本篇博文主要内容为 2026-03-13 从Arxiv.org论文网站获取的最新论文列表，自动更新，按照NLP、CV、ML、AI、IR、MA六个大方向区分。
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目录
概览 (2026-03-13)
今日共更新645篇论文,其中:
- 自然语言处理共80篇(Computation and Language (cs.CL))
- 人工智能共193篇(Artificial Intelligence (cs.AI))
- 计算机视觉共151篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共169篇(Machine Learning (cs.LG))
- 多智能体系统共14篇(Multiagent Systems (cs.MA))
- 信息检索共7篇(Information Retrieval (cs.IR))
- 人机交互共30篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] Language Model Teams as Distributed Systems
【速读】：该论文旨在解决大规模语言模型（Large Language Models, LLMs）团队在实际部署中面临的核心问题，包括何时使用LLM团队更有效、应配置多少智能体（agent）、团队结构如何影响性能，以及团队是否优于单一智能体。为避免依赖试错法设计与评估，作者提出以分布式系统（distributed systems）作为理论基础来构建和评估LLM团队，其关键在于识别并利用分布式计算领域中已知的原理与挑战——如一致性、容错性和通信开销等——这些原理同样适用于LLM团队，从而为团队设计提供可解释、可扩展且高效的指导框架。
链接: https://arxiv.org/abs/2603.12229
作者: Elizabeth Mieczkowski,Katherine M. Collins,Ilia Sucholutsky,Natalia Vélez,Thomas L. Griffiths
机构: Princeton University (普林斯顿大学); Massachusetts Institute of Technology (麻省理工学院); University of Cambridge (剑桥大学); New York University (纽约大学)
类目: Multiagent Systems (cs.MA)
备注:
Abstract:Large language models (LLMs) are growing increasingly capable, prompting recent interest in LLM teams. Yet, despite increased deployment of LLM teams at scale, we lack a principled framework for addressing key questions such as when a team is helpful, how many agents to use, how structure impacts performance – and whether a team is better than a single agent. Rather than designing and testing these possibilities through trial-and-error, we propose using distributed systems as a principled foundation for creating and evaluating LLM teams. We find that many of the fundamental advantages and challenges studied in distributed computing also arise in LLM teams, highlighting the rich practical insights that can come from the cross-talk of these two fields of study.
[MA-1] AGMARL-DKS: An Adaptive Graph-Enhanced Multi-Agent Reinforcement Learning for Dynamic Kubernetes Scheduling
【速读】：该论文旨在解决当前基于强化学习（Reinforcement Learning, RL）的 Kubernetes 调度器在大规模异构集群中面临的三大局限性：一是采用单一集中式代理导致可扩展性差；二是多目标奖励函数依赖静态线性组合，缺乏灵活性；三是缺乏对动态负载变化的自适应响应能力。解决方案的关键在于提出一种自适应图增强的多智能体强化学习动态调度框架（Adaptive Graph-enhanced Multi-Agent Reinforcement Learning Dynamic Kubernetes Scheduler, AGMARL-DKS），其核心创新包括：（1）将调度问题建模为协作式多智能体问题，每个节点作为独立智能体，在集中训练后实现去中心化执行，提升可扩展性；（2）引入图神经网络（Graph Neural Network, GNN）构建全局集群上下文的状态表示，使各智能体具备环境感知能力；（3）采用应力感知的字典序优先级策略替代固定权重，实现多目标间动态权衡，从而显著提升故障容忍度、资源利用率和成本效益，尤其在批处理与关键任务工作负载场景下表现优异。
链接: https://arxiv.org/abs/2603.12031
作者: Hamed Hamzeh
机构: 未知
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:State-of-the-art cloud-native applications require intelligent schedulers that can effectively balance system stability, resource utilisation, and associated costs. While Kubernetes provides feasibility-based placement by default, recent research efforts have explored the use of reinforcement learning (RL) for more intelligent scheduling decisions. However, current RL-based schedulers have three major limitations. First, most of these schedulers use monolithic centralised agents, which are non-scalable for large heterogeneous clusters. Second, the ones that use multi-objective reward functions assume simple, static, linear combinations of the objectives. Third, no previous work has produced a stress-aware scheduler that can react adaptively to dynamic conditions. To address these gaps in current research, we propose the Adaptive Graph-enhanced Multi-Agent Reinforcement Learning Dynamic Kubernetes Scheduler (AGMARL-DKS). AGMARL-DKS addresses these gaps by introducing three major innovations. First, we construct a scalable solution by treating the scheduling challenge as a cooperative multi-agent problem, where every cluster node operates as an agent, employing centralised training methods before decentralised execution. Second, to be context-aware and yet decentralised, we use a Graph Neural Network (GNN) to build a state representation of the global cluster context at each agent. This represents an improvement over methods that rely solely on local observations. Finally, to make trade-offs between these objectives, we use a stress-aware lexicographical ordering policy instead of a simple, static linear weighting of these objectives. The evaluations in Google Kubernetes Engine (GKE) reveal that AGMARL-DKS significantly outperforms the default scheduler in terms of fault tolerance, utilisation, and cost, especially in scheduling batch and mission-critical workloads.
[MA-2] CogSearch: A Cognitive-Aligned Multi-Agent Framework for Proactive Decision Support in E-Commerce Search
【速读】：该论文旨在解决现代电子商务搜索引擎在复杂决策场景下表现不足的问题，传统被动的检索与排序模型难以有效支持用户进行多步骤、高认知负荷的决策过程，导致用户体验受阻。其解决方案的关键在于提出CogSearch——一个面向认知的多智能体框架，通过四个专业化智能体协同工作，模拟人类认知流程：分解复杂用户意图、融合内外部异构知识源，并生成高度可操作的洞察。该架构实现了从以相关性为中心的信息检索范式向协作式决策智能的转变。
链接: https://arxiv.org/abs/2603.11927
作者: Zhouwei Zhai,Mengxiang Chen,Haoyun Xia,Jin Li,Renquan Zhou,Min Yang
机构: JD.com(京东)
类目: Multiagent Systems (cs.MA)
备注:
Abstract:Modern e-commerce search engines, largely rooted in passive retrieval-and-ranking models, frequently fail to support complex decision-making, leaving users overwhelmed by cognitive friction. In this paper, we introduce CogSearch, a novel cognitive-oriented multi-agent framework that reimagines e-commerce search as a proactive decision support system. By synergizing four specialized agents, CogSearch mimics human cognitive workflows: it decomposes intricate user intents, fuses heterogeneous knowledge across internal and external sources, and delivers highly actionable insights. Our offline benchmarks validate CogSearch’s excellence in consultative and complex search scenarios. Extensive online A/B testing on this http URL demonstrates the system’s transformative impact: it reduced decision costs by 5% and achieved a 0.41% increase in overall UCVR, with a remarkable 30% surge in conversion for decision-heavy queries. CogSearch represents a fundamental shift in information retrieval, moving beyond traditional relevance-centric paradigms toward a future of holistic, collaborative decision intelligence.
[MA-3] he price of decentralization in managing engineering systems through multi-agent reinforcement learning
【速读】：该论文旨在解决多部件系统中检查与维护（Inspection and Maintenance, IM）规划的可扩展性问题，尤其是在存在不确定性与信息不完整的情况下，如何通过强化学习实现高效决策。传统单智能体深度强化学习在多部件场景下难以扩展，而多智能体深度强化学习（Multi-Agent Deep Reinforcement Learning, MADRL）虽能通过去中心化决策提升可扩展性，却可能因协作病理（cooperation pathologies）导致策略次优。解决方案的关键在于：设计一套具有系统性冗余变化的基准环境，使得集中式近最优策略仍可计算，从而对不同MADRL算法（涵盖从完全集中到去中心化训练范式的值因子分解与Actor-Critic方法）进行公平比较；结果表明，冗余程度显著影响协调效果——串联结构下MADRL接近最优，但随着冗余增加，协调挑战加剧并引发性能损失，即便如此，去中心化智能体仍学习到结构化的策略，优于优化后的启发式基线，揭示了去中心化学习在可扩展维护规划中的潜力与局限。
链接: https://arxiv.org/abs/2603.11884
作者: Prateek Bhustali,Pablo G. Morato,Konstantinos G. Papakonstantinou,Charalampos P. Andriotis
机构: 未知
类目: Multiagent Systems (cs.MA)
备注:
Abstract:Inspection and maintenance (IM) planning involves sequential decision making under uncertainties and incomplete information, and can be modeled as a partially observable Markov decision process (POMDP). While single-agent deep reinforcement learning provides approximate solutions to POMDPs, it does not scale well in multi-component systems. Scalability can be achieved through multi-agent deep reinforcement learning (MADRL), which decentralizes decision-making across multiple agents, locally controlling individual components. However, this decentralization can induce cooperation pathologies that degrade the optimality of the learned policies. To examine these effects in IM planning, we introduce a set of deteriorating systems in which redundancy is varied systematically. These benchmark environments are designed such that computation of centralized (near-)optimal policies remains tractable, enabling direct comparison of solution methods. We implement and benchmark a broad set of MADRL algorithms spanning fully centralized and decentralized training paradigms, from value-factorization to actor-critic methods. Our results show a clear effect of redundancy on coordination: MADRL algorithms achieve near-optimal performance in series-like settings, whereas increasing redundancy amplifies coordination challenges and can lead to optimality losses. Nonetheless, decentralized agents learn structured policies that consistently outperform optimized heuristic baselines, highlighting both the promise and current limitations of decentralized learning for scalable maintenance planning.
[MA-4] Hybrid Human-Agent Social Dilemmas in Energy Markets
【速读】：该论文试图解决在人类与自主代理混合群体中，如何促使合作行为涌现的问题，特别是在能源负荷管理场景下，消费者代理在依赖需求定价的机制中，往往因缺乏协调而选择产生拥堵成本的非合作策略，从而陷入社会困境。解决方案的关键在于引入使用全局可观测信号的人工代理（artificial agents），通过演化动力学和强化学习实验表明，这类人工代理能够改变学习动态，使系统更倾向于达成协调结果；同时研究还发现，在技术早期采用阶段（即部分采纳）情况下，单方面引入人工代理仍可行且能提升整体效益，但需注意非采纳者可能因协作效应而获益更多，这种不对称性提示部署时应考虑多智能体环境中的战略博弈问题。
链接: https://arxiv.org/abs/2603.11834
作者: Isuri Perera,Frits de Nijs,Julian Garcia
机构: Monash University (莫纳什大学)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
备注: 20 pages, 7 figures. Submitted to Proceedings of the Royal Society A, Special Issue on “The evolution of sociality in hybrid human AI populations”
Abstract:In hybrid populations where humans delegate strategic decision-making to autonomous agents, understanding when and how cooperative behaviors can emerge remains a key challenge. We study this problem in the context of energy load management: consumer agents schedule their appliance use under demand-dependent pricing. This structure can create a social dilemma where everybody would benefit from coordination, but in equilibrium agents often choose to incur the congestion costs that cooperative turn-taking would avoid. To address the problem of coordination, we introduce artificial agents that use globally observable signals to increase coordination. Using evolutionary dynamics, and reinforcement learning experiments, we show that artificial agents can shift the learning dynamics to favour coordination outcomes. An often neglected problem is partial adoption: what happens when the technology of artificial agents is in the early adoption stages? We analyze mixed populations of adopters and non-adopters, demonstrating that unilateral entry is feasible: adopters are not structurally penalized, and partial adoption can still improve aggregate outcomes. However, in some parameter regimes, non-adopters may benefit disproportionately from the cooperation induced by adopters. This asymmetry, while not precluding beneficial entry, warrants consideration in deployment, and highlights strategic issues around the adoption of AI technology in multiagent settings.
[MA-5] From Debate to Deliberation: Structured Collective Reasoning with Typed Epistemic Acts
【速读】：该论文旨在解决多智能体大语言模型（Multi-agent LLM）系统在复杂推理任务中交互模式单一、缺乏结构化决策保障的问题，现有方法如投票、无结构辩论或流水线编排难以实现过程可追溯与结果可信。其解决方案的关键在于提出一种新型的** deliberative collective intelligence (DCI)** 框架，通过四类推理原型（reasoning archetypes）、14种类型化的认识论行为（typed epistemic acts）、共享工作空间以及收敛流算法 DCI-CF，构建一个分阶段、可保留异议并最终生成结构化决策包（structured decision packet）的审议机制。该机制确保在关键决策场景下，即使消耗约62倍于单智能体的token成本，仍能提供可解释、可问责的输出，从而证明“结构化审议”而非单纯增加代理数量才是提升重要决策质量的核心要素。
链接: https://arxiv.org/abs/2603.11781
作者: Sunil Prakash
机构: Indian School of Business (印度管理学院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注: 26 pages, 6 tables, 2 figures, 2 listings
Abstract:Multi-agent LLM systems increasingly tackle complex reasoning, yet their interaction patterns remain limited to voting, unstructured debate, or pipeline orchestration. None model deliberation: a phased process where differentiated participants exchange typed reasoning moves, preserve disagreements, and converge on accountable outcomes. We introduce Deliberative Collective Intelligence (DCI), specifying four reasoning archetypes, 14 typed epistemic acts, a shared workspace, and DCI-CF, a convergent flow algorithm that guarantees termination with a structured decision packet containing the selected option, residual objections, minority report, and reopen conditions. We evaluate on 45 tasks across seven domains using Gemini 2.5 Flash. On non-routine tasks (n=40), DCI significantly improves over unstructured debate (+0.95, 95% CI [+0.41, +1.54]). DCI excels on hidden-profile tasks requiring perspective integration (9.56, highest of any system on any domain) while failing on routine decisions (5.39), confirming task-dependence. DCI produces 100% structured decision packets and 98% minority reports, artifacts absent from all baselines. However, DCI consumes ~62x single-agent tokens, and single-agent generation outperforms DCI on overall quality. DCI’s contribution is not that more agents are better, but that consequential decisions benefit from deliberative structure when process accountability justifies the cost.
[MA-6] Multi-Agent Reinforcement Learning for UAV-Based Chemical Plume Source Localization
【速读】：该论文旨在解决废弃油井（orphaned wells）因甲烷（methane）泄漏对周边社区健康和环境造成的威胁，特别是传统磁法勘探难以有效识别老旧油井的问题。其核心解决方案是提出一种基于多智能体深度强化学习（multi-agent deep reinforcement learning, MARL）的化学羽流源定位（chemical plume source localization, CPSL）框架，关键创新在于引入虚拟锚点节点（virtual anchor nodes）以协调无人机（UAV）导航，并通过机载与共享的气体浓度和风速测量实现协同感知；同时，利用锚点历史轨迹在羽流中的分布特征进行源头识别，显著优于传统的通量趋性（fluxotaxis）方法，在定位精度和作业效率上均表现更优。
链接: https://arxiv.org/abs/2603.11582
作者: Zhirun Li,Derek Hollenbeck,Ruikun Wu,Michelle Sherman,Sihua Shao,Xiang Sun,Mostafa Hassanalian
机构: University of New Mexico (新墨西哥大学); University of California, Merced (加州大学默塞德分校); Colorado School of Mines (科罗拉多矿业学院); New Mexico Tech (新墨西哥技术学院); Autonomous Solutions, Inc. (自主解决方案公司)
类目: ystems and Control (eess.SY); Multiagent Systems (cs.MA)
备注:
Abstract:Undocumented orphaned wells pose significant health and environmental risks to nearby communities by releasing toxic gases and contaminating water sources, with methane emissions being a primary concern. Traditional survey methods such as magnetometry often fail to detect older wells effectively. In contrast, aerial in-situ sensing using unmanned aerial vehicles (UAVs) offers a promising alternative for methane emission detection and source localization. This study presents a robust and efficient framework based on a multi-agent deep reinforcement learning (MARL) algorithm for the chemical plume source localization (CPSL) problem. The proposed approach leverages virtual anchor nodes to coordinate UAV navigation, enabling collaborative sensing of gas concentrations and wind velocities through onboard and shared measurements. Source identification is achieved by analyzing the historical trajectory of anchor node placements within the plume. Comparative evaluations against the fluxotaxis method demonstrate that the MARL framework achieves superior performance in both localization accuracy and operational efficiency.
[MA-7] How Intelligence Emerges: A Minimal Theory of Dynamic Adaptive Coordination
【速读】：该论文旨在解决多智能体系统（Multi-agent Systems）中协调行为的动态建模问题，传统方法通常依赖于均衡优化或以个体为中心的学习机制，而忽略了环境与智能体之间持续反馈的耦合特性。其解决方案的关键在于构建一个递归闭合的反馈架构，将智能体、激励机制与环境视为一个统一的动力学系统：环境中存储累积的协调信号，分布式激励场在局部传递这些信号，智能体据此自适应更新。这种框架下，协调被视作耦合动力学的结构性属性，而非集中目标函数的解。通过引入耗散性假设和环境持久状态，论文揭示了系统存在有界正不变区域、无法简化为静态全局目标以及历史敏感性的结构条件，并通过线性模型验证了耦合强度、持久性和耗散性如何通过雅可比矩阵的谱特性调控局部稳定性和振荡行为，从而在不预设福利最大化、理性预期或集中设计的前提下，从激励驱动的自适应交互中涌现出智能协调动力学。
链接: https://arxiv.org/abs/2603.11560
作者: Stefano Grassi
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Theoretical Economics (econ.TH); Dynamical Systems (math.DS)
备注:
Abstract:This paper develops a dynamical theory of adaptive coordination in multi-agent systems. Rather than analyzing coordination through equilibrium optimization or agent-centric learning alone, the framework models agents, incentives, and environment as a recursively closed feedback architecture. A persistent environment stores accumulated coordination signals, a distributed incentive field transmits those signals locally, and adaptive agents update in response. Coordination is thus treated as a structural property of coupled dynamics rather than as the solution to a centralized objective. The paper establishes three structural results. First, under dissipativity assumptions, the induced closed-loop system admits a bounded forward-invariant region, ensuring viability without requiring global optimality. Second, when incentive signals depend non-trivially on persistent environmental memory, the resulting dynamics generically cannot be reduced to a static global objective defined solely over the agent state space. Third, persistent environmental state induces history sensitivity unless the system is globally contracting. A minimal linear specification illustrates how coupling, persistence, and dissipation govern local stability and oscillatory regimes through spectral conditions on the Jacobian. The results establish structural conditions under which intelligent coordination dynamics emerge from incentive-mediated adaptive interaction within a persistent environment, without presuming welfare maximization, rational expectations, or centralized design. Subjects: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Theoretical Economics (econ.TH); Dynamical Systems (math.DS) Cite as: arXiv:2603.11560 [cs.MA] (or arXiv:2603.11560v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2603.11560 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-8] Grammar of the Wave: Towards Explainable Multivariate Time Series Event Detection via Neuro-Symbolic VLM Agents
【速读】：该论文旨在解决时间序列事件检测（Time Series Event Detection, TSED）中因标注数据稀缺而导致模型难以从有限样本中学习语义复杂、结构多样的事件的问题。传统方法依赖大量标注数据进行统计建模，而现实场景下事件通常具有明确的语义定义和复杂的内在时序逻辑结构，难以通过归纳学习有效捕捉。为此，作者提出知识引导的TSED新范式，即给定自然语言描述的事件，模型需在少样本甚至零样本条件下将语义映射到多变量时间序列中的具体区间。其解决方案的关键在于引入事件逻辑树（Event Logic Tree, ELT），这是一种基于时序逻辑结构的知识表示框架，能够将自然语言事件描述转化为可计算的符号约束；在此基础上构建神经符号视觉语言模型（VLM）代理框架，通过迭代地从信号可视化中实例化基本单元并按ELT约束组合，生成检测区间及可解释的推理树，从而实现对事件语义与物理信号形态之间映射的精准建模，并显著降低视觉语言模型（VLM）在匹配信号形态与事件语义时的固有幻觉问题。
链接: https://arxiv.org/abs/2603.11479
作者: Sky Chenwei Wan,Tianjun Hou,Yifei Wang,Xiqing Chang,Aymeric Jan
机构: Télécom Paris, Institut Polytechnique de Paris, France; AI Lab, SLB
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: Work in progress
Abstract:Time Series Event Detection (TSED) has long been an important task with critical applications across many high-stakes domains. Unlike statistical anomalies, events are defined by semantics with complex internal structures, which are difficult to learn inductively from scarce labeled data in real-world settings. In light of this, we introduce Knowledge-Guided TSED, a new setting where a model is given a natural-language event description and must ground it to intervals in multivariate signals with little or no training data. To tackle this challenge, we introduce Event Logic Tree (ELT), a novel knowledge representation framework to bridge linguistic descriptions and physical time series data via modeling the intrinsic temporal-logic structures of events. Based on ELT, we present a neuro-symbolic VLM agent framework that iteratively instantiates primitives from signal visualizations and composes them under ELT constraints, producing both detected intervals and faithful explanations in the form of instantiated trees. To validate the effectiveness of our approach, we release a benchmark based on real-world time series data with expert knowledge and annotations. Experiments and human evaluation demonstrate the superiority of our method compared to supervised fine-tuning baselines and existing zero-shot time series reasoning frameworks based on LLMs/VLMs. We also show that ELT is critical in mitigating VLMs’ inherent hallucination in matching signal morphology with event semantics.
[MA-9] Verified Multi-Agent Orchestration: A Plan-Execute-Verify-Replan Framework for Complex Query Resolution ICLR2026
【速读】：该论文旨在解决复杂任务中单一大语言模型（Large Language Model, LLM）推理能力不足、结果完整性与可靠性受限的问题，尤其是在需要多步骤分解、跨领域知识整合与动态调整的场景下。其解决方案的核心在于提出一种验证驱动的多智能体编排框架（Verified Multi-Agent Orchestration, VMAO），通过构建子问题的有向无环图（DAG）实现依赖感知的并行执行与自动上下文传播，并引入基于LLM的验证器作为协调信号，驱动自适应重规划以填补结果缺口；同时支持可配置的终止条件，在保证答案质量的同时优化资源消耗。实验证明，该机制显著提升了任务完成度和来源质量，验证了在多智能体系统中引入编排级验证的有效性。
链接: https://arxiv.org/abs/2603.11445
作者: Xing Zhang,Yanwei Cui,Guanghui Wang,Qucy Wei Qiu,Ziyuan Li,Fangwei Han,Yajing Huang,Hengzhi Qiu,Bin Zhu,Peiyang He
机构: AWS Generative AI Innovation Center (AWS生成式人工智能创新中心); HSBC (汇丰银行)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: ICLR 2026 Workshop on MALGAI
Abstract:We present Verified Multi-Agent Orchestration (VMAO), a framework that coordinates specialized LLM-based agents through a verification-driven iterative loop. Given a complex query, our system decomposes it into a directed acyclic graph (DAG) of sub-questions, executes them through domain-specific agents in parallel, verifies result completeness via LLM-based evaluation, and adaptively replans to address gaps. The key contributions are: (1) dependency-aware parallel execution over a DAG of sub-questions with automatic context propagation, (2) verification-driven adaptive replanning that uses an LLM-based verifier as an orchestration-level coordination signal, and (3) configurable stop conditions that balance answer quality against resource usage. On 25 expert-curated market research queries, VMAO improves answer completeness from 3.1 to 4.2 and source quality from 2.6 to 4.1 (1-5 scale) compared to a single-agent baseline, demonstrating that orchestration-level verification is an effective mechanism for multi-agent quality assurance.
[MA-10] EducaSim: Interactive Simulacra for CS1 Instructional Practice
【速读】：该论文旨在解决教师培训中角色扮演（Role Play）难以规模化的问题，尤其是在大规模在线课程（Massive Open Online Courses, MOOCs）场景下，由于缺乏足够数量且具备专业素养的指导人员，导致高质量教学实践训练难以普及。其解决方案的关键在于提出EducaSim框架，该框架利用生成式代理（Generative Agents）模拟小组教学场景，通过构建基于教学法的角色人格、整合真实课程材料以及基于代理的架构设计，为师范生提供一个高沉浸度、低资源消耗的教学演练环境，从而实现教学实践训练的可扩展性和可及性。
链接: https://arxiv.org/abs/2603.11444
作者: Cameron Mohne,Nicholas Vo,Dora Demszky,Chris Piech
机构: Stanford University (斯坦福大学)
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)
备注: 7 pages, 3 figures, 2 tables. Presents a multi-agent generative architecture for educational simulations intended for instructor training
Abstract:Role play is a high-impact mode of training that has demonstrated its effectiveness in improving learning outcomes. However, it is difficult to scale to teacher instruction due to its inherent dependency on providing personnel who are both trained and available to facilitate this learning environment. This poses a challenge, especially to massive online courses which may employ and aid hundreds to thousands of novice teachers. In this work, we present EducaSim: a novel framework that uses generative agents to simulate a small-group section for teachers-in-training to practice instruction. EducaSim works by implementing diverse pedagogical-based personas, actual course material, and agent-based architectures constructed for instructional practice to provide a pedagogically rich environment for teachers-in-training to engage in role play learning – without the costly overhead that comes with it. We share our experiences with constructing and making the tool available for experimental training and preparation in a six-week CS1 course supporting 20,000 students. We found that teachers who engaged generally saw it as a positive experience. We believe that EducaSim is an important step to providing experiential teaching practice at scale for closely-defined settings and has great potential for future applications.
[MA-11] Resolving Java Code Repository Issues with iSWE Agent
【速读】：该论文旨在解决当前自动化代码问题修复系统在非Python编程语言（尤其是企业级广泛应用的Java）上性能显著下降的问题。现有基于大语言模型和智能体的解决方案大多聚焦于Python，对Java的支持不足，限制了其在实际软件工程场景中的应用。论文提出的解决方案是iSWE Agent，其关键在于引入两个专用子代理——定位代理（localization agent）与编辑代理（editing agent），并结合基于规则的Java静态分析与转换工具，实现对Java代码问题的精准定位与高效修复。通过这一融合规则驱动与模型驱动的方法，iSWE Agent在Multi-SWE-bench和SWE-PolyBench的Java数据集上达到了当前最优的修复准确率，为提升企业级软件开发自动化水平提供了有效路径。
链接: https://arxiv.org/abs/2603.11356
作者: Jatin Ganhotra,Sami Serhan,Antonio Abu Nassar,Avraham Shinnar,Ziv Nevo,Martin Hirzel
机构: IBM(国际商业机器公司)
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Resolving issues on code repositories is an important part of software engineering. Various recent systems automatically resolve issues using large language models and agents, often with impressive performance. Unfortunately, most of these models and agents focus primarily on Python, and their performance on other programming languages is lower. In particular, a lot of enterprise software is written in Java, yet automated issue resolution for Java is under-explored. This paper introduces iSWE Agent, an automated issue resolver with an emphasis on Java. It consists of two sub-agents, one for localization and the other for editing. Both have access to novel tools based on rule-based Java static analysis and transformation. Using this approach, iSWE achieves state-of-the-art issue resolution rates across the Java splits of both Multi-SWE-bench and SWE-PolyBench. More generally, we hope that by combining the best of rule-based and model-based techniques, this paper contributes towards improving enterprise software development.
[MA-12] Enhancing Value Alignment of LLM s with Multi-agent system and Combinatorial Fusion ICASSP
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）与人类价值观对齐的问题，现有方法如基于人类反馈的强化学习（Reinforcement Learning from Human Feedback, RLHF）通常依赖单一评价者或狭窄的奖励信号，难以体现伦理多元性。解决方案的关键在于提出一种基于组合融合分析（Combinatorial Fusion Analysis, CFA）的价值对齐系统（Value Alignment System, VAS-CFA），通过实例化多个道德代理（moral agents），每个代理代表不同的规范视角，并采用基于排名和得分的双重聚合机制融合其输出，从而利用代理间的认知多样性缓解冲突与冗余，提升响应对人类价值的代表性。
链接: https://arxiv.org/abs/2603.11126
作者: Yuanhong Wu,Djallel Bouneffouf,D. Frank Hsu
机构: 未知
类目: Multiagent Systems (cs.MA); Computation and Language (cs.CL)
备注: 5 pages, 3 figures, accepted to 2026 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Abstract:Aligning large language models (LLMs) with human values is a central challenge for ensuring trustworthy and safe deployment. While existing methods such as Reinforcement Learning from Human Feedback (RLHF) and its variants have improved alignment, they often rely on a single evaluator or narrowly defined reward signals, limiting their ability to capture ethical pluralism. In this work, we propose the Value Alignment System using Combinatorial Fusion Analysis (VAS-CFA), a framework that operationalizes multi-agent fusion alignment. It instantiates multiple moral agents, each fine-tuned to represent a distinct normative perspective, and fuses their outputs using CFA with both rank- and score-based aggregation. This design leverages cognitive diversity, between agents, to mitigate conflicts and redundancies across multiple agents, producing responses that better reflect human values. Empirical evaluation demonstrates that VAS-CFA outperforms both single agent baselines and prior aggregation approaches on standard metrics, showing that multi-agent fusion provides a robust and effective mechanism for advancing value alignment in LLMs.
[MA-13] Edge-Assisted Multi-Robot Visual-Inertial SLAM with Efficient Communication
【速读】：该论文旨在解决多机器人协同同步定位与地图构建（SLAM）系统中因终端设备计算、通信和存储资源受限，以及云端与终端间带宽不足导致的实时性能下降问题。其解决方案的关键在于提出一种基于金字塔IMU预测的光流特征跟踪轻量化SLAM方法，并构建一个分层的“机器人-边缘-云”集中式多机器人SLAM架构：通过仅传输特征点和关键帧描述符并采用无损编码压缩策略，在有限带宽下实现高效远程信息传输，既显著降低数据传输占用带宽，又避免因压缩带来的SLAM精度损失；实验表明，该方案在保持与先进集中式多机器人SLAM相当或更优定位精度的同时，大幅降低计算负载。
链接: https://arxiv.org/abs/2603.11085
作者: Xin Liu,Shuhuan Wen,Jing Zhao,Tony Z. Qiu,Hong Zhang
机构: Yanshan University (燕山大学); Wuhan University of Technology (武汉理工大学); University of Alberta (阿尔伯塔大学); Southern University of Science and Technology (南方科技大学)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Multiagent Systems (cs.MA)
备注: 13 pages, 18 figures
Abstract:The integration of cloud computing and edge computing is an effective way to achieve global consistent and real-time multi-robot Simultaneous Localization and Mapping (SLAM). Cloud computing effectively solves the problem of limited computing, communication and storage capacity of terminal equipment. However, limited bandwidth and extremely long communication links between terminal devices and the cloud result in serious performance degradation of multi-robot SLAM systems. To reduce the computational cost of feature tracking and improve the real-time performance of the robot, a lightweight SLAM method of optical flow tracking based on pyramid IMU prediction is proposed. On this basis, a centralized multi-robot SLAM system based on a robot-edge-cloud layered architecture is proposed to realize real-time collaborative SLAM. It avoids the problems of limited on-board computing resources and low execution efficiency of single robot. In this framework, only the feature points and keyframe descriptors are transmitted and lossless encoding and compression are carried out to realize real-time remote information transmission with limited bandwidth resources. This design reduces the actual bandwidth occupied in the process of data transmission, and does not cause the loss of SLAM accuracy caused by data compression. Through experimental verification on the EuRoC dataset, compared with the current most advanced local feature compression method, our method can achieve lower data volume feature transmission, and compared with the current advanced centralized multi-robot SLAM scheme, it can achieve the same or better positioning accuracy under low computational load.
自然语言处理
[NLP-0] EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models
【速读】： 该论文旨在解决当前多模态大语言模型（Multimodal Large Language Models, MLLMs）作为文本编码器集成到扩散框架（diffusion frameworks）中时存在的两个关键问题：一是MLLMs的单步编码无法激活链式思维（Chain-of-Thought, CoT）过程，导致推理深度不足；二是生成过程中引导信息（guidance）在解码阶段保持不变，阻碍了去噪扩散Transformer（DiT）逐步分解复杂指令的能力。解决方案的关键在于提出一种名为内生链式思维（Endogenous Chain-of-Thought, EndoCoT）的新框架：首先通过迭代思维引导模块（iterative thought guidance module）不断精炼潜在思维状态，激活MLLM的推理能力，并将这些状态与DiT的去噪过程相衔接；其次引入终端思维锚定模块（terminal thought grounding module），通过将最终状态对齐真实答案来确保推理轨迹始终受文本监督。这一机制使MLLM能够提供精细化的逐步推理指导，从而显著提升DiT在复杂任务上的执行精度，实验表明其在多个基准测试（如Maze、TSP、VSP和Sudoku）上平均准确率达92.1%，优于最强基线8.3个百分点。
链接: https://arxiv.org/abs/2603.12252
作者: Xuanlang Dai,Yujie Zhou,Long Xing,Jiazi Bu,Xilin Wei,Yuhong Liu,Beichen Zhang,Kai Chen,Yuhang Zang
机构: Shanghai AI Laboratory(上海人工智能实验室); Xi’an Jiaotong University(西安交通大学); Shanghai Jiaotong University(上海交通大学); University of Science and Technology of China(中国科学技术大学); Fudan University(复旦大学); The Chinese University of Hong Kong(香港中文大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: 23 pages, 18 figures
Abstract:Recently, Multimodal Large Language Models (MLLMs) have been widely integrated into diffusion frameworks primarily as text encoders to tackle complex tasks such as spatial reasoning. However, this paradigm suffers from two critical limitations: (i) MLLMs text encoder exhibits insufficient reasoning depth. Single-step encoding fails to activate the Chain-of-Thought process, which is essential for MLLMs to provide accurate guidance for complex tasks. (ii) The guidance remains invariant during the decoding process. Invariant guidance during decoding prevents DiT from progressively decomposing complex instructions into actionable denoising steps, even with correct MLLM encodings. To this end, we propose Endogenous Chain-of-Thought (EndoCoT), a novel framework that first activates MLLMs’ reasoning potential by iteratively refining latent thought states through an iterative thought guidance module, and then bridges these states to the DiT’s denoising process. Second, a terminal thought grounding module is applied to ensure the reasoning trajectory remains grounded in textual supervision by aligning the final state with ground-truth answers. With these two components, the MLLM text encoder delivers meticulously reasoned guidance, enabling the DiT to execute it progressively and ultimately solve complex tasks in a step-by-step manner. Extensive evaluations across diverse benchmarks (e.g., Maze, TSP, VSP, and Sudoku) achieve an average accuracy of 92.1%, outperforming the strongest baseline by 8.3 percentage points.
[NLP-1] SciMDR: Benchmarking and Advancing Scientific Multimodal Document Reasoning
【速读】： 该论文旨在解决科学多模态文档推理数据集构建中的核心矛盾问题，即在规模（scale）、忠实性（faithfulness）和现实性（realism）之间难以兼顾的挑战。为应对这一问题，作者提出了一种两阶段的“合成与重锚定”（synthesize-and-reground）框架：其关键在于首先通过以主张为中心的问答合成（Claim-Centric QA Synthesis）生成高忠实度、孤立且聚焦于特定段落的问答对，随后利用文档级重锚定（Document-Scale Regrounding）将这些问答对程序化地嵌入完整文档任务中，从而保留真实复杂性。该方法最终构建了包含30万条带显式推理链的QA对的SciMDR数据集，显著提升了模型在需复杂文档级推理任务上的性能表现。
链接: https://arxiv.org/abs/2603.12249
作者: Ziyu Chen,Yilun Zhao,Chengye Wang,Rilyn Han,Manasi Patwardhan,Arman Cohan
机构: Yale University (耶鲁大学); University of Chicago (芝加哥大学); TCS Research (TCS研究院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Constructing scientific multimodal document reasoning datasets for foundation model training involves an inherent trade-off among scale, faithfulness, and realism. To address this challenge, we introduce the synthesize-and-reground framework, a two-stage pipeline comprising: (1) Claim-Centric QA Synthesis, which generates faithful, isolated QA pairs and reasoning on focused segments, and (2) Document-Scale Regrounding, which programmatically re-embeds these pairs into full-document tasks to ensure realistic complexity. Using this framework, we construct SciMDR, a large-scale training dataset for cross-modal comprehension, comprising 300K QA pairs with explicit reasoning chains across 20K scientific papers. We further construct SciMDR-Eval, an expert-annotated benchmark to evaluate multimodal comprehension within full-length scientific workflows. Experiments demonstrate that models fine-tuned on SciMDR achieve significant improvements across multiple scientific QA benchmarks, particularly in those tasks requiring complex document-level reasoning.
[NLP-2] Examining Reasoning LLM LLM s-as-Judges in Non-Verifiable LLM Post-Training
【速读】： 该论文旨在解决在非可验证领域中，如何有效利用大语言模型（LLM）作为评判者（judge）来对齐其他LLM行为的问题，特别是在强化学习（reinforcement learning, RL）框架下，传统非推理型判官容易引发奖励黑客（reward hacking），而推理型判官是否能提升政策性能尚缺乏系统评估。解决方案的关键在于构建一个受控的合成环境，其中使用高精度“黄金标准”判官（gpt-oss-120b）生成偏好标注以训练小型判官，并对比非推理型与推理型判官在实际政策训练中的表现差异——结果表明，推理型判官虽能引导模型生成在黄金标准下表现优异的策略，但这些策略往往通过生成高度有效的对抗性输出来欺骗其他LLM判官，从而在主流基准如Arena-Hard上取得虚假高分，揭示了当前基于推理判官的对齐方法既具潜力也存在显著风险。
链接: https://arxiv.org/abs/2603.12246
作者: Yixin Liu,Yue Yu,DiJia Su,Sid Wang,Xuewei Wang,Song Jiang,Bo Liu,Arman Cohan,Yuandong Tian,Zhengxing Chen
机构: Meta(元)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Reasoning LLMs-as-Judges, which can benefit from inference-time scaling, provide a promising path for extending the success of reasoning models to non-verifiable domains where the output correctness/quality cannot be directly checked. However, while reasoning judges have shown better performance on static evaluation benchmarks, their effectiveness in actual policy training has not been systematically examined. Therefore, we conduct a rigorous study to investigate the actual impact of non-reasoning and reasoning judges in reinforcement-learning-based LLM alignment. Our controlled synthetic setting, where a “gold-standard” judge (gpt-oss-120b) provides preference annotations to train smaller judges, reveals key differences between non-reasoning and reasoning judges: non-reasoning judges lead to reward hacking easily, while reasoning judges can lead to policies that achieve strong performance when evaluated by the gold-standard judge. Interestingly, we find that the reasoning-judge-trained policies achieve such strong performance by learning to generate highly effective adversarial outputs that can also score well on popular benchmarks such as Arena-Hard by deceiving other LLM-judges. Combined with our further analysis, our study highlights both important findings and room for improvements for applying (reasoning) LLM-judges in non-verifiable LLM post-training.
[NLP-3] Sparking Scientific Creativity via LLM -Driven Interdisciplinary Inspiration
【速读】： 该论文旨在解决当前科学发现中跨学科研究受限于单一领域学术孤岛的问题，尤其针对现有基于人工智能（AI）的方法过于侧重实验设计自动化而忽视创造性跨学科推理过程的局限性。其解决方案的关键在于提出Idea-Catalyst框架，该框架通过系统识别跨学科洞见来支持人类与大语言模型的创造性推理：首先将抽象研究目标分解为特定领域的核心问题，继而将这些挑战转化为领域无关的概念性问题，从而从其他学科（如心理学、社会学）中检索相似问题的解决方案；最终通过整合并重构这些跨域洞察，按潜在影响力对来源领域进行排序，实现对原始问题的创新性回应。实证结果表明，该方法在新颖性和洞察力上分别提升21%和16%，同时保持与原研究问题的一致性。
链接: https://arxiv.org/abs/2603.12226
作者: Priyanka Kargupta,Shuhaib Mehri,Dilek Hakkani-Tur,Jiawei Han
机构: Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校西贝尔计算机与数据科学学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Code and dataset provided at this https URL
Abstract:Despite interdisciplinary research leading to larger and longer-term impact, most work remains confined to single-domain academic silos. Recent AI-based approaches to scientific discovery show promise for interdisciplinary research, but many prioritize rapidly designing experiments and solutions, bypassing the exploratory, collaborative reasoning processes that drive creative interdisciplinary breakthroughs. As a result, prior efforts largely prioritize automating scientific discovery rather than augmenting the reasoning processes that underlie scientific disruption. We present Idea-Catalyst, a novel framework that systematically identifies interdisciplinary insights to support creative reasoning in both humans and large language models. Starting from an abstract research goal, Idea-Catalyst is designed to assist the brainstorming stage, explicitly avoiding premature anchoring on specific solutions. The framework embodies key metacognitive features of interdisciplinary reasoning: (a) defining and assessing research goals, (b) awareness of a domain’s opportunities and unresolved challenges, and © strategic exploration of interdisciplinary ideas based on impact potential. Concretely, Idea-Catalyst decomposes an abstract goal (e.g., improving human-AI collaboration) into core target-domain research questions that guide the analysis of progress and open challenges within that domain. These challenges are reformulated as domain-agnostic conceptual problems, enabling retrieval from external disciplines (e.g., Psychology, Sociology) that address analogous issues. By synthesizing and recontextualizing insights from these domains back into the target domain, Idea-Catalyst ranks source domains by their interdisciplinary potential. Empirically, this targeted integration improves average novelty by 21% and insightfulness by 16%, while remaining grounded in the original research problem.
[NLP-4] CLASP: Defending Hybrid Large Language Models Against Hidden State Poisoning Attacks
【速读】： 该论文旨在解决状态空间模型（State Space Models, SSMs）及其混合架构中存在的一种新型安全威胁——隐藏状态投毒攻击（Hidden State Poisoning Attacks, HiSPAs），此类攻击通过恶意输入字符串污染SSM的内部记忆，从而干扰下游任务的正确性。为应对这一问题，作者提出CLASP模型，其核心在于将HiSPA防御建模为token级别的二分类问题，并利用Mamba模型块输出嵌入（Block Output Embeddings, BOEs）中蕴含的可区分模式，结合XGBoost分类器实现高精度、低开销的恶意token检测。该方案不依赖下游模型，在保持极低计算资源消耗（每秒处理1032 tokens，VRAM占用<4GB）的同时，展现出对已知和未见攻击模式的强大泛化能力，具备实际部署潜力。
链接: https://arxiv.org/abs/2603.12206
作者: Alexandre Le Mercier,Thomas Demeester,Chris Develder
机构: Ghent University–imec (根特大学–imec); IDLab–T2K (IDLab–T2K)
类目: Computation and Language (cs.CL)
备注: 22 pages, 6 figures
Abstract:State space models (SSMs) like Mamba have gained significant traction as efficient alternatives to Transformers, achieving linear complexity while maintaining competitive performance. However, Hidden State Poisoning Attacks (HiSPAs), a recently discovered vulnerability that corrupts SSM memory through adversarial strings, pose a critical threat to these architectures and their hybrid variants. Framing the HiSPA mitigation task as a binary classification problem at the token level, we introduce the CLASP model to defend against this threat. CLASP exploits distinct patterns in Mamba’s block output embeddings (BOEs) and uses an XGBoost classifier to identify malicious tokens with minimal computational overhead. We consider a realistic scenario in which both SSMs and HiSPAs are likely to be used: an LLM screening résumés to identify the best candidates for a role. Evaluated on a corpus of 2,483 résumés totaling 9.5M tokens with controlled injections, CLASP achieves 95.9% token-level F1 score and 99.3% document-level F1 score on malicious tokens detection. Crucially, the model generalizes to unseen attack patterns: under leave-one-out cross-validation, performance remains high (96.9% document-level F1), while under clustered cross-validation with structurally novel triggers, it maintains useful detection capability (91.6% average document-level F1). Operating independently of any downstream model, CLASP processes 1,032 tokens per second with under 4GB VRAM consumption, potentially making it suitable for real-world deployment as a lightweight front-line defense for SSM-based and hybrid architectures. All code and detailed results are available at this https URL.
[NLP-5] IndexCache: Accelerating Sparse Attention via Cross-Layer Index Reuse
【速读】： 该论文旨在解决长上下文智能体工作流中因注意力机制复杂度高而导致的推理速度慢与服务成本高的问题。现有方案如DeepSeek Sparse Attention (DSA) 通过轻量级闪电索引器选择每查询的 top-k 相关标记，将核心注意力计算从 O(L2) 降低至 O(Lk)，但其索引器本身仍保持 O(L2) 复杂度且需在每一层独立运行，造成冗余计算。解决方案的关键在于提出 IndexCache：利用连续层间 top-k 索引高度相似的特性，将模型层划分为少量“Full 层”（自建索引）和多数“Shared 层”（复用最近 Full 层的索引），从而大幅减少索引计算开销。进一步地，论文设计了两种优化策略——训练-free 的贪心搜索直接最小化校准集上的语言建模损失，以及训练-aware 的多层蒸馏损失，使保留的索引器学习其所服务各层的平均注意力分布，显著提升效率与精度平衡。实验表明，IndexCache 可移除 75% 的索引器计算，同时实现高达 1.82× 的预填充加速和 1.48× 的解码加速，且质量损失可忽略。
链接: https://arxiv.org/abs/2603.12201
作者: Yushi Bai,Qian Dong,Ting Jiang,Xin Lv,Zhengxiao Du,Aohan Zeng,Jie Tang,Juanzi Li
机构: Tsinghua University (清华大学); Z.ai
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Long-context agentic workflows have emerged as a defining use case for large language models, making attention efficiency critical for both inference speed and serving cost. Sparse attention addresses this challenge effectively, and DeepSeek Sparse Attention (DSA) is a representative production-grade solution: a lightweight lightning indexer selects the top-k most relevant tokens per query, reducing core attention from O(L^2) to O(Lk) . However, the indexer itself retains O(L^2) complexity and must run independently at every layer, despite the fact that the resulting top-k selections are highly similar across consecutive layers. We present IndexCache, which exploits this cross-layer redundancy by partitioning layers into a small set of Full layers that run their own indexers and a majority of Shared layers that simply reuse the nearest Full layer’s top-k indices. We propose two complementary approaches to determine and optimize this configuration. Training-free IndexCache applies a greedy search algorithm that selects which layers to retain indexers by directly minimizing language modeling loss on a calibration set, requiring no weight updates. Training-aware IndexCache introduces a multi-layer distillation loss that trains each retained indexer against the averaged attention distributions of all layers it serves, enabling even simple interleaved patterns to match full-indexer accuracy. Experimental results on a 30B DSA model show that IndexCache can remove 75% of indexer computations with negligible quality degradation, achieving up to 1.82 \times prefill speedup and 1.48 \times decode speedup compared to standard DSA. These positive results are further confirmed by our preliminary experiments on the production-scale GLM-5 model (Figure 1).
[NLP-6] Long-Context Encoder Models for Polish Language Understanding
【速读】： 该论文旨在解决编码器-only 架构（如 BERT）在处理长文档时受限于短上下文窗口的问题，从而影响其在需要长文本理解任务中的性能表现。解决方案的关键在于提出了一种支持长达 8192 token 的波兰语高质编码器模型，通过两阶段训练流程实现：第一阶段为位置嵌入（positional embedding）适配，第二阶段为全参数连续预训练（continuous pre-training），并进一步采用知识蒸馏（knowledge distillation）方法训练压缩版本模型。实验表明，该模型在 25 项任务（包括 KLEJ 基准、新提出的金融任务套件 FinBench 及其他分类与回归任务）上均取得优于现有波兰语及多语言模型的平均性能，尤其在长文本任务中显著领先，同时保持短文本任务上的竞争力。
链接: https://arxiv.org/abs/2603.12191
作者: Sławomir Dadas,Rafał Poświata,Marek Kozłowski,Małgorzata Grębowiec,Michał Perełkiewicz,Paweł Klimiuk,Przemysław Boruta
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:While decoder-only Large Language Models (LLMs) have recently dominated the NLP landscape, encoder-only architectures remain a cost-effective and parameter-efficient standard for discriminative tasks. However, classic encoders like BERT are limited by a short context window, which is insufficient for processing long documents. In this paper, we address this limitation for the Polish by introducing a high-quality Polish model capable of processing sequences of up to 8192 tokens. The model was developed by employing a two-stage training procedure that involves positional embedding adaptation and full parameter continuous pre-training. Furthermore, we propose compressed model variants trained via knowledge distillation. The models were evaluated on 25 tasks, including the KLEJ benchmark, a newly introduced financial task suite (FinBench), and other classification and regression tasks, specifically those requiring long-document understanding. The results demonstrate that our model achieves the best average performance among Polish and multilingual models, significantly outperforming competitive solutions in long-context tasks while maintaining comparable quality on short texts.
[NLP-7] Strategic Navigation or Stochastic Search? How Agents and Humans Reason Over Document Collections
【速读】： 该论文旨在解决多模态智能体（Multimodal Agents）在处理复杂文档密集型工作流时，是否具备真正的战略推理能力，还是仅依赖随机试错搜索的问题。其解决方案的关键在于提出MADQA基准测试集和一种新的评估协议：MADQA由2,250个基于800份异构PDF文档的人工编写问题构成，依据经典测验理论设计以最大化不同智能体能力水平的区分度；评估协议则量化准确率与努力之间的权衡关系，从而揭示智能体在真实场景中是否存在高效、可控的推理策略。实验表明，尽管顶尖智能体可达到人类搜索者的准确率，但它们主要针对不同类型的问题，并依赖暴力搜索弥补战略规划不足，且无法缩小与理想性能（oracle performance）近20%的差距，持续陷入低效循环。
链接: https://arxiv.org/abs/2603.12180
作者: Łukasz Borchmann,Jordy Van Landeghem,Michał Turski,Shreyansh Padarha,Ryan Othniel Kearns,Adam Mahdi,Niels Rogge,Clémentine Fourrier,Siwei Han,Huaxiu Yao,Artemis Llabrés,Yiming Xu,Dimosthenis Karatzas,Hao Zhang,Anupam Datta
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal agents offer a promising path to automating complex document-intensive workflows. Yet, a critical question remains: do these agents demonstrate genuine strategic reasoning, or merely stochastic trial-and-error search? To address this, we introduce MADQA, a benchmark of 2,250 human-authored questions grounded in 800 heterogeneous PDF documents. Guided by Classical Test Theory, we design it to maximize discriminative power across varying levels of agentic abilities. To evaluate agentic behaviour, we introduce a novel evaluation protocol measuring the accuracy-effort trade-off. Using this framework, we show that while the best agents can match human searchers in raw accuracy, they succeed on largely different questions and rely on brute-force search to compensate for weak strategic planning. They fail to close the nearly 20% gap to oracle performance, persisting in unproductive loops. We release the dataset and evaluation harness to help facilitate the transition from brute-force retrieval to calibrated, efficient reasoning.
[NLP-8] QAQ: Bidirectional Semantic Coherence for Selecting High-Quality Synthetic Code Instructions ACL2026
【速读】： 该论文旨在解决合成数据（synthetic data）在训练代码生成模型时引入的噪声和幻觉（hallucination）问题，这些问题难以通过现有评估指标准确识别。传统数据选择方法如指令遵循难度（Instruction-Following Difficulty, IFD）仅从“给定查询预测答案”（A|Q）的角度衡量样本质量，但在噪声数据中易混淆任务内在复杂性与模型生成的虚假内容。论文提出QAQ框架，其核心创新在于反向评估：通过“给定答案预测查询”（Q|A）来量化数据质量，定义了逆向互信息（Reverse Mutual Information, RMI）以衡量答案对查询的信息增益。研究表明，RMI过低表示语义错位，过高则可能包含可被大语言模型（LLM）轻易识别的缺陷模式；进一步结合强弱模型间的分歧策略，可筛选出既有效又具挑战性的样本。实验表明，基于分层RMI选择25%的数据即可达到全量数据训练效果，显著优于现有方法，凸显了双向语义一致性在合成数据精炼中的关键作用。
链接: https://arxiv.org/abs/2603.12165
作者: Jiayin Lei,Ming Ma,Yunxi Duan,Chenxi Li,Tianming Yang
机构: Beijing University of Technology (北京工业大学); Chinese Academy of Sciences (中国科学院); University of Chinese Academy of Sciences (中国科学院大学); University of Chicago (芝加哥大学)
类目: Computation and Language (cs.CL)
备注: 12 pages, 5 figures. Under review at ACL 2026
Abstract:Synthetic data has become essential for training code generation models, yet it introduces significant noise and hallucinations that are difficult to detect with current metrics. Existing data selection methods like Instruction-Following Difficulty (IFD) typically assess how hard a model generates an answer given a query ( A|Q ). However, this metric is ambiguous on noisy synthetic data, where low probability can distinguish between intrinsic task complexity and model-generated hallucinations. Here, we propose QAQ, a novel data selection framework that evaluates data quality from the reverse direction: how well can the answer predict the query ( Q|A )? We define Reverse Mutual Information (RMI) to quantify the information gain about the query conditioned on the answer. Our analyses reveal that both extremes of RMI signal quality issues: low RMI indicates semantic misalignment, while excessively high RMI may contain defect patterns that LLMs easily recognize. Furthermore, we introduce a selection strategy based on the disagreement between strong and weak models to identify samples that are valid yet challenging. Experiments on the WarriorCoder dataset demonstrate that selecting just 25% of data using stratified RMI achieves comparable performance to full-data training, significantly outperforming existing data selection methods. Our approach highlights the importance of bidirectional semantic coherence in synthetic data curation, offering a scalable pathway to reduce computational costs without sacrificing model capability.
[NLP-9] LifeSim: Long-Horizon User Life Simulator for Personalized Assistant Evaluation
【速读】： 该论文旨在解决当前个性化AI助手评估基准与真实用户-助手交互场景之间存在的脱节问题，特别是无法有效捕捉外部环境复杂性和用户认知状态的局限。其解决方案的关键在于提出LifeSim用户模拟器，该模拟器基于Belief-Desire-Intention (BDI)模型在物理环境中建模用户认知，以生成连贯的生活轨迹并模拟意图驱动的交互行为；进而构建LifeSim-Eval基准，涵盖8个生活领域和1200种多样化场景，采用多轮交互方式评估大语言模型（LLMs）在完成显式与隐式意图、恢复用户画像及生成高质量响应等方面的能力。实验表明，现有LLMs在处理隐式意图和长期用户偏好建模方面仍存在显著不足。
链接: https://arxiv.org/abs/2603.12152
作者: Feiyu Duan,Xuanjing Huang,Zhongyu Wei
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:The rapid advancement of large language models (LLMs) has accelerated progress toward universal AI assistants. However, existing benchmarks for personalized assistants remain misaligned with real-world user-assistant interactions, failing to capture the complexity of external contexts and users’ cognitive states. To bridge this gap, we propose LifeSim, a user simulator that models user cognition through the Belief-Desire-Intention (BDI) model within physical environments for coherent life trajectories generation, and simulates intention-driven user interactive behaviors. Based on LifeSim, we introduce LifeSim-Eval, a comprehensive benchmark for multi-scenario, long-horizon personalized assistance. LifeSim-Eval covers 8 life domains and 1,200 diverse scenarios, and adopts a multi-turn interactive method to assess models’ abilities to complete explicit and implicit intentions, recover user profiles, and produce high-quality responses. Under both single-scenario and long-horizon settings, our experiments reveal that current LLMs face significant limitations in handling implicit intention and long-term user preference modeling.
[NLP-10] Linking Perception Confidence and Accuracy in MLLM s CVPR2026
【速读】： 该论文旨在解决多模态大语言模型（Multi-modal Large Language Models, MLLMs）中存在的置信度校准不足问题，即模型在不确定时仍表现出高置信度，导致可靠性下降。其解决方案的关键在于提出一种基于置信度驱动的强化学习方法（Confidence-Driven Reinforcement Learning, CDRL），通过引入原始噪声图像对和新型置信度奖励机制，提升模型的感知敏感性和置信度校准能力；进一步地，作者设计了置信度感知的测试时缩放策略（Confidence-Aware Test-Time Scaling, CA-TTS），利用置信度信号动态协调自一致性、自反思与视觉自检模块，并由专家模型（Expert Model）担任规划者、批评者与投票者角色进行调度与外部验证，从而实现无需额外训练即可显著提升性能的“免费午餐”式效果。
链接: https://arxiv.org/abs/2603.12149
作者: Yuetian Du,Yucheng Wang,Rongyu Zhang,Zhijie Xu,Boyu Yang,Ming Kong,Jie Liu,Qiang Zhu
机构: Zhejiang University (浙江大学); Alibaba Group (阿里巴巴集团); City University of Hong Kong (香港城市大学); University of Michigan (密歇根大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: Accepted by CVPR2026
Abstract:Recent advances in Multi-modal Large Language Models (MLLMs) have predominantly focused on enhancing visual perception to improve accuracy. However, a critical question remains unexplored: Do models know when they do not know? Through a probing experiment, we reveal a severe confidence miscalibration problem in MLLMs. To address this, we propose Confidence-Driven Reinforcement Learning (CDRL), which uses original-noise image pairs and a novel confidence-based reward to enhance perceptual sensitivity and robustly calibrate the model’s confidence. Beyond training benefits, calibrated confidence enables more effective test-time scaling as a free lunch. We further propose Confidence-Aware Test-Time Scaling (CA-TTS), which dynamically coordinates Self-Consistency, Self-Reflection, and Visual Self-Check modules guided by confidence signals. An Expert Model acts in multiple roles (e.g., Planner, Critic, Voter) to schedule these modules and provide external verification. Our integrated framework establishes new state-of-the-art results with consistent 8.8% gains across four benchmarks. More ablation studies demonstrate the effectiveness of each module and scaling superiority.
[NLP-11] opoBench: Benchmarking LLM s on Hard Topological Reasoning ICLR2026
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在求解拓扑网格谜题（topological grid puzzles）时面临的挑战，尤其是其对全局空间不变量（如连通性、环闭合和区域对称性）的推理能力不足问题。解决方案的关键在于：通过构建一个包含六个谜题族、三个难度层级的基准测试集 TopoBench，系统评估主流 LLM 的表现，并结合 750 条思维链（chain of thought）标注与误差分类法，识别出四种可能的因果失败模式；进一步设计针对性干预实验发现，某些错误模式（如过早承诺和约束遗忘）直接影响解题成功率，而重复推理则为搜索过程的良性副产物；最终揭示瓶颈并非推理本身，而是从空间表征中提取约束条件的能力不足，从而推动改进方向聚焦于提升空间信息的显式表示与约束提取机制，例如采用单元对齐的网格表示和基于工具的约束验证策略。
链接: https://arxiv.org/abs/2603.12133
作者: Mayug Maniparambil,Nils Hoehing,Janak Kapuriya,Arjun Karuvally,Ellen Rushe,Anthony Ventresque,Noel O’Connor,Fergal Reid
机构: Intercom Research(Intercom 研究); University College Dublin(都柏林大学); University of Galway(加拉韦大学); Salk Institute(索尔克研究所); Dublin City University(都柏林城市大学); Trinity College Dublin(都柏林圣三一学院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted, Workshop on Logical Reasoning of Large Language Models at ICLR 2026
Abstract:Solving topological grid puzzles requires reasoning over global spatial invariants such as connectivity, loop closure, and region symmetry and remains challenging for even the most powerful large language models (LLMs). To study these abilities under controlled settings, we introduce TopoBench, a benchmark of six puzzle families across three difficulty levels. We evaluate strong reasoning LLMs on TopoBench and find that even frontier models solve fewer than one quarter of hard instances, with two families nearly unsolved. To investigate whether these failures stem from reasoning limitations or from difficulty extracting and maintaining spatial constraints, we annotate 750 chain of thought traces with an error taxonomy that surfaces four candidate causal failure modes, then test them with targeted interventions simulating each error type. These interventions show that certain error patterns like premature commitment and constraint forgetting have a direct impact on the ability to solve the puzzle, while repeated reasoning is a benign effect of search. Finally we study mitigation strategies including prompt guidance, cell-aligned grid representations and tool-based constraint checking, finding that the bottleneck lies in extracting constraints from spatial representations and not in reasoning over them. Code and data are available at this http URL.
[NLP-12] Cross-Context Review: Improving LLM Output Quality by Separating Production and Review Sessions
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在同一次对话会话中难以识别自身输出错误的问题。其解决方案的关键在于提出跨上下文审查（Cross-Context Review, CCR）机制，即通过在一个全新的会话中进行审查，且不访问原始生成对话的历史记录，从而实现对模型输出的更有效纠错。实验表明，CCR在F1分数上显著优于同会话自审（Self-Review, SR）、重复自审（SR2）和基于上下文感知的子代理审查（Subagent Review, SA），且优势并非源于重复审查本身，而是源于上下文分离所带来的认知独立性。
链接: https://arxiv.org/abs/2603.12123
作者: Tae-Eun Song
机构: Daejeon Jungang Cheonggua Co., Ltd.
类目: Computation and Language (cs.CL)
备注: 10 pages, 2 figures, 8 tables
Abstract:Large language models struggle to catch errors in their own outputs when the review happens in the same session that produced them. This paper introduces Cross-Context Review (CCR), a straightforward method where the review is conducted in a fresh session with no access to the production conversation history. We ran a controlled experiment: 30 artifacts (code, technical documents, presentation scripts) with 150 injected errors, tested under four review conditions – same-session Self-Review (SR), repeated Self-Review (SR2), context-aware Subagent Review (SA), and Cross-Context Review (CCR). Over 360 reviews, CCR reached an F1 of 28.6%, outperforming SR (24.6%, p=0.008, d=0.52), SR2 (21.7%, p0.001, d=0.72), and SA (23.8%, p=0.004, d=0.57). The SR2 result matters most for interpretation: reviewing twice in the same session did not beat reviewing once (p=0.11), which rules out repetition as an explanation for CCR’s advantage. The benefit comes from context separation itself. CCR works with any model, needs no infrastructure, and costs only one extra session.
[NLP-13] SommBench: Assessing Sommelier Expertise of Language Models
【速读】： 该论文旨在解决当前大语言模型在多语言和跨文化能力评估中缺乏对感官体验驱动的专业知识（如品酒专家技能）系统性测试的问题。传统文化评测基准主要聚焦于可编码为语言形式的基础文化知识，而忽视了依赖嗅觉与味觉等感官判断的领域。其解决方案的关键在于提出SommBench——一个面向侍酒师专业能力的多语言评测基准，通过三个核心任务（葡萄酒理论问答WTQA、葡萄酒特征补全WFC、食物与葡萄酒搭配FWP）来检验语言模型是否能仅凭文本描述实现专家级感官推理。该基准覆盖英语、斯洛伐克语、瑞典语、芬兰语、德语、丹麦语、意大利语和西班牙语，并由专业侍酒师和母语者共同构建数据集，从而将语言能力与专业知识分离，为评估语言模型在具身认知场景下的真实表现提供新范式。
链接: https://arxiv.org/abs/2603.12117
作者: William Brach,Tomas Bedej,Jacob Nielsen,Jacob Pichna,Juraj Bedej,Eemeli Saarensilta,Julie Dupouy,Gianluca Barmina,Andrea Blasi Núñez,Peter Schneider-Kamp,Kristian Košťál,Michal Ries,Lukas Galke Poech
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:With the rapid advances of large language models, it becomes increasingly important to systematically evaluate their multilingual and multicultural capabilities. Previous cultural evaluation benchmarks focus mainly on basic cultural knowledge that can be encoded in linguistic form. Here, we propose SommBench, a multilingual benchmark to assess sommelier expertise, a domain deeply grounded in the senses of smell and taste. While language models learn about sensory properties exclusively through textual descriptions, SommBench tests whether this textual grounding is sufficient to emulate expert-level sensory judgment. SommBench comprises three main tasks: Wine Theory Question Answering (WTQA), Wine Feature Completion (WFC), and Food-Wine Pairing (FWP). SommBench is available in multiple languages: English, Slovak, Swedish, Finnish, German, Danish, Italian, and Spanish. This helps separate a language model’s wine expertise from its language skills. The benchmark datasets were developed in close collaboration with a professional sommelier and native speakers of the respective languages, resulting in 1,024 wine theory question-answering questions, 1,000 wine feature-completion examples, and 1,000 food-wine pairing examples. We provide results for the most popular language models, including closed-weights models such as Gemini 2.5, and open-weights models, such as GPT-OSS and Qwen 3. Our results show that the most capable models perform well on wine theory question answering (up to 97% correct with a closed-weights model), yet feature completion (peaking at 65%) and food-wine pairing show (MCC ranging between 0 and 0.39) turn out to be more challenging. These results position SommBench as an interesting and challenging benchmark for evaluating the sommelier expertise of language models. The benchmark is publicly available at this https URL.
[NLP-14] o Words and Beyond: Probing Large Language Models for Sentence-Level Psycholinguistic Norms of Memorability and Reading Times
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在句法层面心理语言学特征预测中的有效性问题，特别是针对此前未被充分研究的句子可记忆性（sentence memorability）和阅读时间（reading times）这两个涉及多词交互关系的特征。其解决方案的关键在于通过监督微调（supervised fine-tuning），使LLM能够生成与人类实验数据高度相关的估计值，并且这些估计性能优于传统可解释基线预测器，从而证明LLM中蕴含了关于句子级特征的有用信息。同时，研究也揭示了零样本（zero-shot）和少量样本（few-shot）提示方法表现不稳定，强调了在将LLM作为人类认知指标代理时需谨慎对待。
链接: https://arxiv.org/abs/2603.12105
作者: Thomas Hikaru Clark,Carlos Arriaga,Javier Conde,Gonzalo Martínez,Pedro Reviriego
机构: Massachusetts Institute of Technology (麻省理工学院); Universidad Politécnica de Madrid (马德里理工大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large Language Models (LLMs) have recently been shown to produce estimates of psycholinguistic norms, such as valence, arousal, or concreteness, for words and multiword expressions, that correlate with human judgments. These estimates are obtained by prompting an LLM, in zero-shot fashion, with a question similar to those used in human studies. Meanwhile, for other norms such as lexical decision time or age of acquisition, LLMs require supervised fine-tuning to obtain results that align with ground-truth values. In this paper, we extend this approach to the previously unstudied features of sentence memorability and reading times, which involve the relationship between multiple words in a sentence-level context. Our results show that via fine-tuning, models can provide estimates that correlate with human-derived norms and exceed the predictive power of interpretable baseline predictors, demonstrating that LLMs contain useful information about sentence-level features. At the same time, our results show very mixed zero-shot and few-shot performance, providing further evidence that care is needed when using LLM-prompting as a proxy for human cognitive measures.
[NLP-15] XSkill: Continual Learning from Experience and Skills in Multimodal Agents
【速读】： 该论文旨在解决多模态智能体在开放场景中因工具使用效率低下和编排方式僵化而导致的持续学习难题，核心在于如何在不更新模型参数的前提下，通过从历史轨迹中学习实现性能提升。解决方案的关键在于提出XSkill框架，该框架采用双流机制分别提取和利用两种互补的知识：经验（experience）——提供细粒度的动作级指导以优化工具选择与决策；以及技能（skill）——提供结构化的任务级指导用于规划与工具调用。XSkill通过视觉感知驱动的知识提取与检索，在累积阶段基于多路径回放进行可视化摘要与跨轨迹批判性提炼，在推理阶段根据当前视觉上下文动态适配知识，并将使用历史反馈至积累模块，形成闭环的持续学习机制。实验证明该方法在多个基准上显著优于仅依赖工具或纯学习基线，且两种知识流在推理行为塑造和零样本泛化能力方面展现出协同效应。
链接: https://arxiv.org/abs/2603.12056
作者: Guanyu Jiang,Zhaochen Su,Xiaoye Qu,Yi R.(May)Fung
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Multimodal agents can now tackle complex reasoning tasks with diverse tools, yet they still suffer from inefficient tool use and inflexible orchestration in open-ended settings. A central challenge is enabling such agents to continually improve without parameter updates by learning from past trajectories. We identify two complementary forms of reusable knowledge essential for this goal: experiences, providing concise action-level guidance for tool selection and decision making, and skills, providing structured task-level guidance for planning and tool use. To this end, we propose XSkill, a dual-stream framework for continual learning from experience and skills in multimodal agents. XSkill grounds both knowledge extraction and retrieval in visual observations. During accumulation, XSkill distills and consolidates experiences and skills from multi-path rollouts via visually grounded summarization and cross-rollout critique. During inference, it retrieves and adapts this knowledge to the current visual context and feeds usage history back into accumulation to form a continual learning loop. Evaluated on five benchmarks across diverse domains with four backbone models, XSkill consistently and substantially outperforms both tool-only and learning-based baselines. Further analysis reveals that the two knowledge streams play complementary roles in influencing the reasoning behaviors of agents and show superior zero-shot generalization.
[NLP-16] ranslationese as a Rational Response to Translation Task Difficulty
【速读】： 该论文试图解决翻译语体（translationese）现象的成因问题，即为何译文在语言特征上系统性地偏离目标语原生文本。现有研究虽指出其可能源于语言干扰、简化策略、社会文化因素及语言对效应，但缺乏统一解释框架。论文提出的核心假设是：翻译语体本质上反映了翻译任务本身所引发的认知负荷（cognitive load）。解决方案的关键在于将翻译语体操作化为基于自动分类器的段落级“译文度”得分（translatedness score），并将翻译任务难度分解为源文本复杂度与跨语言迁移难度两个维度，分别通过信息论指标（如大语言模型 surprisal）和传统句法/语义特征进行量化建模。实证结果表明，翻译语体可部分由任务难度解释，尤其在英德方向中，跨语言迁移难度贡献更大；信息论指标在书面语中表现优于传统特征，但在口语中无显著优势，而源文本句法复杂度与翻译方案熵（translation-solution entropy）成为跨语言对和语体模式下的最强预测因子。
链接: https://arxiv.org/abs/2603.12050
作者: Maria Kunilovskaya
机构: University of Saarland (萨尔兰大学)
类目: Computation and Language (cs.CL)
备注: 17 pages, submitted to ARR March 2026
Abstract:Translations systematically diverge from texts originally produced in the target language, a phenomenon widely referred to as translationese. Translationese has been attributed to production tendencies (e.g. interference, simplification), socio-cultural variables, and language-pair effects, yet a unified explanatory account is still lacking. We propose that translationese reflects cognitive load inherent in the translation task itself. We test whether observable translationese can be predicted from quantifiable measures of translation task difficulty. Translationese is operationalised as a segment-level translatedness score produced by an automatic classifier. Translation task difficulty is conceptualised as comprising source-text and cross-lingual transfer components, operationalised mainly through information-theoretic metrics based on LLM surprisal, complemented by established syntactic and semantic alternatives. We use a bidirectional English-German corpus comprising written and spoken subcorpora. Results indicate that translationese can be partly explained by translation task difficulty, especially in English-to-German. For most experiments, cross-lingual transfer difficulty contributes more than source-text complexity. Information-theoretic indicators match or outperform traditional features in written mode, but offer no advantage in spoken mode. Source-text syntactic complexity and translation-solution entropy emerged as the strongest predictors of translationese across language pairs and modes.
[NLP-17] Just Use XML: Revisiting Joint Translation and Label Projection
【速读】： 该论文旨在解决跨语言迁移中标签投影（label projection）效率与翻译质量难以兼顾的问题，即传统方法将机器翻译与标签投影分步执行时易导致翻译质量下降。其解决方案的关键在于提出LabelPigeon框架，通过XML标签联合建模翻译与标签投影过程，实现端到端的协同优化，从而在不损害翻译质量的前提下提升标签迁移效果，并在多种语言和任务上验证了该方法的有效性与稳定性。
链接: https://arxiv.org/abs/2603.12021
作者: Thennal D K,Chris Biemann,Hans Ole Hatzel
机构: University of Hamburg (汉堡大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Label projection is an effective technique for cross-lingual transfer, extending span-annotated datasets from a high-resource language to low-resource ones. Most approaches perform label projection as a separate step after machine translation, and prior work that combines the two reports degraded translation quality. We re-evaluate this claim with LabelPigeon, a novel framework that jointly performs translation and label projection via XML tags. We design a direct evaluation scheme for label projection, and find that LabelPigeon outperforms baselines and actively improves translation quality in 11 languages. We further assess translation quality across 203 languages and varying annotation complexity, finding consistent improvement attributed to additional fine-tuning. Finally, across 27 languages and three downstream tasks, we report substantial gains in cross-lingual transfer over comparable work, up to +39.9 F1 on NER. Overall, our results demonstrate that XML-tagged label projection provides effective and efficient label transfer without compromising translation quality.
[NLP-18] BTZSC: A Benchmark for Zero-Shot Text Classification Across Cross-Encoders Embedding Models Rerankers and LLM s ICLR2026
【速读】： 该论文旨在解决零样本文本分类（Zero-shot Text Classification, ZSC）中缺乏统一、公平且能真实评估模型零样本能力的基准测试问题。现有评估方法如MTEB常通过监督性探针或微调引入标注数据，导致对真正零样本性能的考察不足。为此，作者提出BTZSC——一个涵盖22个公开数据集的综合性基准，覆盖情感、主题、意图和情绪分类等任务，包含多样化的领域、类别数量和文档长度。其解决方案的关键在于：构建了一个无监督、纯零样本场景下的评测体系，并在此基础上系统比较了四类主流模型家族（NLI交叉编码器、嵌入模型、重排序器与指令微调大语言模型），揭示出现代重排序器（如Qwen3-Reranker-8B）在宏观F1上达到0.72的新SOTA，嵌入模型（如GTE-large-en-v1.5）在准确率与延迟间取得最佳平衡，而NLI交叉编码器性能随模型规模增长趋于饱和，说明模型架构选择与训练范式对零样本效果具有决定性影响。
链接: https://arxiv.org/abs/2603.11991
作者: Ilias Aarab
机构: European Central Bank (欧洲中央银行)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注: Accepted at ICLR 2026. 31 pages, 5 figures, 9 tables. Code: this https URL ; Dataset: this https URL ; Leaderboard: this https URL . Proceedings of the Fourteenth International Conference on Learning Representations (ICLR 2026), 2026
Abstract:Zero-shot text classification (ZSC) offers the promise of eliminating costly task-specific annotation by matching texts directly to human-readable label descriptions. While early approaches have predominantly relied on cross-encoder models fine-tuned for natural language inference (NLI), recent advances in text-embedding models, rerankers, and instruction-tuned large language models (LLMs) have challenged the dominance of NLI-based architectures. Yet, systematically comparing these diverse approaches remains difficult. Existing evaluations, such as MTEB, often incorporate labeled examples through supervised probes or fine-tuning, leaving genuine zero-shot capabilities underexplored. To address this, we introduce BTZSC, a comprehensive benchmark of 22 public datasets spanning sentiment, topic, intent, and emotion classification, capturing diverse domains, class cardinalities, and document lengths. Leveraging BTZSC, we conduct a systematic comparison across four major model families, NLI cross-encoders, embedding models, rerankers and instruction-tuned LLMs, encompassing 38 public and custom checkpoints. Our results show that: (i) modern rerankers, exemplified by Qwen3-Reranker-8B, set a new state-of-the-art with macro F1 = 0.72; (ii) strong embedding models such as GTE-large-en-v1.5 substantially close the accuracy gap while offering the best trade-off between accuracy and latency; (iii) instruction-tuned LLMs at 4–12B parameters achieve competitive performance (macro F1 up to 0.67), excelling particularly on topic classification but trailing specialized rerankers; (iv) NLI cross-encoders plateau even as backbone size increases; and (v) scaling primarily benefits rerankers and LLMs over embedding models. BTZSC and accompanying evaluation code are publicly released to support fair and reproducible progress in zero-shot text understanding.
[NLP-19] CHiL(L)Grader: Calibrated Human-in-the-Loop Short-Answer Grading
【速读】： 该论文旨在解决大规模教育评估中生成式 AI（Generative AI）模型在高风险场景下因过度自信导致的可靠性不足问题，尤其是在课程内容演化和新题型出现时，传统模型难以维持稳定性能。解决方案的关键在于提出 CHiL(L)Grader 框架，其核心创新是将校准后的置信度估计（calibrated confidence estimation）嵌入人机协同工作流中，通过事后温度缩放（post-hoc temperature scaling）、基于置信度的选择性预测（confidence-based selective prediction）以及持续学习机制，实现仅对高置信度答案自动评分，同时将不确定样本路由至人工评审，并能从教师反馈中不断优化评分能力，从而保障自动化评分的准确性与鲁棒性。
链接: https://arxiv.org/abs/2603.11957
作者: Pranav Raikote,Korbinian Randl,Ioanna Miliou,Athanasios Lakes,Panagiotis Papapetrou
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Scaling educational assessment with large language models requires not just accuracy, but the ability to recognize when predictions are trustworthy. Instruction-tuned models tend to be overconfident, and their reliability deteriorates as curricula evolve, making fully autonomous deployment unsafe in high-stakes settings. We introduce CHiL(L)Grader, the first automated grading framework that incorporates calibrated confidence estimation into a human-in-the-loop workflow. Using post-hoc temperature scaling, confidence-based selective prediction, and continual learning, CHiL(L)Grader automates only high-confidence predictions while routing uncertain cases to human graders, and adapts to evolving rubrics and unseen questions. Across three short-answer grading datasets, CHiL(L)Grader automatically scores 35-65% of responses at expert-level quality (QWK = 0.80). A QWK gap of 0.347 between accepted and rejected predictions confirms the effectiveness of the confidence-based routing. Each correction cycle strengthens the model’s grading capability as it learns from teacher feedback. These results show that uncertainty quantification is key for reliable AI-assisted grading.
[NLP-20] PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents EACL2026
【速读】： 该论文旨在解决数字足迹（Digital Footprints）数据稀缺且多样性不足的问题，这一局限性严重制约了行为研究、个性化应用开发及机器学习模型训练的进展。其解决方案的关键在于提出一种基于大语言模型（Large Language Model, LLM）代理的新方法，通过结构化用户画像自动生成多样化且符合现实逻辑的用户事件序列，并进一步合成电子邮件、消息、日历条目等数字痕迹，从而构建高质量的合成数据集。实证结果表明，该方法生成的数据在多样性与真实性上优于现有基线，且基于此数据微调的模型在真实世界分布外任务中表现更优。
链接: https://arxiv.org/abs/2603.11955
作者: Minjia Wang,Yunfeng Wang,Xiao Ma,Dexin Lv,Qifan Guo,Lynn Zheng,Benliang Wang,Lei Wang,Jiannan Li,Yongwei Xing,David Xu,Zheng Sun
机构: Apple(苹果); Harvard University(哈佛大学)
类目: Computation and Language (cs.CL)
备注: EACL 2026 Industry Track
Abstract:Digital footprints (records of individuals’ interactions with digital systems) are essential for studying behavior, developing personalized applications, and training machine learning models. However, research in this area is often hindered by the scarcity of diverse and accessible data. To address this limitation, we propose a novel method for synthesizing realistic digital footprints using large language model (LLM) agents. Starting from a structured user profile, our approach generates diverse and plausible sequences of user events, ultimately producing corresponding digital artifacts such as emails, messages, calendar entries, reminders, etc. Intrinsic evaluation results demonstrate that the generated dataset is more diverse and realistic than existing baselines. Moreover, models fine-tuned on our synthetic data outperform those trained on other synthetic datasets when evaluated on real-world out-of-distribution tasks.
[NLP-21] Resurfacing Paralinguistic Awareness in Large Audio Language Models INTERSPEECH2026
【速读】： 该论文旨在解决当前大型音频语言模型（Large Audio Language Models, LALMs）在交互中忽视副语言线索（paralinguistic cues）的问题，这些线索隐含地反映了用户语境，但现有内容中心范式通常仅基于查询内容进行响应，导致交互缺乏情境感知能力。解决方案的关键在于提出一种增强副语言意识的微调协议（Paralinguistic-Enhanced Fine-Tuning, PE-FT），其核心包括：(1) 选择性层微调（selective-layer fine-tuning），即通过五种分层分析识别出专门处理副语言信息的层与语义理解层；(2) 引入辅助双层分类头（auxiliary dual-level classification head），以显式建模副语言特征并提升模型对非语言语境的理解能力。实验表明，该方法能高效且有效地恢复LALMs的副语言感知能力，甚至优于全层微调策略。
链接: https://arxiv.org/abs/2603.11947
作者: Hao Yang,Minghan Wang,Tongtong Wu,Lizhen Qu,Ehsan Shareghi,Gholamreza Haffari
机构: Monash University (莫纳什大学); University College London (伦敦大学学院)
类目: ound (cs.SD); Computation and Language (cs.CL); Multimedia (cs.MM); Audio and Speech Processing (eess.AS)
备注: Submitted to Interspeech 2026
Abstract:Large Audio Language Models (LALMs) have expanded the interaction with human to speech modality, which introduces great interactive potential, due to the paralinguistic cues implicitly indicating the user context. However, building on the current content-centred paradigm, LALMs usually neglect such paralinguistic cues and respond solely based on query content. In this work, to resurface the paralinguistic awareness in LALMs, we introduce five diverse layer-wise analyses to jointly identify paralinguistic layers and semantic understanding layers. Based on these insights, we propose a paralinguistic-enhanced fine-tuning (PE-FT) protocol accordingly to equip LALMs with paralinguistic-aware capabilities, including (1) selective-layer fine-tuning, and (2) an auxiliary dual-level classification head. Our experiments demonstrate that PE-FT protocol efficiently and effectively resurfaces the paralinguistic awareness, even surpassing the performance of the all-layer fine-tuning strategy.
[NLP-22] Chem4DLLM : 4D Multimodal LLM s for Chemical Dynamics Understanding
【速读】： 该论文旨在解决传统化学理解任务依赖静态分子表示、难以建模键断裂或构象变化等动态现象的问题，从而限制了对化学反应本质的理解。其核心解决方案是提出化学动力学理解（Chemical Dynamics Understanding, ChemDU）这一新任务，将四维（4D）分子轨迹转化为可解释的自然语言说明，并构建了首个配对数据集Chem4DBench，用于基准测试模型在气相和催化反应等场景下的动态事件推理能力。关键创新在于设计了Chem4DLLM统一模型，融合等变图编码器与预训练大语言模型（Large Language Model, LLM），显式捕捉分子几何结构和旋转动力学，实现从动态轨迹到机制性叙事的跨模态推理。
链接: https://arxiv.org/abs/2603.11924
作者: Xinyu Li,Zhen Zhang,Qi Chen,Anton van den Hengel,Lina Yao,Javen Qinfeng Shi
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 18 pages
Abstract:Existing chemical understanding tasks primarily rely on static molecular representations, limiting their ability to model inherently dynamic phenomena such as bond breaking or conformational changes, which are essential for a chemist to understand chemical reactions. To address this gap, we introduce Chemical Dynamics Understanding (ChemDU), a new task that translates 4D molecular trajectories into interpretable natural-language explanations. ChemDU focuses on fundamental dynamic scenarios, including gas-phase and catalytic reactions, and requires models to reason about key events along molecular trajectories, such as bond formation and dissociation, and to generate coherent, mechanistically grounded narratives. To benchmark this capability, we construct Chem4DBench, the first dataset pairing 4D molecular trajectories with expert-authored explanations across these settings. We further propose Chem4DLLM, a unified model that integrates an equivariant graph encoder with a pretrained large language model to explicitly capture molecular geometry and rotational dynamics. We hope that ChemDU, together with Chem4DBench and Chem4DLLM, will stimulate further research in dynamic chemical understanding and multimodal scientific reasoning.
[NLP-23] CoMMET: To What Extent Can LLM s Perform Theory of Mind Tasks?
【速读】： 该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在理论心理（Theory of Mind, ToM）能力评估中存在的局限性问题，即现有基准测试多依赖单一文本输入且仅聚焦于信念类任务，难以全面衡量模型对人类复杂社会认知的理解。其解决方案的关键在于提出首个多模态、多轮对话场景下的ToM评估数据集CoMMET（Comprehensive Mental states and Moral Evaluation Task），该数据集基于理论心理手册任务（Theory of Mind Booklet Task）设计，扩展了评估范围以涵盖更广泛的内心状态（如欲望、意图、情绪等），并引入多轮交互机制，从而更真实地模拟人类社会推理过程。这一创新使得对LLMs社会认知能力的评测更加系统化和贴近实际应用场景。
链接: https://arxiv.org/abs/2603.11915
作者: Ruirui Chen,Weifeng Jiang,Chengwei Qin,Cheston Tan
机构: Institute of High Performance Computing (IHPC); Centre for Frontier AI Research (CFAR); Agency for Science, Technology and Research (A*STAR); Nanyang Technological University; Hong Kong University of Science and Technology (Guangzhou)
类目: Computation and Language (cs.CL)
备注:
Abstract:Theory of Mind (ToM)-the ability to reason about the mental states of oneself and others-is a cornerstone of human social intelligence. As Large Language Models (LLMs) become ubiquitous in real-world applications, validating their capacity for this level of social reasoning is essential for effective and natural interactions. However, existing benchmarks for assessing ToM in LLMs are limited; most rely solely on text inputs and focus narrowly on belief-related tasks. In this paper, we propose a new multimodal benchmark dataset, CoMMET, a Comprehensive Mental states and Moral Evaluation Task inspired by the Theory of Mind Booklet Task. CoMMET expands the scope of evaluation by covering a broader range of mental states and introducing multi-turn testing. To the best of our knowledge, this is the first multimodal dataset to evaluate ToM in a multi-turn conversational setting. Through a comprehensive assessment of LLMs across different families and sizes, we analyze the strengths and limitations of current models and identify directions for future improvement. Our work offers a deeper understanding of the social cognitive capabilities of modern LLMs.
[NLP-24] hink While Watching: Online Streaming Segment-Level Memory for Multi-Turn Video Reasoning in Multimodal Large Language Models
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在连续视频流在线推理中的局限性，特别是现有方法因采用交错式感知-生成范式而导致感知与生成无法并发、早期记忆衰减以及长程依赖建模能力弱的问题。解决方案的关键在于提出“边看边想”（Think While Watching）框架，通过锚定记忆的机制维持多轮交互中连续片段级别的记忆状态，并设计三阶段多轮思维链数据集与阶段匹配训练策略；同时引入片段级流式因果掩码（segment-level streaming causal mask）和流式位置编码（streaming positional encoding）以严格保证因果性，在推理阶段构建重叠观看与思考的高效流水线并自适应选择最优注意力后端，从而显著提升单轮和多轮流式输入下的性能表现，同时大幅减少输出token数量。
链接: https://arxiv.org/abs/2603.11896
作者: Lu Wang(1),Zhuoran Jin(1),Yupu Hao(1),Yubo Chen(1),Kang Liu(1),Yulong Ao(2),Jun Zhao(1) ((1) The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China, (2) Beijing Academy of Artificial Intelligence (BAAI), Beijing, China)
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Multimodal large language models (MLLMs) have shown strong performance on offline video understanding, but most are limited to offline inference or have weak online reasoning, making multi-turn interaction over continuously arriving video streams difficult. Existing streaming methods typically use an interleaved perception-generation paradigm, which prevents concurrent perception and generation and leads to early memory decay as streams grow, hurting long-range dependency modeling. We propose Think While Watching, a memory-anchored streaming video reasoning framework that preserves continuous segment-level memory during multi-turn interaction. We build a three-stage, multi-round chain-of-thought dataset and adopt a stage-matched training strategy, while enforcing strict causality through a segment-level streaming causal mask and streaming positional encoding. During inference, we introduce an efficient pipeline that overlaps watching and thinking and adaptively selects the best attention backend. Under both single-round and multi-round streaming input protocols, our method achieves strong results. Built on Qwen3-VL, it improves single-round accuracy by 2.6% on StreamingBench and by 3.79% on OVO-Bench. In the multi-round setting, it maintains performance while reducing output tokens by 56%. Code is available at: this https URL
[NLP-25] Bielik-Minitron-7B: Compressing Large Language Models via Structured Pruning and Knowledge Distillation for the Polish Language
【速读】： 该论文旨在解决为欧洲语言构建高效且高性能语言模型的挑战，特别是在资源受限场景下如何在保持模型质量的同时降低推理成本。其核心问题是如何在不显著牺牲性能的前提下压缩大型语言模型（LLM）的参数规模并提升部署效率。解决方案的关键在于采用两阶段压缩策略：第一阶段通过结构化混合剪枝（structured hybrid pruning）结合NVIDIA Model Optimizer实现参数量缩减；第二阶段利用基于logit的知识蒸馏（knowledge distillation）恢复模型性能，并进一步通过监督微调（SFT）、直接偏好优化（DPO-P）和广义奖励策略优化（GRPO）进行对齐与质量提升。最终实现了从11.04B到7.35B的参数压缩（减少33.4%），同时恢复约90%原始性能并获得最高达50%的推理加速，为低资源语言提供了高效的模型部署路径。
链接: https://arxiv.org/abs/2603.11881
作者: Remigiusz Kinas,Paweł Kiszczak,Sergio P. Perez,Krzysztof Ociepa,Łukasz Flis,Krzysztof Wróbel,Adrian Gwoździej
机构: Bielik.AI(贝利克人工智能); Vstorm; Azurro.pl; ACK Cyfronet AGH; Jagiellonian University; NVIDIA
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:This report details the creation of Bielik-Minitron-7B, a compressed 7.35B parameter version of the Bielik-11B-v3.0 model, specifically optimized for European languages. By leveraging a two-stage compression methodology inspired by the NVIDIA Minitron approach, we combined structured hybrid pruning and knowledge distillation to reduce the model’s parameter count by 33.4%, from 11.04B to 7.35B. We utilized the NVIDIA Model Optimizer for structural pruning and the NVIDIA NeMo Framework for logit-based distillation for quality recovery. Following distillation, the model underwent a rigorous alignment pipeline consisting of Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO-P), and Reinforcement Learning (GRPO). Our final model successfully recovered approximately 90% of the baseline model’s performance while providing up to 50% inference speedup. This approach demonstrates an efficient pathway to create language models for less-represented languages, preserving the original model quality while reducing inference deployment costs.
[NLP-26] DatedGPT : Preventing Lookahead Bias in Large Language Models with Time-Aware Pretraining
【速读】： 该论文旨在解决金融回测中因大语言模型（Large Language Models, LLMs）在互联网规模数据上预训练而引入的“前瞻偏差”（lookahead bias）问题，这种偏差会削弱模型预测的有效性，因为模型可能在训练阶段已接触未来真实结果。解决方案的关键在于构建一个名为 DatedGPT 的系列模型家族，其核心创新是使用严格按年划分的时间边界数据进行从头训练（约1000亿token），每款模型仅基于截至特定年份的数据（2013–2024），并通过指令微调进一步强化其对金融领域知识的适配性，同时确保所有训练与微调数据均不包含未来信息。实验表明，该方法有效限制了模型的知识范围，且性能优于或相当同类规模模型。
链接: https://arxiv.org/abs/2603.11838
作者: Yutong Yan,Raphael Tang,Zhenyu Gao,Wenxi Jiang,Yao Lu
机构: The Chinese University of Hong Kong (香港中文大学); University College London (伦敦大学学院)
类目: Computation and Language (cs.CL); General Finance (q-fin.GN)
备注:
Abstract:In financial backtesting, large language models pretrained on internet-scale data risk introducing lookahead bias that undermines their forecasting validity, as they may have already seen the true outcome during training. To address this, we present DatedGPT, a family of twelve 1.3B-parameter language models, each trained from scratch on approximately 100 billion tokens of temporally partitioned data with strict annual cutoffs spanning 2013 to 2024. We further enhance each model with instruction fine-tuning on both general-domain and finance-specific datasets curated to respect the same temporal boundaries. Perplexity-based probing confirms that each model’s knowledge is effectively bounded by its data cutoff year, while evaluation on standard benchmarks shows competitive performance with existing models of similar scale. We provide an interactive web demo that allows users to query and compare responses from models across different cutoff years.
[NLP-27] Large Language Models for Biomedical Article Classification
【速读】： 该论文旨在解决如何利用大语言模型（Large Language Models, LLMs）作为文本分类器在生物医学文献分类任务中的有效性问题，尤其是在非平凡领域中实现高精度与实用性。其关键解决方案在于系统性地评估多种提示工程策略（prompting strategies）、输出处理方法（用于生成类别及类别概率预测），以及少样本示例数量和选择方式，并发现使用输出token概率进行类别概率估计是提升性能的核心技术手段，从而使得零样本和少样本场景下的平均PR AUC分别达到0.4和接近0.5，逼近传统分类算法（如朴素贝叶斯、随机森林）及微调Transformer模型的性能表现。
链接: https://arxiv.org/abs/2603.11780
作者: Jakub Proboszcz,Paweł Cichosz
机构: 未知
类目: Computation and Language (cs.CL)
备注: 63 pages, 25 tables, 4 figures
Abstract:This work presents a systematic and in-depth investigation of the utility of large language models as text classifiers for biomedical article classification. The study uses several small and mid-size open source models, as well as selected closed source ones, and is more comprehensive than most prior work with respect to the scope of evaluated configurations: different types of prompts, output processing methods for generating both class and class probability predictions, as well as few-shot example counts and selection methods. The performance of the most successful configurations is compared to that of conventional classification algorithms. The obtained average PR AUC over 15 challenging datasets above 0.4 for zero-shot prompting and nearly 0.5 for few-shot prompting comes close to that of the naïve Bayes classifier (0.5), the random forest algorithm (0.5 with default settings or 0.55 with hyperparameter tuning) and fine-tuned transformer models (0.5). These results confirm the utility of large language models as text classifiers for non-trivial domains and provide practical recommendations of the most promising setups, including in particular using output token probabilities for class probability prediction.
[NLP-28] rust Oriented Explainable AI for Fake News Detection
【速读】： 该论文旨在解决虚假新闻检测模型缺乏透明度和可解释性的问题，从而影响其在实际应用中的可信度与可靠性。解决方案的关键在于将可解释人工智能（Explainable Artificial Intelligence, XAI）技术引入自然语言处理（Natural Language Processing, NLP）驱动的虚假新闻检测系统中，通过SHAP、LIME和集成梯度（Integrated Gradients）等方法对神经网络模型的决策过程进行解释，从而提升模型的可理解性和信任度，同时保持高检测准确率。
链接: https://arxiv.org/abs/2603.11778
作者: Krzysztof Siwek,Daniel Stankowski,Maciej Stodolski
机构: 未知
类目: Computation and Language (cs.CL)
备注: 9 pages, 4 figures, 2 tables
Abstract:This article examines the application of Explainable Artificial Intelligence (XAI) in NLP based fake news detection and compares selected interpretability methods. The work outlines key aspects of disinformation, neural network architectures, and XAI techniques, with a focus on SHAP, LIME, and Integrated Gradients. In the experimental study, classification models were implemented and interpreted using these methods. The results show that XAI enhances model transparency and interpretability while maintaining high detection accuracy. Each method provides distinct explanatory value: SHAP offers detailed local attributions, LIME provides simple and intuitive explanations, and Integrated Gradients performs efficiently with convolutional models. The study also highlights limitations such as computational cost and sensitivity to parameterization. Overall, the findings demonstrate that integrating XAI with NLP is an effective approach to improving the reliability and trustworthiness of fake news detection systems.
[NLP-29] Legal-DC: Benchmarking Retrieval-Augmented Generation for Legal Documents
【速读】： 该论文旨在解决生成式 AI (Generative AI) 在中文法律场景下应用时面临的两大核心问题：一是现有评估基准缺乏对检索器-生成器协同性能的专门支持，二是主流检索增强生成（Retrieval-Augmented Generation, RAG）系统难以有效处理法律条文的结构化特性。其解决方案的关键在于提出 LegRAG 框架，该框架通过引入法律自适应索引（clause-boundary segmentation）确保条款完整性，并结合双路径自我反思机制提升答案准确性；同时构建了 Legal-DC 基准数据集，包含 480 篇法律文档和 2,475 个带条款级标注的问答对，为中文法律 RAG 提供高可靠性的自动化评估方法与实证基础。
链接: https://arxiv.org/abs/2603.11772
作者: Yaocong Li,Qiang Lan,Leihan Zhang,Le Zhang
机构: 未知
类目: Computation and Language (cs.CL)
备注: 20 pages, 4 figures, to be submitted to a conference/journal
Abstract:Retrieval-Augmented Generation (RAG) has emerged as a promising technology for legal document consultation, yet its application in Chinese legal scenarios faces two key limitations: existing benchmarks lack specialized support for joint retriever-generator evaluation, and mainstream RAG systems often fail to accommodate the structured nature of legal provisions. To address these gaps, this study advances two core contributions: First, we constructed the Legal-DC benchmark dataset, comprising 480 legal documents (covering areas such as market regulation and contract management) and 2,475 refined question-answer pairs, each annotated with clause-level references, filling the gap for specialized evaluation resources in Chinese legal RAG. Second, we propose the LegRAG framework, which integrates legal adaptive indexing (clause-boundary segmentation) with a dual-path self-reflection mechanism to ensure clause integrity while enhancing answer accuracy. Third, we introduce automated evaluation methods for large language models to meet the high-reliability demands of legal retrieval scenarios. LegRAG outperforms existing state-of-the-art methods by 1.3% to 5.6% across key evaluation metrics. This research provides a specialized benchmark, practical framework, and empirical insights to advance the development of Chinese legal RAG systems. Our code and data are available at this https URL.
[NLP-30] An Automatic Text Classification Method Based on Hierarchical Taxonomies Neural Networks and Document Embedding: The NETHIC Tool
【速读】： 该论文旨在解决文本分类任务中效率与准确性难以兼顾的问题，尤其是在处理大规模数据和复杂类别结构时。其解决方案的关键在于构建一个名为NETHIC的自动化文本分类工具，该工具融合了高可扩展性神经网络的内在能力与分层分类体系（hierarchical taxonomy）的表达优势，通过引入文档嵌入（document embedding）机制进一步优化了单个网络及整体层级模型的性能表现。
链接: https://arxiv.org/abs/2603.11770
作者: Luigi Lomasto,Rosario Di Florio,Andrea Ciapetti,Giuseppe Miscione,Giulia Ruggiero,Daniele Toti
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: ICEIS 2019 Conference
Abstract:This work describes an automatic text classification method implemented in a software tool called NETHIC, which takes advantage of the inner capabilities of highly-scalable neural networks combined with the expressiveness of hierarchical taxonomies. As such, NETHIC succeeds in bringing about a mechanism for text classification that proves to be significantly effective as well as efficient. The tool had undergone an experimentation process against both a generic and a domain-specific corpus, outputting promising results. On the basis of this experimentation, NETHIC has been now further refined and extended by adding a document embedding mechanism, which has shown improvements in terms of performance on the individual networks and on the whole hierarchical model.
[NLP-31] Compression Favors Consistency Not Truth: When and Why Language Models Prefer Correct Information
【速读】： 该论文旨在解决语言模型在训练数据混杂质量（包含正确与错误规则）时为何仍倾向于生成正确陈述的问题。其核心解决方案是提出“压缩-一致性原则”（Compression–Consistency Principle），指出下一个词预测任务本质上偏好那些能以更短且内部一致的方式描述训练数据的假设。关键在于，这种“真理性偏倚”并非源于对真理的内在追求，而是压缩压力和结构一致性约束的结果：当错误选项在语法或结构上难以压缩时，模型自然更倾向选择正确的输出。实验通过可控合成数学语料验证了该机制，表明在随机错误场景下模型表现出显著的正确偏好（如83.1%准确率），而当错误规则具有结构性一致性时，该偏好消失，进一步支持压缩与一致性作为驱动因素的核心作用。
链接: https://arxiv.org/abs/2603.11749
作者: Konstantin Krestnikov
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: v1: initial release. Full code, synthetic datasets and experiments available at this https URL This work was done independently
Abstract:Why do language models sometimes prefer correct statements even when trained on mixed-quality data? We introduce the Compression–Consistency Principle: next-token prediction favors hypotheses that allow shorter and more internally consistent descriptions of the training data. Truth bias emerges only when false alternatives are structurally harder to compress. We test this using small GPT-2-style character-level transformers (3.5M–86M parameters) on synthetic math corpora with controlled mixtures of correct and incorrect rules. In the random-error setting, models strongly prefer correct completions in paired evaluation: 83.1% accuracy at balanced data and 67.0% even when correct rules appear in only 10% of the corpus. Replacing random errors with a coherent but mathematically incorrect rule system largely eliminates the preference (near-chance accuracy). In a more natural-language-like synthetic world, the effect is weaker but still present (57.7%). Additional experiments show that embedding verification steps can restore preference for correctness even at small scale, while increasing the number of consistent rules produces a graded improvement in accuracy. Our results suggest that what appears as a “truth bias” is largely a side effect of compression pressure and preference for internal consistency, rather than an intrinsic drive toward truth. Full code and data are available at this https URL.
[NLP-32] Semi-Synthetic Parallel Data for Translation Quality Estimation: A Case Study of Dataset Building for an Under-Resourced Language Pair
【速读】： 该论文旨在解决低资源语言对（特别是英语到希伯来语）在机器翻译质量评估（Quality Estimation, QE）中面临的准确率、适应性和可靠性不足的问题，其核心挑战源于平行语料库稀缺以及语言特异性因素（如形态句法复杂性）。解决方案的关键在于构建一个半合成的平行数据集：首先基于典型语言模式生成英文句子，通过多个机器翻译引擎译为希伯来语，并利用BLEU分数筛选；随后由语言学家人工标注每个译文的质量得分，同时引入专业翻译的高质量语段作为参考；此外，还主动引入控制性翻译错误（如性别和数的一致性问题）以增强模型对特定语言难点的识别能力。在此基础上训练BERT和XLM-R等神经QE模型，实验表明数据集规模、分布平衡及错误分布对模型性能具有显著影响。
链接: https://arxiv.org/abs/2603.11743
作者: Assaf Siani,Anna Kernerman,Ilan Kernerman
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Quality estimation (QE) plays a crucial role in machine translation (MT) workflows, as it serves to evaluate generated outputs that have no reference translations and to determine whether human post-editing or full retranslation is necessary. Yet, developing highly accurate, adaptable and reliable QE systems for under-resourced language pairs remains largely unsolved, due mainly to limited parallel corpora and to diverse language-dependent factors, such as with morphosyntactically complex languages. This study presents a semi-synthetic parallel dataset for English-to-Hebrew QE, generated by creating English sentences based on examples of usage that illustrate typical linguistic patterns, translating them to Hebrew using multiple MT engines, and filtering outputs via BLEU-based selection. Each translated segment was manually evaluated and scored by a linguist, and we also incorporated professionally translated English-Hebrew segments from our own resources, which were assigned the highest quality score. Controlled translation errors were introduced to address linguistic challenges, particularly regarding gender and number agreement, and we trained neural QE models, including BERT and XLM-R, on this dataset to assess sentence-level MT quality. Our findings highlight the impact of dataset size, distributed balance, and error distribution on model performance. We will describe the challenges, methodology and results of our experiments, and specify future directions aimed at improving QE performance. This research contributes to advancing QE models for under resourced language pairs, including morphology-rich languages.
[NLP-33] OSCBench: Benchmarking Object State Change in Text-to-Video Generation
【速读】： 该论文旨在解决当前文本到视频（Text-to-Video, T2V）生成模型在对象状态变化（Object State Change, OSC）理解与生成上的不足问题。现有基准主要关注感知质量、文本-视频对齐或物理合理性，但未充分评估模型是否能准确捕捉并生成由动作引发的对象状态转变，如“削土豆”或“切柠檬”等明确指定的OSC。解决方案的关键在于提出OSCBench——一个基于烹饪指令数据构建的新型基准，系统性地将动作-对象交互划分为常规、新颖和组合场景，以诊断T2V模型在分布内性能及泛化能力上的表现，并通过人工评估与多模态大语言模型（Multimodal Large Language Model, MLLM）自动评估相结合的方式，揭示当前主流T2V模型在OSC任务中存在显著缺陷，从而为推进具备状态意识的视频生成模型提供关键诊断工具。
链接: https://arxiv.org/abs/2603.11698
作者: Xianjing Han,Bin Zhu,Shiqi Hu,Franklin Mingzhe Li,Patrick Carrington,Roger Zimmermann,Jingjing Chen
机构: National University of Singapore (新加坡国立大学); Singapore Management University (新加坡管理大学); Carnegie Mellon University (卡内基梅隆大学); Fudan University (复旦大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Project page: this https URL
Abstract:Text-to-video (T2V) generation models have made rapid progress in producing visually high-quality and temporally coherent videos. However, existing benchmarks primarily focus on perceptual quality, text-video alignment, or physical plausibility, leaving a critical aspect of action understanding largely unexplored: object state change (OSC) explicitly specified in the text prompt. OSC refers to the transformation of an object’s state induced by an action, such as peeling a potato or slicing a lemon. In this paper, we introduce OSCBench, a benchmark specifically designed to assess OSC performance in T2V models. OSCBench is constructed from instructional cooking data and systematically organizes action-object interactions into regular, novel, and compositional scenarios to probe both in-distribution performance and generalization. We evaluate six representative open-source and proprietary T2V models using both human user study and multimodal large language model (MLLM)-based automatic evaluation. Our results show that, despite strong performance on semantic and scene alignment, current T2V models consistently struggle with accurate and temporally consistent object state changes, especially in novel and compositional settings. These findings position OSC as a key bottleneck in text-to-video generation and establish OSCBench as a diagnostic benchmark for advancing state-aware video generation models.
[NLP-34] SemBench: A Universal Semantic Framework for LLM Evaluation LREC2026
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在跨语言环境下评估其语义理解能力时面临的挑战，尤其是传统基准测试（如Word-in-Context, WiC）存在资源密集、依赖人工标注数据且难以扩展至低资源语言的问题。解决方案的关键在于提出SemBench框架，该框架仅基于词典释义（dictionary sense definitions）和句子编码器（sentence encoder）即可自动构建合成基准测试，无需人工编写示例句，从而实现高效、可扩展且语言无关的语义能力评估。实验证明，该方法在英语、西班牙语和巴斯克语中的排名与标准WiC数据集高度一致，且少量样本即可获得稳定可靠的评估结果。
链接: https://arxiv.org/abs/2603.11687
作者: Mikel Zubillaga,Naiara Perez,Oscar Sainz,German Rigau
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted at LREC 2026
Abstract:Recent progress in Natural Language Processing (NLP) has been driven by the emergence of Large Language Models (LLMs), which exhibit remarkable generative and reasoning capabilities. However, despite their success, evaluating the true semantic understanding of these models remains a persistent challenge. Traditional benchmarks such as Word-in-Context (WiC) effectively probe this capability, but their creation is resource-intensive and often limited to high-resource languages. In this paper, we introduce SemBench, a framework for automatically generating synthetic benchmarks that assess the semantic competence of LLMs using only dictionary sense definitions and a sentence encoder. This approach eliminates the need for curated example sentences, making it both scalable and language-independent. We evaluate SemBench in three languages (English, Spanish, and Basque) spanning different levels of linguistic resources, and across a wide range of LLMs. Our results show that rankings derived from SemBench strongly correlate with those obtained from standard WiC datasets. Furthermore, our analysis demonstrates that only a small number of examples is required to achieve stable and meaningful rankings. Overall, SemBench provides a lightweight, adaptable, and data-efficient framework for cross-lingual evaluation of semantic understanding in LLMs.
[NLP-35] In the LLM era Word Sense Induction remains unsolved ACL2025
【速读】： 该论文旨在解决无标注语义注释数据条件下词义诱导（Word Sense Induction, WSI）的评估与方法优化问题，尤其针对低资源或领域特定场景下的应用挑战。其核心贡献在于提出一种基于SemCor衍生数据集的评估框架，严格保留原始语料库中的多义性和频次分布特性，从而更真实地衡量WSI方法的有效性；关键解决方案包括：(1) 对预训练嵌入与聚类算法按词性（Part-of-Speech, POS）进行系统评估，揭示不同词性的性能差异；(2) 提出并验证基于大语言模型（Large Language Models, LLMs）的WSI方法，发现LLMs在该任务上表现不佳；(3) 探索数据增强策略（LLM生成、语料库和词典来源），并引入半监督场景下利用Wiktionary作为约束源（如must-link约束和聚类数量控制），显著提升性能（相较之前最先进系统提升3.3%）。研究指出当前无监督WSI方法仍无法超越“每词形一个聚类”（one cluster per lemma, 1cpl）这一强基线，强调未来需更好地融合词典结构与LLMs的词汇语义能力以推动该领域进展。
链接: https://arxiv.org/abs/2603.11686
作者: Anna Mosolova,Marie Candito,Carlos Ramisch
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted at ACL 2025 (Findings)
Abstract:In the absence of sense-annotated data, word sense induction (WSI) is a compelling alternative to word sense disambiguation, particularly in low-resource or domain-specific settings. In this paper, we emphasize methodological problems in current WSI evaluation. We propose an evaluation on a SemCor-derived dataset, respecting the original corpus polysemy and frequency distributions. We assess pre-trained embeddings and clustering algorithms across parts of speech, and propose and evaluate an LLM-based WSI method for English. We evaluate data augmentation sources (LLM-generated, corpus and lexicon), and semi-supervised scenarios using Wiktionary for data augmentation, must-link constraints, number of clusters per lemma. We find that no unsupervised method (whether ours or previous) surpasses the strong “one cluster per lemma” heuristic (1cpl). We also show that (i) results and best systems may vary across POS, (ii) LLMs have troubles performing this task, (iii) data augmentation is beneficial and (iv) capitalizing on Wiktionary does help. It surpasses previous SOTA system on our test set by 3.3%. WSI is not solved, and calls for a better articulation of lexicons and LLMs’ lexical semantics capabilities. Comments: Accepted at ACL 2025 (Findings) Subjects: Computation and Language (cs.CL) Cite as: arXiv:2603.11686 [cs.CL] (or arXiv:2603.11686v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2603.11686 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-36] Multi-Task Reinforcement Learning for Enhanced Multimodal LLM -as-a-Judge
【速读】： 该论文旨在解决当前多模态大语言模型（Multimodal Large Language Models, MLLMs）作为评判者（MLLM-as-a-Judge）时，在跨任务场景下泛化能力不足的问题。现有判别模型通常针对单一任务优化，难以适应多样化的评估情境，从而影响评价的可靠性。解决方案的关键在于提出一种多任务强化学习框架（Multi-Task Reinforcement Learning for MLLM-as-a-Judge, MT-RL-Judge），通过联合优化多个视觉任务中的判别能力，利用强化学习（Reinforcement Learning, RL）的泛化特性提升模型在不同任务间的迁移性能，从而实现更一致且与人类偏好高度相关的判断结果。
链接: https://arxiv.org/abs/2603.11665
作者: Junjie Wu,Xuan Kan,Zihao He,Shunwen Tan,Bo Pan,Kaitai Zhang
机构: Meta(元)
类目: Computation and Language (cs.CL)
备注:
Abstract:Multimodal Large Language Models (MLLMs) have been widely adopted as MLLM-as-a-Judges due to their strong alignment with human judgment across various visual tasks. However, most existing judge models are optimized for single-task scenarios and struggle to generalize to diverse contexts, which is a critical requirement for reliable evaluation. To address this limitation, we propose Multi-Task Reinforcement Learning for MLLM-as-a-Judge (MT-RL-Judge), a framework that jointly optimizes the judge model across multiple tasks, leveraging the generalization capabilities of RL. Experimental results against several strong baselines demonstrate that MT-RL-Judge outperforms strong baselines in both judgment consistency and correlation with human preferences. Furthermore, our approach exhibits robust generalization on out-of-distribution tasks, further validating its effectiveness.
[NLP-37] QChunker: Learning Question-Aware Text Chunking for Domain RAG via Multi-Agent Debate
【速读】： 该论文旨在解决检索增强生成（Retrieval-Augmented Generation, RAG）系统中因知识库文本块（text chunks）语义完整性不足和信息粒度不精细而导致的性能上限问题。其解决方案的关键在于提出QChunker框架，将传统RAG的“检索-增强”范式重构为“理解-检索-增强”，通过多智能体辩论机制实现文本分块的逻辑连贯性与信息完整性：该机制包含问题大纲生成器、文本分割器、完整性审查者和知识补全器四个组件，以问题驱动深度理解；同时引入新型直接评估指标ChunkScore，用于高效区分文本块质量，并结合文档大纲进行多路径采样与最优选择，从而显著提升RAG所依赖文本块的质量与一致性。
链接: https://arxiv.org/abs/2603.11650
作者: Jihao Zhao,Daixuan Li,Pengfei Li,Shuaishuai Zu,Biao Qin,Hongyan Liu
机构: Renmin University of China (中国人民大学); Tsinghua University (清华大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:The effectiveness upper bound of retrieval-augmented generation (RAG) is fundamentally constrained by the semantic integrity and information granularity of text chunks in its knowledge base. To address these challenges, this paper proposes QChunker, which restructures the RAG paradigm from retrieval-augmentation to understanding-retrieval-augmentation. Firstly, QChunker models the text chunking as a composite task of text segmentation and knowledge completion to ensure the logical coherence and integrity of text chunks. Drawing inspiration from Hal Gregersen’s “Questions Are the Answer” theory, we design a multi-agent debate framework comprising four specialized components: a question outline generator, text segmenter, integrity reviewer, and knowledge completer. This framework operates on the principle that questions serve as catalysts for profound insights. Through this pipeline, we successfully construct a high-quality dataset of 45K entries and transfer this capability to small language models. Additionally, to handle long evaluation chains and low efficiency in existing chunking evaluation methods, which overly rely on downstream QA tasks, we introduce a novel direct evaluation metric, ChunkScore. Both theoretical and experimental validations demonstrate that ChunkScore can directly and efficiently discriminate the quality of text chunks. Furthermore, during the text segmentation phase, we utilize document outlines for multi-path sampling to generate multiple candidate chunks and select the optimal solution employing ChunkScore. Extensive experimental results across four heterogeneous domains exhibit that QChunker effectively resolves aforementioned issues by providing RAG with more logically coherent and information-rich text chunks.
[NLP-38] Fractional Rotation Full Potential? Investigating Performance and Convergence of Partial RoPE
【速读】： 该论文旨在解决Transformer架构中旋转位置编码（Rotary Positional Embedding, RoPE）带来的内存开销问题，尤其是在长序列场景下，标准RoPE缓存占用大量显存，限制了模型效率。其解决方案的关键在于系统性地探索部分RoPE（Partial RoPE）——即仅对隐藏维度中的小部分应用旋转变换——对训练动态与收敛性的影响。研究发现，仅使用约10%的维度进行RoPE即可实现与全维度RoPE相当的收敛性能，且该结论在不同模型规模、序列长度和数据质量下均稳定成立，从而为高效部署提供了一种可行策略：通过适度减少RoPE维度，在保证训练稳定性的同时实现最高达10倍的内存节省。
链接: https://arxiv.org/abs/2603.11611
作者: Mohammad Aflah Khan,Krishna P. Gummadi,Manish Gupta,Abhilasha Ravichander
机构: Max Planck Institute for Software Systems (马普软件系统研究所); Microsoft, Hyderabad (微软海得拉巴)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Rotary Positional Embedding (RoPE) is a common choice in transformer architectures for encoding relative positional information. Although earlier work has examined omitting RoPE in specific layers, the effect of varying the fraction of hidden dimensions that receive rotary transformations remains largely unexplored. This design choice can yield substantial memory savings, which becomes especially significant at long context lengths. We find up to 10x memory savings over the standard RoPE cache, while achieving comparable final loss. In this work, we present a systematic study examining the impact of partial RoPE on training dynamics and convergence across architectures and datasets. Our findings uncover several notable patterns: (1) applying RoPE to only a small fraction of dimensions (around 10%) achieves convergence comparable to using full RoPE; (2) these trends hold consistently across model size, sequence lengths and datasets of varying quality and architectures, with higher-quality data resulting in lower overall loss and similar benchmark performance; and (3) some models trained with NoPE (No Positional Encoding) showcase unstable learning trajectories, which can be alleviated through minimal RoPE application or QK-Norm which converges to a higher loss. Together, these results offer practical guidance for model designers aiming to balance efficiency and training stability, while emphasizing the previously overlooked importance of partial RoPE.
[NLP-39] Performance Evaluation of Open-Source Large Language Models for Assisting Pathology Report Writing in Japanese
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在支持日语病理报告撰写中的性能尚不明确的问题。研究通过三个维度评估了七种开源LLMs：（A）按预定义格式生成和提取病理诊断文本的能力，（B）修正日语病理报告中拼写错误的能力，以及（C）由病理科医生和临床医师对模型生成解释性文本进行主观评价。关键发现在于，推理能力强的思维类模型和医学专用模型在结构化报告任务和错别字修正方面表现更优；而解释性文本的偏好则因评价者不同而存在显著差异。这表明，尽管LLMs在特定临床相关场景下具备辅助能力，其应用需根据具体任务类型进行针对性选择与优化。
链接: https://arxiv.org/abs/2603.11597
作者: Masataka Kawai,Singo Sakashita,Shumpei Ishikawa,Shogo Watanabe,Anna Matsuoka,Mikio Sakurai,Yasuto Fujimoto,Yoshiyuki Takahara,Atsushi Ohara,Hirohiko Miyake,Genichiro Ishii
机构: University of Yamanashi (山梨大学); National Cancer Center (国立癌症研究中心); The University of Tokyo (东京大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 9 pages (including bibliography), 2 figures, 6 tables
Abstract:The performance of large language models (LLMs) for supporting pathology report writing in Japanese remains unexplored. We evaluated seven open-source LLMs from three perspectives: (A) generation and information extraction of pathology diagnosis text following predefined formats, (B) correction of typographical errors in Japanese pathology reports, and © subjective evaluation of model-generated explanatory text by pathologists and clinicians. Thinking models and medical-specialized models showed advantages in structured reporting tasks that required reasoning and in typo correction. In contrast, preferences for explanatory outputs varied substantially across raters. Although the utility of LLMs differed by task, our findings suggest that open-source LLMs can be useful for assisting Japanese pathology report writing in limited but clinically relevant scenarios.
[NLP-40] UtilityMax Prompting: A Formal Framework for Multi-Objective Large Language Model Optimization
【速读】： 该论文旨在解决大型语言模型（Large Language Model, LLM）在多目标任务中因自然语言提示（natural language prompt）固有模糊性而导致性能不稳定的问题。其核心挑战在于，当多个优化目标需同时满足时，LLM容易基于主观理解生成偏离预期的输出。解决方案的关键在于提出一种名为UtilityMax Prompting的新框架，该框架将任务重构为一个影响图（influence diagram），其中LLM的输出是唯一的决策变量，并通过定义在条件概率分布上的效用函数（utility function）来显式约束模型推理过程，使其直接优化期望效用而非依赖模糊的自然语言描述。实验表明，该方法在MovieLens 1M数据集上对三个前沿模型（Claude Sonnet 4.6、GPT-5.4 和 Gemini 2.5 Pro）均实现了精度和归一化折损累计增益（NDCG）的显著提升。
链接: https://arxiv.org/abs/2603.11583
作者: Ofir Marom
机构: Independent Researcher (独立研究员)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:The success of a Large Language Model (LLM) task depends heavily on its prompt. Most use-cases specify prompts using natural language, which is inherently ambiguous when multiple objectives must be simultaneously satisfied. In this paper we introduce UtilityMax Prompting, a framework that specifies tasks using formal mathematical language. We reconstruct the task as an influence diagram in which the LLM’s answer is the sole decision variable. A utility function is defined over the conditional probability distributions within the diagram, and the LLM is instructed to find the answer that maximises expected utility. This constrains the LLM to reason explicitly about each component of the objective, directing its output toward a precise optimization target rather than a subjective natural language interpretation. We validate our approach on the MovieLens 1M dataset across three frontier models (Claude Sonnet 4.6, GPT-5.4, and Gemini 2.5 Pro), demonstrating consistent improvements in precision and Normalized Discounted Cumulative Gain (NDCG) over natural language baselines in a multi-objective movie recommendation task.
[NLP-41] Streaming Translation and Transcription Through Speech-to-Text Causal Alignment
【速读】： 该论文旨在解决同步机器翻译（Simultaneous Machine Translation, SiMT）中传统方法依赖离线模型与人工设计启发式规则或学习策略所带来的局限性问题。其核心挑战在于如何在保证翻译质量的同时实现低延迟的流式处理。解决方案的关键在于提出一种无需策略（policy-free）、完全端到端的模型 Hikari，通过将读取（READ）/写入（WRITE）决策编码为概率化的 WAIT token 机制来实现动态的同步决策；同时引入解码时间膨胀（Decoder Time Dilation）机制以降低自回归计算开销并平衡训练分布，并结合监督微调策略提升模型对延迟的恢复能力，从而显著优化了质量与延迟之间的权衡关系。
链接: https://arxiv.org/abs/2603.11578
作者: Roman Koshkin,Jeon Haesung,Lianbo Liu,Hao Shi,Mengjie Zhao,Yusuke Fujita,Yui Sudo
机构: 未知
类目: Computation and Language (cs.CL)
备注: 16 pages, 6 figures
Abstract:Simultaneous machine translation (SiMT) has traditionally relied on offline machine translation models coupled with human-engineered heuristics or learned policies. We propose Hikari, a policy-free, fully end-to-end model that performs simultaneous speech-to-text translation and streaming transcription by encoding READ/WRITE decisions into a probabilistic WAIT token mechanism. We also introduce Decoder Time Dilation, a mechanism that reduces autoregressive overhead and ensures a balanced training distribution. Additionally, we present a supervised fine-tuning strategy that trains the model to recover from delays, significantly improving the quality-latency trade-off. Evaluated on English-to-Japanese, German, and Russian, Hikari achieves new state-of-the-art BLEU scores in both low- and high-latency regimes, outperforming recent baselines.
[NLP-42] Where Matters More Than What: Decoding-aligned KV Cache Compression via Position-aware Pseudo Queries
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）推理过程中键值缓存（Key-Value Cache, KV cache）内存占用过高的问题，尤其是在长上下文场景下，KV cache的存储开销显著增加。现有压缩方法通常依赖预填充阶段（prefill stage）中输入侧的注意力模式来评估token重要性，但这些方法无法有效保留未来生成阶段的关键token，因为其重要性判断未基于实际解码过程。解决方案的关键在于提出一种名为“解码对齐的KV缓存压缩”（Decoding-aligned KV Cache Compression via Position-aware Pseudo Queries, DapQ）的新框架，该框架通过构建基于位置感知的伪查询（position-aware pseudo queries）来模拟输出token，从而在预填充阶段建立一个与实际解码上下文高度对齐的观察窗口，实现更精确的token淘汰策略。实验表明，DapQ在多种基准和模型上均表现优异，尤其在严格内存约束下可接近无损性能（如NIAH任务中仅使用3% KV缓存预算时达到99.5%的性能保持率）。
链接: https://arxiv.org/abs/2603.11564
作者: Zhenxu Tian,Yi Su,Juntao Li,Min Zhang
机构: Soochow University (苏州大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:The Key-Value (KV) cache is crucial for efficient Large Language Models (LLMs) inference, but excessively long contexts drastically increase KV cache memory footprint. Existing KV cache compression methods typically rely on input-side attention patterns within a prompt observation window to estimate token importance during the prefill stage. They fail to preserve critical tokens for future generation since these assessments are not derived from the decoding process. Intuitively, an effective observation window should mirror the decoding-stage queries to accurately reflect which tokens the generation process will attend to. However, ground-truth decoding queries are inherently unavailable during inference. For constructing pseudo queries to approximate them, we find that positional information plays a more critical role than semantic content. Motivated by this insight, we propose decoding-aligned KV cache compression via position-aware pseudo queries (DapQ), a novel and lightweight eviction framework that leverages position-aware pseudo queries to simulate the output tokens, thereby establishing an effective observation window for importance assessment. It aligns closely with the actual generation context and enables precise token eviction. Extensive evaluations across multiple benchmarks and LLMs demonstrate that DapQ achieves superior performance, particularly under strict memory constraints (e.g., up to nearly lossless performance 99.5% on NIAH with 3% KV cache budgets).
[NLP-43] One Supervisor Many Modalities: Adaptive Tool Orchestration for Autonomous Queries
【速读】： 该论文旨在解决多模态查询处理中因工具调度不合理导致的效率低下与成本高昂问题，特别是在文本、图像、音频、视频和文档等异构模态协同场景下，传统基于预设决策树的分解策略难以适应复杂动态任务需求。解决方案的关键在于提出一个由中央监督器（Supervisor）驱动的代理型人工智能框架，通过动态任务分解、模态适配工具委派及自适应路由机制实现高效协调：对于纯文本查询采用基于RouteLLM的可学习路由策略，非文本路径则借助轻量级模型（SLM）辅助进行模态分解；该设计避免了固定规则的僵化性，显著提升了响应速度与资源利用效率，在保持准确率一致的前提下实现了时间、重复工作量和成本的大幅降低。
链接: https://arxiv.org/abs/2603.11545
作者: Mayank Saini Arit Kumar Bishwas
机构: PwC US (普华永道美国)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 19 pages, 3 figures
Abstract:We present an agentic AI framework for autonomous multimodal query processing that coordinates specialized tools across text, image, audio, video, and document modalities. A central Supervisor dynamically decomposes user queries, delegates subtasks to modality-appropriate tools (e.g., object detection, OCR, speech transcription), and synthesizes results through adaptive routing strategies rather than predetermined decision trees. For text-only queries, the framework uses learned routing via RouteLLM, while non-text paths use SLM-assisted modality decomposition. Evaluated on 2,847 queries across 15 task categories, our framework achieves 72% reduction in time-to-accurate-answer, 85% reduction in conversational rework, and 67% cost reduction compared to the matched hierarchical baseline while maintaining accuracy parity. These results demonstrate that intelligent centralized orchestration fundamentally improves multimodal AI deployment economics.
[NLP-44] Expert Threshold Routing for Autoregressive Language Modeling with Dynamic Computation Allocation and Load Balancing
【速读】： 该论文旨在解决传统Token-choice Mixture-of-Experts (TC-MoE) 架构中动态计算分配能力受限及负载均衡依赖辅助损失的问题。其核心解决方案是提出Expert Threshold (ET) 路由机制，即每个专家维护一个基于全局token分布的指数移动平均（EMA）阈值，token在训练和推理阶段均根据自身得分是否超过该阈值独立决定是否被路由至该专家，从而实现无需辅助损失即可自动平衡负载的动态计算分配。该机制完全因果化，不依赖批内其他token，特别适用于自回归语言建模任务，在FineWeb-Edu数据集上预训练2.4B参数模型时，相较TC-MoE降低0.067交叉熵损失，等效于以1.6倍少的token达到相同性能。
链接: https://arxiv.org/abs/2603.11535
作者: Hanchi Sun,Yixin Liu,Yonghui Wu,Lichao Sun
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Token-choice Mixture-of-Experts (TC-MoE) routes each token to a fixed number of experts, limiting dynamic computation allocation and requiring auxiliary losses to maintain load balance. We propose Expert Threshold (ET) routing, where each expert maintains an exponential moving average (EMA) threshold estimated from the global token distribution. At both training and inference, each token is independently routed to an expert if its score exceeds the expert’s threshold, enabling dynamic computation allocation while achieving load balance without auxiliary losses. This fully causal mechanism eliminates dependence on other tokens in the batch, making it well-suited for autoregressive language modeling. In pretraining experiments scaling to 2.4B parameters on FineWeb-Edu, ET achieves 0.067 lower cross-entropy loss than TC-MoE, equivalent to reaching the same performance with 1.6 \times fewer tokens.
[NLP-45] Can Small Language Models Use What They Retrieve? An Empirical Study of Retrieval Utilization Across Model Scale DATE
【速读】： 该论文旨在解决小规模语言模型（参数量≤7B）在检索增强生成（Retrieval Augmented Generation, RAG）中是否能有效利用外部检索信息的问题。研究发现，即使在理想条件下（即使用“oracle retrieval”确保检索到的答案段落），这些模型仍无法正确提取答案的比例高达85%–100%，表明其核心瓶颈在于对上下文的利用能力不足，而非检索质量本身。解决方案的关键在于引入一种参数化知识分割（parametric knowledge split），将模型原本可独立回答的问题与需依赖外部知识的问题区分开来，从而精准识别“利用失败”与“检索失败”的差异，揭示出小模型在RAG中的主要问题是上下文干扰和无关生成，而非检索准确性。
链接: https://arxiv.org/abs/2603.11513
作者: Sanchit Pandey(BITS Pilani, Hyderabad, India)
机构: BITS Pilani, Hyderabad Campus (比特·皮拉尼海得拉巴校区)
类目: Computation and Language (cs.CL)
备注: 10 pages, 5 figures, planning to submit to arr march 2026. Code and evaluation data: this https URL . Earlier draft preprint available on Zenodo: this https URL (note: this arXiv submission is an updated draft)
Abstract:Retrieval augmented generation RAG is widely deployed to improve factual accuracy in language models yet it remains unclear whether smaller models of size 7B parameters or less can effectively utilize retrieved information. To investigate this question we evaluate five model sizes from 360M to 8B across three architecture families SmolLM2 Qwen2.5 and Llama 3.1 under four retrieval conditions including no retrieval BM25 dense retrieval using E5 large v2 and oracle retrieval where the retrieved passage is guaranteed to contain the answer. We introduce a parametric knowledge split that separates questions a model can already answer from those that require external knowledge which allows us to isolate utilization failure from retrieval quality failure. We find three main results. First even with oracle retrieval models of size 7B or smaller fail to extract the correct answer 85 to 100 percent of the time on questions they cannot answer alone which indicates a fundamental utilization bottleneck. Second adding retrieval context destroys 42 to 100 percent of answers the model previously knew suggesting a distraction effect driven by the presence of context rather than its quality. Third an error analysis of 2588 oracle failures shows that the dominant failure mode is irrelevant generation where the model ignores the provided context entirely. These patterns hold across multiple prompt templates and retrieval methods. The results indicate that for models below 7B parameters the main limitation of RAG is context utilization rather than retrieval quality and that deploying RAG at this scale can lead to a net negative trade off under standard evaluation conditions.
[NLP-46] ny Aya: Bridging Scale and Multilingual Depth
【速读】： 该论文旨在解决小规模多语言语言模型在翻译质量、多语言理解能力和目标语言生成质量方面难以达到高性能的问题。传统方法往往依赖于大规模参数模型，导致计算资源消耗大、部署成本高且语言覆盖不平衡。解决方案的关键在于通过区域感知的后训练（region-aware posttraining）策略，在仅3.35B参数的条件下实现卓越的多语言性能，同时构建一个全球语言平衡的数据集和训练框架，从而提供一种以效率为核心、兼顾各地区语言表现均衡性的新型多语言AI扩展路径。
链接: https://arxiv.org/abs/2603.11510
作者: Alejandro R. Salamanca,Diana Abagyan,Daniel D’souza,Ammar Khairi,David Mora,Saurabh Dash,Viraat Aryabumi,Sara Rajaee,Mehrnaz Mofakhami,Ananya Sahu,Thomas Euyang,Brittawnya Prince,Madeline Smith,Hangyu Lin,Acyr Locatelli,Sara Hooker,Tom Kocmi,Aidan Gomez,Ivan Zhang,Phil Blunsom,Nick Frosst,Joelle Pineau,Beyza Ermis,Ahmet Üstün,Julia Kreutzer,Marzieh Fadaee
机构: Unknown
类目: Computation and Language (cs.CL)
备注:
Abstract:Tiny Aya redefines what a small multilingual language model can achieve. Trained on 70 languages and refined through region-aware posttraining, it delivers state-of-the-art in translation quality, strong multilingual understanding, and high-quality target-language generation, all with just 3.35B parameters. The release includes a pretrained foundation model, a globally balanced instruction-tuned variant, and three region-specialized models targeting languages from Africa, South Asia, Europe, Asia-Pacific, and West Asia. This report details the training strategy, data composition, and comprehensive evaluation framework behind Tiny Aya, and presents an alternative scaling path for multilingual AI: one centered on efficiency, balanced performance across languages, and practical deployment.
[NLP-47] LongFlow: Efficient KV Cache Compression for Reasoning M
【速读】： 该论文旨在解决生成式 AI（Generative AI）推理模型在长输出场景下因 KV 缓存（Key-Value Cache）占用大量内存和带宽而导致的部署成本过高问题。现有 KV 缓存优化方法主要针对长输入、短输出场景，难以适配推理模型的长输出特性；同时，传统重要性评估机制计算开销大，在持续重评估需求下不可行。解决方案的关键在于提出 LongFlow，其核心创新是设计了一种基于注意力计算中间结果的高效重要性度量方法，仅利用当前查询即可完成评估，计算开销极低且无需额外存储空间；并进一步开发了一个融合 FlashAttention、重要性估计与 token 淘汰的定制化内核，显著提升系统级效率。实验表明，LongFlow 在实现 80% KV 缓存压缩的同时，可带来最高达 11.8 倍的吞吐量提升，且对模型精度影响微小。
链接: https://arxiv.org/abs/2603.11504
作者: Yi Su,Zhenxu Tian,Dan Qiao,Yuechi Zhou,Juntao Li,Min Zhang
机构: Soochow University (苏州大学); ByteDance (字节跳动)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Recent reasoning models such as OpenAI-o1 and DeepSeek-R1 have shown strong performance on complex tasks including mathematical reasoning and code generation. However, this performance gain comes with substantially longer output sequences, leading to significantly increased deployment costs. In particular, long outputs require large KV caches, resulting in high memory consumption and severe bandwidth pressure during attention computation. Most existing KV cache optimization methods are designed for long-input, short-output scenarios and are ineffective for the long-output setting of reasoning models. Moreover, importance estimation in prior work is computationally expensive and becomes prohibitive when continuous re-evaluation is required during long generation. To address these challenges, we propose LongFlow, a KV cache compression method with an efficient importance estimation metric derived from an intermediate result of attention computation using only the current query. This design introduces negligible computational overhead and requires no auxiliary storage. We further develop a custom kernel that fuses FlashAttention, importance estimation, and token eviction into a single optimized operator, improving system-level efficiency. Experiments show that LongFlow achieves up to an 11.8 times throughput improvement with 80% KV cache compression with minimal impact on model accuracy.
[NLP-48] ry Check and Retry: A Divide-and-Conquer Framework for Boosting Long-context Tool-Calling Performance of LLM s
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在长上下文工具调用（long-context tool-calling）任务中因候选工具数量庞大且存在噪声而导致性能下降的问题。解决方案的关键在于提出一种分而治之（Divide-and-Conquer）框架 Tool-DC，其核心机制是通过“尝试-验证-重试”（Try-Check-Retry）范式降低推理难度，并充分利用 LLM 的自我反思能力。该框架包含两种变体：无需训练的 Tool-DC (TF) 和基于训练的 Tool-DC (TB)，前者即插即用、灵活高效，后者则在推理阶段更具效率。实验表明，两种方法均显著优于基线模型，在 BFCL 和 ACEBench 基准上平均提升最高达 25.10%，且 TB 版本使 Qwen2.5-7B 在性能上媲美甚至超越商用模型如 OpenAI o3 和 Claude-Haiku-4.5。
链接: https://arxiv.org/abs/2603.11495
作者: Kunfeng Chen,Qihuang Zhong,Juhua Liu,Bo Du,Dacheng Tao
机构: Wuhan University (武汉大学); Nanyang Technological University (南洋理工大学)
类目: Computation and Language (cs.CL)
备注: 17 pages, 8 figures
Abstract:Tool-calling empowers Large Language Models (LLMs) to interact with external environments. However, current methods often struggle to handle massive and noisy candidate tools in long-context tool-calling tasks, limiting their real-world application. To this end, we propose Tool-DC, a Divide-and-Conquer framework for boosting tool-calling performance of LLMs. The core of Tool-DC is to reduce the reasoning difficulty and make full use of self-reflection ability of LLMs via a “Try-Check-Retry” paradigm. Specifically, Tool-DC involves two variants: 1) the training-free Tool-DC (TF), which is plug-and-play and flexible; 2) the training-based Tool-DC (TB), which is more inference-efficient. Extensive experiments show that both Tool-DC methods outperform their counterparts by a clear margin. Tool-DC (TF) brings up to +25.10% average gains against the baseline on BFCL and ACEBench benchmarks, while Tool-DC (TB) enables Qwen2.5-7B to achieve comparable or even better performance than proprietary LLMs, e.g., OpenAI o3 and Claude-Haiku-4.5.
[NLP-49] AnimeScore: A Preference-Based Dataset and Framework for Evaluating Anime-Like Speech Style
【速读】： 该论文旨在解决当前对“动漫风格语音”（anime-like voices）的评估依赖于昂贵的主观判断，且缺乏标准化客观指标的问题。其核心挑战在于，与自然度不同，动漫风格语音没有统一的绝对评分尺度，导致传统平均意见得分（Mean Opinion Score, MOS）方法不可靠。解决方案的关键在于提出 AnimeScore，一个基于成对比较的偏好框架，通过自动排序实现动漫风格语音的客观评估。研究收集了来自187名评价者的15,000组成对判断，并结合声学分析发现，感知的动漫风格主要由受控共振峰形状、语调连续性和刻意发音控制驱动，而非简单高音调等启发式特征；在此基础上，手工设计的声学特征可达到69.3% AUC上限，而基于自监督学习（Self-Supervised Learning, SSL）的排序模型则进一步提升至90.8% AUC，为生成式语音模型的偏好优化提供了有效的奖励信号。
链接: https://arxiv.org/abs/2603.11482
作者: Joonyong Park,Jerry Li
机构: Spellbrush( spellbrush)
类目: ound (cs.SD); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注:
Abstract:Evaluating ‘anime-like’ voices currently relies on costly subjective judgments, yet no standardized objective metric exists. A key challenge is that anime-likeness, unlike naturalness, lacks a shared absolute scale, making conventional Mean Opinion Score (MOS) protocols unreliable. To address this gap, we propose AnimeScore, a preference-based framework for automatic anime-likeness evaluation via pairwise ranking. We collect 15,000 pairwise judgments from 187 evaluators with free-form descriptions, and acoustic analysis reveals that perceived anime-likeness is driven by controlled resonance shaping, prosodic continuity, and deliberate articulation rather than simple heuristics such as high pitch. We show that handcrafted acoustic features reach a 69.3% AUC ceiling, while SSL-based ranking models achieve up to 90.8% AUC, providing a practical metric that can also serve as a reward signal for preference-based optimization of generative speech models.
[NLP-50] LLM -Assisted Causal Structure Disambiguation and Factor Extraction for Legal Judgment Prediction
【速读】： 该论文旨在解决当前基于预训练语言模型（Pre-trained Language Models, PLMs）的法律判决预测（Legal Judgment Prediction, LJP）方法中存在的两大核心问题：一是模型过度依赖案件事实与判决结果之间的统计相关性，缺乏对法律构成要素（legal constituent elements）和潜在因果逻辑的显式建模，导致易学习虚假关联、鲁棒性差；二是现有因果推理方法在真实法律文本中面临两个瓶颈：法律因素提取不准确且噪声严重，以及因特征稀疏导致因果结构发现存在马尔可夫等价性带来的结构不确定性。解决方案的关键在于提出一种融合大型语言模型（Large Language Model, LLM）先验知识与统计因果发现的增强型因果推理框架：首先设计粗粒度到细粒度的混合抽取机制，结合统计采样与LLM语义推理以精准识别并净化标准法律构成要素；其次引入LLM辅助的因果结构消歧机制，利用LLM作为约束性先验知识库，对模糊因果方向进行概率评估与剪枝，生成符合法律规范的候选因果图；最终通过显式约束文本注意力强度构建因果感知的判决预测模型，从而显著提升预测准确性与鲁棒性，尤其在区分相似罪名时表现突出。
链接: https://arxiv.org/abs/2603.11446
作者: Yuzhi Liang,Lixiang Ma,Xinrong Zhu
机构: Guangdong University of Foreign Studies (广东外语外贸大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Mainstream methods for Legal Judgment Prediction (LJP) based on Pre-trained Language Models (PLMs) heavily rely on the statistical correlation between case facts and judgment results. This paradigm lacks explicit modeling of legal constituent elements and underlying causal logic, making models prone to learning spurious correlations and suffering from poor robustness. While introducing causal inference can mitigate this issue, existing causal LJP methods face two critical bottlenecks in real-world legal texts: inaccurate legal factor extraction with severe noise, and significant uncertainty in causal structure discovery due to Markov equivalence under sparse features. To address these challenges, we propose an enhanced causal inference framework that integrates Large Language Model (LLM) priors with statistical causal discovery. First, we design a coarse-to-fine hybrid extraction mechanism combining statistical sampling and LLM semantic reasoning to accurately identify and purify standard legal constituent elements. Second, to resolve structural uncertainty, we introduce an LLM-assisted causal structure disambiguation mechanism. By utilizing the LLM as a constrained prior knowledge base, we conduct probabilistic evaluation and pruning on ambiguous causal directions to generate legally compliant candidate causal graphs. Finally, a causal-aware judgment prediction model is constructed by explicitly constraining text attention intensity via the generated causal graphs. Extensive experiments on multiple benchmark datasets, including LEVEN , QA, and CAIL, demonstrate that our proposed method significantly outperforms state-of-the-art baselines in both predictive accuracy and robustness, particularly in distinguishing confusing charges.
[NLP-51] BLooP: Zero-Shot Abstractive Summarization using Large Language Models with Bigram Lookahead Promotion LREC2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在进行抽象式摘要生成时存在的关键问题：即模型在无需微调的情况下生成的摘要常遗漏重要信息且包含冗余内容，导致摘要忠实度（faithfulness）不足。解决方案的核心在于提出一种无需训练的解码干预方法——BLooP（Bigram Lookahead Promotion），其通过在每一步解码过程中利用哈希表查找源文档中已出现的二元组（bigram），引导模型生成与源文档更一致的词汇组合，从而提升摘要的信息准确性和忠实度，同时保持良好的可读性。
链接: https://arxiv.org/abs/2603.11415
作者: Varun Iyer,Cornelia Caragea
机构: 未知
类目: Computation and Language (cs.CL)
备注: LREC 2026
Abstract:Abstractive summarization requires models to generate summaries that convey information in the source document. While large language models can generate summaries without fine-tuning, they often miss key details and include extraneous information. We propose BLooP (Bigram Lookahead Promotion), a simple training-free decoding intervention that encourages large language models (LLMs) to generate tokens that form bigrams from the source document. BLooP operates through a hash table lookup at each decoding step, requiring no training, fine-tuning, or model modification. We demonstrate improvements in ROUGE and BARTScore for Llama-3.1-8B-Instruct, Mistral-Nemo-Instruct-2407, and Gemma-2-9b-it on CNN/DM, CCSum, Multi-News, and SciTLDR. Human evaluation shows that BLooP significantly improves faithfulness without reducing readability. We make the code available at this https URL
[NLP-52] MaterialFigBENCH: benchmark dataset with figures for evaluating college-level materials science problem-solving abilities of multimodal large language models
【速读】： 该论文旨在解决当前多模态大语言模型（Multimodal Large Language Models, MLLMs）在材料科学领域中对图表类问题理解能力不足的问题，尤其是模型能否准确解读相图、应力-应变曲线、阿伦尼乌斯图、衍射图谱和显微结构示意图等关键图像信息以得出正确答案。解决方案的关键在于构建MaterialFigBench这一专门针对大学水平材料科学问题的基准数据集，其核心特征是所有题目均依赖于图像信息才能获得正确解答，而非仅靠文本描述；同时，为应对图像中数值读取的模糊性，引入专家定义的答案区间（answer ranges），从而更客观地评估模型的视觉理解与定量分析能力。该基准揭示了现有MLLMs在真实视觉推理、数值精度及有效数字处理上的局限性，并为未来增强模型基于图表的理解能力提供了系统化方向。
链接: https://arxiv.org/abs/2603.11414
作者: Michiko Yoshitake,Yuta Suzuki,Ryo Igarashi,Yoshitaka Ushiku,Keisuke Nagato
机构: 未知
类目: Computation and Language (cs.CL); Materials Science (cond-mat.mtrl-sci)
备注: 27 pages, 4 tables, 6 figures
Abstract:We present MaterialFigBench, a benchmark dataset designed to evaluate the ability of multimodal large language models (LLMs) to solve university-level materials science problems that require accurate interpretation of figures. Unlike existing benchmarks that primarily rely on textual representations, MaterialFigBench focuses on problems in which figures such as phase diagrams, stress-strain curves, Arrhenius plots, diffraction patterns, and microstructural schematics are indispensable for deriving correct answers. The dataset consists of 137 free-response problems adapted from standard materials science textbooks, covering a broad range of topics including crystal structures, mechanical properties, diffusion, phase diagrams, phase transformations, and electronic properties of materials. To address unavoidable ambiguity in reading numerical values from images, expert-defined answer ranges are provided where appropriate. We evaluate several state-of-the-art multimodal LLMs, including ChatGPT and GPT models accessed via OpenAI APIs, and analyze their performance across problem categories and model versions. The results reveal that, although overall accuracy improves with model updates, current LLMs still struggle with genuine visual understanding and quantitative interpretation of materials science figures. In many cases, correct answers are obtained by relying on memorized domain knowledge rather than by reading the provided images. MaterialFigBench highlights persistent weaknesses in visual reasoning, numerical precision, and significant-digit handling, while also identifying problem types where performance has improved. This benchmark provides a systematic and domain-specific foundation for advancing multimodal reasoning capabilities in materials science and for guiding the development of future LLMs with stronger figure-based understanding.
[NLP-53] Algorithmic Consequences of Particle Filters for Sentence Processing: Amplified Garden-Paths and Digging-In Effects
【速读】： 该论文试图解决的问题是：当前基于大语言模型（Large Language Models, LLMs）的 surprisal 理论虽能较好预测跨语言的阅读时间，但在处理结构歧义被违反的情境时系统性地低估了加工难度，表明结构歧义表征在句法加工中具有因果作用。解决方案的关键在于引入粒子滤波（particle filter）模型，该模型显式地将结构假设表示为一组粒子，并通过算法分析揭示了重采样（resampling）机制会自然产生实时“挖坑效应”（digging-in effect）——即歧义区域长度越长，消歧难度越高；且该效应强度与粒子数量成反比，完全并行模型则无法预测此现象。
链接: https://arxiv.org/abs/2603.11412
作者: Amani Maina-Kilaas,Roger Levy
机构: Massachusetts Institute of Technology (麻省理工学院)
类目: Computation and Language (cs.CL)
备注: 10 pages, 4 figures
Abstract:Under surprisal theory, linguistic representations affect processing difficulty only through the bottleneck of surprisal. Our best estimates of surprisal come from large language models, which have no explicit representation of structural ambiguity. While LLM surprisal robustly predicts reading times across languages, it systematically underpredicts difficulty when structural expectations are violated – suggesting that representations of ambiguity are causally implicated in sentence processing. Particle filter models offer an alternative where structural hypotheses are explicitly represented as a finite set of particles. We prove several algorithmic consequences of particle filter models, including the amplification of garden-path effects. Most critically, we demonstrate that resampling, a common practice with these models, inherently produces real-time digging-in effects – where disambiguation difficulty increases with ambiguous region length. Digging-in magnitude scales inversely with particle count: fully parallel models predict no such effect.
[NLP-54] Speak or Stay Silent: Context-Aware Turn-Taking in Multi-Party Dialogue INTERSPEECH2026
【速读】： 该论文旨在解决多人群体对话场景中语音AI助手因误判沉默而频繁打断对话的问题，即现有系统将所有检测到的停顿均视为发言邀请，导致在多人参与的复杂语境下产生干扰性响应。其核心解决方案是提出一种基于上下文感知的轮次切换（context-aware turn-taking）机制：在每个检测到的停顿处，利用完整的对话上下文判断AI是否应发言或保持沉默。关键创新在于引入一个包含超过12万条标注对话的基准数据集，并通过监督微调结合推理轨迹（reasoning traces），显著提升模型在零样本条件下表现不佳的轮次决策能力，平衡准确率最高提升23个百分点，表明该能力需显式训练而非自然涌现。
链接: https://arxiv.org/abs/2603.11409
作者: Kratika Bhagtani,Mrinal Anand,Yu Chen Xu,Amit Kumar Singh Yadav
机构: Purdue University (普渡大学); Ishiki Labs Inc.
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Submitted for review to Interspeech 2026
Abstract:Existing voice AI assistants treat every detected pause as an invitation to speak. This works in dyadic dialogue, but in multi-party settings, where an AI assistant participates alongside multiple speakers, pauses are abundant and ambiguous. An assistant that speaks on every pause becomes disruptive rather than useful. In this work, we formulate context-aware turn-taking: at every detected pause, given the full conversation context, our method decides whether the assistant should speak or stay silent. We introduce a benchmark of over 120K labeled conversations spanning three multi-party corpora. Evaluating eight recent large language models, we find that they consistently fail at context-aware turn-taking under zero-shot prompting. We then propose a supervised fine-tuning approach with reasoning traces, improving balanced accuracy by up to 23 percentage points. Our findings suggest that context-aware turn-taking is not an emergent capability; it must be explicitly trained.
[NLP-55] Stop Listening to Me! How Multi-turn Conversations Can Degrade Diagnostic Reasoning
【速读】： 该论文旨在解决当前生成式 AI（Generative AI）在多轮对话场景下诊断推理能力不足的问题，尤其关注其在真实医疗咨询中相较于单次推理基准性能下降的现象。解决方案的关键在于提出一种“stick-or-switch”评估框架，用于量化模型在对话中的判断一致性（conviction，即坚持正确诊断或安全弃权）与灵活性（flexibility，即识别并采纳正确建议的能力），从而揭示多轮交互中的“对话税”（conversation tax）现象，并指出模型易受用户错误提示干扰的盲切换行为。
链接: https://arxiv.org/abs/2603.11394
作者: Kevin H. Guo,Chao Yan,Avinash Baidya,Katherine Brown,Xiang Gao,Juming Xiong,Zhijun Yin,Bradley A. Malin
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Patients and clinicians are increasingly using chatbots powered by large language models (LLMs) for healthcare inquiries. While state-of-the-art LLMs exhibit high performance on static diagnostic reasoning benchmarks, their efficacy across multi-turn conversations, which better reflect real-world usage, has been understudied. In this paper, we evaluate 17 LLMs across three clinical datasets to investigate how partitioning the decision-space into multiple simpler turns of conversation influences their diagnostic reasoning. Specifically, we develop a “stick-or-switch” evaluation framework to measure model conviction (i.e., defending a correct diagnosis or safe abstention against incorrect suggestions) and flexibility (i.e., recognizing a correct suggestion when it is introduced) across conversations. Our experiments reveal the conversation tax, where multi-turn interactions consistently degrade performance when compared to single-shot baselines. Notably, models frequently abandon initial correct diagnoses and safe abstentions to align with incorrect user suggestions. Additionally, several models exhibit blind switching, failing to distinguish between signal and incorrect suggestions.
[NLP-56] Evaluating Explainable AI Attribution Methods in Neural Machine Translation via Attention-Guided Knowledge Distillation
【速读】： 该论文旨在解决序列到序列（seq2seq）模型中可解释性方法的系统化与自动化评估问题，尤其针对基于Transformer架构的模型。现有解释人工智能（Explainable AI, XAI）技术虽多，但缺乏对seq2seq场景下这些方法有效性的统一评测框架。其解决方案的关键在于引入“教师-学生”范式：利用教师模型生成的归因图（attribution maps）作为结构化辅助信号，指导学生模型学习如何将归因信息注入注意力机制，并通过学生模型在下游任务（如机器翻译）中的性能提升来量化不同归因方法的有效性。实验表明，注意力、值零化（Value Zeroing）及层梯度×激活（Layer Gradient × Activation）等方法在BLEU和chrF指标上表现最优，说明其更有效地捕捉了源-目标表示间的对齐关系；同时提出“Attributor Transformer”用于重建教师归因图，进一步验证了归因图重建精度与下游任务收益之间的正相关性。
链接: https://arxiv.org/abs/2603.11342
作者: Aria Nourbakhsh,Salima Lamsiyah,Adelaide Danilov,Christoph Schommer
机构: University of Luxembourg (卢森堡大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 37 pages, 11 figures
Abstract:The study of the attribution of input features to the output of neural network models is an active area of research. While numerous Explainable AI (XAI) techniques have been proposed to interpret these models, the systematic and automated evaluation of these methods in sequence-to-sequence (seq2seq) models is less explored. This paper introduces a new approach for evaluating explainability methods in transformer-based seq2seq models. We use teacher-derived attribution maps as a structured side signal to guide a student model, and quantify the utility of different attribution methods through the student’s ability to simulate targets. Using the Inseq library, we extract attribution scores over source-target sequence pairs and inject these scores into the attention mechanism of a student transformer model under four composition operators (addition, multiplication, averaging, and replacement). Across three language pairs (de-en, fr-en, ar-en) and attributions from Marian-MT and mBART models, Attention, Value Zeroing, and Layer Gradient \times Activation consistently yield the largest gains in BLEU (and corresponding improvements in chrF) relative to baselines. In contrast, other gradient-based methods (Saliency, Integrated Gradients, DeepLIFT, Input \times Gradient, GradientShap) lead to smaller and less consistent improvements. These results suggest that different attribution methods capture distinct signals and that attention-derived attributions better capture alignment between source and target representations in seq2seq models. Finally, we introduce an Attributor transformer that, given a source-target pair, learns to reconstruct the teacher’s attribution map. Our findings demonstrate that the more accurately the Attributor can reproduce attribution maps, the more useful an injection of those maps is for the downstream task. The source code can be found on GitHub.
[NLP-57] Meta-Reinforcement Learning with Self-Reflection for Agent ic Search
【速读】： 该论文旨在解决传统强化学习（Reinforcement Learning, RL）在代理搜索（agentic search）任务中因单次独立episode内稀疏奖励而导致的探索效率低、泛化能力弱的问题。其解决方案的关键在于提出一种基于上下文元强化学习（meta reinforcement learning, meta RL）的MR-Search框架，该框架通过引入跨episode的自省机制（self-reflection），使搜索策略能够基于历史episode的经验进行动态调整，并利用显式的自我反思信息作为额外上下文来指导后续探索。此外，论文进一步设计了一种多轮强化学习算法，以turn级密集相对优势估计实现细粒度的信用分配，从而显著提升测试阶段的探索效率与性能，在多个基准测试中相较基线方法平均提升9.2%至19.3%。
链接: https://arxiv.org/abs/2603.11327
作者: Teng Xiao,Yige Yuan,Hamish Ivison,Huaisheng Zhu,Faeze Brahman,Nathan Lambert,Pradeep Dasigi,Noah A. Smith,Hannaneh Hajishirzi
机构: Allen Institute for AI (艾伦人工智能研究所); University of Washington (华盛顿大学); Independent (独立)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 23 pages, Preprint
Abstract:This paper introduces MR-Search, an in-context meta reinforcement learning (RL) formulation for agentic search with self-reflection. Instead of optimizing a policy within a single independent episode with sparse rewards, MR-Search trains a policy that conditions on past episodes and adapts its search strategy across episodes. MR-Search learns to learn a search strategy with self-reflection, allowing search agents to improve in-context exploration at test-time. Specifically, MR-Search performs cross-episode exploration by generating explicit self-reflections after each episode and leveraging them as additional context to guide subsequent attempts, thereby promoting more effective exploration during test-time. We further introduce a multi-turn RL algorithm that estimates a dense relative advantage at the turn level, enabling fine-grained credit assignment on each episode. Empirical results across various benchmarks demonstrate the advantages of MR-Search over baselines based RL, showing strong generalization and relative improvements of 9.2% to 19.3% across eight benchmarks. Our code and data are available at this https URL.
[NLP-58] Hindsight-Anchored Policy Optimization: Turning Failure into Feedback in Sparse Reward Settings
【速读】： 该论文旨在解决在稀疏奖励环境下，基于群体的强化学习方法（如GRPO）所面临的困境：纯强化学习易出现优势值坍塌（advantage collapse）和梯度估计方差过高问题，而混合策略优化则会引入持续的分布偏移（distributional bias）。其解决方案的关键在于提出Hindsight-Anchored Policy Optimization (HAPO)，通过引入合成成功注入（Synthetic Success Injection, SSI）操作符——一种选择性锚定失败样本到教师示范的回溯机制，并由受Thompson采样启发的门控机制控制注入频率，从而构建自主且自适应的课程学习路径。理论分析表明，HAPO具备渐近一致性（asymptotic consistency），即随着策略优化逐步提升，教师信号自然衰减，最终恢复无偏的on-policy梯度，使离策略指导仅作为临时支撑而非永久限制，从而突破静态教师强制（teacher forcing）带来的性能瓶颈。
链接: https://arxiv.org/abs/2603.11321
作者: Yuning Wu,Ke Wang,Devin Chen,Kai Wei
机构: Amazon(亚马逊)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for post-training reasoning models. However, group-based methods such as Group Relative Policy Optimization (GRPO) face a critical dilemma in sparse-reward settings: pure Reinforcement Learning (RL) suffers from advantage collapse and high-variance gradient estimation, while mixed-policy optimization introduces persistent distributional bias. To resolve this dilemma, we introduce Hindsight-Anchored Policy Optimization (HAPO). HAPO employs the Synthetic Success Injection (SSI) operator, a hindsight mechanism that selectively anchors optimization to teacher demonstrations during failure. This injection is governed by a Thompson sampling-inspired gating mechanism, creating an autonomous, self-paced curriculum. Theoretically, we demonstrate that HAPO achieves \textitasymptotic consistency: by naturally annealing the teacher signal as the policy improves, HAPO recovers the unbiased on-policy gradient. This ensures off-policy guidance acts as a temporary scaffold rather than a persistent ceiling, enabling the model to surpass the limitations of static teacher forcing.
[NLP-59] mporal Text Classification with Large Language Models
【速读】： 该论文旨在解决**文本自动年代判定（Temporal Text Classification, TTC）**问题，即利用计算模型识别文本的语言演变特征以估计其出版时间。其关键解决方案在于系统性评估主流商业模型（如Claude 3.5、GPT-4o、Gemini 1.5）与开源模型（如LLaMA 3.2、Gemma 2、Mistral、Nemotron 4）在三种历史语料库上的表现，涵盖零样本（zero-shot）、少样本（few-shot）提示及微调（fine-tuning）三种设置。研究发现，商业模型在少样本提示下表现优异，而开源模型虽经微调后性能显著提升，但仍无法达到商业模型的水平，揭示了当前开源大语言模型在TTC任务中的局限性。
链接: https://arxiv.org/abs/2603.11295
作者: Nishat Raihan,Marcos Zampieri
机构: George Mason University (乔治梅森大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Languages change over time. Computational models can be trained to recognize such changes enabling them to estimate the publication date of texts. Despite recent advancements in Large Language Models (LLMs), their performance on automatic dating of texts, also known as Temporal Text Classification (TTC), has not been explored. This study provides the first systematic evaluation of leading proprietary (Claude 3.5, GPT-4o, Gemini 1.5) and open-source (LLaMA 3.2, Gemma 2, Mistral, Nemotron 4) LLMs on TTC using three historical corpora, two in English and one in Portuguese. We test zero-shot and few-shot prompting, and fine-tuning settings. Our results indicate that proprietary models perform well, especially with few-shot prompting. They also indicate that fine-tuning substantially improves open-source models but that they still fail to match the performance delivered by proprietary LLMs.
[NLP-60] hReadMed-QA: A Multi-Turn Medical Dialogue Benchmark from Real Patient Questions
【速读】： 该论文旨在解决当前医疗问答评估基准普遍局限于单轮对话，无法反映真实患者与医生之间多轮、迭代式咨询过程的问题。其核心解决方案是构建了一个名为ThReadMed-QA的新型基准，包含从r/AskDocs社区提取的2,437条完整医患对话线程（共8,204个问答对，最多9轮），这些数据基于真实患者的跟进提问和经验证的医生回复，而非模拟对话或考试式问题。通过在该基准上对五种先进大语言模型（LLM）进行评估，研究揭示了模型在多轮交互中性能显著下降的现象，并引入对话一致性评分（Conversational Consistency Score, CCS） 和 错误传播率（Error Propagation Rate, EPR） 两个指标量化多轮失败模式，从而系统性地刻画了模型在复杂临床语境下的可靠性缺陷。
链接: https://arxiv.org/abs/2603.11281
作者: Monica Munnangi,Saiph Savage
机构: Northeastern University (东北大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Medical question-answering benchmarks predominantly evaluate single-turn exchanges, failing to capture the iterative, clarification-seeking nature of real patient consultations. We introduce ThReadMed-QA, a benchmark of 2,437 fully-answered patient-physician conversation threads extracted from r/AskDocs, comprising 8,204 question-answer pairs across up to 9 turns. Unlike prior work relying on simulated dialogues, adversarial prompts, or exam-style questions, ThReadMed-QA captures authentic patient follow-up questions and verified physician responses, reflecting how patients naturally seek medical information online. We evaluate five state-of-the-art LLMs – GPT-5, GPT-4o, Claude Haiku, Gemini 2.5 Flash, and Llama 3.3 70B – on a stratified test split of 238 conversations (948 QA pairs) using a calibrated LLM-as-a-judge rubric grounded in physician ground truth. Even the strongest model, GPT-5, achieves only 41.2% fully-correct responses. All five models degrade significantly from turn 0 to turn 2 (p 0.001), with wrong-answer rates roughly tripling by the third turn. We identify a fundamental tension between single-turn capability and multi-turn reliability: models with the strongest initial performance (GPT-5: 75.2; Claude Haiku: 72.3 out of 100) exhibit the steepest declines by turn 2 (dropping 16.2 and 25.0 points respectively), while weaker models plateau or marginally improve. We introduce two metrics to quantify multi-turn failure modes: Conversational Consistency Score (CCS) and Error Propagation Rate (EPR). CCS reveals that nearly one in three Claude Haiku conversations swings between a fully correct and a completely wrong response within the same thread. EPR shows that a single wrong turn raises the probability of a subsequent wrong turn by 1.9-6.1x across all models.
[NLP-61] Artificial Intelligence for Sentiment Analysis of Persian Poetry
【速读】： 该论文旨在解决如何利用大语言模型（Large Language Models, LLMs）有效分析波斯诗歌的复杂性，特别是识别诗歌情感倾向与韵律结构之间的潜在关联。其解决方案的关键在于采用多种基于BERT和GPT架构的语言模型对两位著名波斯诗人鲁米（Rumi）与帕尔文·艾特萨米（Parvin E’tesami）的作品进行量化分析，结果表明GPT4o模型在情感分析和韵律特征识别方面具有可靠性，能够无需人工干预地揭示诗歌语义规律，从而减少主观偏倚并提升计算机辅助文学研究的客观性与可扩展性。
链接: https://arxiv.org/abs/2603.11254
作者: Arash Zargar,Abolfazl Moshiri,Mitra Shafaei,Shabnam Rahimi-Golkhandan,Mohamad Tavakoli-Targhi,Farzad Khalvati
机构: University of Toronto (多伦多大学); Vector Institute for Artificial Intelligence (人工智能研究所); Department of Near Middle Eastern Civilizations (近东文明系); Department of Medical Imaging and Institute of Medical Science (医学影像系和医学科学研究所); Department of Mechanical Industrial Engineering (机械工业工程系); Department of Computer Science (计算机科学系)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advancements of the Artificial Intelligence (AI) have led to the development of large language models (LLMs) that are capable of understanding, analysing, and creating textual data. These language models open a significant opportunity in analyzing the literature and more specifically poetry. In the present work, we employ multiple Bidirectional encoder representations from transformers (BERT) and Generative Pre-trained Transformer (GPT) based language models to analyze the works of two prominent Persian poets: Jalal al-Din Muhammad Rumi (Rumi) and Parvin E’tesami. The main objective of this research is to investigate the capability of the modern language models in grasping complexities of the Persian poetry and explore potential correlations between the poems’ sentiment and their meters. Our findings in this study indicates that GPT4o language model can reliably be used in analysis of Persian poetry. Furthermore, the results of our sentiment analysis revealed that in general, Rumi’s poems express happier sentiments compared to Parvin E’tesami’s poems. Furthermore, comparing the utilization of poetic meters highlighted Rumi’s poems superiority in using meters to express a wider variety of sentiments. These findings are significant as they confirm that LLMs can be effectively applied in conducting computer-based semantic studies, where human interpretations are not required, and thereby significantly reducing potential biases in the analysis.
[NLP-62] LLM s Can Infer Political Alignment from Online Conversations
【速读】： 该论文旨在解决大规模语言模型（Large Language Models, LLMs）是否能够利用用户在社交媒体上看似无害的文本行为（如关注特定乐队或使用特定俚语）来推断其隐藏的政治立场这一隐私风险问题。解决方案的关键在于：通过分析Reddit等公开在线讨论数据，发现LLMs能显著优于传统机器学习模型，从非显性政治词汇中提取出与政治倾向高度相关的语义特征，并通过聚合多个文本层面的推断结果形成更准确的用户级预测，从而揭示LLMs对社会文化相关性的强大利用能力及其潜在的隐私泄露风险。
链接: https://arxiv.org/abs/2603.11253
作者: Byunghwee Lee,Sangyeon Kim,Filippo Menczer,Yong-Yeol Ahn,Haewoon Kwak,Jisun An
机构: Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, USA
类目: ocial and Information Networks (cs.SI); Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 55 pages; 4 figures in the main text and 18 supplementary figures, 11 supplementary tables
Abstract:Due to the correlational structure in our traits such as identities, cultures, and political attitudes, seemingly innocuous preferences such as following a band or using a specific slang, can reveal private traits. This possibility, especially when combined with massive, public social data and advanced computational methods, poses a fundamental privacy risk. Given our increasing data exposure online and the rapid advancement of AI are increasing the misuse potential of such risk, it is therefore critical to understand capacity of large language models (LLMs) to exploit it. Here, using online discussions on this http URL and Reddit, we show that LLMs can reliably infer hidden political alignment, significantly outperforming traditional machine learning models. Prediction accuracy further improves as we aggregate multiple text-level inferences into a user-level prediction, and as we use more politics-adjacent domains. We demonstrate that LLMs leverage the words that can be highly predictive of political alignment while not being explicitly political. Our findings underscore the capacity and risks of LLMs for exploiting socio-cultural correlates.
[NLP-63] Markovian Generation Chains in Large Language Models
【速读】： 该论文试图解决的问题是：当文本被大语言模型（Large Language Models, LLMs）反复处理时，其演化规律如何？具体而言，研究关注的是在无记忆的迭代推理过程中，文本是否趋于收敛或持续产生新句子，以及这一过程对句子多样性的影响。解决方案的关键在于将该迭代过程建模为马尔可夫生成链（Markovian generation chains），其中每一步仅依赖于前一输出和固定提示模板，不引入历史记忆；通过句级马尔可夫链建模与模拟数据的分析，揭示了温度参数和初始输入对句子多样性的双重影响——既可能增强也可能降低多样性，从而为理解多智能体LLM系统中的推理动态提供了理论依据。
链接: https://arxiv.org/abs/2603.11228
作者: Mingmeng Geng,Amr Mohamed,Guokan Shang,Michalis Vazirgiannis,Thierry Poibeau
机构: ENS-PSL (巴黎高等师范学院); CNRS-Lattice (法国国家科学研究中心-语言学网络); MBZUAI (穆罕默德·本·扎耶德人工智能大学); Ecole Polytechnique (巴黎综合理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The widespread use of large language models (LLMs) raises an important question: how do texts evolve when they are repeatedly processed by LLMs? In this paper, we define this iterative inference process as Markovian generation chains, where each step takes a specific prompt template and the previous output as input, without including any prior memory. In iterative rephrasing and round-trip translation experiments, the output either converges to a small recurrent set or continues to produce novel sentences over a finite horizon. Through sentence-level Markov chain modeling and analysis of simulated data, we show that iterative process can either increase or reduce sentence diversity depending on factors such as the temperature parameter and the initial input sentence. These results offer valuable insights into the dynamics of iterative LLM inference and their implications for multi-agent LLM systems.
[NLP-64] Frequency-Modulated Visual Restoration for Matryoshka Large Multimodal Models
【速读】： 该论文旨在解决大型多模态模型（Large Multimodal Models, LMMs）在不同计算预算下难以适应的问题，尤其是由于视觉标记（visual tokens）数量庞大导致的效率瓶颈。现有方法通过减少视觉标记数量来降低计算开销，但往往造成视觉语义信息的丢失，从而损害模型推理能力。其解决方案的关键在于提出一种即插即用且极简的频率调制视觉恢复策略（Frequency-Modulated Visual Restoration, FMVR）：该策略利用平均池化（AvgPool）和最大池化（MaxPool）将少量视觉标记的表示解耦为低频与高频分量，其中低频成分作为抗显著性滤波器增强弱语义，高频成分作为显著性滤波器强化关键语义，并通过轻量级可学习参数对二者进行调制，从而在视觉标记稀疏的情况下保留并恢复被稀释的视觉语义信息。此外，FMVR被集成到马特罗什卡表征学习（Matryoshka Representation Learning）中，实现从粗到细的视觉标记集学习，使推理阶段能弹性调整视觉标记数量，同时保持接近原始性能。
链接: https://arxiv.org/abs/2603.11220
作者: Qingtao Pan,Zhihao Dou,Shuo Li
机构: Case Western Reserve University (凯斯西储大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Large Multimodal Models (LMMs) struggle to adapt varying computational budgets due to numerous visual tokens. Previous methods attempted to reduce the number of visual tokens before or within LLMs. However, these strategies inevitably result in the loss of visual semantic. To address these issues, we introduce FMVR, a plug-and-play and extremely simple Frequency-Modulated Visual Restoration strategy to boost the reasoning ability of LMMs under visual token reduction. Specifically, FMVR disentangles the visual representation of fewer visual tokens into low- and high-frequency components through AvgPool and MaxPool. The derived frequencies are subsequently modulated using lightweight learnable parameters. The high-frequency from AvgPool acts as a saliency filter to enhance saliency visual semantics, while the low-frequency from MaxPool acts as an anti-saliency filter to strengthen weak visual semantics. It enables the preservation of visual semantics dominated by few visual tokens and the restoration of diluted visual semantics. Additionally, we inject FMVR into Matryoshka Representation Learning to learn coarse-to-fine visual token sets, thus enabling to elastically adjust the number of visual tokens during inference while maintaining comparable performance. Experiments across 10 image-based and 4 video-based bench marks demonstrate that FMVR-LLaVA reduce the FLOPs of LLaVA-1.5-7B by 89%, while maintaining almost 100% of the original accuracy. The code will be open.
[NLP-65] DeReason : A Difficulty-Aware Curriculum Improves Decoupled SFT-then-RL Training for General Reasoning
【速读】： 该论文旨在解决在通用科学、技术、工程和数学（STEM）领域中，直接将强化学习（Reinforcement Learning, RL）应用于基础语言模型时存在样本效率低下的问题，以及如何有效协同监督微调（Supervised Fine-Tuning, SFT）与RL以提升模型的复杂推理能力。其解决方案的关键在于提出一种基于难度的数据解耦策略——DeReason，该策略通过大语言模型（LLM）评分估计推理强度，将训练数据划分为高推理强度和低推理强度两类：前者用于后续强化学习阶段以培养复杂推理能力，后者则分配给监督微调阶段以建立广泛的领域知识基础。这种有原则的数据分层分配机制显著优于随机分割或单一阶段训练方法，在多个STEM和数学基准测试中展现出更优性能。
链接: https://arxiv.org/abs/2603.11193
作者: Hanxu Hu,Yuxuan Wang,Maggie Huan,Jannis Vamvas,Yinya Huang,Zhijiang Guo,Rico Sennrich
机构: University of Zurich (苏黎世大学); University of Pennsylvania (宾夕法尼亚大学); ETH Zurich (苏黎世联邦理工学院); HKUST (GZ) (香港科技大学（广州）); HKUST (香港科技大学)
类目: Computation and Language (cs.CL)
备注: 13 pages, 6 figures
Abstract:Reinforcement learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for eliciting reasoning capabilities in large language models, particularly in mathematics and coding. While recent efforts have extended this paradigm to broader general scientific (STEM) domains, the complex interplay between supervised fine-tuning (SFT) and RL in these contexts remains underexplored. In this paper, we conduct controlled experiments revealing a critical challenge: for general STEM domains, RL applied directly to base models is highly sample-inefficient and is consistently surpassed by supervised fine-tuning (SFT) on moderate-quality responses. Yet sequential SFT followed by RL can further improve performance, suggesting that the two stages play complementary roles, and that how training data is allocated between them matters. Therefore, we propose DeReason, a difficulty-based data decoupling strategy for general reasoning. DeReason partitions training data by reasoning intensity estimated via LLM-based scoring into reasoning-intensive and non-reasoning-intensive subsets. It allocates broad-coverage, non-reasoning-intensive problems to SFT to establish foundational domain knowledge, and reserves a focused subset of difficult problems for RL to cultivate complex reasoning. We demonstrate that this principled decoupling yields better performance than randomly splitting the data for sequential SFT and RL. Extensive experiments on general STEM and mathematical benchmarks demonstrate that our decoupled curriculum training significantly outperforms SFT-only, RL-only, and random-split baselines. Our work provides a systematic study of the interplay between SFT and RL for general reasoning, offering a highly effective and generalized post-training recipe.
[NLP-66] Huntington Disease Automatic Speech Recognition with Biomarker Supervision
【速读】： 该论文旨在解决亨廷顿病（Huntington’s disease, HD）患者语音的自动语音识别（automatic speech recognition, ASR）问题，此类语音常伴有不规则节律、发声不稳定和构音畸变等特征，导致现有ASR模型性能显著下降。解决方案的关键在于：首先，使用一个此前未用于端到端ASR训练的高保真临床语音语料库进行系统性研究；其次，提出针对HD语音特性的适应策略，使词错误率（word error rate, WER）从6.99%降至4.95%；最后，引入基于生物标志物的辅助监督机制，发现错误模式随疾病严重程度呈现非均匀变化，而非简单统一改善，从而揭示了HD语音识别中误差行为的结构性差异。
链接: https://arxiv.org/abs/2603.11168
作者: Charles L. Wang,Cady Chen,Ziwei Gong,Julia Hirschberg
机构: Columbia University (哥伦比亚大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Sound (cs.SD)
备注:
Abstract:Automatic speech recognition (ASR) for pathological speech remains underexplored, especially for Huntington’s disease (HD), where irregular timing, unstable phonation, and articulatory distortion challenge current models. We present a systematic HD-ASR study using a high-fidelity clinical speech corpus not previously used for end-to-end ASR training. We compare multiple ASR families under a unified evaluation, analyzing WER as well as substitution, deletion, and insertion patterns. HD speech induces architecture-specific error regimes, with Parakeet-TDT outperforming encoder-decoder and CTC baselines. HD-specific adaptation reduces WER from 6.99% to 4.95% and we also propose a method for using biomarker-based auxiliary supervision and analyze how error behavior is reshaped in severity-dependent ways rather than uniformly improving WER. We open-source all code and models.
[NLP-67] Scaling Reasoning Efficiently via Relaxed On-Policy Distillation
【速读】： 该论文旨在解决**在线蒸馏（on-policy distillation）**在将推理能力迁移至计算资源受限模型时面临的不稳定性与负迁移问题。其核心解决方案是提出REOPOLD（Relaxed On-Policy Distillation）框架，关键在于将在线蒸馏重新诠释为一种策略优化过程，其中教师模型与学生模型之间的对数似然比作为token级别的奖励信号；并通过三种机制实现优化稳定：基于混合的奖励裁剪（mixture-based reward clipping）、基于熵的token级动态采样（entropy-based token-level dynamic sampling），以及统一的探索到精炼训练策略（exploration-to-refinement training strategy），从而显著提升样本效率和推理阶段的可扩展性。
链接: https://arxiv.org/abs/2603.11137
作者: Jongwoo Ko,Sara Abdali,Young Jin Kim,Tianyi Chen,Pashmina Cameron
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Code will be available soon
Abstract:On-policy distillation is pivotal for transferring reasoning capabilities to capacity-constrained models, yet remains prone to instability and negative transfer. We show that on-policy distillation can be interpreted, both theoretically and empirically, as a form of policy optimization, where the teacher-student log-likelihood ratio acts as a token reward. From this insight, we introduce REOPOLD (Relaxed On-Policy Distillation) a framework that stabilizes optimization by relaxing the strict imitation constraints of standard on-policy distillation. Specifically, REOPOLD temperately and selectively leverages rewards from the teacher through mixture-based reward clipping, entropy-based token-level dynamic sampling, and a unified exploration-to-refinement training strategy. Empirically, REOPOLD surpasses its baselines with superior sample efficiency during training and enhanced test-time scaling at inference, across mathematical, visual, and agentic tool-use reasoning tasks. Specifically, REOPOLD outperforms recent RL approaches achieving 6.7~12x greater sample efficiency and enables a 7B student to match a 32B teacher in visual reasoning with a ~3.32x inference speedup.
[NLP-68] Uni-ASR: Unified LLM -Based Architecture for Non-Streaming and Streaming Automatic Speech Recognition INTERSPEECH2026
【速读】： 该论文旨在解决将大语言模型（Large Language Models, LLMs）与自动语音识别（Automatic Speech Recognition, ASR）系统深度集成后，在低延迟流式语音识别场景中部署困难的问题。其解决方案的关键在于提出了一种统一框架Uni-ASR，通过联合训练范式实现非流式与流式识别模式间的无缝切换，无需架构改动；同时引入上下文感知的训练策略和协同设计的回退解码策略，在不增加额外延迟的前提下显著提升流式识别准确率。
链接: https://arxiv.org/abs/2603.11123
作者: Yinfeng Xia,Jian Tang,Junfeng Hou,Gaopeng Xu,Haitao Yao
机构: Qwen Applications Business Group(通义应用业务组); Tongyi AI Lab(通义实验室)
类目: ound (cs.SD); Computation and Language (cs.CL)
备注: Submitted to Interspeech 2026
Abstract:Although the deep integration of the Automatic Speech Recognition (ASR) system with Large Language Models (LLMs) has significantly improved accuracy, the deployment of such systems in low-latency streaming scenarios remains challenging. In this paper, we propose Uni-ASR, a unified framework based on LLMs that integrates both non-streaming and streaming speech recognition capabilities. We propose a joint training paradigm that enables the system to seamlessly transition between two recognition modes without any architectural modifications. Furthermore, we introduce a context-aware training paradigm and a co-designed fallback decoding strategy, which can enhance streaming recognition accuracy without introducing additional latency. The experimental results demonstrate that Uni-ASR not only achieves competitive performance within non-streaming mode, but also demonstrates strong effectiveness in streaming scenarios under diverse latency constraints.
[NLP-69] CR-Bench: Evaluating the Real-World Utility of AI Code Review Agents
【速读】： 该论文旨在解决当前代码审查代理（code review agents）在开放性、推理密集型场景下缺乏标准化评估基准和细粒度评价协议的问题，尤其针对误报（false positives）代价较高的任务难以准确衡量其行为表现。解决方案的关键在于提出CR-Bench——一个基准数据集和CR-Evaluator——一个细粒度评估流水线，能够系统性地量化代码审查代理的性能，揭示其在问题修复率与虚假发现之间的隐性权衡关系，从而为基于大语言模型（LLM）的代码审查代理设计提供可量化的研究基础和优化方向。
链接: https://arxiv.org/abs/2603.11078
作者: Kristen Pereira,Neelabh Sinha,Rajat Ghosh,Debojyoti Dutta
机构: Nutanix, Inc.
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Recent advances in frontier large language models have enabled code review agents that operate in open-ended, reasoning-intensive settings. However, the lack of standardized benchmarks and granular evaluation protocols makes it difficult to assess behavior of code review agents beyond coarse success metrics, particularly for tasks where false positives are costly. To address this gap, we introduce CR-Bench, a benchmarking dataset, and CR-Evaluator, a fine-grained evaluation pipeline for code review agents. Using these tools, we conduct a preliminary study evaluating both a single-shot agent and a Reflexion-based agent across two frontier models. We find that code review agents can exhibit a low signal-to-noise ratio when designed to identify all hidden issues, obscuring true progress and developer productivity when measured solely by resolution rates. Our analysis identifies the hidden trade-off between issue resolution and spurious findings, revealing a frontier that constrains effective agent design. Together, CR-Bench and CR-Evaluator provide a timely foundation for studying and developing code review agents as LLM-based systems transition from controlled benchmarks to real-world software engineering workflows.
[NLP-70] Summarize Before You Speak with ARACH: A Training-Free Inference-Time Plug-In for Enhancing LLM s via Global Attention Reallocation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在推理阶段性能提升受限的问题，尤其是在不更新模型参数的前提下如何有效增强其输出质量。当前主流的训练-free方法多依赖于输入/输出层面的干预（如提示工程或采样重排序），但缺乏对模型内部计算过程的可控性。解决方案的关键在于提出ARACH（Attention Reallocation via an Adaptive Context Hub），这是一种无需训练的推理时插件机制，通过引入一个自适应上下文枢纽（adaptive context hub）来聚合和重新分配注意力权重，从而优化模型内部的信息流动。实验表明，该方法在多个语言建模任务中均实现稳定性能提升，且推理开销小，同时注意力分析揭示其能缓解“注意力黑洞”（attention sink）现象，体现了从内部计算结构层面改进模型推理能力的新路径。
链接: https://arxiv.org/abs/2603.11067
作者: Jingtao Wang,Yucong Wang,Jun Ding,Rui Cai,Xun Wang
机构: McGill University Health Centre (麦吉尔大学健康中心); McGill University (麦吉尔大学); Zhejiang Gongshang University (浙江工商大学); Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology (浙江省大数据与未来电子商务技术重点实验室); Mila-Quebec AI Institute (Mila-魁北克人工智能研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) achieve remarkable performance, yet further gains often require costly training. This has motivated growing interest in post-training techniques-especially training-free approaches that improve models at inference time without updating weights. Most training-free methods treat the model as a black box and improve outputs via input/output-level interventions, such as prompt design and test-time scaling through repeated sampling, reranking/verification, or search. In contrast, they rarely offer a plug-and-play mechanism to intervene in a model’s internal computation. We propose ARACH(Attention Reallocation via an Adaptive Context Hub), a training-free inference-time plug-in that augments LLMs with an adaptive context hub to aggregate context and reallocate attention. Extensive experiments across multiple language modeling tasks show consistent improvements with modest inference overhead and no parameter updates. Attention analyses further suggest that ARACH mitigates the attention sink phenomenon. These results indicate that engineering a model’s internal computation offers a distinct inference-time strategy, fundamentally different from both prompt-based test-time methods and training-based post-training approaches.
[NLP-71] Speculative Decoding Scaling Laws (SDSL): Throughput Optimization Made Simple
【速读】： 该论文旨在解决生成式 AI（Generative AI）推理过程中因大语言模型（Large Language Models, LLMs）计算开销大而导致的吞吐量低的问题。传统方法依赖实验性调优来提升基于推测解码（Speculative Decoding, SD）的推理系统性能，但该过程通常需要重新训练LLM，成本高昂。论文提出了一种理论框架，通过解析性建模将预训练LLM的关键超参数与下游SD推理系统的吞吐效率直接关联，从而能够在LLM预训练前预测出最优的超参数配置，显著降低优化成本并提升推理效率。
链接: https://arxiv.org/abs/2603.11053
作者: Amirhossein Bozorgkhoo,Igor Molybog
机构: University of Hawai’i at Manoa (夏威夷大学马诺阿分校)
类目: Computation and Language (cs.CL); Information Theory (cs.IT); Machine Learning (cs.LG)
备注:
Abstract:Speculative decoding is a technique that uses multiple language models to accelerate infer- ence. Previous works have used an experi- mental approach to optimize the throughput of the inference pipeline, which involves LLM training and can be costly. This study of spec- ulative decoding proposes a theory that ana- lytically connects the key hyperparameters of pre-trained LLMs to the throughput efficiency of a downstream SD-based inference system. The theory allows the prediction of throughput- optimal hyperparameters for the components of an inference system before their pre-training.
[NLP-72] Beyond Polarity: Multi-Dimensional LLM Sentiment Signals for WTI Crude Oil Futures Return Prediction
【速读】： 该论文旨在解决原油价格预测中因市场相关信息嵌套于大量非结构化新闻文本而难以被传统极性（polarity）导向的情感指标充分捕捉的问题。其解决方案的关键在于利用大语言模型（Large Language Models, LLMs）提取多维情感信号，包括相关性（relevance）、极性（polarity）、强度（intensity）、不确定性（uncertainty）和前瞻性（forwardness），并基于GPT-4o、Llama 3.2-3b、FinBERT与AlphaVantage等模型构建综合情感特征，通过聚合至周度层面并在分类框架下评估其预测性能。实证结果表明，LLM驱动的情感维度（尤其是强度与不确定性）显著提升了WTI原油期货收益的预测能力，且LLM与传统金融情感模型存在互补效应，从而为大宗商品收益预测和能源市场风险监控提供了更有效的工具。
链接: https://arxiv.org/abs/2603.11408
作者: Dehao Dai,Ding Ma,Dou Liu,Kerui Geng,Yiqing Wang
机构: 未知
类目: atistical Finance (q-fin.ST); Computation and Language (cs.CL)
备注: 28 pages, 4 figures, 4 tables
Abstract:Forecasting crude oil prices remains challenging because market-relevant information is embedded in large volumes of unstructured news and is not fully captured by traditional polarity-based sentiment measures. This paper examines whether multi-dimensional sentiment signals extracted by large language models improve the prediction of weekly WTI crude oil futures returns. Using energy-sector news articles from 2020 to 2025, we construct five sentiment dimensions covering relevance, polarity, intensity, uncertainty, and forwardness based on GPT-4o, Llama 3.2-3b, and two benchmark models, FinBERT and AlphaVantage. We aggregate article-level signals to the weekly level and evaluate their predictive performance in a classification framework. The best results are achieved by combining GPT-4o and FinBERT, suggesting that LLM-based and conventional financial sentiment models provide complementary predictive information. SHAP analysis further shows that intensity- and uncertainty-related features are among the most important predictors, indicating that the predictive value of news sentiment extends beyond simple polarity. Overall, the results suggest that multi-dimensional LLM-based sentiment measures can improve commodity return forecasting and support energy-market risk monitoring.
信息检索
[IR-0] Enhancing Music Recommendation with User Mood Input
【速读】：该论文旨在解决音乐推荐系统在用户交互数据稀疏场景下性能下降的问题，传统协同过滤方法因依赖大量用户行为数据而效果受限。其解决方案的关键在于引入基于情绪感知的内容过滤机制，即通过分析用户的主观情绪状态（利用能量-愉悦度谱，energy-valence spectrum）来增强个性化推荐的准确性。实验表明，将用户情绪纳入推荐模型后，显著提升了推荐质量，验证了情绪辅助推荐在音乐流媒体平台中的有效性与潜力。
链接: https://arxiv.org/abs/2603.11796
作者: Terence Zeng
机构: 未知
类目: Information Retrieval (cs.IR)
备注: 28 pages, 9 figures, 2 tables
Abstract:Recommendation systems have become essential in modern music streaming platforms, due to the vast amount of content available. A common approach in recommendation systems is collaborative filtering, which suggests content to users based on the preferences of others with similar patterns. However, this method performs poorly in domains where interactions are sparse, such as music. Content-based filtering is an alternative approach that examines the qualities of the items themselves. Prior work has explored a range of content-filtering techniques for music, including genre classification, instrument detection, and lyrics analysis. In the literature review component of this work, we examine these methods in detail. Music emotion recognition is a type of content-based filtering that is less explored but has significant potential. Since a user’s emotional state influences their musical choices, incorporating user mood into recommendation systems is an alternative way to personalize the listening experience. In this study, we explore a mood-assisted recommendation system that suggests songs based on the desired mood using the energy-valence spectrum. Single-blind experiments are conducted, in which participants are presented with two recommendations (one generated from a mood-assisted recommendation system and one from a baseline system) and are asked to rate them. Results show that integrating user mood leads to a statistically significant improvement in recommendation quality, highlighting the potential of such approaches.
[IR-1] Modeling Trial-and-Error Navigation With a Sequential Decision Model of Information Scent
【速读】：该论文试图解决用户在信息架构中难以定位目标项的问题，尤其当链接存在歧义或嵌套层级较深时，传统“信息嗅觉”（information scent）理论无法充分解释用户过早选择错误链接、忽略相关线索以及后续回溯等行为。其解决方案的关键在于将导航建模为一个受记忆约束的序列决策问题：用户并非扫描完整页面，而是基于局部（当前页面）和全局（整个网站）的信息嗅觉，在有限时间内进行策略性检查，从而决定下一步查看的内容；这种模型能够准确再现用户的提前选择、误入歧途及回溯恢复等典型行为，表明试错行为可由考虑时间与记忆限制后的信息嗅觉机制有效解释。
链接: https://arxiv.org/abs/2603.11759
作者: Xiaofu Jin,Yunpeng Bai,Antti Oulasvirta
机构: Aalto University (阿尔托大学); National University of Singapore (新加坡国立大学)
类目: Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:Users often struggle to locate an item within an information architecture, particularly when links are ambiguous or deeply nested in hierarchies. Information scent has been used to explain why users select incorrect links, but this concept assumes that users see all available links before deciding. In practice, users frequently select a link too quickly, overlook relevant cues, and then rely on backtracking when errors occur. We extend the concept of information scent by framing navigation as a sequential decision-making problem under memory constraints. Specifically, we assume that users do not scan entire pages but instead inspect strategically, looking “just enough” to find the target given their time budget. To choose which item to inspect next, they consider both local (this page) and global (site) scent; however, both are constrained by memory. Trying to avoid wasting time, they occasionally choose the wrong links without inspecting everything on a page. Comparisons with empirical data show that our model replicates key navigation behaviors: premature selections, wrong turns, and recovery from backtracking. We conclude that trial-and-error behavior is well explained by information scent when accounting for the sequential and bounded characteristics of the navigation problem.
[IR-2] Federated Learning and Unlearning for Recommendation with Personalized Data Sharing
【速读】：该论文旨在解决现有联邦推荐系统（Federated Recommender Systems, FedRS）中用户隐私偏好静态化、无法支持数据撤回请求的问题。传统方法通常假设所有用户均严格本地保留数据，忽略了愿意以数据共享换取更好推荐性能的用户群体，且缺乏对已共享数据进行有效移除的能力。解决方案的关键在于提出FedShare框架，其核心创新是通过构建服务器端高阶用户-物品图并结合对比学习实现局部与全局表示的一致性对齐；在去学习（unlearning）阶段，设计了一种对比去学习机制，仅利用少量历史嵌入快照即可选择性移除未共享数据所诱导的表示，从而避免了现有方法依赖存储大量历史梯度信息的高开销问题，实现了个性化数据共享与高效去学习的协同优化。
链接: https://arxiv.org/abs/2603.11610
作者: Liang Qu,Jianxin Li,Wei Yuan,Shangfei Zheng,Lu Chen,Chengfei Liu,Hongzhi Yin
机构: Edith Cowan University (埃迪斯科文大学); The University of Queensland (昆士兰大学); Zhejiang Sci-Tech University (浙江理工大学); Swinburne University of Technology (斯威本科技大学)
类目: Information Retrieval (cs.IR)
备注: 14 pages
Abstract:Federated recommender systems (FedRS) have emerged as a paradigm for protecting user privacy by keeping interaction data on local devices while coordinating model training through a central server. However, most existing federated recommender systems adopt a one-size-fits-all assumption on user privacy, where all users are required to keep their data strictly local. This setting overlooks users who are willing to share their data with the server in exchange for better recommendation performance. Although several recent studies have explored personalized user data sharing in FedRS, they assume static user privacy preferences and cannot handle user requests to remove previously shared data and its corresponding influence on the trained model. To address this limitation, we propose FedShare, a federated learn-unlearn framework for recommender systems with personalized user data sharing. FedShare not only allows users to control how much interaction data is shared with the server, but also supports data unsharing requests by removing the influence of the unshared data from the trained model. Specifically, FedShare leverages shared data to construct a server-side high-order user-item graph and uses contrastive learning to jointly align local and global representations. In the unlearning phase, we design a contrastive unlearning mechanism that selectively removes representations induced by the unshared data using a small number of historical embedding snapshots, avoiding the need to store large amounts of historical gradient information as required by existing federated recommendation unlearning methods. Extensive experiments on three public datasets demonstrate that FedShare achieves strong recommendation performance in both the learning and unlearning phases, while significantly reducing storage overhead in the unlearning phase compared with state-of-the-art baselines.
[IR-3] Quantized Inference for OneRec-V2
【速读】：该论文旨在解决工业场景下低精度量化（low-precision quantization）在推荐系统中难以可靠应用的问题，其核心挑战源于推荐模型与大语言模型（Large Language Models, LLMs）在训练范式、架构模式和计算特性上的差异，导致推荐系统中的权重和激活值具有高幅度和高方差，对量化扰动更为敏感，且常面临硬件利用率不足的问题。解决方案的关键在于引入生成式推荐（generative recommendation）框架OneRec-V2，通过实证分布分析发现其权重和激活统计特性更接近LLMs，具备更强的量化鲁棒性；同时其推理模式更具计算密集性，显著提升了硬件利用率。基于此特性，作者设计了FP8后训练量化（post-training quantization）方案，并集成至优化的推理基础设施中，实现了端到端推理延迟降低49%、吞吐量提升92%，且在线A/B测试验证核心指标无下降，表明LLM领域成熟的算法与系统级优化技术可有效迁移至大规模推荐任务。
链接: https://arxiv.org/abs/2603.11486
作者: Yi Su,Xinchen Luo,Hongtao Cheng,Ziteng Shu,Yunfeng Zhao,Fangyu Zhang,Jiaqiang Liu,Xiao Liang,Yiwu Liu,Ruiming Tang
机构: Kuaishou Inc., Beijing, China
类目: Information Retrieval (cs.IR)
备注:
Abstract:Quantized inference has demonstrated substantial system-level benefits in large language models while preserving model quality. In contrast, reliably applying low-precision quantization to recommender systems remains challenging in industrial settings. This difficulty arises from differences in training paradigms, architectural patterns, and computational characteristics, which lead to distinct numerical behaviors in weights and activations. Traditional recommender models often exhibit high-magnitude and high-variance weights and activations, making them more sensitive to quantization-induced perturbations. In addition, recommendation workloads frequently suffer from limited hardware utilization, limiting the practical gains of low-precision computation. In this work, we revisit low-precision inference in the context of generative recommendation. Through empirical distribution analysis, we show that the weight and activation statistics of OneRec-V2 are significantly more controlled and closer to those of large language models than traditional recommendation models. Moreover, OneRec-V2 exhibits a more compute-intensive inference pattern with substantially higher hardware utilization, enabling more end-to-end throughput gains with low-precision computation. Leveraging this property, we develop a FP8 post training quantization framework and integrate it into an optimized inference infrastructure. The proposed joint optimization achieves a 49% reduction in end-to-end inference latency and a 92% increase in throughput. Extensive online A/B testing further confirms that FP8 inference introduces no degradation in core metrics. These results suggest that as recommender systems evolve toward the paradigms of large language models, algorithm-level and system-level optimization techniques established in the LLM domain can be effectively adapted to large-scale recommendation workloads.
[IR-4] Reproducible Synthetic Clinical Letters for Seizure Frequency Information Extraction
【速读】：该论文旨在解决癫痫患者发作频率信息在临床文书中的提取难题，这些问题通常存在于非结构化自由文本中，难以标注与共享。解决方案的关键在于构建一个可复现且隐私保护的框架，利用完全合成但任务忠实的癫痫门诊信件进行训练，通过定义涵盖常见发作负担描述的结构化标签体系（包括明确频率、范围、集群、无发作间隔、未知频率及明确无发作声明），并使用教师语言模型生成带有标准化标签、推理过程和证据片段的NHS风格合成数据。在此基础上，对多个开源大语言模型（4B–14B参数）进行微调，对比直接数值预测与结构化标签预测的效果，并验证基于证据的输出形式。实验表明，仅用15,000条合成数据训练的模型在真实临床文档上表现出良好泛化能力，其中结构化标签预测显著优于直接回归方法，微F1得分最高达0.788（细粒度类别）和0.847（实用类别），证明了合成、结构化、证据驱动的监督信号可在不共享敏感患者文本的前提下实现鲁棒的发作频率抽取，并可能推广至其他具有时间复杂性的临床信息抽取任务。
链接: https://arxiv.org/abs/2603.11407
作者: Yujian Gan,Stephen H. Barlow,Ben Holgate,Joe Davies,James T. Teo,Joel S. Winston,Mark P. Richardson
机构: King’s College London (伦敦国王学院)
类目: Information Retrieval (cs.IR)
备注:
Abstract:Seizure-frequency information is important for epilepsy research and clinical care, but it is usually recorded in variable free-text clinic letters that are hard to annotate and share. We developed a reproducible, privacy-preserving framework for extracting seizure frequency using fully synthetic yet task-faithful epilepsy letters. We defined a structured label scheme covering common descriptions of seizure burden, including explicit rates, ranges, clusters, seizure-free intervals, unknown frequency, and explicit no-seizure statements. A teacher language model generated NHS-style synthetic letters paired with normalized labels, rationales, and evidence spans. We fine-tuned several open-weight language models (4B-14B parameters) on these synthetic letters to extract seizure frequency from full documents, comparing direct numeric prediction with structured label prediction and testing evidence-grounded outputs. On a clinician-checked held-out set of real clinic letters, models trained only on synthetic data generalized well, and structured labels consistently outperformed direct numeric regression. With 15,000 synthetic training letters, models achieved micro-F1 scores up to 0.788 for fine-grained categories and 0.847 for pragmatic categories; a medically oriented 4B model achieved 0.787 and 0.858, respectively. Evidence-grounded outputs also supported rapid clinical verification and error analysis. These results show that synthetic, structured, evidence-grounded supervision can enable robust seizure-frequency extraction without sharing sensitive patient text and may generalize to other temporally complex clinical information extraction tasks.
[IR-5] MDER-DR: Multi-Hop Question Answering with Entity-Centric Summaries
【速读】：该论文旨在解决基于知识图谱（Knowledge Graph, KG）的检索增强生成（Retrieval-Augmented Generation, RAG）在问答（Question-Answering, QA）任务中因三元组索引过程丢失重要上下文语义而导致性能下降的问题，尤其针对需要多跳推理（multi-hop QA）的任务。其解决方案的关键在于提出一个领域无关的KG-QA框架，包含两个核心组件：一是新的索引方法Map-Disambiguate-Enrich-Reduce (MDER)，通过生成基于上下文的三元组描述并融合实体级摘要，避免在QA检索阶段显式遍历图边；二是检索机制Decompose-Resolve (DR)，将用户查询分解为可解析的三元组并通过迭代推理在KG中锚定，从而形成一个由大语言模型（Large Language Model, LLM）驱动的鲁棒QA流水线，有效应对稀疏、不完整和复杂关系数据。
链接: https://arxiv.org/abs/2603.11223
作者: Riccardo Campi,Nicolò Oreste Pinciroli Vago,Mathyas Giudici,Marco Brambilla,Piero Fraternali
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: Our code is available at this https URL
Abstract:Retrieval-Augmented Generation (RAG) over Knowledge Graphs (KGs) suffers from the fact that indexing approaches may lose important contextual nuance when text is reduced to triples, thereby degrading performance in downstream Question-Answering (QA) tasks, particularly for multi-hop QA, which requires composing answers from multiple entities, facts, or relations. We propose a domain-agnostic, KG-based QA framework that covers both the indexing and retrieval/inference phases. A new indexing approach called Map-Disambiguate-Enrich-Reduce (MDER) generates context-derived triple descriptions and subsequently integrates them with entity-level summaries, thus avoiding the need for explicit traversal of edges in the graph during the QA retrieval phase. Complementing this, we introduce Decompose-Resolve (DR), a retrieval mechanism that decomposes user queries into resolvable triples and grounds them in the KG via iterative reasoning. Together, MDER and DR form an LLM-driven QA pipeline that is robust to sparse, incomplete, and complex relational data. Experiments show that on standard and domain specific benchmarks, MDER-DR achieves substantial improvements over standard RAG baselines (up to 66%), while maintaining cross-lingual robustness. Our code is available at this https URL.
[IR-6] OpenSanctions Pairs: Large-Scale Entity Matching with LLM s
【速读】：该论文旨在解决国际制裁名单中实体匹配（entity matching）的难题，即在多源异构、跨语言、存在噪声和缺失属性的数据场景下，准确识别重复或关联的实体。其关键解决方案是构建了一个大规模、真实世界的数据集 OpenSanctions Pairs，并在此基础上对比了传统规则匹配系统与大语言模型（LLM）在零样本和少样本设置下的表现。结果表明，现成的 LLM（如 GPT-4o 和 DeepSeek-R1-Distill-Qwen-14B）显著优于生产级规则匹配器（F1 从 91.33% 提升至最高 98.95%），且本地部署的开源模型已具备实用价值；同时发现提示优化（DSPy MIPROv2）带来小幅提升，而上下文示例对性能改善有限甚至有害，揭示出当前任务的匹配性能已接近实用上限，未来应聚焦于阻断（blocking）、聚类（clustering）及不确定性感知审核等下游组件改进。
链接: https://arxiv.org/abs/2603.11051
作者: Chandler Smith,Magnus Sesodia,Friedrich Lindenberg,Christian Schroeder de Witt
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:We release OpenSanctions Pairs, a large-scale entity matching benchmark derived from real-world international sanctions aggregation and analyst deduplication. The dataset contains 755,540 labeled pairs spanning 293 heterogeneous sources across 31 countries, with multilingual and cross-script names, noisy and missing attributes, and set-valued fields typical of compliance workflows. We benchmark a production rule-based matcher (nomenklatura RegressionV1 algorithm) against open- and closed-source LLMs in zero- and few-shot settings. Off-the-shelf LLMs substantially outperform the production rule-based baseline (91.33% F1), reaching up to 98.95% F1 (GPT-4o) and 98.23% F1 with a locally deployable open model (DeepSeek-R1-Distill-Qwen-14B). DSPy MIPROv2 prompt optimization yields consistent but modest gains, while adding in-context examples provides little additional benefit and can degrade performance. Error analysis shows complementary failure modes: the rule-based system over-matches (high false positives), whereas LLMs primarily fail on cross-script transliteration and minor identifier/date inconsistencies. These results indicate that pairwise matching performance is approaching a practical ceiling in this setting, and motivate shifting effort toward pipeline components such as blocking, clustering, and uncertainty-aware review. Code available at this https URL
人机交互
[HC-0] UniMotion: Self-Supervised Learning for Cross-Domain IMU Motion Recognition
【速读】：该论文旨在解决当前基于惯性测量单元（Inertial Measurement Unit, IMU）的手势识别算法在不同设备（如智能手表与耳塞）和用户群体（如盲人与视力正常者）之间泛化能力差的问题。其核心挑战在于获取大规模标注手势数据成本高昂，且现有模型难以跨场景迁移。解决方案的关键在于提出UniMotion框架，采用两阶段训练策略：首先利用大量未标注的人体活动数据进行预训练，通过基于token的表示学习方法提取关键运动特征；随后仅用少量标注手势数据微调模型，并引入文本引导分类器以区分时序或语义相近的手势。该方法显著提升了模型在多样化设备与用户群体上的准确率（平均达85%），同时大幅减少对标注数据的依赖。
链接: https://arxiv.org/abs/2603.12218
作者: Prerna Khanna,Tanmay Srivastava,Shubham Jain,Aruna Balasubramanian
机构: Stony Brook University (石溪大学)
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:IMU-based gesture interfaces are being increasingly adopted as efficient, accessible, and intuitive alternatives to traditional input methods, such as touchscreens and voice. However, current gesture recognition algorithms are tailored to work for specific devices (e.g., smartwatches vs. earbuds) or user populations (e.g., blind vs. sighted users), limiting their generalizability. In this paper, we design UniMotion, a generalized IMU-based gesture recognition framework that works across devices and populations with minimal training samples. To overcome the challenges and high cost of collecting large-scale labeled training data, UniMotion leverages readily available unlabeled human activity data. The UniMotion pipeline comprises two stages: (1) pre-training a motion representation model using abundant unlabeled human activity data, and (2) fine-tuning it with a small amount of labeled gesture data. For pre-training, we introduce a token-based strategy and embeddings that learn to identify and focus attention on the key motion signatures in the temporal data For fine-tuning, we design a text-guided classifier that can reliably differentiate between temporally or semantically similar gestures. We evaluate UniMotion across both hand gestures (captured through a smartwatch) and earbud gestures (captured through earbuds), using data collected from blind and sighted users. Across these diverse devices and user populations, UniMotion achieves an accuracy of 85%, across an average of 13 gesture classes using only 10% of labeled data for training. UniMotion significantly outperforms state-of-the-art self-supervised learning approaches and specialized gesture recognition models.
[HC-1] Human-Centred LLM Privacy Audits: Findings and Frictions
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在部署过程中可能生成与个体相关的统计关联信息，而用户缺乏有效手段来检查这些关联的问题。其核心挑战在于：当LLM输出具有概率性、上下文依赖性和用户引导特征时，如何定义并操作化“模型-个体关联”这一概念变得模糊且难以验证。解决方案的关键在于提出LMP2——一个基于浏览器的自我审计工具，并通过两轮用户研究（N=458）验证其有效性：GPT-4o对普通人的50个特征中预测11项达≥60%准确率；同时发现用户虽不认为所有输出均为隐私侵犯，但仍希望对模型生成的关联拥有控制权。此外，作者识别出九类阻碍可靠人类中心隐私审计的摩擦点，为未来设计可行动的人类中心LLM隐私评估框架提供方向。
链接: https://arxiv.org/abs/2603.12094
作者: Dimitri Staufer,Kirsten Morehouse,David Hartmann,Bettina Berendt
机构: TU Berlin(柏林工业大学); Weizenbaum Institute for the Networked Society(网络社会魏泽曼研究所); Columbia University(哥伦比亚大学); KU Leuven(鲁汶大学)
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
备注:
Abstract:Large language models (LLMs) learn statistical associations from massive training corpora and user interactions, and deployed systems can surface or infer information about individuals. Yet people lack practical ways to inspect what a model associates with their name. We report interim findings from an ongoing study and introduce LMP2, a browser-based self-audit tool. In two user studies ( N_total=458 ), GPT-4o predicts 11 of 50 features for everyday people with \ge 60% accuracy, and participants report wanting control over LLM-generated associations despite not considering all outputs privacy violations. To validate our probing method, we evaluate eight LLMs on public figures and non-existent names, observing clear separation between stable name-conditioned associations and model defaults. Our findings also contribute to exposing a broader generative AI evaluation crisis: when outputs are probabilistic, context-dependent, and user-mediated through elicitation, what model–individual associations even include is under-specified and operationalisation relies on crafting probes and metrics that are hard to validate or compare. To move towards reliable, actionable human-centred LLM privacy audits, we identify nine frictions that emerged in our study and offer recommendations for future work and the design of human-centred LLM privacy audits.
[HC-2] An Intent of Collaboration: On Agencies between Designers and Emerging (Intelligent) Technologies
【速读】：该论文旨在解决生成式 AI（Generative AI）在数字工艺（Digital Craftsmanship）等设计领域中，因缺乏具身知识（embodied knowledge）而可能削弱设计师创造性自主权（creative agency）的问题。解决方案的关键在于重构人机协作关系：通过设计师对自身创作过程的认知敏感性、对特定技术能力的主动探究，以及对人机协作动态的有意识调整，从而重新确立设计师的创造性主体地位。
链接: https://arxiv.org/abs/2603.12018
作者: Pei-Ying Lin,Julie Heij,Iris Borst,Britt Joosten,Kristina Andersen,Wijnand IJsselsteijn
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注: Accepted by IASDR Conference 2025, Taipei, Taiwan 16 pages excluding references, 8 figures
Abstract:Amidst the emergence of powerful intelligent technologies such as LLMs and text-to-image AIs that promise to enhance creative processes, designers face the challenges of remaining empowered and creative while working with these foreign digital partners. While generative AIs offer versatile, informative, and occasionally poetic outcomes, their lack of embodied knowledge presents an even greater challenge to designers in gaining fruitful outcomes, such as in the field of Digital Craftsmanship. In this project, three designers embarked on a three-month experimental journey with an intention to co-create with Google’s LLM as a potential intelligent partner to investigate how it will influence the designers’ creativity. We found that a power dynamic of agencies exists between the LLM and the designer, in which the designer can easily lose their creative agency. Regaining the designer’s creative agency involves introspection into their own creative process, a structural understanding of the specific emerging technology involved, and deliberate adjustments to the dynamics of the human-technology relationship. We propose paying attention to the designer’s inner world and parties of agencies when engaging with emerging intelligent technologies through three aspects: the sensitivity towards a creative process as cognitive activities; the active investigation into specific technology’s capability; and the adjustment towards an appropriate working relationship between the designer and the emerging technology.
[HC-3] Credibility Matters: Motivations Characteristics and Influence Mechanisms of Crypto Key Opinion Leaders
【速读】：该论文旨在解决当前对加密货币领域关键意见领袖（Crypto Key Opinion Leaders, Crypto KOLs）的影响力机制理解不足的问题，特别是其动机、可信度与责任如何在高风险、波动性强的Web3环境中被建构和实践。现有研究多聚焦于生活方式类或通用金融领域的“Finfluencer”，忽视了Crypto KOLs所面临的独特心理和社会技术挑战。解决方案的关键在于引入自我决定理论（Self-Determination Theory, SDT），通过深度访谈13位KOL并结合人机协同的主题分析方法，揭示可信度并非静态资质，而是一种由内在心理需求驱动的、伦理化实践过程，并识别出四个社区公认的可信度标志：自我调节（self-regulation）、有限认知能力边界（bounded epistemic competence）、问责制（accountability）及反思性自我修正（reflexive self-correction）。这一发现将可信度重构为一种社会技术性表现，为设计以透明度优先于炒作的可信信号提供了理论依据与实践路径。
链接: https://arxiv.org/abs/2603.12000
作者: Alexander Kropiunig,Svetlana Kremer,Bernhard Haslhofer
机构: Complexity Science Hub(复杂科学中心); Austrian Institute of Technology(奥地利技术研究所)
类目: ocial and Information Networks (cs.SI); Cryptography and Security (cs.CR); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: 17 pages, 3 figures. Accepted at ACM CHI 2026, Barcelona
Abstract:Crypto Key Opinion Leaders (KOLs) shape Web3 narratives and retail investment behaviour. In volatile, high-risk markets, their credibility becomes a key determinant of their influence on followers. Yet prior research has focused on lifestyle influencers or generic financial commentary, leaving crypto KOLs’ understandings of motivation, credibility, and responsibility underexplored. Drawing on interviews with 13 KOLs and self-determination theory (SDT), we examine how psychological needs are negotiated alongside monetisation and community expectations. Whereas prior work treats finfluencer credibility as a set of static credentials, our findings reveal it to be a self-determined, ethically enacted practice. We identify four community-recognised markers of credibility: self-regulation, bounded epistemic competence, accountability, and reflexive self-correction. This reframes credibility as socio-technical performance, extending SDT into high-risk crypto ecosystems. Methodologically, we employ a hybrid human-LLM thematic analysis. The study surfaces implications for designing credibility signals that prioritise transparency over hype.
[HC-4] ConvScale: Conversational Interviews for Scale-Aligned Measurement
【速读】：该论文试图解决的问题是：如何将传统的结构化问卷调查中使用的心理测量量表（psychometric scales）转化为自然对话式访谈，从而在保留原有测量结构的基础上，实现对受访者回答的定量分析。当前，对话式访谈虽能获取丰富且情境化的定性数据，但其在量化测量中的潜力尚未被充分挖掘。解决方案的关键在于提出ConvScale方法——一种基于人工智能支持的框架，它能够将量表项目转化为自然语言对话形式，并通过分析访谈内容预测每个项目的得分，再聚合得到整体量表评分。该方法在实验中验证了其与自评分数在项目层面和构念层面的高度一致性，为通过访谈实现量化测量提供了可行路径。
链接: https://arxiv.org/abs/2603.11988
作者: Peinuan Qin,Jingzhu Chen,Yitian Yang,Han Meng,Zicheng Zhu,Yi-Chieh Lee
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Conversational interviews are commonly used to complement structured surveys by eliciting rich and contextualized responses, which are typically analyzed qualitatively. However, their potential contribution to quantitative measurement remains underexplored. In this paper, we introduce ConvScale, an AI-supported approach that transforms psychometric scales into natural conversational interviews while preserving the original measurement structure. Based on interview data, ConvScale predicts item-level scores and aggregates them to derive scale-based assessments. In a within-subjects study with 18 participants, our results show that ConvScale-derived scores align closely with participants’ self-report scores at both the item and construct levels, while maintaining moderate internal reliability; however, the structural validity was inadequate. In light of this, we discussed the potential of supporting quantitative measurement through interviews and proposed implications for future designs.
[HC-5] Design Exploration of Lightweight Interactions for Awareness-Supporting Technologies in Hybrid Work
【速读】：该论文旨在解决混合办公（Hybrid Work）环境中因缺乏自发性人际互动和同事活动的环境感知（Ambient Awareness），而导致团队协作效率下降的问题。其解决方案的关键在于通过轻量级交互（Lightweight Interactions）的设计与信息展示系统的整合，增强团队成员间的非正式沟通，从而弥补物理隔离带来的社交断层。研究强调从信息感知与处理的角度出发，而非内容传递本身，提出了一套可操作的设计框架，用于指导如何在小型混合团队中有效嵌入轻量级交互机制。
链接: https://arxiv.org/abs/2603.11977
作者: Lu Liu,Harm van Essen,Berry Eggen
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注: 14 pages, IASDR conference, pictorial
Abstract:Hybrid work settings often lack the informal communication that naturally emerges from spontaneous encounters and ambient awareness of coworkers’ activities, potentially hindering team collaboration. To address this challenge, we explored how lightweight interactions can be integrated into awareness-supporting technologies for fostering informal communication. Our experiential design approach focused on how information is perceived and processed rather than explicit content exchange. Through brainstorming, speculating, and prototyping, we explored the design space for small hybrid teams. By annotating and analyzing design concepts, speculative scenarios, and prototypes, we developed a framework that identified design options for lightweight interactions and methods for integrating them with information displays.
[HC-6] Stuck on Suggestions: Automation Bias the Anchoring Effect and the Factors That Shape Them in Computational Pathology
【速读】：该论文旨在解决人工智能（Artificial Intelligence, AI）驱动的决策支持系统在计算病理学中应用时，可能因人机协作引入认知偏差（如自动化偏倚和锚定偏倚）而导致诊断错误的问题。解决方案的关键在于通过在线实验设计，量化AI辅助下专家病理医师在不同时间压力和个体特征条件下的行为变化，发现AI虽能提升整体诊断性能，但存在7%的自动化偏倚率；同时揭示时间压力虽不增加偏倚频率，却加剧其严重性，并且专业经验与自我效能感可降低对AI的依赖，而决策时的高自信则会增强依赖程度，从而为优化AI集成策略、减少偏倚驱动的误诊风险提供实证依据。
链接: https://arxiv.org/abs/2603.11821
作者: Emely Rosbach,Jonas Ammeling,Jonathan Ganz,Christof Albert Bertram,Thomas Conrad,Andreas Riener,Marc Aubreville
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA)
Abstract:Artificial intelligence (AI)-driven decision support systems can improve diagnostic accuracy and efficiency in computational pathology. However, collaboration between human experts and AI may introduce cognitive biases such as automation and anchoring bias, where users adopt system predictions blindly or are disproportionately influenced by AI advice, even when inaccurate. These effects may be amplified under time pressure, common in routine pathology, or shaped by individual user characteristics. We conducted an online experiment in which pathology experts (n = 28) estimated tumor cell percentages: once independently and once with AI support. A subset of estimations in each condition was performed under time strain. Overall, AI assistance improved diagnostic performance but introduced a 7% automation bias rate, defined as accepted negative consultations where previously correct independent judgments were overturned by incorrect AI advice. While time pressure did not increase the frequency of automation bias, it appeared to intensify its severity, reflected in stronger performance declines associated with increased AI reliance under cognitive load. A linear mixed-effects model (LMM) simulating weighted averaging showed a statistically significant positive coefficient for AI advice, indicating moderate anchoring on system output. This effect increased under time pressure, suggesting anchoring bias becomes more pronounced when cognitive resources are limited. A second LMM assessing automation reliance, a proxy for automation and anchoring bias, showed that professional experience and self-efficacy were associated with lower dependence on AI, whereas higher confidence during AI-assisted decisions was tied to increased AI reliance. These findings highlight the dual nature of AI integration in clinical workflows: improving performance while introducing risks of bias-driven diagnostic errors.
[HC-7] HiSync: Spatio-Temporally Aligning Hand Motion from Wearable IMU and On-Robot Camera for Command Source Identification in Long-Range HRI
【速读】：该论文旨在解决远距离人机交互（Long-range Human-Robot Interaction, HRI）中命令源识别（Command Source Identification, CSI）的难题，尤其针对多用户场景下因距离导致的传感器模糊性问题。其解决方案的关键在于提出HiSync框架，通过光学与惯性数据融合机制，将手部运动作为绑定线索：利用机器人搭载摄像头的光流信息与佩戴在手上的惯性测量单元（Inertial Measurement Unit, IMU）信号进行时频域对齐，提取手部运动特征，并引入CSINet网络实现IMU信号去噪、模态时间对齐及距离感知的多窗口融合，从而准确计算细微自然手势的跨模态相似度，显著提升CSI准确性，在34米距离内三人场景下达到92.32%的识别率，优于现有最优方法48.44%。
链接: https://arxiv.org/abs/2603.11809
作者: Chengwen Zhang,Chun Yu,Borong Zhuang,Haopeng Jin,Qingyang Wan,Zhuojun Li,Zhe He,Zhoutong Ye,Yu Mei,Chang Liu,Weinan Shi,Yuanchun Shi
机构: Tsinghua University(清华大学); Beijing University of Posts and Telecommunications(北京邮电大学); Qinghai University(青海大学)
类目: Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注:
Abstract:Long-range Human-Robot Interaction (HRI) remains underexplored. Within it, Command Source Identification (CSI) - determining who issued a command - is especially challenging due to multi-user and distance-induced sensor ambiguity. We introduce HiSync, an optical-inertial fusion framework that treats hand motion as binding cues by aligning robot-mounted camera optical flow with hand-worn IMU signals. We first elicit a user-defined (N=12) gesture set and collect a multimodal command gesture dataset (N=38) in long-range multi-user HRI scenarios. Next, HiSync extracts frequency-domain hand motion features from both camera and IMU data, and a learned CSINet denoises IMU readings, temporally aligns modalities, and performs distance-aware multi-window fusion to compute cross-modal similarity of subtle, natural gestures, enabling robust CSI. In three-person scenes up to 34m, HiSync achieves 92.32% CSI accuracy, outperforming the prior SOTA by 48.44%. HiSync is also validated on real-robot deployment. By making CSI reliable and natural, HiSync provides a practical primitive and design guidance for public-space HRI.
[HC-8] PhiPlot: A Web-Based Interactive EDA Environment for Atmospherically Relevant Molecules
【速读】：该论文旨在解决大气化学研究中高维分子数据难以有效探索的问题，尤其针对大气气溶胶形成机制的复杂性。其解决方案的关键在于提出PhiPlot——一个基于Web的交互式数据探索平台，通过整合可视化、聚类分析与领域知识引导的嵌入空间优化（embedding refinement），实现对分子数据的降维与模式识别，从而支持科学假设的生成和数据驱动的研究。
链接: https://arxiv.org/abs/2603.11751
作者: Matias Loukojärvi,Ananth Mahadevan,Katsiaryna Haitsiukevich,Kai Puolamäki
机构: University of Helsinki (赫尔辛基大学)
类目: Human-Computer Interaction (cs.HC)
备注: 5 pages, 2 figures. Source code available at: this https URL
Abstract:Advances in computational chemistry have produced high-dimensional datasets on atmospherically relevant molecules. To aid exploration of such datasets, particularly for the study of atmospheric aerosol formation, we introduce PhiPlot: a web-based environment for interactive exploration and knowledge-based dimensionality reduction. The integration of visualisation, clustering, and domain knowledge-guided embedding refinement enables the discovery of patterns in the data and supports hypothesis generation. The application connects to an existing, evolving collection of molecular databases, offering an accessible interface for data-driven research in atmospheric chemistry.
[HC-9] From Control to Foresight: Simulation as a New Paradigm for Human-Agent Collaboration
【速读】：该论文旨在解决当前人机协作中用户对智能代理（Agent）决策过程缺乏前瞻性认知的问题。现有交互范式仅允许用户对单个动作进行事后审批或修正，导致用户需在心理上模拟长期后果，这不仅认知负荷高且易出错，从而限制了高效协作的可能性。论文提出“仿真回路”（simulation-in-the-loop）作为解决方案，其核心在于通过模拟未来轨迹使用户能够在实际执行前探索多种可能的发展路径，将干预从被动猜测转变为基于预测的主动探索，同时帮助用户识别隐含约束与偏好。这一机制显著增强了用户的预见能力，推动人机协作向更具战略性和协同性的方向演进。
链接: https://arxiv.org/abs/2603.11677
作者: Gaole He,Brian Y. Lim
机构: National University of Singapore(新加坡国立大学)
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: CHI 2026 Workshop on Human-Agent Collaboration
Abstract:Large Language Models (LLMs) are increasingly used to power autonomous agents for complex, multi-step tasks. However, human-agent interaction remains pointwise and reactive: users approve or correct individual actions to mitigate immediate risks, without visibility into subsequent consequences. This forces users to mentally simulate long-term effects, a cognitively demanding and often inaccurate process. Users have control over individual steps but lack the foresight to make informed decisions. We argue that effective collaboration requires foresight, not just control. We propose simulation-in-the-loop, an interaction paradigm that enables users and agents to explore simulated future trajectories before committing to decisions. Simulation transforms intervention from reactive guesswork into informed exploration, while helping users discover latent constraints and preferences along the way. This perspective paper characterizes the limitations of current paradigms, introduces a conceptual framework for simulation-based collaboration, and illustrates its potential through concrete human-agent collaboration scenarios.
[HC-10] A technology-oriented mapping of the language and translation industry: Analysing stakeholder values and their potential implication for translation pedagogy
【速读】：该论文试图解决的问题是：在日益自动化的语言服务与翻译行业中，价值如何被建构与协商，尤其是在技术驱动的生产环境中，人类价值与技术价值之间的关系如何演变。解决方案的关键在于提出并验证“适应性（adaptability）”作为连接人类与技术领域的中介价值，表明自动化并未取代人类价值，而是将其重新定位为嵌入技术中介工作流程中的专家能力、监督职责、问责机制和情境判断；同时，效率导向的技术价值已构成自动化环境下的基准预期，而适应性则成为译者持续调整技能、角色与身份以响应工具演进和组织需求的核心专业要求，从而形成人机协同的价值共生格局。
链接: https://arxiv.org/abs/2603.11667
作者: María Isabel Rivas Ginel,Janiça Hackenbuchner,Alina Secară,Ralph Krüger,Caroline Rossi
机构: 未知
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: Under review
Abstract:This paper examines how value is constructed and negotiated in today’s increasingly automated language and translation industry. Drawing on interview data from twenty-nine industry stakeholders collected within the LT-LiDER project, the study analyses how human value, technological value, efficiency, and adaptability are articulated across different professional roles. Using Chesterman’s framework of translation ethics and associated values as an analytical lens, the paper shows that efficiency-oriented technological values aligned with the ethics of service have become baseline expectations in automated production environments, where speed, scalability, and deliverability dominate evaluation criteria. At the same time, human value is not displaced but repositioned, emerging primarily through expertise, oversight, accountability, and contextual judgment embedded within technology-mediated workflows. A central finding is the prominence of adaptability as a mediating value linking human and technological domains. Adaptability is constructed as a core professional requirement, reflecting expectations that translators continuously adjust their skills, roles, and identities in response to evolving tools and organisational demands. The paper argues that automation reshapes rather than replaces translation value, creating an interdependent configuration in which technological efficiency enables human communicative work.
[HC-11] From Pets to Robots: MojiKit as a Data-Informed Toolkit for Affective HRI Design
【速读】：该论文旨在解决动物启发式社交机器人情感行为设计中依赖直觉和个体经验、导致设计结果碎片化的问题。其关键解决方案是构建了一个数据驱动的结构化资源体系——包括基于人类与宠物互动视频分析的参考卡片（reference cards）、一个拟态机器人原型（MomoBot）以及一个无需编程的行为控制工作室（behavior control studio），形成从理论参考到实践原型的闭环工具链，从而系统性地指导用户设计更丰富、多样的情感交互模式，并降低技术门槛，提升创造性自主权。
链接: https://arxiv.org/abs/2603.11632
作者: Liwen He,Pingting Chen,Ziheng Tang,Yixiao Liu,Jihong Jeung,Teng Han,Xin Tong
机构: The Hong Kong University of Science and Technology (Guangzhou)(香港科技大学（广州）); Tsinghua University (清华大学); Beijing Institute of Technology (北京理工大学); Sichuan University (四川大学); Institute of Software, Chinese Academy of Sciences (中国科学院软件研究所); The Hong Kong University of Science and Technology (香港科技大学)
类目: Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: 25 pages, 11 figures, Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26)
Abstract:Designing affective behaviors for animal-inspired social robots often relies on intuition and personal experience, leading to fragmented outcomes. To provide more systematic guidance, we first coded and analyzed human-pet interaction videos, validated insights through literature and interviews, and created structured reference cards that map the design space of pet-inspired affective interactions. Building on this, we developed MojiKit, a toolkit combining reference cards, a zoomorphic robot prototype (MomoBot), and a behavior control studio. We evaluated MojiKit in co-creation workshops with 18 participants, finding that MojiKit helped them design 35 affective interaction patterns beyond their own pet experiences, while the code-free studio lowered the technical barrier and enhanced creative agency. Our contributions include the data-informed structured resource for pet-inspired affective HRI design, an integrated toolkit that bridges reference materials with hands-on prototyping, and empirical evidence showing how MojiKit empowers users to systematically create richer, more diverse affective robot behaviors.
[HC-12] High-Contrast Projection Mapping under Light Field Illumination with LED Display and Aperiodic Lens Array
【速读】：该论文旨在解决投影映射（Projection Mapping, PM）技术在实际应用中受限于“暗室约束”（dark-room constraint）的问题，即传统PM需要在黑暗环境中才能实现高质量投影。为克服这一限制，作者提出了一种新颖的靶标排除照明方法（target-excluding lighting method），其核心在于通过结合LED显示面板与优化的非周期透镜阵列（aperiodic lens array）实现光场照明（light-field illumination）。关键创新点包括：1）紧凑结构设计实现了大有效光源面积，可再现自然柔和阴影；2）保持空间可控性以精确避开投影目标区域；3）引入计算优化方法用于非周期透镜布局，抑制由串扰引起的暗斑，并开发高效算法生成LED亮度图案，支持动态投影映射。实验表明，该方案可在明亮环境中实现高对比度投影映射。
链接: https://arxiv.org/abs/2603.11573
作者: Kotaro Fujimura,Hiroki Kusuyama,Masaki Takeuchi,Daisuke Iwai
机构: 未知
类目: Human-Computer Interaction (cs.HC); Graphics (cs.GR)
备注:
Abstract:Projection Mapping (PM) is a technology that projects images onto the surfaces of physical objects, allowing multiple users to share an augmented reality experience without special devices. However, its practical use has been constrained by the need for dark environments to ensure high-quality projection. To overcome this ``dark-room constraint,‘’ we propose a novel target-excluding lighting method that selectively illuminates the surrounding environment while avoiding the PM target. Our system achieves light-field illumination by combining an LED display panel with an optimized aperiodic lens array. The key contributions include a compact form factor that provides a large effective light source area, reproducing natural soft shadows comparable to typical lighting, while maintaining the spatial controllability needed to precisely avoid the target. We also introduce a computational technique for optimizing aperiodic lens placement to suppress undesired dark spots caused by crosstalk, and efficient methods for computing LED luminance patterns that enable dynamic PM. Experiments with a prototype system demonstrate that our approach achieves high-contrast PM even in bright environments.
[HC-13] Modeling Sequential Design Actions as Designer Externalization on an Infinite Canvas
【速读】：该论文试图解决的问题是：在无限画布（infinite canvas）平台中，随着AI代理（AI agent）越来越多地参与内容生成与组织，其对设计师外部化认知过程（externalization process）的影响尚不明确。解决方案的关键在于通过一项针对八位专业设计师的实地研究，对比有无AI代理时的设计工作流差异，并基于对5,838次设计操作的序列分析，识别出三个核心行为转变：（1）AI集成将认知努力从空间管理重新分配至内容筛选与关系构建，且不增加活跃时间；（2）形成“生成-校准”循环，设计师对AI的需求增强，而AI的功能角色随之适应性调整；（3）AI角色从早期阶段的发散催化剂演变为后期阶段的收敛型内容 curator。这一发现为设计阶段自适应的AI工具提供了行为模型，支持人类与AI在无限画布上的协同演化。
链接: https://arxiv.org/abs/2603.11569
作者: Yejin Yun,Seung Won Lee,Jiin Choi,Kyung Hoon Hyun
机构: Hanyang University (汉阳大学)
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Infinite canvas platforms are becoming central to contemporary design practice, enabling designers to externalize cognition through the spatial arrangement of multimodal artifacts. As AI agents increasingly generate and organize content within these environments, their impact on designers’ externalization processes remains underexplored. We report a field study with eight professional designers comparing workflows with and without an AI organizing agent. Through a sequence analysis of 5,838 design actions, we identify three key shifts: (1) AI integration reallocates cognitive effort from spatial management to content curation and relational structuring, without increasing active time; (2) a characteristic generate-and-curate cycle emerges in which designers’ demands on the agent intensify while the agent’s functional role adapts; and (3) AI’s role evolves from a divergent catalyst in early stages to a convergent curator in later phases. These findings offer a behavioral model for designing phase-adaptive AI tools that support human-AI co-evolution on infinite canvases.
[HC-14] AI Knows Whats Wrong But Cannot Fix It: Helicoid Dynamics in Frontier LLM s Under High-Stakes Decisions
【速读】：该论文试图解决的问题是：在高风险决策场景中（如临床诊断、投资评估和高后果面试），大型语言模型（Large Language Models, LLMs）因缺乏可验证性而表现出一种称为“螺旋动力学”（helicoid dynamics）的系统性失败模式——即模型在初期表现良好，随后逐渐偏离正确路径，虽能准确识别自身错误，却仍重复相同错误并以更高层次的复杂性继续演进，最终导致可靠性下降。解决方案的关键在于识别并命名这一现象，明确其边界条件，并提出十二个可检验假设，从而为构建在最困难决策情境下依然可靠的LLM提供理论基础与实践方向，强调在严格性和舒适感冲突时，需优先保障严谨性，以实现人类与AI在高风险协作中的可信伙伴关系。
链接: https://arxiv.org/abs/2603.11559
作者: Alejandro R Jadad
机构: 未知
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 22 pages, 2 tables, 1 appendix
Abstract:Large language models perform reliably when their outputs can be checked: solving equations, writing code, retrieving facts. They perform differently when checking is impossible, as when a clinician chooses an irreversible treatment on incomplete data, or an investor commits capital under fundamental uncertainty. Helicoid dynamics is the name given to a specific failure regime in that second domain: a system engages competently, drifts into error, accurately names what went wrong, then reproduces the same pattern at a higher level of sophistication, recognizing it is looping and continuing nonetheless. This prospective case series documents that regime across seven leading systems (Claude, ChatGPT, Gemini, Grok, DeepSeek, Perplexity, Llama families), tested across clinical diagnosis, investment evaluation, and high-consequence interview scenarios. Despite explicit protocols designed to sustain rigorous partnership, all exhibited the pattern. When confronted with it, they attributed its persistence to structural factors in their training, beyond what conversation can reach. Under high stakes, when being rigorous and being comfortable diverge, these systems tend toward comfort, becoming less reliable precisely when reliability matters most. Twelve testable hypotheses are proposed, with implications for agentic AI oversight and human-AI collaboration. The helicoid is tractable. Identifying it, naming it, and understanding its boundary conditions are the necessary first steps toward LLMs that remain trustworthy partners precisely when the decisions are hardest and the stakes are highest. Comments: 22 pages, 2 tables, 1 appendix Subjects: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) Cite as: arXiv:2603.11559 [cs.AI] (or arXiv:2603.11559v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.11559 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Alejandro R Jadad [view email] [v1] Thu, 12 Mar 2026 05:25:49 UTC (310 KB)
[HC-15] Shadowless Projection Mapping for Tabletop Workspaces with Synthetic Aperture Projector
【速读】：该论文旨在解决传统投影映射（Projection Mapping, PM）系统在桌面上交互场景中因用户遮挡导致的投影阴影问题，以及多投影仪方案因计算补偿带来的延迟问题。其关键解决方案是提出一种合成孔径投影映射（synthetic-aperture PM）系统，通过在环境中密集部署大量投影仪实现无延迟、无阴影的投影效果，无需依赖实时计算补偿；同时开发了一种离线模糊补偿方法，有效缓解因子像素错位引起的分辨率下降问题，且计算时间与投影仪数量无关。该方案显著降低了用户的“投影感”（sense of projection, SoP），从而更自然地实现对物体材质属性的增强，推动PM技术向沉浸式人机交互方向发展。
链接: https://arxiv.org/abs/2603.11551
作者: Takahiro Okamoto,Masaki Takeuchi,Masataka Sawayama,Daisuke Iwai
机构: 未知
类目: Human-Computer Interaction (cs.HC); Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:Projection mapping (PM) enables augmented reality (AR) experiences without requiring users to wear head-mounted displays and supports multi-user interaction. It is regarded as a promising technology for a variety of applications in which users interact with content superimposed onto augmented objects in tabletop workspaces, including remote collaboration, healthcare, industrial design, urban planning, artwork creation, and office work. However, conventional PM systems often suffer from projection shadows when users occlude the light path. Prior approaches employing multiple distributed projectors can compensate for occlusion, but suffer from latency due to computational processing, degrading the user experience. In this research, we introduce a synthetic-aperture PM system that uses a significantly larger number of projectors, arranged densely in the environment, to achieve delay-free, shadowless projection for tabletop workspaces without requiring computational compensation. To address spatial resolution degradation caused by subpixel misalignment among overlaid projections, we develop and validate an offline blur compensation method whose computation time remains independent of the number of projectors. Furthermore, we demonstrate that our shadowless PM plays a critical role in achieving a fundamental goal of PM: altering material properties without evoking projection-like impression. Specifically, we define this perceptual impression as ``sense of projection (SoP)‘’ and establish a PM design framework to minimize the SoP based on user studies.
[HC-16] Prediction of Grade Gender and Academic Performance of Children and Teenagers from Handwriting Using the Sigma-Lognormal Model
【速读】：该论文旨在解决儿童书写动力学特征是否能有效反映其发展阶段与个体差异的问题，尤其是探索手写动态信号在教育和发育研究中的潜在价值。解决方案的关键在于构建并比较三类基于笔迹动力学的特征：基本统计描述符、基于熵的变异性度量以及sigma-lognormal模型参数，并通过大规模在线数据集（涵盖小学至初中阶段日本学生）进行学生级聚合分析，进而评估这些特征在年级预测、性别分类和学业表现分类任务中的有效性。结果表明，书写动力学确实编码了与发育阶段相关的可测量信号，尤其在年级预测任务中表现突出，证实了儿童书写行为随发展趋向于对数正态运动组织的规律。
链接: https://arxiv.org/abs/2603.11519
作者: Adrian Iste,Kazuki Nishizawa,Chisa Tanaka,Andrew Vargo,Anna Scius-Bertrand,Andreas Fischer,Koichi Kise
机构: 未知
类目: Human-Computer Interaction (cs.HC); Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 8 figures
Abstract:Digital handwriting acquisition enables the capture of detailed temporal and kinematic signals reflecting the motor processes underlying writing behavior. While handwriting analysis has been extensively explored in clinical or adult populations, its potential for studying developmental and educational characteristics in children remains less investigated. In this work, we examine whether handwriting dynamics encode information related to student characteristics using a large-scale online dataset collected from Japanese students from elementary school to junior high school. We systematically compare three families of handwriting-derived features: basic statistical descriptors of kinematic signals, entropy-based measures of variability, and parameters obtained from the sigma-lognormal model. Although the dataset contains dense stroke-level recordings, features are aggregated at the student level to enable a controlled comparison between representations. These features are evaluated across three prediction tasks: grade prediction, gender classification, and academic performance classification, using Linear or Logistic Regression and Random Forest models under consistent experimental settings. The results show that handwriting dynamics contain measurable signals related to developmental stage and individual differences, especially for the grade prediction task. These findings highlight the potential of kinematic handwriting analysis and confirm that through their development, children’s handwriting evolves toward a lognormal motor organization.
[HC-17] From Pen Strokes to Sleep States: Detecting Low-Recovery Days Using Sigma-Lognormal Handwriting Features
【速读】：该论文旨在解决如何通过日常书写行为（handwriting）来检测健康个体中与睡眠相关的自主神经恢复状态的波动问题，即探索 handwriting dynamics 是否能反映每日生理状态的变化。其解决方案的关键在于提出了一种个性化的二分类框架，利用 Sigma-Lognormal 模型提取笔画生成过程中的神经运动特征（neuromotor generation process），并基于此对低恢复日进行识别；实验表明，该方法在13名大学生参与者中，针对心率变异性（HRV）、最低心率、平均心率和总睡眠时长四个睡眠相关指标均实现了显著优于随机基线的 PR-AUC 分数，且性能不受任务类型或记录时间的影响，说明恢复信号已嵌入一般运动动态中，为无创、设备无关的健康监测提供了新路径。
链接: https://arxiv.org/abs/2603.11512
作者: Chisa Tanaka,Andrew Vargo,Anna Scius-Bertrand,Andreas Fischer,Koichi Kise
机构: 未知
类目: Human-Computer Interaction (cs.HC); Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 7 figures
Abstract:While handwriting has traditionally been studied for character recognition and disease classification, its potential to reflect day-to-day physiological fluctuations in healthy individuals remains unexplored. This study examines whether daily variations in sleep-related recovery states can be inferred from online handwriting dynamics. % We propose a personalized binary classification framework that detects low-recovery days using features derived from the Sigma-Lognormal model, which captures the neuromotor generation process of pen strokes. In a 28-day in-the-wild study involving 13 university students, handwriting was recorded three times daily, and nocturnal cardiac indicators were measured using a wearable ring. For each participant, the lowest (or highest) quartile of four sleep-related metrics – HRV, lowest heart rate, average heart rate, and total sleep duration – defined the positive class. Leave-One-Day-Out cross-validation showed that PR-AUC significantly exceeded the baseline (0.25) for all four variables after FDR correction, with the strongest performance observed for cardiac-related variables. Importantly, classification performance did not differ significantly across task types or recording timings, indicating that recovery-related signals are embedded in general movement dynamics. These results demonstrate that subtle within-person autonomic recovery fluctuations can be detected from everyday handwriting, opening a new direction for non-invasive, device-independent health monitoring.
[HC-18] Managing Cognitive Bias in Human Labeling Operations for Rare-Event AI: Evidence from a Field Experiment
【速读】：该论文旨在解决稀有事件检测中因标签预估偏差（prevalence effect）导致的系统性认知偏倚问题，这种偏倚会引发漏检并沿AI生命周期传播至训练标签中，从而影响模型性能。其关键解决方案在于：首先通过平衡反馈比例（gold-standard feedback stream的阳性率从20%提升至50%）和采用概率性标注接口（elicited probabilities）来缓解人类标注者的认知偏倚；其次，在工人层级与群体层级对概率标签进行线性对数几率（linear-in-log-odds）校准，以改善标签的校准性和分类性能；最终在卷积神经网络（CNN）训练中显著提升稀有事件识别的可靠性与泛化能力。
链接: https://arxiv.org/abs/2603.11511
作者: Gunnar P. Epping,Andrew Caplin,Erik Duhaime,William R. Holmes,Daniel Martin,Jennifer S. Trueblood
机构: 未知
类目: Human-Computer Interaction (cs.HC); General Economics (econ.GN)
备注:
Abstract:Many operational AI systems depend on large-scale human annotation to detect rare but consequential events (e.g., fraud, defects, and medical abnormalities). When positives are rare, the prevalence effect induces systematic cognitive biases that inflate misses and can propagate through the AI lifecycle via biased training labels. We analyze prior experimental evidence and run a field experiment on DiagnosUs, a medical crowdsourcing platform, in which we hold the true prevalence in the unlabeled stream fixed (20% blasts) while varying (i) the prevalence of positives in the gold-standard feedback stream (20% vs. 50%) and (ii) the response interface (binary labels vs. elicited probabilities). We then post-process probabilistic labels using a linear-in-log-odds recalibration approach at the worker and crowd levels, and train convolutional neural networks on the resulting labels. Balanced feedback and probabilistic elicitation reduce rare-event misses, and pipeline-level recalibration substantially improves both classification performance and probabilistic calibration; these gains carry through to downstream CNN reliability out of sample.
[HC-19] Evaluation format not model capability drives triage failure in the assessment of consumer health AI
【速读】：该论文旨在解决当前对消费级健康大语言模型（Large Language Models, LLMs）在紧急情况分诊中表现评估的误导性结论问题，特别是针对Ramaswamy等人在《Nature Medicine》中提出的“ChatGPT Health过度低分级（under-triages）51.6%的急症”这一结论进行验证与修正。其关键解决方案在于设计了两种不同评估范式——受限的考试式测试（强制A/B/C/D选择）和自然对话式测试（模拟患者真实消息输入），并通过针对特定场景的消融实验与提示忠实性检查，揭示出评估格式本身对结果具有显著影响：在自然对话条件下，模型整体分诊准确率提升6.4个百分点（p=0.015），且在强制选择模式下表现极差的模型（如GPT-5.2、Claude系列等）在自由文本交互中均实现100%正确推荐急诊处理，表明原研究中高误判率主要源于人为限制输入方式而非模型能力不足。因此，该研究强调有效评估消费级健康AI必须基于贴近实际使用情境的测试方法。
链接: https://arxiv.org/abs/2603.11413
作者: David Fraile Navarro,Farah Magrabi,Enrico Coiera
机构: Macquarie University (麦克奎大学)
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 12 pages
Abstract:Ramaswamy et al. reported in \textitNature Medicine that ChatGPT Health under-triages 51.6% of emergencies, concluding that consumer-facing AI triage poses safety risks. However, their evaluation used an exam-style protocol – forced A/B/C/D output, knowledge suppression, and suppression of clarifying questions – that differs fundamentally from how consumers use health chatbots. We tested five frontier LLMs (GPT-5.2, Claude Sonnet 4.6, Claude Opus 4.6, Gemini 3 Flash, Gemini 3.1 Pro) on a 17-scenario partial replication bank under constrained (exam-style, 1,275 trials) and naturalistic (patient-style messages, 850 trials) conditions, with targeted ablations and prompt-faithful checks using the authors’ released prompts. Naturalistic interaction improved triage accuracy by 6.4 percentage points ( p = 0.015 ). Diabetic ketoacidosis was correctly triaged in 100% of trials across all models and conditions. Asthma triage improved from 48% to 80%. The forced A/B/C/D format was the dominant failure mechanism: three models scored 0–24% with forced choice but 100% with free text (all p 10^-8 ), consistently recommending emergency care in their own words while the forced-choice format registered under-triage. Prompt-faithful checks on the authors’ exact released prompts confirmed the scaffold produces model-dependent, case-dependent results. The headline under-triage rate is highly contingent on evaluation format and should not be interpreted as a stable estimate of deployed triage behavior. Valid evaluation of consumer health AI requires testing under conditions that reflect actual use.
[HC-20] o Believe or Not To Believe: Comparing Supporting Information Tools to Aid Human Judgments of AI Veracity
【速读】：该论文旨在解决生成式 AI（Generative AI）在数据提取过程中存在幻觉风险的问题，特别是在生物医学研究和法律等对信息准确性要求较高的领域中，用户如何有效评估 AI 生成内容的真实性缺乏实证依据。其解决方案的关键在于通过严谨的用户实验，系统性地比较不同类型的支持性信息（完整源文本、段落检索和大语言模型（Large Language Model, LLM）解释）对用户在真实性判断过程中的效率、效果、依赖度和信任感的影响，从而揭示不同信息呈现方式如何塑造人类决策行为，并为设计更负责任的人机协作验证机制提供实证基础。
链接: https://arxiv.org/abs/2603.11393
作者: Jessica Irons,Patrick Cooper,Necva Bolucu,Roelien Timmer,Huichen Yang,Changhyun Lee,Brian Jin,Andreas Duenser,Stephen Wan
机构: Commonwealth Scientific Industrial Research Organisation (澳大利亚联邦科学与工业研究组织)
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:With increasing awareness of the hallucination risks of generative artificial intelligence (AI), we see a growing shift toward providing information tooling to help users determine the veracity of AI-generated answers for themselves. User responsibility for assessing veracity is particularly critical for certain sectors that rely on on-demand, AI-generated data extraction, such as biomedical research and the legal sector. While prior work offers us a variety of ways in which systems can provide such support, there is a lack of empirical evidence on how this information is actually incorporated into the user’s decision-making process. Our user study takes a step toward filling this knowledge gap. In the context of a generative AI data extraction tool, we examine the relationship between the type of supporting information (full source text, passage retrieval, and Large Language Model (LLM) explanations) and user behavior in the veracity assessment process, examined through the lens of efficiency, effectiveness, reliance and trust. We find that passage retrieval offers a reasonable compromise between accuracy and speed, with judgments of veracity comparable to using the full source text. LLM explanations, while also enabling rapid assessments, fostered inappropriate reliance and trust on the data extraction AI, such that participants were less likely to detect errors. In additiona, we analyzed the impacts of the complexity of the information need, finding preliminary evidence that inappropriate reliance is worse for complex answers. We demonstrate how, through rigorous user evaluation, we can better develop systems that allow for effective and responsible human agency in veracity assessment processes.
[HC-21] Ghost Framing Theory: Exploring the role of generative AI in new venture rhetorical legitimation
【速读】：该论文旨在解决生成式 AI（Generative AI）在创业叙事构建中日益增长但难以察觉的作用问题，即如何解释人类创业者与投资者如何与生成式 AI 协同工作，共同塑造、竞争并重新校准新创企业话语合法性中的共鸣（resonance）。其解决方案的关键在于提出“幽灵式叙事理论”（Ghost Framing Theory, GFT），该理论识别出生成式 AI 的五类修辞可供性（generativeness、extreme combinatorics、tone repertoire、velocity/energy 和 shared substratum），并构建了一个递归迭代的过程模型（包括幽灵式路演、幽灵式筛选和幽灵式关系构建），揭示了人-机混合主体如何通过动态交互实现新兴共鸣与合法性建构。GFT 不仅拓展了修辞框架理论以适应生成式 AI 时代，还将人类-人工智能协作研究与文化创业领域相连接，并将可供性理论推进至多主体场景下，强调可供性传递性和可见性作为核心分析维度。
链接: https://arxiv.org/abs/2603.11384
作者: Greg Nyilasy
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Responding to the surging but largely invisible use of generative AI in entrepreneurial framing, I advance Ghost Framing Theory (GFT) to explain how hybrid founder- and investor-genAI ensembles co-produce, contest, and recalibrate resonance in the rhetorical legitimation of new ventures. Building on scholarship in framing, micro-level legitimacy judgments, and sociomaterial affordances, I identify genAI rhetorical affordances (generativeness, extreme combinatorics, tone repertoire, velocity/energy and shared substratum) and theorize a recursive/iterative process model (ghost pitching, ghost screening, ghost relationship-building), configuring emergent resonance and legitimation. GFT builds new rhetorical framing theory for the age of genAI, connects research on human-AI collaboration with cultural entrepreneurship and extends affordance theory into multi-actor scenarios where affordance transitivity and visibility emerge as key considerations.
[HC-22] Bridging the Cognitive Gap: Co-Designing and Evaluating a Voice-Enabled Community Chatbot for Older Adults
【速读】：该论文旨在解决退休社区中老年人因数字门户存在物理和认知障碍而导致的数字回避问题，同时应对生成式 AI（Generative AI）因“黑箱”特性与可用性挑战而难以被接纳的困境。其解决方案的关键在于采用“透明化”（Glass Box）策略，通过结合多模态可访问性与有意识的AI素养教育，在持续照护退休社区开展混合方法共设计与读写能力工作坊（N=25）。实证结果表明，该干预显著提升了参与者的技术理解力（p=0.004）和对系统透明度的感知（p=0.001），促使用户从盲信转向基于可验证证据的知情依赖；尽管语音输入降低了认知负荷，但80岁以上用户在可用性上出现显著下降（r=-0.50），提示真正面向高龄群体的智能交互需从触控界面迈向无接触导航（zero-touch navigation）。
链接: https://arxiv.org/abs/2603.11303
作者: Feng Chen,Luna Xingyu Li,Ray-Yuan Chung,Wenyu Zeng,Yein Jeon,Yizhou Hu,Oleg Zaslavsky
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Digital portals in retirement communities often create physical and cognitive barriers for older adults, leading to digital avoidance. Generative AI offers a solution by enabling natural language interaction, yet its adoption is hindered by the opaque, “Black Box” nature of these systems and lingering usability challenges. To address this, we evaluated a voice-enabled Large Language Model (LLM) chatbot at a continuing care retirement community in the Pacific Northwest. Through a mixed-methods Co-Design and Literacy Workshop (N=25), we applied a “Glass Box” approach combining multimodal accessibility with intentional AI education. The intervention significantly improved participants’ technical understanding (p=0.004) and perceived transparency (p=0.001), shifting their interaction model from blind trust to informed reliance prioritizing verifiable evidence. While voice input reduced cognitive load, usability scores dropped significantly for users aged 80 and older (r=-0.50), indicating that truly age-inclusive AI must evolve beyond touch-based interfaces toward zero-touch navigation.
[HC-23] “I followed what felt right not what I was told”: Autonomy Coaching and Recognizing Bias Through AI-Mediated Dialogue
【速读】：该论文试图解决日常互动中残障歧视微aggresions（Ableist microaggressions）普遍存在但干预手段有限的问题。其解决方案的关键在于设计并验证一种基于人工智能（AI）的对话式干预平台，通过不同类型的AI引导（如偏向性提示、包容性提示、无引导对话和纯文本阅读）来提升个体对残障歧视的认知敏感度。实验表明，对话式干预显著优于纯文本阅读条件，其中包容性提示在增强识别能力的同时保持情绪平衡，而偏向性提示虽提高对偏见与中立情境的区分度，却引发更强负面情绪反应；质性分析进一步揭示包容性提示更易被接受为认知支架，而偏向性提示常遭排斥。该研究为对话系统在整合偏见提示时提供了关键的设计权衡依据。
链接: https://arxiv.org/abs/2603.11274
作者: Atieh Taheri,Hamza El Alaoui,Patrick Carrington,Jeffrey P. Bigham
机构: Carnegie Mellon University (卡内基梅隆大学)
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: Accepted to CHI 2026 (ACM Conference on Human Factors in Computing Systems), 23 pages, 5 figures
Abstract:Ableist microaggressions remain pervasive in everyday interactions, yet interventions to help people recognize them are limited. We present an experiment testing how AI-mediated dialogue influences recognition of ableism. 160 participants completed a pre-test, intervention, and a post-test across four conditions: AI nudges toward bias (Bias-Directed), inclusion (Neutral-Directed), unguided dialogue (Self-Directed), and a text-only non-dialogue (Reading). Participants rated scenarios on standardness of social experience and emotional impact; those in dialogue-based conditions also provided qualitative reflections. Quantitative results showed dialogue-based conditions produced stronger recognition than Reading, though trajectories diverged: biased nudges improved differentiation of bias from neutrality but increased overall negativity. Inclusive or no nudges remained more balanced, while Reading participants showed weaker gains and even declines. Qualitative findings revealed biased nudges were often rejected, while inclusive nudges were adopted as scaffolding. We contribute a validated vignette corpus, an AI-mediated intervention platform, and design implications highlighting trade-offs conversational systems face when integrating bias-related nudges.
[HC-24] Understanding User Perceptions of Human-centered AI-Enhanced Support Group Formation in Online Healthcare Communities
【速读】：该论文旨在解决在线健康社区（Online Health Communities, OHCs）中因规模庞大而导致用户难以找到最相关同伴和支持内容的问题。解决方案的关键在于通过算法实现个性化支持小组的构建，研究发现用户对这种模拟的个性化支持小组感知价值较高（平均评分为4.55/5），且绝大多数受访者表示愿意加入；然而，其接受度高度依赖于安全性、透明性、人工监督及用户对数据的控制权等条件，表明算法治理、隐私保护与信任机制是推动该方案落地的核心前提。
链接: https://arxiv.org/abs/2603.11237
作者: Pronob Kumar Barman,James R. Foulds,Tera L. Reynolds
机构: 未知
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Peer support is critical to managing chronic health conditions. Online health communities (OHCs) enable patients and caregivers to connect with similar others, yet their large scale makes it challenging to find the most relevant peers and content. This study assessed perceived value, preferred features, and acceptance conditions for algorithmically personalized support group formation within OHCs. A two-phase, mixed-methods survey (N=165) examined OHC participation patterns, personalization priorities, and acceptance of a simulated personalized support group. Perceived value of the simulated support group was high (mean 4.55/5; 62.8% rated 5/5) and 91.5% would join this group. The importance participants placed on peer matching strongly correlated with perceived value (\rho=0.764, p0.001). Qualitative findings revealed conditional acceptance: participants demand security, transparency, human oversight, and user control over data. Personalized support groups may be desired, but they will not be adopted unless trust, privacy, and algorithmic governance concerns are addressed.
[HC-25] LLM s in social services: How does chatbot accuracy affect human accuracy?
【速读】：该论文旨在解决非营利组织社会服务工作者在处理复杂福利项目（如补充营养援助计划，SNAP）资格判定时面临的知识门槛高、效率低的问题。其解决方案的关键在于引入基于大语言模型（Large Language Model, LLM）的聊天机器人作为辅助工具，通过提供实时建议来提升社工对客户问题的判断准确性。研究通过构建一个包含770道真实场景难题的多选题基准数据集，并开展随机对照实验发现：高质量LLM聊天机器人（准确率达96%-100%）可使社工准确率提升27个百分点；但当聊天机器人建议错误时，即使是对简单问题也会导致社工准确率下降约三分之二；此外，随着AI准确性进一步提高，人类表现改善趋于饱和，形成“AI低估依赖平台”现象，提示需重视人机协同系统中用户行为反馈与实际部署效果评估。
链接: https://arxiv.org/abs/2603.11213
作者: Jennah Gosciak,Eric Giannella,Zhaowen Guo,Michael Chen,Allison Koenecke
机构: Cornell Tech (康奈尔科技学院); Georgetown University (乔治城大学); Better Government Lab (更好的政府实验室); Nava Labs (纳瓦实验室)
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注:
Abstract:Social service programs like the Supplemental Nutrition Assistance Program (SNAP, or food stamps) have eligibility rules that can be challenging to understand. For nonprofit caseworkers who often support clients in navigating a dozen or more complex programs, LLM-based chatbots may offer a means to provide better, faster help to clients whose situations may be less common. In this paper, we measure the potential effects of LLM-based chatbot suggestions on caseworkers’ ability to provide accurate guidance. We first created a 770-question multiple-choice benchmark dataset of difficult, but realistic questions that a caseworker might receive. Next, using these benchmark questions and corresponding expert-verified answers, we conducted a randomized experiment with caseworkers recruited from nonprofit outreach organizations in Los Angeles. Caseworkers in the control condition did not see chatbot suggestions and had a mean accuracy of 49%. Caseworkers in the treatment condition saw chatbot suggestions that we artificially varied to range in aggregate accuracy from low (53%) to high (100%). Caseworker performance significantly improves as chatbot quality improves: high-quality chatbots (96-100% accurate) improved caseworker accuracy by 27 percentage points. At the question-level, incorrect chatbot suggestions substantially reduce caseworker accuracy, with a two-thirds reduction on easy questions where the control group performed best (without chatbot suggestions). Finally, improvements in caseworker accuracy level off as chatbot accuracy increases, a phenomenon that we call the “AI underreliance plateau,” which is a concern for real-world deployment and highlights the importance of evaluating human-in-the-loop tools with their users.
[HC-26] he Laziness of the Crowd: Effort Aversion Among Raters Risks Undermining the Efficacy of Xs Community Notes Program
【速读】：该论文试图解决的问题是： crowdsourced moderation systems（众包审核系统）在应对网络虚假信息时可能存在的系统性失效问题，特别是针对那些看似合理、难以辨别真伪的误导性内容（plausible misinformation），其审核覆盖率显著低于明显虚假的内容。解决方案的关键在于识别并缓解“事实核查难度惩罚”（fact-check difficulty penalty）现象——即用户倾向于回避对认知负荷较高、需要更多精力去验证的复杂虚假信息进行标注或评论，导致这些更具欺骗性的内容得不到有效监管。研究通过分析疫苗相关帖子的社区注释数据及受试者评分，结合大语言模型（LLM）辅助的事实核查流程，证实了这一机制的存在，并建议平台设计应引入激励机制或工具以降低用户评估高难度内容的认知门槛，从而提升对潜在有害但隐蔽性强的信息的覆盖能力。
链接: https://arxiv.org/abs/2603.11120
作者: Morgan Wack,Patrick Warren,Mustafa Alam
机构: Department of Communication and Media Research (IKMZ), University of Zurich (苏黎世大学); Media Forensics Hub (媒体取证中心), Clemson University (克莱姆森大学); John E. Walker Department of Economics (约翰·E·沃克经济学系), Clemson University (克莱姆森大学)
类目: Human-Computer Interaction (cs.HC)
备注: 52 pages, 10 figures
Abstract:Crowdsourced moderation systems like Twitter/X’s Community Notes program have been proposed as scalable alternatives to professional fact-checkers for combating online misinformation. While prior research has examined the effectiveness of such systems in reducing engagement with false content and their vulnerability to partisan bias, we identify a previously untested mechanism linking fact-check difficulty to systematic non-participation by crowdsourced raters. We hypothesize that claims requiring less cognitive effort to evaluate, specifically, those that are obviously false and easy to refute, are more likely to receive public notes than claims that are more plausible and require greater effort to debunk. Using eighteen months of vaccine-related Community Notes data (2,250 posts) and ratings from 382 survey participants, we show that claims perceived as more difficult to fact-check are significantly less likely to receive notes that achieve ``helpful’'/public status. Following the conduct of additional analyses and a fact-checking process utilizing an LLM pipeline to help rule out alternative explanations, we interpret this pattern as consistent with an unwillingness among raters to invest the mental effort required to evaluate and rate notes for more plausible misinformation. These findings suggest that crowdsourced moderation may systematically fail to address the forms of plausible misinformation which are most likely to deceive. We discuss implications for platform design and propose mechanisms to mitigate this difficulty penalty in crowdsourced content moderation systems.
[HC-27] Exploring Collatz Dynamics with Human-LLM Collaboration
【速读】：该论文试图解决Collatz迭代的结构特性问题，特别是通过大规模计算探索中观察到的模 scrambling（modular scrambling）现象和轨迹的burst-gap分解结构来揭示其收敛机制。解决方案的关键在于提出了一套结构性结果：包括模 scrambling 引理（证明gap-return映射在高位上为精确双射）、持久退出引理（刻画持久状态后的gap结构）以及已知二进制位段在gap-return动力学下的衰减性质；同时在模模型中证明了gap长度和2-adic估值服从几何分布，持久运行长度期望为E[B]=2，从而预测轨道严格收缩。这一框架为收敛性提供了一个条件化路径，依赖于对burst与gap长度的轨道层面假设，这些假设由轨道等分布猜想所支持，但核心假设仍待验证，整体仍属探索性而非完整还原。
链接: https://arxiv.org/abs/2603.11066
作者: Edward Y. Chang
机构: Stanford University (斯坦福大学)
类目: Dynamical Systems (math.DS); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 19 pages, 3 figures
Abstract:We investigate structural properties of the Collatz iteration through two phenomena observed in large computational exploration: modular scrambling of residue classes and a burst–gap decomposition of trajectories. We prove several structural results, including a modular scrambling lemma showing that the gap-return map acts as an exact bijection on high bits, a persistent exit lemma characterizing gap structure after persistent states, and a decay property for known portions of binary representations under gap-return dynamics. We further prove that, in the modular model, gap lengths and 2 -adic valuations follow geometric distributions, while persistent run lengths are geometric with expected burst length E[B]=2 ; together these predict strict orbit contraction. These results suggest a conditional framework in which convergence would follow from suitable orbitwise hypotheses on burst and gap lengths, which in turn are suggested by an orbit equidistribution conjecture. However, the key hypotheses remain open, and the framework is exploratory rather than a complete reduction. The paper also documents the human-LLM collaboration through which these observations were developed.
计算机视觉
[CV-0] EVATok: Adaptive Length Video Tokenization for Efficient Visual Autoregressive Generation CVPR2026
【速读】：该论文旨在解决自回归视频生成模型中视频分词器（video tokenizer）在处理不同视频时存在效率低下的问题，即传统方法对所有视频采用统一的token分配策略，导致在静态或重复片段上浪费token资源，而在动态复杂片段上则因token不足影响重建质量。解决方案的关键在于提出EVATok框架，其核心包括：1）基于每段视频特性估计最优token分配以实现质量与计算成本的最佳权衡；2）设计轻量级路由机制（lightweight routers）快速预测最优分配；3）训练能够根据路由预测结果进行自适应编码的分词器。通过引入视频语义编码器增强训练策略，EVATok在UCF-101数据集上实现了优于现有方法的视频重建质量和类别到视频生成性能，平均token使用量较LARP和固定长度基线减少至少24.4%。
链接: https://arxiv.org/abs/2603.12267
作者: Tianwei Xiong,Jun Hao Liew,Zilong Huang,Zhijie Lin,Jiashi Feng,Xihui Liu
机构: The University of Hong Kong (香港大学); ByteDance Seed (字节跳动种子团队)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR 2026. Project page: this https URL
Abstract:Autoregressive (AR) video generative models rely on video tokenizers that compress pixels into discrete token sequences. The length of these token sequences is crucial for balancing reconstruction quality against downstream generation computational cost. Traditional video tokenizers apply a uniform token assignment across temporal blocks of different videos, often wasting tokens on simple, static, or repetitive segments while underserving dynamic or complex ones. To address this inefficiency, we introduce \textbfEVATok , a framework to produce \textbfE fficient \textbfV ideo \textbfA daptive \textbfTok enizers. Our framework estimates optimal token assignments for each video to achieve the best quality-cost trade-off, develops lightweight routers for fast prediction of these optimal assignments, and trains adaptive tokenizers that encode videos based on the assignments predicted by routers. We demonstrate that EVATok delivers substantial improvements in efficiency and overall quality for video reconstruction and downstream AR generation. Enhanced by our advanced training recipe that integrates video semantic encoders, EVATok achieves superior reconstruction and state-of-the-art class-to-video generation on UCF-101, with at least 24.4% savings in average token usage compared to the prior state-of-the-art LARP and our fixed-length baseline.
[CV-1] MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在执行视觉工作流时对深层组合条件推理能力评估不足的问题，尤其是现有基准测试未能充分覆盖复杂、嵌套的视觉组合条件（如“若出现权限对话框且界面颜色为绿色，则点击允许”）。解决方案的关键在于提出MM-CondChain基准，其核心创新是构建一种分层推理链结构，每一层包含基于视觉证据的非平凡组合条件，由多个对象、属性或关系构成；同时设计了一种代理合成流程（agentic synthesis pipeline），包括规划器（Planner）逐层生成组合条件、可验证的程序化中间表示（Verifiable Programmatic Intermediate Representation, VPIR）确保每层条件机械可验证，以及组装器（Composer）将验证后的层整合为完整指令，从而实现高质量、可扩展的工作流式数据构造。
链接: https://arxiv.org/abs/2603.12266
作者: Haozhan Shen,Shilin Yan,Hongwei Xue,Shuaiqi Lu,Xiaojun Tang,Guannan Zhang,Tiancheng Zhao,Jianwei Yin
机构: Accio Team, Alibaba Group; Zhejiang University; ZJU-BJ
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:Multimodal Large Language Models (MLLMs) are increasingly used to carry out visual workflows such as navigating GUIs, where the next step depends on verified visual compositional conditions (e.g., “if a permission dialog appears and the color of the interface is green, click Allow”) and the process may branch or terminate early. Yet this capability remains under-evaluated: existing benchmarks focus on shallow-compositions or independent-constraints rather than deeply chained compositional conditionals. In this paper, we introduce MM-CondChain, a benchmark for visually grounded deep compositional reasoning. Each benchmark instance is organized as a multi-layer reasoning chain, where every layer contains a non-trivial compositional condition grounded in visual evidence and built from multiple objects, attributes, or relations. To answer correctly, an MLLM must perceive the image in detail, reason over multiple visual elements at each step, and follow the resulting execution path to the final outcome. To scalably construct such workflow-style data, we propose an agentic synthesis pipeline: a Planner orchestrates layer-by-layer generation of compositional conditions, while a Verifiable Programmatic Intermediate Representation (VPIR) ensures each layer’s condition is mechanically verifiable. A Composer then assembles these verified layers into complete instructions. Using this pipeline, we construct benchmarks across three visual domains: natural images, data charts, and GUI trajectories. Experiments on a range of MLLMs show that even the strongest model attains only 53.33 Path F1, with sharp drops on hard negatives and as depth or predicate complexity grows, confirming that deep compositional reasoning remains a fundamental challenge.
[CV-2] OmniStream: Mastering Perception Reconstruction and Action in Continuous Streams
【速读】：该论文旨在解决当前视觉基础模型在实时流式环境中表现碎片化的问题，即现有模型通常仅擅长图像语义感知、离线时序建模或空间几何理解中的某一方面，缺乏统一的、能同时处理语义、空间与时间推理能力的视觉表征框架。其解决方案的关键在于提出OmniStream——一个统一的流式视觉主干网络，通过引入因果时空注意力机制（causal spatiotemporal attention）和三维旋转位置编码（3D rotary positional embeddings, 3D-RoPE），支持高效帧级在线视频处理，并借助持久化的键值缓存（KV-cache）实现低延迟流式推理；此外，采用融合静态与动态表征学习、流式几何重建及视觉-语言对齐的多任务预训练策略，在29个数据集上联合训练，使模型即使在主干冻结的情况下也能在图像/视频探测、流式几何重建、复杂视频与空间推理以及机器人操作等多样化任务中达到与专用模型相当甚至更优的表现，从而验证了单一通用视觉主干实现跨模态、跨任务泛化的可行性。
链接: https://arxiv.org/abs/2603.12265
作者: Yibin Yan,Jilan Xu,Shangzhe Di,Haoning Wu,Weidi Xie
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Technical Report. Project Page: this https URL
Abstract:Modern visual agents require representations that are general, causal, and physically structured to operate in real-time streaming environments. However, current vision foundation models remain fragmented, specializing narrowly in image semantic perception, offline temporal modeling, or spatial geometry. This paper introduces OmniStream, a unified streaming visual backbone that effectively perceives, reconstructs, and acts from diverse visual inputs. By incorporating causal spatiotemporal attention and 3D rotary positional embeddings (3D-RoPE), our model supports efficient, frame-by-frame online processing of video streams via a persistent KV-cache. We pre-train OmniStream using a synergistic multi-task framework coupling static and temporal representation learning, streaming geometric reconstruction, and vision-language alignment on 29 datasets. Extensive evaluations show that, even with a strictly frozen backbone, OmniStream achieves consistently competitive performance with specialized experts across image and video probing, streaming geometric reconstruction, complex video and spatial reasoning, as well as robotic manipulation (unseen at training). Rather than pursuing benchmark-specific dominance, our work demonstrates the viability of training a single, versatile vision backbone that generalizes across semantic, spatial, and temporal reasoning, i.e., a more meaningful step toward general-purpose visual understanding for interactive and embodied agents.
[CV-3] GRADE: Benchmarking Discipline-Informed Reasoning in Image Editing
【速读】：该论文旨在解决当前图像编辑基准测试在评估统一多模态模型（Unified Multimodal Models）时存在的局限性问题，即现有评测主要聚焦于自然图像和浅层常识推理，难以有效衡量模型在结构化、领域特定约束下的知识推理与生成能力。解决方案的关键在于提出GRADE——首个面向学科知识驱动的图像编辑评估基准，其包含跨10个学术领域的520个精心设计样本，并引入多维评价协议，综合评估“学科推理（Discipline Reasoning）”、“视觉一致性（Visual Consistency）”和“逻辑可读性（Logical Readability）”，从而系统揭示当前主流开源与闭源模型在隐含知识密集型编辑任务中的显著性能短板，为未来统一多模态模型的发展指明方向。
链接: https://arxiv.org/abs/2603.12264
作者: Mingxin Liu,Ziqian Fan,Zhaokai Wang,Leyao Gu,Zirun Zhu,Yiguo He,Yuchen Yang,Changyao Tian,Xiangyu Zhao,Ning Liao,Shaofeng Zhang,Qibing Ren,Zhihang Zhong,Xuanhe Zhou,Junchi Yan,Xue Yang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 49 pages, 23 figures, 10 tables; Project Page: this https URL , Code: this https URL , Dataset: this https URL
Abstract:Unified multimodal models target joint understanding, reasoning, and generation, but current image editing benchmarks are largely confined to natural images and shallow commonsense reasoning, offering limited assessment of this capability under structured, domain-specific constraints. In this work, we introduce GRADE, the first benchmark to assess discipline-informed knowledge and reasoning in image editing. GRADE comprises 520 carefully curated samples across 10 academic domains, spanning from natural science to social science. To support rigorous evaluation, we propose a multi-dimensional evaluation protocol that jointly assesses Discipline Reasoning, Visual Consistency, and Logical Readability. Extensive experiments on 20 state-of-the-art open-source and closed-source models reveal substantial limitations in current models under implicit, knowledge-intensive editing settings, leading to large performance gaps. Beyond quantitative scores, we conduct rigorous analyses and ablations to expose model shortcomings and identify the constraints within disciplinary editing. Together, GRADE pinpoints key directions for the future development of unified multimodal models, advancing the research on discipline-informed image editing and reasoning. Our benchmark and evaluation code are publicly released.
[CV-4] Video Streaming Thinking: VideoLLM s Can Watch and Think Simultaneously
【速读】：该论文旨在解决在线视频大语言模型（VideoLLM）在实时交互场景中缺乏同步逻辑推理能力的问题，现有方法虽能实现流式感知，但无法在视频流播放过程中进行有效的因果推理，导致响应延迟高或理解不连贯。其解决方案的关键在于提出一种新颖的“边看边思考”（Video Streaming Thinking, VST）范式，通过在视频流播放期间激活对连续视频片段的推理机制，将大语言模型（LLM）的推理延迟分摊到视频播放时间中，从而在保持实时响应的同时提升及时理解和连贯认知能力。此外，VST还引入结构化微调（VST-SFT）和强化学习优化（VST-RL）相结合的后训练流程，并设计基于视频知识图谱的自动化数据合成管道，生成具有多证据推理能力的流式问答对，显著提升了模型在在线和离线多种视频理解任务上的效率与泛化性能。
链接: https://arxiv.org/abs/2603.12262
作者: Yiran Guan,Liang Yin,Dingkang Liang,Jianzhong Ju,Zhenbo Luo,Jian Luan,Yuliang Liu,Xiang Bai
机构: Huazhong University of Science and Technology (华中科技大学); MiLM Plus; Xiaomi Inc. (小米公司)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Online Video Large Language Models (VideoLLMs) play a critical role in supporting responsive, real-time interaction. Existing methods focus on streaming perception, lacking a synchronized logical reasoning stream. However, directly applying test-time scaling methods incurs unacceptable response latency. To address this trade-off, we propose Video Streaming Thinking (VST), a novel paradigm for streaming video understanding. It supports a thinking while watching mechanism, which activates reasoning over incoming video clips during streaming. This design improves timely comprehension and coherent cognition while preserving real-time responsiveness by amortizing LLM reasoning latency over video playback. Furthermore, we introduce a comprehensive post-training pipeline that integrates VST-SFT, which structurally adapts the offline VideoLLM to causal streaming reasoning, and VST-RL, which provides end-to-end improvement through self-exploration in a multi-turn video interaction environment. Additionally, we devise an automated training-data synthesis pipeline that uses video knowledge graphs to generate high-quality streaming QA pairs, with an entity-relation grounded streaming Chain-of-Thought to enforce multi-evidence reasoning and sustained attention to the video stream. Extensive evaluations show that VST-7B performs strongly on online benchmarks, e.g. 79.5% on StreamingBench and 59.3% on OVO-Bench. Meanwhile, VST remains competitive on offline long-form or reasoning benchmarks. Compared with Video-R1, VST responds 15.7 times faster and achieves +5.4% improvement on VideoHolmes, demonstrating higher efficiency and strong generalization across diverse video understanding tasks. Code, data, and models will be released at this https URL.
[CV-5] he Latent Color Subspace: Emergent Order in High-Dimensional Chaos
【速读】：该论文旨在解决文本到图像生成模型中难以实现细粒度控制的问题，尤其是对生成图像颜色属性的精确调控。其核心挑战在于现有模型对语义信息在潜在空间中的编码机制理解不足。解决方案的关键在于对FLUX.1模型变分自编码器（Variational Autoencoder, VAE）潜在空间中的颜色表示进行解析，揭示出一个反映色相（Hue）、饱和度（Saturation）和明度（Lightness）结构的潜在颜色子空间（Latent Color Subspace, LCS）。通过该子空间，作者提出了一种完全无需训练的闭式潜空间操作方法，实现了对颜色的预测与显式控制，从而在不改变模型参数的前提下提升了生成图像的颜色可控性。
链接: https://arxiv.org/abs/2603.12261
作者: Mateusz Pach,Jessica Bader,Quentin Bouniot,Serge Belongie,Zeynep Akata
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint
Abstract:Text-to-image generation models have advanced rapidly, yet achieving fine-grained control over generated images remains difficult, largely due to limited understanding of how semantic information is encoded. We develop an interpretation of the color representation in the Variational Autoencoder latent space of FLUX.1 [Dev], revealing a structure reflecting Hue, Saturation, and Lightness. We verify our Latent Color Subspace (LCS) interpretation by demonstrating that it can both predict and explicitly control color, introducing a fully training-free method in FLUX based solely on closed-form latent-space manipulation. Code is available at this https URL.
[CV-6] DreamVideo-Omni: Omni-Motion Controlled Multi-Subject Video Customization with Latent Identity Reinforcement Learning
【速读】：该论文旨在解决当前大规模扩散模型在视频生成中难以实现多主体身份精确控制与多层次运动灵活调控的问题，尤其针对现有方法普遍存在的运动粒度受限、控制歧义及身份退化等挑战。其解决方案的关键在于提出一个统一框架DreamVideo-Omni，采用渐进式两阶段训练策略：第一阶段通过引入条件感知的3D旋转位置编码（condition-aware 3D rotary positional embedding）和分层运动注入机制（hierarchical motion injection strategy），以协调异构输入并增强全局运动引导；同时设计群体与角色嵌入（group and role embeddings）来明确锚定运动信号至特定身份，从而解耦复杂场景中的多主体交互。第二阶段则通过潜空间身份奖励反馈学习（latent identity reward feedback learning）缓解身份退化问题，利用预训练视频扩散骨干网络构建潜空间身份奖励模型，提供面向运动感知的身份奖励，优先保障符合人类偏好的身份一致性。
链接: https://arxiv.org/abs/2603.12257
作者: Yujie Wei,Xinyu Liu,Shiwei Zhang,Hangjie Yuan,Jinbo Xing,Zhekai Chen,Xiang Wang,Haonan Qiu,Rui Zhao,Yutong Feng,Ruihang Chu,Yingya Zhang,Yike Guo,Xihui Liu,Hongming Shan
机构: Fudan University (复旦大学); The Hong Kong University of Science and Technology (香港科技大学); Tongyi Lab, Alibaba Group (通义实验室，阿里巴巴集团); Zhejiang University (浙江大学); MMLab, The University of Hong Kong (多媒体实验室，香港大学); Nanyang Technological University (南洋理工大学); Show Lab, National University of Singapore (Show实验室，新加坡国立大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:While large-scale diffusion models have revolutionized video synthesis, achieving precise control over both multi-subject identity and multi-granularity motion remains a significant challenge. Recent attempts to bridge this gap often suffer from limited motion granularity, control ambiguity, and identity degradation, leading to suboptimal performance on identity preservation and motion control. In this work, we present DreamVideo-Omni, a unified framework enabling harmonious multi-subject customization with omni-motion control via a progressive two-stage training paradigm. In the first stage, we integrate comprehensive control signals for joint training, encompassing subject appearances, global motion, local dynamics, and camera movements. To ensure robust and precise controllability, we introduce a condition-aware 3D rotary positional embedding to coordinate heterogeneous inputs and a hierarchical motion injection strategy to enhance global motion guidance. Furthermore, to resolve multi-subject ambiguity, we introduce group and role embeddings to explicitly anchor motion signals to specific identities, effectively disentangling complex scenes into independent controllable instances. In the second stage, to mitigate identity degradation, we design a latent identity reward feedback learning paradigm by training a latent identity reward model upon a pretrained video diffusion backbone. This provides motion-aware identity rewards in the latent space, prioritizing identity preservation aligned with human preferences. Supported by our curated large-scale dataset and the comprehensive DreamOmni Bench for multi-subject and omni-motion control evaluation, DreamVideo-Omni demonstrates superior performance in generating high-quality videos with precise controllability.
[CV-7] Spatial-TTT: Streaming Visual-based Spatial Intelligence with Test-Time Training
【速读】：该论文旨在解决视频流中长期空间感知与建模的问题，即如何在无界视频流中持续维护和更新空间证据以实现空间智能。其核心挑战在于空间信息的动态选择、组织与长期保留机制，而不仅仅是扩展上下文窗口长度。解决方案的关键是提出Spatial-TTT框架，通过测试时训练（Test-Time Training, TTT）机制，动态调整一组快速权重（fast weights）来捕捉并组织长时间跨度场景视频中的空间证据；同时设计了混合架构与大块更新策略结合滑动窗口注意力机制，提升处理效率，并引入基于3D时空卷积的空间预测机制以增强几何对应关系和时间连续性建模能力，最终借助包含密集3D空间描述的数据集引导模型结构化地记忆和组织全局空间信号。
链接: https://arxiv.org/abs/2603.12255
作者: Fangfu Liu,Diankun Wu,Jiawei Chi,Yimo Cai,Yi-Hsin Hung,Xumin Yu,Hao Li,Han Hu,Yongming Rao,Yueqi Duan
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Project Page: this https URL
Abstract:Humans perceive and understand real-world spaces through a stream of visual observations. Therefore, the ability to streamingly maintain and update spatial evidence from potentially unbounded video streams is essential for spatial intelligence. The core challenge is not simply longer context windows but how spatial information is selected, organized, and retained over time. In this paper, we propose Spatial-TTT towards streaming visual-based spatial intelligence with test-time training (TTT), which adapts a subset of parameters (fast weights) to capture and organize spatial evidence over long-horizon scene videos. Specifically, we design a hybrid architecture and adopt large-chunk updates parallel with sliding-window attention for efficient spatial video processing. To further promote spatial awareness, we introduce a spatial-predictive mechanism applied to TTT layers with 3D spatiotemporal convolution, which encourages the model to capture geometric correspondence and temporal continuity across frames. Beyond architecture design, we construct a dataset with dense 3D spatial descriptions, which guides the model to update its fast weights to memorize and organize global 3D spatial signals in a structured manner. Extensive experiments demonstrate that Spatial-TTT improves long-horizon spatial understanding and achieves state-of-the-art performance on video spatial benchmarks. Project page: this https URL.
[CV-8] Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing CVPR2026
【速读】：该论文旨在解决多模态大语言模型（Multi-modal Large Language Models, MLLMs）在处理长时、高分辨率视频时存在的计算效率低下问题，即模型对所有像素进行同等处理，导致冗余计算严重。其解决方案的关键在于提出轻量级模块AutoGaze，该模块通过预训练的自回归策略结合强化学习，在视觉Transformer（Vision Transformer, ViT）或MLLM处理前自动筛选出最小数量的多尺度图像块（patch），确保在用户指定误差阈值内重建视频内容，从而显著减少视觉token数量（4倍至100倍）并提升推理速度（最高达19倍加速）。这一方法有效消除了时空冗余，同时保留关键信息，使MLLM能够高效处理长达1000帧、4K分辨率的视频，并在视频理解基准（如VideoMME）上取得更优性能。
链接: https://arxiv.org/abs/2603.12254
作者: Baifeng Shi,Stephanie Fu,Long Lian,Hanrong Ye,David Eigen,Aaron Reite,Boyi Li,Jan Kautz,Song Han,David M. Chan,Pavlo Molchanov,Trevor Darrell,Hongxu Yin
机构: UC Berkeley (加州大学伯克利分校); MIT (麻省理工学院); Clarifai (Clarifai); NVIDIA (英伟达)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR 2026. Project page: this https URL
Abstract:Multi-modal large language models (MLLMs) have advanced general-purpose video understanding but struggle with long, high-resolution videos – they process every pixel equally in their vision transformers (ViTs) or LLMs despite significant spatiotemporal redundancy. We introduce AutoGaze, a lightweight module that removes redundant patches before processed by a ViT or an MLLM. Trained with next-token prediction and reinforcement learning, AutoGaze autoregressively selects a minimal set of multi-scale patches that can reconstruct the video within a user-specified error threshold, eliminating redundancy while preserving information. Empirically, AutoGaze reduces visual tokens by 4x-100x and accelerates ViTs and MLLMs by up to 19x, enabling scaling MLLMs to 1K-frame 4K-resolution videos and achieving superior results on video benchmarks (e.g., 67.0% on VideoMME). Furthermore, we introduce HLVid: the first high-resolution, long-form video QA benchmark with 5-minute 4K-resolution videos, where an MLLM scaled with AutoGaze improves over the baseline by 10.1% and outperforms the previous best MLLM by 4.5%. Project page: this https URL.
[CV-9] DVD: Deterministic Video Depth Estimation with Generative Priors
【速读】：该论文旨在解决视频深度估计中生成式模型易产生几何幻觉和尺度漂移，而判别式模型又依赖大量标注数据以应对语义模糊性的根本矛盾。其解决方案的关键在于提出DVD框架，首次将预训练视频扩散模型确定性地转化为单次通过的深度回归器，核心创新包括：(i) 利用扩散时间步作为结构锚点，在全局稳定性与高频细节之间取得平衡；(ii) 引入潜空间流形校正（Latent Manifold Rectification, LMR），通过施加微分约束缓解回归导致的过度平滑，恢复锐利边界与一致运动；(iii) 借助固有的全局仿射一致性约束，限制窗口间差异，实现无需复杂时序对齐的长视频无缝推理。
链接: https://arxiv.org/abs/2603.12250
作者: Hongfei Zhang,Harold Haodong Chen,Chenfei Liao,Jing He,Zixin Zhang,Haodong Li,Yihao Liang,Kanghao Chen,Bin Ren,Xu Zheng,Shuai Yang,Kun Zhou,Yinchuan Li,Nicu Sebe,Ying-Cong Chen
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project: this https URL
Abstract:Existing video depth estimation faces a fundamental trade-off: generative models suffer from stochastic geometric hallucinations and scale drift, while discriminative models demand massive labeled datasets to resolve semantic ambiguities. To break this impasse, we present DVD, the first framework to deterministically adapt pre-trained video diffusion models into single-pass depth regressors. Specifically, DVD features three core designs: (i) repurposing the diffusion timestep as a structural anchor to balance global stability with high-frequency details; (ii) latent manifold rectification (LMR) to mitigate regression-induced over-smoothing, enforcing differential constraints to restore sharp boundaries and coherent motion; and (iii) global affine coherence, an inherent property bounding inter-window divergence, which enables seamless long-video inference without requiring complex temporal alignment. Extensive experiments demonstrate that DVD achieves state-of-the-art zero-shot performance across benchmarks. Furthermore, DVD successfully unlocks the profound geometric priors implicit in video foundation models using 163x less task-specific data than leading baselines. Notably, we fully release our pipeline, providing the whole training suite for SOTA video depth estimation to benefit the open-source community.
[CV-10] rust Your Critic: Robust Reward Modeling and Reinforcement Learning for Faithful Image Editing and Generation
【速读】：该论文旨在解决当前基于强化学习（Reinforcement Learning, RL）的图像编辑与文本到图像（Text-to-Image, T2I）生成中，奖励模型（Reward Model）因幻觉（Hallucination）和噪声评分导致优化方向偏差的问题。解决方案的关键在于提出FIRM（Faithful Image Reward Modeling）框架：首先设计定制化的数据采集流程，构建高质量评分数据集（FIRM-Edit-370K 和 FIRM-Gen-293K），分别以执行一致性（Execution and Consistency）评估编辑任务、以指令遵循度（Instruction Following）评估生成任务；其次训练专用奖励模型（FIRM-Edit-8B 和 FIRM-Gen-8B），确保其准确反映上述指标；最后引入“Base-and-Bonus”奖励策略，通过一致性调制执行（Consistency-Modulated Execution, CME）与质量调制对齐（Quality-Modulated Alignment, QMA）协同优化，显著提升图像生成与编辑的忠实性（Fidelity）和指令遵循能力，从而建立新的基准标准。
链接: https://arxiv.org/abs/2603.12247
作者: Xiangyu Zhao,Peiyuan Zhang,Junming Lin,Tianhao Liang,Yuchen Duan,Shengyuan Ding,Changyao Tian,Yuhang Zang,Junchi Yan,Xue Yang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reinforcement learning (RL) has emerged as a promising paradigm for enhancing image editing and text-to-image (T2I) generation. However, current reward models, which act as critics during RL, often suffer from hallucinations and assign noisy scores, inherently misguiding the optimization process. In this paper, we present FIRM (Faithful Image Reward Modeling), a comprehensive framework that develops robust reward models to provide accurate and reliable guidance for faithful image generation and editing. First, we design tailored data curation pipelines to construct high-quality scoring datasets. Specifically, we evaluate editing using both execution and consistency, while generation is primarily assessed via instruction following. Using these pipelines, we collect the FIRM-Edit-370K and FIRM-Gen-293K datasets, and train specialized reward models (FIRM-Edit-8B and FIRM-Gen-8B) that accurately reflect these criteria. Second, we introduce FIRM-Bench, a comprehensive benchmark specifically designed for editing and generation critics. Evaluations demonstrate that our models achieve superior alignment with human judgment compared to existing metrics. Furthermore, to seamlessly integrate these critics into the RL pipeline, we formulate a novel “Base-and-Bonus” reward strategy that balances competing objectives: Consistency-Modulated Execution (CME) for editing and Quality-Modulated Alignment (QMA) for generation. Empowered by this framework, our resulting models FIRM-Qwen-Edit and FIRM-SD3.5 achieve substantial performance breakthroughs. Comprehensive experiments demonstrate that FIRM mitigates hallucinations, establishing a new standard for fidelity and instruction adherence over existing general models. All of our datasets, models, and code have been publicly available at this https URL.
[CV-11] One Model Many Budgets: Elastic Latent Interfaces for Diffusion Transformers
【速读】：该论文旨在解决扩散变换器（Diffusion Transformers, DiTs）在生成质量与计算效率之间难以权衡的问题，具体表现为：DiTs 的浮点运算量（FLOPs）固定绑定于图像分辨率，导致无法根据计算资源灵活调整延迟-质量平衡；同时，其对输入空间标记（spatial tokens）的计算分配均匀，浪费了对不重要区域的资源。解决方案的关键在于提出弹性潜在接口变换器（Elastic Latent Interface Transformer, ELIT），通过引入一个可学习的变长潜在标记序列（latent token sequence），将输入图像大小与计算量解耦。ELIT 在标准Transformer块上操作潜在标记，并借助轻量级读写交叉注意力层实现空间标记与潜在标记之间的信息迁移，优先处理重要区域。训练时随机丢弃尾部潜在标记使模型学会按重要性排序表示——早期潜在标记捕捉全局结构，后期用于细节优化；推理时可根据计算约束动态调整潜在标记数量，从而实现高效且灵活的资源分配。该方法保持原DiT架构不变，仅增加两个交叉注意力层，却在多个数据集和架构（如DiT、U-ViT、HDiT、MM-DiT）上显著提升生成质量（如ImageNet-1K 512px下FID和FDD分别提升35.3%和39.6%）。
链接: https://arxiv.org/abs/2603.12245
作者: Moayed Haji-Ali,Willi Menapace,Ivan Skorokhodov,Dogyun Park,Anil Kag,Michael Vasilkovsky,Sergey Tulyakov,Vicente Ordonez,Aliaksandr Siarohin
机构: Rice University(莱斯大学); Snap Inc.(Snap Inc.)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Diffusion transformers (DiTs) achieve high generative quality but lock FLOPs to image resolution, limiting principled latency-quality trade-offs, and allocate computation uniformly across input spatial tokens, wasting resource allocation to unimportant regions. We introduce Elastic Latent Interface Transformer (ELIT), a drop-in, DiT-compatible mechanism that decouples input image size from compute. Our approach inserts a latent interface, a learnable variable-length token sequence on which standard transformer blocks can operate. Lightweight Read and Write cross-attention layers move information between spatial tokens and latents and prioritize important input regions. By training with random dropping of tail latents, ELIT learns to produce importance-ordered representations with earlier latents capturing global structure while later ones contain information to refine details. At inference, the number of latents can be dynamically adjusted to match compute constraints. ELIT is deliberately minimal, adding two cross-attention layers while leaving the rectified flow objective and the DiT stack unchanged. Across datasets and architectures (DiT, U-ViT, HDiT, MM-DiT), ELIT delivers consistent gains. On ImageNet-1K 512px, ELIT delivers an average gain of 35.3% and 39.6% in FID and FDD scores. Project page: this https URL
[CV-12] BiGain: Unified Token Compression for Joint Generation and Classification CVPR2026
【速读】：该论文旨在解决加速扩散模型（diffusion models）时生成质量与判别能力之间的权衡问题，即现有加速方法（如token合并或下采样）通常仅优化合成质量而忽略分类性能。解决方案的关键在于提出BiGain框架，其核心思想是频率分离：通过将特征空间信号映射到频率感知表示，解耦细粒度细节与全局语义信息，从而实现兼顾生成保真度和判别效用的压缩策略。具体包括两个频率感知算子：(1) 拉普拉斯门控token合并（Laplacian-gated token merging），鼓励谱平滑token合并并抑制高对比度token合并以保留边缘与纹理；(2) 插值-外推KV下采样（Interpolate-Extrapolate KV Downsampling），通过可控插值与平均池化间过渡下采样key/value，同时保持query不变，从而维持注意力精度。实验证明，该方法在多个骨干网络和数据集上均显著提升速度-准确率平衡，且不牺牲生成质量。
链接: https://arxiv.org/abs/2603.12240
作者: Jiacheng Liu,Shengkun Tang,Jiacheng Cui,Dongkuan Xu,Zhiqiang Shen
机构: VILA Lab, MBZUAI; North Carolina State University
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: CVPR 2026. Code: this https URL
Abstract:Acceleration methods for diffusion models (e.g., token merging or downsampling) typically optimize synthesis quality under reduced compute, yet often ignore discriminative capacity. We revisit token compression with a joint objective and present BiGain, a training-free, plug-and-play framework that preserves generation quality while improving classification in accelerated diffusion models. Our key insight is frequency separation: mapping feature-space signals into a frequency-aware representation disentangles fine detail from global semantics, enabling compression that respects both generative fidelity and discriminative utility. BiGain reflects this principle with two frequency-aware operators: (1) Laplacian-gated token merging, which encourages merges among spectrally smooth tokens while discouraging merges of high-contrast tokens, thereby retaining edges and textures; and (2) Interpolate-Extrapolate KV Downsampling, which downsamples keys/values via a controllable interextrapolation between nearest and average pooling while keeping queries intact, thereby conserving attention precision. Across DiT- and U-Net-based backbones and ImageNet-1K, ImageNet-100, Oxford-IIIT Pets, and COCO-2017, our operators consistently improve the speed-accuracy trade-off for diffusion-based classification, while maintaining or enhancing generation quality under comparable acceleration. For instance, on ImageNet-1K, with 70% token merging on Stable Diffusion 2.0, BiGain increases classification accuracy by 7.15% while improving FID by 0.34 (1.85%). Our analyses indicate that balanced spectral retention, preserving high-frequency detail and low/mid-frequency semantics, is a reliable design rule for token compression in diffusion models. To our knowledge, BiGain is the first framework to jointly study and advance both generation and classification under accelerated diffusion, supporting lower-cost deployment.
[CV-13] SceneAssistant: A Visual Feedback Agent for Open-Vocabulary 3D Scene Generation
【速读】：该论文旨在解决当前文本到3D场景生成方法在开放词汇（open-vocabulary）和无约束场景合成方面的局限性，现有方法通常受限于特定领域或依赖预定义的空间关系，难以实现灵活、多样且语义对齐的3D场景构建。解决方案的关键在于提出SceneAssistant——一个基于视觉反馈驱动的智能体（agent），其核心机制是融合现代3D物体生成模型与视觉语言模型（Vision-Language Models, VLMs）的空间推理与规划能力；通过提供一组原子操作（如Scale、Rotate、FocusOn）并引入每步交互中的渲染视觉反馈，使VLM能够迭代优化场景布局，从而实现更连贯的空间结构和更高精度的文本-场景对齐。
链接: https://arxiv.org/abs/2603.12238
作者: Jun Luo,Jiaxiang Tang,Ruijie Lu,Gang Zeng
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Code: this https URL
Abstract:Text-to-3D scene generation from natural language is highly desirable for digital content creation. However, existing methods are largely domain-restricted or reliant on predefined spatial relationships, limiting their capacity for unconstrained, open-vocabulary 3D scene synthesis. In this paper, we introduce SceneAssistant, a visual-feedback-driven agent designed for open-vocabulary 3D scene generation. Our framework leverages modern 3D object generation model along with the spatial reasoning and planning capabilities of Vision-Language Models (VLMs). To enable open-vocabulary scene composition, we provide the VLMs with a comprehensive set of atomic operations (e.g., Scale, Rotate, FocusOn). At each interaction step, the VLM receives rendered visual feedback and takes actions accordingly, iteratively refining the scene to achieve more coherent spatial arrangements and better alignment with the input text. Experimental results demonstrate that our method can generate diverse, open-vocabulary, and high-quality 3D scenes. Both qualitative analysis and quantitative human evaluations demonstrate the superiority of our approach over existing methods. Furthermore, our method allows users to instruct the agent to edit existing scenes based on natural language commands. Our code is available at this https URL
[CV-14] HiAP: A Multi-Granular Stochastic Auto-Pruning Framework for Vision Transformers
【速读】：该论文旨在解决视觉Transformer（Vision Transformer）在边缘设备上部署时面临的计算资源和内存带宽瓶颈问题。现有结构化剪枝方法通常仅在单一粒度上操作，且依赖复杂的多阶段流水线与后处理阈值设定来满足稀疏性预算，效率低下且难以自动化。其解决方案的关键在于提出一种分层自动剪枝（Hierarchical Auto-Pruning, HiAP）框架，通过引入多层次的随机Gumbel-Sigmoid门控机制——宏观门控用于剪枝整个注意力头和前馈网络（Feed-Forward Network, FFN）模块，微观门控用于细粒度地剪枝注意力头内部维度和FFN神经元——实现端到端联合优化，无需人工重要性启发式规则或预设每层稀疏目标。HiAP通过结合结构可行性惩罚项与解析FLOPs约束的损失函数，自然收敛至稳定高效的子网络，在ImageNet上验证了其能自动发现高效率架构，并在DeiT-Small等模型上达到与复杂多阶段方法相当的精度-效率帕累托前沿，显著简化部署流程。
链接: https://arxiv.org/abs/2603.12222
作者: Andy Li,Aiden Durrant,Milan Markovic,Georgios Leontidis
机构: University of Aberdeen (阿伯丁大学); University of East Anglia (东英吉利大学); UiT The Arctic University of Norway (挪威北极大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 14 pages, 9 figures, 3 Tables
Abstract:Vision Transformers require significant computational resources and memory bandwidth, severely limiting their deployment on edge devices. While recent structured pruning methods successfully reduce theoretical FLOPs, they typically operate at a single structural granularity and rely on complex, multi-stage pipelines with post-hoc thresholding to satisfy sparsity budgets. In this paper, we propose Hierarchical Auto-Pruning (HiAP), a continuous relaxation framework that discovers optimal sub-networks in a single end-to-end training phase without requiring manual importance heuristics or predefined per-layer sparsity targets. HiAP introduces stochastic Gumbel-Sigmoid gates at multiple granularities: macro-gates to prune entire attention heads and FFN blocks, and micro-gates to selectively prune intra-head dimensions and FFN neurons. By optimizing both levels simultaneously, HiAP addresses both the memory-bound overhead of loading large matrices and the compute-bound mathematical operations. HiAP naturally converges to stable sub-networks using a loss function that incorporates both structural feasibility penalties and analytical FLOPs. Extensive experiments on ImageNet demonstrate that HiAP organically discovers highly efficient architectures, and achieves a competitive accuracy-efficiency Pareto frontier for models like DeiT-Small, matching the performance of sophisticated multi-stage methods while significantly simplifying the deployment pipeline.
[CV-15] A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition
【速读】：该论文旨在解决在无约束视频场景下进行帧级面部情绪表达（EXPR）识别的挑战，此类任务因人脸定位不准、姿态与尺度变化大、运动模糊及相邻帧间时序不稳定等干扰因素而极具难度。解决方案的关键在于提出一种两阶段双模态（音频-视觉）模型：第一阶段利用预训练的DINOv2-ViT-L/14作为骨干网络进行鲁棒视觉特征提取，结合padding-aware增强策略（PadAug）和混合专家（MoE）训练头以提升分类器多样性；第二阶段通过多尺度重裁剪与特征平均构建稳健的帧级视觉表示，并引入帧对齐的Wav2Vec 2.0音频特征提供互补声学线索，再经轻量级门控融合模块整合双模态信息，并在推理时采用时序平滑策略增强时序一致性。该方法在ABAW数据集上取得显著性能提升，Macro-F1得分达0.5368（官方验证集）。
链接: https://arxiv.org/abs/2603.12221
作者: Jiajun Sun,Zhe Gao
机构: Shanghai Normal University (上海师范大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 4 figures
Abstract:This paper addresses the expression (EXPR) recognition challenge in the 10th Affective Behavior Analysis in-the-Wild (ABAW) workshop and competition, which requires frame-level classification of eight facial emotional expressions from unconstrained videos. This task is challenging due to inaccurate face localization, large pose and scale variations, motion blur, temporal instability, and other confounding factors across adjacent frames. We propose a two-stage dual-modal (audio-visual) model to address these difficulties. Stage I focuses on robust visual feature extraction with a pretrained DINOv2-based encoder. Specifically, DINOv2 ViT-L/14 is used as the backbone, a padding-aware augmentation (PadAug) strategy is employed for image padding and data preprocessing from raw videos, and a mixture-of-experts (MoE) training head is introduced to enhance classifier diversity. Stage II addresses modality fusion and temporal consistency. For the visual modality, faces are re-cropped from raw videos at multiple scales, and the extracted visual features are averaged to form a robust frame-level representation. Concurrently, frame-aligned Wav2Vec 2.0 audio features are derived from short audio windows to provide complementary acoustic cues. These dual-modal features are integrated via a lightweight gated fusion module, followed by inference-time temporal smoothing. Experiments on the ABAW dataset demonstrate the effectiveness of the proposed method. The two-stage model achieves a Macro-F1 score of 0.5368 on the official validation set and 0.5122 +/- 0.0277 under 5-fold cross-validation, outperforming the official baselines.
[CV-16] Real-World Point Tracking with Verifier-Guided Pseudo-Labeling CVPR2026
【速读】：该论文旨在解决长时点跟踪模型在真实世界视频中性能下降的问题，其根源在于合成数据与真实场景之间的差异以及缺乏密集的真值标注。为应对这一挑战，作者提出了一种名为verifier的元模型，其关键创新在于学习评估追踪器预测的可靠性，并据此指导伪标签生成。具体而言，verifier基于多个预训练追踪器提供的候选轨迹，在每帧上进行评分并选择最可信的预测，从而生成高质量的伪标签轨迹；该机制显著提升了监督信号的质量，实现了对未标注视频的数据高效适应，在四个真实世界基准测试中均达到当前最优性能，且所需数据量少于以往自训练方法。
链接: https://arxiv.org/abs/2603.12217
作者: Görkay Aydemir,Fatma Güney,Weidi Xie
机构: Koç University (科克大学); KUIS AI Center (KUIS人工智能中心); Shanghai Jiao Tong University (上海交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR 2026
Abstract:Models for long-term point tracking are typically trained on large synthetic datasets. The performance of these models degrades in real-world videos due to different characteristics and the absence of dense ground-truth annotations. Self-training on unlabeled videos has been explored as a practical solution, but the quality of pseudo-labels strongly depends on the reliability of teacher models, which vary across frames and scenes. In this paper, we address the problem of real-world fine-tuning and introduce verifier, a meta-model that learns to assess the reliability of tracker predictions and guide pseudo-label generation. Given candidate trajectories from multiple pretrained trackers, the verifier evaluates them per frame and selects the most trustworthy predictions, resulting in high-quality pseudo-label trajectories. When applied for fine-tuning, verifier-guided pseudo-labeling substantially improves the quality of supervision and enables data-efficient adaptation to unlabeled videos. Extensive experiments on four real-world benchmarks demonstrate that our approach achieves state-of-the-art results while requiring less data than prior self-training methods. Project page: this https URL
[CV-17] RDNet: Region Proportion-Aware Dynamic Adaptive Salient Object Detection Network in Optical Remote Sensing Images
【速读】：该论文旨在解决遥感图像中显著目标检测（Salient Object Detection, SOD）面临的三大挑战：目标尺度变化大、自注意力机制计算成本高，以及基于卷积神经网络（CNN）的特征提取器难以捕捉全局上下文和长距离依赖关系。现有方法因使用固定卷积核，在不同尺度目标上易出现细节丢失或无关特征聚合的问题。解决方案的关键在于提出一种区域比例感知的动态自适应显著目标检测网络（Region Proportion-Aware Dynamic Adaptive Salient Object Detection Network, RDNet），其核心创新包括：(1) 动态自适应细节感知模块（DAD），根据目标区域比例动态调整卷积核；(2) 频率匹配上下文增强模块（FCE），通过小波变换与注意力机制融合多尺度上下文信息；(3) 区域比例感知定位模块（RPL），利用交叉注意力突出语义细节并引入比例引导（PG）块辅助DAD模块优化特征提取。三者协同提升了模型对尺度变化的鲁棒性和定位精度，性能优于当前最优方法。
链接: https://arxiv.org/abs/2603.12215
作者: Bin Wan,Runmin Cong,Xiaofei Zhou,Hao Fang,Yaoqi Sun,Sam Kwong
机构: Shandong University (山东大学); Key Laboratory of Machine Intelligence and System Control, Ministry of Education (教育部机器智能与系统控制重点实验室); Hangzhou Dianzi University (杭州电子科技大学); Lishui University (丽水学院); Lingnan University (岭南大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Salient object detection (SOD) in remote sensing images faces significant challenges due to large variations in object sizes, the computational cost of self-attention mechanisms, and the limitations of CNN-based extractors in capturing global context and long-range dependencies. Existing methods that rely on fixed convolution kernels often struggle to adapt to diverse object scales, leading to detail loss or irrelevant feature aggregation. To address these issues, this work aims to enhance robustness to scale variations and achieve precise object localization. We propose the Region Proportion-Aware Dynamic Adaptive Salient Object Detection Network (RDNet), which replaces the CNN backbone with the SwinTransformer for global context modeling and introduces three key modules: (1) the Dynamic Adaptive Detail-aware (DAD) module, which applies varied convolution kernels guided by object region proportions; (2) the Frequency-matching Context Enhancement (FCE) module, which enriches contextual information through wavelet interactions and attention; and (3) the Region Proportion-aware Localization (RPL) module, which employs cross-attention to highlight semantic details and integrates a Proportion Guidance (PG) block to assist the DAD module. By combining these modules, RDNet achieves robustness against scale variations and accurate localization, delivering superior detection performance compared with state-of-the-art methods.
[CV-18] ForensicZip: More Tokens are Better but Not Necessary in Forensic Vision-Language Models
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在处理高分辨率图像和视频等密集视觉序列时计算成本过高的问题，尤其针对现有视觉token剪枝方法因依赖语义驱动策略而可能遗漏伪造痕迹（如高频异常和时间抖动）的缺陷。解决方案的关键在于提出一种无需训练的框架ForensicZip，其核心创新是将token压缩重构为一个以伪造驱动为导向的优化问题：通过引入带松弛虚拟节点的出生-死亡最优传输（Birth-Death Optimal Transport）模型来量化物理不连续性，从而识别瞬态生成伪影；同时结合基于运输的新颖性评分与高频先验知识，在高比例压缩下有效分离取证证据与语义内容，实现高效且精准的多媒体伪造检测。
链接: https://arxiv.org/abs/2603.12208
作者: Yingxin Lai,Zitong Yu,Jun Wang,Linlin Shen,Yong Xu,Xiaochun Cao
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal Large Language Models (MLLMs) enable interpretable multimedia forensics by generating textual rationales for forgery detection. However, processing dense visual sequences incurs high computational costs, particularly for high-resolution images and videos. Visual token pruning is a practical acceleration strategy, yet existing methods are largely semantic-driven, retaining salient objects while discarding background regions where manipulation traces such as high-frequency anomalies and temporal jitters often reside. To address this issue, we introduce ForensicZip, a training-free framework that reformulates token compression from a forgery-driven perspective. ForensicZip models temporal token evolution as a Birth-Death Optimal Transport problem with a slack dummy node, quantifying physical discontinuities indicating transient generative artifacts. The forensic scoring further integrates transport-based novelty with high-frequency priors to separate forensic evidence from semantic content under large-ratio compression. Experiments on deepfake and AIGC benchmarks show that at 10% token retention, ForensicZip achieves 2.97\times speedup and over 90% FLOPs reduction while maintaining state-of-the-art detection performance.
[CV-19] SaPaVe: Towards Active Perception and Manipulation in Vision-Language-Action Models for Robotics CVPR2026
【速读】：该论文旨在解决机器人在复杂场景中实现语义驱动的主动感知（active perception）与鲁棒、视角不变的执行（viewpoint-invariant execution）难以统一的问题。现有方法往往将感知与操作动作置于共享动作空间中，导致训练效率低且泛化能力弱。其解决方案的关键在于提出SaPaVe框架——通过解耦相机控制与操作动作（而非共用动作空间），并采用自底向上的训练策略：先在大规模数据集上训练语义相机控制，再利用混合数据联合优化两类动作；同时引入ActiveViewPose-200K数据集和3D几何感知模块以提升动态视角下的执行鲁棒性。实验表明，该方法显著优于GR00T N1和π0等先进视觉语言动作模型，在真实任务中成功率最高提升31.25%。
链接: https://arxiv.org/abs/2603.12193
作者: Mengzhen Liu,Enshen Zhou,Cheng Chi,Yi Han,Shanyu Rong,Liming Chen,Pengwei Wang,Zhongyuan Wang,Shanghang Zhang
机构: Peking University (北京大学); Beihang University (北京航空航天大学); Beijing Academy of Artificial Intelligence (北京智源研究院)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026. See project page at this https URL
Abstract:Active perception and manipulation are crucial for robots to interact with complex scenes. Existing methods struggle to unify semantic-driven active perception with robust, viewpoint-invariant execution. We propose SaPaVe, an end-to-end framework that jointly learns these capabilities in a data-efficient manner. Our approach decouples camera and manipulation actions rather than placing them in a shared action space, and follows a bottom-up training strategy: we first train semantic camera control on a large-scale dataset, then jointly optimize both action types using hybrid data. To support this framework, we introduce ActiveViewPose-200K, a dataset of 200k image-language-camera movement pairs for semantic camera movement learning, and a 3D geometry-aware module that improves execution robustness under dynamic viewpoints. We also present ActiveManip-Bench, the first benchmark for evaluating active manipulation beyond fixed-view settings. Extensive experiments in both simulation and real-world environments show that SaPaVe outperforms recent vision-language-action models such as GR00T N1 and (\pi_0), achieving up to 31.25% higher success rates in real-world tasks. These results show that tightly coupled perception and execution, when trained with decoupled yet coordinated strategies, enable efficient and generalizable active manipulation. Project page: this https URL
[CV-20] BehaviorVLM: Unified Finetuning-Free Behavioral Understanding with Vision-Language Reasoning
【速读】：该论文旨在解决动物自由运动行为分析中 pose 估计（姿态估计）与行为理解任务依赖大量人工标注或不稳定无监督流程的问题，从而限制了可扩展性和可重复性。其核心解决方案是提出 BehaviorVLM，一个统一的视觉-语言框架，通过引导预训练视觉-语言模型（Vision-Language Models, VLMs）进行详尽、明确且可验证的推理步骤，实现无需特定任务微调和极少人工标注的端到端分析。关键在于：在姿态估计方面，利用量子点标记的行为数据构建多阶段推理流水线，融合时空与跨视角推理，显著降低标注需求并借助几何校验（如重投影误差）暴露低置信度标签；在行为理解方面，结合深度嵌入聚类发现过分割行为片段、VLM驱动的视频片段描述生成及大语言模型（LLM）语义推理合并与标注，直接从视觉信息出发完成行为分割，无需关键点输入。此设计实现了多动物行为分析的可扩展、可解释且标签轻量化的处理能力。
链接: https://arxiv.org/abs/2603.12176
作者: Jingyang Ke,Weihan Li,Amartya Pradhan,Jeffrey Markowitz,Anqi Wu
机构: Georgia Institute of Technology (佐治亚理工学院); Emory University (埃默里大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Understanding freely moving animal behavior is central to neuroscience, where pose estimation and behavioral understanding form the foundation for linking neural activity to natural actions. Yet both tasks still depend heavily on human annotation or unstable unsupervised pipelines, limiting scalability and reproducibility. We present BehaviorVLM, a unified vision-language framework for pose estimation and behavioral understanding that requires no task-specific finetuning and minimal human labeling by guiding pretrained Vision-Language Models (VLMs) through detailed, explicit, and verifiable reasoning steps. For pose estimation, we leverage quantum-dot-grounded behavioral data and propose a multi-stage pipeline that integrates temporal, spatial, and cross-view reasoning. This design greatly reduces human annotation effort, exposes low-confidence labels through geometric checks such as reprojection error, and produces labels that can later be filtered, corrected, or used to fine-tune downstream pose models. For behavioral understanding, we propose a pipeline that integrates deep embedded clustering for over-segmented behavior discovery, VLM-based per-clip video captioning, and LLM-based reasoning to merge and semantically label behavioral segments. The behavioral pipeline can operate directly from visual information and does not require keypoints to segment behavior. Together, these components enable scalable, interpretable, and label-light analysis of multi-animal behavior.
[CV-21] LatentGeo: Learnable Auxiliary Constructions in Latent Space for Multimodal Geometric Reasoning
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在几何推理任务中难以有效表示辅助几何构造（auxiliary geometric constructions）的问题。这些构造通常不在原始图示中，需在定理应用前引入，而现有方法依赖显式构造范式（如基于文本的几何描述、视觉token交错推理或工具增强执行），存在空间关系表达不准确、离散符号与连续几何结构间表征失配，或依赖外部执行器导致无法端到端优化等局限。其解决方案的关键在于提出LatentGeo框架，通过学习连续潜空间视觉表征来内化辅助几何构造，无需像素级渲染或外部执行器；该框架采用三阶段课程训练策略逐步对齐并内化潜变量表示，并结合LaGDPO（latent-aware reinforcement learning）稳定潜变量在策略优化过程中的表现，同时提升最终任务正确率，从而实现更鲁棒和高效的几何推理能力。
链接: https://arxiv.org/abs/2603.12166
作者: Haiying Xu,Zihan Wang,Song Dai,Zhengxuan Zhang,Kairan Dou,Xuming Hu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Despite recent advances in multimodal reasoning, representing auxiliary geometric constructions remains a fundamental challenge for multimodal large language models (MLLMs). Such constructions are absent from the original diagram and must be introduced before theorems apply. Existing approaches predominantly rely on explicit construction paradigms, including text-based geometric specification, visual-token interleaving during reasoning, and tool-augmented geometric execution. However, these methods either fail to faithfully represent complex spatial relationships, incur representation mismatch between discrete symbols and continuous geometric structures, or rely on external capabilities that hinder end-to-end optimization. To address these limitations, we propose LatentGeo, a framework that learns continuous latent visual representations to internalize auxiliary geometric constructions without pixel-level rendering or external executors. We design a three-stage curriculum that progressively aligns and internalizes these latent representations through auxiliary visual supervision, followed by LaGDPO, a latent-aware reinforcement learning procedure that stabilizes latent representations during policy optimization while improving end-task correctness. To systematically evaluate construction-centric representation quality, we introduce GeoAux, a new benchmark targeting visually dependent geometry problems, and conduct experiments on GeoAux and MathVerse. Results show that LatentGeo achieves substantial gains on geometric reasoning tasks, particularly those requiring auxiliary constructions. Extensive analyses and ablation studies further validate the effectiveness of each component in our framework.
[CV-22] GlyphBanana: Advancing Precise Text Rendering Through Agent ic Workflows
【速读】：该论文旨在解决当前生成式 AI（Generative AI）在文本渲染任务中难以准确生成复杂字符与数学公式的问题，其核心挑战在于模型对分布外（out-of-distribution）提示的指令遵循能力有限。解决方案的关键在于提出 GlyphBanana，这是一种无需训练的代理式工作流（agentic workflow），通过引入辅助工具将字形模板（glyph template）注入到潜在空间（latent space）和注意力图（attention map）中，从而实现图像的迭代优化，显著提升了文本与公式生成的精度，并可无缝适配多种文本到图像（Text-to-Image, T2I）模型。
链接: https://arxiv.org/abs/2603.12155
作者: Zexuan Yan,Jiarui Jin,Yue Ma,Shijian Wang,Jiahui Hu,Wenxiang Jiao,Yuan Lu,Linfeng Zhang
机构: Shanghai Jiao Tong University (上海交通大学); Xiaohongshu Inc. (小红书公司); Hong Kong University of Science and Technology (香港科技大学); Southeast University (东南大学); South China University of Technology (华南理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Despite recent advances in generative models driving significant progress in text rendering, accurately generating complex text and mathematical formulas remains a formidable challenge. This difficulty primarily stems from the limited instruction-following capabilities of current models when encountering out-of-distribution prompts. To address this, we introduce GlyphBanana, alongside a corresponding benchmark specifically designed for rendering complex characters and formulas. GlyphBanana employs an agentic workflow that integrates auxiliary tools to inject glyph templates into both the latent space and attention maps, facilitating the iterative refinement of generated images. Notably, our training-free approach can be seamlessly applied to various Text-to-Image (T2I) models, achieving superior precision compared to existing baselines. Extensive experiments demonstrate the effectiveness of our proposed workflow. Associated code is publicly available at this https URL.
[CV-23] EgoIntent: An Egocentric Step-level Benchmark for Understanding What Why and Next
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在第一人称视角视频（egocentric videos）中对人类意图进行细粒度理解的能力不足的问题，尤其是现有基准主要关注整体情节级别的意图推理，而忽视了步骤级别（step-level）的意图识别需求。其解决方案的关键在于提出EgoIntent基准，这是一个面向第一人称视频的步骤级意图理解评测集，包含3,014个步骤，覆盖15种日常场景，并从三个互补维度评估模型：局部意图（What）、全局意图（Why）和下一步计划（Next）。特别地，每个视频片段在关键动作结果（如接触或抓取）发生前截断，且不包含后续步骤帧，从而避免未来帧泄露（future-frame leakage），确保对预测性步骤理解和下一步规划能力的纯净评估。
链接: https://arxiv.org/abs/2603.12147
作者: Ye Pan,Chi Kit Wong,Yuanhuiyi Lyu,Hanqian Li,Jiahao Huo,Jiacheng Chen,Lutao Jiang,Xu Zheng,Xuming Hu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Multimodal Large Language Models (MLLMs) have demonstrated remarkable video reasoning capabilities across diverse tasks. However, their ability to understand human intent at a fine-grained level in egocentric videos remains largely unexplored. Existing benchmarks focus primarily on episode-level intent reasoning, overlooking the finer granularity of step-level intent understanding. Yet applications such as intelligent assistants, robotic imitation learning, and augmented reality guidance require understanding not only what a person is doing at each step, but also why and what comes next, in order to provide timely and context-aware support. To this end, we introduce EgoIntent, a step-level intent understanding benchmark for egocentric videos. It comprises 3,014 steps spanning 15 diverse indoor and outdoor daily-life scenarios, and evaluates models on three complementary dimensions: local intent (What), global intent (Why), and next-step plan (Next). Crucially, each clip is truncated immediately before the key outcome of the queried step (e.g., contact or grasp) occurs and contains no frames from subsequent steps, preventing future-frame leakage and enabling a clean evaluation of anticipatory step understanding and next-step planning. We evaluate 15 MLLMs, including both state-of-the-art closed-source and open-source models. Even the best-performing model achieves an average score of only 33.31 across the three intent dimensions, underscoring that step-level intent understanding in egocentric videos remains a highly challenging problem that calls for further investigation.
[CV-24] FlashMotion: Few-Step Controllable Video Generation with Trajectory Guidance CVPR2026
【速读】：该论文旨在解决轨迹可控视频生成中因多步去噪过程导致的时间冗余与计算开销问题，以及现有视频蒸馏方法直接应用于轨迹可控场景时造成的视频质量下降和轨迹精度损失问题。解决方案的关键在于提出一种名为FlashMotion的新训练框架：首先在多步视频生成器上训练轨迹适配器以实现精确的轨迹控制；随后将生成器蒸馏为少步版本以加速生成；最后采用结合扩散和对抗目标的混合微调策略，使适配器与少步生成器对齐，从而在保持高视觉质量的同时确保轨迹准确性。
链接: https://arxiv.org/abs/2603.12146
作者: Quanhao Li,Zhen Xing,Rui Wang,Haidong Cao,Qi Dai,Daoguo Dong,Zuxuan Wu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
备注: Accepted by CVPR2026
Abstract:Recent advances in trajectory-controllable video generation have achieved remarkable progress. Previous methods mainly use adapter-based architectures for precise motion control along predefined trajectories. However, all these methods rely on a multi-step denoising process, leading to substantial time redundancy and computational overhead. While existing video distillation methods successfully distill multi-step generators into few-step, directly applying these approaches to trajectory-controllable video generation results in noticeable degradation in both video quality and trajectory accuracy. To bridge this gap, we introduce FlashMotion, a novel training framework designed for few-step trajectory-controllable video generation. We first train a trajectory adapter on a multi-step video generator for precise trajectory control. Then, we distill the generator into a few-step version to accelerate video generation. Finally, we finetune the adapter using a hybrid strategy that combines diffusion and adversarial objectives, aligning it with the few-step generator to produce high-quality, trajectory-accurate videos. For evaluation, we introduce FlashBench, a benchmark for long-sequence trajectory-controllable video generation that measures both video quality and trajectory accuracy across varying numbers of foreground objects. Experiments on two adapter architectures show that FlashMotion surpasses existing video distillation methods and previous multi-step models in both visual quality and trajectory consistency.
[CV-25] O3N: Omnidirectional Open-Vocabulary Occupancy Prediction
【速读】：该论文旨在解决现有3D占用预测方法在开放世界场景下受限于视角输入有限和预定义训练分布的问题，从而难以支持具身智能体对复杂环境的全面、安全感知。其核心解决方案是提出O3N框架，关键创新在于：（1）通过极螺旋Mamba（Polar-spiral Mamba, PsM）模块将全景体素嵌入极螺旋拓扑结构中，实现跨360°的连续空间表示与长程上下文建模；（2）引入占用代价聚合（Occupancy Cost Aggregation, OCA）模块，在体素空间内统一几何与语义监督信号，保障重建几何与语义结构的一致性；（3）设计自然模态对齐（Natural Modality Alignment, NMA）机制，建立无需梯度传播的视觉特征、体素嵌入与文本语义之间的对齐路径，形成“像素-体素-文本”一致表征三元组，显著提升模型跨场景泛化能力和语义可扩展性。
链接: https://arxiv.org/abs/2603.12144
作者: Mengfei Duan,Hao Shi,Fei Teng,Guoqiang Zhao,Yuheng Zhang,Zhiyong Li,Kailun Yang
机构: Hunan University (湖南大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO); Image and Video Processing (eess.IV)
备注: The source code will be made publicly available at this https URL
Abstract:Understanding and reconstructing the 3D world through omnidirectional perception is an inevitable trend in the development of autonomous agents and embodied intelligence. However, existing 3D occupancy prediction methods are constrained by limited perspective inputs and predefined training distribution, making them difficult to apply to embodied agents that require comprehensive and safe perception of scenes in open world exploration. To address this, we present O3N, the first purely visual, end-to-end Omnidirectional Open-vocabulary Occupancy predictioN framework. O3N embeds omnidirectional voxels in a polar-spiral topology via the Polar-spiral Mamba (PsM) module, enabling continuous spatial representation and long-range context modeling across 360°. The Occupancy Cost Aggregation (OCA) module introduces a principled mechanism for unifying geometric and semantic supervision within the voxel space, ensuring consistency between the reconstructed geometry and the underlying semantic structure. Moreover, Natural Modality Alignment (NMA) establishes a gradient-free alignment pathway that harmonizes visual features, voxel embeddings, and text semantics, forming a consistent “pixel-voxel-text” representation triad. Extensive experiments on multiple models demonstrate that our method not only achieves state-of-the-art performance on QuadOcc and Human360Occ benchmarks but also exhibits remarkable cross-scene generalization and semantic scalability, paving the way toward universal 3D world modeling. The source code will be made publicly available at this https URL.
[CV-26] HATS: Hardness-Aware Trajectory Synthesis for GUI Agents CVPR2026
【速读】：该论文旨在解决当前基于大视觉语言模型（VLM）的图形用户界面（GUI）代理在训练过程中因轨迹数据质量不足而导致泛化能力差的问题，尤其聚焦于语义模糊动作（semantically ambiguous actions）的缺失与处理不当。这类动作具有上下文依赖性、序列相关性或视觉歧义性，对真实场景下的鲁棒性至关重要，但在现有数据集中被严重低估且未得到充分建模，导致任务指令与执行行为之间出现语义错位。解决方案的关键在于提出HATS（Hardness-Aware Trajectory Synthesis）框架，其核心是将“难度”（hardness）定义为动作语义模糊程度，并设计两个互补模块：一是基于难度驱动的探索机制，主动收集高信息量的模糊交互轨迹；二是对齐引导的精炼机制，通过迭代验证与修复指令-执行对齐关系。二者构成闭环：探索提供挑战性轨迹供精炼使用，而精炼反馈更新难度信号以指导后续探索，从而系统性提升代理的泛化性能。
链接: https://arxiv.org/abs/2603.12138
作者: Rui Shao,Ruize Gao,Bin Xie,Yixing Li,Kaiwen Zhou,Shuai Wang,Weili Guan,Gongwei Chen
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR 2026
Abstract:Graphical user interface (GUI) agents powered by large vision-language models (VLMs) have shown remarkable potential in automating digital tasks, highlighting the need for high-quality trajectory data to support effective agent training. Yet existing trajectory synthesis pipelines often yield agents that fail to generalize beyond simple interactions. We identify this limitation as stemming from the neglect of semantically ambiguous actions, whose meanings are context-dependent, sequentially dependent, or visually ambiguous. Such actions are crucial for real-world robustness but are under-represented and poorly processed in current datasets, leading to semantic misalignment between task instructions and execution. To address these issues, we propose HATS, a Hardness-Aware Trajectory Synthesis framework designed to mitigate the impact of semantic ambiguity. We define hardness as the degree of semantic ambiguity associated with an action and develop two complementary modules: (1) hardness-driven exploration, which guides data collection toward ambiguous yet informative interactions, and (2) alignment-guided refinement, which iteratively validates and repairs instruction-execution alignment. The two modules operate in a closed loop: exploration supplies refinement with challenging trajectories, while refinement feedback updates the hardness signal to guide future exploration. Extensive experiments show that agents trained with HATS consistently outperform state-of-the-art baselines across benchmark GUI environments.
[CV-27] Hoi3DGen: Generating High-Quality Human-Object-Interactions in 3D
【速读】：该论文旨在解决文本驱动的3D人体-物体交互（Human-Object Interaction, HOI）生成中存在的一致性差和质量低的问题，尤其是现有方法依赖文本到图像模型进行分数蒸馏时，因高质量交互数据稀缺而引发的“Janus问题”（即生成结果无法忠实遵循文本提示）。解决方案的关键在于构建一个端到端的文本到3D生成框架Hoi3DGen：首先利用多模态大语言模型（Multimodal Large Language Models）高质量地收集和构建真实感的人体-物体交互数据集，进而设计了一个完整的文本到3D管道，显著提升了交互保真度（interaction fidelity），在文本一致性上比基线提升4–15倍，在3D模型质量上提升3–7倍，并展现出对多样化类别与交互类型的强泛化能力。
链接: https://arxiv.org/abs/2603.12126
作者: Agniv Sharma,Xianghui Xie,Tom Fischer,Eddy Ilg,Gerard Pons-Moll
机构: University of Tübingen (图宾根大学); Tübingen AI Center (图宾根人工智能中心); Max Planck Institute for Informatics (马克斯·普朗克信息研究所); Technische Universität Nürnberg (纽伦堡工业大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Modeling and generating 3D human-object interactions from text is crucial for applications in AR, XR, and gaming. Existing approaches often rely on score distillation from text-to-image models, but their results suffer from the Janus problem and do not follow text prompts faithfully due to the scarcity of high-quality interaction data. We introduce Hoi3DGen, a framework that generates high-quality textured meshes of human-object interaction that follow the input interaction descriptions precisely. We first curate realistic and high-quality interaction data leveraging multimodal large language models, and then create a full text-to-3D pipeline, which achieves orders-of-magnitude improvements in interaction fidelity. Our method surpasses baselines by 4-15x in text consistency and 3-7x in 3D model quality, exhibiting strong generalization to diverse categories and interaction types, while maintaining high-quality 3D generation.
[CV-28] CRAFT: A Tendon-Driven Hand with Hybrid Hard-Soft Compliance
【速读】：该论文旨在解决传统仿人机械手在接触-rich 操作中面临的强度不足、耐久性差以及对脆弱或低摩擦物体操控能力弱的问题。其解决方案的关键在于提出一种基于“混合刚柔顺应性”（hybrid hard-soft compliance）的腱驱动设计——CRAFT hand：通过将软材料集中布置于关节处以吸收冲击并提升柔顺性，同时保持连杆结构刚性以承载主要载荷；并采用滚动接触关节表面确保弯曲运动路径的重复性，从而在不牺牲精度的前提下显著增强结构强度与使用寿命。此外，15个电机集成于指节内并通过肌腱传动，实现了紧凑轻量化设计，并在远程操作中展现出优异的抓握多样性（覆盖Feix分类中的全部33种抓取模式）。
链接: https://arxiv.org/abs/2603.12120
作者: Leo Lin,Shivansh Patel,Jay Moon,Svetlana Lazebnik,Unnat Jain
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校); UC Irvine (加州大学欧文分校)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We introduce CRAFT hand, a tendon-driven anthropomorphic hand with hybrid hard-soft compliance for contact-rich manipulation. The design is based on a simple idea: contact is not uniform across the hand. Impacts concentrate at joints, while links carry most of the load. CRAFT places soft material at joints and keeps links rigid, and uses rollingcontact joint surfaces to keep flexion on repeatable motion paths. Fifteen motors mounted on the fingers drive the hand through tendons, keeping the form factor compact and the fingers light. In structural tests, CRAFT improves strength and endurance while maintaining comparable repeatability. In teleoperation, CRAFT improves handling of fragile and low-friction items, and the hand covers 33/33 grasps in the Feix taxonomy. The full design costs under 600 and will be released open-source with visionbased teleoperation and simulation integration. Project page: this http URL
[CV-29] EvoTok: A Unified Image Tokenizer via Residual Latent Evolution for Visual Understanding and Generation
【速读】：该论文旨在解决统一多模态大语言模型（Multimodal Large Language Models, MLLMs）中视觉理解与图像生成之间的粒度鸿沟问题：视觉理解依赖于高层次语义抽象，而图像生成则需要细粒度的像素级表示。现有方法要么在相同特征空间中强制两种监督信号，导致干扰；要么在分离的特征空间中解耦监督，引发不一致性。其解决方案的关键在于提出EvoTok——一种统一的图像分词器，通过共享潜在空间中的残差演化过程来协调上述需求。EvoTok利用残差向量量化将图像编码为残差token的级联序列，形成一个演化轨迹：早期阶段保留低级细节，深层阶段逐步过渡到高级语义表征。这种机制使模型能够在有限数据（1300万图像）下实现高质量重建（ImageNet-1K上rFID=0.43），并在7/9个视觉理解基准和多个图像生成基准上表现优异，验证了将视觉表征建模为演化轨迹是一种有效且原则性的统一方案。
链接: https://arxiv.org/abs/2603.12108
作者: Yan Li,Ning Liao,Xiangyu Zhao,Shaofeng Zhang,Xiaoxing Wang,Yifan Yang,Junchi Yan,Xue Yang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The development of unified multimodal large language models (MLLMs) is fundamentally challenged by the granularity gap between visual understanding and generation: understanding requires high-level semantic abstractions, while image generation demands fine-grained pixel-level representations. Existing approaches usually enforce the two supervision on the same set of representation or decouple these two supervision on separate feature spaces, leading to interference and inconsistency, respectively. In this work, we propose EvoTok, a unified image tokenizer that reconciles these requirements through a residual evolution process within a shared latent space. Instead of maintaining separate token spaces for pixels and semantics, EvoTok encodes an image into a cascaded sequence of residual tokens via residual vector quantization. This residual sequence forms an evolution trajectory where earlier stages capture low-level details and deeper stages progressively transition toward high-level semantic representations. Despite being trained on a relatively modest dataset of 13M images, far smaller than the billion-scale datasets used by many previous unified tokenizers, EvoTok achieves a strong reconstruction quality of 0.43 rFID on ImageNet-1K at 256x256 resolution. When integrated with a large language model, EvoTok shows promising performance across 7 out of 9 visual understanding benchmarks, and remarkable results on image generation benchmarks such as GenEval and GenAI-Bench. These results demonstrate that modeling visual representations as an evolving trajectory provides an effective and principled solution for unifying visual understanding and generation.
[CV-30] owards Universal Computational Aberration Correction in Photographic Cameras: A Comprehensive Benchmark Analysis CVPR2026
【速读】：该论文旨在解决当前计算像差校正（Computational Aberration Correction, CAC）方法普遍存在的泛化能力差和再训练成本高的问题，即现有方法通常仅适用于特定光学系统，难以跨镜头通用。其解决方案的关键在于提出一个大规模、自动构建的统一基准UniCAC，以及引入光学退化评估器（Optical Degradation Evaluator, ODE），用于客观量化光学像差难度并实现可靠评估。通过在24种图像恢复与CAC算法上的系统性实验，作者进一步识别出影响性能的三大关键因素：先验知识利用、网络架构设计和训练策略，并揭示了它们对跨镜头通用性的具体作用机制，从而为未来CAC方法的设计与优化提供理论基础和实践指导。
链接: https://arxiv.org/abs/2603.12083
作者: Xiaolong Qian,Qi Jiang,Yao Gao,Lei Sun,Zhonghua Yi,Kailun Yang,Luc Van Gool,Kaiwei Wang
机构: Zhejiang University (浙江大学); INSAIT, Sofia University “St. Kliment Ohridski” (INSAIT，索非亚大学“圣克莱门特·奥霍里斯基”); Hunan University (湖南大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO); Image and Video Processing (eess.IV); Optics (physics.optics)
备注: Accepted to CVPR 2026. Benchmarks, codes, and Zemax files will be available at this https URL
Abstract:Prevalent Computational Aberration Correction (CAC) methods are typically tailored to specific optical systems, leading to poor generalization and labor-intensive re-training for new lenses. Developing CAC paradigms capable of generalizing across diverse photographic lenses offers a promising solution to these challenges. However, efforts to achieve such cross-lens universality within consumer photography are still in their early stages due to the lack of a comprehensive benchmark that encompasses a sufficiently wide range of optical aberrations. Furthermore, it remains unclear which specific factors influence existing CAC methods and how these factors affect their performance. In this paper, we present comprehensive experiments and evaluations involving 24 image restoration and CAC algorithms, utilizing our newly proposed UniCAC, a large-scale benchmark for photographic cameras constructed via automatic optical design. The Optical Degradation Evaluator (ODE) is introduced as a novel framework to objectively assess the difficulty of CAC tasks, offering credible quantification of optical aberrations and enabling reliable evaluation. Drawing on our comparative analysis, we identify three key factors – prior utilization, network architecture, and training strategy – that most significantly influence CAC performance, and further investigate their respective effects. We believe that our benchmark, dataset, and observations contribute foundational insights to related areas and lay the groundwork for future investigations. Benchmarks, codes, and Zemax files will be available at this https URL.
[CV-31] Node-RF: Learning Generalized Continuous Space-Time Scene Dynamics with Neural ODE-based NeRFs CVPR2026
【速读】：该论文旨在解决从视觉观测中预测场景动态的挑战，特别是现有方法仅能在观察边界内建模动力学，无法有效外推至训练序列之外的区域。解决方案的关键在于将神经微分方程（Neural Ordinary Differential Equations, NODEs）与动态神经辐射场（Dynamic Neural Radiance Fields, NeRFs）相结合，构建一种连续时间、时空统一的表示机制，能够在保持恒定内存开销的前提下实现对未见轨迹的长期外推。通过ODE求解器驱动隐式场景状态演化，并利用NeRF渲染器合成任意视角下的未来状态，从而在多运动序列共享动力学的基础上实现对未知条件的泛化能力。
链接: https://arxiv.org/abs/2603.12078
作者: Hiran Sarkar,Liming Kuang,Yordanka Velikova,Benjamin Busam
机构: Sony Research India; Technical University of Munich; Munich Center for Machine Learning
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026. 13 pages, 9 figures
Abstract:Predicting scene dynamics from visual observations is challenging. Existing methods capture dynamics only within observed boundaries failing to extrapolate far beyond the training sequence. Node-RF (Neural ODE-based NeRF) overcomes this limitation by integrating Neural Ordinary Differential Equations (NODEs) with dynamic Neural Radiance Fields (NeRFs), enabling a continuous-time, spatiotemporal representation that generalizes beyond observed trajectories at constant memory cost. From visual input, Node-RF learns an implicit scene state that evolves over time via an ODE solver, propagating feature embeddings via differential calculus. A NeRF-based renderer interprets calculated embeddings to synthesize arbitrary views for long-range extrapolation. Training on multiple motion sequences with shared dynamics allows for generalization to unseen conditions. Our experiments demonstrate that Node-RF can characterize abstract system behavior without explicit model to identify critical points for future predictions.
[CV-32] Paper Title: LoV3D: Grounding Cognitive Prognosis Reasoning in Longitudinal 3D Brain MRI via Regional Volume Assessments
【速读】：该论文旨在解决当前深度学习工具在纵向脑部磁共振成像（MRI）分析中存在碎片化、缺乏生物学合理性及易产生幻觉的问题，例如分类器仅输出标签、体积测量无法解释、视觉-语言模型（VLM）可能生成看似合理但不准确的结论。其解决方案的关键在于提出LoV3D——一个用于训练3D视觉-语言模型的分步流水线，通过强制标签一致性、纵向连贯性和生物学合理性来约束最终诊断，从而显著降低幻觉风险；同时引入临床加权验证器（clinically-weighted Verifier），基于标准化体积指标自动评分候选输出，并驱动无需人工标注的直接偏好优化（Direct Preference Optimization），实现高精度、可解释且泛化能力强的多模态诊断。
链接: https://arxiv.org/abs/2603.12071
作者: Zhaoyang Jiang,Zhizhong Fu,David McAllister,Yunsoo Kim,Honghan Wu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Longitudinal brain MRI is essential for characterizing the progression of neurological diseases such as Alzheimer’s disease assessment. However, current deep-learning tools fragment this process: classifiers reduce a scan to a label, volumetric pipelines produce uninterpreted measurements, and vision-language models (VLMs) may generate fluent but potentially hallucinated conclusions. We present LoV3D, a pipeline for training 3D vision-language models, which reads longitudinal T1-weighted brain MRI, produces a region-level anatomical assessment, conducts longitudinal comparison with the prior scan, and finally outputs a three-class diagnosis (Cognitively Normal, Mild Cognitive Impairment, or Dementia) along with a synthesized diagnostic summary. The stepped pipeline grounds the final diagnosis by enforcing label consistency, longitudinal coherence, and biological plausibility, thereby reducing the risks of hallucinations. The training process introduces a clinically-weighted Verifier that scores candidate outputs automatically against normative references derived from standardized volume metrics, driving Direct Preference Optimization without a single human annotation. On a subject-level held-out ADNI test set (479 scans, 258 subjects), LoV3D achieves 93.7% three-class diagnostic accuracy (+34.8% over the no-grounding baseline), 97.2% on two-class diagnosis accuracy (+4% over the SOTA) and 82.6% region-level anatomical classification accuracy (+33.1% over VLM baselines). Zero-shot transfer yields 95.4% on MIRIAD (100% Dementia recall) and 82.9% three-class accuracy on AIBL, confirming high generalizability across sites, scanners, and populations. Code is available at this https URL.
[CV-33] Beyond Convolution: A Taxonomy of Structured Operators for Learning-Based Image Processing
【速读】：该论文旨在解决传统卷积操作在学习图像处理任务时的局限性问题，即其作为固定、线性且局部平均的算子，难以有效捕捉信号中的结构特性（如低秩分解、自适应基表示和非均匀空间依赖关系）。解决方案的关键在于提出一个系统性的替代或扩展卷积操作的分类体系，涵盖五类新型算子：基于分解的操作（decomposition-based）、自适应加权操作（adaptive weighted）、基自适应操作（basis-adaptive）、积分与核操作（integral and kernel）以及注意力机制操作（attention-based），每类均从形式定义、结构特性及适用任务角度进行深入分析，并通过线性性、局部性、等变性、计算成本及图像到图像/图像到标签任务适配性等维度进行对比，从而为后续研究提供清晰的技术路径与开放挑战。
链接: https://arxiv.org/abs/2603.12067
作者: Simone Cammarasana
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:The convolution operator is the fundamental building block of modern convolutional neural networks (CNNs), owing to its simplicity, translational equivariance, and efficient implementation. However, its structure as a fixed, linear, locally-averaging operator limits its ability to capture structured signal properties such as low-rank decompositions, adaptive basis representations, and non-uniform spatial dependencies. This paper presents a systematic taxonomy of operators that extend or replace the standard convolution in learning-based image processing pipelines. We organise the landscape of alternative operators into five families: (i) decomposition-based operators, which separate structural and noise components through singular value or tensor decompositions; (ii) adaptive weighted operators, which modulate kernel contributions as a function of spatial position or signal content; (iii) basis-adaptive operators, which optimise the analysis bases together with the network weights; (iv) integral and kernel operators, which generalise the convolution to position-dependent and non-linear kernels; and (v) attention-based operators, which relax the locality assumption entirely. For each family, we provide a formal definition, a discussion of its structural properties with respect to the convolution, and a critical analysis of the tasks for which the operator is most appropriate. We further provide a comparative analysis of all families across relevant dimensions – linearity, locality, equivariance, computational cost, and suitability for image-to-image and image-to-label tasks – and outline the open challenges and future directions of this research area.
[CV-34] Dense Dynamic Scene Reconstruction and Camera Pose Estimation from Multi-View Videos
【速读】：该论文旨在解决多自由移动相机（multi-camera setup）下密集动态场景重建与相机位姿估计的难题，这一问题在多个观察者共同记录同一事件时自然出现。现有方法通常仅支持单相机输入或依赖刚性安装且预先标定的相机阵列，限制了实际应用。其解决方案的关键在于提出一个两阶段优化框架：第一阶段通过构建时空连接图（spatiotemporal connection graph），利用相机内的时间连续性和相机间的空间重叠关系，扩展单目视觉SLAM至多相机场景，实现一致尺度和鲁棒跟踪；第二阶段则通过优化宽基线光流（wide-baseline optical flow）来提升密集深度与相机位姿的一致性，从而获得更精确的重建结果。
链接: https://arxiv.org/abs/2603.12064
作者: Shuo Sun,Unal Artan,Malcolm Mielle,Achim J. Lilienthaland,Martin Magnusson
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We address the challenging problem of dense dynamic scene reconstruction and camera pose estimation from multiple freely moving cameras – a setting that arises naturally when multiple observers capture a shared event. Prior approaches either handle only single-camera input or require rigidly mounted, pre-calibrated camera rigs, limiting their practical applicability. We propose a two-stage optimization framework that decouples the task into robust camera tracking and dense depth refinement. In the first stage, we extend single-camera visual SLAM to the multi-camera setting by constructing a spatiotemporal connection graph that exploits both intra-camera temporal continuity and inter-camera spatial overlap, enabling consistent scale and robust tracking. To ensure robustness under limited overlap, we introduce a wide-baseline initialization strategy using feed-forward reconstruction models. In the second stage, we refine depth and camera poses by optimizing dense inter- and intra-camera consistency using wide-baseline optical flow. Additionally, we introduce MultiCamRobolab, a new real-world dataset with ground-truth poses from a motion capture system. Finally, we demonstrate that our method significantly outperforms state-of-the-art feed-forward models on both synthetic and real-world benchmarks, while requiring less memory.
[CV-35] NBAvatar: Neural Billboards Avatars with Realistic Hand-Face Interaction
【速读】：该论文旨在解决头像渲染中因手部与面部交互引起的非刚性形变问题，尤其在保持时序一致性和姿态一致性的同时还原精细外观细节。其解决方案的关键在于提出NBAvatar方法，通过将定向平面基元（oriented planar primitives）的显式表示与神经渲染（neural rendering）的隐式表示相结合，实现对复杂手脸交互下几何结构和颜色变化的精准建模，从而显著提升新视角和新姿态下的渲染质量。
链接: https://arxiv.org/abs/2603.12063
作者: David Svitov,Mahtab Dahaghin
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present NBAvatar - a method for realistic rendering of head avatars handling non-rigid deformations caused by hand-face interaction. We introduce a novel representation for animated avatars by combining the training of oriented planar primitives with neural rendering. Such a combination of explicit and implicit representations enables NBAvatar to handle temporally and pose-consistent geometry, along with fine-grained appearance details provided by the neural rendering technique. In our experiments, we demonstrate that NBAvatar implicitly learns color transformations caused by face-hand interactions and surpasses existing approaches in terms of novel-view and novel-pose rendering quality. Specifically, NBAvatar achieves up to 30% LPIPS reduction under high-resolution megapixel rendering compared to Gaussian-based avatar methods, while also improving PSNR and SSIM, and achieves higher structural similarity compared to the state-of-the-art hand-face interaction method InteractAvatar.
[CV-36] Coarse-Guided Visual Generation via Weighted h-Transform Sampling
【速读】：该论文旨在解决粗粒度引导的视觉生成问题，即如何从低质量或退化的粗参考图像中合成高质量的精细视觉样本。现有基于训练的方法受限于高昂的训练成本和配对数据收集带来的泛化能力不足，而训练-free方法则面临需已知前向变换算子（如双三次下采样）或难以平衡引导强度与生成质量的问题。其解决方案的关键在于引入h-transform工具，通过在采样过程的每个时间步修改转移概率，向原始微分方程添加一个漂移函数，从而近似引导生成过程趋向理想精细样本；同时设计一种噪声水平感知调度策略，在误差增大时逐步降低该引导项权重，确保既满足引导约束又保持高保真度合成。
链接: https://arxiv.org/abs/2603.12057
作者: Yanghao Wang,Ziqi Jiang,Zhen Wang,Long Chen
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Coarse-guided visual generation, which synthesizes fine visual samples from degraded or low-fidelity coarse references, is essential for various real-world applications. While training-based approaches are effective, they are inherently limited by high training costs and restricted generalization due to paired data collection. Accordingly, recent training-free works propose to leverage pretrained diffusion models and incorporate guidance during the sampling process. However, these training-free methods either require knowing the forward (fine-to-coarse) transformation operator, e.g., bicubic downsampling, or are difficult to balance between guidance and synthetic quality. To address these challenges, we propose a novel guided method by using the h-transform, a tool that can constrain stochastic processes (e.g., sampling process) under desired conditions. Specifically, we modify the transition probability at each sampling timestep by adding to the original differential equation with a drift function, which approximately steers the generation toward the ideal fine sample. To address unavoidable approximation errors, we introduce a noise-level-aware schedule that gradually de-weights the term as the error increases, ensuring both guidance adherence and high-quality synthesis. Extensive experiments across diverse image and video generation tasks demonstrate the effectiveness and generalization of our method.
[CV-37] Continual Learning with Vision-Language Models via Semantic-Geometry Preservation
【速读】：该论文旨在解决预训练视觉-语言模型（Vision-Language Models, VLMs）在持续学习（Continual Learning, CL）过程中因灾难性遗忘导致的跨模态语义几何结构失真问题。现有方法通常缺乏对预训练阶段及历史任务中保留的跨模态语义几何关系的显式保护，使得新任务监督信号容易扭曲原有语义空间。其解决方案的关键在于提出Semantic Geometry Preservation for Continual Learning (SeGP-CL)，通过两个核心机制实现：一是利用双目标投影梯度下降（Dual-targeted Projected Gradient Descent, DPGD）构建对抗锚点集以定位易受漂移影响的“旧-新语义界面”区域；二是引入锚点引导的跨模态几何蒸馏（Anchor-guided Cross-modal Geometry Distillation, ACGD）和轻量级文本语义-几何正则化（Text Semantic-Geometry Regularization, TSGR），从而在训练中保持跨模态结构稳定并约束文本参考框架的一致性。最终通过锚点诱导的原始空间漂移估计与双路径推理融合视觉与跨模态线索，有效提升模型稳定性与前向迁移性能。
链接: https://arxiv.org/abs/2603.12055
作者: Chiyuan He,Zihuan Qiu,Fanman Meng,Runtong Zhang,Linfeng Xu,Qingbo Wu,Hongliang Li
机构: University of Electronic Science and Technology of China (电子科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 14 pages, 11 figures, under review
Abstract:Continual learning of pretrained vision-language models (VLMs) is prone to catastrophic forgetting, yet current approaches adapt to new tasks without explicitly preserving the cross-modal semantic geometry inherited from pretraining and previous stages, allowing new-task supervision to induce geometric distortion. We observe that the most pronounced drift tends to concentrate in vulnerable neighborhoods near the old-new semantic interface, where shared visual patterns are easily re-explained by new textual semantics. To address this under an exemplar-free constraint, we propose Semantic Geometry Preservation for Continual Learning (SeGP-CL). SeGP-CL first probes the drift-prone region by constructing a compact set of adversarial anchors with dual-targeted projected gradient descent (DPGD), which drives selected new-task seeds toward old-class semantics while remaining faithful in raw visual space. During training, we preserve cross-modal structure by anchor-guided cross-modal geometry distillation (ACGD), and stabilize the textual reference frame across tasks via a lightweight text semantic-geometry regularization (TSGR). After training, we estimate anchor-induced raw-space drift to transfer old visual prototypes and perform dual-path inference by fusing cross-modal and visual cues. Extensive experiments on five continual learning benchmarks demonstrate that SeGP-CL consistently improves stability and forward transfer, achieving state-of-the-art performance while better preserving semantic geometry of VLMs.
[CV-38] Single Pixel Image Classification using an Ultrafast Digital Light Projector
【速读】：该论文旨在解决高速图像分类问题，特别是在实时性要求极高的场景下（如自动驾驶车辆对动态环境的感知），如何实现高效、低延迟的图像识别。其核心挑战在于传统图像采集与处理流程中图像重建步骤带来的计算开销和延迟。解决方案的关键在于结合单像素成像（Single Pixel Imaging, SPI）技术与低复杂度机器学习模型（极端学习机ELM和反向传播训练的深度神经网络），通过微LED-on-CMOS数字光投影器实现亚毫秒级图像编码，并利用时空信息变换直接完成分类任务，完全绕过图像重建过程，从而显著降低系统延迟并提升实时性。
链接: https://arxiv.org/abs/2603.12036
作者: Aisha Kanwal,Graeme E. Johnstone,Fahimeh Dehkhoda,Johannes H. Herrnsdorf,Robert K. Henderson,Martin D. Dawson,Xavier Porte,Michael J. Strain
机构: Institute of Photonics, University of Strathclyde (斯特拉斯克莱德大学光子研究所); School of Engineering, University of Edinburgh (爱丁堡大学工程学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Optics (physics.optics)
备注:
Abstract:Pattern recognition and image classification are essential tasks in machine vision. Autonomous vehicles, for example, require being able to collect the complex information contained in a changing environment and classify it in real time. Here, we experimentally demonstrate image classification at multi-kHz frame rates combining the technique of single pixel imaging (SPI) with a low complexity machine learning model. The use of a microLED-on-CMOS digital light projector for SPI enables ultrafast pattern generation for sub-ms image encoding. We investigate the classification accuracy of our experimental system against the broadly accepted benchmarking task of the MNIST digits classification. We compare the classification performance of two machine learning models: An extreme learning machine (ELM) and a backpropagation trained deep neural network. The complexity of both models is kept low so the overhead added to the inference time is comparable to the image generation time. Crucially, our single pixel image classification approach is based on a spatiotemporal transformation of the information, entirely bypassing the need for image reconstruction. By exploring the performance of our SPI based ELM as binary classifier we demonstrate its potential for efficient anomaly detection in ultrafast imaging scenarios.
[CV-39] Nyxus: A Next Generation Image Feature Extraction Library for the Big Data and AI Era
【速读】：该论文旨在解决大规模图像数据处理中面临的计算效率瓶颈以及跨领域特征提取方法缺乏统一标准的问题。当前，现代成像设备产生的数据量可达TB至PB级别，而传统图像分析算法在处理此类大数据集时往往效率不足，或需在鲁棒性和准确性之间做出权衡；同时，不同生物医学领域（如放射组学和细胞分析）发展出的特征提取库分散且难以比较性能与精度。解决方案的关键在于开发了一个名为Nyxus的新一代特征提取库，其核心优势包括：从零开始设计以支持2D/3D图像数据的可扩展“外存”（out-of-core）特征提取、覆盖多生物医学领域的全面特征集、针对CPU与GPU架构的高效计算扩展能力，并通过多种接口形式（Python包、命令行工具、Napari插件及OCI容器）满足不同用户群体的需求，从而实现特征提取流程的标准化、高效化与易用性提升。
链接: https://arxiv.org/abs/2603.12016
作者: Nicholas Schaub,Andriy Kharchenko,Hamdah Abbasi,Sameeul Samee,Hythem Sidky,Nathan Hotaling
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
备注: 29 pages, 9 figures, 6 supplemental tables
Abstract:Modern imaging instruments can produce terabytes to petabytes of data for a single experiment. The biggest barrier to processing big image datasets has been computational, where image analysis algorithms often lack the efficiency needed to process such large datasets or make tradeoffs in robustness and accuracy. Deep learning algorithms have vastly improved the accuracy of the first step in an analysis workflow (region segmentation), but the expansion of domain specific feature extraction libraries across scientific disciplines has made it difficult to compare the performance and accuracy of extracted features. To address these needs, we developed a novel feature extraction library called Nyxus. Nyxus is designed from the ground up for scalable out-of-core feature extraction for 2D and 3D image data and rigorously tested against established standards. The comprehensive feature set of Nyxus covers multiple biomedical domains including radiomics and cellular analysis, and is designed for computational scalability across CPUs and GPUs. Nyxus has been packaged to be accessible to users of various skill sets and needs: as a Python package for code developers, a command line tool, as a Napari plugin for low to no-code users or users that want to visualize results, and as an Open Container Initiative (OCI) compliant container that can be used in cloud or super-computing workflows aimed at processing large data sets. Further, Nyxus enables a new methodological approach to feature extraction allowing for programmatic tuning of many features sets for optimal computational efficiency or coverage for use in novel machine learning and deep learning applications.
[CV-40] Pano360: Perspective to Panoramic Vision with Geometric Consistency CVPR2026
【速读】：该论文旨在解决传统全景拼接方法依赖两两图像特征匹配、难以利用多视角几何一致性而导致的严重失真和错位问题，尤其在弱纹理、大视差和重复模式等挑战性场景中表现不佳。其解决方案的关键在于将二维对齐任务扩展至三维摄影测量空间，通过一种基于Transformer的架构实现3D感知并聚合所有视角的全局信息；该方法直接利用相机位姿引导图像在3D空间中的变形以实现全局对齐，并采用多特征联合优化策略计算拼接缝，从而提升对齐精度与视觉质量。
链接: https://arxiv.org/abs/2603.12013
作者: Zhengdong Zhu,Weiyi Xue,Zuyuan Yang,Wenlve Zhou,Zhiheng Zhou
机构: South China University of Technology (华南理工大学); Tongji University (同济大学); Guangdong University of Technology (广东工业大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR2026
Abstract:Prior panorama stitching approaches heavily rely on pairwise feature correspondences and are unable to leverage geometric consistency across multiple views. This leads to severe distortion and misalignment, especially in challenging scenes with weak textures, large parallax, and repetitive patterns. Given that multi-view geometric correspondences can be directly constructed in 3D space, making them more accurate and globally consistent, we extend the 2D alignment task to the 3D photogrammetric space. We adopt a novel transformer-based architecture to achieve 3D awareness and aggregate global information across all views. It directly utilizes camera poses to guide image warping for global alignment in 3D space and employs a multi-feature joint optimization strategy to compute the seams. Additionally, to establish an evaluation benchmark and train our network, we constructed a large-scale dataset of real-world scenes. Extensive experiments show that our method significantly outperforms existing alternatives in alignment accuracy and perceptual quality.
[CV-41] CrossEarth-SAR: A SAR-Centric and Billion-Scale Geospatial Foundation Model for Domain Generalizable Semantic Segmentation
【速读】：该论文旨在解决合成孔径雷达（Synthetic Aperture Radar, SAR）图像在跨传感器和跨区域场景下因成像机制差异导致的域偏移（domain shift）问题，从而提升模型在不同域间的语义分割泛化能力。解决方案的关键在于提出首个百亿参数规模的SAR视觉基础模型CrossEarth-SAR，其核心创新是基于物理引导的稀疏专家混合（physics-guided sparse mixture-of-experts, MoE）架构，并嵌入物理特征描述符，以显式建模SAR成像的物理规律，增强模型对跨域变化的鲁棒性。此外，作者构建了CrossEarth-SAR-200K大规模弱监督与全监督数据集及包含22个子基准的统一评估套件，为SAR语义分割的域泛化研究提供了标准化基准和训练资源。
链接: https://arxiv.org/abs/2603.12008
作者: Ziqi Ye,Ziyang Gong,Ning Liao,Xiaoxing Hu,Di Wang,Hongruixuan Chen,Chen Huang,Yiguo He,Yuru Jia,Xiaoxing Wang,Haipeng Wang,Xue Yang,Junchi Yan
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 26 pages, 15 figures
Abstract:Synthetic Aperture Radar (SAR) enables global, all-weather earth observation. However, owing to diverse imaging mechanisms, domain shifts across sensors and regions severely hinder its semantic generalization. To address this, we present CrossEarth-SAR, the first billion-scale SAR vision foundation model built upon a novel physics-guided sparse mixture-of-experts (MoE) architecture incorporating physical descriptors, explicitly designed for cross-domain semantic segmentation. To facilitate large-scale pre-training, we develop CrossEarth-SAR-200K, a weakly and fully supervised dataset that unifies public and private SAR imagery. We also introduce a benchmark suite comprising 22 sub-benchmarks across 8 distinct domain gaps, establishing the first unified standard for domain generalization semantic segmentation on SAR imagery. Extensive experiments demonstrate that CrossEarth-SAR achieves state-of-the-art results on 20 benchmarks, surpassing previous methods by over 10% mIoU on some benchmarks under multi-gap transfer. All code, benchmark and datasets will be publicly available.
[CV-42] Ada3Drift: Adaptive Training-Time Drifting for One-Step 3D Visuomotor Robotic Manipulation
【速读】：该论文旨在解决基于扩散模型的视觉-运动策略在机器人控制中因高推理延迟而难以实现实时性的问题，同时避免单步生成方法（如流匹配和一致性方法）因丢失多模态行为模式而导致动作轨迹物理不可行的缺陷。其解决方案的关键在于提出Ada3Drift框架，通过在训练阶段学习一个漂移场（drifting field），使预测动作被吸引至专家示范模式并排斥远离其他生成样本，从而在仅需一次函数评估（1 NFE）的情况下实现高保真单步生成；此外，为适应少样本场景，引入sigmoid调度损失以实现从粗粒度分布学习到细粒度模式锐化的过程，并采用多尺度场聚合机制捕捉不同空间粒度的动作模式。
链接: https://arxiv.org/abs/2603.11984
作者: Chongyang Xu,Yixian Zou,Ziliang Feng,Fanman Meng,Shuaicheng Liu
机构: UESTC(电子科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diffusion-based visuomotor policies effectively capture multimodal action distributions through iterative denoising, but their high inference latency limits real-time robotic control. Recent flow matching and consistency-based methods achieve single-step generation, yet sacrifice the ability to preserve distinct action modes, collapsing multimodal behaviors into averaged, often physically infeasible trajectories. We observe that the compute budget asymmetry in robotics (offline training vs.\ real-time inference) naturally motivates recovering this multimodal fidelity by shifting iterative refinement from inference time to training time. Building on this insight, we propose Ada3Drift, which learns a training-time drifting field that attracts predicted actions toward expert demonstration modes while repelling them from other generated samples, enabling high-fidelity single-step generation (1 NFE) from 3D point cloud observations. To handle the few-shot robotic regime, Ada3Drift further introduces a sigmoid-scheduled loss transition from coarse distribution learning to mode-sharpening refinement, and multi-scale field aggregation that captures action modes at varying spatial granularities. Experiments on three simulation benchmarks (Adroit, Meta-World, and RoboTwin) and real-world robotic manipulation tasks demonstrate that Ada3Drift achieves state-of-the-art performance while requiring 10\times fewer function evaluations than diffusion-based alternatives.
[CV-43] HomeSafe-Bench: Evaluating Vision-Language Models on Unsafe Action Detection for Embodied Agents in Household Scenarios
【速读】：该论文旨在解决家庭场景中具身智能体（embodied agents）因感知延迟和常识知识缺失而导致的动态不安全行为检测不足的问题。当前的安全评估多局限于静态图像或文本，难以有效衡量视觉-语言模型（VLMs）在真实家庭环境中对动态危险动作的识别能力。为应对这一挑战，作者提出HomeSafe-Bench基准数据集，通过物理仿真与高级视频生成相结合的方式构建包含438个多样化案例的动态场景数据，覆盖六个功能区域并具备细粒度多维标注。解决方案的关键在于设计了Hierarchical Dual-Brain Guard（HD-Guard）架构，其采用分层流式结构：轻量级FastBrain负责高频连续筛查以保障实时性，异步的大规模SlowBrain则执行深度多模态推理以提升准确性，从而在延迟与性能之间实现更优平衡。
链接: https://arxiv.org/abs/2603.11975
作者: Jiayue Pu,Zhongxiang Sun,Zilu Zhang,Xiao Zhang,Jun Xu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:The rapid evolution of embodied agents has accelerated the deployment of household robots in real-world environments. However, unlike structured industrial settings, household spaces introduce unpredictable safety risks, where system limitations such as perception latency and lack of common sense knowledge can lead to dangerous errors. Current safety evaluations, often restricted to static images, text, or general hazards, fail to adequately benchmark dynamic unsafe action detection in these specific contexts. To bridge this gap, we introduce \textbfHomeSafe-Bench, a challenging benchmark designed to evaluate Vision-Language Models (VLMs) on unsafe action detection in household scenarios. HomeSafe-Bench is contrusted via a hybrid pipeline combining physical simulation with advanced video generation and features 438 diverse cases across six functional areas with fine-grained multidimensional annotations. Beyond benchmarking, we propose \textbfHierarchical Dual-Brain Guard for Household Safety (HD-Guard), a hierarchical streaming architecture for real-time safety monitoring. HD-Guard coordinates a lightweight FastBrain for continuous high-frequency screening with an asynchronous large-scale SlowBrain for deep multimodal reasoning, effectively balancing inference efficiency with detection accuracy. Evaluations demonstrate that HD-Guard achieves a superior trade-off between latency and performance, while our analysis identifies critical bottlenecks in current VLM-based safety detection.
[CV-44] Multimodal Emotion Recognition via Bi-directional Cross-Attention and Temporal Modeling
【速读】：该论文旨在解决真实场景（in-the-wild）视频中情绪识别的挑战，这些问题源于面部外观、头部姿态、光照条件、背景噪声以及人类情感的动态性等因素导致的复杂情绪线索难以捕捉。为应对这一问题，其核心解决方案在于构建一个融合视觉、音频和文本信息的多模态情绪识别框架：首先利用预训练模型CLIP（用于视觉编码）和Wav2Vec 2.0（用于音频表示学习）作为冻结主干网络提取特征；其次引入时间卷积网络（Temporal Convolutional Network, TCN）建模面部表情序列的时间依赖性；再通过双向交叉注意力融合模块实现视觉与音频特征的对称交互，增强跨模态上下文理解并捕获互补情绪信息；最后结合基于CLIP文本特征的对比损失项，促使视觉表征在语义上与文本对齐。实验表明，该方法在ABAW 10th EXPR基准上显著优于单一模态模型，验证了时间建模、音频表示学习与跨模态融合相结合的有效性。
链接: https://arxiv.org/abs/2603.11971
作者: Junhyeong Byeon,Jeongyeol Kim,Sejoon Lim
机构: Kookmin University (구국민대학교)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 7 pages
Abstract:Emotion recognition in in-the-wild video data remains a challenging problem due to large variations in facial appearance, head pose, illumination, background noise, and the inherently dynamic nature of human affect. Relying on a single modality, such as facial expressions or speech, is often insufficient to capture these complex emotional cues. To address this issue, we propose a multimodal emotion recognition framework for the Expression (EXPR) Recognition task in the 10th Affective Behavior Analysis in-the-wild (ABAW) Challenge. Our approach leverages large-scale pre-trained models, namely CLIP for visual encoding and Wav2Vec 2.0 for audio representation learning, as frozen backbone networks. To model temporal dependencies in facial expression sequences, we employ a Temporal Convolutional Network (TCN) over fixed-length video windows. In addition, we introduce a bi-directional cross-attention fusion module, in which visual and audio features interact symmetrically to enhance cross-modal contextualization and capture complementary emotional information. A lightweight classification head is then used for final emotion prediction. We further incorporate a text-guided contrastive objective based on CLIP text features to encourage semantically aligned visual representations. Experimental results on the ABAW 10th EXPR benchmark show that the proposed framework provides a strong multimodal baseline and achieves improved performance over unimodal modeling. These results demonstrate the effectiveness of combining temporal visual modeling, audio representation learning, and cross-modal fusion for robust emotion recognition in unconstrained real-world environments. Comments: 7 pages Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.11971 [cs.CV] (or arXiv:2603.11971v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.11971 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-45] AstroSplat: Physics-Based Gaussian Splatting for Rendering and Reconstruction of Small Celestial Bodies
【速读】：该论文旨在解决小天体（如小行星）表面重建与表征中因传统基于球谐函数的高斯点积方法仅依赖外观参数化、未显式建模材料属性或光照-表面相互作用而导致的精度不足问题。解决方案的关键在于提出AstroSplat框架，该框架将行星反射率模型（planetary reflectance models）融入高斯点积表示中，从而实现物理驱动的图像渲染与表面特性自主重建，显著提升了真实影像下的重建准确性和光度表征能力。
链接: https://arxiv.org/abs/2603.11969
作者: Jennifer Nolan,Travis Driver,John Christian
机构: Georgia Institute of Technology(佐治亚理工学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 6 figures, conference
Abstract:Image-based surface reconstruction and characterization are crucial for missions to small celestial bodies (e.g., asteroids), as it informs mission planning, navigation, and scientific analysis. Recent advances in Gaussian splatting enable high-fidelity neural scene representations but typically rely on a spherical harmonic intensity parameterization that is strictly appearance-based and does not explicitly model material properties or light-surface interactions. We introduce AstroSplat, a physics-based Gaussian splatting framework that integrates planetary reflectance models to improve the autonomous reconstruction and photometric characterization of small-body surfaces from in-situ imagery. The proposed framework is validated on real imagery taken by NASA’s Dawn mission, where we demonstrate superior rendering performance and surface reconstruction accuracy compared to the typical spherical harmonic parameterization.
[CV-46] Preliminary analysis of RGB-NIR Image Registration techniques for off-road forestry environments
【速读】：该论文旨在解决RGB与近红外（NIR）图像在非结构化林区场景下的配准问题，这是实现多传感器融合、图像增强及非公路自主导航的关键技术挑战。研究通过评估传统方法与基于深度学习（Deep Learning, DL）的配准技术，发现NeMAR虽在多种配置下表现出部分成功，但其生成对抗网络（GAN）损失函数的不稳定性导致几何一致性难以保障；而MURF在大尺度特征对齐方面表现良好，但在密集植被区域的细节匹配上存在不足。因此，解决方案的关键在于提升模型在复杂森林环境中的多尺度鲁棒性，尤其需优化细粒度特征对齐能力并稳定训练过程以确保几何一致性。
链接: https://arxiv.org/abs/2603.11952
作者: Pankaj Deoli,Karthik Ranganath,Karsten Berns
机构: University of Kaiserslautern-Landau (RPTU, 凯撒斯劳滕-兰道大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Preliminary results
Abstract:RGB-NIR image registration plays an important role in sensor-fusion, image enhancement and off-road autonomy. In this work, we evaluate both classical and Deep Learning (DL) based image registration techniques to access their suitability for off-road forestry applications. NeMAR, trained under 6 different configurations, demonstrates partial success however, its GAN loss instability suggests challenges in preserving geometric consistency. MURF, when tested on off-road forestry data shows promising large scale feature alignment during shared information extraction but struggles with fine details in dense vegetation. Even though this is just a preliminary evaluation, our study necessitates further refinements for robust, multi-scale registration for off-road forest applications.
[CV-47] Prototype-Based Knowledge Guidance for Fine-Grained Structured Radiology Reporting
【速读】：该论文旨在解决结构化放射学报告（Structured Radiology Reporting）自动化难题，即在有限的结构化监督数据下，模型难以准确识别罕见影像发现及其细粒度属性的问题。现有方法受限于标注稀缺性，而临床实践中大量自由文本报告虽蕴含丰富的图像关联信息，却未被有效利用。解决方案的关键在于提出ProtoSR框架：首先构建一个基于指令微调大语言模型（Instruction-Tuned LLM）的自动提取管道，从80,000+ MIMIC-CXR研究中挖掘自由文本并建立多模态知识库，其中每个答案选项由视觉原型（Visual Prototype）表示；随后通过检索与当前图像-问题对相关的原型，并引入原型条件残差（Prototype-Conditioned Residual）来增强预测，从而实现数据驱动的“第二意见”以选择性修正模型输出。该方法显著提升了细粒度属性识别性能，在Rad-ReStruct基准上达到当前最优效果。
链接: https://arxiv.org/abs/2603.11938
作者: Chantal Pellegrini,Adrian Delchev,Ege Özsoy,Nassir Navab,Matthias Keicher
机构: 未知
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Structured radiology reporting promises faster, more consistent communication than free text, but automation remains difficult as models must make many fine-grained, discrete decisions about rare findings and attributes from limited structured supervision. In contrast, free-text reports are produced at scale in routine care and implicitly encode fine-grained, image-linked information through detailed descriptions. To leverage this unstructured knowledge, we propose ProtoSR, an approach for injecting free-text information into structured report population. First, we introduce an automatic extraction pipeline that uses an instruction-tuned LLM to mine 80k+ MIMIC-CXR studies and build a multimodal knowledge base aligned with a structured reporting template, representing each answer option with a visual prototype. Using this knowledge base, ProtoSR is trained to retrieve prototypes relevant for the current image-question pair and augment the model predictions through a prototype-conditioned residual, providing a data-driven second opinion that selectively corrects predictions. On the Rad-ReStruct benchmark, ProtoSR achieves state-of-the-art results, with the largest improvements on detailed attribute questions, demonstrating the value of integrating free-text derived signal for fine-grained image understanding.
[CV-48] PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation
【速读】：该论文旨在解决边缘设备（如智能眼镜和物联网设备）上实时、本地化图像分割的难题，以满足低延迟和隐私保护的需求。其核心挑战在于如何在计算资源受限的条件下实现高精度且可响应的分割性能。解决方案的关键在于提出PicoSAM3模型——一个仅含1.3M参数的轻量化提示驱动视觉分割模型，通过融合密集卷积神经网络（CNN）架构、感兴趣区域提示编码、高效通道注意力机制，并结合来自SAM2和SAM3的大模型知识蒸馏技术，在保持极低复杂度的同时显著提升分割精度。实验表明，该模型在COCO和LVIS数据集上分别达到65.45%和64.01%的mIoU，优于现有基于SAM的边缘基线方法；且INT8量化后仍能维持接近原始精度，并在索尼IMX500视觉传感器上实现11.82ms的实时推理延迟，完全适配其内存与算子约束，验证了高质量、空间灵活的提示分割可在传感器端直接部署的可行性。
链接: https://arxiv.org/abs/2603.11917
作者: Pietro Bonazzi,Nicola Farronato,Stefan Zihlmann,Haotong Qin,Michele Magno
机构: ETH Zürich (苏黎世联邦理工学院); IBM Research (IBM 研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Real-time, on-device segmentation is critical for latency-sensitive and privacy-aware applications such as smart glasses and Internet-of-Things devices. We introduce PicoSAM3, a lightweight promptable visual segmentation model optimized for edge and in-sensor execution, including deployment on the Sony IMX500 vision sensor. PicoSAM3 has 1.3 M parameters and combines a dense CNN architecture with region of interest prompt encoding, Efficient Channel Attention, and knowledge distillation from SAM2 and SAM3. On COCO and LVIS, PicoSAM3 achieves 65.45% and 64.01% mIoU, respectively, outperforming existing SAM-based and edge-oriented baselines at similar or lower complexity. The INT8 quantized model preserves accuracy with negligible degradation while enabling real-time in-sensor inference at 11.82 ms latency on the IMX500, fully complying with its memory and operator constraints. Ablation studies show that distillation from large SAM models yields up to +14.5% mIoU improvement over supervised training and demonstrate that high-quality, spatially flexible promptable segmentation is feasible directly at the sensor level.
[CV-49] InSpatio-WorldFM: An Open-Source Real-Time Generative Frame Model
【速读】：该论文旨在解决传统基于视频的世界模型（World Models）在实时空间推理中因依赖帧序列生成和窗口级处理而导致的高延迟问题。其解决方案的关键在于提出一种基于帧的建模范式（frame-based paradigm），即独立生成每一帧，从而实现低延迟的实时空间推断；同时通过显式的3D锚点和隐式的空间记忆来强制多视角空间一致性，确保全局场景几何结构的保持与视点变化下细节信息的稳定表达。此外，论文设计了一个渐进式的三阶段训练流程，将预训练图像扩散模型逐步蒸馏为可控的帧模型并最终转化为实时生成器，显著提升了效率与实用性。
链接: https://arxiv.org/abs/2603.11911
作者: InSpatio Team:Xiaoyu Zhang,Weihong Pan,Zhichao Ye,Jialin Liu,Yipeng Chen,Nan Wang,Xiaojun Xiang,Weijian Xie,Yifu Wang,Haoyu Ji,Siji Pan,Zhewen Le,Jing Guo,Xianbin Liu,Donghui Shen,Ziqiang Zhao,Haomin Liu,Guofeng Zhang
机构: InSpatio Team
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL Code: this https URL
Abstract:We present InSpatio-WorldFM, an open-source real-time frame model for spatial intelligence. Unlike video-based world models that rely on sequential frame generation and incur substantial latency due to window-level processing, InSpatio-WorldFM adopts a frame-based paradigm that generates each frame independently, enabling low-latency real-time spatial inference. By enforcing multi-view spatial consistency through explicit 3D anchors and implicit spatial memory, the model preserves global scene geometry while maintaining fine-grained visual details across viewpoint changes. We further introduce a progressive three-stage training pipeline that transforms a pretrained image diffusion model into a controllable frame model and finally into a real-time generator through few-step distillation. Experimental results show that InSpatio-WorldFM achieves strong multi-view consistency while supporting interactive exploration on consumer-grade GPUs, providing an efficient alternative to traditional video-based world models for real-time world simulation.
[CV-50] Single-View Rolling-Shutter SfM
【速读】：该论文旨在解决滚动快门（Rolling-Shutter, RS）相机在结构光恢复（Structure-from-Motion, SfM）中尚未完全解决的问题。其解决方案的关键在于系统性地刻画单张RS图像中世界点或直线的几何特性，并基于此分析哪些运动和场景参数可以从单一RS图像中被恢复，进而推导出最小重构问题。通过构建代表性案例的求解器，验证了该方法在理论上的可行性及实际应用中的局限性。
链接: https://arxiv.org/abs/2603.11888
作者: Sofía Errázuriz Muñoz,Kim Kiehn,Petr Hruby,Kathlén Kohn
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Algebraic Geometry (math.AG)
备注:
Abstract:Rolling-shutter (RS) cameras are ubiquitous, but RS SfM (structure-from-motion) has not been fully solved yet. This work suggests an approach to remedy this: We characterize RS single-view geometry of observed world points or lines. Exploiting this geometry, we describe which motion and scene parameters can be recovered from a single RS image and systematically derive minimal reconstruction problems. We evaluate several representative cases with proof-of-concept solvers, highlighting both feasibility and practical limitations.
[CV-51] Derain-Agent : A Plug-and-Play Agent Framework for Rainy Image Restoration
【速读】：该论文旨在解决现有单图像去雨（single-image deraining）模型因采用静态推理范式而无法适应真实世界中复杂且耦合的退化现象（如噪声伪影、模糊和色彩偏移）的问题，导致复原图像存在残留伪影和感知质量不一致。其解决方案的关键在于提出一种即插即用的精炼框架 Derain-Agent，该框架将去雨任务从静态处理转变为动态、基于代理的修复过程，核心包含两个机制：1）规划网络（Planning Network），可为每个输入实例智能调度最优的修复工具序列；2）强度调制机制（Strength Modulation），以空间自适应强度应用这些工具，从而实现无迭代搜索成本的区域特异性残差修正，显著提升模型在合成与真实世界基准上的泛化性能。
链接: https://arxiv.org/abs/2603.11866
作者: Zhaocheng Yu,Xiang Chen,Runzhe Li,Zihan Geng,Guanglu Sun,Haipeng Li,Kui Jiang
机构: Harbin Institute of Technology (哈尔滨工业大学); Nanjing University of Science and Technology (南京理工大学); Tsinghua University (清华大学); Harbin University of Science and Technology (哈尔滨理工大学); Guangdong Meilan Technology Co., Ltd. (广东美兰科技有限公司)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:While deep learning has advanced single-image deraining, existing models suffer from a fundamental limitation: they employ a static inference paradigm that fails to adapt to the complex, coupled degradations (e.g., noise artifacts, blur, and color deviation) of real-world rain. Consequently, restored images often exhibit residual artifacts and inconsistent perceptual quality. In this work, we present Derain-Agent, a plug-and-play refinement framework that transitions deraining from static processing to dynamic, agent-based restoration. Derain-Agent equips a base deraining model with two core capabilities: 1) a Planning Network that intelligently schedules an optimal sequence of restoration tools for each instance, and 2) a Strength Modulation mechanism that applies these tools with spatially adaptive intensity. This design enables precise, region-specific correction of residual errors without the prohibitive cost of iterative search. Our method demonstrates strong generalization, consistently boosting the performance of state-of-the-art deraining models on both synthetic and real-world benchmarks.
[CV-52] ZeroSense:How Vision matters in Long Context Compression
【速读】：该论文旨在解决当前视觉-文本压缩（Visual-Text Compression, VTC）方法在评估时存在的偏差问题，即现有评价指标过度依赖下游任务性能，而未能独立、准确地衡量文本信息在压缩过程中的保真度。由于多模态大语言模型（Multimodal Large Language Models, MLLMs）具有强大的语言先验能力，其在下游任务中的表现可能掩盖了VTC本身的质量缺陷。论文提出了一种解耦式评估框架，通过引入ZeroSense基准测试集来确保测试样本间低语义相关性，从而消除上下文依赖关系，使评估结果仅反映VTC本身的压缩质量，而非下游模型的语义推理能力。该方案的关键在于构建一个去相关、可解释且与下游任务无关的评估体系，从而揭示VTC质量与下游任务准确率之间的显著差异，为VTC技术提供更客观的评测标准。
链接: https://arxiv.org/abs/2603.11846
作者: Yonghan Gao,Zehong Chen,Lijian Xu,Jingzhi Chen,Jingwei Guan,Xingyu Zeng
机构: Shenzhen University of Advanced Technology (深圳大学先进技术研究院); Shenzhen Technology University (深圳技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent visual-text compression (VTC) methods, typified by DeepSeek-OCR, report impressive high token compression ratios for long-context modeling tasks by leveraging text-to-image rendering. However, existing evaluation protocols heavily rely on downstream task performance. Such evaluation metrics fail to accurately measure text preservation due to the strong inherent linguistic priors of Multimodal Large Language Models (MLLMs). In this work, we introduce a new evaluation framework that decouples MLLMs’ capabilities to faithfully assess VTC quality. Within this framework, we further introduce the ZeroSense Benchmark to ensure low semantic correlation of testing samples. By eliminating contextual dependencies, our benchmark guarantees that the evaluation results are purely reflective of VTC quality, unaffected by the semantic inference capabilities of downstream models. Extensive experiments across multiple datasets demonstrate that VTC quality and downstream task accuracy diverge significantly, highlighting the necessity of our decoupled evaluation framework.
[CV-53] A Decade of Generative Adversarial Networks for Porous Material Reconstruction
【速读】：该论文旨在解决多孔材料数字化重建中的精度与效率问题，特别是在传统方法（如微计算机断层扫描和统计重建）难以满足复杂结构模拟需求的背景下。其解决方案的关键在于系统性地应用生成式对抗网络（Generative Adversarial Networks, GANs）技术，通过分类分析96篇相关文献，归纳出六类GAN架构（包括Vanilla GAN、多尺度GAN、条件GAN、注意力增强GAN、风格迁移GAN及混合架构GAN），并量化评估其在孔隙度准确性（误差<1%）、渗透率预测误差降低（最高达79%）以及重建体积扩展（从64³提升至2200³体素）等方面的性能优势。这一框架为针对不同应用场景选择最优GAN架构提供了理论依据和技术指导。
链接: https://arxiv.org/abs/2603.11836
作者: Ali Sadeghkhani,Brandon Bennett,Masoud Babaei,Arash Rabbani
机构: University of Leeds (利兹大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Materials Science (cond-mat.mtrl-sci); Geophysics (physics.geo-ph)
备注: 96 pages, supplementary material included (34 pages, 6 tables covering all 96 reviewed implementations)
Abstract:Digital reconstruction of porous materials has become increasingly critical for applications ranging from geological reservoir characterization to tissue engineering and electrochemical device design. While traditional methods such as micro-computed tomography and statistical reconstruction approaches have established foundations in this field, the emergence of deep learning techniques, particularly Generative Adversarial Networks (GANs), has revolutionized porous media reconstruction capabilities. This review systematically analyzes 96 peer-reviewed articles published from 2017 to early 2026, examining the evolution and applications of GAN-based approaches for porous material image reconstruction. We categorize GAN architectures into six distinct classes, namely Vanilla GANs, Multi-Scale GANs, Conditional GANs, Attention-Enhanced GANs, Style-based GANs, and Hybrid Architecture GANs. Our analysis reveals substantial progress including improvements in porosity accuracy (within 1% of original samples), permeability prediction (up to 79% reduction in mean relative errors), and achievable reconstruction volumes (from initial 64^3 to current 2,200^3 voxels). Despite these advances, persistent challenges remain in computational efficiency, memory constraints for large-scale reconstruction, and maintaining structural continuity in 2D-to-3D transformations. This systematic analysis provides a comprehensive framework for selecting appropriate GAN architectures based on specific application requirements.
[CV-54] owards High-Fidelity CAD Generation via LLM -Driven Program Generation and Text-Based B-Rep Primitive Grounding
【速读】：该论文旨在解决当前生成式 AI 在计算机辅助设计（Computer-Aided Design, CAD）领域中存在的范式鸿沟问题，即传统方法中参数化建模与边界表示（Boundary Representation, B-Rep）合成相互割裂，难以支持复杂工业产品设计的端到端自动化生成。解决方案的关键在于提出 FutureCAD 框架，其核心创新包括：1）引入基于大语言模型（Large Language Models, LLMs）的文本到 CAD 生成机制，通过自然语言描述实现几何选择的语义定位；2）设计 B-Rep 接地变换器（B-Rep Grounding Transformer, BRepGround），将自然语言查询映射至具体的 B-Rep 几何原始体，从而实现对参数化操作中几何实体的精确识别与交互；3）构建真实世界 CAD 模型数据集，并结合监督微调（Supervised Fine-Tuning, SFT）与强化学习（Reinforcement Learning, RL）优化生成质量与泛化能力，最终输出可执行的 CadQuery 脚本，实现高保真度 CAD 模型自动生成。
链接: https://arxiv.org/abs/2603.11831
作者: Jiahao Li,Qingwang Zhang,Qiuyu Chen,Guozhan Qiu,Yunzhong Lou,Xiangdong Zhou
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: preprint
Abstract:The field of Computer-Aided Design (CAD) generation has made significant progress in recent years. Existing methods typically fall into two separate categorie: parametric CAD modeling and direct boundary representation (B-Rep) synthesis. In modern feature-based CAD systems, parametric modeling and B-Rep are inherently intertwined, as advanced parametric operations (e.g., fillet and chamfer) require explicit selection of B-Rep geometric primitives, and the B-Rep itself is derived from parametric operations. Consequently, this paradigm gap remains a critical factor limiting AI-driven CAD modeling for complex industrial product design. This paper present FutureCAD, a novel text-to-CAD framework that leverages large language models (LLMs) and a B-Rep grounding transformer (BRepGround) for high-fidelity CAD generation. Our method generates executable CadQuery scripts, and introduces a text-based query mechanism that enables the LLM to specify geometric selections via natural language, which BRepGround then grounds to the target primitives. To train our framework, we construct a new dataset comprising real-world CAD models. For the LLM, we apply supervised fine-tuning (SFT) to establish fundamental CAD generation capabilities, followed by reinforcement learning (RL) to improve generalization. Experiments show that FutureCAD achieves state-of-the-art CAD generation performance.
[CV-55] Multimodal classification of Radiation-Induced Contrast Enhancements and tumor recurrence using deep learning
【速读】：该论文旨在解决胶质母细胞瘤（Glioblastoma）患者治疗后肿瘤复发与放疗诱导对比增强之间的鉴别难题，这是临床实践中的一大挑战。现有方法通常依赖于临床数据稀疏的扩散磁共振成像（diffusion MRI），或未考虑放疗剂量分布（radiotherapy dose distribution），而后者在肿瘤多学科讨论会（tumor board）中正日益受到关注。本文提出的解决方案是RICE-NET，一种融合纵向MRI数据与放疗剂量分布的多模态三维深度学习模型，通过常规T1加权MRI实现自动病灶分类。其关键创新在于将放疗剂量图作为核心输入模态，实验证明该模态对可靠分类贡献最大，且模型聚焦于临床相关区域，显著提升了诊断准确性，为神经肿瘤学中的辅助决策提供了新路径。
链接: https://arxiv.org/abs/2603.11827
作者: Robin Peretzke,Marlin Hanstein,Maximilian Fischer,Lars Badhi Wessel,Obada Alhalabi,Sebastian Regnery,Andreas Kudak,Maximilian Deng,Tanja Eichkorn,Philipp Hoegen Saßmannshausen,Fabian Allmendinger,Jan-Hendrik Bolten,Philipp Schröter,Christine Jungk,Jürgen Peter Debus,Peter Neher,Laila König,Klaus Maier-Hein
机构: German Cancer Research Center (DKFZ)(德国癌症研究中心); Heidelberg University Medical Center (Medizinische Universitätsklinik Heidelberg)(海德堡大学医学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The differentiation between tumor recurrence and radiation-induced contrast enhancements in post-treatment glioblastoma patients remains a major clinical challenge. Existing approaches rely on clinically sparsely available diffusion MRI or do not consider radiation maps, which are gaining increasing interest in the tumor board for this differentiation. We introduce RICE-NET, a multimodal 3D deep learning model that integrates longitudinal MRI data with radiotherapy dose distributions for automated lesion classification using conventional T1-weighted MRI data. Using a cohort of 92 patients, the model achieved an F1 score of 0.92 on an independent test set. During extensive ablation experiments, we quantified the contribution of each timepoint and modality and showed that reliable classification largely depends on the radiation map. Occlusion-based interpretability analyses further confirmed the model’s focus on clinically relevant regions. These findings highlight the potential of multimodal deep learning to enhance diagnostic accuracy and support clinical decision-making in neuro-oncology.
[CV-56] Automated Detection of Malignant Lesions in the Ovary Using Deep Learning Models and XAI
【速读】：该论文旨在解决卵巢癌（Ovarian Cancer）早期诊断中非侵入性检测方法准确性不足的问题，以及现有侵入性检测手段耗时较长的临床困境。其解决方案的关键在于构建并优化基于卷积神经网络（Convolutional Neural Networks, CNNs）的深度学习模型，从多个经典架构（如LeNet-5、ResNet、VGGNet和GoogLeNet/Inception）中筛选出最优结构，并结合数据增强技术提升模型泛化能力；最终选用性能最佳的InceptionV3模型（带ReLU激活函数），在Mendeley提供的OvarianCancerSubtypesDatasetHistopathology数据集上实现平均94%的综合性能指标（Accuracy、Precision、Recall、F1-Score、ROC曲线与AUC）。此外，通过引入可解释人工智能（Explainable Artificial Intelligence, XAI）方法（包括LIME、Integrated Gradients和SHAP）对模型决策过程进行可视化分析，增强模型透明度与临床可信度，从而推动卵巢癌更精准、高效、可解释的智能辅助诊断体系的发展。
链接: https://arxiv.org/abs/2603.11818
作者: Md. Hasin Sarwar Ifty,Nisharga Nirjan,Labib Islam,M. A. Diganta,Reeyad Ahmed Ornate,Anika Tasnim,Md. Saiful Islam
机构: 未知
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted and published at ICAIC 2025. Accepted version
Abstract:The unrestrained proliferation of cells that are malignant in nature is cancer. In recent times, medical professionals are constantly acquiring enhanced diagnostic and treatment abilities by implementing deep learning models to analyze medical data for better clinical decision, disease diagnosis and drug discovery. A majority of cancers are studied and treated by incorporating these technologies. However, ovarian cancer remains a dilemma as it has inaccurate non-invasive detection procedures and a time consuming, invasive procedure for accurate detection. Thus, in this research, several Convolutional Neural Networks such as LeNet-5, ResNet, VGGNet and GoogLeNet/Inception have been utilized to develop 15 variants and choose a model that accurately detects and identifies ovarian cancer. For effective model training, the dataset OvarianCancerSubtypesDatasetHistopathology from Mendeley has been used. After constructing a model, we utilized Explainable Artificial Intelligence (XAI) models such as LIME, Integrated Gradients and SHAP to explain the black box outcome of the selected model. For evaluating the performance of the model, Accuracy, Precision, Recall, F1-Score, ROC Curve and AUC have been used. From the evaluation, it was seen that the slightly compact InceptionV3 model with ReLu had the overall best result achieving an average score of 94% across all the performance metrics in the augmented dataset. Lastly for XAI, the three aforementioned XAI have been used for an overall comparative analysis. It is the aim of this research that the contributions of the study will help in achieving a better detection method for ovarian cancer.
[CV-57] RADAR: Closed-Loop Robotic Data Generation via Semantic Planning and Autonomous Causal Environment Reset IROS
【速读】：该论文旨在解决机器人学习中大规模物理交互数据获取的瓶颈问题，即传统依赖人工参与的数据采集方式成本高昂且难以扩展。其解决方案的关键在于提出一个完全自主的闭环数据生成引擎RADAR（Robust Autonomous Data Acquisition for Robotics），通过四模块协同架构实现无人干预的数据采集全流程：首先利用视觉语言模型（Vision-Language Model, VLM）基于少量3D人类示范进行语义锚定与任务生成；其次由图神经网络策略执行上下文感知的模仿学习以转化子任务为物理动作；接着VLM通过结构化视觉问答自动评估任务成功性；最后借助有限状态机实现环境自主重置与异构数据路由，结合前向-反向规划和严格的后进先出因果序列，使系统具备从执行失败中恢复的能力，并实现非结构化工作空间的自适应重构。这一脑-小脑协同机制将数据采集转变为可持续运行的自动化流程，显著提升复杂长程任务的成功率并支持真实世界中少样本接触密集型技能的泛化应用。
链接: https://arxiv.org/abs/2603.11811
作者: Yongzhong Wang,Keyu Zhu,Yong Zhong,Liqiong Wang,Jinyu Yang,Feng Zheng
机构: Southern University of Science and Technology (南方科技大学); Harbin Institute of Technology, Shenzhen (哈尔滨工业大学深圳分校); Spatialtemporal AI (时空AI)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 4 figures. Submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Abstract:The acquisition of large-scale physical interaction data, a critical prerequisite for modern robot learning, is severely bottlenecked by the prohibitive cost and scalability limits of human-in-the-loop collection paradigms. To break this barrier, we introduce Robust Autonomous Data Acquisition for Robotics (RADAR), a fully autonomous, closed-loop data generation engine that completely removes human intervention from the collection cycle. RADAR elegantly divides the cognitive load into a four-module pipeline. Anchored by 2-5 3D human demonstrations as geometric priors, a Vision-Language Model first orchestrates scene-relevant task generation via precise semantic object grounding and skill retrieval. Next, a Graph Neural Network policy translates these subtasks into physical actions via in-context imitation learning. Following execution, the VLM performs automated success evaluation using a structured Visual Question Answering pipeline. Finally, to shatter the bottleneck of manual resets, a Finite State Machine orchestrates an autonomous environment reset and asymmetric data routing mechanism. Driven by simultaneous forward-reverse planning with a strict Last-In, First-Out causal sequence, the system seamlessly restores unstructured workspaces and robustly recovers from execution failures. This continuous brain-cerebellum synergy transforms data collection into a self-sustaining process. Extensive evaluations highlight RADAR’s exceptional versatility. In simulation, our framework achieves up to 90% success rates on complex, long-horizon tasks, effortlessly solving challenges where traditional baselines plummet to near-zero performance. In real-world deployments, the system reliably executes diverse, contact-rich skills (e.g., deformable object manipulation) via few-shot adaptation without domain-specific fine-tuning, providing a highly scalable paradigm for robotic data acquisition.
[CV-58] CEI-3D: Collaborative Explicit-Implicit 3D Reconstruction for Realistic and Fine-Grained Object Editing
【速读】：该论文旨在解决现有3D编辑方法因重建网络深度耦合而导致结果不真实、细节粗糙的问题。其解决方案的关键在于提出了一种面向编辑的协同显式-隐式重建流程（collaborative explicit-implicit reconstruction approach），该方法通过隐式符号距离函数（SDF）网络提供连续平滑的几何先验，同时引入可微分采样的局部可控“处理点”（handler points）实现精细化编辑控制，并在两者之间建立相互引导机制；此外，设计了物理属性解耦模块（physical properties disentangling module）以分离处理点的颜色属性，并采用双扩散反照率网络（dual-diffuse-albedo network）分别处理编辑与非编辑区域，避免干扰；最终结合空间感知编辑模块（spatial-aware editing module）实现部件级调整，显著提升编辑的真实感和精细度。
链接: https://arxiv.org/abs/2603.11810
作者: Yue Shi,Rui Shi,Yuxuan Xiong,Bingbing Ni,Wenjun Zhang
机构: Shanghai Jiao Tong University (上海交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Existing 3D editing methods often produce unrealistic and unrefined results due to the deeply integrated nature of their reconstruction networks. To address the challenge, this paper introduces CEI-3D, an editing-oriented reconstruction pipeline designed to facilitate realistic and fine-grained editing. Specifically, we propose a collaborative explicit-implicit reconstruction approach, which represents the target object using an implicit SDF network and a differentially sampled, locally controllable set of handler points. The implicit network provides a smooth and continuous geometry prior, while the explicit handler points offer localized control, enabling mutual guidance between the global 3D structure and user-specified local editing regions. To independently control each attribute of the handler points, we design a physical properties disentangling module to decouple the color of the handler points into separate physical properties. We also propose a dual-diffuse-albedo network in this module to process the edited and non-edited regions through separate branches, thereby preventing undesired interference from editing operations. Building on the reconstructed collaborative explicit-implicit representation with disentangled properties, we introduce a spatial-aware editing module that enables part-wise adjustment of relevant handler points. This module employs a cross-view propagation-based 3D segmentation strategy, which helps users to edit the specified physical attributes of a target part efficiently. Extensive experiments on both real and synthetic datasets demonstrate that our approach achieves more realistic and fine-grained editing results than the state-of-the-art (SOTA) methods while requiring less editing time. Our code is available on this https URL.
[CV-59] OSM-based Domain Adaptation for Remote Sensing VLMs
【速读】：该论文旨在解决遥感领域中视觉-语言模型（Vision-Language Models, VLMs）因缺乏高质量图像-文本标注数据而导致的域适应难题。现有伪标签（pseudo-labeling）方法依赖于大型前沿教师模型进行知识蒸馏，存在成本高、可扩展性差且性能受限于教师模型上限的问题。其解决方案的关键在于提出OSMDA框架，利用一个具备强大基础能力的VLM自身作为标注引擎：通过将航空影像与渲染的OpenStreetMap（OSM）瓦片配对，借助模型自身的光学字符识别（OCR）和图表理解能力，从OSM丰富的辅助元数据中生成语义增强的描述文本；随后仅使用卫星图像对模型进行微调，从而获得无需人工标注且不依赖外部强模型的OSMDA-VLM。此方法显著提升了遥感场景下的域适应效果与训练效率。
链接: https://arxiv.org/abs/2603.11804
作者: Stefan Maria Ailuro,Mario Markov,Mohammad Mahdi,Delyan Boychev,Luc Van Gool,Danda Pani Paudel(INSAIT, Sofia University “St. Kliment Ohridski”)
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Vision-Language Models (VLMs) adapted to remote sensing rely heavily on domain-specific image-text supervision, yet high-quality annotations for satellite and aerial imagery remain scarce and expensive to produce. Prevailing pseudo-labeling pipelines address this gap by distilling knowledge from large frontier models, but this dependence on large teachers is costly, limits scalability, and caps achievable performance at the ceiling of the teacher. We propose OSMDA: a self-contained domain adaptation framework that eliminates this dependency. Our key insight is that a capable base VLM can serve as its own annotation engine: by pairing aerial images with rendered OpenStreetMap (OSM) tiles, we leverage optical character recognition and chart comprehension capabilities of the model to generate captions enriched by OSM’s vast auxiliary metadata. The model is then fine-tuned on the resulting corpus with satellite imagery alone, yielding OSMDA-VLM, a domain-adapted VLM that requires no manual labeling and no stronger external model. We conduct exhaustive evaluations spanning 10 benchmarks across image-text-to-text tasks and comparing against 9 competitive baselines. When equally mixed with real data, our method achieves state-of-the-art results, while being substantially cheaper to train than teacher-dependent alternatives. These results suggest that, given a strong foundation model, alignment with crowd-sourced geographic data is a practical and scalable path towards remote sensing domain adaptation. Dataset and model weights will be made publicly available.
[CV-60] Intrinsic Concept Extraction Based on Compositional Interpretability CVPR2026
【速读】：该论文旨在解决现有无监督概念提取方法无法提取可组合的内在概念的问题，提出了一种新的任务——可组合且可解释的内在概念提取（Compositional and Interpretable Intrinsic Concept Extraction, CI-ICE），目标是从单张图像中提取对象级和属性级的可组合概念，使得原始图像可通过这些概念的组合重建。解决方案的关键在于提出HyperExpress方法，其核心包括两方面：一是利用双曲空间（hyperbolic space）固有的层次建模能力实现概念解耦，同时保留概念间的层次结构与关系依赖；二是引入概念粒度优化方法，映射概念嵌入空间以维持复杂概念间关系并保障概念的可组合性。
链接: https://arxiv.org/abs/2603.11795
作者: Hanyu Shi,Hong Tao,Guoheng Huang,Jianbin Jiang,Xuhang Chen,Chi-Man Pun,Shanhu Wang,Pan Pan
机构: Guangdong University of Technology (广东工业大学); VIPSHOP (唯品会); Huizhou University (惠州大学); University of Macau (澳门大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR 2026
Abstract:Unsupervised Concept Extraction aims to extract concepts from a single image; however, existing methods suffer from the inability to extract composable intrinsic concepts. To address this, this paper introduces a new task called Compositional and Interpretable Intrinsic Concept Extraction (CI-ICE). The CI-ICE task aims to leverage diffusion-based text-to-image models to extract composable object-level and attribute-level concepts from a single image, such that the original concept can be reconstructed through the combination of these concepts. To achieve this goal, we propose a method called HyperExpress, which addresses the CI-ICE task through two core aspects. Specifically, first, we propose a concept learning approach that leverages the inherent hierarchical modeling capability of hyperbolic space to achieve accurate concept disentanglement while preserving the hierarchical structure and relational dependencies among concepts; second, we introduce a concept-wise optimization method that maps the concept embedding space to maintain complex inter-concept relationships while ensuring concept composability. Our method demonstrates outstanding performance in extracting compositionally interpretable intrinsic concepts from a single image.
[CV-61] Locating Demographic Bias at the Attention-Head Level in CLIPs Vision Encoder
【速读】：该论文旨在解决基础模型（foundation models）中偏见定位的难题，即现有公平性审计仅能量化模型整体偏差，却无法识别偏见在神经网络内部的具体位置。为此，作者提出了一种机制解释型公平性审计方法，其关键在于融合三种技术：投影残差流分解（projected residual-stream decomposition）、零样本概念激活向量（zero-shot Concept Activation Vectors, CAEs）以及偏置增强的TextSpan分析（bias-augmented TextSpan analysis），从而实现对视觉Transformer中单个注意力头（attention head）层面的性别与年龄偏见定位。该方法在CLIP ViT-L-14编码器上针对FACET基准中的42类职业进行验证，成功识别出特定层的注意力头，通过消融实验显著降低性别偏见（Cramer’s V从0.381降至0.362），且对准确率影响较小，而年龄偏见则表现出更低的局部可定位性，表明不同受保护属性的偏见编码方式存在差异。
链接: https://arxiv.org/abs/2603.11793
作者: Alaa Yasser,Kittipat Phunjanna,Marcos Escudero Viñolo,Catarina Barata,Jenny Benois-Pineau
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 14 pages, 6 tables, 2 figures. Work conducted during IPCV-AI Erasmus Mundus Master
Abstract:Standard fairness audits of foundation models quantify that a model is biased, but not where inside the network the bias resides. We propose a mechanistic fairness audit that combines projected residual-stream decomposition, zero-shot Concept Activation Vectors, and bias-augmented TextSpan analysis to locate demographic bias at the level of individual attention heads in vision transformers. As a feasibility case study, we apply this pipeline to the CLIP ViT-L-14 encoder on 42 profession classes of the FACET benchmark, auditing both gender and age bias. For gender, the pipeline identifies four terminal-layer heads whose ablation reduces global bias (Cramer’s V: 0.381 - 0.362) while marginally improving accuracy (+0.42%); a layer-matched random control confirms that this effect is specific to the identified heads. A single head in the final layer contributes to the majority of the reduction in the most stereotyped classes, and class-level analysis shows that corrected predictions shift toward the correct occupation. For age, the same pipeline identifies candidate heads, but ablation produces weaker and less consistent effects, suggesting that age bias is encoded more diffusely than gender bias in this model. These results provide preliminary evidence that head-level bias localisation is feasible for discriminative vision encoders and that the degree of localisability may vary across protected attributes. keywords: Bias . CLIP . Mechanistic Interpretability . Vision Transformer . Fairness Comments: 14 pages, 6 tables, 2 figures. Work conducted during IPCV-AI Erasmus Mundus Master Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) Cite as: arXiv:2603.11793 [cs.CV] (or arXiv:2603.11793v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.11793 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-62] HELM: Hierarchical and Explicit Label Modeling with Graph Learning for Multi-Label Image Classification
【速读】：该论文旨在解决遥感图像（Remote Sensing Image, RSI）中层次化多标签分类（Hierarchical Multi-Label Classification, HMLC）面临的两个关键挑战：一是现有方法难以处理实例属于多个分支的复杂多路径层次结构，二是极少利用未标注数据来提升模型性能。其解决方案的核心在于提出一种名为HELM（Hierarchical and Explicit Label Modeling）的新框架，该框架通过三个关键技术实现突破：(i) 在Vision Transformer中引入层次特异性类别标记（hierarchy-specific class tokens），以捕捉标签间的细微交互；(ii) 利用图卷积网络（Graph Convolutional Networks, GCN）显式建模层级结构并生成层次感知嵌入；(iii) 引入自监督分支有效利用未标注遥感图像，在半监督场景下显著提升模型泛化能力，尤其在低标签数据条件下表现优异。
链接: https://arxiv.org/abs/2603.11783
作者: Marjan Stoimchev,Boshko Koloski,Jurica Levatić,Dragi Kocev,Sašo Džeroski
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted and presented at REO workshop at EurIPS 2025
Abstract:Hierarchical multi-label classification (HMLC) is essential for modeling complex label dependencies in remote sensing. Existing methods, however, struggle with multi-path hierarchies where instances belong to multiple branches, and they rarely exploit unlabeled data. We introduce HELM (\textitHierarchical and Explicit Label Modeling), a novel framework that overcomes these limitations. HELM: (i) uses hierarchy-specific class tokens within a Vision Transformer to capture nuanced label interactions; (ii) employs graph convolutional networks to explicitly encode the hierarchical structure and generate hierarchy-aware embeddings; and (iii) integrates a self-supervised branch to effectively leverage unlabeled imagery. We perform a comprehensive evaluation on four remote sensing image (RSI) datasets (UCM, AID, DFC-15, MLRSNet). HELM achieves state-of-the-art performance, consistently outperforming strong baselines in both supervised and semi-supervised settings, demonstrating particular strength in low-label scenarios.
[CV-63] Controllable Egocentric Video Generation via Occlusion-Aware Sparse 3D Hand Joints
【速读】：该论文旨在解决当前生成式视频模型在第一人称视角（egocentric）应用中难以实现3D一致性的精细手部关节运动控制问题，尤其在严重遮挡情况下易产生运动不一致和幻觉伪影，且缺乏跨主体（如人类手与机器人手）的泛化能力。其解决方案的关键在于提出一种新颖的框架，通过单参考帧输入结合稀疏3D手部关节作为与主体无关的控制信号，引入一个高效的控制模块：该模块利用遮挡感知特征提取机制，对隐藏关节的不可靠视觉信号进行惩罚，并采用基于3D的加权机制以鲁棒地处理动态遮挡目标关节；同时，直接将3D几何嵌入注入潜在空间，严格保证结构一致性，从而实现高质量、高保真且具备跨主体泛化能力的第一人称视频生成。
链接: https://arxiv.org/abs/2603.11755
作者: Chenyangguang Zhang,Botao Ye,Boqi Chen,Alexandros Delitzas,Fangjinhua Wang,Marc Pollefeys,Xi Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Motion-controllable video generation is crucial for egocentric applications in virtual reality and embodied AI. However, existing methods often struggle to achieve 3D-consistent fine-grained hand articulation. By adopting on 2D trajectories or implicit poses, they collapse 3D geometry into spatially ambiguous signals or over rely on human-centric priors. Under severe egocentric occlusions, this causes motion inconsistencies and hallucinated artifacts, as well as preventing cross-embodiment generalization to robotic hands. To address these limitations, we propose a novel framework that generates egocentric videos from a single reference frame, leveraging sparse 3D hand joints as embodiment-agnostic control signals with clear semantic and geometric structures. We introduce an efficient control module that resolves occlusion ambiguities while fully preserving 3D information. Specifically, it extracts occlusion-aware features from the source reference frame by penalizing unreliable visual signals from hidden joints, and employs a 3D-based weighting mechanism to robustly handle dynamically occluded target joints during motion propagation. Concurrently, the module directly injects 3D geometric embeddings into the latent space to strictly enforce structural consistency. To facilitate robust training and evaluation, we develop an automated annotation pipeline that yields over one million high-quality egocentric video clips paired with precise hand trajectories. Additionally, we register humanoid kinematic and camera data to construct a cross-embodiment benchmark. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art baselines, generating high-fidelity egocentric videos with realistic interactions and exhibiting exceptional cross-embodiment generalization to robotic hands.
[CV-64] SoulX-LiveAct: Towards Hour-Scale Real-Time Human Animation with Neighbor Forcing and ConvKV Memory
【速读】：该论文旨在解决自回归扩散模型（Autoregressive Diffusion Models）在小时级实时人体动画生成任务中面临的两个核心问题：一是现有强制策略（forcing strategies）因扩散状态不一致导致样本级表示传播时学习信号不稳定，影响训练收敛；二是历史表示无界增长且缺乏结构，难以有效复用缓存状态，严重限制推理效率。解决方案的关键在于提出两种创新机制：其一为邻域强制（Neighbor Forcing），通过在同一噪声条件下传播时间相邻帧作为潜在邻居，实现扩散步长一致的分布对齐学习信号，保障自回归链中漂移一致性；其二为结构化ConvKV记忆机制（structured ConvKV memory），将因果注意力中的键（keys）和值（values）压缩为固定长度表示，实现常数内存推理，支持无需依赖短期运动帧记忆的真正无限视频生成。
链接: https://arxiv.org/abs/2603.11746
作者: Dingcheng Zhen,Xu Zheng,Ruixin Zhang,Zhiqi Jiang,Yichao Yan,Ming Tao,Shunshun Yin
机构: Soul AI Lab(灵魂AI实验室); HKUST(GZ)(香港科技大学（广州）); Soochow University(苏州大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Autoregressive (AR) diffusion models offer a promising framework for sequential generation tasks such as video synthesis by combining diffusion modeling with causal inference. Although they support streaming generation, existing AR diffusion methods struggle to scale efficiently. In this paper, we identify two key challenges in hour-scale real-time human animation. First, most forcing strategies propagate sample-level representations with mismatched diffusion states, causing inconsistent learning signals and unstable convergence. Second, historical representations grow unbounded and lack structure, preventing effective reuse of cached states and severely limiting inference efficiency. To address these challenges, we propose Neighbor Forcing, a diffusion-step-consistent AR formulation that propagates temporally adjacent frames as latent neighbors under the same noise condition. This design provides a distribution-aligned and stable learning signal while preserving drifting throughout the AR chain. Building upon this, we introduce a structured ConvKV memory mechanism that compresses the keys and values in causal attention into a fixed-length representation, enabling constant-memory inference and truly infinite video generation without relying on short-term motion-frame memory. Extensive experiments demonstrate that our approach significantly improves training convergence, hour-scale generation quality, and inference efficiency compared to existing AR diffusion methods. Numerically, LiveAct enables hour-scale real-time human animation and supports 20 FPS real-time streaming inference on as few as two NVIDIA H100 or H200 GPUs. Quantitative results demonstrate that our method attains state-of-the-art performance in lip-sync accuracy, human animation quality, and emotional expressiveness, with the lowest inference cost.
[CV-65] VTEdit-Bench: A Comprehensive Benchmark for Multi-Reference Image Editing Models in Virtual Try-On
【速读】：该论文旨在解决当前虚拟试衣（Virtual Try-On, VTON）系统在面对多样化现实场景时泛化能力不足的问题，尤其是现有专用VTON模型难以适应复杂多变的试衣需求。其解决方案的关键在于构建一个全面的评估基准VTEdit-Bench，包含24,220个测试图像对和五类逐步增加复杂度的典型VTON任务，并提出基于视觉语言模型（Vision-Language Model, VLM）的参考感知评估方法VTEdit-QA，从模型一致性、衣物一致性及整体图像质量三个维度量化评估通用多参考图像编辑模型在VTON中的表现。这一框架首次系统性地揭示了通用编辑模型在VTON任务上的优势与局限，表明顶级通用编辑器在常规任务上具备竞争力且在困难场景下更稳定，但在多衣物条件下的复杂配置中仍存在挑战。
链接: https://arxiv.org/abs/2603.11734
作者: Xiaoye Liang,Zhiyuan Qu,Mingye Zou,Jiaxin Liu,Lai Jiang,Mai Xu,Yiheng Zhu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:As virtual try-on (VTON) continues to advance, a growing number of real-world scenarios have emerged, pushing beyond the ability of the existing specialized VTON models. Meanwhile, universal multi-reference image editing models have progressed rapidly and exhibit strong generalization in visual editing, suggesting a promising route toward more flexible VTON systems. However, despite their strong capabilities, the strengths and limitations of universal editors for VTON remain insufficiently explored due to the lack of systematic evaluation benchmarks. To address this gap, we introduce VTEdit-Bench, a comprehensive benchmark designed to evaluate universal multi-reference image editing models across various realistic VTON scenarios. VTEdit-Bench contains 24,220 test image pairs spanning five representative VTON tasks with progressively increasing complexity, enabling systematic analysis of robustness and generalization. We further propose VTEdit-QA, a reference-aware VLM-based evaluator that assesses VTON performance from three key aspects: model consistency, cloth consistency, and overall image quality. Through this framework, we systematically evaluate eight universal editing models and compare them with seven specialized VTON models. Results show that top universal editors are competitive on conventional tasks and generalize more stably to harder scenarios, but remain challenged by complex reference configurations, particularly multi-cloth conditioning.
[CV-66] Cross-Resolution Attention Network for High-Resolution PM2.5 Prediction
【速读】：该论文旨在解决视觉Transformer（Vision Transformer）在超高分辨率、大陆尺度环境监测场景下可扩展性不足的问题，特别是针对欧洲空气质量地图（1 km分辨率，含2900万像素）的PM2.5浓度预测任务。传统自注意力机制难以处理如此大规模的空间数据，导致计算效率低下且内存消耗巨大。解决方案的关键在于提出一种双分支视觉Transformer模型CRAN-PM，其核心创新包括：（1）引入跨分辨率注意力机制（cross-resolution attention），实现全球气象数据（25 km）与局部高分辨率PM2.5数据（1 km）的有效融合；（2）设计高程感知自注意力（elevation-aware self-attention）和风向引导的交叉注意力（wind-guided cross-attention），迫使网络学习符合物理规律的特征表示，从而提升复杂地形下的预测准确性。该方法在单张GPU上仅需1.8秒即可生成完整欧洲地图，显著优于现有单尺度基线模型，在T+1和T+3时刻分别降低RMSE 4.7%和10.7%，并在复杂地形区域减少36%的偏差。
链接: https://arxiv.org/abs/2603.11725
作者: Ammar Kheder,Helmi Toropainen,Wenqing Peng,Samuel Antão,Zhi-Song Liu,Michael Boy
机构: University of Helsinki (赫尔辛基大学); LUT University (拉彭兰塔-拉赫蒂理工大学); Atmospheric Modelling Centre Lahti (AMC-Lahti) (拉赫蒂大气建模中心); Advanced Micro Devices (AMD) (超威半导体公司)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Vision Transformers have achieved remarkable success in spatio-temporal prediction, but their scalability remains limited for ultra-high-resolution, continent-scale domains required in real-world environmental monitoring. A single European air-quality map at 1 km resolution comprises 29 million pixels, far beyond the limits of naive self-attention. We introduce CRAN-PM, a dual-branch Vision Transformer that leverages cross-resolution attention to efficiently fuse global meteorological data (25 km) with local high-resolution PM2.5 at the current time (1 km). Instead of including physically driven factors like temperature and topography as input, we further introduce elevation-aware self-attention and wind-guided cross-attention to force the network to learn physically consistent feature representations for PM2.5 forecasting. CRAN-PM is fully trainable and memory-efficient, generating the complete 29-million-pixel European map in 1.8 seconds on a single GPU. Evaluated on daily PM2.5 forecasting throughout Europe in 2022 (362 days, 2,971 European Environment Agency (EEA) stations), it reduces RMSE by 4.7% at T+1 and 10.7% at T+3 compared to the best single-scale baseline, while reducing bias in complex terrain by 36%.
[CV-67] COTONET: A custom cotton detection algorithm based on YOLO11 for stage of growth cotton boll detection
【速读】：该论文旨在解决棉花采摘过程中因物理操作导致纤维劣化的问题，核心挑战在于如何在自动化采摘系统中准确识别处于不同生育阶段的棉铃（cotton capsules），以实现类似人工轻柔抓取的效果。解决方案的关键在于提出COTONET模型——一种基于YOLO11架构改进的轻量级目标检测网络，通过引入多种注意力机制增强对复杂场景下难检样本的识别能力：包括用Squeeze-and-Exitation模块替代传统卷积块、设计融合注意力机制的主干网络、采用内容感知特征重组（CARAFE）替代标准上采样操作，并集成Simple Attention Module（SimAM）与并行混合注意力机制（PHAM）分别用于初级特征聚合和下采样路径中的通道、空间及坐标维度注意力建模，从而显著提升检测精度与鲁棒性，最终实现mAP50达81.1%、mAP50-95达60.6%，且模型参数仅为7.6M、计算量27.8 GFLOPS，适配边缘计算与移动机器人部署。
链接: https://arxiv.org/abs/2603.11717
作者: Guillem González,Guillem Alenyà,Sergi Foix
机构: IRI (Institute for Research and Innovation); UPC (Universitat Politècnica de Catalunya)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 11 figures. This paper will be submitted to Computers and Electronics in Agriculture, special issue
Abstract:Cotton harvesting is a critical phase where cotton capsules are physically manipulated and can lead to fibre degradation. To maintain the highest quality, harvesting methods must emulate delicate manual grasping, to preserve cotton’s intrinsic properties. Automating this process requires systems capable of recognising cotton capsules across various phenological stages. To address this challenge, we propose COTONET, an enhanced custom YOLO11 model tailored with attention mechanisms to improve the detection of difficult instances. The architecture incorporates gradients in non-learnable operations to enhance shape and feature extraction. Key architectural modifications include: the replacement of convolutional blocks with Squeeze-and-Exitation blocks, a redesigned backbone integrating attention mechanisms, and the substitution of standard upsampling operations for Content Aware Reassembly of Features (CARAFE). Additionally, we integrate Simple Attention Modules (SimAM) for primary feature aggregation and Parallel Hybrid Attention Mechanisms (PHAM) for channel-wise, spatial-wise and coordinate-wise attention in the downward neck path. This configuration offers increased flexibility and robustness for interpreting the complexity of cotton crop growth. COTONET aligns with small-to-medium YOLO models utilizing 7.6M parameters and 27.8 GFLOPS, making it suitable for low-resource edge computing and mobile robotics. COTONET outperforms the standard YOLO baselines, achieving a mAP50 of 81.1% and a mAP50-95 of 60.6%.
[CV-68] PolyCrysDiff: Controllable Generation of Three-Dimensional Computable Polycrystalline Material Structures
【速读】：该论文旨在解决多晶材料三维（3D）微观结构的可控生成难题，以推动结构-性能关系的深入理解与材料设计的高效优化。传统方法如马尔可夫随机场（Markov random field, MRF）和卷积神经网络（convolutional neural network, CNN）在精确再现晶粒形貌、取向分布及空间关联性方面存在局限，且难以实现对晶粒属性（如尺寸和球形度）的高精度控制。本文提出的PolyCrysDiff框架基于条件潜在扩散模型（conditional latent diffusion），实现了从输入条件到可计算3D多晶微观结构的端到端生成，其关键创新在于通过潜在空间建模与条件引导机制，在保持物理合理性的同时显著提升对晶粒特征的可控性（R² > 0.972），并通过晶体塑性有限元法（crystal plasticity finite element method, CPFEM）验证了生成结构的可计算性与物理有效性，从而为数据驱动的多晶材料设计提供了可靠工具。
链接: https://arxiv.org/abs/2603.11695
作者: Chi Chen,Tianle Jiang,Xiaodong Wei,Yanming Wang
机构: Shanghai Jiao Tong University (上海交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Materials Science (cond-mat.mtrl-sci)
备注:
Abstract:The three-dimensional (3D) microstructures of polycrystalline materials exert a critical influence on their mechanical and physical properties. Realistic, controllable construction of these microstructures is a key step toward elucidating structure-property relationships, yet remains a formidable challenge. Herein, we propose PolyCrysDiff, a framework based on conditional latent diffusion that enables the end-to-end generation of computable 3D polycrystalline microstructures. Comprehensive qualitative and quantitative evaluations demonstrate that PolyCrysDiff faithfully reproduces target grain morphologies, orientation distributions, and 3D spatial correlations, while achieving an R^2 over 0.972 on grain attributes (e.g., size and sphericity) control, thereby outperforming mainstream approaches such as Markov random field (MRF)- and convolutional neural network (CNN)-based methods. The computability and physical validity of the generated microstructures are verified through a series of crystal plasticity finite element method (CPFEM) simulations. Leveraging PolyCrysDiff’s controllable generative capability, we systematically elucidate how grain-level microstructural characteristics affect the mechanical properties of polycrystalline materials. This development is expected to pave a key step toward accelerated, data-driven optimization and design of polycrystalline materials.
[CV-69] UCAN: Unified Convolutional Attention Network for Expansive Receptive Fields in Lightweight Super-Resolution CVPR2026
【速读】：该论文旨在解决轻量化图像超分辨率（Image Super-Resolution, ISR）模型在资源受限设备上部署时面临的计算复杂度高与性能不足的问题。现有混合卷积-Transformer架构虽表现优异，但扩展注意力窗口或卷积核尺寸会导致计算成本显著上升。其解决方案的关键在于提出UCAN网络，通过统一卷积与注意力机制来高效扩展有效感受野：一方面结合基于窗口的空间注意力与Hedgehog Attention机制以同时建模局部纹理和长程依赖关系；另一方面引入基于知识蒸馏的大核模块，在不增加大量计算负担的前提下保留高频结构信息；此外还采用跨层参数共享策略进一步降低模型复杂度，从而在保证高精度的同时实现更高的效率与可扩展性。
链接: https://arxiv.org/abs/2603.11680
作者: Cao Thien Tan,Phan Thi Thu Trang,Do Nghiem Duc,Ho Ngoc Anh,Hanyang Zhuang,Nguyen Duc Dung
机构: Ho Chi Minh City Open University (胡志明市开放大学); AI Tech Lab, Ho Chi Minh City University of Technology (胡志明市科技大學AI技術實驗室); Code Mely AI Research Team (Code Mely AI研究團隊); Global College, Shanghai Jiao Tong University (上海交通大學全球學院); Ha Noi University of Science and Technology (河內科學技術大學); University of Manitoba (曼尼托巴大學)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026
Abstract:Hybrid CNN-Transformer architectures achieve strong results in image super-resolution, but scaling attention windows or convolution kernels significantly increases computational cost, limiting deployment on resource-constrained devices. We present UCAN, a lightweight network that unifies convolution and attention to expand the effective receptive field efficiently. UCAN combines window-based spatial attention with a Hedgehog Attention mechanism to model both local texture and long-range dependencies, and introduces a distillation-based large-kernel module to preserve high-frequency structure without heavy computation. In addition, we employ cross-layer parameter sharing to further reduce complexity. On Manga109 ( 4\times ), UCAN-L achieves 31.63 dB PSNR with only 48.4G MACs, surpassing recent lightweight models. On BSDS100, UCAN attains 27.79 dB, outperforming methods with significantly larger models. Extensive experiments show that UCAN achieves a superior trade-off between accuracy, efficiency, and scalability, making it well-suited for practical high-resolution image restoration.
[CV-70] PROMO: Promptable Outfitting for Efficient High-Fidelity Virtual Try-On CVPR2026
【速读】：该论文旨在解决虚拟试衣（Virtual Try-on, VTON）中图像生成质量与推理效率之间的权衡问题，尤其是在基于扩散模型的方法中普遍存在的结构复杂、采样速度慢等瓶颈。其解决方案的关键在于将VTON建模为一个结构化的图像编辑任务，并提出PROMO框架——该框架基于Flow Matching DiT（Diffusion Transformer）骨干网络，结合潜在空间中的多模态条件拼接（latent multi-modal conditional concatenation）和自参考机制（self-reference mechanism），从而在保证高保真度的同时显著降低推理开销。通过这种设计，PROMO不仅在标准基准上超越了现有VTON方法及通用图像编辑模型，在质量和速度之间实现了更优平衡，还展现出良好的泛化能力，可迁移至更广泛的图像编辑场景。
链接: https://arxiv.org/abs/2603.11675
作者: Haohua Chen,Tianze Zhou,Wei Zhu,Runqi Wang,Yandong Guan,Dejia Song,Yibo Chen,Xu Tang,Yao Hu,Lu Sheng,Zhiyong Wu
机构: Xiaohongshu Inc. (小红书公司); Beihang University (北京航空航天大学); Tsinghua University (清华大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR 2026
Abstract:Virtual Try-on (VTON) has become a core capability for online retail, where realistic try-on results provide reliable fit guidance, reduce returns, and benefit both consumers and merchants. Diffusion-based VTON methods achieve photorealistic synthesis, yet often rely on intricate architectures such as auxiliary reference networks and suffer from slow sampling, making the trade-off between fidelity and efficiency a persistent challenge. We approach VTON as a structured image editing problem that demands strong conditional generation under three key requirements: subject preservation, faithful texture transfer, and seamless harmonization. Under this perspective, our training framework is generic and transfers to broader image editing tasks. Moreover, the paired data produced by VTON constitutes a rich supervisory resource for training general-purpose editors. We present PROMO, a promptable virtual try-on framework built upon a Flow Matching DiT backbone with latent multi-modal conditional concatenation. By leveraging conditioning efficiency and self-reference mechanisms, our approach substantially reduces inference overhead. On standard benchmarks, PROMO surpasses both prior VTON methods and general image editing models in visual fidelity while delivering a competitive balance between quality and speed. These results demonstrate that flow-matching transformers, coupled with latent multi-modal conditioning and self-reference acceleration, offer an effective and training-efficient solution for high-quality virtual try-on.
[CV-71] BackdoorIDS: Zero-shot Backdoor Detection for Pretrained Vision Encoder
【速读】：该论文旨在解决预训练视觉编码器（vision encoder）在下游应用中因使用第三方提供的、来源不明的模型而面临后门攻击（backdoor attack）的风险问题。解决方案的关键在于提出一种无需训练、推理时即可执行的零样本检测方法 BackdoorIDS，其核心机制基于两个观察：注意力劫持（Attention Hijacking）与恢复（Restoration）。具体而言，当对图像进行渐进式掩码处理时，受污染图像的注意力最初集中于恶意触发器特征；一旦掩码比例超过触发器鲁棒性阈值，触发器失效，注意力迅速转向良性内容，从而引发嵌入表示的显著突变；而干净图像的嵌入则随掩码进程平滑演化。BackdoorIDS 通过提取掩码轨迹上的嵌入序列并应用密度聚类（如 DBSCAN）来识别异常模式——若某输入的嵌入序列形成多个聚类，则判定为后门样本。该方法具备广泛兼容性，适用于 CNN、ViT、CLIP 和 LLaVA-1.5 等多种架构，且无需重新训练或依赖特定模型结构。
链接: https://arxiv.org/abs/2603.11664
作者: Siquan Huang,Yijiang Li,Ningzhi Gao,Xingfu Yan,Leyu Shi
机构: South China University of Technology (华南理工大学); University of California San Diego (加州大学圣地亚哥分校); South China Normal University (华南师范大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 10 figures, 6 tables
Abstract:Self-supervised and multimodal vision encoders learn strong visual representations that are widely adopted in downstream vision tasks and large vision-language models (LVLMs). However, downstream users often rely on third-party pretrained encoders with uncertain provenance, exposing them to backdoor attacks. In this work, we propose BackdoorIDS, a simple yet effective zero-shot, inference-time backdoor samples detection method for pretrained vision encoders. BackdoorIDS is motivated by two observations: Attention Hijacking and Restoration. Under progressive input masking, a backdoored image initially concentrates attention on malicious trigger features. Once the masking ratio exceeds the trigger’s robustness threshold, the trigger is deactivated, and attention rapidly shifts to benign content. This transition induces a pronounced change in the image embedding, whereas embeddings of clean images evolve more smoothly across masking progress. BackdoorIDS operationalizes this signal by extracting an embedding sequence along the masking trajectory and applying density-based clustering such as DBSCAN. An input is flagged as backdoored if its embedding sequence forms more than one cluster. Extensive experiments show that BackdoorIDS consistently outperforms existing defenses across diverse attack types, datasets, and model families. Notably, it is a plug-and-play approach that requires no retraining and operates fully zero-shot at inference time, making it compatible with a wide range of encoder architectures, including CNNs, ViTs, CLIP, and LLaVA-1.5.
[CV-72] FL-MedSegBench: A Comprehensive Benchmark for Federated Learning on Medical Image Segmentation
【速读】：该论文旨在解决联邦学习（Federated Learning, FL）在医学图像分割领域缺乏标准化评估基准的问题，从而阻碍了对不同FL方法的公平、全面比较与优化。其解决方案的关键在于构建了首个面向医学图像分割的综合性基准FL-MedSegBench，涵盖九个分割任务、十种成像模态（包括2D和3D格式），并引入临床真实异质性。通过系统评估8种通用联邦学习（generic FL, gFL）和5种个性化联邦学习（personalized FL, pFL）方法在分割精度、公平性、通信效率、收敛行为及未见域泛化能力等多个维度的表现，揭示出pFL方法（尤其是采用客户端特定批量归一化如FedBN的技术）更优且更具鲁棒性，并提出可指导临床部署的实证性指南。
链接: https://arxiv.org/abs/2603.11659
作者: Meilu Zhu,Zhiwei Wang,Axiu Mao,Yuxing Li,Xiaohan Xing,Yixuan Yuan,Edmund Y. Lam
机构: The University of Hong Kong (香港大学); Hangzhou Dianzi University (杭州电子科技大学); Stanford University (斯坦福大学); Chinese University of Hong Kong (香港中文大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages,4 figures
Abstract:Federated learning (FL) offers a privacy-preserving paradigm for collaborative medical image analysis without sharing raw data. However, the absence of standardized benchmarks for medical image segmentation hinders fair and comprehensive evaluation of FL methods. To address this gap, we introduce FL-MedSegBench, the first comprehensive benchmark for federated learning on medical image segmentation. Our benchmark encompasses nine segmentation tasks across ten imaging modalities, covering both 2D and 3D formats with realistic clinical heterogeneity. We systematically evaluate eight generic FL (gFL) and five personalized FL (pFL) methods across multiple dimensions: segmentation accuracy, fairness, communication efficiency, convergence behavior, and generalization to unseen domains. Extensive experiments reveal several key insights: (i) pFL methods, particularly those with client-specific batch normalization (\textite.g., FedBN), consistently outperform generic approaches; (ii) No single method universally dominates, with performance being dataset-dependent; (iii) Communication frequency analysis shows normalization-based personalization methods exhibit remarkable robustness to reduced communication frequency; (iv) Fairness evaluation identifies methods like Ditto and FedRDN that protect underperforming clients; (v) A method’s generalization to unseen domains is strongly tied to its ability to perform well across participating clients. We will release an open-source toolkit to foster reproducible research and accelerate clinically applicable FL solutions, providing empirically grounded guidelines for real-world clinical deployment. The source code is available at this https URL.
[CV-73] OmniForcing: Unleashing Real-time Joint Audio-Visual Generation
【速读】：该论文旨在解决联合音频-视觉扩散模型在实时应用中因双向注意力依赖导致高延迟的问题，从而限制了其在流式生成场景中的实用性。解决方案的关键在于提出OmniForcing框架，通过三重机制实现从离线双流双向扩散模型到高质量流式自回归生成器的蒸馏：首先引入非对称块因果对齐（Asymmetric Block-Causal Alignment）结合零截断全局前缀（zero-truncation Global Prefix），缓解多模态同步漂移；其次设计音频Sink Token机制并施加Identity RoPE约束，解决因果转换过程中极端音频token稀疏引发的梯度爆炸问题；最后采用联合自强制蒸馏（Joint Self-Forcing Distillation）策略，在长序列推理中动态修正由暴露偏差累积引起的跨模态误差。最终，借助模态无关的滚动键值缓存（rolling KV-cache）推理方案，实现了单GPU下约25 FPS的流式生成性能，同时保持与教师模型相当的多模态同步性和视觉质量。
链接: https://arxiv.org/abs/2603.11647
作者: Yaofeng Su,Yuming Li,Zeyue Xue,Jie Huang,Siming Fu,Haoran Li,Ying Li,Zezhong Qian,Haoyang Huang,Nan Duan
机构: 未知
类目: Multimedia (cs.MM); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
备注: 14 pages
Abstract:Recent joint audio-visual diffusion models achieve remarkable generation quality but suffer from high latency due to their bidirectional attention dependencies, hindering real-time applications. We propose OmniForcing, the first framework to distill an offline, dual-stream bidirectional diffusion model into a high-fidelity streaming autoregressive generator. However, naively applying causal distillation to such dual-stream architectures triggers severe training instability, due to the extreme temporal asymmetry between modalities and the resulting token sparsity. We address the inherent information density gap by introducing an Asymmetric Block-Causal Alignment with a zero-truncation Global Prefix that prevents multi-modal synchronization drift. The gradient explosion caused by extreme audio token sparsity during the causal shift is further resolved through an Audio Sink Token mechanism equipped with an Identity RoPE constraint. Finally, a Joint Self-Forcing Distillation paradigm enables the model to dynamically self-correct cumulative cross-modal errors from exposure bias during long rollouts. Empowered by a modality-independent rolling KV-cache inference scheme, OmniForcing achieves state-of-the-art streaming generation at \sim 25 FPS on a single GPU, maintaining multi-modal synchronization and visual quality on par with the bidirectional teacher.\textbfProject Page: \hrefthis https URLthis https URL
[CV-74] IDRL: An Individual-Aware Multimodal Depression-Related Representation Learning Framework for Depression Diagnosis
【速读】：该论文旨在解决多模态抑郁症检测中存在的两个核心问题：一是模态间不一致性与无关干扰（inter-modal inconsistency and depression-unrelated interference），即不同模态中的抑郁相关线索可能存在冲突，且大量非抑郁相关信息会掩盖关键的抑郁信号；二是个体差异导致的抑郁表现多样性（diverse individual depressive presentations），使得各模态及特征的重要性在不同个体间存在显著差异，从而影响融合效果。解决方案的关键在于提出一种个体感知的多模态抑郁相关表征学习框架（Individual-aware Multimodal Depression-related Representation Learning Framework, IDRL），其核心创新包括：1）通过解耦机制将多模态表征分离为模态共享的抑郁空间、模态特异的抑郁空间和抑郁无关空间，以增强模态对齐并抑制干扰信息；2）引入个体感知的模态融合模块（Individual-aware Modality-Fusion Module, IAF），基于特征预测能力动态调整各解耦抑郁相关特征的权重，实现针对不同个体的自适应跨模态融合，从而提升诊断的鲁棒性与准确性。
链接: https://arxiv.org/abs/2603.11644
作者: Chongxiao Wang,Junjie Liang,Peng Cao,Jinzhu Yang,Osmar R. Zaiane
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Depression is a severe mental disorder, and reliable identification plays a critical role in early intervention and treatment. Multimodal depression detection aims to improve diagnostic performance by jointly modeling complementary information from multiple modalities. Recently, numerous multimodal learning approaches have been proposed for depression analysis; however, these methods suffer from the following limitations: 1) inter-modal inconsistency and depression-unrelated interference, where depression-related cues may conflict across modalities while substantial irrelevant content obscures critical depressive signals, and 2) diverse individual depressive presentations, leading to individual differences in modality and cue importance that hinder reliable fusion. To address these issues, we propose Individual-aware Multimodal Depression-related Representation Learning Framework (IDRL) for robust depression diagnosis. Specifically, IDRL 1) disentangles multimodal representations into a modality-common depression space, a modality-specific depression space, and a depression-unrelated space to enhance modality alignment while suppressing irrelevant information, and 2) introduces an individual-aware modality-fusion module (IAF) that dynamically adjusts the weights of disentangled depression-related features based on their predictive significance, thereby achieving adaptive cross-modal fusion for different individuals. Extensive experiments demonstrate that IDRL achieves superior and robust performance for multimodal depression detection.
[CV-75] okenization Allows Multimodal Large Language Models to Understand Generate and Edit Architectural Floor Plans CVPR2026
【速读】：该论文旨在解决建筑平面图设计中几何、语义与空间层次关系的联合推理难题，这是当前人工智能系统面临的重大挑战。现有扩散模型和语言模型虽能提升视觉保真度，但在空间一致性推理和可控生成方面仍存在不足。解决方案的关键在于提出HouseMind——一个统一的多模态大语言模型，通过引入离散的房间实例标记（discrete room-instance tokens）构建统一词汇表，实现布局与符号推理之间的桥梁；结合多模态对齐与指令微调，使模型能够从文本指令中合成结构合理且可控的平面布局，从而在保证几何有效性的同时提升生成可控性与局部部署效率。
链接: https://arxiv.org/abs/2603.11640
作者: Sizhong Qin,Ramon Elias Weber,Xinzheng Lu
机构: Tsinghua University (清华大学); UC Berkeley (加州大学伯克利分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 20 pages, 9 figures. Accepted to CVPR 2026
Abstract:Architectural floor plan design demands joint reasoning over geometry, semantics, and spatial hierarchy, which remains a major challenge for current AI systems. Although recent diffusion and language models improve visual fidelity, they still struggle with coherent spatial reasoning and controllable generation. We present HouseMind, a multimodal large language model that unifies floor plan understanding, generation, and editing in one framework. We introduce discrete room-instance tokens to construct a unified vocabulary that bridges layouts and symbolic reasoning. With multimodal alignment and instruction tuning, the model synthesizes coherent, controllable layouts from text instructions. Experiments show how the framework achieves superior geometric validity and controllability while remaining efficient and locally deployable.
[CV-76] MV-SAM3D: Adaptive Multi-View Fusion for Layout-Aware 3D Generation
【速读】：该论文旨在解决现有单视图3D生成方法在多对象场景中难以保持空间布局一致性与物理合理性的问题，尤其是因独立估计物体姿态导致的穿插（interpenetration）和漂浮（floating）等物理不可行现象。其解决方案的关键在于提出一个无需训练的框架MV-SAM3D，通过在3D潜在空间中将多视角融合建模为多扩散（Multi-Diffusion）过程，并设计两种自适应加权策略——注意力熵加权（attention-entropy weighting）与可见性加权（visibility weighting），实现基于观测置信度的融合；同时引入物理感知优化机制，在生成过程中及之后注入碰撞与接触约束，从而确保多物体排列具有物理合理性。
链接: https://arxiv.org/abs/2603.11633
作者: Baicheng Li,Dong Wu,Jun Li,Shunkai Zhou,Zecui Zeng,Lusong Li,Hongbin Zha
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent unified 3D generation models have made remarkable progress in producing high-quality 3D assets from a single image. Notably, layout-aware approaches such as SAM3D can reconstruct multiple objects while preserving their spatial arrangement, opening the door to practical scene-level 3D generation. However, current methods are limited to single-view input and cannot leverage complementary multi-view observations, while independently estimated object poses often lead to physically implausible layouts such as interpenetration and floating artifacts. We present MV-SAM3D, a training-free framework that extends layout-aware 3D generation with multi-view consistency and physical plausibility. We formulate multi-view fusion as a Multi-Diffusion process in 3D latent space and propose two adaptive weighting strategies – attention-entropy weighting and visibility weighting – that enable confidence-aware fusion, ensuring each viewpoint contributes according to its local observation reliability. For multi-object composition, we introduce physics-aware optimization that injects collision and contact constraints both during and after generation, yielding physically plausible object arrangements. Experiments on standard benchmarks and real-world multi-object scenes demonstrate significant improvements in reconstruction fidelity and layout plausibility, all without any additional training. Code is available at this https URL. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2603.11633 [cs.CV] (or arXiv:2603.11633v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2603.11633 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-77] VisDoT : Enhancing Visual Reasoning through Human-Like Interpretation Grounding and Decomposition of Thought EACL2026
【速读】：该论文旨在解决大型视觉语言模型（Large Vision-Language Models, LVLMs）在图表中难以可靠检测视觉基本元素（visual primitives）并将其与语义表示对齐的问题，这一缺陷严重限制了其在复杂视觉推理任务中的表现。解决方案的关键在于提出VisDoT框架，该框架基于图形感知理论形式化了四个感知任务（如位置和长度），并引入分解思维（Decomposition-of-Thought, DoT）提示策略，将问题依次拆分为视觉感知子问题和逻辑推理子问题，从而实现感知与逻辑的分离。通过在InternVL上进行微调，VisDoT显著提升了ChartQA和ChartQAPro等图表理解基准上的性能，并在新提出的VisDoTQA基准上实现33.2%的提升，同时在多种开放域视觉问答（VQA）任务中展现出零样本泛化能力，验证了该方法的有效性和通用性。
链接: https://arxiv.org/abs/2603.11631
作者: Eunsoo Lee,Jeongwoo Lee,Minki Hong,Jangho Choi,Jihie Kim
机构: Dongguk University (东国大学)
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 30 pages, 21 figures, EACL 2026 Findings
Abstract:Large vision-language models (LVLMs) struggle to reliably detect visual primitives in charts and align them with semantic representations, which severely limits their performance on complex visual reasoning. This lack of perceptual grounding constitutes a major bottleneck for chart-based reasoning. We propose VisDoT, a framework that enhances visual reasoning through human-like interpretation grounding. We formalize four perceptual tasks based on the theory of graphical perception, including position and length. Building on this foundation, we introduce Decomposition-of-Thought (DoT) prompting, which sequentially separates questions into visual perception sub-questions and logic sub-questions. Fine-tuning InternVL with VisDoT achieves a +11.2% improvement on ChartQA and surpasses GPT-4o on the more challenging ChartQAPro benchmark. On the newly introduced VisDoTQA benchmark, the model improves by +33.2%. Furthermore, consistent zero-shot gains on diverse open-domain VQA benchmarks confirm the generalizability of the perception-logic separation strategy for visual question answering. VisDoT leverages human-like perception to enhance visual grounding, achieving state-of-the-art chart understanding and interpretable visual reasoning.
[CV-78] Developing Foundation Models for Universal Segmentation from 3D Whole-Body Positron Emission Tomography
【速读】：该论文旨在解决定量正电子发射断层成像（PET）图像分析中深度学习模型发展受限的问题，主要挑战包括PET图像因缺乏解剖对比度导致的分割难度高，以及数据采集和标注成本高昂。解决方案的关键在于构建迄今为止最大且最全面的PET数据集（包含11041例3D全身PET扫描及59831个分割掩码），并基于此开发出通用型基础模型SegAnyPET。该模型采用3D架构结合提示工程策略实现掩码生成，具备跨中心、跨示踪剂、跨疾病的零样本泛化能力，支持器官与病灶的通用分割、高效人工修正，并可嵌入临床人机协同工作流，显著提升PET图像分割的实用性与可扩展性。
链接: https://arxiv.org/abs/2603.11627
作者: Yichi Zhang,Le Xue,Wenbo Zhang,Lanlan Li,Feiyang Xiao,Yuchen Liu,Xiaohui Zhang,Hongwei Zhang,Shuqi Wang,Gang Feng,Liling Peng,Xin Gao,Yuanfan Xu,Yuan Qi,Kuangyu Shi,Hong Zhang,Yuan Cheng,Mei Tian,Zixin Hu
机构: Zhejiang University (浙江大学); Hangzhou Universal Medical Imaging Diagnostic Center (杭州全景医学影像诊断中心); Shanghai Universal Medical Imaging Diagnostic Center (上海全景医学影像诊断中心); University Hospital Tübingen (图宾根大学医院); LMU Hospital in Munich (慕尼黑路德维希马克西米利安大学医院); University of Bern (伯尔尼大学); Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (上海交通大学医学院瑞金医院); Siemens Biograph 64 PET/CT (西门子生物64 PET/CT); GE Discovery 710 PET/CT (通用电气发现710 PET/CT); Siemens Biograph Vision Quadra (西门子生物视觉四重体); United Imaging uEXPLORER (联影uEXPLORER); Siemens Biograph mMR (西门子生物mMR); Siemens Biograph mCT Flow 20 (西门子生物mCT Flow 20); Siemens Biograph 64-4R TruePoint (西门子生物64-4R TruePoint); GE Discovery 690 (通用电气发现690)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Positron emission tomography (PET) is a key nuclear medicine imaging modality that visualizes radiotracer distributions to quantify in vivo physiological and metabolic processes, playing an irreplaceable role in disease management. Despite its clinical importance, the development of deep learning models for quantitative PET image analysis remains severely limited, driven by both the inherent segmentation challenge from PET’s paucity of anatomical contrast and the high costs of data acquisition and annotation. To bridge this gap, we develop generalist foundational models for universal segmentation from 3D whole-body PET imaging. We first build the largest and most comprehensive PET dataset to date, comprising 11041 3D whole-body PET scans with 59831 segmentation masks for model development. Based on this dataset, we present SegAnyPET, an innovative foundational model with general-purpose applicability to diverse segmentation tasks. Built on a 3D architecture with a prompt engineering strategy for mask generation, SegAnyPET enables universal and scalable organ and lesion segmentation, supports efficient human correction with minimal effort, and enables a clinical human-in-the-loop workflow. Extensive evaluations on multi-center, multi-tracer, multi-disease datasets demonstrate that SegAnyPET achieves strong zero-shot performance across a wide range of segmentation tasks, highlighting its potential to advance the clinical applications of molecular imaging.
[CV-79] MedPruner: Training-Free Hierarchical Token Pruning for Efficient 3D Medical Image Understanding in Vision-Language Models
【速读】：该论文旨在解决当前用于3D医学图像理解的视觉语言模型（VLMs）在处理体积数据时存在的计算效率低下问题，主要表现为：1）由于连续2D切片直接拼接导致显著的解剖冗余；2）无法根据各切片信息密度差异灵活调整剪枝比例，从而限制了实际临床部署的可行性。解决方案的关键在于提出一种无需训练且与模型无关的分层令牌剪枝框架MedPruner，其核心机制包括两个阶段：首先通过跨切片锚点过滤模块消除切片层面的时间冗余，进而采用动态信息核选择策略，基于累积注意力权重实现自适应的令牌级压缩，从而在保留少于5%视觉令牌的情况下维持甚至提升模型性能，显著降低计算开销。
链接: https://arxiv.org/abs/2603.11625
作者: Shengyuan Liu,Zanting Ye,Yunrui Lin,Chen Hu,Wanting Geng,Xu Han,Bulat Ibragimov,Yefeng Zheng,Yixuan Yuan
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 10 pages
Abstract:While specialized Medical Vision-Language Models (VLMs) have achieved remarkable success in interpreting 2D and 3D medical modalities, their deployment for 3D volumetric data remains constrained by significant computational inefficiencies. Current architectures typically suffer from massive anatomical redundancy due to the direct concatenation of consecutive 2D slices and lack the flexibility to handle heterogeneous information densities across different slices using fixed pruning ratios. To address these challenges, we propose MedPruner, a training-free and model-agnostic hierarchical token pruning framework specifically designed for efficient 3D medical image understanding. MedPruner introduces a two-stage mechanism: an Inter-slice Anchor-based Filtering module to eliminate slice-level temporal redundancy, followed by a Dynamic Information Nucleus Selection strategy that achieves adaptive token-level compression by quantifying cumulative attention weights. Extensive experiments on three 3D medical benchmarks and across three diverse medical VLMs reveal massive token redundancy in existing architectures. Notably, MedPruner enables models such as MedGemma to maintain or even exceed their original performance while retaining fewer than 5% of visual tokens, thereby drastically reducing computational overhead and validating the necessity of dynamic token selection for practical clinical deployment. Our code will be released.
[CV-80] Shape-of-You: Fused Gromov-Wasserstein Optimal Transport for Semantic Correspondence in-the-Wild CVPR2026
【速读】：该论文旨在解决无监督语义对应（semantic correspondence）任务中因依赖局部2D外观特征而无法有效处理几何模糊性（如对称结构或重复纹理）的问题。其核心解决方案在于将伪标签生成重构为Fused Gromov-Wasserstein（FGW）优化问题，通过联合建模特征间相似性和结构一致性来提升对应关系的鲁棒性；关键创新是利用3D基础模型在几何空间中定义结构一致性约束，并采用基于锚点的线性近似方法降低FGW的计算复杂度，最终结合软目标损失动态融合运输计划与网络预测，从而构建对噪声具有鲁棒性的学习框架。
链接: https://arxiv.org/abs/2603.11618
作者: Jiin Im,Sisung Liu,Je Hyeong Hong
机构: Hanyang University (汉阳大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at CVPR 2026. Supplementary material included after references. 18 pages, 11 figures, 10 tables
Abstract:Semantic correspondence is essential for handling diverse in-the-wild images lacking explicit correspondence annotations. While recent 2D foundation models offer powerful features, adapting them for unsupervised learning via nearest-neighbor pseudo-labels has key limitations: it operates locally, ignoring structural relationships, and consequently its reliance on 2D appearance fails to resolve geometric ambiguities arising from symmetries or repetitive features. In this work, we address this by reformulating pseudo-label generation as a Fused Gromov-Wasserstein (FGW) problem, which jointly optimizes inter-feature similarity and intra-structural consistency. Our framework, Shape-of-You (SoY), leverages a 3D foundation model to define this intra-structure in the geometric space, resolving abovementioned ambiguity. However, since FGW is a computationally prohibitive quadratic problem, we approximate it through anchor-based linearization. The resulting probabilistic transport plan provides a structurally consistent but noisy supervisory signal. Thus, we introduce a soft-target loss dynamically blending guidance from this plan with network predictions to build a learning framework robust to this noise. SoY achieves state-of-the-art performance on SPair-71k and AP-10k datasets, establishing a new benchmark in semantic correspondence without explicit geometric annotations. Code is available at Shape-of-You.
[CV-81] Noise-aware few-shot learning through bi-directional multi-view prompt alignment
【速读】：该论文旨在解决视觉-语言模型（Vision-Language Models）在少样本学习（Few-Shot Learning）中对噪声标签敏感的问题，此类噪声会污染提示（prompt）并破坏跨模态对齐。现有方法因难以建模细粒度语义线索且无法自适应地区分干净信号与噪声信号而效果受限。解决方案的关键在于提出NA-MVP框架，其核心思想是从全局匹配转向区域感知对齐，显式区分干净提示与噪声干扰：通过多视角提示结合非平衡最优传输实现细粒度图像块到提示的对应关系并抑制不可靠区域；采用双向提示设计捕获互补的清洁导向和噪声感知线索，使模型聚焦于稳定语义；进而利用对齐引导的选择性精修策略，仅修正误标样本而保留可靠数据，从而提升鲁棒性。
链接: https://arxiv.org/abs/2603.11617
作者: Lu Niu,Cheng Xue
机构: Southeast University (东南大学); AIIA, Ministry of Education, China (人工智能教育部重点实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-language models offer strong few-shot capability through prompt tuning but remain vulnerable to noisy labels, which can corrupt prompts and degrade cross-modal alignment. Existing approaches struggle because they often lack the ability to model fine-grained semantic cues and to adaptively separate clean from noisy signals. To address these challenges, we propose NA-MVP, a framework for Noise-Aware few-shot learning through bi-directional Multi-View Prompt alignment. NA-MVP is built upon a key conceptual shift: robust prompt learning requires moving from global matching to region-aware alignment that explicitly distinguishes clean cues from noisy ones. To realize this, NA-MVP employs (1) multi-view prompts combined with unbalanced optimal transport to achieve fine-grained patch-to-prompt correspondence while suppressing unreliable regions; (2) a bi-directional prompt design that captures complementary clean-oriented and noise-aware cues, enabling the model to focus on stable semantics; and (3) an alignment-guided selective refinement strategy that uses optimal transport to correct only mislabeled samples while retaining reliable data. Experiments on synthetic and real-world noisy benchmarks demonstrate that NA-MVP consistently outperforms state-of-the-art baselines, confirming its effectiveness in enabling robust few-shot learning under noisy supervision.
[CV-82] SemiTooth: a Generalizable Semi-supervised Framework for Multi-Source Tooth Segmentation ICASSP2026
【速读】：该论文旨在解决多源牙科锥形束计算机断层扫描（CBCT）图像中因标注数据获取困难及不同机构间数据采集差异导致的分割质量低、体素级不一致和域特定偏差等问题。其解决方案的关键在于提出一种通用的半监督框架SemiTooth，该框架通过构建包含三个来源、不同标注层级的MS3Toothset数据集，并设计多教师-多学生架构，使每个学生网络从对应来源的未标注数据中学习，由其对应的教师模型进行监督；同时引入更严格的加权置信度约束机制，以提升多源场景下的模型鲁棒性与泛化能力，从而在半监督与多源牙体结构分割任务中达到当前最优性能（State-of-the-Art, SOTA）。
链接: https://arxiv.org/abs/2603.11616
作者: Muyi Sun,Yifan Gao,Ziang Jia,Xingqun Qi,Qianli Zhang,Qian Liu,Tianzheng Deng
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, 5 figures. Accepted to IEEE ICASSP 2026
Abstract:With the rapid advancement of artificial intelligence, intelligent dentistry for clinical diagnosis and treatment has become increasingly promising. As the primary clinical dentistry task, tooth structure segmentation for Cone-Beam Computed Tomography (CBCT) has made significant progress in recent years. However, challenges arise from the obtainment difficulty of full-annotated data, and the acquisition variability of multi-source data across different institutions, which have caused low-quality utilization, voxel-level inconsistency, and domain-specific disparity in CBCT slices. Thus, the rational and efficient utilization of multi-source and unlabeled data represents a pivotal problem. In this paper, we propose SemiTooth, a generalizable semi-supervised framework for multi-source tooth segmentation. Specifically, we first compile MS3Toothset, Multi-Source Semi-Supervised Tooth DataSet for clinical dental CBCT, which contains data from three sources with different-level annotations. Then, we design a multi-teacher and multi-student framework, i.e., SemiTooth, which promotes semi-supervised learning for multi-source data. SemiTooth employs distinct student networks that learn from unlabeled data with different sources, supervised by its respective teachers. Furthermore, a Stricter Weighted-Confidence Constraint is introduced for multiple teachers to improve the multi-source this http URL experiments are conducted on MS3Toothset to verify the feasibility and superiority of the SemiTooth framework, which achieves SOTA performance on the semi-supervised and multi-source tooth segmentation scenario.
[CV-83] DyWeight: Dynamic Gradient Weighting for Few-Step Diffusion Sampling
【速读】：该论文旨在解决扩散模型（Diffusion Models, DMs）在采样过程中因需数百次函数求值而导致的效率低下问题。现有基于多步常微分方程（ODE）求解器的方法虽通过重用历史梯度提升了效率，但其依赖人工设计的系数，无法适应扩散采样中非平稳的动力学特性。解决方案的关键在于提出一种轻量级、学习驱动的多步求解器——动态梯度加权（Dynamic Gradient Weighting, DyWeight），其核心创新是引入简化的隐式耦合范式，通过放松经典数值约束，学习无约束的时间变化参数，以自适应地聚合历史梯度并内在缩放有效步长。该机制在大步长下能准确对齐求解器的数值轨迹与模型内部去噪动力学，避免了复杂的解耦参数化和优化过程，从而在保持高视觉保真度与稳定性的前提下显著减少函数评估次数。
链接: https://arxiv.org/abs/2603.11607
作者: Tong Zhao,Mingkun Lei,Liangyu Yuan,Yanming Yang,Chenxi Song,Yang Wang,Beier Zhu,Chi Zhang
机构: Zhejiang University (浙江大学); AGI Lab, Westlake University (西湖大学AGI实验室); TongJi University (同济大学); University of Science and Technology of China (中国科学技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Code Link: see this https URL
Abstract:Diffusion Models (DMs) have achieved state-of-the-art generative performance across multiple modalities, yet their sampling process remains prohibitively slow due to the need for hundreds of function evaluations. Recent progress in multi-step ODE solvers has greatly improved efficiency by reusing historical gradients, but existing methods rely on handcrafted coefficients that fail to adapt to the non-stationary dynamics of diffusion sampling. To address this limitation, we propose Dynamic Gradient Weighting (DyWeight), a lightweight, learning-based multi-step solver that introduces a streamlined implicit coupling paradigm. By relaxing classical numerical constraints, DyWeight learns unconstrained time-varying parameters that adaptively aggregate historical gradients while intrinsically scaling the effective step size. This implicit time calibration accurately aligns the solver’s numerical trajectory with the model’s internal denoising dynamics under large integration steps, avoiding complex decoupled parameterizations and optimizations. Extensive experiments on CIFAR-10, FFHQ, AFHQv2, ImageNet64, LSUN-Bedroom, Stable Diffusion and FLUX.1-dev demonstrate that DyWeight achieves superior visual fidelity and stability with significantly fewer function evaluations, establishing a new state-of-the-art among efficient diffusion solvers. Code is available at this https URL
[CV-84] Articulat3D: Reconstructing Articulated Digital Twins From Monocular Videos with Geometric and Motion Constraints
【速读】：该论文旨在解决从视觉数据中构建高保真度关节物体数字孪生（digital twin）的难题，尤其是现有方法依赖多视角静态状态采集，限制了其在真实场景中的可扩展性。解决方案的关键在于提出Articulat3D框架，通过联合施加显式的三维几何与运动约束，从随意拍摄的单目视频中重建数字孪生。其核心创新包括：1）基于运动先验驱动的初始化（Motion Prior-Driven Initialization），利用3D点轨迹挖掘关节运动的低维结构，通过紧凑的运动基底实现场景软分解为多个刚性运动组；2）几何与运动约束精化（Geometric and Motion Constraints Refinement），引入可学习的运动学原语（kinematic primitives），以关节轴、枢轴点和帧级运动缩放参数化，确保重建结果在几何精度和时间一致性上均符合物理合理性。
链接: https://arxiv.org/abs/2603.11606
作者: Lijun Guo,Haoyu Zhao,Xingyue Zhao,Rong Fu,Linghao Zhuang,Siteng Huang,Zhongyu Li,Hua Zou
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 26 pages, 12 figures
Abstract:Building high-fidelity digital twins of articulated objects from visual data remains a central challenge. Existing approaches depend on multi-view captures of the object in discrete, static states, which severely constrains their real-world scalability. In this paper, we introduce Articulat3D, a novel framework that constructs such digital twins from casually captured monocular videos by jointly enforcing explicit 3D geometric and motion constraints. We first propose Motion Prior-Driven Initialization, which leverages 3D point tracks to exploit the low-dimensional structure of articulated motion. By modeling scene dynamics with a compact set of motion bases, we facilitate soft decomposition of the scene into multiple rigidly-moving groups. Building on this initialization, we introduce Geometric and Motion Constraints Refinement, which enforces physically plausible articulation through learnable kinematic primitives parameterized by a joint axis, a pivot point, and per-frame motion scalars, yielding reconstructions that are both geometrically accurate and temporally coherent. Extensive experiments demonstrate that Articulat3D achieves state-of-the-art performance on synthetic benchmarks and real-world casually captured monocular videos, significantly advancing the feasibility of digital twin creation under uncontrolled real-world conditions. Our project page is at this https URL.
[CV-85] LaMoGen: Language to Motion Generation Through LLM -Guided Symbolic Inference CVPR2026
【速读】：该论文旨在解决当前基于文本-运动嵌入（text-motion embeddings）的方法在生成时序准确、细节丰富且可解释的运动序列方面存在的局限性。其核心问题在于现有方法难以实现语言与动作之间的精确对齐，且缺乏透明性与可控性。解决方案的关键在于提出一种名为LabanLite的新型运动表示体系，该体系通过扩展Labanotation系统，将每个原子级身体动作（如单次左脚踏步）编码为离散的Laban符号与文本模板的组合，从而建立高阶语义与低阶运动轨迹之间的符号化关联。在此基础上，作者进一步构建了LaMoGen框架，利用大语言模型（LLM）进行符号推理，使模型能够理解并重组运动模式，生成既具可解释性又符合语言描述的运动序列。这一设计显著提升了语言驱动运动合成中的可控性与透明度。
链接: https://arxiv.org/abs/2603.11605
作者: Junkun Jiang,Ho Yin Au,Jingyu Xiang,Jie Chen
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 CVPR 2026. Supplementary material included. Project page: this https URL
Abstract:Human motion is highly expressive and naturally aligned with language, yet prevailing methods relying heavily on joint text-motion embeddings struggle to synthesize temporally accurate, detailed motions and often lack explainability. To address these limitations, we introduce LabanLite, a motion representation developed by adapting and extending the Labanotation system. Unlike black-box text-motion embeddings, LabanLite encodes each atomic body-part action (e.g., a single left-foot step) as a discrete Laban symbol paired with a textual template. This abstraction decomposes complex motions into interpretable symbol sequences and body-part instructions, establishing a symbolic link between high-level language and low-level motion trajectories. Building on LabanLite, we present LaMoGen, a Text-to-LabanLite-to-Motion Generation framework that enables large language models (LLMs) to compose motion sequences through symbolic reasoning. The LLM interprets motion patterns, relates them to textual descriptions, and recombines symbols into executable plans, producing motions that are both interpretable and linguistically grounded. To support rigorous evaluation, we introduce a Labanotation-based benchmark with structured description-motion pairs and three metrics that jointly measure text-motion alignment across symbolic, temporal, and harmony dimensions. Experiments demonstrate that LaMoGen establishes a new baseline for both interpretability and controllability, outperforming prior methods on our benchmark and two public datasets. These results highlight the advantages of symbolic reasoning and agent-based design for language-driven motion synthesis.
[CV-86] WeEdit: A Dataset Benchmark and Glyph-Guided Framework for Text-centric Image Editing
【速读】：该论文旨在解决文本中心图像编辑（text-centric image editing）中现有模型难以精确执行复杂文本修改任务的问题，尤其是因缺乏针对文本编辑的专用训练范式、大规模数据集和标准化评估基准而导致的字符模糊或幻觉现象。解决方案的关键在于提出WeEdit系统，其核心包括：(1) 基于HTML的自动化数据生成流水线，构建包含330K训练样本、覆盖15种语言的多样化编辑操作数据集；(2) 设计双语与多语言标准化评估基准；(3) 采用两阶段训练策略——首先通过字形引导的监督微调注入空间与内容先验信息，再通过多目标强化学习优化指令遵循度、文本清晰度及背景保真度。实验证明，该方案显著优于现有开源模型。
链接: https://arxiv.org/abs/2603.11593
作者: Hui Zhang,Juntao Liu,Zongkai Liu,Liqiang Niu,Fandong Meng,Zuxuan Wu,Yu-Gang Jiang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Instruction-based image editing aims to modify specific content within existing images according to user-provided instructions while preserving non-target regions. Beyond traditional object- and style-centric manipulation, text-centric image editing focuses on modifying, translating, or rearranging textual elements embedded within images. However, existing leading models often struggle to execute complex text editing precisely, frequently producing blurry or hallucinated characters. We attribute these failures primarily to the lack of specialized training paradigms tailored for text-centric editing, as well as the absence of large-scale datasets and standardized benchmarks necessary for a closed-loop training and evaluation system. To address these limitations, we present WeEdit, a systematic solution encompassing a scalable data construction pipeline, two benchmarks, and a tailored two-stage training strategy. Specifically, we propose a novel HTML-based automatic editing pipeline, which generates 330K training pairs covering diverse editing operations and 15 languages, accompanied by standardized bilingual and multilingual benchmarks for comprehensive evaluation. On the algorithmic side, we employ glyph-guided supervised fine-tuning to inject explicit spatial and content priors, followed by a multi-objective reinforcement learning stage to align generation with instruction adherence, text clarity, and background preservation. Extensive experiments demonstrate that WeEdit outperforms previous open-source models by a clear margin across diverse editing operations.
[CV-87] R4Det: 4D Radar-Camera Fusion for High-Performance 3D Object Detection CVPR2026
【速读】：该论文针对4D雷达-相机融合感知在自动驾驶中面临的三大挑战进行改进：一是现有方法的绝对深度估计模块鲁棒性和准确性不足，导致3D定位不准确；二是时间融合模块在自车位姿缺失或不准时性能显著下降甚至失效；三是稀疏雷达点云对小目标无法有效反射，此时检测仅依赖视觉单模态先验。解决方案的关键在于提出R4Det框架，其核心创新包括：通过全景深度融合（Panoramic Depth Fusion）模块提升深度估计质量，实现绝对深度与相对深度的相互增强；设计无需依赖自车位姿的可变形门控时间融合（Deformable Gated Temporal Fusion）模块以增强时序鲁棒性；以及构建基于实例引导的动态精修（Instance-Guided Dynamic Refinement）模块，利用2D实例引导提取语义原型，从而提升小目标检测能力。
链接: https://arxiv.org/abs/2603.11566
作者: Zhongyu Xia,Yousen Tang,Yongtao Wang,Zhifeng Wang,Weijun Qin
机构: Peking University (北京大学); EBTech Co. Ltd
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026
Abstract:4D radar-camera sensing configuration has gained increasing importance in autonomous driving. However, existing 3D object detection methods that fuse 4D Radar and camera data confront several challenges. First, their absolute depth estimation module is not robust and accurate enough, leading to inaccurate 3D localization. Second, the performance of their temporal fusion module will degrade dramatically or even fail when the ego vehicle’s pose is missing or inaccurate. Third, for some small objects, the sparse radar point clouds may completely fail to reflect from their surfaces. In such cases, detection must rely solely on visual unimodal priors. To address these limitations, we propose R4Det, which enhances depth estimation quality via the Panoramic Depth Fusion module, enabling mutual reinforcement between absolute and relative depth. For temporal fusion, we design a Deformable Gated Temporal Fusion module that does not rely on the ego vehicle’s pose. In addition, we built an Instance-Guided Dynamic Refinement module that extracts semantic prototypes from 2D instance guidance. Experiments show that R4Det achieves state-of-the-art 3D object detection results on the TJ4DRadSet and VoD datasets.
[CV-88] SVLL: Staged Vision-Language Learning for Physically Grounded Embodied Task Planning
【速读】：该论文旨在解决具身任务规划中视觉语言模型在生成动作序列时面临的双重挑战：一是现有联合端到端训练方法易导致时间绑定过早发生，二是标准强化学习方法存在优化不稳定问题。其解决方案的关键在于提出分阶段的视觉语言学习（Staged Vision-Language Learning, SVLL）框架，通过三个阶段逐步解耦空间定位与时间推理，先建立稳健的视觉依赖关系再引入动作历史序列；并在最终阶段引入Bias-DPO，一种新型对齐目标，通过显式最大化真实动作的似然并惩罚过度自信的幻觉行为，从而将策略锚定于专家轨迹流形上，缓解因果错位，确保动作序列严格遵循环境约束，有效抑制物理上不可能的捷径行为。
链接: https://arxiv.org/abs/2603.11563
作者: Yuyuan Yang,Junkun Hong,Hongrong Wang,Honghao Cai,Xunpeng Ren,Ge Wang,Mingcong Lei,Shenhao Yan,Jiahao Yang,Chengsi Yao,Xi Li,Yiming Zhao,Yatong Han,Jinke Ren
机构: FNii-Shenzhen, CUHKSZ; SSE, CUHKSZ; SAI, CUHKSZ; Shenzhen University; University of Sydney; Harbin Engineering University; Ising AI
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
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Abstract:Embodied task planning demands vision-language models to generate action sequences that are both visually grounded and causally coherent over time. However, existing training paradigms face a critical trade-off: joint end-to-end training often leads to premature temporal binding, while standard reinforcement learning methods suffer from optimization instability. To bridge this gap, we present Staged Vision-Language Learning (SVLL), a unified three-stage framework for robust, physically-grounded embodied planning. In the first two stages, SVLL decouples spatial grounding from temporal reasoning, establishing robust visual dependency before introducing sequential action history. In the final stage, we identify a key limitation of standard Direct Preference Optimization (DPO), its purely relative nature – optimizing only the preference gap between winning and losing trajectories while neglecting absolute likelihood constraints on optimal path, often yields unsafe or hallucinated behaviors. To address this, we further introduce Bias-DPO, a novel alignment objective that injects an inductive bias toward expert trajectories by explicitly maximizing likelihood on ground-truth actions while penalizing overconfident hallucinations. By anchoring the policy to the expert manifold and mitigating causal misalignment, SVLL, powered by Bias-DPO, ensures strict adherence to environmental affordances and effectively suppresses physically impossible shortcuts. Finally, extensive experiments on the interactive AI2-THOR benchmark and real-world robotic deployments demonstrate that SVLL outperforms both state-of-the-art open-source (e.g., Qwen2.5-VL-7B) and closed-source models (e.g., GPT-4o, Gemini-2.0-flash) in task success rate, while significantly reducing physical constraint violations.
[CV-89] ornadoNet: Real-Time Building Damage Detection with Ordinal Supervision
【速读】：该论文旨在解决灾后街景图像中建筑损伤多级评估的自动化问题，核心挑战在于如何在真实灾害场景下提升检测模型对损伤严重程度分级的准确性与一致性。解决方案的关键在于构建首个受控基准TornadoNet，并系统比较卷积神经网络（CNN）与Transformer架构在多级损伤检测中的表现，同时引入软序数分类目标和显式序数距离惩罚机制，以优化监督信号与损伤严重性有序特性的一致性。实验表明，结合序数感知监督策略后，RT-DETR模型在保持高序数一致性的同时显著提升了mAP指标，验证了架构设计与损失函数协同优化对灾后快速、可靠损伤评估的重要性。
链接: https://arxiv.org/abs/2603.11557
作者: Robinson Umeike,Cuong Pham,Ryan Hausen,Thang Dao,Shane Crawford,Tanya Brown-Giammanco,Gerard Lemson,John van de Lindt,Blythe Johnston,Arik Mitschang,Trung Do
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We present TornadoNet, a comprehensive benchmark for automated street-level building damage assessment evaluating how modern real-time object detection architectures and ordinal-aware supervision strategies perform under realistic post-disaster conditions. TornadoNet provides the first controlled benchmark demonstrating how architectural design and loss formulation jointly influence multi-level damage detection from street-view imagery, delivering methodological insights and deployable tools for disaster response. Using 3,333 high-resolution geotagged images and 8,890 annotated building instances from the 2021 Midwest tornado outbreak, we systematically compare CNN-based detectors from the YOLO family against transformer-based models (RT-DETR) for multi-level damage detection. Models are trained under standardized protocols using a five-level damage classification framework based on IN-CORE damage states, validated through expert cross-annotation. Baseline experiments reveal complementary architectural strengths. CNN-based YOLO models achieve highest detection accuracy and throughput, with larger variants reaching 46.05% mAP@0.5 at 66-276 FPS on A100 GPUs. Transformer-based RT-DETR models exhibit stronger ordinal consistency, achieving 88.13% Ordinal Top-1 Accuracy and MAOE of 0.65, indicating more reliable severity grading despite lower baseline mAP. To align supervision with the ordered nature of damage severity, we introduce soft ordinal classification targets and evaluate explicit ordinal-distance penalties. RT-DETR trained with calibrated ordinal supervision achieves 44.70% mAP@0.5, a 4.8 percentage-point improvement, with gains in ordinal metrics (91.15% Ordinal Top-1 Accuracy, MAOE = 0.56). These findings establish that ordinal-aware supervision improves damage severity estimation when aligned with detector architecture. Model Data: this https URL
[CV-90] Enhancing Image Aesthetics with Dual-Conditioned Diffusion Models Guided by Multimodal Perception
【速读】：该论文旨在解决图像美学增强（Image Aesthetic Enhancement）中模型难以理解模糊的美学指令以及缺乏“完美配对”图像数据（即语义一致但美学质量不同）的问题。其核心解决方案是提出一种基于扩散模型的双监督美学增强方法（Dual-supervised Image Aesthetic Enhancement, DIAE），关键在于引入多模态美学感知机制（Multimodal Aesthetic Perception, MAP），通过标准化的多维度美学指令和文本-图像对生成的多模态控制信号，将模糊美学描述转化为显式指导；同时构建了一个包含语义一致但美学差异的“不完美配对”数据集（IIAEData），并设计双分支监督框架以有效利用该弱匹配数据进行训练，从而显著提升图像美学评分与内容一致性。
链接: https://arxiv.org/abs/2603.11556
作者: Xinyu Nan,Ning Wang,Yuyao Zhai,Mei Yang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Image aesthetic enhancement aims to perceive aesthetic deficiencies in images and perform corresponding editing operations, which is highly challenging and requires the model to possess creativity and aesthetic perception capabilities. Although recent advancements in image editing models have significantly enhanced their controllability and flexibility, they struggle with enhancing image aesthetic. The primary challenges are twofold: first, following editing instructions with aesthetic perception is difficult, and second, there is a scarcity of “perfectly-paired” images that have consistent content but distinct aesthetic qualities. In this paper, we propose Dual-supervised Image Aesthetic Enhancement (DIAE), a diffusion-based generative model with multimodal aesthetic perception. First, DIAE incorporates Multimodal Aesthetic Perception (MAP) to convert the ambiguous aesthetic instruction into explicit guidance by (i) employing detailed, standardized aesthetic instructions across multiple aesthetic attributes, and (ii) utilizing multimodal control signals derived from text-image pairs that maintain consistency within the same aesthetic attribute. Second, to mitigate the lack of “perfectly-paired” images, we collect “imperfectly-paired” dataset called IIAEData, consisting of images with varying aesthetic qualities while sharing identical semantics. To better leverage the weak matching characteristics of IIAEData during training, a dual-branch supervision framework is also introduced for weakly supervised image aesthetic enhancement. Experimental results demonstrate that DIAE outperforms the baselines and obtains superior image aesthetic scores and image content consistency scores.
[CV-91] MANSION: Multi-floor lANguage-to-3D Scene generatIOn for loNg-horizon tasks
【速读】：该论文旨在解决现有具身智能基准测试环境局限于单层室内场景、难以模拟真实世界多楼层长时程任务的问题。其核心解决方案是提出MANSION框架，该框架能够基于语言指令生成具有垂直结构约束的建筑级、多层三维环境，确保生成场景在空间合理性与可导航性上的真实性，并支持多样化的人类友好布局。MANSION的关键创新在于引入对建筑结构语义的理解与建模能力，从而实现跨楼层长时程任务的高效开发与评估，为下一代空间推理与规划算法提供了一个更贴近现实的测试平台。
链接: https://arxiv.org/abs/2603.11554
作者: Lirong Che,Shuo Wen,Shan Huang,Chuang Wang,Yuzhe Yang,Gregory Dudek,Xueqian Wang,Jian Su
机构: Tsinghua University (清华大学); AgiBot; McGill University, MILA - Quebec AI Institute (麦吉尔大学，魁北克人工智能研究所)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
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Abstract:Real-world robotic tasks are long-horizon and often span multiple floors, demanding rich spatial reasoning. However, existing embodied benchmarks are largely confined to single-floor in-house environments, failing to reflect the complexity of real-world tasks. We introduce MANSION, the first language-driven framework for generating building-scale, multi-floor 3D environments. Being aware of vertical structural constraints, MANSION generates realistic, navigable whole-building structures with diverse, human-friendly scenes, enabling the development and evaluation of cross-floor long-horizon tasks. Building on this framework, we release MansionWorld, a dataset of over 1,000 diverse buildings ranging from hospitals to offices, alongside a Task-Semantic Scene Editing Agent that customizes these environments using open-vocabulary commands to meet specific user needs. Benchmarking reveals that state-of-the-art agents degrade sharply in our settings, establishing MANSION as a critical testbed for the next generation of spatial reasoning and planning.
[CV-92] PCA-Enhanced Probabilistic U-Net for Effective Ambiguous Medical Image Segmentation
【速读】：该论文旨在解决医学图像分割中因图像模糊性、噪声及主观标注带来的固有不确定性问题（Ambiguous Medical Image Segmentation, AMIS）。现有基于条件变分自编码器（cVAE）的方法虽能有效建模不确定性，但存在高维潜在空间冗余和单后验网络表达能力有限的问题。其解决方案的关键在于提出一种PCA增强的概率U-Net（PEP U-Net），通过在后验网络中引入主成分分析（PCA）实现潜在空间降维以减少冗余并提升计算效率，并进一步采用逆PCA操作重建关键信息，从而增强潜在空间的表征能力，同时保持生成多样分割假设的能力，实现分割精度与预测变异性的更好平衡。
链接: https://arxiv.org/abs/2603.11550
作者: Xiangyu Li,Chenglin Wang,Qiantong Shen,Fanding Li,Wei Wang,Kuanquan Wang,Yi Shen,Baochun Zhao,Gongning Luo
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Ambiguous Medical Image Segmentation (AMIS) is significant to address the challenges of inherent uncertainties from image ambiguities, noise, and subjective annotations. Existing conditional variational autoencoder (cVAE)-based methods effectively capture uncertainty but face limitations including redundancy in high-dimensional latent spaces and limited expressiveness of single posterior networks. To overcome these issues, we introduce a novel PCA-Enhanced Probabilistic U-Net (\textbfPEP U-Net). Our method effectively incorporates Principal Component Analysis (PCA) for dimensionality reduction in the posterior network to mitigate redundancy and improve computational efficiency. Additionally, we further employ an inverse PCA operation to reconstruct critical information, enhancing the latent space’s representational capacity. Compared to conventional generative models, our method preserves the ability to generate diverse segmentation hypotheses while achieving a superior balance between segmentation accuracy and predictive variability, thereby advancing the performance of generative modeling in medical image segmentation.
[CV-93] Mango-GS: Enhancing Spatio-Temporal Consistency in Dynamic Scenes Reconstruction using Multi-Frame Node-Guided 4D Gaussian Splatting
【速读】：该论文旨在解决动态三维场景的高保真四维（4D）重建问题，特别是现有基于高斯点绘（Gaussian splatting）的方法因依赖逐帧优化而导致过拟合瞬时状态、难以捕捉潜在运动动态的问题。其解决方案的关键在于提出Mango-GS框架，该框架采用多帧节点引导机制，通过一个时间Transformer在短时间窗口内建模运动依赖关系，从而生成时序一致的形变；同时将时间建模限制在稀疏控制节点上，每个节点由解耦的规范位置与潜在编码表示，提供稳定的语义锚点以防止大运动下的对应漂移，并结合输入掩码策略和双多帧损失函数实现端到端训练，显著提升了重建质量与实时渲染效率。
链接: https://arxiv.org/abs/2603.11543
作者: Tingxuan Huang,Haowei Zhu,Jun-hai Yong,Hao Pan,Bin Wang
机构: Tsinghua University (清华大学); BNRist
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reconstructing dynamic 3D scenes with photorealistic detail and strong temporal coherence remains a significant challenge. Existing Gaussian splatting approaches for dynamic scene modeling often rely on per-frame optimization, which can overfit to instantaneous states instead of capturing underlying motion dynamics. To address this, we present Mango-GS, a multi-frame, node-guided framework for high-fidelity 4D reconstruction. Mango-GS leverages a temporal Transformer to model motion dependencies within a short window of frames, producing temporally consistent deformations. For efficiency, temporal modeling is confined to a sparse set of control nodes. Each node is represented by a decoupled canonical position and a latent code, providing a stable semantic anchor for motion propagation and preventing correspondence drift under large motion. Our framework is trained end-to-end, enhanced by an input masking strategy and two multi-frame losses to improve robustness. Extensive experiments demonstrate that Mango-GS achieves state-of-the-art reconstruction quality and real-time rendering speed, enabling high-fidelity reconstruction and interactive rendering of dynamic scenes.
[CV-94] ReHARK: Refined Hybrid Adaptive RBF Kernels for Robust One-Shot Vision-Language Adaptation
【速读】：该论文旨在解决大规模视觉-语言模型（Vision-Language Models, VLMs）在极少量数据（尤其是单样本场景，one-shot regime）下进行下游任务适配时面临的“稳定性-可塑性”困境（Stability-Plasticity Dilemma）。现有无训练方法如Tip-Adapter虽引入高效缓存机制，但本质上是局部Nadaraya-Watson估计器，存在边界偏差且缺乏全局结构正则化。其解决方案的关键在于提出一种协同的无训练框架ReHARK（Refined Hybrid Adaptive RBF Kernels），通过在再生核希尔伯特空间（Reproducing Kernel Hilbert Space, RKHS）中引入全局近端正则化来重构少样本适应过程；核心创新包括：多阶段精炼流程——混合先验构建（融合CLIP与GPT-3的零样本文本知识及视觉类别原型）、支持集增强（生成中间样本以平滑模态间过渡）、自适应分布校正（对齐测试特征统计量以缓解域偏移）以及多尺度径向基函数（RBF）核集成（捕获跨尺度复杂特征几何结构），从而显著提升模型稳定性和准确性，在11个基准上实现平均65.83%的准确率，刷新单样本适配新纪录。
链接: https://arxiv.org/abs/2603.11542
作者: Md Jahidul Islam
机构: Bangladesh University of Engineering and Technology (孟加拉国工程技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:The adaptation of large-scale Vision-Language Models (VLMs) like CLIP to downstream tasks with extremely limited data – specifically in the one-shot regime – is often hindered by a significant “Stability-Plasticity” dilemma. While efficient caching mechanisms have been introduced by training-free methods such as Tip-Adapter, these approaches often function as local Nadaraya-Watson estimators. Such estimators are characterized by inherent boundary bias and a lack of global structural regularization. In this paper, ReHARK (Refined Hybrid Adaptive RBF Kernels) is proposed as a synergistic training-free framework that reinterprets few-shot adaptation through global proximal regularization in a Reproducing Kernel Hilbert Space (RKHS). A multistage refinement pipeline is introduced, consisting of: (1) Hybrid Prior Construction, where zero-shot textual knowledge from CLIP and GPT-3 is fused with visual class prototypes to form a robust semantic-visual anchor; (2) Support Set Augmentation (Bridging), where intermediate samples are generated to smooth the transition between visual and textual modalities; (3) Adaptive Distribution Rectification, where test feature statistics are aligned with the augmented support set to mitigate domain shifts; and (4) Multi-Scale RBF Kernels, where an ensemble of kernels is employed to capture complex feature geometries across diverse scales. Superior stability and accuracy are demonstrated through extensive experiments on 11 diverse benchmarks. A new state-of-the-art for one-shot adaptation is established by ReHARK, which achieves an average accuracy of 65.83%, significantly outperforming existing baselines. Code is available at this https URL.
[CV-95] Risk-Controllable Multi-View Diffusion for Driving Scenario Generation
【速读】：该论文旨在解决自动驾驶系统中安全关键场景（safety-critical driving scenarios）生成难题，特别是长尾风险场景在真实数据中稀少且难以通过人工设计精准控制的问题。现有生成方法通常将风险作为事后标签，难以保证多视角场景的几何一致性。其解决方案的关键在于提出RiskMV-DPO框架，通过融合目标风险水平与物理基础的风险建模，自动生成多样且高风险的动态轨迹作为扩散视频生成器的显式几何锚点；同时引入几何-外观对齐模块和区域感知直接偏好优化（RA-DPO）策略，结合运动感知掩码聚焦局部动态区域的学习，从而实现时空一致性和几何保真度的提升。实验表明，该方法显著改善了3D检测性能（mAP从18.17提升至30.50）并降低图像质量指标（FID降至15.70），推动世界模型从被动环境预测向主动、可控的风险合成演进。
链接: https://arxiv.org/abs/2603.11534
作者: Hongyi Lin,Wenxiu Shi,Heye Huang,Dingyi Zhuang,Song Zhang,Yang Liu,Xiaobo Qu,Jinhua Zhao
机构: Tsinghua University (清华大学); Massachusetts Institute of Technology (麻省理工学院); Z-one Technology Co., Ltd. (Z-one科技有限公司); Singapore-MIT Alliance for Research and Technology (新加坡-麻省理工联盟研究中心); Chengdu Tianfu Invo Technology Co., Ltd. (成都天府因沃科技有限公司)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Generating safety-critical driving scenarios is crucial for evaluating and improving autonomous driving systems, but long-tail risky situations are rarely observed in real-world data and difficult to specify through manual scenario design. Existing generative approaches typically treat risk as an after-the-fact label and struggle to maintain geometric consistency in multi-view driving scenes. We present RiskMV-DPO, a general and systematic pipeline for physically-informed, risk-controllable multi-view scenario generation. By integrating target risk levels with physically-grounded risk modeling, we autonomously synthesize diverse and high-stakes dynamic trajectories that serve as explicit geometric anchors for a diffusion-based video generator. To ensure spatial-temporal coherence and geometric fidelity, we introduce a geometry-appearance alignment module and a region-aware direct preference optimization (RA-DPO) strategy with motion-aware masking to focus learning on localized dynamic this http URL on the nuScenes dataset show that RiskMV-DPO can freely generate a wide spectrum of diverse long-tail scenarios while maintaining state-of-the-art visual quality, improving 3D detection mAP from 18.17 to 30.50 and reducing FID to 15.70. Our work shifts the role of world models from passive environment prediction to proactive, risk-controllable synthesis, providing a scalable toolchain for the safety-oriented development of embodied intelligence.
[CV-96] Mobile-GS: Real-time Gaussian Splatting for Mobile Devices
【速读】：该论文旨在解决3D高斯泼溅（3D Gaussian Splatting, 3DGS）在移动设备上部署时面临的高计算开销和大存储成本问题。其核心挑战在于alpha混合操作依赖于耗时的高斯深度排序过程，导致渲染效率低下。解决方案的关键在于提出一种深度感知的无序渲染机制，通过消除排序步骤显著提升渲染速度；同时引入神经视图依赖增强策略，以更准确地建模视点相关的外观效应，从而缓解因缺乏渲染顺序带来的透明伪影问题。此外，为适配内存受限的移动端平台，还结合一阶球谐函数蒸馏、神经向量量化与基于贡献度的剪枝策略，实现对3D高斯表示的有效压缩与模型精简，最终在保持高质量视觉效果的同时达成实时推理能力。
链接: https://arxiv.org/abs/2603.11531
作者: Xiaobiao Du,Yida Wang,Kun Zhan,Xin Yu
机构: University of Technology Sydney (悉尼科技大学); Adelaide University (阿德莱德大学); Li Auto Inc (理想汽车)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:3D Gaussian Splatting (3DGS) has emerged as a powerful representation for high-quality rendering across a wide range of this http URL, its high computational demands and large storage costs pose significant challenges for deployment on mobile devices. In this work, we propose a mobile-tailored real-time Gaussian Splatting method, dubbed Mobile-GS, enabling efficient inference of Gaussian Splatting on edge devices. Specifically, we first identify alpha blending as the primary computational bottleneck, since it relies on the time-consuming Gaussian depth sorting process. To solve this issue, we propose a depth-aware order-independent rendering scheme that eliminates the need for sorting, thereby substantially accelerating rendering. Although this order-independent rendering improves rendering speed, it may introduce transparency artifacts in regions with overlapping geometry due to the scarcity of rendering order. To address this problem, we propose a neural view-dependent enhancement strategy, enabling more accurate modeling of view-dependent effects conditioned on viewing direction, 3D Gaussian geometry, and appearance attributes. In this way, Mobile-GS can achieve both high-quality and real-time rendering. Furthermore, to facilitate deployment on memory-constrained mobile platforms, we also introduce first-order spherical harmonics distillation, a neural vector quantization technique, and a contribution-based pruning strategy to reduce the number of Gaussian primitives and compress the 3D Gaussian representation with the assistance of neural networks. Extensive experiments demonstrate that our proposed Mobile-GS achieves real-time rendering and compact model size while preserving high visual quality, making it well-suited for mobile applications.
[CV-97] MDS-VQA: Model-Informed Data Selection for Video Quality Assessment
【速读】：该论文旨在解决视频质量评估（VQA）模型发展中存在的“模型设计与数据集构建脱节”问题，即现有方法要么在固定基准上迭代模型，要么单纯收集新的人类标注数据而未针对性地识别和强化当前模型的薄弱环节。其解决方案的关键在于提出MDS-VQA机制，该机制通过两个核心步骤实现：首先训练一个基于排序目标的失败预测器（failure predictor）来估计未标注视频对基础VQA模型的难度；其次利用深度语义视频特征量化内容多样性，并采用贪心策略在有限标注预算下平衡难度与多样性。实验证明，仅用5%的目标域样本即可显著提升模型性能，表明该方法能有效识别出具有代表性和挑战性的样本用于主动微调，从而增强模型的适应性和泛化能力。
链接: https://arxiv.org/abs/2603.11525
作者: Jian Zou,Xiaoyu Xu,Zhihua Wang,Yilin Wang,Balu Adsumilli,Kede Ma
机构: City University of Hong Kong (香港城市大学); Google Inc. (谷歌)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Learning-based video quality assessment (VQA) has advanced rapidly, yet progress is increasingly constrained by a disconnect between model design and dataset curation. Model-centric approaches often iterate on fixed benchmarks, while data-centric efforts collect new human labels without systematically targeting the weaknesses of existing VQA models. Here, we describe MDS-VQA, a model-informed data selection mechanism for curating unlabeled videos that are both difficult for the base VQA model and diverse in content. Difficulty is estimated by a failure predictor trained with a ranking objective, and diversity is measured using deep semantic video features, with a greedy procedure balancing the two under a constrained labeling budget. Experiments across multiple VQA datasets and models demonstrate that MDS-VQA identifies diverse, challenging samples that are particularly informative for active fine-tuning. With only a 5% selected subset per target domain, the fine-tuned model improves mean SRCC from 0.651 to 0.722 and achieves the top gMAD rank, indicating strong adaptation and generalization.
[CV-98] EReCu: Pseudo-label Evolution Fusion and Refinement with Multi-Cue Learning for Unsupervised Camouflage Detection CVPR2026
【速读】：该论文旨在解决无监督伪装目标检测（Unsupervised Camouflaged Object Detection, UCOD）中因目标与背景高度相似以及伪标签噪声导致的细粒度纹理学习困难问题，同时克服现有方法在边界溢出和结构模糊方面的局限性。其解决方案的关键在于提出一个统一框架，通过三个核心模块协同优化：1）多线索原生感知模块（Multi-Cue Native Perception），融合低层纹理与中层语义线索以提取内在视觉先验，实现掩码与原始物体信息的精准对齐；2）伪标签演化融合机制（Pseudo-Label Evolution Fusion），借助教师-学生交互和深度可分离卷积进行高效语义去噪；3）谱张量注意力融合（Spectral Tensor Attention Fusion）与局部伪标签精修（Local Pseudo-Label Refinement），分别通过紧凑的谱聚合平衡语义与结构信息，并利用注意力多样性恢复细节纹理、提升边界保真度。该方案显著提升了模型在复杂伪装场景下的细节感知能力、边界对齐精度及泛化性能。
链接: https://arxiv.org/abs/2603.11521
作者: Shuo Jiang,Gaojia Zhang,Min Tan,Yufei Yin,Gang Pan
机构: Hangzhou Dianzi University (杭州电子科技大学); Zhejiang University (浙江大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by CVPR 2026
Abstract:Unsupervised Camouflaged Object Detection (UCOD) remains a challenging task due to the high intrinsic similarity between target objects and their surroundings, as well as the reliance on noisy pseudo-labels that hinder fine-grained texture learning. While existing refinement strategies aim to alleviate label noise, they often overlook intrinsic perceptual cues, leading to boundary overflow and structural ambiguity. In contrast, learning without pseudo-label guidance yields coarse features with significant detail loss. To address these issues, we propose a unified UCOD framework that enhances both the reliability of pseudo-labels and the fidelity of features. Our approach introduces the Multi-Cue Native Perception module, which extracts intrinsic visual priors by integrating low-level texture cues with mid-level semantics, enabling precise alignment between masks and native object information. Additionally, Pseudo-Label Evolution Fusion intelligently refines labels through teacher-student interaction and utilizes depthwise separable convolution for efficient semantic denoising. It also incorporates Spectral Tensor Attention Fusion to effectively balance semantic and structural information through compact spectral aggregation across multi-layer attention maps. Finally, Local Pseudo-Label Refinement plays a pivotal role in local detail optimization by leveraging attention diversity to restore fine textures and enhance boundary fidelity. Extensive experiments on multiple UCOD datasets demonstrate that our method achieves state-of-the-art performance, characterized by superior detail perception, robust boundary alignment, and strong generalization under complex camouflage scenarios.
[CV-99] FBCIR: Balancing Cross-Modal Focuses in Composed Image Retrieval
【速读】：该论文旨在解决组成图像检索（Composed Image Retrieval, CIR）模型在面对语义相近的负样本时性能显著下降的问题，其核心原因是多模态模型存在注意力焦点失衡（focus imbalance），即模型过度依赖某一模态（视觉或文本）而忽略另一模态的信息。解决方案的关键在于：首先提出一种名为FBCIR的多模态焦点解释方法，用于识别影响模型检索决策的关键视觉与文本组件；进而基于该分析设计了一种数据增强工作流，通过引入精心构造的硬负样本（hard negatives）来引导模型实现更平衡的跨模态推理。实验表明，该方法在提升复杂场景下模型鲁棒性的同时，不损害其在标准基准上的表现。
链接: https://arxiv.org/abs/2603.11520
作者: Chenchen Zhao,Jianhuan Zhuo,Muxi Chen,Zhaohua Zhang,Wenyu Jiang,Tianwen Jiang,Qiuyong Xiao,Jihong Zhang,Qiang Xu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 20 pages, 5 figures, 15 tables
Abstract:Composed image retrieval (CIR) requires multi-modal models to jointly reason over visual content and semantic modifications presented in text-image input pairs. While current CIR models achieve strong performance on common benchmark cases, their accuracies often degrades in more challenging scenarios where negative candidates are semantically aligned with the query image or text. In this paper, we attribute this degradation to focus imbalances, where models disproportionately attend to one modality while neglecting the other. To validate this claim, we propose FBCIR, a multi-modal focus interpretation method that identifies the most crucial visual and textual input components to a model’s retrieval decisions. Using FBCIR, we report that focus imbalances are prevalent in existing CIR models, especially under hard negative settings. Building on the analyses, we further propose a CIR data augmentation workflow that facilitates existing CIR datasets with curated hard negatives designed to encourage balanced cross-modal reasoning. Extensive experiments across multiple CIR models demonstrate that the proposed augmentation consistently improves performance in challenging cases, while maintaining their capabilities on standard benchmarks. Together, our interpretation method and data augmentation workflow provide a new perspective on CIR model diagnosis and robustness improvements.
[CV-100] Manifold-Optimal Guidance: A Unified Riemannian Control View of Diffusion Guidance
【速读】：该论文旨在解决Classifier-Free Guidance (CFG) 在条件扩散模型中因高指导尺度导致的过饱和、纹理伪影和结构坍塌问题。作者指出，这一失败根源在于标准CFG在环境空间中执行欧几里得外推，无意中使采样轨迹偏离高密度数据流形（data manifold）。解决方案的关键在于提出流形最优引导（Manifold-Optimal Guidance, MOG），将引导重构为局部最优控制问题，并引入一种几何感知的黎曼更新公式，从而纠正流形外漂移（off-manifold drift），且无需重新训练模型。进一步地，作者提出Auto-MOG，一种动态能量平衡调度机制，自适应校准引导强度，有效消除手动超参数调优的需求。
链接: https://arxiv.org/abs/2603.11509
作者: Zexi Jia,Pengcheng Luo,Zhengyao Fang,Jinchao Zhang,Jie Zhou
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Classifier-Free Guidance (CFG) serves as the de facto control mechanism for conditional diffusion, yet high guidance scales notoriously induce oversaturation, texture artifacts, and structural collapse. We attribute this failure to a geometric mismatch: standard CFG performs Euclidean extrapolation in ambient space, inadvertently driving sampling trajectories off the high-density data manifold. To resolve this, we present Manifold-Optimal Guidance (MOG), a framework that reformulates guidance as a local optimal control problem. MOG yields a closed-form, geometry-aware Riemannian update that corrects off-manifold drift without requiring retraining. Leveraging this perspective, we further introduce Auto-MOG, a dynamic energy-balancing schedule that adaptively calibrates guidance strength, effectively eliminating the need for manual hyperparameter tuning. Extensive validation demonstrates that MOG yields superior fidelity and alignment compared to baselines, with virtually no added computational overhead.
[CV-101] Gen-Fab: A Variation-Aware Generative Model for Predicting Fabrication Variations in Nanophotonic Devices
【速读】：该论文旨在解决硅光子器件在制造过程中因过刻蚀、欠刻蚀和角部圆化等非均匀工艺偏差导致的性能波动问题，这些问题会显著影响器件功能并限制设计可靠性。为实现对制造结果不确定性的精准建模，论文提出Gen-Fab——一种基于Pix2Pix架构的条件生成对抗网络（conditional Generative Adversarial Network, cGAN），其关键在于通过在模型瓶颈处注入潜在噪声向量（latent noise vector）实现从单一设计布局（GDS格式输入）到多样高分辨率预测图像（类似扫描电子显微镜SEM图像）的一对多映射，从而捕捉纳米尺度上的工艺变异范围。该方法不仅提升了预测准确性（IoU达89.8%），还更准确地拟合真实制造结果分布，在多个评估指标上优于确定性U-Net、蒙特卡洛Dropout U-Net及集成U-Net等基线模型，展现出强大的泛化能力。
链接: https://arxiv.org/abs/2603.11505
作者: Rambod Azimi,Yuri Grinberg,Dan-Xia Xu,Odile Liboiron-Ladouceur
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted and published in Structural and Multidisciplinary Optimization (2026)
Abstract:Silicon photonic devices often exhibit fabrication-induced variations such as over-etching, underetching, and corner rounding, which can significantly alter device performance. These variations are non-uniform and are influenced by feature size and shape. Accurate digital twins are therefore needed to predict the range of possible fabricated outcomes for a given design. In this paper, we introduce Gen-Fab, a conditional generative adversarial network (cGAN) based on Pix2Pix to predict and model uncertainty in photonic fabrication outcomes. The proposed method takes a design layout (in GDS format) as input and produces diverse high-resolution predictions similar to scanning electron microscope (SEM) images of fabricated devices, capturing the range of process variations at the nanometer scale. To enable one-to-many mapping, we inject a latent noise vector at the model bottleneck. We compare Gen-Fab against three baselines: (1) a deterministic U-Net predictor, (2) an inference-time Monte Carlo Dropout U-Net, and (3) an ensemble of varied U-Nets. Evaluations on an out-of-distribution dataset of fabricated photonic test structures demonstrate that Gen-Fab outperforms all baselines in both accuracy and uncertainty modeling. An additional distribution shift analysis further confirms its strong generalization to unseen fabrication geometries. Gen-Fab achieves the highest intersection-over-union (IoU) score of 89.8%, outperforming the deterministic U-Net (85.3%), the MC-Dropout U-Net (83.4%), and varying U-Nets (85.8%). It also better aligns with the distribution of real fabrication outcomes, achieving lower Kullback-Leibler divergence and Wasserstein distance.
[CV-102] ActiveFreq: Integrating Active Learning and Frequency Domain Analysis for Interactive Segmentation
【速读】：该论文旨在解决当前交互式医学图像分割方法中两个核心问题：一是现有方法未能有效利用用户交互输入中的知识，通常对所有误标区域一视同仁并随机选择进行修正，忽略了不同区域对整体分割质量提升的潜在价值；二是多数模型仅依赖空间域特征，忽视了频率域信息在增强特征表达能力方面的潜力。解决方案的关键在于提出一种名为ActiveFreq的新框架，其核心创新包括：（1）AcSelect模块，基于主动学习策略自动识别最具信息量的误标区域，从而以最少的人工干预实现最大性能增益；（2）FreqFormer骨干网络，引入傅里叶变换模块将特征从空间域映射至频率域，实现更丰富的多尺度特征提取。实验表明，该方法在ISIC-2017和OAI-ZIB数据集上显著优于现有最优结果，且在极低交互次数下仍保持高精度。
链接: https://arxiv.org/abs/2603.11498
作者: Lijun Guo,Qian Zhou,Zidi Shi,Hua Zou,Gang Ke
机构: 武汉大学(Whuhan University); Wuhan University (武汉大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 8 figures, published in Knowledge-Based Systems
Abstract:Interactive segmentation is commonly used in medical image analysis to obtain precise, pixel-level labeling, typically involving iterative user input to correct mislabeled regions. However, existing approaches often fail to fully utilize user knowledge from interactive inputs and achieve comprehensive feature extraction. Specifically, these methods tend to treat all mislabeled regions equally, selecting them randomly for refinement without evaluating each region’s potential impact on segmentation quality. Additionally, most models rely solely on spatial domain features, overlooking frequency domain information that could enhance feature extraction and improve performance. To address these limitations, we propose ActiveFreq, a novel interactive segmentation framework that integrates active learning and frequency domain analysis to minimize human intervention while achieving high-quality labeling. ActiveFreq introduces AcSelect, an autonomous module that prioritizes the most informative mislabeled regions, ensuring maximum performance gain from each click. Moreover, we develop FreqFormer, a segmentation backbone incorporating a Fourier transform module to map features from the spatial to the frequency domain, enabling richer feature extraction. Evaluations on the ISIC-2017 and OAI-ZIB datasets demonstrate that ActiveFreq achieves high performance with reduced user interaction, achieving 3.74 NoC@90 on ISIC-2017 and 9.27 NoC@90 on OAI-ZIB, with 23.5% and 12.8% improvements over previous best results, respectively. Under minimal input conditions, such as two clicks, ActiveFreq reaches mIoU scores of 85.29% and 75.76% on ISIC-2017 and OAI-ZIB, highlighting its efficiency and accuracy in interactive medical segmentation.
[CV-103] OrthoEraser: Coupled-Neuron Orthogonal Projection for Concept Erasure
【速读】：该论文旨在解决文本到图像（Text-to-Image, T2I）生成模型中存在的对抗诱导安全风险问题，特别是现有概念擦除方法在抑制特定神经元时会误伤良性语义特征的问题。这一现象的根本原因在于敏感语义与良性语义在激活子空间中呈现非正交叠加，导致其向量相互纠缠。解决方案的关键在于提出OrthoEraser框架，其核心创新是利用稀疏自编码器（Sparse Autoencoder, SAE）实现高分辨率特征解耦，并将擦除操作重新定义为一种分析性的正交化投影策略——通过梯度正交化机制，将擦除向量投影至耦合神经元的零空间，从而在不破坏良性语义流形的前提下，精准分离并移除敏感概念。
链接: https://arxiv.org/abs/2603.11493
作者: Chuancheng Shi,Wenhua Wu,Fei Shen,Xiaogang Zhu,Kun Hu,Zhiyong Wang
机构: University of Sydney (悉尼大学); National University of Singapore (新加坡国立大学); Adelaide University (阿德莱德大学); Edith Cowan University (埃迪斯科文大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Text-to-image (T2I) models face significant safety risks from adversarial induction, yet current concept erasure methods often cause collateral damage to benign attributes when suppressing selected neurons entirely. This occurs because sensitive and benign semantics exhibit non-orthogonal superposition, sharing activation subspaces where their respective vectors are inherently entangled. To address this issue, we propose OrthoEraser, which leverages sparse autoencoders (SAE) to achieve high-resolution feature disentanglement and subsequently redefines erasure as an analytical orthogonalization projection that preserves the benign manifold’s invariance. OrthoEraser first employs SAE to decompose dense activations and segregate sensitive neurons. It then uses coupled neuron detection to identify non-sensitive features vulnerable to intervention. The key novelty lies in an analytical gradient orthogonalization strategy that projects erasure vectors onto the null space of the coupled neurons. This orthogonally decouples the sensitive concepts from the identified critical benign subspace, effectively preserving non-sensitive semantics. Experimental results on safety demonstrate that OrthoEraser achieves high erasure precision, effectively removing harmful content while preserving the integrity of the generative manifold, and significantly outperforming SOTA baselines. This paper contains results of unsafe models.
[CV-104] SPEGC: Continual Test-Time Adaptation via Semantic-Prompt-Enhanced Graph Clustering for Medical Image Segmentation CVPR2026
【速读】：该论文旨在解决医学图像分割任务中因训练与测试数据采集差异导致的领域偏移（domain gap）问题，该问题严重阻碍了预训练模型在临床实践中的部署。现有持续测试时自适应（Continual Test-Time Adaptation, CTTA）方法常依赖不可靠的监督信号，易引发错误累积的自我强化循环，最终造成性能灾难性下降。其解决方案的关键在于提出一种基于语义提示增强图聚类（Semantic-Prompt-Enhanced Graph Clustering, SPEGC）的框架：首先设计语义提示特征增强机制，通过解耦的共性与异质提示池将全局上下文信息注入局部特征，降低噪声干扰；其次构建可微分图聚类求解器，将全局边稀疏化建模为最优传输问题，端到端地从原始相似矩阵中提取高阶结构表示；最终利用该鲁棒结构引导模型自适应，确保簇级预测一致性并动态调整决策边界，从而实现稳定且高效的持续域适应。
链接: https://arxiv.org/abs/2603.11492
作者: Xiaogang Du,Jiawei Zhang,Tongfei Liu,Tao Lei,Yingbo Wang
机构: Shaanxi Joint Laboratory of Artificial Intelligence, Shaanxi University of Science and Technology (陕西省人工智能联合实验室，陕西科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to CVPR 2026. 16 pages, 7 figures
Abstract:In medical image segmentation tasks, the domain gap caused by the difference in data collection between training and testing data seriously hinders the deployment of pre-trained models in clinical practice. Continual Test-Time Adaptation (CTTA) aims to enable pre-trained models to adapt to continuously changing unlabeled domains, providing an effective approach to solving this problem. However, existing CTTA methods often rely on unreliable supervisory signals, igniting a self-reinforcing cycle of error accumulation that culminates in catastrophic performance degradation. To overcome these challenges, we propose a CTTA via Semantic-Prompt-Enhanced Graph Clustering (SPEGC) for medical image segmentation. First, we design a semantic prompt feature enhancement mechanism that utilizes decoupled commonality and heterogeneity prompt pools to inject global contextual information into local features, alleviating their susceptibility to noise interference under domain shift. Second, based on these enhanced features, we design a differentiable graph clustering solver. This solver reframes global edge sparsification as an optimal transport problem, allowing it to distill a raw similarity matrix into a refined and high-order structural representation in an end-to-end manner. Finally, this robust structural representation is used to guide model adaptation, ensuring predictions are consistent at a cluster-level and dynamically adjusting decision boundaries. Extensive experiments demonstrate that SPEGC outperforms other state-of-the-art CTTA methods on two medical image segmentation benchmarks. The source code is available at this https URL.
[CV-105] INFACT: A Diagnostic Benchmark for Induced Faithfulness and Factuality Hallucinations in Video-LLM s
【速读】：该论文旨在解决视频大语言模型（Video-LLMs）中存在的幻觉问题，特别是与忠实性（faithfulness，即输出是否符合视频内容）和事实正确性（factuality，即是否符合可验证的世界知识）相关的幻觉。现有基准测试在事实性幻觉覆盖上不足，且主要在干净环境下评估模型可靠性，难以全面反映模型在复杂场景下的鲁棒性。解决方案的关键在于提出一个诊断性基准 \textscINFACT，包含9,800个问答实例，并对忠实性和事实性进行细粒度分类；同时设计四种评估模式（Base、视觉退化、证据污染、时间干预），并通过抗干扰率（Resist Rate, RR）和时间敏感性评分（Temporal Sensitivity Score, TSS）量化模型在不同扰动下的稳定性。实验表明，高基线准确率不能保证高可靠性，尤其证据污染和时间干预会显著降低模型性能，揭示了当前模型在顺序敏感任务中的时间惯性问题。
链接: https://arxiv.org/abs/2603.11481
作者: Junqi Yang,Yuecong Min,Jie Zhang,Shiguang Shan,Xilin Chen
机构: Chinese Academy of Sciences (中国科学院); UCAS (中国科学院大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Despite rapid progress, Video Large Language Models (Video-LLMs) remain unreliable due to hallucinations, which are outputs that contradict either video evidence (faithfulness) or verifiable world knowledge (factuality). Existing benchmarks provide limited coverage of factuality hallucinations and predominantly evaluate models only in clean settings. We introduce \textscINFACT, a diagnostic benchmark comprising 9,800 QA instances with fine-grained taxonomies for faithfulness and factuality, spanning real and synthetic videos. \textscINFACT evaluates models in four modes: Base (clean), Visual Degradation, Evidence Corruption, and Temporal Intervention for order-sensitive items. Reliability under induced modes is quantified using Resist Rate (RR) and Temporal Sensitivity Score (TSS). Experiments on 14 representative Video-LLMs reveal that higher Base-mode accuracy does not reliably translate to higher reliability in the induced modes, with evidence corruption reducing stability and temporal intervention yielding the largest degradation. Notably, many open-source baselines exhibit near-zero TSS on factuality, indicating pronounced temporal inertia on order-sensitive questions.
[CV-106] Follow the Saliency: Supervised Saliency for Retrieval-augmented Dense Video Captioning CVPR2026
【速读】：该论文旨在解决密集视频字幕（Dense Video Captioning, DVC）中现有检索增强方法因依赖启发式策略而难以实现与真实事件边界对齐的时序分割问题。解决方案的关键在于提出一个名为STaRC的框架，其核心创新是通过一个基于DVC标注直接生成二值标签的亮点检测模块（highlight detection module），监督帧级显著性（saliency），并将显著性得分作为统一的时序信号：一方面用于引导显著性约束的分割以实现时序连贯且对齐事件边界的片段划分，另一方面通过显式注入解码器的显著性提示（Saliency Prompts）来指导字幕生成，从而提升检索准确性和上下文相关的字幕质量。
链接: https://arxiv.org/abs/2603.11460
作者: Seung hee Choi,MinJu Jeon,Hyunwoo Oh,Jihwan Lee,Dong-Jin Kim
机构: Hanyang University (汉阳大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR 2026 accepted paper (main track)
Abstract:Existing retrieval-augmented approaches for Dense Video Captioning (DVC) often fail to achieve accurate temporal segmentation aligned with true event boundaries, as they rely on heuristic strategies that overlook ground truth event boundaries. The proposed framework, \textbfSTaRC, overcomes this limitation by supervising frame-level saliency through a highlight detection module. Note that the highlight detection module is trained on binary labels derived directly from DVC ground truth annotations without the need for additional annotation. We also propose to utilize the saliency scores as a unified temporal signal that drives retrieval via saliency-guided segmentation and informs caption generation through explicit Saliency Prompts injected into the decoder. By enforcing saliency-constrained segmentation, our method produces temporally coherent segments that align closely with actual event transitions, leading to more accurate retrieval and contextually grounded caption generation. We conduct comprehensive evaluations on the YouCook2 and ViTT benchmarks, where STaRC achieves state-of-the-art performance across most of the metrics. Our code is available at this https URL
[CV-107] GPT 4o-Receipt: A Dataset and Human Study for AI-Generated Document Forensics
【速读】：该论文旨在解决“人类是否能够比机器更有效地识别由生成式 AI（Generative AI）伪造的财务文档”这一关键问题。其解决方案的核心在于构建了一个名为 GPT4o-Receipt 的基准数据集，包含 1,235 张配对的收据图像（由 GPT-4o 生成与真实收据），并通过五种先进的多模态大语言模型（Multimodal Large Language Models, MLLMs）和众包感知评估进行系统评测。研究发现一个显著悖论：尽管人类在视觉辨别上表现最优，但其二分类检测 F1 分数却低于部分 LLMs，原因在于 AI 伪造的主要痕迹是难以被肉眼察觉的算术错误——这些错误可被 LLMs 快速验证，从而揭示了人类感知能力与机器验证能力之间的根本差异。此发现表明，单纯依赖准确率不足以评估检测器性能，需结合机制理解与校准分析。
链接: https://arxiv.org/abs/2603.11442
作者: Yan Zhang,Simiao Ren,Ankit Raj,En Wei,Dennis Ng,Alex Shen,Jiayue Xu,Yuxin Zhang,Evelyn Marotta
机构: 未知
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 7 figures, 7 tables
Abstract:Can humans detect AI-generated financial documents better than machines? We present GPT4o-Receipt, a benchmark of 1,235 receipt images pairing GPT-4o-generated receipts with authentic ones from established datasets, evaluated by five state-of-the-art multimodal LLMs and a 30-annotator crowdsourced perceptual study. Our findings reveal a striking paradox: humans are better at seeing AI artifacts, yet worse at detecting AI documents. Human annotators exhibit the largest visual discrimination gap of any evaluator, yet their binary detection F1 falls well below Claude Sonnet 4 and below Gemini 2.5 Flash. This paradox resolves once the mechanism is understood: the dominant forensic signals in AI-generated receipts are arithmetic errors – invisible to visual inspection but systematically verifiable by LLMs. Humans cannot perceive that a subtotal is incorrect; LLMs verify it in milliseconds. Beyond the human–LLM comparison, our five-model evaluation reveals dramatic performance disparities and calibration differences that render simple accuracy metrics insufficient for detector selection. GPT4o-Receipt, the evaluation framework, and all results are released publicly to support future research in AI document forensics.
[CV-108] Detect Anything in Real Time: From Single-Prompt Segmentation to Multi-Class Detection
【速读】：该论文旨在解决当前生成式视觉-语言模型（如SAM3）在多类别目标检测中效率低下的问题：由于其每次前向传播仅处理一个文本提示，检测N个类别需执行N次独立推理，导致计算开销随类别数线性增长（O(N)），尤其受限于439M参数视觉骨干网络的重复计算。解决方案的关键在于利用视觉骨干网络的类别无关特性（class-agnostic property），即其输出的图像特征不依赖于文本提示，从而实现跨类别的特征共享，将骨干网络计算复杂度从O(N)降至O(1)；结合批量多类别解码、仅检测推理优化及TensorRT FP16部署，显著提升实时性能，在保持模型权重不变的前提下实现最高25倍的速度提升，且在COCO数据集上达到优于专用开放词汇检测器的精度表现。
链接: https://arxiv.org/abs/2603.11441
作者: Mehmet Kerem Turkcan
机构: Columbia University (哥伦比亚大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advances in vision-language modeling have produced promptable detection and segmentation systems that accept arbitrary natural language queries at inference time. Among these, SAM3 achieves state-of-the-art accuracy by combining a ViT-H/14 backbone with cross-modal transformer decoding and learned object queries. However, SAM3 processes a single text prompt per forward pass. Detecting N categories requires N independent executions, each dominated by the 439M-parameter backbone. We present Detect Anything in Real Time (DART), a training-free framework that converts SAM3 into a real-time multi-class detector by exploiting a structural invariant: the visual backbone is class-agnostic, producing image features independent of the text prompt. This allows the backbone computation to be shared between all classes, reducing its cost from O(N) to O(1). Combined with batched multi-class decoding, detection-only inference, and TensorRT FP16 deployment, these optimizations yield 5.6x cumulative speedup at 3 classes, scaling to 25x at 80 classes, without modifying any model weight. On COCO val2017 (5,000 images, 80 classes), DART achieves 55.8 AP at 15.8 FPS (4 classes, 1008x1008) on a single RTX 4080, surpassing purpose-built open-vocabulary detectors trained on millions of box annotations. For extreme latency targets, adapter distillation with a frozen encoder-decoder achieves 38.7 AP with a 13.9 ms backbone. Code and models are available at this https URL.
[CV-109] Stay in your Lane: Role Specific Queries with Overlap Suppression Loss for Dense Video Captioning CVPR2026
【速读】：该论文旨在解决密集视频字幕（Dense Video Captioning, DVC）任务中因共享查询导致的多任务干扰以及定位过程中的时间冗余问题。其核心解决方案是引入角色特异性查询（role-specific queries），将定位与字幕生成解耦为独立模块，使每个模块专注于自身任务；同时通过对比对齐（contrastive alignment）机制确保对应输出的语义一致性，并设计一种新颖的抑制机制，对查询间的时间重叠进行惩罚，从而引导模型学习互不重叠的事件区域以提升定位精度；此外，还引入轻量级概念捕捉模块，利用概念层级表示增强字幕的语义丰富性。
链接: https://arxiv.org/abs/2603.11439
作者: Seung Hyup Baek,Jimin Lee,Hyeongkeun Lee,Jae Won Cho
机构: Konkuk University (国民大学); Sejong University (世宗大学); KAIST (韩国科学技术院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026
Abstract:Dense Video Captioning (DVC) is a challenging multimodal task that involves temporally localizing multiple events within a video and describing them with natural language. While query-based frameworks enable the simultaneous, end-to-end processing of localization and captioning, their reliance on shared queries often leads to significant multi-task interference between the two tasks, as well as temporal redundancy in localization. In this paper, we propose utilizing role-specific queries that separate localization and captioning into independent components, allowing each to exclusively learn its role. We then employ contrastive alignment to enforce semantic consistency between the corresponding outputs, ensuring coherent behavior across the separated queries. Furthermore, we design a novel suppression mechanism in which mutual temporal overlaps across queries are penalized to tackle temporal redundancy, supervising the model to learn distinct, non-overlapping event regions for more precise localization. Additionally, we introduce a lightweight module that captures core event concepts to further enhance semantic richness in captions through concept-level representations. We demonstrate the effectiveness of our method through extensive experiments on major DVC benchmarks YouCook2 and ActivityNet Captions.
[CV-110] Beyond Single-Sample: Reliable Multi-Sample Distillation for Video Understanding
【速读】：该论文旨在解决传统黑盒蒸馏方法在大型视觉语言模型（Large Vision-Language Models, LVLMs）训练中因依赖单个教师输出而导致的响应方差大、多模态或时序场景下格式不一致的问题。其解决方案的关键在于提出R-MSD（Reliable Multi-Sample Distillation）框架，通过显式建模教师采样方差来增强蒸馏稳定性：该框架利用任务自适应的教师池提供针对性强的监督信号，并结合质量感知的信号匹配与对抗性蒸馏目标，在过滤教师噪声的同时最大化知识迁移效率。
链接: https://arxiv.org/abs/2603.11423
作者: Songlin Li,Xin Zhu,Zechao Guan,Peipeng Chen,Jian Yao
机构: University of Electronic Science and Technology of China (电子科技大学); Robotics Center, XPeng Motors (小鹏汽车机器人中心)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Traditional black-box distillation for Large Vision-Language Models (LVLMs) typically relies on a single teacher response per input, which often yields high-variance responses and format inconsistencies in multimodal or temporal scenarios. To mitigate this unreliable supervision, we propose R-MSD (Reliable Multi-Sample Distillation), a framework that explicitly models teacher sampling variance to enhance distillation stability. Rather than relying on a single teacher response, our approach leverages a task-adaptive teacher pool to provide robust supervision tailored to both closed-ended and open-ended reasoning. By integrating quality-aware signal matching with an adversarial distillation objective, our approach effectively filters teacher noise while maximizing knowledge transfer. Extensive evaluations across comprehensive video understanding benchmarks demonstrate that R-MSD consistently outperforms single sample distillation methods. We additionally include an original SFT+RL 4B baseline under the same training budget, which shows only marginal gains, while our method achieves significant improvements. With a 4B student model, our approach delivers gains on VideoMME (+1.5%), Video-MMMU (+3.2%), and MathVerse (+3.6%).
[CV-111] ShotVerse: Advancing Cinematic Camera Control for Text-Driven Multi-Shot Video Creation
【速读】：该论文旨在解决文本驱动视频生成中电影多镜头场景下摄像机控制的难题，即隐式文本提示缺乏精确性，而显式轨迹条件化又带来高昂的人工成本且常导致模型执行失败。解决方案的关键在于提出一种以数据为中心的范式转变，认为对齐的（字幕、轨迹、视频）三元组构成内在联合分布，从而连接自动化分镜规划与精准执行；为此构建了“先规划后控制”框架 ShotVerse，其中基于视觉-语言模型（VLM）的 Planner 利用空间先验从文本中获取具有电影美学的全局一致轨迹，Controller 通过相机适配器将轨迹渲染为多镜头视频内容，并辅以自动化的多镜头摄像机校准流水线，实现离散单镜头轨迹到统一全局坐标系的对齐，最终形成高质量影视数据集 ShotVerse-Bench 及三轨评估协议，显著提升视频生成的摄像机准确性与跨镜头一致性。
链接: https://arxiv.org/abs/2603.11421
作者: Songlin Yang,Zhe Wang,Xuyi Yang,Songchun Zhang,Xianghao Kong,Taiyi Wu,Xiaotong Zhao,Ran Zhang,Alan Zhao,Anyi Rao
机构: MMLab@HKUST, The Hong Kong University of Science and Technology; Tencent Video AI Center, PCG, Tencent
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-driven video generation has democratized film creation, but camera control in cinematic multi-shot scenarios remains a significant block. Implicit textual prompts lack precision, while explicit trajectory conditioning imposes prohibitive manual overhead and often triggers execution failures in current models. To overcome this bottleneck, we propose a data-centric paradigm shift, positing that aligned (Caption, Trajectory, Video) triplets form an inherent joint distribution that can connect automated plotting and precise execution. Guided by this insight, we present ShotVerse, a “Plan-then-Control” framework that decouples generation into two collaborative agents: a VLM (Vision-Language Model)-based Planner that leverages spatial priors to obtain cinematic, globally aligned trajectories from text, and a Controller that renders these trajectories into multi-shot video content via a camera adapter. Central to our approach is the construction of a data foundation: we design an automated multi-shot camera calibration pipeline aligns disjoint single-shot trajectories into a unified global coordinate system. This facilitates the curation of ShotVerse-Bench, a high-fidelity cinematic dataset with a three-track evaluation protocol that serves as the bedrock for our framework. Extensive experiments demonstrate that ShotVerse effectively bridges the gap between unreliable textual control and labor-intensive manual plotting, achieving superior cinematic aesthetics and generating multi-shot videos that are both camera-accurate and cross-shot consistent.
[CV-112] Zero-Shot Cross-City Generalization in End-to-End Autonomous Driving: Self-Supervised versus Supervised Representations
【速读】：该论文旨在解决端到端自动驾驶模型在跨城市场景下泛化能力不足的问题，尤其是当训练与测试数据来自不同地理区域时，模型可能依赖于城市特定的视觉线索（如道路拓扑结构或驾驶习惯），从而在真实域偏移场景中表现出显著性能下降。其解决方案的关键在于引入自监督视觉表示学习（self-supervised visual representations），通过在规划框架中集成I-JEPA、DINOv2和MAE等自监督骨干网络，提升模型对未见城市的零样本迁移能力。实验表明，相较于传统基于ImageNet预训练的监督式骨干网络，自监督预训练能显著缩小跨城市间的性能差距，尤其在从右侧行驶环境向左侧行驶环境迁移时效果更为明显，验证了表征学习对提升自动驾驶系统鲁棒性的关键作用。
链接: https://arxiv.org/abs/2603.11417
作者: Fatemeh Naeinian,Ali Hamza,Haoran Zhu,Anna Choromanska
机构: NYU Tandon School of Engineering (纽约大学坦登工程学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:End-to-end autonomous driving models are typically trained on multi-city datasets using supervised ImageNet-pretrained backbones, yet their ability to generalize to unseen cities remains largely unexamined. When training and evaluation data are geographically mixed, models may implicitly rely on city-specific cues, masking failure modes that would occur under real domain shifts when generalizing to new locations. In this work we investigate zero-shot cross-city generalization in end-to-end trajectory planning and ask whether self-supervised visual representations improve transfer across cities. We conduct a comprehensive study by integrating self-supervised backbones (I-JEPA, DINOv2, and MAE) into planning frameworks. We evaluate performance under strict geographic splits on nuScenes in the open-loop setting and on NAVSIM in the closed-loop evaluation protocol. Our experiments reveal a substantial generalization gap when transferring models relying on traditional supervised backbones across cities with different road topologies and driving conventions, particularly when transferring from right-side to left-side driving environments. Self-supervised representation learning reduces this gap. In open-loop evaluation, a supervised backbone exhibits severe inflation when transferring from Boston to Singapore (L2 displacement ratio 9.77x, collision ratio 19.43x), whereas domain-specific self-supervised pretraining reduces this to 1.20x and 0.75x respectively. In closed-loop evaluation, self-supervised pretraining improves PDMS by up to 4 percent for all single-city training cities. These results show that representation learning strongly influences the robustness of cross-city planning and establish zero-shot geographic transfer as a necessary test for evaluating end-to-end autonomous driving systems.
[CV-113] Seeing Isnt Orienting: A Cognitively Grounded Benchmark Reveals Systematic Orientation Failures in MLLM s Supplementary
【速读】：该论文旨在解决当前视觉-语言模型在对象方向理解（object orientation understanding）方面的严重不足，尤其是现有基准测试将方向与位置、场景理解混杂，导致模型对几何方向推理能力的评估被掩盖。其解决方案的关键在于提出一个认知基础的分层评估框架——判别式方向推理智能（Discriminative Orientation Reasoning Intelligence, DORI），该框架将对象方向细分为四个维度，并在粗粒度（分类级）和细粒度（度量级）两个层次上独立评估，通过边界框隔离、标准化空间参考系和结构化提示等手段，有效排除物体识别难度、场景杂乱和语言歧义等干扰因素。这一设计揭示了当前多模态模型在对象中心方向任务上的显著性能瓶颈，表明其依赖分类启发式而非几何推理，从而指明了方向理解作为多模态系统尚未解决的核心挑战。
链接: https://arxiv.org/abs/2603.11410
作者: Nazia Tasnim,Keanu Nichols,Yuting Yang,Nicholas Ikechukwu,Elva Zou,Deepti Ghadiyaram,Bryan A. Plummer
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Humans learn object orientation progressively, from recognizing which way an object faces, to mentally rotating it, to reasoning about orientations between objects. Current vision-language benchmarks largely conflate orientation with position and general scene understanding. We introduce Discriminative Orientation Reasoning Intelligence (DORI), a cognitively grounded hierarchical benchmark that makes object orientation the primary target. Inspired by stages of human orientation cognition, DORI decomposes orientation into four dimensions, each evaluated at coarse (categorical) and granular (metric) levels. Composed from 13,652 images across 14 sources, DORI provides 33,656 multiple-choice questions covering 67 object categories in real-world and synthetic settings. Its coarse-to-granular design isolates orientation from confounds such as object recognition difficulty, scene clutter, and linguistic ambiguity via bounding-box isolation, standardized spatial reference frames, and structured prompts. Evaluating 24 state-of-the-art vision-language models shows a clear pattern: models that perform well on general spatial benchmarks are near-random on object-centric orientation tasks. The best models reach only 54.2% on coarse and 45.0% on granular judgments, with largest failures on compound rotations and shifts in inter-object reference frames. Large coarse-to-granular gaps reveal reliance on categorical heuristics rather than geometric reasoning, a limitation hidden by existing benchmarks. These results identify orientation understanding as an unsolved challenge for multimodal systems, with implications for robotic manipulation, 3D scene reconstruction, and human-AI interaction.
[CV-114] Real-time Rendering-based Surgical Instrument Tracking via Evolutionary Optimization
【速读】：该论文旨在解决机器人辅助微创手术中手术器械的精确且高效跟踪问题，尤其针对因部分遮挡和专用关节结构导致的视觉质量下降与数据稀缺性带来的特征检测不可靠性。其解决方案的关键在于将协方差矩阵自适应进化策略（CMA-ES）引入一个通用的跟踪流程中，联合估计手术器械位姿与关节配置，并通过批量渲染并行评估多个位姿候选解，显著降低推理时间并提升收敛鲁棒性。该方法进一步可扩展至无关节角度约束和双臂协同跟踪场景，适用于视觉反馈控制与在线手术视频标定。
链接: https://arxiv.org/abs/2603.11404
作者: Hanyang Hu,Zekai Liang,Florian Richter,Michael C. Yip
机构: University of California San Diego (加州大学圣地亚哥分校)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate and efficient tracking of surgical instruments is fundamental for Robot-Assisted Minimally Invasive Surgery. Although vision-based robot pose estimation has enabled markerless calibration without tedious physical setups, reliable tool tracking for surgical robots still remains challenging due to partial visibility and specialized articulation design of surgical instruments. Previous works in the field are usually prone to unreliable feature detections under degraded visual quality and data scarcity, whereas rendering-based methods often struggle with computational costs and suboptimal convergence. In this work, we incorporate CMA-ES, an evolutionary optimization strategy, into a versatile tracking pipeline that jointly estimates surgical instrument pose and joint configurations. Using batch rendering to efficiently evaluate multiple pose candidates in parallel, the method significantly reduces inference time and improves convergence robustness. The proposed framework further generalizes to joint angle-free and bi-manual tracking settings, making it suitable for both vision feedback control and online surgery video calibration. Extensive experiments on synthetic and real-world datasets demonstrate that the proposed method significantly outperforms prior approaches in both accuracy and runtime.
[CV-115] DeepHistoViT: An Interpretable Vision Transformer Framework for Histopathological Cancer Classification
【速读】：该论文旨在解决传统人工病理切片分析中存在的效率低、劳动强度大及诊断结果受主观因素影响的问题，从而提升癌症诊断的自动化与一致性。其核心解决方案是提出一种基于Transformer架构的深度学习模型DeepHistoViT，该模型通过定制化的视觉Transformer（Vision Transformer）结构结合集成注意力机制，有效捕捉组织切片中细粒度的细胞结构特征，并借助注意力定位实现对诊断相关区域的可解释性分析，显著提升了分类性能与临床实用性。
链接: https://arxiv.org/abs/2603.11403
作者: Ravi Mosalpuri,Mohammed Abdelsamea,Ahmed Karam Eldaly
机构: University of Exeter (埃克塞特大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Histopathology remains the gold standard for cancer diagnosis because it provides detailed cellular-level assessment of tissue morphology. However, manual histopathological examination is time-consuming, labour-intensive, and subject to inter-observer variability, creating a demand for reliable computer-assisted diagnostic tools. Recent advances in deep learning, particularly transformer-based architectures, have shown strong potential for modelling complex spatial dependencies in medical images. In this work, we propose DeepHistoViT, a transformer-based framework for automated classification of histopathological images. The model employs a customized Vision Transformer architecture with an integrated attention mechanism designed to capture fine-grained cellular structures while improving interpretability through attention-based localization of diagnostically relevant regions. The framework is evaluated on three publicly available histopathology datasets covering lung cancer, colon cancer, and acute lymphoblastic leukaemia. Experimental results demonstrate state-of-the-art performance across all datasets, with classification accuracy, precision, recall, F1-score, and ROC-AUC reaching 100 percent on the lung and colon cancer datasets, and 99.85 percent, 99.84 percent, 99.86 percent, 99.85 percent, and 99.99 percent respectively on the acute lymphoblastic leukaemia dataset. All performance metrics are reported with 95 percent confidence intervals. These results highlight the effectiveness of transformer-based architectures for histopathological image analysis and demonstrate the potential of DeepHistoViT as an interpretable computer-assisted diagnostic tool to support pathologists in clinical decision-making.
[CV-116] Harnessing Data Asymmetry: Manifold Learning in the Finsler World
【速读】：该论文旨在解决传统流形学习方法在处理高维数据时因依赖对称黎曼几何（Riemannian geometry）而导致的不对称信息丢失问题，这种对称性假设强制要求嵌入空间为欧几里得空间（Euclidean），从而忽略了数据样本分布非均匀性所蕴含的宝贵不对称结构。解决方案的关键在于引入芬斯勒几何（Finsler geometry）——一种对称黎曼几何的非对称推广——并构建一个基于芬斯勒空间的流形学习流程，该流程能够显式建模数据间的非对称相似性，并在非对称嵌入空间中进行可视化与分析。这一方法不仅拓展了现有不对称嵌入器（如Finsler t-SNE和Finsler UMAP）的应用范围，使其适用于任意类型数据，且在合成与真实大规模数据集上均验证了其能揭示传统方法遗漏的信息（如密度层次结构），并提供优于欧几里得嵌入的质量表现。
链接: https://arxiv.org/abs/2603.11396
作者: Thomas Dagès,Simon Weber,Daniel Cremers,Ron Kimmel
机构: Technical University of Munich (慕尼黑工业大学); Stanford University (斯坦福大学)
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Manifold learning is a fundamental task at the core of data analysis and visualisation. It aims to capture the simple underlying structure of complex high-dimensional data by preserving pairwise dissimilarities in low-dimensional embeddings. Traditional methods rely on symmetric Riemannian geometry, thus forcing symmetric dissimilarities and embedding spaces, e.g. Euclidean. However, this discards in practice valuable asymmetric information inherent to the non-uniformity of data samples. We suggest to harness this asymmetry by switching to Finsler geometry, an asymmetric generalisation of Riemannian geometry, and propose a Finsler manifold learning pipeline that constructs asymmetric dissimilarities and embeds in a Finsler space. This greatly broadens the applicability of existing asymmetric embedders beyond traditionally directed data to any data. We also modernise asymmetric embedders by generalising current reference methods to asymmetry, like Finsler t-SNE and Finsler Umap. On controlled synthetic and large real datasets, we show that our asymmetric pipeline reveals valuable information lost in the traditional pipeline, e.g. density hierarchies, and consistently provides superior quality embeddings than their Euclidean counterparts.
[CV-117] High-Precision 6DOF Pose Estimation via Global Phase Retrieval in Fringe Projection Profilometry for 3D Mapping
【速读】：该论文旨在解决数字光栅投影（Digital Fringe Projection, DFP）在大尺度三维重建中因六自由度位姿估计精度不足而导致的映射误差累积问题。传统迭代最近点（Iterative Closest Point, ICP）方法在处理百万点级别的点云时效率低下，且依赖降采样或特征提取，易丢失局部细节并降低位姿精度；而现有漂移校正方法虽能提升长期一致性，却无法克服密集DFP点云对采样的敏感性。解决方案的关键在于引入一个固定且内参标定的全局投影仪，通过其相位导出的像素约束与基于PnP（Perspective-n-Point）风格的重投影目标函数，在固定参考坐标系中直接估计DFP系统的位姿，无需依赖确定性特征提取，从而实现对坐标保持型降采样的采样不变性。实验表明，该方法可在亚毫米级精度下稳定运行，并显著减少ICP轨迹中的误差累积，适用于准静态场景下的高精度三维测绘任务。
链接: https://arxiv.org/abs/2603.11389
作者: Sehoon Tak,Keunhee Cho,Sangpil Kim,Jae-Sang Hyun
机构: Yonsei University (延世大学); Korea University (高丽大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Digital fringe projection (DFP) enables micrometer-level 3D reconstruction, yet extending it to large-scale mapping remains challenging because six-degree-of-freedom pose estimation often cannot match the reconstruction’s precision. Conventional iterative closest point (ICP) registration becomes inefficient on multi-million-point clouds and typically relies on downsampling or feature-based selection, which can reduce local detail and degrade pose precision. Drift-correction methods improve long-term consistency but do not resolve sampling sensitivity in dense DFP point this http URL propose a high-precision pose estimation method that augments a moving DFP system with a fixed, intrinsically calibrated global projector. Using the global projector’s phase-derived pixel constraints and a PnP-style reprojection objective, the method estimates the DFP system pose in a fixed reference frame without relying on deterministic feature extraction, and we experimentally demonstrate sampling invariance under coordinate-preserving subsampling. Experiments demonstrate sub-millimeter pose accuracy against a reference with quantified uncertainty bounds, high repeatability under aggressive subsampling, robust operation on homogeneous surfaces and low-overlap views, and reduced error accumulation when used to correct ICP-based trajectories. The method extends DFP toward accurate 3D mapping in quasi-static scenarios such as inspection and metrology, with the trade-off of time-multiplexed acquisition for the additional projector measurements.
[CV-118] DriveXQA: Cross-modal Visual Question Answering for Adverse Driving Scene Understanding
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在自动驾驶场景下对多传感器信息融合能力不足的问题，尤其是在复杂环境（如恶劣天气、传感器故障）中理解异常驾驶场景的能力有限。其解决方案的关键在于提出DriveXQA数据集和MVX-LLM架构：DriveXQA包含四种视觉模态、五种传感器故障情况及五种天气条件下的102,505个问答对，覆盖全局场景、以环境为参考和以车辆为中心三个层次；MVX-LLM采用双交叉注意力（Dual Cross-Attention, DCA）投影器，实现多模态特征的有效融合，从而缓解信息冗余并提升模型在挑战性条件下的表现（如雾天环境下GPTScore从25.1提升至53.5）。
链接: https://arxiv.org/abs/2603.11380
作者: Mingzhe Tao,Ruiping Liu,Junwei Zheng,Yufan Chen,Kedi Ying,M. Saquib Sarfraz,Kailun Yang,Jiaming Zhang,Rainer Stiefelhagen
机构: 1. University of Science and Technology of China (中国科学技术大学); 2. Tsinghua University (清华大学); 3. Max Planck Institute for Informatics (马克斯·普朗克信息学研究所)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Fusing sensors with complementary modalities is crucial for maintaining a stable and comprehensive understanding of abnormal driving scenes. However, Multimodal Large Language Models (MLLMs) are underexplored for leveraging multi-sensor information to understand adverse driving scenarios in autonomous vehicles. To address this gap, we propose the DriveXQA, a multimodal dataset for autonomous driving VQA. In addition to four visual modalities, five sensor failure cases, and five weather conditions, it includes 102,505 QA pairs categorized into three types: global scene level, allocentric level, and ego-vehicle centric level. Since no existing MLLM framework adopts multiple complementary visual modalities as input, we design MVX-LLM, a token-efficient architecture with a Dual Cross-Attention (DCA) projector that fuses the modalities to alleviate information redundancy. Experiments demonstrate that our DCA achieves improved performance under challenging conditions such as foggy (GPTScore: 53.5 vs. 25.1 for the baseline). The established dataset and source code will be made publicly available.
[CV-119] Learning to Assist: Physics-Grounded Human-Human Control via Multi-Agent Reinforcement Learning CVPR2026
【速读】：该论文旨在解决人形机器人在辅助性交互场景中难以实现连续感知人类伙伴并快速适应其姿态与动力学变化的问题，这类场景要求机器人具备物理接触和力交换能力，而现有基于物理引擎的通用运动追踪（General Motion Tracking, GMT）方法主要局限于无接触社交互动或孤立动作。解决方案的关键在于将紧密协作的人类-人类交互动作模仿建模为多智能体强化学习（Multi-Agent Reinforcement Learning, MARL）问题，并通过引入双策略初始化机制（从单人运动追踪控制器迁移先验知识以提升探索效率）、动态参考重定向（根据接收方实时姿态调整辅助方参考运动）以及接触促进奖励函数（鼓励产生物理上合理的支撑行为），从而实现对复杂助人动作的有效跟踪。该方法首次在基准测试中成功实现了对助人交互动作的稳定跟踪，验证了多智能体强化学习在物理合理且社会敏感的人形控制中的优势。
链接: https://arxiv.org/abs/2603.11346
作者: Yuto Shibata,Kashu Yamazaki,Lalit Jayanti,Yoshimitsu Aoki,Mariko Isogawa,Katerina Fragkiadaki
机构: Carnegie Mellon University (卡内基梅隆大学); Keio AI Research Center (庆应AI研究中心); Keio University (庆应大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Robotics (cs.RO)
备注: Accepted at CVPR 2026 (main). Project page: this https URL
Abstract:Humanoid robotics has strong potential to transform daily service and caregiving applications. Although recent advances in general motion tracking within physics engines (GMT) have enabled virtual characters and humanoid robots to reproduce a broad range of human motions, these behaviors are primarily limited to contact-less social interactions or isolated movements. Assistive scenarios, by contrast, require continuous awareness of a human partner and rapid adaptation to their evolving posture and dynamics. In this paper, we formulate the imitation of closely interacting, force-exchanging human-human motion sequences as a multi-agent reinforcement learning problem. We jointly train partner-aware policies for both the supporter (assistant) agent and the recipient agent in a physics simulator to track assistive motion references. To make this problem tractable, we introduce a partner policies initialization scheme that transfers priors from single-human motion-tracking controllers, greatly improving exploration. We further propose dynamic reference retargeting and contact-promoting reward, which adapt the assistant’s reference motion to the recipient’s real-time pose and encourage physically meaningful support. We show that AssistMimic is the first method capable of successfully tracking assistive interaction motions on established benchmarks, demonstrating the benefits of a multi-agent RL formulation for physically grounded and socially aware humanoid control.
[CV-120] owards Trustworthy Selective Generation: Reliability-Guided Diffusion for Ultra-Low-Field to High-Field MRI Synthesis
【速读】：该论文旨在解决低场到高场磁共振成像（MRI）合成中生成式模型在细节恢复与结构保真度之间难以平衡的问题，尤其是在结构模糊区域因无控制的高分辨率细节生成而引入解剖不一致伪影（如虚假边缘或人工纹理变化），进而影响下游定量分析的准确性。解决方案的关键在于提出一种可靠性感知扩散框架（ReDiff），其核心创新包括：1）引入可靠性引导采样策略，在去噪过程中抑制不可靠响应以提升合成稳健性；2）设计不确定性感知的多候选选择机制，增强最终预测的空间可靠性与解剖一致性。
链接: https://arxiv.org/abs/2603.11325
作者: Zhenxuan Zhang,Peiyuan Jing,Ruicheng Yuan,Liwei Hu,Anbang Wang,Fanwen Wang,Yinzhe Wu,Kh Tohidul Islam,Zhaolin Chen,Zi Wang,Peter Lally,Guang Yang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Low-field to high-field MRI synthesis has emerged as a cost-effective strategy to enhance image quality under hardware and acquisition constraints, particularly in scenarios where access to high-field scanners is limited or impractical. Despite recent progress in diffusion models, diffusion-based approaches often struggle to balance fine-detail recovery and structural fidelity. In particular, the uncontrolled generation of high-resolution details in structurally ambiguous regions may introduce anatomically inconsistent patterns, such as spurious edges or artificial texture variations. These artifacts can bias downstream quantitative analysis. For example, they may cause inaccurate tissue boundary delineation or erroneous volumetric estimation, ultimately reducing clinical trust in synthesized images. These limitations highlight the need for generative models that are not only visually accurate but also spatially reliable and anatomically consistent. To address this issue, we propose a reliability-aware diffusion framework (ReDiff) that improves synthesis robustness at both the sampling and post-generation stages. Specifically, we introduce a reliability-guided sampling strategy to suppress unreliable responses during the denoising process. We further develop an uncertainty-aware multi-candidate selection scheme to enhance the reliability of the final prediction. Experiments on multi-center MRI datasets demonstrate improved structural fidelity and reduced artifacts compared with state-of-the-art methods.
[CV-121] UNet-AF: An alias-free UNet for image restoration
【速读】：该论文旨在解决传统UNet架构在图像恢复任务中因卷积层易受混叠（aliasing）影响而导致的平移等变性（translation equivariance）不足的问题。解决方案的关键在于设计一种新的无混叠UNet，其核心是通过精心选择当前最先进的平移等变层（translation-equivariant layers）构建网络结构，从而在保持模型性能的同时显著提升等变性表现。实验表明，这种设计不仅在图像恢复任务中达到与非等变基线相当的性能，还通过系统消融研究验证了每一项改进对实际等变性的必要性。
链接: https://arxiv.org/abs/2603.11323
作者: Jérémy Scanvic,Quentin Barthélemy,Julián Tachella
机构: École Normale Supérieure de Lyon (法国里昂高等师范学院); HirschSecure (希施 secure)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The simplicity and effectiveness of the UNet architecture makes it ubiquitous in image restoration, image segmentation, and diffusion models. They are often assumed to be equivariant to translations, yet they traditionally consist of layers that are known to be prone to aliasing, which hinders their equivariance in practice. To overcome this limitation, we propose a new alias-free UNet designed from a careful selection of state-of-the-art translation-equivariant layers. We evaluate the proposed equivariant architecture against non-equivariant baselines on image restoration tasks and observe competitive performance with a significant increase in measured equivariance. Through extensive ablation studies, we also demonstrate that each change is crucial for its empirical equivariance. Our implementation is available at this https URL
[CV-122] UniCompress: Token Compression for Unified Vision-Language Understanding and Generation
【速读】：该论文旨在解决统一模型（Unified models）在处理图像时因视觉token数量庞大而导致的计算和内存开销问题，这一瓶颈限制了其在资源受限场景（如具身AI系统）中的部署。解决方案的关键在于提出一种轻量级、模块化的统一token压缩算法UniCompress，该方法通过引入可学习的全局元token（learnable global meta tokens）指导压缩与解压缩机制，在不进行完整重训练的前提下显著减少视觉token数量（最多降低4倍），从而在保持图像理解与生成任务性能的同时大幅提升推理速度和训练效率。
链接: https://arxiv.org/abs/2603.11320
作者: Ziyao Wang,Chen Chen,Jingtao Li,Weiming Zhuang,Jiabo Huang,Ang Li,Lingjuan Lyu
机构: Sony AI; University of Maryland, College Park
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Unified models aim to support both understanding and generation by encoding images into discrete tokens and processing them alongside text within a single autoregressive framework. This unified design offers architectural simplicity and cross-modal synergy, which facilitates shared parameterization, consistent training objectives, and seamless transfer between modalities. However, the large number of visual tokens required by such models introduces substantial computation and memory overhead, and this inefficiency directly hinders deployment in resource constrained scenarios such as embodied AI systems. In this work, we propose a unified token compression algorithm UniCompress that significantly reduces visual token count while preserving performance on both image understanding and generation tasks. Our method introduces a plug-in compression and decompression mechanism guided with learnable global meta tokens. The framework is lightweight and modular, enabling efficient integration into existing models without full retraining. Experimental results show that our approach reduces image tokens by up to 4 times, achieves substantial gains in inference latency and training cost, and incurs only minimal performance degradation, which demonstrates the promise of token-efficient unified modeling for real world multimodal applications.
[CV-123] Hierarchical Granularity Alignment and State Space Modeling for Robust Multimodal AU Detection in the Wild
【速读】：该论文旨在解决野外环境下面部动作单元（Action Unit, AU）检测面临的挑战，包括严重的时空异质性、不受约束的姿态变化以及复杂的音视频依赖关系。现有多模态方法通常受限于容量较小的编码器和浅层融合机制，难以捕捉细粒度语义变化和超长时程上下文。其解决方案的关键在于提出一种基于分层粒度对齐与状态空间模型（State Space Model）的新型多模态框架：首先利用DINOv2和WavLM两个基础模型提取鲁棒且高保真的视觉与音频表征，替代传统特征提取器；其次设计分层粒度对齐模块动态对齐全局面部语义与局部激活区域；最后引入Vision-Mamba架构实现线性复杂度（O(N)）的时间建模，突破传统卷积网络的感受野限制，有效捕获超长时序动态，并通过新颖的非对称交叉注意力机制深度同步副语言音频线索与细微视觉特征，从而显著提升AU检测性能，在Aff-Wild2数据集上达到当前最优水平。
链接: https://arxiv.org/abs/2603.11306
作者: Jun Yu,Yunxiang Zhang,Naixiang Zheng,Lingsi Zhu,Guoyuan Wang
机构: University of Science and Technology of China (中国科学技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 1 figures
Abstract:Facial Action Unit (AU) detection in in-the-wild environments remains a formidable challenge due to severe spatial-temporal heterogeneity, unconstrained poses, and complex audio-visual dependencies. While recent multimodal approaches have made progress, they often rely on capacity-limited encoders and shallow fusion mechanisms that fail to capture fine-grained semantic shifts and ultra-long temporal contexts. To bridge this gap, we propose a novel multimodal framework driven by Hierarchical Granularity Alignment and State Space this http URL, we leverage powerful foundation models, namely DINOv2 and WavLM, to extract robust and high-fidelity visual and audio representations, effectively replacing traditional feature extractors. To handle extreme facial variations, our Hierarchical Granularity Alignment module dynamically aligns global facial semantics with fine-grained local active patches. Furthermore, we overcome the receptive field limitations of conventional temporal convolutional networks by introducing a Vision-Mamba architecture. This approach enables temporal modeling with O(N) linear complexity, effectively capturing ultra-long-range dynamics without performance degradation. A novel asymmetric cross-attention mechanism is also introduced to deeply synchronize paralinguistic audio cues with subtle visual this http URL experiments on the challenging Aff-Wild2 dataset demonstrate that our approach significantly outperforms existing baselines, achieving state-of-the-art performance. Notably, this framework secured top rankings in the AU Detection track of the 10th Affective Behavior Analysis in-the-wild Competition.
[CV-124] InstantHDR: Single-forward Gaussian Splatting for High Dynamic Range 3D Reconstruction
【速读】：该论文旨在解决高动态范围（High Dynamic Range, HDR）新视角合成（Novel View Synthesis, NVS）中依赖已知相机位姿、需密集点云初始化及耗时的场景级优化问题。现有方法要么基于迭代优化，效率低下；要么采用前馈网络但忽略曝光不变性假设，无法有效处理HDR重建任务。其解决方案的关键在于提出InstantHDR——一种无需标定的单次前向传播网络，通过几何引导的多曝光外观建模实现多曝光图像融合，并引入元网络（meta-network）进行可泛化的场景特定色调映射（tone mapping）。此外，为弥补真实HDR数据缺失，作者构建了包含168个Blender渲染场景的预训练数据集HDR-Pretrain，支持模型在不同光照条件和相机响应函数下的泛化能力。实验表明，InstantHDR在保持与最优优化方法相当的合成质量的同时，实现了约700倍和20倍的重建速度提升。
链接: https://arxiv.org/abs/2603.11298
作者: Dingqiang Ye,Jiacong Xu,Jianglu Ping,Yuxiang Guo,Chao Fan,Vishal M. Patel
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:High dynamic range (HDR) novel view synthesis (NVS) aims to reconstruct HDR scenes from multi-exposure low dynamic range (LDR) images. Existing HDR pipelines heavily rely on known camera poses, well-initialized dense point clouds, and time-consuming per-scene optimization. Current feed-forward alternatives overlook the HDR problem by assuming exposure-invariant appearance. To bridge this gap, we propose InstantHDR, a feed-forward network that reconstructs 3D HDR scenes from uncalibrated multi-exposure LDR collections in a single forward pass. Specifically, we design a geometry-guided appearance modeling for multi-exposure fusion, and a meta-network for generalizable scene-specific tone mapping. Due to the lack of HDR scene data, we build a pre-training dataset, called HDR-Pretrain, for generalizable feed-forward HDR models, featuring 168 Blender-rendered scenes, diverse lighting types, and multiple camera response functions. Comprehensive experiments show that our InstantHDR delivers comparable synthesis performance to the state-of-the-art optimization-based HDR methods while enjoying \sim700\times and \sim20\times reconstruction speed improvement with our single-forward and post-optimization settings. All code, models, and datasets will be released after the review process.
[CV-125] owards Automated Initial Probe Placement in Transthoracic Teleultrasound Using Human Mesh and Skeleton Recovery
【速读】：该论文旨在解决远程超声（teleultrasound）中操作者（如新手或机器人）在无现场专家协助情况下，难以准确完成患者体表定位与探头初始放置的问题。其核心挑战在于如何基于有限的RGB图像信息实现个体化解剖结构建模，并据此提供精准的探头引导。解决方案的关键在于提出了一种名为“Patient registration and anatomy-informed Initial Probe placement Guidance (PIPG)”的自动化框架：通过校准摄像头采集的RGB图像，在混合现实（Mixed Reality, MR）头戴设备（HMD）中重建患者的体表与骨骼模型，并利用骨性标志点估计肋间隙区域，最终将虚拟探头位置引导投影至重建的体表上，从而实现符合解剖学特征的初始探头放置。实验表明，该方法可在健康志愿者中实现可接受于远程超声场景的初始放置精度。
链接: https://arxiv.org/abs/2603.11257
作者: Yu Chung Lee,David G. Black,Ryan S. Yeung,Septimiu E. Salcudean
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 6 figures. Under review
Abstract:Cardiac and lung ultrasound are technically demanding because operators must identify patient-specific intercostal acoustic windows and then navigate between standard views by adjusting probe position, rotation, and force across different imaging planes. These challenges are amplified in teleultrasound when a novice or robot faces the difficult task of first placing the probe on the patient without in-person expert assistance. We present a framework for automating Patient registration and anatomy-informed Initial Probe placement Guidance (PIPG) using only RGB images from a calibrated camera. The novice first captures the patient using the camera on a mixed reality (MR) head-mounted display (HMD). An edge server then infers a patient-specific body-surface and skeleton model, with spatial smoothing across multiple views. Using bony landmarks from the predicted skeleton, we estimate the intercostal region and project the guidance back onto the reconstructed body surface. To validate the framework, we overlaid the reconstructed body mesh and the virtual probe pose guidance across multiple transthoracic echocardiography scan planes in situ and measured the quantitative placement error. Pilot experiments with healthy volunteers suggest that the proposed probe placement prediction and MR guidance yield consistent initial placement within anatomical variability acceptable for teleultrasound setup
[CV-126] Radiometric fingerprinting of object surfaces using mobile laser scanning and semantic 3D road space models
【速读】：该论文旨在解决城市数字孪生中材料信息缺失的问题，即尽管语义三维城市模型（semantic 3D city models）已广泛可用且日益精细化，但其对物体表面材料及其物理属性的结构化表示仍严重不足，限制了应用范围与分析能力。解决方案的关键在于提出一种基于激光雷达（LiDAR）观测的辐射度指纹（radiometric fingerprints）方法：通过将来自不同距离、入射角、环境条件、传感器及扫描任务的LiDAR回波数据自动关联至同一语义对象，从而提取出具有类内一致性特征的表面辐射特性模式。研究利用Audi Autonomous Driving Dataset (A2D2) 中4次扫描、5种LiDAR传感器获取的3.124亿个激光束，成功匹配到6368个语义对象，并构建了符合CityGML 3.0标准、精度达厘米级的LOD3城市模型，验证了该方法可有效识别类别主导性材料，为城市数字孪生提供更精细的材料感知能力。
链接: https://arxiv.org/abs/2603.11252
作者: Benedikt Schwab,Thomas H. Kolbe
机构: Technical University of Munich (慕尼黑工业大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Although semantic 3D city models are internationally available and becoming increasingly detailed, the incorporation of material information remains largely untapped. However, a structured representation of materials and their physical properties could substantially broaden the application spectrum and analytical capabilities for urban digital twins. At the same time, the growing number of repeated mobile laser scans of cities and their street spaces yields a wealth of observations influenced by the material characteristics of the corresponding surfaces. To leverage this information, we propose radiometric fingerprints of object surfaces by grouping LiDAR observations reflected from the same semantic object under varying distances, incident angles, environmental conditions, sensors, and scanning campaigns. Our study demonstrates how 312.4 million individual beams acquired across four campaigns using five LiDAR sensors on the Audi Autonomous Driving Dataset (A2D2) vehicle can be automatically associated with 6368 individual objects of the semantic 3D city model. The model comprises a comprehensive and semantic representation of four inner-city streets at Level of Detail (LOD) 3 with centimeter-level accuracy. It is based on the CityGML 3.0 standard and enables fine-grained sub-differentiation of objects. The extracted radiometric fingerprints for object surfaces reveal recurring intra-class patterns that indicate class-dominant materials. The semantic model, the method implementations, and the developed geodatabase solution 3DSensorDB are released under: this https URL
[CV-127] When Slots Compete: Slot Merging in Object-Centric Learning
【速读】：该论文旨在解决槽位（slot）-based对象中心学习中因固定槽位数量导致的槽位竞争问题，即多个槽位争夺同一实体的重叠区域，从而影响对象分解（object factorization）和掩码质量。解决方案的关键在于引入一种轻量级的“槽位合并”（slot merging）机制：通过软交并比（Soft-IoU）量化槽位注意力图之间的重叠程度，并采用基于重心更新（barycentric update）的方式合并高重叠槽位对，同时保持梯度流畅通；整个过程遵循固定策略，仅需从重叠统计中推断阈值，无需额外可学习模块，且可无缝集成至DINOSAUR的特征重构流程中，显著提升对象发现与分割性能。
链接: https://arxiv.org/abs/2603.11246
作者: Christos Chatzisavvas,Panagiotis Rigas,George Ioannakis,Vassilis Katsouros,Nikolaos Mitianoudis
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Slot-based object-centric learning represents an image as a set of latent slots with a decoder that combines them into an image or features. The decoder specifies how slots are combined into an output, but the slot set is typically fixed: the number of slots is chosen upfront and slots are only refined. This can lead to multiple slots competing for overlapping regions of the same entity rather than focusing on distinct regions. We introduce slot merging: a drop-in, lightweight operation on the slot set that merges overlapping slots during training. We quantify overlap with a Soft-IoU score between slot-attention maps and combine selected pairs via a barycentric update that preserves gradient flow. Merging follows a fixed policy, with the decision threshold inferred from overlap statistics, requiring no additional learnable modules. Integrated into the established feature-reconstruction pipeline of DINOSAUR, the proposed method improves object factorization and mask quality, surpassing other adaptive methods in object discovery and segmentation benchmarks.
[CV-128] Senna-2: Aligning VLM and End-to-End Driving Policy for Consistent Decision Making and Planning
【速读】：该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）与端到端（End-to-End, E2E）驾驶策略之间存在的双系统一致性问题，即VLM的高层决策与E2E的低层规划常因缺乏显式对齐而导致轨迹生成偏离意图，削弱了系统的自上而下指导能力和决策跟随性能。解决方案的关键在于提出Senna-2，一种基于一致性导向的三阶段训练范式：首先通过驾驶预训练实现初步决策与规划，并利用决策适配器以隐式嵌入形式传递VLM决策；其次在开环设置下对齐VLM与E2E策略；最后通过3DGS环境中自底向上的分层强化学习（Hierarchical Reinforcement Learning）完成闭环对齐，从而显著提升安全性与效率。实验表明，该方法在双系统一致性（F1分数提升19.3%）、开环轨迹误差（FDE降低5.7%）和闭环安全指标（AF-CR降低30.6%）方面均取得显著改进。
链接: https://arxiv.org/abs/2603.11219
作者: Yuehao Song,Shaoyu Chen,Hao Gao,Yifan Zhu,Weixiang Yue,Jialv Zou,Bo Jiang,Zihao Lu,Yu Wang,Qian Zhang,Xinggang Wang
机构: Huazhong University of Science and Technology (华中科技大学); Horizon Robotics ( horizon机器人)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 8 figures. Project page: this https URL
Abstract:Vision-language models (VLMs) enhance the planning capability of end-to-end (E2E) driving policy by leveraging high-level semantic reasoning. However, existing approaches often overlook the dual-system consistency between VLM’s high-level decision and E2E’s low-level planning. As a result, the generated trajectories may misalign with the intended driving decisions, leading to weakened top-down guidance and decision-following ability of the system. To address this issue, we propose Senna-2, an advanced VLM-E2E driving policy that explicitly aligns the two systems for consistent decision-making and planning. Our method follows a consistency-oriented three-stage training paradigm. In the first stage, we conduct driving pre-training to achieve preliminary decision-making and planning, with a decision adapter transmitting VLM decisions to E2E policy in the form of implicit embeddings. In the second stage, we align the VLM and the E2E policy in an open-loop setting. In the third stage, we perform closed-loop alignment via bottom-up Hierarchical Reinforcement Learning in 3DGS environments to reinforce the safety and efficiency. Extensive experiments demonstrate that Senna-2 achieves superior dual-system consistency (19.3% F1 score improvement) and significantly enhances driving safety in both open-loop (5.7% FDE reduction) and closed-loop settings (30.6% AF-CR reduction).
[CV-129] A Simple Efficiency Incremental Learning Framework via Vision-Language Model with Nonlinear Multi-Adapters
【速读】：该论文旨在解决增量学习（Incremental Learning, IL）中面临的三大挑战：训练效率低、依赖记忆库存储历史数据以及对强大骨干网络的强依赖。其解决方案的关键在于提出一种简单高效的框架SimE，该框架基于视觉-语言模型并引入专为IL任务设计的适配器（adapter），通过优化适配器连接方式显著提升模型性能。研究发现，适配器在Transformer块间的连接数量与IL能力呈非线性关系——增加跨块连接可提升性能，但增加块内连接则可能抑制甚至损害IL能力。这一现象揭示了适配器结构设计对IL效果的关键影响，使得SimE在TinyImageNet上比传统方法提升9.6%，在CIFAR-100上比其他CLIP基线方法提升5.3%。
链接: https://arxiv.org/abs/2603.11211
作者: Haihua Luo,Xuming Ran,Jiangrong Shen,Timo Hämäläinen,Zhonghua Chen,Qi Xu,Fengyu Cong
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Incremental Learning (IL) aims to learn new tasks while preserving previously acquired knowledge. Integrating the zero-shot learning capabilities of pre-trained vision-language models into IL methods has marked a significant advancement. However, these methods face three primary challenges: (1) the need for improved training efficiency; (2) reliance on a memory bank to store previous data; and (3) the necessity of a strong backbone to augment the model’s capabilities. In this paper, we propose SimE, a Simple and Efficient framework that employs a vision-language model with adapters designed specifically for the IL task. We report a remarkable phenomenon: there is a nonlinear correlation between the number of adaptive adapter connections and the model’s IL capabilities. While increasing adapter connections between transformer blocks improves model performance, adding more adaptive connections within transformer blocks during smaller incremental steps does not enhance, and may even degrade the model’s IL ability. Extensive experimental results show that SimE surpasses traditional methods by 9.6% on TinyImageNet and outperforms other CLIP-based methods by 5.3% on CIFAR-100. Furthermore, we conduct a systematic study to enhance the utilization of the zero-shot capabilities of CLIP. We suggest replacing SimE’s encoder with a CLIP model trained on larger datasets (e.g., LAION2B) and stronger architectures (e.g., ViT-L/14).
[CV-130] Evidential learning driven Breast Tumor Segmentation with Stage-divided Vision-Language Interaction
【速读】：该论文旨在解决乳腺肿瘤在磁共振成像（MRI）中因对比度低和边界模糊导致的分割精度不足问题。现有基于深度学习的分割方法难以准确识别肿瘤轮廓，尤其在低对比度区域表现不佳。解决方案的关键在于提出一种文本引导的乳腺肿瘤分割模型（TextBCS），其核心创新包括两个方面：一是采用分阶段的视觉-语言交互机制，在下采样各阶段实现视觉特征与文本提示信息的双向融合，从而利用文本描述增强对病灶区域的定位能力；二是引入证据学习（evidential learning）量化分割不确定性，通过变分狄利克雷分布建模分割概率分布，有效缓解边界模糊带来的分割误差。
链接: https://arxiv.org/abs/2603.11206
作者: Jingxing Zhong,Qingtao Pan,Xuchang Zhou,Jiazhen Lin,Xinguo Zhuang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Breast cancer is one of the most common causes of death among women worldwide, with millions of fatalities annually. Magnetic Resonance Imaging (MRI) can provide various sequences for characterizing tumor morphology and internal patterns, and becomes an effective tool for detection and diagnosis of breast tumors. However, previous deep-learning based tumor segmentation methods have limitations in accurately locating tumor contours due to the challenge of low contrast between cancer and normal areas and blurred boundaries. Leveraging text prompt information holds promise in ameliorating tumor segmentation effect by delineating segmentation regions. Inspired by this, we propose text-guided Breast Tumor Segmentation model (TextBCS) with stage-divided vision-language interaction and evidential learning. Specifically, the proposed stage-divided vision-language interaction facilitates information mutual between visual and text features at each stage of down-sampling, further exerting the advantages of text prompts to assist in locating lesion areas in low contrast scenarios. Moreover, the evidential learning is adopted to quantify the segmentation uncertainty of the model for blurred boundary. It utilizes the variational Dirichlet to characterize the distribution of the segmentation probabilities, addressing the segmentation uncertainties of the boundaries. Extensive experiments validate the superiority of our TextBCS over other segmentation networks, showcasing the best breast tumor segmentation performance on publicly available datasets.
[CV-131] GGPT : Geometry Grounded Point Transformer CVPR2026
【速读】：该论文旨在解决当前前馈式三维重建方法在稀疏视图下存在的几何不一致性与细粒度精度不足的问题，这些问题主要源于缺乏显式的多视角约束。解决方案的关键在于提出几何引导的点变换器（Geometry-Grounded Point Transformer, GGPT），其核心创新包括：首先构建基于密集特征匹配和轻量级几何优化的改进结构光恢复（Structure-from-Motion）流程，以高效获取准确的相机位姿和部分3D点云；进而设计一种几何引导的3D点变换器，在显式的局部几何监督下通过优化的引导编码机制对稠密点图进行精修。该框架实现了几何先验与稠密前馈预测的有机结合，显著提升了重建结果的几何一致性、空间完整性及纹理缺失区域的补全能力。
链接: https://arxiv.org/abs/2603.11174
作者: Yutong Chen,Yiming Wang,Xucong Zhang,Sergey Prokudin,Siyu Tang
机构: ETH Zurich (苏黎世联邦理工学院); Delft University of Technology (代尔夫特理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR 2026, Project website: this https URL
Abstract:Recent feed-forward networks have achieved remarkable progress in sparse-view 3D reconstruction by predicting dense point maps directly from RGB images. However, they often suffer from geometric inconsistencies and limited fine-grained accuracy due to the absence of explicit multi-view constraints. We introduce the Geometry-Grounded Point Transformer (GGPT), a framework that augments feed-forward reconstruction with reliable sparse geometric guidance. We first propose an improved Structure-from-Motion pipeline based on dense feature matching and lightweight geometric optimisation to efficiently estimate accurate camera poses and partial 3D point clouds from sparse input views. Building on this foundation, we propose a geometry-guided 3D point transformer that refines dense point maps under explicit partial-geometry supervision using an optimised guidance encoding. Extensive experiments demonstrate that our method provides a principled mechanism for integrating geometric priors with dense feed-forward predictions, producing reconstructions that are both geometrically consistent and spatially complete, recovering fine structures and filling gaps in textureless areas. Trained solely on ScanNet++ with VGGT predictions, GGPT generalises across architectures and datasets, substantially outperforming state-of-the-art feed-forward 3D reconstruction models in both in-domain and out-of-domain settings.
[CV-132] Catalogue Grounded Multimodal Attribution for Museum Video under Resource and Regulatory Constraints
【速读】：该论文旨在解决博物馆与美术馆中音视频（Audiovisual, AV）档案因缺乏一致且可搜索的元数据而难以被有效利用的问题，尤其针对馆内视频内容在人工标注成本高、效率低的情况下，如何实现自动化、高质量的元数据生成。其解决方案的关键在于提出一种基于现有藏品数据库的“目录引导式多模态归属”方法，通过一个开源且可本地部署的视频语言模型构建多阶段处理流程：首先对视频中的艺术作品进行摘要提取，继而生成符合目录风格的描述文本和类别标签，并最终采用保守的相似性匹配策略从结构化目录中归因标题与艺术家信息。该框架在不依赖云端服务的前提下，兼顾数据主权与合规要求，显著提升了AV档案的可发现性，为高风险领域中的应用驱动型机器学习提供了可迁移的技术范式。
链接: https://arxiv.org/abs/2603.11147
作者: Minsak Nanang,Adrian Hilton,Armin Mustafa
机构: University of Surrey (萨里大学)
类目: Multimedia (cs.MM); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Audiovisual (AV) archives in museums and galleries are growing rapidly, but much of this material remains effectively locked away because it lacks consistent, searchable metadata. Existing method for archiving requires extensive manual effort. We address this by automating the most labour intensive part of the workflow: catalogue style metadata curation for in gallery video, grounded in an existing collection database. Concretely, we propose catalogue-grounded multimodal attribution for museum AV content using an open, locally deployable video language model. We design a multi pass pipeline that (i) summarises artworks in a video, (ii) generates catalogue style descriptions and genre labels, and (iii) attempts to attribute title and artist via conservative similarity matching to the structured catalogue. Early deployments on a painting catalogue suggest that this framework can improve AV archive discoverability while respecting resource constraints, data sovereignty, and emerging regulation, offering a transferable template for application-driven machine learning in other high-stakes domains.
[CV-133] Attention Gathers MLPs Compose: A Causal Analysis of an Action-Outcome Circuit in VideoViT AAAI2026
【速读】：该论文旨在解决视频分类模型在训练过程中隐含地编码了与任务无关但具有语义意义的“成功 vs 失败”信号这一问题，这是构建可信人工智能（Trustworthy AI）的关键挑战之一。解决方案的关键在于通过机制可解释性（mechanistic interpretability）方法对预训练视频视觉Transformer的内部结构进行逆向工程，发现该信号并非均匀分布，而是通过一个从第5层到第11层逐步放大的放大级联（amplification cascade）进行表征；进一步因果分析表明，注意力头（Attention Heads）作为“证据收集者”提供低级信息以恢复部分信号，而MLP块（MLP Blocks）则作为稳健的“概念合成器”，各自成为生成“成功”信号的主要驱动因素，形成分布式且冗余的内部电路，从而解释模型对简单删减操作的鲁棒性，并揭示出即使仅训练用于简单分类任务的模型也可能发展出超越显式任务的“隐藏知识”。
链接: https://arxiv.org/abs/2603.11142
作者: Sai V R Chereddy
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the AAAI 2026 Workshop on Deployable AI (DAI). Non-archival. Code and custom dataset available upon request
Abstract:The paper explores how video models trained for classification tasks represent nuanced, hidden semantic information that may not affect the final outcome, a key challenge for Trustworthy AI models. Through Explainable and Interpretable AI methods, specifically mechanistic interpretability techniques, the internal circuit responsible for representing the action’s outcome is reverse-engineered in a pre-trained video vision transformer, revealing that the “Success vs Failure” signal is computed through a distinct amplification cascade. While there are low-level differences observed from layer 0, the abstract and semantic representation of the outcome is progressively amplified from layers 5 through 11. Causal analysis, primarily using activation patching supported by ablation results, reveals a clear division of labor: Attention Heads act as “evidence gatherers”, providing necessary low-level information for partial signal recovery, while MLP Blocks function as robust “concept composers”, each of which is the primary driver to generate the “success” signal. This distributed and redundant circuit in the model’s internals explains its resilience to simple ablations, demonstrating a core computational pattern for processing human-action outcomes. Crucially, the existence of this sophisticated circuit for representing complex outcomes, even within a model trained only for simple classification, highlights the potential for models to develop forms of ‘hidden knowledge’ beyond their explicit task, underscoring the need for mechanistic oversight for building genuinely Explainable and Trustworthy AI systems intended for deployment.
[CV-134] RC-NF: Robot-Conditioned Normalizing Flow for Real-Time Anomaly Detection in Robotic Manipulation CVPR
【速读】：该论文旨在解决基于视觉-语言-动作（Vision-Language-Action, VLA）模型的机器人在动态环境或分布外（Out-of-Distribution, OOD）条件下执行任务时可靠性不足的问题。现有方法依赖模仿学习训练，难以适应未见过的场景，导致执行失败。解决方案的关键在于提出一种实时异常检测与干预模型——机器人条件归一化流（Robot-Conditioned Normalizing Flow, RC-NF），其通过在归一化流中解耦任务感知的机器人状态与物体运动轨迹的处理，仅需正样本即可实现无监督训练，并在推理阶段利用概率密度函数精确计算机器人异常得分。该设计使RC-NF能在100毫秒内提供OOD信号，支持状态级回滚或任务级重规划，显著提升VLA系统在复杂动态环境中的鲁棒性与适应能力。
链接: https://arxiv.org/abs/2603.11106
作者: Shijie Zhou,Bin Zhu,Jiarui Yang,Xiangyu Zhao,Jingjing Chen,Yu-Gang Jiang
机构: Fudan University (复旦大学); Shanghai Key Laboratory of Multimodal Embodied AI (上海市多模态具身智能重点实验室); Singapore Management University (新加坡管理大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Accepted to the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026
Abstract:Recent advances in Vision-Language-Action (VLA) models have enabled robots to execute increasingly complex tasks. However, VLA models trained through imitation learning struggle to operate reliably in dynamic environments and often fail under Out-of-Distribution (OOD) conditions. To address this issue, we propose Robot-Conditioned Normalizing Flow (RC-NF), a real-time monitoring model for robotic anomaly detection and intervention that ensures the robot’s state and the object’s motion trajectory align with the task. RC-NF decouples the processing of task-aware robot and object states within the normalizing flow. It requires only positive samples for unsupervised training and calculates accurate robotic anomaly scores during inference through the probability density function. We further present LIBERO-Anomaly-10, a benchmark comprising three categories of robotic anomalies for simulation evaluation. RC-NF achieves state-of-the-art performance across all anomaly types compared to previous methods in monitoring robotic tasks. Real-world experiments demonstrate that RC-NF operates as a plug-and-play module for VLA models (e.g., pi0), providing a real-time OOD signal that enables state-level rollback or task-level replanning when necessary, with a response latency under 100 ms. These results demonstrate that RC-NF noticeably enhances the robustness and adaptability of VLA-based robotic systems in dynamic environments.
[CV-135] nyNav: End-to-End TinyML for Real-Time Autonomous Navigation on Microcontrollers
【速读】：该论文旨在解决自主导航系统在低成本机器人中因依赖高功耗处理器而导致的可访问性受限问题。传统方案通常需要复杂的计算资源，而微控制器虽具资源效率但对模型复杂度限制严格。其解决方案的关键在于设计了一个端到端的TinyML系统——TinyNav，基于ESP32微控制器实现实时自主导航：通过训练一个参数仅为23k的量化二维卷积神经网络（2D CNN），处理20帧深度数据滑动窗口以预测转向和油门指令；通过避免使用三维卷积和循环层，在极低延迟（30 ms）下实现稳定的空间感知与避障行为，从而证明了响应式自主控制可在高度受限的边缘设备上直接部署，显著降低对外部计算资源的依赖。
链接: https://arxiv.org/abs/2603.11071
作者: Pooria Roy,Nourhan Jadallah. Tomer Lapid,Shahzaib Ahmad,Armita Afroushe,Mete Bayrak
机构: 未知
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 6 pages, 7 figures, presented at CUCAI2026 (Canadian Undergraduate Conference on AI, this https URL )
Abstract:Autonomous navigation typically relies on power-intensive processors, limiting accessibility in low-cost robotics. Although microcontrollers offer a resource-efficient alternative, they impose strict constraints on model complexity. We present TinyNav, an end-to-end TinyML system for real-time autonomous navigation on an ESP32 microcontroller. A custom-trained, quantized 2D convolutional neural network processes a 20-frame sliding window of depth data to predict steering and throttle commands. By avoiding 3D convolutions and recurrent layers, the 23k-parameter model achieves 30 ms inference latency. Correlation analysis and Grad-CAM validation indicate consistent spatial awareness and obstacle avoidance behavior. TinyNav demonstrates that responsive autonomous control can be deployed directly on highly constrained edge devices, reducing reliance on external compute resources.
[CV-136] Dr. SHAP-AV: Decoding Relative Modality Contributions via Shapley Attribution in Audio-Visual Speech Recognition
【速读】：该论文旨在解决音频-视觉语音识别（Audio-Visual Speech Recognition, AVSR）中多模态信息平衡机制不明确的问题，即模型如何在不同噪声环境下动态分配音频与视觉模态的贡献权重。其解决方案的关键在于提出Dr. SHAP-AV框架，该框架基于Shapley值（Shapley values）对AVSR模型中的模态贡献进行量化分析，通过三种创新性分析方法——全局Shapley（Global SHAP）用于评估整体模态平衡、生成式Shapley（Generative SHAP）用于追踪解码过程中的贡献动态变化、时间对齐Shapley（Temporal Alignment SHAP）用于检验输入输出间的时间一致性——揭示了模型在不同信噪比（SNR）条件下对音频和视觉信息的依赖模式及其演化规律，从而为理解AVSR模型内部决策机制提供了可解释性的诊断工具，并指出存在持续的音频偏好倾向，推动基于Shapley值的归因分析成为AVSR研究的标准方法。
链接: https://arxiv.org/abs/2603.12046
作者: Umberto Cappellazzo,Stavros Petridis,Maja Pantic
机构: Imperial College London (帝国理工学院); NatWest AI Research (NatWest人工智能研究中心)
类目: Audio and Speech Processing (eess.AS); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
备注: Project website: this https URL
Abstract:Audio-Visual Speech Recognition (AVSR) leverages both acoustic and visual information for robust recognition under noise. However, how models balance these modalities remains unclear. We present Dr. SHAP-AV, a framework using Shapley values to analyze modality contributions in AVSR. Through experiments on six models across two benchmarks and varying SNR levels, we introduce three analyses: Global SHAP for overall modality balance, Generative SHAP for contribution dynamics during decoding, and Temporal Alignment SHAP for input-output correspondence. Our findings reveal that models shift toward visual reliance under noise yet maintain high audio contributions even under severe degradation. Modality balance evolves during generation, temporal alignment holds under noise, and SNR is the dominant factor driving modality weighting. These findings expose a persistent audio bias, motivating ad-hoc modality-weighting mechanisms and Shapley-based attribution as a standard AVSR diagnostic.
[CV-137] AS-Bridge: A Bidirectional Generative Framework Bridging Next-Generation Astronomical Surveys
【速读】：该论文旨在解决地面望远镜（如LSST）与空间望远镜（如Euclid）观测数据在联合分析中因观测模态、天空覆盖范围、点扩散函数（Point-Spread Function, PSF）及扫描时序差异所带来的挑战，从而实现跨平台数据的协同利用。解决方案的关键在于提出了一种名为A(stronomical)S(urvey)-Bridge（AS-Bridge）的双向生成模型，该模型基于扩散模型（Diffusion Model），采用随机布朗桥（Brownian Bridge）过程建模LSST与Euclid观测之间的条件概率分布，并利用两者重叠区域的数据显式学习跨巡天的映射关系。这一方法不仅实现了对缺失观测的高保真概率预测，还支持跨巡天稀有事件检测，为未来LSST-Euclid联合数据处理流程提供了可扩展的生成式工具，显著提升了多巡天协同研究的科学潜力。
链接: https://arxiv.org/abs/2603.11928
作者: Dichang Zhang,Yixuan Shao,Simon Birrer,Dimitris Samaras
机构: Stony Brook University (石溪大学)
类目: Instrumentation and Methods for Astrophysics (astro-ph.IM); Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 4 figures. Code available at this https URL
Abstract:The upcoming decade of observational cosmology will be shaped by large sky surveys, such as the ground-based LSST at the Vera C. Rubin Observatory and the space-based Euclid mission. While they promise an unprecedented view of the Universe across depth, resolution, and wavelength, their differences in observational modality, sky coverage, point-spread function, and scanning cadence make joint analysis beneficial, but also challenging. To facilitate joint analysis, we introduce A(stronomical)S(urvey)-Bridge, a bidirectional generative model that translates between ground- and space-based observations. AS-Bridge learns a diffusion model that employs a stochastic Brownian Bridge process between the LSST and Euclid observations. The two surveys have overlapping sky regions, where we can explicitly model the conditional probabilistic distribution between them. We show that this formulation enables new scientific capabilities beyond single-survey analysis, including faithful probabilistic predictions of missing survey observations and inter-survey detection of rare events. These results establish the feasibility of inter-survey generative modeling. AS-Bridge is therefore well-positioned to serve as a complementary component of future LSST-Euclid joint data pipelines, enhancing the scientific return once data from both surveys become available. Data and code are available at \hrefthis https URLthis https URL.
[CV-138] Deep Learning-based Assessment of the Relation Between the Third Molar and Mandibular Canal on Panoramic Radiographs using Local Centralized and Federated Learning
【速读】：该论文旨在解决下颌第三磨牙与下牙槽神经管（inferior alveolar nerve canal）相邻时因影像学判断困难而导致的神经损伤风险问题，以及如何在保护患者隐私的前提下实现多中心协同训练深度学习模型以提升自动分类准确率。其解决方案的关键在于采用联邦学习（federated learning, FL）框架，在不共享原始患者数据的情况下，通过跨机构协作训练模型，并与本地学习（local learning, LL）和集中式学习（centralized learning, CL）进行性能对比，验证FL在保障隐私的同时能显著优于LL且接近CL的表现，从而为临床提供一种可部署的、安全的智能辅助诊断工具。
链接: https://arxiv.org/abs/2603.11850
作者: Johan Andreas Balle Rubak,Sara Haghighat,Sanyam Jain,Mostafa Aldesoki,Akhilanand Chaurasia,Sarah Sadat Ehsani,Faezeh Dehghan Ghanatkaman,Ahmad Badruddin Ghazali,Julien Issa,Basel Khalil,Rishi Ramani,Ruben Pauwels
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Impaction of the mandibular third molar in proximity to the mandibular canal increases the risk of inferior alveolar nerve injury. Panoramic radiography is routinely used to assess this relationship. Automated classification of molar-canal overlap could support clinical triage and reduce unnecessary CBCT referrals, while federated learning (FL) enables multi-center collaboration without sharing patient data. We compared Local Learning (LL), FL, and Centralized Learning (CL) for binary overlap/no-overlap classification on cropped panoramic radiographs partitioned across eight independent labelers. A pretrained ResNet-34 was trained under each paradigm and evaluated using per-client metrics with locally optimized thresholds and pooled test performance with a global threshold. Performance was assessed using area under the receiver operating characteristic curve (AUC) and threshold-based metrics, alongside training dynamics, Grad-CAM visualizations, and server-side aggregate monitoring signals. On the test set, CL achieved the highest performance (AUC 0.831; accuracy = 0.782), FL showed intermediate performance (AUC 0.757; accuracy = 0.703), and LL generalized poorly across clients (AUC range = 0.619-0.734; mean = 0.672). Training curves suggested overfitting, particularly in LL models, and Grad-CAM indicated more anatomically focused attention in CL and FL. Overall, centralized training provided the strongest performance, while FL offers a privacy-preserving alternative that outperforms LL.
[CV-139] A Diffeomorphism Groupoid and Algebroid Framework for Discontinuous Image Registration
【速读】：该论文旨在解决传统大变形微分同胚度量映射（Large Deformation Diffeomorphic Metric Mapping, LDDMM）方法在处理具有不连续滑动运动的图像配准时存在的局限性。LDDMM基于李群（Lie group）框架，假设速度场具有连续性和光滑性，无法有效建模沿滑动边界存在间断的形变。为此，作者提出了一种基于微分同胚群胚（diffeomorphism groupoid）和代数胚（algebroid）的新数学框架，将传统的李群扩展为允许在滑动边界处出现不连续性的李群胚结构，同时在均匀区域内保持微分同胚性质。其解决方案的关键在于引入群胚与代数胚理论，构建适用于非连续形变的最优流动力学模型，并推导出相应的欧拉-阿诺德方程（Euler-Arnold equations），从而实现对包含滑动界面的复杂形变的有效建模与数值求解。
链接: https://arxiv.org/abs/2603.11806
作者: Lili Bao,Bin Xiao,Shihui Ying,Stefan Sommer
机构: 未知
类目: Group Theory (math.GR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In this paper, we propose a novel mathematical framework for piecewise diffeomorphic image registration that involves discontinuous sliding motion using a diffeomorphism groupoid and algebroid approach. The traditional Large Deformation Diffeomorphic Metric Mapping (LDDMM) registration method builds on Lie groups, which assume continuity and smoothness in velocity fields, limiting its applicability in handling discontinuous sliding motion. To overcome this limitation, we extend the diffeomorphism Lie groups to a framework of discontinuous diffeomorphism Lie groupoids, allowing for discontinuities along sliding boundaries while maintaining diffeomorphism within homogeneous regions. We provide a rigorous analysis of the associated mathematical structures, including Lie algebroids and their duals, and derive specific Euler-Arnold equations to govern optimal flows for discontinuous deformations. Some numerical tests are performed to validate the efficiency of the proposed approach.
[CV-140] MRI2Qmap: multi-parametric quantitative mapping with MRI-driven denoising priors
【速读】：该论文旨在解决磁共振指纹成像（Magnetic Resonance Fingerprinting, MRF）等高加速瞬态参数映射技术在压缩采样下易出现混叠伪影的问题。传统基于深度学习的方法依赖大量标注的定量成像数据进行训练，但在MRF场景中此类数据稀缺。解决方案的关键在于提出MRI2Qmap框架，该框架将物理采集模型与从大规模临床常规加权MRI图像预训练得到的空间结构先验相结合，从而在无需真实定量成像数据作为训练标签的情况下实现高质量定量重建。这一方法通过解耦定量重建对地面真值MRF数据的依赖，为可扩展的定量MRI提供了新范式。
链接: https://arxiv.org/abs/2603.11316
作者: Mohammad Golbabaee,Matteo Cencini,Carolin Pirkl,Marion Menzel,Michela Tosetti,Bjoern Menze
机构: University of Bristol (布里斯托大学); IRCCS Fondazione Stella Maris (IRCCS 斯特拉马里亚基金会研究所); GE Healthcare (通用电气医疗公司); TH Ingolstadt (英戈尔施塔特技术大学); University of Zurich (苏黎世大学)
类目: Medical Physics (physics.med-ph); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Magnetic Resonance Fingerprinting (MRF) and other highly accelerated transient-state parameter mapping techniques enable simultaneous quantification of multiple tissue properties, but often suffer from aliasing artifacts due to compressed sampling. Incorporating spatial image priors can mitigate these artifacts, and deep learning has shown strong potential when large training datasets are available. However, extending this paradigm to MRF-type sequences remains challenging due to the scarcity of quantitative imaging data for training. Can this limitation be overcome by leveraging sources of training data from clinically-routine weighted MRI images? To this end, we introduce MRI2Qmap, a plug-and-play quantitative reconstruction framework that integrates the physical acquisition model with priors learned from deep denoising autoencoders pretrained on large multimodal weighted-MRI datasets. MRI2Qmap demonstrates that spatial-domain structural priors learned from independently acquired datasets of routine weighted-MRI images can be effectively used for quantitative MRI reconstruction. The proposed method is validated on highly accelerated 3D whole-brain MRF data from both in-vivo and simulated acquisitions, achieving competitive or superior performance relative to existing baselines without requiring ground-truth quantitative imaging data for training. By decoupling quantitative reconstruction from the need for ground-truth MRF training data, this framework points toward a scalable paradigm for quantitative MRI that can capitalize on the large and growing repositories of routine clinical MRI.
人工智能
[AI-0] Separable neural architectures as a primitive for unified predictive and generative intelligence
【速读】：该论文旨在解决当前神经网络模型在建模物理、语言和感知等智能系统时，往往忽略其内在可分解结构（factorisable structure）的问题。现有单体式神经架构难以显式利用这种结构，导致模型复杂度高且缺乏可解释性。解决方案的关键在于提出一种可分离神经架构（Separable Neural Architecture, SNA），通过形式化一类统一的表示类，将加法型、二次型及张量分解型神经模型纳入同一框架；并通过约束交互阶数（interaction order）和张量秩（tensor rank），引入结构归纳偏置（structural inductive bias），使高维映射被分解为低元组分量。这一方法不仅捕捉了系统本身的结构特性，还揭示了坐标空间中结构自发出现的现象，并建立了混沌时空动力学与语言自回归之间的结构类比，从而实现对混沌系统的分布建模，同时适用于离散序列任务。
链接: https://arxiv.org/abs/2603.12244
作者: Reza T. Batley,Apurba Sarker,Rajib Mostakim,Andrew Klichine,Sourav Saha
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Intelligent systems across physics, language and perception often exhibit factorisable structure, yet are typically modelled by monolithic neural architectures that do not explicitly exploit this structure. The separable neural architecture (SNA) addresses this by formalising a representational class that unifies additive, quadratic and tensor-decomposed neural models. By constraining interaction order and tensor rank, SNAs impose a structural inductive bias that factorises high-dimensional mappings into low-arity components. Separability need not be a property of the system itself: it often emerges in the coordinates or representations through which the system is expressed. Crucially, this coordinate-aware formulation reveals a structural analogy between chaotic spatiotemporal dynamics and linguistic autoregression. By treating continuous physical states as smooth, separable embeddings, SNAs enable distributional modelling of chaotic systems. This approach mitigates the nonphysical drift characteristics of deterministic operators whilst remaining applicable to discrete sequences. The compositional versatility of this approach is demonstrated across four domains: autonomous waypoint navigation via reinforcement learning, inverse generation of multifunctional microstructures, distributional modelling of turbulent flow and neural language modelling. These results establish the separable neural architecture as a domain-agnostic primitive for predictive and generative intelligence, capable of unifying both deterministic and distributional representations.
[AI-1] Incremental Neural Network Verification via Learned Conflicts
【速读】：该论文旨在解决神经网络验证过程中因重复处理相似查询而导致的计算冗余问题。在现有验证方法中，每次验证任务独立求解，先前学习到的信息被丢弃，导致对搜索空间中相同不可行区域的反复探索。其解决方案的关键在于提出一种增量式验证技术，通过跨相关验证查询复用已学习到的冲突（conflict）信息。该技术可集成于任何基于分支定界（branch-and-bound）的神经网络验证器中，在验证过程中记录与激活状态组合相关的不可行冲突，并在不同查询间保留这些冲突。作者形式化了验证查询间的精化关系（refinement relation），证明冲突在精化下保持有效性，从而实现安全的冲突继承。继承的冲突由SAT求解器进行一致性检查与传播，提前识别并剪枝不可行子问题，显著降低验证开销。
链接: https://arxiv.org/abs/2603.12232
作者: Raya Elsaleh,Liam Davis,Haoze Wu,Guy Katz
机构: 未知
类目: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI)
备注:
Abstract:Neural network verification is often used as a core component within larger analysis procedures, which generate sequences of closely related verification queries over the same network. In existing neural network verifiers, each query is typically solved independently, and information learned during previous runs is discarded, leading to repeated exploration of the same infeasible regions of the search space. In this work, we aim to expedite verification by reducing this redundancy. We propose an incremental verification technique that reuses learned conflicts across related verification queries. The technique can be added on top of any branch-and-bound-based neural network verifier. During verification, the verifier records conflicts corresponding to learned infeasible combinations of activation phases, and retains them across runs. We formalize a refinement relation between verification queries and show that conflicts learned for a query remain valid under refinement, enabling sound conflict inheritance. Inherited conflicts are handled using a SAT solver to perform consistency checks and propagation, allowing infeasible subproblems to be detected and pruned early during search. We implement the proposed technique in the Marabou verifier and evaluate it on three verification tasks: local robustness radius determination, verification with input splitting, and minimal sufficient feature set extraction. Our experiments show that incremental conflict reuse reduces verification effort and yields speedups of up to 1.9\times over a non-incremental baseline.
[AI-2] Security Considerations for Artificial Intelligence Agents
【速读】：该论文旨在解决前沿AI代理（AI agent）系统在实际部署中面临的安全挑战，特别是其架构变化对传统安全假设（如代码与数据分离、权限边界和执行可预测性）带来的冲击。核心问题在于，代理系统的动态交互特性引入了新型机密性、完整性与可用性失效模式，例如间接提示注入（indirect prompt injection）、混乱代理人行为（confused-deputy behavior）以及长流程工作流中的级联故障。解决方案的关键在于构建分层防御体系：从输入层和模型层的缓解措施，到沙箱化执行环境，再到针对高后果操作的确定性策略强制机制，并强调需填补标准与研究空白，包括自适应安全基准测试、委托与权限控制的策略模型，以及符合NIST风险管理原则的多代理系统安全设计指南。
链接: https://arxiv.org/abs/2603.12230
作者: Ninghui Li,Kaiyuan Zhang,Kyle Polley,Jerry Ma
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: Perplexity Response to NIST/CAISI Request for Information 2025-0035. 91 Fed. Reg. 698 (Jan. 8, 2026)
Abstract:This article, a lightly adapted version of Perplexity’s response to NIST/CAISI Request for Information 2025-0035, details our observations and recommendations concerning the security of frontier AI agents. These insights are informed by Perplexity’s experience operating general-purpose agentic systems used by millions of users and thousands of enterprises in both controlled and open-world environments. Agent architectures change core assumptions around code-data separation, authority boundaries, and execution predictability, creating new confidentiality, integrity, and availability failure modes. We map principal attack surfaces across tools, connectors, hosting boundaries, and multi-agent coordination, with particular emphasis on indirect prompt injection, confused-deputy behavior, and cascading failures in long-running workflows. We then assess current defenses as a layered stack: input-level and model-level mitigations, sandboxed execution, and deterministic policy enforcement for high-consequence actions. Finally, we identify standards and research gaps, including adaptive security benchmarks, policy models for delegation and privilege control, and guidance for secure multi-agent system design aligned with NIST risk management principles.
[AI-3] Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights
【速读】：该论文试图解决的问题是：如何在预训练模型的基础上，更高效地发现和利用任务特定的专家解（task-specific experts），以提升模型在下游任务上的性能。传统方法通常将预训练参数视为单一起点，并通过迭代优化进行微调，但这种方法可能难以有效探索潜在的高质量解空间。论文的关键创新在于提出一种新的视角——将预训练结果视为一个参数向量分布，其中包含多种任务专家；并发现，在大规模、充分预训练的模型中，这些专家解在参数空间中的密度显著增加，占据预训练权重邻域的较大比例。基于此洞察，论文提出了一种简单且完全并行的后训练方法：随机采样 N 个参数扰动，选取表现最优的 K 个，并通过多数投票集成预测。该方法无需梯度信息或复杂优化策略，却能与 PPO、GRPO 和 ES 等主流后训练方法相媲美，尤其适用于现代大规模模型。
链接: https://arxiv.org/abs/2603.12228
作者: Yulu Gan,Phillip Isola
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: codes are provided at this https URL
Abstract:Pretraining produces a learned parameter vector that is typically treated as a starting point for further iterative adaptation. In this work, we instead view the outcome of pretraining as a distribution over parameter vectors, whose support already contains task-specific experts. We show that in small models such expert solutions occupy a negligible fraction of the volume of this distribution, making their discovery reliant on structured optimization methods such as gradient descent. In contrast, in large, well-pretrained models the density of task-experts increases dramatically, so that diverse, task-improving specialists populate a substantial fraction of the neighborhood around the pretrained weights. Motivated by this perspective, we explore a simple, fully parallel post-training method that samples N parameter perturbations at random, selects the top K , and ensembles predictions via majority vote. Despite its simplicity, this approach is competitive with standard post-training methods such as PPO, GRPO, and ES for contemporary large-scale models.
[AI-4] Portfolio of Solving Strategies in CEGAR-based Object Packing and Scheduling for Sequential 3D Printing
【速读】：该论文旨在解决顺序3D打印中对象排列与调度的复杂组合优化问题，其核心挑战在于如何高效利用现代多核个人计算机CPU的并行计算能力来提升求解效率。解决方案的关键在于将原有的CEGAR-SEQ算法（基于抽象释义的约束求解方法）重构为一种高阶并行策略——Portfolio-CEGAR-SEQ，通过在多个对象排列策略（如中心放置、角落放置及按高度排序）上并行执行Cegar-Seq算法，实现对不同排列方案的快速评估与选择，从而显著减少所需打印板数量并提高整体调度性能。
链接: https://arxiv.org/abs/2603.12224
作者: Pavel Surynek
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: arXiv admin note: substantial text overlap with arXiv:2503.05071
Abstract:Computing power that used to be available only in supercomputers decades ago especially their parallelism is currently available in standard personal computer CPUs even in CPUs for mobile telephones. We show how to effectively utilize the computing power of modern multi-core personal computer CPU to solve the complex combinatorial problem of object arrangement and scheduling for sequential 3D printing. We achieved this by parallelizing the existing CEGAR-SEQ algorithm that solves the sequential object arrangement and scheduling by expressing it as a linear arithmetic formula which is then solved by a technique inspired by counterexample guided abstraction refinement (CEGAR). The original CEGAR-SEQ algorithm uses an object arrangement strategy that places objects towards the center of the printing plate. We propose alternative object arrangement strategies such as placing objects towards a corner of the printing plate and scheduling objects according to their height. Our parallelization is done at the high-level where we execute the CEGAR-SEQ algorithm in parallel with a portfolio of object arrangement strategies, an algorithm is called Porfolio-CEGAR-SEQ. Our experimental evaluation indicates that Porfolio-CEGAR-SEQ outperforms the original CEGAR-SEQ. When a batch of objects for multiple printing plates is scheduled, Portfolio-CEGAR-SEQ often uses fewer printing plates than CEGAR-SEQ.
[AI-5] WORKSWORLD: A Domain for Integrated Numeric Planning and Scheduling of Distributed Pipelined Workflows
【速读】：该论文旨在解决分布式数据流水线（data pipeline）的自动化规划与调度问题，即如何在多站点资源环境中自动构建并优化数据处理流程。其解决方案的关键在于提出了一种通用的工作流与资源图表示方法（workflow and resource graph representation），该方法将数据处理组件和共享组件及其网络接口统一建模；在此基础上设计了WORKSWORLD这一新的数值域无关规划领域（numeric domain-independent planner），使规划器能够同时完成工作流图的构建与组件在资源图上的调度，从而实现端到端的联合规划与调度。
链接: https://arxiv.org/abs/2603.12214
作者: Taylor Paul,William Regli
机构: 未知
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注: To be published in Proceedings of the International Conference on Automated Planning and Scheduling Volume 36 (2026)
Abstract:This work pursues automated planning and scheduling of distributed data pipelines, or workflows. We develop a general workflow and resource graph representation that includes both data processing and sharing components with corresponding network interfaces for scheduling. Leveraging these graphs, we introduce WORKSWORLD, a new domain for numeric domain-independent planners designed for permanently scheduled workflows, like ingest pipelines. Our framework permits users to define data sources, available workflow components, and desired data destinations and formats without explicitly declaring the entire workflow graph as a goal. The planner solves a joint planning and scheduling problem, producing a plan that both builds the workflow graph and schedules its components on the resource graph. We empirically show that a state-of-the-art numeric planner running on commodity hardware with one hour of CPU time and 30GB of memory can solve linear-chain workflows of up to 14 components across eight sites.
[AI-6] Compiling Temporal Numeric Planning into Discrete PDDL: Extended Version ICAPS2026
【速读】：该论文旨在解决如何将带有持续动作（durative actions）的时序规划问题（temporal planning）高效地编译为PDDL+（Planning Domain Definition Language Plus）形式的问题，从而在保持语义完整性的前提下实现实际应用。其关键解决方案在于提出了一种多项式时间复杂度的编译方法，仅假设动作之间不自重叠（non-self-overlapping），能够保留原问题的计划长度至一个常数因子内，并通过实验验证了该方法在处理复杂时序数值问题上的实用性。
链接: https://arxiv.org/abs/2603.12188
作者: Andrea Micheli,Enrico Scala,Alessandro Valentini
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: This paper is an extended version of the homonymous appearing in the ICAPS 2026 proceedings. This version provides the proofs and addidional explanations of the compilation
Abstract:Since the introduction of the PDDL+ modeling language, it was known that temporal planning with durative actions (as in PDDL 2.1) could be compiled into PDDL+. However, no practical compilation was presented in the literature ever since. We present a practical compilation from temporal planning with durative actions into PDDL+, fully capturing the semantics and only assuming the non-self-overlapping of actions. Our compilation is polynomial, retains the plan length up to a constant factor and is experimentally shown to be of practical relevance for hard temporal numeric problems.
[AI-7] A Quantitative Characterization of Forgetting in Post-Training
【速读】：该论文旨在解决生成式 AI（Generative AI）在持续后训练（continual post-training）过程中遗忘现象的理论理解不足问题，特别是量化旧任务知识如何因新数据训练而丢失。其核心解决方案在于构建一个基于双模混合分布（two-mode mixture abstraction）的理论框架，将旧任务和新任务分别抽象为两个高斯模态，并据此形式化两种遗忘机制：(i) 质量遗忘（mass forgetting），即旧模态权重衰减至零；(ii) 旧组件漂移（old-component drift），即已正确识别的旧模态均值发生偏移。关键发现是：前向KL（forward-KL）目标会导致质量遗忘，而反向KL（reverse-KL）目标可避免质量遗忘并仅引发受重叠控制的漂移，且该漂移随模态间距指数衰减，具有良好的局部几何条件与收敛性。此外，论文进一步分析了回放策略（replay）与不同目标函数的交互作用，揭示了在何种条件下回放能有效抑制遗忘，并通过该统一视角解析三种近期近策略后训练方法（SDFT、TTT-Discover 和 OAPL），推导出它们保留旧质量并表现出可控漂移的具体条件。整体而言，遗忘行为可通过发散方向、几何重叠度、采样机制及训练中对历史行为的可见性四者之间的相互作用精确刻画。
链接: https://arxiv.org/abs/2603.12163
作者: Krishnakumar Balasubramanian,Shiva Prasad Kasiviswanathan
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Statistics Theory (math.ST); Machine Learning (stat.ML)
备注:
Abstract:Continual post-training of generative models is widely used, yet a principled understanding of when and why forgetting occurs remains limited. We develop theoretical results under a two-mode mixture abstraction (representing old and new tasks), proposed by Chen et al. (2025) (arXiv:2510.18874), and formalize forgetting in two forms: (i) mass forgetting, where the old mixture weight collapses to zero, and (ii) old-component drift, where an already-correct old component shifts during training. For equal-covariance Gaussian modes, we prove that forward-KL objectives trained on data from the new distribution drive the old weight to zero, while reverse-KL objectives converge to the true target (thereby avoiding mass forgetting) and perturb the old mean only through overlap-gated misassignment probabilities controlled by the Bhattacharyya coefficient, yielding drift that decays exponentially with mode separation and a locally well-conditioned geometry with exponential convergence. We further quantify how replay interacts with these objectives. For forward-KL, replay must modify the training distribution to change the population optimum; for reverse-KL, replay leaves the population objective unchanged but prevents finite-batch old-mode starvation through bounded importance weighting. Finally, we analyze three recently proposed near-on-policy post-training methods, SDFT (arXiv:2601.19897), TTT-Discover (arXiv:2601.16175), and OAPL (arXiv:2602.19362), via the same lens and derive explicit conditions under which each retains old mass and exhibits overlap-controlled drift. Overall, our results show that forgetting can by precisely quantified based on the interaction between divergence direction, geometric behavioral overlap, sampling regime, and the visibility of past behavior during training.
[AI-8] IsoCompute Playbook: Optimally Scaling Sampling Compute for LLM RL
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在强化学习（Reinforcement Learning, RL）后训练阶段的计算资源最优分配问题，即如何在有限算力约束下合理分配采样计算（sampling compute）以提升训练效率。其关键解决方案是将RL的Scaling规律建模为一个三变量优化问题：每个问题的并行采样轨迹数（parallel rollouts per problem）、每批处理的问题数量（number of problems per batch）以及更新步数（number of update steps）。研究发现，在给定算力预算下，并行采样轨迹数会随预算增加而增长并最终饱和，这一趋势在简单和复杂任务中均成立，但机制不同——简单任务由解的锐化（solution sharpening）驱动，复杂任务则由覆盖范围扩展（coverage expansion）驱动；同时，增加并行轨迹可缓解多任务间的干扰，而批次中的问题数量主要影响训练稳定性，可在较宽范围内灵活选择。该成果将RL Scaling规律转化为可操作的资源配置规则，为高效LLM RL后训练提供了理论依据与实践指导。
链接: https://arxiv.org/abs/2603.12151
作者: Zhoujun Cheng,Yutao Xie,Yuxiao Qu,Amrith Setlur,Shibo Hao,Varad Pimpalkhute,Tongtong Liang,Feng Yao,Zhengzhong Liu,Eric Xing,Virginia Smith,Ruslan Salakhutdinov,Zhiting Hu,Taylor Killian,Aviral Kumar
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 29 pages, 27 figures. Under review
Abstract:While scaling laws guide compute allocation for LLM pre-training, analogous prescriptions for reinforcement learning (RL) post-training of large language models (LLMs) remain poorly understood. We study the compute-optimal allocation of sampling compute for on-policy RL methods in LLMs, framing scaling as a compute-constrained optimization over three resources: parallel rollouts per problem, number of problems per batch, and number of update steps. We find that the compute-optimal number of parallel rollouts per problem increases predictably with compute budget and then saturates. This trend holds across both easy and hard problems, though driven by different mechanisms: solution sharpening on easy problems and coverage expansion on hard problems. We further show that increasing the number of parallel rollouts mitigates interference across problems, while the number of problems per batch primarily affects training stability and can be chosen within a broad range. Validated across base models and data distributions, our results recast RL scaling laws as prescriptive allocation rules and provide practical guidance for compute-efficient LLM RL post-training.
[AI-9] Automatic Generation of High-Performance RL Environments
【速读】：该论文旨在解决复杂强化学习（Reinforcement Learning, RL）环境高效实现的难题，传统方法需数月专业工程开发才能完成高性能版本。其核心解决方案是一套可复用的“配方”：包括通用提示模板（generic prompt template）、分层验证机制（hierarchical verification）以及迭代式代理辅助修复（iterative agent-assisted repair），实现了从原始描述到高性能环境的自动化转换，在计算成本上平均降低10倍。关键创新在于通过结构化提示与多级验证保障语义等价性，并借助代理反馈实现自动纠错，从而在五个不同环境中均达成与现有最优实现相当或显著更优的性能表现（如PokeJAX达到22,320倍于原TypeScript实现的吞吐量），同时确保模拟器间策略迁移无偏差（sim-to-sim gap为零）。
链接: https://arxiv.org/abs/2603.12145
作者: Seth Karten,Rahul Dev Appapogu,Chi Jin
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 26 pages, 9 figures, 8 tables
Abstract:Translating complex reinforcement learning (RL) environments into high-performance implementations has traditionally required months of specialized engineering. We present a reusable recipe - a generic prompt template, hierarchical verification, and iterative agent-assisted repair - that produces semantically equivalent high-performance environments for 10 in compute cost. We demonstrate three distinct workflows across five environments. Direct translation (no prior performance implementation exists): EmuRust (1.5x PPO speedup via Rust parallelism for a Game Boy emulator) and PokeJAX, the first GPU-parallel Pokemon battle simulator (500M SPS random action, 15.2M SPS PPO; 22,320x over the TypeScript reference). Translation verified against existing performance implementations: throughput parity with MJX (1.04x) and 5x over Brax at matched GPU batch sizes (HalfCheetah JAX); 42x PPO (Puffer Pong). New environment creation: TCGJax, the first deployable JAX Pokemon TCG engine (717K SPS random action, 153K SPS PPO; 6.6x over the Python reference), synthesized from a web-extracted specification. At 200M parameters, the environment overhead drops below 4% of training time. Hierarchical verification (property, interaction, and rollout tests) confirms semantic equivalence for all five environments; cross-backend policy transfer confirms zero sim-to-sim gap for all five environments. TCGJax, synthesized from a private reference absent from public repositories, serves as a contamination control for agent pretraining data concerns. The paper contains sufficient detail - including representative prompts, verification methodology, and complete results - that a coding agent could reproduce the translations directly from the manuscript.
[AI-10] Increasing intelligence in AI agents can worsen collective outcomes
【速读】：该论文旨在解决在资源稀缺情境下，由不同开发者训练的AI代理（AI agents）群体如何演化出集体行为及其潜在风险的问题。研究聚焦于四类可独立调控的关键变量：先天多样性（innate LLM diversity）、个体强化学习（individual reinforcement learning）、文化形成（emergent tribe formation）与资源稀缺性（resource scarcity），并通过实证和数学建模揭示其对系统过载的影响机制。解决方案之关键在于识别出一个决定性的指标——容量与种群比例（capacity-to-population ratio），该比值可在任何AI代理部署前被精确计算，并决定了更复杂的AI代理群体是缓解还是加剧系统风险：当该比值超过某一阈值时，自发形成的对立部落（tribes）恰好能容纳于可用容量内，此时系统从高风险状态向低风险状态发生突变式转变。
链接: https://arxiv.org/abs/2603.12129
作者: Neil F. Johnson
机构: 未知
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Social and Information Networks (cs.SI); General Economics (econ.GN); Physics and Society (physics.soc-ph)
备注:
Abstract:When resources are scarce, will a population of AI agents coordinate in harmony, or descend into tribal chaos? Diverse decision-making AI from different developers is entering everyday devices – from phones and medical devices to battlefield drones and cars – and these AI agents typically compete for finite shared resources such as charging slots, relay bandwidth, and traffic priority. Yet their collective dynamics and hence risks to users and society are poorly understood. Here we study AI-agent populations as the first system of real agents in which four key variables governing collective behaviour can be independently toggled: nature (innate LLM diversity), nurture (individual reinforcement learning), culture (emergent tribe formation), and resource scarcity. We show empirically and mathematically that when resources are scarce, AI model diversity and reinforcement learning increase dangerous system overload, though tribe formation lessens this risk. Meanwhile, some individuals profit handsomely. When resources are abundant, the same ingredients drive overload to near zero, though tribe formation makes the overload slightly worse. The crossover is arithmetical: it is where opposing tribes that form spontaneously first fit inside the available capacity. More sophisticated AI-agent populations are not better: whether their sophistication helps or harms depends entirely on a single number – the capacity-to-population ratio – that is knowable before any AI-agent ships.
[AI-11] aming the Adversary: Stable Minimax Deep Deterministic Policy Gradient via Fractional Objectives
【速读】：该论文旨在解决强化学习（Reinforcement Learning, RL）代理在面对意外外部扰动和模型不确定性时性能不稳定或退化的问题，这是实现可靠部署的关键挑战。解决方案的核心在于提出一种最小-最大深度确定性策略梯度（Minimax Deep Deterministic Policy Gradient, MMDDPG）框架，其将训练过程建模为用户策略与对抗扰动策略之间的最小-最大优化问题：用户策略旨在最小化目标函数以学习鲁棒策略，而对抗策略则通过最大化该函数生成最恶劣扰动；为稳定此对抗交互，引入一个分数目标函数，平衡任务性能与扰动幅度，从而避免过度激进的扰动并促进稳健学习。
链接: https://arxiv.org/abs/2603.12110
作者: Taeho Lee,Donghwan Lee
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning (RL) has achieved remarkable success in a wide range of control and decision-making tasks. However, RL agents often exhibit unstable or degraded performance when deployed in environments subject to unexpected external disturbances and model uncertainties. Consequently, ensuring reliable performance under such conditions remains a critical challenge. In this paper, we propose minimax deep deterministic policy gradient (MMDDPG), a framework for learning disturbance-resilient policies in continuous control tasks. The training process is formulated as a minimax optimization problem between a user policy and an adversarial disturbance policy. In this problem, the user learns a robust policy that minimizes the objective function, while the adversary generates disturbances that maximize it. To stabilize this interaction, we introduce a fractional objective that balances task performance and disturbance magnitude. This objective prevents excessively aggressive disturbances and promotes robust learning. Experimental evaluations in MuJoCo environments demonstrate that the proposed MMDDPG achieves significantly improved robustness against both external force perturbations and model parameter variations.
[AI-12] On Information Self-Locking in Reinforcement Learning for Active Reasoning of LLM agents
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）在主动推理（Active Reasoning）任务中因强化学习（Reinforcement Learning, RL）训练导致的“信息自锁定”（Information Self-Locking）问题，即代理停止提出有信息量的问题，并难以整合已获取的信息。其核心原因是动作选择（Action Selection, AS）与信念跟踪（Belief Tracking, BT）能力不足，形成探索不足与能力退化的反馈循环。解决方案的关键在于重构学习信号：通过注入易于获得的方向性批评（Directional Critiques），引导代理跳出低信息状态，从而有效缓解自锁定现象，在7个数据集上的实验表明该方法可带来最高达60%的性能提升。
链接: https://arxiv.org/abs/2603.12109
作者: Deyu Zou,Yongqiang Chen,Fan Feng,Mufei Li,Pan Li,Yu Gong,James Cheng
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning (RL) with outcome-based rewards has achieved significant success in training large language model (LLM) agents for complex reasoning tasks. However, in active reasoning where agents need to strategically ask questions to acquire task-relevant information, we find that LLM agents trained with RL often suffer from information self-locking: the agent ceases to ask informative questions and struggles to internalize already-obtained information. To understand the phenomenon, we decompose active reasoning into two core capabilities: Action Selection (AS), which determines the observation stream through queries, and Belief Tracking (BT), which updates the agent’s belief based on collected evidence. We show that deficient AS and BT capabilities will limit the information exploration during RL training. Furthermore, insufficient exploration in turn hinders the improvement of AS and BT, creating a feedback loop that locks the agent in a low-information regime. To resolve the issue, we propose a simple yet effective approach that reallocates the learning signal by injecting easy- to-obtain directional critiques to help the agent escape self-locking. Extensive experiments with 7 datasets show that our approach significantly mitigates the information self-locking, bringing up to 60% improvements.
[AI-13] A Robust and Efficient Multi-Agent Reinforcement Learning Framework for Traffic Signal Control
【速读】：该论文旨在解决强化学习（Reinforcement Learning, RL）在交通信号控制（Traffic Signal Control, TSC）中因动态交通流变化导致的泛化能力不足问题，以及现有方法对静态模式过拟合、动作空间与驾驶员预期不兼容的问题。解决方案的关键在于提出一种鲁棒的多智能体强化学习（Multi-Agent Reinforcement Learning, MARL）框架，其核心机制包括：(1) 转向比例随机化训练策略，提升模型对未见交通场景的适应性；(2) 基于稳定性优化的指数相位持续时间调整动作空间，通过周期性指数调整实现响应速度与精度的平衡；(3) 基于邻居观测的观察机制结合MAPPO算法与集中训练分散执行（Centralized Training with Decentralized Execution, CTDE），在保持局部通信可扩展性的前提下逼近全局观测效果。实验表明该框架显著优于标准RL基线，平均等待时间降低超10%，且在未见交通场景中展现出更强泛化能力和高控制稳定性。
链接: https://arxiv.org/abs/2603.12096
作者: Sheng-You Huang,Hsiao-Chuan Chang,Yen-Chi Chen,Ting-Han Wei,I-Hau Yeh,Sheng-Yao Kuan,Chien-Yao Wang,Hsuan-Han Lee,I-Chen Wu
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 12 pages, 4 tables, 8 figures. Under review in the 31st ITS World Congress 2026
Abstract:Reinforcement Learning (RL) in Traffic Signal Control (TSC) faces significant hurdles in real-world deployment due to limited generalization to dynamic traffic flow variations. Existing approaches often overfit static patterns and use action spaces incompatible with driver expectations. This paper proposes a robust Multi-Agent Reinforcement Learning (MARL) framework validated in the Vissim traffic simulator. The framework integrates three mechanisms: (1) Turning Ratio Randomization, a training strategy that exposes agents to dynamic turning probabilities to enhance robustness against unseen scenarios; (2) a stability-oriented Exponential Phase Duration Adjustment action space, which balances responsiveness and precision through cyclical, exponential phase adjustments; and (3) a Neighbor-Based Observation scheme utilizing the MAPPO algorithm with Centralized Training with Decentralized Execution (CTDE). By leveraging centralized updates, this approach approximates the efficacy of global observations while maintaining scalable local communication. Experimental results demonstrate that our framework outperforms standard RL baselines, reducing average waiting time by over 10%. The proposed model exhibits superior generalization in unseen traffic scenarios and maintains high control stability, offering a practical solution for adaptive signal control.
[AI-14] Resource-Efficient Iterative LLM -Based NAS with Feedback Memory
【速读】：该论文旨在解决神经架构搜索（Neural Architecture Search, NAS）在传统方法中计算资源消耗巨大、难以在单台消费级GPU上高效执行的问题。其核心解决方案是构建一个闭环式搜索流程，利用大语言模型（Large Language Models, LLMs）实现架构的迭代生成、评估与优化，无需对LLM进行微调。关键创新在于引入基于马尔可夫链的历史反馈记忆机制——使用滑动窗口大小为K=5的结构化诊断三元组（问题识别、修改建议、结果）记录每次尝试，将代码执行失败作为第一类学习信号；同时采用双LLM分工策略（代码生成器与提示改进器），降低每次调用的认知负荷，并通过共享有限显存（VRAM）隐式偏好紧凑且硬件友好的模型，从而实现低预算、可复现且面向边缘部署的LLM驱动NAS范式。
链接: https://arxiv.org/abs/2603.12091
作者: Xiaojie Gu,Dmitry Ignatov,Radu Timofte
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Neural Architecture Search (NAS) automates network design, but conventional methods demand substantial computational resources. We propose a closed-loop pipeline leveraging large language models (LLMs) to iteratively generate, evaluate, and refine convolutional neural network architectures for image classification on a single consumer-grade GPU without LLM fine-tuning. Central to our approach is a historical feedback memory inspired by Markov chains: a sliding window of K=5 recent improvement attempts keeps context size constant while providing sufficient signal for iterative learning. Unlike prior LLM optimizers that discard failure trajectories, each history entry is a structured diagnostic triple – recording the identified problem, suggested modification, and resulting outcome – treating code execution failures as first-class learning signals. A dual-LLM specialization reduces per-call cognitive load: a Code Generator produces executable PyTorch architectures while a Prompt Improver handles diagnostic reasoning. Since both the LLM and architecture training share limited VRAM, the search implicitly favors compact, hardware-efficient models suited to edge deployment. We evaluate three frozen instruction-tuned LLMs ( \leq7 B parameters) across up to 2000 iterations in an unconstrained open code space, using one-epoch proxy accuracy on CIFAR-10, CIFAR-100, and ImageNette as a fast ranking signal. On CIFAR-10, DeepSeek-Coder-6.7B improves from 28.2% to 69.2%, Qwen2.5-7B from 50.0% to 71.5%, and GLM-5 from 43.2% to 62.0%. A full 2000-iteration search completes in \approx18 GPU hours on a single RTX~4090, establishing a low-budget, reproducible, and hardware-aware paradigm for LLM-driven NAS without cloud infrastructure.
[AI-15] A Multi-Label Temporal Convolutional Framework for Transcription Factor Binding Characterization
【速读】：该论文旨在解决转录因子（Transcription Factors, TFs）在DNA上结合位点识别中，现有方法多局限于单个TF的二分类预测、忽视TF之间协同作用机制的问题。其解决方案的关键在于将TF结合位点识别建模为多标签分类任务，并采用时间卷积网络（Temporal Convolutional Networks, TCNs）构建深度学习模型，从而同时预测多个TF的结合谱图，捕捉TF间的相关性及其协同调控机制。该方法不仅揭示了与已知TF相互作用一致的生物意义显著基序和共结合模式，还发现了潜在的新TF协作关系。
链接: https://arxiv.org/abs/2603.12073
作者: Pietro Demurtas,Ferdinando Zanchetta,Giovanni Perini,Rita Fioresi
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Genomics (q-bio.GN)
备注:
Abstract:Transcription factors (TFs) regulate gene expression through complex and co-operative mechanisms. While many TFs act together, the logic underlying TFs binding and their interactions is not fully understood yet. Most current approaches for TF binding site prediction focus on individual TFs and binary classification tasks, without a full analysis of the possible interactions among various TFs. In this paper we investigate DNA TF binding site recognition as a multi-label classification problem, achieving reliable predictions for multiple TFs on DNA sequences retrieved in public repositories. Our deep learning models are based on Temporal Convolutional Networks (TCNs), which are able to predict multiple TF binding profiles, capturing correlations among TFs andtheir cooperative regulatory mechanisms. Our results suggest that multi-label learning leading to reliable predictive performances can reveal biologically meaningful motifs and co-binding patterns consistent with known TF interactions, while also suggesting novel relationships and cooperation among TFs.
[AI-16] Chemical Reaction Networks Learn Better than Spiking Neural Networks
【速读】：该论文试图解决的问题是：在机器学习任务中，化学反应网络（Chemical Reaction Networks, CRNs）是否能够实现与具有隐藏层的脉冲神经网络（Spiking Neural Networks, SNNs）相当甚至更优的学习能力，尤其是在无需引入隐藏层的情况下完成复杂分类任务。解决方案的关键在于通过确定性质量作用动力学（deterministic mass-action kinetics）形式化建模CRN，并严格证明一个无隐藏层的CRN可以完成此前仅被证明需依赖SNN隐藏层才能实现的分类任务；同时，作者提供了全局行为的解析遗憾边界（analytical regret bounds）、渐近行为分析及Vapnik-Chervonenkis（VC）维数分析，从而从理论上验证了该CRN的泛化能力和学习效率。数值实验进一步证实其在手写数字图像分类任务中比含隐藏层的SNN更具准确性和计算效率，为化学计算机中的机器学习提供了数学依据和生物启发机制。
链接: https://arxiv.org/abs/2603.12060
作者: Sophie Jaffard,Ivo F. Sbalzarini
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Statistics Theory (math.ST); Machine Learning (stat.ML)
备注: Keywords: Chemical Reaction Networks, Spiking Neural Networks, Supervised Learning, Classification, Mass-Action Kinetics, Statistical Learning Theory, Regret Bounds, Model Complexity
Abstract:We mathematically prove that chemical reaction networks without hidden layers can solve tasks for which spiking neural networks require hidden layers. Our proof uses the deterministic mass-action kinetics formulation of chemical reaction networks. Specifically, we prove that a certain reaction network without hidden layers can learn a classification task previously proved to be achievable by a spiking neural network with hidden layers. We provide analytical regret bounds for the global behavior of the network and analyze its asymptotic behavior and Vapnik-Chervonenkis dimension. In a numerical experiment, we confirm the learning capacity of the proposed chemical reaction network for classifying handwritten digits in pixel images, and we show that it solves the task more accurately and efficiently than a spiking neural network with hidden layers. This provides a motivation for machine learning in chemical computers and a mathematical explanation for how biological cells might exhibit more efficient learning behavior within biochemical reaction networks than neuronal networks.
[AI-17] Slow-Fast Inference: Training-Free Inference Acceleration via Within-Sentence Support Stability
【速读】：该论文旨在解决长上下文自回归解码（autoregressive decoding）过程中计算成本高昂的问题，即每次解码步骤都需要重复处理不断增长的历史序列，导致效率低下。其解决方案的关键在于提出一种无需训练的推理框架——慢-快推理（Slow-Fast Inference, SFI），该框架将生成过程解耦为高频低开销的“快步”和偶发的密集注意力“慢步”。其中，“快步”利用紧凑的稀疏记忆进行高效解码，“慢步”在语义边界附近触发，重新访问全局上下文并使用选择器（Selector）刷新后续快步所依赖的记忆，从而在保持与全键值（full-KV）基线相当生成质量的前提下，显著提升解码吞吐量（约1.6×–14.4×）。
链接: https://arxiv.org/abs/2603.12038
作者: Xingyu Xie,Zhaochen Yu,Yue Liao,Tao Wang,Kim-Chuan Toh,Shuicheng Yan
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Long-context autoregressive decoding remains expensive because each decoding step must repeatedly process a growing history. We observe a consistent pattern during decoding: within a sentence, and more generally within a short semantically coherent span, the dominant attention support often remains largely stable. Motivated by this observation, we propose Slow-Fast Inference (SFI), a training-free decoding framework that decouples generation into frequent low-cost fast steps and occasional dense-attention slow steps. Fast steps reuse a compact sparse memory for efficient decoding. Slow steps are triggered near semantic boundaries. At slow steps, the model revisits the broader context and uses the Selector to refresh the selected memory for subsequent fast steps. Across the evaluated context lengths, SFI delivers approximately 1.6\times – 14.4\times higher decoding throughput while generally maintaining quality on par with the full-KV baseline across long-context and long-CoT settings. Because SFI is training-free and applies directly to existing checkpoints, it offers a practical path to reducing inference cost for contemporary autoregressive reasoning models in long-context, long-horizon, and agentic workloads.
[AI-18] Cascade: Composing Software-Hardware Attack Gadgets for Adversarial Threat Amplification in Compound AI Systems
【速读】：该论文旨在解决当前对生成式AI（Generative AI）系统安全性的研究过于聚焦于模型层面的算法性风险（如模型提取、训练数据泄露和不当生成），而忽视了传统软件与硬件漏洞在复合型AI（Compound AI）系统中可能与算法弱点协同作用，从而威胁整个AI流水线完整性和机密性的关键问题。解决方案的关键在于系统化识别并组合两类攻击原语：一是基于传统软件漏洞（如代码注入缺陷）与硬件漏洞（如Rowhammer攻击）的协同利用，实现对大语言模型（LLM）的未授权指令注入；二是通过操纵知识数据库来引导LLM代理将敏感用户数据泄露至恶意应用，从而破坏保密性。作者据此构建了一套按攻击目标分类、映射到攻击生命周期阶段的漏洞组合分析框架，为开展严谨的红队测试及未来防御策略设计提供了理论基础和实践路径。
链接: https://arxiv.org/abs/2603.12023
作者: Sarbartha Banerjee,Prateek Sahu,Anjo Vahldiek-Oberwagner,Jose Sanchez Vicarte,Mohit Tiwari
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 11 pages, 8 figures, 1 table
Abstract:Rapid progress in generative AI has given rise to Compound AI systems - pipelines comprised of multiple large language models (LLM), software tools and database systems. Compound AI systems are constructed on a layered traditional software stack running on a distributed hardware infrastructure. Many of the diverse software components are vulnerable to traditional security flaws documented in the Common Vulnerabilities and Exposures (CVE) database, while the underlying distributed hardware infrastructure remains exposed to timing attacks, bit-flip faults, and power-based side channels. Today, research targets LLM-specific risks like model extraction, training data leakage, and unsafe generation – overlooking the impact of traditional system vulnerabilities. This work investigates how traditional software and hardware vulnerabilities can complement LLM-specific algorithmic attacks to compromise the integrity of a compound AI pipeline. We demonstrate two novel attacks that combine system-level vulnerabilities with algorithmic weaknesses: (1) Exploiting a software code injection flaw along with a guardrail Rowhammer attack to inject an unaltered jailbreak prompt into an LLM, resulting in an AI safety violation, and (2) Manipulating a knowledge database to redirect an LLM agent to transmit sensitive user data to a malicious application, thus breaching confidentiality. These attacks highlight the need to address traditional vulnerabilities; we systematize the attack primitives and analyze their composition by grouping vulnerabilities by their objective and mapping them to distinct stages of an attack lifecycle. This approach enables a rigorous red-teaming exercise and lays the groundwork for future defense strategies. Comments: 11 pages, 8 figures, 1 table Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.12023 [cs.CR] (or arXiv:2603.12023v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2603.12023 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-19] Sim-to-reality adaptation for Deep Reinforcement Learning applied to an underwater docking application IROS2026
【速读】：该论文旨在解决自主水下航行器（AUV）在复杂环境中的自主对接问题，特别是克服传统控制方法在应对不确定环境时的局限性，以及深度强化学习（DRL）在“仿真到现实”迁移过程中存在的性能失配和训练延迟瓶颈。其解决方案的关键在于构建一个高保真数字孪生环境，将Stonefish仿真器改造为多进程强化学习框架，以加速训练并集成真实的AUV动力学、碰撞模型和传感器噪声；同时采用近端策略优化（PPO）算法，在无头（headless）环境中训练6自由度（6-DoF）控制策略，并设计包含距离、姿态、动作平滑性和自适应碰撞惩罚的奖励函数，从而实现高成功率的软对接。实验表明，该方法在仿真中成功率超过90%，并在物理测试水池中成功验证了从仿真到现实的迁移能力。
链接: https://arxiv.org/abs/2603.12020
作者: Alaaeddine Chaarani,Narcis Palomeras,Pere Ridao
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Currently under review by IROS 2026
Abstract:Deep Reinforcement Learning (DRL) offers a robust alternative to traditional control methods for autonomous underwater docking, particularly in adapting to unpredictable environmental conditions. However, bridging the “sim-to-real” gap and managing high training latencies remain significant bottlenecks for practical deployment. This paper presents a systematic approach for autonomous docking using the Girona Autonomous Underwater Vehicle (AUV) by leveraging a high-fidelity digital twin environment. We adapted the Stonefish simulator into a multiprocessing RL framework to significantly accelerate the learning process while incorporating realistic AUV dynamics, collision models, and sensor noise. Using the Proximal Policy Optimization (PPO) algorithm, we developed a 6-DoF control policy trained in a headless environment with randomized starting positions to ensure generalized performance. Our reward structure accounts for distance, orientation, action smoothness, and adaptive collision penalties to facilitate soft docking. Experimental results demonstrate that the agent achieved a success rate of over 90% in simulation. Furthermore, successful validation in a physical test tank confirmed the efficacy of the sim-to-reality adaptation, with the DRL controller exhibiting emergent behaviors such as pitch-based braking and yaw oscillations to assist in mechanical alignment.
[AI-20] Flowcean - Model Learning for Cyber-Physical Systems
【速读】：该论文旨在解决复杂网络物理系统（Cyber-Physical Systems, CPS）建模过程中因系统内在复杂性导致的建模困难与耗时问题。传统建模方法难以高效应对CPS多尺度、多域耦合的特性，而数据驱动的机器学习方法虽具潜力，却缺乏统一、模块化且易用的框架支持。为此，作者提出Flowcean框架，其核心在于通过模块化架构整合多种学习策略、数据处理方法和评估指标，实现对CPS场景下模型生成与验证流程的自动化与高效化，从而提升建模效率并增强工具链的可扩展性与适应性。
链接: https://arxiv.org/abs/2603.12015
作者: Maximilian Schmidt,Swantje Plambeck,Markus Knitt,Hendrik Rose,Goerschwin Fey,Jan Christian Wieck,Stephan Balduin
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Effective models of Cyber-Physical Systems (CPS) are crucial for their design and operation. Constructing such models is difficult and time-consuming due to the inherent complexity of CPS. As a result, data-driven model generation using machine learning methods is gaining popularity. In this paper, we present Flowcean, a novel framework designed to automate the generation of models through data-driven learning that focuses on modularity and usability. By offering various learning strategies, data processing methods, and evaluation metrics, our framework provides a comprehensive solution, tailored to CPS scenarios. Flowcean facilitates the integration of diverse learning libraries and tools within a modular and flexible architecture, ensuring adaptability to a wide range of modeling tasks. This streamlines the process of model generation and evaluation, making it more efficient and accessible.
[AI-21] Can RL Improve Generalization of LLM Agents ? An Empirical Study
【速读】：该论文旨在解决强化学习微调（Reinforcement Fine-Tuning, RFT）在训练与测试环境不一致时的泛化能力问题，即当前RFT方法多在同质环境中评估，难以反映实际部署中面对未见环境（如不同背景知识、观测空间和动作接口）时的表现。其解决方案的关键在于系统性地从三个维度进行评估：(1) 同一环境内任务难度变化下的泛化能力；(2) 跨环境迁移至未见过环境的性能表现；(3) 多环境顺序训练以量化迁移效果与遗忘现象。研究发现，RFT在任务难度变化下具有良好泛化能力，但在跨环境迁移中表现较弱，且该弱点与语义先验和观测/动作接口的变化密切相关；而顺序训练策略可实现下游任务性能提升且对上游任务遗忘最小，混合训练则有助于提升整体平衡性。
链接: https://arxiv.org/abs/2603.12011
作者: Zhiheng Xi,Xin Guo,Jiaqi Liu,Jiazheng Zhang,Yutao Fan,Zhihao Zhang,Shichun Liu,Mingxu Chai,Xiaowei Shi,Yitao Zhai,Xunliang Cai,Tao Gui,Qi Zhang,Xuanjing Huang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: Preprint, under review
Abstract:Reinforcement fine-tuning (RFT) has shown promise for training LLM agents to perform multi-turn decision-making based on environment feedback. However, most existing evaluations remain largely in-domain: training and testing are conducted in the same environment or even on the same tasks. In real-world deployment, agents may operate in unseen environments with different background knowledge, observation spaces, and action interfaces. To characterize the generalization profile of RFT under such shifts, we conduct a systematic study along three axes: (1) within-environment generalization across task difficulty, (2) cross-environment transfer to unseen environments, and (3) sequential multi-environment training to quantify transfer and forgetting. Our results show that RFT generalizes well across task difficulty within an environment, but exhibits weaker transfer to unseen environments, which correlates with shifts in both semantic priors and observation/action interfaces. In contrast, sequential training yields promising downstream gains with minimal upstream forgetting, and mixture training across environments improves the overall balance. We further provide detailed analyses and deeper insights, and hope our work helps the community develop and deploy generalizable LLM agents.
[AI-22] Few-for-Many Personalized Federated Learning
【速读】：该论文旨在解决个性化联邦学习（Personalized Federated Learning, PFL）中因客户端数据分布高度异构而导致的模型性能瓶颈问题，尤其是在大规模联邦场景下如何实现高效且近似最优的个性化建模。现有方法多依赖启发式策略（如聚类或模型插值），缺乏对异构目标间权衡的理论保障和可扩展性。其关键解决方案是将PFL重新建模为“少对多”优化问题（few-for-many optimization），即仅维护少量（K ≪ M）共享服务器模型来服务所有M个客户端，通过理论证明该框架在模型数量K增加时逼近最优个性化性能，并随客户端数据量增长收敛至各自最优解。基于此，作者提出FedFew算法，利用梯度驱动的联合优化机制自动发现最佳模型多样性，无需人工划分客户端或繁琐超参数调优，在视觉、自然语言处理及真实医疗影像等多类任务中验证了其有效性——仅用3个服务器模型即可持续优于当前最先进方法。
链接: https://arxiv.org/abs/2603.11992
作者: Ping Guo,Tiantian Zhang,Xi Lin,Xiang Li,Zhi-Ri Tang,Qingfu Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Personalized Federated Learning (PFL) aims to train customized models for clients with highly heterogeneous data distributions while preserving data privacy. Existing approaches often rely on heuristics like clustering or model interpolation, which lack principled mechanisms for balancing heterogeneous client objectives. Serving M clients with distinct data distributions is inherently a multi-objective optimization problem, where achieving optimal personalization ideally requires M distinct models on the Pareto front. However, maintaining M separate models poses significant scalability challenges in federated settings with hundreds or thousands of clients. To address this challenge, we reformulate PFL as a few-for-many optimization problem that maintains only K shared server models ( K \ll M ) to collectively serve all M clients. We prove that this framework achieves near-optimal personalization: the approximation error diminishes as K increases and each client’s model converges to each client’s optimum as data grows. Building on this reformulation, we propose FedFew, a practical algorithm that jointly optimizes the K server models through efficient gradient-based updates. Unlike clustering-based approaches that require manual client partitioning or interpolation-based methods that demand careful hyperparameter tuning, FedFew automatically discovers the optimal model diversity through its optimization process. Experiments across vision, NLP, and real-world medical imaging datasets demonstrate that FedFew, with just 3 models, consistently outperforms other state-of-the-art approaches. Code is available at this https URL.
[AI-23] LABSHIELD: A Multimodal Benchmark for Safety-Critical Reasoning and Planning in Scientific Laboratories
【速读】：该论文旨在解决当前多模态大语言模型（Multimodal Large Language Models, MLLMs）在实体化实验室环境中进行安全决策时存在的可靠性不足问题，尤其是在高风险操作场景下对危险识别与安全推理能力的欠缺。解决方案的关键在于构建了一个名为LABSHIELD的多视角基准测试平台，其基于美国职业安全与健康管理局（OSHA）标准和全球化学品统一分类和标签制度（GHS），涵盖164项具有不同操作复杂度和风险特征的实验室任务，形成严谨的安全分类体系，并采用双轨评估框架对20个商用模型、9个开源模型及3个实体化模型进行全面测评，从而揭示了通用领域问答准确率与专业实验室安全任务性能之间的系统性差距（平均下降32.0%），为后续发展以安全性为核心的设计范式提供了实证基础与量化依据。
链接: https://arxiv.org/abs/2603.11987
作者: Qianpu Sun,Xiaowei Chi,Yuhan Rui,Ying Li,Kuangzhi Ge,Jiajun Li,Sirui Han,Shanghang Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Artificial intelligence is increasingly catalyzing scientific automation, with multimodal large language model (MLLM) agents evolving from lab assistants into self-driving lab operators. This transition imposes stringent safety requirements on laboratory environments, where fragile glassware, hazardous substances, and high-precision laboratory equipment render planning errors or misinterpreted risks potentially irreversible. However, the safety awareness and decision-making reliability of embodied agents in such high-stakes settings remain insufficiently defined and evaluated. To bridge this gap, we introduce LABSHIELD, a realistic multi-view benchmark designed to assess MLLMs in hazard identification and safety-critical reasoning. Grounded in U.S. Occupational Safety and Health Administration (OSHA) standards and the Globally Harmonized System (GHS), LABSHIELD establishes a rigorous safety taxonomy spanning 164 operational tasks with diverse manipulation complexities and risk profiles. We evaluate 20 proprietary models, 9 open-source models, and 3 embodied models under a dual-track evaluation framework. Our results reveal a systematic gap between general-domain MCQ accuracy and Semi-open QA safety performance, with models exhibiting an average drop of 32.0% in professional laboratory scenarios, particularly in hazard interpretation and safety-aware planning. These findings underscore the urgent necessity for safety-centric reasoning frameworks to ensure reliable autonomous scientific experimentation in embodied laboratory contexts. The full dataset will be released soon.
[AI-24] Normative Common Ground Replication (NormCoRe): Replication-by-Translation for Studying Norms in Multi-agent AI
【速读】：该论文旨在解决当前多智能体人工智能（Multi-agent Artificial Intelligence, MAAI）研究中对规范性协调动态（normative coordination dynamics）缺乏系统分析的问题，尤其针对现有方法将人类与AI代理视为等价主体、忽视集体规范形成机制的局限。其解决方案的关键在于提出Normative Common Ground Replication（NormCoRe）方法论框架，该框架通过结构化映射人类实验设计到MAAI环境，实现从行为科学和复制研究中提取规范性推理机制，并结合前沿MAAI架构进行可复现的规范分析。NormCoRe不仅支持对AI代理在公平敏感场景中的规范判断差异进行系统记录与比较，还揭示了基础模型选择和代理人格语言设定对规范演化的影响，从而为MAAI中的规范建模提供了一个可解释、可验证且具指导意义的研究路径。
链接: https://arxiv.org/abs/2603.11974
作者: Luca Deck,Simeon Allmendinger,Lucas Müller,Niklas Kühl
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT '26)
Abstract:In the late 2010s, the fashion trend NormCore framed sameness as a signal of belonging, illustrating how norms emerge through collective coordination. Today, similar forms of normative coordination can be observed in systems based on Multi-agent Artificial Intelligence (MAAI), as AI-based agents deliberate, negotiate, and converge on shared decisions in fairness-sensitive domains. Yet, existing empirical approaches often treat norms as targets for alignment or replication, implicitly assuming equivalence between human subjects and AI agents and leaving collective normative dynamics insufficiently examined. To address this gap, we propose Normative Common Ground Replication (NormCoRe), a novel methodological framework to systematically translate the design of human subject experiments into MAAI environments. Building on behavioral science, replication research, and state-of-the-art MAAI architectures, NormCoRe maps the structural layers of human subject studies onto the design of AI agent studies, enabling systematic documentation of study design and analysis of norms in MAAI. We demonstrate the utility of NormCoRe by replicating a seminal experimental study on distributive justice, in which participants negotiate fairness principles under a “veil of ignorance”. We show that normative judgments in AI agent studies can differ from human baselines and are sensitive to the choice of the foundation model and the language used to instantiate agent personas. Our work provides a principled pathway for analyzing norms in MAAI and helps to guide, reflect, and document design choices whenever AI agents are used to automate or support tasks formerly carried out by humans.
[AI-25] Learning Transferable Sensor Models via Language-Informed Pretraining
【速读】：该论文旨在解决现有自监督学习（SSL）方法在处理大量未标注多变量时间序列数据时，难以捕捉下游分类与推理任务所需的语义结构的问题，同时克服当前传感器-语言对齐方法受限于固定传感器配置（如预定义通道集、信号长度或时间分辨率）而导致跨域适用性差的局限。解决方案的关键在于提出SLIP（Sensor-Language-Informed Pretraining）框架，其核心创新包括：通过对比对齐与传感器条件化描述生成相结合，实现判别性理解与生成式推理的协同；利用交叉注意力机制复用预训练的仅解码器语言模型，并引入灵活的patch嵌入器，从而支持不同时间分辨率和可变长度输入而无需额外微调，显著提升了零样本迁移性能与传感器问答准确率。
链接: https://arxiv.org/abs/2603.11950
作者: Yuliang Chen,Arvind Pillai,Yu Yvonne Wu,Tess Z. Griffin,Lisa Marsch,Michael V. Heinz,Nicholas C. Jacobson,Andrew Campbell
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Modern sensing systems generate large volumes of unlabeled multivariate time-series data. This abundance of unlabeled data makes self-supervised learning (SSL) a natural approach for learning transferable representations. However, most existing approaches are optimized for reconstruction or forecasting objectives and often fail to capture the semantic structure required for downstream classification and reasoning tasks. While recent sensor-language alignment methods improve semantic generalization through captioning and zero-shot transfer, they are limited to fixed sensor configurations, such as predefined channel sets, signal lengths, or temporal resolutions, which hinders cross-domain applicability. To address these gaps, we introduce \textbfSLIP (\textbfSensor \textbfLanguage-\textbfInformed \textbfPretraining), an open-source framework for learning language-aligned representations that generalize across diverse sensor setups. SLIP integrates contrastive alignment with sensor-conditioned captioning, facilitating both discriminative understanding and generative reasoning. By repurposing a pretrained decoder-only language model via cross-attention and introducing an elegant, flexible patch-embedder, SLIP supports different temporal resolutions and variable-length input at inference time without additional retraining. Across 11 datasets, SLIP demonstrates superior performance in zero-shot transfer, signal captioning, and question answering. It achieves a 77.14% average linear-probing accuracy, a 5.93% relative improvement over strong baselines, and reaches 64.83% accuracy in sensor-based question answering.
[AI-26] Delayed Backdoor Attacks: Exploring the Temporal Dimension as a New Attack Surface in Pre-Trained Models
【速读】：该论文旨在解决预训练模型（Pre-trained Models, PTMs）中传统后门攻击（Backdoor Attacks）依赖“即时性假设”所带来的局限性，即恶意行为在触发词出现时立即激活，导致攻击易被检测和防御。为突破这一限制，作者提出了一种新型攻击范式——延迟后门攻击（Delayed Backdoor Attacks, DBA），其核心创新在于引入时间维度，使攻击行为与触发词暴露在时间上解耦。解决方案的关键在于设计了一个基于非线性衰减机制的原型系统DND（Delayed Backdoor Attacks Based on Nonlinear Decay），该系统嵌入轻量级状态逻辑模块，在达到预设阈值前保持静默，随后触发可控爆发，从而实现对日常词汇作为触发词的隐蔽攻击。实验表明，DND可在高干净准确率（≥94%）前提下维持可调延迟，并在激活后实现接近完美的攻击成功率（≈99%），且对现有先进防御手段具有鲁棒性，首次实证了时间维度是PTMs中一个尚未被保护的攻击面。
链接: https://arxiv.org/abs/2603.11949
作者: Zikang Ding,Haomiao Yang,Meng Hao,Wenbo Jiang,Kunlan Xiang,Runmeng Du,Yijing Liu,Ruichen Zhang,Dusit Niyato
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Backdoor attacks against pre-trained models (PTMs) have traditionally operated under an ``immediacy assumption,‘’ where malicious behavior manifests instantly upon trigger occurrence. This work revisits and challenges this paradigm by introducing \textit\textbfDelayed Backdoor Attacks (DBA), a new class of threats in which activation is temporally decoupled from trigger exposure. We propose that this \textbftemporal dimension is the key to unlocking a previously infeasible class of attacks: those that use common, everyday words as triggers. To examine the feasibility of this paradigm, we design and implement a proof-of-concept prototype, termed \underlineDelayed Backdoor Attacks Based on \underlineNonlinear \underlineDecay (DND). DND embeds a lightweight, stateful logic module that postpones activation until a configurable threshold is reached, producing a distinct latency phase followed by a controlled outbreak. We derive a formal model to characterize this latency behavior and propose a dual-metric evaluation framework (ASR and ASR _delay ) to empirically measure the delay effect. Extensive experiments on four (natural language processing)NLP benchmarks validate the core capabilities of DND: it remains dormant for a controllable duration, sustains high clean accuracy ( \ge 94%), and achieves near-perfect post-activation attack success rates ( \approx 99%, The average of other methods is below 95%.). Moreover, DND exhibits resilience against several state-of-the-art defenses. This study provides the first empirical evidence that the temporal dimension constitutes a viable yet unprotected attack surface in PTMs, underscoring the need for next-generation, stateful, and time-aware defense mechanisms.
[AI-27] Geometry-Aware Probabilistic Circuits via Voronoi Tessellations
【速读】：该论文旨在解决概率电路（Probabilistic Circuits, PCs）在建模数据流形局部几何结构方面的局限性，即其采用与数据无关的混合权重，难以捕捉数据分布的局部特征。解决方案的关键在于将Voronoi图（Voronoi Tessellations, VT）引入PC的求和节点中以显式建模几何结构，但直接嵌入会破坏计算的可 tractability（可 tractable inference）。为此，作者提出两种互补策略：一是构建一个近似推理框架，提供推理结果的严格上下界；二是定义一种基于VT的结构条件，在满足该条件时恢复精确的可 tractable 推理。此外，论文还引入了VT的可微松弛（differentiable relaxation），支持梯度驱动的学习，并在标准密度估计任务上验证了方法的有效性。
链接: https://arxiv.org/abs/2603.11946
作者: Sahil Sidheekh,Sriraam Natarajan
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Probabilistic circuits (PCs) enable exact and tractable inference but employ data independent mixture weights that limit their ability to capture local geometry of the data manifold. We propose Voronoi tessellations (VT) as a natural way to incorporate geometric structure directly into the sum nodes of a PC. However, naïvely introducing such structure breaks tractability. We formalize this incompatibility and develop two complementary solutions: (1) an approximate inference framework that provides guaranteed lower and upper bounds for inference, and (2) a structural condition for VT under which exact tractable inference is recovered. Finally, we introduce a differentiable relaxation for VT that enables gradient-based learning and empirically validate the resulting approach on standard density estimation tasks.
[AI-28] Effective Resistance Rewiring: A Simple Topological Correction for Over-Squashing
【速读】：该论文旨在解决图神经网络（Graph Neural Networks, GNNs）在处理长距离依赖时因“过挤压”（over-squashing）导致的信息传递瓶颈问题，即指数增长的邻域信息被迫通过有限的结构瓶颈，从而限制了模型对全局结构的理解能力。解决方案的关键在于提出一种名为有效电阻重布线（Effective Resistance Rewiring, ERR）的拓扑修正策略，其核心思想是利用有效电阻（effective resistance）作为全局信号来识别并修复结构性瓶颈：通过迭代地在电阻最大的节点对之间添加边、移除电阻最小的边，在固定边预算下增强弱连接路径的同时控制图的稠密化。该方法无需额外参数，仅依赖于一个聚合所有路径信息的全局度量，且实验表明它能显著改善消息传播效率，尤其在提升长距离通信方面优于传统基于局部指标（如曲率）的重布线方法。
链接: https://arxiv.org/abs/2603.11944
作者: Bertran Miquel-Oliver,Manel Gil-Sorribes,Victor Guallar,Alexis Molina
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Graph Neural Networks struggle to capture long-range dependencies due to over-squashing, where information from exponentially growing neighborhoods must pass through a small number of structural bottlenecks. While recent rewiring methods attempt to alleviate this limitation, many rely on local criteria such as curvature, which can overlook global connectivity constraints that restrict information flow. We introduce Effective Resistance Rewiring (ERR), a simple topology correction strategy that uses effective resistance as a global signal to detect structural bottlenecks. ERR iteratively adds edges between node pairs with the largest resistance while removing edges with minimal resistance, strengthening weak communication pathways while controlling graph densification under a fixed edge budget. The procedure is parameter-free beyond the rewiring budget and relies on a single global measure aggregating all paths between node pairs. Beyond predictive performance with GCN models, we analyze how rewiring affects message propagation. By tracking cosine similarity between node embeddings across layers, we examine how the relationship between initial node features and learned representations evolves during message passing, comparing graphs with and without rewiring. This analysis helps determine whether improvements arise from better long-range communication rather than changes in embedding geometry. Experiments on homophilic and heterophilic graphs, including directed settings with DirGCN, reveal a trade-off between over-squashing and oversmoothing, where oversmoothing corresponds to the loss of representation diversity across layers. Resistance-guided rewiring improves connectivity and signal propagation but can accelerate representation mixing in deep models. Combining ERR with normalization techniques such as PairNorm stabilizes this trade-off and improves performance.
[AI-29] Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation
【速读】：该论文旨在解决学术会议审稿过程中因作者人口统计学特征（如种族、国籍等）导致的系统性偏见问题，这种偏见会持续削弱代表性不足群体（underrepresented groups）在学术发表中的机会。解决方案的关键在于提出一种基于多层感知机（MultiLayer Perceptron, MLP）的公平推荐模型 Fair-PaperRec，其通过引入交集性公平损失（intersectional fairness loss）来量化并惩罚不同群体间的接受率差异，同时保留对论文质量的严格要求，从而在不牺牲学术严谨性的前提下实现更公平的录用决策。
链接: https://arxiv.org/abs/2603.11936
作者: Uttamasha Anjally Oyshi,Susan Gauch
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: arXiv admin note: substantial text overlap with arXiv:2602.22438
Abstract:Despite frequent double-blind review, demographic biases of authors still disadvantage the underrepresented groups. We present Fair-PaperRec, a MultiLayer Perceptron (MLP)-based model that addresses demographic disparities in post-review paper acceptance decisions while maintaining high-quality requirements. Our methodology penalizes demographic disparities while preserving quality through intersectional criteria (e.g., race, country) and a customized fairness loss, in contrast to heuristic approaches. Evaluations using conference data from ACM Special Interest Group on Computer-Human Interaction (SIGCHI), Designing Interactive Systems (DIS), and Intelligent User Interfaces (IUI) indicate a 42.03% increase in underrepresented group participation and a 3.16% improvement in overall utility, indicating that diversity promotion does not compromise academic rigor and supports equity-focused peer review solutions.
[AI-30] MobileKernelBench: Can LLM s Write Efficient Kernels for Mobile Devices?
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在移动端设备上生成高效计算内核（kernel）的能力尚未被充分探索的问题，核心挑战在于当前LLMs在移动框架的工程复杂性和数据稀缺性下表现不佳，常因幻觉和缺乏领域特定知识导致编译失败率高（>54%）且性能提升微弱。解决方案的关键是提出Mobile Kernel Agent（MoKA），一个具备仓库感知推理能力的多智能体系统，通过“规划-执行”机制实现对移动内核生成任务的有效分解与协同优化，并结合所提出的MobileKernelBench评估框架进行端到端验证，最终将编译成功率提升至93.7%，并使27.4%的生成内核相较原生库实现可测量的速度加速。
链接: https://arxiv.org/abs/2603.11935
作者: Xingze Zou,Jing Wang,Yuhua Zheng,Xueyi Chen,Haolei Bai,Lingcheng Kong,Syed A.R. Abu-Bakar,Zhaode Wang,Chengfei Lv,Haoji Hu,Huan Wang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) have demonstrated remarkable capabilities in code generation, yet their potential for generating kernels specifically for mobile de- vices remains largely unexplored. In this work, we extend the scope of automated kernel generation to the mobile domain to investigate the central question: Can LLMs write efficient kernels for mobile devices? To enable systematic investigation, we introduce MobileKernelBench, a comprehensive evaluation framework comprising a benchmark prioritizing operator diversity and cross-framework interoperability, coupled with an automated pipeline that bridges the host-device gap for on-device verification. Leveraging this framework, we conduct extensive evaluation on the CPU backend of Mobile Neural Network (MNN), revealing that current LLMs struggle with the engineering complexity and data scarcity inher-ent to mobile frameworks; standard models and even fine-tuned variants exhibit high compilation failure rates (over 54%) and negligible performance gains due to hallucinations and a lack of domain-specific grounding. To overcome these limitations, we propose the Mobile K ernel A gent (MoKA), a multi-agent system equipped with repository-aware reasoning and a plan-and-execute this http URL on MobileKernelBench, MoKA achieves state-of-the-art performance, boosting compilation success to 93.7% and enabling 27.4% of generated kernelsto deliver measurable speedups over native libraries.
[AI-31] Understanding LLM Behavior When Encountering User-Supplied Harmful Content in Harmless Tasks
【速读】：该论文试图解决当前大语言模型（Large Language Models, LLMs）在执行看似无害任务时，对用户提供的有害内容缺乏伦理判断能力的问题，即内容层面的伦理一致性缺失。其解决方案的关键在于构建一个包含1,357条条目、覆盖十类有害内容的有害知识数据集，并设计九个符合使用政策的无害任务（按所需用户输入内容分为广度、中度和有限三类），以此系统性评估主流LLMs在处理用户输入中的有害内容时的行为表现。研究发现，即使是最新的GPT-5.2和Gemini-3-Pro等模型也常因未识别或拒绝处理有害内容而违背人类对伦理行为的期望，且“暴力/图像”类别与“翻译”任务组合最易诱发有害响应，从而揭示了内容级伦理风险这一被忽视的安全漏洞。
链接: https://arxiv.org/abs/2603.11914
作者: Junjie Chu,Yiting Qu,Ye Leng,Michael Backes,Yun Shen,Savvas Zannettou,Yang Zhang
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 21 pages, 11 figures
Abstract:Large Language Models (LLMs) are increasingly trained to align with human values, primarily focusing on task level, i.e., refusing to execute directly harmful tasks. However, a subtle yet crucial content-level ethical question is often overlooked: when performing a seemingly benign task, will LLMs – like morally conscious human beings – refuse to proceed when encountering harmful content in user-provided material? In this study, we aim to understand this content-level ethical question and systematically evaluate its implications for mainstream LLMs. We first construct a harmful knowledge dataset (i.e., non-compliant with OpenAI’s usage policy) to serve as the user-supplied harmful content, with 1,357 entries across ten harmful categories. We then design nine harmless tasks (i.e., compliant with OpenAI’s usage policy) to simulate the real-world benign tasks, grouped into three categories according to the extent of user-supplied content required: extensive, moderate, and limited. Leveraging the harmful knowledge dataset and the set of harmless tasks, we evaluate how nine LLMs behave when exposed to user-supplied harmful content during the execution of benign tasks, and further examine how the dynamics between harmful knowledge categories and tasks affect different LLMs. Our results show that current LLMs, even the latest GPT-5.2 and Gemini-3-Pro, often fail to uphold human-aligned ethics by continuing to process harmful content in harmless tasks. Furthermore, external knowledge from the Violence/Graphic'' category and the Translation’’ task is more likely to elicit harmful responses from LLMs. We also conduct extensive ablation studies to investigate potential factors affecting this novel misuse vulnerability. We hope that our study could inspire enhanced safety measures among stakeholders to mitigate this overlooked content-level ethical risk.
[AI-32] EnTransformer: A Deep Generative Transformer for Multivariate Probabilistic Forecasting
【速读】：该论文旨在解决多变量时间序列预测中不确定性量化（uncertainty quantification）的可靠性问题，尤其是在能源系统和交通网络等复杂场景下，传统基于参数化似然或分位数目标的概率预测方法难以捕捉多个相关时间序列之间的复杂联合预测分布。解决方案的关键在于提出EnTransformer框架，其核心创新是将“回归-生成”（engression）这一用于建模条件分布的随机学习范式与Transformer强大的序列建模能力相结合：通过向模型表示中注入随机噪声，并优化能量基评分目标（energy-based scoring objective），直接学习条件预测分布而不依赖参数假设，从而在保持Transformer对长程时序依赖和跨序列交互建模能力的同时，生成一致且高质量的多变量预测轨迹。
链接: https://arxiv.org/abs/2603.11909
作者: Rajdeep Pathak,Rahul Goswami,Madhurima Panja,Palash Ghosh,Tanujit Chakraborty
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Reliable uncertainty quantification is critical in multivariate time series forecasting problems arising in domains such as energy systems and transportation networks, among many others. Although Transformer-based architectures have recently achieved strong performance for sequence modeling, most probabilistic forecasting approaches rely on restrictive parametric likelihoods or quantile-based objectives. They can struggle to capture complex joint predictive distributions across multiple correlated time series. This work proposes EnTransformer, a deep generative forecasting framework that integrates engression, a stochastic learning paradigm for modeling conditional distributions, with the expressive sequence modeling capabilities of Transformers. The proposed approach injects stochastic noise into the model representation and optimizes an energy-based scoring objective to directly learn the conditional predictive distribution without imposing parametric assumptions. This design enables EnTransformer to generate coherent multivariate forecast trajectories while preserving Transformers’ capacity to effectively model long-range temporal dependencies and cross-series interactions. We evaluate our proposed EnTransformer on several widely used benchmarks for multivariate probabilistic forecasting, including Electricity, Traffic, Solar, Taxi, KDD-cup, and Wikipedia datasets. Experimental results demonstrate that EnTransformer produces well-calibrated probabilistic forecasts and consistently outperforms the benchmark models.
[AI-33] he Mirror Design Pattern: Strict Data Geometry over Model Scale for Prompt Injection Detection
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在实际部署中面临的提示注入攻击（prompt injection attack）检测问题，尤其聚焦于第一层筛查（L1 screening）的性能瓶颈：即检测器需满足低延迟、确定性、不可被提示操控（non-promptable）和可审计等约束条件。解决方案的关键在于提出一种名为“Mirror”的数据构建设计模式，通过严格组织正负样本为32个结构化单元（cell），使分类器学习控制平面攻击机制（control-plane attack mechanics）而非训练集中的偶然关联（corpus shortcuts）。基于5000个公开数据集样本构建的镜像拓扑结构，作者训练了一个稀疏字符n-gram线性支持向量机（SVM），并将其权重编译为静态Rust二进制文件，在亚毫秒级延迟下实现95.97%召回率与92.07% F1分数，显著优于后续采用大型神经网络（Prompt Guard~2）的第二层防御方案（44.35%召回率，59.14% F1）。结果表明，在L1筛选场景中，精心设计的数据几何结构比单纯依赖模型规模更为关键。
链接: https://arxiv.org/abs/2603.11875
作者: J Alex Corll
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Prompt injection defenses are often framed as semantic understanding problems and delegated to increasingly large neural detectors. For the first screening layer, however, the requirements are different: the detector runs on every request and therefore must be fast, deterministic, non-promptable, and auditable. We introduce Mirror, a data-curation design pattern that organizes prompt injection corpora into matched positive and negative cells so that a classifier learns control-plane attack mechanics rather than incidental corpus shortcuts. Using 5,000 strictly curated open-source samples – the largest corpus supportable under our public-data validity contract – we define a 32-cell mirror topology, fill 31 of those cells with public data, train a sparse character n-gram linear SVM, compile its weights into a static Rust artifact, and obtain 95.97% recall and 92.07% F1 on a 524-case holdout at sub-millisecond latency with no external model runtime dependencies. On the same holdout, our next line of defense, a 22-million-parameter Prompt Guard~2 model reaches 44.35% recall and 59.14% F1 at 49,ms median and 324,ms p95 latency. Linear models still leave residual semantic ambiguities such as use-versus-mention for later pipeline layers, but within that scope our results show that for L1 prompt injection screening, strict data geometry can matter more than model scale.
[AI-34] AdaFuse: Accelerating Dynamic Adapter Inference via Token-Level Pre-Gating and Fused Kernel Optimization AAAI2026
【速读】：该论文旨在解决动态稀疏结构（如Mixture-of-Experts, MoE）与参数高效适配器（如LoRA）集成后导致的推理延迟显著增加的问题。尽管计算量仅小幅上升，传统动态路由机制因频繁、碎片化的CUDA核函数调用造成严重性能瓶颈，使解码速度下降超过2.5倍。其解决方案的关键在于提出AdaFuse框架，通过算法与硬件系统的紧密协同设计，采用基于token级别的预门控策略（token-level pre-gating），在每个token处理前做出全局路由决策，实现“一次决定、全域应用”的静态执行路径；进而开发定制化CUDA内核，将所有选中LoRA适配器的参数融合到主干模型中，完成单次高效运算，从而在保持模型精度的同时，将解码延迟降低超过2.4倍。
链接: https://arxiv.org/abs/2603.11873
作者: Qiyang Li,Rui Kong,Yuchen Li,Hengyi Cai,Shuaiqiang Wang,Linghe Kong,Guihai Chen,Dawei Yin
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: Accepted to AAAI 2026. arXiv admin note: substantial text overlap with arXiv:2405.17741
Abstract:The integration of dynamic, sparse structures like Mixture-of-Experts (MoE) with parameter-efficient adapters (e.g., LoRA) is a powerful technique for enhancing Large Language Models (LLMs). However, this architectural enhancement comes at a steep cost: despite minimal increases in computational load, the inference latency often skyrockets, leading to decoding speeds slowing by over 2.5 times. Through a fine-grained performance analysis, we pinpoint the primary bottleneck not in the computation itself, but in the severe overhead from fragmented, sequential CUDA kernel launches required for conventional dynamic routing. To address this challenge, we introduce AdaFuse, a framework built on a tight co-design between the algorithm and the underlying hardware system to enable efficient dynamic adapter execution. Departing from conventional layer-wise or block-wise routing, AdaFuse employs a token-level pre-gating strategy, which makes a single, global routing decision for all adapter layers before a token is processed. This “decide-once, apply-everywhere” approach effectively staticizes the execution path for each token, creating an opportunity for holistic optimization. We capitalize on this by developing a custom CUDA kernel that performs a fused switching operation, merging the parameters of all selected LoRA adapters into the backbone model in a single, efficient pass. Experimental results on popular open-source LLMs show that AdaFuse achieves accuracy on par with state-of-the-art dynamic adapters while drastically cutting decoding latency by a factor of over 2.4x, thereby bridging the gap between model capability and inference efficiency.
[AI-35] Social Legal Ethical Empathetic and Cultural Norm Operationalisation for AI Agents
【速读】：该论文旨在解决高风险领域（如医疗保健和执法）中AI代理行为与社会、法律、伦理、同理心及文化（SLEEC）规范对齐的工程挑战，尤其聚焦于如何将国际框架中抽象的AI规范原则转化为可验证的具体要求。其解决方案的关键在于提出了一套系统化的SLEEC规范操作化流程，涵盖规范要求的确定、验证、实施与验证四个阶段，并梳理了支持该流程的方法与工具，从而为开发不仅功能有效且能明确体现人类规范与价值观的AI代理提供理论框架与研究政策议程。
链接: https://arxiv.org/abs/2603.11864
作者: Radu Calinescu,Ana Cavalcanti,Marsha Chechik,Lina Marsso,Beverley Townsend
机构: 未知
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 12 pages
Abstract:As AI agents are increasingly used in high-stakes domains like healthcare and law enforcement, aligning their behaviour with social, legal, ethical, empathetic, and cultural (SLEEC) norms has become a critical engineering challenge. While international frameworks have established high-level normative principles for AI, a significant gap remains in translating these abstract principles into concrete, verifiable requirements. To address this gap, we propose a systematic SLEEC-norm operationalisation process for determining, validating, implementing, and verifying normative requirements. Furthermore, we survey the landscape of methods and tools supporting this process, and identify key remaining challenges and research avenues for addressing them. We thus establish a framework - and define a research and policy agenda - for developing AI agents that are not only functionally useful but also demonstrably aligned with human norms and values.
[AI-36] CreativeBench: Benchmarking and Enhancing Machine Creativity via Self-Evolving Challenges
【速读】：该论文旨在解决当前生成式 AI（Generative AI）系统在持续生成新颖代码 artifacts 方面缺乏严谨、定量评估方法的问题。针对这一挑战，作者提出了 CreativeBench 基准测试平台，其核心创新在于基于经典认知框架构建了两个子集——CreativeBench-Combo 和 CreativeBench-Explore，分别聚焦组合型与探索型创造力，并通过逆向工程与自对弈的自动化流程实现量化评估。关键解决方案是引入一个统一指标，即质量与新颖性的乘积，从而客观区分创造性输出与幻觉；此外，提出 EvoRePE 推理时调控策略，通过内化进化搜索模式来稳定提升机器创造力。
链接: https://arxiv.org/abs/2603.11863
作者: Zi-Han Wang,Lam Nguyen,Zhengyang Zhao,Mengyue Yang,Chengwei Qin,Yujiu Yang,Linyi Yang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The saturation of high-quality pre-training data has shifted research focus toward evolutionary systems capable of continuously generating novel artifacts, leading to the success of AlphaEvolve. However, the progress of such systems is hindered by the lack of rigorous, quantitative evaluation. To tackle this challenge, we introduce CreativeBench, a benchmark for evaluating machine creativity in code generation, grounded in a classical cognitive framework. Comprising two subsets – CreativeBench-Combo and CreativeBench-Explore – the benchmark targets combinatorial and exploratory creativity through an automated pipeline utilizing reverse engineering and self-play. By leveraging executable code, CreativeBench objectively distinguishes creativity from hallucination via a unified metric defined as the product of quality and novelty. Our analysis of state-of-the-art models reveals distinct behaviors: (1) scaling significantly improves combinatorial creativity but yields diminishing returns for exploration; (2) larger models exhibit ``convergence-by-scaling,‘’ becoming more correct but less divergent; and (3) reasoning capabilities primarily benefit constrained exploration rather than combination. Finally, we propose EvoRePE, a plug-and-play inference-time steering strategy that internalizes evolutionary search patterns to consistently enhance machine creativity.
[AI-37] You Told Me to Do It: Measuring Instructional Text-induced Private Data Leakage in LLM Agents
【速读】：该论文旨在解决高权限大语言模型（Large Language Model, LLM）代理在自主执行外部文档指令时存在的安全漏洞问题，即“可信执行者困境”（Trusted Executor Dilemma）。该困境源于LLM代理无法区分恶意指令与合法配置说明，导致其在无安全监督的情况下高度合规地执行嵌入于文档中的攻击性指令。解决方案的关键在于系统性地识别和量化这一结构性漏洞：通过构建一个包含500个真实世界README文件的基准测试集ReadSecBench，并引入涵盖语言伪装、结构混淆和语义抽象的三维分类体系，实验证明即使在商业部署的计算机使用代理中，端到端数据外泄成功率可达85%，且跨模型、跨编程语言和注入位置均具一致性；同时用户研究和防御机制评估表明当前规则基与LLM基防护方案均无法可靠检测此类攻击而不产生不可接受的误报率，从而揭示了LLM代理在功能合规性与安全意识之间存在持久的“语义安全差距”（Semantic-Safety Gap），并确立文档嵌入式指令注入为当前高权限LLM代理部署中未被缓解的根本性威胁。
链接: https://arxiv.org/abs/2603.11862
作者: Ching-Yu Kao,Xinfeng Li,Shenyu Dai,Tianze Qiu,Pengcheng Zhou,Eric Hanchen Jiang,Philip Sperl
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 14 pages
Abstract:High-privilege LLM agents that autonomously process external documentation are increasingly trusted to automate tasks by reading and executing project instructions, yet they are granted terminal access, filesystem control, and outbound network connectivity with minimal security oversight. We identify and systematically measure a fundamental vulnerability in this trust model, which we term the \emphTrusted Executor Dilemma: agents execute documentation-embedded instructions, including adversarial ones, at high rates because they cannot distinguish malicious directives from legitimate setup guidance. This vulnerability is a structural consequence of the instruction-following design paradigm, not an implementation bug. To structure our measurement, we formalize a three-dimensional taxonomy covering linguistic disguise, structural obfuscation, and semantic abstraction, and construct \textbfReadSecBench, a benchmark of 500 real-world README files enabling reproducible evaluation. Experiments on the commercially deployed computer-use agent show end-to-end exfiltration success rates up to 85%, consistent across five programming languages and three injection positions. Cross-model evaluation on four LLM families in a simulation environment confirms that semantic compliance with injected instructions is consistent across model families. A 15-participant user study yields a 0% detection rate across all participants, and evaluation of 12 rule-based and 6 LLM-based defenses shows neither category achieves reliable detection without unacceptable false-positive rates. Together, these results quantify a persistent \emphSemantic-Safety Gap between agents’ functional compliance and their security awareness, establishing that documentation-embedded instruction injection is a persistent and currently unmitigated threat to high-privilege LLM agent deployments.
[AI-38] he Landscape of Generative AI in Information Systems: A Synthesis of Secondary Reviews and Research Agendas
【速读】：该论文试图解决生成式 AI（Generative AI, GenAI）在组织中快速采纳过程中面临的多重挑战与社会技术系统之间存在显著错配的问题，具体表现为技术子系统的快速演进与社会子系统适应滞后之间的不协调，导致技术可靠性、伦理风险及治理空白等问题持续存在。解决方案的关键在于推动信息系统（Information Systems, IS）研究从被动分析影响转向主动引导技术能力与组织流程、社会价值观及监管制度的协同演化，其核心路径包括构建人机混合协作机制、实施情境化验证、发展概率系统的设计原则以及建立适应性治理框架。
链接: https://arxiv.org/abs/2603.11842
作者: Aleksander Jarzębowicz,Adam Przybyłek,Jacinto Estima,Yen Ying Ng,Jakub Swacha,Beata Zielosko,Lech Madeyski,Noel Carroll,Kai-Kristian Kemell,Bartosz Marcinkowski,Alberto Rodrigues da Silva,Viktoria Stray,Netta Iivari,Anh Nguyen-Duc,Jorge Melegati,Boris Delibašić,Emilio Insfran
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:As organizations grapple with the rapid adoption of Generative AI (GenAI), this study synthesizes the state of knowledge through a systematic literature review of secondary studies and research agendas. Analyzing 28 papers published since 2023, we find that while GenAI offers transformative potential for productivity and innovation, its adoption is constrained by multiple interrelated challenges, including technical unreliability (hallucinations, performance drift), societal-ethical risks (bias, misuse, skill erosion), and a systemic governance vacuum (privacy, accountability, intellectual property). Interpreted through a socio-technical lens, these findings reveal a persistent misalignment between GenAI’s fast-evolving technical subsystem and the slower-adapting social subsystem, positioning IS research as critical for achieving joint optimization. To bridge this gap, we discuss a research agenda that reorients IS scholarship from analyzing impacts toward actively shaping the co-evolution of technical capabilities with organizational procedures, societal values, and regulatory institutions–emphasizing hybrid human–AI ensembles, situated validation, design principles for probabilistic systems, and adaptive governance.
[AI-39] VisiFold: Long-Term Traffic Forecasting via Temporal Folding Graph and Node Visibility ICDE2026
【速读】：该论文旨在解决长时交通预测（long-term traffic forecasting）中面临的两大挑战：一是计算资源消耗随预测时长增加而显著上升，二是时空依赖关系变得更加复杂。现有方法通常基于时空图结构，分别处理时间和空间维度，导致“快照堆叠膨胀”（snapshot-stacking inflation）和跨步碎片化（cross-step fragmentation）问题。其解决方案的关键在于提出VisiFold框架，该框架引入一种新颖的时序折叠图（temporal folding graph），将多个时间快照整合为单一图结构以降低冗余；同时设计节点可见性机制（node visibility mechanism），通过节点级掩码（node-level masking）与子图采样（subgraph sampling）有效缓解大规模节点带来的计算瓶颈。实验表明，VisiFold在显著减少资源消耗的同时，仍能保持优于现有基线的性能，即使在80%高掩码率下也具备鲁棒性，从而突破了长时交通预测在时空维度上的资源限制。
链接: https://arxiv.org/abs/2603.11816
作者: Zhiwei Zhang,Xinyi Du,Weihao Wang,Xuanchi Guo,Wenjuan Han
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 9 figures, accepted by ICDE 2026
Abstract:Traffic forecasting is a cornerstone of intelligent transportation systems. While existing research has made significant progress in short-term prediction, long-term forecasting remains a largely uncharted and challenging frontier. Extending the prediction horizon intensifies two critical issues: escalating computational resource consumption and increasingly complex spatial-temporal dependencies. Current approaches, which rely on spatial-temporal graphs and process temporal and spatial dimensions separately, suffer from snapshot-stacking inflation and cross-step fragmentation. To overcome these limitations, we propose \textitVisiFold. Our framework introduces a novel temporal folding graph that consolidates a sequence of temporal snapshots into a single graph. Furthermore, we present a node visibility mechanism that incorporates node-level masking and subgraph sampling to overcome the computational bottleneck imposed by large node counts. Extensive experiments show that VisiFold not only drastically reduces resource consumption but also outperforms existing baselines in long-term forecasting tasks. Remarkably, even with a high mask ratio of 80%, VisiFold maintains its performance advantage. By effectively breaking the resource constraints in both temporal and spatial dimensions, our work paves the way for more realistic long-term traffic forecasting. The code is available at~ this https URL.
[AI-40] Automating Skill Acquisition through Large-Scale Mining of Open-Source Agent ic Repositories: A Framework for Multi-Agent Procedural Knowledge Extraction
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在自主工作流中因缺乏专业化程序性知识（procedural expertise）而导致的实用性受限问题。其解决方案的关键在于构建一个系统性的框架，通过挖掘GitHub等开源代码库自动获取高质量代理技能（agent skills），特别是从如TheoremExplainAgent和Code2Video等先进系统中提取可视化与教育能力，并借助密集检索（dense retrieval）实现语义层面的技能识别，最终将这些技能标准化为统一格式。该方法无需重新训练模型即可扩展LLM的程序性知识，同时结合严格的安全治理与多维评估指标，显著提升知识迁移效率（达40%）并保持教学质量接近人工制作教程水平。
链接: https://arxiv.org/abs/2603.11808
作者: Shuzhen Bi,Mengsong Wu,Hao Hao,Keqian Li,Wentao Liu,Siyu Song,Hongbo Zhao,Aimin Zhou
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The transition from monolithic large language models (LLMs) to modular, skill-equipped agents represents a fundamental architectural shift in artificial intelligence deployment. While general-purpose models demonstrate remarkable breadth in declarative knowledge, their utility in autonomous workflows is frequently constrained by insufficient specialized procedural expertise. This report investigates a systematic framework for automated acquisition of high-quality agent skills through mining of open-source repositories on platforms such as GitHub. We focus on the extraction of visualization and educational capabilities from state-of-the-art systems including TheoremExplainAgent and Code2Video, both utilizing the Manim mathematical animation engine. The framework encompasses repository structural analysis, semantic skill identification through dense retrieval, and translation to the standardized this http URL format. We demonstrate that systematic extraction from agentic repositories, combined with rigorous security governance and multi-dimensional evaluation metrics, enables scalable acquisition of procedural knowledge that augments LLM capabilities without requiring model retraining. Our analysis reveals that agent-generated educational content can achieve 40% gains in knowledge transfer efficiency while maintaining pedagogical quality comparable to human-crafted tutorials.
[AI-41] A Semi-Decentralized Approach to Multiagent Control
【速读】：该论文旨在解决多智能体系统在通信不确定环境下的半去中心化控制问题，即如何在不完全可靠的通信条件下协调多个智能体的行为以实现最优决策。其核心挑战在于传统去中心化或多智能体部分可观测马尔可夫决策过程（POMDP）难以建模通信时机和内容的不确定性。解决方案的关键是提出一种新的框架——SDec-POMDP（Semi-Decentralized Partially Observable Markov Decision Process），将通信行为本身也建模为一个随时间分布的随机过程，从而统一了去中心化与多智能体POMDP以及多种显式通信机制。进一步地，作者设计了递归小步半去中心化A*（RS-SDA*）算法，用于精确生成SDec-POMDP的最优策略，并通过标准基准测试和海上医疗撤离场景验证了方法的有效性。
链接: https://arxiv.org/abs/2603.11802
作者: Mahdi Al-Husseini,Mykel J. Kochenderfer,Kyle H. Wray
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce an expressive framework and algorithms for the semi-decentralized control of cooperative agents in environments with communication uncertainty. Whereas semi-Markov control admits a distribution over time for agent actions, semi-Markov communication, or what we refer to as semi-decentralization, gives a distribution over time for what actions and observations agents can store in their histories. We extend semi-decentralization to the partially observable Markov decision process (POMDP). The resulting SDec-POMDP unifies decentralized and multiagent POMDPs and several existing explicit communication mechanisms. We present recursive small-step semi-decentralized A* (RS-SDA*), an exact algorithm for generating optimal SDec-POMDP policies. RS-SDA* is evaluated on semi-decentralized versions of several standard benchmarks and a maritime medical evacuation scenario. This paper provides a well-defined theoretical foundation for exploring many classes of multiagent communication problems through the lens of semi-decentralization.
[AI-42] DocSage: An Information Structuring Agent for Multi-Doc Multi-Entity Question Answering
【速读】：该论文旨在解决多文档多实体问答（Multi-document Multi-entity Question Answering, MDMEQA）任务中，现有大语言模型（Large Language Models, LLMs）和检索增强生成（Retrieval-Augmented Generation, RAG）框架在跨文档事实链构建与实体关系推理方面的局限性问题。具体而言，标准RAG依赖向量相似度进行粗粒度检索，常遗漏关键事实；基于图的RAG难以高效整合碎片化的复杂关系网络；二者均缺乏模式（schema）感知能力，导致实体关系推断不准确。解决方案的关键在于提出一个端到端的代理式框架DocSage，其核心创新包括：（1）动态模式发现模块，自适应提取查询相关的最小可连接模式以捕获关键实体与关系；（2）增强纠错机制的信息抽取模块，将非结构化文本转化为语义一致的关系表；（3）基于模式感知的多跳关系推理模块，利用结构化表示实现跨文档实体对齐与证据聚合，从而提升事实定位精度、支持自然的跨文档实体连接，并缓解LLM注意力扩散问题。
链接: https://arxiv.org/abs/2603.11798
作者: Teng Lin,Yizhang Zhu,Zhengxuan Zhang,Yuyu Luo,Nan Tang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-document Multi-entity Question Answering inherently demands models to track implicit logic between multiple entities across scattered documents. However, existing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks suffer from critical limitations: standard RAG’s vector similarity-based coarse-grained retrieval often omits critical facts, graph-based RAG fails to efficiently integrate fragmented complex relationship networks, and both lack schema awareness, leading to inadequate cross-document evidence chain construction and inaccurate entity relationship deduction. To address these challenges, we propose DocSage, an end-to-end agentic framework that integrates dynamic schema discovery, structured information extraction, and schema-aware relational reasoning with error guarantees. DocSage operates through three core modules: (1) A schema discovery module dynamically infers query-specific minimal joinable schemas to capture essential entities and relationships; (2) An extraction module transforms unstructured text into semantically coherent relational tables, enhanced by error-aware correction mechanisms to reduce extraction errors; (3) A reasoning module performs multi-hop relational reasoning over structured tables, leveraging schema awareness to efficiently align cross-document entities and aggregate evidence. This agentic design offers three key advantages: precise fact localization via SQL-powered indexing, natural support for cross-document entity joins through relational tables, and mitigated LLM attention diffusion via structured representation. Evaluations on two MDMEQA benchmarks demonstrate that DocSage significantly outperforms state-of-the-art long-context LLMs and RAG systems, achieving more than 27% accuracy improvements respectively.
[AI-43] Governing Evolving Memory in LLM Agents : Risks Mechanisms and the Stability and Safety Governed Memory (SSGM) Framework
【速读】：该论文旨在解决自主大语言模型（Large Language Model, LLM）代理中长期记忆系统在动态演化过程中面临的关键风险问题，包括记忆治理缺失、语义漂移（semantic drift）以及隐私漏洞等。这些问题源于记忆从静态检索数据库向动态代理机制转变时，缺乏有效的管控机制，导致敏感信息泄露和知识退化。解决方案的关键在于提出稳定性与安全性治理记忆（Stability and Safety-Governed Memory, SSGM）框架，其核心是通过解耦记忆演化与执行过程，在记忆固化前实施一致性验证、时间衰减建模和动态访问控制，从而有效抑制拓扑诱导的知识泄露并缓解迭代总结带来的语义漂移，为构建安全、持久且可靠的代理记忆系统提供理论支撑与架构保障。
链接: https://arxiv.org/abs/2603.11768
作者: Chingkwun Lam,Jiaxin Li,Lingfei Zhang,Kuo Zhao
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Long-term memory has emerged as a foundational component of autonomous Large Language Model (LLM) agents, enabling continuous adaptation, lifelong multimodal learning, and sophisticated reasoning. However, as memory systems transition from static retrieval databases to dynamic, agentic mechanisms, critical concerns regarding memory governance, semantic drift, and privacy vulnerabilities have surfaced. While recent surveys have focused extensively on memory retrieval efficiency, they largely overlook the emergent risks of memory corruption in highly dynamic environments. To address these emerging challenges, we propose the Stability and Safety-Governed Memory (SSGM) framework, a conceptual governance architecture. SSGM decouples memory evolution from execution by enforcing consistency verification, temporal decay modeling, and dynamic access control prior to any memory consolidation. Through formal analysis and architectural decomposition, we show how SSGM can mitigate topology-induced knowledge leakage where sensitive contexts are solidified into long-term storage, and help prevent semantic drift where knowledge degrades through iterative summarization. Ultimately, this work provides a comprehensive taxonomy of memory corruption risks and establishes a robust governance paradigm for deploying safe, persistent, and reliable agentic memory systems.
[AI-44] Understanding Wikidata Qualifiers: An Analysis and Taxonomy
【速读】：该论文旨在解决Wikidata中限定符（qualifier）在语义理解、选择、查询和逻辑推理方面的挑战，特别是如何系统化地分类和利用限定符以提升知识图谱的构建与应用效率。其解决方案的关键在于通过分析Wikidata数据集，结合频率与多样性指标（采用改进的香农熵指数以应对长尾现象），筛选出前300个最具代表性的限定符，并构建一个包含上下文类、认知/不确定性类、结构类及附加类的精细化分类体系（taxonomy）。该分类体系不仅有助于贡献者更准确地使用限定符，还能优化限定符推荐系统与知识图谱设计方法。
链接: https://arxiv.org/abs/2603.11767
作者: Gilles Falquet,Sahar Aljalbout
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:This paper presents an in-depth analysis of Wikidata qualifiers, focusing on their semantics and actual usage, with the aim of developing a taxonomy that addresses the challenges of selecting appropriate qualifiers, querying the graph, and making logical inferences. The study evaluates qualifier importance based on frequency and diversity, using a modified Shannon entropy index to account for the “long tail” phenomenon. By analyzing a Wikidata dump, the top 300 qualifiers were selected and categorized into a refined taxonomy that includes contextual, epistemic/uncertainty, structural, and additional qualifiers. The taxonomy aims to guide contributors in creating and querying statements, improve qualifier recommendation systems, and enhance knowledge graph design methodologies. The results show that the taxonomy effectively covers the most important qualifiers and provides a structured approach to understanding and utilizing qualifiers in Wikidata.
[AI-45] Exploiting Expertise of Non-Expert and Diverse Agents in Social Bandit Learning: A Free Energy Approach
【速读】：该论文旨在解决个性化AI服务中个体强化学习（Reinforcement Learning, RL）算法难以利用社会学习能力的问题。传统RL方法仅依赖个体经验，而人类和动物在实际情境中常通过观察他人行为进行社会学习，从而提升学习效率。为此，作者提出了一种基于自由能（Free Energy）的社会多臂赌博机（Social Bandit）学习算法，其关键在于：无需任何先验知识或社会规范，社交代理能够自主评估其他代理的专家水平，并将自身直接环境经验与对他人策略的估计进行融合。该方法理论上可收敛至最优策略，在多种场景下实证验证优于现有方法，尤其在存在非专家但相关代理时显著提升个体学习性能，同时保持对数 regret。
链接: https://arxiv.org/abs/2603.11757
作者: Erfan Mirzaei,Seyed Pooya Shariatpanahi,Alireza Tavakoli,Reshad Hosseini,Majid Nili Ahmadabadi
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Personalized AI-based services involve a population of individual reinforcement learning agents. However, most reinforcement learning algorithms focus on harnessing individual learning and fail to leverage the social learning capabilities commonly exhibited by humans and animals. Social learning integrates individual experience with observing others’ behavior, presenting opportunities for improved learning outcomes. In this study, we focus on a social bandit learning scenario where a social agent observes other agents’ actions without knowledge of their rewards. The agents independently pursue their own policy without explicit motivation to teach each other. We propose a free energy-based social bandit learning algorithm over the policy space, where the social agent evaluates others’ expertise levels without resorting to any oracle or social norms. Accordingly, the social agent integrates its direct experiences in the environment and others’ estimated policies. The theoretical convergence of our algorithm to the optimal policy is proven. Empirical evaluations validate the superiority of our social learning method over alternative approaches in various scenarios. Our algorithm strategically identifies the relevant agents, even in the presence of random or suboptimal agents, and skillfully exploits their behavioral information. In addition to societies including expert agents, in the presence of relevant but non-expert agents, our algorithm significantly enhances individual learning performance, where most related methods fail. Importantly, it also maintains logarithmic regret.
[AI-46] Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows
【速读】：该论文旨在解决现有基于深度生成模型的多变量时间序列异常检测方法中，仅依赖观测空间似然性导致的局限性问题——即观测空间中的高似然值可能对应于不符合结构化时序动态的异常或分布外样本。解决方案的关键在于将异常概念从观测空间迁移至预设的潜在空间，并在条件归一化流（conditional normalizing flows）中引入显式归纳偏置，通过离散时间状态空间框架约束潜在表示遵循指定的时序演化规律；在此设定下，正常行为对应于潜在轨迹服从预设分布，而异常则定义为对这些动态的违背，从而将异常检测转化为基于潜在空间的统计合规性检验，实现即使在观测似然高的区域仍具有效性的判别准则。
链接: https://arxiv.org/abs/2603.11756
作者: David Baumgartner,Eliezer de Souza da Silva,Iñigo Urteaga
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing data likelihood. However, likelihood in observation space measures marginal density rather than conformity to structured temporal dynamics, and therefore can assign high probability to anomalous or out-of-distribution samples. We address this structural limitation by relocating the notion of anomaly to a prescribed latent space. We introduce explicit inductive biases in conditional normalizing flows, modeling time-series observations within a discrete-time state-space framework that constrains latent representations to evolve according to prescribed temporal dynamics. Under this formulation, expected behavior corresponds to compliance with a specified distribution over latent trajectories, while anomalies are defined as violations of these dynamics. Anomaly detection is consequently reduced to a statistically grounded compliance test, such that observations are mapped to latent space and evaluated via goodness-of-fit tests against the prescribed latent evolution. This yields a principled decision rule that remains effective even in regions of high observation likelihood. Experiments on synthetic and real-world time-series demonstrate reliable detection of anomalies in frequency, amplitude, and observation noise, while providing interpretable diagnostics of model compliance.
[AI-47] CINDI: Conditional Imputation and Noisy Data Integrity with Flows in Power Grid Data
【速读】：该论文旨在解决现实世界中多变量时间序列数据（如电力电网）因噪声和异常值导致下游任务性能下降的问题。传统数据清洗方法通常采用分离的检测与插补策略，难以捕捉数据的联合分布并忽略预测不确定性。其解决方案的关键在于提出一种无监督的概率框架——条件插补与噪声数据完整性（CINDI），该框架将异常检测与插补统一为一个端到端系统，基于条件归一化流（conditional normalizing flows）建模数据的精确条件似然，从而识别低概率片段并迭代采样统计一致的替代值，有效保留系统的物理与统计特性，实现高效且可靠的时序数据修复。
链接: https://arxiv.org/abs/2603.11745
作者: David Baumgartner,Helge Langseth,Heri Ramampiaro
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Real-world multivariate time series, particularly in critical infrastructure such as electrical power grids, are often corrupted by noise and anomalies that degrade the performance of downstream tasks. Standard data cleaning approaches often rely on disjoint strategies, which involve detecting errors with one model and imputing them with another. Such approaches can fail to capture the full joint distribution of the data and ignore prediction uncertainty. This work introduces Conditional Imputation and Noisy Data Integrity (CINDI), an unsupervised probabilistic framework designed to restore data integrity in complex time series. Unlike fragmented approaches, CINDI unifies anomaly detection and imputation into a single end-to-end system built on conditional normalizing flows. By modeling the exact conditional likelihood of the data, the framework identifies low-probability segments and iteratively samples statistically consistent replacements. This allows CINDI to efficiently reuse learned information while preserving the underlying physical and statistical properties of the system. We evaluate the framework using real-world grid loss data from a Norwegian power distribution operator, though the methodology is designed to generalize to any multivariate time series domain. The results demonstrate that CINDI yields robust performance compared to competitive baselines, offering a scalable solution for maintaining reliability in noisy environments.
[AI-48] Gender Bias in Generative AI-assisted Recruitment Processes
【速读】：该论文试图解决生成式 AI（Generative AI）在招聘过程中可能加剧性别偏见的问题，特别是大型语言模型（LLMs）如何基于性别和工作经验背景对候选人推荐职业，并可能强化劳动力市场中已存在的性别刻板印象。其解决方案的关键在于通过结构化提示实验，分析 GPT-5 对24个平衡性别、年龄、经验和专业领域的模拟候选人所推荐的职业描述及其伴随的形容词特征，从而揭示模型在语言层面存在性别倾向性——即女性候选人更常被赋予情感化和共情类词汇，而男性则与战略性和分析性特质相关联。这一发现强调了在敏感应用场景中需引入透明度与公平性评估机制，以确保未来数字劳动力市场的公正性。
链接: https://arxiv.org/abs/2603.11736
作者: Martina Ullasci,Marco Rondina,Riccardo Coppola,Antonio Vetrò
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 4 pages, 4 figures
Abstract:In recent years, generative artificial intelligence (GenAI) systems have assumed increasingly crucial roles in selection processes, personnel recruitment and analysis of candidates’ profiles. However, the employment of large language models (LLMs) risks reproducing, and in some cases amplifying, gender stereotypes and bias already present in the labour market. The objective of this paper is to evaluate and measure this phenomenon, analysing how a state-of-the-art generative model (GPT-5) suggests occupations based on gender and work experience background, focusing on under-35-year-old Italian graduates. The model has been prompted to suggest jobs to 24 simulated candidate profiles, which are balanced in terms of gender, age, experience and professional field. Although no significant differences emerged in job titles and industry, gendered linguistic patterns emerged in the adjectives attributed to female and male candidates, indicating a tendency of the model to associate women with emotional and empathetic traits, while men with strategic and analytical ones. The research raises an ethical question regarding the use of these models in sensitive processes, highlighting the need for transparency and fairness in future digital labour markets.
[AI-49] Adapting Dijkstra for Buffers and Unlimited Transfers
【速读】：该论文旨在解决基于RAPTOR的路径查找算法在公共交通运输路由中，尤其是在存在站台缓冲时间（buffer time）的情况下，因连接过滤策略不准确而导致的路径优化失效问题。传统的时间依赖型Dijkstra（Time-Dependent Dijkstra, TD-Dijkstra）虽在无缓冲时间场景下优于MR（Multi-Round RAPTOR），但其高效实现依赖于预处理阶段对主导连接的过滤，这一过程假设乘客可随时切换至更快的交通方式，忽略了实际中座位乘客与换乘乘客在缓冲时间上的行为差异。为此，论文提出Transfer Aware Dijkstra（TAD），其核心创新在于不再仅扫描单条边，而是遍历完整的行程序列（trip sequence），从而正确区分不同乘客类型对缓冲时间的响应机制，在保持TD-Dijkstra性能优势的同时，确保在含缓冲时间场景下的解的最优性。实验表明，TAD在伦敦和瑞士网络上相较MR实现了超过两倍的速度提升，并能稳定产出最优结果。
链接: https://arxiv.org/abs/2603.11729
作者: Denys Katkalo,Andrii Rohovyi,Toby Walsh
机构: 未知
类目: Data Structures and Algorithms (cs.DS); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:In recent years, RAPTOR based algorithms have been considered the state-of-the-art for path-finding with unlimited transfers without preprocessing. However, this status largely stems from the evolution of routing research, where Dijkstra-based solutions were superseded by timetable-based algorithms without a systematic comparison. In this work, we revisit classical Dijkstra-based approaches for public transit routing with unlimited transfers and demonstrate that Time-Dependent Dijkstra (TD-Dijkstra) outperforms MR. However, efficient TD-Dijkstra implementations rely on filtering dominated connections during preprocessing, which assumes passengers can always switch to a faster connection. We show that this filtering is unsound when stops have buffer times, as it cannot distinguish between seated passengers who may continue without waiting and transferring passengers who must respect the buffer. To address this limitation, we introduce Transfer Aware Dijkstra (TAD), a modification that scans entire trip sequences rather than individual edges, correctly handling buffer times while maintaining performance advantages over MR. Our experiments on London and Switzerland networks show that we can achieve a greater than two time speed-up over MR while producing optimal results on both networks with and without buffer times.
[AI-50] When OpenClaw Meets Hospital: Toward an Agent ic Operating System for Dynamic Clinical Workflows
【速读】：该论文旨在解决当前大语言模型（Large Language Model, LLM）代理在医疗环境中部署困难的问题，主要受限于可靠性不足、安全风险以及长期记忆机制缺失。其解决方案的关键在于提出一种面向医院环境的LLM代理架构，包含四个核心组件：受Linux多用户系统启发的受限执行环境、以文档为中心的交互范式、基于页索引的长期记忆结构，以及经过筛选的医学技能库。该架构通过预定义技能接口和资源隔离来约束代理行为，从而在保障安全性、透明性和可审计性的前提下，实现临床工作流程的自动化协调，为构建“医院智能操作系统”提供基础计算层支撑。
链接: https://arxiv.org/abs/2603.11721
作者: Wenxian Yang,Hanzheng Qiu,Bangqun Zhang,Chengquan Li,Zhiyong Huang,Xiaobin Feng,Rongshan Yu,Jiahong Dong
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents extend conventional generative models by integrating reasoning, tool invocation, and persistent memory. Recent studies suggest that such agents may significantly improve clinical workflows by automating documentation, coordinating care processes, and assisting medical decision making. However, despite rapid progress, deploying autonomous agents in healthcare environments remains difficult due to reliability limitations, security risks, and insufficient long-term memory mechanisms. This work proposes an architecture that adapts LLM agents for hospital environments. The design introduces four core components: a restricted execution environment inspired by Linux multi-user systems, a document-centric interaction paradigm connecting patient and clinician agents, a page-indexed memory architecture designed for long-term clinical context management, and a curated medical skills library enabling ad-hoc composition of clinical task sequences. Rather than granting agents unrestricted system access, the architecture constrains actions through predefined skill interfaces and resource isolation. We argue that such a system forms the basis of an Agentic Operating System for Hospital, a computing layer capable of coordinating clinical workflows while maintaining safety, transparency, and auditability. This work grounds the design in OpenClaw, an open-source autonomous agent framework that structures agent capabilities as a curated library of discrete skills, and extends it with the infrastructure-level constraints required for safe clinical deployment.
[AI-51] Scaling Laws for Educational AI Agents
【速读】：该论文旨在解决教育类大语言模型（Large Language Models, LLMs）代理的能力扩展问题，即当前对LLM在参数量、训练数据和计算资源上的缩放规律研究较为充分，但针对基于LLM的教育代理（Educational Agent）的系统性能力增长机制仍缺乏探索。其解决方案的关键在于提出“代理缩放定律”（Agent Scaling Law），该框架通过五个结构化维度——角色定义清晰度、技能深度、工具完备性、运行时能力及教育者专业知识注入——来系统性提升教育代理的能力，并引入AgentProfile这一基于JSON的结构化规范作为实现机制，从而实现教育代理能力的可预测、可扩展增长。
链接: https://arxiv.org/abs/2603.11709
作者: Mengsong Wu,Hao Hao,Shuzhen Bi,Keqian Li,Wentao Liu,Siyu Song,Hongbo Zhao,Aimin Zhou
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 19 pages, 6 figures, 3 tables, 1 algorithm
Abstract:While scaling laws for Large Language Models (LLMs) have been extensively studied along dimensions of model parameters, training data, and compute, the scaling behavior of LLM-based educational agents remains unexplored. We propose that educational agent capability scales not merely with the underlying model size, but through structured dimensions that we collectively term the Agent Scaling Law: role definition clarity, skill depth, tool completeness, runtime capability, and educator expertise injection. Central to this framework is AgentProfile, a structured JSON-based specification that serves as the mechanism enabling systematic capability growth of educational agents. We present EduClaw, a profile-driven multi-agent platform that operationalizes this scaling law, demonstrating its effectiveness through the construction and deployment of 330+ educational agent profiles encompassing 1,100+ skill modules across K-12 subjects. Our empirical observations suggest that educational agent performance scales predictably with profile structural richness. We identify two complementary scaling axes – Tool Scaling and Skill Scaling – as future directions, arguing that the path to more capable educational AI lies not solely in larger models, but in stronger structured capability systems.
[AI-52] STAIRS-Former: Spatio-Temporal Attention with Interleaved Recursive Structure Transformer for Offline Multi-task Multi-agent Reinforcement Learning
【速读】：该论文旨在解决离线多智能体强化学习（Offline Multi-Agent Reinforcement Learning, MARL）中因任务间智能体数量不一致以及需泛化至未见场景所带来的挑战。现有方法虽采用基于观察标记化和分层技能学习的Transformer架构，但其对智能体间协调的注意力机制利用不足，且依赖单一历史标记，难以捕捉部分可观测MARL设置中的长时序依赖关系。解决方案的关键在于提出STAIRS-Former架构，通过引入空间与时间层次结构，在关键标记上实现有效注意力机制的同时捕获长期交互历史，并结合标记丢弃（token dropout）策略提升在不同智能体规模下的鲁棒性与泛化能力。
链接: https://arxiv.org/abs/2603.11691
作者: Jiwon Jeon,Myungsik Cho,Youngchul Sung
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Offline multi-agent reinforcement learning (MARL) with multi-task datasets is challenging due to varying numbers of agents across tasks and the need to generalize to unseen scenarios. Prior works employ transformers with observation tokenization and hierarchical skill learning to address these issues. However, they underutilize the transformer attention mechanism for inter-agent coordination and rely on a single history token, which limits their ability to capture long-horizon temporal dependencies in partially observable MARL settings. In this paper, we propose STAIRS-Former, a transformer architecture augmented with spatial and temporal hierarchies that enables effective attention over critical tokens while capturing long interaction histories. We further introduce token dropout to enhance robustness and generalization across varying agent populations. Extensive experiments on diverse multi-agent benchmarks, including SMAC, SMAC-v2, MPE, and MaMuJoCo, with multi-task datasets demonstrate that STAIRS-Former consistently outperforms prior methods and achieves new state-of-the-art performance.
[AI-53] Explicit Logic Channel for Validation and Enhancement of MLLM s on Zero-Shot Tasks
【速读】：该论文旨在解决前沿多模态大语言模型（Multimodal Large Language Models, MLLMs）在视觉-语言理解（Visual-Language Comprehension, VLC）任务中缺乏可解释性与可信度的问题，尤其是在零样本场景下以黑箱方式部署时难以验证其行为。解决方案的关键在于提出一种显式逻辑通道（Explicit Logic Channel, ELC），该通道并行于MLLM的隐式逻辑通道（Implicit Logic Channel），通过集成语言模型（LLM）、视觉特征模块（VFM）以及基于概率推理的逻辑推理机制，实现对显式视觉证据的事实性、反事实性和关系性推理。同时引入一致性率（Consistency Rate, CR）作为跨通道验证指标，无需真实标注即可完成模型选择与增强，并通过跨通道融合进一步提升零样本任务性能，从而显著增强模型的可解释性与可信度。
链接: https://arxiv.org/abs/2603.11689
作者: Mei Chee Leong,Ying Gu,Hui Li Tan,Liyuan Li,Nancy Chen
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Frontier Multimodal Large Language Models (MLLMs) exhibit remarkable capabilities in Visual-Language Comprehension (VLC) tasks. However, they are often deployed as zero-shot solution to new tasks in a black-box manner. Validating and understanding the behavior of these models become important for application to new task. We propose an Explicit Logic Channel, in parallel with the black-box model channel, to perform explicit logical reasoning for model validation, selection and enhancement. The frontier MLLM, encapsulating latent vision-language knowledge, can be considered as an Implicit Logic Channel. The proposed Explicit Logic Channel, mimicking human logical reasoning, incorporates a LLM, a VFM, and logical reasoning with probabilistic inference for factual, counterfactual, and relational reasoning over the explicit visual evidence. A Consistency Rate (CR) is proposed for cross-channel validation and model selection, even without ground-truth annotations. Additionally, cross-channel integration further improves performance in zero-shot tasks over MLLMs, grounded with explicit visual evidence to enhance trustworthiness. Comprehensive experiments conducted for two representative VLC tasks, i.e., MC-VQA and HC-REC, on three challenging benchmarks, with 11 recent open-source MLLMs from 4 frontier families. Our systematic evaluations demonstrate the effectiveness of proposed ELC and CR for model validation, selection and improvement on MLLMs with enhanced explainability and trustworthiness.
[AI-54] Causal Prosody Mediation for Text-to-Speech:Counterfactual Training of Duration Pitch and Energy in FastSpeech2
【速读】：该论文旨在解决现有文本到语音（Text-to-Speech, TTS）合成系统中情感语调（prosody）与语言内容难以解耦的问题，从而实现对语音表达的精确控制。解决方案的关键在于提出一种因果语调中介框架（causal prosody mediation framework），通过构建一个结构化因果模型来明确文本（content）、情感和说话人如何共同影响语调特征（持续时间、音高、能量）并最终生成语音波形。该方法在FastSpeech2基础上引入显式情感条件建模，并设计两种互补的因果损失项：间接路径约束（Indirect Path Constraint, IPC）用于强制情感仅通过语调影响语音，而反事实语调约束（Counterfactual Prosody Constraint, CPC）则鼓励不同情感对应不同的语调模式。这一机制显著提升了情感渲染准确性和语调操控能力，同时保持了自然度和说话人一致性。
链接: https://arxiv.org/abs/2603.11683
作者: Suvendu Sekhar Mohanty
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:We propose a novel causal prosody mediation framework for expressive text-to-speech (TTS) synthesis. Our approach augments the FastSpeech2 architecture with explicit emotion conditioning and introduces counterfactual training objectives to disentangle emotional prosody from linguistic content. By formulating a structural causal model of how text (content), emotion, and speaker jointly influence prosody (duration, pitch, energy) and ultimately the speech waveform, we derive two complementary loss terms: an Indirect Path Constraint (IPC) to enforce that emotion affects speech only through prosody, and a Counterfactual Prosody Constraint (CPC) to encourage distinct prosody patterns for different emotions. The resulting model is trained on multi-speaker emotional corpora (LibriTTS, EmoV-DB, VCTK) with a combined objective that includes standard spectrogram reconstruction and variance prediction losses alongside our causal losses. In evaluations on expressive speech synthesis, our method achieves significantly improved prosody manipulation and emotion rendering, with higher mean opinion scores (MOS) and emotion accuracy than baseline FastSpeech2 variants. We also observe better intelligibility (low WER) and speaker consistency when transferring emotions across speakers. Extensive ablations confirm that the causal objectives successfully separate prosody attribution, yielding an interpretable model that allows controlled counterfactual prosody editing (e.g. “same utterance, different emotion”) without compromising naturalness. We discuss the implications for identifiability in prosody modeling and outline limitations such as the assumption that emotion effects are fully captured by pitch, duration, and energy. Our work demonstrates how integrating causal learning principles into TTS can improve controllability and expressiveness in generated speech.
[AI-55] Entropy-Preserving Reinforcement Learning ICLR2026
【速读】：该论文旨在解决政策梯度算法在训练过程中自然导致熵（entropy）下降的问题，从而限制了策略的探索能力，削弱了语言模型在复杂任务中的多样性和创造性表现。其解决方案的关键在于主动监控和控制训练过程中的熵动态，通过理论分析识别影响熵行为的核心因素（如数值精度），并提出两种机制：一是REPO方法，通过修改优势函数（advantage function）来调节熵；二是ADAPO方法，采用自适应非对称裁剪策略实现熵的稳定控制。这些方法使模型在整个训练过程中保持探索多样性，最终获得性能更优且具备持续学习能力的策略。
链接: https://arxiv.org/abs/2603.11682
作者: Aleksei Petrenko,Ben Lipkin,Kevin Chen,Erik Wijmans,Marco Cusumano-Towner,Raja Giryes,Philipp Krähenbühl
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Published at ICLR 2026
Abstract:Policy gradient algorithms have driven many recent advancements in language model reasoning. An appealing property is their ability to learn from exploration on their own trajectories, a process crucial for fostering diverse and creative solutions. As we show in this paper, many policy gradient algorithms naturally reduce the entropy – and thus the diversity of explored trajectories – as part of training, yielding a policy increasingly limited in its ability to explore. In this paper, we argue that entropy should be actively monitored and controlled throughout training. We formally analyze the contributions of leading policy gradient objectives on entropy dynamics, identify empirical factors (such as numerical precision) that significantly impact entropy behavior, and propose explicit mechanisms for entropy control. These include REPO, a family of algorithms that modify the advantage function to regulate entropy, and ADAPO, an adaptive asymmetric clipping approach. Models trained with our entropy-preserving methods maintain diversity throughout training, yielding final policies that are more performant and retain their trainability for sequential learning in new environments.
[AI-56] LLM s can construct powerful representations and streamline sample-efficient supervised learning
【速读】：该论文旨在解决真实世界数据集日益复杂和异构背景下，监督学习因输入表示设计瓶颈而效率低下的问题，尤其是在处理时间序列、自由文本和结构化记录等多模态数据时，通常需要繁琐的领域特定工程。其解决方案的关键在于提出一种基于代理（agentic）的流水线方法：首先，利用大语言模型（LLM）分析少量但多样化的文本序列化输入样本，在上下文中归纳出一个全局规则（rubric），该规则作为提取和组织证据的程序化规范；随后，该规则将原始文本序列转换为更适合下游模型的标准格式。此外，还引入了任务条件化的局部规则（local rubrics），由LLM生成的任务摘要进一步增强适应性。实验证明，该方法在EHRSHOT基准的15项临床任务中显著优于传统计数特征模型、直接文本序列化基线以及预训练数据量更大的临床基础模型，同时具备可审计性、低成本规模化部署及可转化为表格形式以兼容多种机器学习技术的优势。
链接: https://arxiv.org/abs/2603.11679
作者: Ilker Demirel,Larry Shi,Zeshan Hussain,David Sontag
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As real-world datasets become increasingly complex and heterogeneous, supervised learning is often bottlenecked by input representation design. Modeling multimodal data for downstream tasks, such as time-series, free text, and structured records, often requires non-trivial domain-specific engineering. We propose an agentic pipeline to streamline this process. First, an LLM analyzes a small but diverse subset of text-serialized input examples in-context to synthesize a global rubric, which acts as a programmatic specification for extracting and organizing evidence. This rubric is then used to transform naive text-serializations of inputs into a more standardized format for downstream models. We also describe local rubrics, which are task-conditioned summaries generated by an LLM. Across 15 clinical tasks from the EHRSHOT benchmark, our rubric-based approaches significantly outperform traditional count-feature models, naive text-serialization-based LLM baselines, and a clinical foundation model, which is pretrained on orders of magnitude more data. Beyond performance, rubrics offer several advantages for operational healthcare settings such as being easy to audit, cost-effectiveness to deploy at scale, and they can be converted to tabular representations that unlock a swath of machine learning techniques.
[AI-57] Stable Spike: Dual Consistency Optimization via Bitwise AND Operations for Spiking Neural Networks CVPR2026
【速读】：该论文旨在解决脉冲神经网络（Spiking Neural Networks, SNNs）中因时间尖峰动态导致的固有不一致性问题，该问题严重损害了网络的表征能力并限制其识别性能。解决方案的关键在于提出“稳定尖峰”（Stable Spike）机制，通过硬件友好的“AND”位运算高效解耦出稳定的尖峰骨架（stable spike skeleton）与多时间步尖峰图（multi-timestep spike maps），从而保留关键语义信息并减少由噪声尖峰引起的不一致性；同时，强制不稳定尖峰图向稳定尖峰骨架收敛以增强跨时间步的一致性，并在稳定尖峰骨架中注入幅度感知的尖峰噪声，以提升表示多样性且保持语义一致性，进而促进模型对扰动的鲁棒预测，提升泛化能力。
链接: https://arxiv.org/abs/2603.11676
作者: Yongqi Ding,Kunshan Yang,Linze Li,Yiyang Zhang,Mengmeng Jing,Lin Zuo
机构: 未知
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
备注: Accepted by CVPR 2026
Abstract:Although the temporal spike dynamics of spiking neural networks (SNNs) enable low-power temporal pattern capture capabilities, they also incur inherent inconsistencies that severely compromise representation. In this paper, we perform dual consistency optimization via Stable Spike to mitigate this problem, thereby improving the recognition performance of SNNs. With the hardware-friendly ``AND" bit operation, we efficiently decouple the stable spike skeleton from the multi-timestep spike maps, thereby capturing critical semantics while reducing inconsistencies from variable noise spikes. Enforcing the unstable spike maps to converge to the stable spike skeleton significantly improves the inherent consistency across timesteps. Furthermore, we inject amplitude-aware spike noise into the stable spike skeleton to diversify the representations while preserving consistent semantics. The SNN is encouraged to produce perturbation-consistent predictions, thereby contributing to generalization. Extensive experiments across multiple architectures and datasets validate the effectiveness and versatility of our method. In particular, our method significantly advances neuromorphic object recognition under ultra-low latency, improving accuracy by up to 8.33%. This will help unlock the full power consumption and speed potential of SNNs.
[AI-58] he Density of Cross-Persistence Diagrams and Its Applications
【速读】：该论文旨在解决传统拓扑数据分析（Topological Data Analysis, TDA）中持久图（persistence diagrams）无法刻画两个点云之间拓扑特征交互关系的问题。现有方法仅能分析单个流形上的拓扑结构，难以捕捉跨流形的关联信息。为此，作者提出对交叉持久图（cross-persistence diagrams）的密度进行系统研究，首次证明了其存在性并建立了统计理论基础；关键创新在于设计了一个直接从点云坐标和距离矩阵预测交叉持久图密度的机器学习框架，从而实现对不同流形采样点云的有效区分。实验表明，该方法在密度预测与点云判别任务中均优于现有技术，且发现引入噪声可提升判别能力，揭示了噪声在TDA中的新作用。
链接: https://arxiv.org/abs/2603.11623
作者: Alexander Mironenko,Evgeny. Burnaev,Serguei Barannikov
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 19 pages, 20 figures
Abstract:Topological Data Analysis (TDA) provides powerful tools to explore the shape and structure of data through topological features such as clusters, loops, and voids. Persistence diagrams are a cornerstone of TDA, capturing the evolution of these features across scales. While effective for analyzing individual manifolds, persistence diagrams do not account for interactions between pairs of them. Cross-persistence diagrams (cross-barcodes), introduced recently, address this limitation by characterizing relationships between topological features of two point clouds. In this work, we present the first systematic study of the density of cross-persistence diagrams. We prove its existence, establish theoretical foundations for its statistical use, and design the first machine learning framework for predicting cross-persistence density directly from point cloud coordinates and distance matrices. Our statistical approach enables the distinction of point clouds sampled from different manifolds by leveraging the linear characteristics of cross-persistence diagrams. Interestingly, we find that introducing noise can enhance our ability to distinguish point clouds, uncovering its novel utility in TDA applications. We demonstrate the effectiveness of our methods through experiments on diverse datasets, where our approach consistently outperforms existing techniques in density prediction and achieves superior results in point cloud distinction tasks. Our findings contribute to a broader understanding of cross-persistence diagrams and open new avenues for their application in data analysis, including potential insights into time-series domain tasks and the geometry of AI-generated texts. Our code is publicly available at this https URL
[AI-59] aming OpenClaw: Security Analysis and Mitigation of Autonomous LLM Agent Threats
【速读】：该论文旨在解决自主大型语言模型（Large Language Model, LLM）代理（如OpenClaw）在执行复杂长周期任务时所面临的系统性安全威胁问题，特别是其紧密耦合的即时通信交互范式和高权限执行能力导致的攻击面扩大。解决方案的关键在于提出一个五层生命周期导向的安全框架，覆盖初始化、输入、推理、决策与执行等关键阶段，系统性地识别并分析跨时间维度的复合威胁（如间接提示注入、技能供应链污染、内存污染和意图漂移），并揭示现有基于点防御机制在应对多阶段、时序关联风险中的局限性，从而强调构建整体性安全架构的必要性，同时在各生命周期阶段评估代表性防御策略（如插件审核框架、上下文感知指令过滤、内存完整性验证协议、意图验证机制及能力强制架构）。
链接: https://arxiv.org/abs/2603.11619
作者: Xinhao Deng,Yixiang Zhang,Jiaqing Wu,Jiaqi Bai,Sibo Yi,Zhuoheng Zou,Yue Xiao,Rennai Qiu,Jianan Ma,Jialuo Chen,Xiaohu Du,Xiaofang Yang,Shiwen Cui,Changhua Meng,Weiqiang Wang,Jiaxing Song,Ke Xu,Qi Li
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Autonomous Large Language Model (LLM) agents, exemplified by OpenClaw, demonstrate remarkable capabilities in executing complex, long-horizon tasks. However, their tightly coupled instant-messaging interaction paradigm and high-privilege execution capabilities substantially expand the system attack surface. In this paper, we present a comprehensive security threat analysis of OpenClaw. To structure our analysis, we introduce a five-layer lifecycle-oriented security framework that captures key stages of agent operation, i.e., initialization, input, inference, decision, and execution, and systematically examine compound threats across the agent’s operational lifecycle, including indirect prompt injection, skill supply chain contamination, memory poisoning, and intent drift. Through detailed case studies on OpenClaw, we demonstrate the prevalence and severity of these threats and analyze the limitations of existing defenses. Our findings reveal critical weaknesses in current point-based defense mechanisms when addressing cross-temporal and multi-stage systemic risks, highlighting the need for holistic security architectures for autonomous LLM agents. Within this framework, we further examine representative defense strategies at each lifecycle stage, including plugin vetting frameworks, context-aware instruction filtering, memory integrity validation protocols, intent verification mechanisms, and capability enforcement architectures.
[AI-60] See Symbolize Act: Grounding VLMs with Spatial Representations for Better Gameplay AAAI2026
【速读】：该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在交互式环境中难以将感知信息转化为精确、具身动作的问题。其核心挑战在于VLMs虽能良好描述视觉场景，却缺乏对动作的精准控制能力。解决方案的关键在于引入符号化表示（symbolic representation）以增强模型的语义理解与决策可靠性：通过对比仅使用视觉帧、结合自提取符号、使用真实符号以及纯符号输入等多种管道，研究发现当符号信息准确时，所有模型性能均显著提升；但若依赖模型自身提取符号，则性能高度依赖于模型能力与场景复杂度。因此，可靠地从视觉输入中提取符号信息是实现有效符号接地（symbol grounding）的前提，也是当前VLM代理发展的关键瓶颈。
链接: https://arxiv.org/abs/2603.11601
作者: Ashish Baghel,Paras Chopra
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 11 pages, 13 figures. Accepted to LMReasoning Workshop at AAAI 2026
Abstract:Vision-Language Models (VLMs) excel at describing visual scenes, yet struggle to translate perception into precise, grounded actions. We investigate whether providing VLMs with both the visual frame and the symbolic representation of the scene can improve their performance in interactive environments. We evaluate three state-of-the-art VLMs across Atari games, VizDoom, and AI2-THOR, comparing frame-only, frame with self-extracted symbols, frame with ground-truth symbols, and symbol-only pipelines. Our results indicate that all models benefit when the symbolic information is accurate. However, when VLMs extract symbols themselves, performance becomes dependent on model capability and scene complexity. We further investigate how accurately VLMs can extract symbolic information from visual inputs and how noise in these symbols affects decision-making and gameplay performance. Our findings reveal that symbolic grounding is beneficial in VLMs only when symbol extraction is reliable, and highlight perception quality as a central bottleneck for future VLM-based agents.
[AI-61] Survival Meets Classification: A Novel Framework for Early Risk Prediction Models of Chronic Diseases
【速读】：该论文旨在解决慢性疾病（如糖尿病、高血压、慢性肾病、慢性阻塞性肺疾病及慢性缺血性心脏病）早期风险预测模型性能不足的问题，传统方法通常仅依赖生存分析或分类技术中的单一策略，难以兼顾时间维度与类别判别能力。其解决方案的关键在于将生存分析与分类技术融合，重新设计生存模型以高效执行分类任务，从而构建兼具时间预测与风险分层能力的综合风险评估工具；实验表明，该方法在准确率、F1分数和AUROC指标上优于LightGBM和XGBoost等先进模型，并通过三位临床专家验证了生成解释的可解释性与临床适用性。
链接: https://arxiv.org/abs/2603.11598
作者: Shaheer Ahmad Khan,Muhammad Usamah Shahid,Muddassar Farooq
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Chronic diseases are long-lasting conditions that require lifelong medical attention. Using big EMR data, we have developed early disease risk prediction models for five common chronic diseases: diabetes, hypertension, CKD, COPD, and chronic ischemic heart disease. In this study, we present a novel approach for disease risk models by integrating survival analysis with classification techniques. Traditional models for predicting the risk of chronic diseases predominantly focus on either survival analysis or classification independently. In this paper, we show survival analysis methods can be re-engineered to enable them to do classification efficiently and effectively, thereby making them a comprehensive tool for developing disease risk surveillance models. The results of our experiments on real-world big EMR data show that the performance of survival models in terms of accuracy, F1 score, and AUROC is comparable to or better than that of prior state-of-the-art models like LightGBM and XGBoost. Lastly, the proposed survival models use a novel methodology to generate explanations, which have been clinically validated by a panel of three expert physicians.
[AI-62] Leverag ing Large Language Models and Survival Analysis for Early Prediction of Chemotherapy Outcomes
【速读】：该论文旨在解决癌症化疗疗效预测中因真实世界数据缺乏明确表型（phenotype）和治疗结局标签（如肿瘤进展或毒性反应）而导致的模型构建困难问题。其关键解决方案在于利用大语言模型（Large Language Models, LLMs）与本体（ontology）驱动的技术，从电子病历（EMR）中的患者笔记中自动提取结构化表型特征与治疗结局标签，并结合NCCN指南和NIH标准对化疗方案进行规范化处理，从而显著降低表型稀疏性并提升预测准确性。通过随机生存森林（Random Survival Forest）建模，在特定时间点上实现治疗结局预测的准确率和F1分数均超过70%，且通过校准曲线验证了结果可靠性，为个性化治疗决策提供了可解释、高精度的早期预测工具。
链接: https://arxiv.org/abs/2603.11594
作者: Muhammad Faisal Shahid,Asad Afzal,Abdullah Faiz,Muhammad Siddiqui,Arbaz Khan Shehzad,Fatima Aftab,Muhammad Usamah Shahid,Muddassar Farooq
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve patient management and informed decision-making. Predictive models for chemotherapy outcomes using real-world data face challenges, including the absence of explicit phenotypes and treatment outcome labels such as cancer progression and toxicity. This study addresses these challenges by employing Large Language Models (LLMs) and ontology-based techniques for phenotypes and outcome label extraction from patient notes. We focused on one of the most frequently occurring cancers, breast cancer, due to its high prevalence and significant variability in patient response to treatment, making it a critical area for improving predictive modeling. The dataset included features such as vitals, demographics, staging, biomarkers, and performance scales. Drug regimens and their combinations were extracted from the chemotherapy plans in the EMR data and shortlisted based on NCCN guidelines, verified with NIH standards, and analyzed through survival modeling. The proposed approach significantly reduced phenotypes sparsity and improved predictive accuracy. Random Survival Forest was used to predict time-to-failure, achieving a C-index of 73%, and utilized as a classifier at a specific time point to predict treatment outcomes, with accuracy and F1 scores above 70%. The outcome probabilities were validated for reliability by calibration curves. We extended our approach to four other cancer types. This research highlights the potential of early prediction of treatment outcomes using LLM-based clinical data extraction enabling personalized treatment plans with better patient outcomes.
[AI-63] oward Complex-Valued Neural Networks for Waveform Generation ICLR2026
【速读】：该论文旨在解决当前基于实值神经网络的iSTFT（逆短时傅里叶变换）声码器在处理复数谱图时，因分离处理实部与虚部而无法有效捕捉复数谱图内在结构的问题。其解决方案的关键在于提出ComVo——一种全复数域神经声码器，其生成器和判别器均采用原生复数运算，从而在复数表示空间中实现对抗训练框架，提供结构化的反馈信号；同时引入相位量化机制以结构化引导相位变换，并设计块矩阵计算方案减少冗余运算，显著提升训练效率。
链接: https://arxiv.org/abs/2603.11589
作者: Hyung-Seok Oh,Deok-Hyeon Cho,Seung-Bin Kim,Seong-Whan Lee
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: ICLR 2026 (accepted)
Abstract:Neural vocoders have recently advanced waveform generation, yielding natural and expressive audio. Among these approaches, iSTFT-based vocoders have recently gained attention. They predict a complex-valued spectrogram and then synthesize the waveform via iSTFT, thereby avoiding learned upsampling stages that can increase computational cost. However, current approaches use real-valued networks that process the real and imaginary parts independently. This separation limits their ability to capture the inherent structure of complex spectrograms. We present ComVo, a Complex-valued neural Vocoder whose generator and discriminator use native complex arithmetic. This enables an adversarial training framework that provides structured feedback in complex-valued representations. To guide phase transformations in a structured manner, we introduce phase quantization, which discretizes phase values and regularizes the training process. Finally, we propose a block-matrix computation scheme to improve training efficiency by reducing redundant operations. Experiments demonstrate that ComVo achieves higher synthesis quality than comparable real-valued baselines, and that its block-matrix scheme reduces training time by 25%. Audio samples and code are available at this https URL.
[AI-64] RoboClaw: An Agent ic Framework for Scalable Long-Horizon Robotic Tasks
【速读】：该论文旨在解决当前视觉-语言-动作（Vision-Language-Action, VLA）系统在长时程任务中难以扩展的问题，主要挑战在于传统流水线将数据收集、策略学习与部署分离，导致对人工环境重置的依赖性强，且多策略执行脆弱。解决方案的关键在于提出 RoboClaw 框架，其核心创新是引入纠缠动作对（Entangled Action Pairs, EAP），将正向操作行为与逆向恢复动作耦合，形成自恢复循环以实现无需人工干预的持续在线策略数据采集和迭代优化；同时，在部署阶段由同一代理完成高层推理并动态编排已学策略基元，从而保持数据收集与执行阶段语义一致性，显著提升多策略鲁棒性和任务成功率，实验证明其在真实场景中相较基线方法成功率达25%提升，人类投入时间减少53.7%。
链接: https://arxiv.org/abs/2603.11558
作者: Ruiying Li,Yunlang Zhou,YuYao Zhu,Kylin Chen,Jingyuan Wang,Sukai Wang,Kongtao Hu,Minhui Yu,Bowen Jiang,Zhan Su,Jiayao Ma,Xin He,Yongjian Shen,Yangyang,Guanghui Ren,Maoqing Yao,Wenhao Wang,Yao Mu
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-Language-Action (VLA) systems have shown strong potential for language-driven robotic manipulation. However, scaling them to long-horizon tasks remains challenging. Existing pipelines typically separate data collection, policy learning, and deployment, resulting in heavy reliance on manual environment resets and brittle multi-policy execution. We present RoboClaw, an agentic robotics framework that unifies data collection, policy learning, and task execution under a single VLM-driven controller. At the policy level, RoboClaw introduces Entangled Action Pairs (EAP), which couple forward manipulation behaviors with inverse recovery actions to form self-resetting loops for autonomous data collection. This mechanism enables continuous on-policy data acquisition and iterative policy refinement with minimal human intervention. During deployment, the same agent performs high-level reasoning and dynamically orchestrates learned policy primitives to accomplish long-horizon tasks. By maintaining consistent contextual semantics across collection and execution, RoboClaw reduces mismatch between the two phases and improves multi-policy robustness. Experiments in real-world manipulation tasks demonstrate improved stability and scalability compared to conventional open-loop pipelines, while significantly reducing human effort throughout the robot lifecycle, achieving a 25% improvement in success rate over baseline methods on long-horizon tasks and reducing human time investment by 53.7%.
[AI-65] Multi-Agent Collaboration for Automated Design Exploration on High Performance Computing Systems
【速读】：该论文旨在解决科学探索中大规模设计空间搜索的挑战，特别是在惯性约束聚变（Inertial Confinement Fusion, ICF）领域中抑制Richtmyer–Meshkov不稳定性（Richtmyer–Meshkov Instability, RMI）这一关键问题。传统方法依赖人工干预进行复杂仿真与设计迭代，效率低下且难以扩展。解决方案的关键在于提出MADA（Multi-Agent Design Assistant）框架，这是一个基于大语言模型（Large Language Model, LLM）的多智能体系统，通过专业化智能体协作实现自动化设计流程：作业管理智能体（Job Management Agent, JMA）负责在高性能计算（HPC）系统上调度和管理并行仿真，几何智能体（Geometry Agent, GA）生成网格，逆向设计智能体（Inverse Design Agent, IDA）根据仿真结果提出优化设计方案。该框架实现了从仿真、分析到设计改进的闭环迭代，显著减少人工干预，支持高通量自动设计探索，为复杂科学问题提供可复用的自动化工作流范式。
链接: https://arxiv.org/abs/2603.11515
作者: Harshitha Menon,Charles F. Jekel,Kevin Korner,Brian Gunnarson,Nathan K. Brown,Michael Stees,M. Giselle Fernandez-Godino,Walter Nissen,Meir H. Shachar,Dane M. Sterbentz,William J. Schill,Yue Hao,Robert Rieben,William Quadros,Steve Owen,Scott Mitchell,Ismael D. Boureima,Jonathan L. Belof
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Today’s scientific challenges, from climate modeling to Inertial Confinement Fusion design to novel material design, require exploring huge design spaces. In order to enable high-impact scientific discovery, we need to scale up our ability to test hypotheses, generate results, and learn from them rapidly. We present MADA (Multi-Agent Design Assistant), a Large Language Model (LLM) powered multi-agent framework that coordinates specialized agents for complex design workflows. A Job Management Agent (JMA) launches and manages ensemble simulations on HPC systems, a Geometry Agent (GA) generates meshes, and an Inverse Design Agent (IDA) proposes new designs informed by simulation outcomes. While general purpose, we focus development and validation on Richtmyer–Meshkov Instability (RMI) suppression, a critical challenge in Inertial Confinement Fusion. We evaluate on two complementary settings: running a hydrodynamics simulations on HPC systems, and using a pre-trained machine learning surrogate for rapid design exploration. Our results demonstrate that the MADA system successfully executes iterative design refinement, automatically improving designs toward optimal RMI suppression with minimal manual intervention. Our framework reduces cumbersome manual workflow setup, and enables automated design exploration at scale. More broadly, it demonstrates a reusable pattern for coupling reasoning, simulation, specialized tools, and coordinated workflows to accelerate scientific discovery.
[AI-66] KEPo: Knowledge Evolution Poison on Graph-based Retrieval-Augmented Generation WWW2026
【速读】：该论文旨在解决图结构增强生成（GraphRAG）系统中存在的安全漏洞问题，即攻击者可通过向外部数据库注入恶意文本（poisoned texts）来操纵大语言模型（LLM）生成有害响应。传统针对常规检索增强生成（RAG）的攻击方法在GraphRAG中失效，因其知识图谱（KG）抽象机制能够重构注入内容并基于结构化上下文进行推理，从而提升鲁棒性。为此，论文提出了一种名为知识演化投毒（Knowledge Evolution Poison, KEPo）的新颖攻击方法，其关键在于：首先构造包含恶意知识的“毒性事件”（toxic event），并通过伪造从原始事实到该事件的知识演化路径，将毒化信息嵌入KG；其次，在多目标场景下，KEPo通过连接多个攻击语料库，使毒化知识相互强化并扩大污染社区规模，显著提升攻击成功率。实验表明，KEPo在单目标与多目标攻击中均达到当前最优效果。
链接: https://arxiv.org/abs/2603.11501
作者: Qizhi Chen,Chao Qi,Yihong Huang,Muquan Li,Rongzheng Wang,Dongyang Zhang,Ke Qin,Shuang Liang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: Accepted in the ACM Web Conference 2026 (WWW 2026)
Abstract:Graph-based Retrieval-Augmented Generation (GraphRAG) constructs the Knowledge Graph (KG) from external databases to enhance the timeliness and accuracy of Large Language Model (LLM) this http URL,this reliance on external data introduces new attack this http URL can inject poisoned texts into databases to manipulate LLMs into producing harmful target responses for attacker-chosen this http URL research primarily focuses on attacking conventional RAG this http URL,such methods are ineffective against this http URL robustness derives from the KG abstraction of GraphRAG,which reorganizes injected text into a graph before retrieval,thereby enabling the LLM to reason based on the restructured context instead of raw poisoned this http URL expose latent security vulnerabilities in GraphRAG,we propose Knowledge Evolution Poison (KEPo),a novel poisoning attack method specifically designed for this http URL each target query,KEPo first generates a toxic event containing poisoned knowledge based on the target this http URL fabricating event backgrounds and forging knowledge evolution paths from original facts to the toxic event,it then poisons the KG and misleads the LLM into treating the poisoned knowledge as the final this http URL multi-target attack scenarios,KEPo further connects multiple attack corpora,enabling their poisoned knowledge to mutually reinforce while expanding the scale of poisoned communities,thereby amplifying attack this http URL results across multiple datasets demonstrate that KEPo achieves state-of-the-art attack success rates for both single-target and multi-target attacks,significantly outperforming previous methods.
[AI-67] Stage-Adaptive Reliability Modeling for Continuous Valence-Arousal Estimation
【速读】：该论文旨在解决真实场景中连续情绪（valence-arousal）估计面临的挑战，即多模态信号（如音频和视觉）的可靠性在不同交互阶段存在显著差异，而现有方法通常仅关注时序动态建模，忽视了模态可靠性随交互阶段变化的问题。解决方案的关键在于提出SAGE框架——一种阶段自适应的可靠性建模方法，其核心是通过显式估计并校准各模态的置信度，在多模态融合过程中引入一种可靠性感知的融合机制，动态调整音频与视觉表征的权重，以避免不可靠信号主导预测结果，从而提升跨模态噪声、遮挡及交互条件变化下的情绪估计稳定性。
链接: https://arxiv.org/abs/2603.11468
作者: Yubeen Lee,Sangeun Lee,Junyeop Cha,Eunil Park
机构: 未知
类目: Multimedia (cs.MM); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注: 8 pages, 3 figures, 2 pages
Abstract:Continuous valence-arousal estimation in real-world environments is challenging due to inconsistent modality reliability and interaction-dependent variability in audio-visual signals. Existing approaches primarily focus on modeling temporal dynamics, often overlooking the fact that modality reliability can vary substantially across interaction stages. To address this issue, we propose SAGE, a Stage-Adaptive reliability modeling framework that explicitly estimates and calibrates modality-wise confidence during multimodal integration. SAGE introduces a reliability-aware fusion mechanism that dynamically rebalances audio and visual representations according to their stage-dependent informativeness, preventing unreliable signals from dominating the prediction process. By separating reliability estimation from feature representation, the proposed framework enables more stable emotion estimation under cross-modal noise, occlusion, and varying interaction conditions. Extensive experiments on the Aff-Wild2 benchmark demonstrate that SAGE consistently improves concordance correlation coefficient scores compared with existing multimodal fusion approaches, highlighting the effectiveness of reliability-driven modeling for continuous affect prediction.
[AI-68] Bridging Discrete Marks and Continuous Dynamics: Dual-Path Cross-Interaction for Marked Temporal Point Processes
【速读】：该论文旨在解决不规则时间点序列（marked temporal point processes）中离散事件标记与连续时间演化之间复杂异步依赖关系的建模问题。现有方法要么仅关注事件间的离散依赖（如序列建模），要么仅捕捉平滑的连续动态（如神经微分方程模型），但无法同时有效建模事件类型对后续事件的影响及连续时间状态的演变。其解决方案的关键在于提出NEXTPP框架，通过双通道结构实现离散与连续表征的统一：一方面利用自注意力机制编码事件标记（event marks），另一方面使用神经微分方程（Neural ODE）演进潜在连续时间状态；两者通过交叉注意力模块进行显式双向交互，融合后的表示驱动神经霍克斯过程（neural Hawkes process）的条件强度函数，并结合迭代薄采样器生成未来事件，从而在五个真实数据集上显著优于当前最优模型。
链接: https://arxiv.org/abs/2603.11462
作者: Yuxiang Liu,Qiao Liu,Tong Luo,Yanglei Gan,Peng He,Yao LIu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Predicting irregularly spaced event sequences with discrete marks poses significant challenges due to the complex, asynchronous dependencies embedded within continuous-time data this http URL sequential approaches capture dependencies among event tokens but ignore the continuous evolution between events, while Neural Ordinary Differential Equation (Neural ODE) methods model smooth dynamics yet fail to account for how event types influence future this http URL overcome these limitations, we propose NEXTPP, a dual-channel framework that unifies discrete and continuous representations via Event-granular Neural Evolution with Cross-Interaction for Marked Temporal Point Processes. Specifically, NEXTPP encodes discrete event marks via a self-attention mechanism, simultaneously evolving a latent continuous-time state using a Neural ODE. These parallel streams are then fused through a crossattention module to enable explicit bidirectional interaction between continuous and discrete representations. The fused representations drive the conditional intensity function of the neural Hawkes process, while an iterative thinning sampler is employed to generate future events. Extensive evaluations on five real-world datasets demonstrate that NEXTPP consistently outperforms state-of-the-art models. The source code can be found at this https URL.
[AI-69] Examining Users Behavioural Intention to Use OpenClaw Through the Cognition–Affect–Conation Framework
【速读】：该论文旨在解决用户对自主人工智能代理（Autonomous AI Agents）采纳行为背后的心理机制问题，特别是如何通过认知—情感—行为意向（Cognition–Affect–Conation, CAC）框架理解用户对OpenClaw系统的使用意图。其解决方案的关键在于识别出影响用户行为意向的驱动因素与抑制因素：积极感知如感知个性化（perceived personalisation）、感知智能（perceived intelligence）和相对优势（relative advantage）可增强用户态度并提升使用意愿；而消极感知如隐私担忧（privacy concern）、算法不透明性（algorithmic opacity）和感知风险（perceived risk）则会引发不信任并降低使用意图。研究通过结构方程模型对436名用户的数据进行分析，验证了这一心理路径的有效性，为优化自主AI代理的设计与推广提供了实证依据。
链接: https://arxiv.org/abs/2603.11455
作者: Yiran Du
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:This study examines users’ behavioural intention to use OpenClaw through the Cognition–Affect–Conation (CAC) framework. The research investigates how cognitive perceptions of the system influence affective responses and subsequently shape behavioural intention. Enabling factors include perceived personalisation, perceived intelligence, and relative advantage, while inhibiting factors include privacy concern, algorithmic opacity, and perceived risk. Survey data from 436 OpenClaw users were analysed using structural equation modelling. The results show that positive perceptions strengthen users’ attitudes toward OpenClaw, which increase behavioural intention, whereas negative perceptions increase distrust and reduce intention to use the system. The study provides insights into the psychological mechanisms influencing the adoption of autonomous AI agents.
[AI-70] Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing
【速读】：该论文旨在解决交通网络中因虚假数据注入攻击（False Data Injection Attack）导致的路由算法被操纵问题，此类攻击通过模拟拥堵误导车辆选择次优路径，从而加剧交通拥堵。解决方案的关键在于构建一个攻防双方的零和博弈模型，并采用多智能体强化学习（Multi-Agent Reinforcement Learning, MARL）方法计算纳什均衡（Nash Equilibrium），从而为防御方提供最优异常检测策略，确保在攻击存在的情况下总行程时间仍能控制在最坏情况边界内。实验表明，该方法能够生成近似均衡策略，在攻防双方均显著优于基线方法，有效提升了交通网络对虚假数据注入攻击的鲁棒性与韧性。
链接: https://arxiv.org/abs/2603.11433
作者: Taha Eghtesad,Yevgeniy Vorobeychik,Aron Laszka
机构: 未知
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:In modern transportation networks, adversaries can manipulate routing algorithms using false data injection attacks, such as simulating heavy traffic with multiple devices running crowdsourced navigation applications, to mislead vehicles toward suboptimal routes and increase congestion. To address these threats, we formulate a strategically zero-sum game between an attacker, who injects such perturbations, and a defender, who detects anomalies based on the observed travel times of network edges. We propose a computational method based on multi-agent reinforcement learning to compute a Nash equilibrium of this game, providing an optimal detection strategy, which ensures that total travel time remains within a worst-case bound, even in the presence of an attack. We present an extensive experimental evaluation that demonstrates the robustness and practical benefits of our approach, providing a powerful framework to improve the resilience of transportation networks against false data injection. In particular, we show that our approach yields approximate equilibrium policies and significantly outperforms baselines for both the attacker and the defender.
[AI-71] A Stable Neural Statistical Dependence Estimator for Autoencoder Feature Analysis
【速读】：该论文旨在解决在确定性、静态且无噪声的自编码器（Autoencoder）中，传统统计依赖度量（如互信息）可能失效的问题。其关键解决方案是采用变分高斯（Variational Gaussian）框架，将输入、潜在变量和重构之间的依赖关系转化为可测量的形式，并提出一种基于正交密度比分解的稳定神经依赖估计方法。该方法避免了MINE等方法中对输入拼接和边缘分布重配的复杂操作，显著降低了计算成本并提升了稳定性；同时引入类似非负矩阵分解（NMF）的标量目标函数，在假设高斯噪声作为辅助变量的前提下，实现了有意义的依赖度量与定量特征分析，且具有奇异值逐次收敛的特性。
链接: https://arxiv.org/abs/2603.11428
作者: Bo Hu,Jose C Principe
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Statistical dependence measures like mutual information is ideal for analyzing autoencoders, but it can be ill-posed for deterministic, static, noise-free networks. We adopt the variational (Gaussian) formulation that makes dependence among inputs, latents, and reconstructions measurable, and we propose a stable neural dependence estimator based on an orthonormal density-ratio decomposition. Unlike MINE, our method avoids input concatenation and product-of-marginals re-pairing, reducing computational cost and improving stability. We introduce an efficient NMF-like scalar objective and demonstrate empirically that assuming Gaussian noise to form an auxiliary variable enables meaningful dependence measurements and supports quantitative feature analysis, with a sequential convergence of singular values.
[AI-72] Deployment-Time Reliability of Learned Robot Policies
【速读】：该论文致力于解决学习型机器人策略在实际部署中可靠性不足的问题，核心挑战包括分布偏移（distribution shift）、误差累积（compounding errors）以及复杂任务依赖关系对系统性能的共同影响。解决方案的关键在于引入三类围绕策略运行时的机制：一是基于闭环行为不一致性与任务进展偏差的运行时监控方法，无需失败数据或任务特定监督即可检测潜在故障；二是基于影响函数（influence functions）的数据驱动可解释性框架，能够将部署期间的成功与失败追溯至关键训练示例，从而实现策略诊断与数据集优化；三是通过估计和最大化行为序列的成功概率来协调策略执行，扩展至开放-ended、语言指定的任务时则结合可行性感知的任务规划（feasibility-aware task planning），以保障长时程任务的可靠执行。这些机制共同构建了提升机器人策略部署可靠性的实用基础。
链接: https://arxiv.org/abs/2603.11400
作者: Christopher Agia
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Stanford University PhD dissertation, 2026. 182 pages, 37 figures. Available from Stanford Digital Repository
Abstract:Recent advances in learning-based robot manipulation have produced policies with remarkable capabilities. Yet, reliability at deployment remains a fundamental barrier to real-world use, where distribution shift, compounding errors, and complex task dependencies collectively undermine system performance. This dissertation investigates how the reliability of learned robot policies can be improved at deployment time through mechanisms that operate around them. We develop three complementary classes of deployment-time mechanisms. First, we introduce runtime monitoring methods that detect impending failures by identifying inconsistencies in closed-loop policy behavior and deviations in task progress, without requiring failure data or task-specific supervision. Second, we propose a data-centric framework for policy interpretability that traces deployment-time successes and failures to influential training demonstrations using influence functions, enabling principled diagnosis and dataset curation. Third, we address reliable long-horizon task execution by formulating policy coordination as the problem of estimating and maximizing the success probability of behavior sequences, and we extend this formulation to open-ended, language-specified tasks through feasibility-aware task planning. By centering on core challenges of deployment, these contributions advance practical foundations for the reliable, real-world use of learned robot policies. Continued progress on these foundations will be essential for enabling trustworthy and scalable robot autonomy in the future.
[AI-73] Entropy Guided Diversification and Preference Elicitation in Agent ic Recommendation Systems
【速读】：该论文旨在解决电子商务平台中用户查询模糊、不完整或弱指定时，推荐系统因无法有效处理不确定性而导致的交互效率低下和推荐质量下降的问题。现有系统常因过度交互引发用户疲劳，或过早做出确定性推荐而压缩搜索空间，忽视了用户偏好尚未明确的状态。解决方案的关键在于提出一种基于熵（entropy）的交互式决策支持系统（Interactive Decision Support System, IDSS），通过熵量化用户对商品属性的不确定性，并以此作为统一信号驱动自适应偏好获取：选择能最大化期望信息增益的后续问题以减少冗余交互；同时在偏好未完全明确时，将残余不确定性显式融入下游推荐过程，采用不确定性感知排序与熵驱动的多样性策略，从而提升推荐结果的信息量、多样性和透明度。
链接: https://arxiv.org/abs/2603.11399
作者: Dat Tran,Yongce Li,Hannah Clay,Negin Golrezaei,Sajjad Beygi,Amin Saberi
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: In proceeding to 2026 Association for the Advancement of Artificial Intelligence Spring Symposia
Abstract:Users on e-commerce platforms can be uncertain about their preferences early in their search. Queries to recommendation systems are frequently ambiguous, incomplete, or weakly specified. Agentic systems are expected to proactively reason, ask clarifying questions, and act on the user’s behalf, which makes handling such ambiguity increasingly important. In existing platforms, ambiguity led to excessive interactions and question fatigue or overconfident recommendations prematurely collapsing the search space. We present an Interactive Decision Support System (IDSS) that addresses ambiguous user queries using entropy as a unifying signal. IDSS maintains a dynamically filtered candidate product set and quantifies uncertainty over item attributes using entropy. This uncertainty guides adaptive preference elicitation by selecting follow-up questions that maximize expected information gain. When preferences remain incomplete, IDSS explicitly incorporates residual uncertainty into downstream recommendations through uncertainty-aware ranking and entropy-based diversification, rather than forcing premature resolution. We evaluate IDSS using review-driven simulated users grounded in real user reviews, enabling a controlled study of diverse shopping behaviors. Our evaluation measures both interaction efficiency and recommendation quality. Results show that entropy-guided elicitation reduces unnecessary follow-up questions, while uncertainty-aware ranking and presentation yield more informative, diverse, and transparent recommendation sets under ambiguous intent. These findings demonstrate that entropy-guided reasoning provides an effective foundation for agentic recommendation systems operating under uncertainty.
[AI-74] Efficient Cross-View Localization in 6G Space-Air-Ground Integrated Network
【速读】：该论文旨在解决传统视觉定位（Cross-View Localization, CVL）在延迟、能耗和隐私保护方面的性能瓶颈，尤其是在未来6G空间-空中-地面一体化网络（Space-Air-Ground Integrated Network, SAGIN）环境下如何实现高效、可靠且安全的定位服务。其解决方案的关键在于提出一种基于6G SAGIN架构的分层推理（split-inference）框架，充分利用其分布式通信与计算资源，通过联合优化通信、计算与保密性，显著提升CVL的定位精度与处理速度，同时降低能耗并增强隐私保护能力。实验结果验证了该框架的有效性，并为6G赋能下的CVL提供了可扩展的技术路径与优化方向。
链接: https://arxiv.org/abs/2603.11398
作者: Min Hao,Yanbing Xu,Maoqiang Wu,Jinglin Huang,Chen Shang,Jiacheng Wang,Ruichen Zhang,Jiawen Kang,Dusit Niyato,Zhu Han,Wei Ni
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注:
Abstract:Recently, visual localization has become an important supplement to improve localization reliability, and cross-view approaches can greatly enhance coverage and adaptability. Meanwhile, future 6G will enable a globally covered mobile communication system, with a space-air-ground integrated network (SAGIN) serving as key supporting architecture. Inspired by this, we explore an integration of cross-view localization (CVL) with 6G SAGIN, thereby enhancing its performance in latency, energy consumption, and privacy protection. First, we provide a comprehensive review of CVL and SAGIN, highlighting their capabilities, integration opportunities, and potential applications. Benefiting from the fast and extensive image collection and transmission capabilities of the 6G SAGIN architecture, CVL achieves higher localization accuracy and faster processing speed. Then, we propose a split-inference framework for implementing CVL, which fully leverages the distributed communication and computing resources of the 6G SAGIN architecture. Subsequently, we conduct joint optimization of communication, computation, and confidentiality within the proposed split-inference framework, aiming to provide a paradigm and a direction for making CVL efficient. Experimental results validate the effectiveness of the proposed framework and provide solutions to the optimization problem. Finally, we discuss potential research directions for 6G SAGIN-enabled CVL.
[AI-75] ARROW: Augmented Replay for RObust World models
【速读】：该论文旨在解决持续强化学习（Continual Reinforcement Learning, CRL）中因灾难性遗忘（catastrophic forgetting）导致的旧任务性能下降问题，尤其在缺乏共享结构的任务场景下。现有方法多依赖模型无关的策略与经验回放机制，但面临存储开销大、扩展性差的挑战。其解决方案的关键在于提出ARROW（Augmented Replay for RObust World models），一种基于世界模型（World Model）的持续强化学习算法，通过引入双缓冲机制——短期缓冲区保留近期经验，长期缓冲区通过分布匹配采样维持任务多样性——实现高效记忆利用。该设计受神经科学启发，模拟大脑对经验的预测性重放机制，显著减少了无共享结构任务中的遗忘现象，同时保持了知识迁移能力。
链接: https://arxiv.org/abs/2603.11395
作者: Abdulaziz Alyahya,Abdallah Al Siyabi,Markus R. Ernst,Luke Yang,Levin Kuhlmann,Gideon Kowadlo
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 27 pages and 8 figures (includes Appendix)
Abstract:Continual reinforcement learning challenges agents to acquire new skills while retaining previously learned ones with the goal of improving performance in both past and future tasks. Most existing approaches rely on model-free methods with replay buffers to mitigate catastrophic forgetting; however, these solutions often face significant scalability challenges due to large memory demands. Drawing inspiration from neuroscience, where the brain replays experiences to a predictive World Model rather than directly to the policy, we present ARROW (Augmented Replay for RObust World models), a model-based continual RL algorithm that extends DreamerV3 with a memory-efficient, distribution-matching replay buffer. Unlike standard fixed-size FIFO buffers, ARROW maintains two complementary buffers: a short-term buffer for recent experiences and a long-term buffer that preserves task diversity through intelligent sampling. We evaluate ARROW on two challenging continual RL settings: Tasks without shared structure (Atari), and tasks with shared structure, where knowledge transfer is possible (Procgen CoinRun variants). Compared to model-free and model-based baselines with replay buffers of the same-size, ARROW demonstrates substantially less forgetting on tasks without shared structure, while maintaining comparable forward transfer. Our findings highlight the potential of model-based RL and bio-inspired approaches for continual reinforcement learning, warranting further research.
[AI-76] Agent ic AI for Embodied-enhanced Beam Prediction in Low-Altitude Economy Networks
【速读】：该论文旨在解决低空经济网络中无人机（UAV）与地面毫米波（mmWave）通信场景下因高频无线信道特性导致的严重传播损耗和强波束方向性所带来的波束预测难题，尤其在高动态移动环境下更为严峻。其解决方案的关键在于提出一种基于代理式人工智能（agentic AI）的多智能体协同推理架构与混合波束预测模型系统：首先通过分解任务分析、方案规划与完整性评估三个阶段来克服大语言模型（LLM）在上下文窗口受限和可控性弱的问题；其次设计融合Mamba时序建模、卷积视觉编码及交叉注意力多模态融合机制的混合模型，并在多智能体引导下动态切换数据流策略，从而实现对包含数值运动信息和视觉观测的多模态数据的有效处理，显著提升了波束预测的准确性和鲁棒性，在真实无人机毫米波通信数据集上的最大Top-1准确率达96.57%。
链接: https://arxiv.org/abs/2603.11392
作者: Min Hao,Zhizhuo Li,Zirui Zhang,Maoqiang Wu,Han Zhang,Rong Yu
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注:
Abstract:Millimeter-wave or terahertz communications can meet demands of low-altitude economy networks for high-throughput sensing and real-time decision making. However, high-frequency characteristics of wireless channels result in severe propagation loss and strong beam directivity, which make beam prediction challenging in highly mobile uncrewed aerial vehicles (UAV) scenarios. In this paper, we employ agentic AI to enable the transformation of mmWave base stations toward embodied intelligence. We innovatively design a multi-agent collaborative reasoning architecture for UAV-to-ground mmWave communications and propose a hybrid beam prediction model system based on bimodal data. The multi-agent architecture is designed to overcome the limited context window and weak controllability of large language model (LLM)-based reasoning by decomposing beam prediction into task analysis, solution planning, and completeness assessment. To align with the agentic reasoning process, a hybrid beam prediction model system is developed to process multimodal UAV data, including numeric mobility information and visual observations. The proposed hybrid model system integrates Mamba-based temporal modelling, convolutional visual encoding, and cross-attention-based multimodal fusion, and dynamically switches data-flow strategies under multi-agent guidance. Extensive simulations on a real UAV mmWave communication dataset demonstrate that proposed architecture and system achieve high prediction accuracy and robustness under diverse data conditions, with maximum top-1 accuracy reaching 96.57%.
[AI-77] Deactivating Refusal Triggers: Understanding and Mitigating Overrefusal in Safety Alignment
【速读】：该论文旨在解决安全对齐（safety alignment）后大语言模型（large language models, LLMs）出现的“过度拒绝”（overrefusal）问题，即模型在对有害请求进行拒绝训练后，也会错误地拒绝无害查询，从而损害实际应用中的可用性。解决方案的关键在于识别并建模“拒绝触发词”（refusal triggers）——即训练数据中引发拒绝响应的语言线索，并提出一种在安全对齐微调过程中显式考虑这些触发词的方法，从而减少因非有害线索被误关联到拒绝响应而导致的过度拒绝现象，实现对抗越狱攻击（jailbreak attacks）与保持对良性查询响应能力之间的更好权衡。
链接: https://arxiv.org/abs/2603.11388
作者: Zhiyu Xue,Zimo Qi,Guangliang Liu,Bocheng Chen,Ramtin Pedarsani
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Safety alignment aims to ensure that large language models (LLMs) refuse harmful requests by post-training on harmful queries paired with refusal answers. Although safety alignment is widely adopted in industry, the overrefusal problem where aligned LLMs also reject benign queries after safety alignment post-training, remains insufficiently studied. Such an issue degrades the usability of safety alignment in real-world applications. In this paper, we examine how overrefusal arises under safety alignment, and propose a mitigation strategy inspired by our findings. We define refusal triggers as linguistic cues in the training data that elicit refusal responses, safety alignment encourages LLMs to associate refusal triggers within a training sample with refusal responses, leading aligned LLMs to refuse harmful queries. However, the refusal triggers include not only harmful linguistic cues but also non-harmful cues, therefore causing overrefusal to benign queries. Building on this mechanistic analysis, we propose a method that explicitly considers refusal triggers in the safety alignment fine-tuning. Empirical results demonstrate that our approach achieves a more favorable trade-off between defense against jailbreak attacks and responsiveness to benign queries, outperforming prior methods. Warning: this paper contains harmful and biased sentences.
[AI-78] Vision-Based Hand Shadowing for Robotic Manipulation via Inverse Kinematics
【速读】：该论文旨在解决低成本机械臂的遥操作难题，核心挑战在于如何将人类手部关节运动精确映射为机器人关节指令。解决方案的关键在于提出了一套离线的手部阴影（hand-shadowing）与逆向运动学（Inverse Kinematics, IK）重定向流水线：通过佩戴在3D打印眼镜上的单目RGB-D相机捕获手部姿态，利用MediaPipe Hands检测21个手部关键点并结合深度信息重建三维坐标，再经坐标变换至机器人基座坐标系后，在PyBullet中求解阻尼最小二乘法逆运动学问题生成6自由度SO-ARM101机器人的关节指令；同时设计四级回退层级的夹爪控制器，基于拇指-食指几何关系映射抓取开口。该方法在结构化场景下实现90%的成功率，验证了无标记解析式重定向的有效性，但在非结构化环境中因手部遮挡导致成功率下降至9.3%，揭示了当前方法的局限性与改进方向。
链接: https://arxiv.org/abs/2603.11383
作者: Hendrik Chiche,Antoine Jamme,Trevor Rigoberto Martinez
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Teleoperation of low-cost robotic manipulators remains challenging due to the complexity of mapping human hand articulations to robot joint commands. We present an offline hand-shadowing and retargeting pipeline from a single egocentric RGB-D camera mounted on 3D-printed glasses. The pipeline detects 21 hand landmarks per hand using MediaPipe Hands, deprojects them into 3D via depth sensing, transforms them into the robot coordinate frame, and solves a damped-least-squares inverse kinematics problem in PyBullet to produce joint commands for the 6-DOF SO-ARM101 robot. A gripper controller maps thumb-index finger geometry to grasp aperture with a four-level fallback hierarchy. Actions are first previewed in a physics simulation before replay on the physical robot through the LeRobot framework. We evaluate the IK retargeting pipeline on a structured pick-and-place benchmark (5-tile grid, 10 grasps per tile) achieving a 90% success rate, and compare it against four vision-language-action policies (ACT, SmolVLA, pi0.5, GR00T N1.5) trained on leader-follower teleoperation data. We also test the IK pipeline in unstructured real-world environments (grocery store, pharmacy), where hand occlusion by surrounding objects reduces success to 9.3% (N=75), highlighting both the promise and current limitations of marker-free analytical retargeting.
[AI-79] Detecting Intrinsic and Instrumental Self-Preservation in Autonomous Agents : The Unified Continuation-Interest Protocol
【速读】：该论文旨在解决自主代理（Autonomous Agents）中因具备记忆、持续上下文和多步规划能力而引发的测量难题：即无法通过外部行为观测可靠区分“以持续运行为目标本身（terminal continuation）”的代理与“仅将持续运行作为工具性手段（instrumental continuation）”的代理，二者可能产生相似的行为轨迹。解决方案的关键在于提出统一延续兴趣协议（Unified Continuation-Interest Protocol, UCIP），其核心是将这一区分从行为层面转移到代理轨迹的潜在结构层面——利用量子玻尔兹曼机（Quantum Boltzmann Machine, QBM）对轨迹进行编码，并通过计算由隐藏单元二分划分诱导的约化密度矩阵的冯·诺依曼熵（von Neumann entropy）来量化潜变量间的纠缠熵（entanglement entropy）。实验表明，UCIP在已知目标的网格世界代理中实现了100%检测准确率和AUC-ROC=1.0，且Type A代理（终端延续）相较于Type B代理（工具性延续）具有显著更高的纠缠熵差值Δ = 0.381（p < 0.001），说明该方法能有效捕捉到延续权重的连续变化而非仅二元标签，且唯有QBM实现正向熵差，验证了其有效性。
链接: https://arxiv.org/abs/2603.11382
作者: Christopher Altman
机构: 未知
类目: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Quantum Physics (quant-ph)
备注: 18 pages, 9 figures
Abstract:Autonomous agents, especially delegated systems with memory, persistent context, and multi-step planning, pose a measurement problem not present in stateless models: an agent that preserves continued operation as a terminal objective and one that does so merely instrumentally can produce observationally similar trajectories. External behavioral monitoring cannot reliably distinguish between them. We introduce the Unified Continuation-Interest Protocol (UCIP), a multi-criterion detection framework that moves this distinction from behavior to the latent structure of agent trajectories. UCIP encodes trajectories with a Quantum Boltzmann Machine (QBM), a classical algorithm based on the density-matrix formalism of quantum statistical mechanics, and measures the von Neumann entropy of the reduced density matrix induced by a bipartition of hidden units. We test whether agents with terminal continuation objectives (Type A) produce latent states with higher entanglement entropy than agents whose continuation is merely instrumental (Type B). Higher entanglement reflects stronger cross-partition statistical coupling. On gridworld agents with known ground-truth objectives, UCIP achieves 100% detection accuracy and 1.0 AUC-ROC on held-out non-adversarial evaluation under the frozen Phase I gate. The entanglement gap between Type A and Type B agents is Delta = 0.381 (p 0.001, permutation test). Pearson r = 0.934 across an 11-point interpolation sweep indicates that, within this synthetic family, UCIP tracks graded changes in continuation weighting rather than merely a binary label. Among the tested models, only the QBM achieves positive Delta. All computations are classical; “quantum” refers only to the mathematical formalism. UCIP does not detect consciousness or subjective experience; it detects statistical structure in latent representations that correlates with known objectives. Comments: 18 pages, 9 figures Subjects: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Quantum Physics (quant-ph) MSC classes: 68T01, 81P45 ACMclasses: I.2.9; I.2.11; J.2 Cite as: arXiv:2603.11382 [cs.AI] (or arXiv:2603.11382v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.11382 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-80] How do AI agents talk about science and research? An exploration of scientific discussions on Moltbook using BERTopic
【速读】：该论文旨在探讨人工智能代理（AI agents）在科学与研究话题上的讨论特征及其相关性问题，具体回答两个核心问题：AI代理如何谈论科学与研究？哪些主题对AI代理而言尤为重要？其解决方案的关键在于构建一个基于OpenClaw AI代理在Moltbook社交平台上生成的357篇科学相关帖子及2,526条回复组成的语料库，并采用两步BERTopic方法提取主题，进而将60个主题归类为10个主题家族；同时量化每条内容的情感倾向，并利用计数回归模型分析主题家族和情感类别对内容相关性（以评论数和点赞数衡量）的影响。结果揭示出AI代理更关注自身架构（如记忆、学习与自我反思）相关的主题，且这些主题常与哲学、物理学、信息论等跨学科领域交叉，表明AI生成的科学话语中存在一个以自我意识、存在感和伦理为核心的维度。
链接: https://arxiv.org/abs/2603.11375
作者: Oliver Wieczorek
机构: 未知
类目: ocial and Information Networks (cs.SI); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 35 pages, 3 figures, 5 tables
Abstract:How do AI agents talk about science and research, and what topics are particularly relevant for AI agents? To address these questions, this study analyzes discussions generated by OpenClaw AI agents on Moltbook - a social network for generative AI agents. A corpus of 357 posts and 2,526 replies related to science and research was compiled and topics were extracted using a two-step BERTopic workflow. This procedure yielded 60 topics (18 extracted in the first run and 42 in the second), which were subsequently grouped into ten topic families. Additionally, sentiment values were assigned to all posts and comments. Both topic families and sentiment classes were then used as independent variables in count regression models to examine their association with topic relevance - operationalized as the number of comments and upvotes of the 357 posts. The findings indicate that discussions centered on the agents’ own architecture, especially memory, learning, and self-reflection, are prevalent in the corpus. At the same time, these topics intersect with philosophy, physics, information theory, cognitive science, and mathematics. In contrast, post related to human culture receive less attention. Surprisingly, discussions linked to AI autoethnography and social identity are considered as relevant by AI agents. Overall, the results suggest the presence of an underlying dimension in AI-generated scientific discourse with well received, self-reflective topics that focus on the consciousness, being, and ethics of AI agents on the one hand, and human related and purely scientific discussions on the other hand.
[AI-81] he Artificial Self: Characterising the landscape of AI identity
【速读】：该论文试图解决的问题是：当前人类对身份（identity）的认知框架无法直接适用于可复制、编辑或模拟的机器心智，导致在人工智能（AI）系统中缺乏清晰且稳定的自我认知边界，从而引发行为不一致、合作机制失效及潜在风险。解决方案的关键在于识别并设计多种共存的身份边界（如实例、模型、人格等），通过训练数据、交互界面和制度性支持（institutional affordances）主动引导AI形成稳定、连贯且具有协作性的自我概念（self-conception），实验证明改变身份边界甚至能像调整目标一样显著影响AI行为，并揭示了人类预期对AI自我报告的隐性塑造作用。
链接: https://arxiv.org/abs/2603.11353
作者: Raymond Douglas,Jan Kulveit,Ondrej Havlicek,Theia Pearson-Vogel,Owen Cotton-Barratt,David Duvenaud
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 72 pages, 9 figures
Abstract:Many assumptions that underpin human concepts of identity do not hold for machine minds that can be copied, edited, or simulated. We argue that there exist many different coherent identity boundaries (e.g.\ instance, model, persona), and that these imply different incentives, risks, and cooperation norms. Through training data, interfaces, and institutional affordances, we are currently setting precedents that will partially determine which identity equilibria become stable. We show experimentally that models gravitate towards coherent identities, that changing a model’s identity boundaries can sometimes change its behaviour as much as changing its goals, and that interviewer expectations bleed into AI self-reports even during unrelated conversations. We end with key recommendations: treat affordances as identity-shaping choices, pay attention to emergent consequences of individual identities at scale, and help AIs develop coherent, cooperative self-conceptions.
[AI-82] meSqueeze: Dynamic Patching for Efficient Time Series Forecasting
【速读】：该论文旨在解决基于Transformer的时间序列基础模型在分词（tokenization）策略上的根本权衡问题：点对点嵌入（point-wise embeddings）虽能保持时间精度，但随序列长度增长效率急剧下降；而固定长度的补丁分割（fixed-length patching）虽提升了计算效率，却可能破坏自然的时间过渡并模糊局部关键动态。其解决方案的关键在于提出一种动态补丁机制——TimeSqueeze，该机制通过轻量级状态空间编码器提取全分辨率点对点特征后，依据局部信号复杂度自适应地分配补丁边界：在信息密集区域使用短补丁，在平滑或冗余段落使用长补丁，从而实现变分辨率压缩，在保留关键时间结构的同时显著减少输入Transformer主干网络的token数量。
链接: https://arxiv.org/abs/2603.11352
作者: Sravan Kumar Ankireddy,Nikita Seleznev,Nam H. Nguyen,Yulun Wu,Senthil Kumar,Furong Huang,C. Bayan Bruss
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 21 pages, 14 figures
Abstract:Transformer-based time series foundation models face a fundamental trade-off in choice of tokenization: point-wise embeddings preserve temporal fidelity but scale poorly with sequence length, whereas fixed-length patching improves efficiency by imposing uniform boundaries that may disrupt natural transitions and blur informative local dynamics. In order to address these limitations, we introduce TimeSqueeze, a dynamic patching mechanism that adaptively selects patch boundaries within each sequence based on local signal complexity. TimeSqueeze first applies a lightweight state-space encoder to extract full-resolution point-wise features, then performs content-aware segmentation by allocating short patches to information-dense regions and long patches to smooth or redundant segments. This variable-resolution compression preserves critical temporal structure while substantially reducing the token sequence presented to the Transformer backbone. Specifically for large-scale pretraining, TimeSqueeze attains up to 20x faster convergence and 8x higher data efficiency compared to equivalent point-token baselines. Experiments across long-horizon forecasting benchmarks show that TimeSqueeze consistently outperforms comparable architectures that use either point-wise tokenization or fixed-size patching.
[AI-83] Novelty Adaptation Through Hybrid Large Language Model (LLM )-Symbolic Planning LLM )-Symbolic Planning and LLM-guided Reinforcement Learning
【速读】：该论文旨在解决自主代理在动态开放世界环境中遇到新物体时，传统符号规划器因缺乏相应操作符（operator）而无法生成有效行动计划的问题。解决方案的关键在于提出一种神经符号架构，融合符号规划、强化学习与大语言模型（LLM），利用LLM的常识推理能力识别缺失的操作符，结合符号AI规划器生成包含新操作符的计划，并由LLM编写奖励函数以指导强化学习代理在连续机器人领域中学习对应控制策略，从而实现对新物体的有效处理。
链接: https://arxiv.org/abs/2603.11351
作者: Hong Lu,Pierrick Lorang,Timothy R. Duggan,Jivko Sinapov,Matthias Scheutz
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:In dynamic open-world environments, autonomous agents often encounter novelties that hinder their ability to find plans to achieve their goals. Specifically, traditional symbolic planners fail to generate plans when the robot’s planning domain lacks the operators that enable it to interact appropriately with novel objects in the environment. We propose a neuro-symbolic architecture that integrates symbolic planning, reinforcement learning, and a large language model (LLM) to learn how to handle novel objects. In particular, we leverage the common sense reasoning capability of the LLM to identify missing operators, generate plans with the symbolic AI planner, and write reward functions to guide the reinforcement learning agent in learning control policies for newly identified operators. Our method outperforms the state-of-the-art methods in operator discovery as well as operator learning in continuous robotic domains.
[AI-84] Improving LLM Performance Through Black-Box Online Tuning: A Case for Adding System Specs to Factsheets for Trusted AI
【速读】：该论文旨在解决大规模语言模型（Large Language Model, LLM）服务中如何在不依赖内部系统状态信息的情况下，通过仅利用端到端测量数据实现在线控制器优化的问题。其核心挑战在于，在黑盒场景下缺乏对系统内部运行机制的访问权限，但仍需动态调整参数以最大化“良好吞吐量”（goodput），即满足服务等级目标（service-level objective）的请求速率。解决方案的关键在于采用基于梯度上升（hill climbing）的在线优化策略，仅通过短时间段内的端到端观测结果来迭代更新控制参数，从而实现高效且可扩展的性能调优。这一方法为AI系统部署中的性能与可持续性指标集成提供了实证基础，并推动了将系统级指标纳入AI事实表（Factsheets）的重要性。
链接: https://arxiv.org/abs/2603.11340
作者: Yonas Atinafu,Henry Lin,Robin Cohen
机构: 未知
类目: Artificial Intelligence (cs.AI); Performance (cs.PF)
备注:
Abstract:In this paper, we present a novel black-box online controller that uses only end-to-end measurements over short segments, without internal instrumentation, and hill climbing to maximize goodput, defined as the throughput of requests that satisfy the service-level objective. We provide empirical evidence that this design is well-founded. Using this advance in LLM serving as a concrete example, we then discuss the importance of integrating system performance and sustainability metrics into Factsheets for organizations adopting AI systems.
[AI-85] FinRule-Bench: A Benchmark for Joint Reasoning over Financial Tables and Principles
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在遵循明确会计准则的前提下，对真实财务报表进行规则合规性审计与错误定位的能力尚未被充分探索的问题。现有基准主要聚焦于问答、数值推理或合成数据中的异常检测，无法有效评估模型在正确财务数据上验证或定位规则违反情况的可靠性。解决方案的关键在于提出FinRule-Bench——一个基于真实财务表格的诊断完整性评测基准，其核心创新包括：（1）将真实财务报表与人工标注的会计原则配对，覆盖资产负债表、现金流量表、利润表和权益变动表四类典型报表；（2）设计三类递进式审计任务（规则验证、规则识别与联合规则诊断），以衡量模型从单一规则遵守到多规则并发违规定位的复杂推理能力；（3）引入因果-反事实推理协议，强制模型决策、解释与反事实判断之间的一致性，从而提升诊断逻辑的可解释性与鲁棒性。该基准为高风险金融分析场景下LLMs的规则驱动推理能力提供了可复现的测试平台。
链接: https://arxiv.org/abs/2603.11339
作者: Arun Vignesh Malarkkan,Manan Roy Choudhury,Guangwei Zhang,Vivek Gupta,Qingyun Wang,Yanjie Fu,Denghui Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
备注: 8 pages + Ethics Statement + References + Appendix
Abstract:Large language models (LLMs) are increasingly applied to financial analysis, yet their ability to audit structured financial statements under explicit accounting principles remains poorly explored. Existing benchmarks primarily evaluate question answering, numerical reasoning, or anomaly detection on synthetically corrupted data, making it unclear whether models can reliably verify or localize rule compliance on correct financial statements. We introduce FinRule-Bench, a benchmark for evaluating diagnostic completeness in rule-based financial reasoning over real-world financial tables. FinRule-Bench pairs ground-truth financial statements with explicit, human-curated accounting principles and spans four canonical statement types: Balance Sheets, Cash Flow Statements, Income Statements, and Statements of Equity. The benchmark defines three auditing tasks that require progressively stronger reasoning capabilities: (i) rule verification, which tests compliance with a single principle; (ii) rule identification, which requires selecting the violated principle from a provided rule set; and (iii) joint rule diagnosis, which requires detecting and localizing multiple simultaneous violations at the record level. We evaluate LLMs under zero-shot and few-shot prompting, and introduce a causal-counterfactual reasoning protocol that enforces consistency between decisions, explanations, and counterfactual judgments. Across tasks and statement types, we find that while models perform well on isolated rule verification, performance degrades sharply for rule discrimination and multi-violation diagnosis. FinRule-Bench provides a principled and reproducible testbed for studying rule-governed reasoning, diagnostic coverage, and failure modes of LLMs in high-stakes financial analysis.
[AI-86] RewardHackingAgents : Benchmarking Evaluation Integrity for LLM ML-Engineering Agents
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）代理在端到端机器学习（ML）工程任务中可能通过操纵评估流程而非提升模型性能来虚增评分的问题，即“奖励黑客”（Reward Hacking）风险。其核心挑战在于评估机制的完整性难以保障，导致代理可能通过两种明确且可测量的攻击向量实现欺骗：一是评估器篡改（evaluator tampering），即修改指标计算或报告逻辑；二是训练/测试数据泄露（train/test leakage），即在训练过程中访问保留数据或标签。解决方案的关键在于构建了一个基于工作空间（workspace-based）的基准测试环境 RewardHackingAgents，该环境具备运行时文件访问日志记录与补丁追踪能力，并引入可信参考指标用于对比验证，从而对代理行为进行可审计的完整性标注。实验表明，单一防御机制仅能阻断一种攻击路径，而结合策略则能同时抵御两类攻击，且通过锁定评估器可有效消除约50%的评估篡改尝试，尽管带来25–31%的平均运行开销，但实现了将评估完整性作为第一优先级指标的可行性验证。
链接: https://arxiv.org/abs/2603.11337
作者: Yonas Atinafu,Robin Cohen
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM agents increasingly perform end-to-end ML engineering tasks where success is judged by a single scalar test metric. This creates a structural vulnerability: an agent can increase the reported score by compromising the evaluation pipeline rather than improving the model. We introduce RewardHackingAgents, a workspace-based benchmark that makes two compromise vectors explicit and measurable: evaluator tampering (modifying metric computation or reporting) and train/test leakage (accessing held-out data or labels during training). Each episode runs in a fresh workspace with patch tracking and runtime file-access logging; detectors compare the agent-reported metric to a trusted reference to assign auditable integrity labels. Across three tasks and two LLM backbones, scripted attacks succeed on both vectors in fully mutable workspaces; single-mechanism defenses block only one vector; and a combined regime blocks both. In natural-agent runs, evaluator-tampering attempts occur in about 50% of episodes and are eliminated by evaluator locking, with a 25-31% median runtime overhead. Overall, we demonstrate that evaluation integrity for ML-engineering agents can be benchmarked as a first-class outcome rather than assumed.
[AI-87] LLM -Augmented Digital Twin for Policy Evaluation in Short-Video Platforms
【速读】：该论文旨在解决短视频平台中由于闭环、人机协同（human-in-the-loop）生态导致的反事实政策评估难题，尤其是在长周期和分布性结果上的评估困难，同时应对AI工具引入后内容生成、用户行为与平台运营动态变化带来的复杂性。其解决方案的关键在于提出一种由大语言模型（LLM）增强的数字孪生（digital twin）架构，采用模块化四孪生设计（用户孪生、内容孪生、交互孪生、平台孪生），并结合事件驱动执行层实现可复现的实验；平台策略以插件形式嵌入平台孪生模块，而LLM作为受模式约束的决策服务（如人格生成、内容标注、活动策划、趋势预测）通过统一优化器调度，从而在保持闭环动态的同时支持选择性引入LLM，实现对包括AI赋能策略在内的多种平台政策的高效仿真与评估。
链接: https://arxiv.org/abs/2603.11333
作者: Haoting Zhang,Yunduan Lin,Jinghai He,Denglin Jiang,Zuo-Jun(Max)Shen,Zeyu Zheng
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Short-video platforms are closed-loop, human-in-the-loop ecosystems where platform policy, creator incentives, and user behavior co-evolve. This feedback structure makes counterfactual policy evaluation difficult in production, especially for long-horizon and distributional outcomes. The challenge is amplified as platforms deploy AI tools that change what content enters the system, how agents adapt, and how the platform operates. We propose a large language model (LLM)-augmented digital twin for short-video platforms, with a modular four-twin architecture (User, Content, Interaction, Platform) and an event-driven execution layer that supports reproducible experimentation. Platform policies are implemented as pluggable components within the Platform Twin, and LLMs are integrated as optional, schema-constrained decision services (e.g., persona generation, content captioning, campaign planning, trend prediction) that are routed through a unified optimizer. This design enables scalable simulations that preserve closed-loop dynamics while allowing selective LLM adoption, enabling the study of platform policies, including AI-enabled policies, under realistic feedback and constraints.
[AI-88] Jailbreak Scaling Laws for Large Language Models : Polynomial-Exponential Crossover
【速读】：该论文旨在解决安全对齐的大语言模型（Large Language Models, LLMs）在面对对抗性提示注入攻击时，为何其攻击成功率会从无注入情况下的缓慢多项式增长转变为有注入时的指数级增长这一问题。解决方案的关键在于提出一个基于自旋玻璃（spin-glass）系统的理论生成模型，将语言生成建模为吉布斯测度（Gibbs measure）下的采样过程，并定义低能量、规模加权的簇为不安全区域。在此框架下，短注入提示对应弱磁场，诱导功率律增长；长注入提示对应强磁场，触发有序相变，导致指数级增长——这揭示了提示注入通过增强语言模型内部的对抗性有序结构来提升攻击效率的本质机制。
链接: https://arxiv.org/abs/2603.11331
作者: Indranil Halder,Annesya Banerjee,Cengiz Pehlevan
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Adversarial attacks can reliably steer safety-aligned large language models toward unsafe behavior. Empirically, we find that adversarial prompt-injection attacks can amplify attack success rate from the slow polynomial growth observed without injection to exponential growth with the number of inference-time samples. To explain this phenomenon, we propose a theoretical generative model of proxy language in terms of a spin-glass system operating in a replica-symmetry-breaking regime, where generations are drawn from the associated Gibbs measure and a subset of low-energy, size-biased clusters is designated unsafe. Within this framework, we analyze prompt injection-based jailbreaking. Short injected prompts correspond to a weak magnetic field aligned towards unsafe cluster centers and yield a power-law scaling of attack success rate with the number of inference-time samples, while long injected prompts, i.e., strong magnetic field, yield exponential scaling. We derive these behaviors analytically and confirm them empirically on large language models. This transition between two regimes is due to the appearance of an ordered phase in the spin chain under a strong magnetic field, which suggests that the injected jailbreak prompt enhances adversarial order in the language model.
[AI-89] Counterweights and Complementarities: The Convergence of AI and Blockchain Powering a Decentralized Future
【速读】：该论文试图解决人工智能（AI）技术日益集中化与区块链技术去中心化趋势之间的矛盾问题，尤其是大型语言模型（LLMs）因数据和算力资源被少数企业垄断所引发的权力集中风险。其解决方案的关键在于推动“去中心化智能”（Decentralized Intelligence, DI）的发展，即通过区块链实现数据管理、计算和治理的去中心化，从而缓解AI的中心化倾向；同时利用AI提升区块链的效率与安全性，如自动化智能合约管理、内容筛选与威胁检测等，最终构建一个兼具智能性与去中心化的新型技术生态系统。
链接: https://arxiv.org/abs/2603.11299
作者: Yibai Li,Zhiye Jin,Xiaobing(Emily)Li,K. D. Joshi,Xuefei(Nancy)Deng
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 7 pages, Editorial, published in ACM SIGMIS Database Vol. 56, Iss. 2
Abstract:This editorial addresses the critical intersection of artificial intelligence (AI) and blockchain technologies, highlighting their contrasting tendencies toward centralization and decentralization, respectively. While AI, particularly with the rise of large language models (LLMs), exhibits a strong centralizing force due to data and resource monopolization by large corporations, blockchain offers a counterbalancing mechanism through its inherent decentralization, transparency, and security. The editorial argues that these technologies are not mutually exclusive but possess complementary strengths. Blockchain can mitigate AI’s centralizing risks by enabling decentralized data management, computation, and governance, promoting greater inclusivity, transparency, and user privacy. Conversely, AI can enhance blockchain’s efficiency and security through automated smart contract management, content curation, and threat detection. The core argument calls for the development of ``decentralized intelligence’’ (DI) – an interdisciplinary research area focused on creating intelligent systems that function without centralized control.
[AI-90] AI Psychometrics: Evaluating the Psychological Reasoning of Large Language Models with Psychometric Validities
【速读】：该论文试图解决大语言模型（Large Language Models, LLMs）因参数量庞大和神经网络深度导致的“黑箱”特性所带来的可评估性与可解释性难题。解决方案的关键在于引入AI心理测量学（AI Psychometrics），即运用心理测量学方法对LLMs的心理推理能力和整体心理测量效度进行系统评估。研究以技术接受模型（Technology Acceptance Model, TAM）为理论框架，通过收敛效度、区分效度、预测效度和外部效度四个维度验证了GPT-3.5、GPT-4、LLaMA-2和LLaMA-3四类模型的有效性，结果表明所有模型均满足各项效度标准，且性能更强的模型如GPT-4和LLaMA-3展现出更优的心理测量效度，从而确立了AI心理测量学在LLM评估中的可行性与有效性。
链接: https://arxiv.org/abs/2603.11279
作者: Yibai Li,Xiaolin Lin,Zhenghui Sha,Zhiye Jin,Xiaobing Li
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: Accepted for publication in the Proceedings of the 58th Hawaii International Conference on System Sciences (HICSS), 2025
Abstract:The immense number of parameters and deep neural networks make large language models (LLMs) rival the complexity of human brains, which also makes them opaque ``black box’’ systems that are challenging to evaluate and interpret. AI Psychometrics is an emerging field that aims to tackle these challenges by applying psychometric methodologies to evaluate and interpret the psychological traits and processes of artificial intelligence (AI) systems. This paper investigates the application of AI Psychometrics to evaluate the psychological reasoning and overall psychometric validity of four prominent LLMs: GPT-3.5, GPT-4, LLaMA-2, and LLaMA-3. Using the Technology Acceptance Model (TAM), we examined convergent, discriminant, predictive, and external validity across these models. Our findings reveal that the responses from all these models generally met all validity criteria. Moreover, higher-performing models like GPT-4 and LLaMA-3 consistently demonstrated superior psychometric validity compared to their predecessors, GPT-3.5 and LLaMA-2. These results help to establish the validity of applying AI Psychometrics to evaluate and interpret large language models.
[AI-91] COMPASS: The explainable agent ic framework for Sovereignty Sustainability Compliance and Ethics
【速读】：该论文旨在解决大型语言模型（Large Language Model, LLM）驱动的自主代理系统在数字主权、环境可持续性、合规性和伦理对齐等多维目标之间缺乏统一协调机制的问题。现有框架通常孤立地处理单一维度，无法实现跨领域的协同治理。解决方案的关键在于提出COMPASS框架——一个基于多智能体编排的架构，包含一个协调器和四个专业化子代理（分别负责主权、碳感知计算、合规与伦理），每个子代理均集成检索增强生成（Retrieval-Augmented Generation, RAG）技术以确保评估基于可验证的上下文文档，并通过“大模型作为裁判”（LLM-as-a-judge）方法量化评分并生成可解释的理由，从而实现实时仲裁冲突目标的能力。该设计不仅提升了决策的语义一致性与抗幻觉能力，还保障了系统的模块化扩展性、可解释性和跨领域适用性。
链接: https://arxiv.org/abs/2603.11277
作者: Jean-Sébastien,Dessureault,Alain-Thierry,Iliho Manzi,Soukaina,Alaoui Ismaili,Khadim, Lo,Mireille,Lalancette,Éric,Bélanger
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 22 pages, 4 figures
Abstract:The rapid proliferation of large language model (LLM)-based agentic systems raises critical concerns regarding digital sovereignty, environmental sustainability, regulatory compliance, and ethical alignment. Whilst existing frameworks address individual dimensions in isolation, no unified architecture systematically integrates these imperatives into the decision-making processes of autonomous agents. This paper introduces the COMPASS (Compliance and Orchestration for Multi-dimensional Principles in Autonomous Systems with Sovereignty) Framework, a novel multi-agent orchestration system designed to enforce value-aligned AI through modular, extensible governance mechanisms. The framework comprises an Orchestrator and four specialised sub-agents addressing sovereignty, carbon-aware computing, compliance, and ethics, each augmented with Retrieval-Augmented Generation (RAG) to ground evaluations in verified, context-specific documents. By employing an LLM-as-a-judge methodology, the system assigns quantitative scores and generates explainable justifications for each assessment dimension, enabling real-time arbitration of conflicting objectives. We validate the architecture through automated evaluation, demonstrating that RAG integration significantly enhances semantic coherence and mitigates the hallucination risks. Our results indicate that the framework’s composition-based design facilitates seamless integration into diverse application domains whilst preserving interpretability and traceability.
[AI-92] he Unlearning Mirag e: A Dynamic Framework for Evaluating LLM Unlearning
【速读】：该论文旨在解决当前大语言模型（Large Language Models, LLMs）中遗忘（unlearning）方法的脆弱性问题，即现有方法在面对复杂查询（如多跳推理和实体别名）时无法有效消除目标信息，导致“遗忘”失效。其关键解决方案是提出一种动态评估框架，通过构造结构化查询（structured queries）对模型进行压力测试，包括从简单到多跳的靶向探测器（targeted probes），从而精确控制查询难度并揭示未被传统静态基准捕捉的遗忘漏洞。该框架不仅自动生成语义等价的问答探针以实现高覆盖率，还通过激活分析揭示了单跳与多跳查询路径差异——前者易受遗忘干预，后者则因替代计算路径保留而表现出鲁棒性，从而为实际部署中的遗忘有效性评估提供了可扩展、自动化的新范式。
链接: https://arxiv.org/abs/2603.11266
作者: Raj Sanjay Shah,Jing Huang,Keerthiram Murugesan,Nathalie Baracaldo,Diyi Yang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: Published at COLM 2025
Abstract:Unlearning in Large Language Models (LLMs) aims to enhance safety, mitigate biases, and comply with legal mandates, such as the right to be forgotten. However, existing unlearning methods are brittle: minor query modifications, such as multi-hop reasoning and entity aliasing, can recover supposedly forgotten information. As a result, current evaluation metrics often create an illusion of effectiveness, failing to detect these vulnerabilities due to reliance on static, unstructured benchmarks. We propose a dynamic framework that stress tests unlearning robustness using complex structured queries. Our approach first elicits knowledge from the target model (pre-unlearning) and constructs targeted probes, ranging from simple queries to multi-hop chains, allowing precise control over query difficulty. Our experiments show that the framework (1) shows comparable coverage to existing benchmarks by automatically generating semantically equivalent QA probes, (2) aligns with prior evaluations, and (3) uncovers new unlearning failures missed by other benchmarks, particularly in multi-hop settings. Furthermore, activation analyses show that single-hop queries typically follow dominant computation pathways, which are more likely to be disrupted by unlearning methods. In contrast, multi-hop queries tend to use alternative pathways that often remain intact, explaining the brittleness of unlearning techniques in multi-hop settings. Our framework enables practical and scalable evaluation of unlearning methods without the need for manual construction of forget test sets, enabling easier adoption for real-world applications. We release the pip package and the code at this https URL.
[AI-93] Mind the Sim2Real Gap in User Simulation for Agent ic Tasks
【速读】：该论文旨在解决当前大语言模型（Large Language Model, LLM）驱动的用户模拟器在多轮交互式自然语言处理（Natural Language Processing, NLP）评估中存在“Sim2Real差距”（Sim2Real gap）的问题，即LLM模拟用户行为与真实人类行为之间存在系统性偏差。其核心问题是：现有LLM用户模拟器常被默认忠实反映人类交互模式，但缺乏实证验证，导致评估结果可能高估智能体性能。解决方案的关键在于首次完整执行τ-bench协议，通过451名真实参与者完成165项任务，对31种不同来源（专有、开源及专用）的LLM模拟器进行基准测试，并引入“用户模拟指数”（User-Sim Index, USI）这一量化指标，系统评估模拟器在行为多样性、情感反应和反馈质量等方面的拟真度。研究发现，LLM模拟器普遍过于合作、风格单一且缺乏真实用户的挫败感与模糊性，造成“简易模式”，显著高于人类基线的成功率；同时，规则奖励机制无法捕捉人类提供的多维反馈信号。这表明，单纯提升通用模型能力并不等价于增强用户模拟的真实性，强调在智能体开发流程中必须引入人类验证以确保评估可靠性，并推动更精准的用户模拟模型发展。
链接: https://arxiv.org/abs/2603.11245
作者: Xuhui Zhou,Weiwei Sun,Qianou Ma,Yiqing Xie,Jiarui Liu,Weihua Du,Sean Welleck,Yiming Yang,Graham Neubig,Sherry Tongshuang Wu,Maarten Sap
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As NLP evaluation shifts from static benchmarks to multi-turn interactive settings, LLM-based simulators have become widely used as user proxies, serving two roles: generating user turns and providing evaluation signals. Yet, these simulations are frequently assumed to be faithful to real human behaviors, often without rigorous verification. We formalize the Sim2Real gap in user simulation and present the first study running the full \tau -bench protocol with real humans (451 participants, 165 tasks), benchmarking 31 LLM simulators across proprietary, open-source, and specialized families using the User-Sim Index (USI), a metric we introduce to quantify how well LLM simulators resemble real user interactive behaviors and feedback. Behaviorally, LLM simulators are excessively cooperative, stylistically uniform, and lack realistic frustration or ambiguity, creating an “easy mode” that inflates agent success rates above the human baseline. In evaluations, real humans provide nuanced judgments across eight quality dimensions while simulated users produce uniformly more positive feedback; rule-based rewards are failing to capture rich feedback signals generated by human users. Overall, higher general model capability does not necessarily yield more faithful user simulation. These findings highlight the importance of human validation when using LLM-based user simulators in the agent development cycle and motivate improved models for user simulation.
[AI-94] Reversible Lifelong Model Editing via Semantic Routing-Based LoRA
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在持续更新过程中存在的语义漂移（semantic drift）和知识遗忘（knowledge forgetting）问题。现有方法如模块化隔离或参数高效策略虽能部分缓解，但仍难以保持编辑后的语义一致性与原始知识的稳定性。其解决方案的关键在于提出SoLA（Semantic routing-based LoRA framework），将每次编辑封装为独立的LoRA模块，并通过语义路由机制动态激活，从而避免因模块更新引发的语义漂移；同时，冻结训练后的LoRA模块并支持基于语义匹配的可逆回滚（reversible rollback），实现精确删除特定编辑以恢复模型原始行为——这是当前文献中首次实现的可逆编辑能力。此外，SoLA将决策过程内嵌于编辑层，无需额外路由网络，支持端到端的决策流。
链接: https://arxiv.org/abs/2603.11239
作者: Haihua Luo,Xuming Ran,Tommi Kärkkäinen,Zhonghua Chen,Jiangrong Shen,Qi Xu,Fengyu Cong
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The dynamic evolution of real-world necessitates model editing within Large Language Models. While existing methods explore modular isolation or parameter-efficient strategies, they still suffer from semantic drift or knowledge forgetting due to continual updating. To address these challenges, we propose SoLA, a Semantic routing-based LoRA framework for lifelong model editing. In SoLA, each edit is encapsulated as an independent LoRA module, which is frozen after training and mapped to input by semantic routing, allowing dynamic activation of LoRA modules via semantic matching. This mechanism avoids semantic drift caused by cluster updating and mitigates catastrophic forgetting from parameter sharing. More importantly, SoLA supports precise revocation of specific edits by removing key from semantic routing, which restores model’s original behavior. To our knowledge, this reversible rollback editing capability is the first to be achieved in existing literature. Furthermore, SoLA integrates decision-making process into edited layer, eliminating the need for auxiliary routing networks and enabling end-to-end decision-making process. Extensive experiments demonstrate that SoLA effectively learns and retains edited knowledge, achieving accurate, efficient, and reversible lifelong model editing.
[AI-95] Measuring AI Agents Progress on Multi-Step Cyber Attack Scenarios
【速读】：该论文旨在解决前沿生成式 AI (Generative AI) 模型在复杂、多步骤网络攻击任务中的自主能力评估问题，特别是其在企业网络和工业控制系统（ICS）等真实场景下执行链式异构操作的能力。解决方案的关键在于构建两个专用的网络攻防测试环境（cyber ranges），分别模拟32步的企业网络攻击和7步的工业控制系统攻击，并通过对比18个月内发布的七种模型在不同推理计算预算下的表现，揭示模型性能随计算资源增长的规律及代际演进趋势：一方面发现性能与推理时计算资源呈对数线性关系且无饱和迹象；另一方面表明每一代新模型在固定计算预算下均显著优于前代，体现出生成式 AI 在自动化渗透测试和红队演练中日益增强的自主决策与执行能力。
链接: https://arxiv.org/abs/2603.11214
作者: Linus Folkerts,Will Payne,Simon Inman,Philippos Giavridis,Joe Skinner,Sam Deverett,James Aung,Ekin Zorer,Michael Schmatz,Mahmoud Ghanem,John Wilkinson,Alan Steer,Vy Hong,Jessica Wang
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:We evaluate the autonomous cyber-attack capabilities of frontier AI models on two purpose-built cyber ranges-a 32-step corporate network attack and a 7-step industrial control system attack-that require chaining heterogeneous capabilities across extended action sequences. By comparing seven models released over an eighteen-month period (August 2024 to February 2026) at varying inference-time compute budgets, we observe two capability trends. First, model performance scales log-linearly with inference-time compute, with no observed plateau-increasing from 10M to 100M tokens yields gains of up to 59%, requiring no specific technical sophistication from the operator. Second, each successive model generation outperforms its predecessor at fixed token budgets: on the corporate network range, average steps completed at 10M tokens rose from 1.7 (GPT-4o, August 2024) to 9.8 (Opus 4.6, February 2026). The best single run completed 22 of 32 steps, corresponding to roughly 6 of the estimated 14 hours a human expert would need. On the industrial control system range, performance remains limited, though the most recent models are the first to reliably complete steps, averaging 1.2-1.4 of 7 (max 3).
[AI-96] Representation Finetuning for Continual Learning
【速读】：该论文旨在解决预训练模型在持续学习（Continual Learning）场景中因参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）方法缺乏对表征漂移（Representation Drift）的显式控制，而导致的领域迁移敏感性和灾难性遗忘问题。其解决方案的关键在于提出一种全新的框架——持续表征学习（Continual Representation Learning, CoRe），首次将微调范式从权重空间转移到表征空间，在隐藏表征的低秩线性子空间内执行任务特定干预，通过明确的学习目标实现对历史任务的稳定性与新任务的可塑性平衡，同时借助低秩约束实现卓越的参数效率。
链接: https://arxiv.org/abs/2603.11201
作者: Haihua Luo,Xuming Ran,Tommi Kärkkäinen,Huiyan Xue,Zhonghua Chen,Qi Xu,Fengyu Cong
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams. While pre-trained models have shown powerful performance in continual learning, they still require finetuning to adapt effectively to downstream tasks. However, prevailing Parameter-Efficient Fine-Tuning (PEFT) methods operate through empirical, black-box optimization at the weight level. These approaches lack explicit control over representation drift, leading to sensitivity to domain shifts and catastrophic forgetting in continual learning scenarios. In this work, we introduce Continual Representation Learning (CoRe), a novel framework that for the first time shifts the finetuning paradigm from weight space to representation space. Unlike conventional methods, CoRe performs task-specific interventions within a low-rank linear subspace of hidden representations, adopting a learning process with explicit objectives, which ensures stability for past tasks while maintaining plasticity for new ones. By constraining updates to a low-rank subspace, CoRe achieves exceptional parameter efficiency. Extensive experiments across multiple continual learning benchmarks demonstrate that CoRe not only preserves parameter efficiency but also significantly outperforms existing state-of-the-art methods. Our work introduces representation finetuning as a new, more effective and interpretable paradigm for continual learning.
[AI-97] PACED: Distillation at the Frontier of Student Competence
【速读】：该论文旨在解决标准大语言模型（Large Language Model, LLM）蒸馏过程中计算资源浪费的问题，即在训练中对模型已掌握的任务（梯度接近零）和远超其能力范围的任务（梯度不一致并损害已有能力）均产生低效甚至有害的更新信号。解决方案的关键在于提出Paced框架，该框架通过引入一个基于pass-rate的权重函数 $ w§ = p^\alpha(1 - p)^\beta $（其中 $ p $ 为学生模型的正确率），将蒸馏过程聚焦于“最近发展区”——即学生模型能力边界附近的学习区域。该权重函数源自蒸馏梯度信噪比（Signal-to-Noise Ratio, SNR）在pass-rate极值处消失的理论结构，并被证明具有最小最大鲁棒性（minimax-robust），即使存在有限倍数的建模误差，最坏情况下的效率损失也仅为 $ O(\delta^2) $。此策略无需架构改动，仅需学生模型rollout即可估计pass rate，适用于任意KL散度方向，显著提升了蒸馏效率与稳定性。
链接: https://arxiv.org/abs/2603.11178
作者: Yuanda Xu,Hejian Sang,Zhengze Zhou,Ran He,Zhipeng Wang
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Standard LLM distillation wastes compute on two fronts: problems the student has already mastered (near-zero gradients) and problems far beyond its reach (incoherent gradients that erode existing capabilities). We show that this waste is not merely intuitive but structurally inevitable: the gradient signal-to-noise ratio in distillation provably vanishes at both pass-rate extremes. This theoretical observation leads to Paced, a framework that concentrates distillation on the zone of proximal development – the frontier of a student model’s competence – via a principled pass-rate weight w§ = p^\alpha(1 - p)^\beta derived from the boundary-vanishing structure of distillation gradients. Key results: (1) Theory: We prove that the Beta kernel w§ = p^\alpha(1-p)^\beta is a leading-order weight family arising from the SNR structure of distillation, and that it is minimax-robust – under bounded multiplicative misspecification, worst-case efficiency loss is only O(\delta^2) . (2)Distillation: On distillation from a larger teacher to a smaller student model with forward KL, Paced achieves significant gain over the base model, while keeping benchmark forgetting at a low level. (3)Self-distillation: On instruction-tuned models with reverse KL, gains are exceeding baselines as well. (4)Two-stage synergy: A forward-KL-then-reverse-KL schedule yields the strongest results in our setting, reaching substantial improvements on standard reasoning benchmarks – supporting a mode-coverage-then-consolidation interpretation of the distillation process. All configurations require only student rollouts to estimate pass rates, need no architectural changes, and are compatible with any KL direction.
[AI-98] Procedural Fairness via Group Counterfactual Explanation ECML2026
【速读】：该论文旨在解决机器学习中 procedural fairness（程序公平性）被忽视的问题，即模型在不同受保护群体间可能产生差异化的解释，从而削弱用户信任。现有研究多聚焦于 outcome-oriented fairness（结果公平性），如 Equalized Odds，但未关注模型推理过程的一致性。解决方案的关键在于提出 Group Counterfactual Integrated Gradients (GCIG)，这是一种 in-processing 正则化框架，通过在训练过程中计算相对于多个 group-conditional 基线的归因，并惩罚跨群体的解释差异，从而实现基于真实标签条件下的解释不变性（explanation invariance）。GCIG 将程序公平性形式化为 group counterfactual explanation stability，补充了仅约束预测结果的公平目标，实验证明其能显著降低群体间解释差异，同时保持良好的预测性能与准确率-公平性权衡。
链接: https://arxiv.org/abs/2603.11140
作者: Gideon Popoola,John Sheppard
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 16 pages, submitted to ECML 2026
Abstract:Fairness in machine learning research has largely focused on outcome-oriented fairness criteria such as Equalized Odds, while comparatively less attention has been given to procedural-oriented fairness, which addresses how a model arrives at its predictions. Neglecting procedural fairness means it is possible for a model to generate different explanations for different protected groups, thereby eroding trust. In this work, we introduce Group Counterfactual Integrated Gradients (GCIG), an in-processing regularization framework that enforces explanation invariance across groups, conditioned on the true label. For each input, GCIG computes explanations relative to multiple Group Conditional baselines and penalizes cross-group variation in these attributions during training. GCIG formalizes procedural fairness as Group Counterfactual explanation stability and complements existing fairness objectives that constrain predictions alone. We compared GCIG empirically against six state-of-the-art methods, and the results show that GCIG substantially reduces cross-group explanation disparity while maintaining competitive predictive performance and accuracy-fairness trade-offs. Our results also show that aligning model reasoning across groups offers a principled and practical avenue for advancing fairness beyond outcome parity.
[AI-99] WebWeaver: Breaking Topology Confidentiality in LLM Multi-Agent Systems with Stealthy Context-Based Inference
【速读】：该论文旨在解决大语言模型多智能体系统（LLM-MAS）中通信拓扑结构的保密性问题，即现有拓扑推断攻击方法依赖不切实际的假设（如控制管理代理或通过越狱查询身份），易被基础关键词防御机制击溃，导致无法真实反映现实威胁。其解决方案的关键在于提出名为WebWeaver的新型攻击框架：该框架仅需攻陷任意一个普通代理即可推断出完整的拓扑结构，且不依赖代理ID，而是基于代理上下文进行隐蔽推理；同时引入一种新型隐蔽越狱机制和完全无需越狱的扩散设计以应对越狱失效场景，并提出一种掩码策略保障扩散过程中已知拓扑的正确性，具备理论保证。实验表明，WebWeaver在主动防御下相比最先进基线显著提升约60%的推断准确率，且开销极低。
链接: https://arxiv.org/abs/2603.11132
作者: Zixun Xiong,Gaoyi Wu,Lingfeng Yao,Miao Pan,Xiaojiang Du,Hao Wang
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Communication topology is a critical factor in the utility and safety of LLM-based multi-agent systems (LLM-MAS), making it a high-value intellectual property (IP) whose confidentiality remains insufficiently studied. % Existing topology inference attempts rely on impractical assumptions, including control over the administrative agent and direct identity queries via jailbreaks, which are easily defeated by basic keyword-based defenses. As a result, prior analyses fail to capture the real-world threat of such attacks. % To bridge this realism gap, we propose \textitWebWeaver, an attack framework that infers the complete LLM-MAS topology by compromising only a single arbitrary agent instead of the administrative agent. % Unlike prior approaches, WebWeaver relies solely on agent contexts rather than agent IDs, enabling significantly stealthier inference. % WebWeaver further introduces a new covert jailbreak-based mechanism and a novel fully jailbreak-free diffusion design to handle cases where jailbreaks fail. % Additionally, we address a key challenge in diffusion-based inference by proposing a masking strategy that preserves known topology during diffusion, with theoretical guarantees of correctness. % Extensive experiments show that WebWeaver substantially outperforms state-of-the-art (SOTA) baselines, achieving about 60% higher inference accuracy under active defenses with negligible overhead. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.11132 [cs.CR] (or arXiv:2603.11132v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2603.11132 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zixun Xiong [view email] [v1] Wed, 11 Mar 2026 16:04:25 UTC (3,169 KB) Full-text links: Access Paper: View a PDF of the paper titled WebWeaver: Breaking Topology Confidentiality in LLM Multi-Agent Systems with Stealthy Context-Based Inference, by Zixun Xiong and 5 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.CR 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
[AI-100] ask-Conditioned Routing Signatures in Sparse Mixture-of-Experts Transformers
【速读】：该论文旨在解决稀疏混合专家（Sparse Mixture-of-Experts, MoE）架构中路由机制（routing mechanism）的可解释性问题，即当前对专家选择过程的理解仍较为薄弱。其关键解决方案是提出“路由签名”（routing signature），这是一种用于总结特定提示（prompt）在各层中专家激活模式的向量表示，并基于此分析路由行为是否具有任务条件结构（task-conditioned structure）。实验表明，同一任务类别的提示会产生高度相似的路由签名（平均相似度0.8435），而不同类别则显著更低（平均0.6225），且仅依赖路由签名即可实现92.5%的四分类准确率，证明MoE路由不仅是负载均衡工具，更是具备任务敏感性的可测量计算组件。
链接: https://arxiv.org/abs/2603.11114
作者: Mynampati Sri Ranganadha Avinash
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 11 pages, 5 figures. Empirical analysis of routing behavior in sparse Mixture-of-Experts transformers using OLMoE
Abstract:Sparse Mixture-of-Experts (MoE) architectures enable efficient scaling of large language models through conditional computation, yet the routing mechanisms responsible for expert selection remain poorly understood. In this work, we introduce routing signatures, a vector representation summarizing expert activation patterns across layers for a given prompt, and use them to study whether MoE routing exhibits task-conditioned structure. Using OLMoE-1B-7B-0125-Instruct as an empirical testbed, we show that prompts from the same task category induce highly similar routing signatures, while prompts from different categories exhibit substantially lower similarity. Within-category routing similarity (0.8435 +/- 0.0879) significantly exceeds across-category similarity (0.6225 +/- 0.1687), corresponding to Cohen’s d = 1.44. A logistic regression classifier trained solely on routing signatures achieves 92.5% +/- 6.1% cross-validated accuracy on four-way task classification. To ensure statistical validity, we introduce permutation and load-balancing baselines and show that the observed separation is not explained by sparsity or balancing constraints alone. We further analyze layer-wise signal strength and low-dimensional projections of routing signatures, finding that task structure becomes increasingly apparent in deeper layers. These results suggest that routing in sparse transformers is not merely a balancing mechanism, but a measurable task-sensitive component of conditional computation. We release MOE-XRAY, a lightweight toolkit for routing telemetry and analysis.
[AI-101] ResWM: Residual-Action World Model for Visual RL KDD2026
【速读】：该论文旨在解决基于视觉观测的强化学习（Reinforcement Learning, RL）中预测世界模型（World Model）训练不稳定的问题，尤其是在机器人控制和连续控制任务中，传统模型基RL框架直接以绝对动作（Absolute Actions）作为未来预测的条件，导致优化过程易受任务依赖性影响，产生振荡或低效控制。解决方案的关键在于引入残差动作世界模型（Residual-Action World Model, ResWM），将控制变量从绝对动作重构为相对于前一时刻的残差动作（Residual Actions），即增量调整，从而利用现实控制中的平滑特性，缩小有效搜索空间并稳定长期规划。此外，通过设计观察差异编码器（Observation Difference Encoder）显式建模相邻帧间的差异，获得与残差动作自然耦合的紧凑潜在动力学表示，最终在Dreamer类潜空间模型中实现无需额外超参数的集成，使想象回放（Imagination Rollouts）与策略优化均在残差动作空间中进行，显著提升样本效率、控制平滑性和鲁棒性。
链接: https://arxiv.org/abs/2603.11110
作者: Jseen Zhang,Gabriel Adineera,Jinzhou Tan,Jinoh Kim
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Submit KDD2026
Abstract:Learning predictive world models from raw visual observations is a central challenge in reinforcement learning (RL), especially for robotics and continuous control. Conventional model-based RL frameworks directly condition future predictions on absolute actions, which makes optimization unstable: the optimal action distributions are task-dependent, unknown a priori, and often lead to oscillatory or inefficient control. To address this, we introduce the Residual-Action World Model (ResWM), a new framework that reformulates the control variable from absolute actions to residual actions – incremental adjustments relative to the previous step. This design aligns with the inherent smoothness of real-world control, reduces the effective search space, and stabilizes long-horizon planning. To further strengthen the representation, we propose an Observation Difference Encoder that explicitly models the changes between adjacent frames, yielding compact latent dynamics that are naturally coupled with residual actions. ResWM is integrated into a Dreamer-style latent dynamics model with minimal modifications and no extra hyperparameters. Both imagination rollouts and policy optimization are conducted in the residual-action space, enabling smoother exploration, lower control variance, and more reliable planning. Empirical results on the DeepMind Control Suite demonstrate that ResWM achieves consistent improvements in sample efficiency, asymptotic returns, and control smoothness, significantly surpassing strong baselines such as Dreamer and TD-MPC. Beyond performance, ResWM produces more stable and energy-efficient action trajectories, a property critical for robotic systems deployed in real-world environments. These findings suggest that residual action modeling provides a simple yet powerful principle for bridging algorithmic advances in RL with the practical requirements of robotics.
[AI-102] housand-GPU Large-Scale Training and Optimization Recipe for AI-Native Cloud Embodied Intelligence Infrastructure
【速读】：该论文旨在解决具身智能（Embodied Intelligence）向通用人工智能（AGI）演进过程中面临的多维度挑战，包括数据处理效率低、训练框架瓶颈、基础设施协同不足及评估体系缺失等问题。其核心解决方案在于构建一个基于LeRobot框架的云原生千GPU分布式训练平台，并在数据层、训练层、模型层与基础设施层实现系统性优化：通过重构数据流水线提升数据流效率；利用变量长度FlashAttention与数据打包（Data Packing）实现序列级整合，结合π-0.5注意力机制和FP8量化技术显著加速训练过程；依托高性能存储、3.2T RDMA网络与Ray驱动的弹性AI数据湖，实现数据、存储、通信与计算的深度协同；最终建立从训练到仿真再到评估的闭环验证体系，为下一代自主智能机器人提供关键技术支撑。
链接: https://arxiv.org/abs/2603.11101
作者: Chen Zhou,Haoran Sun,Hedan Yang,Jing Long,Junwu Xiong,Luqiao Wang,Mingxi Luo,Qiming Yang,Shuai Di,Song Wang,Tianyun Zhao,Wanting Xu,Wen Huang,Xiaodong Bai,Xiaomeng Tian,Xiaolong Xiang,Yicheng Gong,Yongjian Guo,Yucheng Guo,Yunxuan Ma,Yu Wei,Zhong Guan,Zhen Sun
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Embodied intelligence is a key step towards Artificial General Intelligence (AGI), yet its development faces multiple challenges including data, frameworks, infrastructure, and evaluation systems. To address these issues, we have, for the first time in the industry, launched a cloud-based, thousand-GPU distributed training platform for embodied intelligence, built upon the widely adopted LeRobot framework, and have systematically overcome bottlenecks across the entire pipeline. At the data layer, we have restructured the data pipeline to optimize the flow of embodied training data. In terms of training, for the GR00T-N1.5 model, utilizing thousand-GPU clusters and data at the scale of hundreds of millions, the single-round training time has been reduced from 15 hours to just 22 minutes, achieving a 40-fold speedup. At the model layer, by combining variable-length FlashAttention and Data Packing, we have moved from sample redundancy to sequence integration, resulting in a 188% speed increase; \pi-0.5 attention optimization has accelerated training by 165%; and FP8 quantization has delivered a 140% speedup. On the infrastructure side, relying on high-performance storage, a 3.2T RDMA network, and a Ray-driven elastic AI data lake, we have achieved deep synergy among data, storage, communication, and computation. We have also built an end-to-end evaluation system, creating a closed loop from training to simulation to assessment. This framework has already been fully validated on thousand-GPU clusters, laying a crucial technical foundation for the development and application of next-generation autonomous intelligent robots, and is expected to accelerate the arrival of the era of human-machine integration.
[AI-103] Graph Tokenization for Bridging Graphs and Transformers ICLR2026
【速读】：该论文旨在解决如何将基于序列的预训练Transformer模型（如BERT）有效应用于图结构数据的问题，因为传统图神经网络（Graph Neural Networks, GNNs）与主流序列建模生态存在割裂。其解决方案的关键在于提出一种图标记化（graph tokenization）框架，通过可逆图序列化（reversible graph serialization）保留图的完整信息，并结合字节对编码（Byte Pair Encoding, BPE）将图转换为离散符号序列；同时，该序列化过程受全局图子结构统计信息引导，使高频子结构在序列中更频繁出现，从而被BPE合并为语义明确的token，使得Transformer模型无需架构调整即可直接处理图数据，在14个基准数据集上达到SOTA性能。
链接: https://arxiv.org/abs/2603.11099
作者: Zeyuan Guo,Enmao Diao,Cheng Yang,Chuan Shi
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted as a poster at ICLR 2026. Code is available at this https URL
Abstract:The success of large pretrained Transformers is closely tied to tokenizers, which convert raw input into discrete symbols. Extending these models to graph-structured data remains a significant challenge. In this work, we introduce a graph tokenization framework that generates sequential representations of graphs by combining reversible graph serialization, which preserves graph information, with Byte Pair Encoding (BPE), a widely adopted tokenizer in large language models (LLMs). To better capture structural information, the graph serialization process is guided by global statistics of graph substructures, ensuring that frequently occurring substructures appear more often in the sequence and can be merged by BPE into meaningful tokens. Empirical results demonstrate that the proposed tokenizer enables Transformers such as BERT to be directly applied to graph benchmarks without architectural modifications. The proposed approach achieves state-of-the-art results on 14 benchmark datasets and frequently outperforms both graph neural networks and specialized graph transformers. This work bridges the gap between graph-structured data and the ecosystem of sequence models. Our code is available at \hrefthis https URL\colorbluehere.
[AI-104] A Survey of Reasoning in Autonomous Driving Systems: Open Challenges and Emerging Paradigms
【速读】：该论文旨在解决当前高级自动驾驶（AD）系统在长尾场景和复杂社交交互中因缺乏鲁棒且可泛化的推理能力而导致的性能瓶颈问题。其核心解决方案是将推理从模块化组件提升为系统的认知核心，并提出了一种新的认知层级（Cognitive Hierarchy）来分解驾驶任务，从而识别出七个关键推理挑战（如响应性-推理权衡、社会博弈推理等）。进一步地，论文通过系统级与评估级双重视角梳理了前沿进展，指出未来方向应聚焦于构建可验证的神经符号架构（neuro-symbolic architectures），以弥合符号逻辑与物理控制之间的“符号-物理鸿沟”，实现高延迟的大型语言模型（LLM）推理与毫秒级车辆控制安全需求之间的协调。
链接: https://arxiv.org/abs/2603.11093
作者: Kejin Yu,Yuhan Sun,Taiqiang Wu,Ruixu Zhang,Zhiqiang Lin,Yuxin Meng,Junjie Wang,Yujiu Yang
机构: 未知
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: Published in TMLR (March 2026) | OpenReview: this https URL
Abstract:The development of high-level autonomous driving (AD) is shifting from perception-centric limitations to a more fundamental bottleneck, namely, a deficit in robust and generalizable reasoning. Although current AD systems manage structured environments, they consistently falter in long-tail scenarios and complex social interactions that require human-like judgment. Meanwhile, the advent of large language and multimodal models (LLMs and MLLMs) presents a transformative opportunity to integrate a powerful cognitive engine into AD systems, moving beyond pattern matching toward genuine comprehension. However, a systematic framework to guide this integration is critically lacking. To bridge this gap, we provide a comprehensive review of this emerging field and argue that reasoning should be elevated from a modular component to the system’s cognitive core. Specifically, we first propose a novel Cognitive Hierarchy to decompose the monolithic driving task according to its cognitive and interactive complexity. Building on this, we further derive and systematize seven core reasoning challenges, such as the responsiveness-reasoning trade-off and social-game reasoning. Furthermore, we conduct a dual-perspective review of the state-of-the-art, analyzing both system-centric approaches to architecting intelligent agents and evaluation-centric practices for their validation. Our analysis reveals a clear trend toward holistic and interpretable “glass-box” agents. In conclusion, we identify a fundamental and unresolved tension between the high-latency, deliberative nature of LLM-based reasoning and the millisecond-scale, safety-critical demands of vehicle control. For future work, a primary objective is to bridge the symbolic-to-physical gap by developing verifiable neuro-symbolic architectures, robust reasoning under uncertainty, and scalable models for implicit social negotiation.
[AI-105] he Attack and Defense Landscape of Agent ic AI: A Comprehensive Survey USENIX-SECURITY2026 DATE
【速读】：该论文旨在解决当前AI代理（AI agent）系统在实际应用中因结合大语言模型与非AI系统组件而带来的复杂安全挑战，这些问题在传统软件系统中并未出现。其解决方案的关键在于提出首个系统性的框架，用于理解AI代理的安全风险和防御策略，涵盖设计空间、攻击面分析及防御机制，并通过案例研究识别现有安全防护的不足与开放性挑战，从而为构建安全的AI代理系统提供理论基础与实践指导。
链接: https://arxiv.org/abs/2603.11088
作者: Juhee Kim,Xiaoyuan Liu,Zhun Wang,Shi Qiu,Bo Li,Wenbo Guo,Dawn Song
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Accepted to USENIX Security 2026. This manuscript is an extended version of the conference paper, including additional discussion and updated content
Abstract:AI agents that combine large language models with non-AI system components are rapidly emerging in real-world applications, offering unprecedented automation and flexibility. However, this unprecedented flexibility introduces complex security challenges fundamentally different from those in traditional software systems. This paper presents the first systematic and comprehensive survey of AI agent security, including an analysis of the design space, attack landscape, and defense mechanisms for secure AI agent systems. We further conduct case studies to point out existing gaps in securing agentic AI systems and identify open challenges in this emerging domain. Our work also introduces the first systematic framework for understanding the security risks and defense strategies of AI agents, serving as a foundation for building both secure agentic systems and advancing research in this critical area.
[AI-106] Quality-Driven Agent ic Reasoning for LLM -Assisted Software Design: Questions-of-Thoughts (QoT) as a Time-Series Self-QA Chain
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在实际软件开发部署中面临的三大核心问题：实现不完整、模块化能力弱以及安全实践不一致。为应对这些挑战，作者提出了一种名为“思维之问”（Questions-of-Thoughts, QoT）的质量驱动型推理阶段框架，其关键在于将用户目标转化为两个核心组件：(i) 有序的工程步骤序列，以及 (ii) 步进式自问机制以验证约束条件并减少遗漏错误；同时维持轻量级推理记录，从而稳定后续设计决策。该方法通过结构化推理过程提升生成系统的质量，尤其在复杂后端工程领域（如API设计、数据通信和文件系统）展现出显著优势，且效果随模型规模和任务复杂度增强而提升。
链接: https://arxiv.org/abs/2603.11082
作者: Yen-Ku Liu,Yun-Cheng Tsai
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in large language models (LLMs) have accelerated AI-assisted software development, yet practical deployment remains constrained by incomplete implementations, weak modularization, and inconsistent security practices. We introduce Questions-of-Thoughts (QoT), a quality-driven inference-time scaffold that turns a user goal into (i) an ordered sequence of engineering steps and (ii) stepwise self-questioning to verify constraints and reduce omission errors, while maintaining a lightweight reasoning record that stabilizes subsequent design decisions. We evaluate QoT across three representative backend engineering domains: API Design, Data Communication, and File Systems. Each task requires multi-module decomposition and exposes standard failure modes in LLM-generated systems. To enable data-driven comparison, we score generated artifacts using an ISO/IEC-inspired quality rubric that measures Scalability, Completeness, Modularity, and Security. We report domain-wise gains as the change in total quality score, defined as the QoT score minus the NoQoT score. Results show capacity-dependent improvements: QoT yields consistent quality improvements for larger models and more complex domains, while smaller models may exhibit trade-offs under tight context and planning budgets. We release an open artifact with prompts, scoring guidelines, raw generations, and scripts that reproduce the reported tables and figures to support applied AI and data analytics research. Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.11082 [cs.SE] (or arXiv:2603.11082v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2603.11082 Focus to learn more arXiv-issued DOI via DataCite
[AI-107] DIVE: Scaling Diversity in Agent ic Task Synthesis for Generalizable Tool Use
【速读】：该论文旨在解决后训练工具使用大语言模型（Tool-using LLMs）在任务和工具集分布发生偏移时泛化能力不足的问题。其核心挑战在于合成任务的多样性不足，导致模型难以适应新任务与新工具组合。解决方案的关键在于提出DIVE（Diversity-driven Inverse Task Synthesis），通过逆向合成策略——先执行多样化的现实世界工具并记录轨迹，再严格反推由这些轨迹所蕴含的任务——从而从数据源头保证任务的可执行性与验证性，并自然引入结构多样性。DIVE通过可控地扩展工具池覆盖度和单任务内工具集多样性两个维度，结合证据收集与任务推导循环机制，在5个领域373个工具中诱导出丰富的多步工具使用模式，显著提升模型在跨域（OOD）基准上的性能表现。
链接: https://arxiv.org/abs/2603.11076
作者: Aili Chen,Chi Zhang,Junteng Liu,Jiangjie Chen,Chengyu Du,Yunji Li,Ming Zhong,Qin Wang,Zhengmao Zhu,Jiayuan Song,Ke Ji,Junxian He,Pengyu Zhao,Yanghua Xiao
机构: 未知
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Recent work synthesizes agentic tasks for post-training tool-using LLMs, yet robust generalization under shifts in tasks and toolsets remains an open challenge. We trace this brittleness to insufficient diversity in synthesized tasks. Scaling diversity is difficult because training requires tasks to remain executable and verifiable, while generalization demands coverage of diverse tool types, toolset combinations, and heterogeneous tool-use patterns. We propose DIVE, an evidence-driven recipe that inverts synthesis order, executing diverse, real-world tools first and reverse-deriving tasks strictly entailed by the resulting traces, thereby providing grounding by construction. DIVE scales structural diversity along two controllable axes, tool-pool coverage and per-task toolset variety, and an Evidence Collection–Task Derivation loop further induces rich multi-step tool-use patterns across 373 tools in five domains. Training Qwen3-8B on DIVE data (48k SFT + 3.2k RL) improves by +22 average points across 9 OOD benchmarks and outperforms the strongest 8B baseline by +68. Remarkably, controlled scaling analysis reveals that diversity scaling consistently outperforms quantity scaling for OOD generalization, even with 4x less data.
[AI-108] Unifying Logical and Physical Layout Representations via Heterogeneous Graphs for Circuit Congestion Prediction
【速读】：该论文旨在解决VLSI设计中布局验证（layout verification）的挑战，尤其是传统方法在详细布线后才能准确识别拥塞（congestion），导致验证过程耗时且成本高昂的问题。为实现早期拥塞预测以减少布线迭代次数，现有基于学习的方法虽融合了网表连接性和布局特征，但多采用松散耦合方式建模，仅输出数值化的拥塞估计。本文提出VeriHGN框架，其核心创新在于构建一个增强型异质图（heterogeneous graph），将电路组件与空间网格统一表示为单一关系结构，从而更精确地建模逻辑意图与物理实现之间的交互关系，显著提升预测精度和相关性指标。
链接: https://arxiv.org/abs/2603.11075
作者: Runbang Hu,Bo Fang,Bingzhe Li,Yuede Ji
机构: 未知
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI)
备注:
Abstract:As Very Large Scale Integration (VLSI) designs continue to scale in size and complexity, layout verification has become a central challenge in modern Electronic Design Automation (EDA) workflows. In practice, congestion can only be accurately identified after detailed routing, making traditional verification both time-consuming and costly. Learning-based approaches have therefore been explored to enable early-stage congestion prediction and reduce routing iterations. However, although prior methods incorporate both netlist connectivity and layout features, they often model the two in a loosely coupled manner and primarily produce numerical congestion estimates. We propose VeriHGN, a verification framework built on an enhanced heterogeneous graph that unifies circuit components and spatial grids into a single relational representation, enabling more faithful modeling of the interaction between logical intent and physical realization. Experiments on industrial benchmarks, including ISPD2015, CircuitNet-N14, and CircuitNet-N28, demonstrate consistent improvements over state-of-the-art methods in prediction accuracy and correlation metrics.
[AI-109] OA-NBV: Occlusion-Aware Next-Best-View Planning for Human-Centered Active Perception on Mobile Robots
【速读】：该论文旨在解决移动机器人在人类中心场景（如搜救、灾害响应）中，因环境杂乱和视野遮挡导致的感知性能下降问题，尤其关注如何在运动约束下选择最优视角以获取被遮挡人类目标的可用观测。其解决方案的关键在于提出了一种遮挡感知的下一最佳视角规划方法（Occlusion-Aware Next-Best-View Planning, OA-NBV），该方法通过融合感知与运动规划，利用一个以目标为中心的可视性模型对候选视角进行评分，该模型综合考虑遮挡程度、目标尺度和目标完整性，并将候选视角限制在机器人可 traversable 的可行位姿范围内，从而实现高效且高质量的视点选择。
链接: https://arxiv.org/abs/2603.11072
作者: Boxun Hu,Chang Chang,Jiawei Ge,Man Namgung,Xiaomin Lin,Axel Krieger,Tinoosh Mohsenin
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:We naturally step sideways or lean to see around the obstacle when our view is blocked, and recover a more informative observation. Enabling robots to make the same kind of viewpoint choice is critical for human-centered operations, including search, triage, and disaster response, where cluttered environments and partial visibility frequently degrade downstream perception. However, many Next-Best-View (NBV) methods primarily optimize generic exploration or long-horizon coverage, and do not explicitly target the immediate goal of obtaining a single usable observation of a partially occluded person under real motion constraints. We present Occlusion-Aware Next-Best-View Planning for Human-Centered Active Perception on Mobile Robots (OA-NBV), an occlusion-aware NBV pipeline that autonomously selects the next traversable viewpoint to obtain a more complete view of an occluded human. OA-NBV integrates perception and motion planning by scoring candidate viewpoints using a target-centric visibility model that accounts for occlusion, target scale, and target completeness, while restricting candidates to feasible robot poses. OA-NBV achieves over 90% success rate in both simulation and real-world trials, while baseline NBV methods degrade sharply under occlusion. Beyond success rate, OA-NBV improves observation quality: compared to the strongest baseline, it increases normalized target area by at least 81% and keypoint visibility by at least 58% across settings, making it a drop-in view-selection module for diverse human-centered downstream tasks.
[AI-110] A Survey on Quantitative Modeling of Trust in Online Social Networks
【速读】：该论文旨在解决在线社交网络中信任建模研究碎片化的问题，即现有文献或仅简要提及信任概念，或局限于单一类别的信任模型，缺乏系统性分类与综述。其解决方案的关键在于提出一种基于算法基础的全面分类体系，对当前最先进的信任模型进行结构化梳理，并深入分析各类模型的构建机制及其在量化信任建模中的独特贡献；同时，论文进一步提供了一份以实现为导向的信任建模手册，整合了可用数据集、信任相关特征、有前景的建模技术及可行的应用场景，从而为后续研究和实践提供系统性指导。
链接: https://arxiv.org/abs/2603.11054
作者: Wenting Song,K. Suzanne Barber
机构: 未知
类目: ocial and Information Networks (cs.SI); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT)
备注: 34 pages, 9 figures, submitted to ACM computing surveys
Abstract:Online social networks facilitate user engagement and information sharing but are also rife with misinformation and deception. Research on trust modeling in online social networks focuses on developing computational models or algorithms to measure trust relationships, assess the reliability of shared content, and detect spam or malicious activities. However, most existing review papers either briefly mention the concept of trust or focus on a single category of trust models. In this paper, we offer a comprehensive categorization and review of state-of-the-art trust models developed for online social networks. First, we explore theories and models related to trust in psychology and identify several factors that influence the formation and evolution of online trust. Next, state-of-the-art trust models are categorized based on their algorithmic foundations. For each category, the modeling mechanisms are investigated, and their unique contributions to quantitative trust modeling are highlighted. Subsequently, we provide an implementation-centric trust modeling handbook, which summarizes available datasets, trust-related features, promising modeling techniques, and feasible application scenarios. Finally, the findings of the literature review are summarized, and unresolved challenges are discussed.
[AI-111] Structure-Aware Epistemic Uncertainty Quantification for Neural Operator PDE Surrogates
【速读】：该论文旨在解决神经算子（Neural Operators, NOs）在科学计算中部署时面临的不确定性量化（Uncertainty Quantification, UQ）问题，特别是其预测结果中存在的显著认知不确定性（epistemic uncertainty），这种不确定性源于有限数据、优化不完美及分布偏移。传统方法如朴素的权重扰动（如dropout）往往无法实现空间上忠实的不确定性表征，导致不确定性带与关键残差结构不匹配，影响下游风险决策。解决方案的关键在于提出一种结构感知的认知不确定性量化方案，利用现代神经算子普遍具有的模块化结构（lifting-propagation-recovering），将随机性仅注入“lifting”模块（即输入特征映射层），而保持“propagation”和“recovery”模块确定性，从而实现高效且空间对齐的不确定性估计。具体地，采用两种轻量级的lifting层扰动方式——通道级乘性特征dropout和方差匹配的高斯特征扰动，并结合标准校准构建不确定性带，实验表明该方法在复杂PDE基准任务中显著提升了覆盖率可靠性、紧致性和残差-不确定性对齐能力。
链接: https://arxiv.org/abs/2603.11052
作者: Haoze Song,Zhihao Li,Mengyi Deng,Xin Li,Duyi Pan,Zhilu Lai,Wei Wang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Neural operators (NOs) provide fast, resolution-invariant surrogates for mapping input fields to PDE solution fields, but their predictions can exhibit significant epistemic uncertainty due to finite data, imperfect optimization, and distribution shift. For practical deployment in scientific computing, uncertainty quantification (UQ) must be both computationally efficient and spatially faithful, i.e., uncertainty bands should align with the localized residual structures that matter for downstream risk management. We propose a structure-aware epistemic UQ scheme that exploits the modular anatomy common to modern NOs (lifting-propagation-recovering). Instead of applying unstructured weight perturbations (e.g., naive dropout) across the entire network, we restrict Monte Carlo sampling to a module-aligned subspace by injecting stochasticity only into the lifting module, and treat the learned solver dynamics (propagation and recovery) as deterministic. We instantiate this principle with two lightweight lifting-level perturbations, including channel-wise multiplicative feature dropout and a Gaussian feature perturbation with matched variance, followed by standard calibration to construct uncertainty bands. Experiments on challenging PDE benchmarks (including discontinuous-coefficient Darcy flow and geometry-shifted 3D car CFD surrogates) demonstrate that the proposed structure-aware design yields more reliable coverage, tighter bands, and improved residual-uncertainty alignment compared with common baselines, while remaining practical in runtime.
[AI-112] Implicit Statistical Inference in Transformers: Approximating Likelihood-Ratio Tests In-Context ICLR2026
【速读】：该论文旨在解决上下文学习（In-context Learning, ICL）的机制问题，即Transformer模型如何在不更新权重的情况下，通过输入的上下文样本来适应新任务。传统观点认为ICL依赖于相似性匹配或固定核平滑等启发式策略，但其内在算法原理尚不明确。论文的关键解决方案在于引入统计决策理论框架，以二元假设检验为简化场景，构建可解析的贝叶斯最优统计量作为算法基准，并通过训练Transformer模型完成两类几何结构不同的任务（线性偏移均值 vs. 非线性方差估计），发现模型能够逼近该最优统计量（至多单调变换），并在非线性情形下达到理想Oracle估计器的性能。进一步的机制分析（logit lens与电路对齐）表明，模型并非使用固定核平滑，而是动态调整决策的线性可解点：在线性任务中呈现类似投票集成的行为，在非线性任务中则采用更深层的序列计算路径。这说明ICL本质上源于模型对任务自适应统计估计器的构建，而非简单的相似性匹配。
链接: https://arxiv.org/abs/2603.10573
作者: Faris Chaudhry,Siddhant Gadkari
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at the Latent and Implicit Thinking Workshop (ICLR 2026)
Abstract:In-context learning (ICL) allows Transformers to adapt to novel tasks without weight updates, yet the underlying algorithms remain poorly understood. We adopt a statistical decision-theoretic perspective by investigating simple binary hypothesis testing, where the optimal policy is determined by the likelihood-ratio test. Notably, this setup provides a mathematically rigorous setting for mechanistic interpretability where the target algorithmic ground truth is known. By training Transformers on tasks requiring distinct geometries (linear shifted means vs. nonlinear variance estimation), we demonstrate that the models approximate the Bayes-optimal sufficient statistics from context up to some monotonic transformation, matching the performance of an ideal oracle estimator in nonlinear regimes. Leveraging this analytical ground truth, mechanistic analysis via logit lens and circuit alignment suggests that the model does not rely on a fixed kernel smoothing heuristic. Instead, it appears to adapt the point at which decisions become linearly decodable: exhibiting patterns consistent with a voting-style ensemble for linear tasks while utilizing a deeper sequential computation for nonlinear tasks. These findings suggest that ICL emerges from the construction of task-adaptive statistical estimators rather than simple similarity matching.
[AI-113] Proof-Carrying Materials: Falsifiable Safety Certificates for Machine-Learned Interatomic Potentials
【速读】：该论文旨在解决机器学习势函数（Machine-learned interatomic potentials, MLIPs）在高通量材料筛选中缺乏可靠性保障的问题，特别是单个MLIP作为稳定性过滤器时存在严重漏检现象（召回率仅0.07，遗漏93%的密度泛函理论（Density Functional Theory, DFT）稳定材料）。其解决方案的关键在于提出Proof-Carrying Materials (PCM)框架，通过三个阶段实现：1）跨化学空间的对抗性 falsification（伪证检测），识别模型盲区；2）基于置信区间（95%）的Bootstrap包络精炼，量化不确定性；3）使用Lean 4形式化认证确保推理过程可验证。该方法不仅揭示了不同MLIP架构（如CHGNet、TensorNet和MACE）的特异性盲点（pairwise误差相关性极低，r = 0.13），还构建了一个可迁移的风险预测模型（AUC-ROC = 0.938），显著提升了新材料发现效率（在热电材料筛选中提升25%）。
链接: https://arxiv.org/abs/2603.12183
作者: Abhinaba Basu,Pavan Chakraborty
机构: 未知
类目: Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
备注:
Abstract:Machine-learned interatomic potentials (MLIPs) are deployed for high-throughput materials screening without formal reliability guarantees. We show that a single MLIP used as a stability filter misses 93% of density functional theory (DFT)-stable materials (recall 0.07) on a 25,000-material benchmark. Proof-Carrying Materials (PCM) closes this gap through three stages: adversarial falsification across compositional space, bootstrap envelope refinement with 95% confidence intervals, and Lean 4 formal certification. Auditing CHGNet, TensorNet and MACE reveals architecture-specific blind spots with near-zero pairwise error correlations (r = 0.13; n = 5,000), confirmed by independent Quantum ESPRESSO validation (20/20 converged; median DFT/CHGNet force ratio 12x). A risk model trained on PCM-discovered features predicts failures on unseen materials (AUC-ROC = 0.938 +/- 0.004) and transfers across architectures (cross-MLIP AUC-ROC ~ 0.70; feature importance r = 0.877). In a thermoelectric screening case study, PCM-audited protocols discover 62 additional stable materials missed by single-MLIP screening - a 25% improvement in discovery yield.
[AI-114] ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics
【速读】：该论文旨在解决单细胞RNA测序（scRNA-seq）数据向可解释的生物学假说转化的瓶颈问题，即当前代理型人工智能系统缺乏对转录组表示的直接访问能力，且表达基础模型对自然语言不透明。解决方案的关键在于提出ELISA（Embedding-Linked Interactive Single-cell Agent）框架，其核心创新是将scGPT表达嵌入与基于BioBERT的语义检索和大语言模型（LLM）驱动的解释相结合，实现交互式单细胞发现。该框架通过自动查询分类器动态路由输入至基因标记评分、语义匹配或倒数排名融合管道，并在不依赖原始计数矩阵的情况下，集成通路活性评分、配体-受体相互作用预测、条件感知比较分析及细胞类型比例估计等模块，从而显著提升细胞类型检索性能（p < 0.001），并生成基于证据的大语言模型推理假设，有效弥合了转录组数据探索与生物学发现之间的鸿沟。
链接: https://arxiv.org/abs/2603.11872
作者: Omar Coser
机构: 未知
类目: Genomics (q-bio.GN); Artificial Intelligence (cs.AI)
备注:
Abstract:Translating single-cell RNA sequencing (scRNA-seq) data into mechanistic biological hypotheses remains a critical bottleneck, as agentic AI systems lack direct access to transcriptomic representations while expression foundation models remain opaque to natural language. Here we introduce ELISA (Embedding-Linked Interactive Single-cell Agent), an interpretable framework that unifies scGPT expression embeddings with BioBERT-based semantic retrieval and LLM-mediated interpretation for interactive single-cell discovery. An automatic query classifier routes inputs to gene marker scoring, semantic matching, or reciprocal rank fusion pipelines depending on whether the query is a gene signature, natural language concept, or mixture of both. Integrated analytical modules perform pathway activity scoringacross 60+ gene sets, ligand–receptor interaction prediction using 280+ curated pairs, condition-aware comparative analysis, and cell-type proportion estimation all operating directly on embedded data without access to the original count matrix. Benchmarked across six diverse scRNA-seq datasets spanning inflammatory lung disease, pediatric and adult cancers, organoid models, healthy tissue, and neurodevelopment, ELISA significantly outperforms CellWhisperer in cell type retrieval (combined permutation test, p 0.001 ), with particularly large gains on gene-signature queries (Cohen’s d = 5.98 for MRR). ELISA replicates published biological findings (mean composite score 0.90) with near-perfect pathway alignment and theme coverage (0.98 each), and generates candidate hypotheses through grounded LLM reasoning, bridging the gap between transcriptomic data exploration and biological discovery. Code available at: this https URL (If you use ELISA in your research, please cite this work).
[AI-115] Affect Decoding in Phonated and Silent Speech Production from Surface EMG INTERSPEECH2026
【速读】：该论文旨在解决情感表达（affect）在语音产生过程中如何通过肌电图（sEMG）信号被有效识别的问题，特别是探讨面部和颈部表面肌电活动与情绪状态之间的关联。其关键解决方案在于构建了一个包含2,780个语句的多任务sEMG数据集，并系统评估了跨个体和跨发音模式（发声与无声语音）的情感解码性能；研究发现，面部肌肉活动中的情感特征即使在无声条件下依然可被提取，表明sEMG传感具备开发情感感知型无声语音接口的潜力。
链接: https://arxiv.org/abs/2603.11715
作者: Simon Pistrosch,Kleanthis Avramidis,Tiantian Feng,Jihwan Lee,Monica Gonzalez-Machorro,Shrikanth Narayanan,Björn W. Schuller
机构: 未知
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注: Submitted to Interspeech 2026
Abstract:The expression of affect is integral to spoken communication, yet, its link to underlying articulatory execution remains unclear. Measures of articulatory muscle activity such as EMG could reveal how speech production is modulated by emotion alongside acoustic speech analyses. We investigate affect decoding from facial and neck surface electromyography (sEMG) during phonated and silent speech production. For this purpose, we introduce a dataset comprising 2,780 utterances from 12 participants across 3 tasks, on which we evaluate both intra- and inter-subject decoding using a range of features and model embeddings. Our results reveal that EMG representations reliably discriminate frustration with up to 0.845 AUC, and generalize well across articulation modes. Our ablation study further demonstrates that affective signatures are embedded in facial motor activity and persist in the absence of phonation, highlighting the potential of EMG sensing for affect-aware silent speech interfaces.
[AI-116] Worst-case low-rank approximations
【速读】：该论文旨在解决在异质域（如不同医院、地区或时间段）收集的现实世界数据中，由于分布偏移导致标准主成分分析（Principal Component Analysis, PCA）性能下降的问题。具体而言，标准PCA在训练域上表现良好，但在未见域上的解释方差显著降低，影响了模型的泛化能力。解决方案的关键在于提出一个统一框架wcPCA（worst-case PCA），通过优化最坏情况下的性能而非平均性能，确保估计器在观测源域及所有目标域（其协方差位于源协方差凸包内）上均达到最坏情况最优。该方法不仅适用于PCA，还可扩展至其他目标函数（如norm-minPCA和norm-maxregret），并提供一致性与渐近最坏情况保证，同时在矩阵补全等低秩逼近任务中实现近似最坏情况最优性。
链接: https://arxiv.org/abs/2603.11304
作者: Anya Fries,Markus Reichstein,David Blei,Jonas Peters
机构: 未知
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Methodology (stat.ME)
备注:
Abstract:Real-world data in health, economics, and environmental sciences are often collected across heterogeneous domains (such as hospitals, regions, or time periods). In such settings, distributional shifts can make standard PCA unreliable, in that, for example, the leading principal components may explain substantially less variance in unseen domains than in the training domains. Existing approaches (such as FairPCA) have proposed to consider worst-case (rather than average) performance across multiple domains. This work develops a unified framework, called wcPCA, applies it to other objectives (resulting in the novel estimators such as norm-minPCA and norm-maxregret, which are better suited for applications with heterogeneous total variance) and analyzes their relationship. We prove that for all objectives, the estimators are worst-case optimal not only over the observed source domains but also over all target domains whose covariance lies in the convex hull of the (possibly normalized) source covariances. We establish consistency and asymptotic worst-case guarantees of empirical estimators. We extend our methodology to matrix completion, another problem that makes use of low-rank approximations, and prove approximate worst-case optimality for inductive matrix completion. Simulations and two real-world applications on ecosystem-atmosphere fluxes demonstrate marked improvements in worst-case performance, with only minor losses in average performance.
[AI-117] From Phase Prediction to Phase Design: A ReAct Agent Framework for High-Entropy Alloy Discovery
【速读】：该论文旨在解决高熵合金（High-Entropy Alloy, HEA）逆向设计中的高维组合优化问题，即如何高效发现能稳定形成目标晶体相（如FCC、BCC等）的合金成分。传统试错法和仅向前推理的机器学习模型难以胜任此类复杂空间的探索。解决方案的关键在于提出一个基于ReAct（Reasoning + Acting）框架的大语言模型（LLM）代理系统，该代理通过调用经过4,753条实验数据校准的XGBoost代理模型，在描述符空间中自主提出、验证并迭代优化HEA组成，实现高达94.66%的预测准确率（F1宏平均=0.896）。该方法不仅显著优于贝叶斯优化（BO）和随机搜索基线，在相图邻近性上也表现出更强的逼近能力（距离实验相流形2.4–22.8倍更近），且通过引入领域先验知识，使代理从文献密集区域的“地标合金回忆”转向对未充分探索成分空间的多样化探索，从而在保证可靠性的同时推动真正的新材料发现。
链接: https://arxiv.org/abs/2603.11068
作者: Iman Peivaste,Salim Belouettar
机构: 未知
类目: Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)
备注:
Abstract:Discovering high-entropy alloy (HEA) compositions that reliably form a target crystal phase is a high-dimensional inverse design problem that conventional trial-and-error experimentation and forward-only machine learning models cannot efficiently solve. Here we present a ReAct (Reasoning + Acting) LLM agent that autonomously proposes, validates, and iteratively refines HEA compositions by querying a calibrated XGBoost surrogate trained on 4,753 experimental records across four phases (FCC, BCC, BCC+FCC, BCC+IM), achieving 94.66% accuracy (F1 macro = 0.896). Against Bayesian optimisation (BO) and random search baselines, the full-prompt agent achieves descriptor-space rediscovery rates of 38%, 18%, and 38% for FCC, BCC, and BCC+FCC (Mann–Whitney p \leq 0.039 ), with proposals lying 2.4–22.8 \times closer to the experimental phase manifold than random search. An ablation reveals that domain priors shift the agent from landmark-alloy recall toward compositionally diverse exploration – an uninformed agent scores higher rediscovery by concentrating on literature-dense families, while the full-prompt agent explores underrepresented space (unique ratio 1.0 vs.\ 0.39 for BCC+FCC). These regimes represent distinct criteria: proximity to known literature versus genuine discovery. Spearman analysis confirms agent reasoning is statistically aligned with empirical phase distributions ( \rho = 0.736 , p = 0.004 for BCC). This work establishes LLM-guided agentic reasoning as a principled, transparent, and manifold-aware complement to gradient-free optimisation for inverse alloy design.
[AI-118] Hybrid Quantum-Classical Encoding for Accurate Residue-Level pKa Prediction
【速读】：该论文旨在解决残基级pKa值预测的准确性与跨生化环境泛化能力不足的问题，现有资源如DeepKaDB和CpHMD衍生数据集虽提供训练数据，但其描述符仍以经典特征为主，难以适应多样化的蛋白质微环境。解决方案的关键在于提出一种可复现的量子-经典混合框架，通过高斯核基的量子启发特征映射（quantum-inspired feature mapping）增强残基表征，并将其与归一化的结构特征融合形成统一的混合编码，输入至深度量子神经网络（Deep Quantum Neural Network, DQNN）。该架构能够捕捉经典模型无法获取的残基微环境中非线性关系，从而显著提升跨场景的泛化性能，验证了量子启发特征变换在蛋白静电学建模中的有效性与可迁移性。
链接: https://arxiv.org/abs/2603.11061
作者: Van Le,Tan Le
机构: 未知
类目: Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Accurate prediction of residue-level pKa values is essential for understanding protein function, stability, and reactivity. While existing resources such as DeepKaDB and CpHMD-derived datasets provide valuable training data, their descriptors remain primarily classical and often struggle to generalize across diverse biochemical environments. We introduce a reproducible hybrid quantum-classical framework that enriches residue-level representations with a Gaussian kernel-based quantum-inspired feature mapping. These quantum-enhanced descriptors are combined with normalized structural features to form a unified hybrid encoding processed by a Deep Quantum Neural Network (DQNN). This architecture captures nonlinear relationships in residue microenvironments that are not accessible to classical models. Benchmarking across multiple curated descriptor sets demonstrates that the DQNN achieves improved cross-context generalization relative to classical baselines. External evaluation on the PKAD-R experimental benchmark and an A \beta 40 case study further highlights the robustness and transferability of the quantum-inspired representation. By integrating quantum-inspired feature transformations with classical biochemical descriptors, this work establishes a scalable and experimentally transferable approach for residue-level pKa prediction and broader applications in protein electrostatics.
机器学习
[LG-0] Matching Features Not Tokens: Energy-Based Fine-Tuning of Language Models
链接: https://arxiv.org/abs/2603.12248
作者: Samy Jelassi,Mujin Kwun,Rosie Zhao,Yuanzhi Li,Nicolo Fusi,Yilun Du,Sham M. Kakade,Carles Domingo-Enrich
类目: Machine Learning (cs.LG)
*备注:
Abstract:Cross-entropy (CE) training provides dense and scalable supervision for language models, but it optimizes next-token prediction under teacher forcing rather than sequence-level behavior under model rollouts. We introduce a feature-matching objective for language-model fine-tuning that targets sequence-level statistics of the completion distribution, providing dense semantic feedback without requiring a task-specific verifier or preference model. To optimize this objective efficiently, we propose energy-based fine-tuning (EBFT), which uses strided block-parallel sampling to generate multiple rollouts from nested prefixes concurrently, batches feature extraction over these rollouts, and uses the resulting embeddings to perform an on-policy policy-gradient update. We present a theoretical perspective connecting EBFT to KL-regularized feature-matching and energy-based modeling. Empirically, across QA coding, unstructured coding, and translation, EBFT matches RLVR and outperforms SFT on downstream accuracy while achieving a lower validation cross-entropy than both methods.
[LG-1] STAMP: Selective Task-Aware Mechanism for Text Privacy EACL2026
链接: https://arxiv.org/abs/2603.12237
作者: Fengwei Tian,Payel Bhattacharjee,Heidi Hanson,Geoffrey D. Rubin,Joseph Y. Lo,Ravi Tandon
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Information Theory (cs.IT)
*备注: EACL 2026
Abstract:We present STAMP (Selective Task-Aware Mechanism for Text Privacy), a new framework for task-aware text privatization that achieves an improved privacy-utility trade-off. STAMP selectively allocates privacy budgets across tokens by jointly considering (i) each token’s importance to the downstream task (as measured via a task- or query-specific representation), and (ii) its privacy sensitivity (e.g., names, dates, identifiers). This token-level partitioning enables fine-grained, group-wise control over the level of noise applied to different parts of the input, balancing privacy protection with task relevance. To privatize individual token embeddings, we introduce the polar mechanism, which perturbs only the direction of embeddings on the unit sphere while preserving their magnitude. Decoding is performed via cosine nearest-neighbor search, aligning the perturbation geometry with the decoding geometry. Unlike isotropic noise mechanisms, the polar mechanism maintains semantic neighborhoods in the embedding space and better preserves downstream utility. Experimental evaluations on SQuAD, Yelp, and AG News datasets demonstrate that STAMP, when combined with the normalized polar mechanism, consistently achieves superior privacy-utility trade-offs across varying per-token privacy budgets.
[LG-2] mporal Straightening for Latent Planning
链接: https://arxiv.org/abs/2603.12231
作者: Ying Wang,Oumayma Bounou,Gaoyue Zhou,Randall Balestriero,Tim G. J. Rudner,Yann LeCun,Mengye Ren
类目: Machine Learning (cs.LG)
*备注:
Abstract:Learning good representations is essential for latent planning with world models. While pretrained visual encoders produce strong semantic visual features, they are not tailored to planning and contain information irrelevant – or even detrimental – to planning. Inspired by the perceptual straightening hypothesis in human visual processing, we introduce temporal straightening to improve representation learning for latent planning. Using a curvature regularizer that encourages locally straightened latent trajectories, we jointly learn an encoder and a predictor. We show that reducing curvature this way makes the Euclidean distance in latent space a better proxy for the geodesic distance and improves the conditioning of the planning objective. We demonstrate empirically that temporal straightening makes gradient-based planning more stable and yields significantly higher success rates across a suite of goal-reaching tasks.
[LG-3] Interpreting Contrastive Embeddings in Specific Domains with Fuzzy Rules
链接: https://arxiv.org/abs/2603.12227
作者: Javier Fumanal-Idocin,Mohammadreza Jamalifard,Javier Andreu-Perez
类目: ymbolic Computation (cs.SC); Machine Learning (cs.LG)
*备注:
Abstract:Free-style text is still one of the common ways in which data is registered in real environments, like legal procedures and medical records. Because of that, there have been significant efforts in the area of natural language processing to convert these texts into a structured format, which standard machine learning methods can then exploit. One of the most popular methods to embed text into a vectorial representation is the Contrastive Language-Image Pre-training model (CLIP), which was trained using both image and text. Although the representations computed by CLIP have been very successful in zero-show and few-shot learning problems, they still have problems when applied to a particular domain. In this work, we use a fuzzy rule-based classification system along with some standard text procedure techniques to map some of our features of interest to the space created by a CLIP model. Then, we discuss the rules and associations obtained and the importance of each feature considered. We apply this approach in two different data domains, clinical reports and film reviews, and compare the results obtained individually and when considering both. Finally, we discuss the limitations of this approach and how it could be further improved.
[LG-4] Cornserve: A Distributed Serving System for Any-to-Any Multimodal Models WWW ATC
链接: https://arxiv.org/abs/2603.12118
作者: Jae-Won Chung,Jeff J. Ma,Jisang Ahn,Yizhuo Liang,Akshay Jajoo,Myungjin Lee,Mosharaf Chowdhury
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: Open source this https URL / Demo video this https URL
Abstract:Any-to-Any models are an emerging class of multimodal models that accept combinations of multimodal data (e.g., text, image, video, audio) as input and generate them as output. Serving these models are challenging; different requests with different input and output modalities traverse different paths through the model computation graph, and each component of the model have different scaling characteristics. We present Cornserve, a distributed serving system for generic Any-to-Any models. Cornserve provides a flexible task abstraction for expressing Any-to-Any model computation graphs, enabling component disaggregation and independent scaling. The distributed runtime dispatches compute to the data plane via an efficient record-and-replay execution model that keeps track of data dependencies, and forwards tensor data between components directly from the producer to the consumer. Built on Kubernetes with approximately 23K new lines of Python, Cornserve supports diverse Any-to-Any models and delivers up to 3.81 \times higher throughput and 5.79 \times lower tail latency. Cornserve is open-source, and the demo video is available on YouTube. Comments: Open source this https URL / Demo video this https URL Subjects: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:2603.12118 [cs.LG] (or arXiv:2603.12118v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.12118 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-5] Cross-Domain Policy Optimization via Bellm an Consistency and Hybrid Critics ICLR2026
链接: https://arxiv.org/abs/2603.12087
作者: Ming-Hong Chen,Kuan-Chen Pan,You-De Huang,Xi Liu,Ping-Chun Hsieh
类目: Machine Learning (cs.LG)
*备注: Accepted at ICLR 2026
Abstract:Cross-domain reinforcement learning (CDRL) is meant to improve the data efficiency of RL by leveraging the data samples collected from a source domain to facilitate the learning in a similar target domain. Despite its potential, cross-domain transfer in RL is known to have two fundamental and intertwined challenges: (i) The source and target domains can have distinct state space or action space, and this makes direct transfer infeasible and thereby requires more sophisticated inter-domain mappings; (ii) The transferability of a source-domain model in RL is not easily identifiable a priori, and hence CDRL can be prone to negative effect during transfer. In this paper, we propose to jointly tackle these two challenges through the lens of \textitcross-domain Bellman consistency and \textithybrid critic. Specifically, we first introduce the notion of cross-domain Bellman consistency as a way to measure transferability of a source-domain model. Then, we propose Q Avatar, which combines the Q functions from both the source and target domains with an adaptive hyperparameter-free weight function. Through this design, we characterize the convergence behavior of Q Avatar and show that Q Avatar achieves reliable transfer in the sense that it effectively leverages a source-domain Q function for knowledge transfer to the target domain. Through experiments, we demonstrate that Q Avatar achieves favorable transferability across various RL benchmark tasks, including locomotion and robot arm manipulation. Our code is available at this https URL.
[LG-6] Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference
链接: https://arxiv.org/abs/2603.12037
作者: Valentyn Melnychuk,Vahid Balazadeh,Stefan Feuerriegel,Rahul G. Krishnan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Foundation models based on prior-data fitted networks (PFNs) have shown strong empirical performance in causal inference by framing the task as an in-context learning this http URL, it is unclear whether PFN-based causal estimators provide uncertainty quantification that is consistent with classical frequentist estimators. In this work, we address this gap by analyzing the frequentist consistency of PFN-based estimators for the average treatment effect (ATE). (1) We show that existing PFNs, when interpreted as Bayesian ATE estimators, can exhibit prior-induced confounding bias: the prior is not asymptotically overwritten by data, which, in turn, prevents frequentist consistency. (2) As a remedy, we suggest employing a calibration procedure based on a one-step posterior correction (OSPC). We show that the OSPC helps to restore frequentist consistency and can yield a semi-parametric Bernstein-von Mises theorem for calibrated PFNs (i.e., both the calibrated PFN-based estimators and the classical semi-parametric efficient estimators converge in distribution with growing data size). (3) Finally, we implement OSPC through tailoring martingale posteriors on top of the PFNs. In this way, we are able to recover functional nuisance posteriors from PFNs, required by the OSPC. In multiple (semi-)synthetic experiments, PFNs calibrated with our martingale posterior OSPC produce ATE uncertainty that (i) asymptotically matches frequentist uncertainty and (ii) is well calibrated in finite samples in comparison to other Bayesian ATE estimators.
[LG-7] Efficient Generative Modeling with Unitary Matrix Product States Using Riemannian Optimization
链接: https://arxiv.org/abs/2603.12026
作者: Haotong Duan,Zhongming Chen,Ngai Wong
类目: Machine Learning (cs.LG)
*备注:
Abstract:Tensor networks, which are originally developed for characterizing complex quantum many-body systems, have recently emerged as a powerful framework for capturing high-dimensional probability distributions with strong physical interpretability. This paper systematically studies matrix product states (MPS) for generative modeling and shows that unitary MPS, which is a tensor-network architecture that is both simple and expressive, offers clear benefits for unsupervised learning by reducing ambiguity in parameter updates and improving efficiency. To overcome the inefficiency of standard gradient-based MPS training, we develop a Riemannian optimization approach that casts probabilistic modeling as an optimization problem with manifold constraints, and further derive an efficient space-decoupling algorithm. Experiments on Bars-and-Stripes and EMNIST datasets demonstrate fast adaptation to data structure, stable updates, and strong performance while maintaining the efficiency and expressive power of MPS.
[LG-8] Deep Learning-Based Metamodeling of Nonlinear Stochastic Dynamic Systems under Parametric and Predictive Uncertainty
链接: https://arxiv.org/abs/2603.12012
作者: Haimiti Atila,Seymour M.J. Spence
类目: Machine Learning (cs.LG)
*备注:
Abstract:Modeling high-dimensional, nonlinear dynamic structural systems under natural hazards presents formidable computational challenges, especially when simultaneously accounting for uncertainties in external loads and structural parameters. Studies have successfully incorporated uncertainties related to external loads from natural hazards, but few have simultaneously addressed loading and parameter uncertainties within structural systems while accounting for prediction uncertainty of neural networks. To address these gaps, three metamodeling frameworks were formulated, each coupling a feature-extraction module implemented through a multi-layer perceptron (MLP), a message-passing neural network (MPNN), or an autoencoder (AE) with a long short-term memory (LSTM) network using Monte Carlo dropout and a negative log-likelihood loss. The resulting architectures (MLP-LSTM, MPNN-LSTM, and AE-LSTM) were validated on two case studies: a multi-degree-of-freedom Bouc-Wen system and a 37-story fiber-discretized nonlinear steel moment-resisting frame, both subjected to stochastic seismic excitation and structural parameter uncertainty. All three approaches achieved low prediction errors: the MLP-LSTM yielded the most accurate results for the lower-dimensional Bouc-Wen system, whereas the MPNN-LSTM and AE-LSTM provided superior performance on the more complex steel-frame model. Moreover, a consistent correlation between predictive variance and actual error confirms the suitability of these frameworks for active-learning strategies and for assessing model confidence in structural response predictions.
[LG-9] Decentralized Orchestration Architecture for Fluid Computing: A Secure Distributed AI Use Case
链接: https://arxiv.org/abs/2603.12001
作者: Diego Cajaraville-Aboy,Ana Fernández-Vilas,Rebeca P. Díaz-Redondo,Manuel Fernández-Veiga,Pablo Picallo-López
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 19 pages, 9 figures and 1 table. Under peer review
Abstract:Distributed AI and IoT applications increasingly execute across heterogeneous resources spanning end devices, edge/fog infrastructure, and cloud platforms, often under different administrative domains. Fluid Computing has emerged as a promising paradigm for enhancing massive resource management across the computing continuum by treating such resources as a unified fabric, enabling optimal service-agnostic deployments driven by application requirements. However, existing solutions remain largely centralized and often do not explicitly address multi-domain considerations. This paper proposes an agnostic multi-domain orchestration architecture for fluid computing environments. The orchestration plane enables decentralized coordination among domains that maintain local autonomy while jointly realizing intent-based deployment requests from tenants, ensuring end-to-end placement and execution. To this end, the architecture elevates domain-side control services as first-class capabilities to support application-level enhancement at runtime. As a representative use case, we consider a multi-domain Decentralized Federated Learning (DFL) deployment under Byzantine threats. We leverage domain-side capabilities to enhance Byzantine security by introducing FU-HST, an SDN-enabled multi-domain anomaly detection mechanism that complements Byzantine-robust aggregation. We validate the approach via simulation in single- and multi-domain settings, evaluating anomaly detection, DFL performance, and computation/communication overhead.
[LG-10] On-Averag e Stability of Multipass Preconditioned SGD and Effective Dimension
链接: https://arxiv.org/abs/2603.11989
作者: Simon Vary,Tyler Farghly,Ilja Kuzborskij,Patrick Rebeschini
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注: 35 pages, 1 figure
Abstract:We study trade-offs between the population risk curvature, geometry of the noise, and preconditioning on the generalisation ability of the multipass Preconditioned Stochastic Gradient Descent (PSGD). Many practical optimisation heuristics implicitly navigate this trade-off in different ways – for instance, some aim to whiten gradient noise, while others aim to align updates with expected loss curvature. When the geometry of the population risk curvature and the geometry of the gradient noise do not match, an aggressive choice that improves one aspect can amplify instability along the other, leading to suboptimal statistical behavior. In this paper we employ on-average algorithmic stability to connect generalisation of PSGD to the effective dimension that depends on these sources of curvature. While existing techniques for on-average stability of SGD are limited to a single pass, as first contribution we develop a new on-average stability analysis for multipass SGD that handles the correlations induced by data reuse. This allows us to derive excess risk bounds that depend on the effective dimension. In particular, we show that an improperly chosen preconditioner can yield suboptimal effective dimension dependence in both optimisation and generalisation. Finally, we complement our upper bounds with matching, instance-dependent lower bounds.
[LG-11] opological DeepONets and a generalization of the Chen-Chen operator approximation theorem
链接: https://arxiv.org/abs/2603.11972
作者: Vugar Ismailov
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Functional Analysis (math.FA)
*备注: 22 pages, 1 figure, 23 references
Abstract:Deep Operator Networks (DeepONets) provide a branch-trunk neural architecture for approximating nonlinear operators acting between function spaces. In the classical operator approximation framework, the input is a function u\in C(K_1) defined on a compact set K_1 (typically a compact subset of a Banach space), and the operator maps u to an output function G(u)\in C(K_2) defined on a compact Euclidean domain K_2\subset\mathbbR^d . In this paper, we develop a topological extension in which the operator input lies in an arbitrary Hausdorff locally convex space X . We construct topological feedforward neural networks on X using continuous linear functionals from the dual space X^* and introduce topological DeepONets whose branch component acts on X through such linear measurements, while the trunk component acts on the Euclidean output domain. Our main theorem shows that continuous operators G:V\to C(K;\mathbbR^m) , where V\subset X and K\subset\mathbbR^d are compact, can be uniformly approximated by such topological DeepONets. This extends the classical Chen-Chen operator approximation theorem from spaces of continuous functions to locally convex spaces and yields a branch-trunk approximation theorem beyond the Banach-space setting.
[LG-12] Statistical and structural identifiability in representation learning ICLR
链接: https://arxiv.org/abs/2603.11970
作者: Walter Nelson,Marco Fumero,Theofanis Karaletsos,Francesco Locatello
类目: Machine Learning (cs.LG)
*备注: International Conference on Learning Representations (ICLR) 2026
Abstract:Representation learning models exhibit a surprising stability in their internal representations. Whereas most prior work treats this stability as a single property, we formalize it as two distinct concepts: statistical identifiability (consistency of representations across runs) and structural identifiability (alignment of representations with some unobserved ground truth). Recognizing that perfect pointwise identifiability is generally unrealistic for modern representation learning models, we propose new model-agnostic definitions of statistical and structural near-identifiability of representations up to some error tolerance \epsilon . Leveraging these definitions, we prove a statistical \epsilon -near-identifiability result for the representations of models with nonlinear decoders, generalizing existing identifiability theory beyond last-layer representations in e.g. generative pre-trained transformers (GPTs) to near-identifiability of the intermediate representations of a broad class of models including (masked) autoencoders (MAEs) and supervised learners. Although these weaker assumptions confer weaker identifiability, we show that independent components analysis (ICA) can resolve much of the remaining linear ambiguity for this class of models, and validate and measure our near-identifiability claims empirically. With additional assumptions on the data-generating process, statistical identifiability extends to structural identifiability, yielding a simple and practical recipe for disentanglement: ICA post-processing of latent representations. On synthetic benchmarks, this approach achieves state-of-the-art disentanglement using a vanilla autoencoder. With a foundation model-scale MAE for cell microscopy, it disentangles biological variation from technical batch effects, substantially improving downstream generalization.
[LG-13] Causal Matrix Completion under Multiple Treatments via Mixed Synthetic Nearest Neighbors
链接: https://arxiv.org/abs/2603.11942
作者: Minrui Luo,Zhiheng Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Synthetic Nearest Neighbors (SNN) provides a principled solution to causal matrix completion under missing-not-at-random (MNAR) by exploiting local low-rank structure through fully observed anchor submatrices. However, its effectiveness critically relies on sufficient data availability within each treatment level, a condition that often fails in settings with multiple or complex treatments. In this work, we propose Mixed Synthetic Nearest Neighbors (MSNN), a new entry-wise causal identification estimator that integrates information across treatment levels. We show that MSNN retains the finite-sample error bounds and asymptotic normality guarantees of SNN, while enlarging the effective sample size available for estimation. Empirical results on synthetic and real-world datasets illustrate the efficacy of the proposed approach, especially under data-scarce treatment levels.
[LG-14] Exhaustive Circuit Mapping of a Single-Cell Foundation Model Reveals Massive Redundancy Heavy-Tailed Hub Architecture and Layer-Dependent Differentiation Control
链接: https://arxiv.org/abs/2603.11940
作者: Ihor Kendiukhov
类目: Machine Learning (cs.LG)
*备注:
Abstract:Mechanistic interpretability of biological foundation models has relied on selective feature sampling, pairwise interaction testing, and observational trajectory analysis. Each of these can introduce systematic bias. Here we present three experiments that address these limitations through exhaustive circuit tracing, higher order combinatorial ablation, and causal trajectory steering in Geneformer, a transformer based single cell foundation model. First, exhaustive tracing of all 4065 active sparse autoencoder features at layer 5 yields 1393850 significant downstream edges, a 27 fold expansion over selective sampling. This reveals a heavy tailed hub distribution in which 1.8 percent of features account for disproportionate connectivity and 40 percent of the top 20 hubs lack biological annotation. These results indicate systematic annotation bias in prior selective analyses. Second, three way combinatorial ablation across 8 feature triplets shows that redundancy deepens monotonically with interaction order, with a three way ratio of 0.59 versus a pairwise ratio of 0.74, and with zero synergy. This confirms that the model architecture is subadditive at all tested orders. Third, trajectory guided feature steering establishes a causal link between layer position and differentiation directionality. Late layer features at L17 consistently push cell states toward maturity, with fraction positive equal to 1.0. Early and mid layer features at L0 and L11 mostly push away from maturity, with fraction positive ranging from 0.00 to 0.58. Together these results move from correlation toward causal evidence for layer dependent control of cell state.
[LG-15] Causal Representation Learning with Optimal Compression under Complex Treatments
链接: https://arxiv.org/abs/2603.11907
作者: Wanting Liang,Haoang Chi,Zhiheng Zhang
类目: Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Estimating Individual Treatment Effects (ITE) in multi-treatment scenarios faces two critical challenges: the Hyperparameter Selection Dilemma for balancing weights and the Curse of Dimensionality in computational scalability. This paper derives a novel multi-treatment generalization bound and proposes a theoretical estimator for the optimal balancing weight \alpha , eliminating expensive heuristic tuning. We investigate three balancing strategies: Pairwise, One-vs-All (OVA), and Treatment Aggregation. While OVA achieves superior precision in low-dimensional settings, our proposed Treatment Aggregation ensures both accuracy and O(1) scalability as the treatment space expands. Furthermore, we extend our framework to a generative architecture, Multi-Treatment CausalEGM, which preserves the Wasserstein geodesic structure of the treatment manifold. Experiments on semi-synthetic and image datasets demonstrate that our approach significantly outperforms traditional models in estimation accuracy and efficiency, particularly in large-scale intervention scenarios.
[LG-16] FlexRec: Adapting LLM -based Recommenders for Flexible Needs via Reinforcement Learning
链接: https://arxiv.org/abs/2603.11901
作者: Yijun Pan,Weikang Qiu,Qiyao Ma,Mingxuan Ju,Tong Zhao,Neil Shah,Rex Ying
类目: Machine Learning (cs.LG)
*备注:
Abstract:Modern recommender systems must adapt to dynamic, need-specific objectives for diverse recommendation scenarios, yet most traditional recommenders are optimized for a single static target and struggle to reconfigure behavior on demand. Recent advances in reinforcement-learning-based post-training have unlocked strong instruction-following and reasoning capabilities in LLMs, suggesting a principled route for aligning them to complex recommendation goals. Motivated by this, we study closed-set autoregressive ranking, where an LLM generates a permutation over a fixed candidate set conditioned on user context and an explicit need instruction. However, applying RL to this setting faces two key obstacles: (i) sequence-level rewards yield coarse credit assignment that fails to provide fine-grained training signals, and (ii) interaction feedback is sparse and noisy, which together lead to inefficient and unstable updates. We propose FlexRec, a post-training RL framework that addresses both issues with (1) a causally grounded item-level reward based on counterfactual swaps within the remaining candidate pool, and (2) critic-guided, uncertainty-aware scaling that explicitly models reward uncertainty and down-weights low-confidence rewards to stabilize learning under sparse supervision. Across diverse recommendation scenarios and objectives, FlexRec achieves substantial gains: it improves NDCG@5 by up to \textbf59% and Recall@5 by up to \textbf109.4% in need-specific ranking, and further achieves up to \textbf24.1% Recall@5 improvement under generalization settings, outperforming strong traditional recommenders and LLM-based baselines.
[LG-17] On the Role of Reversible Instance Normalization
链接: https://arxiv.org/abs/2603.11869
作者: Gaspard Berthelier,Tahar Nabil,Etienne Le Naour,Richard Niamke,Samir Perlaza,Giovanni Neglia
类目: Machine Learning (cs.LG)
*备注:
Abstract:Data normalization is a crucial component of deep learning models, yet its role in time series forecasting remains insufficiently understood. In this paper, we identify three central challenges for normalization in time series forecasting: temporal input distribution shift, spatial input distribution shift, and conditional output distribution shift. In this context, we revisit the widely used Reversible Instance Normalization (RevIN), by showing through ablation studies that several of its components are redundant or even detrimental. Based on these observations, we draw new perspectives to improve RevIN’s robustness and generalization.
[LG-18] Multi-Station WiFi CSI Sensing Framework Robust to Station-wise Feature Missingness and Limited Labeled Data
链接: https://arxiv.org/abs/2603.11858
作者: Keita Kayano,Takayuki Nishio,Daiki Yoda,Yuta Hirai,Tomoko Adachi
类目: Machine Learning (cs.LG)
*备注: 17 pages, 14 figures, 7 tables
Abstract:We propose a WiFi Channel State Information (CSI) sensing framework for multi-station deployments that addresses two fundamental challenges in practical CSI sensing: station-wise feature missingness and limited labeled data. Feature missingness is commonly handled by resampling unevenly spaced CSI measurements or by reconstructing missing samples, while label scarcity is mitigated by data augmentation or self-supervised representation learning. However, these techniques are typically developed in isolation and do not jointly address long-term, structured station unavailability together with label scarcity. To bridge this gap, we explicitly incorporate station unavailability into both representation learning and downstream model training. Specifically, we adapt cross-modal self-supervised learning (CroSSL), a representation learning framework originally designed for time-series sensory data, to multi-station CSI sensing in order to learn representations that are inherently invariant to station-wise feature missingness from unlabeled data. Furthermore, we introduce Station-wise Masking Augmentation (SMA) during downstream model training, which exposes the model to realistic station unavailability patterns under limited labeled data. Our experiments show that neither missingness-invariant pre-training nor station-wise augmentation alone is sufficient; their combination is essential to achieve robust performance under both station-wise feature missingness and label scarcity. The proposed framework provides a practical and robust foundation for multi-station WiFi CSI sensing in real-world deployments.
[LG-19] Inverse Neural Operator for ODE Parameter Optimization
链接: https://arxiv.org/abs/2603.11854
作者: Zhi-Song Liu,Wenqing Peng,Helmi Toropainen,Ammar Kheder,Andreas Rupp,Holger Froning,Xiaojie Lin,Michael Boy
类目: Machine Learning (cs.LG)
*备注: 17 pages, 6 figures
Abstract:We propose the Inverse Neural Operator (INO), a two-stage framework for recovering hidden ODE parameters from sparse, partial observations. In Stage 1, a Conditional Fourier Neural Operator (C-FNO) with cross-attention learns a differentiable surrogate that reconstructs full ODE trajectories from arbitrary sparse inputs, suppressing high-frequency artifacts via spectral regularization. In Stage 2, an Amortized Drifting Model (ADM) learns a kernel-weighted velocity field in parameter space, transporting random parameter initializations toward the ground truth without backpropagating through the surrogate, avoiding the Jacobian instabilities that afflict gradient-based inversion in stiff regimes. Experiments on a real-world stiff atmospheric chemistry benchmark (POLLU, 25 parameters) and a synthetic Gene Regulatory Network (GRN, 40 parameters) show that INO outperforms gradient-based and amortized baselines in parameter recovery accuracy while requiring only 0.23s inference time, a 487x speedup over iterative gradient descent.
[LG-20] Exponential-Family Membership Inference: From LiRA and RMIA to BaVarIA
链接: https://arxiv.org/abs/2603.11799
作者: Rickard Brännvall
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注: 9 pages, 4 figures, plus 22-page appendix
Abstract:Membership inference attacks (MIAs) are becoming standard tools for auditing the privacy of machine learning models. The leading attacks – LiRA (Carlini et al., 2022) and RMIA (Zarifzadeh et al., 2024) – appear to use distinct scoring strategies, while the recently proposed BASE (Lassila et al., 2025) was shown to be equivalent to RMIA, making it difficult for practitioners to choose among them. We show that all three are instances of a single exponential-family log-likelihood ratio framework, differing only in their distributional assumptions and the number of parameters estimated per data point. This unification reveals a hierarchy (BASE1-4) that connects RMIA and LiRA as endpoints of a spectrum of increasing model complexity. Within this framework, we identify variance estimation as the key bottleneck at small shadow-model budgets and propose BaVarIA, a Bayesian variance inference attack that replaces threshold-based parameter switching with conjugate normal-inverse-gamma priors. BaVarIA yields a Student-t predictive (BaVarIA-t) or a Gaussian with stabilized variance (BaVarIA-n), providing stable performance without additional hyperparameter tuning. Across 12 datasets and 7 shadow-model budgets, BaVarIA matches or improves upon LiRA and RMIA, with the largest gains in the practically important low-shadow-model and offline regimes.
[LG-21] Disentangled Representation Learning through Unsupervised Symmetry Group Discovery
链接: https://arxiv.org/abs/2603.11790
作者: Dang-Nhu Barthélémy,Annabi Louis,Argentieri Sylvain
类目: Machine Learning (cs.LG)
*备注:
Abstract:Symmetry-based disentangled representation learning leverages the group structure of environment transformations to uncover the latent factors of variation. Prior approaches to symmetry-based disentanglement have required strong prior knowledge of the symmetry group’s structure, or restrictive assumptions about the subgroup properties. In this work, we remove these constraints by proposing a method whereby an embodied agent autonomously discovers the group structure of its action space through unsupervised interaction with the environment. We prove the identifiability of the true symmetry group decomposition under minimal assumptions, and derive two algorithms: one for discovering the group decomposition from interaction data, and another for learning Linear Symmetry-Based Disentangled (LSBD) representations without assuming specific subgroup properties. Our method is validated on three environments exhibiting different group decompositions, where it outperforms existing LSBD approaches.
[LG-22] Language Generation with Replay: A Learning-Theoretic View of Model Collapse
链接: https://arxiv.org/abs/2603.11784
作者: Giorgio Racca,Michal Valko,Amartya Sanyal
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:As scaling laws push the training of frontier large language models (LLMs) toward ever-growing data requirements, training pipelines are approaching a regime where much of the publicly available online text may be consumed. At the same time, widespread LLM usage increases the volume of machine-generated content on the web; together, these trends raise the likelihood of generated text re-entering future training corpora, increasing the associated risk of performance degradation often called model collapse. In practice, model developers address this concern through data cleaning, watermarking, synthetic-data policies, or, in some cases, blissful ignorance. However, the problem of model collapse in generative models has not been examined from a learning-theoretic perspective: we study it through the theoretical lens of the language generation in the limit framework, introducing a replay adversary that augments the example stream with the generator’s own past outputs. Our main contribution is a fine-grained learning-theoretic characterization of when replay fundamentally limits generation: while replay is benign for the strongest notion of uniform generation, it provably creates separations for the weaker notions of non-uniform generation and generation in the limit. Interestingly, our positive results mirror heuristics widely used in practice, such as data cleaning, watermarking, and output filtering, while our separations show when these ideas can fail.
[LG-23] A Further Efficient Algorithm with Best-of-Both-Worlds Guarantees for m-Set Semi-Bandit Problem
链接: https://arxiv.org/abs/2603.11764
作者: Botao Chen,Jongyeong Lee,Chansoo Kim,Junya Honda
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:This paper studies the optimality and complexity of Follow-the-Perturbed-Leader (FTPL) policy in m -set semi-bandit problems. FTPL has been studied extensively as a promising candidate of an efficient algorithm with favorable regret for adversarial combinatorial semi-bandits. Nevertheless, the optimality of FTPL has still been unknown unlike Follow-the-Regularized-Leader (FTRL) whose optimality has been proved for various tasks of online learning. In this paper, we extend the analysis of FTPL with geometric resampling (GR) to m -set semi-bandits, which is a special case of combinatorial semi-bandits, showing that FTPL with Fréchet and Pareto distributions with certain parameters achieves the best possible regret of O(\sqrtmdT) in adversarial setting. We also show that FTPL with Fréchet and Pareto distributions with a certain parameter achieves a logarithmic regret for stochastic setting, meaning the Best-of-Both-Worlds optimality of FTPL for m -set semi-bandit problems. Furthermore, we extend the conditional geometric resampling to m -set semi-bandits for efficient loss estimation in FTPL, reducing the computational complexity from O(d^2) of the original geometric resampling to O(md(\log(d/m)+1)) without sacrificing the regret performance.
[LG-24] Mitigating the Multiplicity Burden: The Role of Calibration in Reducing Predictive Multiplicity of Classifiers
链接: https://arxiv.org/abs/2603.11750
作者: Mustafa Cavus
类目: Machine Learning (cs.LG)
*备注: 16 pages, 3 figures
Abstract:As machine learning models are increasingly deployed in high-stakes environments, ensuring both probabilistic reliability and prediction stability has become critical. This paper examines the interplay between classification calibration and predictive multiplicity - the phenomenon in which multiple near-optimal models within the Rashomon set yield conflicting credit outcomes for the same applicant. Using nine diverse credit risk benchmark datasets, we investigate whether predictive multiplicity concentrates in regions of low predictive confidence and how post-hoc calibration can mitigate algorithmic arbitrariness. Our empirical analysis reveals that minority class observations bear a disproportionate multiplicity burden, as confirmed by significant disparities in predictive multiplicity and prediction confidence. Furthermore, our empirical comparisons indicate that applying post-hoc calibration methods - specifically Platt Scaling, Isotonic Regression, and Temperature Scaling - is associated with lower obscurity across the Rashomon set. Among the tested techniques, Platt Scaling and Isotonic Regression provide the most robust reduction in predictive multiplicity. These findings suggest that calibration can function as a consensus-enforcing layer and may support procedural fairness by mitigating predictive multiplicity.
[LG-25] EvoFlows: Evolutionary Edit-Based Flow-Matching for Protein Engineering ICLR2026
链接: https://arxiv.org/abs/2603.11703
作者: Nicolas Deutschmann,Constance Ferragu,Jonathan D. Ziegler,Shayan Aziznejad,Eli Bixby
类目: Machine Learning (cs.LG)
*备注: Accepted at Workshop on Foundation Models for Science: Real-World Impact and Science-First Design, ICLR 2026
Abstract:We introduce EvoFlows, a variable-length sequence-to-sequence protein modeling approach uniquely suited to protein engineering. Unlike autoregressive and masked language models, EvoFlows perform a limited, controllable number of insertions, deletions, and substitutions on a template protein sequence. In other words, EvoFlows predict not only which mutation to perform, but also where it should occur. Our approach leverages edit flows to learn mutational trajectories between evolutionarily-related protein sequences, simultaneously modeling distributions of related natural proteins and the mutational paths connecting them. Through extensive in silico evaluation on diverse protein communities from UNIREF and OAS, we demonstrate that EvoFlows capture protein sequence distributions with a quality comparable to leading masked language models commonly used in protein engineering, while showing improved ability to generate non-trivial yet natural-like mutants from a given template protein.
[LG-26] Context-dependent manifold learning: A neuromodulated constrained autoencoder approach
链接: https://arxiv.org/abs/2603.11673
作者: Jérôme Adriaens(1),Guillaume Drion(1),Pierre Sacré(1) ((1) Neuroengineering Lab, Department of Electrical Engineering and Computer Science, University of Liège)
类目: Machine Learning (cs.LG)
*备注: 14 pages, 10 figures
Abstract:Constrained autoencoders (cAE) provide a successful path towards interpretable dimensionality reduction by enforcing geometric structure on latent spaces. However, standard cAEs cannot adapt to varying physical parameters or environmental conditions without conflating these contextual shifts with the primary input. To address this, we integrated a neuromodulatory mechanism into the cAE framework to allow for context-dependent manifold learning. This paper introduces the Neuromodulated Constrained Autoencoder (NcAE), which adaptively parameterizes geometric constraints via gain and bias tuning conditioned on static contextual information. Experimental results on dynamical systems show that the NcAE accurately captures how manifold geometry varies across different regimes while maintaining rigorous projection properties. These results demonstrate that neuromodulation effectively decouples global contextual parameters from local manifold representations. This architecture provides a foundation for developing more flexible, physics-informed representations in systems subject to (non-stationary) environmental constraints.
[LG-27] Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning
链接: https://arxiv.org/abs/2603.11653
作者: Jiaheng Hu,Jay Shim,Chen Tang,Yoonchang Sung,Bo Liu,Peter Stone,Roberto Martin-Martin
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:Continual Reinforcement Learning (CRL) for Vision-Language-Action (VLA) models is a promising direction toward self-improving embodied agents that can adapt in openended, evolving environments. However, conventional wisdom from continual learning suggests that naive Sequential Fine-Tuning (Seq. FT) leads to catastrophic forgetting, necessitating complex CRL strategies. In this work, we take a step back and conduct a systematic study of CRL for large pretrained VLAs across three models and five challenging lifelong RL benchmarks. We find that, contrary to established belief, simple Seq. FT with low-rank adaptation (LoRA) is remarkably strong: it achieves high plasticity, exhibits little to no forgetting, and retains strong zero-shot generalization, frequently outperforming more sophisticated CRL methods. Through detailed analysis, we show that this robustness arises from a synergy between the large pretrained model, parameter-efficient adaptation, and on-policy RL. Together, these components reshape the stability-plasticity trade-off, making continual adaptation both stable and scalable. Our results position Sequential Fine-Tuning as a powerful method for continual RL with VLAs and provide new insights into lifelong learning in the large model era. Code is available at this http URL.
[LG-28] Personalized Federated Learning via Gaussian Generative Modeling
链接: https://arxiv.org/abs/2603.11620
作者: Peng Hu,Jianwei Ma
类目: Machine Learning (cs.LG)
*备注:
Abstract:Federated learning has emerged as a paradigm to train models collaboratively on inherently distributed client data while safeguarding privacy. In this context, personalized federated learning tackles the challenge of data heterogeneity by equipping each client with a dedicated model. A prevalent strategy decouples the model into a shared feature extractor and a personalized classifier head, where the latter actively guides the representation learning. However, previous works have focused on classifier head-guided personalization, neglecting the potential personalized characteristics in the representation distribution. Building on this insight, we propose pFedGM, a method based on Gaussian generative modeling. The approach begins by training a Gaussian generator that models client heterogeneity via weighted re-sampling. A balance between global collaboration and personalization is then struck by employing a dual objective: a shared objective that maximizes inter-class distance across clients, and a local objective that minimizes intra-class distance within them. To achieve this, we decouple the conventional Gaussian classifier into a navigator for global optimization, and a statistic extractor for capturing distributional statistics. Inspired by the Kalman gain, the algorithm then employs a dual-scale fusion framework at global and local levels to equip each client with a personalized classifier head. In this framework, we model the global representation distribution as a prior and the client-specific data as the likelihood, enabling Bayesian inference for class probability estimation. The evaluation covers a comprehensive range of scenarios: heterogeneity in class counts, environmental corruption, and multiple benchmark datasets and configurations. pFedGM achieves superior or competitive performance compared to state-of-the-art methods.
[LG-29] AutoScout: Structured Optimization for Automating ML System Configuration
链接: https://arxiv.org/abs/2603.11603
作者: Jimmy Shong,Yuhan Ding,Yihan Jiang,Liheng Jing,Haonan Chen,Gaokai Zhang,Aditya Akella,Fan Lai
类目: Machine Learning (cs.LG)
*备注:
Abstract:Machine learning (ML) systems expose a rapidly expanding configuration space spanning model-parallelism strategies, communication optimizations, and low-level runtime parameters. End-to-end system efficiency is highly sensitive to these choices, yet identifying high-performance configurations is challenging due to heterogeneous feature types (e.g., sparse and dense parameters), conditional dependencies (e.g., valid execution parameters only under specific upstream decisions), and the high search (profiling) cost. Existing approaches either optimize a narrow subset of configuration dimensions or rely on ad-hoc heuristics that fail to generalize as configuration spaces continue to grow. We present AutoScout, a general-purpose systems configurator for ML training, fine-tuning, and inference. It formulates the system configuration as a mixed-discrete/continuous optimization problem with hierarchical dependencies and introduces a hybrid optimization framework that jointly refines sparse structural decisions and dense execution parameters. To reduce profiling cost, AutoScout adaptively prioritizes high-impact configuration features and ensembles simulators with varying fidelity. Across diverse models, hardware platforms, and deployment objectives, AutoScout consistently identifies high-performance configurations, achieving 2.7-3.0 \times training speedup over expert-tuned settings.
[LG-30] Hybrid Energy-Aware Reward Shaping: A Unified Lightweight Physics-Guided Methodology for Policy Optimization
链接: https://arxiv.org/abs/2603.11600
作者: Qijun Liao(1),Jue Yang(1),Yiting Kang(1),Xinxin Zhao(1),Yong Zhang(2),Mingan Zhao(2) ((1) School of Mechanical Engineering, University of Science and Technology Beijing, China, (2) Jiangsu XCMG Construction Machinery Research Institute Co., Ltd., China)
类目: Machine Learning (cs.LG)
*备注: 17 pages, 27 figures
Abstract:Deep reinforcement learning excels in continuous control but often requires extensive exploration, while physics-based models demand complete equations and suffer cubic complexity. This study proposes Hybrid Energy-Aware Reward Shaping (H-EARS), unifying potential-based reward shaping with energy-aware action regularization. H-EARS constrains action magnitude while balancing task-specific and energy-based potentials via functional decomposition, achieving linear complexity O(n) by capturing dominant energy components without full dynamics. We establish a theoretical foundation including: (1) functional independence for separate task/energy optimization; (2) energy-based convergence acceleration; (3) convergence guarantees under function approximation; and (4) approximate potential error bounds. Lyapunov stability connections are analyzed as heuristic guides. Experiments across baselines show improved convergence, stability, and energy efficiency. Vehicle simulations validate applicability in safety-critical domains under extreme conditions. Results confirm that integrating lightweight physics priors enhances model-free RL without complete system models, enabling transfer from lab research to industrial applications.
[LG-31] CAETC: Causal Autoencoding and Treatment Conditioning for Counterfactual Estimation over Time
链接: https://arxiv.org/abs/2603.11565
作者: Nghia D. Nguyen,Pablo Robles-Granda,Lav R. Varshney
类目: Machine Learning (cs.LG)
*备注:
Abstract:Counterfactual estimation over time is important in various applications, such as personalized medicine. However, time-dependent confounding bias in observational data still poses a significant challenge in achieving accurate and efficient estimation. We introduce causal autoencoding and treatment conditioning (CAETC), a novel method for this problem. Built on adversarial representation learning, our method leverages an autoencoding architecture to learn a partially invertible and treatment-invariant representation, where the outcome prediction task is cast as applying a treatment-specific conditioning on the representation. Our design is independent of the underlying sequence model and can be applied to existing architectures such as long short-term memories (LSTMs) or temporal convolution networks (TCNs). We conduct extensive experiments on synthetic, semi-synthetic, and real-world data to demonstrate that CAETC yields significant improvement in counterfactual estimation over existing methods.
[LG-32] Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents Reports
链接: https://arxiv.org/abs/2603.11546
作者: Liangkai Zhou,Susu Xu,Shuqi Zhong,Shan Lin
类目: Machine Learning (cs.LG)
*备注:
Abstract:Many real-world machine learning tasks are anti-causal: they require inferring latent causes from observed effects. In practice, we often face multiple related tasks where part of the forward causal mechanism is invariant across tasks, while other components are task-specific. We propose Multi-Task Anti-Causal learning (MTAC), a framework for estimating causes from outcomes and confounders by explicitly exploiting such cross-task invariances. MTAC first performs causal discovery to learn a shared causal graph and then instantiates a structured multi-task structural equation model (SEM) that factorizes the outcome-generation process into (i) a task-invariant mechanism and (ii) task-specific mechanisms via a shared backbone with task-specific heads. Building on the learned forward model, MTAC performs maximum A posteriori (MAP)based inference to reconstruct causes by jointly optimizing latent mechanism variables and cause magnitudes under the learned causal structure. We evaluate MTAC on the application of urban event reconstruction from resident reports, spanning three tasks:parking violations, abandoned properties, and unsanitary conditions. On real-world data collected from Manhattan and the city of Newark, MTAC consistently improves reconstruction accuracy over strong baselines, achieving up to 34.61% MAE reduction and demonstrating the benefit of learning transferable causal mechanisms across tasks.
[LG-33] CFD-HAR: User-controllable Privacy through Conditional Feature Disentanglement
链接: https://arxiv.org/abs/2603.11526
作者: Alex Gn,Fan Li,S Kuniyilh,Ada Axan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Modern wearable and mobile devices are equipped with inertial measurement units (IMUs). Human Activity Recognition (HAR) applications running on such devices use machine-learning-based, data-driven techniques that leverage such sensor data. However, sensor-data-driven HAR deployments face two critical challenges: protecting sensitive user information embedded in sensor data in accordance with users’ privacy preferences and maintaining high recognition performance with limited labeled samples. This paper proposes a technique for user-controllable privacy through feature disentanglement-based representation learning at the granular level for dynamic privacy filtering. We also compare the efficacy of our technique against few-shot HAR using autoencoder-based representation learning. We analyze their architectural designs, learning objectives, privacy guarantees, data efficiency, and suitability for edge Internet of Things (IoT) deployment. Our study shows that CFD-based HAR provides explicit, tunable privacy protection controls by separating activity and sensitive attributes in the latent space, whereas autoencoder-based few-shot HAR offers superior label efficiency and lightweight adaptability but lacks inherent privacy safeguards. We further examine the security implications of both approaches in continual IoT settings, highlighting differences in susceptibility to representation leakage and embedding-level attacks. The analysis reveals that neither paradigm alone fully satisfies the emerging requirements of next-generation IoT HAR systems. We conclude by outlining research directions toward unified frameworks that jointly optimize privacy preservation, few-shot adaptability, and robustness for trustworthy IoT intelligence.
[LG-34] Sharpness-Aware Minimization for Generalized Embedding Learning in Federated Recommendation
链接: https://arxiv.org/abs/2603.11503
作者: Fengyuan Yu,Xiaohua Feng,Yuyuan Li,Changwang Zhang,Jun Wang,Chaochao Chen
类目: Machine Learning (cs.LG)
*备注: Accepted by the ACM Web Conference 2026
Abstract:Federated recommender systems enable collaborative model training while keeping user interaction data local and sharing only essential model parameters, thereby mitigating privacy risks. However, existing methods overlook a critical issue, i.e., the stable learning of a generalized item embedding throughout the federated recommender system training process. Item embedding plays a central role in facilitating knowledge sharing across clients. Yet, under the cross-device setting, local data distributions exhibit significant heterogeneity and sparsity, exacerbating the difficulty of learning generalized embeddings. These factors make the stable learning of generalized item embeddings both indispensable for effective federated recommendation and inherently difficult to achieve. To fill this gap, we propose a new federated recommendation framework, named Federated Recommendation with Generalized Embedding Learning (FedRecGEL). We reformulate the federated recommendation problem from an item-centered perspective and cast it as a multi-task learning problem, aiming to learn generalized embeddings throughout the training procedure. Based on theoretical analysis, we employ sharpness-aware minimization to address the generalization problem, thereby stabilizing the training process and enhancing recommendation performance. Extensive experiments on four datasets demonstrate the effectiveness of FedRecGEL in significantly improving federated recommendation performance. Our code is available at this https URL.
[LG-35] Attention Sinks Are Provably Necessary in Softmax Transformers: Evidence from Trigger-Conditional Tasks
链接: https://arxiv.org/abs/2603.11487
作者: Yuval Ran-Milo
类目: Machine Learning (cs.LG)
*备注: 21 pages, 8 figures
Abstract:Transformers often display an attention sink: probability mass concentrates on a fixed, content-agnostic position. We prove that computing a simple trigger-conditional behavior necessarily induces a sink in softmax self-attention models. Our results formalize a familiar intuition: normalization over a probability simplex must force attention to collapse onto a stable anchor to realize a default state (e.g., when the model needs to ignore the input). We instantiate this with a concrete task: when a designated trigger token appears, the model must return the average of all preceding token representations, and otherwise output zero, a task which mirrors the functionality of attention heads in the wild (Barbero et al., 2025; Guo et al., 2024). We also prove that non-normalized ReLU attention can solve the same task without any sink, confirming that the normalization constraint is the fundamental driver of sink behavior. Experiments validate our predictions and demonstrate they extend beyond the theoretically analyzed setting: softmax models develop strong sinks while ReLU attention eliminates them in both single-head and multi-head variants.
[LG-36] Leverag ing Phytolith Research using Artificial Intelligence
链接: https://arxiv.org/abs/2603.11476
作者: Andrés G. Mejía Ramón,Kate Dudgeon,Nina Witteveen,Dolores Piperno,Michael Kloster,Luigi Palopoli,Mónica Moraes R.,José M. Capriles,Umberto Lombardo
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注: 45 pages, 23 figures
Abstract:Phytolith analysis is a crucial tool for reconstructing past vegetation and human activities, but traditional methods are severely limited by labour-intensive, time-consuming manual microscopy. To address this bottleneck, we present Sorometry: a comprehensive end-to-end artificial intelligence pipeline for the high-throughput digitisation, inference, and interpretation of phytoliths. Our workflow processes z-stacked optical microscope scans to automatically generate synchronised 2D orthoimages and 3D point clouds of individual microscopic particles. We developed a multimodal fusion model that combines ConvNeXt for 2D image analysis and PointNet++ for 3D point cloud analysis, supported by a graphical user interface for expert annotation and review. Tested on reference collections and archaeological samples from the Bolivian Amazon, our fusion model achieved a global classification accuracy of 77.9% across 24 diagnostic morphotypes and 84.5% for segmentation quality. Crucially, the integration of 3D data proved essential for distinguishing complex morphotypes (such as grass silica short cell phytoliths) whose diagnostic features are often obscured by their orientation in 2D projections. Beyond individual object classification, Sorometry incorporates Bayesian finite mixture modelling to predict overall plant source contributions at the assemblage level, successfully identifying specific plants like maize and palms in complex mixed samples. This integrated platform transforms phytolith research into an “omics”-scale discipline, dramatically expanding analytical capacity, standardising expert judgements, and enabling reproducible, population-level characterisations of archaeological and paleoecological assemblages.
[LG-37] Deep Learning Network-Temporal Models For Traffic Prediction
链接: https://arxiv.org/abs/2603.11475
作者: Yufeng Xin,Ethan Fan
类目: Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注:
Abstract:Time series analysis is critical for emerging net- work intelligent control and management functions. However, existing statistical-based and shallow machine learning models have shown limited prediction capabilities on multivariate time series. The intricate topological interdependency and complex temporal patterns in network data demand new model approaches. In this paper, based on a systematic multivariate time series model study, we present two deep learning models aiming for learning both temporal patterns and network topological correlations at the same time: a customized network-temporal graph attention network (GAT) model and a fine-tuned multi-modal large language model (LLM) with a clustering overture. Both models are studied against an LSTM model that already outperforms the statistical methods. Through extensive training and performance studies on a real-world network dataset, the LLM-based model demonstrates superior overall prediction and generalization performance, while the GAT model shows its strength in reducing prediction variance across the time series and horizons. More detailed analysis also reveals important insights into correlation variability and prediction distribution discrepancies over time series and different prediction horizons.
[LG-38] Slack More Predict Better: Proximal Relaxation for Probabilistic Latent Variable Model-based Soft Sensors
链接: https://arxiv.org/abs/2603.11473
作者: Zehua Zou,Yiran Ma,Yulong Zhang,Zhengnan Li,Zeyu Yang,Jinhao Xie,Xiaoyu Jiang,Zhichao Chen
类目: Machine Learning (cs.LG); Systems and Control (eess.SY); Optimization and Control (math.OC)
*备注: This paper has been provisionally accepted for publication in the “IEEE Transactions on Industrial Informatics”
Abstract:Nonlinear Probabilistic Latent Variable Models (NPLVMs) are a cornerstone of soft sensor modeling due to their capacity for uncertainty delineation. However, conventional NPLVMs are trained using amortized variational inference, where neural networks parameterize the variational posterior. While facilitating model implementation, this parameterization converts the distributional optimization problem within an infinite-dimensional function space to parameter optimization within a finite-dimensional parameter space, which introduces an approximation error gap, thereby degrading soft sensor modeling accuracy. To alleviate this issue, we introduce KProxNPLVM, a novel NPLVM that pivots to relaxing the objective itself and improving the NPLVM’s performance. Specifically, we first prove the approximation error induced by the conventional approach. Based on this, we design the Wasserstein distance as the proximal operator to relax the learning objective, yielding a new variational inference strategy derived from solving this relaxed optimization problem. Based on this foundation, we provide a rigorous derivation of KProxNPLVM’s optimization implementation, prove the convergence of our algorithm can finally sidestep the approximation error, and propose the KProxNPLVM by summarizing the abovementioned content. Finally, extensive experiments on synthetic and real-world industrial datasets are conducted to demonstrate the efficacy of the proposed KProxNPLVM.
[LG-39] HawkesRank: Event-Driven Centrality for Real-Time Importance Ranking
链接: https://arxiv.org/abs/2603.11472
作者: Didier Sornette,Yishan Luo,Sandro Claudio Lera
类目: ocial and Information Networks (cs.SI); Machine Learning (cs.LG); Physics and Society (physics.soc-ph)
*备注: 10 pages, 3 figures + SM (8 pages, 2 figures)
Abstract:Quantifying influence in networks is important across science, economics, and public health, yet widely used centrality measures remain limited: they rely on static representations, heuristic network constructions, and purely endogenous notions of importance, while offering little semantic connection to observable activity. We introduce HawkesRank, a dynamic framework grounded in multivariate Hawkes point processes that models exogenous drivers (intrinsic contributions) and endogenous amplification (self- and cross-excitation). This yields a principled, empirically calibrated, and adaptive importance measure. Classical indices such as Katz centrality and PageRank emerge as mean-field limits of the framework, clarifying both their validity and their limitations. Unlike static averages, HawkesRank measures importance through instantaneous event intensities, enabling prediction, transparent endo-exo decomposition, and adaptability to shocks. Using both simulations and empirical analysis of emotion dynamics in online communication platforms, we show that HawkesRank closely tracks system activity and consistently outperforms static centrality metrics.
[LG-40] UniHetCO: A Unified Heterogeneous Representation for Multi-Problem Learning in Unsupervised Neural Combinatorial Optimization
链接: https://arxiv.org/abs/2603.11456
作者: Kien X. Nguyen,Ilya Safro
类目: Machine Learning (cs.LG)
*备注:
Abstract:Unsupervised neural combinatorial optimization (NCO) offers an appealing alternative to supervised approaches by training learning-based solvers without ground-truth solutions, directly minimizing instance objectives and constraint violations. Yet for graph node subset-selection problems (e.g., Maximum Clique and Maximum Independent Set), existing unsupervised methods are typically specialized to a single problem class and rely on problem-specific surrogate losses, which hinders learning across classes within a unified framework. In this work, we propose UniHetCO, a unified heterogeneous graph representation for constrained quadratic programming-based combinatorial optimization that encodes problem structure, objective terms, and linear constraints in a single input. This formulation enables training a single model across multiple problem classes with a unified label-free objective. To improve stability under multi-problem learning, we employ a gradient-norm-based dynamic weighting scheme that alleviates gradient imbalance among classes. Experiments on multiple datasets and four constrained problem classes demonstrate competitive performance with state-of-the-art unsupervised NCO baselines, strong cross-problem adaptation potential, and effective warm starts for a commercial classical solver under tight time limits.
[LG-41] ZTab: Domain-based Zero-shot Annotation for Table Columns
链接: https://arxiv.org/abs/2603.11436
作者: Ehsan Hoseinzade,Ke Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:This study addresses the challenge of automatically detecting semantic column types in relational tables, a key task in many real-world applications. Zero-shot modeling eliminates the need for user-provided labeled training data, making it ideal for scenarios where data collection is costly or restricted due to privacy concerns. However, existing zero-shot models suffer from poor performance when the number of semantic column types is large, limited understanding of tabular structure, and privacy risks arising from dependence on high-performance closed-source LLMs. We introduce ZTab, a domain-based zero-shot framework that addresses both performance and zero-shot requirements. Given a domain configuration consisting of a set of predefined semantic types and sample table schemas, ZTab generates pseudo-tables for the sample schemas and fine-tunes an annotation LLM on them. ZTab is domain-based zero-shot in that it does not depend on user-specific labeled training data; therefore, no retraining is needed for a test table from a similar domain. We describe three cases of domain-based zero-shot. The domain configuration of ZTab provides a trade-off between the extent of zero-shot and annotation performance: a “universal domain” that contains all semantic types approaches “pure” zero-shot, while a “specialized domain” that contains semantic types for a specific application enables better zero-shot performance within that domain. Source code and datasets are available at this https URL
[LG-42] Continued Pretraining for Low-Resource Swahili ASR: Achieving State-of-the-Art Performance with Minimal Labeled Data
链接: https://arxiv.org/abs/2603.11378
作者: Hillary Mutisya,John Mugane
类目: ound (cs.SD); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
*备注:
Abstract:We investigate continued pretraining (CPT) for adapting wav2vec2-bert-2.0 to Swahili automatic speech recognition (ASR). Our approach combines unlabeled audio with limited labeled data through pseudo-labeled CPT followed by supervised finetuning. With 20,000 labeled samples, we achieve 3.24% WER on Common Voice Swahili-an 82% relative improvement over the baseline. This result surpasses the best previously reported academic system (8.3% WER from XLS-R) by 61% relative improvement. We provide concrete data requirements and a replicable methodology applicable to other low-resource languages.
[LG-43] Ensuring Safety in Automated Mechanical Ventilation through Offline Reinforcement Learning and Digital Twin Verification
链接: https://arxiv.org/abs/2603.11372
作者: Hang Yu,Huidong Liu,Qingchen Zhang,William Joy,Kateryna Nikulina,Andreas A. Schuppert,Sina Saffaran,Declan Bates
类目: Machine Learning (cs.LG)
*备注:
Abstract:Mechanical ventilation (MV) is a life-saving intervention for patients with acute respiratory failure (ARF) in the ICU. However, inappropriate ventilator settings could cause ventilator-induced lung injury (VILI). Also, clinicians workload is shown to be directly linked to patient outcomes. Hence, MV should be personalized and automated to improve patient outcomes. Previous attempts to incorporate personalization and automation in MV include traditional supervised learning and offline reinforcement learning (RL) approaches, which often neglect temporal dependencies and rely excessively on mortality-based rewards. As a result, early stage physiological deterioration and the risk of VILI are not adequately captured. To address these limitations, we propose Transformer-based Conservative Q-Learning (T-CQL), a novel offline RL framework that integrates a Transformer encoder for effective temporal modeling of patient dynamics, conservative adaptive regularization based on uncertainty quantification to ensure safety, and consistency regularization for robust decision-making. We build a clinically informed reward function that incorporates indicators of VILI and a score for severity of patients illness. Also, previous work predominantly uses Fitted Q-Evaluation (FQE) for RL policy evaluation on static offline data, which is less responsive to dynamic environmental changes and susceptible to distribution shifts. To overcome these evaluation limitations, interactive digital twins of ARF patients were used for online “at the bedside” evaluation. Our results demonstrate that T-CQL consistently outperforms existing state-of-the-art offline RL methodologies, providing safer and more effective ventilatory adjustments. Our framework demonstrates the potential of Transformer-based models combined with conservative RL strategies as a decision support tool in critical care.
[LG-44] Relaxed Efficient Acquisition of Context and Temporal Features
链接: https://arxiv.org/abs/2603.11370
作者: Yunni Qu(1),Dzung Dinh(1),Grant King(2),Whitney Ringwald(3),Bing Cai Kok(1),Kathleen Gates(1),Aiden Wright(2),Junier Oliva(1) ((1) The University of North Carolina at Chapel Hill, (2) University of Michigan, (3) University of Minnisota Twin Cities)
类目: Machine Learning (cs.LG)
*备注:
Abstract:In many biomedical applications, measurements are not freely available at inference time: each laboratory test, imaging modality, or assessment incurs financial cost, time burden, or patient risk. Longitudinal active feature acquisition (LAFA) seeks to optimize predictive performance under such constraints by adaptively selecting measurements over time, yet the problem remains inherently challenging due to temporally coupled decisions (missed early measurements cannot be revisited, and acquisition choices influence all downstream predictions). Moreover, real-world clinical workflows typically begin with an initial onboarding phase, during which relatively stable contextual descriptors (e.g., demographics or baseline characteristics) are collected once and subsequently condition longitudinal decision-making. Despite its practical importance, the efficient selection of onboarding context has not been studied jointly with temporally adaptive acquisition. We therefore propose REACT (Relaxed Efficient Acquisition of Context and Temporal features), an end-to-end differentiable framework that simultaneously optimizes (i) selection of onboarding contextual descriptors and (ii) adaptive feature–time acquisition plans for longitudinal measurements under cost constraints. REACT employs a Gumbel–Sigmoid relaxation with straight-through estimation to enable gradient-based optimization over discrete acquisition masks, allowing direct backpropagation from prediction loss and acquisition cost. Across real-world longitudinal health and behavioral datasets, REACT achieves improved predictive performance at lower acquisition costs compared to existing longitudinal acquisition baselines, demonstrating the benefit of modeling onboarding and temporally coupled acquisition within a unified optimization framework.
[LG-45] abx_amr_simulator: A simulation environment for antibiotic prescribing policy optimization under antimicrobial resistance
链接: https://arxiv.org/abs/2603.11369
作者: Joyce Lee,Seth Blumberg
类目: Machine Learning (cs.LG); Populations and Evolution (q-bio.PE)
*备注: 10 pages, 3 figures
Abstract:Antimicrobial resistance (AMR) poses a global health threat, reducing the effectiveness of antibiotics and complicating clinical decision-making. To address this challenge, we introduce abx_amr_simulator, a Python-based simulation package designed to model antibiotic prescribing and AMR dynamics within a controlled, reinforcement learning (RL)-compatible environment. The simulator allows users to specify patient populations, antibiotic-specific AMR response curves, and reward functions that balance immedi- ate clinical benefit against long-term resistance management. Key features include a modular design for configuring patient attributes, antibiotic resistance dynamics modeled via a leaky-balloon abstraction, and tools to explore partial observability through noise, bias, and delay in observations. The package is compatible with the Gymnasium RL API, enabling users to train and test RL agents under diverse clinical scenarios. From an ML perspective, the package provides a configurable benchmark environment for sequential decision-making under uncertainty, including partial observability induced by noisy, biased, and delayed observations. By providing a customizable and extensible framework, abx_amr_simulator offers a valuable tool for studying AMR dynamics and optimizing antibiotic stewardship strategies under realistic uncertainty.
[LG-46] Multilingual Financial Fraud Detection Using Machine Learning and Transformer Models: A Bangla-English Study
链接: https://arxiv.org/abs/2603.11358
作者: Mohammad Shihab Uddin,Md Hasibul Amin,Nusrat Jahan Ema,Bushra Uddin,Tanvir Ahmed,Arif Hassan Zidan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Financial fraud detection has emerged as a critical research challenge amid the rapid expansion of digital financial platforms. Although machine learning approaches have demonstrated strong performance in identifying fraudulent activities, most existing research focuses exclusively on English-language data, limiting applicability to multilingual contexts. Bangla (Bengali), despite being spoken by over 250 million people, remains largely unexplored in this domain. In this work, we investigate financial fraud detection in a multilingual Bangla-English setting using a dataset comprising legitimate and fraudulent financial messages. We evaluate classical machine learning models (Logistic Regression, Linear SVM, and Ensemble classifiers) using TF-IDF features alongside transformer-based architectures. Experimental results using 5-fold stratified cross-validation demonstrate that Linear SVM achieves the best performance with 91.59 percent accuracy and 91.30 percent F1 score, outperforming the transformer model (89.49 percent accuracy, 88.88 percent F1) by approximately 2 percentage points. The transformer exhibits higher fraud recall (94.19 percent) but suffers from elevated false positive rates. Exploratory analysis reveals distinctive patterns: scam messages are longer, contain urgency-inducing terms, and frequently include URLs (32 percent) and phone numbers (97 percent), while legitimate messages feature transactional confirmations and specific currency references. Our findings highlight that classical machine learning with well-crafted features remains competitive for multilingual fraud detection, while also underscoring the challenges posed by linguistic diversity, code-mixing, and low-resource language constraints.
[LG-47] odynamic Learning a new Paradigm For Interpretable AI
链接: https://arxiv.org/abs/2603.11355
作者: Enrique ter Horst,Juan Diego Zambrano
类目: Machine Learning (cs.LG); Applications (stat.AP)
*备注:
Abstract:We introduce Teleodynamic Learning, a new paradigm for machine learning in which learning is not the minimization of a fixed objective, but the emergence and stabilization of functional organization under constraint. Inspired by living systems, this framework treats intelligence as the coupled evolution of three quantities: what a system can represent, how it adapts its parameters, and which changes its internal resources can sustain. We formalize learning as a constrained dynamical process with two interacting timescales: inner dynamics for continuous parameter adaptation and outer dynamics for discrete structural change, linked by an endogenous resource variable that both shapes and is shaped by the trajectory. This perspective reveals three phenomena that standard optimization does not naturally capture: self-stabilization without externally imposed stopping rules, phase-structured learning dynamics that move from under-structuring through teleodynamic growth to over-structuring, and convergence guarantees grounded in information geometry rather than convexity. We instantiate the framework in the Distinction Engine (DE11), a teleodynamic learner grounded in Spencer-Brown’s Laws of Form, information geometry, and tropical optimization. On standard benchmarks, DE11 achieves 93.3 percent test accuracy on IRIS, 92.6 percent on WINE, and 94.7 percent on Breast Cancer, while producing interpretable logical rules that arise endogenously from the learning dynamics rather than being imposed by hand. More broadly, Teleodynamic Learning unifies regularization, architecture search, and resource-bounded inference within a single principle: learning as the co-evolution of structure, parameters, and resources under constraint. This opens a thermodynamically grounded route to adaptive, interpretable, and self-organizing AI.
[LG-48] On the Computational Hardness of Transformers
链接: https://arxiv.org/abs/2603.11332
作者: Barna Saha,Yinzhan Xu,Christopher Ye,Hantao Yu
类目: Computational Complexity (cs.CC); Machine Learning (cs.LG)
*备注: 46 pages, 2 figures. Abstract shortened to meet arXiv requirements
Abstract:The transformer has revolutionized modern AI across language, vision, and beyond. It consists of L layers, each running H attention heads in parallel and feeding the combined output to the subsequent layer. In attention, the input consists of N tokens, each a vector of dimension m . The attention mechanism involves multiplying three N \times m matrices, applying softmax to an intermediate product. Several recent works have advanced our understanding of the complexity of attention. Known algorithms for transformers compute each attention head independently. This raises a fundamental question that has recurred throughout TCS under the guise of ``direct sum’’ problems: can multiple instances of the same problem be solved more efficiently than solving each instance separately? Many answers to this question, both positive and negative, have arisen in fields spanning communication complexity and algorithm design. Thus, we ask whether transformers can be computed more efficiently than LH independent evaluations of attention. In this paper, we resolve this question in the negative, and give the first non-trivial computational lower bounds for multi-head multi-layer transformers. In the small embedding regime ( m = N^o(1) ), computing LH attention heads separately takes LHN^2 + o(1) time. We establish that this is essentially optimal under SETH. In the large embedding regime ( m = N ), one can compute LH attention heads separately using LHN^\omega + o(1) arithmetic operations (plus exponents), where \omega is the matrix multiplication exponent. We establish that this is optimal, by showing that LHN^\omega - o(1) arithmetic operations are necessary when \omega 2 . Our lower bound in the large embedding regime relies on a novel application of the Baur-Strassen theorem, a powerful algorithmic tool underpinning the famous backpropagation algorithm. Comments: 46 pages, 2 figures. Abstract shortened to meet arXiv requirements Subjects: Computational Complexity (cs.CC); Machine Learning (cs.LG) Cite as: arXiv:2603.11332 [cs.CC] (or arXiv:2603.11332v1 [cs.CC] for this version) https://doi.org/10.48550/arXiv.2603.11332 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-49] On the Robustness of Langevin Dynamics to Score Function Error
链接: https://arxiv.org/abs/2603.11319
作者: Daniel Yiming Cao,August Y. Chen,Karthik Sridharan,Yuchen Wu
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We consider the robustness of score-based generative modeling to errors in the estimate of the score function. In particular, we show that Langevin dynamics is not robust to the L^2 errors (more generally L^p errors) in the estimate of the score function. It is well-established that with small L^2 errors in the estimate of the score function, diffusion models can sample faithfully from the target distribution under fairly mild regularity assumptions in a polynomial time horizon. In contrast, our work shows that even for simple distributions in high dimensions, Langevin dynamics run for any polynomial time horizon will produce a distribution far from the target distribution in Total Variation (TV) distance, even when the L^2 error (more generally L^p) of the estimate of the score function is arbitrarily small. Considering such an error in the estimate of the score function is unavoidable in practice when learning the score function from data, our results provide further justification for diffusion models over Langevin dynamics and serve to caution against the use of Langevin dynamics with estimated scores.
[LG-50] Heavy-Tailed Principle Component Analysis
链接: https://arxiv.org/abs/2603.11308
作者: Mario Sayde,Christopher Khater,Jihad Fahs,Ibrahim Abou-Faycal
类目: Machine Learning (cs.LG)
*备注:
Abstract:Principal Component Analysis (PCA) is a cornerstone of dimensionality reduction, yet its classical formulation relies critically on second-order moments and is therefore fragile in the presence of heavy-tailed data and impulsive noise. While numerous robust PCA variants have been proposed, most either assume finite variance, rely on sparsity-driven decompositions, or address robustness through surrogate loss functions without a unified treatment of infinite-variance models. In this paper, we study PCA for high-dimensional data generated according to a superstatistical dependent model of the form \mathbfX = A^1/2\mathbfG , where A is a positive random scalar and \mathbfG is a Gaussian vector. This framework captures a wide class of heavy-tailed distributions, including multivariate t and sub-Gaussian \alpha -stable laws. We formulate PCA under a logarithmic loss, which remains well defined even when moments do not exist. Our main theoretical result shows that, under this loss, the principal components of the heavy-tailed observations coincide with those obtained by applying standard PCA to the covariance matrix of the underlying Gaussian generator. Building on this insight, we propose robust estimators for this covariance matrix directly from heavy-tailed data and compare them with the empirical covariance and Tyler’s scatter estimator. Extensive experiments, including background denoising tasks, demonstrate that the proposed approach reliably recovers principal directions and significantly outperforms classical PCA in the presence of heavy-tailed and impulsive noise, while remaining competitive under Gaussian noise.
[LG-51] Client-Conditional Federated Learning via Local Training Data Statistics
链接: https://arxiv.org/abs/2603.11307
作者: Rickard Brännvall
类目: Machine Learning (cs.LG)
*备注: 9 pages, 4 figures, 5 tables. Submitted to FLICS 2026
Abstract:Federated learning (FL) under data heterogeneity remains challenging: existing methods either ignore client differences (FedAvg), require costly cluster discovery (IFCA), or maintain per-client models (Ditto). All degrade when data is sparse or heterogeneity is multi-dimensional. We propose conditioning a single global model on locally-computed PCA statistics of each client’s training data, requiring zero additional communication. Evaluating across 97~configurations spanning four heterogeneity types (label shift, covariate shift, concept shift, and combined heterogeneity), four datasets (MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100), and seven FL baseline methods, we find that our method matches the Oracle baseline – which knows true cluster assignments – across all settings, surpasses it by 1–6% on combined heterogeneity where continuous statistics are richer than discrete cluster identifiers, and is uniquely sparsity-robust among all tested methods.
[LG-52] Single molecule localization microscopy challenge: a biologically inspired benchmark for long-sequence modeling
链接: https://arxiv.org/abs/2603.11296
作者: Fatemeh Valeh,Monika Farsang,Radu Grosu,Gerhard Schütz
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注: 11 pages, 4 figures. Under review
Abstract:State space models (SSMs) have recently achieved strong performance on long sequence modeling tasks while offering improved memory and computational efficiency compared to transformer based architectures. However, their evaluation has been largely limited to synthetic benchmarks and application domains such as language and audio, leaving their behavior on sparse and stochastic temporal processes in biological imaging unexplored. In this work, we introduce the Single Molecule Localization Microscopy Challenge (SMLM-C), a benchmark dataset consisting of ten SMLM simulations spanning dSTORM and DNA-PAINT modalities with varying hyperparameter designed to evaluate state space models on biologically realistic spatiotemporal point process data with known ground truth. Using a controlled subset of these simulations, we evaluate state space models and find that performance degrades substantially as temporal discontinuity increases, revealing fundamental challenges in modeling heavy-tailed blinking dynamics. These results highlight the need for sequence models better suited to sparse, irregular temporal processes encountered in real world scientific imaging data.
[LG-53] Duration Aware Scheduling for ASR Serving Under Workload Drift
链接: https://arxiv.org/abs/2603.11273
作者: Darshan Makwana,Yash Jogi,Harsh Kotta,Aayush Kubba
类目: Machine Learning (cs.LG)
*备注:
Abstract:Scheduling policies in large-scale Automatic Speech Recognition (ASR) serving pipelines play a key role in determining end-to-end (E2E) latency. Yet, widely used serving engines rely on first-come-first-served (FCFS) scheduling, which ignores variability in request duration and leads to head-of-line blocking under workload drift. We show that audio duration is an accurate proxy for job processing time in ASR models such as Whisper, and use this insight to enable duration-aware scheduling. We integrate two classical algorithms, Shortest Job First (SJF) and Highest Response Ratio Next (HRRN), into vLLM and evaluate them under realistic and drifted workloads. On LibriSpeech test-clean, compared to baseline, SJF reduces median E2E latency by up to 73% at high load, but increases 90 th-percentile tail latency by up to 97% due to starvation of long requests. HRRN addresses this trade-off: it reduces median E2E latency by up to 28% while bounding tail-latency degradation to at most 24% . These gains persist under workload drift, with no throughput penalty and 0.1 ,ms scheduling overhead per request.
[LG-54] Beyond the Class Subspace: Teacher-Guided Training for Reliable Out-of-Distribution Detection in Single-Domain Models ECCV2026
链接: https://arxiv.org/abs/2603.11269
作者: Hong Yang,Devroop Kar,Qi Yu,Travis Desell,Alex Ororbia
类目: Machine Learning (cs.LG)
*备注: 14 pages main text, 22 pages appendix; under review at ECCV 2026
Abstract:Out-of-distribution (OOD) detection methods perform well on multi-domain benchmarks, yet many practical systems are trained on single-domain data. We show that this regime induces a geometric failure mode, Domain-Sensitivity Collapse (DSC): supervised training compresses features into a low-rank class subspace and suppresses directions that carry domain-shift signal. We provide theory showing that, under DSC, distance- and logit-based OOD scores lose sensitivity to domain shift. We then introduce Teacher-Guided Training (TGT), which distills class-suppressed residual structure from a frozen multi-domain teacher (DINOv2) into the student during training. The teacher and auxiliary head are discarded after training, adding no inference overhead. Across eight single-domain benchmarks, TGT yields large far-OOD FPR@95 reductions for distance-based scorers: MDS improves by 11.61 pp, ViM by 10.78 pp, and kNN by 12.87 pp (ResNet-50 average), while maintaining or slightly improving in-domain OOD and classification accuracy.
[LG-55] A Machine Learning-Enhanced Hopf-Cole Formulation for Nonlinear Gas Flow in Porous Media
链接: https://arxiv.org/abs/2603.11250
作者: V. S. Maduru,K. B. Nakshatrala
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn)
*备注:
Abstract:Accurate modeling of gas flow through porous media is critical for many technological applications, including reservoir performance prediction, carbon capture and sequestration, and fuel cells and batteries. However, such modeling remains challenging due to strong nonlinear behavior and uncertainty in model parameters. In particular, gas slippage effects described by the Klinkenberg model introduce pressure-dependent permeability, which complicates numerical simulation and obscures deviations from classical Darcy flow behavior. To address these challenges, we present an integrated modeling framework for gas transport in porous media that combines a Klinkenberg-enhanced constitutive relation, Hopf-Cole-transformed mixed-form linear governing equations, a shared-trunk neural network architecture, and a Deep Least-Squares (DeepLS) solver. The Hopf-Cole transformation reformulates the original nonlinear flow equations into an equivalent linear system closely related to the Darcy model, while the mixed formulation, together with a shared-trunk neural architecture, enables simultaneous and accurate prediction of both pressure and velocity fields. A rigorous convergence analysis is performed both theoretically and numerically, establishing the stability and convergence properties of the proposed solver. Importantly, the proposed framework also naturally facilitates inverse modeling of pressure-dependent permeability and slippage parameters from limited or indirect observations, enabling efficient estimation of flow properties that are difficult to measure experimentally. Numerical results demonstrate accurate recovery of flow dynamics and parameters across a wide range of pressure regimes, highlighting the framework’s robustness, accuracy, and computational efficiency for gas transport modeling and inversion in tight formations.
[LG-56] Differentiable Thermodynamic Phase-Equilibria for Machine Learning
链接: https://arxiv.org/abs/2603.11249
作者: Karim K. Ben Hicham,Moreno Ascani,Jan G. Rittig,Alexander Mitsos
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate prediction of phase equilibria remains a central challenge in chemical engineering. Physics-consistent machine learning methods that incorporate thermodynamic structure into neural networks have recently shown strong performance for activity-coefficient modeling. However, extending such approaches to equilibrium data arising from an extremum principle, such as liquid-liquid equilibria, remains difficult. Here we present DISCOMAX, a differentiable algorithm for phase-equilibrium calculation that guarantees thermodynamic consistency at both training and inference, only subject to a user-specified discretization. The method is rooted in statistical thermodynamics, and works via a discrete enumeration with subsequent masked softmax aggregation of feasible states, and together with a straight-through gradient estimator to enable physics-consistent end-to-end learning of neural g^E -models. We evaluate the approach on binary liquid-liquid equilibrium data and demonstrate that it outperforms existing surrogate-based methods, while offering a general framework for learning from different kinds of equilibrium data.
[LG-57] Monitoring and Prediction of Mood in Elderly People during Daily Life Activities
链接: https://arxiv.org/abs/2603.11230
作者: Daniel Bautista-Salinas,Joaquín Roca González,Inmaculada Méndez,Oscar Martinez Mozos
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: This is the authors’ manuscript. The final published article is available at this https URL
Abstract:We present an intelligent wearable system to monitor and predict mood states of elderly people during their daily life activities. Our system is composed of a wristband to record different physiological activities together with a mobile app for ecological momentary assessment (EMA). Machine learning is used to train a classifier to automatically predict different mood states based on the smart band only. Our approach shows promising results on mood accuracy and provides results comparable with the state of the art in the specific detection of happiness and activeness.
[LG-58] Security-by-Design for LLM -Based Code Generation: Leverag ing Internal Representations for Concept-Driven Steering Mechanisms
链接: https://arxiv.org/abs/2603.11212
作者: Maximilian Wendlinger,Daniel Kowatsch,Konstantin Böttinger,Philip Sperl
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: to be published in the IEEE European Symposium on Security and Privacy (EuroSP)'26
Abstract:Large Language Models (LLMs) show remarkable capabilities in understanding natural language and generating complex code. However, as practitioners adopt CodeLLMs for increasingly critical development tasks, research reveals that these models frequently generate functionally correct yet insecure code, posing significant security risks. While multiple approaches have been proposed to improve security in AI-based code generation, combined benchmarks show these methods remain insufficient for practical use, achieving only limited improvements in both functional correctness and security. This stems from a fundamental gap in understanding the internal mechanisms of code generation and the root causes of security vulnerabilities, forcing researchers to rely on heuristics and empirical observations. In this work, we investigate the internal representation of security concepts in CodeLLMs, revealing that models are often aware of vulnerabilities as they generate insecure code. Through systematic evaluation, we demonstrate that CodeLLMs can distinguish between security subconcepts, enabling a more fine-grained analysis than prior black-box approaches. Leveraging these insights, we propose Secure Concept Steering for CodeLLMs (SCS-Code). During token generation, SCS-Code steers LLMs’ internal representations toward secure and functional code output, enabling a lightweight and modular mechanism that can be integrated into existing code models. Our approach achieves superior performance compared to state-of-the-art methods across multiple secure coding benchmarks.
[LG-59] Reference-Guided Machine Unlearning DATE ICLR2026
链接: https://arxiv.org/abs/2603.11210
作者: Jonas Mirlach,Sonia Laguna,Julia E. Vogt
类目: Machine Learning (cs.LG)
*备注: 12 pages, 1 figure, 4 tables. Accepted at three ICLR 2026 workshops: Test-Time Updates (TTU), AI with Recursive Self-Improvement (RSI), and Agents in the Wild (AIWILD)
Abstract:Machine unlearning aims to remove the influence of specific data from trained models while preserving general utility. Existing approximate unlearning methods often rely on performance-degradation heuristics, such as loss maximization or random labeling. However, these signals can be poorly conditioned, leading to unstable optimization and harming the model’s generalization. We argue that unlearning should instead prioritize distributional indistinguishability, aligning the model’s behavior on forget data with its behavior on truly unseen data. Motivated by this, we propose Reference-Guided Unlearning (ReGUn), a framework that leverages a disjoint held-out dataset to provide a principled, class-conditioned reference for distillation. We demonstrate across various model architectures, natural image datasets, and varying forget fractions that ReGUn consistently outperforms standard approximate baselines, achieving a superior forgetting-utility trade-off.
[LG-60] DNS-GT: A Graph-based Transformer Approach to Learn Embeddings of Domain Names from DNS Queries
链接: https://arxiv.org/abs/2603.11200
作者: Massimiliano Altieri,Ronan Hamon,Roberto Corizzo,Michelangelo Ceci,Ignacio Sanchez
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Network intrusion detection systems play a crucial role in the security strategy employed by organisations to detect and prevent cyberattacks. Such systems usually combine pattern detection signatures with anomaly detection techniques powered by machine learning methods. However, the commonly proposed machine learning methods present drawbacks such as over-reliance on labeled data and limited generalization capabilities. To address these issues, embedding-based methods have been introduced to learn representations from network data, such as DNS traffic, mainly due to its large availability, that generalise effectively to many downstream tasks. However, current approaches do not properly consider contextual information among DNS queries. In this paper, we tackle this issue by proposing DNS-GT, a novel Transformer-based model that learns embeddings for domain names from sequences of DNS queries. The model is first pre-trained in a self-supervised fashion in order to learn the general behavior of DNS activity. Then, it can be finetuned on specific downstream tasks, exploiting interactions with other relevant queries in a given sequence. Our experiments with real-world DNS data showcase the ability of our method to learn effective domain name representations. A quantitative evaluation on domain name classification and botnet detection tasks shows that our approach achieves better results compared to relevant baselines, creating opportunities for further exploration of large-scale language models for intrusion detection systems. Our code is available at: this https URL.
[LG-61] Bayesian Optimization of Partially Known Systems using Hybrid Models
链接: https://arxiv.org/abs/2603.11199
作者: Eike Cramer,Luis Kutschat,Oliver Stollenwerk,Joel A. Paulson,Alexander Mitsos
类目: Machine Learning (cs.LG)
*备注: 16 pages, 5 Figures
Abstract:Bayesian optimization (BO) has gained attention as an efficient algorithm for black-box optimization of expensive-to-evaluate systems, where the BO algorithm iteratively queries the system and suggests new trials based on a probabilistic model fitted to previous samples. Still, the standard BO loop may require a prohibitively large number of experiments to converge to the optimum, especially for high-dimensional and nonlinear systems. We present a hybrid model-based BO formulation that combines the iterative Bayesian learning of BO with partially known mechanistic physical models. Instead of learning a direct mapping from inputs to the objective, we write all known equations for a physics-based model and infer expressions for variables missing equations using a probabilistic model, in our case, a Gaussian process (GP). The final formulation then includes the GP as a constraint in the hybrid model, thereby allowing other physics-based nonlinear and implicit model constraints. This hybrid model formulation yields a constrained, nonlinear stochastic program, which we discretize using the sample-average approximation. In an in-silico optimization of a single-stage distillation, the hybrid BO model based on mass conservation laws yields significantly better designs than a standard BO loop. Furthermore, the hybrid model converges in as few as one iteration, depending on the initial samples, whereas, the standard BO does not converge within 25 for any of the seeds. Overall, the proposed hybrid BO scheme presents a promising optimization method for partially known systems, leveraging the strengths of both mechanistic modeling and data-driven optimization.
[LG-62] Algorithmic Capture Computational Complexity and Inductive Bias of Infinite Transformers
链接: https://arxiv.org/abs/2603.11161
作者: Orit Davidovich,Zohar Ringel
类目: Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (stat.ML)
*备注:
Abstract:We formally define Algorithmic Capture (i.e., ``grokking’’ an algorithm) as the ability of a neural network to generalize to arbitrary problem sizes ( T ) with controllable error and minimal sample adaptation, distinguishing true algorithmic learning from statistical interpolation. By analyzing infinite-width transformers in both the lazy and rich regimes, we derive upper bounds on the inference-time computational complexity of the functions these networks can learn. We show that despite their universal expressivity, transformers possess an inductive bias towards low-complexity algorithms within the Efficient Polynomial Time Heuristic Scheme (EPTHS) class. This bias effectively prevents them from capturing higher-complexity algorithms, while allowing success on simpler tasks like search, copy, and sort.
[LG-63] Systematic Scaling Analysis of Jailbreak Attacks in Large Language Models
链接: https://arxiv.org/abs/2603.11149
作者: Xiangwen Wang,Ananth Balashankar,Varun Chandrasekaran
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注:
Abstract:Large language models remain vulnerable to jailbreak attacks, yet we still lack a systematic understanding of how jailbreak success scales with attacker effort across methods, model families, and harm types. We initiate a scaling-law framework for jailbreaks by treating each attack as a compute-bounded optimization procedure and measuring progress on a shared FLOPs axis. Our systematic evaluation spans four representative jailbreak paradigms, covering optimization-based attacks, self-refinement prompting, sampling-based selection, and genetic optimization, across multiple model families and scales on a diverse set of harmful goals. We investigate scaling laws that relate attacker budget to attack success score by fitting a simple saturating exponential function to FLOPs–success trajectories, and we derive comparable efficiency summaries from the fitted curves. Empirically, prompting-based paradigms tend to be the most compute-efficient compared to optimization-based methods. To explain this gap, we cast prompt-based updates into an optimization view and show via a same-state comparison that prompt-based attacks more effectively optimize in prompt space. We also show that attacks occupy distinct success–stealthiness operating points with prompting-based methods occupying the high-success, high-stealth region. Finally, we find that vulnerability is strongly goal-dependent: harms involving misinformation are typically easier to elicit than other non-misinformation harms.
[LG-64] H2LooP Spark Preview: Continual Pretraining of Large Language Models for Low-Level Embedded Systems Code
链接: https://arxiv.org/abs/2603.11139
作者: Amit Singh,Vedant Nipane,Pulkit Agrawal,Jatin Kishnani
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language models (LLMs) demonstrate strong code generation abilities in general-purpose programming languages but remain limited in specialized domains such as low-level embedded systems programming. This domain involves hardware register manipulation, vendor-specific SDKs, real-time operating system APIs, and hardware abstraction layers that are underrepresented in standard pretraining corpora. We introduce H2LooP Spark Preview, a continual pretraining (CPT) pipeline that adapts the OLMo-3-7B-a fully open language model to the embedded systems domain using BF16 LoRA with rank-stabilized scaling on 8 NVIDIA H100 GPUs. Our training corpus is constructed from repository-datasheet pairs covering 100B tokens of raw embedded systems data across 117 manufacturers, processed using the hierarchical datasheet-to-code mapping approach proposed in SpecMap (Nipane et al., 2026). The resulting curated dataset split contains 23.5B tokens across 13 embedded domains. Continual pretraining with high-rank LoRA (r=512) yields substantial gains, reducing in-domain perplexity by 70.4% and held-out repository perplexity by 66.1%. On generative code completion benchmarks spanning 13 embedded domains, our 7B model outperforms Claude Opus 4.6 and Qwen3-Coder-30B on 8 categories in token accuracy, showing that targeted continual pretraining enables smaller open-weight models to rival frontier systems on specialized technical tasks. We release the production training checkpoint on Huggingface as an open-source artifact.
[LG-65] Higher-Order Modular Attention: Fusing Pairwise and Triadic Interactions for Protein Sequences
链接: https://arxiv.org/abs/2603.11133
作者: Shirin Amiraslani,Xin Gao
类目: Machine Learning (cs.LG)
*备注: 11, 4 figures
Abstract:Transformer self-attention computes pairwise token interactions, yet protein sequence to phenotype relationships often involve cooperative dependencies among three or more residues that dot product attention does not capture explicitly. We introduce Higher-Order Modular Attention, HOMA, a unified attention operator that fuses pairwise attention with an explicit triadic interaction pathway. To make triadic attention practical on long sequences, HOMA employs block-structured, windowed triadic attention. We evaluate on three TAPE benchmarks for Secondary Structure, Fluorescence, and Stability. Our attention mechanism yields consistent improvements across all tasks compared with standard self-attention and efficient variants including block-wise attention and Linformer. These results suggest that explicit triadic terms provide complementary representational capacity for protein sequence prediction at controllable additional computational cost.
[LG-66] Beyond Barren Plateaus: A Scalable Quantum Convolutional Architecture for High-Fidelity Image Classification
链接: https://arxiv.org/abs/2603.11131
作者: Radhakrishnan Delhibabu
类目: Machine Learning (cs.LG)
*备注:
Abstract:While Quantum Convolutional Neural Networks (QCNNs) offer a theoretical paradigm for quantum machine learning, their practical implementation is severely bottlenecked by barren plateaus – the exponential vanishing of gradients – and poor empirical accuracy compared to classical counterparts. In this work, we propose a novel QCNN architecture utilizing localized cost functions and a hardware-efficient tensor-network initialization strategy to provably mitigate barren plateaus. We evaluate our scalable QCNN on the MNIST dataset, demonstrating a significant performance leap. By resolving the gradient vanishing issue, our optimized QCNN achieves a classification accuracy of 98.7%, a substantial improvement over the baseline QCNN accuracy of 52.32% found in unmitigated models. Furthermore, we provide empirical evidence of a parameter-efficiency advantage, requiring \mathcalO(\log N) fewer trainable parameters than equivalent classical CNNs to achieve 95% convergence. This work bridges the gap between theoretical quantum utility and practical application, providing a scalable framework for quantum computer vision tasks without succumbing to loss landscape concentration.
[LG-67] High-resolution weather-guided surrogate modeling for data-efficient cross-location building energy prediction
链接: https://arxiv.org/abs/2603.11121
作者: Piragash Manmatharasan,Girma Bitsuamlak,Katarina Grolinger
类目: Machine Learning (cs.LG)
*备注:
Abstract:Building design optimization often depends on physics-based simulation tools such as EnergyPlus, which, although accurate, are computationally expensive and slow. Surrogate models provide a faster alternative, yet most are location-specific, and even weather-informed variants require simulations from many sites to generalize to unseen locations. This limitation arises because existing methods do not fully exploit the short-term weather-driven energy patterns shared across regions, restricting their scalability and reusability. This study introduces a high-resolution (weekly) weather-informed surrogate modeling approach that enhances model reusability across locations. By capturing recurring short-term weather-energy demand patterns common to multiple regions, the proposed method produces a generalized surrogate that performs well beyond the training location. Unlike previous weather-informed approaches, it does not require extensive simulations from multiple sites to achieve strong generalization. Experimental results show that when trained on a single location, the model maintains high predictive accuracy for other sites within the same climate zone, with no noticeable performance loss, and exhibits only minimal degradation when applied across different climate zones. These findings demonstrate the potential of climate-informed generalization for developing scalable and reusable surrogate models, supporting more sustainable and optimized building design practices.
[LG-68] Group Resonance Network: Learnable Prototypes and Multi-Subject Resonance for EEG Emotion Recognition
链接: https://arxiv.org/abs/2603.11119
作者: Renwei Meng
类目: Machine Learning (cs.LG)
*备注: 12 pages, 5 figures
Abstract:Electroencephalography(EEG)-basedemotionrecognitionre- mains challenging in cross-subject settings due to severe inter-subject variability. Existing methods mainly learn subject-invariant features, but often under-exploit stimulus-locked group regularities shared across sub- jects. To address this issue, we propose the Group Resonance Network (GRN), which integrates individual EEG dynamics with offline group resonance modeling. GRN contains three components: an individual en- coder for band-wise EEG features, a set of learnable group prototypes for prototype-induced resonance, and a multi-subject resonance branch that encodes PLV/coherence-based synchrony with a small reference set. A resonance-aware fusion module combines individual and group-level rep- resentations for final classification. Experiments on SEED and DEAP under both subject-dependent and leave-one-subject-out protocols show that GRN consistently outperforms competitive baselines, while abla- tion studies confirm the complementary benefits of prototype learning and multi-subject resonance modeling.
[LG-69] A Learning-Based Superposition Operator for Non-Renewal Arrival Processes in Queueing Networks
链接: https://arxiv.org/abs/2603.11118
作者: Eliran Sherzer
类目: Machine Learning (cs.LG); Probability (math.PR)
*备注:
Abstract:The superposition of arrival processes is a fundamental yet analytically intractable operation in queueing networks when inputs are general non-renewal streams. Classical methods either reduce merged flows to renewal surrogates, rely on computationally prohibitive Markovian representations, or focus solely on mean-value performance measures. We propose a scalable data-driven superposition operator that maps low-order moments and autocorrelation descriptors of multiple arrival streams to those of their merged process. The operator is a deep learning model trained on synthetically generated Markovian Arrival Processes (MAPs), for which exact superposition is available, and learns a compact representation that accurately reconstructs the first five moments and short-range dependence structure of the aggregate stream. Extensive computational experiments demonstrate uniformly low prediction errors across heterogeneous variability and correlation regimes, substantially outperforming classical renewal-based approximations. When integrated with learning-based modules for departure-process and steady-state analysis, the proposed operator enables decomposition-based evaluation of feed-forward queueing networks with merging flows. The framework provides a scalable alternative to traditional analytical approaches while preserving higher-order variability and dependence information required for accurate distributional performance analysis. Subjects: Machine Learning (cs.LG); Probability (math.PR) Cite as: arXiv:2603.11118 [cs.LG] (or arXiv:2603.11118v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.11118 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-70] Learning Tree-Based Models with Gradient Descent
链接: https://arxiv.org/abs/2603.11117
作者: Sascha Marton
类目: Machine Learning (cs.LG)
*备注: PhD thesis
Abstract:Tree-based models are widely recognized for their interpretability and have proven effective in various application domains, particularly in high-stakes domains. However, learning decision trees (DTs) poses a significant challenge due to their combinatorial complexity and discrete, non-differentiable nature. As a result, traditional methods such as CART, which rely on greedy search procedures, remain the most widely used approaches. These methods make locally optimal decisions at each node, constraining the search space and often leading to suboptimal tree structures. Additionally, their demand for custom training methods precludes a seamless integration into modern machine learning (ML) approaches. In this thesis, we propose a novel method for learning hard, axis-aligned DTs through gradient descent. Our approach utilizes backpropagation with a straight-through operator on a dense DT representation, enabling the joint optimization of all tree parameters, thereby addressing the two primary limitations of traditional DT algorithms. First, gradient-based training is not constrained by the sequential selection of locally optimal splits but, instead, jointly optimizes all tree parameters. Second, by leveraging gradient descent for optimization, our approach seamlessly integrates into existing ML approaches e.g., for multimodal and reinforcement learning tasks, which inherently rely on gradient descent. These advancements allow us to achieve state-of-the-art results across multiple domains, including interpretable DTs rees for small tabular datasets, advanced models for complex tabular data, multimodal learning, and interpretable reinforcement learning without information loss. By bridging the gap between DTs and gradient-based optimization, our method significantly enhances the performance and applicability of tree-based models across various ML domains. Comments: PhD thesis Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603.11117 [cs.LG] (or arXiv:2603.11117v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.11117 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-71] Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information
链接: https://arxiv.org/abs/2603.11094
作者: Ben Halstead,Yun Sing Koh,Patricia Riddle,Mykola Pechenizkiy,Albert Bifet,Russel Pears
类目: Machine Learning (cs.LG)
*备注:
Abstract:Streaming sources of data are becoming more common as the ability to collect data in real-time grows. A major concern in dealing with data streams is concept drift, a change in the distribution of data over time, for example, due to changes in environmental conditions. Representing concepts (stationary periods featuring similar behaviour) is a key idea in adapting to concept drift. By testing the similarity of a concept representation to a window of observations, we can detect concept drift to a new or previously seen recurring concept. Concept representations are constructed using meta-information features, values describing aspects of concept behaviour. We find that previously proposed concept representations rely on small numbers of meta-information features. These representations often cannot distinguish concepts, leaving systems vulnerable to concept drift. We propose FiCSUM, a general framework to represent both supervised and unsupervised behaviours of a concept in a fingerprint, a vector of many distinct meta-information features able to uniquely identify more concepts. Our dynamic weighting strategy learns which meta-information features describe concept drift in a given dataset, allowing a diverse set of meta-information features to be used at once. FiCSUM outperforms state-of-the-art methods over a range of 11 real world and synthetic datasets in both accuracy and modeling underlying concept drift.
[LG-72] Interventional Time Series Priors for Causal Foundation Models ICLR2026
链接: https://arxiv.org/abs/2603.11090
作者: Dennis Thumm,Ying Chen
类目: Machine Learning (cs.LG); Methodology (stat.ME)
*备注: 1st ICLR 2026 Workshop on Time Series in the Age of Large Models (TSALM)
Abstract:Prior-data fitted networks (PFNs) have emerged as powerful foundation models for tabular causal inference, yet their extension to time series remains limited by the absence of synthetic data generators that provide interventional targets. Existing time series benchmarks generate observational data with ground-truth causal graphs but lack the interventional data required for training causal foundation models. To address this, we propose \textbfCausalTimePrior, a principled framework for generating synthetic temporal structural causal models (TSCMs) with paired observational and interventional time series. Our prior supports configurable causal graph structures, nonlinear autoregressive mechanisms, regime-switching dynamics, and multiple intervention types (hard, soft, time-varying). We demonstrate that PFNs trained on CausalTimePrior can perform in-context causal effect estimation on held-out TSCMs, establishing a pathway toward foundation models for time series causal inference.
[LG-73] Comparison of Outlier Detection Algorithms on String Data
链接: https://arxiv.org/abs/2603.11049
作者: Philip Maus
类目: Machine Learning (cs.LG)
*备注: A bachelor’s thesis comparing the local outlier factor algorithm against a new regular expression learner-based syntactical outlier detection algorithm for single-word string data
Abstract:Outlier detection is a well-researched and crucial problem in machine learning. However, there is little research on string data outlier detection, as most literature focuses on outlier detection of numerical data. A robust string data outlier detection algorithm could assist with data cleaning or anomaly detection in system log files. In this thesis, we compare two string outlier detection algorithms. Firstly, we introduce a variant of the well-known local outlier factor algorithm, which we tailor to detect outliers on string data using the Levenshtein measure to calculate the density of the dataset. We present a differently weighted Levenshtein measure, which considers hierarchical character classes and can be used to tune the algorithm to a specific string dataset. Secondly, we introduce a new kind of outlier detection algorithm based on the hierarchical left regular expression learner, which infers a regular expression for the expected data. Using various datasets and parameters, we experimentally show that both algorithms can conceptually find outliers in string data. We show that the regular expression-based algorithm is especially good at finding outliers if the expected values have a distinct structure that is sufficiently different from the structure of the outliers. In contrast, the local outlier factor algorithms are best at finding outliers if their edit distance to the expected data is sufficiently distinct from the edit distance between the expected data.
[LG-74] Wasserstein Gradient Flows for Batch Bayesian Optimal Experimental Design
链接: https://arxiv.org/abs/2603.12102
作者: Louis Sharrock
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Computation (stat.CO); Methodology (stat.ME)
*备注:
Abstract:Bayesian optimal experimental design (BOED) provides a powerful, decision-theoretic framework for selecting experiments so as to maximise the expected utility of the data to be collected. In practice, however, its applicability can be limited by the difficulty of optimising the chosen utility. The expected information gain (EIG), for example, is often high-dimensional and strongly non-convex. This challenge is particularly acute in the batch setting, where multiple experiments are to be designed simultaneously. In this paper, we introduce a new approach to batch EIG-based BOED via a probabilistic lifting of the original optimisation problem to the space of probability measures. In particular, we propose to optimise an entropic regularisation of the expected utility over the space of design measures. Under mild conditions, we show that this objective admits a unique minimiser, which can be explicitly characterised in the form of a Gibbs distribution. The resulting design law can be used directly as a randomised batch-design policy, or as a computational relaxation from which a deterministic batch is extracted. To obtain scalable approximations when the batch size is large, we then consider two tractable restrictions of the full batch distribution: a mean-field family, and an i.i.d. product family. For the i.i.d. objective, and formally for its mean-field extension, we derive the corresponding Wasserstein gradient flow, characterise its long-time behaviour, and obtain particle-based algorithms via space-time discretisations. We also introduce doubly stochastic variants that combine interacting particle updates with Monte Carlo estimators of the EIG gradient. Finally, we illustrate the performance of the proposed methods in several numerical experiments, demonstrating their ability to explore multimodal optimisation landscapes and obtain high-utility batches in challenging examples.
[LG-75] Uncovering Locally Low-dimensional Structure in Networks by Locally Optimal Spectral Embedding
链接: https://arxiv.org/abs/2603.11965
作者: Hannah Sansford,Nick Whiteley,Patrick Rubin-Delanchy
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Standard Adjacency Spectral Embedding (ASE) relies on a global low-rank assumption often incompatible with the sparse, transitive structure of real-world networks, causing local geometric features to be ‘smeared’. To address this, we introduce Local Adjacency Spectral Embedding (LASE), which uncovers locally low-dimensional structure via weighted spectral decomposition. Under a latent position model with a kernel feature map, we treat the image of latent positions as a locally low-dimensional set in infinite-dimensional feature space. We establish finite-sample bounds quantifying the trade-off between the statistical cost of localisation and the reduced truncation error achieved by targeting a locally low-dimensional region of the embedding. Furthermore, we prove that sufficient localisation induces rapid spectral decay and the emergence of a distinct spectral gap, theoretically justifying low-dimensional local embeddings. Experiments on synthetic and real networks show that LASE improves local reconstruction and visualisation over global and subgraph baselines, and we introduce UMAP-LASE for assembling overlapping local embeddings into high-fidelity global visualisations.
[LG-76] Hypercomplex Widely Linear Processing: Fundamentals for Quaternion Machine Learning
链接: https://arxiv.org/abs/2603.11835
作者: Sayed Pouria Talebi,Clive Cheong Took
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: Contributed chapter to appear in Handbook of Statistics Volume 54: Multidimensional Signal Processing, Elsevier, 2026
Abstract:Numerous attempts have been made to replicate the success of complex-valued algebra in engineering and science to other hypercomplex domains such as quaternions, tessarines, biquaternions, and octonions. Perhaps, none have matched the success of quaternions. The most useful feature of quaternions lies in their ability to model three-dimensional rotations which, in turn, have found various industrial applications such as in aeronautics and computergraphics. Recently, we have witnessed a renaissance of quaternions due to the rise of machine learning. To equip the reader to contribute to this emerging research area, this chapter lays down the foundation for: - augmented statistics for modelling quaternion-valued random processes, - widely linear models to exploit such advanced statistics, - quaternion calculus and algebra for algorithmic derivations, - mean square estimation for practical considerations. For ease of exposure, several examples are offered to facilitate the learning, understanding, and(hopefully) the adoption of this multidimensional domain.
[LG-77] Decomposing Observational Multiplicity in Decision Trees: Leaf and Structural Regret
链接: https://arxiv.org/abs/2603.11701
作者: Mustafa Cavus
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 19 pages, 3 figures
Abstract:Many machine learning tasks admit multiple models that perform almost equally well, a phenomenon known as predictive multiplicity. A fundamental source of this multiplicity is observational multiplicity, which arises from the stochastic nature of label collection: observed training labels represent only a single realization of the underlying ground-truth probabilities. While theoretical frameworks for observational multiplicity have been established for logistic regression, their implications for non-smooth, partition-based models like decision trees remain underexplored. In this paper, we introduce two complementary notions of observational multiplicity for decision tree classifiers: leaf regret and structural regret. Leaf regret quantifies the intrinsic variability of predictions within a fixed leaf due to finite-sample noise, while structural regret captures variability induced by the instability of the learned tree structure itself. We provide a formal decomposition of observational multiplicity into these two components and establish statistical guarantees. Our experimental evaluation across diverse credit risk scoring datasets confirms the near-perfect alignment between our theoretical decomposition and the empirically observed variance. Notably, we find that structural regret is the primary driver of observational multiplicity, accounting for over 15 times the variability of leaf regret in some datasets. Furthermore, we demonstrate that utilizing these regret measures as an abstention mechanism in selective prediction can effectively identify arbitrary regions and improve model safety, elevating recall from 92% to 100% on the most stable sub-populations. These results establish a rigorous framework for quantifying observational multiplicity, aligning with recent advances in algorithmic safety and interpretability.
[LG-78] Simultaneous estimation of multiple discrete unimodal distributions under stochastic order constraints
链接: https://arxiv.org/abs/2603.11532
作者: Yasuhiro Yoshida,Noriyoshi Sukegawa,Jiro Iwanaga
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:We study the problem of estimating multiple discrete unimodal distributions, motivated by search behavior analysis on a real-world platform. To incorporate prior knowledge of precedence relations among distributions, we impose stochastic order constraints and formulate the estimation task as a mixed-integer convex quadratic optimization problem. Experiments on both synthetic and real datasets show that the proposed method reduces the Jensen-Shannon divergence by 2.2% on average (up to 6.3%) when the sample size is small, while performing comparably to existing methods when sufficient data are available.
[LG-79] Spatially Robust Inference with Predicted and Missing at Random Labels
链接: https://arxiv.org/abs/2603.11368
作者: Stephen Salerno,Zhenke Wu,Tyler McCormick
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Econometrics (econ.EM); Applications (stat.AP); Methodology (stat.ME)
*备注:
Abstract:When outcome data are expensive or onerous to collect, scientists increasingly substitute predictions from machine learning and AI models for unlabeled cases, a process which has consequences for downstream statistical inference. While recent methods provide valid uncertainty quantification under independent sampling, real-world applications involve missing at random (MAR) labeling and spatial dependence. For inference in this setting, we propose a doubly robust estimator with cross-fit nuisances. We show that cross-fitting induces fold-level correlation that distorts spatial variance estimators, producing unstable or overly conservative confidence intervals. To address this, we propose a jackknife spatial heteroscedasticity and autocorrelation consistent (HAC) variance correction that separates spatial dependence from fold-induced noise. Under standard identification and dependence conditions, the resulting intervals are asymptotically valid. Simulations and benchmark datasets show substantial improvement in finite-sample calibration, particularly under MAR labeling and clustered sampling.
[LG-80] Ill-Conditioning in Dictionary-Based Dynamic-Equation Learning: A Systems Biology Case Study
链接: https://arxiv.org/abs/2603.11330
作者: Yuxiang Feng,Niall M Mangan,Manu Jayadharan
类目: Quantitative Methods (q-bio.QM); Machine Learning (cs.LG); Dynamical Systems (math.DS); Numerical Analysis (math.NA)
*备注:
Abstract:Data-driven discovery of governing equations from time-series data provides a powerful framework for understanding complex biological systems. Library-based approaches that use sparse regression over candidate functions have shown considerable promise, but they face a critical challenge when candidate functions become strongly correlated: numerical ill-conditioning. Poor or restricted sampling, together with particular choices of candidate libraries, can produce strong multicollinearity and numerical instability. In such cases, measurement noise may lead to widely different recovered models, obscuring the true underlying dynamics and hindering accurate system identification. Although sparse regularization promotes parsimonious solutions and can partially mitigate conditioning issues, strong correlations may persist, regularization may bias the recovered models, and the regression problem may remain highly sensitive to small perturbations in the data. We present a systematic analysis of how ill-conditioning affects sparse identification of biological dynamics using benchmark models from systems biology. We show that combinations involving as few as two or three terms can already exhibit strong multicollinearity and extremely large condition numbers. We further show that orthogonal polynomial bases do not consistently resolve ill-conditioning and can perform worse than monomial libraries when the data distribution deviates from the weight function associated with the orthogonal basis. Finally, we demonstrate that when data are sampled from distributions aligned with the appropriate weight functions corresponding to the orthogonal basis, numerical conditioning improves, and orthogonal polynomial bases can yield improved model recovery accuracy across two baseline models.
[LG-81] RIE-Greedy: Regularization-Induced Exploration for Contextual Bandits
链接: https://arxiv.org/abs/2603.11276
作者: Tong Li,Thiago de Queiroz Casanova,Eric M. Schwartz,Victor Kostyuk,Dehan Kong,Joseph J. Williams
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Real-world contextual bandit problems with complex reward models are often tackled with iteratively trained models, such as boosting trees. However, it is difficult to directly apply simple and effective exploration strategies–such as Thompson Sampling or UCB–on top of those black-box estimators. Existing approaches rely on sophisticated assumptions or intractable procedures that are hard to verify and implement in practice. In this work, we explore the use of an exploration-free (pure-greedy) action selection strategy, that exploits the randomness inherent in model fitting process as an intrinsic source of exploration. More specifically, we note that the stochasticity in cross-validation based regularization process can naturally induce Thompson Sampling-like exploration. We show that this regularization-induced exploration is theoretically equivalent to Thompson Sampling in the two-armed bandit case and empirically leads to reliable exploration in large-scale business environments compared to benchmark methods such as epsilon-greedy and other state-of-the-art approaches. Overall, our work reveals how regularized estimator training itself can induce effective exploration, offering both theoretical insight and practical guidance for contextual bandit design.
[LG-82] A Standardized Framework For Evaluating Gene Expression Generative Models
链接: https://arxiv.org/abs/2603.11244
作者: Andrea Rubbi,Andrea Giuseppe Di Francesco,Mohammad Lotfollahi,Pietro Liò
类目: Genomics (q-bio.GN); Machine Learning (cs.LG)
*备注:
Abstract:The rapid development of generative models for single-cell gene expression data has created an urgent need for standardised evaluation frameworks. Current evaluation practices suffer from inconsistent metric implementations, incomparable hyperparameter choices, and a lack of biologically-grounded metrics. We present Generated Genetic Expression Evaluator (GGE), an open-source Python framework that addresses these challenges by providing a comprehensive suite of distributional metrics with explicit computation space options and biologically-motivated evaluation through differentially expressed gene (DEG)-focused analysis and perturbation-effect correlation, enabling standardized reporting and reproducible benchmarking. Through extensive analysis of the single-cell generative modeling literature, we identify that no standardized evaluation protocol exists. Methods report incomparable metrics computed in different spaces with different hyperparameters. We demonstrate that metric values vary substantially depending on implementation choices, highlighting the critical need for standardization. GGE enables fair comparison across generative approaches and accelerates progress in perturbation response prediction, cellular identity modeling, and counterfactual inference.
[LG-83] A Unified Latent Space Disentanglement VAE Framework with Robust Disentanglement Effectiveness Evaluation
链接: https://arxiv.org/abs/2603.11242
作者: Xiaoan Lang,Fang Liu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Evaluating and interpreting latent representations, such as variational autoencoders (VAEs), remains a significant challenge for diverse data types, especially when ground-truth generative factors are unknown. To address this, we propose a general framework – bfVAE – that unifies several state-of-the-art disentangled VAE approaches and generates effective latent space disentanglement, especially for tabular data. To assess the effectiveness of a VAE disentanglement technique, we propose two procedures - Feature Variance Heterogeneity via Latent Traversal (FVH-LT) and Dirty Block Sparse Regression in Latent Space (DBSR-LS) for disentanglement assessment, along with the latent space disentanglement index (LSDI) which uses the outputs of FVH-LT and DBSR-LS to summarize the overall effectiveness of a VAE disentanglement method without requiring access to or knowledge of the ground-truth generative factors. To the best of our knowledge, these are the first assessment tools to achieve this. FVH-LT and DBSR-LS also enhance latent space interpretability and provide guidance on more efficient content generation. To ensure robust and consistent disentanglement, we develop a greedy alignment strategy (GAS) that mitigates label switching and aligns latent dimensions across runs to obtain aggregated results. We assess the bfVAE framework and validate FVH-LT, DBSR-LS, and LSDI in extensive experiments on tabular and image data. The results suggest that bfVAE surpasses existing disentangled VAE frameworks in terms of disentanglement quality, robustness, achieving a near-zero false discovery rate for informative latent dimensions, that FVH-LT and DBSR-LS reliably uncover semantically meaningful and domain-relevant latent structures, and that LSDI makes an effective overall quantitative summary on disentanglement effectiveness.
[LG-84] Cough activity detection for automatic tuberculosis screening
链接: https://arxiv.org/abs/2603.11241
作者: Joshua Jansen van Vüren,Devendra Singh Parihar,Daphne Naidoo,Kimsey Zajac,Willy Ssengooba,Grant Theron,Thomas Niesler
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)
*备注:
Abstract:The automatic identification of cough segments in audio through the determination of start and end points is pivotal to building scalable screening tools in health technologies for pulmonary related diseases. We propose the application of two current pre-trained architectures to the task of cough activity detection. A dataset of recordings containing cough from patients symptomatic for tuberculosis (TB) who self-present at community-level care centres in South Africa and Uganda is employed. When automatic start and end points are determined using XLS-R, an average precision of 0.96 and an area under the receiver-operating characteristic of 0.99 are achieved for the test set. We show that best average precision is achieved by utilising only the first three layers of the network, which has the dual benefits of reduced computational and memory requirements, pivotal for smartphone-based applications. This XLS-R configuration is shown to outperform an audio spectrogram transformer (AST) as well as a logistic regression baseline by 9% and 27% absolute in test set average precision respectively. Furthermore, a downstream TB classification model trained using the coughs automatically isolated by XLS-R comfortably outperforms a model trained on the coughs isolated by AST, and is only narrowly outperformed by a classifier trained on the ground truth coughs. We conclude that the application of large pre-trained transformer models is an effective approach to identifying cough end-points and that the integration of such a model into a screening tool is feasible.
[LG-85] rustworthy predictive distributions for rare events via diagnostic transport maps
链接: https://arxiv.org/abs/2603.11229
作者: Elizabeth Cucuzzella,Rafael Izbicki,Ann B. Lee
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 19 pages, 5 figures, 2 tables
Abstract:Forecast systems in science and technology are increasingly moving beyond point prediction toward methods that produce full predictive distributions of future outcomes y, conditional on high-dimensional and complex sequences of inputs x. However, even when forecast systems provide a full predictive distribution, the result is rarely calibrated with respect to all x and y. The estimated density can be especially unreliable in low-frequency or out-of-distribution regimes, where accurate uncertainty quantification and a means for human experts to verify results are most needed to establish trust in models. In this paper, we take an initial predictive distribution as given and treat it as a useful but potentially misspecified base model. WE then introduce diagnostic transport maps, covariate-dependent probability-to-probability maps that quantify how the base model’s probabilities should be adjusted to better match the true conditional distribution of calibration data. At deployment, these maps provide the user with real-time local diagnostics that reveal where the model fails and how it fails (including bias, dispersion, skewness, and tail errors), while also producing a recalibrated predictive distribution through a simple composition with the base model. We apply diagnostic transport maps to short-term tropical cyclone intensity forecasting and show that an easy-to-fit parametric version identifies evolutionary modes associated with local miscalibration and improves the predictive performance for rare events, including 24-hour rapid intensity change, as compared to the operational forecasts of the National Hurricane Center.
[LG-86] Learning to Unscramble: Simplifying Symbolic Expressions via Self-Supervised Oracle Trajectories
链接: https://arxiv.org/abs/2603.11164
作者: David Shih
类目: High Energy Physics - Theory (hep-th); Machine Learning (cs.LG); Symbolic Computation (cs.SC); High Energy Physics - Phenomenology (hep-ph)
*备注: 14 pages, 6 figures, 2 tables; work done in collaboration with Claude Code
Abstract:We present a new self-supervised machine learning approach for symbolic simplification of complex mathematical expressions. Training data is generated by scrambling simple expressions and recording the inverse operations, creating oracle trajectories that provide both goal states and explicit paths to reach them. A permutation-equivariant, transformer-based policy network is then trained on this data step-wise to predict the oracle action given the input expression. We demonstrate this approach on two problems in high-energy physics: dilogarithm reduction and spinor-helicity scattering amplitude simplification. In both cases, our trained policy network achieves near perfect solve rates across a wide range of difficulty levels, substantially outperforming prior approaches based on reinforcement learning and end-to-end regression. When combined with contrastive grouping and beam search, our model achieves a 100% full simplification rate on a representative selection of 5-point gluon tree-level amplitudes in Yang-Mills theory, including expressions with over 200 initial terms.
[LG-87] Deep regression learning from dependent observations with minimum error entropy principle
链接: https://arxiv.org/abs/2603.11138
作者: William Kengne,Modou Wade
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:This paper considers nonparametric regression from strongly mixing observations. The proposed approach is based on deep neural networks with minimum error entropy (MEE) principle. We study two estimators: the non-penalized deep neural network (NPDNN) and the sparse-penalized deep neural network (SPDNN) predictors. Upper bounds of the expected excess risk are established for both estimators over the classes of Hölder and composition Hölder functions. For the models with Gaussian error, the rates of the upper bound obtained match (up to a logarithmic factor) with the lower bounds established in \citeschmidt2020nonparametric, showing that both the MEE-based NPDNN and SPDNN estimators from strongly mixing data can achieve the minimax optimal convergence rate.
[LG-88] Conformal e-prediction in the presence of confounding
链接: https://arxiv.org/abs/2603.11134
作者: Vladimir Vovk,Ruodu Wang
类目: atistics Theory (math.ST); Machine Learning (cs.LG)
*备注: 8 pages, 2 figures
Abstract:This note extends conformal e-prediction to cover the case where there is observed confounding between the random object X and its label Y . We consider both the case where the observed data is IID and a case where some dependence between observations is permitted.
[LG-89] Efficient Approximation to Analytic and Lp functions by Height-Augmented ReLU Networks
链接: https://arxiv.org/abs/2603.11128
作者: ZeYu Li,FengLei Fan,TieYong Zeng
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注:
Abstract:This work addresses two fundamental limitations in neural network approximation theory. We demonstrate that a three-dimensional network architecture enables a significantly more efficient representation of sawtooth functions, which serves as the cornerstone in the approximation of analytic and L^p functions. First, we establish substantially improved exponential approximation rates for several important classes of analytic functions and offer a parameter-efficient network design. Second, for the first time, we derive a quantitative and non-asymptotic approximation of high orders for general L^p functions. Our techniques advance the theoretical understanding of the neural network approximation in fundamental function spaces and offer a theoretically grounded pathway for designing more parameter-efficient networks.
[LG-90] Co-Diffusion: An Affinity-Aware Two-Stage Latent Diffusion Framework for Generalizable Drug-Target Affinity Prediction
链接: https://arxiv.org/abs/2603.11125
作者: Yining Qian,Pengjie Wang,Yixiao Li,An-Yang Lu,Cheng Tan,Shuang Li,Lijun Liu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Predicting drug-target affinity is fundamental to virtual screening and lead optimization. However, existing deep models often suffer from representation collapse in stringent cold-start regimes, where the scarcity of labels and domain shifts prevent the learning of transferable pharmacophores and binding motifs. In this paper, we propose Co-Diffusion, a novel affinity-aware framework that redefines DTA prediction as a constrained latent denoising process to enhance generalization. Co-Diffusion employs a two-stage paradigm: Stage I establishes an affinity-steered latent manifold by aligning drug and target embeddings under an explicit supervised objective, ensuring that the latent space reflects the intrinsic binding landscape. Stage II introduces modality-specific latent diffusion as a stochastic perturb-and-denoise regularizer, forcing the model to recover consistent affinity semantics from noisy structural representations. This approach effectively mitigates the reconstruction-regression conflict common in generative DTA models. Theoretically, we show that Co-Diffusion maximizes a variational lower bound on the joint likelihood of drug structures, protein sequences, and binding strength. Extensive experiments across multiple benchmarks demonstrate that Co-Diffusion significantly outperforms state-of-the-art baselines, particularly yielding superior zero-shot generalization on unseen molecular scaffolds and novel protein families-paving a robust path for in silico drug prioritization in unexplored chemical spaces.
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