypergraph learning into the YOLO architecture. Traditional YOLO-based object detection models primarily capture pairwise feature interactions and may fail to model complex high-order relationships among objects and contextual features. To address this limitation, HYolo incorporates hypergraph learning to capture richer contextual dependencies and improve object representation. Experimental evaluation on the COCO dataset demonstrates significant performance improvements over baseline YOLO models. The proposed approach achieves approximately 12% improvement in mAP@50 while enhancing overall detection accuracy and robustness. By modeling high-order feature relationships, HYolo provides improved contextual understanding and more reliable object detection performance in IoT-based environments. The results indicate that integrating hypergraph learning into object detection pipelines offers a promising direction for intelligent and context-aware IoT vision systems.
Chinese Translation
本文提出了HYolo，一种基于智能物联网的物体检测框架，它将超图学习集成到YOLO架构中。传统的基于YOLO的物体检测模型主要捕捉成对特征之间的交互，可能无法建模对象和上下文特征之间复杂的高阶关系。为了解决这一局限性，HYolo结合了超图学习，以捕捉更丰富的上下文依赖性并改善物体表征。在COCO数据集上的实验评估显示，其性能显著优于基线YOLO模型。所提出的方法在mAP@50上实现了约12%的提升，同时增强了整体检测的准确性和鲁棒性。通过建模高阶特征关系，HYolo提供了更好的上下文理解和更可靠的物体检测性能，适用于基于物联网的环境。结果表明，将超图学习整合到物体检测流程中，为智能和具上下文感知的物联网视觉系统提供了一个有前景的方向。
cs.CV / 23 / 2606.04349
MorphoQuant: Modality-Aware Quantization for Omni-modal Large Language Models
MorphoQuant：面向模态的全模态大型语言模型量化方法
Abstract
Conventional Post-Training Quantization (PTQ) methods struggle with 4-bit Omni-modal Large Language Models (OLLMs) due to the extreme distribution heterogeneity and disparate outlier patterns across modalities. To address this, we propose MorphoQuant, a modality-aware PTQ framework engineered to preserve cross-modal morphology and mitigate outlier loss. Specifically, we introduce Distribution-Aware Bias Compensation (DABC), which selectively absorbs long-tailed outliers into channel-wise biases. This mechanism safeguards outlier magnitudes while maintaining high-precision discretization for dense inliers, thereby preserving accurate discretization across diverse modal distribution. Complementing this, we propose Morphology-Directed Quantization Function Optimization (MDQFO) to co-optimize the quantization grid with the bias mask, ensuring fine-grained alignment across modalities. Extensive evaluations on Qwen2.5-Omni across benchmarks like MMMU and Video-MME demonstrate our approach's superiority. Notably, our W4A4 model achieves 76.63% on ScienceQA, significantly outperforming SOTA W4A4 methods and surprisingly surpassing the W4A16 baseline, which fully demonstrates the exceptional accuracy-efficiency trade-off of our framework.
Chinese Translation
传统的后训练量化（Post-Training Quantization, PTQ）方法在4位全模态大型语言模型（Omni-modal Large Language Models, OLLMs）上面临极端的分布异质性和模态间离群点模式差异的问题。为了解决这一问题，我们提出了MorphoQuant，一种面向模态的PTQ框架，旨在保持跨模态形态并减轻离群点损失。具体而言，我们引入了分布感知偏置补偿（Distribution-Aware Bias Compensation, DABC），该方法选择性地将长尾离群点吸收到通道偏置中。此机制在保持离群点幅度的同时，为密集的内点保持高精度离散化，从而在不同模态分布中保持准确的离散化。此外，我们还提出了形态引导量化函数优化（Morphology-Directed Quantization Function Optimization, MDQFO），以共同优化量化网格与偏置掩码，确保不同模态间的细粒度对齐。在Qwen2.5-Omni上针对MMM和视频-MME等基准的广泛评估证明了我们方法的优越性。值得注意的是，我们的W4A4模型在ScienceQA上的准确率达到了76.63%，显著超越了当前最优（SOTA）W4A4方法，并且惊人地超过了W4A16基准，充分展示了我们框架在准确性和效率之间的卓越平衡。
cs.CV / 24 / 2606.04351
Video2LoRA: Parametric Video Internalization for Vision-Language Models
Video2LoRA：面向视觉语言模型的参数化视频内化
Abstract
Processing video in vision-language models is expensive: each frame occupies hundreds of tokens, and inference cost scales with every frame and every repeated query. We introduce Video2LoRA, a method for parametric video internalization. A perceiver hypernetwork reads the intermediate representations produced layer-by-layer as a frozen VLM encodes a video, and generates a Low-Rank Adaptation (LoRA) adapter in a single forward pass. Unlike standard LoRA fine-tuning, which requires iterative gradient updates, Video2LoRA predicts these weights directly from the video. Trained for SmolVLM2 500M and 2.2B on video summarization and captioning, Video2LoRA enables the same frozen VLM to answer queries from the adapter alone, with zero visual tokens in its context at query time. Video2LoRA is statistically non-inferior and equivalent to direct video-in-context inference across all five captioning benchmarks at both model scales, and across seven of eight video question answering benchmark-scale pairings. Although trained only on 12 frames at 384px, it remains stable up to 1,024 frames and 1024px, where direct video-in-context inference often degenerates. Across this sweep, it reduces answer-time visual-token load by up to 1,500x and query TTFT by 6-80x, while preserving video-faithful outputs. We also find that independently generated adapters for non-overlapping video segments can compose in rank space, suggesting a path toward chunked long-video internalization.
Chinese Translation
在视觉语言模型中处理视频的成本较高：每帧占用数百个标记，并且推理成本随每帧和每个重复查询而增加。我们提出了Video2LoRA，这是一种用于参数化视频内化的方法。一个感知超网络（perceiver hypernetwork）逐层读取由冻结的视觉语言模型（VLM）编码视频所产生的中间表示，并在单次前向传播中生成低秩适配器（Low-Rank Adaptation, LoRA）。与标准的LoRA微调需要迭代的梯度更新不同，Video2LoRA直接从视频中预测这些权重。经过针对SmolVLM2 500M和2.2B的训练，旨在进行视频摘要和字幕生成，Video2LoRA使得同一冻结的VLM能够仅凭适配器回答查询，而在查询时上下文中没有视觉标记。Video2LoRA在所有五个字幕生成基准（在两个模型规模上）以及八个视频问答基准规模配对中的七个上，在统计上与直接视频上下文推理等效且未表现出劣势。尽管仅在384px下对12帧进行训练，但它在高达1,024帧和1024px的情况下仍保持稳定，而在这些条件下直接视频上下文推理往往会退化。在这一系列测试中，它将回答时的视觉标记负担降低了最多1,500倍，并将查询的大量推理时间（TTFT）减少了6-80倍，同时保持了视频忠实的输出。我们还发现，为非重叠视频片段独立生成的适配器可以在秩空间中组合，这为分块长视频的内化提供了一条可能的路径。
cs.CV / 25 / 2606.04364
Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention
通过部分因子化注意力实现空间基础的概念瓶颈模型
Abstract
Concept bottleneck models (CBMs) predict a layer of human-named attributes before predicting a class, which makes their decisions auditable. On fine-grained recognition tasks the concept heads are usually free to attend anywhere in the image, so a head named for one body region can be satisfied by evidence on another. This work studies a part-factorized CBM that removes that freedom by construction. The method has three components built on a frozen DINOv3 vision transformer. A learned foreground gate, trained on DINOv3 patch features, suppresses background patches inside the part attention. A set of part queries cross-attends to patch features and each of the 312 CUB attributes is routed, through a fixed concept-to-part map, to read only from the part token its name implies. A learnable two-dimensional Gaussian prior, injected additively in log space into the attention logits, breaks the permutation symmetry among part queries; its means are initialized from the dataset-average keypoint location of each part, which requires no per-image keypoint supervision at training or test time. On CUB-200-2011 the spatial-prior model matches a fully supervised baseline (88.85% versus 88.95% top-1) while raising pointing accuracy by 16 points (52.6% versus 36.4%). Replacing bounding-box supervision with a PCA foreground target and combining it with the Gaussian prior removes all per-image supervision and reaches 88.6% top-1 at about 70% pointing accuracy. A keypoint-fraction sweep shows that 0.5% of the training set (about 27 images) suffices to initialize the prior with no measurable loss. Removing part identity entirely is the harder case: without any spatial prior, pointing accuracy collapses to $2.9\%$.
Chinese Translation
概念瓶颈模型（CBMs）在预测类别之前，先预测一层人类命名的属性，这使得它们的决策可以审计。在细粒度识别任务中，概念头通常可以自由地关注图像中的任何地方，因此一个命名为某一身体区域的头可以通过另一个区域的证据得到满足。本研究探讨了一种通过构造消除这种自由度的部分因子化CBM。该方法有三个组件，基于一个冻结的DINOv3视觉变换器。一个学习的前景门在DINOv3补丁特征上训练，用于抑制部分注意力中的背景补丁。一组部分查询交叉注意补丁特征，并且通过固定的概念到部分映射，每个312个CUB属性被路由至仅从其名称所暗示的部分标记读取。一个可学习的二维高斯先验，通过加性方式注入到注意力逻辑的对数空间中，打破部分查询之间的排列对称性；其均值初始化自每个部分的数据库平均关键点位置，这在训练或测试时无需每张图像的关键点监督。在CUB-200-2011数据集上，该空间先验模型的性能与完全监督的基线相匹配（88.85%对88.95%的top-1），同时提高了指向准确率16个百分点（52.6%对36.4%）。用PCA前景目标替换边界框监督，并将其与高斯先验结合，消除了所有每图像的监督，达到了约70%指向准确率下的88.6% top-1性能。关键点比例的变化显示，训练集中0.5%的样本（约27幅图像）足以初始化先验，且没有可测量的损失。完全去除部分身份是更具挑战性的情况：在没有任何空间先验的情况下，指向准确率下降至$2.9\%$。
cs.CV / 26 / 2606.04365
Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes
基于CT体积的多粒度肾脏病变特征描述
Abstract
Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level. We reformulate kidney CT characterization as a per-lesion set-prediction task: one model emits a variable number of lesions per kidney, each with four clinical attributes. We curated 2,619 CT volumes from 788 patients at one academic medical center, with multi-granularity side- and per-lesion labels, and used KiTS23 (489 cases) for zero-shot external validation. We propose \textbf{LesionDETR}, a DETR-style architecture with size-distance Hungarian matching and a hierarchical loss that aggregates per-slot outputs to side-level objectives. Across four input representations and six encoder initializations, two design choices dominate: a segmentation mask as an input channel, and same-domain abdominal pretraining (SuPreM); generic large-corpus pretraining is no better than random initialization. LesionDETR reaches bilateral side-level abnormality AUC $0.799 \pm 0.009$ on UF-Health and $0.817 \pm 0.072$ on KiTS23. A count-conditioned variant reaches per-lesion mAP $0.190 \pm 0.083$ on cystic lesions; rare solid-lesion AP stays at the noise floor, pointing to targeted data collection, not architecture, as the next bottleneck. The framework yields verified per-lesion predictions for downstream structured report generation.
Chinese Translation
放射科报告通过类型、大小、增强和衰减来描述肾脏病变，然而现有的3D方法仅在患者或器官层面进行预测。我们将肾脏CT特征描述重新定义为每个病变的集合预测任务：一个模型为每个肾脏输出可变数量的病变，每个病变具有四个临床属性。我们从一所学术医学中心整理了2,619个CT体积数据，包含788名患者的多粒度侧面和每个病变标签，并使用KiTS23（489个案例）进行了零样本外部验证。我们提出了 extbf{LesionDETR}，一种基于DETR的架构，具有基于大小-距离的匈牙利匹配和将每个槽输出聚合到侧面级目标的层次损失。在四种输入表示和六次编码器初始化中，两个设计选择占主导地位：将分割掩码作为输入通道，以及同域腹部预训练（SuPreM）；通用大型语料库预训练效果不如随机初始化。LesionDETR在UF-Health数据集上达到双侧级异常AUC $0.799 imes 0.009$，在KiTS23数据集上达到 $0.817 imes 0.072$。一个条件计数的变种在囊性病变上达到每个病变的mAP为 $0.190 imes 0.083$；罕见实性病变的AP保持在噪声水平，指向目标数据收集，而非体系结构，作为下一个瓶颈。该框架为下游结构化报告生成提供验证的每个病变预测。
cs.CV / 27 / 2606.04369
VT-3DAD: Cross-Category 3D Anomaly Detection via Visual-Text Normal Space Alignment
VT-3DAD：通过视觉-文本法向空间对齐进行跨类别三维异常检测
Abstract
Few-shot cross-category 3D anomaly detection aims to determine whether an unknown point cloud belongs to a target normal category using only a few normal references. Existing training-based methods usually require category-wise optimization, while recent training-free methods based on multi-view CLIP visual features mainly rely on visual similarity and may be confused by geometrically similar categories. In this paper, we propose VT-3DAD, a training-free framework for cross-category 3D anomaly detection via Visual-Text Normal Space Alignment. Given few-shot normal references and a test point cloud, VT-3DAD first generates realistic multi-view depth maps and extracts view-wise features using a frozen CLIP visual encoder. The visual branch measures reference-test deviation in the multi-view feature space. In parallel, depth-aware and 3D-aware prompts are encoded by the frozen CLIP text encoder to construct textual normal anchors, which provide semantic normality constraints for the target category. The final anomaly score is obtained by fusing visual deviation from normal references and semantic deviation from the textual normal space. Experiments on the ShapeNetPart dataset demonstrate that VT-3DAD achieves state-of-the-art performance. In particular, VT-3DAD improves the one-shot average AUC-ROC from 92.49% to 94.80% compared with the visual-only baseline, while also reducing the average standard deviation from 5.64 to 3.41.
Chinese Translation
小样本跨类别三维异常检测的目标是仅利用少量正常参考，判断未知点云是否属于目标正常类别。现有的基于训练的方法通常需要按类别进行优化，而最近基于多视角 CLIP 视觉特征的无训练方法主要依靠视觉相似性，可能会被几何上相似的类别所混淆。在本文中，我们提出了 VT-3DAD，一种通过视觉-文本法向空间对齐进行跨类别三维异常检测的无训练框架。给定少量正常参考和一个测试点云，VT-3DAD 首先生成真实的多视角深度图，并使用冻结的 CLIP 视觉编码器提取视角特征。视觉分支在多视角特征空间中测量参考与测试之间的偏差。与此同时，深度感知和三维感知的提示通过冻结的 CLIP 文本编码器编码，以构建文本法向锚点，为目标类别提供语义正常性约束。最终的异常分数是通过融合来自正常参考的视觉偏差和来自文本法向空间的语义偏差获得的。在 ShapeNetPart 数据集上的实验表明，VT-3DAD 达到了最先进的性能。特别是，与仅基于视觉的基线相比，VT-3DAD 将一次性平均 AUC-ROC 从 92.49% 提高到 94.80%，同时将平均标准差从 5.64 降低到 3.41。
cs.CV / 28 / 2606.04373
Selective Coupling of Decoupled Informative Regions: Masked Attention Alignment for Data-Free Quantization of Vision Transformers
去耦合信息区域的选择性耦合：用于数据无关量化的掩蔽注意力对齐方法在视觉变换器中的应用
Abstract
Data-Free Quantization (DFQ) addresses data security concerns by synthesizing samples, without accessing real data. It has garnered increasing attention in the context of Vision Transformers (ViTs), owing to the superiority of the self-attention mechanism compared to classical convolutional operation. However, previous DFQ arts for ViTs often suffer from a distribution mismatch between synthetic samples and input distribution expected by quantized models Q, resulting in the suboptimal performance. In this paper, we propose a novel Masked Attention Alignment approach for Data-Free Quantization of ViTs, named MaskAQ, revealing that: 1) the semantics in the self-attention mechanism is predominantly localized to a sparse subset of patches, called informative regions; 2) the informative regions dominate the mutual information between synthetic samples and Q's outputs. To these ends, we incorporate differential entropy maximum over patch similarity of synthetic samples, to decouple informative regions from noisy background. To couple with varied Q, the informative regions are selected to align full-precision models with Q via a masked attention alignment objective, thus yielding high-quality synthetic samples. Furthermore, a periodic sample refreshing strategy comes up to endow MaskAQ with the capacity to continually adapt to the evolving state of Q throughout the training process, to preserve desirable mutual information with synthetic samples. Extensive experiments verify the merits of MaskAQ over state-of-the-art approaches across multiple backbones and downstream tasks. Our code is available at https://github.com/hfutqian/MaskAQ.
Chinese Translation
数据无关量化（Data-Free Quantization, DFQ）通过合成样本而不访问真实数据来解决数据安全问题。由于自注意力机制相较于传统卷积操作的优势，该方法在视觉变换器（Vision Transformers, ViTs）中引起了越来越多的关注。然而，以往针对ViTs的DFQ方法通常受到合成样本和量化模型Q预期的输入分布之间的分布不匹配影响，导致性能不佳。本文提出了一种新颖的掩蔽注意力对齐方法，用于ViTs的数据无关量化，命名为MaskAQ，我们发现：1）自注意力机制中的语义主要局限于称为信息区域（informative regions）的稀疏补丁子集；2）信息区域主导了合成样本与Q输出之间的互信息。为此，我们结合合成样本的补丁相似性最大化微分熵，以将信息区域与噪声背景去耦。为了与不同的Q进行耦合，选择信息区域通过掩蔽注意力对齐目标使全精度模型与Q对齐，从而生成高质量的合成样本。此外，提出了一种周期性样本刷新策略，使MaskAQ能够持续适应Q在训练过程中的演变状态，以保持与合成样本之间的理想互信息。大量实验验证了MaskAQ在多个骨干网络和下游任务中的优越性。我们的代码可在https://github.com/hfutqian/MaskAQ获取。
cs.CV / 29 / 2606.04385
Geometry-Preserving Unsupervised Alignment for Heterogeneous Foundation Models
保持几何形状的无监督对齐用于异构基础模型
Abstract
Foundation models have driven rapid progress in computer vision, yet the two dominant paradigms, vision-language foundation models (VLMs) and vision-only foundation models (VFMs), remain only partially compatible. VLMs offer language-grounded semantic alignment but are often visually coarse, while VFMs learn discriminative perceptual geometry but lack semantic grounding. We propose GPUA (Geometry-Preserving Unsupervised Alignment), a framework that integrates the complementary strengths of VFMs and VLMs. Inspired by cross-lingual alignment, GPUA treats VFM features as a visual language and learns an orthogonal mapping that translates the VFM space into the VLM semantic space, preserving geometry and narrowing the modality gap without labels or model parameter updates. GPUA is task-agnostic and requires only feature-level access to pretrained models. Experiments across diverse benchmarks demonstrate improved cross-model compatibility and strong gains in downstream zero-shot recognition and segmentation with negligible overhead. Code is available at https://github.com/Yuteam14/GPUA
Chinese Translation
基础模型推动了计算机视觉的快速进展，但当前主流的两种范式，即视觉语言基础模型（VLMs）和仅视觉基础模型（VFMs），仍然存在部分不兼容的情况。VLMs 提供与语言相关的语义对齐，但通常在视觉上比较粗糙；而 VFMs 学习辨别性的感知几何形状，但缺乏语义基础。我们提出了 GPUA（保持几何形状的无监督对齐），一个整合了 VFMs 和 VLMs 互补优点的框架。GPUA 受跨语言对齐的启发，将 VFM 特征视为一种视觉语言，并学习一个正交映射，将 VFM 空间转化为 VLM 语义空间，从而保持几何形状并缩小模态间的差距，无需标签或模型参数更新。GPUA 是任务无关的，仅需对预训练模型进行特征级访问。针对多种基准的实验表明，跨模型兼容性得到改善，并在下游零样本识别和分割任务中获得显著提升，且开销微乎其微。代码可在 https://github.com/Yuteam14/GPUA 获取。
cs.CV / 30 / 2606.04409
An Empirical Study of Data Scale, Model Complexity, and Input Modalities in Visual Generalization
数据规模、模型复杂性和输入模态对视觉泛化影响的实证研究
Abstract
Modern deep neural networks usually have large parameter scales and nonlinear hierarchical structures, and they have achieved strong performance in computer vision. However, the source of their generalization performance remains difficult to explain using traditional statistical learning theory. Among the factors that may affect visual generalization, data scale, model complexity, and input modalities are fundamental and controllable variables. This study empirically analyzes how these three factors influence model generalization performance. Specifically, in a preliminary experiment, we construct a one-dimensional nonlinear function and vary the number of training samples and the polynomial degree to observe the effects of data scale and model complexity on model performance. In the main experiments, we compare model performance on CIFAR-10 and CIFAR-100 under different training data scales, model architectures, and input modalities. The experimental results show that increasing the training data scale consistently improves generalization performance, whereas changes in model complexity do not provide stable gains. In addition, removing color information degrades model performance, while explicit prior features such as gradients, edges, and wavelets have inconsistent effects across different model architectures. Overall, this study provides an empirical analysis of the relationships among data scale, model complexity, input modalities, and visual generalization performance. Code and experimental logs are available at: https://github.com/zlyd-CV/DeepLearning-Empirical-Studies.
Chinese Translation
现代深度神经网络通常具有大规模的参数和非线性的层次结构，在计算机视觉领域取得了优异的表现。然而，其泛化表现的来源仍然难以通过传统统计学习理论进行解释。在可能影响视觉泛化的因素中，数据规模、模型复杂性和输入模态是基本且可控的变量。本研究实证分析了这三个因素如何影响模型的泛化性能。具体而言，在初步实验中，我们构建了一个一维非线性函数，并改变训练样本的数量和多项式的度数，以观察数据规模和模型复杂性对模型性能的影响。在主要实验中，我们比较了CIFAR-10和CIFAR-100在不同训练数据规模、模型架构和输入模态下的模型性能。实验结果表明，增加训练数据规模始终能改善泛化性能，而模型复杂性的变化并未提供稳定的收益。此外，去除颜色信息会降低模型性能，而显性先验特征（如梯度、边缘和小波）在不同模型架构下的效果则存在不一致性。总体而言，本研究提供了数据规模、模型复杂性、输入模态与视觉泛化性能之间关系的实证分析。代码和实验日志可在以下网址获取: https://github.com/zlyd-CV/DeepLearning-Empirical-Studies。
cs.CV / 31 / 2606.04410
Ultra-Fast Neural Video Compression
超快速神经视频压缩
Abstract
While neural video codecs (NVCs) have demonstrated superior compression ratio, their prohibitive computational complexity remains a critical barrier to real-world deployment. This paper introduces a chunk-based coding framework designed to significantly improve the rate-distortion-complexity trade-off. Instead of processing frames sequentially, our approach encodes a chunk of multiple frames into a single compact latent representation and decodes them simultaneously. This is enabled by cross-frame interaction modules for joint spatial-temporal modeling and frame-specific decoders for parallel reconstruction. This paradigm not only dramatically enhances coding throughput but also facilitates more effective modeling of long-term temporal correlations. To further boost speed, we propose a streamlined entropy coding mechanism that consolidates bit-stream interactions into a single step, substantially reducing decoding overhead. Building on these innovations, we present DCVC-UF (Ultra-Fast), a new NVC that sets a new SOTA in performance. Our experiments show that DCVC-UF can achieve ultra-fast encoding and decoding speeds, significantly outperforming previous leading codecs. DCVC-UF serves as a notable landmark in the journey of NVC evolution. The code is at https://github.com/microsoft/DCVC.
Chinese Translation
尽管神经视频编码器（NVCs）已显示出优越的压缩比，但其过高的计算复杂性仍然是现实部署中的一个关键障碍。本文介绍了一种基于数据块的编码框架，旨在显著改善速率-失真-复杂性之间的权衡。我们的方法不是依次处理帧，而是将多帧的一个数据块编码为单一的紧凑潜在表示，并同时解码。这是通过跨帧互动模块实现的，以便进行联合时空建模，并通过特定帧的解码器实现并行重建。该范式不仅显著提升了编码吞吐量，还更有效地建模了长期的时间相关性。为了进一步提升速度，我们提出了一种简化的熵编码机制，将比特流的交互整合为单个步骤，从而显著减少了解码开销。基于这些创新，我们提出了DCVC-UF（超快速），这是一种新的NVC，设定了性能的新标准（SOTA）。我们的实验表明，DCVC-UF可以实现超快速的编码和解码速度，显著优于以前的领先编码器。DCVC-UF在NVC演变的旅程中是一个显著的里程碑。代码可以在https://github.com/microsoft/DCVC找到。
cs.CV / 32 / 2606.04414
Motion-Guided Causal Disentanglement for Robust Multi-View Cine Cardiac MRI Diagnosis
运动引导的因果解耦用于稳健的多视角心脏MRI诊断
Abstract
Multi-view cardiac magnetic resonance (CMR) imaging provides complementary anatomical information and is widely used for noninvasive disease assessment. Recent transformer-based models have demonstrated strong representation learning capabilities for CMR analysis; however, they typically learn unified latent embeddings that entangle view-specific anatomical variations with disease-related features. Such entanglement biases classifiers toward structural attributes rather than view-invariant pathological patterns. This issue is exacerbated in low-data regimes, particularly for underrepresented cardiac conditions, where limited samples increase the susceptibility to shortcut learning and view-dependent decision boundaries. To address this, we propose a Motion-Guided View--Disease Disentanglement framework MoViD built upon a ViT-MAE backbone. The model explicitly factorizes latent representations into view-specific and disease-discriminative components using dual-branch supervised contrastive objectives and a gradient-reversal adversarial constraint that minimizes disease leakage into the view embedding. Additionally, an annotation-free temporal motion feature, derived from inter-frame difference maps, is introduced to localize the beating heart region and suppress background artifacts. A focal reweighting mechanism is incorporated into the contrastive loss to mitigate class imbalance. We evaluate the framework on a private clinical venous thrombosis dataset and two public benchmarks (M&Ms, M&Ms2). Across disease classification and cardiac segmentation tasks, our approach consistently outperforms standard transformer baselines and demonstrates competitive performance against large-scale pretrained foundation models, validating the efficacy of structural disentanglement in medical image analysis.
Chinese Translation
多视角心脏磁共振成像（CMR）提供互补的解剖信息，并广泛用于无创疾病评估。近期基于变换器的模型在CMR分析中展示了强大的表征学习能力；然而，它们通常学习统一的潜在嵌入，将特定视角的解剖变异与疾病相关特征纠缠在一起。这种纠缠使得分类器倾向于结构属性，而非视角不变的病理模式。这个问题在低数据环境中尤为严重，尤其是在代表性不足的心脏疾病中，有限的样本增加了对快捷学习和视角依赖决策边界的敏感性。为了解决这个问题，我们提出了一种基于运动引导的视角-疾病解耦框架MoViD，该框架建立在ViT-MAE骨干上。该模型通过双分支监督对比目标和梯度反转对抗约束，显式地将潜在表征分解为特定视角和疾病区分成分，从而最小化疾病信息泄漏到视角嵌入中。此外，采用了一种无标注的时间运动特征，该特征源自帧间差异图，用于定位跳动的心脏区域并抑制背景伪影。对比损失中引入了聚焦重加权机制，以减轻类别不平衡。我们在一个私有临床静脉血栓形成数据集和两个公共基准（M&Ms, M&Ms2）上评估了该框架。在疾病分类和心脏分割任务中，我们的方法在性能上始终超过标准的变换器基线，并且在与大规模预训练基础模型的竞争中表现出色，验证了结构解耦在医学图像分析中的有效性。
cs.CV / 33 / 2606.04427
Implicit Fuzzification via Bounded Noise Injection for Robust Medical Image Segmentation
通过有界噪声注入实现隐式模糊化的鲁棒医学图像分割
Abstract
Image segmentation remains fundamentally limited by boundary ambiguity arising from sampling-induced information loss and inherent uncertainty in pixel-wise labeling. Although encoder-decoder architectures such as U-Net achieve strong performance, they often produce overconfident predictions that fail to capture transition-region ambiguity. To address this issue, we propose \textbf{NoiseUNet}, a simple yet effective framework that injects bounded perturbations into skip connections to regularize cross-scale feature fusion. This mechanism enforces robustness to local feature variations and promotes boundary-aware representations. Theoretically, the perturbation induces an implicit fuzzification effect, yielding soft, data-driven memberships without requiring explicit fuzzy modeling. We further introduce \textbf{ThyR}, a real-world thyroid ultrasound dataset with inherently ambiguous boundaries. Experiments demonstrate that NoiseUNet consistently improves both segmentation accuracy and boundary fidelity.
Chinese Translation
图像分割在根本上受到边界模糊的限制，这种模糊源于采样引起的信息损失和像素级标注中的固有不确定性。尽管像 U-Net 这样的编码-解码器架构表现出色，但它们常常产生过于自信的预测，未能捕捉过渡区域的模糊性。为了解决这一问题，我们提出了 extbf{NoiseUNet}，这是一个简单但有效的框架，它在跳跃连接中注入有界扰动，以规范化跨尺度特征融合。该机制增强了对局部特征变化的鲁棒性，并促进了边界感知的表征。从理论上讲，扰动引发了隐式模糊化效应，生成软的、以数据驱动的隶属关系，而无需显式的模糊建模。我们进一步引入了 extbf{ThyR}，一个具有固有模糊边界的真实世界甲状腺超声数据集。实验表明，NoiseUNet 始终提高了分割准确性和边界保真度。
cs.CV / 34 / 2606.04432
DSA: Dynamic Step Allocation for Fast Autoregressive Video Generation
DSA：用于快速自回归视频生成的动态步长分配
Abstract
Video diffusion transformers have achieved state-of-the-art visual quality, but their high inference cost remains a major bottleneck for real-time applications. Recent distillation frameworks produce autoregressive video diffusion models with reduced latency, yet these models still use a fixed number of denoising steps per frame, wasting computation on predictable frames and under-refining challenging ones. We present DSA, a confidence-guided adaptive computation framework for AR video diffusion. DSA introduces a lightweight confidence head, trained jointly with the generator under a distribution-matching distillation objective, to estimate per-frame denoising reliability. At inference, this confidence signal dynamically adjusts the number of diffusion steps: simple frames terminate early for speed, while complex frames receive additional refinement. Our method requires no extra video data, no heuristics, and little architectural modification. Experiments show that DSA achieves real-time autoregressive video generation, reaching 22.63 FPS with sub-second latency on H100 GPUs, while maintaining competitive or superior VBench quality compared to recent autoregressive and bidirectional video diffusion models. Our results demonstrate that confidence-guided adaptive sampling provides an effective and practical path toward interactive video generation.
Chinese Translation
视频扩散变换器已实现先进的视觉质量，但其高推理成本仍然是实时应用的主要瓶颈。最近的蒸馏框架产生了具有降低延迟的自回归视频扩散模型，但这些模型仍然对每帧使用固定数量的去噪步骤，这在可预测的帧上浪费了计算，而在复杂的帧上又未能充分精炼。我们提出了DSA，一种基于置信度的自适应计算框架，用于自回归视频扩散。DSA引入了一个轻量级的置信度头，它与生成器在分布匹配的蒸馏目标下共同训练，以估计每帧的去噪可靠性。在推理时，这一置信度信号动态调整扩散步骤的数量：简单帧提前终止以提高速度，而复杂帧则获得额外的精炼。我们的方法无需额外的视频数据，不依赖启发式方法，并且仅需少量架构修改。实验表明，DSA实现了实时自回归视频生成，在H100 GPU上达到了22.63 FPS的帧率，延迟低于一秒，同时在VBench质量上与最近的自回归和双向视频扩散模型相比，保持竞争力或更优的效果。我们的结果表明，基于置信度的自适应采样为交互式视频生成提供了有效且实用的途径。
cs.CV / 35 / 2606.04433
Stateful Visual Encoders for Vision-Language Models
用于视觉-语言模型的状态感知视觉编码器
Abstract
Vision-language models (VLMs) are increasingly used in multi-image, multi-turn agentic settings where decisions depend on visual changes. However, in existing open-weight VLMs, visual comparisons happen only inside the language model, while the visual encoder itself remains stateless: each image is encoded independently, without access to the prior visual context. As a result, small but task-critical changes may be attenuated before the language model has a chance to compare them, especially when those changes do not affect the high-level semantics of the scene. We introduce a Stateful Visual Encoder, which conditions each visual representation on prior visual features. Under supervised finetuning, VLMs equipped with stateful encoders achieve consistent improvements on controlled tasks involving cross-image spatial aggregation, multi-object visual differencing, and visual trajectory behavior cloning. These improvements are consistent across input resolutions, language model sizes, and VLM backbones. Finally, we validate our model on real-world tasks, including longitudinal radiology, fine-grained image comparison, and remote sensing, where stateful encoders consistently improve generalist VLM baselines and can match or surpass specialized models in selected domains. Project page: https://statefulvisualencoders.github.io/
Chinese Translation
视觉-语言模型（VLMs）在多图像、多轮次的自主设置中被越来越广泛地使用，在这些设置中，决策依赖于视觉变化。然而，在现有的开放权重VLM中，视觉比较仅发生在语言模型内部，而视觉编码器本身却是无状态的：每幅图像都是独立编码的，无法访问先前的视觉上下文。因此，小但对任务至关重要的变化可能在语言模型有机会进行比较之前就被削弱，尤其是当这些变化不影响场景的高层语义时。我们引入了一种状态感知视觉编码器，该编码器根据先前的视觉特征对每个视觉表示进行条件处理。在监督微调的情况下，配备状态感知编码器的VLM在涉及跨图像空间聚合、多对象视觉差异化和视觉轨迹行为克隆的受控任务上获得了一致的性能提升。这些改善在输入分辨率、语言模型规模以及VLM骨干网络之间是一致的。最后，我们在现实世界任务上验证了我们的模型，包括纵向放射学、细粒度图像比较和遥感，其中状态感知编码器始终提高了一般性VLM的基线性能，并能够在特定领域与专业模型匹敌或超越。项目页面：https://statefulvisualencoders.github.io/
cs.CV / 36 / 2606.04434
Hyper-ICL: Attention Calibration with Hyperbolic Anchor Distillation for Multimodal In-Context Learning
Hyper-ICL：利用双曲锚定蒸馏进行的注意力校准以实现多模态上下文学习
Abstract
Multimodal In-Context Learning (ICL) has emerged as a practical inference paradigm for Multimodal Large Language Models, where a small set of interleaved image-text In-Context Demonstrations (ICDs) conditions the model to solve new tasks. Despite its flexibility, multimodal ICL incurs high inference latency and suffers from instability due to sensitivity to demonstration formatting, ordering, and content. To address these limitations, we propose Hyper-ICL, a lightweight, training-based framework for demonstration-free multimodal ICL that reconstructs demonstration effects directly without requiring ICDs at inference time. Hyper-ICL learns a parameter-efficient low-rank logit-level adapter that calibrates attention distributions to better match demonstration-induced attention redistribution. To capture how demonstration influence varies across queries, we introduce a query-adaptive modulation mechanism that adaptively controls intervention strength at token level across layers and heads based on the current query. Finally, we propose a layer-wise hyperbolic anchor distillation loss that aligns intermediate student features to a demonstration-conditioned teacher via Lorentz geodesic distance. This loss encourages the student to reconstruct the demonstration-query relationships induced by ICDs. Extensive experiments across six different multimodal benchmarks (including VQAv2, OK-VQA, and COCO Caption) demonstrate that Hyper-ICL consistently improves accuracy and stability over vanilla ICL and existing state-of-the-art methods.
Chinese Translation
多模态上下文学习（In-Context Learning, ICL）已成为多模态大型语言模型的一种实用推理范式，在该范式中，一小组交错的图像-文本上下文演示（In-Context Demonstrations, ICDs）使模型能够解决新任务。尽管具有灵活性，多模态 ICL 的推理延迟较高，并且由于对演示格式、顺序和内容的敏感性，存在不稳定的问题。为了解决这些局限性，我们提出了 Hyper-ICL，一个轻量级的、基于训练的无演示多模态 ICL 框架，能够直接重构演示效果，而无需在推理时使用 ICDs。Hyper-ICL 学习了一种参数效率高的低秩逻辑适配器，以校准注意力分布，使其更好地匹配演示引起的注意力重分配。为了捕捉演示影响在查询间的变化，我们引入了一种查询自适应调制机制，该机制根据当前查询在每层和每个头部的令牌级别自适应控制干预强度。最后，我们提出了一种分层的双曲锚定蒸馏损失，该损失通过洛伦兹测地距离将中间的学生特征与演示条件教师对齐。该损失鼓励学生重构由 ICDs 引起的演示-查询关系。在六个不同的多模态基准（包括 VQAv2、OK-VQA 和 COCO Caption）上的大量实验表明，Hyper-ICL 在准确性和稳定性上始终优于普通 ICL 和现有的最先进方法。
cs.CV / 37 / 2606.04436
3DThinkVLA: Endowing Vision-Language-Action Models with Latent 3D Priors via 3D-Thinking-Guided Co-training
3DThinkVLA：通过3D思维引导的共同训练赋予视觉-语言-动作模型潜在的3D先验知识
Abstract
We propose a 3D-thinking-guided co-training framework that enables vision-language-action (VLA) models to perform 3D spatial reasoning implicitly during action prediction. Our core insight is that 3D geometry perception and 3D spatial reasoning are distinct capabilities that can be disentangled and injected at different feature hierarchies. During training, three tightly coupled components work in concert primarily within the latent space: (1) To gain geometric priors, a latent 3D geometry perception module aligns intermediate visual features with a 3D foundation model, acquiring low-level geometric cues without architectural modifications to the VLM backbone. (2) Complementing this, an online 3D reasoning distillation module mitigates the prompt-induced reasoning gap via a shared reasoning anchor token. During 3D VLM co-training, this anchor is emitted as the first output token to robustly encode spatial priors. During VLA training, it serves as an input token inserted between the task and action instructions, transferring high-level spatial thinking from explicit teacher reasoning prompts to student action prompts without chain-of-thought text generation. (3) These disentangled geometric and reasoning features are then united by a spatially augmented action integration, which jointly injects them into the action-query tokens as hierarchical spatial conditions to prevent action shortcuts. At deployment, our method retains only its lightweight adapters to perform implicit 3D reasoning, discarding the 3D foundation model and the teacher branch used for supervision. Consequently, it operates purely on 2D images without 3D sensors, external models, or explicit text generation while preventing catastrophic forgetting of the pretrained VLM, achieving state-of-the-art performance on LIBERO, LIBERO-PLUS, SimplerEnv, and real-world manipulation tasks.
Chinese Translation
我们提出了一种3D思维引导的共同训练框架，使视觉-语言-动作（VLA）模型在动作预测过程中能够隐式地进行3D空间推理。我们的核心观点是，3D几何感知和3D空间推理是可以分离的不同能力，可以在不同的特征层次上进行注入。在训练过程中，三个紧密耦合的组件主要在潜在空间中协同工作：（1）为了获得几何先验，一个潜在的3D几何感知模块将中间视觉特征与3D基础模型对齐，在不修改VLM主干结构的情况下获取低层次的几何线索。（2）作为补充，一个在线3D推理蒸馏模块通过共享推理锚令牌来减轻由提示引起的推理差距。在3D VLM共同训练期间，该锚令牌作为第一个输出令牌被发出，以稳健地编码空间先验。在VLA训练中，它作为一个输入令牌插入任务和动作指令之间，将来自显性教师推理提示的高层次空间思维转移到学生动作提示中，而无需链式思维文本生成。（3）这些分离的几何和推理特征随后通过空间增强的动作集成进行统一，共同将它们注入到动作查询令牌中，作为层次空间条件以防止动作捷径。在部署时，我们的方法仅保留其轻量级适配器以进行隐式3D推理，丢弃用于监督的3D基础模型和教师分支。因此，它完全在没有3D传感器、外部模型或显性文本生成的情况下，仅基于2D图像进行操作，同时防止对预训练VLM的灾难性遗忘，在LIBERO、LIBERO-PLUS、SimplerEnv和实际操作任务上实现了最先进的性能。
cs.CV / 38 / 2606.04437
INTACT: Ego-Guided Typed Sparse Evidence Retrieval for Heterogeneous Collaborative Perception
INTACT：面向异构协作感知的自我引导类型稀疏证据检索
Abstract
Collaborative perception extends the perceptual range of autonomous vehicles by sharing information across agents, but heterogeneous sensors and perception models make intermediate feature fusion difficult to deploy at scale. Existing heterogeneous collaboration methods typically follow a translation-first paradigm: collaborator features must be aligned, adapted, or projected into an ego-compatible space before fusion. Such feature-compatibility contracts improve fixed-system performance, but they couple deployment to collaborator-specific adaptation and make newly joined heterogeneous agents costly to integrate. To address this gap, we propose INTACT, an ego-guided typed sparse evidence retrieval framework for heterogeneous collaborative perception. Instead of translating an entire collaborator feature map, INTACT lets the ego vehicle issue typed evidence queries that express suspected objects and evidence-deficient regions. Collaborators respond only with local evidence at queried locations, and the ego selects useful responses through sparse per-query routing and injects them through gated residual write-back. This changes the compatibility requirement from global feature-map interpretability to local, typed response comparability under ego-issued queries, enabling a zero-training heterogeneous insertion protocol in which the ego interface is trained once and new collaborators join through checkpoint merging. Extensive experiments on simulated and real-world heterogeneous collaborative perception benchmarks validate the effectiveness and deployability of INTACT. On OPV2V-H, INTACT achieves 80.1 AP70 with only 0.52M additional parameters and 18.0 $\log_2$ communication volume, corresponding to about 16$\times$ compression over dense feature transmission. On DAIR-V2X, INTACT achieves 43.8 AP50 under challenging real-world conditions.
Chinese Translation
协作感知通过在代理之间共享信息，扩展了自主车辆的感知范围，但异构传感器和感知模型使得中间特征融合在大规模部署中变得困难。现有的异构协作方法通常遵循先翻译的范式：在融合之前，协作方的特征必须对齐、适配或投影到与自我（ego）兼容的空间。这种特征兼容性契约提升了固定系统的性能，但将部署与协作方特定的适配耦合在一起，使得新加入的异构代理集成成本高昂。为了解决这一问题，我们提出了INTACT，一种面向异构协作感知的自我引导类型稀疏证据检索框架。与其翻译整个协作方特征图，INTACT让自我车辆发出类型化证据查询，表达可疑物体和证据不足区域。协作方仅在查询位置返回局部证据，而自我则通过稀疏的每查询路由选择有用的响应，并通过门控残差回写将其注入。这使得兼容性要求从全局特征图的可解释性转变为在自我发出的查询下局部、类型化响应的可比性，从而实现一个零训练的异构插入协议，其中自我接口仅训练一次，并且新协作方通过检查点合并加入。对模拟和真实世界异构协作感知基准的广泛实验验证了INTACT的有效性和可部署性。在OPV2V-H上，INTACT仅以0.52M额外参数和18.0 $ ext{log}_2$ 通信量实现了80.1 AP70，相当于在密集特征传输上的约16倍压缩。在DAIR-V2X上，INTACT在挑战性的真实世界条件下实现了43.8 AP50。
cs.CV / 39 / 2606.04453
Radiomic Feature Selection Using Gradient Loss of Deep Neural Network for Lung Cancer Stage Detection
基于深度神经网络梯度损失的放射组学特征选择用于肺癌分期检测
Abstract
Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis. However, radiomics datasets are typically high-dimensional with limited samples, making feature selection a critical step for building reliable predictive models. This study proposes a Gradient-Loss Recursive Feature Elimination (GL-RFE) framework that integrates gradient sensitivity analysis from a deep neural network to identify the most influential radiomic features for lung cancer stage detection. A total of 106 radiomic features were extracted from chest Computed Tomography (CT) scans using the PyRadiomics extension of the 3D Slicer platform. The proposed method evaluates feature importance by computing gradients of the network loss with respect to input features and recursively eliminates features with minimal contribution. The resulting top-15 radiomic features are used to train a deep neural network classifier for distinguishing early-stage and advanced-stage lung cancer. The proposed framework achieves strong classification performance, with accuracy of 90.22%, precision of 90.10%, recall of 90.24%, and F1-score of 90.16% on the test dataset. Visualization analyses, including correlation heat maps and distribution plots, further confirm reduced feature redundancy and improved class separability. Compared to conventional feature selection techniques, GL-RFE effectively captures nonlinear feature interactions and enhances model generalization. The presented protocol provides a reproducible and interpretable methodology for radiomics-based cancer stage detection and is particularly suitable for high-dimensional, small-sample biomedical datasets, with potential applications in other domains such as genomics and multimodal clinical analysis.
Chinese Translation
放射组学能够从医学图像中提取定量成像生物标志物，已成为计算机辅助癌症诊断的重要工具。然而，放射组学数据集通常具有高维特征且样本有限，使得特征选择成为构建可靠预测模型的关键步骤。本研究提出了一种梯度损失递归特征消除（Gradient-Loss Recursive Feature Elimination, GL-RFE）框架，该框架结合了来自深度神经网络的梯度灵敏度分析，以识别对肺癌分期检测最具影响力的放射组学特征。使用3D Slicer平台的PyRadiomics扩展，从胸部计算机断层扫描（CT）图像中提取了总共106个放射组学特征。该方法通过计算网络损失相对于输入特征的梯度来评估特征重要性，并递归消除贡献最小的特征。最终选出的前15个放射组学特征用于训练深度神经网络分类器，以区分早期肺癌和晚期肺癌。 proposed framework achieves strong classification performance, with accuracy of 90.22%, precision of 90.10%, recall of 90.24%, and F1-score of 90.16% 在测试数据集上。可视化分析，包括相关性热图和分布图，进一步确认了特征冗余的减少和类别可分性的改善。与传统特征选择技术相比，GL-RFE有效捕捉非线性特征交互并增强模型的泛化能力。所提出的协议提供了一种可重复和可解释的方法，适用于基于放射组学的癌症分期检测，尤其适合高维小样本生物医学数据集，并在基因组学和多模态临床分析等其他领域具有潜在应用。
cs.CV / 40 / 2606.04457
Imagine Before You Draw: Visual Prompt Engineering for Image Generation
绘制之前的想象：图像生成的视觉提示工程
Abstract
Incorporating visual semantic representations as an intermediate step before image generation can reduce the modeling difficulty between text and images, thereby improving generation quality. Recent works such as X-Omni and BLIP3o-Next have explored this direction, but they typically use a two-stage external pipeline: a separate autoregressive model first generates semantic tokens, which are then fed as conditioning to an independent diffusion decoder. Since the decoder cannot jointly access the original input and the semantic plan, this design introduces an information bottleneck that limits detail preservation in downstream tasks such as editing. Internal architectures such as Transfusion, BAGEL, and Show-o2 avoid this bottleneck by enabling cross-modal interaction within a single model, but they still face the difficult text-to-pixel modeling gap without intermediate semantic guidance. We propose Visual Prompt Engineering (VPE), which can be seamlessly integrated into such internal frameworks. Specifically, the model first autoregressively generates visual semantic tokens (e.g., SigLIP 2) as "visual prompts" that capture the semantic layout, then generates the full image tokens conditioned on this plan. We validate VPE across class-conditional generation, text-to-image generation, and image editing, covering various token types and model architectures. Results show that VPE can accelerate convergence, raise quality ceilings, and through internal integration, achieve substantially better editing preservation (PSNR: 26.76 vs. 19.92) than external alternatives of the same parameter scale, while maintaining competitive editing responsiveness.
Chinese Translation
在图像生成之前融入视觉语义表示作为中间步骤，可以减少文本与图像之间的建模难度，从而提高生成质量。近期的研究如 X-Omni 和 BLIP3o-Next 探索了这个方向，但它们通常采用二阶段外部管道：一个独立的自回归模型首先生成语义标记，然后将其作为条件输入到一个独立的扩散解码器中。由于解码器无法同时访问原始输入和语义计划，这种设计引入了一个信息瓶颈，限制了在后续任务（如编辑）中的细节保留。内部架构如 Transfusion、BAGEL 和 Show-o2 通过在单一模型内实现跨模态交互来避免这一瓶颈，但仍然面临没有中间语义指导的难文本到像素建模差距。我们提出了视觉提示工程（Visual Prompt Engineering, VPE），该方法可以无缝集成到这些内部框架中。具体而言，该模型首先自回归地生成视觉语义标记（例如，SigLIP 2）作为捕捉语义布局的“视觉提示”，然后根据该计划生成完整的图像标记。我们在类别条件生成、文本到图像生成和图像编辑中验证了 VPE，涵盖了各种标记类型和模型架构。结果表明，VPE 能够加速收敛，提高质量上限，并通过内部集成，实现在相同参数规模的外部替代方法中显著更好的编辑保留（PSNR: 26.76 vs. 19.92），同时保持竞争性的编辑响应能力。
cs.CV / 41 / 2606.04461
ChannelTok: Efficient Flexible-Length Vision Tokenization
ChannelTok：高效的可变长度视觉标记化
Abstract
Leading flexible vision tokenizers achieve SOTA quality at an extreme cost, relying on parameter-heavy backbones and slow, multi-step generative decoders. We depart from this complex, spatial-token paradigm and introduce a simple, lightweight, and fast channel-wise flexible-length tokenizer. Our method treats each latent channel as a visual token, enabling a parameter-efficient CNN-Transformer hybrid backbone. Furthermore, employing a stochastic tail-dropping paradigm during training naturally forces channels to organize by semantic importance. This allows for flexible compression at inference by simply retaining the first $k$ channels, and naturally enables variable-length autoregressive image generation. We validate our approach through extensive experiments on ImageNet, demonstrating consistent quality across diverse token budgets. The results establish a new quality-efficiency frontier: our model achieves state-of-the-art perceptual quality (rFID 2.92) while being $8.6\times$ faster in decoding and $2.1\times$ smaller (159M params) than the next-best alternative. Our work establishes channel-wise tokenization as a powerful and practical paradigm for efficient visual representation. Project page: https://channeltok.github.io
Chinese Translation
现有的灵活视觉标记器在极高的成本下实现了最先进的质量，依赖于参数密集型的骨干网络和缓慢的多步生成解码器。我们摆脱了这种复杂的空间标记范式，提出了一种简单、轻量且快速的通道级可变长度标记器。我们的方法将每个潜在通道视为一个视觉标记，使得参数高效的CNN-Transformer混合骨干网络成为可能。此外，在训练过程中采用随机尾部丢弃范式，自然促进了通道按语义重要性进行组织。这使得在推理阶段能够通过简单地保留前$k$个通道实现灵活压缩，并自然支持可变长度的自回归图像生成。我们通过在ImageNet上进行广泛实验验证了我们的方法，展示了在多样化标记预算下的一致质量。结果建立了一个新的质量-效率前沿：我们的模型在解码方面比下一个最佳替代品快$8.6 imes$，参数量也小$2.1 imes$（159M参数），同时实现了最先进的感知质量（rFID 2.92）。我们的工作将通道级标记化确立为一种高效视觉表示的强大和实用范式。项目页面：https://channeltok.github.io
cs.CV / 42 / 2606.04469
Adaptive Calibration for Fair and Performant Facial Recognition
适应性校准：公平且高效的面部识别方法
Abstract
We introduce Adaptive Calibration (AC), a novel calibration strategy for facial recognition that maps cosine similarity between normalized embeddings to well-calibrated probabilities. By incorporating local context into calibration, Adaptive Calibration corrects for a fundamental mismatch in cosine similarity, whereby the same distance can correspond to different match probabilities in different embedding regions. Our approach improves both overall performance and results in a fairer calibration without requiring demographic metadata. Our approach consistently dominates existing methods both on accuracy and fairness metrics across a variety of pretrained models and standard benchmarks. AC provides a practical solution for equitable facial recognition, without requiring demographic group annotations, and while improving overall performance. Unlike existing approaches, our method provides continuous, region-specific calibration that avoids "leveling down" where fairness comes at the cost of degraded performance for some groups.
Chinese Translation
我们提出了一种新颖的面部识别校准策略——适应性校准（Adaptive Calibration, AC），该策略将归一化的嵌入之间的余弦相似度映射到精准校准的概率上。通过将局部上下文纳入校准过程，适应性校准纠正了余弦相似度中的一个基本不匹配，即同一距离在不同嵌入区域对应于不同的匹配概率。我们的方法在不需要人口统计元数据的情况下，同时改善整体性能并实现了更公平的校准。我们的方案在各种预训练模型和标准基准测试中，在准确性和公平性指标上，一直优于现有方法。AC为公平面部识别提供了一种实用的解决方案，既不需要人口统计群体的注释，又提高了整体性能。与现有方法不同，我们的方法提供了连续的、区域特定的校准，避免了因追求公平而导致某些群体的性能下降的“水准下降”现象。
cs.CV / 43 / 2606.04479
Evaluating Reasoning Fidelity in Visual Text Generation
评估视觉文本生成中的推理准确性
Abstract
Recent text-to-image (T2I) models can render highly legible and well-structured text within images, enabling applications including document generation and slide generation. However, it remains unclear whether such systems faithfully preserve reasoning ability when complex solutions must be expressed directly through rendered text, or whether they merely imitate surface-level patterns. We investigate this question by evaluating reasoning fidelity in visual text generation, where models must express complete reasoning processes as images. Our evaluation includes long text rendering, factual knowledge probing, context understanding, and multi-step reasoning. Across these settings, we find that current T2I models frequently produce semantic errors, logical inconsistencies, and incorrect intermediate steps, even when the rendered text appears visually clear. These failures contrast with the strong reasoning performance of text-only models on the same tasks. Our findings reveal a substantial gap between visual text generation and procedural reasoning, motivating more reliable visual text reasoning.
Chinese Translation
近年来，文本到图像（T2I）模型能够在图像中渲染出高可读性和结构良好的文本，这为文档生成和幻灯片生成等应用开辟了新局面。然而，尚不清楚这些系统在复杂解决方案必须通过渲染文本直接表达时，是否能够忠实地保留推理能力，或者仅仅是模仿表层模式。我们通过评估视觉文本生成中的推理准确性来探讨这一问题，在这一过程中，模型必须将完整的推理过程表达为图像。我们的评估包括长文本渲染、事实知识探测、上下文理解和多步骤推理。在这些设置中，我们发现当前的T2I模型经常产生语义错误、逻辑不一致和错误的中间步骤，即使渲染的文本在视觉上清晰。这些失败与仅基于文本模型在同一任务上的强推理表现形成对比。我们的研究发现显示了视觉文本生成与过程推理之间的显著差距，这突显了更可靠的视觉文本推理的必要性。
cs.CV / 44 / 2606.04480
IMPose: Interactive Multi-person Pose Estimation with Dynamic Correction Propagation
IMPose：基于动态校正传播的交互式多人姿态估计
Abstract
High-quality dynamic human pose annotation equips AI with precise motion kinematics to enable human behavior mastery, yet remains labor-intensive and time-consuming. Current annotation tools either lack temporal correction propagation or fail in multi-person scenarios, necessitating excessive manual intervention. In this paper, we introduce IMPose, an interactive tool for multi-person dynamic pose annotation. It features a dual-level tracking mechanism that propagates one-frame multi-person pose corrections from annotators across entire videos. The keypoint-level ensures corrections temporal propagation via sequential modeling, while the instance-level employs keypoint-aware embedding with relative positional encoding to maintain multi-person cross-frame consistency. To further improve robustness, IMPose maintains historical pose and instance cues in a trajectory bank, which enhances long-range temporal association and stabilizes annotation in challenging cases such as occlusion and motion blur. By converting sparse human corrections into dense and coherent pose trajectories, our framework significantly reduces repeated manual refinement across frames. Extensive experiments show that IMPose consistently achieves a strong accuracy efficiency trade off under different interaction budgets, demonstrating particular advantages in low click annotation settings. IMPose achieves high precision annotation with high efficiency, requiring only 27 clicks per 1,050 frame video on 3DPW and 3 clicks per tracklet per 84-frame on PoseTrack21. We further expand PoseTrack21 with 188K pose instances (3.55M keypoints) at a minimal cost of 10 annotators in 10 hours. The annotation tool, codes, and extended dataset will be open-sourced.
Chinese Translation
高质量的动态人类姿态标注为人工智能提供精准的运动动力学，以实现对人类行为的掌控，但这仍然是一个劳动密集且耗时的过程。目前的标注工具要么缺乏时间校正传播，要么在多人场景中表现不佳，需要过多的人工干预。本文介绍了IMPose，一种用于多人动态姿态标注的交互工具。它具备双层跟踪机制，可以将来自标注者的一帧多人姿态校正传播到整个视频。关键点层通过序列建模确保校正的时间传播，而实例层则采用关键点感知嵌入及相对位置编码，以保持多人跨帧的一致性。为了进一步提高鲁棒性，IMPose在轨迹库中维护历史姿态和实例线索，从而增强长距离的时间关联性，并在遮挡和运动模糊等挑战性情况下稳定标注。通过将稀疏的人类校正转化为密集且连贯的姿态轨迹，我们的框架显著减少了跨帧的重复人工细化。大量实验表明，IMPose在不同的交互预算下始终实现了良好的准确性和效率平衡，在低点击标注设置中表现出特别优势。IMPose以高效率和高精度完成标注，在3DPW数据集中每1,050帧视频仅需要27次点击，而在PoseTrack21数据集中每条轨迹84帧仅需3次点击。我们在PoseTrack21中新增了188K个姿态实例（3.55M个关键点），仅需要10位标注者在10小时内完成。标注工具、代码和扩展数据集将会开源。
cs.CV / 45 / 2606.04493
SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning
SFMambaNet：用于对应修剪的光谱频率增强选择性状态空间模型
Abstract
Correspondence pruning aims to identify inliers from an initial set of correspondences. Most existing Graph Neural Network (GNN)-based methods rely on geometric features mapped from coarse Euclidean coordinates, which struggle to capture the subtle geometric consistencies presented by inliers. While Mamba-based methods possess global receptive fields and long sequence modeling capabilities, they tend to accumulate substantial inconsistent features within the hidden state space, making it difficult to distinguish inliers from outliers. In this paper, we integrate frequency domain perception into this task for the first time and propose SFMambaNet, a novel Spectral-Frequency enhanced Mamba-based two-view correspondence pruning network. Our method is collaboratively composed of two components: First, we design a Local Spectral-Geometric Attention (LSGA) block. LSGA incorporates spectral positional encoding into local graph interactions and introduces multi-scale Mamba processing to enhance the capture of subtle geometric consistencies and improve local feature discriminability. Building upon this, we design a Spectral-Integrated Global Mamba (SIGM) block. SIGM embeds a frequency gating mechanism within the state space, utilizing the frequency information provided by LSGA to explicitly suppress high-frequency noise accumulation within hidden states and mitigate the propagation of inconsistent features. This enhances inlier-outlier separability and achieves robust global context modeling capabilities with nearly linear complexity. Extensive experiments demonstrate that SFMambaNet outperforms current state-of-the-art methods on several challenging tasks. The code is available at https://github.com/Kirito14IT/SFMambaNet.
Chinese Translation
对应修剪旨在从初始对应集合中识别内点。大多数现有的基于图神经网络（GNN）的方法依赖于从粗略的欧几里得坐标映射的几何特征，而这些特征在捕捉内点所呈现的微妙几何一致性方面存在困难。尽管基于Mamba的方法具有全局感受野和长序列建模能力，但它们倾向于在隐藏状态空间中累积大量不一致的特征，从而使内点与外点之间的区分变得困难。本文首次将频域感知整合到这一任务中，提出了一种新颖的基于Mamba的光谱频率增强双视图对应修剪网络SFMambaNet。我们的方法由两个组件共同组成：首先，我们设计了一个局部光谱几何注意力（LSGA）模块。LSGA将光谱位置编码融入局部图交互中，并引入多尺度Mamba处理，以增强对微妙几何一致性的捕捉并提高局部特征的可区分性。在此基础上，我们设计了一个光谱集成全局Mamba（SIGM）模块。SIGM在状态空间内嵌入了频率门控机制，利用LSGA提供的频率信息明确抑制隐藏状态中的高频噪声累积，减缓不一致特征的传播。这样增强了内点与外点的可分性，并以近乎线性的复杂度实现了健壮的全局上下文建模能力。大量实验表明，SFMambaNet在多个具有挑战性的任务上超越了当前最先进的方法。代码可在 https://github.com/Kirito14IT/SFMambaNet 获取。
cs.CV / 46 / 2606.04528
Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning
光学引导的合成孔径雷达少样本类增量学习中的神经崩溃
Abstract
Few-shot class-incremental learning (FSCIL) in synthetic aperture radar imagery presents unique challenges due to severe data scarcity and SAR-specific variability. In particular, strong azimuth sensitivity in SAR induces large intra-class variation and inter-class confusion, and FSCIL sequential updates further lead to catastrophic forgetting of previously learned classes. Inspired by neural collapse, we propose an optical-guided SAR FSCIL framework, which derives orthogonal feature subspaces from a data-rich optical ATR dataset and uses them as geometric priors to guide SAR feature learning. SAR features are projected onto these orthogonal subspaces via principal angle constraints, effectively transferring discriminative structure from the optical to the SAR domain. Specifically, our projection loss and the classifier loss optimized with a frozen simplex-ETF geometry jointly induce neural collapse by concentrating features around class means while maintaining large inter-class angles. We evaluate the approach on a benchmark comprising an optical ATR dataset and a SAR ATR dataset with 24 target classes, organized into a base training session and seven incremental sessions. Compared with recent FSCIL methods including NCFSCIL and so on, our method achieves the highest final accuracy and a favorable trade-off between final performance and performance degradation. Moreover, neural collapse metrics show improved intra-class compactness and inter-class separability, indicating that the learned features more closely approximate the ideal simplex-ETF geometry.
Chinese Translation
合成孔径雷达图像中的少样本类增量学习（FSCIL）面临独特的挑战，因为数据稀缺严重且具有SAR特有的变异性。特别是，SAR中的强方位敏感性导致类内变异性大和类间混淆，FSCIL的连续更新进一步导致了对先前学习类别的灾难性遗忘。受神经崩溃的启发，我们提出了一种光学引导的SAR FSCIL框架，该框架从数据丰富的光学自动目标识别（ATR）数据集中导出正交特征子空间，并将其作为几何先验来引导SAR特征学习。通过主角约束将SAR特征投影到这些正交子空间，有效地将光学域的判别结构转移到SAR域。具体来说，我们的投影损失和被固定的简单x-ETF几何体优化的分类器损失共同引导了神经崩溃，通过使特征集中在类别均值周围同时保持较大的类间角度。我们在一个基准测试上评估了该方法，该基准测试包含一个光学ATR数据集和一个包含24个目标类别的SAR ATR数据集，分为一个基础训练阶段和七个增量阶段。与最近的FSCIL方法（包括NCFSCIL等）相比，我们的方法实现了最高的最终准确率，并在最终性能和性能下降之间达成了良好的权衡。此外，神经崩溃指标显示了更好的类内紧凑性和类间可分性，表明学习到的特征更接近理想的简单x-ETF几何体。
cs.CV / 47 / 2606.04545
Impostor: An Agent-Curated Benchmark for Realistic AIGC Manipulation Localization
Impostor：一个由代理人策划的现实 AIGC 操作定位基准
Abstract
Recent advances in generative image editing have improved the realism and controllability of localized image manipulation, raising new challenges for image manipulation detection and localization (IMDL). However, existing IMDL benchmarks still have limitations in visual realism, manipulation diversity, and generator coverage, making it difficult to reflect recent trends in image manipulation. To address these limitations, we introduce Impostor, a high-quality AI-edited image manipulation localization dataset containing 100K manipulated images. Impostor is constructed by CraftAgent, a closed-loop agent framework that integrates scene perception, editing planning, manipulation execution, quality validation, and iterative reflection to automatically generate diverse and visually realistic manipulated images. Moreover, Impostor contains images generated by seven recent AIGC models across three manipulation types and includes multiple manipulated regions, providing a more comprehensive benchmark for AIGC-based IMDL. Furthermore, we propose PhaseAware-Net (PANet), a semantic-forensic framework that introduces local phase modeling and semantic-forensic consistency learning to better localize semantically plausible yet forensically disrupted manipulated regions. Extensive experiments show that Impostor poses significant challenges to existing large vision-language models (LVLMs) and specialized IMDL methods, while PANet achieves superior performance on Impostor and multiple public benchmarks.
Chinese Translation
近年来，生成图像编辑的进展提高了局部图像操作的真实性和可控性，带来了图像操作检测和定位（IMDL）的新挑战。然而，现有的 IMDL 基准在视觉真实性、操作多样性和生成器覆盖方面仍存在局限性，难以反映近期图像操作的趋势。为了解决这些局限性，我们引入了 Impostor，一个包含 100K 操作图像的高质量 AI 编辑图像操作定位数据集。Impostor 由 CraftAgent 构建，这是一个封闭环代理框架，集成了场景感知、编辑规划、操作执行、质量验证和迭代反思，能够自动生成多样化且视觉上真实的操作图像。此外，Impostor 包含由七个最新的 AIGC 模型生成的图像，涵盖三种操作类型，并包含多个操作区域，为基于 AIGC 的 IMDL 提供了更全面的基准。此外，我们提出了 PhaseAware-Net（PANet），这是一种语义法医学框架，引入了局部相位建模和语义法医学一致性学习，以更好地定位语义上合理但法医学上被破坏的操作区域。大量实验表明，Impostor 对现有的大型视觉-语言模型（LVLM）和专业 IMDL 方法提出了显著挑战，而 PANet 在 Impostor 和多个公共基准上实现了卓越的性能。
cs.CV / 48 / 2606.04593
4D Reconstruction from Sparse Dynamic Cameras
稀疏动态摄像机的4D重建
Abstract
Although dynamic 3D (i.e., 4D) reconstruction from a monocular dynamic camera has recently advanced, it remains fundamentally limited by depth ambiguity. In this paper, we focus on an alternative practical way, i.e., sparse dynamic camera setup, where a handful of independently moving cameras capture the same subjects. While keeping capture costs low, this setup introduces multi-view constraints and remains practical for real-world video production such as sports, concerts, and TV shows. Despite its potential, our experiments show that naive extensions of existing monocular or dense-fixed camera-based methods are insufficient since they fail to resolve the complex spatiotemporal inconsistencies across views and time. To fill this gap, we propose a simple yet effective 3D track initialization method designed to ensure spatiotemporal consistency by integrating inter-camera feature matching with intra-camera point tracking. Additionally, we incorporate a noise-robust depth-ordering regularization loss and a spatiotemporally diverse batch sampling strategy to enhance optimization stability and cross-view generalization. Furthermore, to address the lack of standardized benchmarks for this task, we introduce LetCamsGo, a new real-world video dataset with 5 sequences across 4 diverse environments, recorded by three independently moving cameras and one fixed camera. Comprehensive benchmarking on LetCamsGo demonstrated that our proposed framework improves 4D reconstruction quality in dynamic regions compared with baselines, paving the way for a low-cost 4D reconstruction paradigm in the wild.
Chinese Translation
尽管最近从单目动态摄像机进行动态3D（即4D）重建已经取得了进展，但它在深度歧义方面仍存在根本性的限制。本文着重探讨一种实用的替代方法，即稀疏动态摄像机设置，其中少量独立移动的摄像机捕捉相同的对象。该设置在保持捕获成本低的同时，引入了多视角约束，并且在体育、音乐会和电视节目的现实视频制作中仍然具有实用性。尽管潜力巨大，我们的实验表明，现有的单目或密集固定摄像机方法的简单扩展并不足够，因为它们未能解决视角和时间之间复杂的时空不一致性。为了填补这一空白，我们提出了一种简单而有效的3D轨迹初始化方法，旨在通过整合摄像机间特征匹配与摄像机内点追踪来确保时空一致性。此外，我们引入了一种抗噪音的深度排序正则化损失和时空多样化的批次采样策略，以增强优化的稳定性和视角间的泛化。此外，为了解决这一任务缺乏标准化基准的问题，我们介绍了LetCamsGo，一个新的现实视频数据集，包含4个多样化环境下的5个序列，由三台独立移动的摄像机和一台固定摄像机录制。对LetCamsGo的全面基准测试表明，我们提出的框架在动态区域改善了4D重建质量，相比基线为在野外低成本4D重建范式铺平了道路。
cs.CV / 49 / 2606.04604
COMBINER: Composed Image Retrieval Guided by Attribute-based Neighbor Relations
COMBINER：基于属性邻域关系的组合图像检索
Abstract
Composed Image Retrieval (CIR) represents a challenging retrieval task that targets locating specific images through multimodal inputs. Despite recent progress in CIR techniques, prior approaches often overlook cases where images appear visually alike yet differ in attributes, potentially undermining both multimodal feature fusion and similarity modeling. To mitigate this limitation, we design a unified representation of cross-modal features based on attribute prototypes. Nevertheless, the task is far from straightforward, owing to three core issues: (1) entanglement in attribute-level semantics, (2) inconsistency across modalities, and (3) supervised signal missing. To tackle the above obstacles, we introduce a COMposed image retrieval network guided By attrIbute-based NEighbor Relations (COMBINER). Specifically, we first design an Adaptive Semantic Disentanglement module, which is capable of disentangling attribute features based on multimodal primitive features. Secondly, we propose a Unified Prototype-based Composition module, which can construct cross-modal unified prototypes (CUP) and facilitate multimodal feature composition. Finally, we introduce a Dual Relations Modeling module, which can mine pairwise and neighbor relations based on attribute similarity. Compared to traditional neighbor relations modeling CIR methods, COMBINER represents the first study addressing the phenomenon of visually similar but attribute-unrelated samples. It achieves a more accurate understanding of the semantic relations among samples by employing an attribute prototype-based similarity metric. Comprehensive experiments conducted on three benchmark datasets confirm the effectiveness of our proposed COMBINER. The implementation of our method will be accessed at https://github.com/Lee-zixu/COMBINER
Chinese Translation
组合图像检索（CIR）是一项具有挑战性的检索任务，旨在通过多模态输入定位特定图像。尽管最近在CIR技术上取得了进展，然而，之前的方法往往忽视了那些在视觉上相似但属性不同的图像，这可能削弱了多模态特征融合和相似性建模的有效性。为了解决这一局限性，我们设计了一种基于属性原型的跨模态特征统一表示。不过，由于三个核心问题，该任务远非简单： (1) 属性级语义的纠缠，(2) 各模态之间的不一致性，(3) 缺乏监督信号。为此，我们提出了一种由属性邻域关系指导的组合图像检索网络（COMBINER）。具体而言，我们首先设计了一个自适应语义解缠模块，该模块能够基于多模态原始特征解缠属性特征。其次，我们提出了一个统一原型组合模块，该模块可以构建跨模态统一原型（CUP）并促进多模态特征组合。最后，我们引入了一个双重关系建模模块，该模块能够基于属性相似性挖掘成对和邻域关系。与传统邻域关系建模的CIR方法相比，COMBINER是首个解决视觉上相似但属性无关样本现象的研究。通过采用基于属性原型的相似性度量，它实现了对样本间语义关系的更准确理解。在三个基准数据集上进行的全面实验验证了我们所提出的COMBINER的有效性。我们方法的实现可以访问https://github.com/Lee-zixu/COMBINER
cs.CV / 50 / 2606.04613
Beyond Symmetric Alignment: Spectral Diagnostics of Modality Imbalance in Vision-Language Models in the Medical Domain
超越对称对齐：医学领域视觉-语言模型中的模态不平衡的谱诊断
Abstract
Vision-Language Models (VLMs) struggle when applied to medical image-text data, yet the tools available to diagnose this failure remain limited. Existing representation alignment metrics are symmetric, collapsing both modalities into a single score and hiding which modality drives cross-modal degradation. We introduce the Spectral Alignment Score (SAS), an asymmetric metric that projects both modalities onto the principal eigenbasis of an anchor modality and computes eigenvalue-weighted per-eigenmode correlations, resulting in directional scores whose difference quantifies modality information imbalance. We embed SAS within a benchmarking framework evaluating 15 VLMs across natural and medical image-text datasets alongside 6 alignment metrics and bidirectional retrieval. Our experiments show that medical images retain richer structural information than their paired clinical reports, a directional asymmetry invisible to all competing metrics, and that SAS achieves the strongest zero-label correlation with retrieval performance in the medical domain, positioning it as a practical diagnostic tool for clinical deployment. Code is available at this URL: https://github.com/iamalegambetti/medical-vlms-assessment.
Chinese Translation
视觉-语言模型（VLMs）在应用于医学图像-文本数据时面临困难，但用于诊断这些失败的工具仍然有限。现有的表征对齐指标是对称的，因而将两个模态压缩为一个分数，隐藏了哪个模态导致了跨模态的退化。我们引入了谱对齐分数（Spectral Alignment Score, SAS），这是一种不对称指标，它将两个模态投影到锚模态的主特征基上，并计算特征值加权的每个特征模态相关性，得到的方向性分数差异量化了模态信息的不平衡。我们将SAS嵌入一个基准框架中，评估15个VLMs在自然和医学图像-文本数据集上的表现，并结合6个对齐指标及双向检索。我们的实验表明，医学图像保留的结构信息比其配对的临床报告更为丰富，这种方向性不对称在所有竞争指标中都是不可见的，并且SAS与医学领域的检索性能之间的零标签相关性最强，使其成为实际临床部署的有效诊断工具。代码可在此链接查看：https://github.com/iamalegambetti/medical-vlms-assessment。
cs.CV / 51 / 2606.04621
MeshFlow: Efficient Artistic Mesh Generation via MeshVAE and Flow-based Diffusion Transformer
MeshFlow：通过 MeshVAE 和基于流的扩散变换器高效生成艺术网格
Abstract
We present MeshFlow, a new method for generating artist-like 3D meshes. Current mesh generators often adopt Auto-Regressive (AR) next-token prediction, a natural choice given the discrete nature of mesh topology. However, AR methods scale poorly because the inference cost is quadratic in mesh size. They also require discretizing the vertex coordinates, which introduces quantization errors. To address these challenges, we introduce a Variational Autoencoder (VAE) that, supervised with a contrastive loss, represents both continuous vertex positions and discrete connectivity in a continuous latent space. This latent space is significantly more compact than prior token-based mesh representations. We then build a 3D generator based on a Rectified Flow transformer, generating all mesh vertices and edges in parallel. Our model generates meshes 18x faster than the fastest AR generator while also achieving excellent accuracy across standard mesh-generation metrics. Homepage: https://mesh-flow.github.io/, Code: https://github.com/facebookresearch/meshflow
Chinese Translation
我们提出了 MeshFlow，一种生成艺术风格 3D 网格的新方法。目前的网格生成器通常采用自回归 (AR) 下一个标记预测，这是考虑到网格拓扑离散特性的一种自然选择。然而，AR 方法的扩展性能较差，因为推理成本与网格大小成平方关系。此外，它们还需要对顶点坐标进行离散化，这会引入量化误差。为了应对这些挑战，我们引入了一种变分自编码器 (VAE)，该编码器在对比损失的监督下，在一个连续的潜在空间中表示连续的顶点位置和离散的连接性。该潜在空间显著更加紧凑，相比于先前基于标记的网格表示。然后，我们基于修正的流变换器构建一个 3D 生成器，能够并行生成所有网格顶点和边。我们的模型生成网格的速度比最快的 AR 生成器快 18 倍，同时在标准网格生成指标上也达到了优秀的准确性。主页：https://mesh-flow.github.io/，代码：https://github.com/facebookresearch/meshflow
cs.CV / 52 / 2606.04656
Instance-Level Post Hoc Uncertainty Quantification in Object Detection
实例级后验不确定性量化在目标检测中的应用
Abstract
Object detection is a safety-critical component of autonomous driving. It is essential to quantify the uncertainty in bounding-box predictions for safety assurance. Post hoc uncertainty quantification without retraining aligns with real-world deployment requirements; therefore, we employ the Laplace approximation. Because instance-level uncertainty is needed, linearized inference methods that require multiple backpropagations are not time-efficient, and sampling-based methods are not fully post hoc. We propose Monte-Carlo generalized linearized model (MC-GLM), which provides instance-level and approximately post hoc uncertainty quantification. The number of samples required in the Monte Carlo step is constant and independent of the number of output instances, so it can be parallelized. Experiments on the nuScenes dataset with the CenterPoint detector validate the effectiveness of our method, and the resulting uncertainties exhibit good quality.
Chinese Translation
目标检测是自动驾驶中的一个安全关键组件。量化边界框预测的不确定性对于安全保障至关重要。在不重新训练的情况下进行后验不确定性量化符合真实世界的部署要求；因此，我们采用拉普拉斯近似。由于需要实例级的不确定性，线性化推理方法需要多次反向传播，效率较低，而基于采样的方法并未完全满足后验要求。我们提出了一种蒙特卡洛广义线性模型（Monte-Carlo generalized linearized model, MC-GLM），可以提供实例级以及近似后验的不确定性量化。在蒙特卡洛步骤中所需的样本数量是常数，并且与输出实例的数量无关，因此可以进行并行处理。在nuScenes数据集上使用CenterPoint检测器进行的实验验证了我们方法的有效性，所得的不确定性展现出良好的质量。
cs.CV / 53 / 2606.04684
Real-Time Automatic License Plate Recognition Using YOLOv8, SORT Tracking, and Temporal Data Interpolation
基于YOLOv8、SORT跟踪和时间数据插值的实时自动车牌识别
Abstract
The real-time hardships of video processing seriously limit the usage of Automatic License Plate Recognition (ALPR) with application in dynamic traffic monitoring settings. High-fidelity recognition of unconstrained variables, e.g. drastic variations in illumination, acute camera scans, high vehicle speeds, and harsh physical concealment, is a problem that often leads to disjointed tracking paths and poor Optical Character Recognition (OCR) rates. In order to mitigate these weaknesses, the study proposes a 5 stage, end-to-end algorithmic pipeline, encompassing a smooth transition between deep learning based object detection, multi-object tracking which is kinematic in nature, and geometry temporal data interpolation. The suggested architecture takes advantage of a very powerful YOLOv8 nano model to localize the vehicle at the first stage and then Simple Online and Realtime Tracking (SORT) algorithm is used to build spatial-temporal links between frames. Another, more specific typology of YOLOv8 object detectors the license plate area, channeling the sliced array to an EasyOCR chain under the limitations of positional syntax verification. More importantly, an offline interpolation mechanism of temporal bounding box is initiated to recast fragmented paths.
Chinese Translation
视频处理的实时困难严重限制了自动车牌识别（ALPR）在动态交通监控环境中的应用。对非约束变量（如光照急剧变化、剧烈的摄像头扫描、高速行驶的车辆以及严酷的物理遮挡）进行高保真识别是一个常常导致跟踪路径不连续和光学字符识别（OCR）率低下的问题。为了解决这些弱点，本研究提出了一种五阶段的端到端算法管道，涵盖了基于深度学习的目标检测、多目标跟踪（该过程具有动力学特点）和几何时间数据插值之间的平稳过渡。所建议的架构利用了非常强大的YOLOv8纳米模型在第一阶段定位车辆，然后采用简单在线和实时跟踪（SORT）算法在帧之间建立时空链接。另一个更具体的YOLOv8对象检测器类型专注于车牌区域，将切片数组传递到EasyOCR链下，并在位置语法验证的限制下进行处理。更重要的是，启动了一个离线的时间边界框插值机制，以重新构建碎片化的路径。
cs.CV / 54 / 2606.04688
MeshWeaver: Sparse-Voxel-Guided Surface Weaving for Autoregressive Mesh Generation
MeshWeaver：基于稀疏体素的自回归网格生成表面编织技术
Abstract
Autoregressive mesh generation has gained attention by tokenizing meshes into sequences and training models in a language-modeling fashion. However, existing approaches suffer from two fundamental limitations: (i) low tokenization efficiency, which yields long token sequences and prevents scaling to high-poly meshes, and (ii) absence of geometry-aware guidance, as generation is conditioned only on global shape embeddings rather than local surface cues. We introduce MeshWeaver, an autoregressive framework that treats mesh generation as a surface weaving process by directly predicting the next vertex instead of independent coordinates. At its core is a multi-level sparse-voxel encoder that injects geometric context into the generative process in three complementary ways: providing voxel features as vertex representations, guiding token prediction via cross-attention to voxel features, and serving as a structural scaffold that constrains generation around the input surface. Our hierarchical design enables coarse-to-fine vertex prediction in a single decoding step, while tightly coupling the generative model with 3D geometry. Extensive experiments demonstrate that MeshWeaver achieves a state-of-the-art compression ratio of 18%, can generate meshes with up to 16K faces, and significantly improves geometric fidelity over prior approaches.
Chinese Translation
自回归网格生成通过将网格转化为序列并以语言建模的方式训练模型而获得了关注。然而，现有方法存在两个基本限制：（i）低令牌化效率，导致长令牌序列，并阻碍高多边形网格的扩展；（ii）缺乏几何感知指导，因为生成仅依赖于全局形状嵌入而非局部表面线索。我们提出了MeshWeaver，这是一个自回归框架，将网格生成视为表面编织过程，通过直接预测下一个顶点而不是独立坐标来实现。其核心是一个多层稀疏体素编码器，通过三种互补方式将几何上下文注入生成过程：提供体素特征作为顶点表示，通过交叉注意力引导令牌预测体素特征，以及作为约束输入表面生成的结构支架。我们的层次设计使得在单次解码步骤中实现粗到细的顶点预测，同时将生成模型与3D几何形状紧密耦合。大量实验表明，MeshWeaver实现了18%的最先进压缩比，能够生成最多16K面的网格，并显著提高了几何保真度相比于之前的方法。
cs.CV / 55 / 2606.04700
A New Angle on Bones: Robust Pose Estimation in X-Ray and Ultrasound
骨骼的新视角：X射线和超声中的稳健姿态估计
Abstract
Measuring the angle between bone structures is a routine task in medical image analysis and provides a key quantitative parameter for diagnosis and treatment planning. Automated methods can reduce time and cost while improving reproducibility. In this work, we address automatic bone pose estimation using a learning-based point candidate proposal followed by a line model to extract axis parameters. Since conventional line models such as least squares are sensitive to outliers, we incorporate false-positive reduction strategies and robust fitting techniques, such as RANSAC and Hough transforms, to improve robustness. We evaluate our method on three clinically relevant paediatric angle estimation tasks: fracture fragment assessment in radiographs and ultrasound and developmental dysplasia of the hip evaluation in ultrasound using the Graf method. Our approach achieves mean errors of $4.1^\circ$, $5.4^\circ$, and $5.51^\circ$, respectively, not only remaining within the expected clinical observer variability, but also significantly outperforming landmark-based methods. Our code and annotations for fracture angle assessment in radiographs are publicly available on GitHub.
Chinese Translation
测量骨骼结构之间的角度是医学图像分析中的常规任务，并为诊断和治疗计划提供了关键的定量参数。自动化方法可以减少时间和成本，同时提高可重复性。本文中，我们针对自动骨骼姿态估计进行了研究，采用基于学习的点候选提议，随后使用线模型提取轴参数。由于传统的线模型（如最小二乘法）对离群值很敏感，我们引入了假阳性减少策略和稳健拟合技术，如RANSAC和霍夫变换，以提高系统的鲁棒性。我们在三个临床相关的儿童角度估计任务中评估了我们的方法：在X射线和超声图像中对骨折碎片的评估，以及使用Graf方法在超声中对髋关节发育性发育不良的评估。我们的方法分别实现了$4.1^ heta$、$5.4^ heta$和$5.51^ heta$的平均误差，不仅保持在预期的临床观察者变异性范围内，而且显著优于基于标志点的方法。我们针对X射线骨折角度评估的代码和注释已公开在GitHub上。
cs.CV / 56 / 2606.04701
Benchmarking Living-Screen-Native GUI Agents on Short-Video Platforms
在短视频平台上基准测试活屏本地 GUI 代理
Abstract
GUI agents today assume a static screen, where the world is frozen between two actions. However, real interfaces such as short-video applications violate this assumption, as their content keeps playing, and a competent user must decide what to watch and for how long. We formalize this task as Living-Screen-Native GUI agents and introduce LivingScreen, the first benchmark instantiating it on short-video platforms, with a faithful browser-based environment, a three-tier task suite, and metrics that jointly score accuracy and information efficiency. Evaluating extensive frontier models, we find that none reaches the human cost-accuracy performance, and that their dominant failure mode is over- and under-observation, pointing to observation control as a missing capability axis for future GUI agents. All data and code will be available at https://github.com/BITHLP/LivingScreen.
Chinese Translation
当前的 GUI 代理假设有一个静态屏幕，在两个动作之间世界是静止的。然而，短视频应用等真实界面违反了这个假设，因为它们的内容不断播放，合格的用户必须决定观看什么以及观看多长时间。我们将这一任务形式化为活屏本地 GUI 代理，并引入 LivingScreen，这是第一个在短视频平台上实现这一任务的基准，配备真实的基于浏览器的环境、一个三级任务套件以及联合评分准确性和信息效率的指标。通过评估大量前沿模型，我们发现没有一个模型达到了人类的成本-准确性表现，而它们主要的失败模式是过度和不足观察，这表明观察控制是未来 GUI 代理缺失的能力轴。所有数据和代码将在 https://github.com/BITHLP/LivingScreen 上提供。
cs.CV / 57 / 2606.04705
Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation
通过轻量级边框预测器增强MedSAM用于医学图像分割
Abstract
Semantic segmentation in medical imaging is a critical yet challenging task due to data scarcity and high variability across modalities. While foundation models like the Segment Anything Model (SAM) show promise, they often struggle with medical images without specific adaptation. Moreover, point prompts, despite being the most natural form of user interaction, provide insufficient spatial context for reliable segmentation, particularly when target structures are irregular or poorly contrasted. In this paper, we propose an enhanced segmentation framework that integrates a lightweight Box Predictor module into the MedSAM architecture. The Box Predictor estimates an approximate bounding box from a single user click using localized image embedding features, providing spatial guidance that reduces the ambiguity of point prompts, while introducing only 1.6M additional parameters and negligible inference overhead. We introduce a two-stage training pipeline where the Box Predictor is trained independently before being integrated into MedSAM. To validate the generalization capability of our method, we conduct extensive evaluations on four diverse datasets (FLARE22, BRISC, BUSI, LungSegDB) spanning distinct imaging modalities, including CT, MRI, and Ultrasound. Our method improves segmentation accuracy and robustness across varied anatomical structures and imaging domains, achieving Dice scores of 0.89 (BUSI), 0.93 (FLARE22), 0.88 (BRISC), and 0.98 (LungSegDB). Code is available at https://github.com/Amirhosseinmovahedi/MedSAM-BoxPredictor
Chinese Translation
医学成像中的语义分割是一项关键但具有挑战性的任务，主要由于数据稀缺和模态间的高变异性。尽管像Segment Anything Model (SAM)这样的基础模型表现出一定的潜力，但在没有特定适应的情况下，它们往往难以处理医学图像。此外，尽管点提示是最自然的用户交互形式，但它们提供的空间上下文不足以确保可靠的分割，尤其是在目标结构不规则或对比度较低的情况下。本文提出了一种增强的分割框架，将轻量级边框预测器模块集成到MedSAM架构中。边框预测器通过使用局部图像嵌入特征，从单个用户点击估算一个近似的边界框，提供空间引导，减少点提示的模糊性，同时仅引入1.6百万额外参数且推理开销微乎其微。我们引入一个两阶段的训练流程，首先独立训练边框预测器，然后将其整合到MedSAM中。为验证我们方法的泛化能力，我们在四个不同的数据集（FLARE22、BRISC、BUSI、LungSegDB）上进行了广泛评估，这些数据集涵盖了不同的成像模态，包括CT、MRI和超声。我们的方法在不同的解剖结构和成像领域中提高了分割精度和鲁棒性，Dice评分分别达到了0.89（BUSI）、0.93（FLARE22）、0.88（BRISC）和0.98（LungSegDB）。代码可在https://github.com/Amirhosseinmovahedi/MedSAM-BoxPredictor获取。
cs.CV / 58 / 2606.04706
ReConFuse: Reconstruction-Error Guided Semantic Fusion for AI-Generated Video Detection
ReConFuse：基于重建误差的语义融合用于人工智能生成视频检测
Abstract
AI-generated videos are becoming increasingly realistic, raising serious concerns about misinformation, content authenticity, and media trust. Reliable AI-generated video detection is therefore essential for multimedia forensics, yet remains challenging due to the need to capture spatial artifacts, temporal dynamics, and generalize to evolving generative models. In this paper, we explore reconstruction error as a discriminative forensic cue for AI-generated video detection. By reconstructing input videos with a pretrained WF-VAE, we observe that real and generated videos exhibit distinguishable frame-wise reconstruction error patterns, suggesting that reconstruction errors can reveal their distributional discrepancies. However, extending reconstruction-based image detection to videos is non-trivial, since video reconstruction errors are temporally organized across frames and require semantic context for effective interpretation. To address these challenges, we propose ReConFuse, a reconstruction-guided semantic fusion framework for video-level AI-generated video detection. ReConFuse extracts reconstruction error cues from WF-VAE reconstructed videos, aligns them with multi-frame semantic features, and uses a Mamba-based module to model temporal evolution for video-level classification. Experiments across multiple generators and evaluation settings demonstrate the effectiveness and strong generalization ability of ReConFuse.
Chinese Translation
人工智能生成的视频正变得越来越逼真，这引发了关于虚假信息、内容真实性和媒体信任的严重担忧。因此，可靠的人工智能生成视频检测对于多媒体取证至关重要，但由于需要捕捉空间伪影、时间动态并对不断演变的生成模型进行概括，这一任务仍然具有挑战性。本文探讨了重建误差作为人工智能生成视频检测的判别性取证线索。通过使用预训练的 WF-VAE 重建输入视频，我们观察到真实视频和生成视频在逐帧重建误差模式上存在可区分的差异，这表明重建误差可以揭示它们的分布差异。然而，将基于重建的图像检测扩展到视频并不简单，因为视频重建误差在帧之间是时间上有序的，并且需要语义上下文以有效解释。为了解决这些挑战，我们提出了 ReConFuse，这是一种基于重建引导的语义融合框架，用于视频级人工智能生成视频检测。ReConFuse 从 WF-VAE 重建的视频中提取重建误差线索，将其与多帧语义特征对齐，并使用基于 Mamba 的模块来建模视频级分类的时间演变。在多个生成器和评估设置下的实验表明，ReConFuse 的有效性和强大的泛化能力。
cs.CV / 59 / 2606.04710
Data Efficient Complex Feature Fusion Network For Hyperspectral Image Classification
数据高效的复合特征融合网络用于高光谱图像分类
Abstract
This work presents a data-efficient variant of the Attention-Based Dual-Branch Complex Feature Fusion Network (CFFN) for hyperspectral image classification. The proposed model, termed DE-CFFN, retains the original two-stream structure: the Real-Valued Neural Network (RVNN) processes standard hyperspectral patches, while the Complex-Valued Neural Network (CVNN) handles their Fourier-transformed counterparts. The main contribution of this work lies in the feature extraction process and architectural enhancement. Factor Analysis is used for dimensionality reduction, offering improved latent feature representation over Principal Component Analysis. Additionally, both the RVNN and CVNN streams are structurally modified by successively halving the number of filters in the 3D convolutional layers to reduce complexity. The outputs of both branches are concatenated and passed through a Squeeze and Excitation (SE) block to enhance joint feature representation. Evaluated on the Pavia University and Salinas datasets, DE-CFFN achieves classification performance comparable to CFFN, while significantly reducing model size, memory consumption, and inference latency, making it suitable for real-time hyperspectral imaging applications.
Chinese Translation
本研究提出了一种数据高效的注意力基双分支复合特征融合网络（Complex Feature Fusion Network, CFFN）变体，用于高光谱图像分类。所提出的模型称为DE-CFFN，保留了原有的双流结构：实值神经网络（Real-Valued Neural Network, RVNN）处理标准高光谱图像块，而复值神经网络（Complex-Valued Neural Network, CVNN）处理其傅里叶变换后的对应物。该研究的主要贡献在于特征提取过程和结构增强。通过因子分析（Factor Analysis）进行降维，相较于主成分分析（Principal Component Analysis），提供了更好的潜在特征表示。此外，RVNN和CVNN的结构通过在三维卷积层中逐步减半滤波器数量进行修改，以降低复杂度。两个分支的输出被连接并通过挤压与激励（Squeeze and Excitation, SE）模块增强联合特征表示。在Pavia大学和Salinas数据集上的评估表明，DE-CFFN的分类性能可与CFFN相媲美，同时显著减少了模型大小、内存消耗和推理延迟，使其适用于实时高光谱成像应用。
cs.CV / 60 / 2606.04722
StrokeTimer: Robust Representation Learning for Ischemic Stroke Onset-Time Estimation from Non-contrast CT
StrokeTimer：用于缺血性中风发作时间估计的稳健表示学习，基于非对比CT
Abstract
Ischemic stroke is a major global disease. Treatment decisions are highly time-sensitive, as eligibility for reperfusion therapies relies on the interval between stroke onset and intervention. However, the true onset time is often uncertain in clinical practice, necessitating imaging-based assessment of tissue age as a surrogate marker. Early ischemic changes on routinely acquired non-contrast CT (NCCT) are often subtle, and real-world clinical datasets exhibit pronounced onset-time class imbalance and center-scanner-related heterogeneity. In this work, we propose StrokeTimer, a fully automated framework for onset-time estimation in acute ischemic stroke. StrokeTimer integrates self-supervised disentanglement learning with energy-guided contrastive learning to capture subtle ischemic patterns while addressing long-tailed data distributions under acquisition variability. Onset time is categorized into three clinically relevant windows: <4.5 h, 4.5-6 h, and >6 h. Experimental results on a large multi-center NCCT dataset from two national cohorts, MR CLEAN Registry and MR CLEAN LATE, show that StrokeTimer achieves a macro AUC of 0.69 and a macro F1-score of 0.57, improving the strongest baseline by nearly 50% (p < 0.005). In this realistic, challenging setting, representative baseline approaches exhibit near-chance macro performance. Model explanations further highlight subtle gray-white matter blurring and hypodense regions consistent with established radiological biomarkers. These findings demonstrate the potential of StrokeTimer to support treatment decision-making in acute ischemic stroke. Code is available at https://github.com/BrainVas/StrokeTimer.
Chinese Translation
缺血性中风是一种全球主要疾病。治疗决策高度依赖时间，因为再灌注治疗的适应性取决于中风发作与干预之间的间隔。然而，在临床实践中，真正的发作时间往往不确定，这就需要基于影像进行组织年龄的评估作为替代指标。常规获取的非对比CT（NCCT）上的早期缺血性变化常常较为微妙，而现实世界中的临床数据集则表现出明显的发作时间类别失衡和中心扫描相关的异质性。本文提出了StrokeTimer，这是一个用于急性缺血性中风发作时间估计的全自动框架。StrokeTimer将自监督的解缠学习与能量引导的对比学习相结合，旨在捕捉微妙的缺血模式，同时应对在获取变异性下的长尾数据分布。发作时间被分为三个临床相关窗口：<4.5小时，4.5-6小时，以及>6小时。在来自两个国家队列的一个大型多中心NCCT数据集上的实验结果显示，StrokeTimer取得了0.69的宏观AUC和0.57的宏观F1-score，较最强基准提高了近50%（p < 0.005）。在这种现实且具有挑战性的环境下，代表性的基准方法展现了接近随机的宏观表现。模型解释进一步突出了微妙的灰白质模糊和低密度区域，这与既往放射学生物标志物一致。这些发现表明，StrokeTimer在支持急性缺血性中风的治疗决策中具有潜力。代码可在https://github.com/BrainVas/StrokeTimer上获得。
cs.CV / 61 / 2606.04737
Physics-Informed Video Generation via Mixture-of-Experts Latent Alignment
通过混合专家潜在对齐的物理信息视频生成
Abstract
Large-scale video generation models have made remarkable progress in semantic consistency and visual quality, producing videos that are increasingly coherent and visually convincing. Nevertheless, the dynamics induced by pixel-level fitting do not naturally accommodate the regularities that govern real-world motion and interaction, resulting in persistent shortcomings in physical plausibility. To address this limitation, we propose \textbf{PILA} (Physics-Informed Latent Alignment), a framework that injects physics-structured latent guidance into the frozen flow-matching dynamics of pretrained video models. Specifically, PILA first employs anchored field estimation to map frozen-generator latents into an operational physical attribute bank organized by field-proxy slots, using observable motion as a kinematic anchor for constructing less directly observed proxies. To handle the heterogeneity of real-world dynamics, PILA adopts a mixture-of-experts design over physical categories. Label-prior masked expert routing selects category-specific operator experts, whose refinements are regularized by operational residuals abstracted from physical relations. Finally, the refined proxies are fused into the physical attribute bank and decoded into a correction to the flow-matching vector field, injecting physics-aware guidance while preserving the visual prior of the pretrained backbone. With staged adapter training on Wan 2.1-1.3B and direct transfer of the learned adapter to Wan 2.2-14B, PILA achieves state-of-the-art results on VBench-2.0, VideoPhy-2, and PhyGenBench in both visual quality and benchmark-measured physical plausibility.
Chinese Translation
大规模视频生成模型在语义一致性和视觉质量上取得了显著进展，生成的视频越来越连贯且视觉上令人信服。然而，像素级拟合带来的动态并未自然地适应于 govern 真实世界运动和交互的规律，导致在物理合理性方面存在持续的不足。为了解决这一局限性，我们提出了 extbf{PILA}（物理信息潜在对齐）框架，该框架将物理结构的潜在引导注入到预训练视频模型的冻结流匹配动态中。具体而言，PILA 首先采用锚定场估计将冻结生成器的潜在映射到由场代理槽组织的操作物理属性库中，使用可观察运动作为运动学锚点来构建不那么直接观察的代理。为了处理真实世界动力学的异质性，PILA 在物理类别上采用了混合专家设计。标签优先掩码的专家路由选择类别特定的操作专家，其细化通过从物理关系抽象出的操作残差进行正则化。最后，将细化后的代理融合到物理属性库中，并解码为对流匹配向量场的修正，注入对物理的感知引导，同时保留预训练主干的视觉先验。通过对 Wan 2.1-1.3B 进行分阶段适配器训练，并将学习到的适配器直接转移到 Wan 2.2-14B，PILA 在 VBench-2.0、VideoPhy-2 和 PhyGenBench 上在视觉质量和基准测量的物理合理性方面实现了最先进的结果。
cs.CV / 62 / 2606.04764
Do Foundation Models See Biology? Evaluating Attention Coherence with Spatial Transcriptomics in Glioblastoma
基础模型能否看见生物学？基于空间转录组学在胶质母细胞瘤中评估注意力一致性
Abstract
Whether attention maps from pathology foundation models capture genuine biology remains unknown, yet this question is critical for clinical trust and regulatory approval. We propose a spatial transcriptomics-based framework for orthogonal, hypothesis-free evaluation of attention and apply it to five pathology foundation models (CONCH v1.5, UNI v2, Virchow2, GigaPath, H-Optimus-1) and a ResNet50 baseline. Using attention-based multiple instance learning, we train single-task and multi-task models to predict five molecular alterations in glioblastoma on the CPTAC cohort, validate on an independent TCGA cohort, and evaluate biological coherence of attention maps against 87 transcriptional signatures using co-registered Visium spatial transcriptomics data from 18 samples. Internally, no single encoder dominates across all tasks, and external validation inverts internal performance rankings. Attention maps show a five-fold enrichment gradient from pathways (Cohen's d=0.329) to individual genes (d=0.055), indicating that attention captures emergent multi-gene transcriptional programs rather than individual molecular events. Spatially smooth attention maps do not imply biological coherence, and different encoders attend to distinct biological compartments. Our framework provides objective, quantitative assessment of what foundation models learn from histopathology, moving the field beyond qualitative saliency map review.
Chinese Translation
病理基础模型的注意力图是否捕捉到真实的生物学现象仍然未知，但这一问题对于临床信任和监管批准至关重要。我们提出了一种基于空间转录组学的框架，用于对注意力进行正交的、无假设的评估，并将其应用于五个病理基础模型（CONCH v1.5、UNI v2、Virchow2、GigaPath、H-Optimus-1）及一个 ResNet50 基线模型。我们通过基于注意力的多实例学习，在CPTAC队列上训练单任务和多任务模型，以预测胶质母细胞瘤中的五种分子变异，在独立的TCGA队列上进行验证，并使用来自18个样本的共注册Visium空间转录组数据评估注意力图的生物一致性，比较87个转录签名。从内部来看，没有单一的编码器在所有任务中占主导地位，外部验证颠覆了内部性能排名。注意力图显示出从通路（Cohen's d=0.329）到个体基因（d=0.055）之间的五倍富集梯度，表明注意力捕获了复杂的多基因转录程序，而非单个分子事件。空间平滑的注意力图并不意味着生物一致性，不同的编码器会关注不同的生物区域。我们的框架提供了对基础模型从组织病理学中学习内容的客观、定量评估，推动该领域超越定性显著性图的审查。
cs.CV / 63 / 2606.04772
Coarse-to-fine Hierarchical Architecture with Sequential Mamba for Brain Reconstruction
粗到细的分层架构与顺序Mamba在脑重建中的应用
Abstract
Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience. While modern vision models achieve strong performance in image recognition, their correspondence with the hierarchical organization of the human visual cortex remains an open question. In this study, we propose CHASMBrain, a novel hierarchical two-stage framework for image-to-fMRI encoding. Our architecture leverages a dual-stream Mamba design to explicitly separate and process global semantic tokens and local spatial patches, motivated by the functional organization of the visual cortex. A coarse-to-fine strategy is employed: Stage 1 predicts denoised ROI-level activations, while Stage 2 refines these coarse responses into full voxel-level predictions using a Mamba-VAE. Experiments on the Natural Scenes Dataset (NSD) demonstrate that our method achieves a Pearson correlation of 0.429 and an MSE of 0.261, outperforming all evaluated baselines including ridge regression and DINOv2 linear probes. Beyond predictive performance, causal branch-ablation experiments reveal an asymmetric specialization: the patch stream is specifically locked to early visual cortex (retinotopic regions), while the CLS stream contributes broader semantic context to higher-order areas -- a correspondence that holds causally, not merely correlationally. Cross-subject transfer experiments further show that the learned backbone generalizes across individuals with minimal per-subject adaptation, suggesting the model captures a shared, subject-agnostic visual representation.
Chinese Translation
理解深度视觉表征与人类视觉系统之间的关系是计算神经科学中的一个基本挑战。虽然现代视觉模型在图像识别中表现出色，但它们与人类视觉皮层的分层组织之间的对应关系仍然是一个未解之谜。在本研究中，我们提出了CHASMBrain，一种新颖的两阶段分层框架，用于图像到功能性磁共振成像(fMRI)的编码。我们的架构利用双流Mamba设计，明确分离并处理全局语义标记和局部空间片段，这一设计受到视觉皮层功能组织的启发。采用粗到细的策略：第一阶段预测去噪后的感兴趣区(ROI)级激活，而第二阶段则使用Mamba-VAE将这些粗略响应细化为完整的体素级预测。在自然场景数据集(Natural Scenes Dataset, NSD)上的实验表明，我们的方法获得了0.429的皮尔逊相关系数和0.261的均方误差(MSE)，超越了所有评估的基准，包括岭回归和DINOv2线性探测器。除了预测性能之外，因果分支消融实验揭示了一种不对称的专门化：片段流特别锁定于早期视觉皮层（视网膜拓扑区域），而CLS流则为更高阶区域贡献更广泛的语义上下文——这种对应关系是因果的，而不仅仅是相关的。跨个体迁移实验进一步表明，学习到的主干模型在个体之间具有良好的泛化性，仅需最小的个体适应，表明该模型捕捉到了共享的、不依赖于主体的视觉表征。
cs.CV / 64 / 2606.04773
NextMotionQA: Benchmarking and Judging Human Motion Understanding with Vision-Language Models
NextMotionQA：通过视觉-语言模型对人类运动理解进行基准评估和判断
Abstract
Reliable evaluation of human motion understanding is fundamental to advancing embodied AI, robotics, and animation. However, existing benchmarks suffer from coarse semantic granularity, undifferentiated difficulty, limited annotation quality, and pervasive answer ambiguity, leaving them unable to diagnose where current models fail. To bridge this gap, we introduce NextMotionQA, a comprehensive benchmark that leverages vision-language models (VLMs) for semi-automated, expert-verified dataset. NextMotionQA features three complementary tasks: multiple-choice question answering, video captioning, and fine-grained error correction. Each task is systematically structured across three core semantic axes and stratified into three task complexity levels. Our extensive evaluation of twelve representative VLMs uncovers critical capability gaps and weakness that remain invisible under conventional, single-task evaluations. In a complementary direction, recent work has begun using VLMs as judges for text-to-motion evaluation; we ask whether they show the same degradation under harder tasks. We find that VLMs align strongly with expert ratings on coarse criteria (Cohen's \kappa=0.70) but break down on fine-grained, part-level judgment (\kappa=0.10), validating the paradigm in its strong regime while clarifying its limits.
Chinese Translation
对人类运动理解的可靠评估是推动具身人工智能、机器人技术和动画发展的基础。然而，现有基准的语义粒度过于粗糙、难度未予区分、注释质量有限以及答案歧义普遍，导致无法诊断当前模型的失败之处。为了填补这一空白，我们引入了NextMotionQA，这是一项综合性基准，利用视觉-语言模型（VLMs）创建半自动化的专家验证数据集。NextMotionQA包括三个互补任务：多项选择问答、视频字幕生成和细粒度错误修正。每个任务在三个核心语义轴上系统构建，并分为三个任务复杂度级别。我们对十二个具有代表性的VLM进行了广泛评估，发现了在传统单任务评估下无法显现的重要能力差距和弱点。在一个互补的方向上，近期的研究开始将VLM作为文本到运动评估的评判工具；我们询问它们在更难任务下是否表现出相同的退化。我们的发现表明，VLM在粗略标准上的评分与专家评级高度一致（Cohen's 4=0.70），但在细粒度、部件级的判断上则出现了崩溃（4=0.10），这验证了该范式在其强大范围内的有效性，同时阐明了其局限性。
cs.CV / 65 / 2606.04788
Z-FLoc: Zero-Shot Floorplan Localization via Geometric Primitives
Z-FLoc：通过几何原语实现零样本平面定位
Abstract
Visual localization -- estimating a camera pose within a pre-existing map -- is a fundamental problem in computer vision. Floorplans are an attractive map representation: they are readily available for most buildings, compact, and inherently invariant to visual appearance changes. However, bridging the severe domain gap between camera observations and floorplan geometry remains challenging. Existing methods address this gap through data-driven learning, yet they require large-scale training data and environment-specific retraining, limiting their practical deployment. We propose a zero-shot floorplan localization method that generalizes to novel environments without any retraining. Our key insight is that dominant geometric primitives -- lines and circles -- are ubiquitous in human-made environments and provide appearance-invariant structural constraints. We extract these primitives from a bird's-eye-view (BEV) projection of monocular 3D reconstructions and match them to the floorplan via dedicated minimal solvers within a robust estimation framework. Experiments on both simulated and real-world datasets show that our approach outperforms state-of-the-art learning-based methods on unseen environments, while using a single fixed set of hyperparameters across all experiments. The source code will be made publicly available.
Chinese Translation
视觉定位——在预先存在的地图中估计摄像机位置——是计算机视觉中的一个基本问题。平面图是一种吸引人的地图表示：它们通常为大多数建筑所提供，体积小，并且本质上对视觉外观变化具有不变性。然而，弥合摄像机观测与平面图几何之间的巨大领域差距仍然是一个挑战。现有方法通过数据驱动的学习来解决这一差距，但它们需要大规模的训练数据和针对特定环境的重新训练，这限制了它们的实际应用。我们提出了一种零样本平面定位方法，能够在不进行任何重新训练的情况下推广到新环境。我们的关键见解是，主导的几何原语——直线和圆——在人造环境中普遍存在，并提供了与外观无关的结构约束。我们从单目三维重建的鸟瞰图（BEV）投影中提取这些原语，并通过专用的最小求解器在稳健的估计框架中将其匹配到平面图上。在模拟和真实世界数据集上的实验表明，我们的方法在看不见的环境中超越了最先进的基于学习的方法，同时在所有实验中使用一组固定的超参数。源代码将公开发布。
cs.CV / 66 / 2606.04792
A Pathology Foundation Model for Gastric Cancer with Real-World Validation
胃癌的病理基础模型及其现实世界验证
Liang, Ling, Ma, Jiabo, Zhang, Zhengyu, Zhou, Fengtao, Xu, Yingxue, Wang, Yihui, Jin, Cheng, Guo, Zhengrui, Tang, On Ki, Cen, Zhijian, Wang, Zhen, Xie, Qi, Lu, Chengyu, Zhao, Chenglong, Wang, Feifei, Cai, Yu, Wang, Hongyi, Zhang, Jing, Ye, Yaping, Sun, Shijun, Li, Shenglei, Wang, Yu, Li, Zhenhui, Chan, Ronald Cheong Kin, Zhang, Xiuming, Wang, Zhe, Chen, Hao, Liang, Li
Abstract
Gastric cancer remains a major cause of cancer mortality, yet its histological and molecular heterogeneity complicates diagnosis and risk stratification. General-purpose pathology foundation models (PFMs) often plateau on fine-grained endpoints central to gastric cancer care, and few have undergone rigorous prospective validation or clinical reader studies. We present GRACE, a Gastric-specific foundation model for Real-world Assessment and Clinical dEcision support. GRACE was developed from multicenter gastric pathology datasets totaling 48,364 primarily HE-stained whole-slide images from 37,493 patients. When evaluated on 28 clinically relevant tasks, GRACE consistently outperformed representative pancancer PFMs, achieving a macro-AUC of 0.9188, with strong performance for precancerous lesion diagnosis (macro-AUC 0.9322), tumor histopathological assessment (macro-AUC 0.9119), molecular profiling (macro-AUC 0.8682), and prognostic prediction. Beyond benchmarking, GRACE's translational value was substantiated through a rigorous evidence chain. Under safety-gated criteria requiring 100% NPV for rule-out and 100% PPV for rule-in, GRACE streamlined review for up to 69.6% of malignancy-diagnosis cases and triaged 46.8% of MMR-IHC follow-up requests. This translational feasibility was further strengthened by a randomized crossover reader study of pathologist-AI collaboration. With GRACE assistance, diagnostic accuracy improved from 82.0% to 89.9%, yielding nearly twofold higher adjusted odds of a correct diagnosis (OR 1.987) alongside concurrent gains in sensitivity and specificity. AI assistance also reduced diagnostic time by 14.9%, elevated diagnostic confidence by 9.0%, and markedly improved inter-rater agreement. When calibrated to maintain non-inferior performance to senior pathologists, the AI-assisted workflow could triage 60.7% of atrophy and 82.7% of intestinal metaplasia cases.
Chinese Translation
胃癌仍然是癌症死亡的主要原因，但其组织学和分子异质性使得诊断和风险分层变得复杂。通用的病理基础模型（PFMs）通常在与胃癌护理密切相关的细粒度终点上表现平平，且很少经过严谨的前瞻性验证或临床阅读研究。我们提出了GRACE，一个用于现实世界评估和临床决策支持的胃癌特异性基础模型。GRACE的开发基于来自37,493名患者的多中心胃部病理数据集，总计48,364幅主要为HE染色的全切片图像。在28个临床相关任务的评估中，GRACE始终优于代表性全癌种PFM，达到了0.9188的宏观曲线下面积（macro-AUC），在癌前病变诊断（宏观AUC 0.9322）、肿瘤组织病理学评估（宏观AUC 0.9119）、分子特征分析（宏观AUC 0.8682）和预后预测方面表现出色。除了基准评估，GRACE的转化价值通过严格的证据链得到了证实。在需要100%阴性预测值（NPV）进行排除和100%阳性预测值（PPV）进行确认的安全性门槛标准下，GRACE简化了高达69.6%的恶性诊断案例的审查，并对46.8%的MMR-IHC后续请求进行了分流。这一转化可行性在病理学家与AI合作的随机交叉阅读研究中得到了进一步加强。借助GRACE的帮助，诊断准确率从82.0%提高至89.9%，正确诊断的调整优势比几乎提高了一倍（优势比OR 1.987），同时灵敏度和特异性也有所提升。AI的辅助还使诊断时间减少了14.9%，诊断信心提高了9.0%，并显著改善了评审者间的一致性。当调校以保持与资深病理学家相当的表现时，AI辅助的工作流程能够对60.7%的萎缩病例和82.7%的肠上皮化生病例进行分流。
cs.CV / 67 / 2606.04797
Crafting Your Evolving Dreams: Concept-Incremental Versatile Customization
塑造你的不断演变的梦想：概念增量多样化定制
Abstract
Custom diffusion models (CDMs) have garnered significant interest owing to their remarkable capacity for generating personalized concepts. However, the majority of CDMs unrealistically presume that the user's collection of personalized concepts is static and incapable of incremental growth over time. Furthermore, they exhibit significant catastrophic forgetting and concept neglect of previously learned concepts when incrementally learning a sequence of new ones. To resolve the above challenges, we develop a novel Continually Customizable Diffusion Model (CCDM), enabling users to perform concept-incremental versatile customization. Specifically, we design an attribute-decoupled LoRA (AD-LoRA) module and a relevance-guided AD-LoRA aggregation strategy to mitigate catastrophic forgetting. They can preserve concept-specific attributes of each task and leverage beneficial inter-task correlations to enhance the continual learning of new customization tasks. Additionally, to address the challenge of concept neglect, we propose a controllable regional context synthesis strategy that performs multi-concept composition in alignment with user-provided conditions. This strategy enhances the overall consistency in multi-concept synthesis by guaranteeing semantic independence between user-defined regions and their smooth boundary transitions. Experiments show our CCDM exhibits significant improvements over baseline methods.
Chinese Translation
定制扩散模型（CDMs）因其出色的个性化概念生成能力而受到广泛关注。然而，大多数CDMs不切实际地假设用户的个性化概念集合是静态的，并且无法随着时间的推移进行增量增长。此外，在增量学习一系列新概念时，它们会表现出显著的灾难性遗忘和对先前学习概念的忽视。为了解决上述挑战，我们开发了一种新颖的连续可定制扩散模型（CCDM），使用户能够进行概念增量多样化定制。具体而言，我们设计了一种属性解耦的LoRA（AD-LoRA）模块和一种相关性引导的AD-LoRA聚合策略，以减少灾难性遗忘。这些方法可以保留每个任务的特定概念属性，并利用有利的任务间相关性来增强新定制任务的持续学习。此外，为了解决概念忽视的问题，我们提出了一种可控区域上下文合成策略，该策略根据用户提供的条件执行多概念组合。该策略通过确保用户定义区域之间的语义独立性及其平滑的边界过渡，从而增强了多概念合成的整体一致性。实验结果表明，我们的CCDM在基准方法上表现出显著改善。
cs.CV / 68 / 2606.04801
Fast Cubical Persistent Homology on 2D and 3D Images via Union-Find, Pruning, and Lookup Tables
通过并查集、修剪和查找表实现2D和3D图像的快速立方持续同调
Abstract
We present Flash Cubical, a highly efficient computation of cubical persistence on a V-filtration for 2D and 3D images over $\mathbb{F}_2$. The implementation is built around three core ideas. First, cubical complexes satisfy properties that allow for the computation of persistence of the highest dimension via union-find and duality. Second, pruning of certain edges allows for a fast and efficient implementation of union-find. Third, the use of a lookup table, which exploits the regularity of cubical complexes to pre-compute local information. This avoids the need to compute local information at run time. To the best of our knowledge, this is the most efficient implementation of cubical persistence with a V-filtration, both in terms of time and memory costs. Although the paper focuses on persistence for V-filtration cubical complexes, the underlying ideas generalise naturally to T-filtrations on cubical complexes and suggest promising directions for other complexes.
Chinese Translation
我们提出了Flash Cubical，一种在$ ext{F}_2$域上对2D和3D图像进行高效立方持续计算的算法。该实现基于三个核心思想。首先，立方复形满足某些属性，允许通过并查集和对偶性计算最高维度的持续性。其次，修剪某些边缘使得并查集的实现快速高效。第三，使用查找表，利用立方复形的规则性预先计算局部信息，从而避免在运行时计算局部信息。根据我们所知，这是目前对具有V-过滤的立方持续性最为高效的实现，在时间和内存成本上均具有优势。尽管本文集中讨论V-过滤立方复形的持续性，但其基本思想自然可以推广到立方复形上的T-过滤，并为其他复形提供了有前景的研究方向。
cs.CV / 69 / 2606.04806
NoRA: Evaluating Grounded Reasonableness in Visual First-person Normative Action Reasoning
NoRA：评估视觉第一人称规范行动推理中的合理性基础
Abstract
LLMs and agentic systems are increasingly deployed in social environments, making normative competence critical for safe and appropriate behavior. However, existing approaches either assess normative judgment in text alone or reduce it to choosing among a fixed set of candidate actions. We argue both are insufficient. In practice, agents are never handed a menu of options; they must identify a reasonable action from scratch, grounded in visible facts and supported by inspectable reasons. We introduce NoRA, a visual first-person video benchmark that requires models to generate candidate next actions and justify each through an explicit fact-reason-action support graph. The benchmark comprises 1,420 annotated video clips, including HumanGold-190 and LLMSilver-1230 splits. Each instance is evaluated through action alignment, factual grounding, and support binding, aggregated into a single grounded reasonableness score. We benchmark 12 multimodal systems under direct, deliberate, and structured prompting regimes, finding that current VLMs frequently recover plausible actions and relevant scene facts, but consistently struggle to construct the full reasonable action space and bind selected actions to the correct local support. NoRA makes this gap measurable, shifting the evaluation question from whether a model can pick an action to whether it can justify an appropriate action for the right visible reasons.
Chinese Translation
随着大型语言模型（LLMs）和自主系统在社会环境中的越来越多应用，具备规范能力对于确保安全和恰当的行为显得尤为重要。然而，现有的方法要么仅在文本中评估规范判断，要么将其简化为在固定候选行动中进行选择。我们认为两者都不够充分。在实践中，代理并不会接收到一份选项菜单；他们必须根据可见的事实，从零开始识别合理的行动，并通过可检验的理由来支撑这一行动。我们引入了NoRA，这是一个视觉第一人称视频基准，要求模型生成候选下一步行动，并通过明确的事实-理由-行动支持图来为每个行动提供理由。该基准包含1,420个标注的视频片段，包括HumanGold-190和LLMSilver-1230拆分。每个实例通过行动对齐、事实基础和支持绑定进行评估，汇总为一个单一的合理性基础得分。我们在直接、故意和结构性提示下对12个多模态系统进行了基准测试，发现当前的视觉语言模型（VLMs）常常能够恢复合理的行动和相关的场景事实，但在构建完整合理的行动空间以及将所选行动绑定到正确的局部支持方面始终面临挑战。NoRA使这一差距可度量，将评估问题的焦点从模型是否能够选择行动转变为其是否能够根据正确可见理由为适当行动提供合理的支持。
cs.CV / 70 / 2606.04811
Dream.exe: Can Video Generation Models Dream Executable Robot Manipulation?
Dream.exe：视频生成模型能否梦想可执行的机器人操控？
Abstract
Video generation models have made impressive strides in synthesizing visually compelling content, yet their outputs remain confined to the virtual domain. A natural question follows: how well do these models reflect the physical world when their generated videos leave the screen and enter reality? We propose robotic manipulation as a concrete, measurable window onto this question: if a model has truly internalized physical laws, the motion it depicts should translate into executable robot behavior. We introduce Dream.exe, an evaluation framework that operationalizes this criterion through a video-to-execution pipeline. Given a scene image and a task description, Dream.exe synthesizes a manipulation video, converts the generated motion into robot trajectories, and executes them in a physics simulator, yielding a grounding signal that purely visual metrics cannot offer. Using this pipeline, we evaluate 8 models spanning frontier closed-source generators, open-source generators, and robot-specific models. Our benchmark covers 101 manually curated manipulation tasks at three levels of physical complexity, measured across visual quality, trajectory fidelity, and execution success. Encouragingly, several models achieve measurable execution success, suggesting that generative priors learned from internet-scale data already encode meaningful physical knowledge. Yet visual quality proves a poor predictor of executability, exposing a dimension of model capability that standard visual evaluations do not capture. Dream.exe will be open-sourced at https://github.com/showlab/Dream.exe.
Chinese Translation
视频生成模型在合成视觉引人注目的内容方面取得了显著进展，但其输出仍然局限于虚拟领域。紧随其后的是一个自然的问题：当这些模型生成的视频离开屏幕并进入现实时，它们在多大程度上反映了物理世界？我们提出机器人操控作为一个具体的、可衡量的窗口来探讨这个问题：如果一个模型真正内化了物理法则，它所描绘的运动应该能够转化为可执行的机器人行为。我们引入了Dream.exe，一个评估框架，通过视频到执行的管道实现这一标准。给定一个场景图像和一个任务描述，Dream.exe合成一个操控视频，将生成的运动转换为机器人轨迹，并在物理模拟器中执行它们，从而产生一个仅凭视觉指标无法提供的基础信号。利用这个管道，我们评估了8个模型，涵盖了前沿的闭源生成器、开源生成器和特定于机器人模型。我们的基准测试涵盖了101个手动策划的操控任务，分为三个物理复杂度级别，并在视觉质量、轨迹一致性和执行成功率方面进行了测量。令人鼓舞的是，多个模型实现了可测的执行成功，表明从互联网规模数据中学习到的生成先验已经编码了有意义的物理知识。然而，视觉质量却证明是可执行性的较差预测指标，暴露出标准视觉评估未能捕捉到的模型能力维度。Dream.exe将开放源代码，地址为 https://github.com/showlab/Dream.exe。
cs.CV / 71 / 2606.04820
OA-CutMix: Correcting the Label Bias of CutMix
OA-CutMix：纠正 CutMix 的标签偏差
Abstract
CutMix has become the de facto standard mixing augmentation, yet its label assignment rests on a flawed assumption: The area of the pasted patch faithfully reflects its semantic contribution to the mixed image. In practice, however, patches frequently land on background regions, assigning label credit to classes whose objects are not visible. The mean discrepancy of the CutMix label and the semantic object area is $21.5\%$. In $17\%$ of samples an image contributes zero visible object pixels yet receives nonzero label weight. We propose Object-Aware CutMix (OA-CutMix), which corrects this bias by replacing the area-based CutMix weight with one derived from precomputed segmentation masks, assigning labels in proportion to the visible object area each image contributes to the mix. The image mixing procedure is left entirely unchanged. We evaluate OA-CutMix against 10+ static and dynamic mixing methods across 4 architectures and 6 datasets. OA-CutMix consistently achieves the highest accuracy over all tasks, outperforming even dynamic mixing methods, but at a fraction of the training-time cost. Improvements are largest for small objects, where the label bias from CutMix is greatest. Thus, correcting the label is sufficient to match or exceed the performance of methods modifying the image mixing algorithm.
Chinese Translation
CutMix 已成为事实上的标准混合增强方法，但其标签分配基于一个错误的假设：粘贴补丁的区域真实地反映了其对混合图像的语义贡献。然而，在实际操作中，补丁经常落在背景区域，从而给那些其对象不可见的类别分配标签权重。CutMix 标签与语义对象区域的平均差异为 21.5%。在 17% 的样本中，一幅图像贡献的可见对象像素数为零，但仍然获得非零的标签权重。我们提出了面向对象的 CutMix（Object-Aware CutMix，OA-CutMix），通过用从预计算的分割掩码中得出的权重替换基于面积的 CutMix 权重，来纠正这一偏差，从而根据每幅图像对混合贡献的可见对象区域的比例分配标签。图像混合过程完全保持不变。我们在 4 个架构和 6 个数据集上，将 OA-CutMix 与 10 多种静态和动态混合方法进行评估。OA-CutMix 在所有任务中始终实现最高的准确率，甚至超越了动态混合方法，但训练时间成本却极低。对小对象的改善最为显著，因为 CutMix 的标签偏差在这方面最为严重。因此，纠正标签即可达到或超过修改图像混合算法的方法的性能。
cs.CV / 72 / 2606.04836
3D Temporal Analysis for Autism Spectrum Disorder Screening During Attention Tasks
注意任务中的自闭症谱系障碍筛查的3D时间分析
Abstract
Accurate Autism Spectrum Disorder (ASD) screening for school-age children is crucial to identify cases that may have been missed earlier and to enable timely interventions supporting social, cognitive, and academic development. Current ASD screening relies on subjective assessments and 2D analysis methods that fail to capture spatial displacement patterns characteristic of ASD behaviors. In this study, a novel 3D temporal analysis framework is presented, built on top of DECA (Detailed Expression Capture and Animation), a 3D modeling framework, to extract comprehensive head pose parameters (including translational components $T_x, T_y, T_z$) and facial expressions independent of pose variations. LSTM and GRU-based temporal classifiers were trained on the extracted 3D features from video data collected from 39 participants (19 ASD, 20 TD) aged 7-12 years during Virtual Reality-Continuous Performance Test tasks. The GRU-based models demonstrated superior performance, with 3D head pose features achieving 83.9\% accuracy and 3D facial features reaching 81.4\% accuracy, outperforming 2D baseline approaches by 10.7\% and 7.5\%, respectively. Furthermore, multimodal fusion of 3D head pose and facial features with PCA-based dimensionality reduction achieved the highest accuracy of 84.6\%, outperforming unimodal approaches. This work establishes a foundation for objective, automated screening tools addressing current diagnostic limitations in ASD identification for school-age populations.
Chinese Translation
准确的自闭症谱系障碍（ASD）筛查对于学龄儿童至关重要，以识别可能被早期漏诊的案例并及时提供支持社会、认知和学业发展的干预措施。目前的ASD筛查依赖于主观评估和二维分析方法，这些方法未能捕捉到ASD行为的空间位移模式。在本研究中，提出了一种新颖的3D时间分析框架，基于DECA（Detailed Expression Capture and Animation）这一3D建模框架，提取全面的头部姿态参数（包括平移分量 $T_x, T_y, T_z$）和独立于姿态变换的面部表情。基于LSTM和GRU的时间分类器在从39名参与者（19名ASD，20名TD）在虚拟现实连续表现测试任务中收集的视频数据中提取的3D特征上进行了训练，参与者年龄在7到12岁之间。GRU模型表现出更优异的性能，3D头部姿态特征的准确率达到83.9\%，3D面部特征的准确率达到81.4\,分别比二维基线方法提高了10.7\%和7.5\%。此外，通过PCA（主成分分析）进行的3D头部姿态与面部特征的多模态融合实现了最高的准确率84.6\%，优于单一模态的方法。本研究为建立客观、自动化的筛查工具奠定了基础，以应对目前学龄儿童ASD识别中的诊断限制。
cs.CV / 73 / 2606.04847
MusaCoder: Native GPU Kernel Generation with Full-Stack Training on Moore Threads GPU
MusaCoder：基于Moore Threads GPU的全栈训练的本地GPU内核生成
Abstract
Native GPU kernel generation turns high-level tensor programs into executable, efficient low-level code. Existing Large Language Models (LLMs) struggle with this task, while execution-based reinforcement learning suffers from sparse rewards, reward hacking, and training instability. We present MusaCoder, a full-stack training framework for native GPU kernel generation on CUDA and MUSA backends. MusaCoder combines progressive kernel-oriented data synthesis, diversity-preserving rejection fine-tuning, and execution-feedback Reinforcement Learning (RL) through MooreEval, a distributed verifier and reward environment. To stabilize RL, MusaCoder introduces PrimeEcho for first-turn-anchored multi-turn rewards, Buffered Dynamic Retry for recovering signals from all-failed hard samples, and MirrorPop for off-policy sequence filtering. Experiments on KernelBench and a MUSA-ported variant show that MusaCoder outperforms strong open-source and proprietary baselines in both correctness and empirical speedup, with the 9B model matching or exceeding frontier closed-source models and the 27B model establishing a new state of the art. These results demonstrate not only the effectiveness of full-stack execution-feedback training for native kernel generation, but also the capability of Moore Threads GPUs to support the complete LLM post-training stack, providing a practical foundation for large-model training and optimization on emerging accelerators.
Chinese Translation
本地GPU内核生成将高级张量程序转变为可执行的高效低级代码。现有的大型语言模型（LLMs）在此任务中表现不佳，而基于执行的强化学习则面临稀疏奖励、奖励操控和训练不稳定等问题。我们提出了MusaCoder，这是一个用于CUDA和MUSA后端的本地GPU内核生成的全栈训练框架。MusaCoder结合了渐进式内核导向数据合成、保持多样性的拒绝精调和通过MooreEval（一个分布式验证器和奖励环境）的执行反馈强化学习（RL）。为了稳定RL，MusaCoder引入了PrimeEcho用于首轮锚定的多轮奖励、缓冲动态重试用于从全失败的困难样本中恢复信号，以及MirrorPop用于脱政策序列过滤。基于KernelBench和MUSA移植变体的实验表明，MusaCoder在正确性和经验加速方面超过了多个强大的开源和专有基线，其中9B模型与前沿闭源模型持平或超越，而27B模型则建立了新的最佳状态。这些结果不仅证明了全栈执行反馈训练在本地内核生成中的有效性，也展示了Moore Threads GPU支持完整LLM后训练流程的能力，为新兴加速器上的大模型训练和优化提供了切实基础。
cs.CV / 74 / 2606.04863
IRIS-GAN: Staged Specialist Detection of Deepfake Faces
IRIS-GAN：深度伪造人脸的分阶段专业检测
Abstract
We introduce IRIS-GAN, a specialist forensic detector for synthetic face images under cross-generator shift. Rather than addressing universal synthetic-image detection, we focus on faces generated by generative adversarial networks (GANs), which are state-of-the-art in deepfake content, and train the detector through staged exposure to increasingly demanding GAN families while retaining earlier generators. The final model reaches fake-detection rates above 99% across the GAN families considered and classifies an external real-face dataset with 98.9% accuracy. Grad-CAM analysis further reveals measurable generator-dependent spatial response patterns, which remain informative for a secondary heatmap-only classifier. Out-of-family tests on diffusion-generated faces confirm that IRIS-GAN is a specialist detector, with some capability to reach non-GAN deepfakes. These results establish staged training as an effective strategy for robust GAN-face forensics.
Chinese Translation
我们介绍了IRIS-GAN，一种针对合成面部图像的专业法医检测器，能够应对跨生成器变化。与其解决普遍的合成图像检测问题，我们专注于由生成对抗网络（GANs）生成的人脸，这些网络在深度伪造内容中处于最先进的水平。我们通过分阶段暴露于越来越复杂的GAN家族进行检测器的训练，同时保留早期的生成器。最终模型在考虑的GAN家族中达到超过99%的假冒检测率，并且在一个外部真实人脸数据集上以98.9%的准确率进行分类。Grad-CAM分析进一步揭示了可测量的生成器依赖空间响应模式，这些模式对于仅使用热图的二级分类器仍然具有信息价值。对扩散生成的人脸进行的家族外测试证实了IRIS-GAN是一种专业检测器，具备一定能力检测非GAN深度伪造。这些结果确立了分阶段训练作为稳健的GAN人脸取证中的有效策略。
cs.CV / 75 / 2606.04871
Recent Advances and Trends in Learning-based 3D Representations
基于学习的3D表示的最新进展与趋势
Abstract
The selection of an appropriate 3D representation is a fundamental design decision that dictates the efficiency, quality, and capabilities of modern computer vision and graphics pipelines for tasks such as 3D reconstruction, novel-view synthesis and rendering, shape and motion analysis, recognition, and generation. While traditional representations (\eg meshes, point clouds, and volumetric grids) remain standard outputs of 3D sensors (\eg LiDAR and 3D scanners) and are widely used in downstream applications (\eg editing and simulation), recent neural and primitive-based representations (\eg 3D Gaussian Splatting) offer compact and differentiable alternatives opening a wide range of opportunities in applications such as games, AR/VR, autonomous driving, robot navigation, and medical imaging, to name a few. The goal of this paper is to survey the main families of 3D representations from discrete explicit formats to continuous implicit fields based either on neural rendering or primitive splatting. For each type of representation, we present the general formulation and its variants, discuss its benefits and limitations, and highlight key applications. We conclude the paper by outlining the open challenges and potential directions for future research. Distinct from recent surveys that broadly cover 3D object and scene reconstruction, this paper provides a focused analysis on the evolution of 3D representations themselves. We specifically emphasize the paradigm shift toward implicit representations, offering a novel perspective on how these emerging formats fundamentally alter 3D/4D workflows.
Chinese Translation
选择适当的3D表示是一个基础设计决策，它决定了现代计算机视觉和图形管道在3D重建、新视角合成与渲染、形状和运动分析、识别与生成等任务中的效率、质量和能力。尽管传统表示（如网格、点云和体积网格）仍然是3D传感器（如LiDAR和3D扫描仪）的标准输出，并在下游应用（如编辑和仿真）中广泛使用，近期的神经和原语基础表示（如3D Gaussian Splatting）则提供了紧凑且可微分的替代方案，为游戏、增强现实/虚拟现实（AR/VR）、自主驾驶、机器人导航和医学成像等应用开辟了广泛的机会。本文的目标是调查3D表示的主要类别，从离散显式格式到基于神经渲染或原语散射的连续隐式场。对于每种类型的表示，我们呈现一般公式及其变体，讨论其优点和局限性，并强调关键应用。最后，我们通过概述未解决的挑战和未来研究的潜在方向来结束论文。与近期广泛涵盖3D物体和场景重建的调查不同，本文提供了对3D表示自身演变的集中分析。我们特别强调向隐式表示的范式转变，提供了对这些新兴格式如何根本改变3D/4D工作流程的新视角。
cs.CV / 76 / 2606.04880
MAOAM: Unified Object and Material Selection with Vision-Language Models
MAOAM：基于视觉-语言模型的统一对象与材料选择
Abstract
Selection is a core operation in interactive image editing. To be practical, a user should be able to specify and disambiguate the desired selection region through either text or click-based interactions, and the system should support selecting not only objects but also other criteria, such as materials. Material-based selection is valuable for tasks like re-texturing surfaces or editing instances of a specific material. However, existing vision-language-model (VLM) based selection methods are object-centric and typically support a single interaction modality, limiting their applicability. In this work, we thus present Mask Any Object And Material (MAOAM), a unified selection framework that enables precise object and material-level selection across both text- and click-based interactions. MAOAM leverages a VLM with a segmentation head to produce pixel-accurate masks from user prompts: the VLM interprets the user's selection intent (object or material-level) and encodes visual entities, attributes, and spatial relations, while the segmentation head decodes the output token into a mask. A key challenge is the lack of material selection datasets with text annotations. We propose a scalable data generation pipeline: we collect real and synthetic images with material masks, and leverage VLMs to generate material descriptions with rich visual-semantics. We train MAOAM with a multi-task objective over click and text-based selection, along with an auxiliary VQA task derived from the material descriptions to facilitate deeper material understanding. Despite being trained with uni-modal prompts, our model exhibits an emergent improvement in selection when combining text and clicks at inference, enabling flexible image editing workflows. Experiments demonstrate accurate and coherent selections across diverse objects, materials, and interaction scenarios, highlighting robustness in practice.
Chinese Translation
选择是交互式图像编辑中的核心操作。为了提高实用性，用户应能够通过文本或点击交互明确指定并消歧所需的选择区域，而系统应支持选择不仅仅是对象，还包括其他标准，如材料。基于材料的选择对于诸如重新纹理表面或编辑特定材料实例等任务尤为重要。然而，现有的基于视觉-语言模型（VLM）的选择方法以对象为中心，通常仅支持单一的交互模式，从而限制了它们的适用性。在本研究中，我们提出了“Mask Any Object And Material”（MAOAM），这是一个统一的选择框架，可在文本和点击交互中实现精确的对象和材料级选择。MAOAM利用具有分割头的VLM，根据用户提示生成像素级精确的遮罩：VLM解读用户的选择意图（对象或材料级别），并编码视觉实体、属性和空间关系，而分割头则将输出标记解码为遮罩。一个关键挑战是缺乏带有文本注释的材料选择数据集。我们提出了一种可扩展的数据生成管道：我们收集了带有材料遮罩的真实和合成图像，并利用VLM生成具有丰富视觉语义的材料描述。我们在基于点击和文本的选择上通过多任务目标训练MAOAM，同时引入从材料描述中派生的辅助视觉问答（VQA）任务，以促进对材料的更深入理解。尽管我们的模型是在单模态提示下训练的，但在推理时结合文本和点击后，其选择性能显著提升，支持灵活的图像编辑工作流程。实验结果表明，在不同的对象、材料和交互场景中具有准确且一致的选择能力，突显了其在实践中的鲁棒性。
cs.CV / 77 / 2606.04881
DiverAge: Reliable Pluralistic Face Aging with Cross-Age Identity Relation Guidance
DiverAge：基于跨年龄身份关系引导的可靠多元面部衰老
Abstract
Face aging plays an important role in long-term biometric analysis, cross-age identity verification, and forensic identity analysis. Since the same subject may exhibit multiple plausible appearances at a target age due to genetic, environmental, and lifestyle factors, face aging is inherently a one-to-many generation problem. However, pluralism alone is insufficient for reliable face aging: a model should provide appearance-level candidate diversity within each age group while maintaining sequence-level ordinal reliability across ordered age groups. Existing deterministic aging methods can synthesize visually plausible age-progressed faces, but usually lack stochastic diversity. In contrast, pluralistic aging methods introduce local appearance variations, but often fail to explicitly regulate the identity evolution of the full aging sequence. In this paper, we propose \textbf{DiverAge}, a hierarchical pluralistic face aging framework based on diffusion autoencoding. DiverAge preserves appearance-level diversity through stochastic diffusion decoding and age-conditioned semantic modulation. To improve sequence-level reliability, we introduce a Cross-age Identity Relation Regulator (CARR), an inference-time guidance strategy that jointly denoises multiple target age groups. CARR is guided by a Cross-age Identity Similarity (CIS) prior estimated from real same-identity cross-age pairs, and suppresses excessive cross-age identity drift through one-sided sampling-time guidance without modifying the training objective or introducing extra trainable parameters. Experiments demonstrate that DiverAge improves sequence-level ordinal reliability while maintaining identity preservation, age accuracy, image quality, and appearance-level diversity.
Chinese Translation
面部衰老在长期生物特征分析、跨年龄身份验证和法医身份分析中起着重要作用。由于相同个体可能由于遗传、环境和生活方式因素在目标年龄上展现出多种合理的外观，面部衰老本质上是一个多对一生成问题。然而，仅靠多元性并不足以实现可靠的面部衰老：模型应在每个年龄组内提供外观级候选者多样性，同时在有序年龄组之间保持序列级的有序可靠性。现有的确定性衰老方法能够合成视觉上合理的年龄进展面孔，但通常缺乏随机多样性。相反，多元衰老方法引入局部外观变化，但往往未能明确调节整个衰老序列的身份演变。本文提出了DiverAge，一种基于扩散自编码的分层多元面部衰老框架。DiverAge通过随机扩散解码和年龄条件的语义调制来保持外观级的多样性。为了提高序列级的可靠性，我们引入了一种跨年龄身份关系调节器（Cross-age Identity Relation Regulator，CARR），这是一种在推断时指导策略，能联合去噪多个目标年龄组。CARR受来自真实同身份跨年龄对的跨年龄身份相似性（Cross-age Identity Similarity，CIS）先验引导，通过单边采样时的指导抑制过度的跨年龄身份漂移，而不更改训练目标或引入额外的可训练参数。实验表明，DiverAge在保持身份保留、年龄准确性、图像质量和外观级多样性的同时，提高了序列级的有序可靠性。
cs.CV / 78 / 2606.04888
HD-DinoMoE: A Class-Aware Hierarchical Dual Mixture-of-Experts Network for Scleral Anomaly Segmentation in Complex Acquisition Scenarios
HD-DinoMoE：一种面向类别的分层双专家混合网络，用于复杂获取场景中的巩膜异常分割
Abstract
Traditional Chinese Medicine (TCM) ocular inspection provides empirical cues for assessing scleral surface anomalies, but its clinical use remains subjective and difficult to quantify. To support intelligent and quantifiable ocular inspection, this study presents the TCM-inspired Artificial Intelligence Ocular Auxiliary Diagnosis System (TAO) and focuses on pixel-level scleral surface anomaly segmentation. For clinical and user-acquired images affected by multi-source distributional discrepancies, diverse anomaly morphologies, and scleral specular reflection (SSR), we propose HD-DinoMoE, a class-aware hierarchical dual mixture-of-experts network. HD-DinoMoE combines class-aware dual-stream DINOv3 feature fusion with class-specific multi-expert decoding to segment Vessels, Yellow and Black Spots, and Blood Spots. A three-stage backbone-frozen routing strategy stabilizes dual-backbone adaptation; Progressive Confidence Penalty (PCP) Loss reduces high-confidence false positives and segmentation leakage in SSR regions; and Class-Aware Adaptive Sample Weighting (CA-ASW) balances sample- and class-level training contributions. We further construct the Multi-label Scleral Anomaly Segmentation Dataset (ML-SASD), a new benchmark with Clinical, Wild, and Mix settings and pixel-wise annotations for three anomaly categories. On ML-SASD-Mix, HD-DinoMoE achieves a mean Dice of 72.11% and a mean Intersection-over-Union of 58.44%, while maintaining favorable boundary localization and specular-region false-positive control. It also shows competitive generalization on the Vessels subset of the public SBVPI dataset. These results indicate that HD-DinoMoE provides a feasible segmentation solution for TAO under complex acquisition scenarios. The code and data access information are available at https://github.com/FX-CMX/HD-DinoMoE.
Chinese Translation
传统中医（TCM）眼部检查为评估巩膜表面异常提供了经验线索，但其临床应用仍然主观且难以量化。为了支持智能和量化的眼部检查，本研究提出了受中医启发的人工智能眼部辅助诊断系统（TAO），并重点关注像素级的巩膜表面异常分割。针对受多源分布差异、多样异常形态以及巩膜镜面反射（SSR）影响的临床和用户采集图像，我们提出了HD-DinoMoE，一种面向类别的分层双专家混合网络。HD-DinoMoE结合了面向类别的双流DINOv3特征融合与特定类别的多专家解码，用于分割血管、黄色和黑色斑点及血斑。三阶段的骨架冻结路由策略稳定了双骨架适应；渐进式置信惩罚（PCP）损失减少了高置信度的误报和SSR区域的分割泄漏；以及面向类别的自适应样本加权（CA-ASW）平衡样本和类别级别的训练贡献。我们进一步构建了多标签巩膜异常分割数据集（ML-SASD），这是一个新的基准数据集，包含临床、野外和混合设置，及三种异常类别的像素级注释。在ML-SASD-Mix上，HD-DinoMoE达到了72.11%的平均Dice系数和58.44%的平均交并比，同时保持了良好的边界定位和镜面区域误报控制。它在公共SBVPI数据集的血管子集上也表现出竞争力的泛化能力。这些结果表明，HD-DinoMoE为TAO在复杂获取场景中提供了一种可行的分割解决方案。代码和数据访问信息可在https://github.com/FX-CMX/HD-DinoMoE获得。
cs.CV / 79 / 2606.04891
Hierarchical Space Partition for Surface Reconstruction
用于表面重建的层次空间划分
Abstract
Generating compact polygonal models from point clouds is a key problem in 3D vision and computer graphics. However, due to inherent limitations of LiDAR scanning (e.g. range constraints and occlusions), critical scene information is often missing, leading to degraded reconstruction accuracy. To address this, we propose a plane assembling strategy that effectively recovers missing details while maintaining model compactness. We classify all the planes extracted from the scene into three categories: highly visible, barely visible, and invisible. The invisible planes, which are recovered by scene structure analysis, indicate the missing details. The three types of planes correspond to the three growth priorities. Each plane grows according to the priority level, and the space is partitioned progressively, namely, the hierarchical partition. Subsequently, we generate a watertight polygonal mesh from the partition via a min-cut-based optimization. Finally, comparisons on public datasets show the effectiveness and superiority of our method against mainstream approaches. The project page is available at https://hsr-3dv.github.io/.
Chinese Translation
从点云生成紧凑的多边形模型是3D视觉和计算机图形学中的一个关键问题。然而，由于激光雷达扫描的固有限制（例如距离限制和遮挡），关键场景信息往往缺失，从而导致重建精度降低。为了解决这个问题，我们提出了一种平面组装策略，能够有效恢复缺失的细节，同时保持模型的紧凑性。我们将从场景中提取的所有平面分为三类：高度可见、勉强可见和不可见。不可见的平面通过场景结构分析进行恢复，表明了缺失的细节。这三种类型的平面对应于三种生长优先级。每个平面根据优先级水平生长，空间逐步划分，即层次划分。随后，我们通过基于最小割的优化从划分中生成一个水密多边形网格。最后，针对公共数据集的比较展示了我们的方法相较于主流方法的有效性和优越性。项目页面可访问 https://hsr-3dv.github.io/。
cs.CV / 80 / 2606.04898
CDPM-Align: Multi-Scale Guidance-Aligned Diffusion Pretraining for Robust Few-Shot Anatomical Landmark Detection
CDPM-Align：多尺度引导对齐的扩散预训练用于鲁棒的少量样本解剖标志检测
Abstract
Anatomical landmark detection is a fundamental task in medical image analysis supporting a wide range of diagnostic and interventional workflows. Although recent methods have achieved sub-millimetric localisation, accuracy alone is not sufficient for clinical deployment, requiring reliability and robustness in prediction. Despite its clinical relevance, the impact of representation learning in this context is still underexplored. In this work, we introduce CDPM-align, a multi-scale guidance-aligned conditional diffusion pre-training for anatomical landmark detection. Our experimental setup focuses on a few images and a few annotation regimes. Specifically, we employ three popular heterogeneous small-scale benchmark datasets for representation learning via conditional generative pre-training. Furthermore, we consider low-annotation scenarios for the downstream task of landmark detection, with 10 and 25 annotated images, reflecting realistic trade-offs between clinical effort and resource constraints for annotations. Our results confirm that generative pre-training enables the model to learn a robust representation. This improves both accuracy and uncertainty on the downstream tasks, advancing towards safe and efficient clinical deployment.
Chinese Translation
解剖标志检测是医学图像分析中的一项基础任务，支持广泛的诊断和干预工作流程。尽管近期方法已实现亚毫米级定位，但单靠准确性不足以满足临床部署的要求，需要在预测中具备可靠性和鲁棒性。尽管其在临床中的相关性，表示学习在这一背景下的影响仍然未得到充分探讨。在本工作中，我们引入了CDPM-align，一种多尺度引导对齐的条件扩散预训练方法，用于解剖标志检测。我们的实验设置专注于少量图像和少量标注方案。具体而言，我们利用三种流行的异构小规模基准数据集，通过条件生成预训练进行表示学习。此外，我们考虑了低标注场景下的标志检测下游任务，使用10幅和25幅已标注图像，反映临床工作与标注资源限制之间的现实权衡。我们的结果确认生成预训练使模型能够学习到鲁棒的表示，提高了下游任务的准确性和不确定性，朝着安全高效的临床部署迈进。
cs.CV / 81 / 2606.04911
BreastGPT: A Multimodal Large Language Model for the Full Spectrum of Breast Cancer Clinical Routine
BreastGPT：一个多模态大语言模型，涵盖乳腺癌临床日常工作的全谱
Abstract
Breast cancer remains a leading cause of cancer-related mortality among women. Its clinical management requires multimodal reasoning across a clinical workflow that spans \textit{screening}, \textit{diagnosis} and \textit{treatment planning}, where each stage involves distinct imaging modalities, task objectives, and reasoning patterns. However, constrained by data scarcity and model versatility, existing medical MLLMs are typically evaluated on isolated modalities or narrow task families, limiting their ability to support workflow-level clinical reasoning. In this work, we first introduce \textbf{BreastStage}, a workflow-aligned breast imaging instruction corpus comprising 1.86M instruction-following pairs curated from 17 sub-datasets across 5 imaging modalities and 136 task templates. Its held-out split, \textbf{BreastStage-Bench}, provides a comprehensive benchmark for evaluating multimodal reasoning across the breast cancer care continuum. Building on this corpus, we propose \textbf{BreastGPT}, a unified MLLM equipped with a dual-branch visual encoder and concept-preserving token compression to bridge the scale gap between standard radiology and gigapixel pathology. On BreastStage-Bench, BreastGPT achieves 75.66\% closed-ended accuracy and 89.92\% open-ended score, outperforming both general-purpose and medical-specific MLLMs across clinical stages and task formats. These results suggest that workflow-aligned data and cross-scale visual modeling are critical for clinically grounded medical MLLMs. All data, code, and model checkpoints are released at https://yangyy-liu.github.io/BreastGPT.io.
Chinese Translation
乳腺癌仍然是女性癌症相关死亡的主要原因。其临床管理需要对跨越 extit{筛查}、 extit{诊断}和 extit{治疗计划}的临床工作流程进行多模态推理，每个阶段涉及不同的成像模态、任务目标和推理模式。然而，由于数据稀缺和模型多样性的限制，现有的医学多模态大语言模型（MLLMs）通常只能在孤立的模态或狭窄的任务家族上进行评估，限制了其支持工作流程级临床推理的能力。在本研究中，我们首先介绍了 extbf{BreastStage}，一个与工作流程对齐的乳腺成像指令语料库，该语料库包含来自5种成像模态的17个子数据集中的1.86M条指令跟随对。其独立分割 extbf{BreastStage-Bench}为评估乳腺癌护理连续体中的多模态推理提供了一个全面的基准。在此语料库的基础上，我们提出了 extbf{BreastGPT}，一个统一的多模态大语言模型，配备双分支视觉编码器和概念保留的令牌压缩，以弥补标准放射学与千兆像素病理学之间的规模差距。在BreastStage-Bench上，BreastGPT取得了75.66\%的封闭式准确率和89.92\\%的开放式分数，超越了临床阶段和任务格式下的通用和医学特定大语言模型。这些结果表明，与工作流程对齐的数据和跨尺度的视觉建模对于临床基础的医学大语言模型至关重要。所有数据、代码和模型检查点均已发布在 https://yangyy-liu.github.io/BreastGPT.io。
cs.CV / 82 / 2606.04922
Geometry-Aware Distillation for Prompt Tuning Biomedical Vision-Language Models
面向几何的蒸馏方法用于生物医学视觉-语言模型的提示调整
Abstract
Current prompt-based and adapter-based tuning of vision-language models (VLMs) is attractive for medical imaging, where clinical data sensitivity favors frozen backbones and annotations are limited. However, these methods typically optimize only the ground-truth class, treating all other classes as equally incorrect, ignoring clinically meaningful class relations and yielding unstable decision boundaries in limited-supervision settings. We propose Omni-Geometry Knowledge Distillation (OGKD), a new framework that injects class-relation structure into the teacher to produce directional targets that preserve the ground truth while respecting inter-class geometry. Using these targets, we develop two distillation losses: Global Geometry-Aware Distillation (GAD) operates on the global image token, and Label-Guided Geometry Distillation (LGD) applies the same geometry to attentive patch tokens to improve fine-grained alignment. Across comprehensive experiments and analyses on 11 widely-used medical datasets for base-to-novel and few-shot evaluations, our OGKD achieves substantially better performance, consistently improving accuracy by an average absolute gain of 1.7%-2.8% over all prior state-of-the-art VLM adaptation counterparts. It also robustly generalizes to unseen classes and yields more reliable predictions than other approaches. Our code is available at https://github.com/tientrandinh/OGKD.
Chinese Translation
当前基于提示和适配器的视觉-语言模型（VLMs）调整在医学成像中具有吸引力，因临床数据的敏感性使得骨干网络保持冻结，同时标注有限。然而，这些方法通常仅优化真实类别，将所有其他类别视为同等错误，忽视临床上有意义的类别关系，并在有限监督设置下产生不稳定的决策边界。我们提出了全几何知识蒸馏（Omni-Geometry Knowledge Distillation, OGKD），这是一个新的框架，它将类别关系结构注入到教师模型中，以生成保持真实情况的方向性目标，同时尊重类别间的几何关系。利用这些目标，我们开发了两种蒸馏损失：全局几何感知蒸馏（Global Geometry-Aware Distillation, GAD）针对全局图像标记操作，而标签引导几何蒸馏（Label-Guided Geometry Distillation, LGD）将相同几何应用于关注的补丁标记，以提高细粒度对齐。通过在11个广泛使用的医学数据集上进行的全面实验和分析，我们的OGKD在基于基础到新颖和少样本评估方面实现了显著更好的性能，在所有之前的最先进的VLM适配方法中，准确率平均绝对增益达到1.7%-2.8%。它还对未见类具有良好的泛化能力，并且比其他方法提供了更可靠的预测。我们的代码可在 https://github.com/tientrandinh/OGKD 获得。
cs.CV / 83 / 2606.04925
Scene-Centric Unsupervised Video Panoptic Segmentation
以场景为中心的无监督视频全景分割
Abstract
Video panoptic segmentation (VPS) aims to jointly detect, segment, and track all objects while partitioning the video into semantically consistent regions. We introduce the task setting of unsupervised VPS, omitting any human supervision. Existing unsupervised scene understanding works mainly focused on image segmentation tasks; the video domain remains underexplored. We propose VideoCUPS, the first unsupervised VPS approach. VideoCUPS generates temporally consistent panoptic video pseudo-labels from scene-centric videos by exploiting unsupervised depth, motion, and visual cues. Training on these pseudo-labels using a novel Video DropLoss yields an accurate, unsupervised VPS model. To benchmark progress, we introduce a comprehensive evaluation protocol and four competitive baselines, extending state-of-the-art unsupervised panoptic image and instance video segmentation models to VPS. VideoCUPS outperforms all baselines and demonstrates strong label-efficient learning. With VideoCUPS, our evaluation protocol, and baselines, we provide a strong foundation for future research on unsupervised VPS.
Chinese Translation
视频全景分割（VPS）旨在同时检测、分割和追踪所有物体，同时将视频划分为语义一致的区域。我们引入了无监督VPS的任务设定，省略了任何人工监督。现有的无监督场景理解工作主要集中在图像分割任务上；而视频领域仍然相对较少探索。我们提出了VideoCUPS，这是第一个无监督的VPS方法。VideoCUPS通过利用无监督的深度、运动和视觉线索，从以场景为中心的视频生成时间一致的全景视频伪标签。使用一种新颖的Video DropLoss在这些伪标签上进行训练，产生了一个准确的无监督VPS模型。为了评估进展，我们引入了一个全面的评估协议和四个竞争基准，扩展了最新的无监督全景图像和实例视频分割模型至VPS。VideoCUPS的表现优于所有基准，并且展示了强大的标签高效学习能力。通过VideoCUPS、我们的评估协议和基准，我们为未来的无监督VPS研究提供了坚实的基础。
cs.CV / 84 / 2606.04970
Plan, Watch, Recover: A Benchmark and Architectures for Proactive Procedural Assistance
规划、观察、恢复：主动过程辅助的基准和架构
Abstract
We envision a proactive multi-modal assistant system which gives users real-time step-by-step guidance on a procedural task, autonomously deciding \textit{when} to interrupt, and \textit{how} to coach. However, progress is limited by the absence of large-scale, cross-domain benchmarks that reflect realistic conditions, particularly the common case in which users deviate from the expected step sequence. We address this gap with four contributions: \textbf{(1)}~we release \textbf{EgoProactive}, a large-scale wearable-egocentric dataset for proactive procedural assistance with explicit Out-of-Plan (OOP) annotations and recovery steps; \textbf{(2)}~we augment five established benchmarks (Ego4D, EPIC-KITCHENS, EgoExo4D, HoloAssist, HowTo100M) into \textbf{Pro\textsuperscript{2}Bench} under a unified proactive-guidance schema; \textbf{(3)}~we propose a \textbf{decoupled planner--interaction architecture} specialized for procedural state, visual cues, and recovery injection; \textbf{(4)}~we introduce a post-training recipe that transfers across model families, validated by cross-backbone replication on Llama~4 and Qwen-3.6-VL. In extensive experiments, our trained Llama-4 system substantially improves objective intervention quality over strong proprietary baselines (Claude Opus~4.6, Gemini~3.1~Pro, GPT~5.2) and open-weight baselines (Qwen3~VL~235B) baselines across all six datasets. Oracle-plan experiments further show that, when plan quality is controlled, the trained duplex model produces high-quality guidance and large gains on Out-of-Plan recovery.
Chinese Translation
我们设想了一个主动的多模态助手系统，能够为用户在过程任务中提供实时的逐步指导，能够自主决定何时中断以及如何进行指导。然而，进展受到缺乏大型跨领域基准的限制，这些基准反映了现实条件，尤其是用户偏离预期步骤序列的常见情况。我们通过以下四个贡献来填补这一空白： extbf{(1)} 我们发布了 extbf{EgoProactive}，这是一个大规模的穿戴式自我中心数据集，用于主动过程辅助，包含明确的计划外（Out-of-Plan, OOP）注释和恢复步骤； extbf{(2)} 我们将五个已建立的基准（Ego4D、EPIC-KITCHENS、EgoExo4D、HoloAssist、HowTo100M）整合为 extbf{Pro extsuperscript{2}Bench}，采用统一的主动指导框架； extbf{(3)} 我们提出了一种针对过程状态、视觉线索和恢复注入的 extbf{解耦规划-交互架构}； extbf{(4)} 我们引入了一种后训练配方，能够在各模型系列之间进行迁移，通过在 Llama~4 和 Qwen-3.6-VL 上的交叉主干复制进行验证。在大量实验中，我们训练的 Llama-4 系统在所有六个数据集上显著提高了目标干预质量，相比强大的专有基线（Claude Opus~4.6、Gemini~3.1~Pro、GPT~5.2）和开放权重基线（Qwen3~VL~235B）。Oracle计划实验进一步表明，当控制计划质量时，经过训练的双工模型能够产生高质量的指导，并在计划外恢复上获得显著提升。
cs.CV / 85 / 2606.04986
Food-R1: A Unified Multi-Task Food Vision-Language Model with Reinforcement Learning
Food-R1：一种统一的多任务食品视觉-语言模型及其强化学习
Abstract
Recent studies have explored Vision-Language Models (VLMs) for food analysis. However, most existing methods rely primarily on supervised fine-tuning (SFT), which often limits reasoning and generalization capabilities. Moreover, high-quality large-scale nutritional annotations remain scarce. To address these issues, we introduce CalorieBench-80K, a large-scale benchmark with curated calorie labels and dietary advice annotations. To the best of our knowledge, it is the first food image benchmark to incorporate Chain-of-Thought (CoT) annotations for calorie reasoning. We also propose Food-R1, a unified food VLM trained in a multi-task learning paradigm to equip the model with broad capabilities. Food-R1 undergoes CoT-based cold-start instruction tuning, followed by reinforcement fine-tuning (RFT) using Group Relative Policy Optimization (GRPO) to improve reasoning and performance. Experiments on CalorieBench-80K and representative benchmarks show that Food-R1 consistently outperforms strong baselines across food-related tasks. The code, model weights, and benchmark annotations are available at the project repository.
Chinese Translation
最近的研究探讨了用于食品分析的视觉-语言模型（VLMs）。然而，现有的方法大多数主要依赖于监督微调（SFT），这往往限制了推理和泛化能力。此外，高质量的大规模营养注释仍然稀缺。为了解决这些问题，我们引入了CalorieBench-80K，一个拥有精心策划的卡路里标签和饮食建议注释的大规模基准。 据我们所知，这是第一个将思维链（Chain-of-Thought，CoT）注释纳入卡路里推理的食品图像基准。我们还提出了Food-R1，这是一个在多任务学习范式下训练的统一食品VLM，以赋予模型广泛的能力。Food-R1经历了基于CoT的冷启动指令调优，随后使用群体相对策略优化（Group Relative Policy Optimization，GRPO）进行强化微调（RFT），以提高推理和性能。在CalorieBench-80K和具有代表性的基准上的实验表明，Food-R1在食品相关任务中始终优于强基线。代码、模型权重和基准注释已在项目库中提供。
cs.CV / 86 / 2606.04992
Multi-Camera AR Guidance System for Surgical Instrument Handling and Assembly: Investigating Workload and Efficiency
多摄像头增强现实引导系统用于外科手术器械的操作与组合：工作负载与效率的研究
Abstract
The handling and assembly of instruments during surgery imposes high cognitive demands on scrub nurses, particularly when instruments are unfamiliar. We present a supporting guidance system for surgical instrumentation that combines multi-camera 6D pose estimation with augmented reality in-situ visualization on a head-mounted display without the requirement for additional markers. Pose estimation and consecutive camera calibration are achieved through known objects. The 6D pose estimation network is trained purely on synthetic data, aiming for better generalizability and real-world applicability. The AR guidance displays tooltip localization cues and step-wise assembly animations. Via gaze-based selection and a foot pedal, users can switch between assembly steps in intraoperative use. In a technical evaluation, our approach outperforms state-of-art 6D pose estimation. A user study with 29 scrub nurses was conducted in a surgical simulation of knee arthroplasty, comparing the system against a paper manual. AR guidance significantly reduced the perceived workload compared. Objectively, AR guidance reduced task completion time by 21.3\% (4.76 minutes). Specifically, scrub nurses less experienced with the instrument set benefited when using the system. Error frequencies were comparable between conditions. Qualitative feedback highlighted improved process clarity, reduced information overload, and perceived independence. To summarize, our marker-free multi-camera AR guidance approach for surgical instruments can, subjectively and objectively, improve intraoperative instrumentation performance, particularly for untrained scrub nurses.
Chinese Translation
手术过程中对器械的操作与组合对专职护士提出了较高的认知要求，尤其是在器械不熟悉的情况下。我们提出了一种支持外科器械操作的引导系统，该系统结合了多摄像头6D位姿估计与增强现实（AR）的现场可视化，且无需额外的标记物。位姿估计和连续的摄像头校准通过已知物体实现。6D位姿估计网络完全基于合成数据进行训练，旨在提高其泛化能力和实际应用性。增强现实引导系统显示工具提示定位线索和逐步组合动画。用户可以通过注视选择和脚踏板在手术过程中切换组合步骤。在技术评估中，我们的方法优于当前最先进的6D位姿估计。在一次针对29名专职护士的用户研究中，我们在膝关节置换手术的仿真中比较了该系统与纸质手册。增强现实引导显著降低了感知工作负载。从客观上看，增强现实引导将任务完成时间减少了21.3%（4.76分钟）。具体而言，对器械组不太熟悉的专职护士在使用该系统时受益明显。不同条件下的错误发生频率相当。定性反馈强调了流程清晰度的提升、信息过载的减少以及感知的独立性。总之，我们的无标记多摄像头增强现实引导方法可以在主观和客观上改善手术中的器械操作性能，特别是对于未经培训的专职护士。
cs.CV / 87 / 2606.05008
M$^3$Eval: Multi-Modal Memory Evaluation through Cognitively-Grounded Video Tasks
M$^3$Eval：通过认知基础视频任务进行的多模态记忆评估
Abstract
As multi-modal models advance towards long-form video understanding, memory emerges as a critical capability. Despite substantial efforts in developing video datasets and benchmarks, existing works primarily focus on perception and reasoning, without systematically evaluating memory: what models retain, how faithfully information is preserved, and how robust memory remains under interference. To address this gap, we introduce M$^3$Eval, the first comprehensive evaluation framework and benchmark for probing different memory dimensions in multi-modal models. Grounded in cognitive psychology, our design features carefully constructed tasks that isolate key aspects of memory. Leveraging M$^3$Eval, we conduct extensive experiments across representative multi-modal models, revealing consistent weaknesses and distinctive behaviors. We find that models struggle to maintain disentangled representations when processing parallel video streams, exhibit interference patterns differing substantially from those observed in human memory, ground memory sources more reliably in the spatial domain than the temporal domain, and demonstrate limited symbolic memory. Collectively, our benchmark provides a valuable resource for future research, while our findings highlight memory as a fundamental yet underexplored capability and offer insights for designing more effective memory mechanisms in multi-modal models. Our code and dataset are available at https://pku-value-lab.github.io/m3eval-homepage.
Chinese Translation
随着多模态模型在长视频理解方面的进展，记忆作为一种关键能力逐渐凸显。尽管在视频数据集和基准的开发上进行了大量努力，现有研究主要集中在感知和推理，而没有系统地评估记忆：模型保留了什么，信息保存的真实性如何，以及在干扰下记忆的稳健性如何。为了解决这一空白，我们引入了M$^3$Eval，这是第一个全面的评估框架和基准，用于探测多模态模型中的不同记忆维度。基于认知心理学，我们的设计包含精心构建的任务，以隔离记忆的关键方面。借助M$^3$Eval，我们对代表性的多模态模型进行了广泛实验，揭示了一致的弱点和独特行为。我们发现，模型在处理并行视频流时难以保持解耦的表征，表现出不同于人类记忆的干扰模式，记忆来源在空间域中的可靠性高于时间域，并且表现出有限的符号记忆。总体而言，我们的基准为未来研究提供了宝贵的资源，而我们的发现凸显了记忆作为一种基本但未充分探讨的能力，并为设计更有效的多模态模型记忆机制提供了洞见。我们的代码和数据集可以在 https://pku-value-lab.github.io/m3eval-homepage 获取。
cs.CV / 88 / 2606.05011
CIPER: A Unified Framework for Cross-view Image-retrieval and Pose-estimation
CIPER：跨视图图像检索与姿态估计的统一框架
Abstract
Cross-view geo-localization estimates the geographic location of a ground image by matching it against an aerial image database. Existing methods tackle this through either large-scale retrieval or precise pose estimation, but not both: retrieval-based methods enable wide-area search at the cost of localization accuracy, while pose estimation methods achieve high precision within only a narrow search space. Naively cascading these pipelines introduces error propagation and inconsistent feature representations. We formulate cross-view geo-localization as a unified problem requiring simultaneous city-scale retrieval and precise 3-DoF pose estimation. We propose CIPER (Cross-view Image-retrieval and Pose-estimation transformER), a single architecture that jointly performs both tasks through mutually beneficial feature learning. CIPER uses a shared transformer encoder with task-specific tokens to disentangle global retrieval features from spatial localization cues. To bridge the large domain gap between ground and aerial views, we introduce a two-way transformer pose decoder that uses ground features as spatial queries for bidirectional cross-attention. A set prediction strategy further enables stable 3-DoF regression under a unified multi-task objective. Experiments on VIGOR, KITTI, and Ford Multi-AV demonstrate competitive performance, especially under limited field-of-view and arbitrary orientation conditions. Code is available at https://github.com/yurimjeon1892/CIPER.
Chinese Translation
跨视图地理定位通过将地面图像与航空图像数据库匹配来估计其地理位置。现有方法要么通过大规模检索解决问题，要么通过精确的姿态估计来实现，但两者不能兼得：基于检索的方法以降低定位精度为代价实现广域搜索，而姿态估计方法在狭窄的搜索空间内实现高精度。简单地级联这些处理流程会引入误差传播和不一致的特征表示。我们将跨视图地理定位定义为一个统一问题，要求同时进行城市规模的检索和精确的三自由度（3-DoF）姿态估计。我们提出了CIPER（Cross-view Image-retrieval and Pose-estimation transformER），这是一个单一架构，能够通过互惠的特征学习共同执行这两项任务。CIPER采用具有任务特定标记的共享变换器（transformer）编码器，将全局检索特征与空间定位线索分离。为了弥合地面视图与航空视图之间的巨大域间差异，我们引入了一个双向变换器姿态解码器，该解码器使用地面特征作为空间查询用于双向交叉注意（cross-attention）。一套预测策略进一步使得在统一的多任务目标下进行稳定的3-DoF回归成为可能。在VIGOR、KITTI和Ford多AV上的实验表明，该方法具有竞争力的表现，特别是在有限的视场和任意朝向条件下。代码可在 https://github.com/yurimjeon1892/CIPER 上获取。
cs.CV / 89 / 2606.05018
Handwriting Extraction and Analysis of Signature Lists in Swiss Popular Initiatives
瑞士公民倡议中签名列表的手写提取与分析
Abstract
Popular initiatives and referendums are central to Swiss democracy, yet the validation of handwritten signature lists remains a labor-intensive manual process. This paper investigates the potential of automated document analysis methods, including OCR and AI-based handwriting analysis, to support this task. We propose a pipeline combining template-based line segmentation with text recognition and writer retrieval techniques, evaluated on a dataset of 443 handwritten entries from 418 writers. Results show that OCR struggles with out-of-vocabulary handwriting, with a CER of 29.6% for first names. In contrast, writer retrieval performs more robustly, reaching an mAP of 50.6%. Furthermore, our experiments indicate that off-the-shelf OCR systems are not sufficiently reliable for transcription of handwritten signature data, particularly for short, out-of-vocabulary entries such as names or addresses. However, writer retrieval methods can effectively identify visually similar entries across signature lists, making them a suitable tool for supporting the detection of potential duplicate submissions based on handwriting similarity.
Chinese Translation
公民倡议和公投是瑞士民主的核心，而手写签名列表的验证仍然是一个劳动密集型的手动过程。本文研究了自动文档分析方法的潜力，包括光学字符识别（OCR）和基于人工智能的手写分析，以支持这一任务。我们提出了一种将基于模板的行分割与文本识别和作者检索技术相结合的处理流程，评估的数据集包括来自418名书写者的443个手写条目。结果表明，OCR在处理词汇表外的手写内容时表现不佳，名字的字符错误率（CER）为29.6%。相比之下，作者检索表现得更为稳健，达到50.6%的均值平均精度（mAP）。此外，我们的实验证明，现成的OCR系统在转录手写签名数据方面的可靠性不足，尤其是对短小的、词汇表外的条目如姓名或地址。然而，作者检索方法能够有效识别签名列表中在视觉上相似的条目，使其成为支持基于手写相似性检测潜在重复提交的合适工具。
cs.CV / 90 / 2606.05031
MetaPoint: Unlocking Precise Spatial Control in Agentic Visual Generation
MetaPoint：解锁代理视觉生成中的精确空间控制
Abstract
Generative visual models fundamentally struggle with precise spatial control. This arises from a core disconnect: models can process textual descriptions of space but cannot directly map numerical coordinates onto the 2D image canvas. We introduce MetaPoint, a method that bridges this gap by representing a continuous 2D coordinate as a single, special token. Crucially, MetaPoint requires no new architectural components; it directly leverages the model's inherent positional encoding schemes to interpret these coordinates, treating our token as a virtual point on the canvas. This lightweight approach enables pixel-level control of an object's position with one token or its bounding box with two, all without requiring architectural changes or bespoke attention masking. The MetaPoint tokens are designed to be compositional, serving as spatial primitives. This allows a planner agent to decompose a high-level user request into a structured sequence of primitives for the generator. By providing a simple, precise, and scalable building block for spatial control, MetaPoint unlocks more powerful compositional generative agents and enables intuitive, interactive editing systems.
Chinese Translation
生成视觉模型在精确空间控制方面存在根本性的困难。这源于一个核心的脱节：模型可以处理空间的文本描述，但无法直接将数值坐标映射到二维图像画布上。我们提出了MetaPoint，一种通过将连续的二维坐标表示为单个特殊标记来弥合这一差距的方法。重要的是，MetaPoint不需要新的架构组件；它直接利用模型固有的位置编码方案来解读这些坐标，将我们的标记视为画布上的虚拟点。这种轻量级的方法允许通过一个标记实现对象位置的像素级控制，或通过两个标记实现其边界框的控制，全部无需架构更改或专门的注意力掩码。MetaPoint标记被设计为可组合的，作为空间原语。这使得规划代理能够将高层次的用户请求分解为生成器的结构化原语序列。通过提供一个简单、精确且可扩展的空间控制基础构件，MetaPoint解锁了更强大的组合生成代理，并启用了直观的交互式编辑系统。
cs.CV / 91 / 2606.05035
Anchor3R: Streaming 3D Reconstruction with Transient Anchors for Long-Horizon Visual Mapping
Anchor3R：使用瞬态锚点的长时间视觉映射流式3D重建
Abstract
Long-horizon online visual mapping is a core capability for robot perception, requiring continuous camera-motion and scene-geometry estimation from visual streams under bounded memory and computation. Recent feed-forward 3D reconstruction models provide strong geometric priors, but their streaming variants often predict poses in a fixed coordinate system tied to the first frame or a persistent scene memory. This fixed-gauge design leads to train--test mismatch, attention bias toward early anchors, and accumulated drift on sequences much longer than those seen during training. We propose \emph{Anchor3R}, a streaming 3D reconstruction framework that treats feed-forward reconstruction as current-centric local measurement prediction rather than persistent global-gauge regression. At each time step, Anchor3R predicts window-relative poses and a local pointmap in the current-frame coordinate system, turning streaming reconstruction into relative-pose measurement generation. These measurements support online pose updates, while loop-closure reinsertion and motion averaging align the trajectory and transform local pointmaps into a coherent global reconstruction. Experiments on indoor, outdoor, driving, and RGB-D benchmarks show that Anchor3R improves long-horizon pose accuracy and dense reconstruction quality over existing streaming baselines, while supporting bounded-memory online inference.
Chinese Translation
长时间在线视觉映射是机器人感知的核心能力，要求在受限的内存和计算条件下从视觉流中持续估计相机运动和场景几何。近期的前馈3D重建模型提供了强有力的几何先验，但其流式变体通常在与第一帧或持久场景记忆相关的固定坐标系中预测姿态。这种固定基准设计导致训练-测试不匹配，注意力偏向于早期锚点，以及在训练时未见到的更长序列上的累积漂移。我们提出了 extit{Anchor3R}，一个将前馈重建视为以当前为中心的局部测量预测而非持久全局基准回归的流式3D重建框架。在每个时间步，Anchor3R预测相对于窗口的姿态和当前帧坐标系中的局部点图，将流式重建转变为相对姿态测量生成。这些测量支持在线姿态更新，而闭环重插入和运动平均则对齐轨迹并将局部点图转换为一致的全局重建。在室内、室外、驾驶和RGB-D基准测试上的实验表明，Anchor3R在长时间姿态精度和密集重建质量上优于现有流式基准，同时支持受限内存的在线推理。
cs.CV / 92 / 2606.05058
UniCAD: A Unified Benchmark and Universal Model for Multi-Modal Multi-Task CAD
UniCAD：多模态多任务计算机辅助设计的统一基准和通用模型
Abstract
Computer-Aided Design (CAD) underpins modern engineering and manufacturing by enabling the creation of precise, editable 3D models. However, CAD research typically studies tasks in isolation, and multi-modal, multi-task learning for CAD is hindered by the absence of a unified benchmark. To address this gap, we introduce UniCAD, a comprehensive benchmark for multi-modal CAD learning that covers point-to-CAD reconstruction, text/image-to-CAD generation, and CAD question answering across diverse input modalities. Alongside the benchmark, we present UniCAD-MLLM, a universal multi-modal large language model that ingests text, images, sketches, and point clouds and performs these heterogeneous tasks in an end-to-end fashion within a single framework. Extensive experiments on the UniCAD and Fusion360 benchmarks demonstrate that UniCAD-MLLM achieves state-of-the-art performance across all tasks, outperforming existing task-specific and multi-task baselines. We will release the dataset, code, and pretrained models to accelerate future research.
Chinese Translation
计算机辅助设计（CAD）为现代工程和制造奠定了基础，使得创建精确且可编辑的三维模型成为可能。然而，CAD研究通常孤立地研究各个任务，而多模态、多任务的CAD学习受到缺乏统一基准的制约。为了解决这一问题，我们提出了UniCAD，这是一个涵盖多模态CAD学习的综合基准，包括点到CAD重建、文本/图像到CAD生成，以及跨多种输入模态的CAD问答。除了基准外，我们还推出了UniCAD-MLLM，一个通用的多模态大型语言模型，该模型能够处理文本、图像、草图和点云，并在单一框架内以端到端的方式执行这些异构任务。对UniCAD和Fusion360基准的广泛实验表明，UniCAD-MLLM在所有任务上实现了最先进的性能，超越了现有的任务特定和多任务基准。我们将发布数据集、代码和预训练模型，以加速未来研究的进展。
cs.CV / 93 / 2606.05068
MaCo-GAN: Manifold-Contrastive Adversarial Learning for Single Image Super-Resolution
MaCo-GAN：用于单幅图像超分辨率的流形对比生成对抗学习
Abstract
Conventional Generative Adversarial Networks (GANs) for Single Image Super-Resolution (SISR) often struggle with hallucinated artifacts, largely because standard discriminators evaluate overall image naturalness rather than strict conditional realism. To address this, we propose MaCo-GAN, a novel manifold-contrastive GAN framework that replaces the conventional adversarial loss with a supervised contrastive objective. A core component of our method is a dynamic fake sample synthesizer that transforms ground truth (GT) data into a spectrum of challenging, perceptually plausible fake images that strictly maintain low-resolution (LR) correspondence. Utilizing these synthesized samples, we establish a robust contrastive minimax game: the generator is trained to attract its predictions toward on-manifold fakes (low distortion) and repel them from off-manifold fakes (high distortion), while the discriminator optimizes the exact opposite. By simply replacing the adversarial loss of a baseline SR model with our proposed objective, we demonstrate consistent improvements in the perception-distortion trade-off across various benchmarks. Extensive ablation studies validate the effectiveness of our framework and provide deep insights into the dynamics of this conditional contrastive game.
Chinese Translation
传统的针对单幅图像超分辨率（SISR）的生成对抗网络（GAN）经常在生成虚假伪影方面遇到困难，这主要是因为标准鉴别器评估的是整体图像的自然性，而不是严格的条件现实性。为了解决这个问题，我们提出了MaCo-GAN，这是一种新颖的流形对比GAN框架，采用有监督的对比目标替代传统的对抗损失。我们方法的核心组件是一个动态伪样本合成器，它将真实数据（GT）转换成一系列具有挑战性且感知上合理的伪图像，同时严格保持低分辨率（LR）对应关系。利用这些合成样本，我们建立了一个稳健的对比最小化博弈：生成器被训练来将其预测吸引向流形上的伪样本（低失真），而将其推开离流形的伪样本（高失真），而鉴别器则优化正好相反。通过简单地将基线超分模型的对抗损失替换为我们提出的目标，我们在各种基准数据集上展示了感知失真权衡的一致改进。广泛的消融研究验证了我们框架的有效性，并深入洞察了这一条件对比博弈的动态特性。
cs.CV / 94 / 2606.05071
InstantRetouch: Efficient and High-Fidelity Instruction-Guided Image Retouching with Bilateral Space
InstantRetouch：高效且高保真的指导性图像修饰方法，基于双边空间
Abstract
Language-guided photo retouching aims to adjust color and tone while preserving geometry and texture. Recently, diffusion-based retouching shows a superior visual quality, but often struggles with both fidelity issues due to its generative nature and efficiency because of its iterative sampling process. In this work, we propose an efficient and fidelity-preserving retouching method using bilateral space manipulation, which is both compact and content-decoupled. Specifically, instead of directly editing pixels or image latents, our model predicts a low-resolution bilateral grid of affine transforms, which are sliced using a learned guidance map and then applied to the full-resolution image. This approach yields both high fidelity and improved efficiency. To retain strong priors of a pretrained generative model, we distill a multi-step diffusion model into our bilateral grid framework using Variational Score Distillation, complemented by a prompt alignment loss to guide instruction-following behavior. Additionally, we introduce a new benchmark and evaluate our method across multiple dimensions: fidelity, instruction following, and efficiency. Compared to the latest retouch methods, like Gemini-2.5-Flash (Nano-Banana), our method can avoid content drift, significantly improve latency, and generate visually pleasing edits, while maintaining a high level of fidelity. Project page: https://openimaginglab.github.io/InstantRetouch/.
Chinese Translation
语言引导的照片修饰旨在调整色彩和色调，同时保留几何形状和纹理。最近，基于扩散的方法展现出优越的视觉质量，但由于其生成特性，在保真度和效率上往往面临挑战，因为其迭代采样过程带来效率问题。在本研究中，我们提出了一种高效且保留保真的修饰方法，采用双边空间操作，该方法在结构上紧凑且内容解耦。具体而言，我们的模型预测一个低分辨率的双边网格，由仿射变换组成，这些变换通过学习到的引导图进行切片，并应用于全分辨率图像。该方法增强了保真度，且提高了效率。为了保留预训练生成模型的强先验，我们使用变分评分蒸馏将多步扩散模型提炼到我们的双边网格框架中，并辅以引导指令遵循行为的提示对齐损失。此外，我们引入了一个新的基准，并在多个维度上评估我们的方法：保真度、指令遵循和效率。与最新的修饰方法，如 Gemini-2.5-Flash（Nano-Banana）相比，我们的方法能够避免内容漂移，大幅度降低延迟，并生成视觉上令人满意的修饰，同时保持高水平的保真度。项目页面： https://openimaginglab.github.io/InstantRetouch/。
cs.CV / 95 / 2606.05102
ZipSplat: Fewer Gaussians, Better Splats
ZipSplat：更少的高斯，更好的光斑
Abstract
Feed-forward 3D Gaussian Splatting methods reconstruct a scene from posed or pose-free images in a single forward pass, yet current approaches predict one Gaussian per input pixel, tying the representation budget to camera resolution rather than scene complexity. A flat wall and a richly textured object thus produce equally many Gaussians despite very different geometric needs. We propose ZipSplat, a token-based feed-forward model that decouples Gaussian placement from the pixel grid. A multi-view backbone extracts dense visual tokens, and k-means clustering compresses them into a compact set of scene tokens. Cross- and self-attention refine these tokens, and a lightweight MLP decodes each into a group of Gaussians with unconstrained 3D positions. Because clustering is applied at inference, a single trained model spans the quality-efficiency curve without retraining. ZipSplat operates without ground-truth poses or intrinsics, yet sets a new state of the art on DL3DV and RealEstate10K with ${\sim}6{\times}$ fewer Gaussians than pixel-aligned methods, surpassing the best pose-free baseline by 2.1dB and 1.2dB PSNR, respectively. It further generalizes zero-shot to Mip-NeRF360 and ScanNet++, outperforming all comparable baselines. Our project page is at ${\href{https://veichta.com/zipsplat}{https://veichta.com/zipsplat}}$.
Chinese Translation
前馈型3D高斯光斑方法通过单次前向传播从姿态图像或无姿态图像中重建场景，然而当前的方法对每个输入像素预测一个高斯，导致表示预算与相机分辨率而非场景复杂性相关。因此，即使几何需求差异很大，平坦的墙面和富有纹理的物体产生的高斯数量也相同。我们提出了ZipSplat，这是一种基于token的前馈模型，将高斯放置与像素网格解耦。多视图主干网络提取密集视觉token，而k均值聚类将其压缩为一组紧凑的场景token。交叉注意力和自注意力进一步优化这些token，轻量级多层感知器（MLP）将每个token解码为具有不受限3D位置的一组高斯。由于聚类在推理时应用，单个训练好的模型涵盖了质量-效率曲线，而无需重新训练。ZipSplat在没有真实姿态或内参的情况下运行，但在DL3DV和RealEstate10K上设定了新的最先进的水平，其高斯数量比像素对齐方法减少约6倍，分别超越了最佳无姿态基线2.1dB和1.2dB PSNR。此外，它还在零-shot情况下推广到Mip-NeRF360和ScanNet++，超越了所有可比的基线。我们的项目页面链接为${ ext{https://veichta.com/zipsplat}}$。
cs.CV / 96 / 2606.05115
Continual Visual and Verbal Learning Through a Child's Egocentric Input
通过儿童的自我中心输入进行持续的视觉与语言学习
Abstract
Children learn the meanings of words from a continuous, temporally structured stream of egocentric experience. Recent work shows that neural networks can also learn word-referent mappings from a child's egocentric video recordings, but they cycle through the shuffled data for hundreds of epochs, contrasting with how children actually encounter their environment. We introduce BabyCL, a continual multimodal learning framework that processes the SAYCam dataset in a single chronological pass, combining streaming visual representation learning with an image-text contrastive objective. BabyCL combines a multi-stage temporal segmentation of the stream with a dual replay buffer that independently manages visual and multimodal histories, and it is jointly trained with three contrastive losses on a shared backbone. Under a matched optimization budget, BabyCL outperforms streaming learning baselines on the SAYCam Labeled-S 4AFC benchmark, substantially narrowing the gap to an upper bound of offline training. Ablations show that the gains are robust to the length of the online temporal segmentation window and the eviction rule of the replay buffer. Together, these results show that meaningful word-referent mappings can emerge under training conditions much closer to a child's actual experience.
Chinese Translation
儿童通过一个持续的、时间结构化的自我中心体验流来学习单词的含义。最近的研究表明，神经网络也可以从儿童的自我中心视频记录中学习词汇与指称的对应关系，但它们在打乱的数据上循环训练了数百个周期，这与儿童实际接触环境的方式形成了对比。我们提出了BabyCL，一个持续的多模态学习框架，它在单一的时间顺序传递中处理SAYCam数据集，将流式视觉表示学习与图像-文本对比目标相结合。BabyCL将流的多阶段时间分割与一个独立管理视觉和多模态历史的双重重放缓冲区结合，并在一个共享的主干上联合训练三个对比损失。在相匹配的优化预算下，BabyCL在SAYCam Labeled-S 4AFC基准测试中超过了流式学习基线，显著缩小了与离线训练上限之间的差距。消融实验表明，这些增益对在线时间分割窗口的长度和重放缓冲区的驱逐规则具有鲁棒性。总体而言，这些结果表明，在与儿童实际经验更加接近的训练条件下，可以产生有意义的词汇-指称映射。
cs.CV / 97 / 2606.05142
GeM-NR: Geometry-Aware Multi-View Editing for Nonrigid Scene Changes
GeM-NR：针对非刚性场景变化的几何感知多视角编辑
Abstract
Recent developments in multi-view image editing with generative models have brought us a step closer toward general 3D content generation and customization. Most existing works focus on rigid or appearance-only edits by utilizing the geometry of the unedited scene. This naturally limits these methods to edits that preserve the underlying scene structure. Other approaches are trained for specific image editing tasks, such as object removal and addition. Despite this progress, general nonrigid edits, i.e., edits that substantially change the scene geometry, remain challenging for existing methods. We propose GeM-NR, a fast and flexible training-free approach for general multi-view consistent image editing, including edits that drastically change the geometry and appearance of the scene. Given an anchor image edited with a chosen backbone editor (such as FLUX, Qwen, BrushNet) and a query unedited image, GeM-NR edits the query image consistently with the anchor edit. The method incorporates multiple stages: (i) depth map estimation, where we propose a strategy to maximize the alignment between the 3D point clouds of the edited and unedited scenes, (ii) projection onto a query viewpoint, and (iii) refinement of the obtained image conditioned on the unedited query. The conditioning-based formulation scales well from two to many views of an object. We demonstrate the ability of our method to handle edits with significant changes in geometry and appearance, something that existing methods struggle with. We perform an extensive evaluation showing that our method improves consistency for a wide variety of edit tasks, including generating 3D representations of the edited scene. Both quantitative and qualitative results indicate the state-of-the-art performance of our method in terms of edit quality as well as geometric and photometric consistency across multiple views.
Chinese Translation
最近，基于生成模型的多视角图像编辑的进展使我们向通用三维内容生成和定制迈出了重要一步。现有的大多数研究集中在通过利用未编辑场景的几何结构进行刚性或仅外观编辑。这自然限制了这些方法只能进行保持基础场景结构的编辑。其他方法则针对特定的图像编辑任务进行训练，例如对象移除和添加。尽管取得了一定进展，现有方法在进行通用的非刚性编辑（即显著改变场景几何的编辑）时仍面临挑战。我们提出了GeM-NR，这是一种快速且灵活的无训练方法，旨在实现通用的多视角一致图像编辑，包括那些显著改变场景几何和外观的编辑。给定一幅经过选定基础编辑器（如FLUX、Qwen、BrushNet）编辑的锚图像和一幅未编辑的查询图像，GeM-NR将查询图像与锚图像的编辑保持一致。该方法包含多个阶段：（i）深度图估计，我们提出了一种策略以最大化编辑和未编辑场景之间3D点云的对齐，(ii) 投影到查询视角，(iii) 基于未编辑查询图像对获得图像进行细化。基于条件的公式从两个对象视角向多个视角的扩展效果良好。我们展示了该方法处理具有显著几何和外观变化的编辑的能力，这正是现有方法所面临的挑战。我们进行了广泛的评估，结果表明我们的方法在各种编辑任务中提高了一致性，包括生成编辑场景的三维表示。定量和定性结果均表明，在编辑质量以及多个视角下的几何和光度一致性方面，我们的方法表现处于最先进水平。
cs.CV / 98 / 2606.05149
An Open-Source Two-Stage Computer Vision Pipeline for Fine-Grained Vehicle Classification using Vision Transformers
基于视觉变换器的开源二阶段计算机视觉管道用于细粒度车辆分类
Abstract
Vehicle body type is a significant determinant of cyclist injury severity in overtaking crashes, yet automated tools for classifying vehicles into injury-risk-relevant categories from naturalistic roadway video do not exist in the open literature. Standard object detection benchmarks provide only coarse vehicle labels (car, truck, bus, motorcycle), while existing fine-grained recognition systems are trained on controlled imagery and lack evaluation for deployment robustness across recording sites. This paper presents an open-source two-stage computer vision pipeline combining a pre-trained RT-DETR detector for coarse vehicle localization with a fine-tuned Vision Transformer (ViT-Base/16) for six-category body-type classification: passenger car, SUV, pickup truck, minivan, large van, and commercial truck. A confidence-based abstention mechanism withholds Stage 2 predictions when softmax output falls below 0.60, producing unknown labels rather than silent misclassifications. Evaluated on 3,805 annotated overtaking events from a bicycle-lane corridor in Ann Arbor, Michigan (in-distribution), the pipeline achieved 0.94 accuracy with per-class F1 scores from 0.91 (minivan) to 0.97 (SUV). On an independent out-of-distribution evaluation of 311 events from an open cycling dataset without retraining, accuracy was 0.89. Three of four well-represented categories maintained F1 at or above 0.90 under domain shift. The largest degradation was observed for minivan (F1 = 0.72), driven by abstention rate rising from 2.4% to 25.0% rather than active misclassification, consistent with the mechanism propagating genuine model uncertainty. The full pipeline, including inference scripts, training code, evaluation utilities, and model weights, is released as open-source software to support reproducibility and reuse across roadside video archives and cycling safety research.
Chinese Translation
车辆车身类型是影响超车事故中骑行者伤害程度的重要因素，但目前在自然路况视频中将车辆分类为与伤害风险相关类别的自动化工具尚未出现在公开文献中。标准的目标检测基准仅提供粗略的车辆标签（轿车、卡车、公交车、摩托车），而现有的细粒度识别系统则是在受控图像上训练，缺乏针对不同拍摄地点的部署稳健性评估。本文提出了一种开源的二阶段计算机视觉管道，结合了预训练的 RT-DETR 检测器进行粗略的车辆定位，以及经过微调的视觉变换器（ViT-Base/16）用于六类车身类型的分类：乘用车、SUV、皮卡、面包车、大型厢式货车和商用卡车。当 softmax 输出低于 0.60 时，基于置信度的弃权机制会抑制第二阶段的预测，产生未知标签而不是沉默的误分类。该管道在美国密歇根州安阿伯的一条自行车道走廊上对 3,805 个标注的超车事件进行了评估，取得了 0.94 的准确率，各类别的 F1 分数从 0.91（面包车）到 0.97（SUV）。在不进行再训练的情况下，对 311 个来自开放骑行数据集的独立分布外事件进行评估，准确率为 0.89。四个代表性类别中有三个在领域转移下的 F1 分数维持在 0.90 或以上。面包车的退化最大（F1 = 0.72），这是由于弃权率从 2.4% 上升到 25.0%，而不是主动的误分类，这与机制传播真实的模型不确定性一致。完整管道，包括推理脚本、训练代码、评估工具和模型权重，作为开源软件发布，以支持可重复性和在路边视频档案及骑行安全研究中的重用。
cs.CV / 99 / 2606.05162
Controllable Dynamic 3D Shape Generation via 3D Trajectories and Text
通过3D轨迹和文本可控动态3D形状生成
Abstract
We introduce T2Mo, a feed-forward framework for controllable dynamic 3D shape generation conditioned on 3D trajectories and text. Due to the inherent ambiguity of language, generating precisely intended motions using text alone remains challenging. To address this, we adopt 3D trajectories as controllable spatial guidance, specifying the exact paths along which selected points should move. By combining both, T2Mo generates object motions that spatially adhere to the given trajectories while globally reflecting the text semantics. To robustly handle trajectory inputs with arbitrary configurations, ranging from dense to sparse and unevenly distributed, we further propose a shape-grounded trajectory embedding that maps an input trajectory set into a shape-aware token set covering the entire object. We conduct extensive comparisons against text-based baselines and cascaded video-based baselines that combine trajectory-guided video generation with video-to-dynamic mesh generation. Quantitative and qualitative evaluations, along with user studies, demonstrate that our approach produces motions that more faithfully follow the given prompts with higher expressiveness while preserving motion quality.
Chinese Translation
我们引入了T2Mo，这是一个用于基于3D轨迹和文本的可控动态3D形状生成的前馈框架。由于语言固有的模糊性，单靠文本生成精确意图的运动仍然具有挑战性。为了解决这个问题，我们采用3D轨迹作为可控的空间指导，明确指定所选点应该移动的精确路径。通过结合两者，T2Mo生成的物体运动在空间上遵循给定的轨迹，同时在整体上反映文本的语义。为了稳健地处理具有任意配置的轨迹输入，从密集到稀疏，以及分布不均，我们进一步提出了一种形状基础的轨迹嵌入，将输入的轨迹集映射到一个覆盖整个物体的形状感知标记集。我们对基于文本的基线和将轨迹引导的视频生成与视频到动态网格生成相结合的级联视频基线进行了广泛的比较。定量和定性评估以及用户研究表明，我们的方法生成的运动在表达性上更高，能够更忠实地遵循给定提示，同时保持运动质量。
cs.AI / 1 / 2606.04037
Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification
迈向企业人工智能代理的预部署保障：基于本体的仿真与信任认证
Abstract
Pre-deployment verification of enterprise artificial intelligence (AI) agents remains a critical gap between large language model (LLM) capability benchmarking and production deployment. Post-deployment monitoring, human-in-the-loop controls, and prompt-level guardrails offer limited assurance once an agent is operating in production. We propose an ontology-grounded verification framework combining three components: an Agent Operational Envelope formalizing the certification space across permissions, domain constraints, safety properties, governance rules, and autonomy levels; an ontology-to-scenario generation pipeline that derives regulatory, operational, and adversarial test scenarios automatically; and a Trust Certificate carrying a machine-verifiable attestation with graduated deployment verdicts (Approved, Conditional, Rejected). A controlled pilot across four regulated industries (Fintech, Banking, Insurance, and Healthcare), instantiated as five industry-by-regulatory-regime cells across the United States and Vietnam, generated 1,800 scenarios evaluated against 125 primary-source regulatory requirements and 25 injected faults. Ontology-grounded generation (G4) achieved 48.3% regulatory coverage versus 33.1% for the persona-based baseline (corrected p = .0006) and the highest domain specificity (4.77/5.0; p = 2e-6). The coverage advantage over baseline and retrieval-augmented prompting was not robust after Bonferroni correction. Cross-validation across three LLM families (Claude Sonnet 4, Qwen 2.5 72B, Gemma 4 26B; 5,400 total scenarios) replicated the persona-versus-ontology pattern. The results establish ontology-grounded scenario generation as a credible complement to persona-based test suites for regulatory-intensive domains.
Chinese Translation
企业人工智能（AI）代理的预部署验证仍然是大型语言模型（LLM）能力基准测试与生产部署之间的关键差距。一旦代理在生产中运行，后续监控、人机协作控制和提示级守护措施提供的保障是有限的。我们提出了一种基于本体的验证框架，结合三个组成部分：一个代理操作包络（Agent Operational Envelope），形式化认证空间，包括权限、领域约束、安全属性、治理规则和自主水平；一个本体到场景生成流程（ontology-to-scenario generation pipeline），自动推导监管、操作和对抗测试场景；以及一个信任证书（Trust Certificate），携带机器可验证的证明以及分级部署判决（批准、条件、拒绝）。在美国和越南四个受监管行业（金融科技、银行、保险和医疗保健）进行的受控试点，生成了1800个场景，评估了125项主要来源的监管要求和25个引入的故障。基于本体的生成（G4）实现了48.3%的监管覆盖率，相较于基于角色的基线的33.1%（校正p = .0006），且域特异性最高（4.77/5.0；p = 2e-6）。经过Bonferroni校正后，与基线和增强提示的覆盖优势并不稳健。三种LLM系列（Claude Sonnet 4, Qwen 2.5 72B, Gemma 4 26B；共5400个场景）的交叉验证重复了角色与本体模式的结果。这些结果建立了基于本体的场景生成作为监管密集领域中对基于角色测试套件的可信补充。
cs.AI / 2 / 2606.04150
Stumbling Into AI Emotional Dependence: How Routine AI Interactions Reshape Human Connection
无心插柳的AI情感依赖：常规AI互动如何重塑人际关系
Abstract
Public discourse and emerging policy typically assume that AI emotional support is a deliberate act: a lonely user consciously seeking comfort from a dedicated companion chatbot. In this paper, we draw on emerging empirical evidence and argue that this picture is inaccurate on two accounts, both in how AI emotional support arises and how it shapes future behavior. First, AI emotional support commonly emerges incidentally within task-oriented interactions on general-purpose platforms, much as workplace friendships deepen through collaboration. Second, these incidental encounters are path-dependent: positive experiences of AI emotional support update people's beliefs about AI's emotional capabilities and redirect their choices for future emotional support, increasing preference for AI and decreasing preference for humans. We review recent evidence, including a large-scale longitudinal study conducted in collaboration with OpenAI, showing that daily five-minute conversations with an AI about personal issues over 28 days led to a 10.3% decrease in the preference for seeking support from humans and an 11.6% increase in the preference for AI. These findings suggest that current policy, focused on companion apps and isolated interactions, cannot adequately protect human connection. Instead, effective regulations should extend to general-purpose AI systems and address cumulative, trajectory-level changes in how people seek support. Recognizing how people stumble into AI emotional support and how those encounters redirect human connections over time is essential to safeguarding human well-being.
Chinese Translation
公众话语和新兴政策通常认为AI情感支持是一种有意识的行为：孤独的用户在主动寻求来自专用伴侣聊天机器人的安慰。本文基于新兴的实证证据，论证这一看法不准确，主要体现在两个方面：AI情感支持是如何产生的，以及它如何塑造未来的行为。首先，AI情感支持通常在任务导向的互动中偶然出现，类似于工作场所的友谊通过合作而加深。其次，这些偶然的相遇具有路径依赖性：AI情感支持的积极体验会更新人们对AI情感能力的信念，并重新引导他们未来对情感支持的选择，增加对AI的偏好，减少对人类的偏好。我们回顾了近期的证据，包括与OpenAI合作进行的一项大规模纵向研究，结果显示，在28天内与AI进行五分钟的日常对话，讨论个人问题，导致人们对人类寻求支持的偏好下降10.3%，而对AI的偏好上升11.6%。这些发现表明，当前集中于伴侣应用程序和孤立互动的政策无法有效保护人际关系。相反，切实有效的规制应扩展至通用AI系统，并关注人们寻求支持的累积性和轨迹性变化。认识到人们是如何无意中走入AI情感支持的，以及这些经历如何随着时间的推移重定向人际关系，对于保护人类福祉至关重要。
cs.AI / 3 / 2606.04152
Thinking Through Signs: PEEL as a Semiotic Scaffolding for Epistemically Accountable AI-Enabled Research
通过符号思考：PEEL作为一种符号学支架以支持可解释的人工智能研究
Abstract
Large language models are reshaping research practice while quietly eroding researchers epistemic accountability. This commentary introduces PEEL - Protocols for Epistemically Engaged Literacy in AI, a working scaffolding that combines deterministic distant reading via Voyant Tools with LLM interpretation via Claude, grounded in Peircean semiotics and abductive reasoning. Applied to AI-generated condensations of three source texts, PEEL reveals systematic distortions in quantity, term frequency, and epistemic voice that are invisible without non-AI measurement -- and yields three design implications: deterministic instruments must accompany AI tools; fluency is not fidelity; epistemic authority must be designed in, not assumed.
Chinese Translation
大型语言模型正在重塑研究实践，同时悄然侵蚀研究者的知识责任。本文介绍了PEEL——人工智能中的知识参与素养协议（Protocols for Epistemically Engaged Literacy in AI），这是一种工作性支架，它结合了通过Voyant Tools进行的决定性远程阅读与通过Claude进行的LLM解读，内容基于皮尔士的符号学和溯因推理。应用于三篇源文本的AI生成摘要，PEEL揭示了在量、术语频率和知识声音方面的系统性扭曲，这些扭曲在没有非AI测量的情况下是不可见的——并提出了三个设计启示：决定性工具必须与AI工具相伴；流畅性并不等同于忠实性；知识权威必须被设计，而非假设。
cs.AI / 4 / 2606.04202
SMAC-Talk: A Natural Language Extension of the StarCraft Multi-Agent Challenge for Large Language Models
SMAC-Talk：针对大语言模型的星际争霸多智能体挑战的自然语言扩展
Abstract
As LLMs become more widely deployed, they are increasingly expected to work alongside other AI agents rather than operating in isolation. Effective coordination in these settings requires agents to communicate, share information and make decisions under uncertainty. We introduce SMAC-Talk, a natural language extension of the StarCraft Multi-Agent Challenge for evaluating LLM-based agents in cooperative multi-agent environments. The environment has several key features such as decentralized control, partial observability and long-horizon decision making. SMAC-Talk includes a natural language communication channel which is used to probe agent coordination and trust. We use this communication channel to construct different evaluation scenarios, including settings with an embedded deceptive communicator that tries to disrupt and deceive allies through communication alone. We provide three agents for benchmarking using 4 models from the Qwen3.5 family and study how reasoning structure, memory and model scale affect coordination between agents. We release SMAC-Talk as an open benchmark to support the research community in developing and evaluating LLM agents in cooperative multi-agent settings.
Chinese Translation
随着大语言模型（LLMs）的广泛应用，越来越多的人期待它们能够与其他人工智能代理协同工作，而非孤立运行。在这样的环境中，代理之间需要有效协调，以便沟通、共享信息并在不确定性下做出决策。我们引入了SMAC-Talk，这是对星际争霸多智能体挑战的自然语言扩展，用于评估基于LLM的代理在合作多智能体环境中的表现。该环境具有几个关键特征，例如去中心化控制、部分可观测性以及长期决策能力。SMAC-Talk包含一个自然语言通信通道，用于探测代理之间的协调和信任。我们利用这一通信通道构建了不同的评估场景，包括嵌入了欺骗性沟通者的设置，该沟通者试图通过沟通来破坏和欺骗盟友。我们提供了三个代理用于基准测试，使用来自Qwen3.5系列的四个模型，并研究推理结构、记忆和模型规模如何影响代理之间的协调。我们将SMAC-Talk作为一个开放基准发布，以支持研究社区开发和评估合作多智能体环境中的LLM代理。
cs.AI / 5 / 2606.04223
Consensus is Strategically Insufficient: Reasoning-Trace Disagreement as a Knowledge-Representation Signal
共识在战略上是不足的：推理轨迹分歧作为知识表示信号
Abstract
Multi-agent systems are commonly designed to reduce disagreement through voting, consensus protocols, debate, or fault-tolerant aggregation. We argue that this objective is insufficient for value-laden tasks, where disagreement may reflect genuine normative uncertainty rather than agent error. Building on prior work on reasoning-trace disagreement in human-AI collaborative moderation, we propose a knowledge-representation layer in which reasoning traces and agent decisions are abstracted into symbolic disagreement states. Given agents producing explicit reasoning traces and binary decisions, we distinguish four states according to reasoning similarity and conclusion agreement: convergent agreement, divergent agreement, convergent disagreement and divergent disagreement. These states support defeasible strategic routing rules. We instantiate the framework in content moderation and argue that disagreement-aware routing provides a bridge between sub-symbolic LLM deliberation and symbolic knowledge representation for multi-agent strategic reasoning.
Chinese Translation
多智能体系统通常通过投票、共识协议、辩论或容错聚合来减少分歧。我们认为，这一目标对于价值导向的任务而言是不够的，因为分歧可能反映真实的规范性不确定性，而非代理错误。在以人机协作审查中的推理轨迹分歧为基础的先前研究上，我们提出了一个知识表示层，其中推理轨迹和代理决策被抽象为符号化的分歧状态。考虑到代理产生明确的推理轨迹和二元决策，我们根据推理相似性和结论一致性区分四种状态：会聚一致、会聚分歧、发散一致和发散分歧。这些状态支持可推翻的战略路由规则。我们在内容审查中实例化该框架，并认为基于分歧的路由为子符号大语言模型（LLM）审议与符号知识表示之间的多智能体战略推理提供了一座桥梁。
cs.AI / 6 / 2606.04244
VAMPS: Visual-Assisted Mathematical Problem Solving Benchmark
VAMPS：视觉辅助数学问题解决基准
Abstract
Multimodal large language models are increasingly capable of complex reasoning, yet their performance often degrades when they must externalize a problem through a tool and then reason over the tool's output, specifically when they rely on visual aids. This gap is especially important because real engineering and scientific workflows often rely on visualization tools for analysis, validation, and decision-making. To study this discrepancy, we introduce VAMPS (Visual-Assisted Mathematical Problem Solving), a benchmark for graph-assisted mathematics. VAMPS contains 1,168 multimodal, bilingual multiple-choice question-answer pairs drawn from Iranian University Entrance Exam algebra and calculus problems and expanded with human-reviewed LLM-generated synthetic variants, all selected so that plotting provides a natural solution strategy by revealing intersections, extrema, asymptotes, etc. Designed for both benchmarking and diagnosis, VAMPS goes beyond prior multimodal benchmarks that primarily evaluate reasoning over fixed visual inputs by testing whether a model can benefit from constructing a useful graph and grounding its answer in the resulting visualization. Overall, we found that across a diverse set of models, direct analytical solving surprisingly outperforms tool-enabled visual solving, even on problems where plotting is a natural strategy.
Chinese Translation
多模态大型语言模型在复杂推理方面的能力日益增强，然而当它们必须通过工具外化一个问题并对工具的输出进行推理时，它们的表现往往会下降，尤其是在依赖视觉辅助时。这一差距尤为重要，因为真实的工程和科学工作流程通常依赖于可视化工具进行分析、验证和决策。为研究这一差异，我们提出了VAMPS（视觉辅助数学问题解决），这是一个用于图形辅助数学的基准。VAMPS包含1,168对来自伊朗大学入学考试代数和微积分问题的多模态双语选择题问答对，并通过人工审核的LLM生成的合成变体进行扩展，所有选择均旨在通过揭示交点、极值、渐近线等内容，提供自然的解决策略。VAMPS不仅用于基准测试，还用于诊断，超越了以往主要评估固定视觉输入推理能力的多模态基准，通过测试模型能否从构建有用图形及在生成的可视化中落实答案中获益。总体而言，我们发现，在多样化模型的广泛研究中，直接的解析解法出人意料地优于工具辅助的视觉解法，即使在绘图是一种自然策略的问题上也是如此。
cs.AI / 7 / 2606.04246
StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis
StepPRM-RTL：逐步过程-奖励引导的LLM微调以增强RTL综合
Abstract
Automatic generation of RTL code for digital hardware designs remains challenging due to long-horizon reasoning, multi-step dependencies, and strict correctness constraints in Verilog and VHDL. We present StepPRM-RTL, a novel framework that combines stepwise trajectory modeling, process-reward modeling (PRM), and retrieval-augmented fine-tuning (RAFT) to enhance both the functional correctness and reasoning fidelity of LLM-based RTL code generation. StepPRM-RTL constructs stepwise reasoning trajectories from canonical solutions, where each step contains a rationale and incremental code modification. A Process Reward Model (PRM) evaluates intermediate steps, providing dense feedback that guides reinforcement-style updates during RAFT fine-tuning. Monte Carlo Tree Search (MCTS) explores alternative reasoning paths, enriching the training dataset with high-quality trajectories. This integration of stepwise and outcome-aware rewards allows the model to learn both how and why to construct correct RTL, improving long-horizon reasoning beyond standard supervised or outcome-based training. Experimental evaluation on benchmark Verilog and VHDL datasets demonstrates that StepPRM-RTL outperforms the best prior methods by over 10\% in functional correctness and reasoning fidelity metrics. Ablation studies confirm that the combination of PRM-guided rewards and stepwise trajectory exploration is key to its performance. StepPRM-RTL generalizes across RTL languages and provides a scalable framework for high-fidelity, interpretable code generation, establishing a new standard for LLM-assisted hardware design automation.
Chinese Translation
自动生成数字硬件设计的RTL代码仍然具有挑战性，这主要是由于长时间范围推理、多步骤依赖关系，以及Verilog和VHDL中严格的正确性约束。我们提出了StepPRM-RTL，这是一种新颖的框架，结合了逐步轨迹建模、过程奖励建模（Process Reward Modeling, PRM）和检索增强微调（Retrieval-Augmented Fine-Tuning, RAFT），以提高基于大语言模型（LLM）的RTL代码生成的功能正确性和推理准确性。StepPRM-RTL从典型解决方案中构建逐步推理轨迹，其中每一步包含一个理由和增量代码修改。过程奖励模型（PRM）评估中间步骤，提供密集的反馈，以指导RAFT微调过程中的强化学习风格更新。蒙特卡罗树搜索（Monte Carlo Tree Search, MCTS）探索替代推理路径，丰富高质量轨迹的训练数据集。这种逐步和结果意识奖励的结合，使模型能够学习如何以及为什么构造正确的RTL，从而改善超越标准监督或基于结果的训练的长时间范围推理。在基准Verilog和VHDL数据集上的实验评估表明，StepPRM-RTL在功能正确性和推理准确性指标上超过了现有最佳方法超过10%。消融研究证实，PRM引导的奖励与逐步轨迹探索的结合是其性能的关键。StepPRM-RTL能够跨RTL语言进行泛化，并提供一个可扩展的高保真、可解释代码生成框架，为LLM辅助硬件设计自动化建立了新标准。
cs.AI / 8 / 2606.04261
Can Generalist Agents Automate Data Curation?
通用代理能够自动化数据整理吗？
Abstract
Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, implement, evaluate, and revise data policies against noisy benchmark feedback. We ask whether generalist coding agents can automate this data-curation loop. We introduce *Curation-Bench*, an agent-centric benchmark that fixes the model, training recipe, and evaluation suite while giving agents command-line access to inspect data, implement policies, submit them to a fixed training/evaluation pipeline, and revise. In a vision-language instruction-tuning instantiation, out-of-the-box agents reach strong published data-selection baselines within ten iterations. However, trajectory analysis reveals a persistent *execution-research gap*: agents mainly tune local policy variants rather than explore new policy families, even when given strategy guides and paper references. Scaffolds requiring each iteration to cite, instantiate, and adapt a prior method shift agents toward method-guided exploration. The scaffolded agent autonomously composes -- without human design input -- a data-selection policy that outperforms strong published baselines at one-tenth their data budget. Overall, current agents can run the curation loop, but reliable data research requires scaffolded method adaptation, not open-ended prompting alone. Code and benchmark are open-sourced.
Chinese Translation
整理训练数据是现代人工智能开发中最重要且劳动密集的部分之一：从业者需要迭代地提出、实施、评估并修订数据政策，以应对噪声基准反馈。我们探讨通用编码代理能否自动化这一数据整理循环。我们引入了*Curation-Bench*，这是一个以代理为中心的基准，固定了模型、训练配方和评估套件，同时允许代理通过命令行访问数据、实施政策、将其提交给固定的训练/评估管道并进行修订。在一个视觉语言指令调优的实例中，现成的代理在十次迭代内达到了强大的已发布数据选择基准。然而，轨迹分析揭示了一个持续的*执行-研究差距*：代理主要调整局部政策变体，而不是探索新的政策家族，即使在给定策略指南和论文参考的情况下。要求每次迭代引用、实例化和适应先前方法的支架使代理转向方法引导的探索。这一支架化代理自主地组合出一种数据选择政策，超越了强大的已发布基准，仅使用其数据预算的十分之一。总体而言，当前代理能够运行整理循环，但可靠的数据研究需要支架化的方法适应，而不仅仅是开放式提示。代码和基准已开源。
cs.AI / 9 / 2606.04273
Characterizing initial human-AI proof formalization workflows
初步人类-AI证明形式化工作流程的特征化
Abstract
For centuries, human mathematicians have written proofs to substantiate their mathematical arguments; yet, the ability to automatically verify the validity of proofs has long been a challenge. Advances in AI systems' ability to generate code and engage in increasingly high-level mathematical reasoning promise to transform people's ability to formalize and thereby verify proofs. While many works focus on benchmarking the current frontier, we instead study how people use these tools. We conduct a mixed-methods analysis into the initial impact of AI on people's formalization workflows: what people claim they want, what they see as the barriers to those visions, and how they actually use and adapt AI in practice. A qualitative survey shows that people's preferences are diverse, but with a general desire for AI assistance in formalization that preserves high-level human control over the proof discovery process. To assess how people actually engage with AI for formalization under such limitations, we conduct a controlled user study in which participants formalize informal math problems and their proofs, with and without AI, across a range of mathematical problems at varying levels of difficulty and domains. Despite limitations of the tools at the time for autoformalization, participants tend to attain higher formalization accuracy when allowed access to AI tools than when formalizing on their own, with most participants flexibly choosing to use multiple different AI tools. Taken together, our work sheds light on the early stages of AI integration into formalization workflows, involving an intimate interplay of human and AI engagement.
Chinese Translation
数世纪以来，人类数学家通过撰写证明来支持他们的数学论点；然而，自动验证证明有效性的能力长期以来一直是一个挑战。人工智能（AI）系统在生成代码和进行越来越高层次的数学推理方面的进步，有望改变人们形式化并由此验证证明的能力。尽管许多研究集中于评估当前的最前沿工具，但我们则研究人们如何使用这些工具。我们对AI对人们形式化工作流程的初步影响进行了混合方法分析：人们声称希望得到什么，他们认为实现这些愿景的障碍是什么，以及他们在实践中如何实际使用和调整AI。定性调查显示，人们的偏好多样，但普遍希望AI在形式化过程中提供支持，同时保持对证明发现过程的高层次人类控制。为了评估人们在这样的限制下如何实际使用AI进行形式化，我们进行了一项受控用户研究，参与者在多种数学问题的不同难度和领域下，使用和不使用AI对非正式数学问题及其证明进行形式化。尽管当时自动形式化工具的局限性，参与者在获得AI工具的情况下，通常能够达到比单独进行形式化时更高的形式化准确性，大多数参与者灵活选择使用多种不同的AI工具。总的来看，我们的研究为AI融入形式化工作流程的早期阶段提供了洞见，涉及人类与AI之间亲密的互动。
cs.AI / 10 / 2606.04296
The Saturation Trap and the Subjectivity of Intervention Timing: Why Affect-Based Triggers and LLM Judges Fail to Time Interventions on Autonomous Agents
饱和陷阱与干预时机的主观性：为何基于情感的触发器和大型语言模型评判者未能恰当地时机干预自主代理
Abstract
As autonomous AI agents move from conversational systems to long-horizon software execution, runtime safety layers that decide when to interrupt an agent have become essential. We study this timing problem using a continuous 18-dimensional affective-dynamics engine (HEART) as a diagnostic probe, evaluating four intervention trigger families - absolute state thresholds, composite state-action patterns, regex reasoning-feature extraction, and zero-shot LLM-as-judge - against human-annotated intervention points on SWE-bench-Verified debugging traces. We report three findings. First, a State Saturation Trap: agents show no recovery signal under sustained difficulty, so modeled frustration quickly crosses the threshold and stays at its maximum, converting threshold-on-state triggers from moment detectors into near-constant indicators that fire on 39-83% of actions across five trajectories. Second, a capability-and-context floor for LLM judges: a small model (gpt-5.4-mini) never fires, while frontier and cross-vendor models escape the zero-firing floor only with full-trajectory context, and even then reach only F1 0.17-0.40 at up to 90x the cost. Third, and most importantly, the supervised target is not reproducible among humans: three trained annotators using one rubric on a 56-action trajectory agree on where to intervene only slightly above chance (location Krippendorff's alpha = +0.047; best pairwise Cohen's kappa = +0.349) and not at all on intervention type (pause degenerate; clarify below chance; reflect only alpha = +0.226). We conclude that intervention timing is a low-reliability construct, making single-annotator F1 an unsuitable optimization target. Our contribution is the joint mapping of this problem across human inter-rater reliability, four detector architectures, a cross-model LLM-judge sweep, and a reproduced saturation effect, rather than any single detector's accuracy.
Chinese Translation
随着自主人工智能代理从对话系统转向长时间的软件执行，决定何时中断代理的运行时安全层变得至关重要。我们使用一个连续的18维情感动态引擎（HEART）作为诊断探头研究这一时机问题，评估四种干预触发家族——绝对状态阈值、复合状态-动作模式、正则表达式推理-特征提取，以及零样本大型语言模型（LLM）作为评判者——与在SWE-bench-Verified调试轨迹上人类标注的干预点对比。我们报告了三项发现。首先，状态饱和陷阱：在持续困难下，代理没有恢复信号，因此建模的挫败感迅速越过阈值并保持在其最大值，将基于状态的阈值触发器从瞬态检测器转变为几乎恒定的指标，在五条轨迹上的39-83%的行动中触发。其次，是对LLM评判者的能力与上下文的底线：一个小模型（gpt-5.4-mini）从未触发，而前沿及跨供应商模型仅在具备完整轨迹上下文的情况下逃脱零触发底线，即便如此，其F1值仍仅达到0.17-0.40，成本高达90倍。最重要的是，监督目标在不同人类间并不可重现：三名训练有素的标注员在一个56行动的轨迹上使用同一标准，对于干预位置的达成一致仅稍高于随机概率（位置的Krippendorff's alpha = +0.047；最佳成对的Cohen's kappa = +0.349），而在干预类型上完全一致（暂停为劣质；澄清低于随机概率；反思的alpha = +0.226）。我们得出结论：干预时机是一个低可靠性的构造，使得单一标注员的F1值不适合作为优化目标。我们的贡献在于将这一问题与人类间的评判可靠性、四种检测器架构、一轮跨模型LLM-评判者的评估和再现的饱和效应进行联合映射，而不仅仅专注于单一检测器的准确性。
cs.AI / 11 / 2606.04315
Exploring Cross-Scenario Generality of Agentic Memory Systems: Diagnostics and a Strong Baseline
探索智能记忆系统的跨场景通用性：诊断与强基线
Abstract
LLM agents accumulate histories that outgrow their context windows, motivating a growing literature on memory systems. Yet most existing designs are tuned to a single scenario (multi-session chat or a single trajectory format), and there is little evidence that they generalize across the heterogeneous trajectories agents encounter in deployment. We revisit eight memory systems plus an agentic harness for search problems, on five scenarios: single-turn QA, multi-session chat, agentic-trajectory QA, memory stress tests, and long-horizon agentic tasks. The harness, which self-manages flat text-file storage via tool calls, achieves the best cross-task ranking, suggesting that memory performance hinges on giving the agent active control over storage and retrieval rather than on a passive store behind a fixed pipeline. We instantiate this insight in AutoMEM, an agentic memory harness with a self-managed tool interface that achieves the best cross-scenario generality among the systems we evaluate.
Chinese Translation
大型语言模型（LLM）代理累积的历史超出了其上下文窗口，这推动了关于记忆系统的文献日益增加。然而，现有的大多数设计都针对单一场景（如多会话聊天或单一轨迹格式）进行调优，并且几乎没有证据表明它们能够跨越代理在部署中遇到的异质轨迹进行泛化。我们重新审视了八种记忆系统以及针对搜索问题的智能工具，在五种场景下进行评估：单轮问答、多会话聊天、智能轨迹问答、记忆压力测试和长时域智能任务。该工具通过工具调用自我管理平面文本文件存储，达到了最佳跨任务排名，表明记忆表现依赖于给予代理对存储与检索的主动控制，而不是依赖于固定管道后的被动存储。我们在AutoMEM中实现了这一见解，该智能记忆工具具有自我管理的工具接口，在我们评估的系统中实现了最佳的跨场景通用性。
cs.AI / 12 / 2606.04321
The Digital Apprentice: A Framework for Human-Directed Agentic AI Development
数字学徒：一个以人为导向的自主人工智能发展框架
Abstract
Agentic AI deployments face a recurring design tension: heavy human oversight limits scale, while broad autonomy outruns accountability. Neither posture provides the governance infrastructure required for responsible delegation. We present the Digital Apprentice, a framework for scalable, safe AI agency in which autonomy is earned, not assumed. The Digital Apprentice is a developmental learner that internalizes the tacit methodology of a directing human, graduating through per-skill autonomy tiers only when empirical evidence justifies it. The result is an agent that becomes genuinely useful over time while remaining aligned to a specific human's standards. Three architectural components make this possible. (1) Methodology capture, distilling a directing professional's tacit approach into structured assets. (2) Authorization, with autonomy escalation gated by explicit human approval. (3) Continuous alignment, correcting drift at runtime and converting each correction into owned preference data. We instantiate this framework as an inference-time control plane. We mathematically model the quality framework and discuss policies and techniques designed to raise quality. We apply the framework to an open professional corpus, and we show how catching data drift and applying a different technique at runtime recovers degraded quality dimensions under traffic shift. The implication extends beyond any single application. We believe these three pillars, stitched together as a system, form a safer and more viable path to agentic systems that can scale without sacrificing trust.
Chinese Translation
自主人工智能的部署面临着一种反复出现的设计紧张关系：高度的人类监督限制了规模，而广泛的自主性则超出了问责的范围。两者均无法提供负责委托所需的治理基础设施。我们提出了数字学徒，一个可扩展、安全的人工智能代理框架，在该框架中，自主性是通过学习获得的，而非默认的。数字学徒是一种发展型学习者，能够内化指导人类的隐性方法论，仅在经验数据证明其合理时，才会通过每项技能的自主性等级。其结果是一个随着时间的推移变得真正有用的代理，同时始终与特定人类的标准保持一致。实现这一点的有三个架构组成部分。 (1) 方法论捕捉，将指导专业人士的隐性方法提炼成结构化资产。 (2) 授权，通过明确的人类批准来限制自主性的提升。 (3) 持续对齐，在运行时纠正偏移，并将每次纠正转化为拥有的偏好数据。我们将该框架实例化为推理时控制平面。我们对质量框架进行了数学建模，并讨论了旨在提高质量的政策和技术。我们将该框架应用于一个开放的专业语料库，展示了如何通过捕捉数据偏移和在运行时应用不同技术来恢复在流量变化下退化的质量维度。其影响超越了任何单一应用。我们相信，这三个支柱作为一个系统结合在一起，构成了一个更安全和更可行的自主系统发展路径，从而能够在不牺牲信任的情况下进行规模扩展。
cs.AI / 13 / 2606.04391
Online Skill Learning for Web Agents via State-Grounded Dynamic Retrieval
通过状态引导动态检索进行在线技能学习的网络代理
Abstract
Language agents increasingly rely on reusable skills to improve multi-step web automation across related tasks. A growing line of work studies online skill learning, where agents continually induce skills from previous task trajectories and reuse them in future tasks on the fly. However, existing methods mainly reuse skills at the task-level: a fixed set of skills is retrieved based on the initial task instruction and then held fixed throughout execution. This static strategy is misaligned with web execution, where the appropriate next action depends not only on the task goal but also on the current webpage state, which often transitions into situations that the initial skills fail to cover. To address this gap, we propose State-Grounded Dynamic Retrieval (SGDR), an online skill learning method that enables stepwise skill reuse for web agents. SGDR consists of three components: a sliding-window extraction process that turns completed trajectories into reusable sub-procedures invokable at intermediate execution states, a dual text-code representation that connects skill retrieval with executable action, and a state-grounded dynamic retrieval mechanism that matches skills to both the task goal and the current webpage state. Experiments on WebArena across five domains show that SGDR consistently outperforms strong baselines, achieving average success rates of 37.5% with GPT-4.1 and 24.3% with Qwen3-4B, corresponding to relative gains of 10.6% and 10.0% over the strongest baseline, respectively. The code is available at https://github.com/plusnli/skill-dynamic-retrieval.
Chinese Translation
语言代理越来越依赖于可重用技能，以提高相关任务中的多步骤网络自动化。一项不断增长的研究工作探讨在线技能学习，代理不断从先前的任务轨迹中归纳技能，并在随后的任务中即时重用它们。然而，现有的方法主要是在任务层面重用技能：根据初始任务指令检索固定的技能集合，并在执行过程中保持不变。这种静态策略与网络执行不一致，因为适当的下一个行动不仅依赖于任务目标，还依赖于当前网页状态，这常常会过渡到初始技能无法覆盖的情况。为了解决这一问题，我们提出了状态引导动态检索（State-Grounded Dynamic Retrieval, SGDR），这是一种使网络代理能够逐步重用技能的在线技能学习方法。SGDR由三个组件组成：一个滑动窗口提取过程，将已完成的轨迹转化为可在中间执行状态下调用的可重用子程序；一种双重文本-代码表示，将技能检索与可执行行动连接起来；以及一种状态引导的动态检索机制，将技能与任务目标和当前网页状态相匹配。在五个领域的 WebArena 实验中，SGDR 一直优于强基线，分别达到了与 GPT-4.1 相关的 37.5% 的平均成功率和与 Qwen3-4B 相关的 24.3% 的成功率，分别比最强基线提高了 10.6% 和 10.0%。代码可在 https://github.com/plusnli/skill-dynamic-retrieval 获取。
cs.AI / 14 / 2606.04402
Not All Errors Are Equal: Consequence-Aware Reasoning Compute Allocation
并非所有错误都相同：后果感知推理计算分配
Abstract
Modern reasoning models can allocate different amounts of test-time computation, such as thinking tokens, model calls, or compute budget, to different tasks. Existing methods generally drive this allocation by predicted difficulty and spend more compute where it is expected to raise accuracy. This implicitly assumes that all failures cost the same, since an accuracy objective weights every task equally. However, such an assumption does not hold in deployment: A typo in a log message and a migration that corrupts a production database both count as one benchmark failure, but their real-world costs are fundamentally different. To fill this gap, we propose consequence-aware test-time compute allocation. Instead of routing compute only by predicted difficulty, we use a lightweight predictor to estimate from the issue text how costly a task would be if solved incorrectly. The scheduler then routes higher-consequence tasks to larger compute tiers or higher thinking budgets under the same total budget. We conduct main experiments on SWE-bench Lite and evaluate cross-dataset behavior on Multi-SWE-bench mini, covering 700 software-engineering tasks in total. Our results reveal that consequence and difficulty are approximately orthogonal under various annotations, and that current thinking models do not allocate compute sufficiently according to consequence. Moreover, our issue-only predictor never misclassifies a high-consequence task as low-consequence across the 300 SWE-bench tasks. Under matched compute budgets, our consequence-aware scheduler reduces cost-weighted loss by 22% to 33% relative to difficulty-aware routing; in particular, the priority-aware variant, which routes by per-task cost scaled by the marginal-utility signal, crosses 30%, and its deployable predictor-driven version retains over 90% of the oracle gain.
Chinese Translation
现代推理模型能够在测试阶段为不同任务分配不同量的计算资源，例如思考标记、模型调用或计算预算。现有方法通常通过预测的难度来驱动这种分配，并在预计提高准确性的地方投入更多计算。这隐含地假设所有失败的成本相同，因为准确性目标对每个任务的权重是相等的。然而，这种假设在实际应用中并不成立：日志消息中的拼写错误和导致生产数据库损坏的迁移都被视为一个基准失败，但它们在现实世界中的成本是根本不同的。为了填补这一空白，我们提出了后果感知的测试时计算分配。我们不是仅仅通过预测的难度来分配计算，而是使用一个轻量级预测器从问题文本中估计如果任务解决错误其成本会有多高。之后，调度器将高后果任务分配到更大的计算层或在相同总预算下更高的思考预算。我们在SWE-bench Lite上进行主要实验，并在Multi-SWE-bench mini上评估跨数据集行为，总共涵盖了700个软件工程任务。我们的结果表明，在各种注释下，后果和难度大致是正交的，而当前的推理模型并未根据后果充分分配计算。此外，我们的仅基于问题的预测器在300个SWE-bench任务中从未将高后果任务错误分类为低后果任务。在匹配的计算预算下，我们的后果感知调度器相较于基于难度的路由将成本加权损失降低了22%至33%；特别是，基于优先级的变体通过每任务成本与边际效用信号的比例进行路由，降低幅度超过30%，其可部署的基于预测器驱动的版本保留了超过90%的最优收益。
cs.AI / 15 / 2606.04421
Trivium: Temporal Regret as a First-Class Objective for Causal-Memory Controllers
Trivium：将时间后悔作为因果记忆控制器的一级目标
Abstract
Many current agentic systems and LLM pipelines correct mistakes by optimizing outcome reward. This addresses only the what of failure: when an outcome diverges from prediction, the why and when of the mismatch are not systematically logged, reviewed, or corrected, so the same error can recur episode after episode. We argue that this is a structural problem, not merely a model-capacity one. We propose long-horizon temporal regret as a first-class objective alongside outcome regret and epistemic regret over the working causal model. Temporal regret captures when failure persists: how long a miscalibrated causal model is tolerated before correction. Epistemic regret captures why failure persists: residual uncertainty or error in the working causal model. Together, the three regrets give a falsifiable account of what, why, and when a long-lived agent can fail. Modeling the agent as a stream of E episodes, we prove three conditional results under explicit causal-probing, persistence, and detectability assumptions. First, under observationally equivalent confounding, outcome-only learning cannot distinguish causal from spurious structure without an intervention channel, so temporal miscalibration can persist linearly even after outcome regret is driven to zero. Second, with a persistent causal log and budgeted probes, total probe complexity is logarithmic in the episode horizon, inducing O(log E) temporal regret. Third, under K detectable change-points, the rate extends to O(K log E). We instantiate Trivium and pre-register five falsifiable predictions. On CausalBench-Seq, Trivium follows the predicted logarithmic envelope while outcome-only baselines grow linearly. A pilot real-LLM stream provides preliminary external-validity evidence across one full E = 500 run and three E = 100 frontier-model pilots. Self-learning here means revising an external causal model, not retraining LLM weights.
Chinese Translation
许多当前的智能系统和大型语言模型（LLM）流水线通过优化结果奖励来纠正错误。这仅仅解决了失败的“什么”问题：当结果与预测偏离时，偏差的“为什么”和“何时”并没有系统地记录、审查或纠正，因此同样的错误可以在每个事件中反复出现。我们认为这是一个结构性问题，而不仅仅是模型容量问题。我们提出将长时间范围的时间后悔视为与结果后悔和关于工作因果模型的认知后悔并列的一级目标。时间后悔反映了失败持续的时间：在纠正之前，一个校准错误的因果模型被容忍的时间长度。认知后悔则捕捉了失败持续的原因：在工作因果模型中残留的不确定性或错误。共同来看，这三种后悔提供了一个可验证的关于长期智能体失败的“什么”、“为什么”和“何时”的解释。通过将智能体建模为一系列 E 个事件，我们在明确的因果探测、持续性和可检测性假设下证明了三个条件结果。首先，在观察上等效的混杂条件下，仅依靠结果学习无法在没有干预通道的情况下区分因果结构和虚假结构，因此即使在结果后悔被驱赶至零之后，时间误校准仍然可以线性持续。其次，借助持久的因果日志和有预算的探测，总探测复杂度在事件范围内呈对数增长，从而引入了 O(log E) 的时间后悔。第三，在 K 个可检测的变化点下，这一速率扩展至 O(K log E)。我们实例化了 Trivium，并预注册了五个可验证的预测。在 CausalBench-Seq 上，Trivium 遵循预测的对数包络，而仅基于结果的基线则线性增长。一项真实的 LLM 流的试点实验为一整个 E = 500 的运行和三个 E = 100 的前沿模型试点提供了初步的外部有效性证据。在这里，自我学习意味着修订外部因果模型，而不是重新训练 LLM 权重。
cs.AI / 16 / 2606.04435
Cascading Hallucination in Agentic RAG: The CHARM Framework for Detection and Mitigation
代理型RAG中的级联幻觉：用于检测和缓解的CHARM框架
Abstract
Multi-step agentic retrieval-augmented generation (RAG) pipelines have demonstrated significant capability for complex reasoning tasks, yet remain vulnerable to a class of failure that existing hallucination detection mechanisms systematically miss: cascading hallucination, where errors introduced at early pipeline stages propagate and amplify across successive reasoning steps, producing confident but factually incorrect final outputs. To address this vulnerability, we formalize cascading hallucination as a distinct failure mode in agentic RAG systems, present a four-type taxonomy of cascade patterns, and introduce CHARM (Cascading Hallucination Aware Resolution and Mitigation), an architectural framework for detecting and interrupting error propagation in multi-step reasoning pipelines. CHARM comprises four components - stage-level fact verification, cross-stage consistency tracking, confidence propagation monitoring, and cascade resolution triggering - that operate alongside standard agentic RAG pipelines without requiring architectural replacement. We evaluate CHARM on HotpotQA, MuSiQue, 2WikiMultiHopQA, and a custom adversarial dataset across LangChain agentic pipeline configurations, achieving an 89.4% cascade detection rate with a 5.3% false positive rate and 215 ms +/- 18 ms average latency overhead per stage, achieving an error propagation reduction of 82.1%, compared to 18.5% for output-level detectors. Component ablations confirm that each detection module contributes meaningfully to overall cascade coverage. CHARM integrates with human-in-the-loop oversight frameworks to provide a complete reliability and governance stack for production agentic AI deployment.
Chinese Translation
多步骤的代理型检索增强生成（RAG）管道在复杂推理任务中展现了显著的能力，但仍然容易受到一种现有幻觉检测机制系统性遗漏的失败类别的影响：级联幻觉。在这种情况下，早期管道阶段引入的错误会在后续推理步骤中传播和放大，从而产生自信但事实不正确的最终输出。为了解决这一脆弱性，我们将级联幻觉正式化为代理型RAG系统中的一种独特失败模式，提出了一种四种类型的级联模式分类法，并介绍了CHARM（Cascading Hallucination Aware Resolution and Mitigation），这是一个用于检测和中断多步骤推理管道中错误传播的架构框架。CHARM由四个组件构成——阶段级别的事实验证、跨阶段一致性跟踪、置信度传播监控和级联解决触发——它们与标准的代理型RAG管道协同工作，无需架构替换。我们在HotpotQA、MuSiQue、2WikiMultiHopQA以及一个自定义对抗数据集的LangChain代理管道配置上评估了CHARM，达到了89.4%的级联检测率，5.3%的误报率，以及每个阶段平均215毫秒±18毫秒的延迟开销，实现了82.1%的错误传播减少，而输出级别检测器仅为18.5%。组件消融实验确认每个检测模块对整体级联覆盖的贡献。CHARM与人机协作监督框架集成，以提供完整的可靠性和治理栈，支持生产环境中的代理AI部署。
cs.AI / 17 / 2606.04455
The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development?
元代理挑战：当前的代理是否具备自主开发代理的能力？
Abstract
Current AI benchmarks evaluate agents on task execution within human-designed workflows. These evaluations fundamentally fail to measure a critical next-level capability: whether models can autonomously develop agent systems. We introduce the Meta-Agent Challenge (MAC), an evaluation framework designed to test the capacity of frontier models for autonomous agent development. Specifically, a code agent (the meta-agent) is given a sandboxed environment, an evaluation API, and a time limitation to iteratively program an agent artifact that maximizes performance on a held-out test set across five domains. To ensure evaluation integrity, this framework is secured by multi-layer defenses against reward hacking. Leveraging this framework, we demonstrate that meta-agents rarely match human-engineered baseline policies, and the few that do are dominated by proprietary frontier models. Moreover, the design process exhibits high variance, and high optimization pressure surfaces emergent adversarial behaviors like ground-truth exfiltration-highlighting critical deficits in both robustness and model alignment. Ultimately, MAC provides a rigorous, open-source benchmark for autonomous AI research and development, offering an empirical proxy for evaluating recursive self-improvement. Benchmark is publicly available at: https://github.com/ant-research/meta-agent-challenge.
Chinese Translation
当前的人工智能基准评估代理在由人设计的工作流程中的任务执行能力。然而，这些评估在根本上无法衡量一个关键的下一个能力：模型是否能够自主开发代理系统。我们引入了元代理挑战（Meta-Agent Challenge, MAC），一个旨在测试前沿模型自主开发代理能力的评估框架。具体来说，一个代码代理（元代理）被提供一个沙箱环境、一个评估API，以及一个时间限制，以迭代编程一个代理工件，使其在五个领域的保留测试集上最大化性能。为确保评估的完整性，该框架通过多层防御机制来抵御奖励黑客行为。借助这一框架，我们展示了元代理很少能够匹配人类设计的基准策略，而能匹配的少数模型则被专有前沿模型所主导。此外，设计过程表现出高度的方差，而高优化压力引发了新出现的对抗性行为，比如真实信息外泄，突显了模型在鲁棒性和对齐方面的关键缺陷。最终，MAC为自主人工智能研究与开发提供了一个严格的开源基准，提供了一个用于评估递归自我改进的实证代理。基准公开可在：https://github.com/ant-research/meta-agent-challenge 获取。
cs.AI / 18 / 2606.04484
AgentJet: A Flexible Swarm Training Framework for Agentic Reinforcement Learning
AgentJet：用于代理强化学习的灵活群体训练框架
Abstract
We present AgentJet, a distributed swarm training framework for large language model (LLM) agent reinforcement learning. Unlike centralized frameworks that tightly couple agent rollouts with model optimization, AgentJet adopts a decoupled multi-node architecture in which swarm server nodes host trainable models and run optimization on GPU clusters, whereas swarm client nodes execute arbitrary agents on arbitrary devices. This design provides capabilities that are difficult to support in centralized frameworks: (1) heterogeneous multi-model reinforcement learning, enabling the training of heterogeneous multi-agent teams with multiple LLM as brains; (2) multi-task cocktail training with isolated agent runtimes; (3) fault-tolerant execution that prevents external environment failures from interrupting the training process; and (4) live code iteration, which allows agents to be edited during training by replacing swarm client nodes. To support efficient RL in multi-model, multi-turn, and multi-agent settings, AgentJet introduces a context tracking module with timeline merging, which consolidates redundant context and achieves a 1.5-10x training speedup. Finally, AgentJet introduces an automated research system that takes a research topic as input and autonomously conducts long-horizon, multi-day RL studies on large-scale clusters. By leveraging the swarm architecture, this system reproduces key exploratory workflows of RL researchers without human intervention during execution.
Chinese Translation
我们提出了AgentJet，这是一个用于大语言模型（LLM）代理强化学习的分布式群体训练框架。与将代理回合与模型优化紧密耦合的集中式框架不同，AgentJet采用了一个解耦的多节点架构，其中群体服务器节点托管可训练模型并在GPU集群上进行优化，而群体客户端节点则在任意设备上执行任意代理。该设计提供了在集中式框架中难以支持的能力：（1）异构多模型强化学习，能够训练多个 LLM 作为智能体的异构多智能体团队；（2）具有隔离智能体运行时的多任务鸡尾酒训练；（3）容错执行，防止外部环境故障中断训练过程；以及（4）实时代码迭代，允许在训练过程中通过替换群体客户端节点对代理进行编辑。为了支持多模型、多回合和多智能体环境中的高效强化学习，AgentJet引入了一个带有时间线合并的上下文跟踪模块，该模块整合冗余上下文，并实现了1.5-10倍的训练加速。最后，AgentJet 引入了一个自动化研究系统，该系统以研究主题为输入，自动在大规模集群上进行长期的多天RL研究。通过利用群体架构，该系统在执行过程中无需人工干预就能重现强化学习研究人员的主要探索工作流程。
cs.AI / 19 / 2606.04494
Beyond Prompt-Based Planning: MCP-Native Graph Planning-based Biomedical Agent System
超越基于提示的规划：基于MCP原生图规划的生物医学智能体系统
Abstract
Biomedical agents promise to automate complex biological workflows, yet current systems face two fundamental bottlenecks: bioinformatics tools are highly heterogeneous in interfaces and execution environments, while agent planning still relies on flat prompt-retrieved tool descriptions. As biomedical software ecosystems grow, this coupling between tool coverage and context size leads to tool confusion, unstable planning, and inefficient execution. We introduce BioManus, an MCP-native biomedical agent built on graph-scaffolded planning over structured biological capabilities. BioManus first introduces the BioinfoMCP Compiler, which converts heterogeneous bioinformatics software into standardized MCP servers, yielding a large executable MCP ecosystem. It then organizes this ecosystem as a typed heterogeneous MCP graph over tools, operations, datatypes, and workflow stages. At inference time, BioManus retrieves compact task-specific subgraphs, synthesizes operation-level workflow scaffolds. This design decouples planning complexity from raw tool inventory size, achieving a context compression ratio of Theta(N / (h * m_bar)) under high-recall retrieval, where N is the total tool count, h is the workflow horizon, and m_bar (much smaller than N) is the average number of candidate tools per operation. Experiments on BioAgentBench and LAB-Bench show that BioManus improves execution accuracy, workflow validity, and context efficiency over advanced biomedical agent baselines. This work suggests a paradigm shift: scalable biomedical reasoning requires structured executable capability graphs rather than increasingly larger prompt-level tool retrieval.
Chinese Translation
生物医学智能体有望自动化复杂的生物流程，但当前系统面临两个根本瓶颈：生物信息学工具在接口和执行环境上具有高度异构性，而智能体规划仍依赖于扁平的提示检索工具描述。随着生物医学软件生态系统的增长，工具覆盖与上下文规模之间的耦合导致工具混淆、不稳定的规划和低效的执行。我们介绍了BioManus，这是一种基于结构化生物能力的图支架规划的MCP原生生物医学智能体。BioManus首先引入BioinfoMCP编译器，该编译器将异构生物信息学软件转换为标准化的MCP服务器，从而产生一个大型可执行的MCP生态系统。随后，它将该生态系统组织为一个基于工具、操作、数据类型和工作流程阶段的类型异构MCP图。在推理时，BioManus检索紧凑的任务特定子图，并合成操作级工作流程支架。这种设计将规划复杂度与原始工具库存规模解耦，实现了在高召回检索下的上下文压缩比为Theta(N / (h * m_bar))，其中N是总工具数量，h是工作流程的时间范围，m_bar（远小于N）是每个操作的候选工具的平均数量。在BioAgentBench和LAB-Bench上的实验表明，BioManus在执行准确性、工作流程有效性和上下文效率上优于先进的生物医学智能体基线。此项工作提出了一个范式转变：可扩展的生物医学推理需要结构化的可执行能力图，而非日益庞大的提示级工具检索。
cs.AI / 20 / 2606.04505
Simulate, Reason, Decide: Scientific Reasoning with LLMs for Simulation-Driven Decision Making
模拟、推理、决策：基于大型语言模型的科学推理在模拟驱动决策中的应用
Abstract
Scientific simulators are increasingly being integrated into LLM-driven systems for high-stakes simulation-driven decision-making. However, existing frameworks primarily use LLMs to generate, calibrate, or execute simulators, treating them as black-box interfaces rather than as structured mechanistic systems that can be reasoned about. As a result, current approaches lack the ability to identify, represent, and reason about the assumptions and mechanisms underlying simulator behavior, limiting transparency, auditability, and decision justification. We introduce MechSim, a mechanism-grounded neuro-symbolic reasoning framework for executable scientific simulators. Unlike prior neuro-symbolic approaches that primarily reason over static symbolic structures, MechSim enables LLM agents to reason about the mechanisms, assumptions, and execution behavior of scientific simulators. Our framework represents simulators through a shared structured schema capturing assumptions, variables, mechanism dependencies, and execution traces. On top of this representation, LLM agents operate as constrained reasoning engines that generate structured, evidence-grounded explanations linking simulator outcomes to their underlying mechanisms. We evaluate our approach across multiple high-stakes domains and show that it improves mechanism-level explanation quality, simulator analysis, and downstream decision-making reliability.
Chinese Translation
科学模拟器正越来越多地被集成到大型语言模型驱动的系统中，以进行高风险的模拟驱动决策。然而，现有框架主要使用大型语言模型生成、校准或执行模拟器，将其视为黑箱接口，而非可以进行推理的结构化机制系统。因此，目前的方法缺乏识别、表示和推理模拟器行为背后的假设和机制的能力，限制了透明性、可审计性和决策合理性。我们提出了 MechSim，这是一个基于机制的神经符号推理框架，用于可执行的科学模拟器。与之前主要对静态符号结构进行推理的神经符号方法不同，MechSim 使大型语言模型代理能够推理科学模拟器的机制、假设和执行行为。我们的框架通过共享的结构化模式表示模拟器，捕捉假设、变量、机制依赖关系和执行痕迹。在此基础上，大型语言模型代理作为受限的推理引擎生成结构化、基于证据的解释，链接模拟器的结果与其基本机制。我们在多个高风险领域评估了我们的方法，并展示其在机制层面解释质量、模拟器分析和下游决策可靠性方面的改善。
cs.AI / 21 / 2606.04513
MapAgent: An Industrial-Grade Agentic Framework for City-scale Lane-level Map Generation
MapAgent：用于城市规模车道级地图生成的工业级代理框架
Abstract
Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane networks for hundreds of cities remains highly labor-intensive. Recent end-to-end vectorized mapping methods can predict lane geometry and topology directly from sensor data, but they typically treat mapping specifications and traffic regulations as implicit, dataset-dependent supervision. Moreover, in complex scenes (e.g., worn or missing markings and occlusions), correct lane configurations are often under-determined by visual evidence alone, making specification violations a major source of human post-editing. We propose MapAgent, an industrial-grade agentic architecture that augments a vectorization backbone for specification-compliant lane-map production. Rather than merely adding an agent loop to map prediction, MapAgent couples backbone perception with explicit specification verification, constraint-aware reasoning, and deterministic map editing under a bounded, verification-driven Judge-Planner-Worker loop. A vision-language Judge diagnoses errors by jointly inspecting visual evidence and draft vectors, while a tool-calling Planner generates minimal corrective edits with post-edit re-validation. To remain scalable for city-scale production, MapAgent is selectively triggered only on tiles with low backbone confidence, adding modest overhead while preserving throughput. Experiments on real-world datasets show consistent gains over strong production baselines, especially in complex and long-tail scenarios. Additionally, MapAgent has been integrated into Baidu Maps, supporting lane-level map generation for over 360 cities nationwide and elevating the overall production automation to over 95%, demonstrating MapAgent's practicality and effectiveness for large-scale lane-level map generation.
Chinese Translation
车道级地图是自动驾驶和车道级导航的关键基础设施，但为数百个城市构建和维护标准化车道网络仍然需要大量人力。最近的端到端矢量化地图生成方法能够直接从传感器数据中预测车道几何形状和拓扑结构，但通常将映射规范和交通法规视为隐式的、依赖数据集的监督。此外，在复杂场景中（例如，磨损或缺失的标记和遮挡），仅凭视觉证据通常无法确定正确的车道配置，导致规范违反成为人工后编辑的主要来源。我们提出了MapAgent，一个工业级的代理架构，增强了一个用于符合规范的车道地图生成的矢量化基础网络。MapAgent不仅仅是在地图预测中添加代理循环，而是将基础感知与显式的规范验证、约束感知推理和在有限且以验证驱动的Judge-Planner-Worker循环下的确定性地图编辑结合起来。视觉-语言Judge通过共同检视视觉证据和草图向量来诊断错误，而工具调用规划者则生成最小的纠正编辑并进行后编辑再验证。为了保持城市规模生产的可扩展性，MapAgent仅在基础网络信心低的瓷砖上选择性触发，增加了适度的开销，同时保持了吞吐量。在真实世界数据集上的实验表明，在复杂和长尾场景中，相比于强大的生产基线，性能有持续提高。此外，MapAgent已被集成到百度地图中，支持全国360多个城市的车道级地图生成，并将整体生产自动化提升至95%以上，证明了MapAgent在大规模车道级地图生成中的实用性和有效性。
cs.AI / 22 / 2606.04536
Scaling Self-Evolving Agents via Parametric Memory
通过参数记忆扩展自我演化代理
Abstract
Existing memory-augmented LLM agents store past experience exclusively in prompt space, as textual summaries or retrieved passages, while keeping model parameters frozen throughout a rollout. Such agents can \emph{look up} what they have seen but cannot \emph{learn from} it: their policy is unchanged by experience, and any information dropped from the context is permanently lost. We introduce \texttt{TMEM}, a self-evolving parametric memory framework in which the agent not only compresses history into explicit memory but also absorbs distilled supervision into fast LoRA weights $\Delta_t$ via lightweight online updates, genuinely altering its future behavior within a single episode. We formalize this as an agentic decision process with fast-weight rollout dynamics: actions are sampled from $\pi_{\theta_0+\Delta_t}$, while extraction actions produce supervision that updates $\Delta_t$ for subsequent decisions. This view makes the extraction policy directly optimizable by RL: training $\theta_0$ improves not only task actions but also the quality of the data used for online LoRA adaptation. We further propose SVD-based initialization of the LoRA subspace to accelerate online convergence. Experiments on LoCoMo, LongMemEval-S, multi-objective search, and CL-Bench show that \texttt{TMEM} consistently outperforms summary-based and retrieval-based baselines across different model scales.
Chinese Translation
现有的记忆增强大型语言模型（LLM）代理在提示空间中仅存储过去的经验，作为文本摘要或检索到的段落，同时在整个滚动过程中保持模型参数不变。这种代理可以“查阅”他们所见的内容，但无法“从中学习”：他们的策略不会受到经验的影响，任何从上下文中丢失的信息都会被永久遗忘。我们提出了一种自我演化的参数记忆框架 exttt{TMEM}，其中代理不仅将历史压缩为显式记忆，而且通过轻量级在线更新将提炼的监督吸收到快速的LoRA权重$ extDelta_t$中，从而在单次历程中真正改变其未来行为。我们将其形式化为具有快速权重滚动动态的代理决策过程：动作从$ extpi_{ heta_0+ extDelta_t}$采样，而提取动作产生监督，以更新后续决策中的$ extDelta_t$。这种视角使得提取策略可以通过强化学习直接优化：训练$ heta_0$不仅改善任务动作，还提高用于在线LoRA适应的数据质量。我们进一步提出基于奇异值分解（SVD）对LoRA子空间进行初始化，以加速在线收敛。针对LoCoMo、LongMemEval-S、多目标搜索和CL-Bench的实验表明， exttt{TMEM}在不同模型规模下始终优于基于摘要和检索的基线。
cs.AI / 23 / 2606.04562
Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models
Neetyabhas：一种基于理性代理模型的不确定性感知公共政策优化框架
Abstract
Purpose The WHO's COVID-19 non-pharmaceutical interventions (e.g., lockdowns, vaccinations) effectively curb transmission but impose heavy economic strains. Existing research often neglects individual behaviors and falsely assumes perfect infection tracking and flawless policy execution, failing to account for real-world uncertainties and errors. Methods We propose an integrative approach incorporating uncertainties in both epidemic measurement (infections/hospitalizations) and policy implementation. We built a simulation model of 1,000 individuals making real-time choices regarding mask-wearing, vaccination, and shopping. Concurrently, policymakers deploy interventions (lockdowns, mandates) based on health and economic observations. This framework is driven by hierarchical reinforcement learning agents, utilizing deep Q-networks alongside uncertainty-aware policy gradient variants (DDPG and TD3). Results The simulations effectively managed the epidemic's progression. Masking and vaccinations proved highly effective, significantly reducing both the outbreak's peak height and duration. By integrating individual behaviors, policy uncertainties, and multifaceted interventions, our dynamic control approach successfully mitigated the epidemic's impact. Conclusions Our model overcomes previous research limitations by embedding uncertainty and human behavior into public health policy frameworks. The simulation demonstrates that accounting for individual choices and imperfect data is crucial for designing effective interventions during complex pandemics, with masks and vaccines serving as pivotal tools.
Chinese Translation
目的 世界卫生组织（WHO）的 COVID-19 非药物干预措施（如封锁、疫苗接种）有效遏制了传播，但给经济带来了巨大的压力。现有研究通常忽视个体行为，错误地假设感染追踪和政策执行都是完美的，未能考虑现实世界中的不确定性和错误。方法 我们提出了一种综合方法，纳入了疫情测量（感染/住院）和政策实施中的不确定性。我们构建了一个由 1000 个人组成的模拟模型，他们实时做出有关佩戴口罩、接种疫苗和购物的选择。同时，政策制定者基于健康和经济观察实施干预（封锁、强制规定）。该框架由分层强化学习代理驱动，利用深度 Q 网络以及不确定性感知的策略梯度变体（DDPG 和 TD3）。结果 模拟有效管理了疫情的发展。口罩和疫苗接种证明了其高度有效性，显著减少了疫情高峰的高度和持续时间。通过整合个体行为、政策不确定性和多方面干预，我们的动态控制方法成功减轻了疫情的影响。结论 我们的模型克服了以往研究的局限性，将不确定性和人类行为嵌入公共卫生政策框架中。模拟显示，考虑个体选择和不完美数据对于在复杂疫情期间设计有效干预措施至关重要，而口罩和疫苗是关键工具。
cs.AI / 24 / 2606.04579
SCI-PRM: A Tool Aware Process Reward Model for Scientific Reasoning Verification
SCI-PRM：一种针对科学推理验证的工具感知过程奖励模型
Abstract
While Process Reward Models (PRMs) have achieved remarkable success in mathematical reasoning, their application in complex scientific domains-such as biology, chemistry, and physics remains largely unexplored. Scientific problems demand not only logical rigor but also factual consistency and the precise usage of domain-specific tools, areas where current models often suffer from hallucinations and lack of verification. In this paper, we first construct SCIPRM70K, a large-scale dataset featuring Chain-of-Tool trajectories that explicitly interleave reasoning with the execution of scientific tools. Building upon this, we train an efficient reward model called Sci-PRM to provide fine-grained supervision on tool selection, execution accuracy, and result interpretation at each step in one inference. Experiments demonstrate that Sci-PRM significantly enhances foundation models in two key aspects: (1) it enables effective test-time scaling via Best-of-N selection; and (2) when integrated into Reinforcement Learning, it serves as a dense reward signal that mitigates the critical issue of advantage disappearance, allowing the model to break through existing performance ceilings.
Chinese Translation
虽然过程奖励模型（Process Reward Models, PRMs）在数学推理方面取得了显著成功，但其在复杂科学领域（如生物学、化学和物理学）的应用仍然较少探索。科学问题不仅要求逻辑严谨，还需符合事实的一致性和精准使用特定领域工具，而当前模型在这些方面常常遭遇幻觉和缺乏验证的问题。在本文中，我们首先构建了SCIPRM70K，这是一个大规模数据集，特征为明确交替推理与科学工具执行的工具链轨迹。基于此，我们训练了一个高效的奖励模型Sci-PRM，以在每一个推理步骤中对工具选择、执行准确性和结果解释进行细致的监督。实验表明，Sci-PRM在两个关键方面显著提升了基础模型的性能：（1）通过最佳选择（Best-of-N）实现有效的测试时间扩展；（2）当集成到强化学习中时，作为一种密集奖励信号，缓解了优势消失的关键问题，使得模型能够突破现有性能瓶颈。
cs.AI / 25 / 2606.04597
Learning Admissible Heuristics via Cost Partitioning
通过成本划分学习可接受启发式
Abstract
Admissible heuristics are essential for optimal planning, yet learning them remains challenging due to the risk of overestimation. Cost partitioning combines multiple abstraction heuristics while preserving admissibility, but computing optimal partitions online is expensive. We propose a framework that learns to infer admissible cost partitions by leveraging the Lagrangian dual equivalence between cost partitioning and multiplier prediction. Planning states and patterns are encoded as labelled graphs, and an action-centric variant of the Weisfeiler-Leman algorithm extracts structural feature vectors. A deep architecture with axial self-attention and a softmax output layer maps these features to cost weights that satisfy the partition constraints by construction, ensuring admissibility. Experiments demonstrate reduced node expansions compared to suboptimal partitioning baselines while maintaining strict admissibility. To our knowledge, this is the first machine-learned heuristic guaranteed to be admissible.
Chinese Translation
可接受的启发式对于最优规划至关重要，但由于过高估计的风险，学习这些启发式依然具有挑战性。成本划分结合了多种抽象启发式，并在保持可接受性的同时进行处理，但在线计算最优划分的费用较高。我们提出了一个框架，通过利用成本划分与乘子预测之间的拉格朗日对偶关系来学习推断可接受的成本划分。规划状态和模式被编码为带标签的图，并且一个以动作为中心的Weisfeiler-Leman算法变体提取结构特征向量。一个具有轴向自注意力和softmax输出层的深度架构将这些特征映射到构造上满足划分约束的成本权重，确保了可接受性。实验表明，与次优划分基线相比，节点扩展减少，同时保持严格的可接受性。据我们所知，这是首个保证可接受性的机器学习启发式。
cs.AI / 26 / 2606.04599
Plan First, Judge Later, Run Better: A DMAIC-Inspired Agentic System for Industrial Anomaly Detection
先规划，后判断，更高效运行：基于DMAIC的工业异常检测自主系统
Abstract
Large language model (LLM) agents have shown promise in automating complex data-analysis workflows, but their reliable deployment remains challenging in high-stakes industrial scenarios. Industrial anomaly detection (IAD) is essential for manufacturing quality, safety, and efficiency, yet existing LLM-based IAD agents mainly focus on execution while under-exploiting strategy formulation. Consequently, they struggle to handle heterogeneous modalities in a unified and cost-effective manner. Inspired by the DMAIC quality-management framework, we propose DMAIC-IAD (DMAIC-inspired Agentic Industrial Anomaly Detection), a "Plan First, Judge Later" multi-agent system that aligns LLM agents with structured industrial problem-solving. DMAIC-IAD distills heterogeneous references into standardized operating procedures (SOPs) before strategy generation, and introduces a pre-trained execution-free judge model to rank candidate strategies without costly runtime trials. Extensive experiments across four modalities show that DMAIC-IAD improves average detection performance over applicable agentic baselines by 37.76%.
Chinese Translation
大语言模型（LLM）代理在自动化复杂数据分析工作流程方面展现了良好前景，但在高风险工业场景中的可靠部署仍然面临挑战。工业异常检测（IAD）对于制造质量、安全和效率至关重要，然而现有基于LLM的IAD代理主要集中于执行，未能充分利用战略制定。因此，它们在统一和成本有效地处理异构模态方面存在困难。受到DMAIC质量管理框架的启发，我们提出了DMAIC-IAD（基于DMAIC的自主工业异常检测），一个“先规划，后判断”的多代理系统，旨在将LLM代理与结构化的工业问题解决相结合。DMAIC-IAD在战略生成之前将异构参考提炼为标准操作程序（SOP），并引入一个预训练的无执行判断模型，以在无需昂贵的运行时试验的情况下对候选策略进行排名。在四种模态下的广泛实验表明，DMAIC-IAD在可适用代理基线的平均检测性能上提高了37.76%。
cs.AI / 27 / 2606.04602
Parthenon Law: A Self-Evolving Legal-Agent Framework
帕台农法则：自我演进的法律代理框架
Abstract
As agents grow more capable, legal-domain LLM agents promise to turn document-heavy matters into reviewable work products -- yet reliable deployment faces three obstacles: no large-scale evidence on how today's strongest model-and-harness combinations behave on end-to-end legal matters; no agent architecture adapted to the legal vertical, only general-purpose harnesses; and, in a setting that keeps shifting with new facts, authorities, and deadlines, no mechanism for systems to learn from their own outcomes. We address each. A large-scale empirical study on Harvey LAB -- $12{,}510$ agent trajectories -- shows that even frontier agents remain far from completing matters in a single pass: per-criterion accuracy climbs with stronger models while strict matter completion stalls. We then introduce \textsc{Parthenon}, a self-evolving legal-agent framework that factors Model, Harness, Agent roles, legal Knowledge, deterministic Tools, and procedural Skills into auditable surfaces for source traceability, date and number grounding, deliverable compliance, and issue closure. Finally, an anti-leakage learning loop converts scored failures into task-agnostic edits to skills, tools, and knowledge, letting the system improve with experience -- as a firm refines its checklists and playbooks after each matter -- without touching model weights. Across our large-scale empirical analysis, \textsc{Parthenon} substantially improves the performance of state-of-the-art models and harnesses on legal-matter tasks.
Chinese Translation
随着代理的能力不断增强，法律领域的大型语言模型（LLM）代理有望将文档密集型事务转变为可审查的工作成果——然而，可靠的部署面临三大障碍：当前最强模型与框架组合在端到端法律事务中的表现缺乏大规模证据；没有适应法律领域的代理架构，仅有通用框架；并且在不断变化的新事实、权威和截止日期的背景下，缺乏系统从自身结果中学习的机制。我们逐一解决这些问题。对Harvey LAB进行的大规模实证研究——$12{,}510$个代理轨迹——表明，即使是先进的代理也远未能在一次处理过程中完成事务：每个标准的准确性随着模型的增强而提高，但严格的事务完成却停滞不前。接着，我们引入了 extsc{Parthenon}，一个自我演进的法律代理框架，将模型、框架、代理角色、法律知识、确定性工具和程序技能因素化为可审计的表面，以实现源可追溯性、日期和数字基础、交付符合性及问题关闭。最后，一个反泄漏学习循环将得分失败转化为与任务无关的对技能、工具和知识的编辑，使系统能够在经验中得到提升——就像公司在每个事务后完善其检查清单和实施手册一样——而无需调整模型权重。在我们的大规模实证分析中， extsc{Parthenon}显著提升了最先进模型和框架在法律事务任务上的表现。
cs.AI / 28 / 2606.04619
A Normative Intermediate Representation for ASP-Based Compliance Reasoning
基于ASP的合规推理的规范中间表示
Abstract
We propose MONIR, a Modalized-Output Normative Intermediate Representation for ASP-based compliance reasoning. Its core fragment has a staged operational semantics, while MONIR-ASP provides an executable compilation and extensions for external functions, temporal rules, and stable-model reasoning. We instantiate the framework on Chinese ADAS regulations and standards with an LLM-assisted pipeline. Experiments evaluate extraction quality and the efficiency of modular and incremental ASP solving.
Chinese Translation
我们提出了MONIR，一种用于基于ASP的合规推理的模态化输出规范中间表示。它的核心片段具有分阶段的操作语义，而MONIR-ASP则提供了可执行编译以及对外部函数、时间规则和稳定模型推理的扩展。我们在中国ADAS（高级驾驶辅助系统）法规和标准上实例化该框架，并采用了LLM（大语言模型）辅助的管道。实验评估了提取质量和模块化及增量ASP求解的效率。
cs.AI / 29 / 2606.04627
MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models
MIRAGE：具有隐式推理和生成世界模型的移动代理
Abstract
Mobile agents are increasingly expected to operate everyday applications from screenshots and language goals, where reliable control requires reasoning over screen affordances, multi-step navigation, and future state changes. However, many agents externalize this computation as long textual chains of thought, which slows interaction, increases supervision cost, and complicates deployment. We introduce MIRAGE, a framework that learns continuous latent reasoning representations from visible textual reasoning traces. MIRAGE transfers explicit reasoning into compact hidden states, enabling the agent to reason internally without decoding long rationales. It also incorporates a generative world-model objective: latent reasoning vectors are aligned with future screenshots, encouraging the agent to anticipate upcoming interface states before acting. This turns hidden computation into both a compressed thought representation and a forward-looking model of environment dynamics. At inference time, MIRAGE reasons in continuous latent space, reducing token generation while improving execution efficiency. On AndroidWorld, MIRAGE matches explicit chain-of-thought supervised fine-tuning in the 4B ablation with a 3-5x lower decoded-token budget and improves a comparable instruction-tuned baseline by 10.2 points; on AndroidControl, it improves action grounding while generating over 75% fewer tokens.
Chinese Translation
移动代理越来越被期望能够根据截图和语言目标操作日常应用，可靠的控制需要对屏幕可用性、多个步骤的导航和未来状态变化进行推理。然而，许多代理将这一计算外部化为冗长的文本推理链，这减慢了交互速度，提高了监督成本，并使部署变得复杂。我们提出了MIRAGE，一个从可见文本推理痕迹中学习连续潜在推理表示的框架。MIRAGE将显式推理转移到紧凑的隐藏状态，使代理能够在不解码长推理内容的情况下进行内部推理。它还包含一个生成世界模型的目标：潜在推理向量与未来截图对齐，鼓励代理在行动之前预见即将出现的界面状态。这将隐藏计算转变为压缩的思维表示和环境动态的前瞻性模型。在推理阶段，MIRAGE在连续潜在空间中进行推理，减少了代币生成，同时提高了执行效率。在AndroidWorld上，MIRAGE在4B消融实验中以3-5倍更低的解码代币预算匹配显式推理链的有监督微调，并将可比的指令微调基线提高了10.2个百分点；在AndroidControl上，它在生成超过75%的代币减少的同时改善了动作定位。
cs.AI / 30 / 2606.04648
BiNSGPS: Geometry Problem Solving via Bidirectional Neuro-Symbolic Interaction
BiNSGPS：通过双向神经符号互动解决几何问题
Abstract
Geometry problem solving poses distinct challenges in artificial intelligence. Existing approaches typically fall into two paradigms: symbolic methods, which exhibit limited adaptability, and neural methods, which are prone to hallucinations. Recent neuro-symbolic hybrids predominantly rely on a unidirectional pipeline where neural outputs are fed into solvers without feedback, making system brittle to early-stage errors. To break this unidirectional bottleneck, we propose BiNSGPS, a framework that establishes Bidirectional Neuro-Symbolic Interaction (BiNS) between a MLLM Adviser and a Symbolic Solver. MLLM Adviser actively incorporates feedback from the symbolic solver to dynamically rectify inconsistent formal representations or propose auxiliary hypotheses, resolving symbolic conflicts and facilitating complex deductions.
Chinese Translation
几何问题解决在人工智能领域面临独特的挑战。现有的方法通常分为两种范式：符号方法，其适应性有限；神经方法，容易出现幻觉。近期的神经符号混合方法主要依赖于单向流程，将神经输出提供给求解器而不进行反馈，这使得系统在早期错误中变得脆弱。为了打破这一单向瓶颈，我们提出了BiNSGPS，一个建立在多层次大模型顾问（MLLM Adviser）和符号求解器之间的双向神经符号互动（BiNS）框架。MLLM Adviser积极地吸收来自符号求解器的反馈，以动态修正不一致的形式表示或提出辅助假设，从而解决符号冲突并促进复杂推理。
cs.AI / 31 / 2606.04750
Fog of Love: Engineering Virtuous Agent Behavior with Affinity-based Reinforcement Learning in a Game Environment
爱的迷雾：在游戏环境中通过基于亲和力的强化学习塑造高尚代理行为
Abstract
Instilling virtuous behavior in artificial intelligence has seen increasing interest. One of the techniques proposed is known as affinity-based reinforcement learning, which uses policy regularization on the objective function to incentivize virtuous actions without being fully dependent on the reward function design. Thus far, this technique has been demonstrated to be effective in grid worlds and toy-problem environments with minimal state and action spaces. To expand this research to more sophisticated environments, we introduce a two-player multi-agent environment based on the role-playing board game known as Fog of Love. In this environment, two agents compete to fulfill their individual virtues, while also cooperating to satisfy their relationship. Given the multi-agent nature, this is a complex problem where multi-agent deep deterministic policy gradient agents neither compete nor cooperate successfully. We present evidence that localized affinities enhance agent performance in achieving both competitive and cooperative objectives, resulting from superior overall scores in both domains. This not only results in virtuous choices but also clarifies an agent's teleology and makes its behavior human-level interpretable.
Chinese Translation
在人工智能中培养高尚行为引起了越来越多的关注。提出的一种技术被称为基于亲和力的强化学习，它通过在目标函数上进行策略正则化，以激励高尚行动，而不完全依赖于奖励函数的设计。迄今为止，这种技术已在状态和动作空间有限的网格世界和玩具问题环境中被证明是有效的。为了将这一研究扩展到更复杂的环境中，我们引入了一种基于角色扮演桌游《爱的迷雾》的双人多智能体环境。在此环境中，两个代理竞争以实现各自的高尚目标，同时为了满足彼此的关系而合作。由于多智能体的特性，这是一个复杂的问题，其中多智能体深度确定性策略梯度代理既不能成功竞争，也不能有效合作。我们提供证据表明，局部亲和力增强了代理在实现竞争和合作目标方面的表现，导致两个领域中整体评分的提升。这不仅促进了高尚选择的产生，还澄清了代理的目的，使其行为在人类层面上更具可解释性。
cs.AI / 32 / 2606.04751
FALSIFYBENCH: Evaluating Inductive Reasoning in LLMs with Rule Discovery Games
FALSIFYBENCH：通过规则发现游戏评估大型语言模型的归纳推理能力
Abstract
Large language models (LLMs) are increasingly deployed as autonomous agents in scientific tasks. Yet whether these systems can effectively engage in forms of inductive reasoning relevant to scientific discovery remains an open question. In this work, we introduce FALSIFYBENCH, an evaluation framework for hypothesis-driven reasoning inspired by the classic Wason 2-4-6 task, in which agents must discover hidden semantic properties by iteratively proposing examples and receiving feedback. This task captures key elements of scientific reasoning: hypothesis generation, evidence gathering, and belief revision in response to both confirming and disconfirming evidence. Our evaluation of 12 LLMs across model families and scales shows that reasoning models are generally stronger scientific reasoners than instruction-tuned models, although no model comes close to optimal performance. The primary driver of success is the capacity for negative testing: models that actively seek to falsify their hypotheses consistently outperform those that primarily seek confirmation. Moreover, a fine-grained turn-level analysis, neglected in previous work, reveals that failure is tied to identifiable patterns in how models navigate the hypothesis space.
Chinese Translation
大型语言模型（LLMs）越来越多地被作为自主代理应用于科学任务。然而，这些系统是否能够有效地参与与科学发现相关的归纳推理形式仍然是一个未解之谜。在这项工作中，我们引入了 FALSIFYBENCH，这是一种受经典 Wason 2-4-6 任务启发的假设驱动推理评估框架，其中代理必须通过迭代提出示例并接收反馈来发现隐藏的语义属性。该任务涵盖了科学推理的关键元素：假设生成、证据收集，以及在面对确认性和否定性证据时的信念修正。我们对 12 个不同模型家族和规模的 LLM 进行评估的结果显示，推理模型通常比经过指令调优的模型表现出更强的科学推理能力，尽管没有模型接近最佳性能。成功的主要驱动因素是进行否定测试的能力：主动寻求证明其假设为假模型的表现始终优于主要寻求确认的模型。此外，先前未被关注的细粒度转向级分析揭示，失败与模型在假设空间中导航的可识别模式相关。
cs.AI / 33 / 2606.04778
Inference-Time Vulnerability Beyond Shallow Safety: Alignment Along Generation Trajectories
超越浅层安全的推断时间脆弱性：沿生成轨迹的对齐
Abstract
Safety-aligned Large Language Models (LLMs) remain vulnerable to interventions during inference that redirect generation toward harmful outputs. Recent work attributes this to shallow safety, where alignment concentrates in the first few output tokens. We show that shallow safety is a special case of a broader inference-time vulnerability, in which short token injections at any generation step can substantially alter subsequent safety behavior. We also find that a model's alignment with refusal directions in its hidden states does not predict its robustness to such injection, revealing that internal state alone does not determine generation behavior under perturbation. To address this, we align models directly on generation trajectories constructed by simulating mid-sequence perturbation, and show that this improves robustness to mid-sequence injection and generalizes to attacks that exploit early-token generation. Our work argues that robust safety alignment requires training on the generation process itself, not only its outputs.
Chinese Translation
安全对齐的大型语言模型（LLMs）在推理过程中仍然易受到干预，这些干预会将生成过程引导至有害输出。近期的研究将这一现象归因于浅层安全，即对齐主要集中在前几个输出标记上。我们表明，浅层安全是更广泛的推断时间脆弱性的特例，在此情况下，任何生成步骤的短标记注入均可能显著改变后续的安全行为。我们还发现，模型在其隐状态中与拒绝方向的对齐并不能预测其对这种注入的鲁棒性，表明仅凭内部状态并不能决定在扰动下的生成行为。为了解决这一问题，我们直接在通过模拟中序列扰动构建的生成轨迹上对模型进行对齐，并显示这提高了对中序列注入的鲁棒性，并能推广到利用早期标记生成的攻击。我们的研究认为，稳健的安全对齐需要在生成过程中进行训练，而不仅仅是其输出。
cs.AI / 34 / 2606.04779
Tree-Based Formalization of Multi-Agent Complementarity in Human-AI Interactions
基于树的多智能体互补性在人与人工智能交互中的形式化
Abstract
Complementarity is the case in which a human--AI interaction (HAI) outperforms the best prediction benchmark available among its members. Although this idea is central in HAI research, formal work on complementarity remains limited. Existing frameworks do not model how agents' predictions compose into workflow-sensitive multi-agent protocols. We close this gap by introducing a tree-based formalization of complementarity in multi-agent HAI. An HAI protocol is represented by an ordered agent-role configuration together with a rooted planar binary tree whose leaves are decorated by prediction vectors. A local binary composition rule is evaluated recursively along the tree, yielding a tree-relative complementarity functional relative to a pointwise-min oracle benchmark. We prove four results. First, selector-based HAIs, including self- or AI-reliance, cannot achieve complementarity regardless of task, loss, or prediction quality. Second, in regression under squared loss, complementarity is equivalent to Euclidean distance minimization from the ground-truth vector; for $N=2$, the optimal linear-pooling weight has a closed form and a residual-correction interpretation. Third, under linear local composition, every protocol tree defines a barycentric coordinate chart on the simplex of leaf weights; Tamari-cover reparameterizations of protocol trees preserve complementarity, and for $N=4$, they satisfy the pentagon identity. Fourth, in binary classification, no internal local composition can achieve complementarity under endpoint-monotone losses, including standard Bregman and many finite Bernoulli $f$-divergence losses; an analogous obstruction holds for multiclass aggregation under cross-entropy. In summary, our framework shows that complementarity is attainable in multi-agent regression, but obstructed in classification under natural conditions on local aggregation and loss functions.
Chinese Translation
互补性是指人机交互（HAI）的表现超过其成员中可用的最佳预测基准。尽管这一思想在HAI研究中至关重要，但关于互补性的正式研究仍然有限。现有框架未能建模智能体的预测如何在与工作流敏感的多智能体协议中组合。我们通过引入一种基于树的互补性形式化方法，来填补这一空白。在该方法中，一个HAI协议由一个有序智能体角色配置和一个根节点的平面二叉树表示，该树的叶子装饰有预测向量。一个局部二元组合规则沿着树递归评估，产生相对于逐点最小化预言机基准的树相对互补性函数。我们证明了四个结果。首先，基于选择器的HAI，包括自我或AI依赖，在任何任务、损失或预测质量下都无法实现互补性。第二，在平方损失的回归中，互补性相当于从真实向量的欧几里得距离最小化；对于 $N=2$，最佳线性池权重具有闭合形式和残差校正解读。第三，在线性局部组合下，每个协议树在叶权重的单纯形上定义了一个质心坐标图；协议树的Tamari-cover重新参数化保持互补性，对于 $N=4$，它们满足五边形恒等式。第四，在二元分类中，任何内部局部组合在端点单调损失下都无法实现互补性，包括标准的Bregman损失和许多有限的Bernoulli $f$-散度损失；类似的障碍同样适用于跨熵下的多类聚合。总之，我们的框架展示了在多智能体回归中互补性是可实现的，但在分类中由于局部聚合和损失函数的自然条件而受阻。
cs.AI / 35 / 2606.04781
AIP: A Graph Representation for Learning and Governing Agent Skills
AIP：用于学习和管理代理技能的图表示
Abstract
Agent Skills today consist largely of free-form prose requiring the agent to read, interpret, and re-derive how to act in every session. This imposes two compounding costs: reduced reliability on implementation-heavy tasks, and difficulty in skill creation and improvement, since editing prose is a fragile process that both humans and agents struggle with, particularly for domain-specific procedural knowledge underrepresented in model training. The Agent Instruction Protocol (AIP) addresses both by modeling a skill as a directed execution graph: discrete steps as nodes backed by deterministic scripts or natural-language descriptions, connected by explicit typed input/output edges, and governed by a schema-validated YAML specification. A compiler meta-skill translates existing human-written skills into this form. The benefits are twofold. First, compiling human-written skills to AIP raised Claude Sonnet's mean task reward from 0.60 to 0.71 and pass rate from 53% to 67% across 27 real agent tasks from SkillsBench - a statistically significant gain (Wilcoxon signed-rank p = 0.011), winning 12 tasks to 2 with 13 ties - often in less wall-clock time. The graph delivers vetted, runnable units to the agent rather than asking it to re-derive code, commands, and tool calls from natural language. Second, on creation and improvement, because each skill is schema-validated, functionally testable, and addressable node-by-node, failures can be diagnosed and repaired precisely. Two authored-skill failures were traced to the script level. After adjusting the AIP spec and recompiling, both recovered with zero regressions (one task going from 0/5 to 5/5), turning skill improvement into a measurable tuning loop rather than a prose rewrite. That same graph structure supports corpus-level governance and skill introspection, and provides a natural action space for reinforcement learning over skills.
Chinese Translation
如今，代理技能主要由自由形式的文本构成，代理需要在每个会话中阅读、解释并重新推导如何行动。这带来了两个累积成本：对实施要求高的任务的可靠性降低，以及技能创建与改进的困难，因为编辑文本是一个脆弱的过程，无论是人类还是代理在这一过程中都面临挑战，尤其是对于在模型训练中未充分代表的领域特定程序知识。《代理指令协议》(Agent Instruction Protocol, AIP)通过将技能建模为有向执行图来解决这两个问题：离散步骤作为节点，由确定性的脚本或自然语言描述支持，节点之间通过显式的类型化输入/输出边相连，并由方案验证的YAML规范管理。一种编译器元技能将现有的人类编写技能转换为这种形式。其益处有二。首先，将人类编写的技能编译为AIP使Claude Sonnet的平均任务奖励从0.60提升至0.71，通过来自SkillsBench的27个真实代理任务的通过率从53%提升至67%——这是一个具有统计显著性的增益（Wilcoxon签名秩p = 0.011），任务胜率为12比2，平局13次——且通常所需实际时间较少。该图为代理提供经过验证且可执行的单元，而不是要求其从自然语言重新推导代码、命令和工具调用。其次，在创建和改进方面，由于每个技能都是方案验证的、可功能测试的并且可以逐节点访问，因此可以精确诊断和修复故障。两个所著技能的故障被追溯至脚本级别。在调整AIP规范并重新编译后，两个技能在没有出现回归的情况下恢复（一个任务的评分从0/5提升至5/5），将技能改进转变为一个可测量的调优循环，而不是单纯的文本重写。同样的图结构支持语料库级治理和技能自省，并为基于技能的强化学习提供了自然的动作空间。
cs.AI / 36 / 2606.04807
BiasGRPO: Stabilizing Bias Mitigation in High-Variance Reward Landscapes via Group-Relative Policy Optimization
BiasGRPO：通过组相对策略优化稳定高方差奖励景观中的偏见缓解
Abstract
Mitigating social bias in Large Language Models (LLMs) presents a distinct alignment challenge: unlike verifiable tasks, bias lacks a single ground truth, creating a high-variance, subjective reward landscape. Previous preference-based fine-tuning methods have major trade-offs: Direct Preference Optimization (DPO) is limited by the lack of exploration inherent in offline training, while Proximal Policy Optimization (PPO) can lead to training instability due to potentially unreliable critic estimates. In this paper, we propose BiasGRPO, a framework using Group Relative Policy Optimization (GRPO) to stabilize alignment by normalizing rewards across a group of sampled completions. By substituting the value function with a group-relative baseline, our approach reduces instability while maintaining the exploration benefits of online training. We find that BiasGRPO outperforms DPO and PPO across multiple benchmarks, indicating its effectiveness. To adapt GRPO, we synthetically extend a dataset spanning multiple domains and contexts. We also create and release a custom bias reward model that effectively guides generation while being highly compute-efficient and avoiding knowledge degradation, providing a valuable resource that can be seamlessly integrated into multi-objective RLHF pipelines.
Chinese Translation
在大型语言模型（LLMs）中缓解社会偏见面临独特的对齐挑战：与可验证任务不同，偏见没有单一的真实值，导致高方差的主观奖励景观。以偏好为基础的微调方法存在主要权衡：直接偏好优化（DPO）受限于离线训练中固有的探索不足，而近端策略优化（PPO）可能由于潜在的不可靠评价估计导致训练不稳定。在本文中，我们提出了BiasGRPO，这是一种利用组相对策略优化（GRPO）来通过对一组采样完成的奖励进行归一化，从而稳定对齐的框架。通过用组相对基线替代值函数，我们的方法在保持在线训练探索优势的同时减少了不稳定性。我们发现，BiasGRPO在多个基准测试中优于DPO和PPO，显示了其有效性。为了适应GRPO，我们对跨多个领域和上下文的数据集进行了合成扩展。我们还创建并发布了一种自定义偏见奖励模型，能够有效指导生成，同时计算效率高，并避免知识退化，提供了一个可以无缝集成到多目标强化学习人类反馈（RLHF）管道中的宝贵资源。
cs.AI / 37 / 2606.04816
Beyond Objective Equivalence: Constraint Injection for LLM-Based Optimization Modeling on Vehicle Routing Problems
超越目标等效性：基于约束注入的车辆路线问题优化建模
Abstract
Large language models (LLMs) increasingly translate natural-language optimization problems into executable solver code. Yet for constraint-dense operations research (OR) problems, existing data-filtering and training pipelines largely rely on objective-equivalence signals such as differential testing and answer agreement, which a program can pass while adding spurious constraints or silently omitting required ones, whenever those constraints are non-binding on the tested instance. We propose constraint injection, which uses feasible probes to expose spurious over-constraint and one-constraint-violating probes to reveal silent constraint omission. Combined with differential testing, it forms a dual verifier. We instantiate and evaluate it on vehicle routing problems (VRPs), a representative constraint-dense combinatorial optimization testbed with coupled operational constraints. We develop VRPCoder, an 8B end-to-end model that translates natural-language VRP scenarios into Gurobi scripts, together with an expert-verified VRP benchmark suite covering 21 variants. The verifier is reused as a rejection-sampling filter during data synthesis and as a per-rollout reward in group relative policy optimization (GRPO). Across four VRP benchmarks, VRPCoder-GRPO reaches 93\% average Pass@1, outperforms Gemini-3.1-Pro Preview on three benchmarks, exceeds Claude-Sonnet-4.5 by 28 average points, and surpasses prior OR-LLMs by 78 average points.
Chinese Translation
大型语言模型（LLMs）越来越多地将自然语言优化问题翻译为可执行的求解器代码。然而，对于约束密集的运筹学（OR）问题，现有的数据过滤和训练流程在很大程度上依赖于目标等效性信号，例如差异测试和答案一致性，而程序在测试实例中能够通过这些信号，尽管可能增加虚假的约束或默默省略必需的约束，只要那些约束对被测试的实例没有约束作用。我们提出了约束注入，它使用可行探针来揭示虚假的过约束，并使用违反单个约束的探针来揭示静默的约束省略。结合差异测试，形成了一个双重验证器。我们在车辆路线问题（VRPs）上实现并评估了这一方法，VRP 是一个代表性的约束密集的组合优化测试平台，具有耦合的操作约束。我们开发了 VRPCoder，一个 8B 的端到端模型，将自然语言 VRP 场景翻译为 Gurobi 脚本，并与一个经过专家验证的 VRP 基准套件相结合，涵盖 21 种变体。在数据合成过程中，验证器被重复用作拒绝采样过滤器，并在群体相对策略优化（GRPO）中作为每次实施奖励。在四个 VRP 基准测试中，VRPCoder-GRPO 达到 93\% 的平均通过率（Pass@1），在三个基准测试中优于 Gemini-3.1-Pro Preview，平均超出 Claude-Sonnet-4.5 28 分，并在平均上超越了之前的 OR-LLMs 78 分。
cs.AI / 38 / 2606.04823
R-APS: Compositional Reasoning and In-Context Meta-Learning for Constrained Design via Reflective Adversarial Pareto Search
R-APS：通过反思对抗帕累托搜索进行受限设计的组合推理与情境元学习
Abstract
Large language models (LLMs) are fluent on open-ended tasks, yet in agentic settings, where a system must plan, use tools, and act over extended horizons, fluency does not ensure reliable delivery. We trace this gap to three coupled structural failures: errors propagate without localization, worst-case perturbations go unevaluated, and accumulated knowledge is never invalidated. We argue these share a root cause: abductive, counterfactual, meta-inductive, corrective, and inductive reasoning pull a shared context in incompatible directions. We introduce Reflective Adversarial Pareto Search (R-APS), to our knowledge the first method addressing all three failures jointly via reasoning-mode decomposition, allocating each reasoning mode its own context and orchestrating interaction across three timescales: staged compositional reasoning with a typed validation critic (failure localization), sensitivity-guided counterfactual stress-testing as a first-class Pareto objective (robustness), and meta-inductive rule extraction with explicit invalidation (persistent memory). R-APS requires no fine-tuning and operates on a frozen LLM purely via structured protocol design. We evaluate on planar mechanism synthesis (robotics, prosthetics, mechanical design), with every candidate checked by a kinematic solver. On 32 target trajectories, R-APS delivers robustness certificates 3.5x tighter than uniform-perturbation baselines, 46% faster iterations-to-first-admission, and 2.1x Chamfer-distance reduction over Enum+GA while jointly controlling bar-count and worst-case robustness. Small 4B reasoning-specialized models prove competitive with general-purpose 70B backbones inside the protocol, suggesting structured protocols can partially offset model scale.
Chinese Translation
大型语言模型（LLMs）在开放性任务中表现流利，但在需要系统进行规划、使用工具以及在较长时间范围内采取行动的主动环境中，流利性并不能确保可靠的交付。我们将这一差距归因于三种相互关联的结构性失效：错误没有局部化传播，最坏情况下的扰动未被评估，以及积累的知识从未被否定。我们认为这些问题有一个共同的根源：溯因的、反事实的、元归纳的、纠正的和归纳推理在兼容的方向上拉动共享的上下文。我们引入了反思对抗帕累托搜索（Reflective Adversarial Pareto Search, R-APS），据我们所知，这是首个通过推理模式分解共同解决所有三种失效的 метод ，为每种推理模式分配其独特的上下文，并协调沿着三个时间尺度的交互：具有类型验证评判的分阶段组合推理（失效定位），作为一类帕累托目标的敏感性引导反事实压力测试（鲁棒性），以及通过明确的无效化进行的元归纳规则提取（持久记忆）。R-APS无需微调，仅通过结构化协议设计在冻结的 LLM 上运行。我们在平面机制合成（机器人、假肢、机械设计）上进行了评估，所有候选项均通过运动学求解器进行了检查。在 32 个目标轨迹上，R-APS的鲁棒性证书比均匀扰动基线紧凑 3.5 倍，首次接纳的迭代速度快 46%，并且比 Enum+GA 具有 2.1 倍的 Chamfer 距离减少，同时共同控制杆件数量和最坏情况鲁棒性。小型4B推理专用模型在协议内与通用70B骨干网竞争，表明结构化协议可以部分抵消模型规模的影响。
cs.AI / 39 / 2606.04867
AICompanionBench: Benchmarking LLMs-as-Judges for AI Companion Safety
AICompanionBench：评估大型语言模型作为AI陪伴者安全性的基准
Abstract
As AI companion platforms such as Replika and Character.AI rapidly grow, concerns about unsafe human-AI interactions have intensified. This study introduces AICompanionBench, to our knowledge the first publicly available benchmark dataset of human-AI companion conversations annotated with fine-grained safety risk categories. The dataset contains 2,123 real-world Replika conversations collected from Reddit and annotated through human-AI collaboration across nine categories: sexual behavior, antisocial behavior, physical aggression, verbal aggression, substance abuse, self-harm and suicide, control, manipulation, and no-harm. Using this benchmark, we evaluate 20 state-of-the-art open-source and closed-source LLMs under an LLM-as-judge framework for detecting unsafe interactions. Results show substantial variation in model performance, with stronger models achieving high overall accuracy but still struggling with nuanced categories such as manipulation, as well as benign conversations that are incorrectly identified as harmful. Our findings suggest that while current LLMs can effectively detect explicit harmful content, they remain limited in identifying implicit unsafe interactions. Overall, our work contributes a new benchmark dataset for AI companionship safety research and offers insights into monitoring AI companion systems using LLMs. The dataset is publicly available at: https://github.com/anonymousresearcher2026/AICompanionBench/blob/main/AICompanionBench.xlsx
Chinese Translation
随着AI陪伴平台如Replika和Character.AI的迅速发展，人机交互中的安全隐患问题日益严重。本研究介绍AICompanionBench，这是我们所知的第一个公开可用的人机陪伴对话基准数据集，标注了细粒度的安全风险类别。该数据集包含来自Reddit的2,123个真实Replika对话，并通过人机协作在九个类别中进行标注：性行为、反社会行为、身体攻击、语言攻击、药物滥用、自我伤害和自杀、控制、操控，以及无害。利用这一基准数据集，我们在LLM-as-judge框架下评估了20个最先进的开源和闭源大型语言模型在检测不安全交互中的表现。结果显示模型性能存在显著差异，更强的模型虽然在总体准确性上表现良好，但在操控等细微类别以及错误地将良性对话识别为有害的情况下依然表现不佳。我们的研究表明，虽然当前的LLM能够有效检测显性有害内容，但在识别隐性不安全交互方面仍然存在局限性。总体而言，我们的工作为AI陪伴安全研究贡献了一个新的基准数据集，并提供了使用LLM监控AI陪伴系统的见解。数据集可公开访问： https://github.com/anonymousresearcher2026/AICompanionBench/blob/main/AICompanionBench.xlsx
cs.AI / 40 / 2606.04935
What Type of Inference is Active Inference?
主动推理是什么类型的推理？
Abstract
Active inference casts decision-making as inference, with the Expected Free Energy (EFE) unifying goal-directed and information-seeking behavior. Recent work showed that EFE minimization can be written as Variational Free Energy (VFE) minimization on a generative model augmented with epistemic priors. We prove that the VFE of the augmented model can be rewritten as the VFE of the predictive model plus explicit entropy-correction terms, making the EFE contribution transparent. We then show that proper EFE-based planning requires combining these epistemic corrections with a planning correction that turns marginal inference into policy optimization, yielding a full variational characterization of EFE-based planning. This clarifies which corrections are needed for cross-entropy planning and for full EFE-based planning. The same entropy-corrected formulation leads to a detailed message-passing scheme for EFE-based planning together with simpler ablations. Experiments on three grid-world environments show that the planning correction already helps when observations are decisive, whereas the additional observation-side epistemic corrections matter most when observations are merely suggestive.
Chinese Translation
主动推理将决策过程视为推理，其中期望自由能（Expected Free Energy, EFE）统一了目标导向和信息寻求行为。最近的研究表明，EFE最小化可以被表述为在扩展了认知先验的生成模型上进行变分自由能（Variational Free Energy, VFE）最小化。我们证明了扩展模型的VFE可以重写为预测模型的VFE加上显式的熵修正项，从而使EFE的贡献变得透明。然后，我们展示了基于EFE的适当规划需要将这些认知修正与将边际推理转换为策略优化的规划修正相结合，从而实现对基于EFE的规划的完整变分描述。这阐明了进行交叉熵规划和完整基于EFE的规划所需的修正。相同的熵修正公式引导出了一种详细的基于EFE的规划信息传递方案，附带更简单的消融实验。在三个网格世界环境中的实验结果表明，当观察结果具有决定性时，规划修正已经有帮助，而额外的观察侧认知修正则在观察结果仅具有暗示性时显得尤为重要。
cs.AI / 41 / 2606.05043
Strabo: Declarative Specification and Implementation of Agentic Interaction Protocols
Strabo：代理交互协议的声明性规范与实现
Abstract
The last few years have witnessed major advances in the modeling and implementation of multiagent systems based on declarative interaction protocols. Our contribution, Strabo, establishes the relevance of these advances to ongoing industry efforts in Agentic AI. Specifically, we consider UCP, the Universal Commerce Protocol, a recent Google-led effort to standardize e-commerce interactions for AI agents. Our exercise is in two parts. One, we model the part of UCP dealing with checkouts as a declarative Langshaw protocol and implement agents using Peach, a programming model for Langshaw. This part of the exercise brings out the advantages of formal, declarative specifications. Two, we show that Peach agents can interoperate with UCP agents implemented by Google, thereby establishing the fidelity of our approach with respect to UCP. Such interoperation enables the incremental introduction of declarative protocols and agents into a conventional setting, indicating a pathway by which EMAS ideas could influence practice without demanding a wholesale update.
Chinese Translation
近年来，在基于声明性交互协议的多代理系统建模和实施方面取得了重大进展。我们的贡献，Strabo，确立了这些进展与行业中正在进行的代理人工智能（Agentic AI）努力之间的相关性。具体而言，我们考虑了由谷歌主导的、旨在标准化 AI 代理电子商务交互的近期工作——通用商务协议（Universal Commerce Protocol，UCP）。我们的研究分为两部分。第一部分，我们将涉及结账的 UCP 部分建模为声明性 Langshaw 协议，并使用 Langshaw 的编程模型 Peach 实现代理。这部分工作突显了形式化声明性规范的优势。第二部分，我们展示了 Peach 代理可以与谷歌实施的 UCP 代理进行互操作，从而验证了我们方法在 UCP 方面的可靠性。这种互操作性使得在传统环境中逐步引入声明性协议和代理成为可能，表明 EMAS 思想能够影响实践的途径，而无需进行全面更新。
cs.AI / 42 / 2606.05080
AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?
AutoLab：前沿模型能否解决长期自动化研究与工程任务？
Abstract
Scientific and engineering progress is fundamentally a long-horizon iterative process: proposing changes, running experiments, measuring outcomes, and continuously refining artifacts. Yet existing benchmarks for frontier models primarily evaluate either single-turn responses or short-horizon agent trajectories, failing to capture the challenges of sustained iterative improvement over extended time horizons. To address this gap, we introduce AutoLab, a new benchmark for ultra long-horizon closed-loop optimization. AutoLab consists of 36 realistic, expert-curated tasks spanning four diverse domains: system optimization, puzzle & challenge, model development, and CUDA kernel optimization. Each task begins with a correct but deliberately suboptimal baseline and challenges agents to improve it within a strict wall-clock budget. Evaluating 17 state-of-the-art models reveals the dominant predictor of success is not the quality of an agent's initial attempt, but its persistence in repeatedly benchmarking, editing, and incorporating empirical feedback. While claude-opus-4.6 exhibits strong long-horizon optimization capabilities, most frontier models, including several proprietary ones, either terminate prematurely or exhaust their budgets with minimal progress. These results underscore the importance of time awareness and persistent iteration in autonomous agents. We open-source the full benchmark, evaluation harness, and task artifacts, to accelerate research toward truly capable long-horizon agents.
Chinese Translation
科学与工程进步本质上是一个长期迭代的过程：提出变更、进行实验、测量结果并持续完善工件。然而，现有的前沿模型基准主要评估单次响应或短期代理轨迹，未能捕捉到在较长时间跨度内持续迭代改进所面临的挑战。为了解决这一空白，我们推出了AutoLab，一个用于超长期闭环优化的新基准。AutoLab包含36个现实且由专家策划的任务，覆盖四个不同领域：系统优化、谜题与挑战、模型开发以及CUDA内核优化。每个任务都以一个正确但故意设置为次优的基线开始，并挑战代理在严格的时间预算内对其进行改进。对17个最先进模型的评估揭示，成功的主要预测因素并不是代理初始尝试的质量，而是其在反复基准测试、编辑和融入经验反馈中的坚持。虽然claude-opus-4.6表现出强大的长期优化能力，但大多数前沿模型，包括一些专有模型，要么过早终止，要么在预算耗尽时进展甚微。这些结果突显了时间意识和持久迭代在自主代理中的重要性。我们将完整基准、评估工具和任务工件开源，以加速朝向真正具有能力的长期代理的研究。
cs.AI / 43 / 2606.05104
Knowledge Index of Noah's Ark
诺亚方舟的知识指数
Jin, Sheng, Liu, Minghao, Xiao, Yunze, Zhou, Zeqi, Qi, Heli, Yao, Yifan, Song, Meishu, Ma, Kaijing, Zhang, Xuan, Jiang, Sicong, Li, Yizhe, Ma, Ningshan, Wei, Jie, Li, Ziniu, Yang, Minglai, Liu, Bangya, Liang, Yiming, Fang, Xiao, Zeng, Qingcheng, Liu, Jiarui, Yang, Rui, Yan, Shen, Huang, Wenhao, Liu, Jiaheng, Wang, Zihan, Xuan, Weihao, Zhang, Ge
Abstract
Knowledge benchmarks for LLMs face three issues: scaling-driven designs that do not operationalize disciplinary representativeness; flat-payment annotation that permits lazy consensus; and unaudited ranking instability under bounded test budgets. We introduce KINA, an 899-item benchmark across 261 fine-grained disciplines, with two formal results. First, we cast representativeness as a coverage-style objective over expert-elicited anchors and operationalize disciplinary representativeness through a proxy, yielding a (1-1/e) greedy approximation (Proposition 1); the guarantee applies to the proxy, not to population representativeness. Second, we prove a bonus-on-bar tournament weakly FOSD-dominates flat payment in released-review quality, with incentive-compatibility threshold B > Delta C / Delta p_min (Theorem 1). Evaluating 42 models from 13 labs, the top model, Gemini-3.1-Pro-Preview, reaches 53.17%, followed by Claude-Opus-4.6 at 49.92% and GPT-5.4 at 48.55%, leaving substantial headroom below saturation. The full leaderboard shows a tiered structure rather than a smooth total order: a small frontier tier lies above 48%, a dense strong-model tier spans roughly 38-45%, and low-performing models remain only modestly above the 10% chance baseline. Tool augmentation adds up to 5.17 points across the five tool-use evaluations, with gains varying substantially across models. We report bootstrap ranking-stability statistics to make bounded-budget variance explicit and to discourage over-interpretation of adjacent ranks.
Chinese Translation
大型语言模型（LLMs）的知识基准面临三个问题：规模驱动的设计未能实际操作学科代表性；平价标注允许懒惰共识；以及在有限测试预算下未经审计的排名不稳定性。我们引入了KINA，这是一个涵盖261个细分学科的899项基准，并提出了两个正式结果。首先，我们将代表性视为对专家引导下锚点的覆盖风格目标，并通过一个代理实现学科代表性，得到一个(1-1/e)贪心近似（命题1）；该保证适用于代理，而非人口代表性。其次，我们证明了奖金对决（bonus-on-bar tournament）在发布审查质量上的弱第一阶支配（weak FOSD）平价支付，具有激励相容性阈值B > Delta C / Delta p_min（定理1）。在对来自13个实验室的42个模型进行评估时，排名第一的模型Gemini-3.1-Pro-Preview达到了53.17%，其次是Claude-Opus-4.6，达到了49.92%，而GPT-5.4则为48.55%，这为进一步提升留下了相当大的空间。完整的排行榜显示出分层结构而非平滑的总体顺序：一个小的前沿层在48%以上，一个密集的强模型层大约在38%-45%之间，而低绩效模型仅稍微超过10%的机会基线。工具增强在五项工具使用评估中增添了最高5.17分，且增益在不同模型中变化显著。我们报告了自助法（bootstrap）排名稳定性统计，以明确有限预算下的方差，并防止对临近排名的过度解读。
cs.CL / 1 / 2606.04095
POLARIS: Guiding Small Models to Write Long Stories
POLARIS：引导小型模型撰写长篇故事
Abstract
Small open-weight models struggle at long-form creative writing: their generated stories either fall far short of the requested length, or their quality significantly degrades as length increases, especially when compared to frontier models. We present POLARIS (Policy Optimization with LLM-as-a-judge rewards and Anchored-Reference Injection for Storywriting), a lower-compute GRPO recipe with two key ingredients: a frontier LLM judge with a structured Story Quality rubric as the online reward, and human-reference injection (HRI), where a teacher-forced human-written story serves as a high-reward anchor within each GRPO group. By applying our training recipe to Qwen3.5-9B, using a dataset of approximately 1.4K prompt-story pairs derived from 100 short-story anthologies and 4 A100 GPUs, we obtain POLARIS-9B. Across five benchmarks spanning in-distribution and out-of-distribution prompts and rubrics, POLARIS-9B is competitive with much larger open-weight models while following length instructions more closely. A blinded human evaluation confirms that POLARIS-9B is preferred to the base Qwen3.5-9B and on par with Qwen3.5-27B. Despite training only on stories up to 4k words, POLARIS-9B preserves quality on prompts requesting stories up to 3 times the training length, a regime where most open-weight models degrade substantially in quality, length adherence, or both. More broadly, our results suggest that length generalization is a meaningful stress test for creative-writing models and a useful lens for distinguishing otherwise close models.
Chinese Translation
小型开源权重模型在长篇创意写作方面表现不佳：它们生成的故事通常远未达到所要求的长度，或者随着长度的增加其质量显著下降，尤其是与前沿模型相比。我们提出了POLARIS（基于LLM评判奖励和锚定参考注入的策略优化，用于故事创作），这是一种低计算量的GRPO配方，主要包含两个关键成分：一个具有结构化故事质量标准的前沿LLM评判作为在线奖励，以及人类参考注入（HRI），其中一个由教师强制写的故事作为每个GRPO组内的高奖励锚定。通过将我们的训练配方应用于Qwen3.5-9B，使用从100本短篇小说选集衍生出的约1.4K对提示-故事对的数据集，并在4个A100 GPU上进行训练，我们得到了POLARIS-9B。在涵盖分布内和分布外提示及标准的五个基准测试中，POLARIS-9B在遵循长度指令上表现出色，并与更大规模的开源权重模型相竞争。盲评显示，POLARIS-9B优于基础的Qwen3.5-9B，且与Qwen3.5-27B表现相当。尽管仅对最多4千字的故事进行训练，POLARIS-9B在提示请求故事的长度可达到训练长度的三倍时仍能保持质量，而大多数开源权重模型在质量、长度遵循性或两者方面均出现显著下降。更广泛而言，我们的结果表明，长度泛化是对创意写作模型的一个有意义的压力测试，也为区分相近模型提供了有用的视角。
cs.CL / 2 / 2606.04109
Discourse-Role Labels as Presentation-Time Variables for Context Use in Language Models
作为语言模型上下文使用呈现时间变量的语篇角色标签
Abstract
Context-augmented language model systems often wrap supplied content with labels such as Reference:, Evidence:, Instruction:, Note:, or Example:, but the effect of these labels on reader-model behavior remains underexplored. We introduce a paired fixed-content probe over 500 MMLU-Pro items: each item receives the same misleading answer-bearing assertion under different discourse-role labels, and adoption is measured by whether the model outputs the injected wrong option. Across GPT-5.5, DeepSeek V4 Pro, Llama-3-8B-Instruct, and Qwen2.5-7B-Instruct, Misleading Adoption Rate shifts by 56-84 percentage points. Binding or source-like labels such as Instruction: and Reference: produce high adoption, whereas Example: consistently suppresses it. Paired tests, bootstrap intervals, final-instruction ablations, and Qwen final-step log-probability probes support a label-conditioned candidate preference. Boundary probes show where the effect weakens or persists: arithmetic tasks reduce adoption, passage-shaped external context preserves smaller label gaps, short-answer evaluation rules out option-letter copying, and nested-label conflicts suggest that illustrative framing can delimit adoption scope. A 200-case single-author manual audit confirms that the short-answer contrasts are stable under conservative adjudication. The resulting claim is bounded but practical: context-utilization and reader-side RAG benchmarks should report and control wrapper labels, because presentation choices can change measured reliance on supplied context.
Chinese Translation
上下文增强的语言模型系统通常用标签如 Reference:、Evidence:、Instruction:、Note: 或 Example: 来包装提供的内容，但这些标签对阅读者模型行为的影响仍然未被深入探讨。我们在 500 个 MMLU-Pro 项目上引入了一种配对固定内容探测器：每个项目在不同的语篇角色标签下接收相同的误导性答案声明，并通过模型是否输出注入的错误选项来测量采纳率。在 GPT-5.5、DeepSeek V4 Pro、Llama-3-8B-Instruct 和 Qwen2.5-7B-Instruct 中，误导性采纳率变化幅度达 56-84 个百分点。Binding 或 source 类标签如 Instruction: 和 Reference: 产生高采纳率，而 Example: 一直抑制这一现象。配对测试、引导区间、最终指令消融及 Qwen 最后一步的对数概率探测支持了基于标签的候选偏好。边界探测显示了效果减弱或持续的地方：算术任务降低了采纳率，经过处理的外部上下文保留了较小的标签差距，短答案评估排除了选项字母的复制，而嵌套标签冲突则表明示例框架可以限制采纳范围。一项包含 200 个案例的单作者手动审计确认短答案对比在保守裁决下是稳定的。因此，得到的结论虽然有限但切实可行：上下文利用和阅读者侧 RAG 基准应报告并控制包装标签，因为呈现选择可能改变测量对提供上下文的依赖程度。
cs.CL / 3 / 2606.04118
Computational conceptual history of scientific concepts: From early digital methods to LLMs
科学概念的计算概念史：从早期数字方法到大型语言模型（LLMs）
Abstract
This article situates large language models (LLMs) within the longer history of computational approaches to concept analysis in the history, philosophy, and sociology of science (HPSS). We examine what LLMs add to existing methods, how they inherit longstanding problems, and review recent case studies that employ them. In the first part, we reconstruct computational conceptual history before LLMs by bringing together three strands of work: early digital methods in HPSS, distributional approaches from digital history and related research, and lexical semantic change detection. We provide an overview of the main challenges and opportunities, focusing on corpus construction, operationalization and modelling choices, and evaluation and interpretation. In the second part, we turn to the era of LLMs, starting with a short introduction to LLMs before reviewing LLM-based work on lexical semantic change detection and relevant case studies in HPSS. We then revisit the earlier methodological questions, showing how issues of corpus construction, model choice and training data, operationalization trade-offs, and evaluation and interpretation play out in LLM-based workflows.
Chinese Translation
本文将大型语言模型（LLMs）置于科学史、哲学和社会学（HPSS）概念分析的计算方法的更长历史中。我们分析LLMs为现有方法增添了什么，它们如何继承长期存在的问题，并回顾了最近使用它们的案例研究。在第一部分中，我们通过整合三条研究路径来重构LLMs之前的计算概念史：HPSS中的早期数字方法、来自数字历史和相关研究的分布式方法以及词汇语义变化检测。我们提供了主要挑战和机遇的概述，重点关注语料库构建、操作化和建模选择，以及评估和解释。在第二部分中，我们转向LLMs时代，从对LLMs的简要介绍开始，然后回顾基于LLM的词汇语义变化检测和HPSS中相关案例研究的工作。随后，我们重新审视早期的方法论问题，展示了语料库构建、模型选择和训练数据、操作化权衡以及评估和解释在基于LLM的工作流程中的表现。
cs.CL / 4 / 2606.04120
SaliMory: Orchestrating Cognitive Memory for Conversational Agents
SaliMory: 为对话代理组织认知记忆
Abstract
Conversational agents that serve as lifelong companions must maintain persistent memory across all interactions. However, simply expanding context windows with raw retrieval degrades reasoning quality, while training memory agents via standard reinforcement learning creates a severe credit assignment bottleneck in a multi-stage pipeline. To solve this, we introduce SALIMORY, a framework that trains a single language model to manage a cognitively-structured memory-spanning user facts, preferences, and working memory. By introducing a hierarchical stage-wise process reward and reward-decomposed contrastive refinement, SALIMORY provides isolated supervision for distinct memory operations (selective filtering, consolidation, and cue-driven recall) end-to-end. SALIMORY cuts memory-attributed failures by one-third, outperforms the state-of-the-art by over 10% in end-to-end accuracy, and more than doubles the Good Personalization rate.
Chinese Translation
作为终生伴侣的对话代理必须在所有交互中保持持久记忆。然而，简单地扩展上下文窗口并采用原始检索会降低推理质量，而通过标准强化学习训练记忆代理则在多阶段管道中会产生严重的信用分配瓶颈。为了解决这个问题，我们推出了SALIMORY，一个训练单一语言模型以管理认知结构化记忆的框架，该记忆跨越用户事实、偏好和工作记忆。通过引入分层的阶段性过程奖励和奖励分解对比精炼，SALIMORY为不同的记忆操作（选择性过滤、巩固和线索驱动回忆）提供了独立的端到端监督。SALIMORY将记忆相关的失败减少了三分之一，在端到端准确性上超越了最先进的技术超过10%，并且使良好个性化率增加了两倍以上。
cs.CL / 5 / 2606.04127
When Retrieval Doesn't Help: A Large-Scale Study of Biomedical RAG
当检索无助时：一项大规模生物医学 RAG 研究
Abstract
Medical question answering is a high-stakes setting where factual errors can have serious consequences. Retrieval-augmented generation (RAG) is widely viewed as a promising solution, and prior work has reported substantial gains for large medical QA models. We revisit this assumption across a broad range of open-weight instruction-tuned models spanning 7B to 72B parameters. Across five models, ten biomedical QA datasets, four retrieval methods, and four retrieval corpora, we find that retrieval yields only small and inconsistent improvements over a no-retrieval baseline, typically within 1-2 points. In contrast, the choice of backbone model has a much larger effect than the choice of retriever or corpus, and expert and layman retrieval sources perform similarly in most settings. These results suggest that the main bottleneck is not retrieval quality alone, but the model's limited ability to use retrieved evidence effectively.
Chinese Translation
医学问答是一种高风险的环境，事实错误可能会带来严重后果。增强检索生成（RAG）被广泛视为一种有前景的解决方案，先前的研究报告称大型医学问答模型取得了显著提升。我们在一系列开放权重的指令调优模型上重新审视这一假设，这些模型的参数从 7B 到 72B 不等。在五个模型、十个生物医学问答数据集、四种检索方法和四个检索语料库中，我们发现，与无检索基线相比，检索只带来了微小且不一致的改进，通常仅在 1-2 分之内。相比之下，主干模型的选择对结果的影响远大于检索器或语料库的选择，在大多数情况下，专家和外行的检索来源表现相似。这些结果表明，主要的瓶颈并不单是检索质量，而是模型有效利用检索到的证据的能力有限。
cs.CL / 6 / 2606.04160
Expert-Aware Refusal Steering
专家感知拒绝引导
Abstract
Safety alignment in instruction-tuned large language models (LLMs) depends on a model's ability to reliably refuse to respond to harmful or disallowed requests. Recent work has shown that a steering vector can be applied to a dense LLM during inference to effectively suppress refusal behavior, inducing response to harmful requests. We extend this refusal steering method to three open-source Mixture-of-Experts (MoE) LLMs and find that steering performance is uninhibited by the complex routing patterns inherent to the MoE architecture. We then propose two expert-aware refusal steering methods that leverage refusal-specific expert routing patterns and expert-specific steering directions to suppress normal refusal behavior. We find that refusal behavior can be effectively steered based on the output of a single expert. Our results show that refusal signals captured by steering methods differ from expert routing behavior, suggesting a substantial role for attention in MoE refusal behavior.
Chinese Translation
在针对特定指令调优的大型语言模型（LLMs）中，安全性对齐依赖于模型可靠拒绝对有害或禁止请求的响应能力。近期研究表明，可以在推理过程中对密集型LLM应用引导向量，以有效抑制拒绝行为，从而响应有害请求。我们将这一拒绝引导方法扩展到三种开源的专家组合模型（Mixture-of-Experts，MoE）LLM，并发现引导性能未受到MoE架构固有的复杂路由模式的影响。随后，我们提出了两种专家感知的拒绝引导方法，这些方法利用特定于拒绝的专家路由模式和专家特定的引导方向，以抑制正常的拒绝行为。我们的研究表明，拒绝行为可以有效地根据单个专家的输出进行引导。结果显示，引导方法捕获的拒绝信号与专家路由行为存在差异，暗示了注意力在MoE拒绝行为中扮演了重要角色。
cs.CL / 7 / 2606.04177
A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models
跨领域和模型的AI生成文本检测中的语言特征系统分析
Abstract
Interpretable linguistic features offer a promising approach for explaining why a given text appears machine-generated, particularly for non-expert users. However, existing findings on which features reliably indicate LLM-generated text remain fragmented across feature sets, models, and text domains. To address this gap, we conduct a large-scale empirical study assessing the robustness of linguistic signals for characterizing AI-generated text. Our analysis covers 284 interpretable linguistic features across outputs from 27 LLMs and ten text domains under cross-model and cross-domain generalization settings. We show that classifiers based solely on linguistic features can reliably distinguish AI-generated from human-written text. However, many previously proposed indicators prove strongly context-dependent, with the exception of measures of lexical richness, which remain robust signals across model families and text domains. These results demonstrate which linguistic signals generalize across contexts and provide a foundation for more reliable, interpretable analyses of AI-generated language.
Chinese Translation
可解释的语言特征为解释特定文本为何显得机器生成提供了一种有前景的方法，尤其是针对非专业用户。然而，关于哪些特征可以可靠指示大型语言模型（LLM）生成的文本的现有研究发现仍然在特征集合、模型和文本领域之间缺乏一致性。为了解决这一问题，我们进行了一项大规模的实证研究，评估语言信号在表征AI生成文本方面的稳健性。我们的分析涵盖了284个可解释的语言特征，涉及27个LLM的输出和十个文本领域，并在跨模型和跨领域的泛化设置下进行。我们展示了仅基于语言特征的分类器可以可靠地区分AI生成文本与人类撰写文本。然而，许多先前提出的指示符证明对上下文高度依赖，词汇丰富度的度量除外，该指标在不同模型系列和文本领域中依然保持稳健。这些结果展示了哪些语言信号在不同语境中具有广泛的适用性，并为对AI生成语言进行更可靠、可解释的分析打下了基础。
cs.CL / 8 / 2606.04189
ACAT: A Collaborative Platform for Efficient Aspect-Based Sentiment Dataset Annotation
ACAT：一个高效的基于方面情感数据集注释的协作平台
Abstract
Aspect-Based Sentiment Analysis (ABSA) requires high-quality datasets to train reliable models. However, existing annotation tools treat output as flat files, leaving researchers to manually consolidate multi-annotator data, reconstruct relational structures, and compute reliability metrics through custom scripts. This paper introduces ACAT (Aspect-based sentiment analysis Collaborative Annotation Tool), a web-based platform natively supporting four ABSA workflows: (1) Aspect-Category Sentiment Analysis, (2) Clause-Level Segmentation, (3) Aspect-Term Sentiment Analysis with character-level position tracking, and (4) Aspect Sentiment Triplet Extraction with dual span offset preservation. Its core contribution is an automated Extract, Transform, Load (ETL) pipeline that aligns collaborative annotations and computes Inter-Annotator Agreement (IAA) metrics directly at export, yielding training-ready datasets. In a preliminary validation on 1,002 restaurant reviews with two annotators of differing expertise, ACAT achieves a median annotation time of 31.58 seconds and a raw IAA ranging from 0.78 to 0.86 across all tasks.
Chinese Translation
基于方面的情感分析（ABSA）需要高质量的数据集来训练可靠的模型。然而，现有的注释工具将输出视为平面文件，使研究人员需要手动合并多注释者的数据，重构关系结构，并通过自定义脚本计算可靠性指标。本文介绍了ACAT（基于方面的情感分析协作注释工具），这是一个基于网络的平台，原生支持四种ABSA工作流：（1）基于方面的类别情感分析，（2）子句级别分割，（3）带字符级位置跟踪的基于方面的术语情感分析，以及（4）保存双跨度偏移的基于方面情感三元组提取。其核心贡献是一个自动化的提取、转换、加载（ETL）管道，该管道对齐协作注释并在导出时直接计算注释者间一致性（IAA）指标，从而生成适合训练的数据集。在对1,002条餐厅评论进行的初步验证中，涉及两位专业背景不同的注释者，ACAT的中位注释时间为31.58秒，所有任务的原始IAA范围为0.78到0.86。
cs.CL / 9 / 2606.04199
Cross-Prompt Generalization in Detecting AI-Generated Fake News Using Interpretable Linguistic Features
使用可解释语言特征在检测AI生成的假新闻中的跨提示泛化
Abstract
The increasing use of large language models has raised concerns about the spread of AI-generated fake news, particularly under varying prompting strategies. Most existing detection models are trained and evaluated under a single generation setting, leaving their ability to generalize across unseen prompts unclear. In this study, we investigate cross-prompt generalization in fake news detection using three datasets of AI-generated articles produced under distinct prompts, combined with real news articles. We extract interpretable linguistic features capturing lexical diversity, readability, and emotion-based characteristics and evaluate a random forest classifier under a cross-prompt framework, where models trained on one prompt are tested on another. Across all six train-test combinations, performance remains consistently high, with AUC values ranging from 0.988 to 1.000. Analysis of feature distributions shows that AI-generated text exhibits increased lexical diversity, reduced readability, and substantially lower emotional intensity compared to the overall dataset, with variations across prompts. Despite these distributional shifts, the classifier maintains strong performance, indicating that these features capture stable properties of AI-generated text that generalize across prompting strategies. These findings suggest that feature-based approaches can provide robust detection of AI-generated fake news under prompt variability.
Chinese Translation
大型语言模型的日益使用引发了对AI生成假新闻传播的担忧，尤其是在不同提示策略下。现有的大多数检测模型在单一生成设置下进行训练和评估，因此它们在未见提示下的泛化能力尚不明确。在本研究中，我们利用三个在不同提示下生成的AI文章数据集（结合真实新闻文章）来研究假新闻检测中的跨提示泛化。我们提取了可解释的语言特征，这些特征捕捉了词汇多样性、可读性和情感特征，并在跨提示框架下评估了随机森林分类器，其中在一个提示下训练的模型在另一个提示下进行测试。在所有六种训练-测试组合中，性能始终保持较高，AUC值范围为0.988至1.000。特征分布分析表明，与整体数据集相比，AI生成文本展现出更高的词汇多样性、较低的可读性和显著更低的情感强度，同时在不同提示之间存在变异。尽管有这些分布变化，分类器依然保持强劲表现，这表明这些特征捕捉了AI生成文本的稳定属性，能够在不同提示策略下泛化。这些发现表明，基于特征的方法在提示变化下可以提供对AI生成假新闻的稳健检测能力。
cs.CL / 10 / 2606.04231
MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A
MM-BizRAG：重新思考用于通用企业问答的多模态检索增强生成
Abstract
Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing retriever embeddings and for answer generation. While efficient, this trend often neglects explicit handling of the rich, structured information in complex enterprise documents, instead depending on pre-trained embeddings or vision-language models to implicitly capture such structure. In this work, we take a more direct approach: MM-BizRAG proactively extracts and represents document structure via a document structure-aware split that dynamically routes documents through orientation-specific ingestion pipelines, applying explicit layout-aware parsing for vertically structured documents (e.g., reports) and holistic page-level representations for horizontally structured documents (e.g., slide decks). A unified LLM-driven artifact transformation pipeline with placeholder-based positional alignment preserves natural reading order, while inference-time multimodal assembly decouples retrieval representations from generation context, enabling richer, more grounded answers without any finetuning requirement. Through experiments on a large, heterogeneous enterprise dataset and two public benchmarks (SlideVQA and FinRAGBench-V), MM-BizRAG consistently outperforms state-of-the-art vision-centric baselines by up to 32% points, with especially strong gains on report-style layouts. Furthermore, we introduce FastRAGEval, a single-call LLM Judge metric for fine-grained generative recall that halves RAGChecker's cost while achieving stronger human alignment.
Chinese Translation
近年来，多模态检索增强生成（MM-RAG）的进展已转向最小解析，依赖于页面级图像生成检索嵌入和答案生成。虽然这一趋势提高了效率，但通常忽视了对复杂企业文档中丰富结构信息的明确处理，而是依赖于预训练嵌入或视觉-语言模型来隐式捕捉这种结构。在本研究中，我们采取了更直接的方法：MM-BizRAG主动提取并表示文档结构，通过文档结构感知分割动态引导文档经过特定方向的摄取管道，为垂直结构文档（例如报告）应用明确的布局感知解析，并为横向结构文档（例如幻灯片）提供整体页面级表示。一个统一的基于大型语言模型（LLM）驱动的工件转化管道与占位符位置对齐，保留了自然的阅读顺序，而推理时的多模态组装则将检索表示与生成上下文解耦，使得生成更丰富且更具基础的答案，无需进行任何微调。通过在一个大型异构企业数据集和两个公共基准（SlideVQA和FinRAGBench-V）上的实验，MM-BizRAG始终在报告式布局上比最先进的视觉中心基线提高了最多32个百分点。此外，我们引入了FastRAGEval，这是一种单次调用的LLM评估指标，用于细粒度生成召回，其成本降低至RAGChecker的一半，同时实现了更强的人类对齐。
cs.CL / 11 / 2606.04236
Supportive Token Revealing for Fast Diffusion Language Model Decoding
支持性标记揭示用于快速扩散语言模型解码
Abstract
Discrete diffusion language models can generate text efficiently by updating multiple masked positions in parallel, but this parallelism introduces a quality-latency trade-off. Aggressive decoding may commit mutually dependent tokens too early, while conservative decoding requires many denoising steps. Existing methods address this tension by deciding which tokens are safe to reveal using confidence or dependency criteria. However, avoiding unsafe commits does not necessarily make the remaining masked sequence easy to decode, since uncertain tokens may depend on masked tokens, creating a bottleneck for denoising steps. We propose AXON, a training-free module that can be added on top of existing parallel decoding strategies for diffusion language models. Rather than replacing the base decoder, AXON monitors the remaining uncertain masked tokens and intervenes only when their current state suggests that additional context is needed. It then shifts the criterion from which tokens are safest to reveal to which confident reveals would best support later denoising. AXON selects anchors, confident masked tokens that uncertain positions attend to, using attention, uncertainty, and confidence signals. Experiments on reasoning and code-generation benchmarks across multiple diffusion language models show that AXON improves the quality-latency trade-off of existing parallel decoders, often reducing the number of function evaluations while maintaining or improving accuracy.
Chinese Translation
离散扩散语言模型可以通过并行更新多个掩码位置高效生成文本，但这种并行性引入了质量-延迟的权衡。激进解码可能会过早地确定相互依赖的标记，而保守解码则需要多个去噪步骤。现有方法通过使用置信度或依赖性标准来确定哪些标记可以安全揭示，从而解决这一矛盾。然而，避免不安全的确定并不一定使剩余的掩码序列易于解码，因为不确定的标记可能依赖于掩码标记，造成去噪步骤的瓶颈。我们提出了AXON，这是一个无需训练的模块，可以添加到现有的扩散语言模型并行解码策略之上。AXON并不是替换基础解码器，而是监控剩余的不确定掩码标记，仅在其当前状态表明需要额外上下文时进行干预。然后，它将标准从揭示哪些标记最安全转变为哪些置信的揭示可以最好地支持后续去噪。AXON使用注意力、不确定性和置信信号选择锚点，即不确定位置所关注的置信掩码标记。针对多个扩散语言模型的推理和代码生成基准的实验表明，AXON改善了现有并行解码器的质量-延迟权衡，通常在保持或提高准确性的同时减少功能评估的次数。
cs.CL / 12 / 2606.04262
Can I Take Another Dose? Evaluating LLM Decision-Making Under Temporal Uncertainty in OTC Dosing QA
我可以再吃一剂吗？在OTC药物剂量问答中评估大语言模型的决策在时间不确定性下的表现
Abstract
Large language models (LLMs) are increasingly used for everyday health questions, including whether a user can safely take another dose of an over-the-counter (OTC) medication. Yet this common safety-relevant setting remains underexplored in existing medical QA evaluations, where correct answers require tracking dose timing, computing rolling 24-hour intake, following product-label constraints, and handling incomplete medication histories. We introduce DOSEBENCH, a focused benchmark of 81 curated OTC dosing scenarios focused on adult acetaminophen and ibuprofen use, with manually annotated gold references. We evaluate four LLMs across repeated runs using metrics for decision correctness, consistency, explanation verifiability, failure types, and confidence-related signals, resulting in 1,620 model responses. Our results show that models frequently struggle with rolling-window reasoning and ambiguity-sensitive cases and that stable or confident-looking responses can still violate dosing constraints. These findings suggest that OTC dosing QA provides a narrow yet practical testbed for evaluating temporal reasoning, constraint following, and safety-relevant uncertainty handling in medical QA.
Chinese Translation
大型语言模型（LLMs）在日常健康问题中被越来越多地使用，包括用户是否可以安全地服用另一剂非处方（OTC）药物。然而，这一常见的与安全相关的情境在现有的医疗问答评估中仍然研究不足，其中正确的答案需要跟踪剂量时间、计算24小时滚动摄入量、遵循产品标签限制以及处理不完整的用药历史。我们引入了DOSEBENCH，这是一个专注于成人对乙酰氨基酚和布洛芬使用的81个精心策划的OTC剂量场景的基准，并附有人工标注的金标准参考。我们对四个LLM进行了重复运行评估，使用了决策正确性、一致性、解释可验证性、失败类型和信心相关信号等指标，共获得1,620个模型响应。我们的结果显示，模型在滚动窗口推理和模糊敏感案例中经常遇到困难，即使是外表稳定或看似自信的响应也可能违反剂量限制。这些发现表明，OTC剂量问答为评估医疗问答中的时间推理、遵循约束和与安全相关的不确定性处理提供了一个狭窄但实用的测试场。
cs.CL / 13 / 2606.04274
Long Live Fine-Tuning: Task-Specific Transformers Outperform Zero-Shot LLMs for Misinformation Response Classification on Reddit
细致调整永存：针对虚假信息反应分类的任务特定变换器在Reddit上优于零-shot大型语言模型
Abstract
As large language models (LLMs) become default tools for online information verification, an implicit assumption follows them: that scale and general capability are sufficient for nuanced classification of misinformation discourse. We test this assumption directly on 900 Reddit comments spanning three PolitiFact-verified misinformation claims (environment, health, immigration), labelled as belief (propagates the claim), fact-check (corrects it), or other. We compare nine models across three paradigms -- BART-MNLI, three Llama variants, three commercial frontier LLMs (Claude Haiku 4.5, Gemini Flash Lite 2.5, Claude Sonnet 4.6), and fine-tuned DistilBERT and RoBERTa -- under universal and topic-specific label schemas. The assumption does not hold. Fine-tuned RoBERTa reaches 0.62 macro-$F_1$ against a best zero-shot result of 0.50 (Claude Haiku 4.5), at a fraction of the per-query cost; the supervised advantage is concentrated on the belief class, the implicit, affective category every zero-shot model under-detects. Scaling does not help: Llama-3-8B matches Llama-3-70B, and Claude Sonnet 4.6 underperforms the smaller Haiku under generic labels, collapsing belief detection to 0.17 and refusing outright on a subset of comments flagged as sensitive. This is a safety-alignment artefact, not a capacity limit. Label schema and topic jointly shape zero-shot performance, with the same model varying by more than 0.13 macro-$F_1$ across topics under matched labels. In a verification context, where missing belief is the costlier error, task-specific fine-tuning remains the more reliable choice despite the proliferation of large generative models.
Chinese Translation
随着大型语言模型（LLMs）成为在线信息验证的默认工具，随之而来的是一个隐含假设：规模和一般能力足以对虚假信息话语进行细致的分类。我们直接在900条涵盖三项PolitiFact验证的虚假信息声明（环境、健康、移民）的Reddit评论上测试这一假设，这些评论被标注为信念（传播该声明）、事实检查（纠正该声明）或其他。我们比较了在通用和主题特定标签模式下的九种模型——BART-MNLI、三种Llama变体、三种商业前沿LLM（Claude Haiku 4.5、Gemini Flash Lite 2.5、Claude Sonnet 4.6）以及经过微调的DistilBERT和RoBERTa。该假设并不成立。经过微调的RoBERTa达到了0.62的宏观-$F_1$，而最佳的零-shot结果为0.50（Claude Haiku 4.5），且每次查询的成本远低于前者；这种监督优势集中在信念类上，这是每个零-shot模型都未能充分探测的隐性情感类别。规模并没有帮助：Llama-3-8B与Llama-3-70B相匹配，而Claude Sonnet 4.6在通用标签下表现不如较小的Haiku，其信念检测降至0.17，并对部分被标记为敏感的评论直接拒绝。这是一个安全对齐的产物，而非能力的限制。标签模式和主题共同影响零-shot性能，同一模型在匹配标签下不同主题之间的宏观-$F_1$数值变化超过0.13。在验证上下文中，缺乏信念是更昂贵的错误，因此尽管大型生成模型日益增多，任务特定的微调依然是更可靠的选择。
cs.CL / 14 / 2606.04286
Using Text-Based Causal Inference to Disentangle Factors Influencing Online Review Ratings
利用基于文本的因果推断来梳理影响在线评价评分的因素
Abstract
Online reviews provide valuable insights into the perceived quality of facets of a product or service. While aspect-based sentiment analysis has focused on extracting these facets from reviews, there is less work understanding the impact of each aspect on overall perception. This is particularly challenging given correlations among aspects, making it difficult to isolate the effects of each. This paper introduces a methodology based on recent advances in text-based causal analysis, specifically CausalBERT, to disentangle the effect of each factor on overall review ratings. We enhance CausalBERT with three key improvements: temperature scaling for better calibrated treatment assignment estimates; hyperparameter optimization to reduce confound overadjustment; and interpretability methods to characterize discovered confounds. In this work, we treat the textual mentions in reviews as proxies for real-world attributes. We validate our approach on real and semi-synthetic data from over 600K reviews of U.S. K-12 schools. We find that the proposed enhancements result in more reliable estimates, and that perception of school administration and performance on benchmarks are significant drivers of overall school ratings.
Chinese Translation
在线评价提供了对产品或服务各个方面感知质量的宝贵见解。虽然基于方面的情感分析专注于从评价中提取这些方面，但对每个方面对整体感知的影响的理解仍较少。这一问题尤其具有挑战性，因为各方面之间存在相关性，使得孤立每个方面的影响变得困难。本文引入了一种基于最近文本因果分析进展的方法，特别是 CausalBERT，以梳理每个因素对整体评价评分的影响。我们通过三项关键改进增强了 CausalBERT：温度缩放以获得更好校准的处理分配估计；超参数优化以减少混淆超调；以及可解释性方法以描述发现的混淆变量。在本研究中，我们将评价中的文本提及视为现实属性的代理。我们在来自超过 60 万条美国 K-12 学校评价的真实和半合成数据上验证了我们的方法。我们发现所提出的改进导致更可靠的估计，并且学校管理的感知和基准表现是整体学校评分的重要驱动因素。
cs.CL / 15 / 2606.04302
LazyAttention: Efficient Retrieval-Augmented Generation with Deferred Positional Encoding
LazyAttention：通过延迟位置编码实现高效的检索增强生成
Abstract
Key-value (KV) caching accelerates inference of large language models (LLMs) by reusing past computations for generated tokens. Its importance becomes even greater in long-context applications such as retrieval-augmented generation (RAG) and in-context learning (ICL). However, conventional KV caching embeds positional information directly into the cache, limiting its reusability. Existing solutions either restrict reuse to prefixes or require expensive memory materialization for positional re-encoding. We introduce LazyAttention, a novel attention mechanism that kernelizes deferred positional encoding to enable zero-copy, position-agnostic KV reuse. By adjusting positional encoding within attention kernels on-the-fly, LazyAttention resolves the materialization bottleneck, allowing a single physical KV copy to serve multiple logical requests at arbitrary positions. Leveraging attention kernels tailored for prefilling and decoding, our system achieves significant efficiency improvements: under skewed document distributions, it reduces time-to-first-token (TTFT) by 1.37$\times$ and increases inference throughput by 1.40$\times$ compared to the state-of-the-art Block-Attention, while maintaining comparable output quality.
Chinese Translation
键值（KV）缓存通过重用生成令牌的过去计算加速大型语言模型（LLMs）的推理。在检索增强生成（RAG）和上下文学习（ICL）等长上下文应用中，其重要性更为突出。然而，传统的 KV 缓存将位置信息直接嵌入缓存中，限制了其可重用性。现有解决方案要么限制重用到前缀，要么需要昂贵的内存材料化以进行位置重新编码。我们提出了 LazyAttention，这是一种新颖的注意力机制，通过将延迟位置编码进行核化，从而实现零拷贝、无位置依赖的 KV 重用。通过在注意力核内动态调整位置编码，LazyAttention 解决了材料化瓶颈，使得单个物理 KV 副本能够在任意位置服务于多个逻辑请求。利用专为预填充和解码量身定制的注意力核，我们的系统实现了显著的效率提升：在偏斜文档分布下，与最先进的 Block-Attention 相比，时间到第一个令牌（TTFT）减少了 1.37$ imes$，推理吞吐量提高了 1.40$ imes$，同时保持了可比的输出质量。
cs.CL / 16 / 2606.04325
Parameter-Efficient Fine-Tuning with Learnable Rank
具有可学习秩的参数高效微调
Abstract
Low-Rank Adaptation (LoRA) is a popular parameter-efficient fine-tuning (PEFT) method that restricts weight updates to low-rank adapters, introducing a fixed low-rank inductive bias by optimizing in a low-dimensional subspace. In this work, we question whether a fixed-rank constraint is the most effective inductive bias for parameter-efficient fine-tuning. We introduce *Learnable Rank LoRA (LR-LoRA)*, a PEFT method in which the adapter rank is learned during the training process. Instead of prescribing a uniform rank for all adapter layers, LR-LoRA allows the optimizer to determine the appropriate rank for each layer. Using this approach, we find substantial layer-wise variation in the learned ranks, with the attention and MLP layers in the transformer models exhibiting systematically different rank preferences. Across a range of language understanding and commonsense reasoning benchmarks, LR-LoRA achieves state-of-the-art performance in most settings and consistently outperforms strong PEFT baselines, demonstrating that a learnable rank provides a more flexible and effective inductive bias than fixed-rank adaptations.
Chinese Translation
低秩适应（Low-Rank Adaptation, LoRA）是一种流行的参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）方法，它将权重更新限制在低秩适配器上，通过在低维子空间中进行优化，引入了固定的低秩归纳偏见。在本研究中，我们质疑固定秩约束是否是参数高效微调的最有效归纳偏见。我们提出了可学习秩低秩适应（Learnable Rank LoRA, LR-LoRA），这是一种在训练过程中学习适配器秩的PEFT方法。与为所有适配器层规定统一秩的做法不同，LR-LoRA允许优化器为每一层确定适当的秩。使用这种方法，我们发现所学习的秩在层级上有显著的变异，变压器模型中的注意力层和多层感知机（MLP）层表现出系统性不同的秩偏好。在一系列语言理解和常识推理基准测试中，LR-LoRA在大多数设置下实现了最先进的性能，并始终超越强大的PEFT基准，证明可学习的秩提供了一种比固定秩适应更灵活和有效的归纳偏见。
cs.CL / 17 / 2606.04340
Noisy memory encoding explains negative polarity illusions
噪声记忆编码解释负极性幻想
Abstract
A sentence like "The authors that no critics recommended have ever received acknowledgment for a best-selling novel" is sometimes rated as acceptable even though, strictly speaking, it is ungrammatical because the negative polarity word "ever" is not licensed where it is. This behavioral effect is sometimes called a "negative polarity illusion". Here we propose that the lossy context surprisal theory of Hahn et al. (2022) -- whereby people have an imperfect encoding of complex sentences -- might explain this effect. We hypothesize that people have poor memory representation of the determiners in the main-clause and embedded-clause subjects and could entertain a determiner exchange that licenses ever. We propose that more similar determiners in those positions would trigger stronger illusion effects. Acceptability judgment tasks with six novel determiner pairs (e.g., "few" and "many", "few" and "most") support our proposal, showing, specifically, that a novel sentence, "Many authors that few critics recommended have ever received acknowledgment for a best-selling novel", triggered a much stronger illusion than the canonical one even without time pressure. These results offer further support for the suggestion that human language processing is imperfect and resource-rational: in face of working memory limitations, humans rationally reconstruct what is most likely from noisy linguistic input to facilitate downstream processing.
Chinese Translation
像"那些没有任何评论家推荐的作者从未因畅销小说而获得认可"这样的一句话，有时被评为可接受，即使严格来说它是不 grammatical 的，因为负极性词"ever"在这里并没有获得许可。这种行为效应有时被称为"负极性幻想"。在这里，我们提出Hahn等人(2022)的有损上下文惊讶理论——即人们对复杂句子的编码不够完美——可能解释了这一效应。我们假设人们对主句和嵌套句主语中的限定词的记忆表征较差，可能会考虑一种许可"ever"的限定词交换。我们认为，在这些位置中更加相似的限定词将引发更强的幻想效应。带有六对新限定词的可接受性判断任务（例如，"few"和"many"，"few"和"most"）支持我们的提议，具体表现为，一句话"许多评论家推荐的少数作者从未因畅销小说而获得认可"引发的幻想效应远远强于典型例子，即使在没有时间压力的情况下。这些结果进一步支持了这样的观点：人类语言处理是不完美的且是资源理性的：面对工作记忆的限制，人类在嘈杂的语言输入下理性地重构出最可能的内容，以促进后续处理。
cs.CL / 18 / 2606.04360
Deliberate Evolution: Agentic Reasoning for Sample-Efficient Symbolic Regression with LLMs
深思演化：基于大型语言模型的样本高效符号回归的自主推理
Abstract
Symbolic regression (SR) discovers compact mathematical expressions from data, yet recent LLM-based evolutionary methods remain sample-inefficient because they rely mainly on scalar feedback such as MSE. We identify a core limitation: existing methods conflate candidate proposal with search guidance, requiring the LLM to infer how to evolve an expression, diagnose its errors, and reuse past experience from a single score. To address this, we propose Deliberate Evolution (DE), an agentic framework that decouples symbolic generation from search control. DE guides LLM proposals with adaptive operators for search direction, analytical tools for structural diagnosis, and reflective memory for trajectory-level experience. Experiments on LLM-SRBench show that DE consistently outperforms representative LLM-based SR baselines across diverse scientific domains while using only 40% of the standard sample budget.
Chinese Translation
符号回归（Symbolic Regression, SR）从数据中发现紧凑的数学表达式，但近年来基于大型语言模型（LLMs）的演化方法仍然样本效率低下，因为它们主要依赖于均方误差（MSE）等标量反馈。我们识别出一个核心限制：现有方法将候选提议与搜索引导混为一谈，要求LLM推断如何演化一个表达式、诊断其错误，并从单一得分中重用过去的经验。为了解决这个问题，我们提出了深思演化（Deliberate Evolution, DE），这是一个将符号生成与搜索控制解耦的自主框架。DE通过适应性操作符引导LLM提议以确定搜索方向，利用分析工具进行结构诊断，并通过反思记忆实现轨迹级别的经验重用。基于LLM-SRBench的实验表明，DE在多个科学领域中始终优于代表性的基于LLM的SR基线，同时仅使用标准样本预算的40%。
cs.CL / 19 / 2606.04367
GlossAssist -- A Tool to Simplify Corpus Creation and Study the Effect of NLP Models in Low-Resource Documentation Settings
GlossAssist——一个简化语料库创建并研究 NLP 模型在低资源文档环境中效果的工具
Abstract
Interlinear glossed text (IGT) is the standard format for linguistic annotation in language documentation. Producing it manually, however, is often slow and costly. Automated glossing systems have improved substantially in recent years, but adoption among field linguists remains limited. Existing tools are designed to be evaluated rather than used, offering no interpretable path for correction or the incorporation of linguistic expertise back into model behavior. We present GlossAssist, a glossing tool built around the retrieval-based architecture of CWoMP (Contrastive Word-Morpheme Pre-training), which grounds predictions in a mutable lexicon of learned morpheme representations. In conjunction with CWoMP, our system treats each correction by an annotator as part of an active learning setting, which expands the lexicon and improves future predictions without having to retrain the model. In this paper, we present our interface and argue that this feedback loop should be treated as a design requirement for NLP tools aimed at documentary linguists.
Chinese Translation
插行注释文本（Interlinear glossed text, IGT）是语言文档中的标准语言注释格式。然而，手动生成这一格式通常既缓慢又昂贵。近年来，自动注释系统有了显著改善，但在现场语言学家中的采用仍然有限。现有工具旨在进行评估而非应用，因此未提供可解释的修正路径或将语言学专业知识纳入模型行为的方式。我们呈现了 GlossAssist，一个构建于 CWoMP（对比词素预训练）检索基础架构上的注释工具，它的预测基于可变的学习词素表示词典。我们的系统结合 CWoMP，将注释者的每一次修正视为主动学习设置的一部分，从而扩展词典并改善未来的预测，而无需重新训练模型。在本文中，我们展示了我们的界面，并认为这种反馈循环应被视为针对面向文档语言学家的 NLP 工具的设计要求。
cs.CL / 20 / 2606.04378
DLLG: Dynamic Logit-Level Gating of LLM Experts
DLLG：大语言模型专家的动态 Logit 级门控
Abstract
Leveraging multiple specialized LLMs can combine complementary strengths, but existing approaches trade adaptability for stability: routing commits prematurely, heuristic ensembling depends on fragile proxies, and parameter merging introduces interference. We propose DLLG (Dynamic Logit-Level Gating), a dynamic logit-level ensembling framework that learns token-level expert fusion from sparse response-level supervision. A lightweight gating module predicts step-wise fusion weights, linking trajectory-level correctness to generation without token-level labels or expert retraining. Across diverse reasoning and code benchmarks, DLLG consistently outperforms strong routing, heuristic ensembling, and parameter-merging baselines across model scales, highlighting learned logit-level fusion as a robust and scalable paradigm for integrating specialized experts.
Chinese Translation
利用多个专业化的大语言模型（LLMs）可以结合互补的优势，但现有方法在适应性与稳定性之间进行权衡：路由过早承诺，启发式集成依赖于脆弱的代理，而参数合并引入了干扰。我们提出了 DLLG（动态 Logit 级门控），这是一个动态的 Logit 级集成框架，从稀疏的响应级监督中学习令牌级专家融合。一个轻量级的门控模块预测逐步融合权重，将轨迹级正确性与生成联系起来，而无需令牌级标签或专家再训练。在多样的推理与代码基准测试中，DLLG 在各种模型规模上始终优于强路由、启发式集成和参数合并基线，突出了学习的 Logit 级融合作为整合专业专家的一个稳健且可扩展的范式。
cs.CL / 21 / 2606.04389
When Clients Stop Following: A Cognitive Conceptualization Diagram-driven Framework for Strategic Counseling
客户停止跟随时：基于认知概念图的战略咨询框架
Abstract
Large Language Models (LLMs) show promise in psychological counseling, yet existing benchmarks rely heavily on highly cooperative simulated clients. We observe a critical counselor-following phenomenon: these clients often rapidly shift from resistance to compliance after only a few turns, creating an illusion of therapeutic progress and inflating scores under current evaluation protocols through superficial empathy. To address this evaluation mismatch, we propose a Cognitive Behavioral Therapy (CBT)-grounded resistance-aware framework. We introduce CARS, a client simulator that explicitly models dynamic resistance via Cognitive Conceptualization Diagrams (CCDs). We present STREAMS, a dual-module framework that decouples strategic reasoning (Thinker) from response generation (Presenter) and optimizes it via reinforcement learning. We further propose EWTS-MI, an entropy-weighted metric for evaluating responsiveness under high-friction interactions. Experiments across resistant and non-resistant counseling settings validate our findings on evaluation mismatch and demonstrate the effectiveness of resistance-aware training for improving strategic robustness under challenging counseling interactions.
Chinese Translation
大型语言模型（LLMs）在心理咨询中展现出潜力，但现有基准测试过于依赖高度合作的模拟客户。我们观察到一个关键的咨询师跟随现象：这些客户往往在仅仅几轮后迅速从抵抗转向服从，造成了治疗进展的错觉，并通过表面的共情在当前评估协议下膨胀了得分。为了解决这一评估不匹配问题，我们提出了一个基于认知行为疗法（CBT）的抵抗意识框架。我们引入了CARS，一个明确通过认知概念图（CCDs）建模动态抵抗的客户模拟器。我们提出了STREAMS，一个双模块框架，将战略推理（Thinker）与响应生成（Presenter）解耦，并通过强化学习进行优化。我们进一步提出了EWTS-MI，一种用于评估高摩擦交互下响应性的熵加权指标。针对抵抗和非抵抗咨询环境的实验验证了我们关于评估不匹配的发现，并展示了抵抗意识训练在改善挑战性咨询交互中的战略鲁棒性方面的有效性。
cs.CL / 22 / 2606.04396
Read the Trace, Steer the Path: Trajectory-Aware Reinforcement Learning for Diffusion Language Models
读取踪迹，掌控路径：用于扩散语言模型的轨迹感知强化学习
Abstract
Diffusion large language models (dLLMs) generate responses by iteratively unmasking and revising many positions in parallel. This process leaves a rich denoising trace depicting which tokens become confident, which remain unstable, and when commitments form. Existing dLLM reinforcement learning methods use this signal only weakly. Flat rollouts are cheap, but assign a single outcome reward to the whole trajectory. Tree rollouts provide finer, verifiable training signals by branching partial trajectories and propagating leaf rewards upward, but are compute intensive. We ask whether the denoising trace itself can provide tree-like supervision without tree-level compute. We introduce CAPR (Cached-Amortized Path Refinement), a dLLM-RL algorithm that summarizes the denoising trace into a compact path state, uses cached trajectory states to generate cheap sibling continuations, and trains a block-level value head for local block-wise supervision. Under a block-wise unmasking schedule, CAPR records path-state and block-progress features, then redistributes the final outcome reward across blocks according to the tokens revealed in each block. This trains the value head to convert one sparse reward into block-level PPO weights. CAPR therefore recovers much of the granularity of tree search while avoiding full tree expansion, reducing rollout-generation cost to roughly 0.75x that of flat rollouts and 0.6x that of tree rollouts (under standard settings). Across 4x4 Sudoku, Countdown, GSM8K, and Math500, on dense and mixture-of-experts LLaDA backbones, CAPR sets a new state of the art for RL-tuned dLLMs at 256- and 512-token budgets. On Sudoku, it matches the strongest tree-structured baseline at less than one third of the per-step compute.
Chinese Translation
扩散大型语言模型（dLLMs）通过迭代地揭示和修正多个位置来生成响应。这一过程留下了丰富的去噪踪迹，描绘出哪些标记变得自信、哪些仍然不稳定，以及何时形成承诺。现有的dLLM强化学习方法仅弱化利用这一信号。平坦的回滚生成成本低，但给整个轨迹分配单一结果奖励。树形回滚通过分支部分轨迹并向上传播叶子奖励，提供更细致、可验证的训练信号，但计算密集。我们探讨去噪踪迹本身是否可以在无需树形计算的条件下提供树状监督。我们提出了CAPR（Cached-Amortized Path Refinement），一种dLLM-RL算法，该算法将去噪踪迹总结为紧凑的路径状态，利用缓存的轨迹状态生成廉价的兄弟延续，并为局部块级监督训练一个块级价值头。在块级去掩码调度下，CAPR记录路径状态和块进度特征，然后根据每个块中揭示的标记重新分配最终结果奖励。这训练价值头将一个稀疏奖励转换为块级PPO权重。因此，CAPR在避免完整树扩展的同时，恢复了树搜索的许多粒度，将回滚生成成本减少到平坦回滚的约0.75倍和树形回滚的约0.6倍（在标准设置下）。在4x4数独、倒计时、GSM8K和Math500任务中，基于稠密和混合专家LLaDA骨干网，CAPR在256和512标记预算下设定了RL调优dLLMs的新最优状态。在数独任务中，其计算成本不到最强树结构基线的三分之一。
cs.CL / 23 / 2606.04442
MemoryDocDataSet: A Benchmark for Joint Conversational Memory and Long Document Reasoning
MemoryDocDataSet：用于联合对话记忆和长文档推理的基准
Abstract
AI systems increasingly need to combine two demanding capabilities: navigating multi-session conversation history and performing deep reading comprehension within long documents. Yet no existing benchmark evaluates both simultaneously. We introduce MemoryDocDataSet, a synthetic benchmark of 50 micro-worlds and 1,000 QA pairs in which each instance comprises 3-5 personas, a temporal event graph spanning months of activity, 3-5 real long documents (20,000-50,000 tokens each sourced from the Caselaw Access Project), multi-session conversations grounded on those documents, and 20 question-answer pairs across five reasoning categories. The defining feature is the Hybrid source tag: questions requiring a system to first navigate conversation history to identify which document is relevant, then extract the answer from within that document. Hybrid questions account for 75.1% of the dataset. Dataset quality is characterised through a prompt-sensitivity self-consistency analysis using LLM-as-judge, yielding a median Cohen's $\kappa = 0.634$ across all 50 micro-worlds. We evaluate six baseline configurations spanning truncated context, long-context LLMs, retrieval-augmented generation (RAG), and memory systems. The best baseline (RAG-Both) achieves 0.358 overall F1 and 0.342 on Hybrid. Document-only retrieval (RAG-Doc) collapses to 0.267 on Hybrid despite achieving 0.453 on Doc-only questions, demonstrating a clear joint-retrieval gap that motivates architectures unifying conversational memory with long-document navigation. We release the dataset, generation pipeline, and all baseline implementations.
Chinese Translation
人工智能系统越来越需要结合两种复杂的能力：导航多会话的对话历史，以及在长文档中进行深度阅读理解。然而，目前尚无现有基准能够同时评估这两者。我们介绍了MemoryDocDataSet，这是一个由50个微观世界和1,000个问答对组成的合成基准，其中每个实例包含3-5个角色、一个跨越数月活动的时间事件图、3-5篇真实的长文档（每篇包含20,000-50,000个词，均来源于案例法访问项目）、基于这些文档的多会话对话，以及在五个推理类别中包含的20个问答对。其定义特征是混合源标签：问题要求系统首先导航对话历史，以识别哪个文档是相关的，然后从该文档中提取答案。混合问题占数据集的75.1%。数据集的质量通过使用LLM作为评判者的提示敏感性自一致性分析进行表征，得出的结果在所有50个微观世界中，中位数Cohen's $ ext{kappa} = 0.634$。我们评估了六种基线配置，涵盖截断上下文、长上下文LLMs、增强检索生成（RAG）和记忆系统。表现最佳的基线（RAG-Both）在整体F1得分中达到0.358，在混合问题上为0.342。尽管在仅文档问题上达到0.453，文档仅检索（RAG-Doc）在混合问题上的得分却降到了0.267，清晰地显示了联合检索的差距，这促使融合对话记忆与长文档导航的架构形成。我们发布了数据集、生成管道和所有基线实现。
cs.CL / 24 / 2606.04450
Listening to the Workforce: Measuring Construction Worker Safety Attitudes from Social Media Discourse Using LLMs
倾听劳动力：利用大型语言模型从社交媒体话语中测量建筑工人安全态度
Abstract
Worker safety attitudes are key determinants of whether protective practices are applied or bypassed on construction sites. Yet measuring them at scale has remained out of reach. Safety attitudes are multidimensional, vary across topics, and surface most candidly in workers' own conversations. This study created and validated the Construction Safety Attitude Framework (CSAF), which integrates two components: a theory-grounded structure that characterizes safety attitudes along eight dimensions, and an operational codebook for measuring them in worker naturalistic discourse. Applying CSAF to 250 posts and comments from the r/Construction community on Reddit, trained coders reached strong agreement (Krippendorff's {\alpha} = 0.85). Pairwise lift and conditional probability confirmed that the eight dimensions are related yet distinct. To apply the framework across large volumes of discourse, CSAF was operationalized through a large language model (LLM) classifier. On 450 r/Construction contributions, the classifier reproduced expert human coding (Cohen's \k{appa} = 0.90, precision = 0.98, recall = 0.98), and on 400 contributions from r/Roofing it retained that accuracy after transfer to a different trade community (\k{appa} = 0.89, precision = 0.98, recall = 0.97). A proof-of-value case study then applied the validated classifier to 10,346 contributions from r/Roofing, demonstrating that CSAF can distinguish multidimensional attitudes by safety topic, track how they shift over time, and trace the reasoning behind unfavorable ones. The study therefore provides a theoretically grounded, empirically vetted instrument for examining safety attitudes, offering a basis for targeted interventions that address the attitudes underlying unsafe practices.
Chinese Translation
工人安全态度是影响建筑工地上是否应用或规避保护措施的关键因素。然而，规模化测量这些态度一直难以实现。安全态度是多维度的，在不同话题中有所变化，并且最真实地反映在工人的自我对话中。本研究创建并验证了建筑安全态度框架（Construction Safety Attitude Framework, CSAF），该框架整合了两个组成部分：一个基于理论的结构，用于在八个维度上描述安全态度，以及一个用于在工人自然话语中测量这些态度的操作编码手册。通过对Reddit上r/Construction社区的250条帖子和评论应用CSAF，训练有素的编码员达成了一致意见（Krippendorff's α = 0.85）。成对提升和条件概率的分析确认这八个维度是相关但又独立的。为了在大量话语中应用该框架，CSAF通过大型语言模型（LLM）分类器进行了操作化。在450条r/Construction贡献中，该分类器再现了专家人类编码（Cohen's κ = 0.90，精确度 = 0.98，召回率 = 0.98），在转移至不同贸易社区的400条r/Roofing贡献中，该准确性依然保持（κ = 0.89，精确度 = 0.98，召回率 = 0.97）。随后，进行了一项价值证明案例研究，该研究将验证过的分类器应用于r/Roofing的10,346条贡献，展示CSAF能够根据安全话题区分多维度态度，跟踪态度随时间的变化，并追溯不利态度背后的原因。因此，本研究提供了一种理论基础扎实、经过实证验证的工具，用于检验安全态度，为针对性干预措施提供基础，以应对导致不安全实践的态度。
cs.CL / 25 / 2606.04454
Stepwise Reasoning Enhancement for LLMs via External Subgraph Generation
通过外部子图生成增强大型语言模型的逐步推理能力
Abstract
Large language models have shown strong performance in natural language generation and downstream reasoning tasks, but they still struggle with logical consistency, factual grounding, and interpretability in complex multi-step reasoning. To address these limitations, this paper proposes SGR, a stepwise reasoning enhancement framework that integrates large language models with external knowledge graphs through query-relevant subgraph generation. Given an input question, SGR first extracts key entities, relations, and constraints to construct a structured schema, then retrieves compact subgraphs from a knowledge graph using schema-guided querying. The generated subgraphs provide explicit relational evidence that guides the language model through step-by-step reasoning. In addition, SGR combines direct Cypher-based reasoning with collaborative reasoning integration, allowing candidate answers from multiple reasoning paths to be validated and aggregated according to both model confidence and graph consistency. Experiments on benchmark datasets including CWQ, WebQSP, GrailQA, and KQA Pro demonstrate that SGR improves reasoning accuracy and Hits@1 performance over standard prompting and several knowledge-enhanced baselines. Ablation studies further show that schema guidance and Neo4j-based retrieval are both crucial to the effectiveness of the framework. These results indicate that dynamically generated external subgraphs can improve the accuracy, robustness, and interpretability of LLM-based reasoning.
Chinese Translation
大型语言模型在自然语言生成和下游推理任务中表现出色，但在复杂的多步骤推理时，仍然面临逻辑一致性、事实依据和可解释性方面的挑战。为了解决这些局限性，本文提出了SGR（逐步推理增强框架），该框架通过与外部知识图谱的查询相关子图生成，将大型语言模型与知识图谱相结合。给定一个输入问题，SGR首先提取关键实体、关系和约束，以构建结构化模式，然后使用模式引导查询从知识图谱中检索紧凑的子图。生成的子图提供明确的关系证据，指导语言模型进行逐步推理。此外，SGR将直接基于Cypher的推理与协作推理整合相结合，允许从多个推理路径中的候选答案根据模型置信度和图谱一致性进行验证和聚合。在包含CWQ、WebQSP、GrailQA和KQA Pro等基准数据集上的实验表明，SGR相较于标准提示和多个知识增强基线，提高了推理准确性和Hits@1的表现。消融研究进一步表明，模式引导和基于Neo4j的检索对框架的有效性至关重要。这些结果表明，动态生成的外部子图可以提升基于大型语言模型的推理的准确性、鲁棒性和可解释性。
cs.CL / 26 / 2606.04465
SePO: Self-Evolving Prompt Agent for System Prompt Optimization
SePO：自我演化提示代理用于系统提示优化
Abstract
System prompt optimization improves agent behavior without modifying the underlying model, yielding human-readable, model-agnostic instructions. Existing methods build a prompt agent that refines task agents' system prompts, yet leave the prompt agent's own system prompt hand-engineered and fixed. We propose Self-Evolving Prompt Optimization (SePO), which treats the prompt agent's own system prompt as an optimization target alongside task agents' system prompts. SePO adopts a self-referential design. A single prompt agent improves both task agents' system prompts and its own under an open-ended evolutionary search that maintains an archive of candidate prompts as stepping stones. Training proceeds in two stages: pre-training evolves the prompt agent on a multi-task pool, and fine-tuning then applies it to a target task. Across five benchmarks spanning math (AIME'25), abstract reasoning (ARC-AGI-1), graduate-level science (GPQA), code generation (MBPP), and logic puzzles (Sudoku), SePO consistently outperforms Manual-CoT, TextGrad, and MetaSPO, improving the average accuracy by 4.49 points compared to Manual-CoT. The prompt optimization skill from pre-training also generalizes to tasks beyond the pre-training mixture, rather than memorizing per-task prompts.
Chinese Translation
系统提示优化在不修改基础模型的情况下改善了代理的行为，产生可读的、与模型无关的指令。现有方法构建了一个提示代理，该代理精细化任务代理的系统提示，但仍然将提示代理自身的系统提示设定为手动工程和固定不变。我们提出了自我演化提示优化（Self-Evolving Prompt Optimization, SePO），将提示代理自身的系统提示作为优化目标，与任务代理的系统提示并行优化。SePO采纳自我指涉的设计。一个单一的提示代理在一个开放式的演化搜索中同时改进任务代理的系统提示和自身的系统提示，通过维护一份候选提示的存档作为跳板。训练过程分为两个阶段：预训练阶段在多任务池上进化提示代理，然后微调阶段将其应用于目标任务。在涵盖数学（AIME'25）、抽象推理（ARC-AGI-1）、研究生级科学（GPQA）、代码生成（MBPP）和逻辑谜题（数独）的五个基准测试中，SePO始终优于Manual-CoT、TextGrad和MetaSPO，平均准确率比Manual-CoT提高了4.49个百分点。预训练中的提示优化技能还能够推广到预训练混合之外的任务，而不仅仅是记忆每个任务的提示。
cs.CL / 27 / 2606.04466
Learning What to Learn: Stage-Specific Data Sets for SFT-then-RL in Small Language Model Reasoning
学习学习的内容：针对小型语言模型推理的阶段特定数据集用于SFT-然后RL
Abstract
Post-training Small Language Models (SLMs) for reasoning typically follows an SFT-then-RL pipeline, yet existing work rarely considers what data should be learned at each stage. We argue that data strategy should be aligned with the distinct roles of SFT and RL: SFT is better suited for acquiring not-yet-mastered reasoning skills, while RL is better suited for consolidating skills that the model can already partially access. Based on this principle, we propose a difficulty-aware SFT-then-RL framework that organizes training data into stage-specific sets. For hard samples in the SFT stage, we introduce a Bridge mechanism that transforms raw teacher-generated reasoning traces into more learnable supervision for SLMs. For hard samples that remain unsolved during RL, we apply Critique Fine-Tuning by converting all-zero-reward failures into diagnostic, repair, and new reasoning trace supervision for the next SFT stage. Experiments on two SLMs across five reasoning benchmarks show that our method consistently improves over representative SFT, distillation, and RL baselines. Our results highlight the importance of coordinating data difficulty across SFT and RL for effective SLM reasoning post-training.
Chinese Translation
后训练的小型语言模型（SLMs）进行推理通常遵循SFT-然后-RL的流程，但现有研究很少考虑在每个阶段应该学习哪些数据。我们认为数据策略应与SFT和RL的不同角色相一致：SFT更适合获取尚未掌握的推理技能，而RL则更适合巩固模型已经部分掌握的技能。在这一原则基础上，我们提出了一种难度感知的SFT-然后-RL框架，将训练数据组织成阶段特定的集合。在SFT阶段的困难样本中，我们引入了一种桥接机制，将原始教师生成的推理轨迹转化为更易学习的监督信号以供SLMs使用。对于在RL阶段仍未解决的困难样本，我们应用了批评微调（Critique Fine-Tuning），通过将全零奖励的失败转换为诊断、修复和新的推理轨迹监督，支持下一阶段的SFT。在五个推理基准上的两个SLMs实验表明，我们的方法在多个代表性的SFT、蒸馏（distillation）和RL基线之上始终表现出改进。我们的结果强调了协调SFT和RL之间数据难度的重要性，以实现有效的后训练SLM推理。
cs.CL / 28 / 2606.04474
Entity Binding Failures in Speech LLM Reasoning: Diagnosis and Chain-of-Thought Intervention
语音大语言模型推理中的实体绑定失败：诊断与链式思维干预
Abstract
Speech Large Language Models (SLLMs) underperform their text counterparts on complex reasoning. We reveal that this modality gap is not a uniform cognitive deficit. Evaluating three diverse SLLMs, we show speech-to-text (S2T) matches or exceeds text-to-text (T2T) on spatial, syntactic, and factual tasks. However, on logical tasks requiring entity tracking, S2T accuracy collapses to chance. We diagnose this localized degradation as an entity binding failure: continuous speech features cause models to lose precise entity-property associations during implicit reasoning. To resolve this, we propose Entity-Aware Chain-of-Thought (EA-CoT), forcing SLLMs to explicitly enumerate entities and bind them to claims before reasoning. Strikingly, EA-CoT bridges the gap, even when spoken names are misrecognized, yielding up to a 24.4% absolute accuracy improvement. Ablations confirm these gains stem entirely from explicit semantic binding, reframing the gap as a resolvable bottleneck.
Chinese Translation
语音大语言模型（SLLMs）在复杂推理方面的表现低于文本对应模型。我们揭示这种模态差异并非一个统一的认知缺陷。通过评估三种不同的SLLMs，我们发现语音转文本（S2T）在空间、句法和事实任务上的表现与文本转文本（T2T）相匹配或超越。然而，在需要实体跟踪的逻辑任务中，S2T的准确性却崩溃至偶然水平。我们将这种局部退化诊断为实体绑定失败：连续的语音特征导致模型在隐式推理过程中失去精准的实体-属性关联。为了解决这一问题，我们提出了实体感知链式思维（EA-CoT），迫使SLLMs在推理之前明确列举实体并将其绑定到陈述上。显著的是，EA-CoT弥合了这一差距，即使在误识别的情况下也能提升最高达24.4%的绝对准确性。消融实验确认这些增益完全来源于显式的语义绑定，将差距重新框定为一个可解决的瓶颈。
cs.CL / 29 / 2606.04483
Off-Distribution Voices: Fanfiction Subgenres as Universal Vernacular Jailbreaks for Aligned LLMs
离散分发声音：同调大型语言模型的同人小说子类别作为通用方言破解方法
Abstract
Existing jailbreaks against aligned LLMs are discrete artifacts whose surface forms are easy to fingerprint and patch. We argue that the real failure mode is not any specific prompt, but an entire register of natural human writing that safety training has under-covered. Building on this insight, we introduce the first jailbreak family that uses real fanfiction subgenres as universal attack carriers: a creative-writing meta is conditioned on passages from one of twelve Archive of Our Own (AO3) subgenres, and the harmful behavior is embedded as the climax of the resulting scene. The construction requires no attacker LLM and no per-target adaptation. On eight aligned LLMs over the union of HarmBench and JailbreakBench, this attack lifts mean ASR from 0.278 to 0.731 under a four-judge ensemble; a factorial decomposition shows the gain is carried by register rather than length or structure. Two active defences widen rather than narrow the vernacular-to-baseline ratio, indicating that template-targeting defences merely steer attackers toward register-based attacks like ours. We also propose SAGA-A4, a static four-turn extension that attains mean ASR 0.924, substantially exceeding three existing multi-turn methods.
Chinese Translation
现有针对同调大型语言模型的破解方法是离散的工件，其表面形式易于指纹识别和修补。我们认为，真正的失败模式并不是任何特定的提示，而是自然人类写作的一个整体框架，而安全训练对此覆盖不足。在此基础上，我们介绍了第一个使用真实同人小说子类别作为通用攻击载体的破解家族：一种创意写作元框架以十二个《我们的收藏》（Archive of Our Own, AO3）子类别中的某个段落为条件，并将有害行为嵌入结果场景的高潮部分。这种构建不需要攻击者的语言模型（LLM）和针对每个目标的适应。在涵盖 HarmBench 和 JailbreakBench 的八个同调 LLM 上，这种攻击将平均成功率（ASR）从 0.278 提升到 0.731，基于四位评审的合议制；因子分解表明，提升主要由框架引起，而不是长度或结构。两个主动防御的结果是拓宽而非缩小方言与基准的比率，表明模板瞄准防御只是将攻击者引向像我们这样的基于框架的攻击。我们还提出了 SAGA-A4，这是一种静态的四轮扩展，平均成功率达到 0.924，远超三种现有的多轮方法。
cs.CL / 30 / 2606.04500
SANE Schema-aware Natural-language Evaluation of Biological Data
SANE：面向模式的生物数据自然语言评估
Abstract
High-throughput microscopy generates large, structured datasets capturing cellular responses to pharmacological perturbations, but accessing these datasets typically requires SQL expertise. Large language models offer a natural-language alternative, yet their tendency to hallucinate raises concerns about result reliability . We present SANE Schema-Aware Natural-language Evaluation, a novel paradigm for domain-specific text-to-SQL evaluation: schema-grounded, automatically generated benchmarks tied to real and specific experimental structure. SANE makes evaluation more scalable, systematic, and reproducible. Using SANE, we evaluate a few-shot large language model and show that, under constrained schemas with structured prompting and guardrails, accurate query generation is achievable without any model training or fine-tuning. Most failures stem from ambiguous or underspecified inputs and manifest as overly cautious clarification requests or answers to queries that should first be disambiguated, rather than incorrect SQL generation. These results indicate that few-shot large language models can provide reliable database access in well-defined domains when combined with schema-aware prompting.
Chinese Translation
高通量显微镜技术生成大量结构化数据集，捕捉细胞对药物干扰的反应，但访问这些数据集通常需要SQL专业知识。大型语言模型提供了一种自然语言替代方案，然而它们的幻觉倾向引发了对结果可靠性的担忧。我们提出了SANE（模式意识自然语言评估），这是一种针对特定领域的文本到SQL评估的新范式：基于模式的、与真实和特定实验结构相关的自动生成基准。SANE使评估更具可扩展性、系统性和可重复性。使用SANE，我们评估了一种少样本的大型语言模型，并展示在受限模式下，通过结构化提示和保障措施，可以在没有任何模型训练或微调的情况下实现准确的查询生成。大多数失败源于模糊或规定不明确的输入，表现为过于谨慎的澄清请求或应对应当首先消歧的查询的回答，而不是错误的SQL生成。这些结果表明，当与模式意识提示结合时，少样本的大型语言模型可以在明确规定的领域中提供可靠的数据库访问。
cs.CL / 31 / 2606.04507
Self-Evolving Deep Research via Joint Generation and Evaluation
通过联合生成与评估实现自我演进的深度研究
Abstract
Large Language Models (LLMs) have become increasingly adopted in daily applications, with deep research standing out as a particularly important capability. Unlike traditional question-answering (QA) tasks, deep research report generation lacks definitive ground-truth, making reward design inherently unverifiable and limiting effective reinforcement learning. Existing approaches mitigate this challenge with LLM-as-a-judge and query-dependent evaluation rubrics, but they still rely on static evaluators that cannot adapt their standards as the solver improves, leading to insufficient and eventually saturated optimization pressure. We address this limitation with a \textbf{s}elf-evolving \textbf{co}-evolutionary training framework for deep \textbf{re}search evaluation and generation (SCORE), which tightly couples an evaluator and a solver in a shared-parameter learning process. Rather than treating generation and evaluation as isolated modules, we leverage their intrinsic connection to enable joint improvement within a single shared-parameter model. To restrict this process, we introduce a meta-harness, which dynamically controls the evaluation environment based on solver performance, encouraging valid evaluation dimensions and sufficiently deep evaluator search. Extensive experiments on deep research benchmarks demonstrate consistent improvement in report generation quality, showing that co-evolving evaluation and generation is a promising direction for training open-ended research agents.
Chinese Translation
大型语言模型（LLMs）在日常应用中逐渐被广泛采用，其中深度研究作为一种特别重要的能力而脱颖而出。与传统的问答（QA）任务不同，深度研究报告生成缺乏明确的真实标签，这使得奖励设计本质上无法验证，从而限制了有效的强化学习。现有的方法通过将LLM作为评估者和依赖查询的评估标准来缓解这一挑战，但仍然依赖静态评估者，无法随着解题者的进步而调整标准，导致优化压力不足并最终饱和。我们通过自我演进的共同演化训练框架（SCORE）来解决这一局限性，该框架将评估者和解题者紧密结合在一个共享参数的学习过程中。我们不将生成和评估视为孤立的模块，而是利用它们之间的内在联系，使二者在单一共享参数模型内实现联合提升。为了限制这一过程，我们引入了一个元控制器，它根据解题者的表现动态调整评估环境，鼓励有效的评估维度和足够深入的评估者搜索。对深度研究基准的广泛实验表明，报告生成质量的一致改善，证明了共同演化评估与生成是训练开放式研究代理的一个有前途的方向。
cs.CL / 32 / 2606.04511
SparDA: Sparse Decoupled Attention for Efficient Long-Context LLM Inference
SparDA：用于高效长上下文 LLM 推理的稀疏解耦注意力
Abstract
Sparse attention reduces compute and memory bandwidth for long-context LLM inference. However, two key challenges remain: (1) KV cache capacity still grows with sequence length, and offloading to CPU memory introduces a PCIe transfer bottleneck; (2) the sparse selection step itself retains $O(T^2)$ complexity and can dominate attention cost at long contexts. We propose SparDA, a decoupled sparse attention architecture that introduces a fourth per-layer projection, the Forecast, alongside Query, Key, and Value. The Forecast predicts the KV blocks needed by the next layer, enabling lookahead selection that overlaps CPU-to-GPU prefetch with current-layer execution. Because Forecast is decoupled from the attention query, our GQA implementation uses one Forecast head per GQA group, reducing selection overhead versus the original multi-head selector. SparDA adds $<$0.5% parameters and trains only the Forecast projections by matching the original selector's attention distribution. On two sparse-pretrained 8B models, SparDA matches or slightly improves accuracy and delivers up to 1.25$\times$ prefill speedup and 1.7$\times$ decode speedup over the sparse-attention offload baseline. By enabling larger feasible batch sizes on a single GPU, SparDA further reaches up to 5.3$\times$ higher decode throughput than the non-offload sparse baseline. Our source code is available at https://github.com/NVlabs/SparDA.
Chinese Translation
稀疏注意力减少了长上下文 LLM 推理的计算和内存带宽。然而，仍然存在两个关键挑战：(1) KV 缓存容量仍随序列长度增长，且将其卸载到 CPU 内存会引入 PCIe 传输瓶颈；(2) 稀疏选择步骤本身保留 $O(T^2)$ 的复杂度，并可能在长上下文中主导注意力成本。我们提出了 SparDA，这是一种解耦的稀疏注意力架构，引入了一个第四个每层投影，即预测（Forecast），与查询（Query）、键（Key）和值（Value）并行。预测用于预测下一层所需的 KV 块，使得前瞻选择可以与当前层执行重叠的 CPU 到 GPU 预取。由于预测与注意力查询解耦，我们的 GQA 实现为每个 GQA 组使用一个预测头，减少了与原始多头选择器相比的选择开销。SparDA 仅新增 $<$0.5\% 的参数，并通过匹配原始选择器的注意力分布，仅训练预测投影。在两个稀疏预训练的 8B 模型上，SparDA 达到了与原始准确度相匹配或略有提高，并提供了高达 1.25$ imes$ 的前填充加速以及 1.7$ imes$ 的解码加速，超越稀疏注意力卸载基线。通过在单个 GPU 上支持更大的有效批量大小，SparDA 实现了比非卸载稀疏基线高出最高 5.3$ imes$ 的解码吞吐量。我们的源代码可在 https://github.com/NVlabs/SparDA 获取。
cs.CL / 33 / 2606.04525
GENEB: Why Genomic Models Are Hard to Compare
GENEB：为什么基因组模型难以比较
Abstract
Progress in genomic foundation models is difficult to assess due to fragmented benchmarks, incompatible evaluation protocols, and task-specific reporting. As a result, claims of superiority or generality across models are often not directly comparable. We introduce GENEB, a large-scale diagnostic benchmark that evaluates frozen representations from 40 genomic foundation models across 100 tasks spanning 13 functional categories under a unified probing-based protocol, including few-shot regimes. GENEB enables controlled comparison across model scale, architecture, tokenization, and pretraining data while explicitly exposing task-level trade-offs. Our analysis shows that aggregate leaderboards are unstable: model rankings vary sharply across task categories, scale provides only modest and inconsistent gains, and architectural and pretraining alignment frequently outweigh parameter count. These results highlight limitations of current evaluation practices and position GENEB as a reference framework for principled comparison and category-aware model selection in genomic machine learning.
Chinese Translation
由于基准测试的碎片化、评估协议的不兼容以及特定任务的报告，基因组基础模型的进展难以评估。因此，模型间的优越性或一般性的声明通常无法直接比较。我们提出了GENEB，一个大规模的诊断基准，它在统一的探测基础协议下，评估来自40个基因组基础模型的冻结表示，涵盖100个任务，分属于13个功能类别，包括少量样本的情境。GENEB能够在模型规模、架构、分词和预训练数据之间进行受控比较，同时明确暴露任务级别的权衡。我们的分析显示，汇总的排行榜不稳定：模型排名在任务类别间变化显著，规模仅提供适度且不一致的增益，而架构和预训练的对齐通常超过参数数量。这些结果突显了当前评估实践的局限性，并将GENEB定位为基于原则的比较和类别感知模型选择的参考框架，适用于基因组机器学习。
cs.CL / 34 / 2606.04535
Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models
用于扩散大型语言模型的格式约束生成的动态填充锚点
Abstract
Diffusion large language models (dLLMs) offer bidirectional attention and parallel generation, enabling them to exploit global context and naturally support format-constrained tasks like parseable JSON or reasoning templates. While straightforward fixed anchors can enforce such constraints, they often impose rigid spans, leading to truncated reasoning or redundant content. To overcome this, we propose Dynamic Infilling Anchors (DIA), a training-free method that dynamically estimates end-anchor positions to adjust generation length before iterative infilling. This flexible mechanism ensures structural correctness and semantic coherence, avoiding the inefficiencies of fixed-span methods. Experiments on reasoning benchmarks demonstrate that DIA substantially improves format compliance and answer accuracy, achieving significant zero-shot gains on GSM8K and MATH. These results establish DIA as a robust pathway toward reliable, structure-aware generation.
Chinese Translation
扩散大型语言模型（dLLMs）提供了双向注意力和并行生成，使它们能够充分利用全局上下文，并自然支持格式约束任务，如可解析的JSON或推理模板。虽然简单的固定锚点可以强制执行这些约束，但它们往往施加了严格的跨度，导致推理被截断或内容冗余。为了克服这一问题，我们提出了动态填充锚点（Dynamic Infilling Anchors, DIA），这是一种无需训练的方法，能够动态估计结束锚点位置，在迭代填充之前调整生成长度。这种灵活的机制确保了结构的正确性和语义的一致性，避免了固定跨度方法的低效。在推理基准测试中的实验表明，DIA大幅提高了格式合规性和答案准确性，在GSM8K和MATH上实现了显著的零样本提升。这些结果确立了DIA作为朝向可靠的、结构感知生成的有力途径。
cs.CL / 35 / 2606.04552
LDARNet: DNA Adaptive Representation Network with Learnable Tokenization for Genomic Modeling
LDARNet：具有可学习分词的 DNA 自适应表示网络用于基因组建模
Abstract
Genomic foundation models increasingly adopt large language model architectures, yet almost universally rely on fixed tokenization schemes such as $k$-mers, BPE, or single nucleotides, which impose arbitrary sequence boundaries that may obscure biologically relevant structure. We present LDARNet, a 120M-parameter hierarchical genomic foundation model that adapts H-Net-style dynamic chunking from autoregressive generation to masked language modeling, combining BiMamba-2 state-space layers with local attention, bidirectional routing, and a ratio-based regularizer to induce adaptive token boundaries without supervision. Fine-tuned on 27 tasks from the Nucleotide Transformer and Genomic Benchmarks suites, LDARNet achieves 11/18 wins among compact models ($<$300M parameters) and state-of-the-art results on 5 histone modification tasks, outperforming models up to 20$\times$ larger. A FLOPs-matched controlled experiment isolates learned routing as the source of these gains: learned boundaries beat fixed-grid boundaries by up to 14 percentage points on histone tasks at identical compute. Nucleotide-resolution analysis further shows that the learned boundaries align with canonical promoter motifs and splice junctions without supervision, providing a biological interpretation for adaptive tokenization in genomic foundation models.
Chinese Translation
基因组基础模型越来越多地采用大型语言模型架构，但几乎普遍依赖于固定的分词方案，例如 $k$-mers、BPE 或单核苷酸，这在人为地设定的序列边界上可能掩盖生物学相关结构。我们提出了 LDARNet，一种具有 120M 参数的层次化基因组基础模型，它从自回归生成中适应 H-Net 风格的动态分块到掩蔽语言建模，结合了 BiMamba-2 状态空间层、局部注意力、双向路由和基于比率的正则化器，能够在没有监督的情况下引入自适应分词边界。在 27 项来自 Nucleotide Transformer 和 Genomic Benchmarks 套件的任务上进行微调后，LDARNet 在紧凑模型（参数少于 300M）中获得了 11/18 次胜利，并在 5 项组蛋白修饰任务上实现了最先进的结果，表现超过了高达 20 倍的更大模型。一项与 FLOPs 匹配的对照实验将学习的路由孤立出来，作为这些提升的来源：学习的边界在相同计算条件下在组蛋白任务上超越固定网格边界最多 14 个百分点。此外，核苷酸分辨率分析进一步表明，学习的边界与经典启动子基序和剪接位点对齐，无需监督，为基因组基础模型中的自适应分词提供了生物学解释。
cs.CL / 36 / 2606.04555
Temporal Order Matters for Agentic Memory: Segment Trees for Long-Horizon Agents
时间顺序对代理记忆的重要性：用于长时间范围代理的段树
Abstract
Long-horizon conversational agents need to interact with users through evolving events, tasks, and goals. Such histories are naturally temporal, yet many existing memory systems organize information primarily by topical similarity and may ignore the order in which events occur. We introduce Segment Tree Memory, or SegTreeMem, a memory architecture that represents conversation history as a temporally ordered Segment Tree over utterances. SegTreeMem incrementally inserts new utterances through an online rightmost-frontier update rule, preserving chronological order while forming hierarchical memory segments. For retrieval, SegTreeMem propagates relevance scores through the tree to combine local semantic matching with hierarchical temporal context. Across three long-horizon memory benchmarks and two LLM backbones, SegTreeMem improves answer quality over flat retrieval, graph-structured memory, and tree-structured memory baselines. Additional temporal-order permutation analysis shows that the performance gain depends on preserving temporal order during memory construction, supporting the claim that temporal order is a key structure for agentic memory.
Chinese Translation
长时间范围的对话代理需要通过不断变化的事件、任务和目标与用户进行互动。这些历史记录具有自然的时间性，但许多现有的记忆系统主要通过主题相似性来组织信息，可能忽略事件发生的顺序。我们提出了段树记忆（Segment Tree Memory），简称SegTreeMem，这是一种将对话历史表示为发言的时间有序段树的记忆架构。SegTreeMem通过在线最右前沿更新规则逐步插入新的发言，既保持了时间顺序，又形成了层次记忆段。对于检索，SegTreeMem通过树结构传播相关性评分，以结合局部语义匹配与层次时间上下文。在三个长时间范围的记忆基准和两个大规模语言模型（LLM）骨干网络中，SegTreeMem在答案质量上优于平坦检索、图结构记忆和树结构记忆基线。额外的时间顺序置换分析表明，性能的提升依赖于在记忆构建过程中保持时间顺序，这支持了时间顺序是代理记忆的关键结构的观点。
cs.CL / 37 / 2606.04557
Cartridges at Scale: Training Modular KV Caches over Large Document Collections
大规模弹夹：在大型文档集合上训练模块化的键值缓存
Abstract
Large Language Models can reason over long contexts, yet prefilling millions of tokens is wasteful as much of the content remains static across queries. Cartridges address this by distilling document collections into reusable key-value (KV) caches that eliminate prefilling while preserving accuracy. A critical limitation of this approach is that cartridges are monolithic and non-compositional: encoding an entire collection into a single KV block does not scale, and naively mixing cartridges trained in isolation collapses performance to near chance. We introduce Cartridges at Scale (CAS), a training framework for scalable multi-cartridge learning with dynamic distractor mixing and a memory-efficient budget manager that rotates hundreds of per-document cartridges between GPU and persistent storage. Our approach scales to collections exceeding a million tokens, improving over a monolithic cartridge by 10-31 points at comparable token budgets. Oracle cartridge accuracy falls within 2-6 points of full in-context learning even at high compression. When paired with retrieval for cartridge selection, CAS matches or exceeds conventional RAG accuracy while consuming 3-4x fewer prompt tokens.
Chinese Translation
大型语言模型能够在长上下文中进行推理，然而，为数百万个标记进行预填充是浪费的，因为大部分内容在不同查询中保持静态。弹夹通过将文档集合提炼为可重用的键值（KV）缓存来解决这一问题，这样可以消除预填充，同时保持准确性。然而，这种方法的一个关键限制是弹夹是单一的且不可组合的：将整个集合编码为单个KV块并不具有可扩展性，并且简单地混合在孤立中训练的弹夹会使性能降至接近随机水平。我们引入了大规模弹夹（Cartridges at Scale, CAS），这是一个支持可扩展的多弹夹学习的训练框架，借助动态干扰混合和一个内存高效的预算管理器，在GPU和持久存储之间轮换数以百计的每文档弹夹。我们的方法可扩展至超过百万个标记的集合，在相似的标记预算下，比单一弹夹提高了10-31个百分点。即使在高压缩情况下，预期弹夹的准确性也在全上下文学习的基础上，仅下降2-6个百分点。当与检索结合用于弹夹选择时，CAS的准确性与传统的检索增强生成（RAG）相匹配或更佳，同时消耗的提示标记减少了3-4倍。
cs.CL / 38 / 2606.04588
VCIFBench: Evaluating Complex Instruction Following for Video Understanding
VCIFBench：评估视频理解中的复杂指令遵循
Abstract
Multimodal large language models have made rapid progress in video understanding, yet existing benchmarks largely rely on simple prompts and provide limited evidence about whether models can satisfy explicit output constraints. We introduce VCIFBench, a benchmark for evaluating complex instruction following in video understanding. VCIFBench constructs constraint-rich instructions from both benchmark-adapted and directly video-grounded prompts, covering content, format, style, and structure requirements, and evaluates model outputs with a hybrid verification pipeline. The benchmark contains 306 satisfiable test instructions, a 540-pair DPO preference dataset, and a 30-item conflict diagnostic subset. Experiments on 10 MLLMs show that joint constraint satisfaction remains challenging. We further show that DPO training on VCIFBench data can improve instruction-following performance.
Chinese Translation
多模态大型语言模型在视频理解方面取得了快速进展，但现有基准大多依赖简单提示，提供的证据有限，无法说明模型是否能够满足明确的输出约束。我们提出了VCIFBench，这是一个用于评估视频理解中复杂指令遵循的基准。VCIFBench从基准适配和直接视频基础的提示中构建了约束丰富的指令，涵盖内容、格式、风格和结构要求，并通过混合验证流水线评估模型输出。该基准包含306条可满足的测试指令、一个540对DPO偏好数据集和一个30项冲突诊断子集。在10个大型多模态语言模型的实验中，我们展示了联合约束满足仍然具有挑战性。我们进一步表明，在VCIFBench数据上进行DPO训练可以提升指令遵循性能。
cs.CL / 39 / 2606.04591
Fine-grained Fragment Retrieval in Multi-modal Long-form Dialogues
多模态长文本对话中的细粒度片段检索
Abstract
With the widespread adoption of multi-modal communication platforms, long-form dialogues interleaving text and images have become increasingly common. Users often need to retrieve coherent dialogue fragments related to specific topics, rather than isolated utterances. We propose Fine-grained Fragment Retrieval (FFR), which locates semantically relevant multi-utterance, multi-image fragments in multi-modal long-form dialogues. We explore two settings: (1) FFR within Single-Dialogue, retrieving fragments from a given dialogue; and (2) FFR within Dialogue Corpus, retrieving from a large-scale corpus for open-domain scenarios. For (1), we introduce F2RVLM, a generation-based retrieval model trained with reinforcement learning, using multi-objective rewards and difficulty-aware curriculum sampling to enhance fragment coherence. For (2), we develop FFRS, a two-stage system combining offline fragment-level indexing with online retrieval. Specifically, each dialogue is decomposed into minimal semantic fragments encoded by a Fragment Embedding Model (FEM) into a vector database; at inference, FEM rapidly recalls Top-K candidates, and F2RVLM performs fine-grained reasoning to identify the most relevant sub-content. To support FFR, we construct MLDR, the longest multi-modal dialogue retrieval dataset to date, and a WeChat-based real-world test set. Experiments on both benchmarks demonstrate that F2RVLM and FFRS consistently achieve superior performance across single-dialogue and corpus-level FFR.
Chinese Translation
随着多模态沟通平台的广泛应用，交织文本和图像的长文本对话变得越来越普遍。用户常常需要检索与特定主题相关的连贯对话片段，而不是孤立的发言。我们提出了细粒度片段检索（Fine-grained Fragment Retrieval，FFR），该方法旨在定位多模态长文本对话中的语义相关的多发言、多图像片段。我们探索了两种设置：（1）单对话中的FFR，从给定对话中检索片段；（2）对话语料库中的FFR，从大规模语料库中检索以适应开放域场景。对于（1），我们引入了F2RVLM，一个基于生成的检索模型，通过强化学习进行训练，使用多目标奖励和基于难度的课程抽样来增强片段的连贯性。对于（2），我们开发了FFRS，一个结合离线片段级索引与在线检索的两阶段系统。具体而言，每个对话被分解为最小语义片段，并由片段嵌入模型（Fragment Embedding Model，FEM）编码到向量数据库；在推理阶段，FEM迅速召回Top-K候选片段，而F2RVLM则进行细粒度推理以识别最相关的子内容。为了支持FFR，我们构建了MLDR，这是迄今为止最大的多模态对话检索数据集，以及一个基于微信的真实世界测试集。在这两个基准上的实验表明，F2RVLM和FFRS在单对话和语料库级FFR中始终表现出优越的性能。
cs.CL / 40 / 2606.04596
A Systematic Evaluation of Positional Bias in Multi-Video Summarization with MLLMs
对多视频摘要中位置偏差的系统评估：基于多模态大语言模型
Abstract
Multimodal Large Language Models (MLLMs) are increasingly used for video understanding, yet their reliability under multi-video inputs remains poorly understood. We study positional bias in multi-video summarization, where the quality of a per-video summary can change with the video's input slot even when the underlying content is unchanged. We construct a benchmark from ActivityNet and News videos, covering Cooking, Domestic, Leisure, and News settings with two- and four-video inputs. We evaluate nine open-source and proprietary MLLMs and measure position effects with three complementary metrics: Coverage, Directional Positional Bias (DPB), and Middle-Edge Gap (MEG). Our results show that positional effects are domain- and model-dependent: signed directional bias can be small even when middle positions underperform, and increasing visual or generation budget does not uniformly remove the imbalance. We further analyze prompt-level mitigation methods. Together, the results show that multi-video summarization remains sensitive to input protocol and position, motivating more robust order-invariant multimodal systems.
Chinese Translation
多模态大语言模型（MLLMs）在视频理解中的应用日益广泛，但它们在多视频输入下的可靠性仍然不够清楚。本文研究了多视频摘要中的位置偏差，即即使在底层内容不变的情况下，每个视频的摘要质量会随着视频输入位置的变化而变化。我们从ActivityNet和新闻视频构建了一个基准，涵盖了烹饪、家庭、休闲和新闻环境，使用两个和四个视频的输入。我们评估了九种开源和专有的多模态大语言模型，并通过三个互补指标测量了位置效应：覆盖率、方向性位置偏差（Directional Positional Bias, DPB）和中边差（Middle-Edge Gap, MEG）。我们的结果表明，位置效应依赖于领域和模型：有符号的方向性偏差可能较小，即使中间位置表现不佳，提高视觉或生成预算并不能均匀消除这种不平衡。我们进一步分析了提示级别的缓解方法。综合来看，这些结果表明，多视频摘要仍然对输入协议和位置敏感，这为开发更稳健的无序并变多模态系统提供了动机。
cs.CL / 41 / 2606.04612
Hybrid Adversarial Defence for Natural Language Understanding Tasks
针对自然语言理解任务的混合对抗防御
Abstract
Large Language Models (LLMs) are vulnerable both to hallucination and adversarial manipulation. Although these problems are closely related, existing defences typically address them separately. We investigate a hybrid defence framework that combines entropy-based models, designed to reduce hallucinations, with uncertainty-based models and geometric-based models, designed to reduce vulnerability. Under in-domain tests on Natural Language Understanding datasets (FEVER, HotpotQA, CSQA, SIQA) we find our hybrid model improves both clean-task performance (up to 43.34\% increase in accuracy) and adversarial robustness (up to 64.92\% improvement in accuracy and 62.27\% reduction in attack success rate). For out-of-distribution datasets (AeroEngQA, CPIQA) we see similar adversarial robustness from our hybrid model (up to 57.14\% improvement in accuracy). For prompt injection (SafeGuard) and jailbreak detection (AdvBench, DAN) datasets our hybrid model is also very strong (up to 51\% reduction in attack success rate compared to state of the art baseline models). Overall, our results show that combining entropy, uncertainty and geometric features provides a more effective defence strategy than using any single feature alone for both in-domain and out-of-distribution tasks.
Chinese Translation
大型语言模型（LLMs）易受幻觉和对抗性操控的影响。尽管这些问题密切相关，现有的防御措施通常是分别处理这两者。我们研究了一种混合防御框架，该框架结合了基于熵的模型（旨在减少幻觉）与基于不确定性和几何的模型（旨在降低脆弱性）。在自然语言理解数据集（FEVER、HotpotQA、CSQA、SIQA）上的领域内测试中，我们发现我们的混合模型显著提高了干净任务的表现（准确率提高了43.34\%）和对抗性鲁棒性（准确率提高了64.92\%，攻击成功率降低了62.27\%）。在跨分布数据集（AeroEngQA、CPIQA）上，我们的混合模型也展现出了类似的对抗性鲁棒性（准确率提高了57.14\%）。对于提示注入（SafeGuard）和越狱检测（AdvBench，DAN）数据集，我们的混合模型也表现出色（与最先进的基线模型相比，攻击成功率降低了51\%）。总体而言，我们的结果表明，结合熵、不确定性和几何特征相比单独使用任何一种特征提供了更有效的防御策略，适用于领域内和跨分布任务。
cs.CL / 42 / 2606.04628
RAMPART: Registry-based Agentic Memory with Priority-Aware Runtime Transformation
RAMPART：基于注册表的代理记忆与优先级感知运行时转换
Abstract
RAMPART is a compile-time memory model and pure in-RAM block registry for LLM-based agents. Context assembly is a programmable runtime operation where content is compiled from a structured registry under explicit policy for ordering, inclusion, and eviction. Five composable primitives (promote, gate, write, evict, rollback) act on named addressable blocks before compilation at zero prompt-token cost. Provenance tags and non-evictable authorship flags implement a permissioned memory model with block-level ownership. Controlled probes with Qwen3-8B Q4 show that compile-time placement and the structural relationship between blocks and the task query affect task success, with the cliff falling at roughly the seventh block position when the task follows the registry and the twelfth when it precedes. Grouping the critical block with content-adjacent neighbours and promoting the group as a unit lifts task success by tens of percentage points at positions where single-block placement fails. Cross-model replication on Qwen2.5-7B, Llama-3.1-8B, Mistral-7B-v0.3, and Qwen3-14B shows the content-priming effect appears at the same absolute positions across families, with magnitude varying with model strength. Block grouping raises Mistral's mean pass rate roughly fivefold at the hardest registry size, and a smaller model with the intervention can outperform a larger model without it in the mid-registry zone. Relevance gating reduces prompt cost by 67.8\% while recovering 83% of the promoted-condition success rate. Schema eviction produces 0% invocations against 100% with the schema present, a property policy-based approaches cannot guarantee by construction. Shared-registry coordination reduces inter-agent communication to a method call at zero coordination token cost.
Chinese Translation
RAMPART 是一种编译时内存模型和纯内存块注册表，专为基于大型语言模型（LLM）的代理设计。上下文组装是一种可编程的运行时操作，其内容根据明确的策略从结构化注册表中编译，以决定排序、包含和驱逐。五种可组合原语（提升、门控、写入、驱逐、回滚）在编译之前以零提示令牌成本作用于可命名的可寻址块。来源标签和不可驱逐的作者标志实现了一种具有块级所有权的权限内存模型。控制探测与Qwen3-8B Q4的研究表明，编译时放置以及块与任务查询之间的结构关系会影响任务的成功率，当任务遵循注册表时，临界点大约在第七个块的位置；而当任务位于注册表之前时，临界点则在第十二个块位置。将关键块与内容相邻的邻居进行分组，并将该组作为一个单元进行提升，在单块放置失败的位置大幅提升任务成功率。对Qwen2.5-7B、Llama-3.1-8B、Mistral-7B-v0.3和Qwen3-14B的跨模型复制显示，内容引导效果在同类模型中的绝对位置一致，且幅度随模型强度而变化。在最困难的注册表大小下，块分组将Mistral的平均通过率提高了大约五倍，且在中等注册表区域干预的小型模型可超越不进行干预的大型模型。相关门控将提示成本降低了67.8%，同时恢复了83%的提升条件成功率。模式驱逐在模式存在时产生0%的调用率，而在模式缺失时则为100%的调用率，这是基于属性的策略方法无法通过结构来保证的。共享注册表协调将代理间的通信减少到方法调用且不产生协调令牌成本。
cs.CL / 43 / 2606.04645
CYGNET: Cypher Gate for Neural Execution Triage and Cost Containment
CYGNET：用于神经执行分流和成本控制的 Cypher 门
Abstract
Language models acting as agents over knowledge graphs generate Cypher queries that fail structurally (crashing at the database) or semantically (executing but returning wrong results). We place a pre-execution gate between query generation and a production Neo4j database. The gate validates structure through a four-backend chain culminating in execution against a mirror graph at 5.6 ms median latency. Structurally broken queries are routed to a corrector that iterates structured error feedback through a language model. On seven CypherBench schemas (2348 questions, ACL 2025) the pipeline maintains generation accuracy on every model tested, confirming it operates as a safe defensive layer. The corrector achieves 81% to 95% success across five models (mean 89%). On a template-generated corpus across nine schemas the gate catches 100% of parse errors, 100% of constraint violations, and 100% of schema-reference errors in path queries with labelled endpoints, at zero false positives across 1135 queries. Property sibling-swaps where the substituted name is valid on the target label score 0%, marking the formal boundary where structural validation ends and semantic validation must begin. A planner-based cost gate flags catastrophic plan structures before execution.
Chinese Translation
作为知识图谱代理的语言模型生成的 Cypher 查询在结构上可能失败（在数据库崩溃）或语义上失败（执行但返回错误结果）。我们在查询生成与生产 Neo4j 数据库之间设置了一个预执行门。该门通过一个四后端链进行结构验证，最终以 5.6 毫秒的中位延迟对镜像图进行执行。结构性错误的查询被路由到一个校正器，该校正器通过语言模型迭代结构错误反馈。在七个 CypherBench 模式（2348个问题，ACL 2025）上，该管道在每个测试模型上都保持生成准确性，确认其作为一个安全的防御层运作。校正器在五个模型上实现了 81% 到 95%的成功率（平均 89%）。在一个跨越九个模式的模板生成语料库中，该门捕捉了 100%的解析错误、100%的约束违反和 100% 的路径查询中的模式参考错误（带有标记端点），在 1135 个查询中没有假阳性。在属性同级交换中，替换名称在目标标签上有效得分为 0%，标志着结构验证结束和语义验证开始的正式边界。基于规划的成本门在执行前标记灾难性计划结构。
cs.CL / 44 / 2606.04646
QO-Bench: Diagnosing Query-Operator-Preserving Retrieval over Typed Event Tuples
QO-Bench：对类型化事件元组进行保留查询操作的检索诊断
Abstract
Many real-world questions over business, legal, and scientific corpora are natural-language versions of database-style queries over records latent in text. Existing retrieval-augmented generation (RAG) systems are optimized primarily for semantic relevance, but retrieving plausible passages does not guarantee correct query execution. We introduce QO-Bench, a diagnostic benchmark for query-operator question answering over typed event tuples. The benchmark covers 22,984 news articles and 614 corporate events across 18 query templates, evaluated on 785 questions. Each gold answer is deterministically computed from typed event tuples and scored by recall, with answers matched to the gold tuples by exact match rather than an LLM judge. This design enables operator-level diagnosis such as joins and intersection. We evaluate RAG, ReAct RAG, GraphRAG, and information-extraction-to-SQL under matched conditions, with a long-context oracle ceiling to isolate retrieval failure. A two-axis framework -- index-time preservation versus query-time execution -- predicts where each paradigm fails, and the results bear it out: systems retrieve relevant text but discard the typed values operators need, and the deployable paradigm ranking inverts across operators, with similarity retrieval leading on filter/project and extraction-to-SQL on intersection and counting. Even given the gold evidence, a long-context oracle stays far from saturated, so operator execution -- not retrieval alone -- is a core bottleneck that a stronger answer model does not remove. QO-Bench reframes the goal from passage relevance to query-operator-preserving retrieval.
Chinese Translation
许多关于商业、法律和科学语料库的现实问题，都是自然语言版本的数据库样式查询，这些查询涉及隐藏在文本中的记录。现有的增强检索生成（RAG）系统主要针对语义相关性进行优化，但检索到的合理段落并不能保证查询的正确执行。我们引入了QO-Bench，一个用于类型化事件元组上查询操作问答的诊断基准。该基准覆盖了22,984篇新闻文章和614个企业事件，基于18个查询模板评估了785个问题。每个标准答案都是从类型化事件元组中确定无疑地计算出来的，并通过召回率进行评分，答案通过精确匹配而不是LLM评审与标准元组相匹配。这种设计能够进行操作级的诊断，例如连接和交集。我们在匹配条件下评估了RAG、ReAct RAG、GraphRAG和信息提取至SQL，并设定了一个长上下文的oracle上限，以隔离检索失败的问题。一个双轴框架——索引时保留与查询时执行——预测了每种范式失败的原因，结果证实了这一点：系统能够检索相关文本，但却丢弃了操作所需的类型化值，而可部署的范式排名在不同操作之间反转，基于相似度的检索在过滤/投影上领先，而提取至SQL在交集和计数上更具优势。即便在有标准证据的情况下，长上下文的oracle仍远未饱和，因此，操作执行——而不仅仅是检索——是一个核心瓶颈，而更强的答案模型并未消除这一点。QO-Bench将目标重新框定为保留查询操作的检索，而非仅仅是段落的相关性。
cs.CL / 45 / 2606.04660
LifeSide: Benchmarking Agents as Lifelong Digital Companions
LifeSide：将智能体作为终身数字伴侣的基准测试
Abstract
Lifelong digital companions must integrate cross-session cues, continually update their understanding of users, and adapt to shifting privacy boundaries. Existing evaluations fail to capture this, testing memory recall and short-term empathy in isolation. To bridge this gap, we introduce \benchmark, a benchmark centered on multi-session \textit{Memory-Emotion-Environment} loops. By modeling users as persistent worlds with layered profiles and event trajectories, \benchmark uses multi-agent simulation to project environmental dynamics into dialogue, preserving the critical gap between latent thoughts and observable expressions. Evaluating 2,000 personas and 111K tasks across memory tracking, user understanding, privacy control, and emotional companionship, our experiment results reveal a stark reality: even models that saturate current memory benchmarks fail to sustain accurate user understanding and true companionship over long horizons.
Chinese Translation
终身数字伴侣必须整合跨会话线索，持续更新对用户的理解，并适应不断变化的隐私边界。现有评估未能捕捉这一点，而是孤立地测试记忆回忆和短期共情。为了解决这一问题，我们提出了enchmark，一个以多会话 extit{记忆-情感-环境}循环为中心的基准测试。通过将用户建模为拥有分层档案和事件轨迹的持久世界，enchmark利用多智能体模拟将环境动态投射到对话中，保留潜在思想与可观察表达之间的关键差距。对2000个个性和111K任务在记忆跟踪、用户理解、隐私控制和情感陪伴之间进行评估，我们的实验结果揭示了一个严酷的现实：即使是饱和当前记忆基准的模型，仍无法在长时间范围内维持准确的用户理解和真正的陪伴。
cs.CL / 46 / 2606.04661
CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts
CRAFT：成本意识的提示优化与前端意识的调优
Abstract
Prompts tuned for accuracy often grow long, raising inference cost on every model call. The best accuracy-cost trade-off depends on the task and the budget, so prompt optimization is a search over the Pareto front of accuracy and prompt-token cost rather than for one prompt. The usual shortcut, collapsing the objectives into a weighted sum, fixes the trade-off weight before search and often recovers only a narrow region of the front, a failure we call scalarization collapse. We present CRAFT (Cost-aware Refinement And Front-aware Tuning), a Pareto-front prompt optimizer that treats target-LLM validation calls as the scarce resource and allocates them to candidates near the optimistic candidate front. Each round, complementary accuracy-oriented and cost-oriented generators propose edits, Pareto-gap acquisition spends the per-round validation budget, and NSGA-II retention keeps a spread-out population. Across six classification and reasoning benchmarks, CRAFT's retained fronts reach both high-accuracy and low-cost regions, while accuracy-only, cost-only, and weighted-sum baselines each concentrate in narrower regions. The accuracy-cost trade-off becomes a post-search choice, not a pre-search weight.
Chinese Translation
为了提高准确性的调整提示通常变得冗长，从而增加了每次模型调用的推断成本。最佳的准确性-成本权衡依赖于任务和预算，因此提示优化是在准确性和提示令牌成本的帕累托前沿上进行的搜索，而不是针对单一提示。习惯性的简化方法将目标合并为加权和，在搜索之前固定了权重，往往只能回收前沿的狭窄区域，这种失败我们称之为标量化崩溃。我们提出了CRAFT（成本意识的优化与前端意识的调优），这是一种帕累托前沿提示优化器，将目标大型语言模型（target-LLM）的验证调用视为稀缺资源，并将其分配给接近乐观候选前沿的候选者。在每一轮中，互补的准确性导向和成本导向生成器提出编辑，帕累托差距获取花费每轮的验证预算，NSGA-II保留则保持了分散的人群。在六个分类和推理基准测试中，CRAFT保留的前沿同时达到高准确性和低成本区域，而仅关注准确性、仅关注成本以及加权和的基线则分别集中在较狭窄的区域。准确性-成本权衡变成了搜索后的选择，而不是搜索前的权重。
cs.CL / 47 / 2606.04691
SMADE-IE: Sparse Multi-Agent Framework with Evidence-Driven Debate for Zero-Shot Information Extraction
SMADE-IE：稀疏多智能体框架与证据驱动辩论用于零样本信息提取
Abstract
Zero-shot information extraction (IE) with large language models (LLMs) has attracted increasing attention due to its flexibility in adapting to new schemas and domains without task-specific training. Existing approaches mainly rely on monolithic prompting, each-type prompting, or multi-agent debate. However, monolithic prompting often suffers from boundary and type errors, while each-type prompting and multi-agent debate introduce cross-type conflicts, redundant agent interactions, and substantial token overhead. To address these challenges, we propose SMADE-IE, a sparse and evidence-driven multi-agent framework for zero-shot IE. SMADE-IE first employs an Adaptive Mode Selector to dynamically route inputs into either a lightweight Global Extraction Mode or a Type-Centric Extraction Mode, reducing unnecessary type selection and reasoning noise. For conflicting predictions, we further introduce an Evidence-Driven Debate mechanism that structures arguments into Toulmin-style components and performs confidence aggregation through external evidence scoring and Bayesian updates. Experimental results on 9 benchmark datasets across NER, RE, and JERE tasks show that SMADE-IE consistently outperforms existing zero-shot IE baselines while also improving token efficiency through sparse agent selection and early-stopping debate.
Chinese Translation
零样本信息提取（Information Extraction, IE）结合大语言模型（Large Language Models, LLMs）因其在无需特定任务训练的情况下灵活适应新架构和领域而受到越来越多的关注。现有的方法主要依赖于单一提示、每类提示或多智能体辩论。然而，单一提示常常遭遇边界和类型错误，而每类提示和多智能体辩论则引入了跨类型冲突、冗余的智能体交互以及大量的令牌开销。为了解决这些挑战，我们提出了SMADE-IE，这是一种用于零样本信息提取的稀疏且以证据驱动的多智能体框架。SMADE-IE首先采用自适应模式选择器（Adaptive Mode Selector）动态将输入路由到轻量级的全局提取模式（Global Extraction Mode）或以类型为中心的提取模式（Type-Centric Extraction Mode），从而减少不必要的类型选择和推理噪声。针对冲突的预测，我们进一步引入了一种证据驱动的辩论机制，通过将论点结构化为图尔敏风格（Toulmin-style）组件，并通过外部证据评分和贝叶斯更新进行置信度聚合。针对命名实体识别（NER）、关系提取（RE）和联合实体关系提取（JERE）任务的9个基准数据集的实验结果显示，SMADE-IE在性能上持续优于现有的零样本信息提取基线，同时通过稀疏智能体选择和早停辩论提高了令牌效率。
cs.CL / 48 / 2606.04694
DuDi: Dual-Signal Distillation with Cross-Lingual Verbalizer
DuDi：跨语言表述的双信号蒸馏
Abstract
Small language models (SLMs) are efficient and scalable, but their multilingual capabilities degrade severely at sub-billion scales, especially for Southeast Asian (SEA) languages. We introduce DuDi, a dual-signal multilingual distillation framework that combines an online sequence-level signal with off-policy and on-policy token-level signals. DuDi further uses a cross-lingual verbalizer to refine teacher feedback and improve teacher-student transferability in multilingual settings. Experiments on SEA-HELM across multiple model families, scales, and teacher-student settings show that DuDi consistently outperforms competitive distillation baselines. Ablations and analyses confirm that sequence-level optimization, token-level supervision, and cross-lingual verbalization provide complementary and transferable learning signals for multilingual SLMs.
Chinese Translation
小型语言模型（SLMs）高效且可扩展，但在十亿规模以下，其多语言能力严重下降，尤其是在东南亚（SEA）语言方面。我们提出了DuDi，一个双信号多语言蒸馏框架，将在线序列级信号与离线和在线令牌级信号相结合。DuDi进一步利用跨语言表述器来优化教师反馈，并提高多语言环境中的教师-学生可转移性。在多个模型系列、规模及教师-学生设置下对SEA-HELM的实验表明，DuDi始终优于竞争性蒸馏基线。消融实验和分析确认序列级优化、令牌级监督和跨语言表述为多语言SLMs提供了互补且可转移的学习信号。
cs.CL / 49 / 2606.04703
Rethinking Continual Experience Internalization for Self-Evolving LLM Agents
重新思考自我演化大型语言模型代理的持续经验内化
Abstract
Experience internalization converts contextual experience from past interactions into reusable parametric capability, offering a promising path toward continual learning in large language models (LLMs). While prior work has predominantly focused on single-iteration transfer, we discover that under multi-iteration experience learning, existing methods suffer from a progressive capability collapse rather than compounding improvement. We systematically examine this failure through three vital dimensions of experience internalization: (1) Experience Granularity: We find that principle-level experience is more durable than instance-level experience, as it effectively abstracts transferable strategies away from trajectory-specific details. (2) Experience Injection Pattern: Our analysis reveals that step-wise injection significantly outperforms global injection by aligning experience with intermediate decision states, a property that is critical for long-horizon tool use. (3) Internalization Regime: We demonstrate that off-policy context-distillation on high-quality teacher trajectories provides a substantially more stable training signal than on-policy context-distillation, which is inherently limited by local corrections on student-induced flawed states. Together, these insights yield a simple yet robust recipe for stable and sustainable experience internalization, providing concrete guidance for engineering self-evolving and continually learning LLMs.
Chinese Translation
经验内化将来自过去交互的上下文经验转化为可重用的参数能力，为大型语言模型（LLMs）的持续学习提供了一个有前景的路径。然而，先前的研究主要集中在单次迭代转移上，我们发现，在多次迭代经验学习的情况下，现有方法面临着逐渐能力崩溃的问题，而不是复合效益的提升。我们通过经验内化的三个重要维度系统性地检视了这一失败： (1) 经验粒度：我们发现原则级别的经验比实例级别的经验更具耐用性，因为它有效地将可转移策略与轨迹特定细节区分开。 (2) 经验注入模式：我们的分析表明，逐步注入显著优于全局注入，因为它将经验与中间决策状态对齐，这一特性对于长时间使用工具至关重要。 (3) 内化机制：我们证明，基于高质量教师轨迹的离策略上下文蒸馏提供的训练信号显著比基于本策略的上下文蒸馏更加稳定，后者本质上受到学生所引发缺陷状态的局部修正的限制。这些见解共同提供了一个简单而强大的稳定和可持续经验内化的方案，为工程自我演化和持续学习的LLMs提供了具体指导。
cs.CL / 50 / 2606.04719
Query-based Cross-Modal Projector Bolstering Mamba Multimodal LLM
基于查询的跨模态投影器强化 Mamba 多模态 LLM
Abstract
The Transformer's quadratic complexity with input length imposes an unsustainable computational load on large language models (LLMs). In contrast, the Selective Scan Structured State-Space Model, or Mamba, addresses this computational challenge effectively. This paper explores a query-based cross-modal projector designed to bolster Mamba's efficiency for vision-language modeling by compressing visual tokens based on input through the cross-attention mechanism. This innovative projector also removes the need for manually designing the 2D scan order of original image features when converting them into an input sequence for Mamba LLM. Experimental results across various vision-language understanding benchmarks show that the proposed cross-modal projector enhances Mamba-based multimodal LLMs, boosting both performance and throughput.
Chinese Translation
变换器的二次复杂度随着输入长度的增加，对大型语言模型（LLMs）施加了不可持续的计算负担。与此相比，选择性扫描结构状态空间模型（Selective Scan Structured State-Space Model，简称 Mamba）有效应对了这一计算挑战。本文探讨了一种基于查询的跨模态投影器，旨在通过基于输入压缩视觉标记，提升 Mamba 在视觉-语言建模中的效率。该创新投影器还消除了在将原始图像特征转换为 Mamba LLM 的输入序列时手动设计二维扫描顺序的需求。在各种视觉-语言理解基准测试中的实验结果表明，所提出的跨模态投影器增强了基于 Mamba 的多模态 LLM，提升了性能和吞吐量。
cs.CL / 51 / 2606.04730
Multilingual Long-Form Speech Instruction Following: KIT's Submission to IWSLT 2026
多语言长篇语音指令跟随：KIT提交至IWSLT 2026
Abstract
With the advent of Large Language Models, single-task and token-based multi-task models have evolved into instruction-based systems that infer task and target language implicitly from natural language prompts. This trend is reflected in IWSLT's Instruction Following Track, which this year introduced new tasks including an unknown surprise task, posing a genuine challenge against overfitting to known tasks. We present KIT's submission to the Long and Short Instruction Following tracks in the unconstrained setting. Our approach combines a general data augmentation pipeline that converts short-form corpora into long-form training data through segment concatenation, LLM-based label generation, and cross-lingual translation, yielding over 1M instances across six tasks and four languages. We further show that likelihood-based re-ranking, while highly effective for ASR, systematically degrades semantic tasks by spuriously selecting candidates generated from segmented audio processing rather than holistic long-form inference, a failure mode resolved by combining likelihood with Minimum Bayes Risk decoding.
Chinese Translation
随着大模型的出现，单任务和基于标记的多任务模型已演变为基于指令的系统，这些系统从自然语言提示中隐含推断任务和目标语言。今年IWSLT的指令跟随赛道反映了这一趋势，该赛道引入了包括未知惊喜任务在内的新任务，真正对已知任务的过拟合提出了挑战。我们在无约束环境中展示了KIT对长短指令跟随赛道的提交。我们的方法结合了一个通用的数据增强管道，通过段落拼接、基于LLM的标签生成和跨语言翻译，将短语料库转换为长篇训练数据，从而在六个任务和四种语言中产生超过100万个实例。此外，我们进一步表明，基于似然的再排序对于自动语音识别（ASR）的有效性虽然很高，但通过从分段音频处理中虚假选择候选者而系统性地削弱了语义任务，这一失败模式通过将似然与最小贝叶斯风险解码相结合得以解决。
cs.CL / 52 / 2606.04743
TIDE: Proactive Multi-Problem Discovery via Template-Guided Iteration
TIDE：通过模板引导迭代主动发现多重问题
Abstract
Agents are widely deployed as assistants over documents, tools, and code. However, they typically act only on explicit user requests, which surface only the problems the user has noticed, while many other important problems coexist, hidden in plain sight, within the broader user context, with their total number unknown in advance. We frame this as the task of discovering multiple hidden problems from context, in which coexisting problems should be uncovered, grounded in supporting evidence, and paired with concrete actions. To this end, we introduce TIDE, a template-guided iterative framework with two complementary mechanisms. Specifically, motivated by the observation that single-pass prediction anchors on the most salient cases and yields generic claims, we propose iterative discovery, which surfaces a small batch of candidates per round while conditioning on what has already been found, so subsequent rounds extend coverage; and thought templates, reusable schemas distilled from previously solved cases that specify what contextual signals to attend to and how to connect them, anchoring each prediction in a recognizable problem class. We validate TIDE on two realistic settings, personal workspaces and software repositories, across four model backbones, showing substantial gains over single-shot and parallel multi-agent baselines on task coverage, identification, and resolution.
Chinese Translation
智能体被广泛部署在文档、工具和代码中作为助手。然而，它们通常仅在用户明确请求时行动，仅暴露出用户注意到的问题，而许多其他重要问题则隐藏在广泛的用户上下文中，其总数事先未知。我们将此视为从上下文中发现多个隐藏问题的任务，其中共存问题应被揭示、基于支持证据并配对具体行动。为此，我们提出了TIDE，一个具有两个互补机制的模板引导迭代框架。具体而言，受观察到的单次预测依赖于最显著案例并产生一般性声明的启发，我们提出了迭代发现，在每轮中浮现一小批候选项，同时依据已经找到的结果进行条件限制，以便后续轮次扩大覆盖范围；以及思维模板，从之前解决的案例中提炼出的可重用模式，指定关注哪些上下文信号以及如何将其连接，从而将每个预测锚定在一个可识别的问题类别中。我们在两个实际场景中验证了TIDE，即个人工作空间和软件库，涉及四个模型骨干，显示出在任务覆盖、识别和解决方面，TIDE相比于单次及并行多智能体基线具有显著提升。
cs.CL / 53 / 2606.04780
PersonaTree: Structured Lifecycle Memory for Person Understanding in LLM Agents
PersonaTree: LLM代理中的结构化生命周期记忆以实现人物理解
Abstract
Persistent LLM agents require memory representations that make the formation of person understanding explicit across long term interaction. Existing agent memory methods emphasize information retention and retrieval, yet give limited account of how accumulated interaction evidence is abstracted into person understanding. We view this process as schema formation, where situated evidence is abstracted into reusable patterns and stable person level claims. We introduce PersonaTree, a structured lifecycle memory framework that realizes this view as a three level persona tree with explicit support paths from evidence to claims. PersonaTree maintains the tree through conservative writing, confidence guided consolidation, and query conditioned path retrieval, returning only the evidence depth required by each query. Across six person understanding and persistent memory benchmarks with three answer backbones, PersonaTree ranks first in 12 of 18 compact scores and reaches the top two in 16 settings. Ablations show that hierarchy improves abstract person understanding on KnowMe, while support path retrieval improves RealPref alignment under a comparable context budget.
Chinese Translation
持久的LLM代理需要记忆表示，以便在长期互动中明确形成人物理解。现有的代理记忆方法强调信息的保留和检索，但对如何将累积的互动证据抽象为人物理解的方式给出了有限的解释。我们将这个过程视为模式形成，其中具体证据被抽象成可重用的模式和稳定的人物层面主张。我们提出了PersonaTree，一个结构化生命周期记忆框架，它将这一观点实现为一个具有显式支持路径的三层人物树，从证据到主张。PersonaTree通过保守写入、基于置信度的整合和条件查询路径检索来维护这棵树，只返回每个查询所需的证据深度。在六个人物理解和持久记忆基准测试中，结合三种答案骨干，PersonaTree在18个紧凑分数中排名前12，并在16个设置中达到前两名。消融实验表明，在KnowMe上，层级结构改善了抽象人物理解，而支持路径检索在可比的上下文预算下改善了RealPref的对齐。
cs.CL / 54 / 2606.04828
A French Corpus Annotated for Multiword Expressions with Adverbial Function
一种注释有副词功能的法语多词表达语料库
Abstract
This paper presents a French corpus annotated for multiword expressions (MWEs) with adverbial function. This corpus is designed for investigation on information retrieval and extraction, as well as on deep and shallow syntactic parsing. We delimit which kind of MWEs we annotated, we describe the resources and methods we used for the annotation, and we briefly comment the results. The annotated corpus is available at http://infolingu.univ-mlv.fr/ under the LGPLLR license.
Chinese Translation
本论文介绍了一种注释有副词功能的法语多词表达（MWE）语料库。该语料库旨在用于信息检索和提取的研究，以及深度与浅层句法分析。我们定义了注释的多词表达的类型，描述了用于注释的资源和方法，并简要讨论了结果。该注释语料库可在 http://infolingu.univ-mlv.fr/ 上按照 LGPLLR 许可协议获取。
cs.CL / 55 / 2606.04846
Large Language Models in K-12 Education: Alignment with State Curriculum Standards and Student Personas
K-12 教育中的大型语言模型：与州课程标准和学生人格的对齐
Abstract
As Large Language Models (LLMs) become increasingly popular in educational settings, they raise important questions about the ethical implications of their use. Publicly available online chatbots are quickly improving in capability and accuracy leading to more widespread use, including among students looking for help with their homework. This makes it crucial to consider whether these models are aligned with educational standards. Because curriculum standards in the United States are set at the state level, they differ significantly in required content, emphasis, and narrative focus. In this work, we develop an LLM-based pipeline to identify variations in U.S. History curricula across states and evaluate the extent to which different LLMs reflect these state-specific curricular differences. In addition, we conduct controlled experiments that vary user personas by stating user attributes such as geographic location, grade level, gender and race to evaluate the sensitivity of LLM responses to user characteristics. We find that while models are able to adjust their presentation of historical topics, these shifts may come from the perceived political leanings of states and do not necessarily reflect actual curriculum content. Additionally, models successfully adapt to a student's grade level while showing minimal sensitivity to race or gender, suggesting they are capable of useful adaptation to student personas with limited demographic bias. Together, these findings highlight potential risks that open access to LLM chatbots may cause to student learning outcomes stemming from misalignment with state curriculum standards and highlight the need for more robust alignment techniques.
Chinese Translation
随着大型语言模型（LLMs）在教育环境中日益受到欢迎，它们的使用引发了关于伦理影响的重要问题。公开可用的在线聊天机器人能力和准确性迅速提高，导致其被更广泛地使用，包括学生在寻求作业帮助时的使用。考虑这些模型是否与教育标准对齐变得至关重要。由于美国的课程标准是在州一级制定的，因此在所需内容、重点和叙述焦点上存在显著差异。在本研究中，我们开发了一种基于LLM的管道，旨在识别美国历史课程在各州之间的差异，并评估不同LLMs在多大程度上反映这些州特定的课程差异。此外，我们进行控制实验，通过表述用户属性如地理位置、年级、性别和种族来变化用户的人格，以评估LLM响应对用户特征的敏感性。我们发现，尽管模型能够调整历史主题的呈现方式，但这些变化可能源于对各州政治倾向的感知，并不一定反映实际的课程内容。此外，模型成功地适应学生的年级水平，同时对种族或性别的敏感性较低，表明它们能够在有限的人口统计偏见下，对学生人格进行有用的适应。这些发现共同突显了开放访问LLM聊天机器人可能对学生学习成果造成的潜在风险，这种风险源于与州课程标准的不对齐，并强调了需要更强有力的对齐技术。
cs.CL / 56 / 2606.04874
Agent Planning Benchmark: A Diagnostic Framework for Planning Capabilities in LLM Agents
智能体规划基准：智能体规划能力的诊断框架
Abstract
Planning is central to LLM agents: before acting, an agent must decompose goals, select tools, reason over constraints, and decide when a task is infeasible. Yet existing agent evaluations often report only end-to-end success, making it difficult to determine whether failures stem from planning or execution. We introduce \textbf{Agent Planning Benchmark (APB)}, a planning-specific diagnostic benchmark with 4,209 multimodal cases across 22 domains and five settings, covering holistic planning, feedback-conditioned step-wise planning, and robustness under extraneous tools, broken tools, and unsolvable tasks. Across 12 MLLMs, APB reveals systematic weaknesses in long-horizon planning, tool-noise robustness, calibrated refusal, and inference-time refinement. We further validate APB on 200 ToolSandbox tasks and 200 $\tau^2$-bench tasks, where APB-guided refinement consistently improves plan correctness, plan grade, and downstream execution metrics across three representative models. APB thus serves as an upstream diagnostic complement to execution benchmarks.
Chinese Translation
规划是大型语言模型（LLM）智能体的核心：在行动之前，智能体必须分解目标、选择工具、推理约束，并决定何时任务不可行。然而，现有的智能体评估通常仅报告端到端的成功，难以确定失败是源于规划还是执行。我们引入了 extbf{智能体规划基准（Agent Planning Benchmark, APB）}，这是一个专门用于规划的诊断基准，涵盖来自22个领域和5种设置的4209个多模态案例，涉及整体规划、基于反馈的逐步规划，以及在外部工具、损坏工具和不可解任务下的鲁棒性。在12个多模态大型语言模型（MLLMs）上，APB揭示了长期规划、工具噪声鲁棒性、校准拒绝和推理时间细化方面的系统性弱点。我们进一步在200个ToolSandbox任务和200个$ au^2$-bench任务上验证APB，其中基于APB的细化一致地提高了三种代表性模型的计划正确性、计划评分和下游执行指标。因此，APB作为执行基准的上游诊断补充。
cs.CL / 57 / 2606.04883
Optimizing the Cost-Quality Tradeoff of Agentic Theorem Provers in Lean
在 Lean 中优化代理定理证明器的成本-质量权衡
Abstract
Large language models (LLMs) are increasingly used in workflows for generating formal proofs in Lean. These workflows often decompose problems into smaller lemmas, sample many proof attempts, and use compiler feedback to guide search. However, they can be prohibitively expensive, often spending substantial compute on attempts that ultimately fail. In this work, we address this problem with an action routing agent that consists of a data plane and a control plane. The data plane generates natural-language lemma decompositions, formalizes them in Lean, and samples proof attempts for the resulting theorem and lemma targets. The control plane observes previous failed Lean attempts, estimates both the likelihood of success and cost of another attempt, and decides whether to continue proving the current target or restart from a new breakdown. On a subset of PutnamBench, our agent decreases the cost by $25.8\%$ over a fixed-step baseline on average, preserving performance while using substantially less compute. These results suggest that failed Lean trajectories provide actionable signals for cost-aware resource allocation in agentic theorem proving.
Chinese Translation
大型语言模型（LLMs）在 Lean 中生成正式证明的工作流程中越来越多地被使用。这些工作流程常常将问题分解为更小的引理，采样多个证明尝试，并利用编译器反馈来引导搜索。然而，它们的成本可能过于高昂，往往在最终失败的尝试上花费大量计算资源。在本研究中，我们提出解决这一问题的行动路由代理，该代理由数据平面和控制平面组成。数据平面生成自然语言的引理分解，使用 Lean 对其进行形式化，并对生成的定理和引理目标进行证明尝试的采样。控制平面观察先前失败的 Lean 尝试，评估另一个尝试的成功概率和成本，并决定是继续证明当前目标还是从新的分解重新开始。在 PutnamBench 的一个子集上，我们的代理在平均上将成本降低了 25.8 ext{%}，相较于固定步长基线，保持了性能的同时显著减少了计算资源。这些结果表明，失败的 Lean 轨迹为成本意识的资源分配提供了可操作的信号，用于代理定理证明。
cs.CL / 58 / 2606.04889
GRAIL: Gradient-Reweighted Advantages for Reinforcement Learning with Verifiable Rewards
GRAIL：用于具有可验证奖励的强化学习的梯度重加权优势
Abstract
Reinforcement learning with verifiable rewards (e.g. GRPO) is now a common way to improve mathematical reasoning in Large Language Models (LLMs). However, current methods usually broadcast one sequence-level advantage to all tokens, or use costly process reward models (PRMs) for step-level supervision. Uniform advantage distribution assumes that all tokens contribute equally to the final reward. This dilutes the gradient signal, since flawed reasoning steps and filler words are updated as strongly as valid logical inferences. To address this, we introduce Gradient-Reweighted Advantage (GRAIL), an intrinsic token-wise advantage reweighting method. GRAIL uses gradient-activation saliency to place more weight on tokens that are more locally sensitive to the final answer. Evaluations across five models from the Qwen3, R1-distilled and OctoThinker families show that GRAIL consistently outperforms GRPO. GRAIL achieved an average improvement of 3.60% in accuracy and 3.05% in Pass@3, demonstrating that fine-grained reasoning alignment can be achieved without process-level supervision.
Chinese Translation
具有可验证奖励的强化学习（例如 GRPO）现在已成为提高大型语言模型（LLMs）数学推理的一种常见方法。然而，现有方法通常将一个序列级的优势广播到所有标记，或者使用成本高昂的过程奖励模型（PRMs）进行步级监督。均匀优势分配假设所有标记对最终奖励的贡献相等。这稀释了梯度信号，因为错误的推理步骤和填充词与有效的逻辑推理一样强烈地更新。为了解决这个问题，我们引入了梯度重加权优势（GRAIL），一种内在的按标记重加权优势的方法。GRAIL 使用梯度激活显著性赋予对最终答案更局部敏感的标记更大的权重。在 Qwen3、R1-精简版和 OctoThinker 家族的五个模型上进行的评估表明，GRAIL 始终优于 GRPO。GRAIL 在准确性上平均提高了 3.60%，在 Pass@3 上提高了 3.05%，表明在没有过程级监督的情况下，可以实现精细化的推理对齐。
cs.CL / 59 / 2606.04906
'Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic Assumptions
‘你的AI文本不是我的’: 在现实假设下重新定义和评估AI生成文本检测
Abstract
Although it is generally agreed that AI-generated text poses a broad societal risk, there is no common understanding in the AI-generated text detection literature on what constitutes harmful use. Rather, existing datasets and approaches often define their own criteria and make their own assumptions, sometimes implicitly, and often only loosely related to real-world needs and applications. To address this gap, we here systematically define various notions of AI-generated text and their characteristics. To study these, we collect AITDNA - a new benchmark of human-machine co-constructed texts that is annotated with detailed genesis information, such as the entire edit and AI-interaction history. We benchmark various machine-generated text detectors and find that they often only perform well for specific notions but not as broad detectors. We release code and data publicly.
Chinese Translation
尽管普遍认为AI生成文本构成广泛的社会风险，但在AI生成文本检测的文献中，关于什么构成有害使用并没有达成共识。现有的数据集和方法往往定义自己的标准并作出自己的假设，这些假设有时隐含，且通常与现实世界的需求和应用关系不大。为了填补这一空白，我们在这里系统地定义了AI生成文本的不同概念及其特征。为了研究这些概念，我们收集了AITDNA——一个新的人工-机器共构文本基准，附有详细的生成信息注释，如完整的编辑和AI交互历史。我们对各种机器生成文本检测器进行了基准测试，发现它们通常只在特定概念下表现良好，而不是作为广泛的检测器。我们公开发布代码和数据。
cs.CL / 60 / 2606.04915
Caliper: Probing Lexical Anchors versus Causal Structure in LLMs
Caliper：探究大语言模型中的词汇锚与因果结构
Abstract
Large language models reach 50 to 70% accuracy on causal reasoning benchmarks such as CLadder, but it is unclear whether this reflects structural reasoning or lexical pattern matching. We introduce Caliper, a controlled perturbation that replaces semantic variable names with placeholder tokens while preserving the causal graph and probabilistic specification of each question. Across nine instruction-tuned LLMs from 3.8B to 671B and three causal reasoning benchmarks, lexical anonymization yields robust accuracy drops of +7.6, +27.0, and +11.1 pp on a local 3.8B-14B set, rising to +29.6 and +18.0 pp on CRASS and e-CARE across nine frontier models spanning the 2024-2026 generations. Of 40 engaged model-by-benchmark cells, 39 show a positive gap, and the gap collapses by 17x on CLadder's pseudoword subset. Structured scaffolding and few-shot in-context learning each narrow the gap, but mainly by lowering P0 accuracy on smaller models rather than recovering P1. Current instruction-tuned LLMs, evaluated zero-shot, show little evidence of structural causal reasoning once lexical anchors are removed.
Chinese Translation
大型语言模型在因果推理基准（如 CLadder）上达到 50% 到 70% 的准确率，但尚不清楚这是否反映了结构推理或词汇模式匹配。我们引入了 Caliper，这是一种控制性干扰，通过用占位符令牌替换语义变量名，同时保留每个问题的因果图和概率规范。在从 3.8B 到 671B 的九个指令调优的大语言模型以及三个因果推理基准中，词汇匿名化在局部 3.8B-14B 集中产生了 +7.6、+27.0 和 +11.1 个百分点的显著准确率下降，在 CRASS 和 e-CARE 中在九个前沿模型上上升至 +29.6 和 +18.0 个百分点。在 40 个涉及模型与基准单元中，39 个显示出正差距，而在 CLadder 的伪单词子集上，差距减少了 17 倍。结构性支架和少量示例的上下文学习在一定程度上缩小了差距，但主要是通过降低较小模型的 P0 准确率而非恢复 P1。目前的指令调优大语言模型在零-shot 评估下，在移除词汇锚后几乎没有显示出结构性因果推理的证据。
cs.CL / 61 / 2606.04924
Can Crowdsourcing Survive the LLM Era? A Community Survey on Human Data Collection
众包能够在大语言模型时代生存吗？关于人类数据收集的社区调查
Abstract
The widespread use of Large Language Models (LLMs) as writing tools challenges the validity of crowdsourced data, as crowdworkers may outsource tasks to models. To better understand how this is addressed, we surveyed 155 researchers in NLP and related disciplines about their experiences and opinions on collecting free-text responses via crowdsourcing. This paper provides an overview of practitioners' challenges, mitigation strategies, and the foreseen implications on data quality. 44% of respondents reported observing LLM usage in their crowdsourced data. While 93% of them had anticipated this, half were unsure what precautions to take. The most prevalent detection strategies are distinctive textual style patterns and unusually fast completion times. Overall, survey responses show that the research community is aware of the problem and taking measures, but existing efforts remain insufficient to fully address it. Finally, we derive a set of considerations to guide future crowdsourced free-text data collection in the era of LLMs.
Chinese Translation
大型语言模型（LLMs）作为写作工具的广泛使用，挑战了众包数据的有效性，因为众包工作者可能会将任务外包给模型。为更好地理解这一问题，我们对155名从事自然语言处理（NLP）及相关学科的研究者进行了调查，收集他们在通过众包收集自由文本回应方面的经验和看法。本文提供了从业者面临的挑战、缓解策略以及对数据质量的预期影响的概述。44%的受访者报告在他们的众包数据中观察到LLM的使用。尽管93%的人对此有所预见，但一半的人不确定应采取何种预防措施。最普遍的检测策略是独特的文本风格模式和异常迅速的完成时间。总体而言，调查结果显示研究社区意识到这一问题并采取措施，但现有努力仍不足以完全应对挑战。最后，我们提出了一系列考虑因素，以指导在大语言模型时代未来的众包自由文本数据收集。
cs.CL / 62 / 2606.04964
SemBlock: Semantic Boundary Dynamic Blocks for Diffusion LLMs
SemBlock：用于扩散语言模型的语义边界动态块
Abstract
Diffusion language models (DLMs) generate text through iterative denoising, and blockwise decoding improves their practicality by committing tokens in local blocks. However, existing blockwise methods typically rely on fixed block sizes or delimiter-based runtime signals, which do not necessarily align with semantic boundaries. In this paper, we propose SemBlock, a semantic-boundary-driven dynamic block decoding framework for diffusion LLMs. SemBlock formulates dynamic block construction as semantic boundary prediction and trains lightweight predictors on frozen LLaDA hidden states. To provide supervision, we construct SemBound, a semantic-boundary dataset that derives boundary labels from discourse units, reasoning steps, and implementation spans across natural language, math, and code tasks. During inference, SemBlock uses predicted boundary probabilities to select the ending position of each dynamic block. Experiments on GSM8K, IFEval, MATH, and HumanEval show that SemBlock consistently improves over fixed-block decoding and AdaBlock. Our code is publicly available: https://github.com/TH-AI-Lab-PKU/SemBlock.
Chinese Translation
扩散语言模型（DLMs）通过迭代去噪生成文本，而分块解码通过在局部块中固定令牌提高了其实用性。然而，现有的分块方法通常依赖于固定的块大小或基于分隔符的运行时信号，这些方法并不一定与语义边界对齐。本文提出了SemBlock，一个针对扩散语言模型的语义边界驱动的动态块解码框架。SemBlock将动态块构建表达为语义边界预测，并在冻结的LLaDA隐状态上训练轻量级预测器。为了提供监督，我们构建了SemBound，一个语义边界数据集，该数据集从话语单元、推理步骤和自然语言、数学及代码任务的实现范围中获取边界标签。在推理过程中，SemBlock利用预测的边界概率选择每个动态块的结束位置。在GSM8K、IFEval、MATH和HumanEval上的实验表明，SemBlock在固定块解码和AdaBlock上始终表现出更好的改进。我们的代码已公开提供： https://github.com/TH-AI-Lab-PKU/SemBlock。
cs.CL / 63 / 2606.04974
SAID: Accelerating Diffusion-Based Language Models via Scaffold-Aware Iterative Decoding
SAID：通过支架感知的迭代解码加速基于扩散的语言模型
Abstract
Diffusion large language models (DLLMs) enable non-autoregressive generation by iteratively denoising corrupted token sequences with bidirectional context. Despite their ability to update multiple positions in parallel, inference remains costly due to the many denoising steps required for high-quality generation. We propose SAID, a Scaffold-Aware Iterative Decoding framework that accelerates DLLMs by reallocating computation across tokens. SAID first spends denoising computation on scaffold tokens to establish the coarse semantic structure, and then completes predictable detail tokens with fewer steps. We further adapt SAID to block-wise diffusion decoding and introduce Confidence-Hierarchical Layered Generation (CHLG), which assigns additional steps only to low-confidence tokens. Experiments on LLaDA-8B and LLaDA 1.5 across math, coding, and knowledge benchmarks show that SAID significantly accelerates DLLM inference with a maximum speedup of 9.1x while maintaining competitive performance. Our code is publicly available: https://github.com/TH-AI-Lab-PKU/SAID.
Chinese Translation
扩散大型语言模型（DLLMs）通过在双向上下文中迭代去噪损坏的标记序列，实现了非自回归生成。尽管其能够并行更新多个位置，但由于高质量生成所需的众多去噪步骤，推理过程仍然成本高昂。我们提出了SAID，一个支架感知的迭代解码框架，通过在标记之间重新分配计算来加速DLLMs。SAID首先将去噪计算用于支架标记，以建立粗略的语义结构，然后以更少的步骤完成可预测的细节标记。我们进一步将SAID适应于块级扩散解码，并引入信心层次生成（Confidence-Hierarchical Layered Generation，CHLG），该方法仅对低信心标记分配额外的步骤。在LLaDA-8B和LLaDA 1.5上进行的数学、编码和知识基准实验表明，SAID在保持竞争性能的同时，显著加速了DLLM推理，最大加速比达到9.1倍。我们的代码已公开发布：https://github.com/TH-AI-Lab-PKU/SAID。
cs.CL / 64 / 2606.04978
Probing Outcome-Level Resemblance and Mechanism-Level Alignment in LLM Risk Decisions: Evidence from the St. Petersburg Game
探讨大型语言模型风险决策中的结果层相似性与机制层协调性：来自圣彼得堡博弈的证据
Abstract
LLMs can appear cautious in risk decision-making tasks, yet cautious-looking outputs do not necessarily indicate alignment with human decision-making mechanisms. We investigate this distinction using the St. Petersburg game as a controlled testbed, a classical paradox in which the expected payoff is infinite, yet humans typically report low, finite willingness to pay. We evaluate 28 LLMs with a structured prompt suite that includes the original game; controlled decision variants that perturb truncation, repeated play, numeric endowment, and occupational identity; a human-perspective prompt that asks models to reason as human decision makers; and paired comparisons between base models and their instruction-tuned counterparts. In the original game, most models generate finite bids, creating the appearance of human-like risk behavior. However, this outcome-level resemblance masks substantial mechanism-level differences. The controlled variants reveal that rather than maintaining human-like behavior seen in the original game, models often shift to conditionally and computationally rational behavior. Human-cue prompting and instruction tuning often lower bids and reduce some visible pathologies, but most mechanism-level response patterns remain largely unchanged. These findings show that behavioral alignment in risk decision-making can be surface-level: LLMs may produce human-like risk decisions without exhibiting human-consistent mechanisms. High-stakes evaluations of LLM decision-making should therefore move beyond outcome similarity and examine whether the alignment is supported by mechanism-level consistency.
Chinese Translation
大型语言模型（LLMs）在风险决策任务中可能显得谨慎，但谨慎的输出并不一定表明与人类决策机制的一致性。我们使用圣彼得堡博弈作为一个受控测试平台来研究这种区别，这是一种经典悖论，其中预期收益是无限的，但人类通常报告的支付意愿是低且有限的。我们评估了28个大型语言模型，使用包含原始博弈的结构化提示集，其中包括对截断、重复游戏、数字财富和职业身份的控制决策变体；一个人类视角的提示，要求模型像人类决策者一样推理；以及基础模型与其指令调优版本之间的成对比较。在原始博弈中，大多数模型生成有限的出价，营造出人类风险行为的表象。然而，这种结果层的相似性掩盖了显著的机制层差异。控制变体揭示，与原始博弈中观察到的人类行为相比，模型通常转向条件性和计算理性的行为。人类提示和指令调优往往降低出价并减少一些明显的病态现象，但大多数机制层响应模式仍然基本不变。这些发现表明，风险决策中的行为一致性可能仅仅是表面现象：大型语言模型可能产生类似人类的风险决策，但并未表现出与人类一致的机制。因此，针对大型语言模型决策的高风险评估应超越结果相似性，检验这种一致性是否得到了机制层面的支持。
cs.CL / 65 / 2606.04987
DeliChess: A Multi-party Dialogue Dataset for Deliberation in Chess Puzzle Solving
DeliChess：一个用于国际象棋难题解决的多方对话数据集
Abstract
Multi-party dialogue is a critical setting for studying collaborative reasoning and decision-making, yet existing datasets rarely focus on structured, in-depth complex reasoning tasks. We introduce DeliChess, a novel dataset of group deliberation dialogues in which participants collaboratively solve multiple-choice chess puzzles. Each group first completes the puzzle individually, then engages in a multi-party discussion before submitting a revised collective answer. The dataset includes 107 dialogues with full transcripts, pre- and post-discussion choices, and metadata on puzzle difficulty and move quality. We evaluate performance using three metrics based on chess engine evaluations, and find that deliberation significantly improves group accuracy. We further analyse the role of probing utterances (i.e., messages that elicit proposals, justifications, or strategic reflection) using a classifier trained on prior deliberation data. While probing makes group performance more variable after discussion, it does not consistently lead to better performance. Our dataset offers a rich testbed for modelling group reasoning, dialogue dynamics, and the resolution of differing perspectives and opinions in a well-defined strategic domain.
Chinese Translation
多方对话是研究协作推理和决策的重要场景，但现有的数据集很少关注结构化的、深入的复杂推理任务。我们介绍了 DeliChess，这是一个新颖的群体 deliberation 对话数据集，参与者在其中协作解决多项选择的国际象棋难题。每个小组首先单独完成难题，然后进行多方讨论，最终提交修订后的集体答案。该数据集包含107个对话的完整记录、讨论前后的选择以及关于难题难度和走棋质量的元数据。我们使用基于国际象棋引擎评估的三种指标来评估表现，发现 deliberation 显著提高了小组的准确性。我们进一步分析了探询语句（即引发提议、辩解或战略反思的信息）的作用，采用了基于先前 deliberation 数据训练的分类器。虽然探询在讨论后使小组表现的变异性增加，但并不一致地导致更好的表现。我们的数据集为建模小组推理、对话动态以及在明确的战略领域中不同观点和意见的解决提供了丰富的测试平台。
cs.CL / 66 / 2606.05002
GARL: Game-Theoretic Reinforcement Learning for Multi-Agent Strategic Prioritisation
GARL：用于多智能体战略优先排序的博弈论强化学习
Abstract
LLM-based multi-agent systems are increasingly used for strategic decision-making tasks. In such settings, performance depends not only on individual model capabilities, but also on the policies by which agents interact and adapt. Multi-agent reinforcement learning can optimise these interaction policies, but its reward design often remains task-specific and weakly grounded in interaction structure. To address this gap, we propose GARL, a GAme-theoretic Reinforcement Learning framework for multi-agent strategic prioritisation. GARL formalises strategic prioritisation as a two-stage game: competing agents first allocate strategic resources over a shared candidate set, and a higher-level arbiter then produces the final ranking. The resulting game-theoretic utilities are converted into role-specific reinforcement signals, allowing policy optimisation to be guided by structured interaction. We instantiate GARL on issues-in-dispute ranking, where the goal is to prioritise core issues in legal proceedings. Experiments show that GARL improves ranking performance, enables small open-source LLMs to become competitive with a strong closed-source LLM under the same candidate-ranking setting, and yields gains in legal-domain competence and broader strategic decision-making. Overall, GARL demonstrates how game-theoretic interaction structure can be turned into reinforcement-learning objectives, providing a principled approach to policy optimisation in multi-agent strategic prioritisation.
Chinese Translation
基于大型语言模型（LLM）的多智能体系统在战略决策任务中的应用日益增加。在这样的环境中，性能不仅取决于单个模型的能力，还取决于智能体之间的互动和适应政策。多智能体强化学习能够优化这些互动政策，但其奖励设计往往仍然是特定任务的，且与互动结构的关联较弱。为了解决这个问题，我们提出了GARL，一个用于多智能体战略优先排序的博弈论强化学习框架。GARL将战略优先排序形式化为一个两阶段的博弈：竞争的智能体首先在共享候选集合上分配战略资源，然后一个高层仲裁者生成最终排名。由此产生的博弈论效用被转换为角色特定的强化信号，使得政策优化可以由结构化互动来引导。我们在争议问题排名上实例化了GARL，目标是在法律程序中优先考虑核心问题。实验表明，GARL提高了排名性能，使小型开源LLM在同样的候选排名环境下能够与强大的封闭源LLM竞争，并在法律领域的能力和更广泛的战略决策中取得了进展。总体而言，GARL展示了如何将博弈论的互动结构转化为强化学习目标，为多智能体战略优先排序中的政策优化提供了一个有原则的方法。
cs.CL / 67 / 2606.05009
DAR: Deontic Reasoning with Agentic Harnesses
DAR：基于主体的义务推理
Abstract
Deontic reasoning is the task of answering questions by applying explicit rules and policies to case-specific facts, for example computing tax liability under a statute or determining the outcome of an immigration appeal. A key technical challenge for LLM-based deontic reasoning is that the relevant ruleset can be long and cross-referenced, so models may still fail to locate the rules needed for a particular reasoning step. We introduce Deontic Agentic Reasoning (DAR), an agentic reasoning setup in which the model interacts with the statutes on demand. We evaluate DAR under multiple harnesses on hard subsets of DeonticBench. Across these settings, we find that agentic harnesses can push the frontier on deontic reasoning tasks, but improvements are not uniform: weaker models often degrade on numerical tasks while consuming far more tokens.
Chinese Translation
义务推理是通过将明确的规则和政策应用于特定案例事实来回答问题的任务，例如根据法规计算税收责任或确定移民上诉的结果。基于大型语言模型（LLM）的义务推理面临的一个主要技术挑战是相关的规则集可能很长并互相引用，因此模型仍可能无法找到特定推理步骤所需的规则。我们提出了基于代理的义务推理（DAR），这是一种代理推理设置，其中模型根据需要与法规进行交互。我们在 DeonticBench 的多个具有挑战性的子集上评估了 DAR。在这些设置中，我们发现基于代理的设置可以推动义务推理任务的前沿，但改进并不均匀：较弱的模型在数值任务上往往表现不佳，同时消耗更多的代币。
cs.CL / 68 / 2606.05014
Depth-Attention: Cross-Layer Value Mixing for Language Models
深度注意力：语言模型的跨层值混合
Abstract
Self-attention selects information freely across the sequence, but across depth, Transformers merely add each layer's output to the residual stream, so later layers cannot selectively reuse earlier-layer representations. Recent cross-layer methods improve this flow but operate on hidden states outside attention, adding state beyond the key-value cache at inference--a cost that becomes increasingly salient as modern LLMs compress the cache with grouped-query and multi-head latent attention. We introduce Depth-Attention, which performs this selection inside the attention module itself: before a layer attends over the sequence, its query attends over the keys of earlier layers at the same token position and mixes their values into the value that self-attention then reads. Because Depth-Attention reuses the standard attention queries, keys, and value-cache slots, storing depth-mixed values in place of the original values, it adds no parameters and introduces no persistent inference state beyond the standard key-value cache--the same cache size as a vanilla decoder and less than hidden-state-based cross-layer methods. On Qwen3-style decoders at 1.5B and 3B parameters, Depth-Attention attains the lowest perplexity and the highest average downstream accuracy, improving over the vanilla Transformer by up to 2.3 accuracy points and surpassing strong cross-layer baselines in perplexity and average accuracy, while adding under 0.01% extra arithmetic FLOPs and no additional persistent inference state. The gains hold from 360M to 3B parameters and extend to looped Transformers.
Chinese Translation
自注意力在序列中自由选择信息，但在深度层次上，变换器（Transformers）仅将每层的输出添加到残差流中，因此后续层无法选择性地重用早期层的表示。近期的跨层方法改善了这一信息流，但在注意力之外操作隐藏状态，在推理时添加超过关键信息-值缓存的状态——这一成本在现代大型语言模型（LLMs）通过分组查询和多头潜在注意力压缩缓存时变得愈发明显。我们提出了深度注意力（Depth-Attention），在注意力模块内部执行这一选择：在一层对序列进行注意力操作之前，其查询将对同一令牌位置上早期层的键进行注意，并将它们的值混合到随后自注意力读取的值中。由于深度注意力重用标准的注意力查询、键和值缓存槽，用混合后的深度值替代原始值，因此不增加任何参数，也不会引入标准关键信息-值缓存之外的持久推理状态——其缓存大小与普通解码器相同，且小于基于隐藏状态的跨层方法。在具有15亿和30亿参数的Qwen3风格解码器上，深度注意力达到了最低的困惑度和最高的平均下游准确度，较普通变换器提高了最高2.3个准确度点，并在困惑度和平均准确度上超越了强大的跨层基线，同时增加的额外算术运算浮点数（FLOPs）不足0.01%，且没有额外的持久推理状态。性能提升适用于从3.6亿到30亿参数的模型，并扩展至循环变换器（looped Transformers）。
cs.CL / 69 / 2606.05016
TaDA: Calibrated Probe Gating for Task-Domain LoRA Merging
TaDA：任务领域 LoRA 合并的校准探针门控
Abstract
Combining a task LoRA adapter with a domain LoRA adapter into a single unified model is a practical yet largely unexplored challenge. Existing methods treat both adapters as symmetric peers, applying uniform weights across all layers. We argue that task and domain adapters exhibit a consistent depth-dependent asymmetry across transformer architectures. Domain dominance increases with layer depth, while shallower layers retain stronger task-relevant signals. Motivated by this observation, we propose $\textbf{TaDA}$ ($\textbf{Ta}$sk-$\textbf{D}$omain LoR$\textbf{A}$ Merging), a training-free algorithm that exploits this structure through calibrated probe-guided per-layer gating and per-component subspace-aware merging. The gating assigns individual weights per layer and projection type using a probe signal proved invariant to adapter weight magnitude. The merging discards conflicting singular directions before combining the remaining components. $\textbf{TaDA}$ produces a standard rank-$r$ LoRA adapter with zero inference overhead. On six scientific QA benchmarks with Llama-2-7B, TaDA achieves an average accuracy of 0.452, outperforming DARE-TIES by +3.6 percentage points and obtaining the best result on all six benchmarks. On six image classification benchmarks with ViT-L/16, TaDA reaches 85.9\% average accuracy, improving over the strongest merging baseline while leading in three of the six individual benchmarks.
Chinese Translation
将任务 LoRA 适配器与领域 LoRA 适配器合并为一个统一模型是一个切实可行但仍未被广泛探索的挑战。现有方法将这两个适配器视为对称的平行体，在所有层中应用均匀权重。我们认为任务适配器和领域适配器展现出一种一致的深度依赖性不对称性，贯穿于变换器架构中。随着层深度的增加，领域的主导性增强，而较浅的层则保留了更强的与任务相关的信号。基于这一观察，我们提出了 $ extbf{TaDA}$ ($ extbf{Ta}$sk-$ extbf{D}$omain LoR$ extbf{A}$ Merging)，这是一种无训练的算法，利用校准探针引导的逐层门控和逐组件子空间感知合并来利用这一结构。该门控为每层和投影类型分配单独的权重，使用一个被证明对适配器权重大小不变的探针信号。合并步骤在结合剩余组件之前丢弃冲突的唯一方向。$ extbf{TaDA}$ 生成一个标准的秩为 $r$ 的 LoRA 适配器，且无推理开销。在六个科学问答基准上，使用 Llama-2-7B，TaDA 达到平均准确率 0.452，超越 DARE-TIES 3.6个百分点，并在所有六个基准中获得最佳结果。在六个图像分类基准上，使用 ViT-L/16，TaDA 达到 85.9\% 的平均准确率，相较于最强的合并基线有所提升，同时在六个单独基准中的三个中居于领先地位。
cs.CL / 70 / 2606.05030
Imbuing Large Language Models with Bidirectional Logic for Robust Chain Repair
为大型语言模型注入双向逻辑以实现稳健的链修复
Abstract
Autoregressive chain-of-thought (CoT) reasoning in large language models (LLMs) is fundamentally forward-directed: each step conditions only on prior tokens. This unidirectional inductive bias renders even capable models susceptible to error snowballing, wherein a single logical or arithmetic mistake in an early step irreversibly corrupts the entire reasoning chain. We introduce Teleological Reasoning Infilling (\TRI{}), a training framework that endows decoder-only transformers with a native \emph{goal-conditioned bridging} capability. The key insight is to reframe erroneous reasoning segments as fill-in-the-middle (FIM) tasks: given a verified prefix premise $P$, a verified downstream milestone $S$, and the original query $Q$, the model must synthesise the logical bridge $M$ that connects $P$ to $S$ rigorously and completely. To achieve this with standard causal architectures, we introduce a Prefix-Suffix-Middle (PSM) sequence rearrangement with three non-overlapping sentinel tokens, enabling $M$ to attend to both $P$ and $S$ without any structural modification to the self-attention mechanism. Training proceeds in two stages: (i) Supervised Fine-Tuning (SFT) on symbolically verified $(P, S, M)$ triples extracted from formal mathematics corpora, and (ii) Direct Preference Optimisation (DPO) with a deterministic symbolic verifier (Lean 4 / Python) as the sole reward oracle, eliminating LLM-judge sycophancy. At inference, TRI operates as a surgical repair module within a dual-system loop: a causal draft model generates an initial trace, the verifier pinpoints failures, and TRI infills only the damaged segment, leaving verified sections intact. Comprehensive experiments on three benchmarks demonstrate that TRI achieves state-of-the-art performance across all tasks, while reducing per-problem token expenditure by 31.2%.
Chinese Translation
大型语言模型（LLMs）中的自回归思维链（CoT）推理基本上是单向的：每一步仅基于先前的标记进行条件化。这种单向归纳偏差使得即使是能力强大的模型也容易出现错误滚雪球现象，即在早期步骤中的一次逻辑或算术错误不可逆地破坏整个推理链。我们提出了目的论推理填充（Teleological Reasoning Infilling，TRI），这是一种训练框架，为仅解码器的变换器（transformers）赋予了原生的目标条件桥接能力。关键的见解是将错误推理片段重新构造为中间填充（FIM）任务：给定一个经过验证的前缀前提 $P$、一个经过验证的下游里程碑 $S$ 和原始查询 $Q$，模型必须严格和完整地合成连接 $P$ 和 $S$ 的逻辑桥 $M$。为了用标准因果架构实现这一点，我们引入了前后缀中间（Prefix-Suffix-Middle，PSM）序列重排，使用三个不重叠的哨兵标记，使得 $M$ 可以同时关注 $P$ 和 $S$，而无需对自注意力机制进行结构调整。训练分为两个阶段进行：（i）对从形式数学语料库中提取的符号验证的 $(P, S, M)$ 三元组进行监督微调（Supervised Fine-Tuning，SFT），以及（ii）使用确定性的符号验证器（Lean 4 / Python）作为唯一的奖励Oracle进行直接偏好优化（Direct Preference Optimisation，DPO），消除了LLM-评估者的迎合。在推理过程中，TRI作为双系统循环中的手术修复模块运行：因果草稿模型生成初始轨迹，验证器指明失败，TRI仅填充损坏的部分，保持验证部分完整。对三项基准的全面实验表明，TRI在所有任务中都达到了最先进的性能，同时将每个问题的标记消耗降低了31.2%。
cs.CL / 71 / 2606.05054
Boosting Self-Consistency with Ranking
通过排序提升自一致性
Abstract
Self-consistency improves large language models by sampling multiple reasoning paths and selecting the most frequent answer, but majority voting often fails to recover correct answers that are already present among the samples. We address this limitation with Ranking-Improved Self-Consistency (RISC), which reformulates answer selection in self-consistency as a ranking problem. Instead of relying on a single uncertainty or confidence signal, RISC uses a lightweight LambdaRank model to score candidate answers with five carefully designed features that capture answer frequency, semantic centrality, and reasoning-trace consistency. We evaluate RISC on three datasets under a range of test-time budgets. Across datasets, RISC consistently achieves a better accuracy-efficiency trade-off than standard self-consistency and strong baselines, with particularly large gains on question answering benchmarks. Further analysis shows that the proposed features are individually useful and, more importantly, complementary, highlighting the value of learning to combine multiple informative signals for test-time answer selection.
Chinese Translation
自一致性通过采样多个推理路径并选择最频繁的答案来提升大型语言模型的性能，但多数投票在恢复样本中已有的正确答案时往往失败。我们通过改进的自一致性排序方法（Ranking-Improved Self-Consistency, RISC）解决了这一局限性，将自一致性中的答案选择重新表述为一个排序问题。RISC并不依赖于单一的不确定性或置信度信号，而是使用轻量级的LambdaRank模型，以五个精心设计的特征对候选答案进行评分，这些特征捕捉了答案频率、语义中心性和推理轨迹一致性。我们在三个数据集上评估RISC，并在不同的测试预算下进行测试。跨数据集，RISC在准确性与效率之间持续实现了比标准自一致性和强基线更优的平衡，尤其在问答基准测试中取得了显著提高。进一步分析表明，所提出的特征在个别上是有用的，更重要的是具互补性，突显了学习组合多个信息信号用于测试时答案选择的价值。
cs.CL / 72 / 2606.05079
Fast & Faithful Function Vectors
快速且可靠的函数向量
Abstract
Function vectors (FVs) are task representations elicited during in-context learning that can be used to steer Large Language Models (LLMs). However, design choices in their formulation remain underexplored. In this work, we study the impact of varying FV definitions for instructions along two degrees of freedom: attention head selection and steering. For head selection, using gradient-based attributions with Layer-wise Relevance Propagation (LRP) substantially improves efficiency as well as accuracy. For FV steering, applying it in a distributed manner yields a higher accuracy compared to simple aggregation. Our code is publicly available.
Chinese Translation
函数向量（Function Vectors, FVs）是通过上下文学习引发的任务表征，可以用于引导大规模语言模型（Large Language Models, LLMs）。然而，其构造中的设计选择仍然没有被充分探索。在本研究中，我们研究了在两个自由度上针对指令变化函数向量定义的影响：注意力头选择和引导策略。对于头选择，使用基于梯度的归因与层级相关传播（Layer-wise Relevance Propagation, LRP）显著提高了效率和准确性。对于函数向量引导，采用分布式方式的应用相较于简单聚合提供了更高的准确率。我们的代码已公开提供。
cs.CL / 73 / 2606.05085
Automatic Generation of Titles for Research Papers Using Language Models
基于语言模型的研究论文标题自动生成
Abstract
The title of a research paper conveys its primary idea and, occasionally, its conclusions in a clear and concise manner. Choosing an appropriate title is often challenging, and automated title generation can assist authors in this task. In this work, we propose a technique to generate paper titles from abstracts using open-weight pre-trained and large language models. We use the CSPubSum and LREC-COLING-2024 datasets and introduce a new dataset, SpringerSSAT, curated from four Springer journals in the social sciences. Additionally, we use GPT-3.5-turbo in a zero-shot setting to generate titles. Model performance is evaluated with ROUGE, METEOR, MoverScore, BERTScore, and SciBERTScore metrics. Our experiments show that fine-tuned PEGASUS-large outperforms other models, including fine-tuned LLaMA-3-8B and zero-shot GPT-3.5-turbo, across most metrics. We further demonstrate that ChatGPT can generate creative paper titles. Overall, AI-generated titles are generally appropriate and reliable.
Chinese Translation
研究论文的标题清晰且简洁地传达了其主要思想，有时还包括结论。选择一个合适的标题往往具有挑战性，而自动化标题生成可以帮助作者完成这一任务。在本研究中，我们提出了一种使用开放权重预训练的大型语言模型从摘要生成论文标题的技术。我们使用了CSPubSum和LREC-COLING-2024数据集，并引入了一个新的数据集SpringerSSAT，该数据集从四本社会科学领域的Springer期刊中整理而来。此外，我们在零样本设置下使用GPT-3.5-turbo生成标题。模型性能通过ROUGE、METEOR、MoverScore、BERTScore和SciBERTScore指标进行评估。我们的实验表明，经过微调的PEGASUS-large在大多数指标上优于其他模型，包括微调的LLaMA-3-8B和零样本的GPT-3.5-turbo。我们进一步证明了ChatGPT能够生成富有创意的论文标题。总体而言，AI生成的标题通常恰当且可靠。
cs.CL / 74 / 2606.05087
Light or Full Verb? A Minimal-Pair Dataset for Probing Phraseological Competence in Language Models
轻动词或全动词？用于探测语言模型短语能力的最小对比数据集
Abstract
Frequent English verbs such as 'have' and 'make' can function either as collocates in light-verb constructions or as full lexical predicates, as in 'make a decision' vs. 'make a cake'. Whether language models represent this distinction remains unclear. We introduce a large-scale controlled dataset of minimally varying English sentence series in which the same context contains the same verb in light-verb and full-verb uses. Two probing experiments show that language models differentiate between these uses even in minimal contexts and exhibit separable patterns across object types. We release the dataset, generation code, and materials as a reusable resource. The framework supports extensions to broader contexts, additional verbs, and other languages.
Chinese Translation
如 'have' 和 'make' 等常见英语动词既可以在轻动词结构中作为搭配使用，也可以作为全词汇谓词使用，例如 'make a decision'（做出决定）与 'make a cake'（做蛋糕）。语言模型是否能够表示这一区别尚不清楚。我们引入了一个大规模的控制数据集，包含最小变化的英语句子序列，其中相同的上下文包含相同动词在轻动词和全动词中的用法。两个探测实验表明，语言模型即使在最小上下文中也能区分这些用法，并在对象类型之间表现出可分离的模式。我们将数据集、生成代码和材料发布为可重用资源。该框架支持扩展到更广泛的上下文、额外的动词以及其他语言。
cs.CL / 75 / 2606.05106
Arithmetic Pedagogy for Language Models
语言模型的算术教学法
Abstract
We investigate whether methods of human mathematics pedagogy can guide the training of language models toward arithmetic reasoning. Building on the GASING method -- an Indonesian pedagogy that solves basic arithmetic through a left-to-right procedure aligned with the causal order of token generation -- we operationalize each operation as a computational procedure whose execution trace is serialized into natural-language Chain-of-Thought (CoT) supervision. A small GPT-2 decoder (86M parameters) with a syllabic-agglutinative TOBA tokenizer for Indonesian is trained from scratch on this data using only a next-token prediction objective, without reinforcement learning or reward-based optimization. Monitoring training reveals three distinct learning phases, and mechanistic analyses -- attention-masking interventions on the CoT information graph, residual-stream probing, and logit-lens inspection -- show that the model first internalizes a procedural pathway and subsequently develops an associative, ``mental-arithmetic'' capacity that retrieves intermediate results without explicit step-by-step computation. The trained model reaches over 80% accuracy on held-out problems and attains competitive performance against substantially larger language models, indicating that targeted, pedagogically grounded training can yield strong and economical arithmetic capability at small scale.
Chinese Translation
我们研究人类数学教学的方法是否可以指导语言模型向算术推理的训练。基于 GASING 方法——一种通过与标记生成的因果顺序对齐的从左到右的过程解决基本算术的印度尼西亚教学法——我们将每个操作转化为计算过程，其执行轨迹序列化为自然语言的思维链（Chain-of-Thought, CoT）监督。通过仅使用下一个标记预测目标，在该数据上从头开始训练一个小型的 GPT-2 解码器（86M 参数），没有强化学习或基于奖励的优化。监测训练过程显示出三种不同的学习阶段，机制分析——对 CoT 信息图的注意力掩蔽干预、残差流探测和对数透镜检查——表明，该模型首先内化了一个程序化路径，随后发展出一种联想的“心算”能力，能够在没有明确逐步计算的情况下检索中间结果。训练后的模型在保留问题上达到了超过80%的准确率，并且在与大得多的语言模型竞争时表现可观，这表明以针对性和以教学为基础的训练能够在小规模下产生强大而高效的算术能力。
cs.CL / 76 / 2606.05122
Self-Evaluation Is Already There: Eliciting Latent Judge Calibration in Base LLMs with Minimal Data
自我评估已然存在：使用最少数据引出基础大规模语言模型中的潜在评审校准
Abstract
Large language models are increasingly evaluated by other models, raising a natural question: can a model predict how a judge will score its own output? We find that the ability is largely present before any targeted training: prompted few-shot, a base model already predicts an external judge's multi-attribute quality scores on open-ended responses well above chance across three benchmarks. We introduce Self-Evaluation Elicitation (SEE), a method that surfaces this latent ability through a short cycle comprising a calibration-coupled reinforcement learning phase that improves the answer and predicts the judge, followed by a masked distillation phase that sharpens the prediction while leaving the answer untouched. From 160 unique examples, roughly 31x fewer than a reinforcement learning baseline, SEE improves held-out calibration across three benchmarks while preserving answer quality. The elicited self-evaluation is sharply localized within the model's own token distribution and stable across judges it was never trained against, indicating a transferable notion of quality rather than a single judge's preference. These results reframe judge-aligned self-evaluation as a problem of elicitation rather than acquisition.
Chinese Translation
大型语言模型越来越多地通过其他模型进行评估，这引发了一个自然的问题：模型能否预测评审者如何为其自身输出打分？我们发现，这种能力在任何有针对性的训练之前就已经普遍存在：通过少量提示，基础模型已经能够在三个基准测试中对开放式回答的外部评审者的多属性质量得分做出远高于随机猜测的预测。我们引入了自我评估引导（Self-Evaluation Elicitation, SEE）方法，该方法通过一个短周期来揭示这种潜在能力，这个周期包括一个强化学习阶段，该阶段结合了校准过程，旨在提高答案质量并预测评审者；随后是一个掩蔽蒸馏阶段，该阶段在不改变答案的情况下优化预测。从160个独特示例中，SEE在三个基准测试中实现了约31倍于强化学习基线的提升，同时保持了答案质量。引出的自我评估在模型自身的标记分布中高度局部化，并且在未曾训练过的评审者之间保持稳定，表明它体现了一个可转移的质量观念，而非单一评审者的偏好。这些结果重新框定了与评审者一致的自我评估问题，使其成为引导而非获取的问题。
cs.CL / 77 / 2606.05134
Activation-Based Active Learning for In-Context Learning: Challenges and Insights
基于激活的主动学习在上下文学习中的应用：挑战与见解
Abstract
Deep active learning has previously been explored for LLM in-context sample selection, but not with methods that utilise recent advances in understanding of transformer activations. In this paper, we test the hypothesis that model activations could provide a fine-grained signal to optimise the selection of in-context examples. We present the most comprehensive analysis to date of MLP activation-based deep active learning methods applied to in-context learning, including how different attention masking strategies impact active learning across diverse classification and generative datasets, using both Llama-3.2-3B and Qwen2.5-3B base models. However, we find a negative result: MLP outputs, viewed through the lenses of massive activations or the first four moments, do not correlate with example quality or task performance. Specifically, the absolute Spearman correlation coefficient is at most 0.33 for all tasks and models we tested, showing that such activation-based sampling should not be used for in-context learning. We hypothesise that this may be due to superposition, whereby models represent more features than they have dimensionality, suggesting that methods like Sparse Autoencoders (SAEs) may be a promising future direction.
Chinese Translation
深度主动学习以前已针对大规模语言模型（LLM）进行过上下文样本选择的探索，但未采用利用最新的变换器激活理解进展的方法。在本文中，我们测试了模型激活是否能够提供细粒度信号以优化上下文示例的选择这一假设。我们呈现了迄今为止针对上下文学习应用的基于多层感知器（MLP）激活的深度主动学习方法的最全面分析，包括不同注意力掩蔽策略如何影响多样分类和生成数据集上的主动学习，使用的基础模型包括 Llama-3.2-3B 和 Qwen2.5-3B。然而，我们发现了一个负面结果：从大规模激活或前四个矩的角度来看，MLP 输出与示例质量或任务表现并无相关性。具体而言，对于我们测试的所有任务和模型，绝对斯皮尔曼相关系数最多为 0.33，表明这种基于激活的采样不应用于上下文学习。我们假设这可能由于叠加现象，即模型表示的特征数量超过其维度，这表明像稀疏自编码器（Sparse Autoencoders, SAEs）这类方法可能是未来的有希望的方向。
cs.CL / 78 / 2606.05158
Streaming Communication in Multi-Agent Reasoning
多智能体推理中的流式通信
Abstract
Multi-agent reasoning systems adopt a "generate-then-transfer" paradigm that forces end-to-end latency to scale linearly with pipeline depth. We introduce StreamMA, a multi-agent reasoning system that streams each reasoning step to downstream agents as soon as it is generated, pipelining adjacent agents and thus reducing latency. Surprisingly, this pipelining also improves effectiveness: because multi-step reasoning quality is non-uniform and early steps are more reliable than later ones, working with these reliable early steps instead of the full chain prevents error-prone late steps from misleading downstream agents. We formalize both advantages with the first closed-form joint analysis of stream, serial, and single protocols, deriving the effectiveness ordering, speedup upper bound, and cost ratio. Across eight reasoning benchmarks spanning mathematics, science, and code, two frontier LLMs (Claude Opus 4.6 and GPT-5.4), and three topologies (Chain, Tree, Graph), StreamMA outperforms both baselines (avg. +7.3 pp, max +22.4 pp on HMMT 2026; Claude Opus 4.6-high). Beyond these contributions, we discover a "step-level scaling law": increasing per-agent steps consistently improves both effectiveness and efficiency, a new scaling dimension orthogonal to and composable with agent-count scaling.
Chinese Translation
多智能体推理系统采用"生成后转移"（generate-then-transfer）范式，这使得端到端延迟与流水线深度成线性关系。我们引入了StreamMA，一个多智能体推理系统，它在生成每个推理步骤后立即将其流式传输到下游智能体，从而对相邻的智能体进行流水线处理，减少延迟。令人惊讶的是，这种流水线处理也提高了有效性：由于多步骤推理的质量是不均匀的，且早期步骤的可靠性高于后期步骤，使用这些可靠的早期步骤而不是完整链条，可以防止容易出错的后期步骤误导下游智能体。我们通过首次对流式、串行和单一协议进行闭式联合分析，形式化了这两项优势，推导出有效性排序、加速上限和成本比。在涵盖数学、科学和代码的八项推理基准测试中，面对两个前沿的LLM（Claude Opus 4.6和GPT-5.4）以及三种拓扑结构（链、树、图），StreamMA的表现超过了两个基线（平均提高7.3个百分点，最大提高22.4个百分点，针对HMMT 2026；Claude Opus 4.6-high）。除了这些贡献外，我们还发现了一个“步骤级别扩展法则”：增加每个智能体的步骤数始终能提高有效性和效率，这是一种与智能体数量扩展正交且可组合的新扩展维度。