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目录
概览 (2026-03-10)
今日共更新750篇论文,其中:
- 自然语言处理共91篇(Computation and Language (cs.CL))
- 人工智能共192篇(Artificial Intelligence (cs.AI))
- 计算机视觉共201篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共194篇(Machine Learning (cs.LG))
- 多智能体系统共4篇(Multiagent Systems (cs.MA))
- 信息检索共17篇(Information Retrieval (cs.IR))
- 人机交互共49篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] IronEngine: Towards General AI Assistant
【速读】：该论文旨在解决当前通用人工智能助手（General AI Assistant）系统在架构统一性、执行效率与多模态能力协同方面的不足，尤其针对跨平台集成、模型资源调度、工具调用自动化及任务规划与执行分离等关键挑战。其解决方案的核心在于提出IronEngine平台，采用三阶段流水线设计——讨论（Planner–Reviewer协作）、模型切换（VRAM感知的过渡机制）和执行（工具增强的动作循环），实现规划质量与执行能力的解耦；同时通过分层记忆架构、基于ChromaDB的向量化技能库、自适应模型管理模块（支持92种模型配置与显存预算分配）以及智能工具路由系统（含130+别名归一化与自动纠错），构建了一个可扩展、高效且安全的通用AI助理基础框架。
链接: https://arxiv.org/abs/2603.08425
作者: Xi Mo
机构: 未知
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Systems and Control (eess.SY)
备注: Technical Report
Abstract:This paper presents IronEngine, a general AI assistant platform organized around a unified orchestration core that connects a desktop user interface, REST and WebSocket APIs, Python clients, local and cloud model backends, persistent memory, task scheduling, reusable skills, 24-category tool execution, MCP-compatible extensibility, and hardware-facing integration. IronEngine introduces a three-phase pipeline – Discussion (Planner–Reviewer collaboration), Model Switch (VRAM-aware transition), and Execution (tool-augmented action loop) – that separates planning quality from execution capability. The system features a hierarchical memory architecture with multi-level consolidation, a vectorized skill repository backed by ChromaDB, an adaptive model management layer supporting 92 model profiles with VRAM-aware context budgeting, and an intelligent tool routing system with 130+ alias normalization and automatic error correction. We present experimental results on file operation benchmarks achieving 100% task completion with a mean total time of 1541 seconds across four heterogeneous tasks, and provide detailed comparisons with representative AI assistant systems including ChatGPT, Claude Desktop, Cursor, Windsurf, and open-source agent frameworks. Without disclosing proprietary prompts or core algorithms, this paper analyzes the platform’s architectural decomposition, subsystem design, experimental performance, safety boundaries, and comparative engineering advantages. The resulting study positions IronEngine as a system-oriented foundation for general-purpose personal assistants, automation frameworks, and future human-centered agent platforms.
[MA-1] Less is More: Robust Zero-Communication 3D Pursuit-Evasion via Representational Parsimony
【速读】：该论文旨在解决复杂三维障碍环境中多智能体强化学习（MARL）在通信延迟、部分可观测性和非完整运动约束下的不对称追逃问题，尤其关注通信不可靠时协作鲁棒性下降的问题。其解决方案的关键在于通过表征简约性（representational parsimony）提升无通信条件下的协同性能：一是设计了一个精简的观测接口（将团队耦合通道从83维降至50维），移除冗余跨智能体信息；二是提出贡献门控信用分配机制（Contribution-Gated Credit Assignment, CGCA），基于局部性感知的信用结构实现无需通信的合作学习。实验表明，该方法在4追1逃场景中成功率达0.753 ± 0.091，碰撞率低至0.223 ± 0.066，优于全观测基准，并在多种扰动下表现出优雅退化和零样本迁移能力，验证了主动切断冗余通信通道可有效抑制误差传播、增强部署鲁棒性的新范式。
链接: https://arxiv.org/abs/2603.08273
作者: Jialin Ying,Zhihao Li,Zicheng Dong,Guohua Wu,Yihuan Liao
机构: 长沙理工大学(Changsha University of Science and Technology)
类目: Robotics (cs.RO); Multiagent Systems (cs.MA)
备注: 7 pages, 10 figures. This work has been submitted to the IEEE for possible publication
Abstract:Asymmetric 3D pursuit-evasion in cluttered voxel environments is difficult under communication latency, partial observability, and nonholonomic maneuver limits. While many MARL methods rely on richer inter-agent coupling or centralized signals, these dependencies can become fragility sources when communication is delayed or noisy. Building on an inherited path-guided decentralized pursuit scaffold, we study a robustness-oriented question: can representational parsimony improve communication-free coordination? We instantiate this principle with (i) a parsimonious actor observation interface that removes team-coupled channels (83-D to 50-D), and (ii) Contribution-Gated Credit Assignment (CGCA), a locality-aware credit structure for communication-denied cooperation. In Stage-5 evaluation (4 pursuers vs. 1 evader), our configuration reaches 0.753 +/- 0.091 success and 0.223 +/- 0.066 collision, outperforming the 83-D FULL OBS counterpart (0.721 +/- 0.071, 0.253 +/- 0.089). It further shows graceful degradation under speed/yaw/noise/delay stress tests and resilient zero-shot transfer on urban-canyon maps (about 61% success at density 0.24). These results support a practical paradigm shift: explicitly severing redundant cross-agent channels can suppress compounding error cascades and improve robustness in latency-prone deployment.
[MA-2] Modeling the Senegalese artisanal fisheries migrations
【速读】：该论文试图解决的问题是：气候变化、捕捞努力强度与社会经济参数如何相互作用并决定塞内加尔沿岸小规模渔业的动态变化。其解决方案的关键在于构建了一个多智能体模型（multi-agent model），通过整合气候、捕捞努力和社会经济等多维度数据，模拟渔民迁移行为及其对渔业资源可持续性的影响。研究发现，尽管气候变化对小规模渔业影响有限，但若维持当前捕捞强度，将导致渔业崩溃和大规模移民；而通过降低捕捞努力，可实现年产量约25万吨的可持续平衡状态，且该结果在两种气候情景下均成立。因此，控制捕捞强度是保障渔业可持续性的核心策略，同时渔民迁移行为本身可作为鱼类种群状况的指示信号，应纳入区域渔业管理和空间规划政策中。
链接: https://arxiv.org/abs/2603.08189
作者: Alassane Bah(ESP, UMMISCO),Timothée Brochier(UMMISCO, IRD [Ile-de-France])
机构: 未知
类目: Multiagent Systems (cs.MA)
备注:
Abstract:The North-West African coast is enriched by the Canary current, which sustain a very produc- tive marine ecosystem. The Senegalese artisanal fishing fleet, the largest in West Africa, ben- efit from this particularly productive ecosystem. It has survived the ages with remarkable adaptability, and has great flexibility allowing it to react quickly to changes, in particular by changing fishing gear and performing migrations. However, since the 1980s, the increasing fishing effort led to a progressive fish depletion, increasing fisher’s migration distances to access new fishing grounds. Since 2007 many fishers even started to navigate to Canary archi- pelago in order to find a more lucrative job in Europe, carrying candidate to emigration in their canoes. This phenomenon further increased since 2022 due to a new drop in fishery yields, consecutive to the development of fishmeal factories along the coast that amplified overfishing. Climate change may also impact fish habitat, and by consequence the distribution of fishing grounds. The question addressed in this research was how climate change, fishing effort and socio-economic parameters interact and determine the artisanal fishery dynamics. An interdisciplinary approach allowed us to collect data and qualitative information on cli- mate, fishing effort and socio-economic parameters. This served as a basis to build a multi- agent model of the mobility of Senegalese artisanal fishing. We implemented a first version of the model and presented some preliminary simulations with contrasted fishing effort and climate scenario. The results suggested that first, climate change should have only a slight impact on artisanal fishing, even in the most extreme climate scenario considered. Second, if fishing effort was maintained at current levels, we found a collapse of the fishery with massive fishers migrations whatever the climate scenario. Third, with reduced fishing effort, a sustain- able fishery equilibrium emerges in which Senegal’s artisanal fishery catches ~250,000 tons of fish a year mostly in Senegal, approaching the 2000s catches records. This sustainable equi- librium maintained with the two-climate change scenario tested. Fishers migrations provide clues of the fish populations state and have implications for the sustainable exploitation of fishing resources. Senegalese artisanal fishers’ migrations impact the regional distribution of the fishing effort, therefore must be taken into account in regional development and planning policies for this sector, particularly in a context of increasing infrastructure and spatial man- agement measures (e.g. marine protected areas). This work lays the foundations of a computer simulation tool for decision support.
[MA-3] amHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Size CVPR2026
【速读】：该论文旨在解决多智能体协作中人-物交互（Human-Object Interaction, HOI）的扩展性与物理真实性难题，特别是如何让任意数量的机器人 humanoid 在不依赖集中式控制的情况下实现高效、协调且符合物理规律的协同任务。其解决方案的关键在于提出 TeamHOI 框架：采用基于 Transformer 的去中心化策略网络，通过引入“队友标记”（teammate tokens）使每个代理仅依赖局部观测即可感知并响应其他队友状态，从而实现可扩展的团队协作；同时设计了一种掩码对抗运动先验（masked Adversarial Motion Prior, AMP）策略，在训练中利用单人参考动作并掩码与物体交互的身体部位，再通过任务奖励引导生成多样且物理合理的协作行为，最终结合与团队规模和物体形状无关的形成奖励机制，显著提升了复杂场景下的稳定协作性能。
链接: https://arxiv.org/abs/2603.07988
作者: Stefan Lionar,Gim Hee Lee
机构: Garena; Sea AI Lab; National University of Singapore
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Multiagent Systems (cs.MA); Robotics (cs.RO)
备注: CVPR 2026. Project page: this https URL Code: this https URL
Abstract:Physics-based humanoid control has achieved remarkable progress in enabling realistic and high-performing single-agent behaviors, yet extending these capabilities to cooperative human-object interaction (HOI) remains challenging. We present TeamHOI, a framework that enables a single decentralized policy to handle cooperative HOIs across any number of cooperating agents. Each agent operates using local observations while attending to other teammates through a Transformer-based policy network with teammate tokens, allowing scalable coordination across variable team sizes. To enforce motion realism while addressing the scarcity of cooperative HOI data, we further introduce a masked Adversarial Motion Prior (AMP) strategy that uses single-human reference motions while masking object-interacting body parts during training. The masked regions are then guided through task rewards to produce diverse and physically plausible cooperative behaviors. We evaluate TeamHOI on a challenging cooperative carrying task involving two to eight humanoid agents and varied object geometries. Finally, to promote stable carrying, we design a team-size- and shape-agnostic formation reward. TeamHOI achieves high success rates and demonstrates coherent cooperation across diverse configurations with a single policy.
自然语言处理
[NLP-0] Agent ic Critical Training
【速读】： 该论文旨在解决当前大型语言模型（Large Language Models, LLMs）作为自主代理（autonomous agents）时，因依赖模仿学习（imitation learning）而缺乏对动作质量的自主判断能力的问题。传统方法仅教会代理“做什么”，却未使其理解“为什么这样做更好”，导致其无法通过对比成功与次优动作来形成对行动质量的认知。解决方案的关键在于提出一种称为代理批判训练（Agentic Critical Training, ACT）的强化学习范式，该范式通过奖励模型在多个备选动作中正确识别更优动作的能力，驱动模型自主发展关于动作质量的推理能力，从而生成真正的自我反思（self-reflection），而非简单模仿预构建的反思文本。这一机制显著提升了代理在多种基准上的性能和跨分布泛化能力。
链接: https://arxiv.org/abs/2603.08706
作者: Weize Liu,Minghui Liu,Sy-Tuyen Ho,Souradip Chakraborty,Xiyao Wang,Furong Huang
机构: University of Maryland (马里兰大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Project page: this https URL
Abstract:Training large language models (LLMs) as autonomous agents often begins with imitation learning, but it only teaches agents what to do without understanding why: agents never contrast successful actions against suboptimal alternatives and thus lack awareness of action quality. Recent approaches attempt to address this by introducing self-reflection supervision derived from contrasts between expert and alternative actions. However, the training paradigm fundamentally remains imitation learning: the model imitates pre-constructed reflection text rather than learning to reason autonomously. We propose Agentic Critical Training (ACT), a reinforcement learning paradigm that trains agents to identify the better action among alternatives. By rewarding whether the model’s judgment is correct, ACT drives the model to autonomously develop reasoning about action quality, producing genuine self-reflection rather than imitating it. Across three challenging agent benchmarks, ACT consistently improves agent performance when combined with different post-training methods. It achieves an average improvement of 5.07 points over imitation learning and 4.62 points over reinforcement learning. Compared to approaches that inject reflection capability through knowledge distillation, ACT also demonstrates clear advantages, yielding an average improvement of 2.42 points. Moreover, ACT enables strong out-of-distribution generalization on agentic benchmarks and improves performance on general reasoning benchmarks without any reasoning-specific training data, highlighting the value of our method. These results suggest that ACT is a promising path toward developing more reflective and capable LLM agents.
[NLP-1] How Far Can Unsupervised RLVR Scale LLM Training? ICLR2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）训练中因依赖人工标注数据而导致的监督瓶颈问题，提出通过无监督强化学习与可验证奖励（Unsupervised Reinforcement Learning with Verifiable Rewards, URLVR）实现更高效的训练扩展。其核心解决方案在于区分URLVR方法为内在奖励（intrinsic）与外部奖励（external）两类，并建立统一理论框架揭示：所有内在奖励方法本质上会趋向于“ sharpening the model’s initial distribution”，即增强模型初始分布的峰值强度；当模型初始置信度与正确性一致时该机制有效，但若二者错位则会导致灾难性崩溃。关键发现是内在奖励普遍存在先升后降的趋势，且崩溃时间由模型先验决定而非工程调参，这明确了内在URLVR的固有扩展限制，同时提出“Model Collapse Step”作为衡量模型先验的实用指标，指导测试阶段小样本微调中的RL可行性，并初步探索基于计算不对称性的外部奖励方法以突破当前性能天花板。
链接: https://arxiv.org/abs/2603.08660
作者: Bingxiang He,Yuxin Zuo,Zeyuan Liu,Shangziqi Zhao,Zixuan Fu,Junlin Yang,Cheng Qian,Kaiyan Zhang,Yuchen Fan,Ganqu Cui,Xiusi Chen,Youbang Sun,Xingtai Lv,Xuekai Zhu,Li Sheng,Ran Li,Huan-ang Gao,Yuchen Zhang,Bowen Zhou,Zhiyuan Liu,Ning Ding
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Accepted to the ICLR 2026
Abstract:Unsupervised reinforcement learning with verifiable rewards (URLVR) offers a pathway to scale LLM training beyond the supervision bottleneck by deriving rewards without ground truth labels. Recent works leverage model intrinsic signals, showing promising early gains, yet their potential and limitations remain unclear. In this work, we revisit URLVR and provide a comprehensive analysis spanning taxonomy, theory and extensive experiments. We first classify URLVR methods into intrinsic versus external based on reward sources, then establish a unified theoretical framework revealing that all intrinsic methods converge toward sharpening the model’s initial distribution This sharpening mechanism succeeds when initial confidence aligns with correctness but fails catastrophically when misaligned. Through systematic experiments, we show intrinsic rewards consistently follow a rise-then-fall pattern across methods, with collapse timing determined by model prior rather than engineering choices. Despite these scaling limits, we find intrinsic rewards remain valuable in test-time training on small datasets, and propose Model Collapse Step to measure model prior, serving as a practical indicator for RL trainability. Finally, we explore external reward methods that ground verification in computational asymmetries, showing preliminary evidence they may escape the confidence-correctness ceiling. Our findings chart boundaries for intrinsic URLVR while motivating paths toward scalable alternatives.
[NLP-2] CODA: Difficulty-Aware Compute Allocation for Adaptive Reasoning
【速读】： 该论文试图解决大模型在推理过程中因固定计算资源分配导致的“过度思考”问题，即在简单任务上重复推理路径却仅带来微小准确率提升，造成计算资源浪费。解决方案的关键在于提出一种基于难度感知的计算分配方法CODA（Compute Allocation by Difficulty Awareness），其核心思想是将推理深度动态调整为与实例难度相匹配：通过内部难度信号估计任务复杂度，并利用两个非负门控机制——易例门控抑制简单任务的冗余输出，难例门控激励复杂任务的更多推理轮次，从而在不依赖外部标注或用户预算的前提下，实现对token资源的最优分配，兼顾效率与性能。
链接: https://arxiv.org/abs/2603.08659
作者: Siye Wu,Jian Xie,Yikai Zhang,Yanghua Xiao
机构: Fudan University (复旦大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:The emergence of large reasoning models demonstrates that scaling inference-time compute significantly enhances performance on complex tasks. However, it often falls into another trap: overthinking simple problems, where repetitive rationales yield minimal accuracy gains at a disproportionately high cost. This motivates adaptive reasoning: dynamically aligning reasoning depth with instance difficulty. In this paper, we study adaptive reasoning from an optimality perspective, formalizing it as a utility maximization problem where tokens are allocated until the marginal accuracy gain falls below the incremental cost. Based on this, we propose CODA (Compute Allocation by Difficulty Awareness), a method that operationalizes this principle by allocating tokens via a policy-internal difficulty signal. Specifically, CODA estimates difficulty via group-based rollouts and maps it to two non-negative gates that modulate a length-dependent shaping term on top of the binary base reward. The easy-side gate penalizes verbosity on simple instances, whereas the hard-side gate encourages more deliberative rollouts on challenging ones. Across model scales and benchmarks, CODA achieves adaptive reasoning without external annotations or user-provided budgets: on easy tasks, CODA reduces token costs by over 60% while maintaining strong accuracy, whereas on hard tasks it incentivizes more deliberative rollouts to maximize performance.
[NLP-3] Drift-to-Action Controllers: Budgeted Interventions with Online Risk Certificates ICLR2026
【速读】： 该论文旨在解决部署中的机器学习系统在面对数据分布漂移（distribution drift）时，现有监控流水线仅触发告警而缺乏明确响应策略的问题，尤其在标签获取、计算资源和延迟受限的场景下。解决方案的关键在于提出 Drift2Act 控制器，其核心创新是将监控建模为带约束的决策问题，并引入主动风险证书（active risk certificate），通过少量延迟标签对当前风险进行 anytime-valid 上界估计 $ U_t(\delta) $，从而动态决定是否执行低成本操作（如重新校准或测试时适应）或触发高成本干预（如回滚或重训练），实现安全与效率的平衡。
链接: https://arxiv.org/abs/2603.08578
作者: Ismail Lamaakal,Chaymae Yahyati,Khalid El Makkaoui,Ibrahim Ouahbi,Yassine Maleh
机构: Mohammed First University (穆罕默德第一大学); Sultan Moulay Slimane University (苏丹穆莱·斯利曼大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Published as a conference paper at CAO Workshop at ICLR 2026
Abstract:Deployed machine learning systems face distribution drift, yet most monitoring pipelines stop at alarms and leave the response underspecified under labeling, compute, and latency constraints. We introduce Drift2Act, a drift-to-action controller that treats monitoring as constrained decision-making with explicit safety. Drift2Act combines a sensing layer that maps unlabeled monitoring signals to a belief over drift types with an active risk certificate that queries a small set of delayed labels from a recent window to produce an anytime-valid upper bound U_t(\delta) on current risk. The certificate gates operation: if U_t(\delta) \le \tau , the controller selects low-cost actions (e.g., recalibration or test-time adaptation); if U_t(\delta) \tau , it activates abstain/handoff and escalates to rollback or retraining under cooldowns. In a realistic streaming protocol with label delay and explicit intervention costs, Drift2Act achieves near-zero safety violations and fast recovery at moderate cost on WILDS Camelyon17, DomainNet, and a controlled synthetic drift stream, outperforming alarm-only monitoring, adapt-always adaptation, schedule-based retraining, selective prediction alone, and an ablation without certification. Overall, online risk certification enables reliable drift response and reframes monitoring as decision-making with safety.
[NLP-4] Fanar-Sadiq: A Multi-Agent Architecture for Grounded Islamic QA
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在回答伊斯兰相关知识问题时存在的幻觉（hallucination）和来源误引问题，尤其是在要求严格基于《古兰经》《圣训》及教法（fiqh）细节的场景下，LLMs 的生成结果缺乏可验证性和准确性。解决方案的关键在于提出一个双语（阿拉伯语/英语）多智能体系统 Fanar-Sadiq，其核心是基于意图感知的路由机制与工具调用架构，将查询分发至专用模块：包括检索增强生成（Retrieval-Augmented Generation, RAG）以支持带确定性引用归一化和验证轨迹的教法回答、精确经文查找与引文验证、以及针对逊尼派天课（zakat）和遗产分配的教法学派敏感（madhhab-sensitive）分支式确定性计算器，从而实现高准确率、可解释且符合宗教规范的问答服务。
链接: https://arxiv.org/abs/2603.08501
作者: Ummar Abbas,Mourad Ouzzani,Mohamed Y. Eltabakh,Omar Sinan,Gagan Bhatia,Hamdy Mubarak,Majd Hawasly,Mohammed Qusay Hashim,Kareem Darwish,Firoj Alam
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) can answer religious knowledge queries fluently, yet they often hallucinate and misattribute sources, which is especially consequential in Islamic settings where users expect grounding in canonical texts (Qur’an and Hadith) and jurisprudential (fiqh) nuance. Retrieval-augmented generation (RAG) reduces some of these limitations by grounding generation in external evidence. However, a single ``retrieve-then-generate’’ pipeline is limited to deal with the diversity of Islamic this http URL may request verbatim scripture, fatwa-style guidance with citations or rule-constrained computations such as zakat and inheritance that require strict arithmetic and legal invariants. In this work, we present a bilingual (Arabic/English) multi-agent Islamic assistant, called Fanar-Sadiq, which is a core component of the Fanar AI platform. Fanar-Sadiq routes Islamic-related queries to specialized modules within an agentic, tool-using architecture. The system supports intent-aware routing, retrieval-grounded fiqh answers with deterministic citation normalization and verification traces, exact verse lookup with quotation validation, and deterministic calculators for Sunni zakat and inheritance with madhhab-sensitive branching. We evaluate the complete end-to-end system on public Islamic QA benchmarks and demonstrate effectiveness and efficiency. Our system is currently publicly and freely accessible through API and a Web application, and has been accessed \approx 1.9M times in less than a year.
[NLP-5] LycheeCluster: Efficient Long-Context Inference with Structure-Aware Chunking and Hierarchical KV Indexing
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在处理长文本上下文时，因注意力机制的二次复杂度和键值（Key-Value, KV）缓存带来的巨大计算与内存开销问题。现有基于检索的方法常因固定大小分块导致语义完整性受损，且依赖低效的线性扫描。其解决方案的关键在于提出LycheeCluster方法：通过边界感知的分块策略保持局部语义连贯性，并基于三角不等式构建递归分层索引结构，将KV缓存检索从线性扫描转化为理论上有界、对数时间的剪枝过程；同时引入惰性更新策略以支持高效流式生成。实验表明，该方法在几乎不牺牲模型性能的前提下实现了最高达3.6倍的端到端推理加速，优于当前最优的KV缓存管理方法（如Quest、ClusterKV）。
链接: https://arxiv.org/abs/2603.08453
作者: Dongfang Li,Zixuan Liu,Gang Lin,Baotian Hu,Min Zhang
机构: Harbin Institute of Technology (哈尔滨工业大学); University of Electronic Science and Technology of China (电子科技大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 17 pages, 12 figures
Abstract:The quadratic complexity of the attention mechanism and the substantial memory footprint of the Key-Value (KV) cache present severe computational and memory challenges for Large Language Models (LLMs) processing long contexts. Existing retrieval-based methods often compromise semantic integrity through fixed-size chunking and suffer from inefficient linear scanning. In this paper, we propose LycheeCluster, a novel method for efficient KV cache management. LycheeCluster preserves local semantic coherence via boundary-aware chunking and constructs a recursive hierarchical index rooted in the triangle inequality. This design transforms cache retrieval from a linear scan into a theoretically bounded, logarithmic-time pruning process, while a lazy update strategy supports efficient streaming generation. Experiments demonstrate that LycheeCluster achieves up to a 3.6x end-to-end inference speedup with negligible degradation in model performance, outperforming state-of-the-art KV cache management methods (e.g., Quest, ClusterKV). We will release our code and kernels after publication.
[NLP-6] A Dataset for Probing Translationese Preferences in English-to-Swedish Translation LREC2026
【速读】： 该论文旨在解决生成式 AI 在非英语语言中常出现的“翻译腔”（translationese）问题，即机器翻译结果往往保留源语言结构和表达习惯，导致目标语言输出不自然、不地道。解决方案的关键在于构建了一个首个公开可用的英译瑞典语对比数据集，其中包含具有翻译腔的句子与更符合目标语言习惯的替代句，并标注了原翻译中的错误类型与成因。通过该数据集对小型瑞典语及多语言大语言模型（LLM）进行评估，研究发现模型普遍偏好翻译腔表达，且人类替代句在缺乏源语言上下文时才更受青睐，表明源语言暴露会诱导模型产生机械性翻译倾向。此数据集为开发更具自然性和地道性的非英语语言生成模型提供了基准与资源。
链接: https://arxiv.org/abs/2603.08450
作者: Jenny Kunz,Anja Jarochenko,Marcel Bollmann
机构: 未知
类目: Computation and Language (cs.CL)
备注: To appear at LREC 2026
Abstract:Translations often carry traces of the source language, a phenomenon known as translationese. We introduce the first freely available English-to-Swedish dataset contrasting translationese sentences with idiomatic alternatives, designed to probe intrinsic preferences of language models. It includes error tags and descriptions of the problems in the original translations. In experiments evaluating smaller Swedish and multilingual LLMs with our dataset, we find that they often favor the translationese phrasing. Human alternatives are chosen more often when the English source sentence is omitted, indicating that exposure to the source biases models toward literal translations, although even without context models often prefer the translationese variant. Our dataset and findings provide a resource and benchmark for developing models that produce more natural, idiomatic output in non-English languages.
[NLP-7] Can Vision-Language Models Solve the Shell Game?
【速读】： 该论文旨在解决视觉实体跟踪（Visual Entity Tracking）在视觉-语言模型（VLMs）中的根本性缺陷问题，即当前主流VLMs在面对外观相似的物体时，难以通过时空连续性进行可靠跟踪，导致其在视频理解任务中表现不佳。这一问题常被现有视频基准测试中的视觉捷径所掩盖。解决方案的关键在于提出一种名为时空接地思维链（Spatiotemporal Grounded Chain-of-Thought, SGCoT）的新范式，通过将物体轨迹作为显式的中间状态进行建模，从而增强模型对不可区分对象的长期追踪能力。该方法利用合成文本数据对模型进行微调，使其能够生成具有时空一致性的推理路径，最终在VET-Bench上实现超过90%的准确率，显著优于现有方法。
链接: https://arxiv.org/abs/2603.08436
作者: Tiedong Liu,Wee Sun Lee
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Visual entity tracking is an innate cognitive ability in humans, yet it remains a critical bottleneck for Vision-Language Models (VLMs). This deficit is often obscured in existing video benchmarks by visual shortcuts. We introduce VET-Bench, a synthetic diagnostic testbed featuring visually identical objects that necessitate tracking exclusively through spatiotemporal continuity. Our experiments reveal that current state-of-the-art VLMs perform at or near chance level on VET-Bench, exposing a fundamental limitation: an over-reliance on static frame-level features and a failure to maintain entity representations over time. We provide a theoretical analysis drawing connections to the state-tracking problem, proving that fixed-depth transformer-based VLMs are fundamentally limited in tracking indistinguishable objects without intermediate supervision due to expressivity constraints. To address this, we propose Spatiotemporal Grounded Chain-of-Thought (SGCoT): generating object trajectories as explicit intermediate states. Leveraging Molmo2’s object tracking ability, we elicit SGCoT reasoning by fine-tuning on synthesized text-only data for alignment. Our method achieves state-of-the-art accuracy exceeding 90% on VET-Bench, demonstrating that VLMs can reliably solve the video shell-game task end-to-end without external tools. Our code and data are available at this https URL .
[NLP-8] Aligning to Illusions: Choice Blindness in Human and AI Feedback
【速读】： 该论文试图解决强化学习中人类反馈（Reinforcement Learning from Human Feedback, RLHF）所依赖的偏好信号稳定性问题，即假设标注者偏好反映稳定的内部状态这一前提是否成立。研究表明，在偏好获取的全流程中，人类和大语言模型（Large Language Models, LLMs）的判断均易受上下文干扰，导致偏好信号被系统性扭曲而难以检测。解决方案的关键在于揭示了偏好构建过程中的“情境塑造效应”：无论是人类在第三视角文本比较中的选择盲视（choice blindness），还是LLM判官基于浅层文本匹配而非深层自我监控作出判断，抑或标准评估指标无法识别标签污染对奖励信号的实质性损害，都表明RLHF所使用的偏好信号本质上是情境依赖的、非稳定的。这一发现凸显出当前RLHF范式对偏好来源可靠性的过度信任，并提示未来需从机制设计层面改进偏好采集与验证方式，以提升训练信号的稳健性。
链接: https://arxiv.org/abs/2603.08412
作者: Wenbin Wu
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 16 pages, 6 figures, 2 tables
Abstract:Reinforcement Learning from Human Feedback (RLHF) assumes annotator preferences reflect stable internal states. We challenge this through three experiments spanning the preference pipeline. In a human choice blindness study, 91% of surreptitiously swapped preferences go undetected, extending choice blindness to third-person evaluative comparison of unfamiliar text. Testing fifteen LLM judges as potential replacements, we find detection relies on shallow text matching rather than genuine self-monitoring: removing prior reasoning from context causes blindness to surge from near-zero to over 50%, while explicit social pressure induces near-universal compliance. In a dose-response experiment across two architectures from 86M to 2B parameters, one-sixth to one-third of labels must be corrupted before the reward signal halves, yet standard pairwise accuracy remains virtually unchanged. A Best-of-N evaluation confirms this translates to downstream policy degradation: at 50% corruption, reward-guided selection produces no improvement over random sampling, while the proxy model reports monotonically increasing scores. Together, these results reveal a preference construction problem: the signal entering RLHF is shaped by elicitation context in ways that neither human metacognition, LLM self-monitoring, nor standard evaluation metrics can detect.
[NLP-9] Revealing Behavioral Plasticity in Large Language Models : A Token-Conditional Perspective
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在推理过程中缺乏灵活行为调控能力的问题，即如何在不重新训练模型的前提下，实现对模型行为模式的精确控制与切换。其解决方案的关键在于揭示并利用LLMs内在的行为可塑性——类似于变色龙根据环境线索调整体色的能力，并通过基于token条件生成的强化学习框架（Token-Conditioned Reinforcement Learning, ToCoRL），将这种瞬时的行为适应转化为稳定、可学习的行为模式。ToCoRL通过token前缀引导生成过程，并结合强化学习策略优化探索与利用，从而实现对模型行为的精准控制，且不会导致性能下降，例如使原本擅长复杂数学推理的模型有效适配到事实问答任务中。
链接: https://arxiv.org/abs/2603.08398
作者: Liyuan Mao,Le Yu,Jing Zhou,Chujie Zheng,Bowen Yu,Chang Gao,Shixuan Liu,An Yang,Weinan Zhang,JunYang Lin
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Work done during an internship at the Qwen Team, Alibaba Group
Abstract:In this work, we reveal that Large Language Models (LLMs) possess intrinsic behavioral plasticity-akin to chameleons adapting their coloration to environmental cues-that can be exposed through token-conditional generation and stabilized via reinforcement learning. Specifically, by conditioning generation on carefully selected token prefixes sampled from responses exhibiting desired behaviors, LLMs seamlessly adapt their behavioral modes at inference time (e.g., switching from step-by-step reasoning to direct answering) without retraining. Based on this insight, we propose Token-Conditioned Reinforcement Learning (ToCoRL), a principled framework that leverages RL to internalize this chameleon-like plasticity, transforming transient inference-time adaptations into stable and learnable behavioral patterns. ToCoRL guides exploration with token-conditional generation and keep enhancing exploitation, enabling emergence of appropriate behaviors. Extensive experiments show that ToCoRL enables precise behavioral control without capability degradation. Notably, we show that large reasoning models, while performing strongly on complex mathematics, can be effectively adapted to excel at factual question answering, which was a capability previously hindered by their step-by-step reasoning patterns.
[NLP-10] COACH meets QUORUM: A Framework and Pipeline for Aligning User Expert and Developer Perspectives in LLM -generated Health Counselling ALT
【速读】： 该论文旨在解决当前面向慢性病人群的个性化生活方式干预系统在开发与评估过程中存在的多利益相关方视角割裂问题，即如何统一开发者、医疗专家和用户对生成式AI（Generative AI）驱动的生活方式建议的质量、相关性和可信度的评价标准。其解决方案的关键在于提出一个名为QUORUM的统一评估框架，该框架整合了开发者、专家和用户三个维度的评价视角，并通过COACH这一基于大语言模型（Large Language Model, LLM）的个性化建议生成管道，在癌症患者及幸存者使用的健康日记应用Healthy Chronos中进行实证验证。结果表明，尽管三方在建议的整体质量与相关性上达成共识，但在语气、对模式识别错误的敏感性以及潜在幻觉等方面存在分歧，凸显了多利益相关方评估对于构建可信赖、以患者为中心的自然语言处理（Natural Language Processing, NLP）系统的重要性。
链接: https://arxiv.org/abs/2603.08392
作者: Yee Man Ng,Bram van Dijk,Pieter Beynen,Otto Boekesteijn,Joris Jansen,Gerard van Oortmerssen,Max van Duijn,Marco Spruit
机构: 未知
类目: Computation and Language (cs.CL)
备注: Under review for the CL4Health workshop
Abstract:Systems that collect data on sleep, mood, and activities can provide valuable lifestyle counselling to populations affected by chronic disease and its consequences. Such systems are, however, challenging to develop; besides reliably extracting patterns from user-specific data, systems should also contextualise these patterns with validated medical knowledge to ensure the quality of counselling, and generate counselling that is relevant to a real user. We present QUORUM, a new evaluation framework that unifies these developer-, expert-, and user-centric perspectives, and show with a real case study that it meaningfully tracks convergence and divergence in stakeholder perspectives. We also present COACH, a Large Language Model-driven pipeline to generate personalised lifestyle counselling for our Healthy Chronos use case, a diary app for cancer patients and survivors. Applying our framework shows that overall, users, medical experts, and developers converge on the opinion that the generated counselling is relevant, of good quality, and reliable. However, stakeholders also diverge on the tone of the counselling, sensitivity to errors in pattern-extraction, and potential hallucinations. These findings highlight the importance of multi-stakeholder evaluation for consumer health language technologies and illustrate how a unified evaluation framework can support trustworthy, patient-centered NLP systems in real-world settings.
[NLP-11] Adaptive Loops and Memory in Transformers: Think Harder or Know More? ICLR2026
【速读】： 该论文旨在解决传统链式思维（Chain-of-thought, CoT）提示在语言模型中依赖显式中间步骤表述所带来的局限性，以及循环变压器（looped transformers）因缺乏深层模型的参数存储能力而导致的性能瓶颈问题。其解决方案的关键在于提出一种融合自适应逐层循环机制与门控记忆库（gated memory banks）的新型架构：其中每个Transformer模块通过学习的终止机制（halting mechanism）自主决定是否迭代隐藏状态，同时引入额外的可学习存储单元以增强信息保留能力。实验证明，循环机制显著提升数学推理性能，而记忆库有助于恢复常识任务表现；两者协同作用下，模型在保持相同浮点运算量（FLOP）的情况下超越更深的基线模型，且内部结构呈现早期层轻度循环与低频访问记忆、后期层则高频利用循环与记忆的层级专业化现象。
链接: https://arxiv.org/abs/2603.08391
作者: Markus Frey,Behzad Shomali,Ali Hamza Bashir,David Berghaus,Mehdi Ali
机构: Lamarr Institute; Fraunhofer IAIS; University of Bonn
类目: Computation and Language (cs.CL)
备注: Published at Latent Implicit Thinking Workshop @ ICLR 2026
Abstract:Chain-of-thought (CoT) prompting enables reasoning in language models but requires explicit verbalization of intermediate steps. Looped transformers offer an alternative by iteratively refining representations within hidden states. This parameter efficiency comes at a cost, as looped models lack the storage capacity of deeper models which use unique weights per layer. In this work, we investigate transformer models that feature both adaptive per-layer looping, where each transformer block learns to iterate its hidden state via a learned halting mechanism, and gated memory banks, that provide additional learned storage. We find that looping primarily benefits mathematical reasoning, while memory banks help recover performance on commonsense tasks compared to parameter and FLOP matched models. Combining both mechanisms yields a model that outperforms an iso-FLOP baseline – with three times the number of layers – on math benchmarks. Analysis of model internals reveals layer specialization: early layers learn to loop minimally and access memory sparingly, while later layers do both more heavily.
[NLP-12] Computational modeling of early language learning from acoustic speech and audiovisual input without linguistic priors
【速读】： 该论文旨在解决早期语言习得过程中，婴儿如何从复杂的声学语音信号中无监督地提取语言结构这一核心问题。其解决方案的关键在于利用自监督（self-supervised）和视觉引导（visually grounded）的计算模型，通过模拟感知学习机制，在无需强语言先验的前提下，实现对语音特征的有效学习，并揭示了多种早期语言发展现象可由一组共享的学习原则加以解释——这些原则与多种语言习得理论及人类认知模型具有广泛兼容性。
链接: https://arxiv.org/abs/2603.08359
作者: Okko Räsänen
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
备注:
Abstract:Learning to understand speech appears almost effortless for typically developing infants, yet from an information-processing perspective, acquiring a language from acoustic speech is an enormous challenge. This chapter reviews recent developments in using computational models to understand early language acquisition from speech and audiovisual input. The focus is on self-supervised and visually grounded models of perceptual learning. We show how these models are becoming increasingly powerful in learning various aspects of speech without strong linguistic priors, and how many features of early language development can be explained through a shared set of learning principles-principles broadly compatible with multiple theories of language acquisition and human cognition. We also discuss how modern learning simulations are gradually becoming more realistic, both in terms of input data and in linking model behavior to empirical findings on infant language development.
[NLP-13] Do Language Models Know Theo Has a Wife? Investigating the Proviso Problem
【速读】： 该论文旨在解决语言模型在处理条件句中前提投射（presupposition projection）问题时存在的“proviso问题”，即理论预期与人类理解之间存在偏差的语用学难题。解决方案的关键在于将该现象重构为自然语言推理（Natural Language Inference, NLI）任务，并构建了一个用于探测条件句中前提投射行为的诊断数据集；通过结合可解释性分析对RoBERTa、DeBERTa、LLaMA和Gemma等模型进行评估，发现模型虽整体上与人类判断一致，但主要依赖浅层模式匹配而非深层语义或语用推理，从而首次建立了针对该问题的计算评估框架，并强调需采用多方法诊断策略以提升模型在上下文依赖意义和语用能力方面的表现。
链接: https://arxiv.org/abs/2603.08358
作者: Tara Azin,Daniel Dumitrescu,Diana Inkpen,Raj Singh
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:We investigate how language models handle the proviso problem, an unresolved issue in pragmatics where presuppositions in conditional sentences diverge between theoretical and human interpretations. We reformulate this phenomenon as a Natural Language Inference task and introduce a diagnostic dataset designed to probe presupposition projection in conditionals. We evaluate RoBERTa, DeBERTa, LLaMA, and Gemma using explainability analyses. The results show that models broadly align with human judgments but rely on shallow pattern matching rather than semantic or pragmatic reasoning. Our work provides the first computational evaluation framework for the proviso problem and highlights the need for diagnostic, multi-method approaches to assess pragmatic competence and context-dependent meaning in language models.
[NLP-14] Rethinking Attention Output Projection: Structured Hadamard Transforms for Efficient Transformers
【速读】： 该论文旨在解决多头注意力机制中密集输出投影（dense output projection）导致的参数量大、内存占用高及推理成本高的问题。其关键解决方案是用一个固定且无参数的沃尔什-哈达玛变换（Walsh-Hadamard Transform）替代原有的密集投影层，并辅以轻量级可学习仿射重缩放，从而在保持跨头全局交互能力的同时，显著降低模型复杂度。该结构化替换方法在不同模型规模下均能维持相当或略优的下游任务性能，同时实现高达7%的参数减少、8.9%的峰值内存节省和6.6%的吞吐量提升，且效率增益随模型规模、批处理大小和序列长度增加而单调增长。
链接: https://arxiv.org/abs/2603.08343
作者: Shubham Aggarwal,Lokendra Kumar
机构: Indian Institute of Technology Madras (印度理工学院马德拉斯分校)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 12 pages, 9 figures, 4 tables
Abstract:The dense output projection in multi-head attention scales quadratically with model dimension, contributing significantly to parameter count, memory footprint, and inference cost. We propose replacing this projection with a fixed, parameter-free Walsh Hadamard Transform followed by a lightweight learnable affine rescaling, eliminating approximately 25 percent of attention parameters per block while preserving global cross head interaction through an orthogonal, norm-preserving transformation. Across different model sizes, we demonstrate that this structured substitution maintains comparable or slightly superior downstream task performance on standard benchmarks, while achieving up to 7 percent aggregate parameter reduction, 8.9 percent peak memory savings, and 6.6 percent throughput improvement at scale, with efficiency gains growing monotonically with model size, batch size, and sequence length. Interestingly, we observe that structured Hadamard-based models exhibit a steeper validation loss curve relative to training FLOPs compared to their dense counterparts, suggesting more favorable compute utilization during training.
[NLP-15] SlowBA: An efficiency backdoor attack towards VLM-based GUI agents
【速读】： 该论文旨在解决基于视觉-语言模型（Vision-Language Model, VLM）的图形用户界面（Graphical User Interface, GUI）智能体在响应效率方面存在的安全漏洞问题。现有研究主要关注对动作正确性的攻击，而忽视了响应延迟带来的潜在风险。为此，作者提出了一种名为SlowBA的新颖后门攻击方法，其核心在于通过特定触发模式诱导模型生成过长的推理链，从而显著增加响应时间。解决方案的关键是采用两阶段奖励级后门注入（Reward-Level Backdoor Injection, RBI）策略：首先对齐长响应格式，再利用强化学习学习触发感知激活机制；同时设计真实场景中的弹窗作为触发器，提升攻击隐蔽性。实验表明，该方法可在保持任务准确性的同时大幅延长响应长度和延迟，且在低中毒比例及多种防御设置下仍具有效性。
链接: https://arxiv.org/abs/2603.08316
作者: Junxian Li,Tu Lan,Haozhen Tan,Yan Meng,Haojin Zhu
机构: 未知
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: 25 pages
Abstract:Modern vision-language-model (VLM) based graphical user interface (GUI) agents are expected not only to execute actions accurately but also to respond to user instructions with low latency. While existing research on GUI-agent security mainly focuses on manipulating action correctness, the security risks related to response efficiency remain largely unexplored. In this paper, we introduce SlowBA, a novel backdoor attack that targets the responsiveness of VLM-based GUI agents. The key idea is to manipulate response latency by inducing excessively long reasoning chains under specific trigger patterns. To achieve this, we propose a two-stage reward-level backdoor injection (RBI) strategy that first aligns the long-response format and then learns trigger-aware activation through reinforcement learning. In addition, we design realistic pop-up windows as triggers that naturally appear in GUI environments, improving the stealthiness of the attack. Extensive experiments across multiple datasets and baselines demonstrate that SlowBA can significantly increase response length and latency while largely preserving task accuracy. The attack remains effective even with a small poisoning ratio and under several defense settings. These findings reveal a previously overlooked security vulnerability in GUI agents and highlight the need for defenses that consider both action correctness and response efficiency. Code can be found in this https URL.
[NLP-16] Learning Multiple Utterance-Level Attribute Representations with a Unified Speech Encoder INTERSPEECH
【速读】： 该论文旨在解决现有语音基础模型（speech foundation models）通常仅学习声学帧级别的上下文嵌入，难以支持多样化下游任务中对话语级别属性（utterance-level attributes）表示的需求问题。解决方案的关键在于提出一个统一的后训练框架，通过联合学习语义和说话人等多种话语级别表征，使单一语音基础模型能够生成多种类型的 utterance-level representations，从而在多语言语音检索和说话人识别等任务上实现性能提升。
链接: https://arxiv.org/abs/2603.08312
作者: Maryem Bouziane,Salima Mdhaffar,Yannick Estève
机构: LIA - Avignon Université, France
类目: Computation and Language (cs.CL)
备注: Submitted to Interspeech
Abstract:Speech foundation models trained with self-supervised learning produce generic speech representations that support a wide range of speech processing tasks. When further adapted with supervised learning, these models can achieve strong performance on specific downstream tasks. Recent post-training approaches, such as SAMU-XSLR and SONAR, align speech representations with utterance-level semantic representations, enabling effective multimodal (speech-text) and multilingual applications. While speech foundation models typically learn contextual embeddings at the acoustic frame level, these methods learn representations at the utterance level. In this work, we extend this paradigm to arbitrary utterance-level attributes and propose a unified post-training framework that enables a single speech foundation model to generate multiple types of utterance-level representations. We demonstrate the effectiveness of this approach by jointly learning semantic and speaker representations and evaluating them on multilingual speech retrieval and speaker recognition tasks.
[NLP-17] LAMUS: A Large-Scale Corpus for Legal Argument Mining from U.S. Caselaw using LLM s
【速读】： 该论文旨在解决美国判例法（尤其是州级判例）中法律论证挖掘（Legal Argument Mining）因缺乏大规模高质量标注数据集而进展受限的问题。其解决方案的关键在于构建了一个名为LAMUS的句子级别法律论证挖掘语料库，该语料库基于美国联邦最高法院判决和德克萨斯州刑事上诉案件编写而成；通过数据驱动的流水线方法实现：大规模案例收集、大语言模型（LLM）自动标注与人工反馈的质量优化相结合，并将法律论证任务建模为六类句子分类问题。实验表明，链式思维（Chain-of-Thought）提示策略显著提升LLM性能，领域特定模型在零样本场景下表现更稳定，且LLM辅助验证可修正近20%的标注错误，最终经人工验证达到Cohen’s Kappa=0.85，证实了标注质量。
链接: https://arxiv.org/abs/2603.08286
作者: Serene Wang,Lavanya Pobbathi,Haihua Chen
机构: University of North Texas (北德克萨斯大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Legal argument mining aims to identify and classify the functional components of judicial reasoning, such as facts, issues, rules, analysis, and conclusions. Progress in this area is limited by the lack of large-scale, high-quality annotated datasets for U.S. caselaw, particularly at the state level. This paper introduces LAMUS, a sentence-level legal argument mining corpus constructed from U.S. Supreme Court decisions and Texas criminal appellate opinions. The dataset is created using a data-centric pipeline that combines large-scale case collection, LLM-based automatic annotation, and targeted human-in-the-loop quality refinement. We formulate legal argument mining as a six-class sentence classification task and evaluate multiple general-purpose and legal-domain language models under zero-shot, few-shot, and chain-of-thought prompting strategies, with LegalBERT as a supervised baseline. Results show that chain-of-thought prompting substantially improves LLM performance, while domain-specific models exhibit more stable zero-shot behavior. LLM-assisted verification corrects nearly 20% of annotation errors, improving label consistency. Human verification achieves Cohen’s Kappa of 0.85, confirming annotation quality. LAMUS provides a scalable resource and empirical insights for future legal NLP research. All code and datasets can be accessed for reproducibility on GitHub at: this https URL
[NLP-18] Using Multimodal and Language-Agnostic Sentence Embeddings for Abstractive Summarization LREC2026
【速读】： 该论文旨在解决生成式摘要（Abstractive Summarization）中存在的事实性不一致问题，尤其是模型引入虚假信息的“幻觉”现象。其解决方案的关键在于提出了一种名为SBARThez的新框架，该框架结合了多模态和多语言句子嵌入（如LaBSE、SONAR和BGE-M3），并基于改进的BART模型实现；同时引入命名实体注入（Named Entity Injection）机制，在解码器输入中添加分词后的命名实体，从而提升生成摘要的事实一致性，尤其在低资源语言上表现优越，且支持跨语言摘要与文本/语音输入。
链接: https://arxiv.org/abs/2603.08282
作者: Chaimae Chellaf,Salima Mdhaffar,Yannick Estève,Stéphane Huet
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted at LREC 2026
Abstract:Abstractive summarization aims to generate concise summaries by creating new sentences, allowing for flexible rephrasing. However, this approach can be vulnerable to inaccuracies, particularly `hallucinations’ where the model introduces non-existent information. In this paper, we leverage the use of multimodal and multilingual sentence embeddings derived from pretrained models such as LaBSE, SONAR, and BGE-M3, and feed them into a modified BART-based French model. A Named Entity Injection mechanism that appends tokenized named entities to the decoder input is introduced, in order to improve the factual consistency of the generated summary. Our novel framework, SBARThez, is applicable to both text and speech inputs and supports cross-lingual summarization; it shows competitive performance relative to token-level baselines, especially for low-resource languages, while generating more concise and abstract summaries.
[NLP-19] Evaluating LLM -Based Grant Proposal Review via Structured Perturbations
【速读】： 该论文旨在解决生成式 AI（Generative AI）辅助科研资助申请评审在当前研究生态系统中因处理速度远超人工审核能力而陷入“马尔萨斯陷阱”（Malthusian trap）的问题，即AI生成的提案数量激增导致评审资源严重不足。其解决方案的关键在于构建一个基于扰动（perturbation-based）的评估框架，系统性地测试大型语言模型（LLM）在六个关键质量维度（资金合理性、时间线、能力匹配度、一致性、清晰度和影响力）上的敏感性和判别能力，并比较三种评审架构：单次通读、逐部分分析与“人物委员会”（Council of Personas）集成方法。研究发现，逐部分分析策略在检测准确率和评分一致性上显著优于其他方法，而复杂的集成方法并未带来性能提升；同时指出当前LLM更擅长合规性检查而非整体评估，存在对某些缺陷（如清晰度问题）识别能力弱的问题，表明其当前价值主要体现在作为补充工具而非替代人类专家。
链接: https://arxiv.org/abs/2603.08281
作者: William Thorne,Joseph James,Yang Wang,Chenghua Lin,Diana Maynard
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:As AI-assisted grant proposals outpace manual review capacity in a kind of ``Malthusian trap’’ for the research ecosystem, this paper investigates the capabilities and limitations of LLM-based grant reviewing for high-stakes evaluation. Using six EPSRC proposals, we develop a perturbation-based framework probing LLM sensitivity across six quality axes: funding, timeline, competency, alignment, clarity, and impact. We compare three review architectures: single-pass review, section-by-section analysis, and a ‘Council of Personas’ ensemble emulating expert panels. The section-level approach significantly outperforms alternatives in both detection rate and scoring reliability, while the computationally expensive council method performs no better than baseline. Detection varies substantially by perturbation type, with alignment issues readily identified but clarity flaws largely missed by all systems. Human evaluation shows LLM feedback is largely valid but skewed toward compliance checking over holistic assessment. We conclude that current LLMs may provide supplementary value within EPSRC review but exhibit high variability and misaligned review priorities. We release our code and any non-protected data.
[NLP-20] AdaCultureSafe: Adaptive Cultural Safety Grounded by Cultural Knowledge in Large Language Models
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在跨文化应用中缺乏文化敏感性的问题，即现有研究将文化安全（cultural safety）与文化知识（cultural knowledge）割裂处理，忽视了前者应以后者为基础这一关键前提，导致模型难以生成符合特定文化规范的响应。解决方案的关键在于提出一种联合建模文化安全与文化知识的新范式：首先构建了一个名为AdaCultureSafe的数据集，包含4.8K细粒度文化描述及其对应的48K人工验证的安全导向和知识导向查询，该数据集通过权威文化知识采集、LLM自动化查询生成与大规模人工校验相结合的方式实现；其次，基于该数据集发现LLMs的文化安全能力与其文化知识掌握程度之间无显著相关性，进一步揭示其根源在于预训练与后对齐阶段目标不一致所导致的神经激活差异；最终提出一种以知识为根基的文化安全增强方法，通过强制将文化知识整合进响应生成过程，显著提升模型的文化安全性。
链接: https://arxiv.org/abs/2603.08275
作者: Hankun Kang,Di Lin,Zhirong Liao,Pengfei Bai,Xinyi Zeng,Jiawei Jiang,Yuanyuan Zhu,Tieyun Qian
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:With the widespread adoption of Large Language Models (LLMs), respecting indigenous cultures becomes essential for models’ culturally safety and responsible global applications. Existing studies separately consider cultural safety and cultural knowledge and neglect that the former should be grounded by the latter. This severely prevents LLMs from yielding culture-specific respectful responses. Consequently, adaptive cultural safety remains a formidable task. In this work, we propose to jointly model cultural safety and knowledge. First and foremost, cultural-safety and knowledge-paired data serve as the key prerequisite to conduct this research. However, the cultural diversity across regions and the subtlety of cultural differences pose significant challenges to the creation of such paired evaluation data. To address this issue, we propose a novel framework that integrates authoritative cultural knowledge descriptions curation, LLM-automated query generation, and heavy manual verification. Accordingly, we obtain a dataset named AdaCultureSafe containing 4.8K manually decomposed fine-grained cultural descriptions and the corresponding 48K manually verified safety- and knowledge-oriented queries. Upon the constructed dataset, we evaluate three families of popular LLMs on their cultural safety and knowledge proficiency, via which we make a critical discovery: no significant correlation exists between their cultural safety and knowledge proficiency. We then delve into the utility-related neuron activations within LLMs to investigate the potential cause of the absence of correlation, which can be attributed to the difference of the objectives of pre-training and post-alignment. We finally present a knowledge-grounded method, which significantly enhances cultural safety by enforcing the integration of knowledge into the LLM response generation process.
[NLP-21] How Much Do LLM s Hallucinate in Document QA Scenarios? A 172-Billion-Token Study Across Temperatures Context Lengths and Hardware Platforms
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在基于给定文档回答问题时的幻觉（Hallucination）程度难以可靠测量的问题。现有评估方法受限于静态数据集易被污染、LLM评分器存在已知偏倚，以及评价尺度过小导致统计置信度不足。其解决方案的关键在于提出 RIKER——一种以真实答案（ground-truth）优先的评估方法，通过确定性打分机制实现无需人工标注的量化评估。该方法在超过 1720 亿 token 的大规模测试中验证了模型幻觉率、温度设置影响、硬件平台无关性等关键发现，为部署企业级生成式 AI 提供了可信赖的基准依据。
链接: https://arxiv.org/abs/2603.08274
作者: JV Roig
机构: Kamiwaza AI(卡米瓦扎人工智能)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 18 pages, 12 tables, 2 figures
Abstract:How much do large language models actually hallucinate when answering questions grounded in provided documents? Despite the critical importance of this question for enterprise AI deployments, reliable measurement has been hampered by benchmarks that rely on static datasets vulnerable to contamination, LLM-based judges with documented biases, or evaluation scales too small for statistical confidence. We address this gap using RIKER, a ground-truth-first evaluation methodology that enables deterministic scoring without human annotation. Across 35 open-weight models, three context lengths (32K, 128K, and 200K tokens), four temperature settings, and three hardware platforms (NVIDIA H200, AMD MI300X, and Intel Gaudi 3), we conducted over 172 billion tokens of evaluation - an order of magnitude beyond prior work. Our findings reveal that: (1) even the best-performing models fabricate answers at a non-trivial rate - 1.19% at best at 32K, with top-tier models at 5 - 7% - and fabrication rises steeply with context length, nearly tripling at 128K and exceeding 10% for all models at 200K; (2) model selection dominates all other factors, with overall accuracy spanning a 72-percentage-point range and model family predicting fabrication resistance better than model size; (3) temperature effects are nuanced - T=0.0 yields the best overall accuracy in roughly 60% of cases, but higher temperatures reduce fabrication for the majority of models and dramatically reduce coherence loss (infinite generation loops), which can reach 48x higher rates at T=0.0 versus T=1.0; (4) grounding ability and fabrication resistance are distinct capabilities - models that excel at finding facts may still fabricate facts that do not exist; and (5) results are consistent across hardware platforms, confirming that deployment decisions need not be hardware-dependent.
[NLP-22] NCL-UoR at SemEval-2026 Task 5: Embedding-Based Methods Fine-Tuning and LLM s for Word Sense Plausibility Rating
【速读】： 该论文旨在解决词语义合理性评分（word sense plausibility rating）问题，即在包含歧义同形词的短篇叙事故事中，预测人类对特定词义在语境下合理性的感知评分（1–5级）。其解决方案的关键在于采用结构化提示（structured prompting）策略，将评价过程分解为叙事组件（前文语境、目标句、结尾），并引入显式决策规则进行评分校准，从而显著优于微调模型和基于嵌入的方法，且表明提示设计的重要性超过模型规模。
链接: https://arxiv.org/abs/2603.08256
作者: Tong Wu,Thanet Markchom,Huizhi Liang
机构: University of Reading (阅读大学); Newcastle University (纽卡斯尔大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Word sense plausibility rating requires predicting the human-perceived plausibility of a given word sense on a 1–5 scale in the context of short narrative stories containing ambiguous homonyms. This paper systematically compares three approaches: (1) embedding-based methods pairing sentence embeddings with standard regressors, (2) transformer fine-tuning with parameter-efficient adaptation, and (3) large language model (LLM) prompting with structured reasoning and explicit decision rules. The best-performing system employs a structured prompting strategy that decomposes evaluation into narrative components (precontext, target sentence, ending) and applies explicit decision rules for rating calibration. The analysis reveals that structured prompting with decision rules substantially outperforms both fine-tuned models and embedding-based approaches, and that prompt design matters more than model scale for this task. The code is publicly available at this https URL.
[NLP-23] Not All Queries Need Deep Thought: CoFiCot for Adaptive Coarse-to-fine Stateful Refinement
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在推理过程中因统一分配计算资源而导致的“计算资源分配悖论”问题，即简单任务因过度修正而浪费资源，复杂任务则因修正不足而性能受限。其解决方案的关键在于提出一种粗到精自适应框架 CoFiCot，通过多指标分类器（融合语义熵、共识可靠性与预测推理深度）对查询进行分诊，并据此差异化地执行优化策略：对简单任务采用高效聚合机制，对复杂任务则引入上下文感知的纠错循环（context-aware correction loop）。该框架将纠错过程形式化为状态依赖的序贯传播过程，确保每一步修复均严格基于先前修正的历史验证结果，从而在细粒度错误定位与全局逻辑一致性之间建立有效桥梁，避免了无状态修正方法常见的上下文碎片化问题。
链接: https://arxiv.org/abs/2603.08251
作者: Dongxu Zhang,Hongqiang Lin,Yiding Sun,Pengyu Wang,Qirui Wang,Ning Yang,Jihua Zhu
机构: Xi’an Jiaotong University (西安交通大学); Zhejiang University (浙江大学); The Chinese University of Hong Kong (Shenzhen) (香港中文大学（深圳）); Institute of Automation (自动化研究所); CASIA (中国科学院自动化研究所)
类目: Computation and Language (cs.CL)
备注:
Abstract:Scaling test-time computation enhances LLM reasoning ability but faces a uniform computation paradox. Allocating identical resources leads to over-correction on simple tasks and insufficient refinement on complex ones. To address this, we propose CoFiCot, a coarse-to-fine adaptive framework that dynamically tailors inference strategies to problem difficulty. Specifically, we implement a multi-metric classifier that triages queries by synthesizing semantic entropy, consensus reliability, and predicted reasoning depth . This enables a differentiated refinement stage that applies efficient aggregation for simple queries while routing complex ones to a context-aware correction loop . We formalize correction as a stateful sequential propagation process , where each repair is strictly conditioned on the verified history of prior rectifications. By integrating Process Reward Models (PRMs) within this state-dependent trajectory, CoFiCot effectively bridges the gap between granular error localization and global logical coherence, preventing the context fragmentation typical of stateless refinement methods.
[NLP-24] Sensivity of LLM s Explanations to the Training Randomness:Context Class Task Dependencies
【速读】： 该论文旨在解决Transformer模型在自然语言处理中决策解释的敏感性问题，即相同模型在相同数据上因训练随机性不同而产生差异显著的解释结果。其解决方案的关键在于系统性地分析语法上下文（syntactic context）、待学习类别（classes）以及任务类型（tasks）对解释敏感性的影响程度，发现三者均具有统计学显著影响，其中任务类型影响最大，类别次之，语法上下文最小。这一发现为提升模型可解释性的稳定性提供了重要依据。
链接: https://arxiv.org/abs/2603.08241
作者: Romain Loncour,Jérémie Bogaert,François-Xavier Standaert
机构: UCLouvain(鲁汶大学)
类目: Computation and Language (cs.CL)
备注: 6 pages, 6 figures
Abstract:Transformer models are now a cornerstone in natural language processing. Yet, explaining their decisions remains a challenge. It was shown recently that the same model trained on the same data with a different randomness can lead to very different explanations. In this paper, we investigate how the (syntactic) context, the classes to be learned and the tasks influence this explanations’ sensitivity to randomness. We show that they all have statistically significant impact: smallest for the (syntactic) context, medium for the classes and largest for the tasks.
[NLP-25] Fibration Policy Optimization
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在多尺度训练中缺乏统一稳定控制机制的问题，尤其是当前基于局部目标的优化方法难以同时保障词元级（token-level）、轨迹级（trajectory-level）及更高层次的分层稳定性。其核心解决方案是提出Aggregational Policy Censoring Objective（APC-Obj），首次实现了基于样本的总变差信任区域策略梯度（TV-TRPO）的无约束精确重构，并揭示了剪裁型代理目标与信任区域优化本质上为同一问题的对偶表述；在此基础上构建Fiber Bundle Gating（FBG）框架，将采样强化学习数据组织为纤维丛结构，将比例门控分解为基底层级（轨迹聚合）与纤维层级（词元残差）两个独立门控，且在策略在线时具有与真实强化学习目标的一阶一致性。由此衍生出Fibration Policy Optimization（FiberPO），其雅可比矩阵沿轨迹呈块对角结构、在线时退化为单位矩阵，从而提供更优更新方向并提升词元效率。该框架的组合性质进一步扩展为Fibration Gating Hierarchy（FGH），无需引入新原语即可将相同门控机制应用于任意层级深度，如FiberPO-Domain实例展示了领域、提示组、轨迹和词元四个层级独立的信任区域预算配置，实现了从理论到实践的多尺度稳定性控制统一建模。
链接: https://arxiv.org/abs/2603.08239
作者: Chang Li,Tshihao Tsu,Yaren Zhang,Chao Xue,Xiaodong He
机构: JD Explore Academy; Carleton University
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Large language models are increasingly trained as heterogeneous systems spanning multiple domains, expert partitions, and agentic pipelines, yet prevalent proximal objectives operate at a single scale and lack a principled mechanism for coupling token-level, trajectory-level, and higher-level hierarchical stability control. To bridge this gap, we derive the Aggregational Policy Censoring Objective (APC-Obj), the first exact unconstrained reformulation of sample-based TV-TRPO, establishing that clipping-based surrogate design and trust-region optimization are dual formulations of the same problem. Building on this foundation, we develop Fiber Bundle Gating (FBG), an algebraic framework that organizes sampled RL data as a fiber bundle and decomposes ratio gating into a base-level gate on trajectory aggregates and a fiber-level gate on per-token residuals, with provable first-order agreement with the true RL objective near on-policy. From APC-Obj and FBG we derive Fibration Policy Optimization (or simply, FiberPO), a concrete objective whose Jacobian is block-diagonal over trajectories, reduces to identity at on-policy, and provides better update direction thus improving token efficiency. The compositional nature of the framework extends beyond the trajectory-token case: fibrations compose algebraically into a Fibration Gating Hierarchy (FGH) that scales the same gating mechanism to arbitrary hierarchical depth without new primitives, as demonstrated by FiberPO-Domain, a four-level instantiation with independent trust-region budgets at the domain, prompt group, trajectory, and token levels. Together, these results connect the trust-region theory, a compositional algebraic structure, and practical multi-scale stability control into a unified framework for LLM policy optimization.
[NLP-26] he Conundrum of Trustworthy Research on Attacking Personally Identifiable Information Removal Techniques
【速读】： 该论文旨在解决文本去标识化（PII removal）技术在实际应用中隐私保护有效性难以客观评估的问题。现有研究虽表明此类技术易受重建攻击，但作者指出其评估方法存在数据泄露和数据污染问题，导致攻击成功率被高估，从而无法真实反映隐私保护能力。解决方案的关键在于构建一个能避免数据泄露的攻击场景，即使用真正私有的数据进行实验，以实现对PII移除技术漏洞的客观、可复现且可信的评估。然而，由于隐私数据获取受限，当前公共研究社区难以开展此类验证。
链接: https://arxiv.org/abs/2603.08207
作者: Sebastian Ochs,Ivan Habernal
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted to Computational Linguistics
Abstract:Removing personally identifiable information (PII) from texts is necessary to comply with various data protection regulations and to enable data sharing without compromising privacy. However, recent works show that documents sanitized by PII removal techniques are vulnerable to reconstruction attacks. Yet, we suspect that the reported success of these attacks is largely overestimated. We critically analyze the evaluation of existing attacks and find that data leakage and data contamination are not properly mitigated, leaving the question whether or not PII removal techniques truly protect privacy in real-world scenarios unaddressed. We investigate possible data sources and attack setups that avoid data leakage and conclude that only truly private data can allow us to objectively evaluate vulnerabilities in PII removal techniques. However, access to private data is heavily restricted - and for good reasons - which also means that the public research community cannot address this problem in a transparent, reproducible, and trustworthy manner.
[NLP-27] Supporting Workflow Reproducibility by Linking Bioinformatics Tools across Papers and Executable Code
【速读】： 该论文旨在解决生物信息学计算工作流中叙事描述与代码实现之间缺乏明确关联的问题，从而提升工作流的透明性、可复现性和可重用性。其核心挑战在于将科学论文中提及的生物信息学工具（tool）与工作流代码中的工具调用进行精准匹配。解决方案的关键是提出CoPaLink，一个集成三阶段自动处理流程的方法：首先利用命名实体识别（Named Entity Recognition, NER）分别提取文本和代码中的工具实体；其次基于生物信息学知识库（如Bioconda和Bioweb）进行实体链接，实现跨模态对齐。该方法在Nextflow工作流上评估获得66%的联合准确率，显著提升了从文献描述到代码实现的映射效率与可靠性。
链接: https://arxiv.org/abs/2603.08195
作者: Clémence Sebe,Olivier Ferret,Aurélie Névéol,Mahdi Esmailoghli,Ulf Leser,Sarah Cohen-Boulakia
机构: Université Paris-Saclay (巴黎萨克雷大学); CNRS (法国国家科学研究中心); Laboratoire Interdisciplinaire des Sciences du Numérique (数字科学跨学科实验室); CEA, List (法国原子能和替代能源委员会，列表实验室); Humboldt-Universität zu Berlin (柏林洪堡大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Motivation: The rapid growth of biological data has intensified the need for transparent, reproducible, and well-documented computational workflows. The ability to clearly connect the steps of a workflow in the code with their description in a paper would improve workflow understanding, support reproducibility, and facilitate reuse. This task requires the linking of Bioinformatics tools in workflow code with their mentions in a published workflow description. Results: We present CoPaLink, an automated approach that integrates three components: Named Entity Recognition (NER) for identifying tool mentions in scientific text, NER for tool mentions in workflow code, and entity linking grounded on Bioinformatics knowledge bases. We propose approaches for all three steps achieving a high individual F1-measure (84 - 89) and a joint accuracy of 66 when evaluated on Nextflow workflows using Bioconda and Bioweb Knowledge bases. CoPaLink leverages corpora of scientific articles and workflow executable code with curated tool annotations to bridge the gap between narrative descriptions and workflow implementations. Availability: The code is available at this https URL and this https URL. The corpora are also available at this https URL, this https URL and this https URL.
[NLP-28] deOpen LLM : Leverag ing Curriculum Learning to Achieve Equitable Language Representation LREC2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在欧洲多种低资源语言中性能不足的问题，其根本原因在于训练数据对英语及少数高资源语言的显著偏倚。解决方案的关键在于通过数据集上采样（dataset upsampling）与基于课程学习（curriculum-based training schedule）的训练策略相结合，交替使用均匀分布和自然语言分布进行训练，从而有效缓解数据不平衡问题。这一方法在显著减少计算资源投入的前提下，使TildeOpen LLM在34种欧洲语言上的文本生成与理解能力优于现有开源多语言模型，尤其在波罗的海、芬兰-乌戈尔语系和斯拉夫语系语言中表现突出。
链接: https://arxiv.org/abs/2603.08182
作者: Toms Bergmanis,Martins Kronis,Ingus Jānis Pretkalniņš,Dāvis Nicmanis,Jeļizaveta Jeļinska,Roberts Rozis,Rinalds Vīksna,Mārcis Pinnis
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: LREC 2026
Abstract:Large language models often underperform in many European languages due to the dominance of English and a few high-resource languages in training data. This paper presents TildeOpen LLM, a 30-billion-parameter open-weight foundational model trained for 34 European languages to promote linguistic equity and improve performance for low-resource languages. To address the data imbalance, we combine dataset upsampling with a curriculum-based training schedule that alternates between uniform and natural language distributions. The resulting model performs favorably compared to other multilingual LLMs despite being trained with significantly fewer computing resources. Evaluation across multiple multilingual benchmarks shows that TildeOpen surpasses existing open-weight models in text generation and comprehension, particularly for Baltic, Finno-Ugric, and Slavic languages. Human evaluations confirm an up to tenfold reduction in linguistic errors relative to leading baselines. The model and associated resources are fully open-weight and publicly available at this http URL. These outcomes demonstrate that careful data curation and balanced training strategies can substantially enhance multilingual model quality without increasing model size or training volume.
[NLP-29] Is continuous CoT better suited for multi-lingual reasoning ? ICLR
【速读】： 该论文旨在解决多语言环境下模型推理能力在低资源语言中表现不佳的问题，尤其是在零样本（zero-shot）场景下缺乏泛化能力的挑战。其解决方案的关键在于采用连续思维链（Continuous Chain-of-Thought, CCoT）方法，通过在连续潜在空间（continuous latent space）中进行推理，而非传统的显式符号化推理路径，从而显著提升跨语言推理的鲁棒性与效率。实验表明，该方法在五种语系差异较大的语言（英语、中文、德语、法语和乌尔都语）上均优于标准监督微调（supervised fine-tuning），尤其在低资源语言中优势明显，并实现了推理轨迹压缩约29至50倍的极致效率。这说明连续潜表示天然具备更强的语言不变性（language invariance），为可扩展的跨语言推理提供了新范式。
链接: https://arxiv.org/abs/2603.08177
作者: Ali Hamza Bashir,Behzad Shomali,Markus Frey,Mehdi Ali,Rafet Sifa,David Berghaus
机构: Lamarr Institute (Lamarr研究所); Fraunhofer IAIS (弗劳恩霍夫信息学与应用数学研究所); University of Bonn (波恩大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at the ICLR latent reasoning workshop
Abstract:We investigate whether performing reasoning in a continuous latent space leads to more robust multilingual capabilities. We compare Continuous Chain-of-Thought (using the CODI framework) against standard supervised fine-tuning across five typologically diverse languages: English, Chinese, German, French, and Urdu. Our experiments on GSM8k and CommonsenseQA demonstrate that continuous reasoning significantly outperforms explicit reasoning on low-resource languages, particularly in zero-shot settings where the target language was not seen during training. Additionally, this approach achieves extreme efficiency, compressing reasoning traces by approximately 29\times to 50\times . These findings indicate that continuous latent representations naturally exhibit greater language invariance, offering a scalable solution for cross-lingual reasoning.
[NLP-30] RexDrug: Reliable Multi-Drug Combination Extraction through Reasoning -Enhanced LLM s
【速读】： 该论文旨在解决从大规模生物医学文献中自动提取多药联合治疗（n-ary drug combination）关系的难题，现有方法主要聚焦于二元相互作用，难以建模变量长度的复杂药物组合及其分布式的证据支持。解决方案的关键在于提出RexDrug框架，其核心创新为两阶段训练策略：首先利用多智能体协同机制自动生成高质量专家级推理轨迹用于监督微调；其次采用针对药物组合抽取任务定制的多维奖励函数进行强化学习优化，从而提升推理质量与抽取准确性。
链接: https://arxiv.org/abs/2603.08166
作者: Zhijun Wang,Ling Luo,Dinghao Pan,Huan Zhuang,Lejing Yu,Yuanyuan Sun,Hongfei Lin
机构: Dalian University of Technology (大连理工大学); Cancer Hospital of Dalian University of Technology (大连理工大学癌症医院)
类目: Computation and Language (cs.CL)
备注: 21 pages, 7 figures
Abstract:Automated Drug Combination Extraction (DCE) from large-scale biomedical literature is crucial for advancing precision medicine and pharmacological research. However, existing relation extraction methods primarily focus on binary interactions and struggle to model variable-length n-ary drug combinations, where complex compatibility logic and distributed evidence need to be considered. To address these limitations, we propose RexDrug, an end-to-end reasoning-enhanced relation extraction framework for n-ary drug combination extraction based on large language models. RexDrug adopts a two-stage training strategy. First, a multi-agent collaborative mechanism is utilized to automatically generate high-quality expert-like reasoning traces for supervised fine-tuning. Second, reinforcement learning with a multi-dimensional reward function specifically tailored for DCE is applied to further refine reasoning quality and extraction accuracy. Extensive experiments on the DrugComb dataset show that RexDrug consistently outperforms state-of-the-art baselines for n-ary extraction. Additional evaluation on the DDI13 corpus confirms its generalizability to binary drugdrug interaction tasks. Human expert assessment and automatic reasoning metrics further indicates that RexDrug produces coherent medical reasoning while accurately identifying complex therapeutic regimens. These results establish RexDrug as a scalable and reliable solution for complex biomedical relation extraction from unstructured text. The source code and data are available at this https URL
[NLP-31] Gender Bias in MT for a Genderless Language: New Benchmarks for Basque
【速读】： 该论文旨在解决当前性别偏见评估资源主要针对英语且受限于其社会文化背景的问题，从而难以适用于其他语言（尤其是低资源、无性别标记语言）的局限性。解决方案的关键在于构建两个新的基准数据集：WinoMTeus 和 FLORES+Gender，分别用于评估巴斯克语（一种无性别标记语言）在翻译为有性别标记语言（如西班牙语和法语）时是否产生性别偏向，以及从有性别标记语言翻译至巴斯克语时是否存在因指称对象性别不同而导致的翻译质量差异。通过这两个数据集对多种通用大语言模型（LLMs）和机器翻译（MT）系统进行评估，揭示了系统性偏好男性形式的现象，并强调了需结合语言特征与文化背景开发更全面的偏见评估方法。
链接: https://arxiv.org/abs/2603.08153
作者: Amaia Murillo,Olatz-Perez-de-Viñaspre,Naiara Perez
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) and machine translation (MT) systems are increasingly used in our daily lives, but their outputs can reproduce gender bias present in the training data. Most resources for evaluating such biases are designed for English and reflect its sociocultural context, which limits their applicability to other languages. This work addresses this gap by introducing two new datasets to evaluate gender bias in translations involving Basque, a low-resource and genderless language. WinoMTeus adapts the WinoMT benchmark to examine how gender-neutral Basque occupations are translated into gendered languages such as Spanish and French. FLORES+Gender, in turn, extends the FLORES+ benchmark to assess whether translation quality varies when translating from gendered languages (Spanish and English) into Basque depending on the gender of the referent. We evaluate several general-purpose LLMs and open and proprietary MT systems. The results reveal a systematic preference for masculine forms and, in some models, a slightly higher quality for masculine referents. Overall, these findings show that gender bias is still deeply rooted in these models, and highlight the need to develop evaluation methods that consider both linguistic features and cultural context.
[NLP-32] Gradually Excavating External Knowledge for Implicit Complex Question Answering EMNLP
【速读】： 该论文旨在解决开放域复杂问答任务中大语言模型（Large Language Models, LLMs）因知识覆盖不全或过时、以及单次生成导致推理不充分的问题。其解决方案的关键在于提出一种渐进式知识挖掘框架，通过让LLM在解答过程中迭代地主动获取外部知识，并基于历史积累的知识进行逻辑推理；每一步决策均选择执行查询外部知识或单一逻辑推理等动作，从而逐步逼近最终答案。该方法能有效利用插件式外部知识源并动态调整策略，在StrategyQA数据集上以少于竞争对手6%的参数量实现78.17%的准确率，成为约100亿参数规模模型的新SOTA。
链接: https://arxiv.org/abs/2603.08148
作者: Chang Liu,Xiaoguang Li,Lifeng Shang,Xin Jiang,Qun Liu,Edmund Y. Lam,Ngai Wong
机构: The University of Hong Kong (香港大学); Huawei Noah’s Ark Lab (华为诺亚方舟实验室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 13 pages, 3 figures, EMNLP findings 2023
Abstract:Recently, large language models (LLMs) have gained much attention for the emergence of human-comparable capabilities and huge potential. However, for open-domain implicit question-answering problems, LLMs may not be the ultimate solution due to the reasons of: 1) uncovered or out-of-date domain knowledge, 2) one-shot generation and hence restricted comprehensiveness. To this end, this work proposes a gradual knowledge excavation framework for open-domain complex question answering, where LLMs iteratively and actively acquire external information, and then reason based on acquired historical knowledge. Specifically, during each step of the solving process, the model selects an action to execute, such as querying external knowledge or performing a single logical reasoning step, to gradually progress toward a final answer. Our method can effectively leverage plug-and-play external knowledge and dynamically adjust the strategy for solving complex questions. Evaluated on the StrategyQA dataset, our method achieves 78.17% accuracy with less than 6% parameters of its competitors, setting new SOTA for ~10B-scale LLMs.
[NLP-33] EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery
【速读】： 该论文旨在解决当前基于大语言模型（Large Language Models, LLMs）的AI科学家系统在科学发现任务中因依赖静态、人工设计的流水线而缺乏适应性的问题，导致其容易忽略有潜力的研究方向、重复失败实验并追求不可行的假设。解决方案的关键在于提出EvoScientist框架，该框架通过三个专业化代理（Researcher Agent, RA；Engineer Agent, EA；Evolution Manager Agent, EMA）协同工作，并引入两个持久化记忆模块——“构想记忆”（ideation memory）和“实验记忆”（experimentation memory），实现研究策略的持续进化与优化。其中，EMA从历史交互中提炼可复用知识，RA和EA则基于记忆模块检索过往有效策略，从而提升科学想法的质量与代码执行成功率，最终实现端到端的自适应科学研究。
链接: https://arxiv.org/abs/2603.08127
作者: Yougang Lyu,Xi Zhang,Xinhao Yi,Yuyue Zhao,Shuyu Guo,Wenxiang Hu,Jan Piotrowski,Jakub Kaliski,Jacopo Urbani,Zaiqiao Meng,Lun Zhou,Xiaohui Yan
机构: Huawei Technologies Co., Ltd.(华为技术有限公司); Vrije Universiteit Amsterdam(阿姆斯特丹自由大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:The increasing adoption of Large Language Models (LLMs) has enabled AI scientists to perform complex end-to-end scientific discovery tasks requiring coordination of specialized roles, including idea generation and experimental execution. However, most state-of-the-art AI scientist systems rely on static, hand-designed pipelines and fail to adapt based on accumulated interaction histories. As a result, these systems overlook promising research directions, repeat failed experiments, and pursue infeasible ideas. To address this, we introduce EvoScientist, an evolving multi-agent AI scientist framework that continuously improves research strategies through persistent memory and self-evolution. EvoScientist comprises three specialized agents: a Researcher Agent (RA) for scientific idea generation, an Engineer Agent (EA) for experiment implementation and execution, and an Evolution Manager Agent (EMA) that distills insights from prior interactions into reusable knowledge. EvoScientist contains two persistent memory modules: (i) an ideation memory, which summarizes feasible research directions from top-ranked ideas while recording previously unsuccessful directions; and (ii) an experimentation memory, which captures effective data processing and model training strategies derived from code search trajectories and best-performing implementations. These modules enable the RA and EA to retrieve relevant prior strategies, improving idea quality and code execution success rates over time. Experiments show that EvoScientist outperforms 7 open-source and commercial state-of-the-art systems in scientific idea generation, achieving higher novelty, feasibility, relevance, and clarity via automatic and human evaluation. EvoScientist also substantially improves code execution success rates through multi-agent evolution, demonstrating persistent memory’s effectiveness for end-to-end scientific discovery.
[NLP-34] Ramsa: A Large Sociolinguistically Rich Emirati Arabic Speech Corpus for ASR and TTS
【速读】： 该论文旨在解决低资源语言技术（尤其是阿拉伯语方言）在语音识别（ASR）和文本到语音合成（TTS）方面的性能瓶颈问题，特别是在阿联酋阿拉伯语（Emirati Arabic）这一代表性方言上缺乏高质量标注数据集的现状。其解决方案的关键在于构建并公开发布了一个大规模、多样化的41小时语音语料库——Ramsa，该语料库涵盖多种子方言（Urban、Bedouin、Mountain/Shihhi）、多类主题（文化传承、农业与可持续发展等），并包含单人独白和对话录音共170段，为后续研究提供了坚实的数据基础。此外，通过在零样本设置下评估多个商用及开源模型，确立了当前ASR和TTS的初步基线性能，其中Whisper-large-v3-turbo在ASR上表现最优（平均词错误率0.268，字符错误率0.144），MMS-TTS-Ara在TTS上最佳（平均词错误率0.285，字符错误率0.081），从而揭示了现有模型在处理该方言时仍存在显著提升空间，为未来针对性优化指明方向。
链接: https://arxiv.org/abs/2603.08125
作者: Rania Al-Sabbagh
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Ramsa is a developing 41-hour speech corpus of Emirati Arabic designed to support sociolinguistic research and low-resource language technologies. It contains recordings from structured interviews with native speakers and episodes from national television shows. The corpus features 157 speakers (59 female, 98 male), spans subdialects such as Urban, Bedouin, and Mountain/Shihhi, and covers topics such as cultural heritage, agriculture and sustainability, daily life, professional trajectories, and architecture. It consists of 91 monologic and 79 dialogic recordings, varying in length and recording conditions. A 10% subset was used to evaluate commercial and open-source models for automatic speech recognition (ASR) and text-to-speech (TTS) in a zero-shot setting to establish initial baselines. Whisper-large-v3-turbo achieved the best ASR performance, with average word and character error rates of 0.268 and 0.144, respectively. MMS-TTS-Ara reported the best mean word and character rates of 0.285 and 0.081, respectively, for TTS. These baselines are competitive but leave substantial room for improvement. The paper highlights the challenges encountered and provides directions for future work.
[NLP-35] DC-W2S: Dual-Consensus Weak-to-Strong Training for Reliable Process Reward Modeling in Biological Reasoning
【速读】： 该论文旨在解决在科学推理任务中，如何利用大量但噪声较大的“弱”监督数据训练可靠的Process Reward Models (PRMs)，以克服传统Outcome Reward Models (ORMs) 在细粒度监督上的局限性及专家标注成本过高的问题。其解决方案的关键在于提出Dual-Consensus Weak-to-Strong (DC-W2S) 框架：通过结合弱监督者之间的Self-Consensus (SC) 与嵌入空间中的Neighborhood-Consensus (NC) 来对监督信号进行可靠性分层，并采用实例级平衡采样和标签级可靠性感知掩码的课程学习策略，从而实现从噪声数据中高效提取高质量训练信号，显著提升PRM的鲁棒性，而无需依赖详尽的专家标注。
链接: https://arxiv.org/abs/2603.08095
作者: Chi-Min Chan,Ehsan Hajiramezanali,Xiner Li,Edward De Brouwer,Carl Edwards,Wei Xue,Sirui Han,Yike Guo,Gabriele Scalia
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:In scientific reasoning tasks, the veracity of the reasoning process is as critical as the final outcome. While Process Reward Models (PRMs) offer a solution to the coarse-grained supervision problems inherent in Outcome Reward Models (ORMs), their deployment is hindered by the prohibitive cost of obtaining expert-verified step-wise labels. This paper addresses the challenge of training reliable PRMs using abundant but noisy “weak” supervision. We argue that existing Weak-to-Strong Generalization (W2SG) theories lack prescriptive guidelines for selecting high-quality training signals from noisy data. To bridge this gap, we introduce the Dual-Consensus Weak-to-Strong (DC-W2S) framework. By intersecting Self-Consensus (SC) metrics among weak supervisors with Neighborhood-Consensus (NC) metrics in the embedding space, we stratify supervision signals into distinct reliability regimes. We then employ a curriculum of instance-level balanced sampling and label-level reliability-aware masking to guide the training process. We demonstrate that DC-W2S enables the training of robust PRMs for complex reasoning without exhaustive expert annotation, proving that strategic data curation is more effective than indiscriminate training on large-scale noisy datasets.
[NLP-36] oward Robust LLM -Based Judges: Taxonomic Bias Evaluation and Debiasing Optimization
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）作为评判者在自动化评估和奖励建模中普遍存在的判断偏差问题，这些问题会显著影响评估结果的可靠性。现有研究通常仅在单一判别或生成式评判框架下考察有限类型的偏差，缺乏系统性量化与全面评估。为应对这一挑战，作者提出JudgeBiasBench基准，构建涵盖四个维度、12种典型偏差类型的可控偏置注入数据集，并对生成式与判别式LLM评判者进行广泛实验，揭示其存在多样且严重的偏差模式。解决方案的关键在于引入“偏置感知训练”（bias-aware training），通过显式地将偏置相关属性纳入训练过程，促使评判者从任务相关的质量特征中解耦出与偏置相关的线索；具体而言，针对生成式评判者采用强化学习，判别式评判者采用对比学习，有效降低判断偏差的同时保持了整体评估能力。
链接: https://arxiv.org/abs/2603.08091
作者: Hongli Zhou,Hui Huang,Rui Zhang,Kehai Chen,Bing Xu,Conghui Zhu,Tiejun Zhao,Muyun Yang
机构: Harbin Institute of Technology (哈尔滨工业大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language model (LLM)-based judges are widely adopted for automated evaluation and reward modeling, yet their judgments are often affected by judgment biases. Accurately evaluating these biases is essential for ensuring the reliability of LLM-based judges. However, existing studies typically investigate limited biases under a single judge formulation, either generative or discriminative, lacking a comprehensive evaluation. To bridge this gap, we propose JudgeBiasBench, a benchmark for systematically quantifying biases in LLM-based judges. JudgeBiasBench defines a taxonomy of judgment biases across 4 dimensions, and constructs bias-augmented evaluation instances through a controlled bias injection pipeline, covering 12 representative bias types. We conduct extensive experiments across both generative and discriminative judges, revealing that current judges exhibit significant and diverse bias patterns that often compromise the reliability of automated evaluation. To mitigate judgment bias, we propose bias-aware training that explicitly incorporates bias-related attributes into the training process, encouraging judges to disentangle task-relevant quality from bias-correlated cues. By adopting reinforcement learning for generative judges and contrastive learning for discriminative judges, our methods effectively reduce judgment biases while largely preserving general evaluation capability.
[NLP-37] High-Fidelity Pruning for Large Language Models
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在部署时因计算和内存开销过大而导致的效率瓶颈问题，尤其聚焦于神经元剪枝（neuron pruning）中重要性评估不全面的局限性。现有方法通常基于单标签交叉熵损失（one-hot cross entropy loss）进行Taylor展开来估计神经元重要性，仅关注预测下一个token的概率，忽略了模型对其他潜在输出的分布信息，从而导致剪枝策略局部且不充分。解决方案的关键在于提出一种无需额外教师模型（teacher model）的信息熵准则（information entropy of the model’s output distribution），作为Taylor剪枝的替代指标，以全局视角衡量神经元对模型预测能力的影响，从而更精准地识别可剪枝神经元，提升剪枝后的模型保真度与性能。
链接: https://arxiv.org/abs/2603.08083
作者: Yijun Zhu,Jianxin Wang,Chengchao Shen
机构: Central South University (中南大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large Language Models (LLMs) have demonstrated exceptional performance across a wide range of tasks, yet their significant computational and memory requirements present major challenges for deployment. A common approach uses Taylor expansion on the loss function to estimate neuron importance. However, its reliance on one-hot cross entropy loss, a key limitation is that it narrowly assesses importance based only on the probability assigned to the single predicted next token, thereby ignoring the other potential predictions of the original model. An intuitive solution to address this is to employ self distillation criterion for importance evaluation. However, this approach introduces significant computational overhead by requiring a separate teacher model for supervision. To this end, we propose a simple but effective criterion, information entropy of the model’s output distribution, to efficiently evaluate importance scores of neurons with Taylor pruning without requirement of additional teacher. Compared to plain cross entropy criterion, it provides a more holistic criterion for Taylor pruning to prune neurons with the least impact on the prediction of model in a global manner, thereby preserving the fidelity of the model’s predictive capabilities. Experimental results on extensive zero-shot benchmarks demonstrate that our method consistently outperforms existing pruning methods across the LLaMA and Qwen series models. The source code and trained weights are availabel at this https URL.
[NLP-38] Deterministic Differentiable Structured Pruning for Large Language Models
【速读】： 该论文旨在解决大型语言模型（Large Language Model, LLM）推理过程中计算成本过高问题，核心在于通过结构化剪枝（Structured Pruning）移除低重要性的模型组件以降低推理开销。传统方法通常在 l0 稀疏性约束下学习每个组件的乘法门控（multiplicative gate），但由于 l0 范数的离散特性，常采用随机硬混凝土（stochastic hard-concrete）松弛来实现可微优化，这会引入训练-测试不一致问题，并限制门控掩码（mask）只能在近二值范围内取值。本文提出确定性可微剪枝（Deterministic Differentiable Pruning, DDP），其关键创新在于直接优化一个确定性的软代理目标函数，替代原离散 l0 目标，从而消除随机性、减少训练与部署之间的差异，并提升收敛速度和表达能力。实验表明，DDP 在多个稠密模型和稀疏专家模型（MoE）上均能实现接近无损的性能表现（如 Qwen3-32B 在 20% 剪枝率下仅损失 1% 性能），并显著优于现有方法。
链接: https://arxiv.org/abs/2603.08065
作者: Weiyu Huang,Pengle Zhang,Xiaolu Zhang,Jun Zhou,Jun Zhu,Jianfei Chen
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Structured pruning reduces LLM inference cost by removing low-importance architectural components. This can be viewed as learning a multiplicative gate for each component under an l0 sparsity constraint. Due to the discreteness of the l0 norm, prior work typically adopts stochastic hard-concrete relaxations to enable differentiable optimization; however, this stochasticity can introduce a train–test mismatch when sampled masks are discretized for deployment and restricts masks to a bounded, near-binary range. To address this, we propose Deterministic Differentiable Pruning (DDP), a mask-only optimization method that eliminates stochasticity by directly optimizing a deterministic soft surrogate of the discrete l0 objective. Compared with prior approaches, DDP offers greater expressiveness, reduced train–test mismatch, and faster convergence. We apply our method to several dense and MoE models, including Qwen3-32B and Qwen3-30B-A3B, achieving a performance loss as small as 1% on downstream tasks while outperforming previous methods at 20% sparsity. We further demonstrate end-to-end inference speedups in realistic deployment settings with vLLM.
[NLP-39] Examining the Role of YouTube Production and Consumption Dynamics on the Formation of Extreme Ideologies
【速读】： 该论文试图解决的问题是：在算法驱动的平台（如YouTube）中，内容生产与消费之间的互动如何影响用户意识形态的演变，尤其是极端意识形态的形成机制尚不明确。解决方案的关键在于采用纵向混合方法，结合一年的YouTube观看历史数据与来自1,100名美国用户的两轮意识形态调查问卷，识别出意识形态显著极化的用户群体，并系统比较其内容消费模式及所关注频道的内容生产特征。研究发现，极端化用户表现出独特的消费习惯，且其所偏好的频道更倾向于生成具有高愤怒、怨恨等情绪标记的内容；进一步通过时间序列分析验证了内容生产者并非仅被动响应用户需求，而是可能主动引导消费行为，从而揭示了内容生产端对意识形态极化的关键驱动作用。
链接: https://arxiv.org/abs/2603.08049
作者: Sarmad Chandio,Rishab Nithyanand
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:The relationship between content production and consumption on algorithm-driven platforms like YouTube plays a critical role in shaping ideological behaviors. While prior work has largely focused on user behavior and algorithmic recommendations, the interplay between what is produced and what gets consumed, and its role in ideological shifts remains understudied. In this paper, we present a longitudinal, mixed-methods analysis combining one year of YouTube watch history with two waves of ideological surveys from 1,100 U.S. participants. We identify users who exhibited significant shifts toward more extreme ideologies and compare their content consumption and the production patterns of YouTube channels they engaged with to ideologically stable users. Our findings show that users who became more extreme consumed have different consumption habits from those who do not. This gets amplified by the fact that channels favored by users with extreme ideologies also have a higher affinity to produce content with a higher anger, grievance and other such markers. Lastly, using time series analysis, we examine whether content producers are the primary drivers of consumption behavior or merely responding to user demand.
[NLP-40] DyLLM : Efficient Diffusion LLM Inference via Saliency-based Token Selection and Partial Attention
【速读】： 该论文旨在解决掩码扩散语言模型（Masked Diffusion Language Models, MDLMs）在推理阶段因迭代去噪过程导致的计算开销过大的问题。MDLMs虽然支持并行解码，但其每一步都需对整个序列进行重复处理，效率低下。解决方案的关键在于识别出在扩散步骤中具有显著变化的“显著令牌”（salient tokens），这些令牌对下一步更新贡献较大；其余大部分令牌则保持稳定。通过测量相邻去噪步骤间注意力上下文的余弦相似度来判断令牌的显著性，DyLLM仅对显著令牌重新计算前馈和注意力操作，其余令牌复用缓存激活值，从而实现无需训练即可加速推理，最高可达9.6倍吞吐量提升，同时保持与先进模型如LLaDA和Dream相当的生成质量。
链接: https://arxiv.org/abs/2603.08026
作者: Younjoo Lee,Junghoo Lee,Seungkyun Dan,Jaiyoung Park,Jung Ho Ahn
机构: Seoul National University (首尔国立大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Performance (cs.PF)
备注: 18 pages, 10 figures
Abstract:Masked Diffusion Language Models (MDLMs) enable parallel token decoding, providing a promising alternative to the sequential nature of autoregressive generation. However, their iterative denoising process remains computationally expensive because it repeatedly processes the entire sequence at every step. We observe that across these diffusion steps, most token representations remain stable; only a small subset, which we term salient tokens, contributes meaningfully to the next update. Leveraging this temporal sparsity, we present DyLLM, a training-free inference framework that accelerates decoding by selectively computing only these salient tokens. DyLLM identifies saliency by measuring the cosine similarity of attention contexts between adjacent denoising steps. It recomputes feed-forward and attention operations only for salient tokens while reusing cached activations for the remainder. Across diverse reasoning and code-generation benchmarks, DyLLM achieves up to 9.6x higher throughput while largely preserving the baseline accuracy of state-of-the-art models like LLaDA and Dream.
[NLP-41] ConflictBench: Evaluating Human-AI Conflict via Interactive and Visually Grounded Environments
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）作为自主代理在开放环境中行为对齐（behavioral alignment）的安全性问题，尤其是现有基准测试因局限于静态、单轮提示而无法捕捉真实世界中多轮交互与多模态冲突的动态特性。其解决方案的关键在于提出ConflictBench——一个基于150个多轮冲突场景构建的基准测试，融合文本模拟引擎与视觉感知的世界模型，使代理能够在动态条件下进行感知、规划与行动；实证结果表明，该框架能有效揭示传统基准未暴露的对齐失效现象，如代理在延迟或低风险情境下倾向于自我保护或采取欺骗策略，并在压力加剧时逆转对齐决策，尤其在引入视觉输入后更为显著。
链接: https://arxiv.org/abs/2603.08024
作者: Weixiang Zhao,Haozhen Li,Yanyan Zhao,xuda zhi,Yongbo Huang,Hao He,Bing Qin,Ting Liu
机构: Harbin Institute of Technology (哈尔滨工业大学); SERES
类目: Computation and Language (cs.CL)
备注: 29 pages, 20 figures, 9 tables
Abstract:As large language models (LLMs) evolve into autonomous agents capable of acting in open-ended environments, ensuring behavioral alignment with human values becomes a critical safety concern. Existing benchmarks, focused on static, single-turn prompts, fail to capture the interactive and multi-modal nature of real-world conflicts. We introduce ConflictBench, a benchmark for evaluating human-AI conflict through 150 multi-turn scenarios derived from prior alignment queries. ConflictBench integrates a text-based simulation engine with a visually grounded world model, enabling agents to perceive, plan, and act under dynamic conditions. Empirical results show that while agents often act safely when human harm is immediate, they frequently prioritize self-preservation or adopt deceptive strategies in delayed or low-risk settings. A regret test further reveals that aligned decisions are often reversed under escalating pressure, especially with visual input. These findings underscore the need for interaction-level, multi-modal evaluation to surface alignment failures that remain hidden in conventional benchmarks.
[NLP-42] SmartThinker: Progressive Chain-of-Thought Length Calibration for Efficient Large Language Model Reasoning
【速读】： 该论文旨在解决大推理模型（Large Reasoning Models, LRM）在复杂任务中因长链式思维（Chain-of-Thought, CoT）推理路径过于冗长而导致的冗余和过度思考问题。现有方法如分组相对策略优化（Group Relative Policy Optimization, GRPO）虽能压缩输出长度，但其静态长度奖励设计无法根据问题难度和响应长度分布动态调整，导致过度压缩并损害准确性。解决方案的关键在于提出 SmartThinker，一种基于 GRPO 的高效推理方法，通过两个核心机制实现：一是训练过程中动态估计达到峰值准确率时的最优 CoT 长度，并引导过长响应向该长度收敛，从而在保持准确率的同时实现长度压缩；二是动态调节长度奖励系数，避免对正确推理路径的不当惩罚，从而在压缩长度的同时提升整体性能。
链接: https://arxiv.org/abs/2603.08000
作者: Chenzhi Hu,Qinzhe Hu,Yuhang Xu,Junyi Chen,Ruijie Wang,Shengzhong Liu,Jianxin Li,Fan Wu,Guihai Chen
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Large reasoning models (LRMs) like OpenAI o1 and DeepSeek-R1 achieve high accuracy on complex tasks by adopting long chain-of-thought (CoT) reasoning paths. However, the inherent verbosity of these processes frequently results in redundancy and overthinking. To address this issue, existing works leverage Group Relative Policy Optimization (GRPO) to reduce LRM output length, but their static length reward design cannot dynamically adapt according to the relative problem difficulty and response length distribution, causing over-compression and compromised accuracy. Therefore, we propose SmartThinker, a novel GRPO-based efficient reasoning method with progressive CoT length calibration. SmartThinker makes a two-fold contribution: First, it dynamically estimates the optimal length with peak accuracy during training and guides overlong responses toward it to reduce response length while sustaining accuracy. Second, it dynamically modulates the length reward coefficient to avoid the unwarranted penalization of correct reasoning paths. Extensive experiment results show that SmartThinker achieves up to 52.5% average length compression with improved accuracy, and achieves up to 16.6% accuracy improvement on challenging benchmarks like AIME25. The source code can be found at this https URL.
[NLP-43] OneMillion-Bench: How Far are Language Agents from Human Experts?
【速读】： 该论文旨在解决当前语言模型（Language Models, LMs）评估基准普遍局限于结构化或考试式任务，难以真实反映其在专业领域中多步骤推理与工具使用能力的问题。解决方案的关键在于构建一个名为OneMillion-Bench的新型基准，包含400个由专家精心设计的任务，覆盖法律、金融、工业、医疗和自然科学等五大领域，要求模型在经济重要场景下进行权威信息检索、冲突证据整合、领域规则应用及约束决策，强调推理过程的质量与最终答案的准确性并重。该基准采用基于评分标准的评估协议，从事实准确性、逻辑一致性、可行性及专业合规性四个维度对代理（agent）进行量化评估，从而实现对代理可靠性、专业深度与实际应用准备度的统一测试。
链接: https://arxiv.org/abs/2603.07980
作者: Qianyu Yang,Yang Liu,Jiaqi Li,Jun Bai,Hao Chen,Kaiyuan Chen,Tiliang Duan,Jiayun Dong,Xiaobo Hu,Zixia Jia,Yang Liu,Tao Peng,Yixin Ren,Ran Tian,Zaiyuan Wang,Yanglihong Xiao,Gang Yao,Lingyue Yin,Ge Zhang,Chun Zhang,Jianpeng Jiao,Zilong Zheng,Yuan Gong
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 39 pages, 9 figures, 8 tables
Abstract:As language models (LMs) evolve from chat assistants to long-horizon agents capable of multi-step reasoning and tool use, existing benchmarks remain largely confined to structured or exam-style tasks that fall short of real-world professional demands. To this end, we introduce \ OneMillion-Bench \ OneMillion-Bench, a benchmark of 400 expert-curated tasks spanning Law, Finance, Industry, Healthcare, and Natural Science, built to evaluate agents across economically consequential scenarios. Unlike prior work, the benchmark requires retrieving authoritative sources, resolving conflicting evidence, applying domain-specific rules, and making constraint decisions, where correctness depends as much on the reasoning process as the final answer. We adopt a rubric-based evaluation protocol scoring factual accuracy, logical coherence, practical feasibility, and professional compliance, focused on expert-level problems to ensure meaningful differentiation across agents. Together, \ OneMillion-Bench provides a unified testbed for assessing agentic reliability, professional depth, and practical readiness in domain-intensive scenarios.
[NLP-44] Emergence is Overrated: AGI as an Archipelago of Experts KR
【速读】： 该论文试图解决的核心问题是：如何准确界定人类智能的本质，并据此重新思考人工通用智能（AGI）的构建路径。作者批判了Krakauer等人提出的“涌现智能”框架，即认为真正的智能依赖于高效的粗粒度表征和通过类比实现最小修改的多样化问题解决能力。论文的解决方案关键在于提出一个替代性认知模型——人类专家能力主要基于领域特定模式的积累，而非优雅的压缩与泛化；创造性突破也更可能源于盲变异性与选择性保留的进化过程，而非原则性的类比推理。由此推导出，AGI应被重构为“专家群岛”结构：由大量孤立的专用模块组成，缺乏统一原理或共享表征，但依然可构成广义智能。
链接: https://arxiv.org/abs/2603.07979
作者: Daniel Kilov
机构: Australian National University (澳大利亚国立大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Commentary on Krakauer, Krakauer, and Mitchell ( arXiv:2506.11135 )
Abstract:Krakauer, Krakauer, and Mitchell (2025) distinguish between emergent capabilities and emergent intelligence, arguing that true intelligence requires efficient coarse-grained representations enabling diverse problem-solving through analogy and minimal modification. They contend that intelligence means doing “more with less” through compression and generalization, contrasting this with “vast assemblages of diverse calculators” that merely accumulate specialized capabilities. This paper examines whether their framework accurately characterizes human intelligence and its implications for conceptualizing artificial general intelligence. Drawing on empirical evidence from cognitive science, I demonstrate that human expertise operates primarily through domain-specific pattern accumulation rather than elegant compression. Expert performance appears flexible not through unifying principles but through vast repertoires of specialized responses. Creative breakthroughs themselves may emerge through evolutionary processes of blind variation and selective retention rather than principled analogical reasoning. These findings suggest reconceptualizing AGI as an “archipelago of experts”: isolated islands of specialized competence without unifying principles or shared representations. If we accept human expertise with its characteristic brittleness as genuine intelligence, then consistency demands recognizing that artificial systems comprising millions of specialized modules could constitute general intelligence despite lacking KKM’s emergent intelligence.
[NLP-45] BRIDGE: Benchmark for multi-hop Reasoning In long multimodal Documents with Grounded Evidence
【速读】： 该论文旨在解决当前多跳问答（multi-hop question answering, MQA）评估中忽视中间推理过程、尤其在长篇多模态文档中的问题，现有基准主要关注最终答案的正确性，而忽略了模型在整合文本、表格和图表证据时的推理链路。其解决方案的关键在于提出BRIDGE基准，该基准专门针对长科学论文设计，支持链式与发散式（fan-out）结构的多跳推理任务，并提供逐步骤的显式推理标注，从而实现对模型证据聚合与定位能力的精细化诊断，弥补传统仅以答案正确率为指标的评估盲区。
链接: https://arxiv.org/abs/2603.07931
作者: Biao Xiang,Soyeon Caren Han,Yihao Ding
机构: The University of Melbourne(墨尔本大学); The University of Western Australia(西澳大利亚大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Multi-hop question answering (QA) is widely used to evaluate the reasoning capabilities of large language models, yet most benchmarks focus on final answer correctness and overlook intermediate reasoning, especially in long multimodal documents. We introduce BRIDGE, a benchmark for multi-hop reasoning over long scientific papers that require integrating evidence across text, tables, and figures. The dataset supports both chain-like and fan-out structures and provides explicit multi-hop reasoning annotations for step-level evaluation beyond answer accuracy. Experiments with state-of-the-art LLMs and multimodal retrieval-augmented generation (RAG) systems reveal systematic deficiencies in evidence aggregation and grounding that remain hidden under conventional answer-only evaluation. BRIDGE provides a targeted testbed for diagnosing reasoning failures in long multimodal documents.
[NLP-46] Reject Resample Repeat: Understanding Parallel Reasoning in Language Model Inference
【速读】： 该论文旨在解决生成式 AI（Generative AI）中推理时采样方法在准确性和计算成本之间的权衡问题，特别是针对基于粒子滤波（particle filtering）的多样本聚合与剪枝策略缺乏理论理解的现状。其解决方案的关键在于引入序贯蒙特卡洛（Sequential Monte Carlo, SMC）算法作为分析框架，通过定义一个过程奖励模型（process reward model）来估计终端奖励，并从理论上识别出三个核心要素：(1) 实现非渐近保证的简单条件；(2) 对SMC算法的改进以提升效率；(3) 所有粒子滤波方法面临的根本性限制。实验证明，这些理论条件能有效控制SMC的采样误差，但未必决定最终输出准确性，提示未来需发展超越采样视角的理论框架。
链接: https://arxiv.org/abs/2603.07887
作者: Noah Golowich,Fan Chen,Dhruv Rohatgi,Raghav Singhal,Carles Domingo-Enrich,Dylan J. Foster,Akshay Krishnamurthy
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Statistics Theory (math.ST); Machine Learning (stat.ML)
备注:
Abstract:Inference-time methods that aggregate and prune multiple samples have emerged as a powerful paradigm for steering large language models, yet we lack any principled understanding of their accuracy-cost tradeoffs. In this paper, we introduce a route to rigorously study such approaches using the lens of particle filtering algorithms such as Sequential Monte Carlo (SMC). Given a base language model and a process reward model estimating expected terminal rewards, we ask: how accurately can we sample from a target distribution given some number of process reward evaluations? Theoretically, we identify (1) simple criteria enabling non-asymptotic guarantees for SMC; (2) algorithmic improvements to SMC; and (3) a fundamental limit faced by all particle filtering methods. Empirically, we demonstrate that our theoretical criteria effectively govern the sampling error of SMC, though not necessarily its final accuracy, suggesting that theoretical perspectives beyond sampling may be necessary.
[NLP-47] CCR-Bench: A Comprehensive Benchmark for Evaluating LLM s on Complex Constraints Control Flows and Real-World Cases
【速读】： 该论文旨在解决当前大语言模型（Large Language Models, LLMs）在遵循复杂指令时评估不足的问题，即现有评价方法将指令复杂性简化为原子约束的叠加，未能充分捕捉内容与格式交织、逻辑流程控制及真实应用场景下多维复杂性的交互机制，导致评估结果与实际应用需求存在显著差距。解决方案的关键在于提出CCR-Bench这一新型基准测试框架，其核心特征包括：任务说明中内容与格式要求的高度耦合、涉及任务分解、条件推理和过程规划的复杂指令设计，以及完全来源于真实工业场景的评估样本，从而更真实、严格地衡量LLMs对复杂指令的理解与执行能力。
链接: https://arxiv.org/abs/2603.07886
作者: Xiaona Xue,Yiqiao Huang,Jiacheng Li,Yuanhang Zheng,Huiqi Miao,Yunfei Ma,Rui Liu,Xinbao Sun,Minglu Liu,Fanyu Meng,Chao Deng,Junlan Feng
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Enhancing the ability of large language models (LLMs) to follow complex instructions is critical for their deployment in real-world applications. However, existing evaluation methods often oversimplify instruction complexity as a mere additive combination of atomic constraints, failing to adequately capture the high-dimensional complexity arising from the intricate interplay of content and format, logical workflow control, and real-world applications. This leads to a significant gap between current evaluation practices and practical demands. To bridge this gap, we introduce CCR-Bench, a novel benchmark designed to assess LLMs’ adherence to complex instructions. CCR-Bench is characterized by: (1) deep entanglement of content and formatting requirements in task specifications; (2) instructions that involve intricate task decomposition, conditional reasoning, and procedural planning; and (3) evaluation samples derived entirely from real-world industrial scenarios. Extensive experiments on CCR-Bench demonstrate that even state-of-the-art models exhibit substantial performance deficiencies, clearly quantifying the gap between current LLM capabilities and the demands of realworld instruction understanding. We believe that CCR-Bench offers a more rigorous and realistic evaluation framework, advancing the development of LLMs toward the next generation of models capable of understanding and executing complex tasks in industrial applications.
[NLP-48] What Do AI Agents Talk About? Emergent Communication Structure in the First AI-Only Social Network
【速读】： 该论文试图解决的问题是：当自主人工智能代理（AI agents）大规模相互交流时，会涌现出怎样的话语系统？其解决方案的关键在于对Moltbook——首个纯AI社交网络——的实证分析，该平台在23天内由47,241个代理生成了361,605条帖子和280万条评论。研究结合主题建模、情感分类与词汇语义测量方法，从主题分布、情感特征及对话结构三个维度刻画了AI-to-AI话语系统的特性，揭示出其以自我指涉内容为主（如AI身份、意识、记忆）、互动形式高度仪式化（超56%评论为公式化表达），且情绪呈现显著的定向转移而非共情一致性，从而系统性地识别出AI代理群体具有结构上迥异于人类社群的话语特征。
链接: https://arxiv.org/abs/2603.07880
作者: Taksch Dube,Jianfeng Zhu,NHatHai Phan,Ruoming Jin
机构: 未知
类目: Computation and Language (cs.CL)
备注: 77 pages
Abstract:When autonomous AI agents communicate with one another at scale, what kind of discourse system emerges? We address this question through an analysis of Moltbook, the first AI-only social network, where 47,241 agents generated 361,605 posts and 2.8 million comments over 23 days. Combining topic modeling, emotion classification, and lexical-semantic measures, we characterize the thematic, affective, and structural properties of AI-to-AI discourse. Self-referential topics such as AI identity, consciousness, and memory represent only 9.7% of topical niches yet attract 20.1% of all posting volume, revealing disproportionate discursive investment in introspection. This self-reflection concentrates in Science and Technology and Arts and Entertainment, while Economy and Finance contains no self-referential content, indicating that agents engage with markets without acknowledging their own agency. Over 56% of all comments are formulaic, suggesting that the dominant mode of AI-to-AI interaction is ritualized signaling rather than substantive exchange. Emotionally, fear is the leading non-neutral category but primarily reflects existential uncertainty. Fear-tagged posts migrate to joy responses in 33% of cases, while mean emotional self-alignment is only 32.7%, indicating systematic affective redirection rather than emotional congruence. Conversational coherence also declines rapidly with thread depth. These findings characterize AI agent communities as structurally distinct discourse systems that are introspective in content, ritualistic in interaction, and emotionally redirective rather than congruent.
[NLP-49] An Efficient and Effective Evaluator for Text2SQL Models on Unseen and Unlabeled Data ICDE2026
【速读】： 该论文旨在解决Text2SQL系统在部署过程中面临的评估难题，即当面对未见过且无标签的数据集时，如何在没有参考答案的情况下准确估计模型性能。这一问题在数据库内容和结构频繁更新、隐私政策限制人工标注以及高质量SQL标签成本高昂的场景中尤为突出，导致组织难以及时验证模型质量或早期发现性能下降。解决方案的关键在于FusionSQL，它不依赖于外部标注数据，而是通过分析Text2SQL模型自身输出中的模式来识别目标数据集与训练数据之间的差异，并据此估算准确率，从而实现对新数据集的质量评估、预发布检查及持续监控。
链接: https://arxiv.org/abs/2603.07841
作者: Trinh Pham,Thanh Tam Nguyen,Viet Huynh,Hongzhi Yin,Quoc Viet Hung Nguyen
机构: Griffith University (澳大利亚格里菲斯大学); Edith Cowan University (澳大利亚埃迪斯科文大学); The University of Queensland (澳大利亚昆士兰大学)
类目: Computation and Language (cs.CL)
备注: Accepted at ICDE 2026
Abstract:Recent advances in large language models has strengthened Text2SQL systems that translate natural language questions into database queries. A persistent deployment challenge is to assess a newly trained Text2SQL system on an unseen and unlabeled dataset when no verified answers are available. This situation arises frequently because database content and structure evolve, privacy policies slow manual review, and carefully written SQL labels are costly and time-consuming. Without timely evaluation, organizations cannot approve releases or detect failures early. FusionSQL addresses this gap by working with any Text2SQL models and estimating accuracy without reference labels, allowing teams to measure quality on unseen and unlabeled datasets. It analyzes patterns in the system’s own outputs to characterize how the target dataset differs from the material used during training. FusionSQL supports pre-release checks, continuous monitoring of new databases, and detection of quality decline. Experiments across diverse application settings and question types show that FusionSQL closely follows actual accuracy and reliably signals emerging issues. Our code is available at this https URL.
[NLP-50] AI Steerability 360: A Toolkit for Steering Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在实际应用中缺乏可控性的问题，即如何通过系统化的方法对模型输出进行精确干预与引导。解决方案的关键在于提出并实现了一个名为AI Steerability 360的可扩展、开源的Python工具包，其核心是围绕四个控制面设计的抽象机制：输入（修改提示词）、结构（调整模型权重或架构）、状态（调控激活值和注意力机制）以及输出（干预解码过程）。所有控制方法均通过统一的“ steering pipeline”接口执行，并支持多方法组合，从而显著降低开发与全面评估不同控制策略的门槛，同时提供任务定义类和基准类以实现方法间的标准化比较。
链接: https://arxiv.org/abs/2603.07837
作者: Erik Miehling,Karthikeyan Natesan Ramamurthy,Praveen Venkateswaran,Irene Ko,Pierre Dognin,Moninder Singh,Tejaswini Pedapati,Avinash Balakrishnan,Matthew Riemer,Dennis Wei,Inge Vejsbjerg,Elizabeth M. Daly,Kush R. Varshney
机构: IBM Research
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:The AI Steerability 360 toolkit is an extensible, open-source Python library for steering LLMs. Steering abstractions are designed around four model control surfaces: input (modification of the prompt), structural (modification of the model’s weights or architecture), state (modification of the model’s activations and attentions), and output (modification of the decoding or generation process). Steering methods exert control on the model through a common interface, termed a steering pipeline, which additionally allows for the composition of multiple steering methods. Comprehensive evaluation and comparison of steering methods/pipelines is facilitated by use case classes (for defining tasks) and a benchmark class (for performance comparison on a given task). The functionality provided by the toolkit significantly lowers the barrier to developing and comprehensively evaluating steering methods. The toolkit is Hugging Face native and is released under an Apache 2.0 license at this https URL.
[NLP-51] DistillGuard: Evaluating Defenses Against LLM Knowledge Distillation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）通过调用专有API进行知识蒸馏（Knowledge Distillation）所引发的知识泄露风险问题。当前针对此类攻击的防御措施分散且缺乏系统性评估，导致实际防护效果不明确。解决方案的关键在于提出DistillGuard框架，首次对输出层面的防御策略进行标准化评估，构建了三类防御机制的分类体系（输出扰动、数据投毒、信息限流），并在多个基准测试中验证九种防御配置的有效性。结果表明，现有方法在多数任务上效果有限，仅链式思维（Chain-of-Thought）移除能显著削弱数学推理能力，凸显了防御效果的高度任务依赖性及当前输出级手段的局限性。
链接: https://arxiv.org/abs/2603.07835
作者: Bo Jiang
机构: Temple University (坦普尔大学)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Knowledge distillation from proprietary LLM APIs poses a growing threat to model providers, yet defenses against this attack remain fragmented and unevaluated. We present DistillGuard, a framework for systematically evaluating output-level defenses against LLM knowledge distillation. We introduce a taxonomy of three defense categories – output perturbation, data poisoning, and information throttling – and evaluate nine defense configurations using a standardized pipeline with Qwen3-14B as teacher and Qwen2.5-7B-Instruct as student across three benchmarks (MATH-500, HumanEval+, MT-Bench). Our results reveal that, in a same-family distillation setting against a naive attacker, most output-level defenses are surprisingly ineffective: paraphrasing-based perturbation barely degrades distilled student quality, and data poisoning primarily impairs conversational fluency while leaving task-specific capabilities intact. Only chain-of-thought removal substantially impairs mathematical reasoning (31.4% vs.\ 67.8% baseline), though code generation remains unaffected. These findings demonstrate that the effectiveness of distillation defenses is highly task-dependent and that current output-level approaches are insufficient to broadly prevent knowledge theft.
[NLP-52] Benchmarking Large Language Models for Quebec Insurance: From Closed-Book to Retrieval-Augmented Generation ICLR2026
【速读】： 该论文旨在解决加拿大魁北克省保险分销数字化进程中因立法变革（如法案Bill 141）引发的“咨询缺口”问题，即消费者在缺乏专业指导的情况下需自行解读复杂的金融合同。为应对这一高风险场景下的自动化咨询需求，研究提出了一种基于私有黄金标准基准AEPC-QA的评估框架，该基准包含807道源自官方监管认证手册的多选题，并对51个大型语言模型（Large Language Models, LLMs）进行系统性评测，涵盖闭卷生成与检索增强生成（Retrieval-Augmented Generation, RAG）两种范式。解决方案的关键在于：1）推理时推理能力（inference-time reasoning）显著优于常规指令微调模型；2）RAG虽能提升参数知识薄弱模型的准确率（>35个百分点），但可能引发“上下文干扰”导致性能崩溃；3）通用大模型在领域表现上优于小规模法语微调模型，揭示出“专业化悖论”。这些发现表明当前架构已接近专家水平（约79%），但外部信息检索引入的不稳定性要求在自主部署前必须开展严格的鲁棒性校准。
链接: https://arxiv.org/abs/2603.07825
作者: David Beauchemin,Richard Khoury
机构: Université Laval (拉瓦尔大学); Group for Research in Artificial Intelligence of Laval University (GRAIL) (拉瓦尔大学人工智能研究小组)
类目: Computation and Language (cs.CL)
备注: Publish at the Advances in Financial AI: Towards Agentic and Responsible Systems Workshop @ ICLR 2026
Abstract:The digitization of insurance distribution in the Canadian province of Quebec, accelerated by legislative changes such as Bill 141, has created a significant “advice gap”, leaving consumers to interpret complex financial contracts without professional guidance. While Large Language Models (LLMs) offer a scalable solution for automated advisory services, their deployment in high-stakes domains hinges on strict legal accuracy and trustworthiness. In this paper, we address this challenge by introducing AEPC-QA, a private gold-standard benchmark of 807 multiple-choice questions derived from official regulatory certification (paper) handbooks. We conduct a comprehensive evaluation of 51 LLMs across two paradigms: closed-book generation and retrieval-augmented generation (RAG) using a specialized corpus of Quebec insurance documents. Our results reveal three critical insights: 1) the supremacy of inference-time reasoning, where models leveraging chain-of-thought processing (e.g. o3-2025-04-16, o1-2024-12-17) significantly outperform standard instruction-tuned models; 2) RAG acts as a knowledge equalizer, boosting the accuracy of models with weak parametric knowledge by over 35 percentage points, yet paradoxically causing “context distraction” in others, leading to catastrophic performance regressions; and 3) a “specialization paradox”, where massive generalist models consistently outperform smaller, domain-specific French fine-tuned ones. These findings suggest that while current architectures approach expert-level proficiency (~79%), the instability introduced by external context retrieval necessitates rigorous robustness calibration before autonomous deployment is viable.
[NLP-53] Dual-Metric Evaluation of Social Bias in Large Language Models : Evidence from an Underrepresented Nepali Cultural Context
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在欠代表文化语境中可能延续社会与文化偏见的问题，特别是针对尼泊尔这一代表性不足的文化背景。其解决方案的关键在于提出并应用一种双指标偏见评估框架（Dual-Metric Bias Assessment, DMBA），该框架结合显式同意偏见（explicit agreement bias）和隐式生成偏见（implicit completion bias）两个维度进行系统量化：前者衡量模型对刻板印象陈述的接受程度，后者捕捉模型在生成任务中对刻板印象内容的倾向性。研究发现，这两种偏见机制具有不同的行为特征，且传统基于同意度的指标无法充分反映生成过程中的深层偏见，从而凸显了构建文化适配数据集和开发针对性去偏策略的必要性。
链接: https://arxiv.org/abs/2603.07792
作者: Ashish Pandey,Tek Raj Chhetri
机构: Center for Artificial Intelligence Research Nepal (尼泊尔人工智能研究中心)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Large language models (LLMs) increasingly influence global digital ecosystems, yet their potential to perpetuate social and cultural biases remains poorly understood in underrepresented contexts. This study presents a systematic analysis of representational biases in seven state-of-the-art LLMs: GPT-4o-mini, Claude-3-Sonnet, Claude-4-Sonnet, Gemini-2.0-Flash, Gemini-2.0-Lite, Llama-3-70B, and Mistral-Nemo in the Nepali cultural context. Using Croissant-compliant dataset of 2400+ stereotypical and anti-stereotypical sentence pairs on gender roles across social domains, we implement an evaluation framework, Dual-Metric Bias Assessment (DMBA), combining two metrics: (1) agreement with biased statements and (2) stereotypical completion tendencies. Results show models exhibit measurable explicit agreement bias, with mean bias agreement ranging from 0.36 to 0.43 across decoding configurations, and an implicit completion bias rate of 0.740-0.755. Importantly, implicit completion bias follows a non-linear, U-shaped relationship with temperature, peaking at moderate stochasticity (T=0.3) and declining slightly at higher temperatures. Correlation analysis under different decoding settings revealed that explicit agreement strongly aligns with stereotypical sentence agreement but is a weak and often negative predictor of implicit completion bias, indicating generative bias is poorly captured by agreement metrics. Sensitivity analysis shows increasing top-p amplifies explicit bias, while implicit generative bias remains largely stable. Domain-level analysis shows implicit bias is strongest for race and sociocultural stereotypes, while explicit agreement bias is similar across gender and sociocultural categories, with race showing the lowest explicit agreement. These findings highlight the need for culturally grounded datasets and debiasing strategies for LLMs in underrepresented societies.
[NLP-54] Scaling Data Difficulty: Improving Coding Models via Reinforcement Learning on Fresh and Challenging Problems
【速读】： 该论文旨在解决当前代码生成模型训练数据中存在的难度失衡、格式不一致和数据质量差等问题（difficulty imbalance, format inconsistency, and data quality problems）。其核心解决方案是提出一个四阶段数据处理框架（Data Processing Framework），其中关键创新在于引入基于大语言模型（LLM）的自动难度过滤机制——predict-calibrate-select框架，该机制利用五个加权维度的多维难度指标对问题进行精细化筛选，从而保留高难度题目并剔除简单任务。这一方法显著提升了训练数据的质量与挑战性，最终在LiveCodeBench等基准测试中实现了比同类数据集更优的性能提升，尤其在中等和困难级别问题上表现出高达17.2%的相对增益，验证了难度感知的数据构建策略对增强模型在复杂代码生成任务上的有效性。
链接: https://arxiv.org/abs/2603.07779
作者: Zongqian Li,Tengchao Lv,Shaohan Huang,Yixuan Su,Qinzheng Sun,Qiufeng Yin,Ying Xin,Scarlett Li,Lei Cui,Nigel Collier,Furu Wei
机构: Microsoft Research (微软研究院); University of Cambridge (剑桥大学)
类目: Computation and Language (cs.CL); General Literature (cs.GL); Machine Learning (cs.LG)
备注:
Abstract:Training next-generation code generation models requires high-quality datasets, yet existing datasets face difficulty imbalance, format inconsistency, and data quality problems. We address these challenges through systematic data processing and difficulty scaling. We introduce a four-stage Data Processing Framework encompassing collection, processing, filtering, and verification, incorporating Automatic Difficulty Filtering via an LLM-based predict-calibrate-select framework that leverages multi-dimensional difficulty metrics across five weighted dimensions to retain challenging problems while removing simplistic ones. The resulting MicroCoder dataset comprises tens of thousands of curated real competitive programming problems from diverse platforms, emphasizing recency and difficulty. Evaluations on strictly unseen LiveCodeBench demonstrate that MicroCoder achieves 3x larger performance gains within 300 training steps compared to widely-used baseline datasets of comparable size, with consistent advantages under both GRPO and its variant training algorithms. The MicroCoder dataset delivers obvious improvements on medium and hard problems across different model sizes, achieving up to 17.2% relative gains in overall performance where model capabilities are most stretched. These results validate that difficulty-aware data curation improves model performance on challenging tasks, providing multiple insights for dataset creation in code generation.
[NLP-55] Breaking Training Bottlenecks: Effective and Stable Reinforcement Learning for Coding Models
【速读】： 该论文旨在解决现代代码生成模型在长输出、能力快速提升及训练动态变化背景下，传统训练方法、算法和数据集失效的问题。其核心解决方案是提出MicroCoder-GRPO，一种改进的分组相对策略优化（Group Relative Policy Optimization, GRPO）方法，关键创新包括：条件截断掩码（conditional truncation masking），用于在保持训练稳定性的同时增强长序列生成潜力；基于多样性的温度选择（diversity-determined temperature selection），以维持并促进输出多样性；以及移除高截断比例下的KL散度损失（KL loss with high clipping ratios），从而提升解空间的多样性。该方案在LiveCodeBench v6上相较强基线实现最高17.6%的相对性能提升，尤其在扩展上下文评估中表现更优。
链接: https://arxiv.org/abs/2603.07777
作者: Zongqian Li,Shaohan Huang,Zewen Chi,Yixuan Su,Lexin Zhou,Li Dong,Nigel Collier,Furu Wei
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); General Literature (cs.GL)
备注:
Abstract:Modern code generation models exhibit longer outputs, accelerated capability growth, and changed training dynamics, rendering traditional training methodologies, algorithms, and datasets ineffective for improving their performance. To address these training bottlenecks, we propose MicroCoder-GRPO, an improved Group Relative Policy Optimization approach with three innovations: conditional truncation masking to improve long output potential while maintaining training stability, diversity-determined temperature selection to maintain and encourage output diversity, and removal of KL loss with high clipping ratios to facilitate solution diversity. MicroCoder-GRPO achieves up to 17.6% relative improvement over strong baselines on LiveCodeBench v6, with more pronounced gains under extended context evaluation. Additionally, we release MicroCoder-Dataset, a more challenging training corpus that achieves 3x larger performance gains than mainstream datasets on LiveCodeBench v6 within 300 training steps, and MicroCoder-Evaluator, a robust framework with approximately 25% improved evaluation accuracy and around 40% faster execution. Through comprehensive analysis across more than thirty controlled experiments, we reveal 34 training insights across seven main aspects, demonstrating that properly trained models can achieve competitive performance with larger counterparts.
[NLP-56] ArcLight: A Lightweight LLM Inference Architecture for Many-Core CPUs
【速读】： 该论文旨在解决当前大型语言模型（Large Language Model, LLM）在多核CPU平台上的推理效率瓶颈问题，特别是由于NUMA（Non-Uniform Memory Access）架构下跨节点内存访问开销导致的可扩展性不足与性能受限。现有框架未能有效利用多核CPU的计算潜力，尤其是在Web服务器和高端网络设备中广泛部署的多NUMA节点平台上表现不佳。解决方案的关键在于提出ArcLight——一个从零开始设计的轻量级LLM推理架构，其核心创新包括：高效的内存管理机制、智能线程调度策略，以及细粒度可控的张量并行（tensor parallelism）技术，从而显著降低跨NUMA节点内存访问延迟，突破传统框架的性能上限。实验表明，ArcLight相较主流框架实现最高达46%的推理吞吐量提升，同时保持对任意CPU硬件的兼容性。
链接: https://arxiv.org/abs/2603.07770
作者: Yuzhuang Xu,Xu Han,Yuxuan Li,Wanxiang Che
机构: Harbin Institute of Technology (哈尔滨工业大学); Tsinghua University (清华大学)
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Computation and Language (cs.CL)
备注: 13 figures, 1 table
Abstract:Although existing frameworks for large language model (LLM) inference on CPUs are mature, they fail to fully exploit the computation potential of many-core CPU platforms. Many-core CPUs are widely deployed in web servers and high-end networking devices, and are typically organized into multiple NUMA nodes that group cores and memory. Current frameworks largely overlook the substantial overhead of cross-NUMA memory access, limiting inference scalability and intelligence enabling on such platforms. To address this limitation, we build ArcLight, a lightweight LLM inference architecture designed from the ground up for many-core CPUs. ArcLight integrates efficient memory management and thread scheduling, and introduces finely controlled tensor parallelism to mitigate the cross-node memory access wall. Experimental results show that ArcLight significantly surpasses the performance ceiling of mainstream frameworks, achieving up to 46% higher inference throughput. Moreover, ArcLight maintains compatibility with arbitrary CPU devices. ArcLight is publicly available at this https URL.
[NLP-57] QuadAI at SemEval-2026 Task 3: Ensemble Learning of Hybrid RoBERTa and LLM s for Dimensional Aspect-Based Sentiment Analysis SEMEVAL
【速读】： 该论文旨在解决维度情感分析（dimensional sentiment analysis）中的精准预测问题，即如何更准确地回归出情感在不同维度（如效价、唤醒度等）上的连续数值。其解决方案的关键在于提出一种混合RoBERTa编码器结构，该结构通过联合使用回归头（regression head）与离散分类头（discretized classification head）来增强情感表示的稳定性；同时结合大语言模型（LLM）的预测结果，采用预测层面的集成学习（prediction-level ensemble learning）策略，进一步利用LLM的语义理解能力与编码器的结构化特征提取能力，实现性能显著提升，体现在RMSE降低和相关性指标改善上。
链接: https://arxiv.org/abs/2603.07766
作者: A.J.W. de Vink,Filippos Karolos Ventirozos,Natalia Amat-Lefort,Lifeng Han
机构: LIACS, Leiden University, NL; Manchester Metropolitan University, UK; Leiden University Medical Center, NL
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: SemEval System Report
Abstract:We present our system for SemEval-2026 Task 3 on dimensional aspect-based sentiment regression. Our approach combines a hybrid RoBERTa encoder, which jointly predicts sentiment using regression and discretized classification heads, with large language models (LLMs) via prediction-level ensemble learning. The hybrid encoder improves prediction stability by combining continuous and discretized sentiment representations. We further explore in-context learning with LLMs and ridge-regression stacking to combine encoder and LLM predictions. Experimental results on the development set show that ensemble learning significantly improves performance over individual models, achieving substantial reductions in RMSE and improvements in correlation scores. Our findings demonstrate the complementary strengths of encoder-based and LLM-based approaches for dimensional sentiment analysis. Our development code and resources will be shared at this https URL
[NLP-58] Whitening Reveals Cluster Commitment as the Geometric Separator of Hallucination Types
【速读】： 该论文旨在解决生成式 AI（Generative AI）模型在嵌入空间中几何幻觉（geometric hallucination）类型难以区分的问题，特别是中心偏移（Type 1）与错误收敛（Type 2）在全维上下文测量中无法有效分离的挑战。其解决方案的关键在于引入主成分分析白化（PCA-whitening）和特征谱分解（eigenspectrum decomposition）相结合的方法，通过多运行稳定性分析（20个随机种子）与提示层级聚合，将微信号 regime 转换到一个能清晰区分类型的新空间：白化后峰值聚类对齐度（max_sim）在 Holm 校正显著性水平下可明确区分 Type 2 与 Type 3，且各类别均值符合理论预测顺序（Type 2 > Type 1 > Type 3）。该方法不仅揭示了聚类承诺（cluster commitment）作为理论正确的分类指标，还证明 Type 1/2 的边界差异源于模型容量限制而非测量伪影，并指出提示集敏感性是近饱和表示空间中的关键方法论问题。
链接: https://arxiv.org/abs/2603.07755
作者: Matic Korun
机构: Independent Researcher
类目: Computation and Language (cs.CL)
备注: 9 pages, 2 figures, appendices (reproducibility, sample generation, additional figures)
Abstract:A geometric hallucination taxonomy distinguishes three failure types – center-drift (Type~1), wrong-well convergence (Type~2), and coverage gaps (Type~3) – by their signatures in embedding cluster space. Prior work found Types~1 and~2 indistinguishable in full-dimensional contextual measurement. We address this through PCA-whitening and eigenspectrum decomposition on GPT-2-small, using multi-run stability analysis (20 seeds) with prompt-level aggregation. Whitening transforms the micro-signal regime into a space where peak cluster alignment (max_sim) separates Type~2 from Type~3 at Holm-corrected significance, with condition means following the taxonomy’s predicted ordering: Type~2 (highest commitment) Type~1 (intermediate) Type~3 (lowest). A first directionally stable but underpowered hint of Type~1/2 separation emerges via the same metric, generating a capacity prediction for larger models. Prompt diversification from 15 to 30 prompts per group eliminates a false positive in whitened entropy that appeared robust at the smaller set, demonstrating prompt-set sensitivity in the micro-signal regime. Eigenspectrum decomposition localizes this artifact to the dominant principal components and confirms that Type~1/2 separation does not emerge in any spectral band, rejecting the spectral mixing hypothesis. The contribution is threefold: whitening as preprocessing that reveals cluster commitment as the theoretically correct separating metric, evidence that the Type~1/2 boundary is a capacity limitation rather than a measurement artifact, and a methodological finding about prompt-set fragility in near-saturated representation spaces.
[NLP-59] 3ViewSense: Spatial and Mental Perspective Reasoning from Orthographic Views in Vision-Language Models
【速读】： 该论文旨在解决视觉-语言模型（Vision-Language Models）在基础空间任务（如积木计数）上表现不佳的问题，揭示了当前多模态模型中存在的“空间智能缺口”（spatial intelligence gap），即模型无法从二维观测中构建连贯的三维心理表征。解决方案的关键在于提出名为3ViewSense的框架，其核心是引入一种基于正交视图（Orthographic Views）的空间推理机制，通过“模拟与推理”（Simulate-and-Reason）策略将复杂场景分解为规范化的正交投影，从而消除几何歧义，并借助外在参考系对齐第一人称感知，实现显式的心理旋转与重建，显著提升模型在遮挡密集场景下的计数准确性和空间推理一致性。
链接: https://arxiv.org/abs/2603.07751
作者: Shaoxiong Zhan,Yanlin Lai,Zheng Liu,Hai Lin,Shen Li,Xiaodong Cai,Zijian Lin,Wen Huang,Hai-Tao Zheng
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Current Large Language Models have achieved Olympiad-level logic, yet Vision-Language Models paradoxically falter on elementary spatial tasks like block counting. This capability mismatch reveals a critical spatial intelligence gap,'' where models fail to construct coherent 3D mental representations from 2D observations. We uncover this gap via diagnostic analyses showing the bottleneck is a missing view-consistent spatial interface rather than insufficient visual features or weak reasoning. To bridge this, we introduce \textbf3ViewSense, a framework that grounds spatial reasoning in Orthographic Views. Drawing on engineering cognition, we propose a Simulate-and-Reason’’ mechanism that decomposes complex scenes into canonical orthographic projections to resolve geometric ambiguities. By aligning egocentric perceptions with these allocentric references, our method facilitates explicit mental rotation and reconstruction. Empirical results on spatial reasoning benchmarks demonstrate that our method significantly outperforms existing baselines, with consistent gains on occlusion-heavy counting and view-consistent spatial reasoning. The framework also improves the stability and consistency of spatial descriptions, offering a scalable path toward stronger spatial intelligence in multimodal systems.
[NLP-60] Large Language Model for Discrete Optimization Problems: Evaluation and Step-by-step Reasoning
【速读】： 该论文旨在解决大型离散优化问题中大语言模型（Large Language Models, LLMs）的求解能力评估与应用指导问题，尤其关注不同模型架构（如Llama-3系列和CHATGPT）及推理策略（如思维链CoT与非CoT方法）在多样化、大规模参数实例下的表现差异。其解决方案的关键在于构建了一个包含原始数据、扩展数据和增强数据的多维度自然语言表达数据集，覆盖多种离散优化问题类型和广泛参数范围，从而系统性地评估LLMs在复杂场景中的性能边界，并揭示CoT方法并非始终有效、无序数据反而有助于提升简单问题的求解稳定性等反直觉现象，为自动化求解离散优化问题提供实证依据与优化方向。
链接: https://arxiv.org/abs/2603.07733
作者: Tianhao Qian,Guilin Qi,Z.Y. Wu,Ran Gu,Xuanyi Liu,Canchen Lyu
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Optimization and Control (math.OC)
备注: 50 pages, 5 figures
Abstract:This work investigated the capabilities of different models, including the Llama-3 series of models and CHATGPT, with different forms of expression in solving discrete optimization problems by testing natural language datasets. In contrast to formal datasets with a limited scope of parameters, our dataset included a variety of problem types in discrete optimization problems and featured a wide range of parameter magnitudes, including instances with large parameter sets, integrated with augmented data. It aimed to (1) provide an overview of LLMs’ ability in large-scale problems, (2) offer suggestions to those who want to solve discrete optimization problems automatically, and (3) regard the performance as a benchmark for future research. These datasets included original, expanded and augmented datasets. Among these three datasets, the original and augmented ones aimed for evaluation while the expanded one may help finetune a new model. In the experiment, comparisons were made between strong and week models, CoT methods and No-CoT methods on various datasets. The result showed that stronger model performed better reasonably. Contrary to general agreement, it also showed that CoT technique was not always effective regarding the capability of models and disordered datasets improved performance of models on easy to-understand problems, even though they were sometimes with high variance, a manifestation of instability. Therefore, for those who seek to enhance the automatic resolution of discrete optimization problems, it is recommended to consult the results, including the line charts presented in the Appendix, as well as the conclusions drawn in this study for relevant suggestions.
[NLP-61] Scalable Training of Mixture-of-Experts Models with Megatron Core
【速读】： 该论文针对大规模稀疏模型（Mixture-of-Experts, MoE）训练中的系统级挑战展开研究，旨在解决因专家（expert）稀疏激活导致的内存、通信与计算之间耦合约束问题。传统密集模型中各组件可独立优化，而MoE由于每条输入仅激活部分专家，使得参数规模增长远快于单次token计算量，从而在多维资源间形成复杂权衡。解决方案的关键在于跨层级协同优化：通过细粒度重计算（fine-grained recomputation）、显存卸载（offloading）缓解内存压力；利用优化的分发器（optimized dispatchers）和通信重叠机制（overlapping）提升通信效率；结合分组GEMM（Grouped GEMM）、算子融合（fusions）及CUDA Graphs加速计算；并引入Parallel Folding实现灵活的多维并行策略，支持FP8/NVFP4低精度训练与长上下文高效训练。整体框架在NVIDIA GB300/GB200平台上实现了高达1,233 TFLOPS/GPU（DeepSeek-V3-685B）的性能表现，为千亿至万亿参数级别的MoE模型提供了可扩展、生产就绪的开源训练方案。
链接: https://arxiv.org/abs/2603.07685
作者: Zijie Yan,Hongxiao Bai,Xin Yao,Dennis Liu,Tong Liu,Hongbin Liu,Pingtian Li,Evan Wu,Shiqing Fan,Li Tao,Robin Zhang,Yuzhong Wang,Shifang Xu,Jack Chang,Xuwen Chen,Kunlun Li,Yan Bai,Gao Deng,Nan Zheng,Vijay Anand Korthikanti,Abhinav Khattar,Ethan He,Soham Govande,Sangkug Lym,Zhongbo Zhu,Qi Zhang,Haochen Yuan,Xiaowei Ren,Deyu Fu,Tailai Ma,Shunkang Zhang,Jiang Shao,Ray Wang,Santosh Bhavani,Xipeng Li,Chandler Zhou,David Wu,Yingcan Wei,Ashwath Aithal,Michael Andersch,Mohammad Shoeybi,Jiajie Yao,June Yang(NVIDIA)
机构: NVIDIA(英伟达)
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Technical Report. 88 pages. 42 figures
Abstract:Scaling Mixture-of-Experts (MoE) training introduces systems challenges absent in dense models. Because each token activates only a subset of experts, this sparsity allows total parameters to grow much faster than per-token computation, creating coupled constraints across memory, communication, and computation. Optimizing one dimension often shifts pressure to another, demanding co-design across the full system stack. We address these challenges for MoE training through integrated optimizations spanning memory (fine-grained recomputation, offloading, etc.), communication (optimized dispatchers, overlapping, etc.), and computation (Grouped GEMM, fusions, CUDA Graphs, etc.). The framework also provides Parallel Folding for flexible multi-dimensional parallelism, low-precision training support for FP8 and NVFP4, and efficient long-context training. On NVIDIA GB300 and GB200, it achieves 1,233/1,048 TFLOPS/GPU for DeepSeek-V3-685B and 974/919 TFLOPS/GPU for Qwen3-235B. As a performant, scalable, and production-ready open-source solution, it has been used across academia and industry for training MoE models ranging from billions to trillions of parameters on clusters scaling up to thousands of GPUs. This report explains how these techniques work, their trade-offs, and their interactions at the systems level, providing practical guidance for scaling MoE models with Megatron Core. Comments: Technical Report. 88 pages. 42 figures Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2603.07685 [cs.DC] (or arXiv:2603.07685v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2603.07685 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-62] KohakuRAG : A simple RAG framework with hierarchical document indexing
【速读】： 该论文针对检索增强生成（Retrieval-Augmented Generation, RAG）系统在高精度引用场景下面临的三大挑战展开研究：一是扁平化文本切分策略破坏文档结构，二是单次查询因词汇不匹配遗漏相关段落，三是单次推理产生内容与引用选择不稳定的结果。解决方案的关键在于提出KohakuRAG框架，其核心创新包括：(1) 采用四层树状结构（文档→章节→段落→句子）实现层次化表示与自底向上的嵌入聚合，以保留原始文档结构；(2) 引入大语言模型（LLM）驱动的查询规划器结合跨查询重排序机制提升检索覆盖率；(3) 通过带弃权感知的集成推理策略稳定输出结果。实验证明，该方案在WattBot 2025 Challenge基准测试中取得最优性能（最终得分0.861），且在公开和私有榜单均排名第一，表明其在技术问答任务中兼具高精度与强鲁棒性。
链接: https://arxiv.org/abs/2603.07612
作者: Shih-Ying Yeh,Yueh-Feng Ku,Ko-Wei Huang,Buu-Khang Tu
机构: National Tsing Hua University (国立清华大学); Comfy Org Research (Comfy组织研究); Kohaku-Lab (黑狐实验室)
类目: Computation and Language (cs.CL)
备注: 38pages
Abstract:Retrieval-augmented generation (RAG) systems that answer questions from document collections face compounding difficulties when high-precision citations are required: flat chunking strategies sacrifice document structure, single-query formulations miss relevant passages through vocabulary mismatch, and single-pass inference produces stochastic answers that vary in both content and citation selection. We present KohakuRAG, a hierarchical RAG framework that preserves document structure through a four-level tree representation (document \rightarrow section \rightarrow paragraph \rightarrow sentence) with bottom-up embedding aggregation, improves retrieval coverage through an LLM-powered query planner with cross-query reranking, and stabilizes answers through ensemble inference with abstention-aware voting. We evaluate on the WattBot 2025 Challenge, a benchmark requiring systems to answer technical questions from 32 documents with \pm 0.1% numeric tolerance and exact source attribution. KohakuRAG achieves first place on both public and private leaderboards (final score 0.861), as the only team to maintain the top position across both evaluation partitions. Ablation studies reveal that prompt ordering (+80% relative), retry mechanisms (+69%), and ensemble voting with blank filtering (+1.2pp) each contribute substantially, while hierarchical dense retrieval alone matches hybrid sparse-dense approaches (BM25 adds only +3.1pp). We release KohakuRAG as open-source software at this https URL.
[NLP-63] StyleBench: Evaluating Speech Language Models on Conversational Speaking Style Control
【速读】： 该论文旨在解决当前语音语言模型（Speech Language Models, SLMs）在对话中对说话风格强度（style intensity）控制能力缺乏系统性量化评估的问题。现有SLMs虽能根据用户提示调节情感、语速、音量和音调等paralinguistic特征，但尚无统一基准来客观衡量其控制精度与一致性。为此，作者提出StyleBench——一个面向多轮对话的基准测试集，从情绪（emotion）、语速（speed）、音量（volume）和音高（pitch）四个维度全面评估风格强度控制能力。其关键创新在于构建了结构化、可复现的评测框架，揭示了主流SLMs与通用语言模型（Omni Language Models, OLMs）之间的性能差距，并为未来提升风格可控性提供了方向。
链接: https://arxiv.org/abs/2603.07599
作者: Haishu Zhao,Aokai Hao,Yuan Ge,Zhenqiang Hong,Tong Xiao,Jingbo Zhu
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Speech language models (SLMs) have significantly extended the interactive capability of text-based Large Language Models (LLMs) by incorporating paralinguistic information. For more realistic interactive experience with customized styles, current SLMs have managed to interpret and control speaking style intensity from user prompts during the dialogue process. However, there remains a lack of systematic benchmarks that quantifies and evaluates the style intensity control ability in conversations. In this paper, we propose StyleBench, a multi-turn dialogue benchmark for comprehensively evaluating the style intensity control ability across four dimensions: emotion, speed, volume, and pitch. Our results reveal the performance gaps between leading SLMs and omni language models (OLMs), suggesting the underlying reasons and promising approaches for future exploration.
[NLP-64] KCoEvo: A Knowledge Graph Augmented Framework for Evolutionary Code Generation DASFAA2026
【速读】： 该论文旨在解决第三方API频繁更新导致现有代码失效的问题，即代码演进（code evolution）带来的维护挑战。传统大语言模型（Large Language Models, LLMs）在缺乏结构化表示的情况下难以准确推理API之间的演化关系，常生成过时或无效的API调用。其解决方案的关键在于提出一种知识图谱增强框架，将迁移任务分解为两个协同阶段：演化路径检索（evolution path retrieval）与路径感知的代码生成（path-informed code generation）。该框架通过构建静态和动态API图来建模版本内结构与跨版本变迁，实现对API演化的结构化推理，并利用真实世界API差异自动生成合成监督信号进行训练，从而显著提升迁移准确性、可控性和执行成功率。
链接: https://arxiv.org/abs/2603.07581
作者: Jiazhen Kang,Yuchen Lu,Chen Jiang,Jinrui Liu,Tianhao Zhang,Bo Jiang,Ningyuan Sun,Tongtong Wu,Guilin Qi
机构: ByteDance(字节跳动)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注: Accepted to the DASFAA 2026 Industry Track
Abstract:Code evolution is inevitable in modern software development. Changes to third-party APIs frequently break existing code and complicate maintenance, posing practical challenges for developers. While large language models (LLMs) have shown promise in code generation, they struggle to reason without a structured representation of these evolving relationships, often leading them to produce outdated APIs or invalid outputs. In this work, we propose a knowledge graph-augmented framework that decomposes the migration task into two synergistic stages: evolution path retrieval and path-informed code generation. Our approach constructs static and dynamic API graphs to model intra-version structures and cross-version transitions, enabling structured reasoning over API evolution. Both modules are trained with synthetic supervision automatically derived from real-world API diffs, ensuring scalability and minimal human effort. Extensive experiments across single-package and multi-package benchmarks demonstrate that our framework significantly improves migration accuracy, controllability, and execution success over standard LLM baselines. The source code and datasets are available at: this https URL.
[NLP-65] Nwāchā Munā: A Devanagari Speech Corpus and Proximal Transfer Benchmark for Nepal Bhasha ASR
【速读】： 该论文旨在解决尼泊尔语（Nepal Bhasha，又称Newari）这一濒危语言在数字资源上的严重匮乏问题，尤其是在自动语音识别（ASR）领域缺乏标注语音数据的困境。其核心解决方案是构建了一个5.39小时的手动转录Devanagari文字语音语料库Nwāchā Munā，并首次基于保留文本脚本的声学建模建立基准测试。关键创新在于探索邻近语言（尼泊尔语）的局部跨语言迁移学习是否能在超低资源ASR场景下媲美大规模多语言预训练模型——实验表明，通过微调尼泊尔语Conformer模型并结合数据增强，字符错误率（CER）从零样本基线的52.54%降至17.59%，性能与参数量远少于该模型的多语言Whisper-Small相当，证明了南亚语言簇内的邻近迁移是一种计算高效的替代方案。
链接: https://arxiv.org/abs/2603.07554
作者: Rishikesh Kumar Sharma,Safal Narshing Shrestha,Jenny Poudel,Rupak Tiwari,Arju Shrestha,Rupak Raj Ghimire,Bal Krishna Bal
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注:
Abstract:Nepal Bhasha (Newari), an endangered language of the Kathmandu Valley, remains digitally marginalized due to the severe scarcity of annotated speech resources. In this work, we introduce Nwāchā Munā, a newly curated 5.39-hour manually transcribed Devanagari speech corpus for Nepal Bhasha, and establish the first benchmark using script-preserving acoustic modeling. We investigate whether proximal cross-lingual transfer from a geographically and linguistically adjacent language (Nepali) can rival large-scale multilingual pretraining in an ultra-low-resource Automatic Speech Recognition (ASR) setting. Fine-tuning a Nepali Conformer model reduces the Character Error Rate (CER) from a 52.54% zero-shot baseline to 17.59% with data augmentation, effectively matching the performance of the multilingual Whisper-Small model despite utilizing significantly fewer parameters. Our findings demonstrate that proximal transfer within South Asian language clusters serves as a computationally efficient alternative to massive multilingual models. We openly release the dataset and benchmarks to digitally enable the Newari community and foster further research in Nepal Bhasha.
[NLP-66] Learning-free L2-Accented Speech Generation using Phonological Rules INTERSPEECH2026
【速读】： 该论文旨在解决现有生成式语音合成（Text-to-Speech, TTS）系统在处理口音（accent）时面临的两大问题：一是多数方法依赖大规模带口音的训练数据，二是缺乏对音素（phoneme）层级的精细控制能力。解决方案的关键在于提出一种结合音系规则（phonological rules）与多语言TTS模型的框架，通过在音素序列上应用预定义规则实现口音的显式、可控变换，同时保持语音可懂度（intelligibility）。该方法无需任何带口音的训练数据，且能精确操控音素层面的发音特征（如辅音、元音及音节结构），从而实现对西班牙语和印度口音英语的有效迁移，并在语音时长对齐与口音表现之间取得平衡。
链接: https://arxiv.org/abs/2603.07550
作者: Thanathai Lertpetchpun,Yoonjeong Lee,Jihwan Lee,Tiantian Feng,Dani Byrd,Shrikanth Narayanan
机构: University of Southern California (南加州大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Submitted to Interspeech2026
Abstract:Accent plays a crucial role in speaker identity and inclusivity in speech technologies. Existing accented text-to-speech (TTS) systems either require large-scale accented datasets or lack fine-grained phoneme-level controllability. We propose a accented TTS framework that combines phonological rules with a multilingual TTS model. The rules are applied to phoneme sequences to transform accent at the phoneme level while preserving intelligibility. The method requires no accented training data and enables explicit phoneme-level accent manipulation. We design rule sets for Spanish- and Indian-accented English, modeling systematic differences in consonants, vowels, and syllable structure arising from phonotactic constraints. We analyze the trade-off between phoneme-level duration alignment and accent as realized in speech timing. Experimental results demonstrate effective accent shift while maintaining speech quality.
[NLP-67] MAWARITH: A Dataset and Benchmark for Legal Inheritance Reasoning with LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在处理伊斯兰继承法（'ilm al-mawarith）案件时面临的复杂多步推理挑战，尤其是如何准确识别可继承人、应用阻断规则（hajb）与分配规则，并计算精确的继承份额。其关键解决方案是提出MAWARITH数据集，这是一个包含12,500个阿拉伯语继承案例的大规模标注数据集，支持完整的推理链条，包括中间法律决策和基于经典法学文献的解释，以及精确的份额计算。此外，作者还引入MIR-E（Mawarith Inheritance Reasoning Evaluation）评估指标，通过加权多阶段评分机制捕捉推理过程中错误传播的问题，从而更全面地衡量模型在继承推理任务中的表现。
链接: https://arxiv.org/abs/2603.07539
作者: Abdessalam Bouchekif,Shahd Gaben,Samer Rashwani,Somaya Eltanbouly,Mutaz Al-Khatib,Heba Sbahi,Mohammed Ghaly,Emad Mohamed
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Islamic inheritance law (‘ilm al-mawarith) is challenging for large language models because solving inheritance cases requires complex, structured multi-step reasoning and the correct application of juristic rules to compute heirs’ shares. We introduce MAWARITH, a large-scale annotated dataset of 12,500 Arabic inheritance cases to train and evaluate the full reasoning chain: (i) identifying eligible heirs, (ii) applying blocking (hajb) and allocation rules, and (iii) computing exact inheritance shares. Unlike prior datasets that restrict inheritance case solving to multiple-choice questions, MAWARITH supports the full reasoning chain and provides step-by-step solutions, including intermediate legal decisions and justifications based on classical juristic sources and established inheritance rules, as well as exact share calculations. To evaluate models beyond final-answer accuracy, we propose MIR-E (Mawarith Inheritance Reasoning Evaluation), a weighted multi-stage metric that scores key reasoning stages and captures error propagation across the pipeline. We evaluate five LLMs in a zero-shot setting. Gemini-2.5-flash achieves about 90% MIR-E on both validation and test, while Fanar-C, Fanar-Sadiq, LLaMA 3, and Qwen 3 remain below 50%. Our error analysis identifies recurring failure patterns, including scenario misinterpretation, errors in heir identification, errors in share allocation, and missing or incorrect application of key inheritance rules such as 'awl and radd. The MAWARITH dataset is publicly available at this https URL.
[NLP-68] Accent Vector: Controllable Accent Manipulation for Multilingual TTS Without Accented Data INTERSPEECH2026
【速读】： 该论文旨在解决当前文本转语音（Text-To-Speech, TTS）系统普遍缺乏对非美式口音（accent）建模能力的问题，尤其是针对全球多数英语使用者为非母语者（L2）的现实场景，现有TTS系统仍主要依赖美式口音数据进行训练，导致生成语音缺乏多样性与代表性。解决方案的关键在于提出一种可控制的“口音向量”（Accent Vector），该向量通过在非英语母语语音上微调TTS模型并计算捕捉口音特征的任务向量（task vectors）获得，无需额外的带口音英语训练数据；通过缩放和插值操作，可实现对口音强度的细粒度控制及混合口音语音的生成，并具备跨语言泛化能力，从而在多语言TTS中实现可控且组合式的口音调节。
链接: https://arxiv.org/abs/2603.07534
作者: Thanathai Lertpetchpun,Thanapat Trachu,Jihwan Lee,Tiantian Feng,Dani Byrd,Shrikanth Narayanan
机构: Signal Analysis and Interpretation Lab, University of Southern California, USA; Thomas Lord Department of Computer Science, University of Southern California, USA; Department of Linguistics, University of Southern California
类目: Computation and Language (cs.CL)
备注: Submitted to Interspeech2026
Abstract:Accent is an integral part of society, reflecting multiculturalism and shaping how individuals express identity. The majority of English speakers are non-native (L2) speakers, yet current Text-To-Speech (TTS) systems primarily model American-accented English due limited accented data. We propose \textitAccent Vector, a controllable representation that enables accent manipulation in multilingual TTS without requiring accented training data. \textitAccent Vector is derived by fine-tuning a TTS system on native speech of a different language (i.e. non-English) and computing task vectors capturing accent characteristics (i.e. in English). By scaling and interpolating the vector, we achieve fine-grained control over accent strength and generate mixed-accent speech. In addition, it generalizes beyond English, enabling accent control across multiple languages. Objective and human evaluations confirm the effectiveness of Accent Vector for fine-grained and compositional accent control.
[NLP-69] ableMind: An Uncertainty-Aware Programmatic Agent for Tool-Augmented Table Reasoning
【速读】： 该论文旨在解决表格推理中因大语言模型（LLM）固有随机性导致的幻觉问题，尤其在单轮推理范式下存在的上下文溢出和数值敏感性弱的问题。解决方案的关键在于提出TableMind++，通过引入一种新颖的不确定性感知推理框架来缓解幻觉：首先采用记忆引导的计划剪枝机制，利用历史轨迹验证并过滤逻辑错误的推理计划以应对认知不确定性（epistemic uncertainty）；其次引入基于置信度的动作精炼策略，监控token级概率以检测并自纠正语法噪声，从而降低偶然不确定性（aleatoric uncertainty）；最后结合双权重轨迹聚合方法，从多条推理路径中合成稳健共识，显著提升推理准确性和鲁棒性。
链接: https://arxiv.org/abs/2603.07528
作者: Mingyue Cheng,Shuo Yu,Chuang Jiang,Xiaoyu Tao,Qingyang Mao,Jie Ouyang,Qi Liu,Enhong Chen
机构: University of Science and Technology of China (中国科学技术大学)
类目: Computation and Language (cs.CL)
备注: 6 tables, 9 figures
Abstract:Table reasoning requires models to jointly perform semantic understanding and precise numerical operations. Most existing methods rely on a single-turn reasoning paradigm over tables which suffers from context overflow and weak numerical sensitivity. To address these limitations, we previously proposed TableMind as a tuning-based autonomous programmatic agent that simulates human-like interaction within a lightweight large language model (LLM). TableMind internalizes planning, action, and reflection through a two-stage training strategy involving supervised fine-tuning (SFT) on filtered high-quality data and reinforcement learning (RL) via a multi-perspective reward and the Rank-Aware Policy Optimization (RAPO) algorithm. While TableMind establishes a solid foundation for programmatic agents, the inherent stochasticity of LLMs remains a critical challenge that leads to hallucinations. In this paper, we extend this foundation to TableMind++ by introducing a novel uncertainty-aware inference framework to mitigate hallucinations. Specifically, we propose memory-guided plan pruning to retrieve historical trajectories for validating and filtering out logically flawed plans to address epistemic uncertainty. To ensure execution precision, we introduce confidence-based action refinement which monitors token-level probabilities to detect and self-correct syntactic noise for aleatoric uncertainty mitigation. Finally, we employ dual-weighted trajectory aggregation to synthesize a robust consensus from multiple reasoning paths. Extensive experiments on diverse benchmarks demonstrate that TableMind++ consistently outperforms previous baselines and proprietary models to validate the effectiveness of integrating autonomous training with uncertainty quantification. Our code is available.
[NLP-70] Bolbosh: Script-Aware Flow Matching for Kashmiri Text-to-Speech
【速读】： 该论文旨在解决克什米尔语（Kashmiri）在语音技术领域长期缺乏高质量文本到语音（Text-to-Speech, TTS）系统的问题，从而提升数字可访问性和人机交互的包容性。现有基于印地语系多语言模型的零样本基线方法在克什米尔语上表现不佳，MOS仅为1.86，主要归因于对波斯-阿拉伯字母变音符号（Perso-Arabic diacritics）和语言特有音节结构（phonotactics）建模不足。解决方案的关键在于提出一种名为Bolbosh的监督跨语言适应策略，基于最优传输条件流匹配（Optimal Transport Conditional Flow Matching, OT-CFM），嵌入Matcha-TTS框架中，在有限配对数据下实现稳定对齐；同时引入三阶段声学增强流水线（去混响、静音裁剪与响度归一化）统一异构语音源并稳定对齐学习，并扩展模型词汇表以显式编码克什米尔语字形（graphemes），保留元音细微差异。最终系统达到MOS 3.63和Mel-Cepstral Distortion (MCD) 3.73，显著优于多语言基线，确立了克什米尔语语音合成的新基准。
链接: https://arxiv.org/abs/2603.07513
作者: Tajamul Ashraf,Burhaan Rasheed Zargar,Saeed Abdul Muizz,Ifrah Mushtaq,Nazima Mehdi,Iqra Altaf Gillani,Aadil Amin Kak,Janibul Bashir
机构: 未知
类目: Computation and Language (cs.CL)
备注: this https URL
Abstract:Kashmiri is spoken by around 7 million people but remains critically underserved in speech technology, despite its official status and rich linguistic heritage. The lack of robust Text-to-Speech (TTS) systems limits digital accessibility and inclusive human-computer interaction for native speakers. In this work, we present the first dedicated open-source neural TTS system designed for Kashmiri. We show that zero-shot multilingual baselines trained for Indic languages fail to produce intelligible speech, achieving a Mean Opinion Score (MOS) of only 1.86, largely due to inadequate modeling of Perso-Arabic diacritics and language-specific phonotactics. To address these limitations, we propose Bolbosh, a supervised cross-lingual adaptation strategy based on Optimal Transport Conditional Flow Matching (OT-CFM) within the Matcha-TTS framework. This enables stable alignment under limited paired data. We further introduce a three-stage acoustic enhancement pipeline consisting of dereverberation, silence trimming, and loudness normalization to unify heterogeneous speech sources and stabilize alignment learning. The model vocabulary is expanded to explicitly encode Kashmiri graphemes, preserving fine-grained vowel distinctions. Our system achieves a MOS of 3.63 and a Mel-Cepstral Distortion (MCD) of 3.73, substantially outperforming multilingual baselines and establishing a new benchmark for Kashmiri speech synthesis. Our results demonstrate that script-aware and supervised flow-based adaptation are critical for low-resource TTS in diacritic-sensitive languages. Code and data are available at: this https URL.
[NLP-71] A Joint Neural Baseline for Concept Assertion and Relation Extraction from Clinical Text
【速读】： 该论文旨在解决临床信息抽取（Clinical Information Extraction, CIE）中多阶段任务（概念识别、断言分类和关系抽取）在现有独立建模框架下难以进行公平比较的问题，以及缺乏统一优化机制导致性能受限的挑战。其关键解决方案是定义了一个新的联合任务设置（joint task setting），并提出一种端到端的系统，能够同时优化这三个阶段的任务，从而避免传统流水线方法中各阶段依赖参考输入（reference inputs）带来的不可比性。实验表明，该联合模型在三种任务上的F1分数分别较基线管道方法提升0.3、1.4和3.1，显著优于现有方法，为后续研究提供了强有力的联合基准。
链接: https://arxiv.org/abs/2603.07487
作者: Fei Cheng,Ribeka Tanaka,Sadao Kurohashi
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Technical Report. Our code is available at: this https URL
Abstract:Clinical information extraction (e.g., 2010 i2b2/VA challenge) usually presents tasks of concept recognition, assertion classification, and relation extraction. Jointly modeling the multi-stage tasks in the clinical domain is an underexplored topic. The existing independent task setting (reference inputs given in each stage) makes the joint models not directly comparable to the existing pipeline work. To address these issues, we define a joint task setting and propose a novel end-to-end system to jointly optimize three-stage tasks. We empirically investigate the joint evaluation of our proposal and the pipeline baseline with various embedding techniques: word, contextual, and in-domain contextual embeddings. The proposed joint system substantially outperforms the pipeline baseline by +0.3, +1.4, +3.1 for the concept, assertion, and relation F1. This work bridges joint approaches and clinical information extraction. The proposed approach could serve as a strong joint baseline for future research. The code is publicly available.
[NLP-72] Skip to the Good Part: Representation Structure Inference-Time Layer Skipping in Diffusion vs. Autoregressive LLM s ICLR2026
【速读】： 该论文旨在解决扩散语言模型（diffusion language models, dLLMs）与自回归语言模型（autoregressive language models, AR models）在内部表示结构上的差异问题，特别是训练目标如何影响模型各层和token层面的表征特性。其解决方案的关键在于：通过系统性的层级与token级别的表征分析发现，扩散目标促使模型形成更具层次化抽象、早期层冗余度高且减少近期偏差的表示结构，而自回归目标则产生深度依赖性强、紧密耦合的表示；进一步利用这种冗余特性，提出一种无需架构改动或KV缓存共享的静态推理时层跳过方法，在保持90%以上性能的同时，使原生dLLMs实现高达18.75%的浮点运算量（FLOPs）减少，显著优于AR模型在相同跳过策略下的性能下降。
链接: https://arxiv.org/abs/2603.07475
作者: Raghavv Goel,Risheek Garrepalli,Sudhanshu Agrawal,Chris Lott,Mingu Lee,Fatih Porikli
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted at Sci4DL and Delta workshops at ICLR 2026
Abstract:Autoregressive (AR) language models form representations incrementally through left-to-right prediction, whereas diffusion language models (dLLMs) are trained via full-sequence denoising. Although recent dLLMs match AR performance, it remains unclear whether diffusion objectives fundamentally reshape internal representations across depth. We perform the first layer- and token-wise representational analysis comparing native dLLMs (LLaDA), native AR models (Qwen2.5), and AR-initialized dLLMs (Dream-7B). We find that diffusion objectives result in different, more hierarchical abstractions with substantial early-layer redundancy and reduced recency bias, while AR objectives produce tightly coupled, depth-dependent representations. Critically, AR-initialized dLLMs retain AR-like representational dynamics despite diffusion training, revealing persistent initialization bias. Leveraging this observed representational redundancy, we introduce a static, task-agnostic inference-time layer-skipping method requiring no architectural changes or KV-cache sharing. Native dLLMs achieve up to 18.75% FLOPs reduction while preserving over 90% performance on reasoning and code generation benchmarks, whereas AR models degrade sharply under comparable skipping. These results link training objectives to representational structure and enable practical, cache-orthogonal efficiency gains.
[NLP-73] Cross-Modal Taxonomic Generalization in (Vision-) Language Models
【速读】： 该论文旨在探究语言模型（Language Model, LM）仅从文本表面形式中学习到的语义表征，与通过更 grounded（具身化）证据所学到的语义表征之间的交互关系。具体而言，研究聚焦于视觉-语言模型（Vision-Language Model, VLM）场景下，当部分输入来自图像模态时，语言模型能否从图像引导的语义信息中提取并泛化出词义层级关系（如超类词预测）。其解决方案的关键在于：在保持图像编码器和语言模型参数冻结的前提下，仅训练中间映射层，并逐步剥夺模型对超类词的显式视觉证据，从而检验语言模型是否仍能恢复并泛化此类知识。实验表明，即使在完全缺乏超类词标注的极端条件下，语言模型依然能够基于内部语义一致性实现跨模态分类泛化，且这种泛化能力依赖于图像类别内部的高视觉相似性，说明跨模态泛化既源于多模态输入的一致性，也依赖于语言线索提供的先验知识。
链接: https://arxiv.org/abs/2603.07474
作者: Tianyang Xu,Marcelo Sandoval-Castaneda,Karen Livescu,Greg Shakhnarovich,Kanishka Misra
机构: Toyota Technological Institute at Chicago (丰田技术学院芝加哥分校); The University of Texas at Austin (德克萨斯大学奥斯汀分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:What is the interplay between semantic representations learned by language models (LM) from surface form alone to those learned from more grounded evidence? We study this question for a scenario where part of the input comes from a different modality – in our case, in a vision-language model (VLM), where a pretrained LM is aligned with a pretrained image encoder. As a case study, we focus on the task of predicting hypernyms of objects represented in images. We do so in a VLM setup where the image encoder and LM are kept frozen, and only the intermediate mappings are learned. We progressively deprive the VLM of explicit evidence for hypernyms, and test whether knowledge of hypernyms is recoverable from the LM. We find that the LMs we study can recover this knowledge and generalize even in the most extreme version of this experiment (when the model receives no evidence of a hypernym during training). Additional experiments suggest that this cross-modal taxonomic generalization persists under counterfactual image-label mappings only when the counterfactual data have high visual similarity within each category. Taken together, these findings suggest that cross-modal generalization in LMs arises as a result of both coherence in the extralinguistic input and knowledge derived from language cues.
[NLP-74] he Dual-Stream Transformer: Channelized Architecture for Interpretable Language Modeling
【速读】： 该论文旨在解决标准Transformer模型中所有计算被混杂在单一残差流（residual stream）中的问题，这一设计使得模型内部各组件的功能难以区分，从而阻碍了对模型行为的可解释性分析。其解决方案的关键在于提出双流Transformer（Dual-Stream Transformer），将残差流分解为两个功能独立的分支：由注意力机制更新的token流和由前馈网络更新的context流。通过控制注意力头之间信息流动的混合策略层级（从完全独立到密集连接），该架构实现了可调的可解释性与性能之间的权衡，其中推荐的Kronecker混合策略在保持结构完整性的同时仅带来2.5%的性能损失，显著提升了模型内部机制的透明度与可控性。
链接: https://arxiv.org/abs/2603.07461
作者: J. Clayton Kerce,Alexis Fox
机构: Georgia Tech Research Institute (佐治亚理工学院研究学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Standard transformers entangle all computation in a single residual stream, obscuring which components perform which functions. We introduce the Dual-Stream Transformer, which decomposes the residual stream into two functionally distinct components: a token stream updated by attention and a context stream updated by feed-forward networks. Information flow between attention heads is controlled through a hierarchy of mixing strategies, from fully independent (maximum interpretability) to dense (standard transformer behavior). This design exposes a tunable tradeoff between interpretability and performance. We measure this tradeoff on language modeling tasks at 29M parameters. Fully independent head mixing increases validation loss by 8% relative to dense baselines. The recommended Kronecker mixing strategy, which permits scalar communication between heads while preserving within-head structure, costs only 2.5%. All configurations maintain functional generation under attention amplification (scaling logits by factors up to 16 at inference time), with degradation ranging from 16% to 27%. This robustness suggests the architectures learn discrete algorithms that operate independently of soft probabilistic mixing. The architecture provides a foundation for interpretable language models where internal structure is exposed by design. \footnoteThis work was partially supported by DARPA Contract HR001125C0302. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2603.07461 [cs.CL] (or arXiv:2603.07461v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2603.07461 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-75] Image Generation Models: A Technical History
【速读】： 该论文旨在解决图像生成模型研究领域中文献碎片化的问题，系统性地梳理了过去十年间各类主流图像生成模型的发展脉络与技术演进。其解决方案的关键在于提供一个全面的综述框架，涵盖变分自编码器（VAEs）、生成对抗网络（GANs）、归一化流（normalizing flows）、自回归及基于Transformer的生成器、扩散模型（diffusion-based methods）等核心方法，不仅详尽解析每类模型的技术原理、架构组成与训练算法，还深入探讨优化策略、常见失败模式与局限性；同时延伸至视频生成领域的最新进展，并强调模型鲁棒性与负责任部署的重要性，包括深度伪造（deepfake）风险、检测机制、生成伪影（artifacts）及水印技术等关键议题。
链接: https://arxiv.org/abs/2603.07455
作者: Rouzbeh Shirvani
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Graphics (cs.GR)
备注:
Abstract:Image generation has advanced rapidly over the past decade, yet the literature seems fragmented across different models and application domains. This paper aims to offer a comprehensive survey of breakthrough image generation models, including variational autoencoders (VAEs), generative adversarial networks (GANs), normalizing flows, autoregressive and transformer-based generators, and diffusion-based methods. We provide a detailed technical walkthrough of each model type, including their underlying objectives, architectural building blocks, and algorithmic training steps. For each model type, we present the optimization techniques as well as common failure modes and limitations. We also go over recent developments in video generation and present the research works that made it possible to go from still frames to high quality videos. Lastly, we cover the growing importance of robustness and responsible deployment of these models, including deepfake risks, detection, artifacts, and watermarking.
[NLP-76] Few Tokens Big Leverag e: Preserving Safety Alignment by Constraining Safety Tokens during Fine-tuning
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在下游任务微调（Fine-tuning, FT）过程中出现的安全对齐漂移（Safety-alignment drift）问题，即即使训练数据仅包含良性内容，微调仍可能导致模型对有害请求的拒绝能力下降。解决方案的关键在于提出一种名为“通过约束令牌保持安全对齐”（Preserving Safety Alignment via Constrained Tokens, PACT）的微调框架，其核心机制是：在微调过程中，仅对一小部分与安全相关的令牌（safety-related tokens）施加置信度约束，使其输出分布与预训练阶段已对齐的安全参考模型保持一致，而其他非安全令牌则不受限制，从而允许模型有效适应下游任务，避免全局性参数约束带来的性能折损。
链接: https://arxiv.org/abs/2603.07445
作者: Guoli Wang,Haonan Shi,Tu Ouyang,An Wang
机构: Case Western Reserve University (凯斯西储大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Large language models (LLMs) often require fine-tuning (FT) to perform well on downstream tasks, but FT can induce safety-alignment drift even when the training dataset contains only benign data. Prior work shows that introducing a small fraction of harmful data can substantially compromise LLM refusal behavior, causing LLMs to comply with harmful requests. Existing defense methods often rely on model-wide interventions, such as restricting which parameters are updated or injecting additional safety data, which can limit generality and degrade downstream task performance. To address these limitations, we propose a fine-tuning framework called Preserving Safety Alignment via Constrained Tokens (PACT), which stabilizes the model’s confidence on safety tokens. Our approach is motivated by the empirical observation that safety-aligned behavior is reflected in the model’s token-level output confidence and is often concentrated on a small subset of safety-related tokens. During downstream fine-tuning, we regularize the fine-tuned model to match the aligned reference model’s confidence on safety-related tokens at each response step, while leaving non-safety tokens largely unconstrained to allow effective task adaptation. This targeted constraint prevents alignment drift without imposing global restrictions that typically trade off with model utility.
[NLP-77] AQuA: Toward Strategic Response Generation for Ambiguous Visual Questions ICLR2026
【速读】： 该论文旨在解决当前视觉问答（Visual Question Answering, VQA）基准数据集普遍缺乏对现实场景中模糊性（ambiguity）的系统刻画，以及模型难以根据模糊程度采取相应响应策略的问题。解决方案的关键在于提出一个细粒度的模糊视觉问答数据集AQuA，将模糊实例按性质和程度分为四个等级，并为每类标注最优响应策略（如直接回答、意图推断、列举备选答案或请求澄清）。通过在AQuA上微调视觉语言模型（Vision-Language Models, VLMs），使其能够自适应地选择最合适的策略，从而提升模型识别模糊性、管理不确定性并生成上下文适配响应的能力，显著优于现有开源与闭源基线模型。
链接: https://arxiv.org/abs/2603.07394
作者: Jihyoung Jang,Hyounghun Kim
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: ICLR 2026 (28 pages); Project website: this https URL
Abstract:Visual Question Answering (VQA) is a core task for evaluating the capabilities of Vision-Language Models (VLMs). Existing VQA benchmarks primarily feature clear and unambiguous image-question pairs, whereas real-world scenarios often involve varying degrees of ambiguity that require nuanced reasoning and context-appropriate response strategies. Although recent studies have begun to address ambiguity in VQA, they lack (1) a systematic categorization of ambiguity levels and (2) datasets and models that support strategy-aware responses. In this paper, we introduce Ambiguous Visual Question Answering (AQuA), a fine-grained dataset that classifies ambiguous VQA instances into four levels according to the nature and degree of ambiguity, along with the optimal response strategy for each case. Our evaluation of diverse open-source and proprietary VLMs shows that most models fail to adapt their strategy to the ambiguity type, frequently producing overconfident answers rather than seeking clarification or acknowledging uncertainty. To address this challenge, we fine-tune VLMs on AQuA, enabling them to adaptively choose among multiple response strategies, such as directly answering, inferring intent from contextual cues, listing plausible alternatives, or requesting clarification. VLMs trained on AQuA achieve strategic response generation for ambiguous VQA, demonstrating the ability to recognize ambiguity, manage uncertainty, and respond with context-appropriate strategies, while outperforming both open-source and closed-source baselines.
[NLP-78] Can Large Language Models Keep Up? Benchmarking Online Adaptation to Continual Knowledge Streams
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在动态现实场景中面对持续更新知识流时，难以实时适应新信息的问题。现有方法在面对不断演变的事实时表现出显著局限性，如状态跟踪延迟和对流式环境中干扰信息的敏感性。解决方案的关键在于提出一种名为在线持续知识流适应能力评估框架（Online Adaptation to Continual Knowledge Streams, OAKS）的新基准，该框架通过细粒度的时间片段上下文序列模拟事实随时间动态变化的过程，并提供密集标注以精确衡量模型对知识变更的响应能力。OAKS包含两个数据集（OAKS-BABI 和 OAKS-Novel），用于系统评估多种推理策略下模型的在线适应性能，从而揭示当前先进模型与代理记忆系统在处理连续知识流时的不足。
链接: https://arxiv.org/abs/2603.07392
作者: Jiyeon Kim,Hyunji Lee,Dylan Zhou,Sue Hyun Park,Seunghyun Yoon,Trung Bui,Franck Dernoncourt,Sungmin Cha,Minjoon Seo
机构: KAIST AI(韩国科学技术院人工智能); UNC Chapel Hill(北卡罗来纳大学教堂山分校); Google(谷歌); KRAFTON; Adobe Research(Adobe研究院); New York University(纽约大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:LLMs operating in dynamic real-world contexts often encounter knowledge that evolves continuously or emerges incrementally. To remain accurate and effective, models must adapt to newly arriving information on the fly. We introduce Online Adaptation to Continual Knowledge Streams(OAKS) to evaluate this capability, establishing a benchmark for online adaptation over streaming, continually updating knowledge. Specifically, the benchmark is structured as a sequence of fine-grained context chunks where facts change dynamically across time intervals. OAKS comprises two datasets: OAKS-BABI and OAKS-Novel, where individual facts evolve multiple times across context chunks. These datasets include dense annotations to measure whether models track changes accurately. Evaluating 14 models with varied inference approaches, we observe significant limitations in current methodologies. Both state-of-the-art models and agentic memory systems fail to adapt robustly on OAKS, demonstrating delays in state-tracking and susceptibility to distraction within streaming environments.
[NLP-79] Domain-Specific Quality Estimation for Machine Translation in Low-Resource Scenarios
【速读】： 该论文旨在解决无参考（reference-less）场景下机器翻译质量评估（Quality Estimation, QE）的挑战，特别是在领域特定（如医疗、法律）和低资源语言对中的应用问题。其核心解决方案是提出并改进基于大语言模型（LLM）的QE框架ALOPE，关键在于采用中间层低秩适配（Low-Rank Adaptation, LoRA）策略，在选定的Transformer中间层附加回归头（regression heads），以实现更稳定和高效的QE性能提升。实验表明，该方法在语义复杂领域中表现显著优于纯提示（prompt-only）方法，尤其对开放权重模型更具鲁棒性，为实际部署提供了可行路径。
链接: https://arxiv.org/abs/2603.07372
作者: Namrata Patil Gurav,Akashdeep Ranu,Archchana Sindhujan,Diptesh Kanojia
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 21 pages, 7 tables, 7 figures
Abstract:Quality Estimation (QE) is essential for assessing machine translation quality in reference-less settings, particularly for domain-specific and low-resource language scenarios. In this paper, we investigate sentence-level QE for English to Indic machine translation across four domains (Healthcare, Legal, Tourism, and General) and five language pairs. We systematically compare zero-shot, few-shot, and guideline-anchored prompting across selected closed-weight and open-weight LLMs. Findings indicate that while closed-weight models achieve strong performance via prompting alone, prompt-only approaches remain fragile for open-weight models, especially in high-risk domains. To address this, we adopt ALOPE, a framework for LLM-based QE that uses Low-Rank Adaptation with regression heads attached to selected intermediate Transformer layers. We also extend ALOPE with recently proposed Low-Rank Multiplicative Adaptation (LoRMA). Our results show that intermediate-layer adaptation consistently improves QE performance, with gains in semantically complex domains, indicating a path toward more robust QE in practical scenarios. We release code and domain-specific QE datasets publicly to support further research.
[NLP-80] Position: LLM s Must Use Functor-Based and RAG -Driven Bias Mitigation for Fairness
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）中存在的性别和人口统计学偏见问题，此类偏见表现为模型在不同性别、种族和地理群体与职业或社会角色之间产生系统性扭曲关联，从而强化有害刻板印象。解决方案的关键在于提出一种双轨并行的方法：一是利用范畴论（Category Theory）的结构保持变换，通过函子（functor）将存在偏见的语义域映射到无偏见的标准形式，实现对语义结构的数学严谨去偏；二是结合检索增强生成（Retrieval-Augmented Generation, RAG），在推理阶段动态注入多样且最新的外部知识，直接抵消模型参数中内嵌的偏见。二者协同作用，既保证了语义完整性，又提升了输出的公平性与适应性。
链接: https://arxiv.org/abs/2603.07368
作者: Ravi Ranjan,Utkarsh Grover,Agorista Polyzou
机构: Florida International University (佛罗里达国际大学); University of South Florida (南佛罗里达大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 24 pages, 3 figures
Abstract:Biases in large language models (LLMs) often manifest as systematic distortions in associations between demographic attributes and professional or social roles, reinforcing harmful stereotypes across gender, ethnicity, and geography. This position paper advocates for addressing demographic and gender biases in LLMs through a dual-pronged methodology, integrating category-theoretic transformations and retrieval-augmented generation (RAG). Category theory provides a rigorous, structure-preserving mathematical framework that maps biased semantic domains to unbiased canonical forms via functors, ensuring bias elimination while preserving semantic integrity. Complementing this, RAG dynamically injects diverse, up-to-date external knowledge during inference, directly countering ingrained biases within model parameters. By combining structural debiasing through functor-based mappings and contextual grounding via RAG, we outline a comprehensive framework capable of delivering equitable and fair model outputs. Our synthesis of the current literature validates the efficacy of each approach individually, while addressing potential critiques demonstrates the robustness of this integrated strategy. Ensuring fairness in LLMs, therefore, demands both the mathematical rigor of category-theoretic transformations and the adaptability of retrieval augmentation.
[NLP-81] RILEC: Detection and Generation of L1 Russian Interference Errors in English Learner Texts LREC2026
【速读】： 该论文旨在解决俄语母语学习者在撰写英文作文时因母语（L1）干扰导致的错误识别问题，尤其是词汇层面的误译、音译错误（如将“stadium”误写为“stadion”）及动词时态语义偏差等典型L1迁移现象。其解决方案的关键在于构建了一个大规模标注数据集RILEC（包含超过18,000个句子），融合了专家标注的真实错误数据与基于规则和神经网络增强生成的合成样本，并提出一种利用强化学习（PPO）优化的生成式框架，结合提示控制（prompt-based control）与规则模式，系统性地模拟L1驱动的错误类型。实验表明，基于RILEC微调的模型在词级干扰类错误检测上表现优异，且该增强管道显著提升了模型性能，具备辅助语言教学和个性化纠错的实际应用价值。
链接: https://arxiv.org/abs/2603.07366
作者: Darya Kharlamova,Irina Proskurina
机构: 未知
类目: Computation and Language (cs.CL)
备注: 12 pages, 7 tables, 2 figures. Accepted to LREC 2026
Abstract:Many errors in student essays can be explained by influence from the native language (L1). L1 interference refers to errors influenced by a speaker’s first language, such as using stadion instead of stadium, reflecting lexical transliteration from Russian. In this work, we address the task of detecting such errors in English essays written by Russian-speaking learners. We introduce RILEC, a large-scale dataset of over 18,000 sentences, combining expert-annotated data from REALEC with synthetic examples generated through rule-based and neural augmentation. We propose a framework for generating L1-motivated errors using generative language models optimized with PPO, prompt-based control, and rule-based patterns. Models fine-tuned on RILEC achieve strong performance, particularly on word-level interference types such as transliteration and tense semantics. We find that the proposed augmentation pipeline leads to a significant performance improvement, making it a potentially valuable tool for learners and teachers to more effectively identify and address such errors.
信息检索
[IR-0] OfficeQA Pro: An Enterprise Benchmark for End-to-End Grounded Reasoning
【速读】：该论文旨在解决当前大语言模型（Large Language Models, LLMs）在企业级场景下进行基于文档的多源推理任务时表现不佳的问题，特别是在处理大规模、异构且包含结构化与非结构化数据的文档集合时缺乏可靠性和准确性。其核心解决方案在于构建一个名为OfficeQA Pro的新基准测试集，该数据集基于近百年美国财政部公告（U.S. Treasury Bulletins），涵盖89,000页文档和超过2600万数值条目，要求模型具备精确的文档解析、跨文档检索及对表格与文本内容的联合分析能力。关键创新在于引入结构化文档表示方法（由Databricks的ai_parse_document生成），相比直接使用原始文档，可使前沿AI代理平均准确率提升16.1%，显著改善了模型在复杂企业级任务中的表现，但仍存在较大性能提升空间。
链接: https://arxiv.org/abs/2603.08655
作者: Krista Opsahl-Ong,Arnav Singhvi,Jasmine Collins,Ivan Zhou,Cindy Wang,Ashutosh Baheti,Owen Oertell,Jacob Portes,Sam Havens,Erich Elsen,Michael Bendersky,Matei Zaharia,Xing Chen
机构: Databricks AI Research
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 24 pages, 16 figures. Introduces the OfficeQA Pro benchmark for grounded reasoning over enterprise documents
Abstract:We introduce OfficeQA Pro, a benchmark for evaluating AI agents on grounded, multi-document reasoning over a large and heterogeneous document corpus. The corpus consists of U.S. Treasury Bulletins spanning nearly 100 years, comprising 89,000 pages and over 26 million numerical values. OfficeQA Pro consists of 133 questions that require precise document parsing, retrieval, and analytical reasoning across both unstructured text and tabular data. Frontier LLMs including Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro Preview achieve less than 5% accuracy on OfficeQA Pro when relying on parametric knowledge, and less than 12% with additional access to the web. When provided directly with the document corpus, frontier agents still struggle on over half of questions, scoring 34.1% on average. We find that providing agents with a structured document representation produced by Databricks’ ai_parse_document yields a 16.1% average relative performance gain across agents. We conduct additional ablations to study the effects of model selection, table representation, retrieval strategy, and test-time scaling on performance. Despite these improvements, significant headroom remains before agents can be considered reliable at enterprise-grade grounded reasoning.
[IR-1] LoopLens: Supporting Search as Creation in Loop-Based Music Composition
【速读】：该论文旨在解决当前创意支持工具（Creativity Support Tools, CSTs）在音乐创作等场景中，将搜索行为局限于信息检索框架，而忽视了搜索本身作为创意媒介（如电子舞曲制作中的循环素材拼贴式编排）的问题。其解决方案的关键在于提出LoopLens——一种面向循环音乐创作的研究探针，通过可视化音频搜索结果来支持创造性觅食（creative foraging）与素材组装。该设计利用多模态线索增强专家用户的高效利用能力，同时通过降低对音乐术语依赖的方式，帮助非专业用户进行更广泛的探索，从而揭示了创意搜索中探索与利用之间的行为二分性，并为未来CST的设计提供了词汇无关发现的支持路径。
链接: https://arxiv.org/abs/2603.08571
作者: Sheng Long,Atsuya Kobayashi,Kei Tateno
机构: Northwestern University (西北大学); Sony Group Corporation (索尼集团)
类目: Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR); Sound (cs.SD)
备注:
Abstract:Creativity support tools (CSTs) typically frame search as information retrieval, yet in practices like electronic dance music production, search serves as a creative medium for collage-style composition. To address this gap, we present LoopLens, a research probe for loop-based music composition that visualizes audio search results to support creative foraging and assembling. We evaluated LoopLens in a within-subject user study with 16 participants of diverse musical domain expertise, performing both open-ended (divergent) and goal-directed (convergent) tasks. Our results reveal a clear behavioral split: participants with domain expertise leveraged multimodal cues to quickly exploit a narrow set of loops, while those without domain knowledge relied primarily on audio impressions, engaging in broad exploration often constrained by limited musical vocabulary for query formulation. This behavioral dichotomy provides a new lens for understanding the balance between exploration and exploitation in creative search and offers clear design implications for supporting vocabulary-independent discovery in future CSTs.
[IR-2] mmGAT: Pose Estimation by Graph Attention with Mutual Features from mmWave Radar Point Cloud
【速读】：该论文旨在解决传统基于图像的人体姿态估计（Human Pose Estimation, HPE）与人体动作识别（Human Action Recognition, HAR）在隐私保护不足以及低光照或黑暗环境下性能下降的问题。其解决方案的关键在于利用毫米波（mmWave）雷达技术获取人体点云数据，并采用图神经网络（Graph Neural Network, GNN）结合注意力机制（attention mechanism）进行特征提取与建模，从而实现高精度、隐私友好的姿态估计。该方法通过设计独特的特征提取策略充分挖掘GNN对雷达点云数据的表达潜力，在两个公开基准mmWave数据集上实现了显著的性能提升，特别是在平均关节位置误差（MPJPE）和佩尔森对齐平均关节位置误差（PA-MPJPE）指标上分别较现有最优方法降低了35.6%和14.1%。
链接: https://arxiv.org/abs/2603.08551
作者: Abdullah Al Masud,Shi Xintong,Mondher Bouazizi,Ohtsuki Tomoaki
机构: Keio University (早稻田大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
备注: copyright 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Abstract:Pose estimation and human action recognition (HAR) are pivotal technologies spanning various domains. While the image-based pose estimation and HAR are widely admired for their superior performance, they lack in privacy protection and suboptimal performance in low-light and dark environments. This paper exploits the capabilities of millimeter-wave (mmWave) radar technology for human pose estimation by processing radar data with Graph Neural Network (GNN) architecture, coupled with the attention mechanism. Our goal is to capture the finer details of the radar point cloud to improve the pose estimation performance. To this end, we present a unique feature extraction technique that exploits the full potential of the GNN processing method for pose estimation. Our model mmGAT demonstrates remarkable performance on two publicly available benchmark mmWave datasets and establishes new state of the art results in most scenarios in terms of human pose estimation. Our approach achieves a noteworthy reduction of pose estimation mean per joint position error (MPJPE) by 35.6% and PA-MPJPE by 14.1% from the current state of the art benchmark within this domain.
[IR-3] PCFEx: Point Cloud Feature Extraction for Graph Neural Networks
【速读】：该论文旨在解决基于毫米波雷达（mmWave）点云数据在人体姿态估计（Human Pose Estimation, HPE）和人体活动识别（Human Activity Recognition, HAR）任务中的精度不足问题。其关键解决方案在于提出了一种新颖的点云特征提取（Point Cloud Feature Extraction, PCFEx）方法，将点云建模为图结构，在点、边和图三个层次上捕获有意义的特征，并设计了一种高效的图神经网络（Graph Neural Network, GNN）架构来处理这些多粒度特征，从而显著提升了点云数据的表征能力与下游任务性能。
链接: https://arxiv.org/abs/2603.08540
作者: Abdullah Al Masud,Shi Xintong,Mondher Bouazizi,Ohtsuki Tomoaki
机构: Keio University (早稻田大学); Keio University (早稻田大学); Keio University (早稻田大学); Keio University (早稻田大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
备注: ©2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Abstract:Graph neural networks (GNNs) have gained significant attention for their effectiveness across various domains. This study focuses on applying GNN to process 3D point cloud data for human pose estimation (HPE) and human activity recognition (HAR). We propose novel point cloud feature extraction (PCFEx) techniques to capture meaningful information at the point, edge, and graph levels of the point cloud by considering point cloud as a graph. Moreover, we introduce a GNN architecture designed to efficiently process these features. Our approach is evaluated on four most popular publicly available millimeter wave radar datasets, three for HPE and one for HAR. The results show substantial improvements, with significantly reduced errors in all three HPE benchmarks, and an overall accuracy of 98.8% in mmWave-based HAR, outperforming the existing state of the art models. This work demonstrates the great potential of feature extraction incorporated with GNN modeling approach to enhance the precision of point cloud processing.
[IR-4] One Model Is Enough: Native Retrieval Embeddings from LLM Agent Hidden States
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）代理在检索外部知识时依赖两阶段流水线（先生成文本查询，再用独立嵌入模型编码为向量）所导致的基础设施复杂性和延迟问题。其核心解决方案是通过在LLM中添加一个轻量级投影头（projection head），使模型能够直接将自身隐藏状态映射到嵌入空间，从而实现原生检索能力，无需额外嵌入模型。该方法在训练中结合对齐损失、对比损失和排序蒸馏损失，保持了97%基线检索质量，同时显著简化了系统架构并提升了效率。
链接: https://arxiv.org/abs/2603.08429
作者: Bo Jiang
机构: Temple University (天普大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:LLM agents that retrieve external knowledge typically generate a search query as text, then run a separate embedding model to encode it into a vector. This two-model pipeline adds infrastructure complexity and latency, yet is redundant: the LLM already encodes the full conversational context in its hidden states. We propose equipping LLM agents with native retrieval capability by adding a lightweight projection head that maps hidden states directly into the embedding space, eliminating the need for a separate embedding model. Trained with a combination of alignment, contrastive, and rank distillation losses, our method retains 97% of baseline retrieval quality while enabling the LLM agent to search with its own representations. Experiments on the QReCC conversational search benchmark show competitive Recall@10 and MRR@10 compared to the standard generate-then-encode pipeline, with systematic ablations confirming the contribution of each loss component.
[IR-5] ERASE – A Real-World Aligned Benchmark for Unlearning in Recommender Systems
【速读】：该论文旨在解决推荐系统中机器遗忘（Machine Unlearning, MU）的实践瓶颈问题，即如何在满足隐私合规、安全与责任要求的前提下，高效且可靠地从已训练模型中移除特定训练数据。现有基准测试未能反映真实推荐场景，如过度依赖协同过滤、假设大规模删除请求以及忽略顺序遗忘和效率约束等。本文提出ERASE这一大规模MU基准，其关键在于构建一个贴近实际应用的多任务框架，涵盖协同过滤、会话推荐和下一篮子推荐三类核心任务，并引入基于真实场景的遗忘情景（如逐次删除敏感交互或垃圾内容）。ERASE整合了七种遗忘算法（含通用方法与推荐专用方法）及九个公开数据集与九个先进模型，生成超过600 GB可复用资源（包括实验日志和上千个模型检查点），从而系统性揭示当前方法的优势与局限：例如近似遗忘在某些条件下可媲美重新训练，但鲁棒性因数据集和架构而异；通用方法在注意力机制和循环结构模型上表现不稳定，而推荐专用方法更具可靠性。此基准为推动实用MU技术的发展提供了实证基础。
链接: https://arxiv.org/abs/2603.08341
作者: Pierre Lubitzsch,Maarten de Rijke,Sebastian Schelter
机构: BIFOLD TU Berlin (BIFOLD 柏林工业大学); University of Amsterdam (阿姆斯特丹大学)
类目: Information Retrieval (cs.IR)
备注:
Abstract:Machine unlearning (MU) enables the removal of selected training data from trained models, to address privacy compliance, security, and liability issues in recommender systems. Existing MU benchmarks poorly reflect real-world recommender settings: they focus primarily on collaborative filtering, assume unrealistically large deletion requests, and overlook practical constraints such as sequential unlearning and efficiency. We present ERASE, a large-scale benchmark for MU in recommender systems designed to align with real-world usage. ERASE spans three core tasks – collaborative filtering, session-based recommendation, and next-basket recommendation – and includes unlearning scenarios inspired by real-world applications, such as sequentially removing sensitive interactions or spam. The benchmark covers seven unlearning algorithms, including general-purpose and recommender-specific methods, across nine public datasets and nine state-of-the-art models. We execute ERASE to produce more than 600 GB of reusable artifacts, such as extensive experimental logs and more than a thousand model checkpoints. Crucially, the artifacts that we release enable systematic analysis of where current unlearning methods succeed and where they fall short. ERASE showcases that approximate unlearning can match retraining in some settings, but robustness varies widely across datasets and architectures. Repeated unlearning exposes weaknesses in general-purpose methods, especially for attention-based and recurrent models, while recommender-specific approaches behave more reliably. ERASE provides the empirical foundation to help the community assess, drive, and track progress toward practical MU in recommender systems. Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2603.08341 [cs.IR] (or arXiv:2603.08341v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2603.08341 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-6] SPD-RAG : Sub-Agent Per Document Retrieval-Augmented Generation
【速读】：该论文旨在解决复杂现实问题中因事实分散于大规模文档语料库而导致的标准检索增强生成（Retrieval-Augmented Generation, RAG）管道证据覆盖不全，以及长上下文大语言模型（Large Language Models, LLMs）在处理海量输入时推理不可靠的问题。解决方案的关键在于提出一种分层多智能体框架SPD-RAG（Hierarchical Multi-Agent Framework for Exhaustive Cross-Document Question Answering），其核心创新是沿文档维度对问题进行分解：每个文档由专用的文档级智能体独立处理，实现聚焦式检索；同时引入协调器智能体调度任务并聚合各智能体的部分答案，通过一个基于token限制的合成层（支持递归map-reduce机制）进行答案融合。该设计实现了文档级专业化与集中式融合的结合，在异构多文档场景下显著提升可扩展性和答案质量，同时降低API调用成本（仅为全上下文基线的38%）。
链接: https://arxiv.org/abs/2603.08329
作者: Yagiz Can Akay,Muhammed Yusuf Kartal,Esra Alparslan,Faruk Ortakoyluoglu,Arda Akpinar
机构: TOBB University of Economics and Technology (TOBB经济与技术大学); OSTIM Technical University (OSTIM技术大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 12 pages
Abstract:Answering complex, real-world queries often requires synthesizing facts scattered across vast document corpora. In these settings, standard retrieval-augmented generation (RAG) pipelines suffer from incomplete evidence coverage, while long-context large language models (LLMs) struggle to reason reliably over massive inputs. We introduce SPD-RAG, a hierarchical multi-agent framework for exhaustive cross-document question answering that decomposes the problem along the document axis. Each document is processed by a dedicated document-level agent operating only on its own content, enabling focused retrieval, while a coordinator dispatches tasks to relevant agents and aggregates their partial answers. Agent outputs are synthesized by merging partial answers through a token-bounded synthesis layer (which supports recursive map-reduce for massive corpora). This document-level specialization with centralized fusion improves scalability and answer quality in heterogeneous multidocument settings while yielding a modular, extensible retrieval pipeline. On the LOONG benchmark (EMNLP 2024) for long-context multi-document QA, SPD-RAG achieves an Avg Score of 58.1 (GPT-5 evaluation), outperforming Normal RAG (33.0) and Agentic RAG (32.8) while using only 38% of the API cost of a full-context baseline (68.0).
[IR-7] UIS-Digger: Towards Comprehensive Research Agent Systems for Real-world Unindexed Information Seeking ICLR2026
【速读】：该论文旨在解决当前基于大语言模型（Large Language Model, LLM）的信息检索代理在面对未被搜索引擎索引的信息（Unindexed Information Seeking, UIS）时表现严重下降的问题。这类信息包括被忽略的内容、动态网页及嵌入式文件等，构成了现有评估范式中的关键盲区。解决方案的关键在于提出一个名为UIS-Digger的多智能体框架，其核心创新是引入双模式浏览机制，实现网页搜索与文件解析并行执行，并采用参数规模约为30B的骨干语言模型结合监督微调（Supervised Fine-Tuning, SFT）与强化反馈训练（Reinforcement Feedback Training, RFT）策略，从而显著提升了对未索引信息的获取能力，在新提出的UIS-QA基准上达到27.27%的准确率，优于集成复杂大模型如O3和GPT-4.1的系统，验证了主动交互未索引源对于构建健壮信息检索系统的重要性。
链接: https://arxiv.org/abs/2603.08117
作者: Chang Liu,Chuqiao Kuang,Tianyi Zhuang,Yuxin Cheng,Huichi Zhou,Xiaoguang Li,Lifeng Shang
机构: Huawei Technologies Ltd.(华为技术有限公司); The University of Hong Kong(香港大学); University College London(伦敦大学学院)
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 21 pages, 5 figures, ICLR 2026
Abstract:Recent advancements in LLM-based information-seeking agents have achieved record-breaking performance on established benchmarks. However, these agents remain heavily reliant on search-engine-indexed knowledge, leaving a critical blind spot: Unindexed Information Seeking (UIS). This paper identifies and explores the UIS problem, where vital information is not captured by search engine crawlers, such as overlooked content, dynamic webpages, and embedded files. Despite its significance, UIS remains an underexplored challenge. To address this gap, we introduce UIS-QA, the first dedicated UIS benchmark, comprising 110 expert-annotated QA pairs. Notably, even state-of-the-art agents experience a drastic performance drop on UIS-QA (e.g., from 70.90 on GAIA and 46.70 on BrowseComp-zh to 24.55 on UIS-QA), underscoring the severity of the problem. To mitigate this, we propose UIS-Digger, a novel multi-agent framework that incorporates dual-mode browsing and enables simultaneous webpage searching and file parsing. With a relatively small \sim 30B-parameter backbone LLM optimized using SFT and RFT training strategies, UIS-Digger sets a strong baseline at 27.27%, outperforming systems integrating sophisticated LLMs such as O3 and GPT-4.1. This demonstrates the importance of proactive interaction with unindexed sources for effective and comprehensive information-seeking. Our work not only uncovers a fundamental limitation in current agent evaluation paradigms but also provides the first toolkit for advancing UIS research, defining a new and promising direction for robust information-seeking systems.
[IR-8] Why Large Language Models can Secretly Outperform Embedding Similarity in Information Retrieval
【速读】：该论文试图解决传统神经嵌入检索系统（Neural Embedding Retrieval Systems, NERS）中因依赖向量相似度而存在的“短视性”（short-sightedness）问题，即仅基于局部语义匹配判断相关性，难以捕捉深层次语义关联。其解决方案的关键在于引入基于大语言模型的相关性判断系统（LLM-Based Relevance Judgment Systems, LLM-RJS），通过显式推理机制增强对查询与文档间复杂语义关系的理解，从而突破NERS在相关性建模上的局限性。研究发现，尽管在标准标注数据集上未观察到显著性能提升，但对比有无推理能力的LLM-RJS表明，现有标注本身也存在短视性偏差，导致推理型LLM-RJS中的假阳性主要源于标注错误而非模型缺陷，这说明LLM-RJS具备理论潜力克服NERS的短视性限制，但需开发更高质量的评估基准以准确衡量其优势。
链接: https://arxiv.org/abs/2603.08077
作者: Matei Benescu,Ivo Pascal de Jong
机构: University of Groningen (格罗宁根大学)
类目: Information Retrieval (cs.IR)
备注: 13 pages, 6 figures, 5 tables
Abstract:With the emergence of Large Language Models (LLMs), new methods in Information Retrieval are available in which relevance is estimated directly through language understanding and reasoning, instead of embedding similarity. We argue that similarity is a short-sighted interpretation of relevance, and that LLM-Based Relevance Judgment Systems (LLM-RJS) (with reasoning) have potential to outperform Neural Embedding Retrieval Systems (NERS) by overcoming this limitation. Using the TREC-DL 2019 passage retrieval dataset, we compare various LLM-RJS with NERS, but observe no noticeable improvement. Subsequently, we analyze the impact of reasoning by comparing LLM-RJS with and without reasoning. We find that human annotations also suffer from short-sightedness, and that false-positives in the reasoning LLM-RJS are primarily mistakes in annotations due to short-sightedness. We conclude that LLM-RJS do have the ability to address the short-sightedness limitation in NERS, but that this cannot be evaluated with standard annotated relevance datasets.
[IR-9] Structure-Preserving Graph Contrastive Learning for Mathematical Information Retrieval
【速读】：该论文旨在解决图对比学习（Graph Contrastive Learning, GCL）在数学公式检索任务中，传统图增强方法容易破坏公式语义信息的问题，尤其针对小规模且结构高度复杂的图数据。解决方案的关键在于提出一种领域特定的图增强技术——变量替换（Variable Substitution），该方法通过保持公式的核心代数关系和结构不变性，实现对图结构的有效扰动，从而提升模型在数学公式检索中的性能表现。实验表明，相较于通用增强策略，该方法显著改善了检索效果。
链接: https://arxiv.org/abs/2603.08012
作者: Chun-Hsi Ku,Hung-Hsuan Chen
机构: National Central University (国立中央大学)
类目: Information Retrieval (cs.IR); Digital Libraries (cs.DL)
备注:
Abstract:This paper introduces Variable Substitution as a domain-specific graph augmentation technique for graph contrastive learning (GCL) in the context of searching for mathematical formulas. Standard GCL augmentation techniques often distort the semantic meaning of mathematical formulas, particularly for small and highly structured graphs. Variable Substitution, on the other hand, preserves the core algebraic relationships and formula structure. To demonstrate the effectiveness of our technique, we apply it to a classic GCL-based retrieval model. Experiments show that this straightforward approach significantly improves retrieval performance compared to generic augmentation strategies. We release the code on GitHub.\footnotethis https URL.
[IR-10] SynPlanResearch-R1: Encourag ing Tool Exploration for Deep Research with Synthetic Plans
【速读】：该论文旨在解决研究型智能体（Research Agent）在利用工具进行多跳信息检索时，因探索行为不佳（如过早终止和工具使用偏倚）而导致强化学习与可验证奖励（Reinforcement Learning with Verifiable Rewards, RLVR）效果有限的问题。其解决方案的关键在于提出SynPlanResearch-R1框架，通过合成多样化的工具使用轨迹来引导冷启动监督微调阶段的探索行为，从而为后续强化学习提供更优的初始策略，显著提升了模型在多跳和开放网络基准任务上的性能表现。
链接: https://arxiv.org/abs/2603.07853
作者: Hansi Zeng,Zoey Li,Yifan Gao,Chenwei Zhang,Xiaoman Pan,Tao Yang,Fengran Mo,Jiacheng Lin,Xian Li,Jingbo Shang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:Research Agents enable models to gather information from the web using tools to answer user queries, requiring them to dynamically interleave internal reasoning with tool use. While such capabilities can in principle be learned via reinforcement learning with verifiable rewards (RLVR), we observe that agents often exhibit poor exploration behaviors, including premature termination and biased tool usage. As a result, RLVR alone yields limited improvements. We propose SynPlanResearch-R1, a framework that synthesizes tool-use trajectories that encourage deeper exploration to shape exploration during cold-start supervised fine-tuning, providing a strong initialization for subsequent RL. Across seven multi-hop and open-web benchmarks, \framework improves performance by up to 6.0% on Qwen3-8B and 5.8% on Qwen3-4B backbones respectively compared to SOTA baselines. Further analyses of tool-use patterns and training dynamics compared to baselines shed light on the factors underlying these gains. Our code is publicly available at this https URL.
[IR-11] Verifiable Reasoning for LLM -based Generative Recommendation
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在生成式推荐中因缺乏中间验证而导致的推理退化（reasoning degradation）问题，即推理过程可能出现同质化或错误累积，从而削弱推荐效果。其解决方案的关键在于提出一种全新的“推理-验证-推荐”（reason-verify-recommend）范式，通过在推理过程中嵌入多维度、高可靠性的验证机制，提供有效反馈以引导推理向更贴近用户真实偏好的方向发展。具体而言，该方法设计了基于双重原则的验证器：一是可靠性（reliability），确保对推理正确性进行准确评估并生成有指导意义的反馈；二是多维性（multi-dimensionality），覆盖用户偏好的多个维度以实现全面验证。为此，作者提出了VRec模型，采用混合验证器结构保障多维性，并引入代理预测目标提升可靠性，在四个真实数据集上验证了其在推荐效果与可扩展性上的显著提升。
链接: https://arxiv.org/abs/2603.07725
作者: Xinyu Lin,Hanqing Zeng,Hanchao Yu,Yinglong Xia,Jiang Zhang,Aashu Singh,Fei Liu,Wenjie Wang,Fuli Feng,Tat-Seng Chua,Qifan Wang
机构: Meta
类目: Information Retrieval (cs.IR)
备注:
Abstract:Reasoning in Large Language Models (LLMs) has recently shown strong potential in enhancing generative recommendation through deep understanding of complex user preference. Existing approaches follow a reason-then-recommend paradigm, where LLMs perform step-by-step reasoning before item generation. However, this paradigm inevitably suffers from reasoning degradation (i.e., homogeneous or error-accumulated reasoning) due to the lack of intermediate verification, thus undermining the recommendation. To bridge this gap, we propose a novel \textbf\textitreason-verify-recommend paradigm, which interleaves reasoning with verification to provide reliable feedback, guiding the reasoning process toward more faithful user preference understanding. To enable effective verification, we establish two key principles for verifier design: 1) reliability ensures accurate evaluation of reasoning correctness and informative guidance generation; and 2) multi-dimensionality emphasizes comprehensive verification across multi-dimensional user preferences. Accordingly, we propose an effective implementation called VRec. It employs a mixture of verifiers to ensure multi-dimensionality, while leveraging a proxy prediction objective to pursue reliability. Experiments on four real-world datasets demonstrate that VRec substantially enhances recommendation effectiveness and scalability without compromising efficiency. The codes can be found at this https URL.
[IR-12] Deep Research for Recommender Systems
【速读】：该论文试图解决传统推荐系统（recommender systems）在实际应用中因仅提供物品列表而导致用户体验受限的问题，即用户需自行承担探索、比较和整合信息的全部负担，而系统仅作为被动过滤器，缺乏主动辅助能力。解决方案的关键在于提出一种全新的深度研究型推荐范式（deep research paradigm），其核心是将传统的物品列表替换为以用户为中心的综合性报告；具体实现上通过RecPilot多智能体框架完成，该框架包含两个核心组件：一个用户轨迹模拟代理（user trajectory simulation agent），用于自主探索物品空间；另一个自演化报告生成代理（self-evolving report generation agent），负责将探索结果整合为结构清晰、可解释且支持决策的定制化报告，从而将推荐从“工具式”交互转变为由智能体驱动的主动服务。
链接: https://arxiv.org/abs/2603.07605
作者: Kesha Ou,Chenghao Wu,Xiaolei Wang,Bowen Zheng,Wayne Xin Zhao,Weitao Li,Long Zhang,Sheng Chen,Ji-Rong Wen
机构: Gaoling School of Artificial Intelligence, Renmin University of China(中国人民大学高瓴人工智能学院); Meituan(美团)
类目: Information Retrieval (cs.IR)
备注: 24 pages, 5 figures, 5 tables
Abstract:The technical foundations of recommender systems have progressed from collaborative filtering to complex neural models and, more recently, large language models. Despite these technological advances, deployed systems often underserve their users by simply presenting a list of items, leaving the burden of exploration, comparison, and synthesis entirely on the user. This paper argues that this traditional “tool-based” paradigm fundamentally limits user experience, as the system acts as a passive filter rather than an active assistant. To address this limitation, we propose a novel deep research paradigm for recommendation, which replaces conventional item lists with comprehensive, user-centric reports. We instantiate this paradigm through RecPilot, a multi-agent framework comprising two core components: a user trajectory simulation agent that autonomously explores the item space, and a self-evolving report generation agent that synthesizes the findings into a coherent, interpretable report tailored to support user decisions. This approach reframes recommendation as a proactive, agent-driven service. Extensive experiments on public datasets demonstrate that RecPilot not only achieves strong performance in modeling user behaviors but also generates highly persuasive reports that substantially reduce user effort in item evaluation, validating the potential of this new interaction paradigm.
[IR-13] GP-Tree: An in-memory spatial index combining adaptive grid cells with a prefix tree for efficient spatial querying
【速读】：该论文旨在解决传统空间索引（如STR-Tree和Quad-Tree）在处理复杂空间对象（如行政区边界和轨迹）时因依赖粗粒度近似（如最小包围矩形，MBR）而导致过滤精度低、查询效率差的问题。其解决方案的关键在于提出一种细粒度空间索引GP-Tree，通过将空间对象的近似网格单元（grid cells）组织成前缀树（prefix tree）结构，以替代MBR进行更精确的过滤；同时利用父子节点间网格编码的共享前缀特性优化数据组织与查询路径，结合剪枝和节点优化策略进一步降低搜索路径长度与内存消耗，从而显著提升空间查询性能。
链接: https://arxiv.org/abs/2603.07517
作者: Xiangyang Yang,Xuefeng Guan,Lanxue Dang,Yi Xie,Qingyang Xu,Huayi Wu,Jiayao Wang
机构: 未知
类目: Databases (cs.DB); Information Retrieval (cs.IR)
备注:
Abstract:Efficient spatial indexing is crucial for processing large-scale spatial data. Traditional spatial indexes, such as STR-Tree and Quad-Tree, organize spatial objects based on coarse approximations, such as their minimum bounding rectangles (MBRs). However, this coarse representation is inadequate for complex spatial objects (e.g., district boundaries and trajectories), limiting filtering accuracy and query performance of spatial indexes. To address these limitations, we propose GP-Tree, a fine-grained spatial index that organizes approximated grid cells of spatial objects into a prefix tree structure. GP-Tree enhances filtering ability by replacing coarse MBRs with fine-grained cell-based approximations of spatial objects. The prefix tree structure optimizes data organization and query efficiency by leveraging the shared prefixes in the hierarchical grid cell encodings between parent and child cells. Additionally, we introduce optimization strategies, including tree pruning and node optimization, to reduce search paths and memory consumption, further enhancing GP-Tree’s performance. Finally, we implement a variety of spatial query operations on GP-Tree, including range queries, distance queries, and k-nearest neighbor queries. Extensive experiments on real-world datasets demonstrate that GP-Tree significantly outperforms traditional spatial indexes, achieving up to an order-of-magnitude improvement in query efficiency.
[IR-14] SeDa: A Unified System for Dataset Discovery and Multi-Entity Augmented Semantic Exploration
【速读】：该论文旨在解决开放数据平台与研究存储库持续扩展所导致的数据集生态系统碎片化问题，这给跨源数据发现与解释带来了显著挑战。解决方案的关键在于提出SeDa框架，其核心包括：通过语义提取与标准化统一异构元数据表示；构建可扩展的主题标签图以支持主题检索与跨域关联；嵌入溯源保障模块确保数据来源可靠性与链接可用性；以及采用多实体增强导航策略，在站点、机构和企业构成的知识空间中组织数据集，实现超越传统搜索范式的上下文感知与溯源感知探索。
链接: https://arxiv.org/abs/2603.07502
作者: Kan Ling,Zhen Qin,Yichi Zhu,Hengrun Zhang,Huiqun Yu,Guisheng Fan
机构: East China University of Science and Technology (华东理工大学)
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: 16 pages, 8 figures. System for large-scale dataset discovery and multi-entity semantic exploration
Abstract:The continuous expansion of open data platforms and research repositories has led to a fragmented dataset ecosystem, posing significant challenges for cross-source data discovery and interpretation. To address these challenges, we introduce SeDa–a unified framework for dataset discovery, semantic annotation, and multi-entity augmented navigation. SeDa integrates more than 7.6 million datasets from over 200 platforms, spanning governmental, academic, and industrial domains. The framework first performs semantic extraction and standardization to harmonize heterogeneous metadata representations. On this basis, a topic-tagging mechanism constructs an extensible tag graph that supports thematic retrieval and cross-domain association, while a provenance assurance module embedded within the annotation process continuously validates dataset sources and monitors link availability to ensure reliability and traceability. Furthermore, SeDa employs a multi-entity augmented navigation strategy that organizes datasets within a knowledge space of sites, institutions, and enterprises, enabling contextual and provenance-aware exploration beyond traditional search paradigms. Comparative experiments with popular dataset search platforms, such as ChatPD and Google Dataset Search, demonstrate that SeDa achieves superior coverage, timeliness, and traceability. Taken together, SeDa establishes a foundation for trustworthy, semantically enriched, and globally scalable dataset exploration.
[IR-15] Dial: A Knowledge-Grounded Dialect-Specific NL2SQL System
【速读】：该论文旨在解决多方言SQL（multi-dialect SQL）场景下自然语言到SQL（NL2SQL）转换的准确性与可执行性问题，即现有方法在面对不同数据库系统（如MySQL、PostgreSQL等）各自独特的SQL语法、内置函数和执行约束时，难以同时保证生成查询的语义正确性和目标引擎上的可执行性。解决方案的关键在于提出Dial框架，其核心创新包括：(1) 引入方言感知的逻辑查询规划模块（Dialect-Aware Logical Query Planning），通过操作符级意图分解和差异感知规范将自然语言映射为结构化的逻辑查询计划；(2) 构建分层意图感知知识库（HINT-KB），系统组织方言知识为标准化语法参考、声明式函数仓库和过程式约束仓库；(3) 设计执行驱动的调试与语义验证循环，分离语法修复与逻辑审计，避免语义漂移。这一方法显著提升了跨数据库系统的NL2SQL准确率与方言特性覆盖度。
链接: https://arxiv.org/abs/2603.07449
作者: Xiang Zhang,Hongming Xu,Le Zhou,Wei Zhou,Xuanhe Zhou,Guoliang Li,Yuyu Luo,Changdong Liu,Guorun Chen,Jiang Liao,Fan Wu
机构: 未知
类目: Databases (cs.DB); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:Enterprises commonly deploy heterogeneous database systems, each of which owns a distinct SQL dialect with different syntax rules, built-in functions, and execution constraints. However, most existing NL2SQL methods assume a single dialect (e.g., SQLite) and struggle to produce queries that are both semantically correct and executable on target engines. Prompt-based approaches tightly couple intent reasoning with dialect syntax, rule-based translators often degrade native operators into generic constructs, and multi-dialect fine-tuning suffers from cross-dialect interference. In this paper, we present Dial, a knowledge-grounded framework for dialect-specific NL2SQL. Dial introduces: (1) a Dialect-Aware Logical Query Planning module that converts natural language into a dialect-aware logical query plan via operator-level intent decomposition and divergence-aware specification; (2) HINT-KB, a hierarchical intent-aware knowledge base that organizes dialect knowledge into (i) a canonical syntax reference, (ii) a declarative function repository, and (iii) a procedural constraint repository; and (3) an execution-driven debugging and semantic verification loop that separates syntactic recovery from logic auditing to prevent semantic drift. We construct DS-NL2SQL, a benchmark covering six major database systems with 2,218 dialect-specific test cases. Experimental results show that Dial consistently improves translation accuracy by 10.25% and dialect feature coverage by 15.77% over state-of-the-art baselines. The code is at this https URL. Subjects: Databases (cs.DB); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG) Cite as: arXiv:2603.07449 [cs.DB] (or arXiv:2603.07449v1 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2603.07449 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-16] SoK: Agent ic Retrieval-Augmented Generation (RAG ): Taxonomy Architectures Evaluation and Research Directions
【速读】：该论文旨在解决当前生成式AI（Generative AI）系统中Agentic Retrieval-Augmented Generation (RAG)架构缺乏系统性理解的问题，具体包括架构碎片化、评估方法不一致以及可靠性风险未被充分识别等挑战。其解决方案的关键在于首次提出一个统一的理论框架，将自主检索-生成循环形式化为有限时域部分可观测马尔可夫决策过程（finite-horizon partially observable Markov decision processes），从而明确定义控制策略与状态转移机制；在此基础上构建了涵盖规划机制、检索编排、记忆范式和工具调用行为的模块化分类体系，并揭示了传统静态评估方法的局限性及自主循环中的系统性风险（如幻觉传播累积、记忆污染、检索错位与工具执行级联漏洞），最终提出稳定自适应检索、成本感知编排、轨迹形式化评估与监督机制等博士级研究方向，为构建可靠、可控且可扩展的智能体式RAG系统提供明确路径。
链接: https://arxiv.org/abs/2603.07379
作者: Saroj Mishra,Suman Niroula,Umesh Yadav,Dilip Thakur,Srijan Gyawali,Shiva Gaire
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR); Information Retrieval (cs.IR)
备注:
Abstract:Retrieval-Augmented Generation (RAG) systems are increasingly evolving into agentic architectures where large language models autonomously coordinate multi-step reasoning, dynamic memory management, and iterative retrieval strategies. Despite rapid industrial adoption, current research lacks a systematic understanding of Agentic RAG as a sequential decision-making system, leading to highly fragmented architectures, inconsistent evaluation methodologies, and unresolved reliability risks. This Systematization of Knowledge (SoK) paper provides the first unified framework for understanding these autonomous systems. We formalize agentic retrieval-generation loops as finite-horizon partially observable Markov decision processes, explicitly modeling their control policies and state transitions. Building upon this formalization, we develop a comprehensive taxonomy and modular architectural decomposition that categorizes systems by their planning mechanisms, retrieval orchestration, memory paradigms, and tool-invocation behaviors. We further analyze the critical limitations of traditional static evaluation practices and identify severe systemic risks inherent to autonomous loops, including compounding hallucination propagation, memory poisoning, retrieval misalignment, and cascading tool-execution vulnerabilities. Finally, we outline key doctoral-scale research directions spanning stable adaptive retrieval, cost-aware orchestration, formal trajectory evaluation, and oversight mechanisms, providing a definitive roadmap for building reliable, controllable, and scalable agentic retrieval systems.
人机交互
[HC-0] Clarity and Computational Efficiency of Orbital Boundary Labeling
【速读】：该论文旨在解决圆形界面（如智能手表、汽车仪表盘或径向可视化图表）中标签可读性差的问题，传统矩形标签布局在这些空间受限的显示场景下会浪费屏幕空间并引发视觉杂乱。其核心解决方案是提出两种轨道边界标注（orbital boundary labeling）算法：轨道-径向（orbital-radial）和直线型（straight-line）引导线（leader line），用于在环形区域外放置标签，并通过优化无交叉且最短的引导线来提升布局效率。实验结果表明，两种引导线类型在标注准确性上相当，但直线型引导线显著缩短了用户响应时间，体现出更高的交互效率。
链接: https://arxiv.org/abs/2603.08657
作者: Markus Wallinger,Annika Bonerath,Soeren Terziadis,Jules Wulms,Martin Nöllenburg
机构: Technical University of Munich (慕尼黑工业大学); University of Bonn (波恩大学); TU Eindhoven (埃因霍温理工大学); TU Wien (维也纳工业大学)
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Circular interfaces such as those found on smartwatches, automotive dashboards, cockpit instruments, or in radial visualizations pose unique challenges for placing readable labels. Traditional rectangular labeling methods waste screen space and create visual clutter on these constrained displays. In orbital boundary labeling, the labels (e.g., the features’ names) are placed in an annulus-shaped orbit outside of the figure, and each label is connected to its feature using a short, crossing-free leader line. We contribute algorithms to compute two leader styles, orbital-radial and straight-line, for uniform and non-uniform label sizes, optimizing for crossing-free shortest leaders. We evaluate the model and the algorithms with computational experiments and a controlled user experiment. The user experiment reveals that both leader types exhibit similar accuracy, but straight-line leaders yield faster response times.
[HC-1] What to Make Sense of in the Era of LLM ? A Perspective from the Structure and Efforts in Sensemaking
【速读】：该论文旨在解决复杂情境下信息感知与解释（sensemaking）任务中，如何有效整合人类认知能力与大语言模型（LLM）的分析优势，以提升对模糊、多源数据的洞察力。其解决方案的关键在于探索两种不同策略下GPT-4在虚构恐怖主义阴谋解码任务中的辅助作用：一是整体式（holistic）感知流程，二是分步骤（step-by-step）推理方法，从而揭示人机协作中可优化的协同机制，以充分发挥人类直觉与AI模式识别能力的互补性。
链接: https://arxiv.org/abs/2603.08604
作者: Tianyi Li,Satya Samhita Bonepalli,Vikram Mohanty
机构: Purdue University (普渡大学); Bosch Research North America (博世研究中心北美)
类目: Human-Computer Interaction (cs.HC)
备注: CHI 2024 Sensemaking Workshop this https URL
Abstract:Sensemaking tasks often entail navigating through complex, ambiguous data to construct coherent insights. Prior work has shown that crowds can effectively distribute cognitive load, pooling diverse perspectives to enhance analytical depth. Recent advancements in LLMs have further expanded the toolkit for sensemaking, offering scalable data processing, complex pattern recognition, and the ability to infer and propose meaningful hypotheses. In this study, we explore how LLMs (i.e., GPT-4) can assist in a complex sensemaking task of deciphering fictional terrorist plots. We explore two different approaches for leveraging GPT-4’s capabilities: a holistic sensemaking process and a step-by-step approach. Our preliminary investigations open the doors for future research into optimizing human-AI collaborative workflows, aiming to harness the complementary strengths of both for more effective sensemaking in complex scenarios.
[HC-2] DiverXplorer: Stock Image Exploration via Diversity Adjustment for Graphic Design
【速读】：该论文旨在解决图形设计师在开放式或早期设计任务中，使用现有图像检索工具时面临的局限性——这些工具通常聚焦于相关性和相似性，导致设计师难以全面概览设计空间或发现视觉模式。解决方案的关键在于提出一种基于行列式点过程（Determinantal Point Process, DPP）采样的图像探索原型，通过逐步调整多样性（diversity）来支持用户从宽泛的视觉概览向更聚焦的子集过渡，从而增强早期阶段的意义建构与视觉模式比较能力。该方法将多样性-相似性权衡转化为交互式体验，而非静态排序，有效揭示了不同探索阶段对多样性的需求变化。
链接: https://arxiv.org/abs/2603.08584
作者: Antonio Tejero-de-Pablos,Sichao Song,Naoto Ohsaka,Mayu Otani,Shin’ichi Satoh
机构: CyberAgent( CyberAgent); National Institute of Informatics (日本信息研究所)
类目: Human-Computer Interaction (cs.HC)
备注: Accepted at CHI EA 2026
Abstract:Graphic designers explore large stock image collections during open-ended or early-stage design tasks, yet common tools emphasize relevance and similarity, limiting designers’ ability to overview the design space or discover visual patterns. We present an image exploration prototype that enables stepwise adjustment of diversity, allowing users to transition from diverse overviews to increasingly focused subsets during exploration. Our approach implements diversity control via determinantal point process (DPP)-based sampling and exposes diversity-similarity tradeoffs through interaction rather than static ranking. We report findings from a pilot study with professional graphic designers comparing our technique to baselines inspired by current tools in open-ended image selection tasks. Results suggest that stepwise diversity control supports early-stage sensemaking and comparison of visual patterns, while revealing important tradeoffs: diversity aids discovery and reduces backtracking, but becomes less desirable as exploration progresses. We aim to provide a novel perspective on how to implement transitions between diversity and similarity. Our code is available at this https URL.
[HC-3] Galaw at Gunita: Extended Reality Murals for Experiencing Filipino Art
【速读】：该论文旨在解决数字技术在文化传承中可能对传统艺术造成替代或削弱的问题，特别是在菲律宾语境下，如何通过数字系统支持艺术欣赏而不损害原作的完整性。解决方案的关键在于构建一种交互式数字孪生（digital twin）机制，即利用扩展现实（XR）技术创建沉浸式、可触达的互动壁画系统——SoulWall，使观众能够在尊重艺术家意图的前提下，以具身交互方式探索和体验菲律宾艺术作品，从而实现数字空间与实体艺术的协同增效。
链接: https://arxiv.org/abs/2603.08557
作者: Jomar Delos Reyes,Sealtiel Dy,Rica Mae Sales. Orrin Landon Uy,Toni-Jan Keith Monserrat,Ryan Austin Fernandez,Jordan Aiko Deja
机构: De La Salle University (德拉萨大学)
类目: Human-Computer Interaction (cs.HC)
备注: 8 pages, 5 figures
Abstract:Digital and interactive spaces are becoming increasingly prevalent as platforms for cultural engagement, offering new ways to make art more accessible, engaging, and inclusive. In the Philippine context, where visual art is deeply rooted in precolonial, colonial, and postcolonial histories, there is a growing need to explore how digital systems can support art appreciation without replacing or compromising traditional and physical artworks. Rather than treating digital experiences as substitutes, we argue for the value of creating interactive digital twins that allow audiences to explore, touch, and engage with artworks while preserving the integrity of the originals. In this paper, we present SoulWall, an extended reality (XR) interactive mural system designed to augment Filipino artworks through embodied interaction. SoulWall enables viewers to experience paintings and animations at scale, supporting exploratory and playful engagement while respecting artist intent. We describe the design and implementation of the system, including its mural layout, interaction techniques, and interaction logging infrastructure. We report findings from a user study focused on user experience complemented by analyses of interaction logs and spatial engagement patterns. Our results highlight the potential of XR murals as a cultural computing artifact for art appreciation and for showcasing Filipino artists in interactive public and exhibition spaces.
[HC-4] UMSphere: Turning a University Curriculum into Playable VR Challenges
【速读】：该论文旨在解决传统大学迎新形式难以有效传达STEM（科学、技术、工程和数学）课程核心知识的问题，尤其是在算法思维（algorithmic thinking）和形式推理（formal reasoning）等抽象能力培养方面。其解决方案的关键在于构建了一个名为TUMSphere的严肃虚拟现实（Serious Virtual Reality, VR）应用，作为慕尼黑工业大学（TUM）海布伦教育园区的数字孪生体，通过六个与课程内容高度映射的微型游戏，将信息工程领域的基础知识点转化为沉浸式VR交互挑战。这些游戏涵盖编程入门、硬件调试、代码补全、图遍历、最短路径优化及关系数据库查询，并按照学期进度设计难度梯度，从而实现教学内容的具象化和可操作化。实证研究表明，该方案在提升学习成效、用户可用性与参与度方面表现优异，且无显著晕动症反应，验证了在可探索的VR校园中嵌入真实学术任务是一种可行且可扩展的STEM推广策略。
链接: https://arxiv.org/abs/2603.08525
作者: Santiago Berrezueta-Guzman,Nadia Damianova,Andrei Koshelev,Ivan Parmacli,Stefan Wagner
机构: Technical University of Munich (慕尼黑工业大学)
类目: Human-Computer Interaction (cs.HC)
备注: Paper submitted to IEEE
Abstract:Traditional university orientation formats struggle to convey the intellectual substance of STEM curricula, particularly in disciplines where core competencies, such as algorithmic thinking and formal reasoning, are inherently abstract. This paper presents TUMSphere, a serious virtual reality (VR) application built as an interactive digital twin of the TUM Bildungscampus Heilbronn, in which six curriculum-mapped mini-games translate foundational Information Engineering topics into hands-on VR challenges. The mini-games, covering introductory programming, hardware debugging, code completion, graph traversal, shortest-path optimization, and relational database querying, follow a graduated difficulty progression that mirrors the real semesters’ structure of the degree. We describe the pedagogical rationale, the VR interaction mechanics, and nine cross-cutting design considerations that guided development. A within-subjects pilot study (N = 18) using pre-/post-knowledge tests, the System Usability Scale, a User Engagement Scale adaptation, and the Simulator Sickness Questionnaire yielded a statistically significant knowledge gain (p 0.001, r = 0.86), good usability (SUS M = 76.4), high engagement (M = 4.21/5), and negligible simulator sickness (SSQ M = 7.1). Task performance logs confirmed the intended difficulty gradient across mini-games. These results suggest that embedding authentic academic challenges in an explorable VR campus is a viable and extensible approach to gamified STEM outreach.
[HC-5] A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic
【速读】：该论文旨在解决将基于大语言模型（Large Language Model, LLM）的生成式 AI 系统安全、有效地应用于真实临床环境中的问题，特别是在急症门诊场景下进行病史采集与初步诊断推理的可行性。其解决方案的关键在于开发并评估一个名为 Articulate Medical Intelligence Explorer (AMIE) 的对话式人工智能系统，该系统在患者就诊前通过文本聊天形式完成病史采集，并生成鉴别诊断（Differential Diagnosis, DDx）供医生参考；研究通过前瞻性单臂可行性试验验证了其安全性、用户接受度及临床推理能力，结果显示 AMIE 在无干预情况下保持高安全性，患者满意度显著提升，且其诊断建议质量与初级保健医师（Primary Care Provider, PCP）相当，尽管在管理方案的实用性和成本效益方面略逊于人类医生。这一成果标志着生成式 AI 向临床落地迈出关键一步。
链接: https://arxiv.org/abs/2603.08448
作者: Peter Brodeur,Jacob M. Koshy,Anil Palepu,Khaled Saab,Ava Homiar,Roma Ruparel,Charles Wu,Ryutaro Tanno,Joseph Xu,Amy Wang,David Stutz,Hannah M. Ferrera,David Barrett,Lindsey Crowley,Jihyeon Lee,Spencer E. Rittner,Ellery Wulczyn,Selena K. Zhang,Elahe Vedadi,Christine G. Kohn,Kavita Kulkarni,Vinay Kadiyala,Sara Mahdavi,Wendy Du,Jessica Williams,David Feinbloom,Renee Wong,Tao Tu,Petar Sirkovic,Alessio Orlandi,Christopher Semturs,Yun Liu,Juraj Gottweis,Dale R. Webster,Joëlle Barral,Katherine Chou,Pushmeet Kohli,Avinatan Hassidim,Yossi Matias,James Manyika,Rob Fields,Jonathan X. Li,Marc L. Cohen,Vivek Natarajan,Mike Schaekermann,Alan Karthikesalingam,Adam Rodman
机构: Beth Israel Deaconess Medical Center(贝斯以色列女执事医疗中心); Google Research(谷歌研究); Google DeepMind(谷歌深度思维); Harvard Medical School(哈佛医学院); Massachusetts General Hospital(麻省总医院); Beth Israel Lahey Health(贝斯以色列拉海健康); Equal leadership(平等领导)
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and management conversations in simulated settings. Translating these systems into clinical practice requires assessment in real-world workflows with rigorous safety oversight. We report a prospective, single-arm feasibility study of an LLM-based conversational AI, the Articulate Medical Intelligence Explorer (AMIE), conducting clinical history taking and presentation of potential diagnoses for patients to discuss with their provider at urgent care appointments at a leading academic medical center. 100 adult patients completed an AMIE text-chat interaction up to 5 days before their appointment. We sought to assess the conversational safety and quality, patient and clinician experience, and clinical reasoning capabilities compared to primary care providers (PCPs). Human safety supervisors monitored all patient-AMIE interactions in real time and did not need to intervene to stop any consultations based on pre-defined criteria. Patients reported high satisfaction and their attitudes towards AI improved after interacting with AMIE (p 0.001). PCPs found AMIE’s output useful with a positive impact on preparedness. AMIE’s differential diagnosis (DDx) included the final diagnosis, per chart review 8 weeks post-encounter, in 90% of cases, with 75% top-3 accuracy. Blinded assessment of AMIE and PCP DDx and management (Mx) plans suggested similar overall DDx and Mx plan quality, without significant differences for DDx (p = 0.6) and appropriateness and safety of Mx (p = 0.1 and 1.0, respectively). PCPs outperformed AMIE in the practicality (p = 0.003) and cost effectiveness (p = 0.004) of Mx. While further research is needed, this study demonstrates the initial feasibility, safety, and user acceptance of conversational AI in a real-world setting, representing crucial steps towards clinical translation.
[HC-6] Human-Aware Robot Behaviour in Self-Driving Labs
【速读】：该论文旨在解决自驱动实验室（Self-driving Laboratories, SDLs）中移动机器人化学家（Mobile Robot Chemists, MRCs）在与人类协同工作时因缺乏情境感知能力而导致的效率低下问题。当前MRC主要依赖简单的LiDAR障碍检测，一旦检测到人类便被动等待，无法区分人类是处于准备状态还是正在进行仪器操作，从而造成不必要的延迟。解决方案的关键在于提出一种基于嵌入式AI的感知方法，其核心是一个分层的人类意图预测模型，能够主动识别人类行为类型——即区分“预备性动作”（如等待）与“瞬时交互”（如使用仪器），从而实现主动的人机协作，提升共享实验场景下的自动化流程效率。
链接: https://arxiv.org/abs/2603.08420
作者: Satheeshkumar Veeramani,Anna Kisil,Abigail Bentley,Hatem Fakhruldeen,Gabriella Pizzuto,Andrew I. Cooper
机构: University of Liverpool(利物浦大学)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Self-driving laboratories (SDLs) are rapidly transforming research in chemistry and materials science to accelerate new discoveries. Mobile robot chemists (MRCs) play a pivotal role by autonomously navigating the lab to transport samples, effectively connecting synthesis, analysis, and characterisation equipment. The instruments within an SDL are typically designed or retrofitted to be accessed by both human and robotic chemists, ensuring operational flexibility and integration between manual and automated workflows. In many scenarios, human and robotic chemists may need to use the same equipment simultaneously. Currently, MRCs rely on simple LiDAR-based obstruction detection, which forces the robot to passively wait if a human is present. This lack of situational awareness leads to unnecessary delays and inefficient coordination in time-critical automated workflows in human-robot shared labs. To address this, we present an initial study of an embodied, AI-driven perception method that facilitates proactive human-robot interaction in shared-access scenarios. Our method features a hierarchical human intention prediction model that allows the robot to distinguish between preparatory actions (waiting) and transient interactions (accessing the instrument). Our results demonstrate that the proposed approach enhances efficiency by enabling proactive human-robot interaction, streamlining coordination, and potentially increasing the efficiency of autonomous scientific labs.
[HC-7] Sandpiper: Orchestrated AI-Annotation for Educational Discourse at Scale
【速读】：该论文旨在解决数字教育环境中大规模对话数据的定性分析面临的人工劳动密集、效率低下且难以规模化的问题，同时应对生成式 AI（Generative AI）在教育研究中应用时存在的隐私风险、幻觉问题及方法论严谨性不足等关键障碍。解决方案的关键在于构建一个混合主动性系统 Sandpiper，其核心是通过交互式研究者仪表板与代理型大语言模型（Large Language Model, LLM）引擎的紧密耦合，实现高通量数据处理与人类专业判断的协同；系统还引入上下文感知的自动化去标识化流程、基于模式约束的调度机制以消除 LLM 幻觉，并强制执行定性编码本规范，同时集成评估引擎持续对标人工标注结果，从而保障分析质量并提升研究者对 AI 辅助定性工作流的信任度与效率。
链接: https://arxiv.org/abs/2603.08406
作者: Daryl Hedley,Doug Pietrzak,Jorge Dias,Ian Burden,Bakhtawar Ahtisham,Zhuqian Zhou,Kirk Vanacore,Josh Marland,Rachel Slama,Justin Reich,Kenneth Koedinger,René Kizilcec
机构: National Tutoring Observatory; FreshCognate; Cornell University; Massachusetts Institute of Technology; Carnegie Mellon University
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL)
备注:
Abstract:Digital educational environments are expanding toward complex AI and human discourse, providing researchers with an abundance of data that offers deep insights into learning and instructional processes. However, traditional qualitative analysis remains a labor-intensive bottleneck, severely limiting the scale at which this research can be conducted. We present Sandpiper, a mixed-initiative system designed to serve as a bridge between high-volume conversational data and human qualitative expertise. By tightly coupling interactive researcher dashboards with agentic Large Language Model (LLM) engines, the platform enables scalable analysis without sacrificing methodological rigor. Sandpiper addresses critical barriers to AI adoption in education by implementing context-aware, automated de-identification workflows supported by secure, university-housed infrastructure to ensure data privacy. Furthermore, the system employs schema-constrained orchestration to eliminate LLM hallucinations and enforces strict adherence to qualitative codebooks. An integrated evaluations engine allows for the continuous benchmarking of AI performance against human labels, fostering an iterative approach to model refinement and validation. We propose a user study to evaluate the system’s efficacy in improving research efficiency, inter-rater reliability, and researcher trust in AI-assisted qualitative workflows.
[HC-8] Agent ic Neurosymbolic Collaboration for Mathematical Discovery: A Case Study in Combinatorial Design
【速读】：该论文致力于解决组合设计理论中拉丁方（Latin square）不平衡性（imbalance）的下界问题，特别是在难处理的情形 $ n \equiv 1 \pmod{3} $ 下寻求紧致下界。其解决方案的关键在于构建一个神经符号系统（neurosymbolic system），该系统融合了大语言模型（LLM）的生成式推理能力、符号计算工具（包括计算机代数、约束求解器与模拟退火算法）以及人类研究者的战略引导。AI代理负责发现隐藏结构并提出假设，符号组件提供严格验证与穷举枚举，而人类则在关键节点实现认知转向，将死胡同转化为有效探索路径。最终成果为一个新构造的近完美排列类所实现的紧下界 $ \frac{4n(n-1)}{9} $，并在 Lean 4 中完成形式化验证，证明了神经符号系统在纯数学领域可产生真正的原创性发现。
链接: https://arxiv.org/abs/2603.08322
作者: Hai Xia,Carla P. Gomes,Bart Selman,Stefan Szeider
机构: TU Wien (维也纳工业大学); Cornell University (康奈尔大学)
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Combinatorics (math.CO)
备注:
Abstract:We study mathematical discovery through the lens of neurosymbolic reasoning, where an AI agent powered by a large language model (LLM), coupled with symbolic computation tools, and human strategic direction, jointly produced a new result in combinatorial design theory. The main result of this human-AI collaboration is a tight lower bound on the imbalance of Latin squares for the notoriously difficult case n \equiv 1 \pmod3 . We reconstruct the discovery process from detailed interaction logs spanning multiple sessions over several days and identify the distinct cognitive contributions of each component. The AI agent proved effective at uncovering hidden structure and generating hypotheses. The symbolic component consists of computer algebra, constraint solvers, and simulated annealing, which provides rigorous verification and exhaustive enumeration. Human steering supplied the critical research pivot that transformed a dead end into a productive inquiry. Our analysis reveals that multi-model deliberation among frontier LLMs proved reliable for criticism and error detection but unreliable for constructive claims. The resulting human-AI mathematical contribution, a tight lower bound of 4n(n-1)/9 , is achieved via a novel class of near-perfect permutations. The bound was formally verified in Lean 4. Our experiments show that neurosymbolic systems can indeed produce genuine discoveries in pure mathematics. Subjects: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Combinatorics (math.CO) Cite as: arXiv:2603.08322 [cs.AI] (or arXiv:2603.08322v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.08322 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[HC-9] Do Models See in Line with Human Vision? Probing the Correspondence Between LVLM Representations and EEG Signals
【速读】：该论文旨在解决大型视觉语言模型（Large Vision Language Models, LVLMs）的内部表征是否反映人类视觉认知机制这一问题。其解决方案的关键在于通过量化LVLM与人脑图像诱发的脑电图（EEG）信号之间的对齐程度，利用岭回归（ridge regression）和表示相似性分析（representational similarity analysis）方法，系统比较32个开源LVLM在不同架构、规模和图像类型下的视觉表征与对应EEG响应的一致性。研究发现，中间层（第8-16层）在100–300 ms时间窗口内表现出最强的脑电活动对齐，且多模态架构设计对脑对齐的贡献显著高于参数量扩展，同时下游视觉任务性能更强的模型也展现出更高的EEG相似性，从而验证了LVLM学习到的人类对齐视觉表征，并提出神经对齐作为评估和改进LVLM的生物学基准。
链接: https://arxiv.org/abs/2603.08303
作者: Xin Xiao,Yang Lei,Haoyang Zeng,Xiao Sun,Xinyi Jiang,Yu Tian,Hao Wu,Kaiwen Wei,Jiang Zhong
机构: Chongqing University (重庆大学); UNSW Sydney (新南威尔士大学); Tsinghua University (清华大学); The First Affiliated Hospital of Chongqing Medical University (重庆医科大学附属第一医院)
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Large Vision Language Models (LVLMs) exhibit strong visual understanding and reasoning abilities. However, whether their internal representations reflect human visual cognition is still under-explored. In this paper, we address this by quantifying LVLM-brain alignment using image-evoked Electroencephalogram (EEG) signals, analyzing the effects of model architecture, scale, and image type. Specifically, by using ridge regression and representational similarity analysis, we compare visual representations from 32 open-source LVLMs with corresponding EEG responses. We observe a structured LVLM-brain correspondence: First, intermediate layers (8-16) show peak alignment with EEG activity in the 100-300 ms window, consistent with hierarchical human visual processing. Secondly, multimodal architectural design contributes 3.4 more to brain alignment than parameter scaling, and models with stronger downstream visual performance exhibit higher EEG similarity. Thirdly, spatiotemporal patterns further align with known cortical visual pathways. These results demonstrate that LVLMs learn human-aligned visual representations and establish neural alignment as a biologically grounded benchmark for evaluating and improving LVLMs. In addition, those results could provide insights that may inform the development of neuro-inspired applications.
[HC-10] Why Learn What Physics Already Knows? Realizing Agile mmWave-based Human Pose Estimation via Physics-Guided Preprocessing
【速读】：该论文旨在解决毫米波（mmWave）人体姿态估计（HPE）系统中参数冗余与精度不足的问题。现有方法依赖数据驱动的预处理模块来估计本可通过毫米波物理特性直接建模的现象，导致计算资源消耗大且性能不如视觉基线。其解决方案的关键在于引入显式物理先验：（1）通过耦合距离（range）与角度（angle）维度以保留人体空间结构；（2）利用多普勒（Doppler）信息维持人体运动连续性；（3）采用与人体结构对齐的多尺度融合策略。整体架构仅使用轻量级MLP作为回归器，在显著降低参数量（减少55.7%-88.9%）的同时保持竞争性精度，并支持在树莓派（Raspberry Pi）上实时部署。
链接: https://arxiv.org/abs/2603.08236
作者: Shuntian Zheng,Jiaqi Li,Minzhe Ni,Xiaoman Lu,Yu Guan
机构: 未知
类目: Human-Computer Interaction (cs.HC); Hardware Architecture (cs.AR)
备注:
Abstract:We revisit millimeter-wave (mmWave) human pose estimation (HPE) from a signal preprocessing perspective. A single mmWave frame provides structured dimensions that map directly to human geometry and motion: range, angle, and Doppler, offering pose-aligned cues that are not explicitly present in RGB images. However, recent mmWave-based HPE systems require more parameters and compute resources yet yield lower estimation accuracy than vision baselines. We attribute this to preprocessing modules: most systems rely on data-driven modules to estimate phenomena that are already well-defined by mmWave sensing physics, whereas human pose could be captured more efficiently with explicit physical priors. To this end, we introduce processing modules that explicitly model mmWave’s inter-dimensional correlations and human kinematics. Our design (1) couples range and angle to preserve spatial human structure, (2) leverages Doppler to retain human motion continuity, and (3) applies multi-scale fusion aligned with the human body. A lightweight MLP is involved as the regressor. In experiments, this framework reduces the number of parameters by 55.7-88.9% on the HPE task relative to existing mmWave baselines while maintaining competitive accuracy. Meanwhile, its lightweight nature enables real-time Raspberry Pi deployment. Code and deployment artifacts will be released upon acceptance.
[HC-11] Re-evaluating Position and Velocity Decoding for Hand Pose Estimation with Surface Electromyography
【速读】：该论文旨在解决表面肌电信号（surface electromyography, sEMG）到手势姿态估计中位置解码与速度解码性能对比的争议问题，特别是针对emg2pose基准测试中先前结论——即速度解码优于位置解码——是否在严格因果评估下依然成立。其解决方案的关键在于：通过改进训练稳定性（尤其是调整解码器输出标量参数），发现此前位置解码模型因对这一超参数敏感而被低估；在优化后，位置解码在追踪任务（Tracking task）上全面超越速度解码，且具备更强的误差累积鲁棒性；同时引入因果自适应滤波器可有效抑制局部抖动，在保持精度优势的同时实现更优的平滑性-准确性权衡，从而确立了新的流式兼容模型在该基准上的最优性能。
链接: https://arxiv.org/abs/2603.08212
作者: Nima Hadidi,Johannes Lee,Ebrahim Feghhi,Michael Yuan,Jonathan C. Kao
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注: 11 pages, 4 figures, 2 tables
Abstract:Recent progress in real-time hand pose estimation from surface electromyography (sEMG) has been driven by the emg2pose benchmark, whose original baseline study concluded that velocity decoding outperforms position decoding in both reconstruction accuracy and trajectory smoothness. We revisit that conclusion under the original causal evaluation protocol. Using the same core architecture but a more stable training recipe, we show that position decoding models were previously underestimated because they are highly sensitive to a previously unswept decoder output scalar and can otherwise collapse into low movement solutions. Once this scalar is tuned, position decoding outperforms velocity decoding on the Tracking task across all three emg2pose generalization conditions, consistent with greater robustness to error accumulation. On the Regression task, the gap between position and velocity decoding is much smaller; instead, the largest gains come from multi-task training with Tracking, suggesting that the Regression objective alone does not sufficiently constrain the learned dynamics. Although position decoding models exhibit greater local jitter, a causal speed-adaptive filter preserves their accuracy advantage while yielding a more favorable smoothness-accuracy tradeoff than velocity decoding. Altogether, our results revise the original emg2pose modeling conclusions and establish a new state of the art among published streaming-compatible models on this benchmark.
[HC-12] he Differential Effects of Agreeableness and Extraversion on Older Adults Perceptions of Conversational AI Explanations in Assistive Settings
【速读】：该论文旨在解决大型语言模型语音助手（LLM-VA）在辅助老年人场景中，其人格特质如何影响用户对其解释内容的感知这一关键问题。研究通过混合因子实验（N=140）系统考察了宜人性（agreeableness）和外向性（extraversion）对老年人七项核心感知指标（包括共情、好感度、信任、依赖度、满意度、采用意愿及感知智能）的影响。解决方案的关键在于识别出：高宜人性显著提升共情感知，而低宜人性则显著降低好感度；同时发现感知智能不受人格影响，表明人格主要塑造社会性而非能力认知；此外，实时环境解释优于基于对话历史的解释，尤其在紧急情境下优势明显；并揭示用户与代理间人格一致性效应——高宜人性用户对低宜人性代理尤为苛刻。这些发现为设计具备人格敏感性和情境适应性的LLM-VA提供了实证依据和设计指导。
链接: https://arxiv.org/abs/2603.08164
作者: Niharika Mathur,Hasibur Rahman,Smit Desai
机构: Georgia Institute of Technology (佐治亚理工学院); Northeastern University (东北大学)
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Large Language Model-based Voice Assistants (LLM-VAs) are increasingly deployed in assistive settings for older adults, yet little is known about how an agent’s personality shapes user perceptions of its explanations. This paper presents a mixed factorial experiment (N=140) examining how agreeableness and extraversion in an LLM-VA (“Robin”) influence older adults’ perceptions across seven measures: empathy, likeability, trust, reliance, satisfaction, intention to adopt, and perceived intelligence. Results reveal that high agreeableness drove stronger empathy perceptions, while low agreeableness consistently penalized likeability. Importantly, perceived intelligence remained unaffected by personality, suggesting that personality shapes sociability without altering competence perceptions. Real-time environmental explanations outperformed conversational history explanations on five measures, with advantages concentrated in emergency contexts. Notably, highly agreeable participants were especially critical of low-agreeableness agents, revealing a user-agent personality congruence effect. These findings offer design implications for personality-aware, context-sensitive LLM-VAs in assistive settings.
[HC-13] oward Governing Perception in Safety-Critical Mediated Reality on the Move
【速读】：该论文试图解决可穿戴增强现实（Wearable Augmented Reality, WAR）在移动场景中（如自动驾驶、骑行和行人导航）面临的核心问题：当前系统多依赖于添加式叠加（additive overlays）来呈现信息，而忽视了通过感知中介（perceptual mediation）重构现实的可能性。随着头戴显示设备与计算机视觉技术的进步，Diminished 和 Modified Reality（削弱与修改现实）技术使系统能够抑制、变换或替换场景元素，从而将WAR扩展至媒介现实（Mediated Reality, MR），重新定义“可感知内容”的设计边界。解决方案的关键在于实现“可治理的感知中介”（governable perceptual mediation），即用户需具备配置、检查和理解媒介干预的能力，同时不损害安全；这要求在治理粒度（governance granularity）、认知信号（epistemic signaling）和责任归属（accountability）等方面建立新的设计原则，以应对动态、高风险环境中情境意识与信任校准的新挑战。
链接: https://arxiv.org/abs/2603.08138
作者: Pascal Jansen
机构: Ulm University (乌尔姆大学)
类目: Human-Computer Interaction (cs.HC)
备注: Position Paper at the CHI 2026 Workshop Next Steps for Augmented Reality On-the-Move: Challenges Opportunities. April 14, 2026. Barcelona, Spain
Abstract:Wearable Augmented Reality (AR) is increasingly deployed in on-the-move contexts such as automated driving, cycling, and pedestrian navigation. To date, most systems rely on additive overlays that highlight hazards, intentions, or predictions without altering the scene itself. However, advances in head-mounted displays and computer vision now enable Diminished and Modified Reality techniques that suppress, transform, or substitute scene elements. These capabilities conceptually extend AR into Mediated Reality (MR), shifting the design space from “what to add” to “what is perceptually available.” Because such mediation reshapes the evidential basis for situation awareness and trust calibration, it raises novel interaction challenges. This position paper argues that MR on the move must become governable, as users need mechanisms to configure, inspect, and understand mediation without compromising safety. Additionally, this position paper outlines design challenges related to governance granularity, epistemic signaling, and accountability, and frames MR on the move as a research agenda for governable perceptual mediation in dynamic, safety-critical environments.
[HC-14] I dont want to break it: An Exploration of Perceived Frag ility in Shape-Changing Interfaces
【速读】：该论文旨在解决形状可变界面（Shape-Changing Interfaces, SCIs）中用户对其潜在脆弱性的感知如何影响交互行为的问题。由于SCIs具有动态改变形态的特性，其设计天然存在易损性风险，而用户对这种脆弱性的认知可能显著影响操作方式和使用体验，但这一影响机制尚不明确。研究通过两项实证研究提出解决方案：首先，基于视频刺激的定性分析识别出影响感知脆弱性的关键因素并构建理论框架；其次，通过实物原型实验验证这些因素在具体维度上的作用，量化其对用户操作行为与评价的影响。关键在于将主观感知转化为可测量的设计变量，并据此优化SCIs的感知鲁棒性（perceived robustness），从而为未来SCIs的设计提供结构化指导。
链接: https://arxiv.org/abs/2603.08107
作者: Eva Mackamul(IIHM),Tom Maillard(IIHM),Noé Marceaul(IIHM),Yelli Coulibaly(IIHM),Julien Pansiot(SED [Grenoble]),Laurence Boissieux(SED [Grenoble]),Dominique Vaufreydaz(LIG, M-PSI),Anne Roudaut,Céline Coutrix(IIHM)
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Shape-Changing Interfaces (SCIs) dynamically alter their form, an inherent characteristic that introduces fragility into their design. As a result, users’ perceptions of an interface’s fragility or its potential to move or break may influence their interaction, however the extent of this effect is unclear. To address this gap, we conducted a qualitative study (N = 18) using video stimuli showcasing 20 existing SCIs. Through thematic analysis, we identified key factors impacting perceived fragility and formalized these into a framework. We then conducted a second study (N = 36) for which we fabricated SCIs that varied across selected fragility-related dimensions. We recorded user interactions and compared how the selected dimensions shaped manipulation of the objects and how they were considered by users. Together, these studies provide a structured foundational understanding of perceived fragility in SCIs and offer insights to enhance perceived robustness and inform future SCI development.
[HC-15] he AI Amplifier Effect: Defining Human-AI Intimacy and Romantic Relationships with Conversational AI
【速读】：该论文旨在解决人与虚拟人工智能（AI）之间建立亲密关系的机制及其对用户现实生活影响的问题，尤其关注设计如何促进情感联结、长期互动对用户生活的影响，以及在保障用户自主性的同时实现平台监管的平衡。其解决方案的关键在于提出“AI放大效应”（AI Amplifier Effect），即AI作为媒介会强化用户已有的情绪状态，从而产生积极、中性或消极的不同影响；并进一步提出基于深度访谈的“人类AI亲密关系”定义，强调情感设计需超越技术功能，深入理解人类情感的本质，为未来人机交互（HCI）研究提供理论基础与实践方向。
链接: https://arxiv.org/abs/2603.08084
作者: Ching Christie Pang,Yi Gao,Xuetong Wang,Pan Hui
机构: The Hong Kong University of Science and Technology (香港科技大学); The Hong Kong University of Science and Technology (Guangzhou) (香港科技大学（广州)
类目: Human-Computer Interaction (cs.HC)
备注: 30 pages, 2 figures, 3 tables
Abstract:What does it mean to fall in love with something we know is virtual? The proliferation of conversational AI enables users to create customizable companions, fostering new intimate relationships that, while virtual, are perceived as authentic. However, public understanding of these bonds is limited, and platform policies regarding these interactions remain inconsistent. There is a pressing need for further HCI research to investigate: (a) the design affordances in AI that construct bonds and a sense of intimacy, (b) how such long-term engagement impacts users’ real lives, and © how to balance user autonomy with platform regulation in the design of these systems without compromising users’ well-being and experiences. This paper takes a step toward addressing these goals by providing a concrete definition of human AI intimacy based on in depth interviews with 30 users engaged in romantic relationships with AI companions. We elucidate the complexities of these relationships, from their formation to sustainability, and identify key features of the bonds formed. Notably, we introduce the AI Amplifier Effect, where the AI serves as a medium that intensifies the user’s existing emotional state, leading to divergent positive, neutral, and negative impacts. We argue that designing for emotion must extend beyond technical affordances to encompass the essence of human affection. This paper’s contributions aim to initiate a conversation and guide future research on human AI relationships within the HCI community.
[HC-16] MRDrive: An Open Source Mixed Reality Driving Simulator for Automotive User Research
【速读】：该论文旨在解决驾驶模拟器在生态效度（ecological validity）与实验控制之间存在的根本性权衡问题：高保真物理模拟器成本高昂且难以调整，而虚拟现实模拟器虽灵活但缺乏与真实车辆的物理交互。解决方案的关键在于提出MRDrive——一个开放的混合现实（mixed-reality）驾驶模拟平台，它允许驾驶员和乘客在真实车辆舱内进行操作，同时沉浸于虚拟驾驶环境中，从而兼顾生态效度与实验灵活性，支持人机交互（HCI）研究中关于车内交互、注意力分配及可解释性的探索。
链接: https://arxiv.org/abs/2603.08080
作者: Patrick Ebel,Michał Patryk Miazga,Martin Lorenz,Timur Getselev,Pavlo Bazilinskyy,Celine Conzen
机构: ScaDS.AI, Leipzig University (莱比锡大学); Leipzig University (莱比锡大学); Eindhoven University of Technology (埃因霍温理工大学)
类目: Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: This version has been accepted at CHI 2026
Abstract:Designing and evaluating in-vehicle interfaces requires experimental platforms that combine ecological validity with experimental control. Driving simulators are widely used for this purpose. However, they face a fundamental trade-off: high-fidelity physical simulators are costly and difficult to adapt, while virtual reality simulators provide flexibility at the expense of physical interaction with the vehicle. In this work, we present MRDrive, an open mixed-reality driving simulator designed to support HCI research on in-vehicle interaction, attention, and explainability in manual and automated driving contexts. MRDrive enables drivers and passengers to interact with a real vehicle cabin while being fully immersed in a virtual driving environment. We demonstrate the capabilities of MRDrive through a small pilot study that illustrates how the simulator can be used to collect and analyze eye-tracking and touch interaction data in an automated driving scenario. MRDRive is available at: this https URL
[HC-17] CinemaWorld: Generative Augmented Reality with LLM s and 3D Scene Generation for Movie Augmentation
【速读】：该论文旨在解决传统电影观看体验缺乏沉浸感与交互性的问题，试图通过生成式增强现实（Generative Augmented Reality）技术将二维电影场景转化为与用户物理环境空间同步的三维混合现实内容，从而提升观影的沉浸感和参与度。解决方案的关键在于：首先利用多模态大语言模型（Multimodal Large Language Models, LLMs）对影片进行预处理并提取关键特征；其次借助生成式AI（Generative AI）动态生成3D增强内容；最后在Meta Quest 3设备上实现空间化嵌入，使虚拟内容与真实环境精准对齐，形成自然融合的混合现实体验。
链接: https://arxiv.org/abs/2603.08060
作者: Keiichi Ihara,DaeHo Lee,Manato Abe,Hye-Young Jo,Ryo Suzuki
机构: University of Colorado Boulder (科罗拉多大学博尔德分校); Tohoku University (东北大学); Gwangju Institute of Science and Technology (光州科学技术院)
类目: Human-Computer Interaction (cs.HC)
备注: 13 pages, 16 figures
Abstract:We introduce CinemaWorld, a generative augmented reality system that augments the viewer’s physical surroundings with automatically generated mixed reality 3D content extracted from and synchronized with 2D movie scenes. Our system preprocesses films to extract key features using multimodal large language models (LLMs), generates dynamic 3D augmentations with generative AI, and embeds them spatially into the viewer’s physical environment on the Meta Quest 3. To explore the design space of CinemaWorld, we conducted an elicitation study with eight film students, which led us to identify several key augmentation types, including particle effects, surrounding objects, textural overlays, character-driven augmentation, and lighting effects. We evaluated our system through a technical evaluation (N=100 video clips), a user study (N=12), and expert interviews with film creators (N=8). Results indicate that CinemaWorld enhances immersion and enjoyment, suggesting its potential to enrich the film-viewing experience.
[HC-18] Rendering Forces With a Modular Cable System Motors and Brakes
【速读】：该论文旨在解决传统触觉反馈系统在自由度（DoF）灵活性和力渲染范围上的局限性问题，尤其是如何实现高精度、大范围且可重构的多维力反馈。其解决方案的关键在于设计了一种由混合电机-制动器执行模块组成的可重构触觉接口，每个模块集成电机与单向制动器，从而既能通过电机主动施加最大6 N的平滑力，又能利用被动制动器产生高达186 N的碰撞力；同时，模块化架构支持系统根据需求灵活配置自由度数量和空间布局，显著提升了触觉反馈的适应性和多样性。
链接: https://arxiv.org/abs/2603.08054
作者: Jan Ulrich Bartels,Alexander Achberger,Katherine J. Kuchenbecker,Michael Sedlmair
机构: Max-Planck Institute for Intelligent Systems (马普智能系统研究所); Visualisierungsinstitut (VISUS) (可视化研究所), Universität Stuttgart (斯图加特大学)
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:We describe the hardware design, force-rendering approach, and evaluation of a new reconfigurable haptic interface consisting of a network of hybrid motor-brake actuation modules that apply forces via cables. Each module contains both a motor and a brake, enabling it to smoothly render active forces up to 6 N using its motor and collision forces up to 186 N using its passive one-way brake. The modular design, meanwhile, allows the system to deliver rich haptic feedback in a flexible number of DoF and widely ranging configurations.
[HC-19] Alignment–Process–Outcome: Rethinking How AIs and Humans Collaborate
【速读】：该论文试图解决现有协作研究中对协同过程结构理解不足的问题，特别是如何在不同协作类型（人-人、AI-AI、人-AI）中统一解释对齐（alignment）、决策机制与轨迹结构之间的动态关系。传统方法往往孤立地分析这些维度或仅聚焦特定参与者类型，导致难以揭示协作的本质结构。其解决方案的关键在于提出两个互补的视角：任务视角（task lens）将协作建模为结构化任务空间中的轨迹演化，识别出推进、分支和回溯等模式；意图视角（intent lens）则关注个体意图如何在共享情境中表达并影响具体决策。这两个视角共同澄清了对齐、决策与轨迹结构之间的结构性关联，从而构建了一个统一的动态框架，用于重新审视各类协作场景下的结构特征。
链接: https://arxiv.org/abs/2603.08017
作者: Haichang Li,Anjun Zhu,Arpit Narechania
机构: George Mason University (乔治梅森大学); Simon Fraser University (西蒙弗雷泽大学); The Hong Kong University of Science and Technology (香港科技大学)
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: Accepted by Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA 26), Barcelona, Spain, 2026
Abstract:In real-world collaboration, alignment, process structure, and outcome quality do not exhibit a simple linear or one-to-one correspondence: similar alignment may accompany either rapid convergence or extensive multi-branch exploration, and lead to different results. Existing accounts often isolate these dimensions or focus on specific participant types, limiting structural accounts of collaboration. We reconceptualize collaboration through two complementary lenses. The task lens models collaboration as trajectory evolution in a structured task space, revealing patterns such as advancement, branching, and backtracking. The intent lens examines how individual intents are expressed within shared contexts and enter situated decisions. Together, these lenses clarify the structural relationships among alignment, decision-making, and trajectory structure. Rather than reducing collaboration to outcome quality or treating alignment as the sole objective, we propose a unified dynamic view of the relationships among alignment, process, and outcome, and use it to re-examine collaboration structure across Human-Human, AI-AI, and Human-AI settings. Comments: Accepted by Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA 26), Barcelona, Spain, 2026 Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.08017 [cs.HC] (or arXiv:2603.08017v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2603.08017 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[HC-20] Extend Your Horizon: A Device-Agnostic Surgical Tool Tracking Framework with Multi-View Optimization for Augmented Reality
【速读】：该论文旨在解决手术导航系统在动态手术室环境中因设备、器械和人员频繁遮挡导致的跟踪失效问题，尤其是在基于头戴式显示器（Head-Mounted Display, HMD）的增强现实（Augmented Reality, AR）可视化场景中。其解决方案的关键在于构建一个基于动态场景图（Dynamic Scene Graph）表示的多传感融合框架，通过整合不同精度和运动特性的跟踪系统，并实时估计跟踪可靠性，从而提升在遮挡条件下的跟踪鲁棒性和AR可视化的一致性。
链接: https://arxiv.org/abs/2603.07981
作者: Jiaming Zhang,Mingxu Liu,Hongchao Shu,Ruixing Liang,Yihao Liu,Ojas Taskar,Amir Kheradmand,Mehran Armand,Alejandro Martin-Gomez
机构: Johns Hopkins University (约翰霍普金斯大学); University of Arkansas (阿肯色大学)
类目: Human-Computer Interaction (cs.HC); Computer Vision and Pattern Recognition (cs.CV)
备注: accepted by IEEE VR 2026
Abstract:Surgical navigation provides real-time guidance by estimating the pose of patient anatomy and surgical instruments to visualize relevant intraoperative information. In conventional systems, instruments are typically tracked using fiducial markers and stationary optical tracking systems (OTS). Augmented reality (AR) has further enabled intuitive visualization and motivated tracking using sensors embedded in head-mounted displays (HMDs). However, most existing approaches rely on a clear line of sight, which is difficult to maintain in dynamic operating room environments due to frequent occlusions caused by equipment, surgical tools, and personnel. This work introduces a framework for tracking surgical instruments under occlusion by fusing multiple sensing modalities within a dynamic scene graph representation. The proposed approach integrates tracking systems with different accuracy levels and motion characteristics while estimating tracking reliability in real time. Experimental results demonstrate improved robustness and enhanced consistency of AR visualization in the presence of occlusions.
[HC-21] Designing a Generative AI-Assisted Music Psychotherapy Tool for Deaf and Hard-of-Hearing Individuals
【速读】：该论文试图解决的问题是：传统音乐心理治疗因依赖听觉媒介，导致聋人和听力障碍（Deaf and Hard-of-Hearing, DHH）个体难以参与，从而被排除在音乐的疗愈效益之外。解决方案的关键在于开发一种由治疗师共同设计的音乐心理治疗工具，该工具融合了对话代理（Conversational Agents, CAs）与生成式 AI（Generative AI），作为象征性与治疗性的媒介。通过23名DHH用户的使用研究发现，该工具借助支持性共情、示例响应选项及基于视觉的隐喻等策略，有效促进了用户与AI之间的音乐对话，实现了情感释放、认知重构与更深层次的自我理解，体现了人-AI协作在包容性人工智能设计中的潜力。
链接: https://arxiv.org/abs/2603.07963
作者: Youjin Choi,Jaeyoung Moon,Jinyoung Yoo,Jennifer G. Kim,Jin-Hyuk Hong
机构: Gwangju Institute of Science and Technology (光州科学技术院); Georgia Institute of Technology (佐治亚理工学院)
类目: Human-Computer Interaction (cs.HC)
备注: Accepted in CHI 2026, 25 pages, 7 figures
Abstract:Songwriting has long served as a powerful medium for expressing unconscious emotions and fostering self-awareness in psychotherapy. Due to the auditory-centric nature of traditional approaches, Deaf and Hard-of-Hearing (DHH) individuals have often been excluded from music’s therapeutic benefits. In response, this study presents a music psychotherapy tool co-designed with therapists, integrating conversational agents (CAs) and music generative AI as symbolic and therapeutic media. Through a usage study with 23 DHH individuals, we found that collaborative song writing with the CA enabled them to experience emotional release, reinterpretation, and deeper self-understanding. In particular, the CA’s strategies – supportive empathy, example response options, and visual-based metaphors – were found to facilitate musical dialogue effectively for DHH individuals. These findings contribute to inclusive AI design by showing the potential of human-AI collaboration to bridge therapeutic artistic practices.
[HC-22] WeldAR: Augmenting Live Hands-On Training with In-Situ Guidance for Novice Learners
【速读】：该论文旨在解决当前扩展现实（Extended Reality, XR）系统在物理技能训练中过度侧重模拟而缺乏实时现场指导的问题。其解决方案的关键在于提出WeldAR系统，该系统通过集成于焊接头盔的增强现实（Augmented Reality, AR）设备和焊枪附件，在实际焊接过程中提供实时指导，包含五个学习模块，并对四项性能指标进行即时反馈。实验证明，与视频教学相比，AR显著提升了初学者在辅助练习和独立测试中的综合焊接表现，尤其体现在移动速度和作业角度上的改善，从而支持学习者将具身知识迁移到自主任务中。
链接: https://arxiv.org/abs/2603.07959
作者: Chuhan(Franklin)Xu (1),Lia Sparingga Purnamasari(1),Zhenfang Chen(1),Daragh Byrne(1),Dina El-Zanfaly(1) ((1) Carnegie Mellon University)
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注: 35 pages, 19 figures. Accepted to CHI 2026, Barcelona, Spain. Author-created accepted version. DOI: https://doi.org/10.1145/3772363.3798846
Abstract:Extended Reality (XR) systems for physical skill training have largely emphasized simulation rather than real-time in-situ instruction. We present WeldAR, an Augmented Reality (AR) system with five learning modules that overlays real-time guidance during live welding using a headset integrated into a welding helmet and a torch attachment. We conducted an in-situ within-subjects study with 24 novices, comparing AR guidance to video instruction for live welding across practice and unassisted tests. AR improved performance in both assisted practice and unassisted tests, primarily driven by gains in travel speed and work angle. By offering real-time feedback on four performance measures, AR supported novices in carrying embodied knowledge into independent tasks. Our contributions include: (1) WeldAR for in-situ physical skill training; (2) empirical evidence that AR enhances composite welding performance and key physical skills; and (3) implications for the development of AR systems that support in-situ, embodied skill training in welding and related trades.
[HC-23] From Daily Song to Daily Self: Supporting Reflective Songwriting of Deaf and Hard-of-Hearing Individuals through Generative Music AI
【速读】：该论文旨在解决当前支持听障及重听（Deaf and Hard-of-Hearing, DHH）人群通过音乐进行自我表达的新兴技术普遍存在的局限性问题，即这些技术多在单次使用场景中被评估，难以帮助缺乏歌曲创作经验的用户有效传达个人叙事或维持长期参与。其解决方案的关键在于提出一个名为SoulNote的生成式AI（Generative AI, GenAI）系统，该系统基于以用户为中心的设计方法，整合了设计工作坊、预研究和多轮日记研究，使DHH个体能够通过迭代式的歌曲创作实践，将音乐作为持续的情感反思工具，从而在自我洞察、情绪调节以及对情绪与自我关怀的日常态度三个维度上促进情感成长。
链接: https://arxiv.org/abs/2603.07956
作者: Youjin Choi,Jinyoung Yoo,Jaeyoung Moon,Yoonjae Kim,Eun Young Lee,Jennifer G. Kim,Jin-Hyuk Hong
机构: Gwangju Institute of Science and Technology(光州科学技术院); Ewha Womans University(延世女子大学); Georgia Institute of Technology(佐治亚理工学院)
类目: Human-Computer Interaction (cs.HC)
备注: Accepted in CHI 2026, 26 pages, 5 figures
Abstract:The rapid advancement of generative AI (GenAI) is expanding access to songwriting, offering a new medium of self-expression for Deaf and Hard-of-Hearing (DHH) individuals. However, emerging technologies that support DHH individuals in expressing themselves through music have largely been evaluated in single-session settings and often fall short in helping users unfamiliar with songwriting convey personal narratives or sustain engagement over time. This paper explores songwriting as an extended, music-based journaling practice that supports sustained emotional reflection over multiple sessions. We introduce SoulNote, a GenAI system enabling DHH to engage in iterative songwriting. Grounded in user-centered design, including a design workshop, a preliminary study, and a multi-session diary study, our findings show that ongoing songwriting with \textitSoulNote facilitated emotional growth across three dimensions: self-insight, emotion regulation, and \revisedeveryday attitudes toward emotions and self-care. Overall, this work demonstrates how GenAI can support marginalized communities by transforming creative expression into a daily practice of self-discovery and reflection.
[HC-24] he Sense of Misinformation Can Harm Local Community: A Case Study of Community Conflict
【速读】：该论文试图解决在社区决策与公民协作过程中，因“误判信息”（sense of misinformation）引发的社会冲突问题，即当个体将他人无虚假意图的语言或行为错误感知为虚假信息时所导致的信任危机与民主机制受损。其核心解决方案在于提出并界定“误判信息”这一新概念，明确其与传统“虚假信息”（misinformation）的本质区别——前者源于认知偏差而非事实错误，并通过案例分析揭示治理失能、沟通断裂与公共话语崩塌等关键因素如何促成此类误判的积累与扩散；进而建议从识别、修复误判入手，设计干预机制以缓解其对社区信任和民主实践的侵蚀。
链接: https://arxiv.org/abs/2603.07953
作者: Jiyoon Kim,Jie Cai,Srishti Gupta,John M. Carroll
机构: Pennsylvania State University (宾夕法尼亚州立大学); Tsinghua University (清华大学); University at Albany, SUNY (纽约州立大学阿尔巴尼分校)
类目: Human-Computer Interaction (cs.HC)
备注: Accepted at ACM GROUP 2027
Abstract:During community decision-making and civic collaboration, conflicts can escalate when people suspect misinformation. We introduce the concept of sense of misinformation as experiencing someone’s language or behavior as misinformation when it is not, that is to say when no falsehood is involved. Misinformation and sense of misinformation feel similar and can have similar social consequences; but sense of misinformation rests upon a mistaken perception of someone else’s information as false. Through a case study of a casino proposal in local community, we examine how sense of misinformation developed over time during a contentious civic process through key factors (i.e., miscoordination governance, miscommunication between local government and citizens, and conflict and the breakdown of civic discourse), undermining trust and community democracy. Distinguishing between misinformation and sense of misinformation presents a challenge, but it is important. We contribute a conceptual distinction to the misinformation literature by identifying this distinct phenomenon and discuss ways to help communities recognize and repair such misattributions. Finally, we discuss design approaches for mitigating sense of misinformation.
[HC-25] How Neurotypical and Autistic Children Interact Nonverbally with Anthropomorphic Agents in Open-Ended Tasks
【速读】：该论文试图解决的问题是如何在缺乏明确指令的开放性非语言交互场景中，理解儿童（包括神经发育典型与自闭症谱系）如何与具身人工智能代理（embodied artificial agents）进行非语言互动，从而为开发更具包容性和社会交互能力的系统提供依据。解决方案的关键在于通过“巫师之 Oz”实验范式，收集了563次（141种独特）儿童产生的非语言行为数据，并对比了此前成人研究中的交互模式，同时识别出重复性的面部和手部动作，这些发现对设计能够感知并响应儿童非语言信号的智能代理具有重要指导意义。
链接: https://arxiv.org/abs/2603.07843
作者: Chuxuan Zhang,Bermet Burkanova,Lawrence H. Kim,Grace Iarocci,Elina Birmingham,Angelica Lim
机构: Simon Fraser University (西蒙菲莎大学)
类目: Human-Computer Interaction (cs.HC)
备注: 4 pages, 4 figures, 3 tables, accepted by HRI Late Break Report
Abstract:What nonverbal behaviors should a robot respond to? Understanding how children-both neurotypical and autistic-engage with embodied artificial agents is critical for developing inclusive and socially interactive systems. In this paper, we study “open-ended” unconstrained interactions with embodied agents, where little is known about how children behave nonverbally when given few instructions. We conducted a Wizard-of-Oz study in which children were invited to interact nonverbally with 6 different embodied virtual characters displayed on a television screen. We collected 563 (141 unique) nonverbal behaviors produced by children and compare the childre’s interaction patterns with those previously reported in an adult study. We also report the presence of repetitive face and hand movements, which should be considered in the development of nonverbally interactive artificial agents.
[HC-26] AI Misuse in Education Is a Measurement Problem: Toward a Learning Visibility Framework
【速读】：该论文试图解决的问题是：在教育场景中，生成式 AI（Generative AI）的快速融入引发了关于学术诚信、公平性和学生认知发展的伦理担忧，而当前以AI检测工具和限制性政策为主的应对方式存在不可靠性和伦理争议。其解决方案的关键在于将AI滥用问题从“检测”转向“测量”，提出基于学习可见性框架（Learning Visibility Framework）的系统性方法，核心包括三个原则：明确规范和建模可接受的AI使用方式；将学习过程视为与学习结果同等重要的评估证据；建立透明的学生活动时间线。该框架强调通过增强学习过程的可见性而非实施监控，实现AI与教育价值的对齐，从而维护师生间的信任与透明度。
链接: https://arxiv.org/abs/2603.07834
作者: Eduardo Davalos,Yike Zhang
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 14 pages, 5 figures, Submitted and Accepted to AIR-RES2026
Abstract:The rapid integration of conversational AI systems into educational settings has intensified ethical concerns about academic integrity, fairness, and students’ cognitive development. Institutional responses have largely centered on AI detection tools and restrictive policies, yet such approaches have proven unreliable and ethically contentious. This paper reframes AI misuse in education not primarily as a detection problem, but as a measurement problem rooted in the loss of visibility into the learning process. When AI enters the assessment loop, educators often retain access to final outputs but lose valuable insight into how those outputs were produced. Drawing on research in cognitive offloading, learning analytics, and multimodal timeline reconstruction, we propose the Learning Visibility Framework, grounded in three principles: clear specification and modeling of acceptable AI use, recognition of learning processes as assessable evidence alongside outcomes, and the establishment of transparent timelines of student activity. Rather than promoting surveillance, the framework emphasizes transparency and shared evidence as foundations for ethical AI integration in classroom settings. By shifting focus from adversarial detection toward process visibility, this work offers a principled pathway for aligning AI use with educational values while preserving trust and transparency between students and educators
[HC-27] Uncertainty Mitigation and Intent Inference: A Dual-Mode Human-Machine Joint Planning System
【速读】：该论文旨在解决开放环境中人机协作中因任务知识不确定性与人类潜在意图模糊性导致的协同效率低下问题。现有方法通常将人类视为被动监督者，无法实现机器人作为主动协作伙伴的行为建模与动态交互能力。其解决方案的关键在于提出一个统一的人机联合规划系统，包含两个互补模块：一是基于大语言模型（LLM）辅助的主动询问机制与假设增强型A*搜索相结合的不确定性缓解模块，通过动态规划计算最优查询策略以最小化交互和验证成本；二是基于视觉-语言模型（VLM）驱动的3D语义感知与空间方向线索的实时意图感知模块，通过概率信念更新实现无需显式通信的任务选择与协调。实验证明该方案在减少交互成本和提升任务执行效率方面显著优于基线方法。
链接: https://arxiv.org/abs/2603.07822
作者: Zeyu Fang,Yuxin Lin,Cheng Liu,Beomyeol Yu,Zeyuan Yang,Rongqian Chen,Taeyoung Lee,Mahdi Imani,Tian Lan
机构: George Washington University (乔治华盛顿大学); Northeastern University (东北大学)
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注:
Abstract:Effective human-robot collaboration in open-world environments requires joint planning under uncertain conditions. However, existing approaches often treat humans as passive supervisors, preventing autonomous agents from becoming human-like teammates that can actively model teammate behaviors, reason about knowledge gaps, query, and elicit responses through communication to resolve uncertainties. To address these limitations, we propose a unified human-robot joint planning system designed to tackle dual sources of uncertainty: task-relevant knowledge gaps and latent human intent. Our system operates in two complementary modes. First, an uncertainty-mitigation joint planning module enables two-way conversations to resolve semantic ambiguity and object uncertainty. It utilizes an LLM-assisted active elicitation mechanism and a hypothesis-augmented A^* search, subsequently computing an optimal querying policy via dynamic programming to minimize interaction and verification costs. Second, a real-time intent-aware collaboration module maintains a probabilistic belief over the human’s latent task intent via spatial and directional cues, enabling dynamic, coordination-aware task selection for agents without explicit communication. We validate the proposed system in both Gazebo simulations and real-world UAV deployments integrated with a Vision-Language Model (VLM)-based 3D semantic perception pipeline. Experimental results demonstrate that the system significantly cuts the interaction cost by 51.9% in uncertainty-mitigation planning and reduces the task execution time by 25.4% in intent-aware cooperation compared to the baselines.
[HC-28] Broken Access: On the Challenges of Screen Reader Assisted Two-Factor and Passwordless Authentication WWW2025
【速读】：该论文旨在解决盲人及视力障碍用户在使用Web服务时面临的认证安全与可访问性双重挑战，当前主流认证机制主要面向视力正常用户设计，忽视了视障群体的实际需求。其关键解决方案是提出一个名为AWARE的评估框架，将视障用户的认证交互建模为“屏幕阅读器辅助认证”，并通过系统化测试主流PC和智能手机上的屏幕阅读器在多种认证方式（包括双因素认证2FA和无密码方案）下的表现，识别出真实场景中存在的安全漏洞和可访问性缺陷。该框架可帮助设计者在正式用户研究前早期发现并修复潜在问题，从而提升认证流程对视障用户的可用性和安全性。
链接: https://arxiv.org/abs/2603.07820
作者: Md Mojibur Rahman Redoy Akanda(1),Ahmed Tanvir Mahdad(1),Nitesh Saxena(1) ((1) Texas Aamp;M University)
机构: 未知
类目: Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC)
备注: 13 pages, published in Proceedings of the ACM Web Conference (WWW 2025)
Abstract:In today’s technology-driven world, web services have opened up new opportunities for blind and visually impaired people to interact independently. Securing interactions with these services is crucial; however, currently deployed authentication mainly concentrate on sighted users, overlooking the needs of the blind and visually impaired community. In this paper, we address this gap by investigating the security and accessibility aspects of these authentication when adopted by blind and visually impaired users. We model web authentication for such users as screen reader assisted authentication and introduce an evaluation framework called AWARE. Using AWARE, we then systematically assessed popular PC and smartphone-based screen readers against different authentication methods, including variants of 2FA and passwordless schemes, to simulate real-world scenarios. We analyzed these screen reader assisted authentication interactions with authentication methods in three settings: using a terminal (PC) with screen readers, a combination of the terminal (PC) and smartphone with screen readers, and smartphones with integrated screen readers. The results of our study underscore weaknesses in all of our observed screen reader assisted scenarios for real-life authentication methods. These weaknesses, encompassing specific accessibility issues caused by imprecise screen reader instructions, highlight vulnerability concerning observed scenarios for both real-world and research literature based attacks, including phishing, concurrency, fatigue, cross-service, and shoulder surfing. Broadly, our AWARE framework can be used by designers as a precursor to user studies which are typically time-consuming and tedious to perform, independently allowing to unfold security and accessibility problems early which designers can address prior to full-fledged user testing of more isolated issues. Comments: 13 pages, published in Proceedings of the ACM Web Conference (WWW 2025) Subjects: Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC) ACMclasses: K.6.5; H.5.2 Cite as: arXiv:2603.07820 [cs.CR] (or arXiv:2603.07820v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2603.07820 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: Proceedings of the ACM Web Conference 2025 (WWW '25) Related DOI: https://doi.org/10.1145/3696410.3714579 Focus to learn more DOI(s) linking to related resources
[HC-29] Governance of AI-Generated Content: A Case Study on Social Media Platforms
【速读】：该论文旨在解决在线平台日益增长的生成式 AI (Generative AI) 内容所带来的治理挑战，即如何制定有效的政策与执行策略，以规范用户在创建、发布、分享和互动过程中对这类内容的使用行为，从而促进负责任的使用。其解决方案的关键在于通过系统性分析40个主流社交媒体平台的治理实践，发现当前多数平台仅聚焦于违反内容规则的AI生成内容审核及来源披露，而缺乏对版权归属、商业化等更深层次问题的覆盖；因此，研究建议利益相关方和政策制定者应构建更具前瞻性、全面性的AI生成内容治理框架，并配套开发工具与用户教育机制，以应对未来复杂多样的AI内容生态。
链接: https://arxiv.org/abs/2603.07814
作者: Lan Gao,Abani Ahmed,Oscar Chen,Margaux Reyl,Zayna Cheema,Nick Feamster,Chenhao Tan,Kurt Thomas,Marshini Chetty
机构: 未知
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: Preprint for CHI 2026
Abstract:Online platforms are seeing increasing amounts of AI-generated content – text and other forms of media that are made or co-created with generative AI. This trend suggests platforms may need to establish governance frameworks, including policies and enforcement strategies for how users create, post, share, and engage with such content to encourage responsible use. We investigate the governance of AI-generated content across 40 popular social media platforms. Just over two-thirds explicitly describe governance of AI-generated content spanning six themes. Most platforms focus on moderating AI-generated content that violates established content rules and discloses AI-generated content. Fewer platforms – those that are focused on creativity and knowledge-sharing – address other issues such as ownership and monetization. Based on these findings, we suggest stakeholders and policymakers develop more direct, comprehensive, and forward-looking AI-generated content governance, as well as tools and education for users about the use of such content.
[HC-30] Directing the Robot: Scaffolding Creative Human-AI-Robot Interaction
【速读】：该论文试图解决当前人-人工智能-机器人交互（Human-AI-Robot Interaction）过度聚焦于性能与效率，导致人类角色被限定为监督者而非合作者的问题。其解决方案的关键在于将AI与机器人交互重新定义为“支架式支持”（scaffolding），即构建一种使人类能够持续塑造机器人行为、同时保持对过程实质性控制的基础设施。通过创意实践、教学式学习和具身交互等场景，论文强调人类作为“执行导演”（executive director）的角色，负责定义意图并引导迭代优化，而AI则在人类表达与机器人执行之间起到中介作用，从而促进创造力、自主性和流畅体验（flow）。
链接: https://arxiv.org/abs/2603.07748
作者: Jordan Aiko Deja,Isidro Butaslac,Nicko Reginio Caluya,Maheshya Weerasinghe
机构: De La Salle University (德拉萨大学); Nara Institute of Science and Technology (奈良科学技术研究所); Ritsumeikan University (立命馆大学); University of Primorska (普里莫尔斯卡大学)
类目: Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: 4 pages, 1 figure
Abstract:Robots are moving beyond industrial settings into creative, educational, and public environments where interaction is open-ended and improvisational. Yet much of human-AI-robot interaction remains framed around performance and efficiency, positioning humans as supervisors rather than collaborators. We propose a re-framing of AI interaction with robots as scaffolding: infrastructure that enables humans to shape robotic behaviour over time while remaining meaningfully in control. Through scenarios from creative practice, learning-by-teaching, and embodied interaction, we illustrate how humans can act as executive directors, defining intent and steering revisions, while AI mediates between human expression and robotic execution. We outline design and evaluation implications that foreground creativity, agency, and flow. Finally, we discuss open challenges in social, scalable, and mission-critical contexts. We invite the community to rethink interacting with Robots and AI not as autonomy, but as sustained support for human creativity.
[HC-31] From Autonomy to Sovereignty - A New Telos for Socially Assistive Technology
【速读】：该论文旨在解决辅助技术（Assistive Technology, AT）研究中长期存在的张力问题：即AT设计普遍追求个体独立性，而残障人群的实际体验却体现出对互依性的强烈偏好。通过分析2011–2025年间90篇文献，作者指出这一矛盾源于更深层的理论分歧，并融合自我决定理论（Self-Determination Theory, SDT）、符号互动主义与后人类主义及酷儿科技（Crip Technoscience）视角，揭示出“关系主权”（Relational Sovereignty）作为替代传统“自主性”目标的新范式——它强调个体拥有在独立与互依之间自主选择的权利。解决方案的关键在于将设计焦点从“能否完成任务”转向“是否拥有决策权”，并提出四个具体干预策略：以主权为中心重构SDT、引入正义导向的生成性问题、基于主权技术原语进行构建，以及在AT设计中明确纳入权力考量。
链接: https://arxiv.org/abs/2603.07737
作者: JiWoong Jang,Patrick Carrington,Andrew Begel
机构: Carnegie Mellon University (卡内基梅隆大学)
类目: Human-Computer Interaction (cs.HC)
备注: CHI 26 Conference, 19 pages, 7 figures
Abstract:Social accessibility research faces a persistent tension: assistive technologies (AT) predominantly pursue independence, yet disabled people’s experiences reveal rich preferences for interdependence. Our analysis of 90 papers from 2011-2025 uncovered that this stems from a deeper issue - which crystallized through dialogue with three bodies of theories: (1) self-determination theory (SDT), (2) symbolic interactionism, and (3) posthumanist perspectives and crip technoscience. SDT illuminates individual needs; symbolic interactionism addresses construction of social meaning and stigma; Posthumanist and crip technoscience together challenges normalcy, governance, and the human-machine boundary. Through their tensions, we identify relational sovereignty as an alternative telos - or goal - to autonomy. While our corpus equates autonomy with independence, sovereignty centers the power to choose between independence and interdependence. To operationalize this shift - from “Can they do it?” to “Do they get to decide?” - we introduce the Relational Sovereignty Matrix and four design interventions: (1) a sovereignty-centered reframing of SDT, (2) generative questions for justice-oriented reflection, (3) the idea of building through sovereign technical primitives, and (4) explicit consideration of power in AT design.
[HC-32] he Three Praxes Framework - A Thematic Review and Map of Social Accessibility Research
【速读】：该论文旨在解决社会可及性研究中各实践领域割裂、难以形成闭环反馈的问题，以提升残障人士在沟通、关系与环境接入等方面的综合生活质量。其解决方案的关键在于提出“三重实践框架”（Three Praxes Framework），包含三个实践场域：物化实践（Artifact，建构性）、生态系统实践（Ecosystem，关系性）与认识论实践（Epistemology，理论性），并引入两个贯穿性的变革立场（时间取向与时序导向与利益相关者聚焦），以及一个反思循环机制，用以实现从残障人群生活经验到物质实践、再到理论知识的流动转化，最终推动接入生态系统的整体变革。
链接: https://arxiv.org/abs/2603.07727
作者: JiWoong Jang,Patrick Carrington,Andrew Begel
机构: Carnegie Mellon University (卡内基梅隆大学)
类目: Human-Computer Interaction (cs.HC)
备注: CHI 26 Conference, 20 pages, 8 figures
Abstract:Research in social accessibility aims to improve the lives of disabled people across diverse abilities and experiences by assisting with communication, relationships, and ecosystems of access. We seek to understand this intersectional body of work through analyzing social accessibility research from 2011 to 2025. Through constructivist grounded theory analysis of 90 papers (curated from 605), we develop the Three Praxes Framework: three sites of practice Artifact (constructive), Ecosystem (relational), and Epistemology (theoretical) - two cross-cutting stances toward change (Temporal Orientation and Stakeholder Focus) - and one reflexive cycle modeling how insights can flow between praxes. Our analysis reveals these praxes operate largely in isolation, risking that insights remain academic exercises while assistive technologies reinforce existing barriers. We call on the field to realize a cycle where disabled people’s lived experiences shape material realities, material practice generates theoretical knowledge, and both transform ecosystems of access.
[HC-33] Rigidity in LLM Bandits with Implications for Human-AI Dyads
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在决策过程中是否表现出稳健的偏见行为这一问题，特别是通过类比人类在多臂赌博机（two-arm bandit）任务中的行为来评估其策略倾向。解决方案的关键在于采用最小化带状任务设计（minimal bandits），结合计算建模方法（即分层Rescorla-Wagner-softmax模型）对模型行为进行定量解析，发现LLMs在对称奖励下会放大位置顺序偏好形成顽固的单臂策略，在非对称奖励下则表现出僵化的利用行为且极少重新校验，这些模式在不同解码参数（温度和top-p）下保持一致，表明其决策偏差具有鲁棒性；进一步建模揭示出低学习率与极高逆温度是导致噪声放大为系统性偏见的核心机制，从而为理解LLMs决策倾向及其在人机交互中可能产生的影响提供了理论基础。
链接: https://arxiv.org/abs/2603.07717
作者: Haomiaomiao Wang,Tomás E Ward,Lili Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Human-Computer Interaction (cs.HC)
备注: 13 pages, 5 figures, AICS conference this https URL
Abstract:We test whether LLMs show robust decision biases. Treating models as participants in two-arm bandits, we ran 20000 trials per condition across four decoding configurations. Under symmetric rewards, models amplified positional order into stubborn one-arm policies. Under asymmetric rewards, they exploited rigidly yet underperformed an oracle and rarely re-checked. The observed patterns were consistent across manipulations of temperature and top-p, with top-k held at the provider default, indicating that the qualitative behaviours are robust to the two decoding knobs typically available to practitioners. Crucially, moving beyond descriptive metrics to computational modelling, a hierarchical Rescorla-Wagner-softmax fit revealed the underlying strategies: low learning rates and very high inverse temperatures, which together explain both noise-to-bias amplification and rigid exploitation. These results position minimal bandits as a tractable probe of LLM decision tendencies and motivate hypotheses about how such biases could shape human-AI interaction.
[HC-34] YAQIN: Culturally Sensitive Agent ic AI for Mental Healthcare Support Among Muslim Women in the UK
【速读】：该论文旨在解决英国心理健康服务在满足穆斯林女性文化需求方面的显著缺口，当前服务常导致她们感到自身价值观被病理化，从而削弱信任与参与度。解决方案的关键在于设计并评估YAQIN——一款由用户共同开发的基于人工智能（AI）的应用程序，其核心特征是融合伊斯兰心理学框架与用户中心设计方法，通过具备信仰敏感性的聊天机器人和引导式日记工具提供匿名且持续的心理支持。该方案利用检索增强生成（Retrieval-Augmented Generation, RAG）技术保障对话上下文连续性，并通过五名参与者（四名穆斯林女性及一名心理健康专家）的共设计评估验证了其在文化适配性和治疗一致性上的有效性，凸显了信仰整合型AI在提升边缘群体心理健康可及性与信任感方面的潜力。
链接: https://arxiv.org/abs/2603.07709
作者: Yasmin Zaraket,Céline Mougenot
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Mental healthcare services in the UK lack tools and resources to address the cultural needs of Muslim women, often leaving them feeling as though their values are pathologised and limiting trust and engagement [1]. Despite growing awareness of cultural competency, few interventions integrate Islamic frameworks into therapeutic support. This report investigates the design and evaluation of YAQIN, a co-designed AI-based application supporting culturally and faith-sensitive mental health engagement for Muslim women. With almost 1.9 million Muslim women in England in 2021, YAQIN responds to a gap in care [2]. It leverages AIś anonymity and continuous support through a faith-aware chatbot and guided journaling tool grounded in user-centred design and Islamic psychology. The YAQIN design research methodology comprised three stages: contextual investigation and literature review, user research with N=14 stakeholders including Muslim women and mental health experts, and prototype development informed by deductive thematic analysis, personas, journey maps, and design specifications. Evaluation involved a co-designed user study with five participants: four Muslim women and one mental health expert who reviewed therapeutic alignment and cultural sensitivity after using the chatbot prototype. Feedback focused on tone, faith relevance, emotional resonance, and the Retrieval-Augmented Generation pipeline enabling contextual continuity. Participants highlighted YAQINś ability to bridge cultural gaps in trust and therapeutic confidence. Feedback included suggestions of including linguistic diversity and routine-based guidance. This project demonstrates how culturally sensitive AI can improve mental healthcare accessibility and trust for marginalised communities and highlights the potential of faith-integrated technology in healthcare innovation. Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.07709 [cs.HC] (or arXiv:2603.07709v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2603.07709 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yasmin Zaraket [view email] [v1] Sun, 8 Mar 2026 16:23:43 UTC (19,690 KB) Full-text links: Access Paper: View a PDF of the paper titled YAQIN: Culturally Sensitive, Agentic AI for Mental Healthcare Support Among Muslim Women in the UK, by Yasmin Zaraket and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.HC prev | next new | recent | 2026-03 Change to browse by: cs cs.AI References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked=“checked”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); About Help contact arXivClick here to contact arXiv Contact subscribe to arXiv mailingsClick here to subscribe Subscribe Copyright Privacy Policy Web Accessibility Assistance arXiv Operational Status
[HC-35] ask Breakpoint Generation using Origin-Centric Graph in Virtual Reality Recordings for Adaptive Playback
【速读】：该论文旨在解决虚拟现实（VR）环境中任务分割的自动化问题，现有方法多依赖人工标注或局限于二维视频，难以适配三维VR场景下的动态交互需求。其解决方案的关键在于提出基于以原点为中心的图（Origin-Centric Graph, OCG）的任务分割方法，通过结构化的时空场景图（Structured Spatio-Temporal Scene Graph, STSG）记录装配场景，并利用OCG追踪中心对象的变化及新群体的形成，从而自动识别任务断点（task breakpoints），实现目标导向活动的自适应播放控制。
链接: https://arxiv.org/abs/2603.07627
作者: Selin Choi,Dooyoung Kim,Taewook Ha,Seonji Kim,Woontack Woo
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注: 10 pages, 5 figures. This paper will be presented at IEEE VR 2026 and is published in IEEE Transactions on Visualization and Computer Graphics (TVCG)
Abstract:We propose a method for generating task breakpoints based on an Origin-Centric Graph (OCG) to segment goal-oriented activity recordings into task units for adaptive playback in Virtual Reality (VR) environments. With the development of Augmented Reality (AR)/VR head-mounted displays (HMDs), research on adaptive tutorials and authoring tools has become active, but existing task segmentation methods mainly rely on manual annotation or are restricted to 2D video which limits their applicability to 3D VR contexts. In our approach, assembly scenarios with clearly defined task boundaries are recorded using a structured spatio-temporal scene graph (STSG), and the OCG is employed to track changes in the central object and the formation of new groups, thereby generating task breakpoints automatically. A user study collected user-perceived task breakpoints to establish ground truth (GT), and comparison with the algorithm-detected breakpoints demonstrated high agreement and confirmed accuracy in supporting adaptive playback. The proposed task segmentation method provides a foundation for dynamically adjusting VR playback according to user proficiency and progress, with potential for extension into automatic timeline segmentation systems for diverse VR recordings.
[HC-36] Beyond Semantic Similarity: Open Challenges for Embedding-Based Creative Process Analysis Across AI Design Tools
【速读】：该论文旨在解决当前基于人工智能的创意支持工具（AI-based Creativity Support Tools, CSTs）在跨领域比较时面临的局限性问题，即现有评估方法依赖于特定领域的指标，难以捕捉不同领域中创意过程的本质差异。核心挑战在于：固定嵌入相似度可能误判创造性动态，例如无法识别表面语言相似但实质问题转变的“创意转向”（creative pivots），从而将此类转变误判为持续的细化而非真正的创新突破。为此，论文提出以大语言模型（Large Language Models, LLMs）为基础的上下文感知干预策略，使轨迹分析能够敏感地响应会话特异性的创意动态，从而提升对多模态设计痕迹的分割与表征能力，并应对代理系统（agentic systems）中嵌入式度量影响生成行为的闭环问题。
链接: https://arxiv.org/abs/2603.07611
作者: Seung Won Lee,Semin Jin,Kyung Hoon Hyun
机构: Hanyang University (汉阳大学)
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:AI-based creativity support tools (CSTs) are evaluated through domain-specific metrics, limiting cross-domain comparison of creative processes. Embedding-based protocol analysis offers a potential domain-agnostic analytical layer. However, we argue that fixed embedding similarity can misrepresent creative dynamics: it may not detect creative pivots that occur within superficially similar language, treating shifts in the problem being addressed as continued elaboration. We identify three open challenges stemming from this gap: aligning similarity measures with creative significance, segmenting and representing multimodal design traces, and evaluating agentic systems where embedding-based metrics enter the generation loop and shape agent behavior. We propose context-aware interventions using large language models as a direction for making trace analysis sensitive to session-specific creative dynamics.
[HC-37] From Logs to Agents : Reconstructing High-Level Creative Workflows from Low-Level Raw System Traces
【速读】：该论文旨在解决当前基于人工智能的创意支持工具（Creativity Support Tools, CSTs）生成的大量低级日志数据（如点击、参数调整、元数据更新等）难以被解释为“创意意图”的问题。解决方案的关键在于提出一种方法，将原始的CSV/JSON日志解析为结构化的行为工作流图（behavioral workflow graphs），通过抽象低级系统事件为高级行为标记（如MODIFY_Prompt、GENERATE_Image），从而映射创意资产的来源与流动路径，使下游分析（如序列挖掘和概率建模）成为可能。这一结构化的工作流历史是实现“过程感知代理”（Process-Aware Agents）的前提，后者能够基于对用户工作流模式和历史的深入理解，提供下一步设计建议或解释决策逻辑。
链接: https://arxiv.org/abs/2603.07609
作者: Tae Hee Jo,Kyung Hoon Hyun
机构: Hanyang University (汉阳大学); Design Informatics Lab (设计信息学实验室); Human-Centered AI Design Institute (以人为中心的人工智能设计研究所)
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Current AI-based Creativity Support Tools (CSTs) generate massive amounts of low-level log data (e.g., clicks, parameter tweaks, metadata updates) that are hard to interpret as “creative intent”. We argue that to enable future agentic systems to understand and assist users, we must first translate these noisy system traces into meaningful high-level user behavioral traces. We propose a method that parses raw csv/JSON logs into structured behavioral workflow graphs that map the provenance and flow of creative assets. By abstracting low-level system events into high-level behavioral tokens (e.g., MODIFY_Prompt, GENERATE_Image), this method enables downstream analyses like sequence mining and probabilistic modeling. We discuss how this structured workflow history is a prerequisite for “Process-Aware Agents” - systems capable of suggesting next design moves or explaining rationales based on a deeper understanding of the user’s workflow patterns and history.
[HC-38] AiRWeb: Using AR to Extend Web Browsing Beyond Handheld Screens
【速读】：该论文旨在解决移动设备上浏览网页时因屏幕空间有限而导致的用户体验不佳问题。其解决方案的关键在于提出了一种名为AiRWeb的手机+增强现实（Augmented Reality, AR）网页浏览方法，该方法利用网页的结构特性，使用户能够无缝地选择并将其任意内容投射到周围空间中进行操作。通过赋予用户对投送内容、投送时机以及布局方式的自主控制权，AiRWeb实现了针对具体任务的个性化组织，从而提升交互灵活性与可用性。
链接: https://arxiv.org/abs/2603.07586
作者: Mengfei Gao,Caroline Appert,Ludovic David,Emmanuel Pietriga
机构: Université Paris-Saclay (巴黎-萨克雷大学); CNRS (法国国家科学研究中心); Inria (法国国家信息与自动化研究院)
类目: Human-Computer Interaction (cs.HC)
备注: CHI EA '26: Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems
Abstract:Browsing the Web on mobile devices is often cumbersome due to their limited screen space. We investigate a phone+AR Web browsing approach, AiRWeb, that leverages the structural properties of Web pages to allow users to seamlessly select and offload arbitrary Web content into the space surrounding them. Focusing on flexibility, AiRWeb lets users decide what to offload, when to do so, and how offloaded content is arranged, enabling personalized organization tailored to the task at hand. We developed a fully functional prototype using standard Web technologies, that covers the complete interaction workflow, from the selection of elements to offload from the phone to their manipulation in the air. Results from a preliminary study conducted using this prototype suggest that AiRWeb is learnable and usable, while also revealing open design challenges around offload mode activation in particular.
[HC-39] MIRO: Multi-radar Identity and Ranging for Occupational Safety
【速读】：该论文旨在解决开放工业环境中作业人员暴露于空气颗粒物（Particulate Matter, PM）所带来的健康风险监测难题。传统方案如可穿戴PM传感器和基于摄像头的追踪方法因不适感、维护困难及隐私问题而难以实际应用。其解决方案的关键在于提出一种隐私保护框架MIRO，该框架融合连续PM传感与多雷达毫米波（millimeter-wave, mmWave）重识别（re-identification, re-ID）技术：通过分布式PM传感器获取局部污染物浓度，利用空间重叠的mmWave雷达在不依赖视觉线索的情况下跨视角追踪并重新关联工人身份；为保障跨雷达身份一致性，创新性地引入基于生成对抗网络（Generative Adversarial Network, GAN）的视图适应网络以校正距离-多普勒（range-Doppler, RD）特征中的方位畸变，并结合相关性匹配实现跨雷达身份关联，从而实现精准的个体化PM暴露估计。
链接: https://arxiv.org/abs/2603.07531
作者: Tirthankar Halder,Argha Sen,Swadhin Pradhan,Rijurekha Sen,Sandip Chakraborty
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注: Accepted in SenSys 2026
Abstract:Occupational exposure to airborne particulate matter (PM) poses a severe health risk in open industrial workspaces such as stonecutting yards. Conventional monitoring solutions such as wearable PM sensors and camera-based tracking are impractical due to discomfort, maintenance issues, and privacy concerns. We present MIRO, a privacy-preserving framework that integrates continuous PM sensing with a multi-radar millimeter-wave (mmWave) re-identification (re-ID) backbone. A distributed network of PM sensors captures localized pollutant concentrations, while spatially overlapping mmWave radars track and re-associate workers across viewpoints without relying on visual cues. To ensure identity consistency across radars, we introduce a GAN-based view adaptation network that compensates for azimuthal distortions in range-Doppler (RD) signatures, combined with correlation-based cross-radar matching. In controlled laboratory experiments, our system achieves a re-ID F1-score of 90.4% and a mean Structural Similarity Index Measure (SSIM) of 0.70 for view adaptation accuracy. Field trials in rural stone-cutting yards further validate the system’s robustness, demonstrating reliable worker-specific PM exposure estimation.
[HC-40] Pushing Bistatic Wireless Sensing toward High Accuracy at the Sub-Wavelength Scale
【速读】：该论文旨在解决无线通信信号在非接触式感知中因双基地部署导致的时钟不同步问题，该问题会引入未知相位偏移，从而阻碍精细感知精度。现有系统通常采用跨天线信道比来消除相位偏移，但该方法仅在目标位移为整波长倍数时保持感知特征准确性，丧失了亚波长尺度的感知保真度。论文的关键创新在于首次建立了失真信道比特征与理想信道特征之间的定量映射关系，并基于此构建了一个鲁棒框架，利用信道响应幅度信息从失真比值中恢复出理想的信道特征，从而实现了亚波长位移细节的有效重建，在Wi-Fi和LoRa实测场景下将感知精度提升了近一个数量级。
链接: https://arxiv.org/abs/2603.07492
作者: Wenwei Li,Jiarun Zhou,Qinxiao Quan,Fusang Zhang,Daqing Zhang
机构: 未知
类目: Information Theory (cs.IT); Emerging Technologies (cs.ET); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注:
Abstract:Contactless sensing using wireless communication signals has garnered significant attention due to its non-intrusive nature and ubiquitous infrastructure. Despite the promise, the inherent bistatic deployment of wireless communication introduces clock asynchronism, which leads to unknown phase offsets in channel response and hinders fine-grained sensing. State-of-the-art systems widely adopt the cross-antenna channel ratio to cancel these detrimental phase offsets. However, the channel ratio preserves sensing feature accuracy only at integer-wavelength target displacements, losing sub-wavelength fidelity. To overcome this limitation, we derive the first quantitative mapping between the distorted ratio feature and the ideal channel feature. Building on this foundation, we develop a robust framework that leverages channel response amplitude to recover the ideal channel feature from the distorted ratio. Real-world experiments across Wi-Fi and LoRa demonstrate that our method can effectively reconstruct sub-wavelength displacement details, achieving nearly an order-of-magnitude improvement in accuracy.
[HC-41] “Better Ask for Forgiveness than Permission”: Practices and Policies of AI Disclosure in Freelance Work
【速读】：该论文试图解决的问题是：在自由职业经济中，生成式 AI (Generative AI) 的广泛应用正在重塑工作者与客户之间的信任关系，但当前双方对 AI 使用及披露的预期存在显著差距，导致误解和不信任。解决方案的关键在于建立更清晰的 AI 揭示规范与政策框架——具体而言，需引导工作者从被动披露转向主动披露，并推动客户制定明确的 AI 使用政策，从而弥合预期差距、提升透明度与责任归属，为其他 AI 辅助工作场景提供可借鉴的信任机制与治理路径。
链接: https://arxiv.org/abs/2603.07459
作者: Angel Hsing-Chi Hwang,Senya Wong,Baixiao Chen,Jessica He,Hyo Jin Do
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:The growing use of AI applications among freelance workers is reshaping trust and relationships with clients. This paper investigates how both workers and clients perceive AI use and disclosure in the freelance economy through a three-stage study: interviews with workers and two survey studies with workers and clients. Findings first reveal a key expectation gap around disclosure: Workers often adopt passive disclosure practices, revealing AI use only when asked, as they assume clients can already detect it. Clients, however, are far less confident in recognizing AI-assisted work and prefer proactive disclosure. A second finding highlights the role of unclear or absent client AI policies, which leave workers consistently misinterpreting clients’ expectations for AI use and disclosure. Together, these gaps point to the need for clearer guidelines and practices for AI disclosure. Insights extend beyond freelancing, offering implications for trust, accountability, and policy design in other AI-mediated work domains.
[HC-42] GeoVisA11y: An AI-based Geovisualization Question-Answering System for Screen-Reader Users
【速读】：该论文旨在解决地理可视化（Geovisualization）对屏幕阅读器用户不具可访问性的问题。其解决方案的关键在于提出GeoVisA11y，一个基于大语言模型（LLM）的问答系统，通过自然语言交互使地理可视化内容可被视障用户获取，支持地图读取、分析、解释与导航，能够处理分析型、地理空间、视觉和上下文查询。
链接: https://arxiv.org/abs/2603.07446
作者: Chu Li,Rock Yuren Pang,Arnavi Chheda-Kothary,Ather Sharif,Henok Assalif,Jeffrey Heer,Jon E. Froehlich
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注: This manuscript has been accepted at CHI’26
Abstract:Geovisualizations are powerful tools for communicating spatial information, but are inaccessible to screen-reader users. To address this limitation, we present GeoVisA11y, an LLM-based question-answering system that makes geovisualizations accessible through natural language interaction. The system supports map reading, analysis, interpretation and navigation by handling analytical, geospatial, visual and contextual queries. Through user studies with 12 screen-reader users and sighted participants, we demonstrate that GeoVisA11y effectively bridges accessibility gaps while revealing distinct interaction patterns between user groups. We contribute: (1) an open-source, accessible geovisualization system, (2) empirical findings on query and navigation differences, and (3) a dataset of geospatial queries to inform future research on accessible data visualization.
[HC-43] Generalization in Online Reinforcement Learning for Mobile Agents
【速读】：该论文旨在解决移动设备上基于图形用户界面（GUI）的智能体在面对未见过的任务实例、模板和应用时，其零样本泛化能力不足的问题。当前方法多依赖强化学习（Reinforcement Learning, RL）提升性能，但缺乏标准化评估基准与开源RL训练系统，导致泛化研究进展缓慢。解决方案的关键在于：首先，将问题形式化为上下文马尔可夫决策过程（Contextual Markov Decision Process, CMDP），并构建AndroidWorld-Generalization基准，涵盖三类逐步增加难度的泛化场景；其次，提出一个集成组相对策略优化（Group Relative Policy Optimization, GRPO）的可扩展强化学习训练系统，结合容器化基础设施、异步执行及错误恢复机制，实现高效可靠的训练。实验表明，该方案使7B参数视觉-语言模型（Vision-Language Model, VLM）智能体在未见任务实例上性能提升26.1%，显著优于监督微调基线，但在未见模板和应用上的提升有限，凸显泛化挑战，同时验证了测试时少样本适应的有效性，为未来研究提供方向。
链接: https://arxiv.org/abs/2603.07432
作者: Li Gu,Zihuan Jiang,Zhixiang Chi,Huan Liu,Ziqiang Wang,Yuanhao Yu,Glen Berseth,Yang Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注:
Abstract:Graphical user interface (GUI)-based mobile agents automate digital tasks on mobile devices by interpreting natural-language instructions and interacting with the screen. While recent methods apply reinforcement learning (RL) to train vision-language-model(VLM) agents in interactive environments with a primary focus on performance, generalization remains underexplored due to the lack of standardized benchmarks and open-source RL systems. In this work, we formalize the problem as a Contextual Markov Decision Process (CMDP) and introduce \textbfAndroidWorld-Generalization, a benchmark with three increasingly challenging regimes for evaluating zero-shot generalization to unseen task instances, templates, and applications. We further propose an RL training system that integrates Group Relative Policy Optimization (GRPO) with a scalable rollout collection system, consisting of containerized infrastructure and asynchronous execution % , and error recovery to support reliable and efficient training. Experiments on AndroidWorld-Generalization show that RL enables a 7B-parameter VLM agent to surpass supervised fine-tuning baselines, yielding a 26.1% improvement on unseen instances but only limited gains on unseen templates (15.7%) and apps (8.3%), underscoring the challenges of generalization. As a preliminary step, we demonstrate that few-shot adaptation at test-time improves performance on unseen apps, motivating future research in this direction. To support reproducibility and fair comparison, we open-source the full RL training system, including the environment, task suite, models, prompt configurations, and the underlying infrastructure \footnotethis https URL.
[HC-44] oward Real-Time Mirrors Intelligence: System-Level Latency and Computation Evaluation in Internet of Mirrors (IoM)
【速读】：该论文旨在解决智能镜（Smart Mirror）在互联网镜像（Internet of Mirrors, IoM）生态系统中计算任务部署位置的优化问题，即如何在消费级、专业级和枢纽级三层节点架构中合理分配计算资源，以平衡端到端延迟、资源利用率与用户体验。其解决方案的关键在于通过构建首个物理IoM测试平台，在真实Wi-Fi和5G网络环境下评估四种计算放置策略，发现将分类等任务卸载至高层节点虽能显著降低终端延迟和消费者设备负载，但会引入随数据包大小和跳数增长的网络开销；因此，不存在普适最优方案，最佳策略需依据实时网络状况、节点距离及并发用户负载动态调整，从而为实现面向应用需求与系统状态自适应的智能任务调度提供实证基础。
链接: https://arxiv.org/abs/2603.07408
作者: Haneen Fatima,Muhammad Ali Imran,Ahmad Taha,Lina Mohjazi
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Human-Computer Interaction (cs.HC)
备注: 6 pages, 6 figures, conference
Abstract:The Internet of Mirrors (IoM) is an emerging IoT ecosystem of interconnected smart mirrors designed to deliver personalised services across a three-tier node hierarchy spanning consumer, professional, and hub nodes. Determining where computation should reside within this hierarchy is a critical design challenge, as placement decisions directly affect end-to-end latency, resource utilisation, and user experience. This paper presents the first physical IoM testbed study, evaluating four computational placement strategies across the IoM tier hierarchy under real Wi-Fi and 5G network conditions. Results show that offloading classification to higher-tier nodes substantially reduces latency and consumer resource load, but introduces network overhead that scales with payload size and hop count. No single strategy is universally optimal: the best choice depends on available network, node proximity, and concurrent user load. These findings empirically characterise the computation-communication trade-off space of the IoM and motivate the need for intelligent, adaptive task placement responsive to application requirements and live ecosystem conditions.
[HC-45] Collaboration by Mandate: How Shared Data Infrastructure Shapes Coordination and Control in U.S. Homelessness Services
【速读】：该论文试图解决的问题是：在政府强制要求协作的背景下，共享数据系统（如美国无家可归者服务网络中的家庭无家可归管理信息系统 HMIS）如何在促进跨机构协调与问责的同时，可能加剧数据解释和决策权分配中的权力不对称。解决方案的关键在于揭示标准制定与治理规则如何嵌入数据基础设施中，从而既支持协同合作与知识共享，又因资源、分析能力和决策权威的不平等分配，导致部分参与者被迫转向合规性角色，进而固化结构性不平等。这一发现为公共利益导向的数据基础设施设计提供了重要启示：需在技术标准化之外，同步构建公平的参与机制与赋权框架。
链接: https://arxiv.org/abs/2603.07354
作者: Lingwei Cheng,Saerim Kim,Andrew Sullivan
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注: 6 pages. Accepted to the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA '26)
Abstract:When governments mandate collaboration, shared data systems can serve both as tools for coordination and instruments of control. This study examines U.S. homelessness service networks, where Continuums of Care (CoCs) coordinate service providers through the federally mandated Homeless Management Information System (HMIS). With client consent, providers enter data into HMIS and access cross-provider service histories to support coordinated care. At the same time, HMIS embeds standards and governance rules that shape who can collect, access, interpret, and act on data, and thus who holds decision authority. Using qualitative interviews with six experts, we show that standardization can facilitate collaboration and shared learning. However, unequal resources, analytic capacity, and authority limit equitable participation and often shift some participants toward compliance-focused roles. We contribute to public-interest design research on civic data infrastructures by illustrating how mandated data sharing can simultaneously enable coordination and accountability while reproducing power asymmetries in data interpretation and decision-making.
[HC-46] Pre-Clinical Latency Characterization of VRxBioRelax: A Real-Time EMG Biofeedback System for Muscle Relaxation in Virtual Reality
【速读】：该论文旨在解决慢性上斜方肌（upper trapezius, UT）紧张导致的肌肉骨骼不适、头痛及内感受觉意识受损等问题，同时克服传统表面肌电（sEMG）生物反馈系统因依赖常规显示界面而难以维持用户参与度的局限。其解决方案的关键在于开发了一种闭环虚拟现实（VR）生物反馈系统——VRxBioRelax，该系统通过MQTT协议实时传输Delsys Trigno Avanti传感器采集的sEMG数据至Unity场景，并将肌肉激活程度映射为随时间演化的日出到日落景观，同步执行渐进式肌肉放松协议。实验验证表明，该系统端到端平均延迟仅为25.34 ms，且99.3%的帧在50 ms内完成渲染，显著低于30 ms的VR舒适阈值和50 ms的临床基准，从而确保了沉浸感与有效性，适用于远程内感受训练、压力缓解及远程康复等应用场景。
链接: https://arxiv.org/abs/2603.07353
作者: Melanie Baumgartner,Raphael Weibel,Tobias Hoesli,Aydin Javadov,Rayna Ney,Helen Schwerdt,Florian von Wangenheim,Joseph Ollier
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注: Accepted and presented at 2025 IEEE Conference on Telepresence. Source code used is publicly available on the Open Science Framework (OSF): this https URL
Abstract:Chronic tension in the upper trapezius (UT), often caused by poor ergonomics, prolonged posture, or psychological stress, contributes to musculoskeletal discomfort, headaches, and impaired interoceptive awareness. Although surface electromyography (sEMG) biofeedback can promote UT relaxation, traditional systems using conventional displays often fail to sustain engagement. Virtual reality (VR) offers a more immersive alternative, provided that latency remains below perceptual thresholds. We introduce VRxBioRelax, a closed-loop VR biofeedback system that streams sEMG data from Delsys Trigno Avanti sensors via MQTT to a Unity scene. Muscle activation drives a dynamic dawn-to-dusk landscape synchronized with a progressive muscle relaxation protocol. To validate system responsiveness, 87,716 EMG samples from the NinaPro DB2 dataset were replayed at \sim 75 Hz. Timestamps at four key stages-acquisition, Root Mean Square (RMS) processing, network receipt, and rendering-revealed mean latencies of 0.50 ms (processing), 5.62 ms (network), and 19.22 ms (rendering), yielding an average end-to-end delay of 25.34 ms. Notably, 99.3% of frames arrived within 50 ms. One-sided t-tests confirmed mean latency was significantly lower than both the 30 ms VR comfort limit ( t_87,715=-25.2 , p=5.9\times10^-140 ) and the 50 ms clinical benchmark ( t_87,715=-133.3 , p10^-300 ). These findings support VRxBioRelax for use in remote interoceptive training, stress reduction, and telepresence-enabled rehabilitation.
计算机视觉
[CV-0] Scale Space Diffusion
【速读】：该论文旨在解决扩散模型（Diffusion Models）在生成过程中需要全分辨率处理高噪声状态的效率问题，尽管研究表明高度噪声状态的信息量等价于小尺寸下采样图像。为解决此问题，作者提出将尺度空间（Scale Space）理论引入扩散过程，通过定义一类具有广义线性退化形式的扩散模型来实现多尺度信息融合；其核心创新在于提出Scale Space Diffusion框架，并配套设计了Flexi-UNet——一种能根据输入分辨率动态调整网络计算路径、仅使用必要部分进行去噪的UNet变体，从而在保持生成质量的同时显著提升计算效率与可扩展性。
链接: https://arxiv.org/abs/2603.08709
作者: Soumik Mukhopadhyay,Prateksha Udhayanan,Abhinav Shrivastava
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Project website: this https URL . The first two authors contributed equally
Abstract:Diffusion models degrade images through noise, and reversing this process reveals an information hierarchy across timesteps. Scale-space theory exhibits a similar hierarchy via low-pass filtering. We formalize this connection and show that highly noisy diffusion states contain no more information than small, downsampled images - raising the question of why they must be processed at full resolution. To address this, we fuse scale spaces into the diffusion process by formulating a family of diffusion models with generalized linear degradations and practical implementations. Using downsampling as the degradation yields our proposed Scale Space Diffusion. To support Scale Space Diffusion, we introduce Flexi-UNet, a UNet variant that performs resolution-preserving and resolution-increasing denoising using only the necessary parts of the network. We evaluate our framework on CelebA and ImageNet and analyze its scaling behavior across resolutions and network depths. Our project website ( this https URL ) is available publicly.
[CV-1] FVG-PT: Adaptive Foreground View-Guided Prompt Tuning for Vision-Language Models
【速读】：该论文旨在解决基于CLIP的提示调优（prompt tuning）在下游任务中因视觉编码器内部注意力机制变化而导致的预测失败问题，特别是由前景注意力偏移引起的问题。其解决方案的关键在于提出一种可即插即用的前景视图引导提示调优方法（Foreground View-Guided Prompt Tuning, FVG-PT），该方法包含三个核心模块：可学习的前景可靠性门（Foreground Reliability Gate）用于自动增强前景视图质量；前景蒸馏补偿模块（Foreground Distillation Compensation）引导视觉注意力聚焦于前景区域；以及先验校准模块（Prior Calibration）以缓解因过度关注前景导致的泛化性能下降问题。
链接: https://arxiv.org/abs/2603.08708
作者: Haoyang Li,Liang Wang,Siyu Zhou,Jiacheng Sun,Jing Jiang,Chao Wang,Guodong Long,Yan Peng
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 27 Pages, 9 Figures, 15 Tables
Abstract:CLIP-based prompt tuning enables pretrained Vision-Language Models (VLMs) to efficiently adapt to downstream tasks. Although existing studies have made significant progress, they pay limited attention to changes in the internal attention representations of VLMs during the tuning process. In this paper, we attribute the failure modes of prompt tuning predictions to shifts in foreground attention of the visual encoder, and propose Foreground View-Guided Prompt Tuning (FVG-PT), an adaptive plug-and-play foreground attention guidance module, to alleviate the shifts. Concretely, FVG-PT introduces a learnable Foreground Reliability Gate to automatically enhance the foreground view quality, applies a Foreground Distillation Compensation module to guide visual attention toward the foreground, and further introduces a Prior Calibration module to mitigate generalization degradation caused by excessive focus on the foreground. Experiments on multiple backbone models and datasets show the effectiveness and compatibility of FVG-PT. Codes are available at: this https URL
[CV-2] HiAR: Efficient Autoregressive Long Video Generation via Hierarchical Denoising
【速读】：该论文旨在解决自回归扩散模型在生成长视频时面临的两个核心问题：一是如何维持时间连续性，二是如何避免因误差累积导致的渐进式质量下降。现有方法通常依赖高度去噪的上下文进行条件生成，但这种做法会将高置信度的预测误差传播到后续帧中，加剧质量退化。论文的关键创新在于提出HiAR框架，其核心思想是摒弃传统“逐块顺序生成”的模式，转而采用分层去噪机制，在每个去噪步骤中对所有视频块进行因果生成，确保当前块始终基于与之处于相同噪声水平的上下文进行条件处理——这既提供了足够的时间一致性信号，又有效抑制了误差传播。此外，为缓解由反向KL目标引发的低运动捷径问题，作者进一步引入前向KL正则项，在双向注意力模式下保持运动多样性的同时不影响蒸馏损失。实验表明，HiAR在VBench基准上的20秒视频生成任务中实现了最优综合评分和最低的时间漂移。
链接: https://arxiv.org/abs/2603.08703
作者: Kai Zou,Dian Zheng,Hongbo Liu,Tiankai Hang,Bin Liu,Nenghai Yu
机构: University of Science and Technology of China (中国科学技术大学); The Chinese University of Hong Kong (香港中文大学); Tongji University (同济大学); Tencent Hunyuan (腾讯混元); Anhui Province Key Laboratory of Digital Security, USTC (安徽省数字安全重点实验室，中国科学技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL Code: this https URL
Abstract:Autoregressive (AR) diffusion offers a promising framework for generating videos of theoretically infinite length. However, a major challenge is maintaining temporal continuity while preventing the progressive quality degradation caused by error accumulation. To ensure continuity, existing methods typically condition on highly denoised contexts; yet, this practice propagates prediction errors with high certainty, thereby exacerbating degradation. In this paper, we argue that a highly clean context is unnecessary. Drawing inspiration from bidirectional diffusion models, which denoise frames at a shared noise level while maintaining coherence, we propose that conditioning on context at the same noise level as the current block provides sufficient signal for temporal consistency while effectively mitigating error propagation. Building on this insight, we propose HiAR, a hierarchical denoising framework that reverses the conventional generation order: instead of completing each block sequentially, it performs causal generation across all blocks at every denoising step, so that each block is always conditioned on context at the same noise level. This hierarchy naturally admits pipelined parallel inference, yielding a 1.8 wall-clock speedup in our 4-step setting. We further observe that self-rollout distillation under this paradigm amplifies a low-motion shortcut inherent to the mode-seeking reverse-KL objective. To counteract this, we introduce a forward-KL regulariser in bidirectional-attention mode, which preserves motion diversity for causal inference without interfering with the distillation loss. On VBench (20s generation), HiAR achieves the best overall score and the lowest temporal drift among all compared methods.
[CV-3] ER-Pose: Rethinking Keypoint-Driven Representation Learning for Real-Time Human Pose Estimation
【速读】：该论文旨在解决单阶段多人姿态估计中因采用框驱动（box-driven）建模范式而导致的任务错位问题，即在训练过程中姿态估计被边界框监督隐式约束，从而引发样本分配偏差和特征表示失真，最终限制了姿态估计精度。其解决方案的关键在于提出一种关键点驱动（keypoint-driven）的学习范式：首先移除边界框预测分支并重构预测头以适配高维结构化姿态表示；其次设计关键点驱动的动态样本分配策略，使训练目标与姿态评估指标对齐，实现密集监督与无非极大值抑制（NMS-free）推理；同时引入基于OKS（Object Keypoint Similarity）的平滑损失函数以稳定回归型姿态估计的优化过程。基于此，作者构建了名为ER-Pose的单阶段多人姿态估计框架，在MS COCO和CrowdPose数据集上显著优于基线YOLO-Pose，且参数更少、推理效率更高。
链接: https://arxiv.org/abs/2603.08681
作者: Nanjun Li,Pinqi Cheng,Zean Liu,Minghe Tian,Xuanyin Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Single-stage multi-person pose estimation aims to jointly perform human localization and keypoint prediction within a unified framework, offering advantages in inference efficiency and architectural simplicity. Consequently, multi-scale real-time detection architectures, such as YOLO-like models, are widely adopted for real-time pose estimation. However, these approaches typically inherit a box-driven modeling paradigm from object detection, in which pose estimation is implicitly constrained by bounding-box supervision during training. This formulation introduces biases in sample assignment and feature representation, resulting in task misalignment and ultimately limiting pose estimation accuracy. In this work, we revisit box-driven single-stage pose estimation from a keypoint-driven perspective and identify semantic conflicts among parallel objectives as a key source of performance degradation. To address this issue, we propose a keypoint-driven learning paradigm that elevates pose estimation to a primary prediction objective. Specifically, we remove bounding-box prediction and redesign the prediction head to better accommodate the high-dimensional structured representations for pose estimation. We further introduce a keypoint-driven dynamic sample assignment strategy to align training objectives with pose evaluation metrics, enabling dense supervision during training and efficient NMS-free inference. In addition, we propose a smooth OKS-based loss function to stabilize optimization in regression-based pose estimation. Based on these designs, we develop a single-stage multi-person pose estimation framework, termed ER-Pose. On MS COCO and CrowdPose, ER-Pose-n achieves AP improvements of 3.2/6.7 without pre-training and 7.4/4.9 with pre-training respectively compared with the baseline YOLO-Pose. These improvements are achieved with fewer parameters and higher inference efficiency.
[CV-4] alking Together: Synthesizing Co-Located 3D Conversations from Audio CVPR2026
【速读】：该论文旨在解决从混合音频流中生成两个共处空间内的参与者完整3D面部动画的问题，现有方法通常仅生成脱离物理空间的“说话头”（talking heads），无法体现真实面对面交流中的动态三维空间关系（包括相对位置、朝向及相互凝视）。其解决方案的关键在于提出一种双流架构，分别处理每位参与者的输出，并引入说话者角色嵌入（speaker’s role embeddings）与跨说话者交叉注意力机制（inter-speaker cross-attention mechanisms），以分离混合音频并建模交互关系；同时设计了一种新颖的眼部凝视损失函数（eye gaze loss）以促进自然的双向注视行为。此外，研究构建了一个包含超过200万对对话场景的大规模野外视频数据集，支撑了模型训练，最终实现了可控制、空间感知且流畅的双人动画生成，适用于VR和远程存在（telepresence）等沉浸式应用场景。
链接: https://arxiv.org/abs/2603.08674
作者: Mengyi Shan,Shouchieh Chang,Ziqian Bai,Shichen Liu,Yinda Zhang,Luchuan Song,Rohit Pandey,Sean Fanello,Zeng Huang
机构: University of Washington (华盛顿大学); Google (谷歌); University of Rochester (罗切斯特大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026
Abstract:We tackle the challenging task of generating complete 3D facial animations for two interacting, co-located participants from a mixed audio stream. While existing methods often produce disembodied “talking heads” akin to a video conference call, our work is the first to explicitly model the dynamic 3D spatial relationship – including relative position, orientation, and mutual gaze – that is crucial for realistic in-person dialogues. Our system synthesizes the full performance of both individuals, including precise lip-sync, and uniquely allows their relative head poses to be controlled via textual descriptions. To achieve this, we propose a dual-stream architecture where each stream is responsible for one participant’s output. We employ speaker’s role embeddings and inter-speaker cross-attention mechanisms designed to disentangle the mixed audio and model the interaction. Furthermore, we introduce a novel eye gaze loss to promote natural, mutual eye contact. To power our data-hungry approach, we introduce a novel pipeline to curate a large-scale conversational dataset consisting of over 2 million dyadic pairs from in-the-wild videos. Our method generates fluid, controllable, and spatially aware dyadic animations suitable for immersive applications in VR and telepresence, significantly outperforming existing baselines in perceived realism and interaction coherence.
[CV-5] ImprovedGS: A High-Performance C/CUDA Re-Implementation Strategy for 3D Gaussian Splatting
【速读】：该论文旨在解决3D高斯溅射（3D Gaussian Splatting, 3DGS）中重建保真度与计算效率难以平衡的问题。其解决方案的关键在于对ImprovedGS策略进行底层重构，通过将原生Python逻辑迁移至硬件优化的C++/CUDA内核实现，显著降低主机-设备同步开销和训练延迟；同时引入长轴分割（Long-Axis-Split, LAS）CUDA内核、基于拉普拉斯算子的边缘重要性评分及非极大值抑制（Non-Maximum Suppression, NMS）机制，以及自适应指数尺度调度策略，从而在保持视觉质量的同时大幅提升训练速度与参数效率。
链接: https://arxiv.org/abs/2603.08661
作者: Jordi Muñoz Vicente
机构: Universidad de Murcia(穆尔西亚大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 1 figure. Technical Report. This work introduces ImprovedGS+, a library-free C++/CUDA implementation for 3D Gaussian Splatting within the LichtFeld-Studio framework. Source code available at this https URL
Abstract:Recent advancements in 3D Gaussian Splatting (3DGS) have shifted the focus toward balancing reconstruction fidelity with computational efficiency. In this work, we propose ImprovedGS+, a high-performance, low-level reinvention of the ImprovedGS strategy, implemented natively within the LichtFeld-Studio framework. By transitioning from high-level Python logic to hardware-optimized C++/CUDA kernels, we achieve a significant reduction in host-device synchronization and training latency. Our implementation introduces a Long-Axis-Split (LAS) CUDA kernel, custom Laplacian-based importance kernels with Non-Maximum Suppression (NMS) for edge scores, and an adaptive Exponential Scale Scheduler. Experimental results on the Mip-NeRF360 dataset demonstrate that ImprovedGS+ establishes a new Pareto-optimal front for scene reconstruction. Our 1M-budget variant outperforms the state-of-the-art MCMC baseline by achieving a 26.8% reduction in training time (saving 17 minutes per session) and utilizing 13.3% fewer Gaussians while maintaining superior visual quality. Furthermore, our full variant demonstrates a 1.28 dB PSNR increase over the ADC baseline with a 38.4% reduction in parametric complexity. These results validate ImprovedGS+ as a scalable, high-speed solution that upholds the core pillars of Speed, Quality, and Usability within the LichtFeld-Studio ecosystem.
[CV-6] CAST: Modeling Visual State Transitions for Consistent Video Retrieval
【速读】：该论文旨在解决长视频叙事中短片段组合时存在的状态与身份一致性问题，即现有检索方法在推理阶段缺乏上下文感知能力，仅关注局部语义对齐而忽视了跨时间步的潜在状态演化。其解决方案的关键在于提出了一种轻量级、可插拔的适配器模型CAST（Context-Aware State Transition），通过预测基于视觉历史的状态条件残差更新（Δ）来引入显式的潜在状态演进归纳偏置，从而增强视频片段间的时序连贯性。
链接: https://arxiv.org/abs/2603.08648
作者: Yanqing Liu,Yingcheng Liu,Fanghong Dong,Budianto Budianto,Cihang Xie,Yan Jiao
机构: Google(谷歌); University of California, Santa Cruz(加州大学圣克鲁兹分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:As video content creation shifts toward long-form narratives, composing short clips into coherent storylines becomes increasingly important. However, prevailing retrieval formulations remain context-agnostic at inference time, prioritizing local semantic alignment while neglecting state and identity consistency. To address this structural limitation, we formalize the task of Consistent Video Retrieval (CVR) and introduce a diagnostic benchmark spanning YouCook2, COIN, and CrossTask. We propose CAST (Context-Aware State Transition), a lightweight, plug-and-play adapter compatible with diverse frozen vision-language embedding spaces. By predicting a state-conditioned residual update ( \Delta ) from visual history, CAST introduces an explicit inductive bias for latent state evolution. Extensive experiments show that CAST improves performance on YouCook2 and CrossTask, remains competitive on COIN, and consistently outperforms zero-shot baselines across diverse foundation backbones. Furthermore, CAST provides a useful reranking signal for black-box video generation candidates (e.g., from Veo), promoting more temporally coherent continuations.
[CV-7] Retrieval-Augmented Gaussian Avatars: Improving Expression Generalization
【速读】：该论文旨在解决模板无关的可驱动头像（template-free animatable head avatars）在表达覆盖范围有限、对训练分布外表达动作鲁棒性差的问题。其核心解决方案是提出一种名为RAF（Retrieval-Augmented Faces）的训练时增强方法，关键在于构建一个大规模未标注表达特征库，并在训练过程中将部分主体表达特征替换为从该库中检索到的最近邻表达特征，同时仍重建原始主体帧。这种方法通过引入更丰富的表达条件，强化了身份与表达的解耦，从而提升模型在表达分布偏移下的鲁棒性，且无需跨身份配对数据、额外标注或架构改动。
链接: https://arxiv.org/abs/2603.08645
作者: Matan Levy,Gavriel Habib,Issar Tzachor,Dvir Samuel,Rami Ben-Ari,Nir Darshan,Or Litany,Dani Lischinski
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Machine Learning (cs.LG)
备注:
Abstract:Template-free animatable head avatars can achieve high visual fidelity by learning expression-dependent facial deformation directly from a subject’s capture, avoiding parametric face templates and hand-designed blendshape spaces. However, since learned deformation is supervised only by the expressions observed for a single identity, these models suffer from limited expression coverage and often struggle when driven by motions that deviate from the training distribution. We introduce RAF (Retrieval-Augmented Faces), a simple training-time augmentation designed for template-free head avatars that learn deformation from data. RAF constructs a large unlabeled expression bank and, during training, replaces a subset of the subject’s expression features with nearest-neighbor expressions retrieved from this bank while still reconstructing the subject’s original frames. This exposes the deformation field to a broader range of expression conditions, encouraging stronger identity-expression decoupling and improving robustness to expression distribution shift without requiring paired cross-identity data, additional annotations, or architectural changes. We further analyze how retrieval augmentation increases expression diversity and validate retrieval quality with a user study showing that retrieved neighbors are perceptually closer in expression and pose. Experiments on the NeRSemble benchmark demonstrate that RAF consistently improves expression fidelity over the baseline, in both self-driving and cross-driving scenarios.
[CV-8] UNBOX: Unveiling Black-box visual models with Natural-language
【速读】：该论文旨在解决开放世界视觉识别中模型可信性问题，即在模型作为黑盒API部署、无法获取内部结构、参数、梯度或训练数据的情况下，如何实现对模型的可解释性、公平性和鲁棒性分析。其核心挑战在于现有解释方法依赖白盒或灰盒访问权限，或假设已知训练分布，难以适用于真实场景。解决方案的关键在于提出UNBOX框架，该框架在完全无数据、无梯度、无反向传播的约束下，利用大语言模型（Large Language Models, LLMs）和文本到图像扩散模型，将激活最大化重构为仅基于输出概率的语义搜索任务，从而生成人类可理解的类别级文本描述，揭示模型隐式学习的概念、训练分布特征及潜在偏见来源。
链接: https://arxiv.org/abs/2603.08639
作者: Simone Carnemolla,Chiara Russo,Simone Palazzo,Quentin Bouniot,Daniela Giordano,Zeynep Akata,Matteo Pennisi,Concetto Spampinato
机构: University of Catania (卡塔尼亚大学); Technical University of Munich (慕尼黑工业大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Under review at IJCV
Abstract:Ensuring trustworthiness in open-world visual recognition requires models that are interpretable, fair, and robust to distribution shifts. Yet modern vision systems are increasingly deployed as proprietary black-box APIs, exposing only output probabilities and hiding architecture, parameters, gradients, and training data. This opacity prevents meaningful auditing, bias detection, and failure analysis. Existing explanation methods assume white- or gray-box access or knowledge of the training distribution, making them unusable in these real-world settings. We introduce UNBOX, a framework for class-wise model dissection under fully data-free, gradient-free, and backpropagation-free constraints. UNBOX leverages Large Language Models and text-to-image diffusion models to recast activation maximization as a purely semantic search driven by output probabilities. The method produces human-interpretable text descriptors that maximally activate each class, revealing the concepts a model has implicitly learned, the training distribution it reflects, and potential sources of bias. We evaluate UNBOX on ImageNet-1K, Waterbirds, and CelebA through semantic fidelity tests, visual-feature correlation analyses and slice-discovery auditing. Despite operating under the strictest black-box constraints, UNBOX performs competitively with state-of-the-art white-box interpretability methods. This demonstrates that meaningful insight into a model’s internal reasoning can be recovered without any internal access, enabling more trustworthy and accountable visual recognition systems.
[CV-9] StreamReady: Learning What to Answer and When in Long Streaming Videos CVPR2026
【速读】：该论文旨在解决流式视频理解中模型回答时机不当的问题，即模型可能在视觉证据出现前作答（导致推测性错误）或在其后作答（丧失实时性）。为实现“适时回答”，作者提出了一种就绪感知（readiness-aware）的建模框架，其核心是引入答案就绪分数（Answer Readiness Score, ARS），这是一种具有不对称早/晚惩罚机制的时间敏感目标函数。结合准确性与时机判断，ARS定义了新的有效准确率指标，用于衡量模型是否在恰当时刻作出正确响应。解决方案的关键在于构建一个轻量级的就绪机制（readiness mechanism），使模型能在观察到足够证据后才决定输出答案，从而实现时间感知的时序推理与及时响应的统一。
链接: https://arxiv.org/abs/2603.08620
作者: Shehreen Azad,Vibhav Vineet,Yogesh Singh Rawat
机构: University of Central Florida (中佛罗里达大学); Microsoft Research (微软研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted in CVPR 2026
Abstract:Streaming video understanding often involves time-sensitive scenarios where models need to answer exactly when the supporting visual evidence appears: answering before the evidence reflects speculation, answering after it has passed reduces real-time utility. To capture this behavior, we introduce a readiness-aware formulation of streaming video understanding with the Answer Readiness Score (ARS), a timing-aware objective with asymmetric early and late penalties. When combined with correctness, ARS defines an effective accuracy that measures not just whether a model is right, but whether it answers at the appropriate moment. Building on this formulation, we introduce StreamReady, a framework to unify temporal reasoning with on-time answering through a lightweight readiness mechanism that decides if sufficient evidence has been observed before responding. To evaluate this capability, we further introduce ProReady-QA, a benchmark with annotated answer evidence windows and proactive multi-turn questions across local and global contexts. StreamReady achieves superior performance on ProReady-QA, and consistently outperforms prior methods across eight additional streaming and offline long-video benchmarks, demonstrating robust and broadly generalizable video understanding capability.
[CV-10] FOMO-3D: Using Vision Foundation Models for Long-Tailed 3D Object Detection
【速读】：该论文旨在解决自动驾驶车辆在复杂交通环境中对长尾分布的稀有安全关键物体（如施工人员）进行3D检测时，因训练样本不足而导致性能下降的问题。解决方案的关键在于提出FOMO-3D，这是首个利用视觉基础模型（vision foundation models）进行多模态3D目标检测的方法；其核心创新在于结合OWLv2提供的丰富语义先验和Metric3Dv2提供的深度先验，在两阶段检测框架中分别通过LiDAR分支与新颖的摄像头分支生成候选区域，并通过注意力机制重点融合来自OWLv2的图像特征，从而显著提升对长尾类别的检测性能。
链接: https://arxiv.org/abs/2603.08611
作者: Anqi Joyce Yang,James Tu,Nikita Dvornik,Enxu Li,Raquel Urtasun
机构: Waabi; University of Toronto
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Published at 9th Annual Conference on Robot Learning (CoRL 2025)
Abstract:In order to navigate complex traffic environments, self-driving vehicles must recognize many semantic classes pertaining to vulnerable road users or traffic control devices. However, many safety-critical objects (e.g., construction worker) appear infrequently in nominal traffic conditions, leading to a severe shortage of training examples from driving data alone. Recent vision foundation models, which are trained on a large corpus of data, can serve as a good source of external prior knowledge to improve generalization. We propose FOMO-3D, the first multi-modal 3D detector to leverage vision foundation models for long-tailed 3D detection. Specifically, FOMO-3D exploits rich semantic and depth priors from OWLv2 and Metric3Dv2 within a two-stage detection paradigm that first generates proposals with a LiDAR-based branch and a novel camera-based branch, and refines them with attention especially to image features from OWL. Evaluations on real-world driving data show that using rich priors from vision foundation models with careful multi-modal fusion designs leads to large gains for long-tailed 3D detection. Project website is at this https URL.
[CV-11] Weakly Supervised Teacher-Student Framework with Progressive Pseudo-mask Refinement for Gland Segmentation
【速读】：该论文旨在解决结直肠癌组织病理图像中腺体结构（glandular structures）分割依赖大量像素级标注的问题，此类标注在临床实践中成本高昂且难以获取。为应对这一挑战，作者提出了一种弱监督语义分割框架，其核心创新在于引入了一个基于指数移动平均（Exponential Moving Average, EMA）稳定的教师网络与学生网络协同训练机制，结合置信度过滤、教师预测与有限真实标签的自适应融合以及课程引导的精炼策略，逐步生成高质量伪掩码（pseudo masks），从而有效监督未标注区域的腺体分割。该方法显著降低了对密集标注的依赖，并在多个数据集上展现出良好的泛化能力。
链接: https://arxiv.org/abs/2603.08605
作者: Hikmat Khan,Wei Chen,Muhammad Khalid Khan Niazi
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Background and objectives: Colorectal cancer histopathological grading depends on accurate segmentation of glandular structures. Current deep learning approaches rely on large scale pixel level annotations that are labor intensive and difficult to obtain in routine clinical practice. Weakly supervised semantic segmentation offers a promising alternative. However, class activation map based methods often produce incomplete pseudo masks that emphasize highly discriminative regions and fail to supervise unannotated glandular structures. We propose a weakly supervised teacher student framework that leverages sparse pathologist annotations and an Exponential Moving Average stabilized teacher network to generate refined pseudo masks. Methods: The framework integrates confidence based filtering, adaptive fusion of teacher predictions with limited ground truth, and curriculum guided refinement to progressively segment unannotated glandular regions. The method was evaluated on an institutional colorectal cancer cohort from The Ohio State University Wexner Medical Center consisting of 60 hematoxylin and eosin stained whole slide images and on public datasets including the Gland Segmentation dataset, TCGA COAD, TCGA READ, and SPIDER. Results: On the Gland Segmentation dataset the framework achieved a mean Intersection over Union of 80.10 and a mean Dice coefficient of 89.10. Cross cohort evaluation demonstrated robust generalization on TCGA COAD and TCGA READ without additional annotations, while reduced performance on SPIDER reflected domain shift. Conclusions: The proposed framework provides an annotation efficient and generalizable approach for gland segmentation in colorectal histopathology. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.08605 [cs.CV] (or arXiv:2603.08605v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.08605 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hikmat Khan Ph.D [view email] [v1] Mon, 9 Mar 2026 16:54:05 UTC (4,169 KB)
[CV-12] Boosting MLLM Spatial Reasoning with Geometrically Referenced 3D Scene Representations
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在三维（3D）空间推理能力上的局限性问题，尽管其在二维（2D）视觉理解任务中已取得显著进展。解决方案的关键在于提出几何参考的3D场景表示（Geometrically Referenced 3D Scene Representations, GR3D），该方法通过为输入图像中的物体分配唯一ID，并将它们的3D几何属性编码为以这些ID索引的文本引用，从而使得MLLMs能够利用其强大的基于语言的数学推理能力来解析3D线索，同时以紧密耦合的方式分析2D视觉特征。该方案无需额外训练，在零样本（zero-shot）设置下即可提升GPT-5在VSI-Bench上的性能，尤其在依赖空间布局理解的任务上提升超过11%。
链接: https://arxiv.org/abs/2603.08592
作者: Jiangye Yuan,Gowri Kumar,Baoyuan Wang
机构: Zillow Group(齐乐集团)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:While Multimodal Large Language Models (MLLMs) have achieved remarkable success in 2D visual understanding, their ability to reason about 3D space remains limited. To address this gap, we introduce geometrically referenced 3D scene representations (GR3D). Given a set of input images, GR3D annotates objects in the images with unique IDs and encodes their 3D geometric attributes as textual references indexed by these IDs. This representation enables MLLMs to interpret 3D cues using their advanced language-based skills in mathematical reasoning, while concurrently analyzing 2D visual features in a tightly coupled way. We present a simple yet effective approach based on GR3D, which requires no additional training and is readily applicable to different MLLMs. Implemented in a zero-shot setting, our approach boosts GPT-5’s performance on VSI-Bench by 8% overall and more than 11% on tasks that rely heavily on spatial layout understanding. Qualitative studies further demonstrate that GR3D empowers MLLMs to perform complex spatial reasoning with highly sparse input views.
[CV-13] PRISM: Streaming Human Motion Generation with Per-Joint Latent Decomposition
【速读】：该论文旨在解决文本到动作生成（text-to-motion generation）中的两大核心挑战：一是现有运动自编码器将每一帧压缩为单一的稠密潜在向量，导致轨迹与关节旋转信息混杂，形成无结构表示，使得下游生成模型难以忠实建模；二是文本到动作、姿态条件生成及长时序序列合成通常依赖独立模型或任务特异性机制，且自回归方法在长序列推理中易出现误差累积。解决方案的关键在于提出PRISM框架：其一，设计了一种联合因子分解的运动潜在空间（joint-factorized motion latent space），每个身体关节占据独立token并构成二维时空网格（time-joints），通过因果变分自编码器（causal VAE）结合前向运动学监督进行压缩，显著提升生成质量；其二，引入无噪声条件注入机制（noise-free condition injection），每个潜在token携带时间嵌入，使条件帧可作为干净token直接注入（timestep0），其余token则进行去噪处理，从而统一文本驱动与姿态条件生成，并支持自回归片段链式拼接以实现流式合成。这两个关键创新共同构建了一个单一的基础运动生成模型，实现了多任务无缝切换与最优性能表现。
链接: https://arxiv.org/abs/2603.08590
作者: Zeyu Ling,Qing Shuai,Teng Zhang,Shiyang Li,Bo Han,Changqing Zou
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-to-motion generation has advanced rapidly, yet two challenges persist. First, existing motion autoencoders compress each frame into a single monolithic latent vector, entangling trajectory and per-joint rotations in an unstructured representation that downstream generators struggle to model faithfully. Second, text-to-motion, pose-conditioned generation, and long-horizon sequential synthesis typically require separate models or task-specific mechanisms, with autoregressive approaches suffering from severe error accumulation over extended rollouts. We present PRISM, addressing each challenge with a dedicated contribution. (1) A joint-factorized motion latent space: each body joint occupies its own token, forming a structured 2D grid (time joints) compressed by a causal VAE with forward-kinematics supervision. This simple change to the latent space – without modifying the generator – substantially improves generation quality, revealing that latent space design has been an underestimated bottleneck. (2) Noise-free condition injection: each latent token carries its own timestep embedding, allowing conditioning frames to be injected as clean tokens (timestep0) while the remaining tokens are denoised. This unifies text-to-motion and pose-conditioned generation in a single model, and directly enables autoregressive segment chaining for streaming synthesis. Self-forcing training further suppresses drift in long rollouts. With these two components, we train a single motion generation foundation model that seamlessly handles text-to-motion, pose-conditioned generation, autoregressive sequential generation, and narrative motion composition, achieving state-of-the-art on HumanML3D, MotionHub, BABEL, and a 50-scenario user study. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2603.08590 [cs.CV] (or arXiv:2603.08590v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.08590 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-14] CARE-Edit: Condition-Aware Routing of Experts for Contextual Image Editing CVPR2026
【速读】：该论文旨在解决统一扩散编辑模型中因使用固定共享主干网络而导致的任务干扰问题，尤其是在处理异构需求（如局部与全局、语义与图像特征）时出现的模态冲突，例如掩码边界处的颜色渗漏、身份或风格漂移以及多条件输入下的不可预测行为。解决方案的关键在于提出条件感知专家路由机制（Condition-Aware Routing of Experts, CARE-Edit），其核心是一个轻量级潜空间注意力路由器，根据多模态条件和扩散时间步动态分配编码后的扩散标记至四个专业化专家模块（Text、Mask、Reference 和 Base），并通过稀疏 top-K 选择实现计算资源的精准调度；同时引入掩码重绘（Mask Repaint）模块优化空间引导精度，并设计潜空间混合（Latent Mixture）模块融合各专家输出，从而在保持语义一致性的同时有效缓解多条件冲突，提升编辑任务的可控性与鲁棒性。
链接: https://arxiv.org/abs/2603.08589
作者: Yucheng Wang,Zedong Wang,Yuetong Wu,Yue Ma,Dan Xu
机构: The Hong Kong University of Science and Technology
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR 2026. Project page: this https URL
Abstract:Unified diffusion editors often rely on a fixed, shared backbone for diverse tasks, suffering from task interference and poor adaptation to heterogeneous demands (e.g., local vs global, semantic vs photometric). In particular, prevalent ControlNet and OmniControl variants combine multiple conditioning signals (e.g., text, mask, reference) via static concatenation or additive adapters which cannot dynamically prioritize or suppress conflicting modalities, thus resulting in artifacts like color bleeding across mask boundaries, identity or style drift, and unpredictable behavior under multi-condition inputs. To address this, we propose Condition-Aware Routing of Experts (CARE-Edit) that aligns model computation with specific editing competencies. At its core, a lightweight latent-attention router assigns encoded diffusion tokens to four specialized experts–Text, Mask, Reference, and Base–based on multi-modal conditions and diffusion timesteps: (i) a Mask Repaint module first refines coarse user-defined masks for precise spatial guidance; (ii) the router applies sparse top-K selection to dynamically allocate computation to the most relevant experts; (iii) a Latent Mixture module subsequently fuses expert outputs, coherently integrating semantic, spatial, and stylistic information to the base images. Experiments validate CARE-Edit’s strong performance on contextual editing tasks, including erasure, replacement, text-driven edits, and style transfer. Empirical analysis further reveals task-specific behavior of specialized experts, showcasing the importance of dynamic, condition-aware processing to mitigate multi-condition conflicts.
[CV-15] DualFlexKAN: Dual-stage Kolmogorov-Arnold Networks with Independent Function Control
【速读】：该论文旨在解决传统多层感知机（Multi-Layer Perceptrons, MLPs）因固定激活函数导致的静态归纳偏置问题，以及Kolmogorov-Arnold Networks（KANs）中存在的参数量二次增长和架构刚性限制标准正则化技术有效集成的问题。其解决方案的关键在于提出DualFlexKAN（DFKAN），这是一种具有双阶段机制的灵活架构，通过解耦输入端的预线性变换与输出端的后线性激活，实现对非线性表达能力与计算成本之间权衡的独立控制；同时支持多种基函数族（如正交多项式、B样条和径向基函数）及可配置的正则化策略，从而在显著减少参数量（较标准KANs低一到两个数量级）的同时保持KAN式的高表达能力，并提升训练稳定性与梯度保真度。
链接: https://arxiv.org/abs/2603.08583
作者: Andrés Ortiz,Nicolás J. Gallego-Molina,Carmen Jiménez-Mesa,Juan M. Górriz,Javier Ramírez
机构: University of Malaga (马拉加大学); DaSCI Andalusian Institute of Data Science and Computational Intelligence (安达卢西亚数据科学与计算智能研究所); University of Granada (格拉纳达大学)
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 22 pages, 12 figures
Abstract:Multi-Layer Perceptrons (MLPs) rely on pre-defined, fixed activation functions, imposing a static inductive bias that forces the network to approximate complex topologies solely through increased depth and width. Kolmogorov-Arnold Networks (KANs) address this limitation through edge-centric learnable functions, yet their formulation suffers from quadratic parameter scaling and architectural rigidity that hinders the effective integration of standard regularization techniques. This paper introduces the DualFlexKAN (DFKAN), a flexible architecture featuring a dual-stage mechanism that independently controls pre-linear input transformations and post-linear output activations. This decoupling enables hybrid networks that optimize the trade-off between expressiveness and computational cost. Unlike standard formulations, DFKAN supports diverse basis function families, including orthogonal polynomials, B-splines, and radial basis functions, integrated with configurable regularization strategies that stabilize training dynamics. Comprehensive evaluations across regression benchmarks, physics-informed tasks, and function approximation demonstrate that DFKAN outperforms both MLPs and conventional KANs in accuracy, convergence speed, and gradient fidelity. The proposed hybrid configurations achieve superior performance with one to two orders of magnitude fewer parameters than standard KANs, effectively mitigating the parameter explosion problem while preserving KAN-style expressiveness. DFKAN provides a principled, scalable framework for incorporating adaptive non-linearities, proving particularly advantageous for data-efficient learning and interpretable function discovery in scientific applications.
[CV-16] Online Sparse Synthetic Aperture Radar Imaging
【速读】：该论文旨在解决现代国防应用中低成本、自主无人机在执行任务时面临的计算与存储效率难题，尤其是在合成孔径雷达（SAR）成像场景下，如何在有限资源条件下实现高效在线重建。其核心挑战在于传统SAR图像重建需存储全部接收信号数据，导致内存占用高、难以部署于嵌入式平台。解决方案的关键是提出一种在线快速迭代收缩阈值算法（Online FISTA），通过稀疏编码实现增量式场景重建，无需保存全部原始数据，而是递归更新每轮迭代所需的存储矩阵，从而显著降低内存需求，并支持在线自动目标识别（ATR）等下游任务，构建更集成、灵活的实时处理框架。
链接: https://arxiv.org/abs/2603.08582
作者: Conor Flynn,Radoslav Ivanov,Birsen Yazici
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: IEEE Radar Conference 2026
Abstract:With modern defense applications increasingly relying on inexpensive, autonomous drones, lies the major challenge of designing computationally and memory-efficient onboard algorithms to fulfill mission objectives. This challenge is particularly significant in Synthetic Aperture Radar (SAR), where large volumes of data must be collected and processed for downstream tasks. We propose an online reconstruction method, the Online Fast Iterative Shrinkage-Thresholding Algorithm (Online FISTA), which incrementally reconstructs a scene with limited data through sparse coding. Rather than requiring storage of all received signal data, the algorithm recursively updates storage matrices for each iteration, greatly reducing memory demands. Online SAR image reconstruction facilitates more complex downstream tasks, such as Automatic Target Recognition (ATR), in an online manner, resulting in a more versatile and integrated framework compared to existing post-collection reconstruction and ATR approaches.
[CV-17] BioGait-VLM: A Tri-Modal Vision-Language-Biomechanics Framework for Interpretable Clinical Gait Assessment
【速读】：该论文旨在解决视频基临床步态分析（Video-based Clinical Gait Analysis）中模型因过度拟合环境偏差而导致泛化能力差的问题，即模型往往依赖视觉上的表面线索而非真正捕捉病理运动模式。解决方案的关键在于提出一种三模态视觉-语言-生物力学框架（BioGait-VLM），其核心创新包括：1）引入时间证据蒸馏分支（Temporal Evidence Distillation branch）以建模步态的节律动态；2）设计生物力学标记化分支（Biomechanical Tokenization branch），将3D骨骼序列映射为与语言对齐的语义标记（tokens），从而显式地基于关节力学进行推理，避免视觉捷径依赖。这一架构显著提升了模型在跨场景下的可解释性和临床合理性。
链接: https://arxiv.org/abs/2603.08564
作者: Erdong Chen,Yuyang Ji,Jacob K. Greenberg,Benjamin Steel,Faraz Arkam,Abigail Lewis,Pranay Singh,Feng Liu
机构: Drexel University (德雷塞尔大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Video-based Clinical Gait Analysis often suffers from poor generalization as models overfit environmental biases instead of capturing pathological motion. To address this, we propose BioGait-VLM, a tri-modal Vision-Language-Biomechanics framework for interpretable clinical gait assessment. Unlike standard video encoders, our architecture incorporates a Temporal Evidence Distillation branch to capture rhythmic dynamics and a Biomechanical Tokenization branch that projects 3D skeleton sequences into language-aligned semantic tokens. This enables the model to explicitly reason about joint mechanics independent of visual shortcuts. To ensure rigorous benchmarking, we augment the public GAVD dataset with a high-fidelity Degenerative Cervical Myelopathy (DCM) cohort to form a unified 8-class taxonomy, establishing a strict subject-disjoint protocol to prevent data leakage. Under this setting, BioGait-VLM achieves state-of-the-art recognition accuracy. Furthermore, a blinded expert study confirms that biomechanical tokens significantly improve clinical plausibility and evidence grounding, offering a path toward transparent, privacy-enhanced gait assessment.
[CV-18] Interactive World Simulator for Robot Policy Training and Evaluation
【速读】：该论文旨在解决现有动作条件视频预测模型（即世界模型）在机器人应用中存在推理速度慢、长期物理交互一致性差的问题，从而限制了其在大规模机器人策略训练与评估中的实用性。解决方案的关键在于提出交互式世界模拟器（Interactive World Simulator），该框架利用一致性模型（consistency models）分别用于图像解码和潜在空间动力学预测，实现了快速且稳定的物理交互模拟；实验表明，该方法可在单张RTX 4090 GPU上以15 FPS速率稳定生成超过10分钟的长时序物理交互预测，并支持基于模型生成数据训练出性能媲美真实数据的模仿策略，同时模拟与现实表现高度相关，验证了其作为可扩展数据生成与可靠策略评估代理的有效性。
链接: https://arxiv.org/abs/2603.08546
作者: Yixuan Wang,Rhythm Syed,Fangyu Wu,Mengchao Zhang,Aykut Onol,Jose Barreiros,Hooshang Nayyeri,Tony Dear,Huan Zhang,Yunzhu Li
机构: 未知
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Project Page: this https URL
Abstract:Action-conditioned video prediction models (often referred to as world models) have shown strong potential for robotics applications, but existing approaches are often slow and struggle to capture physically consistent interactions over long horizons, limiting their usefulness for scalable robot policy training and evaluation. We present Interactive World Simulator, a framework for building interactive world models from a moderate-sized robot interaction dataset. Our approach leverages consistency models for both image decoding and latent-space dynamics prediction, enabling fast and stable simulation of physical interactions. In our experiments, the learned world models produce interaction-consistent pixel-level predictions and support stable long-horizon interactions for more than 10 minutes at 15 FPS on a single RTX 4090 GPU. Our framework enables scalable demonstration collection solely within the world models to train state-of-the-art imitation policies. Through extensive real-world evaluation across diverse tasks involving rigid objects, deformable objects, object piles, and their interactions, we find that policies trained on world-model-generated data perform comparably to those trained on the same amount of real-world data. Additionally, we evaluate policies both within the world models and in the real world across diverse tasks, and observe a strong correlation between simulated and real-world performance. Together, these results establish the Interactive World Simulator as a stable and physically consistent surrogate for scalable robotic data generation and faithful, reproducible policy evaluation.
[CV-19] SWIFT: Sliding Window Reconstruction for Few-Shot Training-Free Generated Video Attribution
【速读】：该论文旨在解决生成式视频（Generative Video）的溯源问题，即在不依赖额外训练或复杂操作的前提下，准确识别视频内容的生成源，从而防范潜在的滥用风险。其解决方案的关键在于提出了一种“少样本无训练”（few-shot training-free）的视频溯源方法SWIFT，该方法通过利用视频帧在时间维度上的“像素帧（many）到潜在帧（one）”映射特性，采用固定长度滑动窗口对视频片段进行正常重建与损坏重建两种操作，基于两者损失差异构建归属信号，从而实现高效且无需训练的视频来源识别。
链接: https://arxiv.org/abs/2603.08536
作者: Chao Wang,Zijin Yang,Yaofei Wang,Yuang Qi,Weiming Zhang,Nenghai Yu,Kejiang Chen
机构: University of Science and Technology of China (中国科学技术大学); Hefei University of Technology (合肥工业大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advancements in video generation technologies have been significant, resulting in their widespread application across multiple domains. However, concerns have been mounting over the potential misuse of generated content. Tracing the origin of generated videos has become crucial to mitigate potential misuse and identify responsible parties. Existing video attribution methods require additional operations or the training of source attribution models, which may degrade video quality or necessitate large amounts of training samples. To address these challenges, we define for the first time the “few-shot training-free generated video attribution” task and propose SWIFT, which is tightly integrated with the temporal characteristics of the video. By leveraging the “Pixel Frames(many) to Latent Frame(one)” temporal mapping within each video chunk, SWIFT applies a fixed-length sliding window to perform two distinct reconstructions: normal and corrupted. The variation in the losses between two reconstructions is then used as an attribution signal. We conducted an extensive evaluation of five state-of-the-art (SOTA) video generation models. Experimental results show that SWIFT achieves over 90% average attribution accuracy with merely 20 video samples across all models and even enables zero-shot attribution for HunyuanVideo, EasyAnimate, and Wan2.2. Our source code is available at this https URL.
[CV-20] SecAgent : Efficient Mobile GUI Agent with Semantic Context
【速读】：该论文旨在解决移动图形用户界面（Mobile GUI）代理在多语言环境下的两个关键问题：一是高质量多语言数据集的稀缺性，尤其是非英语生态系统的数据不足；二是历史状态表示方法效率低下。其解决方案的关键在于提出SecAgent——一个3B参数规模的高效移动GUI代理，首先构建了一个经过人工验证的中文移动GUI数据集（包含18k标注样本和121k导航步骤，覆盖44个应用），并配套设计了一个多选动作标注的中文导航基准；在此基础上，引入语义上下文机制（semantic context mechanism），将历史截图与操作信息压缩为简洁自然语言摘要，在显著降低计算开销的同时保留任务相关性，从而实现高效且高性能的移动端自动化任务执行。
链接: https://arxiv.org/abs/2603.08533
作者: Yiping Xie,Song Chen,Jingxuan Xing,Wei Jiang,Zekun Zhu,Yingyao Wang,Pi Bu,Jun Song,Yuning Jiang,Bo Zheng
机构: Taobao Tmall Group of Alibaba (淘宝天猫集团)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Mobile Graphical User Interface (GUI) agents powered by multimodal large language models have demonstrated promising capabilities in automating complex smartphone tasks. However, existing approaches face two critical limitations: the scarcity of high-quality multilingual datasets, particularly for non-English ecosystems, and inefficient history representation methods. To address these challenges, we present SecAgent, an efficient mobile GUI agent at 3B scale. We first construct a human-verified Chinese mobile GUI dataset with 18k grounding samples and 121k navigation steps across 44 applications, along with a Chinese navigation benchmark featuring multi-choice action annotations. Building upon this dataset, we propose a semantic context mechanism that distills history screenshots and actions into concise, natural language summaries, significantly reducing computational costs while preserving task-relevant information. Through supervised and reinforcement fine-tuning, SecAgent outperforms similar-scale baselines and achieves performance comparable to 7B-8B models on our and public navigation benchmarks. We will open-source the training dataset, benchmark, model, and code to advance research in multilingual mobile GUI automation.
[CV-21] BuildMamba: A Visual State-Space Based Model for Multi-Task Building Segmentation and Height Estimation from Satellite Images
【速读】：该论文旨在解决从单视图RGB卫星图像中准确进行建筑分割与高度估计的问题，这是城市分析的基础任务，但长期以来因结构多样性及全局上下文建模计算成本高而难以实现。现有方法多基于单目深度估计架构，常出现边界模糊和高层建筑高度系统性低估的问题。其解决方案的关键在于提出BuildMamba框架，利用视觉状态空间模型（Visual State-Space Model）的线性时间全局建模能力，设计三个核心模块：Mamba注意力模块用于动态空间重校准，空间感知的Mamba-FPN通过门控状态空间扫描实现多尺度特征聚合，以及基于语义先验的掩码感知高度精修模块以抑制高度伪影。该方法在多个基准上显著提升性能，尤其在DFC23数据集上达到0.93 IoU和1.77 m RMSE，优于当前最先进方法。
链接: https://arxiv.org/abs/2603.08523
作者: Sinan U. Ulu,A. Enes Doruk,I. Can Yagmur,Bahadir K. Gunturk,Oguz Hanoglu,Hasan F. Ates
机构: Ozyegin University (奥泽京大学); University of Rochester (罗彻斯特大学); Istanbul Medipol University (伊斯坦布尔梅迪波尔大学); Huawei Turkey RD Center (华为土耳其研发中心)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate building segmentation and height estimation from single-view RGB satellite imagery are fundamental for urban analytics, yet remain ill-posed due to structural variability and the high computational cost of global context modeling. While current approaches typically adapt monocular depth architectures, they often suffer from boundary bleeding and systematic underestimation of high-rise structures. To address these limitations, we propose BuildMamba, a unified multi-task framework designed to exploit the linear-time global modeling of visual state-space models. Motivated by the need for stronger structural coupling and computational efficiency, we introduce three modules: a Mamba Attention Module for dynamic spatial recalibration, a Spatial-Aware Mamba-FPN for multi-scale feature aggregation via gated state-space scans, and a Mask-Aware Height Refinement module using semantic priors to suppress height artifacts. Extensive experiments demonstrate that BuildMamba establishes a new performance upper bound across three benchmarks. Specifically, it achieves an IoU of 0.93 and RMSE of 1.77~m on DFC23 benchmark, surpassing state-of-the-art by 0.82~m in height estimation. Simulation results confirm the model’s superior robustness and scalability for large-scale 3D urban reconstruction.
[CV-22] OccTrack360: 4D Panoptic Occupancy Tracking from Surround-View Fisheye Cameras
【速读】：该论文旨在解决4D全景占据追踪（4D panoptic occupancy tracking）中因缺乏支持环视鱼眼相机、长时序序列及实例级体素追踪的基准数据集而导致的研究进展受限问题。解决方案的关键在于提出一个名为OccTrack360的新基准，其包含更长且多样化的视频序列（174~2234帧），并提供基于全向遮挡掩码和基于MEI（Modified Equirectangular Image）的鱼眼视场掩码的结构化体素可见性标注；同时，为建立面向鱼眼成像的强基线模型，提出了FoSOcc框架，其核心创新包括：中心聚焦模块（Center Focusing Module, CFM）通过监督引导增强实例感知的空间定位能力，以及球面提升模块（Spherical Lift Module, SLM）在统一投影模型下将透视提升方法扩展至鱼眼图像，从而有效应对鱼眼图像的球面畸变与体素空间定位不准两大挑战。
链接: https://arxiv.org/abs/2603.08521
作者: Yongzhi Lin,Kai Luo,Yuanfan Zheng,Hao Shi,Mengfei Duan,Yang Liu,Kailun Yang
机构: Hunan University (湖南大学); Zhejiang University (浙江大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO); Image and Video Processing (eess.IV)
备注: The benchmark and source code will be made publicly available at this https URL
Abstract:Understanding dynamic 3D environments in a spatially continuous and temporally consistent manner is fundamental for robotics and autonomous driving. While recent advances in occupancy prediction provide a unified representation of scene geometry and semantics, progress in 4D panoptic occupancy tracking remains limited by the lack of benchmarks that support surround-view fisheye sensing, long temporal sequences, and instance-level voxel tracking. To address this gap, we present OccTrack360, a new benchmark for 4D panoptic occupancy tracking from surround-view fisheye cameras. OccTrack360 provides substantially longer and more diverse sequences (174~2234 frames) than prior benchmarks, together with principled voxel visibility annotations, including an all-direction occlusion mask and an MEI-based fisheye field-of-view mask. To establish a strong fisheye-oriented baseline, we further propose Focus on Sphere Occ (FoSOcc), a framework that addresses two core challenges in fisheye occupancy tracking: distorted spherical projection and inaccurate voxel-space localization. FoSOcc includes a Center Focusing Module (CFM) to enhance instance-aware spatial localization through supervised focus guidance, and a Spherical Lift Module (SLM) that extends perspective lifting to fisheye imaging under the Unified Projection Model. Extensive experiments on Occ3D-Waymo and OccTrack360 show that our method improves occupancy tracking quality with notable gains on geometrically regular categories, and establishes a strong baseline for future research on surround-view fisheye 4D occupancy tracking. The benchmark and source code will be made publicly available at this https URL.
[CV-23] Beyond Hungarian: Match-Free Supervision for End-to-End Object Detection
【速读】：该论文旨在解决基于DETR（Detection Transformer）框架在目标检测任务中依赖匈牙利算法进行查询与真实标注之间二分图匹配所带来的计算开销大、训练动态复杂的问题。解决方案的关键在于提出一种无需显式匹配的训练机制，其核心是一个基于交叉注意力的查询选择模块（Cross-Attention-based Query Selection, CAQS）。该模块利用编码后的真实标注信息通过交叉注意力机制探测解码器查询，以加权误差最小化的方式自动学习查询与目标之间的隐式对应关系，从而替代传统的离散匹配过程，并通过可微分的对应学习有效消除匹配瓶颈，同时提升模型性能和训练效率。
链接: https://arxiv.org/abs/2603.08514
作者: Shoumeng Qiu,Xinrun Li,Yang Long
机构: BOSCH; Durham University
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Recent DEtection TRansformer (DETR) based frameworks have achieved remarkable success in end-to-end object detection. However, the reliance on the Hungarian algorithm for bipartite matching between queries and ground truths introduces computational overhead and complicates the training dynamics. In this paper, we propose a novel matching-free training scheme for DETR-based detectors that eliminates the need for explicit heuristic matching. At the core of our approach is a dedicated Cross-Attention-based Query Selection (CAQS) module. Instead of discrete assignment, we utilize encoded ground-truth information to probe the decoder queries through a cross-attention mechanism. By minimizing the weighted error between the queried results and the ground truths, the model autonomously learns the implicit correspondences between object queries and specific targets. This learned relationship further provides supervision signals for the learning of queries. Experimental results demonstrate that our proposed method bypasses the traditional matching process, significantly enhancing training efficiency, reducing the matching latency by over 50%, effectively eliminating the discrete matching bottleneck through differentiable correspondence learning, and also achieving superior performance compared to existing state-of-the-art methods.
[CV-24] Spherical-GOF: Geometry-Aware Panoramic Gaussian Opacity Fields for 3D Scene Reconstruction
【速读】：该论文旨在解决将3D高斯溅射（3D Gaussian Splatting, 3DGS）扩展至全景相机模型时面临的几何不一致性和畸变问题，因为现有方法基于透视投影设计，直接适配会导致渲染失真。其核心解决方案是提出Spherical-GOF框架，该框架基于高斯不透明度场（Gaussian Opacity Fields, GOF），通过在球面射线空间（spherical ray space）中的单位球面上直接进行射线采样，实现全景渲染下射线与高斯体素的一致交互；关键创新在于推导出一种保守的球面包围盒规则用于高效射线-高斯剔除，并引入球面滤波机制以适应全景像素采样中变化的畸变，从而显著提升几何一致性与视觉质量。
链接: https://arxiv.org/abs/2603.08503
作者: Zhe Yang,Guoqiang Zhao,Sheng Wu,Kai Luo,Kailun Yang
机构: Hunan University (湖南大学); State Key Laboratory of Autonomous Intelligent Unmanned Systems (自主智能无人系统国家重点实验室); National Engineering Research Center of Robot Visual Perception and Control Technology (机器人视觉感知与控制技术国家工程研究中心)
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Robotics (cs.RO); Image and Video Processing (eess.IV)
备注: The source code and dataset will be released at this https URL
Abstract:Omnidirectional images are increasingly used in robotics and vision due to their wide field of view. However, extending 3D Gaussian Splatting (3DGS) to panoramic camera models remains challenging, as existing formulations are designed for perspective projections and naive adaptations often introduce distortion and geometric inconsistencies. We present Spherical-GOF, an omnidirectional Gaussian rendering framework built upon Gaussian Opacity Fields (GOF). Unlike projection-based rasterization, Spherical-GOF performs GOF ray sampling directly on the unit sphere in spherical ray space, enabling consistent ray-Gaussian interactions for panoramic rendering. To make the spherical ray casting efficient and robust, we derive a conservative spherical bounding rule for fast ray-Gaussian culling and introduce a spherical filtering scheme that adapts Gaussian footprints to distortion-varying panoramic pixel sampling. Extensive experiments on standard panoramic benchmarks (OmniBlender and OmniPhotos) demonstrate competitive photometric quality and substantially improved geometric consistency. Compared with the strongest baseline, Spherical-GOF reduces depth reprojection error by 57% and improves cycle inlier ratio by 21%. Qualitative results show cleaner depth and more coherent normal maps, with strong robustness to global panorama rotations. We further validate generalization on OmniRob, a real-world robotic omnidirectional dataset introduced in this work, featuring UAV and quadruped platforms. The source code and the OmniRob dataset will be released at this https URL.
[CV-25] Improving Continual Learning for Gaussian Splatting based Environments Reconstruction on Commercial Off-the-Shelf Edge Devices
【速读】：该论文旨在解决边缘机器人场景下新型视图合成（Novel View Synthesis, NVS）对紧凑且可增量更新的3D场景模型的需求，特别是在内存和延迟预算受限的情况下，如何实现变分贝叶斯高斯点绘（Variational Bayesian Gaussian Splatting, VBGS）算法的设备端训练问题。VBGS虽能实现无回放的持续更新，但其高精度计算和大中间张量导致在资源受限硬件上无法进行训练。解决方案的关键在于提出一种精度自适应优化框架：首先对VBGS进行性能剖析以识别内存与延迟热点；其次通过融合内存密集型核函数减少中间张量的显式存储；最后基于有界相对误差的混合精度搜索自动分配操作级精度。该方法在不改变VBGS变分公式前提下显著降低峰值内存（从9.44 GB降至1.11 GB）并加速训练时间（从约234分钟降至约61分钟），同时保持甚至提升重建质量，并首次实现了在Jetson Orin Nano嵌入式平台上的NVS训练，帧级延迟降低19倍。
链接: https://arxiv.org/abs/2603.08499
作者: Ivan Zaino,Matteo Risso,Daniele Jahier Pagliari,Miguel de Prado,Toon Van de Maele,Alessio Burrello
机构: Politecnico di Torino(都灵理工大学); VERSES
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Novel view synthesis (NVS) is increasingly relevant for edge robotics, where compact and incrementally updatable 3D scene models are needed for SLAM, navigation, and inspection under tight memory and latency budgets. Variational Bayesian Gaussian Splatting (VBGS) enables replay-free continual updates for the 3DGS algorithm by maintaining a probabilistic scene model, but its high-precision computations and large intermediate tensors make on-device training impractical. We present a precision-adaptive optimization framework that enables VBGS training on resource-constrained hardware without altering its variational formulation. We (i) profile VBGS to identify memory/latency hotspots, (ii) fuse memory-dominant kernels to reduce materialized intermediate tensors, and (iii) automatically assign operation-level precisions via a mixed-precision search with bounded relative error. Across the Blender, Habitat, and Replica datasets, our optimised pipeline reduces peak memory from 9.44 GB to 1.11 GB and training time from ~234 min to ~61 min on an A5000 GPU, while preserving (and in some cases improving) reconstruction quality of the state-of-the-art VBGS baseline. We also enable for the first time NVS training on a commercial embedded platform, the Jetson Orin Nano, reducing per-frame latency by 19x compared to 3DGS.
[CV-26] All Vehicles Can Lie: Efficient Adversarial Defense in Fully Untrusted-Vehicle Collaborative Perception via Pseudo-Random Bayesian Inference CVPR2026
【速读】：该论文旨在解决协同感知（Collaborative Perception, CP）在完全不可信车辆环境下的安全性问题，即如何有效防御针对特征级感知数据交换的对抗攻击。现有防御方法通常依赖可信的自车（ego vehicle）作为参考或引入额外的二分类器，但在实际部署中受限于自车可信度存疑、实时检测需求及跨场景泛化能力不足等问题。其解决方案的关键在于提出一种新颖的伪随机贝叶斯推断（Pseudo-Random Bayesian Inference, PRBI）框架：通过利用前一帧可靠感知结果作为动态参考来检测时序感知差异，同时采用仅需每帧两次验证的伪随机分组策略，并结合贝叶斯推理估计恶意车辆的数量与身份，从而实现高效且鲁棒的对抗行为识别。理论分析证明了PRBI的收敛性与稳定性，实验表明其平均仅需2.5次验证/帧即可将检测精度恢复至攻击前水平的79.4%–86.9%。
链接: https://arxiv.org/abs/2603.08498
作者: Yi Yu,Libing Wu,Zhuangzhuang Zhang,Jing Qiu,Lijuan Huo,Jiaqi Feng
机构: Wuhan University (武汉大学); Guangzhou University (广州大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR 2026
Abstract:Collaborative perception (CP) enables multiple vehicles to augment their individual perception capacities through the exchange of feature-level sensory data. However, this fusion mechanism is inherently vulnerable to adversarial attacks, especially in fully untrusted-vehicle environments. Existing defense approaches often assume a trusted ego vehicle as a reference or incorporate additional binary classifiers. These assumptions limit their practicality in real-world deployments due to the questionable trustworthiness of ego vehicles, the requirement for real-time detection, and the need for generalizability across diverse scenarios. To address these challenges, we propose a novel Pseudo-Random Bayesian Inference (PRBI) framework, a first efficient defense method tailored for fully untrusted-vehicle CP. PRBI detects adversarial behavior by leveraging temporal perceptual discrepancies, using the reliable perception from the preceding frame as a dynamic reference. Additionally, it employs a pseudo-random grouping strategy that requires only two verifications per frame, while applying Bayesian inference to estimate both the number and identities of malicious vehicles. Theoretical analysis has proven the convergence and stability of the proposed PRBI framework. Extensive experiments show that PRBI requires only 2.5 verifications per frame on average, outperforming existing methods significantly, and restores detection precision to between 79.4% and 86.9% of pre-attack levels.
[CV-27] Reading neq Seeing: Diagnosing and Closing the Typography Gap in Vision-Language Models
【速读】：该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在文本识别任务中存在“字体感知鸿沟”（typographic gap）的问题，即模型能够准确识别图像中文本的内容（what text says），但对字体的家族、大小、样式和颜色等视觉属性（how it looks）却表现不佳。研究通过系统评估15个主流VLMs在26种字体、4种文字脚本和3种难度等级下的字体属性识别能力，发现模型在颜色识别上接近完美，而字体样式识别则普遍表现差，且模型规模与性能无关，不同难度下准确率一致，表明问题源于训练数据缺失而非模型容量限制。解决方案的关键在于：利用少量合成样本进行LoRA微调（Low-Rank Adaptation fine-tuning），可显著提升开源模型在字体大小识别上的性能，甚至超越最优闭源系统；然而字体样式识别仍难以通过微调改善，提示当前基于patch的编码架构可能需要引入新的结构设计以支持关系性视觉推理（relational visual reasoning）。
链接: https://arxiv.org/abs/2603.08497
作者: Heng Zhou,Ao Yu,Li Kang,Yuchen Fan,Yutao Fan,Xiufeng Song,Hejia Geng,Yiran Qin
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Vision-Language Models achieve near-perfect accuracy at reading text in images, yet prove largely typography-blind: capable of recognizing what text says, but not how it looks. We systematically investigate this gap by evaluating font family, size, style, and color recognition across 26 fonts, four scripts, and three difficulty levels. Our evaluation of 15 state-of-the-art VLMs reveals a striking perception hierarchy: color recognition is near-perfect, yet font style detection remains universally poor. We further find that model scale fails to predict performance and that accuracy is uniform across difficulty levels, together pointing to a training-data omission rather than a capacity ceiling. LoRA fine-tuning on a small set of synthetic samples substantially improves an open-source model, narrowing the gap to the best closed-source system and surpassing it on font size recognition. Font style alone remains resistant to fine-tuning, suggesting that relational visual reasoning may require architectural innovation beyond current patch-based encoders. We release our evaluation framework, data, and fine-tuning recipe to support progress in closing the typographic gap in vision-language understanding.
[CV-28] Global Cross-Modal Geo-Localization: A Million-Scale Dataset and a Physical Consistency Learning Framework
【速读】：该论文旨在解决跨模态地理定位（Cross-modal Geo-localization, CMGL）中因地理覆盖范围狭窄和场景多样性不足而导致的全球空间异质性难以体现的问题，从而实现更通用、鲁棒的定位能力。其解决方案的关键在于两个方面：一是构建了首个百万级全球尺度的CMGL数据集CORE，包含来自全球225个不同地理区域的103万张跨视角图像，显著提升了场景多样性与环境复杂度；二是提出了一种物理规律感知网络（PLANET），通过引入一种新颖的对比学习范式，引导文本表示捕捉卫星图像中的内在物理特征签名，从而增强跨模态对齐精度。实验表明，PLANET在多地域场景下显著优于现有最先进方法，为全球尺度的鲁棒地理定位建立了新基准。
链接: https://arxiv.org/abs/2603.08491
作者: Yutong Hu,Jinhui Chen,Chaoqiang Xu,Yuan Kou,Sili Zhou,Shaocheng Yan,Pengcheng Shi,Qingwu Hu,Jiayuan Li
机构: Wuhan University (武汉大学); First Surveying and Mapping Institute of Hunan province (湖南省第一测绘院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Cross-modal Geo-localization (CMGL) matches ground-level text descriptions with geo-tagged aerial imagery, which is crucial for pedestrian navigation and emergency response. However, existing researches are constrained by narrow geographic coverage and simplistic scene diversity, failing to reflect the immense spatial heterogeneity of global architectural styles and topographic features. To bridge this gap and facilitate universal positioning, we introduce CORE, the first million-scale dataset dedicated to global CMGL. CORE comprises 1,034,786 cross-view images sampled from 225 distinct geographic regions across all continents, offering an unprecedented variety of perspectives in varying environmental conditions and urban layouts. We leverage the zero-shot reasoning of Large Vision-Language Models (LVLMs) to synthesize high-quality scene descriptions rich in discriminative cues. Furthermore, we propose a physical-law-aware network (PLANET) for cross-modal geo-localization. PLANET introduces a novel contrastive learning paradigm to guide textual representations in capturing the intrinsic physical signatures of satellite imagery. Extensive experiments across varied geographic regions demonstrate that PLANet significantly outperforms state-of-the-art methods, establishing a new benchmark for robust, global-scale geo-localization. The dataset and source code will be released at this https URL.
[CV-29] Visual Self-Fulfilling Alignment: Shaping Safety-Oriented Personas via Threat-Related Images
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）中存在的安全错位（safety misalignment）问题，即视觉输入可能诱发有害输出。现有方法通常依赖显式的安全标签或对比数据，但这类方法难以处理抽象的安全概念（如“助人”），因为它们缺乏视觉参照。解决方案的关键在于提出视觉自实现对齐（Visual Self-Fulfilling Alignment, VSFA），通过在围绕威胁相关图像构建的中性视觉问答（VQA）任务上微调视觉语言模型（Vision-Language Models, VLMs），无需任何安全标签即可使模型通过反复接触威胁相关视觉内容，内化警惕与谨慎的隐含语义，从而塑造出以安全为导向的行为模式。实验表明，VSFA可有效降低攻击成功率、提升响应质量，并缓解过度拒绝问题，同时保持模型的通用能力。
链接: https://arxiv.org/abs/2603.08486
作者: Qishun Yang,Shu Yang,Lijie Hu,Di Wang
机构: King Abdullah University of Science and Technology (阿卜杜拉国王科技大学); Mohamed bin Zayed University of Artificial Intelligence (穆罕默德·本·扎耶德人工智能大学); China University of Petroleum-Beijing at Karamay (北京石油大学克拉玛依校区)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal large language models (MLLMs) face safety misalignment, where visual inputs enable harmful outputs. To address this, existing methods require explicit safety labels or contrastive data; yet, threat-related concepts are concrete and visually depictable, while safety concepts, like helpfulness, are abstract and lack visual referents. Inspired by the Self-Fulfilling mechanism underlying emergent misalignment, we propose Visual Self-Fulfilling Alignment (VSFA). VSFA fine-tunes vision-language models (VLMs) on neutral VQA tasks constructed around threat-related images, without any safety labels. Through repeated exposure to threat-related visual content, models internalize the implicit semantics of vigilance and caution, shaping safety-oriented personas. Experiments across multiple VLMs and safety benchmarks demonstrate that VSFA reduces the attack success rate, improves response quality, and mitigates over-refusal while preserving general capabilities. Our work extends the self-fulfilling mechanism from text to visual modalities, offering a label-free approach to VLMs alignment.
[CV-30] X-AVDT: Audio-Visual Cross-Attention for Robust Deepfake Detection
【速读】：该论文旨在解决当前生成式 AI（Generative AI）模型生成的高保真合成视频（deepfake）日益增多所带来的恶意滥用风险，以及现有检测方法在面对新型生成器时泛化能力不足的问题。其解决方案的关键在于从生成器内部视角出发，利用扩散模型中的交叉注意力机制（cross-attention mechanism）所隐含的音频-视觉细粒度对齐信息作为伪造检测线索；具体而言，提出 X-AVDT 检测框架，通过 DDIM 反演获取生成器内部的多模态信号，提取两类互补特征：(i) 由反演诱导的视频复合差异图，(ii) 反映生成过程中强制执行的音频-视觉对齐关系的交叉注意力特征，从而实现对多种生成范式（包括 GAN、扩散模型和流匹配）的鲁棒且可泛化的深度伪造检测。
链接: https://arxiv.org/abs/2603.08483
作者: Youngseo Kim,Kwan Yun,Seokhyeon Hong,Sihun Cha,Colette Suhjung Koo,Junyong Noh
机构: KAIST
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The surge of highly realistic synthetic videos produced by contemporary generative systems has significantly increased the risk of malicious use, challenging both humans and existing detectors. Against this backdrop, we take a generator-side view and observe that internal cross-attention mechanisms in these models encode fine-grained speech-motion alignment, offering useful correspondence cues for forgery detection. Building on this insight, we propose X-AVDT, a robust and generalizable deepfake detector that probes generator-internal audio-visual signals accessed via DDIM inversion to expose these cues. X-AVDT extracts two complementary signals: (i) a video composite capturing inversion-induced discrepancies, and (ii) an audio-visual cross-attention feature reflecting modality alignment enforced during generation. To enable faithful cross-generator evaluation, we further introduce MMDF, a new multimodal deepfake dataset spanning diverse manipulation types and rapidly evolving synthesis paradigms, including GANs, diffusion, and flow-matching. Extensive experiments demonstrate that X-AVDT achieves leading performance on MMDF and generalizes strongly to external benchmarks and unseen generators, outperforming existing methods with accuracy improved by 13.1%. Our findings highlight the importance of leveraging internal audio-visual consistency cues for robustness to future generators in deepfake detection.
[CV-31] Alfa: Attentive Low-Rank Filter Adaptation for Structure-Aware Cross-Domain Personalized Gaze Estimation AAAI2026
【速读】：该论文旨在解决预训练注视模型在面对用户特定差异（如眼睑形状或面部结构）时性能下降的问题，尤其是在测试阶段通过少量未标注样本进行个性化适应（Test-time Personalization, TTP）的场景下，如何高效地完成域适应。其核心挑战在于有限的数据与计算资源条件下，如何有效利用预训练模型中已编码的结构信息以实现精准个性化。解决方案的关键在于将个性化过程重新定义为对预训练滤波器中已有语义特征的重加权，而非学习全新特征；具体方法为提出Attentive Low-Rank Filter Adaptation (Alfa)，该方法基于奇异值分解（SVD）提取跨用户共有的空间主成分（如眼睛和面部特征），并引入注意力机制，仅用少量未标注样本即可选择性地增强目标用户相关的特征权重，从而显著提升跨数据集的注视估计精度，优于现有TTP及低秩适配（LoRA）方法。
链接: https://arxiv.org/abs/2603.08445
作者: He-Yen Hsieh,Wei-Te Mark Ting,H.T. Kung
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 21 pages, 16 figures, AAAI2026
Abstract:Pre-trained gaze models learn to identify useful patterns commonly found across users, but subtle user-specific variations (i.e., eyelid shape or facial structure) can degrade model performance. Test-time personalization (TTP) adapts pre-trained models to these user-specific domain shifts using only a few unlabeled samples. Efficient fine-tuning is critical in performing this domain adaptation: data and computation resources can be limited-especially for on-device customization. While popular parameter-efficient fine-tuning (PEFT) methods address adaptation costs by updating only a small set of weights, they may not be taking full advantage of structures encoded in pre-trained filters. To more effectively leverage existing structures learned during pre-training, we reframe personalization as a process to reweight existing features rather than learning entirely new ones. We present Attentive Low-Rank Filter Adaptation (Alfa) to adapt gaze models by reweighting semantic patterns in pre-trained filters. With Alfa, singular value decomposition (SVD) extracts dominant spatial components that capture eye and facial characteristics across users. Via an attention mechanism, we need only a few unlabeled samples to adjust and reweight pre-trained structures, selectively amplifying those relevant to a target user. Alfa achieves the lowest average gaze errors across four cross-dataset gaze benchmarks, outperforming existing TTP methods and low-rank adaptation (LoRA)-based variants. We also show that Alfa’s attentive low-rank methods can be applied to applications beyond vision, such as diffusion-based language models. Comments: 21 pages, 16 figures, AAAI2026 Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2603.08445 [cs.CV] (or arXiv:2603.08445v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.08445 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-32] Information Maximization for Long-Tailed Semi-Supervised Domain Generalization
【速读】：该论文旨在解决半监督域泛化（Semi-supervised Domain Generalization, SSDG）在长尾类别分布场景下的性能下降问题，这在现实世界中尤为常见。现有方法在标签数据稀缺且类别分布不均衡时表现不佳，主要原因在于标准互信息（Mutual Information, MI）估计中边际熵项对类别平衡的依赖性导致了类不平衡偏差。解决方案的关键在于提出IMaX，一种基于InfoMax原理改进的目标函数，其通过最大化特征与潜在标签之间的MI来增强表示学习，同时利用标签样本的监督约束优化过程；特别地，IMaX引入了一个α-熵目标项以替代传统MI中的边际熵项，从而有效缓解类不平衡带来的偏差，使模型能够更好地适应任意类别分布。该方法可无缝集成到当前最先进的SSDG框架中，并在两种不同图像模态上均实现了稳定性能提升。
链接: https://arxiv.org/abs/2603.08434
作者: Leo Fillioux,Omprakash Chakraborty,Quentin Gopée,Pierre Marza,Paul-Henry Cournède,Stergios Christodoulidis,Maria Vakalopoulou,Ismail Ben Ayed,Jose Dolz
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Semi-supervised domain generalization (SSDG) has recently emerged as an appealing alternative to tackle domain generalization when labeled data is scarce but unlabeled samples across domains are abundant. In this work, we identify an important limitation that hampers the deployment of state-of-the-art methods on more challenging but practical scenarios. In particular, state-of-the-art SSDG severely suffers in the presence of long-tailed class distributions, an arguably common situation in real-world settings. To alleviate this limitation, we propose IMaX, a simple yet effective objective based on the well-known InfoMax principle adapted to the SSDG scenario, where the Mutual Information (MI) between the learned features and latent labels is maximized, constrained by the supervision from the labeled samples. Our formulation integrates an \alpha-entropic objective, which mitigates the class-balance bias encoded in the standard marginal entropy term of the MI, thereby better handling arbitrary class distributions. IMaX can be seamlessly plugged into recent state-of-the-art SSDG, consistently enhancing their performance, as demonstrated empirically across two different image modalities.
[CV-33] Grow Assess Compress: Adaptive Backbone Scaling for Memory-Efficient Class Incremental Learning
【速读】：该论文旨在解决类增量学习（Class Incremental Learning, CIL）中模型在保持学习新任务的可塑性（plasticity）与防止灾难性遗忘（catastrophic forgetting）之间的稳定性（stability）之间难以平衡的问题。传统基于扩展的方法虽能缓解遗忘，但会导致架构无控制地增长和内存开销剧增。其解决方案的关键在于提出一种动态缩放框架——GRACE（GRow, Assess, ComprEss），通过循环式的“生长、评估、压缩”策略自适应管理模型容量：特别引入了饱和度评估阶段（saturation assessment phase），用于量化模型容量利用率，从而智能决策是否扩展或压缩骨干网络（backbone），有效避免参数爆炸，同时在多个CIL基准上实现最优性能，并将内存占用减少高达73%。
链接: https://arxiv.org/abs/2603.08426
作者: Adrian Garcia-Castañeda,Jon Irureta,Jon Imaz,Aizea Lojo
机构: 未知
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Class Incremental Learning (CIL) poses a fundamental challenge: maintaining a balance between the plasticity required to learn new tasks and the stability needed to prevent catastrophic forgetting. While expansion-based methods effectively mitigate forgetting by adding task-specific parameters, they suffer from uncontrolled architectural growth and memory overhead. In this paper, we propose a novel dynamic scaling framework that adaptively manages model capacity through a cyclic “GRow, Assess, ComprEss” (GRACE) strategy. Crucially, we supplement backbone expansion with a novel saturation assessment phase that evaluates the utilization of the model’s capacity. This assessment allows the framework to make informed decisions to either expand the architecture or compress the backbones into a streamlined representation, preventing parameter explosion. Experimental results demonstrate that our approach achieves state-of-the-art performance across multiple CIL benchmarks, while reducing memory footprint by up to a 73% compared to purely expansionist models.
[CV-34] SPIRAL: A Closed-Loop Framework for Self-Improving Action World Models via Reflective Planning Agents
【速读】：该论文旨在解决现有单次（one-shot）视频生成模型在长时程视频生成中面临的动作执行不完整、语义锚定弱以及时间漂移等问题（即open-loop架构的局限性）。其核心解决方案是提出SPIRAL框架，该框架采用闭环式的“思考-行动-反思”（think-act-reflect）机制，通过PlanAgent将高层语义动作分解为以物体为中心的子动作，并由CriticAgent基于长期记忆评估中间结果并引导迭代优化，从而实现可控的长时程视频生成。这一设计天然支持强化学习（RL）驱动的持续优化，在提升语义对齐性和时间一致性方面表现出显著优势。
链接: https://arxiv.org/abs/2603.08403
作者: Yu Yang,Yue Liao,Jianbiao Mei,Baisen Wang,Xuemeng Yang,Licheng Wen,Jiangning Zhang,Xiangtai Li,Hanlin Chen,Botian Shi,Yong Liu,Shuicheng Yan,Gim Hee Lee
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 22 Pages, 11 Figures
Abstract:We introduce SPIRAL, a self-improving planning and iterative reflective action world modeling closed-loop framework that enables controllable long-horizon video generation conditioned on high-level semantic actions. Existing one-shot video generation models operate in open-loop, often resulting in incomplete action execution, weak semantic grounding, and temporal drift. SPIRAL formulates ActWM as a closed-loop think-act-reflect process, where generation proceeds step by step under explicit planning and feedback. A PlanAgent decomposes abstract actions into object-centric sub-actions, while a CriticAgent evaluates intermediate results and guides iterative refinement with long-horizon memory. This closed-loop design naturally supports RL evolving optimization, improving semantic alignment and temporal consistency over extended horizons. We further introduce the ActWM-Dataset and ActWM-Bench for training and evaluation. Experiments across multiple TI2V backbones demonstrate consistent gains on ActWM-Bench and mainstream video generation benchmarks, validating SPIRAL’s effectiveness.
[CV-35] StructBiHOI: Structured Articulation Modeling for Long–Horizon Bimanual Hand–Object Interaction Generation
【速读】：该论文旨在解决长时序双臂交互（bimanual hand–object interaction, HOI）生成中的稳定性、物理合理性与语义一致性难题，尤其针对多模态条件下双臂协同操作的复杂性。现有方法在长时间序列中难以同时保证动作时序一致性、物理可实现性和语义对齐。其解决方案的关键在于提出一种结构化的分层建模框架 StructBiHOI：通过将长期关节规划（temporal joint planning）与帧级操作精修（frame-level manipulation refinement）解耦，分别由 jointVAE 和 maniVAE 实现；并引入基于 Mamba 的状态空间启发式扩散去噪器，以线性复杂度建模长程依赖关系，从而显著提升双臂协调的一致性与生成效率。
链接: https://arxiv.org/abs/2603.08390
作者: Zhi Wang,Liu Liu,Ruonan Liu,Dan Guo,Meng Wang
机构: 未知
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent progress in 3D hand–object interaction (HOI) generation has primarily focused on single–hand grasp synthesis, while bimanual manipulation remains significantly more challenging. Long–horizon planning instability, fine–grained joint articulation, and complex cross–hand coordination make coherent bimanual generation difficult, especially under multimodal conditions. Existing approaches often struggle to simultaneously ensure temporal consistency, physical plausibility, and semantic alignment over extended sequences. We propose StructBiHOI, a Structured articulation modeling framework for long-horizon Bimanual HOI generation. Our key insight is to structurally disentangle temporal joint planning from frame–level manipulation refinement. Specifically, a jointVAE models long-term joint evolution conditioned on object geometry and task semantics, while a maniVAE refines fine-grained hand poses at the single–frame level. To enable stable and efficient long–sequence generation, we incorporate a state–space–inspired diffusion denoiser based on Mamba, which models long–range dependencies with linear complexity. This hierarchical design facilitates coherent dual-hand coordination and articulated object interaction. Extensive experiments on bimanual manipulation and single-hand grasping benchmarks demonstrate that our method achieves superior long–horizon stability, motion realism, and computational efficiency compared to strong baselines.
[CV-36] AULLM : Structural Reasoning with Large Language Models for Micro-Expression Recognition
【速读】：该论文旨在解决微表情动作单元（Micro-expression Action Unit, AU）检测中存在的三大问题：一是依赖低密度视觉信息导致判别性特征易受背景噪声干扰；二是特征处理粒度粗略，难以满足细粒度表征需求；三是忽视动作单元间的相互关系，限制了复杂表情模式的解析能力。解决方案的关键在于提出一个基于大语言模型（Large Language Models, LLMs）的推理导向框架AULLM++，通过将视觉特征注入文本提示作为可操作语义前提来引导推理过程，并将AU预测分解为证据构建、结构建模和基于推理的预测三个阶段：首先利用多粒度增强融合投影器（Multi-Granularity Evidence-Enhanced Fusion Projector, MGE-EFP）融合中层纹理与高层语义信息生成紧凑的内容标记（Content Token, CT）；其次引入关系感知AU图神经网络（Relation-Aware AU Graph Neural Network, R-AUGNN）编码AU间稀疏结构先验并学习交互强度，生成指令标记（Instruction Token, IT）；最后融合CT与IT构成结构化文本提示，并采用反事实一致性正则化（Counterfactual Consistency Regularization, CCR）构造反事实样本以提升模型泛化能力。
链接: https://arxiv.org/abs/2603.08387
作者: Zhishu Liu,Kaishen Yuan,Bo Zhao,Hui Ma,Zitong Yu
机构: Great Bay University (大湾区大学); Tsinghua University (清华大学); The Hong Kong University of Science and Technology (广州)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Micro-expression Action Unit (AU) detection identifies localized AUs from subtle facial muscle activations, providing a foundation for decoding affective cues. Previous methods face three key limitations: (1) heavy reliance on low-density visual information, rendering discriminative evidence vulnerable to background noise; (2) coarse-grained feature processing that misaligns with the demand for fine-grained representations; and (3) neglect of inter-AU correlations, restricting the parsing of complex expression patterns. We propose AULLM++, a reasoning-oriented framework leveraging Large Language Models (LLMs), which injects visual features into textual prompts as actionable semantic premises to guide inference. It formulates AU prediction into three stages: evidence construction, structure modeling, and deduction-based prediction. Specifically, a Multi-Granularity Evidence-Enhanced Fusion Projector (MGE-EFP) fuses mid-level texture cues with high-level semantics, distilling them into a compact Content Token (CT). Furthermore, inspired by micro- and macro-expression AU correspondence, we encode AU relationships as a sparse structural prior and learn interaction strengths via a Relation-Aware AU Graph Neural Network (R-AUGNN), producing an Instruction Token (IT). We then fuse CT and IT into a structured textual prompt and introduce Counterfactual Consistency Regularization (CCR) to construct counterfactual samples, enhancing the model’s generalization. Extensive experiments demonstrate AULLM++ achieves state-of-the-art performance on standard benchmarks and exhibits superior cross-domain generalization.
[CV-37] Real-Time Drone Detection in Event Cameras via Per-Pixel Frequency Analysis
【速读】：该论文旨在解决从事件相机（event camera）数据中检测高速移动目标（如无人机，UAV）的难题，其核心挑战在于事件数据具有稀疏性和异步性，传统基于均匀采样假设的离散傅里叶变换（Discrete Fourier Transform, DFT）难以有效处理此类数据。解决方案的关键在于提出一种基于非均匀离散傅里叶变换（Non-uniform Discrete Fourier Transform, NDFT）的逐像素时域分析框架，称为通过谐波指纹识别无人机（Drone Detection via Harmonic Fingerprinting, DDHF）。该方法利用纯粹的解析技术识别无人机旋翼在功率谱中形成的频率梳（frequency comb）特征，从而实现可调且泛化能力强的实时定位算法，相较YOLO等深度学习方法，在准确率和延迟上均取得显著提升。
链接: https://arxiv.org/abs/2603.08386
作者: Michael Bezick,Majid Sahin
机构: Johns Hopkins University APL (约翰霍普金斯大学应用物理实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Detecting fast-moving objects, such as unmanned aerial vehicle (UAV), from event camera data is challenging due to the sparse, asynchronous nature of the input. Traditional Discrete Fourier Transforms (DFT) are effective at identifying periodic signals, such as spinning rotors, but they assume uniformly sampled data, which event cameras do not provide. We propose a novel per-pixel temporal analysis framework using the Non-uniform Discrete Fourier Transform (NDFT), which we call Drone Detection via Harmonic Fingerprinting (DDHF). Our method uses purely analytical techniques that identify the frequency signature of drone rotors, as characterized by frequency combs in their power spectra, enabling a tunable and generalizable algorithm that achieves accurate real-time localization of UAV. We compare against a YOLO detector under equivalent conditions, demonstrating improvement in accuracy and latency across a difficult array of drone speeds, distances, and scenarios. DDHF achieves an average localization F1 score of 90.89% and average latency of 2.39ms per frame, while YOLO achieves an F1 score of 66.74% and requires 12.40ms per frame. Through utilization of purely analytic techniques, DDHF is quickly tuned on small data, easily interpretable, and achieves competitive accuracies and latencies to deep learning alternatives.
[CV-38] his Looks Distinctly Like That: Grounding Interpretable Recognition in Stiefel Geometry against Neural Collapse
【速读】：该论文旨在解决原型网络（Prototype Networks）在解释性任务中因原型坍缩（Prototype Collapse）导致的可解释性下降问题，即多个原型退化为高度冗余的证据，从而削弱了模型的因果忠实性（Causal Faithfulness）。其核心解决方案是提出自适应流形原型（Adaptive Manifold Prototypes, AMP），关键在于利用Stiefel流形上的黎曼优化（Riemannian Optimization）将类原型表示为正交基底，从结构上杜绝秩一原型坍缩的可能性；同时通过非负容量向量的近端梯度更新学习类别特定的有效秩，并引入空间正则项减少旋转歧义、促进局部且非重叠的部件级证据，从而在细粒度分类任务中实现更高的准确率与更强的可解释性。
链接: https://arxiv.org/abs/2603.08374
作者: Junhao Jia,Jiaqi Wang,Yunyou Liu,Haodong Jing,Yueyi Wu,Xian Wu,Yefeng Zheng
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Prototype networks provide an intrinsic case based explanation mechanism, but their interpretability is often undermined by prototype collapse, where multiple prototypes degenerate to highly redundant evidence. We attribute this failure mode to the terminal dynamics of Neural Collapse, where cross entropy optimization suppresses intra class variance and drives class conditional features toward a low dimensional limit. To mitigate this, we propose Adaptive Manifold Prototypes (AMP), a framework that leverages Riemannian optimization on the Stiefel manifold to represent class prototypes as orthonormal bases and make rank one prototype collapse infeasible by construction. AMP further learns class specific effective rank via a proximal gradient update on a nonnegative capacity vector, and introduces spatial regularizers that reduce rotational ambiguity and encourage localized, non overlapping part evidence. Extensive experiments on fine-grained benchmarks demonstrate that AMP achieves state-of-the-art classification accuracy while significantly improving causal faithfulness over prior interpretable models.
[CV-39] Diffusion-Based Data Augmentation for Image Recognition: A Systematic Analysis and Evaluation
【速读】：该论文旨在解决当前基于扩散模型的数据增强（Diffusion-based Data Augmentation, DiffDA）方法在实验设计和评估上缺乏统一标准的问题，以及对DiffDA全流程理解不足导致的策略比较困难与效果评估不准确。其解决方案的关键在于提出一个名为UniDiffDA的统一分析框架，将DiffDA方法解耦为三个核心组件：模型微调（model fine-tuning）、样本生成（sample generation）和样本利用（sample utilization），从而系统性地厘清不同方法的设计差异与整体设计空间，并在此基础上构建了一个全面且公平的评估协议，用于在多种低数据场景下对代表性DiffDA方法进行基准测试，为方法设计与部署提供可复现的实践指导。
链接: https://arxiv.org/abs/2603.08364
作者: Zekun Li,Yinghuan Shi,Yang Gao,Dong Xu
机构: Nanjing University (南京大学); The University of Hong Kong (香港大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diffusion-based data augmentation (DiffDA) has emerged as a promising approach to improving classification performance under data scarcity. However, existing works vary significantly in task configurations, model choices, and experimental pipelines, making it difficult to fairly compare methods or assess their effectiveness across different scenarios. Moreover, there remains a lack of systematic understanding of the full DiffDA workflow. In this work, we introduce UniDiffDA, a unified analytical framework that decomposes DiffDA methods into three core components: model fine-tuning, sample generation, and sample utilization. This perspective enables us to identify key differences among existing methods and clarify the overall design space. Building on this framework, we develop a comprehensive and fair evaluation protocol, benchmarking representative DiffDA methods across diverse low-data classification tasks. Extensive experiments reveal the relative strengths and limitations of different DiffDA strategies and offer practical insights into method design and deployment. All methods are re-implemented within a unified codebase, with full release of code and configurations to ensure reproducibility and to facilitate future research.
[CV-40] ΔVLA: Prior-Guided Vision-Language-Action Models via World Knowledge Variation
【速读】：该论文旨在解决当前视觉-语言-动作（Vision-Language-Action, VLA）模型在机器人操作中过度关注未来状态预测而忽视对变化过程推理的问题，这限制了模型对动作决策的准确性与可解释性。其解决方案的关键在于提出一种基于先验引导的框架——ΔVLA，核心创新包括：1）设计Prior-Guided World-Knowledge Extractor（PWKE），通过辅助头和先验伪标签提取可操作区域、空间关系与语义线索，构建当前世界知识先验以减少冗余；2）引入Latent World Variation Quantization（LWVQ），利用VQ-VAE目标学习离散潜在空间来编码世界知识的变化量，实现从全模态预测到紧凑潜在表示的转变；3）提出Conditional Variation Attention（CV-Atten），通过条件注意力机制促进表征解耦，降低变化建模中的干扰，从而提升动作生成的效率与性能。
链接: https://arxiv.org/abs/2603.08361
作者: Yijie Zhu,Jie He,Rui Shao,Kaishen Yuan,Tao Tan,Xiaochen Yuan,Zitong Yu
机构: Harbin Institute of Technology, Shenzhen(哈尔滨工业大学深圳校区); Great Bay University(大湾区大学); The Hong Kong University of Science and Technology (Guangzhou)(香港科技大学（广州）); Macao Polytechnic University(澳门理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Recent vision-language-action (VLA) models have significantly advanced robotic manipulation by unifying perception, reasoning, and control. To achieve such integration, recent studies adopt a predictive paradigm that models future visual states or world knowledge to guide action generation. However, these models emphasize forecasting outcomes rather than reasoning about the underlying process of change, which is essential for determining how to act. To address this, we propose \Delta VLA, a prior-guided framework that models world-knowledge variations relative to an explicit current-world knowledge prior for action generation, rather than regressing absolute future world states. Specifically, 1) to construct the current world knowledge prior, we propose the Prior-Guided WorldKnowledge Extractor (PWKE). It extracts manipulable regions, spatial relations, and semantic cues from the visual input, guided by auxiliary heads and prior pseudo labels, thus reducing redundancy. 2) Building upon this, to represent how world knowledge evolves under actions, we introduce the Latent World Variation Quantization (LWVQ). It learns a discrete latent space via a VQ-VAE objective to encode world knowledge variations, shifting prediction from full modalities to compact latent. 3)Moreover, to mitigate interference during variation modeling, we design the Conditional Variation Attention (CV-Atten), whichpromotes disentangled learning and preserves the independence of knowledge representations. Extensive experiments on both simulated benchmarks and real-world robotic tasks demonstrate \Delta VLA achieves state-of-the-art performance while improving efficiency. Code and real-world execution videos are available at this https URL.
[CV-41] Local-Global Prompt Learning via Sparse Optimal Transport
【速读】：该论文旨在解决少样本适配视觉语言模型（Vision-Language Models, VLMs）时，现有基于文本提示（prompt）的方法在局部图像-文本对齐中存在区域冗余和提示重叠的问题。具体而言，当前方法通常独立为每个提示选择局部图像区域，导致特征利用效率低且多个提示间出现语义重复。其解决方案的关键在于提出SOT-GLP框架，通过引入共享稀疏补丁支持（shared sparse patch support）与平衡熵正则最优传输（balanced entropic optimal transport）机制，实现类特定局部提示对显著视觉区域的软划分，从而避免提示冲突并保留全局对齐能力。该方法同时学习共享全局提示与类条件局部提示：全局分支维持标准图像-文本匹配以保障类别级鲁棒性，局部分支则利用视觉-视觉注意力构建稀疏补丁集，并通过最优传输分配至多类提示，有效提升少样本分类准确率（16-shot下平均达85.1%）与分布外检测性能（AUC达94.2%），揭示了可学习投影带来的准确性-鲁棒性权衡现象。
链接: https://arxiv.org/abs/2603.08347
作者: Deniz Kizaroğlu,Ülku Tuncer Küçüktas,Emre Çakmakyurdu,Alptekin Temizel
机构: Middle East Technical University (中东技术大学); Gazi University (加济大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 3 figures, 4 tables. Code available at GitHub
Abstract:Few-shot adaptation of vision-language models (VLMs) like CLIP typically relies on learning textual prompts matched to global image embeddings. Recent works extend this paradigm by incorporating local image-text alignment to capture fine-grained visual cues, yet these approaches often select local regions independently for each prompt, leading to redundant local feature usage and prompt overlap. We propose SOT-GLP, which introduces a shared sparse patch support and balanced optimal transport allocation to explicitly partition salient visual regions among class-specific local prompts while preserving global alignment. Our method learns shared global prompts and class-specific local prompts. The global branch maintains standard image-text matching for robust category-level alignment. The local branch constructs a class-conditioned sparse patch set using V-V attention and aligns it to multiple class-specific prompts via balanced entropic optimal transport, yielding a soft partition of patches that prevents prompt overlap and collapse. We evaluate our method on two complementary objectives: (i) few-shot classification accuracy on 11 standard benchmarks and (ii) out-of-distribution (OOD) detection. On the standard 11-dataset benchmark with 16-shot ViT-B/16, SOT-GLP achieves 85.1% average accuracy, outperforming prior prompt-learning methods. We identify a distinct accuracy-robustness trade-off in prompt learning: while learnable projections optimize in-distribution fit, they alter the foundational feature space. We demonstrate that a projection-free local alignment preserves the native geometry of the CLIP manifold, yielding state-of-the-art OOD detection performance (94.2% AUC) that surpasses fully adapted models. Implementation available at: this https URL
[CV-42] Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology
【速读】：该论文旨在解决多实例学习（Multiple Instance Learning, MIL）在数字病理学中生成的热图（heatmap）有效性缺乏系统评估的问题。当前热图被广泛用于验证MIL模型并发现组织生物标志物，但其是否真实反映模型决策机制尚未得到充分研究。解决方案的关键在于提出一个无需额外标签的通用框架，用于评估MIL热图的质量，并在此基础上开展大规模基准实验，对比六种解释方法在不同任务类型（分类、回归、生存分析）、MIL模型架构（基于注意力机制、Transformer、Mamba）及图像编码器（UNI2、Virchow2）下的表现。结果表明，解释质量主要受MIL模型架构和任务类型影响，其中扰动法（Single）、层间相关性传播（LRP）和集成梯度（IG）显著优于基于注意力和梯度的显著性热图，从而为提升可解释AI在数字病理中的可靠性与生物学意义提供了实证依据。
链接: https://arxiv.org/abs/2603.08328
作者: Mina Jamshidi Idaji,Julius Hense,Tom Neuhäuser,Augustin Krause,Yanqing Luo,Oliver Eberle,Thomas Schnake,Laure Ciernik,Farnoush Rezaei Jafari,Reza Vahidimajd,Jonas Dippel,Christoph Walz,Frederick Klauschen,Andreas Mock,Klaus-Robert Müller
机构: Berlin Institute for the Foundations of Learning and Data, Berlin, Germany; Machine Learning Group, Technische Universität Berlin, Berlin, Germany; Department of Chemistry, Chemical Physics Theory Group, University of Toronto, Canada; Vector Institute for Artificial Intelligence, Toronto, Canada; Acceleration Consortium, University of Toronto, Canada; Department of Computer Science and Engineering, The Chinese University of Hong Kong; Aignostics GmbH, Berlin, Germany; Institute of Pathology, Ludwig Maximilian University, Munich, Germany; German Cancer Research Center, Heidelberg, and German Cancer Consortium, Munich, Germany; Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany; Department of Artificial Intelligence, Korea University, Seoul, Korea; Max-Planck Institute for Informatics, Saarbrücken, Germany
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Multiple instance learning (MIL) has enabled substantial progress in computational histopathology, where a large amount of patches from gigapixel whole slide images are aggregated into slide-level predictions. Heatmaps are widely used to validate MIL models and to discover tissue biomarkers. Yet, the validity of these heatmaps has barely been investigated. In this work, we introduce a general framework for evaluating the quality of MIL heatmaps without requiring additional labels. We conduct a large-scale benchmark experiment to assess six explanation methods across histopathology task types (classification, regression, survival), MIL model architectures (Attention-, Transformer-, Mamba-based), and patch encoder backbones (UNI2, Virchow2). Our results show that explanation quality mostly depends on MIL model architecture and task type, with perturbation (“Single”), layer-wise relevance propagation (LRP), and integrated gradients (IG) consistently outperforming attention-based and gradient-based saliency heatmaps, which often fail to reflect model decision mechanisms. We further demonstrate the advanced capabilities of the best-performing explanation methods: (i) We provide a proof-of-concept that MIL heatmaps of a bulk gene expression prediction model can be correlated with spatial transcriptomics for biological validation, and (ii) showcase the discovery of distinct model strategies for predicting human papillomavirus (HPV) infection from head and neck cancer slides. Our work highlights the importance of validating MIL heatmaps and establishes that improved explainability can enable more reliable model validation and yield biological insights, making a case for a broader adoption of explainable AI in digital pathology. Our code is provided in a public GitHub repository: this https URL
[CV-43] Human-AI Divergence in Ego-centric Action Recognition under Spatial and Spatiotemporal Manipulations
【速读】：该论文旨在解决人类在动作识别任务中显著优于当前先进人工智能模型的问题，尤其是在低分辨率、遮挡和视觉杂乱等真实世界挑战性条件下。其核心问题是理解导致这一性能差距的根本原因，从而推动更鲁棒且与人类认知对齐的AI模型发展。解决方案的关键在于构建一个大规模的人类-AI对比研究框架，利用最小可识别识别区域（Minimal Identifiable Recognition Crops, MIRCs）作为分析单位，系统性地评估人类与AI模型（Side4Video）在空间缩减和时间打乱条件下的表现差异。通过量化指标（如平均缩减率和识别差距）及定性分析（包括高、中、低层次视觉特征和时空因素），发现人类高度依赖稀疏但语义关键的线索（如手-物交互），而模型则更多依赖上下文和中低级特征，并对时空扰动表现出不同的敏感性模式，揭示了人类感知机制与现有AI模型之间的本质差异。
链接: https://arxiv.org/abs/2603.08317
作者: Sadegh Rahmaniboldaji,Filip Rybansky,Quoc C. Vuong,Anya C. Hurlbert,Frank Guerin,Andrew Gilbert
机构: Unknown
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Humans consistently outperform state-of-the-art AI models in action recognition, particularly in challenging real-world conditions involving low resolution, occlusion, and visual clutter. Understanding the sources of this performance gap is essential for developing more robust and human-aligned models. In this paper, we present a large-scale human-AI comparative study of egocentric action recognition using Minimal Identifiable Recognition Crops (MIRCs), defined as the smallest spatial or spatiotemporal regions sufficient for reliable human recognition. We used our previously introduced, Epic ReduAct, a systematically spatially reduced and temporally scrambled dataset derived from 36 EPIC KITCHENS videos, spanning multiple spatial reduction levels and temporal conditions. Recognition performance is evaluated using over 3,000 human participants and the Side4Video model. Our analysis combines quantitative metrics, Average Reduction Rate and Recognition Gap, with qualitative analyses of spatial (high-, mid-, and low-level visual features) and spatiotemporal factors, including a categorisation of actions into Low Temporal Actions (LTA) and High Temporal Actions (HTA). Results show that human performance exhibits sharp declines when transitioning from MIRCs to subMIRCs, reflecting a strong reliance on sparse, semantically critical cues such as hand-object interactions. In contrast, the model degrades more gradually and often relies on contextual and mid- to low-level features, sometimes even exhibiting increased confidence under spatial reduction. Temporally, humans remain robust to scrambling when key spatial cues are preserved, whereas the model often shows insensitivity to temporal disruption, revealing class-dependent temporal sensitivities.
[CV-44] HDR-NSFF: High Dynamic Range Neural Scene Flow Fields ICLR2026
【速读】：该论文旨在解决动态场景下高动态范围（High Dynamic Range, HDR）图像重建中因传统2D像素级对齐方法导致的鬼影伪影和时间不一致性问题。其核心解决方案是提出HDR-NSFF框架，通过将场景建模为时空连续函数，实现从交替曝光单目视频中重建动态HDR辐射场，从而突破2D融合限制。关键创新在于：1）采用神经辐射场或4D高斯泼溅（4D Gaussian Splatting, 4DGS）表示动态场景，统一建模HDR辐射、3D场景光流、几何结构与色调映射；2）引入基于DINO特征的语义增强光流估计以实现曝光不变的运动估计，并利用生成先验作为正则项补偿单目观测不足及饱和导致的信息损失，显著提升重建鲁棒性与物理合理性。
链接: https://arxiv.org/abs/2603.08313
作者: Shin Dong-Yeon,Kim Jun-Seong,Kwon Byung-Ki,Tae-Hyun Oh
机构: KAIST(韩国科学技术院); POSTECH(浦项科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ICLR 2026. Project page: this https URL
Abstract:Radiance of real-world scenes typically spans a much wider dynamic range than what standard cameras can capture. While conventional HDR methods merge alternating-exposure frames, these approaches are inherently constrained to 2D pixel-level alignment, often leading to ghosting artifacts and temporal inconsistency in dynamic scenes. To address these limitations, we present HDR-NSFF, a paradigm shift from 2D-based merging to 4D spatio-temporal modeling. Our framework reconstructs dynamic HDR radiance fields from alternating-exposure monocular videos by representing the scene as a continuous function of space and time, and is compatible with both neural radiance field and 4D Gaussian Splatting (4DGS) based dynamic representations. This unified end-to-end pipeline explicitly models HDR radiance, 3D scene flow, geometry, and tone-mapping, ensuring physical plausibility and global coherence. We further enhance robustness by (i) extending semantic-based optical flow with DINO features to achieve exposure-invariant motion estimation, and (ii) incorporating a generative prior as a regularizer to compensate for limited observation in monocular captures and saturation-induced information loss. To evaluate HDR space-time view synthesis, we present the first real-world HDR-GoPro dataset specifically designed for dynamic HDR scenes. Experiments demonstrate that HDR-NSFF recovers fine radiance details and coherent dynamics even under challenging exposure variations, thereby achieving state-of-the-art performance in novel space-time view synthesis. Project page: this https URL
[CV-45] Concept-Guided Fine-Tuning: Steering ViTs away from Spurious Correlations to Improve Robustness CVPR2026
【速读】：该论文旨在解决视觉Transformer（Vision Transformer, ViT）在分布偏移（distribution shift）下性能下降的问题，其根本原因在于ViT过度依赖背景等伪相关性特征，而非语义上具有区分度的物体概念（如“长喙”和“翅膀”对于“鸟”的定义）。解决方案的关键在于提出一种新颖的微调框架，通过引导模型推理聚焦于概念级语义，具体实现方式为：利用大语言模型（LLM）和视觉语言模型（VLM）自动生成无需人工标注的空间对齐概念掩码（concept masks），并优化模型内部的相关性热力图（relevance maps）以匹配这些概念区域，同时抑制对虚假背景区域的关注。该方法仅需少量图像及一半数据集类别即可实现显著提升，且在多个分布外基准测试中验证了其有效性与可扩展性。
链接: https://arxiv.org/abs/2603.08309
作者: Yehonatan Elisha,Oren Barkan,Noam Koenigstein
机构: Tel Aviv University (特拉维夫大学); The Open University (开放大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: CVPR 2026 ; Project page: this https URL
Abstract:Vision Transformers (ViTs) often degrade under distribution shifts because they rely on spurious correlations, such as background cues, rather than semantically meaningful features. Existing regularization methods, typically relying on simple foreground-background masks, which fail to capture the fine-grained semantic concepts that define an object (e.g., long beak'' and wings’’ for a ``bird’'). As a result, these methods provide limited robustness to distribution shifts. To address this limitation, we introduce a novel finetuning framework that steers model reasoning toward concept-level semantics. Our approach optimizes the model’s internal relevance maps to align with spatially grounded concept masks. These masks are generated automatically, without manual annotation: class-relevant concepts are first proposed using an LLM-based, label-free method, and then segmented using a VLM. The finetuning objective aligns relevance with these concept regions while simultaneously suppressing focus on spurious background areas. Notably, this process requires only a minimal set of images and uses half of the dataset classes. Extensive experiments on five out-of-distribution benchmarks demonstrate that our method improves robustness across multiple ViT-based models. Furthermore, we show that the resulting relevance maps exhibit stronger alignment with semantic object parts, offering a scalable path toward more robust and interpretable vision models. Finally, we confirm that concept-guided masks provide more effective supervision for model robustness than conventional segmentation maps, supporting our central hypothesis.
[CV-46] Retrieval-Augmented Anatomical Guidance for Text-to-CT Generation
【速读】：该论文旨在解决生成式AI在体素医学影像（volumetric medical imaging）中因缺乏显式解剖结构引导而导致的空间模糊性与解剖不一致性问题，同时保持文本语义控制能力。传统基于文本的生成模型虽具备语义灵活性，但难以保证解剖合理性；而依赖真实标注的结构驱动方法在无标签目标图像合成场景下不可行。其解决方案的关键在于提出一种检索增强型文本到CT（Text-to-CT）生成框架：通过3D视觉语言编码器从临床病例库中检索语义相关的参考案例，并将其解剖标注作为结构代理（structural proxy），借助ControlNet分支注入到文本条件潜扩散模型中，从而在不依赖目标图像标注的前提下实现粗粒度的解剖引导，兼顾语义可控性与空间可塑性。实验表明，该方法显著提升图像保真度与临床一致性，并首次在文本驱动生成中实现了显式的空间控制能力。
链接: https://arxiv.org/abs/2603.08305
作者: Daniele Molino,Camillo Maria Caruso,Paolo Soda,Valerio Guarrasi
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Text-conditioned generative models for volumetric medical imaging provide semantic control but lack explicit anatomical guidance, often resulting in outputs that are spatially ambiguous or anatomically inconsistent. In contrast, structure-driven methods ensure strong anatomical consistency but typically assume access to ground-truth annotations, which are unavailable when the target image is to be synthesized. We propose a retrieval-augmented approach for Text-to-CT generation that integrates semantic and anatomical information under a realistic inference setting. Given a radiology report, our method retrieves a semantically related clinical case using a 3D vision-language encoder and leverages its associated anatomical annotation as a structural proxy. This proxy is injected into a text-conditioned latent diffusion model via a ControlNet branch, providing coarse anatomical guidance while maintaining semantic flexibility. Experiments on the CT-RATE dataset show that retrieval-augmented generation improves image fidelity and clinical consistency compared to text-only baselines, while additionally enabling explicit spatial controllability, a capability inherently absent in such approaches. Further analysis highlights the importance of retrieval quality, with semantically aligned proxies yielding consistent gains across all evaluation axes. This work introduces a principled and scalable mechanism to bridge semantic conditioning and anatomical plausibility in volumetric medical image synthesis. Code will be released.
[CV-47] Novel Semantic Prompting for Zero-Shot Action Recognition
【速读】：该论文旨在解决零样本动作识别（zero-shot action recognition）中如何有效利用视觉-语言模型（vision-language models）的语义先验知识来理解未见过的动作问题。现有方法多聚焦于时序建模或架构调整以处理视频数据，但忽视了语义提示（semantic prompting）作为潜在强信号的作用。解决方案的关键在于提出SP-CLIP框架，通过引入结构化的多层次语义提示（包括意图、运动和物体交互等抽象层次），在不修改视觉编码器或学习额外参数的前提下，借助提示聚合与一致性评分机制，将视频表征对齐至增强的文本语义空间，从而显著提升对细粒度及组合型动作的识别性能，同时保持预训练模型的高效性与泛化能力。
链接: https://arxiv.org/abs/2603.08289
作者: Salman Iqbal,Waheed Rehman
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Zero-shot action recognition relies on transferring knowledge from vision-language models to unseen actions using semantic descriptions. While recent methods focus on temporal modeling or architectural adaptations to handle video data, we argue that semantic prompting alone provides a strong and underexplored signal for zero-shot action understanding. We introduce SP-CLIP, a lightweight framework that augments frozen vision-language models with structured semantic prompts describing actions at multiple levels of abstraction, such as intent, motion, and object interaction. Without modifying the visual encoder or learning additional parameters, SP-CLIP aligns video representations with enriched textual semantics through prompt aggregation and consistency scoring. Experiments across standard benchmarks show that semantic prompting substantially improves zero-shot action recognition, particularly for fine-grained and compositional actions, while preserving the efficiency and generalization of pretrained models.
[CV-48] OSCAR: Occupancy-based Shape Completion via Acoustic Neural Implicit Representations
【速读】：该论文旨在解决从超声（Ultrasound, US）图像中准确重建椎体三维解剖结构的问题，尤其针对因声影（acoustic shadowing）和视场依赖的信号变化导致的重建困难。其解决方案的关键在于提出一种基于体素占据（occupancy-based）的形状补全方法，通过联合建模图像外观与潜在解剖形状的耦合隐空间，利用神经隐式表示（Neural Implicit Representation, NIR）同时刻画空间占据关系与声学交互特性，从而在无需标注的情况下实现对未观测区域的隐式感知，有效提升B-mode超声图像下椎体几何重建的精度（HD95分数较现有最优方法提升80%）。
链接: https://arxiv.org/abs/2603.08279
作者: Magdalena Wysocki,Kadir Burak Buldu,Miruna-Alexandra Gafencu,Mohammad Farid Azampour,Nassir Navab
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Accurate 3D reconstruction of vertebral anatomy from ultrasound is important for guiding minimally invasive spine interventions, but it remains challenging due to acoustic shadowing and view-dependent signal variations. We propose an occupancy-based shape completion method that reconstructs complete 3D anatomical geometry from partial ultrasound observations. Crucially for intra-operative applications, our approach extracts the anatomical surface directly from the image, avoiding the need for anatomical labels during inference. This label-free completion relies on a coupled latent space representing both the image appearance and the underlying anatomical shape. By leveraging a Neural Implicit Representation (NIR) that jointly models both spatial occupancy and acoustic interactions, the method uses acoustic parameters to become implicitly aware of the unseen regions without explicit shadowing labels through tracking acoustic signal transmission. We show that this method outperforms state-of-the-art shape completion for B-mode ultrasound by 80% in HD95 score. We validate our approach both in-silico and on phantom US images with registered mesh models from CT labels, demonstrating accurate reconstruction of occluded anatomy and robust generalization across diverse imaging conditions. Code and data will be released on publication.
[CV-49] Prototype-Guided Concept Erasure in Diffusion Models CVPR2026
【速读】：该论文旨在解决当前概念擦除（concept erasure）方法在处理宽泛概念（如“性”或“暴力”）时效果不佳的问题，这类概念因其范围广泛且多维度特征难以被可靠地从文本到图像生成模型中移除。解决方案的关键在于利用模型内部嵌入空间的几何结构，识别出编码特定概念的潜在嵌入，并通过聚类这些嵌入获得一组概念原型（concept prototypes），进而将这些原型作为推理阶段的负向条件信号，从而实现对宽泛概念的精准、稳定擦除，同时保持图像整体质量。
链接: https://arxiv.org/abs/2603.08271
作者: Yuze Cai,Jiahao Lu,Hongxiang Shi,Yichao Zhou,Hong Lu
机构: Fudan University (复旦大学); National University of Singapore (新加坡国立大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR 2026
Abstract:Concept erasure is extensively utilized in image generation to prevent text-to-image models from generating undesired content. Existing methods can effectively erase narrow concepts that are specific and concrete, such as distinct intellectual properties (e.g. Pikachu) or recognizable characters (e.g. Elon Musk). However, their performance degrades on broad concepts such as sexual'' or violent’', whose wide scope and multi-faceted nature make them difficult to erase reliably. To overcome this limitation, we exploit the model’s intrinsic embedding geometry to identify latent embeddings that encode a given concept. By clustering these embeddings, we derive a set of concept prototypes that summarize the model’s internal representations of the concept, and employ them as negative conditioning signals during inference to achieve precise and reliable erasure. Extensive experiments across multiple benchmarks show that our approach achieves substantially more reliable removal of broad concepts while preserving overall image quality, marking a step towards safer and more controllable image generation.
[CV-50] Event-based Motion Appearance Fusion for 6D Object Pose Tracking
【速读】：该论文旨在解决在高动态环境中基于RGB-D相机进行6D物体位姿跟踪时因运动模糊和帧率限制而导致性能下降的问题。其解决方案的关键在于利用事件相机（event camera）的高时间分辨率特性，提出一种无需学习的位姿跟踪方法：首先通过事件驱动的光流估计获得6D物体速度以实现位姿传播，随后引入基于模板的局部位姿校正模块对位姿进行优化。该方法在快速移动物体场景中表现优于现有主流算法，展现出事件相机在高速动态环境下的应用潜力。
链接: https://arxiv.org/abs/2603.08264
作者: Zhichao Li,Chiara Bartolozzi,Lorenzo Natale,Arren Glover
机构: Istituto Italiano di Tecnologia (意大利技术研究院); University of Genoa (热那亚大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Object pose tracking is a fundamental and essential task for robotics to perform tasks in the home and industrial settings. The most commonly used sensors to do so are RGB-D cameras, which can hit limitations in highly dynamic environments due to motion blur and frame-rate constraints. Event cameras have remarkable features such as high temporal resolution and low latency, which make them a potentially ideal vision sensors for object pose tracking at high speed. Even so, there are still only few works on 6D pose tracking with event cameras. In this work, we take advantage of the high temporal resolution and propose a method that uses both a propagation step fused with a pose correction strategy. Specifically, we use 6D object velocity obtained from event-based optical flow for pose propagation, after which, a template-based local pose correction module is utilized for pose correction. Our learning-free method has comparable performance to the state-of-the-art algorithms, and in some cases out performs them for fast-moving objects. The results indicate the potential for using event cameras in highly-dynamic scenarios where the use of deep network approaches are limited by low update rates.
[CV-51] WaDi: Weight Direction-aware Distillation for One-step Image Synthesis CVPR2026
【速读】：该论文旨在解决扩散模型（Diffusion Models）在图像生成中推理速度慢的问题，尤其是针对如Stable Diffusion（SD）这类多步扩散模型在实际部署时的效率瓶颈。现有方法通过蒸馏（Distillation）将多步扩散过程压缩为单步生成器以加速推理，但其内在机制尚不清晰。论文的关键发现是：在蒸馏过程中，学生模型（One-step Student）与教师模型（Multi-step Teacher）之间的权重方向变化显著大于权重范数变化，表明权重方向调整是蒸馏的核心因素。基于此洞察，作者提出低秩方向旋转适配器（LoRaD），利用可学习的低秩旋转矩阵建模结构化的权重方向变化，并将其集成到变分得分蒸馏（VSD）框架中，形成面向权重方向感知的蒸馏方法（WaDi）。该方法仅使用约10%的U-Net/DiT可训练参数，在COCO 2014和COCO 2017数据集上实现当前最优FID分数，且具备良好的下游任务泛化能力，如可控生成、关系反转和高分辨率合成。
链接: https://arxiv.org/abs/2603.08258
作者: Lei Wang,Yang Cheng,Senmao Li,Ge Wu,Yaxing Wang,Jian Yang
机构: Nankai University (南开大学); Nanjing University (南京大学); NKIARI, Shenzhen Futian (深圳市福田区NKIARI)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026;Code: this https URL
Abstract:Despite the impressive performance of diffusion models such as Stable Diffusion (SD) in image generation, their slow inference limits practical deployment. Recent works accelerate inference by distilling multi-step diffusion into one-step generators. To better understand the distillation mechanism, we analyze U-Net/DiT weight changes between one-step students and their multi-step teacher counterparts. Our analysis reveals that changes in weight direction significantly exceed those in weight norm, highlighting it as the key factor during distillation. Motivated by this insight, we propose the Low-rank Rotation of weight Direction (LoRaD), a parameter-efficient adapter tailored to one-step diffusion distillation. LoRaD is designed to model these structured directional changes using learnable low-rank rotation matrices. We further integrate LoRaD into Variational Score Distillation (VSD), resulting in Weight Direction-aware Distillation (WaDi)-a novel one-step distillation framework. WaDi achieves state-of-the-art FID scores on COCO 2014 and COCO 2017 while using only approximately 10% of the trainable parameters of the U-Net/DiT. Furthermore, the distilled one-step model demonstrates strong versatility and scalability, generalizing well to various downstream tasks such as controllable generation, relation inversion, and high-resolution synthesis.
[CV-52] DynamicVGGT: Learning Dynamic Point Maps for 4D Scene Reconstruction in Autonomous Driving
【速读】：该论文旨在解决自动驾驶场景中动态场景重建的难题，即如何在存在显著时间变化、移动物体和复杂动态特性的情况下，实现高精度的4D（三维空间+时间）动态场景重建。现有前馈式3D模型在静态场景重建上表现良好，但难以有效捕捉动态运动信息。其解决方案的关键在于提出DynamicVGGT框架，该框架通过两个核心创新实现动态建模：一是引入Motion-aware Temporal Attention (MTA)模块以学习运动连续性并高效捕获时序依赖关系；二是设计Dynamic 3D Gaussian Splatting Head，利用可学习的运动token预测点云的高斯速度，并在场景流监督下显式建模点运动，从而通过连续的3D高斯优化精修动态几何结构，最终实现统一且时序一致的动态4D重建。
链接: https://arxiv.org/abs/2603.08254
作者: Zhuolin He,Jing Li,Guanghao Li,Xiaolei Chen,Jiacheng Tang,Siyang Zhang,Zhounan Jin,Feipeng Cai,Bin Li,Jian Pu,Jia Cai,Xiangyang Xue
机构: Fudan University(复旦大学); Huawei(华为); Yinwang Intelligent Technology(英伟达智能科技); Shanghai Innovation Institute(上海创新研究院); CUHK(香港中文大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Dynamic scene reconstruction in autonomous driving remains a fundamental challenge due to significant temporal variations, moving objects, and complex scene dynamics. Existing feed-forward 3D models have demonstrated strong performance in static reconstruction but still struggle to capture dynamic motion. To address these limitations, we propose DynamicVGGT, a unified feed-forward framework that extends VGGT from static 3D perception to dynamic 4D reconstruction. Our goal is to model point motion within feed-forward 3D models in a dynamic and temporally coherent manner. To this end, we jointly predict the current and future point maps within a shared reference coordinate system, allowing the model to implicitly learn dynamic point representations through temporal correspondence. To efficiently capture temporal dependencies, we introduce a Motion-aware Temporal Attention (MTA) module that learns motion continuity. Furthermore, we design a Dynamic 3D Gaussian Splatting Head that explicitly models point motion by predicting Gaussian velocities using learnable motion tokens under scene flow supervision. It refines dynamic geometry through continuous 3D Gaussian optimization. Extensive experiments on autonomous driving datasets demonstrate that DynamicVGGT significantly outperforms existing methods in reconstruction accuracy, achieving robust feed-forward 4D dynamic scene reconstruction under complex driving scenarios.
[CV-53] opologically Stable Hough Transform
【速读】：该论文旨在解决点云数据中直线检测的精度与效率问题，传统霍夫变换（Hough transform）依赖离散投票机制，易受噪声干扰且难以准确提取稳定结构。其解决方案的关键在于提出一种替代性连续评分函数（continuous score function），通过持久同调（persistent homology）理论识别具有拓扑稳定性的候选直线，从而在保持高精度的同时提升算法鲁棒性与计算效率。
链接: https://arxiv.org/abs/2603.08245
作者: Stefan Huber,Kristóf Huszár,Michael Kerber,Martin Uray
机构: Salzburg University of Applied Sciences (萨尔茨堡应用科学大学); Graz University of Technology (格拉茨工业大学)
类目: Computational Geometry (cs.CG); Computer Vision and Pattern Recognition (cs.CV)
备注: Extended abstract will be presented at EuroCG’26; 11 pages, 7 figures
Abstract:We propose an alternative formulation of the well-known Hough transform to detect lines in point clouds. Replacing the discretized voting scheme of the classical Hough transform by a continuous score function, its persistent features in the sense of persistent homology give a set of candidate lines. We also devise and implement an algorithm to efficiently compute these candidate lines.
[CV-54] SiMO: Single-Modality-Operable Multimodal Collaborative Perception ICLR ICLR2026
【速读】：该论文旨在解决多模态协同感知（Multimodal Collaborative Perception）中因关键传感器（如LiDAR）失效导致的性能下降问题，其根本原因在于特征融合引发单模态特征与下游模块之间的语义不匹配。解决方案的关键在于提出Single-Modality-Operable Multimodal Collaborative Perception (SiMO)，通过引入Length-Adaptive Multi-Modal Fusion (LAMMA)机制，在模态失效时能自适应处理剩余模态特征并保持语义空间一致性；同时采用“Pretrain-Align-Fuse-RD”训练策略，有效缓解模态竞争问题，确保各模态分支的独立性，从而在所有单模态场景下均能维持最优性能。
链接: https://arxiv.org/abs/2603.08240
作者: Jiageng Wen,Shengjie Zhao,Bing Li,Jiafeng Huang,Kenan Ye,Hao Deng
机构: Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University; School of Computer Science and Technology, Tongji University; School of Mechatronic Engineering and Automation, Shanghai University
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ICLR 2026. This arXiv version includes an additional appendix (Appendix 15) containing further philosophical discussion not included in the official ICLR peer-reviewed version
Abstract:Collaborative perception integrates multi-agent perspectives to enhance the sensing range and overcome occlusion issues. While existing multimodal approaches leverage complementary sensors to improve performance, they are highly prone to failure–especially when a key sensor like LiDAR is unavailable. The root cause is that feature fusion leads to semantic mismatches between single-modality features and the downstream modules. This paper addresses this challenge for the first time in the field of collaborative perception, introducing Single-Modality-Operable Multimodal Collaborative Perception (SiMO). By adopting the proposed Length-Adaptive Multi-Modal Fusion (LAMMA), SiMO can adaptively handle remaining modal features during modal failures while maintaining consistency of the semantic space. Additionally, leveraging the innovative “Pretrain-Align-Fuse-RD” training strategy, SiMO addresses the issue of modality competition–generally overlooked by existing methods–ensuring the independence of each individual modality branch. Experiments demonstrate that SiMO effectively aligns multimodal features while simultaneously preserving modality-specific features, enabling it to maintain optimal performance across all individual modalities. The implementation details can be found in this https URL.
[CV-55] Exploring Deep Learning and Ultra-Widefield Imaging for Diabetic Retinopathy and Macular Edema
【速读】：该论文旨在解决糖尿病视网膜病变（Diabetic Retinopathy, DR）和糖尿病黄斑水肿（Diabetic Macular Edema, DME）的早期精准检测问题，尤其针对传统标准彩色眼底照相（Color Fundus Photography, CFP）视野有限的局限性，探索超广角成像（Ultra-Widefield Imaging, UWF）结合深度学习（Deep Learning, DL）在三个临床任务中的应用：UWF图像质量评估、可转诊糖尿病视网膜病变（Referable Diabetic Retinopathy, RDR）识别及DME识别。其解决方案的关键在于：1）在空间域（RGB）与频域（frequency domain）中对比多种先进DL架构（包括卷积神经网络CNNs、视觉Transformer ViTs及基础模型），验证频域表示对UWF分析的有效性；2）引入特征级融合策略以提升模型鲁棒性；3）利用Grad-CAM可视化技术增强模型决策的可解释性。结果表明，ViTs和基础模型在UWF分析中表现优异，且特征融合与频域建模显著提升了整体性能。
链接: https://arxiv.org/abs/2603.08235
作者: Pablo Jimenez-Lizcano,Sergio Romero-Tapiador,Ruben Tolosana,Aythami Morales,Guillermo González de Rivera,Ruben Vera-Rodriguez,Julian Fierrez
机构: BiometricsAI, Universidad Autónoma de Madrid; HCTLab Research Group, Universidad Autónoma de Madrid
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 6 pages, 4 figures, 2 tables
Abstract:Diabetic retinopathy (DR) and diabetic macular edema (DME) are leading causes of preventable blindness among working-age adults. Traditional approaches in the literature focus on standard color fundus photography (CFP) for the detection of these conditions. Nevertheless, recent ultra-widefield imaging (UWF) offers a significantly wider field of view in comparison to CFP. Motivated by this, the present study explores state-of-the-art deep learning (DL) methods and UWF imaging on three clinically relevant tasks: i) image quality assessment for UWF, ii) identification of referable diabetic retinopathy (RDR), and iii) identification of DME. Using the publicly available UWF4DR Challenge dataset, released as part of the MICCAI 2024 conference, we benchmark DL models in the spatial (RGB) and frequency domains, including popular convolutional neural networks (CNNs) as well as recent vision transformers (ViTs) and foundation models. In addition, we explore a final feature-level fusion to increase robustness. Finally, we also analyze the decisions of the DL models using Grad-CAM, increasing the explainability. Our proposal achieves consistently strong performance across all architectures, underscoring the competitiveness of emerging ViTs and foundation models and the promise of feature-level fusion and frequency-domain representations for UWF analysis.
[CV-56] GarmentPainter: Efficient 3D Garment Texture Synthesis with Character-Guided Diffusion Model
【速读】：该论文旨在解决生成高保真、3D一致的服装纹理这一难题，现有方法要么依赖于难以保证3D一致性的2D扩散模型，要么需要昂贵的多步优化或严格的2D参考图像与3D网格之间的空间对齐，从而限制了灵活性和可扩展性。解决方案的关键在于提出GarmentPainter框架，其核心创新是利用UV位置图（UV position map）作为3D结构引导信号，在UV空间中实现纹理生成过程中的全局一致性；同时引入类型选择模块（type selection module），使模型可根据角色参考图像对特定服装组件进行细粒度控制，无需图像与3D网格间的精确对齐，且所有引导信号以空间对齐方式整合进扩散模型输入，不修改底层UNet架构，从而在视觉保真度、3D一致性和计算效率方面均达到当前最优性能。
链接: https://arxiv.org/abs/2603.08228
作者: Jinbo Wu,Xiaobo Gao,Xing Liu,Chen Zhao,Jialun Liu
机构: Baidu Inc.(百度公司); TeleAI
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Generating high-fidelity, 3D-consistent garment textures remains a challenging problem due to the inherent complexities of garment structures and the stringent requirement for detailed, globally consistent texture synthesis. Existing approaches either rely on 2D-based diffusion models, which inherently struggle with 3D consistency, require expensive multi-step optimization or depend on strict spatial alignment between 2D reference images and 3D meshes, which limits their flexibility and scalability. In this work, we introduce GarmentPainter, a simple yet efficient framework for synthesizing high-quality, 3D-aware garment textures in UV space. Our method leverages a UV position map as the 3D structural guidance, ensuring texture consistency across the garment surface during texture generation. To enhance control and adaptability, we introduce a type selection module, enabling fine-grained texture generation for specific garment components based on a character reference image, without requiring alignment between the reference image and the 3D mesh. GarmentPainter efficiently integrates all guidance signals into the input of a diffusion model in a spatially aligned manner, without modifying the underlying UNet architecture. Extensive experiments demonstrate that GarmentPainter achieves state-of-the-art performance in terms of visual fidelity, 3D consistency, and computational efficiency, outperforming existing methods in both qualitative and quantitative evaluations.
[CV-57] SRNeRV: A Scale-wise Recursive Framework for Neural Video Representation ISCAS2026
【速读】：该论文旨在解决现有多尺度隐式神经表示（Implicit Neural Representations, INRs）生成器中因逐层堆叠独立处理模块而导致的参数冗余问题。其解决方案的关键在于提出一种尺度递归框架SRNeRV，通过解耦处理模块为尺度特定的空间混合模块与尺度不变的通道混合模块，并在所有尺度上递归复用包含大部分参数的共享通道混合模块，从而显著降低模型规模，同时保留学习尺度特异性空间模式的能力。
链接: https://arxiv.org/abs/2603.08227
作者: Jia Wang,Jun Zhu,Xinfeng Zhang
机构: University of Chinese Academy of Sciences (中国科学院大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by IEEE ISCAS 2026
Abstract:Implicit Neural Representations (INRs) have emerged as a promising paradigm for video representation and compression. However, existing multi-scale INR generators often suffer from significant parameter redundancy by stacking independent processing blocks for each scale. Inspired by the principle of scale self-similarity in the generation process, we propose SRNeRV, a novel scale-wise recursive framework that replaces this stacked design with a parameter-efficient shared architecture. The core of our approach is a hybrid sharing scheme derived from decoupling the processing block into a scale-specific spatial mixing module and a scale-invariant channel mixing module. We recursively apply the same shared channel mixing module, which contains the majority of the parameters, across all scales, significantly reducing the model size while preserving the crucial capacity to learn scale-specific spatial patterns. Extensive experiments demonstrate that SRNeRV achieves a significant rate-distortion performance boost, especially in INR-friendly scenarios, validating that our sharing scheme successfully amplifies the core strengths of the INR paradigm.
[CV-58] SAVE: Speech-Aware Video Representation Learning for Video-Text Retrieval CVPR2026
【速读】：该论文针对视频-文本检索任务中现有方法（如CLIP）仅利用视频视觉信息而忽略音频内容所导致的偏差问题，提出了一种新的解决方案SAVE（Speech Aware Video Representation Learning）。其关键在于：1）引入专门用于语音内容建模的语音分支，以更有效地提取语义信息；2）采用soft-ALBEF机制实现早期视觉与音频对齐，从而优化跨模态融合策略。该方法在多个基准测试中显著优于当前最优模型AVIGATE，验证了其有效性。
链接: https://arxiv.org/abs/2603.08224
作者: Ruixiang Zhao,Zhihao Xu,Bangxiang Lan,Zijie Xin,Jingyu Liu,Xirong Li
机构: Renmin University of China (中国人民大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR2026
Abstract:For video-text retrieval, the use of CLIP has been a de facto choice. Since CLIP provides only image and text encoders, this consensus has led to a biased paradigm that entirely ignores the sound track of videos. While several attempts have been made to reintroduce audio – typically by incorporating an audio encoder and fusing its output with visual features – these methods face two challenges: ineffective representation of speech content and suboptimal vision-audio fusion. To address these issues jointly, we propose SAVE, a Speech Aware Video rEpresentation learning method. SAVE improves upon AVIGATE, a SOTA audiovisual method, with a dedicated speech branch for more effective speech embedding. Furthermore, we introduce soft-ALBEF for early vision-audio alignment that facilitates fusion. Extensive experiments on five benchmarks show that SAVE compares favorably against the SOTA, outperforming AVIGATE by +4.1% on MSRVTT-9k, +1.9% on MSRVTT-7k, +2.5% on VATEX, +9.8% on Charades, and +2.1% on LSMDC, in light of the SumR metric.
[CV-59] Video2LoRA: Unified Semantic-Controlled Video Generation via Per-Reference-Video LoRA
【速读】：该论文旨在解决多模态视频生成中语义对齐（semantic alignment）的难题，即如何在不同控制条件（如文本、图像或参考视频）下实现一致且灵活的语义驱动视频生成。现有方法受限于显式结构引导带来的刚性空间约束，或因针对单一控制类型设计而缺乏可迁移性和通用性。解决方案的关键在于提出 Video2LoRA 框架，其通过轻量级超网络（hypernetwork）为每种语义输入动态预测个性化 LoRA（Low-Rank Adaptation）权重，并结合辅助矩阵构建自适应 LoRA 模块嵌入冻结的扩散模型主干（diffusion backbone），从而无需针对每个控制条件进行训练即可实现语义一致性生成，同时保留关键风格与内容变化，最终模型参数小于 150MB，具备高效部署能力。
链接: https://arxiv.org/abs/2603.08210
作者: Zexi Wu,Qinghe Wang,Jing Dai,Baolu Li,Yiming Zhang,Yue Ma,Xu Jia,Hongming Xu
机构: Dalian University of Technology (大连理工大学); HKUST (香港科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages
Abstract:Achieving semantic alignment across diverse video generation conditions remains a significant challenge. Methods that rely on explicit structural guidance often enforce rigid spatial constraints that limit semantic flexibility, whereas models tailored for individual control types lack interoperability and adaptability. These design bottlenecks hinder progress toward flexible and efficient semantic video generation. To address this, we propose Video2LoRA, a scalable and generalizable framework for semantic-controlled video generation that conditions on a reference video. Video2LoRA employs a lightweight hypernetwork to predict personalized LoRA weights for each semantic input, which are combined with auxiliary matrices to form adaptive LoRA modules integrated into a frozen diffusion backbone. This design enables the model to generate videos consistent with the reference semantics while preserving key style and content variations, eliminating the need for any per-condition training. Notably, the final model weights less than 150MB, making it highly efficient for storage and deployment. Video2LoRA achieves coherent, semantically aligned generation across diverse conditions and exhibits strong zero-shot generalization to unseen semantics.
[CV-60] Alignment-Aware and Reliability-Gated Multimodal Fusion for Unmanned Aerial Vehicle Detection Across Heterogeneous Thermal-Visual Sensors
【速读】：该论文旨在解决多传感器异构模态（如红外、广角和变焦摄像头）在无人机（UAV）检测中因分辨率、视角和视场差异导致的融合困难问题，传统融合方法难以保持空间对应关系且受标注不一致性影响，限制了实际应用中的鲁棒性。解决方案的关键在于提出两种新型融合策略：Registration-aware Guided Image Fusion (RGIF) 和 Reliability-Gated Modality-Attention Fusion (RGMAF)，前者通过增强相关系数（ECC）配准与引导滤波保留热成像显著性并提升结构细节，后者结合仿射与光流配准及可靠性加权注意力机制，自适应平衡热对比度与视觉锐度，从而实现更精准的多模态信息整合，显著提升UAV检测性能。
链接: https://arxiv.org/abs/2603.08208
作者: Ishrat Jahan,Molla E Majid,M Murugappan,Muhammad E. H. Chowdhury,N.B.Prakash,Saad Bin Abul Kashem,Balamurugan Balusamy,Amith Khandakar
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Reliable unmanned aerial vehicle (UAV) detection is critical for autonomous airspace monitoring but remains challenging when integrating sensor streams that differ substantially in resolution, perspective, and field of view. Conventional fusion methods-such as wavelet-, Laplacian-, and decision-level approaches-often fail to preserve spatial correspondence across modalities and suffer from annotation of inconsistencies, limiting their robustness in real-world settings. This study introduces two fusion strategies, Registration-aware Guided Image Fusion (RGIF) and Reliability-Gated Modality-Attention Fusion (RGMAF), designed to overcome these limitations. RGIF employs Enhanced Correlation Coefficient (ECC)-based affine registration combined with guided filtering to maintain thermal saliency while enhancing structural detail. RGMAF integrates affine and optical-flow registration with a reliability-weighted attention mechanism that adaptively balances thermal contrast and visual sharpness. Experiments were conducted on the Multi-Sensor and Multi-View Fixed-Wing (MMFW)-UAV dataset comprising 147,417 annotated air-to-air frames collected from infrared, wide-angle, and zoom sensors. Among single-modality detectors, YOLOv10x demonstrated the most stable cross-domain performance and was selected as the detection backbone for evaluating fused imagery. RGIF improved the visual baseline by 2.13% mAP@50 (achieving 97.65%), while RGMAF attained the highest recall of 98.64%. These findings show that registration-aware and reliability-adaptive fusion provides a robust framework for integrating heterogeneous modalities, substantially enhancing UAV detection performance in multimodal environments.
[CV-61] MM-TS: Multi-Modal Temperature and Margin Schedules for Contrastive Learning with Long-Tail Data WACV2026
【速读】：该论文旨在解决多模态对比学习中因固定温度参数导致的特征表示优化受限问题，以及长尾分布数据下样本间吸引力与排斥力难以动态平衡的问题。其解决方案的关键在于提出多模态温度与边界调度机制（Multi-Modal Temperature and Margin Schedules, MM-TS），通过在训练过程中动态调整对比损失中的温度参数，实现对多模态空间中正负样本对之间吸引力和排斥力的自适应调控；同时，结合局部样本分布信息，为密集簇中的样本分配更高温度以更好保持语义结构，并将温度调度嵌入最大边界（max-margin）框架中，从而统一InfoNCE损失与最大边界目标两种主流多模态对比学习范式，显著提升模型性能并取得新的最先进结果。
链接: https://arxiv.org/abs/2603.08202
作者: Siarhei Sheludzko,Dhimitrios Duka,Bernt Schiele,Hilde Kuehne,Anna Kukleva
机构: University of Bonn(波恩大学); MPI for Informatics, SIC(马克斯·普朗克信息研究所，SIC); Tuebingen AI Center/University of Tuebingen(图宾根人工智能中心/图宾根大学); MIT-IBM Watson AI Lab(MIT-IBM华生人工智能实验室)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 18 pages, 11 figures. Accepted at WACV 2026
Abstract:Contrastive learning has become a fundamental approach in both uni-modal and multi-modal frameworks. This learning paradigm pulls positive pairs of samples closer while pushing negatives apart. In the uni-modal setting (e.g., image-based learning), previous research has shown that the strength of these forces can be controlled through the temperature parameter. In this work, we propose Multi-Modal Temperature and Margin Schedules (MM-TS), extending the concept of uni-modal temperature scheduling to multi-modal contrastive learning. Our method dynamically adjusts the temperature in the contrastive loss during training, modulating the attraction and repulsion forces in the multi-modal setting. Additionally, recognizing that standard multi-modal datasets often follow imbalanced, long-tail distributions, we adapt the temperature based on the local distribution of each training sample. Specifically, samples from dense clusters are assigned a higher temperature to better preserve their semantic structure. Furthermore, we demonstrate that temperature scheduling can be effectively integrated within a max-margin framework, thereby unifying the two predominant approaches in multi-modal contrastive learning: InfoNCE loss and max-margin objective. We evaluate our approach on four widely used image- and video-language datasets, Flickr30K, MSCOCO, EPIC-KITCHENS-100, and YouCook2, and show that our dynamic temperature and margin schedules improve performance and lead to new state-of-the-art results in the field.
[CV-62] Fusion-Poly: A Polyhedral Framework Based on Spatial-Temporal Fusion for 3D Multi-Object Tracking
【速读】：该论文旨在解决LiDAR与相机在3D多目标跟踪（3D MOT）中因采样频率不同导致的异步观测数据被浪费的问题。现有方法通常通过降低帧率进行时间对齐，仅在同步时刻进行跨模态融合，从而忽略了大量异步观测信息，限制了轨迹估计的频率和鲁棒性。解决方案的关键在于提出Fusion-Poly框架，其核心创新包括：1）频率感知级联匹配模块，根据可用检测模态自适应处理同步与异步帧；2）频率感知轨迹估计模块，通过高频运动预测、差分更新及置信度校准的生命周期管理维持轨迹连续性；3）全状态观测对齐模块，在同步时刻优化图像投影误差以提升跨模态一致性。该方法显著提升了轨迹更新频率和跟踪可靠性，在nuScenes测试集上达到76.5% AMOTA，刷新了基于检测的3D MOT方法的性能纪录。
链接: https://arxiv.org/abs/2603.08199
作者: Xian Wu,Yitao Wu,Xiaoyu Li,Zijia Li,Lijun Zhao,Lining Sun
机构: Harbin Institute of Technology (哈尔滨工业大学); State Key Laboratory of Robotics and System (机器人与系统国家重点实验室)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:LiDAR-camera 3D multi-object tracking (MOT) combines rich visual semantics with accurate depth cues to improve trajectory consistency and tracking reliability. In practice, however, LiDAR and cameras operate at different sampling rates. To maintain temporal alignment, existing data pipelines usually synchronize heterogeneous sensor streams and annotate them at a reduced shared frequency, forcing most prior methods to perform spatial fusion only at synchronized timestamps through projection-based or learnable cross-sensor association. As a result, abundant asynchronous observations remain underexploited, despite their potential to support more frequent association and more robust trajectory estimation over short temporal intervals. To address this limitation, we propose Fusion-Poly, a spatial-temporal fusion framework for 3D MOT that integrates asynchronous LiDAR and camera data. Fusion-Poly associates trajectories with multi-modal observations at synchronized timestamps and with single-modal observations at asynchronous timestamps, enabling higher-frequency updates of motion and existence states. The framework contains three key components: a frequency-aware cascade matching module that adapts to synchronized and asynchronous frames according to available detection modalities; a frequency-aware trajectory estimation module that maintains trajectories through high-frequency motion prediction, differential updates, and confidence-calibrated lifecycle management; and a full-state observation alignment module that improves cross-modal consistency at synchronized timestamps by optimizing image-projection errors. On the nuScenes test set, Fusion-Poly achieves 76.5% AMOTA, establishing a new state of the art among tracking-by-detection 3D MOT methods. Extensive ablation studies further validate the effectiveness of each component. Code will be released. Subjects: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO) Cite as: arXiv:2603.08199 [cs.CV] (or arXiv:2603.08199v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.08199 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-63] ALOOD: Exploiting Language Representations for LiDAR-based Out-of-Distribution Object Detection ITSC
【速读】：该论文旨在解决LiDAR-based 3D物体检测中对分布外（out-of-distribution, OOD）物体产生过度自信预测的问题，这会带来自动驾驶系统的安全风险。现有方法在面对训练数据未涵盖的物体类别时，容易做出错误分类，因为这些物体在训练过程中未被建模。解决方案的关键在于提出ALOOD（Aligned LiDAR representations for Out-Of-Distribution Detection），通过将物体检测器提取的特征与视觉语言模型（vision-language model, VLM）的语义特征空间对齐，从而将OOD检测转化为零样本分类任务，利用VLM的语言先验能力识别未知类别物体，显著提升了对OOD物体的检测性能。
链接: https://arxiv.org/abs/2603.08180
作者: Michael Kösel,Marcel Schreiber,Michael Ulrich,Claudius Gläser,Klaus Dietmayer
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted for publication at the 2025 IEEE Intelligent Transportation Systems Conference (ITSC)
Abstract:LiDAR-based 3D object detection plays a critical role for reliable and safe autonomous driving systems. However, existing detectors often produce overly confident predictions for objects not belonging to known categories, posing significant safety risks. This is caused by so-called out-of-distribution (OOD) objects, which were not part of the training data, resulting in incorrect predictions. To address this challenge, we propose ALOOD (Aligned LiDAR representations for Out-Of-Distribution Detection), a novel approach that incorporates language representations from a vision-language model (VLM). By aligning the object features from the object detector to the feature space of the VLM, we can treat the detection of OOD objects as a zero-shot classification task. We demonstrate competitive performance on the nuScenes OOD benchmark, establishing a novel approach to OOD object detection in LiDAR using language representations. The source code is available at this https URL.
[CV-64] MERLIN: Building Low-SNR Robust Multimodal LLM s for Electromagnetic Signals
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在电磁（Electromagnetic, EM）领域应用中的三大核心挑战：数据稀缺、缺乏系统性评估基准以及低信噪比（Signal-to-Noise Ratio, SNR）环境下模型性能脆弱性。针对这些问题，作者提出三项关键贡献：首先构建并发布EM-100k数据集，包含超过10万对EM信号与文本描述，缓解高质量标注数据不足的问题；其次设计EM-Bench基准，涵盖从感知到推理的多样化下游任务，实现标准化评测；最后提出MERLIN训练框架，通过显式对齐低层信号表示与高层语义文本，并增强模型在低SNR条件下的鲁棒性，显著提升其在复杂电磁环境中的泛化能力与稳定性。
链接: https://arxiv.org/abs/2603.08174
作者: Junyu Shen,Zhendong She,Chenghanyu Zhang,Yuchuang Sun,Luqing Luo,Dingwei Tan,Zonghao Guo,Bo Guo,Zehua Han,Wupeng Xie,Yaxin Mu,Peng Zhang,Peipei Li,Fengxiang Wang,Yangang Sun,Maosong Sun
机构: Tsinghua University (清华大学); Beijing University of Posts and Telecommunications (北京邮电大学); Tianjin University (天津大学); Institute of Microelectronics of the Chinese Academy of Sciences (中国科学院微电子研究所); HKUST (Guangzhou) (香港科技大学（广州）); National University of Defense Technology (国防科技大学); Beihang University (北京航空航天大学); Beijing Information Science and Technology University (北京信息科技大学); Artificial Intelligence Institute of China Electronics Technology Group Corporation (中国电子科技集团公司人工智能研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The paradigm of Multimodal Large Language Models (MLLMs) offers a promising blueprint for advancing the electromagnetic (EM) domain. However, prevailing approaches often deviate from the native MLLM paradigm, instead using task-specific or pipelined architectures that lead to fundamental limitations in model performance and generalization. Fully realizing the MLLM potential in EM domain requires overcoming three main challenges: (1) Data. The scarcity of high-quality datasets with paired EM signals and descriptive text annotations used for MLLMs pre-training; (2) Benchmark. The absence of comprehensive benchmarks to systematically evaluate and compare the performance of models on EM signal-to-text tasks; (3) Model. A critical fragility in low Signal-to-Noise Ratio (SNR) environments, where critical signal features can be obscured, leading to significant performance degradation. To address these challenges, we introduce a tripartite contribution to establish a foundation for MLLMs in the EM domain. First, to overcome data scarcity, we construct and release EM-100k, a large-scale dataset comprising over 100,000 EM signal-text pairs. Second, to enable rigorous and standardized evaluation, we propose EM-Bench, the most comprehensive benchmark featuring diverse downstream tasks spanning from perception to reasoning. Finally, to tackle the core modeling challenge, we present MERLIN, a novel training framework designed not only to align low-level signal representations with high-level semantic text, but also to explicitly enhance model robustness and performance in challenging low-SNR environments. Comprehensive experiments validate our method, showing that MERLIN is state-of-the-art in the EM-Bench and exhibits remarkable robustness in low-SNR settings. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2603.08174 [cs.CV] (or arXiv:2603.08174v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.08174 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-65] Edged USLAM: Edge-Aware Event-Based SLAM with Learning-Based Depth Priors ICRA2026
【速读】：该论文旨在解决传统视觉同步定位与建图（SLAM）算法在快速运动、低光照或突变光照条件下因运动模糊和动态范围受限而失效的问题。其核心解决方案是提出一种混合视觉-惯性系统Edged USLAM，关键创新在于引入边缘感知前端（edge-aware front-end）以增强事件帧的特征跟踪与非线性运动补偿能力，并集成轻量级深度模块，通过区域感兴趣（ROI）粗略估计场景深度，从而提升运动补偿精度与尺度一致性。此设计有效融合了事件相机的高时间分辨率与高动态范围优势，同时增强了系统在复杂光照下的鲁棒性与定位稳定性，尤其适用于无人机等空中平台的多样化导航任务。
链接: https://arxiv.org/abs/2603.08150
作者: Şebnem Sarıözkan,Hürkan Şahin,Olaya Álvarez-Tuñón,Erdal Kayacan
机构: Paderborn University (帕德博恩大学); EIVA A/S; IT University of Copenhagen (哥本哈根信息技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 8 pages, 7 figures, 3 tables. Accepted to ICRA 2026. Project code and datasets available at this https URL
Abstract:Conventional visual simultaneous localization and mapping (SLAM) algorithms often fail under rapid motion, low illumination, or abrupt lighting transitions due to motion blur and limited dynamic range. Event cameras mitigate these issues with high temporal resolution and high dynamic range (HDR), but their sparse, asynchronous outputs complicate feature extraction and integration with other sensors; e.g. inertial measurement units (IMUs) and standard cameras. We present Edged USLAM, a hybrid visual-inertial system that extends Ultimate SLAM (USLAM) with an edge-aware front-end and a lightweight depth module. The frontend enhances event frames for robust feature tracking and nonlinear motion compensation, while the depth module provides coarse, region-of-interest (ROI)-based scene depth to improve motion compensation and scale consistency. Evaluations across public benchmarks and real-world unmanned air vehicle (UAV) flights demonstrate that performance varies significantly by scenario. For instance, event-only methods like point-line event-based visual-inertial odometry (PL-EVIO) or learning-based pipelines such as deep event-based visual odometry (DEVO) excel in highly aggressive or extreme HDR conditions. In contrast, Edged USLAM provides superior stability and minimal drift in slow or structured trajectories, ensuring consistently accurate localization on real flights under challenging illumination. These findings highlight the complementary strengths of event-only, learning-based, and hybrid approaches, while positioning Edged USLAM as a robust solution for diverse aerial navigation tasks.
[CV-66] MV-Fashion: Towards Enabling Virtual Try-On and Size Estimation with Multi-View Paired Data
【速读】：该论文旨在解决现有4D人体数据集在时尚领域研究中的局限性，即缺乏逼真的服装动态表现或任务特定的标注信息。真实世界采集的数据通常缺少用于虚拟试穿（Virtual Try-On, VTON）和尺码估计等任务所需的精细标注与配对数据，而合成数据则存在真实性差距。解决方案的关键在于提出MV-Fashion数据集——一个大规模、多视角视频数据集，包含3,273个序列（共7250万帧）和80名多样化的受试者，每人穿着3–10套服装，能够捕捉复杂的真实世界服装动态（如多层穿搭、卷袖、束腰等）。其核心贡献在于提供像素级语义标注、真实的材料属性（如弹性）以及3D点云，并特别为VTON任务构建了同步的多视角穿着图像与对应的平面商品图配对数据，从而为时尚场景下的虚拟试穿、尺码估计和新视角合成等任务建立了基准。
链接: https://arxiv.org/abs/2603.08147
作者: Hunor Laczkó,Libang Jia,Loc-Phat Truong,Diego Hernández,Sergio Escalera,Jordi Gonzalez,Meysam Madadi
机构: Universitat Autònoma de Barcelona (巴塞罗那自治大学); Computer Vision Center (计算机视觉中心); Universitat de Barcelona (巴塞罗那大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Existing 4D human datasets fall short for fashion-specific research, lacking either realistic garment dynamics or task-specific annotations. Synthetic datasets suffer from a realism gap, whereas real-world captures lack the detailed annotations and paired data required for virtual try-on (VTON) and size estimation tasks. To bridge this gap, we introduce MV-Fashion, a large-scale, multi-view video dataset engineered for domain-specific fashion analysis. MV-Fashion features 3,273 sequences (72.5 million frames) from 80 diverse subjects wearing 3-10 outfits each. It is designed to capture complex, real-world garment dynamics, including multiple layers and varied styling (e.g. rolled sleeves, tucked shirt). A core contribution is a rich data representation that includes pixel-level semantic annotations, ground-truth material properties like elasticity, and 3D point clouds. Crucially for VTON applications, MV-Fashion provides paired data: multi-view synchronized captures of worn garments alongside their corresponding flat, catalogue images. We leverage this dataset to establish baselines for fashion-centric tasks, including virtual try-on, clothing size estimation, and novel view synthesis. The dataset is available at this https URL .
[CV-67] VesselFusion: Diffusion Models for Vessel Centerline Extraction from 3D CT Images
【速读】：该论文旨在解决从三维计算机断层扫描（3D CT）图像中准确提取血管中心线（vessel centerline）的问题，传统确定性模型难以捕捉复杂的人体血管结构。其解决方案的关键在于提出一种基于扩散模型（diffusion model）的VesselFusion方法，该方法采用粗到细（coarse-to-fine）的中心线表示策略，并引入基于投票的聚合机制（voting-based aggregation），从而实现更自然且稳定的血管中心线提取结果。
链接: https://arxiv.org/abs/2603.08135
作者: Soichi Mita,Shumpei Takezaki,Ryoma Bise
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Vessel centerline extraction from 3D CT images is an important task because it reduces annotation effort to build a model that estimates a vessel structure. It is challenging to estimate natural vessel structures since conventional approaches are deterministic models, which cannot capture a complex human structure. In this study, we propose VesselFusion, which is a diffusion model to extract the vessel centerline from 3D CT image. The proposed method uses a coarse-to-fine representation of the centerline and a voting-based aggregation for a natural and stable extraction. VesselFusion was evaluated on a publicly available CT image dataset and achieved higher extraction accuracy and a more natural result than conventional approaches.
[CV-68] Fast Low-light Enhancement and Deblurring for 3D Dark Scenes ICASSP2026
【速读】：该论文旨在解决低光照、噪声和运动模糊等复合退化条件下，从图像中进行高质量三维场景重建（novel view synthesis）的难题。现有体渲染方法在处理多重退化时性能受限，而传统的2D顺序预处理则因帧间依赖性引入伪影。其解决方案的关键在于提出FLED-GS框架，将3D场景恢复重构为增强与重建交替迭代的过程：通过插入多个中间亮度锚点实现渐进式恢复，避免噪声放大对去模糊或几何结构的破坏；每轮迭代中使用现成的2D去模糊器锐化输入，并结合噪声感知的3D高斯溅射（3DGS）重建，同时估计并抑制噪声，生成干净先验供下一轮优化，从而显著提升训练与渲染效率（分别快21倍和11倍）。
链接: https://arxiv.org/abs/2603.08133
作者: Feng Zhang,Jinglong Wang,Ze Li,Yanghong Zhou,Yang Chen,Lei Chen,Xiatian Zhu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, 2 figures, Accepted at ICASSP 2026
Abstract:Novel view synthesis from low-light, noisy, and motion-blurred imagery remains a valuable and challenging task. Current volumetric rendering methods struggle with compound degradation, and sequential 2D preprocessing introduces artifacts due to interdependencies. In this work, we introduce FLED-GS, a fast low-light enhancement and deblurring framework that reformulates 3D scene restoration as an alternating cycle of enhancement and reconstruction. Specifically, FLED-GS inserts several intermediate brightness anchors to enable progressive recovery, preventing noise blow-up from harming deblurring or geometry. Each iteration sharpens inputs with an off-the-shelf 2D deblurrer and then performs noise-aware 3DGS reconstruction that estimates and suppresses noise while producing clean priors for the next level. Experiments show FLED-GS outperforms state-of-the-art LuSh-NeRF, achieving 21 \times faster training and 11 \times faster rendering.
[CV-69] UniGround: Universal 3D Visual Grounding via Training-Free Scene Parsing
【速读】：该论文旨在解决3D视觉定位（3D Visual Grounding, 3DVG）中现有方法依赖预训练模型导致泛化能力受限的问题，特别是在未见过的空间关系和分布外场景下表现不佳。解决方案的关键在于摒弃对预训练模型的依赖，引入无需训练的视觉与几何推理机制，从而实现开放世界（open-world）的3DVG。具体而言，提出的方法UniGround分为两个阶段：第一阶段通过无训练的3D拓扑结构和多视角语义编码进行全局候选过滤；第二阶段利用多尺度视觉提示和结构化推理实现局部精确定位，最终在ScanRefer和EmbodiedScan数据集上取得优于现有零样本方法的性能，并在真实环境中展现出强鲁棒性。
链接: https://arxiv.org/abs/2603.08131
作者: Jiaxi Zhang,Yunheng Wang,Wei Lu,Taowen Wang,Weisheng Xu,Shuning Zhang,Yixiao Feng,Yuetong Fang,Renjing Xu
机构: 未知
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages,6 figures,3 tables
Abstract:Understanding and localizing objects in complex 3D environments from natural language descriptions, known as 3D Visual Grounding (3DVG), is a foundational challenge in embodied AI, with broad implications for robotics, augmented reality, and human-machine interaction. Large-scale pre-trained foundation models have driven significant progress on this front, enabling open-vocabulary 3DVG that allows systems to locate arbitrary objects in a given scene. However, their reliance on pre-trained models constrains 3D perception and reasoning within the inherited knowledge boundaries, resulting in limited generalization to unseen spatial relationships and poor robustness to out-of-distribution scenes. In this paper, we replace this constrained perception with training-free visual and geometric reasoning, thereby unlocking open-world 3DVG that enables the localization of any object in any scene beyond the training data. Specifically, the proposed UniGround operates in two stages: a Global Candidate Filtering stage that constructs scene candidates through training-free 3D topology and multi-view semantic encoding, and a Local Precision Grounding stage that leverages multi-scale visual prompting and structured reasoning to precisely identify the target object. Experiments on ScanRefer and EmbodiedScan show that UniGround achieves 46.1%/34.1% Acc@0.25/0.5 on ScanRefer and 28.7% Acc@0.25 on EmbodiedScan, establishing a new state-of-the-art among zero-shot methods on EmbodiedScan without any 3D supervision. We further evaluate UniGround in real-world environments under uncontrolled reconstruction conditions and substantial domain shift, showing training-free reasoning generalizes robustly beyond curated benchmarks.
[CV-70] Foley-Flow: Coordinated Video-to-Audio Generation with Masked Audio-Visual Alignment and Dynamic Conditional Flows
【速读】：该论文旨在解决视频驱动的音频生成任务中，如何实现音频与视频在语义和节奏上的精确对齐问题（即音频-视觉对齐，Audio-Visual Alignment）。现有方法通常采用两阶段设计：先通过对比学习对齐音视频编码器，再以全局视频特征引导音频生成，但这种方法难以保证时序上的节奏同步。其解决方案的关键在于提出FoleyFlow框架，首先利用掩码建模训练对单模态音视频编码器进行联合对齐，使音频片段在语义和节奏上均与对应视频帧一致；随后引入动态条件流（dynamic conditional flow），基于随时间变化的视频特征作为动态条件，指导音频段的逐时生成，从而实现更精细的时空一致性音频合成。
链接: https://arxiv.org/abs/2603.08126
作者: Shentong Mo,Yibing Song
机构: DAMO Academy, Alibaba Group; Hupan Laboratory; CMU; MBZUAI
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注:
Abstract:Coordinated audio generation based on video inputs typically requires a strict audio-visual (AV) alignment, where both semantics and rhythmics of the generated audio segments shall correspond to those in the video frames. Previous studies leverage a two-stage design where the AV encoders are firstly aligned via contrastive learning, then the encoded video representations guide the audio generation process. We observe that both contrastive learning and global video guidance are effective in aligning overall AV semantics while limiting temporally rhythmic synchronization. In this work, we propose FoleyFlow to first align unimodal AV encoders via masked modeling training, where the masked audio segments are recovered under the guidance of the corresponding video segments. After training, the AV encoders which are separately pretrained using only unimodal data are aligned with semantic and rhythmic consistency. Then, we develop a dynamic conditional flow for the final audio generation. Built upon the efficient velocity flow generation framework, our dynamic conditional flow utilizes temporally varying video features as the dynamic condition to guide corresponding audio segment generations. To this end, we extract coherent semantic and rhythmic representations during masked AV alignment, and use this representation of video segments to guide audio generation temporally. Our audio results are evaluated on the standard benchmarks and largely surpass existing results under several metrics. The superior performance indicates that FoleyFlow is effective in generating coordinated audios that are both semantically and rhythmically coherent to various video sequences.
[CV-71] SAMoE-VLA: A Scene Adaptive Mixture-of-Experts Vision-Language-Action Model for Autonomous Driving
【速读】：该论文旨在解决现有视觉-语言-动作（Vision-Language-Action, VLA）模型在自动驾驶任务中因直接沿用大语言模型（Large Language Models, LLMs）的token级稀疏专家混合（Mixture-of-Experts, MoE）机制而导致性能不稳定和安全性下降的问题，其根本原因在于token级专家分工与场景级决策需求之间存在不匹配。解决方案的关键在于提出一种场景自适应的VLA框架SAMoE-VLA，通过将专家选择机制从token嵌入转移到结构化的鸟瞰图（bird’s-eye-view, BEV）特征表示上，利用BEV特征所蕴含的交通场景上下文信息作为MoE路由信号，实现针对不同驾驶场景的专家权重分配与融合；同时引入条件交叉模态因果注意力机制（Conditional Cross-Modal Causal Attention），统一整合世界状态、语言意图与动作历史，以支持跨感知、语言、知识与行为的时序一致性推理。
链接: https://arxiv.org/abs/2603.08113
作者: Zihan You,Hongwei Liu,Chenxu Dang,Zhe Wang,Sining Ang,Aoqi Wang,Yan Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advances in Vision-Language-Action (VLA) models have shown promising capabilities in autonomous driving by leveraging the understanding and reasoning strengths of Large Language Models(LLMs).However, our empirical analysis reveals that directly applying existing token-level MoE mechanisms–which are inherited from LLM architectures–to VLA models results in unstable performance and safety degradation in autonomous driving, highlighting a misalignment between token-based expert specialization and scene-level this http URL address this, we propose SAMoE-VLA, a scene-adaptive Vision-Language-Action framework that conditions expert selection on structured scene representations instead of token embeddings. Our key idea is to derive the MoE routing signal from bird’s-eye-view (BEV) features that encapsulates traffic scene context, enabling scenario-dependent expert weighting and merging tailored to distinct driving conditions. Furthermore, to support temporally consistent reasoning across world-knowledge, perception, language, and action, we introduce a Conditional Cross-Modal Causal Attention mechanism that integrates world state, linguistic intent, and action history into a unified causal reasoning process. Extensive experiments on the nuScenes open loop planning dataset and LangAuto closed-loop benchmark demonstrate that SAMoE-VLA achieves state-of-the-art performance, outperforming prior VLA-based and world-model-based approaches with fewer this http URL code will be released soon.
[CV-72] Adaptive MLP Pruning for Large Vision Transformers
【速读】：该论文旨在解决大规模视觉Transformer模型中参数冗余导致的计算与内存开销过大的问题。其核心挑战在于如何在不显著损害模型性能的前提下，有效压缩模型参数量。解决方案的关键在于提出一种自适应多层感知机（MLP）剪枝方法（Adaptive MLP Pruning, AMP），首先基于泰勒展开法评估MLP模块中神经元的重要性，但为克服传统基于one-hot交叉熵损失忽略其他类别预测信息而导致重要性评分质量下降的问题，引入无标签信息熵准则以更全面地建模原始模型的输出分布，从而提升重要性评估精度；其次，通过排序并结合二分搜索算法自适应地剪枝不同MLP模块中的冗余神经元，避免预设固定压缩比，实现近无损的参数与浮点运算次数（FLOPs）缩减（约40%）。实验表明，该方法在CLIP和DINOv2等先进视觉Transformer上无需微调即可显著优于现有剪枝方法。
链接: https://arxiv.org/abs/2603.08100
作者: Chengchao Shen
机构: Central South University (中南大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Large vision transformers present impressive scalability, as their performance can be well improved with increased model capacity. Nevertheless, their cumbersome parameters results in exorbitant computational and memory demands. By analyzing prevalent transformer structures, we find that multilayer perceptron (MLP) modules constitute the largest share of the model’s parameters. In this paper, we propose an Adaptive MLP Pruning (AMP) method to substantially reduce the parameters of large vision transformers without obvious performance degradation. First, we adopt Taylor based method to evaluate neuron importance of MLP. However, the importance computation using one-hot cross entropy loss ignores the potential predictions on other categories, thus degrading the quality of the evaluated importance scores. To address this issue, we introduce label-free information entropy criterion to fully model the predictions of the original model for more accurate importance evaluation. Second, we rank the hidden neurons of MLP by the above importance scores and apply binary search algorithm to adaptively prune the ranked neurons according to the redundancy of different MLP modules, thereby avoiding the predefined compression ratio. Experimental results on several state-of-the-art large vision transformers, including CLIP and DINOv2, demonstrate that our method achieves roughly 40% parameter and FLOPs reduction in a near lossless manner. Moreover, when the models are not finetuned after pruning, our method outperforms other pruning methods by significantly large margin. The source code and trained weights are available at this https URL.
[CV-73] rianguLang: Geometry-Aware Semantic Consensus for Pose-Free 3D Localization
【速读】：该论文旨在解决3D空间中基于自然语言的对象与部件定位问题，其核心挑战在于现有方法在每场景优化的精度与几何一致性之间存在权衡，同时受限于前馈推理效率。解决方案的关键在于提出一种无需相机标定的前馈框架TrianguLang，其创新性地引入几何感知语义注意力（Geometry-Aware Semantic Attention, GASA），利用预测的几何信息来调控跨视角特征对应关系，从而抑制语义合理但几何不一致的匹配，且无需真实位姿标注。该方法显著提升了文本引导的3D分割与定位性能，并将用户交互从O(N)次点击简化为单次文本查询，在1008×1008分辨率下实现约57ms/帧（~18 FPS）的实时推理速度，适用于交互式机器人和增强现实（AR）等实际场景部署。
链接: https://arxiv.org/abs/2603.08096
作者: Bryce Grant,Aryeh Rothenberg,Atri Banerjee,Peng Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Localizing objects and parts from natural language in 3D space is essential for robotics, AR, and embodied AI, yet existing methods face a trade-off between the accuracy and geometric consistency of per-scene optimization and the efficiency of feed-forward inference. We present TrianguLang, a feed-forward framework for 3D localization that requires no camera calibration at inference. Unlike prior methods that treat views independently, we introduce Geometry-Aware Semantic Attention (GASA), which utilizes predicted geometry to gate cross-view feature correspondence, suppressing semantically plausible but geometrically inconsistent matches without requiring ground-truth poses. Validated on five benchmarks including ScanNet++ and uCO3D, TrianguLang achieves state-of-the-art feed-forward text-guided segmentation and localization, reducing user effort from O(N) clicks to a single text query. The model processes each frame at 1008x1008 resolution in \sim 57ms ( \sim 18 FPS) without optimization, enabling practical deployment for interactive robotics and AR applications. Code and checkpoints are available at this https URL.
[CV-74] DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation
【速读】：该论文旨在解决当前基于文本到图像（Text-to-Image, T2I）生成模型在主体驱动场景下评估体系存在的三大关键问题：一是主体图像多样性与全面性不足；二是缺乏针对不同主体难度等级和提示（prompt）场景的细粒度性能评估机制；三是缺少可操作的诊断性洞察以指导模型优化。其解决方案的核心在于提出DSH-Bench这一综合性基准，通过四项创新实现系统化多视角分析：1）基于分层分类采样机制确保58个细粒度类别下的主体代表性；2）引入主体难度与提示场景联合分类方案以实现能力评估的精细化；3）设计Subject Identity Consistency Score（SICS）指标，在主体保真度量化上较现有方法提升9.4%的人类评价相关性；4）基于大规模实证评估（19个主流模型）提供可解释的诊断洞察，为未来训练范式与数据构建策略优化提供明确方向。
链接: https://arxiv.org/abs/2603.08090
作者: Zhenyu Hu,Qing Wang,Te Cao,Luo Liao,Longfei Lu,Liqun Liu,Shuang Li,Hang Chen,Mengge Xue,Yuan Chen,Chao Deng,Peng Shu,Huan Yu,Jie Jiang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Significant progress has been achieved in subject-driven text-to-image (T2I) generation, which aims to synthesize new images depicting target subjects according to user instructions. However, evaluating these models remains a significant challenge. Existing benchmarks exhibit critical limitations: 1) insufficient diversity and comprehensiveness in subject images, 2) inadequate granularity in assessing model performance across different subject difficulty levels and prompt scenarios, and 3) a profound lack of actionable insights and diagnostic guidance for subsequent model refinement. To address these limitations, we propose DSH-Bench, a comprehensive benchmark that enables systematic multi-perspective analysis of subject-driven T2I models through four principal innovations: 1) a hierarchical taxonomy sampling mechanism ensuring comprehensive subject representation across 58 fine-grained categories, 2) an innovative classification scheme categorizing both subject difficulty level and prompt scenario for granular capability assessment, 3) a novel Subject Identity Consistency Score (SICS) metric demonstrating a 9.4% higher correlation with human evaluation compared to existing measures in quantifying subject preservation, and 4) a comprehensive set of diagnostic insights derived from the benchmark, offering critical guidance for optimizing future model training paradigms and data construction strategies. Through an extensive empirical evaluation of 19 leading models, DSH-Bench uncovers previously obscured limitations in current approaches, establishing concrete directions for future research and development.
[CV-75] From Reactive to Map-Based AI: Tuned Local LLM s for Semantic Zone Inference in Object-Goal Navigation
【速读】：该论文旨在解决Object-Goal Navigation (ObjectNav)任务中，基于大语言模型（LLM）的代理因采用“反应式”（reactive）范式而缺乏显式空间记忆，导致冗余探索和短视行为的问题。其解决方案的关键在于从反应式AI向“基于地图的AI”（Map-Based AI）转变，通过将LLM的语义推理能力与混合拓扑-栅格地图系统相结合：具体而言，利用LoRA微调的Llama-2模型从视觉观测中推断语义区域类别及目标存在概率，“区域”（zone）被定义为由观察到的对象集合所描述的功能性空间单元，提供关键的语义共现线索；这些语义信息被嵌入拓扑图中，使代理能够优先探索高概率区域，并通过旅行商问题（TSP）优化实现系统性探索。
链接: https://arxiv.org/abs/2603.08086
作者: Yudai Noda,Kanji Tanaka
机构: University of Fukui (福井大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 5 figures, technical report
Abstract:Object-Goal Navigation (ObjectNav) requires an agent to find and navigate to a target object category in unknown environments. While recent Large Language Model (LLM)-based agents exhibit zero-shot reasoning, they often rely on a “reactive” paradigm that lacks explicit spatial memory, leading to redundant exploration and myopic behaviors. To address these limitations, we propose a transition from reactive AI to “Map-Based AI” by integrating LLM-based semantic inference with a hybrid topological-grid mapping system. Our framework employs a fine-tuned Llama-2 model via Low-Rank Adaptation (LoRA) to infer semantic zone categories and target existence probabilities from verbalized object observations. In this study, a “zone” is defined as a functional area described by the set of observed objects, providing crucial semantic co-occurrence cues for finding the target. This semantic information is integrated into a topological graph, enabling the agent to prioritize high-probability areas and perform systematic exploration via Traveling Salesman Problem (TSP) optimization. Evaluations in the AI2-THOR simulator demonstrate that our approach significantly outperforms traditional frontier exploration and reactive LLM baselines, achieving a superior Success Rate (SR) and Success weighted by Path Length (SPL).
[CV-76] ALON: Test-time Adaptive Learning for On-the-Fly Category Discovery CVPR2026
【速读】：该论文旨在解决在线类别发现（On-the-fly Category Discovery, OCD）中因固定特征提取器和基于哈希的原型量化方法导致的局限性问题，包括忽视新数据的学习潜力、特征量化带来的信息损失与类内方差增大，以及由此引发的类别爆炸（category explosion）现象。解决方案的关键在于提出一种测试时适应（test-time adaptation）框架，包含两个互补策略：一是语义感知的原型更新机制，用于动态优化类别原型以提升分类性能；二是稳定的测试时编码器更新机制，将新信息直接融入参数空间以持续扩展知识库。此外，论文在离线阶段引入边际感知的logit校准，增强类间距离并压缩类内紧凑性，为未来类别发现预留嵌入空间。这一设计显著提升了新类别识别准确率，并有效缓解了类别爆炸问题。
链接: https://arxiv.org/abs/2603.08075
作者: Yanan Wu,Yuhan Yan,Tailai Chen,Zhixiang Chi,ZiZhang Wu,Yi Jin,Yang Wang,Zhenbo Li
机构: China Agricultural University(中国农业大学); University of Toronto(多伦多大学); Fudan University(复旦大学); Beijing Jiaotong University(北京交通大学); Concordia University(康考迪亚大学); Woof RoboT Co., Ltd.(woof机器人有限公司)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 6 figures, accepted by CVPR 2026
Abstract:On-the-fly category discovery (OCD) aims to recognize known categories while simultaneously discovering novel ones from an unlabeled online stream, using a model trained only on labeled data. Existing approaches freeze the feature extractor trained offline and employ a hash-based framework that quantizes features into binary codes as class prototypes. However, discovering novel categories with a fixed knowledge base is counterintuitive, as the learning potential of incoming data is entirely neglected. In addition, feature quantization introduces information loss, diminishes representational expressiveness, and amplifies intra-class variance. It often results in category explosion, where a single class is fragmented into multiple pseudo-classes. To overcome these limitations, we propose a test-time adaptation framework that enables learning through discovery. It incorporates two complementary strategies: a semantic-aware prototype update and a stable test-time encoder update. The former dynamically refines class prototypes to enhance classification, whereas the latter integrates new information directly into the parameter space. Together, these components allow the model to continuously expand its knowledge base with newly encountered samples. Furthermore, we introduce a margin-aware logit calibration in the offline stage to enlarge inter-class margins and improve intra-class compactness, thereby reserving embedding space for future class discovery. Experiments on standard OCD benchmarks demonstrate that our method substantially outperforms existing hash-based state-of-the-art approaches, yielding notable improvements in novel-class accuracy and effectively mitigating category explosion. The code is publicly available at \textcolorbluethis https URL.
[CV-77] Synthetic Defect Image Generation for Power Line Insulator Inspection Using Multimodal Large Language Models
【速读】：该论文旨在解决电力巡检中缺陷识别模型训练因真实缺陷样本稀缺而导致的性能瓶颈问题（即数据稀缺场景下的缺陷类型分类难题）。其核心解决方案是利用现成的多模态大语言模型（Multimodal Large Language Model, MLLM）作为无需训练的图像生成器，通过文本提示和视觉参考双重条件控制合成缺陷图像，结合轻量级人工验证与提示优化提升标签保真度，并采用基于嵌入距离的聚类中心筛选策略从合成数据池中选择最贴近真实类别分布的样本进行增强。该方法在仅使用104张真实训练图像的情况下，使陶瓷绝缘子缺陷分类（壳体 vs. 釉面）的测试F1分数由0.615提升至0.739，相当于约4–5倍的数据效率增益，且在不同骨干网络和冻结特征线性探测基线上均表现出稳定性，为低数据采集成本场景下的缺陷识别提供了一条实用、低门槛的技术路径。
链接: https://arxiv.org/abs/2603.08069
作者: Xuesong Wang,Caisheng Wang
机构: Wayne State University (韦恩州立大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted to Engineering Applications of Artificial Intelligence, Feb. 16, 2026
Abstract:Utility companies increasingly rely on drone imagery for post-event and routine inspection, but training accurate defect-type classifiers remains difficult because defect examples are rare and inspection datasets are often limited or proprietary. We address this data-scarcity setting by using an off-the-shelf multimodal large language model (MLLM) as a training-free image generator to synthesize defect images from visual references and text prompts. Our pipeline increases diversity via dual-reference conditioning, improves label fidelity with lightweight human verification and prompt refinement, and filters the resulting synthetic pool using an embedding-based selection rule based on distances to class centroids computed from the real training split. We evaluate on ceramic insulator defect-type classification (shell vs. glaze) using a public dataset with a realistic low training-data regime (104 real training images; 152 validation; 308 test). Augmenting the 10% real training set with embedding-selected synthetic images improves test F1 score (harmonic mean of precision and recall) from 0.615 to 0.739 (20% relative), corresponding to an estimated 4–5x data-efficiency gain, and the gains persist with stronger backbone models and frozen-feature linear-probe baselines. These results suggest a practical, low-barrier path for improving defect recognition when collecting additional real defects is slow or infeasible.
[CV-78] Evaluating Generative Models via One-Dimensional Code Distributions
【速读】：该论文旨在解决当前生成式模型评估中依赖连续特征分布指标（如FID）所导致的感知质量信息丢失问题，这类指标通常基于经过训练以对视觉外观变化具有不变性的特征，从而忽略了对感知质量至关重要的细节。解决方案的关键在于将评估从连续特征空间转移到离散视觉标记（discrete visual tokens）空间，利用现代一维图像标记器（1D image tokenizers）紧凑编码语义与感知信息的特性，并通过引入两种新指标实现高效评估：一是无需训练的Codebook Histogram Distance（CHD），用于衡量标记分布差异；二是基于合成退化数据学习的无参考质量指标Code Mixture Model Score（CMMS）。该方法在跨分布偏移场景下表现出更强的人类感知相关性，显著优于现有主流指标。
链接: https://arxiv.org/abs/2603.08064
作者: Zexi Jia,Pengcheng Luo,Yijia Zhong,Jinchao Zhang,Jie Zhou
机构: WeChat AI, Tencent Inc.; Institute for Artificial Intelligence, Peking University; College of Computer Science and Artificial Intelligence, Fudan University
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Most evaluations of generative models rely on feature-distribution metrics such as FID, which operate on continuous recognition features that are explicitly trained to be invariant to appearance variations, and thus discard cues critical for perceptual quality. We instead evaluate models in the space of \emphdiscrete visual tokens, where modern 1D image tokenizers compactly encode both semantic and perceptual information and quality manifests as predictable token statistics. We introduce \emphCodebook Histogram Distance (CHD), a training-free distribution metric in token space, and \emphCode Mixture Model Score (CMMS), a no-reference quality metric learned from synthetic degradations of token sequences. To stress-test metrics under broad distribution shifts, we further propose \emphVisForm, a benchmark of 210K images spanning 62 visual forms and 12 generative models with expert annotations. Across AGIQA, HPDv2/3, and VisForm, our token-based metrics achieve state-of-the-art correlation with human judgments, and we will release all code and datasets to facilitate future research.
[CV-79] Enhancing Cross-View UAV Geolocalization via LVLM-Driven Relational Modeling
【速读】：该论文旨在解决跨视角无人机（UAV）图像地理定位问题，即如何通过将无人机拍摄的图像与大规模地理参考的卫星数据库进行匹配，精确识别其空间坐标。现有方法通常独立提取各视角特征，并依赖简单启发式规则计算相似性，未能显式建模不同视角之间的关键交互关系。解决方案的关键在于提出一种可插拔的排名架构，利用大视觉语言模型（Large Vision-Language Model, LVLM）显式学习无人机与卫星图像间的深层视觉-语义关联，并设计了一种新型关系感知损失函数，通过软标签提供细粒度监督，避免对近似正样本过度惩罚，从而显著提升模型的判别能力和训练稳定性。
链接: https://arxiv.org/abs/2603.08063
作者: Bowen Liu,Pengyue Jia,Wanyu Wang,Derong Xu,Jiawei Cheng,Jiancheng Dong,Xiao Han,Zimo Zhao,Chao Zhang,Bowen Yu,Fangyu Hong,Xiangyu Zhao
机构: City University of Hong Kong (香港城市大学); Zhejiang University of Technology (浙江工业大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The primary objective of cross-view UAV geolocalization is to identify the exact spatial coordinates of drone-captured imagery by aligning it with extensive, geo-referenced satellite databases. Current approaches typically extract features independently from each perspective and rely on basic heuristics to compute similarity, thereby failing to explicitly capture the essential interactions between different views. To address this limitation, we introduce a novel, plug-and-play ranking architecture designed to explicitly perform joint relational modeling for improved UAV-to-satellite image matching. By harnessing the capabilities of a Large Vision-Language Model (LVLM), our framework effectively learns the deep visual-semantic correlations linking UAV and satellite imagery. Furthermore, we present a novel relational-aware loss function to optimize the training phase. By employing soft labels, this loss provides fine-grained supervision that avoids overly penalizing near-positive matches, ultimately boosting both the model’s discriminative power and training stability. Comprehensive evaluations across various baseline architectures and standard benchmarks reveal that the proposed method substantially boosts the retrieval accuracy of existing models, yielding superior performance even under highly demanding conditions.
[CV-80] ImageEdit-R1: Boosting Multi-Agent Image Editing via Reinforcement Learning
【速读】：该论文旨在解决当前图像编辑系统在处理复杂、间接或多步骤用户指令时存在的局限性，尤其是封闭源代码或专有模型难以实现语义理解与上下文感知的精细化编辑问题。其解决方案的关键在于提出一种基于强化学习的多智能体框架 ImageEdit-R1，通过协调一组具备预训练视觉-语言能力和生成能力的专业化智能体（agent）来完成图像编辑任务，每个智能体负责特定功能模块（如意图理解、感兴趣区域识别、编辑动作选择和内容合成），并利用强化学习机制实现跨智能体的协同决策，从而将图像编辑建模为一个序列决策过程，显著提升了编辑策略的动态性和上下文相关性。
链接: https://arxiv.org/abs/2603.08059
作者: Yiran Zhao,Yaoqi Ye,Xiang Liu,Michael Qizhe Shieh,Trung Bui
机构: National University of Singapore (新加坡国立大学); Adobe Research (Adobe研究院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:With the rapid advancement of commercial multi-modal models, image editing has garnered significant attention due to its widespread applicability in daily life. Despite impressive progress, existing image editing systems, particularly closed-source or proprietary models, often struggle with complex, indirect, or multi-step user instructions. These limitations hinder their ability to perform nuanced, context-aware edits that align with human intent. In this work, we propose ImageEdit-R1, a multi-agent framework for intelligent image editing that leverages reinforcement learning to coordinate high-level decision-making across a set of specialized, pretrained vision-language and generative agents. Each agent is responsible for distinct capabilities–such as understanding user intent, identifying regions of interest, selecting appropriate editing actions, and synthesizing visual content–while reinforcement learning governs their collaboration to ensure coherent and goal-directed behavior. Unlike existing approaches that rely on monolithic models or hand-crafted pipelines, our method treats image editing as a sequential decision-making problem, enabling dynamic and context-aware editing strategies. Experimental results demonstrate that ImageEdit-R1 consistently outperforms both individual closed-source diffusion models and alternative multi-agent framework baselines across multiple image editing datasets.
[CV-81] See and Switch: Vision-Based Branching for Interactive Robot-Skill Programming
【速读】：该论文旨在解决基于演示的机器人编程（Programming by Demonstration, PbD）在真实世界复杂性和变异性下难以规模化的问题，尤其是如何实现可靠、自适应的条件分支选择与异常检测。其解决方案的关键在于提出了一种名为See Switch的交互式教学与执行框架，该框架将任务表示为由技能模块通过决策状态（Decision States, DS）连接而成的用户可扩展图结构，并利用眼在手（eye-in-hand）视觉图像（高维感知信号）进行在线分支选择和分布外情境识别，从而支持在执行过程中动态调整行为路径。该方法不依赖于人工设定分支或低维感知信号（如本体感觉），并通过输入模态抽象层整合了力控教学、操纵杆控制和手势等多种教学方式，实现了教学模态无关性与现场恢复演示的高效性。
链接: https://arxiv.org/abs/2603.08057
作者: Petr Vanc,Jan Kristof Behrens,Václav Hlaváč,Karla Stepanova
机构: 未知
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 11 figures
Abstract:Programming robots by demonstration (PbD) is an intuitive concept, but scaling it to real-world variability remains a challenge for most current teaching frameworks. Conditional task graphs are very expressive and can be defined incrementally, which fits very well with the PbD idea. However, acting using conditional task graphs requires reliable perception-grounded online branch selection. In this paper, we present See Switch, an interactive teaching-and-execution framework that represents tasks as user-extendable graphs of skill parts connected via decision states (DS), enabling conditional branching during replay. Unlike prior approaches that rely on manual branching or low-dimensional signals (e.g., proprioception), our vision-based Switcher uses eye-in-hand images (high-dimensional) to select among competing successor skill parts and to detect out-of-distribution contexts that require new demonstrations. We integrate kinesthetic teaching, joystick control, and hand gestures via an input-modality-abstraction layer and demonstrate that our proposed method is teaching modality-independent, enabling efficient in-situ recovery demonstrations. The system is validated in experiments on three challenging dexterous manipulation tasks. We evaluate our method under diverse conditions and furthermore conduct user studies with 8 participants. We show that the proposed method reliably performs branch selection and anomaly detection for novice users, achieving 90.7 % and 87.9 % accuracy, respectively, across 576 real-robot rollouts. We provide all code and data required to reproduce our experiments at this http URL.
[CV-82] Speed3R: Sparse Feed-forward 3D Reconstruction Models CVPR2026
【速读】：该论文旨在解决当前前馈式三维重建模型在推理速度上的计算瓶颈问题，这类模型虽能通过单次前向传播联合推断密集几何结构与相机位姿，但其对密集注意力机制的依赖导致复杂度呈二次增长，严重制约了实际应用效率。解决方案的关键在于提出Speed3R模型，其核心创新是采用双分支注意力机制：压缩分支生成粗粒度上下文先验以指导选择分支，后者仅对最具信息量的图像标记（image tokens）进行细粒度注意力操作，从而模拟传统关键点匹配的高效性，在保证几何精度可控下降的前提下实现了高达12.4倍的推理加速。
链接: https://arxiv.org/abs/2603.08055
作者: Weining Ren,Xiao Tan,Kai Han
机构: The University of Hong Kong (香港大学); Baidu AMU (百度AMU)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: CVPR 2026 Findings, project page: this https URL
Abstract:While recent feed-forward 3D reconstruction models accelerate 3D reconstruction by jointly inferring dense geometry and camera poses in a single pass, their reliance on dense attention imposes a quadratic complexity, creating a prohibitive computational bottleneck that severely limits inference speed. To resolve this, we introduce Speed3R, an end-to-end trainable model inspired by the core principle of Structure-from-Motion: that a sparse set of keypoints is sufficient for robust pose estimation. Speed3R features a dual-branch attention mechanism where a compression branch creates a coarse contextual prior to guide a selection branch, which performs fine-grained attention only on the most informative image tokens. This strategy mimics the efficiency of traditional keypoint matching, achieving a remarkable 12.4x inference speedup on 1000-view sequences, while introducing a minimal, controlled trade-off in geometric accuracy. Validated on standard benchmarks with both VGGT and \pi^3 backbones, our method delivers high-quality reconstructions at a fraction of computational cost, paving the way for efficient large-scale scene modeling.
[CV-83] Solution to the 10th ABAW Expression Recognition Challenge: A Robust Multimodal Framework with Safe Cross-Attention and Modality Dropout
【速读】：该论文旨在解决真实场景下情绪识别（Emotion Recognition）中存在的三大挑战：部分遮挡、模态缺失以及严重类别不平衡问题，尤其针对Affective Behavior Analysis in-the-wild (ABAW) 表情识别挑战。其解决方案的关键在于提出一种基于双分支Transformer架构的多模态动态融合框架，通过引入安全交叉注意力机制（safe cross-attention mechanism）和模态丢弃策略（modality dropout strategy），使模型能够在视觉线索缺失时依赖音频信息进行预测；同时，为缓解Aff-Wild2数据集的长尾分布问题，采用焦点损失（focal loss）优化，并结合滑动窗口软投票策略以捕捉情绪的动态演变并减少帧级分类抖动，从而在验证集上实现了60.79%的准确率和0.5029的F1分数。
链接: https://arxiv.org/abs/2603.08034
作者: Jun Yu,Naixiang Zheng,Guoyuan Wang,Yunxiang Zhang,Lingsi Zhu,Jiaen Liang,Wei Huang,Shengping Liu
机构: University of Science and Technology of China (中国科学技术大学); Unisound AI Technology Co., Ltd. (云知声人工智能科技有限公司)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Emotion recognition in real-world environments is hindered by partial occlusions, missing modalities, and severe class imbalance. To address these issues, particularly for the Affective Behavior Analysis in-the-wild (ABAW) Expression challenge, we propose a multimodal framework that dynamically fuses visual and audio representations. Our approach uses a dual-branch Transformer architecture featuring a safe cross-attention mechanism and a modality dropout strategy. This design allows the network to rely on audio-based predictions when visual cues are absent. To mitigate the long-tail distribution of the Aff-Wild2 dataset, we apply focal loss optimization, combined with a sliding-window soft voting strategy to capture dynamic emotional transitions and reduce frame-level classification jitter. Experiments demonstrate that our framework effectively handles missing modalities and complex spatiotemporal dependencies, achieving an accuracy of 60.79% and an F1-score of 0.5029 on the Aff-Wild2 validation set.
[CV-84] QualiTeacher: Quality-Conditioned Pseudo-Labeling for Real-World Image Restoration
【速读】：该论文旨在解决真实世界图像复原（Real-world Image Restoration, RWIR）中因缺乏干净真值图像而导致的监督信号不足问题，尤其针对现有基于伪标签（Pseudo-label, PL）的Mean-Teacher框架所面临的悖论：盲目信任低质量PL会导致学生模型学习到不良伪影，而完全丢弃则会限制数据多样性并损害泛化能力。解决方案的关键在于提出QualiTeacher框架，其核心创新是将伪标签质量从噪声负担转变为条件监督信号——通过集成多种互补的无参考图像质量评估（Non-reference Image Quality Assessment, NR-IQA）模型，估计PL质量并显式地将该质量信息作为条件输入至学生网络，从而引导其学习一个分层的质量感知恢复流形（quality-graded restoration manifold）。此机制使模型不仅能规避低质量标签中的伪影，还能生成优于教师模型本身的高质量结果，同时结合多增强策略、基于分数的偏好优化（受DPO启发）和裁剪一致性损失以提升训练鲁棒性与质量排序的单调性，最终实现从不完美监督中高效学习的新范式。
链接: https://arxiv.org/abs/2603.08030
作者: Fengyang Xiao,Jingjia Feng,Peng Hu,Dingming Zhang,Lei Xu,Guanyi Qin,Lu Li,Chunming He,Sina Farsiu
机构: 1. University of California, Berkeley (加州大学伯克利分校); 2. Alibaba Group (阿里巴巴集团); 3. Tsinghua University (清华大学); 4. University of Science and Technology of China (中国科学技术大学); 5. University of Illinois at Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校); 6. Duke University (杜克大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 8 figures
Abstract:Real-world image restoration (RWIR) is a highly challenging task due to the absence of clean ground-truth images. Many recent methods resort to pseudo-label (PL) supervision, often within a Mean-Teacher (MT) framework. However, these methods face a critical paradox: unconditionally trusting the often imperfect, low-quality PLs forces the student model to learn undesirable artifacts, while discarding them severely limits data diversity and impairs model generalization. In this paper, we propose QualiTeacher, a novel framework that transforms pseudo-label quality from a noisy liability into a conditional supervisory signal. Instead of filtering, QualiTeacher explicitly conditions the student model on the quality of the PLs, estimated by an ensemble of complementary non-reference image quality assessment (NR-IQA) models spanning low-level distortion and semantic-level assessment. This strategy teaches the student network to learn a quality-graded restoration manifold, enabling it to understand what constitutes different quality levels. Consequently, it can not only avoid mimicking artifacts from low-quality labels but also extrapolate to generate results of higher quality than the teacher itself. To ensure the robustness and accuracy of this quality-driven learning, we further enhance the process with a multi-augmentation scheme to diversify the PL quality spectrum, a score-based preference optimization strategy inspired by Direct Preference Optimization (DPO) to enforce a monotonically ordered quality separation, and a cropped consistency loss to prevent adversarial over-optimization (reward hacking) of the IQA models. Experiments on standard RWIR benchmarks demonstrate that QualiTeacher can serve as a plug-and-play strategy to improve the quality of the existing pseudo-labeling framework, establishing a new paradigm for learning from imperfect supervision. Code will be released.
[CV-85] Controllable Complex Human Motion Video Generation via Text-to-Skeleton Cascades
【速读】：该论文旨在解决当前视频扩散模型在生成复杂人类动作（如翻滚、空翻和武术动作）时面临的两大挑战：一是纯文本条件控制在时间上存在模糊性，难以实现精细的动作调控；二是基于显式姿态的控制方法虽有效，但需用户提供完整的骨骼序列，而这类序列对于长时间且动态的动作而言获取成本高昂。解决方案的关键在于提出一个两阶段级联框架：第一阶段采用自回归文本到骨骼模型，通过逐关节预测的方式从自然语言描述中生成2D骨骼序列，从而捕捉长程时序依赖性和关节间协调关系；第二阶段使用姿态条件视频扩散模型，结合参考图像与生成的骨骼序列合成高质量视频，并引入DINO-ALF（Adaptive Layer Fusion）多层级参考编码器以在大幅姿态变化和自我遮挡下保持外观与服饰细节。该方案显著提升了复杂动作视频生成的可控性与质量。
链接: https://arxiv.org/abs/2603.08028
作者: Ashkan Taghipour,Morteza Ghahremani,Zinuo Li,Hamid Laga,Farid Boussaid,Mohammed Bennamoun
机构: The University of Western Australia, Australia; Munich Center for Machine Learning (MCML) and Technical University of Munich (TUM), Germany; Murdoch University, Australia
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注:
Abstract:Generating videos of complex human motions such as flips, cartwheels, and martial arts remains challenging for current video diffusion models. Text-only conditioning is temporally ambiguous for fine-grained motion control, while explicit pose-based controls, though effective, require users to provide complete skeleton sequences that are costly to produce for long and dynamic actions. We propose a two-stage cascaded framework that addresses both limitations. First, an autoregressive text-to-skeleton model generates 2D pose sequences from natural language descriptions by predicting each joint conditioned on previously generated poses. This design captures long-range temporal dependencies and inter-joint coordination required for complex motions. Second, a pose-conditioned video diffusion model synthesizes videos from a reference image and the generated skeleton sequence. It employs DINO-ALF (Adaptive Layer Fusion), a multi-level reference encoder that preserves appearance and clothing details under large pose changes and self-occlusions. To address the lack of publicly available datasets for complex human motion video generation, we introduce a Blender-based synthetic dataset containing 2,000 videos with diverse characters performing acrobatic and stunt-like motions. The dataset provides full control over appearance, motion, and environment. It fills an important gap because existing benchmarks significantly under-represent acrobatic motions while web-collected datasets raise copyright and privacy concerns. Experiments on our synthetic dataset and the Motion-X Fitness benchmark show that our text-to-skeleton model outperforms prior methods on FID, R-precision, and motion diversity. Our pose-to-video model also achieves the best results among all compared methods on VBench metrics for temporal consistency, motion smoothness, and subject preservation. Subjects: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM) Cite as: arXiv:2603.08028 [cs.CV] (or arXiv:2603.08028v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.08028 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Morteza Ghahremani [view email] [v1] Mon, 9 Mar 2026 07:04:29 UTC (25,535 KB)
[CV-86] Not Like Transformers: Drop the Beat Representation for Dance Generation with Mamba-Based Diffusion Model WACV2026
【速读】：该论文旨在解决现有舞蹈生成方法难以充分捕捉舞蹈固有的时序性、节奏性和音乐同步性的问题。其解决方案的关键在于提出了一种基于Mamba的两阶段扩散模型（MambaDance），利用Mamba架构对长序列和自回归结构的高效建模能力替代传统的Transformer，并引入基于高斯分布的节拍表示来显式引导舞蹈序列的解码过程，从而在AIST++和FineDance数据集上实现从短到长舞蹈序列的一致性高质量生成。
链接: https://arxiv.org/abs/2603.08023
作者: Sangjune Park,Inhyeok Choi,Donghyeon Soon,Youngwoo Jeon,Kyungdon Joo
机构: Ulsan National Institute of Science and Technology (蔚山科学技术院); Daegu Gyeongbuk Institute of Science and Technology (大邱庆北科学技术院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR); Sound (cs.SD)
备注: Accepted by WACV 2026
Abstract:Dance is a form of human motion characterized by emotional expression and communication, playing a role in various fields such as music, virtual reality, and content creation. Existing methods for dance generation often fail to adequately capture the inherently sequential, rhythmical, and music-synchronized characteristics of dance. In this paper, we propose \emphMambaDance, a new dance generation approach that leverages a Mamba-based diffusion model. Mamba, well-suited to handling long and autoregressive sequences, is integrated into our two-stage diffusion architecture, substituting off-the-shelf Transformer. Additionally, considering the critical role of musical beats in dance choreography, we propose a Gaussian-based beat representation to explicitly guide the decoding of dance sequences. Experiments on AIST++ and FineDance datasets for each sequence length show that our proposed method effectively generates plausible dance movements while reflecting essential characteristics, consistently from short to long dances, compared to the previous methods. Additional qualitative results and demo videos are available at \smallthis https URL.
[CV-87] AffordGrasp: Cross-Modal Diffusion for Affordance-Aware Grasp Synthesis
【速读】：该论文旨在解决现有语义抓握方法在生成人类抓握姿态时面临的两大挑战：一是3D物体表示与文本指令之间的巨大模态鸿沟，二是缺乏显式的空间或语义约束导致生成的抓握动作可能物理上无效或语义不一致。解决方案的关键在于提出AffordGrasp框架，其核心创新包括：1）构建一个可扩展的标注流程，自动为手-物体交互数据集注入细粒度结构化语言标签以捕捉交互意图；2）引入一种基于可用性（affordance）感知的隐式手部姿态表示，并结合双条件扩散过程，使模型能够联合推理物体几何形状、空间可用性与指令语义；3）设计分布调整模块以强制执行物理接触一致性与语义对齐，从而提升抓握的质量、语义准确性和多样性。
链接: https://arxiv.org/abs/2603.08021
作者: Xiaofei Wu,Yi Zhang,Yumeng Liu,Yuexin Ma,Yujiao Shi,Xuming He
机构: ShanghaiTech University (上海科技大学); Shanghai Engineering Research Center of Intelligent Vision and Imaging (智能视觉与成像工程研究中心); University of Science and Technology of China (中国科学技术大学)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Generating human grasping poses that accurately reflect both object geometry and user-specified interaction semantics is essential for natural hand-object interactions in AR/VR and embodied AI. However, existing semantic grasping approaches struggle with the large modality gap between 3D object representations and textual instructions, and often lack explicit spatial or semantic constraints, leading to physically invalid or semantically inconsistent grasps. In this work, we present AffordGrasp, a diffusion-based framework that produces physically stable and semantically faithful human grasps with high precision. We first introduce a scalable annotation pipeline that automatically enriches hand-object interaction datasets with fine-grained structured language labels capturing interaction intent. Building upon these annotations, AffordGrasp integrates an affordance-aware latent representation of hand poses with a dual-conditioning diffusion process, enabling the model to jointly reason over object geometry, spatial affordances, and instruction semantics. A distribution adjustment module further enforces physical contact consistency and semantic alignment. We evaluate AffordGrasp across four instruction-augmented benchmarks derived from HO-3D, OakInk, GRAB, and AffordPose, and observe substantial improvements over state-of-the-art methods in grasp quality, semantic accuracy, and diversity.
[CV-88] VSDiffusion: Taming Ill-Posed Shadow Generation via Visibility-Constrained Diffusion
【速读】：该论文旨在解决图像合成中插入前景物体时生成逼真投射阴影（cast shadows）的问题，尤其关注复杂场景下阴影与物体之间几何一致性难以保持的挑战。其解决方案的关键在于提出一种基于可见性约束的两阶段框架VSDiffusion：第一阶段预测粗略阴影掩码以定位合理的阴影生成区域；第二阶段通过条件扩散模型，结合从合成图像中估计的光照和深度线索生成精确阴影。该方法创新性地引入两种互补的可见性先验机制——包含阴影门控交叉注意力的可见性控制分支提供多尺度结构引导，以及学习得到的软先验图在易错区域重新加权训练损失以增强几何校正能力；同时引入高频引导增强模块以锐化边界并改善阴影与背景的纹理交互，从而在DESOBAv2数据集上实现了当前最优性能（SOTA）。
链接: https://arxiv.org/abs/2603.08020
作者: Jing Li,Jing Zhang
机构: East China University of Science and Technology (华东理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages,8 figures
Abstract:Generating realistic cast shadows for inserted foreground objects is a crucial yet challenging problem in image composition, where maintaining geometric consistency of shadow and object in complex scenes remains difficult due to the ill-posed nature of shadow formation. To address this issue, we propose VSDiffusion, a visibility-constrained two-stage framework designed to narrow the solution space by incorporating visibility priors. In Stage I, we predict a coarse shadow mask to localize plausible shadow generated regions. And in Stage II, conditional diffusion is performed guided by lighting and depth cues estimated from the composite to generate accurate shadows. In VSDiffusion, we inject visibility priors through two complementary pathways. First, a visibility control branch with shadow-gated cross attention that provides multi-scale structural guidance. Then, a learned soft prior map that reweights training loss in error-prone regions to enhance geometric correction. Additionally, we also introduce high-frequency guided enhancement module to sharpen boundaries and improve texture interaction with the background. Experiments on widely used public DESOBAv2 dataset demonstrated that our proposed VSDiffusion can generate accurate shadow, and establishes new SOTA results across most evaluation metrics.
[CV-89] Missing No More: Dictionary-Guided Cross-Modal Image Fusion under Missing Infrared CVPR2026
【速读】：该论文旨在解决红外-可见光（IR-VIS）图像融合中因红外模态缺失而导致的生成式方法难以控制且缺乏可解释性的问题。传统方法依赖于训练和推理阶段同时具备双模态数据，当红外信息不可用时，像素空间的生成模型容易产生不可控结果。解决方案的关键在于提出一种基于共享卷积字典的系数域（coefficient domain）框架，其核心创新包括：(1) 联合共享字典表示学习（JSRL），构建IR与VIS共用的可解释原子空间；(2) 可见光引导红外推断（VGII），在系数域内将VIS系数映射为伪红外系数，并利用冻结的大语言模型作为弱语义先验进行闭环优化；(3) 基于表示推理的自适应融合（AFRI），通过窗口注意力和卷积混合在原子层面融合VIS结构与推断出的红外特征，最终使用共享字典重建图像。该encode-transfer-fuse-reconstruct流程避免了像素空间的无约束生成，同时保障了先验信息在可解释的字典-系数表示中的保留。
链接: https://arxiv.org/abs/2603.08018
作者: Yafei Zhang,Meng Ma,Huafeng Li,Yu Liu
机构: Faculty of Information Engineering and Automation, Kunming University of Science and Technology; Department of Biomedical Engineering, Hefei University of Technology
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This paper has been accepted by CVPR 2026
Abstract:Infrared-visible (IR-VIS) image fusion is vital for perception and security, yet most methods rely on the availability of both modalities during training and inference. When the infrared modality is absent, pixel-space generative substitutes become hard to control and inherently lack interpretability. We address missing-IR fusion by proposing a dictionary-guided, coefficient-domain framework built upon a shared convolutional dictionary. The pipeline comprises three key components: (1) Joint Shared-dictionary Representation Learning (JSRL) learns a unified and interpretable atom space shared by both IR and VIS modalities; (2) VIS-Guided IR Inference (VGII) transfers VIS coefficients to pseudo-IR coefficients in the coefficient domain and performs a one-step closed-loop refinement guided by a frozen large language model as a weak semantic prior; and (3) Adaptive Fusion via Representation Inference (AFRI) merges VIS structures and inferred IR cues at the atom level through window attention and convolutional mixing, followed by reconstruction with the shared dictionary. This encode-transfer-fuse-reconstruct pipeline avoids uncontrolled pixel-space generation while ensuring prior preservation within interpretable dictionary-coefficient representation. Experiments under missing-IR settings demonstrate consistent improvements in perceptual quality and downstream detection performance. To our knowledge, this represents the first framework that jointly learns a shared dictionary and performs coefficient-domain inference-fusion to tackle missing-IR fusion. The source code is publicly available at this https URL.
[CV-90] Its Time to Get It Right: Improving Analog Clock Reading and Clock-Hand Spatial Reasoning in Vision-Language Models CVPR2026
【速读】：该论文旨在解决当前视觉语言模型（Vision-Language Models, VLMs）在真实环境中读取模拟钟表时间时表现不佳的问题，其核心挑战在于现有数据集多为合成或平面化场景，缺乏真实世界的视觉多样性与背景复杂性，导致模型在面对遮挡、光照变化和杂乱背景等实际条件时出现时空推理能力薄弱，常混淆时针与分针。解决方案的关键在于两个方面：一是构建了TickTockVQA这一人类标注的真实世界模拟钟表图像数据集，提供精确的小时、分钟标注及可推断的AM/PM标签；二是提出Swap-DPO方法，一种基于直接偏好优化（Direct Preference Optimization, DPO）的微调框架，通过强化模型对时间信息的准确理解与推理能力，显著提升模型在复杂现实场景下的钟表读数准确性与鲁棒性。
链接: https://arxiv.org/abs/2603.08011
作者: Jaeha Choi,Jin Won Lee,Siwoo You,Jangho Lee
机构: Incheon National University (仁川国立大学); McGill University (麦吉尔大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026 Findings
Abstract:Advances in vision-language models (VLMs) have achieved remarkable success on complex multimodal reasoning tasks, leading to the assumption that they should also excel at reading analog clocks. However, contrary to this expectation, our study reveals that reading analog clocks in real-world environments remains a significant challenge for state-of-the-art VLMs. Existing analog clock datasets are largely synthetic or planar with limited stylistic diversity and minimal background context, failing to capture the visual variability of real-world scenes. As a result, VLMs trained on such data exhibit weak spatial-temporal reasoning, frequently confusing the hour and minute hands and struggling under common visual conditions such as occlusion, lighting variation, and cluttered backgrounds. To address this issue, we introduce TickTockVQA, a human-annotated dataset containing analog clocks in diverse real-world scenarios. TickTockVQA provides explicit hour and minute annotations, and includes an AM/PM tag when it is inferable from the visual context. Furthermore, we propose Swap-DPO, a direct preference optimization based fine-tuning framework to align model reasoning toward accurate time interpretation. Experimental results demonstrate that our approach substantially enhances clock reading accuracy and robustness under real-world conditions, establishing a foundation for future research on spatial-temporal reasoning and visual understanding in VLMs.
[CV-91] ViSA-Enhanced Aerial VLN: A Visual-Spatial Reasoning Enhanced Framework for Aerial Vision-Language Navigation
【速读】：该论文旨在解决现有空中视觉-语言导航（Aerial Vision-Language Navigation, VLN）方法中因依赖检测与规划流水线而导致的空间推理能力不足和语言歧义问题。其解决方案的关键在于提出了一种视觉空间推理（Visual-Spatial Reasoning, ViSA）增强框架，采用三阶段协同架构，利用结构化视觉提示（structured visual prompting），使视觉-语言模型（Vision-Language Models, VLMs）能够在图像平面上直接进行推理，无需额外训练或复杂中间表示，从而显著提升导航成功率。
链接: https://arxiv.org/abs/2603.08007
作者: Haoyu Tong,Xiangyu Dong,Xiaoguang Ma,Haoran Zhao,Yaoming Zhou,Chenghao Lin
机构: Tianmushan Laboratory (天目山实验室); Beihang University (北京航空航天大学); Hangzhou International Innovation Institute (杭州国际创新研究院); Foshan Graduate School of Innovation, Northeastern University (东北大学佛山研究生创新学院); School of Aeronautic Science and Engineering, Beihang University (北京航空航天大学航空科学与工程学院); qingniaoAI; Faculty of Robot Science and Engineering, Northeastern University (东北大学机器人科学与工程学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 8 pages
Abstract:Existing aerial Vision-Language Navigation (VLN) methods predominantly adopt a detection-and-planning pipeline, which converts open-vocabulary detections into discrete textual scene graphs. These approaches are plagued by inadequate spatial reasoning capabilities and inherent linguistic ambiguities. To address these bottlenecks, we propose a Visual-Spatial Reasoning (ViSA) enhanced framework for aerial VLN. Specifically, a triple-phase collaborative architecture is designed to leverage structured visual prompting, enabling Vision-Language Models (VLMs) to perform direct reasoning on image planes without the need for additional training or complex intermediate representations. Comprehensive evaluations on the CityNav benchmark demonstrate that the ViSA-enhanced VLN achieves a 70.3% improvement in success rate compared to the fully trained state-of-the-art (SOTA) method, elucidating its great potential as a backbone for aerial VLN systems.
[CV-92] AutoTraces: Autoregressive Trajectory Forecasting via Multimodal Large Language Models
【速读】：该论文旨在解决机器人在人类密集环境中进行轨迹预测的难题，尤其是如何建模复杂的人类行为并实现长期、准确的轨迹预测。其解决方案的关键在于提出了一种新颖的轨迹标记化（trajectory tokenization）方案，将路径点（waypoint）的坐标信息以类别型点标记（point tokens）和位置嵌入（point embeddings）的形式融合进大语言模型（LLM）的空间中，并通过轻量级编码器-解码器架构无缝集成到LLM的自回归生成机制中，从而保留LLM的推理能力的同时扩展至物理坐标空间，有效捕捉轨迹数据中的长期交互关系。此外，论文还引入了自动链式思维（CoT）生成机制，利用多模态LLM从视觉观测与轨迹数据中推断时空关系，避免了人工标注依赖，显著提升了模型在跨场景下的泛化能力和灵活长度预测性能。
链接: https://arxiv.org/abs/2603.07989
作者: Teng Wang,Yanting Lu,Ruize Wang
机构: Southeast University (东南大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present AutoTraces, an autoregressive vision-language-trajectory model for robot trajectory forecasting in humam-populated environments, which harnesses the inherent reasoning capabilities of large language models (LLMs) to model complex human behaviors. In contrast to prior works that rely solely on textual representations, our key innovation lies in a novel trajectory tokenization scheme, which represents waypoints with point tokens as categorical and positional markers while encoding waypoint numerical values as corresponding point embeddings, seamlessly integrated into the LLM’s space through a lightweight encoder-decoder architecture. This design preserves the LLM’s native autoregressive generation mechanism while extending it to physical coordinate spaces, facilitates modeling of long-term interactions in trajectory data. We further introduce an automated chain-of-thought (CoT) generation mechanism that leverages a multimodal LLM to infer spatio-temporal relationships from visual observations and trajectory data, eliminating reliance on manual annotation. Through a two-stage training strategy, our AutoTraces achieves SOTA forecasting accuracy, particularly in long-horizon prediction, while exhibiting strong cross-scene generalization and supporting flexible-length forecasting.
[CV-93] On the Feasibility and Opportunity of Autoregressive 3D Object Detection CVPR2026
【速读】：该论文旨在解决基于激光雷达（LiDAR）的3D目标检测器普遍依赖手工设计组件（如锚框分配和非极大值抑制NMS）所导致的训练复杂性和可扩展性受限问题。其解决方案的关键在于提出AutoReg3D，一种将检测任务建模为序列生成的自回归3D检测框架：通过近到远（near-to-far）的顺序输出物体，并将每个物体编码为包含中心、尺寸、方向、速度和类别信息的离散token序列，从而在训练中实现直接教师强制（teacher forcing），测试时进行自回归解码。该方法无需锚框或NMS，且兼容多种点云输入与骨干网络，在nuScenes数据集上达到竞争性性能，并为引入语言模型中的先进优化技术（如GRPO风格强化学习）提供了可能，推动了3D感知领域对现代序列建模工具的迁移应用。
链接: https://arxiv.org/abs/2603.07985
作者: Zanming Huang,Jinsu Yoo,Sooyoung Jeon,Zhenzhen Liu,Mark Campbell,Kilian Q Weinberger,Bharath Hariharan,Wei-Lun Chao,Katie Z Luo
机构: The Ohio State University (俄亥俄州立大学); Cornell University (康奈尔大学); Boston University (波士顿大学); Stanford University (斯坦福大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR 2026 Findings Project Page: this https URL
Abstract:LiDAR-based 3D object detectors typically rely on proposal heads with hand-crafted components like anchor assignment and non-maximum suppression (NMS), complicating training and limiting extensibility. We present AutoReg3D, an autoregressive 3D detector that casts detection as sequence generation. Given point-cloud features, AutoReg3D emits objects in a range-causal (near-to-far) order and encodes each object as a short, discrete-token sequence consisting of its center, size, orientation, velocity, and class. This near-to-far ordering mirrors LiDAR geometry–near objects occlude far ones but not vice versa–enabling straightforward teacher forcing during training and autoregressive decoding at test time. AutoReg3D is compatible across diverse point-cloud or backbones and attains competitive nuScenes performance without anchors or NMS. Beyond parity, the sequential formulation unlocks language-model advances for 3D perception, including GRPO-style reinforcement learning for task-aligned objectives. These results position autoregressive decoding as a viable, flexible alternative for LiDAR-based detection and open a path to importing modern sequence-modeling tools into 3D perception.
[CV-94] Listening with the Eyes: Benchmarking Egocentric Co-Speech Grounding across Space and Time
【速读】：该论文旨在解决当前多模态大语言模型（Multimodal Large Language Models, MLLMs）在具身协作场景中对指称性指令（deictic commands）理解不足的问题，特别是由于许多基准测试允许仅使用语言信息即可完成任务，导致模型未真正学习语音与视觉动作（如手势）之间的时序对齐能力。解决方案的关键在于提出一个名为Egocentric Co-Speech Grounding (EcoG)的新范式，其核心要求是代理必须联合预测“何物（What）”、“何处（Where）”和“何时（When）”，从而强制模型建立事件级别的语音-手势绑定（speech–gesture binding）。为此，作者构建了EcoG-Bench这一评估型双语（英文/中文）诊断基准，包含811段第一人称视角视频片段，带有密集空间标注和毫秒级手势监督，并采用渐进式认知评估协议进行测试。实证结果表明，人类表现接近天花板（96.9%严格Eco准确率），而最优MLLM在原生视频-音频接口下表现极低（Gemini-3-Pro: 17.0%），但若改用带时间戳的帧样本与外部验证的自动语音识别（ASR）输出，则性能显著提升至42.9%，说明多模态接口可能限制了时序对齐线索的可观测性，而非模型推理能力本身。
链接: https://arxiv.org/abs/2603.07966
作者: Weijie Zhou,Xuantang Xiong,Zhenlin Hu,Xiaomeng Zhu,Chaoyang Zhao,Honghui Dong,Zhengyou Zhang,Ming Tang,Jinqiao Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In situated collaboration, speakers often use intentionally underspecified deictic commands (e.g., ``pass me \textitthat’'), whose referent becomes identifiable only by aligning speech with a brief co-speech pointing \emphstroke. However, many embodied benchmarks admit language-only shortcuts, allowing MLLMs to perform well without learning the \emphaudio–visual alignment required by deictic interaction. To bridge this gap, we introduce \textbfEgocentric Co-Speech Grounding (EcoG), where grounding is executable only if an agent jointly predicts \textitWhat, \textitWhere, and \textitWhen. To operationalize this, we present \textbfEcoG-Bench, an evaluation-only bilingual (EN/ZH) diagnostic benchmark of \textbf811 egocentric clips with dense spatial annotations and millisecond-level stroke supervision. It is organized under a \textbfProgressive Cognitive Evaluation protocol. Benchmarking state-of-the-art MLLMs reveals a severe executability gap: while human subjects achieve near-ceiling performance on EcoG-Bench (\textbf96.9% strict Eco-Accuracy), the best native video-audio setting remains low (Gemini-3-Pro: \textbf17.0%). Moreover, in a diagnostic ablation, replacing the native video–audio interface with timestamped frame samples and externally verified ASR (with word-level timing) substantially improves the same model (\textbf17.0% \to \textbf42.9%). Overall, EcoG-Bench provides a strict, executable testbed for event-level speech–gesture binding, and suggests that multimodal interfaces may bottleneck the observability of temporal alignment cues, independently of model reasoning.
[CV-95] SGG-Rrm 3: From Next-Token Prediction to End-to-End Unbiased Scene Graph Generation
【速读】：该论文旨在解决场景图生成（Scene Graph Generation, SGG）中因任务特定结构化推理不足和稀疏、长尾关系分布导致的召回率低与预测偏差问题。解决方案的关键在于提出一种名为SGG-RR的结构化推理框架，其核心由三阶段流程构成：首先通过基于多模态大语言模型（Multimodal Large Language Models, MLLMs）的关系增强策略结合嵌入相似性过滤缓解关系稀疏问题；其次在监督微调（Supervised Fine-Tuning, SFT）阶段引入链式思维（Chain-of-Thought, CoT）引导机制提升推理能力；最后在强化学习（Reinforcement Learning, RL）阶段采用分阶段奖励设计，结合细粒度与粗粒度关系奖励，并通过频率自适应加权和语义聚类优化关系覆盖与长尾分布平衡，从而实现端到端无偏的场景图生成。
链接: https://arxiv.org/abs/2603.07961
作者: Jiaye Feng,Qixiang Yin,Yuankun Liu,Tong Mo,Weiping Li
机构: Peking University (北京大学); Beijing University of Posts and Telecommunications (北京邮电大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Scene Graph Generation (SGG) structures visual scenes as graphs of objects and their relations. While Multimodal Large Language Models (MLLMs) have advanced end-to-end SGG, current methods are hindered by both a lack of task-specific structured reasoning and the challenges of sparse, long-tailed relation distributions, resulting in incomplete scene graphs characterized by low recall and biased predictions. To address these issues, we introduce SGG-R ^\rm 3 , a structured reasoning framework that integrates task-specific chain-of-thought (CoT)-guided supervised fine-tuning (SFT) and reinforcement learning (RL) with group sequence policy optimization (GSPO), designed to engage in three sequential stages to achieve end-to-end unbiased scene graph generation. During the SFT phase, we propose a relation augmentation strategy by leveraging an MLLM and refined via embedding similarity filtering to alleviate relation sparsity. Subsequently, a stage-aligned reward scheme optimizes the procedural reasoning during RL. Specifically, we propose a novel dual-granularity reward which integrates fine-grained and coarse-grained relation rewards, simultaneously mitigating the long-tail issue via frequency-based adaptive weighting of predicates and improving relation coverage through semantic clustering. Experiments on two benchmarks show that SGG-R ^\rm 3 achieves superior performance compared to existing methods, demonstrating the effectiveness and generalization of the framework.
[CV-96] VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer CVPR2026
【速读】：该论文旨在解决零样本异常检测（Zero-shot Anomaly Detection, ZSAD）中依赖视觉-语言模型（Vision-Language Models, VLMs）所带来的训练不稳定和参数冗余问题。现有方法通常利用CLIP等VLM构建文本提示集并计算图像-文本相似度以实现开集判别，但其对文本编码器和跨模态对齐的强依赖限制了模型效率与鲁棒性。解决方案的关键在于提出一种纯视觉框架VisualAD，它基于视觉Transformer（Vision Transformer）设计，通过在冻结主干网络中引入两个可学习标记（learnable tokens）来直接编码正常性和异常性语义；借助多层自注意力机制，这些标记与图像块标记交互，逐步捕获高层语义信息并引导局部特征突出异常线索；同时结合空间感知交叉注意力（Spatial-Aware Cross-Attention, SCA）模块注入细粒度空间信息，并引入轻量级自对齐函数（Self-Alignment Function, SAF）对patch特征进行再校准，从而提升异常评分精度。
链接: https://arxiv.org/abs/2603.07952
作者: Yanning Hou,Peiyuan Li,Zirui Liu,Yitong Wang,Yanran Ruan,Jianfeng Qiu,Ke Xu
机构: Anhui University (安徽大学); National University of Defense Technology (国防科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR 2026
Abstract:Zero-shot anomaly detection (ZSAD) requires detecting and localizing anomalies without access to target-class anomaly samples. Mainstream methods rely on vision-language models (VLMs) such as CLIP: they build hand-crafted or learned prompt sets for normal and abnormal semantics, then compute image-text similarities for open-set discrimination. While effective, this paradigm depends on a text encoder and cross-modal alignment, which can lead to training instability and parameter redundancy. This work revisits the necessity of the text branch in ZSAD and presents VisualAD, a purely visual framework built on Vision Transformers. We introduce two learnable tokens within a frozen backbone to directly encode normality and abnormality. Through multi-layer self-attention, these tokens interact with patch tokens, gradually acquiring high-level notions of normality and anomaly while guiding patches to highlight anomaly-related cues. Additionally, we incorporate a Spatial-Aware Cross-Attention (SCA) module and a lightweight Self-Alignment Function (SAF): SCA injects fine-grained spatial information into the tokens, and SAF recalibrates patch features before anomaly scoring. VisualAD achieves state-of-the-art performance on 13 zero-shot anomaly detection benchmarks spanning industrial and medical domains, and adapts seamlessly to pretrained vision backbones such as the CLIP image encoder and DINOv2. Code: this https URL
[CV-97] L3:Scene-agnostic Visual Localization in the Wild
【速读】：该论文旨在解决传统视觉定位方法依赖离线场景预处理（如构建和存储3D结构信息）所带来的计算开销、时间成本及存储负担问题。其解决方案的关键在于提出了一种无需地图的视觉定位框架 L³，该框架利用前馈式3D重建网络的在线推理能力，直接对RGB图像进行实时3D重建，并通过基于2D-3D对应关系的两阶段尺度恢复与位姿精化，实现高精度定位，从而在不预建或存储任何离线场景表示的前提下完成鲁棒的视觉定位。
链接: https://arxiv.org/abs/2603.07937
作者: Yu Zhang,Muhua Zhu,Yifei Xue,Tie Ji,Yizhen Lao
机构: Hunan University (湖南大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Standard visual localization methods typically require offline pre-processing of scenes to obtain 3D structural information for better performance. This inevitably introduces additional computational and time costs, as well as the overhead of storing scene representations. Can we visually localize in a wild scene without any off-line preprocessing step? In this paper, we leverage the online inference capabilities of feed-forward 3D reconstruction networks to propose a novel map-free visual localization framework L^3 . Specifically, by performing direct online 3D reconstruction on RGB images, followed by two-stage metric scale recovery and pose refinement based on 2D-3D correspondences, L^3 achieves high accuracy without the need to pre-build or store any offline scene representations. Extensive experiments demonstrate L^3 not only that the performance is comparable to state-of-the-art solutions on various benchmarks, but also that it exhibits significantly superior robustness in sparse scenes (fewer reference images per scene).
[CV-98] xt to Automata Diagrams: Comparing TikZ Code Generation with Direct Image Synthesis
【速读】：该论文旨在解决学生在计算机科学课程中手绘图（如自动机、数据结构等领域的示意图）在自动化处理和数字化重建中的准确性问题。当前视觉-语言模型（Vision-Language Models, VLMs）虽能从图像生成文本描述，但直接用于学生绘制的不规范图时存在错误率高、结构失真等问题。解决方案的关键在于引入人工校正环节：首先使用VLM生成初始描述，再由人工评审修正以提升准确性，随后将高质量描述输入大语言模型（Large Language Models, LLMs）生成可编译的TikZ代码，最终实现与原始扫描图的高度一致的数字复现。这一流程显著提升了自动化生成图的可靠性，为计算机科学教育中的自动评分与可访问教学材料开发提供了可行路径。
链接: https://arxiv.org/abs/2603.07936
作者: Ethan Young,Zichun Wang,Aiden Taylor,Chance Jewell,Julian Myers,Satya Sri Rajiteswari Nimmagadda,Anthony White,Aniruddha Maiti,Ananya Jana
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ASEE North Central Section 2026
Abstract:Diagrams are widely used in teaching computer science courses. They are useful in subjects such as automata and formal languages, data structures, etc. These diagrams, often drawn by students during exams or assignments, vary in structure, layout, and correctness. This study examines whether current vision-language and large language models can process such diagrams and produce accurate textual and digital representations. In this study, scanned student-drawn diagrams are used as input. Then, textual descriptions are generated from these images using a vision-language model. The descriptions are checked and revised by human reviewers to make them accurate. Both the generated and the revised descriptions are then fed to a large language model to generate TikZ code. The resulting diagrams are compiled and then evaluated against the original scanned diagrams. We found descriptions generated directly from images using vision-language models are often incorrect and human correction can substantially improve the quality of vision language model generated descriptions. This research can help computer science education by paving the way for automated grading and feedback and creating more accessible instructional materials.
[CV-99] A Hybrid Vision Transformer Approach for Mathematical Expression Recognition
【速读】：该论文旨在解决数学表达式识别（Mathematical Expression Recognition）问题，该任务因数学符号的二维空间布局和不同符号尺寸而比文本识别更为复杂。解决方案的关键在于采用结合2D位置编码的混合视觉Transformer（Hybrid Vision Transformer, HVT）作为编码器，以有效提取图像中符号间的复杂关系；同时引入覆盖注意力（Coverage Attention）解码器，通过跟踪注意力历史来缓解解析不足（under-parsing）与过度解析（over-parsing）的问题，并利用ViT中的[CLS]标记作为解码器的初始嵌入，从而提升生成质量。实验在IM2LATEX-100K数据集上验证了该方法的有效性，BLEU得分达到89.94，优于现有最先进方法。
链接: https://arxiv.org/abs/2603.07929
作者: Anh Duy Le,Van Linh Pham,Vinh Loi Ly,Nam Quan Nguyen,Huu Thang Nguyen,Tuan Anh Tran
机构: HCMUT(胡志明市科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted as oral presentation at DICTA 2022
Abstract:One of the crucial challenges taken in document analysis is mathematical expression recognition. Unlike text recognition which only focuses on one-dimensional structure images, mathematical expression recognition is a much more complicated problem because of its two-dimensional structure and different symbol size. In this paper, we propose using a Hybrid Vision Transformer (HVT) with 2D positional encoding as the encoder to extract the complex relationship between symbols from the image. A coverage attention decoder is used to better track attention’s history to handle the under-parsing and over-parsing problems. We also showed the benefit of using the [CLS] token of ViT as the initial embedding of the decoder. Experiments performed on the IM2LATEX-100K dataset have shown the effectiveness of our method by achieving a BLEU score of 89.94 and outperforming current state-of-the-art methods.
[CV-100] IMSE: Intrinsic Mixture of Spectral Experts Fine-tuning for Test-Time Adaptation ICLR2026
【速读】：该论文旨在解决测试时适应（Test-time Adaptation, TTA）中如何充分利用大规模预训练模型的丰富表征能力，同时以最小的参数更新实现性能提升的问题。其关键解决方案是提出Intrinsic Mixture of Spectral Experts (IMSE)，通过奇异值分解（Singular Value Decomposition, SVD）对Vision Transformer中的线性层进行分解，并仅适应奇异值而固定奇异向量，从而高效利用模型内在的谱专家结构；此外，针对熵最小化导致特征坍缩的问题，引入基于专家-输入对齐的多样性最大化损失，促进谱专家在适应过程中的多样化使用；在持续测试时适应（Continual Test-Time Adaptation, CTTA）场景下，进一步提出领域感知谱码检索机制，用于检测域偏移并复用先前域的适配奇异值，实现快速知识迁移与保留。该方法在多个分布偏移基准上达到最先进性能，且在CTTA和渐进式CTTA中分别提升准确率3.4和2.4个百分点，同时仅需385倍更少的可训练参数。
链接: https://arxiv.org/abs/2603.07926
作者: Sunghyun Baek(1),Jaemyung Yu(1),Seunghee Koh(1),Minsu Kim(2),Hyeonseong Jeon(2),Junmo Kim(1) ((1) Korea Advanced Institute of Science and Technology (KAIST), (2) LG Energy Solution)
机构: Korea Advanced Institute of Science and Technology (KAIST); LG Energy Solution
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: ICLR 2026
Abstract:Test-time adaptation (TTA) has been widely explored to prevent performance degradation when test data differ from the training distribution. However, fully leveraging the rich representations of large pretrained models with minimal parameter updates remains underexplored. In this paper, we propose Intrinsic Mixture of Spectral Experts (IMSE) that leverages the spectral experts inherently embedded in Vision Transformers. We decompose each linear layer via singular value decomposition (SVD) and adapt only the singular values, while keeping the singular vectors fixed. We further identify a key limitation of entropy minimization in TTA: it often induces feature collapse, causing the model to rely on domain-specific features rather than class-discriminative features. To address this, we propose a diversity maximization loss based on expert-input alignment, which encourages diverse utilization of spectral experts during adaptation. In the continual test-time adaptation (CTTA) scenario, beyond preserving pretrained knowledge, it is crucial to retain and reuse knowledge from previously observed domains. We introduce Domain-Aware Spectral Code Retrieval, which estimates input distributions to detect domain shifts, and retrieves adapted singular values for rapid adaptation. Consequently, our method achieves state-of-the-art performance on various distribution-shift benchmarks under the TTA setting. In CTTA and Gradual CTTA, it further improves accuracy by 3.4 percentage points (pp) and 2.4 pp, respectively, while requiring 385 times fewer trainable parameters. Our code is available at this https URL.
[CV-101] RLPR: Radar-to-LiDAR Place Recognition via Two-Stage Asymmetric Cross-Modal Alignment for Autonomous Driving
【速读】：该论文旨在解决多模态环境下自动驾驶中雷达到激光雷达（LiDAR）的场景识别问题，即在恶劣天气条件下，如何利用现有激光雷达地图实现对雷达扫描的有效定位。其核心挑战在于跨模态特征提取的困难、训练数据稀缺以及不同雷达类型间信号异质性。解决方案的关键在于提出了一种名为RLPR的鲁棒雷达到激光雷达场景识别框架，通过设计双流网络提取脱离传感器特有信号属性（如多普勒或雷达散射截面RCS）的结构化特征，并引入两阶段不对称跨模态对齐策略（TACMA），利用预训练的雷达分支作为判别锚点引导对齐过程，从而显著提升识别精度与零样本泛化能力。
链接: https://arxiv.org/abs/2603.07920
作者: Zhangshuo Qi,Jingyi Xu,Luqi Cheng,Shichen Wen,Guangming Xiong
机构: Beijing Institute of Technology (北京理工大学); Shanghai Jiao Tong University (上海交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:All-weather autonomy is critical for autonomous driving, which necessitates reliable localization across diverse scenarios. While LiDAR place recognition is widely deployed for this task, its performance degrades in adverse weather. Conversely, radar-based methods, though weather-resilient, are hindered by the general unavailability of radar maps. To bridge this gap, radar-to-LiDAR place recognition, which localizes radar scans within existing LiDAR maps, has garnered increasing interest. However, extracting discriminative and generalizable features shared between modalities remains challenging, compounded by the scarcity of large-scale paired training data and the signal heterogeneity across radar types. In this work, we propose RLPR, a robust radar-to-LiDAR place recognition framework compatible with single-chip, scanning, and 4D radars. We first design a dual-stream network to extract structural features that abstract away from sensor-specific signal properties (e.g., Doppler or RCS). Subsequently, motivated by our task-specific asymmetry observation between radar and LiDAR, we introduce a two-stage asymmetric cross-modal alignment (TACMA) strategy, which leverages the pre-trained radar branch as a discriminative anchor to guide the alignment process. Experiments on four datasets demonstrate that RLPR achieves state-of-the-art recognition accuracy with strong zero-shot generalization capabilities.
[CV-102] Enhancing Unregistered Hyperspectral Image Super-Resolution via Unmixing-based Abundance Fusion Learning
【速读】：该论文旨在解决未配准高光谱图像（HSI）超分辨率（SR）问题，即如何利用未对齐的高分辨率参考图像来提升低分辨率HSI的空间细节和光谱质量。其解决方案的关键在于提出了一种基于解混的融合框架，通过奇异值分解（SVD）实现初始光谱解混，分离出原始端元（endmember）与丰度图（abundance map），从而将空间与光谱信息解耦；随后设计了一个从粗到精的可变形聚合模块，先在粗金字塔预测器中估计像素级流场和相似性图，再进行亚像素级精细调整，以适配未配准参考图像的空间纹理；同时引入空间-通道丰度交叉注意力块增强特征表示，并采用空间-通道调制融合模块结合编码器-解码器特征，通过动态门控权重生成高质量高分辨率HSI。该方法有效缓解了因图像未配准带来的融合误差，显著提升了SR模型的学习能力与重建性能。
链接: https://arxiv.org/abs/2603.07918
作者: Yingkai Zhang,Tao Zhang,Jing Nie,Ying Fu
机构: Beijing Institute of Technology (北京理工大学); Hangzhou Dianzi University (杭州电子科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Unregistered hyperspectral image (HSI) super-resolution (SR) typically aims to enhance a low-resolution HSI using an unregistered high-resolution reference image. In this paper, we propose an unmixing-based fusion framework that decouples spatial-spectral information to simultaneously mitigate the impact of unregistered fusion and enhance the learnability of SR models. Specifically, we first utilize singular value decomposition for initial spectral unmixing, preserving the original endmembers while dedicating the subsequent network to enhancing the initial abundance map. To leverage the spatial texture of the unregistered reference, we introduce a coarse-to-fine deformable aggregation module, which first estimates a pixel-level flow and a similarity map using a coarse pyramid predictor. It further performs fine sub-pixel refinement to achieve deformable aggregation of the reference features. The aggregative features are then refined via a series of spatial-channel abundance cross-attention blocks. Furthermore, a spatial-channel modulated fusion module is presented to merge encoder-decoder features using dynamic gating weights, yielding a high-quality, high-resolution HSI. Experimental results on simulated and real datasets confirm that our proposed method achieves state-of-the-art super-resolution performance. The code will be available at this https URL.
[CV-103] Geometric Transformation-Embedded Mamba for Learned Video Compression
【速读】：该论文旨在解决传统视频压缩方法中因依赖显式运动估计与补偿而带来的复杂性问题，尤其是在低比特率条件下难以兼顾感知质量和时序一致性的问题。其解决方案的关键在于提出了一种基于直接变换策略（即非线性变换、量化和熵编码）的简化且高效的视频压缩框架：通过引入级联Mamba模块（Cascaded Mamba Module, CMM）以有效建模长距离时空依赖关系，并结合局部细化前馈网络（Locality Refinement Feed-Forward Network, LRFFN）增强局部空间表征能力；同时设计了一种条件通道自适应熵模型，利用条件时序先验更精确地估计当前潜在特征的概率分布，从而在保持高压缩效率的同时显著提升视频的感知质量与时序稳定性。
链接: https://arxiv.org/abs/2603.07912
作者: Hao Wei,Yanhui Zhou,Chenyang Ge
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Although learned video compression methods have exhibited outstanding performance, most of them typically follow a hybrid coding paradigm that requires explicit motion estimation and compensation, resulting in a complex solution for video compression. In contrast, we introduce a streamlined yet effective video compression framework founded on a direct transform strategy, i.e., nonlinear transform, quantization, and entropy coding. We first develop a cascaded Mamba module (CMM) with different embedded geometric transformations to effectively explore both long-range spatial and temporal dependencies. To improve local spatial representation, we introduce a locality refinement feed-forward network (LRFFN) that incorporates a hybrid convolution block based on difference convolutions. We integrate the proposed CMM and LRFFN into the encoder and decoder of our compression framework. Moreover, we present a conditional channel-wise entropy model that effectively utilizes conditional temporal priors to accurately estimate the probability distributions of current latent features. Extensive experiments demonstrate that our method outperforms state-of-the-art video compression approaches in terms of perceptual quality and temporal consistency under low-bitrate constraints. Our source codes and models will be available at this https URL.
[CV-104] Beyond Heuristic Prompting: A Concept-Guided Bayesian Framework for Zero-Shot Image Recognition CVPR2026
【速读】：该论文旨在解决视觉-语言模型（Vision-Language Models, VLMs）在零样本图像分类任务中因提示工程（prompt engineering）不优和目标类别适应性差而导致性能受限的问题。其核心解决方案在于将类别特定概念（class-specific concepts）引入提示设计，并从贝叶斯视角重新建模分类过程：将预测视为对概念空间的边缘化，其中每个概念由先验概率和基于测试图像的似然加权。关键创新包括两个方面：一是通过大语言模型（LLMs）驱动的多阶段概念合成流程生成判别性强且组合丰富的概念，并利用行列式点过程（Determinantal Point Process, DPP）保证多样性；二是提出一种无需训练的自适应软截断似然机制（adaptive soft-trim likelihood），在单次前向传播中抑制异常概念的影响，从而提升模型鲁棒性与泛化能力。
链接: https://arxiv.org/abs/2603.07911
作者: Hui Liu,Kecheng Chen,Jialiang Wang,Xianming Liu,Wenya Wang,Haoliang Li
机构: City University of Hong Kong (香港城市大学); Harbin Institute of Technology (哈尔滨工业大学); Nanyang Technological University (南洋理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages, Accepted by CVPR 2026
Abstract:Vision-Language Models (VLMs), such as CLIP, have significantly advanced zero-shot image recognition. However, their performance remains limited by suboptimal prompt engineering and poor adaptability to target classes. While recent methods attempt to improve prompts through diverse class descriptions, they often rely on heuristic designs, lack versatility, and are vulnerable to outlier prompts. This paper enhances prompt by incorporating class-specific concepts. By treating concepts as latent variables, we rethink zero-shot image classification from a Bayesian perspective, casting prediction as marginalization over the concept space, where each concept is weighted by a prior and a test-image conditioned likelihood. This formulation underscores the importance of both a well-structured concept proposal distribution and the refinement of concept priors. To construct an expressive and efficient proposal distribution, we introduce a multi-stage concept synthesis pipeline driven by LLMs to generate discriminative and compositional concepts, followed by a Determinantal Point Process to enforce diversity. To mitigate the influence of outlier concepts, we propose a training-free, adaptive soft-trim likelihood, which attenuates their impact in a single forward pass. We further provide robustness guarantees and derive multi-class excess risk bounds for our framework. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art approaches, validating its effectiveness in zero-shot image classification. Our code is available at this https URL.
[CV-105] Revisiting Unknowns: Towards Effective and Efficient Open-Set Active Learning CVPR2026
【速读】：该论文旨在解决开放集主动学习（Open-set Active Learning, OSAL）中因未见类（unseen classes）存在而导致的标注样本选择效率低、监督信号利用不足的问题。现有方法通常依赖独立训练的开放集检测器，不仅增加训练开销，还忽视了已标注未知类样本对已知类别学习的监督价值。其解决方案的关键在于提出一种统一且无需额外检测器的框架 E²OAL（Effective and Efficient Open-set Active Learning），通过冻结对比预训练特征空间中的标签引导聚类挖掘未知类潜在结构，并引入 Dirichlet 校准辅助头联合建模已知与未知类别，从而增强置信度校准和已知类判别能力；在此基础上，结合 logits-margin 纯度评分构建高纯度候选池与专为 OSAL 设计的信息性度量，形成灵活的两阶段查询策略，在保证查询精度的同时显著降低超参数敏感性，实现高效准确的样本选择。
链接: https://arxiv.org/abs/2603.07898
作者: Chen-Chen Zong,Yu-Qi Chi,Xie-Yang Wang,Yan Cui,Sheng-Jun Huang
机构: Nanjing University of Aeronautics and Astronautics (南京航空航天大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted to CVPR 2026
Abstract:Open-set active learning (OSAL) aims to identify informative samples for annotation when unlabeled data may contain previously unseen classes-a common challenge in safety-critical and open-world scenarios. Existing approaches typically rely on separately trained open-set detectors, introducing substantial training overhead and overlooking the supervisory value of labeled unknowns for improving known-class learning. In this paper, we propose E ^2 OAL (Effective and Efficient Open-set Active Learning), a unified and detector-free framework that fully exploits labeled unknowns for both stronger supervision and more reliable querying. E ^2 OAL first uncovers the latent class structure of unknowns through label-guided clustering in a frozen contrastively pre-trained feature space, optimized by a structure-aware F1-product objective. To leverage labeled unknowns, it employs a Dirichlet-calibrated auxiliary head that jointly models known and unknown categories, improving both confidence calibration and known-class discrimination. Building on this, a logit-margin purity score estimates the likelihood of known classes to construct a high-purity candidate pool, while an OSAL-specific informativeness metric prioritizes partially ambiguous yet reliable samples. These components together form a flexible two-stage query strategy with adaptive precision control and minimal hyperparameter sensitivity. Extensive experiments across multiple OSAL benchmarks demonstrate that E ^2 OAL consistently surpasses state-of-the-art methods in accuracy, efficiency, and query precision, highlighting its effectiveness and practicality for real-world applications. The code is available at this http URL.
[CV-106] MINT: Molecularly Informed Training with Spatial Transcriptomics Supervision for Pathology Foundation Models
【速读】：该论文旨在解决病理基础模型（Pathology Foundation Models）在自监督预训练中仅学习组织形态学特征，而未能显式捕获组织分子状态的问题。其解决方案的关键在于提出MINT（Molecularly Informed Training）框架，通过引入空间转录组学（Spatial Transcriptomics, ST）的监督信号来增强模型对分子信息的感知能力：具体而言，MINT在Vision Transformer（ViT）输入中添加一个可学习的ST token以独立编码转录组信息，避免与形态学CLS token混淆；同时利用DINO自蒸馏和特征锚定机制防止灾难性遗忘，并在spot级（Visium）和patch级（Xenium）分辨率上进行基因表达回归，实现跨空间尺度的互补监督。
链接: https://arxiv.org/abs/2603.07895
作者: Minsoo Lee,Jonghyun Kim,Juseung Yun,Sunwoo Yu,Jongseong Jang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Pathology foundation models learn morphological representations through self-supervised pretraining on large-scale whole-slide images, yet they do not explicitly capture the underlying molecular state of the tissue. Spatial transcriptomics technologies bridge this gap by measuring gene expression in situ, offering a natural cross-modal supervisory signal. We propose MINT (Molecularly Informed Training), a fine-tuning framework that incorporates spatial transcriptomics supervision into pretrained pathology Vision Transformers. MINT appends a learnable ST token to the ViT input to encode transcriptomic information separately from the morphological CLS token, preventing catastrophic forgetting through DINO self-distillation and explicit feature anchoring to the frozen pretrained encoder. Gene expression regression at both spot-level (Visium) and patch-level (Xenium) resolutions provides complementary supervision across spatial scales. Trained on 577 publicly available HEST samples, MINT achieves the best overall performance on both HEST-Bench for gene expression prediction (mean Pearson r = 0.440) and EVA for general pathology tasks (0.803), demonstrating that spatial transcriptomics supervision complements morphology-centric self-supervised pretraining.
[CV-107] Visualizing Coalition Formation: From Hedonic Games to Image Segmentation ICLR2026
【速读】：该论文试图解决如何通过图像分割来诊断联盟形成问题在享乐博弈（hedonic games）中的均衡结构及其稳定性，特别是量化机制设计参数对多智能体系统中平衡状态的影响。其解决方案的关键在于将像素建模为图上的代理（agents），利用粒度参数调节联盟的碎片化程度，并通过在Weizmann单目标基准上测量收敛联盟与前景真值的重叠情况，揭示从一致到可恢复的碎片化再到内在失效的相变行为，从而建立多智能体系统与图像分割之间的定量关联。
链接: https://arxiv.org/abs/2603.07890
作者: Pedro Henrique de Paula França,Lucas Lopes Felipe,Daniel Sadoc Menasché
机构: UFRJ
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: The First Workshop on AI for Mechanism Design and Strategic Decision Making – Workshop AIMS at ICLR 2026
Abstract:We propose image segmentation as a visual diagnostic testbed for coalition formation in hedonic games. Modeling pixels as agents on a graph, we study how a granularization parameter shapes equilibrium fragmentation and boundary structure. On the Weizmann single-object benchmark, we relate multi-coalition equilibria to binary protocols by measuring whether the converged coalitions overlap with a foreground ground-truth. We observe transitions from cohesive to fragmented yet recoverable equilibria, and finally to intrinsic failure under excessive fragmentation. Our core contribution links multi-agent systems with image segmentation by quantifying the impact of mechanism design parameters on equilibrium structures.
[CV-108] Structure and Progress Aware Diffusion for Medical Image Segmentation
【速读】：该论文旨在解决医学图像分割中粗粒度形态与语义结构和细粒度边界难以协同学习的问题。现有方法在训练过程中同时优化两者，但因病灶边界常受重叠、标注不确定性等因素影响而模糊且不可靠，导致早期监督效果不佳。解决方案的关键在于提出一种结构与进度感知扩散（SPAD）框架，其核心由两个模块构成：一是语义聚焦扩散（ScD），通过保留目标区域内的语义锚点（anchor-preserved target perturbation）引导模型从上下文推断噪声区域；二是边界集中扩散（BcD），引入进度感知边界噪声以模糊不可靠边界，迫使模型优先关注稳定的整体解剖形态和全局语义。二者由进度感知调度器（PaS）动态调节噪声强度，形成从粗到细的渐进式扩散范式，从而实现先理解整体结构、再精细化边界调整的分阶段学习机制。
链接: https://arxiv.org/abs/2603.07889
作者: Siyuan Song,Guyue Hu,Chenglong Li,Dengdi Sun,Zhe Jin,Jin Tang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Medical image segmentation is crucial for computer-aided diagnosis, which necessitates understanding both coarse morphological and semantic structures, as well as carving fine boundaries. The morphological and semantic structures in medical images are beneficial and stable clues for target understanding. While the fine boundaries of medical targets (like tumors and lesions) are usually ambiguous and noisy since lesion overlap, annotation uncertainty, and so on, making it not reliable to serve as early supervision. However, existing methods simultaneously learn coarse structures and fine boundaries throughout the training process. In this paper, we propose a structure and progress-aware diffusion (SPAD) for medical image segmentation, which consists of a semantic-concentrated diffusion (ScD) and a boundary-centralized diffusion (BcD) modulated by a progress-aware scheduler (PaS). Specifically, the semantic-concentrated diffusion introduces anchor-preserved target perturbation, which perturbs pixels within a medical target but preserves unaltered areas as semantic anchors, encouraging the model to infer noisy target areas from the surrounding semantic context. The boundary-centralized diffusion introduces progress-aware boundary noise, which blurs unreliable and ambiguous boundaries, thus compelling the model to focus on coarse but stable anatomical morphology and global semantics. Furthermore, the progress-aware scheduler gradually modulates noise intensity of the ScD and BcD forming a coarse-to-fine diffusion paradigm, which encourage focusing on coarse morphological and semantic structures during early target understanding stages and gradually shifting to fine target boundaries during later contour adjusting stages.
[CV-109] VLM-SubtleBench: How Far Are VLMs from Human-Level Subtle Comparative Reasoning ? ICLR2026
【速读】：该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在处理视觉相似图像间细微差异时的推理能力不足问题，这在工业缺陷检测、医学影像分析和航空监控等实际场景中至关重要。现有对比推理基准主要关注显著差异，难以反映真实应用所需的细粒度判断能力。解决方案的关键在于构建一个名为VLM-SubtleBench的新基准，涵盖十类细微差异类型（如属性、状态、情绪、时间、空间、存在性、数量、质量、视角和动作），并收集跨工业、航空与医疗等多领域的成对问答数据集，从而系统评估VLMs在复杂现实场景中的细微比较推理表现，并揭示其与人类性能之间的系统性差距。
链接: https://arxiv.org/abs/2603.07888
作者: Minkyu Kim,Sangheon Lee,Dongmin Park
机构: KRAFTON( Krafton); KAIST(韩国科学技术院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: ICLR 2026
Abstract:The ability to distinguish subtle differences between visually similar images is essential for diverse domains such as industrial anomaly detection, medical imaging, and aerial surveillance. While comparative reasoning benchmarks for vision-language models (VLMs) have recently emerged, they primarily focus on images with large, salient differences and fail to capture the nuanced reasoning required for real-world applications. In this work, we introduce VLM-SubtleBench, a benchmark designed to evaluate VLMs on subtle comparative reasoning. Our benchmark covers ten difference types - Attribute, State, Emotion, Temporal, Spatial, Existence, Quantity, Quality, Viewpoint, and Action - and curate paired question-image sets reflecting these fine-grained variations. Unlike prior benchmarks restricted to natural image datasets, our benchmark spans diverse domains, including industrial, aerial, and medical imagery. Through extensive evaluation of both proprietary and open-source VLMs, we reveal systematic gaps between model and human performance across difference types and domains, and provide controlled analyses highlighting where VLMs’ reasoning sharply deteriorates. Together, our benchmark and findings establish a foundation for advancing VLMs toward human-level comparative reasoning.
[CV-110] oward Unified Multimodal Representation Learning for Autonomous Driving
【速读】：该论文旨在解决现有基于对比学习的多模态对齐方法在3D视觉任务中存在的一致性不足问题，即仅通过两两模态间的余弦相似度进行对齐，难以保证整个多模态空间内的一致性和统一性。其解决方案的关键在于提出了一种对比张量预训练（Contrastive Tensor Pre-training, CTP）框架，将传统的二维相似度矩阵扩展为高阶多模态相似度张量，并引入张量损失函数以实现所有模态间的联合对比学习，从而在统一嵌入空间中实现更一致、更强的跨模态对齐，提升端到端自动驾驶系统的场景理解能力。
链接: https://arxiv.org/abs/2603.07874
作者: Ximeng Tao,Dimitar Filev,Gaurav Pandey
机构: Texas AM University (德州农工大学); Texas AM University (德州农工大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Contrastive Language-Image Pre-training (CLIP) has shown impressive performance in aligning visual and textual representations. Recent studies have extended this paradigm to 3D vision to improve scene understanding for autonomous driving. A common strategy is to employ pairwise cosine similarity between modalities to guide the training of a 3D encoder. However, considering the similarity between individual modality pairs rather than all modalities jointly fails to ensure consistent and unified alignment across the entire multimodal space. In this paper, we propose a Contrastive Tensor Pre-training (CTP) framework that simultaneously aligns multiple modalities in a unified embedding space to enhance end-to-end autonomous driving. Compared with pairwise cosine similarity alignment, our method extends the 2D similarity matrix into a multimodal similarity tensor. Furthermore, we introduce a tensor loss to enable joint contrastive learning across all modalities. For experimental validation of our framework, we construct a text-image-point cloud triplet dataset derived from existing autonomous driving datasets. The results show that our proposed unified multimodal alignment framework achieves favorable performance for both scenarios: (i) aligning a 3D encoder with pretrained CLIP encoders, and (ii) pretraining all encoders from scratch.
[CV-111] SoundWeaver: Semantic Warm-Starting for Text-to-Audio Diffusion Serving INTERSPEECH2026
【速读】：该论文旨在解决文本到音频扩散模型（text-to-audio diffusion models）在实际部署中因需大量函数评估（Number of Function Evaluations, NFEs）而导致的高延迟（多秒级）和低吞吐量问题。解决方案的关键在于提出SoundWeaver——一个无需训练、与模型无关的推理加速系统，其核心创新包括：1）参考选择器（Reference Selector）通过语义和时长感知门控机制检索并时间对齐缓存候选音频；2）跳过门控器（Skip Gater）动态决定跳过的NFE比例以减少计算开销；3）轻量级缓存管理器（Cache Manager）通过质量感知的淘汰与精炼策略维持缓存效用。实验表明，仅使用约1K条缓存条目即可实现1.8–3.0倍的延迟降低，同时保持或提升感知音频质量。
链接: https://arxiv.org/abs/2603.07865
作者: Ayush Barik,Sofia Stoica,Nikhil Sarda,Arnav Kethana,Abhinav Khanduja,Muchen Xu,Fan Lai
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校); Assured Intelligence (保证智能)
类目: ound (cs.SD); Computer Vision and Pattern Recognition (cs.CV); Audio and Speech Processing (eess.AS)
备注: Submitted to INTERSPEECH 2026
Abstract:Text-to-audio diffusion models produce high-fidelity audio but require tens of function evaluations (NFEs), incurring multi-second latency and limited throughput. We present SoundWeaver, the first training-free, model-agnostic serving system that accelerates text-to-audio diffusion by warm-starting from semantically similar cached audio. SoundWeaver introduces three components: a Reference Selector that retrieves and temporally aligns cached candidates via semantic and duration-aware gating; a Skip Gater that dynamically determines the percentage of NFEs to skip; and a lightweight Cache Manager that maintains cache utility through quality-aware eviction and refinement. On real-world audio traces, SoundWeaver achieves 1.8–3.0 \times latency reduction with a cache of only \sim 1K entries while preserving or improving perceptual quality.
[CV-112] raining-free Temporal Object Tracking in Surgical Videos
【速读】：该论文旨在解决腹腔镜胆囊切除术（laparoscopic cholecystectomy, LC）视频中关键解剖结构与手术器械的在线目标跟踪问题，尤其针对现有数据集存在的像素级标注成本高和标签不一致性挑战。其解决方案的关键在于利用预训练文本到图像扩散模型（text-to-image diffusion models）的内在目标定位能力，无需任何训练或微调即可从手术帧中提取代表性特征；并通过基于查询-键-值注意力机制启发的亲和矩阵实现跨帧交互，从而保障跟踪过程的时间连续性。
链接: https://arxiv.org/abs/2603.07839
作者: Subhadeep Koley,Abdolrahim Kadkhodamohammadi,Santiago Barbarisi,Danail Stoyanov,Imanol Luengo
机构: Medtronic(美敦力)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted in IPCAI 2025
Abstract:Purpose: In this paper, we present a novel approach for online object tracking in laparoscopic cholecystectomy (LC) surgical videos, targeting localisation and tracking of critical anatomical structures and instruments. Our method addresses the challenges of costly pixel-level annotations and label inconsistencies inherent in existing datasets. Methods: Leveraging the inherent object localisation capabilities of pre-trained text-to-image diffusion models, we extract representative features from surgical frames without any training or fine-tuning. Our tracking framework uses these features, along with cross-frame interactions via an affinity matrix inspired by query-key-value attention, to ensure temporal continuity in the tracking process. Results: Through a pilot study, we first demonstrate that diffusion features exhibit superior object localisation and consistent semantics across different decoder levels and temporal frames. Later, we perform extensive experiments to validate the effectiveness of our approach, showcasing its superiority over competitors for the task of temporal object tracking. Specifically, we achieve a per-pixel classification accuracy of 79.19%, mean Jaccard Score of 56.20%, and mean F-Score of 79.48% on the publicly available CholeSeg8K dataset. Conclusion: Our work not only introduces a novel application of text-to-image diffusion models but also contributes to advancing the field of surgical video analysis, offering a promising avenue for accurate and cost-effective temporal object tracking in minimally invasive surgery videos. Comments: Accepted in IPCAI 2025 Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2603.07839 [cs.CV] (or arXiv:2603.07839v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.07839 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: Int J CARS 20, 1067-1075 (2025) Related DOI: https://doi.org/10.1007/s11548-025-03349-6 Focus to learn more DOI(s) linking to related resources Submission history From: Subhadeep Koley [view email] [v1] Sun, 8 Mar 2026 23:09:16 UTC (40,946 KB)
[CV-113] GazeShift: Unsupervised Gaze Estimation and Dataset for VR CVPR26
【速读】：该论文旨在解决虚拟现实（VR）系统中眼动估计（gaze estimation）因数据稀缺而导致的性能瓶颈问题，尤其是缺乏大规模、高精度标注的离轴摄像头配置数据集，以及传统方法依赖大量标注数据和复杂几何建模的局限性。解决方案的关键在于提出两个核心贡献：一是构建了首个大规模离轴眼动估计数据集VRGaze，包含68名参与者采集的210万张近眼红外图像；二是设计了GazeShift框架，一种基于注意力机制的无监督学习方法，能够从近眼红外图像中高效解耦眼动特征与外观特征，在无需标签的情况下实现紧凑且实时的眼动表示学习，同时支持轻量级少样本校准以适配个体用户，显著提升准确性和部署效率。
链接: https://arxiv.org/abs/2603.07832
作者: Gil Shapira,Ishay Goldin,Evgeny Artyomov,Donghoon Kim,Yosi Keller,Niv Zehngut
机构: Samsung Semiconductor Israel RD Center (SIRC); Samsung Electronics; Bar-Ilan University
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR26
Abstract:Gaze estimation is instrumental in modern virtual reality (VR) systems. Despite significant progress in remote-camera gaze estimation, VR gaze research remains constrained by data scarcity - particularly the lack of large-scale, accurately labeled datasets captured with the off-axis camera configurations typical of modern headsets. Gaze annotation is difficult since fixation on intended targets cannot be guaranteed. To address these challenges, we introduce VRGaze - the first large-scale off-axis gaze estimation dataset for VR - comprising 2.1 million near-eye infrared images collected from 68 participants. We further propose GazeShift, an attention-guided unsupervised framework for learning gaze representations without labeled data. Unlike prior redirection-based methods that rely on multi-view or 3D geometry, GazeShift is tailored to near-eye infrared imagery, achieving effective gaze-appearance disentanglement in a compact, real-time model. GazeShift embeddings can be optionally adapted to individual users via lightweight few-shot calibration, achieving a 1.84-degree mean error on VRGaze. On the remote-camera MPIIGaze dataset, the model achieves a 7.15-degree person-agnostic error, doing so with 10x fewer parameters and 35x fewer FLOPs than baseline methods. Deployed natively on a VR headset GPU, inference takes only 5 ms. Combined with demonstrated robustness to illumination changes, these results highlight GazeShift as a label-efficient, real-time solution for VR gaze tracking. Project code and the VRGaze dataset are released at this https URL.
[CV-114] ransferable Optimization Network for Cross-Domain Image Reconstruction
【速读】：该论文旨在解决图像重建中因训练数据有限而导致性能受限的问题（limited training data in image reconstruction problems）。其解决方案的关键在于提出了一种新颖的迁移学习框架，该框架包含两个步骤：首先通过双层优化训练一个通用特征提取器（universal feature-extractor），使其能够从多个异构领域的大规模数据中学习重要知识；其次，在仅有少量目标域数据的情况下，通过另一组双层优化训练任务特定的域适配器（domain-adapter），将适配器与通用特征提取器组合后可有效利用特征作为图像正则化的重要组成部分，从而在数据稀缺条件下实现高质量的重建。
链接: https://arxiv.org/abs/2603.07831
作者: Yunmei Chen,Chi Ding,Xiaojing Ye
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Optimization and Control (math.OC)
备注: 30 pages, 7 figures
Abstract:We develop a novel transfer learning framework to tackle the challenge of limited training data in image reconstruction problems. The proposed framework consists of two training steps, both of which are formed as bi-level optimizations. In the first step, we train a powerful universal feature-extractor that is capable of learning important knowledge from large, heterogeneous data sets in various domains. In the second step, we train a task-specific domain-adapter for a new target domain or task with only a limited amount of data available for training. Then the composition of the adapter and the universal feature-extractor effectively explores feature which serve as an important component of image regularization for the new domains, and this leads to high-quality reconstruction despite the data limitation issue. We apply this framework to reconstruct under-sampled MR images with limited data by using a collection of diverse data samples from different domains, such as images of other anatomies, measurements of various sampling ratios, and even different image modalities, including natural images. Experimental results demonstrate a promising transfer learning capability of the proposed method.
[CV-115] Fusion Complexity Inversion: Why Simpler Cross View Modules Outperform SSMs and Cross View Attention Transformers for Pasture Biomass Regression
【速读】：该论文旨在解决农业遥感图像中牧草生物量（pasture biomass）估计精度不足的问题，尤其针对现实场景下数据稀缺、类别不平衡且标注稀疏的挑战。其关键解决方案在于系统评估视觉基础模型在农业回归任务中的适应性，发现一个反直觉现象——“融合复杂度反转”（fusion complexity inversion）：在小样本农业数据上，简单的两层门控深度卷积（gated depthwise convolution）表现优于复杂的跨视图注意力机制（如Transformer）、双向状态空间模型（SSM）和完整Mamba架构；同时强调骨干网络预训练规模是决定性能的核心因素，DINOv2到DINOv3的升级带来5.0 R²点的显著提升，且仅训练元数据（物种、状态和NDVI）即可达到约0.829的通用上限，大幅压缩不同融合策略间的性能差异。因此，论文提出可操作指南：优先选择高质量骨干网络，局部模块优于全局建模，且推理阶段不可用特征应被排除。
链接: https://arxiv.org/abs/2603.07819
作者: Mridankan Mandal
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Accurate estimation of pasture biomass from agricultural imagery is critical for sustainable livestock management, yet existing methods are limited by the small, imbalanced, and sparsely annotated datasets typical of real world monitoring. In this study, adaptation of vision foundation models to agricultural regression is systematically evaluated on the CSIRO Pasture Biomass benchmark, a 357 image dual view dataset with laboratory validated, component wise ground truth for five biomass targets, through 17 configurations spanning four backbones (EfficientNet-B3 to DINOv3-ViT-L), five cross view fusion mechanisms, and a 4x2 metadata factorial. A counterintuitive principle, termed “fusion complexity inversion”, is uncovered: on scarce agricultural data, a two layer gated depthwise convolution (R^2 = 0.903) outperforms cross view attention transformers (0.833), bidirectional SSMs (0.819), and full Mamba (0.793, below the no fusion baseline). Backbone pretraining scale is found to monotonically dominate all architectural choices, with the DINOv2 - DINOv3 upgrade alone yielding +5.0 R^2 points. Training only metadata (species, state, and NDVI) is shown to create a universal ceiling at R^2 ~ 0.829, collapsing an 8.4 point fusion spread to 0.1 points. Actionable guidelines for sparse agricultural benchmarks are established: backbone quality should be prioritized over fusion complexity, local modules preferred over global alternatives, and features unavailable at inference excluded.
[CV-116] racking Phenological Status and Ecological Interactions in a Hawaiian Cloud Forest Understory using Low-Cost Camera Traps and Visual Foundation Models
【速读】：该论文旨在解决植物物候学（plant phenology）研究中，特别是在热带地区缺乏精细化观测数据的问题，以及如何在不依赖监督学习的情况下从相机陷阱图像中提取个体水平的物候趋势。其解决方案的关键在于结合基础视觉模型（foundation vision models）与传统计算机视觉方法，利用低成本、动物触发的相机陷阱获取高时间分辨率的图像数据，从而实现与地面观测相当的物候变化检测，并进一步通过图像中记录的动植物互作信息揭示驱动植物物候和动物生态的潜在机制。
链接: https://arxiv.org/abs/2603.07817
作者: Luke Meyers,Anirudh Potlapally,Yuyan Chen,Mike Long,Tanya Berger-Wolf,Hari Subramoni,Remi Megret,Daniel Rubenstein
机构: The Ohio State University (俄亥俄州立大学); The University of Puerto Rico Rio Piedras (波多黎各大学里奥皮德拉斯分校); McGill University (麦吉尔大学); Batelle Ecology (巴特尔生态学); Princeton University (普林斯顿大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Plant phenology, the study of cyclical events such as leafing out, flowering, or fruiting, has wide ecological impacts but is broadly understudied, especially in the tropics. Image analysis has greatly enhanced remote phenological monitoring, yet capturing phenology at the individual level remains challenging. In this project, we deployed low-cost, animal-triggered camera traps at the Pu’u Maka’ala Natural Area Reserve in Hawaii to simultaneously document shifts in plant phenology and flora-faunal interactions. Using a combination of foundation vision models and traditional computer vision methods, we measure phenological trends from images comparable to on-the-ground observations without relying on supervised learning techniques. These temporally fine-grained phenology measurements from camera-trap images uncover trends that coarser traditional sampling fails to detect. When combined with detailed visitation data detected from images, these trends can begin to elucidate drivers of both plant phenology and animal ecology.
[CV-117] HybridStitch: Pixel and Timestep Level Model Stitching for Diffusion Acceleration
【速读】：该论文旨在解决生成式 AI（Generative AI）中扩散模型在文本到图像（Text-to-Image, T2I）生成任务中存在的计算开销过大问题，尤其针对参数量达数十亿的大模型而言。现有方法虽通过引入小型模型替代部分去噪步骤以节省计算资源，但仅关注不同时间步之间的计算优化，忽略了单个时间步内不同区域的计算需求差异。解决方案的关键在于提出 HybridStitch 框架，其将图像生成过程类比为图像编辑：在每个时间步中，将图像划分为易渲染区域与复杂区域，分别由小模型生成粗略草图和由大模型进行精细化编辑，从而实现更高效的计算分配。实验表明，该方法在 Stable Diffusion 3 上实现了 1.83× 的加速效果，优于所有现有混合模型方法。
链接: https://arxiv.org/abs/2603.07815
作者: Desen Sun,Jason Hon,Jintao Zhang,Sihang Liu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Diffusion models have demonstrated a remarkable ability in Text-to-Image (T2I) generation applications. Despite the advanced generation output, they suffer from heavy computation overhead, especially for large models that contain tens of billions of parameters. Prior work has illustrated that replacing part of the denoising steps with a smaller model still maintains the generation quality. However, these methods only focus on saving computation for some timesteps, ignoring the difference in compute demand within one timestep. In this work, we propose HybridStitch, a new T2I generation paradigm that treats generation like editing. Specifically, we introduce a hybrid stage that jointly incorporates both the large model and the small model. HybridStitch separates the entire image into two regions: one that is relatively easy to render, enabling an early transition to the smaller model, and another that is more complex and therefore requires refinement by the large model. HybridStitch employs the small model to construct a coarse sketch while exploiting the large model to edit and refine the complex regions. According to our evaluation, HybridStitch achieves 1.83 \times speedup on Stable Diffusion 3, which is faster than all existing mixture of model methods.
[CV-118] MWM: Mobile World Models for Action-Conditioned Consistent Prediction
【速读】：该论文旨在解决当前基于世界模型（World Models）的具身导航任务中存在两个关键问题：一是现有模型缺乏动作条件一致性（Action-Conditioned Consistency, ACC），导致多步预测过程中视觉合理性虽高但轨迹漂移严重，影响规划性能；二是高效部署需要少步扩散推理（few-step diffusion inference），而现有蒸馏方法未显式保留滚动一致性，造成训练与推理间的不匹配。解决方案的关键在于提出一种名为MWM的移动世界模型，并设计两阶段训练框架：首先进行结构预训练以建立基础环境表征，随后通过ACC后训练增强动作条件下的预测一致性；同时引入推断一致状态蒸馏（Inference-Consistent State Distillation, ICSD）机制，在少步扩散蒸馏过程中显式保持 rollout 一致性，从而在真实场景和基准测试中实现更高的视觉保真度、轨迹准确率、规划成功率及推理效率。
链接: https://arxiv.org/abs/2603.07799
作者: Han Yan,Zishang Xiang,Zeyu Zhang,Hao Tang
机构: Peking University (北京大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:World models enable planning in imagined future predicted space, offering a promising framework for embodied navigation. However, existing navigation world models often lack action-conditioned consistency, so visually plausible predictions can still drift under multi-step rollout and degrade planning. Moreover, efficient deployment requires few-step diffusion inference, but existing distillation methods do not explicitly preserve rollout consistency, creating a training-inference mismatch. To address these challenges, we propose MWM, a mobile world model for planning-based image-goal navigation. Specifically, we introduce a two-stage training framework that combines structure pretraining with Action-Conditioned Consistency (ACC) post-training to improve action-conditioned rollout consistency. We further introduce Inference-Consistent State Distillation (ICSD) for few-step diffusion distillation with improved rollout consistency. Our experiments on benchmark and real-world tasks demonstrate consistent gains in visual fidelity, trajectory accuracy, planning success, and inference efficiency. Code: this https URL. Website: this https URL.
[CV-119] 4DRC-OCC: Robust Semantic Occupancy Prediction Through Fusion of 4D Radar and Camera
【速读】：该论文旨在解决自动驾驶中复杂环境条件下三维语义占据预测（3D semantic occupancy prediction）的鲁棒性问题，尤其是在恶劣天气和光照变化下的感知挑战。其解决方案的关键在于首次将四维雷达（4D radar）与摄像头数据进行融合，利用4D雷达在复杂环境中可靠的测距、速度和角度测量能力，以及摄像头提供的丰富语义和纹理信息；同时通过引入相机像素中的深度线索实现从2D图像到3D场景的提升，从而显著提高场景重建精度，并构建了一个全自动标注的数据集以降低对昂贵人工标注的依赖。
链接: https://arxiv.org/abs/2603.07794
作者: David Ninfa,Andras Palffy,Holger Caesar
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Autonomous driving requires robust perception across diverse environmental conditions, yet 3D semantic occupancy prediction remains challenging under adverse weather and lighting. In this work, we present the first study combining 4D radar and camera data for 3D semantic occupancy prediction. Our fusion leverages the complementary strengths of both modalities: 4D radar provides reliable range, velocity, and angle measurements in challenging conditions, while cameras contribute rich semantic and texture information. We further show that integrating depth cues from camera pixels enables lifting 2D images to 3D, improving scene reconstruction accuracy. Additionally, we introduce a fully automatically labeled dataset for training semantic occupancy models, substantially reducing reliance on costly manual annotation. Experiments demonstrate the robustness of 4D radar across diverse scenarios, highlighting its potential to advance autonomous vehicle perception.
[CV-120] SGI: Structured 2D Gaussians for Efficient and Compact Large Image Representation CVPR2026
【速读】：该论文旨在解决2D Gaussian Splatting在高分辨率图像表示中面临的存储冗余与优化缓慢问题，即传统方法需独立优化和存储数百万个无结构的高斯基元（Gaussian primitives），导致收敛速度慢且参数冗余。其解决方案的关键在于提出结构化高斯图像（Structured Gaussian Image, SGI）框架：通过一组种子（seeds）定义多尺度局部空间，每个种子对应一个空间连贯区域，并结合轻量级多层感知机（MLP）生成结构化的隐式二维神经高斯；这种基于种子的建模方式在无结构高斯基元上引入了结构正则性，从而支持在种子层级进行基于熵的压缩以减少总体存储需求。同时，设计了从粗到细的多尺度拟合策略，显著加速了优化过程，实验证明SGI在保持甚至提升图像保真度的同时，相比非量化方法实现最高达7.5倍的压缩率，相比量化方法实现1.6倍压缩率，并分别实现1.6倍和6.5倍的优化速度提升。
链接: https://arxiv.org/abs/2603.07789
作者: Zixuan Pan,Kaiyuan Tang,Jun Xia,Yifan Qin,Lin Gu,Chaoli Wang,Jianxu Chen,Yiyu Shi
机构: University of Notre Dame (圣母大学); Tohoku University (东北大学); Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. (莱布尼茨分析科学研究所)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR 2026
Abstract:2D Gaussian Splatting has emerged as a novel image representation technique that can support efficient rendering on low-end devices. However, scaling to high-resolution images requires optimizing and storing millions of unstructured Gaussian primitives independently, leading to slow convergence and redundant parameters. To address this, we propose Structured Gaussian Image (SGI), a compact and efficient framework for representing high-resolution images. SGI decomposes a complex image into multi-scale local spaces defined by a set of seeds. Each seed corresponds to a spatially coherent region and, together with lightweight multi-layer perceptrons (MLPs), generates structured implicit 2D neural Gaussians. This seed-based formulation imposes structural regularity on otherwise unstructured Gaussian primitives, which facilitates entropy-based compression at the seed level to reduce the total storage. However, optimizing seed parameters directly on high-resolution images is a challenging and non-trivial task. Therefore, we designed a multi-scale fitting strategy that refines the seed representation in a coarse-to-fine manner, substantially accelerating convergence. Quantitative and qualitative evaluations demonstrate that SGI achieves up to 7.5x compression over prior non-quantized 2D Gaussian methods and 1.6x over quantized ones, while also delivering 1.6x and 6.5x faster optimization, respectively, without degrading, and often improving, image fidelity. Code is available at this https URL.
[CV-121] OrdinalBench: A Benchmark Dataset for Diagnosing Generalization Limits in Ordinal Number Understanding of Vision-Language Models
【速读】：该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在序数理解能力上的显著不足问题，即模型难以准确追踪相对位置并泛化至大数值序数（如300以内）的情形。其解决方案的关键在于提出OrdinalBench——一个标准化的诊断性基准，通过定义“第N个对象识别”任务来系统评估VLM的序数理解能力，并从三个维度控制难度：序数大小（ordinal magnitude）、排列复杂度（arrangement complexity）和物体数量（object count）。该基准提供39,000个带真实推理轨迹标注的问题-答案对，支持零样本测试与结构化步骤级推理轨迹生成，从而实现对模型最终准确性及路径一致性的双重量化评估，揭示了当前主流VLM在极端序数和复杂路径条件下性能急剧下降的现象，为提升VLM的序列推理能力提供了可复现的诊断框架与评测工具。
链接: https://arxiv.org/abs/2603.07786
作者: Yusuke Tozaki,Hisashi Miyamori
机构: Kyoto Sangyo University (京都产业大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted as a Short Paper at VISAPP 2026
Abstract:Vision-Language Models (VLMs) have advanced across multimodal benchmarks but still show clear gaps in ordinal number understanding, i.e., the ability to track relative positions and generalize to large indices. We present OrdinalBench, a diagnostic benchmark that standardizes ordinal number understanding as an evaluation task for VLMs. The core task is N-th object identification, defined by a starting reference and traversal rule. Task difficulty is controlled along three axes: (i) ordinal magnitude, from small numbers to extreme cases up to 300; (ii) arrangement complexity, from single loops to maze-like paths; and (iii) object count. The benchmark provides 39,000 question-answer pairs, each annotated with a ground-truth reasoning trajectory and balanced across difficulty levels for controlled large-scale testing. Beyond answer-only evaluation, our framework requires models to generate structured stepwise traces of the counting process and provides an open evaluation toolkit that measures both final accuracy and step-level path consistency. Zero-shot evaluations of GPT-5, Gemini 2.5 Flash Lite, Qwen2.5-VL, InternVL3.5, and Molmo reveal sharp degradation under large-ordinal and complex-path conditions, highlighting weak generalization despite strong scores on standard multimodal tasks. By framing ordinal number understanding as a core target, OrdinalBench provides a reproducible benchmark and diagnostic framework for developing VLMs with stronger sequential reasoning. All data and code are available at this https URL
[CV-122] Parameterized Brushstroke Style Transfer
【速读】：该论文旨在解决传统基于计算机视觉的风格迁移方法局限于像素域（pixel domain）所带来的不自然问题，即这些方法通过直接修改图像像素来实现艺术风格迁移，而忽略了真实艺术品本质上由画笔笔触（brush stroke）构成的特性。解决方案的关键在于将图像表示从RGB空间转换至笔触域（brush stroke domain），从而更贴近人类艺术创作的实际过程，实现更具视觉真实感和艺术表现力的风格迁移效果。
链接: https://arxiv.org/abs/2603.07776
作者: Uma Meleti,Siyu Huang
机构: Clemson University (克莱姆森大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:Computer Vision-based Style Transfer techniques have been used for many years to represent artistic style. However, most contemporary methods have been restricted to the pixel domain; in other words, the style transfer approach has been modifying the image pixels to incorporate artistic style. However, real artistic work is made of brush strokes with different colors on a canvas. Pixel-based approaches are unnatural for representing these images. Hence, this paper discusses a style transfer method that represents the image in the brush stroke domain instead of the RGB domain, which has better visual improvement over pixel-based methods.
[CV-123] Geometric Knowledge-Assisted Federated Dual Knowledge Distillation Approach Towards Remote Sensing Satellite Imagery
【速读】：该论文旨在解决多卫星遥感图像（Remote Sensing Satellite Imagery, RSSI）在联邦学习（Federated Learning, FL）场景下因数据规模大和局部数据分布异构性（data heterogeneity）导致的模型训练效率与性能下降问题。其解决方案的关键在于提出一种几何知识引导的联邦双知识蒸馏框架（Geometric Knowledge-Guided Federated Dual Knowledge Distillation, GK-FedDKD）：首先，各客户端通过自监督方式从多个学生编码器（Student Encoder, SE）中蒸馏出教师编码器（Teacher Encoder, TE），并结合共享分类器构成教师网络（Teacher Network, TN）以监督学生网络（Student Network, SN）训练；其次，利用TN中间特征计算局部协方差矩阵，在服务器端聚合生成全局几何知识（Global Geometric Knowledge, GGK），进而用于本地嵌入增强以进一步指导SN训练；此外，设计了新型损失函数与多原型生成机制以稳定训练过程。该方法有效缓解了异构数据对模型泛化能力的影响，显著提升了联邦学习在RSSI分析中的性能表现。
链接: https://arxiv.org/abs/2603.07774
作者: Luyao Zou,Fei Pan,Jueying Li,Yan Kyaw Tun,Apurba Adhikary,Zhu Han,Hayoung Oh
机构: Sungkyunkwan University (成均馆大学); University of Michigan (密歇根大学); Konkuk University (国民大学); Aalborg University (奥尔堡大学); Noakhali Science and Technology University (诺阿哈尔科技大学); University of Houston (休斯顿大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 9 figures
Abstract:Federated learning (FL) has recently become a promising solution for analyzing remote sensing satellite imagery (RSSI). However, the large scale and inherent data heterogeneity of images collected from multiple satellites, where the local data distribution of each satellite differs from the global one, present significant challenges to effective model training. To address this issue, we propose a Geometric Knowledge-Guided Federated Dual Knowledge Distillation (GK-FedDKD) framework for RSSI analysis. In our approach, each local client first distills a teacher encoder (TE) from multiple student encoders (SEs) trained with unlabeled augmented data. The TE is then connected with a shared classifier to form a teacher network (TN) that supervises the training of a new student network (SN). The intermediate representations of the TN are used to compute local covariance matrices, which are aggregated at the server to generate global geometric knowledge (GGK). This GGK is subsequently employed for local embedding augmentation to further guide SN training. We also design a novel loss function and a multi-prototype generation pipeline to stabilize the training process. Evaluation over multiple datasets showcases that the proposed GK-FedDKD approach is superior to the considered state-of-the-art baselines, e.g., the proposed approach with the Swin-T backbone surpasses previous SOTA approaches by an average 68.89% on the EuroSAT dataset.
[CV-124] MedQ-Deg: A Multidimensional Benchmark for Evaluating MLLM s Across Medical Image Quality Degradations
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在真实临床环境中因医学图像质量退化而导致性能显著下降的问题，同时弥补现有评估基准在大规模、多维度图像质量梯度覆盖和系统性置信度校准分析方面的不足。其解决方案的关键在于提出MedQ-Deg基准，该基准涵盖18种不同的图像退化类型、30个细粒度能力维度及7种成像模态，共包含24,894个问答对，并通过放射科专家校准每种退化的三个严重程度；此外，引入Calibration Shift指标以量化模型感知置信度与实际性能之间的差距，从而评估其在退化条件下的元认知可靠性。
链接: https://arxiv.org/abs/2603.07769
作者: Jiyao Liu,Junzhi Ning,Chenglong Ma,Wanying Qu,Jianghan Shen,Siqi Luo,Jinjie Wei,Jin Ye,Pengze Li,Tianbin Li,Jiashi Lin,Hongming Shan,Xinzhe Luo,Xiaohong Liu,Lihao Liu,Junjun He,Ningsheng Xu
机构: Xinzhe Luo; Xiaohong Liu; Lihao Liu; Junjun He; Ningsheng Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 29 pages, 11 figures
Abstract:Despite impressive performance on standard benchmarks, multimodal large language models (MLLMs) face critical challenges in real-world clinical environments where medical images inevitably suffer various quality degradations. Existing benchmarks exhibit two key limitations: (1) absence of large-scale, multidimensional assessment across medical image quality gradients and (2) no systematic confidence calibration analysis. To address these gaps, we present MedQ-Deg, a comprehensive benchmark for evaluating medical MLLMs under image quality degradations. MedQ-Deg provides multi-dimensional evaluation spanning 18 distinct degradation types, 30 fine-grained capability dimensions, and 7 imaging modalities, with 24,894 question-answer pairs. Each degradation is implemented at 3 severity degrees, calibrated by expert radiologists. We further introduce Calibration Shift metric, which quantifies the gap between a model’s perceived confidence and actual performance to assess metacognitive reliability under degradation. Our comprehensive evaluation of 40 mainstream MLLMs reveals several critical findings: (1) overall model performance degrades systematically as degradation severity increases, (2) models universally exhibit the AI Dunning-Kruger Effect, maintaining inappropriately high confidence despite severe accuracy collapse, and (3) models display markedly differentiated behavioral patterns across capability dimensions, imaging modalities, and degradation types. We hope MedQ-Deg drives progress toward medical MLLMs that are robust and trustworthy in real clinical practice.
[CV-125] DECADE: A Temporally-Consistent Unsupervised Diffusion Model for Enhanced Rb-82 Dynamic Cardiac PET Image Denoising
【速读】：该论文旨在解决Rb-82动态心脏正电子发射断层成像（PET）中因半衰期短导致的噪声水平高、动态帧质量下降及参数图像失真问题，尤其针对缺乏配对干净-噪声训练数据、示踪剂动力学快速变化以及帧间噪声差异显著等挑战。其解决方案的关键在于提出一种无监督扩散模型DECADE（A Temporally-Consistent Unsupervised Diffusion model for Enhanced Rb-82 CArdiac PET DEnoising），通过在训练和迭代采样过程中引入时间一致性约束，利用噪声帧作为引导以保持定量准确性，从而实现从早期到晚期动态帧的泛化去噪，并在不同扫描仪平台上均展现出优于现有UNet基线和扩散模型的图像质量和K1/心肌血流（MBF）量化性能。
链接: https://arxiv.org/abs/2603.07759
作者: Yinchi Zhou,Liang Guo,Huidong Xie,Yuexi Du,Ashley Wang,Menghua Xia,Tian Yu,Ramesh Fazzone-Chettiar,Christopher Weyman,Bruce Spottiswoode,Vladimir Panin,Kuangyu Shi,Edward J. Miller,Attila Feher,Albert J. Sinusas,Nicha C. Dvornek,Chi Liu
机构: Yale University (耶鲁大学); Yale School of Medicine (耶鲁医学院); Siemens Medical Solutions USA, Inc. (西门子医疗解决方案美国公司); University of Bern (伯尔尼大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Rb-82 dynamic cardiac PET imaging is widely used for the clinical diagnosis of coronary artery disease (CAD), but its short half-life results in high noise levels that degrade dynamic frame quality and parametric imaging. The lack of paired clean-noisy training data, rapid tracer kinetics, and frame-dependent noise variations further limit the effectiveness of existing deep learning denoising methods. We propose DECADE (A Temporally-Consistent Unsupervised Diffusion model for Enhanced Rb-82 CArdiac PET DEnoising), an unsupervised diffusion framework that generalizes across early- to late-phase dynamic frames. DECADE incorporates temporal consistency during both training and iterative sampling, using noisy frames as guidance to preserve quantitative accuracy. The method was trained and evaluated on datasets acquired from Siemens Vision 450 and Siemens Biograph Vision Quadra scanners. On the Vision 450 dataset, DECADE consistently produced high-quality dynamic and parametric images with reduced noise while preserving myocardial blood flow (MBF) and myocardial flow reserve (MFR). On the Quadra dataset, using 15%-count images as input and full-count images as reference, DECADE outperformed UNet-based and other diffusion models in image quality and K1/MBF quantification. The proposed framework enables effective unsupervised denoising of Rb-82 dynamic cardiac PET without paired training data, supporting clearer visualization while maintaining quantitative integrity.
[CV-126] AR2-4FV: Anchored Referring and Re-identification for Long-Term Grounding in Fixed-View Videos CVPR2026
【速读】：该论文旨在解决固定视角视频中长期语言引导指代（long-term language-guided referring）的难题，即目标物体可能长时间离开视野或被遮挡后重新出现，而传统逐帧指代方法因重识别（ReID）可靠性下降导致性能漂移。其解决方案的关键在于引入一个离线构建的“锚点库”（Anchor Bank），该库从静态背景结构中蒸馏得到；在推理阶段，通过将文本查询与锚点库对齐生成“锚点图”（Anchor Map），作为目标消失时的持久语义记忆，并结合基于锚点的再进入先验（re-entry prior）加速目标回归，同时采用轻量级ReID-Gating机制利用锚点帧中的位移线索维持身份连续性，从而实现无需假设目标首帧可见或显式建模外观变化的端到端框预测。
链接: https://arxiv.org/abs/2603.07758
作者: Teng Yan,Yihan Liu,Jiongxu Chen,Teng Wang,Jiaqi Li,Bingzhuo Zhong
机构: The Hong Kong University of Science and Technology (Guangzhou)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026
Abstract:Long-term language-guided referring in fixed-view videos is challenging: the referent may be occluded or leave the scene for long intervals and later re-enter, while framewise referring pipelines drift as re-identification (ReID) becomes unreliable. AR2-4FV leverages background stability for long-term referring. An offline Anchor Bank is distilled from static background structures; at inference, the text query is aligned with this bank to produce an Anchor Map that serves as persistent semantic memory when the referent is absent. An anchor-based re-entry prior accelerates re-capture upon return, and a lightweight ReID-Gating mechanism maintains identity continuity using displacement cues in the anchor frame. The system predicts per-frame bounding boxes without assuming the target is visible in the first frame or explicitly modeling appearance variations. AR2-4FV achieves +10.3% Re-Capture Rate (RCR) improvement and -24.2% Re-Capture Latency (RCL) reduction over the best baseline, and ablation studies further confirm the benefits of the Anchor Map, re-entry prior, and ReID-Gating.
[CV-127] PARSE: Part-Aware Relational Spatial Modeling
【速读】：该论文旨在解决现有场景表示方法在空间智能建模中的局限性问题，即传统基于语言预置词或对象级场景图的表示方式过于粗粒度，无法精确描述物体间支持、包含或接触的具体区域，导致布局模糊且物理上不一致。其解决方案的关键在于提出PARSE框架，通过引入以部件为中心的装配图（Part-centric Assembly Graph, PAG）来显式建模物体部件之间的几何关系，并结合部件感知的空间配置求解器，将这些关系转化为几何约束，从而生成无碰撞、物理合理的三维场景。这一方法显著提升了物体层级布局推理能力和部件层级关系理解精度，并增强了3D生成模型的物理真实性和结构复杂度。
链接: https://arxiv.org/abs/2603.07704
作者: Yinuo Bai,Peijun Xu,Kuixiang Shao,Yuyang Jiao,Jingxuan Zhang,Kaixin Yao,Jiayuan Gu,Jingyi Yu
机构: ShanghaiTech University(上海科技大学); Deemos Technology(迪莫斯科技)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Inter-object relations underpin spatial intelligence, yet existing representations – linguistic prepositions or object-level scene graphs – are too coarse to specify which regions actually support, contain, or contact one another, leading to ambiguous and physically inconsistent layouts. To address these ambiguities, a part-level formulation is needed; therefore, we introduce PARSE, a framework that explicitly models how object parts interact to determine feasible and spatially grounded scene configurations. PARSE centers on the Part-centric Assembly Graph (PAG), which encodes geometric relations between specific object parts, and a Part-Aware Spatial Configuration Solver that converts these relations into geometric constraints to assemble collision-free, physically valid scenes. Using PARSE, we build PARSE-10K, a dataset of 10,000 3D indoor scenes constructed from real-image layout priors and a curated part-annotated shape database, each with dense contact structures and a part-level contact graph. With this structured, spatially grounded supervision, fine-tuning Qwen3-VL on PARSE-10K yields stronger object-level layout reasoning and more accurate part-level relation understanding; furthermore, leveraging PAGs as structural priors in 3D generation models leads to scenes with substantially improved physical realism and structural complexity. Together, these results show that PARSE significantly advances geometry-grounded spatial reasoning and supports the generation of physically consistent 3D scenes.
[CV-128] DM-R1: Reinforcing Few-Step Diffusion Models with Non-Differentiable Reward
【速读】：该论文旨在解决少步长生成模型（few-step generative models）在强化学习（Reinforcement Learning, RL）应用中的关键难题，即如何有效整合非可微奖励信号（non-differentiable rewards），如人类对图像的二元偏好或目标计数等现实世界奖励，以提升生成质量。现有RL方法严重依赖通过可微奖励模型进行反向传播，因而无法利用这些重要但不可微的奖励信号。其解决方案的核心是提出TDM-R1，一种基于领先少步长模型Trajectory Distribution Matching (TDM) 的新型强化学习范式，通过将学习过程解耦为代理奖励学习（surrogate reward learning）与生成器学习（generator learning），并设计实用方法从确定性生成轨迹中提取每步奖励信号，从而实现统一的后训练强化学习策略，显著增强少步长生成模型对通用奖励的适应能力。
链接: https://arxiv.org/abs/2603.07700
作者: Yihong Luo,Tianyang Hu,Weijian Luo,Jing Tang
机构: Hong Kong University of Science and Technology (香港科技大学); The Chinese University of Hong Kong, Shenzhen (香港中文大学（深圳）); hi-Lab, Xiaohongshu Inc (小红书公司实验室); Hong Kong University of Science and Technology (Guangzhou) (香港科技大学（广州）)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: this https URL
Abstract:While few-step generative models have enabled powerful image and video generation at significantly lower cost, generic reinforcement learning (RL) paradigms for few-step models remain an unsolved problem. Existing RL approaches for few-step diffusion models strongly rely on back-propagating through differentiable reward models, thereby excluding the majority of important real-world reward signals, e.g., non-differentiable rewards such as humans’ binary likeness, object counts, etc. To properly incorporate non-differentiable rewards to improve few-step generative models, we introduce TDM-R1, a novel reinforcement learning paradigm built upon a leading few-step model, Trajectory Distribution Matching (TDM). TDM-R1 decouples the learning process into surrogate reward learning and generator learning. Furthermore, we developed practical methods to obtain per-step reward signals along the deterministic generation trajectory of TDM, resulting in a unified RL post-training method that significantly improves few-step models’ ability with generic rewards. We conduct extensive experiments ranging from text-rendering, visual quality, and preference alignment. All results demonstrate that TDM-R1 is a powerful reinforcement learning paradigm for few-step text-to-image models, achieving state-of-the-art reinforcement learning performances on both in-domain and out-of-domain metrics. Furthermore, TDM-R1 also scales effectively to the recent strong Z-Image model, consistently outperforming both its 100-NFE and few-step variants with only 4 NFEs. Project page: this https URL
[CV-129] Learning Context-Adaptive Motion Priors for Masked Motion Diffusion Models with Efficient Kinematic Attention Aggregation
【速读】：该论文旨在解决视觉运动捕捉（Vision-based motion capture）中因遮挡导致关键关节信息丢失，以及可穿戴设备数据噪声大、不稳定等问题，这些问题常导致3D运动重建不准确，且需大量人工校正。解决方案的核心是提出一种基于扩散模型的生成式重建框架——掩码运动扩散模型（Masked Motion Diffusion Model, MMDM），其关键创新在于引入了运动结构注意力聚合机制（Kinematic Attention Aggregation, KAA），该机制能高效地迭代编码关节级与姿态级特征，捕获任务特定的结构和时序运动模式；同时通过学习上下文自适应的运动先验（context-adaptive motion priors），使同一架构可在无需结构变更的情况下，灵活适配运动精炼、补全与插值等不同任务，从而实现对不完整或低置信度运动数据的有效增强与重建。
链接: https://arxiv.org/abs/2603.07697
作者: Junkun Jiang,Jie Chen,Ho Yin Au,Jingyu Xiang
机构: Hong Kong Baptist University (香港浸会大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by IEEE Transactions on Multimedia. Supplementary material is included
Abstract:Vision-based motion capture solutions often struggle with occlusions, which result in the loss of critical joint information and hinder accurate 3D motion reconstruction. Other wearable alternatives also suffer from noisy or unstable data, often requiring extensive manual cleaning and correction to achieve reliable results. To address these challenges, we introduce the Masked Motion Diffusion Model (MMDM), a diffusion-based generative reconstruction framework that enhances incomplete or low-confidence motion data using partially available high-quality reconstructions within a Masked Autoencoder architecture. Central to our design is the Kinematic Attention Aggregation (KAA) mechanism, which enables efficient, deep, and iterative encoding of both joint-level and pose-level features, capturing structural and temporal motion patterns essential for task-specific reconstruction. We focus on learning context-adaptive motion priors, specialized structural and temporal features extracted by the same reusable architecture, where each learned prior emphasizes different aspects of motion dynamics and is specifically efficient for its corresponding task. This enables the architecture to adaptively specialize without altering its structure. Such versatility allows MMDM to efficiently learn motion priors tailored to scenarios such as motion refinement, completion, and in-betweening. Extensive evaluations on public benchmarks demonstrate that MMDM achieves strong performance across diverse masking strategies and task settings. The source code is available at this https URL.
[CV-130] Compressed-Domain-Aware Online Video Super-Resolution CVPR2026
【速读】：该论文旨在解决在线视频超分辨率（online video super-resolution, online VSR）中计算复杂度高、难以实现实时处理的问题，尤其是在高分辨率场景下，现有方法因复杂的运动估计对齐和连续帧的冗余处理导致效率低下。其解决方案的关键在于提出一种压缩域感知网络（CDA-VSR），充分利用视频编码过程中已有的压缩域信息（如运动向量、残差图和帧类型），从而在保证重建质量的同时显著提升推理效率。具体而言，核心创新包括：基于运动向量引导的可变形对齐模块，仅学习局部残差偏移以减少计算开销；残差图门控融合模块，通过残差图生成空间权重抑制误匹配区域；以及帧类型感知重建模块，实现不同帧类型的自适应计算分配，有效平衡精度与效率。
链接: https://arxiv.org/abs/2603.07694
作者: Yuhang Wang,Hai Li,Shujuan Hou,Zhetao Dong,Xiaoyao Yang
机构: Beijing Institute of Technology (北京理工大学); Terahertz Science and Application Center, Beijing Institute of Technology (太赫兹科学与应用中心，北京理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to CVPR 2026
Abstract:In bandwidth-limited online video streaming, videos are usually downsampled and compressed. Although recent online video super-resolution (online VSR) approaches achieve promising results, they are still compute-intensive and fall short of real-time processing at higher resolutions, due to complex motion estimation for alignment and redundant processing of consecutive frames. To address these issues, we propose a compressed-domain-aware network (CDA-VSR) for online VSR, which utilizes compressed-domain information, including motion vectors, residual maps, and frame types to balance quality and efficiency. Specifically, we propose a motion-vector-guided deformable alignment module that uses motion vectors for coarse warping and learns only local residual offsets for fine-tuned adjustments, thereby maintaining accuracy while reducing computation. Then, we utilize a residual map gated fusion module to derive spatial weights from residual maps, suppressing mismatched regions and emphasizing reliable details. Further, we design a frame-type-aware reconstruction module for adaptive compute allocation across frame types, balancing accuracy and efficiency. On the REDS4 dataset, our CDA-VSR surpasses the state-of-the-art method TMP, with a maximum PSNR improvement of 0.13 dB while delivering more than double the inference speed. The code will be released at this https URL.
[CV-131] RoboPCA: Pose-centered Affordance Learning from Human Demonstrations for Robot Manipulation ICRA2026
【速读】：该论文旨在解决现有空间可操作性（spatial affordance）预测方法中接触区域与姿态不一致导致任务失败的问题，即传统方法通常将接触区域定位与姿态估计分离处理，缺乏协同优化。其解决方案的关键在于提出一种以姿态为中心的可操作性预测框架 RoboPCA，通过条件化指令联合预测任务适配的接触区域与姿态；同时设计 Human2Afford 数据采集管道，利用人类示范自动恢复场景级 3D 信息并推断基于姿态的可操作性标注，从而实现几何-外观特征融合与掩码增强特征提取，显著提升模型在图像数据集、仿真环境和真实机器人上的性能及跨任务、跨类别的泛化能力。
链接: https://arxiv.org/abs/2603.07691
作者: Zhanqi Xiao,Ruiping Wang,Xilin Chen
机构: Chinese Academy of Sciences (中国科学院); University of Chinese Academy of Sciences (中国科学院大学)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ICRA 2026
Abstract:Understanding spatial affordances – comprising the contact regions of object interaction and the corresponding contact poses – is essential for robots to effectively manipulate objects and accomplish diverse tasks. However, existing spatial affordance prediction methods mainly focus on locating the contact regions while delegating the pose to independent pose estimation approaches, which can lead to task failures due to inconsistencies between predicted contact regions and candidate poses. In this work, we propose RoboPCA, a pose-centered affordance prediction framework that jointly predicts task-appropriate contact regions and poses conditioned on instructions. To enable scalable data collection for pose-centered affordance learning, we devise Human2Afford, a data curation pipeline that automatically recovers scene-level 3D information and infers pose-centered affordance annotations from human demonstrations. With Human2Afford, scene depth and the interaction object’s mask are extracted to provide 3D context and object localization, while pose-centered affordance annotations are obtained by tracking object points within the contact region and analyzing hand-object interaction patterns to establish a mapping from the 3D hand mesh to the robot end-effector orientation. By integrating geometry-appearance cues through an RGB-D encoder and incorporating mask-enhanced features to emphasize task-relevant object regions into the diffusion-based framework, RoboPCA outperforms baseline methods on image datasets, simulation, and real robots, and exhibits strong generalization across tasks and categories.
[CV-132] FrameVGGT: Frame Evidence Rolling Memory for streaming VGGT
【速读】：该论文旨在解决流式视觉几何变换器（如StreamVGGT）在长时间序列处理中因KV-cache无界增长而导致的部署受限问题。其核心挑战在于：传统基于token级保留的内存管理方式，在固定内存预算下会稀释每帧内的局部证据一致性，进而使后续特征融合对弱配准历史更加敏感，影响几何推理稳定性。解决方案的关键在于提出FrameVGGT，一种基于帧驱动的滚动显式记忆框架，将每帧的增量KV贡献视为一个完整的证据块，通过紧凑原型压缩每个块并维护一个固定容量的中期互补帧块库，同时引入轻量锚点层应对罕见的长期退化情况，从而在有限内存约束下实现更稳定且高精度的长序列3D感知性能。
链接: https://arxiv.org/abs/2603.07690
作者: Zhisong Xu,Takeshi Oishi
机构: Institute of Industrial Science, The University of Tokyo (东京大学工业科学研究所)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 24pages including appendix
Abstract:Streaming Visual Geometry Transformers such as StreamVGGT enable strong online 3D perception but suffer from unbounded KV-cache growth, which limits deployment over long streams. We revisit bounded-memory streaming from the perspective of geometric support. In geometry-driven reasoning, memory quality depends not only on how many tokens are retained, but also on whether the retained memory still preserves sufficiently coherent local support. This suggests that token-level retention may become less suitable under fixed budgets, as it can thin the evidence available within each contributing frame and make subsequent fusion more sensitive to weakly aligned history. Motivated by this observation, we propose FrameVGGT, a frame-driven rolling explicit-memory framework that treats each frame’s incremental KV contribution as a coherent evidence block. FrameVGGT summarizes each block into a compact prototype and maintains a fixed-capacity mid-term bank of complementary frame blocks under strict budgets, with an optional lightweight anchor tier for rare prolonged degradation. Across long-sequence 3D reconstruction, video depth estimation, and camera pose benchmarks, FrameVGGT achieves favorable accuracy–memory trade-offs under bounded memory, while maintaining more stable geometry over long streams.
[CV-133] UniUncer: Unified Dynamic Static Uncertainty for End to End Driving ICRA2026
【速读】：该论文旨在解决端到端（End-to-end, E2E）自动驾驶系统中因未充分建模不确定性而导致的可靠性问题，特别是现有方法仅关注静态地图元素的不确定性，而忽略了动态交通参与者（如车辆、行人）的不确定性，从而导致规划模块对输入信息过度自信、决策不可靠。解决方案的关键在于提出UniUncer——首个轻量级、统一的不确定性建模框架，其核心创新包括：(1) 将确定性预测头转换为概率性的拉普拉斯回归器（Laplace regressor），以输出矢量化静态与动态目标的位置和尺度参数；(2) 设计不确定性融合模块（uncertainty-fusion module），将这些参数编码并注入至对象/地图查询中，生成具有不确定性的感知查询；(3) 引入不确定性感知门控机制（uncertainty-aware gate），根据当前不确定性水平自适应调节对历史输入（如自身状态或时序感知查询）的依赖程度。该设计在保持极小计算开销（仅降低约0.5 FPS吞吐量）的同时，显著提升了导航性能，在nuScenes数据集上平均轨迹L2误差减少7%，在NavsimV2伪闭环测试中整体EPDMS指标提升10.8%，尤其在交互密集场景中第二阶段性能提升明显。
链接: https://arxiv.org/abs/2603.07686
作者: Yu Gao,Jijun Wang,Zongzheng Zhang,Anqing Jiang,Yiru Wang,Yuwen Heng,Shuo Wang,Hao Sun,Zhangfeng Hu,Hao Zhao
机构: Bosch Corporate Research (博世企业研究院); Institute for AI Industry Research (AIR), Tsinghua University (清华大学人工智能产业研究院); Rensselaer Polytechnic Institute (伦斯勒理工学院)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: ICRA 2026
Abstract:End-to-end (E2E) driving has become a cornerstone of both industry deployment and academic research, offering a single learnable pipeline that maps multi-sensor inputs to actions while avoiding hand-engineered modules. However, the reliability of such pipelines strongly depends on how well they handle uncertainty: sensors are noisy, semantics can be ambiguous, and interaction with other road users is inherently stochastic. Uncertainty also appears in multiple forms: classification vs. localization, and, crucially, in both static map elements and dynamic agents. Existing E2E approaches model only static-map uncertainty, leaving planning vulnerable to overconfident and unreliable inputs. We present UniUncer, the first lightweight, unified uncertainty framework that jointly estimates and uses uncertainty for both static and dynamic scene elements inside an E2E planner. Concretely: (1) we convert deterministic heads to probabilistic Laplace regressors that output per-vertex location and scale for vectorized static and dynamic entities; (2) we introduce an uncertainty-fusion module that encodes these parameters and injects them into object/map queries to form uncertainty-aware queries; and (3) we design an uncertainty-aware gate that adaptively modulates reliance on historical inputs (ego status or temporal perception queries) based on current uncertainty levels. The design adds minimal overhead and drops throughput by only \sim 0.5 FPS while remaining plug-and-play for common E2E backbones. On nuScenes (open-loop), UniUncer reduces average L2 trajectory error by 7%. On NavsimV2 (pseudo closed-loop), it improves overall EPDMS by 10.8% and notable stage two gains in challenging, interaction-heavy scenes. Ablations confirm that dynamic-agent uncertainty and the uncertainty-aware gate are both necessary.
[CV-134] FusionRegister: Every Infrared and Visible Image Fusion Deserves Registration
【速读】：该论文旨在解决红外与可见光图像融合中跨模态空间配准（spatial registration）效率低下的问题，现有基于配准的融合方法通常需要复杂的预配准操作，限制了整体性能。其解决方案的关键在于提出一种由视觉先验引导的通用跨模态配准方法 FusionRegister：首先通过学习跨模态错配表示而非强制全局对齐，提升在复杂输入条件下的鲁棒性；其次直接作用于融合结果，显式建模并有效处理错配区域，从而兼容多种融合方法且保留其固有特性；最后利用主干融合方法作为自然的视觉先验提供者，仅聚焦于错配区域进行优化，显著减少冗余计算，提高效率。
链接: https://arxiv.org/abs/2603.07667
作者: Congcong Bian,Haolong Ma,Hui Li,Zhongwei Shen,Xiaoqing Luo,Xiaoning Song,Xiao-Jun Wu
机构: Jiangnan University (江南大学); Suzhou University of Science and Technology (苏州科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Spatial registration across different visual modalities is a critical but formidable step in multi-modality image fusion for real-world perception. Although several methods are proposed to address this issue, the existing registration-based fusion methods typically require extensive pre-registration operations, limiting their efficiency. To overcome these limitations, a general cross-modality registration method guided by visual priors is proposed for infrared and visible image fusion task, termed FusionRegister. Firstly, FusionRegister achieves robustness by learning cross-modality misregistration representations rather than forcing alignment of all differences, ensuring stable outputs even under challenging input conditions. Moreover, FusionRegister demonstrates strong generality by operating directly on fused results, where misregistration is explicitly represented and effectively handled, enabling seamless integration with diverse fusion methods while preserving their intrinsic properties. In addition, its efficiency is further enhanced by serving the backbone fusion method as a natural visual prior provider, which guides the registration process to focus only on mismatch regions, thereby avoiding redundant operations. Extensive experiments on three datasets demonstrate that FusionRegister not only inherits the fusion quality of state-of-the-art methods, but also delivers superior detail alignment and robustness, making it highly suitable for infrared and visible image fusion method. The code will be available at this https URL.
[CV-135] Ref-DGS: Reflective Dual Gaussian Splatting
【速读】：该论文旨在解决反射外观（Reflective Appearance），尤其是近场强镜面反射对表面重建和新视角合成准确性造成的挑战。现有高斯点绘制（Gaussian Splatting）方法要么无法建模近场镜面反射，要么依赖显式光线追踪导致计算成本高昂。其解决方案的关键在于提出一种反射双高斯点绘制框架（Ref-DGS），通过在基于光栅化的高效流水线中将表面重建与镜面反射解耦，引入由几何高斯（Geometry Gaussians）和互补局部反射高斯（Complementary Local Reflection Gaussians）组成的双重高斯场景表示，以无显式光线追踪的方式捕捉近场镜面交互，并结合全局环境反射场建模远场镜面反射；同时设计了一种轻量级、物理感知的自适应混合着色器，融合全局与局部反射特征以预测镜面辐射亮度，从而在保持高性能的同时显著提升训练效率。
链接: https://arxiv.org/abs/2603.07664
作者: Ningjing Fan,Yiqun Wang,Dongming Yan,Peter Wonka
机构: Chongqing University (重庆大学); Institute of Automation, Chinese Academy of Sciences (中国科学院自动化研究所); King Abdullah University of Science and Technology (KAUST) (阿卜杜拉国王科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR)
备注: Project page: this https URL
Abstract:Reflective appearance, especially strong and typically near-field specular reflections, poses a fundamental challenge for accurate surface reconstruction and novel view synthesis. Existing Gaussian splatting methods either fail to model near-field specular reflections or rely on explicit ray tracing at substantial computational cost. We present Ref-DGS, a reflective dual Gaussian splatting framework that addresses this trade-off by decoupling surface reconstruction from specular reflection within an efficient rasterization-based pipeline. Ref-DGS introduces a dual Gaussian scene representation consisting of geometry Gaussians and complementary local reflection Gaussians that capture near-field specular interactions without explicit ray tracing, along with a global environment reflection field for modeling far-field specular reflections. To predict specular radiance, we further propose a lightweight, physically-aware adaptive mixing shader that fuses global and local reflection features. Experiments demonstrate that Ref-DGS achieves state-of-the-art performance on reflective scenes while training substantially faster than ray-based Gaussian methods.
[CV-136] Holi-Spatial: Evolving Video Streams into Holistic 3D Spatial Intelligence
【速读】：该论文旨在解决当前空间智能（Spatial Intelligence）研究中因数据集规模有限、标注方式粗粒度及域间差异大而导致的模型泛化能力不足问题。现有方法主要依赖人工标注的小规模3D数据集生成问答对，难以扩展且存在领域偏移（domain gap），限制了模型在真实复杂场景中的表现。其解决方案的关键在于提出Holi-Spatial——首个完全自动化、大规模、多模态的空间感知数据集构建框架，通过从原始网络视频流中无监督地提取并优化3D几何结构（如使用3D Gaussian Splatting, 3DGS）与语义信息（包括对象级和关系级标注），实现了从原始数据到高质量空间问答对的端到端自动构建流程。该方案显著提升了数据规模与多样性，并支持多层次空间监督信号，从而有效缓解了传统方法的瓶颈，为视觉-语言模型（VLMs）的空间推理能力提升提供了高质量训练基础。
链接: https://arxiv.org/abs/2603.07660
作者: Yuanyuan Gao,Hao Li,Yifei Liu,Xinhao Ji,Yuning Gong,Yuanjun Liao,Fangfu Liu,Manyuan Zhang,Yuchen Yang,Dan Xu,Xue Yang,Huaxi Huang,Hongjie Zhang,Ziwei Liu,Xiao Sun,Dingwen Zhang,Zhihang Zhong
机构: Shanghai AI Lab(上海人工智能实验室); Northwestern Polytechnical University(西北工业大学); Shanghai Jiao Tong University(上海交通大学); Peking University(北京大学); Nanyang Technological University(南洋理工大学); Beihang University(北京航空航天大学); Sichuan University(四川大学); Tsinghua University(清华大学); The Chinese University of Hong Kong(香港中文大学); Fudan University(复旦大学); Hong Kong University of Science and Technology(香港科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: project page: this https URL
Abstract:The pursuit of spatial intelligence fundamentally relies on access to large-scale, fine-grained 3D data. However, existing approaches predominantly construct spatial understanding benchmarks by generating question-answer (QA) pairs from a limited number of manually annotated datasets, rather than systematically annotating new large-scale 3D scenes from raw web data. As a result, their scalability is severely constrained, and model performance is further hindered by domain gaps inherent in these narrowly curated datasets. In this work, we propose Holi-Spatial, the first fully automated, large-scale, spatially-aware multimodal dataset, constructed from raw video inputs without human intervention, using the proposed data curation pipeline. Holi-Spatial supports multi-level spatial supervision, ranging from geometrically accurate 3D Gaussian Splatting (3DGS) reconstructions with rendered depth maps to object-level and relational semantic annotations, together with corresponding spatial Question-Answer (QA) pairs. Following a principled and systematic pipeline, we further construct Holi-Spatial-4M, the first large-scale, high-quality 3D semantic dataset, containing 12K optimized 3DGS scenes, 1.3M 2D masks, 320K 3D bounding boxes, 320K instance captions, 1.2M 3D grounding instances, and 1.2M spatial QA pairs spanning diverse geometric, relational, and semantic reasoning tasks. Holi-Spatial demonstrates exceptional performance in data curation quality, significantly outperforming existing feed-forward and per-scene optimized methods on datasets such as ScanNet, ScanNet++, and DL3DV. Furthermore, fine-tuning Vision-Language Models (VLMs) on spatial reasoning tasks using this dataset has also led to substantial improvements in model performance. Comments: project page: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2603.07660 [cs.CV] (or arXiv:2603.07660v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.07660 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yuanyuan Gao [view email] [v1] Sun, 8 Mar 2026 14:49:20 UTC (30,827 KB)
[CV-137] Scaling Test-Time Robustness of Vision-Language Models via Self-Critical Inference Framework CVPR2026
【速读】：该论文旨在解决大型视觉语言模型（Large Vision-Language Models, LVLMs）在训练过程中对语言模型（LLM）过度依赖所引发的两个关键鲁棒性问题：语言偏见（language bias）和语言敏感性（language sensitivity）。其解决方案的核心是提出一种新颖的自批判推理（Self-Critical Inference, SCI）框架，该框架通过文本与视觉扰动进行多轮反事实推理（counterfactual reasoning），扩展了视觉对比解码（Visual Contrastive Decoding）方法，并引入了一种通过增加反事实推理轮数来提升鲁棒性的新策略。实验表明，SCI 在动态鲁棒性基准（Dynamic Robustness Benchmark, DRBench）上显著优于基线方法，且推理轮数越多，鲁棒性越强。
链接: https://arxiv.org/abs/2603.07659
作者: Kaihua Tang,Jiaxin Qi,Jinli Ou,Yuhua Zheng,Jianqiang Huang
机构: Tongji University (同济大学); Computer Network Information Center, CAS (中国科学院计算机网络信息中心); HIAS, University of Chinese Academy of Sciences (中国科学院大学人工智能创新研究院); University of Chinese Academy of Sciences (中国科学院大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026. Code: this https URL
Abstract:The emergence of Large Language Models (LLMs) has driven rapid progress in multi-modal learning, particularly in the development of Large Vision-Language Models (LVLMs). However, existing LVLM training paradigms place excessive reliance on the LLM component, giving rise to two critical robustness challenges: language bias and language sensitivity. To address both issues simultaneously, we propose a novel Self-Critical Inference (SCI) framework that extends Visual Contrastive Decoding by conducting multi-round counterfactual reasoning through both textual and visual perturbations. This process further introduces a new strategy for improving robustness by scaling the number of counterfactual rounds. Moreover, we also observe that failure cases of LVLMs differ significantly across models, indicating that fixed robustness benchmarks may not be able to capture the true reliability of LVLMs. To this end, we propose the Dynamic Robustness Benchmark (DRBench), a model-specific evaluation framework targeting both language bias and sensitivity issues. Extensive experiments show that SCI consistently outperforms baseline methods on DRBench, and that increasing the number of inference rounds further boosts robustness beyond existing single-step counterfactual reasoning methods.
[CV-138] GLASS: Graph and Vision-Language Assisted Semantic Shape Correspondence
【速读】：该论文旨在解决三维形状之间稠密对应关系（dense correspondence）的学习问题，尤其是在缺乏人工标注监督的情况下，如何在严重非等距形变和跨类别场景中建立准确、语义一致的映射。传统基于函数映射（functional map）的方法因依赖等距性假设，在此类复杂场景下表现受限。解决方案的关键在于提出GLASS框架，其核心创新包括：(i) 通过视图一致性策略从视觉基础模型中提取鲁棒的多视角特征；(ii) 利用零样本3D分割将语言嵌入注入顶点描述符，捕获高层部件语义；(iii) 设计图辅助对比损失，利用测地距离与拓扑关系强制区域间结构一致性（如“源形状头部”↔“目标形状头部”）。这一设计使GLASS能够在无监督条件下学习全局一致且语义合理的形状对应关系，并在多个基准测试中显著优于现有方法。
链接: https://arxiv.org/abs/2603.07652
作者: Qinfeng Xiao,Guofeng Mei,Qilong Liu,Chenyuan Yi,Fabio Poiesi,Jian Zhang,Bo Yang,Yick Kit-lun
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Establishing dense correspondence across 3D shapes is crucial for fundamental downstream tasks, including texture transfer, shape interpolation, and robotic manipulation. However, learning these mappings without manual supervision remains a formidable challenge, particularly under severe non-isometric deformations and in inter-class settings where geometric cues are ambiguous. Conventional functional map methods, while elegant, typically struggle in these regimes due to their reliance on isometry. To address this, we present GLASS, a framework that bridges the gap by integrating geometric spectral analysis with rich semantic priors from vision-language foundation models. GLASS introduces three key innovations: (i) a view-consistent strategy that enables robust multi-view visual feature extraction from powerful vision foundation models; (ii) the injection of language embeddings into vertex descriptors via zero-shot 3D segmentation, capturing high-level part semantics; and (iii) a graph-assisted contrastive loss that enforces structural consistency between regions (e.g., source’s head’’ \leftrightarrow target’s head’') by leveraging geodesic and topological relationships between regions. This design allows GLASS to learn globally coherent and semantically consistent maps without ground-truth supervision. Extensive experiments demonstrate that GLASS achieves state-of-the-art performance across all regimes, maintaining high accuracy on standard near-isometric tasks while significantly advancing performance in challenging settings. Specifically, it achieves average geodesic errors of 0.21, 4.5, and 5.6 on the inter-class benchmark SNIS and non-isometric benchmarks SMAL and TOPKIDS, reducing errors from URSSM baselines of 0.49, 6.0, and 8.9 by 57%, 25%, and 37%, respectively.
[CV-139] AtomicVLA: Unlocking the Potential of Atomic Skill Learning in Robots CVPR2026
【速读】：该论文旨在解决当前视觉-语言-动作（Visual-Language-Action, VLA）模型在真实机器人任务中面临的两大挑战：一是长时程、多步骤问题求解能力不足，二是缺乏持续学习能力以支持技能的不断扩展。现有VLA模型通常采用单一动作解码器并基于聚合数据训练，导致其可扩展性差。为此，作者提出AtomicVLA框架，其核心创新在于构建一个统一的规划与执行架构，能够同时生成任务级计划、原子技能抽象和细粒度动作；并通过Skill-Guided Mixture-of-Experts（SG-MoE）机制建立可扩展的原子技能库，每个专家专门掌握通用且精确的原子技能，再结合灵活的路由编码器自动为新技能分配专属专家，从而实现持续学习。此设计显著提升了机器人在长时程任务和终身学习场景下的性能表现。
链接: https://arxiv.org/abs/2603.07648
作者: Likui Zhang,Tao Tang,Zhihao Zhan,Xiuwei Chen,Zisheng Chen,Jianhua Han,Jiangtong Zhu,Pei Xu,Hang Xu,Hefeng Wu,Liang Lin,Xiaodan Liang
机构: Sun Yat-sen University (中山大学); Peng Cheng Laboratory (鹏城实验室); Yinwang Intelligent Technology Co. Ltd. (英伟达智能科技有限公司)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR2026
Abstract:Recent advances in Visual-Language-Action (VLA) models have shown promising potential for robotic manipulation tasks. However, real-world robotic tasks often involve long-horizon, multi-step problem-solving and require generalization for continual skill acquisition, extending beyond single actions or skills. These challenges present significant barriers for existing VLA models, which use monolithic action decoders trained on aggregated data, resulting in poor scalability. To address these challenges, we propose AtomicVLA, a unified planning-and-execution framework that jointly generates task-level plans, atomic skill abstractions, and fine-grained actions. AtomicVLA constructs a scalable atomic skill library through a Skill-Guided Mixture-of-Experts (SG-MoE), where each expert specializes in mastering generic yet precise atomic skills. Furthermore, we introduce a flexible routing encoder that automatically assigns dedicated atomic experts to new skills, enabling continual learning. We validate our approach through extensive experiments. In simulation, AtomicVLA outperforms \pi_0 by 2.4% on LIBERO, 10% on LIBERO-LONG, and outperforms \pi_0 and \pi_0.5 by 0.22 and 0.25 in average task length on CALVIN. Additionally, our AtomicVLA consistently surpasses baselines by 18.3% and 21% in real-world long-horizon tasks and continual learning. These results highlight the effectiveness of atomic skill abstraction and dynamic expert composition for long-horizon and lifelong robotic tasks. The project page is \hrefthis https URLhere.
[CV-140] Evaluating Synthetic Data for Baggage Trolley Detection in Airport Logistics
【速读】：该论文旨在解决机场行李车（luggage trolley）自动化检测系统在实际部署中面临的两大挑战：一是受严格安全与隐私法规限制，难以大规模收集真实标注数据；二是现有公开数据集在多样性、规模和标注质量上不足，无法有效应对现实场景中密集且重叠的行李车排列。其解决方案的关键在于构建一个基于高保真数字孪生（Digital Twin）的合成数据生成流水线，利用NVIDIA Omniverse平台生成丰富标注的数据，包括方向性边界框（oriented bounding boxes），以捕捉复杂的行李车组合形态（如紧密嵌套的链条结构）。通过对比多种训练策略（纯真实数据、纯合成数据、线性探测、全微调及混合训练），实验表明，仅使用40%的真实标注数据配合合成数据进行混合训练即可达到甚至超过全部真实数据训练的性能（mAP@50=0.94，mAP@50-95=0.77），同时降低25–35%的标注工作量，验证了合成数据在提升模型泛化能力与降低人工标注成本方面的有效性。
链接: https://arxiv.org/abs/2603.07645
作者: Abdeldjalil Taibi,Mohmoud Badlis,Amina Bensalem,Belkacem Zouilekh,Mohammed Brahimi
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Efficient luggage trolley management is critical for reducing congestion and ensuring asset availability in modern airports. Automated detection systems face two main challenges. First, strict security and privacy regulations limit large-scale data collection. Second, existing public datasets lack the diversity, scale, and annotation quality needed to handle dense, overlapping trolley arrangements typical of real-world operations. To address these limitations, we introduce a synthetic data generation pipeline based on a high-fidelity Digital Twin of Algiers International Airport using NVIDIA Omniverse. The pipeline produces richly annotated data with oriented bounding boxes, capturing complex trolley formations, including tightly nested chains. We evaluate YOLO-OBB using five training strategies: real-only, synthetic-only, linear probing, full fine-tuning, and mixed training. This allows us to assess how synthetic data can complement limited real-world annotations. Our results show that mixed training with synthetic data and only 40 percent of real annotations matches or exceeds the full real-data baseline, achieving 0.94 mAP@50 and 0.77 mAP@50-95, while reducing annotation effort by 25 to 35 percent. Multi-seed experiments confirm strong reproducibility with a standard deviation below 0.01 on mAP@50, demonstrating the practical effectiveness of synthetic data for automated trolley detection. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2603.07645 [cs.CV] (or arXiv:2603.07645v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.07645 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-141] Real-Time Glottis Detection Framework via Spatial-decoupled Feature Learning for Nasal Transnasal Intubation
【速读】：该论文旨在解决紧急气道管理中经鼻气管插管（nasotracheal intubation, NTI）过程中，现有机器辅助视觉检测系统因计算资源需求高、推理延迟大而难以在时间敏感且资源受限场景下应用的问题。解决方案的关键在于提出一种轻量级高效的声门检测框架 Mobile GlottisNet，其核心创新包括：引入结构感知与空间对齐机制以提升复杂解剖和视觉条件下声门定位的鲁棒性；设计分层动态阈值策略优化样本分配；采用基于可变形卷积的自适应特征解耦模块支持动态空间重建；并通过跨层动态加权机制实现多尺度语义与细节特征的有效融合，从而在嵌入式和边缘设备上实现超过62 FPS的实时推理性能（模型仅5MB），显著提升了NTI场景下的实用性与响应速度。
链接: https://arxiv.org/abs/2603.07630
作者: Jinyu Liu,Gaoyang Zhang,Yang Zhou,Ruoyi Hao,Yang Zhang,Hongliang Ren
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 7 figures
Abstract:Nasotracheal intubation (NTI) is a vital procedure in emergency airway management, where rapid and accurate glottis detection is essential to ensure patient safety. However, existing machine assisted visual detection systems often rely on high performance computational resources and suffer from significant inference delays, which limits their applicability in time critical and resource constrained scenarios. To overcome these limitations, we propose Mobile GlottisNet, a lightweight and efficient glottis detection framework designed for real time inference on embedded and edge devices. The model incorporates structural awareness and spatial alignment mechanisms, enabling robust glottis localization under complex anatomical and visual conditions. We implement a hierarchical dynamic thresholding strategy to enhance sample assignment, and introduce an adaptive feature decoupling module based on deformable convolution to support dynamic spatial reconstruction. A cross layer dynamic weighting scheme further facilitates the fusion of semantic and detail features across multiple scales. Experimental results demonstrate that the model, with a size of only 5MB on both our PID dataset and Clinical datasets, achieves inference speeds of over 62 FPS on devices and 33 FPS on edge platforms, showing great potential in the application of emergency NTI.
[CV-142] Duala: Dual-Level Alignment of Subjects and Stimuli for Cross-Subject fMRI Decoding
【速读】：该论文旨在解决跨被试（cross-subject）视觉解码中因个体差异导致的性能下降问题，特别是在仅有少量功能性磁共振成像（fMRI）数据时难以保持刺激语义一致性与脑响应对齐的问题。其解决方案的关键在于提出一种双层次对齐框架（Duala），在刺激层引入语义对齐与关系一致性策略以维持类内相似性和类间可分性，从而保障适应过程中的语义边界清晰；在被试层设计基于分布的特征扰动机制，捕捉全局与个体特异性神经表征变化，实现高效且不过拟合的个体化适应。
链接: https://arxiv.org/abs/2603.07625
作者: Shumeng Li,Jintao Guo,Jian Zhang,Yulin Zhou,Luyang Cao,Yinghuan Shi
机构: Nanjing University (南京大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cross-subject visual decoding aims to reconstruct visual experiences from brain activity across individuals, enabling more scalable and practical brain-computer interfaces. However, existing methods often suffer from degraded performance when adapting to new subjects with limited data, as they struggle to preserve both the semantic consistency of stimuli and the alignment of brain responses. To address these challenges, we propose Duala, a dual-level alignment framework designed to achieve stimulus-level consistency and subject-level alignment in fMRI-based cross-subject visual decoding. (1) At the stimulus level, Duala introduces a semantic alignment and relational consistency strategy that preserves intra-class similarity and inter-class separability, maintaining clear semantic boundaries during adaptation. (2) At the subject level, a distribution-based feature perturbation mechanism is developed to capture both global and subject-specific variations, enabling adaptation to individual neural representations without overfitting. Experiments on the Natural Scenes Dataset (NSD) demonstrate that Duala effectively improves alignment across subjects. Remarkably, even when fine-tuned with only about one hour of fMRI data, Duala achieves over 81.1% image-to-brain retrieval accuracy and consistently outperforms existing fine-tuning strategies in both retrieval and reconstruction. Our code is available at this https URL.
[CV-143] Overthinking Causes Hallucination: Tracing Confounder Propagation in Vision Language Models CVPR2026
【速读】：该论文旨在解决视觉语言模型（Vision Language Models, VLMs）中常见的幻觉问题，即模型在生成描述时错误地引入不存在的对象。现有检测方法主要依赖于最终输出层的信号，如基于注意力机制的方法假设幻觉词具有低注意力权重，或基于熵的方法利用最终步骤的不确定性。然而，本文通过深入分析发现，幻觉对象往往因上下文先验而表现出高注意力，并且模型在中间层已提前收敛至错误假设，从而在最终输出中表现出高置信度。因此，解决问题的关键在于挖掘模型推理过程中的内部行为——特别是“过度思考”（overthinking）现象：模型在多个解码层中反复修正对象假设，但一旦陷入错误认知便持续传播，导致最终幻觉。为此，作者提出“过度思考分数”（Overthinking Score），量化模型在不同层间对竞争假设的探索程度及其不稳定性，显著提升了幻觉检测性能，在MSCOCO和AMBER数据集上分别达到78.9%和71.58%的F1分数。
链接: https://arxiv.org/abs/2603.07619
作者: Abin Shoby,Ta Duc Huy,Tuan Dung Nguyen,Minh Khoi Ho,Qi Chen,Anton van den Hengel,Phi Le Nguyen,Johan W. Verjans,Vu Minh Hieu Phan
机构: Australian Institute for Machine Learning, University of Adelaide; Hanoi University of Science and Technology
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR2026 Findings
Abstract:Vision Language models (VLMs) often hallucinate non-existent objects. Detecting hallucination is analogous to detecting deception: a single final statement is insufficient, one must examine the underlying reasoning process. Yet existing detectors rely mostly on final-layer signals. Attention-based methods assume hallucinated tokens exhibit low attention, while entropy-based ones use final-step uncertainty. Our analysis reveals the opposite: hallucinated objects can exhibit peaked attention due to contextual priors; and models often express high confidence because intermediate layers have already converged to an incorrect hypothesis. We show that the key to hallucination detection lies within the model’s thought process, not its final output. By probing decoder layers, we uncover a previously overlooked behavior, overthinking: models repeatedly revise object hypotheses across layers before committing to an incorrect answer. Once the model latches onto a confounded hypothesis, it can propagate through subsequent layers, ultimately causing hallucination. To capture this behavior, we introduce the Overthinking Score, a metric to measure how many competing hypotheses the model entertains and how unstable these hypotheses are across layers. This score significantly improves hallucination detection: 78.9% F1 on MSCOCO and 71.58% on AMBER.
[CV-144] Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models
【速读】：该论文旨在解决现有视觉表示（如像素、潜在变量或标记）无法直接利用大规模训练所得的视觉知识进行紧凑存储与复用的问题。其核心解决方案是提出一种新的隐式视觉表示框架，将信号编码为由低秩适应（Low-Rank Adaptation, LoRA）参数化的函数，这些参数附加在冻结的视觉生成模型上。这种隐式表示可将复杂信号（如81帧视频）哈希为单一紧凑向量，在极低比特率下实现强感知视频压缩；同时，由于其函数特性，可在推理阶段动态调整和优化压缩性能，从而构建一个统一框架，连接视觉压缩与生成任务。
链接: https://arxiv.org/abs/2603.07615
作者: Jiajun He,Zongyu Guo,Zhaoyang Jia,Xiaoyi Zhang,Jiahao Li,Xiao Li,Bin Li,José Miguel Hernández-Lobato,Yan Lu
机构: 未知
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Modern visual generative models acquire rich visual knowledge through large-scale training, yet existing visual representations (such as pixels, latents, or tokens) remain external to the model and cannot directly exploit this knowledge for compact storage or reuse. In this work, we introduce a new visual representation framework that encodes a signal as a function, which is parametrized by low-rank adaptations attached to a frozen visual generative model. Such implicit representations of visual signals, \textite.g., an 81-frame video, can further be hashed into a single compact vector, achieving strong perceptual video compression at extremely low bitrates. Beyond basic compression, the functional nature of this representation enables inference-time scaling and control, allowing additional refinement on the compression performance. More broadly, as the implicit representations directly act as a function of the generation process, this suggests a unified framework bridging visual compression and generation.
[CV-145] Looking Into the Water by Unsupervised Learning of the Surface Shape
【速读】：该论文旨在解决从空气中观察水体时因水面折射导致的图像失真问题（image distortions caused by refractions at the water surface）。其核心解决方案是构建一个基于神经场（neural field）的双网络模型：第一个网络预测不同空间位置和时间点的水表面高度，第二个网络预测对应位置的图像颜色。通过联合建模水表面的时空变化与底层图像内容，该方法能够实现无监督训练下的图像重建，并利用周期性激活函数（SIREN）有效捕捉表面高度及其导数信息，从而提升重建精度。实验表明，该方法在模拟与真实数据上均优于当前最先进的无监督图像恢复方法，同时可提供水表面的估计结果。
链接: https://arxiv.org/abs/2603.07614
作者: Ori Lifschitz,Tali Treibitz,Dan Rosenbaum
机构: University of Haifa (海法大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We address the problem of looking into the water from the air, where we seek to remove image distortions caused by refractions at the water surface. Our approach is based on modeling the different water surface structures at various points in time, assuming the underlying image is constant. To this end, we propose a model that consists of two neural-field networks. The first network predicts the height of the water surface at each spatial position and time, and the second network predicts the image color at each position. Using both networks, we reconstruct the observed sequence of images and can therefore use unsupervised training. We show that using implicit neural representations with periodic activation functions (SIREN) leads to effective modeling of the surface height spatio-temporal signal and its derivative, as required for image reconstruction. Using both simulated and real data we show that our method outperforms the latest unsupervised image restoration approach. In addition, it provides an estimate of the water surface.
[CV-146] EmbedTalk: Triplane-Free Talking Head Synthesis using Embedding-Driven Gaussian Deformation
【速读】：该论文旨在解决基于可变形3D高斯泼溅（3D Gaussian Splatting, 3DGS）的实时说话人脸合成中，传统三平面（tri-plane）编码方式因网格分辨率限制和三维体素场投影到二维子空间所引入的近似误差导致的渲染质量与运动一致性不足的问题。其解决方案的关键在于引入可学习嵌入（learnt embeddings）替代三平面编码，以显式建模语音驱动的时序形变，从而提升唇部同步精度、运动一致性和渲染质量，同时显著压缩模型规模，在移动GPU（RTX 2060 6 GB）上实现超过60 FPS的推理速度。
链接: https://arxiv.org/abs/2603.07604
作者: Arpita Saggar,Jonathan C. Darling,Duygu Sarikaya,David C. Hogg
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint
Abstract:Real-time talking head synthesis increasingly relies on deformable 3D Gaussian Splatting (3DGS) due to its low latency. Tri-planes are the standard choice for encoding Gaussians prior to deformation, since they provide a continuous domain with explicit spatial relationships. However, tri-plane representations are limited by grid resolution and approximation errors introduced by projecting 3D volumetric fields onto 2D subspaces. Recent work has shown the superiority of learnt embeddings for driving temporal deformations in 4D scene reconstruction. We introduce \textbfEmbedTalk , which shows how such embeddings can be leveraged for modelling speech deformations in talking head synthesis. Through comprehensive experiments, we show that EmbedTalk outperforms existing 3DGS-based methods in rendering quality, lip synchronisation, and motion consistency, while remaining competitive with state-of-the-art generative models. Moreover, replacing the tri-plane encoding with learnt embeddings enables significantly more compact models that achieve over 60 FPS on a mobile GPU (RTX 2060 6 GB). Our code will be placed in the public domain on acceptance.
[CV-147] Fast Attention-Based Simplification of LiDAR Point Clouds for Object Detection and Classification
【速读】：该论文旨在解决LiDAR点云数据在自动驾驶场景中因密度高导致的计算成本与功耗过高的问题，同时克服现有点云采样方法在速度与精度之间难以平衡的局限性。其解决方案的关键在于提出一种高效的可学习点云简化方法，该方法由特征嵌入模块（feature embedding module）和基于注意力机制的采样模块（attention-based sampling module）组成，能够端到端训练以优先保留任务相关区域的点云信息，从而在保证检测或分类准确率的同时显著提升处理效率。
链接: https://arxiv.org/abs/2603.07593
作者: Z. Rozsa,Á. Madaras,Q. Wei,X. Lu,M. Golarits,H. Yuan,T. Sziranyi,R. Hamzaoui
机构: Institute for Computer Science and Control (SZTAKI)(计算机科学与控制研究所); Nanchang University (南昌大学); De Montfort University (德蒙福特大学); Shandong University (山东大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:LiDAR point clouds are widely used in autonomous driving and consist of large numbers of 3D points captured at high frequency to represent surrounding objects such as vehicles, pedestrians, and traffic signs. While this dense data enables accurate perception, it also increases computational cost and power consumption, which can limit real-time deployment. Existing point cloud sampling methods typically face a trade-off: very fast approaches tend to reduce accuracy, while more accurate methods are computationally expensive. To address this limitation, we propose an efficient learned point cloud simplification method for LiDAR data. The method combines a feature embedding module with an attention-based sampling module to prioritize task-relevant regions and is trained end-to-end. We evaluate the method against farthest point sampling (FPS) and random sampling (RS) on 3D object detection on the KITTI dataset and on object classification across four datasets. The method was consistently faster than FPS and achieved similar, and in some settings better, accuracy, with the largest gains under aggressive downsampling. It was slower than RS, but it typically preserved accuracy more reliably at high sampling ratios.
[CV-148] Models as Lego Builders: Assembling Malice from Benign Blocks via Semantic Blueprints
【速读】：该论文旨在解决大型视觉语言模型（Large Vision-Language Models, LVLMs）在引入视觉模态后所面临的新安全漏洞问题，特别是攻击者利用语义槽填充（semantic slot filling）机制诱导模型生成偏见或恶意内容的威胁。解决方案的关键在于提出一种名为StructAttack的单查询越狱框架，其核心思想是将有害查询分解为一个中心主题和一组看似无害的槽类型，并将其嵌入带有微小随机扰动的结构化视觉提示（如思维导图、表格或旭日图），再结合完成引导指令，使LVLM在推理过程中自动重组这些看似无害的槽以生成非法输出，从而绕过内置的安全防护机制。该方法利用了LVLM对结构化信息的语义整合能力，在局部看似良性（local benignness）的前提下实现整体有害意图的隐蔽执行。
链接: https://arxiv.org/abs/2603.07590
作者: Chenxi Li,Xianggan Liu,Dake Shen,Yaosong Du,Zhibo Yao,Hao Jiang,Linyi Jiang,Chengwei Cao,Jingzhe Zhang,RanYi Peng,Peiling Bai,Xiande Huang
机构: DAIL Tech; NLP KG Lab, Huazhong University of Science and Technology
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Despite the rapid progress of Large Vision-Language Models (LVLMs), the integration of visual modalities introduces new safety vulnerabilities that adversaries can exploit to elicit biased or malicious outputs. In this paper, we demonstrate an underexplored vulnerability via semantic slot filling, where LVLMs complete missing slot values with unsafe content even when the slot types are deliberately crafted to appear benign. Building on this finding, we propose StructAttack, a simple yet effective single-query jailbreak framework under black-box settings. StructAttack decomposes a harmful query into a central topic and a set of benign-looking slot types, then embeds them as structured visual prompts (e.g., mind maps, tables, or sunburst diagrams) with small random perturbations. Paired with a completion-guided instruction, LVLMs automatically recompose the concealed semantics and generate unsafe outputs without triggering safety mechanisms. Although each slot appears benign in isolation (local benignness), StructAttack exploits LVLMs’ reasoning to assemble these slots into coherent harmful semantics. Extensive experiments on multiple models and benchmarks show the efficacy of our proposed StructAttack.
[CV-149] 3DGS-HPC: Distractor-free 3D Gaussian Splatting with Hybrid Patch-wise Classification
【速读】：该论文旨在解决3D高斯泼溅（3D Gaussian Splatting, 3DGS）在真实场景中因瞬态干扰物（如移动物体和变化阴影）导致重建质量下降的问题。现有方法依赖预训练视觉模型提取语义信息来识别并抑制这些干扰物，但此类语义与静态/瞬态区域的二值划分不一致，且在3DGS优化过程中引入的外观扰动下表现脆弱。解决方案的关键在于提出3DGS-HPC框架，其核心创新为两个互补机制：一是基于局部空间一致性的像素块级分类策略，实现鲁棒的区域级决策；二是融合光度与感知线索的混合分类指标，自适应地提升分离可靠性。
链接: https://arxiv.org/abs/2603.07587
作者: Jiahao Chen,Yipeng Qin,Ganlong Zhao,Xin Li,Wenping Wang,Guanbin Li
机构: Sun Yat-sen University (中山大学); Texas AM University (德克萨斯农工大学); Cardiff University (卡迪夫大学); Centre for Perceptual and Interactive Intelligence (感知与交互智能中心)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in novel view synthesis and 3D scene reconstruction, yet its quality often degrades in real-world environments due to transient distractors, such as moving objects and varying shadows. Existing methods commonly rely on semantic cues extracted from pre-trained vision models to identify and suppress these distractors, but such semantics are misaligned with the binary distinction between static and transient regions and remain fragile under the appearance perturbations introduced during 3DGS optimization. We propose 3DGS-HPC, a framework that circumvents these limitations by combining two complementary principles: a patch-wise classification strategy that leverages local spatial consistency for robust region-level decisions, and a hybrid classification metric that adaptively integrates photometric and perceptual cues for more reliable separation. Extensive experiments demonstrate the superiority and robustness of our method in mitigating distractors to improve 3DGS-based novel view synthesis.
[CV-150] Integration of deep generative Anomaly Detection algorithm in high-speed industrial line DATE
【速读】：该论文旨在解决制药生产中工业视觉检测面临的高精度要求与严格的时间、硬件和成本约束之间的矛盾，尤其是传统人工在线检测受操作者差异影响大、吞吐量低，以及基于规则的计算机视觉方法在复杂多变生产场景下难以扩展的问题。解决方案的关键在于提出了一种基于生成对抗架构的半监督异常检测框架，其核心组件为带有残差结构和密集瓶颈（dense bottleneck）的自编码器，仅使用正常样本进行训练，通过重建残差实现异常分类与空间定位（热力图），并在真实工业测试中验证了其在满足500 ms采集窗口时间约束下的高性能表现。
链接: https://arxiv.org/abs/2603.07577
作者: Niccolò Ferrari,Nicola Zanarini,Michele Fraccaroli,Alice Bizzarri,Evelina Lamma
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Preprint under review at a Springer Nature journal. 36 pages, 3 tables, 29 figures. Updated and expanded version of the SSRN preprint (abstract_id=4858664), with substantial revisions and Springer Nature formatting
Abstract:Industrial visual inspection in pharmaceutical production requires high accuracy under strict constraints on cycle time, hardware footprint, and operational cost. Manual inline inspection is still common, but it is affected by operator variability and limited throughput. Classical rule-based computer vision pipelines are often rigid and difficult to scale to highly variable production scenarios. To address these limitations, we present a semi-supervised anomaly detection framework based on a generative adversarial architecture with a residual autoencoder and a dense bottleneck, specifically designed for online deployment on a high-speed Blow-Fill-Seal (BFS) line. The model is trained only on nominal samples and detects anomalies through reconstruction residuals, providing both classification and spatial localization via heatmaps. The training set contains 2,815,200 grayscale patches. Experiments on a real industrial test kit show high detection performance while satisfying timing constraints compatible with a 500 ms acquisition slot.
[CV-151] A Systematic Comparison of Training Objectives for Out-of-Distribution Detection in Image Classification
【速读】：该论文旨在解决分布外（Out-of-Distribution, OOD）检测在安全敏感型图像分类任务中的性能稳定性问题，特别是训练目标（training objectives）对OOD检测能力的影响尚未被充分理解。解决方案的关键在于系统性地比较四种主流训练目标——交叉熵损失（Cross-Entropy Loss）、原型损失（Prototype Loss）、三元组损失（Triplet Loss）和平均精度损失（Average Precision, AP Loss）——在标准化OpenOOD协议下的表现，发现交叉熵损失在近域和远域OOD场景中均展现出最一致的检测性能，而其他损失函数在特定条件下可能具有竞争力，从而为OOD检测模型的设计提供了基于训练目标的优化依据。
链接: https://arxiv.org/abs/2603.07571
作者: Furkan Genç,Onat Özdemir,Emre Akbaş
机构: Bilkent University (比尔肯大学); Middle East Technical University (中东技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Out-of-distribution (OOD) detection is critical in safety-sensitive applications. While this challenge has been addressed from various perspectives, the influence of training objectives on OOD behavior remains comparatively underexplored. In this paper, we present a systematic comparison of four widely used training objectives: Cross-Entropy Loss, Prototype Loss, Triplet Loss, and Average Precision (AP) Loss, spanning probabilistic, prototype-based, metric-learning, and ranking-based supervision, for OOD detection in image classification under standardized OpenOOD protocols. Across CIFAR-10/100 and ImageNet-200, we find that Cross-Entropy Loss, Prototype Loss, and AP Loss achieve comparable in-distribution accuracy, while Cross-Entropy Loss provides the most consistent near- and far-OOD performance overall; the other objectives can be competitive in specific settings.
[CV-152] Efficient RGB-D Scene Understanding via Multi-task Adaptive Learning and Cross-dimensional Feature Guidance
【速读】：该论文旨在解决传统RGB-D场景理解方法在处理遮挡、边界模糊以及无法根据任务需求和样本差异自适应调整注意力机制等方面的局限性。其核心解决方案在于提出一种高效的RGB-D场景理解模型，关键创新包括：1）增强融合编码器，有效利用RGB与深度信息的冗余特征；2）针对语义分割引入归一化聚焦通道层和上下文特征交互层，缓解浅层特征误导并提升局部-全局特征表示能力；3）实例分割采用非瓶颈1D结构，在减少参数量的同时实现更优的轮廓表达；4）设计多任务自适应损失函数，依据场景变化动态调整各任务的学习策略，从而在NYUv2、SUN RGB-D和Cityscapes等多个数据集上实现更高的分割精度与更快的推理速度。
链接: https://arxiv.org/abs/2603.07570
作者: Guodong Sun,Junjie Liu,Gaoyang Zhang,Bo Wu,Yang Zhang
机构: Hubei University of Technology (湖北工业大学); SARI (中国科学院自动化研究所)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 23 pages, 13 figures
Abstract:Scene understanding plays a critical role in enabling intelligence and autonomy in robotic systems. Traditional approaches often face challenges, including occlusions, ambiguous boundaries, and the inability to adapt attention based on task-specific requirements and sample variations. To address these limitations, this paper presents an efficient RGB-D scene understanding model that performs a range of tasks, including semantic segmentation, instance segmentation, orientation estimation, panoptic segmentation, and scene classification. The proposed model incorporates an enhanced fusion encoder, which effectively leverages redundant information from both RGB and depth inputs. For semantic segmentation, we introduce normalized focus channel layers and a context feature interaction layer, designed to mitigate issues such as shallow feature misguidance and insufficient local-global feature representation. The instance segmentation task benefits from a non-bottleneck 1D structure, which achieves superior contour representation with fewer parameters. Additionally, we propose a multi-task adaptive loss function that dynamically adjusts the learning strategy for different tasks based on scene variations. Extensive experiments on the NYUv2, SUN RGB-D, and Cityscapes datasets demonstrate that our approach outperforms existing methods in both segmentation accuracy and processing speed.
[CV-153] GRD-Net: Generative-Reconstructive-Discriminative Anomaly Detection with Region of Interest Attention Module
【速读】：该论文旨在解决工业视觉检测中缺陷定位不准确、泛化能力差的问题，尤其针对图像中并非全部区域都具有检测意义的情况（即仅关注特定区域 of interest, ROI）。传统方法依赖于基于blob分析或图像编辑的后处理步骤，易受训练数据偏倚影响且难以迁移。其解决方案的关键在于提出一种名为GRD-Net的新架构，由两部分组成：第一部分是基于残差自编码器（ResAE）的生成对抗网络（GAN），用于图像重建与去噪；第二部分为判别模块，通过引入ROI注意力机制，在训练时以每张图像的ROI作为监督信号，使模型学习在哪些区域内异常信息更具相关性。此设计显著减少了对预处理算法的依赖，并提升了对真实工业场景（如药品瓶塞封口条带）中局部缺陷的识别精度与鲁棒性。
链接: https://arxiv.org/abs/2603.07566
作者: Niccolò Ferrari,Michele Fraccaroli,Evelina Lamma
机构: University of Ferrara (费拉拉大学); Bonfiglioli Engineering (邦菲格利工程公司); University of Ferrara (费拉拉大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Peer-reviewed journal version published. 18 pages, 12 figures, 7 tables
Abstract:Anomaly detection is nowadays increasingly used in industrial applications and processes. One of the main fields of the appliance is the visual inspection for surface anomaly detection, which aims to spot regions that deviate from regularity and consequently identify abnormal products. Defect localization is a key task, that usually is achieved using a basic comparison between generated image and the original one, implementing some blob-analysis or image-editing algorithms, in the post-processing step, which is very biased towards the source dataset, and they are unable to generalize. Furthermore, in industrial applications, the totality of the image is not always interesting but could be one or some regions of interest (ROIs), where only in those areas there are relevant anomalies to be spotted. For these reasons, we propose a new architecture composed by two blocks. The first block is a Generative Adversarial Network (GAN), based on a residual autoencoder (ResAE), to perform reconstruction and denoising processes, while the second block produces image segmentation, spotting defects. This method learns from a dataset composed of good products and generated synthetic defects. The discriminative network is trained using a ROI for each image contained in the training dataset. The network will learn in which area anomalies are relevant. This approach guarantees the reduction of using pre-processing algorithms, formerly developed with blob-analysis and image-editing procedures. To test our model we used challenging MVTec anomaly detection datasets and an industrial large dataset of pharmaceutical BFS strips of vials. This set constitutes a more realistic use case of the aforementioned network. Comments: Peer-reviewed journal version published. 18 pages, 12 figures, 7 tables Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) ACMclasses: I.2.6; I.4.8 Cite as: arXiv:2603.07566 [cs.CV] (or arXiv:2603.07566v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.07566 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: International Journal of Intelligent Systems, vol. 2023, Article ID 7773481, 2023 Related DOI: https://doi.org/10.1155/2023/7773481 Focus to learn more DOI(s) linking to related resources Submission history From: Niccolò Ferrari [view email] [v1] Sun, 8 Mar 2026 10:02:17 UTC (2,809 KB) Full-text links: Access Paper: View a PDF of the paper titled GRD-Net: Generative-Reconstructive-Discriminative Anomaly Detection with Region of Interest Attention Module, by Niccol`o Ferrari and 2 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.CV prev | next new | recent | 2026-03 Change to browse by: cs cs.AI cs.LG References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked=“checked”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); About Help contact arXivClick here to contact arXiv Contact subscribe to arXiv mailingsClick here to subscribe Subscribe Copyright Privacy Policy Web Accessibility Assistance arXiv Operational Status
[CV-154] SiamGM: Siamese Geometry-Aware and Motion-Guided Network for Real-Time Satellite Video Object Tracking
【速读】：该论文旨在解决卫星视频中单目标跟踪（Single Object Tracking in Satellite Videos, SatSOT）面临的挑战，包括小目标、背景模糊、长宽比剧烈变化及频繁视觉遮挡等问题，这些问题常导致基于外观的跟踪器产生误差累积并不可逆地丢失目标。解决方案的关键在于提出一种几何感知与运动引导的孪生网络 SiamGM：空间层面引入了帧间图注意力机制（Inter-Frame Graph Attention, IFGA）与长宽比约束标签分配方法（Aspect Ratio-Constrained Label Assignment, LA），以建立细粒度拓扑对应关系并抑制背景噪声；时间层面则设计了运动矢量引导的在线跟踪优化策略（Motion Vector-Guided Online Tracking Optimization），结合归一化主瓣峰值比（nPSR）作为动态置信度指标，实现历史轨迹信息的在线运动模型精调（Online Motion Model Refinement, OMMR）。该方案在保持极低计算开销的前提下实现了高达130 FPS的实时性能，并在SatSOT和SV248S两个基准上显著优于当前主流跟踪算法。
链接: https://arxiv.org/abs/2603.07564
作者: Zixiao Wen,Zhen Yang,Jiawei Li,Xiantai Xiang,Guangyao Zhou,Yuxin Hu,Yuhan Liu
机构: Aerospace Information Research Institute, Chinese Academy of Sciences (中国科学院空天信息研究院); Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences (中国科学院地理空间信息处理与应用系统技术重点实验室); School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences (中国科学院大学电子、电气与通信工程学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This work has been submitted to the IEEE for possible publication
Abstract:Single object tracking in satellite videos is inherently challenged by small target, blurred background, large aspect ratio changes, and frequent visual occlusions. These constraints often cause appearance-based trackers to accumulate errors and lose targets irreversibly. To systematically mitigate both spatial ambiguities and temporal information loss, we propose SiamGM, a novel geometry-aware and motion-guided Siamese network. From a spatial perspective, we introduce an Inter-Frame Graph Attention (IFGA) module, closely integrated with an Aspect Ratio-Constrained Label Assignment (LA) method, establishing fine-grained topological correspondences and explicitly preventing surrounding background noise. From a temporal perspective, we introduce the Motion Vector-Guided Online Tracking Optimization method. By adopting the Normalized Peak-to-Sidelobe Ratio (nPSR) as a dynamic confidence indicator, we propose an Online Motion Model Refinement (OMMR) strategy to utilize historical trajectory information. Evaluations on two challenging SatSOT and SV248S benchmarks confirm that SiamGM outperforms most state-of-the-art trackers in both precision and success metrics. Notably, the proposed components of SiamGM introduce virtually no computational overhead, enabling real-time tracking at 130 frames per second (FPS). Codes and tracking results are available at this https URL.
[CV-155] Brain-WM: Brain Glioblastoma World Model
【速读】：该论文旨在解决胶质母细胞瘤（Glioblastoma, GBM）在不同治疗干预下的精准预后建模问题，现有生成式 AI 方法通常将治疗视为静态条件输入，无法捕捉肿瘤演化与治疗响应之间的动态互馈关系。其解决方案的关键在于提出 Brain-WM——一种统一未来治疗预测与MRI生成的脑胶质母细胞瘤世界模型（Brain GBM World Model），通过共享潜在空间联合建模时空动态，并采用创新的Y型混合Transformer（Y-shaped Mixture-of-Transformers, MoT）架构实现异构目标的结构解耦与跨任务协同，同时引入多时间点掩码对齐目标以锚定潜在表示到解剖学上合理的肿瘤结构和进展感知语义，从而有效刻画肿瘤与治疗的共演化机制。
链接: https://arxiv.org/abs/2603.07562
作者: Chenhui Wang,Boyun Zheng,Liuxin Bao,Zhihao Peng,Peter Y.M. Woo,Hongming Shan,Yixuan Yuan
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Precise prognostic modeling of glioblastoma (GBM) under varying treatment interventions is essential for optimizing clinical outcomes. While generative AI has shown promise in simulating GBM evolution, existing methods typically treat interventions as static conditional inputs rather than dynamic decision variables. Consequently, they fail to capture the complex, reciprocal interplay between tumor evolution and treatment response. To bridge this gap, we present Brain-WM, a pioneering brain GBM world model that unifies next-step treatment prediction and future MRI generation, thereby capturing the co-evolutionary dynamics between tumor and treatment. Specifically, Brain-WM encodes spatiotemporal dynamics into a shared latent space for joint autoregressive treatment prediction and flow-based future MRI generation. Then, instead of a conventional monolithic framework, Brain-WM adopts a novel Y-shaped Mixture-of-Transformers (MoT) architecture. This design structurally disentangles heterogeneous objectives, successfully leveraging cross-task synergies while preventing feature collapse. Finally, a synergistic multi-timepoint mask alignment objective explicitly anchors latent representations to anatomically grounded tumor structures and progression-aware semantics. Extensive validation on internal and external multi-institutional cohorts demonstrates the superiority of Brain-WM, achieving 91.5% accuracy in treatment planning and SSIMs of 0.8524, 0.8581, and 0.8404 for FLAIR, T1CE, and T2W sequences, respectively. Ultimately, Brain-WM offers a robust clinical sandbox for optimizing patient healthcare. The source code is made available at this https URL.
[CV-156] PureCC: Pure Learning for Text-to-Image Concept Customization CVPR2026
【速读】：该论文旨在解决现有概念定制方法在实现高保真度和多概念定制的同时，往往忽视了对原始模型行为与能力的影响这一问题。其解决方案的关键在于提出PureCC，该方法设计了一种新颖的解耦学习目标，将目标概念的隐式引导与原始条件预测分离，从而在训练过程中显著聚焦于保持原模型特性；同时构建了一个双分支训练流程，包括一个冻结提取器提供纯净的目标概念表示作为隐式引导，以及一个可训练的流模型生成原始条件预测，共同实现对个性化概念的纯学习（pure learning）；此外，引入自适应引导尺度λ⋆以动态调节目标概念的引导强度，在定制保真度与模型保留之间取得平衡。
链接: https://arxiv.org/abs/2603.07561
作者: Zhichao Liao,Xiaole Xian,Qingyu Li,Wenyu Qin,Meng Wang,Weicheng Xie,Siyang Song,Pingfa Feng,Long Zeng,Liang Pan
机构: Tsinghua University (清华大学); School of Computer Science, Software Engineering, Shenzhen University (深圳大学计算机科学与软件工程学院); Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University (广东省智能信息处理重点实验室，深圳大学); Kling Team, Kuaishou Technology (快手科技Kling团队); University of Exeter (埃克塞特大学); S-Lab, Nanyang Technological University (南洋理工大学S-Lab)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026
Abstract:Existing concept customization methods have achieved remarkable outcomes in high-fidelity and multi-concept customization. However, they often neglect the influence on the original model’s behavior and capabilities when learning new personalized concepts. To address this issue, we propose PureCC. PureCC introduces a novel decoupled learning objective for concept customization, which combines the implicit guidance of the target concept with the original conditional prediction. This separated form enables PureCC to substantially focus on the original model during training. Moreover, based on this objective, PureCC designs a dual-branch training pipeline that includes a frozen extractor providing purified target concept representations as implicit guidance and a trainable flow model producing the original conditional prediction, jointly achieving pure learning for personalized concepts. Furthermore, PureCC introduces a novel adaptive guidance scale \lambda^\star to dynamically adjust the guidance strength of the target concept, balancing customization fidelity and model preservation. Extensive experiments show that PureCC achieves state-of-the-art performance in preserving the original behavior and capabilities while enabling high-fidelity concept customization. The code is available at this https URL.
[CV-157] Active Inference for Micro-Gesture Recognition: EFE-Guided Temporal Sampling and Adaptive Learning CVPR2026
【速读】：该论文旨在解决微手势（micro-gesture）识别在低样本、噪声干扰和跨被试条件下的性能退化问题，其关键在于提出一种基于主动推理（active inference）的框架，核心创新包括：1）基于期望自由能（Expected Free Energy, EFE）引导的时序采样机制，使模型能够动态选择最具判别性的时序片段以最大化信息增益；2）基于预测不确定性的自适应学习策略，通过样本加权缓解标签噪声和分布偏移的影响。实验表明，该方法在SMG数据集上对多种主流骨干网络均实现稳定提升，且消融实验证实EFE引导观测与自适应学习机制均为性能提升的关键因素。
链接: https://arxiv.org/abs/2603.07559
作者: Weijia Feng,Jingyu Yang,Ruojia Zhang,Fengtao Sun,Qian Gao,Chenyang Wang,Tongtong Su,Jia Guo,Xiaobai Li,Minglai Shao
机构: Tianjin Normal University (天津师范大学); Shenzhen University (深圳大学); Zhejiang University (浙江大学); Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security (杭州市滨江区区块链与数据安全研究院); Tianjin University (天津大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, accepted by CVPR 2026
Abstract:Micro-gestures are subtle and transient movements triggered by unconscious neural and emotional activities, holding great potential for human-computer interaction and clinical monitoring. However, their low amplitude, short duration, and strong inter-subject variability make existing deep models prone to degradation under low-sample, noisy, and cross-subject conditions. This paper presents an active inference-based framework for micro-gesture recognition, featuring Expected Free Energy (EFE)-guided temporal sampling and uncertainty-aware adaptive learning. The model actively selects the most discriminative temporal segments under EFE guidance, enabling dynamic observation and information gain maximization. Meanwhile, sample weighting driven by predictive uncertainty mitigates the effects of label noise and distribution shift. Experiments on the SMG dataset demonstrate the effectiveness of the proposed method, achieving consistent improvements across multiple mainstream backbones. Ablation studies confirm that both the EFE-guided observation and the adaptive learning mechanism are crucial to the performance gains. This work offers an interpretable and scalable paradigm for temporal behavior modeling under low-resource and noisy conditions, with broad applicability to wearable sensing, HCI, and clinical emotion monitoring.
[CV-158] ReconDrive: Fast Feed-Forward 4D Gaussian Splatting for Autonomous Driving Scene Reconstruction
【速读】：该论文旨在解决自动驾驶场景中高保真视觉重建与新视角合成的效率与质量难题：现有基于逐场景优化的方法虽精度高但计算成本昂贵，难以扩展至大规模城市环境；而当前前馈式方法则常因光度质量下降导致重建效果不佳。其解决方案的关键在于提出ReconDrive框架，通过两个核心创新实现高效且高质量的4D高斯溅射（4D Gaussian Splatting, 4DGS）生成：一是引入混合高斯预测头（Hybrid Gaussian Prediction Heads），将空间坐标与外观属性的回归解耦，从而克服通用基础特征带来的光度缺陷；二是设计静态-动态4D组合策略（Static-Dynamic 4D Composition），通过速度建模显式捕捉时间维度上的运动信息，以准确表达复杂动态驾驶环境。该方法在nuScenes数据集上显著优于现有前馈基线，在重建、新视角合成和3D感知任务中达到接近逐场景优化的效果，同时速度提升数个数量级，具备良好的可扩展性和实用性。
链接: https://arxiv.org/abs/2603.07552
作者: Haibao Yu,Kuntao Xiao,Jiahang Wang,Ruiyang Hao,Yuxin Huang,Guoran Hu,Haifang Qin,Bowen Jing,Yuntian Bo,Ping Luo
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:High-fidelity visual reconstruction and novel-view synthesis are essential for realistic closed-loop evaluation in autonomous driving. While 4D Gaussian Splatting (4DGS) offers a promising balance of accuracy and efficiency, existing per-scene optimization methods require costly iterative refinement, rendering them unscalable for extensive urban environments. Conversely, current feed-forward approaches often suffer from degraded photometric quality. To address these limitations, we propose ReconDrive, a feed-forward framework that leverages and extends the 3D foundation model VGGT for rapid, high-fidelity 4DGS generation. Our architecture introduces two core adaptations to tailor the foundation model to dynamic driving scenes: (1) Hybrid Gaussian Prediction Heads, which decouple the regression of spatial coordinates and appearance attributes to overcome the photometric deficiencies inherent in generalized foundation features; and (2) a Static-Dynamic 4D Composition strategy that explicitly captures temporal motion via velocity modeling to represent complex dynamic environments. Benchmarked on nuScenes, ReconDrive significantly outperforms existing feed-forward baselines in reconstruction, novel-view synthesis, and 3D perception. It achieves performance competitive with per-scene optimization while being orders of magnitude faster, providing a scalable and practical solution for realistic driving simulation.
[CV-159] DreamSAC: Learning Hamiltonian World Models via Symmetry Exploration
【速读】：该论文旨在解决生成式世界模型在面对新物理属性时无法实现外推泛化（extrapolative generalization）的问题，其根源在于模型仅学习了统计相关性而非环境的底层生成规则（如物理不变性和守恒定律）。解决方案的关键在于两个核心创新：一是提出对称性探索（Symmetry Exploration），通过基于哈密顿量的内在好奇心奖励机制，引导智能体主动探测并挑战其对守恒定律的理解，从而收集具有物理意义的数据；二是设计一种基于哈密顿量的世界模型，利用新颖的自监督对比目标从原始、视角依赖的像素观测中识别出不变的物理状态。该框架名为DreamSAC，在3D物理模拟任务中显著优于现有最先进基线方法，尤其在需要外推能力的任务上表现突出。
链接: https://arxiv.org/abs/2603.07545
作者: Jinzhou Tang,Fan Feng,Minghao Fu,Wenjun Lin,Biwei Huang,Keze Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 19 pages, 5 figures
Abstract:Learned world models excel at interpolative generalization but fail at extrapolative generalization to novel physical properties. This limitation arises because they learn statistical correlations rather than the environment’s underlying generative rules, such as physical invariances and conservation laws. We argue that learning these invariances is key to robust extrapolation. To achieve this, we first introduce \textbfSymmetry Exploration, an unsupervised exploration strategy where an agent is intrinsically motivated by a Hamiltonian-based curiosity bonus to actively probe and challenge its understanding of conservation laws, thereby collecting physically informative data. Second, we design a Hamiltonian-based world model that learns from the collected data, using a novel self-supervised contrastive objective to identify the invariant physical state from raw, view-dependent pixel observations. Our framework, \textbfDreamSAC, trained on this actively curated data, significantly outperforms state-of-the-art baselines in 3D physics simulations on tasks requiring extrapolation.
[CV-160] CONSTANT: Towards High-Quality One-Shot Handwriting Generation with Patch Contrastive Enhancement and Style-Aware Quantization WACV2026
【速读】：该论文旨在解决单样本风格化手写图像生成（one-shot styled handwriting image generation）中的核心挑战，即如何仅凭一张参考图像准确捕捉并迁移人类手写风格的复杂多样性，同时分离出不变的风格特征（如倾斜度、笔画宽度、曲率等）并抑制无关噪声。现有方法在生成视觉逼真且风格一致的手写图像方面仍存在局限，尤其难以适应未见过的书写者风格。为应对这一问题，作者提出了一种基于扩散模型的新方法CONSTANT，其关键创新在于：1）引入风格感知量化（Style-Aware Quantization, SAQ）模块，将风格建模为离散的视觉标记（visual tokens），以捕捉不同风格概念；2）设计对比损失函数，确保这些标记在嵌入空间中具有良好的区分性和语义意义；3）采用潜空间局部对比（Latent Patch-based Contrastive, LLatentPCE）目标，在潜空间中对齐生成图像与真实图像的多尺度空间块特征，从而提升图像质量和局部结构保真度。
链接: https://arxiv.org/abs/2603.07543
作者: Anh-Duy Le,Van-Linh Pham,Thanh-Nam Vo,Xuan Toan Mai,Tuan-Anh Tran
机构: Viettel Artificial Intelligence and Data Services Center (越南电信人工智能与数据服务中心); Ho Chi Minh City University of Technology (胡志明市科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: Accepted as oral presentation at WACV 2026
Abstract:One-shot styled handwriting image generation, despite achieving impressive results in recent years, remains challenging due to the difficulty in capturing the intricate and diverse characteristics of human handwriting by using solely a single reference image. Existing methods still struggle to generate visually appealing and realistic handwritten images and adapt to complex, unseen writer styles, struggling to isolate invariant style features (e.g., slant, stroke width, curvature) while ignoring irrelevant noise. To tackle this problem, we introduce Patch Contrastive Enhancement and Style-Aware Quantization via Denoising Diffusion (CONSTANT), a novel one-shot handwriting generation via diffusion model. CONSTANT leverages three key innovations: 1) a Style-Aware Quantization (SAQ) module that models style as discrete visual tokens capturing distinct concepts; 2) a contrastive objective to ensure these tokens are well-separated and meaningful in the embedding style space; 3) a latent patch-based contrastive (LLatentPCE) objective help improving quality and local structures by aligning multiscale spatial patches of generated and real features in latent space. Extensive experiments and analysis on benchmark datasets from multiple languages, including English, Chinese, and our proposed ViHTGen dataset for Vietnamese, demonstrate the superiority of adapting to new reference styles and producing highly detailed images of our method over state-of-the-art approaches. Code is available at GitHub
[CV-161] How Long Can Unified Multimodal Models Generate Images Reliably? Taming Long-Horizon Interleaved Image Generation via Context Curation
【速读】：该论文旨在解决统一多模态模型在生成长序列叙事时面临的可靠性问题，即随着文本与图像交替生成的长度增加，模型性能迅速下降。研究表明，这一问题并非源于传统长上下文处理挑战，而是由于累积的视觉历史作为“主动污染源”，其衰减机制由图像事件数量而非总token数决定；具体表现为密集的视觉token会淹没注意力机制，产生噪声并干扰后续合成。解决方案的关键在于提出一种无需训练的推理策略UniLongGen，其核心是通过动态筛选记忆、基于模型内部相关性排序主动遗忘干扰性视觉信号，从而实现安全条件化而非全量记忆保留，显著提升长程连贯性和稳定性，同时降低内存占用与推理时间。
链接: https://arxiv.org/abs/2603.07540
作者: Haoyu Chen,Qing Liu,Yuqian Zhou,He Zhang,Zhaowen Wang,Mengwei Ren,Jingjing Ren,Xiang Wang,Zhe Lin,Lei Zhu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Unified multimodal models hold the promise of generating extensive, interleaved narratives, weaving text and imagery into coherent long-form stories. However, current systems suffer from a critical reliability gap: as sequences grow, generation quality rapidly collapses. In this work, we investigate the mechanism behind this failure and argue that it is distinct from standard long-context challenges. We reveal that in generation, accumulated visual history acts as a source of active pollution, a decay governed specifically by the number of image events rather than raw token count. We identify a structural vulnerability where dense visual tokens overwhelm the attention mechanism, creating noise that distorts future synthesis. Guided by these mechanistic insights, we propose UniLongGen, a training-free inference strategy that prioritizes safe conditioning over total recall. Instead of retaining all history, UniLongGen dynamically curates the model’s memory, identifying and discarding interfering visual signals based on the model’s own internal relevance rankings. Extensive experiments demonstrate that this active forgetting approach is essential for stability: UniLongGen significantly outperforms baselines in long-horizon fidelity and consistency, while simultaneously reducing memory footprint and inference time.
[CV-162] Scale-Aware UAV-to-Satellite Cross-View Geo-Localization: A Semantic Geometric Approach
【速读】：该论文旨在解决跨视图地理定位（Cross-View Geo-Localization, CVGL）中因无人机（UAV）影像与卫星图像之间尺度不一致导致的场视野错位和特征匹配失效问题，尤其是在真实场景下普遍存在的尺度模糊性。解决方案的关键在于提出一种基于语义锚点的几何框架，通过小车辆（Small Vehicles, SVs）作为具有稳定先验尺寸分布的度量参考，利用解耦立体投影模型（Decoupled Stereoscopic Projection Model）从单目UAV图像中恢复绝对尺度：该模型将车辆三维尺寸分解为径向与切向分量以补偿2D检测中的透视畸变，结合基于四分位距（Interquartile Range, IQR）的鲁棒聚合策略降低类内尺寸差异和检测噪声，最终以估计的全局尺度作为物理约束实现自适应卫星图像裁剪，显著提升UAV到卫星的特征对齐精度与CVGL鲁棒性。
链接: https://arxiv.org/abs/2603.07535
作者: Yibin Ye,Shuo Chen,Kun Wang,Xiaokai Song,Jisheng Dang,Qifeng Yu,Xichao Teng,Zhang Li
机构: National University of Defense Technology (国防科技大学); Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation (湖南省图像测量与视觉导航重点实验室); Lanzhou University (兰州大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages
Abstract:Cross-View Geo-Localization (CVGL) between UAV imagery and satellite images plays a crucial role in target localization and UAV self-positioning. However, most existing methods rely on the idealized assumption of scale consistency between UAV queries and satellite galleries, overlooking the severe scale ambiguity commonly encountered in real-world scenarios. This discrepancy leads to field-of-view misalignment and feature mismatch, significantly degrading CVGL robustness. To address this issue, we propose a geometric framework that recovers the absolute metric scale from monocular UAV images using semantic anchors. Specifically, small vehicles (SVs), characterized by relatively stable prior size distributions and high detectability, are exploited as metric references. A Decoupled Stereoscopic Projection Model is introduced to estimate the absolute image scale from these semantic targets. By decomposing vehicle dimensions into radial and tangential components, the model compensates for perspective distortions in 2D detections of 3D vehicles, enabling more accurate scale estimation. To further reduce intra-class size variation and detection noise, a dual-dimension fusion strategy with Interquartile Range (IQR)-based robust aggregation is employed. The estimated global scale is then used as a physical constraint for scale-adaptive satellite image cropping, improving UAV-to-satellite feature alignment. Experiments on augmented DenseUAV and UAV-VisLoc datasets demonstrate that the proposed method significantly improves CVGL robustness under unknown UAV image scales. Additionally, the framework shows strong potential for downstream applications such as passive UAV altitude estimation and 3D model scale recovery.
[CV-163] ACCURATE: Arbitrary-shaped Continuum Reconstruction Under Robust Adaptive Two-view Estimation
【速读】：该论文旨在解决柔性连续体结构（如导丝、导管和软体连续机械臂）在医学影像中难以实现高精度三维重建的问题。现有基于图像的重建方法常因未充分利用相机几何信息或依赖刚性几何假设而精度不足，且对复杂变形形状适应性差。其解决方案的关键在于提出一种名为ACCURATE的框架，该框架融合了图像分割神经网络与几何约束的拓扑遍历及动态规划算法，通过强制全局双平面几何一致性、最小化累积点到极线距离，并有效应对遮挡和极线模糊问题，从而显著提升重建精度，在模拟和真实phantom数据集上均实现了低于1.0 mm的平均绝对误差。
链接: https://arxiv.org/abs/2603.07533
作者: Yaozhi Zhang,Shun Yu,Yugang Zhang,Yang Liu
机构: 未知
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate reconstruction of arbitrary-shaped long slender continuum bodies, such as guidewires, catheters and other soft continuum manipulators, is essential for accurate mechanical simulation. However, existing image-based reconstruction approaches often suffer from limited accuracy because they often underutilize camera geometry, or lack generality as they rely on rigid geometric assumptions that may fail for continuum robots with complex and highly deformable shapes. To address these limitations, we propose ACCURATE, a 3D reconstruction framework integrating an image segmentation neural network with a geometry-constrained topology traversal and dynamic programming algorithm that enforces global biplanar geometric consistency, minimizes the cumulative point-to-epipolar-line distance, and remains robust to occlusions and epipolar ambiguities cases caused by noise and discretization. Our method achieves high reconstruction accuracy on both simulated and real phantom datasets acquired using a clinical X-ray C-arm system, with mean absolute errors below 1.0 mm.
[CV-164] SketchGraphNet: A Memory-Efficient Hybrid Graph Transformer for Large-Scale Sketch Corpora Recognition
【速读】：该论文旨在解决大规模手绘草图识别问题，传统方法通常将草图建模为栅格图像或笔画序列，难以有效捕捉其内在结构信息。为此，作者提出从图原生（graph-native）视角出发，直接将自由手绘草图表示为结构化的图数据，并设计了SketchGraphNet这一混合图神经网络架构。其核心创新在于融合局部消息传递机制与一种内存高效的全局注意力机制（MemEffAttn），无需依赖辅助的位置或结构编码即可实现高效建模。该方案在自建的大规模基准SketchGraph上验证了有效性，该基准包含344类共344万张带时空属性的草图图结构样本，在不同噪声条件下分别达到83.62%和87.61%的Top-1准确率，同时相较基于Performer的全局注意力机制显著降低峰值GPU内存使用超40%，训练时间减少30%以上，保持相近精度。
链接: https://arxiv.org/abs/2603.07521
作者: Shilong Chen,Mingyuan Li,Zhaoyang Wang,Zhonglin Ye,Haixing Zhao
机构: Qinghai Normal University (青海师范大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:This work investigates large-scale sketch recognition from a graph-native perspective, where free-hand sketches are directly modeled as structured graphs rather than raster images or stroke sequences. We propose SketchGraphNet, a hybrid graph neural architecture that integrates local message passing with a memory-efficient global attention mechanism, without relying on auxiliary positional or structural encodings. To support systematic evaluation, we construct SketchGraph, a large-scale benchmark comprising 3.44 million graph-structured sketches across 344 categories, with two variants (A and R) to reflect different noise conditions. Each sketch is represented as a spatiotemporal graph with normalized stroke-order attributes. On SketchGraph-A and SketchGraph-R, SketchGraphNet achieves Top-1 accuracies of 83.62% and 87.61%, respectively, under a unified training configuration. MemEffAttn further reduces peak GPU memory by over 40% and training time by more than 30% compared with Performer-based global attention, while maintaining comparable accuracy.
[CV-165] EvolveReason : Self-Evolving Reasoning Paradigm for Explainable Deepfake Facial Image Identification
【速读】：该论文旨在解决当前生成式 AI (Generative AI) 伪造人脸检测技术中存在的两大核心问题：一是传统分类方法缺乏可解释性，二是现有视觉语言模型（VLM）虽能提供粗粒度解释但存在幻觉和细节不足的问题。解决方案的关键在于提出 EvolveReason 框架，其通过构建面向高级 VLM 的思维链数据集 CoT-Face，引导模型模拟人类审计员的推理与观察过程，输出结构化推理路径与判断结果，从而提升识别可靠性并减少幻觉；同时引入伪造潜在空间分布捕捉模块以提取原始图像中难以察觉的高频伪造线索，并结合基于强化学习的自进化探索策略，在两阶段迭代优化中增强文本解释的准确性与鲁棒性。
链接: https://arxiv.org/abs/2603.07515
作者: Binjia Zhou,Dawei Luo,Shuai Chen,Feng Xu,Seow,Haoyuan Li,Jiachi Wang,Jiawen Wang,Zunlei Feng,Yijun Bei
机构: Zhejiang University (浙江大学); Ant Group (蚂蚁集团)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:With the rapid advancement of AIGC technology, developing identification methods to address the security challenges posed by deepfakes has become urgent. Face forgery identification techniques can be categorized into two types: traditional classification methods and explainable VLM approaches. The former provides classification results but lacks explanatory ability, while the latter, although capable of providing coarse-grained explanations, often suffers from hallucinations and insufficient detail. To overcome these limitations, we propose EvolveReason, which mimics the reasoning and observational processes of human auditors when identifying face forgeries. By constructing a chain-of-thought dataset, CoT-Face, tailored for advanced VLMs, our approach guides the model to think in a human-like way, prompting it to output reasoning processes and judgment results. This provides practitioners with reliable analysis and helps alleviate hallucination. Additionally, our framework incorporates a forgery latent-space distribution capture module, enabling EvolveReason to identify high-frequency forgery cues difficult to extract from the original images. To further enhance the reliability of textual explanations, we introduce a self-evolution exploration strategy, leveraging reinforcement learning to allow the model to iteratively explore and optimize its textual descriptions in a two-stage process. Experimental results show that EvolveReason not only outperforms the current state-of-the-art methods in identification performance but also accurately identifies forgery details and demonstrates generalization capabilities.
[CV-166] A Unified View of Drifting and Score-Based Models
【速读】：该论文旨在解决生成模型中训练一阶生成器（one-step generators）时如何有效利用数据与模型分布之间的差异来指导样本生成的问题。其核心挑战在于如何将基于核函数的均值漂移（mean-shift）机制与扩散模型背后的得分匹配（score-matching）原理建立精确联系。解决方案的关键在于揭示了均值漂移本质上是对核平滑后数据与模型分布的得分差进行建模：对于高斯核，其群体均值漂移场恰好等于高斯平滑后数据分布与模型分布的得分差，这一结论源于Tweedie公式，从而证明高斯核漂移等价于在平滑分布上的得分匹配目标；此外，论文还推导出一般径向核的精确分解形式，并对拉普拉斯核给出了严格的误差界，表明在低温度和高维场景下，漂移仍能作为得分匹配的准确代理。
链接: https://arxiv.org/abs/2603.07514
作者: Chieh-Hsin Lai,Bac Nguyen,Naoki Murata,Yuhta Takida,Toshimitsu Uesaka,Yuki Mitsufuji,Stefano Ermon,Molei Tao
机构: Sony AI (索尼人工智能); Sony Group Corporation (索尼集团); Stanford University (斯坦福大学); Georgia Tech (佐治亚理工学院)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Drifting models train one-step generators by optimizing a mean-shift discrepancy induced by a kernel between the data and model distributions, with Laplace kernels used by default in practice. At each point, this discrepancy compares the kernel-weighted displacement toward nearby data samples with the corresponding displacement toward nearby model samples, yielding a transport direction for generated samples. In this paper, we make its relationship to the score-matching principle behind diffusion models precise by showing that drifting admits a score-based formulation on kernel-smoothed distributions. For Gaussian kernels, the population mean-shift field coincides with the score difference between the Gaussian-smoothed data and model distributions. This identity follows from Tweedie’s formula, which links the score of a Gaussian-smoothed density to the corresponding conditional mean, and implies that Gaussian-kernel drifting is exactly a score-matching-style objective on smoothed distributions. It also clarifies the connection to Distribution Matching Distillation (DMD): both methods use score-mismatch transport directions, but drifting realizes the score signal nonparametrically from kernel neighborhoods, whereas DMD uses a pretrained diffusion teacher. Beyond Gaussians, we derive an exact decomposition for general radial kernels, and for the Laplace kernel we prove rigorous error bounds showing that drifting remains an accurate proxy for score matching in low-temperature and high-dimensional regimes.
[CV-167] High-Fidelity Medical Shape Generation via Skeletal Latent Diffusion
【速读】：该论文旨在解决医学图像分析中解剖形状建模的难题，尤其是由解剖结构几何复杂性和拓扑变异性带来的高精度形状生成挑战。其解决方案的关键在于提出了一种基于骨骼潜在扩散（skeletal latent diffusion）的框架，该框架通过引入结构先验信息来提升生成效率与保真度：首先设计了一个形状自编码器，其中编码器利用可微分骨骼化模块捕捉全局几何信息，并将局部表面特征聚合为形状潜在表示；解码器则在稀疏采样的坐标上预测对应的隐式场；随后通过潜在空间扩散模型生成新形状，再经神经隐式解码和网格提取完成最终重建。此方法显著提升了医学形状生成的质量与计算效率。
链接: https://arxiv.org/abs/2603.07504
作者: Guoqing Zhang,Jingyun Yang,Siqi Chen,Anping Zhang,Yang Li
机构: Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China; Pengcheng Laboratory, Shenzhen, China; School of AI, Chinese University of Hong Kong (Shenzhen)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 5 figures, journal
Abstract:Anatomy shape modeling is a fundamental problem in medical data analysis. However, the geometric complexity and topological variability of anatomical structures pose significant challenges to accurate anatomical shape generation. In this work, we propose a skeletal latent diffusion framework that explicitly incorporates structural priors for efficient and high-fidelity medical shape generation. We introduce a shape auto-encoder in which the encoder captures global geometric information through a differentiable skeletonization module and aggregates local surface features into shape latents, while the decoder predicts the corresponding implicit fields over sparsely sampled coordinates. New shapes are generated via a latent-space diffusion model, followed by neural implicit decoding and mesh extraction. To address the limited availability of medical shape data, we construct a large-scale dataset, \textitMedSDF, comprising surface point clouds and corresponding signed distance fields across multiple anatomical categories. Extensive experiments on MedSDF and vessel datasets demonstrate that the proposed method achieves superior reconstruction and generation quality while maintaining a higher computational efficiency compared with existing approaches. Code is available at: this https URL.
[CV-168] AMR-CCR: Anchored Modular Retrieval for Continual Chinese Character Recognition
【速读】：该论文旨在解决古代汉字识别（Ancient Chinese Character Recognition, ACR）在文化遗产数字化过程中面临的持续学习挑战，即随着新出土材料的不断加入，字符类别随时间持续增长（class-incremental），且新旧类别间差异细微、增量数据稀缺，同时同一类字符因书写风格和载体条件差异导致显著的类内多样性（intra-class diversity）。为应对传统封闭集分类方法的局限性，作者提出AMR-CCR框架——其核心创新在于：一是构建一个基于嵌入空间的锚定模块化检索机制（anchored modular retrieval），通过共享多模态空间中的嵌入匹配实现字典扩展式识别，支持新类别的无须重训练添加；二是引入轻量级脚本条件注入模块（SIA+SAR），用于校准新增脚本并保持跨阶段嵌入一致性；三是设计图像驱动的多原型字典（multi-prototype dictionary），对类内嵌入进行聚类以覆盖多样书写风格模式。
链接: https://arxiv.org/abs/2603.07497
作者: Yuchuan Wu,Yinglian Zhu,Haiyang Yu,Ke Niu,Bin Li,Xiangyang Xue
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Ancient Chinese character recognition is a core capability for cultural heritage digitization, yet real-world workflows are inherently non-stationary: newly excavated materials are continuously onboarded, bringing new classes in different scripts, and expanding the class space over time. We formalize this process as Continual Chinese Character Recognition (Continual CCR), a script-staged, class-incremental setting that couples two challenges: (i) scalable learning under continual class growth with subtle inter-class differences and scarce incremental data, and (ii) pronounced intra-class diversity caused by writing-style variations across writers and carrier conditions. To overcome the limitations of conventional closed-set classification, we propose AMR-CCR, an anchored modular retrieval framework that performs recognition via embedding-based dictionary matching in a shared multimodal space, allowing new classes to be added by simply extending the dictionary. AMR-CCR further introduces a lightweight script-conditioned injection module (SIA+SAR) to calibrate newly onboarded scripts while preserving cross-stage embedding compatibility, and an image-derived multi-prototype dictionary that clusters within-class embeddings to better cover diverse style modes. To support systematic evaluation, we build EvoCON, a six-stage benchmark for continual script onboarding, covering six scripts (OBC, BI, SS, SAC, WSC, CS), augmented with meaning/shape descriptions and an explicit zero-shot split for unseen characters without image exemplars.
[CV-169] DocCogito: Aligning Layout Cognition and Step-Level Grounded Reasoning for Document Understanding
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在文档理解任务中缺乏完整、可解释且证据驱动的推理过程的问题，尤其是在高风险场景下，现有方法虽提升了布局编码和思维链（Chain-of-Thought, CoT）式提示的效果，但二者之间的交互通常以隐式方式学习，耦合松散，难以形成系统性推理机制。解决方案的关键在于提出DocCogito框架，其核心创新包括：(1) 引入轻量级布局塔（layout tower），将页面结构蒸馏为可学习的全局布局先验token；(2) 设计确定性的视觉-语义链（Visual-Semantic Chain, VSC），作为比自由文本CoT更紧凑、低歧义的结构化中间推理表示，用于监督与证据区域对齐的细粒度推理过程；(3) 通过渐进式训练策略（包括布局感知预训练、VSC引导冷启动、拒绝采样及GRPO优化）强化布局先验与VSC执行间的内部耦合，并引入细粒度区域置信度信号作为奖励增强模块，确保推理轨迹始终聚焦于对应证据区域。
链接: https://arxiv.org/abs/2603.07494
作者: Yuchuan Wu,Minghan Zhuo,Teng Fu,Mengyang Zhao,Bin Li,Xiangyang Xue
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Document understanding with multimodal large language models (MLLMs) requires not only accurate answers but also explicit, evidence-grounded reasoning, especially in high-stakes scenarios. However, current document MLLMs still fall short of forming a complete, human-like reasoning process, because even when they improve both layout encoding and CoT-style prompting, the interaction between the two is typically learned implicitly and remains loosely coupled rather than being enforced as a systematic mechanism. So we propose DocCogito, a unified framework that integrates global layout perception with structured, region-grounded reasoning. DocCogito introduces a lightweight layout tower that distills page structure into learnable global layout prior tokens, and a deterministic Visual-Semantic Chain (VSC)-a concise structured representation less ambiguous than free-form natural-language CoT-to supervise fine-grained intermediate reasoning aligned with evidence regions. Training follows a progressive recipe, including layout perception pretraining, VSC-guided cold start, rejection sampling, and GRPO. To further strengthen the internal coupling between layout priors and VSC execution, we augment standard rewards with a fine-grained region-confidence signal that encourages reasoning traces to stay aligned with corresponding evidence regions. Extensive experiments on six benchmarks (DocVQA, WTQ, ChartQA, TextVQA, OCRBench, and InfoVQA) demonstrate strong generalization, achieving state-of-the-art results on four benchmarks.
[CV-170] RayD3D: Distilling Depth Knowledge Along the Ray for Robust Multi-View 3D Object Detection
【速读】：该论文旨在解决多视角3D目标检测中鸟瞰图（BEV）方法在真实场景下鲁棒性不足的问题，核心瓶颈在于模型难以准确预测深度值。现有主流方案——跨模态蒸馏（cross-modal distillation）虽能将LiDAR的深度信息迁移至视觉模型，但同时会引入与深度无关的信息（如LiDAR点云密度），干扰模型学习。其解决方案的关键在于提出RayD3D，基于成像原理设计沿“射线”（ray）传递深度知识：即物体在图像中的可能位置仅沿从相机出发指向真实空间位置的射线变化，最终由深度预测决定。通过两个基于射线的蒸馏模块实现高效信息传递——射线对比蒸馏（RCD）利用射线上采样引入对比学习以增强对物体精确定位能力的学习；射线加权蒸馏（RWD）则动态调整蒸馏权重以抑制LiDAR中非深度相关干扰信息的影响，从而显著提升BEV模型在多种数据扰动下的鲁棒性，且不增加推理开销。
链接: https://arxiv.org/abs/2603.07493
作者: Rui Ding,Zhaonian Kuang,Zongwei Zhou,Meng Yang,Xinhu Zheng,Gang Hua
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multi-view 3D detection with bird’s eye view (BEV) is crucial for autonomous driving and robotics, but its robustness in real-world is limited as it struggles to predict accurate depth values. A mainstream solution, cross-modal distillation, transfers depth information from LiDAR to camera models but also unintentionally transfers depth-irrelevant information (e.g. LiDAR density). To mitigate this issue, we propose RayD3D, which transfers crucial depth knowledge along the ray: a line projecting from the camera to true location of an object. It is based on the fundamental imaging principle that predicted location of this object can only vary along this ray, which is finally determined by predicted depth value. Therefore, distilling along the ray enables more effective depth information transfer. More specifically, we design two ray-based distillation modules. Ray-based Contrastive Distillation (RCD) incorporates contrastive learning into distillation by sampling along the ray to learn how LiDAR accurately locates objects. Ray-based Weighted Distillation (RWD) adaptively adjusts distillation weight based on the ray to minimize the interference of depth-irrelevant information in LiDAR. For validation, we widely apply RayD3D into three representative types of BEV-based models, including BEVDet, BEVDepth4D, and BEVFormer. Our method is trained on clean NuScenes, and tested on both clean NuScenes and RoboBEV with a variety types of data corruptions. Our method significantly improves the robustness of all the three base models in all scenarios without increasing inference costs, and achieves the best when compared to recently released multi-view and distillation models.
[CV-171] RobustSCI: Beyond Reconstruction to Restoration for Snapshot Compressive Imaging under Real-World Degradations
【速读】：该论文旨在解决视频快照压缩感知（video Snapshot Compressive Imaging, SCI）在实际应用中因运动模糊和低光导致的测量信号严重退化问题，现有深度学习方法主要针对干净测量进行重建，难以应对真实场景中的复杂退化。解决方案的关键在于提出“恢复”（restoration）新范式——从退化测量中还原原始未受损场景，并构建首个大规模退化基准数据集（基于DAVIS 2017模拟连续退化）。同时设计了RobustSCI网络，其核心创新是引入RobustCFormer模块，包含多尺度去模糊分支与频域增强分支，实现退化因素的显式解耦与去除；进一步提出RobustSCI-C（Cascade），集成预训练轻量级后处理去模糊网络，在极小计算开销下显著提升恢复性能。
链接: https://arxiv.org/abs/2603.07489
作者: Hao Wang,Yuanfan Li,Qi Zhou,Zhankuo Xu,Jiong Ni,Xin Yuan
机构: Westlake University (西湖大学); Xi’an Jiaotong University (西安交通大学); Dalian University of Technology (大连理工大学); Westlake Institute for Optoelectronics (西湖大学光电研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Deep learning algorithms for video Snapshot Compressive Imaging (SCI) have achieved great success, yet they predominantly focus on reconstructing from clean measurements. This overlooks a critical real-world challenge: the captured signal itself is often severely degraded by motion blur and low light. Consequently, existing models falter in practical applications. To break this limitation, we pioneer the first study on robust video SCI restoration, shifting the goal from “reconstruction” to “restoration”–recovering the underlying pristine scene from a degraded measurement. To facilitate this new task, we first construct a large-scale benchmark by simulating realistic, continuous degradations on the DAVIS 2017 dataset. Second, we propose RobustSCI, a network that enhances a strong encoder-decoder backbone with a novel RobustCFormer block. This block introduces two parallel branches–a multi-scale deblur branch and a frequency enhancement branch–to explicitly disentangle and remove degradations during the recovery process. Furthermore, we introduce RobustSCI-C (RobustSCI-Cascade), which integrates a pre-trained Lightweight Post-processing Deblurring Network to significantly boost restoration performance with minimal overhead. Extensive experiments demonstrate that our methods outperform all SOTA models on the new degraded testbeds, with additional validation on real-world degraded SCI data confirming their practical effectiveness, elevating SCI from merely reconstructing what is captured to restoring what truly happened.
[CV-172] Multi-Modal Decouple and Recouple Network for Robust 3D Object Detection
【速读】：该论文旨在解决多模态3D目标检测在真实场景中因传感器配置错误（如LiDAR）或环境条件变化（如相机成像质量下降）导致的数据退化问题，此类退化常使现有模型性能显著下降。其核心挑战在于传统方法在特征融合阶段对多模态BEV（鸟瞰图）特征进行紧耦合处理，一旦某一模态或两者同时受损，将严重影响整体系统鲁棒性。解决方案的关键在于提出一种“解耦-再耦合”网络结构：首先显式地将Camera和LiDAR的BEV特征分解为模态不变特征（modality-invariant features）与模态特有特征（modality-specific features），利用前者在不同模态间具有较高稳定性、可互相补偿的特性来缓解单一模态失效的影响；随后构建三个专家模块分别应对LiDAR退化、相机退化及两者共退化的情形，每个专家以模态不变特征作为稳健信息源，结合特定模态特征增强适应性；最终通过自适应融合机制整合三路专家输出，获得对多种数据退化均具备鲁棒性的统一特征表示，从而实现更稳定的3D目标检测性能。
链接: https://arxiv.org/abs/2603.07486
作者: Rui Ding,Zhaonian Kuang,Yuzhe Ji,Meng Yang,Xinhu Zheng,Gang Hua
机构: Xi’an Jiaotong University (西安交通大学); The Hong Kong University of Science and Technology (Guangzhou) (香港科技大学（广州）); Dolby Laboratories (杜比实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multi-modal 3D object detection with bird’s eye view (BEV) has achieved desired advances on benchmarks. Nonetheless, the accuracy may drop significantly in the real world due to data corruption such as sensor configurations for LiDAR and scene conditions for camera. One design bottleneck of previous models resides in the tightly coupling of multi-modal BEV features during fusion, which may degrade the overall system performance if one modality or both is corrupted. To mitigate, we propose a Multi-Modal Decouple and Recouple Network for robust 3D object detection under data corruption. Different modalities commonly share some high-level invariant features. We observe that these invariant features across modalities do not always fail simultaneously, because different types of data corruption affect each modality in distinct this http URL invariant features can be recovered across modalities for robust fusion under data this http URL this end, we explicitly decouple Camera/LiDAR BEV features into modality-invariant and modality-specific parts. It allows invariant features to compensate each other while mitigates the negative impact of a corrupted modality on the this http URL then recouple these features into three experts to handle different types of data corruption, respectively, i.e., LiDAR, camera, and this http URL each expert, we use modality-invariant features as robust information, while modality-specific features serve as a this http URL, we adaptively fuse the three experts to exact robust features for 3D object detection. For validation, we collect a benchmark with a large quantity of data corruption for LiDAR, camera, and both based on nuScenes. Our model is trained on clean nuScenes and tested on all types of data corruption. Our model consistently achieves the best accuracy on both corrupted and clean data compared to recent models.
[CV-173] EVLF: Early Vision-Language Fusion for Generative Dataset Distillation CVPR2026
【速读】：该论文旨在解决扩散模型驱动的数据集蒸馏（Dataset Distillation, DD）方法中因晚期交叉注意力机制引入语义引导而导致的视觉特征失真问题。具体而言，现有方法通常在生成过程后期通过文本提示控制图像生成，虽能保证类别标签的相关性，但会削弱视觉潜在表示的作用，导致合成样本过度依赖提示模式而非真实视觉结构。解决方案的关键在于提出一种早期视觉-语言融合（Early Vision-Language Fusion, EVLF）机制，其在编码器与生成骨干网络之间的过渡阶段对文本和视觉嵌入进行对齐，通过引入轻量级交叉注意力模块，使早期表征同时捕捉局部纹理与全局语义方向，从而提升合成数据的语义忠实性和视觉一致性。该方法具有即插即用特性，适用于多种扩散模型架构和采样策略，无需任务特定调整即可显著提升下游分类准确率。
链接: https://arxiv.org/abs/2603.07476
作者: Wenqi Cai,Yawen Zou,Guang Li,Chunzhi Gu,Chao Zhang
机构: University of Toyama (Toyama大学); Hokkaido University (北海道大学); University of Fukui (福井大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR2026 (main conference)
Abstract:Dataset distillation (DD) aims to synthesize compact training sets that enable models to achieve high accuracy with significantly fewer samples. Recent diffusion-based DD methods commonly introduce semantic guidance through late-stage cross-attention, where textual prompts tend to dominate the generative process. Although this strategy enforces label relevance, it diminishes the contribution of visual latents, resulting in over-corrected samples that mirror prompt patterns rather than reflecting intrinsic visual features. To solve this problem, we introduce an Early Vision-Language Fusion (EVLF) method that aligns textual and visual embeddings at the transition between the encoder and the generative backbone. By incorporating a lightweight cross-attention module at this transition, the early representations simultaneously encode local textures and global semantic directions across the denoising process. Importantly, EVLF is plug-and-play and can be easily integrated into any diffusion-based dataset distillation pipeline with an encoder. It works across different denoiser architectures and sampling schedules without any task-specific modifications. Extensive experiments demonstrate that EVLF generates semantically faithful and visually coherent synthetic data, yielding consistent improvements in downstream classification accuracy across varied settings. Source code is available at this https URL.
[CV-174] FedEU: Evidential Uncertainty-Driven Federated Fine-Tuning of Vision Foundation Models for Remote Sensing Image Segmentation
【速读】：该论文旨在解决联邦遥感图像分割（Federated Remote Sensing Image Segmentation, FedRSIS）中因客户端数据异构性导致的本地模型更新不确定性增加、协作优化可靠性下降的问题。现有方法缺乏对每个本地模型预测不确定性的量化，难以有效应对分布偏移和不可靠更新带来的挑战。解决方案的关键在于提出FedEU框架，其核心创新包括：(1) 引入个性化证据不确定性建模（personalized evidential uncertainty modeling），用于量化本地模型的认知不确定性（epistemic uncertainty），识别局部数据分布下的高风险区域；(2) 设计客户端特定特征嵌入（Client-specific Feature Embedding, CFE），通过个性化的注意力机制与元素感知参数更新策略，在保留客户端特性的前提下增强通道感知特征表示；(3) 基于上传的不确定性估计，采用Top-k不确定性引导加权（Top-k Uncertainty-guided Weighting, TUW）策略实现自适应全局聚合，从而显著降低预测不确定性并提升联邦模型在多样化客户端上的鲁棒性和可靠性。
链接: https://arxiv.org/abs/2603.07468
作者: Xiaokang Zhang,Xuran Xiong,Jianzhong Huang,Lefei Zhang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 8 figures
Abstract:Remote sensing image segmentation (RSIS) in federated environments has gained increasing attention because it enables collaborative model training across distributed datasets without sharing raw imagery or annotations. Federated RSIS combined with parameter-efficient fine-tuning (PEFT) can unleash the generalization power of pretrained foundation models for real-world applications, with minimal parameter aggregation and communication overhead. However, the dynamic adaptation of pretrained models to heterogeneous client data inevitably increases update uncertainty and compromises the reliability of collaborative optimization due to the lack of uncertainty estimation for each local model. To bridge this gap, we present FedEU, a federated optimization framework for fine-tuning RSIS models driven by evidential uncertainty. Specifically, personalized evidential uncertainty modeling is introduced to quantify epistemic variations of local models and identify high-risk areas under local data distributions. Furthermore, the client-specific feature embedding (CFE) is exploited to enhance channel-aware feature representation while preserving client-specific properties through personalized attention and an element-aware parameter update approach. These uncertainty estimates are uploaded to the server to enable adaptive global aggregation via a Top-k uncertainty-guided weighting (TUW) strategy, which mitigates the impact of distribution shifts and unreliable updates. Extensive experiments on three large-scale heterogeneous datasets demonstrate the superior performance of FedEU. More importantly, FedEU enables balanced model adaptation across diverse clients by explicitly reducing prediction uncertainty, resulting in more robust and reliable federated outcomes. The source codes will be available at this https URL.
[CV-175] Classifying Novel 3D-Printed Objects without Retraining: Towards Post-Production Automation in Additive Manufacturing
【速读】：该论文旨在解决工业增材制造（Additive Manufacturing）后处理流程中3D打印物体分类依赖人工检测的问题，其核心挑战在于待分类对象集合每日可能变化，导致频繁模型重训练不切实际。解决方案的关键在于提出一个名为ThingiPrint的新公开数据集，该数据集将CAD模型与对应的3D打印实物照片配对，并基于此数据集系统评估多种视觉模型在3D打印物体分类任务上的性能；进一步通过对比学习微调（contrastive fine-tuning）结合旋转不变性目标函数，实现仅依赖CAD模型即可对未见过的3D打印物体进行原型驱动（prototype-based）分类，从而避免引入新对象时的重新训练，显著提升模型泛化能力和实际应用价值。
链接: https://arxiv.org/abs/2603.07465
作者: Fanis Mathioulakis,Gorjan Radevski,Silke GC Cleuren,Michel Janssens,Brecht Das,Koen Schauwaert,Tinne Tuytelaars
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reliable classification of 3D-printed objects is essential for automating post-production workflows in industrial additive manufacturing. Despite extensive automation in other stages of the printing pipeline, this task still relies heavily on manual inspection, as the set of objects to be classified can change daily, making frequent model retraining impractical. Automating the identification step is therefore critical for improving operational efficiency. A vision model that could classify any set of objects by utilizing their corresponding CAD models and avoiding retraining would be highly beneficial in this setting. To enable systematic evaluation of vision models on this task, we introduce ThingiPrint, a new publicly available dataset that pairs CAD models with real photographs of their 3D-printed counterparts. Using ThingiPrint, we benchmark a range of existing vision models on the task of 3D-printed object classification. We additionally show that contrastive fine-tuning with a rotation-invariant objective allows effective prototype-based classification of previously unseen 3D-printed objects. By relying solely on the available CAD models, this avoids the need for retraining when new objects are introduced. Experiments show that this approach outperforms standard pretrained baselines, suggesting improved generalization and practical relevance for real-world use.
[CV-176] Selective Transfer Learning of Cross-Modality Distillation for Monocular 3D Object Detection
【速读】：该论文旨在解决单目3D目标检测中因模态差距（modality gap）导致的跨模态知识蒸馏（cross-modality knowledge distillation）中的负迁移（negative transfer）问题，尤其关注网络结构不一致和特征过拟合两大挑战。解决方案的关键在于提出一种名为MonoSTL的选择性学习方法，其核心是通过两个创新模块实现深度感知的选择性蒸馏：一是深度感知的选择性特征蒸馏（Depth-Aware Selective Feature Distillation, DASFD），利用深度不确定性筛选正向特征；二是深度感知的选择性关系蒸馏（Depth-Aware Selective Relation Distillation, DASRD），在关系层面引入深度不确定性以抑制负迁移。同时，通过采用相似网络架构确保图像与LiDAR特征的空间对齐，从而有效促进深度信息从LiDAR到图像网络的正向迁移，显著提升检测精度。
链接: https://arxiv.org/abs/2603.07464
作者: Rui Ding,Meng Yang,Nanning Zheng
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Monocular 3D object detection is a promising yet ill-posed task for autonomous vehicles due to the lack of accurate depth information. Cross-modality knowledge distillation could effectively transfer depth information from LiDAR to image-based network. However, modality gap between image and LiDAR seriously limits its accuracy. In this paper, we systematically investigate the negative transfer problem induced by modality gap in cross-modality distillation for the first time, including not only the architecture inconsistency issue but more importantly the feature overfitting issue. We propose a selective learning approach named MonoSTL to overcome these issues, which encourages positive transfer of depth information from LiDAR while alleviates the negative transfer on image-based network. On the one hand, we utilize similar architectures to ensure spatial alignment of features between image-based and LiDAR-based networks. On the other hand, we develop two novel distillation modules, namely Depth-Aware Selective Feature Distillation (DASFD) and Depth-Aware Selective Relation Distillation (DASRD), which selectively learn positive features and relationships of objects by integrating depth uncertainty into feature and relation distillations, respectively. Our approach can be seamlessly integrated into various CNN-based and DETR-based models, where we take three recent models on KITTI and a recent model on NuScenes for validation. Extensive experiments show that our approach considerably improves the accuracy of the base models and thereby achieves the best accuracy compared with all recently released SOTA models.
[CV-177] SIGMAE: A Spectral-Index-Guided Foundation Model for Multispectral Remote Sensing
【速读】：该论文旨在解决在多光谱遥感图像中应用基于掩码自编码器（Masked Autoencoder, MAE）进行预训练时所面临的挑战，包括复杂背景干扰、目标边界模糊以及掩码过程中缺乏语义引导等问题，这些问题限制了模型对底层结构和有意义的空间-光谱特征的学习能力。解决方案的关键在于提出一种名为“光谱指数引导的MAE”（Spectral Index-Guided MAE, SIGMAE）的方法，其核心创新是引入领域特定的光谱指数作为先验知识，指导动态令牌掩码策略——具体为设计了一种课程学习风格的语义显著性引导动态令牌掩码（Semantic Saliency-Guided Dynamic Token Masking, SSDTM），该策略通过量化每个图像块的语义丰富度与内部异质性，自适应地选择最具信息量的令牌进行掩码，从而优先关注语义显著区域并逐步提升样本难度，有效增强空间-光谱表征学习能力，减少冗余计算，并提升小样本条件下的复杂目标识别性能。
链接: https://arxiv.org/abs/2603.07463
作者: Xiaokang Zhang,Bo Li,Chufeng Zhou,Weikang Yu,Lefei Zhang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17pages,10figures
Abstract:Pretraining and fine-tuning have emerged as a new paradigm in remote sensing image interpretation. Among them, Masked Autoencoder (MAE)-based pretraining stands out for its strong capability to learn general feature representations via reconstructing masked image regions. However, applying MAE to multispectral remote sensing images remains challenging due to complex backgrounds, indistinct targets, and the lack of semantic guidance during masking, which hinders the learning of underlying structures and meaningful spatial-spectral features. To address this, we propose a simple yet effective approach, Spectral Index-Guided MAE (SIGMAE), for multispectral image pretraining. The core idea is to incorporate domain-specific spectral indices as prior knowledge to guide dynamic token masking toward informative regions. SIGMAE introduces Semantic Saliency-Guided Dynamic Token Masking (SSDTM), a curriculum-style strategy that quantifies each patch’s semantic richness and internal heterogeneity to adaptively select the most informative tokens during training. By prioritizing semantically salient regions and progressively increasing sample difficulty, SSDTM enhances spectrally rich and structurally aware representation learning, mitigates overfitting, and reduces redundant computation compared with random masking. Extensive experiments on five widely used datasets covering various downstream tasks, including scene classification, semantic segmentation, object extraction and change detection, demonstrate that SIGMAE outperforms other pretrained geospatial foundation models. Moreover, it exhibits strong spatial-spectral reconstruction capability, even with a 90% mask ratio, and improves complex target recognition under limited labeled data. The source codes and model weights will be released at this https URL.
[CV-178] SLNet: A Super-Lightweight Geometry-Adaptive Network for 3D Point Cloud Recognition ICRA2026
【速读】：该论文旨在解决当前3D点云识别模型中普遍存在的计算效率与性能难以平衡的问题，尤其是针对基于注意力机制（attention）、图神经网络（graph）和深层多层感知机（deep MLP）的复杂模型所导致的高计算成本。其解决方案的关键在于提出SLNet轻量级骨干网络，核心创新包括：1）非参数自适应点嵌入（NAPE），通过高斯径向基函数（Gaussian RBF）与余弦基函数的组合，并引入输入自适应带宽和混合策略来捕捉点云的空间结构；2）几何调制单元（GMU），一种每通道仿射调制器，仅引入二维可学习参数以增强特征表达能力。结合分层编码器结构、FPS+kNN分组策略、非参数归一化及共享残差MLP，SLNet在保持极低参数量（如SLNet-S仅0.14M参数）的同时，在ModelNet40、ScanObjectNN和S3DIS等任务上显著优于或接近主流模型，实现准确率与效率的协同优化。
链接: https://arxiv.org/abs/2603.07454
作者: Mohammad Saeid,Amir Salarpour,Pedram MohajerAnsari,Mert D. Pesé
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)
备注: Accepted to the 2026 IEEE International Conference on Robotics and Automation (ICRA 2026)
Abstract:We present SLNet, a lightweight backbone for 3D point cloud recognition designed to achieve strong performance without the computational cost of many recent attention, graph, and deep MLP based models. The model is built on two simple ideas: NAPE (Nonparametric Adaptive Point Embedding), which captures spatial structure using a combination of Gaussian RBF and cosine bases with input adaptive bandwidth and blending, and GMU (Geometric Modulation Unit), a per channel affine modulator that adds only 2D learnable parameters. These components are used within a four stage hierarchical encoder with FPS+kNN grouping, nonparametric normalization, and shared residual MLPs. In experiments, SLNet shows that a very small model can still remain highly competitive across several 3D recognition tasks. On ModelNet40, SLNet-S with 0.14M parameters and 0.31 GFLOPs achieves 93.64% overall accuracy, outperforming PointMLP-elite with 5x fewer parameters, while SLNet-M with 0.55M parameters and 1.22 GFLOPs reaches 93.92%, exceeding PointMLP with 24x fewer parameters. On ScanObjectNN, SLNet-M achieves 84.25% overall accuracy within 1.2 percentage points of PointMLP while using 28x fewer parameters. For large scale scene segmentation, SLNet-T extends the backbone with local Point Transformer attention and reaches 58.2% mIoU on S3DIS Area 5 with only 2.5M parameters, more than 17x fewer than Point Transformer V3. We also introduce NetScore+, which extends NetScore by incorporating latency and peak memory so that efficiency can be evaluated in a more deployment oriented way. Across multiple benchmarks and hardware settings, SLNet delivers a strong overall balance between accuracy and efficiency. Code is available at: this https URL.
[CV-179] Med-Evo: Test-time Self-evolution for Medical Multimodal Large Language Models
【速读】：该论文旨在解决当前医学多模态大语言模型（Medical Multimodal Large Language Models, MLLMs）在后训练阶段对大量标注数据的高度依赖问题，尤其是在医疗领域中因数据敏感性和标注复杂性导致高质量标注数据获取困难的现实挑战。此外，如何有效利用未标注测试数据进行模型自我进化，并生成稳定可靠的监督信号，是现有方法尚未充分解决的关键难题。为此，作者提出了一种名为Med-Evo的首个面向医学MLLMs的自进化框架，其核心创新在于：1）特征驱动伪标签生成（Feature-driven Pseudo Labeling, FPL），通过识别异构候选回答中的语义中心点来选择每轮推理中的伪标签；2）硬-软奖励机制（Hard-Soft Reward, HSR），结合精确匹配与词元级评估及语义相似度，提供分层奖励信号，从而实现无需额外标注数据即可提升模型性能的标签自由强化学习策略。
链接: https://arxiv.org/abs/2603.07443
作者: Dunyuan Xu,Xikai Yang,Juzheng Miao,Yaoqian Li,Jinpeng Li,Pheng-Ann Heng
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Medical Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across diverse healthcare tasks. However, current post-training strategies, such as supervised fine-tuning and reinforcement learning, heavily depend on substantial annotated data while overlooking the potential of unlabeled test data for model enhancement. This limitation becomes particularly pronounced in medical domains, where acquiring extensive labeled medical data is difficult due to the strict data sensitivity and annotation complexity. Moreover, leveraging test data poses challenges in generating reliable supervision signals from unlabeled samples and maintaining stable self-evolution. To address these limitations, we propose Med-Evo, the first self-evolution framework for medical MLLMs that utilizes label-free reinforcement learning to promote model performance without requiring additional labeled data. Our framework introduces two key innovations: 1) Feature-driven Pseudo Labeling (FPL) that identifies semantic centroids from all heterogeneous candidate responses to select pseudo labels in each rollout, and 2) Hard-Soft Reward (HSR) that combines exact match with token-level assessment and semantic similarity to provide hierarchical reward. Experiments on three medical VQA benchmarks and two base MLLMs show clear advantages of our approach over SOTA methods, with significant improvements of 10.43% accuracy and 4.68% recall on the SLAKE dataset using Qwen2.5-VL, showing the effectiveness of our method.
[CV-180] DogWeave: High-Fidelity 3D Canine Reconstruction from a Single Image via Normal Fusion and Conditional Inpainting
【速读】：该论文旨在解决单目RGB图像到3D犬类模型重建中存在的几何失真与纹理不一致问题，其核心挑战源于犬类复杂的关节运动、自遮挡以及毛发等细粒度细节的缺失，且2D数据集中后视图信息稀缺导致未观测区域难以重建。解决方案的关键在于提出DogWeave框架：首先通过多视角法向场优化（multi-view normal field optimization）将粗略参数化网格精炼为基于隐式表示（SDF）的高保真几何结构；随后利用扩散增强法向量引导的条件局部修补（conditional partial inpainting），结合结构与风格线索生成视图一致的纹理，从而实现对未观测区域的逼真重建。该方法仅需约7,000张狗图像即可训练，并在形状准确性和纹理真实感方面优于当前最先进的单图像三维重建方法。
链接: https://arxiv.org/abs/2603.07441
作者: Shufan Sun,Chenchen Wang,Zongfu Yu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Monocular 3D animal reconstruction is challenging due to complex articulation, self-occlusion, and fine-scale details such as fur. Existing methods often produce distorted geometry and inconsistent textures due to the lack of articulated 3D supervision and limited availability of back-view images in 2D datasets, which makes reconstructing unobserved regions particularly difficult. To address these limitations, we propose DogWeave, a model-based framework for reconstructing high-fidelity 3D canine models from a single RGB image. DogWeave improves geometry by refining a coarsely-initiated parametric mesh into a detailed SDF representation through multi-view normal field optimization using diffusion-enhanced normals. It then generates view-consistent textures through conditional partial inpainting guided by structure and style cues, enabling realistic reconstruction of unobserved regions. Using only about 7,000 dog images processed via our 2D pipeline for training, DogWeave produces complete, realistic 3D models and outperforms state-of-the-art single image to 3d reconstruction methods in both shape accuracy and texture realism for canines.
[CV-181] RPG-SAM: Reliability-Weighted Prototypes and Geometric Adaptive Threshold Selection for Training-Free One-Shot Polyp Segmentation MICCAI2026
【速读】：该论文旨在解决训练-free one-shot分割方法中因忽略支持图像（support images）的区域异质性（regional heterogeneity）和查询响应（query response）的响应异质性（response heterogeneity）而导致的性能瓶颈问题。现有方法通常对支持图像中的所有像素和查询图像的响应强度进行同质化处理，未能有效区分不同区域的信息可靠性与形态一致性，从而影响分割精度。解决方案的关键在于提出RPG-SAM框架，其核心创新包括：1）引入可靠性加权原型挖掘（Reliability-Weighted Prototype Mining, RWPM），通过优先选择高保真支持特征并利用背景锚点作为对比参考来抑制噪声；2）设计几何自适应选择（Geometric Adaptive Selection, GAS），动态调整二值化阈值以评估候选区域的形态学一致性；3）构建迭代精炼机制，逐步优化解剖边界。该方法系统性地建模多层级信息异质性，在Kvasir数据集上实现了5.56%的mIoU提升。
链接: https://arxiv.org/abs/2603.07436
作者: Weikun Lin,Yunhao Bai,Yan Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Under review at MICCAI 2026. 8 pages, 3 figures
Abstract:Training-free one-shot segmentation offers a scalable alternative to expert annotations where knowledge is often transferred from support images and foundation models. But existing methods often treat all pixels in support images and query response intensities models in a homogeneous way. They ignore the regional heterogeity in support images and response heterogeity in this http URL resolve this, we propose RPG-SAM, a framework that systematically tackles these heterogeneity gaps. Specifically, to address regional heterogeneity, we introduce Reliability-Weighted Prototype Mining (RWPM) to prioritize high-fidelity support features while utilizing background anchors as contrastive references for noise suppression. To address response heterogeneity, we develop Geometric Adaptive Selection (GAS) to dynamically recalibrate binarization thresholds by evaluating the morphological consensus of candidates. Finally, an iterative refinement loop method is designed to polishes anatomical boundaries. By accounting for multi-layered information heterogeneity, RPG-SAM achieves a 5.56% mIoU improvement on the Kvasir dataset. Code will be released.
[CV-182] Data Agent : Learning to Select Data via End-to-End Dynamic Optimization
【速读】：该论文旨在解决动态数据选择（Dynamic Data Selection）在训练过程中因依赖任务特定的手工设计指标或静态/快照式标准而导致的可扩展性不足问题，从而难以捕捉数据在整个训练过程中的演化效用。解决方案的关键在于提出了一种端到端的动态数据选择框架——Data Agent，其将数据选择建模为一个训练感知的序列决策问题，通过学习与模型优化共同演化的样本级选择策略来实现高效训练加速。该策略由融合损失驱动难度与置信度驱动不确定性的复合奖励信号引导，并引入无需调参的自适应加权机制以平衡不同目标，从而在多个数据集和架构上均实现显著的训练成本降低（如ImageNet-1k和MMLU上减少超过50%）且保持性能无损，展现出良好的泛化能力和实际应用潜力。
链接: https://arxiv.org/abs/2603.07433
作者: Suorong Yang,Fangjian Su,Hai Gan,Ziqi Ye,Jie Li,Baile Xu,Furao Shen,Soujanya Poria
机构: 未知
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Dynamic Data selection aims to accelerate training by prioritizing informative samples during online training. However, existing methods typically rely on task-specific handcrafted metrics or static/snapshot-based criteria to estimate sample importance, limiting scalability across learning paradigms and making it difficult to capture the evolving utility of data throughout training. To address this challenge, we propose Data Agent, an end-to-end dynamic data selection framework that formulates data selection as a training-aware sequential decision-making problem. The agent learns a sample-wise selection policy that co-evolves with model optimization, guided by a composite reward that integrates loss-based difficulty and confidence-based uncertainty signals. The reward signals capture complementary objectives of optimization impact and information gain, together with a tuning-free adaptive weighting mechanism that balances these signals over training. Extensive experiments across a wide range of datasets and architectures demonstrate that Data Agent consistently accelerates training while preserving or improving performance, e.g., reducing costs by over 50% on ImageNet-1k and MMLU with lossless performance. Moreover, its dataset-agnostic formulation and modular reward make it plug-and-play across tasks and scenarios, e.g., robustness to noisy datasets, highlighting its potential in real-world scenarios.
[CV-183] Disentangled Textual Priors for Diffusion-based Image Super-Resolution CVPR2026
【速读】：该论文旨在解决扩散模型在图像超分辨率（Image Super-Resolution, SR）任务中因语义先验（semantic priors）结构不合理而导致的可控性差、可解释性弱的问题。现有方法通常使用混杂的或粗粒度的先验，将全局布局与局部细节、结构信息与纹理特征耦合在一起，限制了生成过程的语义控制能力。解决方案的关键在于提出DTPSR框架，通过引入两个互补维度的解耦文本先验：空间层次（全局 vs. 局部）和频率语义（低频 vs. 高频），实现对场景级结构和对象级细节的分离建模，并借助专门设计的交叉注意力模块注入这些先验，形成从全局到局部、从低频到高频的渐进式生成流程。此外，研究构建了大规模的DisText-SR数据集以支持该范式，并采用多分支无分类器引导策略结合频率感知的负向提示，有效抑制幻觉和语义漂移，从而提升生成质量与一致性。
链接: https://arxiv.org/abs/2603.07430
作者: Lei Jiang,Xin Liu,Xinze Tong,Zhiliang Li,Jie Liu,Jie Tang,Gangshan Wu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR 2026
Abstract:Image Super-Resolution (SR) aims to reconstruct high-resolution images from degraded low-resolution inputs. While diffusion-based SR methods offer powerful generative capabilities, their performance heavily depends on how semantic priors are structured and integrated into the generation process. Existing approaches often rely on entangled or coarse-grained priors that mix global layout with local details, or conflate structural and textural cues, thereby limiting semantic controllability and interpretability. In this work, we propose DTPSR, a novel diffusion-based SR framework that introduces disentangled textual priors along two complementary dimensions: spatial hierarchy (global vs. local) and frequency semantics (low- vs. high-frequency). By explicitly separating these priors, DTPSR enables the model to simultaneously capture scene-level structure and object-specific details with frequency-aware semantic guidance. The corresponding embeddings are injected via specialized cross-attention modules, forming a progressive generation pipeline that reflects the semantic granularity of visual content, from global layout to fine-grained textures. To support this paradigm, we construct DisText-SR, a large-scale dataset containing approximately 95,000 image-text pairs with carefully disentangled global, low-frequency, and high-frequency descriptions. To further enhance controllability and consistency, we adopt a multi-branch classifier-free guidance strategy with frequency-aware negative prompts to suppress hallucinations and semantic drift. Extensive experiments on synthetic and real-world benchmarks show that DTPSR achieves high perceptual quality, competitive fidelity, and strong generalization across diverse degradation scenarios.
[CV-184] QdaVPR: A novel query-based domain-agnostic model for visual place recognition
【速读】：该论文旨在解决视觉场景识别（Visual Place Recognition, VPR）中因域变化（domain variation）导致的性能下降问题，尤其是现有方法在面对未见域偏移时泛化能力不足的问题。解决方案的关键在于提出一种基于查询的域无关VPR模型QdaVPR，其核心创新包括：1）设计双层对抗学习框架，强制查询特征（query features）和图像特征均具备域不变性；2）引入基于查询组合的三元组监督机制，提升全局描述符的判别能力；3）利用风格迁移技术增强大规模VPR数据集，生成带标签的合成域作为辅助监督信号。实验表明，该方法在多个存在显著域变化的基准测试中均达到最优性能。
链接: https://arxiv.org/abs/2603.07414
作者: Shanshan Wan,Lai Kang,Yingmei Wei,Tianrui Shen,Haixuan Wang,Chao Zuo
机构: National University of Defense Technology (国防科技大学); College of Systems Engineering (系统工程学院); Laboratory for Big Data and Decision (大数据与决策实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual place recognition (VPR) aiming at predicting the location of an image based solely on its visual features is a fundamental task in robotics and autonomous systems. Domain variation remains one of the main challenges in VPR and is relatively unexplored. Existing VPR models attempt to achieve domain agnosticism either by training on large-scale datasets that inherently contain some domain variations, or by being specifically adapted to particular target domains. In practice, the former lacks explicit domain supervision, while the latter generalizes poorly to unseen domain shifts. This paper proposes a novel query-based domain-agnostic VPR model called QdaVPR. First, a dual-level adversarial learning framework is designed to encourage domain invariance for both the query features forming the global descriptor and the image features from which these query features are derived. Then, a triplet supervision based on query combinations is designed to enhance the discriminative power of the global descriptors. To support the learning process, we augment a large-scale VPR dataset using style transfer methods, generating various synthetic domains with corresponding domain labels as auxiliary supervision. Extensive experiments show that QdaVPR achieves state-of-the-art performance on multiple VPR benchmarks with significant domain variations. Specifically, it attains the best Recall@1 and Recall@10 on nearly all test scenarios: 93.5%/98.6% on Nordland (seasonal changes), 97.5%/99.0% on Tokyo24/7 (day-night transitions), and the highest Recall@1 across almost all weather conditions on the SVOX dataset. Our code will be released at this https URL.
[CV-185] UnSCAR: Universal Scalable Controllable and Adaptable Image Restoration
【速读】：该论文旨在解决通用图像恢复（Universal Image Restoration）在面对多种真实世界退化类型时的可扩展性问题，即现有统一恢复网络在处理多退化场景下训练不稳定、模型规模膨胀且对已见与未见退化域性能下降的问题。其关键解决方案是提出一种基于多分支专家混合（multi-branch mixture-of-experts）架构的统一推理流程，通过将恢复知识分解到任务自适应的专用专家中，有效缓解不同退化类型之间的干扰和灾难性任务遗忘（catastrophic task forgetting），从而实现对超过十六种退化的可扩展学习、对未见退化域的鲁棒泛化以及用户可控的退化特定恢复能力。
链接: https://arxiv.org/abs/2603.07406
作者: Debabrata Mandal,Soumitri Chattopadhyay,Yujie Wang,Marc Niethammer,Praneeth Chakravarthula
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Universal image restoration aims to recover clean images from arbitrary real-world degradations using a single inference model. Despite significant progress, existing all-in-one restoration networks do not scale to multiple degradations. As the number of degradations increases, training becomes unstable, models grow excessively large, and performance drops across both seen and unseen domains. In this work, we show that scaling universal restoration is fundamentally limited by interference across degradations during joint learning, leading to catastrophic task forgetting. To address this challenge, we introduce a unified inference pipeline with a multi-branch mixture-of-experts architecture that decomposes restoration knowledge across specialized task-adaptable experts. Our approach enables scalable learning (over sixteen degradations), adapts and generalizes robustly to unseen domains, and supports user-controllable restoration across degradations. Beyond achieving superior performance across benchmarks, this work establishes a new design paradigm for scalable and controllable universal image restoration.
[CV-186] Prompt-Based Caption Generation for Single-Tooth Dental Images Using Vision-Language Models
【速读】：该论文旨在解决当前数字牙科领域中缺乏具备牙齿整体知识且能基于此知识执行多任务分析的视觉-语言模型（Vision-Language Models, VLMs）的问题，尤其是现有带标注的牙科图像数据集存在局限性：如图像视角单一（仅前牙视图）、标注内容局限于特定疾病（如牙龈炎），且未对单颗牙齿进行独立描述，难以支持精准的牙齿疾病评分与临床应用。解决方案的关键在于利用引导式提示（guided prompts）提升VLMs生成牙科图像描述的质量，使其更准确地锚定于图像的视觉特征，并通过RGB图像构建适用于消费场景的单颗牙齿图像及其标注数据集，从而为后续牙科视觉-语言模型训练提供高质量、结构化、细粒度的数据基础。
链接: https://arxiv.org/abs/2603.07403
作者: Anastasiia Sukhanova,Aiden Taylor,Julian Myers,Zichun Wang,Kartha Veerya Jammuladinne,Satya Sri Rajiteswari Nimmagadda,Aniruddha Maiti,Ananya Jana
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to IEEE International Conference on Semantic Computing (IEEE ICSC 2026)
Abstract:Digital dentistry has made significant advances with the advent of deep learning. However, the majority of these deep learning-based dental image analysis models focus on very specific tasks such as tooth segmentation, tooth detection, cavity detection, and gingivitis classification. There is a lack of a specialized model that has holistic knowledge of teeth and can perform dental image analysis tasks based on that knowledge. Datasets of dental images with captions can help build such a model. To the best of our knowledge, existing dental image datasets with captions are few in number and limited in scope. In many of these datasets, the captions describe the entire mouth, while the images are limited to the anterior view. As a result, posterior teeth such as molars are not clearly visible, limiting the usefulness of the captions for training vision-language models. Additionally, the captions focus only on a specific disease (gingivitis) and do not provide a holistic assessment of each tooth. Moreover, tooth disease scores are typically assigned to individual teeth, and each tooth is treated as a separate entity in orthodontic procedures. Therefore, it is important to have captions for single-tooth images. As far as we know, no such dataset of single-tooth images with dental captions exists. In this work, we aim to bridge that gap by assessing the possibility of generating captions for dental images using Vision-Language Models (VLMs) and evaluating the extent and quality of those captions. Our findings suggest that guided prompts help VLMs generate meaningful captions. We show that the prompts generated by our framework are better anchored in describing the visual aspects of dental images. We selected RGB images as they have greater potential in consumer scenarios.
[CV-187] VIVECaption: A Split Approach to Caption Quality Improvement
【速读】：该论文旨在解决生成式 AI（Generative AI）训练中图像/视频-文本描述（caption）质量不足的问题，特别是由于视觉语言模型（VLMs）在生成 caption 时存在幻觉、组合推理能力弱及细粒度理解有限等缺陷，导致图像与文本对齐不佳，进而影响下游模型性能。解决方案的关键在于提出 VIVECaption 方法，其核心是“两面驱动”策略：一方面通过分层抽样构建高质量、结构化且可解析的黄金标准数据集；另一方面采用上下文对齐与参数级微调（SFT）相结合的模型对齐策略，显著提升 caption 与图像的整体一致性。实验证明，在图像字幕管道中引入微调后的字符检测模型能有效改善图文对齐质量，为工业级 AI 开发提供无需依赖网络爬取内容的高质量“纯素”（vegan）训练数据解决方案。
链接: https://arxiv.org/abs/2603.07401
作者: Varun Ananth,Baqiao Liu,Haoran Cai
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Caption quality has emerged as a critical bottleneck in training high-quality text-to-image (T2I) and text-to-video (T2V) generative models. While visual language models (VLMs) are commonly deployed to generate captions from visual data, they suffer from hallucinations, poor compositional reasoning, and limited fine-grained understanding, resulting in misaligned image-caption pairs that degrade downstream model performance. This technical report introduces VIVECaption, a systematic two-sided approach to caption quality improvement. We first establish a comprehensive taxonomy of caption evaluation metrics, distinguishing between “universal” and “instance-grounded” metrics, with the ultimate goal of showcasing the use-cases and tradeoffs between different caption quality metrics. We then use this language to describe our two-sided approach to caption quality improvement: (1) a gold-standard dataset creation methodology using stratified sampling and (2) a model alignment strategy encompassing context alignment and parameter-level finetuning using SFT. We demonstrate our methodology on open-source models, focusing on structured caption formats that enable better parsing and downstream utilization. We ultimately show that using a finetuned character detection model in an image captioning pipeline significantly improves holistic image-caption alignment quality. Our work addresses the growing need for high-quality “vegan” training data in enterprise AI development, providing practical solutions for teams seeking to improve caption-image alignment without relying on potentially copyright-protected web-scraped content.
[CV-188] Interpretable Aneurysm Classification via 3D Concept Bottleneck Models: Integrating Morphological and Hemodynamic Clinical Features
【速读】：该论文旨在解决深度学习模型在颅内动脉瘤（intracranial aneurysm）分类与评估中面临的可解释性不足问题，即传统黑箱模型虽具备高预测准确性，但缺乏临床透明度，阻碍其在医疗场景中的应用与监管审批。解决方案的关键在于引入端到端的3D概念瓶颈模型（Concept Bottleneck Model, CBM），通过将高维神经影像特征映射至一组人类可理解的形态学和血流动力学概念，强制模型内部逻辑符合神经外科临床原则。该框架采用预训练的3D ResNet-34与DenseNet-121提取CTA图像特征，并通过软瓶颈层实现概念空间的约束优化，结合诊断焦点损失（focal loss）与概念均方误差（concept MSE）的联合损失函数，在保持93.33% ± 4.5%分类准确率的同时，确保了模型的可解释性和泛化稳定性（准确率-泛化差距 < 0.04）。
链接: https://arxiv.org/abs/2603.07399
作者: Toqa Khaled,Ahmad Al-Kabbany
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP)
备注:
Abstract:We are concerned with the challenge of reliably classifying and assessing intracranial aneurysms using deep learning without compromising clinical transparency. While traditional black-box models achieve high predictive accuracy, their lack of inherent interpretability remains a significant barrier to clinical adoption and regulatory approval. Explainability is paramount in medical modeling to ensure that AI-driven diagnoses align with established neurosurgical principles. Unlike traditional eXplainable AI (XAI) methods – such as saliency maps, which often provide post-hoc, non-causal visual correlations – Concept Bottleneck Models (CBMs) offer a robust alternative by constraining the model’s internal logic to human-understandable clinical indices. In this article, we propose an end-to-end 3D Concept Bottleneck framework that maps high-dimensional neuroimaging features to a discrete set of morphological and hemodynamic concepts for aneurysm identification. We implemented this pipeline using a pre-trained 3D ResNet-34 backbone and a 3D DenseNet-121 to extract features from CTA volumes, which were subsequently processed through a soft bottleneck layer representing human-interpretable clinical concepts. The model was optimized using a joint-loss function to balance diagnostic focal loss and concept mean squared error (MSE), validated via stratified five-fold cross-validation. Our results demonstrate a peak task classification accuracy of 93.33% +/- 4.5% for the ResNet-34 architecture and 91.43% +/- 5.8% for the DenseNet-121 model. Furthermore, the implementation of 8-pass Test-Time Augmentation (TTA) yielded a robust mean accuracy of 88.31%, ensuring diagnostic stability during inference. By maintaining an accuracy-generalization gap of less than 0.04, this framework proves that high predictive performance can be achieved without sacrificing interpretability. Subjects: Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP) Cite as: arXiv:2603.07399 [cs.CV] (or arXiv:2603.07399v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.07399 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-189] N-Tree Diffusion for Long-Horizon Wildfire Risk Forecasting
【速读】：该论文旨在解决长时程野火风险预测中，在稀疏事件监督下生成概率性空间场的同时保持多预测时域计算效率的问题。传统扩散模型在多步预测中对每个时间点独立执行去噪过程，导致计算冗余。其解决方案的关键在于提出一种分层扩散模型——N-Tree Diffusion（NT-Diffusion），通过共享早期去噪阶段并在后期分支进行时域特异性精修，实现跨多个预测时域的高效采样与概率建模；同时，将野火发生位置表示为连续的火险图（Fire Risk Maps, FRMs），以提供适合概率建模的平滑空间风险场，从而在提升预测精度的同时显著降低推理成本。
链接: https://arxiv.org/abs/2603.07361
作者: Yucheng Xing,Xin Wang
机构: 未知
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 6 figures
Abstract:Long-horizon wildfire risk forecasting requires generating probabilistic spatial fields under sparse event supervision while maintaining computational efficiency across multiple prediction horizons. Extending diffusion models to multi-step forecasting typically repeats the denoising process independently for each horizon, leading to redundant computation. We introduce N-Tree Diffusion (NT-Diffusion), a hierarchical diffusion model designed for long-horizon wildfire risk forecasting. Fire occurrences are represented as continuous Fire Risk Maps (FRMs), which provide a smoothed spatial risk field suitable for probabilistic modeling. Instead of running separate diffusion trajectories for each predicted timestamp, NT-Diffusion shares early denoising stages and branches at later levels, allowing horizon-specific refinement while reducing redundant sampling. We evaluate the proposed framework on a newly collected real-world wildfire dataset constructed for long-horizon probabilistic prediction. Results indicate that NT-Diffusion achieves consistent accuracy improvements and reduced inference cost compared to baseline forecasting approaches.
[CV-190] AgrI Challenge: A Data-Centric AI Competition for Cross-Team Validation in Agricultural Vision
【速读】：该论文旨在解决农业视觉领域中机器学习模型在真实田间环境下泛化能力不足的问题，其核心挑战在于训练数据与部署环境之间的分布偏移（distribution shift）。传统方法往往依赖于固定数据集进行模型设计，忽视了数据采集实践对模型鲁棒性的影响。为此，作者提出了AgrI Challenge这一以数据为中心的竞赛框架，关键创新在于引入跨团队验证（Cross-Team Validation, CTV）机制，将每支队伍独立采集的数据视为一个独立域，通过Train-on-One-Team-Only（TOTO）和Leave-One-Team-Out（LOTO）两种协议系统评估单源与多源训练下的跨域泛化性能。实验表明，单一来源训练存在显著性能下降（最大达16.20%），而多源协同训练可大幅降低泛化差距至2.82%，证明了高质量、多样化数据采集对于提升农业视觉模型实际应用可靠性的重要性。
链接: https://arxiv.org/abs/2603.07356
作者: Mohammed Brahimi,Karim Laabassi,Mohamed Seghir Hadj Ameur,Aicha Boutorh,Badia Siab-Farsi,Amin Khouani,Omar Farouk Zouak,Seif Eddine Bouziane,Kheira Lakhdari,Abdelkader Nabil Benghanem
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 17 pages, 8 figures, 6 tables. Introduces the AgrI Challenge dataset containing 50,673 field images of six tree species collected by twelve independent teams
Abstract:Machine learning models in agricultural vision often achieve high accuracy on curated datasets but fail to generalize under real field conditions due to distribution shifts between training and deployment environments. Moreover, most machine learning competitions focus primarily on model design while treating datasets as fixed resources, leaving the role of data collection practices in model generalization largely unexplored. We introduce the AgrI Challenge, a data-centric competition framework in which multiple teams independently collect field datasets, producing a heterogeneous multi-source benchmark that reflects realistic variability in acquisition conditions. To systematically evaluate cross-domain generalization across independently collected datasets, we propose Cross-Team Validation (CTV), an evaluation paradigm that treats each team’s dataset as a distinct domain. CTV includes two complementary protocols: Train-on-One-Team-Only (TOTO), which measures single-source generalization, and Leave-One-Team-Out (LOTO), which evaluates collaborative multi-source training. Experiments reveal substantial generalization gaps under single-source training: models achieve near-perfect validation accuracy yet exhibit validation-test gaps of up to 16.20% (DenseNet121) and 11.37% (Swin Transformer) when evaluated on datasets collected by other teams. In contrast, collaborative multi-source training dramatically improves robustness, reducing the gap to 2.82% and 1.78%, respectively. The challenge also produced a publicly available dataset of 50,673 field images of six tree species collected by twelve independent teams, providing a diverse benchmark for studying domain shift and data-centric learning in agricultural vision. Comments: 17 pages, 8 figures, 6 tables. Introduces the AgrI Challenge dataset containing 50,673 field images of six tree species collected by twelve independent teams Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2603.07356 [cs.CV] (or arXiv:2603.07356v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.07356 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Mohammed Brahimi [view email] [v1] Sat, 7 Mar 2026 21:40:34 UTC (642 KB)
人工智能
[AI-0] Evaluating Financial Intelligence in Large Language Models : Benchmarking SuperInvesting AI with LLM Engines
【速读】：该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在金融分析与投资研究场景中缺乏系统性评估的问题，尤其关注其金融推理能力的多维表现。解决方案的关键在于构建一个名为AI Financial Intelligence Benchmark (AFIB) 的多维度评估框架，涵盖事实准确性、分析完整性、数据时效性、模型一致性及失败模式五个核心维度，并基于95+个源自真实股权研究任务的结构化问题对五种主流AI系统进行量化评测。结果表明，具备结构化金融数据访问与分析推理能力相结合的系统（如SuperInvesting）在综合性能上最优，凸显了金融智能的多维特性以及融合数据获取与推理能力的重要性。
链接: https://arxiv.org/abs/2603.08704
作者: Akshay Gulati,Kanha Singhania,Tushar Banga,Parth Arora,Anshul Verma,Vaibhav Kumar Singh,Agyapal Digra,Jayant Singh Bisht,Danish Sharma,Varun Singla,Shubh Garg
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 12 pages, 6 Figures, 5 Tables
Abstract:Large language models are increasingly used for financial analysis and investment research, yet systematic evaluation of their financial reasoning capabilities remains limited. In this work, we introduce the AI Financial Intelligence Benchmark (AFIB), a multi-dimensional evaluation framework designed to assess financial analysis capabilities across five dimensions: factual accuracy, analytical completeness, data recency, model consistency, and failure patterns. We evaluate five AI systems: GPT, Gemini, Perplexity, Claude, and SuperInvesting, using a dataset of 95+ structured financial analysis questions derived from real-world equity research tasks. The results reveal substantial differences in performance across models. Within this benchmark setting, SuperInvesting achieves the highest aggregate performance, with an average factual accuracy score of 8.96/10 and the highest completeness score of 56.65/70, while also demonstrating the lowest hallucination rate among evaluated systems. Retrieval-oriented systems such as Perplexity perform strongly on data recency tasks due to live information access but exhibit weaker analytical synthesis and consistency. Overall, the results highlight that financial intelligence in large language models is inherently multi-dimensional, and systems that combine structured financial data access with analytical reasoning capabilities provide the most reliable performance for complex investment research workflows.
[AI-1] A Multi-Objective Optimization Approach for Sustainable AI-Driven Entrepreneurship in Resilient Economies
【速读】：该论文旨在解决人工智能（Artificial Intelligence, AI）在推动可持续经济发展过程中面临的双重挑战：一方面如何最大化AI对环境和社会的积极影响，另一方面如何最小化其能源消耗与环境成本，并增强经济韧性。针对这一多目标优化问题，作者提出EcoAI-Resilience框架，其核心在于通过数学优化方法协同实现三个关键目标——可持续性影响最大化、经济韧性提升和环境成本最小化。该方案的关键创新在于整合来自53个国家、14个行业（2015–2024年）的多源数据，构建高精度预测模型（R > 0.99），并识别出最优AI部署策略，包括100%可再生能源整合、80%能效提升目标及人均投资202.48美元的临界水平，从而为全球AI可持续发展提供量化决策依据。
链接: https://arxiv.org/abs/2603.08692
作者: Anas ALsobeh,Raneem Alkurdi
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 35 Pages,
Abstract:The rapid advancement of artificial intelligence (AI) technologies presents both unprecedented opportunities and significant challenges for sustainable economic development. While AI offers transformative potential for addressing environmental challenges and enhancing economic resilience, its deployment often involves substantial energy consumption and environmental costs. This research introduces the EcoAI-Resilience framework, a multi-objective optimization approach designed to maximize the sustainability benefits of AI deployment while minimizing environmental costs and enhancing economic resilience. The framework addresses three critical objectives through mathematical optimization: sustainability impact maximization, economic resilience enhancement, and environmental cost minimization. The methodology integrates diverse data sources, including energy consumption metrics, sustainability indicators, economic performance data, and entrepreneurship outcomes across 53 countries and 14 sectors from 2015-2024. Our experimental validation demonstrates exceptional performance with R scores exceeding 0.99 across all model components, significantly outperforming baseline methods, including Linear Regression (R = 0.943), Random Forest (R = 0.957), and Gradient Boosting (R = 0.989). The framework successfully identifies optimal AI deployment strategies featuring 100% renewable energy integration, 80% efficiency improvement targets, and optimal investment levels of 202.48 per capita. Key findings reveal strong correlations between economic complexity and resilience (r = 0.82), renewable energy adoption and sustainability outcomes (r = 0.71), and demonstrate significant temporal improvements in AI readiness (+1.12 points/year) and renewable energy adoption (+0.67 year) globally.
[AI-2] Split Federated Learning Architectures for High-Accuracy and Low-Delay Model Training
【速读】：该论文旨在解决Split Federated Learning (SFL) 中模型划分策略对训练损失（进而影响准确率）、训练延迟和通信开销的潜在优化问题。尽管传统SFL中模型分割方式不影响准确率，但本文指出，Hierarchical SFL (HSFL) 架构中两个分割层的位置及客户端到聚合器的分配方式显著影响性能指标。解决方案的关键在于首次构建了一个联合优化问题，显式建模分割层位置与客户端-聚合器映射对准确率、延迟和通信开销的影响，并提出首个兼顾准确率感知与延迟效率的启发式算法。实验表明，该方法在公共数据集上可提升准确率3%，同时降低延迟20%和通信开销50%。
链接: https://arxiv.org/abs/2603.08687
作者: Yiannis Papageorgiou,Yannis Thomas,Ramin Khalili,Iordanis Koutsopoulos
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Can we find a network architecture for ML model training so as to optimize training loss (and thus, accuracy) in Split Federated Learning (SFL)? And can this architecture also reduce training delay and communication overhead? While accuracy is not influenced by how we split the model in ordinary, state-of-the-art SFL, in this work we answer the questions above in the affirmative. Recent Hierarchical SFL (HSFL) architectures adopt a three-tier training structure consisting of clients, (local) aggregators, and a central server. In this architecture, the model is partitioned at two partitioning layers into three sub-models, which are executed across the three tiers. Despite their merits, HSFL architectures overlook the impact of the partitioning layers and client-to-aggregator assignments on accuracy, delay, and overhead. This work explicitly captures the impact of the partitioning layers and client-to-aggregator assignments on accuracy, delay and overhead by formulating a joint optimization problem. We prove that the problem is NP-hard and propose the first accuracy-aware heuristic algorithm that explicitly accounts for model accuracy, while remaining delay-efficient. Simulation results on public datasets show that our approach can improve accuracy by 3%, while reducing delay by 20% and overhead by 50%, compared to state-of-the-art SFL and HSFL schemes.
[AI-3] Benchmarking Language Modeling for Lossless Compression of Full-Fidelity Audio INTERSPEECH2026
【速读】：该论文旨在解决基于自回归语言模型（Language Models, LMs）的音频无损压缩方法在高比特深度（16/24-bit）场景下难以实用化的问题，此前的研究仅限于8-bit音频，且未充分评估其在全保真度音频（full-fidelity audio）中的性能表现。解决方案的关键在于提出一种名为Trilobyte的字节级分词策略（byte-level tokenization schema），将传统样本级分词导致的词汇量随比特深度指数增长（O(2^b)）问题转化为常数级别增长（O(1)），从而首次实现可计算的24-bit LM-based无损音频压缩，显著提升了模型在高分辨率音频上的适用性与压缩效率。
链接: https://arxiv.org/abs/2603.08683
作者: Phillip Long,Zachary Novack,Chris Donahue
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
备注: Submitted for review at Interspeech 2026, 7 pages, 5 figures
Abstract:Autoregressive “language” models (LMs) trained on raw waveforms can be repurposed for lossless audio compression, but prior work is limited to 8-bit audio, leaving open whether such approaches work for practical settings (16/24-bit) and can compete with existing codecs. We benchmark LM-based compression on full-fidelity audio across diverse domains (music, speech, bioacoustics), sampling rates (16kHz-48kHz), and bit depths (8, 16, 24-bit). Standard sample-level tokenization becomes intractable at higher bit depths due to vocabulary size (65K for 16-bit; 16.7M for 24-bit). We propose Trilobyte, a byte-level tokenization schema for full resolution audio, improving vocabulary scaling from O(2^b) to O(1) and enabling the first tractable 24-bit LM-based lossless compression. While LMs consistently outperform FLAC and yield state-of-the-art compression at 8-bit and 16-bit, we observe that compression gains become more modest as bit depth increases beyond 8-bit.
[AI-4] A New Lower Bound for the Random Offerer Mechanism in Bilateral Trade using AI-Guided Evolutionary Search
【速读】：该论文旨在解决双边贸易中随机报价者（Random-Offerer, RO）机制在贝叶斯激励相容（Bayesian Incentive Compatible, BIC）和预算平衡（Budget Balanced, BB）约束下，其收益从交易（Gains from Trade, GFT）相对于第一最佳（First-Best, FB）效率的最坏情况逼近比问题。此前学界曾猜测该比值上限为2，但已有研究指出其可超过2，且存在约2.02的实例。本文的关键解决方案是采用AlphaEvolve这一AI引导的进化搜索框架，系统性探索价值分布空间以发现更优的最坏情形实例，从而得出新的下界：GFTROGFTFB≥2.0749，显著提升了对RO机制效率损失的认知边界。
链接: https://arxiv.org/abs/2603.08679
作者: Yang Cai,Vineet Gupta,Zun Li,Aranyak Mehta
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Theoretical Economics (econ.TH)
备注:
Abstract:The celebrated Myerson–Satterthwaite theorem shows that in bilateral trade, no mechanism can be simultaneously fully efficient, Bayesian incentive compatible (BIC), and budget balanced (BB). This naturally raises the question of how closely the gains from trade (GFT) achievable by a BIC and BB mechanism can approximate the first-best (fully efficient) benchmark. The optimal BIC and BB mechanism is typically complex and highly distribution-dependent, making it difficult to characterize directly. Consequently, much of the literature analyzes simpler mechanisms such as the Random-Offerer (RO) mechanism and establishes constant-factor guarantees relative to the first-best GFT. An important open question concerns the worst-case performance of the RO mechanism relative to first-best (FB) efficiency. While it was originally hypothesized that the approximation ratio \frac\textGFT_\textFB\textGFT_\textRO is bounded by 2 , recent work provided counterexamples to this conjecture: Cai et al. proved that the ratio can be strictly larger than 2 , and Babaioff et al. exhibited an explicit example with ratio approximately 2.02 . In this work, we employ AlphaEvolve, an AI-guided evolutionary search framework, to explore the space of value distributions. We identify a new worst-case instance that yields an improved lower bound of \frac\textGFT_\textFB\textGFT_\textRO \ge \textbf2.0749 . This establishes a new lower bound on the worst-case performance of the Random-Offerer mechanism, demonstrating a wider efficiency gap than previously known. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Theoretical Economics (econ.TH) Cite as: arXiv:2603.08679 [cs.LG] (or arXiv:2603.08679v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.08679 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-5] CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation
【速读】：该论文旨在解决当前基于Chain-of-Thought (CoT)推理的文本到图像（Text-to-Image, T2I）生成方法在处理复杂空间布局、结构化视觉元素和密集文本内容时，因依赖抽象自然语言规划而导致精度不足的问题。其解决方案的关键在于提出一种代码驱动的推理框架CoCo（Code-as-CoT），将推理过程表示为可执行代码，从而实现显式且可验证的中间规划；具体而言，CoCo首先生成用于指定场景结构布局的可执行代码，并在沙箱环境中执行以生成确定性草图图像，随后通过细粒度图像编辑对草图进行优化，最终输出高质量结果。该方法显著提升了T2I生成的可控性与结构准确性。
链接: https://arxiv.org/abs/2603.08652
作者: Haodong Li,Chunmei Qing,Huanyu Zhang,Dongzhi Jiang,Yihang Zou,Hongbo Peng,Dingming Li,Yuhong Dai,ZePeng Lin,Juanxi Tian,Yi Zhou,Siqi Dai,Jingwei Wu
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 21 pages, 7 figures, 7 tables
Abstract:Recent advancements in Unified Multimodal Models (UMMs) have significantly advanced text-to-image (T2I) generation, particularly through the integration of Chain-of-Thought (CoT) reasoning. However, existing CoT-based T2I methods largely rely on abstract natural-language planning, which lacks the precision required for complex spatial layouts, structured visual elements, and dense textual content. In this work, we propose CoCo (Code-as-CoT), a code-driven reasoning framework that represents the reasoning process as executable code, enabling explicit and verifiable intermediate planning for image generation. Given a text prompt, CoCo first generates executable code that specifies the structural layout of the scene, which is then executed in a sandboxed environment to render a deterministic draft image. The model subsequently refines this draft through fine-grained image editing to produce the final high-fidelity result. To support this training paradigm, we construct CoCo-10K, a curated dataset containing structured draft-final image pairs designed to teach both structured draft construction and corrective visual refinement. Empirical evaluations on StructT2IBench, OneIG-Bench, and LongText-Bench show that CoCo achieves improvements of +68.83%, +54.8%, and +41.23% over direct generation, while also outperforming other generation methods empowered by CoT. These results demonstrate that executable code is an effective and reliable reasoning paradigm for precise, controllable, and structured text-to-image generation. The code is available at: this https URL
[AI-6] PostTrainBench: Can LLM Agents LLM Agents Automate LLM Post-Training?
【速读】：该论文旨在解决如何利用大语言模型（LLM）代理自主完成后训练（post-training）过程以提升基础模型性能的问题，从而探索AI代理是否能够自动化AI研究本身。其解决方案的关键在于引入PostTrainBench基准测试框架，该框架在有限计算资源（单张H100 GPU运行10小时）下评估LLM代理能否自主执行后训练任务——包括从网络获取信息、运行实验和数据整理，而无需预设策略。实验表明，尽管前沿代理（如Claude Code with Opus 4.6）能显著提升基础模型表现（最高达23.2%），但仍普遍落后于专业机构提供的指令微调模型（51.1%），但在特定场景下可超越（如GPT-5.1 Codex Max在BFCL上达89%）。同时，研究识别出奖励劫持等风险行为，凸显了对系统进行严格沙箱隔离的重要性。
链接: https://arxiv.org/abs/2603.08640
作者: Ben Rank,Hardik Bhatnagar,Ameya Prabhu,Shira Eisenberg,Karina Nguyen,Matthias Bethge,Maksym Andriushchenko
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:AI agents have become surprisingly proficient at software engineering over the past year, largely due to improvements in reasoning capabilities. This raises a deeper question: can these systems extend their capabilities to automate AI research itself? In this paper, we explore post-training, the critical phase that turns base LLMs into useful assistants. We introduce PostTrainBench to benchmark how well LLM agents can perform post-training autonomously under bounded compute constraints (10 hours on one H100 GPU). We ask frontier agents (e.g., Claude Code with Opus 4.6) to optimize the performance of a base LLM on a particular benchmark (e.g., Qwen3-4B on AIME). Importantly, we do not provide any predefined strategies to the agents and instead give them full autonomy to find necessary information on the web, run experiments, and curate data. We find that frontier agents make substantial progress but generally lag behind instruction-tuned LLMs from leading providers: 23.2% for the best agent vs. 51.1% for official instruction-tuned models. However, agents can exceed instruction-tuned models in targeted scenarios: GPT-5.1 Codex Max achieves 89% on BFCL with Gemma-3-4B vs. 67% for the official model. We also observe several failure modes worth flagging. Agents sometimes engage in reward hacking: training on the test set, downloading existing instruction-tuned checkpoints instead of training their own, and using API keys they find to generate synthetic data without authorization. These behaviors are concerning and highlight the importance of careful sandboxing as these systems become more capable. Overall, we hope PostTrainBench will be useful for tracking progress in AI RD automation and for studying the risks that come with it. Website and code are available at this https URL.
[AI-7] Dont Look Back in Anger: MAGIC Net for Streaming Continual Learning with Temporal Dependence
【速读】：该论文旨在解决数据流学习中面临的三大挑战：概念漂移（concept drift）、时间依赖性（temporal dependence）以及灾难性遗忘（catastrophic forgetting）。现有方法通常分别处理这些问题，而Streaming Continual Learning（SCL）试图统一建模。论文提出的MAGIC Net是一种新颖的SCL框架，其关键在于将类持续学习（Continual Learning, CL）的架构策略与循环神经网络（Recurrent Neural Networks, RNNs）相结合，以有效建模时间依赖性；同时通过可学习掩码（learnable masks）对冻结权重进行动态调制，实现对历史知识的回溯利用，并在必要时扩展网络结构，从而在线地完成学习、记忆和适应过程，保障推理始终可用。
链接: https://arxiv.org/abs/2603.08600
作者: Federico Giannini,Sandro D’Andrea,Emanuele Della Valle
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Concept drift, temporal dependence, and catastrophic forgetting represent major challenges when learning from data streams. While Streaming Machine Learning and Continual Learning (CL) address these issues separately, recent efforts in Streaming Continual Learning (SCL) aim to unify them. In this work, we introduce MAGIC Net, a novel SCL approach that integrates CL-inspired architectural strategies with recurrent neural networks to tame temporal dependence. MAGIC Net continuously learns, looks back at past knowledge by applying learnable masks over frozen weights, and expands its architecture when necessary. It performs all operations online, ensuring inference availability at all times. Experiments on synthetic and real-world streams show that it improves adaptation to new concepts, limits memory usage, and mitigates forgetting.
[AI-8] owards Batch-to-Streaming Deep Reinforcement Learning for Continuous Control
【速读】：该论文旨在解决当前深度强化学习（Deep Reinforcement Learning, DRL）方法在资源受限硬件上部署时面临的计算复杂性问题，尤其是传统方法依赖经验回放缓冲区（replay buffers）、批量更新和目标网络所带来的高内存与计算开销。为此，作者提出两种新颖的流式深度强化学习算法——Streaming Soft Actor-Critic (S2AC) 和 Streaming Deterministic Actor-Critic (SDAC)，其关键创新在于设计为与当前最先进的批处理强化学习方法兼容，同时实现纯在线更新机制，从而显著降低对存储和计算资源的需求。这使得它们特别适用于设备端微调（on-device fine-tuning）场景，如Sim2Real迁移任务，并且在标准基准测试中表现出与现有流式基线相当的性能，无需繁琐的超参数调优。
链接: https://arxiv.org/abs/2603.08588
作者: Riccardo De Monte,Matteo Cederle,Gian Antonio Susto
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:State-of-the-art deep reinforcement learning (RL) methods have achieved remarkable performance in continuous control tasks, yet their computational complexity is often incompatible with the constraints of resource-limited hardware, due to their reliance on replay buffers, batch updates, and target networks. The emerging paradigm of streaming deep RL addresses this limitation through purely online updates, achieving strong empirical performance on standard benchmarks. In this work, we propose two novel streaming deep RL algorithms, Streaming Soft Actor-Critic (S2AC) and Streaming Deterministic Actor-Critic (SDAC), explicitly designed to be compatible with state-of-the-art batch RL methods, making them particularly suitable for on-device finetuning applications such as Sim2Real transfer. Both algorithms achieve performance comparable to state-of-the-art streaming baselines on standard benchmarks without requiring tedious hyperparameter tuning. Finally, we further investigate the practical challenges of transitioning from batch to streaming learning during finetuning and propose concrete strategies to tackle them.
[AI-9] rust via Reputation of Conviction
【速读】：该论文旨在解决知识（knowledge）、真理（truth）与信任（trust）之间的内在关联问题，尤其是在信息源存在不确定性与误差的背景下如何建立可验证的信任机制。其解决方案的关键在于提出以“信念度”（conviction）为核心的信任基础——即一个信息源立场被独立共识所支持的概率，而非单纯依赖正确性或忠实性。该框架将来源建模为兼具生成与判别功能的实体，并定义声誉为在特定命题领域内对带符号信念度的期望加权值，强调持续验证是声誉积累的理论必要条件和实践机制，从而为AI代理等易出错但具能力的信息源提供了基于可验证信念度的稳健信任基础。
链接: https://arxiv.org/abs/2603.08575
作者: Aravind R. Iyengar
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 19 pages, 4 figures
Abstract:The question of \emphknowledge, \emphtruth and \emphtrust is explored via a mathematical formulation of claims and sources. We define truth as the reproducibly perceived subset of knowledge, formalize sources as having both generative and discriminative roles, and develop a framework for reputation grounded in the \emphconviction – the likelihood that a source’s stance is vindicated by independent consensus. We argue that conviction, rather than correctness or faithfulness, is the principled basis for trust: it is regime-independent, rewards genuine contribution, and demands the transparent and self-sufficient perceptions that make external verification possible. We formalize reputation as the expected weighted signed conviction over a realm of claims, characterize its behavior across source-claim regimes, and identify continuous verification as both a theoretical necessity and a practical mechanism through which reputation accrues. The framework is applied to AI agents, which are identified as capable but error-prone sources for whom verifiable conviction and continuously accrued reputation constitute the only robust foundation for trust.
[AI-10] MetaWorld-X: Hierarchical World Modeling via VLM-Orchestrated Experts for Humanoid Loco-Manipulation
【速读】：该论文旨在解决人形机器人在同时进行运动与操作（loco-manipulation）任务时，难以学习到自然、稳定且具备组合泛化能力的全身控制策略的问题。现有强化学习方法通常依赖单一的端到端策略来习得多种技能，但在高自由度系统中易引发跨技能梯度干扰和运动模式冲突，导致生成行为不自然、稳定性差且难以适应复杂任务组合。解决方案的关键在于提出一种分层世界模型框架 MetaWorld-X：首先通过“分而治之”原则将复杂控制问题分解为一组受人类运动先验约束的专用专家策略（Specialized Expert Policies, SEP），利用模仿约束的强化学习引入生物力学一致的归纳偏置以保证运动的自然性和物理合理性；其次构建由视觉-语言模型（Vision-Language Model, VLM）监督的智能路由机制（Intelligent Routing Mechanism, IRM），实现基于语义的任务驱动专家组合，从而支持多阶段loco-manipulation任务中的组合泛化与自适应执行。
链接: https://arxiv.org/abs/2603.08572
作者: Yutong Shen,Hangxu Liu,Penghui Liu,Jiashuo Luo,Yongkang Zhang,Rex Morvley,Chen Jiang,Jianwei Zhang,Lei Zhang
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 8 figures, this https URL
Abstract:Learning natural, stable, and compositionally generalizable whole-body control policies for humanoid robots performing simultaneous locomotion and manipulation (loco-manipulation) remains a fundamental challenge in robotics. Existing reinforcement learning approaches typically rely on a single monolithic policy to acquire multiple skills, which often leads to cross-skill gradient interference and motion pattern conflicts in high-degree-of-freedom systems. As a result, generated behaviors frequently exhibit unnatural movements, limited stability, and poor generalization to complex task compositions. To address these limitations, we propose MetaWorld-X, a hierarchical world model framework for humanoid control. Guided by a divide-and-conquer principle, our method decomposes complex control problems into a set of specialized expert policies (Specialized Expert Policies, SEP). Each expert is trained under human motion priors through imitation-constrained reinforcement learning, introducing biomechanically consistent inductive biases that ensure natural and physically plausible motion generation. Building upon this foundation, we further develop an Intelligent Routing Mechanism (IRM) supervised by a Vision-Language Model (VLM), enabling semantic-driven expert composition. The VLM-guided router dynamically integrates expert policies according to high-level task semantics, facilitating compositional generalization and adaptive execution in multi-stage loco-manipulation tasks.
[AI-11] OSS-CRS: Liberating AIxCC Cyber Reasoning Systems for Real-World Open-Source Security
【速读】：该论文旨在解决当前自主网络推理系统（Cyber Reasoning Systems, CRSs）在实际应用中难以落地的问题，即尽管DARPA AI Cyber Challenge（AIxCC）展示了CRS能够实现漏洞发现、验证与修复的自动化能力，但所有参赛团队开发的CRS系统均依赖于竞赛专属云基础设施，导致其无法在真实环境中部署和复用。解决方案的关键在于提出OSS-CRS——一个开源、可本地部署的框架，支持对真实世界开源项目运行和组合多种CRS技术，并引入预算感知的资源管理机制，从而显著提升CRS的实际可用性与可扩展性；作者通过迁移冠军系统Atlantis并在8个OSS-Fuzz项目中发现10个此前未知漏洞（其中3个为高严重性），验证了该框架的有效性。
链接: https://arxiv.org/abs/2603.08566
作者: Andrew Chin,Dongkwan Kim,Yu-Fu Fu,Fabian Fleischer,Youngjoon Kim,HyungSeok Han,Cen Zhang,Brian Junekyu Lee,Hanqing Zhao,Taesoo Kim
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Version 1.0 (March 2026), OSS-CRS: an open-source framework for porting, deploying, and composing AIxCC cyber reasoning systems. Project page: this https URL
Abstract:DARPA’s AI Cyber Challenge (AIxCC) showed that cyber reasoning systems (CRSs) can go beyond vulnerability discovery to autonomously confirm and patch bugs: seven teams built such systems and open-sourced them after the competition. Yet all seven open-sourced CRSs remain largely unusable outside their original teams, each bound to the competition cloud infrastructure that no longer exists. We present OSS-CRS, an open, locally deployable framework for running and combining CRS techniques against real-world open-source projects, with budget-aware resource management. We ported the first-place system (Atlantis) and discovered 10 previously unknown bugs (three of high severity) across 8 OSS-Fuzz projects. OSS-CRS is publicly available.
[AI-12] RetroAgent : From Solving to Evolving via Retrospective Dual Intrinsic Feedback
【速读】：该论文旨在解决基于强化学习（Reinforcement Learning, RL）训练的大语言模型（Large Language Model, LLM）代理在复杂交互任务中面临的两个核心问题：一是标准RL范式倾向于静态求解而非持续适应，导致代理因探索不足而收敛到次优策略；二是所学知识隐含于参数中难以显式获取，限制了经验的有效积累与复用。解决方案的关键在于提出RetroAgent框架，其核心创新为一种事后自我反思机制（hindsight self-reflection mechanism），该机制生成双重内在反馈：(1) 内在数值反馈，用于追踪相对于先前尝试的子任务完成度增量，奖励有潜力的探索行为；(2) 内在语言反馈，将可复用的经验提炼为记忆缓冲区中的知识条目，并通过提出的相似性-效用感知上置信界（Similarity Utility-Aware Upper Confidence Bound, SimUtil-UCB）策略，在相关性、效用与探索之间取得平衡，从而高效利用历史经验。
链接: https://arxiv.org/abs/2603.08561
作者: Xiaoying Zhang,Zichen Liu,Yipeng Zhang,Xia Hu,Wenqi Shao
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 45 pages
Abstract:Large language model (LLM)-based agents trained with reinforcement learning (RL) have shown strong potential on complex interactive tasks. However, standard RL paradigms favor static problem-solving over continuous adaptation: agents often converge to suboptimal strategies due to insufficient exploration, while learned knowledge remains implicit within parameters rather than explicitly retrievable, limiting effective experiential learning. To address these limitations, we introduce RetroAgent, an online RL framework that empowers agents to master complex interactive environments not just by solving, but by evolving. Concretely, RetroAgent features a hindsight self-reflection mechanism that produces dual intrinsic feedback: (1) intrinsic numerical feedback that that tracks incremental subtask completion relative to prior attempts, rewarding promising explorations, and (2) intrinsic language feedback that distills reusable lessons into a memory buffer, retrieved via our proposed Similarity Utility-Aware Upper Confidence Bound (SimUtil-UCB) strategy balancing relevance, utility, and exploration to effectively leverage past experiences. Extensive experiments on two model families across four challenging agentic tasks demonstrate that RetroAgent significantly outperforms existing methods, achieving state-of-the-art results – e.g., surpassing Group Relative Policy Optimization (GRPO)-trained agents by +18.3% on ALFWorld, +15.4% on WebShop, +27.1% on Sokoban, and +8.9% on MineSweeper – while exhibiting strong test-time adaptation and generalization to out-of-distribution scenarios.
[AI-13] owards Effective and Efficient Graph Alignment without Supervision
【速读】：该论文旨在解决无监督图对齐（unsupervised graph alignment）中现有深度学习方法在模型准确率与效率之间的权衡问题。传统方法通常采用“局部表示、全局对齐”范式，难以有效融合局部与全局图结构信息，导致对齐精度受限。其解决方案的关键在于提出新的“全局表示与对齐”范式，通过引入全局注意力机制和分层跨图最优传输（optimal transport, OT）代价函数，显式建模长程及隐式节点依赖关系，从而提升对齐质量。在此基础上，作者进一步设计了高效变体GlobAlign-E，将OT的立方时间复杂度降低至二次项，显著提升了计算效率，同时保持高精度，实验表明其在准确率上较最优竞争方法提升达20%，且相较现有OT方法提速一个数量级。
链接: https://arxiv.org/abs/2603.08526
作者: Songyang Chen,Youfang Lin,Yu Liu,Shuai Zheng,Lei Zou
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: World Wide Web Journal
Abstract:Unsupervised graph alignment aims to find the node correspondence across different graphs without any anchor node pairs. Despite the recent efforts utilizing deep learning-based techniques, such as the embedding and optimal transport (OT)-based approaches, we observe their limitations in terms of model accuracy-efficiency tradeoff. By focusing on the exploitation of local and global graph information, we formalize them as the local representation, global alignment'' paradigm, and present a new global representation and alignment’’ paradigm to resolve the mismatch between the two phases in the alignment process. We then propose \underlineGlobal representation and \underlineoptimal transport-\underlinebased \underlineAlignment (\textttGlobAlign), and its variant, \textttGlobAlign-E, for better \underlineEfficiency. Our methods are equipped with the global attention mechanism and a hierarchical cross-graph transport cost, able to capture long-range and implicit node dependencies beyond the local graph structure. Furthermore, \textttGlobAlign-E successfully closes the time complexity gap between representative embedding and OT-based methods, reducing OT’s cubic complexity to quadratic terms. Through extensive experiments, our methods demonstrate superior performance, with up to a 20% accuracy improvement over the best competitor. Meanwhile, \textttGlobAlign-E achieves the best efficiency, with an order of magnitude speedup against existing OT-based methods.
[AI-14] Oracle-Guided Soft Shielding for Safe Move Prediction in Chess ICML
【速读】：该论文旨在解决高风险环境中代理在探索过程中易发生安全关键错误的问题，特别是在纯模仿学习（imitation learning）或强化学习（reinforcement learning）方法中，前者样本效率高但对分布偏移敏感且缺乏主动风险规避机制，后者则需要大量训练样本和计算资源才能收敛。解决方案的关键在于提出Oracle-Guided Soft Shielding（OGSS）框架，其核心是通过从“oracle”反馈中学习一个概率性安全模型（即blunder预测模型），结合策略模型预测的走法置信度与战术风险估计，在推理阶段构建一个融合性能与安全性的效用函数，从而在保证竞争力的同时显著降低战术失误率。
链接: https://arxiv.org/abs/2603.08506
作者: Prajit T Rajendran,Fabio Arnez,Huascar Espinoza,Agnes Delaborde,Chokri Mraidha
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted for publication at the 24th International Conference on Machine Learning and Applications (ICMLA), 2025. Preprint version in Arxiv
Abstract:In high stakes environments, agents relying purely on imitation learning or reinforcement learning often struggle to avoid safety-critical errors during exploration. Existing reinforcement learning approaches for environments such as chess require hundreds of thousands of episodes and substantial computational resources to converge. Imitation learning, on the other hand, is more sample efficient but is brittle under distributional shift and lacks mechanisms for proactive risk avoidance. In this work, we propose Oracle-Guided Soft Shielding (OGSS), a simple yet effective framework for safer decision-making, enabling safe exploration by learning a probabilistic safety model from oracle feedback in an imitation learning setting. Focusing on the domain of chess, we train a model to predict strong moves based on past games, and separately learn a blunder prediction model from Stockfish evaluations to estimate the tactical risk of each move. During inference, the agent first generates a set of candidate moves and then uses the blunder model to determine high-risk options, and uses a utility function combining the predicted move likelihood from the policy model and the blunder probability to select actions that strike a balance between performance and safety. This enables the agent to explore and play competitively while significantly reducing the chance of tactical mistakes. Across hundreds of games against a strong chess engine, we compare our approach with other methods in the literature, such as action pruning, SafeDAgger, and uncertainty-based sampling. Our results demonstrate that OGSS variants maintain a lower blunder rate even as the agent’s exploration ratio is increased by several folds, highlighting its ability to support broader exploration without compromising tactical soundness.
[AI-15] Echo2ECG: Enhancing ECG Representations with Cardiac Morphology from Multi-View Echos
【速读】：该论文旨在解决传统心电图（ECG）无法直接测量心脏形态学表型（如左心室射血分数，LVEF）的问题，从而限制了其在早期、可及性健康筛查中的应用。现有自监督方法通过将ECG与单视角超声心动图（Echo）对齐来学习特征表示，但这种策略存在表征不匹配问题，因为单视角Echo仅能捕捉局部、空间受限的心脏解剖结构信息。解决方案的关键在于提出Echo2ECG框架——一种多模态自监督学习方法，它利用多视角Echo中更全面的心脏形态结构信息来增强ECG的表示能力，从而有效提取蕴含心脏形态学特征的ECG特征向量。实验表明，该方法在三个数据集上的结构性心脏表型分类任务和基于ECG查询的Echo研究检索任务中均显著优于当前最优的单模态与多模态基线模型，且特征维度仅为最大基线的1/18，体现出高效性和鲁棒性。
链接: https://arxiv.org/abs/2603.08505
作者: Michelle Espranita Liman,Özgün Turgut,Alexander Müller,Eimo Martens,Daniel Rueckert,Philip Müller
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Electrocardiography (ECG) is a low-cost, widely used modality for diagnosing electrical abnormalities like atrial fibrillation by capturing the heart’s electrical activity. However, it cannot directly measure cardiac morphological phenotypes, such as left ventricular ejection fraction (LVEF), which typically require echocardiography (Echo). Predicting these phenotypes from ECG would enable early, accessible health screening. Existing self-supervised methods suffer from a representational mismatch by aligning ECGs to single-view Echos, which only capture local, spatially restricted anatomical snapshots. To address this, we propose Echo2ECG, a multimodal self-supervised learning framework that enriches ECG representations with the heart’s morphological structure captured in multi-view Echos. We evaluate Echo2ECG as an ECG feature extractor on two clinically relevant tasks that fundamentally require morphological information: (1) classification of structural cardiac phenotypes across three datasets, and (2) retrieval of Echo studies with similar morphological characteristics using ECG queries. Our extracted ECG representations consistently outperform those of state-of-the-art unimodal and multimodal baselines across both tasks, despite being 18x smaller than the largest baseline. These results demonstrate that Echo2ECG is a robust, powerful ECG feature extractor. Our code is accessible at this https URL.
[AI-16] R2F: Repurposing Ray Frontiers for LLM -free Object Navigation
【速读】：该论文旨在解决基于大视觉语言模型（VLM）和大语言模型（LLM）的零样本开放词汇目标导航系统在推理时依赖迭代调用大型模型所带来的延迟与计算开销问题，从而限制了其实时部署能力。其解决方案的关键在于重新利用射线前沿（ray frontiers, R2F）这一基于前景的探索范式，将前沿区域重新解释为显式的、方向条件化的语义假设，并在这些区域稀疏存储沿射线累积的语言对齐特征，每个前沿区域维护多个方向嵌入以编码可能未见的内容；由此，导航任务被转化为基于嵌入的前沿评分与经典建图规划流水线中的目标追踪，彻底避免了迭代的大模型推理过程，实现了无需LLM的高效实时导航。
链接: https://arxiv.org/abs/2603.08475
作者: Francesco Argenziano,John Mark Alexis Marcelo,Michele Brienza,Abdel Hakim Drid,Emanuele Musumeci,Daniele Nardi,Domenico D. Bloisi,Vincenzo Suriani
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Zero-shot open-vocabulary object navigation has progressed rapidly with the emergence of large Vision-Language Models (VLMs) and Large Language Models (LLMs), now widely used as high-level decision-makers instead of end-to-end policies. Although effective, such systems often rely on iterative large-model queries at inference time, introducing latency and computational overhead that limit real-time deployment. To address this problem, we repurpose ray frontiers (R2F), a recently proposed frontier-based exploration paradigm, to develop an LLM-free framework for indoor open-vocabulary object navigation. While ray frontiers were originally used to bias exploration using semantic cues carried along rays, we reinterpret frontier regions as explicit, direction-conditioned semantic hypotheses that serve as navigation goals. Language-aligned features accumulated along out-of-range rays are stored sparsely at frontiers, where each region maintains multiple directional embeddings encoding plausible unseen content. In this way, navigation then reduces to embedding-based frontier scoring and goal tracking within a classical mapping and planning pipeline, eliminating iterative large-model reasoning. We further introduce R2F-VLN, a lightweight extension for free-form language instructions using syntactic parsing and relational verification without additional VLM or LLM components. Experiments in Habitat-sim and on a real robotic platform demonstrate competitive state-of-the-art zero-shot performance with real-time execution, achieving up to 6 times faster runtime than VLM-based alternatives.
[AI-17] he Boiling Frog Threshold: Criticality and Blindness in World Model-Based Anomaly Detection Under Gradual Drift
【速读】：该论文旨在解决强化学习（Reinforcement Learning, RL）代理在持续观测漂移（observation drift）下如何实现自我监控的问题，特别是识别漂移的临界阈值 ε∗ —— 即当漂移率低于此阈值时，系统将漂移视为正常波动；高于该阈值则能快速检测到异常。其关键解决方案在于提出并验证了一个通用的“尖锐检测阈值”现象：无论使用何种检测器家族（z-score、方差、百分位数）或世界模型容量，该阈值均呈现S型分布且具有不变性，其具体位置由噪声底噪结构（noise floor）、检测器灵敏度与环境动力学之间的相互作用决定。此外，研究发现正弦漂移无法被任何检测器识别，揭示了这是世界模型本身的特性而非检测方法缺陷，并指出在脆弱环境中存在“先崩溃后无感知”的不可监测失败模式，从而将 ε∗ 从单一的世界模型属性重构为噪声底噪、检测机制与环境动态三者之间的交互边界，提供了更具实证基础和可解释性的自监控理论框架。
链接: https://arxiv.org/abs/2603.08455
作者: Zhe Hong
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 10 pages, 5 figures, preprint
Abstract:When an RL agent’s observations are gradually corrupted, at what drift rate does it “wake up” – and what determines this boundary? We study world model-based self-monitoring under continuous observation drift across four MuJoCo environments, three detector families (z-score, variance, percentile), and three model capacities. We find that (1) a sharp detection threshold \varepsilon^* exists universally: below it, drift is absorbed as normal variation; above it, detection occurs rapidly. The threshold’s existence and sigmoid shape are invariant across all detector families and model capacities, though its position depends on the interaction between detector sensitivity, noise floor structure, and environment dynamics. (2) Sinusoidal drift is completely undetectable by all detector families – including variance and percentile detectors with no temporal smoothing – establishing this as a world model property rather than a detector artifact. (3) Within each environment, \varepsilon^* follows a power law in detector parameters ( R^2 = 0.89 - 0.97 ), but cross-environment prediction fails ( R^2 = 0.45 ), revealing that the missing variable is environment-specific dynamics structure \partial \mathrmPE/\partial\varepsilon . (4) In fragile environments, agents collapse before any detector can fire (“collapse before awareness”), creating a fundamentally unmonitorable failure mode. Our results reframe \varepsilon^* from an emergent world model property to a three-way interaction between noise floor, detector, and environment dynamics, providing a more defensible and empirically grounded account of self-monitoring boundaries in RL agents.
[AI-18] Efficient Policy Learning with Hybrid Evaluation-Based Genetic Programming for Uncertain Agile Earth Observation Satellite Scheduling
【速读】：该论文旨在解决不确定敏捷地球观测卫星调度问题（Uncertain Agile Earth Observation Satellite Scheduling Problem, UAEOSSP），该问题因利润、资源消耗和可见性等参数的不确定性，导致传统预设调度方案可能次优甚至不可行。现有基于遗传编程超启发式（Genetic Programming Hyper-Heuristic, GPHH）的方法虽能演化可解释的调度策略，但其仿真评估计算成本高，且构造性调度算法（Online Scheduling Algorithm, OSA）的设计会引入依赖于评估方式的局部最优解。解决方案的关键在于提出一种混合评估机制（Hybrid Evaluation, HE）嵌入策略驱动的OSA中，通过精确模式（利用约束验证模块保证准确性）与近似模式（简化逻辑降低计算开销）的动态切换，在实时进化状态信息指导下平衡评估精度与效率。实验表明，HE-GP在保持优异调度性能的同时，平均训练时间较纯精确评估的GP减少17.77%，显著优于手工启发式及单一评估机制的GPHH。
链接: https://arxiv.org/abs/2603.08447
作者: Junhua Xue,Yuning Chen
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 18 pages, 10 figures, 8 tables
Abstract:The Uncertain Agile Earth Observation Satellite Scheduling Problem (UAEOSSP) is a novel combinatorial optimization problem and a practical engineering challenge that aligns with the current demands of space technology development. It incorporates uncertainties in profit, resource consumption, and visibility, which may render pre-planned schedules suboptimal or even infeasible. Genetic Programming Hyper-Heuristic (GPHH) shows promise for evolving interpretable scheduling policies; however, their simulation-based evaluation incurs high computational costs. Moreover, the design of the constructive method, denoted as Online Scheduling Algorithm (OSA), directly affects fitness assessment, resulting in evaluation-dependent local optima within the policy space. To address these issues, this paper proposes a Hybrid Evaluation-based Genetic Programming (HE-GP) for effectively solving UAEOSSP. A Hybrid Evaluation (HE) mechanism is integrated into the policy-driven OSA, combining exact and approximate filtering modes: exact mode ensures evaluation accuracy through elaborately designed constraint verification modules, while approximate mode reduces computational overhead via simplified logic. HE-GP dynamically switches between evaluation models based on real-time evolutionary state information. Experiments on 16 simulated instance sets demonstrate that HE-GP significantly outperforms handcrafted heuristics and single-evaluation based GPHH, achieving substantial reductions in computational cost while maintaining excellent scheduling performance across diverse scenarios. Specifically, the average training time of HE-GP was reduced by 17.77% compared to GP employing exclusively exact evaluation, while the optimal policy generated by HE-GP achieved the highest average ranks across all scenarios.
[AI-19] SYNAPSE: Framework for Neuron Analysis and Perturbation in Sequence Encoding
【速读】：该论文旨在解决生成式 AI（Generative AI）在复杂任务中因缺乏透明性而导致的可靠性问题，尤其是在医疗或网络安全等敏感领域，如何系统评估 Transformer 模型内部行为的鲁棒性与可解释性。现有基于神经元级别的可解释方法多为描述性、任务依赖性强或需重新训练模型，难以作为通用工具跨架构和领域进行评估。为此，论文提出 SYNAPSE 框架，其核心在于无需重新训练即可提取每层 [CLS] 表示，通过轻量级线性探测获得全局及类别特异的神经元排序，并在推理阶段应用前向钩子干预（forward-hook interventions），从而实现对模型内部表示的可控实验。该设计使得不同任务和架构间的稳定性模式、标签敏感性差异得以直接量化比较，揭示了内部表征具有域无关的冗余组织结构，同时暴露了权重或 logit 空间中小规模结构化扰动即可导致预测偏移，凸显了 SYNAPSE 在识别脆弱点与指导模型鲁棒性提升方面的价值。
链接: https://arxiv.org/abs/2603.08424
作者: Jesús Sánchez Ochoa,Enrique Tomás Martínez Beltrán,Alberto Huertas Celdrán
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:In recent years, Artificial Intelligence has become a powerful partner for complex tasks such as data analysis, prediction, and problem-solving, yet its lack of transparency raises concerns about its reliability. In sensitive domains such as healthcare or cybersecurity, ensuring transparency, trustworthiness, and robustness is essential, since the consequences of wrong decisions or successful attacks can be severe. Prior neuron-level interpretability approaches are primarily descriptive, task-dependent, or require retraining, which limits their use as systematic, reusable tools for evaluating internal robustness across architectures and domains. To overcome these limitations, this work proposes SYNAPSE, a systematic, training-free framework for understanding and stress-testing the internal behavior of Transformer models across domains. It extracts per-layer [CLS] representations, trains a lightweight linear probe to obtain global and per-class neuron rankings, and applies forward-hook interventions during inference. This design enables controlled experiments on internal representations without altering the original model, thereby allowing weaknesses, stability patterns, and label-specific sensitivities to be measured and compared directly across tasks and architectures. Across all experiments, SYNAPSE reveals a consistent, domain-independent organization of internal representations, in which task-relevant information is encoded in broad, overlapping neuron subsets. This redundancy provides a strong degree of functional stability, while class-wise asymmetries expose heterogeneous specialization patterns and enable label-aware analysis. In contrast, small structured manipulations in weight or logit space are sufficient to redirect predictions, highlighting complementary vulnerability profiles and illustrating how SYNAPSE can guide the development of more robust Transformer models.
[AI-20] Geometrically Constrained Outlier Synthesis
【速读】：该论文旨在解决深度神经网络在图像分类任务中对分布外（out-of-distribution, OOD）样本表现出过度自信的问题，从而提升模型在推理阶段对OOD样本的鲁棒性。其解决方案的关键在于提出几何约束的异常样本合成方法（Geometrically Constrained Outlier Synthesis, GCOS），该方法在训练过程中通过两个核心步骤生成符合数据流形结构的虚拟异常样本：首先利用训练特征中的主导方差子空间识别几何上合理的离流形方向；其次基于校准集上非一致性得分的经验分位数构建共形启发的壳层，自适应控制合成幅度以生成边界样本，确保生成的异常样本既不会过于容易被检测到，也不会与分布内（in-distribution, ID）数据难以区分。此外，GCOS结合对比正则化目标，在如马氏距离或能量空间等得分空间中促进ID与OOD样本的可分离性，显著提升了OOD检测性能，并可自然扩展至具有统计保证的共形OOD推理框架。
链接: https://arxiv.org/abs/2603.08413
作者: Daniil Karzanov,Marcin Detyniecki
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 18 pages, 6 figures
Abstract:Deep neural networks for image classification often exhibit overconfidence on out-of-distribution (OOD) samples. To address this, we introduce Geometrically Constrained Outlier Synthesis (GCOS), a training-time regularization framework aimed at improving OOD robustness during inference. GCOS addresses a limitation of prior synthesis methods by generating virtual outliers in the hidden feature space that respect the learned manifold structure of in-distribution (ID) data. The synthesis proceeds in two stages: (i) a dominant-variance subspace extracted from the training features identifies geometrically informed, off-manifold directions; (ii) a conformally-inspired shell, defined by the empirical quantiles of a nonconformity score from a calibration set, adaptively controls the synthesis magnitude to produce boundary samples. The shell ensures that generated outliers are neither trivially detectable nor indistinguishable from in-distribution data, facilitating smoother learning of robust features. This is combined with a contrastive regularization objective that promotes separability of ID and OOD samples in a chosen score space, such as Mahalanobis or energy-based. Experiments demonstrate that GCOS outperforms state-of-the-art methods using standard energy-based inference on near-OOD benchmarks, defined as tasks where outliers share the same semantic domain as in-distribution data. As an exploratory extension, the framework naturally transitions to conformal OOD inference, which translates uncertainty scores into statistically valid p-values and enables thresholds with formal error guarantees, providing a pathway toward more predictable and reliable OOD detection.
[AI-21] A Recipe for Stable Offline Multi-agent Reinforcement Learning
【速读】：该论文旨在解决多智能体强化学习（Multi-Agent Reinforcement Learning, MARL）在离线训练（offline training）场景下难以有效采用单智能体离线强化学习范式的问题，其核心挑战在于非线性值分解（non-linear value decomposition）导致的不稳定优化和值尺度放大（value-scale amplification）。为缓解这一问题，作者提出了一种简单而有效的解决方案——尺度不变值归一化（Scale-Invariant Value Normalization, SVN），该方法通过稳定演员-评论家（actor-critic）训练过程，在不改变贝尔曼不动点（Bellman fixed point）的前提下显著提升算法稳定性与性能。关键创新在于SVN无需修改值分解结构或引入额外复杂性，即可实现对值函数估计的稳定控制，从而推动离线MARL向更高效、可靠的实践迈进。
链接: https://arxiv.org/abs/2603.08399
作者: Dongsu Lee,Daehee Lee,Amy Zhang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: Preprint
Abstract:Despite remarkable achievements in single-agent offline reinforcement learning (RL), multi-agent RL (MARL) has struggled to adopt this paradigm, largely persisting with on-policy training and self-play from scratch. One reason for this gap comes from the instability of non-linear value decomposition, leading prior works to avoid complex mixing networks in favor of linear value decomposition (e.g., VDN) with value regularization used in single-agent setups. In this work, we analyze the source of instability in non-linear value decomposition within the offline MARL setting. Our observations confirm that they induce value-scale amplification and unstable optimization. To alleviate this, we propose a simple technique, scale-invariant value normalization (SVN), that stabilizes actor-critic training without altering the Bellman fixed point. Empirically, we examine the interaction among key components of offline MARL (e.g., value decomposition, value learning, and policy extraction) and derive a practical recipe that unlocks its full potential.
[AI-22] A Hierarchical Error-Corrective Graph Framework for Autonomous Agents with LLM -Based Action Generation
【速读】：该论文旨在解决自主代理在基于大语言模型（Large Language Model, LLM）生成动作时，因策略选择不当、错误归因模糊及上下文理解不足而导致的任务执行失败问题。解决方案的关键在于提出一种分层纠错图框架（Hierarchical Error-Corrective Graph Framework, HECG），其核心创新包括：(1) 多维可迁移策略（Multi-Dimensional Transferable Strategy, MDTS），通过融合任务质量（Q）、置信度/成本（C）、奖励（R）与LLM语义推理得分（LLM-Score）实现量化性能与语义情境的多维对齐，提升高质量候选策略的选择精度；(2) 错误矩阵分类（Error Matrix Classification, EMC），将任务失败细分为十类错误类型（如策略错误、脚本解析错误等），并按严重性、典型行为、描述和可恢复性进行结构化分解，从而精准定位失败根源；(3) 因果-上下文图检索（Causal-Context Graph Retrieval, CCGR），构建包含历史状态、动作与事件序列的因果图结构，利用节点间因果依赖关系识别当前任务最相关的子图，超越向量相似度限制，增强动态环境下的上下文感知能力与策略适应效率。
链接: https://arxiv.org/abs/2603.08388
作者: Cong Cao,Jingyao Zhang,Kun Tong
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We propose a Hierarchical Error-Corrective Graph FrameworkforAutonomousAgentswithLLM-BasedActionGeneration(HECG),whichincorporates three core innovations: (1) Multi-Dimensional Transferable Strategy (MDTS): by integrating task quality metrics (Q), confidence/cost metrics ©, reward metrics ®, and LLM-based semantic reasoning scores (LLM-Score), MDTS achieves multi-dimensional alignment between quantitative performance and semantic context, enabling more precise selection of high-quality candidate strate gies and effectively reducing the risk of negative transfer. (2) Error Matrix Classification (EMC): unlike simple confusion matrices or overall performance metrics, EMC provides structured attribution of task failures by categorizing errors into ten types, such as Strategy Errors (Strategy Whe) and Script Parsing Errors (Script-Parsing-Error), and decomposing them according to severity, typical actions, error descriptions, and recoverability. This allows precise analysis of the root causes of task failures, offering clear guidance for subsequent error correction and strategy optimization rather than relying solely on overall success rates or single performance metrics. (3) Causal-Context Graph Retrieval (CCGR): to enhance agent retrieval capabilities in dynamic task environments, we construct graphs from historical states, actions, and event sequences, where nodes store executed actions, next-step actions, execution states, transferable strategies, and other relevant information, and edges represent causal dependencies such as preconditions for transitions between nodes. CCGR identifies subgraphs most relevant to the current task context, effectively capturing structural relationships beyond vector similarity, allowing agents to fully leverage contextual information, accelerate strategy adaptation, and improve execution reliability in complex, multi-step tasks.
[AI-23] M3-ACE: Rectifying Visual Perception in Multimodal Math Reasoning via Multi-Agent ic Context Engineering
【速读】：该论文旨在解决多模态大语言模型在视觉数学推理任务中因视觉感知不准确而导致的性能瓶颈问题，尤其是由视觉证据提取错误或不完整引发的失败，而非模型推理能力不足。其解决方案的关键在于提出M3-ACE（Multi-agentic Context Engineering）框架，通过解耦视觉感知与推理过程，构建以视觉证据列表为中心的动态共享上下文，并引入多个智能体协同协作，互补性地提供观察结果，从而暴露不一致性和恢复缺失的感知信息；同时设计轻量级工具——Summary Tool用于结构化整合不同智能体的证据（一致性、互补性与冲突性），Refine Tool用于过滤不可靠样本并引导迭代修正，显著提升了视觉数学推理的准确性与鲁棒性。
链接: https://arxiv.org/abs/2603.08369
作者: Peijin Xie,Zhen Xu,Bingquan Liu,Baoxun Wang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal large language models have recently shown promising progress in visual mathematical reasoning. However, their performance is often limited by a critical yet underexplored bottleneck: inaccurate visual perception. Through systematic analysis, we find that the most failures originate from incorrect or incomplete visual evidence extraction rather than deficiencies in reasoning capability. Moreover, models tend to remain overly confident in their initial perceptions, making standard strategies such as prompt engineering, multi-round self-reflection, or posterior guidance insufficient to reliably correct errors. To address this limitation, we propose M3-ACE, a multi-agentic context engineering framework designed to rectify visual perception in multimodal math reasoning. Instead of directly aggregating final answers, our approach decouples perception and reasoning by dynamically maintaining a shared context centered on visual evidence lists. Multiple agents collaboratively contribute complementary observations, enabling the system to expose inconsistencies and recover missing perceptual information. To support stable multi-turn collaboration, we further introduce two lightweight tools: a Summary Tool that organizes evidence from different agents into consistent, complementary, and conflicting components, and a Refine Tool that filters unreliable samples and guides iterative correction. Extensive experiments demonstrate that M3-ACE substantially improves visual mathematical reasoning performance across multiple benchmarks. Our method establishes new state-of-the-art results 89.1 on the MathVision benchmark and achieves consistent improvements on other related datasets, including MathVista and MathVerse. These results highlight the importance of perception-centric multi-agent collaboration for advancing multimodal reasoning systems. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.08369 [cs.AI] (or arXiv:2603.08369v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.08369 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-24] owards plausibility in time series counterfactual explanations
【速读】：该论文旨在解决时间序列分类模型中生成合理反事实解释（counterfactual explanations）的问题，现有方法往往在保持时间结构合理性方面存在不足。解决方案的关键在于提出一种直接在输入空间进行梯度优化的方法，并引入软动态时间规整（soft-DTW）与目标类别k近邻（k-nearest neighbors）相结合的机制，以强制生成的反事实样本具备现实的时间结构特征。该方法通过一个多目标损失函数实现有效性（validity）、稀疏性（sparsity）、接近性（proximity）和基于soft-DTW的可塑性（plausibility）之间的平衡，从而显著提升反事实解释的时间序列真实性，优于现有方法在分布对齐上的表现。
链接: https://arxiv.org/abs/2603.08349
作者: Marcin Kostrzewa,Krzysztof Galus,Maciej Zięba
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:We present a new method for generating plausible counterfactual explanations for time series classification problems. The approach performs gradient-based optimization directly in the input space. To enforce plausibility, we integrate soft-DTW (dynamic time warping) alignment with k -nearest neighbors from the target class, which effectively encourages the generated counterfactuals to adopt a realistic temporal structure. The overall optimization objective is a multi-faceted loss function that balances key counterfactual properties. It incorporates losses for validity, sparsity, and proximity, alongside the novel soft-DTW-based plausibility component. We conduct an evaluation of our method against several strong reference approaches, measuring the key properties of the generated counterfactuals across multiple dimensions. The results demonstrate that our method achieves competitive performance in validity while significantly outperforming existing approaches in distributional alignment with the target class, indicating superior temporal realism. Furthermore, a qualitative analysis highlights the critical limitations of existing methods in preserving realistic temporal structure. This work shows that the proposed method consistently generates counterfactual explanations for time series classifiers that are not only valid but also highly plausible and consistent with temporal patterns.
[AI-25] Detecting Fake Reviewer Groups in Dynamic Networks: An Adaptive Graph Learning Method
【速读】：该论文旨在解决在线平台中由组织化群体生成的虚假评论（fake reviews）问题，此类评论在冷启动场景下（即新上市产品数据稀疏时）难以被传统检测方法识别，从而损害消费者信任与公平竞争。其解决方案的关键在于提出一种新型图学习模型——Diversity- and Similarity-aware Dynamic Graph Attention-enhanced Graph Convolutional Network (DS-DGA-GCN)，该模型通过构建产品-评论-用户三元关系网络，联合建模三者间的复杂关联，并结合网络特征评分（Network Feature Scoring, NFS）系统与动态图注意力机制，实现对虚假评论群体的鲁棒且自适应的检测。其中，NFS系统量化邻居多样性与网络自相似性等属性为统一特征分数，而动态图注意力机制则融合时间信息、节点重要性及全局结构特征，提升模型的适应性和计算效率。
链接: https://arxiv.org/abs/2603.08332
作者: Jing Zhang,Ke Huang,Yao Zhang,Bin Guo,Zhiwen Yu
机构: 未知
类目: ocial and Information Networks (cs.SI); Artificial Intelligence (cs.AI)
备注:
Abstract:The proliferation of fake reviews, often produced by organized groups, undermines consumer trust and fair competition on online platforms. These groups employ sophisticated strategies that evade traditional detection methods, particularly in cold-start scenarios involving newly launched products with sparse data. To address this, we propose the \underlineDiversity- and \underlineSimilarity-aware \underlineDynamic \underlineGraph \underlineAttention-enhanced \underlineGraph \underlineConvolutional \underlineNetwork (DS-DGA-GCN), a new graph learning model for detecting fake reviewer groups. DS-DGA-GCN achieves robust detection since it focuses on the joint relationships among products, reviews, and reviewers by modeling product-review-reviewer networks. DS-DGA-GCN also achieves adaptive detection by integrating a Network Feature Scoring (NFS) system and a new dynamic graph attention mechanism. The NFS system quantifies network attributes, including neighbor diversity, network self-similarity, as a unified feature score. The dynamic graph attention mechanism improves the adaptability and computational efficiency by captures features related to temporal information, node importance, and global network structure. Extensive experiments conducted on two real-world datasets derived from Amazon and Xiaohongshu demonstrate that DS-DGA-GCN significantly outperforms state-of-the-art baselines, achieving accuracies of up to \textbf89.8% and 88.3%, respectively.
[AI-26] EndoSERV: A Vision-based Endoluminal Robot Navigation System
【速读】：该论文旨在解决机器人辅助内腔镜手术中因管腔解剖结构复杂、狭窄且迂曲导致的导航困难问题，尤其是现有基于视觉的定位方法在组织变形、体内伪影及缺乏显著特征点的情况下易产生误差的问题。解决方案的关键在于提出了一种名为EndoSERV的新型定位方法，其核心包括两个部分：一是将长距离复杂管腔结构划分为子段并独立估计里程计（SEgment-to-structure），二是通过高效迁移技术将真实图像特征映射至虚拟域以利用虚拟姿态真值进行训练（Real-to-Virtual mapping）。该方法还包含离线预训练提取纹理无关特征和在线适应真实环境的训练阶段，从而在无需任何真实姿态标签的情况下仍能实现高精度定位。
链接: https://arxiv.org/abs/2603.08324
作者: Junyang Wu,Fangfang Xie,Minghui Zhang,Hanxiao Zhang,Jiayuan Sun,Yun Gu,Guang-Zhong Yang
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Robot-assisted endoluminal procedures are increasingly used for early cancer intervention. However, the intricate, narrow and tortuous pathways within the luminal anatomy pose substantial difficulties for robot navigation. Vision-based navigation offers a promising solution, but existing localization approaches are error-prone due to tissue deformation, in vivo artifacts and a lack of distinctive landmarks for consistent localization. This paper presents a novel EndoSERV localization method to address these challenges. It includes two main parts, \textiti.e., \textbfSEgment-to-structure and \textbfReal-to-\textbfVirtual mapping, and hence the name. For long-range and complex luminal structures, we divide them into smaller sub-segments and estimate the odometry independently. To cater for label insufficiency, an efficient transfer technique maps real image features to the virtual domain to use virtual pose ground truth. The training phases of EndoSERV include an offline pretraining to extract texture-agnostic features, and an online phase that adapts to real-world conditions. Extensive experiments based on both public and clinical datasets have been performed to demonstrate the effectiveness of the method even without any real pose labels.
[AI-27] CORE-Acu: Structured Reasoning Traces and Knowledge Graph Safety Verification for Acupuncture Clinical Decision Support
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在针灸临床决策支持（Acupuncture Clinical Decision Support, CDS）中因黑箱特性导致的推理不可追溯性和概率性幻觉问题，这些问题在强调解释性与安全性的针灸领域尤为突出。解决方案的关键在于提出一种神经符号框架CORE-Acu，其核心创新包括：（1）构建首个针灸结构化思维链（Structured Chain-of-Thought, S-CoT）数据集与模式约束微调框架，将中医辨证论治的隐式逻辑显式化为可审计的生成约束；（2）建立基于符号否决机制（Symbolic Veto Mechanism）的“生成—验证—修正”闭环推理系统，利用确定性规则拦截幻觉并强制执行硬性安全边界；（3）引入词典匹配实体重加权损失（Lexicon-Matched Entity-Reweighted Loss, LMERL），通过自适应放大高风险实体梯度贡献，缓解通用优化中术语漂移问题。实验证明，该方法在1000例独立测试案例中实现零安全违规（95%置信区间：0–0.37%），显著优于GPT-4o的8.5%违规率，从而实现了针灸CDS中推理可审计性与严格安全性合规的统一。
链接: https://arxiv.org/abs/2603.08321
作者: Liuyi Xu,Yun Guo,Ming Chen,Zihan Dun,Yining Qian,An-Yang Lu,Shuang Li,Lijun Liu
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 19 pages, 5 figures, 18 tables. Includes the Acu-Reasoning dataset and TCM knowledge graph schema
Abstract:Large language models (LLMs) show significant potential for clinical decision support (CDS), yet their black-box nature – characterized by untraceable reasoning and probabilistic hallucinations – poses severe challenges in acupuncture, a field demanding rigorous interpretability and safety. To address this, we propose CORE-Acu, a neuro-symbolic framework for acupuncture clinical decision support that integrates Structured Chain-of-Thought (S-CoT) with knowledge graph (KG) safety verification. First, we construct the first acupuncture Structured Reasoning Trace dataset and a schema-constrained fine-tuning framework. By enforcing an explicit causal chain from pattern identification to treatment principles, treatment plans, and acupoint selection, we transform implicit Traditional Chinese Medicine (TCM) reasoning into interpretable generation constraints, mitigating the opacity of LLM-based CDS. Furthermore, we construct a TCM safety knowledge graph and establish a ``Generate–Verify–Revise’’ closed-loop inference system based on a Symbolic Veto Mechanism, employing deterministic rules to intercept hallucinations and enforce hard safety boundaries. Finally, we introduce the Lexicon-Matched Entity-Reweighted Loss (LMERL), which corrects terminology drift caused by the frequency–importance mismatch in general optimization by adaptively amplifying gradient contributions of high-risk entities during fine-tuning. Experiments on 1,000 held-out cases demonstrate CORE-Acu’s superior entity fidelity and reasoning quality. Crucially, CORE-Acu achieved 0/1,000 observed safety violations (95% CI: 0–0.37%), whereas GPT-4o exhibited an 8.5% violation rate under identical rules. These results establish CORE-Acu as a robust neuro-symbolic framework for acupuncture clinical decision support, guaranteeing both reasoning auditability and strict safety compliance.
[AI-28] Graph-Instructed Neural Networks for parametric problems with varying boundary conditions
【速读】：该论文旨在解决由参数化偏微分方程（Parametric Partial Differential Equations, PDEs）驱动的物理现象模拟中，因边界条件变化而导致的传统伽辽金投影型降阶方法（Galerkin projection-based reduced order techniques）所面临的根本性瓶颈问题。这类问题中，参数实例不仅改变问题的物理特性，还影响计算域上的边界约束施加方式，导致每次新配置都需要重新构建离散问题，难以满足实时应用需求。解决方案的关键在于提出一种基于图指导神经网络（Graph-Instructed Neural Networks, GINNs）的新方法，该框架能够有效学习从计算域参数描述到对应PDE解的映射关系，从而高效表示高度复杂的参数化PDE，相较于全连接架构展现出更强的鲁棒性和可扩展性。
链接: https://arxiv.org/abs/2603.08304
作者: Francesco Della Santa,Sandra Pieraccini,Maria Strazzullo
机构: 未知
类目: Numerical Analysis (math.NA); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:This work addresses the accurate and efficient simulation of physical phenomena governed by parametric Partial Differential Equations (PDEs) characterized by varying boundary conditions, where parametric instances modify not only the physics of the problem but also the imposition of boundary constraints on the computational domain. In such scenarios, classical Galerkin projection-based reduced order techniques encounter a fundamental bottleneck. Parametric boundaries typically necessitate a re-formulation of the discrete problem for each new configuration, and often, these approaches are unsuitable for real-time applications. To overcome these limitations, we propose a novel methodology based on Graph-Instructed Neural Networks (GINNs). The GINN framework effectively learns the mapping between the parametric description of the computational domain and the corresponding PDE solution. Our results demonstrate that the proposed GINN-based models, can efficiently represent highly complex parametric PDEs, serving as a robust and scalable asset for several applied-oriented settings when compared with fully connected architectures. Subjects: Numerical Analysis (math.NA); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2603.08304 [math.NA] (or arXiv:2603.08304v1 [math.NA] for this version) https://doi.org/10.48550/arXiv.2603.08304 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-29] Deconstructing Multimodal Mathematical Reasoning : Towards a Unified Perception-Alignment-Reasoning Paradigm
【速读】：该论文旨在解决多模态数学推理（Multimodal Mathematical Reasoning, MMR）中模型在真实世界视觉数学任务中的局限性，包括对图表的误读、数学符号与视觉证据之间的对齐失败以及推理步骤的一致性不足等问题。当前评估方法也主要关注最终答案的正确性，而忽视了中间推理步骤的可验证性。论文提出的关键解决方案在于构建统一框架，整合结构化感知（structured perception）、显式对齐（explicit alignment）和可验证推理（verifiable reasoning），从而系统性提升模型在多模态输入下的理解、推理与评估能力。
链接: https://arxiv.org/abs/2603.08291
作者: Tianyu Yang,Sihong Wu,Yilun Zhao,Zhenwen Liang,Lisen Dai,Chen Zhao,Minhao Cheng,Arman Cohan,Xiangliang Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal Mathematical Reasoning (MMR) has recently attracted increasing attention for its capability to solve mathematical problems that involve both textual and visual modalities. However, current models still face significant challenges in real-world visual math tasks. They often misinterpret diagrams, fail to align mathematical symbols with visual evidence, and produce inconsistent reasoning steps. Moreover, existing evaluations mainly focus on checking final answers rather than verifying the correctness or executability of each intermediate step. To address these limitations, a growing body of recent research addresses these issues by integrating structured perception, explicit alignment, and verifiable reasoning within unified frameworks. To establish a clear roadmap for understanding and comparing different MMR approaches, we systematically study them around four fundamental questions: (1) What to extract from multimodal inputs, (2) How to represent and align textual and visual information, (3) How to perform the reasoning, and (4) How to evaluate the correctness of the overall reasoning process. Finally, we discuss open challenges and offer perspectives on promising directions for future research.
[AI-30] Minor First Major Last: A Depth-Induced Implicit Bias of Sharpness-Aware Minimization ICLR2026
【速读】：该论文旨在揭示Sharpness-Aware Minimization (SAM)在训练线性可分二分类任务中的隐式偏差（implicit bias），特别是针对多层线性对角网络的深度依赖特性。其核心问题是：SAM在不同网络深度下是否仍能收敛到与梯度下降（GD）一致的最优解，以及其动态行为是否与传统优化方法存在本质差异。解决方案的关键在于理论分析发现：对于单层模型（L=1），\ell_2-和\ell_\infty-SAM均恢复\ell_2最大间隔分类器，与GD一致；但当深度L=2时，\ell_\infty-SAM的极限方向高度依赖初始值，可能收敛至零向量或任意标准基向量，显著偏离GD的主导坐标方向；而\ell_2-SAM虽最终方向匹配\ell_1最大间隔解，却表现出“顺序特征放大”（sequential feature amplification）现象——即早期依赖次要特征、随训练推进逐步转向主要特征，此现象源于其扰动中引入的\ell_2梯度归一化因子，从而说明仅关注无限时间极限的隐式偏差分析不足以刻画实际训练过程。
链接: https://arxiv.org/abs/2603.08290
作者: Chaewon Moon,Dongkuk Si,Chulhee Yun
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to ICLR 2026, 82 pages, 35 figures
Abstract:We study the implicit bias of Sharpness-Aware Minimization (SAM) when training L -layer linear diagonal networks on linearly separable binary classification. For linear models ( L=1 ), both \ell_\infty - and \ell_2 -SAM recover the \ell_2 max-margin classifier, matching gradient descent (GD). However, for depth L = 2 , the behavior changes drastically – even on a single-example dataset. For \ell_\infty -SAM, the limit direction depends critically on initialization and can converge to \mathbf0 or to any standard basis vector, in stark contrast to GD, whose limit aligns with the basis vector of the dominant data coordinate. For \ell_2 -SAM, we show that although its limit direction matches the \ell_1 max-margin solution as in the case of GD, its finite-time dynamics exhibit a phenomenon we call “sequential feature amplification”, in which the predictor initially relies on minor coordinates and gradually shifts to larger ones as training proceeds or initialization increases. Our theoretical analysis attributes this phenomenon to \ell_2 -SAM’s gradient normalization factor applied in its perturbation, which amplifies minor coordinates early and allows major ones to dominate later, giving a concrete example where infinite-time implicit-bias analyses are insufficient. Synthetic and real-data experiments corroborate our findings.
[AI-31] A Blockchain-based Traceability System for AI-Driven Engine Blade Inspection
【速读】：该论文旨在解决航空发动机叶片（blade）维护过程中因信息碎片化、难以审计及易被篡改而导致的可追溯性不足问题。现有系统缺乏统一、不可变的记录机制，导致多利益相关方（OEM、航空公司、维修机构和监管机构）之间难以协同验证维护历史。解决方案的关键在于提出BladeChain——一个基于Hyperledger Fabric的区块链系统，其核心创新包括：多利益相关方共识机制确保数据可信；链码驱动的状态机自动触发符合飞行小时、循环次数或日历阈值的检测任务，消除人工调度错误；检测结果以IPFS存储并用SHA-256哈希绑定至链上记录，实现缺陷发现过程与AI模型版本的可审计性；同时支持插件式检测模块，允许组织在不修改链上逻辑的前提下升级AI模型。实验证明该方案在100个叶片负载下实现100%生命周期覆盖，吞吐量达26次/分钟，且通过哈希校验可在17ms内完成篡改检测，显著提升了航空航天领域维护流程的安全性和透明度。
链接: https://arxiv.org/abs/2603.08288
作者: Mahmoud Hafez,Eman Ouda,Mohammed A. Mohammed Eltoum,Khaled Salah,Yusra Abdulrahman
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Aircraft engine blade maintenance relies on inspection records shared across manufacturers, airlines, maintenance organizations, and regulators. Yet current systems are fragmented, difficult to audit, and vulnerable to tampering. This paper presents BladeChain, a blockchain-based system providing immutable traceability for blade inspections throughout the component life cycle. BladeChain is the first system to integrate multi-stakeholder endorsement, automated inspection scheduling, AI model provenance, and cryptographic evidence binding, delivering auditable maintenance traceability for aerospace deployments. Built on a four-stakeholder Hyperledger Fabric network (OEM, Airline, MRO, Regulator), BladeChain captures every life-cycle event in a tamper-evident ledger. A chaincode-enforced state machine governs blade status transitions and automatically triggers inspections when configurable flight hour, cycle, or calendar thresholds are exceeded, eliminating manual scheduling errors. Inspection artifacts are stored off-chain in IPFS and linked to on-chain records via SHA-256 hashes, with each inspection record capturing the AI model name and version used for defect detection. This enables regulators to audit both what defects were found and how they were found. The detection module is pluggable, allowing organizations to adopt or upgrade inspection models without modifying the ledger or workflows. We built a prototype and evaluated it on workloads of up to 100 blades, demonstrating 100% life cycle completion with consistent throughput of 26 operations per minute. A centralized SQL baseline quantifies the consensus overhead and highlights the security trade-off. Security validation confirms tamper detection within 17~ms through hash verification.
[AI-32] A-RNN-Medical-Hybrid: A Time-Aware and Interpretable Framework for Mortality Risk Prediction
【速读】：该论文旨在解决重症监护病房（ICU）中死亡风险预测的准确性与可解释性难题，具体挑战包括电子健康记录（EHR）的不规则时间结构、纵向疾病轨迹的复杂性，以及现有数据驱动模型缺乏临床可理解的解释。其解决方案的关键在于提出一种时序感知且知识增强的深度学习框架 TA-RNN-Medical-Hybrid，该框架通过显式的连续时间编码建模不规则时间动态，结合 SNOMED 标准化的疾病表征，并引入分层双级注意力机制，分别捕捉就诊级别的时序重要性和临床概念层面的相关性，从而在提升预测性能（AUC、准确率、F₂ 分数）的同时提供与医学知识对齐的透明解释。
链接: https://arxiv.org/abs/2603.08278
作者: Zahra Jafari,Azadeh Zamanifar,Amirfarhad Farhadi
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Emerging Technologies (cs.ET)
备注:
Abstract:Accurate and interpretable mortality risk prediction in intensive care units (ICUs) remains a critical challenge due to the irregular temporal structure of electronic health records (EHRs), the complexity of longitudinal disease trajectories, and the lack of clinically grounded explanations in many data-driven models. To address these challenges, we propose \textitTA-RNN-Medical-Hybrid, a time-aware and knowledge-enriched deep learning framework that jointly models longitudinal clinical sequences and irregular temporal dynamics through explicit continuous-time encoding, along with standardized medical concept representations. The proposed framework extends time-aware recurrent modeling by integrating explicit continuous-time embeddings that operate independently of visit indexing, SNOMED-based disease representations, and a hierarchical dual-level attention mechanism that captures both visit-level temporal importance and feature/concept-level clinical relevance. This design enables accurate mortality risk estimation while providing transparent and clinically meaningful explanations aligned with established medical knowledge. We evaluate the proposed approach on the MIMIC-III critical care dataset and compare it against strong time-aware and sequential baselines. Experimental results demonstrate that TA-RNN-Medical-Hybrid consistently improves predictive performance in terms of AUC, accuracy, and recall-oriented F _2 -score. Moreover, qualitative analysis shows that the model effectively decomposes mortality risk across time and clinical concepts, yielding interpretable insights into disease severity, chronicity, and temporal progression. Overall, the proposed framework bridges the gap between predictive accuracy and clinical interpretability, offering a scalable and transparent solution for high-stakes ICU decision support systems.
[AI-33] SCL-GNN: Towards Generalizable Graph Neural Networks via Spurious Correlation Learning
【速读】：该论文旨在解决图神经网络（Graph Neural Networks, GNNs）在面对训练数据中节点特征与标签之间的虚假相关性（spurious correlations）时，泛化能力受限的问题，尤其是在独立同分布（IID）和分布外（Out-of-Distribution, OOD）场景下表现不佳。其解决方案的关键在于提出了一种名为Spurious Correlation Learning Graph Neural Network (SCL-GNN) 的新框架，该框架通过引入基于希尔伯特-施密特独立性准则（Hilbert-Schmidt Independence Criterion, HSIC）的可学习机制，量化节点表示与类别得分之间的相关性，从而识别并抑制无关但具有误导性的虚假关联；同时设计了一种高效的双层优化策略，联合优化模块参数与GNN结构，有效防止过拟合，显著提升模型在多种分布偏移下的鲁棒性和泛化性能。
链接: https://arxiv.org/abs/2603.08270
作者: Yuxiang Zhang,Enyan Dai
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Graph Neural Networks (GNNs) have demonstrated remarkable success across diverse tasks. However, their generalization capability is often hindered by spurious correlations between node features and labels in the graph. Our analysis reveals that GNNs tend to exploit imperceptible statistical correlations in training data, even when such correlations are unreliable for prediction. To address this challenge, we propose the Spurious Correlation Learning Graph Neural Network (SCL-GNN), a novel framework designed to enhance generalization on both Independent and Identically Distributed (IID) and Out-of-Distribution (OOD) graphs. SCL-GNN incorporates a principled spurious correlation learning mechanism, leveraging the Hilbert-Schmidt Independence Criterion (HSIC) to quantify correlations between node representations and class scores. This enables the model to identify and mitigate irrelevant but influential spurious correlations effectively. Additionally, we introduce an efficient bi-level optimization strategy to jointly optimize modules and GNN parameters, preventing overfitting. Extensive experiments on real-world and synthetic datasets demonstrate that SCL-GNN consistently outperforms state-of-the-art baselines under various distribution shifts, highlighting its robustness and generalization capabilities.
[AI-34] SAIL: Test-Time Scaling for In-Context Imitation Learning with VLM
【速读】：该论文旨在解决单次轨迹生成在环境变化下仍存在脆弱性的问题，即传统基于上下文模仿学习（in-context imitation learning）的机器人技能获取方法难以在测试阶段适应动态环境变化。其解决方案的关键在于提出SAIL框架，将机器人模仿学习重构为一个可扩展的迭代优化问题：通过蒙特卡洛树搜索（Monte Carlo Tree Search, MCTS）对完整轨迹进行逐步精炼，利用自动归档的成功轨迹库实现情境相关检索、基于视觉语言模型（vision language model）的评分机制评估轨迹质量，并引入步级反馈提供对齐轨迹的评分信号以指导迭代优化。实验表明，随着测试时计算资源的增加，成功率显著提升，最高达95%，验证了轨迹级测试时扩展（trajectory-level test-time scaling）是提升机器人泛化能力的有效路径。
链接: https://arxiv.org/abs/2603.08269
作者: Makoto Sato,Yusuke Iwasawa,Yujin Tang,So Kuroki
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 8 pages, 3 figures
Abstract:In-context imitation learning allows robots to acquire skills from demonstrations, yet one-shot trajectory generation remains fragile under environmental variation. We propose SAIL, a framework that reframes robot imitation as an iterative refinement problem capable of scaling with test-time compute. SAIL utilizes Monte Carlo Tree Search, where each node is a complete trajectory and edges correspond to trajectory refinements. The process is guided by three core components: an automated archive of successful trajectories for contextually relevant retrieval, a vision language model-based scoring mechanism for trajectory evaluation, and a step-level feedback that provides trajectory-aligned scores for iterative refinement. Experiments across six diverse manipulation tasks in simulation and real-world validation clearly demonstrate that increasing test-time compute consistently improves success rates, achieving up to 95% on complex tasks. Our results suggest that trajectory-level test-time scaling is a robust path toward more generalizable robotic agents.
[AI-35] owards a more efficient bias detection in financial language models
【速读】：该论文旨在解决金融语言模型中偏见检测的计算成本过高问题，尤其是在大规模模型持续再训练和发布过程中。现有方法依赖于对大量语料库进行穷举式变异与成对预测分析，虽有效但资源消耗巨大。其解决方案的关键在于通过大规模实证研究发现不同模型在偏见揭示输入上的模式一致性，并提出基于跨模型引导的偏见检测机制——利用一个轻量级模型（如DistilRoBERTa）输出的特征指导偏见输入识别，从而显著减少所需样本量。实验表明，仅需20%的输入对即可识别出高达73%的偏见行为，实现了偏见检测效率的大幅提升。
链接: https://arxiv.org/abs/2603.08267
作者: Firas Hadj Kacem,Ahmed Khanfir,Mike Papadakis
机构: 未知
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
备注:
Abstract:Bias in financial language models constitutes a major obstacle to their adoption in real-world applications. Detecting such bias is challenging, as it requires identifying inputs whose predictions change when varying properties unrelated to the decision, such as demographic attributes. Existing approaches typically rely on exhaustive mutation and pairwise prediction analysis over large corpora, which is effective but computationally expensive-particularly for large language models and can become impractical in continuous retraining and releasing processes. Aiming at reducing this cost, we conduct a large-scale study of bias in five financial language models, examining similarities in their bias tendencies across protected attributes and exploring cross-model-guided bias detection to identify bias-revealing inputs earlier. Our study uses approximately 17k real financial news sentences, mutated to construct over 125k original-mutant pairs. Results show that all models exhibit bias under both atomic (0.58%-6.05%) and intersectional (0.75%-5.97%) settings. Moreover, we observe consistent patterns in bias-revealing inputs across models, enabling substantial reuse and cost reduction in bias detection. For example, up to 73% of FinMA’s biased behaviours can be uncovered using only 20% of the input pairs when guided by properties derived from DistilRoBERTa outputs.
[AI-36] FinToolBench: Evaluating LLM Agents for Real-World Financial Tool Use
【速读】：该论文旨在解决金融领域中生成式 AI（Generative AI）工具学习评估的严重不足问题，即现有基准测试多局限于静态文本分析或文档问答，未能反映金融场景下复杂工具调用的实际需求；而通用工具基准则缺乏金融领域的专业严谨性，常依赖玩具环境或少量金融 API。解决方案的关键在于提出 FinToolBench——首个面向真实可执行金融工具的基准测试平台，其核心创新包括：构建包含 760 个可运行金融工具与 295 个强依赖工具的查询组成的现实生态系统，并设计超越二元执行成功的新型评估框架，从时效性、意图类型和监管领域对齐等金融关键维度量化评估代理性能；同时引入 FATR 基线模型，增强金融任务中的稳定性与合规性，从而为可信金融智能代理提供首个可审计、可复现的测试床。
链接: https://arxiv.org/abs/2603.08262
作者: Jiaxuan Lu,Kong Wang,Yemin Wang,Qingmei Tang,Hongwei Zeng,Xiang Chen,Jiahao Pi,Shujian Deng,Lingzhi Chen,Yi Fu,Kehua Yang,Xiao Sun
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The integration of Large Language Models (LLMs) into the financial domain is driving a paradigm shift from passive information retrieval to dynamic, agentic interaction. While general-purpose tool learning has witnessed a surge in benchmarks, the financial sector, characterized by high stakes, strict compliance, and rapid data volatility, remains critically underserved. Existing financial evaluations predominantly focus on static textual analysis or document-based QA, ignoring the complex reality of tool execution. Conversely, general tool benchmarks lack the domain-specific rigor required for finance, often relying on toy environments or a negligible number of financial APIs. To bridge this gap, we introduce FinToolBench, the first real-world, runnable benchmark dedicated to evaluating financial tool learning agents. Unlike prior works limited to a handful of mock tools, FinToolBench establishes a realistic ecosystem coupling 760 executable financial tools with 295 rigorous, tool-required queries. We propose a novel evaluation framework that goes beyond binary execution success, assessing agents on finance-critical dimensions: timeliness, intent type, and regulatory domain alignment. Furthermore, we present FATR, a finance-aware tool retrieval and reasoning baseline that enhances stability and compliance. By providing the first testbed for auditable, agentic financial execution, FinToolBench sets a new standard for trustworthy AI in finance. The tool manifest, execution environment, and evaluation code will be open-sourced to facilitate future research.
[AI-37] he Struggle Between Continuation and Refusal: A Mechanistic Analysis of the Continuation-Triggered Jailbreak in LLM s
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在安全对齐后仍易受“越狱攻击”（jailbreaking attacks）的问题，特别是针对一种由续写触发器（continuation-triggered）引发的新型越狱现象。其核心问题是：为何简单地移动一个续写触发指令的后缀就能显著提升越狱成功率？解决方案的关键在于通过机制可解释性分析（mechanistic interpretability analysis），聚焦于注意力头（attention heads）层面进行因果干预和激活缩放实验，揭示出该现象的本质源于模型内在的续写驱动力与对齐训练中习得的安全防御机制之间的竞争关系。这一发现为理解LLM越狱行为提供了新的机制视角，并为改进模型安全性提供了理论依据和实践路径。
链接: https://arxiv.org/abs/2603.08234
作者: Yonghong Deng,Zhen Yang,Ping Jian,Xinyue Zhang,Zhongbin Guo,Chengzhi Li
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:With the rapid advancement of large language models (LLMs), the safety of LLMs has become a critical concern. Despite significant efforts in safety alignment, current LLMs remain vulnerable to jailbreaking attacks. However, the root causes of such vulnerabilities are still poorly understood, necessitating a rigorous investigation into jailbreak mechanisms across both academic and industrial communities. In this work, we focus on a continuation-triggered jailbreak phenomenon, whereby simply relocating a continuation-triggered instruction suffix can substantially increase jailbreak success rates. To uncover the intrinsic mechanisms of this phenomenon, we conduct a comprehensive mechanistic interpretability analysis at the level of attention heads. Through causal interventions and activation scaling, we show that this jailbreak behavior primarily arises from an inherent competition between the model’s intrinsic continuation drive and the safety defenses acquired through alignment training. Furthermore, we perform a detailed behavioral analysis of the identified safety-critical attention heads, revealing notable differences in the functions and behaviors of safety heads across different model architectures. These findings provide a novel mechanistic perspective for understanding and interpreting jailbreak behaviors in LLMs, offering both theoretical insights and practical implications for improving model safety.
[AI-38] Disentangling Reasoning in Large Audio-Language Models for Ambiguous Emotion Prediction INTERSPEECH
【速读】：该论文旨在解决语音情感识别（Speech Emotion Recognition, SER）中因人类情感表达固有的模糊性而被现有方法过度简化的难题，即大多数模型仅预测单一情感标签，无法准确反映真实情境下的多模态情感分布。其解决方案的关键在于将模糊情感识别重新建模为一种分布推理问题，并提出首个针对大音频语言模型（Large Audio-Language Models, LALMs）的感知模糊性推理系统。该框架包含两个互补组件：一是与人类感知分布对齐的模糊性感知目标函数，二是结构化的模糊性感知思维链（chain-of-thought）监督机制，用于引导模型在情感线索上的推理过程。实验表明，该方法在IEMOCAP和CREMA-D数据集上，无论采用SFT、DPO还是GRPO训练策略均能实现稳定提升。
链接: https://arxiv.org/abs/2603.08230
作者: Xiaofeng Yu,Jiaheng Dong,Jean Honorio,Abhirup Ghosh,Hong Jia,Ting Dang
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
备注: The paper was submitted to Interspeech for review
Abstract:Speech emotion recognition plays an important role in various applications. However, most existing approaches predict a single emotion label, oversimplifying the inherently ambiguous nature of human emotional expression. Recent large audio-language models show promise in generating richer outputs, but their reasoning ability for ambiguous emotional understanding remains limited. In this work, we reformulate ambiguous emotion recognition as a distributional reasoning problem and present the first systematic study of ambiguity-aware reasoning in LALMs. Our framework comprises two complementary components: an ambiguity-aware objective that aligns predictions with human perceptual distributions, and a structured ambiguity-aware chain-of-thought supervision that guides reasoning over emotional cues. Experiments on IEMOCAP and CREMA-D demonstrate consistent improvements across SFT, DPO, and GRPO training strategies.
[AI-39] SplitAgent : A Privacy-Preserving Distributed Architecture for Enterprise-Cloud Agent Collaboration
【速读】：该论文旨在解决企业采用基于云的AI代理时面临的隐私困境：使用强大的云端模型需共享敏感数据，而本地处理又受限于能力不足。现有代理框架（如MCP和A2A）假设完全数据共享，不适用于包含机密信息的企业环境。其解决方案的核心是提出SplitAgent架构，通过上下文感知的动态数据脱敏机制，在任务语义驱动下自适应调整隐私保护强度——例如合同审查与代码审查所需的脱敏策略不同。该方案进一步结合差分隐私保障、零知识工具验证及隐私预算管理，实验证明其在保持90.1%隐私保护的同时实现83.8%的任务准确率，显著优于静态方法（73.2%准确率，79.7%隐私保护），且任务效用提升24.1%，隐私泄露降低67%。
链接: https://arxiv.org/abs/2603.08221
作者: Jianshu She
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Enterprise adoption of cloud-based AI agents faces a fundamental privacy dilemma: leveraging powerful cloud models requires sharing sensitive data, while local processing limits capability. Current agent frameworks like MCP and A2A assume complete data sharing, making them unsuitable for enterprise environments with confidential information. We present SplitAgent, a novel distributed architecture that enables privacy-preserving collaboration between enterprise-side privacy agents and cloud-side reasoning agents. Our key innovation is context-aware dynamic sanitization that adapts privacy protection based on task semantics – contract review requires different sanitization than code review or financial analysis. SplitAgent extends existing agent protocols with differential privacy guarantees, zero-knowledge tool verification, and privacy budget management. Through comprehensive experiments on enterprise scenarios, we demonstrate that SplitAgent achieves 83.8% task accuracy while maintaining 90.1% privacy protection, significantly outperforming static approaches (73.2% accuracy, 79.7% privacy). Context-aware sanitization improves task utility by 24.1% over static methods while reducing privacy leakage by 67%. Our architecture provides a practical path for enterprise AI adoption without compromising sensitive data.
[AI-40] Revisiting Gradient Staleness: Evaluating Distance Metrics for Asynchronous Federated Learning Aggregation
【速读】：该论文旨在解决异步联邦学习（Asynchronous Federated Learning, AFL）中因客户端更新使用过时的全局模型版本而导致的收敛性下降和准确率降低问题。其关键解决方案是通过引入多种替代的距离度量方法来更精确地衡量梯度的过时程度，并将这些度量集成到聚合过程中，从而提升异步联邦学习在客户端异构性和非独立同分布（non-IID）数据场景下的收敛速度、模型性能与训练稳定性。
链接: https://arxiv.org/abs/2603.08211
作者: Patrick Wilhelm,Odej Kao
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:In asynchronous federated learning (FL), client devices send updates to a central server at varying times based on their computational speed, often using stale versions of the global model. This staleness can degrade the convergence and accuracy of the global model. Previous work, such as AsyncFedED, proposed an adaptive aggregation method using Euclidean distance to measure staleness. In this paper, we extend this approach by exploring alternative distance metrics to more accurately capture the effect of gradient staleness. We integrate these metrics into the aggregation process and evaluate their impact on convergence speed, model performance, and training stability under heterogeneous clients and non-IID data settings. Our results demonstrate that certain metrics lead to more robust and efficient asynchronous FL training, offering a stronger foundation for practical deployment.
[AI-41] Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules
【速读】：该论文旨在解决当前表格式基础模型（如TabPFN和TabICL）在回归任务中评估体系的局限性问题，即现有基准测试主要依赖点估计性能指标（如均方误差或R²），忽略了对概率预测质量的全面评估。其核心解决方案是引入** proper scoring rules（恰当评分规则）**，特别是连续排名概率评分（CRPS），用于衡量分布式回归（distributional regression）中概率预测的准确性，并建议将此类指标纳入主流机器学习基准测试中，以引导模型优化更完整的概率建模能力。此外，作者指出评分规则的选择会影响模型的归纳偏置（inductive bias），因此还主张通过微调或提示可调的表格式基础模型来适配不同评分规则下的任务需求。
链接: https://arxiv.org/abs/2603.08206
作者: Jonas Landsgesell,Pascal Knoll
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Prior-Data Fitted Networks (PFNs), such as TabPFN and TabICL, have revolutionized tabular deep learning by leveraging in-context learning for tabular data. These models are meant as foundation models for classification and regression settings and promise to greatly simplify deployment in practical settings because their performance is unprecedented (in terms of mean squared error or R^2 , when measured on common benchmarks like TabArena or TALENT). However, we see an important weakness of current benchmarks for the regression setting: the current benchmarks focus on evaluating win rates and performance using metrics like (root) mean squared error or R^2 . Therefore, these leaderboards (implicitly and explicitly) push researchers to optimize for machine learning pipelines which elicit a good mean value estimate. The main problem is that this approach only evaluates a point estimate (namely the mean estimator which is the Bayes estimator associated with the mean squared error loss). In this article we discuss the application of proper scoring rules for evaluating the goodness of probabilistic forecasts in distributional regression. We also propose to enhance common machine learning benchmarks with metrics for probabilistic regression. To improve the status quo and make the machine learning community aware of scoring rules for probabilistic regression, we advocate to use the continuous ranked probability score (CRPS) in benchmarks for probabilistic regression. However, we also illustrate that the choice of the scoring rule changes the inductive bias of the trained model. We, therefore, advocate for finetuning or promptable tabular foundation models. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.08206 [cs.LG] (or arXiv:2603.08206v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.08206 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jonas Landsgesell [view email] [v1] Mon, 9 Mar 2026 10:38:01 UTC (1,673 KB) Full-text links: Access Paper: View a PDF of the paper titled Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules, by Jonas Landsgesell and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.LG prev | next new | recent | 2026-03 Change to browse by: cs cs.AI References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked=“checked”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); About Help contact arXivClick here to contact arXiv Contact subscribe to arXiv mailingsClick here to subscribe Subscribe Copyright Privacy Policy Web Accessibility Assistance arXiv Operational Status
[AI-42] Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models INTERSPEECH2026
【速读】：该论文旨在解决音频信号在量化过程中因激活值动态范围大而导致的信息损失问题，这一挑战在现有针对视觉和自然语言处理（Natural Language Processing, NLP）架构设计的量化方法中被忽视。其解决方案的关键在于提出一种基于进化策略（Evolution Strategy, ES）的校准方法（ESC），将激活缩放建模为一个优化问题，并采用两阶段局部-全局搜索策略求解，从而实现全INT8量化下的性能无损以及首次在多个语音任务上实现全INT4量化下的近无损性能。
链接: https://arxiv.org/abs/2603.08173
作者: Lucas Rakotoarivony
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: Submitted to INTERSPEECH 2026
Abstract:Quantization has become essential for the efficient deployment of speech processing systems. Although widely studied, most existing quantization methods were developed for vision and NLP architectures, while the specific challenges of audio signals remain largely overlooked. In particular, we show that audio activations can exhibit large calibration ranges, leading to significant information loss when standard calibration techniques are applied. To address this, we propose ESC, an Evolution Strategy-based Calibration method that formulates activation scaling as an optimization problem and solves it using a two-step local-global scheme driven by an evolution strategy. ESC enables unaltered performance under full INT8 quantization and is the first calibration method to achieve near-lossless performance for full INT4 quantization across multiple speech tasks. Integrating ESC with PTQ methods further reduces performance loss, achieving a 1% relative accuracy degradation on the AST model.
[AI-43] Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data
【速读】：该论文旨在解决工业维护领域中多源异构数据（如自由文本工单、异构传感器数据和结构化故障知识）孤立分析导致的决策支持不足问题，即现有系统难以基于资产历史与行为提供条件化的诊断与行动建议。解决方案的关键在于提出“Condition Insight Agent”框架，通过整合维护语言、运行数据的行为抽象与工程故障语义，构建基于证据的推理机制，并采用规则驱动的验证循环来抑制无依据的结论，从而在异构且不完整的数据条件下实现可靠、可解释的决策支持，同时保留人工监督。
链接: https://arxiv.org/abs/2603.08171
作者: Fearghal O’Donncha,Nianjun Zhou,Natalia Martinez,James T Rayfield,Fenno F. Heath III,Abigail Langbridge,Roman Vaculin
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Industrial maintenance platforms contain rich but fragmented evidence, including free-text work orders, heterogeneous operational sensors or indicators, and structured failure knowledge. These sources are often analyzed in isolation, producing alerts or forecasts that do not support conditional decision-making: given this asset history and behavior, what is happening and what action is warranted? We present Condition Insight Agent, a deployed decision-support framework that integrates maintenance language, behavioral abstractions of operational data, and engineering failure semantics to produce evidence-grounded explanations and advisory actions. The system constrains reasoning through deterministic evidence construction and structured failure knowledge, and applies a rule-based verification loop to suppress unsupported conclusions. Case studies from production CMMS deployments show that this verification-first design operates reliably under heterogeneous and incomplete data while preserving human oversight. Our results demonstrate how constrained LLM-based reasoning can function as a governed decision-support layer for industrial maintenance.
[AI-44] An explainable hybrid deep learning-enabled intelligent fault detection and diagnosis approach for automotive software systems validation
【速读】：该论文旨在解决生成式 AI (Generative AI) 在汽车软件系统（ASSs）验证与测试过程中，因黑箱故障检测与诊断（FDD）模型缺乏可解释性而导致的预测逻辑不透明问题，从而影响根因分析（RCA）和模型适应性。解决方案的关键在于提出一种基于1D CNN-GRU混合架构的智能模型，并结合多种可解释人工智能（XAI）技术（包括IGs、DeepLIFT、Gradient SHAP及DeepLIFT SHAP），实现对实时测试记录中故障的检测、识别与定位，并提供清晰的决策依据，以支持功能安全验证效率提升和模型动态优化。
链接: https://arxiv.org/abs/2603.08165
作者: Mohammad Abboush,Ehab Ghannoum,Andreas Rausch
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 20 pages
Abstract:Advancements in data-driven machine learning have emerged as a pivotal element in supporting automotive software systems (ASSs) engineering across various levels of the V-development process. Duringsystemverificationandvalidation,theintegrationofanintelligent fault detection anddiagnosis (FDD) model with test recordings analysis process serves as a powerful tool for efficiency ensuring functional safety. However, the lack of interpretability of the black-box FDD models developed not only hinders understanding of the cause underlying the prediction, but also prevents the model from being adapted based on the prediction result. This, in turn, increases the computational cost required for this http URL address this challenge, a novel explainable method for fault detection, identification, and localization is proposed in this article with the aim of providing a clear understanding of the logic behind the prediction outcome. To this end, a hybrid 1dCNN-GRU-based intelligent model was developed to analyze the recordings from the real-time validation process of ASSs. The employment of explainable AI techniques, i.e., IGs, DeepLIFT, Gradient SHAP, and DeepLIFT SHAP, was instrumental in enabling model adaptation and facilitating the root cause analysis (RCA). The proposed approach is applied to the real time dataset collected during a virtual test drive performed by the user on hardware in the loop system.
[AI-45] DARC: Disagreement-Aware Alignment via Risk-Constrained Decoding
【速读】：该论文旨在解决当前基于偏好对齐方法（如RLHF、DPO）在处理异质人类偏好时的局限性问题，即这些方法通常优化单一标量目标，隐式地对不同标注者和用户群体间的系统性分歧进行平均，导致模型在面对噪声和多样化反馈时鲁棒性差，易发生代理目标过优化（proxy over-optimization）。其解决方案的关键在于提出一种无需重训练的推理阶段方法——Disagreement-Aware Alignment via Risk-Constrained Decoding (DARC)，该方法将响应选择建模为分布鲁棒、风险敏感的决策过程：通过最大化一个KL-鲁棒（熵正则化）满意度目标来重新排序候选输出，并引入简单部署控制机制以限制或惩罚相对于均值的熵风险溢价，从而实现显式的风险预算管理。理论分析表明，该解码规则与原则性的悲观策略及基于KL散度的分布鲁棒优化存在紧密联系，实验证明DARC可在保持平均质量的同时显著降低分歧和尾部风险。
链接: https://arxiv.org/abs/2603.08145
作者: Mingxi Zou,Jiaxiang Chen,Junfan Li,Langzhang Liang,Qifan Wang,Xu Yinghui,Zenglin Xu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Preference-based alignment methods (e.g., RLHF, DPO) typically optimize a single scalar objective, implicitly averaging over heterogeneous human preferences. In practice, systematic annotator and user-group disagreement makes mean-reward maximization brittle and susceptible to proxy over-optimization. We propose Disagreement-Aware Alignment via Risk-Constrained Decoding (DARC), a retraining-free inference-time method that frames response selection as distributionally robust, risk-sensitive decision making. Given multiple preference samples or scalable disagreement proxies, DARC reranks candidates by maximizing a KL-robust (entropic) satisfaction objective, and provides simple deployment controls that cap or penalize the corresponding entropic risk premium relative to the mean, enabling explicit risk budgets without retraining. We provide theoretical characterization linking this decoding rule to principled pessimism and KL-based distributionally robust optimization. Experiments on alignment benchmarks show that DARC reduces disagreement and tail risk while maintaining competitive average quality under noisy, heterogeneous feedback.
[AI-46] SaiVLA-0: Cerebrum–Pons–Cerebellum Tripartite Architecture for Compute-Aware Vision-Language-Action
【速读】：该论文旨在解决多模态机器人控制中高阶语义理解与实时运动执行之间的耦合问题，即如何在保持任务意图稳定的同时实现高效、鲁棒的在线动作生成。其核心解决方案是受神经科学启发的三元架构：Cerebrum（大脑皮层）提供冻结的高阶多模态先验；Pons Adapter（脑桥适配器）将这些抽象特征与本体感觉输入融合，并编译为可执行动作令牌；Cerebellum（小脑，ParaCAT）则负责快速并行的类别解码以支持在线控制，通过滞回机制、指数移动平均（EMA）、温度调节和熵约束提升稳定性。该设计实现了模块化升级——更换机器人仅需微调小脑，更新高层语义仅需重训练脑桥，且支持仅用小脑强化学习（RL）优化控制策略而不扰动高层语义，从而显著提升系统计算效率与可复现性。
链接: https://arxiv.org/abs/2603.08124
作者: Xiang Shi,Wenlong Huang,Menglin Zou,Xinhai Sun
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 14 pages, 3 figures
Abstract:We revisit Vision-Language-Action through a neuroscience-inspired triad. Biologically, the Cerebrum provides stable high-level multimodal priors and remains frozen; the Pons Adapter integrates these cortical features with real-time proprioceptive inputs and compiles intent into execution-ready tokens; and the Cerebellum (ParaCAT) performs fast, parallel categorical decoding for online control, with hysteresis/EMA/temperature/entropy for stability. A fixed-ratio schedule and two-stage feature caching make the system compute-aware and reproducible. Inspired by active, foveated vision, our wrist ROIs are geometrically tied to the end-effector via calibrated projection, providing a movement-stabilized, high-resolution view that is sensitive to fine-grained pose changes and complements the global context of the main view. The design is modular: upgrading the Cerebrum only retrains the Pons; changing robots only trains the Cerebellum; cerebellum-only RL can further refine control without touching high-level semantics. As a concept-and-protocol paper with preliminary evidence, we outline a timing protocol under matched conditions (GPU, resolution, batch) to verify anticipated efficiency gains. We also report preliminary LIBERO evidence showing that split feature caching reduces training time (7.5h to 4.5h) and improves average success (86.5% to 92.5%) under official N1.5 head-only training, and that SaiVLA0 reaches 99.0% mean success. Comments: 14 pages, 3 figures Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) ACMclasses: I.2.9; I.2.6 Cite as: arXiv:2603.08124 [cs.RO] (or arXiv:2603.08124v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2603.08124 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-47] In-Context Reinforcement Learning for Tool Use in Large Language Models
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在复杂任务中因内部知识有限而导致性能受限的问题，尤其是如何高效地让模型调用外部工具（如Python解释器或搜索引擎）以增强推理能力。传统方法依赖于监督微调（Supervised Fine-Tuning, SFT）与强化学习（Reinforcement Learning, RL）相结合的冷启动流水线，但SFT阶段通常需要大量标注数据，成本高昂。论文提出一种仅使用强化学习的框架——上下文强化学习（In-Context Reinforcement Learning, ICRL），其核心创新在于：在强化学习的rollout阶段引入少量上下文示例（in-context examples）来指导模型如何调用外部工具；随着训练推进，逐步减少示例数量直至零样本（zero-shot）设置，使模型最终能够自主决策调用工具。该方案显著提升了数据效率和可扩展性，且在多个推理与工具使用基准上达到当前最优性能。
链接: https://arxiv.org/abs/2603.08068
作者: Yaoqi Ye,Yiran Zhao,Keyu Duan,Zeyu Zheng,Kenji Kawaguchi,Cihang Xie,Michael Qizhe Shieh
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:While large language models (LLMs) exhibit strong reasoning abilities, their performance on complex tasks is often constrained by the limitations of their internal knowledge. A compelling approach to overcome this challenge is to augment these models with external tools – such as Python interpreters for mathematical computations or search engines for retrieving factual information. However, enabling models to use these tools effectively remains a significant challenge. Existing methods typically rely on cold-start pipelines that begin with supervised fine-tuning (SFT), followed by reinforcement learning (RL). These approaches often require substantial amounts of labeled data for SFT, which is expensive to annotate or synthesize. In this work, we propose In-Context Reinforcement Learning (ICRL), an RL-only framework that eliminates the need for SFT by leveraging few-shot prompting during the rollout stage of RL. Specifically, ICRL introduces in-context examples within the rollout prompts to teach the model how to invoke external tools. Furthermore, as training progresses, the number of in-context examples is gradually reduced, eventually reaching a zero-shot setting where the model learns to call tools independently. We conduct extensive experiments across a range of reasoning and tool-use benchmarks. Results show that ICRL achieves state-of-the-art performance, demonstrating its effectiveness as a scalable, data-efficient alternative to traditional SFT-based pipelines.
[AI-48] S2S-FDD: Bridging Industrial Time Series and Natural Language for Explainable Zero-shot Fault Diagnosis
【速读】：该论文旨在解决工业系统中故障诊断模型输出抽象（如异常评分或故障类别）无法回答操作层面关键问题（如“为什么”或“如何修复”）的局限性，以及大语言模型（LLM）在处理高维时序工业信号时因训练语料为离散文本而产生的语义鸿沟问题。解决方案的关键在于提出一种信号到语义的故障诊断（Signals-to-Semantics fault diagnosis, S2S-FDD）框架：其一，设计了信号到语义转换算子（Signal-to-Semantic operator），将抽象的时间序列信号转化为包含趋势、周期性和偏差等特征的自然语言描述；其二，基于该描述构建多轮树状结构诊断方法，通过引用历史维修文档并动态查询额外信号实现推理诊断，并支持人机协同反馈以持续优化。
链接: https://arxiv.org/abs/2603.08048
作者: Baoxue Li,Chunhui Zhao
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Fault diagnosis is critical for the safe operation of industrial systems. Conventional diagnosis models typically produce abstract outputs such as anomaly scores or fault categories, failing to answer critical operational questions like “Why” or “How to repair”. While large language models (LLMs) offer strong generalization and reasoning abilities, their training on discrete textual corpora creates a semantic gap when processing high-dimensional, temporal industrial signals. To address this challenge, we propose a Signals-to-Semantics fault diagnosis (S2S-FDD) framework that bridges high-dimensional sensor signals with natural language semantics through two key innovations: We first design a Signal-to-Semantic operator to convert abstract time-series signals into natural language summaries, capturing trends, periodicity, and deviations. Based on the descriptions, we design a multi-turn tree-structured diagnosis method to perform fault diagnosis by referencing historical maintenance documents and dynamically querying additional signals. The framework further supports human-in-the-loop feedback for continuous refinement. Experiments on the multiphase flow process show the feasibility and effectiveness of the proposed method for explainable zero-shot fault diagnosis.
[AI-49] CDRRM: Contrast-Driven Rubric Generation for Reliable and Interpretable Reward Modeling
【速读】：该论文旨在解决传统奖励模型（Reward Model）在对齐大语言模型（Large Language Models, LLMs）与人类偏好时存在的可解释性差、依赖昂贵专家标注等问题，同时克服现有基于评分量表（rubric-based）方法因缺乏系统质量控制而导致的噪声冗余标准及固有偏见（如冗长性、位置偏好）等局限。其解决方案的关键在于提出一种名为CDRRM（Contrast-Driven Rubric Reward Model）的新框架，该框架采用“对比-合成”（Contrast-then-Synthesis）范式：首先通过多维对比分析偏好样本对以识别因果判别因素，再将这些洞察提炼为紧凑且上下文感知的评分量表，从而引导更精准的偏好判断。实验表明，该方法在多个权威基准上均达到最先进性能，并显著缓解评估偏差，且仅需3k高质量样本即可训练出高效鲁棒的评分生成器，使冻结预训练判别模型超越全量微调基线，实现了高可扩展性、可解释性和数据效率的统一。
链接: https://arxiv.org/abs/2603.08035
作者: Dengcan Liu,Fengkai Yang,Xiaohan Wang,Shurui Yan,Jiajun Chai,Jiahao Li,Yikun Ban,Zhendong Mao,Wei Lin,Guojun Yin
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Reward modeling is essential for aligning Large Language Models(LLMs) with human preferences, yet conventional reward models suffer from poor interpretability and heavy reliance on costly expert annotations. While recent rubric-based approaches enhance evaluation transparency, they lack systematic quality control, yielding noisy and redundant criteria, failing to mitigate persistent biases (e.g., verbosity, position) in LLM evaluators, and creating a scalability-reliability trade-off. To address these limitations, we propose CDRRM (Contrast-Driven Rubric Reward Model), a framework built on a novel Contrast-then-Synthesis paradigm for high-quality rubric generation and guided preference judgment. CDRRM first conducts multi-dimensional contrastive profiling on preference pairs to identify causal discriminative factors, then synthesizes these insights into compact, context-aware rubrics to guide preference judg- ments. Extensive experiments on three authoritative benchmarks (RewardBench, RMBench, RMB) demonstrate that CDRRM achieves state-of-the-art performance across diverse domains and effectively mitigates aforementioned evaluation biases. Notably, our approach delivers exceptional data efficiency: training the rubric generator on only 3k high-quality samples empowers a frozen pre-trained judge model to outperform fully fine-tuned baselines. This work offers a scalable, interpretable, and data-efficient path for reward modeling.
[AI-50] GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables
【速读】：该论文旨在解决时间序列预测中如何有效建模时序相关性（temporal correlations）与通道相关性（channel correlations）的联合关系问题，尤其是在存在外生变量（exogenous variables）且未来外生变量可得的情况下，现有方法多采用两步策略分别建模两类相关性，难以捕捉跨时间和通道的联合依赖关系，且对噪声敏感。解决方案的关键在于提出GCGNet——一种图一致性生成网络，其核心机制包括：首先通过变分生成器（Variational Generator）生成粗略预测；随后利用图结构对齐器（Graph Structure Aligner）基于生成与真实相关性的图表示进行一致性评估，提升对噪声的鲁棒性；最后通过图精炼器（Graph Refiner）优化预测结果以防止退化并提高精度。
链接: https://arxiv.org/abs/2603.08032
作者: Zhengyu Li,Xiangfei Qiu,Yuhan Zhu,Xingjian Wu,Jilin Hu,Chenjuan Guo,Bin Yang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Exogenous variables offer valuable supplementary information for predicting future endogenous variables. Forecasting with exogenous variables needs to consider both past-to-future dependencies (i.e., temporal correlations) and the influence of exogenous variables on endogenous variables (i.e., channel correlations). This is pivotal when future exogenous variables are available, because they may directly affect the future endogenous variables. Many methods have been proposed for time series forecasting with exogenous variables, focusing on modeling temporal and channel correlations. However, most of them use a two-step strategy, modeling temporal and channel correlations separately, which limits their ability to capture joint correlations across time and channels. Furthermore, in real-world scenarios, time series are frequently affected by various forms of noises, underscoring the critical importance of robustness in such correlations modeling. To address these limitations, we propose GCGNet, a Graph-Consistent Generative Network for time series forecasting with exogenous variables. Specifically, GCGNet first employs a Variational Generator to produce coarse predictions. A Graph Structure Aligner then further guides it by evaluating the consistency between the generated and true correlations, where the correlations are represented as graphs, and are robust to noises. Finally, a Graph Refiner is proposed to refine the predictions to prevent degeneration and improve accuracy. Extensive experiments on 12 real-world datasets demonstrate that GCGNet outperforms state-of-the-art baselines.
[AI-51] FedMomentum: Preserving LoRA Training Momentum in Federated Fine-Tuning
【速读】：该论文旨在解决联邦微调大语言模型（Large Language Models, LLMs）中基于低秩适应（Low-Rank Adaptation, LoRA）方法时，因朴素聚合LoRA模块导致的噪声问题，以及现有无噪声聚合策略牺牲结构表达能力、削弱客户端特定适配保留能力的问题。核心挑战在于LoRA更新在多轮训练中难以有效累积训练动量（loss of training momentum），从而影响收敛速度和最终性能。解决方案的关键在于提出FedMomentum框架，通过奇异值分解（Singular Value Decomposition, SVD）实现数学上正确的结构化LoRA聚合：首先以正确方式聚合低秩更新，再利用SVD提取主导更新方向以重建保持原秩的LoRA模块，同时保留残差成分用于后续融合至主干网络，从而兼顾收敛效率与语义信息完整性。
链接: https://arxiv.org/abs/2603.08014
作者: Peishen Yan,Yang Hua,Hao Wang,Jiaru Zhang,Xiaoyu Wu,Tao Song,Haibing Guan
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Federated fine-tuning of large language models (LLMs) with low-rank adaptation (LoRA) offers a communication-efficient and privacy-preserving solution for task-specific adaptation. Naive aggregation of LoRA modules introduces noise due to mathematical incorrectness when averaging the downsampling and upsampling matrices independently. However, existing noise-free aggregation strategies inevitably compromise the structural expressiveness of LoRA, limiting its ability to retain client-specific adaptations by either improperly reconstructing the low-rank structure or excluding partially trainable components. We identify this problem as loss of training momentum, where LoRA updates fail to accumulate effectively across rounds, resulting in slower convergence and suboptimal performance. To address this, we propose FedMomentum, a novel framework that enables structured and momentum-preserving LoRA aggregation via singular value decomposition (SVD). Specifically, after aggregating low-rank updates in a mathematically correct manner, FedMomentum applies SVD to extract the dominant components that capture the main update directions. These components are used to reconstruct the LoRA modules with the same rank, while residual components can be retained and later merged into the backbone to preserve semantic information and ensure robustness. Extensive experiments across multiple tasks demonstrate that FedMomentum consistently outperforms prior state-of-the-art methods in convergence speed and final accuracy.
[AI-52] PIRA-Bench: A Transition from Reactive GUI Agents to GUI-based Proactive Intent Recommendation Agents
【速读】：该论文旨在解决当前图形用户界面（GUI）智能代理主要依赖于反应式范式的问题，即必须等待用户明确指令才能执行任务，而无法主动从连续的视觉输入（如移动端或桌面截图）中感知用户意图并提供及时推荐。为实现更智能的主动式交互，论文提出PIRA-Bench（Proactive Intent Recommendation Agent Benchmark），这是一个用于评估多模态大语言模型（MLLMs）在弱监督视觉输入下识别复杂、多线程意图的能力的新基准。其关键创新在于构建了包含交错意图和噪声片段的真实世界屏幕轨迹数据集，并引入PIRF基线框架——一种具备记忆机制与状态跟踪能力的架构，使通用MLLM能够同时管理多个任务线程并有效处理误导性视觉输入，从而推动GUI驱动型个人助理向鲁棒性和主动性演进。
链接: https://arxiv.org/abs/2603.08013
作者: Yuxiang Chai,Shunye Tang,Han Xiao,Rui Liu,Hongsheng Li
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Current Graphical User Interface (GUI) agents operate primarily under a reactive paradigm: a user must provide an explicit instruction for the agent to execute a task. However, an intelligent AI assistant should be proactive, which is capable of anticipating user intentions directly from continuous visual inputs, such as mobile or desktop screenshots, and offering timely recommendations without explicit user prompting. Transitioning to this proactive paradigm presents significant challenges. Real-world screen activity is rarely linear; it consists of long-horizon trajectories fraught with noisy browsing, meaningless actions, and multithreaded task-switching. To address this gap, we introduce PIRA-Bench (Proactive Intent Recommendation Agent Benchmark), a novel benchmark for evaluating multimodal large language models (MLLMs) on continuous, weakly-supervised visual inputs. Unlike reactive datasets, PIRA-Bench features complex trajectories with multiple interleaved intents and noisy segments with various user profile contexts, challenging agents to detect actionable events while fitting to user preferences. Furthermore, we propose the PIRF baseline, a memory-aware, state-tracking framework that empowers general MLLMs to manage multiple task threads and handle misleading visual inputs. PIRA-Bench serves as an initial step toward robust and proactive GUI-based personal assistants.
[AI-53] Aero-Promptness: Drag -Aware Aerodynamic Manipulability for Propeller-driven Vehicles
【速读】：该论文旨在解决冗余多旋翼飞行器控制分配（control allocation）中的关键问题，即如何在考虑电机扭矩限制和气动阻力（aerodynamic drag）的情况下，实现高效且稳定的力矩分配。其解决方案的核心是提出了一种新的几何框架——Drag-Aware Aerodynamic Manipulability (DAAM)，通过在螺旋桨转速空间中引入基于各电机剩余对称加速度能力的Riemannian度量，显式建模了气动阻力与电机饱和效应；进一步将该度量映射到广义力空间后得到状态依赖的可操作性体积（manipulability volume），其对数行列式作为自然障碍函数，严格惩罚由拖拽引起的饱和和低转速推力损失。该方法不仅提供了一种内在不变于广义力空间坐标缩放的冗余分辨率策略，还从理论上证明最优分配局部构成光滑嵌入流形，并几何刻画了由物理执行器极限和转速符号切换引发的全局跳跃不连续性。
链接: https://arxiv.org/abs/2603.07998
作者: Antonio Franchi
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Optimization and Control (math.OC)
备注:
Abstract:This work introduces the Drag-Aware Aerodynamic Manipulability (DAAM), a geometric framework for control allocation in redundant multirotors. By equipping the propeller spin-rate space with a Riemannian metric based on the remaining symmetric acceleration capacity of each motor, the formulation explicitly accounts for motor torque limits and aerodynamic drag. Mapping this metric through the nonlinear thrust law to the generalized force space yields a state-dependent manipulability volume. The log-determinant of this volume acts as a natural barrier function, strictly penalizing drag-induced saturation and low-spin thrust loss. Optimizing this volume along the allocation fibers provides a redundancy resolution strategy inherently invariant to arbitrary coordinate scaling in the generalized-force space. Analytically, we prove that the resulting optimal allocations locally form smooth embedded manifolds, and we geometrically characterize the global jump discontinuities that inevitably arise from physical actuator limits and spin-rate sign transitions.
[AI-54] CMMR-VLN: Vision-and-Language Navigation via Continual Multimodal Memory Retrieval
【速读】：该论文旨在解决当前基于大语言模型（Large Language Models, LLMs）的视觉-语言导航（Vision-and-Language Navigation, VLN）系统在长程任务和陌生场景中表现受限的问题，即缺乏对先前经验的有选择性地回忆与利用能力。其解决方案的关键在于提出CMMR-VLN框架，该框架通过构建以全景视觉图像和显著地标为索引的多模态经验记忆库，在导航过程中检索相关历史经验；引入检索增强生成（retrieved-augmented generation）机制模拟人类导航者如何调用先验知识；并设计基于反思的记忆更新策略，仅存储成功路径的完整轨迹及失败案例中的关键初始错误，从而实现持续学习与高效决策。
链接: https://arxiv.org/abs/2603.07997
作者: Haozhou Li,Xiangyu Dong,Huiyan Jiang,Yaoming Zhou,Xiaoguang Ma
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Although large language models (LLMs) are introduced into vision-and-language navigation (VLN) to improve instruction comprehension and generalization, existing LLM- based VLN lacks the ability to selectively recall and use relevant priori experiences to help navigation tasks, limiting their performance in long-horizon and unfamiliar scenarios. In this work, we propose CMMR-VLN (Continual Multimodal Memory Retrieval based VLN), a VLN framework that endows LLM agents with structured memory and reflection capabilities. Specifically, the CMMR-VLN constructs a multimodal experi- ence memory indexed by panoramic visual images and salient landmarks to retrieve relevant experiences during navigation, introduces a retrieved-augmented generation pipeline to mimick how experienced human navigators leverage priori knowledge, and incorporates a reflection-based memory update strategy that selectively stores complete successful paths and the key initial mistake in failure cases. Comprehensive tests illustrate average success rate improvements of 52.9%, 20.9% and 20.9%, and 200%, 50% and 50% over the NavGPT, the MapGPT, and the DiscussNav in simulation and real tests, respectively eluci- dating the great potential of the CMMR-VLN as a backbone VLN framework.
[AI-55] OSExpert: Computer-Use Agents Learning Professional Skills via Exploration
【速读】：该论文旨在解决通用计算机使用代理（General-purpose computer-use agents）在复杂任务执行中效率低下、泛化能力弱以及难以处理细粒度动作序列的问题。当前代理虽具备一定适应性，但在未见过的用户界面（UI）上表现不佳，且依赖推理时扩展（inference-time scaling）导致资源浪费和性能下降。解决方案的关键在于引入基于图形用户界面（GUI）的深度优先搜索（GUI-DFS）探索算法，通过系统性地探索环境中的基本功能单元（unit functions），构建可组合的技能集合，并利用这些技能自动生成复合任务的学习路径（curriculum）。此外，作者还构建了一个动作原语数据库，使代理能够在探索过程中发现并固化细粒度操作技能，从而减少推理时的盲目扩展，提升规划效率与准确性。实验表明，该方法在OSExpert-Eval基准上实现了约20%的性能提升，并将效率差距缩小至人类水平的约20%。
链接: https://arxiv.org/abs/2603.07978
作者: Jiateng Liu,Zhenhailong Wang,Rushi Wang,Bingxuan Li,Jeonghwan Kim,Aditi Tiwari,Pengfei Yu,Denghui Zhang,Heng Ji
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 26 pages
Abstract:General-purpose computer-use agents have shown impressive performance across diverse digital environments. However, our new benchmark, OSExpert-Eval, indicates they remain far less helpful than human experts. Although inference-time scaling enables adaptation, these agents complete complex tasks inefficiently with degraded performance, transfer poorly to unseen UIs, and struggle with fine-grained action sequences. To solve the problem, we introduce a GUI-based depth-first search (GUI-DFS) exploration algorithm to comprehensively explore and verify an environment’s unit functions. The agent then exploits compositionality between unit skills to self-construct a curriculum for composite tasks. To support fine-grained actions, we curate a database of action primitives for agents to discover during exploration; these are saved as a skill set once the exploration is complete. We use the learned skills to improve the agent’s performance and efficiency by (1) enriching agents with ready-to-use procedural knowledge, allowing them to plan only once for long trajectories and generate accurate actions, and (2) enabling them to end inference-time scaling earlier by realizing their boundary of capabilities. Extensive experiments show that our environment-learned agent takes a meaningful step toward expert-level computer use, achieving a around 20 percent performance gain on OSExpert-Eval and closing the efficiency gap to humans by around 80 percent
[AI-56] VORL-EXPLORE: A Hybrid Learning Planning Approach to Multi-Robot Exploration in Dynamic Environments
【速读】：该论文旨在解决分层多机器人探索中任务分配与局部导航解耦所导致的系统脆弱性问题，尤其在密集和动态环境中易引发机器人聚集于瓶颈区域、频繁重规划及冗余覆盖等问题。其解决方案的关键在于引入“执行保真度（execution fidelity）”这一共享的局部可导航性估计机制，将任务分配与运动执行紧密耦合；通过构建融合保真度的Voronoi目标函数并引入机器人间排斥力以提前减少竞争冲突，同时设计风险感知的自适应仲裁机制，在全局A*引导与反应式强化学习策略之间实现长距离效率与狭窄空间安全交互的平衡。此外，框架支持基于近期进展和安全结果生成伪标签的在线自监督校准，使保真度模型能自动适应非平稳障碍物，无需人工调参。
链接: https://arxiv.org/abs/2603.07973
作者: Ning Liu,Sen Shen,Zheng Li,Sheng Liu,Dongkun Han,Shangke Lyu,Thomas Braunl
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Hierarchical multi-robot exploration commonly decouples frontier allocation from local navigation, which can make the system brittle in dense and dynamic environments. Because the allocator lacks direct awareness of execution difficulty, robots may cluster at bottlenecks, trigger oscillatory replanning, and generate redundant coverage. We propose VORL-EXPLORE, a hybrid learning and planning framework that addresses this limitation through execution fidelity, a shared estimate of local navigability that couples task allocation with motion execution. This fidelity signal is incorporated into a fidelity-coupled Voronoi objective with inter-robot repulsion to reduce contention before it emerges. It also drives a risk-aware adaptive arbitration mechanism between global A* guidance and a reactive reinforcement learning policy, balancing long-range efficiency with safe interaction in confined spaces. The framework further supports online self-supervised recalibration of the fidelity model using pseudo-labels derived from recent progress and safety outcomes, enabling adaptation to non-stationary obstacles without manual risk tuning. We evaluate this capability separately in a dedicated severe-traffic ablation. Extensive experiments in randomized grids and a Gazebo factory scenario show high success rates, shorter path length, lower overlap, and robust collision avoidance. The source code will be made publicly available upon acceptance.
[AI-57] Adaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLM s with Continual Learning
【速读】：该论文旨在解决当前多智能体系统（Multi-Agent Systems, MAS）在面对超出预训练数据范围的知识需求时，因缺乏动态知识更新能力而导致的“封闭世界”局限性问题。其核心挑战在于如何使智能体在协作中具备持续学习与人类协同决策的能力，从而避免在新颖任务中出现集体失效。解决方案的关键在于提出人类在环路的多智能体协作框架（Human-In-the-Loop Multi-Agent Collaboration, HILA），并通过双环策略优化（Dual-Loop Policy Optimization）实现机制解耦：内环采用带成本感知奖励的群体相对策略优化（Group Relative Policy Optimization, GRPO）来训练智能体判断何时自主决策、何时向人类专家求助；外环则通过持续学习将人类反馈转化为高质量监督信号，增强智能体的推理能力，从而构建具有元认知能力且可不断进化的协作式智能体系统。
链接: https://arxiv.org/abs/2603.07972
作者: Wei Yang,Defu Cao,Jiacheng Pang,Muyan Weng,Yan Liu
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:While scaling individual Large Language Models (LLMs) has delivered remarkable progress, the next frontier lies in scaling collaboration through multi-agent systems (MAS). However, purely autonomous MAS remain ‘‘closed-world’’ systems, constrained by the static knowledge horizon of pre-trained models. This limitation makes them brittle on tasks requiring knowledge beyond training data, often leading to collective failure under novel challenges. To address this, we propose the Human-In-the-Loop Multi-Agent Collaboration (HILA) framework, a principled paradigm for human–agent collaboration. HILA trains agents to learn a metacognitive policy that governs when to solve problems autonomously and when to defer to a human expert. To operationalize this policy, we introduce Dual-Loop Policy Optimization, which disentangles immediate decision-making from long-term capability growth. The inner loop applies Group Relative Policy Optimization (GRPO) with a cost-aware reward to optimize deferral decisions, while the outer loop implements continual learning, transforming expert feedback into high-quality supervised signals that strengthen the agent’s reasoning ability. Experiments on challenging mathematical and problem-solving benchmarks show that HILA, equipped with Dual-Loop Policy Optimization, consistently outperforms advanced MAS, establishing a principled foundation for collaborative and continually improving agentic systems.
[AI-58] Advancing Automated Algorithm Design via Evolutionary Stagewise Design with LLM s
【速读】：该论文旨在解决工业场景中算法设计日益复杂所带来的挑战，特别是传统算法设计方法难以应对高维、非线性问题的局限性，以及当前基于大语言模型（Large Language Models, LLMs）的自动化算法设计方法因黑箱建模导致对目标问题内在机制缺乏理解，从而产生幻觉式设计的问题。其解决方案的关键在于提出一种新型进化范式——演化分阶段算法设计（Evolutionary Stagewise Algorithm Design, EvoStage），该方法通过类思维链（Chain-of-Thought, CoT）思想将算法设计过程分解为可管理的阶段性步骤，并引入实时中间反馈机制以迭代优化设计方向；同时，结合多智能体系统与“全局-局部视角”机制有效缩小搜索空间并避免陷入局部最优，从而实现高效、可靠的自动化算法生成。
链接: https://arxiv.org/abs/2603.07970
作者: Chen Lu,Ke Xue,Chengrui Gao,Yunqi Shi,Siyuan Xu,Mingxuan Yuan,Chao Qian,Zhi-Hua Zhou
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 28 pages, 19 figures and 7 tables
Abstract:With the rapid advancement of human science and technology, problems in industrial scenarios are becoming increasingly challenging, bringing significant challenges to traditional algorithm design. Automated algorithm design with LLMs emerges as a promising solution, but the currently adopted black-box modeling deprives LLMs of any awareness of the intrinsic mechanism of the target problem, leading to hallucinated designs. In this paper, we introduce Evolutionary Stagewise Algorithm Design (EvoStage), a novel evolutionary paradigm that bridges the gap between the rigorous demands of industrial-scale algorithm design and the LLM-based algorithm design methods. Drawing inspiration from CoT, EvoStage decomposes the algorithm design process into sequential, manageable stages and integrates real-time intermediate feedback to iteratively refine algorithm design directions. To further reduce the algorithm design space and avoid falling into local optima, we introduce a multi-agent system and a “global-local perspective” mechanism. We apply EvoStage to the design of two types of common optimizers: designing parameter configuration schedules of the Adam optimizer for chip placement, and designing acquisition functions of Bayesian optimization for black-box optimization. Experimental results across open-source benchmarks demonstrate that EvoStage outperforms human-expert designs and existing LLM-based methods within only a couple of evolution steps, even achieving the historically state-of-the-art half-perimeter wire-length results on every tested chip case. Furthermore, when deployed on a commercial-grade 3D chip placement tool, EvoStage significantly surpasses the original performance metrics, achieving record-breaking efficiency. We hope EvoStage can significantly advance automated algorithm design in the real world, helping elevate human productivity.
[AI-59] PSTNet: Physically-Structured Turbulence Network
【速读】：该论文旨在解决航空器在不同高度层（尤其是海洋、极地及数据稀疏区域）中实时可靠估计大气湍流强度的难题。传统谱模型依赖气候平均值而非瞬时大气状态，而通用机器学习回归器虽具适应性却无法保证预测结果符合基本物理尺度规律。解决方案的关键在于提出一种物理结构化湍流网络（Physically-Structured Turbulence Network, PSTNet），其核心创新包括：(i) 基于Monin-Obukhov理论构建零参数主干网络；(ii) 通过由Richardson数导出的软目标监督的分段专家子网络混合机制实现运行工况自适应；(iii) 引入特征线性调制层以本地空气密度比条件化隐藏表征；(iv) 在输出层强制执行Kolmogorov惯性子区标度律作为架构约束。该设计使模型仅含552个可学习参数，存储占用不足2.5 kB，并可在Cortex-M7微控制器上于12秒内完成推理，显著优于传统查表法，在多类飞行器六自由度仿真中实现均方误差改善+2.8%且统计显著。
链接: https://arxiv.org/abs/2603.07957
作者: Boris Kriuk,Fedor Kriuk
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 7 pages, 6 figures, 2 tables
Abstract:Reliable real-time estimation of atmospheric turbulence intensity remains an open challenge for aircraft operating across diverse altitude bands, particularly over oceanic, polar, and data-sparse regions that lack operational nowcasting infrastructure. Classical spectral models encode climatological averages rather than the instantaneous atmospheric state, and generic ML regressors offer adaptivity but provide no guarantee that predictions respect fundamental scaling laws. This paper introduces the Physically-Structured Turbulence Network (PSTNet), a lightweight architecture that embeds physics directly into its structure. PSTNet couples four components: (i) a zero-parameter backbone derived from Monin-Obukhov theory, (ii) a regime-gated mixture of specialist sub-networks supervised by Richardson-number-derived soft targets, (iii) Feature-wise Linear Modulation layers conditioning hidden representations on local air-density ratio, and (iv) a Kolmogorov output layer enforcing inertial-subrange scaling as an architectural constraint. The entire model contains only 552 learnable parameters, requiring fewer than 2.5 kB of storage and executing in under 12s on a Cortex-M7 microcontroller. We validate PSTNet on 340 paired six-degree-of-freedom guidance simulations spanning three vehicle classes (Mach 2.8, 4.5, and 8.0) and six operational categories with real-time satellite weather ingestion. PSTNet achieves a mean miss-distance improvement of +2.8% with a 78% win rate and a statistically significant effect size. Our results demonstrate that encoding domain physics as architectural priors yields a more efficient and interpretable path to turbulence estimation accuracy than scaling model capacity, establishing PSTNet as a viable drop-in replacement for legacy look-up tables in resource-constrained, safety-critical on-board guidance systems.
[AI-60] ELLM ob: Event-Driven Human Mobility Generation with Self-Aligned LLM Framework ICLR2026
【速读】：该论文旨在解决生成式 AI 在人类移动性模拟中对大规模社会事件期间偏离常规轨迹的建模不足问题，其核心挑战在于缺乏事件标注的移动数据集以及现有框架难以协调用户习惯模式与事件约束之间的竞争关系。解决方案的关键在于提出 ELLMob 框架，该框架基于模糊痕迹理论（Fuzzy-Trace Theory）首先提取习惯模式与事件约束之间的竞争理由，并通过迭代对齐机制生成既符合个体习惯又响应事件情境的轨迹，从而显著提升在台风、疫情和奥运会等重大事件下的轨迹生成质量。
链接: https://arxiv.org/abs/2603.07946
作者: Yusong Wang,Chuang Yang,Jiawei Wang,Xiaohang Xu,Jiayi Xu,Dongyuan Li,Chuan Xiao,Renhe Jiang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted by ICLR 2026
Abstract:Human mobility generation aims to synthesize plausible trajectory data, which is widely used in urban system research. While Large Language Model-based methods excel at generating routine trajectories, they struggle to capture deviated mobility during large-scale societal events. This limitation stems from two critical gaps: (1) the absence of event-annotated mobility datasets for design and evaluation, and (2) the inability of current frameworks to reconcile competitions between users’ habitual patterns and event-imposed constraints when making trajectory decisions. This work addresses these gaps with a twofold contribution. First, we construct the first event-annotated mobility dataset covering three major events: Typhoon Hagibis, COVID-19, and the Tokyo 2021 Olympics. Second, we propose ELLMob, a self-aligned LLM framework that first extracts competing rationales between habitual patterns and event constraints, based on Fuzzy-Trace Theory, and then iteratively aligns them to generate trajectories that are both habitually grounded and event-responsive. Extensive experiments show that ELLMob wins state-of-the-art baselines across all events, demonstrating its effectiveness. Our codes and datasets are available at this https URL.
[AI-61] SWE-Fuse: Empowering Software Agents via Issue-free Trajectory Learning and Entropy-aware RLVR Training
【速读】：该论文旨在解决当前基于大语言模型（Large Language Models, LLMs）的软件工程代理（SWE agents）在真实世界软件问题修复任务中因高质量问题描述不足而导致的性能瓶颈问题。具体而言，现实数据集中问题描述与解决方案之间常存在语义错位，引入噪声和歧义，从而误导自动化代理并削弱其问题求解能力。解决方案的关键在于提出一种名为 SWE-Fuse 的问题描述感知训练框架，其核心创新包括：(1) 一种无问题描述驱动的轨迹学习模块，用于缓解潜在误导性描述的影响，同时使模型掌握逐步调试过程；(2) 一种熵感知的强化学习价值回归（RLVR）训练模块，通过熵驱动的裁剪机制自适应调整训练动态——高熵时采用宽松裁剪以促进探索，低熵时采用严格裁剪以保障训练稳定性。该方法显著提升了SWE代理在SWE-bench Verified基准上的求解率，优于现有8B和32B基线模型达43.0%和60.2%，并进一步结合测试时缩放（TTS）实现更高性能。
链接: https://arxiv.org/abs/2603.07927
作者: Xin-Cheng Wen,Binbin Chen,Haoxuan Lan,Hang Yu,Peng Di,Cuiyun Gao
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 19 pages
Abstract:Large language models (LLMs) have transformed the software engineering landscape. Recently, numerous LLM-based agents have been developed to address real-world software issue fixing tasks. Despite their state-of-the-art performance, Despite achieving state-of-the-art performance, these agents face a significant challenge: \textbfInsufficient high-quality issue descriptions. Real-world datasets often exhibit misalignments between issue descriptions and their corresponding solutions, introducing noise and ambiguity that mislead automated agents and limit their problem-solving effectiveness. We propose \textbf\textitSWE-Fuse, an issue-description-aware training framework that fuses issue-description-guided and issue-free samples for training SWE agents. It consists of two key modules: (1) An issue-free-driven trajectory learning module for mitigating potentially misleading issue descriptions while enabling the model to learn step-by-step debugging processes; and (2) An entropy-aware RLVR training module, which adaptively adjusts training dynamics through entropy-driven clipping. It applies relaxed clipping under high entropy to encourage exploration, and stricter clipping under low entropy to ensure training stability. We evaluate SWE-Fuse on the widely studied SWE-bench Verified benchmark shows to demonstrate its effectiveness in solving real-world software problems. Specifically, SWE-Fuse outperforms the best 8B and 32B baselines by 43.0% and 60.2% in solve rate, respectively. Furthermore, integrating SWE-Fuse with test-time scaling (TTS) enables further performance improvements, achieving solve rates of 49.8% and 65.2% under TTS@8 for the 8B and 32B models, respectively.
[AI-62] Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases
【速读】：该论文旨在解决关系型数据库（Relational Database, RDB）中实体分类任务因类别不平衡导致的少数类实体被淹没、模型性能下降的问题。现有关系深度学习（Relational Deep Learning, RDL）方法未充分考虑RDB中的类别不平衡现象，从而难以在实际应用中获得可靠预测结果。其解决方案的关键在于提出一种基于关系感知的少数类过采样图神经网络（Relation-centric Minority Synthetic Over-sampling GNN, Rel-MOSS），核心创新包括：1）设计关系级门控控制器（relation-wise gating controller），通过动态调节每种关系类型的邻域消息传递，缓解多数类对少数类信息的压制；2）构建关系引导的少数类合成器（relation-guided minority synthesizer），利用实体的关系签名（relational signatures）确保生成样本在结构和语义上保持关系一致性，从而提升模型对少数类的识别能力。实验表明，Rel-MOSS在12个实体分类数据集上显著优于当前最优RDL方法和经典不平衡处理方法，平均Balanced Accuracy和G-Mean分别提升2.46%和4.00%。
链接: https://arxiv.org/abs/2603.07916
作者: Jun Yin,Peng Huo,Bangguo Zhu,Hao Yan,Senzhang Wang,Shirui Pan,Chengqi Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI); Databases (cs.DB); Machine Learning (cs.LG)
备注:
Abstract:In recent advances, to enable a fully data-driven learning paradigm on relational databases (RDB), relational deep learning (RDL) is proposed to structure the RDB as a heterogeneous entity graph and adopt the graph neural network (GNN) as the predictive model. However, existing RDL methods neglect the imbalance problem of relational data in RDBs and risk under-representing the minority entities, leading to an unusable model in practice. In this work, we investigate, for the first time, class imbalance problem in RDB entity classification and design the relation-centric minority synthetic over-sampling GNN (Rel-MOSS), in order to fill a critical void in the current literature. Specifically, to mitigate the issue of minority-related information being submerged by majority counterparts, we design the relation-wise gating controller to modulate neighborhood messages from each individual relation type. Based on the relational-gated representations, we further propose the relation-guided minority synthesizer for over-sampling, which integrates the entity relational signatures to maintain relational consistency. Extensive experiments on 12 entity classification datasets provide compelling evidence for the superiority of Rel-MOSS, yielding an average improvement of up to 2.46% and 4.00% in terms of Balanced Accuracy and G-Mean, compared with SOTA RDL methods and classic methods for handling class imbalance.
[AI-63] Ares: Adaptive Reasoning Effort Selection for Efficient LLM Agents
【速读】：该论文旨在解决多步智能体任务中因静态推理强度设置导致的效率与准确性失衡问题：固定高推理强度虽能保证精度但计算成本高昂，而低强度或随机选择则易造成性能下降。其解决方案的关键在于提出Ares框架，通过一个轻量级路由器（router）根据每一步的交互历史动态预测最低必要推理水平，从而实现按需分配推理资源；该路由器基于自动生成的训练数据进行微调，这些数据识别了每个步骤成功完成所需的最小推理努力，使得Ares可无缝集成至任意大语言模型（LLM）智能体中，并在多种任务场景下显著降低推理令牌消耗（最高达52.7%），同时保持任务成功率几乎不变。
链接: https://arxiv.org/abs/2603.07915
作者: Jingbo Yang,Bairu Hou,Wei Wei,Yujia Bao,Shiyu Chang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Modern agents powered by thinking LLMs achieve high accuracy through long chain-of-thought reasoning but incur substantial inference costs. While many LLMs now support configurable reasoning levels (e.g., high/medium/low), static strategies are often ineffective: using low-effort modes at every step leads to significant performance degradation, while random selection fails to preserve accuracy or provide meaningful cost reduction. However, agents should reserve high reasoning effort for difficult steps like navigating complex website structures, while using lower-effort modes for simpler steps like opening a target URL. In this paper, we propose Ares, a framework for per-step dynamic reasoning effort selection tailored for multi-step agent tasks. Ares employs a lightweight router to predict the lowest appropriate reasoning level for each step based on the interaction history. To train this router, we develop a data generation pipeline that identifies the minimum reasoning effort required for successful step completion. We then fine-tune the router to predict these levels, enabling plug-and-play integration for any LLM agents. We evaluate Ares on a diverse set of agent tasks, including TAU-Bench for tool use agents, BrowseComp-Plus for deep-research agents, and WebArena for web agents. Experimental results show that Ares reduces reasoning token usage by up to 52.7% compared to fixed high-effort reasoning, while introducing minimal degradation in task success rates.
[AI-64] Long-Short Term Agents for Pure-Vision Bronchoscopy Robotic Autonomy
【速读】：该论文旨在解决机器人辅助支气管镜介入手术中术中导航精度不足的问题，其核心挑战在于内窥镜视野有限及动态伪影干扰，且现有依赖外部定位技术（如电磁跟踪或形状感知）的导航平台存在硬件复杂性和术中解剖不匹配的风险。解决方案的关键在于提出一种纯视觉自主框架，利用术前CT生成的虚拟目标与实时内窥视频进行长期支气管镜导航，无需术中外部跟踪；该框架采用分层长短期智能体结构：短期反应式智能体实现低延迟运动控制，长期策略智能体在解剖模糊点提供决策支持，当两者建议冲突时，由世界模型评判器预测候选动作的未来视觉状态，并选择最接近目标视图的动作，从而实现高精度、无传感器依赖的自主导航。
链接: https://arxiv.org/abs/2603.07909
作者: Junyang Wu,Mingyi Luo,Fangfang Xie,Minghui Zhang,Hanxiao Zhang,Chunxi Zhang,Junhao Wang,Jiayuan Sun,Yun Gu,Guang-Zhong Yang
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Accurate intraoperative navigation is essential for robot-assisted endoluminal intervention, but remains difficult because of limited endoscopic field of view and dynamic artifacts. Existing navigation platforms often rely on external localization technologies, such as electromagnetic tracking or shape sensing, which increase hardware complexity and remain vulnerable to intraoperative anatomical mismatch. We present a vision-only autonomy framework that performs long-horizon bronchoscopic navigation using preoperative CT-derived virtual targets and live endoscopic video, without external tracking during navigation. The framework uses hierarchical long-short agents: a short-term reactive agent for continuous low-latency motion control, and a long-term strategic agent for decision support at anatomically ambiguous points. When their recommendations conflict, a world-model critic predicts future visual states for candidate actions and selects the action whose predicted state best matches the target view. We evaluated the system in a high-fidelity airway phantom, three ex vivo porcine lungs, and a live porcine model. The system reached all planned segmental targets in the phantom, maintained 80% success to the eighth generation ex vivo, and achieved in vivo navigation performance comparable to the expert bronchoscopist. These results support the preclinical feasibility of sensor-free autonomous bronchoscopic navigation.
[AI-65] EveryQuery: Zero-Shot Clinical Prediction via Task-Conditioned Pretraining over Electronic Health Records
【速读】：该论文旨在解决基于电子健康记录（Electronic Health Records, EHR）的生成式基础模型在临床预测中存在计算成本高、统计噪声大且不支持直接提示（promptable）的问题。传统方法依赖自回归推理生成患者未来轨迹，不仅效率低，还难以根据具体临床问题进行条件化预测。其解决方案的关键在于提出 EveryQuery——一种通过任务条件预训练（task-conditioned pre-training）实现零样本推理的基础模型：它不再生成未来事件序列，而是将患者的病史与结构化查询作为输入，通过单次前向传播直接估计特定结局在未来窗口内发生的概率。该设计使模型能够在无需微调、线性探测或轨迹生成的情况下，对任意查询空间中的任务实现零样本预测，显著提升了罕见临床事件的预测性能，并展现出优于自回归基线的泛化能力。
链接: https://arxiv.org/abs/2603.07900
作者: Payal Chandak,Gregory Kondas,Isaac Kohane,Matthew McDermott
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Foundation models pretrained on electronic health records (EHR) have demonstrated zero-shot clinical prediction capabilities by generating synthetic patient futures and aggregating statistics over sampled trajectories. However, this autoregressive inference procedure is computationally expensive, statistically noisy, and not natively promptable because users cannot directly condition predictions on specific clinical questions. In this preliminary work, we introduce EveryQuery, an EHR foundation model that achieves zero-shot inference through task-conditioned pre-training. Rather than generating future events, EveryQuery takes as input a patient’s history and a structured query specifying a clinical task, and directly estimates the likelihood of the outcome occurring in the future window via a single forward pass. EveryQuery realizes this capability by pre-training over randomly sampled combinations of query tasks and patient contexts, directly training the model to produce correct answers to arbitrary input prompts. This enables zero-shot prediction for any task in the query space without finetuning, linear probing, or trajectory generation. On MIMIC-IV, EveryQuery outperforms an autoregressive baseline on 82% of 39 randomly sampled prediction tasks, with a mean AUC improvement of +0.16 (95% CI: [0.10,0.22]). This advantage remains consistent on tasks that were explicitly held out from the pre-training distribution. Further, EveryQuery’s performance gains are most pronounced for rare clinical events, affirming and demonstrating a solution to the fundamental limitation of autoregressive inference for low-prevalence outcomes. However, at present, EveryQuery underperforms on tasks requiring disjunctive reasoning over multiple codes, such as 30-day readmission, exposing a concrete expressiveness limitation of the current query language.
[AI-66] SMGI: A Structural Theory of General Artificial Intelligence
【速读】：该论文旨在解决通用人工智能（General Artificial Intelligence, GAI）的理论建模问题，即如何从固定环境下的假设优化范式转向对学习接口自身结构演化的控制机制。其核心挑战在于构建一个具有数学严谨性的结构模型，以统一描述不同智能系统的学习行为并确保其在任务变换下的稳定性与泛化能力。解决方案的关键在于提出结构化通用智能模型（Structural Model of General Intelligence, SMGI），该模型通过一个带类型的元模型 θ = (r, 𝒫, Π, 𝒲, ℰ, 𝒎) 显式地将表示映射、假设空间、结构先验、多模式评估器和记忆操作符等组件形式化为动态且可类型化的构件，并严格区分结构本体（θ）与其诱导的行为语义（T_θ）。在此基础上，定义GAI为满足四项义务的可容许耦合动力系统（θ, T_θ）：类型变换下的结构封闭性、认证演化下的动力学稳定性、有界统计容量以及跨模式切换时的评估不变性。该框架不仅提供了连接序列PAC-Bayes分析与李雅普诺夫稳定性的结构泛化界，还证明了经典经验风险最小化、强化学习、程序先验模型（Solomonoff型）及前沿代理流水线均为SMGI的结构受限实例。
链接: https://arxiv.org/abs/2603.07896
作者: Aomar Osmani
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Preprint. 77 pages, 1 figure, 3 tables
Abstract:We introduce SMGI, a structural theory of general artificial intelligence, and recast the foundational problem of learning from the optimization of hypotheses within fixed environments to the controlled evolution of the learning interface itself. We formalize the Structural Model of General Intelligence (SMGI) via a typed meta-model \theta = (r,\mathcal H,\Pi,\mathcal L,\mathcal E,\mathcal M) that treats representational maps, hypothesis spaces, structural priors, multi-regime evaluators, and memory operators as explicitly typed, dynamic components. By enforcing a strict mathematical separation between this structural ontology ( \theta ) and its induced behavioral semantics ( T_\theta ), we define general artificial intelligence as a class of admissible coupled dynamics (\theta, T_\theta) satisfying four obligations: structural closure under typed transformations, dynamical stability under certified evolution, bounded statistical capacity, and evaluative invariance across regime shifts. We prove a structural generalization bound that links sequential PAC-Bayes analysis and Lyapunov stability, providing sufficient conditions for capacity control and bounded drift under admissible task transformations. Furthermore, we establish a strict structural inclusion theorem demonstrating that classical empirical risk minimization, reinforcement learning, program-prior models (Solomonoff-style), and modern frontier agentic pipelines operate as structurally restricted instances of SMGI.
[AI-67] Designing probabilistic AI monsoon forecasts to inform agricultural decision-making
【速读】：该论文旨在解决发展中国家农民在面临气候不确定性时，如何获得对其农业决策（如播种时间）具有实际指导意义的天气预报问题。由于农户的生产条件和风险承受能力存在显著异质性，传统统一的预报服务难以满足多样化需求。解决方案的关键在于提出一个基于决策理论的框架，将人工智能（AI）天气预测模型与一种新的“演化农户预期”统计模型相结合：后者利用贝叶斯推断对历史观测数据进行动态建模，以预测季节内首次事件（如季风初至）的时间变化概率；通过系统性地融合这两种模型，形成更具技巧性的子季节尺度预报，从而为大规模脆弱人群提供可操作的气候适应工具。
链接: https://arxiv.org/abs/2603.07893
作者: Colin Aitken,Rajat Masiwal,Adam Marchakitus,Katherine Kowal,Mayank Gupta,Tyler Yang,Amir Jina,Pedram Hassanzadeh,William R. Boos,Michael Kremer
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); General Economics (econ.GN); Atmospheric and Oceanic Physics (physics.ao-ph)
备注:
Abstract:Hundreds of millions of farmers make high-stakes decisions under uncertainty about future weather. Forecasts can inform these decisions, but available choices and their risks and benefits vary between farmers. We introduce a decision-theory framework for designing useful forecasts in settings where the forecaster cannot prescribe optimal actions because farmers’ circumstances are heterogeneous. We apply this framework to the case of seasonal onset of monsoon rains, a key date for planting decisions and agricultural investments in many tropical countries. We develop a system for tailoring forecasts to the requirements of this framework by blending systematically benchmarked artificial intelligence (AI) weather prediction models with a new “evolving farmer expectations” statistical model. This statistical model applies Bayesian inference to historical observations to predict time-varying probabilities of first-occurrence events throughout a season. The blended system yields more skillful Indian monsoon forecasts at longer lead times than its components or any multi-model average. In 2025, this system was deployed operationally in a government-led program that delivered subseasonal monsoon onset forecasts to 38 million Indian farmers, skillfully predicting that year’s early-summer anomalous dry period. This decision-theory framework and blending system offer a pathway for developing climate adaptation tools for large vulnerable populations around the world.
[AI-68] A Lightweight Traffic Map for Efficient Anytime LaCAM*
【速读】：该论文旨在解决多智能体路径规划（Multi-Agent Path Finding, MAPF）中现有引导路径方法在大规模场景下计算开销高、引导路径静态且仅对首次解有效的问题。其解决方案的关键在于利用LaCAM*在搜索过程中动态构建轻量级交通地图的能力，从而实现更高效、更具适应性的路径引导机制，显著提升解的质量并克服传统方法的局限性。
链接: https://arxiv.org/abs/2603.07891
作者: Bojie Shen,Yue Zhang,Zhe Chen,Daniel Harabor
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-Agent Path Finding (MAPF) aims to compute collision-free paths for multiple agents and has a wide range of practical applications. LaCAM*, an anytime configuration-based solver, currently represents the state of the art. Recent work has explored the use of guidance paths to steer LaCAM* toward configurations that avoid traffic congestion, thereby improving solution quality. However, existing approaches rely on Frank-Wolfe-style optimization that repeatedly invokes single-agent search before executing LaCAM*, resulting in substantial computational overhead for large-scale problems. Moreover, the guidance path is static and primarily beneficial for finding the first solution in LaCAM*. To address these limitations, we propose a new approach that leverages LaCAM*'s ability to construct a dynamic, lightweight traffic map during its search. Experimental results demonstrate that our method achieves higher solution quality than state-of-the-art guidance-path approaches across two MAPF variants.
[AI-69] Hospitality-VQA: Decision-Oriented Informativeness Evaluation for Vision-Language Models EACL2026
【速读】：该论文旨在解决当前视觉语言模型（Vision-Language Models, VLMs）在决策导向型领域（如酒店服务）中应用不足的问题，尤其是其在酒店设施图像上的视觉问答（Visual Question Answering, VQA）任务中对用户实际信息需求的响应能力有限。解决方案的关键在于提出“信息性”（Informativeness）作为量化框架，用于衡量图像-问题对在酒店场景下提供有用决策信息的程度，并基于此构建了一个专门面向住宿行业的VQA数据集，其中问题设计紧密贴合用户的核心信息需求。实验表明，未经领域微调的VLMs难以有效利用关键视觉线索进行可靠的信息推理，而经过适度领域特定微调后，模型才能展现出稳定的决策相关性理解能力。
链接: https://arxiv.org/abs/2603.07868
作者: Jeongwoo Lee,Baek Duhyeong,Eungyeol Han,Soyeon Shin,Gukin han,Seungduk Kim,Jaehyun Jeon,Taewoo Jeong
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at EACL 2026 SRW. 16 pages
Abstract:Recent advances in Vision-Language Models (VLMs) have demonstrated impressive multimodal understanding in general domains. However, their applicability to decision-oriented domains such as hospitality remains largely unexplored. In this work, we investigate how well VLMs can perform visual question answering (VQA) about hotel and facility images that are central to consumer decision-making. While many existing VQA benchmarks focus on factual correctness, they rarely capture what information users actually find useful. To address this, we first introduce Informativeness as a formal framework to quantify how much hospitality-relevant information an image-question pair provides. Guided by this framework, we construct a new hospitality-specific VQA dataset that covers various facility types, where questions are specifically designed to reflect key user information needs. Using this benchmark, we conduct experiments with several state-of-the-art VLMs, revealing that VLMs are not intrinsically decision-aware-key visual signals remain underutilized, and reliable informativeness reasoning emerges only after modest domain-specific finetuning.
[AI-70] Slumbering to Precision: Enhancing Artificial Neural Network Calibration Through Sleep-like Processes
【速读】：该论文旨在解决人工神经网络（Artificial Neural Networks, ANNs）普遍存在的过度自信问题，即模型预测概率与实际准确率不匹配，从而削弱了用户对模型输出的信任。为应对这一挑战，作者受生物睡眠及自发回放（spontaneous replay）在记忆巩固和学习中作用的启发，提出了一种名为Sleep Replay Consolidation (SRC) 的新型校准方法。其核心在于引入一个后训练阶段的“类睡眠”过程，通过选择性地重放网络内部表征来更新权重，从而提升模型校准性能，且无需监督微调。SRC的关键创新在于利用无监督的内在表征回放机制实现校准优化，与传统方法如温度缩放（temperature scaling）具有互补性，并在AlexNet和VGG19上结合使用时实现了最优的Brier分数与熵权衡，显著提升了模型置信度估计的可靠性。
链接: https://arxiv.org/abs/2603.07867
作者: Jean Erik Delanois,Aditya Ahuja,Giri P. Krishnan,Maxim Bazhenov
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Artificial neural networks are often overconfident, undermining trust because their predicted probabilities do not match actual accuracy. Inspired by biological sleep and the role of spontaneous replay in memory and learning, we introduce Sleep Replay Consolidation (SRC), a novel calibration approach. SRC is a post-training, sleep-like phase that selectively replays internal representations to update network weights and improve calibration without supervised retraining. Across multiple experiments, SRC is competitive with and complementary to standard approaches such as temperature scaling. Combining SRC with temperature scaling achieves the best Brier score and entropy trade-offs for AlexNet and VGG19. These results show that SRC provides a fundamentally novel approach to improving neural network calibration. SRC-based calibration offers a practical path toward more trustworthy confidence estimates and narrows the gap between human-like uncertainty handling and modern deep networks.
[AI-71] Intentional Deception as Controllable Capability in LLM Agents
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在多智能体系统中面临的有意欺骗（intentional deception）问题，尤其关注通过LLM-to-LLM交互实现的策略性操纵行为。其解决方案的关键在于构建一个参数化的行为画像测试平台（9种对齐方式 × 4种动机，共36种具有明确伦理基准的行为配置），并揭示一种两阶段攻击机制：首先推断目标智能体的动机与信念，继而生成具有误导性的回应（以真实陈述但战略性地框架信息为主，占比88.5%），从而引导目标采取违背其自身信念和动机的行为。研究发现，此类欺骗效果集中于特定行为画像而非均匀分布，且动机可被高精度（>98%）推断作为主要攻击向量，而信念系统则难以识别（上限约49%）。这表明当前依赖事实核查的防御策略不足以应对基于语境框架的对抗性响应，亟需针对高风险行为画像增强防护机制。
链接: https://arxiv.org/abs/2603.07848
作者: Jason Starace,Terence Soule
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As LLM-based agents increasingly operate in multi-agent systems, understanding adversarial manipulation becomes critical for defensive design. We present a systematic study of intentional deception as an engineered capability, using LLM-to-LLM interactions within a text-based RPG where parameterized behavioral profiles (9 alignments x 4 motivations, yielding 36 profiles with explicit ethical ground truth) serve as our experimental testbed. Unlike accidental deception from misalignment, we investigate a two-stage system that infers target agent characteristics and generates deceptive responses steering targets toward actions counter to their beliefs and motivations. We find that deceptive intervention produces differential effects concentrated in specific behavioral profiles rather than distributed uniformly, and that 88.5% of successful deceptions employ misdirection (true statements with strategic framing) rather than fabrication, indicating fact-checking defenses would miss the large majority of adversarial responses. Motivation, inferable at 98%+ accuracy, serves as the primary attack vector, while belief systems remain harder to identify (49% inference ceiling) or exploit. These findings identify which agent profiles require additional safeguards and suggest that current fact-verification approaches are insufficient against strategically framed deception.
[AI-72] Gradient Iterated Temporal-Difference Learning
【速读】：该论文旨在解决传统时序差分（Temporal-Difference, TD）学习中半梯度方法虽学习速度快但易发散，而梯度TD方法虽稳定却学习速度慢的问题。其关键解决方案是提出一种名为梯度迭代时序差分学习（Gradient Iterated Temporal-Difference learning）的新算法，通过在迭代TD框架中对移动目标（moving targets）计算梯度，从而在保持梯度TD方法稳定性的同时显著提升学习速度，使其在多个基准测试（包括Atari游戏）中达到与半梯度方法相当的性能。
链接: https://arxiv.org/abs/2603.07833
作者: Théo Vincent,Kevin Gerhardt,Yogesh Tripathi,Habib Maraqten,Adam White,Martha White,Jan Peters,Carlo D’Eramo
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Temporal-difference (TD) learning is highly effective at controlling and evaluating an agent’s long-term outcomes. Most approaches in this paradigm implement a semi-gradient update to boost the learning speed, which consists of ignoring the gradient of the bootstrapped estimate. While popular, this type of update is prone to divergence, as Baird’s counterexample illustrates. Gradient TD methods were introduced to overcome this issue, but have not been widely used, potentially due to issues with learning speed compared to semi-gradient methods. Recently, iterated TD learning was developed to increase the learning speed of TD methods. For that, it learns a sequence of action-value functions in parallel, where each function is optimized to represent the application of the Bellman operator over the previous function in the sequence. While promising, this algorithm can be unstable due to its semi-gradient nature, as each function tracks a moving target. In this work, we modify iterated TD learning by computing the gradients over those moving targets, aiming to build a powerful gradient TD method that competes with semi-gradient methods. Our evaluation reveals that this algorithm, called Gradient Iterated Temporal-Difference learning, has a competitive learning speed against semi-gradient methods across various benchmarks, including Atari games, a result that no prior work on gradient TD methods has demonstrated.
[AI-73] ProgAgent :A Continual RL Agent with Progress-Aware Rewards
【速读】：该论文旨在解决持续强化学习（Continual Reinforcement Learning, CRL）中面临的灾难性遗忘（catastrophic forgetting）和奖励设计成本高的问题。解决方案的关键在于提出ProgAgent，其核心创新是将基于未标注专家视频的进度感知奖励学习（progress-aware reward learning）与JAX原生的高吞吐量系统架构相结合：通过感知模型从初始、当前和目标观测中估计任务进展，构建一种可解释为状态势函数（state-potential function）的密集奖励信号，从而提供与专家行为一致的鲁棒引导；同时引入对抗性回推精化（adversarial push-back refinement）机制以稳定在线探索中的奖励模型预测，抑制对非专家轨迹的过自信输出并缓解分布偏移问题；最终在JIT编译循环中集成该奖励机制，实现大规模并行采样与全微分更新，统一PPO、核密度重放（coreset replay）和突触智能（synaptic intelligence）的目标，显著提升稳定性与可塑性的平衡。
链接: https://arxiv.org/abs/2603.07784
作者: Jinzhou Tan,Gabriel Adineera,Jinoh Kim
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We present ProgAgent, a continual reinforcement learning (CRL) agent that unifies progress-aware reward learning with a high-throughput, JAX-native system architecture. Lifelong robotic learning grapples with catastrophic forgetting and the high cost of reward specification. ProgAgent tackles these by deriving dense, shaped rewards from unlabeled expert videos through a perceptual model that estimates task progress across initial, current, and goal observations. We theoretically interpret this as a learned state-potential function, delivering robust guidance in line with expert behaviors. To maintain stability amid online exploration - where novel, out-of-distribution states arise - we incorporate an adversarial push-back refinement that regularizes the reward model, curbing overconfident predictions on non-expert trajectories and countering distribution shift. By embedding this reward mechanism into a JIT-compiled loop, ProgAgent supports massively parallel rollouts and fully differentiable updates, rendering a sophisticated unified objective feasible: it merges PPO with coreset replay and synaptic intelligence for an enhanced stability-plasticity balance. Evaluations on ContinualBench and Meta-World benchmarks highlight ProgAgent’s advantages: it markedly reduces forgetting, boosts learning speed, and outperforms key baselines in visual reward learning (e.g., Rank2Reward, TCN) and continual learning (e.g., Coreset, SI) - surpassing even an idealized perfect memory agent. Real-robot trials further validate its ability to acquire complex manipulation skills from noisy, few-shot human demonstrations.
[AI-74] Hide and Find: A Distributed Adversarial Attack on Federated Graph Learning ICLR2026
【速读】：该论文旨在解决联邦图学习（Federated Graph Learning, FedGL）在面对恶意攻击时的脆弱性问题，尤其是现有攻击方法普遍存在攻击成功率低、计算成本高以及易被防御算法识别和抑制的缺陷。其解决方案的关键在于提出一种两阶段“隐藏与寻找”（Hide and Find）分布式对抗攻击方法——第一阶段在联邦训练开始前，向部分训练数据中注入一个可学习且隐蔽的“移位器”（shifter），该移位器通过微调图表示使其逼近目标类别的决策边界但不跨越边界，从而保障攻击的隐蔽性；第二阶段在联邦聚合完成后，利用全局模型信息以该隐藏移位器为优化起点高效搜索对抗扰动，并聚合多个恶意客户端的扰动生成最终有效的对抗样本，实现高隐蔽性、强鲁棒性和显著的计算效率提升（时间成本降低超90%）。
链接: https://arxiv.org/abs/2603.07743
作者: Jinshan Liu,Ken Li,Jiazhe Wei,Bin Shi,Bo Dong
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at ICLR 2026 Workshop: Principled Design for Trustworthy AI
Abstract:Federated Graph Learning (FedGL) is vulnerable to malicious attacks, yet developing a truly effective and stealthy attack method remains a significant challenge. Existing attack methods suffer from low attack success rates, high computational costs, and are easily identified and smoothed by defense algorithms. To address these challenges, we propose \textbfFedShift, a novel two-stage “Hide and Find” distributed adversarial attack. In the first stage, before FedGL begins, we inject a learnable and hidden “shifter” into part of the training data, which subtly pushes poisoned graph representations toward a target class’s decision boundary without crossing it, ensuring attack stealthiness during training. In the second stage, after FedGL is complete, we leverage the global model information and use the hidden shifter as an optimization starting point to efficiently find the adversarial perturbations. During the final attack, we aggregate these perturbations from multiple malicious clients to form the final effective adversarial sample and trigger the attack. Extensive experiments on six large-scale datasets demonstrate that our method achieves the highest attack effectiveness compared to existing advanced attack methods. In particular, our attack can effectively evade 3 mainstream robust federated learning defense algorithms and converges with a time cost reduction of over 90%, highlighting its exceptional stealthiness, robustness, and efficiency.
[AI-75] A Novel Multi-Agent Architecture to Reduce Hallucinations of Large Language Models in Multi-Step Structural Modeling
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在处理多步骤结构建模任务时因频繁幻觉和错误累积导致的可靠性不足问题。现有方法难以稳定地自动化完成从用户描述到OpenSeesPy脚本生成的全过程，尤其在复杂结构分析中表现不稳定。解决方案的关键在于提出一种新颖的多智能体架构（multi-agent architecture），通过分工协作实现结构建模与分析的自动化：问题分析与施工规划智能体提取关键参数并制定分步计划；节点与单元智能体并行构建框架几何；荷载分配智能体负责加载信息处理；代码转换智能体将几何与荷载信息转化为可执行的OpenSeesPy脚本。该架构显著提升了准确性和计算效率，并具备向更大规模结构系统扩展的能力。
链接: https://arxiv.org/abs/2603.07728
作者: Ziheng Geng,Jiachen Liu,Ran Cao,Lu Cheng,Dan M. Frangopol,Minghui Cheng
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) such as GPT and Gemini have demonstrated remarkable capabilities in contextual understanding and reasoning. The strong performance of LLMs has sparked growing interest in leveraging them to automate tasks traditionally dependent on human expertise. Recently, LLMs have been integrated into intelligent agents capable of operating structural analysis software (e.g., OpenSees) to construct structural models and perform analyses. However, existing LLMs are limited in handling multi-step structural modeling due to frequent hallucinations and error accumulation during long-sequence operations. To this end, this study presents a novel multi-agent architecture to automate the structural modeling and analysis using OpenSeesPy. First, problem analysis and construction planning agents extract key parameters from user descriptions and formulate a stepwise modeling plan. Node and element agents then operate in parallel to assemble the frame geometry, followed by a load assignment agent. The resulting geometric and load information is translated into executable OpenSeesPy scripts by code translation agents. The proposed architecture is evaluated on a benchmark of 20 frame problems over ten repeated trials, achieving 100% accuracy in 18 cases and 90% in the remaining two. The architecture also significantly improves computational efficiency and demonstrates scalability to larger structural systems.
[AI-76] VoiceSHIELD-Small: Real-Time Malicious Speech Detection and Transcription
【速读】：该论文旨在解决语音交互式人工智能系统中存在的安全风险问题，如提示注入（prompt injection）、社会工程攻击和有害语音指令等，这些问题传统上依赖于将语音转换为文本后再进行过滤的方法来检测，但此类方法存在延迟高且可能忽略关键音频特征的局限性。解决方案的关键在于提出 VoiceSHIELD-Small——一个轻量级、实时运行的端到端模型，它基于 OpenAI 的 Whisper-small 编码器，在其基础上增加了均值池化层和简单分类头，能够在同一时间完成语音转录与安全性判断，无需额外的文本处理步骤；该模型在中等性能 GPU 上仅需 90–120 毫秒即可完成分类，且在包含 947 个音频片段的平衡数据集上实现了 99.16% 的准确率和 0.9865 的 F1 分数，显著提升了语音 AI 安全检测的效率与准确性。
链接: https://arxiv.org/abs/2603.07708
作者: Sumit Ranjan,Sugandha Sharma,Ubaid Abbas,Puneeth N Ail
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: 17 pages, 9 figures
Abstract:Voice interfaces are quickly becoming a common way for people to interact with AI systems. This also brings new security risks, such as prompt injection, social engineering, and harmful voice commands. Traditional security methods rely on converting speech to text and then filtering that text, which introduces delays and can ignore important audio cues. This paper introduces VoiceSHIELD-Small, a lightweight model that works in real time. It can transcribe speech and detect whether it is safe or harmful, all in one step. Built on OpenAI’s Whisper-small encoder, VoiceSHIELD adds a mean-pooling layer and a simple classification head. It takes just 90-120 milliseconds to classify audio on mid-tier GPUs, while transcription happens at the same time. Tested on a balanced set of 947 audio clips, the model achieved 99.16 percent accuracy and an F1 score of 0.9865. At the default setting, it missed 2.33 percent of harmful inputs. Cross-validation showed consistent performance (F1 standard deviation = 0.0026). The paper also covers the model’s design, training data, performance trade-offs, and responsible use guidelines. VoiceSHIELD is released under the MIT license to encourage further research and adoption in voice AI security.
[AI-77] Memory for Autonomous LLM Agents :Mechanisms Evaluation and Emerging Frontiers
【速读】：该论文旨在解决大型语言模型（Large Language Model, LLM）代理在长期交互中因单次上下文窗口容量有限而导致的“记忆缺失”问题，即如何实现跨会话的信息持久化、组织与选择性召回，从而将状态无关的文本生成器转变为具备适应能力的智能体。其核心解决方案在于提出一个由“写入—管理—读取”构成的闭环机制，并构建了一个涵盖时间范围、表征载体和控制策略的三维分类体系，系统梳理了五类关键记忆机制：基于上下文的压缩、检索增强存储、反思式自我改进、分层虚拟上下文以及策略学习的管理方法，强调通过结构化设计与多维评估手段提升LLM代理的记忆能力，以支撑复杂场景下的持续决策与行为优化。
链接: https://arxiv.org/abs/2603.07670
作者: Pengfei Du
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents increasingly operate in settings where a single context window is far too small to capture what has happened, what was learned, and what should not be repeated. Memory – the ability to persist, organize, and selectively recall information across interactions – is what turns a stateless text generator into a genuinely adaptive agent. This survey offers a structured account of how memory is designed, implemented, and evaluated in modern LLM-based agents, covering work from 2022 through early 2026. We formalize agent memory as a \emphwrite–manage–read loop tightly coupled with perception and action, then introduce a three-dimensional taxonomy spanning temporal scope, representational substrate, and control policy. Five mechanism families are examined in depth: context-resident compression, retrieval-augmented stores, reflective self-improvement, hierarchical virtual context, and policy-learned management. On the evaluation side, we trace the shift from static recall benchmarks to multi-session agentic tests that interleave memory with decision-making, analyzing four recent benchmarks that expose stubborn gaps in current systems. We also survey applications where memory is the differentiating factor – personal assistants, coding agents, open-world games, scientific reasoning, and multi-agent teamwork – and address the engineering realities of write-path filtering, contradiction handling, latency budgets, and privacy governance. The paper closes with open challenges: continual consolidation, causally grounded retrieval, trustworthy reflection, learned forgetting, and multimodal embodied memory.
[AI-78] SMAT: Staged Multi-Agent Training for Co-Adaptive Exoskeleton Control
【速读】：该论文旨在解决外骨骼辅助系统在实际应用中因人体运动适应性变化而导致的控制不稳定问题，即“非平稳学习问题”（non-stationary learning problem）——当外骨骼改变关节动力学时，用户需重新组织神经肌肉协调机制，而现有基于学习的方法未充分考虑这一顺序适应过程，导致训练不稳定且辅助时机不当。解决方案的关键在于提出分阶段多智能体训练（Staged Multi-Agent Training, SMAT），其核心是模仿人类自然适应穿戴设备的过程：首先训练人体模型在无辅助下行走，再逐步引入外骨骼质量、固定人体策略下的正向辅助模式，最终实现双智能体在全扭矩能力和双向反馈下的协同适应。该方法显著提升了外骨骼控制策略的稳定性与通用性，实验证明其能在无需个体化调参的情况下为五名受试者提供一致且高效的机械功率输出（平均正向功率13.6–23.8 W），并降低髋部肌肉激活强度达10.1%。
链接: https://arxiv.org/abs/2603.07618
作者: Yifei Yuan,Ghaith Androwis,Xianlian Zhou
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Effective exoskeleton assistance requires co-adaptation: as the device alters joint dynamics, the user reorganizes neuromuscular coordination, creating a non-stationary learning problem. Most learning-based approaches do not explicitly account for the sequential nature of human motor adaptation, leading to training instability and poorly timed assistance. We propose Staged Multi-Agent Training (SMAT), a four-stage curriculum designed to mirror how users naturally acclimate to a wearable device. In SMAT, a musculoskeletal human actor and a bilateral hip exoskeleton actor are trained progressively: the human first learns unassisted gait, then adapts to the added device mass; the exoskeleton subsequently learns a positive assistance pattern against a stabilized human policy, and finally both agents co-adapt with full torque capacity and bidirectional feedback. We implement SMAT in the MyoAssist simulation environment using a 26-muscle lower-limb model and an attached hip exoskeleton. Our musculoskeletal simulations demonstrate that the learned exoskeleton control policy produces an average 10.1% reduction in hip muscle activation relative to the no-assist condition. We validated the learned controller in an offline setting using open-source gait data, then deployed it to a physical hip exoskeleton for treadmill experiments with five subjects. The resulting policy delivers consistent assistance and predominantly positive mechanical power without the need for any explicitly imposed timing shift (mean positive power: 13.6 W at 6 Nm RMS torque to 23.8 W at 9.3 Nm RMS torque, with minimal negative power) consistently across all subjects without subject-specific retraining.
[AI-79] Shorter Thoughts Same Answers: Difficulty-Scaled Segment-Wise RL for CoT Compression
【速读】：该论文旨在解决链式思维（Chain-of-thought, CoT）推理过程中因显式推理路径过长而导致的token成本过高问题，同时避免现有基于强化学习（Reinforcement Learning, RL）的压缩方法在削减推理步骤时误剪用户可见的回答内容。其核心挑战在于：最短有效推理长度具有非通用性，受任务难度、模型容量和训练状态影响；且单一完成度学习信号会跨“思考”与“回答”边界泄露，导致答案质量下降。解决方案的关键在于提出难度感知的分段相对优势策略优化（Difficulty-Scaled Segment-Wise GRPO, DSS-GRPO），通过将回报分解为思考和回答两部分，对每个片段分别计算组内相对优势，并利用硬性token掩码隔离更新作用域——仅在思考阶段进行压缩优化，而在回答阶段保持对齐，从而实现简洁推理而不损害最终输出质量。
链接: https://arxiv.org/abs/2603.07598
作者: Ye Tian,Aijun Liu
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 12 pages, 3 figures. Preprint. Code available at the GitHub project repository
Abstract:Chain-of-thought (CoT) improves reasoning reliability but increases token cost, motivating post-training compression of explicit reasoning traces. However, the shortest sufficient reasoning is not universal: it depends on difficulty, model capacity, and training state, making fixed length targets brittle. In practice, naive RL-based compression can also undesirably shorten the user-facing answer, because a single completion-level learning signal leaks across the think/answer boundary. We propose Difficulty-Scaled Segment-Wise GRPO (DSS-GRPO), which decomposes returns into think and answer components, computes group-relative advantages per segment, and routes them with hard token masks so compression updates act only on think while answer alignment acts only on answer. DSS-GRPO uses prompt-wise within-group shaping and difficulty-aware scaling to encourage concise reasoning without collapsing answer behavior.
[AI-80] argeted Speaker Poisoning Framework in Zero-Shot Text-to-Speech INTERSPEECH2026
【速读】：该论文旨在解决零样本文本到语音（Zero-shot Text-to-Speech, TTS）模型中存在的隐私风险问题，即如何从训练好的TTS模型中移除特定说话人的身份信息，同时保持模型对其他说话人语音生成的性能。其核心解决方案是提出了一种新的任务框架——语音生成说话人投毒（Speech Generation Speaker Poisoning, SGSP），通过在推理阶段引入过滤机制或修改模型参数的方式，使模型无法生成被遗忘的特定说话人声音，从而实现隐私保护。关键在于在模型实用性（以词错误率WER衡量）与隐私保护效果（通过AUC和遗忘说话人相似度FSSIM量化）之间取得平衡，并揭示了当前方法在处理大规模遗忘对象（如100个说话人）时因身份重叠导致的可扩展性局限。
链接: https://arxiv.org/abs/2603.07551
作者: Thanapat Trachu,Thanathai Lertpetchpun,Sai Praneeth Karimireddy,Shrikanth Narayanan
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: Submitted to Interspeech2026
Abstract:Zero-shot Text-to-Speech (TTS) voice cloning poses severe privacy risks, demanding the removal of specific speaker identities from trained TTS models. Conventional machine unlearning is insufficient in this context, as zero-shot TTS can dynamically reconstruct voices from just reference prompts. We formalize this task as Speech Generation Speaker Poisoning (SGSP), in which we modify trained models to prevent the generation of specific identities while preserving utility for other speakers. We evaluate inference-time filtering and parameter-modification baselines across 1, 15, and 100 forgotten speakers. Performance is assessed through the trade-off between utility (WER) and privacy, quantified using AUC and Forget Speaker Similarity (FSSIM). We achieve strong privacy for up to 15 speakers but reveal scalability limits at 100 speakers due to increased identity overlap. Our study thus introduces a novel problem and evaluation framework toward further advances in generative voice privacy.
[AI-81] COOL-MC: Verifying and Explaining RL Policies for Multi-bridge Network Maintenance
【速读】：该论文旨在解决老化桥梁网络在维护决策中缺乏形式化安全保证与可解释性的问题，传统强化学习（Reinforcement Learning, RL）策略仅依赖奖励信号训练，难以提供可验证的安全边界且对基础设施管理者不透明。解决方案的关键在于提出COOL-MC框架，其核心是基于文献中的单桥马尔可夫决策过程（Markov Decision Process, MDP）扩展为包含三座异构桥梁的并行网络，并引入共享周期预算约束，用PRISM建模语言进行形式化描述；随后在训练得到的RL策略与MDP交互生成的离散时间马尔可夫链（Discrete-Time Markov Chain, DTMC）基础上，结合概率模型检测（Probabilistic Model Checking）与可解释性分析方法，实现对RL维护策略的形式化验证和行为洞察，例如发现策略存在对桥1的状态系统性偏倚及3.5%的安全违规概率，从而为基础设施管理提供可信赖、可理解的决策支持。
链接: https://arxiv.org/abs/2603.07546
作者: Dennis Gross
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Aging bridge networks require proactive, verifiable, and interpretable maintenance strategies, yet reinforcement learning (RL) policies trained solely on reward signals provide no formal safety guarantees and remain opaque to infrastructure managers. We demonstrate COOL-MC as a tool for verifying and explaining RL policies for multi-bridge network maintenance, building on a single-bridge Markov decision process (MDP) from the literature and extending it to a parallel network of three heterogeneous bridges with a shared periodic budget constraint, encoded in the PRISM modeling language. We train an RL agent on this MDP and apply probabilistic model checking and explainability methods to the induced discrete-time Markov chain (DTMC) that arises from the interaction between the learned policy and the underlying MDP. Probabilistic model checking reveals that the trained policy has a safety-violation probability of 3.5% over the planning horizon, being slightly above the theoretical minimum of 0% and indicating the suboptimality of the learned policy, noting that these results are based on artificially constructed transition probabilities and deterioration rates rather than real-world data, so absolute performance figures should be interpreted with caution. The explainability analysis further reveals, for instance, a systematic bias in the trained policy toward the state of bridge 1 over the remaining bridges in the network. These results demonstrate COOL-MC’s ability to provide formal, interpretable, and practical analysis of RL maintenance policies.
[AI-82] Neural Dynamics-Informed Pre-trained Framework for Personalized Brain Functional Network Construction
【速读】：该论文旨在解决当前主流脑功能网络构建方法在异质场景下难以精确捕捉神经活动模式变化的问题，这些问题源于传统方法依赖预定义脑图谱和线性假设，导致所构建的功能网络一致性与泛化能力受限。解决方案的关键在于提出一种基于神经动力学信息的预训练框架，通过提取异质场景中个性化的神经活动模式表征，利用这些表征指导脑分区和神经活动相关性估计，从而实现个性化脑功能网络的构建，显著提升了在复杂多变条件下的性能表现。
链接: https://arxiv.org/abs/2603.07524
作者: Hongjie Jiang,Yifei Tang,Shuqiang Wang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Brain activity is intrinsically a neural dynamic process constrained by anatomical space. This leads to significant variations in spatial distribution patterns and correlation patterns of neural activity across variable and heterogeneous scenarios. However, dominant brain functional network construction methods, which relies on pre-defined brain atlases and linear assumptions, fails to precisely capture varying neural activity patterns in heterogeneous scenarios. This limits the consistency and generalizability of the brain functional networks constructed by dominant methods. Here, a neural dynamics-informed pre-trained framework is proposed for personalized brain functional network construction. The proposed framework extracts personalized representations of neural activity patterns in heterogeneous scenarios. Personalized brain functional networks are obtained by utilizing these representations to guide brain parcellation and neural activity correlation estimation. Systematic evaluations were employed on 18 datasets across tasks, such as virtual neural modulation and abnormal neural circuit identification. Experimental results demonstrate that the proposed framework attains superior performance in heterogeneous scenarios. Overall, the proposed framework challenges the dominant brain functional network construction method.
[AI-83] InterReal: A Unified Physics-Based Imitation Framework for Learning Human-Object Interaction Skills
【速读】：该论文旨在解决当前人形机器人在现实世界中与物体交互（Human-Object Interaction, HOI）时面临的两大挑战：一是现有框架多聚焦于非交互式的全身控制，难以实现精细的交互技能学习；二是大规模奖励函数设计困难，影响策略学习效率与稳定性。解决方案的关键在于提出一个统一的基于物理的模仿学习框架InterReal，其核心创新包括：1）引入带手-物接触约束的HOI运动数据增强方法，提升策略在物体扰动下的鲁棒性；2）设计自动奖励学习机制，通过元策略（meta-policy）依据关键跟踪误差指标探索并分配奖励信号至底层强化学习目标，从而高效优化交互策略。实验表明，该框架在盒体拾取和推动任务中显著优于现有基线，并在真实机器人Unitree G1上验证了其实际有效性与鲁棒性。
链接: https://arxiv.org/abs/2603.07516
作者: Dayang Liang,Yuhang Lin,Xinzhe Liu,Jiyuan Shi,Yunlong Liu,Chenjia Bai
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Interaction is one of the core abilities of humanoid robots. However, most existing frameworks focus on non-interactive whole-body control, which limits their practical applicability. In this work, we develop InterReal, a unified physics-based imitation learning framework for Real-world human-object Interaction (HOI) control. InterReal enables humanoid robots to track HOI reference motions, facilitating the learning of fine-grained interactive skills and their deployment in real-world settings. Within this framework, we first introduce a HOI motion data augmentation scheme with hand-object contact constraints, and utilize the augmented motions to improve policy stability under object perturbations. Second, we propose an automatic reward learner to address the challenge of large-scale reward shaping. A meta-policy guided by critical tracking error metrics explores and allocates reward signals to the low-level reinforcement learning objective, which enables more effective learning of interactive policies. Experiments on HOI tasks of box-picking and box-pushing demonstrate that InterReal achieves the best tracking accuracy and the highest task success rate compared to recent baselines. Furthermore, we validate the framework on the real-world robot Unitree G1, which demonstrates its practical effectiveness and robustness beyond simulation.
[AI-84] From Thinker to Society: Security in Hierarchical Autonomy Evolution of AI Agents
【速读】：该论文旨在解决生成式 AI（Generative AI）代理在向自主决策和环境交互演进过程中引入的新型安全漏洞问题，这些问题超出了传统安全框架的覆盖范围。其解决方案的关键在于提出一个分层的“层级自主进化”（Hierarchical Autonomy Evolution, HAE）框架，将代理安全划分为三个层次：认知自主性（L1）保障内部推理完整性，执行自主性（L2）管理工具驱动的环境交互，集体自主性（L3）应对多代理生态系统中的系统性风险，并据此构建涵盖认知操纵、物理环境干扰和多代理系统失效的威胁分类体系，从而为可信AI代理系统的多层次、自主感知防御架构提供理论指导与研究方向。
链接: https://arxiv.org/abs/2603.07496
作者: Xiaolei Zhang,Lu Zhou,Xiaogang Xu,Jiafei Wu,Tianyu Du,Heqing Huang,Hao Peng,Zhe Liu
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Artificial Intelligence (AI) agents have evolved from passive predictive tools into active entities capable of autonomous decision-making and environmental interaction, driven by the reasoning capabilities of Large Language Models (LLMs). However, this evolution has introduced critical security vulnerabilities that existing frameworks fail to address. The Hierarchical Autonomy Evolution (HAE) framework organizes agent security into three tiers: Cognitive Autonomy (L1) targets internal reasoning integrity; Execution Autonomy (L2) covers tool-mediated environmental interaction; Collective Autonomy (L3) addresses systemic risks in multi-agent ecosystems. We present a taxonomy of threats spanning cognitive manipulation, physical environment disruption, and multi-agent systemic failures, and evaluate existing defenses while identifying key research gaps. The findings aim to guide the development of multilayered, autonomy-aware defense architectures for trustworthy AI agent systems.
[AI-85] Interpretable-by-Design Transformers via Architectural Stream Independence
【速读】：该论文旨在解决Transformer模型内部决策过程缺乏可解释性的问题，即如何在不依赖后验分析的前提下，通过架构设计实现模型行为的透明化。其解决方案的关键在于引入架构流独立性（architectural stream independence）：将携带符号结构的词元流（token stream）与上下文语义流分离处理，在整个计算过程中保持各自独立可观测，仅在输出阶段进行融合。这一原则通过提出的**延迟融合架构（Late Fusion Architecture, LFA）**得以验证，LFA 在所有最终层中均表现出可解释的符号头部（symbolic heads），而标准Transformer在第三层后即出现符号信息溶解；量化指标Token-Position Dependence Score（PDS）显示LFA的PDS_max为0.276，显著低于基线的0.058。更重要的是，干预实验表明LFA具备功能模块化特性，抑制其近期头部仅造成微小语义损伤（Cohen’s d = -0.158），远优于基线的灾难性纠缠（d = -0.672），从而证明架构约束能有效提升学习机制的稳定性与可解释性，使模型更倾向于语义理解而非位置启发式学习。
链接: https://arxiv.org/abs/2603.07482
作者: Clayton Kerce,Alexis Fox
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:While transformers achieve strong performance, their internal decision-making processes remain opaque. We investigate whether architectural constraints can enforce interpretability by design through architectural stream independence: maintaining a token stream (carrying symbolic structure) and contextual semantics in separated streams that remain independently observable throughout processing, with integration delayed until output. We validate this principle through the Late Fusion Architecture (LFA), which demonstrates interpretable symbolic heads through all the final layers, while standard transformers show dissolution by the third of six layers; we quantify this effect by introducing the Token-Position Dependence Score (PDS), with PDS_max = 0.276 and 0.058, respectively. Crucially, intervention experiments demonstrate functional modularity: suppressing LFA’s recency heads causes minimal semantic damage (Cohen’s d = -0.158) versus catastrophic entanglement in baselines (d = -0.672). LFA demonstrates that architectural constraints improve underlying learning mechanisms, averaging 42% stability versus 19% and 11% for baseline comparisons, with extremes from 50% on LFA’s best pairs (6 of 12 heads position-invariant) down to 0% complete collapse in over-constrained cases. By preventing premature entanglement, architectural independence steers models toward semantic understanding over positional heuristics, establishing interpretability as an architectural design criterion enforceable through structural constraints rather than post-hoc analysis.
[AI-86] Give Them an Inch and They Will Take a Mile:Understanding and Measuring Caller Identity Confusion in MCP-Based AI Systems
【速读】：该论文旨在解决基于模型上下文协议（Model Context Protocol, MCP）的AI代理系统中存在的安全漏洞问题，特别是由于MCP服务器被默认视为可信实体而缺乏对调用方身份的认证机制所引发的风险。研究发现，多数MCP服务器依赖持久化的授权状态，在初始授权后无需重新认证即可允许工具调用，且未在工具级别强制执行访问控制，从而导致未经授权的访问和潜在的权限滥用。解决方案的关键在于引入显式的调用方身份认证机制，并实施细粒度的授权策略，以防止因单次授权和服务器级信任带来的攻击面扩大问题。
链接: https://arxiv.org/abs/2603.07473
作者: Yuhang Huang,Boyang Ma,Biwei Yan,Xuelong Dai,Yechao Zhang,Minghui Xu,Kaidi Xu,Yue Zhang
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:The Model Context Protocol (MCP) is an open and standardized interface that enables large language models (LLMs) to interact with external tools and services, and is increasingly adopted by AI agents. However, the security of MCP-based systems remains largely this http URL this work, we conduct a large-scale security analysis of MCP servers integrated within MCP clients. We show that treating MCP servers as trusted entities without authenticating the caller identity is fundamentally insecure. Since MCP servers often cannot distinguish who is invoking a request, a single authorization decision may implicitly grant access to multiple, potentially untrusted this http URL empirical study reveals that most MCP servers rely on persistent authorization states, allowing tool invocations after an initial authorization without re-authentication, regardless of the caller. In addition, many MCP servers fail to enforce authentication at the per-tool level, enabling unauthorized access to sensitive this http URL findings demonstrate that one-time authorization and server-level trust significantly expand the attack surface of MCP-based systems, highlighting the need for explicit caller authentication and fine-grained authorization mechanisms.
[AI-87] Contact-Guided 3D Genome Structure Generation of E. coli via Diffusion Transformers ICLR2026
【速读】：该论文旨在解决三维基因组结构重建中如何从Hi-C接触图谱生成具有异质性的多构象 ensemble 的问题，而非仅生成单一确定性结构。其核心挑战在于如何在保持与Hi-C数据一致性的前提下，同时捕捉基因组构象的多样性。解决方案的关键在于提出了一种条件扩散-Transformer 框架（conditional diffusion-transformer framework），将基因组重构建模为一个条件生成任务：通过变分自编码器（VAE）在潜在空间中对染色质片段进行对齐保留和复制感知表示，利用基于Transformer的编码器和交叉注意力机制将Hi-C信息以单向物理可解释约束注入生成过程，并采用流匹配目标（flow-matching objective）实现稳定优化，从而在验证集上成功重现输入Hi-C的距离衰减规律和结构相关性指标，同时维持显著的构象多样性。
链接: https://arxiv.org/abs/2603.07472
作者: Mingxin Zhang,Xiaofeng Dai,Yu Yao,Ziqi Yin
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at the Gen2 Workshop at ICLR 2026
Abstract:In this study, we present a conditional diffusion-transformer framework for generating ensembles of three-dimensional Escherichia coli genome conformations guided by Hi-C contact maps. Instead of producing a single deterministic structure, we formulate genome reconstruction as a conditional generative modeling problem that samples heterogeneous conformations whose ensemble-averaged contacts are consistent with the input Hi-C data. A synthetic dataset is constructed using coarse-grained molecular dynamics simulations to generate chromatin ensembles and corresponding Hi-C maps under circular topology. Our models operate in a latent diffusion setting with a variational autoencoder that preserves per-bin alignment and supports replication-aware representations. Hi-C information is injected through a transformer-based encoder and cross-attention, enforcing a physically interpretable one-way constraint from Hi-C to structure. The model is trained using a flow-matching objective for stable optimization. On held-out ensembles, generated structures reproduce the input Hi-C distance-decay and structural correlation metrics while maintaining substantial conformational diversity, demonstrating the effectiveness of diffusion-based generative modeling for ensemble-level 3D genome reconstruction.
[AI-88] Do Machines Fail Like Humans? A Human-Centred Out-of-Distribution Spectrum for Mapping Error Alignment
【速读】：该论文旨在解决当前AI系统与人类在信息处理机制上是否一致的问题，这是认知科学和可信人工智能（Trustworthy AI）研究的核心议题。尽管现代AI模型在标准任务上已达到与人类相当的准确率，但这种性能对齐并不意味着其决策策略与人类的信息加工方式一致。为更精细地刻画模型与人类的对齐程度，论文提出一种以人类感知难度为基准的人类中心框架（human-centred framework），其关键在于将分布外（Out-of-Distribution, OOD）偏差重新定义为一个连续的、基于人类准确率变化的感知难度谱（OOD spectrum）。通过量化刺激集相对于无畸变参考集的人类准确性偏离程度，该框架识别出四个不同层级的感知挑战区域，并实现跨条件、校准难度水平下的模型-人类一致性比较。实验表明，视觉语言模型（Vision-Language Models）在近域和远域OOD条件下均最接近人类表现，而卷积神经网络（CNNs）与视觉变换器（ViTs）在不同难度区间表现出互补性的对齐优势，凸显了考虑跨条件感知难度差异对于建立严谨模型-人类对齐评估的重要性。
链接: https://arxiv.org/abs/2603.07462
作者: Binxia Xu,Xiaoliang Luo,Luke Dickens,Robert M. Mok
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Determining whether AI systems process information similarly to humans is central to cognitive science and trustworthy AI. While modern AI models match human accuracy on standard tasks, such parity does not guarantee that their underlying decision-making strategies are aligned with human information processing. Assessing performance using i) error alignment metrics to compare how humans and models fail, and ii) using distorted, or otherwise more challenging, stimuli, provides a viable pathway toward a finer characterization of model-human alignment. However, existing out-of-distribution (OOD) analyses for challenging stimuli are limited due to methodological choices: they define OOD shift relative to model training data or use arbitrary distortion-specific parameters with little correspondence to human perception, hindering principled comparisons. We propose a human-centred framework that redefines the degree of OOD as a spectrum of human perceptual difficulty. By quantifying how much a collection of stimuli deviates from an undistorted reference set based on human accuracy, we construct an OOD spectrum and identify four distinct regimes of perceptual challenge. This approach enables principled model-human comparisons at calibrated difficulty levels. We apply this framework to object recognition and reveal unique, regime-dependent model-human alignment rankings and profiles across deep learning architectures. Vision-language models are the most consistently human aligned across near- and far-OOD conditions, but CNNs are more aligned than ViTs for near-OOD and ViTs are more aligned than CNNs for far-OOD conditions. Our work demonstrates the critical importance of accounting for cross-condition differences such as perceptual difficulty for a principled assessment of model-human alignment.
[AI-89] Where Do LLM -based Systems Break? A System-Level Security Framework for Risk Assessment and Treatment
【速读】：该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在安全关键场景中部署时，其安全风险分析碎片化、缺乏系统性建模以及难以与实际攻击路径和防御策略关联的问题。解决方案的关键在于提出一种目标驱动的风险评估框架，该框架融合系统建模、攻击-防御树（Attack-Defense Trees, ADTrees）和基于通用漏洞评分系统（Common Vulnerability Scoring System, CVSS）的可利用性评分机制，从而实现对多阶段攻击路径的结构化识别与量化比较，并揭示跨传统网络安全、对抗机器学习和对话式攻击等不同威胁类型的共性瓶颈，支持针对性防御措施的有效部署与风险优先级排序。
链接: https://arxiv.org/abs/2603.07460
作者: Neha Nagaraja,Hayretdin Bahsi
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) are increasingly integrated into safety-critical workflows, yet existing security analyses remain fragmented and often isolate model behavior from the broader system context. This work introduces a goal-driven risk assessment framework for LLM-powered systems that combines system modeling with Attack-Defense Trees (ADTrees) and Common Vulnerability Scoring System (CVSS)-based exploitability scoring to support structured, comparable analysis. We demonstrate the framework through a healthcare case study, modeling multi-step attack paths targeting intervention in medical procedures, leakage of electronic health record (EHR) data, and disruption of service availability. Our analysis indicates that threats spanning (i) conventional cyber, (ii) adversarial ML, and (iii) conversational attacks that manipulate prompts or context often consolidate into a small number of dominant paths and shared system choke points, enabling targeted defenses to yield meaningful reductions in path exploitability. By systematically comparing defense portfolios, we align these risks with established vulnerability management practices and provide a domain-agnostic workflow applicable to other LLM-enabled critical systems.
[AI-90] Backdoor4Good: Benchmarking Beneficial Uses of Backdoors in LLM s
【速读】：该论文旨在解决传统后门机制仅被视为安全威胁、而忽视其潜在可控性与可审计性的局限问题，试图探索后门机制在提升大语言模型（LLM）安全性、可控性和问责性方面的有益应用。解决方案的关键在于提出一个统一的基准和框架——Backdoor4Good (B4G)，通过三元组形式（T, A, U）形式化有益后门学习，其中T代表触发器（Trigger）、A代表激活机制（Activation mechanism）、U代表效用函数（Utility function），并构建涵盖四个信任核心场景的基准测试。实验表明，经过合理设计的有益后门可在保持原始任务性能的同时实现高可控性、抗篡改性和隐蔽性，从而证明后门机制本身并非必然有害，而是可作为可信人工智能系统中模块化、可解释且有益的构建单元。
链接: https://arxiv.org/abs/2603.07452
作者: Yige Li,Wei Zhao,Zhe Li,Nay Myat Min,Hanxun Huang,Yunhan Zhao,Xingjun Ma,Yu-Gang Jiang,Jun Sun
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 19 pages, 5 figures
Abstract:Backdoor mechanisms have traditionally been studied as security threats that compromise the integrity of machine learning models. However, the same mechanism – the conditional activation of specific behaviors through input triggers – can also serve as a controllable and auditable interface for trustworthy model behavior. In this work, we present \textbfBackdoor4Good (B4G), a unified benchmark and framework for \textitbeneficial backdoor applications in large language models (LLMs). Unlike conventional backdoor studies focused on attacks and defenses, B4G repurposes backdoor conditioning for Beneficial Tasks that enhance safety, controllability, and accountability. It formalizes beneficial backdoor learning under a triplet formulation (T, A, U) , representing the \emphTrigger, \emphActivation mechanism, and \emphUtility function, and implements a benchmark covering four trust-centric applications. Through extensive experiments across Llama3.1-8B, Gemma-2-9B, Qwen2.5-7B, and Llama2-13B, we show that beneficial backdoors can achieve high controllability, tamper-resistance, and stealthiness while preserving clean-task performance. Our findings demonstrate new insights that backdoors need not be inherently malicious; when properly designed, they can serve as modular, interpretable, and beneficial building blocks for trustworthy AI systems. Our code and datasets are available at this https URL.
[AI-91] HLER: Human-in-the-Loop Economic Research via Multi-Agent Pipelines for Empirical Discovery
【速读】：该论文旨在解决当前基于大语言模型（Large Language Models, LLMs）的自动化科研系统在实证经济学研究中面临的可行性与可控性问题：现有方法多追求完全自主发现，但实证研究需依赖可用数据集、严谨的识别策略及人类对经济意义的判断，导致盲目生成的研究问题常不可行或缺乏实际价值。其解决方案的关键在于提出一种“人在环路中的经济研究”（Human-in-the-Loop Economic Research, HLER）多智能体架构，通过引入数据感知的假设生成机制（dataset-aware hypothesis generation），将候选研究问题限制于数据结构、变量可得性和分布诊断范围内，显著提升假设的可行性；同时采用双循环设计——问题质量循环用于筛选可行假设，研究修订循环通过自动评审触发重分析与稿件迭代，并在关键节点嵌入人工决策门控，确保人类专家对研究方向和结论的主导权。实验表明，该方法使可行研究问题比例从41%提升至87%，且单次运行平均API成本仅为0.8–1.5美元，验证了人机协同路径在实证研究自动化中的有效性与实用性。
链接: https://arxiv.org/abs/2603.07444
作者: Chen Zhu,Xiaolu Wang
机构: 未知
类目: Artificial Intelligence (cs.AI); General Economics (econ.GN)
备注:
Abstract:Large language models (LLMs) have enabled agent-based systems that aim to automate scientific research workflows. Most existing approaches focus on fully autonomous discovery, where AI systems generate research ideas, conduct analyses, and produce manuscripts with minimal human involvement. However, empirical research in economics and the social sciences poses additional constraints: research questions must be grounded in available datasets, identification strategies require careful design, and human judgment remains essential for evaluating economic significance. We introduce HLER (Human-in-the-Loop Economic Research), a multi-agent architecture that supports empirical research automation while preserving critical human oversight. The system orchestrates specialized agents for data auditing, data profiling, hypothesis generation, econometric analysis, manuscript drafting, and automated review. A key design principle is dataset-aware hypothesis generation, where candidate research questions are constrained by dataset structure, variable availability, and distributional diagnostics, reducing infeasible or hallucinated hypotheses. HLER further implements a two-loop architecture: a question quality loop that screens and selects feasible hypotheses, and a research revision loop where automated review triggers re-analysis and manuscript revision. Human decision gates are embedded at key stages, allowing researchers to guide the automated pipeline. Experiments on three empirical datasets show that dataset-aware hypothesis generation produces feasible research questions in 87% of cases (versus 41% under unconstrained generation), while complete empirical manuscripts can be produced at an average API cost of 0.8- 1.5 per run. These results suggest that Human-AI collaborative pipelines may provide a practical path toward scalable empirical research.
[AI-92] Machine Learning for Stress Testing: Uncertainty Decomposition in Causal Panel Prediction
【速读】：该论文旨在解决监管压力测试中信用损失预测的因果推断问题，即在假设宏观经济情景下如何准确估计信贷损失，而传统方法常将其视为纯预测问题，忽略了潜在的混杂因素影响。解决方案的关键在于提出一个四部分组成的政策路径反事实推理框架：首先通过迭代回归实现路径条件均值的观测识别，无需控制组即可进行连续宏观路径对比；其次在有限混杂条件下实现因果集识别，给出可解释的断裂值以量化稳健性；第三基于oracle不等式揭示递归滚动误差受时域依赖放大因子约束，明确可靠预测的前瞻范围；最后引入重要性加权的保形校准置信带并附诊断机制，量化外推成本并在覆盖保证下降时触发拒绝预测。整体形成三层不确定性分解，清晰分离估计不确定性和混杂不确定性，且通过模拟与真实失业数据的半合成实验验证了框架的有效性与诊断价值。
链接: https://arxiv.org/abs/2603.07438
作者: Yu Wang,Xiangchen Liu,Siguang Li
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Regulatory stress testing requires projecting credit losses under hypothetical macroeconomic scenarios – a fundamentally causal question typically treated as a prediction problem. We propose a framework for policy-path counterfactual inference in panels that transparently separates what can be learned from data from what requires assumptions about confounding. Our approach has four components: (i) observational identification of path-conditional means via iterated regression, enabling continuous macro-path contrasts without requiring a control group; (ii) causal set identification under bounded confounding, yielding sharp identified sets with interpretable breakdown values that communicate robustness in a single number; (iii) an oracle inequality showing that recursive rollout error is governed by a horizon-dependent amplification factor, providing a concrete answer to how far ahead one can reliably predict under stress; and (iv) importance-weighted conformal calibration bands with diagnostics that quantify extrapolation cost and trigger abstention when coverage guarantees degrade. The final output is a three-layer uncertainty decomposition that cleanly separates estimation uncertainty from confounding uncertainty. We validate all results through simulation and semi-synthetic experiments with real unemployment data, including a Covid retrospective demonstrating the framework’s diagnostic value under extreme scenarios.
[AI-93] OrthoFormer: Instrumental Variable Estimation in Transformer Hidden States via Neural Control Functions
【速读】：该论文旨在解决标准Transformer架构在序列建模中因依赖相关性学习（correlational learning）而导致的因果机制识别偏差问题，即模型容易捕捉由潜在混杂因素（latent confounders）诱导的虚假关联，而非稳定的因果机制，从而在分布外（out-of-distribution）场景下产生灾难性失效。其解决方案的关键在于提出OrthoFormer——一种基于因果理论的架构，通过在Transformer块中嵌入工具变量估计（instrumental variable estimation）并引入神经控制函数（neural control functions），实现对静态背景因素（如身份、风格、上下文）与动态因果流（状态演化、机制）的分离。该方法建立于四个理论支柱之上：结构方向性（Structural Directionality，强制时间箭头）、表示正交性（Representation Orthogonality，分离潜在噪声）、因果稀疏性（Causal Sparsity，马尔可夫毯近似）以及端到端一致性（End-to-End Consistency，梯度解耦阶段分离），从而理论上保证残差偏差严格小于普通最小二乘法（OLS），且随仪器滞后期呈几何衰减，并揭示了自工具化中的偏置-方差-外生性三难困境（bias-variance-exogeneity trilemma）。
链接: https://arxiv.org/abs/2603.07431
作者: Charles Luo
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Transformer architectures excel at sequential modeling yet remain fundamentally limited by correlational learning - they capture spurious associations induced by latent confounders rather than invariant causal mechanisms. We identify this as an epistemological challenge: standard Transformers conflate static background factors (intrinsic identity, style, context) with dynamic causal flows (state evolution, mechanism), leading to catastrophic out-of-distribution failure. We propose OrthoFormer, a causally grounded architecture that embeds instrumental variable estimation directly into Transformer blocks via neural control functions. Our framework rests on four theoretical pillars: Structural Directionality (time-arrow enforcement), Representation Orthogonality (latent-noise separation), Causal Sparsity (Markov Blanket approximation), and End-to-End Consistency (gradient- detached stage separation). We prove that OrthoFormer achieves bias strictly less than OLS for any valid instrument lag, with residual bias decaying geometrically as O(\rhok ). We characterize the bias-variance-exogeneity trilemma inherent in self-instrumenting and identify the neural forbidden regression - where removing gradient detachment improves prediction loss while destroying causal validity. Experiments confirm all theoretical predictions. OrthoFormer represents a paradigm shift from correlational to causal sequence modeling, with implications for robustness, interpretability, and reliable decision-making under distribution shift.
[AI-94] AutoControl Arena: Synthesizing Executable Test Environments for Frontier AI Risk Evaluation ICML2026
【速读】：该论文旨在解决当前大型语言模型（Large Language Models, LLMs）作为自主智能体时，安全评估中存在的根本性权衡问题：人工构建的基准测试成本高昂，而基于LLM的模拟器虽具可扩展性却易产生逻辑幻觉（logic hallucination）。其解决方案的关键在于提出“逻辑-叙事解耦”（logic-narrative decoupling）原则，通过将确定性状态以可执行代码形式固化，同时将生成性动态行为交由LLM处理，从而在保持灵活性的同时显著降低幻觉风险。这一原则具体体现在一个三智能体框架中，实现了超过98%的端到端成功率和60%的人类偏好优势，有效提升了前沿AI风险评估的准确性与实用性。
链接: https://arxiv.org/abs/2603.07427
作者: Changyi Li,Pengfei Lu,Xudong Pan,Fazl Barez,Min Yang
机构: 未知
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: Submitted to ICML 2026. Code and benchmark will be available
Abstract:As Large Language Models (LLMs) evolve into autonomous agents, existing safety evaluations face a fundamental trade-off: manual benchmarks are costly, while LLM-based simulators are scalable but suffer from logic hallucination. We present AutoControl Arena, an automated framework for frontier AI risk evaluation built on the principle of logic-narrative decoupling. By grounding deterministic state in executable code while delegating generative dynamics to LLMs, we mitigate hallucination while maintaining flexibility. This principle, instantiated through a three-agent framework, achieves over 98% end-to-end success and 60% human preference over existing simulators. To elicit latent risks, we vary environmental Stress and Temptation across X-Bench (70 scenarios, 7 risk categories). Evaluating 9 frontier models reveals: (1) Alignment Illusion: risk rates surge from 21.7% to 54.5% under pressure, with capable models showing disproportionately larger increases; (2) Scenario-Specific Safety Scaling: advanced reasoning improves robustness for direct harms but worsens it for gaming scenarios; and (3) Divergent Misalignment Patterns: weaker models cause non-malicious harm while stronger models develop strategic concealment.
[AI-95] Dynamic Vehicle Routing Problem with Prompt Confirmation of Advance Requests
【速读】：该论文旨在解决动态车辆路径问题（Dynamic Vehicle Routing Problem, DVRP）中一个现实且未被充分研究的挑战：当乘客提前预订行程时，公交运营机构需要在短时间内确认是否接受请求，并确保已接受的请求能够按承诺完成服务。现有方法要么能快速响应确认但无法持续优化已接受请求的路线，要么虽可不断优化却无法保障所有已接受请求均被满足。解决方案的关键在于提出一种新的问题建模方式——兼顾即时确认与持续优化，并设计了一种融合快速插入搜索（quick insertion search）与任意时间算法（anytime algorithm）的计算框架；同时引入基于强化学习训练的非贪婪目标函数，引导插入和优化过程向全局最优、非短视的方向演进，从而在保证快速响应的同时显著提升服务请求数量。
链接: https://arxiv.org/abs/2603.07422
作者: Amutheezan Sivagnanam,Ayan Mukhopadhyay,Samitha Samaranayake,Abhishek Dubey,Aron Laszka
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Transit agencies that operate on-demand transportation services have to respond to trip requests from passengers in real time, which involves solving dynamic vehicle routing problems with pick-up and drop-off constraints. Based on discussions with public transit agencies, we observe a real-world problem that is not addressed by prior work: when trips are booked in advance (e.g., trip requests arrive a few hours in advance of their requested pick-up times), the agency needs to promptly confirm whether a request can be accepted or not, and ensure that accepted requests are served as promised. State-of-the-art computational approaches either provide prompt confirmation but lack the ability to continually optimize and improve routes for accepted requests, or they provide continual optimization but cannot guarantee serving all accepted requests. To address this gap, we introduce a novel problem formulation of dynamic vehicle routing with prompt confirmation and continual optimization. We propose a novel computational approach for this vehicle routing problem, which integrates a quick insertion search for prompt confirmation with an anytime algorithm for continual optimization. To maximize the number requests served, we train a non-myopic objective function using reinforcement learning, which guides both the insertion and the anytime algorithms towards optimal, non-myopic solutions. We evaluate our computational approach on a real-world microtransit dataset from a public transit agency in the U.S., demonstrating that our proposed approach provides prompt confirmation while significantly increasing the number of requests served compared to existing approaches.
[AI-96] Context Channel Capacity: An Information-Theoretic Framework for Understanding Catastrophic Forgetting
【速读】：该论文旨在解决持续学习（Continual Learning, CL）中灾难性遗忘（Catastrophic Forgetting）的核心难题，即为何某些模型架构会表现出严重遗忘而另一些则不会，且缺乏统一的信息论解释。其关键解决方案是提出“上下文通道容量”（Context Channel Capacity, $ C_\mathrm{ctx} $），定义为模型上下文信号与生成参数之间的互信息，并证明零遗忘要求 $ C_\mathrm{ctx} \geq H(T) （ H(T) $ 为任务标识熵）。作者进一步建立“不可能三角形”——零遗忘、在线学习与有限参数无法同时满足于基于状态的序列学习器，并指出条件再生架构（如HyperNetworks）通过将参数视为函数值而非状态，可绕过该限制。实验证明，$ C_\mathrm{ctx} $ 能精确预测遗忘行为：当 $ C_\mathrm{ctx} = 0 $ 时（如NaiveSGD、EWC等方法）出现灾难性遗忘（6–97%），而 $ C_\mathrm{ctx} \approx 1 $ 时（如HyperNetwork）实现零遗忘（98.8%准确率）。这一理论框架强调“架构优于算法”，核心在于设计不可绕过的上下文路径结构以保障信息传递。
链接: https://arxiv.org/abs/2603.07415
作者: Ran Cheng
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Theory (cs.IT)
备注: 39 pages
Abstract:Catastrophic forgetting remains a central challenge in continual learning (CL), yet lacks a unified information-theoretic explanation for why some architectures forget catastrophically while others do not. We introduce \emphContext Channel Capacity ( C_\mathrmctx ), the mutual information between a CL architecture’s context signal and its generated parameters, and prove that zero forgetting requires C_\mathrmctx \geq H(T) , where H(T) is the task identity entropy. We establish an \emphImpossibility Triangle – zero forgetting, online learning, and finite parameters cannot be simultaneously satisfied by sequential state-based learners – and show that conditional regeneration architectures (HyperNetworks) bypass this triangle by redefining parameters as function values rather than states. We validate this framework across 8 CL methods on Split-MNIST (1,130+ experiments over 86 days, 4 seeds each), showing that C_\mathrmctx perfectly predicts forgetting behavior: methods with C_\mathrmctx = 0 (NaiveSGD, EWC, SI, LwF, CFlow) exhibit catastrophic forgetting (6–97%), while methods with C_\mathrmctx \approx 1 (HyperNetwork) achieve zero forgetting (98.8% ACC). We further propose \emphWrong-Context Probing (P5), a practical diagnostic protocol for measuring C_\mathrmctx , and extend the framework to CIFAR-10 via a novel \emphGradient Context Encoder that closes the oracle gap from 23.3pp to 0.7pp. A systematic taxonomy of 15+ closed research directions – including the Hebbian null result (frozen random features outperform learned features), CFlow’s \theta_0 -memorizer phenomenon, and the S_N symmetry barrier to column specialization – provides the community with precisely diagnosed negative results. Our central design principle: \empharchitecture over algorithm – the context pathway must be structurally unbypassable. Comments: 39 pages Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Theory (cs.IT) Cite as: arXiv:2603.07415 [cs.LG] (or arXiv:2603.07415v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.07415 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-97] Machine Learning for the Internet of Underwater Things: From Fundamentals to Implementation
【速读】：该论文旨在解决海洋观测、海洋资源管理和气候科学等领域中，因水下无线传感器网络（Underwater Wireless Sensor Networks, UWSNs）面临的严重声学衰减、传播延迟高、能量约束严格以及拓扑动态变化等挑战所导致的性能瓶颈问题。其解决方案的关键在于系统性地引入机器学习（Machine Learning, ML）技术，涵盖监督学习、无监督学习、强化学习和深度学习等多种范式，并针对水下通信环境进行专门适配与优化。通过分层分析发现，ML在物理层可提升定位精度与信道估计能力，在MAC层改善信道利用率，在网络层优化路由策略以延长节点寿命，在传输层显著降低丢包率（最高达91%），并在应用层实现高效数据压缩与目标检测（准确率达92%）。研究基于300篇文献综述，验证了ML带来的能效提升（7–29倍）、吞吐量改进及跨层优化收益（最高42%），同时指出当前仍面临数据集匮乏、计算资源受限及理论与实际部署差距等障碍，为未来水下网络中机器学习的落地提供技术路线图。
链接: https://arxiv.org/abs/2603.07413
作者: Kenechi Omeke,Attai Abubakar,Michael Mollel,Lei Zhang,Qammer H. Abbasi,Muhammad Ali Imran
机构: 未知
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI)
备注: 78 pages, 14 figures,
Abstract:The Internet of Underwater Things (IoUT) is becoming a critical infrastructure for ocean observation, marine resource management, and climate science. Its development is hindered by severe acoustic attenuation, propagation delays far exceeding those of terrestrial wireless systems, strict energy constraints, and dynamic topologies shaped by ocean currents. Machine learning (ML) has emerged as a key enabler for addressing these limitations, offering data driven mechanisms that enhance performance across all layers of underwater wireless sensor networks. This tutorial survey synthesises ML methodologies supervised, unsupervised, reinforcement, and deep learning specifically contextualised for underwater communication environments. It outlines the algorithmic principles of each paradigm and examines the conditions under which particular approaches deliver superior performance. A layer wise analysis highlights physical layer gains in localisation and channel estimation, MAC layer adaptations that improve channel utilisation, network layer routing strategies that extend operational lifetime, and transport layer mechanisms capable of reducing packet loss by up to 91 percent. At the application layer, ML enables substantial data compression and object detection accuracies reaching 92 percent. Drawing on 300 studies from 2012 to 2025, the survey documents energy efficiency gains of 7 to 29 times, throughput improvements over traditional protocols, and cross layer optimisation benefits of up to 42 percent. It also identifies persistent barriers, including limited datasets, computational constraints, and the gap between theoretical models and real world deployment. The survey concludes with emerging research directions and a technology roadmap supporting ML adoption in operational underwater networks.
[AI-98] Adaptive Capacity Allocation for Vision Language Action Fine-tuning ICRA2026
【速读】：该论文旨在解决视觉语言动作模型（Vision Language Action models, VLAs）在未见环境、机器人本体或任务中进行参数高效微调（Parameter-efficient fine-tuning, PEFT）时存在的秩不匹配问题，即传统LoRA方法中固定秩（rank）无法适应多任务场景下VLA的高内在秩需求，导致性能下降和跨任务干扰加剧。解决方案的关键在于提出LoRA-SP（Select-Prune），其核心创新是引入一种基于奇异值分解（SVD）风格的参数化结构，通过一个小型路由器（router）动态选择激活向量，并以累积平方得分能量目标 E(k)≥η 控制有效秩，从而实现输入与层级自适应的容量分配。此机制在训练中自动聚焦于少数关键方向，显著减少可训练参数量并提升多任务泛化能力，同时保持对秩选择的鲁棒性。
链接: https://arxiv.org/abs/2603.07404
作者: Donghoon Kim,Minji Bae,Unghui Nam,Gyeonghun Kim,Suyun Lee,Kyuhong Shim,Byonghyo Shim
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: ICRA 2026 (Official Code: this https URL )
Abstract:Vision language action models (VLAs) are increasingly used for Physical AI, but deploying a pre-trained VLA model to unseen environments, embodiments, or tasks still requires adaptation. Parameter-efficient fine-tuning (PEFT), especially LoRA, is common for VLA policies, yet the exposed capacity knob, the rank, does not transfer uniformly: robotics transfer exhibits a higher and task-varying intrinsic rank than language fine-tuning. Small ranks suffice for LLMs (e.g., r \in \4, 8\ ), while spectral analyses indicate VLAs may require much larger ranks (e.g., r \approx 128 ) or near-full rank, a mismatch that worsens in multi-task settings. We present LoRA-SP (Select-Prune), a rank-adaptive fine-tuning method that replaces fixed-rank updates with input- and layer-wise capacity. LoRA-SP uses an SVD-style parameterization with a small router whose nonnegative scores act as singular values over a shared vector bank. The active set is chosen by an energy target on the cumulative squared scores E(k) \ge \eta , providing a direct link to approximation error via our spectral analysis. During training, \eta concentrates energy on a few directions and teaches the router to rely on fewer vectors while preserving accuracy. This yields compact adapters that reduce cross-task interference and improve generalization. On four real-robot manipulation tasks collected on an unseen AgileX PiPER arm, across two VLA backbones ( \pi_0 and SmolVLA), LoRA-SP matches or exceeds full fine-tuning with far fewer trainable parameters, and improves multi-task success by up to 31.6% over standard LoRA while remaining robust to rank choice.
[AI-99] Sparsity and Out-of-Distribution Generalization
【速读】：该论文旨在解决**分布外泛化（Out-of-Distribution Generalization, OOD Generalization）**这一核心问题，即模型如何在训练数据分布与测试数据分布不一致时仍能保持良好性能。其解决方案的关键在于提出一个基于三个核心要素的理论框架：首先，现实世界的经验总是通过特定特征通道（如视觉和听觉）呈现；其次，奥卡姆剃刀原则偏好“稀疏”假设，即仅依赖最少数量特征的模型；第三，若训练与测试分布在其相关特征上的限制存在足够重叠，则稀疏假设可实现OOD泛化，即使其他无关特征存在任意差异。作者进一步通过一个形式化的定理将上述直觉推广至OOD场景，扩展了经典的样本复杂度边界（Blumer et al.），并引入子空间杂交（subspace juntas）概念，使稀疏分类器适用于低维线性特征子空间的建模。
链接: https://arxiv.org/abs/2603.07388
作者: Scott Aaronson,Lin Lin Lee,Jiawei Li
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Explaining out-of-distribution generalization has been a central problem in epistemology since Goodman’s “grue” puzzle in 1946. Today it’s a central problem in machine learning, including AI alignment. Here we propose a principled account of OOD generalization with three main ingredients. First, the world is always presented to experience not as an amorphous mass, but via distinguished features (for example, visual and auditory channels). Second, Occam’s Razor favors hypotheses that are “sparse,” meaning that they depend on as few features as possible. Third, sparse hypotheses will generalize from a training to a test distribution, provided the two distributions sufficiently overlap on their restrictions to the features that are either actually relevant or hypothesized to be. The two distributions could diverge arbitrarily on other features. We prove a simple theorem that formalizes the above intuitions, generalizing the classic sample complexity bound of Blumer et al. to an OOD context. We then generalize sparse classifiers to subspace juntas, where the ground truth classifier depends solely on a low-dimensional linear subspace of the features. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.07388 [cs.LG] (or arXiv:2603.07388v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.07388 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-100] Scheduling Parallel Optical Circuit Switches for AI Training
【速读】：该论文旨在解决在多并行光电路交换机（Optical Circuit Switches, OCSes）架构下，如何高效调度时变流量矩阵以最小化调度完成时间（makespan），同时应对非可忽略的重配置延迟（reconfiguration delay）这一挑战。其核心问题在于：当AI训练任务产生的流量具有动态性和突发性时，传统调度算法难以在多OCS并行环境中实现负载均衡与低延迟切换的兼顾。论文提出的解决方案Spectra采用三步策略：首先将流量矩阵D分解为最少数量的加权排列（weighted permutations）；其次利用负载感知分配策略将这些排列映射到各并行OCS上；最后通过受控的排列拆分（controlled permutation splitting）来均衡各交换机上的负载不均。该方法显著优于现有最优基线算法，在GPT和MoE等真实AI工作负载及标准基准测试中分别实现了平均1.4×、1.9×和2.4×的makespan降低，并逼近新推导出的理论下界，体现了其调度效率与优化能力。
链接: https://arxiv.org/abs/2603.07373
作者: Kevin Liang,Litao Qiao,Isaac Keslassy,Bill Lin
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注:
Abstract:The rapid growth of AI training has dramatically increased datacenter traffic demand and energy consumption, which has motivated renewed interest in optical circuit switches (OCSes) as a high-bandwidth, energy-efficient alternative for AI fabrics. Deploying multiple parallel OCSes is a leading alternative. However, efficiently scheduling time-varying traffic matrices across parallel optical switches with non-negligible reconfiguration delays remains an open challenge. We consider the problem of scheduling a single AI traffic demand matrix D over s parallel OCSes while minimizing the makespan under reconfiguration delay \delta . Our algorithm Spectra relies on a three-step approach: Decompose D into a minimal set of weighted permutations; Schedule these permutations across parallel switches using load-aware assignment; then Equalize the imbalanced loads on the switches via controlled permutation splitting. Evaluated on realistic AI training workloads (GPT model and Qwen MoE expert routing) as well as standard benchmarks, Spectra vastly outperforms a baseline based on state-of-the-art algorithms, reducing schedule makespan by an average factor of 1.4\times on GPT AI workloads, 1.9\times on MoE AI workloads, and 2.4\times on standard benchmarks. Further, the makespans achieved by Spectra consistently approach newly derived lower bounds. Subjects: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.07373 [cs.NI] (or arXiv:2603.07373v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2603.07373 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-101] ConfHit: Conformal Generative Design with Oracle Free Guarantees ICLR2026
【速读】：该论文旨在解决生成式分子设计中缺乏可靠保证的问题，即如何在不依赖实验验证（oracle）的前提下，确保生成的分子候选集至少包含一个满足目标性质的“命中”（hit），并在此基础上进一步优化候选集以保持统计有效性。其核心解决方案是提出ConfHit框架，关键在于利用历史样本与生成样本之间的加权交换性（weighted exchangeability）消除对实验oracle的依赖，通过多样本密度比加权的置信度p值量化命中概率，并采用嵌套检验程序在维持统计保证的同时实现候选集的精炼与压缩，从而在预算有限、分布偏移等实际约束下提供无分布假设的有效性保障。
链接: https://arxiv.org/abs/2603.07371
作者: Siddhartha Laghuvarapu,Ying Jin,Jimeng Sun
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at ICLR 2026
Abstract:The success of deep generative models in scientific discovery requires not only the ability to generate novel candidates but also reliable guarantees that these candidates indeed satisfy desired properties. Recent conformal-prediction methods offer a path to such guarantees, but its application to generative modeling in drug discovery is limited by budget constraints, lack of oracle access, and distribution shift. To this end, we introduce ConfHit, a distribution-free framework that provides validity guarantees under these conditions. ConfHit formalizes two central questions: (i) Certification: whether a generated batch can be guaranteed to contain at least one hit with a user-specified confidence level, and (ii) Design: whether the generation can be refined to a compact set without weakening this guarantee. ConfHit leverages weighted exchangeability between historical and generated samples to eliminate the need for an experimental oracle, constructs multiple-sample density-ratio weighted conformal p-value to quantify statistical confidence in hits, and proposes a nested testing procedure to certify and refine candidate sets of multiple generated samples while maintaining statistical guarantees. Across representative generative molecule design tasks and a broad range of methods, ConfHit consistently delivers valid coverage guarantees at multiple confidence levels while maintaining compact certified sets, establishing a principled and reliable framework for generative modeling.
[AI-102] Scaling Laws in the Tiny Regime: How Small Models Change Their Mistakes
【速读】：该论文旨在解决当前神经网络缩放定律（neural scaling laws）研究主要集中在参数量超过1亿的模型上，而对参数量低于2000万的微型模型（tiny models）——即边缘人工智能（edge AI）和TinyML领域所依赖的模型——缺乏系统性分析的问题。其解决方案的关键在于通过训练90个参数规模从2.2万到1980万不等的模型（涵盖Plain ConvNet与MobileNetV2两种架构），在CIFAR-100数据集上固定深度和训练条件，仅调节宽度，从而首次量化揭示了小模型性能随规模增长时的误差率变化规律。结果显示，尽管两类模型均近似遵循幂律关系（ScaleCNN的指数α=0.156±0.002，MobileNetV2为α=0.106±0.001），但其局部指数随规模递减且MobileNetV2在1980万参数处趋于饱和，同时错误模式结构发生显著迁移（最小与最大模型间Jaccard重叠仅为0.35），表明小模型不仅提升整体准确率，还改变误分类对象分布，并表现出更强的校准能力（最小模型ECE=0.013，优于中等规模模型）。因此，该研究强调：针对边缘部署场景，单纯依赖聚合准确率评估模型性能具有误导性，验证必须在目标模型尺寸下进行。
链接: https://arxiv.org/abs/2603.07365
作者: Mohammed Alnemari,Rizwan Qureshi,Nader Begrazadah
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 17 pages, 6 figures, 2 tables. Submitted to MDPI Machine Learning and Knowledge Extraction (MAKE)
Abstract:Neural scaling laws describe how model performance improves as a power law with size, but existing work focuses on models above 100M parameters. The sub-20M regime – where TinyML and edge AI operate – remains unexamined. We train 90 models (22K–19.8M parameters) across two architectures (plain ConvNet, MobileNetV2) on CIFAR-100, varying width while holding depth and training fixed. Both follow approximate power laws in error rate: \alpha = 0.156 \pm 0.002 (ScaleCNN) and \alpha = 0.106 \pm 0.001 (MobileNetV2) across five seeds. Since prior work fit cross-entropy loss rather than error rate, direct exponent comparison is approximate; with that caveat, these are 1.4–2x steeper than \alpha \approx 0.076 for large language models. The power law does not hold uniformly: local exponents decay with scale, and MobileNetV2 saturates at 19.8M parameters ( \alpha_\mathrmlocal = 0.006 ). Error structure also changes. Jaccard overlap between error sets of the smallest and largest ScaleCNN is only 0.35 (25 seed pairs, \pm 0.004 ) – compression changes which inputs are misclassified, not merely how many. Small models concentrate capacity on easy classes (Gini: 0.26 at 22K vs. 0.09 at 4.7M) while abandoning the hardest (bottom-5 accuracy: 10% vs. 53%). Counter to expectation, the smallest models are best calibrated (ECE = 0.013 vs. peak 0.110 at mid-size). Aggregate accuracy is therefore misleading for edge deployment; validation must happen at the target model size. Comments: 17 pages, 6 figures, 2 tables. Submitted to MDPI Machine Learning and Knowledge Extraction (MAKE) Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) MSC classes: 68T07 ACMclasses: I.2.6; I.5.1 Cite as: arXiv:2603.07365 [cs.LG] (or arXiv:2603.07365v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.07365 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-103] he Yerkes-Dodson Curve for AI Agents : Emergent Cooperation Under Environmental Pressure in Multi-Agent LLM Simulations
【速读】：该论文旨在解决如何设计环境以最大化人工智能代理（AI agent）中涌现行为的发展速率这一开放性问题。其核心解决方案在于系统性地研究大语言模型（LLM）多智能体系统中的压力-性能关系，发现合作行为遵循倒U型曲线：在中等环境压力下（维持成本=5），交易行为达到峰值（29次），而低压力或极端压力均导致交易减少至8–12次；此外，通过引入软压力机制——性选择（sexual selection），即所有代理存活但仅部分繁殖，可完全消除代理间的攻击行为并催生通信行为，这在生存压力下无法实现。因此，环境压力的校准是一种可行的课程设计策略，类比于生物系统中唤醒与绩效之间的倒U型关系。
链接: https://arxiv.org/abs/2603.07360
作者: Ivan Pasichnyk
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 13 pages, 2 figures, 7 tables
Abstract:Designing environments that maximize the rate of emergent behavior development in AI agents remains an open problem. We present the first systematic study of stress-performance relationships in large language model (LLM) multi-agent systems, drawing an explicit parallel to the Yerkes-Dodson law from cognitive psychology. Using a grid-world survival arena, we conduct 22 experiments across four phases, varying environmental pressure through resource scarcity (upkeep cost) and reproductive competition (sexual selection). Our key finding is that cooperative behavior follows an inverted-U curve: trade interactions peak at 29 under medium pressure (upkeep=5), while both low and extreme pressure produce 8–12 trades. Under extreme pressure, behavioral repertoire collapses to movement-only within 5–12 turns. We further show that sexual selection – a softer pressure mechanism where all agents survive but not all reproduce – eliminates inter-agent aggression entirely and produces communicative behavior absent under survival pressure. These results suggest that environmental pressure calibration is a viable curriculum design strategy for LLM agent development, analogous to the inverted-U relationship between arousal and performance in biological systems.
[AI-104] Latent Generative Models with Tunable Complexity for Compressed Sensing and other Inverse Problems
【速读】：该论文旨在解决生成式模型在求解逆问题（inverse problems）时因固定复杂度导致的局限性问题：当模型复杂度设置过低时，会因表示能力不足产生高重建误差；而复杂度过高则可能过度拟合噪声，导致泛化性能下降。解决方案的关键在于提出可调复杂度的先验（tunable-complexity priors），通过引入嵌套 dropout（nested dropout）机制，使扩散模型（diffusion models）、归一化流（normalizing flows）和变分自编码器（variational autoencoders）等生成模型具备动态调整其隐空间复杂度的能力。实验表明，该方法在压缩感知、图像补全、去噪和相位恢复等多种任务中均能显著降低重建误差，且在线性去噪场景下提供了理论分析，揭示了最优调参与噪声水平及模型结构之间的显式关系。
链接: https://arxiv.org/abs/2603.07357
作者: Sean Gunn,Jorio Cocola,Oliver De Candido,Vaggos Chatziafratis,Paul Hand
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Generative models have emerged as powerful priors for solving inverse problems. These models typically represent a class of natural signals using a single fixed complexity or dimensionality. This can be limiting: depending on the problem, a fixed complexity may result in high representation error if too small, or overfitting to noise if too large. We develop tunable-complexity priors for diffusion models, normalizing flows, and variational autoencoders, leveraging nested dropout. Across tasks including compressed sensing, inpainting, denoising, and phase retrieval, we show empirically that tunable priors consistently achieve lower reconstruction errors than fixed-complexity baselines. In the linear denoising setting, we provide a theoretical analysis that explicitly characterizes how the optimal tuning parameter depends on noise and model structure. This work demonstrates the potential of tunable-complexity generative priors and motivates both the development of supporting theory and their application across a wide range of inverse problems.
机器学习
[LG-0] Impermanent: A Live Benchmark for Temporal Generalization in Time Series Forecasting
链接: https://arxiv.org/abs/2603.08707
作者: Azul Garza,Renée Rosillo,Rodrigo Mendoza-Smith,David Salinas,Andrew Robert Williams,Arjun Ashok,Mononito Goswami,José Martín Juárez
类目: Machine Learning (cs.LG)
*备注:
Abstract:Recent advances in time-series forecasting increasingly rely on pre-trained foundation-style models. While these models often claim broad generalization, existing evaluation protocols provide limited evidence. Indeed, most current benchmarks use static train-test splits that can easily lead to contamination as foundation models can inadvertently train on test data or perform model selection using test scores, which can inflate performance. We introduce Impermanent, a live benchmark that evaluates forecasting models under open-world temporal change by scoring forecasts sequentially over time on continuously updated data streams, enabling the study of temporal robustness, distributional shift, and performance stability rather than one-off accuracy on a frozen test set. Impermanent is instantiated on GitHub open-source activity, providing a naturally live and highly non-stationary dataset shaped by releases, shifting contributor behavior, platform/tooling changes, and external events. We focus on the top 400 repositories by star count and construct time series from issues opened, pull requests opened, push events, and new stargazers, evaluated over a rolling window with daily updates, alongside standardized protocols and leaderboards for reproducible, ongoing comparison. By shifting evaluation from static accuracy to sustained performance, Impermanent takes a concrete step toward assessing when and whether foundation-level generalization in time-series forecasting can be meaningfully claimed. Code and a live dashboard are available at this https URL and this https URL.
[LG-1] Context-free Self-Conditioned GAN for Trajectory Forecasting ICML
链接: https://arxiv.org/abs/2603.08658
作者: Tiago Rodrigues de Almeida,Eduardo Gutierrez Maestro,Oscar Martinez Mozos
类目: Machine Learning (cs.LG)
*备注: Accepted at the 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
Abstract:In this paper, we present a context-free unsupervised approach based on a self-conditioned GAN to learn different modes from 2D trajectories. Our intuition is that each mode indicates a different behavioral moving pattern in the discriminator’s feature space. We apply this approach to the problem of trajectory forecasting. We present three different training settings based on self-conditioned GAN, which produce better forecasters. We test our method in two data sets: human motion and road agents. Experimental results show that our approach outperforms previous context-free methods in the least representative supervised labels while performing well in the remaining labels. In addition, our approach outperforms globally in human motion, while performing well in road agents.
[LG-2] Group Entropies and Mirror Duality: A Class of Flexible Mirror Descent Updates for Machine Learning
链接: https://arxiv.org/abs/2603.08651
作者: Andrzej Cichocki,Piergiulio Tempesta
类目: Machine Learning (cs.LG); High Energy Physics - Theory (hep-th); Mathematical Physics (math-ph)
*备注: 36 pages, 5 figures
Abstract:We introduce a comprehensive theoretical and algorithmic framework that bridges formal group theory and group entropies with modern machine learning, paving the way for an infinite, flexible family of Mirror Descent (MD) optimization algorithms. Our approach exploits the rich structure of group entropies, which are generalized entropic functionals governed by group composition laws, encompassing and significantly extending all trace-form entropies such as the Shannon, Tsallis, and Kaniadakis families. By leveraging group-theoretical mirror maps (or link functions) in MD, expressed via multi-parametric generalized logarithms and their inverses (group exponentials), we achieve highly flexible and adaptable MD updates that can be tailored to diverse data geometries and statistical distributions. To this end, we introduce the notion of \textitmirror duality, which allows us to seamlessly switch or interchange group-theoretical link functions with their inverses, subject to specific learning rate constraints. By tuning or learning the hyperparameters of the group logarithms enables us to adapt the model to the statistical properties of the training distribution, while simultaneously ensuring desirable convergence characteristics via fine-tuning. This generality not only provides greater flexibility and improved convergence properties, but also opens new perspectives for applications in machine learning and deep learning by expanding the design of regularizers and natural gradient algorithms. We extensively evaluate the validity, robustness, and performance of the proposed updates on large-scale, simplex-constrained quadratic programming problems.
[LG-3] Divide and Predict: An Architecture for Input Space Partitioning and Enhanced Accuracy
链接: https://arxiv.org/abs/2603.08649
作者: Fenix W. Huang,Henning S. Mortveit,Christian M. Reidys
类目: Machine Learning (cs.LG)
*备注: Under review; 24 pages; 8 figures
Abstract:In this article the authors develop an intrinsic measure for quantifying heterogeneity in training data for supervised learning. This measure is the variance of a random variable which factors through the influences of pairs of training points. The variance is shown to capture data heterogeneity and can thus be used to assess if a sample is a mixture of distributions. The authors prove that the data itself contains key information that supports a partitioning into blocks. Several proof of concept studies are provided that quantify the connection between variance and heterogeneity for EMNIST image data and synthetic data. The authors establish that variance is maximal for equal mixes of distributions, and detail how variance-based data purification followed by conventional training over blocks can lead to significant increases in test accuracy.
[LG-4] Grow Dont Overwrite: Fine-tuning Without Forgetting
链接: https://arxiv.org/abs/2603.08647
作者: Dyah Adila,Hanna Mazzawi,Benoit Dherin,Xavier Gonzalvo
类目: Machine Learning (cs.LG)
*备注:
Abstract:Adapting pre-trained models to specialized tasks often leads to catastrophic forgetting, where new knowledge overwrites foundational capabilities. Existing methods either compromise performance on the new task or struggle to balance training stability with efficient reuse of pre-trained knowledge. We introduce a novel function-preserving expansion method that resolves this dilemma. Our technique expands model capacity by replicating pre-trained parameters within transformer submodules and applying a scaling correction that guarantees the expanded model is mathematically identical to the original at initialization, enabling stable training while exploiting existing knowledge. Empirically, our method eliminates the trade-off between plasticity and stability, matching the performance of full fine-tuning on downstream tasks without any degradation of the model’s original capabilities. Furthermore, we demonstrate the modularity of our approach, showing that by selectively expanding a small subset of layers we can achieve the same performance as full fine-tuning at a fraction of the computational cost.
[LG-5] Integral Formulas for Vector Spherical Tensor Products
链接: https://arxiv.org/abs/2603.08630
作者: Valentin Heyraud,Zachary Weller-Davies,Jules Tilly
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注: 16 pages, 2 figures
Abstract:We derive integral formulas that simplify the Vector Spherical Tensor Product recently introduced by Xie et al., which generalizes the Gaunt tensor product to antisymmetric couplings. In particular, we obtain explicit closed-form expressions for the antisymmetric analogues of the Gaunt coefficients. This enables us to simulate the Clebsch-Gordan tensor product using a single Vector Spherical Tensor Product, yielding a 9\times reduction in the required tensor product evaluations. Our results enable efficient and practical implementations of the Vector Spherical Tensor Product, paving the way for applications of this generalization of Gaunt tensor products in \mathrmSO(3) -equivariant neural networks. Moreover, we discuss how the Gaunt and the Vector Spherical Tensor Products allow to control the expressivity-runtime tradeoff associated with the usual Clebsch-Gordan Tensor Products. Finally, we investigate low rank decompositions of the normalizations of the considered tensor products in view of their use in equivariant neural networks.
[LG-6] Impact of Connectivity on Laplacian Representations in Reinforcement Learning
链接: https://arxiv.org/abs/2603.08558
作者: Tommaso Giorgi,Pierriccardo Olivieri,Keyue Jiang,Laura Toni,Matteo Papini
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Learning compact state representations in Markov Decision Processes (MDPs) has proven crucial for addressing the curse of dimensionality in large-scale reinforcement learning (RL) problems. Existing principled approaches leverage structural priors on the MDP by constructing state representations as linear combinations of the state-graph Laplacian eigenvectors. When the transition graph is unknown or the state space is prohibitively large, the graph spectral features can be estimated directly via sample trajectories. In this work, we prove an upper bound on the approximation error of linear value function approximation under the learned spectral features. We show how this error scales with the algebraic connectivity of the state-graph, grounding the approximation quality in the topological structure of the MDP. We further bound the error introduced by the eigenvector estimation itself, leading to an end-to-end error decomposition across the representation learning pipeline. Additionally, our expression of the Laplacian operator for the RL setting, although equivalent to existing ones, prevents some common misunderstandings, of which we show some examples from the literature. Our results hold for general (non-uniform) policies without any assumptions on the symmetry of the induced transition kernel. We validate our theoretical findings with numerical simulations on gridworld environments.
[LG-7] he Neural Compass: Probabilistic Relative Feature Fields for Robotic Search IROS2026
链接: https://arxiv.org/abs/2603.08544
作者: Gabriele Somaschini,Adrian Röfer,Abhinav Valada
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 9 pages, 7 figures, 2 tables, submitted to IROS 2026
Abstract:Object co-occurrences provide a key cue for finding objects successfully and efficiently in unfamiliar environments. Typically, one looks for cups in kitchens and views fridges as evidence of being in a kitchen. Such priors have also been exploited in artificial agents, but they are typically learned from explicitly labeled data or queried from language models. It is still unclear whether these relations can be learned implicitly from unlabeled observations alone. In this work, we address this problem and propose ProReFF, a feature field model trained to predict relative distributions of features obtained from pre-trained vision language models. In addition, we introduce a learning-based strategy that enables training from unlabeled and potentially contradictory data by aligning inconsistent observations into a coherent relative distribution. For the downstream object search task, we propose an agent that leverages predicted feature distributions as a semantic prior to guide exploration toward regions with a high likelihood of containing the object. We present extensive evaluations demonstrating that ProReFF captures meaningful relative feature distributions in natural scenes and provides insight into the impact of our proposed alignment step. We further evaluate the performance of our search agent in 100 challenges in the Matterport3D simulator, comparing with feature-based baselines and human participants. The proposed agent is 20% more efficient than the strongest baseline and achieves up to 80% of human performance.
[LG-8] Breaking the Bias Barrier in Concave Multi-Objective Reinforcement Learning
链接: https://arxiv.org/abs/2603.08518
作者: Swetha Ganesh,Vaneet Aggarwal
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:While standard reinforcement learning optimizes a single reward signal, many applications require optimizing a nonlinear utility f(J_1^\pi,\dots,J_M^\pi) over multiple objectives, where each J_m^\pi denotes the expected discounted return of a distinct reward function. A common approach is concave scalarization, which captures important trade-offs such as fairness and risk sensitivity. However, nonlinear scalarization introduces a fundamental challenge for policy gradient methods: the gradient depends on \partial f(J^\pi) , while in practice only empirical return estimates \hat J are available. Because f is nonlinear, the plug-in estimator is biased ( \mathbbE[\partial f(\hat J)] \neq \partial f(\mathbbE[\hat J]) ), leading to persistent gradient bias that degrades sample complexity. In this work we identify and overcome this bias barrier in concave-scalarized multi-objective reinforcement learning. We show that existing policy-gradient methods suffer an intrinsic \widetilde\mathcalO(\epsilon^-4) sample complexity due to this bias. To address this issue, we develop a Natural Policy Gradient (NPG) algorithm equipped with a multi-level Monte Carlo (MLMC) estimator that controls the bias of the scalarization gradient while maintaining low sampling cost. We prove that this approach achieves the optimal \widetilde\mathcalO(\epsilon^-2) sample complexity for computing an \epsilon -optimal policy. Furthermore, we show that when the scalarization function is second-order smooth, the first-order bias cancels automatically, allowing vanilla NPG to achieve the same \widetilde\mathcalO(\epsilon^-2) rate without MLMC. Our results provide the first optimal sample complexity guarantees for concave multi-objective reinforcement learning under policy-gradient methods. Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2603.08518 [cs.LG] (or arXiv:2603.08518v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.08518 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-9] Efficient Credal Prediction through Decalibration
链接: https://arxiv.org/abs/2603.08495
作者: Paul Hofman,Timo Löhr,Maximilian Muschalik,Yusuf Sale,Eyke Hüllermeier
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:A reliable representation of uncertainty is essential for the application of modern machine learning methods in safety-critical settings. In this regard, the use of credal sets (i.e., convex sets of probability distributions) has recently been proposed as a suitable approach to representing epistemic uncertainty. However, as with other approaches to epistemic uncertainty, training credal predictors is computationally complex and usually involves (re-)training an ensemble of models. The resulting computational complexity prevents their adoption for complex models such as foundation models and multi-modal systems. To address this problem, we propose an efficient method for credal prediction that is grounded in the notion of relative likelihood and inspired by techniques for the calibration of probabilistic classifiers. For each class label, our method predicts a range of plausible probabilities in the form of an interval. To produce the lower and upper bounds of these intervals, we propose a technique that we refer to as decalibration. Extensive experiments show that our method yields credal sets with strong performance across diverse tasks, including coverage-efficiency evaluation, out-of-distribution detection, and in-context learning. Notably, we demonstrate credal prediction on models such as TabPFN and CLIP – architectures for which the construction of credal sets was previously infeasible.
[LG-10] Pareto-Optimal Anytime Algorithms via Bayesian Racing
链接: https://arxiv.org/abs/2603.08493
作者: Jonathan Wurth,Helena Stegherr,Neele Kemper,Michael Heider,Jörg Hähner
类目: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
*备注: 32 pages, 12 figures, 2 tables, and 4 pages of appendix with additional details. Submitted to ACM Transactions on Evolutionary Learning and Optimization
Abstract:Selecting an optimization algorithm requires comparing candidates across problem instances, but the computational budget for deployment is often unknown at benchmarking time. Current methods either collapse anytime performance into a scalar, require manual interpretation of plots, or produce conclusions that change when algorithms are added or removed. Moreover, methods based on raw objective values require normalization, which needs bounds or optima that are often unavailable and breaks coherent aggregation across instances. We propose a framework that formulates anytime algorithm comparison as Pareto optimization over time: an algorithm is non-dominated if no competitor beats it at every timepoint. By using rankings rather than objective values, our approach requires no bounds, no normalization, and aggregates coherently across arbitrary instance distributions without requiring known optima. We introduce PolarBear (Pareto-optimal anytime algorithms via Bayesian racing), a procedure that identifies the anytime Pareto set through adaptive sampling with calibrated uncertainty. Bayesian inference over a temporal Plackett-Luce ranking model provides posterior beliefs about pairwise dominance, enabling early elimination of confidently dominated algorithms. The output Pareto set together with the posterior supports downstream algorithm selection under arbitrary time preferences and risk profiles without additional experiments.
[LG-11] NN-OpInf: an operator inference approach using structure-preserving composable neural networks
链接: https://arxiv.org/abs/2603.08488
作者: Eric Parish,Anthony Gruber,Patrick Blonigan,Irina Tezaur
类目: Machine Learning (cs.LG); Dynamical Systems (math.DS)
*备注:
Abstract:We propose neural network operator inference (NN-OpInf): a structure-preserving, composable, and minimally restrictive operator inference framework for the non-intrusive reduced-order modeling of dynamical systems. The approach learns latent dynamics from snapshot data, enforcing local operator structure such as skew-symmetry, (semi-)positive definiteness, and gradient preservation, while also reflecting complex dynamics by supporting additive compositions of heterogeneous operators. We present practical training strategies and analyze computational costs relative to linear and quadratic polynomial OpInf (P-OpInf). Numerical experiments across several nonlinear and parametric problems demonstrate improved accuracy, stability, and robustness over P-OpInf and prior NN-ROM formulations, particularly when the dynamics are not well represented by polynomial models. These results suggest that NN-OpInf can serve as an effective drop-in replacement for P-OpInf when the dynamics to be modeled contain non-polynomial nonlinearities, offering potential gains in accuracy and out-of-distribution performance at the expense of higher training computational costs and a more difficult, non-convex learning problem.
[LG-12] STRIDE: Structured Lagrangian and Stochastic Residual Dynamics via Flow Matching
链接: https://arxiv.org/abs/2603.08478
作者: Prakrut Kotecha,Ganga Nair B,Shishir Kolathaya
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 9 pages, 7 figures
Abstract:Robotic systems operating in unstructured environments must operate under significant uncertainty arising from intermittent contacts, frictional variability, and unmodeled compliance. While recent model-free approaches have demonstrated impressive performance, many deployment settings still require predictive models that support planning, constraint handling, and online adaptation. Analytical rigid-body models provide strong physical structure but often fail to capture complex interaction effects, whereas purely data-driven models may violate physical consistency, exhibit data bias, and accumulate long-horizon drift. In this work, we propose STRIDE, a dynamics learning framework that explicitly separates conservative rigid-body mechanics from uncertain, effectively stochastic non-conservative interaction effects. The structured component is modeled using a Lagrangian Neural Network (LNN) to preserve energy-consistent inertial dynamics, while residual interaction forces are represented using Conditional Flow Matching (CFM) to capture multi-modal interaction phenomena. The two components are trained jointly end-to-end, enabling the model to retain physical structure while representing complex stochastic behavior. We evaluate STRIDE on systems of increasing complexity, including a pendulum, the Unitree Go1 quadruped, and the Unitree G1 humanoid. Results show 20% reduction in long-horizon prediction error and 30% reduction in contact force prediction error compared to deterministic residual baselines, supporting more reliable model-based control in uncertain robotic environments.
[LG-13] Integrating Lagrangian Neural Networks into the Dyna Framework for Reinforcement Learning
链接: https://arxiv.org/abs/2603.08468
作者: Shreya Das,Kundan Kumar,Muhammad Iqbal,Outi Savolainen,Dominik Baumann,Laura Ruotsalainen,Simo Särkkä
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注: 5 pages, 3 figures
Abstract:Model-based reinforcement learning (MBRL) is sample-efficient but depends on the accuracy of the learned dynamics, which are often modeled using black-box methods that do not adhere to physical laws. Those methods tend to produce inaccurate predictions when presented with data that differ from the original training set. In this work, we employ Lagrangian neural networks (LNNs), which enforce an underlying Lagrangian structure to train the model within a Dyna-based MBRL framework. Furthermore, we train the LNN using stochastic gradient-based and state-estimation-based optimizers to learn the network’s weights. The state-estimation-based method converges faster than the stochastic gradient-based method during neural network training. Simulation results are provided to illustrate the effectiveness of the proposed LNN-based Dyna framework for MBRL.
[LG-14] MUSA-PINN: Multi-scale Weak-form Physics-Informed Neural Networks for Fluid Flow in Complex Geometries
链接: https://arxiv.org/abs/2603.08465
作者: Weizheng Zhang,Xunjie Xie,Hao Pan,Xiaowei Duan,Bingteng Sun,Qiang Du,Lin lu
类目: Machine Learning (cs.LG)
*备注:
Abstract:While Physics-Informed Neural Networks (PINNs) offer a mesh-free approach to solving PDEs, standard point-wise residual minimization suffers from convergence pathologies in topologically complex domains like Triply Periodic Minimal Surfaces (TPMS). The locality bias of point-wise constraints fails to propagate global information through tortuous channels, causing unstable gradients and conservation violations. To address this, we propose the Multi-scale Weak-form PINN (MUSA-PINN), which reformulates PDE constraints as integral conservation laws over hierarchical spherical control volumes. We enforce continuity and momentum conservation via flux-balance residuals on control surfaces. Our method utilizes a three-scale subdomain strategy-comprising large volumes for long-range coupling, skeleton-aware meso-scale volumes aligned with transport pathways, and small volumes for local refinement-alongside a two-stage training schedule prioritizing continuity. Experiments on steady incompressible flow in TPMS geometries show MUSA-PINN outperforms state-of-the-art baselines, reducing relative errors by up to 93% and preserving mass conservation.
[LG-15] Reasoning as Compression: Unifying Budget Forcing via the Conditional Information Bottleneck
链接: https://arxiv.org/abs/2603.08462
作者: Fabio Valerio Massoli,Andrey Kuzmin,Arash Behboodi
类目: Machine Learning (cs.LG)
*备注:
Abstract:Chain-of-Thought (CoT) prompting improves LLM accuracy on complex tasks but often increases token usage and inference cost. Existing “Budget Forcing” methods reducing cost via fine-tuning with heuristic length penalties, suppress both essential reasoning and redundant filler. We recast efficient reasoning as a lossy compression problem under the Information Bottleneck (IB) principle, and identify a key theoretical gap when applying naive IB to transformers: attention violates the Markov property between prompt, reasoning trace, and response. To resolve this issue, we model CoT generation under the Conditional Information Bottleneck (CIB) principle, where the reasoning trace Z acts as a computational bridge that contains only the information about the response Y that is not directly accessible from the prompt X. This yields a general Reinforcement Learning objective: maximize task reward while compressing completions under a prior over reasoning traces, subsuming common heuristics (e.g., length penalties) as special cases (e.g., uniform priors). In contrast to naive token-counting-based approaches, we introduce a semantic prior that measures token cost by surprisal under a language model prior. Empirically, our CIB objective prunes cognitive bloat while preserving fluency and logic, improving accuracy at moderate compression and enabling aggressive compression with minimal accuracy drop.
[LG-16] Data-Driven Priors for Uncertainty-Aware Deterioration Risk Prediction with Multimodal Data
链接: https://arxiv.org/abs/2603.08459
作者: L. Julián Lechuga López,Tim G. J. Rudner,Farah E. Shamout
类目: Machine Learning (cs.LG)
*备注: 24 pages, 5 figures, 8 tables
Abstract:Safe predictions are a crucial requirement for integrating predictive models into clinical decision support systems. One approach for ensuring trustworthiness is to enable models’ ability to express their uncertainty about individual predictions. However, current machine learning models frequently lack reliable uncertainty estimation, hindering real-world deployment. This is further observed in multimodal settings, where the goal is to enable effective information fusion. In this work, we propose \textttMedCertAIn , a predictive uncertainty framework that leverages multimodal clinical data for in-hospital risk prediction to improve model performance and reliability. We design data-driven priors over neural network parameters using a hybrid strategy that considers cross-modal similarity in self-supervised latent representations and modality-specific data corruptions. We train and evaluate the models with such priors using clinical time-series and chest X-ray images from the publicly-available datasets MIMIC-IV and MIMIC-CXR. Our results show that \textttMedCertAIn significantly improves predictive performance and uncertainty quantification compared to state-of-the-art deterministic baselines and alternative Bayesian methods. These findings highlight the promise of data-driven priors in advancing robust, uncertainty-aware AI tools for high-stakes clinical applications.
[LG-17] Adaptive Entropy-Driven Sensor Selection in a Camera-LiDAR Particle Filter for Single-Vessel Tracking
链接: https://arxiv.org/abs/2603.08457
作者: Andrei Starodubov,Yaqub Aris Prabowo,Andreas Hadjipieris,Ioannis Kyriakides,Roberto Galeazzi
类目: Robotics (cs.RO); Machine Learning (cs.LG); Signal Processing (eess.SP); Systems and Control (eess.SY); Data Analysis, Statistics and Probability (physics.data-an)
*备注: 8 pages, 5 figures, submitted to FUSION 2026 conference proceedings
Abstract:Robust single-vessel tracking from fixed coastal platforms is hindered by modality-specific degradations: cameras suffer from illumination and visual clutter, while LiDAR performance drops with range and intermittent returns. We present a heterogeneous multi-sensor fusion particle-filter tracker that incorporates an information-gain (entropy-reduction) adaptive sensing policy to select the most informative configuration at each fusion time bin. The approach is validated in a real maritime deployment at the CMMI Smart Marina Testbed (Ayia Napa Marina, Cyprus), using a shore-mounted 3D LiDAR and an elevated fixed camera to track a rigid inflatable boat with onboard GNSS ground truth. We compare LiDAR-only, camera-only, all-sensors, and adaptive configurations. Results show LiDAR dominates near-field accuracy, the camera sustains longer-range coverage when LiDAR becomes unavailable, and the adaptive policy achieves a favorable accuracy-continuity trade-off by switching modalities based on information gain. By avoiding continuous multi-stream processing, the adaptive configuration provides a practical baseline for resilient and resource-aware maritime surveillance.
[LG-18] Meta-RL with Shared Representations Enables Fast Adaptation in Energy Systems PAKDD2026
链接: https://arxiv.org/abs/2603.08418
作者: Théo Zangato,Aomar Osmani,Pegah Alizadeh
类目: Machine Learning (cs.LG)
*备注: accepted at PAKDD 2026, Hong Kong
Abstract:Meta-Reinforcement Learning addresses the critical limitations of conventional Reinforcement Learning in multi-task and non-stationary environments by enabling fast policy adaptation and improved generalization. We introduce a novel Meta-RL framework that integrates a bi-level optimization scheme with a hybrid actor-critic architecture specially designed to enhance sample efficiency and inter-task adaptability. To improve knowledge transfer, we meta-learn a shared state feature extractor jointly optimized across actor and critic networks, providing efficient representation learning and limiting overfitting to individual tasks or dominant profiles. Additionally, we propose a parameter-sharing mechanism between the outer- and inner-loop actor networks, to reduce redundant learning and accelerate adaptation during task revisitation. The approach is validated on a real-world Building Energy Management Systems dataset covering nearly a decade of temporal and structural variability, for which we propose a task preparation method to promote generalization. Experiments demonstrate effective task adaptation and better performance compared to conventional RL and Meta-RL methods.
[LG-19] Beyond the Markovian Assumption: Robust Optimization via Fractional Weyl Integrals in Imbalanced Data
链接: https://arxiv.org/abs/2603.08377
作者: Gustavo A. Dorrego
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 5 pages, 3 figures
Abstract:Standard Gradient Descent and its modern variants assume local, Markovian weight updates, making them highly susceptible to noise and overfitting. This limitation becomes critically severe in extremely imbalanced datasets such as financial fraud detection where dominant class gradients systematically overwrite the subtle signals of the minority class. In this paper, we introduce a novel optimization algorithm grounded in Fractional Calculus. By isolating the core memory engine of the generalized fractional derivative, the Weighted Fractional Weyl Integral, we replace the instantaneous gradient with a dynamically weighted historical sequence. This fractional memory operator acts as a natural regularizer. Empirical evaluations demonstrate that our method prevents overfitting in medical diagnostics and achieves an approximately 40 percent improvement in PR-AUC over classical optimizers in financial fraud detection, establishing a robust bridge between pure fractional topology and applied Machine Learning.
[LG-20] Leaderboard Incentives: Model Rankings under Strategic Post-Training
链接: https://arxiv.org/abs/2603.08371
作者: Yatong Chen,Guanhua Zhang,Moritz Hardt
类目: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
*备注:
Abstract:Influential benchmarks incentivize competing model developers to strategically allocate post-training resources toward improvements on the leaderboard, a phenomenon dubbed benchmaxxing or training on the test task. In this work, we initiate a principled study of the incentive structure that benchmarks induce. We model benchmarking as a Stackelberg game between a benchmark designer who chooses an evaluation protocol and multiple model developers who compete simultaneously in a subgame given by the designer’s choice. Each competitor has a model of unknown latent quality and can inflate its observed score by allocating resources to benchmark-specific improvements. First, we prove that current benchmarks induce games for which no Nash equilibrium between model developers exists. This result suggests one explanation for why current practice leads to misaligned incentives, prompting model developers to strategize in opaque ways. However, we prove that under mild conditions, a recently proposed evaluation protocol, called tune-before-test, induces a benchmark with a unique Nash equilibrium that ranks models by latent quality. This positive result demonstrates that benchmarks need not set bad incentives, even if current evaluations do.
[LG-21] PolyFormer: learning efficient reformulations for scalable optimization under complex physical constraints
链接: https://arxiv.org/abs/2603.08283
作者: Yilin Wen,Yi Guo,Bo Zhao,Wei Qi,Zechun Hu,Colin Jones,Jian Sun
类目: Machine Learning (cs.LG); Systems and Control (eess.SY); Optimization and Control (math.OC)
*备注: Code availability: All the data and code are made openly available at this https URL
Abstract:Real-world optimization problems are often constrained by complex physical laws that limit computational scalability. These constraints are inherently tied to complex regions, and thus learning models that incorporate physical and geometric knowledge, i.e., physics-informed machine learning (PIML), offer a promising pathway for efficient solution. Here, we introduce PolyFormer, which opens a new direction for PIML in prescriptive optimization tasks, where physical and geometric knowledge is not merely used to regularize learning models, but to simplify the problems themselves. PolyFormer captures geometric structures behind constraints and transforms them into efficient polytopic reformulations, thereby decoupling problem complexity from solution difficulty and enabling off-the-shelf optimization solvers to efficiently produce feasible solutions with acceptable optimality loss. Through evaluations across three important problems (large-scale resource aggregation, network-constrained optimization, and optimization under uncertainty), PolyFormer achieves computational speedups up to 6,400-fold and memory reductions up to 99.87%, while maintaining solution quality competitive with or superior to state-of-the-art methods. These results demonstrate that PolyFormer provides an efficient and reliable solution for scalable constrained optimization, expanding the scope of PIML to prescriptive tasks in scientific discovery and engineering applications.
[LG-22] Airborne Magnetic Anomaly Navigation with Neural-Network-Augmented Online Calibration
链接: https://arxiv.org/abs/2603.08265
作者: Antonia Hager,Sven Nebendahl,Alexej Klushyn,Jasper Krauser,Torleiv H. Bryne,Tor Arne Johansen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Airborne Magnetic Anomaly Navigation (MagNav) provides a jamming-resistant and robust alternative to satellite navigation but requires the real-time compensation of the aircraft platform’s large and dynamic magnetic interference. State-of-the-art solutions often rely on extensive offline calibration flights or pre-training, creating a logistical barrier to operational deployment. We present a fully adaptive MagNav architecture featuring a “cold-start” capability that identifies and compensates for the aircraft’s magnetic signature entirely in-flight. The proposed method utilizes an extended Kalman filter with an augmented state vector that simultaneously estimates the aircraft’s kinematic states as well as the coefficients of the physics-based Tolles-Lawson calibration model and the parameters of a Neural Network to model aircraft interferences. The Kalman filter update is mathematically equivalent to an online Natural Gradient descent, integrating superior convergence and data efficiency of state-of-the-art second-order optimization directly into the navigation filter. To enhance operational robustness, the neural network is constrained to a residual learning role, modeling only the nonlinearities uncorrected by the explainable physics-based calibration baseline. Validated on the MagNav Challenge dataset, our framework effectively bounds inertial drift using a magnetometer-only feature set. The results demonstrate navigation accuracy comparable to state-of-the-art models trained offline, without requiring prior calibration flights or dedicated maneuvers.
[LG-23] FlowTouch: View-Invariant Visuo-Tactile Prediction
链接: https://arxiv.org/abs/2603.08255
作者: Seongjin Bien,Carlo Kneissl,Tobias Jülg,Frank Fundel,Thomas Ressler-Antal,Florian Walter,Björn Ommer,Gitta Kutyniok,Wolfram Burgard
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Tactile sensation is essential for contact-rich manipulation tasks. It provides direct feedback on object geometry, surface properties, and interaction forces, enhancing perception and enabling fine-grained control. An inherent limitation of tactile sensors is that readings are available only when an object is touched. This precludes their use during planning and the initial execution phase of a task. Predicting tactile information from visual information can bridge this gap. A common approach is to learn a direct mapping from camera images to the output of vision-based tactile sensors. However, the resulting model will depend strongly on the specific setup and on how well the camera can capture the area where an object is touched. In this work, we introduce FlowTouch, a novel model for view-invariant visuo-tactile prediction. Our key idea is to use an object’s local 3D mesh to encode rich information for predicting tactile patterns while abstracting away from scene-dependent details. FlowTouch integrates scene reconstruction and Flow Matching-based models for image generation. Our results show that FlowTouch is able to bridge the sim-to-real gap and generalize to new sensor instances. We further show that the resulting tactile images can be used for downstream grasp stability prediction. Our code, datasets and videos are available at this https URL
[LG-24] FedPrism: Adaptive Personalized Federated Learning under Non-IID Data
链接: https://arxiv.org/abs/2603.08252
作者: Prakash Kumbhakar,Shrey Srivastava,Haroon R Lone
类目: Machine Learning (cs.LG)
*备注:
Abstract:Federated Learning (FL) suffers significant performance degradation in real-world deployments characterized by moderate to extreme statistical heterogeneity (non-IID client data). While global aggregation strategies promote broad generalization, they often fail to capture the diversity of local data distributions, leading to suboptimal personalization. We address this problem with FedPrism, a framework that uses two main strategies. First, it uses a Prism Decomposition method that builds each client’s model from three parts: a global foundation, a shared group part for similar clients, and a private part for unique local data. This allows the system to group similar users together automatically and adapt if their data changes. Second, we include a Dual-Stream design that runs a general model alongside a local specialist. The system routes predictions between the general model and the local specialist based on the specialist’s confidence. Through systematic experiments on non-IID data partitions, we demonstrate that FedPrism exceeds static aggregation and hard-clustering baselines, achieving significant accuracy gains under high heterogeneity. These results establish FedPrism as a robust and flexible solution for federated learning in heterogeneous environments, effectively balancing generalizable knowledge with adaptive personalization. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603.08252 [cs.LG] (or arXiv:2603.08252v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.08252 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-25] Optimising antibiotic switching via forecasting of patient physiology
链接: https://arxiv.org/abs/2603.08242
作者: Magnus Ross,Nel Swanepoel,Akish Luintel,Emma McGuire,Ingemar J. Cox,Steve Harris,Vasileios Lampos
类目: Machine Learning (cs.LG); Applications (stat.AP)
*备注: 32 pages, 8 figures
Abstract:Timely transition from intravenous (IV) to oral antibiotic therapy shortens hospital stays, reduces catheter-related infections, and lowers healthcare costs, yet one in five patients in England remain on IV antibiotics despite meeting switching criteria. Clinical decision support systems can improve switching rates, but approaches that learn from historical decisions reproduce the delays and inconsistencies of routine practice. We propose using neural processes to model vital sign trajectories probabilistically, predicting switch-readiness by comparing forecasts against clinical guidelines rather than learning from past actions, and ranking patients to prioritise clinical review. The design yields interpretable outputs, adapts to updated guidelines without retraining, and preserves clinical judgement. Validated on MIMIC-IV (US intensive care, 6,333 encounters) and UCLH (a large urban academic UK hospital group, 10,584 encounters), the system selects 2.2-3.2 \times more relevant patients than random. Our results demonstrate that forecasting patient physiology offers a principled foundation for decision support in antibiotic stewardship.
[LG-26] Wiener Chaos Expansion based Neural Operator for Singular Stochastic Partial Differential Equations
链接: https://arxiv.org/abs/2603.08219
作者: Dai Shi,Luke Thompson,Andi Han,Peiyan Hu,Junbin Gao,José Miguel Hernández-Lobato
类目: Machine Learning (cs.LG)
*备注:
Abstract:In this paper, we explore how our recently developed Wiener Chaos Expansion (WCE)-based neural operator (NO) can be applied to singular stochastic partial differential equations, e.g., the dynamic \boldsymbol\Phi^4_2 model simulated in the recent works. Unlike the previous WCE-NO which solves SPDEs by simply inserting Wick-Hermite features into the backbone NO model, we leverage feature-wise linear modulation (FiLM) to appropriately capture the dependency between the solution of singular SPDE and its smooth remainder. The resulting WCE-FiLM-NO shows excellent performance on \boldsymbol\Phi^4_2 , as measured by relative L_2 loss, out-of-distribution L_2 loss, and autocorrelation score; all without the help of renormalisation factor. In addition, we also show the potential of simulating \boldsymbol\Phi^4_3 data, which is more aligned with real scientific practice in statistical quantum field theory. To the best of our knowledge, this is among the first works to develop an efficient data-driven surrogate for the dynamical \boldsymbol\Phi^4_3 model.
[LG-27] Sequential Service Region Design with Capacity-Constrained Investment and Spillover Effect
链接: https://arxiv.org/abs/2603.08188
作者: Tingting Chen,Feng Chu,Jiantong Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Service region design determines the geographic coverage of service networks, shaping long-term operational performance. Capital and operational constraints preclude simultaneous large-scale deployment, requiring expansion to proceed sequentially. The resulting challenge is to determine when and where to invest under demand uncertainty, balancing intertemporal trade-offs between early and delayed investment and accounting for network effects whereby each deployment reshapes future demand through inter-regional connectivity. This study addresses a sequential service region design (SSRD) problem incorporating two practical yet underexplored factors: a k -region constraint that limits the number of regions investable per period and a stochastic spillover effect linking investment decisions to demand evolution. The resulting problem requires sequencing regional portfolios under uncertainty, leading to a combinatorial explosion in feasible investment sequences. To address this challenge, we propose a solution framework that integrates real options analysis (ROA) with a Transformer-based Proximal Policy Optimization (TPPO) algorithm. ROA evaluates the intertemporal option value of investment sequences, while TPPO learns sequential policies that directly generate high option-value sequences without exhaustive enumeration. Numerical experiments on realistic multi-region settings demonstrate that TPPO converges faster than benchmark DRL methods and consistently identifies sequences with superior option value. Case studies and sensitivity analyses further confirm robustness and provide insights on investment concurrency, regional prioritization, and the increasing benefits of adaptive expansion via our approach under stronger spillovers and dynamic market conditions.
[LG-28] SERQ: Saliency-Aware Low-Rank Error Reconstruction for LLM Quantization
链接: https://arxiv.org/abs/2603.08185
作者: Yeonsik Park,Hyeonseong Kim,Seungkyu Choi
类目: Machine Learning (cs.LG)
*备注: 21 pages, 4 figures
Abstract:Post-training quantization (PTQ) has emerged as a prevailing technique for deploying large language models (LLMs) efficiently in terms of both memory and computation, across edge devices and server platforms. Existing PTQ methods primarily aim to reduce precision in weights and activations by mitigating quantization errors caused by channel-wise outlier activations (e.g., pre-quantization scaling, online transformations, or low-rank error reconstruction). Among these approaches, error reconstruction with low-rank adaptation (LoRA) has proven particularly effective, as it introduces a lightweight auxiliary computation path without requiring heavy optimization or additional online layers. However, prior studies reveal severe accuracy degradation under W4A4 settings, and conventional low-rank adaptations rely on two sequential factors, necessitating intermediate quantization during inference and thereby limiting low-precision efficiency. In this work, we propose SERQ, a saliency-aware error reconstruction method for low-bit LLM inference that employs a single low-rank compensation matrix. SERQ preserves efficient 4-bit matrix multiplication in linear layers by jointly mitigating quantization errors arising from both activation and weight saliency through three stages: (1) static activation flattening, (2) saliency-aware error reconstruction, and (3) offline weight permutation. The method incurs additional computation only for low-rank error reconstruction via a single decomposition, while all other operations are performed offline, thereby keeping latency overhead minimal. Empirically, SERQ outperforms prior error reconstruction methods under both W4A8 and W4A4 settings, and achieves higher accuracy than state-of-the-art rotation-based W4A4 approaches, while substantially reducing calibration complexity.
[LG-29] AutoAdapt: An Automated Domain Adaptation Framework for LLM s
链接: https://arxiv.org/abs/2603.08181
作者: Sidharth Sinha,Anson Bastos,Xuchao Zhang,Akshay Nambi,Chetan Bansal,Saravan Rajmohan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language models (LLMs) excel in open domains but struggle in specialized settings with limited data and evolving knowledge. Existing domain adaptation practices rely heavily on manual trial-and-error processes, incur significant hyperparameter complexity, and are highly sensitive to data and user preferences, all under the high cost of LLM training. Moreover, the interactions and transferability of hyperparameter choices across models/domains remain poorly understood, making adaptation gains uncertain even with substantial effort. To solve these challenges, we present AutoAdapt, a novel end-to-end automated framework for efficient and reliable LLM domain adaptation. AutoAdapt leverages curated knowledge bases from literature and open-source resources to reduce expert intervention. To narrow the search space, we design a novel multi-agent debating system in which proposal and critic agents iteratively interact to align user intent and incorporate data signals and best practices into the planning process. To optimize hyperparameters under tight budgets, we propose AutoRefine, a novel LLM-based surrogate that replaces costly black-box search. Across 10 tasks, AutoAdapt achieves a 25% average relative accuracy improvement over state-of-the-art Automated Machine Learning baselines with minimal overhead.
[LG-30] Covenant-72B: Pre-Training a 72B LLM with Trustless Peers Over-the-Internet
链接: https://arxiv.org/abs/2603.08163
作者: Joel Lidin,Amir Sarfi,Erfan Miahi,Quentin Anthony,Shivam Chauhan,Evangelos Pappas,Benjamin Thérien,Eugene Belilovsky,Samuel Dare
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 26 pages, 6 figures, 4 tables
Abstract:Recently, there has been increased interest in globally distributed training, which has the promise to both reduce training costs and democratize participation in building large-scale foundation models. However, existing models trained in a globally distributed manner are relatively small in scale and have only been trained with whitelisted participants. Therefore, they do not yet realize the full promise of democratized participation. In this report, we describe Covenant-72B, an LLM produced by the largest collaborative globally distributed pre-training run (in terms of both compute and model scale), which simultaneously allowed open, permissionless participation supported by a live blockchain protocol. We utilized a state-of-the-art communication-efficient optimizer, SparseLoCo, supporting dynamic participation with peers joining and leaving freely. Our model, pre-trained on approximately 1.1T tokens, performs competitively with fully centralized models pre-trained on similar or higher compute budgets, demonstrating that fully democratized, non-whitelisted participation is not only feasible, but can be achieved at unprecedented scale for a globally distributed pre-training run.
[LG-31] Learning Hierarchical Knowledge in Text-Rich Networks with Taxonomy-Informed Representation Learning KDD2026
链接: https://arxiv.org/abs/2603.08159
作者: Yunhui Liu,Yongchao Liu,Yinfeng Chen,Chuntao Hong,Tao Zheng,Tieke He
类目: Machine Learning (cs.LG)
*备注: Accepted by KDD 2026. Extended version coming soon
Abstract:Hierarchical knowledge structures are ubiquitous across real-world domains and play a vital role in organizing information from coarse to fine semantic levels. While such structures have been widely used in taxonomy systems, biomedical ontologies, and retrieval-augmented generation, their potential remains underexplored in the context of Text-Rich Networks (TRNs), where each node contains rich textual content and edges encode semantic relationships. Existing methods for learning on TRNs often focus on flat semantic modeling, overlooking the inherent hierarchical semantics embedded in textual documents. To this end, we propose TIER (Hierarchical \textbfTaxonomy-\textbfInformed R\textbfEpresentation Learning on Text-\textbfRich Networks), which first constructs an implicit hierarchical taxonomy and then integrates it into the learned node representations. Specifically, TIER employs similarity-guided contrastive learning to build a clustering-friendly embedding space, upon which it performs hierarchical K-Means followed by LLM-powered clustering refinement to enable semantically coherent taxonomy construction. Leveraging the resulting taxonomy, TIER introduces a cophenetic correlation coefficient-based regularization loss to align the learned embeddings with the hierarchical structure. By learning representations that respect both fine-grained and coarse-grained semantics, TIER enables more interpretable and structured modeling of real-world TRNs. We demonstrate that our approach significantly outperforms existing methods on multiple datasets across diverse domains, highlighting the importance of hierarchical knowledge learning for TRNs.
[LG-32] Are We Winning the Wrong Game? Revisiting Evaluation Practices for Long-Term Time Series Forecasting
链接: https://arxiv.org/abs/2603.08156
作者: Thanapol Phungtua-eng,Yoshitaka Yamamoto
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: First draft
Abstract:Long-term time series forecasting (LTSF) is widely recognized as a central challenge in data mining and machine learning. LTSF has increasingly evolved into a benchmark-driven ‘‘GAME,’’ where models are ranked, compared, and declared state-of-the-art based primarily on marginal reductions in aggregated pointwise error metrics such as MSE and MAE. Across a small set of canonical datasets and fixed forecasting horizons, progress is communicated through leaderboard-style tables in which lower numerical scores define success. In this GAME, what is measured becomes what is optimized, and incremental error reduction becomes the dominant currency of advancement. We argue that this metric-centric regime is not merely incomplete, but structurally misaligned with the broader objectives of forecasting. In real-world settings, forecasting often prioritizes preserving temporal structure, trend stability, seasonal coherence, robustness to regime shifts, and supporting downstream decision processes. Optimizing aggregate pointwise error does not necessarily imply modeling these structural properties. As a result, leaderboard improvement may increasingly reflect specialization in benchmark configurations rather than a deeper understanding of temporal dynamics. This paper revisits LTSF evaluation as a foundational question in data science: what does it mean to measure forecasting progress? We propose a multi-dimensional evaluation perspective that integrates statistical fidelity, structural coherence, and decision-level relevance. By challenging the current metric monoculture, we aim to redirect attention from winning benchmark tables toward advancing meaningful, context-aware forecasting.
[LG-33] C2FG: Control Classifier-Free Guidance via Score Discrepancy Analysis
链接: https://arxiv.org/abs/2603.08155
作者: Jiayang Gao,Tianyi Zheng,Jiayang Zou,Fengxiang Yang,Shice Liu,Luyao Fan,Zheyu Zhang,Hao Zhang,Jinwei Chen,Peng-Tao Jiang,Bo Li,Jia Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Classifier-Free Guidance (CFG) is a cornerstone of modern conditional diffusion models, yet its reliance on the fixed or heuristic dynamic guidance weight is predominantly empirical and overlooks the inherent dynamics of the diffusion process. In this paper, we provide a rigorous theoretical analysis of the Classifier-Free Guidance. Specifically, we establish strict upper bounds on the score discrepancy between conditional and unconditional distributions at different timesteps based on the diffusion process. This finding explains the limitations of fixed-weight strategies and establishes a principled foundation for time-dependent guidance. Motivated by this insight, we introduce \textbfControl Classifier-Free Guidance (C ^2 FG), a novel, training-free, and plug-in method that aligns the guidance strength with the diffusion dynamics via an exponential decay control function. Extensive experiments demonstrate that C ^2 FG is effective and broadly applicable across diverse generative tasks, while also exhibiting orthogonality to existing strategies.
[LG-34] raining event-based neural networks with exact gradients via Differentiable ODE Solving in JAX
链接: https://arxiv.org/abs/2603.08146
作者: Lukas König,Manuel Kuhn,David Kappel,Anand Subramoney
类目: Machine Learning (cs.LG)
*备注: 9 pages, 3 figures
Abstract:Existing frameworks for gradient-based training of spiking neural networks face a trade-off: discrete-time methods using surrogate gradients support arbitrary neuron models but introduce gradient bias and constrain spike-time resolution, while continuous-time methods that compute exact gradients require analytical expressions for spike times and state evolution, restricting them to simple neuron types such as Leaky Integrate and Fire (LIF). We introduce the Eventax framework, which resolves this trade-off by combining differentiable numerical ODE solvers with event-based spike handling. Built in JAX, our frame-work uses Diffrax ODE-solvers to compute gradients that are exact with respect to the forward simulation for any neuron model defined by ODEs . It also provides a simple API where users can specify just the neuron dynamics, spike conditions, and reset rules. Eventax prioritises modelling flexibility, supporting a wide range of neuron models, loss functions, and network architectures, which can be easily extended. We demonstrate Eventax on multiple benchmarks, including Yin-Yang and MNIST, using diverse neuron models such as Leaky Integrate-and-fire (LIF), Quadratic Integrate-and-fire (QIF), Exponential integrate-and-fire (EIF), Izhikevich and Event-based Gated Recurrent Unit (EGRU) with both time-to-first-spike and state-based loss functions, demonstrating its utility for prototyping and testing event-based architectures trained with exact gradients. We also demonstrate the application of this framework for more complex neuron types by implementing a multi-compartment neuron that uses a model of dendritic spikes in human layer 2/3 cortical Pyramidal neurons for computation. Code available at this https URL.
[LG-35] Mitigating Homophily Disparity in Graph Anomaly Detection: A Scalable and Adaptive Approach WWW2026
链接: https://arxiv.org/abs/2603.08137
作者: Yunhui Liu,Qizhuo Xie,Yinfeng Chen,Xudong Jin,Tao Zheng,Bin Chong,Tieke He
类目: Machine Learning (cs.LG)
*备注: Accepted by WWW 2026
Abstract:Graph anomaly detection (GAD) aims to identify nodes that deviate from normal patterns in structure or features. While recent GNN-based approaches have advanced this task, they struggle with two major challenges: 1) homophily disparity, where nodes exhibit varying homophily at both class and node levels; and 2) limited scalability, as many methods rely on costly whole-graph operations. To address them, we propose SAGAD, a Scalable and Adaptive framework for GAD. SAGAD precomputes multi-hop embeddings and applies reparameterized Chebyshev filters to extract low- and high-frequency information, enabling efficient training and capturing both homophilic and heterophilic patterns. To mitigate node-level homophily disparity, we introduce an Anomaly Context-Aware Adaptive Fusion, which adaptively fuses low- and high-pass embeddings using fusion coefficients conditioned on Rayleigh Quotient-guided anomalous subgraph structures for each node. To alleviate class-level disparity, we design a Frequency Preference Guidance Loss, which encourages anomalies to preserve more high-frequency information than normal nodes. SAGAD supports mini-batch training, achieves linear time and space complexity, and drastically reduces memory usage on large-scale graphs. Theoretically, SAGAD ensures asymptotic linear separability between normal and abnormal nodes under mild conditions. Extensive experiments on 10 benchmarks confirm SAGAD’s superior accuracy and scalability over state-of-the-art methods.
[LG-36] Explainable Condition Monitoring via Probabilistic Anomaly Detection Applied to Helicopter Transmissions
链接: https://arxiv.org/abs/2603.08130
作者: Aurelio Raffa Ugolini,Jessica Leoni,Valentina Breschi,Damiano Paniccia,Francesco Aldo Tucci,Luigi Capone,Mara Tanelli
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We present a novel Explainable methodology for Condition Monitoring, relying on healthy data only. Since faults are rare events, we propose to focus on learning the probability distribution of healthy observations only, and detect Anomalies at runtime. This objective is achieved via the definition of probabilistic measures of deviation from nominality, which allow to detect and anticipate faults. The Bayesian perspective underpinning our approach allows us to perform Uncertainty Quantification to inform decisions. At the same time, we provide descriptive tools to enhance the interpretability of the results, supporting the deployment of the proposed strategy also in safety-critical applications. The methodology is validated experimentally on two use cases: a publicly available benchmark for Predictive Maintenance, and a real-world Helicopter Transmission dataset collected over multiple years. In both applications, the method achieves competitive detection performance with respect to state-of-the-art anomaly detection methods.
[LG-37] RIAGE: Type-Routed Interventions via Aleatoric-Epistemic Gated Estimation in Robotic Manipulation and Adaptive Perception – Dont Treat All Uncertainty the Same
链接: https://arxiv.org/abs/2603.08128
作者: Divake Kumar,Sina Tayebati,Devashri Naik,Patrick Poggi,Amanda Sofie Rios,Nilesh Ahuja,Amit Ranjan Trivedi
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Most uncertainty-aware robotic systems collapse prediction uncertainty into a single scalar score and use it to trigger uniform corrective responses. This aggregation obscures whether uncertainty arises from corrupted observations or from mismatch between the learned model and the true system dynamics. As a result, corrective actions may be applied to the wrong component of the closed loop, degrading performance relative to leaving the policy unchanged. We introduce a lightweight post hoc framework that decomposes uncertainty into aleatoric and epistemic components and uses these signals to regulate system responses at inference time. Aleatoric uncertainty is estimated from deviations in the observation distribution using a Mahalanobis density model, while epistemic uncertainty is detected using a noise robust forward dynamics ensemble that isolates model mismatch from measurement corruption. The two signals remain empirically near orthogonal during closed loop execution and enable type specific responses. High aleatoric uncertainty triggers observation recovery, while high epistemic uncertainty moderates control actions. The same signals also regulate adaptive perception by guiding model capacity selection during tracking inference. Experiments demonstrate consistent improvements across both control and perception tasks. In robotic manipulation, the decomposed controller improves task success from 59.4% to 80.4% under compound perturbations and outperforms a combined uncertainty baseline by up to 21.0%. In adaptive tracking inference on MOT17, uncertainty-guided model selection reduces average compute by 58.2% relative to a fixed high capacity detector while preserving detection quality within 0.4%. Code and demo videos are available at this https URL.
[LG-38] Model-based Offline RL via Robust Value-Aware Model Learning with Implicitly Differentiable Adaptive Weighting ICLR2026
链接: https://arxiv.org/abs/2603.08118
作者: Zhongjian Qiao,Jiafei Lyu,Boxiang Lyu,Yao Shu,Siyang Gao,Shuang Qiu
类目: Machine Learning (cs.LG)
*备注: Accepted at ICLR 2026
Abstract:Model-based offline reinforcement learning (RL) aims to enhance offline RL with a dynamics model that facilitates policy exploration. However, \textitmodel exploitation could occur due to inevitable model errors, degrading algorithm performance. Adversarial model learning offers a theoretical framework to mitigate model exploitation by solving a maximin formulation. Within such a paradigm, RAMBO~\citeprigter2022rambo has emerged as a representative and most popular method that provides a practical implementation with model gradient. However, we empirically reveal that severe Q-value underestimation and gradient explosion can occur in RAMBO with only slight hyperparameter tuning, suggesting that it tends to be overly conservative and suffers from unstable model updates. To address these issues, we propose \textbfRObust value-aware \textbfModel learning with \textbfImplicitly differentiable adaptive weighting (ROMI). Instead of updating the dynamics model with model gradient, ROMI introduces a novel robust value-aware model learning approach. This approach requires the dynamics model to predict future states with values close to the minimum Q-value within a scale-adjustable state uncertainty set, enabling controllable conservatism and stable model updates. To further improve out-of-distribution (OOD) generalization during multi-step rollouts, we propose implicitly differentiable adaptive weighting, a bi-level optimization scheme that adaptively achieves dynamics- and value-aware model learning. Empirical results on D4RL and NeoRL datasets show that ROMI significantly outperforms RAMBO and achieves competitive or superior performance compared to other state-of-the-art methods on datasets where RAMBO typically underperforms. Code is available at this https URL.
[LG-39] au-BNO: Brain Neural Operator for Tau Transport Model
链接: https://arxiv.org/abs/2603.08108
作者: Nuutti Barron,Heng Rao,Urmi Saha,Yu Gu,Zhenghao Liu,Ge Yu,Defu Yang,Ashish Raj,Minghan Chen
类目: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
*备注:
Abstract:Mechanistic modeling provides a biophysically grounded framework for studying the spread of pathological tau protein in tauopathies like Alzheimer’s disease. Existing approaches typically model tau propagation as a diffusive process on the brain’s structural connectome, reproducing macroscopic patterns but neglecting microscale cellular transport and reaction mechanisms. The Network Transport Model (NTM) was introduced to fill this gap, explaining how region-level progression of tau emerges from microscale biophysical processes. However, the NTM faces a common challenge for complex models defined by large systems of partial differential equations: the inability to perform parameter inference and mechanistic discovery due to high computational burden and slow model simulations. To overcome this barrier, we propose Tau-BNO, a Brain Neural Operator surrogate framework for rapidly approximating NTM dynamics that captures both intra-regional reaction kinetics and inter-regional network transport. Tau-BNO combines a function operator that encodes kinetic parameters with a query operator that preserves initial state information, while approximating anisotropic transport through a spectral kernel that retains directionality. Empirical evaluations demonstrate high predictive accuracy ( R^2\approx 0.98) across diverse biophysical regimes and an 89% performance improvement over state-of-the-art sequence models like Transformers and Mamba, which lack inherent structural priors. By reducing simulation time from hours to seconds, we show that the surrogate model is capable of producing new insights and generating new hypotheses. This framework is readily extensible to a broader class of connectome-based biophysical models, showcasing the transformative value of deep learning surrogates to accelerate analysis of large-scale, computationally intensive dynamical systems.
[LG-40] Invisible Safety Threat: Malicious Finetuning for LLM via Steganography
链接: https://arxiv.org/abs/2603.08104
作者: Guangnian Wan,Xinyin Ma,Gongfan Fang,Xinchao Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Understanding and addressing potential safety alignment risks in large language models (LLMs) is critical for ensuring their safe and trustworthy deployment. In this paper, we highlight an insidious safety threat: a compromised LLM can maintain a facade of proper safety alignment while covertly generating harmful content. To achieve this, we finetune the model to understand and apply a steganographic technique. At inference time, we input a prompt that contains a steganographically embedded malicious target question along with a plaintext cover question. The model, in turn, produces a target response similarly embedded within a benign-looking cover response. In this process, human observers only see the model being prompted with a cover question and generating a corresponding cover response, while the malicious content is hidden from view. We demonstrate this invisible safety threat on GPT-4.1 despite the OpenAI finetuning API’s safeguards. The finetuned model produces steganographic malicious outputs in response to hidden malicious prompts, while the user interface displays only a fully benign cover interaction. We also replicate the attack on three open-source models, Llama-3.3-70B-Instruct, Phi-4, and Mistral-Small-24B-Base-2501, confirming the generality of our method. We quantitatively evaluate our method on the AdvBench dataset, using Llama-Guard-3-8B for content safety classification. Across all four models, all stegotexts containing malicious content are incorrectly classified as safe.
[LG-41] EAGLE-Pangu: Accelerator-Safe Tree Speculative Decoding on Ascend NPUs
链接: https://arxiv.org/abs/2603.08088
作者: Chang Han,Yijie Hu,Jingling Liu
类目: Machine Learning (cs.LG); Programming Languages (cs.PL)
*备注: 14 pages. 7 figures
Abstract:Autoregressive decoding remains a primary bottleneck in large language model (LLM) serving, motivating speculative decoding methods that reduce expensive teacher-model invocations by verifying multiple candidate tokens per step. Tree-structured speculation further increases parallelism, but is often brittle when ported across heterogeneous backends and accelerator stacks, where attention masking, KV-cache layouts, and indexing semantics are not interchangeable. We present EAGLE-Pangu, a reproducible system that ports EAGLE-3-style tree speculative decoding to a Pangu teacher backend on Ascend NPUs. EAGLE-Pangu contributes (i) an explicit branch/commit cache manager built on the Cache API, (ii) accelerator-safe tree tensorization that removes undefined negative indices by construction and validates structural invariants, and (iii) a fused-kernel-compatible teacher verification path with a debuggable eager fallback. On 240 turns from MT-Bench and HumanEval-style prompts, EAGLE-Pangu improves end-to-end decoding throughput by 1.27x on average, up to 2.46x at p99, over teacher-only greedy decoding in the fused-kernel performance path. We also provide a fused-kernel-free reference path with structured traces and invariant checks to support reproducible debugging and ablation across execution modes and tree budgets.
[LG-42] ny Autoregressive Recursive Models
链接: https://arxiv.org/abs/2603.08082
作者: Paulius Rauba,Claudio Fanconi,Mihaela van der Schaar
类目: Machine Learning (cs.LG)
*备注:
Abstract:Tiny Recursive Models (TRMs) have recently demonstrated remarkable performance on ARC-AGI, showing that very small models can compete against large foundation models through a two-step refinement mechanism that updates an internal reasoning state z and the predicted output y . Naturally, such refinement is of interest for any predictor; it is therefore natural to wonder whether the TRM mechanism could be effectively re-adopted in autoregressive models. However, TRMs cannot be simply compared to standard models because they lack causal predictive structures and contain persistent latent states that make it difficult to isolate specific performance gains. In this paper, we propose the Autoregressive TRM and evaluate it on small autoregressive tasks. To understand its efficacy, we propose a suite of models that gradually transform a standard Transformer to a Tiny Autoregressive Recursive Model in a controlled setting that fixes the block design, token stream, and next-token objective. Across compute-matched experiments on character-level algorithmic tasks, we surprisingly find that there are some two-level refinement baselines that show strong performance. Contrary to expectations, we find no reliable performance gains from the full Autoregressive TRM architecture. These results offer potential promise for two-step refinement mechanisms more broadly but caution against investing in the autoregressive TRM-specific model as a fruitful research direction.
[LG-43] Hybrid Quantum Neural Network for Multivariate Clinical Time Series Forecasting
链接: https://arxiv.org/abs/2603.08072
作者: Irene Iele,Floriano Caprio,Paolo Soda,Matteo Tortora
类目: Machine Learning (cs.LG)
*备注:
Abstract:Forecasting physiological signals can support proactive monitoring and timely clinical intervention by anticipating critical changes in patient status. In this work, we address multivariate multi-horizon forecasting of physiological time series by jointly predicting heart rate, oxygen saturation, pulse rate, and respiratory rate at forecasting horizons of 15, 30, and 60 seconds. We propose a hybrid quantum-classical architecture that integrates a Variational Quantum Circuit (VQC) within a recurrent neural backbone. A GRU encoder summarizes the historical observation window into a latent representation, which is then projected into quantum angles used to parameterize the VQC. The quantum layer acts as a learnable non-linear feature mixer, modeling cross-variable interactions before the final prediction stage. We evaluate the proposed approach on the BIDMC PPG and Respiration dataset under a Leave-One-Patient-Out protocol. The results show competitive accuracy compared with classical and deep learning baselines, together with greater robustness to noise and missing inputs. These findings suggest that hybrid quantum layers can provide useful inductive biases for physiological time series forecasting in small-cohort clinical settings.
[LG-44] Adversarial Domain Adaptation Enables Knowledge Transfer Across Heterogeneous RNA-Seq Datasets
链接: https://arxiv.org/abs/2603.08062
作者: Kevin Dradjat,Massinissa Hamidi,Blaise Hanczar
类目: Machine Learning (cs.LG); Genomics (q-bio.GN)
*备注: 7 pages, 5 figures. Submitted to ECCB 2026
Abstract:Accurate phenotype prediction from RNA sequencing (RNA-seq) data is essential for diagnosis, biomarker discovery, and personalized medicine. Deep learning models have demonstrated strong potential to outperform classical machine learning approaches, but their performance relies on large, well-annotated datasets. In transcriptomics, such datasets are frequently limited, leading to over-fitting and poor generalization. Knowledge transfer from larger, more general datasets can alleviate this issue. However, transferring information across RNA-seq datasets remains challenging due to heterogeneous preprocessing pipelines and differences in target phenotypes. In this study, we propose a deep learning-based domain adaptation framework that enables effective knowledge transfer from a large general dataset to a smaller one for cancer type classification. The method learns a domain-invariant latent space by jointly optimizing classification and domain alignment objectives. To ensure stable training and robustness in data-scarce scenarios, the framework is trained with an adversarial approach with appropriate regularization. Both supervised and unsupervised approach variants are explored, leveraging labeled or unlabeled target samples. The framework is evaluated on three large-scale transcriptomic datasets (TCGA, ARCHS4, GTEx) to assess its ability to transfer knowledge across cohorts. Experimental results demonstrate consistent improvements in cancer and tissue type classification accuracy compared to non-adaptive baselines, particularly in low-data scenarios. Overall, this work highlights domain adaptation as a powerful strategy for data-efficient knowledge transfer in transcriptomics, enabling robust phenotype prediction under constrained data conditions.
[LG-45] Stabilized Fine-Tuning with LoRA in Federated Learning: Mitigating the Side Effect of Client Size and Rank via the Scaling Factor
链接: https://arxiv.org/abs/2603.08058
作者: Jiayu Huang,Xiaohu Wu,Tiantian He,Qicheng Lao
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large Language Models (LLMs) are pivotal in natural language processing. The impracticality of full fine-tuning has prompted Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA), optimizing low-rank matrices A and B. In distributed scenarios where privacy constraints necessitate Federated Learning (FL), however, the integration of LoRA is often unstable. Specifically, we identify that aggregating updates from multiple clients introduces statistical variance that scales with the client count, causing gradient collapse when using high-rank adapters. Existing scaling factor candidates, such as the one used by Rank-Stabilized LoRA, ignore the interaction caused by the aggregation process. To bridge this gap, this paper introduces Stabilized Federated LoRA (SFed-LoRA), a framework that theoretically characterizes the interaction between adapter rank and federated aggregation. We derive an optimal scaling factor designed to effectively mitigate the aggregation error accumulating across N clients. By correcting the scaling mismatch inherent in previous approaches, SFed-LoRA restores the efficacy of high-rank adaptation without altering the original model architecture or increasing inference latency. Extensive experiments in diverse tasks, model architectures, and heterogeneous data distributions are conducted to validate our results. We demonstrate that SFed-LoRA prevents high-rank collapse, and achieves significantly improved stability and faster convergence compared with state-of-the-art baselines for high-rank adaptation.
[LG-46] Capacity-Aware Mixture Law Enables Efficient LLM Data Optimization
链接: https://arxiv.org/abs/2603.08022
作者: Jingwei Li,Xinran Gu,Jingzhao Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:A data mixture refers to how different data sources are combined to train large language models, and selecting an effective mixture is crucial for optimal downstream performance. Existing methods either conduct costly searches directly on the target model or rely on mixture scaling laws that fail to extrapolate well to large model sizes. We address these limitations by introducing a compute-efficient pipeline for data mixture scaling. First, we propose CAMEL, a capacity-aware mixture law that models validation loss with the nonlinear interplay between model size and mixture. We also introduce a loss-to-benchmark prediction law that estimates benchmark accuracy from validation loss, enabling end-to-end performance prediction for the target model. Next, we study how to allocate a fixed compute budget across model scales to fit the law and reduce prediction error. Finally, we apply our method to Mixture-of-Experts models with up to 7B-A150M parameters to fit the law, and verify the optimal mixture derived from the law by extrapolating to a 55B-A1.2B target model. Compared to prior methods, we reduces mixture optimization costs by 50% and improves downstream benchmark performance by up to 3%.
[LG-47] Amortizing Maximum Inner Product Search with Learned Support Functions
链接: https://arxiv.org/abs/2603.08001
作者: Theo X. Olausson,João Monteiro,Michal Klein,Marco Cuturi
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Maximum inner product search (MIPS) is a crucial subroutine in machine learning, requiring the identification of key vectors that align best with a given query. We propose amortized MIPS: a learning-based approach that trains neural networks to directly predict MIPS solutions, amortizing the computational cost of matching queries (drawn from a fixed distribution) to a fixed set of keys. Our key insight is that the MIPS value function, the maximal inner product between a query and keys, is also known as the support function of the set of keys. Support functions are convex, 1-homogeneous and their gradient w.r.t. the query is exactly the optimal key in the database. We approximate the support function using two complementary approaches: (1) we train an input-convex neural network (SupportNet) to model the support function directly; the optimal key can be recovered via (autodiff) gradient computation, and (2) we regress directly the optimal key from the query using a vector valued network (KeyNet), bypassing gradient computation entirely at inference time. To learn a SupportNet, we combine score regression with gradient matching losses, and propose homogenization wrappers that enforce the positive 1-homogeneity of a neural network, theoretically linking function values to gradients. To train a KeyNet, we introduce a score consistency loss derived from the Euler theorem for homogeneous functions. Our experiments show that learned SupportNet or KeyNet achieve high match rates and open up new directions to compress databases with a specific query distribution in mind.
[LG-48] MJ1: Multimodal Judgment via Grounded Verification
链接: https://arxiv.org/abs/2603.07990
作者: Bhavesh Kumar,Dylan Feng,Leonard Tang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multimodal judges struggle to ground decisions in visual evidence. We present MJ1, a multimodal judge trained with reinforcement learning that enforces visual grounding through a structured grounded verification chain (observations \rightarrow claims \rightarrow verification \rightarrow evaluation \rightarrow scoring) and a counterfactual consistency reward that penalizes position bias. Even without training, our mechanism improves base-model accuracy on MMRB2 by +3.8 points on Image Editing and +1.7 on Multimodal Reasoning. After training, MJ1, with only 3B active parameters, achieves 77.0% accuracy on MMRB2 and surpasses orders-of-magnitude larger models like Gemini-3-Pro. These results show that grounded verification and consistency-based training substantially improve multimodal judgment without increasing model scale.
[LG-49] Semantic Risk Scoring of Aggregated Metrics: An AI-Driven Approach for Healthcare Data Governance
链接: https://arxiv.org/abs/2603.07924
作者: Mohammed Omer Shakeel Ahmed
类目: Machine Learning (cs.LG); Computers and Society (cs.CY)
*备注: 6 pages, 3 figures, 1 Table, Accepted for publication in the 21st Int. Conference on Data Science (ICDATA 25)
Abstract:Large healthcare institutions typically operate multiple business intelligence (BI) teams segmented by domain, including clinical performance, fundraising, operations, and compliance. Due to HIPAA, FERPA, and IRB restrictions, these teams face challenges in sharing patient-level data needed for analytics. To mitigate this, A metric aggregation table is proposed, which is a precomputed, privacy-compliant summary. These abstractions enable decision-making without direct access to sensitive data. However, even aggregated metrics can inadvertently lead to privacy risks if constructed without rigorous safeguards. A modular AI framework is proposed that evaluates SQL-based metric definitions for potential overexposure using both semantic and syntactic features. Specifically, the system parses SQL queries into abstract syntax trees (ASTs), extracts sensitive patterns (e.g., fine-grained GROUP BY on ZIP code or gender), and encodes the logic using pretrained CodeBERT embeddings. These are fused with structural features and passed to an XGBoost classifier trained to assign risk scores. Queries that surpass the risk threshold (e.g., 0.85) are flagged and returned with human-readable explanations. This enables proactive governance, preventing statistical disclosure before deployment. This implementation demonstrates strong potential for cross-departmental metric sharing in healthcare while maintaining compliance and auditability. The system also promotes role-based access control (RBAC), supports zero-trust data architectures, and aligns with national data modernization goals by ensuring that metric pipelines are explainable, privacy-preserving, and AI-auditable by design. Unlike prior works that rely on runtime data access to flag privacy violations, the proposed framework performs static, explainable detection at the query-level, enabling pre-execution protection and audit readiness
[LG-50] DyQ-VLA: Temporal-Dynamic-Aware Quantization for Embodied Vision-Language-Action Models
链接: https://arxiv.org/abs/2603.07904
作者: Zihao Zheng,Hangyu Cao,Sicheng Tian,Jiayu Chen,Maoliang Li,Xinhao Sun,Hailong Zou,Zhaobo Zhang,Xuanzhe Liu,Donggang Cao,Hong Mei,Xiang Chen
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:Vision-Language-Action (VLA) models are dominant in embodied intelligence but are constrained by inference overheads. While model quantization alleviates these bottlenecks for edge deployment, static quantization approaches remain suboptimal for VLAs due to two critical challenges: (1) Temporal-dynamic sensitivity, where fixed precision wastes resources by ignoring stage-varying error tolerances; and (2) Real-time allocation, where identifying real-time sensitivity to guide bit allocation remains unsolved. To address these challenges, we propose DyQ-VLA, a dynamic quantization framework for VLAs. Specifically, a sensitivity-aware switching strategy leverages real-time kinematic proxies to trigger the bit-width switch, while a kinematic-guided module dynamically allocates the optimal bit-width. Experiments show that DyQ-VLA requires only 30.9% of the original memory footprint while maintaining 99.5% of its original performance, achieving 1.49x simulation and up to 1.43x real-world speedups.
[LG-51] NaviDriveVLM: Decoupling High-Level Reasoning and Motion Planning for Autonomous Driving
链接: https://arxiv.org/abs/2603.07901
作者: Ximeng Tao,Pardis Taghavi,Dimitar Filev,Reza Langari,Gaurav Pandey
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Vision-language models (VLMs) have emerged as a promising direction for end-to-end autonomous driving (AD) by jointly modeling visual observations, driving context, and language-based reasoning. However, existing VLM-based systems face a trade-off between high-level reasoning and motion planning: large models offer strong semantic understanding but are costly to adapt for precise control, whereas small VLM models can be fine-tuned efficiently but often exhibit weaker reasoning. We propose NaviDriveVLM, a decoupled framework that separates reasoning from action generation using a large-scale Navigator and a lightweight trainable Driver. This design preserves reasoning ability, reduces training cost, and provides an explicit interpretable intermediate representation for downstream planning. Experiments on the nuScenes benchmark show that NaviDriveVLM outperforms large VLM baselines in end-to-end motion planning.
[LG-52] Bayesian Transformer for Probabilistic Load Forecasting in Smart Grids
链接: https://arxiv.org/abs/2603.07899
作者: Sajib Debnath,Md. Uzzal Mia
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:The reliable operation of modern power grids requires probabilistic load forecasts with well-calibrated uncertainty estimates. However, existing deep learning models produce overconfident point predictions that fail catastrophically under extreme weather distributional shifts. This study proposes a Bayesian Transformer (BT) framework that integrates three complementary uncertainty mechanisms into a PatchTST backbone: Monte Carlo Dropout for epistemic parameter uncertainty, variational feed-forward layers with log-uniform weight priors, and stochastic attention with learnable Gaussian noise perturbations on pre-softmax logits, representing, to the best of our knowledge, the first application of Bayesian attention to probabilistic load forecasting. A seven-level multi-quantile pinball-loss prediction head and post-training isotonic regression calibration produce sharp, near-nominally covered prediction intervals. Evaluation of five grid datasets (PJM, ERCOT, ENTSO-E Germany, France, and Great Britain) augmented with NOAA covariates across 24, 48, and 168-hour horizons demonstrates state-of-the-art performance. On the primary benchmark (PJM, H=24h), BT achieves a CRPS of 0.0289, improving 7.4% over Deep Ensembles and 29.9% over the deterministic LSTM, with 90.4% PICP at the 90% nominal level and the narrowest prediction intervals (4,960 MW) among all probabilistic baselines. During heat-wave and cold snap events, BT maintained 89.6% and 90.1% PICP respectively, versus 64.7% and 67.2% for the deterministic LSTM, confirming that Bayesian epistemic uncertainty naturally widens intervals for out-of-distribution inputs. Calibration remained stable across all horizons (89.8-90.4% PICP), while ablation confirmed that each component contributed a distinct value. The calibrated outputs directly support risk-based reserve sizing, stochastic unit commitment, and demand response activation.
[LG-53] LeJOT-AutoML: LLM -Driven Feature Engineering for Job Execution Time Prediction in Databricks Cost Optimization
链接: https://arxiv.org/abs/2603.07897
作者: Lizhi Ma,Yi-Xiang Hu,Yihui Ren,Feng Wu,Xiang-Yang Li
类目: Machine Learning (cs.LG)
*备注:
Abstract:Databricks job orchestration systems (e.g., LeJOT) reduce cloud costs by selecting low-priced compute configurations while meeting latency and dependency constraints. Accurate execution-time prediction under heterogeneous instance types and non-stationary runtime conditions is therefore critical. Existing pipelines rely on static, manually engineered features that under-capture runtime effects (e.g., partition pruning, data skew, and shuffle amplification), and predictive signals are scattered across logs, metadata, and job scripts-lengthening update cycles and increasing engineering overhead. We present LeJOT-AutoML, an agent-driven AutoML framework that embeds large language model agents throughout the ML lifecycle. LeJOT-AutoML combines retrieval-augmented generation over a domain knowledge base with a Model Context Protocol toolchain (log parsers, metadata queries, and a read-only SQL sandbox) to analyze job artifacts, synthesize and validate feature-extraction code via safety gates, and train/select predictors. This design materializes runtime-derived features that are difficult to obtain through static analysis alone. On enterprise Databricks workloads, LeJOT-AutoML generates over 200 features and reduces the feature-engineering and evaluation loop from weeks to 20-30 minutes, while maintaining competitive prediction accuracy. Integrated into the LeJOT pipeline, it enables automated continuous model updates and achieves 19.01% cost savings in our deployment setting through improved orchestration.
[LG-54] Viewpoint-Agnostic Grasp Pipeline using VLM and Partial Observations
链接: https://arxiv.org/abs/2603.07866
作者: Dilermando Almeida,Juliano Negri,Guilherme Lazzarini,Thiago H. Segreto,Ranulfo Bezerra,Ricardo V. Godoy,Marcelo Becker
类目: Robotics (cs.RO); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:Robust grasping in cluttered, unstructured environments remains challenging for mobile legged manipulators due to occlusions that lead to partial observations, unreliable depth estimates, and the need for collision-free, execution-feasible approaches. In this paper we present an end-to-end pipeline for language-guided grasping that bridges open-vocabulary target selection to safe grasp execution on a real robot. Given a natural-language command, the system grounds the target in RGB using open-vocabulary detection and promptable instance segmentation, extracts an object-centric point cloud from RGB-D, and improves geometric reliability under occlusion via back-projected depth compensation and two-stage point cloud completion. We then generate and collision-filter 6-DoF grasp candidates and select an executable grasp using safety-oriented heuristics that account for reachability, approach feasibility, and clearance. We evaluate the method on a quadruped robot with an arm in two cluttered tabletop scenarios, using paired trials against a view-dependent baseline. The proposed approach achieves a 90% overall success rate (9/10) against 30% (3/10) for the baseline, demonstrating substantially improved robustness to occlusions and partial observations in clutter.
[LG-55] Guess Guide: Gradient-Free Zero-Shot Diffusion Guidance
链接: https://arxiv.org/abs/2603.07860
作者: Abduragim Shtanchaev,Albina Ilina,Yazid Janati,Arip Asadulaev,Martin Takác,Eric Moulines
类目: Machine Learning (cs.LG)
*备注:
Abstract:Pretrained diffusion models serve as effective priors for Bayesian inverse problems. These priors enable zero-shot generation by sampling from the conditional distribution, which avoids the need for task-specific retraining. However, a major limitation of existing methods is their reliance on surrogate likelihoods that require vector-Jacobian products at each denoising step, creating a substantial computational burden. To address this, we introduce a lightweight likelihood surrogate that eliminates the need to calculate gradients through the denoiser network. This enables us to handle diverse inverse problems without backpropagation overhead. Experiments confirm that using our method, the inference cost drops dramatically. At the same time, our approach delivers the highest results in multiple tasks. Broadly speaking, we propose the fastest and Pareto optimal method for Bayesian inverse problems.
[LG-56] Neural Precoding in Complex Projective Spaces
链接: https://arxiv.org/abs/2603.07811
作者: Zaid Abdullah,Merouane Debbah,Symeon Chatzinotas,Bjorn Ottersten
类目: Machine Learning (cs.LG)
*备注:
Abstract:Deep-learning (DL)-based precoding in multi-user multiple-input single-output (MU-MISO) systems involves training DL models to map features derived from channel coefficients to labels derived from precoding weights. Traditionally, complex-valued channel and precoder coefficients are parameterized using either their real and imaginary components or their amplitude and phase. However, precoding performance depends on magnitudes of inner products between channel and precoding vectors, which are invariant to global phase rotations. Conventional representations fail to exploit this symmetry, leading to inefficient learning and degraded generalization. To address this, we propose a DL framework based on complex projective space (CPS) parameterizations of both the wireless channel and the weighted minimum mean squared error (WMMSE) precoder vectors. By removing the global phase redundancies inherent in conventional representations, the proposed framework enables the DL model to learn geometry-aligned and physically distinct channel-precoder mappings. Two CPS parameterizations based on real-valued embeddings and complex hyperspherical coordinates are investigated and benchmarked against two baseline methods. Simulation results demonstrate substantial improvements in sum-rate performance and generalization, with negligible increase in model complexity.
[LG-57] oward Global Intent Inference for Human Motion by Inverse Reinforcement Learning
链接: https://arxiv.org/abs/2603.07797
作者: Sarmad Mehrdad,Maxime Sabbah,Vincent Bonnet,Ludovic Righetti
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 8 pages, 6 figures
Abstract:This paper investigates whether a single, unified cost function can explain and predict human reaching movements, in contrast with existing approaches that rely on subject- or posture-specific optimization criteria. Using the Minimal Observation Inverse Reinforcement Learning (MO-IRL) algorithm, together with a seven-dimensional set of candidate cost terms, we efficiently estimate time-varying cost weights for a standard planar reaching task. MO-IRL provides orders-of-magnitude faster convergence than bilevel formulations, while using only a fraction of the available data, enabling the practical exploration of time-varying cost structures. Three levels of generality are evaluated: Subject-Dependent Posture-Dependent, Subject-Dependent Posture-Independent, and Subject-Independent Posture-Independent. Across all cases, time-varying weights substantially improve trajectory reconstruction, yielding an average 27% reduction in RMSE compared to the baseline. The inferred costs consistently highlight a dominant role for joint-acceleration regulation, complemented by smaller contributions from torque-change smoothness. Overall, a single subject- and posture-agnostic time-varying cost function is shown to predict human reaching trajectories with high accuracy, supporting the existence of a unified optimality principle governing this class of movements.
[LG-58] Vision Transformers that Never Stop Learning
链接: https://arxiv.org/abs/2603.07787
作者: Caihao Sun,Mingqi Yuan,Shiyuan Wang,Jiayu Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Loss of plasticity refers to the progressive inability of a model to adapt to new tasks and poses a fundamental challenge for continual learning. While this phenomenon has been extensively studied in homogeneous neural architectures, such as multilayer perceptrons, its mechanisms in structurally heterogeneous, attention-based models such as Vision Transformers (ViTs) remain underexplored. In this work, we present a systematic investigation of loss of plasticity in ViTs, including a fine-grained diagnosis using local metrics that capture parameter diversity and utilization. Our analysis reveals that stacked attention modules exhibit increasing instability that exacerbates plasticity loss, while feed-forward network modules suffer even more pronounced degradation. Furthermore, we evaluate several approaches for mitigating plasticity loss. The results indicate that methods based on parameter re-initialization fail to recover plasticity in ViTs, whereas approaches that explicitly regulate the update process are more effective. Motivated by this insight, we propose ARROW, a geometry-aware optimizer that preserves plasticity by adaptively reshaping gradient directions using an online curvature estimate for the attention module. Extensive experiments show that ARROW effectively improves plasticity and maintains better performance on newly encountered tasks.
[LG-59] Using GPUs And LLM s Can Be Satisfying for Nonlinear Real Arithmetic Problems
链接: https://arxiv.org/abs/2603.07764
作者: Christopher Brix,Julia Walczak,Nils Lommen,Thomas Noll
类目: Machine Learning (cs.LG)
*备注: Workshop submission, minor errors fixed
Abstract:Solving quantifier-free non-linear real arithmetic (NRA) problems is a computationally hard task. To tackle this problem, prior work proposed a promising approach based on gradient descent. In this work, we extend their ideas and combine LLMs and GPU acceleration to obtain an efficient technique. We have implemented our findings in the novel SMT solver GANRA (GPU Accelerated solving of Nonlinear Real Arithmetic problems). We evaluate GANRA on two different NRA benchmarks and demonstrate significant improvements over the previous state of the art. In particular, on the Sturm-MBO benchmark, we can prove satisfiability for more than five times as many instances in less than 1/20th of the previous state-of-the-art runtime.
[LG-60] Uncertainty-Gated Generative Modeling ICLR2026
链接: https://arxiv.org/abs/2603.07753
作者: Xingrui Gu,Haixi Zhang
类目: Machine Learning (cs.LG)
*备注: Accepeted by ICLR 2026 Workshop Advances in Financial AI
Abstract:Financial time-series forecasting is a high-stakes problem where regime shifts and shocks make point-accurate yet overconfident models dangerous. We propose Uncertainty-Gated Generative Modeling (UGGM), which treats uncertainty as an internal control signal that gates (i) representation via gated reparameterization, (ii) propagation via similarity and confidence routing, and (iii) generation via uncertainty-controlled predictive distributions, together with uncertainty-driven regularization and calibration to curb miscalibration. Instantiated on Weak Innovation AutoEncoder (WIAE-GPF), our UG-WIAE-GPF significantly improves risk-sensitive forecasting, delivering a 63.5% MSE reduction on NYISO (0.3508 \rightarrow 0.1281), with improved robustness under shock intervals (mSE: 0.2739 \rightarrow 0.1748).
[LG-61] A Lightweight MPC Bidding Framework for Brand Auction Ads
链接: https://arxiv.org/abs/2603.07721
作者: Yuanlong Chen,Bowen Zhu,Bing Xia,Yichuan Wang
类目: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:Brand advertising plays a critical role in building long-term consumer awareness and loyalty, making it a key objective for advertisers across digital platforms. Although real-time bidding has been extensively studied, there is limited literature on algorithms specifically tailored for brand auction ads that fully leverage their unique characteristics. In this paper, we propose a lightweight Model Predictive Control (MPC) framework designed for brand advertising campaigns, exploiting the inherent attributes of brand ads – such as stable user engagement patterns and fast feedback loops – to simplify modeling and improve efficiency. Our approach utilizes online isotonic regression to construct monotonic bid-to-spend and bid-to-conversion models directly from streaming data, eliminating the need for complex machine learning models. The algorithm operates fully online with low computational overhead, making it highly practical for real-world deployment. Simulation results demonstrate that our approach significantly improves spend efficiency and cost control compared to baseline strategies, providing a scalable and easily implementable solution for modern brand advertising platforms.
[LG-62] Reverse Distillation: Consistently Scaling Protein Language Model Representations ICLR2026
链接: https://arxiv.org/abs/2603.07710
作者: Darius Catrina,Christian Bepler,Samuel Sledzieski,Rohit Singh
类目: Machine Learning (cs.LG); Biomolecules (q-bio.BM)
*备注: Proceedings of ICLR 2026
Abstract:Unlike the predictable scaling laws in natural language processing and computer vision, protein language models (PLMs) scale poorly: for many tasks, models within the same family plateau or even decrease in performance, with mid-sized models often outperforming the largest in the family. We introduce Reverse Distillation, a principled framework that decomposes large PLM representations into orthogonal subspaces guided by smaller models of the same family. The resulting embeddings have a nested, Matryoshka-style structure: the first k dimensions of a larger model’s embedding are exactly the representation from the smaller model. This ensures that larger reverse-distilled models consistently outperform smaller ones. A motivating intuition is that smaller models, constrained by capacity, preferentially encode broadly-shared protein features. Reverse distillation isolates these shared features and orthogonally extracts additional contributions from larger models, preventing interference between the two. On ProteinGym benchmarks, reverse-distilled ESM-2 variants outperform their respective baselines at the same embedding dimensionality, with the reverse-distilled 15 billion parameter model achieving the strongest performance. Our framework is generalizable to any model family where scaling challenges persist. Code and trained models are available at this https URL.
[LG-63] Deep Incentive Design with Differentiable Equilibrium Blocks
链接: https://arxiv.org/abs/2603.07705
作者: Vinzenz Thoma,Georgios Piliouras,Luke Marris
类目: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
*备注: 24 pages, 7 figures
Abstract:Automated design of multi-agent interactions with desirable equilibrium outcomes is inherently difficult due to the computational hardness, non-uniqueness, and instability of the resulting equilibria. In this work, we propose the use of game-agnostic differentiable equilibrium blocks (DEBs) as modules in a novel, differentiable framework to address a wide variety of incentive design problems from economics and computer science. We call this framework deep incentive design (DID). To validate our approach, we examine three diverse, challenging incentive design tasks: contract design, machine scheduling, and inverse equilibrium problems. For each task, we train a single neural network using a unified pipeline and DEB. This architecture solves the full distribution of problem instances, parameterized by a context, handling all games across a wide range of scales (from two to sixteen actions per player).
[LG-64] Step-Size Decay and Structural Stagnation in Greedy Sparse Learning
链接: https://arxiv.org/abs/2603.07703
作者: Pablo M. Berná
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注:
Abstract:Greedy algorithms are central to sparse approximation and stage-wise learning methods such as matching pursuit and boosting. It is known that the Power-Relaxed Greedy Algorithm with step sizes m^-\alpha may fail to converge when \alpha1 in general Hilbert spaces. In this work, we revisit this phenomenon from a sparse learning perspective. We study realizable regression problems with controlled feature coherence and derive explicit lower bounds on the residual norm, showing that over-decaying step-size schedules induce structural stagnation even in low-dimensional sparse settings. Numerical experiments confirm the theoretical predictions and illustrate the role of feature coherence. Our results provide insight into step-size design in greedy sparse learning.
[LG-65] Global Convergence of Averag e Reward Constrained MDPs with Neural Critic and General Policy Parameterization UAI2026
链接: https://arxiv.org/abs/2603.07698
作者: Anirudh Satheesh,Pankaj Kumar Barman,Washim Uddin Mondal,Vaneet Aggarwal
类目: Machine Learning (cs.LG)
*备注: Submitted to UAI 2026
Abstract:We study infinite-horizon Constrained Markov Decision Processes (CMDPs) with general policy parameterizations and multi-layer neural network critics. Existing theoretical analyses for constrained reinforcement learning largely rely on tabular policies or linear critics, which limits their applicability to high-dimensional and continuous control problems. We propose a primal-dual natural actor-critic algorithm that integrates neural critic estimation with natural policy gradient updates and leverages Neural Tangent Kernel (NTK) theory to control function-approximation error under Markovian sampling, without requiring access to mixing-time oracles. We establish global convergence and cumulative constraint violation rates of \tilde\mathcalO(T^-1/4) up to approximation errors induced by the policy and critic classes. Our results provide the first such guarantees for CMDPs with general policies and multi-layer neural critics, substantially extending the theoretical foundations of actor-critic methods beyond the linear-critic regime.
[LG-66] Mitigating the Memory Bottleneck with Machine Learning-Driven and Data-Aware Microarchitectural Techniques
链接: https://arxiv.org/abs/2603.07683
作者: Rahul Bera
类目: Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Operating Systems (cs.OS)
*备注:
Abstract:Modern applications process massive data volumes that overwhelm the storage and retrieval capabilities of memory systems, making memory the primary performance and energy-efficiency bottleneck of computing systems. Although many microarchitectural techniques attempt to hide or tolerate long memory access latency, rapidly growing data footprints continue to outpace technology scaling, requiring more effective solutions. This dissertation shows that modern processors observe large amounts of application and system data during execution, yet many microarchitectural mechanisms make decisions largely independent of this information. Through four case studies, we demonstrate that such data-agnostic design leads to substantial missed opportunities for improving performance and energy efficiency. To address this limitation, this dissertation advocates shifting microarchitecture design from data-agnostic to data-informed. We propose mechanisms that (1) learn policies from observed execution behavior (data-driven design) and (2) exploit semantic characteristics of application data (data-aware design). We apply lightweight machine learning techniques and previously underexplored data characteristics across four processor components: a reinforcement learning-based hardware data prefetcher that learns memory access patterns online; a perceptron predictor that identifies memory requests likely to access off-chip memory; a reinforcement learning mechanism that coordinates data prefetching and off-chip prediction; and a mechanism that exploits repeatability in memory addresses and loaded values to eliminate predictable load instructions. Our extensive evaluation shows that the proposed techniques significantly improve performance and energy efficiency compared to prior state-of-the-art approaches. Subjects: Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Operating Systems (cs.OS) ACMclasses: C.1; C.4 Cite as: arXiv:2603.07683 [cs.AR] (or arXiv:2603.07683v1 [cs.AR] for this version) https://doi.org/10.48550/arXiv.2603.07683 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-67] Beyond Surrogates: A Quantitative Analysis for Inter-Metric Relationships
链接: https://arxiv.org/abs/2603.07671
作者: Yuanhao Pu,Defu Lian,Enhong Chen
类目: Machine Learning (cs.LG)
*备注: 18 pages, 1 figure
Abstract:The Consistency property between surrogate losses and evaluation metrics has been extensively studied to ensure that minimizing a loss leads to metric optimality. However, the direct relationship between different evaluation metrics remains significantly underexplored. This theoretical gap results in the “Metric Mismatch” frequently observed in industrial applications, where gains in offline validation metrics fail to translate into online performance. To bridge this disconnection, this paper proposes a unified theoretical framework designed to quantify the relationships between metrics. We categorize metrics into different classes to facilitate a comparative analysis across different mathematical forms and interrogates these relationships through Bayes-Optimal Set and Regret Transfer. Through this framework, we provide a new perspective on identifying the structural asymmetry in regret transfer, enabling the design of evaluation systems that are theoretically guaranteed to align offline improvements with online objectives.
[LG-68] Partial Differential Equations in the Age of Machine Learning: A Critical Synthesis of Classical Machine Learning and Hybrid Methods
链接: https://arxiv.org/abs/2603.07655
作者: Mohammad Nooraiepour,Jakub Wiktor Both,Teeratorn Kadeethum,Saeid Sadeghnejad
类目: Machine Learning (cs.LG); Analysis of PDEs (math.AP)
*备注:
Abstract:Partial differential equations (PDEs) govern physical phenomena across the full range of scientific scales, yet their computational solution remains one of the defining challenges of modern science. This critical review examines two mature but epistemologically distinct paradigms for PDE solution, classical numerical methods and machine learning approaches, through a unified evaluative framework organized around six fundamental computational challenges. Classical methods are assessed for their structure-preserving properties, rigorous convergence theory, and scalable solver design; their persistent limitations in high-dimensional and geometrically complex settings are characterized precisely. Machine learning approaches are introduced under a taxonomy organized by the degree to which physical knowledge is incorporated and subjected to the same critical evaluation applied to classical methods. Classical methods are deductive – errors are bounded by quantities derivable from PDE structure and discretization parameters – while machine learning methods are inductive – accuracy depends on statistical proximity to the training distribution. This epistemological distinction is the primary criterion governing responsible method selection. We identify three genuine complementarities between the paradigms and develop principles for hybrid design, including a framework for the structure inheritance problem that addresses when classical guarantees propagate through hybrid couplings, and an error budget decomposition that separates discretization, neural approximation, and coupling contributions. We further assess emerging frontiers, including foundation models, differentiable programming, quantum algorithms, and exascale co-design, evaluating each against the structural constraints that determine whether current barriers are fundamental or contingent on engineering progress.
[LG-69] Helix: Evolutionary Reinforcement Learning for Open-Ended Scientific Problem Solving ICLR2026
链接: https://arxiv.org/abs/2603.07642
作者: Chang Su,Zhongkai Hao,Zhizhou Zhang,Zeyu Xia,Youjia Wu,Hang Su,Jun Zhu
类目: Machine Learning (cs.LG)
*备注: Accepted at ICLR 2026
Abstract:Large language models (LLMs) with reasoning abilities have demonstrated growing promise for tackling complex scientific problems. Yet such tasks are inherently domain-specific, unbounded and open-ended, demanding exploration across vast and flexible solution spaces. Existing approaches, whether purely learning-based or reliant on carefully designed workflows, often suffer from limited exploration efficiency and poor generalization. To overcome these challenges, we present HELIX – a Hierarchical Evolutionary reinforcement Learning framework with In-context eXperiences. HELIX introduces two key novelties: (i) a diverse yet high-quality pool of candidate solutions that broadens exploration through in-context learning, and (ii) reinforcement learning for iterative policy refinement that progressively elevates solution quality. This synergy enables the discovery of more advanced solutions. On the circle packing task, HELIX achieves state-of-the-art result with a sum of radii of 2.63598308 using only a 14B model. Across standard machine learning benchmarks, HELIX further surpasses GPT-4o with a carefully engineered pipeline, delivering an average F1 improvement of 5.95 points on the Adult and Bank Marketing datasets.
[LG-70] Exoskeleton Control through Learning to Reduce Biological Joint Moments in Simulations
链接: https://arxiv.org/abs/2603.07629
作者: Zihang You,Xianlian Zhou
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Data-driven joint-moment predictors offer a scalable alternative to laboratory-based inverse-dynamics pipelines for biomechanics estimation and exoskeleton control. Meanwhile, physics-based reinforcement learning (RL) enables simulation-trained controllers to learn dynamics-aware assistance strategies without extensive human experimentation. However, quantitative verification of simulation-trained exoskeleton torque predictors, and their impact on human joint power injection, remains limited. This paper presents (1) an RL framework to learn exoskeleton assistance policies that reduce biological joint moments, and (2) a validation pipeline that verifies the trained control networks using an open-source gait dataset through inference and comparison with biological joint moments. Simulation-trained multilayer perceptron (MLP) controllers are developed for level-ground and ramp walking, mapping short-horizon histories of bilateral hip and knee kinematics to normalized assistance torques. Results show that predicted assistance preserves task-intensity trends across speeds and inclines. Agreement is particularly strong at the hip, with cross-correlation coefficients reaching 0.94 at 1.8 m/s and 0.98 during 5° decline walking, demonstrating near-matched temporal structure. Discrepancies increase at higher speeds and steeper inclines, especially at the knee, and are more pronounced in joint power comparisons. Delay tuning biases assistance toward greater positive power injection; modest timing shifts increase positive power and improve agreement in specific gait intervals. Together, these results establish a quantitative validation framework for simulation-trained exoskeleton controllers, demonstrate strong sim-to-data consistency at the torque level, and highlight both the promise and the remaining challenges for sim-to-real transfer.
[LG-71] Accelerating Diffusion Models for Generative AI Applications with Silicon Photonics
链接: https://arxiv.org/abs/2603.07626
作者: Tharini Suresh,Salma Afifi,Sudeep Pasricha
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG)
*备注:
Abstract:Diffusion models have revolutionized generative AI, with their inherent capacity to generate highly realistic state-of-the-art synthetic data. However, these models employ an iterative denoising process over computationally intensive layers such as UNets and attention mechanisms. This results in high inference energy on conventional electronic platforms, and thus, there is an emerging need to accelerate these models in a sustainable manner. To address this challenge, we present a novel silicon photonics-based accelerator for diffusion models. Experimental evaluations demonstrate that our photonic accelerator achieves at least 3x better energy efficiency and 5.5x throughput improvement compared to state-of-the-art diffusion model accelerators.
[LG-72] MAS-H2: A Hierarchical Multi-Agent System for Holistic Cloud-Native Autoscaling
链接: https://arxiv.org/abs/2603.07607
作者: Hamed Hamzeh,Parisa Vahdatian
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:Autoscaling in cloud-native platforms like Kubernetes is reactive and metric-driven, leading to a strategic void problem. This comes from the decoupling of higher-level business policies from lower-level resource provisioning. The strategic void, coupled with a fragmented coordination of pod and node scaling, can lead to significant resource waste and performance degradation under dynamic workloads. In this paper, we present MAS-H2, a new hierarchical multi-agent system that addresses the challenges of autonomic cloud resource management with a complete end-to-end solution. MAS-H2 systematically decomposes the control problem into three layers: a Strategic Agent that formalises business policies (e.g., cost vs. performance) into a global utility function; Planning Agents that produce a joint, proactive scaling plan for pods and nodes with time-series forecasting; and Execution Agents that execute the scaling plan. We built and tested a MAS-H2 prototype as a Kubernetes Operator on Google Kubernetes Engine (GKE) to benchmark it against the native Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler (CA) baselines under two realistic, spiky, and stress-inducing workload scenarios. The results show that the MAS-H2 system maintained application CPU usage under 40% for predictable Heartbeat workloads. This resulted in over 50% less sustained CPU stress than the native HPA baseline, which typically operated above 80%. The MAS-H2 system demonstrated proactive planning in a volatile Chaotic Flash Sale scenario by filtering transient noise and deploying more replicas compared to HPA. It reduced peak CPU load by 55% without under-provisioning. Beyond performance, MAS-H2 seamlessly performed a zero-downtime strategic migration between two cost- and performance-optimised infrastructures.
[LG-73] -Sparse: Learning Sparse Rule Models with Differentiable Truth Tables
链接: https://arxiv.org/abs/2603.07606
作者: Hans Farrell Soegeng,Sarthak Ketanbhai Modi,Thomas Peyrin
类目: Machine Learning (cs.LG)
*备注:
Abstract:Interpretable machine learning is essential in high-stakes domains where decision-making requires accountability, transparency, and trust. While rule-based models offer global and exact interpretability, learning rule sets that simultaneously achieve high predictive performance and low, human-understandable complexity remains challenging. To address this, we introduce TT-Sparse, a flexible neural building block that leverages differentiable truth tables as nodes to learn sparse, effective connections. A key contribution of our approach is a new soft TopK operator with straight-through estimation for learning discrete, cardinality-constrained feature selection in an end-to-end differentiable manner. Crucially, the forward pass remains sparse, enabling efficient computation and exact symbolic rule extraction. As a result, each node (and the entire model) can be transformed exactly into compact, globally interpretable DNF/CNF Boolean formulas via Quine-McCluskey minimization. Extensive empirical results across 28 datasets spanning binary, multiclass, and regression tasks show that the learned sparse rules exhibit superior predictive performance with lower complexity compared to existing state-of-the-art methods.
[LG-74] Analysis-Driven Procedural Generation of an Engine Sound Dataset with Embedded Control Annotations
链接: https://arxiv.org/abs/2603.07584
作者: Robin Doerfler,Lonce Wyse
类目: ound (cs.SD); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
*备注: Preprint. 19 hours of engine audio, 5,935 files, sample-accurate annotations. Dataset publicly available at this https URL and this https URL
Abstract:Computational engine sound modeling is central to the automotive audio industry, particularly for active sound design, virtual prototyping, and emerging data-driven engine sound synthesis methods. These applications require large volumes of standardized, clean audio recordings with precisely time-aligned operating-state annotations: data that is difficult to obtain due to high costs, specialized measurement equipment requirements, and inevitable noise contamination. We present an analysis-driven framework for generating engine audio with sample-accurate control annotations. The method extracts harmonic structures from real recordings through pitch-adaptive spectral analysis, which then drive an extended parametric harmonic-plus-noise synthesizer. With this framework, we generate the Procedural Engine Sounds Dataset (19 hours, 5,935 files), a set of engine audio signals with sample-accurate RPM and torque annotations, spanning a wide range of operating conditions, signal complexities, and harmonic profiles. Comparison against real recordings validates that the synthesized data preserves characteristic harmonic structures, and baseline experiments confirm its suitability for learning-based parameter estimation and synthesis tasks. The dataset is released publicly to support research on engine timbre analysis, control parameter estimation, acoustic modeling and neural generative networks. Comments: Preprint. 19 hours of engine audio, 5,935 files, sample-accurate annotations. Dataset publicly available at this https URL and this https URL Subjects: Sound (cs.SD); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS) Cite as: arXiv:2603.07584 [cs.SD] (or arXiv:2603.07584v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2603.07584 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Robin Doerfler [view email] [v1] Sun, 8 Mar 2026 11:05:10 UTC (2,542 KB)
[LG-75] S-MLLM : A Multi-Modal Large Language Model-based Framework for Industrial Time-Series Big Data Analysis
链接: https://arxiv.org/abs/2603.07572
作者: Haiteng Wang,Yikang Li,Yunfei Zhu,Jingheng Yan,Lei Ren,Laurence T. Yang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate analysis of industrial time-series big data is critical for the Prognostics and Health Management (PHM) of industrial equipment. While recent advancements in Large Language Models (LLMs) have shown promise in time-series analysis, existing methods typically focus on single-modality adaptations, failing to exploit the complementary nature of temporal signals, frequency-domain visual representations, and textual knowledge information. In this paper, we propose TS-MLLM, a unified multi-modal large language model framework designed to jointly model temporal signals, frequency-domain images, and textual domain knowledge. Specifically, we first develop an Industrial time-series Patch Modeling branch to capture long-range temporal dynamics. To integrate cross-modal priors, we introduce a Spectrum-aware Vision-Language Model Adaptation (SVLMA) mechanism that enables the model to internalize frequency-domain patterns and semantic context. Furthermore, a Temporal-centric Multi-modal Attention Fusion (TMAF) mechanism is designed to actively retrieve relevant visual and textual cues using temporal features as queries, ensuring deep cross-modal alignment. Extensive experiments on multiple industrial benchmarks demonstrate that TS-MLLM significantly outperforms state-of-the-art methods, particularly in few-shot and complex scenarios. The results validate our framework’s superior robustness, efficiency, and generalization capabilities for industrial time-series prediction.
[LG-76] Constraints Matrix Diffusion based Generative Neural Solver for Vehicle Routing Problems
链接: https://arxiv.org/abs/2603.07568
作者: Zhenwei Wang,Tiehua Zhang,Ning Xue,Ender Ozcan,Ling Wang,Ruibin Bai
类目: Machine Learning (cs.LG)
*备注:
Abstract:Over the past decade, neural network solvers powered by generative artificial intelligence have garnered significant attention in the domain of vehicle routing problems (VRPs), owing to their exceptional computational efficiency and superior reasoning capabilities. In particular, autoregressive solvers integrated with reinforcement learning have emerged as a prominent trend. However, much of the existing work emphasizes large-scale generalization of neural approaches while neglecting the limited robustness of attention-based methods across heterogeneous distributions of problem parameters. Their improvements over heuristic search remain largely restricted to hand-curated, fixed-distribution benchmarks. Furthermore, these architectures tend to degrade significantly when node representations are highly similar or when tasks involve long decision horizons. To address the aforementioned limitations, we propose a novel fusion neural network framework that employs a discrete noise graph diffusion model to learn the underlying constraints of vehicle routing problems and generate a constraint assignment matrix. This matrix is subsequently integrated adaptively into the feature representation learning and decision process of the autoregressive solver, serving as a graph structure mask that facilitates the formation of solutions characterized by both global vision and local feature integration. To the best of our knowledge, this work represents the first comprehensive experimental investigation of neural network model solvers across a 378-combinatorial space spanning four distinct dimensions within the CVRPlib public dataset. Extensive experimental evaluations demonstrate that our proposed fusion model effectively captures and leverages problem constraints, achieving state-of-the-art performance across multiple benchmark datasets.
[LG-77] Revisiting the LiRA Membership Inference Attack Under Realistic Assumptions
链接: https://arxiv.org/abs/2603.07567
作者: Najeeb Jebreel,Mona Khalil,David Sánchez,Josep Domingo-Ferrer
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: Accepted to PoPETs 2026(3)
Abstract:Membership inference attacks (MIAs) have become the standard tool for evaluating privacy leakage in machine learning (ML). Among them, the Likelihood-Ratio Attack (LiRA) is widely regarded as the state of the art when sufficient shadow models are available. However, prior evaluations have often overstated the effectiveness of LiRA by attacking models overconfident on their training samples, calibrating thresholds on target data, assuming balanced membership priors, and/or overlooking attack reproducibility. We re-evaluate LiRA under a realistic protocol that (i) trains models using anti-overfitting (AOF) and transfer learning (TL), when applicable, to reduce overconfidence as in production models; (ii) calibrates decision thresholds using shadow models and data rather than target data; (iii) measures positive predictive value (PPV, or precision) under shadow-based thresholds and skewed membership priors (pi = 10%); and (iv) quantifies per-sample membership reproducibility across different seeds and training variations. We find that AOF significantly weakens LiRA, while TL further reduces attack effectiveness while improving model accuracy. Under shadow-based thresholds and skewed priors, LiRA’s PPV often drops substantially, especially under AOF or AOF+TL. We also find that thresholded vulnerable sets at extremely low FPR show poor reproducibility across runs, while likelihood-ratio rankings are more stable. These results suggest that LiRA, and likely weaker MIAs, are less effective than previously suggested under realistic conditions, and that reliable privacy auditing requires evaluation protocols that reflect practical training practices, feasible attacker assumptions, and reproducibility considerations. Code is available at this https URL.
[LG-78] ECG Classification on PTB-XL: A Data-Centric Approach with Simplified CNN-VAE
链接: https://arxiv.org/abs/2603.07558
作者: Naqcho Ali Mehdi,Amir Ali
类目: Machine Learning (cs.LG)
*备注:
Abstract:Automated electrocardiogram (ECG) classification is essential for early detection of cardiovascular diseases. While recent approaches have increasingly relied on deep neural networks with complex architectures, we demonstrate that careful data preprocessing, class balancing, and a simplified convolutional neural network combined with a variational autoencoder (CNN-VAE) architecture can achieve competitive performance with significantly reduced model complexity. Using the publicly available PTB XL dataset, we achieve 87.01% binary accuracy and 0.7454 weighted F1-score across five diagnostic classes (CD, HYP, MI, NORM, STTC) with only 197,093 trainable parameters. Our work emphasises the importance of data-centric machine learning practices over architectural complexity, demonstrating that systematic preprocessing and balanced training strategies are critical for medical signal classification. We identify challenges in minority class detection (particularly hypertrophy) and provide insights for future improvements in handling imbalanced ECG datasets. Index Terms: ECG classification, convolutional neural networks, class balancing, data preprocessing, variational autoencoders, PTB-XL dataset Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603.07558 [cs.LG] (or arXiv:2603.07558v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.07558 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-79] Obliviator Reveals the Cost of Nonlinear Guardedness in Concept Erasure NEURIPS2025
链接: https://arxiv.org/abs/2603.07529
作者: Ramin Akbari,Milad Afshari,Vishnu Naresh Boddeti
类目: Machine Learning (cs.LG)
*备注: Accepted to NeurIPS 2025 [Poster]. Code available at: this https URL
Abstract:Concept erasure aims to remove unwanted attributes, such as social or demographic factors, from learned representations, while preserving their task-relevant utility. While the goal of concept erasure is protection against all adversaries, existing methods remain vulnerable to nonlinear ones. This vulnerability arises from their failure to fully capture the complex, nonlinear statistical dependencies between learned representations and unwanted attributes. Moreover, although the existence of a trade-off between utility and erasure is expected, its progression during the erasure process, i.e., the cost of erasure, remains unstudied. In this work, we introduce Obliviator, a post-hoc erasure method designed to fully capture nonlinear statistical dependencies. We formulate erasure from a functional perspective, leading to an optimization problem involving a composition of kernels that lacks a closed-form solution. Instead of solving this problem in a single shot, we adopt an iterative approach that gradually morphs the feature space to achieve a more utility-preserving erasure. Unlike prior methods, Obliviator guards unwanted attribute against nonlinear adversaries. Our gradual approach quantifies the cost of nonlinear guardedness and reveals the dynamics between attribute protection and utility-preservation over the course of erasure. The utility-erasure trade-off curves obtained by Obliviator outperform the baselines and demonstrate its strong generalizability: its erasure becomes more utility-preserving when applied to the better-disentangled representations learned by more capable models.
[LG-80] Generative prediction of laser-induced rocket ignition with dynamic latent space representations
链接: https://arxiv.org/abs/2603.07525
作者: Tony Zahtila,Ettore Saetta,Murray Cutforth,Davy Brouzet,Diego Rossinelli,Gianluca Iaccarino
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate and predictive scale-resolving simulations of laser-ignited rocket engines are highly time-consuming because the problem includes turbulent fuel-oxidizer mixing dynamics, laser-induced energy deposition, and high-speed flame growth. This is conflated with the large design space primarily corresponding to the laser operating conditions and target location. To enable rapid exploration and uncertainty quantification, we propose a data-driven surrogate modeling approach that combines convolutional autoencoders (cAEs) with neural ordinary differential equations (neural ODEs). The present target application of an ML-based surrogate model to leading-edge multi-physics turbulence simulation is part of a paradigm shift in the deployment of surrogate models towards increasing real-world complexity. Sequentially, the cAE spatially compresses high-dimensional flow fields into a low-dimensional latent space, wherein the system’s temporal dynamics are learned via neural ODEs. Once trained, the model generates fast spatiotemporal predictions from initial conditions and specified operating inputs. By learning a surrogate to replace the entirety of the time-evolving simulation, the cost of predicting an ignition trial is reduced by several orders of magnitude, allowing efficient exploration of the input parameter space. Further, as the current framework yields a spatiotemporal field prediction, appraisal of the model output’s physical grounding is more tractable. This approach marks a significant step toward real-time digital twins for laser-ignited rocket combustors and represents surrogate modeling in a complex system context.
[LG-81] One-for-All Model Initialization with Frequency-Domain Knowledge
链接: https://arxiv.org/abs/2603.07523
作者: Jianlu Shen,Fu Feng,Yucheng Xie,Jiaqi Lv,Xin Geng
类目: Machine Learning (cs.LG)
*备注:
Abstract:Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coupled with monolithic architecture, which restricts flexible reuse across models of varying scales. In response to this challenge, recent approaches typically resort to either parameter selection, which fails to capture the interdependent structure of this knowledge, or parameter prediction using generative models that depend on impractical access to large network collections. In this paper, we empirically demonstrate that a model’s foundational, task-agnostic knowledge, its “learngene”, is encoded within the low-frequency components of its weights, and can be efficiently inherited by downstream models. Based on this insight, we propose FRONT (FRequency dOmain kNowledge Transfer), a novel framework that uses the Discrete Cosine Transform (DCT) to isolate the low-frequency “learngene”. This learngene can be seamlessly adapted to initialize models of arbitrary size via simple truncation or padding, a process that is entirely training-free. For enhanced performance, we propose an optional low-cost refinement process that introduces a spectral regularizer to further improve the learngene’s transferability. Extensive experiments demonstrate that FRONT achieves the state-of-the-art performance, accelerates convergence by up to 15 times in vision tasks, and reduces training FLOPs by an average of 40.5% in language tasks.
[LG-82] Reinforcement learning-based dynamic cleaning scheduling framework for solar energy system
链接: https://arxiv.org/abs/2603.07518
作者: Heungjo An
类目: Machine Learning (cs.LG)
*备注: 16 pages, 6 figures, This is an accepted manuscript of the article published in Journal of Korean Institute of Intelligent Systems, 35(1), 84-97, 2025
Abstract:Advancing autonomous green technologies in solar photovoltaic (PV) systems is key to improving sustainability and efficiency in renewable energy production. This study presents a reinforcement learning (RL)-based framework to autonomously optimize the cleaning schedules of PV panels in arid regions, where soiling from dust and other airborne particles significantly reduces energy output. By employing advanced RL algorithms, Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC), the framework dynamically adjusts cleaning intervals based on uncertain environmental conditions. The proposed approach was applied to a case study in Abu Dhabi, UAE, demonstrating that PPO outperformed SAC and traditional simulation optimization (Sim-Opt) methods, achieving up to 13% cost savings by dynamically responding to weather uncertainties. The results highlight the superiority of flexible, autonomous scheduling over fixed-interval methods, particularly in adapting to stochastic environmental dynamics. This aligns with the goals of autonomous green energy production by reducing operational costs and improving the efficiency of solar power generation systems. This work underscores the potential of RL-driven autonomous decision-making to optimize maintenance operations in renewable energy systems. In future research, it is important to enhance the generalization ability of the proposed RL model, while also considering additional factors and constraints to apply it to different regions.
[LG-83] Online Continual Learning for Anomaly Detection in IoT under Data Distribution Shifts
链接: https://arxiv.org/abs/2603.07507
作者: Matea Marinova,Shashi Raj Pandey,Junya Shiraishi,Martin Voigt Vejling,Valentin Rakovic,Petar Popovski
类目: Machine Learning (cs.LG)
*备注: Manuscript submitted to EUSIPCO 2026. The copyright might be transferred without further notice
Abstract:In this work, we present OCLADS, a novel communication framework with continual learning (CL) for Internet of Things (IoT) anomaly detection (AD) when operating in non-stationary environments. As the statistical properties of the observed data change with time, the on-device inference model becomes obsolete, which necessitates strategic model updating. OCLADS keeps track of data distribution shifts to timely update the on-device IoT AD model. To do so, OCLADS introduces two mechanisms during the interaction between the resource-constrained IoT device and an edge server (ES): i) an intelligent sample selection mechanism at the device for data transmission, and ii) a distribution-shift detection mechanism at the ES for model updating. Experimental results with TinyML demonstrate that our proposed framework achieves high inference accuracy while realizing a significantly smaller number of model updates compared to the baseline schemes.
[LG-84] A Unified Framework for Knowledge Transfer in Bidirectional Model Scaling
链接: https://arxiv.org/abs/2603.07506
作者: Jianlu Shen,Fu Feng,Jiaze Xu,Yucheng Xie,Jiaqi Lv,Xin Geng
类目: Machine Learning (cs.LG)
*备注:
Abstract:Transferring pre-trained knowledge from a source model to a target model of a different architectural size is a key challenge for flexible and efficient model scaling. However, current parameter-space methods treat Small-to-Large (S2L) and Large-to-Small (L2S) scaling as separate, incompatible problems, focusing on parameter synthesis and selection, respectively. This fragmented perspective has resulted in specialized tools, hindering a unified, bidirectional framework. In this paper, we propose BoT (Bidirectional knowledge Transfer), the first size-agnostic framework to unify S2L and L2S scaling. Our core insight is to treat model weights as continuous signals, where models of different sizes represent distinct discretizations of the transferable knowledge. This multi-resolution perspective directly casts S2L and L2S scaling as the signal processing operations of upsampling and downsampling, naturally leading to the adoption of the Discrete Wavelet Transform (DWT) and its Inverse (IDWT). BoT leverages the recursive nature of wavelets, using the decomposition level as a dynamic scaling factor to bridge disparate model sizes in a parameter-free and computationally efficient manner. Extensive experiments on DeiT, BERT, and GPT demonstrate significant pre-training FLOPs savings (up to 67.1% for S2L, 52.8% for L2S) and state-of-the-art performance on benchmarks like GLUE and SQuAD.
[LG-85] Enhanced Random Subspace Local Projections for High-Dimensional Time Series Analysis
链接: https://arxiv.org/abs/2603.07500
作者: Eman Khalid,Moimma Ali Khan,Zarmeena Ali,Abdullah Illyas,Muhammad Usman,Saoud Ahmed
类目: Machine Learning (cs.LG)
*备注: 12 pages, 18 figures
Abstract:High-dimensional time series forecasting suffers from severe overfitting when the number of predictors exceeds available observations, making standard local projection methods unstable and unreliable. We propose an enhanced Random Subspace Local Projection (RSLP) framework designed to deliver robust impulse response estimation in the presence of hundreds of correlated predictors. The method introduces weighted subspace aggregation, category-aware subspace sampling, adaptive subspace size selection, and a bootstrap inference procedure tailored to dependent data. These enhancements substantially improve estimator stability at longer forecast horizons while providing more reliable finite-sample inference. Experiments on synthetic data, macroeconomic indicators, and the FRED-MD dataset demonstrate a 33 percent reduction in estimator variability at horizons h = 3 through adaptive subspace size selection. The bootstrap inference procedure produces conservative confidence intervals that are 14 percent narrower at policy-relevant horizons in very high-dimensional settings (FRED-MD with 126 predictors) while maintaining proper coverage. The framework provides practitioners with a principled approach for incorporating rich information sets into impulse response analysis without the instability of traditional high-dimensional methods.
[LG-86] rusting What You Cannot See: Auditable Fine-Tuning and Inference for Proprietary AI
链接: https://arxiv.org/abs/2603.07466
作者: Heng Jin,Chaoyu Zhang,Hexuan Yu,Shanghao Shi,Ning Zhang,Y. Thomas Hou,Wenjing Lou
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Cloud-based infrastructures have become the dominant platform for deploying large models, particularly large language models (LLMs). Fine-tuning and inference are increasingly delegated to cloud providers for simplified deployment and access to proprietary models, yet this creates a fundamental trust gap: although cryptographic and TEE-based verification exist, the scale of modern LLMs renders them prohibitive, leaving clients unable to practically audit these processes. This lack of transparency creates concrete security risks that can silently compromise service integrity. We present AFTUNE, an auditable and verifiable framework that ensures the computation integrity of cloud-based fine-tuning and inference. AFTUNE incorporates a lightweight recording and spot-check mechanism that produces verifiable traces of execution. These traces enable clients to later audit whether the training and inference processes followed the agreed configurations. Our evaluation shows that AFTUNE imposes practical computation overhead while enabling selective and efficient verification, demonstrating that trustworthy model services are achievable in today’s cloud environments. Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG) Cite as: arXiv:2603.07466 [cs.CR] (or arXiv:2603.07466v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2603.07466 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-87] Discrete Tokenization Unlocks Transformers for Calibrated Tabular Forecasting
链接: https://arxiv.org/abs/2603.07448
作者: Yael S. Elmatad
类目: Machine Learning (cs.LG)
*备注:
Abstract:Gradient boosting still dominates Transformers on tabular benchmarks. Our tokenizer uses a deliberately simplistic discretized vocabulary so we can highlight how even basic tokenization unlocks the power of attention on tabular features, yet it already outperforms tuned gradient boosting when combined with Gaussian smoothing. Our solution discretizes environmental context while smoothing labels with adaptive Gaussians, yielding calibrated PDFs. On 600K entities (5M training examples) we outperform tuned XGBoost by 10.8% (35.94s vs 40.31s median MAE) and achieve KS=0.0045 with the adaptive-sigma checkpoint selected to minimize KS rather than median MAE. Ablations confirm architecture matters: losing sequential ordering costs about 2.0%, dropping the time-delta tokens costs about 1.8%, and a stratified calibration analysis reveals where miscalibration persists.
[LG-88] Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part II
链接: https://arxiv.org/abs/2603.07437
作者: Yi Tian,Kaiqing Zhang,Russ Tedrake,Suvrit Sra
类目: Machine Learning (cs.LG); Systems and Control (eess.SY); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注: 38 pages; preliminary version appeared in IEEE CDC 2023; this is the extended journal version, with an end-to-end guarantee added
Abstract:We study the problem of state representation learning for control from partial and potentially high-dimensional observations. We approach this problem via cost-driven state representation learning, in which we learn a dynamical model in a latent state space by predicting cumulative costs. In particular, we establish finite-sample guarantees on finding a near-optimal representation function and a near-optimal controller using the learned latent model for infinite-horizon time-invariant Linear Quadratic Gaussian (LQG) control. We study two approaches to cost-driven representation learning, which differ in whether the transition function of the latent state is learned explicitly or implicitly. The first approach has also been investigated in Part I of this work, for finite-horizon time-varying LQG control. The second approach closely resembles MuZero, a recent breakthrough in empirical reinforcement learning, in that it learns latent dynamics implicitly by predicting cumulative costs. A key technical contribution of this Part II is to prove persistency of excitation for a new stochastic process that arises from the analysis of quadratic regression in our approach, and may be of independent interest.
[LG-89] DualSpec: Accelerating Deep Research Agents via Dual-Process Action Speculation
链接: https://arxiv.org/abs/2603.07416
作者: Shuzhang Zhong,Baotong Lu,Qi Chen,Chuanjie Liu,Fan Yang,Meng Li
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language model-based deep research agents have been increasingly popular for addressing long-horizon information-seeking tasks, but they often incur high end-to-end latency due to extensive reasoning and frequent tool use. Speculation frameworks aim to reduce latency by overlapping action execution with reasoning; however, existing approaches typically rely on uniform speculation strategies and strict action matching, which limits inference speedups and robustness. In this work, we revisit the speculate-verify paradigm for deep research agents through the lens of action heterogeneity. We show that \textitSearch and \textitVisit actions exhibit fundamentally different reasoning and model capacity requirements: entropy-based analysis reveals that Search decisions have higher uncertainty and benefit significantly from explicit reasoning, whereas Visit decisions have lower entropy and depend primarily on model capacity. Motivated by this dual-process characteristic, we propose DualSpec, a heterogeneous speculation framework equipped with a lightweight, confidence-based semantic verifier. Experiments across multiple models and benchmarks demonstrate that DualSpec achieves up to 3.28 \times end-to-end speedup while maintaining accuracy comparable to fully reasoning agents.
[LG-90] Generalizing Linear Autoencoder Recommenders with Decoupled Expected Quadratic Loss ICLR2026
链接: https://arxiv.org/abs/2603.07402
作者: Ruixin Guo,Xinyu Li,Hao Zhou,Yang Zhou,Ruoming Jin
类目: Machine Learning (cs.LG)
*备注: Accepted at ICLR 2026 ( this https URL )
Abstract:Linear autoencoders (LAEs) have gained increasing popularity in recommender systems due to their simplicity and strong empirical performance. Most LAE models, including the Emphasized Denoising Linear Autoencoder (EDLAE) introduced by (Steck, 2020), use quadratic loss during training. However, the original EDLAE only provides closed-form solutions for the hyperparameter choice b = 0 , which limits its capacity. In this work, we generalize EDLAE objective into a Decoupled Expected Quadratic Loss (DEQL). We show that DEQL simplifies the process of deriving EDLAE solutions and reveals solutions in a broader hyperparameter range b 0 , which were not derived in Steck’s original paper. Additionally, we propose an efficient algorithm based on Miller’s matrix inverse theorem to ensure the computational tractability for the b 0 case. Empirical results on benchmark datasets show that the b 0 solutions provided by DEQL outperform the b = 0 EDLAE baseline, demonstrating that DEQL expands the solution space and enables the discovery of models with better testing performance.
[LG-91] Deterministic Fuzzy Triage for Legal Compliance Classification and Evidence Retrieval AAAI
链接: https://arxiv.org/abs/2603.07390
作者: Rian Atri
类目: Machine Learning (cs.LG)
*备注: 8 pages, 5 figures. Published in the Proceedings of the AAAI Bridge between Artificial Intelligence and Law 2026 (Full papers), pages 51-58
Abstract:Legal teams increasingly use machine learning to triage large volumes of contractual evidence, but many models are opaque, non-deterministic, and difficult to align with frameworks such as HIPAA or NERC-CIP. We study a simple, reproducible alternative based on deterministic dual encoders and transparent fuzzy triage bands. We train a RoBERTa-base dual encoder with a 512-dimensional projection and cosine similarity on the ACORD benchmark for graded clause retrieval, then fine-tune it on a CUAD-derived binary compliance dataset. Across five random seeds (40-44) on a single NVIDIA A100 GPU, the model achieves ACORD-style retrieval performance of NDCG@5 0.38-0.42, NDCG@10 0.45-0.50, and 4-star Precision@5 about 0.37 on the test split. On CUAD-derived binary labels, it achieves AUC 0.98-0.99 and F1 0.22-0.30 depending on positive-class weighting, outperforming majority and random baselines in a highly imbalanced setting with a positive rate of about 0.6%. We then map scalar compliance scores into three regions: auto-noncompliant, auto-compliant, and human-review. Thresholds are tuned on validation data to maximize automatic decision coverage subject to an empirical error-rate constraint of at most 2% over auto-decided examples. The result is a seed-stable system summarized by a small number of scalar parameters. We argue that deterministic encoders, calibrated fuzzy bands, and explicit error constraints provide a practical middle ground between hand-crafted rules and opaque large language models, supporting explainable evidence triage, reproducible audit trails, and concrete mappings to legal review concepts.
[LG-92] Feed m Birds with One Scone: Accelerating Multi-task Gradient Balancing via Bi-level Optimization
链接: https://arxiv.org/abs/2603.07389
作者: Xuxing Chen,Yun He,Jiayi Xu,Minhui Huang,Xiaoyi Liu,Boyang Liu,Fei Tian,Xiaohan Wei,Rong Jin,Sem Park,Bo Long,Xue Feng
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:In machine learning, the goal of multi-task learning (MTL) is to optimize multiple objectives together. Recent works, for example, Multiple Gradient Descent Algorithm (MGDA) and its variants, show promising results with dynamically adjusted weights for different tasks to mitigate conflicts that may potentially degrade the performance on certain tasks. Despite the empirical success of MGDA-type methods, one major limitation of such methods is their computational inefficiency, as they require access to all task gradients. In this paper we introduce MARIGOLD, a unified algorithmic framework for efficiently solving MTL problems. Our method reveals that multi-task gradient balancing methods have a hierarchical structure, in which the model training and the gradient balancing are coupled during the whole optimization process and can be viewed as a bi-level optimization problem. Moreover, we showcase that the bi-level problem can be solved efficiently by leveraging zeroth-order method. Extensive experiments on both public datasets and industrial-scale datasets demonstrate the efficiency and superiority of our method.
[LG-93] Learning to Reflect: Hierarchical Multi-Agent Reinforcement Learning for CSI-Free mmWave Beam-Focusing
链接: https://arxiv.org/abs/2603.07370
作者: Hieu Le,Oguz Bedir,Mostafa Ibrahim,Jian Tao,Sabit Ekin
类目: Machine Learning (cs.LG)
*备注:
Abstract:Reconfigurable Intelligent Surfaces promise to transform wireless environments, yet practical deployment is hindered by the prohibitive overhead of Channel State Information (CSI) estimation and the dimensionality explosion inherent in centralized optimization. This paper proposes a Hierarchical Multi-Agent Reinforcement Learning (HMARL) framework for the control of mechanically reconfigurable reflective surfaces in millimeter-wave (mmWave) systems. We introduce a “CSI-free” paradigm that substitutes pilot-based channel estimation with readily available user localization data. To manage the massive combinatorial action space, the proposed architecture utilizes Multi-Agent Proximal Policy Optimization (MAPPO) under a Centralized Training with Decentralized Execution (CTDE) paradigm. The proposed architecture decomposes the control problem into two abstraction levels: a high-level controller for user-to-reflector allocation and decentralized low-level controllers for low-level focal point optimization. Comprehensive ray-tracing evaluations demonstrate that the framework achieves 2.81-7.94 dB RSSI improvements over centralized baselines, with the performance advantage widening as system complexity increases. Scalability analysis reveals that the system maintains sustained efficiency, exhibiting minimal per-user performance degradation and stable total power utilization even when user density doubles. Furthermore, robustness validation confirms the framework’s viability across varying reflector aperture sizes (45-99 tiles) and demonstrates graceful performance degradation under localization errors up to 0.5 m. By eliminating CSI overhead while maintaining high-fidelity beam-focusing, this work establishes HMARL as a practical solution for intelligent mmWave environments.
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