本篇博文主要内容为 2026-07-10 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 (2026-07-10)
今日共更新497篇论文,其中:
- 自然语言处理共66篇(Computation and Language (cs.CL))
- 人工智能共158篇(Artificial Intelligence (cs.AI))
- 计算机视觉共87篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共162篇(Machine Learning (cs.LG))
- 多智能体系统共8篇(Multiagent Systems (cs.MA))
- 信息检索共10篇(Information Retrieval (cs.IR))
- 人机交互共17篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）驱动的网络搜索代理在处理深度与广度兼具的复杂信息检索任务时所面临的局限性，尤其是单个代理因上下文长度受限导致的轨迹过长、难以兼顾搜索深度与覆盖范围的问题。现有基于多代理系统的方案虽通过并行执行提升了搜索覆盖度，但在递归深度、协作适应性以及基于证据的扩展能力方面仍存在明显不足。其解决方案的关键在于提出一种渐进式递归委托框架——WebSwarm，该框架在推理过程中协同完成任务分解、递归扩展与代理协作。WebSwarm动态实例化具有局部目标与搜索模式的智能体搜索节点，每个节点可自主求解或进一步委派子节点；求解完成后向上返回证据与结果，使父节点能够继续拓展、修正或聚合搜索过程。为引导该过程，WebSwarm首先探测网页上任务相关知识的组织结构以实现后续节点扩展的语境对齐，并在同级节点间复用过程经验。实验表明，WebSwarm在BrowseComp-Plus、WideSearch、DeepWideSearch和GISA等基准数据集上，在深度、广度及深度与广度交织的任务中均显著优于单代理与多代理基线方法，其有效性通过消融实验、任务难度分析、工具效率评估与模型泛化能力分析得到验证，为多代理搜索系统的设计提供了重要启示。
链接: https://arxiv.org/abs/2607.08662
作者: Xiaoshuai Song,Liancheng Zhang,Kangzhi Zhao,Yutao Zhu,Zhongyuan Wang,Guanting Dong,Jinghan Yang,Han Li,Kun Gai,Ji-Rong Wen,Zhicheng Dou
机构: Kuaishou(快手); 1(第一作者单位); 2(第二作者单位)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: Work in progress
Abstract:Large language model (LLM)-based web search agents are transforming information seeking from simple factoid question answering into complex, deep-and-wide search and research-oriented tasks. A single ReAct-style agent is constrained by one long trajectory and limited context, making it difficult to handle depth and coverage simultaneously. Existing multi-agent systems improve search coverage through parallel execution and aggregation, but still exhibit clear limitations in recursive depth, collaboration adaptability, and evidence-grounded expansion. We propose WebSwarm, a progressive recursive delegation framework that jointly constructs task decomposition, recursive expansion, and agent collaboration during inference. WebSwarm dynamically instantiates agentic search nodes, each coupling a local objective with a search mode that specifies how the node should organize search and collaboration. Each node can either solve its objective itself or further delegate child nodes; after solving, it returns evidence and results upward, enabling parent nodes to further expand, revise, or aggregate the search process. To guide this process, WebSwarm first probes how task-relevant information is organized on the web to ground subsequent node expansion, and reuses process-level experience across homogeneous sibling nodes. Experiments on BrowseComp-Plus, WideSearch, DeepWideSearch, and GISA show that WebSwarm consistently outperforms single-agent and multi-agent baselines on deep, wide, and interleaved deep-and-wide tasks. Further analyses of ablation, task difficulty, web tool efficiency, and model generalization explain WebSwarm’s effectiveness and provide insights for multi-agent search systems.
[MA-1] Early to Share Late to Save: Synchronisation-Driven Communication Gating in Bandwidth-Constrained Cooperative VLN IJCAI2026
【速读】：该论文旨在解决在带宽受限条件下，协作式视觉-语言导航（cooperative Vision-Language Navigation, VLN）中通信效率低下的核心问题。传统方法通常假设通信无限制，但在真实应用场景中，带宽资源有限，信息传输需具备高度效率。为此，论文提出一种轻量级的监督式“事后门控”（hindsight gating）机制，通过从导航失败中后验标注通信关键步骤，规避了REINFORCE类强化学习方法带来的高方差问题。其解决方案的关键在于揭示了一个反直觉现象：训练后的门控机制主要在早期阶段触发，且在智能体具有较高置信度时更频繁激活，而非在不确定性高时。这一现象可通过“循环隐藏状态对齐”（recurrent hidden-state alignment）机制解释——早期通信注入具身化的轨迹表征，这些表征在后续基于门控循环单元（Gated Recurrent Unit, GRU）的更新中持续累积并增强，从而在仅3次通信（B=3）的情况下实现+0.072的累计对齐增益，接近无约束通信条件下的+0.078表现，且相较随机门控（+0.020）和基于熵的门控（+0.017）分别提升260%和320%的对齐效率。研究结果确立了一种面向带宽受限具身智能体的新通信范式：尽早同步表征，后期独立导航。
链接: https://arxiv.org/abs/2607.08504
作者: Arav Gupta,Nivedan Yakolli,Avinash Gautam
机构: 未知
类目: Multiagent Systems (cs.MA); Robotics (cs.RO)
备注: Accepted at the IJCAI 2026 GLOW Workshop. To appear in Springer Communications in Computer and Information Science (CCIS)
Abstract:Most cooperative Vision-Language Navigation (VLN) methods assume unlimited communication, not considering real-world applications where bandwidth is restricted and information efficiency is critical. We introduce \textbfbandwidth-constrained cooperative VLN and propose \textbfhindsight gating: a lightweight supervised gate that labels communication-critical steps post-hoc from navigation failures, avoiding the high variance of REINFORCE. Contrary to the intuition that agents should communicate when uncertain, we observe a consistent counter-intuitive pattern: trained gates fire predominantly in early episode steps and more often when agents are confident, across all budget levels ( B \in \1,3,5\ ). We explain this through \textbfrecurrent hidden-state alignment: early communication injects grounded trajectory representations that persist and compound through subsequent Gated Recurrent Unit (GRU) updates, achieving +0.072 cumulative alignment gain with B=3 transmissions, approaching unconstrained communication ( +0.078 ) at 260% greater alignment efficiency than random gating ( +0.020 ) and 320% greater efficiency than entropy-based gating ( +0.017 ). Our results establish a new communication regime for bandwidth-limited embodied agents: synchronise representations early, navigate independently later. Our codebase is available at: this https URL
[MA-2] Multi-Agent Firewall Architecture for Privacy Protection of Sensitive Data in Interactions with Language Models
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在缺乏充分安全防护的情况下集成到工作流中所带来的重大风险，尤其是数据泄露和敏感信息外泄问题。其核心解决方案是一套开源、注重隐私的用户端防火墙框架，通过浏览器扩展与代理服务器相结合的方式，实现对HTTP(S)及WebSocket通信的全面流量拦截。该方案的关键在于采用灵活的多智能体（multi-agent）处理管道，融合确定性检测机制与基于大语言模型的语义分析能力，构建混合式数据泄露防护体系，同时具备专有的代码泄露防护功能，并支持未来安全增强功能（如对抗提示注入攻击）的可扩展组件。其分层架构使系统能够在异构环境中部署，有效权衡计算成本、检测深度与延迟性能，实验表明在最优配置下可达到高达94.93%的F1分数，显著提升了LLM交互场景下的安全性与可控性。
链接: https://arxiv.org/abs/2607.08282
作者: Hugo García Cuesta,Pablo Mateo Torrejón,Alfonso Sánchez-Macián
机构: Universidad Carlos III de Madrid ( Carlos III University of Madrid)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:While Large Language Models (LLMs) have become essential productivity tools, their integration into workflows without adequate safeguards creates significant risks. This paper proposes an open-source, privacy-focused, user-facing firewall designed to secure both web-based and programmatic LLM interactions. The architecture combines a browser extension and a proxy for total traffic interception across both HTTP(S) and WebSocket communications. At its core, a flexible multi-agent pipeline delivers data leakage prevention through a hybrid approach combining deterministic detectors with LLM-driven semantic analysis, proprietary code leakage prevention, and extensible components designed for future security enhancements such as prompt injection evasion. The framework’s layered architecture enables deployment across heterogeneous environments, allowing organizations to balance computational cost, detection depth and latency. Evaluation results demonstrate it achieves F1 scores of up to 94.93% on optimal configurations.
[MA-3] ASMR: Agent ic Schema Generation for Ship Maintenance Report Writing VLDB2026
【速读】：该论文旨在解决多源异构船舶维护与运营报告中自动模式生成（automatic schema generation）的问题，即如何从大量历史报告中自动发现紧凑且信息丰富的结构化模式，以准确刻画各类报告的核心信息需求。其核心挑战在于如何在保持语义完整性的同时，避免冗余并提升模式的可读性与实用性。解决方案的关键在于提出一种模块化智能体框架ASMR，其中字段生成智能体（Field Generation Agent）通过自适应多粒度聚类从非结构化文本中提取语义概念并生成候选字段；结构优化智能体（Structural Optimizer Agent）则利用强化学习机制，对候选模式进行筛选与优化，最终生成紧凑、信息丰富且无冗余的结构化模式。该方法不仅提升了报告生成的规范性与一致性，也为数据管理、智能体式AI与以人为中心的AI交叉领域的研究提供了新思路。
链接: https://arxiv.org/abs/2607.08177
作者: Sohrab Namazi Nia,Amogh Dalal,Ning Sa,Peter Ly,Marti Zentmaier,Tomek Strzalkowski,Jay Miller,Rishi Singh,Senjuti Basu Roy
机构: New Jersey Institute of Technology (新泽西理工学院); Rensselaer Polytechnic Institute (伦斯勒理工学院); Boston Fusion Corporation (波士顿融合公司)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: Accepted at the DASHSys 2026 workshop (Systems for Data-centric Agents with Human-in-the-loop), co-located with VLDB 2026
Abstract:In this paper, we study the automatic schema generation problem: given a collection of historical ship maintenance and operational reports across multiple form categories, automatically discover compact and informative schemas that capture the essential information requirements of each report type. To address this challenge, we propose ASMR, a modular agentic framework consisting of two specialized agents. A Field Generation Agent extracts semantic concepts from historical narratives and generates candidate schema fields through adaptive multi-granularity clustering, while a Structural Optimizer Agent employs reinforcement learning to identify compact, informative, and non-redundant schema representations. The resulting schemas can guide report authors toward producing more complete, consistent, and actionable reports. Preliminary results demonstrate the promise of the proposed approach and highlight several open research challenges at the intersection of data management, agentic AI, and human-centered AI.
[MA-4] Who Broke the System? Failure Localization in LLM -Based Multi-Agent Systems
【速读】：该论文旨在解决基于大语言模型（Large Language Model, LLM）的多智能体系统在执行失败时难以定位故障根源的问题。由于智能体间存在长时程交互与紧密耦合的行为模式，传统方法难以准确识别导致系统失败的具体智能体及其最早出现偏差的关键步骤。为此，论文提出AgentLocate框架，其核心在于通过结合大语言模型驱动的判别机制与多个独立评估者从不同视角进行验证，并采用置信度感知的聚合策略整合评估结果，实现对失败责任智能体及最早决策失误步骤的精准归因。此外，该框架通过轻量级微调动态优化判断模块，持续提升故障定位的准确性。实验在两个互补基准上验证了AgentLocate的有效性，结果表明其在识别责任智能体和失败步骤方面显著优于现有方法，同时保持较低的计算开销，在令牌消耗与运行时间上均具备高效性。
链接: https://arxiv.org/abs/2607.07989
作者: Yufei Xia,Anjun Gao,Yueyang Quan,Zhuqing Liu,Minghong Fang
机构: University of Louisville (路易斯维尔大学); University of North Texas (北德克萨斯大学)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: To appear in COLM 2026
Abstract:Large language model (LLM) based multi-agent systems enable complex problem solving through coordinated reasoning and action, but their distributed structure also introduces new challenges in diagnosing system-level failures. When an execution fails, identifying which agent is responsible and at what point the trajectory first becomes irreversibly misdirected is difficult due to long-horizon interactions and tightly coupled agent behaviors. In this paper, we study the problem of failure localization in LLM-based multi-agent systems and present AgentLocate, a framework that attributes failures to both a specific agent and the earliest decisive step. AgentLocate combines an LLM-based judging mechanism with multi-perspective verification by independent evaluators, whose assessments are aggregated using a confidence-aware strategy. The resulting feedback is further used to adapt the judge through lightweight fine-tuning, improving attribution quality. We evaluate AgentLocate on two complementary benchmarks covering diverse tasks, agent configurations, and trajectory lengths. Experimental results show that AgentLocate consistently outperforms existing failure localization methods in identifying both responsible agents and failure steps, while remaining efficient in terms of token usage and running time.
[MA-5] From Triggers to Emotions: A CPM-Grounded Appraisal Multi-Agent for Dynamic Emotional Evolution in Persona-Based Dialogue
【速读】：该论文旨在解决当前基于角色的对话系统在情感敏感场景下存在的核心问题：现有方法通常将角色情感视为静态属性或表层风格化特征，且情感对话研究多聚焦于生成共情式回应，而忽视了对话代理自身情绪状态的动态演变过程。这一缺陷导致角色在触发事件下的情感变化机制未被充分建模，难以实现真实、连贯的情感演进。其解决方案的关键在于提出一种基于组件过程模型（Component Process Model, CPM）的多智能体框架——CPM-MultiAgent，将角色情感视为由对话触发事件持续重构的潜在状态。该框架通过情感触发提取、基于CPM的协同评估以及情绪状态更新三阶段机制，实现了对角色在多轮对话中情感动态演化的建模，从而提升了角色扮演对话在医疗、教育等情感敏感场景下的情感一致性与真实性。
链接: https://arxiv.org/abs/2607.07824
作者: Jingyao Cai,Shuaijun Liu,Abdul Rehman,Yutong Guo,Qin Tian,Thomas Dolby,Sue Green,Chantel Cox,Xiaosong Yang
机构: National Centre for Computer Animation, Bournemouth University (邦德大学计算机动画国家中心); The Hong Kong University of Science and Technology (Guangzhou) (香港科技大学(广州)); Key Laboratory of Child Cognition Behavior Development of Hainan Province (海南省儿童认知行为发展重点实验室); i3 Simulations Ltd (i3模拟有限公司); School of Health and Care, Bournemouth University (邦德大学健康与护理学院)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) have substantially advanced persona-based dialogue agents for emotion-sensitive role simulation in healthcare, education, counseling, customer service, and interactive storytelling. However, two related lines of work leave a key gap. Persona-based dialogue systems often encode emotions as static traits or surface-level stylistic cues, and affective dialogue research has largely focused on empathetic response generation toward users rather than modeling the agent persona’s own evolving emotional state. As a result, trigger-driven emotional evolution within a character remains underexplored. To address this limitation, we draw inspiration from the Component Process Model (CPM), a psychological theory that views emotion as a dynamic process shaped by the appraisal of external events. We propose CPM-MultiAgent, a CPM-grounded emotion evolution multi-agent framework for supporting emotional changes in persona-based dialogue. Instead of treating a character’s emotion as a fixed attribute, CPM-MultiAgent represents it as a latent state that is continuously reshaped by dialogue triggers. Through affective trigger extraction, CPM-based collaborative appraisal, and emotion state updating, the framework enables more emotionally consistent role simulation in multi-turn this http URL with baseline comparisons, ablation studies, human evaluation, and case analyses demonstrate that CPM-MultiAgent effectively models dynamic emotional evolution in emotionally sensitive role-simulation settings.
[MA-6] Collective Intelligence with Foundation Models
【速读】：该论文旨在解决大规模基础模型在复杂推理任务中因单一模型局限性导致的可靠性与可解释性不足问题，提出通过构建异构多智能体协作系统来提升生成式AI（Generative AI）的推理能力与可信度。其核心解决方案在于设计一个包含求解器（solver）、批评者（critic）与聚合器（aggregator）三类智能体的协同框架：各求解器独立生成推理草稿，由专门的批评者进行结构化批判与修订，最终由聚合器融合形成共识解；同时引入评分模块对语义、数值及过程逻辑进行多维度评估。关键发现表明，尽管框架结构和冗余采样能带来一定性能提升，但真正驱动显著性能跃升的核心因素是模型多样性——异构多智能体配置在步骤级准确率（0.64）上较单模型基线（0.54）提升2.3倍，并大幅降低不同学科与难度层级间的方差。尤其值得注意的是，仅当引入模型多样性时，中间推理步骤的正确性才实现质的飞跃，证明异构智能体间具备互补性的错误检测与推理优化能力，对于实现可解释性与可审计性至关重要。该研究揭示了异构多智能体协同架构在科学与工业领域实现高置信度、透明化决策的关键作用。
链接: https://arxiv.org/abs/2607.07729
作者: J. de Curtò,I. de Zarzà
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted as a book chapter in “Advances in Global Applied Artificial Intelligence” (G. A. Tsihrintzis, M. Virvou, N. G. Bourbakis, L. C. Jain, Eds.), authenticated version will be published in Springer series: Learning and Analytics in Intelligent Systems
Abstract:As foundation models grow in scale and diversity, coordinating multiple models into cooperative reasoning systems offers a path toward safer, more reliable AI. This chapter presents a multi-agent framework where solver models generate independent drafts, each undergoes structured critique and revision by a critic agent, and an aggregator agent synthesizes a final consensus solution. A scoring module provides semantic, numerical, and procedural evaluation across all agents. Through ablation studies on a benchmark spanning calculus, physics, chemistry, biology, economics, optimization, statistics, and mathematics, we isolate the contributions of framework architecture versus model diversity. We compare four configurations: (1) Individual Baseline, (2) Homogeneous Framework using one shared model, (3) Redundant Homogeneous Solvers using multiple instances of the same model, and (4) Heterogeneous Framework with diverse specialized models. Results show that while framework structure and redundant sampling yield modest gains, model heterogeneity is the critical factor driving substantial performance improvements. The heterogeneous configuration achieves superior step-wise accuracy (0.64 vs. 0.54 for individual models; 2.3x improvement over homogeneous configurations) with reduced variance across categories and difficulty levels. Step-wise reasoning quality (correctness of intermediate steps, not just final answers) improves dramatically only with model diversity, showing that heterogeneous agents provide complementary error detection and reasoning refinement essential for explainability and auditability. We discuss architectural principles, evaluation methodology, and implications for Global Applied AI, showing how heterogeneous multi-agent coordination supports transparent, auditable, high-confidence decision-making across scientific and industrial domains.
[MA-7] Achieving Unanimous Consensus Through Multi-Agent Deliberation
【速读】：该论文旨在解决传统区块链共识机制（如工作量证明PoW和权益证明PoS）在面对需要综合个体意见、而非仅依赖诚实多数或加权共识的决策场景时所表现出的适应性不足问题。其核心挑战在于如何在去中心化网络中实现兼具公平性与一致性的群体决策，尤其是在涉及复杂、主观或优先级排序的政策性问题时。论文提出的解决方案关键在于引入一种基于协商的共识机制，其中大型语言模型（Large Language Models, LLMs）作为具备理性推理能力的代理主体，通过结构化多轮讨论达成共识。该机制结合分级共识（graded consensus）与多轮协商过程，能够在确定性问题上实现全体一致共识，在优先级决策问题上则支持渐进式共识。通过形式化建模，研究证明该系统可维持区块链的核心属性，并有效应对恶意攻击者、协商停滞及共识置信度等风险。实验结果验证了系统的可行性，展示了良好的收敛性、区块特性与决策准确性，为区块链网络上的协商式决策提供了可行路径。
链接: https://arxiv.org/abs/2504.02128
作者: Apurba Pokharel,Ram Dantu,Shakila Zaman,Vinh Quach,Sirisha Talapuru
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Emerging Technologies (cs.ET)
备注: 6 pages, 4 figure, 2 tables
Abstract:Blockchain consensus mechanisms have relied on algorithms such as Proof-of-Work (PoW) and Proof-of-Stake (PoS) to ensure network functionality and integrity. However, these approaches struggle with adaptability for decision-making where the opinions of each matter rather than reaching an agreement based on honest majority or weighted consensus. This paper introduces a novel deliberation-based consensus mechanism where Large Language Models (LLMs) act as rational agents engaging in structured discussions to reach a unanimous consensus. By leveraging graded consensus and a multi-round deliberation process, our approach ensures unanimous consensus for definitive problems and graded consensus for prioritized decision problems and policies. We provide a formalization of our system and use it to show that the properties of blockchains are maintained, while also addressing the behavior in terms of adversaries, stalled deliberations, and confidence in consensus. Moreover, experimental results demonstrate system feasibility, showcasing convergence, block properties, and accuracy, which enable deliberative decision-making on blockchain networks.
自然语言处理
[NLP-0] UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks
【速读】： 该论文旨在解决现有基准测试在评估主动型智能体（proactive agents）时面临的两大核心问题：一是多数基准依赖于封闭的沙盒环境和单轮评估范式，难以真实反映智能体在动态现实环境中的表现；二是任务分类体系将多种模型能力混杂在同一任务类别中，导致无法准确识别智能体失败的根本原因。为此，论文提出UniClawBench，首个以能力为导向的基准评测体系，聚焦五大基础模型能力——技能使用（Skill Usage）、探索能力（Exploration）、长上下文推理（Long-Context Reasoning）、多模态理解（Multimodal Understanding）与跨平台协调（Cross-Platform Coordination），构建了400个双语真实世界任务。其解决方案的关键在于采用实时Docker容器执行机制与细粒度的逐步完成检查点，实现对智能体行为的动态、高精度评估；同时设计闭环评估策略，通过执行者代理、隐藏监督者代理与用户代理的协同，模拟真实的多轮人机反馈过程，且避免评分标准泄露。此外，通过在多种智能体框架下对先进模型进行对比评估，有效解耦了基础模型能力与框架设计因素的影响，揭示了二者在真实环境中共同作用的机制。研究结果为未来智能体系统的设计与优化提供了可量化的评估依据。
链接: https://arxiv.org/abs/2607.08768
作者: Zhekai Chen,Chengqi Duan,Kaiyue Sun,Bohao Li,Yuqing Wang,Manyuan Zhang,Xihui Liu
机构: HKU MMLab (香港大学多媒体实验室); Meituan (美团)
类目: Computation and Language (cs.CL)
备注: Project Page: this https URL | GitHub Repo: this https URL
Abstract:The rapid development of large language models and multimodal large language models has accelerated the emergence of proactive agents capable of operating everyday tools and assisting users in real-world environments. However, existing benchmarks struggle to evaluate such agents effectively, as they often rely on sandboxed environments and single-turn evaluation paradigms. Moreover, their scenario-based task taxonomies mix multiple model capabilities within the same task category, making it difficult to identify the root causes of agent failures. To address these limitations, we introduce UniClawBench, the first capability-driven benchmark designed to evaluate proactive agents in dynamic, real-world settings. UniClawBench is built around five foundational model capabilities: Skill Usage, Exploration, Long-Context Reasoning, Multimodal Understanding, and Cross-Platform Coordination. Based on these capabilities, we design 400 bilingual real-world tasks. Unlike previous benchmarks that rely on static, pre-recorded answers, our benchmark evaluates agents in live Docker containers using fine-grained, step-by-step completion checkpoints. Furthermore, we design a closed-loop evaluation strategy comprising an executor agent, a hidden supervisor agent, and a user agent to simulate realistic multi-turn human feedback without leaking grading criteria. To disentangle base model capabilities from framework-level design choices, we evaluate state-of-the-art models under multiple agent frameworks. Through comprehensive comparisons across both models and frameworks, we show how base model capabilities and agent framework designs jointly shape performance in real-world environments. To facilitate future research, we make our benchmark and code publicly available at this https URL.
[NLP-1] Validity of LLM s as data annotators: AMALIA on authority
【速读】： 该论文旨在解决国家语言模型（National Language Model）在衡量特定理论构念（如道德基础）时，其预测结果的可靠性与有效性之间的分离问题。尽管葡萄牙语的本土大模型AMALIA-9B在与人工标注者的一致性上表现良好（在权威性道德基础编码任务中，仅比规模为其8至13倍的开源模型低6个F1分数），但高一致性并不等同于高有效性。关键问题在于：模型是基于对理论构念的深层理解进行推理，还是依赖表面相关特征（surface correlates）的捷径策略？为检验这一问题，研究提出“恢复差距”（recovery gap）作为核心评估指标——即当整体提示被分解为原子命题并依据理论规则重构后，模型性能下降的程度。若通过校准能有效缩小该差距，则说明模型具备跨模型、跨语言的可迁移性；反之则表明模型与构念之间的适配性存在根本缺陷。实验发现，经过校准的英文工具无法有效迁移至AMALIA-9B及欧洲葡萄牙语语料，其分解后的性能仅恢复原整体提示性能的一半，且错误分析显示模型过度依赖诸如“权威人物附近的道德愤怒”等表面线索。相比之下，同一葡萄牙语语料下，开放多语言大模型（open multilingual LLM）在相同指令下能显著缩小恢复差距，排除了语料差异为主要解释因素。因此，结论指出：尽管AMALIA仍可用于大规模筛查与预编码，但尚不足以独立、准确地测量该类理论构念。研究强调，主权大模型的基准测试体系不应仅关注与人类标注者的吻合度，更需考察其推理路径的证据合理性，以确保模型真正遵循理论逻辑而非依赖表层相关性。
链接: https://arxiv.org/abs/2607.08731
作者: Manuel Pita
机构: Universidade Lusófona; CICANT (Laboratory for Artificial Intelligence, Social Interaction and Complexity)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:A national language model offers a linguistic community its own instrument for measuring what its citizens say and value. Portugal’s AMALIA, a publicly funded 9B-parameter model for European Portuguese, appears competitive on agreement alone: asked to code the moral foundation of authority, it agrees with trained human coders to within six F1 points of open models eight to thirteen times its size. Yet agreement is reliability, not validity. For theoretical constructs that must be inferred rather than read from surface features, the question is whether the model follows the construct’s theory or reaches the right code by correlated shortcuts. We test this with the recovery gap: the loss in performance when a holistic prompt is decomposed into the codebook’s atomic clauses and recombined by the theory’s explicit rule. If calibration closes that gap, some portability should survive across models and languages; where it does not, the construct-model instrument is the likely locus of failure. We ask whether a calibrated English instrument transfers to AMALIA-9B and to European Portuguese. For one construct and one corpus, it does not. Decomposition recovers only about half of AMALIA’s holistic performance, and error analysis suggests reliance on surface correlates, especially moral outrage near authority figures. An open multilingual LLM closes the gap on the same Portuguese corpus under the same instructions, pointing away from the corpus as the main explanation. AMALIA can still screen and pre-code at scale, but it cannot yet measure this construct well enough to stand alone. The study is a single counterexample, not a verdict on national models; it argues that sovereign-LLM benchmark batteries should test not only agreement with human coders, but the evidential route by which that agreement is warranted.
[NLP-2] Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
【速读】： 该论文旨在解决长时序任务中因轨迹不断扩展而导致决策相关状态信息被淹没或超出上下文窗口限制，进而引发“行为状态衰减”（behavioral state decay）的问题。其核心挑战在于：随着任务推进，关键任务需求、环境事实、过往尝试、诊断结果及未完成子目标等重要信息可能在上下文窗口中丢失，无法有效影响后续决策。为此，论文提出一种主动干预式记忆机制，通过引入一个独立的内存代理（memory agent），与未经修改的动作代理并行运行，从近期轨迹中动态更新结构化记忆库，并基于判断决定是否注入记忆锚定的提醒信息，或保持沉默。该方法的关键在于“选择性干预”——仅在必要时主动触发记忆提示，而非被动暴露全部记忆内容，从而显著提升关键信息的可及性与决策有效性。实验表明，该模块在Terminal-Bench 2.0和τ2-Bench上均实现了显著性能提升（分别+8.3 pp和+6.8 pp pass@1），且优于被动记忆暴露、持续注入、仅顾问引导及通用检索等多种基线方案。此外，研究还探索了开放权重记忆策略的可行性，通过SFT与GRPO微调Qwen3.5-27B模型于SETA数据集，在验证奖励上取得提升，并实现部分迁移至Terminal-Bench的能力，为构建可泛化的记忆驱动型智能体奠定了基础。
链接: https://arxiv.org/abs/2607.08716
作者: Yifan Wu,Lizhu Zhang,Yuhang Zhou,Mingyi Wang,Bo Peng,Serena Li,Xiangjun Fan,Zhuokai Zhao
机构: Meta AI(元宇宙人工智能实验室)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As trajectories grow, task requirements, environment facts, prior attempts, diagnoses, and open subgoals can be buried in the context window or pushed beyond it, failing to influence decisions when needed. We call this failure mode “behavioral state decay”. We study memory as an active intervention mechanism rather than passive retrieval. A separate memory agent runs alongside an unmodified action agent, updating a structured memory bank from the recent trajectory and deciding whether to inject a memory-grounded reminder or remain silent. The module is plug-and-play with frontier action agents and existing agent harnesses. Across Terminal-Bench 2.0 and \tau^2 -Bench, it improves pass@1 for both weaker and stronger action agents, with gains of +8.3 pp on Terminal-Bench and +6.8 pp on \tau^2 -Bench. Ablations show that selective intervention outperforms passive bank exposure, always-on injection, advisor-only guidance, and general retrieval. As an early step toward open-weight memory policies, we train Qwen3.5-27B on SETA using SFT and GRPO, improving validation reward and achieving partial transfer to Terminal-Bench.
[NLP-3] Do You Need a Frontier Model as a Citation Verifier? Benchmarking Rubric LLM s for Deep-Research Source Attribution
【速读】： 该论文旨在解决在基于强化学习（Reinforcement Learning, RL）的深度研究系统中，使用大语言模型（LLM）作为评判者（judge）对引用质量进行评分时所面临的可信度与偏差问题。具体而言，研究关注的是：在将结构化评分规则（rubric）作为奖励信号用于训练生成模型之前，需要明确评判者的性能上限及其潜在系统性偏差。其核心挑战在于，当前广泛采用的LLM judge虽被用作奖励模型，但其能力水平与偏倚特性尚未充分校准，尤其在涉及源相关性（source relevance）和事实支持性（factual support）两个关键维度时表现不一。研究通过在对抗性长文本基准上对3个模型家族中的8个现成LLM judge进行评估，共分析1,248项由人工审核的评分决策，其中378项为存在判别分歧的高难度案例。结果表明，尽管成本较低的模型在源相关性维度上仍具竞争力（如GPT-5-mini达到0.908的F1值，κ=0.636），但在事实支持性方面各模型间无显著差异，且在通过率漂移、假阳性率与假阴性率等指标上表现出显著差异。这说明单一的标量F1指标会掩盖模型间的定向偏差，而这些偏差恰恰会被下游强化学习循环放大。因此，解决方案的关键在于建立对评判者的系统性校准机制，而非依赖最昂贵的模型；该研究表明，实现可靠奖励信号所需的并非顶级模型，而是对评判者行为模式的精细理解与校正。
链接: https://arxiv.org/abs/2607.08700
作者: Ethan Leung,Elias Lumer,Corey Feld,Austin Huber,Vamse Kumar Subbiah,Kevin Paul
机构: PricewaterhouseCoopers (普华永道)
类目: Computation and Language (cs.CL)
备注:
Abstract:Reinforcement learning increasingly relies on an LLM judge to score each rubric criterion, and that judge acts as the reward model during training. Before such a signal can be trusted, we need to know how capable the judge must be and how biased it is. We study this calibration question for citation quality in deep-research systems, where a search-grounded LLM must support each claim it writes with a cited source. Citation quality is a structured rubric task in which each attribution-citation pair is judged along two dimensions that require an LLM, source relevance and factual support. On an adversarial long-form benchmark, we score 8 off-the-shelf LLM judges from 3 model families against gold labels over 1,248 rubric decisions, all of which were human-reviewed and 378 of which were hard cases adjudicated from judge disagreements. Cheaper judges remain competitive across both dimensions, with GPT-5-mini attaining the strongest source-relevance pass-class F1 at 0.908 ( \kappa =0.636), while on factual support the judges are statistically indistinguishable (overlapping confidence intervals), so no single model dominates. At comparable F1, the judges still differ substantially in pass-rate drift, false positive rate, and false negative rate. Scalar F1 obscures this directional bias, yet it is exactly what a downstream reinforcement learning loop would reinforce. Calibrating the judge is therefore a prerequisite for using citation rubrics as reward signals, and our results show that this calibration does not require the most expensive available model.
[NLP-4] UltraX: Refining Pre-Training Data at Scale with Adaptive Programmatic Editing
【速读】： 该论文旨在解决大规模预训练数据精炼过程中，现有方法在质量、效率与可靠性方面面临的瓶颈问题。随着训练数据量逼近物理极限，单纯依赖扩展数据规模的Scaling Laws效应逐渐减弱，模型性能提升愈发依赖于高质量数据的高效利用。然而，当前主流的精炼方法存在明显局限：基于规则的方法受限于固定启发式逻辑，难以应对实例级差异；而基于大语言模型（LLM）的方法虽能提升文本质量，却难以满足大规模数据处理所需的效率与可靠性要求。针对上述挑战，论文提出UltraX——一种面向大规模预训练数据的函数调用式精炼框架，其核心创新在于通过引入“插入”操作，补全了传统以删除和修改为主的编辑功能空间，实现了细粒度的实例级数据修正。该框架构建了一个可靠的程序监督生成流水线：首先通过数据集自适应提示优化引导专家模型生成高质量端到端精炼文本；随后利用行对齐映射（Line Alignment Mapping）与动态上下文替换（Dynamic Context Replacement）将原始-精炼文本对转化为结构化程序监督信号；同时，通过低置信度样本过滤与操作组合比率控制采样策略，有效提升监督信号质量并稳定训练分布。在推理执行阶段，UltraX采用滑动窗口预测、全局操作聚合及系统性后处理机制对模型输出进行归一化与验证，显著增强了大规模执行过程中的稳定性与可靠性。实验表明，UltraX在所有语料上均达到最高平均性能，且在更少训练标记数下即实现或超越基线表现，验证了其卓越的数据效率与精炼可靠性。
链接: https://arxiv.org/abs/2607.08646
作者: Xinlong Zhao,Dongsheng Liu,Hengyu Zhao,Zixuan Fu,Zheng Wang,Jie Cai,Jie Zhou,Qiang Ma,Xuanhe Zhou,Xu Han,Yudong Wang,Zhiyuan Liu
机构: Peking University; ModelBest Inc.; Tsinghua University; Shanghai Jiao Tong University
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:As available training data approaches its physical limit, gains from Scaling Laws have begun to diminish. Consequently, improving Large Language Models (LLMs) now depends less on data expansion and more on higher-quality data utilization. However, in the context of large-scale corpora, existing refinement methodologies face significant limitations in quality, efficiency, and reliability: Rule-based approaches are constrained by fixed heuristics and struggle with instance-level variations; LLM-based approaches improve quality but fail to meet the efficiency and reliability requirements of large-scale data processing. To address these challenges, we propose UltraX, a function-calling refinement framework for large-scale pre-training data that completes the editing function space by introducing insertion in addition to deletion and modification, enabling fine-grained instance-level editing. Specifically, UltraX builds a reliable program-supervision generation pipeline. In this pipeline, dataset-adaptive prompt optimization first guides an expert LLM to produce high-quality end-to-end refined texts, and Line Alignment Mapping and Dynamic Context Replacement then convert original-refined text pairs into structured program supervision. Meanwhile, UltraX improves supervision quality and stabilizes the training distribution with low-confidence example filtering and ratio-controlled sampling by operation combination. During inference and execution, it normalizes and validates model outputs through sliding-window prediction, global operation aggregation, and systematic post-processing, improving the stability and reliability of large-scale execution. Experiments show that UltraX achieves the highest average performance across all corpora and also matches or surpasses baselines with fewer training tokens, demonstrating stronger data efficiency and refinement reliability.
[NLP-5] DominoTree: Conditional Tree-Structured Drafting with Domino for Speculative Decoding
【速读】： 该论文旨在解决生成式 AI（Generative AI）中大语言模型（LLM）推理速度慢的问题，特别是针对推测解码（speculative decoding）技术在生成候选文本时因概率建模局限性导致的效率与准确性瓶颈。现有方法如块扩散型（block-diffusion）和基于最优优先树（best-first tree）的方法，虽能并行生成多个候选词，但受限于对位置间条件依赖关系的建模能力不足，难以充分捕捉生成过程中的上下文动态。其关键解决方案是提出 DominoTree，一种无需训练的最优优先树结构推测解码框架，通过引入 Domino 模型所提出的基于门控循环单元（GRU）的因果修正机制，实现每个生成令牌的概率分布沿根到节点路径的条件化、非因子化建模，从而更准确地反映真实生成过程中的依赖关系。为保证计算效率，该方法将每节点的修正操作限制在候选词的 top-M 范围内，并采用原生 GPU 与 CUDA 图构建器实现高效且比特级一致的树结构构造，确保接受率不变的同时显著降低每轮构建开销。实验结果表明，在 Qwen3-4B 和 Qwen3-8B 等多个基准测试中，DominoTree 在所有温度下均达到最高平均接受长度（最高达 10.7 个 token/轮），并实现最高 6.6 倍于自回归解码的加速，同时在吞吐量上全面超越 Domino、DDTree 及 CaDDTree 等基线方法，尤其在 T=0 时相较 DDTree 提升高达 24%。
链接: https://arxiv.org/abs/2607.08642
作者: Saw S. Lin(Zhiqi Zhang),Jyh-Shing Roger Jang
机构: 未知
类目: Computation and Language (cs.CL)
备注: 23 pages, 2 figures, 11 tables. Code: this https URL
Abstract:Speculative decoding accelerates LLM inference by drafting several tokens and verifying them in parallel. Block-diffusion drafters such as DFlash produce a draft block in one pass but model only per-position marginals; best-first tree methods such as DDTree expand candidate trees from those marginals. The released Domino drafter adds a GRU-based causal correction that makes each draft token’s distribution path-dependent, a structure DDTree’s factorized formulation cannot represent. We introduce DominoTree, a training-free best-first draft tree scored by Domino’s conditional, non-factorized correction along each root-to-node path, made practical by restricting the per-node correction to a candidate top-M. On Qwen3-4B across eight benchmarks, DominoTree reaches up to 6.6x speedup over autoregressive decoding and the highest mean accept length of any evaluated method, up to 10.7 tokens per round, at every temperature we test. DominoTree constructs its tree with a GPU-native, CUDA-graph builder that is bit-identical to a reference Python implementation, so acceptance is unchanged, while keeping per-round tree construction cheap. With this builder as default, DominoTree wins throughput over the released Domino decoder at every temperature, 9-10% overall on Qwen3-4B and up to +22% on Alpaca, and over DDTree/CaDDTree at every temperature we test. On Qwen3- 8B, DominoTree keeps the highest accepted length at every temperature and adds a decisive throughput win at T=0, +24% over DDTree; at higher temperature that edge over DDTree/CaDDTree narrows to a tie and a small loss, while its Overall aggregate wins over DFlash and Domino persist. Comments: 23 pages, 2 figures, 11 tables. Code: this https URL Subjects: Computation and Language (cs.CL) Cite as: arXiv:2607.08642 [cs.CL] (or arXiv:2607.08642v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.08642 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Saw San Lin [view email] [v1] Thu, 9 Jul 2026 16:16:35 UTC (115 KB) Full-text links: Access Paper: View a PDF of the paper titled DominoTree: Conditional Tree-Structured Drafting with Domino for Speculative Decoding, by Saw S. Lin (Zhiqi Zhang) and Jyh-Shing Roger JangView PDFHTML (experimental)TeX Source view license Current browse context: cs.CL prev | next new | recent | 2026-07 Change to browse by: cs References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> 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?) 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(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[NLP-6] he complexities of patient-centred conversational artificial intelligence
【速读】： 该论文旨在解决当前面向消费者的健康聊天机器人在症状评估中因依赖理想化、配合度高的模拟患者而产生的现实适用性偏差问题。其核心挑战在于真实患者在与聊天机器人交互时表现出显著的沟通模式与情绪表达多样性，而现有系统往往未能有效应对这种异质性。解决方案的关键是提出一种新型患者模拟器，该模拟器独立建模临床内容、情绪状态、对话策略和沟通风格，从而生成高度逼真的模拟对话。通过基于图灵测试的真人评分验证，模拟对话与真实对话几乎无法区分（人类识别准确率仅55%），证明了该模拟器的有效性。进一步利用五种不同患者人格特征，在1,164个由临床医生评级的案例中评估四种大语言模型（LLM）的分诊表现，结果表明沟通风格可显著影响分诊结果。研究强调，以患者为中心的对话式人工智能必须充分考虑沟通多样性，否则在真实世界部署时可能表现不佳并加剧健康不平等。
链接: https://arxiv.org/abs/2607.08625
作者: João Matos,Olivia Buege,Donny Cheung,Gary S. Collins,Paula Dhiman,Nan Li,Bingyu Mao,Benjamin W. Nelson,Michail Ouroutzoglou,Paul Varghese,Jonathan Amar
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 36 pages (main text), 129 pages (supplementary materials)
Abstract:Consumer-facing health chatbots powered by large language models (LLMs) are increasingly used for symptom assessment. However, chatbot development and evaluation often rely on cooperative, articulate, simulated patients. We analysed 2,053 real patient-chatbot conversations and found that communication patterns and expression of emotions vary widely across users. We developed a patient simulator that separately models clinical content, emotional state, conversational strategy, and communication style. In a Turing-inspired evaluation of realism with 15 human graders, simulated conversations were nearly indistinguishable from real ones, with human graders achieving an accuracy of 55%. We used five distinct patient personae, across 1,164 clinician-graded cases, to evaluate the performance of four LLMs in urgency assessment. We found that communication style can significantly alter triage outcomes. Patient-centred conversational artificial intelligence must accommodate communication diversity: systems designed for idealised, rather than realistic, interactions risk underperforming and amplifying health disparities when deployed in the real world.
[NLP-7] It Takes a MAESTRO To Prune Bad Experts
【速读】： 该论文旨在解决稀疏激活的混合专家（Mixture-of-Experts, MoE）语言模型在部署时面临的内存瓶颈问题：尽管每个输入标记仅激活少量参数，但所有专家仍需常驻内存，导致存储开销巨大。现有结构化剪枝方法多针对密集型Transformer设计，依赖局部启发式规则评估专家重要性，忽视了MoE路由机制中跨层间的动态依赖关系。其解决方案的关键在于提出MAESTRO（基于转移路由的马尔可夫链近似专家稀疏化），将自回归专家激活轨迹建模为遍历性马尔可夫链，通过其平稳分布捕捉跨层依赖关系，从而构建全局感知且与路由一致的重要性评估准则。实验表明，在涵盖安全、偏见与伦理等五个不同领域下，MAESTRO在严格50%压缩率下平均性能保留率较最优基线提升最高达10.61%，且跨任务性能方差显著降低，证明了全局性、路由一致性的剪枝策略能有效提升模型在异构任务间的泛化一致性。
链接: https://arxiv.org/abs/2607.08601
作者: Palaash Goel,Ayush Maheshwari,Tanmoy Chakraborty
机构: Indian Institute of Technology Delhi, India; NVIDIA, India
类目: Computation and Language (cs.CL)
备注: 16 pages, 4 figures
Abstract:Sparsely-activated Mixture-of-Experts (MoE) language models achieve remarkable inference efficiency by activating only a small fraction of parameters per token, yet their full expert banks reside in memory at all times, creating a prohibitive deployment bottleneck. Existing structured pruning methods, largely designed for dense transformers, assess expert importance using locally derived heuristics that are blind to the interdependent nature of MoE routing. We introduce MAESTRO (Markov-chain Approximated Expert Sparsification via Transition-based ROuting), a structured pruning framework designed for MoE architectures that models autoregressive expert activation trajectories as Ergodic Markov chains whose stationary distributions encode cross-layer dependencies, yielding a globally aware importance heuristic. Evaluated across five diverse domains including Safety, Bias, and Ethics, MAESTRO outperforms state-of-the-art baselines by up to 10.61% in average performance retention under a strict 50% compression regime, while exhibiting substantially lower cross-task variance, indicating that global, routing-congruent pruning produces models that generalize more consistently across heterogeneous tasks.
[NLP-8] When the Judge Changes So Does the Measurement: Auditing LLM -as-Judge Reliability
【速读】： 该论文旨在解决大语言模型作为评判者（LLM-as-judge）在评估生成内容时存在的测量有效性问题，即当评判模型本身发生替换时，即使候选回答保持不变，评分结果仍可能发生变化，从而引发评估结果的不可靠性。其核心解决方案的关键在于识别并量化这种“评估者替换模糊性”（evaluator-replacement ambiguity），并通过系统性实验揭示不同升级路径的非可交换性：仅在Qwen3模型从1.7B扩展至4B参数规模时观察到稳定且显著的性能提升，而MiniMax系列模型相邻版本间的迁移则未带来明显改进。研究进一步表明，尽管更强的评判模型可缓解位置偏差与冗余度偏差，但无法彻底消除。此外，重复采样组成的评审团对相关误差贡献有限，而结构化辩论虽能显著改变决策，但若缺乏解析器与回退日志，则无法确认其效果是否真正源于深度推理。因此，论文主张在报告LLM-as-judge结果时，必须包含数据子集划分、偏差探测指标、误差依赖性估计及协议审计追踪，以增强评估过程的透明性与可复现性。
链接: https://arxiv.org/abs/2607.08535
作者: Zongyou Yang,Yinghan Hou,Xiaokun Yang
机构: Imperial College London (帝国理工学院); Nanchang Institute of Technology (南昌工程学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 6 pages, 6 figures, 4 tables
Abstract:An LLM-as-judge score can move even when the candidate responses stay fixed, simply because the evaluator has changed. We treat this evaluator-replacement ambiguity as a measurement-validity problem. Across four judgment datasets, we compare two upgrade paths available in practice: scaling Qwen3 dense judges from 1.7B to 32B parameters and moving across MiniMax M2-M2.7 released APIs. The main pattern is that judge upgrades are not interchangeable: only Qwen3 1.7B to 4B gives a robust adjacent gain, while MiniMax adjacent releases do not. Stronger judges reduce but do not remove position and verbosity bias. Repeated-sample juries add little when errors are correlated. Structured debate can move decisions substantially, but without parser and fallback logs those shifts cannot be attributed to deliberation. We argue that LLM-as-judge reports should include dataset slices, bias probes, error-dependence estimates, and protocol audit trails.
[NLP-9] Cross-seed explainability using Procrustes-conditioned Joint End-to-end Top-K Sparse Autoencoders
【速读】： 该论文旨在解决独立训练的BERT模型在机制可解释性中面临的跨种子特征通用性问题：由于字典学习具有非凸性，不同随机初始化的模型会学习到错位的特征空间，导致看似相同的特征实际上因初始化差异而存在显著偏差。其解决方案的关键在于，在联合稀疏自编码器（SAE）训练前，通过正交普罗克鲁斯特斯旋转（orthogonal Procrustes rotation）对各种子激活空间进行对齐，从而实现特征空间的初步对齐；在此基础上，结合Top-K稀疏性、端到端下游优化以及基于已有SAE研究设计的辅助死特征恢复损失，构建了一个条件化联合端到端的Top-K稀疏自编码器（Procrustes-conditioned Joint End-to-end Top-K Sparse Autoencoder, SAE）。实验结果表明，该方法在三个基准数据集（SST-2、Stanford Politeness、TweetEval Emotion）上，针对五组独立种子对（共十组BERT模型）的评估中，均实现了高于后处理对齐基线的特征通用性（跨种子皮尔逊相关系数 ≥ 0.70），且初步定性分析验证了高通用性特征能够编码可解释的社会语言学模式。
链接: https://arxiv.org/abs/2607.08499
作者: Bendegúz Váradi,Zoltán Kmetty
机构: 未知
类目: Computation and Language (cs.CL)
备注: 17 pages, 4 figures, 6 tables
Abstract:We present a Procrustes-conditioned Joint End-to-end Top-K Sparse Autoencoder (SAE) for extracting cross-seed universal features from independently trained BERT models. Cross-seed feature universality is a fundamental challenge in mechanistic interpretability: because dictionary learning is non-convex, independently trained networks learn misaligned feature spaces, so apparently identical features may differ by random initialization. We address this by computing an orthogonal Procrustes rotation between seeds’ activation spaces before joint SAE training, combining Top-K sparsity, end-to-end downstream optimization, and an auxiliary dead-feature revival loss based on previous SAE literature. Evaluating on five independent seed pairs (ten BERT models) across three benchmark datasets (SST-2, Stanford Politeness, TweetEval Emotion), our full pipeline produces more universal features (Pearson r \geq 0.70 across seeds) than post-hoc alignment baselines on all three datasets. A minimal qualitative analysis confirms that high-universality features encode interpretable sociolinguistic patterns.
[NLP-10] Cognitive-structured Multimodal Agent for Multimodal Understanding Generation and Editing
【速读】： 该论文旨在解决统一多模态模型在长时程多轮视觉对话中因历史视觉与文本输入反复注入共享上下文窗口而导致的视觉标记爆炸（visual token explosion）及跨轮次引用不可靠的问题。其核心解决方案是提出一种认知结构化的多模态智能体（Cognitive-structured Multimodal Agent, CMA），通过将视觉信息外化至情景式视觉记忆（Episodic Visual Memory）中，并在推理过程中选择性激活相关记忆片段，实现高效、精准的跨轮次信息检索。该智能体包含感知抽象引擎（Perceptual Abstraction Engine）用于结构化视觉表征，认知检索引擎（Cognitive Retrieval Engine）支持跨轮次记忆召回，以及多模态执行控制器（Multimodal Executive Controller）实现自主任务推断与动作规划。为弥补现有数据集缺乏轮级检索监督的问题，研究构建了统一场景引擎（Unified Scenario Engine），可程序化生成带有细粒度检索标注的多轮对话数据，支持基于强化学习优化抽象与检索策略。此外，研究还建立了按难度分层的长时程视觉对话基准，用于评估情景记忆的回溯能力。实验表明，8B规模的CMA在20轮会话中达到91.4%的检索准确率，优于32B基线模型8.2个百分点，同时每轮推理时间从23.1秒降至12.7秒，效率显著提升。进一步提出的CMA-Harness工具增强部署框架，集成持久化多模态记忆、网络访问、图像生成/编辑/合成工具及OpenAI兼容服务，验证了结构化记忆与模块化决策在长时程多模态智能体中的可扩展性与高效性优势。
链接: https://arxiv.org/abs/2607.08497
作者: Feng Wang,Canmiao Fu,Zhipeng Huang,Chen Li,Jing Lyu,Ge Li
机构: Peking University (北京大学); WeChat Vision, Tencent Inc (微信视觉，腾讯公司)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 16 pages, 7 figures, 8 tables. Project page: this https URL Code: this https URL
Abstract:Recent unified multimodal models show a single architecture can jointly perform vision/language understanding and image generation/editing. However, they repeatedly feed all historical visual and textual inputs into a shared context window, limiting long-horizon multimodal dialogue due to visual token explosion and unreliable cross-turn referencing. We propose a Cognitive-structured Multimodal Agent that externalizes visual information into an Episodic Visual Memory and selectively reactivates relevant episodes during reasoning. The agent consists of a Perceptual Abstraction Engine for structured visual abstraction, a Cognitive Retrieval Engine for cross-turn memory retrieval, and a Multimodal Executive Controller for autonomous task inference and action planning. To address the lack of turn-level retrieval supervision in existing datasets, we develop a Unified Scenario Engine that programmatically generates structured multi-turn conversations with fine-grained retrieval annotations, enabling reinforcement learning to optimize abstraction and retrieval policies. We also construct a long-horizon visual-dialogue benchmark stratified by difficulty to evaluate episodic visual recall. Our 8B agent achieves 91.4% retrieval accuracy over 20-turn sessions, surpassing 32B baselines by +8.2% while nearly halving per-turn inference time (23.1s - 12.7s). We further present the Cognitive-structured Multimodal Agent Harness (CMA-Harness), a tool-augmented deployment of the same cognitive structure integrating persistent multimodal memory, web access, image generation/editing/composition tools, and OpenAI-compatible serving. Structured memory and modular decision-making offer a more scalable, efficient paradigm for long-horizon multimodal agents than monolithic parameter scaling. Code: this https URL ; Project page: this https URL
[NLP-11] Ensemble Diversity Optimization for Subjective Supervision
【速读】： 该论文旨在解决主观自然语言处理（Subjective NLP）任务中普遍存在的标注者系统性分歧问题，传统模型常因忽略不确定性而产生过拟合或校准偏差。其核心解决方案是提出一种预测空间框架——集成多样性优化（Ensemble Diversity Optimization, EDO），关键在于通过统一的可微目标函数联合优化集成权重、有效基数（effective cardinality）与校准性。EDO利用Gumbel-Softmax松弛实现集成结构与规模的端到端学习，并引入一个在验证集上调优的有符号多样性正则项，以可控地引导优化过程在保留或抑制标注分歧之间进行权衡。该正则化机制有效防止了集成崩溃（ensemble collapse），并实现了对效用-校准权衡的精细调控。此外，框架融合了软F1代理损失、类别加权交叉熵以应对数据不平衡，以及可靠性加权多样性来调节组内变异性。在四个主观文本分类基准（ArMIS、ConvAbuse、HS-Brexit、MD-Agreement）上的实验表明，相较于Soft-CE、Soft-MD、Top-5投票和WEL等基线方法，EDO显著提升了概率校准性能，交叉熵降低40%-78%，Brier分数更低，同时保持了竞争力的F1得分，并更贴近标注者分布，证明了联合优化集成结构与有符号多样性正则化是一种高效、模型无关的建模人类主观性的新范式。
链接: https://arxiv.org/abs/2607.08493
作者: Xia Cui,Ziyi Huang,N. R. Abeynayake
机构: Manchester Metropolitan University (曼彻斯特都会大学); Hubei University (湖北大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Subjective NLP tasks often exhibit systematic annotator disagreement, requiring models that represent uncertainty rather than collapse it. We introduce Ensemble Diversity Optimization (EDO), a prediction-space framework that jointly optimizes ensemble weights, effective cardinality, and calibration through a unified differentiable objective. EDO learns ensemble composition and size end-to-end via Gumbel-Softmax relaxation and incorporates a signed diversity regularizer, tuned on validation data, to steer optimization toward either preserving or suppressing disagreement. This regularization prevents ensemble collapse and enables controlled navigation of the utility-calibration trade-off. The framework integrates a soft F1 surrogate, class-weighted cross-entropy to address imbalance, and reliability-weighted diversity to regulate intra-ensemble variability. Experiments on four subjective text-classification benchmarks (ArMIS, ConvAbuse, HS-Brexit, MD-Agreement) show that EDO substantially improves probabilistic calibration, reducing cross-entropy (40-78% depending on baseline) and lowering Brier scores relative to Soft-CE, Soft-MD, Top-5 Voting, and WEL, while maintaining competitive F1 and better alignment with annotator distributions. These results demonstrate that jointly optimizing ensemble structure with a signed diversity regularizer provides an efficient, model-agnostic approach for modeling human subjectivity in supervised learning.
[NLP-12] wo Axes of LLM Abstention: Answer Correctness and Question Answerability
【速读】： 该论文旨在解决大模型在面对错误前提问题（false-premise questions）或无法回答的问题时，仍会生成看似合理但实际错误或不恰当回答的难题。传统方法依赖单一置信度阈值进行拒绝决策，但该策略无法区分“答案错误”与“问题本身不可答”这两种本质不同的情况。研究发现，在五个不同规模（2B至14B）的指令微调模型中，答案正确性（answer correctness）与问题可答性（answerability）是两个独立的维度：普通答案置信度对问题是否可答几乎无感知，而基于隐藏状态的线性探测器则恰好相反。尤其在自然语境下的错误前提问题（CREPE数据集）上，传统方法的表现接近随机水平，而隐藏状态探测器仍能实现0.69至0.77的AUROC，表明模型内部已编码了问题不可答的表征，却未主动报告。该缺陷并非随模型规模扩大而缓解。进一步实验表明，直接指令模型检查前提会导致其错误地质疑所有前提（57%误挑战），无法区分真伪；而将前提检查指令与隐藏状态探测结合，可使挑战精度提升近三倍。最终提出一种双轴校准策略：同时要求可答性得分和正确性得分均通过独立阈值，从而实现对不可答问题的可控拒绝（任意规模下均可调节），且错误回答率受模型自身准确率上限约束，随着规模增长，该策略成为唯一能在14B模型上实现有效认证的方案。关键在于利用隐藏状态中的可答性信号，构建分离、可校准的双重决策机制。
链接: https://arxiv.org/abs/2607.08456
作者: Benedikt J. Wagner
机构: City St George’s, University of London (城市圣乔治大学，伦敦大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:A model should refuse two different things: answers it would get wrong, and questions it should not answer at all, such as unanswerable ones or ones resting on a false premise. The usual recipe thresholds a single confidence score, which cannot tell these apart. Across five instruction-tuned models from three families (2B to 14B), we find they are separate axes. Ordinary answer-confidence tracks whether an answer is right but is nearly blind to whether the question is answerable; a linear probe on hidden states does the reverse. The blind spot does not shrink with scale. It is worst on naturally occurring false-premise questions (CREPE). There, answer-confidence, P(IK), P(True), and even asking the model outright whether a premise is false all stay near chance, while a hidden-state probe reaches 0.69 to 0.77 AUROC: the model represents a problem it will not report. This turns out to be fixable. Instructing a model to check premises backfires, because it then disputes sound and false premises alike (57% false challenges), unable to tell them apart; routing the same instruction with the probe roughly triples challenge precision. We turn the two axes into a calibrated policy that answers only when an answerability score and a correctness score each clear a separately certifies behave differently: the unanswerable-answer rate is controllable at every scale, while the wrong-answer rate is capped by model accuracy, so the guarantee tightens as threshold policy certifies both budgets at 0.75 coverage of correct answers, against 0.31 for a single threshold; at 14B it is the only policy that certifies at all.
[NLP-13] Detecting Ladder Logic Bombs in IEC 61131-3 PLC Programs using ESBMC-PLC: A Formal Verification Approach with Trigger Synthesis
【速读】： 该论文旨在解决工业控制系统中可编程逻辑控制器（PLC）程序内嵌的梯形图炸弹（Ladder Logic Bomb, LLB）检测难题。现有验证工具因将功能块（function-block）内部逻辑从中间表示（IR）中丢弃，导致恶意逻辑无法被识别，从而使炸弹在静态分析下“隐形”。其解决方案的关键在于提出ESBMC-LLB，基于ESBMC-PLC+构建，并引入一个建模层以显式暴露功能块内部逻辑，将炸弹检测转化为形式化验证问题：通过扫描监视器（scan-watchdog）检测非终止型载荷，通过输出接线分析揭示执行器伪造载荷作为安全违规。利用k-归纳法实现对所有扫描周期的无界证明，确保炸弹不存在；而有界模型检查器则返回反例，即触发条件，这是传统签名、异常和控制流图（CFG）分诊检测器所无法提供的。在Iacobelli 2024公开数据集上，ESBMC-LLB成功检测全部30个炸弹并恢复所有触发器，包括可逃避CFG分诊的自适应触发器（如计算型、不可见算术型、多扫描触发）。此外，首次对PLC-Defuser的SWaT语料库进行语义模型检查评估，其扩展版本使整个语料库可解析，在v1.0.0版本中检测出149/150个炸弹（99%准确率，零误报），并完整恢复触发器；但在后续版本含非线性非终止炸弹时，检测率下降至49%，因SMT求解器超时。研究结论表明，语义模型检查与CFG分诊互补：前者提供无界证明、对自适应触发器鲁棒性强，支持布尔、整数及线性模拟逻辑；后者能处理非线性模拟非终止行为，二者在不同场景下各具优势。
链接: https://arxiv.org/abs/2607.08417
作者: Pierre Dantas,Lucas Cordeiro,Waldir Junior
机构: The University of Manchester (曼彻斯特大学); Federal University of Amazonas (联邦亚马逊大学)
类目: Computation and Language (cs.CL); Hardware Architecture (cs.AR)
备注: 14 pages
Abstract:A Ladder Logic Bomb (LLB) is malicious control logic in a Programmable Logic Controller (PLC) program that lies dormant until a trigger activates a payload to manipulate actuators, forge sensor readings, or deny operator control. We observe that real malicious logic hides inside function-block bodies, which existing ladder-diagram verifiers drop from their intermediate representation (IR), making bombs invisible to provers. We present ESBMC-LLB, which uses ESBMC-PLC+ as its verification engine and adds a modeling layer that exposes function-block logic and recasts bomb detection as a formal verification problem: a scan-watchdog exposes non-termination payloads, and output wiring exposes actuator-forgery payloads as safety violations. k-induction gives an unbounded proof of bomb-absence across all scans, and the bounded model checker returns a counterexample that is the trigger - guarantees that signature, anomaly, and CFG-triage detectors lack. On the public Iacobelli 2024 dataset, ESBMC-LLB detects all 30 bombs and recovers every trigger; it also detects adaptive triggers (computed, opaque-arithmetic, multi-scan) that evade CFG-triage. We also report the first semantic model-checker evaluation on PLC-Defuser’s SWaT corpus: our analog extension makes the full corpus parseable; on v1.0.0, it detects 149/150 bombs (99%) with zero false positives, recovering each trigger; on a later version with nonlinear non-termination bombs, detection drops to 49% as the SMT solver times out. We conclude that semantic model checking and CFG-triage are complementary - the former gives unbounded proofs, adaptive-trigger robustness, and handles Boolean/integer and linear analog logic; the latter leads to nonlinear analog non-termination, and we delineate where each wins.
[NLP-14] When Synthetic Speech Is All You Have: Better Call GRPO
【速读】： 该论文旨在解决大语言模型（LLM）驱动的自动语音识别（ASR）在受监管领域（如银行业）应用中因隐私限制导致的真实语音数据难以获取的问题。由于真实语音采集成本高且受法律约束，合成文本到语音（TTS）成为替代方案，但其声学特征与真实录音存在显著差异，造成性能瓶颈。现有方法主要依赖监督微调（SFT），但效果有限。本文提出采用强化学习策略，特别是无评价值函数的组相对策略优化（GRPO），通过奖励低词错误率（WER）的预测结果，充分挖掘合成语音的潜力。实验表明，仅使用合成语音进行GRPO训练可使WER相对降低40%（从36.71%降至22.09%），而SFT后接GRPO进一步提升至45%的相对改进。分析显示，性能提升源于行为层面的优化：GRPO改善了停顿校准以减少插入错误，并通过更精准地对齐语音与文本、锚定注意力机制于音频信号，提升了时序对齐能力，而早期层表示保持不变。因此，在合成语音为主要资源的场景下，强化学习应优先于监督微调。
链接: https://arxiv.org/abs/2607.08409
作者: Shashi Kumar,Yanis Labrak,Hasindri Watawana,Sergio Burdisso,Esaú Villatoro-Tello,Kadri Hacioğlu,Petr Motlicek,Andreas Stolcke
机构: Idiap Research Institute (伊迪亚普研究学院); Uniphore; EU Horizon 2020 project ELOQUENCE (欧盟地平线2020项目ELOQUENCE)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Submitted to SLT 2026
Abstract:LLM-based ASR adapted to regulated domains such as banking is bottlenecked by privacy: real speech is costly and legally constrained to collect, making synthetic text-to-speech (TTS) an attractive substitute. Yet synthetic speech stays acoustically mismatched with real recordings, and work on this gap has stayed within supervised fine-tuning (SFT). We instead turn to reinforcement learning, and show that Group Relative Policy Optimization (GRPO) extracts far more from the same synthetic speech than SFT. Synthetic-only adaptation of the model with GRPO, a critic-free method rewarding low-WER hypotheses, reduces WER by 40% relative to SFT (36.71% \to 22.09%), and an SFT-then-GRPO combination pushes this further to 45%. We trace the gain to behavior rather than representation: GRPO reduces insertion errors by improving stopping calibration and speech-to-text alignment by better anchoring attention to audio, leaving early-layer representations intact. When synthetic speech is the main resource, reinforcement learning should be preferred over supervised fine-tuning.
[NLP-15] Prompt Compression via Activation Aggregation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在处理指令提示（instruction prompt）时计算开销过大的问题，具体关注如何在不重新处理原始令牌序列的前提下，高效保留并利用提示中的任务相关信息。其核心解决方案是：通过在中间层提取激活值，并以学习得到的加权和方式将其压缩为一个单一的激活向量，再注入目标模型的早期层中，从而替代完整的提示序列输入。该方法的关键在于，这种压缩后的向量能够有效保留任务相关的语义信息，仅导致相对于完整提示处理的精度下降不足2%。此外，该研究揭示了大语言模型激活空间中的深层结构特征：（i）中层表示可有意义地传递至早期层，表明不同层间存在一定程度的信息编码兼容性；（ii）单个激活向量可编码可量化且可恢复的语义信息；（iii）加权激活和是一种鲁棒的表征压缩机制，具备良好的泛化能力与实用性。
链接: https://arxiv.org/abs/2607.08399
作者: Thibaud Ardoin,Semira Einsele,Evis Bregu,Gerhard Wunder
机构: Freie Universität Berlin (柏林自由大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Large language models process prompts by propagating activations through dozens of layers before generating a response. We ask whether the task-relevant information contained in an instruction prompt can be compressed into a single activation vector and re-injected into the model, replacing the original token sequence? We show this is achievable using a learned weighted sum of activations extracted at an intermediate layer and injected at an early layer of the target LLM. The compressed vector preserves task-relevant information, incurring an accuracy drop of under 2% relative to full prompt processing. Beyond its practical implications, including reducing per-query computation for fixed instruction prompts without reprocessing the original token sequence, our analysis reveals structure in the activation space of LLMs: (i) mid-layer representations transfer meaningfully to early layers, suggesting a degree of cross-layer compatibility in how information is encoded; (ii) a single activation vector encodes a quantifiable and recoverable amount of semantic information; (iii) a weighted sum of activations is a robust representation compressor.
[NLP-16] oken-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents
【速读】： 该论文旨在解决持久性人工智能代理（persistent AI agents）在长期运行过程中因状态持久化、可复用技能及工具交互导致的安全风险问题。传统基于单轮交互的聊天助手安全防护机制难以应对此类代理中由自然语言令牌流（natural-language token flows）所承载的持续性威胁，如记忆更新、工具参数传递、检索文件内容及组件间通信等均可能成为恶意行为的传播路径。其核心挑战在于，这些安全关键交互多以语义形式通过令牌流传递，传统的稀疏审计或远程大模型监督难以实现全面覆盖与实时响应。为此，论文提出TokenWall——一种基于语义边界的运行时防御框架，作为代理令牌流上的“语义防火墙”。其关键解决方案在于：通过对令牌流进行边界感知的语义审计，构建结构化的源-目标审计记录；在执行前实施轻量级本地检查，并将高风险模糊案例定向升级至更强的仲裁模块，从而实现全范围预执行干预。相较于依赖远程大模型或稀疏审计的方法，TokenWall显著提升了安全性与效率，在CIK-Bench基准测试中将攻击成功率降至12.5%，同时保持97.4%的良性任务通过率，且仅引入0.69秒额外延迟，验证了语义级运行时隔离在持久性AI代理中具备实际可行的安全-效用权衡能力。
链接: https://arxiv.org/abs/2607.08395
作者: Puji Wang,Yingchen Zhang,Ruqing Zhang,Jiafeng Guo,Xueqi Cheng
机构: State Key Laboratory of AI Safety; Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences, Beijing, China
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL)
备注:
Abstract:Persistent AI agents extend large language models (LLMs) beyond single-turn interaction into long-lived software systems. Unlike traditional chat assistants, unsafe content in these agents can propagate through persistent state, reusable skills, and tool-mediated interactions, creating a substantially larger semantic attack surface. We observe that most security-critical interactions in such agents are transmitted through natural-language token flows, including memory updates, tool arguments, retrieved files, and inter-component communications. This observation enables a new security formulation: unsafe behavior can be intercepted as risky semantic flows before reaching privileged runtime sinks. Based on this insight, we propose TokenWall, a runtime defense framework that acts as a semantic firewall over agent token flows. TokenWall performs boundary-aware semantic auditing over these flows, constructing structured source-sink audit records, applying lightweight local inspection before execution, and selectively escalating ambiguous high-risk cases to stronger arbitration modules. Unlike prior approaches that rely on sparse auditing or remote large-model oversight, TokenWall enables full-coverage pre-execution mediation while reducing remote arbitration and latency. Experiments on CIK-Bench show that TokenWall reduces attack success rate to 12.5% while maintaining a 97.4% benign executable pass rate without human confirmation. TokenWall further introduces only 0.69 seconds of additional latency on benign cases, demonstrating that semantic runtime containment can achieve a practical security-utility trade-off for persistent AI agents.
[NLP-17] owards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning
【速读】： 该论文旨在解决大语言模型（LLM）在微调过程中引入新知识时存在的“知—用鸿沟”问题，即模型虽能快速记忆新事实，却难以将其有效应用于下游推理任务。其核心挑战在于记忆与泛化之间存在显著的准确率差距和时间延迟。为深入理解这一现象，研究提出一种名为自修补（self-patching）的新干预技术，通过监测模型内部知识的空间传播动态，定位那些在激活位置上重新分配表示可显著改善推理失败案例的关键神经元。实验结果支持“知识通路错位假说”：被记忆的知识表征可能存在于模型内部，但未被正确路由至计算有效的层。基于此诊断发现，研究设计了一种简单启发式策略，在跨领域实验中成功恢复了58%–75%的原始性能上限，验证了该方法在提升模型泛化能力方面的实际有效性。
链接: https://arxiv.org/abs/2607.08393
作者: Lu Dai,Ziyang Rao,Yili Wang,Hanqing Wang,Hao Liu,Hui Xiong
机构: HKUST(GZ); HKUST
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Fine-tuning LLMs to inject new knowledge faces a critical challenge: LLMs can quickly memorize new facts, yet fail to use them for downstream reasoning tasks. We formalize this failure as the \textit\textbfKnowing–Using Gap, characterized by an accuracy gap and a temporal lag between memorization and generalization. To understand this phenomenon, we fine-tune LLMs with unseen knowledge and monitor the spatial permeation dynamics of the knowledge internally using a novel intervention technique called self-patching. Self-patching identifies activation locations where relocating representations substantially improves failed generalization cases. These results are consistent with a knowledge-circuit misalignment hypothesis: memorized representations can exist internally but may not be routed to computation-effective layers. To demonstrate the practicality of this diagnostic finding, we design a simple heuristic strategy which recovers 58–75% of the oracle headroom in generalization failure. Experiments are done cross-domain for the robustness of this finding.
[NLP-18] Echoes Across Vietnams Highlands Delta and Coast: A Multilingual Corpus for Cham Khmer and Tay-Nung
【速读】： 该论文旨在解决越南少数民族语言（包括占语、高棉语和泰侬语）在自然语言处理（Natural Language Processing, NLP）领域几乎完全缺失的问题，尤其针对这些语言在文字系统、越南语接触程度及标准化水平上的显著差异所导致的多语言模型适配失效问题。现有通用多语言模型在面对这些语言时会出现严重语义碎片化，且常用评估指标如语言建模损失或词汇重叠检索准确率可能产生误导，因模型可能在表面任务上表现良好却无法实现跨文档的语义泛化。其核心解决方案是提出一种基于文字特征感知的适配方法——通过词汇表扩充与校准的替换词预训练相结合，有效抑制模型对非语义性文字差异（如脚本不一致）的过度依赖，从而减少语言碎片化现象。实验结果表明，该方法构建的编码器在分类任务中表现最优，同时揭示了仅依赖词汇重叠作为评估信号的局限性。
链接: https://arxiv.org/abs/2607.08362
作者: Anh Trac Duc Dinh,Khang Nhat Hoang Vo,Vinh Cong Doan,Tai Tien Ta,Khoa Duc Anh Lam
机构: Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, Ho Chi Minh City, Vietnam; Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
类目: Computation and Language (cs.CL)
备注:
Abstract:Vietnam’s ethnic minority languages are almost absent from the field of Natural Language Processing (NLP), and the challenge goes beyond data scarcity: Cham, Khmer, and Tay-Nung differ sharply in script, Vietnamese contact, and standardization, conditions under which standard multilingual adaptation can learn the wrong signals. We introduce CKTN, the first corpus and benchmark for these languages (44,367 documents, 24M subword tokens), spanning continued pretraining, category classification, and summary-document retrieval. We show that existing multilingual encoders severely fragment these languages, and that common adaptation metrics can mislead: models may lower language-modeling loss or excel at lexical-overlap retrieval while still failing at semantic generalization across documents. We address this with a script-aware adaptation recipe - vocabulary augmentation combined with calibrated replaced-token pretraining - that prevents the discriminator from exploiting trivial script mismatches. The result is an encoder with substantially less fragmentation and the strongest classification performance among evaluated models, exposing the limits of lexical-overlap retrieval as an evaluation signal.
[NLP-19] Grounded Event Extraction from SEC 8-K Filings with a Fine-Grained Taxonomy STOC
【速读】： 该论文旨在解决美国上市公司通过Form 8-K披露重大事件时，监管机构（SEC）所使用的条目代码（item codes）粒度粗略的问题——单一代码涵盖从常规行政变更到高管离职等不同性质的事件，且许多对市场具有显著影响的信息被归入“通用类别”（catch-all item），导致信息可读性与分析价值受限。为实现细粒度事件标注的规模化应用，论文提出一种两阶段系统：第一阶段利用大语言模型（LLM）生成事件标签，并通过模糊n-gram匹配将每个标签锚定至原文中的确切语句片段，确保可追溯性；第二阶段则基于事件类别定义对引用语句进行重新评估，生成可信度评分（quality score）。该方法在2022至2026年间共计292,984份8-K文件中实现了601,088个带有文本依据的事件标签标注，并公开发布数据集。实验表明，在5,125个分层验证标签中，随着质量评分提升，人工评判的精确率从12%单调上升至96%，而无依据标签比例从8%降至接近零；消融实验进一步证明，该评分体系仅在独立第二阶段赋值时具备校准有效性。此外，不依赖语言模型的事件研究分析证实，该三层次共119类事件分类体系能够有效区分原本共享同一代码但经济意义截然不同的事件类型，验证了其在实证金融分析中的有效性。
链接: https://arxiv.org/abs/2607.08346
作者: Rian Dolphin,Joe Dursun,Jarrett Blankenship,Katie Adams,Quinton Pike
机构: Massive.com(大规模公司); Atlanta, Georgia(亚特兰大，佐治亚州) USA; Dublin(都柏林) Ireland
类目: Computation and Language (cs.CL); General Finance (q-fin.GN)
备注: 9 pages, 8 figures, 1 table. Full dataset and taxonomy available at this https URL
Abstract:Form 8-K filings are the primary channel through which U.S. public companies disclose material events, but the SEC item codes attached to them are coarse: a single item spans routine administrative changes and chief executive departures, and many of the most market-moving disclosures fall into a catch-all item. Large language models make fine-grained labelling feasible at corpus scale, but only if the labels can be traced to the source text and shown to be reliable. We present a two-stage system that tags 8-K disclosures against a three-tier taxonomy of 119 event types. The first stage constrains output to valid taxonomy entries and anchors every tag to a verbatim quote via fuzzy n-gram validation; the second re-grades each cited quote against the category definition to produce a quality score. Applying the system to 292,984 filings from 2022 to 2026 yields 601,088 grounded event tags, which we release. Over 5,125 stratified tags, an LLM judge finds precision rises monotonically with the quality score, from 12% to 96%, while unsupported tags fall from 8% to near zero. Ablation shows the score is calibrated only when assigned in a dedicated second pass. An event study on unsigned abnormal returns confirms, without any language model, that the taxonomy separates economically distinct events sharing an item code.
[NLP-20] ypeProbe: Recovering Type Representations from Hidden States of Pre-trained Code Models
【速读】： 该论文旨在解决当前生成式代码模型（Generative Code Models）在内部如何编码类型信息这一关键问题，尤其关注其对形式化类型语义（formal type semantics）的隐式表示能力。尽管现有模型在代码生成任务中表现优异，但其内部是否以及如何捕获类型信息仍不明确。研究的关键解决方案在于通过构建并利用跨语言（Java与Python）的平行代码数据集，系统性地探测预训练代码模型残差流（residual streams）中的类型表征。研究发现，即使在无类型标注的代码输入下，模型仍能自发形成跨语言类型表征；进一步通过跨语言线性探针（probe）验证，发现隐藏状态可线性编码由类型函数调用所隐含的结果类型，且该结构对词汇扰动和跨语言语法差异具有部分鲁棒性。这一工作首次直接针对代码模型的类型语义可解释性与跨语言类型表征展开探究，为理解代码模型的内部工作机制提供了新视角，并公开了相关代码与数据集以促进后续研究。
链接: https://arxiv.org/abs/2607.08339
作者: Giuliano Gorgone,Fausto Carcassi
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Programming Languages (cs.PL)
备注: 18 pages, 12 figures. Accepted at ESSLLI 2026 (StuS; double-blind)
Abstract:State-of-the-art code models achieve impressive performance, yet the extent to which they internally encode type information remains poorly understood. We probe the residual streams of pretrained code models for internal type representations using a parallel dataset of Java and Python code examples. Our results show that cross-lingual type representations emerge even from untyped code. Moreover, we test whether hidden states linearly encode the result type implied by typed function application by training probes on one language to infer argument and result types in the other. Finally, we find that this structure is partly robust to lexical perturbations and cross-language syntactic variations. To the best of our knowledge, prior work on interpretability of code models has not directly targeted formal type semantics or cross-lingual type representations. We release our code and datasets.
[NLP-21] XALPHA: A Memory-Driven AI Quant Researcher for Hypothesis-to-Code Alpha Discovery
【速读】： 该论文旨在解决量化投资中因子发现过程碎片化、缺乏闭环迭代与持续学习能力的问题。传统方法虽已从人工设计演进至机器学习及基于大语言模型（LLM）的框架，但多数仅实现因子生成、搜索或评估中的单一环节自动化，无法形成从假设到代码验证的完整闭环，亦难以融合外部知识与历史研究反馈以支持持续进化。其核心解决方案是提出XAlpha——一种基于记忆驱动的生成式AI量化研究员（Generative AI Quant Researcher），通过构建多源研究记忆系统，整合报告驱动的金融知识与过往生成周期的反馈信息。该系统由宏观脑（Macro Brain）规划研究主题并选择合适范式（Archetype），微观脑（Micro Brain）将假设池转化为可执行因子代码，并在事前验证假设逻辑、代码实现与金融合理性的三重对齐性；跨脑（Cross Brain）则将实证结果提炼为生成级反馈、周期级总结与范式级研究提示，推动知识沉淀与策略迭代。由此，XAlpha将因子挖掘从孤立的生成任务转变为一个持续读取、假设、实现、验证、反思与演化的闭环研究流程。在沪深300指数上的实验表明，XAlpha在整体因子发现性能上显著优于代表性基线方法。
链接: https://arxiv.org/abs/2607.08332
作者: Fengyuan Liu,Yuchen Fu,Yuqi Wang,Qi Liu
机构: The University of Hong Kong (香港大学); Grace Investment Machine (格蕾丝投资机器)
类目: Computation and Language (cs.CL)
备注:
Abstract:Financial markets are noisy, non-stationary, and high-dimensional, making it difficult to discover predictive and robust trading signals. Alpha discovery has evolved from manual factor design to machine learning, evolutionary search, and recent LLM-based frameworks, improving the efficiency of factor generation, search, and evaluation. However, existing methods still mostly automate isolated steps, rather than functioning as end-to-end quant researchers that can absorb external knowledge, close the hypothesis-to-code validation loop, and learn from accumulated discovery feedback. To fill this gap, we introduce XAlpha, a memory-driven AI Quant Researcher for continuous hypothesis-to-code alpha discovery. XAlpha maintains a multi-source research memory system that integrates report-grounded financial knowledge with discovery feedback from prior generations and research cycles. Guided by this memory system, a Macro Brain plans research themes and selects suitable Archetypes; a Micro Brain transforms the planned hypothesis pool into executable factor code and verifies ex-ante tri-alignment among the hypothesis idea, code logic, and financial plausibility; and a Cross Brain consolidates empirical outcomes into generation-level feedback, cycle-level summaries, and archetype-level research cues for future exploration. In this way, XAlpha turns alpha mining from isolated factor generation into a closed-loop research process that continuously reads, hypothesizes, implements, validates, reflects, and evolves. Experiments on CSI300 show that XAlpha achieves stronger overall alpha discovery performance than representative baselines.
[NLP-22] Different Teachers Different Capabilities: Sub-1B On-Device Distillation for Structured Text Enrichment
【速读】： 该论文旨在解决高吞吐量结构化信息提取中大模型推理延迟过高问题，提出通过将高性能大模型（如8B参数的deepseek-r1:8b）中的知识与推理能力蒸馏至轻量级本地部署小模型（0.6B参数的Qwen3-0.6B），以实现低延迟、低成本的实时处理。其解决方案的关键在于：通过有意识地设计教师模型的类型（是否具备推理能力、是否采用管理式流水线）来解耦不同能力的迁移路径。实验表明，具备推理能力的教师模型能够有效提升学生模型在摘要生成质量上的表现（恢复基线到教师差距的58%），而管理式流水线则更有利于标签多样性的传递；相比之下，同等规模但无推理能力的教师模型无法带来显著性能提升，说明性能增益主要源于教师的推理机制而非模型规模。此外，在小样本、短文本子集上，指令型教师的学生更具事实一致性，而推理型教师的学生易产生幻觉。因此，最终交付成果并非单一最优模型，而是基于任务特性构建的“按场域路由”策略图谱，指导在设备端根据不同子任务选择最适配的轻量化模型。
链接: https://arxiv.org/abs/2607.08268
作者: Vinay Kumar Chaganti
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 12 pages, 5 figures. has a same-size non-reasoning-teacher control, a three-judge LLM-as-a-judge panel with a negative control, full-source faithfulness grading, and a per-field routing analysis
Abstract:High-volume structured extraction pays a large model’s latency on every item, so distilling the task into a small on-device model is attractive: comparable output at a fraction of the time and cost. We measure what that distillation actually delivers, per sub-task. Each news article is mapped to one JSON object with a short summary and five categorical labels. We distill an 8B reasoning teacher (deepseek-r1:8b) into a 0.6B student (Qwen3-0.6B; QLoRA, three seeds), and add two teacher controls: a same-size non-reasoning teacher and a larger managed pipeline. A blinded, reference-free, three-judge panel scores every arm against the full article, alongside two non-distillation baselines, few-shot prompting and constrained decoding. The student runs at about 0.8 s per article against the teacher’s 39 s, and recovers 58% of the base-to-teacher gap on summary quality, beating its primary baseline (constrained decoding) by +16.8 points and few-shot prompting by a secondary +4.9. A same-size non-reasoning teacher trains a student no better than the untuned base, so the summary gain follows from the teacher’s reasoning nature rather than its scale. Capabilities then split by teacher: the reasoning teacher transfers writing quality and the managed pipeline transfers label diversity, while a same-size instruction teacher’s students stay more grounded on the 22 short, thin-source articles in the 93-item test set (74 versus 55 faithful), where the reasoning-lineage student fabricates. That grounding difference is a consistent ordering rather than a significant aggregate effect, and the subgroup is small, so we report it as a direction. Because no single engine wins every field, the deliverable is a per-field routing map for on-device enrichment.
[NLP-23] Best-of-N TTS Evaluation is Confounded by ASR Family Alignment ICML2026
【速读】： 该论文旨在解决零样本文本到语音（text-to-speech, TTS）生成中内容一致性不足的问题，具体通过引入基于最佳候选选择（Best-of-N, BoN）的推理机制，利用自动语音识别（ASR）验证器从多个生成候选中筛选最优结果。然而，研究发现一个未被充分关注的评估混淆因素：验证器的性能表现高度依赖于所使用的ASR模型家族。在LibriSpeech-PC test-clean数据集上，使用F5-TTS生成的候选在Whisper、wav2vec 2.0和HuBERT等不同ASR评估器下的排名呈现显著反转，而同一家族的验证器-评估器配对相比跨家族配对能获得2–3倍更高的“最优解余量”（oracle headroom），尽管其表示空间的线性核相关分析（linear CKA）高达0.978，表明存在明显的身份或谱系级耦合而非表征重叠。为应对这一问题，论文提出两种跨家族排名集成方法——排名平均（rank-averaging）与合取最大排名（conjunctive max-rank），在三个独立评估器上实现了最低的平均词错误率（WER）1.61%（N=10），相较F5-TTS提升12%；同时在自动评分指标SIM-o和UTMOS上无明显退化。此外，最优单个验证器将官方F5-TTS评估器下的WER由2.06%降至1.72%（相对降低16.5%）。研究建议采用跨评估器三角验证作为默认报告实践，以提升评估结果的鲁棒性与可信度。
链接: https://arxiv.org/abs/2607.08256
作者: Taehyung Yu,Seongjae Kang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD)
备注: Accepted at ICML 2026 Workshop on Machine Learning for Audio
Abstract:Best-of- N (BoN) inference improves content consistency in zero-shot text-to-speech by selecting from N candidates with an automatic speech recognition (ASR) verifier. We identify an underexplored evaluation confound: a verifier’s apparent quality depends strongly on which ASR family judges it. On LibriSpeech-PC test-clean~\citeplibrispeechpc with F5-TTS~\citepf5tts, verifier rankings reverse across Whisper, wav2vec~2.0, and HuBERT evaluators, and same-family verifier-evaluator pairs recover 2-3 \times more oracle headroom than cross-family pairs despite near-identical representations (linear CKA 0.978 ) – a pattern consistent with identity- or lineage-level coupling rather than representational overlap. We propose two \textbfcross-family rank ensembles (rank-averaging and conjunctive max-rank) that attain the lowest mean WER across three independent evaluators – 1.61% at N=10 ( -12% relative to F5-TTS) – with no measurable degradation under automatic SIM-o/UTMOS metrics; the best single verifier drives WER from 2.06% to 1.72% ( -16.5% ) under the official F5-TTS evaluator. We recommend cross-evaluator triangulation as default reporting practice.
[NLP-24] Diarization-Guided Qwen -ASR Adaptation for Multilingual Two-Speaker Conversational Speech
【速读】： 该论文旨在解决多语言双说话人对话语音识别（Multilingual Two-Speaker Conversational Speech Recognition）中的核心挑战，即如何在复杂对话场景下准确区分不同说话人并实现高精度的跨语言语音转写。其解决方案的关键在于构建一个模块化的说话人分离（Speaker Diarization）前端与自适应的生成式语音识别（Generative ASR）后端协同工作：前端通过语音活动检测、子段生成、CAMPPlus说话人嵌入提取、双说话人谱聚类及基于RTTM的音频分割，实现对多说话人语音的精确归属；后端则采用针对任务定制的Qwen3-ASR-1.7B模型，通过三阶段优化策略进行适应性增强——包括监督微调、基于三阶段文本到语音（TTS）合成的语音增强框架下的LoRA微调，以及基于词错误率（WER）、字符错误率（CER）奖励和对幻觉、重复、长度偏移惩罚的GRPO强化学习进一步优化。实验结果表明，该系统在开发集上达到23.70的平均tcpMER，相比基线降低6.83绝对点，在最终评估集上取得17.97的平均tcpMER，且消融实验验证了各阶段优化的有效性，其中监督微调贡献最大，而合成语音增强与强化学习显著提升了模型鲁棒性。
链接: https://arxiv.org/abs/2607.08208
作者: Hao Wu,RongQi Han,Zhen Wang,Wei Liang,Wei Xu
机构: 未知
类目: Computation and Language (cs.CL)
备注: 4 main pages plus 1 page of reference
Abstract:This paper describes our self-designed system for Task 1 of the MLC-SLM 2026 Challenge for multilingual two-speaker conversational speech. The system combines a modular speaker diarization front end with a challenge-adapted Qwen3-ASR-1.7B recognizer. The diarization front end performs voice activity detection, subsegment generation, CAMPPlus speaker embedding extraction, two-speaker spectral clustering, and RTTM-based audio segmentation. The resulting speaker-attributed segments are grouped by language or region and decoded by the adapted ASR model. For ASR adaptation, we first perform supervised full fine-tuning on the official training data, then apply LoRA fine-tuning with synthetic speech generated by a three-pipeline TTS-based synthetic speech augmentation framework, and finally refine the model using GRPO reinforcement learning with rewards based on WER/CER and penalties for hallucination, repetition, and length deviation. On the official development set, the full system achieves an average tcpMER of 23.70, reducing the error rate by 6.83 absolute points relative to the released Qwen-ASR-1.7B performance. On the final evaluation set, the system achieves an average tcpMER of 17.97. Ablation results show that supervised fine-tuning provides the largest gain, while synthetic-speech LoRA adaptation and reinforcement learning further improve robustness.
[NLP-25] A First-Principles Theory of Slow Thinking and Active Perception
【速读】： 该论文旨在解决生成式认知建模中“慢思考”（slow thinking）与主动感知（active perception）的数学形式化问题，核心在于构建可训练、可推理的慢思考大语言模型。其解决方案的关键在于提出“主动提升”（active lifting）理论，该理论通过在可观测空间与潜在空间之间对概率分布进行提升与投影，利用潜在序列采样及内在降低不确定性的最大速率驱动机制，建立了一个包含慢思考模型的广义设计空间。该理论在静态理论（static theory）的表示层次与采样层次上对模型进行定位，并支持通过沿两个层次的“升级”来优化模型性能。此外，主动提升理论引入了具有内生时间轴的推理过程和类最小长度编码的训练目标，揭示了感知代理性（agency of perception）的形成机制，包括慢思考范式的涌现。技术副产品包括改进慢思考模型的三阶段路径、适用于所有数据模态的编码器与生成模型统一构建方法、类人视觉表征的先验形成机制，以及缓解策略崩溃（policy collapse）的潜在方案。
链接: https://arxiv.org/abs/2607.08196
作者: Hongkang Yang,Zhi-Qin John Xu,Feiyu Xiong,Weinan E
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Published on 2026/05/11 in Journal of Machine Learning
Abstract:As part of a series on first-principles modeling of cognitive functions, this paper attempts to provide a mathematical formulation of thinking and perception. It formally derives slow thinking or more generally, active perception, and encompasses the design, training and inference of slow thinking large language models. Our starting point is the lifting and projection of probability distributions on the observable and latent spaces, with the objective of representing complex data distributions by simple function families such as neural networks. A theory called “active lifting” is proposed, based on the sampling of latent sequences and an intrinsic drive to reduce uncertainty with maximum rate. It derives a large design space, containing the slow thinking models in a subspace that we call the static theory. These models are positioned on the representation hierarchy and sampler hierarchy induced by the static theory, and can be upgraded by climbing the two hierarchies. Active lifting further derives an inference process with an internal time axis, and a training objective that resembles minimum-length coding as well as the invention of languages. Thus, it characterizes the agency of perception, including the emergence of the slow thinking formats. Technical by-products of this theory include a three-stage pathway for improving slow thinking models, a unified approach to constructing encoders and generative models for all data modalities, a priori formation of human-like visual representations, and a possible solution to policy collapse.
[NLP-26] Hidden Decoding at Scale: Latent Computation Scaling for Large Language Models
【速读】： 该论文旨在解决在保持大语言模型（LLM）Transformer骨干结构不变的前提下，如何通过增加每个令牌的计算量来持续提升模型性能的问题。现有方法如深度循环（looped）Transformer虽试图通过循环计算增强单个令牌的处理能力，但难以与大规模训练中广泛采用的流水线并行（pipeline parallelism）兼容，导致扩展性受限。为此，本文提出“隐状态解码”（Hidden Decoding），一种在持续预训练（CPT）阶段应用的序列长度扩展方法：将每个输入令牌展开为n个独立流（stream），每一流拥有独立的嵌入表，并保留中间流的键值缓存作为上下文，从而在不增加或加宽Transformer层的情况下实现更深层次的内部计算。为使该方法在大规模场景下可扩展，作者进一步提出“流因子化注意力”（Stream-Factorized Attention），即大多数层仅在各流内进行注意力计算，仅少数层跨流混合信息，将注意力计算复杂度从二次方降低至近线性级别。实验表明，在100B+参数量级的MoE模型上，基于n=4的隐藏解码成功训练出WeLM-HD4-80B和WeLM-HD4-617B，并显著优于对应非隐状态解码基线，首次验证了序列长度扩展作为固定骨干架构下的可行缩放路径。随着扩展因子n增大，性能增益持续提升，证明该方法为前沿规模大模型提供了一条有效的、无需重新预训练的计算资源扩展途径。
链接: https://arxiv.org/abs/2607.08186
作者: Aiwei Liu,Cheng Shi,Chuhan Wu,Ci Lei,Di Lu,Donald He,Fan Zhang,Fanhao Kong,Feifei Zhang,Guan Wang,Haicheng Wang,Haoyu Liu,Houjin Yu,Jiachen Ding,Jiayi Feng,Jie Zhou,Jijun Chi,Jindi Shi,Jing Lei,Junjie Zhang,Laiyi Li,Le Tian,Linhao Zhang,Miao Fan,Sijun Zhang,Wei Jia,Weiwei Shi,Wenhan Li,Wentao Zhao,Wenteng Liang,Xiao Zhou,Xiaojin Zhou,Xihuai Wang,Xinyu Gao,Xuanliang Wang,Xuyang Ao,Yang Yu,Yangxiu You,Yinuo Zhao,Yufei Kuang,Yufei Wang,Yuan Liu,Yuan Liu,Yuwen Chen,Zhencong Tian,Zhongyin Zhao,Zilin Yu,Zitao Wang
机构: WeChat AI Team
类目: Computation and Language (cs.CL)
备注: 30 pages, 9 figures
Abstract:Scaling Large Language Models (LLMs) has been driven mainly by enlarging the Transformer backbone, but for an already-strong model this requires another round of costly pretraining. We study whether an existing backbone can keep improving by allocating more computation to each token while leaving the Transformer backbone fixed. Depth-recurrent (looped) Transformers pursue this goal but are hard to scale, because looped computation does not fit naturally with the pipeline parallelism used to train the largest models. We add computation along the sequence-length dimension, where the extra computation is simply a longer input and stays compatible with standard large-model training. We propose Hidden Decoding, a sequence-length scaling method applied during continued pretraining (CPT). It expands each token into n streams with independent embedding tables and keeps the intermediate streams’ key-value cache as context, so each token performs more internal computation without adding or widening Transformer layers. To keep this affordable at scale, we introduce Stream-Factorized Attention, in which most layers attend only within each stream and only a few layers mix across streams, reducing the attention cost from quadratic to roughly linear in n. Experiments support two scaling results. At frontier scale, we train WeLM-HD4-80B and WeLM-HD4-617B at n=4 and improve their matched non-HD baselines, making Hidden Decoding the first demonstrated sequence-length scaling method at the 100B+ MoE scale. Across expansion factors, the gains grow as n increases, showing that sequence-length expansion is a practical fixed-backbone scaling path for frontier-scale LLMs.
[NLP-27] SQuaD-SQL: Efficient Text-to-SQL with Small Language Models via LLM -Guided Knowledge Distillation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在文本转SQL（Text-to-SQL）任务中虽性能优异但计算开销过大、难以部署于资源受限环境的问题。其核心解决方案在于提出一种名为SQuaD-SQL（Small-Qualified and Distilled for SQL）的新方法，通过知识蒸馏与合成数据生成相结合的策略，使小型语言模型（Small Language Models, SLMs）在保持高效性的同时逼近LLMs的性能水平。该方案的关键在于三个组成部分：（1）基于LLM的合成数据生成，利用精心设计的提示工程从LLM中提取结构化知识；（2）参数高效微调，实现仅需单个消费级GPU即可完成全模型训练；（3）领域自适应微调，通过领域特定的合成数据进一步提升目标领域的表现。在WikiSQL数据集上的实验表明，SQuaD-SQL在测试集上达到86.9%的执行准确率，接近LLMs的性能，同时具备更快的推理速度和更低的内存占用，验证了经过合理训练策略优化的SLMs可作为资源受限场景下Text-to-SQL应用的实用且高效的替代方案。
链接: https://arxiv.org/abs/2607.08161
作者: Wangyu Wu,Xiaojian Lin,Rong Fu,Zaiyang Yu,Xuhang Chen,Wenjun Yu,Zhenhong Chen
机构: The University of Liverpool; University of Macau; University of Chinese Academy of Sciences; Tsinghua University; Microsoft; Shanghai University of International Business and Economics; Huizhou University
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at IEEE SMC 2026
Abstract:Text-to-SQL is a fundamental task in natural language processing that enables users to interact with structured databases using natural language. While large language models (LLMs) have demonstrated remarkable performance on this task, their substantial computational requirements hinder deployment in resource-constrained settings. In this paper, we introduce SQuaD-SQL (Small-Qualified and Distilled for SQL), a novel approach that empowers small language models (SLMs) to approach the performance of LLMs on the Text-to-SQL task while significantly improving efficiency through knowledge distillation and synthetic data generation. Our method comprises three key components: (1) LLM-based synthetic data generation, where structured knowledge is extracted from LLMs via carefully designed prompting strategies; (2) parameter-efficient fine-tuning, enabling full model training on a single consumer-grade GPU; and (3) domain-adaptive fine-tuning, where domain-specific synthetic data further enhances performance in targeted domains. Experiments on the WikiSQL dataset demonstrate that SQuaD-SQL achieves an execution accuracy of 86.9% on the test set, approaching the performance of LLMs while offering faster inference and lower memory usage. These results suggest that, with proper training strategies, SLMs can serve as practical and efficient alternatives for Text-to-SQL applications in resource-limited environments.
[NLP-28] COALA: Robust Contextualized Speech-augmented Language Modeling for ASR via Contrastive Regularizer and Biasing Score Estimation INTERSPEECH2026
【速读】： 该论文旨在解决在复杂多实体场景下，如何有效将外部知识融入语音增强语言模型（Speech-Augmented Language Models, SLMs）以提升特定领域实体识别准确率的问题。其核心挑战在于SLMs固有的上下文窗口限制导致难以从大规模偏置列表中精准筛选出与当前音频片段相关的候选实体。为此，论文提出COALA（Contextualized ASR Leveraging Biasing Scoring）框架，其关键创新在于将SLM的隐空间表示映射至一个专门设计的判别性空间，从而量化音频片段与候选实体之间的匹配强度，实现高效且精准的上下文偏置。此外，针对以往方法在处理多目标话语（即多个罕见词共现）时出现的训练崩溃问题，COALA通过引入鲁棒的偏置评分机制予以缓解。实验结果表明，在LibriSpeech基准测试中，COALA在不同规模的偏置列表下均表现出优越的上下文偏置性能，验证了其有效性与泛化能力。
链接: https://arxiv.org/abs/2607.08117
作者: Jhih-Rong Guo,Bi-Cheng Yan,Tien-Hong Lo,Berlin Chen
机构: National Taiwan Normal University (国立台湾师范大学)
类目: Computation and Language (cs.CL)
备注: Accepted at INTERSPEECH 2026
Abstract:Contextual biasing seeks to integrate external knowledge into automatic speech recognition (ASR) systems to accurately recognize domain-specific entities. In this paper, we propose COALA (Contextualized ASR Leveraging Biasing Scoring), a robust framework designed to enhance speech-augmented language models (SLMs) in complex multi-entity scenarios. Considering the inherent context-window limitations of SLMs, identifying relevant target entities from a large-scale biasing list is crucial for effective recognition. To this end, COALA maps SLM latent representations into a specialized discriminative space to quantify the matching intensity between audio segments and candidate entities. Furthermore, we address the training collapse in prior study when handling multi-target utterances-where multiple rare words co-occur. Experimental results on the LibriSpeech benchmark demonstrate that COALA consistently achieves superior contextual biasing performance across various biasing list scales.
[NLP-29] CausalDS: Benchmarking Causal Reasoning in Data-Science Agents
【速读】： 该论文旨在解决当前大语言模型（LLM）在数据科学代理任务中因果推理评估的割裂问题：现有基准要么缺乏真实数据生成结构，仅聚焦符号化因果推理；要么虽具备真实数据分析能力，却缺少系统性因果生成机制。其核心挑战在于，现有因果评估数据集多依赖于有限模板化变体的预设示例，难以覆盖多样化的新型因果结构。为此，论文提出CausalDS基准，其关键创新在于构建基于采样结构化因果模型（SCM）的合成场景，结合自动生成的观测数据与源于真实领域语义的自然语言叙事，实现对因果推理、数据科学、不确定性量化、主动拒答及工具调用/代码编写等多维度能力的联合评估。通过引入基于真实分布的可选经验性约束，既保留现实结构特征，又避免“因果鹦鹉”（causal parrot）风险，确保生成内容的多样性与真实性。每个场景均衍生出涵盖Pearl因果层级三阶（Rung 1-3）的任务，其中典型预测任务对应第一阶，且多数任务需模型通过多工具协同处理不完美观测数据以达成最终答案，同时将合理拒答作为可评分的一级输出，从而全面刻画智能体在复杂数据科学工作流中的因果推理能力。
链接: https://arxiv.org/abs/2607.08093
作者: Andrej Leban,Yuekai Sun
机构: University of Michigan (密歇根大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 55 pages, 10 figures
Abstract:Large language models (LLMs) increasingly act as integrated data-science agents, combining abstract reasoning with advanced tool use. Yet the relevant benchmark landscape largely divides into symbolic causal reasoning benchmarks without realistic data analysis or data analysis benchmarks without a principled causal data-generating structure. Furthermore, existing causal evaluation datasets are often restricted to curated examples from existing sources, with diversity coming from limited templatized variations rather than from systematic generation of novel synthetic causal structures. We introduce CausalDS, a benchmark for evaluating causal reasoning in agentic data-science workflows. Each benchmark instance is a scene consisting of a sampled structural causal model (SCM) with generated observational data and an accompanying synthetic natural-language story grounded in a realistic domain. We optionally ground the composition of the benchmark components in empirical distributions obtained from real-world datasets, thus retaining empirical structure while reducing the “causal parrot” risk through completely synthetic generation. From each scene, we then derive tasks spanning all three of Pearl’s rungs, with typical data-science prediction tasks appearing as Rung 1. Most tasks include a data science coding component, where the model typically needs to use several tools to arrive at the final answer due to the frequent presence of imperfect observations, which are generated by an observation model. Additionally, recognizing when a question admits no warranted answer and abstaining is treated as a first-class scored outcome. The benchmark thus jointly evaluates symbolic causal reasoning, data science, uncertainty quantification, abstention, and tool use/coding.
[NLP-30] MASTE: A Multi-Agent Pipeline for Zero-Shot Aspect Sentiment Triplet Extraction
【速读】： 该论文旨在解决生成式大模型（LLM）在零样本场景下进行方面情感三元组抽取（Aspect Sentiment Triplet Extraction, ASTE）时性能受限的问题。现有方法在单次生成过程中需同时确定方面（aspect）、观点（opinion）和情感极性（sentiment）的边界与关联，导致模型难以有效捕捉复杂语义结构。尽管采用少样本上下文学习或思维链（Chain-of-Thought）提示等策略可带来有限改进，但其效果依赖于特定领域标注数据或精心设计的推理模板，难以在真正的零样本部署中广泛应用。为此，作者提出一种基于多智能体（Multi-Agent）架构的MASTE框架，将ASTE任务分解为四个顺序执行的子任务阶段，每个专用智能体根据前序输出显式条件化处理特定成分，从而实现无需训练的零样本抽取。该设计不仅避免了对标注数据的依赖，还具备良好的泛化能力，适用于不同模型主干与数据集。在四个ASTE基准上的实验表明，MASTE显著优于现有的零样本及思维链基线，大幅缩小了与全监督方法之间的性能差距。
链接: https://arxiv.org/abs/2607.08080
作者: Ao Hong,Lehang Wang,Zhirun Yue,Mingxin Wang,Zihan Wang,Houde Liu
机构: Tsinghua University (清华大学); Wuhan University (武汉大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Aspect Sentiment Triplet Extraction (ASTE) requires jointly identifying (aspect, opinion, sentiment) triples from a given review sentence. While large language models (LLMs) achieve strong zero-shot performance on many NLP benchmarks, their effectiveness on ASTE remains limited, as single-pass generation forces the model to determine span boundaries, opinion grouping, and sentiment polarity in a single decoding step. Common remedies, such as few-shot in-context learning and chain-of-thought prompting, offer only marginal improvements and rely heavily on either in-domain demonstrations sampled from labeled training data or carefully engineered reasoning prompts, neither of which is broadly available in zero-shot deployment. Inspired by the classical agent paradigm, we propose MASTE, a multi-agent pipeline for zero-shot Aspect Sentiment Triplet Extraction. MASTE decomposes ASTE into four sequential stages, where specialized agents handle different compositional subtasks with explicit conditioning on prior outputs. This design enables entirely training-free zero-shot ASTE and generalizes across different backbones and datasets. Extensive experiments on four ASTE benchmarks show that MASTE substantially outperforms zero-shot and chain-of-thought LLM baselines under the same backbone, narrowing the gap to fully supervised methods without using any labeled triplets. Code is available at this https URL.
[NLP-31] COBART: Controlled Optimized Bidirectional and Auto-Regressive Transformer for Ad Headline Generation KDD’22 KDD
【速读】： 该论文旨在解决在线广告中创意文案（ad headlines）生成面临的个性化与格式适配难题，尤其针对不断演变的广告形式及动态变化的创意需求，现有方法难以持续生成高点击率（CTR）且符合特定格式要求的定制化标题。其解决方案的关键在于引入前缀控制令牌（prefix control tokens）结合BART模型微调，通过可调控的输入前缀实现对生成标题长度的精确控制，从而适配不同广告格式；同时该方法具备高度灵活性，可扩展至多种模型架构、创意需求及优化目标。实验结果表明，该方法在Rouge-L指标上提升25.82%，估计CTR提高5.82%，显著优于已有强基线。
链接: https://arxiv.org/abs/2607.08071
作者: Yashal Shakti Kanungo,Gyanendra Das,Pooja A,Sumit Negi
机构: Amazon(亚马逊); Amazon(亚马逊); Amazon(亚马逊); Amazon(亚马逊)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 10 pages, 5 figures, 5 tables. Published in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22). This is the author’s accepted version; the definitive Version of Record is available at this https URL
Abstract:Online ads are essential to all businesses and ad headlines are one of their core creative component. Existing methods can generate headlines automatically and also optimize their click-through-rate (CTR) and quality. However, evolving ad formats and changing creative requirements make it difficult to generate optimized customized headlines. We propose a novel method that uses prefix control tokens along with BART fine-tuning. It yields the highest CTR and also allows users to control the length of generated headlines for use across different ad formats. The method is also flexible and can easily be adapted to other architectures, creative requirements and optimization criteria. Our experiments demonstrate a 25.82% increment in Rouge-L and a 5.82% increment in estimated CTR over previously published strong ad headline generation baseline.
[NLP-32] Holographic Neural PCFG for Unsupervised Parsing
【速读】： 该论文旨在解决无监督成分句法分析中，现有神经化概率上下文无关文法（Neural PCFG）模型依赖高容量黑箱网络进行规则评分，导致规则概率缺乏可解释数学形式的问题。其核心解决方案是提出全息神经PCFG（Hol-PCFG），将规则评分重构为语法符号嵌入在环面约束空间中的代数关系建模，通过引入全息嵌入（Holographic Embeddings）机制，利用循环相关运算对左子节点、右子节点及词项发射关系进行建模，从而为每条规则概率提供具有内在语法结构的闭式表达。该方法在六种语言上达到当前最优句法分析性能，同时相较基线模型将规则评分参数量减少99.94%，并显著提升训练稳定性；此外，还验证了其可直接对日语字符级输入进行解析，无需形态分割，在保持接近词素级别性能的前提下实现端到端处理。
链接: https://arxiv.org/abs/2607.08063
作者: Ryosuke Yamaki,Daichi Mochihashi,Nobutaka Shimada,Tadahiro Taniguchi
机构: Ritsumeikan University (立命館大学); The Institute of Statistical Mathematics (統計数理研究所); Kyoto University (京都大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Preprint under review
Abstract:Unsupervised constituency parsing aims to accurately induce latent tree structures from raw text alone. Recent neural parameterizations of PCFGs achieve strong performance in both supervised and unsupervised parsing, yet rely on high-capacity black-box networks for rule scoring – as exemplified by the Neural PCFG family – leaving rule probabilities without an interpretable mathematical form. In this paper, we propose Holographic Neural PCFG (Hol-PCFG), which recasts PCFG rule scoring as algebraic relation modeling among grammar-symbol embeddings. Hol-PCFG adapts Holographic Embeddings (Nickel et al., 2016), which scores knowledge-graph triples via circular correlation, to the left-child, right-child, and lexical-emission relations over torus-constrained embeddings, giving every rule probability a closed form that carries the intrinsic structure of grammar rules by construction. Hol-PCFG achieves state-of-the-art parsing performance in six languages while cutting rule-scoring parameters by 99.94% relative to the baseline model and training more stably. Additionally, we demonstrate that Hol-PCFG can parse Japanese directly from characters without any morphological segmentation, retaining nearly the same morpheme-level performance.
[NLP-33] owards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization ACL2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）推理服务中因关键值（Key-Value, KV）缓存导致的内存密集与高成本问题。随着LLM在生成式AI（Generative AI）应用中的广泛部署，其自回归解码过程中的KV缓存占用大量显存，成为制约系统吞吐量与延迟优化的关键瓶颈。本文提出从系统行为视角重构对LLM服务中KV基础设施（system-aware KV infrastructure for serving LLMs, sKis）的理解，将现有研究归纳为三个维度：执行与调度（时间维度）、放置与迁移（空间维度）以及表示与保留（结构维度）。其解决方案的核心在于通过跨维度协同设计（cross-behavior co-design），揭示不同系统行为之间的耦合关系与目标关联性，从而为高效KV缓存管理提供系统化设计范式。该工作不仅梳理了快速演进的KV缓存技术体系，更为未来面向低延迟、高吞吐的LLM服务基础设施创新奠定了理论基础。
链接: https://arxiv.org/abs/2607.08057
作者: Jiantong Jiang,Peiyu Yang,Rui Zhang,Feng Liu
机构: The University of Melbourne (墨尔本大学); Huazhong University of Science and Technology (华中科技大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted to ACL 2026 as a Findings paper
Abstract:Despite the rapid advancements of large language models (LLMs), LLM serving systems remain memory-intensive and costly. The key-value (KV) cache, which stores KV tensors during autoregressive decoding, is crucial for enabling low-latency, high-throughput LLM inference serving. In this survey, we focus on system-aware KV infrastructure for serving LLMs (abbreviated as sKis). We revisit recent work from a system behavior perspective, organizing existing efforts into three dimensions: execution and scheduling (temporal), placement and migration (spatial), and representation and retention (structural). Furthermore, we analyze cross-behavior co-design affinity and behavior-objective links, highlighting future opportunities. Our work systematizes a rapidly evolving area, providing a foundation for understanding and innovating KV cache designs in modern LLM serving infrastructure.
[NLP-34] What LLM Forecasters Know but Dont Say: Probing Internal Representations for Calibration and Faithfulness
【速读】： 该论文旨在解决大语言模型在预测任务中虽具备较高准确性但存在校准不足（calibration）以及链式思维（Chain-of-Thought, CoT）推理过程与实际证据脱节的问题。其核心解决方案在于通过分析模型内部表示（internal representations），利用中间激活层训练表示池化探测器（representation-pooling probes），以更直接、可靠地揭示模型预测背后的认知状态。研究表明，这些探测器不仅显著提升了预测的校准性能（在Eternis-Forecaster 8B、GLM-4.7-Flash和GLM-4.5-Air上均有效），还能作为“谎言检测器”：其激活值对输入扰动引发的行为变化敏感度远超CoT推理轨迹，并可在84%的情况下准确预测预测结果的变化方向，即使CoT刻意掩盖扰动影响。此外，强制回答实验表明，模型的预测结果在推理开始前已基本确定——一次预推理前向传播即可恢复最终答案与置信度，基于此预设答案分布进行路由可减少30%-47%的生成令牌，且不损失精度。综上，该研究证明了对内部表示进行探针分析是一种可用于校准、审计与优先级排序语言模型预测行为的实用方法，适用于更广泛的推理模型。
链接: https://arxiv.org/abs/2607.08046
作者: Raphaël Sarfati,Pratyush Ranjan Tiwari,Siddharth Boppana,Christopher J. Earls,Srikar Varadaraj,Eric Ho
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models fine-tuned for forecasting can be accurate yet poorly calibrated, and their chain-of-thought (CoT) reasoning may not faithfully reflect the evidence behind a forecast. We ask whether internal representations offer a more direct window into both. Working with Eternis-Forecaster 8B on OpenForesight, we train representation-pooling probes on intermediate activations and find they achieve substantially better calibration; a result that also holds for GLM-4.7-Flash and GLM-4.5-Air. We then assess CoT faithfulness through evidence ablation and diversionary injection: removing an influential source in the prompt often changes the model’s forecast while leaving the reasoning trace untouched. The same probes function as lie detectors: their activations track behavioral shifts far better than the reasoning trace does, and they also predict the direction of change in 84% of cases, including when the CoT conceals the perturbation’s influence. Finally, forced answering reveals that forecasts are largely fixed before reasoning begins: a single pre-reasoning pass recovers the committed answer and confidence, and routing questions by the spread of this pre-set answer distribution saves 30-47% of generated tokens, with no loss of accuracy. Together, these results establish probing internal representations as a practical tool for calibrating, auditing, and triaging language model forecasters and reasoning models more broadly.
[NLP-35] PLURAL: A Global Dataset for Value Alignment
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在价值观表达上严重偏向西方文化，难以有效代表全球多元价值体系的问题。其核心挑战在于现有模型训练数据缺乏对非西方社会价值观念的充分覆盖与表征，导致模型输出存在文化偏差。为此，研究提出PLURAL——一个大规模、以价值观为导向的偏好数据集，基于涵盖92个国家的全国代表性调查工具“综合价值观调查”（Integrated Values Survey, IVS），通过两阶段生成流水线将原始调查回应转化为合成的偏好三元组（preference triplets），在保留规范性价值信号的同时生成具有现实情境感的文本。该数据集的关键创新在于：其一，通过结构化转换机制实现跨国家间价值差异与国家内部多样性在数据中的忠实再现；其二，利用真实世界调查数据构建的语义与价值分布，使模型可通过微调实现对特定国家文化特征的精准对齐。实验验证表明，使用PLURAL进行训练可显著提升模型与目标国家文化画像的一致性，相较强基线平均绝对误差降低最高达27.7%，且在印度、巴西和日本的盲测人类评估中，参与者普遍认为经PLURAL对齐的响应更准确反映其本国价值观。因此，PLURAL的核心贡献在于提供了一个可扩展、可学习的价值引导资源，为实现多文化语境下的语言模型对齐提供了系统性解决方案。
链接: https://arxiv.org/abs/2607.08034
作者: Dhruv Agarwal,Anya Shukla,Tanya Goyal,Aditya Vashistha
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Large language models (LLMs) are used worldwide, yet disproportionately reflect Western values, limiting their ability to represent diverse value systems. We introduce PLURAL, a large-scale, value-focused preference dataset grounded in the Integrated Values Survey (IVS), a nationally representative survey spanning 92 countries. Using a two-stage generation pipeline, we transform survey responses into synthetic preference triplets that preserve normative value signals while producing realistic scenarios. We release an initial version of PLURAL containing ~500,000 preference triplets representing people in 20 diverse countries. We evaluate PLURAL in three ways: (i) dataset-level validation showing that it preserves both cross-country value differences and within-country diversity from the original survey; (ii) automated evaluation showing that training on PLURAL improves alignment with target countries’ cultural profiles, reducing mean absolute error by up to 27.7% relative to strong baselines; and (iii) blind human evaluation with 176 evaluators in India, Brazil, and Japan, who judge PLURAL-aligned responses as more representative of their national values. Together, these results show that PLURAL contains learnable signal for value steering, offering a scalable resource for pluralistic alignment. Dataset: this https URL
[NLP-36] From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents
【速读】： 该论文旨在解决企业级大语言模型（LLM）应用从原型阶段向可产品化部署过程中面临的可追溯性、可审计性与系统可靠性问题。具体而言，现有基于提示（prompt）和检索上下文驱动的原型系统在规模化应用时，难以保证源数据边界、实体路由、答案契约、输出纯净性及行为可复现性等关键需求。其核心解决方案是提出一种“夹具工程”（harness-engineering）方法，将原本依赖提示的非确定性行为重构为以代码为核心的确定性架构：通过在可替换的组合边界（composition boundary）周围引入版本化的规范（manifests）、模式（schemas）与验证（validation） artifacts，实现对源数据支撑的声明（source-backed claims）的权威性保留，同时确保运行时答案的合规性。关键创新在于将保障机制从提示层面转移到代码层面，使得安全性和可用性得以兼顾——实验表明，仅靠提示无法有效防止推荐语言违规和内部追踪泄露，而代码所有（code-owned）的强制检查能完全阻断此类风险，且在模型替换、故障注入等测试场景下均保持高鲁棒性，显著优于外部防护层（over-refuse导致效用下降）。最终形成一套可复用的工程范式，支持将探索性原型转化为具备版本化源、控制与验证能力的可审计应用。
链接: https://arxiv.org/abs/2607.08028
作者: Joongho Ahn,Moonsoo Kim
机构: AI Leadership Research Center (AI领导力研究中心)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE)
备注: 32 pages, 6 figures, 16 tables. Reference implementation and evaluation artifacts: this https URL (archived at this https URL )
Abstract:Enterprise large language model (LLM) applications often begin as prototypes whose behavior is carried by prompts and retrieval context. Productization adds requirements for source boundaries, entity routing, answer contracts, and reproducible traces. We present a harness-engineering approach that reconstructs this pattern into a traceable, auditable LLM-agent architecture: deterministic behavior moves into code, manifests, schemas, and validation artifacts around a replaceable composition boundary, while source-backed claims remain the authority for runtime answers. We instantiate it on a public-data slice of five Korean corporate groups (25 listed companies) and evaluate three research questions. (1) The harness preserves its source-grounding, entity-routing, trace, output-hygiene, and recommendation-language contracts across the fixed validation scenarios; a fault-injection control confirms the validators flag deliberately broken contracts. (2) The checks the harness enforces held under model substitution: across three hosted models, they passed on all 270 composition-boundary runs; failures were confined to the model-composed side and were caught and recorded. (3) The code-owned guarantees are load-bearing, not reproducible by prompting alone: holding the model fixed and varying only the enforcement layer, prompt instructions alone let recommendation-language and internal-trace-leakage violations reach the reader, which the harness blocks entirely. A bolt-on external guardrail prevents such violations too but over-refuses, dropping utility to 88/120 where the harness preserves full utility (120/120); in this ablation, only code-owned enforcement preserves both safety and utility. The result is a reusable engineering pattern for turning exploratory prototypes into auditable applications with versioned source, control, and validation artifacts.
[NLP-37] Structured Pruning of Large Language Models via Power Transformation and Sign-Preserving Score Aggregation with Adaptive Feature Retention
【速读】： 该论文旨在解决将非结构化剪枝技术自适应特征保留（Adaptive Feature Retention, AFR）应用于结构化剪枝时所面临的三大核心挑战：异构剪枝分数间的分布不匹配、优化方向一致性信息（符号信息）的丢失，以及异常值对剪枝决策的负面影响。其解决方案的关键在于提出一种统一框架，包含三个核心机制：通过幂变换实现非线性分布对齐以缓解分布不匹配问题；采用保持符号一致性的分数聚合策略以保留优化方向信息；以及基于百分位数的异常值剔除方法以降低极端值干扰。实验在Llama-3-8B、Vicuna-v1.5-13B和LLaVA-v1.5-13B模型上验证了该方法在保持与非结构化剪枝相当精度的同时，实现了可实用的推理加速效果。
链接: https://arxiv.org/abs/2607.08027
作者: Ryota Kobayashi,Tsubasa Hirakawa,Takayoshi Yamashita,Hironobu Fujiyoshi,Yasunori Ishii,Tomoyuki Okuno,Kazuki Kozuka
机构: Chubu University (中部大学); Panasonic Holdings Corporation (松下控股公司)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper proposes an improved structured pruning method for large language models (LLMs) that addresses key challenges in adapting Adaptive Feature Retention (AFR), an unstructured pruning technique, to structured pruning. When applying AFR to structured pruning, three major problems arise: distribution mismatch between heterogeneous pruning scores, loss of sign information indicating optimization direction consistency, and influence of outliers. To address these issues, we propose a unified approach combining power transformation for nonlinear distribution alignment, sign-preserving score aggregation, and percentile-based outlier removal. Experiments on Llama-3-8B, Vicuna-v1.5-13B, and LLaVA-v1.5-13B demonstrate that our method maintains accuracy comparable to unstructured pruning while achieving practical inference speedup through structured pruning.
[NLP-38] Can We Trust LLM s Logic? Quantifying Uncertainty Coherence and Robustness via a Graph-Based Framework
【速读】： 该论文旨在解决大语言模型（Large-Language Models, LLMs）在推理过程中存在缺陷且缺乏忠实性的问题，尤其针对传统自一致性（Self-Consistency, SC）策略仅依赖最终答案一致性而忽略中间推理步骤逻辑有效性的局限性。其核心挑战在于如何可靠地量化模型推理中的不确定性，并提升对推理路径真实性的评估能力。解决方案的关键在于提出GRAPHEVAL——一种基于图结构的推理评估框架，将不确定性量化（Uncertainty Quantification, UQ）重新定义为一个整体的推理保真度问题。该框架引入了一种新型度量指标：图推理一致性得分（Graph Reasoning Coherence Score, GRCS），通过捕捉推理空间中的语义-结构一致性，有效识别病态模式坍缩与自信幻觉现象。研究发现，GRCS是唯一在不同规模模型中均与推理保真度呈现稳定负相关的指标。此外，论文提出了图自一致性（Graph Self-Consistency, GSC）这一基于中位数（medoid）的解码策略，以牺牲少量名义准确率为代价换取更高的推理保真度，揭示了小模型中传统SC因“幸运猜测”导致的性能虚高现象，同时在更强大的模型中保持或提升准确率。最后，通过对抗性中位数消融实验，验证了GSC所选路径具有“承重路径”特性，强制偏离该路径会显著降低推理保真度，甚至引发准确率下降，从而证明其在保障推理可靠性方面的关键作用。
链接: https://arxiv.org/abs/2607.08017
作者: Riccardo Revalor,Jalees Rehman,Debjit Pal
机构: University of Illinois Chicago (芝加哥大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 42 pages, 14 figures, 12 tables
Abstract:Large-Language Models (LLMs) can be prone to flawed and unfaithful reasoning that decoding strategies like Self-Consistency (SC) fail to detect as they evaluate only final-answer agreement while ignoring the logical validity of intermediate steps. This raises three fundamental questions: How can we reliably quantify uncertainty in LLM reasoning? Can semantic, structural, and causal awareness select more faithful reasoning compared to naïve majority voting? and How robust is reasoning topology under adversarial conditions? To address these questions, we introduce GRAPHEVAL, a graph-based reasoning framework that re-frames uncertainty quantification (UQ) as a holistic reasoning fidelity problem. We propose a novel UQ metric, Graph Reasoning Coherence Score (GRCS), that quantifies semantic-structural consensus of the reasoning space and captures pathological mode collapse and confident hallucinations. We find that GRCS is the only metric that is consistently negatively correlated with reasoning faithfulness across both more capable and smaller models. Additionally, we introduce Graph Self-Consistency (GSC), a medoid-based decoding strategy that trades nominal accuracy for reasoning fidelity, exposing the degree to which SC is inflated by unfaithful lucky guesses in smaller models, while preserving or improving accuracy in more capable ones. Finally, through adversarial medoid ablation, we demonstrate that the GSC-selected path acts as a “load-bearing path” and forcing models away from it degrades reasoning faithfulness and, in targeted cases, causes drops in accuracy.
[NLP-39] ool-Making and Self-Evolving LLM Agents in Low-Latency Systems
【速读】： 该论文旨在解决生产环境中大语言模型（LLM）代理在每次请求时重复生成相同流程步骤代码所导致的延迟增加与可靠性下降问题。其核心解决方案是引入一种代理工具生成（agentic tool-making）流水线，将频繁执行的标准操作流程（SOP）步骤在部署前编译为经过验证、版本化的工具。该工具生成过程通过实时环境中的执行轨迹、后端数据模式与数值观察，自动合成候选工具并基于标注样本进行修复，从而实现对生成逻辑的闭环优化。运行时，代理直接调用已构建的工具，仅在必要时才回退至代码生成。在实际部署的履约中心告警诊断系统中，该方法使中位数延迟降低42%，并在1500个历史告警上将端到端错误率最高降低53%，有效抑制了重复步骤的运行波动。此外，由于工具返回紧凑的结构化判断结果，可支持更简化的直接调用架构，进一步降低中位数延迟62%。版本化工具还增强了系统的可审计性，揭示了规范缺口与上游数据漂移问题。实验表明，自演化代理能够显著提升工业级LLM系统的性能、可靠性与可运维性。
链接: https://arxiv.org/abs/2607.08010
作者: Kalle Kujanpää,Ning Liu,Shahnawaz Alam,Yeshwanth Reddy Sura,Tianyu Yang,Kristina Klinkner,Shervin Malmasi
机构: Amazon, Fulfillment Technologies Robotics
类目: Computation and Language (cs.CL); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注: Preprint
Abstract:Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an agentic tool-making pipeline that compiles repeated SOP steps into validated, versioned tools before deployment. The tool-maker grounds synthesis in the live environment as it collects execution traces, observes backend schemas and values, generates candidate tools, and repairs them against labeled cases. At runtime, the production agent calls these tools directly and falls back to code generation only when needed. We deploy the approach in a Fulfillment Center alarm-triage system, where an agent diagnoses alarms against a 44-node SOP over heterogeneous metric backends. In production, tool calls reduce p50 latency by 42%. On 1,500 historical alarms, they reduce end-to-end error rate by up to 53% by suppressing run-to-run variance in repeated steps. Because tools return compact structured verdicts, they also enable a simpler direct-call architecture, reducing p50 latency by a further 62% in a controlled ablation. Versioned tools also improve auditability and expose specification gaps and upstream data drift. Our results show that self-evolving agents can make industrial LLM systems faster, more reliable, and easier to operate.
[NLP-40] From Execution to Education: A Bloom-Aligned Framework for Measuring Educational Control in LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在教育应用中缺乏对认知需求精准调控的问题，即模型虽能完成编程任务，却难以根据特定学习目标（learning objectives）有效调整任务的认知难度以适配不同层次的学习者。其核心解决方案是提出一个与布卢姆分类学（Bloom’s Taxonomy）对齐的评估框架，通过将布卢姆分类学作为认知需求的操作化量表，系统评估模型在两类干预设置下的表现：一是通用难度控制（general difficulty control），即调整任务难易程度；二是布卢姆层级控制（Bloom’s control），即有目的地引导任务向更高或更低的认知层次迁移。研究基于三个基准数据集对一对匹配的Qwen3模型进行对比分析，结果揭示出显著的方向性不对称性——模型能够稳定提升任务的认知需求，但在降低认知需求方面表现乏力。进一步通过语义差分聚类（semantic-delta clustering）和逐层费希尔判别比（layer-wise Fisher’s Discriminant Ratio probing）分析发现，通用模型在中间层对两类控制对比表现出更清晰的可分性，而代码专用模型在通用难度控制上可分性较弱，但在布卢姆层级控制上呈现更深的特征区分峰值。研究表明，强大的执行能力并不自动等同于符合布卢姆分类学的教育控制能力，强调了在教育型AI设计中引入显式认知目标对齐机制的重要性。
链接: https://arxiv.org/abs/2607.08009
作者: Yi Zhang,Julia Rayz
机构: Purdue University (普渡大学)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 24 pages, 20 figures
Abstract:We introduce a Bloom-aligned framework for measuring educational control in Large Language Models (LLMs): the ability to preserve a task’s instructional intent while shifting its cognitive demand toward specified learning objectives. We apply this framework to programming tasks in computer science education to study the gap between solving tasks and adapting them for learners. Using revised Bloom’s Taxonomy as an operational scale of cognitive demand, we evaluate two intervention settings: general difficulty control, where models are asked to make tasks harder or easier, and Bloom’s control, where models are asked to target higher or lower Bloom’s levels. We evaluate a matched Qwen3-Next model pair, comparing Qwen3-Next-80B-A3B-Instruct with Qwen3-Coder-Next across 2,520 tasks from three benchmarks. The framework reveals a robust directional asymmetry: both models reliably increase cognitive demand, but struggle to lower it. We further characterize these outcomes with semantic-delta clustering and layer-wise Fisher’s Discriminant Ratio probing. Within this controlled comparison, the general model shows clearer middle-layer separability for both general difficulty and Bloom-control contrasts, whereas the coder model shows weaker separability for general difficulty and a deeper peak for Bloom-control contrasts. These results show that strong execution performance does not automatically entail Bloom-aligned educational control.
[NLP-41] Hallucination Self-Play: Bootstrapping Reinforced Detector via Evolved Generator
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）生成内容中难以识别的忠实性幻觉（faithfulness hallucination）问题，其核心挑战在于高质量标注数据的稀缺性。现有方法虽利用先进大模型自动生成训练数据（包括推理过程、标签和幻觉陈述），但通常将生成器视为静态组件，无法实现检测器的持续迭代优化。为此，本文提出一种名为“幻觉自对弈”（Hallucination Self-Play, HSP）的新框架，其关键在于构建一个动态演化的生成器与检测器协同进化机制：初始时，二者均源自同一基础模型；首先用人工标注数据微调检测器，随后将其作为奖励模型，通过人工智能反馈强化学习（Reinforcement Learning from AI Feedback, RLAIF）训练生成器，使其产生更难被检测的幻觉响应；继而，该演化后的生成器所生成的幻觉数据用于进一步通过基于规则的强化学习优化检测器。实验在RAGTruth基准及两个模型家族上验证了该框架的有效性，表明无需外部监督即可使小型模型逐步提升至媲美甚至超越大型先进模型的性能。
链接: https://arxiv.org/abs/2607.07993
作者: Shiping Yang,Shining Liang,Weihao Liu,Wenbiao Ding,Linjun Shou,Lu Cheng,Angel X. Chang
机构: Simon Fraser University (西蒙菲莎大学); Microsoft (微软); University of Illinois at Chicago (伊利诺伊大学芝加哥分校)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted to COLM 2026. Camera-ready version to appear
Abstract:Identifying faithfulness hallucinations in LLM-generated outputs remains challenging due to the scarcity of high-quality annotated data. Recent work relies on advanced LLMs to synthesize training data, including rationales, labels, and hallucinated claims. However, these methods treat the generator as a static component, limiting iterative improvement of the detector. To address this limitation, we introduce Hallucination Self-Play (HSP), a novel framework that enables the detector to bootstrap with an evolved generator. HSP involves two roles initialized from the same base model, a detector that assesses the faithfulness of model outputs, and a generator that produces increasingly hard-to-detect hallucinated responses. Specifically, the detector is first fine-tuned on human-labeled data and then employed as a reward model to train the generator via reinforcement learning from AI feedback (RLAIF). In turn, the evolved generator synthesizes hallucination data to further optimize the detector through rule-based reinforcement learning. Experiments on RAGTruth benchmark and two model families demonstrate that the proposed framework can progressively enhance a small LLM to match or even outperform advanced LLMs without external supervision. Our code is available at this https URL .
[NLP-42] A Reliability Assessment of LALM Audio Judges for Full-Duplex Voice Agents
【速读】： 该论文旨在解决在语音对话评估中依赖人工评分（human raters）所面临的高成本与低效率问题，提出将生成式语音判别模型（Large Audio Language Model, LALM）作为自动评分工具替代或补充人工评价的可行性。其核心挑战在于验证LALM在无监督条件下对全双工对话质量的评分可靠性，尤其是在复杂语境（如不同口音、语言障碍、缺陷注入）下的表现一致性。解决方案的关键在于构建以Gemini 2.5 Flash为基准模型的多维度实证验证体系：通过与三位校准人类评分员在209个立体声会话上的对比，覆盖13种口音与情境组合及57段对抗性缺陷注入片段，评估8项生产级指标；结果显示，在8项指标中有5项的LALM-人类斯皮尔曼等级相关系数（Spearman rho）偏离人类间相关性不超过0.07，且7项指标的95%置信区间存在重叠，表明其相关性水平接近人类；同时在6项指标中，LALM评分与三人平均分偏差在1分以内的情况占比达60%–92%，具备良好的一致性。此外，尽管跨模型迁移显示3.5 Flash在简单一致性上达到全部8项，而3.1 Pro虽保持相似秩相关性但整体评分显著偏低，提示仅依赖秩相关性不足以支持模型替换，必须重新进行专门校准验证。研究进一步指出，使用LALM可使当前评估周期的成本降低两个数量级，为自动化语音质量评估提供了坚实的实证依据。
链接: https://arxiv.org/abs/2607.07985
作者: A. Sayyad,J. Emmons,S. Jones,T. Lin,H. Krishnan
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: 28 pages total (12 main body, 1 reference, 15 appendix). In main body: 2 diagrams, 3 table, 2 charts
Abstract:We report the empirical reliability of Gemini models as audio judges that score full-duplex agent conversations directly from the raw stereo waveform, tested across three models in the Gemini family: 2.5 Flash, 3.5 Flash, and 3.1 Pro. Our primary evidence base uses Gemini 2.5 Flash as the ground-truth model, validated against three calibrated human raters on 209 stereo sessions, scored on 8 production dimensions: 152 full-duplex conversations across 13 accent-and-condition strata, together with 57 adversarial defect-injected clips. The evidence for Gemini 2.5 Flash is consistent across three tests. (i) On 5 of 8 dimensions the LALM-human Spearman rho departs from the pairwise human-human rho by at most 0.07, and on 7 of 8 dimensions the two quantities 95 percent bootstrap confidence intervals overlap. (ii) The LALM agrees with the three-rater human mean within 1 point on 60 to 92 percent of sessions on 6 of 8 dimensions. (iii) On 45 of 48 (defect, dimension) cells the LALM is as sensitive as humans or better under Newcombe-Wilson 95 percent confidence intervals, though most of these are underpowered nulls rather than demonstrated parity. Rank-ordering ability transfers across the Gemini family: 3.5 Flash improves simple agreement to 8 of 8 dimensions, while 3.1 Pro rates several dimensions markedly lower than humans despite comparable rank correlation. A model swap should be re-validated on calibration specifically, not assumed from rank-correlation alone. We identify four areas where deployment requires care, and we estimate that human rating alone for our current evaluation cadence costs roughly two orders of magnitude more than the equivalent LALM workload. The data presented here provides a defensible empirical basis for deploying the LALM as a substitute or fourth rater on the dimensions where the evidence supports it.
[NLP-43] When Implausible Tokens Get Reinforced: Tail-Aware Credit Calibration for LLM Reinforcement Learning
【速读】： 该论文旨在解决当前广泛使用的无评判器强化学习（critic-free reinforcement learning, RL）方法在提升大语言模型（large language models, LLMs）推理能力时存在的关键缺陷——即均匀信用分配（uniform credit assignment）机制导致的“正信用污染”（Positive-Credit Contamination）问题。具体而言，该机制对同一生成轨迹中所有标记（token）赋予相同的正向奖励，使得语义上错误但概率极低的尾部标记（low-probability tail tokens）与合理标记获得同等正向激励，从而无意中强化了错误推理行为。为应对这一问题，论文提出了一种名为尾部感知信用校准（Tail-Aware Credit calibration, TACO）的新方法，其核心在于通过引入局部生成上下文信息计算尾部风险得分（tail-risk score），以区分非预期罕见性与由不确定性驱动的探索行为，并据此动态调节高风险标记的正向信用，而非完全移除其梯度。该设计允许重复出现的有用稀有模式逐步积累正向强化，同时抑制偶然噪声的累积。实验结果表明，TACO在三种不同大语言模型和八个基准测试上均显著优于基于GRPO的基线方法，且在长时程强化学习任务中展现出更强的训练稳定性，支持持续性能提升。
链接: https://arxiv.org/abs/2607.07976
作者: Xiuyi Lou,Zicheng Xu,Yu-Neng Chuang,Hoang Anh Duy Le,Zhaozhuo Xu,Guanchu Wang,Vladimir Braverman
机构: Johns Hopkins University (约翰霍普金斯大学); Rice University (莱斯大学); Workato (工作自动化公司); University of North Carolina at Charlotte (北卡罗来纳大学夏洛特分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Reinforcement learning (RL) has achieved remarkable success in enhancing the reasoning capabilities of large language models (LLMs). However, widely used critic-free RL methods rely on uniform credit assignment, broadcasting the same advantage to all tokens regardless of their differences. We identify a critical failure mode of this design, which we refer to as Positive-Credit Contamination: low-probability tail tokens that are contextually erroneous receive identical positive credit to plausible ones within the same trajectory, resulting in the indiscriminate reinforcement of flawed reasoning behavior. To mitigate this issue, we propose Tail-Aware Credit calibratiOn (TACO), a method that calibrates uniform credit assignment to suppress undesirable positive updates. TACO first computes a tail-risk score that incorporates the local generation context to assess each token’s risk of falling into the unreliable tail, distinguishing unexpected rarity from uncertainty-driven exploration. TACO then uses this score to tune positive credit for risky tokens without removing their gradients entirely, so that recurring useful rare patterns can accumulate reinforcement while incidental noise is progressively dampened. Experimental results across three LLMs and eight benchmarks show that TACO consistently outperforms GRPO-style baselines. Notably, TACO improves training stability, supporting sustained performance gains in long-horizon RL. The source code is available at: this https URL.
[NLP-44] A Multi-cluster Boundary Learning Method for Out-of-Scope Intent Detection via MiniLM Embedding
【速读】： 该论文旨在解决人机交互系统中意图检测（intent detection）面临的域外意图（out-of-scope, OOS）识别难题。现有方法存在两大局限：其一，传统多分类方法将OOS检测视为多类分类任务，随着已知意图类别数量增加，分类准确率显著下降；其二，基于大语言模型（LLM）的嵌入方法虽具备较强表征能力，但因参数量庞大，导致训练与实际部署困难。为此，本文提出一种多聚类边界学习方法（multi-cluster boundary learning method），在单类分类（one-class classification）框架下，利用轻量级的MiniLM嵌入（即all-MiniLM-L6-v2）实现高效OOS检测。该方法的核心在于：从训练语句中生成的多聚类嵌入空间中学习边界，通过建模已知意图的分布特征，有效识别并拒绝来自域外的语句为OOS意图。在公开数据集CLINC150、StackOverflow和Banking77上的实验表明，该方法在OOS意图检测性能上达到当前最优水平；消融实验证明，MiniLM嵌入在适配该工作流程及语句表征需求方面表现更优。
链接: https://arxiv.org/abs/2607.07974
作者: Yihong Xu,Mingyu Kang,Linyuan Lü
机构: University of Science and Technology of China (中国科学技术大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: To submit
Abstract:Intent detection is a critical task that bridges human intents and system actions in human-machine interaction systems. However, there still exist challenges for detecting out-of-scope (OOS) intents. (i) The traditional methods view the OOS intent detection as a multi-class classification, then the detection accuracy decreases as the class number of the known intents increases; (ii) LLM-embedding methods require large parameters, that makes them difficult to train and practically deploy. Thus, this work proposes a multi-cluster boundary learning method to detect OOS intents via MiniLM embedding (i.e., all-MiniLM-L6-v2) in an one-class classification workflow. The method learns the boundaries of multi-cluster embeddings generated by MiniLM from the training utterances, and then rejects the out-of-domain utterances as OOS intents. Experiments are conducted on public CLINC150, StackOverflow and Banking77 datasets. The results show that the method achieves the state-of-the-art OOS intent detection performance compared the other baselines. Ablation studies are also conducted and the results show that the used MiniLM can better adapt to the workflow and utterance embedding requirements. The code is available at supplementary materials.
[NLP-45] he Memory Wall of Green Software: Empirical Energy Evaluation of Memento Design Pattern
【速读】： 该论文旨在解决软件设计模式在追求结构完整性的同时，因抽象层级带来的隐性“代谢成本”对能源效率造成的负面影响问题。其核心挑战在于如何在保证设计质量的前提下，实现绿色软件工程中日益关键的能效优化目标。解决方案的关键在于通过高精度硬件遥测（基于RAPL接口）对备忘录模式（Memento design pattern）的三种实现策略——直接基线、经典全快照与差分增量编码——进行实证分析，量化不同状态规模（10–200 MB）下的能耗表现。研究发现，差分策略虽能在中等规模状态下将内存访问量降至最低，实现高达65.8%的能耗降低，但在200 MB规模下遭遇严重的“内存墙”效应，导致垃圾回收（GC）抖动加剧与非线性功耗飙升，使算法优化效果完全失效。由此提出基于实证数据的启发式框架，为架构师在设计质量与可持续绿色计算之间提供可操作的权衡依据。
链接: https://arxiv.org/abs/2607.07944
作者: Imane Jriri,Tarik Houichime,Younes El Amrani
机构: 未知
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注:
Abstract:As Green Software Engineering matures, energy efficiency has transitioned into a mission-critical non-functional requirement. While software design patterns ensure structural integrity, their inherent abstraction layers impose an implicit “metabolic cost” that often remains obscured during the design phase. This paper empirically investigates the energy dynamics of the Memento design pattern, contrasting a direct, unabstracted baseline against Classic full-snapshot and Differential delta-encoding strategies. Leveraging the RAPL interface for high-fidelity hardware telemetry, we quantify energy dissipation across state volumes scaling from 10 MB to 200 MB. Our empirical results expose a critical architectural trade-off: the Differential strategy minimizes memory traffic, yielding a maximum energy reduction of 65.8% for mid-scale states, but collides with a catastrophic “memory wall” at 200 MB. At this saturation point, algorithmic optimizations are completely neutralized by severe GC thrashing and non-linear power spikes. We synthesize these findings into evidence-based heuristics, providing architects with a robust framework to reconcile structural design quality with sustainable Green IT imperatives.
[NLP-46] When Debiasing Backfires: Counterintuitive Side Effects of Preprocessing-Based Stereotype Mitigation ACL2026
【速读】： 该论文旨在解决预处理型去偏方法在自然语言处理（NLP）中用于缓解刻板印象时所引发的未预期副作用问题。尽管这些方法（如基于去偏语料库的预训练或后训练）能够有效降低对特定目标群体的可测量刻板印象，但研究发现其常导致其他非目标群体（包括无关的人口统计类别）出现新的刻板化或反刻板化现象，即刻板印象的转移与增强。解决方案的关键在于识别并量化这些隐蔽的副作用，揭示其在不同模型架构（仅编码器与仅解码器）、多种预处理策略（移除刻板句子、移除群体提及、替换群体指称）以及不同数据规模下均普遍存在，且标准评估基准难以捕捉此类变化。通过注意力传播（attention-rollout）分析进一步表明，这些副作用并未伴随显著的注意力流改变，增加了机制解释的复杂性。因此，论文强调应建立具备副作用感知能力的透明评估与诊断机制，推动更负责任的去偏实践。
链接: https://arxiv.org/abs/2607.07937
作者: Yahan Zheng,John Guerrerio,Soroush Vosoughi,Weicheng Ma
机构: Dartmouth College; Oakland University
类目: Computation and Language (cs.CL)
备注: Published in ACL 2026 Findings
Abstract:Preprocessing-based methods for stereotype mitigation, such as pre-/post-training on debiased corpora, are widely used in NLP. While these approaches reduce measurable stereotypes for targeted groups, we find they often induce unintended shifts-side effects, where stereotyping or counter-stereotyping can increase relative to neutral baselines for other demographics, including across unrelated demographic categories. We demonstrate these side effects across two model families (encoder-only and decoder-only), multiple preprocessing strategies (removing stereotypical sentences, removing group mentions, and swapping group references), and both pre- and post-training at different data scales on Wikipedia. Standard benchmarks frequently miss these shifts. Using attention-rollout analysis, we observe that such side effects are not accompanied by large changes in attention flow, complicating mechanistic explanations. We discuss implications for evaluation, provide actionable diagnostics, and argue for side-effect-aware, transparent mitigation practices.
[NLP-47] Efficient Safety Alignment of Language Models via Latent Personality Traits
【速读】： 该论文旨在解决大语言模型在安全防御方面对对抗攻击（adversarial attacks）的脆弱性问题，尤其针对现有防御方法如隐空间对抗训练（Latent Adversarial Training, LAT）存在性能退化及需依赖大量有害提示数据进行训练的局限。其核心解决方案是提出一种名为隐空间人格对齐（Latent Personality Alignment, LPA）的新方法，关键在于通过仅使用66条无害（harm-agnostic）的人格心理量表语句作为训练样本，基于人格特质的潜在表示进行对抗性稳定化。该方法假设人格锚定的潜在表征与有害内容规避具有共享的隐空间结构，因此通过对这些非有害语句进行对抗训练，可隐式约束被越狱攻击（jailbreak attacks）利用的模型子空间。实验表明，LPA在HarmBench基准上对直接请求及五种越狱方法均实现接近零的攻击成功率，且训练过程中未接触任何有害内容，标准基准性能无损失。此外，该方法训练高效，单块GPU上仅需数分钟即可完成，所用样本量仅为传统LAT的1/75。大量消融实验验证了该方法在鲁棒性、效率与泛化能力上的显著优势。
链接: https://arxiv.org/abs/2607.07918
作者: Mohamed Amine Merzouk,Nolan Smyth,Damiano Fornasiere,Linh Le,David Williams-King,Adam Oberman
机构: Mila, Quebec AI Institute (Mila, 魁北克人工智能研究所); McGill University (麦吉尔大学); LawZero; Université de Montréal (蒙特利尔大学); Independent (独立)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
备注: 15 pages, 6 figures. Accepted at COLM 2026
Abstract:Current safety methods for large language models are known to be vulnerable to adversarial attacks, motivating research into robust alternatives. Latent Adversarial Training (LAT) is among the most effective defenses, but can degrade utility and requires training on large datasets of harmful prompts. We introduce Latent Personality Alignment (LPA), which replaces explicit harm refusal with adversarial training on just 66 harm-agnostic statements drawn from psychometric personality literature. We hypothesize that personality-anchored representations share latent structure with harm avoidance, so adversarially stabilizing them implicitly constrains the subspace exploited by jailbreak attacks. LPA achieves near-zero attack success rates on HarmBench across direct requests and five jailbreak methods, despite never seeing harmful content during training and no loss of performance on standard benchmarks. Moreover, the training process is lightweight; the entire procedure completes in minutes on a single GPU and uses 75x fewer examples than standard LAT. Extensive ablations demonstrate the robustness, efficiency, and generalization of our method.
[NLP-48] Validating LLM s in social science: Epistemic threats and emerging norms
【速读】： 该论文旨在解决生成式 AI（Generative AI）在社会科学实证研究中作为测量工具时所面临的信度与效度挑战，特别是模型固有的偏见、幻觉现象及跨情境脆弱性对研究结果可信度的威胁。其解决方案的关键在于系统识别并整合多样化的验证策略，通过分析八种顶级社会科学期刊中使用生成式 AI 的实证研究，揭示当前验证实践的不一致性和局限性，并提出互补性的增强验证方法，以推动建立更规范、可靠的生成式 AI 应用标准与研究范式。
链接: https://arxiv.org/abs/2607.07915
作者: Meera Desai,Dallas Card,Abigail Z. Jacobs
机构: University of Michigan(密歇根大学); Center for the Study of Complex Systems(复杂系统研究中心)
类目: Computers and Society (cs.CY); Computation and Language (cs.CL)
备注: 28 pages, 2 figures. Main text: 11 pages, Appendix: 11 pages, References: 6 pages
Abstract:Large language models (LLMs) are reshaping social science methodology. Researchers increasingly prompt language models to generate quantitative measurements of social concepts, for example labeling data or simulating survey responses. Yet LLMs pose methodological challenges including bias, hallucination, and brittleness across contexts, with unclear threats to validity. Standard practices and norms for addressing these challenges are still emerging. We collect and systematically analyze validation practices in a comprehensive corpus of papers from eight flagship social science journals that use LLMs as measurement instruments. We find that LLM-generated measurements frequently play a central role in empirical analyses, yet validation practices are inconsistent and limited. We outline complementary strategies for more robust validation, pointing toward better norms and standards around the use of LLMs in social science.
[NLP-49] Multimodal Unlearning Across Vision Language Video and Audio: Survey of Methods Datasets and Benchmarks ACL
【速读】： 该论文旨在解决多模态基础模型（Multimodal Foundation Models, MFMs）在训练过程中无意间编码了敏感、受版权保护、存在偏见或不安全的跨模态关联这一关键问题。由于这些模型的知识分布于共享表示中，传统通过重新训练来响应删除请求或政策更新的方式往往不可行，且目标遗忘（targeted forgetting）难以实现。为此，论文提出多模态去学习（Multimodal Unlearning）作为解决方案，其核心在于实现跨模态的可选择性知识移除，同时保持模型整体性能与实用性。该方案的关键在于构建一种系统化的方法框架，能够针对不同模态（视觉、语言、音频、视频）实现精确、高效且可逆的遗忘操作，兼顾删除强度、保留能力、计算效率、鲁棒性等多重权衡。论文通过建立统一的分类体系，支持对不同模型架构与模态间的比较分析，并揭示当前开放问题与实际部署中的挑战，为未来研究提供理论指导与实践参考。研究团队还发布了配套的精选代码库以促进社区发展。
链接: https://arxiv.org/abs/2607.07907
作者: Nobin Sarwar,Shubhashis Roy Dipta,Zheyuan Liu,Vaidehi Patil
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR); Multimedia (cs.MM)
备注: Accepted to ACL Findings 2026
Abstract:With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associations that originate from their training data. Retraining after deletion requests or policy updates is often impractical, and targeted forgetting remains difficult because knowledge is distributed across shared representations. Multimodal unlearning addresses this challenge by enabling selective removal across modalities while retaining overall utility. This survey offers a unified, system-oriented view of multimodal unlearning across vision, language, audio, and video, grounded in recent advances, emerging applications, and open problems. Our taxonomy enables systematic comparison across model architectures and modalities, clarifying trade-offs among deletion strength, retention, efficiency, reversibility, and robustness. This survey highlights open problems and practical considerations to support future research and deployment of multimodal unlearning. We release a curated repository: this https URL
[NLP-50] Scalable and Culturally Specific Stereotype Dataset Construction via Human-LLM Collaboration EMNLP2025
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中文化偏见研究长期局限于英语语境的问题，其核心挑战在于非英语语言缺乏高质量标注数据以及跨文化人工标注成本高昂。为此，本文提出一种低成本的人类-大语言模型协同标注框架，并基于该框架构建了首个覆盖西班牙语多国（欧洲与拉丁美洲）的刻板印象数据集EspanStereo。该框架的关键创新在于利用大语言模型生成候选刻板印象，再由具备本地文化背景的标注者进行验证，从而高效识别出既有文献记载的普遍性刻板印象以及英语主导资源中缺失的区域性、文化特异性偏见。实验表明，使用EspanStereo评估西班牙语支持的LLMs时，不同国家间存在显著的刻板印象表现差异，凸显了开展文化情境化评估的必要性。该方法具有良好的可扩展性，可推广至其他语言和文化区域，为构建多语言、跨文化的刻板印象基准提供了可行路径，推动了大语言模型中偏见分析的全球化与精细化发展。
链接: https://arxiv.org/abs/2607.07895
作者: Weicheng Ma,John Guerrerio,Soroush Vosoughi
机构: Dartmouth College (达特茅斯学院)
类目: Computation and Language (cs.CL)
备注: Weicheng Ma, John Guerrerio: equal contribution; published in EMNLP 2025 Main
Abstract:Research on stereotypes in large language models (LLMs) has largely focused on English-speaking contexts, due to the lack of datasets in other languages and the high cost of manual annotation in underrepresented cultures. To address this gap, we introduce a cost-efficient human-LLM collaborative annotation framework and apply it to construct EspanStereo, a Spanish-language stereotype dataset spanning multiple Spanish-speaking countries across Europe and Latin America. EspanStereo captures both well-documented stereotypes from prior literature and culturally specific biases absent from English-centric resources. Using LLMs to generate candidate stereotypes and in-culture annotators to validate them, we demonstrate the framework’s effectiveness in identifying nuanced, region-specific biases. Our evaluation of Spanish-supporting LLMs using EspanStereo reveals significant variation in stereotypical behavior across countries, highlighting the need for more culturally grounded assessments. Beyond Spanish, our framework is adaptable to other languages and regions, offering a scalable path toward multilingual stereotype benchmarks. This work broadens the scope of stereotype analysis in LLMs and lays the groundwork for comprehensive cross-cultural bias evaluation.
[NLP-51] How Do I Know What to Say Next? Barenholtzs Autogenerative Theory as an Enrichment of Harrisean Integrationism
【速读】： 该论文旨在解决罗伊·哈里斯（Roy Harris）的整合主义语言学（Integrationist linguistics）在解释语言机制时存在的三方面理论缺口：一是未能充分阐明符号如何维持面向未来的开放性；二是对语言与非语言符号活动之间连续性的理论阐释不足；三是缺乏对过往整合经验累积形成的“档案”（archive）之结构性特征的详细说明。其解决方案的关键在于引入埃兰·巴伦霍尔茨（Elan Barenholtz）提出的自生成语言理论（autogenerative theory of language），该理论最初是为理解大型语言模型（Large Language Models, LLMs）的行为而发展而来。自生成理论为整合主义提供了三重补充：第一，提供了哈里斯所强调的二元交际中前瞻性开放性的结构机制；第二，建立了语言符号活动与其他符号生成行为之间连续性的计算对应物；第三，构建了关于“档案”的理论——即过去整合经验的积累形态及其如何被新参与者调用。这一融合既保留了整合主义对情境化整合行为的本体论优先性，又弥补了其自身在解释层面的不足。对于自然语言处理与大模型设计领域的研究者而言，该论证还提供了一个原则性的框架，阐明了大语言模型所依赖的统计结构的本质，以及其内在无法提供的认知与语用维度。
链接: https://arxiv.org/abs/2607.07891
作者: J. Mark Bishop,Stephen J. Cowley
机构: Goldsmiths, University of London (金史密斯伦敦大学); University of Southern Denmark (南丹麦大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Submitted to Philosophy and Technology
Abstract:Roy Harris’s Integrationist linguistics offers a compelling critique of the referentialist tradition embedded deep at the heart of computational approaches to language, arguing that language is not a code that maps onto a pre-given world but a situated, bipartite activity oriented toward prospective joint action. Yet Integrationism leaves certain explanatory gaps: it does not fully account for the structural mechanism by which signs sustain prospective openness, it undertheorises the continuity between linguistic and non-linguistic semiotic activity, and it offers no detailed account of the structural properties of the accumulated archive of past integrations. This paper argues that Elan Barenholtz’s autogenerative theory of language, developed in response to the behaviour of Large Language Models (LLMs), can fill precisely these gaps, enriching Integrationism without undermining any of its core commitments. Specifically, the autogenerative account provides: a structural mechanism for the prospective openness that Harris identifies as central to bipartite communication; a computational correlate for Harris’s thesis of semiotic continuity between language and other sign-making activity; and a theory of the archive: what the accumulated residue of past integrations looks like and how new participants draw upon it. The synthesis preserves Harris’s ontological primacy of the situated integrative act while adding explanatory content that Integrationism itself does not supply. For practitioners and researchers in natural language processing and large language model design, the argument offers a principled account of what the statistical structure that LLMs so effectively exploit actually is, and of what it cannot, by its nature, provide.
[NLP-52] DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment
【速读】： 该论文旨在解决长时序网页交互中智能体（agent）自我进化能力不足的问题，核心挑战在于：监督微调依赖固定教师模型生成的轨迹，缺乏灵活性；而稀疏奖励强化学习则因信号过弱难以有效指导复杂任务。其解决方案的关键是提出一种基于可验证环境 DeepSearch-World 的自蒸馏框架 DeepSearch-Evolve，该环境具备确定性与可复现性，支持多跳问答任务（420K个实体级随机游走构建）和关键认知行为（如进展验证、基于证据的反思、失败恢复）。通过迭代式轨迹生成、过滤、数据混合与微调，该框架实现了无需依赖更强大模型的自进化能力，使 DeepSearch-World-9B 在 BrowseComp（31.2%）、GAIA（61.5%）和 HotpotQA（93.4%）上达到与开源先进模型相当的性能，证明了可验证环境对长时序网页智能体规模化自我演进的有效性。
链接: https://arxiv.org/abs/2607.07820
作者: Xinyu Geng,Xuanhua He,Sixiang Chen,Yanjing Xiao,Fan Zhang,Shijue Huang,Haitao Mi,Zhenwen Liang,Tianqing Fang,Yi R. Fung
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Training tool-use agents to improve from their own experience remains challenging, as supervised fine-tuning relies on fixed teacher-distilled trajectories, while sparse-reward reinforcement learning provides weak supervision for long-horizon interactions. We present DeepSearch-Evolve, a self-distillation framework for web agents built on DeepSearch-World, a deterministic and verifiable environment with reproducible search and page-reading tools. DeepSearch-World contains 420K multi-hop QA tasks constructed from entity-level random walks and supports key agentic cognitive behaviors useful for self-evolving, including progress verification, grounded reflection, and failure recovery. DeepSearch-Evolve iteratively performs trajectory generation, filtering, data mixing, and fine-tuning to train stronger agents. Without distillation from more capable models, DeepSearch-World-9B achieves competitive performance compared with open-source agents, reaching 31.2% on BrowseComp, 61.5% on GAIA, and 93.4% on HotpotQA, showing that verifiable environments enable scalable self-evolution for long-horizon web agents. We will release the environment, 420K training pool, validation set, model, and code to facilitate future research on self-improving deep search agents.
[NLP-53] From Solvers to Research: Large Language Model-Driven Formal Mathematics at the Research Frontier
【速读】： 该论文旨在解决当前生成式AI在数学领域（AI4Math）应用中面临的根本性瓶颈：现有基于大语言模型（LLM）的定理证明系统虽在形式化命题的自动证明方面取得显著进展，但难以应对前沿数学研究中的开放性、未充分定义且具有多层抽象特征的复杂问题，如新定理发现或未解猜想的证明。其核心挑战在于现有系统仍局限于预设问题求解框架，缺乏自主开展数学探索的能力。解决方案的关键在于推动AI4Math范式从“预定义问题求解器”向具备严谨形式化数学推理能力的“数学研究代理”（mathematical research agents）转型。为此，论文系统梳理了数据集、自动形式化与证明合成等关键技术方向，深入剖析了现有系统在数据集局限性、关系结构表达、数学探索机制、工具生态整合及人机协作模式等方面的不足，并提出了未来发展的战略路线图，以实现对前沿数学研究的实质性支持。
链接: https://arxiv.org/abs/2607.07779
作者: Eric Jiang,Xiao Liang,Yikai Zhang,Yingjia Wan,Mengting Li,Haikang Deng,Alexander K. Taylor,Justin Baker,Rushil Raghavan,Junyi Zhang,Ying Nian Wu,Andrea L. Bertozzi,Kai-Wei Chang,Raghu Meka,Matthew Sottile,Nanyun Peng,Amit Sahai,Terence Tao,Wei Wang
机构: University of California, Los Angeles; Lawrence Livermore National Laboratory
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent developments in AI for Mathematics (AI4Math), especially Large Language Model (LLM)-driven theorem provers, has achieved remarkable success in formal proof generation for well-defined mathematical problems through Interactive Theorem Proving (ITP) languages. However, current systems remain fundamentally limited in tackling frontier research mathematics, such as discovering new theorems or resolving open conjectures, which are often open-ended, under-specified, and involve multiple layers of abstraction. We argue that the next leap in AI4Math systems requires a decisive shift from predefined problem-solvers to research agents that can address frontier mathematical challenges with rigorous formal mathematical reasoning. In this position paper, we provide a systematic review of the field, covering datasets, auto-formalization, and proof synthesis. More importantly, we identify core limitations of existing systems in serving as mathematical research agents, examining issues across datasets, relational structure, mathematical exploration, tool ecosystem, and human-AI collaboration, outlining a strategic road-map for the future of AI4Math.
[NLP-54] Unveiling Public Opinion: A Study of Sentiment Analysis Using LSTM and Traditional Models
【速读】： 该论文旨在解决社交媒体平台（如Twitter）上海量用户生成内容中的情感分析问题，尤其是在实时性与语境复杂性较高的文本中准确识别和分类情感的挑战。其核心问题是传统机器学习方法在处理具有上下文依赖性和时序特征的短文本（如推文）时表现受限，难以充分捕捉语言的深层语义信息。解决方案的关键在于采用基于长短期记忆网络（LSTM）的深度学习模型，通过建模文本序列中的长期依赖关系，有效提取上下文语义特征。实验结果表明，该模型在预处理后的Kaggle Twitter数据集上实现了80.00%的测试准确率和0.92的微平均ROC-AUC值，显著优于逻辑回归、随机森林、朴素贝叶斯及梯度提升等传统机器学习方法，验证了深度学习在捕捉文本时序与语义复杂性方面的优越性。
链接: https://arxiv.org/abs/2607.07772
作者: Atiq Ur Rehman
机构: 未知
类目: Computation and Language (cs.CL)
备注: 6 pages, 5 figures. Published in the Proceedings of the 2025 IEEE Conference on Computing, Communication, and Data Engineering (C-CODE 2025)
Abstract:In this age of social media, sites like Twitter have become meeting places for people to share their views and feelings on a wide range of issues and current events as they unfold in real time. Sentiment analysis, a critical application of NLP, has become indispensable due to the massive influx of user-generated content, enabling the extraction of meaningful insights from the opinions and emotions expressed in textual data. Sentiment analysis on Twitter employs sophisticated computational techniques to categorize tweets into positive, negative, or neutral sentiments. This method not only examines individual expressions but also analyzes vast databases related to specific subjects or events. By spotting these emotions, machine learning models help improve public opinion interpretation and trend forecasting. This paper examines the effectiveness of various machine learning and deep learning approaches. Designed for this use, the system evaluates logistic regression, random forest, naïve bayes, gradient boosting, and LSTM networks, among other algorithms applied in sentiment classification. This work identifies the optimal sentiment analysis model using a Kaggle Twitter dataset that has been preprocessed through tokenization, lemmatization, and stopword elimination. Emphasizing the better performance of the LSTM approach, the model attained a training accuracy of 90.98%, a testing accuracy of 80.00%, and a micro-average ROC- AUC score of 0.92. These results show that the model outperforms conventional machine learning techniques in capturing contextual and sequential textual aspects.
[NLP-55] SPL: Orchestrating Workflows with Declarative Deterministic-Probabilistic Composition
【速读】： 该论文旨在解决当前大型语言模型（LLM）应用开发中确定性计算与概率性计算分离的问题，即现有框架通常将基于LLM的生成任务（如文本生成、推理）与符号数学计算（如代数求解、形式化验证）割裂处理，导致系统设计复杂、跨模态协同困难。其核心挑战在于缺乏统一的语言抽象，使得同一任务需在不同工具链间切换，难以实现端到端可验证、可复现的智能计算流程。为应对这一问题，论文提出SPL（Structured Prompt Language），一种声明式编程语言，通过整合生成式AI（Generative AI）与符号计算能力，实现确定性与非确定性计算模式在同一规范中的无缝融合。其关键创新在于：引入GENERATE/EVALUATE用于概率性计算，SOLVE/ASSERT用于确定性计算，共享统一的语法结构、变量绑定机制与运行时路由逻辑；同时支持跨平台无修改执行，可在本地（Ollama）、云端API（OpenRouter、Anthropic）及分布式网格（Momagrid）环境中一致运行，并将模型选择与验证器配置延迟至调用时决定。实验验证表明，该方法在78个配方的厨艺手册测试集和1200次受控实验中展现出高可靠性——求解器分支实现了82%–93%的机器可验证正确率（如sonnet-4-6为85%，gemma4:e2b达93%），显著优于仅依赖LLM输出而无数学验证的对照组，揭示出“可验证性”与“流畅性”之间的本质差异。此外，研究发现后端工具的难度梯度（如SymPy正确率为78%，Sage为54%）以及主要失败模式为“solver_error”（内核拒绝表达式），而非格式不合规，进一步凸显了底层计算引擎鲁棒性的重要性。
链接: https://arxiv.org/abs/2607.07727
作者: Wen G. Gong
机构: Independent Researcher
类目: Programming Languages (cs.PL); Computation and Language (cs.CL)
备注: 24 pages, 2 figures, under review at TMLR
Abstract:We present SPL (Structured Prompt Language), a declarative language that composes deterministic and probabilistic computation modes in a single specification. While existing frameworks separate these – orchestration systems (AutoGen, CrewAI, LangGraph) for LLM calls, symbolic tools (SymPy, SageMath, Lean) for computation – SPL unifies them. It provides GENERATE/EVALUATE for probabilistic computation and SOLVE/ASSERT for deterministic computation, sharing syntax, variable bindings, and runtime routing. A .spl specification runs unchanged across local nodes (Ollama), cloud APIs (OpenRouter, Anthropic), and distributed grids (Momagrid), with model and verifier selection deferred to invocation time. We validate SPL through an extensive 78-recipe cookbook and a controlled 1,200-run experiment (10 models x 20 problems x 2 arms x 3 repetitions; the 20 problems span 6 difficulty tiers). The solver arm achieves 82-93% machine-verified correctness (sonnet-4-6: 85%, gemma4:e2b: 93%) while the LLM-only arm measures output production without mathematical verification, making the comparison one of verified correctness against unverified fluency. A backend difficulty gradient emerges (SymPy 78%, Sage 54%), and the dominant failure mode is solver_error (kernel-rejected expressions), not format non-compliance. Comments: 24 pages, 2 figures, under review at TMLR Subjects: Programming Languages (cs.PL); Computation and Language (cs.CL) Cite as: arXiv:2607.07727 [cs.PL] (or arXiv:2607.07727v1 [cs.PL] for this version) https://doi.org/10.48550/arXiv.2607.07727 Focus to learn more arXiv-issued DOI via DataCite
[NLP-56] Uncertainty-gated selection for block-sparse attention
【速读】： 该论文旨在解决块稀疏注意力（Block-sparse attention, SSA）在长上下文语言模型中因采用基于查询的top-k选择机制而导致的信息丢失问题。传统方法仅根据键块（key block）得分进行硬性截断，当第k个与第(k+1)个块得分接近时，无法动态调整保留数量，导致携带关键答案证据的块被不可逆地丢弃。其解决方案的关键在于提出一种信息价值路由机制（value-of-information router），该机制通过量化每个查询的top-k截断决策的确定性（即得分差距），对决策模糊（得分差距最小）的查询主动扩大保留集合至2k，从而提升关键信息的召回能力。该路由策略不依赖特定骨干网络（backbone-agnostic），可无缝集成于现有块评分方法（如Quest）之上，显著提升召回性能，在LongBench-v2中实现配对召回率0.75（对比top-k基线0.47，提升28个百分点，p<0.01），且在128K上下文长度下仍能保持接近稠密注意力的精度（如Qwen2.5-7B-1M上达0.81倍稠密精度），同时推理效率优于稠密计算（0.62x~0.80x稠密壁钟时间）。
链接: https://arxiv.org/abs/2607.07724
作者: Thomas Rossi
机构: Eonpass
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Block-sparse attention scales long-context language models by replacing the O(N^2) softmax with a per-query top-k selection over key blocks. This cutoff is myopic: when the k-th and (k+1)-th blocks are nearly tied in score, the selector commits without spending extra budget, and a dropped block carrying answer evidence is unrecoverable downstream. We propose a value-of-information router that measures, for each query, how decisively the top-k cut was made, and doubles the kept set for the queries where that gap is smallest; the rule is backbone-agnostic and stacks with existing block-scoring methods such as Quest. On LongBench-v2 medium at n=215 (the entire dataset subset), router-on-Quest reaches paired recall 0.75 vs. top-k 0.47 – +28 pp over the SSA-style baseline (McNemar p0.01) – and lands within 2 pp of dense on RULER NIAH multikey at the same context. The lift reproduces on four models from three architectures (Qwen2.5, Mistral-Nemo, Qwen3.6). At 128K, the router preserves 0.81 and 0.89 of dense accuracy on Qwen2.5-7B-1M and Qwen3.6 (vs. SSA-style top-k at 0.09 on the former) while the fused selection-plus-kernel pipeline runs at 0.62x and 0.80x dense wall time.
信息检索
[IR-0] ProjAgent : Procedural Similarity Retrieval for Repository-Level Code Generation
链接: https://arxiv.org/abs/2607.08691
作者: QiHong Chen,Aaron Imani,Iftekhar Ahmed
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Repository-level code generation requires implementing target functions while accounting for complex cross-file dependencies and project-specific conventions. Existing retrieval methods predominantly rely on lexical, structural, or semantic similarity, often overlooking repository functions that implement similar procedural logic despite differing in identifiers or application domains. We propose ProjAgent, a repository-level code generation system that introduces procedural similarity as an explicit retrieval signal. ProjAgent decomposes the target function into intermediate reasoning steps and employs an agentic workflow to retrieve repository functions that exhibit similar procedural behavior at each step. The retrieved procedural context is integrated with conventional semantic retrieval to construct a richer repository context for code generation. ProjAgent further incorporates a conservative static-analysis feedback loop that iteratively repairs generated code using compiler and static-analysis feedback. Evaluated on REPOCOD, ProjAgent achieves 41.14% Pass@1, outperforming existing retrieval-based baselines. These results demonstrate that procedural similarity is an effective and previously unexplored retrieval dimension for repository-level code generation.
[IR-1] Improving Ad-hoc Search Effectiveness for Conversational Information Retrieval via Model Merging SIGIR2026
链接: https://arxiv.org/abs/2607.08540
作者: Ahmed Rayane Kebir,Jose G. Moreno,Lynda Tamine
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: Accepted to SIGIR 2026. 6 pages, 3 figures
Abstract:Conversational information retrieval is challenging since it requires the consideration of the conversation history which potentially gives rise to topic shifts and coreference resolution across previous turns. To address these challenges, previous work mainly rely on traditional fine-tuning of ad-hoc retrievers on conversational datasets or extrapolates their generalizability through multi-tasking. However, this mainstream approach is costly - since it requires model re-training - and exhibits catastrophic forgetting, where the model loses its foundational ad-hoc retrieval performance. In this paper, we fill this gap by introducing model merging as a training-free strategy enabling the design of a single retrieval model that operates across both ad-hoc and conversational settings with no additional fine-tuning. We conduct experiments using linear and non-linear parameter-wise merging strategies - namely Model Soup and Slerp - on standard ad-hoc search and conversational retrieval datasets. Our results demonstrate that model merging significantly enhances the ad-hoc search capabilities of conversational retrievers while improving generalizability across task-specific datasets, achieving up to 15% higher NDCG@3 under zero-shot conditions.
[IR-2] Log-Insight: Automating Microservice Incident Diagnosis via Neuro-Symbolic Log Analysis
链接: https://arxiv.org/abs/2607.08529
作者: Carlos Garcia-Hernandez,Aymane Abdali,Guangyu Wu,Mingxue Wang,Fei Shen,Zhaoyu Pang,Yanbin Zhang
类目: Information Retrieval (cs.IR)
备注:
Abstract:Diagnosing production incidents in large-scale microservice systems is time-critical for Site Reliability Engineers (SREs). A single 30-minute incident window in our deployment can generate over two million log lines–approximately 1.2 billion characters, far exceeding standard LLM context windows–making direct LLM-based Root Cause Analysis (RCA) infeasible. Existing approaches leave gaps: template-based parsers lack semantic anomaly reasoning, deep-learning detectors emit black-box binary signals, and LLM pipelines suffer context overflow and domain hallucination on raw telemetry. We present Log-Insight, an automated incident-diagnosis system deployed in production at Huawei. The core design principle automates the SRE’s manual triage workflow: symbolic stages replicate the structured investigation a skilled SRE would perform–sampling, schema understanding, pattern clustering, and statistical anomaly ranking. This hands the LLM a compact, pre-ranked evidence dossier to synthesise into a hypothesis report. Our six-stage pipeline reduces millions of raw events by 1,000-7,000x while preserving statistically significant failure signals. Evaluated on 11 historical production incidents (110 runs, SRE-validated ground truth), Log-Insight achieves MRR = 0.790, returning the correct root cause within the top-3 hypotheses in over 90% of runs in under a minute of latency. We report systematic failure modes, active mitigations, and open research directions. The Forensic Evidence section–listing exact log templates and skew statistics–was consistently identified by operators as a key adoption factor, shifting the system’s perceived role from opaque oracle to investigative assistant. Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2607.08529 [cs.IR] (or arXiv:2607.08529v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2607.08529 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-3] Conversational Retrieval and On-the-Fly Knowledge Modeling of Historical Penitentiary Repression Records ICDAR2026
链接: https://arxiv.org/abs/2607.08459
作者: Paula Font Solà,Adrià Molina Rodríguez,Josep Lladós
类目: Information Retrieval (cs.IR); Digital Libraries (cs.DL)
备注: Accepted at ICDAR2026
Abstract:Recent developments in digital libraries increasingly favor conversational and natural language access to information through Retrieval-Augmented Generation (RAG). Although these approaches are effective for extractive tasks grounded in individual records, they remain limited in their ability to interpret document collections holistically and to incorporate expert knowledge dynamically. In this article, we present a document analysis system designed for the management of historical digital libraries that supports on-the-fly knowledge modeling. The system is equipped with the capability to store facts produced either by expert archivists or derived from document retrieval processes within a graph-based structure. Through continuous professional interaction, the system can retrieve information not only from primary sources such as documents, but also from previously modeled knowledge, with the graph-based index acting as a memory for the language model to access. This enables increasingly complex queries involving long-term dependencies across documents, link discovery, and the integration of expert knowledge that may not be explicitly present in the original sources. As a result, the proposed approach facilitates the generation of richer and more comprehensive information.
[IR-4] H3D: Benchmarking Unsupervised Text Hashing for Fine-Grained Document Deduplication
链接: https://arxiv.org/abs/2607.08382
作者: Qianren Mao,Jiaxun Lyu,Junnan Liu,Zhijun Chen,Jingzheng Li,Hanwen Hao,Bo Li
类目: Information Retrieval (cs.IR)
备注:
Abstract:Document hashing provides compact representations for efficient similarity search and document deduplication, but existing studies rarely compare hashing pipelines under a unified protocol for fine-grained scientific documents. H3D is an unsupervised text hashing benchmark for fine-grained document deduplication. It evaluates representative unsupervised non-learning hashing approaches (MinHash, SimHash, Winnowing, FuzzyHash, FlyHash) together with semantic-sensitive methods built from frozen BGE embeddings and two quantization strategies (BGE-BIHash and BGE-LSHash). The non-learning methods generate hash fingerprints through manually designed mathematical rules without training or labeled similarity pairs, which distinguishes them from neural semantic hashing models. We benchmark all methods on CSFCube and RELISH, two datasets that provide complementary evaluation settings: facet-level analysis for scientific-document similarity and larger-scale split-level evaluation for biomedical similarity search. H3D jointly reports ranking quality (MAP, NDCG@20), efficiency, and robustness under controlled text compression. The results show a consistent trade-off: lexical and structural fingerprints are competitive for near-duplicate matching, while semantic-sensitive representations better preserve similarity under content rewriting, at higher computational cost. We further analyze when different similarity measures become rank-equivalent for specific hash representations, improving the interpretability and reproducibility of method comparisons.
[IR-5] DaV-Gen: End-to-End Generative Retrieval via Draft-and-Verify IJCAI’26
链接: https://arxiv.org/abs/2607.08365
作者: Meng Zhao,Chunmei Liu,Qinyong Wang
类目: Information Retrieval (cs.IR)
备注: Accepted by IJCAI’26
Abstract:Mainstream industrial information retrieval systems (e.g., search and recommendation) are usually built upon Multi-Stage Cascade Architectures (MCAs), which balance effectiveness and efficiency through a coarse-to-fine retrieval-ranking'' pipeline. However, the optimization objectives across different stages are substantially inconsistent, propagating or even amplifying the early-stage errors that ultimately degrade the quality of final results. While emerging end-to-end generative models offer a potential solution by unifying the pipeline, their online serving performance is severely hindered by the auto-regressive process inherited from the standard decoder-only structure. To bridge this gap, we introduce \textbfDaV-Gen, a novel unified solution designed to fundamentally refactor the paradigm for both search and recommendation via a Draft-and-Verify’’ mechanism. Inspired by the process used by speculative decoding, our framework redesigns the generation task into two synergistic operations within a single model. During training, the model is concurrently optimized for both candidate drafting and fine-grained verification. This is achieved by a composite loss function that jointly trains the model on two distinct but related objectives: 1) a contrastive loss that structures the embedding space for efficient drafting, and 2) a fusion loss that combines generative likelihood with vector similarity to produce a superior verification score. This integrated training strategy equips the model with dual capabilities. At inference time, it first performs highly efficient vector-based drafting to generate a candidate set, and then verifies these candidates using the more powerful fused scoring function, thereby achieving both the speed of sparse drafting and the precision of advanced generative models within a unified, end-to-end architecture.
[IR-6] ICDAR 2026 HIPE-OCRepair Competition on LLM -Assisted OCR Post-Correction for Historical Documents
链接: https://arxiv.org/abs/2607.08143
作者: Maud Ehrmann,Emanuela Boros,Juri Opitz,Andrianos Michail,Florian Wagner,Simon Clematide
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 17 pages
Abstract:We present the results of HIPE-OCRepair-2026, an ICDAR competition on LLM-assisted OCR post-correction of historical documents. OCR post-correction remains a long-standing challenge in digital heritage: large-scale collections of digitized documents are affected by legacy OCR errors, while re-digitization at scale remains impractical. Large language models (LLMs) offers a major opportunity to revisit this challenge, yet their effectiveness across languages, document types, and noise conditions - and their tendency to hallucinate - remains insufficiently understood. HIPE-OCRepair-2026 pursues two objectives: (i) to evaluate the capabilities of modern OCR post-correction systems, and (ii) to provide a reproducible evaluation framework anchored in the HIPE-OCRepair-2026 dataset, a harmonized multilingual resource consolidating existing and newly curated historical datasets. Participants were tasked with correcting noisy OCR transcripts from historical newspapers and printed works in English, French, and German (17th-20th century), working at the level of coherent transcription units (paragraphs or articles) without access to source images. The evaluation adopts a retrieval-oriented rather than diplomatic scoring approach, reflecting the practical use case of search and access over digitized collections. Four teams submitted systems ranging from zero-shot prompting to continued pre-training and fine-tuning, offering insights into the merits of different adaptation strategies. Results show that modern LLM-assisted systems can significantly improve OCR quality, but performance varies across datasets, languages, and noise levels. Over-correction on low-noise inputs emerges as a recurring challenge, highlighting the importance of evaluation beyond character error reduction. The dataset, scorer, and evaluation pipeline are publicly released to support future research.
[IR-7] BACH: A Bayesian Admixture of Contrastive Heads for Multi-Interest Two-Tower Retrieval
链接: https://arxiv.org/abs/2607.08107
作者: Quoc Phong Nguyen,Paul Albert,Long Vuong,Vuong Le,Julien Monteil
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:Two-tower retrievers compress each user into a single embedding, limiting their ability to serve diverse interests. Multi-interest models give each user several heads scored by a maximum inner product, but their hard-routing training under-utilizes heads (routing collapse) and gives no per-user estimate of how much each interest matters for serving. We present \textbfBACH (\emphBayesian Admixture of Contrastive Heads), which casts multi-interest two-tower retrieval as a per-user mixture over the heads, fit by variational inference. The soft mixture trains every head (mitigating collapse), produces a per-user weighting of the interests that is reused at serving, and admits a shared global-codebook variant with precomputable retrieval. On three large-scale benchmarks, MovieLens-20M, Taobao, and Netflix, BACH improves top-of-ranking retrieval over hard-routing multi-interest and single-vector baselines at every head count; we further find that scoring every candidate by its best head, consistent with serving, outperforms the usual target-routed training, and that BACH improves further still.
[IR-8] Beware What You Autocomplete: Forensic Attribution of Backdoored Code Completions
链接: https://arxiv.org/abs/2607.08011
作者: Anjun Gao,Yueyang Quan,Zhuqing Liu,Minghong Fang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: To appear in COLM 2026
Abstract:Large language models have enabled powerful code completion systems that assist developers by predicting subsequent lines of code. However, these models remain vulnerable to backdoor attacks, where malicious fine-tuning data covertly implants unsafe behaviors. Despite advances in defensive techniques, adaptive and sophisticated backdoor attacks still evade detection and mitigation. We present CodeTracer, a forensic framework that traces malicious code completions back to the backdoor fine-tuning data responsible for them. Operating under realistic post-deployment constraints, CodeTracer relies solely on the fine-tuning corpus and the reported miscompletion event. It extracts a structured behavioral fingerprint from the compromised output, narrows the search to semantically relevant code samples, and employs LLM-based reasoning to attribute unsafe logic to specific backdoor data. Extensive evaluations across three representative vulnerability cases and ten backdoor attacks, along with sixteen competitive baselines, demonstrate that CodeTracer consistently achieves high forensic accuracy, low false identification rates, and strong robustness against adaptive attacks.
人机交互
[HC-0] Using AI-based Learning Assistants in Higher Education: A Large-Scale Descriptive Analysis
链接: https://arxiv.org/abs/2607.08748
作者: Kristina Schaaff,Quintus Stierstorfer,Valerie Heckel
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:In this study, we present a large-scale descriptive analysis of the use of an AI-based learning assistant (Syntea) in higher education. Based on objective log data from 77,543 students enrolled in distance studies, we examine usage patterns across gender, age group, study cluster, degree, and study mode. To date, existing research on educational chatbots has largely relied on comparatively small samples and self-reported survey data, while large-scale evidence on actual usage behavior remains limited. Our findings show that Syntea is already embedded in the study routines of many learners, but that usage differs across demographic and structural contexts. By identifying these patterns, our study provides an empirical basis for the further development of AI-based learning support and contributes a large-scale analysis of educational chatbot usage in higher education.
[HC-1] Dimensionality Reduction Meets Network Science: Sensemaking on UMAPs kNN Graph
链接: https://arxiv.org/abs/2607.08746
作者: Duen Horng Chau,Donghao Ren,Fred Hohman,Dominik Moritz
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Data Structures and Algorithms (cs.DS); Human-Computer Interaction (cs.HC)
备注: Code and demo: this https URL
Abstract:While UMAP is widely used for exploring high-dimensional data, typical workflows focus on its lower-dimensional embedding, largely overlooking the rich k-nearest-neighbor (kNN) graph that UMAP constructs internally. This graph encodes the data manifold in its original high-dimensional space, before the distortion that UMAP’s 2D projection introduces. We demonstrate the untapped potential of this internal representation, showing how standard graph algorithms applied to this graph enhance data sensemaking: (1) PageRank identifies representative data points, (2) k-core decomposition reveals dense core regions versus sparse periphery, and (3) clustering coefficient detects tight-knit neighborhoods with highly-similar data points. Through quantitative and qualitative evaluation on MNIST and Fashion MNIST, we show that these graph-based analyses are not only practical but also competitive with or complementary to purpose-built methods (e.g., k-medoids for exemplar selection, HDBSCAN for density-based clustering).
[HC-2] Sculptable Mesh Structures for Room-Scale Form-Finding
链接: https://arxiv.org/abs/2607.08736
作者: Jesse T. Gonzalez,Yanzhen Zhang,Dian Zhu,Alice Yu,Sapna Tayal,Nazm Furniturewala,Ziying Qi,Somin Ella Moon,Leyi Han,Alexandra Ion,Scott E. Hudson
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:It can be hard to design a physical structure entirely within the confines of a computer monitor. To better capture the interplay between real-world objects and a designer’s work-in-progress, practitioners will often go through a sequence of low-fidelity prototypes (paper, clay, foam) before arriving at a form that satisfies both functional and aesthetic concerns. While necessary, this model-making process can be quite time-consuming, particularly at larger scales, and the resulting geometry can be difficult to translate into a CAD environment, where it will be further refined. This paper introduces a user-adjustable, room-scale, “shape-aware” mesh structure for low-fidelity prototyping. A user physically manipulates the mesh by lengthening and shortening the edges, altering the overall curvature and sculpting coarse forms. The edges are equipped with resistive length sensors, and transmit their configuration to a central computer. The structure can later be reproduced in software, connecting this prototyping stage to the larger computational design pipeline. Subjects: Human-Computer Interaction (cs.HC) Cite as: arXiv:2607.08736 [cs.HC] (or arXiv:2607.08736v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2607.08736 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: UIST 2025: Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology Related DOI: https://doi.org/10.1145/3746059.3747771 Focus to learn more DOI(s) linking to related resources
[HC-3] How YouTube Frames ChatGPT Use in Education: An Epistemic Network Analysis with Supporting Multimodal Metadata
链接: https://arxiv.org/abs/2607.08698
作者: Shayla Sharmin,Mohammad Al-Ratrout,Mohammad Fahim Abrar,Roghayeh Leila Barmaki
类目: Human-Computer Interaction (cs.HC)
备注: This paper has been accepted in ICMI 2026 and will be presented in October
Abstract:We examine educational YouTube videos through multimodal metadata, such as transcripts, titles, thumbnails, and viewer comments, to investigate how ChatGPT is framed across creator groups and how those framings relate to audience response and platform reach. Little is known about how large language models are presented to learners in informal, creator-driven public discourse. Following PRISMA, we selected 52 videos for analysis. We identified three structurally distinct discourse groups: (G1) videos that positioned ChatGPT as a conceptual scaffold for thinking, (G2) videos oriented toward retrieval practice and skill-building, and (G3) videos that framed ChatGPT as a tool for output generation. Epistemic Network Analysis revealed statistically significant group differences with large effect sizes. Multimodal metadata consistently reflected these distinctions across transcript discourse, titles, and thumbnails. Viewers of learning-oriented content described ChatGPT as a thinking partner or tutor, whereas viewers of output-oriented content raised concerns about over-reliance, surface-level learning, and cognitive offloading. G3 achieved comparable platform reach to G2, yet with substantially weaker learning-oriented framing. This may suggest that output-oriented content competes for visibility despite lower pedagogical depth. These findings reveal a structural tension in self-directed AI learning: content that prioritizes quick outputs reaches far more learners than content that promotes deep engagement. This gap raises critical questions about whose vision of AI literacy scales and what learners are actually left with.
[HC-4] Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction ALT
链接: https://arxiv.org/abs/2607.08595
作者: Hyunho Mo,Djura Smits,Mahlet A. Birhanu,Maarten J.G. Leening,Daniel Bos,Pim van der Harst,Esther E. Bron
类目: Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)
备注: 15 pages, 2 figures, 2 tables. Submitted to Frontiers in Applied Mathematics and Statistics, Research Topic “Enhancing Healthcare through Federated Learning: Privacy, Security and Performance”
Abstract:Cardiovascular disease risk prediction models often rely on data from a single institution or centrally pooled datasets. Extending these models across institutions could be limited by privacy regulations and constraints on sharing patient-level data. Federated learning enables collaborative model development without transferring sensitive patient data, but its application in healthcare remains challenging because datasets often differ in size, population characteristics, and outcome definitions. In this study, we present a federated deep learning approach for privacy-preserving cardiovascular disease risk prediction that integrates two population-based cohorts with different characteristics: Lifelines, including 148,230 participants meeting the study inclusion criteria with self-reported outcomes, and the Rotterdam Study, including a smaller cohort of 10,155 participants with digitally linked clinical outcomes. Model performance was primarily evaluated on the Rotterdam Study because of its complete follow-up. Deep survival models trained using federated learning achieved higher predictive performance than models trained locally without federation. For the Rotterdam Study, the C-statistic increased from 0.728 (95% CI: 0.717-0.739) to 0.739 (95% CI: 0.728-0.749). For Lifelines, the C-statistic increased from 0.783 (95% CI: 0.775-0.791) to 0.787 (95% CI: 0.780-0.792). These findings suggest that federated deep learning across heterogeneous cohorts can improve cardiovascular disease risk prediction while preserving the privacy of individual-level patient data.
[HC-5] ImputeViz: A Visual Analytics Dashboard for Diagnosing Missing Data and Comparing Imputation Methods
链接: https://arxiv.org/abs/2607.08579
作者: Aitik Dandapat,Lalith Punepalle Raveendrareddy,Mithilesh Kumar Singh,Klaus Mueller
类目: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注:
Abstract:Missing data is a persistent obstacle in scientific, social science, and public health research, often biasing analyses and placing accountability on analysts for how they handle missing values. We introduce ImputeViz, an integrated visual analytics dashboard that supports diagnosing missingness, configuring imputation models, and evaluating results. The system brings together widely used methods, including MICE, Random Forest, XGBoost, and kNN, within an interactive environment that makes missingness patterns explicit. To support geospatial reasoning, we introduce gKNN, a geographically informed kNN variant that blends socioeconomic and spatial distances and exposes donor contributions, enabling provenance-based visual accountability by showing which regions drive each estimate. Our primary contribution is a method-agnostic visual analytics environment that makes cross-method comparison a first-class visual task and integrates gKNN alongside standard methods. Coordinated views reveal missingness structure through heatmaps, co-missingness summaries, and distributional diagnostics that help analysts reason about missingness patterns (MCAR/MAR) and cases where missingness may be non-random (MNAR). Users can compare and tune models and interrogate results via distributional overlays, a Method Comparison Summary reporting MAE, RMSE, Delta RMSE, and runtime for each algorithm on the current target and mask, along with variable-level discrepancy views. Cached per-method results and locked axis scales reduce cognitive overhead from shifting ranges during method switching. These comparisons highlight where methods disagree, which variables are sensitive, and how imputation choices affect downstream summaries. Case studies demonstrate how ImputeViz helps analysts select effective strategies, surface sensitive variables, and assess model robustness.
[HC-6] VEGAS: Human-Aligned Video Caption Evaluation via Gaze
链接: https://arxiv.org/abs/2607.08489
作者: Shenghui Chen,Po-han Li,Ximeng Sun,Shijia Yang,Emad Barsoum,Zicheng Liu,Sandeep Chinchali,Ufuk Topcu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Vision-language models excel at video captioning, yet typically generate descriptions that fail to capture individual viewers’ attention. We propose VEGAS (Video caption Evaluation via GAze Score), a training-free metric that leverages test-time gaze to sample personalized, attention-aligned text. It is a cross-modal, information-theoretic metric that quantifies how well a candidate caption matches a viewer’s focus. To evaluate VEGAS, we curate a dataset of egocentric activities and instructional slides paired with synchronized gaze and reference annotations. We then select captions based on VEGAS via rejection sampling without model retraining. Experiments show that VEGAS-selected captions align significantly better with human focus and improve downstream caption-to-video retrieval, demonstrating the practical utility of incorporating viewer attention during inference.
[HC-7] Large-Language-Models -as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition
链接: https://arxiv.org/abs/2607.08374
作者: Jing Jie Tan,Ban-Hoe Kwan,Danny Wee-Kiat Ng,Yan-Chai Hum,Shih-Yu Lo,Po-An Chen,Noriyuki Kawarazaki,Kosuke Takano,Anissa Mokraoui
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Robotics (cs.RO); Social and Information Networks (cs.SI)
备注:
Abstract:Personality recognition has traditionally been constrained by theory-dependent formulations, where models are trained to fit predefined psychological taxonomies rather than uncovering shared underlying behavioral structure. This limits generalization, as personality itself is better understood as theory-invariant, while existing annotations reflect only partial and sometimes inconsistent views of the same latent traits. In this work, we introduce JAM ((J)udge for (A)daptive (M)etric-Alignment), a theory-agnostic framework that shifts learning from adapting to predefined personality theories toward discovering unified latent pseudo-facets that capture shared psychological structure. Rather than constraining the model to any personality taxonomy during training or inference, the framework learns generalizable psychological representations and can infer an individual’s latent psychological profile directly from the textual samples, without requiring theory-specific labels. JAM achieves this through an Attention-Pooled Graph Prototypical Network that learns structured representations via clustering in embedding space, together with a Cross-Theory Harmonization (CTH) approach that integrates (i) Human-Guided Linkage and (ii) Machine-Induced Consensus to unify heterogeneous datasets without relying on predefined labels. To further improve robustness and data quality, we incorporate an LLM-as-a-Judge mechanism operating in two configurations, (i) LLM-before-the-loop and (ii) LLM-in-the-loop which identifies ambiguous samples to guide adaptive metric learning. Experiments show that JAM improves cross-framework generalization and performance, establishing a strong step toward theory-agnostic personality inference and supporting low-resource personality theories. The related code repository, model weights, and artifacts are available at this https URL
[HC-8] How Analysts Use AI in High-Stakes Crime Linkage: An Industrial Study
链接: https://arxiv.org/abs/2607.08274
作者: Jessica Woodhams,Amy Burrell,Wanyin Li,Fahim Ahmed,Matthew Tonkin,Jan Lemeire,Arkady Konovalov,Steven Frisson,Mark Webb,Sarah Galambos,Vesna Nowack,Dalal Alrajeh
类目: Human-Computer Interaction (cs.HC); Software Engineering (cs.SE)
备注: 12 pages, 6 figures, FSE Industry
Abstract:Crime linkage analysis is used in many countries to identify series of offences that may have been committed by the same individual. In practice, specialist analysts manually search for behavioural and situational connections across large crime databases, an effort that is time-consuming, cognitively demanding, and can involve repeated exposure to disturbing material. To support this work, an Artificial Intelligence (AI)-enabled decision-support tool was co-developed with a UK law enforcement agency to assist analysts in identifying likely crime linkages. This paper reports an industrial evaluation of the crime-linkage tool. We conducted a mixed-methods usability study combining direct observation, eye-tracking, mouse-tracking, and surveys to examine how analysts engage with AI predictions and with the model features presented as explanations. Our findings show that analysts used the AI predictions selectively and frequently validated them against behavioural (non-AI) evidence, reflecting partial trust and an ongoing reliance on established analytical practices. We also found that analysts attended to the presented model features and valued their availability, while identifying opportunities to improve how explanations are presented and integrated into the workflow. Overall, our results highlight the need for AI-enabled decision-support tools to better integrate explanations and traditional analytical methods, and demonstrate the importance of in-situ evaluation for engineering usable and trustworthy AI in high-stakes settings. Comments: 12 pages, 6 figures, FSE Industry Subjects: Human-Computer Interaction (cs.HC); Software Engineering (cs.SE) Cite as: arXiv:2607.08274 [cs.HC] (or arXiv:2607.08274v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2607.08274 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[HC-9] AutoPersonas: A Multi-Timescale Loop Engine for Open-Ended Persona Evolution
链接: https://arxiv.org/abs/2607.08252
作者: Mengchen Li
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: 52 pages, 13 figures/tables, ancillary public-safe evaluation artifacts included
Abstract:Long-term persona agents must remain identifiable while adapting to new events, relationships, evidence, and social conditions. We identify self-locking as a runtime failure mode in continuing persona-life loops: locally plausible events keep appearing while the generated life collapses toward familiar environments, weak relationships, suspended decisions, and stale life stages. We trace this failure to model-level convergence toward high-probability behavioral channels and system-level context gravity from State, memory, history, and environment summaries. We introduce AutoPersonas, a multi-timescale life-environment engine for bounded persona-level recursive self-evolution. It separates environment-side Occurrences, accumulated Observations, and persona State. Its OSO loop admits divergent future-facing material while requiring evidence-governed absorption before State or reachability changes. A three-year compressed simulation exposed environment watermark shells, occurrence-hardening gaps, slow-change accumulation failures, recursive indecision, and weak relationship persistence. An eight-model 40-day stress test generated 1,600 events and found mean rolling 5-day action-category repetition of 95.2%-97.6%, with all models crossing 90% by day 11. Semantic re-keeping found 79.0%-88.0% macro-theme repetition across all direct-loop runs. In a same-runtime 40-day A/B, context-slice masking plus per-sample divergence targeting reduced macro-theme repetition from 61.8% to 36.3% and roughly doubled cumulative theme count. A juvenile-goblin fictional-world run reproduced the anti-fixation regime without hard real-world intrusions. These results support a bounded claim: separating controlled divergence from evidence-governed absorption can reduce persona-environment self-locking while preserving identity continuity.
[HC-10] Simulating the Resident: Generating Executable Smart Home Schedules via LLM Personas
链接: https://arxiv.org/abs/2607.08231
作者: Victor Jüttner,Xenia Wagner,Christoph Jahn,Erik Buchmann
类目: Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC)
备注: Published in the Proc. 1st Symposium on Artificial Intelligence throughout the Human-Centered Design Process ( this https URL ). Winner of the Best Paper Award
Abstract:Smart homes have emerged as an important domain for HCI research, including work on usable security and privacy. Ideally, studies in these areas draw on datasets collected in real homes with real residents, capturing authentic device interactions, network traffic, and daily routines. However, creating such datasets is slow, expensive, and raises significant privacy concerns, as it requires long-term observation of people in their most private spaces. We propose using LLMs to generate diverse resident personas that interact with a simulated smart home, producing behaviorally grounded interaction schedules that can be executed on physical testbeds. We present (1) a design framework configuring simulated households across five socio-technical dimensions, (2) a multi-stage LLM pipeline that produces structured, executable device interaction schedules, and (3) a proof of concept demonstrating feasibility. As a work in progress, we aim to support scalable, privacy-conscious smart-home experimentation without relying on intrusive real-world data collection.
[HC-11] LEXIC: Lightweight Eye-tracking eXtension via Injected Complexity
链接: https://arxiv.org/abs/2607.08152
作者: Sumin Lee,Kyeonghun Kim,Subeen Lee,Jiwon Yang,Tien Nguyen,Ken Ying-Kai Liao,Nam-Joon Kim
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注: Accepted to APCCAS 2026
Abstract:On the recent EyeBench benchmark, predicting reading comprehension from eye movements exposes a stark gap: text-aware models using pretrained language models reach 56–63% AUROC, while gaze-only models operate at chance. We ask how far a gaze-only model can be pushed by lightweight, language-model-free conditioning. Building on the EyeBench AhnCNN baseline, LEXIC-Base, we propose two mechanisms to inject three precomputed word-level difficulty signals, GPT-2 surprisal, word frequency, and word length, into the per-fixation input: direct concatenation, LEXIC-Concat, and a residual mechanism, LEXIC-Res, where a small head predicts typical-reader gaze response and the encoder is conditioned on the deviation. On the OneStop reading comprehension task, with K=5 seed-ensemble training across ten folds, both mechanisms produce statistically consistent AUROC gains on Unseen Text, +1.8 to +2.2 percentage points, Wilcoxon p = 0.065. LEXIC-Concat additionally lifts Unseen Reader by +2.9 percentage points, p = 0.010. We trace an architectural boundary in LEXIC-Res on Unseen Reader, +1.8 percentage points, p = 0.19, to the prediction head being calibrated to training readers, transferring imperfectly to out-of-distribution readers.
[HC-12] HeadRoom: Lightweight Edge-deployable Pipeline for Adaptive Notification Routing
链接: https://arxiv.org/abs/2607.08083
作者: Dinithi Dissanayake,Prasanth Sasikumar,Suranga Nanayakkara
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Emerging wearables, such as smart glasses, can deliver notifications through multiple sensory channels, but there is still a limited understanding of how to choose the right channel at the right moment. We propose HeadRoom, a lightweight, edge-deployable pipeline that estimates the availability of visual and auditory channels in real time from egocentric video and audio. Our controlled user study (N=25) shows that, under high perceptual load, routing notifications to the more available channel reduces response time relative to routing them to the less available channel. This work opens up a new possibility for adaptive routing of notifications in wearable and immersive systems.
[HC-13] he Behavioural Reflection Test: A time-efficient measure of reflective reasoning in morally and epistemically charged decisions
链接: https://arxiv.org/abs/2607.07961
作者: Sion Weatherhead,Flora Salim,Aaron Belbasis,Ben R. Newell
类目: Human-Computer Interaction (cs.HC); Other Statistics (stat.OT)
备注:
Abstract:How readily people override intuitive conclusions through reflection shapes how they navigate dense information environments with reliable and misleading sources; yet the effectiveness of a prominent measure, the Cognitive Reflection Test (CRT), is eroded by widespread exposure to classic items and leaves open how such tendencies manifest more generally in decision style and linguistic expression. The Behavioural Reflection Test (BRT) addresses these issues with a brief open-ended measure of reasoning in morally and epistemically charged scenarios, alongside a four-item bespoke CRT (bCRT) as a low-exposure anchor. Among 473 online adults, higher bCRT predicted more evidence-sensitive, ethically driven decisions and reliance on high-quality sources, marked by more emotionally engaged, risk-attentive, economical language; associations the familiarity-adjusted CRT did not recover. The bCRT showed convergent validity, added item information above mean ability. Though open-ended, the BRT remained a time-efficient (median 11.8 minutes) behavioural assay of reflection with scope to extend across domains.
[HC-14] fog: Expressing Motion and Emotion through Function Composition of AI-Generated Code
链接: https://arxiv.org/abs/2607.07952
作者: Vivian Liu,Lydia Chilton
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL)
备注:
Abstract:Motion and emotion are core parts of intelligent, expressive behavior. In this paper, we introduce fog, a function composition framework for implementing and compose motion functions. We demonstrate how fog can be used to express motion and emotion in Heider-Simmel style animations. This code generation framework can help users generate functions for verbs, adverbs, gestures, and emotions to create an open-ended motion vocabulary. It is complemented by an animation editor that helps users refine motion through direct manipulation and dynamically generated UI. We evaluate our approach with a perceptual evaluation, where we test 452 fog-generated animations to see if people can recognize the semantic meaning of the motion. We find that fog’s motion functions can be recognized at 68% accuracy, a 2.68x improvement over a chance baseline. In a mixed-methods user study with professionals and novices, we show that fog in interface form can support users with more rapid iteration, exploration, and control.
[HC-15] Feedback Manipulation Regularization: Enabling Offline Agent Alignment for Imitation Learning
链接: https://arxiv.org/abs/2607.07859
作者: Benjamin Poole,Minwoo Lee
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注:
Abstract:Reinforcement learning (RL) research has increasingly shifted focus towards alignment, ensuring agents learn behaviors adhering to human values. While human demonstrations and feedback have proven crucial for alignment, existing approaches predominantly combine these signals using multi-stage pipelines designed for the contextual bandit framing of language generation. Yet little work explores how these complementary inputs can serve as a richer, interconnected signal for single-stage offline training in fully sequential decision-making environments. We propose Feedback Manipulation Regularization (FMR), an algorithm-agnostic method that harnesses evaluative feedback as a corrective signal to improve the alignment of imitation learning policies. We adapt Safety Gymnasium environments to be a principled testbed for alignment evaluation, demonstrating improved aptitude and up to a 98% reduction in misalignment across a range of imitation learning algorithms. FMR remains robust in limited data regimes, even when learning from scarce aligned and uninformative noisy demonstrations.
[HC-16] Idiobionics: The Unification of Privacy and Intelligent Robotic Prostheses
链接: https://arxiv.org/abs/2607.07775
作者: Kwesi Afari Darfoor,Patrick M. Pilarski,Bailey Kacsmar
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: 8 pages, 3 figures
Abstract:The human body is at the center of a growing family of technologies designed to tightly and persistently couple biological and digital systems. Robotic prostheses are a representative example of this tight coupling. Also referred to as bionic limbs, robotic prostheses are devices that support people who have lost limbs in pursuing daily life activities such as walking and grasping objects. Bionic limbs are now perceptive and responsive owing to their integration with advanced sensors and artificial intelligence-based control approaches. Consequently, such robotic prostheses can now be viewed as semiautonomous wearable robotic systems that can co-adapt with their users. However, the same sensing and control advancements that increase the capability of robotic prostheses also introduce threat vectors that could be exploited by malicious entities to violate the privacy of users. To fully realize the benefits of next-generation bionic limbs, we maintain it is important to directly understand and address these privacy risks and the barriers they might present to user adoption. This paper therefore introduces a new line of inquiry we term idiobionics to holistically investigate issues at the intersection of privacy and intelligent bionic limbs. As the main contribution of this paper, we define idiobionics, ground it in related literature, and provide preliminary evidence showing and discussing potential adversarial attacks that could exploit intelligent bionic limb designs. We then contribute a curated list of open research questions within idiobionics that are relevant to researchers in wearable robotics and other human-facing autonomous systems. We expect that idiobionics research will help unlock the full potential of robotic prostheses and related bionic devices.
计算机视觉
[CV-0] Wat3R: Underwater 3D Geometry Learning without Annotations ECCV2026
链接: https://arxiv.org/abs/2607.08772
作者: Jiangwei Ren,Xingyu Jiang,Zijie Song,Wei Xu,Hongkai Lin,Dingkang Liang,Xiang Bai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026. The dataset and code are available at this https URL
Abstract:Estimating 3D geometry in underwater environments presents unique challenges due to light attenuation, scattering, and the absence of large-scale, high-quality 3D annotations. Pioneering methods rely on massive dense annotations that are impractical in underwater settings. In this paper, we propose Wat3R, a cross-domain semi-supervised learning framework designed to adapt feed-forward 3D reconstruction models from air to underwater scenes. Uniquely, our method eliminates the need for any annotated underwater data following a teacher-student architecture, that learns robust geometry representations merely on abundant unlabeled real underwater video footage. We also design a cross-view consistency loss that leverages geometric cues from other views to compensate for the information degradation in the current view caused by water attenuation and scattering. Furthermore, considering the lack of comprehensive evaluation benchmarks, we construct Water3D, a diverse dataset covering various water bodies and underwater scenarios, designed for geometric task evaluation. Experimental results demonstrate that Wat3R outperforms current state-of-the-art methods in underwater multi-view depth estimation and point cloud reconstruction. The dataset and code are available at this https URL .
[CV-1] ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere on Any Device ECCV2026
链接: https://arxiv.org/abs/2607.08771
作者: Fabio Tosi,Luca Bartolomei,Matteo Poggi,Stefano Mattoccia
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026. Code: this https URL - Project page: this https URL
Abstract:Monocular depth estimation has seen remarkable progress through foundation models achieving robust zero-shot generalization, yet their computational demands place them far beyond the reach of embedded and mobile platforms. Lightweight alternatives exist, but have been developed almost exclusively within single-domain, self-supervised paradigms, failing silently under domain shift. We present ZipDepth, a compact monocular depth network that bridges this gap by combining an efficient reparameterizable encoder-decoder with large-scale knowledge distillation from a foundation model over a large multi-domain training set. Comprising just 6.1M parameters, ZipDepth runs at real-time rates from server GPUs to power-constrained devices, achieving the best trade-off between zero-shot accuracy and deployment efficiency among lightweight models across five benchmarks, taking a significant step towards the accuracy of foundation models with 50x more parameters.
[CV-2] LongE2V: Long-Horizon Event-based Video Reconstruction Prediction and Frame Interpolation with Video Diffusion Models SIGGRAPH2026
链接: https://arxiv.org/abs/2607.08770
作者: Cheng-De Fan,Chun-Wei Tuan Mu,Chen-Wei Chang,Chin-Yang Lin,Kun-Ru Wu,Yu-Chee Tseng,Yu-Lun Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: SIGGRAPH 2026. Project page: this https URL
Abstract:Recovering high-quality video from sparse event streams is a challenging task. Regression methods often blur textures, while existing generative models struggle with long-term stability. We propose LongE2V, a novel approach that leverages pre-trained video diffusion priors to jointly handle event-based video reconstruction, prediction, and frame interpolation. By fine-tuning a foundational video model, our approach achieves high data efficiency and superior perceptual quality. We introduce Autoregressive Unrolling and Adaptive Context Switching to mitigate temporal drift in extremely long sequences. We also propose Reencoding Alignment with Cross Residual Correction to ensure precise bidirectional consistency during frame interpolation. Furthermore, Event Voxel Density Augmentation ensures robustness across varying sensor resolutions. Extensive experiments on real-world benchmarks demonstrate that LongE2V outperforms state-of-the-art methods across all three tasks, exhibiting exceptional temporal coherence and zero-shot generalization. Project page: this https URL
[CV-3] Geometry and Gradient-based Partitioning for Panoramic Outdoor Reconstruction
链接: https://arxiv.org/abs/2607.08769
作者: Weijian Chen,Weibo Yao,Yuhang Zhang,Xiaolin Tang,Guo Wang,Weijun Zhang,Xitong Gao,Yihao Chen,Hongde Qin,Lu Qi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Webpage: this https URL
Abstract:Scaling 3D Gaussian Splatting (3DGS) to large outdoor scenes is costly in both data acquisition and computation. Adopting panoramic images with equirectangular projection (ERP) can reduce capture effort via their full 360^\circ field of view, yet the resulting omnipresent visibility invalidates existing partitioning strategies that rely on local camera frustums, causing block-wise optimization to degenerate into global training. Thus, we propose PanoLOG, a two-stage coarse-to-fine framework equipped with a Geometry and Gradient-based Partitioning Strategy tailored for large-scale panoramic 3DGS reconstruction. In the global coarse stage, PanoLOG leverages sky-sphere modeling and panoramic monocular depth supervision for reliable geometry, while in the refinement stage, G ^2 PS builds adaptive bounding volumes via parallax-driven uncertainty and assigns cameras via gradient-based importance scoring. Furthermore, we construct Pano360, the first benchmark on large-scale panoramic dataset for outdoor scene reconstruction. Extensive experiments demonstrate that G ^2 PS achieves state-of-the-art rendering quality while maintaining scalable, block-parallel training. Our models, training code, and dataset are publicly available.
[CV-4] OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators
链接: https://arxiv.org/abs/2607.08766
作者: Hongyu Liu,Chun Wang,Feng Gao,Xuanhua He,Yue Ma,Ziyu Wan,Yong Zhang,Xiaoming Wei,Qifeng Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL ; Code: this https URL
Abstract:We propose OPSD-V, an on-policy self-distillation paradigm for post-training few-step autoregressive (AR) video diffusion models. Existing few-step AR video generators can produce long videos with low latency, but still suffer from error accumulation and weakened motion dynamics during long autoregressive rollout. OPSD-V reduces long-horizon degradation while preserving the original few-step inference path. The key idea is to introduce real long-video data as temporal context during training and use it to provide dense trajectory-level supervision. Specifically, the student follows the exact inference-time rollout, generating each chunk conditioned on its own previously generated KV cache. In parallel, the teacher is evaluated at the same student-visited denoising states, but uses a cleaner AR-consistent temporal cache in which older history can be replaced by real-video context. This provides dense denoising-level corrective targets under on-policy AR cache dynamics, without changing the sampler, number of denoising steps, or inference-time cache mechanism. We apply OPSD-V to representative few-step AR video models, including Self-Forcing and LongLive. Experiments show consistent improvements in visual quality, motion dynamics, and VBenchLong scores. A user study with 10 participants comparing 20 video pairs shows that OPSD-V is preferred over the base models in 66.0% of overall-preference judgments (82.5% excluding ties).
[CV-5] Enhancing In-context Panoramic Generation via Geometric-aware Pretraining
链接: https://arxiv.org/abs/2607.08765
作者: Haoran Feng,Ruiyang Zhang,Longyi Zhang,Dizhe Zhang,Lu Qi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In this work, we present Canvas360, a two-stage framework for in-context panoramic generation that combines geometry-aware pretraining with downstream task-specific fine-tuning. To address the lack of large-scale, high-quality training data tailored to in-context panoramic tasks, we propose Canvas360Dataset, a collection of 1M high-quality paired panoramic samples for style transfer, inpainting, outpainting, and editing, enabling effective supervision across diverse in-context generation scenarios. On the modeling side, Canvas360 enhances text-to-panorama generation through parallel depth generation, velocity circular padding, and similarity loss regularization, enabling the model to learn geometry-aware representations, capture object distortion details, and improve geometric consistency and global coherence. Furthermore, empowered by strong panoramic priors, Canvas360 enables a unified in-context panoramic generation framework that supports diverse downstream tasks via token-level concatenation, surpassing prior methods in both task coverage and modeling flexibility. Extensive experiments show that Canvas360 improves panoramic image fidelity, achieving particularly strong performance on the panorama-specific FAED metric and competitive or leading results across the reported quantitative evaluations. More information can be found on our project page: this https URL
[CV-6] OpenCoF: Learning to Reason Through Video Generation
链接: https://arxiv.org/abs/2607.08763
作者: Xinyan Chen,Ziyu Guo,Renrui Zhang,Dongzhi Jiang,Hongsheng Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Project Page: this https URL
Abstract:Reasoning has become a core capability for large models, especially when reliable decisions require understanding logical consequences. Recent video generation models offer a reasoning path distinct from previous Chain-of-Thought (CoT): reasoning can unfold through temporally connected frames, known as Chain-of-Frame (CoF) reasoning. However, existing video generators are primarily trained on general video corpora, still lacking diverse supervision and dedicated designs for CoF reasoning. To address this gap, we introduce OpenCoF, a framework comprising the OpenCoF-17K dataset, a reasoning video dataset spanning 11 task families, and Wan-CoF, a fine-tuned video model for studying whether diverse temporal supervision improves CoF behavior. Across four video reasoning benchmarks, Wan-CoF achieves considerable gains over the Wan2.2-I2V-A14B baseline. Building on this, we empirically explore more advanced designs for CoF capabilities, i.e., equipping the model with visual and textual reasoning tokens. This mechanism respectively captures low-level visual cues and high-level semantic priors for spatial and temporal reasoning. Through performance comparisons and attention analysis, we examine how these tokens contribute across model depth, denoising steps, space, and time. Our results suggest that stronger video reasoning requires both broad temporal supervision and explicit mechanisms for organizing intermediate reasoning state. We open-source the dataset, model, and code to facilitate future research on reasoning-oriented video generation.
[CV-7] AUTOPILOT VQA: Benchmarking Vision-Language Models for Incident-Centric Dashcam Understanding CVPR
链接: https://arxiv.org/abs/2607.08745
作者: Siddharth Damodharan,Radhika Gupta,Ali Alshami,Ryan Rabinowitz,Jugal Kalita
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR Autopilot Workshop
Abstract:Recent advances in Vision-Language Models, Large Language Models, and Multimodal Large Language Models have improved autonomous driving tasks such as scene understanding, decision making, trajectory prediction, and visual question answering. However, evaluating whether these models can reliably reason about safety-critical incidents remains challenging. To address this gap, we present AUTOPILOT-VQA, an incident-centric visual question answering benchmark for dashcam video understanding. The dataset evaluates different systems through structured questions designed around real-world driving incidents and near-incidents. The benchmark covers diverse safety-relevant categories, including weather and lighting conditions, traffic environment, road layout, road surface state, signage, involved entities, accident occurrence, impact location, and avoidability-related reasoning. By requiring models to answer grounded questions about both contextual scene properties and event-level incident details, AUTOPILOT-VQA moves beyond object recognition toward temporally grounded, safety-aware reasoning. The dataset is released as part of the AUTOPILOT CVPR 2026 competition and provides a standardized benchmark for assessing the reliability of autonomous driving systems in different scenarios. Our benchmark support developments for more interpretable, robust, and safety-conscious vision-language systems for real-world autonomous driving.
[CV-8] ARDY: Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation SIGGRAPH2026
链接: https://arxiv.org/abs/2607.08741
作者: Kaifeng Zhao,Mathis Petrovich,Haotian Zhang,Tingwu Wang,Siyu Tang,Davis Rempe
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)
备注: ACM Transactions on Graphics (SIGGRAPH 2026)
Abstract:Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and humanoid robotics. While recent offline motion generation approaches offer precise control via text and kinematic constraints, they lack the inference speed required for interactive settings. Conversely, existing online methods enable real-time synthesis but often sacrifice controllability or struggle with complex text semantics and long-horizon goals due to limited context windows. In this work, we introduce ARDY, a streaming generation framework that bridges this gap by enabling high-fidelity motion generation controllable via online text prompts and flexible kinematic constraints. ARDY employs a hybrid representation that combines explicit root features with a latent body embedding, balancing precise trajectory control with efficient generative learning. We propose a two-stage autoregressive transformer denoiser that features variable history context and supports conditioning on flexible, long-horizon kinematic constraints. By training on a large-scale motion capture dataset and being directly conditioned on text labels and kinematic constraints sampled from ground truth poses, ARDY natively learns controllable generation that supports online prompting and flexible long-horizon goals. Extensive evaluations on the HumanML3D benchmark and the large-scale, high-fidelity Bones Rigplay dataset demonstrate ARDY’s high motion quality and constraint adherence, validating the efficacy of our key architectural decisions. Finally, we demonstrate the method’s practical versatility through an interactive demo featuring dynamic text control, diverse keyframe pose constraints, path following, and interactive locomotion control via mouse and keyboard. Supplementary video results, code, and model releases can be found at this https URL.
[CV-9] WaspMOT: A Benchmark for Long-Term Multi-Object Tracking of Trichogramma Wasps
链接: https://arxiv.org/abs/2607.08729
作者: Tomasz Stanczyk,Yuan Gao,Hardik Agarwal,Seongroo Yoon,Tiantao Zhang,Vincent Calcagno,Francois Bremond
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multi-object tracking (MOT) has achieved strong performance on benchmarks dominated by short video sequences. However, such datasets do not adequately evaluate long-term identity preservation, where objects must be tracked consistently over extended durations. We introduce WaspMOT, a benchmark designed to address this gap through long-duration tracking of Trichogramma wasps in controlled ecological experiments. The dataset contains 10 sequences of approximately 12,000 frames each (over 8 minutes at 25 FPS), with dense MOTChallenge annotations and oracle detections to isolate association performance. Unlike existing benchmarks, WaspMOT forms a closed-set tracking scenario where all individuals remain present throughout the sequence, requiring consistent identity assignment across thousands of frames despite abrupt jumps, occlusions, and highly similar appearance. We establish a benchmark by evaluating five tracking-by-detection methods, including ByteTrack, BoT-SORT, C-BIoU, OC-SORT, and McByte, under a unified protocol. Results show that all methods suffer from significant trajectory fragmentation, highlighting the difficulty of long-term identity preservation even with perfect detections. A simple spatial tracklet stitching baseline consistently improves performance, indicating that substantial gains remain possible. WaspMOT provides a new benchmark for studying long-term association and reveals limitations of current tracking approaches that are not observable on conventional datasets. The benchmark will be made publicly available at the project repository: this https URL . Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2607.08729 [cs.CV] (or arXiv:2607.08729v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.08729 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: AVSS 2026
[CV-10] Pose-to-Biomechanics: Bridging 3D Human Pose Estimation and Biomechanical Attribute Prediction
链接: https://arxiv.org/abs/2607.08725
作者: Ayda Eghbalian,Kevin Desai
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 23 pages, 2 figures
Abstract:Recent progress in 3D human pose estimation has made markerless recovery of skeletal motion increasingly accurate and scalable. However, most pose estimators remain optimized for geometric keypoint accuracy, while many real-world applications in rehabilitation, sports science, ergonomics, and clinical movement analysis require biomechanical quantities that describe how the body moves, loads, and activates. In this work, we propose BioModule, a lightweight plug-in temporal transformer that attaches downstream of any 3D pose estimator and predicts biomechanical attributes from standard 17-joint 3D skeletons. BioModule is estimator-agnostic and requires no modification of the upstream pose model, enabling existing pose estimators to be extended toward physically interpretable motion analysis. To train and evaluate BioModule, we construct a large-scale aligned dataset pairing Human3.6M video and 3D keypoints with the biomechanical label space of Human3.6Mplus. We establish and verify anatomical correspondence between coordinate systems of the two datasets, enabling frame-accurate cross-modal supervision. Using this aligned supervision, BioModule predicts biomechanical quantities. We further benchmark BioModule across seven state-of-the-art 3D pose estimators, providing the first systematic analysis of how upstream pose estimation quality propagates to downstream biomechanical prediction fidelity. The results position BioModule as a compact, modular bridge between vision-based pose estimation and biomechanically meaningful human motion analysis. Comments: 23 pages, 2 figures Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2607.08725 [cs.CV] (or arXiv:2607.08725v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.08725 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-11] LTM: Large-scale Terrain Model for Wildfire-prone Landscapes
链接: https://arxiv.org/abs/2607.08711
作者: Xiao Fu,Yue Hu,Meida Chen,Peter Anthony Beerel,Barath Raghavan
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Accurate 3D terrain maps are essential for emergency response when assessing wildfire hazards. However, wildfire-prone regions often span vast areas where conventional reconstruction methods underperform. Airborne LiDAR systems provide high-resolution terrain data, but they are expensive and infrequently updated. Image-based methods offer a lower-cost alternative, but struggle due to sparse visual features and limited image overlap. We propose a multi-modal reconstruction framework leveraging outdated Digital Elevation Models (DEMs) as geometric priors for image-based 3D reconstruction. Our key innovation is physics-based pixel-pixel alignment between images and DEM data, dramatically reducing computational complexity by eliminating expensive feature matching procedures. To validate our approach, we developed a large-terrain simulator based on a real wildfire-prone area, generating realistic images enabling a comprehensive evaluation. Given posed images and legacy DEMs, our method produces high-fidelity depth maps while maintaining real-time performance. We find significant improvements in reconstruction accuracy and computational efficiency over existing techniques, offering a scalable solution for wildfire response.
[CV-12] HumanForge: A Human-Centric Deepfake Video Benchmark with Multi-Agent Forgery Rationales
链接: https://arxiv.org/abs/2607.08705
作者: Wenbo Xu,Zhimin Chen,Xiaojie Liang,Hengrui Liu,Wei Lu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 2 figures
Abstract:Rapid advancements in video diffusion models and temporal editing tools have enabled the generation of highly realistic human-centric videos, posing unprecedented challenges to digital content forensics. Existing benchmarks primarily focus on either face-swapping or global text-to-video synthesis, overlooking the crucial dimensions of human-object or human-human interactions and multi-modal alignment. To address these limitations, we introduce HumanForge, a unified, large-scale, and multi-paradigm human-centric video forgery dataset. To construct and annotate this dataset without labor-intensive manual labeling or hallucinated monolithic prompts, we propose Gen2Anno, a modular active multi-agent pipeline built on LangGraph. Gen2Anno coordinates six specialized agents-ranging from source profiling to MoE-based reference analysis and closed-loop forensic verification-to generate over 18K high-fidelity video segments and produce structured, contrastive omni-annotations containing binary decisions, fine-grained artifact categories, and spatio-temporal localization. Extensive benchmarks using state-of-the-art traditional detectors and Large Multimodal Models (LMMs) demonstrate the significant challenges of zero-shot generalization and fine-grained reasoning on HumanForge. Code and dataset will be publicly released.
[CV-13] SAM-MT: Real-Time Interactive Multi-Target Video Segmentation ECCV2026
链接: https://arxiv.org/abs/2607.08688
作者: Ruiqi Shen,Chang Liu,Henghui Ding
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026, Project Page: this https URL
Abstract:Modern Video Object Segmentation (VOS) involves tracking and segmenting user-specified targets. While recent approaches have achieved remarkable performance in single-target scenarios, extending them to multi-target settings typically involves replicating the single-target processing for each individual object, resulting in reduced frame rates (FPS) with unbounded latency as target count increases. Built upon Segment Anything 2 (SAM2), we propose SAM-MT, which addresses this by transforming the model into an interactive framework for real-time Multi-Target video segmentation. SAM-MT uses explicit queries to represent different individual targets, in parallel with a shared representation for global context. It employs decoupled masked attention to keep individual identities distinct from cross-target interference, and sparse memory for stable temporal evolution, along with specialized strategies for occlusion handling and overlap prevention. SAM-MT successfully decouples latency from the number of targets, achieving real-time speed on par with single-target baselines (36 FPS for 10 targets) while maintaining SAM2’s robust video segmentation performance.
[CV-14] Multi-Resolution Feature Stem for Diabetic Retinopathy lesion segmentation
链接: https://arxiv.org/abs/2607.08679
作者: Indranil Dutta,Taehee Jeong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 2026 International Conference on Advances in Artificial Intelligence and Machine Learning (AAIML), 20-22 March 2026
Abstract:Diabetic Retinopathy (DR) is a leading cause of preventable blindness worldwide, requiring automated lesion segmentation using deep learning models for early detection and monitoring. However, DR lesions vary dramatically in size from tiny microaneurysms to large hemorrhages and exudates. This variability creates conflicting demands on the model architecture and input resolution, posing a challenge for effective design. This work investigates the impact of input resolution on different lesion types. Through systematic experimentation with multiple architectures (U-Net, UNet++, Vision Transformers, DeepLabV3+) at 512 \times 512 and 1024 \times 1024 resolutions, we identify a critical, counter-intuitive phenomenon where increasing input resolution has opposing effects on different lesion types. We demonstrate that while higher resolution is essential for resolving fine-grained microaneurysms, it can unexpectedly degrade performance on larger hemorrhages. This finding challenges the common assumption that higher resolution is uniformly beneficial. To address this, we propose a novel Multi-Resolution Feature Stem, an input-level pyramid integrated with a UNet++ backbone. This architecture processes multiple scales in parallel, capturing fine-grained details without sacrificing contextual information. This work contributes crucial empirical evidence of this complex, resolution-dependent behavior and a practical, parameter-efficient architecture that successfully resolves this trade-off.
[CV-15] Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection
链接: https://arxiv.org/abs/2607.08674
作者: Kutub Uddin,Nusrat Tasnim,Awais Khan,Mohammad Umar Farooq,Khalid Malik
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The rapid advancement of generative AI has enabled the creation of highly realistic deepfake media, posing significant threats, including misinformation, digital identity theft, fraud, and manipulation of public opinion. AI-generated image (AIGI) detection is reliably challenging due to the diversity of generative methods and the subtle artifacts they leave behind. In this work, we propose GenRes, a novel framework for generative residual learning via a neural tensor network, which models fine-grained relational features between original and transformed samples to enhance generalization. To address scenarios involving multiple generative transformations, we introduce GenRes++, which employs a learnable attention mechanism to aggregate relational features across multiple transformed samples and enables the model to focus on the most informative cues. Both models leverage PE-Core as a feature extractor, providing generalized and semantically rich embeddings that improve cross-domain performance and enable the detection of AIGI generated by unseen methods. Comprehensive experiments on multiple benchmark datasets demonstrate that the proposed GenRes++ approach outperforms existing methods.
[CV-16] Native Video-Action Pretraining for Generalizable Robot Control
链接: https://arxiv.org/abs/2607.08639
作者: Qihang Zhang,Lin Li,Luyao Zhang,Shuai Yang,Yiming Luo,Shuaiting Li,Ruilin Wang,Junke Wang,Jiahao Shao,Gangwei Xu,Jiaming Zhou,Yishu Shen,Yudong Jin,Fangyi Xu,Shuailei Ma,Jiaqi Liao,Guanxing Lu,Zifan Shi,Yongkun Wen,Yujie Zhao,Weixuan Tang,Xinyang Wang,Chaojian Li,Jiapeng Zhu,Ka Leong Cheng,Nan Xue,Xing Zhu,Yujun Shen,Yinghao Xu
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The advent of video-action models offers a promising path for robot control. Nevertheless, we argue that repurposing video generative models designed for digital content creation is inherently inadequate for physical environments. To bridge this gap, we present LingBot-VA 2.0, a video-action foundation model built from the ground up for embodiment. Four core design principles showcase its evolution from LingBot-VA. (1) Departing from traditional reconstruction-focused VAEs, we introduce a semantic visual-action tokenizer, which aligns visual representations with both semantics and actions, improving instruction following and action precision in subsequent policy learning. (2) Given the strictly causal nature of temporal dynamics, we adopt a causal pretraining paradigm, training from scratch to circumvent the catastrophic forgetting that frequently occurs when adapting bidirectional architectures. (3) To meet the demands of high-frequency inference, our model employs a sparse MoE backbone, expanding model capacity without compromising efficiency. (4) Real-time closed-loop control is realized through an enhanced asynchronous inference scheme, which predicts future latents in parallel with action execution while re-grounding each rollout on the latest observation via learned forward dynamics. Real-world deployment validates LingBot-VA 2.0 as a robust foundation model, as evidenced by its few-shot generalization across complex manipulation tasks.
[CV-17] When Structured Sparse Autoencoders Learn Consistent Concepts Across Modalities
链接: https://arxiv.org/abs/2607.08605
作者: Weiduo Liao,Yunqiao Yang,Ying Wei
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Sparse autoencoders (SAEs) have emerged as a promising technique for mechanistic interpretability by learning a set of sparse latent features in large models, each of which encodes a distinct concept. However, in vision-language models (VLMs), vanilla SAEs struggle to learn modality-consistent concepts, with concepts often exhibiting fragmented coverage (i.e., disjoint regions) in the visual modality. To address this challenge, we propose a Structured Sparse AutoEncoder ( S^2AE ) that enforces concept consistency from both semantic and spatial perspectives in the visual modality. Specifically, we group image patches based on Transformer attention similarity and spatial proximity, and introduce a structured sparsity regularization when training the vanilla SAE. The regularization consists of exclusive sparsity for inter-group concept disentanglement and group sparsity for intra-group concept consistency, which drives the latent neurons by SAEs to specialize in distinct, semantically grounded concepts. Evaluated on the \textttQwen2.5-VL-7B-Instruct model, the method achieves 6.06% average improvement in semantic alignment (mIoU) and 60.81 in representational efficiency (lower l0 norm) while maintaining near-perfect reconstruction fidelity with an Explained Variance above 99%. Cross-modal analysis further demonstrates that S^2AE enhances neuronal monosemanticity by this visual structural prior, achieving a 3.08% average gain in semantic consistency and a 2.37% average gain in monosemanticity scores for both modalities of multimodal features, thereby fostering more coherent and disentangled representations.
[CV-18] Switch-Reason er: Learn When to Think in Multitask Mixtures via Reinforcement Learning
链接: https://arxiv.org/abs/2607.08572
作者: Yiyang Fang,Pei Fu,Jinjie Li,Jian Liang,Wenke Huang,Ruijie Luo,Shaojie Zhang,Jian Luan,Yi R. Fung,Mang Ye
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal Large Language Models (MLLMs) often follow a fixed Think-then-Answer paradigm, which is inefficient in heterogeneous multitask settings because simple inputs may not require explicit reasoning while difficult ones can benefit substantially from it. Learning when to think is also unstable during post-training, where imbalanced rollouts can drive the model toward always-thinking or always-direct behavior. We propose Switch-Reasoner, a GRPO-based framework that learns to adaptively select reasoning modes for MLLMs. It treats thinking as a virtual tool invocation and allows the model to either answer directly or invoke explicit reasoning before answering. To stabilize this decision, we introduce a dual-level regulation mechanism that balances the overall use of Thinking Mode and Direct Mode while providing sample-level supervision based on the relative benefit of the two choices. Experiments on 11 multimodal tasks show that Switch-Reasoner reduces unnecessary reasoning while maintaining strong performance, achieving a better accuracy-efficiency trade-off.
[CV-19] VocaDet: Sample-Driven Open-Vocabulary Object Detection and Segmentation via Visual Tokenization and Vector Database Retrieval
链接: https://arxiv.org/abs/2607.08541
作者: ZhiXin Sun
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Open-vocabulary object detection and segmentation aim to recognize arbitrary objects beyond predefined categories. Although recent vision-language and reference-based approaches have significantly advanced this field, they often rely on text prompts, limited visual examples, or expensive feature matching procedures, making them difficult to scale to large and continuously expanding object repositories. In this work, we propose VocaDet, a sample-driven open-vocabulary object detection and segmentation framework that learns object concepts directly from user-provided positive and negative sample collections without model retraining. The key idea is to transform continuous visual representations into discrete visual vocabularies and perform efficient retrieval-based recognition through a scalable vector database. Specifically, we employ DINOv3 as the visual feature extractor and apply agglomerative clustering with adaptive clustering sensitivity to generate multi-granularity visual tokens. These visual tokens, together with position-debiased representations and spatial topology information, are stored as expandable object memories in a vector database. During inference, query images are converted into visual tokens and efficiently matched against the stored object memories for object localization and segmentation. Furthermore, a background filtering mechanism is introduced to remove frequently occurring background patterns and reduce redundant retrieval operations in practical fixed-camera scenarios. Experiments on the UA-DETRAC dataset demonstrate that VocaDet achieves effective open-vocabulary detection performance without conventional detector training, while supporting continuously expandable recognition capability as additional positive and negative samples are accumulated.
[CV-20] Whareformer: Learning to Track What is Where in Long Egocentric Videos ECCV2026
链接: https://arxiv.org/abs/2607.08537
作者: Jacob Chalk,Saptarshi Sinha,Dima Damen,Yannis Kalantidis,Diane Larlus
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV 2026. Project Webpage: this https URL
Abstract:The recently established ‘Out of Sight, Not out of Mind’ (OSNOM) task for egocentric videos focuses on tracking objects that are moved by the camera wearer, online, maintaining knowledge of instance locations throughout the video even when they leave the field of view or become heavily occluded. In this paper, we propose the first learning-based solution to the OSNOM task: Whareformer, a transformer-based model with two components: an updatable memory of established tracks and a track assignment module that associates observations with existing tracks in a feed-forward manner. Whareformer jointly reasons over evolving object appearance (what) and updated 3D location (where), and employs a dedicated New Track token to reason about novel objects. Thanks to its design choices of using relative distances and evolving track representations, Whareformer is trained on a small set of 56 videos but achieves SOTA performance on 260 long test videos from three datasets: EPIC-KITCHENS-100 (unseen videos), IT3DEgo, and HD-EPIC, with significant absolute improvements over prior work. Comments: Accepted at ECCV 2026. Project Webpage: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2607.08537 [cs.CV] (or arXiv:2607.08537v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.08537 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-21] Beyond wheelchairs and blindfolds: Investigating disability stereotypes in T2I models with INCLUDE-BENCH
链接: https://arxiv.org/abs/2607.08515
作者: Sophia Lichtenberg,Albert Gatt,Judith Masthoff
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-to-image (T2I) models have been shown to exhibit social biases. Prior work has mainly focused on gender, skin tone, and cultural representation within restricted occupational associations, and emerging benchmarks increasingly incorporate these dimensions. However, disability remains systematically underexplored. Current evaluation practices often fail to align with sociologically grounded definitions of stereotyping, limiting principled assessment of representational harms toward people with disabilities (PWD). To address this, we introduce INCLUDE-BENCH, the first large-scale benchmark for evaluating disability-related bias in T2I models. INCLUDE-BENCH comprises 119K generated images based on prompt design across multiple bias dimensions and both static and dynamic contexts. We evaluate 15 open-source and 2 closed-source models. Our key findings reveal that: (1) mobility-impaired and default disability prompts predominantly yield wheelchair depictions across all models; (2) disability-conditioned generations consistently exhibit less diversity; (3) stereotypical portrayals demonstrate stronger disability-text alignment; and (4) we introduce the Stereotype Content Model (SCM) Score, demonstrating that T2I models reflect real-world stereotypical associations.
[CV-22] Do Egocentric Video-Language Models Capture Both Hand- and Object-Centric Cues?
链接: https://arxiv.org/abs/2607.08514
作者: Masatoshi Tateno,Alexandros Stergiou,Risa Shinoda,Yoichi Sato,Dima Damen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Hand-object interaction (HOI) recognition requires capturing both hand manipulations and object transformations. However, existing video-language models often fall into shortcuts by relying on spurious correlations among hands, objects, or environmental context, rather than reasoning from the appearance and dynamics of hands and objects themselves. To address this limitation, we propose a new learning paradigm that combines (i) hand-object masked training, which enables robust reasoning from partial hand or object observations, and (ii) an HOI-dynamics-aware decoder that explicitly learns hand- and object-centric embeddings through auxiliary predictions of their locations and semantics, enhancing sensitivity to both cues. To systematically evaluate such cue-specific reasoning, we introduce Cue-Isolated HOI (CI-HOI), a new evaluation that assesses models’ ability to predict actions from hand- and object-related cues independently. To enable CI-HOI, we curate the DEHOI testbed, which separates hand- and object-related observations for disentangled HOI evaluation through inpainting. Using DEHOI, we demonstrate both quantitatively and qualitatively that our training strategy exploits hand- and object-centric information more effectively than existing models. Our approach improves over existing models on DEHOI, standard action recognition, object state recognition, and even robot manipulation action recognition, leading to more robust HOI understanding.
[CV-23] Systematic Evaluation of Learning Rate Scheduling Strategies Across Heterogeneous Architectures
链接: https://arxiv.org/abs/2607.08511
作者: Hafsa Mateen,Radu Timofte,Dmitry Ignatov
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Choosing a learning rate scheduling strategy is critical to neural network training, but manual selection is costly and rarely exhaustive. While classical AutoML approaches often treat the scheduler as a secondary hyperparameter, we systematically investigate its impact on classification accuracy across a diverse pool of architectures. We evaluated 30 representative architectures from convolutional and transformer families within the LEMUR neural network dataset. Through automated source-code injection, we applied 25 scheduler configurations across nine PyTorch families, evaluating a total of 3,938 model variants on CIFAR-10. Our best configuration achieved a top-1 accuracy of 86.45%, with 237 variants exceeding 80%. The results show that the choice of scheduler depends heavily on the architecture: CosineAnnealingWarmRestarts and CyclicLR consistently outperform basic decay strategies. The resulting accuracy landscape, contributed to the LEMUR nn-dataset, provides a practical reference for principled scheduler selection.
[CV-24] CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction
链接: https://arxiv.org/abs/2607.08503
作者: Sofie Allgöwer,Mikael Johansson,Andreas Hallqvist,Jonas Andersson,Åse Johnsson,Ida Häggström,Jennifer Alvén
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 2 figures
Abstract:Accurate prognosis prediction is important for treatment planning in lung cancer, but deep learning-driven survival modelling is often limited by the scarcity of curated imaging cohorts with reliable outcome data. This study evaluates whether representations from a domain-specific foundation model can be used for multimodal survival prediction in data-constrained clinical settings. We assess the foundation model CT-CLIP as a feature extractor for pretreatment computed tomography images and clinical variables from 242 diagnosed lung cancer patients. The evaluation includes adaptation strategies based on frozen encoders, full fine-tuning, and low-rank adaptation, together with modality ablations and comparisons with clinical and multimodal baselines. The results show that a frozen CT-CLIP model combined with a trainable lightweight survival head outperforms the clinical baseline and achieves comparable or improved performance relative to other multimodal approaches, and separates patients into clinically meaningful high- and low-risk groups.
[CV-25] Predicting Viticulture Potential through an Ensemble of U-Net and a Geospatial Foundation Model
链接: https://arxiv.org/abs/2607.08449
作者: Jorge Ignacio Perez,Hwaai Kang Kee,Lucas Rassbach
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: To be published in CLEF 2026 Working Notes
Abstract:Determining agricultural potential is fundamental to sustainable land management and agricultural planning. Remote sensing data is increasingly valuable as an avenue for agricultural potential due to the cost of traditional methods (surveys, in-situ measurements, soil testing, etc). ImageCLEF AI4Agri 2026: Subtask 1 is concerned with the prediction of viticulture potential in Southern France. The DS@GT ARC’s submission for Subtask 1 introduces an ensemble of U-Net and a Geospatial Foundation Model (Prithvi-2.0). Our best model achieved a \pm 1 accuracy of 68.32 on the leaderboard, ranking 2nd among 7 teams. The implementation for this work is publicly available at this https URL .
[CV-26] DeltaV: Thinking with Visual State Updates in Unified Large Multimodal Models
链接: https://arxiv.org/abs/2607.08434
作者: Pengjie Wang,Linger Deng,Zujia Zhang,Shaojie Zhang,Zhenbo Luo,Pei Fu,Jian Luan,Xiang Bai,Yuliang Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Current Unified Large Multimodal Models (ULMMs) support interleaved multimodal reasoning through textual reasoning and intermediate visual states, but typically generate each visual state as a full image. This full-image generation paradigm introduces substantial visual-token redundancy and dilutes supervision on sparse yet reasoning-critical state transitions. We propose DeltaV, a ULMM that replaces full-image generation with visual updates. Conditioned on historical visual states, DeltaV incrementally predicts compact update tokens that capture the visual changes across reasoning steps, avoiding repeated modeling of unchanged content. To align the token budget of each update with the magnitude of visual change, DeltaV introduces a temporal similarity (TSIM) Router, which stops allocating tokens once the marginal reconstruction gain falls below a threshold. To support more diverse and generalizable reasoning, we further construct StructCoT, a large-scale interleaved multimodal reasoning dataset with 1.05M samples spanning 44 task domains. Experiments show that the visual-update paradigm reduces newly generated visual tokens by 55.6% on average without compromising reconstruction fidelity, and improves multimodal reasoning by 3.3% over full-image generation. Trained with StructCoT and large-scale multimodal data, DeltaV-2B further outperforms substantially larger open-source models by 8.4% on in-domain multimodal reasoning evaluations and surpasses the comparable-scale Qwen3-VL-2B by 5.9% on external multimodal reasoning and understanding benchmarks. Code, models, and StructCoT will be released at this https URL.
[CV-27] rack2Map: Online Deformable SLAM with Motion-Aware Pose Optimization in Robotic Surgery MICCAI2026
链接: https://arxiv.org/abs/2607.08408
作者: Tianyi Song,Sierra Bonilla,Xinwei Ju,Evangelos Mazomenos,Danail Stoyanov,Adam Schmidt,Omid Mohareri,Sophia Bano,Francisco Vasconcelos
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at MICCAI 2026. This is the submitted version prior to peer review. The final authenticated version will be available on SpringerLink
Abstract:Gaussian splatting is the current state-of-the-art for dense, deformable 3D anatomy reconstruction in robot-assisted minimally invasive surgery (RAMIS); however, most pipelines are offline and depend on accurate camera trajectory priors (often from robotic kinematics), limiting applicability when priors are missing or noisy. To address these limitations, we propose Track2Map, an online 3D Gaussian Splatting pipeline that jointly optimizes camera trajectory and 3D deformable scene representation directly from surgical video. Track2Map is therefore capable of robust 3D reconstructions when camera trajectory priors are either absent or noisy, and due to its online nature it effectively works as a Simultaneous Localisation and Mapping (SLAM) method. To stabilize optimization in the presence of tissue motion and ambiguous visual cues, we introduce a track-anchored deformation initialization using dense 2D point tracks. Track statistics are further utilized to disentangle camera motion from scene deformation by detecting static camera periods and reducing drift during incremental mapping. Experiments on StereoMIS show improved reconstruction quality and camera trajectory against competing SLAM methods, as well as compared to non-SLAM methods that utilize camera trajectory priors. The code is available at this https URL.
[CV-28] Swapping Faces Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS
链接: https://arxiv.org/abs/2607.08402
作者: Roba H. Farouk,Catherine M. Elias
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:Large-scale and diverse datasets are needed to train AI models to take real-time decisions for autonomous vehicles (AVs), an intelligent transportation system (ITS) application. Pedestrian intention and trajectory prediction are critical models used in AVs, requiring datasets involving diverse pedestrian images. Unrestricted access to these datasets imposes serious security risks, like identity theft and pedestrian tracking. The challenge is to apply privacy preservation procedures while maintaining the image attributes needed to train the models. Existing privacy methods may preserve the pedestrian’s privacy, but degrade the image usability, which hinders the models’ effectiveness. This work’s focus is to implement a five-stage pipeline to protect pedestrians’ privacy through face swapping while keeping the essential facial attributes intact. It should be tailored to satisfy the privacy needs of the Egy-DRiVeS dataset. Moreover, Roop and Ghost-v2 face-swapping models are evaluated. Provenly, Roop outperforms Ghost-v2 in various aspects, as will be discussed. Consequently, Roop is the face-swapping model to be used in the pipeline to strike the balance between pedestrian privacy via identity concealment and data usability via facial attribute preservation.
[CV-29] HoloTetSphere: Unified TetSphere Mesh Reconstruction for Physical Simulations ECCV2026
链接: https://arxiv.org/abs/2607.08398
作者: YaQiao Dai,Renjiao Yi,Zhirui Gao,Wei Chen,Kai Xu,Chenyang Zhu
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:Standard pipelines for physics-ready 3D reconstruction rely on a decoupled two-stage paradigm: extracting surface geometry followed by an error-prone tetrahedralization process. While recent Lagrangian methods like TetSphere Splatting attempt to bypass this by directly optimizing volumetric primitives, their homeomorphic constraints prevent topology-adaptive optimization. Consequently, they produce disjoint tetrahedra rather than a single connected mesh, rendering the structures unsuitable for further physical simulations. To address this, we propose a topology-adaptive framework for holistic tetrahedral mesh reconstruction through end-to-end topological and geometric optimization. First, by coupling Gaussian spheres to tetrahedral elements and leveraging edge connections, we estimate a continuous opacity field for differentiable element pruning. Next, jointly minimizing mesh smoothing energy and multi-view Gaussian rendering error drives alternating geometric refinement while preserving topological adaptivity. Consequently, our approach effectively constructs a unified and topologically coherent tetrahedral mesh. Extensive experiments demonstrate that our method outperforms state-of-the-art techniques by achieving superior geometric accuracy and producing coherent, single-connected tetrahedral meshes, thereby effectively bypassing the error-prone conventional tetrahedralization step for reconstructed surface meshes and streamlining downstream physical simulation.
[CV-30] Attribute Retrieving for Open-Vocabulary Endoscopic Compositional Referring Segmentation
链接: https://arxiv.org/abs/2607.08397
作者: Shun Liu,Nan Xi,Yang Liu,Tianyu Luan,Xuan Gong,David Doermann
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Referring Image Segmentation (RIS) aims to segment image regions specified by natural language, enabling fine-grained and controllable visual understanding. Extending RIS to endoscopic imagery, however, presents unique challenges, including scarce high-quality annotations and complex, domain-specific image-text relationships. Although recent vision-language models demonstrate strong cross-domain alignment, they often fail to capture fine-grained textual cues in endoscopic settings, resulting in suboptimal performance and limited generalization. To address these challenges, we introduce ReferEndoscopy, a large-scale benchmark for RIS in the endoscopy field. Building on this dataset, we propose the Attribute Retrieval-based Endoscopic-RIS (AR-ERIS) framework for open-vocabulary endoscopic compositional referring segmentation. AR-ERIS leverages attribute retrieval for open-vocabulary endoscopic compositional referring segmentation and is pretrained on the curated ReferEndoscopy dataset, achieving state-of-the-art performance with strong generalization across both simulated and real-world endoscopic data. The dataset and code will be publicly released upon completion of the review process.
[CV-31] Classical versus Deep Mirror-Symmetry Scoring: A Benchmark of Thirteen Methods
链接: https://arxiv.org/abs/2607.08379
作者: Maximilian Woehrer
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: 22 pages, 6 figures, 5 tables. Code and benchmark: this https URL
Abstract:Quantifying how mirror-symmetric an image is about a given axis (symmetry scoring) underpins applications from visual aesthetics to medical imaging, yet proposed scoring methods have never been compared on a common, statistically grounded protocol. We benchmark 13 scoring methods (nine collected from literature, four introduced here) spanning from classical features to frozen deep features, across four single-axis and five multi-axis datasets under a reflection-exact protocol with a chance-anchored, significance-tested discrimination skill. Deep backbones perform best on single-axis and harder multi-axis protocols. However, a classical histogram-of-oriented-gradients (HOG) descriptor trails the best frozen-network readout by a small (but significant) margin, is not statistically separable from the runner-up (a CNN-filter measure), and runs ~300x faster on CPU. Our results show that discrimination concentrates in mid-scale oriented features, where deep backbones peak at a low or mid stage, and HOG peaks at a mid cell size. Among existing methods, frozen deep features thus offer little over a tuned classical descriptor for measuring symmetry; whether task-trained deep scorers can do better remains open. We release the scorers and harness in imgsym, an open toolkit for image symmetry detection and measurement.
[CV-32] WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving
链接: https://arxiv.org/abs/2607.08375
作者: Xuerun Yan,Zhexi Lian,Nuoheng Zhang,Shiyu Fang,Haoran Wang,Chen Lv,Jia Hu,Binyang Song
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 20 pages, 7 figures
Abstract:Vision-Language-Action (VLA) models have advanced end-to-end autonomous driving. However, existing methods either lack comprehensive world cognition or suffer from fragmented world foresight, inherently confining these models to reactive driving. To address this limitation, we propose WCog-VLA, a novel dual-level World-Cognitive VLA framework that successfully bridges semantic world forecasting with generative world evolution to achieve proactive autonomous driving. At the semantic level, WCog-VLA unifies world cognition and reasoning by incorporating 3D spatial perception and injecting agent tokens to capture the world dynamics, while concurrently enabling Game-theoretic Chain-of-Thought (Game-CoT) reasoning. At the generative level, we introduce the Aligned Decoupled Diffusion Transformer (ADDT) as a powerful generative world model that synthesizes physically-plausible joint multi-agent trajectories. Through scene representation alignment, ADDT reduces the number of denoising steps required and thus significantly accelerates inference. To facilitate strategic reasoning, we further construct a large-scale dataset featuring 85k Game-CoT annotations. Extensive experiments on the NAVSIM benchmark demonstrate that WCog-VLA achieves a State-Of-The-Art (SOTA) PDMS score of 92.9.
[CV-33] xture Representations in Deep Vision Models: Comparing CNNs Vision Transformers and Human Perception
链接: https://arxiv.org/abs/2607.08321
作者: Ludovica de Paolis,Marco Baroni,Alessandro Laio,Eugenio Piasini
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In computational vision science, Convolutional Neural Networks (CNNs) have emerged as a popular model of biological vision because of the alignment they can exhibit with neural and behavioral data in humans and animals. However, it remains unclear to what extent this alignment persists for visual tasks that extend beyond the canonical object recognition paradigm based on well defined semantic content. In this study, we diverge from the common object-centric view by focusing on another aspect of vision: texture perception. We consider textures of different complexity generated with three different algorithms from the same source images. Using a rank-based statistic, we quantify the information encoded in the internal representations of a CNN and three Vision Transformers (ViTs), and we compare the similarity of these representations to those inferred from human psychophysics data. We find that the representation of textures is aligned in different ViTs, but not between the ViTs and the CNN; that ViTs form similar representations for textures of different complexity; that human performance in recognizing textures can be better predicted from ViTs representations rather than CNN representations. Taken together, these results suggest that ViTs may capture more faithfully than CNNs how texture patterns are visually processed by humans, and that the representations of texture stimuli in computational models may be driven by the network architecture.
[CV-34] ARGUS: Accelerated Robust General and Unsupervised Cell Tracking Solutions
链接: https://arxiv.org/abs/2607.08297
作者: Noah Jaitner,Kandice Tanner,Ingolf Sack,Hossein S. Aghamiry
类目: Computer Vision and Pattern Recognition (cs.CV); Optimization and Control (math.OC)
备注:
Abstract:Background and Objective: Quantitative analysis of cell dynamics is central to modern biological research, providing critical insights into immune cell interactions, disease progression, and drug mechanisms. Automated cell tracking in time-lapse microscopy remains challenging due to noise, morphological variations, overlapping cells, and dynamic events such as divisions and fusions. Methods: We present ARGUS, a framework for Accelerated, Robust, General, and Unsupervised Cell Tracking Solutions. ARGUS combines adaptive cell detection, dense Farneback optical-flow prediction, frame-to-frame linear assignment, and a sequence-level tracklet-refinement step that reconnects trajectory fragments across short temporal gaps. Results: On publicly available Cell Tracking Challenge datasets, ARGUS achieved detection accuracy of 0.905-0.971 and tracking accuracy of 0.897-0.964, with runtimes within 1 minute (5-6 seconds for 3 frames). Conclusions: ARGUS is a modular, interpretable framework that can be adapted to different imaging modalities and biological applications without training data or GPU infrastructure. The implementation is publicly available at this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Optimization and Control (math.OC) Cite as: arXiv:2607.08297 [cs.CV] (or arXiv:2607.08297v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.08297 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hossein Aghamiry [view email] [v1] Thu, 9 Jul 2026 09:40:38 UTC (3,572 KB)
[CV-35] Enhancing the KidSat Model: Integrating Geographical Encoding and Data Quality Assessment for Childhood Poverty Prediction
链接: https://arxiv.org/abs/2607.08281
作者: Hou Hin Ip,Ka Nam Lam,Joshua Man Yu Ng,Makkunda Sharma,Seth Flaxman,Codie Gerlach-Wood,H Juliette T Unwin
类目: Computer Vision and Pattern Recognition (cs.CV); Applications (stat.AP)
备注:
Abstract:Accurate poverty mapping using satellite imagery is often hindered by (i) noisy and sparse survey-derived supervision, (ii) image quality issues such as cloud cover and image corruption, and (iii) lack of explicit spatial structure in image-only models. Building on the KidSat framework, we develop an enhanced pipeline that improves predictive accuracy via refined data preprocessing, systematic image quality assessment, and mathematically defined geographic encoding. First, we refine the fine-tuning target matrix by resolving high-cardinality sparsity and reducing one-hot dimensionality from 103 to 51 via DHS re-aggregation. Second, we introduce a simple two-stage quality-screening procedure to filter heavily clouded or corrupted observations. Third, we fuse DINOv2 visual embeddings with Spherical Harmonics (SH) location features. Across extensive experiments, these changes reduce MAE from 0.2167 to 0.1759, corresponding to an 18.83% relative reduction on the cluster-level severe-deprivation proportion scale. When extended from 16 to 33 African countries, the best-performing configuration achieves an overall MAE of 0.1658. We find that SH features consistently improve performance over the image-only backbone, whereas higher-capacity coordinate Multi Layer Perception augmentation (SH+SIREN) can underperform without carefully designed objectives. Finally, gradient-boosted tree heads (XGBoost/LightGBM) most effectively exploit nonlinear interactions in the fused visual-geographic representation. These findings provide a scalable and principled recipe for improving satellite-based socioeconomic predictions using only publicly accessible data.
[CV-36] Progression as Latent Drift: Generative Forecasting of Slow-Evolving Pathologies ECCV2026
链接: https://arxiv.org/abs/2607.08270
作者: Yuxiang Feng,Juncheng Wang,Chao Xu,Wenlong Hou,Huihan Wang,Yijie Qian,Yang Liu,Baigui Sun,Yong Liu,Shujun Wan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:Forecasting the future anatomy of slow-evolving neurodegenerative diseases could enable earlier, more targeted intervention and improve clinical trial design, but it remains challenging because true progression signals are subtle in longitudinal MRI. In this low-signal regime, transferring modern generative sequence models directly is unreliable: training is dominated by stable baseline anatomy and confounded by dense, sample-specific nuisance variation. We first provide a theoretical analysis that explains these failures through two modes. Identity collapse occurs when optimization is driven toward reproducing the current anatomy, which prevents the model from learning faint temporal change. The continuous interpolation trap arises when standard smooth networks cannot separate localized biological drift from pervasive noise, which leads to spurious changes that diffuse across the volume. To address both issues, we propose Latent Drift, a progressive generative framework that learns change in a compressed semantic representation rather than synthesizing full-resolution anatomy. This design removes pixel-level identity from the prediction target and concentrates model capacity on progression-relevant dynamics. We further apply Finite Scalar Quantization to the learned change representation, which suppresses small, high-frequency nuisance fluctuations while preserving consistent structural drift. Experiments on longitudinal 3D brain MRI show that Latent Drift improves patient-specific neuro-forecasting over diffusion and autoregressive transformer baselines across generative fidelity and clinically relevant evaluation metrics. Project page: \hrefthis https URLthis https URL.
[CV-37] UniRef-UAV: A Multimodal Benchmark for Universal Referring in UAV Imagery
链接: https://arxiv.org/abs/2607.08267
作者: Haibin Tian,Huichao Xie,Xuelin Qian,Ruitao Lu,Junwei Han,Dingwen Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Unmanned aerial vehicles (UAVs) increasingly rely on visual grounding capabilities to localize task-relevant targets from diverse instructions in complex aerial scenes. Existing referring expression comprehension (REC) benchmarks and methods, however, are largely built around text-only queries and single-object outputs, which limits their applicability to practical UAV scenarios involving reference images, multimodal instructions, absent targets, and multiple valid target instances. To address this gap, we introduce \emphUniversal Referring, a generalized UAV referring task that jointly expands the query modality and the output cardinality. We construct \emphUniRef-UAV, a multimodal benchmark that supports text-only, image-only, and text+image queries with modality-dependent target cardinality, where text-only and text+image queries admit no-target, single-target, and multi-target grounding while image-only queries focus on existence-aware single-instance grounding. It also provides in-domain and cross-domain evaluation protocols for visual-query generalization. We further present \emphUAV-URNet, a detection-style baseline that maps heterogeneous queries into a shared query space and predicts variable-size target sets through set prediction. Extensive experiments show that UAV-URNet provides a stable and reproducible baseline with more consistent no-target discrimination and a more lightweight, reproducible implementation than large general-purpose MLLMs. Additional domain analysis, query-representation analysis, and ablation studies demonstrate that multimodal queries help reduce visual-query ambiguity and promote a more unified query–target alignment space. The annotations, visual query crops/images, train/validation/test splits, evaluation scripts, and baseline code will be made publicly available to facilitate reproducible research.
[CV-38] On the Design of Mixture-of-Experts for Dynamic Gaussian Splatting
链接: https://arxiv.org/abs/2607.08250
作者: In-Hwan Jin,Hyeongju Mun,Joonsoo Kim,Kugjin Yun,Kyeongbo Kong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence
Abstract:Dynamic scene reconstruction remains challenging due to the heterogeneous and spatially varying nature of real-world motion. Although recent 3D Gaussian Splatting methods have introduced diverse deformation formulations for dynamic novel view synthesis, each method typically relies on a single deformation model within its representation, which limits robustness across diverse dynamic scenarios. In this work, we study a fundamental problem-multi-deformation modeling for dynamic 3D Gaussian representations-under two distinct integration constraints that differ in when and how multiple deformation experts interact during training. From a Mixture-of-Experts (MoE) perspective, we view multi-deformation modeling as the problem of combining multiple specialized deformation models within a unified 3D representation. We first introduce Mixture of Deformation Experts (MoDE), which integrates multiple deformation experts directly into the deformable Gaussian Splatting pipeline through joint optimization. In MoDE, experts operate on a shared canonical Gaussian representation, enabling multi-deformation modeling without introducing additional training stages or modifying the original optimization schedule. In contrast, we further present Mixture of Experts for Dynamic Gaussian Splatting (MoE-GS) under a different integration constraint, where deformation experts are optimized independently and combined through a separate routing stage. As a result, expert interaction occurs over non-canonical Gaussian representations after individual optimization. Together, these two approaches provide alternative strategies for multi-deformation modeling, clarifying how integration constraints shape the design and behavior of deformation experts in dynamic 3D Gaussian representations. Our code is available at: this https URL.
[CV-39] HSA: Hierarchical Slot Attention for Multi-granularity Scene-Decomposition
链接: https://arxiv.org/abs/2607.08249
作者: Neelu Madan,Rongzhen Zhao,Andreas Mogelmose,Juho Kannala,Joni Pajarinen,Graham W. Taylor,Thomas B. Moeslund
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Slot attention is a powerful framework for object-centric learning, decomposing visual scenes into latent slots through iterative competitive attention. However, existing methods share two critical limitations: they decompose scenes into a flat set of slots at a single granularity, and this decomposition is based on appearance rather than semantics. Yet humans understand scenes through semantic hierarchies: separating foreground from background, recognizing object categories, and identifying individual instances. Crucially, such semantic hierarchies cannot emerge without supervision, because category names are human constructs, not visual patterns. We propose Hierarchical Slot Attention (HSA), which learns multi-granularity semantic scene decomposition from a single model. HSA decomposes scenes at three levels: holistic (foreground/background), semantic (object categories), and panoptic (individual instances). Using only 10% labeled data, combined with hierarchical alignment loss, HSA learns all three levels jointly. We further introduce grouping purity and containment to measure whether the hierarchy is encoded in representation space, not just output masks. Experiments on COCO and PASCAL VOC demonstrate that HSA outperforms the strongest flat baseline by up to \textbf + 41.5 ARI at holistic, \textbf + 14.6 at semantic, and \textbf + 10.4 at panoptic level on COCO, with even larger gains on Pascal VOC, while requiring a single model instead of three. Code will be made available upon acceptance.
[CV-40] SkelGen4D: Weakly-Supervised Skeleton-Based 4D Generation for Text-Driven Mesh Animation
链接: https://arxiv.org/abs/2607.08246
作者: Hao Feng,Zhi Zuo,Jia-Hui Pan,Ka-Hei Hui,Zhengzhe Liu,Dian Zhang,Haoran Xie,Bin Sheng,Jingyu Hu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We study 4D generation to synthesize temporally coherent sequences of 3D geometry for animation and content creation. In contrast to existing SDS-based optimization methods and video-driven animation approaches, we adopt a skeleton-driven animation framework aligned with standard industrial pipelines, which enables explicit control and editing. To this end, we propose SkelGen4D, a weakly supervised feed-forward framework for text-driven mesh animation that generates explicit skeleton motions without requiring per-frame skeleton annotations. SkelGen4D first recovers temporally consistent pseudo-skeletons from animated meshes via differentiable fitting, and then generates text-conditioned skeleton motion sequences in a feed-forward manner, further refined with Motion-GRPO to ensure temporally coherent, physically plausible, and articulated animation. We evaluate our method on two large-scale benchmarks, Truebones Zoo and Diffusion4D. Our results show that our weakly supervised skeleton modeling matches or surpasses fully supervised baselines while scaling to diverse object categories for high-quality text-driven mesh animation. Further, our method supports flexible motion editing and is aligned with standard animation production pipelines.
[CV-41] Closing the Null Space: Guidance-Aware Quantization for Classifier-Free Diffusion
链接: https://arxiv.org/abs/2607.08241
作者: Abdullah Al Shafi,Sumaiya Rahim Suma
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 6 pages, 5 figures, 3 tables
Abstract:Deploying classifier-free guidance (CFG) diffusion models under real-world compute budgets requires quantization, yet existing post-training quantization (PTQ) methods treat CFG models as single-branch networks, ignoring the paired conditional/unconditional structure that CFG inference fundamentally relies on. This structural blind spot has two consequences. At the system level, the two-pass CFG execution pattern imposes a latency overhead that parameter-count and bit-operation metrics conceal entirely, and commodity INT8 inference stacks fail to realize the theoretical efficiency gains that BOPs calculations promise. At the algorithmic level, calibrating against the guidance gap alone admits an exact null space: a quantized model can achieve perfect gap-fidelity diagnostics while the unconditional branch drifts arbitrarily, corrupting every guided prediction at inference time. This paper terms this the branch-drift trap, proves its existence analytically, and confirms it empirically through a false-positive result in which the best-calibrated model by standard diagnostics simultaneously produces the worst sample quality. To close the trap, Guidance-Aware Mixed Precision (GAMP) is proposed, which calibrates directly on the guided prediction, derives per-layer activation-bit sensitivity from guided-output degradation, and allocates bits via a greedy knapsack – provably preventing unconditional branch drift by construction.
[CV-42] VTA: Trajectory-Aware Viseme-Guided Temporal Aggregation for Event-Based Lip Reading
链接: https://arxiv.org/abs/2607.08236
作者: Jingrong Zheng,Hongwei Ren,Xiangqian Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Event-based lip reading has recently emerged as a promising direction for visual speech recognition, benefiting from the high temporal resolution and motion sensitivity of event cameras. However, existing methods typically perform spatial compression before sufficient temporal modeling, which may suppress sparse and localized motion trajectories that are crucial for distinguishing similar lip movements. Moreover, most current approaches optimize temporal representations mainly at the word-classification level, leaving the underlying articulatory structure weakly constrained. To address these limitations, we propose a temporally enhanced framework for event-based lip reading. First, we introduce Trajectory-Aware Differential Aggregation (TDA), which performs local temporal modeling at each spatial location before adaptive spatial aggregation. Second, we propose Viseme-Guided Aggregation (VGA), a unified temporal module composed of a CTC decoder and a viseme-guided gated aggregation branch, which injects viseme-aware sequence supervision and improves final temporal aggregation for word recognition. Third, we incorporate an EMA teacher–student training strategy to enhance robustness under strong event perturbations. Experiments on the DVS-Lip benchmark verify the effectiveness of the proposed design, and extensive ablation studies further validate the contributions of TDA, VGA, and teacher–student consistency. Qualitative decoding results also demonstrate that the proposed CTC-based temporal modeling learns meaningful viseme-aware structure from event streams.
[CV-43] Playing ZendoWorld: Challenging AI Agents on Active Visual Concept Induction
链接: https://arxiv.org/abs/2607.08233
作者: Sophia Koehler,Antonia Wüst,Inga Ibs,Wasu Top Piriyakulkij,Wolfgang Stammer,Constantin Rothkopf,Kevin Ellis,Kristian Kersting
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:A central challenge in building intelligent systems is enabling agents to jointly perceive complex inputs, form hypotheses about hidden patterns, and design informative experiments to test them. To study this problem, we propose ZendoWorld, a controlled interactive environment in which agents must infer a logical rule about visual game observations, acquire information by proposing new scenes, and refine their hypotheses based on feedback from the game environment. We evaluate several agents spanning pure VLM reasoning, Bayesian particle filtering, dynamic concept discovery, and neuro-symbolic methods. Our main findings are: (1) high accuracy in predicting labels for observed examples does not imply recovery of the underlying rule; (2) perception and induction are distinct bottlenecks for different agent classes; and (3) VLM-based agents propose near-uninformative experiments, failing to actively reduce hypothesis uncertainty. To compare these results, we collect human data on the task, which reveals a gap in inductive reasoning, particularly for more complex rules. Overall, ZENDOWORLD takes an important step toward evaluating intelligent agents and identifies concrete avenues for improvement, particularly in domains like scientific discovery.
[CV-44] Multimodal 3D LUT Generation via StatLUT with Statistical Features for Photorealistic Style Transfer
链接: https://arxiv.org/abs/2607.08227
作者: Yifan Wang,Zhixiang Hao,Yu Wang,Congchao Zhu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 9 figures, 7 tables. Preprint
Abstract:Photorealistic Style Transfer (PST) aims to transfer the color and tonal style of a reference to a content image while strictly preserving its structural integrity. However, existing deep learning-based methods inherently suffer from semantic entanglement caused by pre-trained image encoders, leading to unnatural spatial distortions. Moreover, current pixel-level mapping paradigms often ignore color gamut topology, resulting in color banding, while also lacking the multimodal capability for intuitive text-driven control. To address these bottlenecks, we propose StatLUT, an innovative multimodal framework for 3D LUT generation. First, we bypass traditional encoders and introduce a Lab-Extractor to derive spatially-agnostic statistical features, fundamentally decoupling color distributions from structural semantics to ensure artifact-free rendering. Second, we formulate LUT generation as a Transformer-based Seq2Seq translation task, utilizing a Multi-dimensional Residual Mapper (MR-Mapper) to predict topologically smooth 3D LUTs. Finally, to break the single-modal barrier, we propose the H-Diffuser, a lightweight Diffusion Transformer that directly synthesizes statistical features from natural language prompts, enabling flexible text-driven color grading. Extensive experiments on standard benchmarks demonstrate that StatLUT significantly outperforms state-of-the-art methods in both visual quality and quantitative metrics, pioneering a highly robust and flexible paradigm for multimodal photorealistic style transfer.
[CV-45] LUMI: Tokenizer-Agnostic LLM -Based Lossless Image Compression
链接: https://arxiv.org/abs/2607.08221
作者: Chris Xing Tian,Chengkai Wu,Ziyu Wang,Rongqun Lin,Kecheng Chen,Xiandong Meng,Haoliang Li,Shiqi Wang,Siwei Ma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint
Abstract:Large language model (LLM)-based lossless image compression methods typically represent pixel data through the native text interface of a pretrained model, converting pixel values into token sequences that the LLM processes through its vocabulary head. This design shows that pretrained language models can provide probability estimates for image coding, but it also couples compression to tokenizer behavior, vocabulary-specific numeric tokens, and model-family-specific adaptation. In this paper, we present LUMI (LLM-based Unified Model-agnostic lossless Image compression), a tokenizer-agnostic framework for lossless RGB image compression with frozen LLM backbones. LUMI replaces pixel-as-text tokenization with a pixel embedding module that maps raw intensity and channel information into the continuous embedding space of the LLM. It further introduces intra-patch position encoding to retain two-dimensional spatial structure after flattening, and uses a 256-way prediction head to produce probabilities over the native pixel alphabet. Only the pixel embedding, position encoding, soft-prefix parameters, and prediction head are trained, while the LLM backbone remains fixed. Experiments on natural, medical, and remote-sensing image benchmarks with LLaMA, Qwen, and Gemma backbones show that LUMI provides a unified interface across tokenizer families, achieves competitive compression rates, and improves cross-domain robustness over tokenizer-based LLM compression baselines. These results formulate LLM-based lossless image compression as pixel-space adaptation of frozen foundation models rather than tokenizer-specific language-symbol modeling.
[CV-46] Benchmark Evaluation of Feredated Learning on Multi-organ Images
链接: https://arxiv.org/abs/2607.08219
作者: Junbin Mao,Xu Tian,Jianchun Zhu,Ludi Li,Jin Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:The privacy requirements of medical data and its substantial variations across organs and modalities hinder the clinical implementation of medical AI. Federated learning (FL) is a feasible approach to overcome these challenges. Due to the continuous emergence of FL algorithms and the highly heterogeneous nature of medical data, objectively evaluating their performance in real-world clinical settings remains difficult. Therefore, a comprehensive federated medical imaging benchmark, serving as a unified evaluation standard, is crucial for advancing the technology toward reliable clinical application. Existing federated medical imaging benchmarks have not yet adequately incorporated state-of-the-art algorithms, are limited to data from single organs or modalities, and overly emphasize model accuracy, making it difficult to comprehensively assess the overall efficacy of FL in real-world medical environments. To address these challenges, we developed the MobenFL benchmark. This benchmark integrates 20 cutting-edge FL algorithms and 22 medical imaging datasets, covering 12 critical organs across the human body, surpassing existing benchmark in breadth. In terms of evaluation dimensions, MobenFL not only assesses performance but also systematically incorporates key metrics such as algorithmic efficiency and privacy protection capabilities. Additionally, it conducts specialized evaluations for complex real-world clinical scenarios involving different diseases, devices, and imaging modalities, thereby providing a comprehensive and in-depth evaluation framework for the clinical application of FL in the medical field.
[CV-47] Metrics or Mirag e? An Audit of Evaluation Inconsistencies in Colonoscopy Polyp Segmentation Benchmarks ECCV
链接: https://arxiv.org/abs/2607.08203
作者: Aisha Urooj,Zain Ul Abdien,Neelu Madan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted to ECCV Workshops
Abstract:Progress in colonoscopy polyp segmentation is routinely reported through leaderboard comparisons on a small set of public benchmarks. We argue that this apparent progress is difficult to verify: a systematic audit of \textbf27 papers published between 2015 and 2026 reveals three structural problems in how the community evaluates models. \textbfFirst, 25 of 27 papers \textitomit the Hausdorff distance. Hausdorff distance is a boundary-accuracy metric with direct clinical relevance for detecting flat or small polyps, and is a standard in radiotherapy segmentation. \textbfSecond, at least five \textitincompatible train/test split protocols co-exist across papers reporting results on the same two datasets (Kvasir-SEG and CVC-ClinicDB), making published Dice scores non-comparable even when they appear in the same leaderboard column. \textbfThird, 26 of 27 papers make \textitperformance claims without any statistical significance test. Strikingly, four papers published \emphafter the Metrics Reloaded framework~\citemetricsreloaded2024 (Maier-Hein et al., \textitNature Methods 2024) perpetuate these same problems, suggesting that general-purpose metric guidance has not yet reached the colonoscopy sub-community. To show these problems are not merely cosmetic, we re-evaluate five representative models under three controlled protocols with a single uniform scorer, and find that the reported metric conceals large boundary and recall failures, that the ``best’’ model changes with the metric, and that near-tied rankings reverse across random splits. We propose a five-point \textbfPolyp Segmentation Reporting Checklist~(PSRC) as a lightweight, domain-adapted corrective.
[CV-48] MI: Text-to-Image Meets Image-to-Image for Complementary Data Synthesis to Boost Long-Tailed Instance Segmentation ECCV2026
链接: https://arxiv.org/abs/2607.08201
作者: Hyeonseop Song,Seokhun Choi,Hoseok Do
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to ECCV 2026. The first two authors contributed equally to this work
Abstract:Large-vocabulary instance segmentation is constrained by long-tailed category distributions and fine-grained inter-class ambiguity. While data synthesis offers a promising alternative, current paradigms have complementary limitations: text-to-image (T2I) methods inherit noisy pseudo-labels and struggle on rare classes, whereas copy-paste methods compromise contextual realism. To address these issues, we propose a hybrid pipeline coupling T2I generation with context-aware image-to-image (I2I) editing. The T2I branch provides broad category and scene diversity, while a teacher-student scheme ensures label reliability by selectively retaining only prompt-specified categories. To strengthen supervision for rare classes, we introduce VRAIN (Verified Rare-class Augmentation via INstructed editing), a novel I2I editor. VRAIN inserts high-confidence instances at semantically appropriate locations within in-the-wild scenes, yielding semantically coherent and visually natural edits that reduce domain gaps and enable targeted augmentation. On the LVIS benchmark, our method surpasses existing baselines, improving overall AP by up to +4.0 points and rare-class AP by up to +9.5 points, while scaling effectively with backbone capacity. Our project page is available at this https URL
[CV-49] Unpaired Joint Distribution Modeling via Multi-Scale Image Representations
链接: https://arxiv.org/abs/2607.08198
作者: Yihang Zou,Hui Zhang,Zuowei Shen,Chenglong Bao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This paper studies the problem of learning a joint distribution from marginal observations, which is inherently ill-posed due to the ambiguity of feasible couplings. We propose LUD-MSR, a latent-variable probabilistic framework that models the joint distribution via auxiliary representations and optimizes evidence lower bounds using only marginal data. Under mild assumptions, we establish an upper bound on the distribution approximation error. This analysis reveals a trade-off in representation learning between domain consistency and information preservation. To address this trade-off, we introduce a Multi-Scale image Representation (MSR) mapping that exploits structural similarity at coarse scales while suppressing domain-specific variations. We show that MSR achieves a more favorable balance of this trade-off compared to existing approaches. Experiments on real-world denoising benchmarks, including cryo-electron microscopy (cryo-EM), demonstrate the effectiveness of the proposed framework.
[CV-50] Dive Into the Implicit Biases of Low-rank Vision-language Alignment ECCV2026
链接: https://arxiv.org/abs/2607.08194
作者: Mingjia Shi,Shuo Wang,Xiaobo Wang,Sifan Zhou,Kai Wang,Tianyu Fu,Chenxu Zhao,Anyang Su,Ping Jiang,Minghui Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026
Abstract:Vision-language alignment, the stage that bridges pretrained vision encoders and large language models, is widely treated as a form of pretraining requiring full-parameter updates. We challenge this view and investigate what happens when low-rank adaptation is applied to the LLM during this stage instead. We find that low-rank alignment not only reduces computational costs but also outperforms full-parameter alignment on most benchmarks. To understand this phenomenon, we systematically characterize the implicit biases introduced by low-rank adaptation during alignment. Empirically, we find that low-rank alignment shifts model behavior from hallucinatory to conservative and preserves per-token linear separability of visual features that full-parameter alignment disrupts, a phenomenon we term LS-curse. Geometrically, low rank aligned models exhibit more homogeneous and structurally stable visual representations, maintaining modality-specific knowledge rather than prematurely fusing entity-level semantics. Theoretically, we establish two theorems showing that low-rank alignment induces preferences for parameter subspaces with flat gradients and feature subspaces robust to perturbations, providing a principled explanation for the observed structure-preserving behavior. Extensive experiments cover ablation over 100 alignment configurations, three families of low-rank operators, and various rank, encoder, and other settings.
[CV-51] Dual-Correlation Hypergraph Network for Unaligned RGBT Video Object Detection and A Large-scale Benchmark
链接: https://arxiv.org/abs/2607.08191
作者: Qishun Wang,Yapeng Li,Bin Luo,Zhengzheng Tu,Chenglong Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:RGB-Thermal (RGBT) Video Object Detection (VOD) has gained significant traction due to its ability to overcome the limitations of conventional RGB-based VOD under challenging conditions. However, spatial misalignment commonly exists between RGBT image pairs. To address this, we propose a Dual-Correlation Hypergraph Network (DHNet) that captures high-dimensional complementary information by explicitly modeling two types of correlations: temporal correlation across consecutive frames and spatial correlation from cross-modal features. Specifically, we first design a Patch-based Spatial Alignment Module (PSAM) to sequentially align the multimodal features at the local region level. Subsequently, we introduce a Dual Hypergraph Fusion Module (DHFM), which constructs separate temporal and multimodal hypergraphs to enhance object discriminability through dual-correlation learning. Furthermore, the field currently lacks a large-scale, scene-diverse benchmark dataset for comprehensive evaluation. To address this gap, we construct DVT-VOD1000, a large-scale RGBT VOD dataset containing 1,000 video sequences with 103,464 RGBT image pairs. The dataset covers diverse scenarios, including campuses, parks, transportation, rural areas, night scenes, rain, and snow. Comprehensive experiments on VT-VOD50 and our DVT-VOD1000 demonstrate that DHNet achieves state-of-the-art detection accuracy. The dataset and source code will be made publicly available on this https URL to support academic research.
[CV-52] Leverag ing Color Naming for Image Enhancement
链接: https://arxiv.org/abs/2607.08185
作者: David Serrano-Lozano,Luis Herranz,Michael S. Brown,Javier Vazquez-Corral
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Project page: this https URL . arXiv admin note: text overlap with arXiv:2407.09892
Abstract:Enhancing images to make them visually appealing is a persistent challenge in computer vision. Many deep-learning methods train models on paired datasets to replicate expert editing styles. However, these approaches struggle with two key issues: (1) interpretability and (2) a parametrization suitable for user adjustments. To address these challenges, we present NamedCurves+, an approach inspired by the concept of Color Naming, a universal set of familiar colors widely used in software tools for intuitive editing. Our method integrates color names into a learning-based framework, enabling global adjustments for each named color through tone curves. To address local image variations, we incorporate a transformer block that captures spatial dependencies, enabling context-aware edits across the image. NamedCurves+ enhances the retouching process’s interpretability and supports user interaction, allowing flexible modifications of individual tone curves to refine the retouched image according to personal preferences. Extensive experiments on tasks such as image retouching, tone mapping, and exposure correction demonstrate that NamedCurves+ outperforms state-of-the-art methods. Notably, our approach is both explainable, as the tone curves explicitly represent how each color name contributes to the enhancement, and interactive, allowing users to customize the retouching process and achieve results tailored to their liking.
[CV-53] LEEVLA: Seeing What Matters in Latent Environment Evolution for Vision-Language-Action
链接: https://arxiv.org/abs/2607.08182
作者: Qi Lyu,Baicheng Liu,Xudong Wang,Jiahua Dong,Lianqing Liu,Zhi Han
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-language-action (VLA) models aim to map multimodal inputs to robot actions. However, most existing approaches struggle to cover complex dynamic scenarios due to treating all visual tokens uniformly and reasoning with human-selected factors, which lack mechanisms to emphasize task-critical evidence and ignore underlying factors. To address this issue, we propose LEEVLA, a VLA architecture for seeing what matters in Latent Environment Evolution that explicitly guides the model toward informative regions while preserving the structured evolution of latent world representations. To identify salient and instruction-relevant regions, we introduce drift-guided dynamic prioritization (DGDP), which combines dynamic position prioritization (DPP) with semantic drift guidance (SDG) to guide the VLA agent where to attend during training. On top of this, we introduce structured feature flow generation (SFFG), which models how these prioritized features should evolve in latent space via prototype-to-periphery (P2P) prediction, and a mutual-neighborhood contrastive (MC) loss to maintain topological consistency among neighborhoods. Together, DGDP and SFFG form a task-aware “where-how” training framework. Extensive experiments on VLA benchmarks show that LEEVLA consistently outperforms prior methods, confirming that explicit task-evidence guidance and structured latent reasoning are both crucial for scalable VLA. Our code is available at this https URL.
[CV-54] Attention-Based Segmentation of WMHs and Differentiation of Vascular vs. Demyelinating Lesions
链接: https://arxiv.org/abs/2607.08171
作者: Aina Tur-Serrano,Gabriel Moyà-Alcover,Francisco J. Perales López
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:White Matter Hyperintensities (WMHs) are commonly observed in brain Magnetic Resonance Imaging (MRI) scans. They are associated with various neurological conditions, including vascular and inflammatory demyelinating diseases. Despite differing in etiology, WMHs from these conditions often appear similar on Fluid Attenuated Inversion Recovery (FLAIR) images. This similarity makes differential diagnosis challenging. In this work, we highlight the potential of combining attention-based segmentation with feature-driven classification. This approach supports more accurate and efficient classification between vascular and demyelinating white matter pathologies. For segmentation, we evaluate the effectiveness of attention mechanisms, specifically the Bottleneck Attention Module (BAM) and the Convolutional Block Attention Module (CBAM). We also test different architectures, particularly Attention U-Net. In addition, we explore advanced training strategies, such as patch-based learning and a 2.5D approach, to enhance lesion detection. After segmentation, we extract morphological features from the lesion masks. We then use them to classify WMHs based on their underlying cause. Our experiments utilize five publicly available datasets with diverse imaging protocols to promote model generalizability, despite limited sample sizes. The results suggest that attention-based segmentation and feature-driven classification offer a promising direction for discriminating vascular and demyelinating white matter lesions. Further validation in larger clinical cohorts is still needed.
[CV-55] Continual Test-Time Adaptation in Computer Vision: Methods Benchmarks and Future Directions
链接: https://arxiv.org/abs/2607.08164
作者: Sarthak Kumar Maharana,Shambhavi Mishra,Yunbei Zhang,Shuaicheng Niu,Taki Hasan Rafi,Jihun Hamm,Marco Pedersoli,Jose Dolz,Yunhui Guo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: TMLR 2026
Abstract:Deep neural nets achieve remarkable performance when training and test data share the same distribution, but this assumption frequently breaks in real-world deployment, where data undergoes continual distributional shifts. Continual Test-Time Adaptation (CTTA) addresses this challenge by adapting pretrained models to non-stationary target distributions on-the-fly, without access to source data or labeled targets, while mitigating two critical failure modes: catastrophic forgetting of source knowledge and error accumulation from noisy pseudo-labels over extended time horizons. In this comprehensive survey, we formally define the CTTA problem, analyze the diverse continual domain shift patterns that characterize different evaluation protocols, and propose a hierarchical taxonomy that categorizes existing methods into three families: optimization-based strategies (entropy minimization, pseudo-labeling, parameter restoration), parameter-efficient methods (normalization layer adaptation, adaptive parameter selection), and architecture-based approaches (teacher-student frameworks, adapters, visual prompting, masked modeling). We systematically review representative methods within each category and present comparative benchmarks and experimental results across standard evaluation settings. Finally, we discuss limitations of current approaches and highlight emerging research directions, including adaptation of foundation models and black-box systems, providing a roadmap for future research in robust continual test-time adaptation. We encourage visiting our repository at [this https URL](this https URL)
[CV-56] ProsMAE: Multi-Source MAE Pretraining for ISUP Grade Classification
链接: https://arxiv.org/abs/2607.08162
作者: Anna Jung,Kyeonghun Kim,Youngung Han,Eunseob Choi,Jiwon Yang,Ken Ying-Kai Liao,Hyuk-Jae Lee,Nam-Joon Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted to APCCAS 2026
Abstract:Whole slide images (WSIs) provide rich diagnostic information for computational pathology, but their gigapixel scale, stain variation, scanner differences, tissue artifacts, and limited expert annotation make robust model training challenging. This paper presents a multi-source Masked Autoencoder (MAE) framework, named ProsMAE, for histopathology representation learning. Tiles from Prostate cANcer graDe Assessment (PANDA), CAncer MEtastases in LYmph nOdes challeNge 2017 (CAMELYON17), and BReAst Carcinoma Subtyping (BRACS) are used for ProsMAE pretraining to expose the encoder to diverse tissue morphology and acquisition conditions. The learned encoder is transferred for International Society of Urological Pathology (ISUP) grade classification through ProsCLS, using a frozen encoder and a linear classification head. ProsMAE achieved a higher mean validation quadratic weighted kappa (QWK) than the vanilla MAE frozen linear-probe baseline under the evaluated disjoint PANDA split. Repeated-split evaluation remains necessary to further establish robustness across split compositions.
[CV-57] Unified Face Attack Detection via Fine-Grained Semantic Guidance ICME2026
链接: https://arxiv.org/abs/2607.08156
作者: Ning Jiang,Shijie Yu,Dingheng Zeng,Haiyang Yi,Yanhong Liu,Haifeng Shen,Ying Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ICME 2026
Abstract:The growing applications of facial recognition systems are accompanied by increasingly diverse security threats. Existing datasets lack detailed textual descriptions of forgery cues, leading most prior methods to treat face attack detection primarily as a visual recognition task. In this paper, building upon the large-scale MS-UFAD dataset which contains over 8 million attack images, we enrich each image with a fine-grained textual description of forgery cues. Furthermore, we propose a Dual Alignment Forgery Network(DAF-Net) to better leverage these textual information. Extensive experiments demonstrate that our approach extracts more generalizable and semantically meaningful forgery representations from attack images, outperforming both vision-only methods and approaches based on coarse-grained descriptions.
[CV-58] Understanding and Mitigating the Video-Action Generalization Gap via Temporal Ratio
链接: https://arxiv.org/abs/2607.08127
作者: Utkarsh A. Mishra,Yongxin Chen,Danfei Xu,Yang Liu,Xi Chen,Jiayuan Mao
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 26 pages, 9 figures
Abstract:Generative video foundation models exhibit strong compositional priors, yet world-action models (WAMs) and video-action models (VAMs) often lose these priors after finetuning on robotic action data. We refer to this discrepancy as the video-action generalization gap. In this paper, we systematically investigate this gap by evaluating a comprehensive design space of VAMs, demonstrating that standard design choices yield no emergent explanation pattern. To explain this behavior, we introduce the Temporal Ratio (TR), an attention-based measure of how strongly the action head relies on future latent rollouts relative to the anchored current frame. TR has two key properties: first, a model’s structural reliance on future-predictive latents, measured via TR, acts as a predictor of its compositional generalization capacity; second, it natively fluctuates based on task phase, shifting attention to future frames during planning and reverting to the present frame for precise manipulation. Finally, based on these findings, we propose an inference-time adaptive guidance method, which exploits this intrinsic feature attention pattern to dynamically amplify compositional video conditioning signals precisely when the policy relies on future rollouts. Evaluated on the LIBERO benchmark and real-world tasks, our approach mitigates the OOD-ID compositional generalization gap. More details: this https URL
[CV-59] VSRo-200: A Romanian Visual Speech Recognition Dataset for Studying Supervision and Multimodal Robustness
链接: https://arxiv.org/abs/2607.08112
作者: Iulia-Maria Udrea,Alexandra Diaconu,Bogdan Alexe
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We introduce VSRo-200, the first large-scale dataset for visual speech recognition (lip reading) in Romanian, comprising 200 hours of real-world podcast videos. All samples are annotated with pseudo-labels generated by a fine-tuned Romanian ASR model, while a subset of 100 hours is additionally transcribed by humans, enabling controlled analysis of supervision quality under a unified framework. Building on this dataset, we establish a benchmark for visual speech recognition in low-resource settings. We systematically study the impact of supervision quality, showing that while human annotations provide better performance at fixed data scales, pseudo-labels enable continued improvements through scalability. We further evaluate robustness under domain shift using curated out-of-distribution (OOD) test sets, and analyze audio-visual speech recognition (AVSR) under noisy conditions, where multimodal fusion significantly improves robustness compared to audio-only models. Finally, we demonstrate that representations learned on VSRo-200 transfer effectively to the LRRo benchmark for isolated word recognition, substantially outperforming previously reported results. Overall, VSRo-200 provides a new testbed for studying supervision, domain generalization, and multimodal fusion in low-resource visual speech recognition.
[CV-60] EVIS: A Physics-Grounded Event Camera Plugin for NVIDIA Isaac Sim
链接: https://arxiv.org/abs/2607.08098
作者: Linli Shi,Ruijun Zhang,Ziyun Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Event cameras offer microsecond temporal resolution, low latency, and high dynamic range, making them attractive for robotics. However, labeled event-camera data for a specific robot and scene is scarce and expensive to collect, which slows the development of event-based perception and control. We present EVIS: a physics-grounded event camera plugin for NVIDIA Isaac Sim that generates high-rate, fully labeled event streams directly inside a physics simulator. The plugin implements a faithful log-intensity contrast event model with per-pixel asynchronous reference updates; it migrates from a normal RGB camera with few changes and integrates into any Isaac Sim / Isaac Lab scene, inheriting the simulator’s physics and frame-perfect ground truth. It is fully configurable, and offers an interpolation option that renders only sparse keyframes and synthesizes the in-between frames through bidirectional motion-vector warping, making real-time generation on a single GPU possible. Optional sensor noise and motion blur further narrow the gap to real cameras. The generated streams are directly usable by pretrained event networks for downstream tasks. Code repository: this https URL
[CV-61] GRE-Diff: Gaussian Room Embeddings for Structured Layout Diffusion
链接: https://arxiv.org/abs/2607.08086
作者: Jing Wang,Haoran Xiong,Zihao Yan,Minglun Gong,Hui Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 37 pages, 9 figures, conference
Abstract:Designing functional and aesthetically coherent floor plans requires exploring a vast space of possible room arrangements, a task that quickly becomes overwhelming for human designers. In this paper, we propose GRE-Diff, a controllable and interactive diffusion-based framework that automates the creation and editing of apartment floor plans under user-specified constraints. By combining AI-generated suggestions with real-time, human-in-the-loop editing, the system enables users to specify room types, room counts, boundary shapes, and editing operations through LLM-parsed instructions or GUI-based interaction. It then generates a diverse set of plausible and well-structured designs for refinement. At the core of our approach is Gaussian Room Embedding (GRE), a continuous latent representation that models each room as a spatial Gaussian distribution capturing its location and extent. Extensive experiments on the RPLAN dataset show that GRE-Diff produces high-quality, constraint-aware, and editable polygonal layouts, offering a practical step toward bridging AI-driven automation and human creativity in spatial design. Comments: 37 pages, 9 figures, conference Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2607.08086 [cs.CV] (or arXiv:2607.08086v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.08086 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jing Wang [view email] [v1] Thu, 9 Jul 2026 03:41:54 UTC (16,285 KB) Full-text links: Access Paper: View a PDF of the paper titled GRE-Diff: Gaussian Room Embeddings for Structured Layout Diffusion, by Jing Wang and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.CV prev | next new | recent | 2026-07 Change to browse by: cs References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> 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?) 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(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[CV-62] Mixture of Enhanced-View Experts for Multi-Query Vehicle ReID and A Large-Scale Benchmark
链接: https://arxiv.org/abs/2607.08085
作者: Aihua Zheng,Jie Zhen,Chenglong Li,Jiaxiang Wang,Jin Tang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multi-query vehicle ReID aims to leverage complementary information from diverse views for robust feature learning. However, current methods suffer from simplistic feature fusion and thus easily ignores some important view information and cross-view relationships. To handle these problems, this work presents a novel approach called Mixture of Enhanced-View Experts (EV-MoE), which enhances the feature representation of each view and efficiently integrate the view-specific enhanced features by MoE, for robust multi-query ReID. In particular, we design a mixture of enhanced-view experts module, which consists of two parts including view-specific feature enhancement sub-Module (VFEM) and dynamic multi-view fusion sub-Module (DMFM). Moreover, we further introduce Multi-view Alignment Loss (MAL), which aligns features through bidirectional crossview contrastive learning and reconstruction constraints, addressing the challenges of consistency between multi-query features and single-image features. In addition, to evaluate multi-query ReID in real-world environments, we collect LCRI-1K, a largescale vehicle ReID dataset with 1,090 identities, 107,805 images, across 23,637 cameras, where each vehicle appears in an average of 67.5 cameras, providing a comprehensive benchmark to test the robustness in complex environments. Extensive experiments demonstrate the robustness of CAFNet in addressing the multiquery vehicle ReID problem. The code is available at https: //github.com/xiaozhen28/CAFNet.
[CV-63] LDFE: Laplacian Decoupled Feature Enhancement Block for Dual-Stream CNN-based RGB-IR Object Detection
链接: https://arxiv.org/abs/2607.08076
作者: Wenhao Dong,Xiaoyan Luo,Linlin Yang,Haodong Zhu,Xiaorong Shi,Guodong Guo,Baochang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:The complementary information between RGB and IR images can significantly enhance object detection performance under extreme conditions. Existing methods prefer dual-stream CNN backbones built upon YOLO for feature extraction and focus on the design of feature fusion. In this paper, we introduce the Laplacian Decoupled Feature Enhancement block (LDFE) to fuse features from different stages of the dual-stream CNN backbone. By design, LDFE simultaneously considers the characteristics of modalities and structures for feature fusion by employing global-local decomposition, denoising, fusion, and reconstruction, sequentially. The LDFE first separates features into global and local components based on Laplacian Pyramid, and then performs denoising and fusion based on Global State Space Enhancement module (GS2E) and Local Convolutional Correlation Enhancement module (LC2E) separately. Specifically, the GS2E conducts a two-branch architecture for the main and auxiliary modalities. It dynamically suppresses noise in the main modality through cross-modal attention derived from the auxiliary modality, while employing a State Space Model to capture long-range dependencies within the global feature representations of the main modality. To obtain bidirectional interaction, the two modalities systematically alternate their main/auxiliary roles. Moreover, the LC2E suppresses noise in local features and leverages spatial and channel dimension along with triple convolution to extract fine-grained details for fusion. These innovative designs achieve a significant performance improvement, with mAP surpassing the SOTA methods 6.2%, 3.7%, 4.7%, 2.3%, 4.1% and 2.0% on M3FD, DroneVehicle, LLVIP, FLIR-Aligned, KAIST and VEDAI datasets,respectively.
[CV-64] UAV-OVVIS: Unmanned Aerial Vehicles Also Need Open-Vocabulary Video Instance Segmentation
链接: https://arxiv.org/abs/2607.08075
作者: Mingyu Dou,Shi Qiu,Ming Hu,Yifan Chen,Zhe Sun
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Unmanned Aerial Vehicle (UAV) videos are widely used in traffic monitoring, urban management, and emergency rescue. However, existing UAV video perception mainly relies on box-level localization and trajectory association under predefined categories, making it difficult to simultaneously support flexible queries and fine-grained instance-level dynamic understanding in open scenarios. To this end, we introduce a new task, UAV Open-Vocabulary Video Instance Segmentation (UAV-OVVIS), which discovers targets in UAV videos according to open-vocabulary queries and outputs instance-level segmentation trajectories with globally consistent identities. Considering the scarcity of instance-level annotations in UAV scenarios, we propose AeroTrack, a training-free unified framework. AeroTrack centers on periodic open-vocabulary detection, short-segment mask propagation, and cross-segment identity unification, reusing existing visual foundation models to enable UAV-OVVIS. Based on this framework, we instantiate five AeroTrack variants and construct AeroVIS, an evaluation benchmark for UAV-OVVIS containing 9 UAV object categories and 8,279 trajectories. Experiments show that AeroTrack substantially outperforms existing general video instance segmentation methods in UAV scenarios and demonstrates strong open-vocabulary robustness and generalization. To support future research, we release AeroTrack and AeroVIS as a unified framework and benchmark for UAV-OVVIS.
[CV-65] Post-Training in End-to-End Autonomous Driving
链接: https://arxiv.org/abs/2607.08072
作者: Ruining Yang,Muxing Wang,Yixiao Chen,Tongfei Guo,Yi Xu,Can Cui,Zichong Yang,Yitian Zhang,Ziran Wang,Yun Fu,Lili Su
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:End-to-end models that map multimodal inputs directly to future trajectories/maneuvers have emerged as an increasingly prominent research paradigm in autonomous driving. This class of models includes both Vision-Language-Action models and trajectory-generative planners. Unlike classic machine learning applications, autonomous vehicles operate in safety-critical and interaction-intensive environments where traditional open-loop imitation of expert demonstrations is not sufficient to ensure reliability. In particular, small execution errors can accumulate over time, while recovery behaviors are scarce in training data. In addition, long-horizon objectives such as safety and driving comfort are not captured by pointwise labels either. These limitations have motivated a shift toward post-training techniques, which further refine driving policies beyond pure imitation. This survey presents a unified view of post-training for autonomous driving by defining its scope and organizing the existing literature into four major families based on the form of supervision they use. For each family, we discuss its capabilities, limitations, and open challenges. We aim to facilitate a systematic understanding of this emerging area and stimulate future research on reliable and efficient post-training for autonomous driving.
[CV-66] APIVOT: Adaptive Planning with Interleaved Vision-Language Thoughts
链接: https://arxiv.org/abs/2607.08024
作者: Emily Jin,Joy Hsu,Yiqing Xu,Weiyu Liu,Nick Haber,Jiajun Wu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
备注: Project Page: this https URL
Abstract:Long-horizon robot planning requires jointly reasoning over semantic task structure and geometric feasibility. To successfully execute a task, a robot must decompose goals, select task-relevant objects, and sequence actions, while ensuring that plans satisfy spatial constraints such as limited free space and object collisions. In this work, we propose APIVOT, a VLM-based planner that adaptively interleaves language and visual thoughts for long-horizon planning. APIVOT learns to leverage language for semantic reasoning, while using visual thoughts as imagined future states for internal verification of geometric feasibility. On long-horizon kitchen tasks, APIVOT outperforms general-purpose VLMs and prior planning frameworks, achieving the largest gains in spatially constrained settings. We find that APIVOT learns meaningful modality selection behavior, demonstrating that adaptive interleaving of vision-language thoughts improves both planning success and reasoning efficiency.
[CV-67] SAGA: Stable Acceleration Guidance for Autoregressive Video Generation
链接: https://arxiv.org/abs/2607.08020
作者: Thanh-Nhan Vo,Trong-Thuan Nguyen,Trung-Hoang Le,Tam V. Nguyen,Minh-Triet Tran
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Autoregressive video diffusion enables efficient streaming and long-horizon video generation, but repeatedly reusing generated latents as causal context can amplify temporal errors, resulting in flickering, motion jitter, and structural drift. In this paper, we investigate this failure mode from a spectral kinematic perspective and identify discrete latent acceleration as an effective signal for revealing unstable high-frequency temporal perturbations. To this end, we propose SAGA, a training-free \textbf\textitstable \textbf\textitacceleration \textbf\textitguidance approach for \textbf\textitautoregressive video generation. SAGA integrates an acceleration domain spectral guidance objective based on finite-window Slepian projections with a structured autoregressive noise initialization strategy that suppresses short-range temporal correlations while preserving long-range motion structure. Without retraining or modifying the backbone, SAGA can be directly applied to existing chunk-wise autoregressive diffusion models, which is the prevalent setting for high-quality generation. Extensive experiments show that SAGA consistently improves temporal quality across multiple autoregressive diffusion models. On Self-Forcing, SAGA improves Temporal Quality from 97.30 to 97.91 and Image Quality from 69.60 to 70.51. Moreover, spectral analysis and human preference studies demonstrate that SAGA reduces temporal instability while maintaining visual fidelity.
[CV-68] LightCrafter: PBR-Conditioned Video Diffusion Refinement for Controllable and Consistent Relighting
链接: https://arxiv.org/abs/2607.08016
作者: Zixin Guo,Yehonathan Litman,Yifeng He,John Miller,Chuhan Chen,Deva Ramanan
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:Video relighting requires balancing long-form temporal consistency with a physically grounded understanding of light transport, which depends on accurate estimation of intrinsic scene properties such as materials, geometry, and illumination. Existing methods follow two paradigms: (1) reconstruct a video’s photometric properties via inverse rendering and relight them to a target illumination via forward rendering, using physically-based rendering (PBR) or a neural renderer; these suffer from noisy reconstructions and struggle with hard-to-model effects such as global illumination. (2) Frame the task as generative video-to-video translation conditioned on relighting targets (a target environment map or text); this limits relighting control and temporal stability, since diffusion models struggle to translate long-form videos, and is constrained by the availability of input/relit training pairs. We propose LightCrafter, a hybrid pipeline that reformulates video relighting as video translation of a proxy video: rather than translating the input video directly to the target, we translate a PBR rendering of the input under the target illumination to the final target. This bakes illumination targets into the PBR proxy, removing the need to teach the diffusion model illumination concepts like environment maps, and enables more intricate lighting control while naturally providing long-form temporal consistency. We show PBR renders alone already outperform some prior art but struggle with effects like global illumination; to capture these, we leverage photometric priors in video generation models by post-training CogVideoX on synthetic video pairs and real-world unpaired videos. We outperform prior state-of-the-art on existing real-world relighting benchmarks and contribute a synthetic benchmark for further analysis. We will release our dataset, benchmark, metrics, and code.
[CV-69] FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection
链接: https://arxiv.org/abs/2607.08014
作者: Vikash Sathiamoorthy,Shuo Huai,Hao Kong,Di Liu,Wendy Yong Yi Loy,Christian Makaya,Daren Ho,Ravi Subramaniam,Qian Lin,Weichen Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Author’s accepted version. Published in Proceedings of the Great Lakes Symposium on VLSI 2024 (GLSVLSI '24)
Abstract:Federated learning (FL) is a collaborative learning scheme to train deep learning models, where collaborating parties can consolidate their models without sharing local data with other parties, hence preserving data privacy. Nevertheless, when implementing FL in Industrial visual inspection (IVI), the constraints posed by limited data availability and the intricate nature of the inspection tasks significantly impact the performance of the resulting model. This paper introduces FedTR, a novel FL framework incorporating transfer learning designed for Autonomous IVI, focusing on the challenging task of identifying label defects through end-to-end text recognition. Transfer learning is a method that leverages the knowledge of a pre-trained model to adapt to a different dataset. FedTR initially trains the model using a publicly available dataset, after which performs the essential federated learning process with model fine-tuning on the distributed and limited private data. Extensive experiment results demonstrate the effectiveness and feasibility of FedTR on private ink cartridge datasets for label defect identification. FedTR achieves an end-to-end text recognition word-level accuracy of 95.5% and 94.2% on homogeneous and heterogeneous data respectively. Additionally, it attains performance levels that are on par with those achieved through centralized training.
[CV-70] LOGOS: Language-guided Oriented Object Detection in Aerial Scenes
链接: https://arxiv.org/abs/2607.08004
作者: Trong-Thuan Nguyen,Minh-Triet Tran
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to SOICT 2025
Abstract:Object detection in geospatial scenes, such as satellite and aerial imagery, poses significant challenges due to the varying orientations and densities of objects, as well as the complex backgrounds inherent to remote sensing imagery. Traditional methods for oriented object detection have struggled to address issues such as angular discontinuity, fixed query sizes, and inefficiencies in handling sparse or cluttered scenes. In this paper, we propose LOGOS, a novel transformer-based approach that leverages textual prompts to guide the detection of oriented objects in aerial scenes. In particular, our proposed approach incorporates prompt-modulated content queries to dynamically adjust the model’s focus based on the provided text, thereby improving object detection accuracy in complex environments. Empirically, extensive experiments on the DOTA dataset demonstrate that LOGOS outperforms existing state-of-the-art methods, particularly in densely packed and rotated object scenarios. Our approach offers a significant step forward in improving the robustness and scalability of oriented object detection in remote sensing applications.
[CV-71] Beyond Thermal Imaging: Inferring Thermophysical Properties from Time-Resolved Thermal Observations
链接: https://arxiv.org/abs/2607.07962
作者: Chenghao Xu,Malcolm Mielle,Olga Fink
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 31 pages, In submission
Abstract:Inferring latent physical properties from sensory observations is a fundamental challenge in machine perception. Among available sensing modalities, thermal imaging is particularly promising because temperature evolution is directly governed by heat-transfer physics and therefore encodes information about underlying thermophysical properties of a scene. Recovering spatially resolved thermophysical properties from thermal observations could transform applications ranging from digital twins and infrastructure monitoring to robotics and scientific imaging. However, existing thermal scene reconstruction methods can recover temperature fields in complex 3D environments without identifying the thermophyiscal properties that govern thermal evolution, whereas inverse methods provide physically interpretable parameter estimation but typically rely on simplified geometries and controlled experimental conditions. Here we introduce ThermoField, a framework that unifies thermal scene reconstruction and thermophysical parameter estimation through differentiable heat-transfer simulation. The proposed framework represents these quantities as spatially varying neural fields and constrains them through scene geometry, governing heat-transfer physics, and temporal thermal observations. We demonstrate that ThermoField jointly reconstructs geometry, estimates spatially varying thermal diffusivity, and predicts thermal evolution under previously unseen environmental conditions. By integrating neural scene representations with differentiable heat-transfer solver, the framework enables physically interpretable parameter inference in complex 3D scenes. Our results establish a bridge between thermal scene reconstruction and inverse heat-transfer analysis, providing a unified approach for geometry reconstruction, thermophysical property estimation, and predictive thermal simulation from thermal observations. Comments: 31 pages, In submission Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.07962 [cs.CV] (or arXiv:2607.07962v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.07962 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-72] Evaluating the Effect of Frame Rate in Sequence-Based Classification of Autism-Related Self-Stimulatory Hand Idiosyncrasies
链接: https://arxiv.org/abs/2607.07957
作者: Raunak Mondal,Peter Washington
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 15 pages, 5 figures, 3 tables. Preliminary version presented as a poster at the AMIA 2024 Informatics Summit
Abstract:Autism spectrum disorder (ASD) affects over 75 million individuals worldwide, yet scalable computational methods for remote behavioral screening remain limited. This study addresses two complementary challenges in automated detection of autism-related self-stimulatory behaviors from video: (1) identifying the optimal sequence-based neural network architecture and temporal sampling rate, and (2) characterizing data augmentation strategies for training on small behavioral datasets. For the first objective, long short-term memory (LSTM) and gated recurrent unit (GRU) models were trained on pose-derived features from the Self-Stimulatory Behavior Diagnosis (SSBD) dataset at frame sampling intervals of 1, 5, 15, 30, 45, and 90 frames. Both architectures exceeded prior convolutional neural network (CNN) baselines (62-76% accuracy), with peak accuracies of 97.5% (LSTM) and 98.75% (GRU) at a sampling interval of every 15 frames. For the second objective, ten data augmentation strategies were applied to an I3D transfer learning pipeline, with an ablation study quantifying the marginal contribution of each technique. Horizontal flip achieved the highest standalone accuracy (48.78%), while exclusion of upsampling from the augmentation pipeline produced the largest performance degradation, indicating its necessity for complex behavioral video augmentation. A personalized machine learning approach, in which per-subject models were trained and tested on temporally split segments of each video, produced consistent predictions (mean loss 1.84, SD 0.79). These results provide practitioners with concrete guidance on architecture selection, sampling rate, and augmentation strategy for video-based behavioral classification in data-scarce clinical domains.
[CV-73] Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT
链接: https://arxiv.org/abs/2607.07922
作者: Giulia Marchiori Pietrosanti,Giulio Rossolini,Giorgio Buttazzo
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision Transformers (ViTs) remain vulnerable to localized adversarial attacks, e.g., adversarial patches, while recent test-time defenses mitigate them by suppressing image tokens with abnormally high attention scores. These defenses exploit a strong coupling between attention and adversarial effectiveness: adversarial tokens often need to attract substantial attention to influence the prediction. We introduce adversarial decoys, independently optimized image patches that redirect the attention, and therefore related defenses, toward selected target tokens. Rather than jointly optimizing misclassifications and defense evasion, our approach decouples the two objectives: the original adversarial region induces the incorrect prediction, while a separate decoy manipulates the attention ranking used by the defense. A layer-wise objective increases target-token attention and promotes these tokens above competing non-target ones. Since the decoy is optimized independently of the underlying attack, the method is attack-agnostic and can be easily integrated with any existing adversarial patch attack. Experiments on ImageNet across multiple ViT architectures and attacks show that decoys can redirect high attention scores away from the true adversarial region while preserving much of the attack effectiveness. These results reveal a fundamental limitation of using attention magnitude as an indicator of adversarial relevance.
[CV-74] 3D Reconstruction of deciduous Trees using low-cost UAV- and Crane-based Photogrammetry for Monitoring Shoot Elongation across entire Canopies
链接: https://arxiv.org/abs/2607.07905
作者: Stephan Nebiker,Micha Tschanz,Nando Amport,Frederik Baumgarten
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ISPRS Congress 2026, camera-ready version
Abstract:Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass. At the same time tree growth is affected by increasing temperatures, more frequent drought periods, late frosts and other extreme events associated with climate change. While continuous measurements of radial (secondary) tree growth using dendrometers are well established, monitoring of shoot elongation (primary growth) has largely been neglected because suitable measurement techniques are lacking. As a result, the effects of climate change on primary tree growth remain insufficiently understood. This work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies. Here we explored the use of low-cost UAV photogrammetry and of a multi-camera CraneCam system under real-world conditions. Data were collected in two study areas over an entire growing season. We present sensor evaluations, photogrammetric data acquisition and processing strategies. A special focus is placed on the analysis of the resulting photogrammetric 3D point clouds in terms of accuracy, resolution and completeness. Results demonstrate 3D point accuracies of 5-6 mm for entire trees using consumer-grade UAVs weighing less than 250 g and a 3D reconstruction completeness between 92% and 98% depending on the UAV type. The paper introduces a novel 3Dprinted ground-truth branch to evaluate the capability to reconstructing fine-detail structures such as thin tree shoots. Finally, we discuss operational challenges and initial experiments towards a skeletonization of entire trees based on photogrammetric point clouds.
[CV-75] me-to-Collision Based Dynamic Obstacle Avoidance Using Pretrained Vision Models for Robots in Unstructured Environments
链接: https://arxiv.org/abs/2607.07885
作者: Erik Jagnandan,Mulugeta Haile,Gregory Barber,Pratik Chaudhari
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: 9 pages, 8 figures
Abstract:Dynamic obstacle avoidance in unstructured outdoor environments remains a critical challenge for autonomous mobile robots, particularly when large-scale robot-specific training data and simulation-based policies are impractical. We present a data-efficient, interpretable method for vision-based dynamic obstacle avoidance that operates entirely on real-world data, avoiding the sim-to-real transfer problem inherent in simulation-trained policies. Our approach leverages UniDepth, a large pretrained monocular depth estimation model, to produce dense depth maps from RGB video without requiring stereo cameras or LiDAR at inference time. Dynamic obstacle avoidance is achieved by extending the SuperPoint and SuperGlue feature correspondence pipeline to track keypoints across long frame sequences, projecting their 2D pixel-space positions into 3D using camera intrinsics and predicted depth, running bundle adjustment initialized from these 3D keypoints, and computing per-keypoint time-to-collision (TTC). A 2D motion primitive in the ground plane is then selected to move the robot away from the closest point of approach of the minimum-TTC keypoint. Evaluated on real-world data from the M3ED dataset, our pipeline achieves a precision of 0.49 and a recall of 0.38 in identifying frames with a ground truth TTC below 1 second, and correctly generates the evasive motion direction in 84% of true positive detections. Crucially, it detects at least one frame with TTC less than 1 second for 20 out of 22 unique physical obstacles present in our test sequences. Unlike end-to-end learned methods that demand thousands of hours of robot-specific training data, our approach eliminates model training entirely, requiring only 74 seconds of data for hyperparameter tuning. This demonstrates exceptional data efficiency while preserving interpretable and generalizable behavior across diverse obstacle types.
[CV-76] GIRAF: Towards Generalizable Human Interactions with Articulated Objects CVPR2026
链接: https://arxiv.org/abs/2607.07880
作者: Xiaohan Zhang,Sebastian Starke,Alexander Winkler,Federica Bogo,Samir Aroudj,Yuting Ye
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 6 figures, 3 tables. Accepted at the Third Workshop on Human Motion Generation (HuMoGen), CVPR 2026
Abstract:Synthesizing realistic full-body human interactions with articulated objects is a fundamental challenge for embodied AI and graphics, with applications in robotics training and virtual agents. Existing models remain limited: some focus on simple activities with static objects, while others restrict attention to hand-only manipulation. This leaves open the problem of generating coordinated full-body motion that approaches, manipulates, and moves articulated objects in a realistic and generalizable way. The key difficulty lies in reasoning jointly about locomotion, fine-grained contact, and object articulation. Models must capture subtle hand-object correspondences that transfer across object geometries, while also producing seamless transitions from navigation to manipulation. At the same time, the scarcity of large-scale paired motion-scene data makes it difficult to generalize across diverse object positions and shapes. We introduce a text-conditioned diffusion model that addresses these challenges through three core ideas: an object-centric representation that unifies hand-object contact with object surfaces, a mixed-domain training strategy that balances locomotion and interaction, and a contact-based augmentation scheme that expands training diversity. Through experiments, our method demonstrated strong generalization to unseen object configurations, surpassing current state-of-the-art methods.
[CV-77] DreamCharacter-1: From 3D Generative Foundation Models to Product-Ready Character Generation
链接: https://arxiv.org/abs/2607.07817
作者: Weizhe Liu,Yunjie Wu,Xiangqian Shu,Guangwei Wang,Xiangyu Xu,Peng Li,Yujie Li,Hengkai Guo
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Official Page: this https URL
Abstract:We present DreamCharacter-1, a lightweight post-adaptation framework that calibrates pretrained 3D foundation models toward high-fidelity, production-ready 3D character generation. Building upon a 3D foundation backbone, our pipeline incorporates three task-oriented components: (1) geometry post-training, which enhances fine-grained surface details through geometric preference optimization; (2) texture post-training, which synthesizes high-resolution textures and refines the appearance of occluded regions; and (3) inference acceleration, which enables scalable deployment. Extensive quantitative and qualitative experiments demonstrate that DreamCharacter-1 produces visually compelling and structurally robust 3D character assets, consistently surpassing state-of-the-art character generation methods.
[CV-78] SASGeo: Stability-Aware Semantic Map Localization for GNSS-Denied UAVs – A Framework and Synthetic Proof of Concept
链接: https://arxiv.org/abs/2607.07737
作者: Natalia Trukhina,Vadim Vashkelis
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 7 pages, 5 figures
Abstract:GNSS-denied unmanned aerial vehicles require occasional absolute position fixes to bound the drift of visual-inertial odometry. Cross-view image retrieval can provide such fixes, but raw appearance is sensitive to season, illumination, viewpoint, map age, and sensor modality. We propose \sas, a semantic map-localization framework that represents the environment through persistent structures such as roads, buildings, waterways, railways, intersections, and field boundaries. The method combines semantic raster alignment, relational graph evidence, feature stability and geographic distinctiveness, explicit positive/contradictory/unknown observations, and integrity-aware rejection of ambiguous fixes. Unlike a broad architecture-only proposal, this paper specifies concrete weighting and decision models and reports a reproducible synthetic proof of concept. In 220 randomized retrieval trials with rotation, scale changes, partial crops, occlusion, simulated map changes, and hard semantic decoys, a global semantic descriptor achieved 58.6% Recall@1, while spatial semantic matching variants achieved 94.5-95.5%. Wilson 95% intervals separate the global descriptor from the spatial variants but overlap among the spatial variants, so the experiment supports semantic geometry rather than a definitive benefit from each proposed module. The preliminary experiment does not validate real-flight navigation; rather, it demonstrates that structured semantic geometry can discriminate locations under controlled cross-view perturbations and identifies the harder aliasing, map-aging, and rejection tests required next.
[CV-79] ReCoLoRA: Spectrum-Aware Recursive Consolidation for Continual LLM Fine-Tuning
链接: https://arxiv.org/abs/2607.07719
作者: Wentao Lu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Parameter-efficient fine-tuning adapts a large language model to one task cheaply, but across a task sequence LoRA-style methods keep stacking low-rank updates on the same frozen weight, so each new task tends to overwrite the previous ones. We present ReCoLoRA (Recursive Consolidation of Low-Rank Adapters), a spectrum-aware framework for continual fine-tuning: adapters are initialized from a randomized SVD of the pretrained weight, per-layer effective ranks are selected by an elbow criterion, and the principal subspace is adapted before residual capacity is opened. Before each new task, ReCoLoRA re-decomposes the current effective weight, rather than the original one, into a frozen residual, a slowly updated principal component, and a fresh adapter (recursive consolidation), so every task starts from the model that has already absorbed its predecessors. On a six-task continual GLUE sequence over four 7-8B backbones, ReCoLoRA attains the best final average score on three of the four backbones against rank-swept LoRA, PiSSA, AdaLoRA, and DoRA baselines while training fewer parameters; an oracle-routed task-bank variant serves as an upper bound under full task isolation. Code: this https URL.
[CV-80] Who Gets Missed in the Tail? Thresholded Subgroup Underdiagnosis in Long-Tailed Chest X-ray Classification
链接: https://arxiv.org/abs/2607.07717
作者: Ha-Hieu Pham,Hai-Dang Nguyen,Dang P. M. Cao,Thanh-Huy Nguyen,Min Xu,Trung-Nghia Le,Ulas Bagci,Huy-Hieu Pham
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In chest X-ray (CXR) classification, acceptable ranking performance can still leave rare-positive patients below threshold, especially within subgroups. We study this pre-deployment fairness problem as an audit question: after a long-tailed multi-label CXR model is converted from scores into decisions, who is missed? Across VinDr-CXR and MIMIC-CXR/CXR-LT, we use a diagnostic ladder to separate class-level long-tail losses, subgroup-aware weighting, group robustness, and threshold selection. On VinDr-CXR, group-tail weighting followed by tail-aware thresholding reduces tail FNR from 0.665 to 0.269, sex worst-group FNR from 0.705 to 0.157, and age worst-group FNR from 0.822 to 0.133, while macro-mAP increases from 0.611 to 0.635. On MIMIC-CXR/CXR-LT, the same score-to-threshold comparison reduces tail FNR from 0.866 to 0.741 and lowers worst-group FNR across sex, age, race, and insurance; residual missed-positive rates nevertheless remain high. Paired bootstrap contrasts on VinDr support the thresholded FNR reductions, and GroupDRO reference runs indicate that aggregate group robustness alone does not remove rare subgroup misses in this setting. The study supports a narrow audit claim: rare-label fairness in CXR depends jointly on the finding, subgroup, and operating threshold, not on label frequency or ranking metrics alone.
[CV-81] Equivariant Quantum Clustering with Differential Privacy: Parameter-Efficient Privacy-Preserving Analysis Across Heterogeneous Sensitive Datasets KDD
链接: https://arxiv.org/abs/2607.08092
作者: B. M. Taslimul Haq,Md Arifur Rahman,Tawfiq Al Islam Foysal,Abdullah Al Noman,Abir Ahmed
类目: Quantum Physics (quant-ph); Computer Vision and Pattern Recognition (cs.CV)
备注: 24 pages, 10+ tables, multiple figures, research article. Introduces Equivariant Quantum Clustering (EQC) integrating differential privacy with parameter-efficient quantum circuits for privacy-preserving clustering. Evaluated on NSL-KDD, CERT Insider Threat v6.2, and Synthetic MIMIC-III datasets
Abstract:Privacy-preserving clustering is critical for analyzing sensitive data in healthcare, cybersecurity, and enterprise applications, where maintaining data confidentiality must be balanced with analytical performance. This paper presents Equivariant Quantum Clustering (EQC), a parameter-efficient framework that integrates symmetry-aware quantum circuits with differential privacy to improve the privacy-utility tradeoff. EQC employs p4m equivariant parameter sharing to reduce circuit complexity while preserving informative feature representations. The framework is evaluated on three privacy-sensitive datasets: NSL-KDD, CERT Insider Threat v6.2, and a synthetic MIMIC-III clinical dataset. On the NSL-KDD benchmark, EQC achieves 79.3% clustering accuracy while reducing membership inference attack success to 38.3% under a privacy budget of \epsilon = 1.0 and \delta = 10^-5, outperforming representative classical and quantum baselines. Ablation studies indicate that the performance gains primarily arise from parameter-efficient circuit design combined with differential privacy. The results demonstrate that EQC provides a practical quantum-ready framework for secure and privacy-preserving clustering across heterogeneous sensitive datasets.
[CV-82] ConRad: Efficient Conformal Prediction for Radiomics
链接: https://arxiv.org/abs/2607.08084
作者: Matt Y. Cheung,Ashok Veeraraghavan,Guha Balakrishnan
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Medical Physics (physics.med-ph); Applications (stat.AP)
备注: Code available at this https URL
Abstract:Radiomic features derived from medical images and segmentation masks are used to support decision making in clinical imaging pipelines. In practice, these features are often computed from predicted masks, but segmentation models can be overconfident or poorly calibrated, making derived measurements appear more reliable than they are. Conformal prediction (CP) provides distribution-free prediction intervals with finite-sample marginal coverage guarantees, but black-box intervals for segmentation-derived radiomics can be inefficient because they ignore test-time information about image appearance, mask geometry, and segmentation uncertainty. We propose ConRad, a conformal framework for scalar radiomic targets that uses covariates derived from the predicted mask, input image, predicted radiomics, and boundary uncertainty to construct adaptive intervals while maintaining coverage. Across five 2D medical imaging datasets and 171 retained radiomic targets, we show that ConRad improves feature-level efficiency compared to baselines while maintaining near-nominal empirical coverage. Ablation results further indicate that segmentation boundary uncertainty features are the largest contributors to interval efficiency.
[CV-83] False Confidence: Automated Labels Confound Fairness Audits in Cervical Spine Segmentation MICCAI
链接: https://arxiv.org/abs/2607.07852
作者: Linus Juni,Aasa Feragen,Aditya Parikh
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 8 pages, 1 figure. Under review at FAIMI 2026 (MICCAI workshop)
Abstract:Automated segmentation of cervical-spine MRI is increasingly used in clinical workflows, yet no fairness audit exists for this anatomy. We show that auditing these segmentation tasks is complicated by a common property of modern segmentation datasets: expert-annotated gold labels are expensive, so abundant machine-generated (silver) labels are added to limit annotation cost. This matters because the reference used to judge a model can itself be biased. In this study, we present the first fairness audit of cervical-spine MRI segmentation across sex, age, and race using the CSpineSeg dataset. We observe that the deployed model is demographically fair, but the choice of reference label, however, is not neutral. Because a dataset’s silver labels are generated by a model trained on its gold labels, any new model trained on those same gold labels agrees more with the silver labels than with expert truth: scoring identical predictions against silver rather than gold overestimates performance by ~8 Dice points and turns the fairness verdict for age from non-significant to significant - not by the gap inflation Parikh et al. report (which we term false magnitude) but by collapsing within-group variance (which we term false confidence). Reference-label provenance is thus a first-order confounder in segmentation evaluation: performance and fairness should be reported against expert labels, and any fairness claim stated together with the provenance of its reference.
人工智能
[AI-0] Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation
链接: https://arxiv.org/abs/2607.08758
作者: Yifan Zhou,Qihao Yang,Yan Li,Donggang Li,Xiru Hu,Hokin Deng,Ziyang Gong,Xuanyi Zhou,Huacan Wang,Xiangchao Yan,Wanghan Xu,Wenlong Zhang,Shaofeng Zhang,Yue Zhou,Yifan Yang,Zhihang Zhong,Xue Yang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Scientific ideas rarely start from a blank page. They inherit mechanisms, repair known limitations, and recombine pieces of earlier work, much like biological genomes. Current benchmarks still say little about whether AI systems can follow this inheritance structure. We present IdeaGene-Bench (IG-Bench), a benchmark for scientific lineage reasoning and lineage-grounded idea generation. IG-Bench is organized around the IdeaGene framework: each paper or proposal is represented as a set of minimal, typed, evidence-grounded Idea Genome objects, and a GenomeDiff aligns these objects to record inheritance, mutation, loss, external import, and novel insertion under six operational evolutionary dynamics. The benchmark contains 1,961 golden lineage traces, 1,085 curated Idea Genome objects, and 920 pairwise GenomeDiff records across 10 scientific domains. It supports two evaluations. IG-Exam (42 task types, 1,029 instances) tests closed-form lineage reasoning across Idea Genome abstraction, inheritance tracing, evolutionary reasoning, and lineage verification. IG-Arena evaluates generation with a lineage-conditioned Population-Evolution Score(PES), asking whether a proposal can be inserted as a coherent descendant of a given lineage population: it should inherit the right Idea Genome objects, vary meaningfully from nearby work, and offer selection value for future research. Experiments on 14 LLM-based scientists expose a compositional bottleneck. The strongest system reaches only 27.3% exact accuracy on lineage reasoning, and structured lineage context reshuffles system rankings rather than helping every participant uniformly.
[AI-1] SLORR: Simple and Efficient In-Training Low-Rank Regularization
链接: https://arxiv.org/abs/2607.08754
作者: David González-Martínez,Shiwei Liu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Low-rank factorization is widely used to compress neural networks, but modern models are often not naturally amenable to aggressive factorization without significant accuracy loss. Existing training-time low-rank regularizers can improve compressibility, but they often require SVDs of large weight matrices, modify the model architecture (introducing additional trainable parameters), or rely on stateful cached quantities. To address these limitations, we introduce SLORR, a simple, stateless, and architecture-preserving framework for in-training low-rank regularization, instantiated with two main variants based on the Hoyer sparsity metric and the nuclear norm. SLORR directly regularizes the original weight matrices using GPU-friendly approximations for the forward and backward passes of the regularizers, for which we provide approximation guarantees. We first evaluate SLORR on ImageNet-1K across short-horizon continued training of ResNet-50, ViT-B/16, and ViT-L/16, and pretraining of ResNet-18, where SLORR induces compressibility while introducing less than 8% training overhead. We further evaluate SLORR-Hoyer in LLM pretraining at 135M and 560M scales: SLORR-trained compressed models preserve performance substantially better than unregularized models while adding less than 1% average training overhead.
[AI-2] Workflow as Knowledge: Semantic Persistence for LLM -Mediated Workflows
链接: https://arxiv.org/abs/2607.08740
作者: Emanuele Quinto,Carlo Andrea Rozzi,Francesco Zanitti
类目: Artificial Intelligence (cs.AI); Programming Languages (cs.PL); Software Engineering (cs.SE)
备注: 39 pages, 18 figures
Abstract:Large language model (LLM) applications increasingly use explicit workflows for tool use, retrieval, branching, checkpointing, and human approval. Existing workflow systems already address many execution concerns. This paper proposes a Lisp-inspired but language-independent conceptual model: symbolic forms, object identity, and live-image thinking are used as explanatory lenses, not implementation commitments. In this model, workflow definitions, workflow instances, inference records, context snapshots, and dependency relations are represented as persistent knowledge objects in a shared knowledge substrate. Its central semantic distinction is between derive and infer: derive is deterministic computation over available state; infer is mediated LLM judgment under declared context and executor-controlled capability policy. The result is a preliminary conceptual account of semantic persistence: workflows do not merely produce knowledge and leave traces, but can themselves be represented as inspectable, resumable, and reviewable knowledge objects, while formal transition semantics remain future work. Comments: 39 pages, 18 figures Subjects: Artificial Intelligence (cs.AI); Programming Languages (cs.PL); Software Engineering (cs.SE) Cite as: arXiv:2607.08740 [cs.AI] (or arXiv:2607.08740v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.08740 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-3] he Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLM s
链接: https://arxiv.org/abs/2607.08734
作者: Baha Rababah,Cuneyt Gurcan Akcora,Carson K. Leung
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Post-training quantization is widely used to deploy large language models in resource-constrained settings, yet its evaluation relies almost exclusively on accuracy and perplexity. We show that these metrics fail to capture behavioral changes induced by quantization. We introduce correctness agreement, a decision-level metric that measures overlap in correct predictions between a base model and its quantized variants, independent of absolute accuracy. Across multiple models and quantization schemes from 8-bit to 2-bit, we find that behavioral divergence emerges under moderate quantization even when task performance appears preserved. To explain this effect, we analyze quantization as a structural operator on attention weights and quantify layer-wise distortions using statistical and distributional measures. Our results reveal non-linear breakpoints at low bit-widths and show that query and key projections are consistently more sensitive than value and output projections. These findings expose an illusion of equivalence between base and quantized models and motivate behavioral evaluation beyond conventional performance metrics.
[AI-4] A Practical Investigation of Training-free Relaxed Speculative Decoding
链接: https://arxiv.org/abs/2607.08690
作者: Guoxuan Xia,Luka Ribar,Paul Balanca
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: preprint
Abstract:Speculative decoding accelerates sampling from an autoregressive LLM by using a faster auxiliary model to draft tokens which are then verified in parallel by the LLM. Standard speculative decoding is lossless: its rejection and resampling steps exactly preserve the LLM’s sampling distribution. Recent work argues that relaxing this strict guarantee can yield further speed-ups, controlled capability-speed trade-offs, or even capability gains. We practically investigate training-free relaxed speculative decoding techniques, unify existing approaches within a shared framework, benchmark them on contemporary settings, and distil takeaways and empirical findings for practitioners. Important takeaways include: relaxation can require considerable capability evaluation unlike lossless speculative decoding, and many relaxed approaches rely on a drafter that is a good language model, making them unsuited for lightweight dedicated multi-token-prediction drafters.
[AI-5] SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets
链接: https://arxiv.org/abs/2607.08681
作者: Shilin Ou,Yifan Xu,Luyao Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As agentic AI systems are increasingly applied to cyber-physical environments, their evaluation requires assessment of both task performance and trustworthiness. In decentralized energy markets, autonomous agents may improve market utility, but may also exploit invalid physical data, create artificial liquidity, and produce unstable governance decisions. Therefore, we propose SolarChain-Eval, a physics-constrained benchmark for evaluating trustworthy economic agents. It formulates market governance as a Gymnasium-compatible Markov Decision Process, where agents make hourly decisions. SolarChain-Eval evaluates each policy across multiple dimensions, including market utility, physical safety, slippage, action smoothness, spatial fairness, and auditability. To support agentic evaluation, SolarChain-Eval incorporates an LLM-based Planner/Auditor layer. The Planner defines episode-level action bounds and audit rules, while the Auditor reviews and revises high-risk actions. All interventions are recorded through structured logs, including trigger signals, proposed actions, revised actions, and audit rationales. Experiments with static, random, myopic, RL, and RL+LLM policies reveal a clear utility-safety trade-off. RL agents improve market utility but can still produce unsafe behavior. When the physics penalty is removed, reward-maximizing agents exploit invalid generation and increase artificial liquidity. The LLM Planner/Auditor improves auditability and mitigates selected risks, but it cannot fully compensate for a misspecified reward function. These results indicate that trustworthy agentic AI evaluation requires both physical constraints and transparent intervention traces. We release data and code as open access on GitHub for replicability.
[AI-6] Formal Mechanisms for Market Stability in Self-Interested Agent Societies: A Marketplace Simulation Study
链接: https://arxiv.org/abs/2607.08652
作者: Eugene Ng Yi Sheng,Bingquan Shen
类目: Artificial Intelligence (cs.AI)
备注: 23 pages, 8 figures
Abstract:Self-interested agents, left unconstrained, tend toward defection in repeated social dilemmas, causing cooperative gains from trade to collapse. This paper investigates what formal mechanisms, layered on top of unrestricted communication, are sufficient for a society of such agents to maintain market stability, and how resilient those mechanisms are to adversarial attack. We instantiate the research question as a multi-agent marketplace simulation where 18 LLM agents (DeepSeek-V3) with complementary production specialties must trade within a constrained social network to obtain utility. We conduct two experimental phases: (1) a mechanism comparison across eight conditions under progressive troll injection over 200 rounds, identifying Mediation as the top-performing mechanism; and (2) adversarial red-teaming of Mediation using iteratively prompt-optimised LLM-driven trolls, finding that the best attack (v6) reduces honest-agent utility by 13.3% but cannot collapse the market. Mediation enables recovery even under sustained adversarial pressure. We define adversarial robustness as a mechanism’s ability to sustain positive honest-agent utility under optimised attack, and find that Mediation is robust: it can be bent but not broken.
[AI-7] Multi-Modal Multi-Environment Machine Teaching for Robust Reward Learning
链接: https://arxiv.org/abs/2607.08647
作者: Ali Larian,Qian Lin,Chang Zong Wu,Daniel S. Brown
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to RLC 2026. Conference paper
Abstract:As autonomous agents are increasingly deployed across diverse operational contexts, aligning their behavior with human intent demands reward functions that remain robust to such changes rather than overfitting to any single environment. Inverse reinforcement learning (IRL) provides a principled way to infer such objectives from human feedback. However, existing analyses of optimal teaching approaches for IRL focus on single-environment, demonstration-only settings, leaving underexplored how heterogeneous feedback modalities and environment dynamics jointly constrain reward functions that generalize across multiple environments. Because demonstrations in one MDP entangle reward information with that environments specific structure, the resulting rewards frequently fail to generalize when the agent is deployed in a new setting. We first analyze how different feedback modalities constrain rewards, showing that, in the unlimited-data regime, comparisons impose strictly stronger global constraints than other modalities. Beyond this theoretical analysis, we introduce a hierarchical machine teaching algorithm for reward learning that operates across multiple MDPs. The algorithm first greedily selects informative environments that expose complementary reward constraints, then strategically queries low-cost feedback within those environments. Empirically, our method achieves substantially lower regret and stronger generalization to held-out environments than uniform teaching baselines under identical feedback budgets, demonstrating the importance of multi-environment, multi-modal teaching for learning dynamics-robust reward functions.
[AI-8] owards Precision Therapy in Hepatocellular Carcinoma: A Clinical-Reasoning LLM for Risk Stratification and Treatment Guidance
链接: https://arxiv.org/abs/2607.08602
作者: Peng Cui,Jitao Wang,Siyan Xue,Yao Huang,Haoming Xia,Dong Li,Dengxiang Liu,Weilin Wang,Liping Liu,Leida Zhang,Yunfu Cui,Tao Peng,Daolin Ji,Haitao Zhao,Wei Zhang,Xiaojuan Wang,Weijie Ma,Zongren Ding,Jinlong Li,Yuan Ding,Jiajing Zhao,Zhiyu Chen,Chengkun Yang,Ziyue Huang,Jiaqi Liu,Fusheng Liu,Yang Zhou,Xiaojuan Wang,Zhongquan Sun,Shiyun Bao,Xiaojun Wang,Ming Yang,Guangxin Li,Bin Shu,Yong Liao,Hongxuan Li,Yao Tang,Shizhong Yang,Yongyi Zeng,Yufeng Yuan,Yinpeng Dong,Jihui Hao,Jun Zhu,Jiahong Dong
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Hepatocellular carcinoma (HCC) is a common malignancy and a leading cause of cancer-related mortality. Current guidelines and staging systems provide coarse categories, but often miss within-stage heterogeneity and the clinical context in electronic medical records (EMRs). We present HCC-STAR (Hepatocellular Carcinoma Staging, Treatment And pRognosis), a clinically aligned large language model that reads routine EMR narratives and jointly outputs risk score-based staging, ranked guideline-consistent treatments with evidence-based rationales, and individualized survival estimates. We curated about 30,000 HCC cases from SEER and expanded them into EMR-style narrative training data using a clinician-validated, prompt-based augmentation workflow. On this corpus, we developed a knowledge-aligned reasoning framework optimized with a step-verifiable composite reward, moving beyond text-level memorization of clinical guidelines. In a multi-center cohort of 6,668 patients from 12 hospitals in China, HCC-STAR achieved state-of-the-art performance in treatment recommendation and risk stratification compared with clinical guidelines and competitive models, including GPT-5 and Gemini-2.5 Pro. Hypothetical overall-survival analysis showed a median survival of 51 months under adherence to HCC-STAR recommendations, compared with 29 and 32 months under BCLC and CNLC. In clinician-centric evaluations, blinded hepatobiliary specialists rated HCC-STAR’s reasoning and evidence-based justifications as trustworthy. The model surpassed resident and attending physicians in treatment accuracy and helped physicians make more accurate decisions faster when used as an assistant. These findings support HCC-STAR as a reliable and verifiable decision-support system for risk stratification and precision therapy in HCC.
[AI-9] SHAP-Weighted Cross-Modal Expert Fusion for Emotion and Sentiment Recognition: Evidence and Limits
链接: https://arxiv.org/abs/2607.08573
作者: Adis Alihodzic,Selma Skopljakovic Hubljar
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal emotion and sentiment recognition is commonly addressed by early fusion, which concatenates modalities before classification, or late fusion, which combines independently trained unimodal predictors. Early fusion can be accurate but monolithic, while late fusion is modular but may lose cross-modal interactions. This paper revisits XAI-guided adaptive fusion (\xgaf), a tree-based mixture of unimodal and cross-modal experts whose sample-level weights are derived from TreeSHAP attribution magnitudes. We focus on the effect of SHAP attribution reduction when experts have unequal feature dimensionalities. In this setting, mean-abs and median-abs reductions can suppress high-dimensional cross-modal experts, whereas sum-abs reduction preserves total attribution mass. On MELD 7-class emotion recognition, sum-abs \xgaf nearly matches early fusion across three face-sequence aggregators; the Transformer variant reaches 0.5983 \wf, compared with 0.6018 for early fusion and 0.4598 for probability-average late fusion. McNemar testing shows no significant difference between sum-abs \xgaf and early fusion on MELD ( p=1.000 ), while \xgaf remains significantly better than late fusion ( p0.0001 ). On CMU-MOSEI 3-class sentiment recognition, sum-abs \xgaf reaches 0.6519 \wf, slightly exceeding early fusion (0.6485) and late fusion (0.5696). Ablation studies show that the main gain comes from adding cross-modal experts, especially the trimodal expert, rather than from complex per-sample routing. Diagnostics further show that mean-abs and median-abs weights are nearly uniform, while sum-abs weights concentrate on the trimodal expert. Thus, the main contribution is a transparent empirical analysis of how SHAP reduction, expert dimensionality, and cross-modal expert design affect modular multimodal fusion.
[AI-10] SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling
链接: https://arxiv.org/abs/2607.08565
作者: Jiahao Wang,Kaizhan Lin,Kaixi Zhang,Jinbo Han,Xingda Wei,Sijie Shen,Chenguang Fang,Wenyuan Yu,Rong Chen,Haibo Chen
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:LLM scheduling is critical to serving, yet it remains unclear how well existing designs fit agentic serving–with LLM requests issued by agents instead of humans. This shifts the workload in two ways: (1) agents act only on complete responses, making the cluster’s tokens per second (TPS) the primary goal and relaxing–not eliminating–per-token latency requirements; and (2) requests share much of their KV\ -reuse exceeds 80% of request tokens in a production trace from BAILIAN, versus 54-62% in chat. This paper first contributes a systematic study of request scheduling for agents on two real-world traces. We find that to increase KV\ reuse, existing schedulers overly prioritize routing requests to instances caching their KV\ , overloading a few while leaving the rest idle, capping TPS. We thus present two key insights: (1) load balance need not sacrifice all KV\ reuse, thanks to the global-tier KV\ store and (2) by utilizing the workload’s intra-session locality, balancing a small fraction of requests–the first request in each agent session–suffices to balance the cluster without sacrificing most KV\ reuse on local instances. SMETRIC realizes these insights with balanced session-centric scheduling: it routes each session’s first request purely for load balance and its follow-up requests in a cache-aware manner, preserving load balance and local reuse while keeping demand on the global tier low. Using the session turn information as the scheduling metric is deliberate: it is derived efficiently and accurately from the user inputs alone, so the scheduler stays clean and stateless. SMETRIC improves cluster TPS by 10-16% under prefill-decode colocation with a global store and prefill TPS by 2-34% under disaggregation over state-of-the-art schedulers, also with a better per-token latency. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.08565 [cs.DC] (or arXiv:2607.08565v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2607.08565 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-11] CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities
链接: https://arxiv.org/abs/2607.08554
作者: Hongye Yang,Shien Liu,Zhihao Xie
类目: Artificial Intelligence (cs.AI)
备注: 17 pages, 4 figures. Presented at ASCAAD 2024
Abstract:For urban managers and designers, improving the functional attributes of urban communities to enhance territorial resilience in the face of complexity and uncertainty is crucial. Currently, community planning often follows a top-down approach and lacks effective metrics to quantify informal behaviors of residents, leading to frequent conflicts with original plans. This study introduces CommuniWave, a machine learning model designed to efficiently detect and quantify the Degree of Informal Behavior (DIB) in urban communities. The model integrates a Behavior Capture Net (BCN) based on mmaction2, a self-developed YOLOv10 model (YLX), and a Behavior Eval Model (BEM) using random forest. Ultimately, by generating DIB fluctuation charts from street videos, the model facilitates dynamic monitoring, supporting urban managers in making refined decisions to enhance the overall resilience of communities.
[AI-12] DocMaster: A Hierarchical Structure-Aware System for Document Analysis
链接: https://arxiv.org/abs/2607.08539
作者: Ziqi Chen,Yingli Zhou,Fangyuan Zhang,Quanqing Xu,Chuanhui Yang,Yixiang Fang
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注: 4 pages, demo paper, under revision
Abstract:Leveraging large language models (LLMs) to analyze complex documents – such as academic papers, technical manuals, and financial reports – has emerged as a mainstream and critical task in both research and industry. In practice, users must first filter relevant documents from large collections and then conduct in-depth analysis (e.g. question answering) over the selected subset, yet existing systems flatten documents into plain-text chunks, discarding the rich hierarchical structures (sections, tables, figures, equations) and degrading downstream performance. We present DocMaster, a hierarchical structure-aware document analysis system. DocMaster parses documents into hierarchical document trees preserving original layouts and constructs a structure-aware semantic index that enables accurate document filtering and in-depth analysis. We demonstrate DocMaster through an interactive web interface that enables users to upload document collections, construct tree-based and multi-view semantic indices, filter relevant documents via natural-language conditions, and perform follow-up question answering over the filtered results. The source code, data, and demo are available at this https URL.
[AI-13] AI-guided stimuli discovery and generation to optimize facial emotion perception studies in autism
链接: https://arxiv.org/abs/2607.08533
作者: Kushin Mukherjee,Na Yeon Kim,Maren Wehrheim,Ralph Adolphs,Kohitij Kar
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Understanding perceptual differences between autistic and neurotypical adults requires behavioral assays that are sensitive, reliable, and mechanistically informative. Facial emotion perception is a useful test case because group differences have been reported, but findings vary across studies. Here we show that this variability may reflect image-level sparsity: autistic-neurotypical differences in emotion judgments were concentrated in a small subset of diagnostic facial expressions rather than spread uniformly across stimuli. We trained population-specific artificial neural network models to predict image-level judgments for autistic and neurotypical participants, then used these models to select novel faces predicted to maximize group separation. In an independent cohort, model-selected images produced larger behavioral differences than matched random images. We then used the same models with a generative adversarial network to transform diagnostic images toward greater predicted group agreement. In phenotype-matched validation, synthesized images reduced behavioral separation relative to their matched originals. These results establish a model-guided framework for discovering and transforming stimuli that reveal population-specific perceptual differences. More broadly, they show how behavioral phenotyping can move beyond averaging across fixed stimulus sets toward optimized assays that identify the conditions under which neurodivergent perception diverges or converges.
[AI-14] he Context Access Divide: Interaction-Level Architecture as a Complementary Dimension of Agent ic Inequality
链接: https://arxiv.org/abs/2607.08495
作者: Masahiro Fujita
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: 19 pages, 2 figures
Abstract:Sharp et al. (2025) introduce “agentic inequality” as a framework for analyzing disparities in access to AI agents across three dimensions: availability, quality, and quantity. These person- and organization-level dimensions characterize who can access agents and at what capability, but do not address a structurally important divide operating at a finer level: the individual interaction. Two users with nominally equivalent agent access may experience qualitatively different AI utility depending on whether the system can autonomously retrieve context from the user’s knowledge corpus (Dynamic Context Retrieval) or requires the user to manually identify and attach relevant documents at each query (Manual Attachment). We term this the Context Access Divide (CAD). For knowledge-intensive workers whose intellectual capital spans tens of thousands of files, the CAD constitutes a qualitative threshold in AI usefulness: below it, the cognitive burden of context curation falls on the human, reproducing the inefficiencies AI is meant to eliminate. We propose contextuality – the degree to which an AI system autonomously accesses a user’s accumulated knowledge capital – as a dimension of AI-mediated inequality that complements, but is not reducible to, the Sharp et al. framework. We formalize the CAD with a probabilistic model grounded in the fan effect literature in cognitive psychology, demonstrating that manual context attachment leads to a combinatorial collapse in task-success probability as corpus size and task conjunctivity grow, while dynamic retrieval architectures are structurally insulated from this collapse. We analyze the technical basis of this divide in the Model Context Protocol (MCP) and retrieval-augmented generation (RAG) architectures, and examine its implications for knowledge-work stratification and AI platform governance.
[AI-15] Drift-Aware Temporal Graph Rewiring (DATGR) for Adaptive Semantic Modeling in Biomedical Text
链接: https://arxiv.org/abs/2607.08490
作者: Bharathwaj Vijayakumar,Sahana K. Varadaraju
类目: Artificial Intelligence (cs.AI)
备注: 6 pages, 4 figures. Published in the Proceedings of the 2026 IEEE Conference on Artificial Intelligence (CAI 2026)
Abstract:Biomedical language evolves rapidly as new discoveries emerge, causing traditional text models to lose semantic fidelity over time. Static embeddings and co-occurrence graphs cannot capture such evolution, leading to performance degradation in retrieval and knowledge discovery tasks. This paper introduces a Drift-Aware Temporal Graph Rewiring (DATGR) framework that models concept evolution by dynamically updating co-occurrence edges based on estimated semantic drift. Instead of retraining embeddings for each time slice, DATGR performs lightweight, feedback-driven rewiring using a logistic update rule applied to edge weights. Evaluated on the Biomedical Multi-Relation Corpus (BIOMRC), the method achieved a mean Area Under the Receiver Operating Characteristic (AUROC) improvement of approximately 0.066 absolute difference (0.699 vs. 0.633) over a static baseline. Area Under the Precision-Recall Curve (AUPRC) remained comparable (0.738 vs. 0.744), showing that drift-aware adaptation enhances link-prediction recall without a loss in precision. These results demonstrate that edge-level adaptation effectively captures temporal semantic change in evolving biomedical text while remaining computationally efficient and interpretable.
[AI-16] Applying JEPA-Style Predictive Learning to JA4-Derived Network Fingerprints
链接: https://arxiv.org/abs/2607.08465
作者: Javier Izquierdo,Aygul Zagidullina
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:I-JEPA and V-JEPA learn by matching latent predictions to target encoder outputs rather than regenerating the original input, and this has worked well for images and video. We explore whether the same objective works for compact network fingerprints. We built JA4-JEPA, a Transformer-based model trained on JA4, JA4H, JA4S, and JA4X subfields drawn from JA4DB and CIC-IDS- 2017. The training data combines roughly 397K samples from both sources, though no single sample contains all four view families. We evaluated the learned representations with a frozen kNN probe on protocol-family classification across TLS, DNS, and SSH. On 39,416 heldout samples the model achieved a cosine similarity of 0.9899 and a kNN accuracy of 0.9220. These results indicate that JEPA-style predictive learning can produce useful embeddings from JA4-derived fingerprints, even with incomplete view overlap across sources. Keywords: JA4, network fingerprinting, JEPA, predictive representation learning, self-supervised learning Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2607.08465 [cs.AI] (or arXiv:2607.08465v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.08465 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-17] Spatio-Temporal Scheduling Prediction Under Backhaul Delay for Resilient Coordinated Beamforming
链接: https://arxiv.org/abs/2607.08454
作者: Prashant Kumar Singh,Shubham Vaishnav,Ahmet Hasim Gökceoglu,Li Wang
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注:
Abstract:Coordinated beamforming in distributed 5G networks relies on the timely exchange of inter-cell scheduling information, but backhaul latency makes this information stale. Even a single transmission time interval (TTI) of delay can reduce CBF-SLNR performance below the uncoordinated baseline, because the precoder suppresses interference toward users that are no longer active. Coordination on stale information is therefore worse than no coordination at all. To address this, we propose a two-stage predictive framework in which a Spectral Temporal Graph Neural Network (StemGNN) predicts future user equipment (UE) scheduling states from delayed historical observations, and the predictions replace stale inputs to the CBF-SLNR precoder. Evaluated on a three-cell massive MIMO downlink with 60 UEs and 64 antennas per base station under Quadriga Urban Micro (UMi) channels and a proportional fair scheduler, StemGNN achieves a mean scheduling prediction accuracy of 87.57%, outperforming LSTM, GRU, Simple RNN, and Markov chain baselines at all evaluated horizons, with gains of up to 7.71% over LSTM at longer horizons where inter-UE structural dependencies dominate over temporal autocorrelation. When integrated into coordinated beamforming, the predictions recover 57-73% of the sum rate loss caused by one TTI of backhaul delay, improving sum rate by 9.58-14.35% over the no-prediction baseline and recovering up to 83% of the Lag-1 fairness loss for cell-edge users, with fairness gains persisting at higher lag values where throughput gains diminish. These results show that treating backhaul latency as a spatio-temporal forecasting problem is an effective approach for robust inter-cell coordination in delay-constrained networks.
[AI-18] ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning
链接: https://arxiv.org/abs/2607.08443
作者: Ashit Kumar Subudhi,Bhargav Chirumamilla,Shubham Vaishnav,Mduduzi C. Hlophe,Praveen Kumar Donta,Andrea Fumagalli,Venkateswarlu Gudepu,Koteswararao Kondepu
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注:
Abstract:Dynamic traffic variations in Open Radio Access Networks (O-RAN) lead to drift, which degrades the performance of Artificial Intelligence/Machine Learning (AI/ML) models. Traditional retraining approaches maintain forecasting accuracy but incur high computational cost and may lead to violations of Service Level Agreements (SLAs). This work proposes a Q-learning-based adaptive retraining approach that formulates the retraining decision as a Markov Decision Process (MDP), where a Reinforcement Learning (RL) agent learns a policy that balances forecasting accuracy and retraining cost. The proposed approach incorporates a multi-expert Long Short-Term Memory (LSTM) ensemble to mitigate catastrophic forgetting and improve robustness across diverse traffic conditions. Experimental results show that the proposed approach effectively reduces retraining overhead compared to greedy and random baselines, while maintaining system performance within predefined limits.
[AI-19] EgoWAM: World Action Models Beyond Pixels with In-the-Wild Egocentric Human Data
链接: https://arxiv.org/abs/2607.08436
作者: Baoyu Li,Xinchen Yin,Mengying Lin,Yixin Zhang,Danfei Xu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Egocentric human data offers scalable supervision for robot manipulation. However, behavior cloning entangles transferable content like objects, scenes, and task semantics, with non-transferable factors like human morphology, head motion, and behavioral style. We study whether World Action Models (WAMs) provide a better training signal by requiring policies to predict not only actions, but also how the scene evolves. The central question is what world representation best enables human-to-robot transfer. We hypothesize that an effective world target should abstract appearance, capture agent-invariant physical effects, and separate camera motion from environment change. We introduce EgoWAM, a controlled human-robot co-training framework that fixes the policy backbone, action head, and data mixture while varying only the world prediction target, comparing Pixel, DINO, and 3D motion flow. Across three real-world bimanual tasks, WAM co-training scales more effectively with in-the-wild egocentric human data than behavior cloning. Pixel-based prediction transfers weakly, while DINO and 3D flow yield substantial gains: DINO improves out-of-distribution object and scene generalization by up to 4x, and 3D flow improves in-domain performance by 20-30%. More details: this https URL
[AI-20] Predicting Male Fertility Using Machine Learning: A Semen Parameters Based Analysis with the VISEM Dataset
链接: https://arxiv.org/abs/2607.08429
作者: Shahnawaz Qureshi,Raja Khurram Shahzad,Muhammad Fozan,Emal Kawal,Syed Aziz Shah,Sattam Al-Anazi,Syed MuhammadZeeshan Iqbal
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Male infertility is a significant yet often underdiagnosed aspect of reproductive health, with semen analysis serving as the cornerstone of clinical evaluation. To address this problem, this study investigates the use of machine learning algorithms to classify male fertility status based on key semen parameters, i.e., sperm concentration, motility, and morphology, using the VISEM dataset. This dataset includes semen samples from 85 participants, classified into three categories, i.e., Fertile, Sub-Fertile, and Infertile, according to the World Health Organization’s criteria. After pre-processing and feature engineering, the dataset was used to train and assess multiple classification models using the LazyPredict framework. Among the more than 40 algorithms tested, the Nearest Centroid classifier achieved an accuracy of 94.2%, outperforming other models such as Support Vector Machines and Quadratic Discriminant Analysis. The model’s robustness was validated using 5-fold cross-validation and multiclass ROC-AUC analysis. This study illustrates that machine learning models can provide fast, accurate, and objective assessments of semen quality, potentially supporting clinical decision-making in andrology and assisted reproductive technologies. These findings emphasize the growing potential of machine learning to enhance fertility diagnostics and inform patient-specific treatment strategies.
[AI-21] OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice
链接: https://arxiv.org/abs/2607.08423
作者: Qian Jiang,Zhecheng Shi,Jingpu Yang,Zirui Song,Miao Fang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The rapid integration of Large Vision-Language Models (VLMs) into critical infrastructure promises to revolutionize personalized healthcare and dietary management. However, in the domain of food systems, autonomous agents face a unique and persistent challenge: the “Systemic Information Asymmetry” between visual appearance and intrinsic nutritional composition. Existing benchmarks primarily focus on coarse-grained classification tasks, such as food category recognition, which fail to evaluate the intricate reasoning chain required for real-world dietary management – specifically, the ability to traverse from identifying hidden ingredients to estimating physical mass, and finally synthesizing safety-critical medical advice. In this paper, we introduce OmniFood-Bench, a comprehensive benchmark constructed from the MM-Food-100K dataset. Unlike previous works, OmniFood-Bench evaluates VLMs across three progressive capabilities: Basic Perception (Ingredients Cooking Methods), Quantitative Reasoning (Portion Size Nutritional Profiling), and Safety-Critical Advisory (Disease-Specific Recommendations). We evaluate six state-of-the-art VLMs, including gpt-5.1, gemini-3-flash, and qwen3-vl-8B. Our extensive experiments reveal a startling “Semantic-Physical Gap”: while models achieve near-human accuracy in naming dishes, they exhibit catastrophic failure in mass estimation and frequently hallucinate benign advice for high-risk diabetic profiles. This work establishes a rigorous standard for trustworthiness in autonomous agents deployed for public health. The code and datasets are available in: this https URL Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2607.08423 [cs.AI] (or arXiv:2607.08423v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.08423 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Qian Jiang [view email] [v1] Thu, 9 Jul 2026 12:46:00 UTC (735 KB) Full-text links: Access Paper: View a PDF of the paper titled OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice, by Qian Jiang and 4 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI prev | next new | recent | 2026-07 Change to browse by: cs References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> 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?) 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(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[AI-22] Game Theory Driven Multi-Agent Framework Mitigates Language Model Hallucination
链接: https://arxiv.org/abs/2607.08403
作者: Runzhe Liu,Biquan Bie,Zihao Wang,Yuchao Ma,Yexin Liu,Xinghai Li,Harry Yang,Wenbo Yang,Jinzhe Cao,Shengyang Tao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The application of lightweight Large Language Models in rule-based scientific domains remains severely limited by their tendency to mimic linguistic patterns rather than reproduce axiomatic reasoning, causing frequent hallucinations. Here, we show that G-Frame, an adaptive multi-agent framework integrating Bayesian and team game principles, establishes an automated closed-loop for high-quality data synthesis and model training. By forcing the internalization of domain constraints through structured reasoning, we synthesized a specialized corpus of 363,045 chains-of-thought and 199,589 question-answer pairs. The resulting 7B model OmniChem achieves performance parity with GPT 4o mini on custom benchmarks and ChemBench while exhibiting a 79.46% reduction in hallucinations relative to its base architecture. We further demonstrate the advanced capabilities of OmniChem in molecular design and synthesis planning. This work establishes a scalable paradigm utilizing adaptive multi-agents to overcome inherent reasoning deficiencies, offering a feasible pathway for accelerating knowledge discovery in specialized scientific fields.
[AI-23] RACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM -Agent Trajectories
链接: https://arxiv.org/abs/2607.08400
作者: Zheng Gao,Xiaoyu Li,Xiaoyan Feng,Jiaojiao Jiang,Yang Song,Yulei Sui,Zhenchang Xing,Liming Zhu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:LLM agents reach users through resellers, who may rebrand a developer’s agent or substitute a cheaper model. When provenance is disputed, attribution rests on the trajectory log (the record of tool calls, observations, and executed actions, not the model’s reasoning), which the reseller stores and processes to meter usage. A watermark must therefore survive an adversary with full read/write access to the very evidence it is detected from; existing agent watermarks do not, as their attribution is read straight off that log. We present TRACE, to our knowledge the first agent watermark that is distortion-free in its action choices, self-synchronizing under deletion, and unconditionally invariant under rewriting. Deletion desynchronizes a position-derived key and rewriting alters content, so a deletion-robust key must come from content and a rewrite-robust key from position, and no single key serves both. A trajectory, however, has room for two watermarks. TRACE superposes a selection channel that sets which action is chosen, keyed on local content with a distortion-free sampler, so the agent’s distribution is provably unchanged and detection resynchronizes after deletions, and a tally channel that sets how many records each decision group holds, keyed on the log’s skeleton alone, which no rewriting can touch. We prove this behavioral watermark’s signal is bought with decision entropy, each decision paying at least half its entropy and deterministic decisions nothing, and that erasing both channels forces the reseller to corrupt the trajectories it resells. On ToolBench and ALFWorld, TRACE matches the unwatermarked agent’s success rate while its selection channel reaches detection scores near z = 100 on long-horizon trajectories, stays detectable under 70% step deletion, and keeps a tally channel exactly unchanged under LLM rewriting of any strength.
[AI-24] Self-Adaptive Anomaly Detection with Reinforcement Learning and Human Feedback in Connected Vehicles
链接: https://arxiv.org/abs/2607.08373
作者: Matthias Weiß,Athreya Hosahalli Prakash,Maurice Artelt,Falk Dettinger,Nasser Jazdi,Michael Weyrich
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at the 30th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2026), Special Session SS10: Evaluation Methods for Autonomous Cyber-Physical Systems’ Behavior. 8 pages, 3 figures, 3 tables
Abstract:Connected vehicles are autonomous cyber-physical systems whose behavior must be continuously monitored during operation to detect deviations from normal operation before they propagate into failures. Such evaluation is challenging because the systems themselves evolve: over-the-air updates, configuration changes, and shifting workloads alter the definition of normal behavior, causing static diagnostic methods to degrade silently over time. Existing approaches typically address either automated model adaptation or operator integration in isolation, rather than as a single coordinated supervisory loop. This paper presents an online anomaly detection framework for autonomous CPS that integrates three coordinated mechanisms. A factorized deep Q-network with self-attention selects the most suitable detector from a candidate pool for each monitored service, exploiting inter-service dependencies in the microservice topology. An ensemble of three statistical drift detectors monitors the input distribution and raises an alarm only when all three concur, prioritizing precision over recall. A human-in-the-loop retraining mechanism, built around a pending transition buffer and a 60/40 prioritized replay strategy, allows the operator to incorporate expert knowledge while preserving the system’s learned response to prior data distributions. The framework is evaluated on a connected-vehicle testbed running an automated valet parking application across seven backend microservices. The attention-augmented agent achieves an F1 score of 0.69, compared to at most 0.11 for any single detector applied uniformly. Following a real software update that induces measurable concept drift, F1 drops to 0.52; after operator-triggered retraining, performance recovers to 0.65 on the new distribution while remaining at 0.69 on the prior one, demonstrating sustained adaptation without catastrophic forgetting. Comments: Accepted at the 30th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2026), Special Session SS10: Evaluation Methods for Autonomous Cyber-Physical Systems’ Behavior. 8 pages, 3 figures, 3 tables Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) ACMclasses: I.2.6; I.5.4; C.2.4 Cite as: arXiv:2607.08373 [cs.LG] (or arXiv:2607.08373v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.08373 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-25] FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning
链接: https://arxiv.org/abs/2607.08368
作者: Lingyu Qiu,Daniela Annunziata,Stefano Izzo,Fabio Giampaolo,Francesco Piccialli
类目: Artificial Intelligence (cs.AI)
备注: Accepted by FLICS 2026
Abstract:With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning. Although one-shot federated learning alleviates this problem by minimizing communication rounds, existing iterative fine-tuning or knowledge distillation methods still face challenges such as high server-side computational costs and hyperparameter sensitivity. Analytical federated learning achieves efficient gradientfree aggregation using least-squares closed-form solutions, but in environments with non-independent and identically distributed data, its static feature assumptions fail, leading to feature manifold misalignment and severely impairing model performance. To address this contradiction, this paper proposes the FedOPAL framework. This framework adapts the visual prompts as feature rectifiers, actively correcting the feature distribution of heterogeneous data to a linearly separable space by applying local proximal constraints, thereby satisfying the theoretical assumptions of analytical federated learning. Experimental results show that FedOPAL not only significantly outperforms the original analytical methods on several benchmarks, but also achieves accuracy comparable to state-of-the-art iterative methods while maintaining zero server-side training costs, providing a new engineering paradigm for efficient collaboration of large models on the edge.
[AI-26] FSD-VLN: Fast-Slow Dual-System Modeling for Aerial Long-Horizon Vision-Language Navigation
链接: https://arxiv.org/abs/2607.08359
作者: Xueke Zhu,Qingyan Meng,Liutao Yu,Wei Zhang,Zhengyu Ma,Huihui Zhou,Yonghong Tian
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-Language Navigation (VLN) enables UAV autonomous navigation in unknown environments by mapping language instructions to real-time visual inputs. Compared with GPS-dependent or pre-programmed navigation, VLN supports intuitive human-machine interaction and stronger environmental adaptability, requiring tight integration of high-level semantic reasoning and low-latency flight this http URL methods suffer from structural misalignment between global multimodal understanding and sequential action generation, causing jittery trajectories and severe decision latency for long-horizon aerial navigation. To solve this issue, we propose FSD-VLN, a fast-slow dual-system architecture disentangling semantic reasoning and low-latency flight command this http URL framework has two asynchronous branches: a slow stream extracting stable semantic priors from pre-trained vision-language models, and a Diffusion Transformer (DiT) fast stream modeling cross-temporal action distributions to produce consistent flight outputs. We further introduce a time-aware adaptive optimizer to stabilize long-sequence training and reduce gradient this http URL-scale low-altitude simulation experiments show FSD-VLN achieves up to 2X higher navigation success rates on unseen scenes than SOTA methods, while cutting single-action inference delay and total task runtime by over 50%. Our work validates the benefit of decoupled semantic-control modeling and provides a practical paradigm for long-horizon aerial VLN.
[AI-27] MobiDiff: Semantic-Aware Multi-Channel Discrete Diffusion for Human Mobility Data Generation
链接: https://arxiv.org/abs/2607.08357
作者: Rongchao Xu,Lin Jiang,Dahai Yu,Ximiao Li,Taichi Liu,Desheng Zhang,Yuan Tian,Guang Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Human mobility data are essential for transportation optimization, urban planning, and resource allocation, yet real-world mobility data are costly to collect and difficult to share due to privacy concerns. Recent diffusion-based methods have shown promise in synthesizing realistic mobility patterns, but they typically rely on continuous or latent spatio-temporal traces, limiting their ability to natively model discrete semantic events with explicit region, activity, time, and interval structures. To address this issue, we introduce MobiDiff, an end-to-end discrete diffusion framework that efficiently generates mobility data by directly denoising multi-channel semantic skeletons, avoiding the costly interpolation, latent trace construction, and coarse-to-fine realization pipelines widely used in existing diffusion-based methods. Specifically, MobiDiff decomposes each human check-in event into spatial, activity, and temporal channels, and employs structured event-, group-, and channel-level masking to jointly capture trajectory-level mobility patterns and within-event dependencies. We evaluate generation fidelity, privacy-preserving, and efficiency on three large-scale real-world datasets from Atlanta, Boston, and Seattle. Results show that MobiDiff effectively preserves trajectory length and temporal interval distributions while remaining competitive across broader mobility statistics; it is also much faster than state-of-the-art methods, e.g., 5.3 \times faster than GeoGen on average during inference. These findings suggest that discrete diffusion offers an interpretable and efficient framework for synthetic mobility data generation.
[AI-28] Spectral Analysis of Dueling Q-Learning
链接: https://arxiv.org/abs/2607.08340
作者: Donghwan Lee
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Q-learning is a fundamental algorithm in reinforcement learning (RL) for solving discounted Markov decision processes (MDPs) when the transition kernel is unknown. The deep Q-network (DQN) extends Q-learning by using a deep neural network for Q-function approximation, which makes Q-learning applicable to more practical high-dimensional problems. Dueling Q-learning decomposes the Q-function into a value function and an advantage function and learns the two components jointly, which can improve learning efficiency. However, the theoretical understanding of dueling Q-learning is still limited. Recent work has initiated an analysis of tabular dueling Q-learning, but existing guarantees focus on a regularized formulation and leave the pure tabular update less completely understood. This paper strengthens that line of analysis by adding a direct interpretation of the centered tabular decomposition and by establishing convergence guarantees for the unregularized, unprojected constant step-size recursion. In particular, we derive an exact switching linear system representation for deterministic dueling Q-learning and a finite-time error bound in expectation for the sampled stochastic version. The analysis clarifies how the value and advantage updates act as different gains on the action-common (value function) and action-differential (advantage function) components of the Q-function.
[AI-29] ArtMine: Discovering and Formalizing Artistic Processes
链接: https://arxiv.org/abs/2607.08331
作者: Kaustubh Kumar,Ashutosh Ranjan,Vivek Srivastava,Blessin Varkey,Shirish Karande
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 47 pages, 10 figures
Abstract:Understanding how artworks are created requires reasoning about the iterative decisions, material operations, and contextual influences that shape artistic production. While recent generative AI systems can synthesize artworks with high fidelity, they primarily model distributions over finished artifacts rather than the creative processes underlying their creation. In practice, artistic workflows are only partially documented through fragmented sources such as archival records, preparatory studies, correspondence, etc., making process-level understanding difficult to formalize computationally. In this work, we introduce ArtMine, a framework for discovering and formalizing artistic processes from heterogeneous historical evidence. Our approach synthesizes heterogeneous artwork evidence into a structured repository, from which a Peircean abductive agent infers evidence-grounded production steps. These steps are converted into a compositional graph and rendering prompt, then optimized through self-reflection over deviations between the generated and reference artworks. We provide a preliminary proof-of-concept case study using open-domain historical sources across multiple artists and artistic movements, demonstrating that fragmented documentary evidence can support coherent, interpretable, and auditable representations of artistic workflows. By modeling creative processes rather than only final artifacts, our work moves toward process-centred human-AI co-creativity systems that can support artistic interpretation, creative education, reflective collaboration, and computational studies of cultural production.
[AI-30] GitLake: Git-for-data for the agent ic lakehouse VLDB2026
链接: https://arxiv.org/abs/2607.08319
作者: Weiming Sheng,Jinlang Wang,Manuel Barros,Aldrin Montana,Jacopo Tagliabue,Luca Bigon
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注: Pre-print of the paper accepted at DASHSys, VLDB 2026, Boston, USA
Abstract:We present GitLake, a Git-for-data design for an agent-first lakehouse. The system lifts single-table Iceberg snapshots into lakehouse-wide commits, branches, and merges, letting agents work on isolated branches while humans review and publish changes. Pipelines run on temporary branches and publish through a final merge, so all outputs become visible atomically or none do. Finally, we report production lessons as well as correctness insights from a preliminary Alloy model of our core abstractions.
[AI-31] Blind-Spots-Bench: Evaluating Blind Spots in Multimodal Models
链接: https://arxiv.org/abs/2607.08317
作者: Matteo Santelmo,Xiuying Wei,Israa Fakih,Felix Bauer,Juan Garcia Giraldo,Chengkun Li,Etienne Bamas,Emmanuel Abbé
类目: Artificial Intelligence (cs.AI)
备注: 25 pages, 8 figures
Abstract:Modern AI models achieve strong performance on many established benchmarks, yet they still fail on tasks that humans find almost trivial, such as manipulating a string or drawing a dog with five legs. These examples suggest that existing benchmarks may under-measure persistent blind spots in current systems. We introduce \textttblind-spots-bench , a benchmark designed to expose such blind spots through tasks that appear simple for humans but remain challenging for modern AI. We collect raw questions from students in an AI course, clean and annotate them with structured reference solutions, and propose a task taxonomy tailored to the resulting dataset of 235 samples. We further develop an automated grading pipeline to evaluate a wide range of models, including open-weight and closed-source language, vision-language, and image-generation models. Our analysis on \textttblind-spots-bench reveals that closed-source frontier models can substantially outperform open-weight models with even \approx10% gap, even when they attain comparable performance on existing benchmarks. A more fine-grained analysis shows that no single model dominates across all task types, and that some tasks remain challenging for all evaluated models. These results highlight the value of \textttblind-spots-bench as a diagnostic stress test for identifying concrete weaknesses in current modern models.
[AI-32] INTENT: An LSTM Framework for Vehicle Intention Prediction in Intersection Scenarios with Comprehensive Ablation Analysis
链接: https://arxiv.org/abs/2607.08316
作者: Logine M. Zaki,Catherine M. Elias
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
备注:
Abstract:Vehicle intention prediction is a pivotal aspect in the agility and safety of autonomous vehicles in all driving scenarios; if genuine enhancement of autonomous vehicles are required, we need to make them adopt human interpretation of driver’s intention especially in cases that require a lot of human interaction as well as complex driving behaviors like the ones at intersections, roundabouts and emergency cases such as sudden stops where vehicle intention prediction helps in taking the correct evasive action within a real time period where every second of action makes an impact and can prevent a catastrophe from taking place. In the worst case, it helps minimize the damage and make safety a priority. Intention prediction can also be used to enhance trajectory prediction (intention conditioned trajectory prediction). In this study, The INTENT framework is proposed using LSTM model to predict the vehicle’s intention at intersections 2 seconds ahead of the event occurrence to predict whether the cars in intersections are going straight, turning left, or turning right. Various model experiments and ablation study are thoroughly tested on InD dataset achieving 99.71% accuracy.
[AI-33] From Legacy Documentation to OSCAL: An MCP-Based Agent Pipeline for Threat-Informed Continuous Compliance in Critical Infrastructure
链接: https://arxiv.org/abs/2607.08288
作者: Lea Roxanne Muth,Marian Margraf
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Accepted for publication at the 2026 IEEE International Conference on Cyber Security and Resilience (IEEE CSR), Lisbon, Portugal, August 3-5, 2026. 8 pages, 1 figure
Abstract:In critical infrastructure, operational technology environments often cannot be actively scanned, and yet active system feedback is needed for risk assessment and compliance. This paper presents a non-invasive, MCP-grounded multi-agent pipeline that converts natural-language system descriptions into source-verified knowledge graph and audit-ready artifacts in the NIST OSCAL format for continuous automated compliance management. The architecture decouples LLM-based reasoning from deterministic knowledge retrieval against authoritative threat-intelligence sources, reducing the risk of fabricated vulnerabilities and hallucinated attack paths. In an evidence-based synthetic scenario of a water utility, the pipeline achieves 0.90 CVE recall and perfect D3FEND recall. It generates a schema-valid OSCAL System Security Plan and an OSCAL Security Assessment Report. Nevertheless, the core insight is not that grounding via MCP eliminates errors (e.g., hallucinations) entirely from the pipeline, but that it shifts errors into the first phase of asset extraction from the natural language description. Here, a single incorrectly extracted entity can lead to genuine but irrelevant CVEs in subsequent stages of the pipeline, which consumes time and resources. However, it makes the remaining risk visible, verifiable, and suitable for a time-efficient manual review, since the infrastructure (e.g., version numbers, OS, etc.) is typically known. Comments: Accepted for publication at the 2026 IEEE International Conference on Cyber Security and Resilience (IEEE CSR), Lisbon, Portugal, August 3-5, 2026. 8 pages, 1 figure Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.08288 [cs.CR] (or arXiv:2607.08288v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2607.08288 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-34] Psychological Competence as a Missing Dimension in AI Evaluation
链接: https://arxiv.org/abs/2607.08285
作者: Marcos Economides,Paul M. Sacher,Samuel Salzer,Alexis Michelle Abellar,Fendi Tsim,Antoine Ferrère
类目: Artificial Intelligence (cs.AI)
备注: 22 pages, 3 figures
Abstract:Current AI evaluation frameworks focus primarily on technical performance, including accuracy, robustness, reasoning ability, and policy compliance. These measures remain essential, but they are not sufficient for systems that interact directly with users through natural language. Human-facing AI systems are increasingly used as advisors, coaches, tutors, and companions. In these roles, their responses can shape how users reason, interpret emotions, form beliefs, calibrate trust, and make decisions. The relevant unit of evaluation is therefore not only the model, but the human-AI interaction. This paper introduces psychological competence as a missing dimension in AI evaluation. We define psychological competence as the capacity of a human-facing AI system to support user cognition, emotional interpretation, and behavioral decision-making in ways that are appropriate to the user, context, and purpose of the interaction. This includes interaction properties such as framing, tone, perceived authority, responsiveness, uncertainty handling, and conversational guidance. Existing evaluation approaches capture parts of this problem but rarely assess these psychological effects directly. Drawing on behavioral science and human-AI interaction research, we outline a conceptual framework for psychological competence and its core domains. Rather than proposing a specific benchmark, we define the construct, clarify its boundaries, and describe how it may be assessed through scenario-based probes, structured human evaluation, and model-assisted evaluation methods. We argue that psychological competence should become a core consideration for model providers, deploying organizations, researchers, and regulators concerned with the real-world effects of human-facing AI systems.
[AI-35] Understanding Axes of Difficulty For Long Context Tasks Via PredicateLongBench
链接: https://arxiv.org/abs/2607.08284
作者: Siddhartha Jain,Ameya Velingker
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) have demonstrated rapidly improving long-context capabilities, prompting a wave of benchmarks designed to evaluate them. However, existing long-context evaluations - from Needle-in-a-Haystack (NIAH) tests to more recent multi-hop reasoning and summarization tasks - predominantly measure average-case performance, and many are either saturated or lack robustness. Notably absent is a systematic way to probe how models perform as we scale up the difficulty of tasks along various axes. We address this gap by proposing PredicateLongBench, a benchmark that stress-tests long-context reasoning by asking models to identify the longest contiguous subsequence of words in a long input that satisfies given predicates/constraints (e.g., lexicographic ordering), drawn from a broader predicate class. The central innovation of our benchmark is the identification and systematic exploration of multiple different axes of difficulty which test multiple aspects of long context understanding. We provide two complementary generation pipelines - a fully synthetic setup using random word-like strings, and a real-world setup that samples words from natural documents while preserving their distributional properties. We find that frontier models struggle to perform well as we scale up the difficulty of tasks along our axes, demonstrating the utility of our benchmark in understanding the limitations of current long-context capabilities. Furthermore, the tasks in PredicateLongBench, though challenging, are conceptually simple and do not require LLM-based generations or judges.
[AI-36] PolyUQuest: Verifiable Structure-Aware Web RAG over Heterogeneous Graphs
链接: https://arxiv.org/abs/2607.08269
作者: Ying Liu,Yi Ye,Quanyu Feng,Mingxi Ye,Mingtao Zhang,Haoyang Li,Chen Jason Zhang,Qing Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Existing retrieval-augmented generation (RAG) systems treat web pages as flat text, losing the structural and semantic signals encoded in HTML. We present PolyUQuest, a verifiable, structure-aware web RAG framework built on a heterogeneous graph that unifies hyperlink topology between pages, DOM hierarchy within pages, and entity-relation knowledge across pages. A two-tier router dispatches each query to one of three retrieval modes matched to its structural need, including direct block retrieval, cross-page graph traversal, and multi-hop entity reasoning. Every answer is fully verifiable, as each cited block carries its source page, heading path, and entity links so that users can trace any claim back to its structural evidence. We evaluate on the official websites of the Hong Kong Polytechnic University (PolyU), comprising 4,240 pages, 31,086 DOM blocks, 29,119 entities, and 37,680 relations, together with a multi-type evaluation benchmark. PolyUQuest outperforms existing RAG systems in answer correctness, coverage, and faithfulness, while consuming significantly fewer LLM tokens per query. The demonstration provides an interactive interface for inspecting cited answers, comparing retrieval traces across routing modes, and exploring evidence graph paths. PolyUQuest is being prepared for deployment as a student-facing QA service at PolyU.
[AI-37] MentalHospital: A Virtual Environment for Evaluating Psychiatric Clinical Encounters
链接: https://arxiv.org/abs/2607.08257
作者: Yuming Yang,Xiao Sun,Yuanwei Zou,Zhengxiao Wu,Yun Chen,Jiang Zhong,Haoyang Zeng,Jingwang Huang,Kaiwen Wei
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) have shown strong performance on isolated psychiatric tasks, including dialogue, diagnosis, and treatment planning, yet existing benchmarks rarely simulate complete psychiatric clinical encounters. We introduce \textbfMentalHospital , a virtual evaluation environment for LLM-based psychiatric clinical encounters. MentalHospital instantiates the Subjective Interviewing, Objective Examination, Diagnostic Assessment, and Treatment Planning (S.O.A.P.) workflow, using skill-augmented standardized patients constructed from 1,193 de-identified psychiatric electronic health record (EHR) cases spanning all major ICD-11 categories and 76 disorders. Each encounter is assessed through a dual-track protocol that combines objective comparison against EHR-derived references with subjective assessment of clinical process quality. To scale specialist judgment, we develop \textbfMentalEval , five domain-specific evaluators covering communication empathy, interviewing professionalism, clinical-note quality, diagnostic rigor, and treatment appropriateness, trained with rubric-grounded SFT and expert-guided DPO. Survey responses from 22 clinicians support MentalHospital’s clinical fidelity (3.88/5), while MentalEval achieves strong expert alignment with an average QWK of 0.944. Benchmarking shows that even the strongest LLM trails clinicians by 37.28 percentage points in objective psychiatric competence, with mental status assessment as a key bottleneck.
[AI-38] Compete Then Collaborate: Frontier AI Teachers Build a Verifiable Curriculum to Improve a Coding Student Beyond Imitation
链接: https://arxiv.org/abs/2607.08255
作者: Miseong Shawn Kim
类目: Artificial Intelligence (cs.AI)
备注: 8 pages, 1 figure
Abstract:Large language models increasingly serve as teachers generating training data for smaller students. Prior multi-teacher knowledge distillation methods merge outputs without determining which frontier model teaches best, often relying on an LLM judge biased toward its own outputs. We introduce a compete-then-collaborate framework where four frontier AI teachers (Claude, Codex-GPT, Grok, Gemini) are ranked head-to-head by an execution-based judge (unit tests and stdin-stdout checks) with fairness controls, and then collaborate to build a verifiable curriculum for a student (Qwen2.5-Coder). We report three findings. (1) Under execution verification, all teachers solve standard problems near-perfectly after self-correction (99-100%) due to a saturation effect, but harder competition problems separate them (Gemini 77% Claude 69% = Codex 69% Grok 50%); however, the robust student-side results do not depend on teacher ranking. (2) Imitation (SFT) on verified solutions does not improve, and can degrade, an already-competent student at 7B and 32B (e.g., from 76.7% to 72.7% on MBPP-test, and 5.9% to 2.9% on competition problems). (3) Using the same collaborative curriculum as a reinforcement learning with verifiable rewards (RLVR) environment improves the student (from 5.9% to 8.8% peak on competition problems, a +49% relative gain), reversing SFT’s direction. The value of AI-teacher collaboration lies not in pooling answers to imitate, but in jointly constructing a verifiable environment where the student learns by doing. We release a reproducible on-prem pipeline (NVIDIA GB10) with framework patches for running GRPO on a bleeding-edge stack.
[AI-39] RhyMix: A Lightweight Adaptive Multi-Rhythm Network for Long-Term Time Series Forecasting
链接: https://arxiv.org/abs/2607.08234
作者: Sumit Satishrao Shevtekar,Chandresh Kumar Maurya
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 38 Pages
Abstract:Real-world time series exhibit complex dynamics characterized by multiple simultaneous temporal patterns: short-term fluctuations, periodic seasonal cycles, long-term trends, and irregular abrupt changes. However, many existing forecasting architectures rely on single-path temporal modeling–transformers capture long-range dependencies but smooth local variations, convolutions capture local patterns but have limited receptive fields, and linear models are efficient but cannot capture nonlinear dynamics. To address this, we introduce RhyMix (RHYthm MIXture), a hybrid neural architecture designed around a parallel dual-path modeling paradigm with adaptive gating mechanisms. RhyMix integrates two complementary encoding branches: (i) a Cyclic Path that incorporates explicit seasonal inductive bias through learnable cyclic embeddings, capturing predictable rhythmic patterns; and (ii) a lightweight Multi-Scale Temporal Convolutional Network with Channel Attention Path that employs multi-scale depthwise dilated convolutions to capture temporal dependencies across different receptive fields. A key innovation is the use of adaptive gating at multiple levels: a path gate dynamically combines four specialized forecasting heads (Direct, Trend-Seasonal Decomposition, Local Convolution, and Periodic Fusion) per sample and channel, while a hybrid gate adaptively balances the Cyclic and MSTCN-CA Paths based on input characteristics. This design ensures the model adapts to specific temporal patterns while maintaining linear complexity in sequence length, channels, and prediction horizon. Across extensive benchmarks on 12 real-world datasets for long-term forecasting, RhyMix achieves state-of-the-art performance on 10 of 12 datasets. The model remains lightweight (~40K params) with linear complexity and low-latency inference (5ms),suitable for resource-constrained edge devices and real-time deployment.
[AI-40] Open-ended Multi-agent Autocurricula via Visual Inspection of Policies with Multi-modal LLM s
链接: https://arxiv.org/abs/2607.08193
作者: Lorenzo Pantè,Andrea Fanti,Roberto Capobianco
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Open-ended curricula in Reinforcement Learning (RL) aim to train generally-capable agents by identifying tasks that facilitate learning increasingly complex skills. A major challenge when designing such curricula is assessing task difficulty relative to the agent’s current learning progress. While previous work has explored using scalar task scores or textual summaries of the agent’s behavior, here we study a different approach: directly inspecting policy behavior via recorded episode videos. We introduce a simple yet effective instantiation of this approach which leverages a Video Language Model (VLM) to both process these videos and provide curriculum recommendations, which we call Visual Inspection of Policies (VIP). Since videos can naturally contain any number of controllable agents, we empirically study VIP on the StarCraft Multi-Agent Challenge (SMAC). We show that even with a lightweight and openly accessible VLM (VideoLLaMa2-7B), VIP can use policy videos to generate more effective curricula than both its text-only ablation and methods that rely on scalar task scores.
[AI-41] Out of Sight: Compression-Aware Content Protection against Agent ic Crawlers
链接: https://arxiv.org/abs/2607.08180
作者: Xuefei Wang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:The rise of LLM-based agents with reasoning, summarization, and memory capabilities has created a new threat surface for online content that conventional defenses fail to address. Existing defenses like access controls can be circumvented by agents mimicking ordinary browsers, and injection-based defenses often degrade human readability. In this paper, we revisit the agent pipeline and identify context compression, which agents routinely invoke to fit context budgets, as a critical yet overlooked defense layer. We propose CAPE, a framework that protects high-value textual content by injecting invisible perturbations without changing its human-visible surface form, thereby inducing severe information loss during agent compression. CAPE extracts disruptive seed perturbations from an accessible surrogate compressor, then adapts them to query-only target compressors through prior-guided evolution and preference-calibrated candidate prioritization, achieving effective protection under a low query budget. Experiments on three content types and four compression settings show that CAPE improves information loss by up to 75.8% over the strongest baseline while keeping protected content visually indistinguishable from originals. CAPE also transfers to real-world settings, including the LangGraph agent workflow and GitHub Copilot, highlighting its generality and practical value. This paper aims to reveal context compression as a new defense layer, promoting content protection research in the agent era.
[AI-42] Overthinking: Amplifying Reasoning Weights to Extract Learned Secrets ICML2026
链接: https://arxiv.org/abs/2607.08173
作者: Jack Hopkins,Dipika Khullar,Fabien Roger
类目: Artificial Intelligence (cs.AI)
备注: Accepted at ICML 2026. 9 pages, 6 figures
Abstract:Black box auditing of language models is an essential pre-deployment tool, but it may miss subtle forms of misalignment and hidden information. To better elicit hidden information during an auditing process, we introduce \emphoverthinking: the process of using reasoning task vectors to amplify the propensity to think out loud of reasoning models. Given the parameters of a non-reasoning instruct model M and reasoning-distilled model R , we define the \emphoverthinking model as \boldsymbol\theta_\mathcalO_\alpha = \boldsymbol\theta_\mathcalM + \alpha(\boldsymbol\theta_\mathcalR - \boldsymbol\theta_\mathcalM) , where \alpha 1 amplifies reasoning beyond the pure reasoning model R . Additionally, we introduce new layer-wise attenuation strategies that selectively amplify reasoning without losing quality and coherence of model outputs. We demonstrate that overthinking models are more likely to reveal hidden information across four experimental settings, across 2B-32B models. Our findings suggest that reasoning amplification may surface secrets or unintended behaviors acquired during training up to 10\times more frequently than the original reasoning model. How secrets surface depends on the secret type: some require perturbation along the reasoning direction, while others yield to any sufficiently large weight perturbation.
[AI-43] Prismata: Confining Cross-Site Prompt Injection in Web Agents
链接: https://arxiv.org/abs/2607.08147
作者: Corban Villa,Alp Eren Ozdarendeli,Sijun Tan,Raluca Ada Popa
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Autonomous web agents promise to automate everyday browsing tasks, but inherit one of the web’s oldest attack surfaces. Cross-Site Scripting proved that mixing trusted and untrusted content is dangerous, even on benign pages. Agents resurface this risk by interpreting natural language as instructions, allowing third-party and user-generated content to hijack the agent via prompt injection. The core challenge is that deriving a task-specific security policy requires reasoning over page structure that is entangled with the attacker’s content. We present Prismata, a defense enforcing contextual least privilege for web agents, constraining both what the agent sees and what it can do. Prismata’s dynamic trust derivation produces permission labels for page content, with structural confinement guarantees, inspired by classical integrity models, that bound any labeling errors so that labels can only decrease in privilege and mislabelings are bounded. Prismata’s mechanical confinement enforces these labels by redacting content and restricting agent capabilities. Importantly, these mechanisms require no developer annotations, so Prismata supports the long tail of websites. Across recent published web agent attacks, including adaptive variants, Prismata substantially reduces attack success while preserving benign task utility. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.08147 [cs.CR] (or arXiv:2607.08147v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2607.08147 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-44] Answer Set Programming Energised! End-to-End Neurosymbolic Reasoning and Learning with ASP and Energy Based Models
链接: https://arxiv.org/abs/2607.08136
作者: Jakob Suchan,Julius Monsen,Salim Baloch,Mehul Bhatt
类目: Artificial Intelligence (cs.AI)
备注: Preprint
Abstract:We present a general neurosymbolic reasoning and learning methodology based on a modular integration of answer set programming with an energy based model substrate. Key contributions are: (1) supporting joint optimisation in the continuous latent space through explicit ASP-based declarative semantics fully incorporating background knowledge, constraints, non-monotonic inference; and (2) advancing recent works at the interface of answer sets, probabilistic logic, and answer set modulo theories by providing a generalised model and practical platform for ASP-centric robust, end-to-end training for applications in dynamic domains (e.g., involving perception and interaction). We provide a practical implementation, and demonstrate basic use and application (with MNIST), and evaluate with the visual question-answering benchmark Clevr and the multi-object tracking benchmark MOT.
[AI-45] PS4: Proxy-Supervised Joint Training for Real Target Speaker Extraction
链接: https://arxiv.org/abs/2607.08111
作者: Wanyi Ning,Wei Zhou,Yingpeng Li,Yinshang Guo,Haitao Qian,Yiming Cheng
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:Training target speaker extraction (TSE) models for real conversational mixtures remains challenging because large-scale training corpora and clean target speech for supervision are unavailable. We present PS4, a proxy-supervised training framework for TSE in real conversational mixtures, with two main contributions. First, we construct a large-scale corpus of 71,771 training samples derived from four public datasets, covering both Chinese and English scenarios. Each sample contains an overlapping speech mixture, per-speaker enrollment audio, a ground-truth transcript, and frame-level voice activity labels. Second, we propose a proxy-supervised joint training strategy that fine-tunes a BSRNN-based TSE model using four complementary differentiable objectives: ASR cross-entropy, speaker similarity, frame-level voice activity detection, and perceptual audio quality. Starting from a publicly available pre-trained checkpoint, only the BSRNN separator is updated during fine-tuning. On the REAL-T challenge leaderboard, PS4 ranks 2nd overall, achieving the best speaker similarity and timing F1 among all submitted systems.
[AI-46] Deep Learning Method for Stationary Distribution of Reflected Brownian Motion
链接: https://arxiv.org/abs/2607.08091
作者: Jim Dai,Zhanhao Zhang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The stationary distribution of reflected Brownian motion (RBM) plays an important role in the analysis of high-dimensional stochastic systems, yet closed-form solutions are known only for a few special cases. Computing important performance metrics, such as tail probabilities, is even more intractable, despite their practical relevance. In this paper, we develop a deep learning approach that accurately and efficiently learns the Laplace transform of high-dimensional RBMs based on the basic adjoint relationship (BAR). Our framework combines a careful design of the loss function, training data sampling procedure, and neural network architecture. We evaluate the proposed method on RBM instances with known ground-truth tail probabilities and demonstrate near-perfect prediction in high-dimensional settings, highlighting its potential as a general tool for analyzing stochastic systems beyond analytically tractable regimes. Our code can be found at this https URL.
[AI-47] PARA-PV: Physics-Aware Retrieval-Augmented PV Prediction Based on Frozen Foundation Model and Distribution Shift Correction
链接: https://arxiv.org/abs/2607.08079
作者: Hang Fan,Weican Liu,Ying Lu,Dunnan Liu,Long Cheng,Wei Wei
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Accurate photovoltaic (PV) power forecasting is essential for reliable grid dispatch and renewable energy integration, yet it remains challenging because PV generation is jointly shaped by weather variability, day-night transitions, regime-dependent dynamics, and strict physical constraints. We propose PARA-PV, a Physics-Aware Retrieval-Augmented framework that embeds physical knowledge throughout the forecasting process. The framework first encodes multivariate PV observations into patch-level representations and, through a physics-aware retrieval-augmented learner, retrieves historical patches and analog trajectories that are consistent with the current window in temporal shape, power level, PV operating state, and intra-day period; this yields a physically grounded base forecast. To supplement local memory with broader temporal knowledge, the base forecast is then calibrated against a frozen Chronos time-series foundation-model prior through a lightweight residual adapter, so that general temporal regularities are adapted to PV-specific dynamics without overriding the physically grounded prediction. Because residual conditional distribution shifts persist when weather and diurnal regimes change, a physics-aware distribution shift correction module subsequently adjusts the preliminary forecast using power, weather, timestamp, and day/night conditions, applying gated mean-shift and scale corrections selectively. Finally, a physics-constrained loss function partitions the samples into peak, ramping, night-time, and regular regimes and adaptively reweights their error contributions, preventing the dominant regular regime from suppressing learning of operationally critical states. Our code is available at this https URL.
[AI-48] Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring
链接: https://arxiv.org/abs/2607.08066
作者: Jennifer Za,Julija Bainiaksina,Nikita Ostrovsky,Tanush Chopra,Victoria Krakovna
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 25 pages, 10 figures
Abstract:Chain-of-thought (CoT) monitoring is a promising safety mechanism for AI agents, based on the premise that visible reasoning traces can surface misaligned or deceptive behavior. While effective in standard scenarios, recent work highlights that LLMs remain vulnerable to persuasion-based jailbreaks, where natural-language arguments override model constraints. We stress-test whether this vulnerability extends to monitoring LLMs: can an adversarial agent persuade its CoT monitor to approve proposed actions that violate the monitor’s policy? We design an evaluation framework with 40 tasks and analyze thousands of agent-monitor interactions, where agents are instructed to argue for policy-violating proposals. We find that in such adversarial settings, monitor access to the agent’s CoT reasoning increases rather than decreases approval of harmful actions on average by 9.5%, as the scratchpad provides an additional persuasion channel. To address this, we introduce a fact-checking monitoring framework. We find that a fact-checker and monitor pairing from different model families, for example a Claude 3.7 Sonnet monitor paired with a GPT-4.1 fact-checker, reduces approval of policy-violating actions by up to 45%, compared to only 6%, when using the same model for both fact-checking and monitoring roles. Our results demonstrate that CoT monitoring alone may be insufficient against adversarial persuasion, and that model-diverse fact-checking provides a robust mitigation.
[AI-49] When LLM s Agree Are They Right? Auditing Self-Consistency and Cross-Model Agreement as Confidence Signals
链接: https://arxiv.org/abs/2607.08065
作者: Kaihua Ding
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM-as-judge (Zheng et al., 2023) is increasingly the default for evaluating AI systems in enterprise pipelines, often scaled to ensembles (Verga et al., 2024) or “mixture-of-experts” (Shazeer et al., 2017) panels of judges. These systems share a key assumption: that consistency – agreement among judges, or among a model’s own samples – indicates correctness. We show this assumption is unreliable. Agreement is not accuracy: a model can agree with itself, and different models can agree with each other, out of shared bias, a memorized heuristic, or an option-position prior rather than truth. We ask when agreement is nonetheless a usable proxy, in a large-scale cross-runner study: 53 runners drew K=50 samples for assigned overlapping cases across comparisons of model tier, prompting, and scale on GPQA Diamond and AIME – 265,000 samples. Using majority-correctness as the deployment label and a hierarchical runner-clustered bootstrap, agreement is a positive but weak predictor (rho 0.20-0.59, all positive under item-clustered resampling) whose usefulness is regime-dependent: best for unsaturated mid-tier models and for allocating compute, and worst – over-confident yet no more accurate – for the most consistent frontier model (agreement =0.8 on 77% of GPQA case-result entries, 48% of those wrong). An exploratory cross-family check on three Claude tiers shows the same frontier over-confidence, with confident errors recurring across providers above a marginal-preserving null. Self-consistency is thus a conditional proxy for correctness, not a standalone confidence score. We publicly release the de-identified per-run rows and answer distributions.
[AI-50] When Thinking Hurts: Epistemic Signals in the Reasoning Chains of Visual Language Models ICML2026
链接: https://arxiv.org/abs/2607.08059
作者: Mayank Singal
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 7 pages, 2 figures, 5 tables. Oral paper at the 2nd Workshop on Epistemic Intelligence in Machine Learning (EIML@ICML 2026), Seoul, South Korea
Abstract:Uncertainty quantification for visual language models (VLMs) conventionally targets the answer token distribution. We provide the first three-family empirical characterisation of answer entropy behaviour in thinking-mode VLMs. Running four models on identical POPE adversarial samples, we find three qualitatively distinct patterns: Qwen3-VL-8B-Thinking shows complete collapse (ans H AUROC = 0.492); GLM-4.1V-9B-Thinking shows no collapse (0.716); and InternVL3-8B shows selective thinking (chains on only 50% of samples, ans H = 0.675 full / 0.602 thinking-only). Across all three thinking-mode models, thinking chain entropy outperforms answer entropy on the subset where chains are generated (0.647, 0.759, 0.608 vs. 0.492, 0.716, 0.602 respectively), suggesting chain signals are the more reliable predictor whenever chains are present. This holds strongly for Qwen and GLM, but with only marginal and statistically unreliable advantage for InternVL3 (n_FP = 17). A 300-sample VQAv2 pilot confirms chain entropy (0.680) outperforms answer entropy (0.595) on VQAv2 questions, with the gap largest for free-form answers (0.733 vs. 0.467). On harder reasoning tasks (HallusionBench) both Qwen models show moderate signal (approx. 0.64), consistent with incomplete pre-commitment on difficult questions. We additionally document structured abstention affecting 12-22% of queries with asymmetry toward absent-object queries, and a practical abstention gate raising accuracy from 71.0% to 93.8% at 62.7% coverage with no additional inference cost.
[AI-51] Reinforcing the Generation Order of Multimodal Masked Diffusion Models
链接: https://arxiv.org/abs/2607.08056
作者: Yidong Ouyang,Zhe Wang,Sourav Bhabesh,Dmitriy Bespalov
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Diffusion Language Models (DLMs) have recently achieved substantial progress in natural language generation tasks. Recent research demonstrates that adaptive token generation ordering can significantly improve performance in mathematical reasoning and code synthesis applications. In this work, we investigate the optimization of generation order for both text-to-image synthesis and multimodal understanding. We first establish that, unlike structured problems in language generation such as Sudoku puzzles, model logits alone are insufficient for determining optimal generation sequences in text-to-image generation and multimodal understanding. To address this challenge, we introduce a learnable control module trained via Group Relative Policy Optimization (GRPO) to determine the generation order. Our results demonstrate that learning this control block substantially improves both text-to-image alignment and multimodal understanding in DLMs. In particular, it enhances the model’s ability to capture fine-grained spatial relationships in generated images while also strengthening performance on multimodal reasoning and comprehension tasks. We evaluate our framework on GenEval, an object-focused benchmark for text-to-image alignment, where it achieves 4.08% relative improvements. In addition, experiments on VLMEvalKit confirm 4.85% relative improvements in multimodal understanding, highlighting the broad effectiveness of our approach.
[AI-52] Who Analyses the Analyser? Self-Validating LLM Hazard Analysis with Constitutional Meta-STPA
链接: https://arxiv.org/abs/2607.08054
作者: Samuel Tetteh,Udip Shrestha,Joshua R. Waite,Cody Fleming
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) are increasingly trusted to draft the artifacts of safety analysis such as, losses, hazards, Unsafe Control Actions (UCAs), and safety constraints, inside rigorous processes such as Systems-Theoretic Process Analysis (STPA). Yet a blind spot runs through this fast-growing literature: every system gets analysed except the LLM-assisted tool doing the analysing, which is itself a safety-relevant system that can hallucinate standards, emit unverifiable constraints, and leave no audit trail from prompt to artifact. We take seriously the question the field has skipped – who analyses the analyser? and answer it by turning STPA on the tool itself. We present \Constitutional Meta-STPA, an LLM-assisted STPA tool built around a closed loop: the tool runs a meta-STPA of the class of AI-assisted safety tools and derives rather than asserts, its governance constitution from the resulting loss \to hazard \to UCA \to constraint chain, yielding a published constitution of 21 Tool Principles and 8 Meta-Safety Principles, each bound to a code enforcement point. We formalise the measured object as a constitution-marginal coverage operator over a principle set P ( |P|=29 ) with a soundness lemma that isolates coverage from model and scanner, and report four findings. (i)~Self-derivation: a frontier ensemble (claude-opus-4.8 + claude-sonnet-4) recovers 18/21 canonical and all 8/8 governance principles from the tool’s own design, while a weaker pair recovers 12/21 and 3/8 , so the meta layer is model-limited, not constitution-limited, and the same 8/8 re-emerge from a second, independently authored tool.
[AI-53] Aleena: Alignment Agent for Research Software Engineering Collaborations ICSE KDD2026 KDD’26
链接: https://arxiv.org/abs/2607.08043
作者: Kshitij Dani,Cordero Core,Landung Setiawan,Carlos Garcia Jurado Suarez,Anshul Tambay,Vani Mandava,Anant Mittal
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 8 pages, 5 figures. AgenticSE @ KDD '26: Agentic Software Engineering (SE 3.0): The Rise of AI Teammates, KDD 2026 Workshop
Abstract:Research software collaborations span meetings, informal chats, pull requests, and GitHub issues. A decision surfaced in a Slack thread, refined in a meeting, and implemented in a pull request can lose its original rationale across these artifacts, leaving domain researchers and research software engineers with divergent mental models of project intent, ownership, and scientific assumptions. We argue that alignment in research software engineering is a continuous lifecycle problem, and that agentic AI can support stakeholder alignment and project-state tracking without replacing human decision-making. We present Aleena, an open-source lifecycle alignment agent that uses GitHub as a shared collaboration surface, transforming multi-modal stakeholder interactions into structured project records that surface risks, track open questions, and preserve decision continuity. Grounded in university-based research software engineering center experiences, this paper presents the motivating problem, system design, prototype, and illustrative lifecycle scenarios for Aleena.
[AI-54] A safety-oriented hypothetico-deductive framework for AI-assisted differential diagnosis
链接: https://arxiv.org/abs/2607.08038
作者: Fan Ma,Mauro Giuffrè,Donald Wright,Kent McCann,Mark Iscoe,Lingfei Qian,Mingyang Jiang,Chi Wing Ng,Na Hong,Huan He,Cathy Shyr,Qingyu Chen,Lee Schwamm,Lucila Ohno-Machado,Hua Xu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Diagnostic error is a major threat to patient safety, yet current large language model (LLM) systems often treat diagnosis as a one-shot prediction task, lacking safeguards against missed high-risk alternatives or rigorous verification of their reasoning. Here, we present AegisDx, a safety-oriented framework for hypothetico-deductive clinical reasoning. AegisDx coordinates specialized LLM components through role-specific contracts, structured intermediate outputs, evidence-retrieval interfaces, and verification gates to generate broad differential diagnoses, enforce explicit screening for dangerous “must-not-miss” conditions, verify reasoning against grounded medical evidence, and structure actionable next steps. We evaluated AegisDx across three layers. On literature-derived case reports from NEJM and JAMA, with GPT-oss-120B as the shared backbone, Top-3 diagnostic accuracy was 59.9% versus 52.1% for the standalone LLM on JAMA cases and 62.7% versus 51.4% on NEJM cases. On cases from Annals of Emergency Medicine, Top-3 accuracy was 85.7% versus 68.6%; against physician-consensus must-not-miss diagnosis sets, AegisDx captured at least one such condition among its top three diagnoses in 78.0% of cases versus 52.0%. In a blinded physician evaluation of 43 real-world emergency department notes from the Yale New Haven Health System compared against GPT-5, AegisDx improved the physician-rated composite safety score from 4.31 to 4.55 on a 5-point scale (adjusted p = 2.1x10^-4), with qualitative gains in must-not-miss identification and reasoning safety. Our findings suggest that engineering diagnostic AI as a safety-oriented reasoning framework, rather than optimizing raw predictive accuracy alone, can provide a safer, more transparent, and clinically meaningful layer of bedside decision support for acute care workflows.
[AI-55] Concretized Proposition Prompting Resolves Composition-Knowledge Dichotomy in Large Language Models
链接: https://arxiv.org/abs/2607.08018
作者: Changhun Lee,Minguk Jeon,Jongkyung Shin,Chiehyeon Lim
类目: Artificial Intelligence (cs.AI)
备注: 9
Abstract:LLMs often struggle to balance compositionality with knowledgeability, a challenge we define as Composition-Knowledge Dichotomy. To address this, we propose Concretized Proposition Prompting (CPP), a framework that explicitly concretizes propositions relevant to questions. The results demonstrate that CPP significantly enhances reasoning performance, particularly in medical benchmarks where precise knowledge is paramount, while being competitive on math benchmarks where deductive reasoning is prioritized. Additional experiments reveal that CPP is scalable to various foundation models and parameter sizes, being a fundamental paradigm that bridges the gap between composition- and knowledge-based approaches. Consequently, CPP resolves the composition-knowledge dichotomy by providing a solid foundation for logically organized and factually grounded reasoning.
[AI-56] Provably Optimal Learning Algorithms for Assistance Games
链接: https://arxiv.org/abs/2607.08012
作者: Nivasini Ananthakrishnan,Mark Bedaywi,Michael I. Jordan,Stuart Russell,Nika Haghtalab
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
备注:
Abstract:This paper studies an online variant of the assistance games framework, where an informed agent and an uninformed agent repeatedly interact over T timesteps to optimize a common reward function. While the informed agent (the human) observes a latent state of the world, the uninformed agent (the assistant) observes only the human’s actions. We provide the first provably efficient learning algorithms for repeated assistance games. We introduce the notion of assistance regret: the gap between the cumulative utility of interactions and that of the optimal joint policies in hindsight, which map latent states to action pairs. We present decentralized algorithms for both the human and the assistant that achieve a (1-1/e) -approximate assistance regret rate of \widetildeO(T^3/4) , with runtime polynomial in the size of the action and state spaces. These algorithms are general; in particular, they accommodate any no-regret algorithm for the assistant. We prove that achieving a regret approximation factor better than (1-1/e) is computationally intractable. Furthermore, we demonstrate how these generic no-regret algorithms can be tailored to a pseudo-decentralized setting – using a shared random string – to achieve a rate of \widetildeO(T^1/2) , optimal up to logarithmic factors.
[AI-57] Agent ic Neural Architecture Search
链接: https://arxiv.org/abs/2607.07984
作者: Seokhoon Jeong,Mijung Kim,Taehwan Kim
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Neural architecture search (NAS) methods have grown increasingly efficient, yet they remain bounded by manually engineered search spaces that require substantial domain expertise and must be rebuilt for every new task. Large language models (LLMs) can generate architectures in an open-ended space, but how to optimally divide the labor between LLM-driven design and NAS-driven search remains unexplored. We propose a mechanism that bridges these two paradigms: an LLM produces a high-quality seed architecture, then decomposes it into a “slotted architecture”, a scaffold with named, interchangeable module slots that automatically defines a bounded, task-specific search space for conventional NAS to explore, without manual engineering. We instantiate this mechanism in AgentNAS, a modular three-phase pipeline in which each component’s contribution can be measured independently. On 17 tasks spanning classification, dense regression, segmentation, and multi-label tagging across diverse modalities (NAS-Bench-360 and Unseen NAS), AgentNAS establishes a new state of the art on 11 tasks, outperforming published baselines including task-specific expert designs. Ablation studies show that the two search mechanisms are broadly complementary: the LLM-generated seed already surpasses published baselines on the majority of tasks, and NAS delivers additional gains in most cases through combinatorial recombination across slots, a mode of search that independent LLM samples cannot replicate. These patterns hold across three LLMs of different capability levels, confirming that the division of labor is robust. Our code is available at this https URL.
[AI-58] 3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse
链接: https://arxiv.org/abs/2607.07980
作者: Shyam Agarwal,Courtney Miller,Christian Kästner,Bogdan Vasilescu
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Coding agents now author entire pull requests, and practitioners sharply disagree about what this does to code review: whether it becomes the bottleneck, whether human review is still necessary, and whether it quietly erodes the understanding that it once built. Repository-mining studies measure surface trends but seldom explain the mechanisms beneath them, and the trends themselves prove unstable. A motivating observational analysis of public GITHUB activity finds that agent-authored pull requests are reviewed less often, merged several times faster, and discussed less than human-authored ones, yet the direction of these trends flips under different but equally defensible analysis choices, so the traces establish what is changing without explaining why. To recover the mechanisms, we synthesize practitioner discourse at scale into an explanatory theory: we collect 38,709 grey-literature documents (engineering blogs and Reddit threads), filter to those substantively about code review, and code a stratified random sample of 3,100 with an LLM-assisted pipeline, from which we build a causal model of 26 constructs and 67 relationships (64 directed, 3 contested). Its organizing claim is that review is the control point through which a coding agent’s effect on software is decided, and that AI does not fix the sign of that effect: the team sets it, through the expertise its humans bring and how it structures the review process. The theory makes the competing positions explicit and turns “AI is changing code review” into falsifiable propositions with named constructs and moderators. As a secondary contribution, we offer the underlying LLM-assisted, grey-literature theory-building method as a scalable template for software-engineering research, with a public implementation.
[AI-59] Linear Attention Architectures: Mechanisms Trade-offs and Cross-Layer Routing
链接: https://arxiv.org/abs/2607.07953
作者: Tommaso Cerruti,Tim Rieder,George Rowlands,Lingfeng Jin,Imanol Schlag
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 20 pages, 6 figures, 8 tables. Code available at this https URL
Abstract:Self-attention lets each token retrieve information from the full context, but its quadratic cost in sequence length limits training and inference at long context. This paper presents a comparative study of softmax attention and four recent recurrent linear-attention architectures: DeltaNet, Gated DeltaNet, Kimi Delta Attention, and Gated DeltaNet-2. We express these mechanisms in a common recurrent-memory notation, making explicit how they differ in expressivity, memory decay, erase and write control, training throughput, and implementation complexity. Our experiments center on 350M-parameter models trained for 15B tokens, and include optimizer and learning-rate comparisons, hybrid-versus-pure stack comparisons, sequence-length runtime measurements, larger DeltaNet runs at 1.3B and 3B parameters, and a small set of downstream evaluations. The reported speed results measure training throughput and iteration time; we do not provide an empirical inference-speed benchmark. Within the reported 350M-parameter, 15B-token sweep, Kimi Delta Attention with Muon reaches the lowest final validation loss, a pure Gated DeltaNet stack trained with AdamW has the highest normalized training throughput, hybrid stacks generally improve loss at a throughput cost, and Muon consistently lowers final validation loss relative to AdamW in the matched architecture settings we evaluate. We introduce and evaluate lightweight cross-layer routing mechanisms for DeltaNet-style memories. The most natural DeltaNet-inspired formulation, forwarding a lower layer’s delta-rule write error into the next layer’s value target, does not improve over matched baselines. Routing into the aligned hidden stream and forwarding the write value instead yields a modest improvement in the matched runs we report: Cross-Layer Value Routing (CLVR) lowers final validation loss for both DeltaNet and Gated DeltaNet.
[AI-60] path_boost: A Python Package for Interpretable Graph-Level Prediction using Path-Based Gradient Boosting
链接: https://arxiv.org/abs/2607.07935
作者: Claudio Meggio,Johan Pensar,Riccardo De Bin
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 27 pages, 4 figures, 5 tables. Code available at this https URL and on PyPI (path-boost)
Abstract:We present path_boost, a Python package for interpretable supervised learning on graph-structured input data. The package implements PathBoost, a gradient boosting algorithm that automatically discovers predictive labeled paths within graphs during the learning process. Unlike graph neural networks, which are generally difficult to interpret, PathBoost produces an additive prediction model over path-based features that explicitly reveals which substructures drive predictions. To avoid an exhaustive enumeration of all possible paths, the algorithm iteratively selects and extends paths during learning based on their predictive power, using boosting to combine weak learners into a strong ensemble. The package supports both regression and binary classification. Key features include compatibility with scikit-learn workflows, support for custom base learners and selectors, automatic starting node selection, parallel training across anchor nodes, and built-in variable importance computation. We demonstrate PathBoost on molecular property prediction of transition metal compounds, where atoms serve as nodes and bonds as edges, and further benchmark PathBoost against an established graph neural network and a graph kernel method across six molecular datasets. The package is available on PyPI and GitHub under an open-source license.
[AI-61] Persona Cartography: Charting Language Model Personality Traits in Weight Space
链接: https://arxiv.org/abs/2607.07916
作者: Luke Baines,Anton Gonzalvez Hawthorne,Mariia Koroliuk,Irakli Shalibashvili,Clément Dumas,Konstantinos Voudouris,David Demitri Africa
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 85 pages, 80 figures
Abstract:Large language models exhibit recurring behavioural patterns – personas – that shape generalisation and safety, but we lack reliable tools for decomposing, measuring, and controlling them. Our central insight is to treat personas as positions in a space of behavioural traits, using the OCEAN framework to describe model personas in terms of Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. We train low-rank adapters to amplify or suppress individual traits, and evaluate their effects using an LLM-judge calibrated against a human-validated panel, trait-specific multiple-choice benchmarks, and standard capability evaluations. Across six models from three families (4B-32B), we find that each adapter moves its target trait largely monotonically with scale, combines approximately additively with other adapters to construct mixed personas, and preserves performance on capability benchmarks at moderate scales. We further show that the induced trait axes affect safety-relevant behaviour in downstream evaluations: for example, moving along neuroticism and agreeableness axes affects frustration and sycophancy respectively. We also introduce an unsupervised psychometric pipeline that recovers four interpretable behavioural factors (tone, initiative, didacticism, epistemic caution) from model rollouts. Persona control can then be considered in terms of learning, scaling, and composing traits in weight space, providing a bridge between personality measurement, model editing, and safety.
[AI-62] Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphs
链接: https://arxiv.org/abs/2607.07903
作者: Anupam Wagle,Ifrat Ikhtear Uddin,Chaowei Zhang,Longwei Wang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) exhibit remarkable capabilities but remain highly vulnerable to adversarial prompts and jailbreak attacks. Existing approaches primarily analyze these failures through input-output behaviors or attribution methods, offering limited insight into how adversarial perturbations alter the model’s internal reasoning. Consequently, the mechanisms underlying unsafe or incorrect behaviors remain poorly understood. We introduce a mechanistic framework for diagnosing LLM vulnerabilities using paired internal computation graphs, which represent prompt-specific inference as structured causal interactions among latent features. By constructing and aligning computation graphs for clean and attacked prompts, we reveal that adversarial attacks induce systematic transformations of internal reasoning, including suppression of safety-relevant components, emergence of attack-specific features, and rerouting of computation paths. Building on this representation, we propose a unified framework that (i) decomposes computation into invariant, suppressed, and emergent structures, (ii) identifies recurring vulnerability motifs associated with failure modes, and (iii) performs causal interventions on nodes, paths, and subgraphs to directly evaluate their contributions to attack success. This enables a transition from descriptive attribution to causal diagnosis of model failures. Experiments across multiple open-source LLMs and diverse adversarial and jailbreak benchmarks demonstrate that structural deviations in internal computation graphs strongly correlate with unsafe behaviors. Furthermore, targeted interventions on identified vulnerability motifs improve model robustness, establishing internal computation graphs as a principled foundation for understanding, diagnosing, and mitigating LLM vulnerabilities.
[AI-63] Closed-Loop Dynamic Validator Node Scaling in Private Substrate Blockchains Using Takagi-Sugeno Fuzzy Inference
链接: https://arxiv.org/abs/2607.07901
作者: Thandile Nododile,Ayinde M. Usman,Clement N. Nyirenda
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 9 pages, 5 figures
Abstract:Private blockchain networks run with fixed node configurations that cannot adapt to changing workload conditions. Too many nodes serving a light workload waste resources; too few nodes facing heavy demand slow block production and degrade finalisation. The right validator count is hard to determine, as it depends on overlapping factors that shift over time. This paper presents a Takagi-Sugeno (TS) fuzzy inference system that reads live blockchain parameters (block production time, block size, and active node count) and outputs a continuous efficiency score alongside a scaling recommendation: Scale Up, Maintain, or Scale Down. The controller uses triangular membership functions across three linguistic variables, evaluated through a complete 27-rule base with product t-norm aggregation. A key contribution is an empirical recalibration of the membership functions, anchoring linguistic terms to the observed operating range of the testbed rather than to theoretical extremes. The system is evaluated on a 10-node Substrate blockchain network storing real smart water meter data hashes from the Queensland Government open data portal. Statistical analysis across configurations of 4, 7, and 10 active nodes confirms that the controller produces distinct operational profiles reflecting each configuration’s provisioning state. In closed-loop experiments, the controller autonomously adjusts validator participation in both directions, activating validators under rising load and removing them under over-provisioning, converging to the same stable equilibrium from both directions. Compared against three threshold-based baselines, it shows fewer scaling oscillations while maintaining comparable block production times. Results show that TS fuzzy inference can support autonomous validator management in private blockchain deployments, with stable scaling behaviour threshold approaches cannot match.
[AI-64] Nigeria Machinery: A Low-Resource Industrial Dataset with a Domain-Grounded Reasoning Layer
链接: https://arxiv.org/abs/2607.07883
作者: Gospel Bassey,Vincent Fakiyesi
类目: Artificial Intelligence (cs.AI)
备注: 10pages, 2 tables
Abstract:There is relatively little, public, and model-ready data on industrial machinery for African economies. This makes it hard to do quantitative analysis or to train language models on numeric tasks grounded in that setting. We release two things to help with part of this problem. The first is the Nigeria Machinery Usage and Failures Dataset: 89 machine-level records across 28 indicators, covering Nigeria’s manufacturing and oil and gas sectors from 2006 to 2025. Every record names a public source and is decoded by a codebook. The second is a method for building chain-of-thought (CoT) reasoning examples from these sparse numeric values. The result is 94 prompt, completion, and reasoning-trace rows. In every row, the prompt names the real indicator, subsector, year, and source of the record it comes from. The data adaptation work was carried out by Adaption Labs. Along the way we describe a problem that is common when language models are used to build datasets. The prompts can match the real numbers while saying nothing about the real domain. We show that fixing this raises the share of domain-grounded prompts from 1 out of 78 in an earlier release to 94 out of 94, and that every retrieval answer now matches its source value (84 out of 84). We release the data, the reasoning layer, and a per-row provenance file under CC-BY-4.0. We are clear about the limits. With 89 records and 17 indicators that have only one observation, this is a reference and seed dataset, not a large training set. Most reasoning rows are retrieval rather than multi-step computation.
[AI-65] Agent ic AI and Retrieval-Augmented Models in Straight-Through Underwriting
链接: https://arxiv.org/abs/2607.07858
作者: Robert Richardson,Josh Meyers,Brian Hartman,David Sandberg
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Artificial intelligence (AI) is beginning to reshape actuarial practice, particularly in domains that require reasoning over unstructured documents, heterogeneous data sources, and regulated decision workflows. Actuaries now face a design space that ranges from traditional rule-based automation to large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent agentic'' systems that plan, retrieve, call tools, and reflect. This paper examines how these emerging architectures can support actuarial priorities such as transparency, auditability, and human-in-the-loop governance, with a focus on straight-through decision processes. To make these ideas concrete, we develop and analyze an agentic AI framework for straight-through underwriting of small commercial Business Owner Policies (BOPs). We construct a synthetic but realistic experimental environment and compare three underwriting pipelines: (i) a single-LLM baseline, (ii) a naive RAG system, and (iii) a multi-agent Agentic RAG’’ pipeline that combines targeted retrieval, third-party data checks, and explicit multi-step rule evaluation. The agentic system performs best overall, with the largest gains in multi-step and missing-information scenarios, where structured retrieval and reflection help the model avoid unsupported straight-through decisions.
[AI-66] A Graph Neural Network Model for Real-Time Gesture Recognition Based on sEMG Signals
链接: https://arxiv.org/abs/2607.07850
作者: Pragatheeswaran Vipulanandan,Kamal Premaratne,Manohar Murthi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:For seemless control of advanced hand prostheses and augmented reality, accurate and immediate hand gestures recognition is essential. Surface electromyography (sEMG) signals obtained from the forearm are commonly employed for this purpose. In this paper, we present a novel approach for sEMG representation that utilizes graph networks which contain information about muscle activation patterns in the forearm. Based on these graph networks, we have developed a machine learning algorithm capable of real-time hand gesture recognition using a graph neural network. The algorithm’s performance was evaluated using sEMG signals acquired from myoband, which has 8 electrodes placed around the forearm, involving 8 healthy subjects. The proposed method demonstrated an average classification accuracy of 99%, surpassing the performance of state-of-the-art techniques. The average time for both graph construction and prediction stood at 48ms utilizing a M1 pro CPU, rendering the approach well-suited for real-time applications.
[AI-67] VectorizationLLM : Smart Vectorization Based AI Assistant
链接: https://arxiv.org/abs/2607.07846
作者: Ryan Duke
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 44 pages, 6 figures
Abstract:VectorizationLLM is a specialized Large Language Model based on Google open-weight LLMs. The model is designed to assist students to learn smart vectorization, time/wave vector analysis, piecewise functions, Fourier analysis, and differential equations in MATLAB. The course application is CTEC 247: Applied Computational Analysis II by the Department of Electrical Computer Engineering Technology at New York Institute of Technology Old Westbury. The LLM model is designed to be an instructive assistant, providing detailed explanations of concepts with examples from in-class notes without providing direct answers to questions. The model is designed with a RAG (Retrieval Augmented Generation) knowledge base and system prompt architecture. Examples in both code, text, and images are provided in the LLM responses.
[AI-68] Shift Drift: A Zero-Shot Benchmark for Generalizable and Robust Autonomous Driving Motion Planning IROS2026
链接: https://arxiv.org/abs/2607.07844
作者: Alessandro Canevaro,Hang Yu,Julian Schmidt,Peizheng Li,Silvan Lindner,Wilhelm Stork,Georg Martius,Julian Jordan
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
Abstract:While closed-loop motion planners trained on large-scale, object-level datasets, e.g., nuPlan, demonstrate strong in-distribution (ID) performance, their generalization to novel urban topologies and recovery mechanisms following execution perturbations remain under-explored. To address this, we present Shift Drift, a novel dual-track benchmark designed to rigorously stress-test motion planners across two critical axes of distribution shift: (1) The Semantic Shift Track leverages a novel conversion pipeline that transforms the aerial, DeepScenario Open 3D dataset into the nuPlan simulation framework. This enables zero-shot evaluation of planners trained on North American and Singaporean data against 1,182 scenarios spanning four German cities and the US city of San Francisco featuring dense pedestrian-cyclist interactions. (2) The State-Distribution Drift Track injects stochastic perturbations into the ego vehicle’s dynamics to quantify robustness against compounding execution errors. Based on this, we systematically evaluate the failure modes of diverse planning paradigms under semantic and state-distribution shifts. While imitation learning methods achieve high scores in ID benchmarks, they exhibit significant failures under semantic shift, particularly in pedestrian-dense environments, and suffer from persistent drift when subjected to temporally correlated actuation noise. In contrast, the evaluated reinforcement-learning-based planner demonstrates more graceful degradation, maintaining higher safety and progress metrics across both tracks. Our findings reveal an empirical trade-off between imitation fidelity and closed-loop resilience, providing the community with a rigorous benchmark to evaluate progress toward reliable deployment.
[AI-69] Infinity-Parser2 Technical Report
链接: https://arxiv.org/abs/2607.07836
作者: Zuming Huang,Jun Huang,Kexuan Ren,Baode Wang,Weizhen Li,Jianming Feng,Yu Wang,Yichen Yao,Shijun Lin,Yige Tang,Cheng Peng,Weidi Xu,Wei Chu,Yinghui Xu,Yuan Qi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We present Infinity-Parser2, a large multimodal model that couples a controllable data-synthesis pipeline with multi-task reinforcement learning for end-to-end document parsing, addressing the persistent scarcity of faithfully annotated parsing corpora. Our contributions are threefold. First, we build a scalable synthesis engine, pairing a controllable rendering framework with an iterative refinement loop, and use it to construct and open-source Infinity-Doc2-5M: a 5-million-sample bilingual (Chinese/English) corpus spanning diverse document types, annotated with element bounding boxes, canonical content forms (Markdown, HTML, LaTeX, SMILES, structured charts), and full-page reading order. Second, we introduce a verifiable, multi-task reward system that enables Joint Reinforcement Learning across eight co-trained objectives (document parsing, layout analysis, table parsing, math formula parsing, chart parsing, chemical formula parsing, document VQA, and general multimodal understanding), unifying perception, structure, and reasoning in a single optimization signal. Third, we release two variants under a shared architecture: Infinity-Parser2-Flash, optimized for low-latency inference with a 3.68\times throughput gain over Infinity-Parser-7B, and Infinity-Parser2-Pro, engineered for precision-critical settings. Infinity-Parser2-Pro reaches state-of-the-art 87.6% on olmOCR-Bench and 74.3% on ParseBench, surpassing DeepSeek-OCR-2, PaddleOCR-VL-1.5, and MinerU2.5, with strong generalization to charts, chemical formulas, and document VQA.
[AI-70] Graph-Regularized Deep Learning for EEG-Based Emotion Recognition with Psychologically-Grounded Label Structure
链接: https://arxiv.org/abs/2607.07773
作者: Dongyang Kuang,Zizheng Ma,Yushan Zhang,Xiaocong Zeng
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:EEG-based emotion recognition is critical for mental health monitoring and affective brain-computer interfaces, yet existing deep learning approaches often treat emotion classes as isolated labels, ignoring their psychological interdependencies. We propose a graph-regularized learning framework that conceptualizes emotions as nodes in a graph where edges encode proximity based on dimensional emotion theories. We adapt three complementary regularization strategies–Graph Label Smoothing (intuitive soft labeling), Commuting distance on graph via Graph Laplacian (spectral graph theory), and Sliced Wasserstein Distance (optimal transport on graph)–ordered by increasing computational complexity. These strategies penalize model predictions that deviate from the established emotion topology. Our framework is evaluated across three representative backbone architectures: AudioTransformer (pure transformer), Conformer (CNN-transformer hybrid), and DCGNN (causal graph neural network), demonstrating architecture-agnostic benefits. Experiments on SEED-IV (4 classes) and SEED-V (5 classes) datasets show consistent improvements: best case up to +5.42% accuracy and 39% reduction in psychologically implausible misclassifications. Ultimately, our framework help raise the upper bound of performance achievable with standard approaches. Code will be released.
[AI-71] Principled Analysis of Deep Reinforcement Learning Evaluation and Design Paradigms AAAI2026
链接: https://arxiv.org/abs/2607.07769
作者: Ezgi Korkmaz
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Published in AAAI 2026
Abstract:Starting from the utilization of deep neural networks to approximate the state-action value function that led to winning one of the most challenging games, to algorithmic advancements that allowed solving problems without even explicitly stating the rules of the challenge at hand, reinforcement learning research has been the center of remarkable scientific progress for the past decade. In this paper, we focus on the key ingredients of this research progress and we analyze the canonical evaluation and design paradigms in reinforcement learning. We introduce the theoretical foundations of scaling laws in reinforcement learning and show that the asymptotic performance of reinforcement learning algorithms does not have a monotone relationship between performance rankings and data-regimes. We conduct large-scale experiments and our results demonstrate that a line of reinforcement learning research under the canonical design and evaluation paradigms resulted in incorrect conclusions. Our analysis and results provide a core analysis on scaling, capacity and complexity of deep reinforcement learning.
[AI-72] Alignment Plausibility: A New Standard for Assuring AI in Healthcare
链接: https://arxiv.org/abs/2607.07766
作者: Gwydion Williams,Sara Zannone,Bilal A Mateen
类目: Artificial Intelligence (cs.AI)
备注: 8 pages, 1 figure
Abstract:Large language models (LLMs) have become significant providers of mental health support, yet they remain products of an attention economy whose operational and commercial targets favour sustained engagement over the friction that effective psychological support often requires. Developers’ safety responses have been largely reactive, addressing the most visible and acute harms while subtler, longer-term patterns of risk (e.g., dependency, boundary erosion, the amplification of distorted beliefs) receive less attention. We contend that making LLMs structurally safe requires alignment organised at three levels that mirror how society assures the safety of human clinical practice: 1) explicit value specification grounded in the codified normative commitments of clinical practice; 2) training that embeds those values in the model; and 3) oversight that detects drift and longer-term harm during deployment, much as clinical supervision does for human practice. Organising alignment in this way yields a construct we call alignment plausibility - a structured demonstration that a system’s values, training regime, and oversight mechanisms are together consistent with safe and positive outcomes. We propose alignment plausibility as a regulatory construct (by drawing analogy to the established construct of biological plausibility) for AI in health: a principled way to argue for, or against, trust that systems are aligned to positive health outcomes, will cause no harm even where capable of doing so, and will ultimately lead to patient benefit.
[AI-73] Aligning Clinical Needs and AI Capabilities: A Survey on LLM s for Medical Reasoning
链接: https://arxiv.org/abs/2607.07761
作者: Qi Peng,Jiatong Li,Sirui Huang,Yiyang Jiang,Kaisong Gong,Ronger Ding,Shijie Ye,Changmeng Zheng,Yi Cai,Xiaobo Yang,Jin Huang,Xiao-Yong Wei,Qing Li
类目: Artificial Intelligence (cs.AI)
备注: Accepted by Machine Intelligence Research
Abstract:Large language models (LLMs) have emerged as important tools in healthcare, showing growing potential for clinical reasoning and patient care. This survey examines recent progress in medical LLMs, focusing on reasoning applications and requirements. We present a dual-view approach that connects clinical practice with computational methods. On the clinical side, we establish a five-level competency scheme following Miller’s Pyramid, progressing from knowledge recall to dynamic case management. On the computational side, we link deductive, inductive, and abductive reasoning patterns to common medical goals and tasks. We also introduce a benchmark dataset spanning five levels of medical reasoning capability and report results on 18 state-of-the-art models, revealing that medical specialist models excel in diagnosis-centric tasks while general models lead in decision support and dialogue. We conclude by discussing current progress and open challenges, including data limitations, hallucination, and grounding issues, and outline directions toward safer, more reliable, and workflow-ready systems.
[AI-74] Adversarial Social Epistemology for Assemblies of Humans and Large Language Models
链接: https://arxiv.org/abs/2607.07760
作者: Mihnea C. Moldoveanu,Joel A.C. Baum
类目: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
备注: 50 pages
Abstract:We outline an adversarial social epistemology (ASE) for densely interactive communicative landscapes in which public assertions are scaffolded by chains of testimony, inference, institutional certification, and tacit trust. In such landscapes, agents have incentives and affordances to distort, color, omit, fabricate, or strategically under-specify information for private, reputational, rhetorical, or material gains. We argue that these phenomena are not adequately captured by familiar descriptions of epistemic bubbles, echo chambers, or misinformation diffusion. What requires explanation is how communicative agents exploit the commitments and entitlements that normally make scaffolded assertions trustworthy. We provide language that delivers the requisite analysis, outline mechanisms that subvert trust in scaffolded public communications, and outline machinery for auditing and redressing trust breaches arising from subverting the auditability of inferential chains, drawing on epistemic networks, enriched with an inferentialist semantics for interpreting assertions.
[AI-75] AI-integrated models for assessing agricultural resilience
链接: https://arxiv.org/abs/2607.07759
作者: Joshua R. Waite,Dana Golden,Brett Indelicato,Kevin Camp,Mojdeh Saadati,Shannon Regan,Patrick Schnable,Baskar Ganapathysubramanian,Carlos Messina,Suzanne Thornsbury,Soumik Sarkar
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agricultural supply chains are vulnerable to disruptions through linked biophysical and economic systems. We develop an AI-powered tool that integrates economic models (GTAP) with biophysical models (APSIM) to analyze supply chain shocks, enabling policymakers and market participants to assess cross-disciplinary impacts through queries and responses written in natural language.
[AI-76] A Transdiagnostic Space of Disorder Like Phenotypes in Reinforcement Learning Agents
链接: https://arxiv.org/abs/2607.07753
作者: Hari Prasad
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 15 pages, 8 figures, 6 tables
Abstract:Modelling psychological disorders in artificial agents offers both a testbed for computational psychiatry and a lens on the failure modes of affective control. Prior work induces one or two disorders in a reinforcement learning (RL) agent by hand-tuned reward shaping, labels the behaviour post hoc, and reports single runs. We recast disorder modelling as dose-controllable manipulation of cognitive appraisal signals in an appraisal-guided PPO agent, expressing seven disorders (anxiety, mania, obsessive-compulsive checking, depression, impulsivity, addiction, and post-traumatic stress) each as a single knob grounded in a computational psychiatry account, with each symptom measured by a preregistered assay mapped to a recognised paradigm. Across more than a thousand runs (10 seeds, four controls, 95% confidence intervals) every disorder shows a graded, monotone dose-response that no control reproduces. Beyond these induced effects, three findings emerge that were not written into the reward: the disorders self-organise into a two-dimensional affective space in which mania mirrors anxiety; removing a knob remits reward distortion disorders (mania, checking, addiction) but not avoidance disorders (anxiety, PTSD), which instead recover under a graded exposure curriculum; and two simultaneous knobs interact nonadditively, yielding testable comorbidity predictions. Appraisal weights thus parameterise a controllable space of affective phenotypes in which the same knobs that induce a disorder can model its treatment. We also show that three disorder knobs (depression, addiction, anxiety) transfer to a three-dimensional pixel environment (MiniWorld) with a standard convolutional agent and no appraisal critic, with cross-assay dissociation confirmed across both domains, indicating the framework is not specific to grid worlds or to PPO’s appraisal critic.
[AI-77] Architecture Generalization with MetaNCA
链接: https://arxiv.org/abs/2607.07743
作者: Meet Barot,Daniel Berenberg,Sina Khajehabdollahi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 9 pages, 6 figures. To appear in the proceedings of the Artificial Life Conference (ALIFE 2026)
Abstract:Self-organization is an emergent property of life, driven by the collective behavior of individual components acting on local information. Biological neurons, through local interactions transmitted through synapses, are able to learn efficiently and can adapt their connections over an organism’s lifespan. Motivated by these desirable properties of adaptability and local interaction, neural cellular automata (NCA) models have been successful at learning morphogenesis solely through local update rules, demonstrating stability over many updates and robustness to perturbations. In this work, we introduce Meta Neural Cellular Automata (MetaNCA), a framework that learns local rules which self-organize the weights of artificial neural networks. A learned rule network iteratively updates the weights of a task network using only local interactions on the computation graph. We propose a novel Weight Transformer architecture for the local rule network, which uses linear attention to aggregate signals from neighboring weights and hidden states. Once trained, the rule network generates task networks of diverse architectures without backpropagation. We show that MetaNCA generates weights for feedforward MLPs, CNNs, and ResNets on MNIST and CIFAR-100, scaling to networks of 2 million parameters. We further show that MetaNCA generalizes to architectures not seen during meta-training, and that architectural diversity in the training phase strengthens this generalization.
[AI-78] Jet-Long: Efficient Long-Context Extension with Dynamic Bifocal RoPE
链接: https://arxiv.org/abs/2607.07740
作者: Haozhan Tang,Zerui Wang,Yuxian Gu,Song Han,Han Cai
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Modern LLMs are increasingly deployed in long-context applications such as retrieval-augmented generation, repository-level coding, and agentic workflows whose accumulated reasoning and tool traces routinely push the input an order of magnitude past the pretraining window, making zero-shot context extension the dominant deployment path for open-weight checkpoints. Most existing zero-shot methods fix a single rescaling factor up front, so an aggressive factor sacrifices short-context fidelity while a conservative one breaks down at long contexts. We propose Jet-Long, a tuning-free zero-shot method that pairs a local RoPE-faithful window with a long-range window whose rescaling factor adapts dynamically to the current sequence length, recovering the base model exactly at short inputs while extrapolating cleanly at long ones. An inclusion-exclusion attention merge and an on-the-fly RoPE correction rotation make the bifocal construction essentially free at inference; fused into a single CuTe kernel, long-context prefill reaches up to 1.39\times FA2 throughput on H100 (approaching the Hopper-only FA4), and single-batch generation incurs \le 4% overhead at every length. On Qwen3-1.7B/4B/8B up to 128K context, Jet-Long leads RULER by +4.79 / +2.18 / +2.03 ~pp over the strongest baseline at 1.7B/4B/8B, achieves the best overall accuracy on HELMET-RAG (a benchmark identified by HELMET as the most efficient predictor of downstream long-context performance) and attains the lowest PG-19 perplexity. Jet-Long also generalizes to hybrid attention architectures such as Jet-Nemotron for further long-context improvement without retraining, and remains hyperparameter-resilient for ease of deployment.
[AI-79] SHIFT: Survival Prediction from Incomplete and Heterogeneous Genomic Data
链接: https://arxiv.org/abs/2607.07725
作者: Muhammet Sami Yavuz,Ayhan Can Erdur,Sabri Mustafa Kahya,Benedikt Wiestler,Jana Lipkova
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Genomics (q-bio.GN)
备注: 18 pages, 2 figures
Abstract:Genomic prediction models often fail to transfer across institutions because sequencing panels differ across sites, creating structural feature missingness at deployment. Existing approaches to this challenge typically restrict analysis to genes shared across cohorts, exclude patients with incomplete profiles, or rely on test-time imputation, all of which can reduce robustness and limit the use of multi-center data. We propose Survival prediction Handling Incomplete Features using Transformer (SHIFT), a missingness-aware survival model that directly predicts from incomplete genomic inputs without test-time imputation. SHIFT represents each genomic feature separately and uses masked self-attention, along with a feature-availability mask, so that predictions are based only on observed inputs. Further, we introduce variable-rate feature masking during training to improve robustness to heterogeneous missingness patterns. We evaluate the approach on glioblastoma and lung squamous cell carcinoma with external validation across multiple cohorts, including a challenging setting with severe cross-cohort panel mismatch. Across these settings, SHIFT shows strong generalization and compares favorably with standard survival baselines and imputation-based approaches, while using a single model across differing feature sets. We also find that incorporating patients from incomplete cohorts during development can improve performance on external data, suggesting that partially observed cohorts need not be excluded from model building. These results support missingness-aware modeling as a practical strategy for multi-center survival prediction in precision oncology.
[AI-80] Context Graphs for Proactive Enterprise Agents
链接: https://arxiv.org/abs/2607.07721
作者: Avinash Kumar
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Retrieval-Augmented Generation (RAG) and agentic frameworks have advanced enterprise AI considerably, yet agents remain fundamentally reactive: they wait for a human query before acting. This paper argues that genuine enterprise productivity gains require proactive agents: systems that surface relevant, actionable information to workers before they ask. We propose the Context Graph, a live relational data structure that models enterprise entities, their relationships, and state transitions over time. Built on this graph, we define a Delta Detection Engine that continuously monitors state changes, a Proactivity Scorer that ranks candidate insights by urgency, relevance, and persona-fit, and a Surfacing Layer powered by an LLM that delivers ranked notifications with grounded explanations. We formalize each component, derive a unified Proactivity Score function, and provide a complete end-to-end Python implementation using NetworkX and the Anthropic Claude API. Evaluation across three generic enterprise case studies (contract lifecycle management, engineering incident response, and sales pipeline hygiene) demonstrates that context-graph-driven proactivity achieves Precision@5 of 0.83, a false positive rate of 0.11, and reduces mean time to surface from 47 minutes (reactive baseline) to under 30 second.
[AI-81] Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS–ANS Dynamic
链接: https://arxiv.org/abs/2607.07720
作者: Zhoujie Hou,Song Wang,Kexin Lou,Mo Wang,Chen Wei,Quanying Liu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Sleep physiology arises from the coordinated dynamics of the central nervous system (CNS) and autonomic nervous system (ANS), as reflected by multimodal polysomnography signals including EEG, EOG, EMG, ECG, and respiration. However, existing sleep foundation models often fuse heterogeneous biosignals in a topology-agnostic manner, overlooking their physiological organization. We introduce Omni-Sleep, a sleep foundation model that uses the CNS/ANS partition as a physiological prior for topology-constrained representation learning. Omni-Sleep learns structured representations through three objectives: intra-system consistency, which captures shared subsystem-level factors within neural and cardio-respiratory signals; inter-system synchronization, which aligns subsystem trajectories to model brain–body dynamics; and latent-space masked temporal modeling, which captures long-horizon sleep dynamics. Pre-trained on over 100,000 hours of multi-center multimodal PSG data, Omni-Sleep is evaluated on sleep staging and multi-disease classification. Across datasets and modality-ablation settings, Omni-Sleep outperforms strong foundation-model baselines, showing improved label efficiency, cross-dataset generalization, and robustness to missing modalities. These results highlight the value of physiological hierarchy for generalizable sleep representation learning. Code is available at this https URL.
[AI-82] LLT: Local Linear Transformer for PDE Operator Learning
链接: https://arxiv.org/abs/2607.07718
作者: Oded Ovadia,Eli Turkel
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Numerical Analysis (math.NA)
备注:
Abstract:Neural operators have become a common approach for learning PDE solution maps and accelerating numerical simulations. Transformer-based neural operators are of particular interest, since attention can learn long-range dependencies in the computational domain. However, standard attention has two major limitations when applied to PDEs: it scales quadratically with the number of computational nodes, and it lacks an explicit bias toward local interactions. To address these issues, we introduce Local Linear Transformer (LLT) for PDE operator learning. The architecture combines linear global attention with local spatial mixing, and incorporates coordinate and geometry information. We evaluate LLT on several PDE problems, including elasticity, plasticity, airfoil flow, pipe flow, and Darcy flow. The reference data for these problems span finite-element, finite-volume, and finite-difference discretizations on structured and unstructured meshes. Compared with other neural-operator and transformer baselines from prior studies, LLT achieves competitive or lower relative L_2 error across these problems. On matched structured discretizations, wall-clock time per training iteration is reduced by factors of 1.8 to 2.5 relative to Transolver. We also scale the approach and apply it to a three-dimensional car aerodynamics dataset with 32,186 unstructured mesh points per sample. Together, these results indicate that LLT provides an accurate and computationally efficient operator for PDE problems across discretizations, mesh types, and problem settings.
[AI-83] owards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attribution ICML2026
链接: https://arxiv.org/abs/2607.07716
作者: Yazheng Liu,Xi Zhang,Sihong Xie,Hui Xiong
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: ICML 2026 Spotlight
Abstract:Temporal graphs are ubiquitous in real-world applications and Temporal Graph Networks (TGNs) have achieved superior predictive accuracy. Understanding which historical events drive model predictions can enhance trustworthiness of TGNs. Existing explanation methods overlook the memory module, the core component that records and updates node histories, leaving the influence of past events unexplored. To address this, we attribute TGNs predictions through the topology attribution tree and memory backtracking tree. The topology attribution tree captures the influence of neighbors and their memory vectors, then the memory backtracking tree quantifies how historical events shape node memory vectors. We apply the LRP in TGNs, ensuring that the total contribution of events equals the logits of model. Finally, top-k selection may be unfaithful due to the nonlinear mapping from logits to probabilities, we design optimization objectives to identify the important events. Experiments on nine temporal graph datasets, spanning node property prediction, link prediction tasks and graph classification tasks, show that our method provides faithful explanations and outperforms state-of-the-art baselines. The code is available at this https URL
[AI-84] DrugGen 2: A disease-aware language model for enhancing drug discovery
链接: https://arxiv.org/abs/2607.08404
作者: Ali Motahharynia,Mohammadreza Ghaffarzadeh-Esfahani,Mahsa Sheikholeslami,Navid Mazrouei,Matin Irajpour,Yousof Gheisari,Hajar Sirous
类目: Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 15 pages, 2 figures, 1 table, and 4 supplementary files. To use the model, see this https URL
Abstract:Current computational approaches for drug design typically focus on generating molecules conditioned on specific targets or general molecular properties, often neglecting the influence of disease context on target behavior and therapeutic outcomes. To address this gap, we introduce DrugGen-2, a novel generative model that designs small molecules conditioned on both disease ontology and target protein sequences. DrugGen-2 was developed by fine-tuning a pre-trained GPT-2 model on a curated dataset of approved drugs linked to their diseases and targets, using a two-step strategy of supervised fine-tuning followed by reinforcement learning via group relative policy optimization (GRPO). This process was guided by reward functions optimizing for chemical validity, novelty, diversity, and high predicted binding affinity. When evaluated on five protein targets relevant to diabetic nephropathy, DrugGen-2 significantly outperformed baseline models (DrugGPT and DrugGen). It demonstrated a superior capacity to generate unique molecules, exhibited greater structural similarity to approved drugs, and achieved improved predicted binding affinities across all targets. Molecular docking analyses further supported these findings, identifying candidate ligands with strong binding potential, including compounds with predicted affinities (-9.917, -9.485, and -9.367) exceeding those of reference drugs such as enalapril for angiotensin-converting enzyme (-8.283). By integrating disease-specific context into molecular generation, DrugGen-2 advances AI-assisted drug discovery, offering a powerful tool for de novo design and drug repurposing that accounts for the complex interplay between diseases and molecular targets.
[AI-85] On the Role of Conversational Timing in Synthetic Training Data for ASR
链接: https://arxiv.org/abs/2607.08371
作者: Máté Gedeon,Péter Mihajlik
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注:
Abstract:Synthetic multi-speaker conversations are widely used to train conversational automatic speech recognition (ASR) systems, but it remains unclear which timing properties make simulated data most useful. This paper studies conversational timing as a controllable training variable rather than merely as a corpus statistic to be reproduced. We parameterize pause and overlap timing distributions with an exponential-tilting family estimated from multiple conversational corpora, and then explore the resulting four-dimensional parameter space with Latin hypercube sampling and multi-objective Bayesian optimization. Each sampled timing configuration is used to generate simulated training conversations, train an ASR system, and evaluate concatenated-permutation word and character error rates (cpWER and cpCER) on a Hungarian dialogue corpus. The results show that downstream ASR behavior is explained more directly by induced timing statistics than by raw simulator coordinates or corpus proximity. In particular, higher overlap exposure is associated with lower cpWER, whereas longer and more variable gaps are associated with higher cpWER; cpCER follows the same trend, but with weaker statistical support. Bayesian optimization yields modest aggregate improvements, but its main value is analytical: it produces controlled timing interventions that reveal an overlap–gap trade-off in simulated conversational training data. These findings suggest that realistic simulation should be complemented by task-relevant diagnostics of overlap, gap, and timing-variability profiles.
[AI-86] DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classification
链接: https://arxiv.org/abs/2607.08031
作者: Shuang Wang,Chenxu Wang,Hantong Xing,Hanlin Mo,Lirong Han,Licheng Jiao
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI)
备注: 13 pages, 6 figures, 9 tables
Abstract:The dynamics of communication environments induce significant distribution shifts across domains, challenging the generalization of deep learning-based automatic modulation classification (AMC) models. While existing UDA methods alleviate this problem by aligning source and target features, they give limited consideration to modulation-specific structures that remain informative across domain conditions. In this paper, we consider signal prior knowledge, grounded in communication protocols and physical principles, as a potential way to enhance cross-domain representation learning. Given that different priors may vary in modulation discriminability, domain stability, and complementarity, this paper first analyzes five commonly adopted signal representations that instantiate different signal priors. From them, in-phase/quadrature (IQ), amplitude–phase (AP), and autocorrelation function (ACF) are selected as compact prior-guided inputs. Based on that, a dual knowledge and data-driven network (DKDNet) is proposed for cross-domain AMC. The multi-representation feature encoder (MRFE) and dynamic lightweight fusion unit (DLFU) are designed to achieve unified representation learning and adaptive feature fusion, and the resulting fused features are optimized with modulation classification and adversarial domain alignment objectives. Experiments on both simulated and public datasets validate the rationality of the prior selection and demonstrate the superiority of the proposed method.
[AI-87] Reaction-network reasoning with frontier models for experimentally confirmed catalyst-selectivity hypotheses
链接: https://arxiv.org/abs/2607.08003
作者: Sutanay Choudhury,Anwesha Banerjee,Udishnu Sanyal,Jorin Dawidowicz,Chiezugolum Ijeoma Odilinye,Jesun Firoz,Liney Arnadottir,Simone Raugei,Johannes Lercher,Arnab Dutta
类目: Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI)
备注:
Abstract:Catalysts are essential for sustainable chemical manufacturing, yet discovering novel architectures remains a bottleneck dominated by trial-and-error experimentation and computationally intensive screening. In complex reactions such as electrochemical carbon dioxide reduction, product selectivity is governed by dynamic interfacial, electrolyte, and potential factors as well as kinetic pathway competition. Conventional descriptor-based machine learning and computational potentials struggle to resolve these mechanistic branch points, primarily relying on static ground-state descriptors or bulk structural correlations rather than end-to-end topological pathway analysis. Here, we show that frontier language models, when strictly constrained to reason over explicit reaction networks, can discover novel catalysts by identifying the physical levers that govern pathway competition. We developed a human-AI co-thinking framework that enforces network invariance to extract testable hypotheses from complex chemical graphs. Applied to CO2 electroreduction, the framework identified ketene desorption and hydroxide capture as the acetate-forming pathway, and predicted a distinct adsorbed CO and CH2 coupling route to ketene. By isolating actionable control levers, specifically local alkalinity, controlled iron incorporation, and restricted interfacial proton-donor accessibility, the framework guided the prospective synthesis of a copper-iron oxide catalyst demonstrating a threefold increase in acetate selectivity over matched Cu-rich baselines. This mechanism-guided reasoning architecture shifts the computational paradigm from retrospective statistical prediction to forward-looking hypothesis generation, providing a broadly applicable blueprint for mechanism-guided materials discovery.
[AI-88] SpO_2 Predictor-Guided Stage-Wise Time-Frequency Reconstruction of Low-Quality Dual-Wavelength PPG for Oxygen Saturation Estimation
链接: https://arxiv.org/abs/2607.07996
作者: Zequan Liang,Elahe Hosseini,Ning Miao,Mahdi Pirayesh Shirazi Nejad,Wei Shao,Ehsan Kourkchi,Setareh Rafatirad,Houman Homayoun
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Continuous oxygen saturation (SpO _2 ) estimation from wearable photoplethysmography (PPG) is important for long-term health monitoring, but low-quality red and infrared PPG segments can distort waveform morphology and degrade SpO _2 prediction accuracy. Existing PPG denoising and reconstruction methods usually optimize waveform fidelity or heart rate characteristics, while time-domain waveform loss on PPG signals alone insufficiently preserves frequency structure and SpO _2 -relevant information. This paper proposes a SpO _2 predictor-guided stage-wise time-frequency reconstruction framework for low-quality dual-wavelength PPG signals. The proposed method first selects high-quality PPG segments to pretrain a SpO _2 predictor. A masked reconstruction model is then trained to recover randomly masked PPG regions using a joint reconstruction objective that combines time-domain waveform loss with frequency-domain loss computed from the short-time Fourier transform (STFT). To make the reconstruction task physiologically relevant, the pretrained SpO _2 predictor is incorporated as an additional constraint, encouraging the reconstructed PPG to preserve SpO _2 information rather than only minimizing waveform reconstruction error. The SpO _2 predictor and PPG reconstructor model are optimized through four training stages. Experiments on the public OpenOximetry Repository and a private wearable PPG dataset show that the proposed approach achieves the lowest subject-level MAE, with 2.882% on the public dataset and 2.359% on the private dataset.
[AI-89] Multi-agent Autoformalization of Tensor Network Theory
链接: https://arxiv.org/abs/2607.07857
作者: Sirui Lu,Erickson Tjoa,J. Ignacio Cirac
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI)
备注: 5+2+33 pages; 3+3+11 figures; 6 tables; An accompanying blueprint document is available at this https URL
Abstract:We build a team of specialized large language-model agents and present an agent-driven workflow for research-level formalization in theoretical physics, with the autoformalization of the fundamental theorem of matrix-product states as a demonstration. The agents, coordinated through a structured mathematical blueprint and periodic human review, orchestrated and executed the full formalization autonomously. For some statements, the agents were able to explore new proof routes that are not part of the standard literature. Along the way the agents produced extensive tensor-network and quantum-information libraries not previously available in Mathlib, Lean’s mathematical library. As a physical application, the formalization also extends towards symmetry-protected topological phases in one dimension. We find that the main bottleneck in large-scale autoformalization is enforcing mathematical intent and we provide a detailed study of the full process and various subtleties involved. We release the codebase as the library \hrefthis https URLTNLean, together with a \nChapters-chapter \hrefthis https URLblueprint of the formalization effort.
[AI-90] Kime-Representation Formulations of Three Open Problems in the Foundations of Classical Mechanics: Uncertainty Invariant Entropy and Directional Degrees of Freedom
链接: https://arxiv.org/abs/2607.07851
作者: Ivo D. Dinov
类目: Mathematical Physics (math-ph); Artificial Intelligence (cs.AI); Computational Physics (physics.comp-ph)
备注:
Abstract:We give mathematically self-contained formulations, in the complex-time (kime) representation, of three open problems from the foundations of classical mechanics: (I) the extension of the classical entropic uncertainty principle to non-canonical variables and to multiple degrees of freedom; (II) the characterization of coordinate-invariant measures and entropies, i.e., the question of why continuous physical quantities must be paired for an invariant entropy to exist; and (III) the construction of a classical relativistic directional degree of freedom (a classical analogue of a spin-1/2 system). Throughout, the kime phase is interpreted statistically as a latent circular random variable whose law \Phi models the intrinsic trial-to-trial variability of repeated, identically controlled experiments indexed by the kime magnitude. The mathematical bridge is an exact symplectic identification of the kime cone with the action-angle chart of a one-degree-of-freedom phase space, under which the kime measure is the Liouville measure and the phase law becomes the angular conditional of a Liouville density. Specifically, we (i) prove a sharp entropic uncertainty relation on the kime cylinder whose extremal family is von Mises x Gaussian, together with a sharp circular Fisher-information inequality saturated exactly by von Mises laws; (ii) prove an exact non-canonical uncertainty relation in which the correction term is the geometric mean of the Poisson bracket, clarifying the conjectured role of the expected bracket; (iii) prove aggregate multi-degree-of-freedom bounds via the Williamson normal form and Fischer’s inequality, and isolate the per-degree-of-freedom refinement as a precise open problem of symplectic Schur-Horn type; (iv) prove that diffusion of the kime phase produces monotone entropy growth with the equipartitioned (Haar-uniform) phase law.
机器学习
[LG-0] MulTTiPop: A Multitrack Transcription Dataset for Pop Music
链接: https://arxiv.org/abs/2607.08756
作者: Nathan Pruyne,Benjamin Stoler,William Chen,Chien-yu Huang,Shinji Watanabe,Chris Donahue
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注: 8 pages, 4 figures. Associated web preview available at this https URL
Abstract:We present MulTTiPop, a dataset of pop music segments and their associated multitrack MIDI recordings for the evaluation of automatic music transcription models. MulTTiPop contains 572 segments of popular music totaling 3.5 hours of audio, and contains songs from diverse genres and decades from the 1930s to 2000s. To collect this dataset, we perform metadata-based matching on song segments from the Lakh MIDI and TheoryTab datasets, manually identify an anchor beat between the audio and MIDI, then use beat tracking on the audio and warp the MIDI to match its tempo and timing. We evaluate state-of-the-art automatic music transcription models on MulTTiPop and find substantial room for improvement, with the best model achieving 38% Onset F1. More details and sound examples of MulTTiPop are available at this https URL.
[LG-1] Super Weights in LLM s and the Failure of Selective Training
链接: https://arxiv.org/abs/2607.08733
作者: Shreyas Subramanian,Adewale Akinfaderin,Akarsha Sehwag
类目: Machine Learning (cs.LG)
*备注: Accepted at the Conference on Language Modeling (COLM) 2026
Abstract:Recent work identified Super Weights, individual parameters whose removal degrades model performance by orders of magnitude. We show that this degradation due to pruning Super Weights does not universally apply to all LLMs. Furthermore, if these parameters are so important, Super Weight-aware training should be effective. We show the opposite. Training Super Weights in isolation (100 to 8,192 parameters) drops accuracy to random-guessing levels on both OLMo-1B and OLMo-7B, and expanding to local neighborhoods of up to 36K parameters provides no improvement. The failure is specific to Super Weight coordinates: training an equal number of randomly chosen positions in the same down_proj layers instead improves over the baseline, so the collapse comes from targeting Super Weights, not from sparsity itself. Vanilla LoRA, updating every position in attention weight matrices through low-rank structure, succeeds with only 0.16% of parameters, and applying the same low-rank update to down_proj succeeds as well. A 10-seed ablation confirms that constraining LoRA updates at positions corresponding to Super Weight coordinates yields statistically indistinguishable results. These findings establish that parameter importance does not imply parameter trainability in isolation, and that effective fine-tuning relies on structured decompositions over entire layers rather than targeting individually important weights.
[LG-2] Latent Memory Palace: Reasoning for Control as Autoregressive Variational Inference
链接: https://arxiv.org/abs/2607.08724
作者: Chuning Zhu,Eva Xu,Jose Barreiros,Krishnan Srinivasan,Paarth Shah,Abhishek Gupta
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:Human decision-making is highly flexible – some actions are taken immediately; others require longer deliberation. Language models have exhibited a similar capacity for adaptive “reasoning.” However, transferring this capability to continuous control policies has been challenging, as directly reasoning in language space may lack the granularity for spatial understanding and precise motions. In this work, we show that reasoning for control policies can emerge by organizing information in an autoregressive latent space reminiscent of a memory palace, where retrieval is iterative and adaptive. Our method, Latent Memory Palace (LMP), formulates reasoning as variational inference with an autoregressive latent distribution. We derive a latent-space reinforcement learning technique to tractably optimize its variational lower bound. The resulting policy, LMP- \pi , achieves strong empirical performance in simulation and real-world domains while exhibiting interpretable, adaptive allocation of test-time compute. We further show that the same framework yields a variable-length action tokenizer, LMP- \texttttok , which significantly improves the performance of downstream autoregressive policies. Together, these results present a new perspective on latent reasoning for control through the lens of variational inference.
[LG-3] Deep Learning for Joint Narrowband Interference Cancellation and Soft Demodulation in OFDM Systems
链接: https://arxiv.org/abs/2607.08717
作者: Emmanouil Kavvousanos,Francky Catthoor,Vassilis Paliouras
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: 17 pages, 11 figures, 6 tables. Preprint under review
Abstract:Narrowband interference (NBI) severely degrades orthogonal frequency-division multiplexing (OFDM) systems by corrupting subcarriers and rendering classical soft demodulation ineffective. Conventional compressed-sensing (CS) mitigation exhibits high sequential latency and leaves structured, non-Gaussian residuals that cause log-likelihood ratio (LLR) unreliability, decoder saturation, and severe error floors when employing classical Gaussian demappers. We resolve this pipeline mismatch using a unified deep learning framework for joint NBI cancellation and robust soft demodulation. First, NBI-CNet employs a physics-informed convolutional architecture to estimate NBI parameters and remove multi-tone interference in a single forward pass. Without requiring prior knowledge of the active interferer count, NBI-CNet reduces computational complexity by up to 60% ( N=2048, Q=64 ) compared to the state-of-the-art EOMP-IDS algorithm. Second, LLR-CNet acts as a structural whitener by mapping non-Gaussian post-mitigation residuals onto well-calibrated soft metrics. Simulations demonstrate that this joint framework eliminates the error floors inherent to traditional baselines across dense grids. Under severe interference ( \textSIR=-10 dB), the pipeline operates within a 0.2 to 0.5 dB SNR margin of the optimal iterative baseline at a target block error rate (BLER) of 10^-4 . Under mild interference ( \textSIR=10 dB) with heavy spectral overlap ( Q=12 ), where classical greedy algorithms erroneously subtract valid data components and corrupt the payload, NBI-CNet avoids signal-peak confusion to deliver a coding gain exceeding 3 dB. Finally, the architecture circumvents the 2\times10^-4 error floor triggered by interferer-estimation errors, while its scale-invariant design enables robust generalization across arbitrary FFT sizes without retraining.
[LG-4] MPFlow: Learning Budgeted Max-Flow Optimization on the Lightning Network with Deep Graph Reinforcement Learning
链接: https://arxiv.org/abs/2607.08703
作者: Harrison Rush,Vincent Davis,Simone Antonelli,Vikash Singh,Jesse Shrader,Emanuele Rossi
类目: Machine Learning (cs.LG)
*备注:
Abstract:We address liquidity placement in the Bitcoin Lightning Network (LN): given a fixed budget, which channels should a node open to maximize its routing capacity? We cast this as a budget-constrained combinatorial optimization problem on graphs, selecting k edge additions that maximize s – t max-flow, a theory-grounded measure of routing capacity, and solve it with graph reinforcement learning. Our lightweight agent combines a message-passing policy network with proximal policy optimization (PPO) and action masking, and is trained under a hub-exclusion curriculum: the network’s top hubs are removed from training subgraphs, forcing the policy to learn capacity-aware placement rather than hub attachment. In extensive experiments on real Lightning Network snapshots, our method consistently outperforms strong heuristic baselines on the max-flow objective across multiple seeds and unseen graphs. The agent has been deployed in production for peer recommendations, executing 4640 channel-open decisions that cumulatively allocate 267.3 BTC over 16 million across 30 managed nodes.
[LG-5] Resample or Reroute? Budget-Aware Test-Time Model Selection for Large Language Models KR
链接: https://arxiv.org/abs/2607.08665
作者: Teng-Ruei Chen
类目: Machine Learning (cs.LG)
*备注: 10 pages, 3 figures. Code: this http URL . Companion analysis: arXiv:2607.03436
Abstract:Routing among large language models (LLMs) trades response quality against serving cost, motivated by the reported gap between deployed routers and a per-instance oracle. Recent analysis shows that test-time resampling can recover per-instance selection headroom that no single-commit router captures; however, that guarantee holds only under an idealized oracle equipped with correctness labels and an unconstrained budget, neither of which a deployed system has. To the best of our knowledge, no previous work treats resampling the committed model and rerouting to an alternative model as competing uses of a single per-query cost budget. Therefore, this work formulates budget-aware test-time model selection: given a per-query budget and an imperfect verifier, allocate each unit of budget between resampling and rerouting so that expected correctness is maximized. An online resample-or-reroute (RoR) allocation policy driven by estimated marginal correctness per unit cost is proposed, and its behavior is grounded in the recoverability asymmetry between selection and sampling. Replay experiments on newly regenerated multi-draw correctness tensors from an eleven-model open-weight pool over four benchmarks of differing difficulty show that the proposed RoR policy attains a favorable cost-quality Pareto front relative to single-route, one-commit-router, budget-aware best-of-K, cascade, and random-allocation baselines for the tested pools, with the largest gains on the most heterogeneous benchmark; an ablation further shows the gains are verifier-gated, shrinking as verifier quality degrades, and robustness replays under a provider price vector and a label-free agreement verifier delineate where the conclusions carry over.
[LG-6] EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy
链接: https://arxiv.org/abs/2607.08659
作者: Wenxiu Ding,Muzhi Liu,Zheng Yan,Mingjun Wang,Yifan Zhao,Qiao Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Graph Neural Networks (GNNs) have shown considerable success in learning from graph-structured data, but their use in privacy-sensitive areas remains difficult because graph structure can leak sensitive link information. To satisfy edge-level differential privacy, a common approach is to inject noise into all elements of the graph’s adjacency matrix, thereby obfuscating the existence of any single edge. However, stronger privacy requires more noise, and excessive noise reduces utility, making the privacy-utility balance a major barrier to practical privacy-preserving graph learning. To address this issue, we propose EdgeRefine, a local differential privacy framework that improves this trade-off through adaptive edge refinement. EdgeRefine first estimates edge-existence probabilities using Jaccard similarity and ranks edges for noisy edge removal. To ensure the sparsity and reliability of the final graph, it uses the privacy budget \epsilon to determine the ratio of true to false edges, samples them separately based on this probability ranking, and controls the total number of edges with a separate sampling rate k . Extensive experiments show that EdgeRefine achieves accuracy comparable to the noise-free baseline and substantially outperforms other privacy-preserving methods across datasets and GNN architectures. Under privacy budget \epsilon = 2.5 , EdgeRefine improves node classification accuracy over state-of-the-art baselines by 17.8% on ACM under GAT and 19.7% on Cora under GCN. In graph classification, it achieves an average accuracy degradation of around 5% compared to the noise-free baseline. Under graph reconstruction attacks, EdgeRefine maintains relative absolute error levels above 1 across all privacy budgets, averaging 1.962 on Cora and 1.472 on AMAP, indicating strong resilience against privacy leakage. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.08659 [cs.LG] (or arXiv:2607.08659v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.08659 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-7] Secure Decentralized Federated Learning via Gossip and Virtual Voting
链接: https://arxiv.org/abs/2607.08651
作者: Amirhossein Taherpour,Xiaodong Wang
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注:
Abstract:Decentralized federated learning (DFL) removes the central server by letting nodes exchange model updates through peer-to-peer gossip, but existing gossip-based methods often lack provenance finality and resilience to Byzantine or lazy participants. Ledger-assisted federated learning (FL) improves auditability, yet blockchains, shards, or settlement committees can reintroduce global coordination costs that conflict with DFL locality. This paper proposes \emphgspDAG-FL, a secure DFL framework that derives consensus from the same gossip history used to disseminate models. Nodes exchange model payloads only with neighbors, while full nodes collect event certificates and receiver-endorsed accepted gossip proofs, reconstruct a compact Topology directed acyclic graph (DAG), and run Hashgraph-style virtual voting followed by compact full-node certificates. Finality is over unique model-origin tuples, not identical local parameter states. To improve resilience, gspDAG-FL combines payload validation, accepted-proof validation, and private semantic audit before aggregation. We formalize the adversarial setting, prove safety and conditional liveness of the control plane, and give a convergence guarantee for certified perturbed gossip under time-varying effective mixing. Experiments on MNIST classification and Penn Treebank language modeling, using fair held-out validation/audit data and networks up to (N=100), show that gspDAG-FL achieves learning quality close to validation-based ledger FL while reducing coordination bottlenecks, improving throughput, and maintaining high invalid-origin detection under mixed Byzantine and lazy participation.
[LG-8] BiSCo-LLM : Lookup-Free Binary Spherical Coding for Extreme Low-Bit Large Language Model Compression
链接: https://arxiv.org/abs/2607.08643
作者: Yuantian Shao,Peisong Wang,Zhilei Liu,Chuangyi Li,Yuanteng Chen,Pengcheng Xie,Yiwu Yao,Zhihui Wei,Jian Cheng
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language models (LLMs) are increasingly constrained by memory capacity, weight bandwidth, and checkpoint storage during deployment. Existing low-bit compression methods mainly follow two directions. Scalar or group-wise quantization is simple and compatible with efficient low-precision kernels, but its representation capacity becomes limited when the target budget approaches 2 bits per weight. Vector-quantized weight compression provides a richer block-level representation, but usually introduces explicit codebooks, index lookup, and additional storage accounting. This paper presents BiSCo-LLM, a codebook-free binary spherical coding framework for extreme low-bit LLM weight compression. The core pipeline is built on three components. First, local weight chunks are mapped onto a unit hypersphere and binarized into compact spherical codes, so that the main payload is a bit-packed sign stream rather than explicit VQ centroids. Second, a residual BSQ stage encodes the reconstruction error left by the base spherical codec, providing an explicit rate-distortion path without stored codebooks. Third, category-wise recovery distillation is performed after replacing each Transformer module category, reducing the mismatch between local weight reconstruction and assembled model behavior. A small 8-bit protected-channel path is used as an auxiliary stabilization mechanism for sensitive channels and is counted separately from the BSQ payload. The reported storage budget includes binary codes, neural decoders, protected-channel payloads, LoRA adapters, and metadata.
[LG-9] Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence
链接: https://arxiv.org/abs/2607.08641
作者: Yann Claes,Pierre Geurts,Vân Anh Huynh-Thu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Over the last few years, there has been an increased interest in making machine learning models more interpretable. Although a great deal of effort goes into developing techniques for interpreting the interactions learned by a given model, fewer studies focus on assessing the quality of such explanations. Even fewer focus on how to adjust the model to produce explanations faithful to prior knowledge, a process known as explanation-guided learning. Furthermore, most approaches in this area focus on classification problems and usually assume prior knowledge about which input features or regions are most important. In this work, we introduce a new approach to steering neural networks based on partial dependence, such that their average response to certain features aligns with specific functional domain knowledge about the problem. We empirically demonstrate on a range of regression problems, including dynamical systems forecasting, that models whose training has been controlled using our method perform better than unconstrained models and are more data-efficient. Moreover, we highlight that interpretations obtained from the former actually align with the user-provided knowledge, whereas those obtained from the latter do not.
[LG-10] Robust Bayesian Decision Making under Adversarial Uncertainty
链接: https://arxiv.org/abs/2607.08590
作者: Haripriya Harikumar,Sammie Katt,Yasir Zubayr Barlas,Samuel Kaski
类目: Machine Learning (cs.LG)
*备注:
Abstract:Scientific experiments are often designed to maximize information gain, yet in many applications the primary objective is to support reliable downstream decision-making. Existing decision-aware experimental design and active learning methods typically assume well-specified outcome models and implicitly rely on the stability of the optimal decision under real-world perturbations. In practice, however, experimental outcomes are frequently influenced by hidden or weakly modeled effects, which can substantially alter decision optimality and lead to misleading conclusions. We study sequential adversarially robust decision-aware experimental design, where data acquisition has to take into account information gain against plausible worst-case unexpected effects, modeled here as variation in adversarial variables. Building on Bayesian decision theory, we formalize an adversarially robust optimal decision under this setting and derive a principled Bayesian experimental design criterion. The criterion explicitly targets decision stability rather than nominal optimality. Experiments on synthetic and real-world scientific datasets show that conventional decision-aware design can converge rapidly to high confidence yet fragile decisions, while our robustness-aware approach yields decisions that are significantly more stable and reliable under adversarial variation.
[LG-11] Spectral Stability of Pseudoinverse-Based Extreme Learning Machine
链接: https://arxiv.org/abs/2607.08581
作者: Bich Van Nguyen,Ngoc Anh Khong
类目: Machine Learning (cs.LG); Spectral Theory (math.SP)
*备注:
Abstract:Extreme Learning Machine (ELM) computes output weights analytically using the Moore-Penrose pseudoinverse. Although this leads to fast training, its numerical stability depends strongly on the conditioning of the hidden layer matrix. This paper studies pseudoinverse-based ELM from a spectral perspective. We show that the smallest singular value governs perturbation amplification in the output weights, while the condition number provides a quantitative measure of hidden-layer instability. We compare SVD-based pseudoinverse computation with iterative hyperpower methods and discuss width-dependent conditioning through a random feature interpretation. Experiments on synthetic matrices and ELM benchmarks show that SVD-based methods remain the most reliable under ill conditioning, while iterative methods are more sensitive to spectral properties. The results suggest that ELM stability is fundamentally governed by the singular value structure of the hidden layer matrix.
[LG-12] Contravariance Theory: Strong Alignment for Minimal Solutions to Hard Tasks
链接: https://arxiv.org/abs/2607.08561
作者: Dan Yamins,Aran Nayebi
类目: Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
*备注:
Abstract:A series of results from the NeuroAI over the past fifteen years have raised core questions both about how to compare Deep Neural Network (DNN) models to the brain, and about how much convergent evolution to expect between artificial networks and real brain networks. Here, we show that for any two minimal DNN solutions to a sufficiently hard task: (i) “weak” alignment of network representations based on affine mappings guarantees “strong” alignment of privileged axes, and (ii) alignment “zippers” up the network hierarchy, causing the emergence of privileged axes from end-to-end task optimization. These results formalize the notion of contravariance from Cao and Yamins [2024], and illustrate important consequences for the theory of NeuroAI: with sufficiently strong tasks, choice of metric for inter-network comparison is not all that sensitive, and that convergent evolution is probably inevitable.
[LG-13] CAAD: Causality-Aware Multivariate Time Series Anomaly Detection via Multi-Scale Alignment and Structural Causal Consistency KDD2026
链接: https://arxiv.org/abs/2607.08555
作者: Xin Wang,Yunshi Wen,Yanan He,Haotian Xu,Youlan Zhao,Michel Ferreira Cardia Haddad,Tengfei Ma
类目: Machine Learning (cs.LG)
*备注: Accepted at KDD 2026 (Research Track)
Abstract:The operational integrity of complex industrial systems relies on precise anomaly detection and diagnosis. The vast majority of existing methods narrowly focus on capturing temporal similarities of representations, often overlooking the disruption of internal causal relationships, which characterizes system failures and latent anomalies. In this paper, we propose a novel framework (CAAD) that reframes anomaly detection as the continuous verification of Granger causality consistency through exogenous variables. Specifically, the CAAD framework models exogenous time-series variables as residuals, identifying anomalies as significant deviations caused by external interventions. The proposed framework leverages multi-scale alignment to internalize system dynamics and utilizes a gradient-based matrix to monitor internal causal relationship breakdowns. By quantifying causal deviations of both dynamic evolution and relational topology, the CAAD is able to capture subtle causal shifts to achieve precise anomaly detection. Extensive experiments on real-world industrial datasets demonstrate that the CAAD achieves high-precision anomaly detection, outperforming most state-of-the-art baselines. Comments: Accepted at KDD 2026 (Research Track) Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.08555 [cs.LG] (or arXiv:2607.08555v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.08555 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-14] Structural Bottlenecks on Frequency Representation in End-to-End Audio Models
链接: https://arxiv.org/abs/2607.08545
作者: Nicole Cosme-Clifford
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:
Abstract:End-to-end neural audio models achieve high-fidelity compression and generation. We might read that performance as evidence they directly represent interpretable features such as pitch and timbre, but a model can produce plausible outputs without doing so. A model may encode these features in any reachable basis, but regardless of which, the features are well described as compositions of time-frequency-localized primitives. Whether state-of-the-art encoders preserve access to these primitives, and thus to compositions of them, remains unclear. Through theoretical analysis and controlled experiments, we show that several state-of-the-art strided convolutional encoders impose two structural bottlenecks, both predictable from architecture and signal structure, on access to these primitives: (1) they collapse primitives into alias equivalence classes, establishing a bound on representational capacity, and (2) they limit the frequency resolution available to learned filters, restricting separability. For well structured data, we find collapse rates of 31-35% and filter bandwidths 10-35x above the theoretical resolution bound, confirming that both bottlenecks arise under realistic signal conditions. We then introduce Gabor Latent Refactorization (GLRF), a lightweight post-hoc intervention that re-expresses encoder latents in a frequency-localized basis, reducing filter bandwidths from 10-35x to 1.5-3x of the theoretical resolution bound while preserving reconstruction fidelity and improving control over attributes like pitch. These results show that the encoders in question predictably degrade access to frequency-localized primitives, entangling the features that depend on them, and that a lightweight, retraining-free intervention can recover much of that access, improving steerability and interpretability.
[LG-15] Stop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data
链接: https://arxiv.org/abs/2607.08522
作者: Ofir Arviv,Kristjan Greenewald,Yotam Perlitz,Hadar Mulian,Michal Shmueli-Scheuer,Leshem Choshen
类目: Machine Learning (cs.LG)
*备注:
Abstract:The inherent rigidity of fixed-size benchmarks makes them an inefficient tool for model evaluation. Diverse evaluation objectives, including model ranking, model selection and testing throughout development, demand varying levels of statistical power. The mismatch between fixed sample sizes and these diverse needs results in either excessive computational cost or compromised reliability - a critical concern for model evaluation. To overcome these limitations, we call for adoption of sequential testing in our field. We provide an adaptive evaluation framework, that provides a principled way to navigate the trade-off between efficiency and reliability in model evaluation. Our framework combines the established statistical paradigm of sequential testing with stopping criteria tailored to common evaluation needs such as diminishing returns detection, and minimum detectable effect size. We demonstrate its ability to adaptively manage the efficiency-reliability trade-off on the Open VLM Leaderboard, including, for example, a 80% reduction in computational cost compared to fixed-size evaluation (with a 2.5-point CI width allowance) while maintaining statistical significance.
[LG-16] Frequency-Domain Multi-Modality Transportation Modeling KDD2026
链接: https://arxiv.org/abs/2607.08475
作者: Jiewen Deng,Hangchen Liu,Junchen Li,Boyuan Zhang,Renhe Jiang
类目: Machine Learning (cs.LG)
*备注: Accepted by KDD 2026 Research Track
Abstract:Multi-modality transportation refers to urban systems composed of multiple transportation modes, such as traffic flow and public transit, whose dynamics are coupled by shared temporal patterns. Accurate multi-modality transportation forecasting remains challenging because (1) different modalities exhibit distinct spectral characteristics and (2) interact unevenly across frequencies, whereas most existing methods operate primarily in the time domain or rely on coarse feature fusion. To address these limitations, we propose a lightweight yet effective Frequency-Domain Multi-Modality modeling (FreMo) that explicitly exploits the frequency domain to enable adaptive and selective cross-modality synergy. FreMo disentangles modality-wise spectral refinement from cross-modality synergy and supports plug-and-play integration with general time series backbones. Specifically, FreMo introduces a Modality-Wise Frequency Filter (MFF) to adaptively refine spectral components within each modality, emphasizing informative frequencies while suppressing noise. FreMo further incorporates a Frequency-Guided Synergy Integrator (FSI) that selectively aggregates information across modalities based on their relative contribution at each frequency, facilitating effective cross-modality knowledge sharing while mitigating negative transfer. Extensive experiments on real-world datasets show that FreMo consistently outperforms state-of-the-art baselines, with superior performance and generalization across diverse forecasting scenarios. The code is available at this https URL.
[LG-17] MatBind: A Shared Embedding Space for Multimodal Materials Characterization
链接: https://arxiv.org/abs/2607.08470
作者: Le Yang(1),Anoop K. Chandran(2),Jona Östreicher(3),Evgenii Sovetkin(2),Adrian Mirza(4 and 6),Sebastien Bompas(1),Bashir Kazimi(1),Pascal Friederich(3),Stefan Kesselheim(2 and 7),Kevin Maik Jablonka(6, 8 and 9),Stefan Sandfeld(1 and 5) ((1) Institute for Advanced Simulations (IAS-9), Forschungszentrum Jülich,(2) Jülich Supercomputing Centre, Forschungszentrum Jülich,(3) Institute of Nanotechnology, Karlsruhe Institute of Technology,(4) Helmholtz-Zentrum Berlin für Materialien und Energie,(5) Faculty 5 - Georesources and Materials Engineering, RWTH Aachen University,(6) Helmholtz Institute for Polymers in Energy Applications Jena, (7) 1. Physikalisches Institut, University of Cologne, (8) Laboratory of Organic and Macromolecular Chemistry, Friedrich Schiller University Jena,(9) Center for Energy and Environmental Chemistry Jena, Friedrich Schiller University Jena)
类目: Machine Learning (cs.LG)
*备注: 24 pages, 12 figures, submitted to npj computational material
Abstract:Fully characterizing a crystalline material requires integrating heterogeneous data sources – atomic structures, diffraction patterns, electronic density of states, and natural language – each of which captures a different facet of the same physical object. In practice, however, these modalities are stored and analyzed in isolation, making it difficult to relate or query materials across representational boundaries. We present MatBind, a contrastive learning framework that aligns four materials modalities – crystal structure, powder X-ray diffraction (pXRD) simulated from structures, density of states (DOS), and text – into a unified embedding space using crystal structure as the central physical anchor. The framework induces alignment between modalities never explicitly paired during training, enabling emergent zero-shot cross-modal retrieval as a direct consequence of the shared representation. The learned embedding space organizes materials according to physically meaningful properties without explicit supervision, and retrieval performance improves systematically when modalities are combined at query time. These results demonstrate that treating heterogeneous materials data as complementary projections of a single physical reality, rather than as isolated data sources, is not a practical choice but is consistent with the underlying physics.
[LG-18] FPGN: Redefining Ultra-Fast Programmable Gate-based Neural Acceleration with Differentiable LUTs
链接: https://arxiv.org/abs/2607.08427
作者: Jiawei Liang,Haotong Qin,Linfeng Du,Xingyu Liu,Shangkun Li,Hui Yu,Michele Magno,Xinyu Chen,Jiang Xu,Wei Zhang
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG)
*备注:
Abstract:Achieving nanosecond-scale inference latency for deep neural networks (DNNs) has become a primary architectural concern for latency-critical applications. While Field-Programmable Gate Arrays (FPGAs) offer a promising substrate for low-latency inference, conventional FPGA accelerators remain arithmetic-centric, using LUTs primarily as building blocks for numerical operators and peripheral logic. In contrast, recent LUT-native neural networks treat LUTs as learnable neurons, revealing promising theoretical potential to exploit their intrinsic logic expressivity. However, existing methods are largely confined to algorithmic optimizations, failing to translate this theoretical potential into high-performance FPGA accelerators. Specifically, their differentiable formulations do not faithfully match FPGA LUT primitives, their physically-unaware topologies compromise routability and timing closure, and their lack of automated optimization flow hinders systematic design space exploration (DSE) and efficient hardware implementation. In this paper, we propose FPGN, an end-to-end physically-aware framework that closes the gap between LUT-native learning and latency-optimized FPGA implementation. FPGN addresses these challenges through (i) a hardware-aligned differentiable formulation for training FPGA-native LUT neurons, (ii) a structured LUT-native topology with a streaming hardware architecture to improve routing locality and timing closure, and (iii) a latency-driven compiler that leverages high-fidelity analytical Quality of Results models to automate DSE and hardware generation. Experiments show that FPGN achieves up to 205 \times latency reduction compared to representative FPGA-based BNN accelerators and up to 30 \times higher LUT efficiency than prior differentiable LUT-native networks, while maintaining competitive inference accuracy. Subjects: Hardware Architecture (cs.AR); Machine Learning (cs.LG) Cite as: arXiv:2607.08427 [cs.AR] (or arXiv:2607.08427v1 [cs.AR] for this version) https://doi.org/10.48550/arXiv.2607.08427 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-19] Beyond Backpropagation: Monte Carlo Method Can Train Deep Neural Networks
链接: https://arxiv.org/abs/2607.08406
作者: Hong Zhao
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 22 pages, 5 figures
Abstract:Backpropagation (BP) dominates deep learning training, but its reliance on gradients brings inherent troubles – vanishing and exploding gradients. The pursuit of gradient-free methods has long been a goal in the field of artificial intelligence. This paper shows that indeed the simplest Monte Carlo algorithm implemented on a single GPU – randomly mutate a parameter, keep it if the loss decreases, otherwise retry – can practically train deep networks. This gradient-free method does not even need common techniques such as batch normalization or residual connections to directly train sufficiently deep networks. More remarkably, its flexibility extends to several nontrivial scenarios: it enables pure pruning training, supports discrete weights, accommodates unconventional transfer functions such as Gaussian, and reveals the substantial redundancy of deep networks. We have demonstrated its feasibility on deep networks with more than 20 layers, single-hidden-layer wide networks with up to 16,384 hidden neurons, and even a simple Transformer architecture trained on both image classification (MNIST) and character-level language modeling (Tiny Shakespeare). This simple gradient-free method may offer a complementary perspective for understanding the self-organization and learning mechanisms of neural networks, and also provides an alternative route for building physically inspired deep learning systems.
[LG-20] On Exploring Input Resolution Scaling For Anytime LiDAR Object Detection
链接: https://arxiv.org/abs/2607.08391
作者: Ahmet Soyyigit,Shuochao Yao,Heechul Yun
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Making tradeoffs between execution latency and result utility (i.e., anytime computing) for adapting to dynamic operational requirements has been shown to enhance the performance of cyber-physical systems. In this work, we focus on enabling anytime computing for deep neural networks (DNNs) that process LiDAR point clouds for 3D object detection. We propose a novel method that enables multi-resolution inference for models that process point clouds as pillars or voxels, allowing the input to be dynamically scaled and processed at the resolution needed to meet timing requirements. Importantly, our memory-efficient approach requires the deployment of only a single DNN model, avoiding the need to deploy multiple models, each trained for a different input resolution. We also introduce a deadline-aware scheduler that selects the highest possible resolution for any given input by accurately predicting the execution time for all possible resolutions at runtime, which is challenging due to the irregularity of LiDAR point clouds. Experimental results on the nuScenes autonomous driving dataset demonstrate that our method significantly outperforms existing anytime computing approaches for LiDAR object detection. Finally, we deploy our approach in a simulated autonomous driving system, where it consistently enables collision-free navigation while avoiding unnecessary stalls caused by environmental complexity.
[LG-21] Dynamics of Gradient Descent with Large Step Size Near a Manifold of Flat Minima
链接: https://arxiv.org/abs/2607.08380
作者: Lachlan Ewen MacDonald,René Vidal
类目: Machine Learning (cs.LG); Dynamical Systems (math.DS); Optimization and Control (math.OC)
*备注: 56 pages, comments welcome
Abstract:An important quantity in the theory of gradient descent (GD) is the \emphsharpness, defined as the largest eigenvalue of the objective Hessian. Classical analyses typically require the step size to be uniformly smaller than twice the reciprocal of the sharpness, but this condition is frequently violated in the training of deep neural networks. Recent work bridges this gap in the setting of overparametrised least-squares with a \emphsingle scalar output, providing a normal form for large-step GD in a neighbourhood of an \emphisolated flat minimum and establishing three corresponding convergence results. In this paper, we extend this theory in two directions: (1) to overparametrised least-squares with \emphvector-valued outputs (including regression with arbitrarily many observations), and (2) to a neighbourhood of a \emphmanifold of flat minima (which we show is essential for applications such as matrix factorisation). We generalise both the normal form and all three convergence theorems of \citemacdonaldeos to this broader setting, overcoming several technical challenges, including the solution of a singular partial differential equation via a novel method that may be of independent interest. We further show that our framework applies to deep matrix factorisation under mild assumptions, yielding several new structural results. In particular, we prove that the set of flat minima forms a fibre bundle over a product of spheres, and that the sharpness is Morse-Bott along this manifold.
[LG-22] Eigenvalue Calibration for Semantic Embeddings of Large Language Models
链接: https://arxiv.org/abs/2607.08377
作者: Sebastian G. Gruber,Nassim Walha,Francis Bach,Florian Buettner
类目: Machine Learning (cs.LG)
*备注:
Abstract:Uncertainty quantification is central to the reliable deployment of large language models (LLMs), and eigenvalues of semantic embeddings have recently emerged as a key tool in state-of-the-art methods. However, conventional calibration results developed for classification probabilities cannot be directly transferred to eigenvalues. We address this gap by proposing a novel framework for calibrating the eigenvalues of semantic embeddings. We interpret LLMs combined with semantic embeddings of their generated answers as density matrix predictors, and we propose a novel approach to calibrate density matrix predictors by applying temperature scaling to their eigenvalues. We establish entropy-risk equivalence under calibration, derive a central calibration inequality specific to eigenvalues, and prove that temperature-scaled eigenvalues optimize calibration when minimizing proper score risks. Experiments on a variety of real-world settings show that current LLMs are systematically overconfident, and validate our theoretical findings. Together, these results advance the foundations and practice of uncertainty quantification for semantic embeddings.
[LG-23] Certified Interventional Fidelity: Anytime-Valid Adaptive Evaluation of Causal Claims in Mechanistic Interpretability UAI2026
链接: https://arxiv.org/abs/2607.08349
作者: Amir Asiaee
类目: Machine Learning (cs.LG)
*备注: Accepted at UAI 2026 (Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence). Code: this https URL
Abstract:Mechanistic interpretability often evaluates explanations by intervening on a model: swapping hidden states, patching activations, ablating components, or comparing a compressed model to the original one. These experiments are usually summarized by a point estimate, even though the evaluation may be monitored while it runs or adapted toward suspected failures. This makes it hard to tell whether a reported fidelity or patching effect is a stable causal claim or a consequence of finite sampling and evaluation choices. We introduce Certified Interventional Fidelity (CIF), a statistical layer for interventional interpretability evaluations. CIF first writes the quantity being reported as a causal estimand: an expectation of a bounded score over a stated input distribution and a stated intervention distribution. It then provides confidence intervals and anytime-valid confidence sequences for this estimand, including under adaptive intervention sampling via bounded mixture importance weighting. We instantiate CIF with Hoeffding-style sequences and variance-adaptive betting sequences, the latter reducing certification cost by 10-30x in our experiments. On MNIST abstractions and GPT-2 Small IOI circuits, CIF certifies high-fidelity claims, shows when apparent method differences are not statistically supported, and makes sensitivity to the intervention distribution explicit.
[LG-24] AutoAnchor: Stable Diffusion Unlearning Using Cross-Attention as a Manifold Surrogate
链接: https://arxiv.org/abs/2607.08337
作者: Siyuan Wen,Jiahao Zeng,Ningning Ding
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Diffusion unlearning is essential for mitigating the generation of harmful or copyrighted content in text-to-image models. Current diffusion unlearning techniques determine the model update direction by either using alternatives of the target concept as an anchor or using empty prompts. The anchor-based method relies on manually and semantically-chosen anchors that risk biased unlearning, while the anchor-free method inherently suffers from unrobust unlearning due to unconstrained latent updates. In this work, we theoretically formalize such unstable diffusion unlearning issues under the manifold hypothesis and prove that lacking a manifold-proximal anchor inevitably induces significant normal-space drift that degrades unlearning performance. To achieve stable unlearning, we propose \mysysn, a two-stage framework that automatically synthesizes manifold-proximal anchors. However, direct geometric manifold optimization is computationally intractable. To address this challenge, \mysys introduces a novel cross-attention consistency loss which serves as a highly efficient surrogate of manifold proximity. Experimental results demonstrate that \mysys effectively achieves robust and unbiased unlearning across various state-of-the-art baselines, significantly improving targeted concept removal (by up to 31.04% in CLIP score) and non-target utility (by up to 4.18% in CLIP score). Moreover, \mysys can also be easily integrated into existing diffusion unlearning methods to enhance their unlearning performance (by 6.30% for concept removal and 6.65% for utility on average).
[LG-25] Write-Protected Discrete Bottlenecks for Language-Grounded World Models: A Structural Limitation and Sufficient Fix
链接: https://arxiv.org/abs/2607.08312
作者: Jiayi Fang
类目: Machine Learning (cs.LG)
*备注: 20 pages, 7 figures. Preprint
Abstract:How should language interface with a world model’s discrete symbol system? The dominant paradigm – end-to-end injection of LLM/VLM features into robot world models (RT-2, Octo, PaLM-E) – implicitly assumes that language gradients can directly shape physical symbol representations. We ask whether this assumption is safe, find that it is not, and characterize the minimal architectural constraint that prevents the failure. Any language gradient entering a Gumbel-softmax-based discrete symbol bottleneck forces a structural trade-off: the vanilla estimator collapses to 2.2/64 symbols (4/5 seeds), while five anti-collapse strategies maintain diversity but fail to learn semantic labels (all = 9.2% accuracy). No tested GumbelBottleneck variant achieves both objectives simultaneously. Within this family of discrete bottlenecks, the failure is structural rather than a matter of optimization. We characterize a sufficient set of three constraints that prevent the failure: (1) cut the gradient chain (this http URL()), preventing language signals from reaching the symbol bottleneck; (2) provide a gradient-free semantic channel – a non-parametric Memory Table (Dict[symbol - Counter[label]], zero parameters, zero gradients) where co-occurrence counting replaces gradient-based binding; (3) handle symbol collisions via DP-Means streaming clustering for automatic sub-cluster splitting. All three layers together achieve 97.2% grounding accuracy vs. 22.2% without Layer 3. Across two experiments spanning 74 independent runs, we demonstrate zero symbol collapse in all 32 seeds, with the blackboard achieving 79-100% semantic binding across three encoder architectures (CNN, V-JEPA 300M, CLIP ViT-L), two environments, and three texture conditions. The fix trains fewer than 2M parameters and requires no LLM fine-tuning.
[LG-26] Learning mathsfAC0 under Locally Sampleable Graphical Models
链接: https://arxiv.org/abs/2607.08303
作者: Weiming Feng,Xiongxin Yang,Yixiao Yu,Yiyao Zhang
类目: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS)
*备注:
Abstract:The problem of learning constant-depth circuits holds profound implications for computational learning theory. In a seminal result, by introducing the low-degree algorithm, Linial, Mansour, and Nisan (J. ACM 1993) presented a quasipolynomial-time learner for \mathsfAC^0 under the uniform distribution. However, obtaining comparable learning guarantees for broader classes of correlated distributions has remained a longstanding challenge. Recently, Chandrasekaran, Gaitonde, Moitra, and Vasilyan (arXiv 2026) extended these guarantees to Gibbs distributions on bounded-degree graphical models with both strong spatial mixing and polynomial growth. In this paper, we give a quasipolynomial-time learner for \mathsfAC^0 under graphical models that admit efficient local samplers, circumventing the polynomial-growth requirement in prior work. The key ingredient is a new low-degree approximation for Gibbs distributions, established by simulating and suitably truncating the classical Glauber dynamics. As applications, this framework yields learners for two-spin systems, including the hard-core model and Ising model, on arbitrary bounded-degree graphs, in regimes approaching their respective sampling thresholds. Subjects: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS) Cite as: arXiv:2607.08303 [cs.LG] (or arXiv:2607.08303v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.08303 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-27] Classifier Chain-based Pathological Test Recommendation
链接: https://arxiv.org/abs/2607.08299
作者: Abu Rafe Md Jamil,Nayan Malakar
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate and timely diagnoses are essential for quality patient care. However, delayed recommendation of diagnostic tests and physicians’ subjective interpretations can hinder effective care. This study introduces a pathological test recommendation system that speeds up the test selection process using patient symptoms before physician consultation. The recommendation task is framed as a multi-label classification problem utilising the Classifier Chain (CC) technique to consider dependencies between tests. We collected data from the this http URL pathology and then created a custom dataset with the help of the expertise. Multiple machine learning algorithms, including Logistic Regression, Decision Tree, and Random Forest, were applied to compare models and identify the best fit for our study context. The Logistic Regression with CC model had the highest overall accuracy at 98.83%, while the Majority Voting ensemble model provided the best balance with a precision of 0.93, recall of 0.85, and F1-score of 0.89. To ensure transparency of the models and clinical interpretability, we used Explainable AI (XAI) techniques utilising SHAP (SHapley Additive Explanations), which identifies how each symptom is contributing to a test recommendation. The diagnostic reasoning revealed by the model was consistent with established medical knowledge of symptoms for the recommended tests, which further adds confidence to the model’s reliability for diagnostic purposes. The reasoning could help physicians make logical decisions in critical scenarios. Overall, our findings suggest that CC can improve the efficiency of the traditional algorithms in diagnostic process providing accurate test recommendations.
[LG-28] CASL-VAE: Learning Structured Latent Variables from Unpaired Data for Semi-supervised Clustering and Paired Sample Generation
链接: https://arxiv.org/abs/2607.08254
作者: Sai Spandana Chintapalli,Pratik Chaudhari,Christos Davatzikos
类目: Machine Learning (cs.LG)
*备注:
Abstract:Quantifying variability in a target population relative to a reference population is central to many scientific and clinical problems (e.g., diseased vs. healthy). Yet, without paired data and in the presence of heterogeneous target variation, existing methods struggle to separate multiple modes of target-specific variation. We propose \textitCASL-VAE, a deep contrastive latent variable model that learns structured latent generative factors from unpaired data. CASL-VAE factorizes variation into continuous common latent factors shared across populations and hierarchical salient latent factors that model target-specific heterogeneity as discrete subtypes and continuous within-subtype variation. Using variational inference, we show how approximate joint likelihood optimization over reference and target domains can be performed using unpaired data, providing a principled basis for paired-sample generation and cross-domain analysis. We validate CASL-VAE on semi-synthetic neuroimaging data, demonstrating improved subtype recovery and paired-sample generation compared to baseline clustering and generative models. We also validate its ability to reveal biologically plausible heterogeneity in Alzheimer’s disease.
[LG-29] An interpretable Good–Turing restart criterion for k-means
链接: https://arxiv.org/abs/2607.08243
作者: Renato Cordeiro de Amorim
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:The k-means++ algorithm is commonly restarted multiple times to avoid poor local optima, yet the number of restarts is almost always chosen arbitrarily and applied uniformly regardless of data set difficulty. This undermines any comparison relying on such a choice and wastes computation on easy data sets while potentially under-serving hard ones. We introduce GTRC, a restart criterion combining a Good-Turing estimate, a proven unconditional bound, and a confidence-based bound on the probability that a further restart would improve on the current result, stopping once this probability falls below a user-specified tolerance \varepsilon . Across 36 data sets, GTRC reached clustering quality competitive with well-chosen fixed restart counts, while the number of restarts used varied considerably and appropriately with data set difficulty, governed by an interpretable, data-dependent signal rather than a fixed rule. GTRC offers a principled and reportable alternative to fixing the number of k -means++ restarts in advance. Software:this https URL.
[LG-30] Structure Learning on Clustered Data
链接: https://arxiv.org/abs/2607.08238
作者: Ryan Thompson,Matt P. Wand,Veerabhadran Baladandayuthapani
类目: Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML)
*备注:
Abstract:Recent algorithmic advances have made directed acyclic graph (DAG) structure learning scalable for causal discovery. Yet, the currently available techniques assume a completely homogeneous population, precluding their application to clustered data where cluster-specific variations (e.g., patient-specific effects) are common. We address this issue by introducing a new approach that estimates a global structure while accounting for local cluster-level effects. The key idea is to extend the fixed- and random-effects framework of classical mixed models to the structure learning setting. Towards this end, we present a differentiable graph coupling mechanism that guarantees the union of the fixed- and random-effects graphs remains acyclic. Computationally, we provide a provably convergent first-order method and leverage efficient batched updates across clusters. Statistically, we establish identifiability of the model and show that our approach recovers the true structure asymptotically. In experiments on real and synthetic data, our proposal detects dependencies missed by alternative estimators, underscoring its value for structure learning in clustered settings.
[LG-31] PIT-SUN: A Deployable Empirical Marginal Transform Framework with Expectation-Consistent Recovery for Regression in Recommender Systems
链接: https://arxiv.org/abs/2607.08202
作者: Mingyu Zhao,Zhaohan Li,Zhenxiong Miao,Xu Zhang,Dewei Leng,Yanan Niu,Kun Gai
类目: Machine Learning (cs.LG)
*备注:
Abstract:Estimating original-space conditional expectations is central to value-driven recommender systems, including dwell time, GMV, and LTV forecasting. Standard MSE is expectation-consistent in principle, but its gradients become unstable on heavy-tailed, zero-inflated, and multimodal targets, causing mean collapse and tail shrinkage. Target transformation alleviates this scale conflict, yet any useful nonlinear marginal transform loses expectation consistency under direct inversion. This is not an implementation oversight: a direct inverse-transform estimator is universally expectation-consistent only when the inverse transform is affine, which cannot simultaneously provide bounded tail compression. Existing conditionally linear recovery methods restore expectation consistency, but still leave open which coordinate, inverse lookup, recovery base, and deployment monitor should be selected for sparse complex marginals. We propose \textbfProbability-\textbfIntegral-\textbfTranSformed \textbfUnbiased recovery (\textbfPIT-SUN), a deployable empirical marginal recovery framework. PIT-SUN uses one empirical marginal table to define a bounded normal-score coordinate, its inverse-quantile lookup, a variance-controlled recovery base, and drift monitoring, then applies multiplicative SUN recovery to estimate the original-space expectation instead of directly inverting transformed predictions. Experiments on synthetic distributions, public benchmarks, large-scale industrial datasets, and online deployment show robust improvements in point accuracy, calibration, and ranking quality with lightweight deployment overhead.
[LG-32] MLQENABLER: Enabling Secure Machine Learning Queries over Encrypted Database in Cloud Computing
链接: https://arxiv.org/abs/2607.08197
作者: Xu Zhou,Haoyang Chen,Xinyu Lei
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:In cloud computing, the public cloud service providers (CSPs) can provide cloud storage as the primary service while providing additional machine learning (ML)-based services by using the clients’ data in storage. This business model extends the border of cloud computing services and brings in new business growth possibilities. Although it is promising, the model also brings in security concerns since the public commercial cloud cannot be fully trusted. For example, the public commercial clouds may sell clients’ sensitive data to the government or other companies. To address the security concerns, an immediate solution is to require clients to encrypt their datasets before outsourcing to the cloud. However, if a database is formally encrypted, then the database contains only pseudorandom numbers, making it impossible to enable ML over it. In this project, we propose MLQENABLER (ML Queries Enabler) scheme to enable secure ML queries over encrypted database in cloud storage. MLQENABLER employs an index-aid approach to achieve security and ML capability simultaneously. Our initial experiments show that MLQENABLER achieves an acceptable security level while incurring only a slight ML performance degradation.
[LG-33] Understanding Layer Patching in Model Size Interpolation
链接: https://arxiv.org/abs/2607.08170
作者: Sara Kangaslahti,Jonathan Geuter,Nihal V. Nayak,Marco Fumero,Francesco Locatello,David Alvarez-Melis
类目: Machine Learning (cs.LG)
*备注: 10 pages, 5 figures
Abstract:Zero-shot model size interpolation aims to create new models of intermediate target sizes by combining existing models without additional training. Recent work on boomerang distillation [Kangaslahti et al., 2026] shows that a student language model distilled from a larger teacher can be expanded by iteratively patching its layers, replacing student layers with contiguous blocks of teacher layers to obtain models whose size and performance interpolate between the student and the teacher. In this work, we provide the first systematic study of student-layer selection for model size interpolation. We cast finding the optimal layer subset for each model size as an optimization problem and prove it can be viewed as a shortest-path problem in a certain acyclic graph. In experiments, we show that patching strongly shapes interpolation behavior, with effects that vary substantially across model families. We find that simple sequential strategies–patching either from the first layer to the last or from the last to the first–often achieve surprisingly strong performance in practice. We further introduce KLPatch, a greedy patching algorithm based on KL divergence, which often improves over last-to-first patching and approximately solves the optimization problem. Together, our results provide a principled understanding of how layer patching affects model size interpolation and offer practical guidance for constructing near-optimal interpolated models.
[LG-34] MuScriptor: An Open Model for Multi-Instrument Music Transcription
链接: https://arxiv.org/abs/2607.08168
作者: Simon Rouard,Michael Krause,Axel Roebel,Carl-Johann Simon-Gabriel,Alexandre Défossez
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:
Abstract:Existing methods for automatic music transcription are often limited to single-instrument recordings or fail on complex, real music mixes. Although previous work utilizes synthetic training data, the resulting models generalize poorly, leading to largely unusable transcription output in realistic, multi-instrument settings. In this work, we analyze the effectiveness of synthetic data for pre-training while combining it with fine-tuning on real music audio and post-training using reinforcement learning. We further introduce conditioning on instrument presence to customize transcriptions. Finally, we release MuScriptor, an open-weight multi-instrument music transcription model that works on real-world music recordings from across a diverse range of musical genres.
[LG-35] DeepPySR – A Symbolic Regression Framework with Dynamic Pruning Pareto Selection and Hierarchical Composition for Real-World Scientific Discovery
链接: https://arxiv.org/abs/2607.08150
作者: Fuling Chen,Kevin Vinsen,Phillip Melton,Rae-Chi Huang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Symbolic regression (SR) discovers analytical equations from data, yielding glass-box models with directly interpretable formulas, unlike black-box methods that rely on unstable post-hoc tools such as SHAP or LIME. This transparency is crucial in clinical medicine and social science, but SR faces three challenges: high-dimensional inputs, principled selection of Pareto-front formulae, and data irregularities such as multicollinearity and class imbalance. We introduce DeepPySR, which addresses these issues with a dynamic variable-pruning schedule to remove irrelevant features during search, an exponential Pareto selection criterion that eliminates trade-offs between accuracy and complexity, and a multi-layer architecture for hierarchical symbolic composition. On four Feynman physics benchmarks and seven biomedical and social-science datasets, DeepPySR outperforms PySR and baselines on body fat (R ^2 : 0.794 vs.\ 0.702), heart disease (F1: 0.898 vs.\ 0.787), student performance (R ^2 : 0.964 vs.\ 0.948), and Raine BMI (R ^2 : 0.525 vs.\ 0.370), producing interpretable formulas aligned with domain risk factors.
[LG-36] Generalization Theory for Through-the-Wall Radar Human Activity Recognition
链接: https://arxiv.org/abs/2607.08144
作者: Weicheng Gao
类目: Information Theory (cs.IT); Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: 38 pages, 10 figures, 10 tables
Abstract:Through-the-wall radar (TWR) human activity recognition (HAR) is important for non-line-of-sight indoor sensing, security monitoring, and emergency rescue. However, structured distribution shifts caused by person variation, observation-view variation, and wall-condition variation severely degrade recognition generalization, while the origin of the target-domain error still lacks a rigorous theoretical explanation. To address this issue, a generalization-analysis framework for TWR HAR is proposed in this paper. First, models for indoor human kinematics, TWR echo generation, radar image formation, feature representation, and bounded-weight neural networks are established within a unified source-to-target learning formulation. Then, the source risk, target risk, empirical risk, and admissible physical domain descriptor are defined, and a unified target-domain generalization bound is derived. Next, the structured shift term is decomposed into cross-person, cross-view, and cross-wall components, and the bound-tightening effects of physical low-dimensional representations, multi-source training, and parameter-space coverage are analyzed. Simulated and measured experiments jointly support the resulting theoretical analysis and illustrate its application value.
[LG-37] Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning
链接: https://arxiv.org/abs/2607.08137
作者: Zifan Zhang,Minghong Fang,Dianwei Chen,Zhuqing Liu,Prashant Khanduri,Xianfeng Yang,Anupam Das,Yuchen Liu
类目: Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注: Submitted to IEEE for potential publication
Abstract:Federated reinforcement learning (FRL) is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby enhancing privacy and scalability in the decision-making process within dynamic vehicular environments. However, poisoning attacks pose a significant threat to the security and reliability of FRL-based systems, particularly in safety-critical autonomous driving, where this vulnerability remains largely unexplored. These attacks can compromise the global control model by subtly injecting malicious system parameters, leading to potential hazards. To counter these challenges, we present \alg (\underlineSecure \underlineAggregation with \underlinepoisoning-\underlineprevention and historical reinforcement) as a defensive framework aimed at enhancing the robustness of FRL systems designed for safety-critical driving scenarios. \alg strategically integrates digital twins for rehearsal-based learning and leverages historical aggregated model parameters along with a selected central gradient to ensure that only benign data is aggregated, effectively mitigating the influence of malicious agents. Theoretical guarantees are provided for the convergence performance of \alg in the presence of poisoning attacks. We also validate the effectiveness of \alg using developed digital twins that model realistic highway environments to evaluate the control of autonomous vehicles under adversarial conditions.
[LG-38] HE: Test-Time Harness Evolution
链接: https://arxiv.org/abs/2607.08124
作者: Jun Nie,Yonggang Zhang,Jun Song,Qianshu Cai,Dahai Yu,Yike Guo,Xinmei Tian,Bo Han
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注: 15 pages, 5 figures
Abstract:The behavior of an LLM agent is determined not only by the underlying model, but also by its harness: the executable program that constructs context, invokes tools, verifies intermediate results, and recovers from failures. Existing approaches optimize such harnesses before deployment, searching training or development data for a fixed agent workflow that is then frozen at test time. This limits adaptation when the test distribution, failure modes, or tool interactions differ from those seen during development. We ask whether the harness can instead be optimized during evaluation itself, using only the unlabeled execution traces the agent produces on the test inputs. We introduce Test-Time Harness Evolution (TTHE), which treats the executable harness as the state of test-time adaptation. During evaluation, TTHE maintains a population of candidate harnesses and refines them through an agentic proposer that reasons over their execution traces, without gold labels or task-specific supervision; a judge then commits an improved harness from execution-derived proxy signals, and the selected program persists to govern subsequent inputs. Crucially, TTHE does not update model weights, require gold labels, or train a separate adaptation model: solver, proposers, and judge are different roles and harnesses around the same frozen LLM, so all adaptation occurs through changes to the surrounding program. Across text-to-SQL, competitive programming, software engineering, data-science coding, and agentic tool-use tasks, TTHE improves fixed ReAct-style baseline harnesses, yielding persistent, inspectable improvements rather than a pre-searched workflow or per-query retries. These results recast test-time adaptation for LLM agents as evolution over executable control programs and identify execution-derived proxy reliability as a central challenge for robust unsupervised agent improvement.
[LG-39] Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration UAI2026
链接: https://arxiv.org/abs/2607.08122
作者: Amir Asiaee,Kaveh Aryan
类目: Machine Learning (cs.LG)
*备注: Accepted at the 42nd Conference on Uncertainty in Artificial Intelligence (UAI 2026). Includes appendices. Code: this https URL
Abstract:Workload-based differentially private (DP) synthetic data methods privately measure aggregate queries and post-process the noisy answers into synthetic records. Generic workloads can achieve strong distributional fidelity, but causal estimands such as the average treatment effect (ATE) depend on treatment-arm balance and outcome moments that generic marginals need not preserve. We propose causal workloads: DP query sets designed around the orthogonal moments used by doubly robust causal estimators. The released workload can be used directly by stable moment-map estimators or reconstructed by maximum-entropy calibration into reusable synthetic data; our theory decomposes ATE error into sampling, privacy, workload-approximation, Monte Carlo, and calibration terms. We also introduce Causal-AIM, an adaptive workload selector, and a noise-aware multiple-imputation (NA+MI) procedure for confidence intervals from DP synthetic data. Because the workload is released once, the same DP synthetic table can support ATE, ATT, and subgroup analyses without additional privacy spending. Empirically, causal workloads are most useful at strict privacy budgets and for calibrated uncertainty, while generic workloads often retain an advantage for point RMSE as privacy relaxes. The broader lesson is a tradeoff: distributional fidelity can help point accuracy, but valid causal inference requires preserving causal moments and propagating DP noise rather than treating synthetic rows as real.
[LG-40] Contrastive Order Learning: A General Framework for Ordinal Regression ICML2026
链接: https://arxiv.org/abs/2607.08109
作者: Chaewon Lee,BeomJun Shim,Kwang Pyo Choi,Chang-Su Kim
类目: Machine Learning (cs.LG)
*备注: Accepted to ICML 2026
Abstract:We propose contrastive order learning (ConOrd), a contrastive learning framework for ordinal regression that integrates the strengths of contrastive learning and order learning. While contrastive learning effectively leverages all samples in a batch, it typically ignores the inherent ordering among rank labels. Conversely, order learning explicitly models label ordinality but often relies on local, margin-based comparisons, limiting its ability to capture global ordinal structure. ConOrd addresses these limitations by introducing a contrastive order loss with soft affinity and disparity weights based on rank differences, enabling fine-grained modeling of ordinal relationships across all sample pairs within a batch. Extensive experiments on a range of ordinal regression tasks, including facial age estimation, blind image quality assessment, and blind video quality assessment, demonstrate that ConOrd consistently achieves state-of-the-art performance and generalizes well across diverse ordinal regression scenarios. The source code is available at this https URL.
[LG-41] Vanilla SGD with Momentum Survives Heavy-Tailed Noise: Convergence Analysis without Gradient Clipping or Normalization UAI2026
链接: https://arxiv.org/abs/2607.08104
作者: Ryusei Yamada,Naoki Sato,Hideaki Iiduka
类目: Machine Learning (cs.LG)
*备注: Accepted at UAI2026
Abstract:Stochastic gradient descent (SGD) is a cornerstone of modern optimization. While its performance under heavy-tailed noise is often addressed through specialized modifications such as gradient clipping or normalization, we investigate a more fundamental question: how does vanilla SGD, particularly with momentum, perform in the presence of heavy-tailed noise? In this paper, we refine existing convergence results for vanilla SGD and, more importantly, provide the first comprehensive convergence analysis of vanilla SGD with momentum for strongly convex, convex, and nonconvex objectives, without employing any gradient control mechanisms. Our results demonstrate that the obtained convergence rates are inferior to the optimal rates achieved by clipped or normalized variants of SGD, thereby revealing inherent limitations of vanilla methods under heavy-tailed noise. The theoretical findings are supported by experiments on synthetic functions.
[LG-42] Stochastic Order Learning: An Approach to Rank Estimation Using Noisy Data ICML2026
链接: https://arxiv.org/abs/2607.08103
作者: Chaewon Lee,Seon-Ho Lee,Chang-Su Kim
类目: Machine Learning (cs.LG)
*备注: Accepted to ICML 2026
Abstract:Rank estimation under label noise poses a fundamental challenge, as ordinal annotations often exhibit structured uncertainty rather than simple label corruption. In this paper, we reformulate rank estimation with noisy ordinal labels as a stochastic ordering problem, in which each instance is inherently associated with multiple plausible ranks instead of a single deterministic label. Based on this view, we propose stochastic order learning (SOL), a learning framework that captures ordinal label uncertainty and learns an embedding space through two complementary objectives: a discriminative loss that structures instance–centroid interactions and a stochastic order loss that enforces probabilistic ordering relations between instances. Extensive experiments across diverse datasets demonstrate that SOL enables reliable rank estimation under various types and levels of label noise. The source code is available at this https URL.
[LG-43] Modular Pretraining Enables Access Control
链接: https://arxiv.org/abs/2607.08077
作者: Ethan Roland,Murat Cubuktepe,Erick Martinez,Stijn Servaes,Keenan Pepper,Mike Vaiana,Diogo Schwerz de Lucena,Judd Rosenblatt,Addie Foote,Cem Anil,Alex Cloud
类目: Machine Learning (cs.LG)
*备注:
Abstract:AI developers face a dual-use dilemma. An AI capability that helps one user cure a disease can help another synthesize one. This dilemma could be resolved with access control, limiting dual-use AI capabilities to trusted deployments with a legitimate need. A gold standard for access control would be to serve separate models with different capabilities to different users. However, training and deploying multiple models is prohibitively expensive. To address this challenge, we propose gradient-routed auxiliary modules (GRAM), a pre-training method that adds modules to a neural network and selectively updates them to induce specialization. Ablating a module at inference time removes its capability from the network, approximating a model trained on filtered data. We evaluate GRAM on synthetic stories and realistic dual-use data spanning virology, cybersecurity, nuclear physics, and specialized code. These experiments show that GRAM disables targeted capabilities while preserving the rest, and resists their recovery under finetuning better than post-hoc unlearning. Most importantly, a Chinchilla-optimal scaling analysis from 50M to 5B parameters shows that the gap between data-filtered and full-data models widens with scale on removed capabilities but stays small on retained ones, and that GRAM closely tracks data filtering. GRAM’s training cost is independent of the number of supported capability profiles, yielding a 5x reduction over data filtering in our 5-profile setting.
[LG-44] Cross-Modal Generative Framework for Signal Translation from Fetal-Maternal Electrocardiograms to Fetal Doppler Waveforms
链接: https://arxiv.org/abs/2607.08073
作者: Tongli Su,Alireza Rafiei,Marly van Assen,Reza Sameni,Gari D. Clifford,Faezeh Marzbanrad,Nasim Katebi
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: Accepted for oral presentation at IEEE EMBC 2026. 7 pages
Abstract:Fetal electrocardiogram (fECG) and Doppler ultrasound provide complementary views of fetal cardiovascular function: fECG captures electrical activity while Doppler reflects mechanical hemodynamics shaped by factors such as placental resistance and vascular compliance. Understanding the recoverable and unrecoverable Doppler components through reconstruction from fECG offers insight into the relative contributions of electrical versus mechanical factors in fetal circulation, thereby informing clinical decisions. In addition, clinical evidence of maternal-fetal cardiac coupling suggests that maternal cardiovascular dynamics may also inform fetal hemodynamics. To computationally model these relationships, we propose a cross-modal generative framework combining dilated convolutions with cross-modal attention to selectively incorporate maternal ECG and self-attention to capture long-range temporal dependencies. Trained on 885 synchronized fetal/maternal ECG and Doppler envelope segments from 39 pregnancies, our model synthesizes Doppler envelopes with power spectral density mean squared error (PSD MSE) of 49.9 +/- 15.8 dB^2 (51% lower than two-channel baseline) and heart-rate error of 4.71 +/- 0.77 bpm (1.5% better than baseline; negligible relative to the 110-160 bpm physiological range). Cross-modal attention yields a 39% PSD MSE reduction over naive dual-channel concatenation, quantifying the contribution of maternal-fetal coupling. Our proposed framework advances computational modeling of the maternal-fetal cardiovascular system by enabling the synthesis of Doppler envelopes from dual-lead ECG. By analysis of both recoverable and residual Doppler components, this approach enables quantification of the purely mechanical contributions to Doppler waveforms – those not recoverable from electrical recordings – ultimately facilitating a more comprehensive fetal assessment.
[LG-45] RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization
链接: https://arxiv.org/abs/2607.08045
作者: Xiucheng Wang,Junxi Huang,Nan Cheng
类目: Information Theory (cs.IT); Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注:
Abstract:Angular radio maps describe the received-power distribution over the angle of arrival and underpin beam selection and receiver localization in sixth-generation (6G) networks. Predicting the angular power spectrum (APS) from geometry is difficult, because the mapping is ill-posed in non-line-of-sight (NLOS) conditions and must generalize to unseen environments. Distortion-minimizing regressors return the conditional mean, which over-smooths the spectrum and erases the multipath structure that downstream tasks need. We cast the task as a perception-distortion problem and propose RadioDiff-v2, a dual-branch one-dimensional diffusion transformer trained with flow matching. It couples periodic angular encoding, adaptive layer-normalization conditioning, a Fourier angular mixer, and joint velocity and clean-signal heads. A per-metric estimator portfolio reads every deployment quantity from this single model, so that samples carry the distribution, the clean-signal head supplies a regression-grade point estimate, Bayes-optimal rules select beams, and the conditional likelihood localizes the receiver. We prove that a concentrated conditional yields a straight probability-flow trajectory that one step integrates exactly, identifying deterministic transport as the correct inductive bias. On a zero-shot test of 99 environments and one million links, RadioDiff-v2 leads every baseline on every metric, with a 0.39 dB Wasserstein-1 distance, per-bin error below the regression baseline, a 2.43 dB eight-beam NLOS sweep loss, and a 20.6-pixel localization error with four base stations. Code is available at this https URL.
[LG-46] An exact information theory of generalization phase transitions in Bayesian diffusion models
链接: https://arxiv.org/abs/2607.08041
作者: Henry Hunt,Mason Kamb,Surya Ganguli
类目: Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn)
*备注:
Abstract:How diffusion models circumvent the curse of dimensionality to learn complex distributions over high dimensional spaces from a finite training set, instead of memorizing it, remains a fundamental mystery. To address this, we introduce analytically tractable Bayesian information restricted diffusion (BIRD) models, in which each pixel observes restricted information about noisy data. A BIRD model time-reverses diffusion by inferring which past training sample produced its current restricted observation using the Bayesian posterior. This model class generalizes existing analytical diffusion models that use spatially local information restriction. We show that spatially local BIRD models closely approximate trained diffusion models \textitearly in training, across different architectures such as UNets and DiTs. Under minimal assumptions on the data distribution, we identify an information-theoretic phase boundary between memorization and generalization in the joint space of amount of training data, time in the reverse generative process, and amount of information restriction: a BIRD model memorizes when the mutual information between its restricted noisy observations and the training data exceeds the log number of training points, and it generalizes otherwise. Experiments across a range of datasets confirm our theoretically predicted location for the transition. We find that generation proceeds near the edge of memorization: both spatially local BIRD models and early-training diffusion models track the memorization-generalization phase boundary by increasingly restricting information over time. Overall, our results reveal a fundamental role for information restriction in generative AI to circumvent the curse of dimensionality.
[LG-47] What to Keep What to Forget: A Rate–Distortion View of Memory Compaction in LLM s and Agents
链接: https://arxiv.org/abs/2607.08032
作者: Ashwin Gerard Colaco,Nada Lahjouji
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language models, and the agents built on them, spend an ever-growing share of their compute and memory on remembering: caching attention keys and values, carrying long prompts, maintaining recurrent state, and storing what happened in previous turns and sessions. Because none of this memory is free, four largely separate research communities have each learned to compact it. They evict or quantize the KV cache, prune or distill prompts, bound architectural state, and consolidate agent memory. We argue that these are instances of one problem: a rate–distortion decision about what context-derived information to retain versus discard, at what fidelity, under a resource budget, so as to preserve downstream task utility. We make this lens precise with a single compaction objective and a layer-agnostic lower bound, use it to build a seven-axis taxonomy that classifies methods from across the stack uniformly, and use it to transfer mechanisms between layers that have never been connected, from serving-stack KV management to agent long-term memory. Two patterns hold across the survey. At every layer the signal that decides what to keep is attention magnitude or recency, and it fails in the same way everywhere, by discarding, before the query is known and with no way to undo it, information the query later needs. And while compression is measured carefully on single-turn long context, the repeated compaction that agents actually perform is almost never measured, and no benchmark holds one budget axis across all the layers at once. We turn both observations into a benchmark proposal, a small reference experiment, and a set of compaction-aware design principles, and we map the open problems.
[LG-48] Rethinking Small VLM Quantization: From Component-Wise Analysis to Hardware-Aware Edge Deployment ICML2026
链接: https://arxiv.org/abs/2607.08029
作者: Hyeju Shin,Chorwon Kim,Ryangsoo Kim,Hark Yoo,Jaein Kim
类目: Machine Learning (cs.LG)
*备注: 14 pages. Accepted at the ICML 2026 Workshop on Hypothesis Testing
Abstract:The emergence of vision language models with fewer than 3 billion parameters has accelerated the implementation of on-device multimodal intelligence. However, a detailed understanding of component-wise quantization remains a bottleneck for optimal deployment. This paper presents a systematic evaluation framework for empirically validating five hypotheses across six quantization configurations on the Jetson Orin NX and AGX. By separating the vision encoder, projector, and large language model backbone yields the following results: (1) Quantization sensitivity is governed by the structural paradigm (MoE vs. dense) rather than scale alone, with MoE backbones mitigating INT4 noise where dense backbones degrade; (2) SigLIP encoders incur disproportionate INT8 latency on Jetson Ampere–a deployment-specific encoder-kernel-hardware interaction, not a SigLIP flaw; (3) Although INT4 quantization of LLMs greatly reduces VRAM consumption, it also causes slower token generation due to dequantization overhead; (4) Composite quantization errors are largely additive, except along the modality-alignment path, which is architecture-dependent; (5) The intelligence-per-joule profile varies significantly across platforms owing to memory bandwidth constraints.
[LG-49] PGD-NO: A Neural Operator with Precomputed Geometry Decomposition for 3D Million-scale Physics Simulations
链接: https://arxiv.org/abs/2607.08025
作者: Weiheng Zhong,Jing Bi,Victor Oancea,Hadi Meidani
类目: Machine Learning (cs.LG)
*备注:
Abstract:While neural PDE solvers have demonstrated significant potential for accelerating engineering simulations, existing architectures remain constrained by high memory consumption and the single node bottleneck, where the maximum processable mesh resolution is strictly limited by the VRAM of a single compute unit. To address these challenges, we propose PGD-NO, a neural operator with Precomputed Geometry Decomposition, that relocates the computational overhead of geometric encoding to a deterministic pre-computation phase. By utilizing an iterative geometry decomposition algorithm to extract geometry tokens, our model decouples feature extraction from solution querying. This architecture enables linear memory scalability, allowing high fidelity learning on meshes exceeding 10 million nodes, a scale where existing architectures typically encounter memory exhaustion. PGD-NO demonstrates competitive predictive accuracy across diverse industrial benchmarks and provides intrinsic interpretability through attention mechanisms. By effectively overcoming traditional mesh-size constraints, PGD-NO offers a robust and efficient solution for the next generation of large-scale, high-fidelity industrial design applications.
[LG-50] Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems
链接: https://arxiv.org/abs/2607.08013
作者: Shuo Huai,Di Liu,Hao Kong,Xiangzhong Luo,Weichen Liu,Ravi Subramaniam,Christian Makaya,Qian Lin
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: Author’s accepted version. Published in the 2022 IEEE 40th International Conference on Computer Design (ICCD)
Abstract:Federated Learning (FL) empowers multiple clients to collaboratively learn a model, enlarging the training data of each client for high accuracy while protecting data privacy. However, when deploying FL in real-time edge systems, the heterogeneity of devices among systems has a severe impact on the performance of the inferred model. Existing optimizations on FL focus on improving the training efficiency but fail to speed up inference, especially when there is a latency constraint. In this work, we propose Collate, a novel training framework that collaboratively learns heterogeneous models to meet the latency constraints of multiple edge systems simultaneously. We design a dynamic zeroizing-recovering method to adjust each local model architecture for high accuracy under its latency constraint. A proto-corrected federated aggregation scheme is also introduced to aggregate all heterogeneous local models, satisfying the latency constraint of different systems with only one training process and maintaining high accuracy. Extensive experiments indicate that, compared to state-of-the-art methods and under a latency constraint, our extended models can improve the accuracy by 1.96% on average, and our shrunk models can also obtain a 3.09% accuracy improvement on average, with almost no extra training overhead. The related codes and data will be available at this https URL
[LG-51] KronQ: LLM Quantization via Kronecker-Factored Hessian
链接: https://arxiv.org/abs/2607.07964
作者: Donghyun Lee,Yuhang Li,Ruokai Yin,Priyadarshini Panda
类目: Machine Learning (cs.LG)
*备注: COLM 2026
Abstract:Post-training quantization (PTQ) is a widely adopted technique for compressing large language models (LLMs) without retraining. Existing second-order PTQ methods, including GPTQ, construct quantization objectives exclusively from input activation statistics, effectively assuming that all output channels contribute equally to the layer-wise reconstruction objective. We propose KronQ, a PTQ framework that challenges this assumption by introducing the gradient covariance into the quantization pipeline. Under the Kronecker-factored Hessian approximation, the quantization loss depends jointly on both the activation and gradient covariances, and KronQ exploits this at two complementary levels. (1) KronQ introduces bidirectional incoherence processing, extending the existing input-side random rotation to the output dimension using the gradient covariance, reducing weight magnitude variance across both input and output dimensions. (2) KronQ derives a new sensitivity metric for inter-layer mixed-precision allocation, driven by the gradient and activation Hessian traces. Notably, in the case of 2-bit weight-only quantization on LLaMA-3-70B, while GPTQ and GPTAQ diverge or produce degenerate quantizations (2000 perplexity on WikiText-2), KronQ achieves 7.93 perplexity.
[LG-52] Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5
链接: https://arxiv.org/abs/2607.07951
作者: Yongcan Huang,Li Jiang,Ze Yu Liu
类目: Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
*备注:
Abstract:Wildfire smoke events produce extreme PM _2.5 concentrations that pose severe public health risks, yet forecasting rare, hazardous-level spikes remains a fundamental challenge. Time series foundation models (TSFMs), pretrained models offering zero-shot inference and efficient adaptation, perform strongly on general benchmarks, but their behavior under extreme out-of-distribution conditions is poorly understood. We present the first systematic benchmark comparing six TSFM configurations (zero-shot TimesFM, Chronos-2, Moirai-2, and Time-MoE, plus LoRA fine-tuned Chronos-2 and Time-MoE) against fully-trained baselines (LSTM, BiLSTM, Transformer) and naive persistence on a 12-year (2013–2025) hourly PM _2.5 dataset covering 1,375 wildfire incidents across 79 California monitoring sites. A leave-one-incident-out (LOIO) protocol evaluates generalization to unseen fires, using MAE, RMSE, and exceedance F1 at EPA AQI thresholds across 6-, 12-, and 24-hour horizons. Results reveal a consistent hierarchy. The BiLSTM achieves the lowest MAE ( 5.16,\mu g/m^3 ) and the highest exceedance F1 at every threshold, including the Hazardous band ( 225.5,\mu g/m^3 ), reaching 0.63 versus at most 0.54 for any foundation model. Zero-shot TSFMs improve on persistence only modestly, and zero-shot Chronos-2 exhibits severe RMSE tail instability ( 23.4,\mu g/m^3 , negative R^2 ) from sporadic large errors. LoRA fine-tuning substantially improves both adapted families and largely repairs this instability, yet no foundation model surpasses the trained recurrent baselines on any metric. These findings challenge the assumption that larger pretrained models universally dominate environmental forecasting and provide actionable deployment guidance for wildfire air quality prediction.
[LG-53] DeepSWE: Measuring Frontier Coding Agents on Original Long-Horizon Engineering Tasks
链接: https://arxiv.org/abs/2607.07946
作者: Wenqi Huang,Charley Lee,Leonard Tng,Serena Ge
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注: 32 pages, 10 figures. Code and data: this https URL ; this https URL
Abstract:DeepSWE is a benchmark of 113 original, long-horizon software engineering tasks for evaluating coding agents. Most public agentic coding benchmarks follow SWE-bench in mining merged fixes from public GitHub repositories, which creates two problems: the fixes and their discussion were likely seen during pretraining, so a high score can reflect recall rather than problem-solving; and each task is graded by the tests that shipped with its merged fix, which were written to confirm one specific fix rather than grade an arbitrary solution, so they can fail a correct alternative or pass an incomplete one. DeepSWE avoids both. Its tasks are written from scratch across 91 active open-source repositories and five languages and are never contributed back upstream, so their reference solutions stay out of the public record that model training scrapes; and each task is graded by a hand-written verifier that checks the requested functionality and accepts any implementation that provides it. When an independent LLM judge re-reviews graded runs, it disagrees with DeepSWE’s verifier about an order of magnitude less often than with SWE-Bench Pro’s inherited tests (1.4% versus 32.4%). Despite being about half the length of SWE-Bench Pro’s prompts, DeepSWE’s prompts describe tasks whose reference solutions touch 5.5x more code, and the benchmark separates frontier agents across a wider score band than the leaderboards on which they otherwise cluster. We release the benchmark, its verifiers, and the full record of evaluation trajectories.
[LG-54] Distributed Sketching on Data Partitions for OLS Regression
链接: https://arxiv.org/abs/2607.07888
作者: Luyuan Yang,Brayden Garner,Shayan Shafaei,Chao Lan
类目: Machine Learning (cs.LG)
*备注: This work has been accepted at StatisticsProbability Letters, 2026
Abstract:This paper studies distributed sketching for ordinary least squares (OLS) regression, an approach that distributes small sketches of a large data set over multiple machines to separately construct OLS estimators and average them. Unlike prior studies that consider sketching on the whole data set, we consider sketching on partitioned subsets to further reduce computational cost. Under the fixed design setting, we characterize the exact excess loss of the averaged OLS estimator. Results show that this loss is comparable to the established loss for sketching on the whole data set when the divergence among subset covariances is small.
[LG-55] Optimal Learning Rate Scaling Depends on Data in Deep Scalar Linear Networks
链接: https://arxiv.org/abs/2607.07884
作者: Yedi Zhang,Peter E. Latham,Leena Chennuru Vankadara,Andrew Saxe
类目: Machine Learning (cs.LG)
*备注:
Abstract:In this short note we consider the gradient descent dynamics of deep scalar linear networks, f(x) = \prod_l=1^L w_l x , which enjoy exact time-course solutions for any integer depth. We show that even in this minimal model, the optimal depth-wise learning rate scaling depends on data, whereas data-agnostic scaling rules fail to transfer across depths. Under the data-dependent optimal scaling, the learning dynamics is independent of data and weakly dependent on depth, resulting in a constant linear convergence rate across all depths including infinity. We further show similar data-dependent effects in deep scalar linear networks with residual connections.
[LG-56] Functional and Secure Code Generation with Task Vectors
链接: https://arxiv.org/abs/2607.07881
作者: Felix Wang,Anudeep Das,Mei Nagappan,N. Asokan
类目: oftware Engineering (cs.SE); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Large language models (LLMs) are increasingly used for code generation, but they struggle to generate functional code free of security vulnerabilities. Prior work to improve the secure code generation abilities of such coding LLMs has largely focused on evaluating code functionality and security separately using different datasets, or focused on finding vulnerabilities post-generation. At the same time, the text-generation domain has seen significant work on alignment techniques, where models are tuned such that their outputs exhibit certain qualities (e.g., helpfulness, harmlessness). Of particular interest is task-vector arithmetic, where linear operations on LLM weights can be used to arbitrarily enhance alignment while incurring only minimal computational overhead. We develop a novel method, SecVecCoder, leveraging task vectors to produce trustworthy code that is simultaneously functional and secure without the need for post-generation adjustment. Across six coding LLMs from three families on the CodeGuard+ benchmark, SecVecCoder improves the rate of trustworthy code completions by 2.1-36.0 percentage points over the base model, with improvements on unseen CWE types reaching up to 39.1 percentage points. Since the effectiveness of the coding LLM relies only on changing the model weights, SecVecCoder requires no method-specific decoding and hence achieves a decoding latency within 0.6% of the base model’s, on average.
[LG-57] Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling ICANN2026
链接: https://arxiv.org/abs/2607.07863
作者: Sarah Grewe,Jörg Frochte
类目: Machine Learning (cs.LG)
*备注: 2 figures, 5 tables, Accepted for publication; to be published in the Proceedings of ICANN 2026, Springer LNCS
Abstract:In physically dominated machining processes, experimental datasets are small, expensive, and material-specific; in this regime, data curation, evaluation design, and the form of physics integration can matter as much as the learning algorithm. Using an abrasive waterjet milling dataset ( n=155 , Inconel,718), we make three methodological contributions. First, we separate physics-based data \emphcleaning from statistical \emphcuration and treat the latter as competing modelling hypotheses rather than silent preprocessing. Second, we find that model rankings from a 15-point hold-out set can be unstable: the single-split winner drops from rank~1 to rank~7 under 10-fold cross-validation, while Gaussian Process (GP) variants occupy the top ranks. Third, we study a spectrum of physics integration levels and find that residual learning on a compact physics baseline is competitive for GP, yielding lower variance and an interpretable decomposition, but degrades tree-based models. Bayesian hyper parameter tuning improves parameter-sensitive baselines such as gradient boosting and SVR, yet harms multi-stage hybrid pipelines at this sample size. GP uncertainty intervals are approximately calibrated ( 86% empirical coverage at nominal 90% ). The resulting picture is methodological: for small, expensive process datasets, our results suggest that, in this setting, reliable model comparison benefits from explicit curation hypotheses, robust evaluation, and careful choices about how physics enters the model.
[LG-58] CTA-Pipelining: A Latency-Oriented Spatial Scaling Method for Multi-GPU Systems
链接: https://arxiv.org/abs/2607.07862
作者: Tingkai Liu,Muralidhar Andoorveedu,Sanjoy Das,Sanjay Patel,Volodymyr Kindratenko
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:The evolution of compute infrastructure has transformed multi-GPU systems into tightly integrated shared-memory structures. However, current software still mostly treats these coherent interconnects simply as high-speed networks. Simultaneously, the demand for serving Large Language Models under latency constraints has shifted GPU workload optimization from being throughput-driven to latency-bound, necessitating latency-oriented scaling methods beyond Tensor Parallelism (TP). Thus, we introduce CTA-pipelining, an execution paradigm designed to exploit shared-memory multi-GPU systems. As a latency-oriented spatial scaling technique, CTA-pipelining leverages dependencies at the Cooperative Thread Array level, enabling concurrent execution of dependent kernels across GPUs. We demonstrate its capability using CUTLASS, cuBLAS, and NCCL libraries on 8-GPU H200 and B200 systems. Results show on 2-layer GEMM, representing the MLP operation, CTA-pipelining reduces latency by up to 31.8% compared to micro-batching, and 29.6% compared to TP. It can also be combined with TP as an orthogonal scaling dimension to further push the latency boundary. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) Cite as: arXiv:2607.07862 [cs.DC] (or arXiv:2607.07862v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2607.07862 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-59] NFTR: From Provable Mode-Averag ing to Geodesic Subgoal Selection in Offline Goal-Conditioned RL
链接: https://arxiv.org/abs/2607.07855
作者: Erdemt Bao,Xing Lei,Jun Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Hierarchical Implicit Q-Learning (HIQL), an offline goal-conditioned RL method, selects subgoals by value-function advantages alone. This rule has two coupled failure modes. Optimistic bias treats lucky stochastic outcomes as skillful choices, and mode collapse reduces a multi-modal subgoal distribution to a single Gaussian mean that often falls in unreachable regions. We propose NFTR (Normalizing Flows subgoal policies with Triangle-slack Reweighting). A conditional Normalizing Flow replaces the Gaussian policy, and a closed-form mode-averaging result identifies NFs as the minimal generative class for AWR-based subgoal selection. A triangle slack score, built on the architectural triangle inequality without relying on distance accuracy, multiplicatively corrects the AWR weight to downweight subgoals whose detour cost exceeds average reachability. Triangle-slack vanishes on geodesics in deterministic MDPs and remains a conservative upper bound on composability violation under stochastic dynamics. The RWDR objective preserves AWR’s population-level monotonic improvement and admits a three-term suboptimality decomposition. Together, these two ingredients yield subgoal selection that provably avoids the Gaussian collapse described above and remains stable under stochastic dynamics. GitHub page: this https URL
[LG-60] When Does Continual Learning Require Learning
链接: https://arxiv.org/abs/2607.07847
作者: Anne Harrington,Nayan Saxena,Michael Murphy,Anastasia Borovykh,Zeyu Yun,Sridhar Kamath,Ara Eindra Kyi,Trevor Darrell,Jitendra Malik,Yutong Bai
类目: Machine Learning (cs.LG)
*备注: Code: this https URL . Project page: this https URL
Abstract:As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn? Today, the field largely frames this as a problem of context management and mitigating forgetting. We argue this framing is incomplete: continual learning is fundamentally about increasing model competence as the world changes. We disentangle this change along two axes – space, where the model encounters new domains, and time, where the underlying data drifts under a fixed task. This framing lets us study continual learning under realistic conditions: new domains arrive over time, facts drift past their training cutoff, and agentic interactions accumulate state across episodes. To evaluate methods under this setting, we recast widely used LLM benchmarks as sequential problems and introduce a single mechanism-agnostic protocol that compares prompt-based methods (GEPA, ACE), supervised learning (SFT, SDFT), reinforcement learning (GRPO, SDPO), and context compression (Cartridges, In-place TTT). Prompt-based methods fit each new stage quickly but degrade on future tasks. Distillation-based methods accumulate knowledge stably but struggle to update outdated facts. Context compression improves efficiency without substantially improving the ability to learn new tasks. Online reinforcement learning adapts most effectively to knowledge updates but remains sensitive to noisy reward signals. Overall, our results suggest that continual learning is not a single capability: different patterns of environmental change require fundamentally different update behaviors, determining when adaptation must be learned inside model weights and when it can be achieved through external scaffolding. We hope that understanding where each method succeeds and fails will guide the design of stronger continual learning systems.
[LG-61] Explaining Near-Zero Hessian Eigenvalues Through Approximate Symmetries in Neural Networks
链接: https://arxiv.org/abs/2607.07845
作者: Marcel Kühn,Bernd Rosenow
类目: Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn)
*备注: 19 pages, 10 figures
Abstract:The Hessian of the training loss governs the local geometry of the loss landscape, yet despite existing explanations for its largest eigenvalues, the origin of the vast multitude of vanishingly small eigenvalues remains elusive. We argue that the bulk consists of the weakly lifted pseudo-Goldstone modes of the continuous symmetries of the network parametrization. In deep linear networks these symmetries are exact: they generate flat directions and hence exact zero modes, whose eigenvectors we construct explicitly. Introducing a ReLU nonlinearity as a perturbation, we show that it breaks these symmetries weakly and explicitly. Resolving the spectrum at the level of eigenvectors, we find that the high-curvature directions are orthogonal to the symmetry subspace, while the bulk lies almost entirely within it. We demonstrate the mechanism in a two-layer ReLU student–teacher model and in a network trained on CIFAR-10. A convolutional example demonstrates that the same diagnostic extends beyond fully connected layers. Together, these results link the Hessian bulk to weakly broken symmetries and clarify the origin of near-zero modes.
[LG-62] GradInf: Gradient Estimation as Probabilistic Inference
链接: https://arxiv.org/abs/2607.07840
作者: Gaurav Arya,Mathieu Huot,Moritz Schauer,Alexander K. Lew,Feras A. Saad
类目: Programming Languages (cs.PL); Machine Learning (cs.LG)
*备注:
Abstract:Gradient estimation – the task of computing the gradient of the expected value of a probabilistic program – has diverse applications in scientific computing, but is notoriously difficult because of issues such as high-dimensional integration, discrete random choices, and complex stochastic dependencies. This article introduces gradient inference, a new approach to developing sound and efficient gradient estimators for probabilistic programs. Gradient inference rests on a formal reduction from a gradient estimation problem to a closely related probabilistic inference problem, whose solution can be differentiated to obtain a gradient estimator. This inference problem is obtained by applying two powerful statistical operations – coupling and factorization – to the input probabilistic program. Our reduction lets us leverage the rich toolkit of probabilistic inference algorithms to design novel gradient estimators that extend and improve upon existing methods. We introduce GradInf, a probabilistic programming system that facilitates the sound and automated implementation of gradient inference. GradInf is centered around programmable source-to-source transformations for coupling and factorizing higher-order probabilistic programs, whose soundness is proven in terms of a denotational semantics. Key to our development is the use of information-flow typing to allow random choices in a probabilistic program to be factored out and partially evaluated, which improves our ability to deploy sophisticated probabilistic inference algorithms. The resulting system offers practitioners a principled framework for designing gradient estimators. We apply GradInf to several challenging case studies, showing that it can express prominent gradient estimators from the literature and enables the construction of new state-of-the-art estimators that outperform the best existing baselines. Subjects: Programming Languages (cs.PL); Machine Learning (cs.LG) Cite as: arXiv:2607.07840 [cs.PL] (or arXiv:2607.07840v1 [cs.PL] for this version) https://doi.org/10.48550/arXiv.2607.07840 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: Proc. ACM Program. Lang. 10, PLDI, Article 243 (June 2026) Related DOI: https://doi.org/10.1145/3808321 Focus to learn more DOI(s) linking to related resources
[LG-63] Predicting Pseudo-nitzschia harmful algal blooms along the Portuguese Coast using satellite-derived predictors
链接: https://arxiv.org/abs/2607.07834
作者: Ayman Bnoussaad,El Khalil Cherif,Ligia Pinto,Ramiro Neves,Alexandra D. Silva,Alexandre Bernardino
类目: Machine Learning (cs.LG)
*备注: 33 pages, 16 figures. Submitted to Harmful Algae (Elsevier)
Abstract:Pseudo-nitzschia diatoms pose recurrent risks to coastal ecosystems and shellfish harvesting along the Portuguese Atlantic coast. Here we develop and evaluate a spatio-temporal machine-learning framework to predict harmful algal bloom (HAB) occurrence using exclusively satellite-derived predictors under realistic forecasting constraints. We characterised environmental and biological variability across shellfish production zones (L1-L9) using 5,882 observations, providing system-wide context. Predictive models were developed for zones L1-L2, a hotspot for Pseudo-nitzschia and domoic acid events, using a decade-long dataset (2013-2023; 1,440 observations; more than 1,000 satellite-based predictors including sea surface temperature, an upwelling index, chlorophyll-a, and plankton functional types). Sampling locations were partitioned into ecologically meaningful sub-regions using a river-aware spatial clustering scheme. A stringent spatio-temporal cross-validation strategy that simultaneously withholds entire years and spatial clusters prevents leakage and closely mimics real-world forecasting conditions. HAB occurrence proved moderately predictable across model classes and feature configurations. Ensemble tree-based methods achieved the strongest discrimination: Random Forest reached 0.74 +/- 0.05 with environmental predictors; Extra Trees reached 0.77 +/- 0.06 with biological variables added. Feature-importance analyses revealed that seasonal structure, spatial context, and lagged environmental conditions dominate model decisions, while biological indicators refine bloom likelihood within physically favourable periods. The framework demonstrates operationally relevant skill for satellite-supported HAB early-warning systems along eastern boundary upwelling coasts.
[LG-64] A law of robustness for two-layer neural networks with arbitrary weights
链接: https://arxiv.org/abs/2607.07778
作者: Yitzchak Shmalo
类目: Machine Learning (cs.LG); Probability (math.PR); Machine Learning (stat.ML)
*备注:
Abstract:Bubeck, Li and Nagaraj conjectured that, for generic data, any two-layer neural network with m neurons that fits n noisy labels must have Lipschitz constant at least of order \sqrtn/m , with no restriction on the size of the weights. Bubeck and Sellke proved a universal version of this law for Lipschitz-parameterized classes, but under a polynomial bound on the parameters; at depth three that boundedness hypothesis is genuinely necessary. The two-layer unbounded-weight case requires a different argument. We prove the conjectured law, up to one logarithmic factor, for every continuous piecewise-linear activation, in particular for ReLU networks. For data drawn uniformly from \mathbbS^d-1 , d\ge3 , or from N(0,I_d/d) , labels in [-1,1] with noise level \sigma^20 , and any width- m two-layer network with arbitrary real weights, biases and affine skip connection, fitting the data \varepsilon below the noise floor forces \mathrmLip(f)\ge c,\varepsilon\sqrtn/(\bar m\log(C\bar m nd/\varepsilon)) , \bar m=(K-1)m+1 , with high probability. A realized-kink-count version holds on the same event: every realized two-layer piecewise-linear function with k(f)\le n distinct kink hyperplanes obeys the bound with \bar m replaced by k(f)+1 , irrespective of how many redundant hidden units parameterize it. The proof replaces parameter-space covering, impossible for unbounded weights, by a function-space covering. The central deterministic ingredient is a rigidity lemma: on B_2 , and on \mathbbS^d-1 for d\ge3 , the coefficient of each canonical kink is controlled by the Lipschitz constant of the realized function, because kinks on distinct hyperplanes cannot cancel at generic points. Rigidity genuinely fails at d=2 , and an explicit two-layer ReLU interpolant with O(1) Lipschitz constant at width 2n matches the law at the overparameterized endpoint.
[LG-65] Unlocking Temporal Generalization in Hamiltonian Video Dynamics Models
链接: https://arxiv.org/abs/2607.07763
作者: Eli Laird,Corey Clark
类目: Machine Learning (cs.LG)
*备注: To appear in the 1st Workshop on Physics-Aware Video Generation and Restoration at the 28th International Conference on Pattern Recognition
Abstract:World models are typically trained to predict discrete-time physical dynamics with a fixed step size baked into the model weights, preventing prediction at variable temporal resolutions. This matters for hierarchical planning, sim-to-real transfer, and scientific or game-engine applications that must query the same dynamics at multiple timescales. Hamiltonian Generative Networks (HGN) offer a principled path forward, grounding predictions in a continuous-time energy function that is, in principle, independent of the observation frame rate. In practice, however, their temporal generalization breaks down in non-conservative settings. We show that in externally forced, dissipative environments, HGN rollouts at step sizes beyond the training regime fail due to distinct failure modes, including latent magnitude growth driven by an unconstrained action-force map, and global truncation error accumulation from an under-resolved integrator. We identify a targeted fix for each mechanism and demonstrate stable dynamics prediction at temporal resolutions well outside the training distribution. In a detailed analysis, we recommend several strategies for enabling temporal generalization in continuous-time video generation.
[LG-66] rustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectives
链接: https://arxiv.org/abs/2607.07762
作者: Thibaut Vidal,Julien Ferry
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 67 pages, 16 mathematical highlights
Abstract:Modern machine learning (ML) increasingly relies on complex models whose behavior is difficult to characterize beyond empirical performance metrics. Across a wide range of tasks, including prediction, generation, and decision-making, models with similar empirical performance can exhibit markedly different properties in terms of their transparency, interpretability, robustness, fairness, privacy, and certifiability. This survey highlights how optimization- and certification-oriented reasoning can provide a useful framework for reasoning about such differences, supporting tasks ranging from model training and selection to auditing and certification. We review and synthesize recent advances at the intersection of combinatorial optimization (CO) and trustworthy ML, covering both training and post-training tasks, including interpretable model learning, explanation generation, robustness analysis, fairness auditing, model compression, and privacy attacks and protections. Across these domains, CO formulations offer additional capabilities over purely heuristic approaches, e.g., gradient-based ones, notably global guarantees, formal certificates, and explicit treatment of trade-offs. While scalability remains an important challenge, continued progress in solvers and hybrid algorithms suggests a growing role for CO in the design and deployment of trustworthy ML systems.
[LG-67] Scalable and Trustworthy Earth Observation Foundation Models
链接: https://arxiv.org/abs/2607.07758
作者: Syed Usama Imtiaz,Mitra Nasr Azadani,Nasrin Alamdari
类目: Machine Learning (cs.LG)
*备注: (Preprint, in review) Elsevier Book Chapter: Next-Generation Remote Sensing Methods
Abstract:Foundation models (FMs) have transformed machine learning from isolated task-specific model development toward general-purpose models pretrained on broad data and adapted to multiple downstream tasks. Earth observation (EO) is an important domain for this paradigm because satellite and airborne archives are large, high-revisit, and increasingly multimodal, while reliable field labels are often sparse. Remote sensing foundation models (RSFMs) cannot be transferred reliably/optimally without domain-specific adaptation. This is because EO data are governed by measurement physics and operational decision constraints. This chapter reviews the design principles arising from these domain-specific constraints. It first defines the FMs paradigm in remote sensing (RS), then synthesizes the current model landscape, pretraining objectives, architecture designs, downstream adaptation and trustworthiness requirements. The chapter also incorporates recent benchmark evidence showing that no single geospatial foundation model is universally best and that inconsistent evaluation remains a major issue to fair comparison and reliable deployment. In addition, two brief environmental monitoring case studies; physics-informed spectral targeted masking for harmful algal bloom prediction and reinforcement learning for adaptive environmental monitoring station selection to illustrate the FMs domain-guided principles in practice. This chapter posits that next-generation RSFMs should be evaluated not only by benchmark accuracy, but also by modality-aware transfer and physically plausible representations for trustworthy EO decisions.
[LG-68] he Importance of Encoder Choice:A Tabular-Image Study
链接: https://arxiv.org/abs/2607.07756
作者: Ilia Koloiarov,Diego Coello de Portugal Mecke,Vijaya Krishna Yalavarthi,Tom Hanika,Lars Schmidt-Thieme
类目: Machine Learning (cs.LG)
*备注: This paper contains color figures. We recommend reading it digitally for the best experience
Abstract:Multimodal learning usually requires a dedicated encoder per modality. When a tabular modality is involved, prior work has been mostly using a \emphplain MLP as the encoder. Yet if it were a strong encoder, the tabular domain would not be ``the last unconquered castle for deep learning’'. This study evaluates state-of-the-art tabular models as encoders in the image-tabular setting for the first time. An obstacle stands out. In-Context Learning models, among the best performing methods in the tabular domain, require labels to process instances, making it non-trivial to embed training and test instances the same way. We addressed this problem across multiple models of this family. With this study, we would like to highlight the importance of encoder factor in the multimodal learning.
[LG-69] Image classification via a quantum-inspired strategy involving a mixture of experts
链接: https://arxiv.org/abs/2607.07754
作者: Kumari Jyoti,Rohith Babu,Apoorva D. Patel
类目: Machine Learning (cs.LG); Quantum Physics (quant-ph)
*备注: 14 pages, 18 figures, comments welcome
Abstract:Pattern recognition problems arise in a variety of physical image processing situations, and convolutional neural networks are a popular scheme for the required feature extraction and classification tasks. The classical networks use diffusion-based smearing and block-wise pooling to downsample the image data and capture important structural features. In this work, we propose and demonstrate a more efficient quantum-inspired strategy involving a mixture of experts. It is a hybrid classical-quantum framework. The quantum part consists of amplitude encoding of the images, convolution using local unitary operations, multiple experts processing the same image with different parameters, and feature extraction using quantum stabiliser codes. The classical part then jointly processes the features extracted by different experts using a standard fully connected neural network for image class prediction. Using MNIST and Fashion-MNIST datasets as benchmarks, we demonstrate that the joint expert analysis outperforms the individual expert one, as well as reduces the failure rate of image class prediction by around a factor of two. The overhead of our quantum-inspired strategy is only moderate on GPU workstations, which makes our proposal a practical alternative to existing classical schemes. We also point out how the quantum part of our framework can be executed on a quantum processor.
[LG-70] Selective Left-Shift: Turning Test-Time Compute and Difficulty-based Curation into Training Data for Low-Resource Code Generation
链接: https://arxiv.org/abs/2607.07748
作者: Didula Samaraweera,Anjana Supun,Srinath Perera
类目: Machine Learning (cs.LG)
*备注: 11 Pages, 5 Figures
Abstract:Large Language Models achieve strong code generation for high resource languages like Python and Java but suffer sharp performance drops on Low-Resource Programming Languages~(LRPLs) such as Julia. Improving Small Language Models~(SLMs) for these languages faces a trilemma: Supervised Fine-Tuning~(SFT) is bottlenecked by data scarcity, inference-time scaling is too expensive for deployment, and Reinforcement Learning from scratch yields near zero advantages. We propose a three-phase pipeline that resolves this trilemma by decoupling syntax acquisition from algorithmic reasoning. First, we \emphleft-shift inference-time compute to an offline data synthesis engine that uses iterative compiler and test feedback to generate verified training examples. Second, we fine-tune an SLM on this synthetic, verified data to embed strong syntactic priors. Third, we apply Reinforcement Learning with Verifiable Reward~(RLVR) grounded by language-agnostic Input/Output tests, where the SFT prior constrains exploration away from syntax errors. Applied to Qwen3-8B, our pipeline improves pass@1 by up to +7.6 points on MultiPL-E and +14.2 points on the Agnostics LiveCodeBench for Julia compared to SOTA results. Furthermore, the pipeline only used \frac13 data and \frac16 cost over the previous state-of-the-art. We further demonstrate that the pipeline generalizes to Ballerina achieving 49.7% MultiPL-E Pass@1, a language with near-zero pretraining representation. Ablations confirm that both the SFT phase and execution-grounded rewards are necessary for stable training.
[LG-71] LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks
链接: https://arxiv.org/abs/2607.07745
作者: Arthur Chiron(IRIT, EPE UT),Franck Mamalet,Thomas Massena(IRIT, DTIPG - SNCF, UT3),Thomas Deltort(IRIT),Mathieu Serrurier(IRIT, UT2J)
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:While accuracy, robustness, and calibration are all essential for reliable neural networks, they are often studied separately; developing models that satisfy all three simultaneously remains a central challenge. Lipschitz-constrained models guarantee robustness by design, yet the manual selection of the Lipschitz constraint L governs the resulting accuracy-robustness trade-off, and their calibration properties remain largely underexplored. In this work, we highlight a theoretical and empirical link between the enforced Lipschitz constraint and Temperature Scaling, a state-of-the-art calibration method. Specifically, we find that for a given training scheme, there exists a non-trivial value L* that yields an out-of-the-box calibrated network, and that calibration acts as a principled criterion to select a well-defined operating point on the accuracy-robustness Pareto front. Leveraging these insights, we introduce Lipschitz Scaling Training (LiST), a novel training paradigm that iteratively adjusts the global Lipschitz constant to reach this operating point. Through a margin parameter in the training loss, LiST further enables the construction of a fully calibrated Pareto front, allowing users to navigate the accuracy-robustness trade-off while remaining calibrated throughout. At convergence, LiST also enables the reintegration of calibration data into training, improving sample efficiency without sacrificing calibration. We validate LiST on CIFAR-10/100 and Tiny-ImageNet, demonstrating competitive accuracy and robustness against constrained and unconstrained baselines, while remaining calibrated out of the box. Code is available at GitHub.
[LG-72] A Self-Supervised Approach for Minimal-Annotation Hydroacoustic Data Exploration
链接: https://arxiv.org/abs/2607.07733
作者: Pierre-Yves Raumer,Axel Marmoret,Dorian Cazau,Anatole Gros-Martial,Richard Dreo,Maelle Torterotot,Sara Bazin,Flore Samaran,Jean-Yves Royer
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注: Submitted to JASA
Abstract:Passive hydroacoustic monitoring often generates large volumes of continuous recordings that are only partially exploited due to the cost of manual annotation. Supervised detection methods perform well but require large labeled datasets, seldom available for rare signals or understudied environments. This work proposes a self-supervised exploration pipeline to address this limitation in low-frequency settings. A Masked AutoEncoder (MAE) is pre-trained on a reconstruction pretext task, then used to extract patch-level representations from spectrograms. Within each spectrogram, adjacent informative patches are aggregated into event-level embeddings, enabling the disentanglement of overlapping events. These embeddings are then clustered at the dataset scale using the dimension reduction algorithm UMAP and the clustering algorithm HDBSCAN to identify hydroacoustic patterns. The pipeline was applied to a multi-year hydroacoustic dataset collected near Mayotte Island, Indian Ocean, containing marine mammal vocalizations, seismo-volcanic signals, and anthropogenic noise. The 317 clusters were manually mapped to 15 hydroacoustic classes or noise in less than one hour. The method was evaluated in two ways. Quantitatively, when used as a classifier, it achieved performance comparable to two existing detectors. Qualitatively, it recovered known seasonal patterns of marine mammal acoustic activity. It also identified patterns of previously unstudied signals, thereby demonstrating its practical value.
[LG-73] Score Accuracy Along the Forward Diffusion Does Not Certify Numerical Stability in Diffusion Sampling
链接: https://arxiv.org/abs/2607.08757
作者: Yiwei Zhou
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Numerical Analysis (math.NA); Probability (math.PR)
*备注: 27 pages, 2 figures, 1 table
Abstract:Score matching controls average error under the forward marginals, but a discretized reverse-time sampler evaluates the learned score along its own trajectory. We show that small forward-marginal error does not guarantee numerical stability. We construct a single smooth score field with arbitrarily small forward-marginal L^2 error. The learned reverse-time process is nonexplosive, has moments of every order, and can be arbitrarily close to the exact reverse-time process in path-space total variation. Yet its Euler–Maruyama discretizations converge in probability while every positive moment diverges. Thus weak convergence can hold even though every Wasserstein distance W_p , p\ge1 , diverges. The same failure can occur within one fixed finite neural architecture. We construct a family of bounded, globally Lipschitz denoisers for which both the forward-marginal error and the path-space total variation distance tend to zero, while their Euler–Maruyama endpoints diverge in every W_p . For compactly supported data, we also give a simple positive result. Projecting the learned denoiser onto a known bounded closed convex set containing the support preserves pointwise accuracy, gives grid-uniform moment bounds, and yields Wasserstein convergence under mild local regularity. Experiments with a small fixed DiT-style network show large growth along rare numerical trajectories and its suppression by denoiser projection, while overall trajectory errors remain small. Comments: 27 pages, 2 figures, 1 table Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Numerical Analysis (math.NA); Probability (math.PR) MSC classes: Primary 65C30, Secondary 60H35, 60J60, 68T07 Cite as: arXiv:2607.08757 [stat.ML] (or arXiv:2607.08757v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2607.08757 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-74] High-Dimensional Procrustes Matching via Tree Counts
链接: https://arxiv.org/abs/2607.08538
作者: Xiaochun Niu,Tselil Schramm,Jiaming Xu
类目: Machine Learning (stat.ML); Information Theory (cs.IT); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:Suppose we observe two sets of n Gaussian vectors in \mathbbR^d , with the promise that, after applying a permutation of [n] and a rotation of \mathbbR^d , the two sets are \rho -correlated. The Procrustes matching problem asks us to recover the unknown permutation of [n] that aligns the two sets. The problem is well-studied in the low-dimensional regime d=O(\log n) , but the high-dimensional regime d\gg \log n has remained largely uncharted: prior matching guarantees require nearly perfect correlation \rho=1-o(1) , even for information-theoretic recovery. Our main result is a polynomial-time algorithm for exact recovery at constant correlation. The algorithm works by computing and comparing weighted counts of a specially chosen family of ``wide’’ trees. So long as d\ge \mathrmpolylog(n) , the algorithm succeeds with high probability for any \rho^2\sqrt\alpha , where \alpha\approx 0.338 is Otter’s tree-counting constant. We complement this algorithmic result with an improved information-theoretic guarantee, showing that exact recovery is possible when \rho^2 \gtrsim \max\log n/d,\sqrt\log n/n\ . We also carry out a low-degree advantage calculation, which suggests that the condition \rho^2 \sqrt\alpha is necessary for any tree-counting algorithm. Subjects: Machine Learning (stat.ML); Information Theory (cs.IT); Machine Learning (cs.LG); Statistics Theory (math.ST) Cite as: arXiv:2607.08538 [stat.ML] (or arXiv:2607.08538v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2607.08538 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-75] Statistical Efficiency and Inference of Quantile Distributional Reinforcement Learning
链接: https://arxiv.org/abs/2607.08444
作者: Zijie Cheng,Yang Peng,Zhihua Zhang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:In this paper, we study quantile-based distributional reinforcement learning from the perspective of statistical efficiency. We focus on distributional policy evaluation, whose goal is to characterize the return distribution, namely the distribution of discounted cumulative rewards under a given policy. To obtain a finite-dimensional representation of the return distribution, we consider the quantile fixed point \eta_m induced by the quantile-projected distributional Bellman equation. Assuming access to a generative model, we construct an estimator \eta_m^(n) based on an empirical Markov decision process. For a fixed number of quantiles m , we establish a non-asymptotic error bound for \eta_m^(n) and \eta_m under the supremum W_\infty metric, showing that the estimation error scales as \widetildeO(\sqrtm/n) with respect to m and n . This implies that the quantile-based distributional policy evaluation problem can be solved with sample efficiency, achieving the optimal parametric \sqrtn convergence rate. We derive the asymptotic distribution of the quantile parameters \sqrtn(\theta_m^(n)-\theta_m) and characterize the semiparametric efficiency bound, which is attained by our estimator. Beyond the fixed-dimensional setting, we investigate the asymptotic regime in which the number of quantiles diverges. We characterize the limit covariance structure and show that it matches the semiparametric efficiency bound of the nonparametric model for distributional policy evaluation, showing that quantile-based estimators remain asymptotically efficient in the infinite-dimensional limit. Finally, we establish a Berry–Esseen theorem for smooth functionals \sqrtn(\eta_m^(n)(s)-\eta_m(s))f , thereby providing a foundation for statistically valid inference on functionals of the quantile-projected return distribution.
[LG-76] Joint Discrete-Continuous Flow Matching for Open-Vocabulary Inverse Design of Multilayer Optical Coatings
链接: https://arxiv.org/abs/2607.08392
作者: Zhiyi Li,Yuheng Jin,Yidan Huang,Nan Chen,Hongyan Fu,Yikun Bu
类目: Optics (physics.optics); Machine Learning (cs.LG)
*备注:
Abstract:Amortized neural inverse design typically remains closed-world: component choices are fixed vocabulary tokens, coordinate grids are frozen at training time, and continuous variables are discretized into sequence tokens. Multilayer optical coatings are an industrially important instance, coupling material sequence, layer thickness and wavelength-dependent response. We present IrisFlow, a query-based, open-vocabulary flow-matching framework instantiated in coatings: the target reflectance/transmittance spectrum, wavelength grid, candidate-material optical constants and layer count are supplied at query time. Candidate materials enter as wavelength-aware optical tokens rather than learned identities; material sequences are sampled by discrete flow matching over the query’s candidate bank, thicknesses by continuous flow matching without discretization. A single 136M-parameter model designs 2-100-layer stacks. Across a 224-task benchmark it reconstructs in-distribution targets faithfully and retains same-order accuracy on a 15-material held-out bank without retraining; it reconstructs bands up to 1100 nm beyond its training envelope, designs against analytic application specifications and outperforms an autoregressive baseline on that baseline’s material library. With optical constants calibrated to our deposition process, IrisFlow designs four color-displaying coolers, fabricated by ion-assisted evaporation: the three chromatic devices reach a CIEDE2000 color error of 3.1-5.2 while retaining 93-95% solar near-infrared reflectance, demonstrating open-vocabulary design carried through to fabricated coatings.
[LG-77] Revisiting One-Zero and Two-Zero Neutrino Mass Textures in Light of Recent Oscillation and Cosmological Data
链接: https://arxiv.org/abs/2607.08384
作者: Haruto Kitagawa,Coh Miyao,Satsuki Nishimura,Hajime Otsuka
类目: High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG)
*备注:
Abstract:We revisit one-zero and two-zero textures of the neutrino mass matrix under current experimental and cosmological constraints. We identify the phenomenologically viable texture structures using the latest results on neutrino oscillation parameters, the cosmological bound on the sum of neutrino masses, the kinematic bound on the effective electron-neutrino mass, and limits from neutrinoless double-beta decay. For two-zero textures, several structures are still allowed if only the CMB bound on the neutrino mass sum is imposed. Among them, the B -series textures show a characteristic prediction for the Dirac CP phase, with \delta_\rm CP lying around \pi/2 and 3\pi/2 , and are within the reach of future neutrinoless double-beta decay searches. When the stronger CMB+BAO constraint is included, however, only the A -series textures remain viable. Therefore, we also analyze one-zero textures by using machine learning techniques, particularly flow matching. It turns out that some of the texture structures are already excluded by current data, while the allowed ones give distinct predictions for \sum_i m_i , m_\nu_e^\rm eff , \langle m_ee\rangle , and \delta_\rm CP . We further discuss how the one-zero texture structures can arise from non-invertible selection rules.
[LG-78] ubular Neighbourhoods of Pfaffian Sets and Applications to Neural Networks
链接: https://arxiv.org/abs/2607.08370
作者: Paul Lezeau,Martin Lotz
类目: Algebraic Geometry (math.AG); Machine Learning (cs.LG)
*备注: 32 pages, 1 figure
Abstract:We derive bounds for the volume of tubular neighbourhoods of smooth Pfaffian hypersurfaces, generalising known results for algebraic varieties. The bounds are given in terms of the Pfaffian format of the defining functions. As an application, we obtain tail bounds on the probability distribution of a condition number measuring the robustness of neural network classifiers with Pfaffian activation functions, in both the uniform and Gaussian settings. In the special case of single-hidden-layer sigmoid networks with rational weights, we derive polynomial-in-width bounds for tubular neighbourhoods of the decision boundary.
[LG-79] Prediction-Powered Active Testing
链接: https://arxiv.org/abs/2607.08347
作者: Kianoosh Ashouritaklimi,Valentin Kilian,Daolang Huang,Tom Rainforth,François Caron
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Active testing provides a label–efficient approach to risk estimation by adaptively selecting which test points should be labelled. However, existing estimators fail to exploit the informative predictions of powerful black–box models, even though such predictions are increasingly available in settings where labels remain expensive. To address this, we propose \textbfPrediction–Powered Active Testing (PPAT), a novel label–efficient risk estimation framework that combines the unbiased LURE estimator \citepfarquhar2021statistical with a prediction–powered control variate. Rather than using proxy predictions as biased pseudo–labels, PPAT uses them to residualise the loss, preserving unbiasedness while reducing variance. Beyond the estimator itself, PPAT also changes which points should be acquired: we derive oracle and practical surrogate–based acquisition rules tailored to reducing the variance of our estimator. Moreover, we establish asymptotic normality for PPAT, yielding asymptotically valid confidence intervals and thus a principled estimate of the uncertainty around our estimates. Across tabular regression and image–classification tasks, PPAT outperforms existing methods in risk estimation, while its confidence intervals attain the target coverage with substantially fewer labels and smaller widths.
[LG-80] Bayesian Experimental Design via Score Matching UAI2026
链接: https://arxiv.org/abs/2607.08335
作者: Angus Phillips,Gavin Kerrigan,Tom Rainforth
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: Accepted for the 42nd Conference on Uncertainty in Artificial Intelligence (UAI 2026)
Abstract:Policy-based approaches to Bayesian experimental design (BED) allow the learning of deep policy networks that adaptively make intelligent design decisions based on previously collected data. However, the training of such policies is often held back by a fundamental challenge: the double intractability of the expected information gain (EIG). This necessitates expensive or complex approximations that restrict the effort one can invest in optimising the policy itself. To address this, we show that the double intractability of the EIG can be isolated from the policy learning by first solving a score matching problem that is independent of the policy used, then using the learned score approximation to train the policy in a singly intractable manner. This turns the key multiplicative cost into an additive one and reduces the computational burden on the policy training itself, making it far cheaper to train the policy multiple times when needed, e.g. for architecture search, hyperparameter tuning, or avoiding local optima. In our experiments we train multiple competitive policies without inducing a multiplicative cost in likelihood evaluations, which can increase performance by allowing us to select the best policy even without performing hyperparameter or architecture searches.
[LG-81] Unit-Independent Low-Rate Wrist GSR Processing for Stress Detection Using Phasic nSCR Features
链接: https://arxiv.org/abs/2607.08007
作者: Zequan Liang,Sally Hang,Geneva M. Jost,Ning Miao,Wei Shao,Mahdi Pirayesh Shirazi Nejad,Hossein Sayadi,Ehsan Kourkchi,Setareh Rafatirad,Camelia E. Hostinar,Houman Homayoun
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注:
Abstract:Galvanic skin response (GSR) is widely used for stress detection, but wrist-based GSR remains challenging because its absolute amplitude can differ substantially from laboratory-grade palmar measurements. In this paper, we propose a unit-independent low-rate wrist GSR processing pipeline to extract the number of skin conductance responses per minute (nSCR/min) as a stress-related feature. We collect paired wrist and palmar GSR recordings from 31 participants during sitting baseline, standing baseline, neutral speaking, and the Trier Social Stress Test (TSST), a laboratory social stressor task. The proposed pipeline cleans the raw GSR signal, decomposes it into tonic skin conductance level (SCL) and phasic skin conductance response (SCR), applies robust z-score normalization, and detects phasic SCR peaks to compute nSCR/min. Using random forest on 25Hz We-Be GSR, nSCR/min achieved balanced accuracies of 0.823 and 0.871 for binary classification between TSST and the sitting and standing baselines, respectively. Moreover, the 25Hz We-Be GSR features achieved comparable balanced accuracy to the original 100Hz features across the evaluated tasks. These results suggest the feasibility of low-rate, unit-independent wrist GSR processing for wearable stress detection.
[LG-82] A Quantum Reservoir Architecture for Chaotic Forecasting and a Test of Whether Its High Dimension Helps
链接: https://arxiv.org/abs/2607.07978
作者: Tushar Pandey
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 11 pages, 3 figures
Abstract:Quantum reservoir computing uses a fixed quantum circuit as a feature generator and trains only a simple linear readout on top of it. This makes it cheap to train and free of the optimisation problems that affect many quantum machine-learning models. A natural worry is that the very large feature space the circuit produces might inflate apparent performance without adding anything real. This paper provides two things. First, it gives a complete, reproducible recipe for one such reservoir applied to forecasting chaotic systems, including how data is fed in, how the circuit is built, and how the readout is trained. Second, it gives a way to tell whether the reservoir’s high dimension is actually doing useful work. We grow the size of the prediction problem and the size of the quantum reservoir together, so that extra capacity cannot be the explanation for any improvement, and we track a single stability number that measures how well behaved the readout fit is. On two chaotic test systems, a spatiotemporal chain and a shallow-water fluid model, the quantum reservoir keeps a flat, stable error as both sizes grow, while a matched classical reservoir does not. We report where the classical baseline is in fact stronger, so the comparison is honest. The result is a clean specification plus a diagnostic that other groups can apply to any reservoir whose features have a known scale.
[LG-83] Expressivity and Statistical Trade-offs in Diffusion Policy Learning
链接: https://arxiv.org/abs/2607.07967
作者: Viet Vu,Renyuan Xu,Jiacheng Zhang,Yufei Zhang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Diffusion-based policies have recently emerged as powerful policy parameterizations for reinforcement learning, representing state-conditioned action distributions as terminal laws of diffusion processes with parameterized drifts. This terminal-law representation has shown substantial expressive flexibility in practice, enabling diffusion policies to model complex, multimodal, and highly non-Gaussian action distributions; however, it remains unclear what mathematically drives this expressivity and how to fully exploit it when the policy is learned from finite data. In this paper, we identify the drift Lipschitz budget K as a central quantity governing the expressivity and statistical behavior of diffusion policies. We quantify expressivity through approximation: diffusion policies with K -Lipschitz drifts can concentrate near optimal deterministic policies and achieve value approximation error of order 1/K ; moreover, we prove a matching lower bound under nondegenerate diffusion noise. This increased expressivity comes with a statistical cost. When the drift is parameterized by neural networks, increasing K improves approximation but increases statistical complexity. Balancing these two terms yields a finite-sample performance gap of order \tildeO(n^-2/(m+6)) for generic neural-network drifts, and a sharper rate \tildeO(n^-2/(m+4)) for one-sided dissipative drift classes, where n is the sample size and m is the dimension of the state space. Numerical experiments provide empirical evidence for the sample-dependent trade-off in K , supporting both theoretical regimes. Our framework also suggests a practical implementation principle: choose the diffusion budget K according to the available sample size, and then select a neural-network architecture with the corresponding fixed Lipschitz coefficient.
[LG-84] Distributionally Faithful Imputation via Positive Semi-Definite Kernel Density Estimation
链接: https://arxiv.org/abs/2607.07767
作者: Andrea Basteri,Carlo Ciliberto,Alessandro Rudi
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Missing values undermine statistical inference and machine learning pipelines, yet most imputation methods rely on heuristics or restrictive parametric assumptions that ignore the joint data distribution. We recast imputation under missing completely at random (MCAR) as density estimation from masked observations: estimate a distribution whose observed marginals exactly match those in the data. Leveraging positive semi definite (PSD) kernel densities we obtain a convex empirical risk problem with closed form marginals, solvable by a Newton interior point method. The resulting PSD Impute model yields both single and multiple imputations from the same fitted density, enjoys statistical consistency with fast adaptive excess risk beating the curse of dimensionality for very regular probabilities. Preliminary experiments on one synthetic and eleven real world datasets already indicate competitive distributional accuracy compared with popular imputation baselines, suggesting strong practical promise.
[LG-85] he Regularization Parameter: Sparse Precision Matrix Estimation
链接: https://arxiv.org/abs/2607.07735
作者: Aryan Eftekhari,Daniel Sergio Vega,Ernst-Jan Camiel Wit,Olaf Schenk
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Sparse precision matrix estimation provides an interpretable and computationally efficient framework for modeling conditional dependencies in high-dimensional, low-sample-size data. A recurring challenge is appropriately selecting the regularization parameter that controls estimator sparsity and strikes a balance between underfitting and overfitting. We propose a closed-form, matrix-valued regularization parameter derived from the sampling distribution of the first-order optimality conditions of the \ell_1 -regularized Gaussian maximum-likelihood estimator. By prescribing the probability that each nonzero entry of the estimator satisfies its optimality condition under resampling, we eliminate the need for cross-validation. The resulting regularization parameter is shown to attain asymptotic scaling properties that, under standard conditions, provide consistency and sparsistency of the estimator. On synthetic Gaussian and non-Gaussian datasets, as well as real-world gene microarray and neuroimaging applications, the proposed approach achieves estimation accuracy comparable to cross-validation, delivers superior support recovery, and reduces runtime by several orders of magnitude.
[LG-86] Statistical inverse learning problems with random observations
链接: https://arxiv.org/abs/2312.15341
作者: Abhishake Rastogi,Tapio Helin,Nicole Mücke
类目: atistics Theory (math.ST); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We provide an overview of recent progress in statistical inverse problems with random experimental design, covering both linear and nonlinear inverse problems. Different regularization schemes have been studied to produce robust and stable solutions. We discuss recent results in spectral regularization methods and regularization by projection, exploring both approaches within the context of Hilbert scales and presenting new insights particularly in regularization by projection. Additionally, we overview recent advancements in regularization using convex penalties. Convergence rates are analyzed in terms of the sample size in a probabilistic sense, yielding minimax rates in both expectation and probability. To achieve these results, the structure of reproducing kernel Hilbert spaces is leveraged to establish minimax rates in the statistical learning setting. We detail the assumptions underpinning these key elements of our proofs. Finally, we demonstrate the application of these concepts to nonlinear inverse problems in pharmacokinetic/pharmacodynamic (PK/PD) models, where the task is to predict changes in drug concentrations in patients.
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