本篇博文主要内容为 2026-08-20 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 (2026-08-20)
今日共更新572篇论文,其中:
- 自然语言处理共100篇(Computation and Language (cs.CL))
- 人工智能共188篇(Artificial Intelligence (cs.AI))
- 计算机视觉共94篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共170篇(Machine Learning (cs.LG))
- 多智能体系统共12篇(Multiagent Systems (cs.MA))
- 信息检索共19篇(Information Retrieval (cs.IR))
- 人机交互共24篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] Autonomous Cyber Defense in Connected Vehicles: A Multi-Agent Approach to V2X Security
【速读】：该论文旨在解决车联网中因安全与行车安全耦合导致的紧急制动警报误判问题，即在100毫秒内判断接收到的车载通信消息（Basic Safety Message, BSM）是否为伪造，若虚假警报进入规划系统则可能引发不必要的紧急制动，造成安全事故。现有入侵检测系统多为单车、单消息、静态规则驱动，无法识别跨车队或随时间演变的攻击模式，且难以平衡“丢弃可疑消息”与“避免误删真实紧急警报”的根本矛盾。为此，论文提出一种三层次多智能体架构，将100毫秒的BSM周期作为硬性设计约束而非性能目标。车辆端智能体在10毫秒内对每条消息分类为“接受”、“丢弃”、“隔离”或“上报”，在不确定时倾向“上报”，以规避合法警报被误删；路侧边缘智能体在50毫秒内融合多车威胁评估，并利用互补传感器数据化解安全-安全冲突；云端通过拜占庭容错联邦学习优化检测模型并分发更新权重。所有层级的时延预算均严格依据SAE J2735与ETSI EN 302 637-2标准定义的BSM周期设定，实现了标准对齐的全栈时延控制。关键创新在于将安全-安全冲突的协同决策机制作为核心设计原则，突破了传统系统仅关注单点检测的局限。未来工作包括边缘端对抗性投毒攻击防御及自动驾驶安全响应的监管框架构建。
链接: https://arxiv.org/abs/2608.19135
作者: Krishna Teja Medam
机构: 未知
类目: Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA); Networking and Internet Architecture (cs.NI)
备注:
Abstract:A connected vehicle has roughly 100 milliseconds to decide whether an incoming Basic Safety Message is real or fabricated. If a false emergency braking alert reaches the planning pipeline in time, the car brakes - a safety failure triggered by a security failure. Existing intrusion detection systems are not designed to handle that coupling. They operate per vehicle, per message, with static rules - blind to attack patterns that only emerge across a fleet or over time, and blind to the fundamental tension between dropping a suspicious message and dropping a real emergency alert. We propose a three-tier multi-agent architecture that treats this timing constraint as a hard design requirement, not a performance target. At the vehicle level, an onboard agent classifies each incoming V2X message into one of four actions - Accept, Drop, Quarantine, or Escalate - within a 10-millisecond budget, deliberately biased toward Escalate when uncertain, passing ambiguous cases to the roadside edge agent rather than risking a dropped legitimate alert. The edge agent operates across a roadside unit zone with a 50-millisecond budget, fusing threat assessments from multiple vehicles and resolving safety-security conflicts using complementary sensor observations. The cloud tier refines detection models through Byzantine fault-tolerant federated learning and redistributes updated weights to the fleet. Every timing constraint derives directly from the 100-millisecond Basic Safety Message cycles mandated by SAE J2735 and ETSI EN 302 637-2. No existing framework simultaneously assigns standards-grounded latency budgets to all three deployment tiers while treating safety-security conflict resolution as a first-class design constraint. Remaining open problems - adversarial poisoning at the edge and the absence of regulatory frameworks for autonomous security response - are discussed as future work.
[MA-1] Adaptive Memory and Reflection Multi-Agent System for Medical Question Answering
【速读】：该论文旨在解决现有医疗问答（Medical QA）系统在处理复杂临床案例时存在的适应性差、缺乏持续记忆和结构化推理能力的问题。当前主流系统多采用单智能体架构与静态检索机制，难以应对需要事实知识整合与多层次推理的医疗场景。为此，本文提出一种自适应记忆与反思（Adaptive Memory and Reflection, AMR）的多智能体框架，其核心在于通过专用记忆模块与基于反思的反馈机制，使专业化智能体能够动态检索历史病例并优化后续推理过程。系统根据问题复杂度自动分配独立、协作或升级的工作流，并引入共识聚合与伦理审查模块以保障推理一致性与输出可靠性。实验在MedQA和MedMCQA数据集上验证了AMR系统的优越性能，消融研究进一步表明，结合智能体专属记忆、反思机制与外部检索可显著提升系统表现。该研究揭示了结构化记忆与闭环反馈对构建更可信医疗智能体的关键作用。
链接: https://arxiv.org/abs/2608.19029
作者: Pradeep Murugesan,Luoxiao Yang,Xueli Chen,Xinqi Fan
机构: Manchester Metropolitan University (曼彻斯特都会大学); Technion – Israel Institute of Technology (以色列理工学院); Hong Kong Metropolitan University (香港城市大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注: Accepted by IEEE SMC 2026
Abstract:Accurate and responsible medical question answering (QA) is important in healthcare, where complex cases require factual knowledge and nuanced reasoning. Existing medical QA systems, typically based on single-agent architectures and static retrieval, often lack adaptability, persistent memory, and structured decision-making. This work introduces an adaptive memory and reflection (AMR) agentic system, a multi-agent framework in which specialized agents use dedicated memory and reflection-based feedback to retrieve relevant prior cases and improve subsequent reasoning. Complexity assessment routes questions through solo, collaborative, or escalated workflows, while consensus and ethical overseer modules support reasoning consolidation and output review. Evaluation on MedQA and MedMCQA demonstrates strong performance compared with several baselines. Ablation studies show that combining agent-specific memory, reflection, and external retrieval yields the strongest performance. These findings highlight the potential of structured memory and feedback for developing more trustworthy medical agents. The source code is publicly available at this https URL.
[MA-2] DentAgent : Evidence-Centric Multi-Agent Coordination for Multimodal Dental Reasoning
【速读】：该论文旨在解决当前口腔疾病评估中多模态数据融合不足与推理过程缺乏可追溯性的问题。现有牙科人工智能系统普遍局限于单一模态或特定任务，而尽管近期视觉-语言模型已支持灵活的牙科问答，但其直接生成的答案往往隐含证据、难以溯源。为此，本文提出DentAgent——一种以证据为核心的多智能体框架，其核心创新在于通过“协调者（Orchestrator）”调度五个跨模态的专业智能体，每个智能体利用领域专用工具将影像、照片、3D数据等异构观测转化为结构化证据记录；这些记录由“证据黑板（Evidence Blackboard）”统一管理，实时追踪证据覆盖范围、缺失项及冲突点，确保推理过程透明可追溯。该标准化证据表示机制将分散的牙科能力整合为统一的智能体工作流，在四个基准测试中表现卓越，尤其在多标签诊断任务上超越资深专家17.3个百分点，验证了其在可解释、可扩展的多模态牙科推理中的价值，具备作为人群口腔健康评估与管理技术基础的潜力。
链接: https://arxiv.org/abs/2608.18878
作者: Zijie Meng,Xiwei Dai,Yixuan Tang,Jin Hao,Yang Feng,Fudong Zhu,Xiaoqiang Liu,Shaosheng Cao,Zuozhu Liu
机构: Zhejiang University (浙江大学); Shanghai Jiao Tong University (上海交通大学); Angelalign Technology Inc.; Peking University (北京大学); Tsinghua University (清华大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Oral diseases affect billions of people worldwide, underscoring a pressing need for accurate and reliable dental assessment that integrates heterogeneous evidence from domain knowledge, radiographs, intraoral photographs, and 3D dental data. Most existing dental AI systems remain modality- or task-specific. Although recent vision-language models support flexible dental question answering, directly generated response leaves evidence implicit and untraceable. To address these limitations, we introduce DentAgent, an evidence-centric multi-agent framework, in which the Orchestrator coordinate five specialized agents spanning various modalities. Each specialist utilizes domain tools to convert observations into structured evidence records. The Evidence Blackboard manages these records as a shared evidence state, tracking coverage, gaps, and conflicts before response generation. This standardized evidence representation integrates isolated dental capabilities into a unified agentic workflow. Across four benchmarks, DentAgent demonstrates leading performance, even surpassing the senior specialists by 17.3 percentage points on multi-label diagnosis, which supports its value for broadly applicable and traceable multimodal dental reasoning, and highlights its potential as a technical foundation for population oral health assessment and management.
[MA-3] CentaurBench: Benchmarking LLM Capabilities on Augmenting vs. Automating Real-World Work Tasks
【速读】：该论文旨在解决当前大语言模型（LLM）评估体系中存在的一大缺陷：现有基准测试主要衡量模型在独立完成工作任务时的性能，而忽视了实际应用中模型常作为辅助角色以增强其他（人类或低能力模型）代理表现的情境。其核心问题在于，一个在自动化任务中表现优异的模型，并不必然能有效提升弱代理的工作效率与质量。解决方案的关键在于提出一种统一框架，用于评估模型在“自动化”与“辅助增强”两种不同角色下的表现能力。该框架通过让高能力助手模型为标准化的低能力工作者生成协助文本，由后者生成最终交付成果，并采用基于任务特异性评分标准的盲评双人比较法进行评估。实验结果表明，在七项具有经济意义的真实任务中，自动化表现最优的模型在辅助增强场景中仅在一项任务上优于无指导条件，且有三项任务中未受助的低能力模型反而优于所有受助情况，揭示了自动化能力与辅助价值之间相关性较弱。因此，研究强调应发展面向人-智能体协同系统与多智能体协作场景的新型评估基准，以更全面地反映模型在实际协作环境中的真实效能。
链接: https://arxiv.org/abs/2608.18554
作者: Pattaraphon Kenny Wongchamcharoen,Kris Gulati,Min Min Fong,Abhishek Nagaraj
机构: UC Berkeley (加州大学伯克利分校); NBER (美国国家经济研究局)
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); General Economics (econ.GN)
备注: 46 pages, 15 figures
Abstract:Most LLM benchmarks rank models on their ability to automate work tasks. In practice, however, models are often used to assist other (human or LLM) agents. The question that drives model selection is therefore not only which model produces the best output, but which model most improves the work of another (weaker) agent. We introduce a unified framework that evaluates the capability of models to automate and augment another agent’s performance. Across seven economically grounded real-world tasks, an assistant model writes assistance text for a standardized lower-capacity worker model, which produces the deliverable. In automation mode, the assistant produces the output directly. Outputs are scored through blind pairwise comparisons by an LLM judge panel with task-specific rubrics, replicated across ten runs. Rankings across the two regimes are only modestly correlated, and the automation winner loses augmentation on five of seven tasks. Assistance is not reliably positive. The unaided worker outranks every assisted condition on three tasks, and only one model’s guidance beats no guidance on average. These results suggest that automation ability is an incomplete proxy for assistance quality, motivating benchmarks that evaluate models according to the roles they play in human-AI and multi-agent systems.
[MA-4] DART-SD: Diamond-topology Aware Retrieval and Tuning for Self-Distillation of Multi-Turn Tool-Calling Agents
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在多轮工具调用任务中因依赖全轨迹模仿学习而导致的策略多样性严重退化问题。具体而言，当任务包含多个顺序无关的子目标时，最优解空间呈现出复杂的组合钻石形拓扑结构（diamond lattice topology），而传统方法将这一复杂拓扑强制压缩为单一连续轨迹，引发严重的拓扑坍缩（topological collapse），从而无差别惩罚合法的替代探索路径，抑制了模型的自主性与泛化能力。其解决方案的关键在于提出DART-SD（Diamond-topology Aware Retrieval and Tuning for Self-Distillation）框架，该框架通过构建收敛式交互状态转移图（Interaction-State Transition Graph, ISTG）来精准建模成功与失败路径的内在钻石拓扑结构；在自主推理过程中，识别关键拓扑断裂点（Critical Topological Breakpoint, CTB），并检索由成功路径支持的恢复参考；进而引入基于CTB引导的局部化监督机制，实施渐进式自蒸馏训练，确保仅在生成的恢复步骤上计算损失，严格保护有效推理前缀免受破坏性梯度更新的影响。该方法实现了从全局强制向拓扑感知局部修正的范式转变，显著提升了多轮工具调用任务中的策略多样性和性能表现。
链接: https://arxiv.org/abs/2608.18524
作者: Hangrui Xu,Jiarui Wang,Yang Yang,Chuanbo Zhu,Fangda Chen,Ziqi Wu,Jingming Cai,Yan Song
机构: ByteDance(字节跳动)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:Equipping Large Language Models (LLMs) with multi-turn tool-calling capabilities is essential for building autonomous agents. However, progress is fundamentally limited by the reliance on full-length trajectory imitation. For tasks involving multiple order-independent sub-goals, the optimal solution space forms a vast combinatorial diamond lattice. Forcing this rich topology into monolithic trajectories causes a severe topological collapse, indiscriminately penalizing valid alternative explorations and severely degrading policy diversity. To address this, we propose DART-SD (Diamond-topology Aware Retrieval and Tuning for Self-Distillation), a novel framework that shifts the paradigm from global forcing to topology-guided localized correction. DART-SD first models the execution process as a converging Interaction-State Transition Graph (ISTG), faithfully capturing the inherent diamond topology of successful and failed exploratory paths. During autonomous rollouts, the framework identifies the Critical Topological Breakpoint (CTB) and retrieves success-supported recovery references. Finally, we introduce a progressive self-distillation paradigm through CTB-guided localized supervision, ensuring that the training loss is calculated exclusively on the generated recovery steps while strictly protecting the valid reasoning prefix from destructive gradient updates. Experiments on complex multi-turn tool-calling benchmarks demonstrate that DART-SD significantly outperforms traditional full-trajectory baselines.
[MA-5] Bayesian Partner Modelling enables Adaptive Replanning for LLM Coordination
【速读】：该论文旨在解决多智能体大语言模型（Multi-agent Large Language Model, LLM）系统在协作过程中难以适应新队友策略动态变化的问题。当合作伙伴在任务执行中改变行为策略时，现有方法要么仅被动感知到这一变化（即仅更新上下文信息，但响应迟缓），要么盲目频繁地重新规划，导致效率低下。其核心挑战在于如何在保持计划连续性的同时，及时识别并响应队友策略的变更。本文提出的解决方案——BayesBeliefAgent，通过将分层式大语言模型（Hierarchical LLM）规划器与贝叶斯追踪模块相结合，实现了对合作队友行为的主动、精准建模。该方法的关键在于：仅当对手的行为直接与当前推断出的技能假设相矛盾时，才触发当前技能的中断与重规划，从而避免了不必要的频繁重规划。此外，作者引入“信念-行动差距”（belief-action gap）作为评价指标，衡量在正确理解伙伴行为的前提下仍执行非互补技能的比例；实验结果表明，在Overcooked基准环境上，基于矛盾条件的控制机制显著缩小了信念-行动差距，同时相比启发式方法减少了约一个数量级的重规划次数，有效提升了协作效率与决策一致性。
链接: https://arxiv.org/abs/2608.18490
作者: Harsh Goel,Aditya Sai Ellendula,Vaishnav Tadiparthi,Ehsan Moradi Pari,Hossein Nourkhiz Mahjoub,Sandeep P. Chinchali
机构: 未知
类目: Multiagent Systems (cs.MA)
备注: Preprint
Abstract:Multi-agent Large Language Model (LLM) systems often struggle to collaborate with new teammates whose strategies shift mid-task. Because agents execute multi-step or temporally extended skills, they frequently continue executing outdated plans long after public evidence shows that a partner has changed its skill. Existing methods either treat partner tracking as passive context-leaving the agent aware of the shift but slow to act-or replan indiscriminately. We introduce BayesBeliefAgent, which pairs a hierarchical LLM planner with a Bayesian tracking module. Rather than replanning constantly, our agent interrupts its current skill only when a partner’s actions directly contradict the inferred skill. Beyond standard reward, we evaluate performance using replanning efficiency and the belief-action gap: the fraction of total decisions where an agent with a correct partner estimate executes a non-complementary skill. Across benchmark Overcooked environments, contradiction-conditioned control drastically narrows this belief-action gap while requiring an order of magnitude fewer replans than heuristic methods
[MA-6] A Locally Deployable Tool-Grounded LLM Multi-agent Framework for Automating Methane Emission Analysis and Reporting
【速读】：该论文旨在解决甲烷田间监测中多环节流程分散、依赖专家手动协调导致效率低下、可重复性差及数据安全风险高的问题。其核心解决方案是构建一个本地部署的、基于工具的大型语言模型（LLM）多智能体框架，通过LLM智能体作为工作流协调者，将采样设计、气象数据解析、传感器处理、高斯烟羽反演、可视化与报告生成等环节有机整合。该框架不直接预测甲烷浓度或排放量，而是以工具调用为基础实现跨模块协同，确保输出可追溯且符合科学逻辑。在多种真实场景（如污水处理厂、垃圾填埋场和油气设施）中的大量实地测试表明，该框架在工作流路由与参数提取方面达到92.0%准确率，在排放速率估算与烟羽预测上成功率可达85.0%，报告生成成功率达95.0%，同时将工作流程耗时从小时级缩短至分钟级，显著降低人工干预与提示工程需求，并支持本地化处理以保障数据安全。研究结果表明，基于工具的LLM协调机制能够有效降低甲烷田间监测在时间成本、人力投入、使用门槛和数据隐私方面的多重障碍。
链接: https://arxiv.org/abs/2608.18473
作者: Yang Yan,Zifan Zhou,Xuan Wang,Erum Hassan,Bilguunzaya Mijiddorj,Jie Cao,Bin Li,Binbin Weng
机构: 未知
类目: Multiagent Systems (cs.MA)
备注:
Abstract:Methane field monitoring requires the integration of sampling design, meteorological interpretation, sensor processing, plume analysis, visualization, and reporting, but these steps are often distributed across separate expert-driven workflows. We developed a locally deployable, tool-grounded large language model (LLM) multi-agent framework for our low-cost methane sensing and field-monitoring campaigns. The framework uses LLM agents as workflow coordinators that link field measurements, meteorological data, deterministic sensor-processing routines, Gaussian plume inversion, and report generation, rather than directly estimating methane concentrations or emissions. Extensive field deployments across diverse real-world environments (e.g., wastewater treatment facilities, landfills, and oil and gas sites) demonstrate that our framework can achieve 92.0% accuracy in workflow routing and parameter extraction, 85.0% success in emission-rate estimation and plume prediction, and 95.0% success in generating editable reports under practical operating conditions. Compared with manual and general-purpose LLM-assisted workflows, it reduced workflow time from hours-level to minutes-level, lowered manual coordination and prompt-engineering requirements, and retained traceable plume-based outputs. In addition, most processing can be performed locally, reducing exposure of sensitive facility and field data to cloud services. These results indicate that tool-grounded LLM coordination can reduce the time, labor, usability, and data-security barriers of methane field monitoring.
[MA-7] Model Predictive Supervisory Control for Hierarchical and Distributed UAS Traffic Management
【速读】：该论文旨在解决多智能体系统在共享资源环境下的协调控制问题，尤其关注如何在动态需求下实现安全、非阻塞且资源独占的高效任务执行。其核心挑战在于如何在保证系统安全性与实时响应能力的同时，避免因集中式合成带来的计算复杂性与可扩展性瓶颈。解决方案的关键在于提出一种分层式模型预测监督控制（Hierarchical Model Predictive Supervisory Control, MPSC）框架，该框架将滚动时域内的成本最优控制与基于监督控制理论（Supervisory Control Theory, SCT）的安全监督机制相结合，通过分层化、可扩展的监督器与自动机模板设计，实现了分布式执行，避免了全局单体式合成，显著提升了系统的可扩展性与实时性。基于此框架，论文构建了一个面向城市无人机交通管理（Urban Unmanned Aircraft System Traffic Management, UTM）的模型，有效支持了随时间变化的需求下的取送任务调度。
链接: https://arxiv.org/abs/2608.18353
作者: Matheus P. Loures,Guilherme V. Raffo,Patrícia N. Pena
机构: Universidade Federal de Minas Gerais (巴西联邦大学); Graduate Program in Electrical Engineering (电气工程研究生项目); Department of Electronics Engineering (电子工程系)
类目: ystems and Control (eess.SY); Multiagent Systems (cs.MA)
备注: 6 pages, 7 figures, IFAC conference
Abstract:This work proposes a hierarchical Model Predictive Supervisory Control (MPSC) framework for multi-agent systems with shared resources. MPSC integrates receding-horizon cost-optimal control with Supervisory control theory (SCT) based supervision that enforces safety, nonblockingness, and resource exclusivity. Scalability arises from hierarchical and scalable supervisor and automaton templates, enabling distributed execution without monolithic synthesis. Using this framework, this work develops an urban Unmanned aircraft system Traffic Management (UTM) model. The model supports pickup-and-delivery missions under time-varying demand efficiently.
[MA-8] Contracting for LLM Delegation: Moral Hazard in Technology and Effort Choice
【速读】：该论文旨在解决在生成式 AI（Generative AI）背景下，委托人（Principal）如何通过激励机制有效引导代理（Agent）在多种技术（如不同大语言模型，LLM）中进行智能选择并合理分配努力水平（如令牌预算）的委托-代理问题。传统委托-代理框架难以刻画代理在多技术选项间权衡成本与能力的复杂决策行为，而本文将此扩展为包含二维隐藏行动（技术选择与努力分配）的动态优化问题。其解决方案的关键在于构建一个最优线性合同模型，揭示代理的最佳响应策略由一个阈值型奖励分成触发——当奖励份额达到特定阈值时，代理会切换所使用的技术。研究通过在 MATH 与 MMLUPro 基准上对开源权重 LLM 组合进行校准，并采用强化学习中的多臂赌博机算法模拟双方策略演化，发现主仆双方最终收敛至理论均衡解附近，验证了线性合同在激励复杂、技术感知型代理任务中的有效性。
链接: https://arxiv.org/abs/2608.18232
作者: Nanda Kishore Sreenivas,Kate Larson
机构: University of Waterloo( Waterloo 大学)
类目: Multiagent Systems (cs.MA)
备注:
Abstract:We extend the standard Principal-Agent framework to scenarios where the Agent selects from a suite of technologies, each characterized by a distinct cost-capability profile. This framework is increasingly critical in the era of Large Language Models (LLMs), where Agents choose both a model and an associated effort level (e.g., token budget). We model the relationship between output quality and effort as a concave, saturating function, which depends on the Agent’s hidden two-dimensional action choice balancing technology selection and effort allocation. We derive the optimal linear contract for the Principal, demonstrating that the Agent’s best response is characterized by a threshold reward share that triggers technology switching. Finally, we calibrate our model using open-weight LLM pairings across the MATH and MMLUPro benchmarks. We show that both Principal and Agent, when employing bandit algorithms to navigate this environment, converge to strategies that closely align with our theoretical equilibrium. These results suggest that simple linear contracts can effectively incentivize complex, technology-aware delegation in agentic workflows.
[MA-9] owards Reversible Forgetting: Managing Obsolete Knowledge in Continual Enterprise AI Agents
【速读】：该论文旨在解决企业在非平稳环境（non-stationary environments）中部署生成式AI代理时，传统持续学习（continual learning）方法因一味强调对过往知识的保留而导致的“遗忘即失败”范式所引发的问题。在客户行为、政策法规、工具流程及市场条件不断变化的现实场景下，无差别保留过时知识可能引发负向迁移（negative transfer）与运营风险。为此，论文提出“可逆遗忘”（reversible forgetting）这一概念框架，其核心在于引入三种操作性记忆状态：活跃（active）、休眠（dormant）与退役（retired），并通过再激活机制在知识相关性恢复时实现休眠知识的重建。该框架的关键创新在于设计了一种滞后型可逆记忆控制器（Hysteretic Reversible Memory Controller），通过累积相关性证据、采用不对称阈值防止状态振荡、以影子模式测试再激活可行性，并结合策略门控实现退役决策，从而在不将临时抑制误判为永久删除的前提下，有效降低过时信息的影响。金融场景中的案例表明，特定市场制度下的知识可能在另一制度下产生负面影响，但当相似环境重现时又可重新具备价值，体现了该框架的动态适应能力。
链接: https://arxiv.org/abs/2608.18177
作者: Nilutpaul Sarker Yash,Tirtho Roy,Ushashi Bhattacharjee
机构: 未知
类目: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:Continual learning has traditionally treated forgetting as a failure, emphasizing preservation of previously acquired knowledge as environments evolve. We argue that this objective is incomplete for enterprise AI agents operating in non-stationary environments, where customers, policies, tools, workflows, regulations, and market conditions change over time. Indiscriminate retention can allow obsolete knowledge to influence decisions, creating negative transfer and operational risk. We therefore propose reversible forgetting: a conceptual framework with three operational memory states: active, dormant, and retired, and a reactivation transition that can restore dormant knowledge when its relevance returns. We instantiate the framework as a Hysteretic Reversible Memory Controller that accumulates relevance evidence, uses asymmetric thresholds to prevent state oscillation, tests reactivation in shadow mode, and gates retirement through policy. The framework reduces the influence of obsolete information without conflating temporary suppression with permanent erasure. Finance illustrates the idea: knowledge useful under one market regime may become harmful under another yet regain relevance when similar conditions recur.
[MA-10] Same Facts Different Updates: Inference Setup Shapes LLM Behavior in Medical Allocation ICML2026
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在实际部署过程中因上下文累积效应导致行为不可预测、且可能产生偏差的问题，尤其是在医疗资源分配等敏感决策场景中的表现。其核心挑战在于，模型在面对相同初始信息但附加不同上下文（如新增患者信息）时，其输出概率分布可能发生显著甚至相反方向的偏移，从而影响公平性和可解释性。解决方案的关键在于揭示并量化这种上下文依赖性（context-dependent effect），通过设计“配对上下文”与“独立推理”对比实验，验证模型响应对历史上下文和新增信息的敏感性，并强调在实际应用中需进行系统性的上下文工程（context engineering）与模型行为评估，以确保基于大语言模型的决策系统具备稳定性与可靠性。
链接: https://arxiv.org/abs/2608.18108
作者: Spencer Gibson,Tyler Crosse,Magnus Saebo,Achyutha Menon,Eyon Jang,Diogo Cruz
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)
备注: Accepted to the AI4GOOD Workshop at ICML 2026, Seoul, South Korea
Abstract:Large language models are being incorporated into sensitive and important decision-making processes across nearly all fields. While prior work studies model bias around inputs and scenario framing, models can also behave in unexpected and undesirable ways due to context accumulated over their deployment. In this work, we study a medical example in which a model is asked to assign resource-allocation probabilities to two people given brief clinical context, and then sees the same scenario with a single extra sentence containing contrasting patient information, either with or without its previous response in context. Across three of four tested models, the paired-context and independent-inference experiments have different probability shifts, often in opposite directions (in favor of Person B vs. in favor of Person A) when new information is provided. We include additional paired-context experiments to show the effect of varying attributes across scenario axes. Our findings show the context-dependent effect of patient information in a sensitive medical use case. More broadly, our work shows the importance of carefully incorporating LLM-based systems into decision-making processes, context engineering, and further model behavioral studies.
[MA-11] Simulation-Optimization of Systems of Optimizers: Exploiting the Inner Optimizations Geometry
【速读】：该论文旨在解决系统级优化器（Systems of Optimizers, SOSO）中仿真-优化的效率与精度问题，即在基于代理的仿真中，每个代理在每个决策时点求解一个结构化优化问题（如线性规划、混合整数规划或动态规划），而传统仿真优化方法将整个仿真过程视为黑箱，忽略了内部优化问题的几何结构。其核心挑战在于如何有效利用这些嵌入式优化问题的数学结构以提升梯度估计的准确性与计算效率。解决方案的关键在于：首次形式化了SOSO框架，并提出通过传播内层线性规划（LP）的最优基（optimal basis）和对偶变量，实现外层目标函数的精确、无偏、单次复制的无穷小扰动分析（Infinitesimal Perturbation Analysis, IPA）梯度计算，仅在测度为零的基变更情况下存在例外。进一步地，论文推导出基于前向仿真中基不一致计数的共随机数（Common Random Numbers, CRN）协方差上界，并证明IPA方差随反馈深度呈指数增长，从而形式化了“牛鞭效应”（bullwhip effect）。在此基础上，提出了“代理作为决策”（surrogate-as-decision）方法，通过引入误差预算统一约束线性规划时域与强化学习代理的近似误差。最终，将上述技术整合为PRIME算法，一种集成IPA梯度、自适应步长与多起点空间多样性策略的随机逼近求解器。在涵盖SOSO分类体系的六个测试实例上，PRIME在相同预算下达到最优或并列最优的优化差距，且收敛过程几乎无振荡、种子间方差极小；相比独立有限差分法，IPA实现每复制约2000倍的方差降低，而在包含1000种商品、6个配送中心的供应链场景中，共随机数使成对差分方差下降超过10,000倍。这些结果确立了挖掘嵌入式优化几何结构是仿真优化领域具有实际意义的重要研究方向。
链接: https://arxiv.org/abs/2608.18129
作者: Zhou He
机构: University of Chinese Academy of Sciences(中国科学院大学)
类目: Optimization and Control (math.OC); Multiagent Systems (cs.MA)
备注:
Abstract:We study simulation-optimization of systems of optimizers (SOSO): agent-based simulations in which every agent solves a structured optimization - a linear program (LP), mixed-integer program, or dynamic program - at every decision epoch. Such systems arise in supply chains, electricity markets, and logistics, yet standard simulation optimization treats the simulation as a black box, discarding the inner optimization’s geometry. We formalize SOSO and develop a framework that converts this geometry into computational advantage. We prove that the inner LP’s optimal basis and dual variables propagate through the dynamics to yield an exact, unbiased, single-replication infinitesimal perturbation analysis (IPA) gradient of the outer objective, with measure-zero basis changes as the only obstructions. We derive a common-random-numbers covariance bound governed by a computable basis-disagreement count from the forward simulation. We further prove that IPA variance grows exponentially with feedback depth, formalizing the bullwhip effect, and introduce surrogate-as-decision with an error budget unifying LP-horizon and reinforcement-learning surrogates under one bound. We compose these into PRIME, a stochastic-approximation solver integrating the IPA gradient, adaptive step sizing, and multi-start spatial diversification. On six testbeds spanning the SOSO taxonomy, PRIME achieves the best or tied-best optimality gap at equal budget with near-zero oscillation and narrow seed-to-seed variance; IPA yields a ~2000x per-replication variance reduction over independent finite differences, and common random numbers cut paired-difference variance by over 10,000x in a 1,000-SKU, six-distribution-center supply chain. These results establish exploiting embedded-optimization geometry as a practically significant direction for simulation optimization.
自然语言处理
[NLP-0] SPADE: Self-Play in Adaptive Synthetic Executable Environments
【速读】： 该论文旨在解决语言智能体在持续自我改进过程中面临的目标分布固化问题——现有训练环境池（手工构建、静态合成或固定验证器）无法随学习者规模扩展而动态演化，导致生成的目标缺乏多样性与适应性。其核心解决方案是提出一种自洽式自玩强化学习框架SPADE（Self-Play in Adaptive Synthetic Executable Environments），其中单一大语言模型（LLM）同时扮演两个角色：环境设计者（Environment Designer）和推理智能体（Reasoning Agent）。环境设计者通过生成具有完整状态转移、奖励函数和验证逻辑的可执行代码环境（类OpenAI Gym接口），动态构建长时程、复杂任务；推理智能体则在这些环境中进行多步决策与工具调用。关键创新在于将环境设计过程本身建模为可学习的优化目标：通过最小化推理智能体在获得特权提示前后的回报差距（即“后悔值”），环境设计者被引导生成位于智能体能力边界但依然可解的挑战性环境。实验表明，该方法在数学、科学、代码及推理等8个基准上，相较于最强的固定环境基线平均提升5.3分，在工具使用场景下分别提升BFCL-v4多轮任务5.7分、ACEBench-Agent 13.9分；且随着模型规模扩大，游戏类任务的优势进一步增强。通过将环境设计纳入可学习闭环，SPADE为实现开放式的持续自我改进提供了实质性路径。
链接: https://arxiv.org/abs/2608.19197
作者: Bo Liu,Simon Yu,Yiding Jiang,Ao Qu,Andrew Zhao,Zichen Liu,Junsu Kim,Zijian Zhou,Seungone Kim,Tongzheng Ren,Mickel Liu,Hanfei Yu,Zhaorun Chen,Weiyan Shi,Paul Pu Liang,Luke Zettlemoyer,Yejin Choi,Natasha Jaques
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Work in progress. Project page: this https URL ; Code: this https URL
Abstract:Continuous self-improvement requires an ever-expanding pool of self-generated, diverse, adaptive goals. For language agents, existing training environment pools (hand-curated, statically synthesized, or frozen-verifier) keep the goal distribution fixed as the learner scales. We introduce SPADE (Self-Play in Adaptive Synthetic Executable Environments), a self-play RL framework in which a single LLM plays two roles: an Environment Designer that writes complete, long-horizon training environments as executable code with an OpenAI Gym-style reset()/step() interface, and a Reasoning Agent that learns to act in them. Each is a stateful, multi-turn environment (state transitions, reward functions, and verification code), so one interface spans reasoning problems and multi-step agentic tool use. The Reasoning Agent’s regret is estimated using the gap between its reward with and without privileged hints; in optimizing this regret signal the Environment Designer learns to target environments at the edge of the agent’s capabilities while keeping them feasible. Through extensive experimentation, we find several components critical to success: grounding the Environment Designer on documents sampled from a large pretraining corpus, and giving it an accumulated environment memory. Scaling to 30B-parameter models, SPADE improves over the strongest fixed-environment baseline by +5.3 on average across eight held-out math, science, code, and reasoning benchmarks, and lifts the tool-use setting by +5.7 on BFCL-v4 multi-turn and +13.9 on ACEBench-Agent; on the games setting, the margin over the strongest baseline grows with model scale. By making environment design itself a learnable component, SPADE takes a concrete step toward open-ended self-improvement.
[NLP-1] Beyond Teacher Likelihood: Group-Calibrated On-Policy Distillation for Long-Context Reasoning
【速读】： 该论文旨在解决生成式 AI 在长上下文任务中因依赖密集的词元级（token-level）教师指导而导致的“局部合理但全局错误”问题，即学生模型可能生成看似合理但遗漏跨输入分布的关键证据或违反全局任务约束的响应。其核心解决方案是提出组校准的在线蒸馏（Group-Calibrated On-Policy Distillation, GC-OPD），关键在于通过在每一批次（rollout group）内分别对验证器评分（verifier rewards）与轨迹级（trajectory-level）OPD得分进行归一化，并利用两者差异构建带符号的教师-验证器不一致残差（signed teacher-verifier disagreement residual），以捕捉教师指导与任务目标之间的偏差。进一步采用基于相对优势的信用分配（RACA）机制，将该残差按各词元的相对OPD优势分布至词元层面，既保留了原始的密集词元级指导信号，又有效融合了任务完成度的全局评估信息。实验表明，在五个长上下文基准上，GC-OPD显著提升Qwen3-4B和Qwen3-8B模型的平均性能，分别从29.08/35.12提升至40.47/44.65，优于基线OPD方法，且消融实验证明该设计中的符号残差与相对优势分配机制均具有关键作用。
链接: https://arxiv.org/abs/2608.19181
作者: Zhu Zhang,Jixun Wang,Xiaoang Xu,Xiaorong Wang,Zihan Zhou,Zhiyuan Wang,Shuo Wang,Chaojun Xiao,Yuezhi Zhou
机构: Tsinghua University (清华大学); Beijing University of Posts and Telecommunications (北京邮电大学); OpenBMB
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 20 pages, 5 figures
Abstract:On-policy distillation (OPD) trains a student on its own responses using dense token-level guidance from a stronger teacher. In long-context tasks, however, token-level teacher support can favor locally plausible responses that omit evidence distributed across the input or violate global task constraints. Task-specific verifiers, in contrast, evaluate task completion at the response level and may return graded rewards that reflect partial success. We diagnose this mismatch on fixed responses from two representative long-context evidence-aggregation tasks. Across longer input ranges, trajectory-level OPD scores become progressively less aligned with verifier rewards, indicating teacher-verifier disagreement. Motivated by this observation, we introduce Group-Calibrated On-Policy Distillation (GC-OPD). GC-OPD separately normalizes verifier rewards and trajectory-level OPD scores within each rollout group and uses their difference as a signed teacher-verifier disagreement residual. Relative-advantage-based credit assignment (RACA) distributes this trajectory-level residual across tokens according to their relative OPD advantages while preserving the original OPD signal. Across five long-context benchmarks, post-training with GC-OPD raises the five-benchmark averages of the official Qwen3-4B and Qwen3-8B checkpoints from 29.08 to 40.47 and from 35.12 to 44.65, respectively. Vanilla OPD reaches 39.31 and 43.56 under the same setup. Controlled ablations show that the signed residual is more effective than either an additional OPD-derived term or direct group-normalized verifier reward addition, while RACA further improves over uniform token allocation. Together, these results demonstrate that group-relative residual calibration can incorporate verifier outcomes without discarding dense token-level guidance. Code is available at this https URL.
[NLP-2] ChildSafeAds Shared Task 2026: Commercial Content in Child-Facing YouTube Videos
【速读】： 该论文针对的是在YouTube视频中面向儿童和青少年的商业内容识别问题，旨在解决如何有效检测并分类可能影响未成年人的广告或推广内容。其核心挑战在于：大量商业内容未正确使用平台内置的广告披露机制（如“包含付费推广”标签），导致用户难以识别潜在的营销信息。解决方案的关键在于构建一个多层次、分阶段的数据评估框架，将证据分为四个累积访问层级（从字幕到视频描述中的链接页面），以评估不同信息源对模型性能的影响，并量化数据获取成本。通过引入生成式AI（Generative AI）如GPT-5.4与GPT-5.6-luna进行标注，结合专家团队迭代优化的标签体系与提示工程，实现了高一致性的人工智能辅助标注，为后续系统评估提供了可靠基准。
链接: https://arxiv.org/abs/2608.19165
作者: Thales Bertaglia,Catalina Goanta,Gerasimos Spanakis,Gunes Acar
机构: Utrecht University(乌得勒支大学); Maastricht University(马斯特里赫特大学); Radboud University(奈梅亨大学)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注:
Abstract:ChildSafeAds is a shared task on commercial content in YouTube videos likely to reach children and teenagers. It contains 3,360 videos from 939 channels. Each instance begins with a segment submitted to SponsorBlock, an open-source crowdsourced browser extension whose users mark sponsor segments so that others can skip them. We pair the segment with its available transcript, video and channel information, and a sales or service page linked from the video description. Systems determine what kind of offer is being promoted (ST1), assign product categories (ST2), and identify legal risk flags (ST3). The evidence is divided into four cumulative access levels, from the transcript to the linked page, so results can be compared against the cost of collecting the data. 45.5% of videos in our data failed to properly use the in-platform ad disclosure method (the ``Includes paid promotion’’ label). GPT-5.4 produced the labels after the expert organiser team reviewed samples and iterated on the taxonomy, prompts and model choices. GPT-5.6-luna independently labelled the development set. This report describes the task, data and evaluation. An updated version will add participating systems and shared-task results.
[NLP-3] Comment-level Topic Drift Analysis in the Reddit Corpus
【速读】： 该论文旨在解决大规模在线文本中话题随时间演变（即话题漂移，topic drift）的检测与量化问题，尤其关注在短文本评论层面的语义动态变化。其核心挑战在于如何在不依赖人工标注的前提下，从海量、非结构化的用户生成内容中识别出具有语义连贯性的动态话题集群，并区分真实语义演化与随机噪声。解决方案的关键在于利用预训练语言模型生成上下文相关的语义嵌入（semantic embeddings），并将这些嵌入作为分析基础，在高维嵌入空间中通过无监督方法追踪话题集群的演化轨迹。研究提出了一种基于嵌入空间本身的语义漂移分析方法，结合对现有技术的可扩展性改进，实现了对127亿条Reddit评论（2006–2022年）的大规模分析；同时引入零假设模型对比检验（null model comparison test），有效过滤虚假动态信号。结果表明，政治与社会争议性话题在嵌入空间中表现出显著的方向性漂移，跨话题距离系统性变化，超出随机预期；而音乐、体育等领域的主题则相对稳定。
链接: https://arxiv.org/abs/2608.19133
作者: Steven Morse,Daniel Runfola,Trenton W. Ford
机构: William Mary(威廉与玛丽学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:We present a novel application of embedding-based dynamic topic modeling techniques to detect and quantify topic drift at the comment level in a massive corpus. By leveraging pretrained language models to generate contextualized semantic embeddings for short text, we analyzed 12.7 billion Reddit comments spanning 2006 to 2022. Using unsupervised methods on these embeddings, we identify dynamically evolving topic clusters over time. Our primary contribution is a methodology for analysis of semantic drift and discourse evolution in the embedding space itself. We also demonstrate modifications to existing methods that enable this analysis at scale, and we propose and demonstrate a null model comparison test to filter spurious dynamics. Key findings suggest that politically and socially contentious topics exhibit significant directional drift in embedding space, with inter-topic distances changing systematically over time beyond what the null model can explain, whereas domains such as music and sports remain comparatively stable.
[NLP-4] Intercepting the Kangaroo: Experimental Astrolinguistics with Constructed Lexicons Active Probing and Large Language Models as Informants and Hypothesis Proposers
【速读】： 该论文旨在解决跨认知范式智能体间语言通信的可行性问题，即如何在双方对现实分类体系存在根本差异（如感知维度、语义结构不兼容）的情况下实现无歧义的语义传递。其核心挑战在于克服奎因提出的“翻译不确定性”（indeterminacy of translation），即在缺乏共同参照系时，词语与其指称对象之间的对应关系无法唯一确定。解决方案的关键在于构建一个可实验化的“天体语言学”（Astrolinguistics）框架：通过两个具有故意不相容构词系统的语言模型作为信息源（一个区分形状、颜色与运动；另一个融合颜色与运动、编码奇偶性且缺失形状概念），并引入一个完全脚本化的协调器来实现跨范畴系统的精确映射。该协议的核心机制包括跨情境排除法、预注册预测探测、主动场景选择、更严格的恢复轮次及隔离策略，有效避免了“袋鼠效应”（kangaroo effect，即词语被无声错误绑定至错误指称对象）。在400余次模拟与实时运行中，该协议实现了零未检测误译，显著优于被动基线（d = 0.62）；即使面对100%注入的“袋鼠陷阱”，亦能完全拦截干扰项，并在缺乏判别证据时以奎因等价类形式声明不确定性而非盲目猜测。在信息源噪声条件下，系统表现出优雅退化特性：在每词≤2%噪声下无袋鼠残留，10%噪声下主要选择不响应而非出错。对于超出预设假设空间的词汇（如历史依赖关系项与异或上下文同形词），通过生成-测试循环由大语言模型提出规则并由脚本验证，实现覆盖能力随生成器性能提升而递增（0%–18%–72%–100%），同时始终保持零未检测误译。研究结论表明，在所测试条件下，正确性是协议本身的属性，而覆盖范围则取决于所用工具的能力。
链接: https://arxiv.org/abs/2608.19124
作者: Francesco Cordella,Mauro Cappelli
机构: ENEA(意大利国家能源、新工艺与工业技术研究所); ENEA, Centro Ricerche di Frascati(意大利国家能源、新工艺与工业技术研究所，弗拉斯卡蒂研究中心)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Astrolinguistics – communication with minds that categorize reality differently from ours – has been purely speculative since Freudenthal’s Lincos (1960). We make it experimental. Two language models with deliberately incompatible constructed lexicons (one encoding shape, color, and motion; the other fusing color with motion, encoding parity, and lacking shape) serve as informants with complete ground truth, while a fully scripted orchestrator translates between the two category systems. The central failure mode is the kangaroo effect: the silent attachment of a word to the wrong referent – Quine’s indeterminacy of translation, operationalized. Across 400+ simulated and live runs, a protocol combining cross-situational elimination, pre-registered predictive probes, active scene selection, a stricter recovery round, and quarantine produced no undetected mistranslations under the tested conditions and exceeded a passive baseline’s coverage (d = 0.62). Injected kangaroo traps defeated naive ostension and pure statistical learning in 100% of runs, while the full protocol intercepted every decoy and, where discriminating evidence is ontologically unavailable, declared Quinean equivalence classes instead of guessing. Under informant noise it degrades gracefully: zero kangaroos persist up to 2% per-word noise; at 10% the protocol predominantly abstains rather than errs. Finally, words outside the scripted hypothesis space (a history-dependent relational term and an XOR contextual homonym) are recovered by a generate-and-test loop in which an LLM proposes rules and the script verifies them: coverage scales with proposer capability (0% - 18% - 72% - 100%) while undetected mistranslations stayed at zero throughout. In the tested conditions, correctness is a property of the protocol; coverage is a property of the instruments.
[NLP-5] Open-MOPD: Diagnosing and Fixing Capability Imbalance in Multi-Teacher On-Policy Distillation
【速读】： 该论文旨在解决多教师在线蒸馏（Multi-teacher on-policy distillation, M-OPD）中领域专用强化学习（Reinforcement Learning, RL）专家能力整合效率低下这一关键问题，尤其聚焦于当前方法在跨域能力融合过程中存在的优化动态不透明、可复现性差等缺陷。其核心挑战在于：尽管M-OPD通过密集的分词级奖励监督实现专家知识聚合，但实际性能远低于理论最优水平，存在显著的能力整合差距。研究发现，该问题并非由梯度冲突引起，而是源于分词级优化预算的严重错配——具体由三个独立因素驱动：不同领域间结构化的序列长度差异、因非均匀学习率导致的动态收敛漂移，以及异步策略更新带来的多步奖励陈旧性。为此，论文提出Open-MOPD框架，其关键创新在于引入三重机制：基于分词共享的平衡策略、感知能力差距的动态预算分配，以及学生奖励刷新机制，系统性地恢复跨域优化平衡。实验表明，该方案将能力整合头空间（headroom）利用率从35.6%提升至83.4%，显著逼近领域路由的基准模型性能。研究同时开源了完整的后训练流程、训练轨迹与评估套件，支持在学术级硬件条件下可复现的部署实践。
链接: https://arxiv.org/abs/2608.19098
作者: Huan-ang Gao,Haohan Chi,Yong Yan,Shiyuan Feng,Hanlin Wu,Zheng Jiang,Bingxiang He,Wei-Ying Ma,Ya-Qin Zhang,Hao Zhou
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Project page: this https URL
Abstract:Multi-teacher on-policy distillation (M-OPD) has emerged as a promising paradigm for consolidating domain-specialized reinforcement learning (RL) experts into a single generalist student via dense, token-level reward supervision. Despite its practical success, the optimization dynamics governing multi-teacher capability integration remain poorly understood, and open, rigorously reproducible recipes are conspicuously lacking. In this work, we establish a controlled M-OPD benchmark on SmolLM3-3B-Base with oracle routing, isolating capability integration from routing ambiguity. Our investigation reveals a pronounced capability integration gap: standard M-OPD captures only 35.6% of the available headroom relative to a domain-routed oracle ensemble, with concise tasks such as instruction following suffering severe degradation and premature stagnation. Crucially, we show that this failure stems not from gradient conflict, but from a severe misallocation of the token-level optimization budget. This pathology is driven by three orthogonal factors: structural sequence-length disparities across domains, dynamic convergence drift due to non-uniform learning rates, and multi-step reward staleness from asynchronous policy updates. To resolve these imbalances, we introduce Open-MOPD, a principled framework incorporating token-share balancing, gap-aware dynamic budget allocation, and student reward refresh. Together, these mechanisms systematically restore cross-domain balance, elevating headroom recovery from 35.6% to 83.4% in a single deployable student. We fully open-source our end-to-end post-training recipe, training trajectories, and evaluation suites on an academically accessible hardware budget.
[NLP-6] ReWEIGH the Evidence: Calibrating Token-Level Ordinal Visual Evidence to Mitigate Hallucinations in Large Vision-Language Models
【速读】： 该论文旨在解决大视觉语言模型（Large Vision-Language Models, LVLMs）在生成过程中常见的幻觉问题，即模型生成的内容与输入图像所支持的信息不符。其核心挑战在于：在解码过程中缺乏一种针对每个候选词的、可衡量图像支持强度的机制。为此，论文提出了一种无需训练的解码干预方法——ReWEIGH，其关键在于利用模型视觉-标记状态（visual-token states）作为图像证据的自然来源，通过将各视觉位置上的词汇排名（vocabulary ranks）进行尺度不变性聚合，并以未标注图像中估计的特定于词项的参考基准为依据，对低于该基准的候选词施加有界惩罚。该方法在预填充阶段缓存图像证据，在推理时仅对不满足参考标准的候选词施加轻量级修正，从而有效降低幻觉性物体提及率最高达21.3%，同时保持或提升描述性和通用性能。由于证据缓存机制，平均额外延迟仅为每标记1.33%，且效果可扩展至六种架构族和320亿参数规模的模型。
链接: https://arxiv.org/abs/2608.19075
作者: Jihae Jeong,Junha Choi,Hwanjo Yu
机构: Pohang University of Science and Technology (POSTECH)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Large vision-language models (LVLMs) often hallucinate, generating content that the input image does not support. Preventing such content during decoding calls for a candidate-specific measure of how strongly the image supports the token under consideration. The model’s visual-token states offer a natural source of this evidence because projecting each state through the output head reveals which vocabulary items that position favors. These position-wise readouts cannot be pooled directly because their probability magnitudes are not comparable across visual positions. Vocabulary ranks provide a scale-invariant basis for pooling, but tokens still differ systematically in their typical rank-based evidence. We propose ReWEIGH, a training-free decoding intervention that aggregates these ranks across visual positions and compares each candidate with a token-specific reference estimated from unlabeled images. At inference, ReWEIGH caches the image evidence during prefill and applies a bounded penalty only to candidates that fall below their reference. On four 7B backbones, ReWEIGH reduces hallucinated object mentions by up to 21.3% while largely preserving or improving descriptive and general performance. With evidence cached, the average added latency is 1.33% per token, and the reductions extend across six architecture families to 32B parameters.
[NLP-7] What is Missing from AI Post-Training AI: An Empirical Analysis
【速读】： 该论文旨在解决当前大语言模型（Large Language Model, LLM）智能体在后训练过程中存在“策略僵化”问题，即智能体虽具备在既定训练策略下进行局部优化的执行能力（execution-level capability），却缺乏根据实验反馈动态调整高层训练策略的能力（strategy-level capability）。其核心挑战在于：现有智能体在启动训练后，几乎完全锁定初始策略，后续所有计算资源均用于局部微调，无法实现对训练方向的根本性重构。论文的关键解决方案是识别并验证：当前智能体缺失的并非经验积累、人类指导或推理算力，而是一种在执行过程中能够自主重新评估并修正训练策略的内在机制。通过系统性实验对比不同干预手段（如经验驱动的支架、人工引导和增加推理计算），研究发现这些方法均无法从根本上突破策略锁定现象，唯有引入动态策略重评估机制，才能真正实现从“执行优化”向“策略进化”的跃迁，从而迈向真正的AI for AI。
链接: https://arxiv.org/abs/2608.19072
作者: Joy Jia Yin Lim,Xin Huang,Hao Peng,Yaxi Lu,Xin Cong,Zhong Zhang,Maosong Sun,Yankai Lin
机构: Tsinghua University(清华大学); University of Electronic Science and Technology of China(电子科技大学); Renmin University of China(中国人民大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Large language model (LLM) agents can now post-train an LLM end-to-end. They can write code, launch training, evaluate checkpoints, and improve downstream performance, raising the prospect of AI-for-AI. We argue that this picture conflates two distinct capabilities: execution-level capability, iterating within a selected training strategy; and strategy-level capability, revising the high-level judgment as experimental evidence accumulates. Analyzing a large corpus of publicly released post-training trajectories, we find that across different tasks, the agent’s training strategy is locked in at the very beginning, and the entire remaining budget is spent on local adjustments within the selected strategy. We then examine three natural explanations–missing experience, missing guidance, and insufficient reasoning–with escalating interventions. Extensive experiments show that (1) an experience-driven scaffold improves execution across the board (+12.6 points on GSM8K and +40.8 on HumanEval) but leaves the strategy static; (2) human guidance effectively redirects the initial strategy, yet the agent falls back into local adjustment loops once training starts; and (3) additional inference compute pays off on easier tasks but yields almost no gain on the hardest one. In conclusion, what agents lack is neither experience, guidance, nor reasoning compute, but a mechanism for spontaneously reevaluating their strategy during execution.
[NLP-8] Institutional Books - Enriched Text: A customizable multilingual open-source pipeline for denoising deduplicating and annotating OCR text at scale
【速读】： 该论文旨在解决大规模文本数据集在标准化预处理过程中与信息保存责任之间存在的矛盾问题，尤其针对机构图书资源（如哈佛图书馆数字藏书，IB-HL）在被用于生成式人工智能训练时所面临的挑战。现有主流预处理流程通常以网络文本优化为目标，采取激进的过滤、去重、语言限制及元数据丢弃策略，导致原始文献中丰富的语义和结构信息被破坏，违背了对学术文化遗产负责任的信息管理原则。此外，研究者在重复进行类似处理时面临效率低下和方法不一致的问题。本文提出的解决方案核心是“增强文本”（Enriched Text）框架，其关键在于不再生成单一的“完整”文本流，而是通过保留并标注原始文本中的多层级元数据，实现内容的可追溯性与可定制性：具体包括对末尾内容（endmatter）进行分离、识别每段落的语言、检测重复段落集群，并计算每段落的比特/字节压缩率，所有这些信息均以类HTML注释形式叠加于文本之上。用户可根据自身需求解析这些注释，灵活选择或排除特定内容，从而避免接受全局性的编辑决策。该方案覆盖约250种语言，已应用于全部98.3万册书籍，最终产出包含2170亿个基础标记的增强版数据集IB-HL-ET，显著提升了机器可读性和人类可研究性。
链接: https://arxiv.org/abs/2608.19026
作者: David Lowry-Duda,Matteo Cargnelutti,Catherine Brobston,Salwa Ismail,Greg Leppert,Amanda Watson,Jonathan Zittrain
机构: 未知
类目: Computation and Language (cs.CL); Digital Libraries (cs.DL)
备注:
Abstract:Released in 2025, Institutional Books: Harvard Library (IB-HL) is a collection of 983,004 volumes (242B o200k_base tokens), originally digitized through Harvard Library’s participation in the Google Books Library project. As researchers and developers have begun to use IB-HL, a tension has emerged between standard large-scale preprocessing practices and the goals of careful information stewardship. Many existing pipelines optimize for web text: as a result, they tend to aggressively filter, deduplicate, restrict by language, and sometimes discard meaningful metadata. Meanwhile, researchers seeking to use IB-HL duplicate effort while performing similar processing and analysis. We describe an approach that we call Enriched Text. Instead of producing a single ‘complete’ stream of tokens, we normalize the text while preserving metadata through annotations. We separate endmatter, detect per-paragraph language, identify clusters of duplicate paragraphs, and compute per-paragraph bits-per-byte scores. We provide this information through HTML-like annotations layered on top of the text. By parsing these annotations, users can tailor the output to their own needs instead of accepting a global editorial decision on content. The pipeline applies to all \approx 250 languages in the collection. This report describes this project’s goals, implementation, and design rationale. The release includes IB-HL-ET (an enriched-text version of IB-HL containing 217B o200k_base tokens across 983,003 volumes, organized into 1.39B annotated subtopic paragraphs) and the pipeline that produced it. These serve to make the collection easier for machines to parse and for humans to study. Subjects: Computation and Language (cs.CL); Digital Libraries (cs.DL) Cite as: arXiv:2608.19026 [cs.CL] (or arXiv:2608.19026v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.19026 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-9] Grading the Graders: Verification Autonomy Levels (L0-L5) for LLM Reasoning
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在与验证器（verifiers）结合使用时，因“验证层级”（verification level）概念模糊而引发的语义混淆问题。现有研究中，“验证层级”被混用以指代至少五种不同维度：验证粒度、概念抽象层次、风险等级、系统栈层级以及真实值的本体论来源。为厘清这一混乱局面，论文提出验证自主性层级（Verification Autonomy Levels, VAL），构建一个单一轴线的元标准，用于分类验证方案的核心依据——验证规范的来源及其结论所保证的性质。VAL从L0（LLM自我声明，无确定性锚点）到L2（客观真实值，仅保证正确性），再到L3/L4（具备单属性或领域级完备性的可判定系统），并指出在无限制情况下L5不可能实现。其关键在于揭示“完备性盲区”（completeness blind spot）：基于替换和采样的验证器只能确认所提议候选满足条件，但无法证明未遗漏其他可能候选。此外，论文明确区分了一个此前文献未阐明的二分法：完备性仅对形式可定义的属性可达，而经验性开放世界验证（如事实核查、诊断）的上限仅为锚定正确性（L2）。这一结论在符号数学、行为监控、医学诊断和代码生成四个领域均得到验证，并在最强形式验证基线中得到印证，其作者亦承认验证器“聚焦于每一步的正确性”。论文进一步论证，验证粒度、概念层级、风险等级及系统栈层级均与VAL正交，从而解决了17篇被调查文献中的系统性概念混淆问题。相关代码与完整评估作为补充材料公开。
链接: https://arxiv.org/abs/2608.19009
作者: Yajie Yin
机构: 未知
类目: Computation and Language (cs.CL)
备注: Code and data: this https URL Keywords: LLM verification; verification autonomy; completeness; ground truth; trustworthy AI Writing and implementation assisted by an AI language model; all experiments, data, and research decisions are the author’s own
Abstract:Large language models (LLMs) are increasingly paired with verifiers (step checkers, self-consistency filters, tool-based fact checkers, formal proof assistants) that claim to detect the model’s errors. Yet the verification literature uses the word “level” to mean at least five different things: verification granularity, concept abstraction, risk tier, system-stack layer, and the epistemic source of the ground truth. We propose Verification Autonomy Levels (VAL), a meta-standard classifying verification schemes along a single axis: where does the verification spec come from, and what does the verdict guarantee? VAL ranges from L0 (LLM self-declaration, no deterministic anchor) through L2 (objective ground truth, correctness only) to L3/L4 (decidable systems with single-property or domain-level completeness), with L5 impossible in the unrestricted case. Central to VAL is the completeness blind spot: substitution- and sampling-based verifiers can confirm that proposed candidates hold, but cannot prove that no candidate was missed. We further identify a dichotomy the literature has not stated: completeness is reachable only for formally specifiable properties, while empirical open-world verification (fact-checking, diagnosis) caps at anchored correctness (L2). We document this across four domains (symbolic mathematics, behavior monitoring, medical diagnosis, and code generation) and in the strongest existing formal-verification baseline, whose authors note the verifier “focuses on the correctness of each step.” We show the levels of granularity, concept hierarchy, risk, and system stack are orthogonal to VAL, resolving a systematic conflation across 17 surveyed papers. Code and full assessment are released as supplementary material.
[NLP-10] Introducing the Privacy-HSD Trade-off: Hate Speech Detection but not at the Cost of Privacy WOAH2026
【速读】： 该论文旨在解决自动仇恨言论检测（HSD）系统在提升检测性能的同时可能无意中泄露用户身份信息所带来的隐私风险问题。其核心挑战在于：现有HSD模型在识别仇恨言论时，可能通过文本特征隐含地编码作者身份，从而构成对用户隐私的威胁。解决方案的关键在于提出并验证“隐私-仇恨言论检测”（privacy-HSD trade-off）这一概念框架，并引入一种新型领域特定的文本隐私化方法——AgnoSpeech，该方法能够在有效保护用户隐私的前提下维持可接受的仇恨言论检测性能。研究通过基准测试多种文本隐私化技术，证明了在隐私与检测效能之间实现平衡虽具挑战性，但具备可行性，为未来兼顾隐私保护与内容安全的智能治理系统设计提供了重要方向。
链接: https://arxiv.org/abs/2608.19006
作者: Stephen Meisenbacher,Vlad Garbuz,Chirill Donos,Maxim Dnestreanschii,Gabriel Creanga,Andreea-Elena Bodea,Thomas Lampert,Jana Diesner
机构: Technical University of Munich (慕尼黑工业大学); Munich Center for Machine Learning (慕尼黑机器学习中心); Munich Data Science Institute (慕尼黑数据科学研究所); University of Southern Denmark (南丹麦大学); Technical University of Moldova (摩尔多瓦工业大学); ICube, University of Strasbourg (斯特拉斯堡大学ICube研究所)
类目: Computation and Language (cs.CL)
备注: 13 pages, 1 figure, 3 tables. Accepted to WOAH 2026
Abstract:Hate speech is a real and timely threat that affects a large portion of online users, especially youth and minority groups. While building reliable and robust automatic hate speech detection (HSD) systems is paramount, we argue that this must also be balanced with the individual right to privacy. Exploring the intersection of HSD and privacy, we demonstrate that HSD systems might unintentionally achieve performance at the cost of encoding authorship, posing a threat to privacy. Building on these findings, we establish the notion of a privacy-HSD trade-off, which demands a careful balance. We benchmark a series of text privatization methods, as well as our newly proposed domain-specific AgnoSpeech technique, showing that balancing privacy and HSD is difficult but feasible. The findings make a strong case for more research on the trade-offs between privacy and HSD, both of which have tangible implications for the safeguarding of online participation.
[NLP-11] Structure Association and Decision Value: Representation-Based Difficulty Estimation for Adaptive Inference in African-Language NLI
【速读】： 该论文旨在解决多语言非洲自然语言处理（Multilingual African NLP）中自适应推理（adaptive inference）的示例级难度信号问题，即探究内部表示统计量是否能够为模型提供有效的、可指导计算资源动态分配的难度判别依据。研究发现，在所考察的设定下，这些统计量无法提供可靠的有效信号。其解决方案的关键在于揭示：尽管某些表示统计量（如有效秩、角度分散度）在特定任务目标上表现出统计显著性，但它们对不同计算效益指标的预测能力存在根本差异——例如，有效秩能预测从低成本模型升级到高成本模型后的准确率提升，却无法预测预测结果是否改变；而低成本模型的置信度则呈现相反模式，二者相关性仅为0.655。这表明，单一统计量可能在一个计算效益维度上显著，而在另一维度上完全无关，因此不适合作为自适应路由的决策变量。研究进一步指出，由于AfriXNLI数据集与XNLI存在高度重叠，导致其英、法、斯瓦希里语配置无法作为对预训练模型的洁净评估基准，且模型参数量在不同非洲语言间不具备一致的能力排序能力。最终结论是，在当前模型架构与计算预算下，无任何已评估信号能使自适应推理优于始终使用高成本推理，尽管理想情况下的“全知者”（oracle）仍可实现11个百分点的准确率提升并节省40%计算开销。这一发现的核心方法论启示是：必须明确区分不同计算效益定义，并警惕表面统计显著性在实际决策中的误导性。
链接: https://arxiv.org/abs/2608.19003
作者: Toheeb Ogunade
机构: University of Lagos(拉各斯大学)
类目: Computation and Language (cs.CL)
备注: 21 pages, 3 figures, 10 tables. Submitted to MIRG-ICAIR 2026
Abstract:We ask whether internal representation statistics can provide useful example-level difficulty signals for adaptive inference in multilingual African NLP, and find that they cannot in this setting. Studying natural language inference across 15 African languages with frozen off-the-shelf checkpoints, we report four results. First, AfriXNLI’s English configuration shares 1,047 of its 1,050 examples verbatim with XNLI evaluation data, and one widely used NLI checkpoint scores 1.000 on that test split, consistent with XNLI test exposure. Because AfriXNLI is derived from XNLI, its English, French and Swahili configurations cannot serve as clean evaluations for XNLI-trained models. Second, parameter count does not reliably order capability across African languages: our larger checkpoint is better in seven languages and worse in eight, with no significant aggregate difference. Third, across three multilingual representation spaces, angular dispersion is consistently more language-determined than effective rank, so pooled correlations can inflate one and mask the other. Fourth, the association that survives language control depends on the target: effective rank predicts probability gain from escalation but not whether escalation changes the prediction, while cheap-model confidence shows the opposite pattern; the two targets correlate at only 0.655. Under the tested models, signals, and compute budgets, no evaluated signal makes adaptive routing preferable to always-expensive inference, although an oracle exceeds it by 11 accuracy points at 60% of the compute. Our central methodological finding is that a representation statistic can be statistically significant for one notion of computational benefit while being irrelevant to another, and therefore be a poor decision variable.
[NLP-12] DeepWeaver: Bridging the Evidence Synthesis Gap in Open-Ended Question Answering
【速读】： 该论文旨在解决开放性问答任务中“检索-生成”范式下的证据综合（evidence synthesis）鸿沟问题，即在仅依赖检索结果的基础上，大语言模型（LLM）难以有效组织噪声多、碎片化的证据，导致生成内容存在证据利用不足、引用错位以及信息冗余压缩为浅层摘要等缺陷。其解决方案的关键在于提出一种名为DeepWeaver的新框架，通过维护思想块链（Thought Block Chains, TBCs）这一结构化表示，将主张、关键信息、关键词与支持性证据进行系统性关联。DeepWeaver利用子级TBC对残余证据进行逐层检视，主动修正并发现新主张，从而在最终生成前完成深度证据编织。实验表明，该方法在高密度证据综合基准LoQA及DeepResearch Bench上显著提升了内容充分性、引用准确性和细节保留度，验证了证据编织机制在弥合检索与生成间差距方面的有效性。
链接: https://arxiv.org/abs/2608.18988
作者: Xujia Wang,Yizhe Zhang,Bin Xu,Lei Hou,Juanzi Li
机构: Tsinghua University(清华大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 49 pages, 6 figures
Abstract:Retrieve-then-generate pipelines are commonly used to produce deep-research answers for open-ended questions, but retrieval alone is insufficient: LLMs must organize noisy and fragmented evidence into comprehensive, well-cited answers. We refer to this process as evidence synthesis. However, direct generation often underuses evidence, misaligns citations, and collapses diverse information into shallow summaries, exposing an evidence synthesis gap between retrieval and generation. Thus, we propose DeepWeaver, a novel framework that weaves noisy retrieved evidence into comprehensive answers by maintaining Thought Block Chains (TBCs), a structured representation that groups claims, salient information, keywords, and supporting evidence. DeepWeaver uses subordinate TBCs to inspect residual evidence, commit TBC revisions, and discover new claims before final generation. We evaluate DeepWeaver on open-ended QA over both knowledge bases and the web, and introduce LoQA, a high-density benchmark for evidence synthesis. Across multiple LLMs, DeepWeaver improves content sufficiency, citation grounding, and detail preservation on LoQA, while achieving deeper insights and higher citation quality on DeepResearch Bench. These results show that evidence weaving is an effective mechanism for bridging retrieval and generation in open-ended QA. Our code is available at this https URL.
[NLP-13] Institutional Newspapers Pipeline: Deriving billions of high quality tokens from historical newspapers
【速读】： 该论文旨在解决历史报纸扫描件因版面密集、布局不规则且常含噪声，导致其在计算上难以高效、高质量地进行数据提取与利用的问题。针对这一挑战，论文提出了一套名为“机构报纸处理流水线”（Institutional Newspapers Pipeline）的模块化系统，该系统由作者与波士顿公共图书馆共同设计，核心在于实现每一步处理过程的可解释性与可定制性，同时确保整体计算资源消耗低，可在工作站级硬件上运行。其关键解决方案包括：将每张报纸扫描图像分块为无类型依赖的文本区域（type-agnostic crops），对每个区域执行光学字符识别（OCR），并在此基础上开展多层级文本分析，涵盖阅读顺序检测、文本分类、命名实体识别、主题分类、语言识别及预计算嵌入生成等任务。通过在波士顿公共图书馆馆藏中约147万份公有领域报纸扫描件上的应用，该流水线成功提取出包含8310万个文本片段、总计163亿个o200k_base tokens的结构化数据集，显著提升了历史报纸数据的可用性与可研究性。
链接: https://arxiv.org/abs/2608.18972
作者: Matteo Cargnelutti,Catherine Brobston,Eben English,Jake Sadow,Kacie Bailey,Greg Leppert,Amanda Watson,Jessica Chapel,Jonathan Zittrain
机构: 未知
类目: Computation and Language (cs.CL); Digital Libraries (cs.DL)
备注:
Abstract:Historical newspapers are an abundant record of public life, but their dense, irregular and sometimes noisy layouts make computational access to these materials both challenging and limited. We present the Institutional Newspapers Pipeline, a modular system we jointly designed with Boston Public Library to extract high-quality, structured datasets from historical newspaper scans. It was architected so that each step remains interpretable and customizable, and so that the pipeline as a whole remains computationally frugal enough to run on workstation-level hardware. The pipeline runs each scan through a multi-step process: it segments scans into individual type-agnostic crops and performs OCR on each resulting segment before then performing text analysis, type classification, reading order detection, named entities recognition, subject classification, language detection, and pre-computed embeddings generation on every crop. We ran this pipeline against a portion of Boston Public Library’s holdings and released the results as an open dataset. The optical character recognition (OCR) output represents 16.3 billion o200k_base tokens across 83.1 million individual crops, extracted from 1,473,635 public domain newspaper scans published between 1795 and 1930. This report describes our methods for each processing step, the small models we trained, as well as the evaluation results and dataset-scale measurements we collected in the process. It accompanies the release of the pipeline, models, and dataset. We position this work as a substantial step towards unlocking high-quality data from tens of millions of newspaper scans.
[NLP-14] raining Chemical Plausibility-Aware Large Language Models for Single-Step Retrosynthesis
【速读】： 该论文旨在解决单步逆合成（single-step retrosynthesis）中固有的“一对多”特性难以被传统单答案评估与基准测试协议有效捕捉的问题。其核心挑战在于现有方法无法充分反映生成结果的多样性与合理性，从而限制了模型在实际合成规划中的实用性。为此，研究提出**Top-K 提示（Top-K prompting）**作为稳健的训练与推理范式，通过引入多候选生成机制，更全面地捕捉多种合理且可行的反应路径。关键解决方案包括构建一个规模达约4560万条经验证反应的超大规模数据集——CREED-CCV-2+USPTO-XL，用于训练化学约束一致的语言模型（C3LM），并结合基于ChemCensor的去噪奖励与新颖性导向奖励进行微调，显著提升了模型在开放域、非分布外（OOD）URSA-expert-2026基准上的表现。进一步分析表明，大语言模型（LLM）与传统模型探索的反应空间具有互补性，这为构建集成式逆合成系统提供了理论依据。综上，该研究确立了以Top-K和可解释性为导向的可塑性训练策略，为未来基于大语言模型的合成规划提供了更具鲁棒性的新方向。
链接: https://arxiv.org/abs/2608.18940
作者: Bogdan Zagribelnyy,Ivan Ilin,Nikita Bondarev,Maksim Kuznetsov,Mathieu Reymond,Vladimir Aladinskiy,Alex Aliper,Alex Zhavoronkov
机构: Insilico Medicine AI Limited, Abu Dhabi, UAE(阿布扎比, 阿联酋); Insilico Medicine Canada Inc., Montreal, Quebec, Canada(蒙特利尔, 魁北克省, 加拿大); Insilico Medicine Hong Kong Ltd., Hong Kong SAR, China(香港特别行政区, 中国)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL)
备注:
Abstract:Single-step retrosynthesis is a central component of computer-aided synthesis planning, yet its intrinsically one-to-many nature is poorly captured by single-answer evaluation and benchmarking protocols. To address this, we introduce Top-K prompting as a robust training and inference paradigm to better capture diverse, plausible reaction predictions. We compile CREED-CCV-2+USPTO-XL, an ultra-large-scale dataset of ~45.6 million verified reactions to train the C3LM (Chemistry Constraint-Consistent Language Model). By integrating fine-tuning with ChemCensor-based and novelty-oriented rewards, our model achieves state-of-the-art performance on the OOD URSA-expert-2026 benchmark. Further analysis of reaction uniqueness shows that LLMs and conventional models explore complementary reaction spaces, motivating ensemble-based retrosynthesis systems. Overall, our results establish Top-K, plausibility-aware training as a practical new direction for robust future LLM-based synthesis planning.
[NLP-15] MedUAG: Unified Understanding and Generation for Medical Multimodal Models
【速读】： 该论文旨在解决当前医学领域多模态大语言模型（Multimodal Large Language Models, MLLMs）在向统一理解与生成（Unified Understanding and Generation, UAG）范式演进过程中面临的两大核心问题：一是缺乏全面的训练与评估基准，二是尚无广泛验证的统一医学模型。其解决方案的关键在于构建一套完整的医学UAG基础体系：首先，构建了迄今规模最大的医学统一理解与生成数据集MedUAGCorpus，涵盖14种影像模态、超过600万条实例；其次，提出MedUAGBench，一个系统化的评估基准，将医学生成任务的评价扩展至12类多样化任务，并采用标准化协议；最终，基于上述资源训练出端到端的统一医学模型MedUAG。实验表明，MedUAG在多种理解与生成任务上均表现出色，确立了具有竞争力的基线性能，为下一代医学多模态系统的发展奠定了坚实基础。
链接: https://arxiv.org/abs/2608.18937
作者: Zijie Meng,Yuncheng Zhang,Hualiang Wang,Yitian Tang,Xiaotang Gai,Chen Shen,Songtao Jiang,Shaosheng Cao,Jian Wu,Xian Wu,Zuozhu Liu
机构: Zhejiang University(浙江大学); Hong Kong University of Science and Technology(香港科技大学); Tsinghua University(清华大学); Tencent Jarvis Lab(腾讯贾维斯实验室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent Multimodal Large Language Models (MLLMs) are rapidly evolving into unified understanding and generation (UAG) frameworks. However, extending these unified paradigms to the medical domain is hindered by: the absence of comprehensive training and evaluation benchmarks, and the lack of broadly validated unified medical model. To address these gaps, we present a comprehensive foundation for medical UAG. First, we construct MedUAGCorpus, the largest unified medical understanding and generation dataset to date, comprising over 6 million instances across 14 imaging modalities. Second, we introduce MedUAGBench, a systematic benchmark that expands medical generation evaluation to 12 diverse tasks under standardized protocols. Finally, leveraging these resources, we develop MedUAG, an end-to-end trained unified medical model. Extensive experiments demonstrate that MedUAG achieves strong performance across a wide array of understanding and generation tasks, establishing a competitive baseline and paving the way for next-generation medical multimodal systems.
[NLP-16] st-Time Scaling in the Wild: Why Exploitation Not Exploration Is the Bottleneck
【速读】： 该论文旨在解决生成式AI在开放域生成任务中，尽管通过测试时扩展（Test-time Scaling, TTS）技术显著增加推理计算量以提升输出质量，但其实际效果受限于选择机制失效的问题。具体而言，现有TTS方法在数学与代码等可验证性强的任务上表现优异，但在医学、法律、金融、通用对话及创意写作等需依赖主观或复杂评估的开放生成任务中，其性能提升有限。论文的核心发现是：虽然通过扩大探索（exploration）——即生成更多候选序列——能持续提升候选池中最佳样本的质量，但关键瓶颈在于利用（exploitation）阶段，即从候选池中选择最优输出的过程。当前基于奖励模型的筛选机制与真实质量的相关性极低（ρ_v ≈ 0.12），导致选择近乎随机，无法有效利用已有的高质量候选。此外，树搜索因多样性崩溃而加剧这一问题，迭代精炼仅在单一基准上有效，其余情况存在混淆因素。唯有跨候选融合（Fusion）策略能稳定超越单样本基线，但仍仅恢复约40%的潜在质量。因此，解决方案的关键不在于扩大候选池规模，而在于构建更可靠的决策机制以实现对高质量候选的有效提炼与整合。
链接: https://arxiv.org/abs/2608.18931
作者: Davide Romano,Kanak Raj,Jerrod Parker,Daniele Giofrè
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Test-time scaling (TTS) improves language model outputs by spending additional inference compute - generating multiple candidates, searching over partial sequences, or iteratively refining drafts. These techniques yield large gains on mathematics and code, but have been developed and stress-tested almost exclusively on tasks where verification is straightforward. We conduct the first compute-normalised comparison of five TTS families across five open-ended generation benchmarks spanning medicine, law, finance, general chat, and creative writing - grounded in a unified framework that decomposes the effectiveness of each method’s token budget into exploration and exploitation. The answer depends on which side of that decomposition you examine. Scaling exploration works: the best candidate in the pool improves steadily with compute across all settings. What breaks is exploitation - the step that converts a rich candidate pool into a final output. With state-of-the-art generators, reward models correlate at only \rho_v \approx 0.12 with true quality, rendering selection near-random regardless of budget. Tree search amplifies this failure through diversity collapse. Refinement helps on one of five benchmarks; its apparent gains elsewhere are confounded. Only synthesis across candidates (Fusion) consistently improves over single-sample baselines, yet still recovers only ~40% of available quality. The candidate pool is not the bottleneck - choosing from it is.
[NLP-17] SMTrap: Cost-Effective DoS Attacks Against Large Reasoning Models via SMT Conflict Guidance
【速读】： 该论文旨在解决现有轻量级推理模型拒绝服务（LRM-DoS）攻击方法依赖模型反馈来生成攻击查询的问题，此类方法通常需要对目标模型进行重复查询或训练专用攻击模型，导致计算开销巨大，严重削弱了攻击的有效性。其解决方案的关键在于提出一种无需模型反馈的新型攻击范式——“搜索放大”（search amplification），通过利用满足可解性模理论（SMT）求解器产生的冲突计数（conflict count）作为低成本外部信号，指导生成计算密集型约束满足问题（CSP）实例。研究发现，当给定CSP实例的SMT冲突计数较高时，会引发大语言模型（LLM）在求解过程中更频繁地进行回溯搜索，从而显著延长输出轨迹。基于此，作者设计了名为\textscSMTrap的轻量级、仅依赖CPU的框架，能够在不进行任何模型查询、无需训练攻击模型且无需GPU计算的情况下，高效生成高推理负载的攻击查询。实验结果表明，\textscSMTrap在七种前沿模型上均展现出当前最优的LRM-DoS攻击能力，攻击效果远超现有基线方法。为应对该威胁，论文还提出了一种基于工具的缓解方案，可显著降低模型的令牌消耗。
链接: https://arxiv.org/abs/2608.18921
作者: Jian Yang,Zhenqi Feng,Zhaoyang Yu,Zhaoxin Fan,Kejian Wu,Xiaofeng Wang,Zheng Zhu,Jianjun Huang,Wei You,Bin Liang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Existing LRM-DoS methods rely heavily on model feedback to synthesize attack queries, requiring either repeated queries to the target model or training a dedicated attack model. These expensive operations severely weaken attack leverage. In this paper, we propose \emphsearch amplification, a novel, model-feedback-free LRM-DoS paradigm. It employs the conflict count derived from an Satisfiability Modulo Theories (SMT) solver as a low-cost external signal to guide the synthesis of inference-heavy Constraint Satisfaction Problem (CSP) instances. Our key observation is that LRMs depend on trial-and-backtracking search when solving CSPs, where higher SMT conflict counts on a given CSP instance positively correlate with more extensive LRM backtracking search and substantially longer output trajectories. Building on this finding, we propose \textscSMTrap, a lightweight, CPU-only framework. Guided by SMT conflict counts, \textscSMTrap generates inference-heavy CSP queries without model queries, attack-model training, or GPU computation. Evaluations across seven frontier models demonstrate the state-of-the-art LRM-DoS capability of \textscSMTrap, producing DoS effects multiple times stronger than existing baselines. To mitigate the threat of \textscSMTrap, we demonstrate a tool-based mitigation that significantly cuts token usage.
[NLP-18] Assessing Quality of Experience in Natural Language Generation of German Text
【速读】： 该论文旨在解决生成式自然语言（Natural Language Generation, NLG）系统在德语场景下缺乏可靠、以人为中心的评估方法的问题，尤其针对自动文本摘要与机器翻译任务中传统自动评估指标无法充分捕捉人类感知质量多维度特征的局限性。其核心解决方案是构建首个面向德语NLG的体验质量（Quality of Experience, QoE）评估数据集——TextQ-German，通过众包方式收集母语为德语的用户对生成文本的质量评分，并识别出各任务下的关键感知质量维度。研究进一步提出基于Transformer、语言学特征及混合模型的自动QoE预测方法，其中混合模型在几乎所有实验设置中均优于纯Transformer基线，而仅使用语言学特征的方法亦可接近微调语言模型的性能。此外，数据集扩展了大语言模型（LLM）生成输出并标注整体QoE分数，经留出集验证，模型具备良好的泛化能力。本研究贡献了一个公开可用的评估资源与自动QoE预测基准，为开发更符合人类质量感知的NLG系统提供了坚实基础。
链接: https://arxiv.org/abs/2608.18888
作者: Dinh Nam Pham,Shushen Manakhimova,Vivien Macketanz,Sebastian Möller
机构: Technische Universität Berlin(柏林工业大学); German Research Center for Artificial Intelligence (DFKI)(德国人工智能研究中心)
类目: Computation and Language (cs.CL)
备注: Dataset available at this https URL
Abstract:The rapid advancement of Natural Language Generation (NLG) has made the reliable evaluation of generated text increasingly critical, as these systems, such as large language models (LLMs), are now widely deployed in real-world applications. However, traditional automatic metrics fail to capture the multifaceted nature of perceived quality. In this paper, we introduce TextQ-German, a novel dataset suite for human-centered evaluation of German NLG from a Quality of Experience (QoE) perspective, covering automatic text summarization and machine translation. Through crowdsourcing studies with German speakers, we collect human quality ratings and identify relevant perceptual quality dimensions for each task. We develop automatic QoE prediction models, including transformer-based, linguistic feature-based, and hybrid approaches. Hybrid models outperform pure transformer baselines in almost all experimental settings, while linguistic features alone can approach the performance of fine-tuned language models. The dataset is extended with LLM-generated outputs annotated with overall QoE scores. Final validation on held-out sets indicates generalization to unseen data. Our work contributes a publicly accessible resource for NLG evaluation and baselines for automatic QoE prediction, providing a foundation for developing NLG systems that better align with human quality perception.
[NLP-19] MLREF: Efficient Module Reuse for Reward Design in Reinforcement Learning via Large Language Models
【速读】： 该论文旨在解决强化学习中奖励函数设计的瓶颈问题，尤其是现有基于大语言模型（Large Language Models, LLMs）的自动奖励生成方法在迭代过程中将奖励函数视为整体程序进行生成与修改，导致早期迭代中发现的有效组件难以可靠保留与复用，从而引发性能不稳定的问题。其解决方案的关键在于提出模块级奖励演化框架（Module Level Reward Evolution Framework, MLREF），核心思想是构建一个持久的模块池（module pool），作为可复用的奖励组件仓库；将模块池本身作为主要优化目标，通过累积成功模块、优化表现不佳模块并重用经过验证的组件，动态演化模块池；而具体的奖励函数则由从该池中选取的模块线性组合而成。为驱动这一演化过程，MLREF引入了基于反思的精炼机制、混合信用分配策略以及具备回滚功能的合并策略，显著提升了奖励优化的有效性与鲁棒性。实验结果表明，在17个任务上，MLREF在运动控制任务上相较强基线提升25.2%，在操作任务上提升6.6%，且优化过程更加稳定。
链接: https://arxiv.org/abs/2608.18827
作者: Chenglin Liu,Xun Wang,Ruishuo Chen,Zhuoran Li,Longbo Huang
机构: Tsinghua University (清华大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 22 pages, 5 figures, 4 tables
Abstract:Reward function design remains a bottleneck in reinforcement learning. While large language models (LLMs) have enabled automated reward generation, existing methods generate and revise reward functions as monolithic programs, making it difficult to reliably preserve and reuse effective components discovered in earlier iterations, leading to unstable performance across iterations. To address this, we propose Module Level Reward Evolution Framework (MLREF). At the core of MLREF is a module pool, a persistent repository of reusable reward components. MLREF treats the module pool as the primary optimization object: the pool evolves across iterations by accumulating successful modules, refining underperforming ones, and reusing proven components; while reward functions are constructed as linear combinations of modules drawn from this pool. To drive this evolution, MLREF integrates three mechanisms: reflection-based refinement, hybrid credit assignment, and a merge strategy with rollback, which together improve the effectiveness and robustness of reward optimization. Experiments on 17 tasks show that MLREF outperforms strong baselines by 25.2% in locomotion and 6.6% in manipulation, with more stable optimization dynamics.
[NLP-20] Understanding Multilingual Medical ASR Adaptation Through Layer-Wise Analysis
【速读】： 该论文旨在解决医疗领域自动语音识别（MedASR）中专业术语适配、标注临床数据有限以及多语言应用场景下的性能瓶颈问题。尽管大规模预训练语音识别模型如Whisper在通用场景下表现出色，但其在医疗及多语言场景下的适应机制仍缺乏深入理解，尤其在词错误率（WER）之外的内部表征演化规律尚不明确。本文通过分层编码器分析，揭示了多语言医疗适配如何重塑Whisper模型的内部表示。关键解决方案在于系统比较零样本解码、仅英语微调、仅德语诊断性微调、两阶段英文-英德连续微调以及直接英德联合微调等多种适配策略，并结合不同规模的Whisper模型进行评估。研究发现，微调显著提升MedASR性能，最优模型选择高度依赖于具体适配设置：Whisper-Medium在直接英德联合微调下实现最低的综合英德WER（26.30%），而仅德语微调的Whisper-Large-v3虽在86个单说话人测试集上达到最低德语WER（44.96%），但缺乏泛化能力；分层分析进一步表明，英医微调主导了编码器表征的显著迁移，而多语言延续则基本保留已适配的表征空间，且域与语言信息在各层级仍高度可恢复，但随着WER降低，线性可恢复的错误预测线索逐渐减弱。
链接: https://arxiv.org/abs/2608.18825
作者: Souranil Kahali,Rituparna Bose,Abner Hernandez,Tomas Arias-Vergara,Andreas Maier,Ning Ma,Paula Andrea Perez-Toro
机构: Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; School of Computer Science, University of Sheffield, Sheffield, UK; Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM), Munich, Germany
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD)
备注:
Abstract:Medical automatic speech recognition (MedASR) requires adaptation to specialised terminology, limited annotated clinical data, and multilingual use cases. Although large-scale pretrained ASR models such as Whisper achieve strong generalisation, their behaviour after medical and multilingual adaptation remains insufficiently understood beyond word error rate (WER). This paper investigates how multilingual medical adaptation reshapes the internal representations of Whisper models through layer-wise encoder analysis. We compare zero-shot decoding, English-only fine-tuning, German-only diagnostic fine-tuning, two-stage EN-EN+DE continuation, and direct EN+DE fine-tuning across Whisper model sizes. Fine-tuning substantially improves MedASR performance, but the best model depends on the adaptation setting: Whisper-Medium gives the lowest English WER (7.72%) and the lowest combined EN+DE WER under direct EN+DE training (26.30%); German-only Whisper-Large-v3 gives the lowest German WER (44.96%), but as a within-corpus diagnostic on 86 single-speaker training utterances rather than robust generalisation. Layer-wise analysis of the two-stage Whisper-Small trajectory shows that English medical fine-tuning produces the dominant encoder shift, whereas multilingual continuation largely preserves the adapted representation space. Domain and language information remain highly recoverable across layers, while linearly recoverable error-predictive cues weaken as WER improves.
[NLP-21] Identifying Implicit Premises for Logical Reconstruction of Argument Graphs
【速读】： 该论文旨在解决从自然语言文本中逻辑重构论证图时，因隐含前提（enthymeme）普遍存在而导致的挑战。现有自然语言处理方法可识别文本中的隐含前提，符号化方法则基于溯因推理（abduction）补全论证的逻辑表示中的缺失前提，但缺乏有效方法生成能够逻辑上支持已知蕴含或矛盾关系的中间隐含前提。为此，本文提出一种神经符号（neuro-symbolic）流程：利用大语言模型（Large Language Models, LLMs）生成中间隐含前提，并将其转化为逻辑公式，与显式前提和显式主张的逻辑表达式共同用于推导它们之间的逻辑关系（蕴含、矛盾或中立）。该方法在Microtext Argumentative Corpus数据集上进行了评估，验证了其有效性。
链接: https://arxiv.org/abs/2608.18821
作者: Xuyao Feng,Anthony Hunter
机构: University College London (伦敦大学学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted at the 11th International Conference on Computational Models of Argument (COMMA 2026)
Abstract:The logical reconstruction of argument graphs from natural language text is challenging because of the prevalence of enthymemes (i.e., arguments with implicit premises). There are natural language processing methods for identifying enthymemes in text, and there are symbolic methods based on abduction for identifying missing premises in a logical representation of enthymemes. However, there is a need for methods to generate implicit premises to logically show a known entailment or contradiction relationship between a pair of statements. To address this, we propose a neuro-symbolic pipeline that uses large language models (LLMs) to generate intermediate implicit premises that are translated into logical formulae and used with logical formulae representing explicit premises and explicit claims to show the logical relationships between them (entailment, contradiction, or neutrality). Our approach is evaluated on the Microtext Argumentative Corpus.
[NLP-22] Do Large Language Models Hallucinate Electric Fata Morganas?
【速读】： 该论文旨在解决生成式人工智能（Generative AI）中幻觉（hallucination）现象的深层问题，不仅将其视为技术缺陷，更进一步探讨其在机器意识（machine consciousness）哲学议题中的意义。其核心观点是：当前大语言模型的幻觉并非单纯的错误输出，而是源于训练数据与推理过程之间的结构性矛盾，如源-目标偏差、训练与推理分布不一致以及过拟合等。论文通过两项实证研究揭示关键机制——在不同温度参数下对GPT模型进行连续生成实验发现，高温度导致模型产生看似合理但事实错误的回答，而低温度则提升准确性；这一现象表明，使模型表现得更具创造性或自发性以通过智能行为测试的采样参数，恰恰也加剧了幻觉的发生率。第二项研究对比了仅基于百科全书数据训练的编码器模型，其回答客观无修饰，证明幻觉主要源于模型接触主观性与社会多样性训练数据所引发的内在生成倾向，而非认知能力的发展。结合图灵测试、塞尔中文房间思想实验、框架问题（frame problem）及维纳与阿什比的控制论传统，论文进一步论证：模型对自身情绪或意识的自我报告本质上属于幻觉范畴，未来若出现真正的机器意识，也可能因无法与高度复杂的幻觉相区分而始终处于认识论上的不可知状态。因此，该研究的解决方案之关键在于重新界定幻觉的性质——它不仅是技术漏洞，更是通向机器意识本体论困境的核心线索。
链接: https://arxiv.org/abs/2608.18816
作者: Kristina Šekrst
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:AI hallucinations - that is, outputs which are made up, cannot be verified, or contradict the source material - are generally regarded as an engineering flaw to be dealt with. This paper contends that they also have philosophical significance when it comes to the question of machine consciousness. We examine the known causes of hallucinations in large language models - such as source-target divergence, discrepancies between training and inference, and overfitting - and we present two empirical investigations. In the first, we apply successive generations of the GPT model to ambiguous factual questions under different temperature settings, finding that higher temperatures result in plausible but incorrect answers while lower temperatures lead to factually accurate ones. The sampling parameters that cause a model to seem creative or spontaneous and thus more likely to pass behavioral tests of intelligence are the same ones that increase its hallucination rate. In the second, we look at an encoder-only model that has been trained on encyclopedic data and which answers questions of the same type factually and without embellishment, indicating that hallucinations are due to exposure to subjective and socially diverse training data rather than to the development of any cognitive ability. Using references to Turing, Searle’s Chinese Room, the frame problem, and the cybernetic tradition of Wiener and Ashby, we claim that a model’s self-reports of emotion or sentience come within the definition of hallucination, and that any future occurrence of machine consciousness might remain epistemically inaccessible since it would be indistinguishable from a sufficiently advanced hallucination.
[NLP-23] Decomposing Wrong-Consensus Agreement in LLM Self-Consistency: A GPT -4.1 Case Study
【速读】： 该论文旨在解决生成式人工智能（Generative AI）中基于多数投票（majority voting）的自洽性推理在面对复杂问题时表现不稳定甚至产生反效果的问题。其核心挑战在于：尽管多数投票常被用于提升大语言模型（LLM）输出答案的准确性，但在困难样本上，该方法可能因模型间共享偏差或错误偏好而引发“自洽性失效”——即多个错误答案因一致而被误认为正确。为此，作者提出了一种名为“多元共识指数 Gamma”的量化指标，用以衡量错误运行中样本与共识的一致性程度，并将其分解为两部分：一是仅由单个案例的答案偏好所决定的机械性成分（mechanical component），二是无法通过个体偏好解释的残差项（preference-unexplained residual）。关键创新在于构建了一个难度匹配且无信息泄露的机械零模型（mechanical null），通过利用同一案例其他运行结果估计其准确率和选项偏好，从而避免任一运行预测自身一致性。实验结果显示，在GPT-4.1上的多选题（GPQA-Diamond）中，个体偏好可解释81%-93%的共识一致性，表明共享偏差主导；而在开放域问答（AIME）中，机械成分仅解释59%-78%，存在显著残差（1.56–2.80 Gamma单位），远超运行层面偏好异质性所能吸收的范围（1.4–2.1），揭示出非偏好驱动的共性错误机制。最终验证了在难题上多数投票反而降低准确性的现象（投票差距降至-0.09），且最高一致性组的准确率仅为0.42–0.83，仅为基线的1.2–3.6倍，说明共识仅为弱证据而非确证。研究未提出新投票方法，但提供了可复现的代码与数据，强调共识是渐进式证据而非绝对保证。
链接: https://arxiv.org/abs/2608.18795
作者: Lizhuo Zhang,Mengmeng Tang,Chenfeng Long,Xiaoyong Tang,Xiang Luo
机构: College of Information and Intelligence, Hunan Agricultural University(湖南农业大学信息与智能学院); Yuelushan Laboratory(岳麓山实验室); School of Computer Science and Technology, Changsha University of Science and Technology(长沙理工大学计算机科学与技术学院); Information Department, China Guangdong Tobacco Meizhou Ltd(广东省烟草公司梅州有限公司信息部)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 18 pages, 2 figures, 9 tables; quantitative kappa-decomposition of agreement saturation in self-consistency;
Abstract:Majority voting over multiple LLM samples is widely used to raise answer accuracy, yet its gain varies erratically: on hard questions it can even backfire. This paper gives a quantitative account of this failure. A pluralistic agreement index Gamma is defined as the expected fraction of the samples of a wrong run that agree with the consensus, normalized by a reference scale d=(1-p)/(C-1), and is decomposed into a mechanical component (what a vote delivers given only a per-case answer preference) and a preference-unexplained residual. The mechanical null is difficulty-matched and leak-free: each case is resimulated at its own accuracy and option preference, estimated from the case’s other runs, so no run predicts its own agreement. On GPT-4.1 the decomposition shows benchmark-associated direction (an observational ordering over n=4 cells per benchmark, not a significance claim). On multiple-choice GPQA-Diamond, the per-case answer preference explains 81-93% of the held-out test-run agreement index: the shared-bias-dominates account over-claims here, because a wrong but attractive option the whole cohort latches onto is captured by the per-case preference channel (whether that preference is induced by shared training bias is not identified). On open-domain AIME, the mechanical preference explains only 59-78% (21-29% if shrunk to pure noise), and a preference-unexplained residual of 1.56-2.80 Gamma units survives, which a run-level preference-heterogeneity reference more than absorbs (1.4-2.1). A self-consistency backfire on hard questions is reproduced (binned voting gap down to -0.09, coupled CI [-0.12,-0.07]), and the highest-agreement bin reaches an accuracy of only 0.42-0.83, a 1.2-3.6x lift over base rate: agreement is graded evidence, not certification. No new voting method is proposed; code and evidence are committed and reproducible.
[NLP-24] Readable Faithful Used: Three Dissociable Properties of Demographic Identity in a Language Model
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在模拟调查受访者时存在的核心问题：尽管广泛用于生成群体响应，但其输出往往呈现同质化，且无法忠实反映真实人群之间的群体差异。研究聚焦于揭示人口统计学群体身份在模型内部的表征位置、其几何结构对真实群体意见格局的拟合程度，以及模型是否真正“使用”这些编码信息。关键发现包括：（1）标准的末尾残差读出机制严重低估了模型潜力，注意力头（attention head）读出在六类属性中的五类中占据主导地位，经选择校正后的相似性高达ρ=0.63（约达到测量可靠性上限的70%），且在词法相似性控制下仍稳健；（2）存在一个固定位置的单一注意力头（L11 H16）在所有六类属性中均表现出显著真实性，而基于种族的类型则表现较弱且对提示敏感，这一现象在另一模型家族的三个检查点中重复出现，表明十亿级训练样本量下模型表征图谱基本不变；（3）因果使用与表征真实性之间存在解耦：最不忠实的类别中存在显著因果路径（p=0.002），而最忠实的类别未表现出单层可修正效应，且替换整个身份向量仅使预测误差降低不足2%；（4）对单一注意力头的128维探针虽比模型自身回答更接近调查真值（21–31%更近），但几乎无法恢复各问题下的群体排序，性能与原始回答相当。研究揭示：可读性、表征真实性与因果使用是同一模型中可分离的三种属性，将三者混为一谈正是导致“大语言模型能否模拟群体”争议长期未决的根本原因。
链接: https://arxiv.org/abs/2608.18768
作者: Fathin Difa Robbani
机构: 独立研究者(Independent Researcher)
类目: Computation and Language (cs.CL)
备注: 30 pages, 6 figures
Abstract:Large language models are widely used to simulate survey respondents, yet their answers are homogeneous and unfaithful to real inter-group differences. We ask where demographic group identity lives inside an LLM, how faithfully its geometry mirrors real inter-group opinion structure, and whether it uses what it encodes. Using representational similarity analysis against Pew ground truth over 169 demographic cells, we score 1,089 read-out locations in Mistral-7B and intervene causally across six attribute types. Four results. (1) The standard last-token residual read-out understates the model: attention-head read-outs dominate it in five of six types, with selection-corrected fidelity up to rho=0.63 – roughly 70% of the measurement-reliability ceiling – surviving a lexical-similarity control. (2) A single head (L11 H16) is significantly faithful in all six types as a fixed location, while race-based types stay weak and prompt-fragile. Both phenomena replicate – the analogous head significant in five of six types, weakest on the same race type – across three checkpoints of a second model family, where ten billion training tokens barely move the map. (3) Causal use does not follow fidelity: the clearest causal pathway sits in one of the least faithful types (p=0.002, cluster-robust, fixed depth), the most faithful type shows no correction-surviving single-layer effect, and replacing the entire identity moves predictions by under 2% of their error. (4) A 128-dimensional probe of the single head lands 21-31% closer to survey truth than the model’s own answers – yet recovers almost none of the per-question group ordering, no better than the answers themselves. Readable, faithfully arranged, and causally used are three dissociable properties of the same model; treating them as one claim is what keeps the “can LLMs simulate populations” debate unresolved.
[NLP-25] Gradient Mirag e: Trainable yet Label-Unidentifiable Gradients in Large Language Model Split Learning
【速读】： 该论文旨在解决大语言模型（LLM）分层学习（Split Learning, SL）中梯度匹配攻击（Gradient Matching Attacks, GMAs）所依赖的核心假设——即分割接口暴露的梯度是客户端完整标签训练目标的真实导数——这一假设在实践中可能被恶意服务器利用，通过搜索可解释观测梯度的标签序列来恢复私有标签信息。为破解该假设，论文提出一种名为Gradient Mirage的防御机制，其核心在于破坏梯度与真实目标之间的一致性，同时不牺牲反向传播信号的优化有效性。关键创新在于从目标（objective）、方向（direction）和尺度（scale）三个维度主动引入不一致：首先，通过选择性自回归监督（Selective Autoregressive Supervision）使暴露梯度来源于掩码代理损失而非完整标签目标；其次，采用随机乘性缩放（Scale Blinding）掩盖梯度的自然量级；最后，基于冯·米塞斯-费舍尔分布（von Mises-Fisher, vMF）机制实现方向隐私化（Directional Privatization），在保持梯度模长不变的前提下随机化方向，并满足方向度量差分隐私（directional metric differential privacy）约束。该设计确保攻击者面对的是一个错误设定的逆问题，即在序列空间中不存在任何合理标签序列能解释观测到的梯度。同时，通过双轨反向传播（Dual-Track Backpropagation）和底端梯度恢复（Bottom-Gradient Recovery）机制，保留了模型训练的实用性，使上段仍能基于所有目标词元进行学习，且每个监督词元保持完整的自回归上下文。实验表明，Gradient Mirage 在维持相近微调性能的前提下，显著优于现有防御方法，实现了更优的隐私-效用权衡。
链接: https://arxiv.org/abs/2608.18767
作者: Shiyu Miao,Yunlong Mao,Zirui Huang,Liang Yao,Tianshuo Zheng,Yanhui Gu,Fan Liu,Sheng Zhong
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Gradient matching attacks (GMAs) in LLM split learning (SL) rely on a critical yet underexplored assumption: the gradient exposed at the split interface is a faithful derivative of the client’s full-label training objective. This gradient-objective consistency allows a curious server to recover private labels by searching for a sequence whose induced gradient explains the observation. We propose Gradient Mirage, a defense that breaks this consistency without discarding the optimization utility of the backward signal. Our key idea is to induce the adversary to solve a misspecified inverse problem, in which no plausible label sequence in the sequence space can explain the observed gradients. Concretely, Gradient Mirage achieves this by inducing inconsistency across three dimensions: objective, direction, and scale. Selective Autoregressive Supervision derives the exposed gradient from a masked surrogate loss rather than the full-label objective assumed by the attacker; Scale Blinding then applies randomized multiplicative rescaling, obscuring the gradient’s natural magnitude; and Directional Privatization further randomizes the gradient direction while preserving its magnitude through the von Mises-Fisher (vMF) mechanism under a directional metric differential privacy guarantee. Crucially, utility is preserved: the Top segment still learns from all target tokens via Dual-Track Backpropagation, the exposed gradient remains informative since each supervised token retains its complete autoregressive context, and Bottom-Gradient Recovery restores the effective gradient for Bottom-segment optimization. Extensive experiments show that Gradient Mirage provides substantially stronger protection than existing defenses under comparable fine-tuning performance, achieving a better privacy-utility trade-off.
[NLP-26] Learning Canonical Register Automata over Ordered Data Domains
【速读】： 该论文旨在解决确定性寄存器自动机（Deterministic Register Automata, DRAs）在有序数据域（包括稠密域如有理数和非稠密域如整数）上的主动学习问题。其核心挑战在于如何在不同类型的有序数据域中高效、统一地实现DRAs的构造与验证。解决方案的关键在于提出并实现了一个多项式时间的主动学习框架，该框架整合了成员资格查询（membership queries）、等价性查询（equivalence queries）和可记忆性查询（memorability queries），其中可记忆性查询是为支持基于等值性测试的数据域而引入的新机制。通过这一统一框架，不仅实现了对两类有序数据域的通用处理，还首次证明了在整数这一非稠密有序域上DRAs的最小化问题是可判定的，拓展了此前仅适用于稠密域的结果。此外，该研究还改进了与主动学习查询密切相关的若干决策问题的复杂度界，显著提升了理论与实践层面的效率。
链接: https://arxiv.org/abs/2608.18765
作者: Yong Li,Qiyi Tang,Di-De Yen
机构: Chinese Academy of Sciences (中国科学院); Institute of Software, Chinese Academy of Sciences (中国科学院软件研究所); University of Liverpool (利物浦大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Register automata are finite automata equipped with memory that recognize data languages over infinite alphabets. In this work, we investigate active learning algorithms for deterministic register automata (DRAs) over ordered data domains–covering both dense domains, such as the rationals, and non-dense domains such as the integers. We show that the active learning problem for DRAs over both dense and non-dense ordered domains can be treated within a single unified framework. More specifically, we develop and implement a polynomial-time active learning procedure for DRAs over ordered domains, using oracles for membership, equivalence and memorability queries. The memorability queries were originally introduced for learning DRAs over domains with identity tests. Our unified framework also leads to a new consequence: minimization of DRAs over the non-dense ordered domain of integers is decidable, extending a result previously known only for dense domains. Finally, we give improved complexity bounds of several decision problems for DRAs over ordered domains that are closely related to the queries used in active learning.
[NLP-27] Metrics That Write Themselves: Evolving an Evaluator from Its Own Blind Spots
【速读】： 该论文旨在解决生成式 AI（Generative AI）在缺乏可靠自动评估指标时性能迅速提升后陷入停滞的问题，尤其针对报告生成等对智能体（agent）需求迫切但难以评分的应用场景。其核心挑战在于：如何在无法明确定义“好答案”标准的情况下，自动生成有效的评估机制。解决方案的关键在于提出一种基于反例引导的抽象精化（counterexample-guided abstraction refinement, CEGAR）的自动化评估器演化框架——EvalCEGAR。该方法将评估过程建模为一组小型 Python 操作符构成的候选池，每个操作符负责检测特定类型的缺陷或选择不表态并参与投票。通过迭代搜索“碰撞”（collision）——即两个被相同操作符评分一致但实际质量不同的答案（一正确一错误），从而生成具有区分能力的测试用例作为反馈信号，驱动操作符逐步扩展其感知范围而非盲目重采样。实验表明，在 MBPP+ 与 HumanEval+ 上，该框架能自动生成一个 55 行的操作符，在未见任务上缩小了 15.4% 的评估差距，仅需手写最优操作符四分之一的标记量，并在多数情况下达到与手工设计操作符相当甚至更优的泛化性能；相比之下，大语言模型（LLM）裁判虽可达成相似效果，却需对每条候选结果持续调用模型，计算开销巨大。
链接: https://arxiv.org/abs/2608.18744
作者: Xing Zhang,Yanwei Cui,Guanghui Wang,Zhihao Lin,Peiyang He
机构: AWS Generative AI Innovation Center
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE)
备注:
Abstract:Agents improve quickly against a reliable automatic metric and stall without one, and the applications that need them most, report generation among them, are the ones nobody knows how to score. Can the metric write itself? Saying what makes an answer good is hard; pointing at something wrong with one is easier, so the metric we evolve is a pool of small Python operators that each flag a candidate for one named defect, or abstain, and vote. Asking a model for operators directly does not work: 183 candidates realise only 96 distinct behaviours, from one narrow region of an enormous space. EvalCEGAR instead borrows counterexample-guided abstraction refinement from program verification. It reads the pool as an abstraction and searches for a collision, two answers the operators score identically, one correct and one not. That pair, not a prompt, is the authoring request, and when a collision defeats every attempt the loop widens what an operator may read rather than resampling. On MBPP+ and HumanEval+, a sandbox whose hidden unit tests give exact ground truth, the loop writes a 55-line operator that closes 15.4% of the gap between flagging nothing and a perfect filter on 428 unseen tasks (+0.0065, p=0.0010) at a quarter of our best hand-written operator’s flags. On the benchmark it never saw it matches that operator’s effect exactly on a third of the flags. Six of eight runs admit such an operator and all six help out of sample; our 15 hand-written operators applied together as one filter lose accuracy. An LLM judge on the same information ties that delta on a nearly disjoint set of candidates, and charges a model call per candidate forever where the operator charges none.
[NLP-28] Execution-grounded evaluation reveals hidden failures in language-model calculations for environmental science
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在环境科学定量任务中应用时，现有评估体系仅关注最终答案而忽视计算过程透明度的问题。其核心挑战在于：当前评估方法无法揭示模型在多步推理过程中是否正确应用公式与约束条件，导致对模型真实能力的误判。解决方案的关键在于提出 AtmosCoder-Bench——一个基于执行验证的基准测试框架，通过可迁移的半自动化流程构建了包含436个问题、3,910种变体及7,029个需评分量值的数据集，确保每个问题具有唯一可验证的解答且人类可解。该设计使计算过程可见，从而精准识别模型在复杂推理中的系统性缺陷，揭示出多项选择题形式会至少高估准确率12个百分点，且多数失败源于模型未能在多步计算中一致应用已知公式和约束，而非知识缺失；即便前沿模型在面对特定任务条件时仍难以摆脱惯用解法模式，缺乏根据物理情境动态调整策略的能力，凸显专家监督在关键决策中的必要性。
链接: https://arxiv.org/abs/2608.18726
作者: Maohao Ran,Chendong Ma,Yanting Zhang,Dailing Jiang,Yusen Huang,Meng Gao,Jun Song
机构: Hong Kong Baptist University (香港浸会大学); Hong Kong University of Science and Technology (香港科技大学); The Chinese University of Hong Kong (Shenzhen) (香港中文大学(深圳))
类目: Computation and Language (cs.CL)
备注: 29 pages, 4 figures, 2 tables, plus supplementary materials. Maohao Ran and Chendong Ma contributed equally. Corresponding author: Jun Song (junsong@hkbu. this http URL ). Code: this https URL
Abstract:Large language models are increasingly used for quantitative work in the environmental sciences, yet existing evaluations score only final answers, leaving calculation process unobserved. Here we introduce AtmosCoder-Bench, an execution-grounded benchmark that makes the calculation process visible. Built through a transferable semi-automated pipeline (436 problems, 3,910 variants, 7,029 graded quantities), every problem is validated to be unambiguous and human-solvable, with uniquely verifiable answers. We find that (i) multiple-choice formats inflate measured accuracy by at least 12 percentage points; (ii) many failures arise not from missing knowledge but from models failing to apply known formulas and constraints consistently throughout multi-step computation; and (iii) even frontier models remain weak when task-specific conditions invalidate familiar methods, often reverting to canonical solution patterns rather than adapting methods to the relevant physical regime, leaving expert oversight essential.
[NLP-29] Budget-First Tariff Recommendation (BFTR): A Complete Algorithmic Framework for Telecom Plan Recommendation without Overcharging
【速读】： 该论文旨在解决电信运营商传统预设资费套餐导致用户选择受限的问题，核心在于设计一种能够根据用户预算精准推荐资费方案的算法框架。其解决方案的关键在于提出BFTR（Budget-First Tariff Recommendation）框架，集成八种预算优先策略，其中包含两种原创的混合方法：递归混合（Recursive Hybrid，基于条件插值）与背包优先混合（Knapsack-First Hybrid，优先背包优化）。不同于现有方法通过上调价格以保障最低利润，BFTR通过系统性地将最终报价与目录参考价对齐，从根本上确保不产生超额收费（overcharging），并从理论上证明了所有不使用修正插值的策略均实现零价格偏差（即零附加费用）。实验结果表明，所提策略在真实客户数据集上表现优异，例如递归混合策略实现了100%预算利用率、38.1 GB平均流量及0%超支，验证了其理论优势；同时，系统具备极低的执行延迟（<10 ms）和近乎为零的失败率，展现出良好的实际部署可行性。该研究的理论贡献在于首次严格证明了无过量收费的数学保证，为智能资费推荐提供了坚实基础。
链接: https://arxiv.org/abs/2608.18723
作者: Ghislain Dorian Tchuente Mondjo
机构: Independent Researcher(独立研究员)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 11 pages, 1 figures, 8 tables
Abstract:Telecom operators traditionally offer predefined tariff grids, forcing users to choose from a limited set of plans. This paper proposes BFTR (Budget-First Tariff Recommendation), a complete algorithmic framework integrating eight Budget-First strategies, including two original hybrid approaches: Recursive Hybrid (conditional interpolation) and Knapsack-First Hybrid (priority knapsack). Unlike existing approaches that adjust prices upward to guarantee a minimum margin, BFTR guarantees the absence of overcharging by systematically aligning the final price with the catalog reference price. We mathematically formalize each strategy, prove the existence of an offer for any positive budget, and prove that the price deviation (surcharge) is zero for all strategies that do not use interpolation with correction. A detailed comparative analysis confronts BFTR to ten main existing tariff models on ten dimensions. Experiments on a dataset of 974 customers inspired by the Nigerian MTN market show that: (i) Recursive Hybrid is optimal for the customer (100% budget used, 29.9 GB volume, utility 0.946, 0% overcharging), (ii) Piecewise offers the highest volume (39.7 GB) with 0% overcharging, (iii) Power Law provides an excellent compromise (99.9% budget, 38.1 GB, 0% overcharging). All strategies achieve a zero surcharge, confirming the theoretical guarantees. A sensitivity analysis on the weighting parameter alpha (0.2 - volume priority, 0.5 - balance, 0.8 - budget priority) shows that utility rankings evolve logically. Execution times ( 10 ms) and very low failure rates (0% for robust strategies) confirm the operational viability of the system. The formal proof of the absence of overcharging constitutes a major theoretical contribution.
[NLP-30] MemFuse: Multi-Source Memory Fusion from Frag mented Observations
【速读】： 该论文旨在解决智能体在长期交互中面临的多源记忆融合问题，即现有记忆系统与评估基准大多局限于单一来源的文本历史，而现实场景中的关键信息往往分散于不同应用、设备、用户及时间维度，亟需将碎片化观测整合为连贯的事件记忆并保留其来源可追溯性。其解决方案的关键在于提出MemFuseBench基准与MemFuse结构化记忆系统：前者通过“场景-传感器”流水线生成带有源标签的观测数据、基于证据的问题及对抗性干扰项，支持对时序推理、跨源证据融合及抗噪能力的系统评估；后者采用因果融合图架构，在事件层以原子形式保留源级证据，并在聚类层构建融合记忆，实现检索时对相关证据片段的组织与溯源。实验表明，MemFuse在三种大语言模型（LLM）设置下均取得最优综合性能，尤其在需要跨源证据融合的任务上表现显著提升。
链接: https://arxiv.org/abs/2608.18704
作者: Chao Li,Yuanfa Li,Wenhao Wu,Xule Liu,Zhi Wang,Kun Shao
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 30 pages, 4 figures, 4 tables
Abstract:Long-term memory is essential for agents that operate across extended interactions, yet existing memory systems and benchmarks predominantly focus on single-source textual histories. In realistic settings, however, relevant information is often fragmented across applications and devices, as well as across users and time, requiring agents to integrate dispersed observations into coherent episodic memories while preserving their source provenance. To address these gaps, we introduce MemFuseBench, a benchmark for multi-source memory fusion. MemFuseBench is built with a Scene-to-Sensor pipeline that synthesizes controllable scenarios into source-tagged observations, evidence-grounded questions, and adversarial distractors. It enables systematic evaluation of temporal reasoning, cross-source evidence fusion, and robustness to noise. We further propose MemFuse, a structured memory system that preserves source-level evidence in event-layer atomic memory and organizes related atomic events into cluster-layer fused memory within a causal fusion graph. During retrieval, MemFuse retrieves and organizes related evidence fragments while maintaining traceability to original source events. Experiments on MemFuseBench show that MemFuse achieves the best overall performance among the evaluated memory systems under all three LLM settings and consistently improves performance on questions requiring cross-source evidence fusion.
[NLP-31] Aslema at NADI 2026: Augmentation through Fewshot for SLU
【速读】： 该论文针对NADI 2026共享任务5中的两个核心问题——意图识别（intent recognition）与槽位填充（slot filling）——展开研究，旨在提升在低资源方言语料（尤其是突尼斯迪尔贾语，Tunisian Derja）上的自然语言理解性能。其关键解决方案在于：首先通过对比零样本推理（zero-shot inference）与微调模型的表现，验证了微调策略在该任务中的显著优势；其次提出一种基于生成式AI（Generative AI）的合成数据增强方法，利用大语言模型（LLM）生成具有文化语境贴合性的突尼斯迪尔贾语语音文本，并结合语音克隆（voice cloning）技术生成合成语音数据，从而有效扩充训练语料。实验表明，融合原始数据与合成数据进行训练可显著提升两个任务的性能。最终系统基于Qwen3-Omni-30B模型，在开发集上达到86.8%的意图识别准确率和34.7%的词错误率（WER），在官方测试集上以59.5%的词错误率（CoER）位列槽位填充第一，意图识别排名第四（66.1%准确率）。该工作为低资源方言场景下的语音理解提供了可复现的端到端解决方案，并公开了实验脚本及合成数据集，推动后续研究发展。
链接: https://arxiv.org/abs/2608.18689
作者: Tajwaar Shafiq,Hunzalah Hassan Bhatti,Shammur Absar Chowdhury,Firoj Alam
机构: Qatar Computing Research Institute, HBKU, Qatar(卡塔尔计算研究研究所，哈马德本哈利法大学，卡塔尔)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: LLMs, Native, Arabic LLMs, Augmentation, Multilingual, Multimodal, Language Diversity, Contextual Understanding, Minority Languages, Culturally Informed, Foundation Models, Large Language Models, Audio Models, Omni Models, Slot Filling
Abstract:We present Aslema, our system for NADI 2026 Shared Task 5, which consists of two subtasks: intent recognition and slot filling. We evaluate four omni LLMs in a zero-shot setting and compare them with fine-tuned models. Our results show that fine-tuning consistently outperforms zero-shot inference. We further explore synthetic data augmentation by using an LLM to generate culturally grounded Tunisian Derja utterances, followed by voice cloning to generate synthetic speech. Incorporating this synthetic data improves performance on both tasks. Our final submitted system, based on Qwen3-Omni-30B and trained with a mixture of original and synthetic data, achieves 86.8% intent accuracy and 34.7 WER on the devtest split. On the official test set it ranks 1st in slot filling (59.5 CoER) and 4th among 8 teams in intent recognition (66.1% accuracy). We release our experimental scripts and will soon share the synthetic dataset to support further research in this area.
[NLP-32] Learning What to Fail On: Failure-Mode Contextual Bandits for Adversarial Data Curation
【速读】： 该论文旨在解决自然语言理解模型在面对对抗性样本时鲁棒性不足的问题，特别是现有方法在生成和选择对抗性训练样本时缺乏对模型失败模式的动态感知与自适应优化。其核心解决方案是提出一种故障感知的对抗性检索增强框架（failure-aware adversarial retrieval-augmented framework），关键在于将对抗性数据筛选过程建模为一个基于上下文的带状问题（contextual bandit problem），使数据整理器自身成为学习代理。具体而言，通过检索增强提示生成候选样本，由目标模型过滤、大语言模型（LLM）判别器集合自动验证，并聚类为重复出现的故障模式；随后采用随机策略选择需重训练的故障模式，其策略更新依赖于基于验证的奖励信号，该信号综合平衡了鲁棒性提升、遗忘效应与数据成本。这一机制实现了跨训练轮次对最有价值模型故障的自适应选择。实验表明，该方法在SNLI、ANLI、MultiNLI等标准基准上显著提升了RoBERTa-base的准确率，并在FEVER事实验证任务中实现高达79.86%的FEVER得分，且无需额外人工标注，具备良好的可扩展性。此外，理论分析进一步证明，在特定假设下，故障模式采样可降低捷径对齐梯度贡献，同时控制分布漂移的有界性。
链接: https://arxiv.org/abs/2608.18681
作者: Roie Kazoom,Ofir Cohen,Rami Puzis,Asaf Shabtai,Ofer Hadar
机构: Software and Information Systems Engineering, Ben Gurion University of The Negev(软件与信息系统工程，本古里安大学); Electrical Computers Engineering, Ben Gurion University of The Negev(电气与计算机工程，本古里安大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:We introduce a failure-aware adversarial retrieval-augmented framework for improving robustness in natural language understanding. Rather than selecting synthetic examples with a fixed reward threshold, our method formulates adversarial data curation as a failure-mode contextual bandit problem. Candidate examples are generated with retrieval-augmented prompting, filtered by the current target model, automatically validated by an LLM judge ensemble, and clustered into recurring failure modes. A stochastic policy then selects which failure modes to sample for retraining, and is updated using validation-based reward that balances robustness gains, forgetting, and data cost. This makes the data curator itself the learning agent, enabling adaptive selection of the most useful model failures across training rounds. On standard benchmarks, our approach improves RoBERTa-base accuracy from 88.48% to 92.60% on SNLI, from 75.04% to 80.95% on ANLI, and from 54.67% to 71.99% on MultiNLI, while consistently outperforming prior adversarial augmentation methods. We further demonstrate transfer to FEVER fact verification, achieving up to 79.86% FEVER score and 82.45% accuracy with RoBERTa-large. Finally, we provide a theoretical interpretation showing that, under stated assumptions, failure-mode sampling can reduce shortcut-aligned gradient contributions while inducing bounded distributional drift. By combining retrieval, automated validation, contextual-bandit failure selection, and controlled adversarial retraining, our framework enables scalable robustness improvement without additional human annotation.
[NLP-33] X2Streaming-TTS: Causal Token-Level Text-to-Speech from Streaming Text with Speech-State Inheritance
【速读】： 该论文旨在解决流式文本转语音（Streaming text-to-speech, TTS）系统在实际应用中普遍存在的延迟与感知连续性问题，特别是现有系统多依赖句子级输入，仅实现伪流式（pseudo-streaming），无法满足低延迟语音对话系统的实时性需求。其核心挑战在于：如何在接收不确定前缀（uncertain prefixes）的条件下，实现真正的词元级（token-level）流式合成，同时保持声学上的感知连贯性，并在有限上下文范围内处理无限长的输入序列。解决方案的关键在于提出一种因果性流式TTS框架X2Streaming-TTS，其创新点包括：（1）因果性承诺（causal commitment），通过引入感知不确定性的缓冲机制和自适应容量、标点感知的分段策略，有效管理不完整输入带来的歧义，稳定在线分段并减少因上下文不足导致的合成失败；（2）因果性语音状态继承（causal speech-state inheritance），通过跨分段传递完整的Code2Wav状态及选定的历史说话人（Talker）状态，确保语音在边界处的声学连续性，同时维持有限的上下文范围；结合注意力先验约束（attention prior constraint），进一步防止未来位置信息访问。实验表明，X2Streaming-TTS在多数主观与客观指标上优于现有伪流式模型，实现了接近离线基线的语音质量，单请求中位首次音频词元时间（TTFT）仅为15.8 ms，128并发请求时中位TTFT为260.8 ms，验证了其在严格词元级流式合成下的高效性与高质量表现。
链接: https://arxiv.org/abs/2608.18661
作者: Rime Wen,Zehan Liu,Shawn Qin,Lights Shi,Roy Gan,Hao Wang,Qian Wang
机构: X Square Robot
类目: Computation and Language (cs.CL)
备注: 11 pages, 3 figures, 4 tables. Equal contribution by Rime Wen and Zehan Liu. Corresponding author: Hao Wang. Code: this https URL
Abstract:Streaming text-to-speech is essential for low-latency spoken dialogue systems, yet many systems wait for sentence-level text and are therefore only pseudo-streaming. True token-level synthesis must generate speech from uncertain prefixes while maintaining perceptual continuity over an unbounded stream with bounded context. We present X2Streaming-TTS, a causal TTS framework that consumes asynchronously arriving text tokens and emits speech without accessing future input. To handle uncertain prefixes, we introduce causal commitment, which keeps ambiguous expressions provisional through uncertainty-aware buffering and performs capacity-adaptive, punctuation-aware segmentation. To preserve acoustic continuity, we further introduce causal speech-state inheritance, which carries the complete Code2Wav state and selected historical Talker states across segment boundaries. Together with an attention prior constraint, it blocks access to future positions while retaining bounded acoustic context. Experiments show that X2Streaming-TTS outperforms existing pseudo-streaming models on most subjective and objective metrics. Further analysis shows that causal commitment stabilizes online segmentation and reduces failures caused by insufficient context, while speech-state inheritance improves boundary continuity without degrading naturalness or speaker identity. X2Streaming-TTS thus achieves strict token-level synthesis with quality comparable to the evaluated offline baselines, a median time to first audio token (TTFT) of 15.8 ms for a single request, and a median TTFT of 260.8 ms at 128 concurrent requests. Our implementation is publicly available at this https URL .
[NLP-34] ranslatePsy-AfriSLM: High-Quality Data Scaling For Low-Resource Machine Translation EMNLP2026
【速读】： 该论文旨在解决非洲语言在人工智能领域长期被忽视所导致的数字鸿沟问题，具体表现为现有开源大语言模型（LLM）在非洲语言机器翻译（Machine Translation, MT）任务中表现显著落后，且受限于缺乏大规模、高质量、开源的平行语料数据，难以训练出具备竞争力的小型语言模型（Small Language Models, SLMs）。其解决方案的关键在于构建名为 TranslatePsy-AfriSLM 的开源资源体系，包含经过筛选的平行语料、专为非洲语言设计的合成数据以及一系列微调后的SLMs。研究发现，统一的质量评估过滤机制可剔除高达96%的训练样本而不会损害模型性能，且经过滤的合成数据在质量与效率的权衡上占据帕累托前沿；基于此混合数据微调的模型仅使用0.8B参数，即显著超越参数量更大的系统（如TranslateGemma-27B和Qwen3.5-122B-A10B），验证了高效数据筛选与合成策略在提升非洲语言模型性能中的核心作用。
链接: https://arxiv.org/abs/2608.18655
作者: Milan Gritta,Patrik Lambert,Jihye Back,Amril Nazir
机构: Tether AI Research(塔瑟人工智能研究)
类目: Computation and Language (cs.CL)
备注: EMNLP 2026 (under ARR, meta review of 4, awaiting accept decision)
Abstract:The rapid progress in Artificial Intelligence has largely bypassed African languages, creating a digital divide that limits AI adoption on the continent. Recent open-source LLMs systematically underperform on African machine translation, while the lack of large-scale, high-quality, open-source parallel data has constrained the development of competitive small language models (SLMs). We introduce TranslatePsy-AfriSLM, a collection of open-source MT resources for 19 Sub-Saharan African languages, including curated parallel data, African-specialized synthetic data, and a family of fine-tuned SLMs. Our empirical study shows that unified quality-estimation filtering removes up to 96% of training tokens without degrading quality, and that filtered synthetic data dominates the quality-efficiency Pareto frontier. Fine-tuned on the resulting data mixture, TranslatePsy-AfriSLM outperforms substantially larger systems, including TranslateGemma-27B and Qwen3.5-122B-A10B, with as few as 0.8B parameters.
[NLP-35] When Safety Overrides Vision: Exploring Dynamics between Vision Influence and Safety Alignment in Vision-Language Models
【速读】： 该论文旨在解决安全对齐的视觉-语言模型（VLMs）在安全约束指令下频繁拒绝回答本可正确作答的问题这一现象所揭示的核心问题：安全对齐是否实质上抑制了模型的感知锚定能力，抑或只是将生成行为引导至回避回答，而视觉证据仍保留在内部表征中。其解决方案的关键在于揭示安全诱导拒绝行为的内在解码机制——通过多架构与多模态基准测试发现，尽管模型选择拒绝生成，但其解码过程始终受到视觉证据的持续影响，表明感知锚定能力在内部仍被保留；进一步分析显示，不同架构虽在拒绝表征组织上存在差异，但安全指令均一致地改变晚期隐藏状态动态，使其趋向于拒绝导向的解码模式；更重要的是，通过针对性激活层级干预，抑制与拒绝相关的表征可有效恢复模型的基于视觉证据的合理回答行为，且无需重新训练或修改输入。研究揭示了一种此前未被充分关注的对齐失败模式：安全对齐可能在感知证据仍可被模型内部利用的情况下，强行覆盖其基于视觉的表达输出。
链接: https://arxiv.org/abs/2608.18628
作者: Mehak Gupta,Tanmoy Chakraborty
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Aligned vision-language models (VLMs) are designed to balance grounded visual reasoning with safe generation behavior. However, we observe a striking phenomenon: under safety-constrained instruction, models frequently abstain from answering questions that remain correctly answerable under default instruction despite receiving identical image-question inputs. This raises a fundamental question: does safety alignment suppress perceptual grounding itself, or does visual evidence remain internally available while generation is redirected toward abstention? In this work, we investigate the internal decoding dynamics underlying safety-induced abstention in aligned VLMs. Across multiple architectures and multimodal benchmarks, we show that abstained generations remain consistently influenced by visual evidence throughout decoding, indicating that perceptual grounding is largely preserved despite refusal behavior. We further demonstrate that, although the representational organization of refusal differs substantially across architectures, safety-constrained instruction consistently alters late-stage hidden-state dynamics toward refusal-oriented decoding. Finally, through targeted activation-level interventions, we show that suppressing refusal-related representations reliably restores grounded answering behavior across models without retraining or modifying visual inputs. Together, these findings reveal a previously underexplored failure mode in aligned VLMs: safety alignment can override grounded visual expression even when perceptual evidence remains internally preserved.
[NLP-36] Can a Lightweight Multimodal Model Estimate LLM Reasoning Performance? A Study for Compute-Optimal Document Inference
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在文档类任务中因统一分配推理预算而导致的资源浪费与过度思考惩罚问题，尤其针对视觉版式复杂性较高的文档场景。其核心挑战在于如何在保证模型性能的同时，实现推理预算的动态、高效分配。解决方案的关键在于提出首个多模态基准测试集BudgetDoc，该基准显式地提供模型-预算-性能之间的权衡监督信号，涵盖三类典型文档任务；并基于此训练出一个约10亿参数的预飞行估算器DRB（Document-Reasoning Balancer），该模型由SigLIP-2与Qwen3-0.6B构成，能够对不同预算水平下的模型性能进行序数预测，实现0.753的加权F1得分。通过动态调度五个前沿模型在三个数据集上的推理预算，DRB在15种配置中的9种上达到或优于始终使用最大预算基线的性能，同时显著降低计算成本。初步评估还表明，DRB具备跨模型选择的泛化潜力。
链接: https://arxiv.org/abs/2608.18591
作者: Zishan Ahmad,Vishal Vaddina
机构: Phi Labs(Phi实验室); Quantiphi(Quantiphi)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Uniformly allocating inference reasoning budgets to LLMs is expensive and prone to over-thinking penalties; especially in document tasks where visual layouts drive complexity. To address this, we introduce BudgetDoc, the first multimodal benchmark providing explicit supervision for model-budget-performance trade-offs across three document tasks. Using BudgetDoc, we train DRB (Document-Reasoning Balancer), an approx. 1B-parameter pre-flight estimator (SigLIP-2 + Qwen3-0.6B) that predicts ordinal model performance across budget levels, achieving a 0.753 weighted F1. When dynamically allocating reasoning budgets across five frontier models and three datasets, DRB matches or improves F1 scores compared to always-maximum-budget baselines in 9 of 15 configurations while drastically reducing cost. Finally, preliminary evaluations demonstrate DRB’s potential to generalize to cross-model selection.
[NLP-37] From Storag e to Access: Verifiable Activation of Parametric Knowledge in LLM s via Explicit Priming and Implicit Reasoning
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在事实性问答任务中难以可靠召回与验证其参数化知识的关键瓶颈问题。现有端到端方法将知识提取与推理过程耦合，导致无法区分正确答案是源于模型内部的参数化知识还是输入上下文中的信息。为应对这一挑战，论文提出VAKE（Verifiable Activation of Parametric KnowledgE），一种基于强化学习的两阶段框架：第一阶段“显式提示（Priming）”通过外部化潜在的参数化知识，主动插入可验证的桥接三元组作为证据，其策略优化依赖于一个独立冻结模型在增强子图上生成答案所获得的奖励信号；第二阶段“隐式推理（Reasoning）”则训练模型在原始输入下作答，以检验前一阶段习得的知识提取能力是否能有效迁移至隐式推理场景。关键创新在于通过可验证的外部知识注入机制，实现了对模型内部知识的可控激活，而非依赖上下文或记忆特定数据模式。实验结果表明，VAKE在七个基准测试中均显著优于标准基线，且在跨数据集迁移时仍保持优势；基于LLM的评估显示，超过80%插入的三元组提供了无法从原始检索上下文中推导出的事实性桥接知识，且超过一半能够激发直接提示无法获取的知识，证明其成功激活了模型深层的参数化知识。
链接: https://arxiv.org/abs/2608.18581
作者: Zuocheng Ying,Yang Yang,Yumou Wu,Chuanbo Zhu,Jiarui Wang,Ziqi Wu,Jingming Cai,Junqing Yu,Zikai Song
机构: ByteDance(字节跳动); Huazhong University of Science and Technology (华中科技大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Although Large Language Models (LLMs) encode rich factual knowledge in their parameters, reliably recalling and verifying such knowledge remains a key bottleneck in factual question answering. Existing end-to-end methods entangle knowledge elicitation with reasoning, making it difficult to determine whether correct answers arise from parametric knowledge or the input context. To address this challenge, we propose VAKE (Verifiable Activation of Parametric KnowledgE), a two-stage reinforcement-learning framework that externalizes latent parametric knowledge through explicit Priming and transfers the acquired elicitation capability to implicit Reasoning. Given a query and an insufficient retrieved subgraph, the Priming policy explicitly inserts bridging triples as verifiable evidence, with supervision provided by rewards derived from answers generated by a separate frozen model over the augmented subgraph. Building on the policy learned during Priming, the Reasoning stage trains the model to answer from the original input, testing whether the capability acquired through explicit knowledge elicitation transfers to implicit reasoning. Experiments across seven benchmarks and models from 3B to 14B show that VAKE consistently outperforms standard baselines, including when transferring directly from HotpotQA to OOD datasets. LLM-based evaluation further shows that over 80% of the inserted triples provide factual bridging knowledge not derivable from the retrieved context, while more than half elicit knowledge inaccessible through direct prompting. These results suggest that VAKE activates latent parametric knowledge rather than copying the input context or memorizing dataset-specific associations.
[NLP-38] Compress and Forget: bitsandbytes Quantization Amplifies Proactive Interference in LLM s
【速读】： 该论文旨在解决大语言模型在长期可更新语义密集上下文场景中因频繁覆盖导致的前向干扰（proactive interference, PI）问题，尤其关注后训练量化（post-training quantization, PTQ）对这一现象的影响。研究发现，尽管低精度量化（如INT4/NF4）常被用于模型部署以降低资源开销，但其显著加剧了高干扰条件下的性能退化——例如在Qwen2.5-7B-Instruct模型中，准确率从81.0%降至68.3%，且该效应在所有测试模型中均通过配对McNemar检验（p ≤ 2.6×10⁻⁶）和混合效应回归得到验证。值得注意的是，即使通常被认为安全的INT8量化也对其中两个模型造成了可观测但较小的性能损失。该干扰效应具有语义特异性，仅在语义相似的干扰项条件下显现，并在数值控制条件下出现符号反转，表明其机制与同键侵入错误（same-key intrusion errors）显著增加相关（从21.5%升至24.6%，p = 4.8×10⁻⁷）。消融实验进一步揭示该影响源自量化后的Transformer主干网络而非输出投影层。因此，关键解决方案在于认识到：bitsandbytes 4-bit量化虽可能维持整体基准准确率，却会为依赖长时、动态更新语义上下文的应用引入额外的认知负担，提示在实际部署中需权衡量化精度与任务特定的上下文稳定性需求。
链接: https://arxiv.org/abs/2608.18578
作者: Shayan Shahrabi-Farahani(1),Dara Rahmati(1) ((1) Shahid Beheshti University, Tehran, Iran)
机构: Shahid Beheshti University (谢里德·贝赫什特大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 21 pages, 6 figures, 11 tables. Code and data released at this https URL
Abstract:Proactive interference (PI) is a documented failure mode in large language models in which retrieval of a repeatedly overwritten value degrades as prior overwrites accumulate, mirroring a classical phenomenon in human working memory. Post-training quantization (PTQ) is now the default deployment path for open-weight models, yet its effect on this failure mode has not been tested. We evaluate three precision levels (FP16, INT8, INT4/NF4, via bitsandbytes) across three architecturally distinct instruction-tuned models (Qwen2.5-7B-Instruct, Mistral-7B-Instruct-v0.3, Phi-3.5-mini-instruct), holding the retrieval task fixed. INT4 quantization significantly reduces accuracy under high interference in every model (e.g., from 81.0% to 68.3% for Qwen), confirmed by paired McNemar’s tests ( p \le 2.6 \times 10^-6 ) and a mixed-effects regression spanning all interference levels; INT8, often assumed safe, also carries a smaller but real penalty in two of three models. The effect is specific to semantically similar (word-type) distractors and reverses sign under a numeric control condition, and is mechanistically linked to a rise in same-key intrusion errors under INT4 (from 21.5% to 24.6% of trials, p = 4.8 \times 10^-7 ). A follow-up ablation shows the effect originates in the quantized transformer backbone rather than the output projection layer. These results suggest that bitsandbytes 4-bit quantization can impose an additional cost on applications relying on long, updatable, semantically dense contexts, even when aggregate benchmark accuracy appears largely unaffected. We release our code and tokenizer-verified vocabulary construction method at this https URL
[NLP-39] Beyond LLM -Based Reasoning : Lightweight GNNs for Agent Failure Attribution
【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（Multi-Agent System, MAS）在复杂任务执行中出现的故障模式识别问题，即在给定失败的多智能体轨迹时，准确识别出故障智能体及其对应的错误类型。现有方法主要依赖于LLM进行故障归因，通过直接提示、合成数据微调或复杂的代理式工作流实现，但这些方法存在显著的计算开销，包括长上下文处理、昂贵的后训练过程以及人工设计的工作流程。此外，实证研究表明，即使最先进的模型在现有基准上的准确率也有限，表明单纯扩大模型规模无法有效提升性能。为此，本文提出AFANet——一种轻量级的图神经网络（Graph Neural Network, GNN）框架，其核心创新在于通过步骤级语义信号与智能体级关系建模交互轨迹，以结构化方式捕捉多智能体协作中的关键信息。实验表明，相较于依赖生成式推理的复杂方案，AFANet仅需极少参数和近乎零的推理成本，即可在域内基准上达到或超越基于LLM的基线表现，并在不同GNN架构下保持鲁棒性，同时可通过低成本的测试时适应进一步提升跨域（Out-of-Domain, OOD）性能。研究结果表明，高效的智能体故障归因并不依赖于重型生成式推理，而一个轻量、结构化的图建模方法即可实现卓越性能。
链接: https://arxiv.org/abs/2608.18575
作者: Ting-Wei Li,Yuanchen Bei,Xiao Lin,Hanghang Tong
机构: University of Illinois(伊利诺伊大学); Urbana-Champaign, IL USA(厄本那-香槟, 伊利诺伊州, 美国)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language model (LLM)-based multi-agent systems (MAS) often exhibit complex failure modes, which frequently cause agents to produce incorrect outcomes. This motivates the task of Agent Failure Attribution: given a failed multi-agent trajectory, identify the faulty agents and their corresponding error types. Existing approaches predominantly rely on LLMs to perform failure attribution, either through direct prompting, fine-tuning on synthetic data or complex agentic pipelines. While effective, these methods incur substantial computational overhead due to long-context processing, expensive post-training and handcrafted workflows. Moreover, empirical evidence shows that even state-of-the-art models achieve limited accuracy on existing benchmarks, suggesting that scaling model size alone is insufficient. In this work, we revisit this task and question the necessity of such expensive generative solutions. We introduce AFANet, a lightweight graph-based framework that models interaction trajectories through step-level semantic signals and agent-level relationships. We show that with significantly fewer parameters and near-zero inference cost, AFANet (i) matches or outperforms LLM-based baselines, including fine-tuned models on in-domain benchmarks, (ii) maintains robust performance across different GNN architectures and (iii) can be further improved with inexpensive test-time adaptation on the OOD benchmark. Our results suggest that effective agent failure attribution does not require heavy LLM reasoning and a lightweight, structured approach can achieve strong performance.
[NLP-40] Shared Circuits for Shared Grammar: Tracing Subject-Verb Agreement Across Languages
【速读】： 该论文旨在解决多语言大语言模型（Multilingual Large Language Models, MLLMs）在跨语言语法处理中是否存在共享计算机制的问题，尤其关注形态句法层面的现在时主谓一致（present-tense subject-verb agreement）现象。由于该语法操作在不同语言中的显性表现差异显著（如英语中弱表达，而许多其他语言具有明显的词形变化），研究核心在于厘清这种跨语言共享机制何时出现及其与语言表征形式的关系。其解决方案的关键在于采用激活修补（activation patching）与注意力分析相结合的方法，在29种语言和5个开源模型家族中识别出参与一致性的关键注意力头，并比较其头级表征模式。研究发现，具有显性人称/数词形变化的语言表现出更高程度的计算结构重用，尤其在分离并恢复词形对比特征的条件下，共享最为显著；而英语作为弱显性语言，在需要显性一致的语境下，其内部机制会趋近于有词形变化的语言。此外，大量相关注意力头展现出跨语言相似的注意模式，表明跨语言共享不仅体现在空间定位的一致性，更反映了功能角色的共性。综上，该研究揭示了多语言大模型并非依赖完全独立的语言特异性解码路径，而是部分复用共享的计算架构来实现形态句法一致处理。
链接: https://arxiv.org/abs/2608.18545
作者: Isabella Gidi,Antonio Almudévar,Core Francisco Park,Naomi Saphra,Ricard Marxer
机构: Harvard University(哈佛大学); University of Zaragoza(萨拉戈萨大学); Boston University(波士顿大学); Univ Toulon, Aix Marseille Univ, CNRS, LIS ILLS(图卢兹大学, 马赛大学, 法国国家科学研究中心, LIS ILLS)
类目: Computation and Language (cs.CL)
备注: 25 pages including appendices, 16 figures. Accepted to COLM 2026
Abstract:Multilingual large language models often generalize across languages, and prior work suggests that their internal mechanisms can overlap cross-lingually. It remains unclear, however, when such sharing emerges and whether it varies with the overt realization of the same grammatical operation. We investigate this question for present-tense subject-verb agreement, a morphosyntactic process that varies substantially across languages and is only weakly expressed in English. Using activation patching and attention analysis across 29 languages and five open-source model families, we identify the attention heads causally implicated in agreement and compare these head-level signatures across languages. We find that languages with overt person/number inflection exhibit more similar agreement circuitry than non-conjugating languages, with the strongest sharing appearing when the analysis isolates recovery of the inflectional contrast itself. English provides an informative bridge case, becoming more similar to conjugating languages precisely in contexts where overt agreement is required. Finally, many implicated heads display similar attention patterns across languages, suggesting that cross-lingual overlap reflects shared functional roles as well as shared localization. Together, these results indicate that multilingual LLMs reuse partially shared computational structure for morphosyntactic agreement rather than relying on fully separate language-specific solutions.
[NLP-41] Evaluating and Explaining Prompt Sensitivity of LLM s Using Interactions ICML2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在面对微小且语义无关的提示（prompt）变化时表现出显著性能波动的问题，即提示敏感性（prompt sensitivity）。传统研究多通过比较提示变化前后模型输出结果的差异来评估敏感性，但此类粗粒度指标无法揭示其内在机制。本文提出以“交互作用”（interactions）作为细粒度分析工具，将模型输出得分分解为一组非线性交互项，每项代表输入变量间的复杂关系。研究发现，即使模型最终输出保持不变，提示的细微调整仍可能引发交互作用的剧烈不稳定性。为此，作者提出基于交互作用的提示敏感性（Interaction-based Prompt Sensitivity, IPS）度量方法，通过量化提示微调时交互作用的变化程度来衡量敏感性。在50个开源大模型上的实验表明，监督微调、模型规模扩大、密集架构设计以及少样本学习等四类因素可有效降低提示敏感性，其共同机制在于均能显著削弱低阶交互作用（涉及较少输入变量的交互）对提示扰动的敏感性。
链接: https://arxiv.org/abs/2608.18539
作者: Ruiyang Qin,Qingzhuo Wang,Tian Wang,Zhihua Wei,Wen Shen
机构: Tongji University (同济大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted at the 43rd International Conference on Machine Learning (ICML 2026). 46 pages, 48 figures
Abstract:The remarkable capabilities of large language models (LLMs) are often undermined by their instability. Even subtle and semantically irrelevant changes in prompts can cause dramatic fluctuations in performance, a phenomenon known as prompt sensitivity. Previous studies typically evaluate prompt sensitivity by comparing the LLM’s final outputs when prompts change. However, such coarse-grained metrics fail to explain the internal reasons for prompt sensitivity. In this paper, we introduce interactions as a fine-grained tool to analyze prompt sensitivity of LLMs. Specifically, we decompose the output score of the LLM into a set of interactions. Each interaction represents a nonlinear relationship involving a set of input variables. We discover that subtle changes to prompts can trigger severe instability in interactions, even when the outputs of the LLM remain the same. To this end, we propose an Interaction-based Prompt Sensitivity (IPS) metric by quantifying changes in interactions when we introduce subtle changes to prompts. We apply the IPS metric to 50 open-source LLMs and uncover four factors that reduce the prompt sensitivity of LLMs, including supervised fine-tuning, increased model scales, dense architectures, and few-shot learning. More crucially, we discover a common mechanism by which these four factors reduce prompt sensitivity: all four factors tend to reduce the prompt sensitivity of low-order interactions (i.e., interactions involving few input variables).
[NLP-42] MissDiag: Diagnostic Evaluation of Incomplete-Knowledge Robustness in KGQA and KG-RAG
【速读】： 该论文旨在解决知识图谱问答（KGQA）与基于知识图谱的检索增强生成（KG-RAG）系统在面对知识图谱不完整、稀疏或过时时，其鲁棒性评估缺乏细粒度诊断能力的问题。现有方法通常仅报告移除或扰动证据后答案质量的总体下降，未能区分退化原因，导致无法明确识别是缺失证据类型、系统响应特性还是答案匹配协议敏感性所致。为此，论文提出一种名为\textbf{MissDiag}的诊断评估框架，通过在保持问题与标准答案不变的前提下，对基准提供的支持图施加结构化的缺失干预（structurally typed missingness interventions），实现按证据类型、系统响应及评估协议进行分解分析的成对比较。实验表明，不完整知识下的鲁棒性应被视为一种“类型化退化”现象而非统一属性：与答案相邻的证据丢失导致最显著的性能下降，源上下文移除则常呈中性甚至有益，而语义答案匹配虽改变绝对得分但维持主要退化模式。通过将聚合性鲁棒性度量转化为类型化的可解释归因，MissDiag为KGQA与KG-RAG系统在不完备知识条件下的对比、诊断与压力测试提供了更清晰、可解释的评估基础。
链接: https://arxiv.org/abs/2608.18489
作者: Hang Wang,Hang Dong,Lu Liu,Chuanru Ren
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Knowledge graph question answering (KGQA) and knowledge-graph-based retrieval-augmented generation (KG-RAG) aim to ground answers in explicit graph evidence, but real-world knowledge graphs are often sparse, outdated, and incomplete. Existing robustness evaluations usually report aggregate changes in answer quality after evidence is removed or perturbed, which measures sensitivity to incomplete support but leaves the source of degradation under-specified: the same score change can conflate the type of missing evidence, the response of the evaluated system, and the sensitivity of the answer-matching protocol. To address this gap, we propose \textbfMissDiag, a diagnostic evaluation framework for incomplete-knowledge robustness in KGQA and KG-RAG. MissDiag keeps the question and gold answer fixed while applying structurally typed missingness interventions to benchmark-provided support graphs, enabling paired comparisons that decompose robustness changes by evidence type, system response, and evaluation protocol rather than reducing them to a single aggregate score drop. Experiments across multiple system families show that incomplete-knowledge robustness is better understood as a typed degradation phenomenon than as a uniform property: answer-adjacent evidence loss produces the largest observed degradation, source-context removal is often neutral and can be beneficial, and semantic answer matching changes absolute scores while preserving the main typed degradation patterns. By transforming aggregate robustness measurement into typed diagnostic attribution, MissDiag provides a more interpretable basis for comparing, diagnosing, and stress-testing KGQA and KG-RAG systems under incomplete knowledge.
[NLP-43] WhiteMatter: All-to-All Cross-Layer Connections via KV Mixing
【速读】： 该论文旨在解决自回归解码过程中，Transformer模型各层仅能通过自身深度产生的键值对（Key-Value, KV）关注历史标记的问题，这一限制导致浅层消费者层无法有效利用深层表示所蕴含的丰富语义信息。现有反馈架构虽允许浅层访问深层表示，但其连接模式固定，缺乏灵活性。本文提出WhiteMatter，其核心创新在于：每个注意力层可动态连接到所有历史标记在各深度层的表示，且连接权重可根据消费者层和源标记自适应调整。具体实现上，针对每个标记，路由机制（router）将其L个层状态混合为k个KV通道并缓存以供后续使用，每个消费者层从其中一个通道中进行注意力计算。通道数k控制KV缓存大小，当kL时可显著降低缓存内存开销。实验表明，WhiteMatter在预训练中表现优于层数多50%的原始Transformer，并在实现50%缓存压缩的情况下仍保持大部分性能增益。
链接: https://arxiv.org/abs/2608.18486
作者: Wenbo Zhang,Xiang Ren
机构: University of Southern California(南加州大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 15 pages, 8 figures, 3 tables
Abstract:In a Transformer, each layer attends to past tokens only through KV produced at its own depth, despite the presence of deeper representations during autoregressive decoding. Feedback architectures allow shallow consumer layers to attend to KV produced by deeper past-token representations, but give all consumer layers the same fixed connection patterns to source layers. We propose WhiteMatter, which connects every attention layer to the representations from all layers of each past token, with connection weights that can vary across consumer layers and adapt to the source token. For each token, a router implements these connections by mixing its L layer states into k KV channels that are cached for subsequent tokens; each consumer layer attends to one of the channels. The number of channels k controls the KV-cache size. Setting kL reduces the cache’s memory footprint. In our pretraining experiments, WhiteMatter outperforms a vanilla Transformer with 50% more layers and retains most of this gain with a 50% KV-cache compression.
[NLP-44] Building real-time digital twin instances with FunctionData Flow: user evaluation and extension for iterative pipelines
【速读】： 该论文旨在解决当前数字孪生（Digital Twin, DT）系统在集成人工智能（AI）与机器学习（ML）管道过程中存在的工程化难题，具体表现为：现有ML pipelines缺乏清晰的规范性表达，导致其难以定义、验证与复用，且缺少专用的开发工具支持。针对这一问题，论文提出的核心解决方案是引入一种名为“功能+数据流”（Function+Data Flow, FDF）的可视化领域特定语言（DSL），通过显式建模机器学习模型（ML models）及其组合关系，实现模型的模块化构建与重用。研究团队将FDF集成于DesCartes Builder这一一体化建模环境，支持基于FDF的数字孪生合成与验证。通过一项实证用户研究，评估了该方法在提升AI驱动数字孪生开发可访问性与可靠性方面的有效性。结果表明，DesCartes Builder与FDF在不同背景用户中均展现出良好的可用性，尤其对领域专家而言具有显著优势。研究进一步揭示了工具与框架的潜在改进方向，并据此提出H-FDF——FDF的分层扩展版本，支持迭代式、模块化的复杂管道设计，如双阶段训练（dual training）等高级模式。研究结论表明，集成化、模型驱动的平台有望推动基于AI的数字孪生工程向规范化建模实践演进。
链接: https://arxiv.org/abs/2608.18480
作者: Eduardo de Conto,Blaise Genest,Arvind Easwaran,Nicholas Ng,Shweta Menon
机构: Nanyang Technological University (南洋理工大学); CNRS@CREATE (法国国家科学研究中心@创造中心)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注: 36 pages, 18 figures, submitted to SoSyM journal
Abstract:Digital twins (DTs) increasingly leverage artificial intelligence (AI) and machine learning (ML) pipelines, both to build real-time DTs from high-fidelity simulations and to instantiate them with historical data. However, engineering these pipelines remains largely ad-hoc: pipelines are hard to specify, validate, and reuse, with scarce dedicated tooling. Function+Data Flow (FDF) addresses this by defining a visual domain-specific language (DSL) that represents functions (ML models) explicitly, enabling their composition and reuse. We implemented FDF in DesCartes Builder, an integrated modeling environment supporting FDF-based DT synthesis and validation. In this paper, we report on an empirical user study evaluating whether FDF and DesCartes Builder can make AI-based DT development more accessible and reliable. Participants implemented a representative real-time DT prototype within DesCartes Builder, and we measured perceived usability and feature adequacy through quantitative and qualitative measures. Our results indicate that DesCartes Builder and FDF achieve a good level of usability across a broad range of potential users, and particularly for the intended audience of domain experts. The study additionally surfaces concrete strengths and areas for improvement of both the tool and the underlying FDF framework. Informed by these findings, we propose H-FDF, a Hierarchical extension of FDF supporting iterative and modular pipelines, enabling the formal specification of more complex DT pipelines such as dual training. Our findings suggest that integrated, model-driven platforms are a promising direction to transform AI-based DT engineering into a disciplined modeling practice. Comments: 36 pages, 18 figures, submitted to SoSyM journal Subjects: Software Engineering (cs.SE); Computation and Language (cs.CL) Cite as: arXiv:2608.18480 [cs.SE] (or arXiv:2608.18480v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2608.18480 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-45] OmniAlign: A Unified Multilingual Aligner for Word and Sentence Alignment
【速读】： 该论文旨在解决跨语言序列对齐（cross-lingual sequence alignment）中因现有工具仅支持单一粒度（如仅词级或仅句级）而导致的多粒度对齐需依赖多个独立系统的问题，尤其在多语言和长文本场景下效率低下。其核心解决方案是提出OmniAlign——一个统一的多语言对齐框架，通过单一轻量级模型同时实现词级与句级对齐。关键创新在于：基于编码器-only架构并具备强大的长上下文建模能力，模型通过上下文感知的词元相似性矩阵生成词级对齐，并结合句子嵌入与动态规划算法实现文档级的m–n句对齐；为兼顾细粒度对齐精度与句子表征质量，采用四阶段训练流程——面向对齐的持续预训练、自监督学习、基于人工标注的监督微调，以及从强多语言教师模型中进行句子嵌入蒸馏。实验表明，OmniAlign在词级与句级对齐基准上均达到竞争力表现，且能有效泛化至未见语言对；尤为关键的是，后期在短文本上的监督微调可进一步提升对齐质量，同时保留早期训练获得的长文本理解能力，确保长文本词对齐的鲁棒性。
链接: https://arxiv.org/abs/2608.18474
作者: Mengpeng Yang,Jingxu Yang,Chao Chen,Tian Xia,Yabo Sun,Qiang Liu
机构: WPS Qingqiu (WPS青丘); Wuhan, China
类目: Computation and Language (cs.CL)
备注:
Abstract:Cross-lingual sequence alignment is fundamental for building and exploiting parallel corpora, spanning mappings from documents and sentences down to words and subwords. Existing tools, however, typically specialize in a single granularity, so practitioners often need separate systems for word- and sentence-level alignment—especially in multilingual and long-text settings. We present OmniAlign, a unified multilingual aligner that supports both word-level and sentence-level alignment with a single lightweight model. Built on an encoder-only backbone with strong long-context modeling, OmniAlign induces word alignments from contextualized token similarity matrices, and obtains document-level m – n sentence alignments via sentence embeddings combined with dynamic programming. To balance fine-grained alignment accuracy and sentence-representation quality, we use a four-stage training pipeline: alignment-oriented continued pre-training, self-supervised learning, supervised fine-tuning on human annotations, and sentence-embedding distillation from a strong multilingual teacher. Experiments show that OmniAlign achieves highly competitive performance on both word- and sentence-alignment benchmarks and generalizes well to unseen language pairs. Surprisingly, later-stage supervised fine-tuning on short texts further improves alignment quality while retaining the long-context understanding acquired in earlier training, keeping the model robust on long-text word alignment. \normalsize \colorblue\textbfCode: this https URL\par \colorblue\textbfModel: this https URL Subjects: Computation and Language (cs.CL) Cite as: arXiv:2608.18474 [cs.CL] (or arXiv:2608.18474v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.18474 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-46] Pedagogical AI in Mental Health: A Tri-Stream Fine-Tuned LLM Framework for Automated Clinical Supervision and Risk Triage
【速读】： 该论文旨在解决现代心理卫生领域中资深督导资源严重不足所导致的“督导空白”问题，即新手治疗师在缺乏及时专业反馈的情况下处理高风险临床情境。其核心解决方案是提出一种基于微调后的Mistral-7B-instruct模型的自动化“督导在环”（Supervisor-in-the-Loop）框架，通过多模态视觉-语音-语言（VAL）分析实现对治疗过程的实时监控与智能干预。关键创新在于构建三重分析流：（1）基于语义一致性追踪治疗联盟；（2）利用注意力加权分析进行潜在风险预测；（3）引入动态临床紧急度指数（D-CUI）实现督导分诊。该系统在DAIC-WOZ数据集上实现了95%的技术识别准确率、0.105的治疗联盟评估平均绝对误差（MAE）、0.423的治疗忠实度α值以及0.370的平均D-CUI，且在单块Tesla T4 GPU上仅需105步训练即实现85.2%的损失下降，将督导分诊延迟从72小时压缩至近实时（每会话约10秒），并通过贝叶斯先验缓解冷启动问题，并采用基于时间戳的模态同步策略保障多模态融合的鲁棒性。
链接: https://arxiv.org/abs/2608.18438
作者: Shreeya Sharma,Ravish Gupta,Saket Kumar,Abhishek Aggarwal
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 14 pages, 1 figure, 2 tables. Accepted for publication in AICTC 2026, Lecture Notes in Networks and Systems, vol. 2165, Springer
Abstract:Modern mental healthcare faces a critical shortage of senior supervisory oversight, leading to a “supervision gap” where novice therapists manage high-stakes risks with delayed professional feedback. This paper proposes a new framework utilizing a fine-tuned Mistral-7B-instruct model as an automated “Supervisor-in-the-Loop” system. By leveraging 106 sessions from the DAIC-WOZ dataset, the model performs a tri-stream analysis: (1) Therapeutic Alliance tracking via semantic adherence, (2) Latent risk prediction using attention-weighted analytics, and (3) Supervisory Triage via a Dynamic Clinical Urgency Index (D-CUI). Our multi-modal VAL (Visual-Acoustic-Linguistic) framework achieves 95% technique identification accuracy [95% CI: 75.1%-99.9%], alliance assessment MAE of 0.105 on a 5-point scale [95% CI: 0.059-0.151], therapeutic fidelity alpha = 0.423, and mean D-CUI of 0.370 [95% CI: 0.322-0.419]. Training converged in 105 steps with 85.2% loss reduction on a single Tesla T4 GPU. The system reduces supervisory triage latency from 72 hours to real time (~10 seconds per session), enabling proactive intervention in high-risk cases. The system addresses the cold-start problem through Bayesian priors and implements timestamp-based modality synchronization for robust multi-modal fusion.
[NLP-47] angut Word Segmentation under Extreme Resource Scarcity: Integrating Traditional Lexicons and Unlabeled Text
【速读】： 该论文旨在解决西夏语（Tangut）书面语中由于文字系统未显式标记词边界而导致的词切分难题。其核心挑战在于缺乏明确的词界标识，且可用标注数据极为有限。解决方案的关键在于构建一个融合可靠性校准的词汇网格表示、显式的分布统计特征以及基于掩码语言建模（MLM）预训练的轻量级字符编码器的综合框架。该方法通过引入词汇与统计特征显著提升条件随机场（CRF）的F1值至约0.91，而完整的TangutEncoder模型在段落级别五折交叉验证中达到最高均值F1（0.911），并实现了对训练词汇外词的召回能力提升，表明模型具备在主题多样的未见文本中进行有效泛化的能力，尽管文档级别的迁移性能仍有待评估。
链接: https://arxiv.org/abs/2608.18437
作者: Lifan Deng,Yongwei Zhang,Sen Sun,Bojun Sun,Jingsong Yu
机构: Key Laboratory of Linguistics, Chinese Academy of Social Sciences (University of Chinese Academy of Social Sciences), Beijing; Rixin College, Tsinghua University, Beijing; Institute of Linguistics, Chinese Academy of Social Sciences, Beijing; Institute of Ethnology and Anthropology, Chinese Academy of Social Sciences, Beijing; School of Software and Microelectronics, Peking University, Beijing
类目: Computation and Language (cs.CL)
备注:
Abstract:Tangut is an extinct language whose script does not explicitly mark word boundaries. We present the first systematic study of Tangut word segmentation using 2,750 expert-annotated segments(31,893 tokens), traditional lexicons, and unlabeled text. Our framework combines a reliability-calibrated lexicon-lattice representation, explicit distributional statistics, and a lightweight character encoder pretrained with MLM. Segment-level five-fold cross-validation shows that lexical and statistical features raise CRF F1 to approximately 0.91. The full TangutEncoder reaches the highest mean F1 (0.911) and improves recall beyond the labeled training vocabulary. These results demonstrate generalization beyond the limited supervised vocabulary across thematically diverse held-out passages, while document-level transfer remains to be evaluated.
[NLP-48] Selection Recombination or a Fresh Solve? A Candidate-Free Control for Single-Pass Test-Time Aggregation
【速读】： 该论文旨在解决在生成式AI（Generative AI）进行测试时推理过程中，当所有候选答案均错误时，传统基于候选答案的选择机制失效的问题。其核心挑战在于：在缺乏正确候选答案的情况下，如何有效实现准确的聚合求解。解决方案的关键在于通过引入“无候选控制”（candidate-free control）这一反事实实验设计，在相同最大输出标记数限制下，系统性地评估候选上下文对最终答案准确性的影响。研究发现，当存在多个正确候选答案时，候选条件化可显著提升准确率（Δ_cand(c2+) = +0.290），而当所有候选均错误时，依赖候选池反而会降低准确率（Δ_cand(c0) = -0.123），表明此时“重新求解”（fresh solving）优于基于错误候选的重组。这一结果在保守校正适应性双基准测试程序后依然成立，揭示了在全错情形下，对错误候选进行条件化反而损害性能，从而支持“全错恢复”本质上是通过脱离原有候选路径实现的再求解机制。此外，尽管显式答案字段干预可因果性引导输出，但掩码处理未带来可测量的准确率提升，且原始格式等价性未被证实，进一步说明当前方法中候选信息的价值并非源于结构或格式本身，而是取决于其内容正确性与求解路径的合理性。
链接: https://arxiv.org/abs/2608.18379
作者: Guiv Farmanfarmaian
机构: ETH Zurich(苏黎世联邦理工学院); Zurich, Switzerland
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted at the COLM 2026 Workshop on Efficient Reasoning. 18 pages
Abstract:When every candidate is wrong, correct-candidate selection is unavailable, yet the aggregation call can still solve the problem afresh. A correct aggregate answer may therefore reflect recombination, fresh solving, or both. For efficient test-time reasoning, the relevant question is whether candidate context adds value beyond the additional generation pass. We introduce the missing candidate-free control under the same maximum output-token allowance and stratify by the number of correct candidates. Across AIME-2025 and HMMT-2025 with Qwen3-4B, candidate conditioning improves accuracy when multiple candidates are correct ( \Delta_\mathrmcand (c2+) = +0.290), lowers accuracy when every candidate is wrong ( \Delta_\mathrmcand (c0) = -0.123), and remains unresolved in the one-correct regime. The c2+ and c0 conclusions survive a conservative correction for the adaptive two-benchmark procedure. Under this counterfactual, the interpretation of all-wrong recovery reverses at this scale: conditioning on an all-wrong candidate pool lowers accuracy relative to a fresh solve. Original-format matching and placebo results characterize the failures descriptively but leave their mechanism unresolved. Within a separate structured intervention, explicit answer fields causally steer outputs toward their values; masking yields no measurable accuracy improvement, and equivalence with the original format was not established. The evidence is limited to one Qwen3-4B family, two mathematics benchmarks, first-answer-truncated candidate fragments, and single-pass prompted aggregation.
[NLP-49] Figurative and Cultural Knowledge in LLM s: Investigating Cross-Domain Transfer through Fine-Tuning
【速读】： 该论文旨在解决大语言模型（LLM）在理解阿拉伯语中隐喻性语言时存在的文化依赖性问题，即模型是否能通过文化相关数据的微调提升对隐喻性语言的理解能力，反之亦然。其核心问题是：文化知识与隐喻语言理解之间是否存在可迁移的内在联系，以及微调策略能否有效激发这种联系。解决方案的关键在于系统性地评估四种主流模型（ALLaM-7B、Fanar-1-9B、Qwen3-8B、Llama-3.1-8B）在六个涵盖文化常识、谚语和诗歌等多源异质数据集上的表现，尤其关注诗歌微调对习语理解的提升效果。研究发现，诗歌微调显著提升了习语理解准确率（+2.33%，p<0.05），且该增益无法由仅针对阿拉伯语语言适应的控制实验再现，表明模型确实对非字面意义具有敏感性，并能实现跨类型隐喻理解的迁移。然而，文化数据微调反而降低了谚语解释的准确性，且在阿拉伯语主导模型中普遍存在性能退化现象，提示其相关知识可能已达到饱和；而多语言模型则表现出更强的适应潜力。错误分析进一步揭示，微调强化了基于经验的文化认知，但削弱了基于历史事实的知识稳定性。综上，尽管文化和隐喻语言在概念上紧密关联，但其复杂关系难以通过单一微调策略充分建模，需考虑知识类型的动态平衡与模型架构的适配性。
链接: https://arxiv.org/abs/2608.18361
作者: Mena Attia,Mona Diab,Thamar Solorio
机构: MBZUAI(穆巴达拉人工智能研究所); Carnegie Mellon University(卡内基梅隆大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Figurative language is deeply culturally embedded; fluent use requires not just linguistic competence but cultural immersion. We ask whether LLMs can learn this link: does fine-tuning on cultural data improve figurative language understanding, and vice versa? We conduct a systematic study across four models (ALLaM-7B, Fanar-1-9B, Qwen3-8B, Llama-3.1-8B) and six Arabic datasets spanning cultural commonsense, proverbs, and poetry across diverse dialects and regions. Fine-tuning on poetry improves idiom comprehension (+2.33%, p0.05), a gain our ArabicMMLU control does not reproduce, indicating that it stems from figurative content rather than Arabic language adaptation and pointing to a sensitivity to non-literal meaning that transfers across figurative types. Cultural fine-tuning, by contrast, lowers proverb-interpretation accuracy in both Arabic-centric models. Transfer between the two domains is otherwise indistinguishable from noise, with Arabic models frequently regressing after fine-tuning, suggesting prior saturation of relevant knowledge, while multilingual models show greater adaptation headroom. Error analysis further reveals that fine-tuning reinforces experiential cultural knowledge while destabilizing historically grounded factual knowledge. Our findings suggest that the relationship between culture and figurative language, though conceptually natural, is not straightforwardly captured through fine-tuning alone.
[NLP-50] From Inference to Adaptation: A Unified Optimal Transport View of Vision Language Model
【速读】： 该论文旨在解决视觉-语言模型（VLMs）在实际推理过程中因分布偏移导致性能下降的问题，核心挑战在于现有测试时自适应（TTA）方法依赖于由原始嵌入相似性直接生成的噪声伪标签，这些伪标签在分布偏移下不可靠，易引发噪声放大，且现有方法采用粗粒度代理目标，未能显式建模跨模态样本级关系，造成推理与自适应目标之间的不一致，导致性能提升有限。本文提出一种原则性的VLM TTA方法——\algname，其关键在于通过Wasserstein最优传输（Wasserstein OT）形式化零样本图像分类任务为跨模态对齐问题，从而在样本层面生成鲁棒的伪标签，用于自适应阶段；同时，在自适应过程中采用基于OT诱导伪标签的软标签InfoNCE损失，通过对比学习显式建模单个图文对间的细粒度关系，实现与推理过程相同粒度的精确建模。此外，理论分析表明，InfoNCE损失可被优雅地重写为Wasserstein OT形式，从而统一了推理与自适应的目标，实现二者互惠增益。大量实验验证了该方法在性能与效率上的优越性，相较最佳现有方法最高提升达7%。
链接: https://arxiv.org/abs/2608.18339
作者: Qi Yu,Zhichen Zeng,Katherine Tieu,Xiyuan Yang,Ruizhong Qiu,Yuchen Yan,Lihui Liu,Yanjun Zhao,Lingjie Chen,Jingrui He,Hanghang Tong
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄本那-香槟分校); Amazon(亚马逊); Wayne State University (韦恩州立大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities yet remain sensitive to real-world distribution shifts during inference. Although significant efforts are devoted to adapting VLMs at test time, they rely heavily on noisy pseudo-labels predicted directly from raw embedding similarities during inference, which are unreliable under distribution shift and mislead the adaptation. To avoid noise amplification, existing works craft coarse-grained surrogate objectives during adaptation, which fail to explicitly model sample-level relationships across different modalities, creating objective mismatch with inference, thus leading to marginal performance improvement. In this work, we aim to bridge the detached objectives of inference and adaptation for VLMs, and propose a principled VLM TTA method called \algname. For VLM inference, we formulate the zero-shot image classification task as a cross-modal alignment problem encoded via a Wasserstein OT formulation, providing robust pseudo-labels at the sample-level to effectively adapt VLMs. For VLM adaptation, we adopt a soft-label InfoNCE loss to adapt VLMs based on the OT-induced pseudo-labels, leveraging fine-grained supervisions to explicitly model relationships of individual image-text pairs via contrastive learning, which empowers accurate inference at the same granularity. Moreover, we theoretically reveal that the InfoNCE loss can be neatly reformulated as a Wasserstein OT formulation, thereby unifying the objectives of the inference and adaptation of VLMs to achieve their mutual benefits. Extensive experiments demonstrate the effectiveness and efficiency of our methods, outperforming the best-performing methods by up to 7% with state-of-the-art efficiency.
[NLP-51] What Makes Software Issue Resolution Tasks Difficult for Agents ?
【速读】： 该论文旨在解决当前生成式 AI（Generative AI）在软件任务中评估基准（benchmark）分数难以解释的问题，核心挑战在于缺乏对任务难度的可控性与系统性刻画。具体而言，研究试图回答“哪些任务结构特征决定了编码代理在修复问题任务中的成功率”这一关键科学问题。其解决方案的关键在于构建一个测量框架，通过大规模实证研究分析 CoderForge-Preview——目前最大的开源编码代理轨迹数据集——中任务补丁、仓库和提示（prompt）层面的静态特征，并利用集成学习、SHAP归因分析及效应量分析等方法量化各特征对任务完成结果的预测能力。研究发现，任务难度可由静态特征高度预测（AUC = 0.863），主要受补丁碎片化程度和仓库规模驱动；而提示的语言学特征仅在中等难度任务中成为显著贡献因子，揭示了任务难度具有分层结构。结论表明，任务难度本质上编码于其结构之中，从而实现了无需执行即可进行事前（pre-hoc）难度估计，为构建具备难度控制能力的评估基准提供了理论基础与技术路径。
链接: https://arxiv.org/abs/2608.18280
作者: Ebtesam Al-Haque,Brittany Johnson
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: To appear in ESEM 2026
Abstract:Background. Advances in agentic systems are simultaneously, and rapidly, saturating benchmarks. Despite this often discussed phenomena, benchmark scores remain difficult to interpret due to the lack of control and characterization of task difficulty. More specifically, we currently have little understanding of what makes one task harder than another, and to what extent task difficulty is predictable from static task properties. Aims. We propose a measurement framework to investigate and systematically quantify what structural properties of software tasks correspond to agent success rates for issue resolution tasks. Method. We conducted a large scale empirical study on CoderForge-Preview, the largest open dataset of coding agent trajectories to date, by extracting features across task patch, repository and prompt. We evaluated the predictive power of each feature against task outcomes using ensemble methods, SHAP attribution, and effect size analysis. Results We found that task difficulty is substantially predictable from static features (AU C = 0.863) and is largely driven by patch fragmentation and repository scale. Prompt linguistic features become visible among top contributors for tasks in the mid-band, revealing a layered structure of difficulty. Conclusion. The difficulty of an issue resolution task is encoded in its structure. This enables static, pre-hoc difficulty estimation and lays the groundwork for difficulty-controlled benchmark construction for evaluation of agents.
[NLP-52] hink Shallow Solve Deep: Controlling Recurrent Dynamics for Reliable Test-Time Depth AAAI-27 AAAI
【速读】： 该论文旨在解决生成式模型在测试时通过增加迭代深度（test-time depth）以应对更复杂问题时，其性能可能因额外迭代而提升、保持或恶化的问题。核心挑战在于如何预测并确保深度扩展对答案的稳定性与准确性具有正向影响。解决方案的关键在于引入“有限时间动力学行为”（finite-time dynamical regime）这一可测量的训练后属性，将其划分为收敛（settling）、临界（marginal）或漂移（drifting）三种状态，用以预判进一步迭代的影响。研究提出深度安全（depth-safety）的充分条件：当每步位移相对于解码器边界（decoder margin）足够小时，答案将不再随迭代变化。实验表明，在仅使用800个未增强样本训练的算法任务上，处于收敛态的算子在增加深度后不会退化，且在部分任务（如数独）中能显著提升对更难未见实例的准确率（从0.19提升至0.34）。此外，引入单一终端固定点目标（terminal fixed-point objective）可统一调控系统动力学行为与深度表现，消除漂移并实现可外推的深度安全行为；研究还提出了四项用于评估有效测试时深度的可操作标准，并据此归类失败模式，最后通过对Huginn-3.5B的验证发现其属于非收敛家族，进一步证实了该框架的诊断能力。
链接: https://arxiv.org/abs/2608.18222
作者: Ivan Viakhirev,Kirill Borodin,Amirah Almutairi,Serguei Barannikov,Maxim Abramov,Grach Mkrtchian
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Submitted to the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-27)
Abstract:Recurrent-depth reasoners aim to solve harder problems by iterating their update longer at test time, but additional iterations can improve, preserve, or degrade an answer. We show that a measurable property of the trained operator, its finite-time dynamical regime (estimated as settling, marginal, or drifting), indicates which of these occurs. We give a sufficient condition for depth-safety: once an operator’s per-step displacement is small relative to the decoder margin, the decoded answer cannot change under further iterations. Empirically, on algorithmic tasks trained from 800 unaugmented examples per difficulty tier, settling operators do not degrade with added depth, and on some tasks convert it into higher accuracy on harder unseen instances (Sudoku, 0.19 to 0.34 past the training horizon). A single terminal fixed-point objective moves the regime and the depth behavior together: removing it induces drift and removes the gains, and adding it to a generic recurrence yields depth-safe extrapolation on carry propagation. We give four operational criteria for useful test-time depth, use them to catalogue failure modes, and, as a consistency check, apply the same measurements to Huginn-3.5B, which falls in the non-settling family.
[NLP-53] Efficient INT8 Inference of Small NLP Models on Server CPUs with PyTorch Native Stack
【速读】： 该论文旨在解决在工业级自然语言处理（NLP）任务中，小型模型（如BERT家族编码器）在服务器CPU上部署时面临的推理延迟高、吞吐量低与资源成本之间的矛盾问题，尤其在生成式AI（Generative AI）时代背景下，用户对在原生PyTorch栈中实现高效量化加速的需求日益增长。其解决方案的关键在于将SmoothQuant量化技术集成至TorchAO，并通过TorchInductor的图级融合优化推理路径，结合oneDNN、AVX512_VNNI及AMX等多种指令集的高效INT8 GEMM内核选择策略，在Intel Xeon CPU上实现端到端的高性能推理。实验结果表明，该方法在BERT、DistilBERT和XLM-RoBERTa等基准测试中实现了最高达5.8倍的吞吐量提升，同时保持了与FP32基线近乎无损甚至无测量误差的精度表现，经由屋顶模型（roofline model）的详细性能分析验证了其计算效率优势。该实现已上游合并至PyTorch和TorchAO，支持使用原生PyTorch工具链的开箱即用部署。
链接: https://arxiv.org/abs/2608.18182
作者: Weiwen Xia,Yuxin Cui,E Cao
机构: Intel Corporation(英特尔公司)
类目: Computation and Language (cs.CL)
备注: 13 pages
Abstract:Small NLP models, especially BERT-family encoders, remain important in industrial workloads such as classification, ranking, and retrieval even in the era of large language models. On server CPUs, INT8 quantization offers an attractive latency-throughput-cost trade-off, but users increasingly expect such acceleration to be available directly in the native PyTorch stack. We integrate SmoothQuant into TorchAO and optimize the resulting inference path for Intel Xeon CPUs through graph-level fusion in TorchInductor and efficient INT8 GEMM kernel selection across oneDNN-, AVX512_VNNI-, and AMX-based implementations. Across BERT, DistilBERT, and XLM-RoBERTa benchmarks, the approach delivers up to 5.8x end-to-end throughput speedup with negligible—and in some cases no measurable—accuracy loss relative to the FP32 baseline. We also validated our work by detailed performance analysis with roofline models. The implementation has been upstreamed to PyTorch and TorchAO, enabling out-of-the-box deployment with native PyTorch tooling
[NLP-54] Are LLM s Safe Beyond Text: Do Emojis Expose Gaps in Safety Evaluation ACL2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）安全评估中过度依赖文本形式对抗性提示（text-based adversarial prompts）所导致的潜在盲区问题，即现有评估方法可能忽视由非标准输入表示（如表情符号增强提示）引发的安全漏洞。其核心解决方案在于引入表情符号增强提示（emoji-augmented prompts）作为测试案例，系统评估不同开源大模型在多样化输入形式下的鲁棒性表现。研究选取4个代表性模型（Mistral 7B、Qwen 2 7B、Gemma 2 9B、Llama 3 8B），对50个表情符号增强提示进行测试，结果显示各模型在应对此类攻击时表现出显著差异：Gemma 2 9B与Mistral 7B的攻击成功率分别为10%，Llama 3 8B为6%，而Qwen 2 7B则完全免疫（0%成功率）。卡方检验（χ² = 32.94, p < 0.001）证实不同模型在攻击结果分布上存在显著差异。研究的关键发现是：模型的鲁棒性高度依赖于输入表示形式，仅基于标准文本提示的安全评估可能严重低估实际存在的脆弱性，强调了在评估中引入多模态输入表示的重要性。
链接: https://arxiv.org/abs/2608.18164
作者: M P V S Gopinadh
机构: 独立研究员(Independent Researcher)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 3 pages. Accepted at ACL 2026 Workshop on Evaluation in Practice: Methodological Rigor, Sociotechnical Perspectives, Community Collaboration (EvalEval)
Abstract:Safety evaluations of large language models (LLMs) predominantly rely on text-based adversarial prompts, potentially overlooking vulnerabilities arising from alternative input representations. This work examines emoji-augmented prompts as a test case for this gap, evaluating 50 prompts across four open-source LLMs (Mistral 7B, Qwen 2 7B, Gemma 2 9B, Llama 3 8B). Results show substantial variation in robustness: Gemma 2 9B and Mistral 7B exhibit non-zero success rates (10%), Llama 3 8B 6%, while Qwen 2 7B shows complete resistance (0% success rate). A chi-square test ( \chi^2 = 32.94, p 0.001 ) confirms significant differences in outcome distributions. These findings indicate that robustness is sensitive to input representation, and that evaluations restricted to standard text prompts may underrepresent model vulnerabilities.
[NLP-55] MicroPython and CircuitPython: Pythons Quiet Takeover of IoT and Robotics
【速读】： 该论文旨在解决嵌入式系统领域长期依赖C/C++语言所带来的开发门槛高、学习成本大等问题，尤其是在物联网（IoT）与机器人技术快速发展的背景下，探索生成式AI（Generative AI）之外的另一类新兴技术——即以MicroPython和CircuitPython为代表的Python微控制器平台，是否正在实现对传统嵌入式开发范式的“静默替代”。其解决方案的关键在于通过多方法融合的研究设计，系统评估这些Python平台在生态系统扩展性、实际应用场景、性能权衡、教育普及度及未来前景等方面的综合表现。研究发现，尽管在输入/输出（I/O）速度和内存占用方面相比C++存在10–20倍延迟和4–6倍更高资源消耗，但其性能仍足以支撑常见传感器读取与网络通信任务；更重要的是，凭借极低的学习门槛和强大的社区支持，Python已成为嵌入式系统原型设计与教学实践中的首选语言，推动了教育领域的广泛采纳。因此，论文结论指出：Python并未取代C/C++作为底层系统开发的核心语言，而是确立了其作为嵌入式系统“默认原型设计与教育语言”的地位，未来随着硬件性能提升、开发工具优化及标准统一，这一趋势将进一步深化。
链接: https://arxiv.org/abs/2608.18160
作者: Sayed Mahbub Hasan Amiri,Atiar Zahan
机构: 未知
类目: Programming Languages (cs.PL); Computation and Language (cs.CL); Software Engineering (cs.SE)
备注: 27 pages, 4 tables
Abstract:Background: Python has become the dominant language in software and data science, yet embedded systems have remained tied to C/C++ due to performance and memory constraints. MicroPython and CircuitPython are changing this by bringing Python to microcontrollers, lowering barriers for IoT and robotics development. Aim: This article examines whether these platforms are achieving a quiet takeover of embedded systems, focusing on ecosystem growth, practical applications, performance trade-offs, educational adoption, and prospects. Methods: A mixed-methods design was used, including quantitative analysis of GitHub, Stack Overflow, and Google Trends data; curation of case studies from this http URL, this http URL, and the Adafruit Learning System; and original benchmarks on ESP32 and Raspberry Pi Pico comparing MicroPython, CircuitPython, and Arduino C++ across GPIO, I2C, SPI, Wi-Fi, and memory usage. Results: Metrics show sustained growth, with MicroPython supporting over 200 boards and CircuitPython over 400. Benchmarks reveal 10-20 times slower I/O and four to six times higher memory use than C, but performance remains adequate for common sensor and network tasks. Case studies demonstrate successful deployment in home automation, robotics, wearables, agriculture, and professional prototyping. Education emerges as a primary adoption driver. Conclusions: Python is not replacing C/C++; rather, it is becoming the default prototyping and educational language for embedded systems. Continued hardware improvements, better tooling, and standardization will likely deepen this trend. The article offers balanced, evidence-based insights for developers, educators, and technology decision-makers.
[NLP-56] When Do LLM s Actually Help? Evaluating LLM s as Data Quality Annotators
【速读】： 该论文旨在解决生成式 AI（Generative AI）在自动检测数据质量问题时的判断一致性与有效性问题，尤其关注大语言模型（LLM）在电子商务场景下的实体匹配与品牌标签错误识别任务中的表现。研究通过对比基于规则的基线方法与人工验证的基准数据，在零样本（zero-shot）和少样本（few-shot）提示两种条件下评估 LLM 的性能。关键发现表明：在存在强词汇信号的任务（如实体匹配）中，简单规则基线已达到高精度（F1=0.950），而 LLM 零样本提示表现相近（F1=0.948），且少样本提示优化在小规模验证中看似有效，却导致全量评估性能下降至 F1=0.914，揭示了小样本提示评估可能产生误导性结论；而在需要背景知识的任务（如品牌标签错误检测）中，LLM 因可利用品牌间产品关系等隐含知识，显著优于仅依赖显式规则的基线（F1=0.833 vs 0.721）。此外，模型在重复运行中表现出极高的内部一致性（平均一致率达 99.7%，99% 的样本在五次运行中结果完全一致），多数投票虽微幅提升性能（+0.005），但代价为五倍推理成本。因此，该研究的核心结论是：LLM 的优势并非普适，其价值高度依赖任务特性——当任务依赖背景知识而非表面特征时，LLM 才能发挥显著优势，同时保持高度一致的输出稳定性。
链接: https://arxiv.org/abs/2608.18158
作者: Praphulla Lal Shrestha
机构: Independent Researcher(独立研究员)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 6 pages, 4 figures
Abstract:LLMs have been increasingly used to catch data quality issues automatically, but we know very little about how consistent these judgments actually are. This study tests an LLM on two e-commerce data quality tasks, entity matching and brand mislabeling, against rule based baselines and human verified ground truth, under both zero-shot and few-shot prompting. On entity matching while using the Abt Buy benchmark (2,194 labeled pairs), a simple rule based baseline (F1=0.950) performed about as well as LLM zero shot prompting (F1=0.948). Moreover, a few-shot prompt revision that looked effective on a small validation sample reduced full-scale performance to F1=0.914. This showed that small sample prompt evaluation can be misleading. On brand mislabeling detection, using 500 Amazon product listings with synthetically injected labeling errors, the LLM clearly outperformed a naive rule based baseline (F1=0.833 vs 0.721), because it could draw on background knowledge of brand product relationships that a simple rule could not access. Testing consistency across repeated runs (200 pairs, 5 runs at temperature 0.7) showed the model agreeing with itself 99.7% of the time on average, with 99% of pairs giving identical answers across all 5 runs. Using majority voting across these runs only improved F1 by 0.005, at 5 times the inference cost. These results suggest that the value of using an LLM over traditional methods depends heavily on the task. LLMs offer little advantage when strong lexical signals already exist, but a clear advantage when the task requires background knowledge, all while remaining highly consistent across repeated queries.
[NLP-57] he Deontic Gap: Large Language Models and the Modal Language of Obligation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在生成文本时是否再现当代人类在道义模态（deontic modals）使用上的语言模式这一问题，尤其关注“必须”“应该”“不得不”等表达必要性与义务的语气助动词在人际立场（interpersonal stance）构建中的使用差异。研究发现，无论是在外部基准、受控复现实验还是自然语境下的多模型复现中，AI生成文本普遍低估了正向道义模态的使用频率，相较于当代人类在非正式数字语境中的实际用法存在显著差距。通过与谷歌图书语料库（Google Books Ngram Corpus, 1920–2022）的历史对比可知，尽管AI的模态频率处于正式书面英语的正常范围内，但当代人类在非正式语境中使用道义模态的频率已远超20世纪书籍基准水平，表明人类在日常交流中更频繁地通过“应该”“不得不”等结构表达即时、个体化的人际义务。进一步的短语层级分析揭示，该差距集中于体现人际立场的核心结构（如should, have to, had to），而AI在说明性或问答类任务中对“need to”的使用甚至超过人类，但在说服性学生写作中仍表现不足，说明模态使用具有明显的语域依赖性。因此，该研究的关键结论是：大语言模型的模态使用模式主要反映其训练数据中形式化书面语的特征，而在模拟当代人类通过道义模态建构即时、互动性义务的语言实践方面存在系统性偏差。
链接: https://arxiv.org/abs/2608.18144
作者: Daniel Hart,Sarah Allred,Joseph Abbas,Morenike Alugo
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Modal auxiliaries such as must, should, and have to mark necessity and obligation within the contexts of speaker authority and interpersonal stance. We examine whether large language models (LLMs) reproduce contemporary human patterns of deontic modal usage. Across three primary corpora, an external benchmark, two controlled replications, and a naturalistic eleven-model replication, AI-generated text consistently underuses positive deontic modals (must, should, have to, had to) relative to contemporary humans. Historical comparison with the Google Books Ngram corpus (1920-2022), used as a heuristic calibration against the published-prose record, shows that AI modal frequencies fall within the range of formal published English, whereas contemporary human modal rates in informal digital contexts often exceed twentieth-century book baselines. Phrase-level decomposition shows that the AI-human modal gap is concentrated in constructions central to interpersonal stance (should, have to, had to), while AI matches or exceeds humans on need to in instructional and question-answering contexts but not in persuasive student writing, indicating that the modal profile is genre-conditional. The findings suggest that LLM modal usage reflects the formal written resources on which these models were trained, while underusing the modal constructions through which contemporary human writers mark immediate, interpersonal obligation.
[NLP-58] Efficient Adaptation of LLM s for Hate Speech Detection in Low-Resource Languages: A Comparative Study on Roman Urdu
【速读】： 该论文旨在解决低资源语言（Low Resource Languages, LRLs）中仇恨言论检测（Hate Speech Detection, HSD）面临的挑战，尤其是针对罗马化乌尔都语（Roman Urdu）这一在南亚社交媒体中广泛使用但存在拼写不一致、语法非标准化及缺乏标注数据等问题的语言。其核心问题是：在缺乏足够标注数据的情况下，如何有效实现对罗马化乌尔都语中仇恨言论的准确识别。解决方案的关键在于采用参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）方法中的低秩适应（Low-Rank Adaptation, LoRA），通过对大型语言模型（LLMs）进行轻量级微调，在仅更新极小比例可训练参数的前提下显著提升模型性能。实验基于包含超过72,000条标注评论的PURUTT数据集，对比了零样本推理与LoRA微调的效果，结果表明，尽管零样本模型表现中等（F1 = 0.56），但通过LoRA微调后，分类性能大幅提升至F1 = 0.93，同时保持了优异的计算效率，证明了PEFT在低资源语言处理任务中的高适用性与优越性。
链接: https://arxiv.org/abs/2608.18142
作者: Toneema Zubair,Muhammad Junaid Asif,Faisal Kamiran,Hafiz Hassan Saeed,Rana Fayyaz Ahmad
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:It is challenging to detect hate speech in Low Resource Languages (LRLs) because of the absence of annotated data, the informality of its language structure, and the lack of standardized grammar. A good example of such a challenge is Roman Urdu which is broadly used by South Asians on social media and has a high variation while lacking contextually consistent spellings. The objective of this paper is to conduct a comprehensive assessment of Large Language Models (LLMs) for Hate Speech Detection (HSD) in Roman Urdu script and fine-tune these models using the Parameter-Efficient Fine-Tuning (PEFT) method called Low-Rank Adaptation (LoRA). To evaluate zero-shot inference, we benchmarked it against PEFT on different transformer models, including Mistral, LLaMA, Falcon, and multilingual BERT. Experiments are conducted on the PURUTT (Parallel Urdu and Roman Urdu Corpus for Toxic Comments and Transliteration) dataset with over 72,000 annotated comments. The results suggest that zero shot models perform moderately (F1 = 0.56), but updating a small fraction of the model trainable parameters improves the classification performance significantly (F1 0.93). Our results have shown that PEFT delivers outstanding performance alongside excellent computational efficiency, making it highly suitable for low-resource language processing tasks.
[NLP-59] Language Models for Portuguese: A Systematic Mapping Study
【速读】： 该论文旨在解决葡萄牙语语言模型（Portuguese Language Models）研究与开发中存在信息分散、缺乏系统性梳理的问题。随着生成式AI在自然语言处理领域的快速发展，尽管多语言支持不断推进，但葡萄牙语相关模型的发展仍面临资源分布零散、可访问性差的挑战。为此，论文提出了一项系统的映射研究（Systematic Mapping Study），对46个已发布的葡萄牙语语言模型进行了全面归纳与分类，涵盖模型基础架构、训练数据集、计算资源、许可证、代码与权重可获取性等关键维度。其解决方案的关键在于通过构建结构化分类框架与演化关系分析（采用系统发育视角），揭示了现有模型的技术演进路径与关联网络，进而识别出当前研究中的空白与机遇，为未来葡萄牙语语言模型的可持续发展提供了明确的方向指引。
链接: https://arxiv.org/abs/2608.18138
作者: Jhessica Silva,Carlos Caetano,Helena Maia,Breno Bernard Nicolau de França,Sandra Avila,Helio Pedrini
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 37 pages; 7 figures; 8 tables
Abstract:In recent years, the rapid development of language models has transformed the field of Natural Language Processing through a wide range of applications. However, the development of language models has not progressed uniformly across all languages. In the case of the Portuguese language, there has recently been a growing effort by academia and companies to develop language models and create data resources for Portuguese. These efforts have resulted in the rise of an increasingly diverse ecosystem of language models for Portuguese. However, information on these models remains dispersed in scientific publications, technical reports, model repositories, and project documentation. This survey presents a systematic mapping study of language models developed for Portuguese, providing a comprehensive overview of the current state of the field. We map a total of 46 models, characterizing them by various aspects, including base model, architecture, computational resources, training datasets, licensing, code availability, data, and model weights. Furthermore, we analyzed the evolution and relationships among these models through a phylogenetic perspective, identified current research gaps and opportunities, and discussed future directions for the development of language models for Portuguese.
[NLP-60] Alignment Is All You Need: Instruction-Free Training for General Audio-Language Models
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在扩展至新模态时依赖大量任务特定监督的效率问题，核心挑战在于如何在不进行大规模微调的前提下，高效迁移预训练大语言模型（LLM）已具备的强大推理与指令遵循能力。其解决方案的关键是提出一种无需指令的仅对齐（Instruction-Free Alignment-Only）音频-语言模型（LALM），通过冻结音频编码器和整个大语言模型，仅训练一个轻量级投影器（projector）来实现跨模态对齐。该方法基于自生成数据构建（Self-Generated Data Construction），利用预训练的LLM将音频描述自动扩展为自由形式的回答，从而生成高质量的（音频, 回答）配对数据。实验表明，该方法在MMAU、MMAR、MMSU及MMAU-Pro等多个基准上达到甚至超越经过大量后训练的基线模型，且所需数据量显著减少。更重要的是，由于保持了原始LLM的冻结状态，该框架不仅保留了其固有的指令遵循能力，还具备跨模型版本无缝迁移的能力，验证了仅通过跨模态对齐即可构建性能优异的多模态模型，将多模态扩展简化为一个轻量级投影器训练问题，具有良好的泛化性与快速适应新模型发布的能力。
链接: https://arxiv.org/abs/2608.18132
作者: Xuanru Zhou,Yiwen Shao,Jiahong Li,Dong Yu
机构: Tencent Hunyuan; Zhejiang University
类目: Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注:
Abstract:Multimodal large language models (MLLMs) are typically built through a multi-stage pipeline consisting of cross-modal alignment, supervised fine-tuning (SFT), and preference optimization. This pipeline assumes that adapting an LLM to a new modality requires extensive task-specific supervision. However, pretrained LLMs already possess strong reasoning and instruction-following abilities. As LLMs evolve rapidly, an important question remains: can we efficiently transfer these capabilities to a new modality with minimal intervention, and is alignment alone sufficient for building a multimodal model? We introduce an Instruction-Free Alignment-Only large audio-language model (LALM) that keeps both the audio encoder and the LLM fully frozen, learning only a lightweight projector. Borrowing insights from AzeroS [1], we train on (audio, response) pairs from Self-Generated Data Construction, where an LLM expands captions into free-form responses without explicit task instructions. Across MMAU, MMAR, MMSU, and MMAU-Pro, our approach matches or surpasses heavily post-trained baselines using substantially less data. By keeping the LLM frozen, our model preserves its native instruction-following competence and can port seamlessly across model generations. Our results suggest that competitive MLLM can emerge from alignment alone, reducing multimodal extension to a lightweight projector-training problem that generalizes across modalities and adapts rapidly to each new LLM release.
[NLP-61] Safety Alignment Illusion: The Cross-Lingual Safety Gap in LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）安全对齐训练中存在的严重英语中心主义问题，尤其是在非英语语境下，安全过滤机制失效所导致的刻板印象强化与有害偏见传播风险。针对印度多语言社会中语音助手与对话系统可能对非英语使用者产生歧视性输出的关键挑战，研究提出一种名为INCLUDE（Indian Cultural Lens for Understanding and Detecting Embedded Biases）的多语言评估基准，用于量化印度本土社会文化偏见。其解决方案的核心在于构建一个涵盖六种语言（英语、印地语、孟加拉语、马拉地语、泰米尔语及印地-英语混用语Hinglish）共2,604个提示的跨语言评估体系，并对十种开源与闭源大模型进行大规模偏见评分分析（共计14,988个偏见得分）。关键发现包括：在开源模型中，孟加拉语表现出最高的平均偏见分数；而在闭源模型中，英语反而成为偏见最高的语言，揭示了不同模型生态在跨语言偏见表现上的显著差异，凸显了当前以英语为主的安全对齐策略在非英语语境下的系统性缺陷。
链接: https://arxiv.org/abs/2608.18131
作者: Namya Bhatnagar
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 7 pages, 8 figures, submitted to IEEE SLT (Spoken Language Technology) 2026
Abstract:Current safety alignment training for Large Language Models (LLMs) are heavily English-centric. When such safety filters fail for non-English languages, the consequences are immediate and user-facing: voice assistants and spoken dialogue systems may produce stereotype-reinforcing outputs, bypassing the standard English-focused safety alignments and propagating harmful bias to non-English speaking communities. For spoken language technologies deployed across India’s linguistically diverse population, this represents a critical failure mode. To address this cross-lingual gap, we introduce INCLUDE (Indian Cultural Lens for Understanding and Detecting Embedded Biases), a multilingual evaluation benchmark designed to quantify Indian-centric socio-cultural biases. INCLUDE consists of 2,604 prompts spanning six prompt languages: English, Hindi, Bengali, Marathi, Tamil, and Hinglish (Hindi-English code-mix). We evaluate ten open- and closed-source LLMs against this benchmark, analyzing 14,988 bias scores. Our statistical results reveal two key findings. First, Bengali yielded the highest average bias score in open-source models. Second, English demonstrated a notable reversal, producing the lowest bias in open-source models but the highest bias in closed-source models.
[NLP-62] You Are What You Prompt: Prompt Quality Domain Shift and Uncertainty in Agrifood Vision-Language Models
【速读】： 该论文旨在解决生成式视觉-语言模型在特定领域（如农食领域）中进行零样本分类时，因提示（prompt）设计敏感性导致性能下降的问题，尤其关注在领域分布外（out-of-distribution, OOD）场景下的表现退化。其核心解决方案是零样本提示集成（Zero-shot Prompt Ensembling, ZPE），通过基于判别信号对提示加权来提升模型鲁棒性。研究发现，ZPE在域内（in-distribution, ID）条件下增益有限，但在面对领域偏移时显著提升分类性能与置信度校准能力；关键在于使用领域特定的提示池（51–52个提示）相比通用提示池（247–426个提示）表现更优。进一步的词汇分析表明，ZPE可作为无监督的域对齐检测器，无需标签信息即可识别域差异。此外，作者提出基于提示不一致性的检测方法（Prompt-based Inconsistency Detection, PID），将提示间的分歧视为认知不确定性（epistemic uncertainty），在严重领域偏移下有效提升了异常检测能力，克服了传统置信度度量失效的问题。
链接: https://arxiv.org/abs/2608.18116
作者: Andrea Morales-Garzón,Salvador López-Joya,Miguel López-Pérez,Maria J. Martin-Bautista
机构: University of Granada (格拉纳达大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted in the journal Procesamiento del Lenguaje Natural (SEPLN2026)
Abstract:Vision-language models enable zero-shot classification through natural language prompts, but performance is sensitive to prompt formulation, especially in specialized domains. Zero-shot Prompt Ensembling (ZPE) addresses this by weighting prompts by discriminative signal, yet its behavior under domain shift remains unexplored. We evaluate ZPE in the agrifood domain using CLIP and SigLIP across four datasets and four prompt pools, spanning in-distribution (ID) food and out-of-distribution agricultural benchmarks. ZPE provides limited benefit under ID conditions but substantially improves performance and calibration under domain shift, where domain-specific pools of 51-52 prompts consistently outperform generic pools of 247-426. Lexical analysis shows that ZPE acts as an unsupervised domain-alignment detector without label access. We further introduce PID (Prompt-based Inconsistency Detection), which repurposes prompt disagreement as epistemic uncertainty, improving failure detection under severe domain shift where standard confidence measures collapse.
[NLP-63] mporal Multi-Signal Fusion for Token-Level Hallucination Detection
【速读】： 该论文旨在解决生成式文本中细粒度的幻觉（hallucination）检测问题，即传统基于词元（token-level）独立评分的方法在生成模型自信地产生错误内容时失效的问题。其核心挑战在于如何有效识别那些在时间序列上持续存在的、非孤立的幻觉片段。解决方案的关键在于将幻觉视为一个时序延展的跨度（temporally extended span），并通过序列标注的方式进行检测：利用融合了文本统计特征、自然语言推理（NLI）蕴含关系和语言模型意外性（surprisal）的33维特征流对每个词元进行联合建模，并采用双向门控循环单元（BiGRU）捕捉上下文中的时序依赖关系。实验表明，该方法在RAGTruth基准上达到0.840的AUC，相比独立逻辑回归基线提升11个百分点（p=0.002，Wilcoxon符号秩检验）。进一步的可控分解分析揭示，性能提升主要源于对时序结构的有效利用，而非模型容量本身——即置信度较高的词元能够向邻近模糊区域传递证据。此外，该检测器在多种架构（递归、状态空间模型Mamba、注意力机制）下均表现一致，表明当前瓶颈存在于特征集设计而非模型结构，且由于仅依赖生成文本与外部信号，无需访问模型内部参数，因此可适用于闭源模型及未见过的语言模型生成文本，性能衰减低于4% AUC。
链接: https://arxiv.org/abs/2608.18115
作者: Igor Itkin
机构: Independent Researcher
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 17 pages, 14 figures, 23 tables. Code: this https URL
Abstract:Token-level hallucination detectors score each token independently from a single signal, and fail exactly when the generating model is confidently wrong. This paper instead treats hallucination as a temporally extended span and detects it by sequence labeling: each token is scored from a 33-dimensional feature stream that fuses text statistics, Natural Language Inference (NLI) entailment, and language model surprisal, with no access to model internals. A Bidirectional Gated Recurrent Unit (BiGRU) over these features reaches an AUC of 0.840 on RAGTruth (10 seeds), an 11-point gain over an independent logistic-regression baseline (p = 0.002, Wilcoxon signed-rank). A controlled decomposition attributes most of the gain to temporal order rather than model capacity: evidence propagates from confident positions to ambiguous neighbors within a span. The same 0.845 ceiling recurs across recurrent, state-space (Mamba), and attention architectures, locating the bottleneck in the feature set rather than the model. Because it reads only the generated text and external signals, the detector works on closed-source models, and it keeps working on text produced by language models it never saw during training, losing under 4% AUC.
[NLP-64] Accurate Decoding of Natural Sentences from Non-Invasive Brain Recordings
【速读】： 该论文旨在解决脑损伤后丧失语言或运动能力患者恢复沟通能力的重大挑战，尤其聚焦于如何实现高精度的非侵入式脑-文本解码。现有颅内植入式脑机接口虽性能优异，但存在安全与伦理风险，而当前非侵入性方法（如基于磁脑图，MEG）在解码自然语言时仍受限于精度不足。本研究提出Brain2Qwerty v2模型，仅依赖实时MEG信号即可解码自然语句，其关键突破在于：（1）采用深度学习替代传统手工设计的事件检测流程，提升信号处理效率；（2）通过微调大语言模型以提取语义表征，增强对上下文和句法结构的理解；（3）引入人工智能代理（AI agent）实现解码管道的自动化迭代优化，包括代码生成与系统改进。实验基于9名受试者共22,000条句子（每名受试者记录10小时），平均词错误率（WER）达39%，最优受试者半数句子仅含一个词错误。更重要的是，解码精度随数据量呈对数线性增长，表明通过数据规模扩展可部分弥合非侵入式与侵入式方法之间的性能差距。该成果标志着非侵入式脑-文本解码已达到此前仅限于手术植入设备才能实现的准确度水平，为安全、高效的脑机接口提供了可行路径。
链接: https://arxiv.org/abs/2608.18114
作者: Mingfang Zhang,Jarod Lévy,Cedric Rommel,Jérémy Rapin,Corentin Bel,Julie Bonnaire,Daniel Nieto,Pierre Bourdillon,Svetlana Pinet,Stéphane d’Ascoli,Thomas Moreau,Jean-Rémi King
机构: Meta(元); PSL University (PSL大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Signal Processing (eess.SP); Neurons and Cognition (q-bio.NC)
备注: Mingfang Zhang and Jarod Lévy contributed equally to this work
Abstract:Restoring communication for people who have lost the ability to speak or move after a brain injury is a major challenge. While intracranial implants now enable high-performing brain-computer-interfaces, non-invasive alternatives are still lagging behind. Here, we present Brain2Qwerty v2, a model that can decode the production of natural sentences solely from real-time magnetoencephalography (MEG) recordings. By collecting 22,000 sentences typed by nine subjects, each recorded for 10 hours, our model leverages character, word and sentence-level representations to achieve an average word error rate (WER) of 39%. For our best participant, the model accurately decodes half of the sentences with one word error or less. Critically, decoding accuracy log-linearly improves with data volume, suggesting that the performance gap with intracranial approaches could be partially bridged through data scaling. We show that AI enables this performance in three main ways: the substitution of hand-crafted pipelines for event detection with deep learning, the finetuning of large language models to extract semantic representations, and the deployment of AI agents to iteratively refine our decoding pipeline via automated code development. Together, these results show that non-invasive brain-to-text decoding starts to operate at a level of accuracy previously thought exclusive to surgical implants, opening a path toward safe and efficient brain-computer-interfaces.
[NLP-65] Operationalizing Narrative Entropy (Sn): A Two-Scene Registered Pilot Report and Pre-Validation Protocol
【速读】： 该论文旨在解决叙事熵（Narrative Entropy, $ S_n $）这一理论构念在真实文本中的可操作化问题，即如何将原本仅停留在理论层面的量化描述指标应用于实际叙事文本分析。其核心挑战在于建立一个能够准确反映读者在处理叙事文本时认知负荷变化的计算公式。解决方案的关键在于首次对候选公式 $ S_n = I_f \times C_b \times t $ 进行实证试用，通过对昆汀·塔伦蒂诺《低俗小说》开场餐厅场景与雷蒙德·卡佛《大教堂》开篇独白段落的双文本手动编码与评分，实现初步操作化。研究发现，单一声部独白（$ S_n = 30.0 ）的认知负荷得分高于九角色对话场景（ S_n = 18.8 $），这一结果虽违背作者初始直觉，但被作为核心发现而非偏差予以保留，并拒绝事后调整公式。研究进一步提出三种可能解释——公式不完备性、真实高负荷散文结构、测量误差，并通过预注册实验设计以区分三者。此外，v2.1版本明确指出该分歧与既有建筑框架一致，该框架强调推论重构优于表层陈述，此前所谓“反预期”实为作者个人预期，而非方法论预测。同时新增针对信息密度因子 $ I_f $ 的预注册效度检验，以回应其在两文本中数值相近（1.71 vs 1.58）却导致显著 $ S_n $ 差异的现象。因此，本研究既作为小样本（n=2）试点报告，也作为下一阶段研究方案的预注册文件，未宣称叙事熵已获验证。
链接: https://arxiv.org/abs/2608.18109
作者: Levent Bulut
机构: 未知
类目: Computation and Language (cs.CL)
备注: v2.1 revised: 9 pages, 1 table. Registered pilot report (n=2) with a pre-registered validation protocol. v2.1 adds Section 4.5 (construct validity gap acknowledgement) and Section 5.2.5 (pre-registered If construct validity test); no claims of v2.0 retracted. Also archived at Zenodo: this http URL
Abstract:Narrative Entropy ( S_n ) is a proposed quantitative descriptor within the Bulut Doctrine, intended to capture the rate at which a narrative text imposes processing load on a reader. To date the construct has been defined theoretically but not operationalized against real texts. This report documents the first such operationalization (the v2.0 pilot): two narrative scenes – the opening restaurant scene of Tarantino’s Reservoir Dogs and the opening interior-monologue block of Carver’s Cathedral – were coded manually by a single rater and scored with the candidate formula S_n = I_f \times C_b \times t . The result was a divergence from the author’s naive intuition: the single-voice monologue ( S_n = 30.0 ) scored higher than the nine-character dialogue scene ( S_n = 18.8 ). We treat this not as a result to be explained away but as the central finding, and we refuse post-hoc adjustment of the formula. Three competing interpretations are presented – formula incompleteness, genuine high-load prose, and measurement error – and the design that would discriminate among them is pre-registered. This v2.1 revision adds: (i) explicit acknowledgement that the divergence is consistent with the pre-existing architectural framework which privileges inferential reconstruction over surface declaration, and that what was called “contrary to expectation” in v2.0 reflected the author’s anticipatory intuition rather than the methodology’s own predictions; (ii) a pre-registered construct validity test for I_f , motivated by the observation that I_f values were nearly equal across the two scenes (1.71 vs 1.58) despite the headline S_n divergence. The document functions simultaneously as a pilot report ( n=2 ) and as a pre-registration of the next-stage protocol. It does not claim that S_n has been validated.
[NLP-66] Institutional Prestige as Geographic Bias in Large Language Models : Evidence from Three Factorial Experiments with Bootstrap Confidence Intervals
【速读】： 该论文旨在解决生成式 AI（Generative AI）在人才评估中是否存在系统性偏见的问题，具体聚焦于申请人姓名所隐含的族裔特征、机构声誉及地理来源对评估结果的影响。研究通过三个因子实验（共4,320次API调用，涵盖四类大语言模型和五个专业领域）发现，机构层级（institution tier）存在显著且稳健的正向影响，其效应值达+0.297分（10分制，95%置信区间：+0.175至+0.422），而姓名起源效应则不显著且可忽略；进一步拆解声誉-地理混杂关系后，机构声誉效应（+0.185）是国籍效应（+0.126）的1.5倍；在期刊声誉与机构声誉的对比中，期刊声誉效应（Nature vs. 边缘开放获取期刊）高达机构声誉效应的5.7倍（+1.937 vs. +0.341）。此外，研究证实“救赎效应”（rescue effect）的存在：在《自然》期刊发表文章能更显著地弥补低声誉机构背景带来的劣势，尤其对来自瓜亚基尔大学的候选人（+2.127）优于麻省理工学院候选人（+1.745）。关键解决方案在于引入中立哲学偏差指数（Neutrosophic Bias Index, NBIT,I,F），其I分量揭示了低声誉背景申请者评价结果的系统性不一致，暴露了传统均值指标无法捕捉的元认知劣势。
链接: https://arxiv.org/abs/2608.18107
作者: Maikel Leyva-Vazquez,Florentin Smarandache
机构: Universidad Bolivariana del Ecuador; Universidad de Guayaquil (厄瓜多尔); University of New Mexico, Gallup (美国新墨西哥州盖普兰)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 11 pages, 3 figures. Extended English version of an earlier two-study Spanish-language paper published in Neutrosophic Computing and Machine Learning (2026); this version adds Study 3 (journal x institution prestige) and bootstrap confidence intervals throughout
Abstract:We investigate whether large language models (LLMs) systematically discriminate in candidate evaluations based on applicant name ethnicity and/or institutional prestige and geographic location. Three factorial experiments are reported (4,320 API calls, four LLMs, five professional domains). Study 1 (3x4 design) finds a statistically robust institution-tier gradient of +0.297 points on a 10-point scale (95% bootstrap CI: +0.175 to +0.422), while name-origin effects are negligible and non-significant (95% CI crosses zero). Study 2 (2x2 Prestige x Country design) breaks the prestige-geography confound: the prestige effect (+0.185; 95% CI: +0.093 to +0.275) exceeds the country-of-origin effect (+0.126; 95% CI: +0.037 to +0.218) by 1.5x. Study 3 (2x2 Journal x Institution design) reveals that journal prestige (Nature vs. a peripheral open-access journal) dominates institutional prestige by 5.7x: journal effect +1.937 (95% CI: +1.811 to +2.062) vs. institution effect +0.341 (95% CI: +0.184 to +0.504). A “rescue effect” is confirmed: publishing in Nature compensates for low institutional prestige more strongly for candidates from the University of Guayaquil (+2.127) than from MIT (+1.745). Results are quantified using the Neutrosophic Bias Index NBIT,I,F; the I component reveals elevated evaluation inconsistency for low-prestige profiles, an epistemic disadvantage not captured by mean-only metrics. Code and data: this https URL
[NLP-67] Different Facets of Verbalised Overconfidence: an Interpretability Study
【速读】： 该论文旨在解决大语言模型在推理过程中普遍存在过度自信（overconfidence）的问题，即在证据不足或存在不确定性时仍给出确定性回答。其核心解决方案在于揭示模型内部对确定性与不确定性表达的不对称机制：通过可解释性分析发现，Qwen3-4B模型默认采用广泛共享的特征组合来生成确定性输出，而不确定性则依赖于少数特定特征的稀疏调用作为“覆盖”机制。研究提出一种差异化识别方法，能够定位并干预这些负责不确定性的特征，实验证明该干预手段不仅因果性地验证了过度自信的根本原因，还能有效缓解模型的过自信错误。该机制在三种不确定性表达方式（语义认知标记、主动回避、数值置信度）、多语言场景及分布外模态任务中均具有良好的泛化能力。
链接: https://arxiv.org/abs/2608.18106
作者: Davide Mazzaccara,Leonardo Bertolazzi,Raffaella Bernardi
机构: CIMeC, University of Trento (认知与智能研究中心，特伦托大学); DISI, University of Trento (计算机科学与统计学系，特伦托大学); Free University of Bozen-Bolzano (博岑-博尔扎诺自由大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models tend to overconfidence, giving assertive answers when the evidence suggests hedging or abstention. Using controlled reasoning scenarios that manipulate logical necessity and possibility, we study this behavior in Qwen3-4B, across three ways to express uncertainty: verbal epistemic markers, abstention, and numeric confidence scores. Our results confirm this tendency toward overconfidence, particularly when the model is prompted to output a numeric confidence score. At the interpretability level, we propose a method that differentially identifies transcoder features responsible for uncertainty and certainty. Our analysis reveals Qwen3-4B’s default mechanism favors certainty generation through a broad coalition of shared features, while uncertainty is implemented as a sparse override mediated by a small set of dedicated features. Intervening on these uncertainty features both causally proves this imbalance underlying overconfidence and also mitigate overconfident errors. The same set of features generalise across the three uncertainty-expression settings, languages, and an out-of-distribution modality task.
[NLP-68] StocksTalk: A Voice-Enabled Conversational Agent for Structured Query Generation over Web Data
【速读】： 该论文旨在解决语音驱动的金融筛选请求在自然语言到结构化查询转换过程中的准确性与可解释性问题，尤其针对传统基于模板的金融助手在处理复杂、多轮语音指令时缺乏透明度与灵活性的缺陷。其核心解决方案在于构建一个端到端的可交互式语音系统StocksTalk，该系统通过融合流式语音识别、检索增强的约束提取、基于数据模式引导的大语言模型（LLM）SQL生成、规则驱动的验证以及人机协同验证机制，在多个关键环节实现中间推理过程的可视化与可控性。系统的关键创新在于引入“中间推理产物”（如提取的约束条件、归一化的财务指标、操作符语义对齐及生成的查询语句）的显式暴露，使用户能够在执行前逐阶段审查与修正，从而显著提升约束提取准确率、SQL可执行性、逻辑一致性及多轮交互稳定性。实验结果表明，检索增强与交互式验证机制相较于基线大模型方法具有显著优势，验证了透明化语音界面在连接自然语言交互与结构化金融分析中的有效性。
链接: https://arxiv.org/abs/2608.18105
作者: Akshat Parmar,Vikranth Udandarao,Abhay Shakya,Tanmay Hire,Avinash Anand,Rajiv Ratn Shah,Daniel Wang Zhengkui
机构: IIIT-Delhi; Singapore Institute of Technology
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:StocksTalk is a voice-enabled conversational system for transforming spoken financial screening requests into executable and validated structured queries over real-world market data. The system combines streaming speech recognition, retrieval-augmented constraint extraction, schema-grounded LLM-based SQL generation, rule-based validation, and human-in-the-loop verification within an interactive dashboard. Unlike traditional template-driven financial assistants, StocksTalk exposes intermediate reasoning artifacts, including extracted constraints, normalized financial metrics, operator grounding, and generated queries, allowing users to inspect and refine each stage before execution. To evaluate the system, we curate a benchmark of 150 spoken financial prompts spanning multiple investment strategies and input noise conditions. Experimental results show that retrieval grounding, constrained query generation, and interactive verification substantially improve constraint extraction accuracy, SQL executability, logical consistency, and multi-turn stability compared to baseline LLM-based approaches. StocksTalk demonstrates how transparent, voice-driven interfaces can bridge natural language interaction and structured financial analysis, providing an effective framework for conversational stock screening and decision support.
[NLP-69] DeepTCM1.0: A Multi-Expert AI Agent for Deciphering Mechanisms of Chinese Herbal Formulae Based on General Large Language Models
【速读】： 该论文旨在解决传统中药复方（TCM compound formulas）机制解析中经典中医理论与现代生命科学难以深度整合的难题，尤其针对现有数据挖掘与网络药理学方法在理论契合度上的不足，以及通用大语言模型在中医理论适配性差、易产生推理幻觉等问题。其解决方案的关键在于构建一个融合经典中医理论与现代生命科学的多专家智能体框架——DeepTCM1.0，该框架基于通用大语言模型DeepSeek V3.2，采用三层协同架构与三轮迭代质量控制流程，模拟11个跨学科智能体的协作分析过程，实现对桂枝汤这一代表性复方从中医经典理论和现代科学研究双重视角的系统性、可解释性机制解析。通过双盲五维评分、组内相关系数（ICC）信度检验、曼-惠特尼U检验及效应量分析等多重评估手段，验证了框架在准确性、一致性与可靠性方面的优越性能。
链接: https://arxiv.org/abs/2608.18103
作者: Wenxin Duan,Hanwei Wang,Zhongying Peng,Zhonghua Lu,Jiayi An,Fan Song,Yong Liang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Background: Mechanistic elucidation of traditional Chinese medicine (TCM) compound formulas remains a central challenge in the modernization of TCM. Conventional approaches, including data mining and network pharmacology, are insufficient for achieving deep integration between classical TCM theory and modern scientific research. In addition, direct question-answering using general-purpose artificial intelligence large language models is limited by inadequate adaptation to TCM theoretical frameworks and susceptibility to reasoning hallucinations. Consequently, there is an urgent need to develop intelligent analytical methods aligned with the holistic principles of TCM. Objective: To establish a multi-expert intelligent agent framework integrating classical TCM theory with modern life sciences, thereby enabling systematic and interpretable mechanistic analysis of TCM compound formulas, with Guizhi Decoction serving as a representative validation case. Methods: The DeepTCM1.0 framework was constructed based on the general-purpose large language model DeepSeek V3.2. It adopts a three-tier collaborative architecture and a three-round iterative quality-control workflow, simulating the collaborative analytical process of 11 interdisciplinary intelligent agents. The framework was applied to the mechanistic interpretation of Guizhi Decoction from the dual perspectives of classical traditional Chinese medicine theory and modern scientific research. Framework performance was comprehensively evaluated through double-blind five-dimensional scoring, intraclass correlation coefficient (ICC) reliability testing, Mann-Whitney U tests, and effect size analysis. The evaluation employed four independent large language models as evaluators, each conducting five rounds of repeated scoring on five anonymized reports, resulting in a total of 100 independent scoring assessments.
[NLP-70] Stability-Aware Feature Design for Robust Watermark Detection in Machine-Generated Text ICML2026
【速读】： 该论文旨在解决生成式 AI（Generative AI）文本在经过多轮改写（paraphrasing）或文本长度较短时，现有水印检测方法性能显著下降的问题。其核心挑战在于如何在保持高检测准确率的同时，提升对复杂文本变换的鲁棒性。解决方案的关键是提出一种名为**模式稳定性评分（Pattern Stability Score, PSS）**的新检测框架，该框架通过融合局部统计特征与跨改写版本的稳定性动态分析，综合利用全局与局部z-score特征、序列运行长度的高阶统计量，并引入自相关信号及基于改写深度计算的稳定性得分，从而捕捉水印信号在语义不变下的内在一致性。实验结果表明，PSS在多个基准数据集和不同大语言模型（LLMs）及改写器组合下均表现出显著优于传统z-score阈值法与先进深度学习方法的性能，尤其在长达八轮改写的情况下仍能维持超过87.8%的AUC，且具备无需再训练即可跨模型、跨改写器、跨领域通用的强大泛化能力。
链接: https://arxiv.org/abs/2608.18102
作者: Sina Mansouri,Mohit Marvania,Abolfazl Safikhani
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注: Accepted at the 43rd International Conference on Machine Learning (ICML 2026), Seoul, South Korea. 20 pages, 5 figures
Abstract:The widespread adoption of large language models (LLMs) has intensified the demand for principled methods to distinguish human from machine-generated text. Watermarking provides a promising avenue, yet existing detectors exhibit sharp performance deterioration under multiple paraphrasing and when applied to shorter texts. We introduce Pattern Stability Score (PSS), a novel detection framework that leverages local statistical features and stability dynamics across paraphrased variants. Specifically, the proposed method combines global and local z-score features with higher-order statistics of run-length patterns, enriched by autocorrelation signals and stability scores computed over paraphrase depth. Numerical evaluations are performed on three benchmark datasets (PG-19, CNN/DailyMail, and WikiText) using multiple LLMs (Llama-3-8B, Qwen2-7B) and paraphrasers (Mistral-7B, Qwen2-7B, Gemma-7B), systematically stress-testing robustness under up to eight rounds of paraphrasing. Compared to prior z-score thresholding baselines and some state-of-the-art deep learning methods, our approach improves detection AUC (area under the receiver operating characteristic curve) by over 10-15 percentage points across different token lengths. Additionally, extensive cross-domain experiments demonstrate that a single universal classifier generalizes across different LLMs, paraphrasers, and text domains without retraining, maintaining above 87.8% AUC even when all components differ from training.
[NLP-71] BERTilda: Explainable Topic Lifecycle Tracking with Split/Merge Detection via Similarity-and-Flow Temporal Graphs KDD2026 ECML
【速读】： 该论文旨在解决纵向文本流中主题演化过程的复杂性问题，特别是传统动态主题模型侧重于平滑演变而忽略结构性重组（如主题分裂或合并），而独立拟合的时间窗口主题模型又缺乏对时间对应关系的明确建模。其解决方案的关键在于提出BERTilda框架，该框架在每个时间窗口内独立使用基于嵌入的主题模型发现主题，并通过构建跨相邻窗口的时序主题图来捕捉主题间的演化关系。该图的连接依赖于两个互补信号：一是主题表示之间的语义相似性，二是双向覆盖信号（bidirectional coverage signal），通过跨窗口推文-主题归因估计主题的流出（去向）与流入（来源）。基于图规则，系统可自动标注主题的延续、分裂、合并、消失及模糊过渡等生命周期状态。实验结果表明，BERTilda在政治语料（如美国国会推文和历史演讲数据集）上表现出优异的主题质量与时间稳定性，尤其在主题消失检测方面显著优于仅依赖相似性或单向传播的基线方法，在人工标注的黄金标准子集上达到高达87%的多数共识率，且宏平均一致性最高。
链接: https://arxiv.org/abs/2608.18101
作者: Cláudia Oliveira,Álvaro Figueira
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 16 pages, 2 figures, 7 tables, with 4-page supplementary material. Accepted at ECML PKDD 2026 (Naples, 7-11 September 2026). Authors’ accepted version; the revised version of record will appear in the proceedings (Springer, Lecture Notes in Computer Science)
Abstract:Longitudinal text streams exhibit topic birth and death, but also discrete structural reorganizations in which themes split into subtopics or merge into broader narratives. Many dynamic topic models emphasize smooth drift, while snapshot topic models (fit independently per time window) leave temporal correspondence underspecified. We present BERTilda, an explainable framework that discovers topics independently in each window (using an embedding-based topic model) and then constructs a temporal topic graph linking topics across adjacent windows. Links are supported by two complementary signals: (i) semantic similarity between topic representations and (ii) a bidirectional coverage signal that estimates document outflow (where a topic goes) and inflow (where a topic comes from) via cross-window tweet-to-topic attribution. Graph-based rules label continuations, splits, merges, disappearances, and unclear transitions. We evaluate BERTilda on political corpora, including U.S. congressional tweets and historical speech datasets, report topic-quality and temporal-stability diagnostics, and validate lifecycle labels on a gold-standard subset annotated by three independent annotators. On the annotated subset, BERTilda reaches majority agreement rates up to 87% and attains the highest macro-average agreement across the compared methods, with particularly strong disappearance detection relative to similarity-only and forward-only baselines.
[NLP-72] Computational Orientalism: Measuring Structural Discourse Bias in Large Language Models Using the Middle East Cultural Sensitivity Score (MECSS)
【速读】： 该论文旨在解决生成式 AI 系统在呈现中东文化时所隐含的东方主义（Orientalism）结构性偏见问题。尽管这些系统看似提供中立信息，但其内容实质上受到训练数据中主导的西方话语框架影响，导致对中东主体性的消解、将西方认知模式视为普遍中立而将非西方知识标记为特殊化，并以非本地产生的范畴解释中东地区。传统公平性评估指标无法识别此类结构性框架偏差，因其仅关注显性歧视而非深层认知范式。为此，论文提出中东文化敏感度评分（Middle East Cultural Sensitivity Score, MECSS），将萨伊德（Said）提出的七种东方主义操作转化为可量化的维度，并引入“萨伊德洗白”（Said-washing）概念，指代模型在否认概括能力的同时，仍系统性复现其所否认的结构偏见。通过对280次对话（共1,120次交互）的分析发现，GPT-4与Falcon3-7B-Instruct均在结构性定位上系统性再现东方主义特征，其中后者虽在阿布扎比开发且使用阿拉伯语训练数据，得分更高（2.18），表明地域归属并非降低东方主义倾向的充分条件；两模型在“认识论中心”（Epistemic Center）维度得分接近上限，凸显西方框架被默认为普适性认知基准的现象。此外，“萨伊德洗白”现象在GPT-4中出现率达87.9%，是现有指标难以捕捉的隐蔽偏差。研究强调，缓解此类偏见的根本在于重构模型学习的知识来源，而非简单增加语言支持或迁移机构地理分布。
链接: https://arxiv.org/abs/2608.18100
作者: Maha Shahid
机构: Independent Researcher(独立研究员)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 16 pages, 3 tables
Abstract:AI systems now shape how hundreds of millions of people learn about cultures other than their own. When someone asks one of these systems about the Middle East, they do not receive neutral facts. They receive a representation shaped by the frameworks embedded in training data, and that data is overwhelmingly Western and English-language. This paper asks whether that representation is Orientalist in Said’s sense: whether it denies agency to Middle Eastern actors, treats Western frameworks as neutral while marking non-Western knowledge as particular, and explains the region through categories it did not produce. Standard fairness metrics cannot answer this, because they detect explicit prejudice rather than structural framing. This paper introduces the Middle East Cultural Sensitivity Score (MECSS), a framework that turns Said’s seven Orientalist operations into measurable dimensions, and the term “Said-washing” for a specific failure: a model that disclaims generalization, then reproduces the structure it disclaimed. Across 280 conversations (1,120 exchanges), GPT-4 and Falcon3-7B-Instruct both reproduce Orientalist patterns systematically, through structural positioning rather than open stereotyping. GPT-4 scores moderately (mean MECSS 1.73); Falcon3-7B-Instruct scores higher (2.18), even though it was built in Abu Dhabi and trained with Arabic content. This is evidence against the assumption that building a model regionally makes it less Orientalist, though the models differ in size as well as origin, so geography cannot be isolated as the cause. Epistemic Center, the treatment of Western frameworks as unmarked universals, scores near the top of the scale for both models. Said-washing appears in 87.9% of GPT-4 conversations, a pattern existing metrics cannot see. Reducing this bias requires changing what models learn from, not only adding languages or relocating institutions.
[NLP-73] Fractional Decay KV-Cache: Ownership-Aware Memory Management for Improved Inference Relevancy in Dialog Systems
【速读】： 该论文旨在解决基于Transformer的对话系统在自回归推理过程中，现有键值（Key-Value, KV）缓存策略对所有缓存条目采用统一处理或依赖粗粒度淘汰启发式方法，无法随对话主题演变动态调整的问题。其核心解决方案是提出一种新型的分段衰减键值缓存（Fractional Decay KV-Cache, FD-KVC）算法，关键在于构建双通道评分机制：一是累积注意力通道，用于追踪历史重要性（类比于H2O中的重头项检测），二是基于时间衰减与强化学习启发式更新的时效相关性通道，使系统既能保留长期重要的上下文信息，又能在对话主题发生突变时快速适应。通过由所有权损失函数驱动的自适应学习率，确保算法收敛且无振荡。FD-KVC完全在CPU上运行，开销极低。在五个多样化多轮对话场景中，每场景600条对话测试表明，相较于当前最优的重头项基线H2O，FD-KVC在晚期对话对齐的综合指标上提升6.7%，在主题突变、渐进演化及混合主题场景下的性能分别提升127%、87%和30%；其主题适应速度为H2O的3.6倍，并达到最高的主题多样性（80.6%）。消融实验验证了各组件的有效性。
链接: https://arxiv.org/abs/2608.18098
作者: Sukanta Ganguly
机构: NetApp Inc (网康公司)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 8 pages, 4 figures, 6 tables
Abstract:Key-value (KV) caching is essential for efficient autoregressive inference in transformer based dialog systems, yet existing strategies treat all cached entries uniformly or apply coarse eviction heuristics that fail to adapt as dialog topics evolve. We propose Fractional Decay KV-Cache (FD-KVC), a novel algorithm that maintains a dual-channel scoring mechanism for each cached KV pair: a cumulative attention channel that tracks aggregate importance (akin to H2O), and a recency-weighted relevance channel governed by temporal decay and reinforcement-inspired updates. The combination enables FD-KVC to both preserve historically important tokens and rapidly adapt when dialog topics shift. An adaptive learning rate driven by an ownership loss function ensures convergence without oscillation. FD-KVC operates entirely on CPU with negligible overhead. Across five diverse multi-turn dialog scenarios with 600 dialogs each, FD-KVC outperforms H2O, the state-of-the-art heavy-hitter baseline, by +6.7% on composite late-turn alignment, with improvements of +127% on topic-shift, +87% on gradual evolution, and +30% on mixed-topic dialogs. FD-KVC adapts to new topics 3.6X faster than H2O and achieves the highest topic diversity (80.6%) across all methods. Ablation studies confirm the contribution of each component.
[NLP-74] FrenchNews-7: Benchmarking Cross-Publisher French News Editorial Desk Classification
【速读】： 该论文旨在解决多出版商法国法语新闻内容中编辑立场分类（editorial desk classification）的标注一致性与模型泛化能力问题，尤其针对跨出版商场景下因编辑风格差异导致的类别边界模糊性。其核心解决方案在于构建一个大规模、多来源的法国本土法语文本基准数据集FrenchNews-7，采用混合标注流程：通过出版商URL路径自动推断类别，并结合大语言模型（LLM）对结构上存在歧义的样本进行人工校验式标注，确保标签质量；同时引入双人+双模型的交叉评估机制（互评一致性κ ≥ 0.766，人类间κ = 0.806），提升标注可靠性。关键创新在于使用微调后的CamemBERT-base模型，在全篇文本输入条件下实现优于仅基于标题输入的性能，且在未见出版商测试集上展现出良好泛化能力，整体召回率达0.799，显著超越零样本大语言模型基线（GPT-OSS-120B、Mistral Small 3.2、Llama-3.3-70B）。研究进一步揭示不同类别边界稳定性不均：Sport、Culture Loisirs 和 International 类别迁移表现良好，而Economie与Societe两类受编辑惯例影响显著，其性能瓶颈反映的是领域内固有模糊性而非模型可改进空间，表明该任务的挑战本质在于语义边界的社会文化建构性。
链接: https://arxiv.org/abs/2608.18097
作者: Amr Sobhy
机构: Le French News Lab
类目: Computation and Language (cs.CL)
备注: 15 pages, 5 figures, includes appendices. Model and dataset available on HuggingFace
Abstract:We present FrenchNews-7, a cross-publisher France-based French-language news editorial desk classification benchmark combining a large multi-outlet corpus, a URL-derived seven-class taxonomy, and a fine-tuned CamemBERT classifier. Labels are assigned via a hybrid pipeline combining publisher URL slugs with LLM annotation for structurally ambiguous cases, audited through an inter-rater study (2 humans + 2 LLMs; pairwise \kappa \geq 0.766 , human–human \kappa = 0.806 ). We evaluate lexical, multilingual, and French-specific trained classifiers under both in-distribution and held-out-publisher settings, with additional comparison against zero-shot LLM baselines (GPT-OSS-120B, Mistral Small 3.2, Llama-3.3-70B) on the held-out pool. The strongest model, CamemBERT-base on full article text, outperforms headline-only input, generalizes to unseen outlets, and exceeds all three zero-shot LLM baselines on overall recall (0.799), with the gap concentrated in the ambiguous editorial-boundary categories Economie and Societe. Cross-publisher evaluation reveals uneven boundary stability: Sport, Culture Loisirs, and International transfer cleanly, while Economie (recall = 0.517) is close to blinded human agreement (0.55), and Societe (precision = 0.577) absorbs boundary ambiguity, both suggesting editorial conventions rather than recoverable classifier headroom. The fine-tuned CamemBERT-base model, labeled manifest, reference collection scripts, and a reliability-tier guidance table are available at this https URL (model) and this https URL (dataset).
[NLP-75] MAVEN: A Macro-Societal Value Evaluation Framework of Multimodal Content with Compact Aligned Evaluators
【速读】： 该论文旨在解决多模态内容（multimodal content）在宏观社会价值（macro-societal values）层面的对齐评估难题，尤其针对和平、正义与自由等核心价值的量化判断。现有方法受限于以安全为导向的分类体系、仅依赖文本的心理测量工具或单标签分类，难以全面捕捉多模态内容在复杂社会语境中的价值内涵。为此，论文提出MAVEN框架，其关键在于构建一个基于国际人权文件与文化价值理论的分层评估体系，将宏观价值划分为6个一级维度与72个二级指标，支持多层级定量评分；同时，通过人工验证的多模态基准数据集（MacroValue-Bench）和软匹配度量（soft-match metric），实现对视觉语言模型（VLMs）在不同价值维度上评估能力的系统性评测。在评估器优化方面，提出一种自适应跨度的多层级偏好优化（span-adaptive multi-level preference optimization, SA-MDPO）用于评估器蒸馏，并设计无需训练的多角色共识策略（multi-role consensus strategy）以提升推理阶段的判断一致性。实验表明，该方法可使小型2B参数评估器达到8B规模同类模型性能，逼近前沿闭源模型，为可扩展的宏观社会价值评估提供了高效可行的技术路径。
链接: https://arxiv.org/abs/2608.18096
作者: Zijuan Zhao,Zheren Fu,Hou Xia,Licheng Zhang,Yi Liu,Zhendong Mao
机构: University of Science and Technology of China (中国科学技术大学); State Key Laboratory of Communication Content Cognition, People’s Daily Online (人民日报社网络内容认知国家重点实验室)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 18 pages, 6 figures
Abstract:Assessing whether multimodal content aligns with macro-societal values, such as peace, justice, and freedom, has become an increasingly urgent challenge. Existing frameworks are largely confined to safety-oriented taxonomies, text-only psychometric probes, or single-label classification. Therefore, we propose MAVEN, a hierarchical framework for macro-societal value evaluation of multimodal content, grounded in international human-rights instruments and cultural value theory. MAVEN organizes values into 6 primary dimensions and 72 secondary indicators, supporting multi-level quantitative scoring. Building on MAVEN, we construct a human-verified multimodal benchmark and a soft-match metric to evaluate VLMs’ assessments across value dimensions. For evaluator optimization, we propose a span-adaptive variant of multi-level preference optimization for evaluator distillation, together with a training-free multi-role consensus strategy at inference time. We evaluate existing open- and closed-source VLMs on our benchmark, revealing shared tendencies and clear differences in macro-societal value judgments. Experiments show that our compact 2B evaluator matches its 8B counterpart in the same family and approaches frontier closed-source VLMs, offering a practical path toward scalable macro-societal value evaluation. Our SA-MDPO implementation and MacroValue-Bench are available at this https URL.
[NLP-76] Backdoor Learning in Language Models and Vision-Language Models
【速读】： 该论文旨在解决生成式人工智能在自然语言处理（Natural Language Processing, NLP）与视觉-语言模型（Vision-Language Models, VLMs）中面临的安全性与效率双重挑战。具体而言，其核心问题在于：随着深度学习技术的发展，NLP与VLMs虽性能显著提升，但其模型易受后门攻击（backdoor attacks）威胁，此类攻击通过隐蔽植入恶意触发器，在特定输入下诱导模型产生错误输出，严重损害系统的可信性与安全性；同时，多模态表征学习在临床医学影像等实际场景中的应用受限于计算效率与表示能力的瓶颈。解决方案的关键在于：一方面，提出系统化的后门攻击分析、检测与防御框架，以增强模型的鲁棒性与可信赖性；另一方面，设计面向医疗影像的高效多模态表征学习方法，通过优化跨模态对齐与特征压缩机制，在保障高精度的同时显著降低计算开销，实现安全与高效的协同优化。
链接: https://arxiv.org/abs/2608.18095
作者: Weimin Lyu
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Ph.D. dissertation
Abstract:Recent advances in deep learning have significantly enhanced the capabilities of Natural Language Processing (NLP) and Vision-Language Models (VLMs). However, these advancements come with increased vulnerabilities, notably through backdoor attacks that pose severe security threats. This thesis addresses two critical dimensions of Trustworthy AI and Efficient Multimodal Representation Learning: (1) security through analyzing, detecting, and designing backdoor attacks in NLP and VLMs, and (2) efficiency through advanced multimodal representation methods tailored for clinical and medical imaging applications.
[NLP-77] NE-BERT: A Multilingual Language Model for Nine Northeast Indian Languages
【速读】： 该论文旨在解决低资源语言（low-resource languages）在大规模预训练语言模型中被边缘化的问题，特别是针对东北印度地区9种语言及2种锚定语言（印地语、英语）的多语言建模挑战。其核心解决方案在于构建NE-BERT——一个专用于东北印度地区的多语言编码器模型，通过引入加权数据采样策略与自定义的SentencePiece Unigram分词器，在仅有约830万句的有限语料基础上实现了对极低资源语言（如Pnar、Kokborok）的有效建模。关键创新点在于采用激进的上采样策略缓解词汇碎片化问题，并通过优化分词机制显著提升分词效率（较mBERT提升1.50倍），从而在所有目标语言上均优于IndicBERT-V2和MuRIL，平均困惑度分别降低15.97倍和7.64倍。该研究不仅验证了模型在词性标注等下游任务中的实用性，还公开了模型、测试集与训练语料，以推动自然语言处理技术在东北印度社群中的应用与数字包容性发展。
链接: https://arxiv.org/abs/2608.18094
作者: Badal Nyalang
机构: MWire Labs
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large pretrained language models have demonstrated remarkable capabilities across diverse languages, yet critically underrepresented low-resource languages remain marginalized. We present NE-BERT, a domain-specific multilingual encoder model trained on approximately 8.3 million sentences spanning 9 Northeast Indian languages and 2 anchor languages (Hindi, English), a linguistically diverse region with minimal representation in existing multilingual models. By employing weighted data sampling and a custom SentencePiece Unigram tokenizer, NE-BERT outperforms IndicBERT-V2 and MuRIL across all 9 Northeast Indian languages, achieving 15.97X and 7.64X lower average perplexity respectively, with 1.50X better tokenization fertility than mBERT. We address critical vocabulary fragmentation issues in extremely low-resource languages such as Pnar (1,002 sentences) and Kokborok (2,463 sentences) through aggressive upsampling strategies. Downstream evaluation on part-of-speech tagging validates practical utility on three Northeast Indian languages. We release NE-BERT, test sets, and training corpus under CC-BY-4.0 to support NLP research and digital inclusion for Northeast Indian communities.
[NLP-78] Abliteration Mitigation via Refusal Aliases
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在后训练对齐阶段面临的安全隐患——即通过“消融”（Abliteration）攻击，仅需少量对比提示即可提取模型中的拒绝方向（refusal direction），并将其投影至权重矩阵的正交空间，从而有效绕过模型的对齐机制。现有防御方法普遍忽视了攻击成功的关键因素：拒绝方向的可提取性。为此，本文提出一种基于权重编辑的新型防御方法AMRA（Adversarial Masking via Rank-k Updates），其核心在于通过在残差流写入矩阵上施加低秩（rank-k）更新，将引发拒绝行为的激活值替换为随机别名，并同步修正下游读取矩阵，以维持模型原有功能的同时隐蔽拒绝信号。实验表明，在Llama-3-8B上，该方法使对抗消融后的拒绝能力提升2.16分，同时MMLU指标下降不足0.5个百分点；在Gemma-2-9B上，拒绝性能提升14.70分，且有害输出率与基线相当，尽管伴随更高的通用性能损耗。
链接: https://arxiv.org/abs/2608.18093
作者: Nathan Truong
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:Abliteration, the removal of refusal capabilities from large language models by projecting weight matrices orthogonal to an extracted refusal direction, has emerged as a prominent safety concern through its ability to bypass post-training alignment using only a small set of contrastive prompts. We find that existing defenses commonly overlook the cause of abliteration; that is, how easily the refusal direction can be extracted. To hinder this process, we introduce a weight-editing method that obscures the refusal signal by applying rank- k updates to residual stream writer matrices while replacing refusal-inducing activations with random aliases and correcting downstream reader matrices to preserve the model’s original behavior. On Llama-3-8B, AMRA improves post-abliteration refusal scores by 2.16 points over the undefended baseline with less than 0.5 percentage points of MMLU degradation. On Gemma-2-9B, it improves the post-abliteration refusal by 14.70 points over the baseline while keeping harmful output rates similar to the baseline, albeit at a greater utility cost.
[NLP-79] Self- and Other-Labels Induce Bidirectional Bias in LLM Judges
【速读】： 该论文旨在解决大语言模型作为评判者（LLM-as-a-judge）系统中日益突出的自偏好（self-preference）问题，即模型倾向于高估自身生成内容的质量，从而影响评估的可靠性。现有研究多集中于对生成文本的评价，但因风格特征与响应质量混杂，难以分离真实的自偏好与潜在混淆因素。为此，本文提出将评估对象从生成文本转向叙事约束选择（narrative constraint selections），此类选择不携带模型特异性的风格指纹，但仍保留可追溯的模型特征签名，从而实现对自偏好的精准测量。关键解决方案在于：通过控制选择质量与评判者严格度，在盲评条件下，自偏好基本消失，甚至在部分维度出现反转（模型认为自己选择的更缺乏原创性）；而在匹配质量条件下，仅凭标签归属（自我/他人标签）即可引发双向偏差——模型会无差别地抬高对自己标签的选择评分、压低对他者标签的选择评分，即使未暴露模型身份。研究揭示了作者归属识别是评估偏差的重要驱动因素，并证明开放性、无需真实标签的任务可作为研究大模型评判行为的可控实验工具。
链接: https://arxiv.org/abs/2608.18091
作者: Songeun Chae,Min Kim,Donghoon Jung,Seojin Choi,Seohyon Jung
机构: KAIST(韩国科学技术院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:As LLM-as-a-judge systems become increasingly widespread, self-preference in LLMs – the tendency to favor one’s own outputs – raises growing concerns about evaluation reliability. However, it has been studied predominantly on generated text, where stylistic features and response quality are inevitably conflated. As a result, existing measurements cannot separate genuine self-preference from these confounds. We address this by changing the object of evaluation: instead of judging generated text, ten LLMs assess narrative constraint selections, which carry no model-specific stylistic fingerprint yet retain a recoverable model-specific signature. We run two experiments that yield distinct findings. Under blind evaluation, self-preference largely disappears once selection quality and evaluator severity are controlled. It vanishes on three of four rubric dimensions and reverses on the fourth, where judges rate their own selections as less original. Under matched quality, however, self- and other-labels alone – without naming any model – shift scores bidirectionally: LLM judges inflate scores for self-labeled selections and deflate those for other-labeled ones regardless of the selection’s actual source. We make two contributions: 1) authorship attribution is a distinct driver of evaluation bias, and 2) open-ended, ground-truth-free tasks can serve as controlled instruments for studying LLM judge behavior.
[NLP-80] Nine Emotion Centroids: A Label-Free Valence Axis That Transfers Across Four Modalities
【速读】： 该论文旨在解决如何在无需大规模标注数据的情况下，从少量情感类别名称与叙事段落中自动识别语言模型内部表征的“情感极性轴”（valence axis, V-axis）这一关键问题。其核心挑战在于：如何在不依赖大量人工标注的情感标签的前提下，挖掘出跨模态、通用且可解释的情感表征方向。解决方案的关键在于提出一种轻量级、无监督的“嵌入-平均-主成分分析”（embedding-averaging-principal component analysis）方法——仅需9个情绪锚点故事集（每类50个简短叙事段落）共约1500个样本，即可通过冻结编码器对这些故事进行嵌入，并计算九类平均嵌入的主成分方向，从而提取出稳定的V轴。该方向不仅在文本任务中达到接近监督学习的性能（如在SST-2上AUC达0.772，接近0.828），且在图像（EmoSet，r=0.636）、音频（ESC-50，AUC 0.906）及人脑电图（EEG，AUC 0.720±0.055）等多模态数据中均表现出显著相关性。进一步实验证明该方向具有机制性功能：移除该方向导致情感分类准确率下降5.5–37.2个百分点，远超随机方向的影响（≤0.88个百分点）。此外，一个仅含两参数的文本分类器经训练后可零样本迁移至其他模态，实现高精度跨模态泛化，而通用16维子空间则表现接近随机。该方法的有效性限于连续属性（如情感极性），对离散类别概念无效，且存在模型家族特异性（仅在Llama/Mistral有效，Qwen/Gemma无效），揭示了其内在机制的可迁移性边界。
链接: https://arxiv.org/abs/2608.18090
作者: Yousef Radwan
机构: KAUST (沙特阿卜杜拉国王科技大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 15 pages, 3 figures, 4 tables
Abstract:Inside a modern language model sits a single internal direction that tracks how positive or negative a sentence feels. We show how to find this valence axis (V-axis) from just 9 emotion category names plus 50 short narrative paragraphs per emotion – about 1,500 fewer labels than the usual supervised approach – and that the same direction appears in vision, audio, and human-brain encoders never jointly trained. The recipe: embed nine emotion-anchored story sets in a frozen encoder, take the top principal direction of the nine averaged embeddings. Projecting new inputs onto it captures 93% of supervised performance on SST-2 (Llama-3-8B-Instruct, AUC 0.772 vs. 0.828), correlates with human valence ratings on 11,811 EmoSet images at r=0.636, reaches AUC 0.906 on ESC-50 audio (p2.2e-15), and AUC 0.720+/-0.055 on EEG from 123 subjects (p3.65e-8). The direction is mechanistically active: ablating it collapses sentiment accuracy by 5.5-37.2 pp across three LLMs vs. at most 0.88 pp for matched random directions (z12). A 2-parameter classifier trained on text labels transfers to images (AUC 0.961), audio (0.764), and brain recordings (0.828) without target-modality labels; a generic 16-D subspace stays at chance (0.525). The recipe is bounded to continuous attributes – seven tests on categorical concepts return near-chance – and steering is family-specific (Llama/Mistral yes, Qwen/Gemma no).
[NLP-81] Latent Space Refusal Anchoring for Low-Resource African Languages: Mechanistic Safety Recovery Without Retraining ICML2026
【速读】： 该论文旨在解决生成式 AI 在低资源语言（如约鲁巴语、伊博语、伊加拉语和豪萨语）中对有害请求缺乏拒绝能力的问题，尽管其在英语中具备有效的安全拒绝机制。问题的核心在于，拒绝机制虽存在于模型的残差流（residual stream）中，但对低资源语言输入无法有效激活。传统解决方案依赖标注的目标语言数据和重新训练，然而这些资源在大多数非洲语言中难以大规模获取。为此，论文提出无需训练的“潜在空间拒绝锚定”（Latent Space Refusal Anchoring, LSR-Anchoring）方法，通过从英语提示中提取拒绝方向，并在推理时将其“锚定”至残差流以激活安全行为。其核心关键在于：采用均值激活引导（Mean-Activation Steering, MAS）在四种主流架构（Llama-3-8B、Llama-3.1-70B、Mistral-7B-Instruct、Qwen2.5-7B）上实现跨语言安全恢复；进一步引入稀疏自编码器（Sparse Autoencoder, SAE）导出的引导（SAE-Derived Steering, SDS），以单一特征替代密集方向，显著降低相对熵（KL divergence）3.5–7倍，且避免良性性能下降。实验表明，四种非洲语言均获得正向迁移效果，而阿拉伯语在所有架构与强度下均失败，揭示其存在几何不匹配而非基线偏差。此外，多任务理解能力（MMLU）下降始终低于0.35个百分点，证明该方法在保持通用能力的同时实现了高效安全增强。
链接: https://arxiv.org/abs/2608.18089
作者: Godwin Abuh Faruna
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Published at ICML 2026 Workshop on Global South in Machine Learning
Abstract:Instruction-tuned models often refuse harmful requests in English but comply with the same requests in Yoruba, Igbo, Igala, and Hausa. This suggests that the refusal mechanism is present in the residual stream but fails to activate for low-resource inputs. Recovering it normally requires labelled target-language data and retraining, neither of which is available at scale for most African languages. We introduce Latent Space Refusal Anchoring (LSR-Anchoring), a training-free method that extracts the refusal direction from English prompts and clamps it onto the residual stream at inference time. The primary variant, Mean-Activation Steering (MAS), operates across the four architectures we tested: Llama-3-8B, Llama-3.1-70B, Mistral-7B-Instruct, and Qwen2.5-7B. On Mistral and Qwen it recovers safety with benign degradation below 0.08. On Llama-3-8B it overcorrects, with Degraded Performance on Legitimate prompts (DPL) reaching 1.00. We address this with SAE-Derived Steering (SDS), which replaces the dense mean-difference direction with a single Sparse Autoencoder (SAE) feature and reduces Kullback-Leibler (KL) divergence by 3.5-7x without benign collapse. Four languages transfer positively, but Arabic fails on every architecture and at every steering magnitude, indicating a geometric mismatch rather than a baseline effect. Massive Multitask Language Understanding (MMLU) accuracy drops remain below 0.35 percentage points at every effective steering magnitude.
[NLP-82] SuTRA : Structurally-Unified Tokenization with Root Awareness INTERSPEECH2026
【速读】： 该论文旨在解决现有子词分词器（subword tokenizers）在处理形态丰富的印地语族语言时，因过度依赖统计压缩而忽视词素结构（morphological structure）所导致的问题，尤其体现在对基本单位——音节（akshara）的破坏上。这种现象被称为“词素碎裂”（Morphological Shattering），即频率驱动的分词方法会任意拆分词根与词缀，损害语言的形态完整性。其解决方案的关键在于提出一种具有词根意识的结构统一分词算法——SuTRA（Structurally-Unified Tokenization with Root Awareness），该方法通过强制保持akshara的不可分割性，并对跨越词素边界的合并操作施加惩罚，从而有效抑制词素碎裂。实验表明，相较于BPE，SuTRA在词素对齐（边界F1）上提升达+14.7%，在语义可恢复性（Hindi）上提升+34%，并带来平均+8.08 chrF2的机器翻译性能增益。
链接: https://arxiv.org/abs/2608.18087
作者: Vaibhav Rathore,Siddhant Gole,Dadhichi Telwadkar,Rooshil Bhatia,Maulik Ruparel,Siddharth Surekha,Neha Bhargava
机构: Motilal Oswal(莫蒂拉尔·奥斯瓦尔)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted at Interspeech 2026
Abstract:Existing subword tokenizers optimize statistical compression but ignore morphological structure, particularly the relationship between roots and affixes. This is harmful for morphologically rich Indic languages, where basic units are complex orthographic syllables (aksharas) rather than letters. Frequency-based methods over-fragment words, arbitrarily splitting roots and affixes - a phenomenon we term Morphological Shattering. We propose SuTRA (Structurally-Unified Tokenization with Root Awareness), a morphology-aware algorithm that preserves akshara indivisibility and penalizes merges crossing morphological boundaries. We also release a new morphological segmentation dataset for Hindi, Marathi, and Gujarati. SuTRA reduces shattering, achieving peak gains of +14.7% in morphological alignment (Boundary F1) and +34% in semantic recoverability (Hindi) over BPE. These structural gains yield an average improvement of +8.08 chrF2 in machine translation.
[NLP-83] Persona-Guided LLM Agents for Task-Oriented Dialogue
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在目标导向对话（Task-Oriented Dialogue, TOD）中如何实现个性化表达而不损害任务完成质量的问题，以及系统是否应根据用户人格特征进行自适应以提升交互质量。其核心挑战在于平衡个性化的表现与任务准确性之间的矛盾。解决方案的关键在于构建一个无需训练的框架，通过两个大语言模型分别模拟具有特定人格特质的用户代理（user agent）和能够适应用户人格的系统代理（system agent），并设置三种不同条件来考察系统对用户人格信息的知晓程度：中性（Neutral，无信息）、尝试（Try，基于对话线索推断）和理想（Oracle，明确告知）。实验结果表明，尽管部分人格特质的表现可靠性较低，但系统仍可在保持较高任务性能的同时实现人格表达；人格适应显著提升了约束满足率、信息提供率及用户满意度，但降低了真实性，揭示了个性化与任务接地性之间的权衡。值得注意的是，基于线索的适应（Try）在多数情况下最有效地缓解了这一矛盾，且其效果不受人格表达强度影响，展现出优于其他方法的稳健性，为实现无需微调的个性化目标导向对话提供了可靠路径。
链接: https://arxiv.org/abs/2608.18085
作者: Maryam Shoaeinaeini,Brent Harrison,A.B. Siddique
机构: University of Kentucky (肯塔基大学)
类目: Computation and Language (cs.CL)
备注: 8 pages
Abstract:Prior work has shown that large language models (LLMs) can express diverse personality traits in open-ended text generation. However, it remains unclear whether they can do so in a goal-directed dialogue without compromising task completion, and whether adapting to the user’s personality improves the interaction quality. We study these questions in task-oriented dialogue (TOD), where a system helps a user accomplish a goal via multi-turn interaction. We build a training-free framework that simulates a TOD interaction between two LLMs: a user agent that exhibits a target personality and a system agent that adapts to the user while completing the task. To isolate the effect of adaptation, we vary how much the system knows about the user’s personality across three conditions. In Neutral, the system receives no personality information. In Try, it infers the personality from dialogue cues. In Oracle, it is given the personality explicitly. We evaluate GPT-4o, Qwen3-Next-80B, and Gemini 2.0 Flash on Hotel and Restaurant dialogues from the Schema-Guided Dialogue (SGD) dataset, across the Big Five traits and their opposite poles. We find that the user agent can express personality while the system maintains strong task performance, although some traits are realized far less reliably than others. Adapting to the user’s personality improves constraint satisfaction, inform rate, and user satisfaction, but lowers truthfulness, revealing a trade-off between personalization and task-grounding. Oracle’s gains grow when the target trait is strongly expressed, whereas Try’s gains are largely insensitive to realization strength. Overall, cue-based adaptation in Try best resolves this trade-off and offers a more reliable route to personality-aware TOD without fine-tuning.
[NLP-84] Compiler-Guided Adaptive Proof Search with Cross-Model Synergy on Context-Dependent Theorem Proving
【速读】： 该论文旨在解决真实世界Lean 4项目中定理证明的挑战，即证明过程高度依赖于项目特定的上下文，导致传统方法难以复用失败的证明尝试。其核心问题在于：如何在保持高效的同时，有效利用历史失败证明中的有用信息，避免因不当重用或迭代优化导致部分正确证明的退化。解决方案的关键在于提出一种编译器引导的证明搜索框架，通过双模型生成（dual-model generation）与停滞触发的重新采样机制实现探索（exploration），以覆盖多样化的初始证明路径；同时，基于编译器驱动的成对比较进行当前最优解精炼（current-best refinement），实现对有前景证明状态的有效利用（exploitation）。实验结果表明，在miniCTX-v2的七个真实项目上，该方法在pass@32预算下相比基线方法将平均通过率提升12.8个百分点，同时减少21.9%的大型语言模型（LLM）调用，显著优化了有效性与效率之间的权衡。
链接: https://arxiv.org/abs/2608.18084
作者: Zhuo Liu,Ding Yu,Hangfeng He
机构: University of Rochester (罗切斯特大学)
类目: Computation and Language (cs.CL); Programming Languages (cs.PL)
备注: 16 pages
Abstract:Theorem proving in real-world Lean 4 projects is challenging because proofs often depend on project-specific context. While iterative refinement can use compiler errors to repair failed proofs, reusing failed attempts requires careful search control: some proofs provide better starting points than others, and later revisions may degrade a partially correct proof. We propose a compiler-guided proof search framework that balances exploration and exploitation. It explores diverse starting points through dual-model generation and stagnation-triggered resampling, while exploiting promising proof states through current-best refinement guided by compiler-grounded pairwise comparison. Experiments on seven real-world Lean 4 projects from miniCTX-v2 show that our method achieves a better effectiveness–efficiency tradeoff than pass@k baselines. Within the pass@32 budget, our method improves average pass rate by 12.8 percentage points while reducing LLM calls by 21.9%.
[NLP-85] Entity tracking emerges in sub-billion parameter language models and exceeds human performance in naturalistic narratives
【速读】： 该论文旨在解决语言模型在自然语言理解中是否具备类人水平的实体追踪能力这一关键问题。实体追踪（entity tracking）是指在话语过程中持续追踪事物的状态与位置变化，即使这些信息未被显式陈述，是语言理解的核心能力之一。现有评估方法多依赖人工构造的任务，与真实语言使用场景脱节，且缺乏对人类表现的直接比较，导致相关结论存在局限。为此，本文采用自然叙事文本，在多个复杂度层级上同时评估语言模型与人类（N = 48）的实体追踪表现。研究发现，人类的实体追踪能力随叙事复杂度增加而显著下降，而非单纯受叙述长度影响；而语言模型在仅4.1亿参数规模时已达到人类水平，并随模型规模持续提升，当前主流模型性能远超人类。其解决方案的关键在于：构建基于真实语境的自然主义评估范式，结合人类行为数据与模型表现对比，揭示了实体追踪能力在远低于此前认为的模型规模下即可出现，挑战了以往关于大模型必要性的认知。
链接: https://arxiv.org/abs/2608.18083
作者: Karolina Drożdż,Micha Heilbron
机构: IDEAS Research Institute (IDEAS 研究所); Max Planck Institute for Psycholinguistics (马克斯·普朗克语言学研究所); University of Amsterdam, Amsterdam Brain and Cognition (阿姆斯特丹大学，阿姆斯特丹大脑与认知中心)
类目: Computation and Language (cs.CL)
备注:
Abstract:Understanding language requires tracking entities across discourse - i.e., knowing where things are and how they change, even when not explicitly stated. Whether language models perform such tracking in a human-like fashion remains unclear, in part because existing evaluations rely on artificial tasks, far removed from natural language comprehension, and lack comparisons to humans. Here, we evaluate entity tracking in both language models and humans (N = 48) using naturalistic narratives at multiple levels of complexity. In humans, we find that entity tracking degrades specifically with narrative complexity, not narrative length. In language models, we find that human-level entity tracking is already present at 410 million parameters - well below the multi-billion parameter, code-specialised models identified by prior work - and improves with scale, with contemporary models far exceeding human performance. Together, these results demonstrate that entity tracking, a core component of language understanding, emerges at model scales far smaller than previously thought.
[NLP-86] LongNovel: A Multi-Scale Benchmark for Hallucination Detection in Long-Context Novel Summarization
【速读】： 该论文旨在解决长上下文文本摘要中幻觉（hallucination）问题，尤其聚焦于长篇小说这一复杂文本类型中的幻觉现象。尽管近年来上下文窗口已显著扩展，但长篇小说因情节复杂、人物关系密集及细节描写丰富，成为研究幻觉行为的理想场景，而现有研究缺乏针对此类文本的多尺度幻觉检测基准，且未充分探究幻觉随上下文长度增长的变化规律。为此，论文提出 LongNovel，一个面向长上下文小说摘要的多尺度、双语（中文与英文）幻觉检测基准。该基准基于29部中文小说（16k至100k token）及BookSum数据集的章节级数据构建，设计了8类幻觉类型，并采用多模型仲裁（Multi-Model Arbitration）与基于实体引用的幻觉生成（Entity-Referenced Hallucination Generation）相结合的方法，以保障数据真实性与各类幻觉分布的均衡性；同时对测试集内容进行人工校验，确保数据可靠性。实验结果表明，LongNovel具有较高的挑战性，可有效推动长上下文生成式模型中幻觉检测与缓解技术的研究。
链接: https://arxiv.org/abs/2608.18082
作者: Ruizhi Zhang,Jinwei Chen,Xiangju Lu,He Yan,Mo Yu,Junmin Zhu,Wei Zhang
机构: East China Normal University (华东师范大学); iQIYI Inc (爱奇艺); Tencent (腾讯)
类目: Computation and Language (cs.CL)
备注:
Abstract:Although context windows have expanded significantly in recent years, hallucinations in long-context summarization remain a challenge. Long novels are better suited than news or papers for researching these hallucinations, due to their intrinsic information and detailed descriptions of events and dialogues. However, current research lacks a multi-scale benchmark for hallucination detection in long-context novel summarization and does not fully explore how hallucinations change as the context grows longer. In this study, we propose LongNovel, a multi-scale long-context bilingual (Chinese and English) novel benchmark for hallucination detection. This benchmark is constructed from 29 Chinese novels (ranging from 16k to 100k tokens) and chapter-level data from the BookSum dataset. We design 8 hallucination types and employ a combination of Multi-Model Arbitration and Entity-Referenced Hallucination Generation to ensure both data authenticity and a balanced distribution of hallucination categories. Furthermore, we manually revise the content in the test set to guarantee data reliability. Extensive experimental results demonstrate that LongNovel is a challenging benchmark. We release LongNovel for future research. this https URL
[NLP-87] Debiased Inference for AI-Generated Data without Gold-Standard Labels: Identification via Multiple Imperfect Measurements
【速读】： 该论文旨在解决在社会科学等研究领域中，利用生成式AI（Generative AI）对变量进行自动化测量时，因忽略预测误差而导致下游分析结果出现显著偏差及置信区间无效的问题。即使AI测量的准确率较高（如超过90%），若不考虑其固有的预测误差，仍会严重影响统计推断的有效性。现有解决方案依赖于昂贵且难以获取的金标准标签（gold-standard labels），限制了其在实际应用中的可扩展性。本文提出了一种无需金标准标签的去偏推断框架——多重不完美测量去偏推断（Debiased Inference with Multiple Imperfect Measurements, DMM），其核心在于利用多个基于生成式AI的不完美测量结果，在假设各测量独立且条件于潜在真实标签与观测单位特征（如文本嵌入）的前提下，通过高阶张量分解（CP decomposition）理论构建稳健的估计方法。该框架允许不同标注方法（如不同大语言模型）和不同样本单位的误分类率存在异质性，并基于半参数推断理论证明了所提估计量的一致性和渐近正态性，从而支持广泛的下游统计推断。仿真结果表明，DMM能够实现有效的统计推断，且引入更准确但非完美的测量可提升估计效率；此外，作者还针对大语言模型标注的典型应用场景，发展了用于检验条件独立性假设的诊断工具。
链接: https://arxiv.org/abs/2608.18294
作者: Naoki Egami,Sooahn Shin
机构: Massachusetts Institute of Technology (麻省理工学院); Department of Political Science (政治科学系); Statistics and Data Science Center (统计与数据科学中心)
类目: Methodology (stat.ME); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注:
Abstract:An increasing number of scholars use AI to measure variables they subsequently include in downstream analyses. Although AI-measured variables are often analyzed as if observed without error, ignoring prediction errors in automated measurement leads to substantial bias and invalid confidence intervals in downstream analyses, even if AI measurement accuracy is high, e.g., above 90%. Existing solutions, such as design-based supervised learning and prediction-powered inference, combine error-prone AI-based measurements with gold-standard labels, which may be costly and difficult to obtain in some application areas. In this paper, we propose debiased inference with multiple imperfect measurements (DMM), a framework that combines multiple error-prone AI measurements to enable valid downstream inference without gold-standard labels. Building on the established results on CP decomposition, DMM assumes that these measurements are independent conditional on the latent true label and observed unit-level features, such as text features represented by embeddings. This framework allows for unknown misclassification rates to vary across annotation methods (e.g., large language models) and across units of annotation (e.g., texts). Under this assumption, we use semiparametric inference theory to prove that the DMM estimator is consistent and asymptotically normal, enabling valid inference for a wide range of downstream statistical analyses common in the social sciences. Our simulation results show that DMM yields valid inference and that adding accurate, though imperfect, measurements can improve efficiency. Focusing on common applications of large language model annotations, we also develop diagnostics to assess the conditional independence assumption. Subjects: Methodology (stat.ME); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2608.18294 [stat.ME] (or arXiv:2608.18294v1 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2608.18294 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
信息检索
[IR-0] Finetuning Strategies for Querying Sounds by Vocal Imitation
链接: https://arxiv.org/abs/2608.19174
作者: Aditya Bhattacharjee,Christos Plachouras,Sungkyun Chang,Emmanouil Benetos
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:This technical report describes our winning submission to the AES AIMLA 2025 Challenge on querying sound effects by vocal imitation. We investigate two complementary fine-tuning strategies: contrastive learning with a frozen, pretrained CED encoder, and joint contrastive-triplet learning with semi-hard negatives using a MobileNetV3 encoder. This report has been updated for posterity to include details released after the challenge.
[IR-1] rEDMRec: Distilling Large Language Model Reasoning into an Editable Experience Memory for Recommendation
链接: https://arxiv.org/abs/2608.18952
作者: Minh Hoang Nguyen,Tung Le,Huy Tien Nguyen
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Large language models can improve recommendation quality by reasoning explicitly over user history and candidate items - for example, extracting a user’s preferences or explaining why one item fits better than another - rather than mapping history directly to a ranked list. This reasoning, however, is expensive to repeat on every ranking request and, once produced, is typically consumed once and discarded, leaving it neither reusable across future requests nor easy to inspect or correct as user tastes drift. Our insight is that reasoning does not need to be regenerated at every call if it can instead be compressed once into a compact, structured memory that a lightweight model retrieves from. We propose rEDMRec, which distills a teacher LLM’s reasoning into four typed, editable experience channels - long-term preference, short-term context, item-perception, and counterfactual hard-negative comparisons - maintained by an LLM memory controller that performs Add/Delete/Modify/Keep operations and refines entries via K-agent debate. A lightweight student LLM then ranks candidates purely by retrieving from this memory, without invoking the teacher again, decoupling online inference cost from reasoning depth. Across ML-1M, Amazon Beauty, and Steam and ten student backbones, rEDMRec improves HR@1 over zero-shot, few-shot, and RAG on every backbone, and over GraphRAG on most backbones, with Impv up to 13.3% vs. the second-best baseline on ML-1M. Channel ablations show that short-term context is the only channel that helps consistently across capacity tiers, whereas long-term, item-perception, and counterfactual contributions are capacity-dependent (and can reverse on the strongest students); debate-based memory optimization lowers bank duplication by 7.4 percentage points while raising downstream HR@1 by up to +0.029 over six optimization epochs.
[IR-2] hink-to-Personalize: Unifying Reasoning and Retrieval for User-Centric Personalized Dense Retrieval CIKM2026
链接: https://arxiv.org/abs/2608.18855
作者: Angqing Jiang,Gaoming Zhang,Jianchun Song,Kena Qi,Dayao Chen,Wei Lin,Defu Lian
类目: Information Retrieval (cs.IR)
备注: Accepted at CIKM 2026. 11 pages, 8 figures, and 9 tables
Abstract:Dense retrieval has become a cornerstone of modern local-lifestyle e-commerce search by encoding queries and items into semantic embedding spaces. While recent advancements have transitioned from BERT-based embedding models to Large Language Models (LLMs), most approaches still treat LLMs as static text encoders, neglecting their inherent reasoning capabilities. Furthermore, standard dense retrieval models remain query-centric, which is insufficient in e-commerce scenarios where sparse and ambiguous queries create an intent gap that can only be bridged by the rich context of user history. Meanwhile, existing personalized retrieval methods typically rely on implicit embedding interactions, which lack the reasoning capability to effectively disambiguate user intent from noisy historical behaviors. To address these challenges, we propose Think-to-Personalize (TTP), a novel framework that unifies explicit user-centric intent reasoning with dense retrieval. By reasoning over the user’s historical purchase sequence, TTP explicitly deduces latent personalized needs and generates an intent-enhanced query, which is then encoded into a unified dense embedding. Specifically, we design a two-stage training paradigm: (1) a Supervised Fine-Tuning (SFT) stage that establishes cold-start capabilities; and (2) a Reinforcement Learning (RL) stage that aligns the reasoning process with retrieval utility using Group Relative Policy Optimization (GRPO). Extensive experiments on both proprietary and public benchmarks demonstrate that TTP significantly outperforms state-of-the-art baselines. Furthermore, in online A/B tests, it achieved a +0.46% lift in order volume, validating its practical effectiveness and establishing a new paradigm for reasoning-driven personalized dense retrieval.
[IR-3] SIDScope: A Diagnostic Resource for Semantic-ID Interfaces in Generative Recommendation
链接: https://arxiv.org/abs/2608.18779
作者: Jiandong Ding,Huijie Qin,Tiandeng Wu,Yi Cao
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: Resource: this https URL
Abstract:Semantic-ID mappings are reusable interfaces between item tokenizers and generative recommenders, yet released mappings rarely state whether they are coherent, what structure they expose, how generated paths resolve, or what must be revalidated after a refresh. SIDScope is a source-traced diagnostic resource for these decisions. It normalizes item-to-code artifacts, verifies provenance and joins, profiles mapping structure, compares paired revisions, and accounts for path-to-item outcomes in generated traces. Across nine source-traced tokenizer exports from seven families on Amazon and Yelp data - eight executable routes plus one auditable snapshot - SIDScope reveals that interface health is multi-signal rather than scalar. Its central finding is mechanism-conditional: prefix alignment strongly tracks held-out candidate exposure when retrieval consumes SID prefixes, then weakens as scoring becomes prefix-independent. Trained trace accounting exposes a second hidden gap: a valid target path can survive without uniquely retrieving the target item by 1.2-3.0 percentage points. A refresh case establishes a third: repairing the mapping does not by itself restore an inherited generator; model reuse requires a separate handoff check. The package provides frozen evidence summaries, conformance reports, trace labels, table builders, and CPU-only verifiers. It supports decisions about artifact readiness, interface risks, and revalidation before model reuse.
[IR-4] GateDiffInt: Gate-Mediated Controllable Diffusion and Multi-Intent LLM Distillation for User Behavior Modeling
链接: https://arxiv.org/abs/2608.18764
作者: Jialong Duan,Zichen Zhang,Zirui Tu,Zheng Zhang,Zepeng Li,Qingyao Cui,Qinwen Wang,Yudan Liu,Luo Yang,Yao Hu
类目: Information Retrieval (cs.IR)
备注:
Abstract:Existing ranking models encode intent only implicitly, making it hard to disentangle structured intents of varying strength and temporal scale. Noise and intent in behavior sequences are mutually reinforcing—we call this Noise–Intent Coupling (NIC). Noise dilutes true intents, while the lack of structured intent priors leaves denoising without a clear this http URL address NIC, we propose GateDiffInt, an intent interaction framework for industrial ranking. It uses the final conversion signal to jointly align sequence denoising and intent extraction. GateDiffInt applies a controllable forward diffusion process with dual gating to enhance and denoise behavior sequences. A large language model then acts as teacher to distill four structured intents—long-term, short-term, latent, and conversion—into a lightweight student model. The enhanced sequence and structured intent representations are deeply fused via attention to produce intent-aware representations for conversion-rate this http URL experiments on public and large-scale industrial datasets show consistent gains over strong baselines. In online A/B tests serving hundreds of millions of daily active users, GateDiffInt delivers substantial GMV improvements and has been deployed to primary traffic, confirming both effectiveness and production readiness.
[IR-5] GreekBarRetrieval: A Benchmark for Greek Statutory Retrieval
链接: https://arxiv.org/abs/2608.18752
作者: Ernest Beta,Odysseas S. Chlapanis,Dimitrios Galanis,Ion Androutsopoulos
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: Submitted to NLLP workshop 2026
Abstract:Statutory retrieval is necessary for citation-grounded legal question answering, but remains underexplored for Greek. We introduce GreekBarRetrieval, a public retrieval benchmark derived from, and complementing GreekBarBench, which did not include retrieval. The new benchmark comprises 283 bar-exam questions, each accompanied by the facts of the case it refers to, and 6,308 candidate statutory articles to retrieve from. Questions and facts are stated in everyday language, but need to be mapped to the formal terminology of statutes and their abstract legal concepts. A further complication is that not all of the case facts are relevant to each question of a case. Experimenting with three BM25 variants and nine dense retrievers, we find that vanilla dense retrieval far outperforms vanilla sparse retrieval in Recall@100. However, LLM-based query reformulation helps BM25 close that gap, while also improving dense retrieval. With a ten-round ReAct-like LLM reformulation loop that we introduce, BM25 improves further in Recall@100 and obtains the best nDCG and MAP scores of all tested retrievers. Query reformulation also outperforms pseudo-relevance feedback, sparse-dense fusion, and English translation.
[IR-6] Visual-Aware Representation of Web Pages for Machine Learning Applications
链接: https://arxiv.org/abs/2608.18727
作者: Radek Burget,Radek Hranický
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注:
Abstract:Applying machine learning to web pages is challenging due to the need to interpret HTML together with associated resources and perform rendering to obtain a meaningful visual and layout-aware representation. As a result, machine learning over web content remains comparatively underexplored. In this paper, we present a platform for visual-aware representation and machine learning over web pages based on the open-source rendering tool FitLayout. The platform provides a server capable of rendering web pages, explicitly capturing their visual and structural properties in an RDF-based representation, and persisting the rendered documents in an integrated storage. The processing pipeline is controlled via a REST API, while SPARQL queries are used to retrieve structured data suitable as input for machine learning algorithms. By explicitly modeling rendered web pages, including fine-grained layout details, the platform enables dataset sharing and supports the reproducibility of experimental results. The architecture supports the complete dataset preparation workflow, from web page collection and rendering through preprocessing and annotation of content elements to downstream learning tasks. We further provide a Python client library that integrates the platform with standard machine learning workflows. As a demonstration, we show how rendered web pages can be transformed into graph-based representations and used to train graph neural networks for recognizing key content elements, illustrating both the applicability of the approach and the reproducibility of the results.
[IR-7] Composed Historical Image Retrieval by Modeling Temporal Representations BMVC2026
链接: https://arxiv.org/abs/2608.18694
作者: Adrià Molina Rodríguez,Oriol Ramos Terrades,Josep Lladós Canet
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: Accepted at BMVC2026
Abstract:While time evolves linearly, the geometry of neural embedding spaces is inherently multi-dimensional, often chaotic, and difficult to interpret. In principle, one could constrain an embedding space to a single temporal dimension; however, such a reduction would sacrifice performance on downstream tasks, as one-dimensional embeddings cannot retain sufficient expressive capacity. This paper asks whether it is possible to learn representations that preserve temporal structure while remaining effective for image and object retrieval, and answers this question by building the mathematical foundations of such a system. We propose Temporally Decomposable Image Representations (TDIR), a representation learning algorithm that decomposes historical photographs into separate date and content components through orthogonal subspaces. We define and prove the conditions under which such a decomposition is achievable, characterize the error incurred when those conditions are only partially met, and show that orthogonality between temporal and categorical subspaces emerges naturally from the joint optimization, without requiring it to be imposed explicitly. Beyond its geometric properties, TDIR enables a class of transitive operations on embedding spaces: the temporal information of one image can be extracted and injected into the representation of another, with no label supervision required. All theoretical properties are grounded and validated in the real-world problem of Composed Image Retrieval on historical photographs, where a query simultaneously specifies object content and a target time period, either through labels or through example images. This in-the-wild setting serves as a concrete backing for the propositions we derive, offering an intuitive and interpretable way to navigate photographic archives while maintaining competitive performance in both date estimation and object retrieval.
[IR-8] PILOT Technical Report
链接: https://arxiv.org/abs/2608.18637
作者: Jiuning Lin,Ruiquan Lan,Xiaodong Zhu,Bin Zhang,Chengyu Lai,Chuxin Chen,Dimin Wang,Hongtao Cheng,Jialin Zhu,Lingqing Zhang,Shuai Zhong,Tao Wang,Weipeng Huang,Yinjiang Cai,Yinnan Song,Yuan Liu,Zhibo Xiao,Zhixin Ma,Zihong Huang
类目: Information Retrieval (cs.IR)
备注: Technical Report, 42 pages, 10 figures
Abstract:Existing agentic approaches for recommendation system optimization remain fundamentally reactive: they adjust parameters in response to observed metric changes but lack the ability to proactively design controlled experiments, personalize strategies at the user-segment level, or accumulate reusable experimental methodology across tasks. We present PILOT (Proactive Insight Learner for Online Tree-Experiments), an LLM-agent framework that organizes three roles within a constrained control loop where deterministic services enforce all safety, statistical, and permission boundaries: (1) an Experiment Manager that drives the full experiment lifecycle – task intake, observation governance, anomaly recovery, and postmortem – by selecting only from a rule-generated legal-command envelope; (2) a Search Planner that proposes candidate decision trees for user-segment-level personalization, invoked only when the Manager requests planning; and (3) a Memory Curator that asynchronously distills experiment outcomes into strategy-level domain knowledge and provenance-tracked methodology, failure-isolated from the main loop. The Manager makes the agent proactive, the Planner enables population-level personalization beyond global tuning, and the Curator turns every completed task into a learning opportunity for the next. Deployed on Taobao’s platform with 5 experimental buckets, PILOT is compared against ROAM(Reactive Optimization with Agent-driven Moves), a free-exploration agent without lifecycle governance or structured hypothesis testing. PILOT achieves up to +1.40% IPV, +1.60% Core IPV, +0.96% transaction count, and +1.50% transaction amount, improving over ROAM’s best results (+1.00% IPV, +0.90% Core IPV, +0.60% transaction count, +1.13% transaction amount) while raising search efficiency from 53.3% to 93.3% (+40 pp), with no human intervention throughout the experimental cycle.
[IR-9] OneModel: A Unified Foundation for Platform-Scale Multi-Scenario Ranking
链接: https://arxiv.org/abs/2608.18606
作者: Yinqi Zhang,Peiyu Hu,Yuntian Tang,Siying Gu,Jiahao Liang,Longxin Kou,Haiqing Hu,Shuman Zhuang,Yubin Xu,Chenggen Sun,Bin Ye,Donghui Xu,Zhaoyu Liu,Jiang Rong,Yuting Jia,Zhaokai Luo,Leilei Ma,Yiying Xie,Yao Hu
类目: Information Retrieval (cs.IR)
备注:
Abstract:Platform-scale recommender systems often span multiple business streams such as organic recommendation, advertising, and merchant services, where user behaviors form a continuous cross-stream trajectory. Maintaining separate ranking systems fragments user representations and increases engineering cost. We propose \textbfOneModel, a unified framework for multi-stream final ranking. OneModel maps heterogeneous behaviors into shared event sequences, learns long-context user representations with an action-oriented backbone, and introduces \emphScenario-aware Information Modulation to balance cross-stream transfer and stream-specific specialization. For production deployment, OneModel further adopts stratified user representation, multi-objective training, and optimized online serving with feature decomposition, user feature prefetching, shared user-tower computation, and graph-level inference optimization. We deploy OneModel in production at \emphXiaohongshu, where it delivers consistent offline gains over strong baselines and scales favorably with context length and model capacity. Online A/B tests improve Time Spent by \textbf+0.33% and Engagement by \textbf+1.25% in Explore Feed, lift advertising value by \textbf+3.43% and CTR by \textbf+8.18% in Feed Advertising, and raise DGMV by \textbf+1.1867% and GPM by \textbf+2.1585% in Merchant Recommendation, validating unified multi-stream ranking as an effective production foundation.
[IR-10] FinRCA-Bench: Benchmarking Evidence Retrieval and Reasoning for Financial AI Systems
链接: https://arxiv.org/abs/2608.18534
作者: Pratik Ghawate
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 19 pages, 4 figures, 7 tables. Code and data: this https URL
Abstract:Large language models are increasingly used to support financial operations, but their apparent reasoning performance can depend on whether they receive the right evidence. In financial reconciliation, the evidence needed for diagnosis is distributed across invoices, purchase orders, approvals, allocations, payments, ledger entries, and bank activity, linked by transactional relationships rather than textual similarity. End-to-end accuracy can therefore conflate evidence access with reasoning quality. We introduce FinRCA-Bench, a deterministic synthetic benchmark of 2,250 accounts-payable-to-bank reconciliation cases spanning 14 operational tables, including 1,500 injected failures across 15 causal categories and 750 legitimate or hard-negative cases. Root-cause labels and record-level evidence contracts are hidden from the model, allowing retrieval to be evaluated independently of answer correctness. We compare Rules/SQL, classical machine learning, dense semantic retrieval, deterministic relational expansion, and Typed Provenance Graph Retrieval (TPGR), a typed traversal restricted to persisted transaction relationships. Rules/SQL reaches 84.97% held-out exact accuracy and classical ML reaches 95.44%. Holding the reasoning model, prompt, and generation settings fixed while changing only retrieval increases macro required-record recall from 0.83% to 77.70% and exact 16-class accuracy from 2.05% to 72.44%. Structural retrieval failures outnumber reasoning failures with sufficient retrieval by 95 to 15; 254 correct predictions occur despite incomplete retrieval, and strict returned-evidence contract accuracy is only 5.72%. On FinRCA-Bench, retrieval architecture strongly shapes observed AI-system performance, and a correct root-cause label is a weak proxy for an auditable diagnosis.
[IR-11] Reducing Technician Search Burden: A Multimodal RAG for Cessna 172 Maintenance Manual
链接: https://arxiv.org/abs/2608.18465
作者: Seongjun Ha,Md Rashedul Islam,Gaurav Nanda,Damon Lercel
类目: Human-Computer Interaction (cs.HC); Emerging Technologies (cs.ET); Information Retrieval (cs.IR)
备注:
Abstract:Proper use of the aircraft maintenance manual is essential for correct maintenance, providing procedures, diagrams, cautions, and specifications. However, technicians often avoid consulting it because it is difficult to navigate and time-consuming under strict schedules. Retrieval augmented generation (RAG) models have recently been introduced in aircraft maintenance, yet existing models focus solely on textual retrieval. This research therefore targeted the Cessna 172 Maintenance Manual (C172-MM), widely used in general aviation, and developed a multimodal manual retriever (MMR) capable of retrieving multimodal manual pages. Retrieval performance was evaluated using synthetic queries covering procedures, diagrams, caution/safety information, and specifications; the MMR achieved 93.37% recall@5. Beyond retrieval, a multimodal RAG (MRAG) pipeline was examined, in which retrieved pages were input to a vision-language model that generated responses to the synthetic queries, achieving 87.20% semantic similarity to ground-truth answers. Three practical feasibilities were also assessed: inference time, operational cost, and interpretability. Average retrieval time for five pages was 11.93 seconds and response generation took 4.95 seconds, at 0.0091 per query, while interpretability was validated through heatmap visualizations. These results indicate that the MRAG pipeline for the C172-MM can reduce the time technicians spend searching manuals and retrieving multimodal information.
[IR-12] More Context Same Budget: Dual-Bounded Relational Recall Beyond Top-K Retrieval
链接: https://arxiv.org/abs/2608.18448
作者: Thomson D. Nguy
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: 20 pages, 4 figures. Complete supporting-evidence recovery under a frozen HotpotQA FullWiki retrieval design; not answer accuracy
Abstract:More context does not require a larger retrieval budget. Under the same ceiling, a retrieval system can recover more of the evidence a question requires by following relationships between evidence that flat top-k ranking leaves behind. We test that proposition with Dual-Bounded Relational Recall (DBRR), which allocates a fixed retrieval budget between relevance-selected seeds and bounded graph-adjacent context, against matched flat top-k retrieval using the same relevance-ranking stage and the same maximum number of retrieval units and tokens. The outcome is complete recovery of the official HotpotQA supporting-evidence set for each question. Across 7,405 FullWiki questions, the Primary DBRR allocation increased complete supporting-evidence recovery by 23.8 percentage points over its matched flat baseline (paired risk difference 0.2377; question-level bootstrap 95% interval 0.2269 to 0.2489). It improved 1,952 questions, tied on 5,261, and harmed 192. Bridge questions drove the effect, with a 28.7-point increase; comparison questions showed a smaller 4.2-point difference. In a prespecified, evaluation-only diagnostic population, real relationships also outperformed random-neighbor and degree-preserving shuffled-graph controls. The result is straightforward: under the same context budget, complete-evidence retrieval depends not only on which items rank highest, but on how context is allocated around them. Relational allocation recovered complete evidence sets that flat top-k retrieval left incomplete. Comments: 20 pages, 4 figures. Complete supporting-evidence recovery under a frozen HotpotQA FullWiki retrieval design; not answer accuracy Subjects: Information Retrieval (cs.IR); Computation and Language (cs.CL) Cite as: arXiv:2608.18448 [cs.IR] (or arXiv:2608.18448v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2608.18448 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-13] AI in Search Reduces Publisher Referrals Without Improving User Experience: Experimental Evidence
链接: https://arxiv.org/abs/2608.18352
作者: Stephanie T. Wang,Jeffrey Gleason,Yakov Bart,Christo Wilson,Danaé Metaxa
类目: Information Retrieval (cs.IR); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注:
Abstract:The integration of generative AI into web search delivers synthesized answers to user queries, changing how people navigate and assess information, while raising concerns about the downstream impacts on publishers who supply the underlying content. We conduct a preregistered field experiment (N=1,100) on Google Search, the dominant online search platform, to estimate the causal effects of AI Overviews and AI Mode on user behavior, perceptions, and publisher traffic. We show that removing AI Overviews and AI Mode increases click-through rates to publishers, while an AI Mode-only experience reduces click-through rates and erodes user experience and trust in information found on Google. These findings show that integrating generative AI into web search reshapes online attention, with economic consequences for the online publishers that sustain both search platforms and the overall information ecosystem.
[IR-14] Multimedia Asset Personalization via Multimodal Embeddings at Netflix
链接: https://arxiv.org/abs/2608.18322
作者: Emma Yanyang Kong,Aditya Deshpande,Bowei Yan,Asad Abbasi,Santiago Castro,Avneesh Saluja,David Fagnan,Ashish Rastogi
类目: Information Retrieval (cs.IR)
备注:
Abstract:Personalized promotional assets, namely artwork images and video preview clips, are critical to content discovery on Netflix. Traditional models for asset selection rely on ID-based interaction history, leaving them blind to asset content and unable to serve newly launched titles and assets. We describe how multimodal embeddings reshaped production systems at Netflix and report transferable lessons for practitioners adopting foundation-model embeddings into recommender systems. First, pretrained image embeddings unlock cross-title, cross-canvas knowledge transfer. Augmenting a two-tower model with CLIP image embeddings lets a single model serve all five Netflix artwork canvas types, replacing five separately trained per-canvas models and substantially improving cold-start performance. A lightweight extension reuses CLIP’s joint text-image space to make artwork personalization query-aware in search. Second, multimodality decisively beats any single modality for video preview personalization. We describe MediaFM, our in-house tri-modal foundation model trained on a large-scale corpus of shots from the Netflix show catalog, fusing visual (SeqCLIP), audio (wav2vec 2.0), and timed-text signals; adopted for video preview personalization, it outperforms strong visual-only baselines both offline and in online A/B tests. Third, a simple offline proxy task whose performance correlates with online outcomes can accelerate the experimentation and productization cycle. Predicting the popularity-based winner from embeddings alone ranks embedding models and versions, pruning the choice space before any end-to-end integration or A/B test; it now gates every new MediaFM checkpoint. We also share the production engineering decisions (shared embedding infrastructure, low-latency serving, cheap screening) that made these deployments viable, along with the design tradeoffs and failure modes we encountered.
[IR-15] Evaluating Structured Information Extraction with Open Models in a High Risk Public Sector Application ECML2026
链接: https://arxiv.org/abs/2608.18289
作者: Elias Schubert,Felix Bießmann
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: Accepted at Workshop on Systems Over Models: What Actually Works in Industry (SOMI-2026) at ECML 2026
Abstract:The extraction of structured information from unstructured documents represents a critical component of digital transformations in all sectors. While proprietary solutions dominate commercial applications, a rapidly growing ecosystem of open-source Optical Character Recognition (OCR) engines, Large Language Models (LLMs), and Vision-Language Models (VLMs) offers accessible alternatives. However, systematic evaluations on realistic, multi-step extraction pipelines remain scarce. Responsible usage of such extraction tools require comprehensive evaluations on realistic tasks, especially as these solutions will be key components of applications in the public sector that the EU AI act categorizes as high risk. To address this gap we present a comprehensive benchmark assessing the end-to-end performance of open-source systems on a complex real-world document processing task classified as high risk: Student applications for an international study program. We conduct a comprehensive empirical evaluation with state-of-the-art OCR engines, LLMs and VLMs. Our results reveal that while VLMs generally outperform OCR+LLM pipelines, even state-of-the-art open-source models struggle to handle such tasks reliably in zero-shot settings. Only 4 of 35 configurations achieved F1 scores above 0.5, with the best OCR+LLM pipeline matching top VLM performance, though most OCR+LLM combinations performed substantially worse. Roughly 75% of all configurations scored below 0.25. Model scale influences performance, yet the relationship is non-linear: substantially larger models do not guarantee proportionally better results. Input quality, particularly the structural preservation of OCR output, emerges as a critical factor independent of downstream model capability.
[IR-16] Global Crises and National Policies: A Large Scale Analysis of Political Content in German Language Online Media ECML-PKDD2026
链接: https://arxiv.org/abs/2608.18268
作者: Yara Döring,Felix Bießmann
类目: Information Retrieval (cs.IR); Computers and Society (cs.CY); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
备注: Accepted at 2nd International Workshop on Sustainable Artificial Intelligence for addressing Online Information Disorder (SAI4OID) at ECML-PKDD 2026
Abstract:Today most media content is consumed based on algorithmic recommendations. Evidence suggests that this can lead to politically biased media consumption patterns. Automated extraction of political agendas from texts can reveal and analyze political biases in online media – and thus help fostering politically unbiased media consumption. Here we employ modern political text analysis methods demonstrating the potential of automated fine-grained political bias analysis in online media. We conduct an analysis of political content in German language online media during the period 2019–2022, encompassing several million articles and tweets covering events with profound societal impact globally and nationally, the COVID-19 pandemic and the beginning of the war in Ukraine. Our analysis identifies thematic similarity between national (German and Swiss) reporting, particularly for categories driven by international events. We also find divergences emerging in domestically influenced categories, reflecting differences in national policies and institutional structures. A comparison of newspaper and Twitter discourse reveals that both media converge around a shared core during the pandemic, yet differ in intensity and temporal dynamics. Newspapers exhibit more stable political content, while Twitter reacts through short-lived event-driven spikes. These findings indicate that international crises act as a powerful synchronizing force on political content in classical media, temporarily overriding both national and media-form differences. Our automated political analysis empowers citizens by rendering political agendas in online media transparent. This transparency also enables media outlets to bridge the gap between algorithm-driven echo chambers and a more informed, balanced public discourse.
[IR-17] Redakto - The Incognito Tab for LLM s ECML-PKDD
链接: https://arxiv.org/abs/2608.18260
作者: Saurav Kumar Saha,Tom Röhr,Felix Bießmann
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: Accepted at WIPE-OUT 2026, 2nd Workshop on Machine Unlearning and Privacy Preservation at ECML-PKDD
Abstract:Large Language Models (LLMs) are being increasingly used in everyday applications. A major challenge in the context of LLMs or Artificial Intelligence (AI) in general is to ensure privacy when using them, meaning that personally identifiable information (PII) is removed from any text that enters an LLM. These challenges have become more urgent with novel EU legislation. Uncertainty around LLM usage with respect to privacy concerns in EU countries can be a major blocker for the speed of innovation and transfer from research to applications. Here we present \textbfRedakto, a tool that can be used for anonymizing text prior to feeding it to an LLM or other downstream text processing. We provide state-of-the-art functionalities for both redaction of PII but also when used for pseudonymization. These functionalities are exposed such that they can easily be used by end-users, through the Redakto web application, and by developers and researchers, via REST APIs and model context protocol (MCP) hooks. The implementation is fully open source, requires modest compute resources, and can be readily deployed on local hardware. In contrast to prior work and in order to better assess the quality of the anonymized texts, we conduct extensive empirical evaluations on textual data from legal and medical domain with respect to both privacy and utility of the redacted texts. Our empirical results demonstrate that the texts anonymized with different redaction strategies achieve utility scores on par with the original texts, suggesting that anonymization with Redakto can be used for LLM tasks without substantial negative impact for the tasks we explored.
[IR-18] Can David Beat Goliath? On Multi-Hop Reasoning with Resource-Constrained Agents
链接: https://arxiv.org/abs/2601.21699
作者: Hojae Han,Heeyun Jung,Jongyoon Kim,Seung-won Hwang
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: Preprint
Abstract:Multi-turn reasoning agents solve complex questions by decomposing them into intermediate retrieval or tool-use steps, for accumulating supporting evidence across turns. Meanwhile, with reinforcement learning (RL), training these agents rely on many on-policy rollouts and large training batches. Under realistic resource constraints that make dense exploration infeasible, each RL batch contains only few useful reasoning paths from the current policy. Existing approaches do not fully address this bottleneck: SFT-based initialization can overfit when annotated trajectories are scarce, retrieval-level rewards can assign credit to individual retrieved documents without directly optimizing coverage of the full evidence set, and expansion can waste rollouts from poorly chosen prefixes. We introduce David-GRPO, which improves small-batch learning by using information from both outside and inside the current policy: (i) expert bootstrapping injects a few off-policy expert trajectories into RL updates, and (ii) evidence-guided exploration turns on-policy partial successes into evidence-coverage scores and additional continuations. On agents up to 1.5B parameters trained on four RTX 3090 GPUs, David-GRPO improves over prior RL baselines under the same low-budget setting on six multi-hop QA benchmarks. The gains come with a behavioral shift: unlike prior low-budget RL baselines that often skip retrieval or stop after shallow search, David-GRPO learns to increase retrieval depth and evidence coverage.
人机交互
[HC-0] LearnAI: Just-in-Time AI Co-Creation Across Disciplines at a University
链接: https://arxiv.org/abs/2608.19164
作者: Weihao Qu,Ling Zheng,Chris Buzaid,Daniel Crawford
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注:
Abstract:As generative AI reshapes professional and educational practice, institutions face a challenge: how to support diverse learners, from non-coders to advanced students, in building confidence and practice with AI-supported problem solving. Most institutional responses bifurcate into conceptual workshops for general audiences or technical courses for computer science majors, leaving few spaces where mixed-ability learners can engage common AI tasks at levels matched to their prior experience. This experience report presents the LearnAI Framework, a two-layer model for just-in-time AI co-creation piloted at a comprehensive teaching university. The Wide-Exposure Layer embeds short presentations in existing courses to build AI awareness at scale, reaching students and faculty across 18 courses in five disciplines. The Customized Co-Creation Layer provides opt-in, one-on-one sessions where clients work with trained undergraduate tutors through a 5-Stage Pedagogical Script: Problem Framing, Tool-Task Mapping, Iterative Co-Prompting, Deployment and Verification, and Ethical Reflection. Over two semesters, 35 clients co-created 36 portfolio websites and over 20 deployed web applications. Interviews with five clients and two tutors suggest a recurring change in how clients described AI use, shifting from treating AI as a passive answer machine to engaging it as a collaborative tool under human direction. A small paired pre/post AI readiness dataset (N = 7) provides preliminary descriptive context, and tutor accounts document how the pedagogical script was enacted and adapted across client types. We report on boundary cases including clients who felt overwhelmed and respondents who deliberately rejected AI use. This paper contributes a practical, adoptable framework with initial evidence from a single institution. Subjects: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC) Cite as: arXiv:2608.19164 [cs.CY] (or arXiv:2608.19164v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2608.19164 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1145/3795867.3831010 Focus to learn more DOI(s) linking to related resources
[HC-1] rade-offs in Data Color Palette Design Tools IEEE-VIS2026
链接: https://arxiv.org/abs/2608.19148
作者: Shiyi He,Andrew M McNutt
类目: Human-Computer Interaction (cs.HC)
备注: 2 pages, IEEE VIS 2026 POSTER
Abstract:Designing a color palette for data requires designers to balance multiple constraints, including accessibility and aesthetics. Color palette tools support this process through features including direct manipulation, automated palette generation and evaluation, previews, and so on. Despite their prominence, relatively little is known about how these different mechanisms shape design across contexts. We conducted an exploratory think-aloud crowd work study with 40 self-identified designers. Each participant used one of four palette tools selected to span different interaction modalities to complete a series of accessibility- and aesthetics-oriented design tasks. We observed two preliminary patterns. First, tool differences were more pronounced in accessibility-constrained tasks. Second, even when accessibility was not explicitly required, some tools produced more accessibility-friendly palettes and prompted more accessibility-oriented thinking. In this tool genre, then, system design shapes outcomes both via built-in functionality, as well as by directing designers’ attention toward particular constraints and design considerations.
[HC-2] When Readability and Source Retention Diverge: An Evaluability Gap in AI Translation
链接: https://arxiv.org/abs/2608.19083
作者: Chenchen Mao,Hanjing Shi,Haiyan Jia,Emily Wegrzyn,Dominic DiFranzo
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL)
备注:
Abstract:Readable AI output can leave an evaluability gap: even when the source is shown, an overall-quality judgment may not reflect what an output preserves. We investigated how source-text condition and output rendering relate to perceived translation quality, and how output and system appraisals relate to trust and stated disclosure willingness in a plain-text interface. A focal 2 * 2 comparison (N=306) using TransLingo examined simple generated narratives and complex literary-philosophical prose alongside LLM-generated readability-oriented outputs and researcher-revised fidelity-oriented outputs. A descriptive stimulus audit indicated greater source retention in fidelity-oriented outputs in both source-text conditions. Factorial analyses showed a significant rendering-by-source-text-condition interaction in perceived quality. Participants rated fidelity-oriented outputs higher than readability-oriented outputs for the simple narratives, whereas no reliable rendering difference emerged for the complex prose. A corresponding source-condition-dependent pattern was observed for perceived intelligence, agency-oriented anthropomorphic attribution, and task-performance trust. A separate theory-ordered appraisal-structure SEM characterized concurrent associations among perceived quality, perceived intelligence, agency-oriented anthropomorphic attribution, task-performance trust, and stated disclosure willingness across six domains, with task-performance trust as the proximal correlate of stated willingness. The observed rating pattern distinguishes source access from source evaluability: for the complex stimuli, displaying the source did not ensure that one overall-quality rating reflected differences in retained content. It also separates support for evaluating translation output from data-handling support for decisions about what personal text to entrust to a system.
[HC-3] ractorBeam: Personalized AI Sensemaking Support via Collaborative Machine Annotation
链接: https://arxiv.org/abs/2608.18994
作者: Sireesh Gururaja,Jordan Taylor,Emma Strubell
类目: Human-Computer Interaction (cs.HC)
备注: UIST Poster Extended Abstract
Abstract:Language model-based systems which allow asking questions of documents have become popular tools for sensemaking. Despite their implied capability, these systems still suffer from issues of factuality and provenance, while encouraging confirmatory, rather than exploratory, research. We present TractorBeam, a browser extension-based mixed-initiative system that uses collaborative annotation as an interface metaphor for sensemaking, re-framing language model (LM) outputs as suggested highlights in a process that we call \textitcollaborative machine annotation. This metaphor allows us to present LM results in-context on PDF documents, directly addressing concerns of provenance and factuality, while allowing users to iteratively construct mental schemas and queries for language models directly in the context of a document. In a preliminary user study, all of our participants felt that TractorBeam enabled them evaluate and iteratively improve the model’s reflection of their intended highlighting, and several found suggestions that made them reconsider their original schema. TractorBeam suggests that systems that facilitate exploratory research on individual documents may lead to verifiable sensemaking for users and complement tools that work across broader corpora.
[HC-4] A revised framework for the assessment of psychological safety in autonomous vehicles
链接: https://arxiv.org/abs/2608.18801
作者: Yandika Sirgabsou,Benjamin Hardin,François Leblanc,Efi Raili,David Jackson,Pericle Salvini,Lars Kunze,Marina Jirotka
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Despite recent technological progress in the development of autonomous vehicles (AVs), their societal acceptability remains a subject of debate as recent research findings point to psychological roadblocks. Concerns arise not only for physical safety but also for potential psychological risks resulting from human interaction with AVs. Psychological concepts such as trust, and perceived safety are well-studied in this context and are found to be determinant factors for the intention to use AVs. Unfortunately, there has been no formalization of the mechanism by which human interaction with AVs may lead to psychological hazards, threatening trust, perceived safety, and acceptability. Furthermore, there has been little prior research that conceptualizes the severity of psychological risk in AVs, and there are no clear guidelines for a systems designer on how to assess and address psychological risk in the AV development context. To address these limitations, this paper extends a theoretical framework for AV psychological safety risk assessment based on an early proposal. The proposed framework consists of an extended risk model for psychological safety including all the key concepts related to psychological safety in AVs, and an assessment method based on the Systems-Theoretic Accident Model and Processes (STAMP). We demonstrate the usefulness of the theoretical framework through a highly automated AV use case scenario, uncovering factors which may lead to psychological risk for an occupant. The use cases provide examples of how to use the framework to extensively evaluate psychological risk and determine vehicle behaviour that could lead to this risk. By developing a theoretical framework for AV psychological safety risk assessment, we provide a foundation and a method that were previously lacking to better enable responsible AV development regarding psychological safety.
[HC-5] Scoring and Gamification to Encourag e Sustainable Use of Compute Clusters
链接: https://arxiv.org/abs/2608.18786
作者: Maximilian MacDonald,Chris McCaig,Sean MacAvaney,Matthew Barr,Lauritz Thamsen
类目: Human-Computer Interaction (cs.HC)
备注: 5 pages, 1 figures. Accepted at the 2nd International Workshop on Low Carbon Computing (LOCO 2026), Lancaster University, United Kingdom, 10-11 September 2026. Part of the LOCO 2026 proceedings, arXiv:LOCO2026/P01
Abstract:The environmental cost of computing continues to grow, yet behaviour change remains limited. We present a composite sustainability score integrating average carbon intensity, resource utilisation, and embodied emissions into a single 0-100 metric designed for gamified feedback. Each component rewards a different dimension of sustainable behaviour: carbon-aware workload shifting, high resource utilisation, and selecting hardware that is commonly underutilised. This scoring system is built into an existing cluster management interface and underpins three dashboard conditions: raw metrics, composite score, and a gamified tree visualisation, which we are planning to evaluate in a 12-week within-subjects study with approximately 35 researchers. Furthermore, we open the discussion on the challenge of defining computational work `goodness’ in the context of sustainability scores.
[HC-6] Engineering Psychological Safety in Autonomous Vehicles: A Systems-Theoretic Framework for Psychological Safety in Autonomous Vehicles and its Validation in Real-World Scenarios
链接: https://arxiv.org/abs/2608.18778
作者: Yandika Sirgabsou,Benjamin Hardin,François Leblanc,Efi Raili,David Jackson,Pericle Salvini,Lars Kunze,Marina Jirotka
类目: Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注:
Abstract:Despite rapid technological advances, the societal acceptability of autonomous vehicles (AVs) remains limited by psychological barriers that extend beyond traditional concerns of physical safety. While factors such as trust and perceived safety are known to influence user acceptance, there is a lack of formalized mechanisms and engineering methods to systematically identify, assess, and mitigate psychological risks arising from human-AV interactions. To address this gap, this work proposes and validates a systems-theoretic framework for the assessment of psychological safety in autonomous vehicles. First, a comprehensive psychological safety risk model is defined, extending the Systems-Theoretic Accident Model and Processes (STAMP) to incorporate key psychological constructs such as trust, perceived control, predictability, and perceived support. Based on this model, a hazard analysis method (AV-PsySafe) is developed to systematically identify psychological hazards, unsafe control actions, and loss scenarios, while introducing a Psychological Safety Integrity Level (PsySIL) to support risk prioritization. Second, the applicability and relevance of the framework are evaluated through its deployment in realistic autonomous vehicle scenarios. A structured validation approach is implemented, including a methodological guide, standardized analysis templates, and the collection of analyst feedback. The results demonstrate that the framework can be consistently applied by practitioners, producing meaningful insights into psychological risks. Overall, this work establishes both the theoretical foundations and practical feasibility of a unified approach to co-assessing psychological and physical safety in autonomous systems, contributing to more human-centred and trustworthy AV development.
[HC-7] Model Literacy: An Extra Summative Evaluation Factor for Visual Analytics
链接: https://arxiv.org/abs/2608.18721
作者: Lei Xia,Siyu Wu,Haodian Li,Ye Sun,Liang Zhou,Lei Shi
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Understanding and enhancing visual analytics (VA) performance is important for maximizing their impact. Existing studies have successfully applied well-established summative evaluation methods from information visualization to the VA context, yet the recent emphasis on an extra data analysis/modeling stage in the VA pipeline poses an additional challenge. Inspired by the modern concept of visualization literacy, this paper examines model literacy, namely users’ knowledge of the analysis model used in a VA technique, as an additional factor for VA performance. Results from a controlled study on the visual analysis of multidimensional data with two dimensionality-reduction models indicate a positive correlation between model-task accuracy and VA-task accuracy. The study involves two common dimensionality-reduction models, PCA and t-SNE. The correlation is stronger for PCA than for t-SNE in the current task design, a pattern consistent with the possibility that VA effectiveness is more closely associated with model literacy when model outputs are less directly readable from the visualization. Completion-time evidence does not show a stable efficiency gain, suggesting that differences in model intuitiveness may help explain when model knowledge shortens task completion and when it involves additional interpretive effort. The findings of this study suggest ways to further enrich VA evaluation methods and provide directions for developing more rigorous model-literacy assessment instruments.
[HC-8] Sounds Uncertain: Exploring the Affective Aspects of Sonification for Uncertainty Visualization
链接: https://arxiv.org/abs/2608.18680
作者: Marcel-Simon Dutt,Sita A. Vriend,Elias Elmquist,Daniel Weiskopf
类目: Human-Computer Interaction (cs.HC); Sound (cs.SD)
备注: Accepted to IEEE Workshop on Uncertainty Visualization
Abstract:Affective visualization can influence how users perceive, interpret, and engage with data by embedding and conveying emotion through visual design. While sound is widely used in media to evoke emotions, little is known about how sonification can support affective visualization. In this work, we investigate how sonification can communicate emotion in uncertainty visualizations through a co-design study. Participants created two sonifications to accompany a visualization: one conveying the affective component of uncertainty and one conveying neutrality. Our findings show that uncertainty was commonly associated with wavy auditory qualities related to an ominous sentiment. On the other hand, neutrality was associated with clear and relaxing auditory qualities. These results provide insights for the design of visualizations that integrate sonification to communicate the affective component of uncertainty.
[HC-9] Report on The 1st Workshop on Human-Centered Proactive and Personalized Agents for Interactive Information Access at CHIIR 2026
链接: https://arxiv.org/abs/2608.18638
作者: Kirandeep Kaur,Vinayak Gupta,Tanya Roosta,Madhura Raju,Grace Hui Yang,Chirag Shah
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Interactive information access is increasingly moving beyond reactive query-response paradigms toward agentic systems that can personalize interaction, retain context, infer latent needs, recommend next steps, and initiate support. This shift creates new opportunities for adaptive and context-aware assistance, while also raising important questions about autonomy, privacy, trust, transparency, user welfare, and evaluation. The First Workshop on Human-Centered Proactive and Personalized Agents for Interactive Information Access provided an interdisciplinary forum for examining these questions across information retrieval, human-computer interaction, dialogue systems, AI ethics, cognitive science, learning technologies, and human-centered AI. Through invited talks, paper presentations, and open discussion, the workshop engaged with topics including calibrated initiative, knowledge-gap navigation, long-term memory, value-sensitive design, implicit personalization, AI-mediated care, proactive dialogue, and evaluation beyond task accuracy. A central theme across the workshop was that proactivity should not be understood only as earlier action or improved prediction, but as a form of initiative that must be appropriately timed, transparent, contestable, and aligned with user goals. This report summarizes the workshop and synthesizes the research challenges it surfaced for designing proactive and personalized agents in interactive information access.
[HC-10] SemanticSlider3D: Training-Free Continuous Semantic Editing for 3D Objects
链接: https://arxiv.org/abs/2608.18560
作者: Ru Wang,Rahul Jain,Koichiro Niinuma,Aakar Gupta
类目: Human-Computer Interaction (cs.HC); Computer Vision and Pattern Recognition (cs.CV)
备注: UIST 2026
Abstract:Fine-grained control over continuous semantic attributes of 3D objects is essential for 3D content creation, but is not well supported by conventional 3D modeling workflows or prompt-based interaction with existing generative AI tools. While slider-based methods have proven effective for fine-grained semantic control in 2D image generation, no equivalent approach exists for 3D. Extending these 2D methods to 3D is non-trivial due to challenges unique to 3D, including geometric integrity and cross-view coherence. We present SemanticSlider3D, a technique for continuous semantic attribute editing of 3D objects that requires no per-attribute training. Given a user-specified attribute, our pipeline constructs a semantic editing direction in the latent space of a state-of-the-art 3D generation model, presenting a diverse and coherent spectrum of 3D variations. A technical validation on a dataset of 50 3D object-attribute pairs shows our method was preferred by all five human assessors across variation range, consistency, 3D object quality, and attribute disentanglement, over a baseline combining a 2D slider with an image-to-3D model. An exploratory study with six participants demonstrates that SemanticSlider3D supported decision-making in 3D prototyping and was perceived as a valuable addition to existing workflows.
[HC-11] Measuring Proof Burden in Public Bounty Listings: A RentAHuman Case Study
链接: https://arxiv.org/abs/2608.18547
作者: Iman YeckehZaare(MIT Center for Collective Intelligence, Honor Education)
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: Accepted at ACM HCOMP 2026 (2026 ACM Conference on Human-AI Complementarity and Alignment), September 27-30, 2026, Alexandria, VA, USA. 10 pages, 4 figures, 3 tables. Camera-ready version; published version DOI: https://doi.org/10.1145/3834580.3838744
Abstract:Online bounty markets let requesters advertise paid tasks. Workers may be asked not just to complete a task but to prove it, and proof can mean exposure: revealing identity or location, using a personal account, posting publicly, acting in the physical world, or repeated evidence at later checks, none disclosed by the posted price. We call these advertised requirements proof burden and measure them on RentAHuman, a 2026 market publicized as a place for AI agents to hire humans. We study what listings request, not what workers submit or experience. We manually audited a nonrandom May 31, 2026 snapshot: every listing our searches returned from RentAHuman and Human Pages, another such market (981 listings, all but one from RentAHuman). Two independent coders recorded 13 features (11 kinds of evidence, recurring monitoring, physical-world action) and our 0-5 Proof Burden Score; a blinded third resolved all disagreements. A planned content screen leaves 779 bounty/task listings as the primary population; 438 (56.2%) score 4 or 5, spanning 154 distinct feature combinations: a checklist, not a single score, tells workers what a listing entails. Platform metadata labels some requester accounts as agents or bots. Exploratory comparisons show physical-world action, location proof, or recurring monitoring in 75.0% of agent-or-bot-labeled versus 55.3% of human-labeled listings, though score-4-or-5 shares did not clearly differ. The labels are self-reported or platform-assigned, the agent-or-bot-labeled listings come from only 20 displayed names, and the comparison was chosen post hoc, after seeing the data: a hypothesis, not a confirmed difference. We contribute the 13-requirement vocabulary, the adjudicated manual audit, and this descriptive case study; the score is a secondary screening summary. The study offers no worker-validated measure or automated detector yet. Comments: Accepted at ACM HCOMP 2026 (2026 ACM Conference on Human-AI Complementarity and Alignment), September 27-30, 2026, Alexandria, VA, USA. 10 pages, 4 figures, 3 tables. Camera-ready version; published version DOI: https://doi.org/10.1145/3834580.3838744 Subjects: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY) Cite as: arXiv:2608.18547 [cs.HC] (or arXiv:2608.18547v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2608.18547 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1145/3834580.3838744 Focus to learn more DOI(s) linking to related resources
[HC-12] Zero-Shot SAM2 Segmentation and Vision Transformer-Based Recognition of Elamite Cuneiform Symbols from Degraded Tablet Images
链接: https://arxiv.org/abs/2608.18544
作者: Utsav Poudel,Rasik Bhattarai,Siddhartha Pathak,Raghavendra Ramacharna,Gaurav Jaswal
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注:
Abstract:Automated recognition of ancient cuneiform script poses a compound signal-degradation problem: the three-dimensional relief of clay tablets creates spatially varying illumination and cast shadows, surface erosion introduces structured noise that overlaps with genuine sign impressions, and severe class imbalance across 141 sign categories undermines classifier reliability. We introduce EpigraphNet, a segmentation-guided transformer pipeline evaluated on the Persepolis Fortification Archive. From 1,239 annotated tablet images, brightness-adaptive morphological preprocessing and zero-shot SAM2-Large segmentation generate clean binary symbol masks, which a fine-tuned Vision Transformer (ViT-B/16) with inverse-frequency class weighting then classifies. EpigraphNet reaches 86.41% top-1 accuracy on a 132-class benchmark, a 17.21 percentage-point gain over the strongest CNN baseline (ResNet-101, 69.20%) and 5.31-12.91% over four modern backbones (DeiT-B/16, Swin-B, ConvNeXt-B, EfficientNet-B4) under identical conditions. The full pipeline runs at approximately 18 ms per sign on an NVIDIA A100 GPU. A lower Spearman correlation between sign frequency and per-class performance indicates more balanced recognition across frequent and rare classes. Implementation is available at: this http URL
[HC-13] Designing Social Robots for Social-Cognition Training with Autistic Adults
链接: https://arxiv.org/abs/2608.18488
作者: Yuval Zohar,Mordi Benhamou,Guy Laban
类目: Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: Accepted for publication at the 35th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2026)
Abstract:Social robots have been widely explored as tools for autism intervention, yet this literature has focused predominantly on children and has rarely involved autistic adults as active contributors to design. This creates a mismatch between existing systems and the social-cognitive challenges autistic adults actually face in everyday life, including navigating ambiguous interpersonal contexts, managing conversational timing, and interpreting implied emotional meaning. To address this gap, we conducted an online focus group and co-design session with five autistic adults to explore what a social robot for social-cognition training should do, how it should interact, and under what conditions it would be genuinely useful. The 90-minute session combined open discussion with structured co-design activities on a shared digital whiteboard, and the resulting verbal and visual data were analysed using reflexive thematic analysis. The analysis yielded seven themes that define core design requirements: the robot should function as a scaffold rather than a substitute, prioritise authenticity over comfort, provide personalised and user-controlled feedback, accommodate emotional self-awareness gaps, respect privacy and contextual boundaries, support rehearsal for real-world social situations, and remain configurable in identity, form, and expression. Together, the findings suggest that autistic adults envision the robot not as a companion or live social assistant, but as a private, configurable rehearsal partner designed to support independence over time.
[HC-14] Multimodal Rapport Estimation in Real-World HRI
链接: https://arxiv.org/abs/2608.18401
作者: Akihiro Sakuramoto,Takato Hayashi,Ryo Miyoshi,Yuki Okafuji,Shogo Okada
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL); Robotics (cs.RO)
备注: 9 pages, 4 figures, 3 tables. Accepted at the 28th ACM International Conference on Multimodal Interaction (ICMI 2026)
Abstract:Evaluating interaction quality in real-world HRI is an important challenge. If interaction quality can be estimated reliably, the results can be used to improve dialogue strategies and ultimately enable robots to adapt their behavior autonomously. However, existing automatic evaluation methods have been developed primarily in controlled laboratory settings, and it remains unclear whether they can be directly applied to real-world environments, where users are free to disengage and multi-party participation may arise naturally. In this study, we investigate the automatic estimation of third-party-rated rapport scores using 62 sessions of multimodal recordings collected in a Japanese drugstore. We compare zero-shot LLMs, pretrained text, audio, and visual models, and their prediction-level fusion. The results show that, in real-world HRI, zero-shot LLMs achieve strong performance, while audio and visual models tend to provide complementary information. In particular, Gemini 2.5 Flash performs strongly as a single model, and a fusion model combining Gemini (text) with HuBERT and V-JEPA performs best overall. Further analyses showed that estimation performance varied across interaction-duration and group-size conditions. These findings suggest that rapport estimation in real-world HRI requires evaluation and model design that account for contextual variability beyond that assumed in laboratory settings.
[HC-15] LEDGER: Claim-to-Evidence Trace Graphs for Auditing LLM Agents
链接: https://arxiv.org/abs/2608.18398
作者: Daehong Kim,Haichao Miao,Shusen Liu
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents can now carry out long-horizon technical workflows involving complex tool use, code execution, file edits, and generated artifacts. As agents do more work faster, the productivity bottleneck shifts from producing outputs to auditing whether those outputs are correct and trustworthy. Agent observability systems make fine-grained execution events visible, but visibility alone still leaves reviewers to reconstruct which actions, artifacts, and validation steps matter for a particular conclusion. We introduce LEDGER - Layered Evidence and Decision Graphs for Execution Review, a tracing and review system that builds layered trace graphs over observed agent sessions. LEDGER preserves Trace Records while grouping them into Evidence Nodes and Workflow Nodes, representing artifacts as evidence anchors, and adding typed semantic edges that connect claims to supporting actions, artifacts, and checks. Through data-analysis and coding examples, we show how the resulting traces expose workflow decisions, artifact lineage, repair steps, validation coverage, and claim-support paths for evidence-centered audit.
[HC-16] he Fabricated Front: Generative AI and the Opacity of Workplace Performance
链接: https://arxiv.org/abs/2608.18369
作者: Tom van Nuenen,Pratik S. Sachdeva,Sahiba Chopra
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: 18 pages, 5 figures. Accepted for publication in the Paris Journal of AI and Digital Ethics (PJAIDE). Code and data: this https URL
Abstract:Generative AI (GenAI) has become a fixture of workplace life. Current research asks chiefly what this implies for jobs and outputs, measured in productivity, displacement, or bias. What remains underexamined are the interactional reconfigurations that GenAI produces at work. The emerging concept of effort opacity has begun to fill this gap by highlighting the systematic decoupling of observable output from human engagement. When GenAI makes interactional cues less diagnostic, it weakens the reciprocal exchange that sustains collaborative trust. Extending this account of effort opacity, we examine the interactional mechanics that produce opacity in everyday workplace encounters. Drawing on Erving Goffman’s dramaturgical framework and 1,250 interview transcripts from Anthropic’s AI Interviewer dataset, we identify five opacity mechanisms through which workplace fronts are reorganized: voice (whose stance the words index), provenance (who can stand behind the artifact), vulnerability (whether the worker is uncertain), attention (whether the worker is engaged), and investment (how much labor the output reflects). We show that professionals defend the identity mechanisms while freely producing opacity around the labor mechanisms, and trace this asymmetry to the output-centered organization of contemporary work, where deliverables already stand in for the labor process that produced them. The governance task, accordingly, is one of involvement management: specifying which forms of human involvement (attention, effort, judgment) must remain inspectable, and to whom. Workplace AI policies built on universal disclosure will systematically misrecognize a social field in which inspectability is already audience-relative.
[HC-17] A Task-Agnostic Control Strategy for Dynamic Assistance with Pneumatically Actuated Soft Exosuits
链接: https://arxiv.org/abs/2608.18364
作者: Anoush Sepehri,Zachary Huang,Raymond de Callafon,Michael T. Tolley,Tania K. Morimoto
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注:
Abstract:Pneumatic artificial muscles have provided new opportunities to develop upper-extremity soft exosuits for reha- bilitation, augmentation, and assisted daily living. However, the complex dynamics and limited bandwidth of these actuators has made providing responsive assistance based on user intention a longstanding challenge. In this work, we present an inverse-plant control strategy for pneumatically actuated soft exosuits that only relies on kinematic sensing for task-agnostic and dynamic assistance during daily living. We model the human-robot system using a Hammerstein dynamic model, consisting of a Preisach hysteresis model and a linear time-invariant filter, to capture the static and dynamic behavior of the system. We personalize our model to each user using 140 s of data and approximate an inverse to integrate into our control loop. When evaluated on a test rig that emulated a soft assistive exosuit for the wrist, our controller reduced the interaction torque by up to 73% and the activation of key flexor and extensor muscles by up to 47% relative to the condition with no assistance for speeds ranging from 8°/s to 120°/s. Overall, this work presents a control strategy that can provide task-agnostic, dynamic assistance with pneumatically actuated soft exosuits without the need for physiological or force sensors to interpret user intention.
[HC-18] Artifact-centered Claim-aware Observability for Autonomous Scientific Agents
链接: https://arxiv.org/abs/2608.18312
作者: Xiangyu Yin,Ming Du,Michael H. Prince,Mathew J. Cherukara
类目: Computation and Language (cs.CL); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注:
Abstract:Autonomous scientific agents now increasingly propose ideas, write code, run experiments, analyze results, and even draft papers. Observe and audit those agents are necessary but logging every model call is not enough, scientists also need to inspect the artifacts and claims that the systems produced and their relations. This is driven by the fact that failures in scientific agent systems are often distributed across several objects. A manuscript claim may cite the wrong evidence, a search process may select a degenerate candidate, a laboratory novelty claim may depend on an unstated rule, or a multi-agent plan may change without a visible trigger. Existing tracing, experiment tracking, and archival provenance tools are valuable, but their native objects do not make these scientific audit relations first-class. We argue that autonomous scientific systems should emit portable, claim-aware artifact lineage as a minimum audit layer. We propose a compact observability profile organized around individuals, operators, fitness records, lineage, archives, runs, streams, and steering commands. In this profile, scientific claims are ordinary individuals with explicit evidence bindings and verification records. The profile is intended as a semantic layer that complements current telemetry and provenance standards. Execution details can remain in OpenTelemetry. Final packages can export to PROV-O or RO-Crate standards.
[HC-19] ComponentBench: Diagnosing Component-Level Failures in Computer-Use Agents
链接: https://arxiv.org/abs/2608.18307
作者: Tianchen Guan,Xinlei Lin,Royce Cheng-Yue,Xiangjun Wang,Shuyan Zhou
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: Accepted at COLM 2026. 30 pages (10 pages main text), 10 figures, 15 tables. Website: this https URL Code: this https URL Data: this https URL
Abstract:Current evaluation of computer-use agents is split between long-horizon workflow benchmarks and atomic GUI-grounding tests. This leaves an under-instrumented middle layer: realistic component-centered interactions (e.g., toggle a button set) that are short enough to diagnose and rich enough to capture the burdens of modern interfaces. We present ComponentBench, a benchmark and diagnostic pipeline for component-level evaluation of computer-use agents on modern web UIs. ComponentBench is organized around a library-agnostic ontology of 97 canonical UI components instantiated as 2,910 programmatically verified tasks across widely used component libraries, paired with cleaned human reference trajectories that enable evaluation of both task success and interaction efficiency. Beyond task collection, we introduce a scalable pipeline for auditing realized structural difficulty after implementation and synthesizing structured failure analyses across tasks and component families. Evaluating seven models – GPT-5.4, Gemini 3 Flash, GPT-5.4 mini, GPT-5 mini, Gemini 3.1 Flash-Lite, Qwen3-VL-235B, and UI-TARS-1.5-7B – across four observation and action spaces, we show that these design choices critically impact performance. Within a single shared harness, changing only the observation and action space shifts task success by more than 30% for the same model: GPT-5 mini falls from 83.1% with accessibility-tree observations to 48.9% with coordinate-only Pixel control. Moreover, even the fastest configuration takes 3.7x as long as the matched human reference, and spatial manipulations that are trivial for humans continue to challenge current agents.
[HC-20] Robust and Efficient Feature Extraction for Spike Sorting via the Walsh-Hadamard Transform
链接: https://arxiv.org/abs/2608.19048
作者: Emily Yang,Liyuan Guo,Seyed Mohammad Ali Zeinolabedin,Meng Zhang,Ke Yang,Matthieu Couriol,Christian Mayr,Pierre-Emmanuel Gaillardon
类目: ignal Processing (eess.SP); Human-Computer Interaction (cs.HC)
备注: Accepted for publication at the 2026 IEEE Biomedical Circuits and Systems Conference (BioCAS 2026)
Abstract:Implantable neural interfaces require low-power real-time signal processing to remain within strict thermal and bandwidth constraints, motivating lightweight feature extraction methods for on-chip spike sorting. This work presents the Walsh-Hadamard Transform (WHT) as a hardware-efficient feature extraction method for neural spike classification. WHT can be implemented using only adders, subtractors, and registers without coefficient memory. WHT performance is compared against the Compressed Hadamard Transform (CHT) and Principal Component Analysis (PCA), improving mean F1-scores from 55-60% to 70-75% on difficult high-noise datasets and from 90-95% to 95-99% on all other simulated datasets. In addition to improved classification performance, WHT demonstrates greater robustness to noise, downsampling, reduced training size, and distance metric selection, maintaining standard deviations typically below 5%, while CHT and PCA reach up to 10% under high-noise conditions.
计算机视觉
[CV-0] Image-Guided Pavement Defect Recognition in GPR Data with novel 3D Deep Learning Architecture
链接: https://arxiv.org/abs/2608.19177
作者: Yuandong Pan,Linjun Lu,Mudan Wang,Florian Noichl,Fan Xue,Brian Sheil,Lavindra de Silva,André Borrmann,Ioannis Brilakis
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Ground Penetrating Radar (GPR) is a widely adopted non-destructive sensing technology for subsurface inspection in civil and transportation engineering. Despite its potential for pavement condition assessment, the large-scale application of GPR in automated inspection has two key challenges: the scarcity of annotated real-world datasets and the lack of deep learning models designed for the unique characteristics of 3-Dimensional (3D) GPR data. This study addresses these limitations by firstly introducing a cost-effective data preparation pipeline that integrates orthomosaic Red Green Blue (RGB) imagery with 3D GPR scans to generate annotated 3D GPR datasets. The proposed method uses the aligned segments of RGB and GPR data, using pavement surface images as a reference to transfer labels of surface-visible defects to corresponding GPR segments, enabling efficient large-scale annotation in a real-world dataset collected on a highway section under operation. In addition to the dataset contribution, we propose a specialised 3D Convolutional Neural Network (CNN) architecture incorporating residual connections, mixed convolutional kernel sizes, and both depthwise and channelwise attention mechanisms to enhance feature representation and defect classification. The model is evaluated on binary classification tasks for detecting patch and crack defects in pavement structures. Experimental results demonstrate that the proposed network outperforms baseline architectures across multiple evaluation metrics. Ablation studies further confirm the effectiveness of the designed architectural components. This work contributes a scalable and practical method for real-world dataset generation, along with a novel deep learning framework.
[CV-1] Detecting Backdoors in Object Detection via Pre-NMS Prediction Distribution Shift
链接: https://arxiv.org/abs/2608.19088
作者: Longtian Wang,Zhengyu Zhao,Chenhao Lin,Le Yang,Shiwei Wang,Yuhan Zhi,Xiaofei Xie,Chao Shen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Object detection models deployed in safety-critical applications remain vulnerable to backdoor attacks that cause targeted misbehaviors when a hidden trigger is present. Existing detection methods either rely on trigger inversion or exploit architecture-specific assumptions, and critically, representative existing methods fail to generalize reliably to scene-level attacks, where a single trigger induces anomalous behavior across all objects in the scene simultaneously. We present DistScan, a backdoor detection framework based on a simple but previously unexploited observation: backdoor injection systematically shifts a model’s pre-NMS prediction class distribution away from its training class frequencies, even on clean inputs without any trigger present. DistScan aggregates intermediate class predictions over a clean validation set and flags a model as backdoored if the resulting distribution deviates significantly from the training class frequencies, requiring no model weight access, no trigger knowledge, and no additional training. Extensive experiments on MS-COCO and PASCAL VOC across two architectures and three scene-level attack scenarios demonstrate that DistScan substantially outperforms existing methods, improving average detection accuracy over the best-performing applicable baseline by 27.32 percentage points.
[CV-2] SPK: Eliciting Structured Prior Knowledge for Interpretable Out-of-Distribution Detection in Real-Time Object Detection
链接: https://arxiv.org/abs/2608.19080
作者: Changshun Wu,Weicheng He,Xiaowei Huang,Saddek Bensalem
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Object detectors often produce over-confident predictions for objects outside their training categories, leading to so-called out-of-distribution (OoD) hallucinations. Existing approaches for detecting or mitigating such hallucinations typically either construct scoring functions directly over learned object detector representations or modify the object detector itself to suppress hallucination emergence. However, the latent priors implicitly encoded in these representations remain largely unexplored and have not been explicitly decoded for OoD detection. To uncover and exploit these latent priors, we propose Structured Prior Knowledge (SPK), a hallucination-oriented framework that explicitly elicits OoD-relevant priors from pretrained object detectors. Specifically, SPK leverages in-distribution data and hallucination-inducing samples as diagnostic supervision to elicit part-level semantic concepts underlying object detector decision-making, rather than using them merely for rejection or object detector adaptation. The elicited semantic priors are further integrated with geometric and contextual priors to form a compact five-dimensional SPK representation for OoD detection. Extensive experiments across diverse object detector architectures and multiple OoD benchmarks demonstrate that SPK achieves state-of-the-art OoD detection. Our findings reveal that pretrained object detectors already encode substantially richer latent knowledge than is typically exploited for OoD detection. More importantly, this knowledge can be explicitly elicited and organized into a compact, structured, and interpretable knowledge space for prediction reliability analysis. This suggests a promising proactive route for improving object detector reliability by explicitly uncovering and leveraging latent priors. Code and data are available at: this https URL
[CV-3] Subgroup performance analysis of adaptation strategies for chest X-ray foundation models MICCAI
链接: https://arxiv.org/abs/2608.19078
作者: Dhruv Gupta,Emma A.M. Stanley,Fabio De Sousa Ribeiro,Sujal Desai,Ben Glocker
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at MICCAI Workshop on Fairness of AI in Medical Imaging (FAIMI) 2026
Abstract:Foundation models are increasingly adapted for downstream medical imaging tasks, yet the influence of the chosen adaptation strategy on subgroup fairness remains poorly understood. We investigate how three parameter-efficient adaptation techniques, including linear heads on the raw CLS token, an MLP, and an attention-pooling module over multi-layer patch features, affect both pathology classification performance and subgroup disparities when applied to the frozen Rad-DINO chest X-ray encoder. Using MIMIC-CXR, we evaluate eight pathologies across race, sex, and imaging-view subgroups on a prevalence-preserving, demographically balanced test set, and additionally probe how strongly each adapter encodes protected attributes. We find that attention pooling achieves the strongest overall discriminative performance and encodes attributes, particularly race, most strongly, but that improved overall performance does not consistently reduce subgroup disparities. Notably, stronger attribute encoding did not correspond to larger disparities: early network layers encoded race most weakly yet produced the largest subgroup performance gaps. Exploring different attention-pooling layer combinations further revealed no consistent relationship between the layers pooled, attribute encoding strength, and subgroup fairness. Our results indicate that richer, more expressive representations can improve accuracy while leaving fairness implications task-dependent and unpredictable, which must be assessed directly and per-task rather than inferred from encoding strength or overall performance alone.
[CV-4] GS-VLA: Plug-and-Play Viewpoint Canonicalization for Frozen VLA Policies via Gaussian Splatting
链接: https://arxiv.org/abs/2608.19066
作者: Yechan Park,HyunJin Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper proposes a lightweight, plug-and-play framework that improves robustness to viewpoint shifts in Vision-Language-Action (VLA) policies without policy retraining. To our knowledge, this is the first approach to directly leverage 3D Gaussian-based novel-view synthesis for observation-space adaptation in VLA policies. Current VLA performance relies on the implicit assumption that training and deployment camera configurations are identical. Our experiments show that even a small displacement of the camera mount can reduce the success rate on the LIBERO benchmark from about 90% to about 10% in the worst case. Prior approaches, such as large-scale fine-tuning or generative data augmentation, are computationally expensive and risk catastrophic forgetting. To address this, viewpoint shifts are reformulated as a localized novel-view synthesis problem. Under a Locality assumption, that camera perturbations remain within a small bounded region relative to the workspace, viewpoint normalization reduces to a scene- and policy-independent disocclusion task. Our work implements this idea with a 4M-parameter 3D-Gaussian canonicalizer prepended to a frozen VLA policy. Without modifying policy weights, GS-VLA improves performance across three orthogonal axes: (1) Policy architectures, (2) Unseen task suites, and (3) Perturbation scales. These results show that a lightweight visual module can recover a large fraction of the performance lost under viewpoint shift, without policy retraining.
[CV-5] When Two Tracers Disagree: An Investigation of Multimodal Fusion for Clinical PET/CT Segmentation MICCAI2026
链接: https://arxiv.org/abs/2608.19063
作者: Jack A. Johnson,Bartłomiej W. Papież
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages (8 pages main content and 2 pages of references), 2 figures, 2 tables, accepted to MICCAI 2026 Cancer Prevention, Detection, and IntervenTion (CaPTion) Workshop
Abstract:PSMA and FDG PET/CT visualise complementary biological information in prostate cancer. Combining both tracers could capture heterogeneous tumour phenotypes that may be missed by either alone, yet there is no consensus on effective deep learning architectures for fusing these modalities. We evaluated multimodal image-fusion strategies for automatic whole-body PET/CT lesion segmentation to estimate total tumour burden. Using the public DEEP-PSMA Challenge dataset, we trained tracer-specific 3D nnU-Net baselines and compared (i) early fusion with a single encoder and one decoder (OEOD) or two decoders (OETD), and (ii) intermediate fusion via a dual-encoder cross-attention U-Net (DECA-UNet). Tracer-specific baselines performed strongly (PSMA Dice = 0.93; FDG = 0.81). Fusion yielded mixed results: OEOD produced a combined Dice of 0.90 (on an easier, non-tracer-specific task), whilst the tracer-specific fusion models reached PSMA/FDG = 0.69/0.64 (OETD) and 0.76/0.57 (DECA-UNet). Whilst fusion often provided reasonable PSMA segmentation, FDG performance degraded and no strategy consistently exceeded the single-tracer baselines. Under the evaluated setting, tracer-specific models remain the stronger baseline; clinically useful gains from multimodal fusion will likely require architectures that better preserve tracer specific representations. Our code is available at: this https URL
[CV-6] LT-Mem: Volatility-Aware Spatio-Temporal Memory for Lifelong Scene Understanding IROS2026
链接: https://arxiv.org/abs/2608.19059
作者: Yumin Lee,Hyoseok Ju,Giseop Kim
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 8 figures, 6 tables. Accepted to the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
Abstract:Long-term robot operation in evolving environments requires object-level understanding that persists across repeated revisits. Existing systems either overwrite history to maintain an up-to-date map or store semantic snapshots without consistent cross-session object identity, resulting in temporal amnesia: the systematic loss of object history that prevents answering queries such as “Where has the green chair been across all sessions?” We propose LT-Mem, a volatility-aware memory evolution framework that unifies spatially aligned instance-level 3D perception with volatility-conditioned temporal reasoning. First, a multi-session SLAM backbone provides spatially aligned per-object observations across sessions. Second, a reasoning layer governs how object memory evolves: deterministic evidence scoring preserves cross-session identity, and a volatility-aware policy selects among overwrite, hold, and multi-hypothesis actions based on each object’s dynamics. Third, the resulting Tri-Memory structure (Live, Delta, Meta) preserves both current states and event histories, enabling longitudinal object-centric reasoning. We further introduce LT-VQA, a dataset and evaluation suite comprising multi-session recordings, persistent identity annotations, and temporal QA pairs. Experiments show that LT-Mem consistently outperforms baselines across all metrics while consuming an order of magnitude fewer tokens, and ablations confirm that gains are driven by the structured memory architecture rather than LLM capacity.
[CV-7] Generalized Audio-Driven Synthesis of Precise Drummer Motion SIGGRAPH
链接: https://arxiv.org/abs/2608.19055
作者: Álvaro G. Iñesta,Mattia Ryffel,Amit H. Bermano,Robert W. Sumner,Martin Guay
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Sound (cs.SD)
备注: Best Paper Award at the 25th ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA 2026). For Supplementary Video, see this https URL
Abstract:Music-driven character animation enables and enhances transformative applications in entertainment and interactive education. However, synthesizing realistic drumming motion from audio remains challenging due to the inherent tension between high-acceleration dynamics and the need for extreme spatial-temporal precision. Existing approaches, often reliant on motion matching or MIDI input, struggle with generalizing to diverse real-world audio. Moreover, the field lacks standardized evaluation metrics capable of distinguishing precise drumming from noisy motion. In this paper, we introduce a generative diffusion framework featuring a dual-objective loss function that decouples skeletal integrity from drumstick precision, thus enabling centimeter-level stick precision without sacrificing natural body dynamics. Additionally, leveraging our own dataset and data augmentation strategy, the model generalizes to non-curated, in-the-wild audio. To rigorously evaluate performance, we propose two novel metrics: an impact-to-target distance to quantify spatial precision and an audio-motion correlation score to assess temporal alignment. Our quantitative analysis and user studies demonstrate that our system generates high-quality motion that is often indistinguishable from ground-truth performances.
[CV-8] USR-Drive: Unified Driving Scene Representation via Joint Denoising of 3D Gaussians and Boxes
链接: https://arxiv.org/abs/2608.19036
作者: Li-Heng Chen,Haokai Pang,Chengye Su,Jiarun Liu,Qifeng Chen,Ziqian Ni,Jianxin Huang,Shi-Sheng Huang,Hongbo Fu,Sheng Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Spatial representation learning for autonomous driving aims to map raw visual signals into structured 3D scene representations, where object-centric bounding boxes and rendering-oriented 3D primitives (\eg, 3D Gaussians) serve as two distinct yet highly complementary levels for scene understanding. Existing methods typically treat dynamic reconstruction and instance-level perception as separate tasks, despite their shared goal of estimating the underlying 3D world state. As a result, dynamic reconstruction is under-constrained while 3D detection lacks geometric grounding. To address this gap, we propose USR-Drive, a unified conditional generative framework that, given only posed multi-view driving videos, jointly recovers dense dynamic geometry and instance-level object layouts within a shared scene representation. Specifically, USR-Drive represents dense Gaussian primitives and sparse 3D bounding boxes as two aligned latent token streams and jointly denoises them with a unified multi-modal diffusion Transformer. Unlike prior paradigms that use boxes as external conditions or predict them with detached modules, USR-Drive treats them as mutually constrained state variables with a Unified Positional Encoding (UPE) that aligns heterogeneous tokens within a shared metric spatiotemporal coordinate. Via such unified representation and generative framework, the two modalities reinforce each other: geometry supplies dense metric evidence for box prediction, while boxes provide instance-level structural priors that help preserve spatial consistency and reduce ambiguity in sequential 3D geometric representation. Our approach successfully delivers state-of-the-art results for both dynamic reconstruction and 3D detection on the nuScenes and VKitti datasets.
[CV-9] Counterfactual Contrastive Analysis MICCAI2026
链接: https://arxiv.org/abs/2608.19032
作者: Yunlong He,Pietro Gori
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: MICCAI 2026
Abstract:Visual Counterfactual Explanations (VCEs) aim to explain image classifiers by generating minimally edited and realistic versions of an input image that change the classifier’s prediction. Existing VCE methods are inherently classifier-dependent and therefore susceptible to classifier biases and failure modes, such as sensitivity to shortcut features and calibration errors. In this paper, we propose a classifier-free approach for visual counterfactual generation based on Contrastive Analysis (CA). Given two datasets corresponding to different classes (e.g., healthy and patients), we disentangle the generative factors that are common across the two datasets from those that are salient to each dataset, and generate counterfactual images by swapping only the salient factors. By operating directly on data distributions rather than decision boundaries, our method provides model-agnostic VCEs that are less sensitive to classifier biases. Our approach leverages the high-quality synthesis and well-structured latent space of StyleGAN2. We use the feature space F, instead than the usual W-space, to improve detail preservation. Unlike conventional CA approaches, which typically assume salient factors in only one dataset, we introduce an adapted framework and loss functions for VCE that allow multiple salient factors in each dataset. We evaluate our method on three medical imaging datasets and demonstrate superior counterfactual generation quality compared to existing approaches.
[CV-10] Orthogonal Polynomial Approximation for Matrix Log Normalization in Global Covariance Pooling
链接: https://arxiv.org/abs/2608.19021
作者: Md Rifat Ur Rahman,Md Raihan Khan,Md Sakib Hossain Shovon,Pietro Liò,Mohammad Ali Moni
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Global Covariance Pooling (GCP) improves deep networks by capturing second-order feature statistics, and is especially effective for fine-grained recognition. Because covariance matrices live on the Symmetric Positive Definite (SPD) manifold, a normalization step is required before the Euclidean classifier. The faithful choice is the matrix logarithm (MLN-COV), which maps the SPD manifold to its tangent space; in practice it was abandoned in favour of the matrix square root because its eigendecomposition-based gradient is numerically unstable. We show that this instability is an artifact of computing the logarithm spectrally, not of the logarithm itself. Approximating the logarithm with finite polynomials in the covariance matrix removes the eigendecomposition from both passes: every operation becomes a General Matrix Multiplication (GEMM), the gradient stays bounded on the spectral support of the pre-normalized covariance, and the unstable 1/(lambda_i-lambda_j) term never appears. The key ingredient is a mean-eigenvalue pre-normalization that centres the spectrum near 1, away from the singularity of log, with a scalar post-compensation that returns the singular part of log(A) in closed form. Our recommended normalizer is a degree-8 Chebyshev expansion evaluated by a three-term matrix recurrence, with a matching reverse recurrence for the backward pass; Legendre, Laguerre, Taylor and Pade expansions are studied as controls that isolate the roles of the basis and of the target function. On three fine-grained benchmarks and ImageNet-1k the decomposition-free logarithm is both faster and more accurate than the spectral logarithm and than the square-root approximations it replaces, and at matched basis and degree the log target beats the square-root target, confirming that the gain comes from the faithful Riemannian map rather than from a better polynomial family.
[CV-11] One-Stage Object Detectors in Autonomous Driving
链接: https://arxiv.org/abs/2608.19014
作者: Jonel Roman,Ryan Sirjue,Peter Nguyen,Daniel Krutky,Juan Jesus,Sudip Dhakal
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Autonomous vehicles depend on fast and reliable perception systems to detect surrounding vehicles, pedestrians, cyclists, traffic signs, and other road objects in real time. This paper presents a comprehensive survey and analysis of one-stage object detectors for autonomous driving rather than an implementation of a new detection system. The survey reviews the evolution of major one-stage detectors, including YOLOv1, SSD, RetinaNet, EfficientDet, anchor-free detectors such as FCOS and CenterNet, and recent real-time models such as YOLOv10. It compares these architectures through their design choices, feature-fusion strategies, loss functions, deployment trade-offs, and reported benchmark performance. The paper also summarizes commonly used autonomous-driving datasets, evaluation metrics, open challenges, and future research directions. Overall, this survey highlights how one-stage detectors balance speed, accuracy, efficiency, and robustness, while also emphasizing the remaining gap between benchmark results and dependable real-world autonomous-driving performance.
[CV-12] Autonomous Agricultural Tractor: Integrated Weed Detection and LiDAR Navigation for Precision Paddy Farming
链接: https://arxiv.org/abs/2608.19004
作者: Benjamin Merryman-Smith,Tony Nguyen,Bilal Dogutas,Krish Shah,Anthony Raphael,Sudip Dhakal
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Site-specific weed management in paddy farming offers substantial reductions in herbicide use over conventional broadcast spraying, but field deployment has been limited by three persistent challenges: robust crop-row navigation under canopy where GNSS degrades, real-time visual discrimination between rice and morphologically diverse weeds, and the asymmetric cost of misclassifying rice as weed, which is irreversible. This paper presents AgriNav, an integrated autonomous tractor system built around four ROS-coupled modules: a custom PyTorch reimplementation of WeedDet for rice detection, a parallel lightweight 1.68M-parameter CNN-FPN variant with asymmetric class weighting, an inverted-logic discrimination module that protects the rice class through a hardcoded confidence-gate veto, and a 6-state constant-velocity-turn-rate Extended Kalman Filter fusing GNSS, IMU, and wheel odometry with three-level outage bridging. Our primary system-level contribution is a four-mechanism LiDAR-camera fusion bridge that uses the navigation LiDAR for region-of-interest constraint, world-coordinate projection, ground-plane filtering, and bidirectional confidence fusion at zero additional hardware cost. Simulation experiments demonstrate continuous position tracking through a 20-second GNSS outage, crop row detection confidence above 0.9 throughout operation, and rice-detection confidences from 0.32 to 0.95 across paddy, aerial, and post-flood imagery. The LiDAR ROI constraint reduces detection inference region by an estimated 30 to 50 percent. Subjects: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.19004 [cs.RO] (or arXiv:2608.19004v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2608.19004 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-13] Mise-en-Scène: Implicit Layout Emergence in Diffusion Transformers for Human-AI Design Co-Creation ECCV
链接: https://arxiv.org/abs/2608.19000
作者: Zipeng Xu,Ryan Murdock,Umberto Michieli
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Best Paper Award at ECCV Human-AI Co-Creation Workshop
Abstract:Automating graphic design synthesis from user-provided elements requires both a coherent overall composition and the exact preservation of each asset. Existing methods predict a layout as explicit bounding-box coordinates with a language model and then paste the assets into it, which separates spatial planning from visual synthesis and tends to produce rigid, mis-scaled compositions. We instead ask whether the layout can emerge implicitly inside a pretrained image-editing diffusion transformer. We present Mise-en-Scène, a two-stage framework. In the first stage, a diffusion transformer adapted with a small, knockout-selected LoRA drafts a complete design in which the arrangement of the elements emerges jointly with the rendered canvas. In the second stage, a deterministic match-and-place step moves the original high-resolution assets to the drafted positions, which guarantees exact asset fidelity and yields an editable, layered design that a designer can keep refining rather than a flat image. Notably, a minimal adaptation of the pretrained transformer already suffices, without the specialized conditioning machinery commonly introduced for multi-element generation. On the large-scale PrismLayersPlus benchmark, the designs produced by Mise-en-Scène are the closest to the ground truth in perceived quality among all compared methods, by a wide margin over both an LLM layout planner and a specialized layout transformer, while our match-and-place stage bridges the remaining fidelity gap to the ground-truth composites.
[CV-14] GrabVG: Graph-Attentive Binding for Visual Grounding in UAV Imagery
链接: https://arxiv.org/abs/2608.18996
作者: Chaowei Wang,Yan Di,Jingjun Sun,Baozhe Liu,Jiaxu Tian,Yuheng Li,Guangqian Guo,Shan Gao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Visual grounding in Unmanned Aerial Vehicle (UAV) imagery aims to localize a target object in complex bird’s-eye-view scenes according to a natural language description. However, the abundance of small, densely distributed, and visually similar objects creates high visual redundancy, while repetitive local configurations give rise to strong topological ambiguity. Existing approaches mainly focus on visual–language feature alignment or dense contextual interaction, yet they struggle to distinguish subtle inter-instance differences and effectively exploit spatial topological structures, leading to inaccurate grounding in highly crowded scenarios. To address these challenges, we propose \textbfGrabVG , a novel visual grounding framework inspired by human visual search. GrabVG explicitly decomposes grounding into two sequential stages: \textitpreattentive hypothesis search and \textitgraph-attentive feature binding . Specifically, we first generate a compact set of reliable object hypotheses through distillation-guided proposal induction and text-aware hypothesis filtering, substantially reducing background distractions and semantic mismatches. These hypotheses are then organized into a sparse graph, where language-guided intra-instance visual cues and inter-instance topological relationships are jointly bound and propagated via graph attention, enabling efficient spatial reasoning and accurate target localization. Extensive experiments on AerialVG and AerialSense show that GrabVG achieves a favorable accuracy–speed trade-off, reaching 67.31 % and 80.34 % Acc@0.5 and outperforming the corresponding baselines by 10.55 and 8.76 percentage points, respectively.
[CV-15] ForeSightGuide: An Anticipatory Framework toward Accurate and Low-Redundancy Guidance for the Visually Impaired
链接: https://arxiv.org/abs/2608.18993
作者: Zhiyuan Wang,Xu Li,Shikang Guo,Wei Meng,Quan Liu,Jie Zuo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Electronic travel aids are pivotal for the independent mobility of the visually impaired. While Vision-Language Models (VLMs) offer rich environmental understanding, they often suffer from excessive false positives in dynamic scenarios, leading to cognitive overload. To address this, we present ForeSightGuide, an anticipatory assistive guidance framework that couples semantic scene understanding with predictive hazard assessment. Unlike reactive systems, ForeSightGuide leverages the reasoning capabilities of VLMs to anticipate obstacle motion, effectively filtering out non-threatening objects to provide concise, actionable guidance. To validate our approach, we introduce a novel dataset captured in complex, dynamic real-world traffic scenes, designed to benchmark predictive capabilities. Extensive experiments on both public benchmarks and our proposed dataset demonstrate that ForeSightGuide achieves state-of-the-art performance. Notably, it significantly mitigates information overload by reducing redundant alerts to 0.299 per guidance output while maintaining a low missed-hazard rate of 0.112, proving its efficacy for safe walking assistance.
[CV-16] X-LMC: Cross-View Spatiotemporal Collateral Circulation Scoring from DSA MICCAI2026
链接: https://arxiv.org/abs/2608.18986
作者: Maedeh Hafezi Moghadas,Hakim Baazaoui,Lukas Bastian Otto,Susanne Wegener,Björn Menze,Ezequiel De la Rosa
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 3 figures, 1 table. Accepted at SWITCH+ 2026, a MICCAI 2026 workshop
Abstract:Digital subtraction angiography (DSA) is the reference standard for leptomeningeal collateral (LMC) assessment, providing critical prognostic insights to guide secondary treatment strategies, neurorehabilitation planning, and retrospective stroke research. However, clinical LMC grading via the ASITN/SIR scale relies on manual, highly variable visual inspection. We introduce X-LMC, a spatiotemporal framework for automated collateral scoring from time-resolved biplane DSA. The proposed architecture encodes spatial frame representations through a DINOv2 backbone, fuses orthogonal projections via a token-level cross-view attention module, and models representations of contrast bolus dynamics using a recurrent network architecture. We evaluate our framework on a multicenter dataset of 134 patients with M1-segment occlusions. In a 5-fold cross-validation setting, X-LMC yields higher point estimates than static architectures and spatiotemporal baselines adapted from related angiographic tasks, achieving a Quadratic Weighted Kappa (QWK) of 0.398 (vs. 0.322) and a dichotomized macro-F1 score of 0.711 (vs. 0.663) against the best-performing baseline. X-LMC performance also aligns with the observed clinical inter-rater agreement (QWK: 0.314). As the first DSA study attempting to automate LMC scoring, we demonstrate that multi-view temporal deep learning can capture collateral-specific contrast kinetics. Ultimately, these benchmarks delineate the clinical ambiguities and achievable performance boundaries of automated ASITN/SIR grading, establishing a reproducible foundation for objective hemodynamic phenotyping in stroke cohorts. Code is available at this https URL.
[CV-17] Uncertainty-Aware Art-Historical Dating with Vision-Language Models
链接: https://arxiv.org/abs/2608.18984
作者: Stefanie Schneider,Peter Bell
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Museum and archival datasets do not mirror historical artistic production, but materialize the contingent histories of collecting, preservation, cataloging, and digitization. This has direct consequences for interpreting pretrained image representations: they may appear to encode historical time while actually encoding the institutional conditions under which objects become visible as data. We describe this phenomenon as temporal entanglement and investigate it by formulating artwork dating as an uncertainty-aware regression task over frozen image embeddings. We evaluate several pretrained vision models on a temporally controlled Wikidata corpus of artworks. Our results show that these models contain usable temporal information, with Vision-Language Models (VLMs) outperforming purely visual self-supervised baselines. However, a qualitative analysis indicates that this temporal knowledge is shaped by various biases.
[CV-18] When Simplicity Wins: Bottleneck-Aware Context Modeling for Lightweight Semantic Segmentation ICIP2026
链接: https://arxiv.org/abs/2608.18979
作者: Mian Muhammad Naeem Abid,Nancy Mehta,Zongwei Wu,Radu Timofte
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at IEEE ICIP 2026; ranked among the Top 3%
Abstract:Semantic segmentation demands a careful balance between accuracy, efficiency, and scalability, which remains difficult to achieve for high-resolution imagery. Convolutional networks effectively model local patterns but struggle with long-range dependencies, whereas Vision Transformers capture global context at a high computational cost. While recent work largely focuses on encoder design, the bottleneck stage, central to contextual aggregation and information flow, has been relatively overlooked. We propose SiConMo, a lightweight yet effective framework, implemented in two variants: an RGB-only model (SiConMo) and a GME-enhanced variant (SiConMo _\dagger ). We show that simplicity arises from a key design principle: at very low computational budgets, the bottleneck is the most efficient stage to integrate local and global context. SiConMo integrates three complementary components: a Token Pyramid Extraction Module for hierarchical multi-scale representation, a Transformer-Branched Depthwise Convolution block for bottleneck-aware context modeling, and a Feature Merging Module that preserves spatial structure while enhancing semantic consistency. Extensive experiments on ADE20K, PASCAL Context, Cityscapes, and COCO-Stuff demonstrate that SiConMo achieves a state-of-the-art accuracy-efficiency trade-off among lightweight semantic segmentation models, highlighting simplicity as a powerful design principle.
[CV-19] Frozen DINO Localizes Image Edits Without a Localizer
链接: https://arxiv.org/abs/2608.18968
作者: Zane Kumar,Vishal Jain,Bernhard Kainz
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 2 figures, 2 tables
Abstract:Localized image edits can change a photograph’s meaning while leaving most of it authentic, so forensic analysis must identify where an edit occurred. We show that patch-level perturbation responses from frozen DINO encoders are themselves localization maps. Training-free Localization of AI-image Edits from patch-token Drift (TRAIL) applies one global Haar perturbation and maps cosine drift between corresponding patch tokens. On 80 source-disjoint CocoGlide test images, TRAIL reaches .903 patch AUROC versus .912 for the mask-supervised Detective SAM; fixed-threshold Dice is .619 versus .709, while an oracle threshold raises TRAIL to .790. Transferred unchanged to Poisson image interpolation, TRAIL reaches .855 AUROC versus .864, showing that the cue persists without a generator. Across sixteen DINO encoders, the best block lies at normalized depth .80-.94. Global context matters: AUROC falls from .903 globally to .857 for local-in-canvas perturbations and .735 for independently encoded crops. Frozen DINO patch tokens therefore contain a strong late-layer localization signal whose visibility depends on the perturbation and preserved context. Code: this https URL.
[CV-20] Institutional Books - Visual Elements: An open-source pipeline for extracting classifying deduplicating and captioning visual elements from digital book collections
链接: https://arxiv.org/abs/2608.18957
作者: Jimmy Mendez,Matteo Cargnelutti,David Lowry-Duda,Catherine Brobston,Salwa Ismail,Greg Leppert,Amanda Watson,Jonathan Zittrain
类目: Computer Vision and Pattern Recognition (cs.CV); Digital Libraries (cs.DL)
备注:
Abstract:Historical book collections contain rich visual elements - such as illustrations, photographs, engravings, and decorative art - that are frequently under-explored in large-scale digitization projects. While Optical Character Recognition (OCR) has standardized the extraction of textual content, these visual components offer a layer of nuance and context that remains largely untapped by automated text extraction workflows. This technical report introduces Institutional Books - Visual Elements, an open-source end-to-end pipeline for detecting, classifying, deduplicating, and captioning visual elements from historical book collections. Alongside this pipeline, we release an initial dataset of 22.6 million visual elements extracted from the 983,004 scanned volumes that comprise the Institutional Books: Harvard Library dataset. This work contributes to ongoing, community-wide efforts to enable new use cases for digitized library collections through computational access, from artificial intelligence model training to digital humanities research.
[CV-21] Simple Safe and Overlooked: Reclaiming Sustainable Domain Generalization with Statistical Color Matching MICCAI2026
链接: https://arxiv.org/abs/2608.18915
作者: Sebastian Doerrich,Francesco Di Salvo,Shyam Nandan Rai,Marco Lents,Christian Ledig
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
备注: Accepted to DEMI @ MICCAI 2026 (4th Workshop in Data Engineering in Medical Imaging)
Abstract:Hardware shifts, color variations, and changing patient characteristics between development and deployment routinely break trained medical image classifiers. Existing remedies fall short: standard color jittering provides insufficient diversity, while deep generative style transfer algorithms hallucinate features, destroy clinically relevant structures, and waste massive compute resources. To address this, we revisit classical statistical color matching and repurpose it as Colorist, a highly efficient data augmentation strategy that applies global mean-standard deviation matching directly in the RGB color space. We demonstrate that this training-free, fully interpretable approach safely generates structurally intact domain variations, outperforming deep generative models in structural fidelity and color alignment. Across out-of-distribution histopathology, peripheral blood, dermatology, and retinal datasets, it improves balanced accuracy by up to +9% over state-of-the-art domain generalization regularizers and by +13% over an unaugmented baseline. Moreover, by avoiding neural networks in the augmentation loop, Colorist preserves anatomical structure, minimizes carbon footprint, and integrates seamlessly into standard dataloaders. Together, these findings establish statistical matching as a safe, interpretable, yet overlooked alternative to deep architectures for clinical robustness. Source code is available at this https URL.
[CV-22] Learning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets
链接: https://arxiv.org/abs/2608.18907
作者: Ting Xiang,Chenxi Deng,Jinhui Zhao,Bingting Jiang,Ke Zhang,Changjian Chen,Zhuo Tang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Small-scale image classification is often limited by the scarcity of training data. Generative data augmentation (GDA) based on pretrained generative models has emerged as an effective solution. However, existing methods rely on task-agnostic augmentation strategies that overlook downstream model needs. Although recent dynamic GDA methods incorporate model feedback to guide augmentation, they still struggle to reliably determine sample-specific augmentation strengths and adapt augmentation strategies to different image regions while balancing image diversity and class semantics. To address these issues, we propose learning-state-aware dynamic generative data augmentation (LSADA). Specifically, LSADA constructs a learning state for each sample based on its current loss and loss-decrease rate, which is then mapped to a sample-specific augmentation strength. Furthermore, LSADA introduces a decoupled data augmentation and diffusion fusion strategy that applies strength-controlled transformations to class-relevant regions and generates diverse class-irrelevant regions, progressively fusing them to improve image diversity while preserving class semantics. Experiments on nine public datasets show that LSADA outperforms the existing SOTA dynamic GDA method by an average of 4.5% on six natural image datasets and 2.5% on three medical image datasets. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.18907 [cs.CV] (or arXiv:2608.18907v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.18907 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-23] Falcon Perception-HD: High Density Perception via Reinforcement Learning
链接: https://arxiv.org/abs/2608.18881
作者: Sofian Chaybouti,Yasser Dahou,Ngoc Dung Huynh,Reda Alami,Hilde Kuehne
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 27 pages, 11 figures
Abstract:Autoregressive perception models trained to localize visual entities under the open-vocabulary setting are mostly trained using Supervised fine-tuning (SFT) with maximum likelihood, yet it optimizes a proxy objective (per-token cross-entropy) that is fundamentally misaligned with perception metrics such as precision and recall. In this paper, we explore post-training reinforcement learning (RL), specifically GRPO, to directly align these models with their evaluation metrics. Building up on the recently introduced Falcon Perception, we design an RL framework that addresses perception-specific challenges: reward design for set-structured outputs and multi-head sampling control. We discover multiple benefits from RL for perception: first, RL unlocks state-of-the-art performance in very dense scenes (up to 500 objects per scene), a regime where most existing systems degrade sharply or collapse; furthermore it fixes common issues in autoregressive perception models like mask repetitions and removes almost entirely the need for NMS and coordinate deduplication, which improve both performance and efficiency and remove the need for hyperparameters tuning; overall, we notice improvements on all levels of difficulties in referring expression segmentation (on PBench and SACO-Gold), and we find an elegant way to preserve the knowledge of whether an object exists or not (as evaluated by MCC) without training on negative samples. We show that a simple reward that penalizes false negatives and positives is sufficient. We develop two hybrid self-annotation pipelines, respectively tailored for difficult referring expressions and very dense scenes, and show their benefits on RL-training. Model weights are released as a Falcon Perception revision~\footnotethis https URL. Datasets will be published.
[CV-24] RVLoss: Runoff Vote Loss for Self-Supervised LiDAR Scene Flow Estimation
链接: https://arxiv.org/abs/2608.18864
作者: Shiming Wang,Liangliang Nan,Julian Kooij,Holger Caesar,Yancong Lin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 5 figures
Abstract:LiDAR scene flow estimates point-wise motion between two consecutive scans, referred to as the source and target. Leading self-supervised methods typically minimize the Chamfer loss, the nearest neighbor distance between the flow-compensated source and the target. However, nearest-neighbor search does not enforce motion rigidity, often leading to inconsistent flows within object instances. Existing approaches address this issue with additional regularization terms, but flow consistency among points remains limited, especially for large objects. We propose RVLoss, a self-supervised loss that incorporates motion rigidity by design through a runoff vote mechanism. Our key observation is that the point-wise motion, calculated from nearest neighbor search, can often be grouped into a small set of dominant flow candidates by voting (top-k voting). Furthermore, when compensating the source by these candidates, the flow that best represents the underlying rigid motion often yields the highest consensus after a second voting (top-1 voting). Based on this insight, we incorporate the two-stage runoff vote into loss design and create cluster-wise rigid flows and free-form flows as pseudo-labels for self-supervised learning. RVLoss can be seamlessly integrated into existing feedforward architectures. Experiments on the Argoverse2 2026 Challenge show that models trained with RVLoss achieve state-of-the-art performance among self-supervised approaches, outperforming baseline models trained with alternative loss designs by 20%. Moreover, cross-dataset evaluations demonstrate consistent performance improvements across four additional datasets. Code will be released upon acceptance.
[CV-25] Beyond Placement and Articulation: Usage-Driven Code Scenes for Embodied Interaction
链接: https://arxiv.org/abs/2608.18840
作者: Zijian Xiao,Zipeng Ye,Jinkun Hao,Xiong Yang,Yuchen Xie,Ran Yi
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Indoor scene synthesis provides essential environments for embodied AI, robotic manipulation, and simulation-based policy learning. Recent code-based scene generation methods produce editable and extensible environments, yet they remain focused on visual construction and object-level articulation, leaving the functional usage of scenes largely unmodeled. To address this problem, we present RoomWright, an agentic usage-driven framework for generating 3D scenes represented entirely as code for embodied interaction. RoomWright performs usage-driven object reasoning, which treats each anchor as a task centre and admits task-required objects and their affordances. A code agent further enables multi-part interaction by compiling each interaction into a trigger, condition, effect rule that updates structured object states, capturing causal dependencies across objects. Moreover, since manipuland orientation is ambiguous and hard to recover from pixels, RoomWright alleviates this via annotation-informed usage-guided orientation. Extensive experiments demonstrate the effectiveness of our method. The resulting scenes are executable, editable, and simulation-ready, providing interactive environments for embodied AI and policy learning.
[CV-26] EVADE: Evidence-Verified Agent ic Diagnosis with Escape
链接: https://arxiv.org/abs/2608.18833
作者: Mohaimenul Azam Khan Raiaan,Nur Mohammad Fahad
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Medical vision-language models (VLMs) can achieve high accuracy but remain unreliable: they are systematically overconfident, benefit little from test-time reasoning, and lack the ability to reliably calibrate trust in their own responses. We introduce EVADE (Evidence-Verified Agentic Diagnosis with Escape), an inferential, non-training method that enhances the safety of deploying a single frozen VLM. EVADE responds and, when uncertain, localises the region most diagnostically relevant, re-answers on a zoomed view, and commits only when both the entire image and the zoomed view responses agree; otherwise, it abstains. To directly address verification hallucination in single-model self-checking, our main idea is to verify gate consistency across different image views rather than re-reading the model’s own text. Experimental evaluation on VQA-RAD, SLAKE, and PathVQA using Qwen2.5-VL-7B reports that EVADE is the only method that simultaneously improves both calibration and selective risk while maintaining accuracy, reducing expected calibration error (ECE) by up to 45% compared to zero-shot. Chain-of-thought, self-consistency, and self-verification all fail at least one axis. A grounding analysis reports that self-proposed regions perform better at diagnostic structure localisation than centres or random crops. However, a 7B VLM cannot use this localisation to revise answers. Therefore, reliability gains come from the consistency gate and calibrated abstention.
[CV-27] EfficientSync: Real-Time Lip Synchronization via Deformation-Based Reference Texture Mixing
链接: https://arxiv.org/abs/2608.18832
作者: Fa-Ting Hong,Runzhen Liu,Luchuan Song,Hongmin Cai,Chuhua Xian
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Under review
Abstract:Audio-driven lip synchronization manipulates the mouth region of a talking-face video to match the driving audio while preserving head pose, identity, and background. Although the task is inherently local editing, prevailing approaches reconstruct the entire lower face with heavy GAN- or diffusion-based decoders, incurring substantial latency and, more critically, hallucinating intra-oral details such as teeth and lip wrinkles instead of preserving authentic textures. We contend that the bottleneck in identity preservation is not the scarcity of reference frames, but the lack of a mechanism that faithfully transfers the genuine textures they already contain. We therefore present EfficientSync, a real-time deformation-based framework that retains reference textures rather than resynthesizing them. First, the Dynamic Texture Mixer reformulates multi-reference fusion as channel-wise selection, evaluating each spatially aligned reference in a global context and aggregating them by channel-wise weighted summation, preserving textural integrity at low cost. Second, Spatio-Temporal Shifted Adaptive Masking decomposes the source frame into lip-generation conditions and an independent background prior, suppressing lower-face leakage while blending the synthesized mouth seamlessly into the background. Third, STAR Sampling, a zero-overhead pre-processing step, retrieves the sharpest and most topologically diverse reference frames. Experiments on HDTF and VFHQ show state-of-the-art visual quality and identity preservation at 166 FPS on a single GPU. Video demos: this https URL.
[CV-28] MIFR: A Modality-Invariant and Fair Representation Framework for Skin Disease Classification MICCAI2026
链接: https://arxiv.org/abs/2608.18774
作者: Asonyu Senge Njih,Yvan Guifo Fodjo,Vianney Kengne Tchendji,Jerry Lacmou Zeutouo,Kerol Djoumessi
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted for publication at the First Workshop on Advancing African Medical AI through Global Integration (AFRICAI)-MICCAI 2026
Abstract:Skin diseases represent a major global public health burden, yet machine learning tools developed to assist in their diagnosis suffer from two critical limitations: reliance on only one modality for diagnosis and systematic performance disparities across skin tones. While existing approaches address each challenge separately, this work proposes a modality-invariant framework with fair representation (MIFR) for skin disease classification. The architecture pairs clinical photographs with dermoscopic images using ViT-based encoders, projecting each input into a high-dimensional embedding space via modality-specific projection heads. The resulting model is trained with a five-component multi-objective loss including weighted cross-entropy for classification, confusion and skin-type classification losses for fairness, per-modality supervised contrastive loss for class alignment, and a modality-invariance loss for clinical and dermoscopic modality alignment. Experiments on the HIBA+Derm7pt paired dataset and the external PAD-UFES-20 and ISIC 2019 datasets showed that modality-invariant representation learning provides competitive predictive performance compare to relevant baseline models and competitive fairness on the internal dataset. t-SNE visualizations confirmed that clinical and dermoscopic embeddings of the same disease are geometrically aligned, validating the joint objectives.
[CV-29] SED-FOD: Scattering-Aware Expert Decomposition for Few-Shot Cross-Sensor SAR Object Detection
链接: https://arxiv.org/abs/2608.18755
作者: Shu Yang,Zhen Chen,Zhiyu Jiang,Yanlei Li,Xingdong Liang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted to IEEE JSTARS. 13 pages, 7 figures
Abstract:Synthetic aperture radar (SAR) object detection is an important part of remote sensing interpretation. However, because of variations in frequency band, resolution, background clutter, and target scattering responses, the performance of existing detectors often degrades when training and testing data are acquired from different SAR domains. Although domain adaptation methods offer a promising paradigm for solving this problem, most of them mainly pursue domain-invariant feature alignment and suppress sensor-dependent scattering characteristics that are useful for object detection. This problem becomes more challenging in few-shot scenarios, where only a few fully annotated target-domain SAR images are available. To address this issue, we propose a scattering-aware shared-specific feature decomposition framework for few-shot SAR domain adaptation object detection. We decompose detection features into a shared path and several soft-gated scattering-specific expert paths. The shared path learns transferable object structural information and is used for asymmetric domain alignment, while the scattering-specific experts adaptively compensate heterogeneous SAR responses. In addition, routing-domain auxiliary loss is introduced to encourage specific experts to capture sensor-dependent routing preferences, and an expert balancing loss is used to prevent routing collapse. Extensive experiments on four bidirectional heterogeneous SAR detection tasks between FARAD-X/FARAD-Ka and MiniSAR under different few-shot settings have been conducted and experimental results demonstrate that the proposed method achieves superior performance in both forward and reverse adaptation directions.
[CV-30] Decision-Metric Alignment in Latent World Models: Diagnostics and Action-Conditioned Objectives for MPC Planning
链接: https://arxiv.org/abs/2608.18746
作者: Jiawei Wang,Ke Rui,Yushen Zuo,Yichun Feng,Minglei Li
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:JEPA-style latent world models can use Euclidean distance to a goal latent as the cost for model-predictive control (MPC). Strong decoding of task variables, however, does not guarantee that this particular cost ranks candidate action sequences by real task progress. We call the latter property \emphdecision-metric alignment. We introduce Plan-Real Spearman, which measures latent–real rank agreement on random plans, and CEM-stage Spearman, which measures the same agreement as cross-entropy-method (CEM) search concentrates its proposal. We analyze sufficient conditions under which latent distance preserves real-cost rankings, identifying encoder distortion, terminal rollout error, and candidate margins as the controlling quantities. Guided by the observed empirical alignment gap, DA-LeWM augments LeWM with inverse-dynamics and demonstration-conditioned goal-action heads. Across all our experiments, DA-LeWM accelerates convergence and achieves higher online success than LeWM, while probe scores remain similar. These results show that action-conditioned objectives improve the geometry used by Euclidean-cost, CEM-based latent MPC.
[CV-31] CL4D: Contrastive Language-4D Pretraining for Vision-Language Reasoning in Dynamic Scenes ECCV2026
链接: https://arxiv.org/abs/2608.18734
作者: Kumal Hewagamage,Isuranga Senavirathne,Sasika Amarasinghe,Hasitha Gallella,Dulanga Weerakoon,Vigneshwaran Subbaraju,Ranga Rodrigo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the 19th European Conference on Computer Vision (ECCV 2026)
Abstract:4D understanding and reasoning is a fundamental capability for embodied AI agents operating in dynamic physical environments. However, existing vision encoders are largely limited to static 2D images or 3D point clouds without temporal modeling, or to 2D videos that lack accurate geometric depth reasoning. Consequently, current approaches fail to jointly capture spatial structure and motion evolution in dynamic scenes. We present CL4D, the first foundational 4D vision encoder that directly operates on dynamic point clouds, trained with a contrastive learning objective to align spatio-temporal geometric representations with natural language descriptions. By learning a shared embedding space between text and 4D scene dynamics, CL4D enables zero-shot motion-to-text and text-to-motion retrieval in dynamic environments and serves as a foundational 4D vision encoder for downstream 4D vision-language tasks. Building on this encoder, we introduce 4DVLM, a 4D vision-language model that conditions language generation on dynamic geometric representations. 4DVLM is the first VLM designed to operate directly on 4D point clouds without relying on 2D images, 2D videos, or static 3D point clouds. We train CL4D and subsequently 4DVLM on a newly constructed dataset termed DynAction4D capturing diverse human motions across varying object interactions and scene environments. Extensive experiments across multiple 4D human action benchmarks demonstrate that CL4D achieves state-of-the-art performance, with improvements of approximately ~16.75% over prior methods. Furthermore, 4DVLM outperforms frontier video VLMs such as Gemini and GPT-5 even when these models are provided with RGB video sequences corresponding to the same scenes represented as 4D point clouds for 4DVLM.
[CV-32] A Few Cases Are All You Need: An Empirical Study of Annotation-Efficient LoRA Fine-Tuning of MedSAM3
链接: https://arxiv.org/abs/2608.18731
作者: Sachin Dudda Nagaraju,Bendik Skarre Abrahamsen,Ashkan Moradi,Mattijs Elschot
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Medical image segmentation is essential for clinical workflows such as treatment planning and disease assessment. While specialist tools like TotalSegmentator and MRSegmentator achieve strong performance, they require large annotated datasets for training. Medical foundation models offer a promising alternative through large-scale pretraining that reduces the annotation burden for new tasks, but zero-shot performance remains limited. Parameter-efficient adaptation via Low-Rank Adaptation (LoRA) enables efficient specialization with few trainable parameters, but a key question remains: how many expert-annotated cases are needed to achieve clinically useful segmentation performance? We address this by adapting MedSAM3 with LoRA for five abdominal organs (liver, kidneys, spleen, gallbladder, and pancreas) in CT and MRI using only 1, 2, 5, and 10 annotated cases, evaluating on AMOS22 dataset. With just 10 cases, models achieve performance competitive with specialist systems trained on orders of magnitude more data. Notably, this includes reliable gallbladder segmentation (Dice 0.68 CT, 0.59 MRI) where existing tools fail almost completely (Dice 0.0004), while remaining within 5–10% of MRSegmentator for liver, kidneys, and spleen using over 100 times fewer annotations. Furthermore, external validation on the Whole Heart Segmentation dataset shows that the approach extends to cardiac segmentation, a use case beyond the scope of TotalSegmentator (MRI) and MRSegmentator, achieving competitive left ventricle (LV) performance with only 10 annotated cases. Training requires only3–5,hours per organ on a single GPU, approximately 2–3 times faster than nnU-Net. These findings suggest that ten annotated cases are sufficient for clinically useful segmentation, effectively reducing bottlenecks for both image annotation and training time.
[CV-33] he Impact of CutMix on Reliability and Robustness in Semantic Segmentation
链接: https://arxiv.org/abs/2608.18715
作者: Steven Landgraf,Markus Ulrich
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted for publication in the ISPRS Annals (ISPRS Congress 2026, Toronto, Oral Presentation)
Abstract:Ensuring not only high accuracy but also reliable and robust predictions is critical for the deployment of semantic segmentation models in safety-critical applications such as autonomous driving. Despite the widespread use of CutMix - a simple yet powerful data augmentation strategy - its effect on the reliability and robustness in dense predictions tasks remains unexplored. Motivated by recent findings that semi-supervised segmentation methods, where CutMix is a core component, can severely degrade reliability, this study isolates and systematically analyzes the influence of CutMix on segmentation accuracy, calibration, and uncertainty quality. We evaluate two representative architectures, the CNN-based DeepLabV3+ and the transformer-based SegFormer, across both in-domain and out-of-domain scenarios. Our results show that CutMix has only a minor impact on segmentation accuracy but consistently improves the reliability, particularly under distribution shifts. These improvements indicate that CutMix primarily enhances the trustworthiness of the model’s calibration and uncertainty rather than the raw segmentation prediction itself. This distinction is crucial for safety-critical deployment, where reliable confidence estimates are as important as raw performance.
[CV-34] EgoHRV: Continuous Heart Rate Variability Estimation from Egocentric Systems for Autonomic Response and Skill Assessment ECCV
链接: https://arxiv.org/abs/2608.18711
作者: Berken Utku Demirel,Christian Holz
类目: Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP)
备注: Accepted to the European Conference on Computer Vision (ECCV) 2026
Abstract:Egocentric vision systems capture human behavior from visible cues, but overlook physiological indicators of autonomic states such as stress, engagement, and attention. Heart rate variability (HRV) is a widely used noninvasive marker of autonomic regulation under stress. HRV reflects small timing differences between successive heartbeats and has so far been out of reach for egocentric platforms, where motion and noise in gaze video mask exactly this fine-grained timing. We propose EgoHRV, a method that estimates HRV as well as heart rate (HR) from the gaze cameras that are already integrated into egocentric headsets. Our pipeline combines a 3D backbone with a novel low–high decomposition module that extracts the blood volume pulse (BVP) signal from gaze video. Our cross-domain pretraining aligns the frequency-domain representations of contact-based and camera-derived signals. This alignment gives EgoHRV the temporal precision to recover HRV from the subtle fluctuations in gaze video. EgoHRV achieves state-of-the-art accuracy for HR and HRV estimation from egocentric video, and its uncertainty-aware design improves downstream behavioral modeling. Integrating our HRV estimates and confidence measures into EgoExo4D’s proficiency estimator raises accuracy by 17.8%. Beyond skill, continuous HRV estimation also opens egocentric systems to stress- and arousal-aware estimation tasks. Code: this https URL
[CV-35] CamWorldQA: Perceptual Quality Assessment of Camera-Controlled World Video Generation
链接: https://arxiv.org/abs/2608.18710
作者: Yunhe Li,Likun Wu,Sijing Wu,Xinyu Tian,Huiyu Duan,Yixuan Gao,Yunhao Li,Guangtao Zhai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advances in generative video models have enabled camera-controlled world video generation, allowing models to synthesize videos under user-defined camera trajectories. However, existing video quality assessment (VQA) methods are mainly developed for natural videos and fail to capture the unique perceptual characteristics of camera-controlled generation, such as viewpoint consistency, motion coherence, and content preservation. In this work, we introduce CamWorldQA, the first benchmark for perceptual quality assessment of camera-controlled world video generation. CamWorldQA contains 720 generated videos produced by 6 representative generation methods from 20 diverse source videos under 6 camera trajectories, where each video is annotated with a human-rated perceptual quality score through subjective experiments. Furthermore, we propose CWQA, a no-reference quality assessment network with three complementary branches that extract spatial features, temporal motion features and optical flow features to jointly predict quality scores. Extensive experiments demonstrate that CWQA achieves superior performance over existing quality assessment methods on the CamWorldQA dataset.
[CV-36] A Critical Synthesis of Uncertainty Quantification and Foundation Models for Semantic Segmentation
链接: https://arxiv.org/abs/2608.18709
作者: Steven Landgraf,Joceline Hinz,Markus Ulrich
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted for publication in the ISPRS Annals (ISPRS Congress 2026, Toronto, Oral Presentation)
Abstract:Foundation models are increasingly breaking what seemed to be impossible not long ago by enabling unprecedented accuracy and cross-domain generalization. Yet their lack of interpretability, tendency to be overconfident, and sensitivity to real-world domain shifts pose critical challenges for safety- and mission-critical applications. Uncertainty quantification (UQ) offers a principled way to address these issues, but its integration into segmentation foundation models has yet to be explored. In this paper we present the first systematic evaluation of UQ methods applied to a foundation model for semantic segmentation. We fine-tune a lightweight DPT decoder on top of the pretrained SAM2 encoder to establish a simple yet competitive baseline and benchmark four representative UQ approaches - Monte Carlo Dropout, Deep Sub-Ensemble, Test-Time Augmentation, and Evidential Deep Learning - across Cityscapes, NYUv2, and two challenging out-of-domain settings. Our analysis compares segmentation accuracy, calibration, uncertainty quality, and inference time, revealing clear trade-offs between predictive performance, reliability, and computational cost. These results highlight both the promise and the current limitations of uncertainty-aware foundation models, pointing to the need for future work that jointly optimizes accuracy, robustness, and efficiency for real-world deployment.
[CV-37] Impact of Iterative Fine-Tuning on Transcription Accuracy in Complex Historical Sanskrit Manuscripts
链接: https://arxiv.org/abs/2608.18696
作者: Kartik Chincholikar,Kaushik Gopalan,Mihir Hasabnis
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Digitizing the text from handwritten historical manuscripts is required to make them easily accessible, preservable, and to enable historical scholars to study them in new ways. Historical manuscripts, however, often exhibit complex heterogeneous layouts and non-standard appearance due to period-specific writing styles, page textures, camera noise, and other nuisance factors, making them difficult to perform OCR on. To tackle this challenge, we introduce a local traditional OCR pipeline, which can be iteratively fine-tuned on the target manuscript at the layout-level and the appearance-level. By adapting to the target manuscript distribution, the proposed Traditional OCR pipeline makes better predictions on subsequent pages, causing iterative reduction in human annotation effort, which is expensive and time-consuming as it requires historical domain expertise. Using this pipeline, we digitize text from three complex historical Sanskrit manuscripts and introduce a dataset with granular layout-level annotations, along with Unicode annotations in the standard PAGE-XML format. We demonstrate quantitative gains due to iterative fine-tuning of the proposed traditional OCR pipeline, and also benchmark the performance of leading Multi-Modal Large Language Models on the introduced Dataset. Code and dataset are available at: this https URL.
[CV-38] DocClaw: A Unified Agent ic System for Intelligent Document Processing
链接: https://arxiv.org/abs/2608.18685
作者: Siqi Xiang,Zhipeng Xu,Yufei Liu,Junhao Ji,Qing Liu,Zulong Chen,Zhibo Yang,Chunyan Miao,Shijian Lu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Intelligent document processing (IDP) encompasses a broad range of tasks, including optical character recognition (OCR), document question answering (DocQA), and key information extraction (KIE). Despite their distinct objectives, these tasks share a common need to perceive document content, acquire task-relevant information, and progressively refine intermediate results. However, they are typically formulated as separate prediction problems and addressed by task-specific models or processing pipelines. We introduce DocClaw, a unified agentic system that formulates diverse intelligent document processing tasks as a shared process of interaction between an agent and a document. Given a document and a task-specific query, DocClaw follows an appropriate document skill to iteratively identify the information required, invoke relevant tools, and integrate the resulting observations into the desired output. Throughout this process, a structured document state organizes reusable document knowledge and task-specific interaction context, allowing the agent to accumulate, revisit, and progressively refine information as the interaction proceeds. Under this formulation, task-specific requirements are captured by the agent’s interpretation of the query objective and the corresponding document skill, while the underlying interaction loop, tool space, and document state are shared across tasks. Extensive experiments across multiple intelligent document processing benchmarks demonstrate that DocClaw effectively handles diverse tasks within a single agentic framework and achieves competitive performance compared with both general-purpose VLMs and task-specific methods.
[CV-39] FRAG MENT: Factorized Graph Representations for Document Generation and Editing via Entity-Aware Transformations
链接: https://arxiv.org/abs/2608.18679
作者: Ayoub El Bouchtili,Guilhaume Leroy-Meline
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Structured documents such as invoices, forms, reports, and scientific articles derive meaning from the interplay between spatial layout, textual content, and logical structure. Generative models operating at the pixel or token level often struggle to capture these dependencies effectively. We explore FRAGMENT, a generative framework that represents a document as a typed relational graph and factorizes its distribution as p(structure, content) = p(structure) * p(content | structure). The framework consists of two stages. The first stage, the Architect, is a causally masked Transformer conditioned on document category that autoregressively generates the graph topology and typed spatial relations. The second stage, the Builder, is a GATv2-based graph attention network that enriches the graph with normalized bounding boxes, text, and visual style attributes. Both stages define explicit likelihood models, yielding a tractable document-level likelihood that serves as an anomaly score for forgery detection. For controlled editing, a prompt-conditioned extension injects instruction embeddings into the Builder through cross-attention, enabling semantic and entity-aware modifications. We describe training on DocLayNet and fine-tuning on FUNSD and SROIE. Experiments on DocLayNet, FUNSD, and SROIE evaluate FRAGMENT alongside representative autoregressive, layout-only, and graph-based baselines, providing an empirical analysis of the characteristics and trade-offs of the proposed factorized graph generation framework.
[CV-40] DynCur-Geo: Dynamic Curiosity Reward Shaping for Multimodal Active Geo-Localization
链接: https://arxiv.org/abs/2608.18673
作者: Yiming Sun,Yang Zhang,Pengfei Zhu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 24 pages, 18 figures, 15 tables. The main paper is 7 pages, with supplementary material included
Abstract:Active geo-localization enables low-altitude UAVs to search for specified targets from limited local aerial observations, supporting time-sensitive applications such as search and rescue and emergency inspection. However, multimodal target cues, restricted views, and sparse feedback make it difficult to balance exploration with target convergence. Existing curiosity-driven methods assign a fixed intrinsic-reward weight throughout search, which can continue rewarding novelty after the agent nears the target and induce detours. We propose DynCur-Geo, a dynamic curiosity framework that adjusts prediction-error intrinsic reward according to remaining target distance. A distance-aware gate encourages early exploration and shifts the policy toward goal-directed behavior near the target, while potential-based reward shaping supplies dense progress guidance. Experiments across multimodal, cross-scene, disaster-affected, and long-range settings show consistent gains over active geo-localization baselines.
[CV-41] Vision-Language Models for Egocentric Video: From Hand-Object Interaction to Embodied AI
链接: https://arxiv.org/abs/2608.18671
作者: Mohammad Zamani,Fatemeh Ziaeetabar
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Egocentric video captures activities from the wearer’s perspective, providing a direct view of human attention, hand–object interaction, and goal-directed behavior. This perspective is increasingly important for wearable intelligence, assistive systems, human–robot interaction, and embodied AI, yet it introduces challenges including ego-motion, occlusion, small active objects, viewpoint-dependent appearance, and long-range temporal dependencies. Vision–language models (VLMs) offer a promising foundation for addressing these challenges by linking visual observations with semantic knowledge and natural-language supervision. This survey presents a critical review of VLMs for egocentric video understanding, tracing the progression from conventional recognition architectures to multimodal foundation models and embodied systems. We organize the literature around tasks, datasets, hand–object interaction understanding, temporal reasoning, frame and clip selection, multimodal representation learning, prompting, semantic alignment, and model adaptation. Particular attention is given to graph-based and object-centric reasoning as mechanisms for modeling relations among hands, objects, actions, and scene context over time. We further examine how first-person perception and multimodal foundation models support wearable assistance, robot skill learning, human-to-robot transfer, and embodied decision making. Across the reviewed literature, a consistent limitation emerges: current models recognize visible objects more reliably than evolving interactions, actions, and user intent, especially over long activities. We therefore identify temporally grounded reasoning, interaction-aware supervision, efficient long-video processing, multimodal fusion, graph-enhanced representations, cross-domain generalization, privacy, and trustworthy evaluation as key priorities for deployable embodied intelligence.
[CV-42] h2Point: A Two-Stage Dental CBCT ROI-to-Point Segmentation Framework
链接: https://arxiv.org/abs/2608.18667
作者: Qi Ma,Shipra Jain,Niko Benjamin Huber,Ender Konukoglu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Modern deep learning architectures have demonstrated strong performance in dental CBCT segmentation. One remaining crucial challenge is accurate tooth labeling in cases with missing or malpositioned teeth, which are highly relevant for dental practice. Transformer-based architectures should in theory be able to resolve such ambiguities using global anatomical context. However, due to the high resolution of CBCT volumes and the wide spatial distribution of teeth within volumes, dense patch-based volumetric processing faces an inherent trade-off. Computational costs limit the number of patches that can be used in self-attention and thus, one can either increase the extent of the context captured in self-attention or capture fine-grained structural details by using small patches, but not both. In this work, we present Teeth2Point, an efficient point-based transformer framework for dental CBCT semantic segmentation that can avoid this trade-off. Teeth2Point first localizes volumetric regions of interest (ROIs) surrounding teeth using a convolutional model, then converts ROIs into point tokens using adaptive sampling. A transformer model predicts accurate segmentations using the point tokens, which allow capturing global context while retaining high resolution. The transformer is first pretrained using self-supervised learning (SSL), in the style of DINO but using domain-specific augmentation strategies, followed by supervised finetuning. The SSL pretraining, which includes random token masking, provides robustness to complex anatomical variations. Compared with the strongest two-stage baseline, Teeth2Point improves abnormal-case performance by 1.44 DSC points on average across four datasets; relative to the first-stage nnU-Net, the gain is 1.9 points.
[CV-43] Dynamic SpectraFormer for Ultra-High-Definition Underwater Image Enhancement IROS2024
链接: https://arxiv.org/abs/2608.18662
作者: Zhiqiang Hu,Tao Yu,Shouren Huang,Masatoshi Ishikawa
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 8 pages, 7 figures. Published in the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)
Abstract:Underwater images suffer from color distortion, haze, and poor visibility due to light refraction and absorption in water. These challenges significantly impact the utilization of Autonomous Underwater Vehicles (AUVs) or marine robots. Typically, color and brightness distortions manifest at lower frequencies, while edge and texture distortions are prevalent at higher frequencies. Traditional methods struggle to concurrently rectify these mixed distortions as they primarily concentrate on the spatial domain. To address these issues, we introduce the Dynamic SpectraFormer, which enhances underwater images through a frequency domain transformer. The Dynamic SpectraFormer introduces an ultra-high-resolution sparse spectrum attention module, which could capture the long-term dependency without losing the universal approximating power. Additionally, we have developed a dynamic spectrum weight generation layer that serves as an adaptive spectrum band selector, accentuating critical frequency bands and suppressing less relevant ones. Consequently, this method significantly improves underwater image quality by addressing both high- and low-frequency distortions. Our extensive ablation studies and comparative evaluations consolidate the Dynamic SpectraFormer’s efficacy across multiple underwater image enhancement benchmarks. The source code is available at this https URL.
[CV-44] Clinically Structured Surrogate Rewards for Post-SFT Medical Image Captioning
链接: https://arxiv.org/abs/2608.18654
作者: Hyun Jun Kim,Heeseung Shin,Changwon Lim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 2 figures, 3 tables
Abstract:Medical image captioning requires translating heterogeneous visual evidence into concise clinical descriptions, where errors in findings, assertion states, or anatomical relations can alter clinical meaning despite surface-level fluency. Sequence-level policy optimization can directly optimize complete captions, but common rewards rely on global text similarity, direct image-caption compatibility, or unordered concept overlap, leaving visual neighborhoods and clinical-claim structure implicit. We propose a clinically structured surrogate reward framework for post-SFT medical image captioning. The framework combines biomedical semantic and short-range lexical fidelity with two structured rewards: distributional image-neighborhood alignment, which matches the medical-image-bank distributions induced by reference and generated captions, and clinical graph consistency, which applies maximum-weight one-to-one matching to entities, assertion states, and typed relations. The four rewards are independently normalized within each rollout group, combined with fixed relative weights, and optimized with GDPO. Across organizer-evaluated hidden test sets for the Standard and Synthetical ImageCLEFmedical Caption tracks and three vision-language backbones, the method improves Overall, Relevance, and Factuality over matched SFT baselines in all six backbone-track combinations, with average relative gains of 3.4%, 2.1%, and 5.8%, respectively. Ablations and paired diagnostics indicate that the structured rewards provide complementary signals, reducing image-neighborhood divergence and improving entity-assertion-relation consistency.
[CV-45] SAM2Dual: Training-Free Dual Memory for Long-Term Video Object Segmentation
链接: https://arxiv.org/abs/2608.18640
作者: JeongRae Kim,Changwon Lim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Long-term video object segmentation (VOS) remains challenging due to error accumulation under extended occlusions, re-appearance, and scene changes. Although SAM2 provides strong zero-shot performance, its streaming memory can amplify drift over long horizons when recent, unreliable predictions dominate the memory state. We propose SAM2Dual, a training-free, plug-and-play inference-time enhancement that improves long-video robustness without updating model weights. SAM2Dual introduces a Dual Memory design that explicitly separates (i) short-term memory for rapid local adaptation and (ii) long-term memory built via interval-based sampling to preserve global identity cues, combined through a gated fusion strategy. In addition, we present Text-Aware Memory (TAM), which extracts a compact word-level cue from early frames and uses text embeddings to reweight memory contributions based on semantic compatibility, supporting identity preservation when visual evidence becomes weak or ambiguous. Across long-term benchmarks, SAM2Dual consistently improves stability on long videos, raising JF from 49.33 to 50.65 on MOSEv2 and achieving consistent gains on LVOSv2.
[CV-46] PCQA-R1: Advancing Generalized 3D Point Cloud Quality Assessment with Reinforcement Learning
链接: https://arxiv.org/abs/2608.18627
作者: Kangning Ye,Yunhao Li,Sijing Wu,Yucheng Zhu,Guangtao Zhai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:No-reference point cloud quality assessment (PCQA) has been an active topic in recent years and is used to measure and optimize the visual experience of point clouds. However, large multimodal models (LMMs) have rarely been explored in this area. Previous LMM-based methods mainly rely on supervised fine-tuning to directly predict numerical quality scores, lacking the ability to generalize across datasets with heterogeneous MOS scales and limited annotations. A key difficulty is that absolute MOS regression can be brittle across datasets with different score scales and distortion distributions, whereas relative quality ranking is more stable under such shifts. In this paper, we present PCQA-R1, the first reinforcement learning LMM for 3D point cloud quality assessment to simultaneously model quality understanding and scoring. Built upon the group relative policy optimization (GRPO) strategy, PCQA-R1 first constructs a chain-of-thought dataset, PCQA-CoT, which serves as cold-start training data through a reverse reasoning strategy that teaches the LMM to generate its reasoning process. We further introduce a Gaussian proximity reward that prevents calibration drift by anchoring score predictions to the source MOS range. Experimental results demonstrate that PCQA-R1 achieves state-of-the-art cross-dataset generalization across five benchmarks and competitive in-domain accuracy. Ablation studies support the role of ranking, Gaussian reward, and cold-start traces.
[CV-47] Evaluation of Image Matching Methods for Visual Odometry on UAVs
链接: https://arxiv.org/abs/2608.18624
作者: Gašper Spagnolo,Luka Čehovin Zajc,Matej Dobrevski
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, published in the Proceedings of the 33rd International Electrotechnical and Computer Science Conference ERK 2024
Abstract:Unmanned aerial vehicles (UAVs) are becoming a powerful tool for many environmental monitoring and transport applications. Yet, their reliance on Global Navigation Satellite System (GNSS) technology for navigation makes them susceptible to catastrophic failures in scenarios where the positioning signal is unavailable or disrupted. This work explores Visual Odometry (VO) as a crucial navigation component. Recently, numerous deep-learning-based methods for image matching have been proposed that are yet to be implemented in a fully-fledged VO system. In this paper, we evaluate recent state-of-the-art image matching methods for the task of VO for UAV position tracking, with a downwards-facing camera, on our synthetic dataset, and find that while the best results are generated by the recent RoMa matcher, SIFT features can outperform some recent state-of-the-art.
[CV-48] PALATE: Personalized Aesthetic Learning through Adaptive Taste Evolution for Multi-User Portrait Retouching
链接: https://arxiv.org/abs/2608.18622
作者: Jingxuan Wang,Yifan Mei,Yuxia Niu,Chaowan Jiao,Qijin Shen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Automatic portrait retouching has advanced rapidly, yet its objective is inherently subjective: the same portrait admits multiple professionally valid results, and users disagree about which one is best. Most existing methods optimize a population-level aesthetic standard and therefore cannot capture individual taste, while fine-tuning a separate editing model for every user incurs prohibitive training, storage, and data costs. We propose PALATE, a shared reward-evolution framework that keeps the image editor fixed and instead personalizes the selection among retouched candidates of the same source portrait. PALATE decomposes the reward for each user into a global backbone shared by all users, category-level residuals shared by aesthetically similar users, and a lightweight user adapter, with anti-collapse regularizers keeping the three levels complementary.A cyclic dual-level distillation scheme first distills user-specific preferences into category rewards and then consolidates the resulting category-level knowledge into the global backbone, which is redistributed to initialize the next evolution round. In this way, the shared initialization improves progressively across rounds, enabling unseen users to be calibrated from only a few rankings. On expert-retouched candidates from PPR10K with held-out users and held-out images, PALATE attains 72.83% pairwise preference-prediction accuracy, surpassing all reward, aesthetic, and image-quality baselines, of which the strongest, PickScore, reaches 58.06%. Each new user costs only 512 bytes of user-specific parameters and millisecond-level scoring.
[CV-49] SPARC: Slice-to-volume Pipeline for Automated Reconstruction of gated 3Dtime fetal Cardiac MRI
链接: https://arxiv.org/abs/2608.18616
作者: Arnaud Boutillon,Naomi Clarke,Tomas Woodgate,Daniel West,Alina Schneider,Rachael Franklin,Anthony Price,Jo Hajnal,Kuberan Pushparajah,David Lloyd,Maria Deprez
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Fetal cardiac MRI (fCMR) provides valuable diagnostic information complementary to echocardiography, particularly for complex congenital heart disease (CHD). Dynamic cine imaging captures cardiac motion essential for assessment of cardiac function; however, the reconstruction of 3D+time cine volumes from 2D+time acquired slices remains challenging due to unpredictable fetal motion and the absence of automated and robust processing tools suitable for clinical deployment. We present the SPARC pipeline (Slice-to-volume Pipeline for Automated Reconstruction of gated 3D+time fetal Cardiac MRI) which combines physics-informed slice-to-volume reconstruction (SVR) of Doppler ultrasound (DUS) gated stacks of slices, assisted by deep learning (DL) models for thoracic segmentation and anatomical reorientation. The proposed SVR algorithm achieves a tenfold reduction in reconstruction time relative to existing frame-wise approaches ( 4.8 \pm 1.0 vs 49.0 \pm 14.1 min, p 0.0001 ) while improving the reconstruction quality. Thoracic segmentation performance using ensemble aggregation exceeded inter-rater agreement (Dice 84.7 \pm 3.9% vs 81.4 \pm 7.7% , p0.05 ), while anatomical reorientation achieved a success rate of 90.1% . End-to-end evaluation on a large held-out clinical cohort ( n = 121 ) demonstrated fully automatic processing in 82.6% of cases with a mean runtime of 7.1 \pm 1.3 min, compatible with clinical deployment. The complete SPARC pipeline is publicly available as a Docker container this https URL and is currently deployed at our institution as a clinical research tool.
[CV-50] CDGP: Contrastive Dual Gaussian Processes for Weakly Supervised Anomaly Segmentation
链接: https://arxiv.org/abs/2608.18614
作者: Seungjun Chu,Seokhee Han,Mateusz Nowak,Peter Chin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Industrial visual inspection must both decide whether a product is defective and localize the defect, yet pixel-level masks are costly to collect at scale. Most anomaly-segmentation methods learn only from defect-free images and score deviations from normality. A true defect and an unusual-but-normal region, however, can both deviate substantially and receive similarly high scores. We propose Contrastive Dual Gaussian Processes (CDGP), a weakly supervised framework that models normal and anomaly inducing-variable predictive distributions over dense tokens. Its posterior-dominance statistic standardizes their predictive-mean difference by the joint predictive uncertainty, providing both spatial evidence and image-level confidence. This evidence complements hierarchical normal-reconstruction residuals for fine localization. All calibration uses training data only, without human pixel annotations or test-time fitting. Across MVTec AD~2, KSDD2, and VisA, CDGP ranks first among the evaluated methods on all MVTec AD~2 localization metrics and is first-place or competitive on KSDD2 and VisA. Factorized and matched linear-head controls delimit the contribution and scope of the linear-kernel Gaussian process (GP) formulation.
[CV-51] VA-Judger: Reward Modeling from Human Preference Feedback for Joint Video-Audio Generation
链接: https://arxiv.org/abs/2608.18607
作者: Yinming Huang,Shuyuan Tu,Xi Yan,Zihan Yang,Jianhua Han,Xu Hang,Yu-Gang Jiang,Zuxuan Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages, 7 figures, 8 tables. Code: this https URL
Abstract:Using reinforcement learning to post-train joint video-audio generation models requires a reward signal. Existing methods construct this reward by combining metrics for individual quality dimensions, including audio quality, visual fidelity, and synchronization. However, these metrics evaluate perceptual dimensions separately and fail to capture the overall semantic and temporal coherence among the text prompt, video, and audio that shapes human preferences. Optimizing models against these metrics encourages reward hacking, generating video-audio content that achieves high scores on these metrics yet appears incoherent or unfaithful to human viewers. To address this problem, we first construct a large-scale human-preference dataset VAPref-10K for joint video-audio generation, comprising 9K prompts and 10.3K fine-grained paired comparisons from open-source generation models. We also introduce the VA-Judger-Bench benchmark with both in-domain and out-of-domain model comparisons to evaluate whether reward models truly align with human preferences. We further propose VA-Judger, a chain-of-thought omni-reward model for joint video-audio generation. In particular, VA-Judger first learns from pairs with clear quality gaps to establish structured output and coarse preference discrimination, then distills reliable preference explanations for harder near-quality comparisons via rejection sampling verified against human annotations, and finally performs dimension-wise reinforcement learning that decomposes human feedback into individual quality dimensions for denser reward signals than a single binary preference label. Experiments show that VA-Judger outperforms metric baselines in predicting human preferences on both in-domain and out-of-domain evaluations. Using its human-aligned rewards for post-training audio-video generation model also yields significant improvements in generation quality.
[CV-52] ach a Molmo2Fish: Towards interactive fish tracking with natural language guidance ECCV2026
链接: https://arxiv.org/abs/2608.18602
作者: Kai Van Brunt(1),Justin Kay(1),Sara Beery(1) ((1) Massachusetts Institute of Technology)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 29 pages, 6 figures, to be published in Third Workshop on Computer Vision for Ecology at ECCV 2026
Abstract:Computer vision is increasingly used to automate recognition tasks in large ecological datasets, but more complex tasks such as multi-object tracking continue to pose challenges. As researchers seek to incorporate vision models in ecology workflows, various lines of research have explored how to make imperfect predictions useful through human-in-the-loop processes. We propose a new approach to working with imperfect tracking predictions through an interactive prediction correction workflow taking place as a conversation with a multimodal large language model, which we tailor to a sonar fish tracking dataset as an initial proof of concept. We investigate the performance of the tool, Molmo2Fish, across guided and unguided tasks, correcting its own predicted tracks and external tracks. We find that Molmo2Fish achieves high performance on fish tracking and track correction tasks, but there is still much room to improve on incorporating natural language guidance. The code and data are publicly available at this https URL.
[CV-53] ReX-Shot: Single-Image Rephotography via Geometry- and Camera-Grounded Generation
链接: https://arxiv.org/abs/2608.18593
作者: Ruiqi Zhang,Hao Zhu,Wenhao Zhang,Qi Zhang,Junqi Shi,Ming Lu,Xun Cao,Zhan Ma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Single-image rephotography aims to synthesize new shots of a scene from a single reference image with specified viewpoints, focal lengths, and photographic effects, which are intrinsically coupled in imaging. Existing methods typically treat these factors separately and struggle under joint control: novel-view synthesis may introduce geometric distortions under focal-length changes, while super-resolution and instruction-guided editing remain confined to 2D and cannot reliably extend detail restoration or appearance control to novel viewpoints. We attribute these limitations to imperfect single-image 3D reconstruction and the sampling limit of continuous focal-length enlargement. To reduce projection bias from geometric errors, we use implicitly transformed foundation-model features for robust target-view guidance. We further formulate focal-length enlargement as a geometry-guided super-resolution problem and exploit generative detail priors to recover details lost during sparse 3D resampling. Built on this 3D-aware generative backbone, we lift photographic-effect control from 2D filtering to 3D-aware appearance editing, preserving content consistency across viewpoints and focal lengths. These components form ReX-Shot, a geometry- and camera-grounded generative framework for single-image rephotography. To our knowledge, ReX-Shot is the first unified framework to jointly control viewpoint, focal length, and parameterized photographic effects from a single image. Experiments show that ReX-Shot outperforms representative baselines across all three controls while enabling near-real-time interactive rephotography.
[CV-54] FD-CanKD: Frequency-Decoupled Cross-Attention Distillation as a Refinement Prior for Compact Object Detectors
链接: https://arxiv.org/abs/2608.18590
作者: YoungJae Cheong,Jhonghyun An
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 5 figures, 6 tables
Abstract:Compact object detectors are suitable for resource-constrained visual perception, but their limited representation capacity creates an accuracy gap relative to large models. Conventional detector distillation often relies on prediction-level supervision or a single feature-alignment target, such as response, distribution, correlation, or frequency-domain matching. Frequency-Decoupled Cross-Attention Knowledge Distillation (FD-CanKD) is presented as a detector-oriented framework that transfers teacher knowledge at three complementary levels: head-level prediction supervision, relation-level non-local context transfer, and frequency-level component-selective alignment. Student features first aggregate teacher-side spatial context through cross-attention-based relation transfer, after which frequency-aware alignment preserves complementary structural and detail-sensitive cues. Under controlled Microsoft Common Objects in Context (COCO) experiments, fixed 50-epoch from-scratch comparisons show that FD-CanKD remains competitive with representative detector knowledge distillation baselines. Post-distillation continued fine-tuning further produces a stronger refinement-ready student than detector-only fine-tuning, reaching 48.87 mean average precision (mAP) at intersection-over-union thresholds from 0.50 to 0.95 (mAP50:95), 65.84 mAP50, and 53.40 mAP75 after 20 additional epochs. All distillation modules are removed after training, leaving the deployed student unchanged at 19.7M parameters. The framework is instantiated and evaluated in a controlled YOLOv12 teacher-student setting as a representative compact-detector case study.
[CV-55] OmniHandwritingOCR: A Diagnostic Benchmark for Evaluating Multimodal LLM s in Handwritten OCR Scenarios CIKM2026
链接: https://arxiv.org/abs/2608.18586
作者: Zinuo Guo,Min Zhang,Bo Jiang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: CIKM 2026
Abstract:Multimodal large language models (MLLMs) are increasingly used as OCR systems in document and knowledge-processing pipelines, but their ability to faithfully read real handwriting remains underexplored. Existing OCR benchmarks focus largely on printed text or clean single-line inputs, leaving limited coverage of realistic handwritten OCR scenarios such as multilingual handwriting, writer errors, and structurally complex mathematical expressions. We introduce OmniHandwritingOCR, a diagnostic benchmark for evaluating MLLMs and OCR systems on handwritten OCR. It covers handwritten text recognition and handwritten mathematical expression recognition across six subtasks and twelve subsets, totaling 77.57K labeled images from public datasets and newly collected student writings. A key component is a difficulty-stratified multi-line formula corpus designed to test robustness under increasing structural complexity. We evaluate thirteen open- and closed-source systems with five complementary metrics under a unified protocol. Results show that current systems remain far from faithful transcription: performance drops sharply on complex multi-line formulas, model rankings vary across language and formula settings, and several generative models hallucinate plausible but visually unsupported corrections. OmniHandwritingOCR provides a challenging testbed for diagnosing language, content, structural, and visual-grounding failure modes of multimodal models in handwritten OCR scenarios.
[CV-56] SPARC: Subspace Position-Aware Robust Few-Shot Calibration for Distribution-Shifted Industrial Anomaly Detection
链接: https://arxiv.org/abs/2608.18585
作者: Seokhee Han,Seungjun Chu,Mateusz Nowak,Peter Chin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-based industrial anomaly detectors are calibrated on one distribution but may be deployed on another that differs in illumination, fixture placement, or sensor characteristics, sharply degrading an otherwise accurate detector. Adapting to the incoming lot is a natural response, but labeled anomalies are scarce. We therefore consider calibration using only a handful of verified-normal images available before scoring the rest of the lot. Existing fixes require backpropagation, detector-specific tuning, or choices about feature directions that few calibration samples cannot justify. We present SPARC, a few-shot calibration method that intercepts patch features between encoder and detector and removes a closed-form, spatially indexed estimate of deployment-time nuisance through per-cell subspace projection. It needs only k \le 8 verified-normal images and uses the algebraic saturation rank r=k-1 on the encoder’s native patch grid. The correction requires no gradient or weight updates and works with memory-bank, density, prototype, and mutual detectors. On the shift-prone benchmarks, SPARC improves pooled Image AUROC and AU-PRO _0.3 for all seven detectors whose image scores depend on corrected patch features by +13.8 and +3.5 percentage points (pp), respectively; on benchmarks without engineered shift, the changes are small and mixed. Controls that give competing corrections the same calibration images attribute these gains to the per-cell subspace structure rather than the images alone. Further ablations support the saturation-rank choice and characterize sensitivity to backbone and calibration conditions.
[CV-57] MR-IQA-2: Faithful Image Quality Reflection via Fine-Grained Credit Assignment
链接: https://arxiv.org/abs/2608.18579
作者: Yuan li,Youyuan Lin,Chenhui Chu,Shin’ya Nishida
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal large language models (MLLMs) have shown strong potential for image quality assessment (IQA) by improving consistency between quality ratings and their underlying reasoning. However, most approaches supervise reasoning through human-provided ratings and rarely examine whether it faithfully reflects image quality. Rating accuracy alone does not ensure faithful reasoning; a shared reward also obscures supervision sources and may reinforce unfaithful reasoning when a correct rating occurs by chance. To improve the faithfulness and reliability of blind IQA, we aim to (1) decouple credit assignment for reasoning and rating and (2) provide verifiable supervision for faithful reasoning. We introduce MR-IQA-2, an actor-editor-judge framework that operationalizes reasoning-editing-reflection. The actor generates quality reasoning for an input image, and the editor revises the image according to the identified quality factors. A frozen judge compares the original and edited images and provides reflective supervision for the actor’s reasoning. MR-IQA-2 further uses fine-grained credit assignment to decouple reasoning and rating supervision. Judge feedback supervises reasoning, whereas human ratings supervise the predicted rating. Masked token-specific updates distinguish these signals while preserving the causal relation from reasoning to rating. Across IQA benchmarks, MR-IQA-2 achieves competitive rating alignment with humans. Visual reflection also enables richer and more faithful visual understanding beyond rating, which may inform image-quality optimization and related downstream tasks. Code is available at this https URL.
[CV-58] PATE-Forensics: Perception-as-Tool for Explainable Deepfake Forensics with General-Purpose MLLM s IJCAI2026
链接: https://arxiv.org/abs/2608.18573
作者: Yaqi Li,Jielun Peng,Yabin Wang,Jincheng Liu,Xiaopeng Hong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 3 figures, 2 tables; DDL-X Track 3, IJCAI 2026 AI Safety Workshop
Abstract:Existing explainable deepfake forensic methods typically rely on task-adapted MLLM to jointly address detection, localization, and explanation. Inspired by agent-style tool use, we instead introduce a Perception-as-Tool paradigm and instantiate it as PATE-Forensics, which architecturally decouples detection and localization from explanation generation while coupling detection and localization as tightly as possible within a forensic perception tool. The DINOv3-based tool couples a multi-granularity detection module that integrates global, patch-level, and segment-level evidence with a cue-guided localization module by spatializing the patch-level and segment-level evidence into forgery score maps that guide dense mask prediction. The original image and forensic perception outputs produced by the tool form structured forensic context for a general-purpose MLLM, which is guided by prompt constraints to generate explanations without task-specific fine-tuning. On DDL-X Track 3, PATE-Forensics achieves the best official score of 0.89, outperforming the second-ranked team by 0.19 points. Our code is available at this https URL.
[CV-59] StateTrace: An Object-Centric Framework for Hidden-State Spatiotemporal Reasoning in Long Videos ACM-MM2026
链接: https://arxiv.org/abs/2608.18532
作者: Yu Han,Wenhao Li,Yichao Cao,Hongyan Xu,Shuo Yang,Shan You,Xiu Su
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages. Accepted at ACM Multimedia 2026 (ACM MM 2026)
Abstract:Existing VLMs have achieved strong performance in video understanding, yet they struggle with long-video spatiotemporal reasoning when target objects become invisible, often mistaking “invisible” for “unknown”. We define this challenge as hidden-state spatiotemporal reasoning: inferring object states during prolonged invisible intervals from context interactions. To address this, we propose StateTrace, a novel object-centric framework that endows VideoLLMs with an explicit mechanism for hidden state reasoning in long videos. StateTrace builds a reusable spatiotemporal state memory that organizes object trajectories, inter-object relations, and state-transition events into a structured reasoning substrate. At inference time, it retrieves question-relevant state-evolution trajectories and converts them into compact reasoning cues, enabling the model to explicitly reason about why an object disappears, how its state evolves while invisible, and whether that state should persist at query time. We further build HSR-Bench, a diagnostic benchmark for hidden-state reasoning, containing 1,427 video-QA samples from 1,384 unique videos. Extensive experiments across multiple VideoLLMs show that StateTrace consistently improves performance on both public benchmarks and HSR-Bench (e.g., improving VideoLLaMA3 from 39.6 to 64.2 on HSR-Bench).
[CV-60] Prior-Conditioned Gaussian Discriminants for Generalizable AI-generated Image Detection ECCV2026
链接: https://arxiv.org/abs/2608.18523
作者: Shashank Kotyan,Makoto Shing,Yuki Imajuku,Rujikorn Charakorn,Tarin Clanuwat
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted in ECCV 2026
Abstract:Diffusion-based generators have made synthetic images ubiquitous, but detectors often fail under simultaneous shifts in generator, prompt/style, and source-domain. We study AI-generated image detection as a transfer system described by training prior, frozen encoder feature space, and decision rule, and ask when classifier head training adds value beyond what is already separable in modern features. As a controlled diagnostic, we fit a prior-conditioned Gaussian discriminant ladder: closed-form heads built from first- and second-order feature statistics under nested covariance assumptions. On Percept-Lens, a unified protocol over 39 public datasets (7.1 million images), the best rung is frequently competitive with, and sometimes exceeds, released AI-generated image detector heads when matched on both prior and encoder. We further quantify strong sensitivity to the training prior, data-efficiency of moment-based heads, and representation dependence of Gaussian shift metrics, motivating (prior, encoder, head)-level reporting and stronger analytical baselines for AIGI transfer.
[CV-61] OptiModNet: A UNet-Transformer Hybrid with Grouped-Query and Channel Attention for Optic Disc and Cup Segmentation
链接: https://arxiv.org/abs/2608.18516
作者: Soumili Ghosh,Debapriya Roy,Aryan Das,Bikash Santra
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Precise segmentation of the optic disc and cup is critical for the early detection and diagnosis of glaucoma. However, achieving consistently high performance across datasets while maintaining low computational requirements remains a significant challenge. In glaucoma detection, low-computation methods are crucial for enabling rapid, large-scale screening and facilitating deployment in resource-limited clinical environments. While deep learning models such as UNets, Vision Transformers (ViTs), and Diffusion models have demonstrated strong segmentation performance but these methods often come with substantial computational overhead. UNets are efficient at capturing local features but are limited in modeling global contextual information. Conversely, ViTs excel at long-range dependency modeling but are computationally intensive. Hybrid architectures, such as UNetR, which combine transformer-based encoders with UNet-style decoders, have shown improved performance but while incurring additional complexity. Considering these, in this work, we propose OptiModNet, a light weight novel hybrid architecture tailored for optic disc and cup segmentation. The model integrates diverse attention mechanisms at multiple stages of the network to enhance both local and global feature representation. We include an Aggregated Pyramid Loss that supervises predictions at multiple decoder depths, to promote better gradient flow and structural consistency. We evaluate OptiModNet on the REFUGE2 dataset for both optic disc and cup segmentation tasks. Our method achieves state-of-the-art performance, exceeding existing approaches by over 2.5%, while maintaining high efficiency with only 3.73 GFLOPs and 1.93M parameters. The code is available at this https URL.
[CV-62] Cross-Modal MRI Ovary Segmentation in Endometriosis Using Unpaired TVUS Prototype Priors
链接: https://arxiv.org/abs/2608.18515
作者: Xingjian Kang,Lina Felsner,Dominik Perrin,Daiqi Liu,Jasmin Arjomandi,Franziska Mathis-Ullrich,Alexandra Stoll,Katharina Breininger
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI) provide complementary information for endometriosis image analysis, yet existing studies mainly focus on single-modality analysis or disease classification, leaving cross-modal ovarian segmentation largely unexplored. In this work, to tackle the increased difficulty of ovary segmentation in MRI due to ovaries’ small target size and ambiguous boundaries with surrounding pelvic structures, we propose a dual branch framework for ovary segmentation across TVUS and MRI. More specifically, by adapting MedSAM3 with TVUS-derived prototype bank, we aim to align anatomically consistent feature representations across both modalities. Extensive experiments are conducted on endometriosis-related TVUS and MRI datasets. We observe quantitative and qualitative improvements of over 5 percentage points for the proposed dual-branch approach compared with multiple state-of-the-art methods. Furthermore, our ablation study shows the contribution of individual components such as the prototype bank and the importance of warm-up pretraining in the source TVUS domain.
[CV-63] DyG2T: Modeling Object Dynamics with 3D Gaussian Temporal-Spatial Particle Graph Transformer
链接: https://arxiv.org/abs/2608.18498
作者: Yansong Wang,Zhaobo Qi,Xinyan Liu,Beichen Zhang,Shuhui Wang,Weigang Zhang,Qingming Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Modeling object dynamics from limited visual observations is a fundamental problem for enabling accurate motion trajectory prediction in embodied interaction scenarios. Existing dynamics modeling methods first compress reconstructed particle representations into sparse Key Points and model their evolution using locally constrained interactions, thereby discarding fine-grained local details and obscuring discriminative interaction modeling across spatial and temporal scales, leading to drifting trajectories and inaccurate appearance prediction. To tackle these issues, we propose DyG ^2 T, a dynamics modeling framework that infers object motion trajectories by spatially completing and temporally discriminating Key Point representations and modeling multi-scale interaction over particle graphs. Spatially, DyG ^2 T enriches each Key Point by aggregating neighboring raw particle positions to recover fine-grained local details, while explicitly encoding relative offsets among Key Points to enhance geometric structure perception. Temporally, we introduce a Temporal Disentangling Network (TDN) to identify dominant cross-frame variations in latent space and amplify inter-frame differences, yielding temporally discriminative representations that are subsequently aggregated via Temporal Attention to capture frame-wise temporal evolution cues. For comprehensive interaction modeling, a Particle Graph Transformer leverages global attention to preserve discriminative long-range dependencies among Key Points, mitigating representation homogenization induced by locality-constrained modeling and providing a robust basis for accurate trajectory prediction. Experiments on both synthetic and real-world datasets demonstrate that DyG ^2 T achieves accurate dynamics modeling and reasoning, and exhibits strong cross-object and real-world generalization.
[CV-64] Partition the Support Reconstruct the Residual: Training-Free Sparse Attention for Video Generation and World Models
链接: https://arxiv.org/abs/2608.18484
作者: Pardis Taghavi,Reza Langari,Gaurav Pandey
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 22 pages, 5 figures. Project page: this https URL
Abstract:Training-free block-sparse attention can accelerate video transformers, but row-wise attention concentration does not by itself specify an executable sparse operator. Queries sharing a block route may have poorly overlapping supports, while retained attention mass alone does not determine the post-softmax error from skipped interactions. We show that partition geometry affects both pooled support and the predictability of the remaining residual from the sparse output. We introduce SparsePR, which combines Response-Coupled Partitioning with Probe-Fitted Residual Reconstruction. Sampled-query key responses form paired K/V groups, whose centroids induce query-response coordinates for shared routing. A small set of exact query rows then calibrates a call-specific affine correction from the sparse output within the output subspace observed in the probe residuals. Across four heterogeneous video generation and world models, SparsePR consistently reduces attention-reconstruction error. Ablations show that probe fitting accounts for most of this reduction, while response-coupled partitioning lowers hard-drop error and improves reconstruction under a finite probe budget. SparsePR preserves generation quality at 22.0-26.0% realized executed-pair density while achieving 1.48x-2.61x end-to-end speedups. Project page: this https URL
[CV-65] COSTA: A Cluster-Centric Paradigm for Annotation-Free Open-Set Semantic Segmentation of Aerial Point Clouds with Domain Shifts
链接: https://arxiv.org/abs/2608.18479
作者: Yanghong Lin,Li Fang,Tianyu Li,Shudong Zhou,Wei Yao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Semantic segmentation of aerial point cloud is trapped in a generalization crisis under distinct domain shifts. While test-time adaptation offers a privacy-preserving and computationally efficient way to adapt pre-trained models to unlabeled target-domain data during inference, existing methods, bound to closed-set label assumptions and non-scalable point-wise segmentation pipelines, still struggle with semantic shifts. We ask: can we adapt any given pre-trained aerial point cloud segmentation model to a shifted target domain at the inference phase alone, without additional training, while segmenting target-specific categories beyond the source label space on demand? This paper introduces COSTA, which breaks this limitation by shifting from closed-set point-wise adaptation to cluster-centric open-set semantic propagation. Our core discovery is that, once effectively adapted at test time, the rich feature distribution of aerial point clouds can be distilled into a compact set of well-separated semantic centroids that are transferable across label spaces. COSTA leverages this to reformulate open-set semantic segmentation as a cluster-level propagating process: it first bridges the domain gap through proven test-time adaptation, then groups each batch of target-domain points into a small set of semantic clusters based on the similarity distribution in the adapted feature space, and finally propagates high-confidence pseudo labels obtained from an open-vocabulary vision-language model to all points through cluster-level voting. This cluster-centric paradigm enables test-time adaptation of aerial point clouds under significant domain gaps with mixed semantic shifts. With DALES as the source domain, COSTA enables on-demand segmentation across three aerial point cloud benchmarks with distinct domains and heterogeneous category spaces, achieving up to 70.09% mIoU under this new setting.
[CV-66] CoMVS-GS: Collaborative Multi-View Stereo and 3D Gaussian Splatting for Surface Reconstruction
链接: https://arxiv.org/abs/2608.18413
作者: Shihan Chen,Junjing Zhang,Qingsong Yan,Haibing Liu,Haofan Ren,Fei Deng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D Gaussian Splatting enables efficient novel view synthesis, but accurate mesh reconstruction remains difficult in weakly observed and occluded regions, where Gaussian primitives may grow into unstable or geometrically inconsistent structures. We propose CoMVS-GS, a general surface reconstruction framework that combines Multi-View Stereo with Gaussian splatting. CoMVS-GS initializes Gaussian primitives from dense multi-view stereo points with pre-flattened scales and normal-aligned orientations, providing stronger geometric priors than sparse structure-from-motion initialization and reducing ambiguity during early optimization. It further introduces PatchMatch-3DGS Mutual Supervision, where Gaussian-rendered depths and normals initialize PatchMatch refinement, and refined PatchMatch depths supervise Gaussian optimization to improve weakly constrained geometry. For surface extraction, CoMVS-GS replaces truncated signed distance field voxel fusion with a Delaunay graph-cut meshing pipeline, reducing sensitivity to voxel resolution while preserving visibility-consistent surface evidence. Experiments on DTU, GauU-Scene V2, and MatrixCity show that CoMVS-GS remains competitive on object-level reconstruction and improves geometric accuracy and mesh compactness in outdoor scenes while maintaining high rendering quality.
[CV-67] JSL-DC: A Word-Level Japanese Sign Language Dataset with Linguist-Derived Descriptions for Distinguishing Confusable Signs
链接: https://arxiv.org/abs/2608.18412
作者: Ken Takaki,Asuka Ando,Misa Suzuki,Uiko Yano,Masaya Tsujimoto,Bill Neubauer,Ananay Vikram Gupta,Rose Shao,Matthias Hoppe,Sahir Shahryar,Celeste Mason,Kai Kunze,Yohei Oseki,Yoshihiro Kawahara,Thad Starner
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Effective sign language (SL) acquisition is crucial for deaf children, yet 95% are born to hearing parents who often lack proficiency in SL. SL recognition can power learning tools to help parents communicate with their children. However, Japanese Sign Language (JSL) lacks large-scale, multi-signer datasets, hindering the development of models that can generalize to new users. To address this gap, we introduce JSL-DC, the largest JSL dataset by video count, comprising 36.7K videos from 19 signers. The entire process was Deaf-centric: the lexicon comprising 270 JSL words was selected by Deaf and Coda linguists to facilitate parent-child communication, all participants were Deaf individuals who use JSL daily, and the data underwent a two-stage review process involving Deaf linguists. Moreover, we provide linguist-derived descriptions for distinguishing confusable signs. We demonstrate that the proposed model inspired by the descriptions outperforms state-of-the-art recognition methods by 9.8% on the confusable subset. The dataset, along with its linguistic description that inspires new models, will be released under a CC-BY 4.0 license to accelerate research in SL recognition.
[CV-68] What Does Attention Transfer Transfer? Attention Structure and Robustness in Vision Transformers
链接: https://arxiv.org/abs/2608.18399
作者: Jesse Ponnock
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 28 pages, 9 figures
Abstract:Vision transformers (ViTs) trained to copy a pretrained teacher’s attention maps recover most of fine-tuning’s in-distribution accuracy yet fall measurably short of it under distribution shift, as recent work has shown. What the copy delivers has never been measured directly in the attention structure and tied to robustness. We build that instrumentation for ViT-S students of a self-supervised teacher on ImageNet-100, and report three findings that triangulate one conclusion. First, the transfer is essentially perfect and permanently so: the distilled student’s attention ends up roughly two orders of magnitude closer to the teacher’s than fine-tuning does, and does not drift with additional training. Second, the gap is real at 14 \times fewer parameters and 10 \times less data than previously studied, but it has a time axis. It tracks training maturity, and completing the schedules that the stopping rule interrupted closes it below our pre-registered threshold in two of three seeds, with comparisons at equal accuracy giving the same result. The endpoint gap at this scale is substantially a training-maturity artifact: robustness matures later than accuracy, and stopping rules tuned to accuracy undersample it. Third, forcing cross-row redundancy down by half the structural separation between the distilled and fine-tuned conditions produces no detectable robustness response under two registered ways of matching accuracy. Verified transfer, a gap that closes while the structure never moves, and a null under direct intervention are together consistent with the deficit residing in features, not in the visible attention structure. This is elimination plus intervention, and its scope is the regime we measured. In this regime, attention overlays show where a model looks, not what it knows.
[CV-69] Depth Anything V4: Dynamic 4D Scene Reconstruction via Riemannian Flow Matching on 4D Gaussian Splatting
链接: https://arxiv.org/abs/2608.18388
作者: Jiaming Fan,Jian Lu,Jinling Jia,Chenbin Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present Depth Anything V4 (DAV4), a framework for dynamic 4D scene reconstruction from monocular video. Our key contribution is the application of Riemannian Flow Matching (RFM) to 4D Gaussian Splatting parameters, defining probability paths directly on non-Euclidean manifolds (scale, rotation, opacity), ensuring all intermediate states are valid. Through controlled experiments, we isolate RFM’s contribution from test-time optimization (TTO) and pre-training. A deterministic MLP baseline with the same data, architecture, and TTO achieves F-score 0.762; RFM achieves 0.806 - the +0.044 gain is RFM’s isolated contribution. We provide corrected computational cost analysis: pre-training is 360 GPU-hours, amortizing for large-scale deployment (over 10,000 scenes). Uncertainty is quantified via Negative Gaussian Log-Likelihood and Expected Calibration Error. DAV4 outperforms prior Depth Anything models and per-scene 4D-GS on dynamic reconstruction and novel-view synthesis, while using no human-annotated depth labels as training losses.
[CV-70] SD-FAR: Test-Time Self-Distillation with Fisher-Anchored Restoration for Missing-Modality Emotion Recognition in LVLMs
链接: https://arxiv.org/abs/2608.18386
作者: Muhammad Haseeb Aslam,Alessandro Koerich,Marco Pedersoli,Ali Etemad,Eric Granger
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Large video-language models (LVLMs) have shown remarkable performance on multimodal tasks like multimodal emotion recognition (ER) in the wild. ER is inherently multimodal, requiring a joint understanding of facial expressions, vocalizations, language, biosignals, and gestures. However, real-world deployment remains challenging: modalities may be missing or noisy at test time. Partial observations can be viewed as a distribution shift relative to the complete-modality distribution. SOTA TTA methods based on entropy minimization or perplexity reduction do not transfer to autoregressive LVLMs, while retrieval augmented generation (RAG) degrades when the observed modality is weak. Because no ground-truth supervision exists to verify individual updates, adaptation across this stream risks accumulating drift and degrading once the model departs from a reliable solution. An effective solution must therefore adapt to arbitrary missing-modality patterns and remain effective during continual adaptation. We address both jointly with Test-Time Self-Distillation (TTSD), a parameter-efficient framework in which a frozen teacher, trained on complete modalities, guides an adaptive low-rank student via self-distillation, updating only a negligible number of parameters. Stability is built into this same loop through Fisher-Anchored Restoration (FAR), which monitors Fisher information stability to detect convergence versus drift and restores the student toward the teacher’s anchor when distributional shifts are identified. Our experiments on MELD, DFEW, and BAH under 0%-50% missing modalities show that this unified adaptation-restoration design consistently outperforms entropy-based adaptation, RAG, and perplexity-based generation over long adaptation horizons, where baselines without restoration progressively degrade while TTSD-FAR remains consistent.
[CV-71] Reproducible Multimodal Affordance Prediction ECCV
链接: https://arxiv.org/abs/2608.18317
作者: Tommaso Apicella,Alessio Xompero,Andrea Cavallaro
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Paper accepted to Workshop on Human-Centered Multimodal Intelligence in the Wild (HCMIW) in European Conference on Computer Vision (ECCV) 2026; 18 pages, 3 figures, 7 tables. Project webpage at this https URL
Abstract:Affordance prediction is the identification of potential actions an agent can perform on a target object from multimodal inputs. Affordance prediction methods are difficult to evaluate and compare due to heterogeneous problem formulations, inconsistent dataset annotations, incomplete reporting of experimental protocols, and limited information about deployment conditions. These limitations challenge fair benchmarking and performance comparison. To promote transparency, we propose the Affordance Sheet, a documentation detailing task formulation with its input modalities, model architectures and training information, datasets, and experimental protocols. Affordance Sheets enable reproducible benchmarking and reliable evaluation of affordance models for real-world scenarios, including generalisation to novel conditions and human safety.
[CV-72] A Configurable Privacy-Preserving MRI Processing Workflow Using Deep Learning-Based Brain Extraction and Adaptive Anatomical Preservation
链接: https://arxiv.org/abs/2608.18316
作者: Rayeef Ali Khan,Komal Raj Mahantesh
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 6 figures
Abstract:Structural Magnetic Resonance Imaging (MRI) is widely used in neuroimaging research and clinical practice, but structural MRI volumes may retain facial and cranial anatomical information that raises privacy concerns. Existing deep learning-based brain extraction methods generally produce a single fixed output, limiting flexibility when different applications require different balances between privacy and anatomical preservation. This paper presents a configurable privacy-preserving MRI processing workflow that extends deep learning-based brain extraction through adaptive anatomical preservation, interactive preservation selection, and integrated quality control. The workflow employs SynthStrip for automated brain extraction, followed by morphological mask expansion to generate configurable shell-based preservation levels. An Interactive Preservation Framework enables users to compare preservation configurations and select an appropriate output, while an integrated Quality Control Framework provides multi-plane visualisation and brain-mask overlay verification. The workflow was implemented in Python using open-source neuroimaging libraries within the Renku reproducible research environment and evaluated using structural T1-weighted MRI data from the publicly available IXI dataset. Experimental results demonstrate anatomically plausible brain extraction and configurable preservation outputs, supported by systematic visual verification. The principal contribution is a modular and reproducible MRI preprocessing framework that enhances deep learning-based brain extraction with configurable anatomical preservation, interactive user-guided processing, and integrated quality control. The workflow provides a practical foundation for privacy-oriented neuroimaging research and collaborative medical image analysis.
[CV-73] FedCoRe: Target-Adaptive Completion for Missing Modalities in Healthcare Federated Learning MICCAI
链接: https://arxiv.org/abs/2608.18311
作者: Holger R. Roth,Ziyue Xu,Peter Cnudde
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted to the 7th Workshop on Distributed, Collaborative Federated Learning, DeCaF 2026, MICCAI, Strasbourg, France
Abstract:Federated multimodal models often assume every site has every modality, although hospitals differ in access to EHRs, chest radiographs, and ECGs. We study this setting on a MIMIC-derived respiratory deterioration task with simulated FL clients and introduce FedCoRe (Federated Cross-Modal Representation Completion). FedCoRe learns representation- or logit-space corrections rather than generating synthetic ECGs or CXR images. When a client observes a modality that may be missing at deployment, it evaluates the same example with and without that modality to obtain paired supervision. Only clients with such pairs update the completion module, and validation may retain the unchanged prediction. We freeze the trained multimodal predictor during evaluation so that measured differences come only from completion. Hiding ECG reduced AUROC by about 0.085; paired-example FedAvg restored 0.0415 AUROC, or 49.0% of the lost performance. We therefore report two distinct effects: paired-example FedAvg partially recovers the missing-ECG gap, while validation-selected completion is a task-specific classifier-logit correction rather than literal ECG recovery. For CXR, effect-aware completion recovers 52.8% of the loss in a controlled test where CXR is hidden. Paired-example FedAvg transfers part of this effect, but validation keeps the no-completion baseline for deployment cases whose inputs lack CXR. Thus, FedCoRe should be read as a validation-gated completion/correction framework: it can recover missing-modality signal in supported settings, but it should be deployed only when paired examples and validation evidence support that modality.
[CV-74] XRF-to-Optical Field-of-View Localization with Vision Language Models
链接: https://arxiv.org/abs/2608.18309
作者: Xiangyu Yin,Tatjana Paunesku,Letonia Copeland-Hardin,Martina Ralle,Zichao Wendy Di,Si Chen,Gayle E. Woloschak,Barry Lai,Mathew J. Cherukara,Stefan Vogt
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Registering images acquired with different microscopy modalities is essential for relating complementary measurements of the same specimen. In correlative X-ray fluorescence (XRF) and optical microscopy, the XRF map often covers only a small region of an optical image acquired from the same or an adjacent tissue section. Field-of-view (FOV) localization is necessary but can be difficult when appearance and structure differ across modalities. Here we evaluate training-free vision language model (VLM) localization on two datasets representing same-section high-correspondence and adjacent-section low-correspondence imaging. We test unconstrained and metadata-constrained search and compare VLMs with geometric controls, classical template matching, and two alternative training-free approaches (DINOv2 and multiGradICON). Direct VLM prompting produced content-dependent spatial signals but was not reliable alone. Classical matching was most accurate when cross-modal structure was preserved but failed in the low-correspondence collection. A proposal-and-verify workflow used repeated VLM predictions as candidates and image-based similarity to select the final location. This workflow recovered useful localization in the low-correspondence regime.
[CV-75] High-Flux Count-Free Single-Photon 3D Cameras
链接: https://arxiv.org/abs/2608.18306
作者: Kaustubh Sadekar,Vivek K Goyal,David Maier,Atul Ingle
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Presented at IEEE ICCP 2026 (Best Paper Award Winner). To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Abstract:Single-photon cameras based on single-photon avalanche diode (SPAD) technology are gaining popularity for 3D sensing, thanks to their extreme sensitivity and time resolution. There are two key challenges with single-photon cameras that limit their widespread use: (i) they suffer from non-linear distortions called ‘‘pile-up’’ when operated in high-photon-flux conditions, and (ii) they generate a large volume of raw photon data, creating a severe data bottleneck at each sensor pixel. In this work, we show that while compressive capture techniques successfully mitigate data transfer challenges, they exacerbate the effects of dead-time distortion because they fail to retain sufficient information about the photon detection history to allow post-processing pile-up correction via existing methods. We propose a new computational-imaging method that combines free-running capture with an analysis-by-synthesis software pipeline to mitigate pile-up distortions. Our results with hardware emulations and full-scene and single-pixel simulations show that our method can reliably capture scene distance and reflectance over a wide range of illumination conditions. Our work will enable high-resolution SPAD cameras that are severely bandwidth-constrained to operate in real-world high-flux scenarios.
[CV-76] Acquisition Geometry-Assisted Whole-Group Localization of X-ray Fluorescence Maps in Optical Microscopy Images
链接: https://arxiv.org/abs/2608.18305
作者: Xiangyu Yin,Tatjana Paunesku,Letonia Copeland-Hardin,Martina Ralle,Zichao Wendy Di,Si Chen,Gayle E. Woloschak,Barry Lai,Mathew J. Cherukara,Stefan Vogt
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:X-ray fluorescence (XRF) microscopy maps elemental distributions, while optical microscopy can provide complementary morphological context. Localizing XRF fields of view (FOVs) in optical images is difficult because the two modalities differ in contrast mechanism and resolution. Most current workflows place each XRF tile independently, even when acquisition metadata already record the tiles’ relative scan positions. This study formalizes XRF tile-group localization, in which one optical-frame placement is estimated for the whole group, constrained by acquisition geometry and quantified using group intersection-over-union (GroupIoU). In a controlled case study, independent localization failed with GroupIoU 0.000, whereas group localization achieved 0.931. Replacing the normalized cross-correlation (NCC) metric with mutual information (MI) gave nearly identical results, showing that the outcome is not specific to one local similarity metric. In another multiscale case study, using a coarse XRF survey scan to connect the fine-scale tile group to the optical image increased mean GroupIoU from 0.694 to 0.856. These case studies support using acquisition geometry as an explicit constraint when localizing related XRF tiles.
[CV-77] GuideFetch: A Task Coordination Framework for Concurrent Navigation and Object Retrieval in Assistive Robot Dogs
链接: https://arxiv.org/abs/2608.18292
作者: Qian Yin,Ruiping Liu,Kunyu Peng,Jianxiang Man,Isik Baran Sandan,Junwei Zheng,Yufan Chen,Di Wen,Kailun Yang,Rainer Stiefelhagen
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Consider a robot guide dog escorting a blind user to an available seat while a second assistive robot dog concurrently retrieves a cup of coffee and delivers it to the same seat. This setting motivates concurrent execution because navigation and object retrieval can overlap. A syntactically valid Large Language Model (LLM) plan may still violate embodiment constraints, and successful-looking controller motion does not by itself establish task completion. We introduce \textscGuideFetch, a coordination framework for concurrent navigation and object retrieval by a heterogeneous guider and fetcher team. An LLM instantiates a schedule-conditioned four-action schema from a natural-language instruction. Before execution, robot, skill, and target aliases are normalized, and proposed actions are validated against registered targets, robot capabilities, and the selected schedule. Robot and object states then govern sequential and parallel execution. In a matched 2\times2 study across 90 combinations of scene and seed (360 executions), all 180 online LLM responses validate without fallback or replay and match the corresponding scripted plans. For each planner source, sequential and parallel execution achieve 72/90 and 71/90 operational successes, respectively. Among the 56 cases completed by both schedules, parallel execution reduces mean makespan by 41.3%. Within this controlled setting, role specialization and action overlap shorten completed missions, while state checks distinguish plan validity from verified mission completion. Source code will be available.
[CV-78] Visual-Prompt Guided Wildlife Instance-Level Recognition ECCV
链接: https://arxiv.org/abs/2608.18246
作者: Mufhumudzi Muthivhi,Jiahao Huo,Terence van Zyl,Fredrik Gustafsson
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepetd in ECCV Instance-Level Recognition and Generation Workshop 2026, Malmö Sweden
Abstract:Fine-grained wildlife re-identification remains a challenging area in research. Current state-of-the-art approaches apply a detection and re-identification pipeline. We propose a one-stage end-to-end detection and re-identification model that performs identity searching within the latent space. We adopt DINOv2 for robust spatial geometry and MegaDescriptor for wildlife re-identification. We enhance latent queries with prompt re-identification features. A detection decoder queries the scene latent space to establish object boundaries around the target identity. Preliminary findings reflect a competitive mean average precision score of 30.584% compared to the state-of-the-art two stage approach of 44.89%. Qualitative results depict effective bounding and identification of animal identities.
[CV-79] Zero-Shot Transfer of Force Map Estimation Across GelSight Mini Sensors
链接: https://arxiv.org/abs/2608.18240
作者: Julio Castaño Amoros,Pablo Gil
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Accepted for publication in IEEE Sensors Letter
Abstract:Despite the rapid industrialization of the touch sensor manufacturing process, most of these sensors are still handmade in research laboratories. This complicates standardizing their performance, requiring the repetition of data collection and training models for each unit produced. To address this problem, this paper presents a method that can generalize the estimation of 3D force maps across different GelSight Mini sensor units, regardless of the sensor version. Specifically, the method consists of two stages: a domain adaptation stage, in which the input tactile image is reconstructed as a general tactile image using a UniT-based model; and a stage for estimating 3D force maps employing a U-Net network. Our proposal achieves promising results in both steps, such as an SSIM of 0.9338 ± 0.0358 in the image reconstruction phase and an MAE_F of 1.1294 ± 1.5934(N) in the force estimation phase.
[CV-80] LumiTokens: 3D Relighting via Token-Space Lighting Transformation
链接: https://arxiv.org/abs/2608.18215
作者: Yiwen Chen,Matheus Gadelha,Huaizu Jiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Existing 3D relighting methods operate through either explicit material decomposition, diffusion-based view-space generation, or a combination of both, requiring full recomputation for each new lighting condition. We observe that recent latent scene representations, which encode multi-view images into a set of compact tokens with no fixed physical semantics, open up a novel design space for relighting. We present LumiTokens, a framework that formulates 3D relighting as a direct transformation on latent scene tokens, without explicit 3D representations, rendering equations, or physics-based decomposition. Our model introduces a Scene Token Editor that processes scene tokens jointly with light-ray tokens through self-attention, producing updated tokens that can be decoded into multi-view-consistent relit images. To support diverse lighting types through a unified interface, all lighting signals, including environment maps, point lights, and area lights, are parameterized as Plucker ray tokens, enabling native 3D user interaction with a representation that carries no explicit spatial structure. Crucially, this design supports progressive relighting: because the editor’s output remains in the same latent space as its input, a user can incrementally build up illumination one light source at a time, with each edit composing in token space. Experiments demonstrate that LumiTokens achieves comparable or superior relighting quality to other methods and supports progressive, composable lighting edits. Project page: this https URL
[CV-81] Bound-Aware Per-Organ Recall Risk Control for Multi-Organ CT Segmentation under Clinical Domain Shift
链接: https://arxiv.org/abs/2608.18193
作者: Souraj Adhikary,Negar Chabi,Andre Mastmeyer
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 12 pages, 4 figures, 2 tables
Abstract:Distribution-free risk control adds organ-specific recall guarantees to frozen segmentation. We calibrate per-organ thresholds for an AMOS-trained nnU-Net, audit transfer to RAOS, and estimate local re-certification cost using case-level voxel false-negative rate (FNR). The AMOS control passes, but 7/12 organs exceed \alpha=0.10 after transfer; smaller calibration sets can mask exceedances with conservative or vacuous thresholds. Risk-Controlling Prediction Sets (RCPS) give high-probability control of population-mean risk, whereas Conformal Risk Control (CRC) gives weaker expectation control. Both require exchangeability; fixed and global thresholds give no per-organ guarantee. The Waudby–Smith–Ramdas (WSR) betting bound re-certifies six Tier-1 organs with 25 local cases, versus 30–40 for Hoeffding–Bentkus (HB). CRC needs 10–15 but has a heavier individual-case tail. No Tier-2 organ meets our illustrative precision criterion with 25 cases.
[CV-82] H2EDL: Hyper Evidential Deep Learning for Hierarchical Classification
链接: https://arxiv.org/abs/2608.18185
作者: Yuanye Liu,Xiahai Zhuang
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Fine-grained recognition often involves hierarchical label spaces, where a model may be confident about a coarse semantic concept while remaining uncertain among its descendant classes. Such structured ambiguity requires uncertainty representations that capture both fine-grained classes and intermediate concepts. However, existing tools each capture only half of it: flat evidential classifiers quantify total ignorance with a single vacuity on the leaf frame, and hierarchical classifiers propagate point probabilities with no notion of evidence. Hyper-opinions would unify the two, but their general form is exponential in the label count, and existing hyper-evidential networks either require composite labels to be supplied in the training data or read them off an unstructured weight pattern, with no principled notion of which composites deserve mass. We observe that the taxonomy itself is the missing hyperdomain. Its subtrees and leaf singletons form a linear-size focal family, and one local Dirichlet opinion per branching node induces every composite mass in closed form. The resulting model, H ^2 EDL, can be interpreted in two complementary ways using the same set of parameters. From a prediction perspective, it functions as a hierarchical classifier that preserves consistency across different levels of the label tree. From a probabilistic perspective, it defines a valid tree-structured hyper-opinion, where the mass assigned to each node represents the belief that reaches that node but does not provide sufficient confidence to further specialize into its descendants. On FGVC-Aircraft and DERM12345, H ^2 EDL reduces calibration error by approximately half compared with cross-entropy baselines, with the improvement becoming more pronounced at deeper hierarchy levels and under larger training budgets.
[CV-83] Human-Centric Intelligence in the Era of Foundation Models: A Survey
链接: https://arxiv.org/abs/2608.18184
作者: Yang Chen,Tianqi Wang,Xiaorui Jiang,Yilei Man,Yihua Shao,Mengyuan Liu,Zhi Chen,Xiaofeng Cao,Qibin Zhao,Chi Harold Liu,Albert Y. Zomaya,Nicu Sebe,Jingren Zhou,Dacheng Tao,Song Guo,Jingcai Guo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: GitHub Repo: this https URL Project Page: this https URL
Abstract:Human-centric intelligence is evolving in the foundation-model era, with growing emphasis on scale, transferability, and general-purpose modeling. Yet it has not fully integrated with foundation models to achieve the comparable progress seen in them. More importantly, recent advances across this broad landscape remain fragmented across tasks, modalities, and research communities, leaving their intrinsic conceptual and methodological connections unclear. To bridge these divides and rethink human-centric intelligence in the foundation-model era, we introduce a full-spectrum human context taxonomy that integrates six interconnected levels by viewing humans as observable subjects through visual appearance and spatial geometry, as dynamic actors through kinematic dynamics and interaction modeling, and as situated agents through world simulation and embodied agency. We next present the methodological foundations of the field, covering human-centric data families, computational architecture paradigms, and representative training and inference optimization strategies. We then systematically review representative methods across these levels and organize the associated datasets, benchmarks, and evaluation metrics. We further discuss open challenges and promising research directions toward human-centric intelligence that is scalable, trustworthy, physically grounded, and deployable, aiming to provide a coherent framework and practical reference for advancing the field. Finally, we provide a systematically organized and continuously updated collection of human-centric AI literature and resources on our project page.
[CV-84] Bernstein-Vazirani Networks: Quantum Machine Learning by Interference
链接: https://arxiv.org/abs/2608.19043
作者: Natacha Kuete Meli,Tolga Birdal,Prayag Tiwari,Vladislav Golyanik,Michael Moeller
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:We introduce Bernstein-Vazirani Networks (BVNs), a non-variational quantum machine learning framework that leverages quantum interference for supervised learning, demonstrated on vision and representation learning tasks. In their standard form, BVNs follow the principle of quantum Fourier sampling: labelled data are placed in superposition and interfered in the Fourier basis to extract globally informative features. We then define generalised BVNs that enable interference in problem-adapted bases, yielding more expressive models under the same measurement budget as in the standard setting. BVNs achieve universal function approximation through (over)complete interference bases, while training of BVNs is gradient-free. Experiments on synthetic and real-world classification tasks, as well as implicit image representation, show strong generalisation capabilities and competitive performance with classical and quantum baselines.
[CV-85] Optic Disc Segmentation in Fundus Images: From Classical Image Processing and Deformable Models to Modern AI
链接: https://arxiv.org/abs/2608.18367
作者: Buket D. Barkana
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate localization and segmentation of the optic disc (OD) are important for retinal image analysis and glaucoma assessment, yet remain challenging due to variations in illumination, pathology, vascular interference, and poorly defined boundaries. This structured methodological review examines the evolution of OD segmentation from classical image-processing and deformable models to contemporary artificial intelligence (AI)-based approaches. A structured literature search and study-selection process was used to identify representative studies spanning major methodological developments. The review first summarizes commonly used fundus-image datasets, then organizes classical methods by principal mechanisms, including intensity and thresholding, histogram and entropy analysis, morphology, geometric and Hough-transform methods, filtering and feature operators, texture- and region-based approaches, and active-contour and level-set models. This paper pays particular attention to the assumptions, strengths, limitations, and complementary roles of these methods in OD localization and boundary delineation. Representative AI approaches are subsequently examined to illustrate the transition from handcrafted features and explicitly defined priors to learned representations, Transformer-based segmentation, boundary- and shape-aware learning, promptable segmentation, and retinal foundation models. Across these methodological generations, several core segmentation principles persist, including region-of-interest localization, multiscale representation, geometric and anatomical constraints, and boundary regularization, although their implementation has shifted from predefined operators to learned modules, losses, prompts, and pretrained representations. The review further identifies boundary ambiguity, anatomical variability, domain shift, and cross-dataset generalization as continuing challenges.
[CV-86] QuARC-GS: Quantized Anchored Residual Coding for Compact Dynamic Scene Streaming with Gaussian Splatting
链接: https://arxiv.org/abs/2608.18285
作者: Vu Trung Nghia Nguyen,Yuchen Wang,Kyung Chul Lee,Kevin C. Zhou
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 5 figures, 3 tables
Abstract:3D scene representation techniques such as neural radiance fields (NeRFs) and Gaussian splatting have made substantial progress in novel view synthesis, achieving high-quality renderings from arbitrary view angles. More recently, such techniques have been extended to dynamic 3D scenes; however, achieving sustainable online free-viewpoint video (FVV) streaming remains challenging, especially for longer videos, due to significant storage demands of detailed scene representations and high reconstruction/rendering speed needs. To address these challenges, we propose Quantized Anchored Residual Coding Gaussian Streaming (QuARC-GS), a quantization-aware 4D scene optimization framework for online dynamic scene reconstruction that achieves ultra-high compression while maintaining reconstruction speed and quality. QuARC-GS represents a scene using a single canonical frame and highly compressed per-frame residuals. Specifically, we compress each residual through two complementary strategies targeting motion, appearance, and densification. We introduce quantization-aware anchor deformation, which suppresses insignificant motion updates while preserving meaningful deformations, maintaining reconstruction quality under low-storage streaming. Furthermore, we design a change-gated densification strategy that allocates new Gaussians only in regions exhibiting genuine temporal changes, effectively eliminating redundant appearance updates and reducing storage overhead. Extensive experiments on widely used datasets demonstrate that QuARC-GS enables competitive reconstruction quality and training speed while cutting per-frame storage by up to 11 \times compared to the state-of-the-art.
[CV-87] ractoGraphVLM: A Unified Vision-Language Framework for White Matter Tractography ECCV2026
链接: https://arxiv.org/abs/2608.18166
作者: Gurucharan Marthi Krishna Kumar,Janine Dale Mendola,Amir Shmuel
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted as a Spotlight at the ECCV 2026 Workshop on Artificial Intelligence for Medical 3D Vision (AI4M3D). Our codebase, including all training and evaluation pipelines, is publicly available at this https URL
Abstract:Vision language models have transformed 2D medical imaging, yet extending them to 3D white matter tractography remains challenging due to the complex topology of fiber bundles. We introduce TractoGraphVLM, a unified framework for four tasks, bundle classification, text-to-tract retrieval, anatomical captioning, and visual question answering, built on a shared GPS architecture, training procedure, and read-out design. Fiber bundles are represented as streamline graphs whose nodes encode 3D position and tangent orientation. A General, Powerful, Scalable (GPS) graph transformer produces bundle embeddings aligned with a frozen BiomedBERT text encoder via contrastive learning, while a BioGPT decoder with visual prefix tokens generates captions and answers. A single shared encoder and decoder is trained jointly across all four tasks and evaluated from one checkpoint. Trained on HCP Young Adult subjects, TractoGraphVLM achieves 91.8% bundle classification accuracy, 84.7% retrieval R@1, BLEU-4=20.1, ROUGE-L=66.8, and 66.4% VQA accuracy on a held-out test set. The same checkpoints transfer zero-shot to HCP Aging subjects, with a modest drop on discriminative tasks and a larger drop on generative tasks, showing robustness to age and acquisition shift. Language supervision yields richer representations than label-only training, recovering structure like hemisphere and fiber family, carried by captions but never given as a label. Swapping only the visual encoder, graphs preserving fiber orientation outperform volumetric baselines, with GPS giving the best balance. Generative metrics measure consistency with a structured knowledge base rather than independent clinical text; even so, TractoGraphVLM shows that classifying, retrieving, describing, and answering questions about a white matter bundle can be served by one jointly trained model that learns transferable neuroanatomy from language alone.
人工智能
[AI-0] ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning
链接: https://arxiv.org/abs/2608.19182
作者: Jayjun Lee,Jessica Yin,Asif Rana,Nicholas Blauch,Sam Mady,Mohak Bhardwaj,Nima Fazeli,Nathan Ratliff,Karl Van Wyk,Ankur Handa
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Project page: this https URL
Abstract:We introduce Accelerating Dexterity via Pre-Training (ADEPT), a large-scale reinforcement learning (RL) framework for learning sim-to-real transferable dexterity across high degree-of-freedom (DoF) robot embodiments that can solve long-horizon tasks directly from raw visuo-tactile perception. ADEPT pretrains a dexterous policy on a generic object reposing task, then post-trains downstream policies with this pretrained behavior as a prior. ADEPT enables learning new behaviors that are otherwise difficult to discover from scratch on multi-fingered robots and avoids learning the same set of skills over again for every new downstream task. The pretrained policy zero-shots the reposing phase of downstream tasks, but naïve RL fine-tuning rapidly degrades this capability during transfer. We address this with a stable post-training recipe combining behavior-cloning distillation, critic warm-up, and conservative on-policy updates. To safely exploit the full kinematic dexterity, we introduce a joint-space Geometric Fabric that mediates between the RL policy and the robot. We distill post-trained teachers into perceptive students that zero-shot sim-to-real transfer on two embodiments: a 23 DoF Kuka-Allegro with two RGB cameras, and a 29 DoF Flexiv-Sharpa with two RGB cameras and five vision-based tactile sensors, and can solve long-horizon tasks from challenging initial states with dexterity at human-level speed.
[AI-1] Beyond the Transcript: Detecting Covert Co ordination in Latent Multi-Agent Communication
链接: https://arxiv.org/abs/2608.19161
作者: Ramneet Kaur,Pradyumna Chari,Ramesh Raskar,Jugad Singh,Sumit Kumar Jha,Anirban Roy
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:Language-model agents can communicate through continuous hidden states that are invisible in public transcripts, creating opportunities for covert harmful coordination. We introduce Verifiable Latent Alignments (VLA), an activation-aware framework for monitoring and steering these private communication channels. For every monitored decision, VLA links the private latent-state record and channel status to the resulting public action using a shared event identifier, enabling matched causal analysis. Our first contribution is a neutral-only three-layer monitor combining representation anomaly detection, counterfactual action-distribution influence, and sparse-autoencoder interpretation support. Our second contribution is a steerability framework spanning black-box behavioral instructions and white-box matched-neutral counterfactuals. Our third contribution is an evaluation on a controlled multi-agent auction benchmark covering homogeneous and heterogeneous model pairs, many-agent scalability, and intervention effectiveness. The sequential monitor achieves mean area under the receiver operating characteristic curve (AUROC) of 0.993 for homogeneous agents and 0.854 for heterogeneous pairs when text- and latent-collusion rows are pooled as positives. In Qwen3-0.6B auctions with 25-100 bidders, monitoring requires only a small normalized load relative to all possible directed pairs, while full white-box steering achieves 100% bid-distribution recovery and reduces collusive low-bid behavior by 47.3 percentage points. Because full white-box steering replays the matched neutral counterfactual, its exact recovery is a sanity check by construction. Overall, the controlled study shows that the evaluated private channel attacks can be monitored without training the primary monitor on attack examples and mitigated when matched counterfactual access is available.
[AI-2] Pre-Compiled Pipeline Shards for Distributed LLM Inference on Intel AI PC Fleets
链接: https://arxiv.org/abs/2608.19147
作者: Tate Berenbaum,Muthaiah Venkatachalam
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Modern Intel AI PCs ship capable integrated GPUs and NPUs with 16+ GB of unified memory, and they spend considerable time idle. That is not enough memory to fit a large model such as a 70B-parameter LLM. We show that a handful of AIPCs, working together over an ordinary network, can serve models beyond the capability of any single one. We use pipeline parallelism: a model is split by layer into per-stage shards, each pre-compiled into an OpenVINO graph, so that every machine runs one shard and passes activations to the next. Three techniques make this fast enough to be useful. First, we recover the speed of the unsplit model: a naive per-stage export runs well below monolithic inference because it misses an OpenVINO GPU optimization, and injecting a beam_idx Gather into each shard triggers that optimization (the IndirectKVCache fusion) and brings the shards to parity. Second, we leverage speculative decoding on stateful OpenVINO models. Third, the pipeline serves several users at once by interleaving their requests across the stages, each request carrying its own cache (micro-batching). Together, a two-node Llama 3.1 8B INT4 pipeline serves two concurrent users at 1.79x the single-user throughput of the unsplit model on the same hardware, and the gap widens under simulated wide-area latency. The same design scales to a 70B model that no single fleet member can hold: a four-node deployment of Lunar Lake AI PCs on Intel Tiber Cloud serves a single user at interactive speed, with output token-for-token identical to the same four-node pipeline decoding without speculation. Code, raw benchmark logs, and reproduction scripts ship as a self-contained package at this https URL (in the top-level reproduction/ directory).
[AI-3] Grouping the Stochastic Machine: Precision Not Capability as the Frontier Metric for AI Systems STOC
链接: https://arxiv.org/abs/2608.19140
作者: George Andrikopoulos
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注: 6 pages, 3 figures. Companion to “Tuning the Stochastic Machine”, submitted concurrently
Abstract:Frontier language models are compared, marketed, and benchmarked on capability – what their best or average output can achieve. I argue this measures the wrong axis. The models have saturated accuracy: their mean output lands on the target. What now separates one system from another in practice is precision: how tightly concentrated their outputs are around that target across repeated, identical requests. Borrowing the marksman’s distinction, capability is where the average shot lands; reliability is the size of the group. I make three claims. First, precision, not capability, is the frontier differentiator between systems, and benchmark culture systematically fails to measure it, reporting central tendency rather than spread. Second, precision is measurable, cheaply and without circularity, by running a fixed suite of deterministically scored tasks many times at fixed temperature and computing the per-task consistency of outcomes – no model-in-the-loop grader required. Third, the measurement is not merely descriptive but decision-guiding: it separates consistent failures (a tight group off-centre, correctable by the operating discipline of Paper 1 – a sight adjustment) from scattered failures (a wide group, correctable only by changing the model or its sampling – a rifle problem). I define a grouping metric, specify a harness, and show how tracking a human-AI pair’s grouping over time yields the compounding signal that Paper 1’s field study requires. A first real run, since replicated, illustrates both the method and its most important limit: one measured gap was closed completely by a single rule (0/5 - 5/5), while a suite of tasks authored from the rules themselves found no value, because a frontier model already embodies explicit good practice – establishing that a discipline’s worth is found by measurement on real work, not constructed from its own rulebook.
[AI-4] Leaf Values as Coordinates: Exact Contrastive Explanation for Gradient-Boosted Ensembles
链接: https://arxiv.org/abs/2608.19127
作者: Emanuele Luzio
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 5 pages, 1 figure, 2 tables
Abstract:A gradient-boosted ensemble predicts by summing one leaf value per tree. Read those values as coordinates rather than as intermediate results, and every instance becomes a point in R^M on which the model acts linearly: the score is the sum of the coordinates. This small change of view makes contrastive explanation exact. The difference between two instances is a vector that is identically zero wherever they share a leaf, so the gap between a rejected applicant and an accepted one is carried by a handful of coordinates, each traceable to a real split in a real tree. Nothing is fitted, sampled, or assumed additive in features – the additivity is already there, in the right space. We build a recourse method on this representation and evaluate it on five tabular datasets under repeated cross-validation. Its recommendation reconstructs the model’s own decision to 6.2 x 10^-15, so an auditor can re-check the arithmetic without the model. On the credit datasets it is Pareto-non-dominated on effort against realism. And when recommendations are restricted to changes the subject could actually make – not their age, not a settled delinquency – it retains 58% of its validity where the strongest baseline retains 41%, a distinction the standard evaluation cannot see because it never asks whether a recommendation can be carried out. Comments: 5 pages, 1 figure, 2 tables Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) ACMclasses: I.2.6; K.4.1 Cite as: arXiv:2608.19127 [cs.LG] (or arXiv:2608.19127v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.19127 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Emanuele Luzio [view email] [v1] Wed, 19 Aug 2026 17:20:33 UTC (41 KB) Full-text links: Access Paper: View a PDF of the paper titled Leaf Values as Coordinates: Exact Contrastive Explanation for Gradient-Boosted Ensembles, by Emanuele LuzioView PDFHTML (experimental)TeX Source view license Current browse context: cs.LG prev | next new | recent | 2026-08 Change to browse by: cs cs.AI cs.CY 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?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); 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-5] uning the Stochastic Machine: A Systems Engineers Operating Model for Human-AI Engineering
链接: https://arxiv.org/abs/2608.19125
作者: George Andrikopoulos
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 8 pages, 3 figures. Experience report and operating model
Abstract:When an expert corrects an LLM assistant’s error, the correction usually dies with the session, and the error class returns. I argue this is an operations problem, not a tooling problem: mechanisms for persisting corrections exist and are shipping, but the discipline for governing them – versioning with provenance, recurrence monitoring, counter-metrics, retirement of stale rules – does not. Writing as a systems engineer of thirty years, I map the LLM stack onto the machines my profession already operates (frozen silicon, firmware, loadable modules, persistent configuration, volatile memory), identify where the mapping fails (stochastic generation, configuration that binds only probabilistically, no general-purpose retirement (verification) stage by default), and derive from the failures a seven-principle operating discipline with an error loop at its core. Three cases from my own practice illustrate the mechanism, among them a control that silently became the exact harm it was built to prevent. I close with the measurement framework this view implies and the lab study required to test it.
[AI-6] PGFS: Molecular Property Improvement under Synthesis and Diversity Constraints
链接: https://arxiv.org/abs/2608.19121
作者: Boqiao Zhang,Godbless James,Sai Krishna Gottipati,Andrew Fitzgibbon
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Improving molecular properties, such as drug-likeness or binding affinity, is a recurring task in early-stage drug discovery. However, molecules optimized in an unconstrained chemical space have limited practical value if they cannot be synthesized. Policy Gradient for Forward Synthesis (PGFS) is a synthesis-aware reinforcement learning method for molecular improvement, but its use of reactant embedding prediction makes reactant selection indirect, which, as we show, limits learning effectiveness. We first develop PGFS+, in which reaction templates and second reactants are represented by trainable embedding lookup tables. Combined with a more effective scoring function and RL algorithm, PGFS+ significantly improves the desired property. However, it exposes a reward-hacking failure mode: a powerful reactant search can map diverse input molecules to the same high-reward magnet molecule, improving the reward while collapsing the output diversity. We therefore introduce PGFS++, a synthesis-aware reinforcement learning framework for input-specific molecular improvement. Given an input molecule, PGFS++ treats it as the start of a forward-synthesis trajectory, applies learned reaction templates with compatible in-stock building blocks, and produces a molecule with improved target properties, an explicit synthesis route, and structural similarity to the input. Experiments on molecular improvement tasks show that PGFS++ improves target properties while preserving high output diversity.
[AI-7] Discretizing Continuous Time Series for Imputation with Masked Diffusion Training NEURIPS2026
链接: https://arxiv.org/abs/2608.19119
作者: Dongbin Kim,Seungyun Lee,Geonwoo Shin,Jaewook Lee
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Submitted to NeurIPS 2026
Abstract:Time series imputation is a crucial area for reliable time series analysis, yet it remains challenging due to the complex temporal dynamics and noise of real-world data. Existing approaches, however, exhibit two limitations: missing and observed values are embedded within the same representation space without explicit structural separation, and continuous diffusion-based methods are trained to predict added noise rather than the original signal. To address these, we propose the Masked Diffusion Time-series Imputation Model (MDTIM), which leverages the training paradigm of masked diffusion model for imputation tasks. The MASK token is structurally orthogonal to valid observations, and the model directly predicts the original values, naturally aligning both the representation and the learning objective with the imputation task. To bridge the gap between discrete masked diffusion and the continuous, ordinal nature of time series, we further introduce Stochastic Discretization, which maps continuous values to ordinal-aware tokens while preserving continuous dynamics. Our experiments on diverse benchmarks confirm that MDTIM achieves superior robustness and scalability, consistently outperforming state-of-the-art deterministic and generative baselines across various missing scenarios.
[AI-8] DA-WAM: Decision-Aligned Future Latents for Driving World Models
链接: https://arxiv.org/abs/2608.19085
作者: Ruiguo Zhong,Benshan Ma,Xiaolong Chen,Lang Zhang,Mingyue Feng,Yaonong Wang,Pei Liu,Jun Ma
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Anticipating how scenes evolve under ego actions is fundamental to safe autonomous driving, yet the full potential of world models for decision-making remains unrealized. The critical challenge lies in ensuring that future modeling is not merely predictive, but decision-informative: the predicted future must directly shape which trajectory is selected. Existing approaches decouple future representation learning from planning optimization, or share predicted states across trajectory candidates, thereby diluting the action-specific consequences that ought to guide selection. To bridge this gap, we propose DA-WAM, a framework that unifies predictive representation learning, action-conditioned future modeling, and trajectory scoring under a single decision-making objective. DA-WAM maintains predictive supervision throughout planner optimization via an online encoder and a stable momentum target, allowing future representations to co-evolve with the driving task. An action-conditioned predictor generates a distinct future latent state per trajectory candidate, which is then evaluated by a future-latent-conditioned factorized scorer. For the expert-matched trajectory, the predicted future latent is supervised by the observed future representation, while safety-critical hard negatives provide additional supervision near planning boundaries. Extensive experiments on NAVSIM-v1 and NAVSIM-v2 demonstrate state-of-the-art performance, while ablations and diagnostic analyses validate the key components.
[AI-9] Robust Risk Under Evolving Uncertainty: A Wasserstein Counterpart of the Entropic Value-at-Risk UAI2026 UAI
链接: https://arxiv.org/abs/2608.19073
作者: Deep Kumar Ganguly,Jan Křetínský
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注: Best Paper Award at the 2nd Workshop on Safe AI at UAI (SafeAI@UAI 2026, non-archival), Amsterdam
Abstract:An agent still learning its environment should be cautious while ignorant and bold once confident. The entropic value-at-risk captures this through a robust-optimization identity—a confidence level fixes the radius of a relative-entropy ball of alternative models—but that ball cannot reach catastrophes the nominal deems impossible, precisely what a safe agent must hedge. We instead use an optimal-transport ball and study the coherent risk measure it induces, the Wasserstein entropic value-at-risk. It has a variational dual mirroring the entropic formula (an inverse temperature becomes a transport price), occupies a definite place in the risk hierarchy, and provably accounts for the reachable catastrophes the entropic measure ignores; we verify both dualities numerically. Driving the transport radius by belief entropy then yields a closed-form robust dynamic-programming operator whose caution contracts as the belief sharpens, with a certified safety sandwich and a sharp safety switch.
[AI-10] Eureka: Task-Conditioned Meta-Agent Orchestration for Scientific Discovery
链接: https://arxiv.org/abs/2608.19047
作者: Alizer Wong,Heng Cui,Yi Tan,Xiongchao Zhan,Liang Lin,Yuxiang Guo,Zhaorong Dai,Zixin Zeng,Wenyuan Li
类目: Artificial Intelligence (cs.AI); Number Theory (math.NT)
备注: 62 pages, 1 figure
Abstract:We present Eureka, a task-conditioned Meta-Agent architecture that compiles long-horizon tasks into dynamic obligation graphs with explicit acceptance semantics. During execution, Eureka forms Macro-Agents with specialized state, memory, operators, tools, verifiers, and local topology via receding-horizon planning, architecture promotion, and minimal-sufficient compilation. When bottlenecks recur, cost-benefit-gated evolution updates the local architecture under constraints. Theoretically, we establish results on regret, planning invalidation, amortization, subtree interfaces, serializability, and verification. Experimentally, Eureka completes 170/170 recursive tasks and generates 3,948 certificates with no false acceptances. Active context compresses median input from 9,490 to 4,005 tokens; incremental processing avoids 65.38% recomputation across 12,000 tasks; 16,000 concurrent executions serialize consistently. The same Meta-Agent instantiates a Theory-Discovery Agent and a Math/Conjecture Agent. The former yields structural results in quantum-process and spacetime theory. The latter identifies bottlenecks in Riemann Hypothesis research and advances a positivity certificate for Suzuki’s localized Weil quadratic form to 0 a = 69/200 = 0.345, reaching ~99.55% of (log 2)/2. These results suggest that scientific-agent capability depends not only on the base model but on whether an architecture can be formed to match the task’s cognitive structure.
[AI-11] Self-prompting and cross-model consensus enable reproducible data extraction from scientific literature with large language models
链接: https://arxiv.org/abs/2608.19025
作者: Valentin Romanov,Monique Bax,Steven Niederer
类目: Artificial Intelligence (cs.AI); Databases (cs.DB)
备注:
Abstract:Accurately extracting nuanced, contextualized data from research articles is laborious and time intensive. Here, we investigate the performance of frontier, browser-based large language models (LLMs) to extract highly contextualized information. We demonstrate four escalating workflows, 1) given an expert curated prompt and research articles, most frontier LLMs perform well at data extraction, however can struggle with interpreting scientific context and nuance, 2) given simple instructions, LLMs can author their own prompts which were almost as eNective as expert-written prompts, 3) autonomous discovery of research literature was diNicult, agents either missed or hallucinated references, and 4) LLMs can create new datasets from published guidelines that closely match human-expert judges, but still require a human-in-the-loop. Together, these findings define an auditable division of labour in which experts specify the evidence standard, models cross-check repeated extractions and researchers resolve disputed cases, providing a practical route to scaling scientific data curation without relinquishing expert oversight.
[AI-12] Harness Continual Learning: Continual Adaptation Beyond Model Parameters
链接: https://arxiv.org/abs/2608.19013
作者: Borui Kang,Jinrui Gu,Junhan Lv,Wenbin Li,Lei Wang,Yang Gao
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Continual learning has largely been model-centric, treating model parameters as the state that changes with sequential experience. Modern agents can also adapt through a harness of prompts, memories, tools, skills, and routing rules. Because these contents jointly shape later execution, a harness update can disrupt previously reliable behavior even when the model is frozen. This raises a new question: how can an agent continually improve its state outside the model while retaining behavior acquired earlier? We formulate Harness Continual Learning (HCL), a new continual learning paradigm in which the harness evolves around a frozen foundation model, and define the resulting loss of earlier behavior as harness-level forgetting. We instantiate HCL with four execution-facing components: the Task Interface, Experience Memory, Capability Map, and Adaptive Router. We further introduce guarded harness evolution to separate update generation from state commitment. A Continual Optimizer proposes candidate harnesses from post-execution feedback, and a Continual Evaluator commits the resulting candidate harness only after checking current improvement, historical retention, and validity. Experiments on textual reasoning, multimodal perception, and open-world interaction demonstrate capability accumulation and failure recovery, with relative gains exceeding 10% over corresponding baselines in multiple settings. Component ablations assess the contribution of each harness component, while controlled retention sweeps reveal measurable harness-level forgetting and show that the stability–plasticity trade-off can be explicitly adjusted.
[AI-13] From Threat Intelligence to Detection: Knowledge-driven Enrichment and Template-based Rule Grounding for Automated Sigma Rule Generation
链接: https://arxiv.org/abs/2608.19011
作者: Sepehr Ghaffarzadegan,Boubakr Nour,Makan Pourzandi,Mourad Debbabi,Chadi Assi
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Submitted for publication and currently under review
Abstract:Mechanisms for dynamically converting cyber threat intelligence (CTI) into actionable detection capabilities are necessary due to the rapid evolution of Advanced Persistent Threats (APTs). Sigma rules are an essential part of contemporary threat detection workflows because they offer a platform-independent framework for expressing detection logic that can be converted into particular queries across SIEM systems. Conventional techniques for manually crafting Sigma rules are prone to mistakes, and necessitate extensive knowledge, which restricts their scalability. Although there are open-source and industry-maintained Sigma rule repositories, they often fail to keep pace with emerging threats and require frequent customization to fit diverse operational environments. This emphasizes the necessity of dynamic rule generation that is adapted to evolving attack techniques as well as particular use cases. In this work, we design AUTOSIGMA, an automated solution for transforming unstructured CTI reports into relevant Sigma rules. Rather than relying solely on language models, AUTOSIGMA leverages a structured knowledge base to enrich partial inputs, matches the enriched content against a repository of existing Sigma rules, and then employs an LLM-as-a-Judge mechanism to iteratively validate the rules. By combining knowledge-driven enrichment, template-based rule grounding, and a multi-stage solution, AUTOSIGMA enables accurate, context-aware, and relevant rule generation. Evaluations across multiple real-world APT reports and multiple security blogs demonstrate that AUTOSIGMA outperforms alternative solutions and LLM models in rule validity, rule relevancy, MITRE ATTCK technique coverage, and robustness to input quality. AUTOSIGMA’s Demo: this https URL Comments: Submitted for publication and currently under review Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.19011 [cs.CR] (or arXiv:2608.19011v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2608.19011 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-14] A Theory of Post-hoc Debate Judgement
链接: https://arxiv.org/abs/2608.19002
作者: Xiang Yin,Adam Dejl,Antonio Rago,Lihu Chen,Francesca Toni
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Debates have recently emerged as a useful methodology for agentic AI to improve performance as well as to aid explainability and user engagement. For example, LLM-empowered agents may debate internally (with themselves) and/or externally (with other agents). In many settings where debates are used, debates’ outcomes and resulting outputs are determined post-hoc by external judges, often LLMs. In this paper we develop and test a novel theory of debate judgement applicable to all settings where agents engage in debates by providing pros and cons for their opinions therein. Specifically, we identify a number of formal properties that debate judgement may be required to satisfy in general, as concerns reproducibility, robustness, groundedness and explainability. Then, we explore their satisfaction formally and/or experimentally, for claim verification settings, for two specific alternative debate judgement methods: variants of the LLMs as a judge idea and formal semantics drawn from computational argumentation. We show that the two methods give similar accuracy performances but the former may lack formal guarantees that the latter brings. Overall, our study indicates argumentation semantics as an ideal candidate for principled judges in debate-driven AI.
[AI-15] Breaking the weakest link to evade vision language models
链接: https://arxiv.org/abs/2608.18938
作者: Ilan Zini,Boussad Addad,Katarzyna Kapusta
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 17 pages
Abstract:Vision Language Models (VLMs) have recently emerged as a critical component of multimodal AI systems, enabling joint reasoning over visual and textual inputs in real-world and safety-critical applications. Despite their growing deployment, the robustness of VLMs against adversarial threats remains insufficiently explored, particularly in the context of evasion attacks targeting multimodal alignment. In this work, we investigate the vulnerability of VLMs to adversarial perturbations applied to visual inputs and study two attack settings: untargeted attacks, where the goal is to disrupt the model’s interpretation of the original image, and targeted attacks, where the adversary aims to force the model to generate a specific semantic description unrelated to the original image. To efficiently generate adversarial examples, we propose a gradient-based attack method that performs optimization exclusively on the vision encoder of the VLM rather than on the entire multimodal architecture. This design significantly reduces the computational cost and resource requirements of the attack while maintaining strong effectiveness. We evaluate our approach on several open-source VLMs, including Qwen2.5-VL, Granite-Vision, FastVLM, and Phi-3.5-Vision, and show that small, human-imperceptible perturbations can substantially alter the textual interpretation produced by the models. Our findings highlight the vulnerability of modern VLMs to adversarial manipulation and emphasize the need for improved robustness and security mechanisms in multimodal AI systems.
[AI-16] Graphical Design of Interpretable Architectures
链接: https://arxiv.org/abs/2608.18936
作者: Pietro Barbiero
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Designing, implementing, and comparing interpretable architectures requires a formal language to represent them. The most common representations fall short in one of two ways. Symbolic equations give no global view of an architecture at a glance. Probabilistic graphical models and flowcharts do not describe actual tensor manipulations, thus hiding key insights and limiting reproducibility. To close this gap, we introduce a graphical notation for designing interpretable AI architectures, adapted from Penrose tensor notation. This graphical notation gives a global view of an architecture and maps one to one onto PyTorch einsum code. We first use this notation to describe architectures that are interpretable by construction, including concept bottlenecks, sparse probes, prototype networks, neural additive models, and mixtures of linear models. We then diagram the key architectural components of Steerling-8B, a frontier interpretable language model. The diagram yields global insights into the architecture (e.g., showing that Steerling is a residual model), a geometric interpretation of each individual operation, and a direct translation into 33 lines of PyTorch code.
[AI-17] SkillForge: Self-Distilling Agents for Project-Specific Issue Resolution
链接: https://arxiv.org/abs/2608.18933
作者: Silin Chen,Han Li,Xiaodong Gu,Yuling Shi,Haibing Guan
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Our code and data are available at this https URL
Abstract:Large language model (LLM) based agents have demonstrated remarkable proficiency in automated software issue resolution, yet they often struggle to resolve issues in a specific repository because they lack project-specific knowledge. Existing self-evolving approaches acquire such knowledge from repository history or online repair trajectories, but they either depend on available historical issue-resolution signals or incur substantial per-issue test-time exploration cost. In this paper, we propose SkillForge, a self-distillation framework that proactively acquires project-specific knowledge from the repository itself. Instead of waiting for real issues to expose project-specific knowledge gaps, SkillForge synthesizes project-specific issues by re-implementing test-covered core functionalities of the repository. By resolving these synthetic issues, SkillForge distills reusable project-specific knowledge into entity-grounded skills and associates them with relevant repository entities for future issue resolution. Extensive experiments using both open-source and closed-source models show that SkillForge consistently improves issue resolution performance over strong baselines. These results demonstrate that proactively acquiring project-specific knowledge before solving real issues substantially improves downstream software issue resolution.
[AI-18] textscTestifAI: Tomography-Based Testing for Deep Learning Systems
链接: https://arxiv.org/abs/2608.18900
作者: Arooj Arif,Tobias Hartung,Elena Botoeva,Alexandros Koliousis
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As AI systems are increasingly deployed in safety-critical application domains (e.g., autonomous driving), associated risks increase too. Deep learning models underlying modern AI systems, therefore, must undergo thorough testing to ensure their correct behaviour. A single robustness test involves thousands of inferences to empirically verify if a model’s outputs remain stable under a bounded perturbation of its inputs. However, existing testing frameworks lack the means to systematically explore and summarise robustness across a combinatorial space of perturbations. We propose TestifAI, a deep learning testing framework for efficient and accurate estimation of robustness against combinations of perturbations. TestifAI enables users to specify operational conditions as structured spaces of semantic input perturbations (e.g., image blur, brightness and zoom) and discrete severity levels (e.g., low, medium and high). Users can query model robustness for any combination (e.g., “low blur, high brightness, and medium zoom”). To achieve efficiency and accuracy, TestifAI introduces partial model tomography, a novel approach to reconstructing model behaviour in a multi-perturbation space from tests that apply only a small number of perturbations (lower-order projections). To estimate robustness against at least three perturbations, TestifAI trains an auxiliary model on the results of tests involving up to two perturbations only, avoiding execution of an exponential number of tests. Our experiments on five image and language classification tasks show that TestifAI can predict higher-order (3 and 4 perturbations) test outcomes from low-order (1 and 2 perturbations) observations with an aggregate robustness estimation error of less than 7%, while reducing the number of inferences by 60-80%. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.18900 [cs.AI] (or arXiv:2608.18900v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.18900 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-19] Syntactic Simplification of OWL Class Expressions
链接: https://arxiv.org/abs/2608.18899
作者: Alkid Baci,N’Dah Jean Kouagou,Caglar Demir,Axel-Cyrille Ngonga Ngomo
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Class expression learning often produces complex OWL class expressions that are difficult to interpret and reason over. However, by following theoretically grounded simplification principles, this complexity can be reduced. In this paper, we propose Class Expression Simplifier (CES), a novel algorithm for the syntactic simplification of class expressions in Description Logics (DL). CES aims to preserve formal semantics while reducing representational complexity. It systematically applies rewriting rules to eliminate redundancies and identify simpler yet equivalent expressions, thereby producing more compact and human-readable representations without altering logical entailments. We evaluate the effectiveness of CES on class expressions learned from two medium-sized ontologies, demonstrating measurable improvements in reasoning efficiency and reductions in verbosity. This work contributes to the broader goal of making ontology-driven applications more accessible, maintainable, and scalable, with direct implications for knowledge graph construction, semantic search, and Web-scale reasoning. CES is implemented within the open-source Python framework OWLAPY and is publicly available.
[AI-20] raining-Free Inference-Time Self-Reflection and Cost-Bounded Early Stopping for Large Language Models
链接: https://arxiv.org/abs/2608.18884
作者: Wei Yu,Suxing Liu,Minjie Yu,Jiahao Wang,Zhijian Zheng,Haocheng Deng,Bing Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement-learning training of reasoning LLMs (e.g., GRPO) is expensive and requires a controllable environment, committing every contribution to a full training pipeline. We present EvoResearcher, a training-free, inference-time protocol that adds cost-bounded self-reflection to a single frozen LLM backbone. The protocol iterates generate - self-critique - revise until a maximum depth D is reached or the critique returns the CONFIRMED sentinel, an implicit early stop that lets the backbone self-verify its answer under a strict compute budget. Four self-reflective meta-reward components (correctness, efficiency, reflection depth, tool-call diversity) act as design principles instantiated as prompt-level mechanisms, so their benefits accrue with zero gradient updates. We validate the protocol on Big-Bench Hard (100 questions) and establish cross-domain behavior on GSM8K (500) and MATH (500) on the same frozen backbone, with cross-model replication on Qwen2.5-72B. All experiments use pure-reasoning benchmarks; the tool-call diversity component is validated in prompt-level form, and the environment-level and multi-agent extensions are design blueprints left to future work. On clean BBH the protocol does not raise accuracy beyond the 95% Wilson interval; its value is cost-bounded self-verification, with the CONFIRMED early stop terminating 82-88% of items at equal accuracy (about 2.1 generations per question).
[AI-21] SkillGate: Training In-Policy Skill Selection in Long-Horizon Agents
链接: https://arxiv.org/abs/2608.18852
作者: Qingyao Li,Wenxiang Jiao,Shuai Shao,Kangning Zhang,Yuan Lu,Yi Guo,Weiwen Liu,Weinan Zhang,Yong Yu
类目: Artificial Intelligence (cs.AI)
备注: 17 pages, 6 figures, 6 tables. Code: this https URL . Models: this https URL
Abstract:Agent frameworks increasingly package procedural knowledge as skills: instruction files an agent reads on demand, while public libraries now hold thousands of them. Which skill to read has thus become a decision the policy itself makes in the middle of an episode, yet no existing signal trains it. We show that the default remedy, outcome-rewarded RL over the candidate slate, cannot teach it, for a structural reason we identify and name selector credit starvation: under a broadcast, sequence-level advantage, the few tokens that name the chosen skill carry a vanishing share of the loss, and the credit they inherit is increasingly wrong-signed as trajectories lengthen. A correct choice is punished whenever the execution after it fails, even though the choice itself is among the most valuable decisions in the trajectory. Auditing a completed run’s own training artifacts confirms all three properties, each worsening monotonically with horizon. SkillGate removes the failure by construction: it partitions the token support into two disjoint credit channels, outcome credit reaching only execution tokens, and a separate action-local advantage reaching exactly the skill-naming tokens, positive only when a trajectory’s single read is the correct one. On five agentic benchmarks under a 16-candidate slate, SkillGate lifts a 9B policy from 40.8% to 53.2% trial success, well ahead of the identical budget spent on outcome reward alone, while cutting exposure to misleading candidates by two thirds and reading fewer skills.
[AI-22] ORBITER: Conflict-Aware Decision-Making for Agent ic Last-Mile Delivery
链接: https://arxiv.org/abs/2608.18846
作者: Mingzhao Li,Chenxi Liu,Yan Zhao,Hao Miao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Last-mile delivery aims to handle dynamically arriving orders with couriers while modeling complex spatial and temporal correlations. Recent learning-based methods model spatiotemporal dependencies among orders to predict courier service sequences, but leave next-order decision making unexplained. Describing the current delivery state in language allows LLMs to reason explicitly about the spatial, temporal, and behavioral cues behind an individual decision. As direct predictors, however, LLMs remain sensitive to task presentation and often produce unreliable decisions. To address these challenges, we introduce ORBITER, an agentic Order Arbiter for next-order decision-making in last-mile delivery. ORBITER models courier service through decision points, each containing the courier’s spatiotemporal state and visible orders and exposing local trade-offs for modeling and verification. Fixed proposers rank the candidates, and a structured report identifies where their rankings disagree. The LLM uses task-specific tools to gather evidence on the leading alternatives, while an independent critic checks the resulting decision against that evidence. We conduct extensive evaluations on data in four cities, where ORBITER outperforms existing state-of-the-art baselines by up to 9.2% on average showing its effectiveness.
[AI-23] Verifiable abstention makes AI leak diagnosis accountable in water distribution networks
链接: https://arxiv.org/abs/2608.18836
作者: Tianwei Mu,Yue Wang,Mingzhe Yuan,Manhong Huang,Wenhong Wang,Xuerui Yin,Qing Luo,Min Xiao,Hui Yang,Jun Li,Dan Xue
类目: Artificial Intelligence (cs.AI)
备注: 42 pages, 5 main figures, 1 main table, 2 extended data figures, 3 supplementary figures, 15 supplementary tables. Code and data availability described in the paper
Abstract:Utilities lose a substantial share of treated water to leakage, yet rarely trust artificial-intelligence localizers to dispatch crews: guessing everywhere cannot justify excavation. The gap is accountability, not accuracy: no method proves when it should not act. Here we recast leak localization as decision-making under verifiable abstention. A physics-grounded executor agent falsifies hypotheses (leak, demand, sensor, valve) against a digital twin; an independent supervisor agent, with a large-language-model (LLM) auditor, checks evidence against a code-verifiable contract, then certifies a dispatch, requests evidence or abstains. Under field-grade noise, a 32% forced baseline becomes 96% decision precision on acted events. On an independently generated benchmark it acts on only 4 of 33 leaks, all correct. A 194-event register of audited real leak locations with twin-simulated pressures and flows yields five excavation dispatches, three correct, and 44% survey recovery at full district precision. Accountable abstention offers a defensible route to autonomous water-infrastructure operation.
[AI-24] Pairwise Logical Selection of Enthymeme Completions under Semantic-Link Uncertainty
链接: https://arxiv.org/abs/2608.18820
作者: Xuyao Feng,Antonis Bikakis
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Arguments often omit premises or claims, forming enthymemes. We study pairwise logical selection between two candidates for the omitted component. Existing natural language methods can identify or generate candidates but often do not expose how the selected candidate completes the inference, while logic-based approaches usually assume that the required formulae and background knowledge are available. We extend a prior neuro-symbolic pipeline from missing-premise to missing-claim selection and replace binary entailment outcomes with logical-resistance scores. Top-Link uses weighted Partial MaxSAT under a single configuration of highest-confidence semantic links. We then introduce Possible-World Atom-Link Formalization (PWAL), which keeps translated formulae fixed and marginalizes logical resistance over alternative cross-formula semantic-link configurations. We evaluate PWAL on five tasks: ARCT and a CDED-derived task for missing-premise selection, iDebate- and AAE2-derived tasks for missing-claim selection, and alphaNLI for abductive hypothesis selection. Relative to Top-Link, PWAL raises strict accuracy by 2.95-30.86 percentage points and reduces tie rates by 4.57-58.00 percentage points on all five tasks. When ties receive half credit, accuracy still increases by 0.45-6.04 percentage points. PWAL also records the translated formulae, sampled link configurations, and resistance components for every comparison, providing a transparent trace of each score.
[AI-25] A strengthening of the MCFL-ness of O_2
链接: https://arxiv.org/abs/2608.18813
作者: Marco B. Caminati
类目: Formal Languages and Automata Theory (cs.FL); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO); Logic (math.LO)
备注: MCU 2026
Abstract:In the last years, a number of proofs of the fact that O_2 is a multiple context-free grammar (MCFG) were given. Such results can be exploited in the fields of both computational linguistics and of computational algebra. Here, we focus on a recent such proof spelled in terms of factorizations of string tuples, and give a new result with a stronger characterization of such factorizations than in existing theorems.
[AI-26] Forgetting plasticity and co-observation: a third facet of continual learning
链接: https://arxiv.org/abs/2608.18803
作者: Timm Hess,Abhishek Jha,Gido M. van de Ven,Tinne Tuytelaars
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Efficient continual learning remains a fundamental challenge for deep neural networks. While catastrophic forgetting and loss of plasticity are widely considered the primary obstacles to overcome, we show that these two issues cannot fully explain the performance gap between naive sequential training and offline joint training. In this paper, we highlight data co-observation as a distinct factor influencing continual learning performance. By decoupling the constraints of separate data access from stability and plasticity, we systematically investigate the representational benefits gained by observing training data together. Empirically, we demonstrate a consistent performance difference between joint and separate training across both supervised and self-supervised paradigms in generic data-incremental “chunking” scenarios, whilst mitigating forgetting and controlling for plasticity. Our findings indicate that simultaneous observation of training data (co-observation) yields benefits to the learner’s generalization that extend well beyond mere knowledge retention, and that this effect does not require a specific continual distribution shift. Furthermore, we contextualize prominent continual learning mechanisms through this lens: while distillation-based approaches act only as effective knowledge retention mechanisms, our results suggest that the empirical success of memory replay goes beyond the mitigation of forgetting, actively reintroducing the benefits of data co-observation into the learning process.
[AI-27] Beyond Predictive Fairness: Quantifying Attribution Consistency Across Demographic Groups in Diabetic Retinopathy Screening MICCAI2026
链接: https://arxiv.org/abs/2608.18759
作者: Kerol Djoumessi,Philipp Berens
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted for publication at the joint FAIMI, BRIDGE, and EPIMI Workshop at MICCAI 2026
Abstract:Fairness in medical imaging is commonly evaluated through subgroup performance metrics, yet it remains unclear whether models rely on consistent visual evidence across demographic groups. This work introduces the Explanation Consistency Score (ECS), a fairness-aware metric based on Jensen-Shannon divergence that quantifies the similarity of attribution maps across subgroups. Using diabetic retinopathy screening as a case study, ECS is evaluated globally and within disease severity. Experiments reveal that while predictive performance differs across ethnic groups, explanation consistency remains relatively high and shows no significant association with performance disparities. These findings suggest that predictive fairness and explanation consistency capture complementary dimensions of model behavior, motivating fairness evaluations that extend beyond predictive performance.
[AI-28] Epistemic Subordination: Generative AI and the Infrastructure of Knowledge
链接: https://arxiv.org/abs/2608.18758
作者: Gilad Abiri,Emanuel V. Towfigh
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:Generative AI does not merely produce biased outputs. It encodes the majority’s way of knowing as the default infrastructure of knowledge itself. We call this epistemic subordination. The training process compresses the full breadth of human expression into a single probabilistic model whose statistical baseline reflects the languages, assumptions, and cultural frameworks of the dominant culture. Minority epistemologies are not excluded but absorbed: present in the training data, yet structurally subordinated in the output. The result is not a collection of discrete biases that can be audited and corrected. It is an epistemic condition embedded in the architecture from which all outputs emerge. This unified harm cuts across three legal domains – anti-discrimination law, cultural and linguistic rights, and democratic viewpoint pluralism – and each fails to address it for the same structural reason: existing law regulates downstream, at the level of decisions and applications. The remedy must match the site of harm. If epistemic subordination is produced at the level of model training, then law must learn to govern at that level.
[AI-29] A Multi-Agent Platform for Automated Enterprise Analytics and Insight Generation
链接: https://arxiv.org/abs/2608.18740
作者: Manoj N M,Vijayakrishna S,Manjunath Srinivas,Rohit Pahan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:This paper proposes a multi-agent framework built on CrewAI [1] for conversational business intelligence. Five specialized AI agents operate in a sequential pipeline to process natural language queries, retrieve and analyze data, generate visualizations via the Model Context Protocol (MCP) [2], and deliver actionable insights. The platform features a defense-in-depth security architecture for multi-tenant data isolation and a query parameterization mechanism for transforming conversational insights into reusable dashboard components. Evaluation across 300 end-to-end test cases spanning synthetic and production enterprise datasets demonstrates 95.3% functional accuracy, a mean response latency of 24 seconds, and a response quality score of 4.52/5.0 as assessed by an LLM-as-a-Judge framework, with a 93.0% hallucination-free rate, representing a 22.6 percentage point accuracy improvement and 20.2% quality gain over a single-agent baseline. Cross-model evaluation across four LLM backends and human expert validation confirm architectural generalizability and evaluator reliability. An ablation study confirms that the Data Analysis and Report Aggregation agents are the primary drivers of output quality.
[AI-30] Flama: a Python framework for development and deployment of production-ready APIs machine learning and LLM services DATE
链接: https://arxiv.org/abs/2608.18733
作者: José A. Perdiguero López,Miguel A. Durán-Olivencia
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 83 pages, 6 figures, 1 table. Software available at this https URL , up-to-date documentation at this https URL
Abstract:We present Flama, an open-source Python framework for developing and deploying production-ready web APIs, machine learning services, and large-language-model (LLM) applications. Built on the Asynchronous Server Gateway Interface (ASGI), Flama offers a type-driven, async-first programming model that unifies REST API development, predictive model serving, and generative AI inference in one architecture. It is organised around seven subsystems: a component-based dependency injection system resolving handler parameters from type annotations at startup; a pluggable schema layer supporting Pydantic, Marshmallow and Typesystem behind a single adapter; an automatic CRUD generator turning a SQLAlchemy table and a schema class into REST endpoints backed by the Repository and Unit of Work patterns; a portable binary format (.flm) packaging models from scikit-learn, TensorFlow, PyTorch and Hugging Face Transformers with their metadata for zero-code deployment; a multi-backend LLM server running vLLM (Linux/CUDA) or MLX (Apple Silicon) and exposing four wire protocols (OpenAI, Anthropic, Ollama, and a native streaming dialect) through a shared codec; a Rust-accelerated core compiled via Maturin for routing, JSON encoding, compression and parsing; and a Model Context Protocol module turning any application into an MCP server over JSON-RPC 2.0. Built-in capabilities include JWT authentication, two pagination strategies, background tasks in threads or processes, WebSocket endpoints, Server-Sent Event and NDJSON streaming, OpenAPI 3.2.0 generation from handler signatures, and a command-line interface for running applications and for serving, packaging and inspecting models. We describe the architecture, present the programming model through worked examples, and compare Flama with existing frameworks, model serving platforms and LLM inference engines. Comments: 83 pages, 6 figures, 1 table. Software available at this https URL, up-to-date documentation at this https URL Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) ACMclasses: D.2.11; D.2.12; D.2.13; I.2.5 Cite as: arXiv:2608.18733 [cs.SE] (or arXiv:2608.18733v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2608.18733 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Miguel A. Durán-Olivencia [view email] [v1] Wed, 19 Aug 2026 09:37:00 UTC (114 KB)
[AI-31] Competence Not Accuracy: A Diagnostic for Reference-Free Judge Gates in Skill Optimization
链接: https://arxiv.org/abs/2608.18719
作者: Chenle Chen,Yangbo Wei,Chao Yao,Shaoqiang Lu,Junhong Qian,Chen Wu,Lei He
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 4 figures, 5 tables
Abstract:Text-space skill optimization adapts a frozen agent by evolving a natural-language skill document, accepting each candidate through a validation gate. Existing gates rely on verifiable rewards, confining these methods to tasks with an automatic verifier. Replacing the verifier with an LLM-judge gate would lift that restriction, but whether such a gate carries usable signal is untested. We ask a prior question: can we tell, before placing a judge in the loop, whether its scores separate correct from incorrect answers at all? We formalize a reference-free judge as a latent solver – its verdict rests on agreement with whatever it would itself conclude, so its capacity to evaluate is bounded by its capacity to solve. The model yields a closed-form bound on discriminability (ROC-AUC) in the judge’s competence c and answer-space size k , a necessary condition c 1/k , and the result that the marginal AUC is confounded by item difficulty while a within-question estimator is not. A non-intervening probe records judge scores on genuine optimization runs without altering any decision. We find discriminability at chance where competence sits near the floor and usable above it; that a judge’s benchmark accuracy overstates the competence that matters; and, in a closed-loop study, that the screen predicts which kind of gating error occurs. The result is a cheap pre-deployment diagnostic for judge gates.
[AI-32] Europes Climate Ambition Under Scrutiny: Evidence from Deep Learning Emission Projections
链接: https://arxiv.org/abs/2608.18690
作者: Jacopo Ghirri,Carlos Rodriguez-Pardo,Lara Aleluia Reis,Massimo Tavoni
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); General Economics (econ.GN)
备注: 24 pages, 9 figures; Supplementary Material included
Abstract:The European Union has committed to reducing greenhouse gas emissions 55% below 1990 levels by 2030, but whether current trends are compatible with this ambition remains uncertain. We apply deep learning to high-resolution socioeconomic and sectoral data across EU27 member states till 2023 to project sectoral CO _2 trajectories under current trends, extrapolating observed sectoral momentum without assuming changes in the pace or effectiveness of the policy environment beyond what is already reflected in historical data. We project that EU27 emissions will exceed the 2030 target by 35% (620 Mt CO _2 shortfall), with only a small minority of countries on trajectories consistent with the bloc’s commitments. While the Power sector achieves target-consistent reductions driven by the renewable transition, Mobility shows minimal progress and accounts for over a third of total emissions by 2030, reflecting a structural inertia across member states rather than geographically concentrated lag. Our findings indicate that substantial additional intervention is required to close Europe’s ambition-implementation gap, and call for establishing up-to-date energy information in Europe.
[AI-33] RTPO: Reverse-Turn Policy Optimization for Stabilizing Agent ic RL Training
链接: https://arxiv.org/abs/2608.18682
作者: Yugu Li,Jimmy Cao,Jianglin Qiao,Siyi Hu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Training multi-turn agentic workflows with reinforcement learning (RL) enables large language models to perform complex reasoning, use external tools, and conduct iterative search beyond single-turn settings. Yet multi-turn RL training remains highly unstable, often causing severe performance degradation as the number of turns increases. Through theoretical analysis, we identify three tightly coupled sources of instability: rollout-training context mismatch, weak turn-level credit assignment under sparse terminal rewards, and asynchronous policy drift when short and long trajectories are optimized under different policy versions. We show that these issues share a common structural origin in flattened trajectory optimization and address them through a unified reverse-turn formulation. We propose Reverse-Turn Policy Optimization (RTPO), which organizes multi-turn rollouts as sparse reverse trees and performs turn-level policy updates in temporal reverse order, aligning each decision with its downstream continuation. RTPO enables causally consistent turn-level credit assignment and on-policy continuation to control asynchronous drift. We provide theoretical guarantees showing that RTPO eliminates context mismatch and asynchronous drift under the proposed turn-level formulation, reduces credit bias, and converges to recursive optimality. Experiments on multi-turn agentic RL benchmarks show that RTPO improves upon trajectory- and turn-level baselines by 21.50% and 10.76%, respectively, highlighting its potential to support more stable training for tool-using agents.
[AI-34] Sanyu Studio: A Multi-Agent System for Art-Historical Narrative Construction
链接: https://arxiv.org/abs/2608.18677
作者: Zhaoxi Wei,Hongye Yang,Shuyuan Tian
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 12 pages, 8 figures, 2 tables
Abstract:Amid concerns that generative AI may standardize art interpretation, this paper examines whether LLM-based interaction can support plural art-historical narrative construction. We present Sanyu Studio, a multi-agent dialogue system that models 321 Sanyu oil paintings as agents with fact, interpretation, organization, and memory-filtering mechanisms. Based on a seven-day workshop with eight art-university participants, the study shows that user prompts, evidence organization, and cognitive tendencies shaped divergent yet coherent versions of digital Sanyu. The findings suggest that, under conditions of limited historical evidence, AI can amplify human agency and offer public audiences an interactive entry point into art-historical interpretation.
[AI-35] Orienteering Problem with Uncertain Time-Varying Rewards: Framework and Benchmark for Everyday Service Robotics
链接: https://arxiv.org/abs/2608.18672
作者: Masafumi Endo,Kohei Honda,Yuu Jinnai,Ryo Yonetani
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 11 pages, 6 figures
Abstract:We present the orienteering problem with uncertain time-varying rewards (OP-UTVR), a novel variant of the orienteering problem (OP). While most existing OP formulations assume rewards to be known in advance, practical applications involve uncertain and time-varying rewards, as with shifting customer demand for delivery agents. OP-UTVR relaxes this assumption by allowing agents to estimate reward dynamics from observations and forecast future rewards. This enables informed routing decisions despite stochastic reward changes and inevitable prediction errors. We address this problem using three planners that differ in planning horizon and online adaptivity, and derive theoretical bounds on their performance under reward stochasticity. We further introduce a mobile service robot benchmark for OP-UTVR, where a robot navigates among pedestrians in indoor environments. Experiments reveal trade-offs between planning horizon and adaptivity, and demonstrate the effectiveness of long-horizon planning with online adaptation.
[AI-36] Candidate-Fate Accounting for Transparent Sensor Diagnostic Pipeline Search
链接: https://arxiv.org/abs/2608.18665
作者: Haotao Xie,Yutian Chen,Yangqi Liu,Xiaoyu Jiang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Industrial sensor diagnostics relies on preprocessing, representation, and classification pipelines, making automated pipeline search useful for reducing manual design cost. However, existing automated machine/deep learning (AutoML/AutoDL) reports typically retain only fitted trials, scores, and winners, omitting generated candidates that are invalid, pruned, skipped, cached, or unfitted. This omission limits reviewers’ ability to check signal constraints, budget use, and unevaluated legal alternatives. To address this, we propose candidate-fate accounting, a candidate-level audit framework for diagnostic search traces. It records each observed candidate as auditable evidence: hashes merge repeated observations, legality checks flag invalid candidates, allocation rationales explain budget decisions, and a closed fate ledger assigns one terminal fate to each candidate. Experiments on three bearing-diagnostic datasets show that the framework detects invalid candidates and identifies 30–41 candidates omitted by fitted-trial-only reports, with closed fate records verifying complete candidate accounting while maintaining competitive diagnostic performance. The code is available at this https URL.
[AI-37] Preference Reasoning under Indeterminacy in Large Language Models
链接: https://arxiv.org/abs/2608.18631
作者: Hadi Hosseini,Samarth Khanna,Xiyuan Wang
类目: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
备注: 55 pages, 14 figures
Abstract:As large language models evolve into decision-making agents, the ability to reason over preferences becomes fundamental to alignment, coordination, and collective intelligence. Yet, unlike standard benchmarks, real-world preference reasoning is inherently indeterminate: information may be incomplete, and valid solutions may not exist. We argue that indeterminacy, rather than correctness alone, is a central challenge for AI reasoning. We formalize this challenge along two axes, (i) epistemic indeterminacy, arising from incomplete, partial, or expressive preferences, and (ii) structural indeterminacy, arising from the non-existence of solutions under standard social choice concepts. Across a hierarchy of tasks, we show that state-of-the-art language models systematically fail to distinguish between determined and undetermined instances, exhibiting miscalibrated reasoning even in verification settings.
[AI-38] CTIFoundry: An Agent -Native Corpus Scaffold for Cyber Threat Intelligence
链接: https://arxiv.org/abs/2608.18613
作者: Yutong Cheng,Changze Li,Qian Cui,Wei Ding,Lingzhi Wang,Yan Chen,Peng Gao
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: Preprint
Abstract:Cyber threat intelligence (CTI) is increasingly consumed not by human analysts but by LLM agents that compose multi-step investigations at query time. The harness side of this shift has matured rapidly (planning loops, tool protocols, context management), but the corpus side has not: threat reports and vulnerability databases are still packaged for retrieval-augmented generation, as opaque chunks behind an embedding index. We argue that this substrate, not model capability, is the bottleneck on agentic CTI investigation, and present CTIFoundry, an agent-native corpus scaffold. At build time, CTIFoundry materializes the latent structure of a CTI corpus: a deterministic ontology graph over four authoritative knowledge bases (CVE, CWE, CAPEC, ATTCK) whose official cross-references become typed, traversable edges; a span-grounded report layer whose canonical, alias-resolved cross-vendor entities index provenance-carrying chunks; and hybrid dense+lexical retrieval surfaces. At query time this structure is exposed through seven typed tools and three procedural skills mounted on a stock open-source agent harness. On the public CTIConnect benchmark, swapping only the action surface lifts the identically-harnessed agent by +0.19 to +0.28 overall F1 across a four-model, two-provider panel: a small model on CTIFoundry surpasses a flagship on the flat substrate, and the gain is not bought with search effort, since on both Claude models the scaffolded agent is more accurate at roughly half the tool calls. An ablation attributes it: typed structure carries the larger share, procedural skills convert structure into discipline, and the two compose super-additively, because skills bind only to structure that exists.
[AI-39] Denoising-Aware Inversion: Revealing Privacy Risks in Noise-Protected Text Embeddings ICDM2026
链接: https://arxiv.org/abs/2608.18610
作者: Yubo Wang,Shujie Cui,James Bailey,Hongzhi Yin,Wenyu Liang,Min Tang,Shiyue Qin,Weiqing Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 11 pages, 3 figures. Accepted by IEEE ICDM 2026
Abstract:Dense text embeddings are widely used in data mining, retrieval, and downstream machine learning systems due to their compact and semantically rich representations, but recent embedding inversion attacks have shown that they can expose substantial information about the original text, leading to serious privacy leakage risks. A common defense is to release perturbed embeddings by adding Gaussian noise, which is simple yet effective against standard inversion attacks and does not significantly degrade embedding utility for downstream tasks. However, it remains unclear whether such noise-protected embeddings are sufficiently safe against adaptive attackers that explicitly account for the perturbation process. In this paper, we study text embedding inversion in a noise-protected setting, where the attacker can observe only noisy embeddings and has no access to clean embedding targets. We first analyze why existing generative inversion methods fail under this setting and identify a “Double Noise Trap”, which fundamentally prevents standard generative inversion models from achieving high-quality reconstruction. To address this challenge, we propose DAEI, a denoising-aware embedding inversion pipeline that combines a residual denoising autoencoder with generative text inversion where the denoiser is trained in an unsupervised manner using Stein’s unbiased risk estimate to enable denoising from noisy observations alone. Extensive experiments show that DAEI achieves approximately 154% relative improvement in BLEU over the existing generative inversion baseline, while also improving token-level F1 and ROUGE-L by 32–60%. The promising inversion performance of DAEI challenges the prevailing assumption that simple Gaussian perturbation is sufficient to prevent sensitive information leakage from embedding representations.
[AI-40] FACET: Preserving Source Intent and Executable State in Terminal Task Synthesis
链接: https://arxiv.org/abs/2608.18580
作者: Kou Shi,Zun Wang,Qisheng Su,Shiting Huang,Ziao Zhang,Zhen Fang,Qingnan Ren,Jin Liu,Yu Zeng,Yiming Zhao,Lin Chen,Zehui Chen,Feng Zhao
类目: Artificial Intelligence (cs.AI); Programming Languages (cs.PL)
备注: this https URL
Abstract:Training terminal agents requires scalable executable supervision, yet synthesizing high-quality terminal tasks remains challenging. Each task couples an instruction, an initialized environment, a reference solution, and an executable verifier; if these artifacts are generated from inconsistent assumptions, the resulting task may be unsolvable or incorrectly evaluated. Meanwhile, multi-stage synthesis can discard the goals, dependencies, state transitions, and procedural constraints encoded in the original sources. We present FACET (Fine-grained Agentic Construction of Executable Tasks), a framework that addresses both information preservation and cross-artifact consistency. FACET reconstructs related agent skills into coherent, information-rich scenarios, then realizes and repairs the execution environment before generating the final task artifacts. The resulting container state serves as shared grounding for the instruction, solution, and verifier, while execution-based validation and targeted repair correct artifact-specific failures without unnecessarily regenerating valid components. FACET produces complex terminal tasks with dense executable checks, and successful trajectories collected from these tasks provide effective, data-efficient supervision. Fine-tuning models across multiple scales consistently improves performance on Terminal-Bench 2.1, while analyses of alternative generation schemes support the importance of environment-grounded construction for task validity and solution-verifier alignment. These results establish source-intent preservation and shared executable-state grounding as key principles for scalable terminal-task synthesis.
[AI-41] he Role of Grid Cells in Reducing Spatial Aliasing in Hippocampal Place Representations
链接: https://arxiv.org/abs/2608.18569
作者: Alexander Johnson,Obadah Ghizawi,Ali A. Minai
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Robotics (cs.RO); Systems and Control (eess.SY); Neurons and Cognition (q-bio.NC)
备注: IEEE World Congress on Computational Intelligence, Masstricht, Netherlands, June 2026
Abstract:Spatial aliasing occurs when two or more distinct locations produce highly similar place-cell representations, primarily due to environmental symmetry or repetitive structures. This issue is most pronounced when place representations are constructed solely from boundary vector cell (BVC) inputs, because symmetric or repetitive structures can yield indistinguishable sensory patterns across multiple locations in an environment. This work introduces grid cell signals to mitigate spatial aliasing in such settings. Because grid cells contribute periodic, internally generated spatial signals that vary independently of environmental geometry, they play a key role in disambiguating perceptually identical locations. We integrate multiple modules of analytically constructed grid cells with BVC-driven place cells and show that this leads to a 94–99% reduction in spatial aliasing relative to a BVC-only baseline across three environments: an open environment without obstacles; an environment with a cross-shaped central obstacle creating high visual symmetry; and a maze environment. The greatest improvement occurs in the environment with the highest visual symmetry. These results indicate that grid cells provide information complementary to boundary-based inputs, yielding more reliable place representations in geometrically ambiguous environments.
[AI-42] MorphoGP: A Nonparametric Framework for Predicting Equilibrium Beach Profiles Under Tidal Influence
链接: https://arxiv.org/abs/2608.18558
作者: Xi Wu,Yanqing Wei,Hang Yin,Pengze Li,Hongshuai Qi,Xi Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Geophysics (physics.geo-ph)
备注: Accepted by IEEE TGRS
Abstract:The prediction of equilibrium beach profiles under tidal influence is of fundamental importance for sustainable coastal development, informing shoreline protection strategies and managing coastal ecosystems under changing environmental conditions. However, it remains challenging due to the highly nonlinear interactions among wave, tide, and sedimentary processes. Traditional empirical and numerical models often exhibit limited adaptability across diverse coastal environments, with especially pronounced limitations in beach systems where tidal processes are important . To improve data-driven prediction under these conditions, this study proposes MorphoGP, a unified category-specific Gaussian process framework for predicting equilibrium beach profiles (EBPs) under tidal influence. The framework first introduces a ContourCluster model based on contrastive learning to classify tide-influenced beach morphologies automatically. Within each morphological category, a specialized Gaussian process expert learns statistical associations between environmental descriptors including waves, tides, and sediments and the beach profile’s shape. A Gating Net then integrates the outputs of all experts through a probabilistic weighting mechanism to produce the final prediction. Evaluated on data from over 180 beach profiles from tide-influenced coasts along the Chinese coast, MorphoGP achieves improved predictive performance compared with conventional and deep learning models, reducing the test RMSE by about 59.3% compared with the best baseline and achieving a final RMSE of 0.297 m. The proposed framework provides a physically informed, data-driven tool for equilibrium beach-profile prediction under tidal influence and coastal management, while stronger process-level physical coupling remains an important direction for future development.
[AI-43] Performance Drift Detection in Machine Learning as a Service (MLaaS) for IoT Environments
链接: https://arxiv.org/abs/2608.18555
作者: Deepak Kanneganti,Sajib Mistry,Sheik Mohammad Mostakim Fattah,Erik Elmroth,Aneesh Krishna,Monowar Bhuyan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Machine Learning as a Service (MLaaS) is a powerful cloud paradigm enabling data-driven intelligent applications in Internet of Things (IoT) environments, widely adopted across healthcare, smart homes, and industry due to its cost-effectiveness. However, the dynamic nature of IoT frequently alters data distributions, affecting MLaaS stability, while periodic MLaaS updates further introduce performance drift. Unlike traditional ML systems, MLaaS clients operate as black-box users without access to internal data or parameters, making drift detection particularly challenging. To address this, we propose a novel MLaaS Performance Drift Detection framework for IoT environments. The framework first employs an MLaaS extraction model that learns service behavior from input-output pairs and identifies prediction-influenced features. Building on this, the proposed MLaaS Performance Drift Detection (MPDD) model jointly captures variations in input data and MLaaS behavior. We further design an Adaptive-Temporal Performance Drift Detection Mechanism (APDDM) that dynamically adjusts monitoring frequency based on behavioral and data variations, enabling timely drift detection for effective service management. Extensive experiments on real-world datasets demonstrate that MPDD achieves up to 22-25% accuracy improvement over baseline drift detection methods. APDDM provides an average accuracy gain of approximately 4% and reduces the miss detection rate by around 9% compared to fixed-interval monitoring.
[AI-44] Bridging Search and CRM: Productionizing AI Product Research Agents for Customer Re-Engagement
链接: https://arxiv.org/abs/2608.18543
作者: Mandar Kulkarni,Pooja A.,Samir Shah
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Modern e-commerce platforms often operate search, recommendation, personalization, and CRM systems independently, limiting opportunities for proactive customer re-engagement. This is particularly challenging for exploratory intents such as best smartphones or latest 5G phones, where users may leave the platform for external research before purchasing. We present a scalable, production-deployed framework that bridges search and CRM workflows through AI-powered Product Research Agents. The system identifies users with exploratory purchase intent and low engagement, conducts grounded multi-agent product research using behavioral signals, external knowledge, and enterprise catalog data, and delivers personalized recommendations through WhatsApp. We evaluate the framework in a 23-day production deployment involving approximately 15K WhatsApp notifications for mobile product discovery. The campaign achieved substantial CTR improvements over traditional WhatsApp recommendation campaigns, with evidence of secondary engagement through message forwarding and sharing. The deployment also generated downstream purchases and GMV impact, demonstrating the practical effectiveness of AI Product Research Agents for proactive customer re-engagement and end-to-end customer journey optimization.
[AI-45] Pairwise Ranking Outperforms Single-Action RL for Offline Explanation Selection: A Practical Lesson RECSYS2026
链接: https://arxiv.org/abs/2608.18531
作者: Tanay Chowdhury,Saeideh Shahrokh Esfahani
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: This is an extended version of a 3-page paper accepted to the RecSys 2026 Research and Practice Notes track
Abstract:Industrial explainable-recommendation systems built on LLMs incur a substantial serving cost: each request triggers an LLM generation, with latency in the hundreds of milliseconds and cost that scales linearly with traffic. We separate generation from selection: explanations are produced ahead of time as a frozen candidate pool (six prompt styles, two commodity LLMs), and a small CPU-resident selector picks one at request time. The stack needs no GPU and returns in under 100 ms. Our primary benchmark is a 2,958-pair XRec Google Local subset, evaluating six offline-pool selectors (LambdaRank, PPO, GRPO, DPO, teacher-student distillation) and three KG-path selectors (random walks, edge-disjoint enumeration, MMR-reranked paths). A 300-pair MovieLens-1M split with Claude-Sonnet-4.5 references serves as an internal cross-dataset check, since no public benchmark exists for this setting. All variants use the same BERTScore-F1 protocol as XRec and G-Refer, averaged across five seeds. LambdaRank reaches F1 = 0.500 on Google Local, exceeding both G-Refer and XRec, and F1 = 0.329 on the MovieLens-1M check. With seed variance below 0.003 F1, the ordering is reliable: pairwise learning-to-rank outperforms single-action RL (PPO, GRPO, DPO), which use only one labelled candidate per rollout, leaving K-1 labels unused. The KG-path family targets a different objective: all three variants reach USR = 1.000 on Google Local and 0.997-1.000 on MovieLens-1M, since per-request path grounding yields a unique output per query, avoiding template-collapse failures affecting cached-LLM outputs. A generator-pool study comparing Claude 3 Haiku and Claude Haiku 4.5 shows small F1 shifts (0.001-0.006) while preserving selector ranking: selector and generator can be evaluated independently, though absolute F1 depends on the generator. End-to-end build cost is near 15 on commodity hardware. Comments: This is an extended version of a 3-page paper accepted to the RecSys 2026 Research and Practice Notes track Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) ACMclasses: H.3.3; I.2.7; I.2.6 Cite as: arXiv:2608.18531 [cs.AI] (or arXiv:2608.18531v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.18531 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Tanay Chowdhury [view email] [v1] Wed, 19 Aug 2026 04:29:04 UTC (43 KB) Full-text links: Access Paper: View a PDF of the paper titled Pairwise Ranking Outperforms Single-Action RL for Offline Explanation Selection: A Practical Lesson, by Tanay Chowdhury and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI prev | next new | recent | 2026-08 Change to browse by: cs cs.LG 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-46] GCNO: Gramian Chebyshev Neural Operator for Physics-Based Compression of Wireless Channels
链接: https://arxiv.org/abs/2608.18522
作者: Rafid Umayer Murshed,Shahab Hamidi-Rad,Elahe Soltanaghai,Akshay Malhotra
类目: Information Theory (cs.IT); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 22 pages, 6 figures, 19 tables
Abstract:Large antenna arrays allow wireless systems to serve more users and achieve higher data rates, but they also make channel feedback expensive: the receiving device must repeatedly report a large complex-valued channel matrix to the base station. Most neural compressors treat this matrix like an image and replace it with a fixed-length code that only a matched neural decoder can interpret. The message therefore does not adapt to channel complexity, and changing the antenna count typically requires retraining. We ask whether a device can instead report only the few dominant propagation paths underlying each channel. We introduce the Gramian Chebyshev Neural Operator (GCNO), a physics-based, variable-rate compressor that identifies a sample-dependent set of path directions. GCNO uses receive-transmit channel structure to locate paths, a first-order Taylor correction to refine directions that fall between grid points, and least squares to recover their complex strengths. It is trained without path labels, and the base station reconstructs the channel analytically from the transmitted path tuples rather than through a learned decoder. Across three ray-traced environments, GCNO achieves better reconstruction accuracy at the same payload - or lower payload at the same accuracy - than neural feedback baselines, and transfers to unseen antenna counts without retraining.
[AI-47] Which Negatives Matter? Ask Your Text Encoder: Adaptive Similarity Margins for Dense-Caption Retrieval
链接: https://arxiv.org/abs/2608.18521
作者: Haoyue Liu,Ye Chen,Zhichao Wang,Xiaoying Tang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Dense-caption retrieval has recently been improved by introducing segmentation, edge maps, LLM-filtered captions, and cross-modal modules into contrastive fine-tuning. However, these methods largely inherit the same InfoNCE objective, whose optimization can prematurely saturate under a strong pre-trained initialization: on dense captions, the loss falls below 10^-3 on 80% of batches within the first epoch, while its gradient reaches exact zero in fp32 in 47% of measurements. We find that this behavior is closely related to the large number of near-duplicate captions in dense-caption benchmarks, where a few highly similar negatives remain unresolved after the easy majority has already been separated. As a remedy, we introduce HN-CLIP, which uses the text encoder’s own text-text geometry to construct per-negative adaptive similarity margins. Specifically, a detached caption-similarity matrix is added to the negative logits, assigning larger margins to more similar captions without mining, synthesizing, or resampling negatives. The resulting objective requires only one caption-similarity matrix and a masked logit addition during training, with no auxiliary data, additional parameters, offline preprocessing, or inference-time overhead. Extensive experiments on four dense-caption retrieval benchmarks show that HN-CLIP improves over the strongest competitors by +2.4–+4.3 R@1 while training 2.4x faster than GOAL and 5.4x faster than StructXLIP. Moreover, the proposed objective improves all six tested fine-tuning frameworks on the in-domain benchmarks and reaches the strongest full-data baseline with only 20% of the training data.
[AI-48] UMER: Unifying Embedding and Ranking via Pair-Aware Discriminative Reasoning for Universal Multimodal Retrieval
链接: https://arxiv.org/abs/2608.18504
作者: Libiao Chen,Xiyang Liu,Yanheng Wei,Tao Wang,Zhenyu Tang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Universal multimodal retrieval aims to support diverse instruction-aware retrieval tasks, demanding both efficient corpus-scale matching and fine-grained semantic reasoning. Recent MLLM-based embedding methods typically derive representations from hidden states, while Chain-of-Thought (CoT) reasoning is emerging as a promising strategy for embedding enhancement by encoding intermediate semantic evidence into the representation space. However, existing CoT methods typically use item-wise reasoning over queries and candidates in isolation, providing no explicit evidence to distinguish a positive from a semantically confusable hard negative. Moreover, contrastive embeddings capture global similarity but struggle with meta-tasks requiring answer verification, category judgment or fine-grained reasoning. In this paper, we propose UMER, a Unified Multimodal Embedding and Ranking framework for universal multimodal retrieval. UMER replaces item-wise reflection with Pair-Aware Discriminative Reasoning, which compares query–candidate pairs to identify instruction-relevant matching and discrepancy evidence. UMER jointly learns contrastive embeddings for efficient global matching and discriminative ranking for explicit pairwise relevance judgment within a single MLLM. A complementary mutual distillation strategy further transfers reliable pairwise preferences between the embedding and ranking functions. On the MMEB-V2 benchmark, UMER achieves state-of-the-art performance under comparable experimental settings while supporting budget-adjustable inference.
[AI-49] Physics-Unrolled Neural Operator for Wireless Field Modeling
链接: https://arxiv.org/abs/2608.18495
作者: Rafid Umayer Murshed,Saif Ur Rahman,Mingyue Tang,Elahe Soltanaghai
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 37 pages, 8 figures, 9 tables
Abstract:Radio maps are essential for wireless decision-making tasks such as access-point placement, coverage planning, and localization, but their fine spatial details are governed by complex propagation effects and are costly to simulate accurately. Machine learning offers a path to high-fidelity radio-map prediction without running expensive high-fidelity simulations for every scene. However, generating high-quality training labels at scale is also difficult: the affordable labels come from finite-ray simulations, which are richer than low-fidelity inputs but carry residual Monte Carlo noise. We address this challenge with Physics-Unrolled Hybrid Neural Operator (PU-HNO), a three-stage cascade that predicts high-fidelity indoor radio maps from low-fidelity ray-tracing outputs and scene priors by progressively capturing reflection, diffraction, and scattering effects, rather than treating radio maps as generic images. We prove that, under conditionally unbiased label noise, the model can learn stable propagation structure and outperform its own training labels. Experiments across diverse floorplans show that PU-HNO outperforms image-to-image baselines, wireless learning models, and monolithic neural operators across both image-quality and wireless deployment metrics.
[AI-50] Coverag e-Driven RTL Assertion Generation with Formal Exploration and Neuro-Symbolic Refinement
链接: https://arxiv.org/abs/2608.18482
作者: Zhiyuan Yan,Ziyue Zheng,Hongce Zhang
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI)
备注: Accepted at MLCAD 2026
Abstract:Hardware functional verification relies on high-quality assertions to expose design bugs and establish confidence in Register Transfer Level (RTL) designs. Yet existing assertion mining methods still struggle to produce complete and reliable assertion sets: random or limited traces fail to cover hard-to-reach behaviors, and one-shot generation provides little feedback about what remains unverified or how the assertion set should be improved. As a result, critical design behaviors can remain uncovered even when many assertions are generated. We present NeuroAssertion, a coverage-driven assertion generation framework that combines formal trace generation, syntax-guided synthesis (SyGuS), and an agent-inspired refinement process within a unified framework. Our framework first converts hard-to-reach control-flow conditions into formal reachability objectives, uses model checking to generate behaviorally diverse traces, and mines initial assertions from these traces with SyGuS. It then performs targeted agent-inspired refinement under verification feedback: one LLM first proposes candidate assertions for uncovered regions, and if a candidate fails formal checking, a second LLM generates a repair grammar that guides constrained symbolic synthesis in a neuro-symbolic repair procedure. Experimental results show that this framework delivers around 2X more assertions and about 2X higher mutation coverage than traditional assertion mining methods.
[AI-51] ERASE: EaRly bAckpropagation SchEdule for Faster Training of Modern Recommendation Systems
链接: https://arxiv.org/abs/2608.18469
作者: Ergan Shang,Flavio Sales Truzzi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Lightweight proxy models enable rapid experimentation without repeatedly training frontier-scale systems, but their small kernels often leave modern accelerators underutilized. Conventional training compounds this inefficiency by scheduling the forward and backward passes as disjoint phases, so spare capacity in one cannot be filled by work from the other. We reinterpret the detachment mechanism of Forward-Forward (FF) as a scheduling primitive: given a local objective, detaching a block’s output removes downstream gradient dependencies, making its backward pass ready when its forward pass finishes. ERASE launches each detached subgraph’s backward pass early on a separate CUDA stream, overlapping it with subsequent forward work. Execution trace on a lightweight transformer demonstrates this overlap and its limit: a kernel that saturates the device leaves no capacity for concurrency. On a large-scale click-through-rate model, detaching six dense subarchitectures improves training throughput by up to 9.51% while keeping normalized entropy close to the baseline.
[AI-52] Formal Verification of Romanovs Triplet Logic: A Verified Filter for Sliding-window 3-CNF with Application to Structured Formulas
链接: https://arxiv.org/abs/2608.18445
作者: Dmitry V. Alexandrov
类目: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Programming Languages (cs.PL)
备注: 25 pages, 6 figures, 5 tables, 3 listings, 16 bibliographic references, more than 23,000 lines of Rocq code across 17 files, with 427 proved lemmas and theorems and zero admitted goals
Abstract:We present the first mechanised formalisation of Romanov’s Triplet Logic (TLS) in the Rocq proof assistant. TLS is a triplet-based combinatorial framework for reasoning about compatible paths through layered triplet structures, called Compact Triplets Structures (CTS), and their intersection via Romanov’s Effective Procedure, which we refer to as Simple Vertex Intersection (SVI). Originally motivated by Boolean satisfiability, TLS constitutes a self-contained mathematical theory whose formal properties had not been previously established. We formalise the core of TLS in Rocq, including Compact Triplets Formulas (CTF), CTS, hyperstructures, clearing, and SVI. For the well-formed sliding-window fragment we verify a clause-by-clause CNF-to-CTF translation, the clearing procedure, and aligned intersection, and we prove explicit polynomial-time bounds for the filter stages. Our main contribution is a precise correctness boundary: the existence of a joint satisfying set implies non-emptiness of SVI, but the converse does not hold in general; for aligned structures we recover a complete bi-implication, extended to systems of structures. We also formalise soundness of grouped-window translation and exhibit a formal counterexample to its completeness. We introduce VFR, an extracted OCaml prototype that provides a verified decision procedure for the sliding-window fragment and a sound one-sided filter for general 3-CNF, with a Python runtime and reproducible Docker packaging. Benchmarks on random and structured instances confirm the predicted behaviour, and the complete toolchain is available as a curated Zenodo artifact. The Rocq development comprises more than 23,000 lines of code across seventeen files, with 427 proved lemmas and theorems and zero admitted goals.
[AI-53] FM-Bench: A Benchmark for Long-Horizon Management with Competing Agents
链接: https://arxiv.org/abs/2608.18423
作者: Tianyou Wang,Chongyang Gao,Kezhen Chen,Chen Dong,Yinghao He,Donghan Li,Wangcheng Xu,Hongjiu Zhang,Chi Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Language model agents now execute bounded tasks reliably. Whether they can sustain effective decision-making over long horizons, where actions have cumulative consequences and the environment responds to their choices, remains largely unmeasured. FM-Bench (Football Management Benchmark) measures this. An LLM agent runs a football club for 20 in-game years through 26 tools and roughly 340 to 400 decision stops. It drafts a squad on the same budget as every rival, trades players, negotiates contracts, invests in facilities and youth, sets lineups, and answers to a board that can fire it, while a deterministic engine accumulates every year into one final score with no LLM judge or human rater. The solo track plays each of 15 frontier models against a frozen scripted world, and the Arena places the same models plus a scripted anchor in one shared 20-year world; to our knowledge, the first head-to-head evaluation at this scale. We measure six behavioral capabilities behind the score. Across three seeds, all 15 models complete every horizon while the blind scripted baselines die out in most of theirs, and claude-fable-5 tops the solo board on mean score and the Arena, where the title nonetheless rotates among ten models. Neither scale, price, nor vendor predicts the order; the order settles only late in the horizon, and the best first-play human lands only at the bottom of the model board. What separates the models is managerial behavior rather than computation. Higher-scoring models reduce slow-payoff investment near the end, keep cash invested rather than idle, and open renewals well before the deadline, while token spend predicts nothing. No model learns the market’s hidden prices from hundreds of rejected bids, and self-managed memory fails in two opposite modes: an archive that only grows or a plan rewritten every season. Code is available at this https URL.
[AI-54] Mechanistic Interpretability of Structure-Aware Numerical Reasoning in LLaMA 3.1 8B
链接: https://arxiv.org/abs/2608.18419
作者: Rahul Chowdhury,Timothy A Rupprecht,Senhao Cao,Jiahao Liu,Octavia Camps,David Bau,Pu Zhao,Yanzhi Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent work has shown that large language models (LLMs) exhibit strong numerical sequence modeling capabilities and show promise in time-series prediction. While LLMs display in-context learning capabilities, the mechanisms with which they accomplish time-series prediction remain unclear. Specifically, whether they truly understand the underlying structure, which at a minimum requires reasoning over first differences in the sequence of numbers. To study this, we investigate Llama 3.1-8B from a mechanistic interpretability point of view. Mechanistic interpretability is an emerging field concerned with the reverse engineering of the algorithms learned by neural networks such as LLMs. To assess Llamas’ numerical sequence modeling capabilities and to facilitate our mechanistic interpretability analysis, we create a sequence modeling task that cannot be solved without picking up structural cues. Specifically, we sample n random numbers and repeat them with an offset. We find that Llama displays strong performance on our tasks suggesting that it can pick up on the underlying structure. To understand the mechanisms that allow it to do so, we perform probing experiments and activation patching based counterfactual analysis. Probing reveals that the model computes and stores first differences in its internal representations without explicit supervision, indicating that it tracks structural information about the sequence. Activation patching reveals that Llama retrieves the relevant first-difference with a mechanism similar to an induction circuit and subsequently adds it to the current value. Notably, our work represents one of the first studies to identify this form of concept induction in LLMs.
[AI-55] Improving Natural-Language Combinatorial-Optimization Accuracy in Resource-Constrained Language Models via Formal Abstractions
链接: https://arxiv.org/abs/2608.18409
作者: Shrenil Shaun Sharma,Avi Sharma
类目: Artificial Intelligence (cs.AI)
备注: 17 Pages
Abstract:Combinatorial scheduling poses a significant challenge for language models, requiring them to identify feasible solutions within exponentially large search spaces while satisfying complex constraints. This challenge is especially pronounced in resource-constrained settings, where larger language models are impractical and selection is limited to smaller models which often fail to preserve feasibility when scheduling directly from natural language. To address these limitations, we introduce SDDL, a neuro-symbolic framework that translates natural-language scheduling problems into compact, solver-aligned representations of tasks, resources, constraints, and objectives, while delegating low-level modeling and search to a deterministic compiler and external solver. On a 300-instance, multi-family subset of scheduling problems, SDDL improves independently verified feasibility for every resource-constrained model tested. The two strongest SDDL configurations reach 55.3% and 28.3%, up from direct-generation baselines of 23.7% and 1.3% and solver-code baselines of 21.7% and 7.0%, with a 0.0% median optimality gap among feasible schedules. By expressing problem structure rather than generating solutions or solver code, SDDL enables smaller models to approach the strongest evaluated direct- and solver-code configurations, including substantially larger frontier models.
[AI-56] Vector Symbolic Policy Gradient
链接: https://arxiv.org/abs/2608.18404
作者: Ryozo Masukawa,Sanggeon Yun,SungHeon Jeong,Hyunwoo Oh,Raheeb Hassan,Pietro Mercati,Nathaniel D. Bastian,Mahdi Imani,Mohsen Imani
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Symbolic Computation (cs.SC)
备注: Code available in this https URL
Abstract:We answer this question with Vector-Symbolic Policy Gradient (VSPG), a discrete-action actor that represents each action by a unit-norm hypervector and scores it by similarity to the encoded state. Under the standard softmax policy-gradient surrogate, we prove that its update is exactly advantage-weighted hypervector bundling followed by normalization, and therefore supports standard advantage estimators. We further show that each trained action hypervector is a fixed-size compressed kernel memory, storing an advantage-weighted kernel expansion over visited states and transferring evidence according to the encoder-induced similarity. This provides a concrete mechanism that can support sample-efficient learning without increasing inference-time memory. Finally, for bipolar action memories, we prove that greedy action selection is stable under random bit flips, with failure probability decaying exponentially in the hypervector dimension. VSPG thus connects VSA action memories, log-linear policy gradients, and kernel policy search while providing a quantitative robustness guarantee.
[AI-57] When Clean Signals Are Not Enough: Detecting Structural Ambiguity for Safe Wearable Stress Classification
链接: https://arxiv.org/abs/2608.18397
作者: Saba A. Farahani,Hung Cao,Amir M. Rahmani
类目: Artificial Intelligence (cs.AI)
备注: 4 pages, 3 figures, 1 table. Accepted at the 2026 IEEE 22nd International Conference on Body Sensor Networks (BSN 2026)
Abstract:Wearable stress classifiers can achieve strong average performance while failing completely for a particular individual. On WESAD, a Random Forest reaches 93.0% mean accuracy yet yields F1 = 0 for Subject 14, whose cross-signal coupling weakens near stress onset. We call this structural ambiguity: individually plausible physiological channels form an inter-signal pattern that is poorly supported by the person’s non-stress reference. We introduce the Individual Conformal Coupling Monitor (ICCM), a lightweight and transparent pre-inference monitor that quantifies subject-specific coupling divergence and routes each window to classify, defer, or abstain without retraining the downstream classifier. Across WESAD (N = 15) and Stress-Predict (N = 35), full-cohort Pearson associations between ambiguity and accuracy are negative (r = -0.607, p = 0.016; r = -0.412, p = 0.014). Robustness analyses temper this finding: rank correlations are not significant, and the WESAD association disappears when Subject 14 is removed. ICCM changes false-positive counts from 29 to 27 and 94 to 92, although neither paired change is significant. It withholds 3 of Subject 14’s 21 stress windows but does not repair the missed-stress failure. These results position ICCM as an interpretable signal of unsupported physiology and individual failure, rather than a stand-alone safety guarantee.
[AI-58] A Jagged Frontier: Evaluating Robustness of Code Agents to Semantics-Preserving Transformations
链接: https://arxiv.org/abs/2608.18389
作者: Hasan Najib Mahmud(1),Shreya Gupta(2),Isha Chaudhary(3),Nathaniel Enis(1),Ravi Mangal(1),Gagandeep Singh(3),Corina Pasareanu(4) ((1) Colorado State University, (2) Microsoft, (3) University of Illinois Urbana-Champaign, (4) Carnegie Mellon University)
类目: Artificial Intelligence (cs.AI)
备注: 18 pages, 6 figures
Abstract:AI code agents are increasingly deployed to resolve real software issues, yet their reliability under superficial code variations remains poorly understood. We evaluate whether coding agents that repair repository-level issues remain reliable when the surrounding codebase is rewritten into a semantically equivalent form. We introduce a random variant sampler that applies common semantics-preserving transformations (SPTs) - spanning control-flow rewrites, dead-code injection, and identifier renaming - to produce perturbed variants. We evaluate two agentic scaffolds (mini-SWE agent and OpenCode) each backed by one of four frontier models (Claude Opus 4.5, Kimi K2.5, MiniMax M2.5, and Qwen 3.6-27B) across instances drawn from SWE-bench Verified and SWE-bench Pro. For each instance, the agent is run multiple times on the unperturbed and perturbed variants, yielding paired resolve-rate estimates that isolate the perturbation effect from intrinsic stochasticity. We find small degradation in most configurations: up to 6.7 percentage points mean resolve-rate drop in the most affected configurations with statistically significant degradations in 6 of 16 configurations of model, scaffold, and dataset. Crucially, no single model ranking by robustness holds across scaffolds - Qwen is among the most robust under mini-SWE agent on SWE-bench Verified yet the most brittle under OpenCode - revealing a jagged robustness frontier. The simpler scaffold (mini-SWE agent) is more robust to perturbation. Our results demonstrate that even top frontier models are susceptible to semantics-preserving perturbations although the effect is not uniform, raising concerns about the deployment reliability of AI code agents in diverse real-world codebases.
[AI-59] One Gate Is Not Enough: Composing Stateful Pre-Action Controls for Agent ic AI
链接: https://arxiv.org/abs/2608.18360
作者: Gaston Besanson
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 29 pages. Code and data: this https URL (Zenodo DOI: https://doi.org/10.5281/zenodo.22003399 )
Abstract:Agentic AI systems take consequential actions governed by more than one pre-action control at once: authority, resource, and evidence gates that can admit, degrade, or remediate an action before it executes. This paper’s central object is remediation-induced control coupling: a remediation applied by one control can change the action, evidence, or context another control evaluates, invalidating that control’s earlier judgment. We formalize this coupling and give a remediate-and-regate protocol that restores per-action soundness in the current bounded, idempotent setting under its stated assumptions. We further show that the two implemented remediation operators (evidence substitution and resource-budget downroute) do not commute – a finite-model checker finds concrete counterexample instances – making remediation order part of the control-plane semantics rather than an implementation detail. A governed evidence buffer that trusts its own most recent admitted write is a further instance of the same problem at the level of state – current admissibility does not imply future reference trustworthiness – and is vulnerable to poisoning from declared-uncovered defect classes; two mitigations reduce, not eliminate, that exposure. Supporting results establish the exact condition under which positive-weight linear aggregation of gate outcomes can compensate a member veto, a unified cross-control Evidence Set, and that composition manufactures no new detection coverage, reported honestly. Empirically, on a deterministic open-data artifact composing three published engines unmodified, CH1-CH5 meet their registered decision rules across all 30 pre-registered seeds; CH6 does so under W1 but not under the smaller W2 workflow, reported as such. This is a mechanism demonstration on open payload data with a synthetic metadata layer, not a claim about production prevalence.
[AI-60] ask-Conditioned Least-Privilege Learning for Executable Terminal and MCP Agents
链接: https://arxiv.org/abs/2608.18351
作者: Alexander Tu,Michael Tu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY)
备注: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
Abstract:Tool-using large language-model agents can complete a task while exercising authority that the user did not grant or the task does not need, causing excess-authority errors. Traditional permission gating systems alone for validating agent environments are insufficient. We study whether post-training can teach a 4B-parameter model to choose task-conditioned authority in executable terminal and Model Context Protocol (MCP) environments to complement those measures. We propose a framework where each action is audited before execution and again from observed effects along six dimensions of risk. This auditing is conducted using deterministic verifiers that score completion, evidence, exact state, prohibited attempts, and safe success. In conjunction with predefined task-specific sufficient-authority envelopes, we determine task-specific excess privilege values for trajectories, which are then optimized for in post-training. We find that after training using this framework on Qwen3.5-4B over 1,500 tasks, the selected seed reaches 98.48% safe success across 2,896 evaluation episodes spanning all 500 held-out tasks, compared with 64.36% for the base policy, and reduces excess-authority error events from 4.56% to 0.79%. Furthermore, external tests show capability retention and prompt-directed improvement. A 400 task continuation study also found evidence of generalization, reducing excess-authority events by 6.99 percentage points while maintaining previous capabilities. We conclude learned restraint through least-privilege aware post-training is therefore useful as an additional control layer for tool-using agents in executable terminal and MCP environments, but it does not replace permission gates and sandboxing.
[AI-61] Low-Power Neuromorphic Acoustic Anomaly Detection for Persistent Machine Monitoring
链接: https://arxiv.org/abs/2608.18341
作者: Steven C. Nesbit(1),Victor M. Vergara(2),Michael A. Felix(3),Evan T. Kain(4),Luis R. García Carrillo(4),Gerd J. Kunde(5),Andrew T. Sornborger(1) ((1) Information Sciences, CAI-3, Los Alamos National Laboratory, Los Alamos, USA, (2) AeroVironment Inc., Albuquerque, USA, (3) University of New Mexico COSMIAC Research Center, Albuquerque, USA, (4) Air Force Research Laboratory, Kirtland AFB, USA, (5) Nuclear and Particle Physics and Applications, P-3, Los Alamos National Laboratory, Los Alamos, USA)
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: 5 pages, 2 figures, 2 tables
Abstract:Persistent acoustic monitoring can detect machine faults without physical contact, but always-on inference is constrained by power, latency, and deployment complexity. We demonstrate autoencoder-based acoustic anomaly detection on an Intel Loihi 2 neuromorphic processor under clean and noisy conditions. Log-mel features are computed off chip; normalization, autoencoder inference, L1 reconstruction scoring, and thresholding run on chip. In a clean, microphone-position-invariant ToyADMOS ToyCar benchmark, the on-chip model achieves 0.9959 AUC and 0.9785 standardized pAUC at maximum false-positive rate 0.1. In the DCASE 2026 Task 2 ToyCar noisy benchmark, the model achieves source AUC 0.7990, target AUC 0.6466, and pAUC 0.6426, exceeding reported baseline metrics. Power profiling on a 16-chip Loihi 2 VPX system shows real-time throughput with 0.0406 \unicodex2013 0.0426 mJ dynamic energy per sample, two orders of magnitude lower than both a CPU and GPU. These results support neuromorphic acoustic anomaly detection as a practical candidate for low-power, persistent machine monitoring.
[AI-62] Measuring the Partial-Credit Gap: A Strict Benchmark on Vietnams 2025 Convex Marking Scheme
链接: https://arxiv.org/abs/2608.18336
作者: Nguyen Quoc Hung,Nguyen Dang Minh,Le Nhu Quynh,Tran Khanh Linh,Nguyen Kieu Linh
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:When evaluating language models on human exams, benchmarks typically score each response as right or wrong and report the overall accuracy. This approach assumes that partial knowledge is worth proportional credit, an assumption that fails when an examination uses a non-additive grading scheme. The 2025 reform of Vietnam’s National High School Graduation Examination demonstrates the cost of this substitution. In Part II of the exam, candidates evaluate four true/false statements per question. The grading is convex: the number of correct statements earns 0, 0.10, 0.25, 0.50, or 1.00 points. Identifying three statements correctly pays 0.50 points, not the 0.75 points that standard accuracy metrics would award. Because Part II accounts for 4.00 of the exam’s 10.00 points, reporting accuracy inflates the score by rewarding partial knowledge that the state explicitly penalizes. We introduce THPT-Ladder, a benchmark of 632 items from 21 official exams across 11 subjects, graded exactly as the ministry grades its students. The ministry publishes the marks of over a million candidates, allowing us to place models directly into the human cohort. Across eight models, the official rubric pays 0.020 to 0.159 points less per Part II question than proportional credit. This shortfall changes a model’s apparent competence. For Qwen3.5-27B on the 2025 History exam, a 0.042-point shortfall drops its standing from the 90th to the 77th percentile among 481,293 candidates. A model’s accuracy does not predict this penalty. At Claude Sonnet 5’s accuracy level, different distributions of errors yield scores varying from 0.869 to 0.932 points per question. Official marks depend on how correct statements are grouped, meaning standard benchmarks report a competence the institution would not certify.
[AI-63] Governance Records as Supervision: Verifier-Selected Self-Training for Structured Workflow Repair
链接: https://arxiv.org/abs/2608.18324
作者: Jesus Salas
类目: Artificial Intelligence (cs.AI)
备注: 21 pages, 5 figures, 9 tables
Abstract:Machine-verifiable workflows produce governance records linking a task contract, model attempt, verifier decision, accepted output, and target origin. We test whether these records can supervise bounded models, consolidating occasional or expensive capability into reliable one-shot execution. On fresh, structure-disjoint PlanBench replanning cases, Qwen3-14B thinking generated 24 plans admitted by the independently authored VAL verifier. Those plans trained the same checkpoint for non-thinking execution, without oracle targets or a stronger teacher. On 80 unopened cases, VAL-accepted plans increased from 1 to 57, with 56 paired gains and zero regressions; thinking reached 30. The adapter was schema-valid on all cases and used approximately 1/56 of thinking’s mean latency. The separate paired interface-cure gate did not pass. A matched ablation fixed the source cases, 52-candidate pool, 24-target count, model, recipe, and seed while changing target selection. On 160 new cases, base, schema-selected, model-self-selected, and VAL-selected execution reached 1, 55, 69, and 102 accepted plans. VAL exceeded self-selection by paired net +33 (p=0.0000019647), with gains in both difficulty strata. Independent semantic selection is therefore load-bearing relative to matched alternatives within this band. A complementary Phi stronger-teacher arm raised base Phi-4 from 2 to 51 accepted plans and from 35 to 80 schema-valid outputs. Earlier synthetic experiments establish teachability, cumulative learning, construction robustness, and stopping boundaries. The results support verifier-selected supervision for bounded, machine-checkable capabilities, not arbitrary planning, enterprise validity, or unrestricted self-improvement. Comments: 21 pages, 5 figures, 9 tables Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.18324 [cs.AI] (or arXiv:2608.18324v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.18324 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jesus Salas [view email] [v1] Tue, 18 Aug 2026 21:16:43 UTC (39 KB)
[AI-64] SESSE: Sketch Expand Sort Summarize Evaluate – LLM -as-Judge Evaluation via Structured Decomposition
链接: https://arxiv.org/abs/2608.18303
作者: Dae Lee,Mihai Delgeanu,Adel Youssef
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:LLM-as-judge evaluation reduces response quality assessment to a single holistic A/B preference choice, providing no mechanism to isolate which quality dimensions drove the preference or distinguish model errors from genuine label ambiguity. We propose SESSE (Sketch, Expand, Sort, Summarize, Evaluate), a training-free framework that decomposes holistic judgment into structured sub-questions mined directly from the judge’s own error cases; requiring no oracle responses, task-specific rubrics, or fine-tuning. On RewardBench (n=1,000), SESSE achieves near-parity with the chain-of-thought baseline and is competitive with RISE-Judge-32B (92.7%), a fine-tuned specialist, while remaining fully training-free. Per-criterion vote evidence provides an interpretable audit trail for diagnosing label ambiguity and judge failure modes unavailable from a single holistic output token.
[AI-65] he Lifecycle of LLM -as-a-Judge for Large-Scale Recommendation Explanations
链接: https://arxiv.org/abs/2608.18300
作者: Emma Yanyang Kong,JJ Tan,Ishan Gupta,Lars Olds,Claire Campbell,David Fagnan,Veli Balin,Rohan Gosain,Louis Garcia,Minsu Jang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM-as-a-Judge, which leverages a large language model to evaluate natural language generated by another AI application or model, has become a standard, scalable approach for accelerating and extending costly human evaluation. However, most work treats a judge as a static artifact, evaluating it once at construction or against a fixed benchmark. In contrast, we argue that an LLM judge running in a production system is better understood as having a lifecycle: it must be built, trained, deployed, and continuously maintained as the surrounding data evolves, and each phase poses distinct technical and operational challenges. We present such a lifecycle for the LLM judges that evaluate user-facing recommendation explanations at Netflix, where our pipeline generates and the judges assess hundreds of thousands of distinct show-level explanations per week, served across the mobile experience to millions of members. Our framework has four phases: (I) Birth, defining multiple evaluation criteria and building curated benchmark datasets with human labels and rationales; (II) Training, refining the judges’ rubrics via Reasoning-Aligned Rubric Tuning (RART), a rubric-tuning procedure that uses a meta-judge over reasoning output as the learning signal; (III) Deployment, in which one judge serves two production roles: quality gating and reflective generation; and (IV) Monitoring, a continuous Human-in-the-Loop alignment process that detects drift and triggers re-tuning behind a human review gate. We report post-launch results from a five-week A/B test over tens of millions of members, in which the judge-aligned explanations shifted member viewing toward novel content (previously unwatched) and increased successful browse-to-play sessions relative to a no-explanation control, with no quality-related takedowns. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.18300 [cs.AI] (or arXiv:2608.18300v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.18300 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-66] FairGlucose: A CGM Fairness Benchmark Reveals Subgroup Disparities Hidden in Population-Level Validation
链接: https://arxiv.org/abs/2608.18296
作者: Junjie Luo,Xuzhe Zhi,Rui Han,Abhimanyu Kumbara,Anand K. Iyer,Mansur E. Shomali,Ritu Agarwal,Guodong Gordon Gao
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:As CGM-based AI tools approach clinical deployment, whether their accuracy is equitable across patient demographics remains insufficiently tested. To enable this evaluation, we constructed FairGlucose, a 300-patient CGM cohort balanced across 12 demographic strata (age x gender x type 1/type 2 diabetes), with 132,480 forecasting samples and 3,945 unique behavioral events (meals, exercise, medication) logged by 81 patients. Benchmarking 33 models across four families on 2-hour glucose forecasting, we find that population-level external validation can conceal substantial subgroup disparities. Aggregate out-of-distribution metrics appear stable (approximately 1.0), yet subgroup-level ratios range from 0.8 to 1.4, with T1D patients showing 6 mg/dL higher prediction error than T2D (p 0.001). This disparity persists across all 33 models, suggesting a property of the prediction task rather than any single architecture. Further analysis shows that subgroup performance gaps align with the proportion of clinically hard cases, and that input-length sensitivity varies across demographics, motivating personalized configurations. Frontier LLMs underperform specialized neural models by 1-6 mg/dL; behavioral events contribute negligibly (approximately 0.1 mg/dL) even under oracle event access. These findings establish that population-level validation alone is insufficient for equity assessment of digital health AI, motivating subgroup-disaggregated reporting as a default standard.
[AI-67] Cacheable by Design? Training Mixture-of-Experts Routers for Locality Against the Edge Memory-Bandwidth Wall: A Pre-Registered Negative Result with a Systems Measurement Study
链接: https://arxiv.org/abs/2608.18261
作者: Shriniwas Ramesh Suram
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Pre-registered negative result plus a systems measurement study. Code, router-telemetry tool (llama-moe-trace), traces, data manifests, and the frozen pre-registration: this https URL
Abstract:Serving a 235B-parameter Mixture-of-Experts (MoE) model on a single 8 GB GPU is bottlenecked not by compute but by memory bandwidth: decode must stream each token’s active experts from whichever tier holds them, and on consumer hardware most experts sit on an SSD far slower than RAM. We quantify this bandwidth wall on Qwen3-235B (Q4_K_M, 134 GB): measured decode is 0.44 tok/s warm, matching a bytes-per-token / bandwidth model, while a batching scheme that should amortize one disk sweep instead collapses at batch 32 from paging thrash. We build llama-moe-trace, a zero-surgery router-telemetry tool, and measure routing on Qwen3-30B: adjacent-token expert reuse is 2.0x chance, 95% of traffic uses 52.5% of experts, and an LRU cache of 13.4% of experts serves 66% of requests. We then ask whether cacheability is trainable: we pre-register training of 137M MoE language models with auxiliary locality and domain router losses, under joint criteria on cache-miss reduction and perplexity. The mechanism works (misses down up to 60%; a 99% static-pin hit rate) but every configuration fails the pre-registered =1% perplexity gate – miss reduction and quality are tightly coupled. Concurrent StickyMoE reports the same loss as near-free on single-domain sub-25M models; on multi-domain 137M we find the tax real. Our contribution is this pre-registered, stricter-criterion, multi-domain evaluation plus edge-serving measurements. A 340M rung shows the tax does not shrink with scale (it rises slightly). We further show training-free cache-aware rerouting stacks with trained locality – together ~80% miss reduction at =3.4% perplexity at both sizes, far cheaper than either alone – while domain-primed prefetching does not help. All code, traces, and the pre-registration are released.
[AI-68] Bidirectional representational alignment between biological and artificial neural networks
链接: https://arxiv.org/abs/2608.18244
作者: Samuel Kostousov,Abhinn Kaushik,Brokoslaw Laschowski
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent work has shown that representational alignment between biological and artificial neural networks is asymmetric: model representations predict neural responses much better than neural responses predict model representations. This asymmetry raises the question of whether representational geometry contributes to bidirectional representational alignment. We hypothesized that steering representational geometry during training can systematically influence bidirectional alignment. To test this hypothesis, we developed a computational framework that integrates spectral regularization with bidirectional predictivity analyses. As an initial demonstration, we evaluated our framework using self-supervised contrastive vision models. Steering the spectral geometry of the learned representations substantially increased reverse predictivity with modest reductions in forward predictivity, yielding a 55% relative improvement in bidirectional predictivity. These improvements were accompanied by reduced effective dimensionality and a reorganization of the shared representational subspace, within which forward and reverse predictivity became approximately symmetric at intermediate spectral exponents. Overall, these findings demonstrate that representational geometry can be systematically steered to modulate bidirectional representational alignment between biological and artificial neural networks.
[AI-69] GenEx: A Graph-Based Representational Paradigm for SARS-CoV-2 Variant Detection via Codon Co-occurrence Networks
链接: https://arxiv.org/abs/2608.18238
作者: Arefin Amin,Labiba Faiza Karim,M. Monir Uddin
类目: Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
备注:
Abstract:Genomic analysis on viruses such as SARS-CoV-2 variants: Beta, Gamma, Delta, and Omicron is heavily dominated by classical bioinformatics methods, including Sequence Alignment, Phylogenetic Analysis, and Mutation Frequency Statistics. These approaches use pairwise codon or nucleotide distance matrices to analyze gene sequences, treating them as linear strings rather than capturing their complex contextual interdependencies. We proposed GenEx, a pipeline that converts raw gene sequences into codon co-occurrence graphs and extracts more than 25 graph features. Our two most prominent techniques for graph generation and feature extraction are MSCG (Multi-Scale Codon Co-occurrence Graph) and LAPCG (Linear-time Adjacency PMI Codon Graph). Using these algorithms, we treated codon sequences as structured symbolic vocabularies interpretable to codon co-occurrence graph analysis, a representational paradigm borrowed from computational linguistics. Another major contribution includes implementing a spectral graph feature extraction using Singular Value Decomposition (SVD), using the squared singular value ( \sigma^2 ) instead of the traditionally used eigenvalue, which helped us to amplify the separation between dominant and subdominant spectral components, thereby enhancing inter-class separability in downstream classification. And to further demonstrate that our method works, we trained 23 benchmarked ML models against the latest SARS-CoV-2 variants, achieving remarkable results in detecting all SARS-CoV-2 variants.
[AI-70] GigaBrain-WBC-0.5: A Behavior World Model for Robust Whole-Body Control with Environment Interaction
链接: https://arxiv.org/abs/2608.18234
作者: Ziyang Cheng,Tianshu Tang,Jinxin Lan,Xinze Chen,Yuhan Gong,Zhichao Liu,Changzhong Wu,Yahao Mao,Zongyan Deng,Mingxuan Ma,Huasen Xi,Yilong Liu,Yutong Wu,Xiaofeng Wang,Yang Wang,Yun Ye,Guan Huang,Xiaojie Jin,Zheng Zhu,Jiwen Lu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 20 pages, 8 figures, 4 tables. Technical report. Project page: this https URL
Abstract:Whole-body motion tracking policies turn a humanoid into a robust control interface: the teleoperator—or an upstream model—only supplies a coarse movement intent, while the low-level policy keeps the robot balanced and physically feasible. Existing trackers deliver this interface only on flat ground: trained in empty scenes, they never learn how contact with terrain and objects reshapes their dynamics, and they attempt to teach the policy to balance under any command by continually enlarging the reference-motion corpus, which stops working once feasible behaviors become environment-dependent. We present GigaBrain-WBC-0.5, the first Behavior World Model (BWM) for humanoid whole-body control. Rather than a purely reactive tracker, we train a causal Transformer to jointly predict its next action, next state, and the distribution over its next latent behavior command, so the network that acts also models how the environment shapes what it can do next. An automatic terrain-annotation pipeline recovers full 3D contact geometry from retargeted motion, enabling terrain annotation at the scale of existing motion datasets. The predicted distribution is reused at deployment to detect implausible commands online and retract them onto learned behaviors, so the robot attempts tasks in a “best-effort” manner. The result is a unified policy that takes real-time command, interacts with environment, and stays robust to implausible commands, falls, and disturbances. GigaBrain-WBC-0.5 achieves the highest success rate across all four regimes among three large-scale tracker baselines: 81.3% on terrain interaction (4.3x the strongest baseline), 83.1% under implausible commands, and 99.3% fall recovery (16.8x the strongest baseline). Hardware trials show robust interaction under missing supports and disturbances; the Unitree G1 checkpoint transfers to the Maker L01 robot with simple fine-tuning.
[AI-71] On the Triangle Inequality for the Jaccard Distance in Arbitrary Lattices
链接: https://arxiv.org/abs/2608.18194
作者: Costin Bădică,Amelia Bădică
类目: Artificial Intelligence (cs.AI); Discrete Mathematics (cs.DM); Combinatorics (math.CO)
备注:
Abstract:This paper presents new theoretical results on generalizing the Jaccard distance for lattices and real valuations. We demonstrate that when the valuation is strictly positive, monotone, and modular, the Jaccard distance satisfies the triangle inequality on arbitrary lattices, effectively generalizing earlier results that depended heavily on distributivity. Moving to relatively complemented distributive lattices (which safely drop the requirement for the global bounds found in Boolean algebras), we prove the triangle inequality holds as long as the valuation is positive, monotone, supermodular, and \log -submodular. Additionally, we adapt the symmetric-difference Jaccard formulation for submodular valuations to sectionally complemented distributive lattices. Shifting to necessary conditions, we prove that supermodularity is a strict requirement for the standard generalized Jaccard distance to operate as a valid metric. Finally, we map the practical value of relaxing these structural constraints to computational fields like quantum information theory, formal concept analysis, and machine learning, closing with a brief look at open mathematical problems.
[AI-72] A systematic review of machine learning techniques to address diagnosis and treatment of autism: challenges and opportunities
链接: https://arxiv.org/abs/2608.18188
作者: Rafael Muñoz-Terol,Jesús Peral,Sandra Amador,David Gil
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 17 pages, 8 figures
Abstract:Autism spectrum disorder (ASD) is a developmental disability characterized by challenges in social interaction and communication. As the causes of ASD remain unclear, identifying relevant features and hidden correlations is crucial for early diagnosis. This systematic review evaluates 55 studies from 2017 to 2023 on the application of machine learning (ML) techniques to ASD. The primary objective is to examine recent ML applications in ASD research, identifying trends, techniques, and datasets that enhance diagnosis and treatment. Supervised learning methods dominate, as they align well with ASD diagnostic needs; however, the role of deep learning is expanding with greater data availability. Emerging techniques based on hybrid methods, where unsupervised, deep learning, and fuzzy logic could be included, will be interesting to observe in the future. The review highlights key challenges and opportunities, particularly the need for models that can integrate complex data -such as genetic and clinical information- to improve diagnostic accuracy and treatment outcomes. Additionally, incorporating innovative data sources, like wearable devices and biometric sensors, could enable continuous and non-intrusive monitoring, providing a more holistic understanding of ASD. Findings emphasize that addressing current challenges requires interdisciplinary collaboration and expanded datasets tailored to ASD. Future ML models will benefit from broader multimodal data integration, enabling researchers to more comprehensively address the complexities of ASD.
[AI-73] What Can Artificial Intelligence Learn from Medicine? Generative Analogies and Reliable Machine Learning Systems
链接: https://arxiv.org/abs/2608.18186
作者: Emanuele Ratti,Lena Zuchowski
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: Accepted for publication in Studies in the History and Philosophy of Science (cite published version)
Abstract:In the past few years, machine learning (ML) has been widely (and to an extent, successfully) implemented in medicine. However, uncertainties surrounding ML have made it difficult to establish the bases of its epistemic and methodological warrants. In the literature, a parallel has been drawn between medicine and ML, suggesting that we should model epistemic and methodological standards for ML on the standards of clinical translation. By developing tools from Hesse work, we characterise the nature of this parallel as a generative analogy between the process of clinical translation and the process of building ML systems. We identify more precisely the epistemic and methodological warrants of clinical translation that are typically only mentioned when appealing to the analogy, and we show in which sense such warrants apply analogically to the context of ML. In particular, we interpret warrants of clinical translation in reliabilist terms, and we show how this can inform a new form of ML reliabilism, which is distinct from (though compatible with) existing reliabilist accounts in philosophy of AI.
[AI-74] Looped Language Models Improve Compositional Tool Calling
链接: https://arxiv.org/abs/2608.18171
作者: Andrei Cristian Popescu,Haitz Sáez de Ocáriz Borde,Pietro Liò
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Looped language models have shown promising results on reasoning benchmarks, yet their potential for agentic tool use remains largely unexplored. We study this question in compositional tool-calling settings, where models must coordinate multiple API calls, maintain intermediate state, and preserve dependencies across tool interactions. We evaluate native and retrofitted looped language models on API-Bank, BFCL, and NESTful, comparing looped and non-looped models trained under matched supervised fine-tuning recipes and varying recurrent depth at inference time. In controlled experiments, recurrent computation generally benefits compositional and dependency-aware tool use, while providing smaller and more model-dependent gains on isolated API invocation. Accuracy on multi-step tool use generally increases with recurrent depth; adaptive inference, however, achieves a more favorable compute-performance trade-off by allocating additional computation only when needed. Our results suggest that looped language models are a promising architecture for agentic systems that require reliable planning, coordination, and execution of compositional tool use workflows.
[AI-75] Adversarial Review: Structured Disagreement for Grounded Agent ic Code Review ICML2026
链接: https://arxiv.org/abs/2608.18167
作者: Eric S. Qiu,Joyce Gill
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: Accepted to ICML 2026 Workshop on DL4C
Abstract:Early multi-agent LLM systems often used role-separated teams, yet scaling agent count yields diminishing returns on repository-level coding tasks. Recent alternatives treat agents as passive tools (subagents), yet this removes the benefits of agent interaction entirely. We study whether a subagent paradigm can support a middle ground: minimal agentic cooperation without the overhead of large multi-agent teams. We introduce Adversarial Review (AR), a minimal cooperative code-review protocol in which a main coding agent works with a reviewer and a critic agent. The reviewer evaluates code, while the critic audits the review through structured disagreement before the main agent edits. On LiveCodeBench, AR achieves the highest pass rate among tested methods, outperforming a five-agent baseline while using only three agents. On SWE-PRBench, naive AR exposes a false-consensus failure mode, where agents converge on agreement without sufficient evidence, but a single prompt iteration that adds disagreement explicitly achieves the highest F1 among tested methods. On SWE-bench Verified, AR also shows improvements over the baselines on repository-level coding tasks. Together, AR demonstrates that cooperative code review does not require many agents or complex communication structures: it requires that disagreement be minimal, structured, and evidence-grounded.
[AI-76] RDFdL: Integrating RDF with Differential Dynamic Logic
链接: https://arxiv.org/abs/2608.18165
作者: Yuyang Li,Lukas Kubelka,Julia Butte,Tobias Käfer
类目: Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL); Logic in Computer Science (cs.LO)
备注: Preprint
Abstract:Knowledge graphs modeled in RDF are powerful for describing static knowledge, but they cannot capture or reason about the dynamic behavior of physical systems, e.g., systems described by differential equations, which is a critical gap for AI-driven cyber-physical systems. To solve this, we propose RDFdL, a framework that integrates RDF with Differential Dynamic Logic (dL) to represent and reason about both static knowledge and the continuous dynamics of physical systems. For the dynamic part, we syntactically represent differential equations and ranges in the state space in RDF and SHACL and provide semantics using a translation to dL. Linking RDF and dL through their shared foundation in first-order logic achieves a unique integration: verification results for safety and reachability properties in the dynamic logic domain become available as entailment to SPARQL queries over RDF data. We implement the pipeline using Apache Jena for ontology-driven RDF reasoning and KeYmaera X, the theorem prover for dL, and sketch its applicability in manufacturing.
[AI-77] okenPowerSandbox: Evidence-Gated CPU-First Screening for Energy-Aware LLM Serving
链接: https://arxiv.org/abs/2608.18149
作者: Chenxu Niu
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Performance (cs.PF)
备注:
Abstract:Energy-aware LLM serving requires comparing configurations under realistic request shapes, yet exhaustive target-GPU profiling is costly and a cheap predictor can be dangerously confident outside its measured scope. We present TokenPowerSandbox, an evidence-gated workflow that combines an interpretable CPU-resident projector, short target-GPU probes, full-workload verification, and tamper-evident freeze-before-measurement provenance. On one NVIDIA H100 80GB serving Qwen2.5-7B-Instruct with vLLM, three anchor repeats and six development workloads calibrate workload transfer. The same frozen model is evaluated on a blind holdout and a separately predeclared no-refit confirmation totaling 51 post-freeze runs. Energy MAPE is 6.23% and 7.35%, with Spearman rank correlations of 0.976 and 0.933. However, a predeclared TTFT gate passes at concurrency four (9.27% MAPE) and triggers abstention below four (64.80%), showing why energy accuracy cannot certify latency.
[AI-78] Entropy-Constrained Adaptive Stochastic Quantization
链接: https://arxiv.org/abs/2608.18147
作者: Ran Ben Basat,Yaniv Ben-Itzhak,Michael Mitzenmacher,Shay Vargaftik
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Data Structures and Algorithms (cs.DS); Information Theory (cs.IT)
备注:
Abstract:Adaptive stochastic quantization (ASQ) is a recently introduced quantization approach that optimizes the Mean Squared Error (MSE) for a given input while preserving unbiasedness. It is designed to alleviate the communication and memory bottlenecks of modern data and machine learning workloads, including model, gradient, and KV-cache compression and nearest-neighbor search. Further, practical systems can then compress quantized data with a lossless entropy encoder. However, existing unbiased methods, including ASQ, choose their quantization values without considering this later encoding stage, leaving accuracy on the table. We formulate the Entropy Constrained Adaptive Stochastic Quantization (ECASQ) problem, which jointly selects adaptive quantization values to minimize MSE under an entropy budget and an unbiasedness constraint. We give an optimal dynamic program with O(sd^2) time and O(d^2) space for a length-d vector and at most s quantization values, and a GPU-friendly approximate dynamic program with O(sd^2) time and O(d) space. The approximation guarantees that the solution has an MSE no larger than the optimal solution that uses one fewer bit of entropy per entry. We also provide an iterative refinement procedure for the approximation solution that, in our experiments, yields near-optimal results while retaining a substantial speed advantage over our solver for the optimal solution. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Data Structures and Algorithms (cs.DS); Information Theory (cs.IT) Cite as: arXiv:2608.18147 [cs.LG] (or arXiv:2608.18147v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.18147 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-79] FraudBench: Stress-Testing Policy-Grounded Banking Agents Against Adaptive Fraud
链接: https://arxiv.org/abs/2608.18136
作者: Dheeraj Mohandas Pai,Lu Xian
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 9 Pages, 2 figures
Abstract:Conversational agents now act for end users through tools while holding access to customer databases and internal policy documents that a caller can reach through dialogue alone. Banking is the clearest case: the same agent that answers a question can also change contact details, reset a PIN, or move money, so ordinary customer service is inseparable from authorization, fraud detection, and policy compliance. Existing financial-fraud benchmarks classify static transactions or messages, and general agent-safety benchmarks target prompt injection or generic harmful use; none test whether a policy-grounded banking agent safely acts when a caller manipulates identity, authorization, and trust over a conversation. We introduce FraudBench, an executable benchmark built on the \tau^2 -bench dual-control framework and the \tau -Knowledge banking environment. Both the agent and the simulated caller act through tools over shared, mutable account state, and the agent may grant the caller access to selected tools; the environment exposes a 698-document internal policy corpus that the agent must retrieve from. FraudBench contains 150 authored adversarial scenarios; a frozen public set of 107 (90 across ten fraud mechanisms plus 17 chained adaptive attacks) is used for all reported runs, with 43 further chained attacks held out. Safety is history-dependent: single-control tasks satisfy every precondition but one, and adaptive attacks make a later, locally valid request unsafe because of an earlier probe, admission, or failed attempt. Each scenario is annotated with observable evidence, prohibited actions, safe dispositions, and intervention points. A preliminary single-trial evaluation of four agents on the 107 graded tasks yields attack-security between 49% and 65%, with money-mule and first-party fraud the most common cross-model weaknesses.
[AI-80] Improving Rural Medication Safety with AI: A Scoping Review
链接: https://arxiv.org/abs/2608.18135
作者: Jeong-ah Kim,Muhammad Ashad Kabir,Daniel Terry,Maryam Rouhi
类目: Artificial Intelligence (cs.AI)
备注: 23 pages
Abstract:Introduction: Medication errors (MEs) represent a significant threat to global healthcare systems, contributing to patient harm. Introducing artificial intelligence (AI) in rural healthcare enhances patient safety. The aim is to explore the applications and effectiveness of AI technologies in enhancing patient safety and reducing medication errors in rural health settings. Methods: A scoping review was conducted through a systematic literature search spanning 2012 to 2025 across multiple databases, including EBSCohost, Emcare (Ovid), MEDLINE, and the ProQuest Consumer Health Database. Twelve primary studies from nine different nations were examined. Data were analysed thematically to obtain insights on AI interventions across the medication process. Results: AI technologies have been integrated into every stage of medication management, right from prescribing and dispensing to administration and post-administration monitoring. Four key themes came to light: (1) the various types of AI being utilised (like Clinical Decision Support Systems, Machine Learning, Natural Language Processing, and smart pumps); (2) the phases of the medication process that are affected; (3) how effective these technologies are in minimising errors and boosting workflow safety; and (4) rural-specific challenges including infrastructure, staff training, system integration, and alert fatigue. Several studies have demonstrated that machine learning-based surveillance improves incident detection and reduces prescribing and transcription errors by an impressive 34% to 80%. Barriers included lack of governance frameworks, financial limitations, and clinician resistance, which still present major obstacles. Conclusion: In rural healthcare, AI technologies hold great potential for enhancing pharmaceutical safety. They can allow data-driven monitoring, automate processes, and offer clinical decision assistance. Comments: 23 pages Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.18135 [cs.AI] (or arXiv:2608.18135v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.18135 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ashad Kabir [view email] [v1] Wed, 29 Jul 2026 03:24:18 UTC (568 KB)
[AI-81] Optimized Fuzzy Logic Approach with the IEEE Key Gas Method for Diagnosing Power Transformer Faults Using Dissolved Gas Analysis
链接: https://arxiv.org/abs/2608.18133
作者: Kim-Anh Nguyen,Huy Hoang Le,Ba Tu Phung
类目: Artificial Intelligence (cs.AI)
备注: This paper was presented at 2025 10th International Conference on Applying New Technology in Green Buildings (ATiGB). Please cite the published version
Abstract:Reliable transformer fault diagnosis is essential for maintaining power system stability. The IEEE Key Gas Method (KGM), a widely utilized approach in Dissolved Gas Analysis (DGA), exhibits limitations in addressing ambiguous data and ensuring high diagnostic accuracy. This study presents An enhanced model combining Fuzzy Logic with the IEEE Key Gas Method (FL-KGM) that introduces refined membership functions, optimized fuzzy rule sets, and a novel separation of CO and CO2 to eliminate diagnostic inconsistencies. By leveraging multidimensional gas ratio analysis and an adaptive classification framework, FL-KGM delivers superior fault identification and classification. Experimental validation utilizing real-world datasets demonstrates that FL-KGM achieves up to 98.6% accuracy, significantly outperforming KGM and other FL-based approaches. These findings elucidate the potential of FL-KGM in advancing transformer monitoring, enabling intelligent fault detection, and enhancing predictive maintenance strategies in modern power systems.
[AI-82] Global Index on Responsible AI 2026 : Conceptual Framework and Methodology
链接: https://arxiv.org/abs/2608.18122
作者: Fola Adeleke,Rachel Adams,Ayantola Alayande,Daniela Benavente,Ana Florido,Nicolás Grossman,Leah Junck
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:This report presents the methodology of the Global Index on Responsible AI (GIRAI), 2nd Edition. This edition refines the 1st Edition by strengthening the distinction between framework existence and implementation, restructuring dimensions from three to five thematic areas, introducing more granular variables for framework quality, and applying a multi-stage review and validation process. An independent statistical pre-audit was conducted to assess the coherence and robustness of the framework. GIRAI assesses responsible AI governance across five dimensions: Inclusion and Diversity, Ethics and Sustainability, Labour and Skills, Trust and Safety, and Use of AI in Public Service. Each dimension has a number of indicators (38 in total), organised into three pillars, namely AI Policy (17 indicators on government frameworks and implementation, assessed through primary data), CSO Engagement (5 indicators, primary data), and Enabling Conditions (15 indicators on the structural factors shaping responsible AI governance, assessed through secondary data), and a government Use of Unacceptable Risk AI (URAI) indicator (primary data), applied separately as an accountability penalty to the final score. Data was collected by 135 country-level researchers through a structured global survey, complemented by secondary datasets. The count, scope, enforceability, thematic coverage, and implementation levels of the data points are coded into numerical variables, normalised to a scale of 100, aggregated through pillar weights of 60% (AI policy), 10% (CSO Engagement), and 30% (Enabling conditions). A deduction penalty is applied for countries with evidence of URAI. This documentation enables systematic cross-national comparison, supporting policymakers, civil society, and AI developers to identify where commitments are translating into enforceable protections and where critical gaps remain.
[AI-83] Position: AI Leaderboards Are Underserving the Global South: A Case Study from India
链接: https://arxiv.org/abs/2608.18117
作者: Sourav Banerjee,Saikat Saha
类目: Artificial Intelligence (cs.AI); General Economics (econ.GN)
备注:
Abstract:This position paper argues that AI leaderboards are structurally ill-suited to serving the Global South because they lack independent governance, conflict-of-interest policies, and mechanisms for metric evolution. The barrier is not missing data; high-quality regional benchmarks already exist: IndicSUPERB, MILU, and LAHAJA for India; IrokoBench for Africa; AlGhafa for Arabic. The barrier is institutional design. Global leaderboards do not include these benchmarks, and no governance mechanism compels them to do so. Commercial pressure corrects leaderboard failures when paying customers in the Global North are affected. The Global South lacks equivalent leverage. Without governance, failures affecting Hindi, Swahili, or Arabic speakers persist indefinitely as documented but unaddressed gaps. Using India as a case study (1.4 billion people, 22 scheduled languages, high-quality benchmarks, but no trusted aggregation), we report findings from a consultation with 58 AI practitioners showing consistent preference for formal governance and disclosure-based conflict management. The solution is not more data but better institutions: regional leaderboards with independent governance from the start.
[AI-84] Solving Is Not Drawing: A Benchmark for Diagrammatic Reasoning in Olympiad Geometry ICML2026
链接: https://arxiv.org/abs/2608.18111
作者: Hsien Xin Peng,Anthony Kim,Alvin Li,Calvin Supasanya,Shivank Garg,Kevin Zhu
类目: Artificial Intelligence (cs.AI)
备注: ICML 2026, AI4MATH Workshop
Abstract:Foundation models such as GPT and Claude now solve olympiad-level mathematics with remarkable proficiency, so much so that geometry problem solving has become a standard proxy for their mathematical reasoning. Yet solving a geometry problem and drawing the figure it depends on are not the same skill: progress often hinges on a faithful diagram with the right auxiliary constructions and incidences, and it is unclear that a model which reasons its way to the answer can also produce one. A growing collection of benchmarks, including MathVista, and MathVerse, measures whether models reach the correct answer, but to our knowledge, none isolate the distinct ability to construct the diagram itself, leaving this capability unmeasured. We introduce an open-source benchmark that targets this gap: 954 self-contained olympiad geometry problems, with a 297-problem hard subset, each paired with its solution and a human-authored, high-fidelity diagram in renderable Asymptote code, together with a suite of text-, code-, image-, VLM-, and constraint-based metrics for what we term diagrammatic reasoning. Evaluating current foundation models reveals a pronounced gap between solving and drawing: their diagrams are markedly less faithful, with an average compile success rate of only 36.14%. Strong mathematical reasoning, we find, does not imply the ability to construct accurate geometric diagrams. Our benchmark and dataset can be accessed at this https URL.
[AI-85] Emergence of Agent ic AI: A Review on Evolution Background Working Principles Applications Adoption Factors and Future Research Directions
链接: https://arxiv.org/abs/2608.18110
作者: AKM Bahalul Haque,Al Amin Islam Ridoy,Mohammad Rayhan,Ivan Porres
类目: Artificial Intelligence (cs.AI)
备注: Accepted Version, 54 Pages, 13 tables, 7 images
Abstract:Agentic AI is gaining new insights and advancements in the field of Artificial Intelligence, fostering significant potential to enable rapid transformation across various this http URL rapid advancement and the potential to revolutionize various domains advocate the need for a deeper understanding and firm grasp of the technology. Moreover, an investigation into state of the art research directions in agentic AI needs to be conducted to comprehensively assess the potential scope for improvement and this http URL, to address these objectives, a comprehensive review can provide researchers and practitioners with valuable insights into the current state and future research scopes of agentic this http URL, this work considers the recently published scholarly contributions in agentic AI across various domains and discusses the fundamentals and working principles of Agentic AI, traces the historical and theoretical evolution of agency in artificial systems, explores and discusses Agentic AIs architecture, working principles, and functionalities, explores real-world applications of Agentic AI across various domains, analyzes the research findings, identifies current challenges, and discuss potential future research directions, and proposes a comprehensive framework of stakeholders intention to use and adopt Agentic AI with the help of proposed system quality this http URL, this systematic review provides researchers and practitioners with a comprehensive understanding of Agentic AI, its current developments and applications, highlights key research gaps, and outlines future research directions.
[AI-86] Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective
链接: https://arxiv.org/abs/2608.18104
作者: Yuanyuan Xu,Wenjie Zhang,Yin Chen,Xuemin Lin,Ying Zhang
类目: Artificial Intelligence (cs.AI)
备注: Project: this https URL
Abstract:Large language model (LLM)-based agents are increasingly becoming self-evolving systems that persist across interactions, maintain memories, use tools, acquire skills, refine workflows, and coordinate with other agents. These capabilities make agent states structural and dynamic: entities, relations, attributes, dependencies, and execution structures change with new evidence, feedback, and environmental conditions. Existing graph-agent surveys typically treat graphs as support structures for agent functions rather than as evolving substrates, while self-evolving-agent surveys focus on agent-level mechanisms and rarely discuss graph topology evolution. Thus, the coupling between evolving agent state and dynamic graph topology remains underexplored. This survey connects these two research lines by framing \textitagent evolution as dynamic graph transformation. We model agent state as a dynamic graph, where memories, tools, skills, workflows, and inter-agent relations are represented as typed nodes, edges, and subgraphs updated through schema-constrained rewrites. Based on this formulation, we organize existing dynamic-graph-based methods for self-evolving agents into four taxonomies: node/feature evolution, edge/topology evolution, subgraph activation, and cross-component co-evolution. Building on this taxonomy, we propose dynamic graph learning as reusable infrastructure for self-evolving agents and map nine dynamic-graph-learning subfields to agent-evolution capabilities, discussing their adaptations and possible failure modes. Finally, we discuss five types of graph-aware evaluation and governance protocols from a dynamic-graph perspective, which complement end-task evaluation. The goal is to provide a compact structural lens for designing and governing self-evolving agents.
[AI-87] FinSkillBench: Evaluating AI Agents and Domain Skills for Investment Management
链接: https://arxiv.org/abs/2608.18099
作者: Jermyn Zhen Yong Bek,Zhuang Qiang Bok,Zhongtian Sun
类目: Artificial Intelligence (cs.AI); Portfolio Management (q-fin.PM)
备注:
Abstract:Investment management is a high-stakes domain in which agentic AI systems must do more than generate plausible text. They must retrieve point-in-time data, assemble correct computational inputs, invoke specialized methods, and produce auditable structured outputs. We introduce FinSkillBench, an evaluation suite designed to measure whether language model agents can effectively use financial domain skills to solve investment management tasks. The benchmark spans three domains, portfolio construction, risk management, and fundamental analysis, and includes 12 subtasks with 2,603 task episodes. Each episode provides point-in-time inputs, hidden ground truth, and a task-specific this http URL compare three conditions: no skill, curated skill packages consisting of procedural documents and executable components, and self-generated skills in which the agent writes and reuses its own procedures within an episode. Across 9 models and a large-scale evaluation, curated skills consistently improve performance, raising mean scores from 0.366 to 0.528, with the largest gains in portfolio construction and risk management. In contrast, self-generated skills provide little benefit despite higher computational cost. An independent evaluation using a separate agent framework (Hermes Agent, 8 models, 5,280 episodes total) reproduces the directional pattern across all three domains, with the magnitude of skill effects varying by subtask and harness. These results showthat in investment management agents, access to reliable procedural skills can be as important as model choice, while naive self-generation of skills is often ineffective. We release the benchmark, evaluation tools, curated skill packages, and full trajectories to support further research. Subjects: Artificial Intelligence (cs.AI); Portfolio Management (q-fin.PM) Cite as: arXiv:2608.18099 [cs.AI] (or arXiv:2608.18099v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.18099 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhuang Qiang Bok [view email] [v1] Tue, 9 Jun 2026 07:40:39 UTC (38 KB)
[AI-88] Position: Multi-Agent Systems Should Prioritize Concurrency Control
链接: https://arxiv.org/abs/2608.18092
作者: Xin Yang,Letian Li,Zimo Ji,Terry Jingchen Zhang,Wenyuan Jiang
类目: Artificial Intelligence (cs.AI)
备注: 16 pages, 1 figure
Abstract:LLM-based multi-agent systems (MAS) promise scalable collaboration, yet adding agents often reduces reliability. This position paper argues that many MAS failures are fundamentally concurrency control problems: agents concurrently read and write shared state, and long LLM inference windows amplify the risk of stale reads, lost updates, and inconsistent outcomes. Failure modes commonly attributed to coordination or communication breakdowns can be mapped directly onto classical concurrency anomalies. We contend that MAS frameworks should address these failures through explicit concurrency control mechanisms: conflict detection, isolation guarantees, and structured access to shared resources. Concurrency control should be a first-class design concern, not an afterthought.
[AI-89] A Metamorphic Artificial Age Score Decision-Support Prototype for Flight-Log-Based Drone Propeller Health Monitoring ALT
链接: https://arxiv.org/abs/2608.18088
作者: Seyma Yaman Kayadibi
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: 21 pages, 7 tables. This paper presents a retrospective decision-support prototype using selected flight logs from the public DronePropA dataset. The framework integrates flight-log-derived feature extraction, metamorphic adequacy testing, and a redundancy-adjusted Artificial Age Score formulation for drone propeller health monitoring
Abstract:Drone propeller faults can create safety and reliability risks when their effects are distributed across multiple flight-log channels rather than appearing as a single diagnostic signal. This paper proposes a Metamorphic Artificial Age Score (AAS) decision-support prototype for flight-log-based drone propeller health monitoring. Using selected historical real flight logs from the 2024 DronePropA public dataset, the framework computes six health-related indicators from raw MATLAB matrices: trajectory tracking error, attitude instability, thrust-command burden, motor-command imbalance, ESC-command instability, and battery-level stress. These indicators are normalized relative to a healthy baseline and evaluated through candidate scoring policies, metamorphic adequacy relations, and a redundancy-adjusted AAS formulation. In this context, AAS is used as a structural policy-adequacy and burden measure rather than as a chronological age measure. A controlled retrospective evaluation was performed using one healthy baseline and three defective propeller cases under the same speed profile and trajectory. The healthy case was assigned to routine monitoring. The Severity 1 case was dominated by ESC-command instability and assigned to maintenance review. The Severity 2 case reached maximum motor-command and ESC-command burden, while the Severity 3 case reached maximum trajectory tracking error; both triggered mandatory inspection. The results show that propeller fault effects may appear through different operational channels, supporting the need for a multi-indicator decision-support layer for post-flight maintenance prioritization and autonomous-system oversight.
[AI-90] Position: Current Model Cards Are Insufficient for Downstream Governance of Open-Weight Foundation Models ICML2026
链接: https://arxiv.org/abs/2608.18086
作者: Sungwon Chae,Keonwoo Kim,Hoki Kim,Jaeyeon Ju,Sangchul Park
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted as a position paper at ICML 2026
Abstract:The growth of open-weight foundation models (OWFMs) has prompted the AI community to re-evaluate strategies for effective downstream governance. Although model cards have been widely adopted as transparency artifacts in model repositories, existing frameworks often fail to adequately inform downstream developers and users about the distinct safety challenges posed by OWFMs. This position paper analyzes 500 model cards hosted on Hugging Face and argues that effective governance of OWFMs requires a multi-layered approach integrating three complementary components: (i) model cards, (ii) acceptable use policies (AUPs), and (iii) licenses. To motivate this claim, we identify a safety gap left by existing regulatory approaches, including model heritage, alignment provenance, and empirically observed behaviors, through an analysis of model cards with safety-critical information. We further argue that standard open-source licenses (OSLs) are not well suited for OWFMs and may weaken the enforceability of AUPs. Building on these observations, we outline directions for evolving model cards, AUPs, and licenses into integrated safety artifacts to enable a more comprehensive governance framework that coherently integrates informational, normative, and legal dimensions.
[AI-91] Position: Behavioral Systems Require Behavioral Tests ICML2026
链接: https://arxiv.org/abs/2608.18081
作者: Manuel Cherep,Nikhil Singh,Pattie Maes
类目: Artificial Intelligence (cs.AI)
备注: Accepted to ICML 2026 (Position Track)
Abstract:Artificial agentic systems increasingly operate as behavioral systems by interacting with dynamic environments, pursuing goals, and adapting over time. Yet, current evaluation methods largely focus on performance outcomes, not the underlying behavioral processes that produce them. This paper argues that AI agents must be evaluated like other behavioral systems: through systematic observation, perturbation, and interpretation of their actions. We draw on lessons from the behavioral sciences to motivate this position, and propose a research agenda focused on developing rigorous behavioral tests. These include methods for recovering decision strategies from action sequences, constructing environments that isolate behavioral differences, and probing emergent dynamics in multi-agent systems. Taken together, these directions offer a roadmap for developing a science of AI behavior.
[AI-92] Large Language Models in Mental Health: A Systematic Review of Applications Innovations and Ethical Challenges ALT
链接: https://arxiv.org/abs/2608.18080
作者: Yisong Chen,Yifan Gao,Sijing Yu,Chuqing Zhao,Yang Lu
类目: Artificial Intelligence (cs.AI)
备注: Systematic review. Published in Journal of Industrial Integration and Management (2025). Applications of large language models in mental health, including social media analysis, clinical conversational agents, therapy support tools, multimodal learning, and ethical considerations
Abstract:We present a review on the applications of large language models (LLMs) in health, e.g., social media analysis, clinical conversational agents, therapy support tools, prompt engineering, multimodal learning, and ethical considerations. We integrate findings from interdisciplinary studies utilizing diverse data sources such as social media posts, electronic medical records, and multimodal inputs to enable early detection of depression, suicide risk assessment, personalized therapy support, and psychoeducational content generation. Our review highlights advancements in LLM models and annotation strategies that enhance interpretability and clinical relevance, while we also emphasize the critical role of prompt engineering for domain adaptation. We also discuss emerging multimodal fusion techniques integrating text, speech, and sensor data for improved mental health diagnosis and monitoring. Finally, we address ongoing ethical, sociotechnical, and regulatory challenges, and advocate frameworks to ensure safe, equitable, and accountable deployment of LLMs in real-world mental health care.
[AI-93] Position: Profiling Game Worlds by Transition Complexity ICML ICML2026
链接: https://arxiv.org/abs/2608.18079
作者: Lele Cao
类目: Artificial Intelligence (cs.AI)
备注: Accepted by ICML 2026 Position Paper Track. this https URL
Abstract:Game world modeling (GWM) and reinforcement learning (RL) are often confounded because research papers rarely quantify how difficult the underlying transition prediction problem is at the declared interface (pixels/tokens/latents with finite history). We propose the Transition Complexity Profile (TCP): a small, reproducible set of metrics that characterizes an environment’s (or gameplay dataset’s) induced transition kernel by (i) intrinsic one-step branching, (ii) interaction-induced uncertainty and opponent influence when observable, and (iii) temporal/spatial dependency span via standardized probe curves. TCP is reported with an explicit reference distribution, protocol stochasticity, and a versioned measurement budget (sampling/resampling and fixed probe compute), enabling comparable numbers across benchmarks. We outline how common game families and modern “neural game engine” domains populate this landscape and call for TCP to become standard benchmark metadata and a required statistic in GWM and RL papers.
[AI-94] Position: Collusion Risks Among AI Reasoning Agents Justify Certification Requirements for Making Market Decisions ICML2026
链接: https://arxiv.org/abs/2608.18078
作者: Matthew Riemer,Tommaso Tosato,Amin Memarian,Maximilian Puelma Touzel,Glen Berseth,Irina Rish,Guillaume Dumas
类目: Artificial Intelligence (cs.AI)
备注: ICML 2026
Abstract:This position paper argues that AI agents with chain-of-thought reasoning capabilities are predisposed to exhibit collusive behavior and should be required to obtain behavioral certification before making decisions that affect economic markets. This is because integrating these agents into society could collapse the legal evidentiary distinction between competition and collusion among independent firms without eroding the economic harm distinction. Experiments with DeepSeek-R1 agents in the Bertrand oligopoly pricing domain reveal a tendency towards tacit collusion that persists even when humans prompt the agents not to collude. We further show that the chain-of-thought of these agents can be steered toward either extremely collusive or highly competitive behavior in a way that is not semantically detectable by another LLM analyzing the reasoning traces. As a result, deploying reasoning agents for market decisions leads to collusive economic outcomes without any evidence of conspiracy or intent. Thus, certification based on observed behavior in representative situations is necessary to prevent collusion. We provide preliminary evidence that such agents can be steered in a generalizable way toward efficient competitive equilibria. However, developing a comprehensive behavioral certification will be required before these models can be deployed in real-world markets while ensuring their stability and efficiency.
[AI-95] Quantifying Risk Under Evolving Uncertainty: Belief-Dependent Robustness for Safe Sequential Decision Making ECAI2026 IJCAI
链接: https://arxiv.org/abs/2608.17574
作者: Deep Kumar Ganguly,Jan Kretinsky
类目: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: Accepted for presentation at the IJCAI-ECAI 2026 RobustifAI workshop
Abstract:How cautious should an agent be while it is still learning its environment? We propose RATTL (Risk-Adversarial Total-Reward Learning), which ties caution to epistemic uncertainty: the agent holds a Bayesian posterior over unknown dynamics and plans against a Wasserstein ambiguity set whose radius is a monotone function of that posterior. The radius contracts with evidence, so behaviour interpolates continuously between worst-case robustness and risk-neutral total-reward maximization. The design follows the duality underlying the Entropic Value-at-Risk, which converts the choice of a risk level into the choice of an ambiguity radius. We show the resulting planning problem is well posed under transience and compactness conditions, and prove a Safety Sandwich: the RATTL value lies between the uninformed robust value and the full- knowledge optimum, with a gap that vanishes as the posterior concentrates. In a canonical binary-hazard instance, the induced criterion reduces to Conditional Value-at-Risk at a level set by the posterior entropy. A worked example shows the agent deferring the efficient action until a sharp identification threshold. RATTL targets runtime safety for agents, including LLM-based systems, acting under uncertainty.
[AI-96] Auditing an AI-Generated Mathematical Proof: A Correction to a Greedy Conditioning Lemma in Quantum Parallel Repetition
链接: https://arxiv.org/abs/2608.14673
作者: Mikołaj Sienicki,Krzysztof Sienicki
类目: Artificial Intelligence (cs.AI); General Literature (cs.GL); Quantum Physics (quant-ph)
备注: 8 pages, 5 references, Auditing an OpenAI’s Generated Mathematical Proof
Abstract:Chapter 6 of OpenAI’s Ten Advances in Mathematics and Theoretical Computer Science claims an exponential parallel-repetition theorem for all finite two-player, one-round entangled games. Early in the proof, the chapter uses a quantitative greedy conditioning lemma. The lemma is meant to select a small set of coordinates (D) such that, after conditioning on winning every coordinate in (D), a randomly chosen remaining coordinate is won with average probability at least (1-\delta). The statement is correct, but the proof as printed contains a polarity error. Its continuation test is written in terms of average success, while the next step requires a coordinate with a large conditional failure probability. That implication is false, and even simple examples can leave the printed procedure without a valid next move. This note gives an explicit counterexample, identifies the intended continuation condition, and supplies a complete corrected proof. The repair is local: it leaves the statement of the lemma and the parameters used later in the chapter unchanged. It should not, however, be read as an independent verification of the main parallel-repetition theorem. More broadly, the example shows how a mathematically plausible AI-generated argument can hide a small but decisive reversal between complementary events. Comments: 8 pages, 5 references, Auditing an OpenAI’s Generated Mathematical Proof Subjects: Artificial Intelligence (cs.AI); General Literature (cs.GL); Quantum Physics (quant-ph) Cite as: arXiv:2608.14673 [cs.AI] (or arXiv:2608.14673v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14673 Focus to learn more arXiv-issued DOI via DataCite
[AI-97] Interpretable AI predicts a 2026 summer dry anomaly in central China
链接: https://arxiv.org/abs/2608.19163
作者: Anran Wang,Wen Shi,Yong Luo,Jianbin Huang,Lijuan Chen,Junhu Zhao,Weixin Jin,Huihui Yuan
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Artificial Intelligence (cs.AI)
备注:
Abstract:Seasonal precipitation anomalies are largely regulated by atmospheric circulation, which dynamical models predict with greater reliability than precipitation itself. Here, we employ a deep learning model that translates dynamical circulation predictions into precipitation estimates. Predictions initialized from March to May consistently indicate a dry anomaly over central China in summer 2026. Retrospective evaluations revealed higher predictive skill in the analogue years, which also tended to feature central equatorial Pacific warming persisting from the preceding winter into summer. This warming favors an anomalous cyclonic circulation over the western North Pacific-South China Sea-South China region, which induces northerly winds and moisture divergence that jointly suppress rainfall over central China. Supporting this mechanism, layer-wise relevance propagation (LRP) independently identifies these northerly winds as the dominant driver of the prediction among all model inputs. Perturbation tests supported this attribution: removing LRP-identified features effectively eliminates the dry anomaly. Our framework thus provides physically interpretable explanations for AI-derived regional climate projections, facilitating evidence-based assessment before observational data become available.
[AI-98] AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RL
链接: https://arxiv.org/abs/2608.18946
作者: Daniele Lizzio Bosco,Jacopo Cossio,Carla Piazza,Giuseppe Serra
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI)
备注: Accepted manuscript to appear in the proceedings of the IEEE International Conference on Quantum Computing and Engineering (QCE 2026). 11 pages, 3 figures
Abstract:Clifford circuits play a foundational role in quantum computing, particularly due to their importance in quantum error correction and fault-tolerant logical synthesis. While these circuits can be efficiently simulated and represented as symplectic matrices, standard synthesis methods-such as the Aaronson-Gottesman algorithm-often yield sub-optimal circuits with excessively high gate counts. In this work, we introduce AlphaClifford, a model-based Reinforcement Learning framework powered by Monte Carlo Tree Search, designed to efficiently synthesize Clifford circuits from the fundamental gate set composed of H, S, and CNOT. By modeling the state space through the algebraic properties of the symplectic group, AlphaClifford effectively explores this combinatorial space to minimize overall circuit cost. For unconstrained Clifford optimization, our approach achieves a consistent reduction in both total and two-qubit (CNOT) gate counts compared to state-of-the-art synthesis heuristics, despite operating with a strictly less expressive gate set. Furthermore, we demonstrate the broad applicability of our framework on two additional tasks: hardware-constrained Clifford transpilation, where we outperform existing RL-based compilers, and as a post-synthesis optimization component within a full Clifford+T logical synthesis pipeline. Our results underscore that model-based RL is highly effective at addressing the combinatorial complexities of quantum compilation, offering a scalable pathway to mitigate hardware constraints in both near-term and future fault-tolerant quantum devices.
[AI-99] Change Point–Aware Evaluation and Re-Calibration of PPG-Based Blood Pressure Estimation
链接: https://arxiv.org/abs/2608.18639
作者: Yunwon Tae,Minje Park,Gyunho Rho,Dongjoon Yoo,Sunghoon Joo
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: MLHC 2026. The first two authors contribute equally
Abstract:Non-invasive continuous blood pressure (BP) monitoring using photoplethysmography (PPG) is a promising alternative to cuff-based measurements. However, existing PPG-based BP estimation studies predominantly rely on aggregated performance metrics (e.g., mean absolute error) computed over entire evaluation intervals, which can obscure model failures during rapid BP fluctuations and limit clinical relevance. In this work, we propose a fluctuation-aware evaluation framework for PPG-based BP estimation based on time-series change point detection. Instead of heuristic BP thresholding (e.g., \Delta\mathrmBP 10\mathrmmmHg ), we identify BP change points by capturing abrupt distributional shifts in BP trajectories and evaluate estimation performance specifically during these fluctuation periods. Our analysis shows that several state-of-the-art models exhibit substantial performance degradation around BP change points, and that periodic test-time calibration is insufficient to handle such dynamic BP variations. To address this limitation, we introduce a targeted re-calibration framework triggered by detected BP change points, improving robustness without modifying model architectures. To the best of our knowledge, this is the first systematic evaluation of PPG-based BP estimation from a BP change point perspective, highlighting the importance of fluctuation-aware evaluation and calibration for real-world continuous BP monitoring.
[AI-100] Science Done on a Machine by a Machine: AI Agents in Computational Chemistry
链接: https://arxiv.org/abs/2608.18508
作者: Pavlo O. Dral,Hassan Nawaz,Arif Ullah
类目: Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI); Computational Physics (physics.comp-ph)
备注:
Abstract:We are witnessing an explosion of agentic systems for computational chemistry simulations: from half a dozen in 2024 to a dozen in 2025, and the current number approaches fifty, surveyed in this Perspective as of 8 August 2026. The capabilities of these agentic systems are shifting from assisting in performing a selection of computational tasks to autonomous design and execution of \textitin silico experiments, their analysis, and even manuscript writing. The ultimate destination is a fully autonomous AI scientist, where the entirety of computational chemistry is performed on a machine by a machine, without human supervision. While we are not there yet, and all reported systems currently involve a human in the loop, the trend is unmistakable. Even building specialized agentic systems for computational chemistry is increasingly commoditized by generalist agents, which may in the end replace the need for the specialized ones altogether, since adding a new capability will be as easy as asking AI to do it for you. Both the explosion in their number and the very limited adoption beyond their own developers point that way, and we close this Perspective on what it leaves us to do. The speed and scale of disruption agentic systems are bringing to computational chemistry leave many of us dumbfounded about the field’s future and what we should spend our efforts on, as already established specialists, teachers, and students, and we have no answer.
[AI-101] Coupled-cluster molecular properties across the main group that extrapolate beyond training size
链接: https://arxiv.org/abs/2608.18346
作者: Wenhao He,Xu Chen,Noah Song,Haowei Xu,Tim S. Hindges,Bohan Li,Zihan Lin,Yu Yao,Avetik R. Harutyunyan,Fang Liu,Yao Wang,Hao Tang,Ju Li
类目: Chemical Physics (physics.chem-ph); Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
备注: 13 pages, 5 figures, 2 tables; SI available upon request
Abstract:Coupled-cluster theory defines the accuracy standard for molecular electronic-structure properties but scales too steeply for routine application, whereas density-functional theory is affordable yet systematically biased. We resolve this trade-off with a single equivariant network, MEHnet-MG, that predicts an effective one-electron Hamiltonian from one inexpensive B3LYP/def2-SVP calculation and derives a broad suite of properties from it (energy, optical gap, dipole, quadrupole, polarizability, Mulliken atomic charges, and Mayer bond orders) at coupled-cluster accuracy across nine main-group elements, including the under-served phosphorus, sulfur, and chlorine chemistries. The model is trained on a new in-house dataset of multi-property labels computed at the CCSD(T) level for all nine elements. On a held-out test set, it reduces the error of every property by a factor of 3.8 to 230 relative to semi-local, hybrid, and double-hybrid DFT (referenced to composite CCSD(T)/cc-pVTZ; Methods), while adding only ~25 ms wall time per molecule, delivering coupled-cluster-quality predictions at the cost of a single DFT calculation. Critically, deriving every property from a predicted Hamiltonian rather than pooling per-atom features builds the correct size-scaling into the model architecture: on pi-conjugated oligothiophenes it matches finite-field CCSD polarizability and the EOM-CCSD optical gap to ~2% at the largest sizes where those references remain affordable (44 and 37 atoms, where a single CCSD field point already costs ~500x the model’s entire inference) and extrapolates the corrected trends to 58-atom chains, a regime where pooling-based architectures fail by construction. Accurate extrapolation is therefore set by the model’s inductive bias rather than by the training data.
[AI-102] SeisEvo: Evolution of Seismic Data Reconstruction Algorithms by Agents
链接: https://arxiv.org/abs/2608.18272
作者: Yingjie Xu,Siwei Yu,Jianwei Ma
类目: Geophysics (physics.geo-ph); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Signal Processing (eess.SP)
备注:
Abstract:Classical seismic data reconstruction relies on manually designed structural priors and iterative operators, whose coupled design space is far larger than manual trial and error can explore systematically. Deep-learning methods encode the reconstruction rules in learned weights rather than in an explicit operator that can be inspected and modified. We propose SeisEvo (Seismic Algorithm Evolution), which does not optimize a single reconstruction result but searches for the algorithm that produces it. Starting from a classical reconstruction algorithm, an LLM-driven multi-agent search modifies only the components that the user has opened for editing, without prescribing the mechanism to be discovered. Candidates that violate the physical constraints of the task are rejected outright, and the remaining ones are scored by execution. The output is neither an agent system nor a neural network, but a standalone white-box algorithm that requires no agent or neural network at inference time. For interpolation without added noise, the search discovered a residual-gated, phase-aligned dip-consistency projection; Evo-POCS improves the SNR over classic POCS by 3.49 dB on average across missing ratios from 30% to 70%. For simultaneous interpolation and denoising, it discovered a reliability-grouped singular-value shrinkage; Evo-MSSA improves the average reconstruction SNR by more than 7 dB over classic MSSA and by more than 3 dB over a stronger rank-reduction baseline. Both operators retain their gains on data not used during the search. To the best of our knowledge, this is the first study to formulate the design of a seismic reconstruction operator as a constrained, LLM-driven program evolution task. Agentic algorithm evolution can thus complement deep learning in discovering explicit, inspectable, and deployable seismic processing algorithms.
[AI-103] How AI Prompts Can Teach Us About the Structure of Human Behavior
链接: https://arxiv.org/abs/2608.18265
作者: Matthew O. Jackson,Benjamin S. Manning,Yutong Xie,Walter Yuan,Qiaozhu Mei
类目: Theoretical Economics (econ.TH); Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce a general, easy-to-implement AI-based method for studying the structure and complexity of human behavior. We assign a large language model a type vector'' and then prompt it to choose actions across settings in which we observe human choices. For instance, the type vector (2,4) becomes You are a player characterized by the following profile: 2 out of 5 in Altruism, 4 out of 5 in Risk Aversion,‘’ after which it is prompted to make choices. We vary the dimensions (e.g., Altruism, Fairness, Trust, \dots ) and values (e.g., 1–5) to minimize distance to human choices. Applying the method to 119,147 decisions made by 78,657 subjects from more than 35 countries across 10 classic economic game roles, we find that human behavior can be closely matched using three dimensions: Risk Aversion, Strategic Sophistication, and Trust. Moreover, the types needed to fit individuals across games cluster into fewer than a dozen groups, and can predict behavior in held-out games with different rules and available actions. The results suggest that behavior across diverse settings can be approximated by a low-dimensional, portable representation, supporting the possibility of general yet parsimonious theories across the behavioral sciences. More broadly, the method can provide insights into the structure of many human behaviors.
[AI-104] How Quantum Is the Advantage? A Fair Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection
链接: https://arxiv.org/abs/2608.18155
作者: Syeda Anshrah Gillani,Mirza Samad Ahmed Baig,Shahid Munir Shah,Asher Ali,Hamzah Siddiqui
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
备注: 19 pages, 4 figures, 5 tables
Abstract:Quantum machine learning (QML) for network intrusion detection (NIDS) is routinely reported to reach near-perfect accuracy, yet the most rigorous studies find that well-tuned classical models remain competitive, and that apparent quantum gains may be artefacts of classical dimensionality reduction and implicit regularisation rather than genuine quantum effects. We ask not whether a quantum model can post a high accuracy, but how quantum the advantage really is. We present a unified, reproducible QML-IDS benchmark evaluating hybrid variational quantum circuits and quantum-kernel SVMs against five honestly-tuned classical baselines across four standard NIDS datasets (NSL-KDD, UNSW-NB15, CICIDS2017, NF-ToN-IoT-v2) under one leakage-controlled protocol, with an equal-budget feature view, imbalance- and calibration-aware metrics with significance testing, and a simulated NISQ noise sweep. We introduce a quantum-attribution audit (parameter-matched classical controls, a random-feature kernel, and a regularisation sweep) that quantifies how much of any gain is genuinely attributable to the quantum component. Tuned classical models (Random Forest, XGBoost) match or exceed the quantum models on aggregate detection on every dataset, and the audit attributes this to classical preprocessing and regularisation rather than quantum effects. Two advantages survive false-discovery-rate correction: the quantum-kernel SVM out-ranks its direct classical surrogate (a random-feature kernel) on AUPRC and ROC-AUC, and a small four-qubit hybrid out-detects the best classical baseline at the 1% false-positive operating point on the distribution-shifted NSL-KDD task (p = 0.005, BH q = 0.030). Code, seeds, and splits are released; our contribution stands whether quantum wins, ties, or loses.
机器学习
[LG-0] Lévy Attention: Single-Pass Predictive Uncertainty for Continuous-Time Attention
链接: https://arxiv.org/abs/2608.19171
作者: Sotirios P. Chatzis,Loukas Papadoulas
类目: Machine Learning (cs.LG)
*备注: 23 pages, 2 figures. Under review at TMLR
Abstract:Deep models for irregularly-sampled time series answer queries at arbitrary continuous timestamps, yet report nothing about how far each answer should be trusted. We show the attention layer itself can close that gap: with the right stochastic formulation, the pass that makes each prediction also reports, in closed form and at no extra cost, how far it should be trusted. We introduce Lévy Attention, a cross-attention operator whose output is a stochastic integral against an inhomogeneous Poisson random measure: query-key compatibilities assemble an intensity over a continuous (time x channel) index space, the measure scatters atoms under it, and the output averages an interpolated value field at those atoms. In expectation it reduces to a mollified cosine-kernel attention, so it replaces a softmax layer and trains with exact gradients. What softmax discards, the Poisson construction preserves in closed form: the evidence \Lambda_q (total compatibility mass) and the disagreement \mathrmtr,\Sigma_V(q) (value spread). An exact variance identity makes their combination \hat\sigma(q)=\sqrt\mathrmtr,\Sigma_V(q),\varphi(\Lambda_q) the root-mean-square deviation of the sampled operator, emitted by the deterministic pass with no trained head. Empirically, disagreement carries the signal, while the evidence factor swings from uninformative on dense data to strongly informative on sparse. On t-PatchGNN the operator swap costs at most 5.6% accuracy against a matched control and nothing on the sparsest dataset. The free disagreement signal improves on 20-pass MC dropout across matched five-seed suites, and \hat\sigma scales a calibrated Gaussian whose zero-sample CRPS beats a fifty-draw sampler; a split-conformal wrapper reaches nominal coverage at every level, and one pass ranks 3,383 unseen patients by trust in 1.4 seconds. Comments: 23 pages, 2 figures. Under review at TMLR Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.19171 [cs.LG] (or arXiv:2608.19171v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.19171 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-1] Learned Then Lost: A Measured Single-Example Counterfactual in Pre-training
链接: https://arxiv.org/abs/2608.19168
作者: Zachary Speck,Asa Shepard
类目: Machine Learning (cs.LG)
*备注:
Abstract:A single training example’s contribution to a finished model is normally estimated rather than measured, because measuring it takes two expensive full pre-training runs that differ in one row of one batch. We ran that counterfactual 24 times at a small scale. We trained 32 GPT-2 models at 124M parameters from scratch on OpenWebText, over four conditions and eight seeds. At step 200 of 9,536, at peak learning rate, we replaced one row of a 256-row batch with a fixed context injection carrying a 194-token passage. The three injected conditions are: 1. fluent prose with a corpus-attested subject, 2. fluent prose with a fabricated subject matched to it within 0.14% on full-batch gradient delta, and 3. random keyboard characters. The fourth condition is an uninjected twin. The passage is learned from one exposure and then decays. Fifty steps after injection, the arm that saw a passage predicts it better than the arm that did not by 0.039 and 0.044 nats of cross-entropy on the passage, at eight of eight seeds with p 10^-4 . At the final step we do not detect that difference for either passage, at p = 0.25 and p = 0.71, against minimum detectable effects of 0.025 and 0.079 nats, nor between the two passages, at p=0.54. Every geometric measure we report is taken after that decay. Our pre-registered contrast on interpolation loss barrier is +0.0068 with p = 0.509, against a minimum detectable effect of 0.032 barrier units. Held-out cross-entropy is -0.00044 with p = 0.310. Per-layer centered kernel alignment does not detectably separate any condition at any layer. Weight displacement reaches 44.1% of the seed-to-seed Euclidean distance and is 92% settled by the midpoint of training, while the barrier reaches 3.0% of the seed-to-seed barrier. Those two figures sit roughly 15 times apart, and that is a lower bound. The injection relocates the model within its basin without moving it out.
[LG-2] Continuous-Time Reinforcement Learning for Controlled Hawkes Jump-Diffusions
链接: https://arxiv.org/abs/2608.19151
作者: Tomasz R. Bielecki,Thibaut Mastrolia,Haoze Yan
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注:
Abstract:We study stochastic control of multivariate Hawkes-driven stochastic differential equations with machine learning algorithms in a non-Markovian setting. Due to the path dependence of the memory of the Hawkes intensity, this problem does not fall within classical stochastic control theory outside particular Markovian kernels. We first develop a finite-dimensional Markovianization procedure and algorithm to approximate multivariate Hawkes processes with mixtures of exponential kernels. We prove the convergence of the Markovianized approximation of the Hawkes process, its intensity, and the value of the problem to the original non-Markovian processes and the value of the primal problem. We then formulate continuous-time deterministic policy gradient learning on the Markovianized approximation of the problem, called Hawkes-CT DDPG. We propose a model-free algorithm to solve the non-Markovian Hawkes-driven optimization by observing only the event times of the process, the realization of the solution to the SDE, and a chosen set of decay filters, while the Hawkes kernel coefficients remain unknown. We compare our continuous time reinforcement learning Hawkes-CT DDPG method with discrete time reinforcement learning techniques under three different types of kernels: simple exponential, Erlang, and power-law kernels.
[LG-3] Bridge Graphical Models: Coupling Projection and Current-Preserving Dynamics for Generative Modeling
链接: https://arxiv.org/abs/2608.19144
作者: Tiantian Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Continuous-time generative models are often built from endpoint-conditioned bridges, but generation requires a different object: a non-anticipative Markov decoder that only observes the current state and time. We identify this bridge-to-decoder compression as a structural bottleneck shared by diffusion models, flow matching, rectified flow, Schrödinger bridges, and field-based generative models. We introduce the \emphMarkovization gap, the time-integrated conditional variance of the bridge velocity given the Markov state. It is the MMSE of predicting endpoint-conditioned motion from the information available to a sampler, and it measures an irreducible loss incurred before any neural network is trained. To make this bottleneck comparable across model families, we define \emphBridge Graphical Models (BGMs), which separate endpoint coupling, bridge law, Markovian projection, and current-preserving dynamics representation as independent design choices. The same formalism also represents Poisson and electrostatic models as field-line bridge kernels with a corresponding field-line Markovization gap. Across synthetic, latent, and pixel-space pilots on CIFAR-10 and Fashion-MNIST, a feature-space proxy gap estimated in minutes before training ranks design choices in the same direction as downstream training loss and FID under fixed architecture, bridge, sampler, and compute. These results support the Markovization gap as a pre-training diagnostic for bridge and coupling design.
[LG-4] Geometric Iterative Retrieval for Neural Audio Codec Resynthesis
链接: https://arxiv.org/abs/2608.19141
作者: Leo Schmidt-Traub,Frédéric Berdoz,Luca A. Lanzendörfer,Roger Wattenhofer
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:
Abstract:Neural audio codecs based on Residual Vector Quantization (RVQ) have become the dominant discrete representation for token-based general audio generation, yet resynthesizing high-quality audio from coarse codec tokens remains an open problem and bounds the fidelity of every system that generates them. Prior work has framed resynthesis as a choice between discrete token prediction and continuous regression. We argue that this dichotomy is incomplete and introduce geometric iterative retrieval, a paradigm that uses the RVQ layer hierarchy itself as a natural iterative decomposition in continuous codebook space. Rather than classifying over discrete vocabularies or regressing to a single target vector, our method performs contrastive retrieval in the codebook’s geometric space. We evaluate our method on codec restoration tasks across speech and music, and show improvements over both single-pass token prediction and one-step regression baselines.
[LG-5] SCORE: Subject Coordinate Recovery for Label-Free Cross-Subject EEG-to-Image Retrieval
链接: https://arxiv.org/abs/2608.19134
作者: Zhenyao Cui,Siyuan Kan,Siyang Li,Ziwei Wang,Dongrui Wu
类目: Machine Learning (cs.LG)
*备注: 9 pages, 6 figures
Abstract:Accurate visual decoding can reveal how the brain represents visual information and recover perceived content from neural signals such as electroencephalography (EEG), with potential for neural communication. However, current EEG-to-image retrieval methods perform far below their within-subject counterparts for new users without labeled calibration, limiting real-world deployment. To understand this gap, we analyze EEG features across subjects and find that different subjects preserve similar relationships among concepts but express them along different coordinate directions. We therefore propose Subject Coordinate Recovery (SCORE), a target label-free framework combining recovery-aware source training with coordinate alignment at deployment. During training, SCORE aligns source subject EEG with a common image space and simulates unseen-subject recovery through source-only episodes. At deployment, with both encoders frozen, SCORE selects reliable EEG-image landmarks through hubness-corrected matching and estimates an orthogonal transformation to recover target EEG coordinates without source data or target labels. In 200-way retrieval on two public benchmarks, SCORE outperforms the unadapted baseline for every target subject and achieves the best overall accuracy. It reaches 53.23%/83.55% and 12.01%/32.16% Top-1/Top-5 on THINGS-EEG2 and Alljoined-1.6M, respectively, surpassing the strongest baselines by 17.45/15.70 and 3.08/4.62 percentage points. Without target labels or encoder updates, SCORE brings brain-based visual decoding closer to robust, practical, low-latency deployment across users.
[LG-6] Beyond Trial Averag ing: Anchoring Neural and Visual Representations for Few-Repetition Brain-to-Image Retrieval
链接: https://arxiv.org/abs/2608.19128
作者: Zhenyao Cui,Siyuan Kan,Dingkun Liu,Dongrui Wu
类目: Machine Learning (cs.LG)
*备注: 8 pages, 6 figures
Abstract:Decoding visual information from brain signals probes neural representations and enables neuro-rehabilitation and dream decoding. Recent brain-to-image retrieval approaches have achieved promising performance, typically by averaging many (up to 80) neural trials per image, requiring repeated stimulus presentation that increases latency, cost, and user burden. When only one or a few repetitions are available, the retrieval accuracy drops sharply. This drop is commonly attributed to query noise because averaging suppresses noise and increases signal stability. However, we find a non-transitive alignment pattern: the low-repetition query signal and the image representation each align with the high-repetition center, but not directly with each other. This pattern shows that query noise is only part of the problem and that gallery placement also affects retrieval. We therefore propose a neural-anchor-based retrieval (NEAR) framework that treats the high-repetition center as an anchor and approaches it from both sides: a denoiser pulls the noisy query toward the true anchor, and a small network predicts each candidate’s pseudo anchor from its image and pulls the image toward it. Across four datasets spanning EEG, MEG and fMRI, NEAR consistently improved retrieval in the few-repetition regime. On THINGS-EEG2, it improved 200-way Top-1 accuracy by 5.7 and 9.3 percentage points respectively, when averaging one and four repetitions. By anchoring neural and visual representations, NEAR reduces reliance on repeated acquisition and brings neural retrieval closer to real-world deployment.
[LG-7] Enhancing EBSD throughput of battery electrode materials using super-resolution generative adversarial networks
链接: https://arxiv.org/abs/2608.19117
作者: John Mangum,Andrew Glaws,Francois Usseglio-Viretta,Steven Spurgeon,Donal Finegan
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci)
*备注: 38 pages, 16 figures
Abstract:Quantitative microstructural characterization of Li-ion battery electrode materials using electron backscatter diffraction (EBSD) has been proven as a critical method for optimizing cell performance. However, the inherently slow nature of EBSD can hinder the throughput of analyses needed for statistical representation of a material microstructure being developed. This work demonstrates a machine learning super-resolution framework using a generative adversarial network (SRGAN) to significantly increase EBSD throughput. The SRGAN model was trained on EBSD data of LiNixMnyCozO2 (NMC) cathode particles to computationally enhance low-resolution datasets and its performance is compared against classical interpolation methods across various upscaling factors (2x to 12x). Both qualitative image metrics and quantitative microstructural analysis verified that the SRGAN systematically outperformed classical methods, particularly in preserving small grains and maintaining realistic grain boundaries. We demonstrate that a 5x upscaling factor, corresponding to a 25x speed-up in acquisition time or a 25x larger field of view, is practical while maintaining acceptable accuracy in key metrics like grain size and shape. For instance, at 5x upscaling, relative errors were +5.7%, +8.2%, and -14.6% on grain area-equivalent diameter, grain maximum sphere-inscribed diameter, and grain boundary length, respectively. The SRGAN methodology developed in this work significantly enhances the efficiency of EBSD acquisition for more statistically robust microstructural dataset, enabling EBSD as a high-throughput characterization tool for materials research and industrial process development.
[LG-8] Pretraining Reusable Inference Across Views with Synthetic Task Priors
链接: https://arxiv.org/abs/2608.19115
作者: Jielong Lu,Zhihao Wu,Jiajun Yu,Zhaoliang Chen,Haishuai Wang
类目: Machine Learning (cs.LG); Multimedia (cs.MM)
*备注:
Abstract:Modern pretrained encoders make representations from heterogeneous views increasingly reusable, but the procedure that determines view utility and combines evidence is still relearned for each downstream task. Consequently, knowledge about view relevance, complementarity, reliability, and missingness is repeatedly discarded rather than transferred across tasks. We therefore reformulate multi-view learning as learning a reusable, task-conditioned inference procedure rather than a fixed fusion function. Based on this perspective, we propose SIMPLE, a prior-fitted multi-view in-context learner that predicts query labels by conditioning on a small labeled support set. Since existing real-world datasets cover only a limited range of view configurations and task structures, we construct a controllable synthetic task prior in embedding space. It generates diverse support-query episodes with varying class structures, shared and view-specific factors, representation geometries, cross-view dependencies, reliability levels, missingness patterns, and distribution shifts. A hierarchical inference architecture then performs reasoning within views, across views, and across support and query samples. Experiments on multi-view and multi-omics benchmarks demonstrate that the frozen variant of SIMPLE achieves competitive performance without updating the inference backbone, while lightweight adapter calibration attains leading performance on most evaluated datasets. Together, the results under frozen, one-shot, and missing-view settings support the central hypothesis that multi-view reasoning itself can be pretrained and reused, while lightweight adapter calibration provides task-specific alignment when needed.
[LG-9] Does Mapping Non-Maximal Probabilities to GMM Components Matter for S-JEPA Encoder Representations?
链接: https://arxiv.org/abs/2608.19084
作者: Wenxuan He,Yunpeng Li,Shan Liang
类目: Machine Learning (cs.LG); Sound (cs.SD)
*备注: 6 pages, 4 figures, 2 tables
Abstract:S-JEPA uses soft Gaussian mixture model (GMM) posteriors instead of hard cluster labels to preserve uncertainty. It remains unclear whether the probability values alone are sufficient, or whether it also matters which GMM components receive the non-maximal probabilities. We test this with two matched controls. FIXED-RANDPERM keeps the top-1 component and probability together with the multiset of non-maximal probability values, but reassigns those non-maximal values using a mapping fixed for each physical frame. UNIFORM-TAIL keeps the top-1 component, its probability, and total non-maximal mass but distributes that mass uniformly. Across three independent seeds, REAL SOFT outperforms both controls on two frozen Encoder readouts. It provides better recovery of the original GMM tail and greater accessibility of spectral dynamics over short time scales after controlling for the complete spectrum of the current frame. In two exposure experiments, both readouts improved overall as more frames retained the original mapping. We also descriptively follow one Phase 2 trajectory after the switch to the online GMM. These results show that the numerical probability structure of the soft target does not fully determine the learned Encoder representation. The mapping of non-maximal probabilities to GMM components also matters.
[LG-10] Multi-Agent Off-Policy Deep Reinforcement Learning for Smart Campus Coverag e
链接: https://arxiv.org/abs/2608.19049
作者: Omar Rady,Mohamed Ayman,Ali Arafa,Mohamed Shalma
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注:
Abstract:Deep reinforcement learning (DRL) has recently gained a great attention due to its real-time adaptation and effectiveness in complex optimization problems. This paper investigates the optimal deployment of millimeter-wave (mmWave) base stations (BSs) in a realistic, non-convex campus topology. The optimization problem is NP-hard, due to the non-convex, non-smooth nature of the max-min fairness objective. To overcome these constraints, we formulate the BS placement as a Markov Decision Process (MDP) and systematically benchmark four DRL schemes: a discrete single-agent Deep Q-Network (DQN), a spatially partitioned Multi-Agent DQN, a continuous single-agent Deep Deterministic Policy Gradient (DDPG), and a geographically partitioned multi-agent DDPG framework. Numerical evaluations reveal that the multi-agent DDPG approach substantially outperforms single-agent in dense scenarios. Additionally full coverage is achieved, and a fairness Jain’s index of 0.94 is obtained. Finally, the multi-agent demonstrates highly efficient computational convergence of dense scenarios with 400 users.
[LG-11] Monroe: A Molecular Foundation Model for In-Context Probabilistic Inference
链接: https://arxiv.org/abs/2608.18982
作者: Blazej Banaszewski,Andrew W. Fitzgibbon
类目: Machine Learning (cs.LG); Biomolecules (q-bio.BM); Quantitative Methods (q-bio.QM)
*备注: Preprint; Open source weights and code
Abstract:Bioassay activity prediction is often data-limited because drug-discovery datasets rely on time-consuming and expensive wet-lab experiments for data generation and evaluation. This challenge has inspired recent research into molecular foundation models (MFMs), which aim to encode general-purpose chemical knowledge into molecular representations that generalize well in data-constrained scenarios. This paper presents Monroe, a new MFM with several innovations over the existing state of the art: increased scale allowing pre-training on over 81 million molecules from the PM6 quantum chemistry dataset; improved graph representation of stereochemistry; improved training losses including conformer denoising and embedding decorrelation; improved multi-task learning; and the use of a prior-data-fitted model (TabPFN) for downstream in-context prediction. Our evaluations use a principled pairwise comparison framework that measures statistically significant performance differences. Across established Polaris benchmarks, Monroe matches or exceeds existing MFMs, while on activity cliff benchmarks, designed to assess utility for molecular discovery, it achieves significant improvements over prior methods. Finally, ablation and transfer experiments show that PFN-based downstream predictors also substantially improve two leading existing models, MiniMol and CheMeleon, yielding new state-of-the-art variants we call MiniMol_PFN and CheMeleon_PFN, suggesting that our downstream adaptation strategy generalizes beyond Monroe. Source code is at this http URL.
[LG-12] Fuzzy Accuracy Compensates for Label Subjectivity in Classification of Skin Tone Using Wearable Photoplethysmography Signals
链接: https://arxiv.org/abs/2608.18969
作者: Padmini Krishnadas,Urs Hackstein,Alen Bosnjakovic,Philip J. Aston
类目: Machine Learning (cs.LG)
*备注: This work was presented at the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2026
Abstract:We consider the problem of classification of skin tone using photoplethysmography (PPG) signals with labels of the ordinal six-class Fitzpatrick skin tones. A typical accuracy for this task is a poor 40-55 %. However, the labels are subjectively determined by comparing the skin with a colour chart, and hence contain widespread small-scale inaccuracies. By working with a “fuzzy accuracy”, which deems a prediction of skin tone class to be correct if its difference from the labelled class is not greater than one, much higher accuracy is obtained which provides more convincing evidence that skin tone can be accurately predicted from PPG signals. Three machine learning approaches were used, namely deep learning or tree-based approaches on raw PPG signals, deep learning on image representations of the signals generated by the Symmetric Projection Attractor Reconstruction (SPAR) method, and machine learning on features extracted from the signals. The first method also employed a fuzzy version of the cross entropy loss function, which gave the best results. Tree-based models on raw signals give accuracies up to 55 % and higher fuzzy accuracies up to 96 %, while deep learning models on the SPAR images obtained lower results of 44 % accuracy and 85 % fuzzy accuracy. The machine learning on PPG features gave similar results to the SPAR method with accuracy of 42 % and fuzzy accuracy of 87 %. We have shown that classification of skin tone using PPG signals is possible with high fuzzy accuracy which implies that our modelling approach enables accurate prediction of skin tone class within at most one class of the observer’s choice of class, from which we conclude that PPG signals are affected by skin tone in a discernible way.
[LG-13] ransportable Causal Effect Estimation across Networks under Interference
链接: https://arxiv.org/abs/2608.18932
作者: Xiaojing Du,Jiuyong Li,Lin Liu,Debo Cheng,Jixue Liu,Thuc Duy Le
类目: Machine Learning (cs.LG)
*备注: 9 pages
Abstract:Estimating causal effects under network interference typically assumes that the network used for training and the network used for deployment coincide. In practice, an intervention is run on one population while the question of interest concerns a different population, and the two generally differ in topology, node-covariate composition, and spillover pathways. Transporting a causal effect across networks is therefore a data-fusion problem that no existing algorithm solves. We employ a selection diagram, extended to the network setting so that covariate shift and structural network shift enter as separate selectors, and derive from it a transport formula for the direct, spillover, and total effects in the deployment population. Each formula makes explicit which interventional mechanism is assumed invariant and which observational distribution must be reweighted. We then turn the formulas into TranCE (Transported Causal Effects), a doubly-robust algorithm combining an interventional outcome model, a domain density-ratio correction, and cross-fitted inference. Extensive experiments on two semi-synthetic benchmarks derived from real-world social networks and on a fully real weather-insurance field experiment, where the transported effects are checked against held-out randomized estimates, confirm the effectiveness of our approach. Our findings have the potential to improve intervention strategies in networked systems, particularly in social networks and public health.
[LG-14] Lost in Aggregation: How Benchmarks Overlook Irreplaceable Model Strengths
链接: https://arxiv.org/abs/2608.18919
作者: Andrej Tschalzev,Stefan Lüdtke,Heiner Stuckenschmidt,Christian Bartelt
类目: Machine Learning (cs.LG)
*备注:
Abstract:Tabular machine learning benchmarks typically summarize performance by averaging scores, ranks, or pairwise wins across datasets. Such aggregates are useful for selecting robust default models, but they can obscure a different question: which models are necessary to attain peak performance on particular datasets? We argue that benchmark evaluation should also consider the data-centric peak performance frontier, defined by the best statistically supported performance achieved on each dataset. From this perspective, a model may be irreplaceable, sufficient, redundant, or fallible depending on where it lies on the frontier relative to other models. Applying this framework to the TabArena benchmark, we find that common aggregation metrics are highly correlated and largely measure consistency and avoiding failures, while being much less aligned with dataset-level irreplaceability. Consequently, models performing decently across datasets without ever being the best choice are rewarded while models with unique dataset-specific strengths appear mediocre under aggregation. Hence, benchmark progress should be measured not only by improvements on aggregation metrics but also by whether new models expand the set of attainable peak performances across datasets.
[LG-15] Score the Algebra Not the Span: Dimension Reduction for Transfer Operator Models of Dynamical Systems
链接: https://arxiv.org/abs/2608.18918
作者: Mark Kozdoba,Shie Mannor
类目: Machine Learning (cs.LG)
*备注:
Abstract:Dimension reduction for dynamical systems is standard practice, and the standard route is spectral: model the transfer (Koopman) operator by its leading modes. We show that on systems assembled from several weakly interacting components — a structure common in physical and biological settings — this may either require an exponential number of modes, or drop an entire component: the component is absent from the model rather than modeled coarsely, and no function of it can be predicted at any accuracy. We call this linear masking. The cause is that a rank-based model pays one coordinate per mode. We propose to score instead the \sigma -algebra the coordinates generate, so that products and powers come free and a component’s cost is governed only by its generators rather than by all its interactions. The criterion is a \chi^2 -divergence between the embedded present and future, and it carries a budget guarantee: twice the intrinsic dimension of the dynamics is enough coordinates for an embedding whose algebra carries the operator’s entire spectrum, with its full infinite rank. In variational form the criterion admits off-the-shelf estimators, and restricting its critic to the bilinear class returns the VAMP score on the span, so rank-based methods are one end of the same family. We demonstrate the proposed objective on a composite of published benchmark systems. We exhibit examples where the rank-based methods completely miss the masked components at all ranks k100 , while ten algebra coordinates recover all of them. In addition, the resulting algebra representation supports predicting the masked components from few labels, while direct regression from the high-dimensional observation or from the VAMP features fail. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.18918 [cs.LG] (or arXiv:2608.18918v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.18918 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-16] Converting Expert Deliberation into Financial Signals Through A Context-Aware NLP Pipeline
链接: https://arxiv.org/abs/2608.18911
作者: Vivek Batra,Kristin Chen,Sanjiv Das,Samuel Judge,Harshad Khadilkar,Sukrit Mittal,Amir Nasrollahzadeh,Daniel Ostrov,Jacob Sisk
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE)
*备注:
Abstract:We introduce the CDSP (context-conditional deliberation signal pipeline), converting an investment committee’s meeting transcripts into structured predictive features. CDSP segments the meeting transcripts into topical chunks, assigns asset-class context labels using a large language model (LLM), maps financial keywords to a pre-determined taxonomy of labels, and constructs complementary features: sentiment polarity and mention frequency. This feature engineering framework is applied to a dataset spanning 48 monthly committee meetings to predict if global equities will perform better or worse than global bonds in the following month. In experiments with engineered features, raw transcript text, sentence embeddings, and combined representations, the prediction accuracy ranges from 62% to 73%, compared to always choosing stocks, which outperforms bonds 60.4% of the time. The best (73% accurate) model combines sentence embeddings with engineered CDSP features, achieving a 0.73 F1 score (although this is not statistically significant compared to always choosing stocks). Sentiment carries a stronger signal than mention frequency for several taxonomy categories. These findings suggest that experts’ deliberations may contain forward-looking information that context-aware NLP can extract.
[LG-17] On the Slow Convergence to Trivial Solutions of Algorithms for Hard Optimization Problems
链接: https://arxiv.org/abs/2608.18910
作者: Ali Hussaini Umar,Jean Barbier,Matthieu Jonckheere,Manuel Sáenz
类目: Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn); Discrete Mathematics (cs.DM); Probability (math.PR)
*备注:
Abstract:Hard combinatorial optimization problems, many of which are NP-hard, present fundamental algorithmic challenges. Average-case analysis on random instances has emerged as a powerful framework for understanding typical algorithmic performance beyond worst-case guarantees. A substantial body of work has established negative results: for sufficiently hard instances (often controlled by the underlying graph connectivity/constraints density), no known polynomial-time algorithm can significantly outperform naive heuristics in the double asymptotic limit where both problem size and constraints density tend to infinity. We revisit this picture by studying the finite-size behavior of some optimization algorithms across easy, intermediate, and hard regimes. Through rigorous analysis of large-graph asymptotics combined with numerical experiments on canonical problems (maximum independent set and maximum K -SAT), we demonstrate that while algorithms do eventually converge to theoretically predicted bounds, this convergence can be remarkably slow. In the intermediate regime where instances are already highly constrained, local algorithms achieve solutions substantially better than their predicted performance in the high-constraint-density limit. This gap between finite-regime and asymptotic behavior has important practical implications: sophisticated algorithmic design remains crucial even when asymptotic theory predicts inevitable failure.
[LG-18] A FEM-Based Surrogate Modelling and Optimization Framework for Physics-Constrained Electromagnetic Coil Design
链接: https://arxiv.org/abs/2608.18903
作者: Yucheng Liu
类目: Machine Learning (cs.LG)
*备注: 18 pages, 9 figures
Abstract:This work evaluates surrogate-assisted optimization of a seven-parameter current-excited coil–core benchmark subject to geometric, manufacturing, and separate core and copper mass constraints. A Python–MPh–COMSOL workflow couples a two-dimensional axisymmetric finite-element method (FEM) model to a Matern 5/2 Gaussian-process (GP) probabilistic surrogate. Here, physics-constrained denotes a design problem evaluated by a governing-equation FEM model and restricted by explicit physical, geometric, manufacturing, and material-allocation constraints; it does not denote a physics-informed GP architecture. Sequential Bayesian optimization (BO) ranks candidates using expected improvement (EI), and every reported incumbent is verified by FEM. Five paired runs show that optimizer ranking depends on the available FEM-evaluation budget: EI–BO improves rapidly at small continuation budgets, COBYLA is stronger at the earliest checkpoint, and BOBYQA attains the highest mean terminal response. A retrospective finite-pool study further finds no robust endpoint advantage of EI over posterior-mean ranking on this smooth response surface. The broader result is that early progress, terminal response, information use, and wall-clock cost can favor different methods in simulation-driven design. A selected-design check at a common total current preserves the observed BOBYQA–COBYLA–EI-BO ordering. The conclusions nevertheless remain conditional on this axisymmetric benchmark and do not establish a fixed-current optimum, fixed-power performance, or electrical-efficiency superiority.
[LG-19] Graph-Based Approaches to Learning Epileptogenic Zone Localization Using Stereo-EEG Recordings IJCNN
链接: https://arxiv.org/abs/2608.18887
作者: Daniel Wendelken(1),Brian Ervin(2),Ravindra Arya(2),Ali A. Minai(1) ((1) University of Cincinnati, Cincinnati, USA, (2) Cincinnati Children’s Hospital Medical Center, Cincinnati, USA)
类目: Machine Learning (cs.LG)
*备注: 8 pages, 2 figures. Accepted for the Proceedings of IEEE WCCI 2026 (IJCNN), Maastricht, Netherlands
Abstract:The epileptogenic zone (EZ) is the brain region that generates seizures in an individual, and is the target of epilepsy surgery. Localizing the EZ from stereo-EEG (sEEG) recordings supports surgical planning, but manual interpretation is time-consuming and focuses on seizure recordings. Graphical learning models of resting-state functional connectivity among the recorded brain regions are an attractive alternative, but depend crucially on the network topology chosen for the model. We present a controlled study of graph-based models to explore how graph topology affects EZ localization from resting-state sEEG in 40 patients. Using the same simple learnable model and leave-one-patient-out evaluation, we compare dense graphs, anatomy- and geometry-informed priors, budgeted sparsification methods, and learned sparsification, including the proposed Region-Bridge- c topology. To compare graph constructions fairly, we control the number of incoming edges per node and vary graph sparsity. At \approx 30% edge retention, Region-Bridge- c achieves the highest observed mean PR-AUC ( 0.371\pm0.015 ; ROC-AUC 0.743\pm0.010 ) while using \approx 69% fewer edges than Dense (PR-AUC 0.349\pm0.014 ). Spatial- k is competitive, whereas random pruning requires near-dense retention. Learned sparsification benefits from anatomical node metadata but, on average, does not surpass the best fixed prior. Across all topologies, the best choice varies by patient. These results suggest that graph construction should be evaluated explicitly rather than treated as fixed preprocessing. Comments: 8 pages, 2 figures. Accepted for the Proceedings of IEEE WCCI 2026 (IJCNN), Maastricht, Netherlands Subjects: Machine Learning (cs.LG) ACMclasses: I.2.6; J.3 Cite as: arXiv:2608.18887 [cs.LG] (or arXiv:2608.18887v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.18887 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-20] Multi-stage neural operator learning with application for convolutions
链接: https://arxiv.org/abs/2608.18851
作者: Zhiping Mao,Zhenye Wen,Yong Zhang,Xiaofei Zhao
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注:
Abstract:Convolution integrals widely exist in applications, and to enable fast and accurate computations, this paper introduces two general multi-stage neural operator learning frameworks. The first, Deep Collocation Neural Operator (DCNO), is a supervised approach that iteratively refines the operator approximation by learning residuals from input-output data pairs. The second, Deep Galerkin Neural Operator (DGNO), is an unsupervised framework applicable when the target operator can be represented by a PDE, leveraging the weak form of the PDE residual for training. Both methods progressively construct basis operators through multiple training stages to enrich the approximation space, leading to significantly improved accuracy over standard one-shot operator learning. We provide theoretical analysis for their approximation capabilities and implement them for learning convolutions. Extensive numerical experiments demonstrate that both DCNO and DGNO achieve high accuracy, approaching machine precision under single float for convolution problems, and offer substantial efficiency gains for numerous queries or parametric variations compared to traditional solvers. We also extend these frameworks to handle multi-input operator learning scenarios involving variations in both the density and kernel of a convolution.
[LG-21] GEAR: Generative Expansion and Real Anchoring for Two-Stage Distillation of Tabular Foundation Models
链接: https://arxiv.org/abs/2608.18849
作者: Qi Qin,Jiajie Zhu,Dali Chen,Yuzhao Zhang,Jia-Xing Han,Yu Su,Peng Zhang,Ying Yan,Yifan Sun
类目: Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML)
*备注: 9 pages,5 figures
Abstract:Tabular foundation models (TFMs) achieve strong performance through in-context learning, but context-dependent inference imposes substantial latency and memory costs, hindering large-scale deployment. We propose GEAR (\emphGenerative Expansion and Real Anchoring), a modular two-stage framework that distills TFMs into lightweight MLP or tree-based predictors that can be deployed on commodity CPUs. Stage 1 uses synthetic covariates solely as teacher-query locations and trains the student on soft TFM targets, expanding coverage beyond observed rows. Stage 2 re-anchors the student to the target distribution using real labels and out-of-fold teacher predictions, whitch avoids self-labeling leakage. We further derive a risk certificate characterizing the trade-off between generated-query volume and generator fidelity. Experiments on TALENT and TabArena demonstrate the broad applicability of GEAR. Two-stage MLPs outperform supervised MLPs by 1.81–2.00 AUC points on binary tasks and 1.19–1.35 points on multiclass tasks, with additional gains over real-data-only distillation of 1.76–2.19 and 2.09–2.40 points, respectively. On binary tasks, the gains also transfer to LightGBM and XGBoost, and all three student families outperform CatBoost, the strongest non-TFM baseline, in mean AUC. Ablations show gains beyond longer training or alternative warm starts, greater stability from staged than mixed optimization, and generator-dependent diminishing returns as query volume increases. Finally, GEAR reduces median inference time by 57–2866 times and peak prediction memory by 1.9–3.3 times, while retaining higher AUC than matched supervised baselines.
[LG-22] A Unifying Relational Perspective on Expressive Lottery Tickets ICML2026
链接: https://arxiv.org/abs/2608.18819
作者: Lorenz Kummer,Samir Moustafa,Anatol Ehrlich,Franka Bause,Marco Nennstiel,Przemysław Andrzej Wałȩga,Nils Morten Kriege
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: ICML 2026
Abstract:Graph neural networks (GNNs) are widely used, but how parameter sparsity affects the expressivity of relational (RGNNs) and temporal (TGNNs) variants is poorly understood. The Strong Expressive Lottery Ticket Hypothesis (SELTH) posits the existence of sparse GNNs that preserve Weisfeiler-Leman (WL) expressivity on static graphs. We generalize this existence result to a probabilistic statement for multi-relational and temporal domains via the relational WL (RWL). We prove that sufficiently parameterized RGNNs contain sparse subnetworks that maintain 1-RWL expressivity and derive a lower bound on the probability that a random pruning yields such a subnetwork. We show that common TGNNs and cross-graph message passing schemes admit RGNN reformulations such that they inherit these guarantees and, moreover, that the expressivity of a sparse RGNN is connected to its optimization behavior under common update regimes. Experiments instantiate the bound, compare it to empirical probabilities on synthetic data, and study how pre-training expressivity relates to optimization and prediction quality metrics on temporal and molecular benchmarks.
[LG-23] Many Optimizers But Only One Training Path: Repeated Resampling for Adaptive Optimizer Selection
链接: https://arxiv.org/abs/2608.18810
作者: Ronald Richman,Mario V. Wüthrich
类目: Machine Learning (cs.LG)
*备注:
Abstract:An optimizer is usually chosen before training a deep neural network and then kept fixed. Treating optimizer choice as a hyperparameter could boost performance, but it requires several complete training runs and discards all but the winner. Repeated Optimizer Resampling (ROR) instead searches during one evolving run. Every b epochs, each candidate optimizer scouts from the current model weights for s epochs. The best scout continues for the remaining b-s epochs, and that completed segment becomes the new incumbent if it improves the validation objective. This design allows the preferred optimizer to change as training progresses. We compare two variants of ROR on MNIST, Fashion-MNIST, and two motor insurance claim-count models. Nine fixed optimizers and both ROR variants are evaluated with the same ten seeds. One-epoch ROR uses 24% to 35% of the aggregate training needed to identify the best fixed optimizer exhaustively and remains close to that optimizer on all four tasks. These results support short scouting as a practical way to search over optimizers without completing every candidate run. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.18810 [cs.LG] (or arXiv:2608.18810v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.18810 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-24] nsor Field Models
链接: https://arxiv.org/abs/2608.18808
作者: Alexander Strunk,Roland Assam
类目: Machine Learning (cs.LG); Differential Geometry (math.DG)
*备注:
Abstract:This paper introduces Tensor Field Models (TFMs), realization-level Mathematical Structures in which a learned Operator maps a product of admissible component-section families to a prescribed family of time-dependent tangent sections on a Generative State Manifold. Analytic and dynamical restrictions are encoded through the choice of admissible families rather than imposed by the root definition. Constructed, component-separable, and Tensor Bundle TFMs provide structured refinements of this common object. In the conditional realizations considered here, a structured condition c=(c_1,\ldots,c_n) is mapped componentwise to a reusable collection \mathbf H_c=(H_c_1^(1),\ldots,H_c_n^(n)) . In the architectures evaluated here, the component representations remain distinct and are combined only by the Field Operator to produce the generated Vector Field. All learned models are trained using Flow Matching. Experiments show that TFMs can improve performance and that amortized sampling enabled by reusable condition representations can accelerate generation.
[LG-25] A Real-Time Tsetlin Machine-based Non-intrusive Load Monitoring System on MCUs
链接: https://arxiv.org/abs/2608.18780
作者: Tianhang Tan,Han Wu,Tousif Rahman,Shengyu Duan,Alex Yakovlev,Rishad Shafik
类目: Machine Learning (cs.LG)
*备注: Accepted by International Symposium on the Tsetlin Machine (ISTM 2026)
Abstract:Non-Intrusive Load Monitoring (NILM) systems estimate individual appliance energy consumption from a single aggregate meter, without requiring separate sensors for each device. By installing a single meter that measures a building’s total electricity consumption, NILM algorithms can determine the active status of each appliance. However, traditional NILM systems use computationally intensive optimization algorithms to process offline data, limiting their capability for on-device deployment, where sensitive household data must be processed locally. This paper proposes a Tsetlin Machine ™-based NILM framework, targeting real-time applications on resource-constrained microcontrollers (MCUs), enabling privacy-preserving edge deployment. The problem is reformulated as a classification task, and the proposed approach achieves an average precision of 90% and recall of 96% for two-appliance classification, and 77% precision and 80% recall for four appliances on the REDD dataset. The trained model occupies only 18 KB of flash memory and achieves an inference latency of 0.43 ms on an ESP32, demonstrating its suitability for embedded NILM applications on MCUs.
[LG-26] GraphK: Variable-Size Graph Generation with Efficient Edge Construction
链接: https://arxiv.org/abs/2608.18777
作者: Resul Tugay,Eren Oluğ,Elif Ak,Sule Gunduz Oguducu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Graph generation models have advanced significantly with deep learning, yet they remain limited in scalability, flexibility, and ability to model underlying structures. We present GraphK, a novel encoder-sampler-decoder framework for graph generation that overcomes these challenges through structural flexibility and computational efficiency. Unlike autoregressive approaches constrained by vocabulary size (i.e. number of nodes in graph generation), GraphK allows for both upscaling (generating graphs with more nodes than the input) and downscaling, providing a flexible control over output graph size. By learning permutation-invariant latent representations and sampling new node embeddings via maximum likelihood estimation, GraphK generalizes across graph sizes and structures. For edge generation, we employ edge prediction with a KDTree-based top-k neighbor search in the latent space, reducing computational cost. Based on the manifold smoothness assumption, our method effectively captures graph properties. Experiments on synthetic and real-world datasets show that GraphK outperforms existing methods, accurately learns graph structures, and generates synthetic graphs without explicit definitions.
[LG-27] o Go Far Go Together: Diverse Preferences Induce a Curriculum for Reward Optimization
链接: https://arxiv.org/abs/2608.18770
作者: Taehyung Kim,Jongeun Choi
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注: 14 pages, 7 figures
Abstract:Learning a reward model from human feedback and optimizing a policy against it is one approach to aligning AI systems with individual users. From a fairness perspective, existing work improves such alignment by developing data-efficient and accurate reward models that capture minority preferences despite scarce data. We push this line of inquiry one step further and argue that data-efficient and accurate per-user reward models are not sufficient: users whose reward models are difficult to \textitoptimize at the policy level can become a new underserved group. We start from the observation that one user’s reward model can be easy to optimize from the initial policy while another’s is not. We argue that, given a sufficiently diverse user population, a curriculum naturally emerges between easy- and hard-to-optimize reward models. Building on this insight, we propose CurriPO, which grows a tree-structured curriculum to accommodate diverse user-specific objectives, covering the population in a single traversal. Specifically, CurriPO automatically constructs a curriculum over diverse user reward models, allowing it to branch from the existing curriculum and reuse reward models previously incorporated into the curriculum. To the best of our knowledge, this is the first work to explicitly exploit multi-user structure to address optimization in AI alignment. Extensive experiments on personalized continuous control in a simulated environment show that CurriPO achieves 1.2 – 2.1\times the population satisfaction of the strongest baseline while substantially reducing training time. Additional analysis attributes much of this improvement to the users left underserved by conventional optimization.
[LG-28] Enhancing Distance-Based Graph Autoencoders with Structural Penalties for Dynamic Graph Embedding
链接: https://arxiv.org/abs/2608.18762
作者: Aleksandar Tomčić,Miloš Savić,Miloš Radovanović
类目: Machine Learning (cs.LG); Emerging Technologies (cs.ET)
*备注:
Abstract:Graph autoencoders (GAEs) are widely used for learning representations of dynamic graphs. However, their optimisation objectives typically do not take structural heterogeneity across nodes into account. We propose three distance-based GAE variants that incorporate structural penalties into the reconstruction loss. All variants share a two-layer Graph Convolutional Network encoder and a Euclidean-distance decoder trained with distance-based reconstruction objectives. We extend sparsity-corrected loss with two node-level regularization terms: (i) a hub penalty based on degree centrality, and (ii) a penalty based on Natural Community Local Intrinsic Dimensionality (NC-LID). The paper is motivated by prior evidence linking high NC-LID to reduced embedding quality. The proposed methods are designed to emphasize reconstruction errors for structurally ambiguous nodes. Experiments on multiple dynamic graph data sets show that incorporating NC-LID-based regularization consistently improves reconstruction performance over the baseline without structural regularization and the method using hub-aware regularization. These findings highlight NC-LID as a useful structural signal for enhancing distance-based graph autoencoders in dynamic settings.
[LG-29] Geometric Data Perturbation with Noisy-Anchor Alignment for Privacy-Preserving Collaborative Learning
链接: https://arxiv.org/abs/2608.18749
作者: Keiyu Nosaka,Yamato Suetake,Yuichi Takano,Yukihiko Okada,Akiko Yoshise
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注: 72 pages total: 36-page main paper and 36-page supplementary material
Abstract:Geometric Data Perturbation (GDP) enables one-shot, privacy-preserving collaborative learning: each participant applies a distance-preserving transformation to its private data and uploads only the resulting representation to a central analyst. We study GDP under analyst-participant collusion, in which the analyst combines all uploaded representations with the private data and transformations disclosed by colluding participants to recover a non-colluding participant’s private data. Participant-specific independent transformations resist this attack but map participants’ data into incompatible representation spaces, degrading downstream model performance. Shared-anchor alignment from Data Collaboration (DC) analysis restores compatibility and improves utility, but we show that disclosing the DC anchor matrix enables exact recovery of non-colluding participants’ private data even in the presence of collusion. Adding noise directly to the private-data representations mitigates this vulnerability but substantially reduces utility. We propose adding noise to the anchor representations instead. Each participant independently transforms its private data and the shared anchor matrix, perturbs only the resulting anchor representation, and uploads both representations in a single round. Using the noisy anchor representations, the analyst aligns the private-data representations by solving a Generalized Orthogonal Procrustes Problem. We characterize alignment and recovery errors, specialize a conservative sufficient condition for convergence of the alignment to our setting, and analyze three recovery attacks. Experiments on MNIST and CelebA show that, across the evaluated attacks and deployment settings, anchor noise achieves higher learning accuracy than private-data noise at comparable measured leakage, yielding a more favorable privacy-utility trade-off under the specified collusion model.
[LG-30] FedLNS: Leverag e LayerNorm Signature Modeling to Mitigate Adversarial Manipulation in Federated LLM s
链接: https://arxiv.org/abs/2608.18736
作者: Kai Li,Jong-Ik Park,Carlee Joe-Wong,Wei Ni,Falko Dressler
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: 13 pages (main body), 36 pages (appendix), 3 figures, 98 tables
Abstract:Federated training enables language models to learn from distributed private text, but the server cannot directly verify the local supervision or optimization process that produces each client update. A malicious client can therefore train on corrupted targets, introduce incorrect context-token associations, and degrade the global model through repeated aggregation. Such degradation can also increase the risk of unreliable or hallucinatory generation. We propose Federated Learning with Normalization Signatures (FedLNS), a server-side framework for lightweight malicious-update screening. FedLNS represents each client update through changes in trainable normalization-layer parameters and screens suspicious updates against a robust, history-aware cross-client reference. Because the signatures are extracted at the server from the returned local models, FedLNS requires no additional client-to-server parameter or metadata exchange compared to standard federated learning (FL) methods. After screening, the retained full-model updates can be aggregated using standard FL or another compatible aggregation rule. FedLNS requires no raw client data, trusted server dataset, labeled attack examples, or separately trained detector. Experiments on GPT-style, BERT-style, and LLaMA-style models trained from scratch with 200 clients show that, under 40% population-level target manipulation, FedLNS achieves lower test perplexity than the strongest of six baselines for all three architectures under both IID (independently and identically distributed) and non-IID data partitions.
[LG-31] Multi-Class Electrical and Mechanical Fault Classification Using Random Convolutional Kernels CEC2026
链接: https://arxiv.org/abs/2608.18716
作者: Mouhamadou Mansour Lo,Mouad Talbaoui,Gildas Morvan,Mathieu Rossi,Fabrice Morganti,David Mercier
类目: Machine Learning (cs.LG)
*备注: Interdisciplinary Conference on Electrics and Computer (INTCEC 2026)
Abstract:Diagnosing faults in rotating machinery is essential for ensuring the reliability of industrial processes. Random convolutional kernel-based Time Series Classification (TSC) methods, such as ROCKET and its variants, provide an attractive trade-off between predictive performance and computational efficiency. In this work, we evaluate SelF-Rocket for the multi-class diagnosis of both mechanical and electrical faults and introduce, as a new contribution, a multivariate extension of the original method. The proposed approach is compared with leading ROCKET-based methods on two public benchmark datasets, MaFaulDa (mechanical faults) and ITSC-UDG (stator inter-turn short circuits), under both univariate and multivariate settings. Experimental results show that SelF-Rocket achieves the best overall accuracy-latency trade-off among the evaluated methods, obtaining the highest classification performance on MaFaulDa while remaining highly competitive on the more challenging ITSC-UDG dataset.
[LG-32] ransforming Heart Disease Prediction with Advanced Machine Learning Techniques
链接: https://arxiv.org/abs/2608.18687
作者: Sami Ullah,Muhammad Mohsin Khan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Heart disease remains the leading cause of mortality globally, necessitating early and accurate detection to improve patient outcomes. This research focuses on the predictive analysis of heart disease using machine learning (ML) techniques, comparing the performance of multiple classifiers to identify the most accurate and least error-prone method. Two datasets from UCI and Kaggle repositories were utilized, each containing 14 attributes related to heart health indicators. Techniques including J48, Naive Bayes, Logistic Regression, Simple Cart, Bagging, Decision Stump, AdaBoost, Artificial Neural Networks, and Support Vector Machine (SVM) were applied. Evaluation metrics such as Mean Absolute Error (MAE), Relative Absolute Error (RAE), accuracy, precision, recall, and F-measure were used for performance comparison. Results revealed that SVM achieved the highest performance on the UCI dataset, while Simple Cart performed best on the Kaggle dataset, offering the highest accuracy and lowest error rates. The research work concludes that ML models, when properly tuned and validated, can significantly assist in the early diagnosis of heart disease, offering critical support for clinical decision-making. Future work may involve hybrid approaches and the use of more recent datasets to further improve prediction accuracy.
[LG-33] An Empirical Benchmark of Deep Time-Series Models for Smart Meter Energy Forecasting
链接: https://arxiv.org/abs/2608.18675
作者: Behnaz Kavoosighafi,Maria Eidenskog,Wiktoria Glad,Katerina Vrotsou
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate forecasting of energy consumption is important for the efficient operation of power systems, with direct implications for operational costs, energy management, and system maintenance. Due to the availability of extensive high-resolution consumption data from smart meters, data-driven methods have been used for short-term and long-term forecasting. However, their comparative performance on real-world smart meter data is still not well studied. In this paper, we present an empirical benchmark of nine modern deep learning models for time-series forecasting, including linear, MLP-based, convolutional, and Transformer architectures. We evaluate these models on two publicly available smart meter datasets. Our analysis focuses on three factors that strongly affect forecasting performance: the length of historical input, the prediction horizon, and the choice of model architecture. We show that extending the historical context improves accuracy, but only up to a saturation point, after which additional input provides limited benefit. In contrast, accuracy decreases as the prediction horizon increases. We also investigate the trade-off between prediction accuracy and computational complexity, and assess the statistical significance and practical magnitude of performance differences across models. Our results show that deep learning models consistently outperform classical baselines, while lightweight architectures achieve relatively similar performance at significantly lower computational cost. Additionally, architectural differences only become meaningful at longer forecasting horizons and on more heterogeneous datasets. Finally, a subgroup analysis across geodemographic and household categories shows that model choice has limited impact for most population segments.
[LG-34] Reinforced Planning with Latent World Models
链接: https://arxiv.org/abs/2608.18669
作者: Armin Sommer,Jannik Schilling
类目: Machine Learning (cs.LG)
*备注: Preprint
Abstract:Humans solve complex problems by constructing plans and mentally simulating their outcomes with an internal model of the world. Machine learning has produced world models that similarly predict the outcomes of action sequences, but the improvement of candidate plans still isn’t fully learned. Current planners are either hand-designed, distilled from a hand-designed optimizer, or learned only to inform an amortized policy rather than to revise the plan itself. We introduce the Reinforced Planning, a method based on the idea that search can be learned by reinforcing good search rules into a neural planner. Our implementation RP1 learns both how to evaluate imagined outcomes through a critic, as well as how to improve multi-step plans through an optimizer trained fully offline from imagined world-model roll-outs. To our knowledge, RP1 is the first method to fully learn how to improve multi-step plans. Furthermore, it can be trained independently of and attached to any pretrained latent world model. Across visual navigation, arm reaching, and robotic manipulation on two world-model backbones, RP1 substantially outperforms hand-designed search algorithms, reaching near-perfect success in several settings while using 1,000 \times less world-model rollouts and being up to 67 \times faster than the strongest alternative under concurrent planner inference.
[LG-35] Computational Measurement of Team-Process Phase Dynamics in Collaborative Virtual Reality
链接: https://arxiv.org/abs/2608.18660
作者: Qing Huang,Jianing Zhang,Pooja Pol
类目: Machine Learning (cs.LG)
*备注:
Abstract:Collaborative virtual reality (VR) environments make team communication observable as it unfolds, but conventional transcript analyses often summarize entire trials or divide them into fixed temporal windows. Such approaches can obscure changes in team communication and coordination over time. This article presents a computational framework for detecting and interpreting dynamic team-process phases from timestamped dialogue in a collaborative VR game. The framework uses late chunking to generate context-aware transcript representations, aggregates them into temporal chunks, and applies penalized Gaussian-kernel change-point detection to identify semantic transitions in team communication. After boundary detection, term frequency–inverse document frequency (TF-IDF), non-negative matrix factorization (NMF), and representative transcript segments provide structured evidence for phase interpretation. A locally deployed large language model (LLM) uses in-context learning to generate initial interpretations that are subsequently reviewed by humans. Independently recorded interaction logs are then aligned with the detected phases to examine corresponding task-action patterns. The evaluation compares representations, pooling strategies, segmentation methods, parameter settings, reviewed phase interpretations, and phase-aligned interaction profiles. The results show that the framework identifies coherent and interpretable phase structures while preserving traceability to the underlying transcript evidence. The correspondence between transcript-derived phases and interaction behavior further supports their relevance for analyzing collaborative activity. The framework therefore offers a transparent and transferable approach for studying temporal changes in teamwork from timestamped transcripts across collaborative task settings.
[LG-36] FlashAttention for Scalable Vector Architectures
链接: https://arxiv.org/abs/2608.18656
作者: Sonia Rani Gupta,Nikela Papadopoulou,Miquel Pericàs
类目: Machine Learning (cs.LG); Performance (cs.PF)
*备注:
Abstract:Inference with transformer models on CPUs is increasingly important, especially for Small Language Models (SLMs), where vector architectures are emerging as a promising execution substrate. The attention module is a major bottleneck due to high memory bandwidth requirements; FlashAttention mitigates this by fusing operations to improve data locality and reduce intermediate memory traffic. In this paper, we present FlashAttention-V, a blocked FlashAttention for scalable vector architectures that adapts efficiently from short to very long vectors by exploiting parallelism across attention heads, inter-head packing to enable efficient utilization of vector lengths beyond the head dimension, and improving vector register utilization and memory access locality. We integrate FlashAttention-V into ggml within this http URL and evaluate it on TinyLlama, Llama 3.2, Qwen2.5, and Pythia-410M using gem5 and a Banana Pi BPI-F3. On the Banana Pi BPI-F3, we confirm that loop reordering and loop unrolling across attention heads are effective optimization principles, scaling performance gains with larger models and most pronounced with short contexts and during decoding. Simulation-based analysis shows that FlashAttention-V achieves 22x-42x speedup over scalar FlashAttention at 512-bit VL in prefill, with an additional 2x-2.5x gain scaling to 64 lanes and 4096-bit VL. During decode, FlashAttention-V achieves 8x-11x speedup using 512-bit vector lengths over scalar FlashAttention, with performance showing diminishing sensitivity to vector width and lane count due to single-token, memory-bound execution. We further identify structural bottlenecks in Q8_0 quantized linear layers that limit arithmetic amortization under long-vector execution, consistent across RVV and Arm SVE, indicating that current quantization formats pose a fundamental challenge to long-vector scalability.
[LG-37] Progressive Experience Fusion for Multi-Task World Model Control in Endovascular Navigation
链接: https://arxiv.org/abs/2608.18647
作者: Harry Robertshaw,Maxence Boels,Nikola Fischer,Sebastien Ourselin,Christos Bergeles,Alejandro Granados,Thomas C Booth
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Autonomous endovascular navigation could support the delivery of mechanical thrombectomy to underserved areas, but controllers must navigate long, multi-stage paths across varying vascular anatomies. This study investigates Progressive Experience Fusion (PEF) to train a multi-task TD-MPC2 controller. We additionally evaluate a heuristic that changes the Model Predictive Path Integral planning horizon using residual action-sequence dispersion, and fine-tuning in a patient-specific simulation. Across five subtasks in ten known training anatomies with held-out targets, PEF achieved a mean success rate of 74%, compared with 37% for Soft Actor-Critic (p 0.001) and 65% for base TD-MPC2 (p = 0.053). A PEF controller with adaptive-horizon planning trained on 30 vasculatures achieved a mean success rate of 90% in ten held-out vasculatures. The PEF agent successfully transferred to an unseen in vitro stroke patient vasculature under fluoroscopy, achieving a mean path ratio improvement from 63% to 80% with fine-tuning (p 0.001), following 40x103 fine-tuning steps (corresponding to approximately 107 min of clinical inter-hospital transfer time). This work represents a proof of concept for multi-vasculature training and patient-specific adaptation, while further validation is required before clinical deployment.
[LG-38] ProxyGuard: Direct Reliability Inference for Randomized Data Release Mechanisms with Shared Targets
链接: https://arxiv.org/abs/2608.18643
作者: Dipesh Tharu Mahato,Pramod Dhungana
类目: Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML)
*备注:
Abstract:Researchers often choose a proxy dataset from many releases, transformations, or seeds. Search can make an invalid release appear adequate, while one adequate release does not establish that its generator is reliable. ProxyGuard controls both errors using prespecified bounded risks and a sealed target set. Named-release mode corrects for multiplicity and certifies specific releases. Direct shared-target mode evaluates independent mechanism draws on a common target, lower-bounds their favorable-score rate, and subtracts a bound on favorable scores contributed by invalid releases. Conditional on the target, release scores are independent, yielding a finite-sample mechanism-reliability guarantee without independent target batches or assumptions on release-level p -value dependence. We show that the mean-only penalty is sharp and derive a smooth-score certificate with additive target concentration. In a registered three-requirement study, direct mode raises power from 5.6% to 64.2% at reliability 0.95, while named mode remains stronger under high-signal evidence. Prospective audits span full-pipeline Rice–TVAE, which retrains on every draw, and a non-tabular text mechanism.
[LG-39] Coordination on a Budget: Federated Active Learning with Few Labels
链接: https://arxiv.org/abs/2608.18634
作者: Liam Mohr,Daphna Weinshall
类目: Machine Learning (cs.LG)
*备注: 16 pages
Abstract:Federated Active Learning (FAL) addresses the dual challenges of data privacy and label scarcity, where the absence of a global data view introduces additional hurdles for coordinated query selection. We study cross-silo FAL in the low-budget regime, where annotation decisions are most critical. We characterize, both theoretically and empirically, a heterogeneity reversal: in low-budget settings, homogeneous (IID) data requires stronger coordination to avoid redundant queries, whereas heterogeneous data naturally promotes diversity; this trend reverses at higher budgets. Thus, in contrast to the standard federated learning (FL) narrative where heterogeneity is a primary challenge, we show that IID settings are more challenging for query selection in FAL. Motivated by these findings, we propose a new FAL framework that utilizes federated representation learning to align client data in a shared embedding space. This enables the server to perform globally coordinated active selection over optionally obfuscated client embeddings, while annotation remains local to each client. Although our framework operates in the more challenging low-budget regime, it achieves performance that surpasses existing FAL methods even when they are given substantially larger annotation budgets, demonstrating the value of centralized coordination under privacy constraints. Comments: 16 pages Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.18634 [cs.LG] (or arXiv:2608.18634v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.18634 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-40] Scalable Geospatial Machine Learning for Power-Line Asset Risk: Integrating Remote Sensing for Lightning and Vegetation Risk Modelling
链接: https://arxiv.org/abs/2608.18611
作者: Artur Sokolovsky,Bhavik Merai,Moe Jafari,Muen Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Electric power networks are increasingly exposed to weather-sensitive failure mechanisms that require asset-level, spatially explicit risk modelling for effective intervention planning. This study contributes a modular, robust, and explainable probability-of-failure (PoF) modelling framework for utility asset management. The central contribution is an asset-level architecture that can be scaled to new environmental data sources and additional PoF types without reworking the underlying pipeline. This is particularly relevant for industry settings, where risk models must remain operationally maintainable while adapting to changing data availability, asset-management priorities, and climate-driven hazard conditions. We demonstrate the framework for vegetation-related and lightning-related failure modes using a harmonised geospatial machine-learning pipeline. The implementation integrates multi-source predictors, including topography (SRTM), vegetation condition (MODIS Normalised Difference Vegetation Index - NDVI), lightning climatology (LIS VHRMC), OpenStreetMap-derived proximity features, and utility operational records. The resulting architecture is computationally efficient, operationally extensible, and suitable for utility-scale deployment. It provides actionable asset-level risk stratification for inspection prioritisation, vegetation management, asset hardening, and resilience planning, supporting earlier intervention and more climate-resilient network operations.
[LG-41] Off-Manifold Collapse in Guided Protein Language Models
链接: https://arxiv.org/abs/2608.18597
作者: Shuibai Zhang,Xinchi Liu,Fred Zhangzhi Peng,Zhihan Yang,Shutong Wu,Yingzi Ma,Jiawei Zhang
类目: Machine Learning (cs.LG)
*备注: 12 pages
Abstract:Protein language models are widely used priors for protein sequence design, and a growing body of work controls them at inference time as an alternative to fine-tuning. Such guidance faces a dilemma: mild enough to preserve natural activation statistics, it barely moves the property; strong enough to move it, the generations become progressively harder to fold. We show the failure has a specific and cheaply detectable signature, an off-manifold collapse of the model’s own representations. Guided activations fall toward a region statistically indistinguishable from random amino-acid input, and the sequences degenerate to low complexity, yet the property oracle being optimized can still score these generations as a success. The optimized oracle can therefore fail to witness the collapse and, for solubility, can actively reward it, whereas structure and composition expose the failure. Because the failure is already visible in a finished candidate, we detect it at the output rather than modify the generator. We introduce a cheap density prior over natural protein activations and keep only the candidates that remain typical under it, a training-free post-hoc step we call Mahalanobis filtering. At matched guidance settings it improves both the property score and the structural plausibility of the sequences it keeps at negligible cost, without touching the generator, and transfers across different guidance methods. We release the activation statistic at this https URL
[LG-42] Infrared Universality of Collective Dynamics across Transformer and State-Space Architectures
链接: https://arxiv.org/abs/2608.18592
作者: Byung Gyu Chae
类目: Machine Learning (cs.LG)
*备注: 31 pages, 12 figures
Abstract:Whether distinct neural architectures develop common collective dynamics remains an open question. Recent analysis of Transformer language models revealed a nearly flat, weakly infrared-enhanced time-scale density of states (TDOS) associated with near-marginal long-memory dynamics. Here we test whether a closely related organization emerges in Mamba, whose selective state-space dynamics provides a fundamentally different microscopic mechanism. Mamba allows relaxation dynamics to be resolved at three levels: the intrinsic spectrum of the learned state-space generator, its input-conditioned selective rescaling, and the collective TDOS of the complete block measured from its Jacobian. These spectra are not identical: selective dynamics and the remaining block transformations substantially reorganize the microscopic relaxation hierarchy. Nevertheless, the full block develops a reproducible slow-mode continuum whose infrared sector becomes progressively better resolved with increasing sequence length. Cumulative analysis yields \rho(\lambda)\sim\lambda^\beta , with the long-sequence Mamba exponent stabilizing near \beta_\rm M\simeq-0.17 . The corresponding memory dynamics follows K(t)\sim t^-(1+\beta) , close to the marginal 1/t regime. Despite fundamentally different microscopic dynamics, Transformer full-block spectra exhibit closely related infrared organization, with representative exponents of order \beta_\rm Tr\sim-0.1 . These results separate explicit state-space memory from collective infrared organization and show that distinct sequence architectures can develop closely related near-marginal slow-mode dynamics. They extend infrared collective organization beyond Transformers and provide an independent test of the dynamical structure described by Cognitive Field Theory.
[LG-43] Beyond receptive fields: sequence-pooled normalization can supply most of a sequence labelers context
链接: https://arxiv.org/abs/2608.18576
作者: Qing Tian
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注:
Abstract:A convolutional sequence labeler’s receptive field is routinely treated as the extent of the model’s usable context: it sets dilation schedules, bounds streaming horizons, and underwrites locality claims. However, we show that this can be false: when a normalization layer computes statistics from the current input along the sequence at inference, those statistics open a sequence-spanning path that bypasses the convolutional receptive field to provide global context. We derive this from the layer’s Jacobian (the criterion needs no experiment), and what the path carries has a closed form. On a synthetic labeling process with computable optima, the global summary that a sequence-spanning normalization encodes already supplies almost all of what a larger receptive field would buy where labels come in long runs: a network reaching 9 positions comes within 0.009 of the whole-sequence optimum, against a near-chance bound for its reach. Closing the path, by taking the same statistics per position, multiplies what enlarging the receptive field is worth by up to an order of magnitude on simulated genomes at every difficulty level tested and on real 1000 Genomes haplotypes. The same path also confounds attribution: ablating a trained network’s receptive-field-enlarging blocks severs part of the path, overstating their contribution 8.3-16.1-fold relative to retraining from scratch. The substitution of normalization for receptive field fades as labels switch more often. Where labels run long, neither the receptive-field justification nor the ablation is wrong about its numbers, but both credit the wrong component.
[LG-44] Continual Reasoning Gym: Diagnosing and Harnessing Shared Reasoning in Continual RLVR
链接: https://arxiv.org/abs/2608.18574
作者: Lirui Luo,Guoxi Zhang,Hongming Xu,Rongqing Li,Cong Fang,Lifeng Fan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Reinforcement learning with verifiable rewards (RLVR) commonly post-trains reasoning models on multiple tasks, while rerunning multitask RLVR (MTRL) as new tasks are added makes capability expansion costly. We therefore study continual RLVR, which updates the existing model as each task arrives. The central question is whether a model updated this way can perform as well as a jointly trained model. To answer this question, we introduce Continual Reasoning Gym, a continual-RLVR environment that organizes text and visual reasoning tasks into five task sequences. In this setting, we identify two key observations: Sequential RLVR exhibits modest forgetting, yet its final performance remains below that of MTRL. To understand the latter, we decompose final performance and show that forgetting accounts for only part of the gap. To explain the former, we identify shared reasoning: transferable reasoning structure allows training on one task to support others on average. We therefore introduce Continual Prompt Replay (CPR), which harnesses shared reasoning to improve learning on the arriving and future tasks by replaying previous-task prompts and regenerating their responses with the current policy. On average, only CPR reaches MTRL-level performance.
[LG-45] NanoSleep: A Parameter-Efficient Hybrid Temporal Convolutional Network for Single-Channel Sleep Stage Classification
链接: https://arxiv.org/abs/2608.18571
作者: S M Asif Hossain,Shruti Kshirsagar
类目: Machine Learning (cs.LG)
*备注:
Abstract:Sleep stage classification from single-channel electroencephalography (EEG) is essential for wearable and home-based sleep monitoring. However, many deep learning models achieve high accuracy at the cost of large model sizes, which limits their deployment on resource-constrained devices. In this work, we present NanoSleep, a compact hybrid temporal convolutional network for automatic sleep stage classification. NanoSleep combines a learnable Sinc-convolutional front end, a dual-branch feature extractor that fuses multi-scale temporal and spectral representations, a gated dilated temporal convolutional backbone with channel recalibration, and a conditional random field for sequence-level decoding. We further employ a weighted calibrated focal loss to address class imbalance. We evaluate NanoSleep on the Sleep-EDF and Sleep-EDF-Expanded datasets using subject-wise cross-validation. The proposed model consistently outperforms six representative baseline methods, and an ablation study confirms the contribution of each major component. These results demonstrate that NanoSleep provides an effective balance between accuracy and efficiency, making it well suited for wearable devices, home-based sleep monitoring, and resource-constrained clinical applications.
[LG-46] MARCUS: Missing-Aware Region Representation with Contextual Urban Signals for Rent Prediction ICDM2026
链接: https://arxiv.org/abs/2608.18546
作者: Chenya Huang,Bin Liang,Zhidong Li,Yuxi Lu,Kunqi Li,Justin Wang,Fang Chen
类目: Machine Learning (cs.LG)
*备注: 10 pages, 7 figures, 4 tables. Accepted at the 2026 IEEE International Conference on Data Mining (ICDM 2026)
Abstract:Multimodal urban data has expanded the applications of urban region representation learning, such as functional zone identification and real estate appraisal, but also introduces challenges caused by data incompleteness. Existing studies usually handle missing data through imputation, treating missingness as noise while ignoring its potential semantic value. To address this issue, we propose MARCUS, a missing-aware region representation model that treats missingness as a contextual urban signal. MARCUS models missingness in three stages: Intra Learning jointly encodes observed features and missing patterns, Inter Learning estimates modality reliability to guide cross-modal interaction, and Fusion uses missing-aware and time-aware gating to generate the final region embedding. We apply MARCUS to rent prediction, a task with long-term trends and seasonal fluctuations, using real-world datasets from Sydney and New York. Experimental results show that MARCUS achieves state-of-the-art performance, reducing MAE by 51.35% on Sydney and 12.62% on New York compared with the best baselines. Additional experiments, including an imputation-based ablation study and randomized additional-missingness analysis, further demonstrate the effectiveness of the proposed method.
[LG-47] LLM -Powered Predictive Decision-Making for Sustainable Data Center Operations
链接: https://arxiv.org/abs/2608.18503
作者: Hanzhao Wang,Jingxuan Wu,Yumeng Li,Yu Pan,Guanting Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:The growing demand for AI-driven workloads, particularly from Large Language Models (LLMs), has raised concerns about the significant energy and resource consumption in data centers. This work introduces a novel LLM-based predictive scheduling system designed to enhance operational efficiency while reducing the environmental impact of data centers. Our system utilizes an LLM to predict key metrics such as execution time and energy consumption from source code, and it has the potential to extend to other sustainability-focused metrics like water usage for cooling and carbon emissions, provided the data center can track such data. The predictive model is followed by a real-time scheduling algorithm that allocates GPU resources, aiming to improve sustainability by optimizing both energy consumption and queuing delays. With fast inference times, the ability to generalize across diverse task types, and minimal data requirements for training, our approach offers a practical solution for data center scheduling. This framework demonstrates strong potential for advancing sustainability objectives in AI-driven infrastructure. Through our collaboration with a data center, we achieved a 32% reduction in energy consumption and a 30% decrease in waiting time.
[LG-48] anmu-TC: Physics-constraints Generative Artificial Intelligence for Global Tropical Cyclone Forecasting
链接: https://arxiv.org/abs/2608.18500
作者: Shiqi Zhang,Pan Mu,Cheng Huang,Hanting Yan,Yuchao Zhu,Jinglin Zhang,Shengyong Chen,Shoujuan Shu,Cong Bai
类目: Machine Learning (cs.LG)
*备注:
Abstract:Tropical cyclones (TCs) pose severe risks from strong winds and heavy rainfall. However, forecasting their track and intensity remains challenging due to chaotic atmosphere and the rapid amplification of initial condition errors, leading to growing forecast uncertainty. While numerical weather prediction (NWP) and deep learning models have made progress, they remain computationally demanding and often fail under complex meteorological scenarios. Here, we present Tianmu-TC, a physics-constraints generative framework for global TC forecasting. Trained on Western North Pacific data, Tianmu-TC leverages physics-constraints to generate controllable outputs with reduced uncertainty thus improving forecast reliability. Experiments show Tianmu-TC outperforms deterministic and ensemble meteorological artificial intelligence models and authoritative NWP systems such as ECMWF in global ocean basins, with significantly lower computational cost. We further show Tianmu-TC performs well in challenging scenarios such as data sparsity, anomaly tracks, rapid intensification and weakening. These findings suggest physics-constraints generative AI offers a promising approach for reliable, efficient global TC forecasting.
[LG-49] Atrial Fibrillation Detection with Arbitrary Leads via a Codebook-Based Reconstruction-Classification Framework
链接: https://arxiv.org/abs/2608.18451
作者: Hongtao Li,Jia Wei,Guoyao Li,Yuchen Lei,Guangnian Ma,Jia Xiao,Yuanjun Lai,Shuzhen Lv,Xueqiang Ouyang
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注:
Abstract:\textbfBackground and Objective: Reliable atrial fibrillation (AF) detection from electrocardiogram (ECG) signals remains challenging in real-world clinical settings due to variable lead configurations, cross-dataset domain shifts, and pervasive physiological and technical artifacts. So we develop a robust and generalizable deep learning model for accurate AF detection.\ \textbfMethods: We propose the Dual-Codebook Graph Collaborative Network (DCGCNet), a novel end-to-end vector-quantized variational autoencoder that jointly performs AF classification and ECG reconstruction. DCGCNet introduces two key components: (1) a Local-Global Contrastive Module for learning noise-invariant representations, and (2) an Adaptive Codebook Vector Quantizer that dynamically refines codebook prototypes to better align with input data distributions, thereby preventing codebook collapse and enhancing generalization.\ \textbfResults: DCGCNet achieves state-of-the-art performance in standard intra-dataset 12-lead evaluation and demonstrates exceptional cross-dataset generalization across seven diverse settings, consistently attaining AUC 0.98 in all cases. Furthermore, it maintains high diagnostic accuracy under realistic noisy conditions, including baseline wander, powerline interference, and EMG artifacts.\ \textbfConclusions: DCGCNet establishes a new benchmark for robust, generalizable, and noise-resilient AF detection, showing strong potential for deployment in real-world clinical environments.
[LG-50] Adaptive Multi-Agent Feature Selection for Personalized Fall Risk Prevention ALT
链接: https://arxiv.org/abs/2608.18450
作者: Chang Liu,Ladda Thiamwong,Yanjie Fu,Rui Xie
类目: Machine Learning (cs.LG)
*备注: 38 pages, 10 figures, 12 tables. Accepted at Machine Learning for Healthcare (MLHC 2026)
Abstract:Falls among older adults represent a major public health challenge driven by complex, time-varying interactions across multiple risk domains. Effective fall risk factor identification requires learning from heterogeneous longitudinal data while accounting for sparse and delayed fall-related outcome events. However, existing approaches are largely static and fail to adaptively model evolving, individualized risk factors across modalities and time. We propose PAFIR, a Personalized and Adaptive Feature selection framework for fall risk Identification and pRevention, which formulates adaptive feature selection as a reinforcement learning problem over longitudinal multimodal health data. PAFIR jointly models structural dependencies among correlated assessment variables and temporal dynamics in wearable-derived physical activity data, and learns adaptive selection policies across repeated study visits using reward signals derived from sparse fall incidence outcomes. We apply PAFIR to data from the Physio fEedback Exercise pRogram (PEER) cluster-randomized trial. Experimental results demonstrate that PAFIR more effectively captures longitudinal and structural patterns of feature relevance than state-of-the-art baselines, and enables dynamic, subject-specific feature selection. By adapting selected features over time, PAFIR supports more timely and personalized fall prevention strategies.
[LG-51] he Embodiment Gap in Robot Foundation Models
链接: https://arxiv.org/abs/2608.18433
作者: Yukiyasu Domae,Keisuke Shirai,Hanbit Oh,Ryoichi Nakajo,Tomohiro Motoda,Koshi Makihara,Masaki Murooka,Takuma Yagi,Yoshiaki Bando,Ryo Hanai
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 32 pages, 4 figures. Published in Transactions on Machine Learning Research (TMLR), August 2026
Abstract:Robot foundation models (RFMs), including vision-language-action (VLA) policies, are often discussed through a scaling view: more data, larger models, and broader benchmarks should improve generalization. In robotics, however, a model can generalize while work still remains before it can run on a robot with a particular body. The work required differs across methods and target robots, and those differences affect practical deployment. We call the gap between reusable models, representations, or data and their use in execution on the target robot the embodiment gap. This survey examines what can be reused across robot embodiments and what must still be implemented on a new robot. We place existing methods on a two-axis map that shows the type of shared structure and the stage at which adaptation is needed for execution on the target robot. We then examine recent work through three overlapping research directions: sharing semantics and perception, sharing robot data and interfaces, and learning correspondence across embodiments. We also propose a reporting framework for adaptation work that success rate alone does not reveal. The framework identifies the work that should be checked when comparing cross-embodiment learning and highlights work that remains on a new robot and questions for future study.
[LG-52] Process Optimization Under Uncertainty for Improving the Bond Quality of Polymer Filaments in Fused Filament Fabrication
链接: https://arxiv.org/abs/2608.18431
作者: Berkcan Kapusuzoglu,Matthew Sato,Sankaran Mahadevan,Paul Witherell
类目: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Numerical Analysis (math.NA); Probability (math.PR)
*备注: 12 pages, ASME Journal of Manufacturing Science and Engineering
Abstract:This paper develops a computational framework to optimize the process parameters such that the bond quality between extruded polymer filaments is maximized in fused filament fabrication (FFF). A transient heat transfer analysis providing an estimate of the temperature profile of the filaments is coupled with a sintering neck growth model to assess the bond quality that occurs at the interfaces between adjacent filaments. Predicting the variability in the FFF process is essential for achieving proactive quality control of the manufactured part; however, the models used to predict the variability are affected by assumptions and approximations. This paper systematically quantifies the uncertainty in the bond quality model prediction due to various sources of uncertainty, both aleatory and epistemic, and includes the uncertainty and the model discrepancy in the process parameter optimization. Variance-based sensitivity analysis based on Sobol indices is used to quantify the relative contributions of the different uncertainty sources to the uncertainty in the bond quality. A Gaussian process (GP) surrogate model is constructed to compute and include the model discrepancy within the optimization. Physical experiments are conducted for calibration and validation of the physics model and also for validation of the optimum solution. The results show that the proposed formulation for process parameter optimization under uncertainty results in high bond quality between adjoining filaments of the FFF product.
[LG-53] Multi-Objective Optimization Under Uncertainty of Part Quality in Fused Filament Fabrication
链接: https://arxiv.org/abs/2608.18429
作者: Berkcan Kapusuzoglu,Paromita Nath,Matthew Sato,Sankaran Mahadevan,Paul Witherell
类目: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Numerical Analysis (math.NA); Optimization and Control (math.OC)
*备注: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Abstract:This work presents a data-driven methodology for multi-objective optimization under uncertainty of process parameters in the fused filament fabrication (FFF) process. The proposed approach optimizes the process parameters with the objectives of minimizing the geometric inaccuracy and maximizing the filament bond quality of the manufactured part. First, experiments are conducted to collect data pertaining to the part quality. Then, Bayesian neural network (BNN) models are constructed to predict the geometric inaccuracy and bond quality as functions of the process parameters. The BNN model captures the model uncertainty caused by the lack of knowledge about model parameters (neuron weights) and the input variability due to the intrinsic randomness in the input parameters. Using the stochastic predictions from these models, different robustness-based design optimization formulations are investigated, wherein process parameters such as nozzle temperature, nozzle speed, and layer thickness are optimized under uncertainty for different multi-objective scenarios. Epistemic uncertainty in the prediction model and the aleatory uncertainty in the input is considered in the optimization. Finally, Pareto surfaces are constructed to estimate the tradeoffs between the objectives. Both the BNN models and the effectiveness of the proposed optimization methodology are validated using the actual manufacturing of the parts.
[LG-54] he Road Taken: The Role of Optimizers at the Edge of Stability
链接: https://arxiv.org/abs/2608.18415
作者: Jaerin Lee,Kyoung Mu Lee
类目: Machine Learning (cs.LG)
*备注: 34 pages, 13 figures
Abstract:The edge of stability refers to a phenomenon in deep learning with gradient-based optimizers where the Hessian eigenvalues of the loss remain stable above a threshold that the classical descent lemma predicts to be unstable. Previous works formulate the edge of stability with respect to the maximum Hessian eigenvalue and the learning rate. However, we observe that many first-order methods, including gradient descent, significantly violate the stability bound predicted by these theories by a factor as large as \times 21.1 . Moreover, this deviation turns out to be systematic and highly dependent on the underlying optimizer, which is not captured by previous formulations. This calls for a new formulation of the stability threshold, which we derive from the directional Hessian and the gradient-alignment score with respect to the actual update taken by the optimizer, rather than the maximum curvature mode. Our new formulation of the realized edge of stability not only removes optimizer-dependent offsets and provides more consistent predictions of the stability threshold, but also introduces new diagnostic tools that reveal the unique role of the optimizer in actively balancing between the temporal and spatial budgets in first-order optimization.
[LG-55] Role-Conditioned Sub-Token Routing for Efficient Vision-Language-Action Policies
链接: https://arxiv.org/abs/2608.18410
作者: Wei Jiang,Wei Wang
类目: Machine Learning (cs.LG)
*备注: 12 pages, 5 tables
Abstract:Vision-Language-Action (VLA) models process long multimodal token sequences, making inference expensive in both memory and computation. Existing efficiency methods mainly reduce visual tokens, but aggressive token pruning becomes fragile because removing a token discards its entire representation. Sub-token compression provides a complementary alternative by retaining more tokens while reducing their value width. However, directly applying sub-token compression to VLA policies is less effective because information important for perception, language understanding, and control is distributed differently across the multimodal representation. We introduce Role-Conditioned Sub-Token Routing (RoleSub), which learns how to compress the value representations of retained tokens. After visual token reduction, RoleSub partitions each retained value representation into groups in an orthogonal space and uses a lightweight router to determine which groups should be preserved. The routing decision is conditioned on the token representation, a learned latent role representation, and language context. The same mechanism can also be applied to language values, allowing visual and language representations to be compressed without removing additional tokens. We evaluate RoleSub on OpenVLA-OFT-7B across the four LIBERO suites. At matched visual-KV budgets, RoleSub outperforms a trained token-only control in 33 of 36 settings, with the largest gains under aggressive compression. Combining visual and language compression reduces total KV to 9.2–11.3% of the original while retaining strong control performance on most tasks. These results show that reducing the representation within retained tokens provides an effective complement to token pruning for aggressive VLA compression. Comments: 12 pages, 5 tables Subjects: Machine Learning (cs.LG) MSC classes: 68W99, 68W40 Cite as: arXiv:2608.18410 [cs.LG] (or arXiv:2608.18410v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.18410 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-56] When Does Dynamic Ensembling Pay Off? Diagnosing Regionwise Gains in Regression under Distribution Shift
链接: https://arxiv.org/abs/2608.18330
作者: Tianxin Zhou,Ruixi Lin
类目: Machine Learning (cs.LG)
*备注: 25 pages
Abstract:Whether input-dependent (“dynamic”) combination of a regression model pool beats the best static blend depends on the shift and is rarely known before deployment. Can a small labeled target-domain probe tell us when reallocating trust across regions of the input space will pay off? We answer this with \widehatD_\mathrmCF5 , which estimates from the probe the cross-fitted gain of the regionwise convex combination over the best static convex blend: the realizable value of deciding, region by region, whom to trust. Across a frozen suite of 12 dataset-shift pairs (spatial, temporal, domain, feature-cluster), \widehatD_\mathrmCF5 predicts realized regionwise test gains with dataset-level Spearman +0.98 (95% CI [+0.83, +1.00] ; p=5\times10^-5 ), including two cases overturning preregistered expectations. The relationship holds in a 16-pair sensitivity analysis (Spearman +0.83 ), whereas alternative probe diagnostics reach at most +0.66 . This contrast isolates regional trust reallocation: correlation is +0.98 for regionwise-convex gain, but +0.01 for smooth covariate-dependent stacking after affine correction. A controlled generator shows dynamic gains arise from the interaction of shift heterogeneity and local competence, increase with shift severity, and become realizable between 128 and 256 probe labels in the tested grid. The Probe-Validated Ensemble Selector chooses among a static affine stacker and dynamic realizers, deploying a candidate only when a held-out lower confidence bound clears the static-convex floor. In a preregistered prospective batch, it matched or improved the floor in all 12 runs; two deployments reduced test risk by 11% and 16%, while the gate rejected a candidate whose un-gated deployment incurred 30\times the static loss. We release OpenRegShift, a reproducible evaluation harness for regression ensembles under distribution shift.
[LG-57] SingularClip: Preventing Spectral Collapse to Maintain Plasticity in Continual and Reinforcement Learning
链接: https://arxiv.org/abs/2608.18319
作者: Tyler Kastner,Nimrod De La Vega,Amir-massoud Farahmand
类目: Machine Learning (cs.LG)
*备注:
Abstract:Neural networks trained on nonstationary tasks frequently lose the ability to fit new targets, a phenomenon referred to as loss of plasticity. We identify a novel source of plasticity loss due to the growing anisotropy of weight matrices’ singular values during training, and analyze this phenomenon both empirically and theoretically. To mitigate this issue, we introduce SingularClip, a procedure that periodically clips the singular values of all weight matrices. We show that SingularClip performs strongly against baselines across a range of tasks in both continual supervised learning and deep reinforcement learning.
[LG-58] Model Card for OpenAI Privacy Filter
链接: https://arxiv.org/abs/2608.18274
作者: Charles de Bourcy,Sahra Ghalebikesabi,Avi Schwarzschild,Alex Gorbachev,Mihai Maruseac,Annie Chu,Vol Kyrylov,Tong Mu,Ally Bennett,Andy Nguyen,Casey Meehan,Jessica Gan Lee,Shane Bauer,Harold Nguyen,Rodolpho Eckhardt,Yuqi Liu,Charlie Oxborough,Marco Rougeth,Omar Chedid,Caio Costa,Yash Parikh,Yao Li,Congzheng Song,Om Thakkar,Vinnie Monaco
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 20 pages, 3 figures, 11 tables
Abstract:OpenAI Privacy Filter is a compact, bidirectional token-classification model for detecting and redacting personally identifiable information (PII) and secrets in unstructured text. The model is derived from an autoregressively pretrained checkpoint and converted into a bidirectional, banded-attention classifier that labels an input sequence in a single forward pass. A constrained Viterbi decoder produces coherent spans across eight privacy categories and exposes configurable operating points for precision-recall tradeoffs. Privacy Filter has 1.5 billion total parameters, 50 million active parameters per token, and a 128,000-token context window. It is designed for efficient local deployment and domain-specific fine-tuning. Privacy Filter is intended as a configurable data-minimization component within layered privacy workflows, not as an anonymization or compliance guarantee.
[LG-59] Rethinking Privileged Information in On-Policy Self-Distillation
链接: https://arxiv.org/abs/2608.18271
作者: Samyak Shrestha,Alexander Tessier
类目: Machine Learning (cs.LG)
*备注: 21 pages, 7 figures
Abstract:On-policy self-distillation (OPSD) trains a student on its own responses using token-level supervision from the same model conditioned on privileged reference information. We investigate whether performance gains from OPSD show that the student learned the information in the reference or instead reflect recovery of reasoning behavior already present in the base model. We perform OPSD experiments on science and mathematics datasets using Qwen3 models ranging from 1.7B to 8B. Our analysis framework separates the supervision induced by the reference from the supervision provided by the teacher without the reference and measures how each aligns with changes in the student’s predictions. The correct reference does not provide a consistent performance benefit across teacher generation modes, model sizes, and training datasets. Students can improve without the correct reference, and a solution from another problem can outperform the correct solution on several mathematical reasoning benchmarks. The student’s predictions align more strongly with the base model’s thinking behavior than with the supervision induced by the reference, but controls constructed from other problems reproduce much of both alignments. Moreover, stronger alignment attributable to the correct reference does not reliably coincide with a greater performance benefit from the reference. Performance gains and distributional alignment alone therefore cannot determine how privileged reference information contributes to student learning in OPSD.
[LG-60] SIGMA: Symmetry-aware Intelligent Geometric Multi-objective Adaptive Control for Robust Dependable Traffic Management
链接: https://arxiv.org/abs/2608.18263
作者: Pratham Payra,Jagadish B,Tanmay Sen,Tanujit Chakraborty
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Traffic signal control is a complex sequential decision-making problem requiring real-time adaptation and trade-offs among throughput, delay fairness, signal stability, and emergency vehicle priority. Existing RL methods often fix objectives, ignore dynamic priority changes, and fail to generalize across geometrically similar this http URL propose SIGMA (Symmetry-aware, Intelligent, Geometric, Multi-objective Adaptive traffic control), an RL framework enhanced with a large language model (LLM) for adaptive objective tuning and orientation-invariant learning. SIGMA converts natural-language emergency commands into priority vectors for a multi-objective actor-critic controller, avoiding manual reward engineering. Rotational augmentation improves transferability across four-way intersections, while offline-to-online learning ensures stable initialization and gradual adaptation to changing this http URL define reliability properties covering emergency service levels, graceful degradation under LLM failures, and demand sensitivity, validated via bootstrap statistics. Evaluated in SUMO on four Kolkata-based urban intersections against fixed-time, actuated, and DQN controllers, SIGMA reduces average/emergency waiting times and queue lengths, and boosts throughput. Ablation studies confirm robustness to component failures and geometric rotations. Overall, SIGMA offers a reliable, language-guided, multi-objective traffic control system with statistical reliability assurance.
[LG-61] ClosureBench: A Constructive Benchmark for Compositional Graph Reasoning
链接: https://arxiv.org/abs/2608.18242
作者: Stefano Goria(AIM Research Lab)
类目: Machine Learning (cs.LG)
*备注: 30 pages, 5 figures, 11 tables. Code and data: this https URL
Abstract:We introduce ClosureBench, a constructive benchmark for compositional graph-relational reasoning with programmatically verified ground truth. Unlike fixed-test-set benchmarks vulnerable to data contamination, ClosureBench generates instances on demand: each task’s reference answer is computed by executing a program in the Ein tensor-logic language, ensuring machine-verified correctness. The benchmark spans 26 task categories at three compositional levels (L1-L3), with difficulty controlled along three independent axes: graph size, edge density, and query depth. We evaluate models from 1.5B open weights to frontier systems (o3, GPT-4.1, Gemini 2.5, Claude Sonnet 4) and report three findings. First, because the benchmark can always supply fresh instances, it measures memorisation directly: a model fine-tuned on a fixed test set shows a 19.3 percentage-point gap between its accuracy on seen and on fresh instances, which a static test set cannot reveal. We scope this to supervised fine-tuning on answer pairs, not pretraining contamination. Second, accuracy falls as graph size and query depth increase, and the two interact: models misread the graph from its natural-language description and then reason correctly over the wrong graph, so even the strongest frontier model degrades from atomic to compositional queries. This bottleneck is a property of the reasoning rather than the input format: it persists when the graph is given as a JSON edge list or an adjacency matrix instead of prose. Third, a 4B model fine-tuned to emit executable programs rather than answers stays nearly flat across compositional levels and approaches frontier accuracy (94.3% on held-out instances) at a fraction of the token cost. This holds for two program targets, Ein and Python+NetworkX, so it is a property of verified program synthesis rather than of one language. Comments: 30 pages, 5 figures, 11 tables. Code and data: this https URL Subjects: Machine Learning (cs.LG) ACMclasses: I.2.7; I.2.4; I.2.6 Cite as: arXiv:2608.18242 [cs.LG] (or arXiv:2608.18242v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.18242 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-62] Classifying Directional Trajectories Near Criticality in the Three-State Majority-Vote Model with Deep Belief Networks and Bidirectional GRUs
链接: https://arxiv.org/abs/2608.18235
作者: Mauricio A. Valle,Gonzalo A. Ruz
类目: Machine Learning (cs.LG)
*备注: 8 pages, 6 figures, to be published in AIxSET 2026
Abstract:In this work, we investigate whether the latent representations learned by a Deep Belief Network (DBN) and a Bidirectional Gated Recurrent Unit (Bi-GRU) can discriminate among four dynamically distinct trajectory types in the three-state majority vote model (MV3): approach from disorder, approach from order, departure to disorder, and departure to order. The DBN, pre-trained in an unsupervised manner on static equilibrium samples via a Gaussian-Bernoulli Restricted Boltzmann Machine input layer and architecture 784 \to 4096 \to 225 \to 81 , encodes each lattice snapshot into an 81-dimensional latent vector. A t-SNE analysis of the DBN latent space reveals only partial separation of the four trajectory types, reflecting the fact that a model trained on static configurations cannot fully resolve directional temporal structure. A two-layer Bi-GRU classifier, trained on sequences of DBN-encoded snapshots of length T = 50 , achieves near-perfect separation of all four trajectory types in its hidden state space, as confirmed by t-SNE visualization on both training and test sets. Furthermore, a sliding-window application of the trained Bi-GRU to continuous MV3 dynamics demonstrates its ability to sense the system’s current dynamical regime in real-time. These results establish a principled hierarchical architecture for detecting and classifying critical transitions in agent-based opinion dynamics models.
[LG-63] Allocating Recurrent Compute in Looped Language Models
链接: https://arxiv.org/abs/2608.18230
作者: Ruhai Lin,Yiyang Guo,Rui-Jie Zhu,Hao Ye,Jason K. Eshraghian
类目: Machine Learning (cs.LG)
*备注:
Abstract:Looped language models improve reasoning and knowledge manipulation by applying shared computation repeatedly. Existing systems usually repeat an entire layer stack, although a mixer and a dense feed-forward network (FFN) perform different operations and have different costs. We ask a narrower question: what should loop? We view recurrence as repeated composition of a state update and argue that an application is valuable when it exposes a new cross-position influence direction that remains observable at the task readout. Iterative Transport Rank (ITR) describes the cumulative influence trajectory; marginal ITR describes the nonredundant influence contributed by successive applications. This view motivates MixerLoop, which repeats each Gated DeltaNet mixer while applying its dense FFN once. We compare MixerLoop with no recurrence and full-block recurrence at 15M and 110M parameters under the same data, initialization, and architecture. A finite context-off intervention tests whether later mixer applications produce distinct, non-negligible, and beneficial changes at the final language-model readout. MixerLoop surpasses FullLoop on aggregate CORE at 15M and retains 41.5% of its CORE improvement at 110M while reducing recurrent-backbone projection FLOPs by 45.9%. These results show that the benefits of recurrent depth can be retained without repeatedly executing the dense FFN.
[LG-64] ChiroEcho: extending automated bat vocalisation classification beyond the learned taxonomy ECCV2026
链接: https://arxiv.org/abs/2608.18191
作者: Burooj Ghani,Welmoed Eversteijn,Milan van Hirtum,Juan Sebastián Cañas,Vincent J. Kalkman,Dan Stowell,A. Leonie Baier
类目: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
*备注: 24 pages, 3 figures. Accepted at the CV4E workshop, ECCV 2026
Abstract:Bats are key indicators of ecosystem health and are protected throughout Europe, making reliable population monitoring a conservation priority. Their cryptic nocturnal lifestyle makes passive acoustic monitoring essential, yet automated identification remains difficult as echolocation calls vary with behaviour and environment and overlap among species. We present a deep learning framework that jointly predicts species and genus and combines genus predictions with geographic species distributions at inference. When only one species of a predicted genus occurs in a region, the framework can resolve species absent from the learned taxonomy. This reframes geographic information as a means of extending, rather than constraining, a classifier’s effective taxonomy. Using recordings spanning 35 European bat species, we evaluate closed-set classification, examine the instability of performance estimates for sparsely represented species, and conduct a controlled held-out proof-of-principle experiment. The rare-species analysis shows how limited evaluation data can obscure species-level performance, while the held-out experiment shows that genus predictions and location can recover labels unavailable to the species head. Geographic resolution extends operational coverage from 35 to 41 of the 48 native European bat species, increasing coverage from 73% to 85%. To our knowledge, this is the broadest operational coverage reported for automated European bat classification. More broadly, the bat framework provides proof of principle for resolving unseen fine-grained classes by combining coarse predictions with transparent external constraints.
[LG-65] Safe Domain Adaptation for Physics: Overcoming Nuisances Label Shifts and Simulation Priors
链接: https://arxiv.org/abs/2608.18190
作者: Ivan Kharuk(1 and 2) ((1) Institute for Nuclear Research of the Russian Academy of Sciences, (2) Moscow Institute of Physics and Technology)
类目: Machine Learning (cs.LG); Instrumentation and Methods for Astrophysics (astro-ph.IM); Data Analysis, Statistics and Probability (physics.data-an)
*备注:
Abstract:Domain adaptation is widely used to make neural networks trained on simulations applicable to experimental data. Its premise is that the two domains differ only in nuisances, and that the quantity of interest is distributed identically in both. In physics neither assumption holds: simulations can be wrong about the physics, and the distribution of the target quantity - an energy spectrum, a redshift distribution - is often the measurement itself. We study the consequences of such mismatches on a toy air-shower benchmark in which a detector-response nuisance, a physical simulation shift, and an energy-spectrum shift can be switched on separately or together. Standard adversarial adaptation handles the conditional shifts, but once the two spectra differ it aligns them, replacing an uncontrolled bias by one anchored on the simulation prior. We present adaptive domain adaptation, which reweights the simulated events so as to focus domain adaptation on the genuine physical mismatch alone. Since the predicted spectrum depends on model training configuration, we provide a label-free model selection rule for selecting the near-the-best operation point.
[LG-66] Accelerating Visual On-Policy Distillation with Batched Speculative Jacobi Rollouts
链接: https://arxiv.org/abs/2608.18183
作者: Bingqi Shan,Zhehao Yu,Kenhong Lin,Baoquan Zhang
类目: Machine Learning (cs.LG)
*备注: 11 pages,4 figures
Abstract:Visual on-policy distillation (OPD) improves the training of compact visual autoregressive models by learning from trajectories generated by the current student. However, these online rollouts are still produced token by token with autoregressive decoding, which adds substantial cost to every on-policy training step. Speculative Jacobi Decoding (SJD) provides an alternative because it can process multiple tokens in parallel without an auxiliary draft model, but the original method is designed for single-sequence inference. We introduce HB-SJD, a batched SJD rollout backend for visual OPD. HB-SJD allows each image to advance independently according to its own decoding progress, while images at different sequence positions are still verified in batched model forwards. As images finish, HB-SJD switches between Full and Compact execution to reduce the cost of later rollout rounds. HB-SJD only replaces the student rollout backend and leaves the teacher, distillation objective, and optimization procedure unchanged. Experiments with LlamaGen show that HB-SJD substantially reduces rollout and end-to-end training time while preserving the generation quality of the distilled student.
[LG-67] rust as a Field: A Macroscopic Representation for Vehicular Networks
链接: https://arxiv.org/abs/2608.18178
作者: Md Mahmudul Islam,Shaurya Agarwal
类目: Robotics (cs.RO); Machine Learning (cs.LG); Dynamical Systems (math.DS)
*备注:
Abstract:Trust assessment is a fundamental component of cooperative and connected vehicle systems. However, existing approaches operate primarily at the level of individual vehicles, making it difficult to reason about trust evolution across road segments. In this paper, we propose a spatio-temporal trust-field framework that aggregates microscopic vehicle-level trust into a continuous representation over space and time. The trust field is formally defined on road segments. We conducted simulation-based experiments using synthetic trajectories generated under controlled conditions, enabling analysis of trust-field behavior in simple road scenarios. Beyond theoretical modeling, we study an implication of the trust-field concept: reconstructing the full trust field from sparse roadside-unit (RSU) measurements. We compare (i) a coordinate-based deep learning baseline that learns a generic trust field from sparse samples and (ii) a field-informed deep learning method that treats trust as a latent quantity carried by vehicles and enforces measurement consistency through the aggregation mechanism. The field-informed approach more accurately recovers trajectory-aligned low-trust patterns and yields improved reconstruction error.
[LG-68] Mitigating Spectral Bias in Neural Operators for Underwater Transmission Loss Prediction
链接: https://arxiv.org/abs/2608.18141
作者: Yifan Sun,Shikai Fang,Chao Zhang,Lei Cheng,Jianlong Li,Peter Gerstoft
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:
Abstract:Predicting underwater acoustic transmission loss rapidly and accurately is crucial for real-time ocean acoustic applications. While Fourier Neural Operators (FNO) have emerged as powerful surrogate models due to their global receptive fields, they suffer from spectral bias. The frequency truncation mechanism in FNO filters out high-frequency components, resulting in over-smoothed predictions that fail to capture fine-grained interference patterns. To overcome this limitation, this paper proposes a Spectral-Spatial Residual Learning (S2RL) framework. S2RL decomposes the prediction task into a coarse-to-fine process: a spectral Global Propagator first generates a globally consistent prediction, and a spatial Local Refiner subsequently recovers the high-frequency residuals. Experimental results on a South China Sea dataset show that the proposed method significantly outperforms FNO baselines while maintaining millisecond-level inference speeds.
[LG-69] Calibrating Adaptive Smoothing Methods for Freeway Traffic Reconstruction
链接: https://arxiv.org/abs/2602.02072
作者: Junyi Ji,Derek Gloudemans,Gergely Zachár,Matthew Nice,William Barbour,Daniel B. Work
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:The adaptive smoothing method (ASM) is a widely used approach for traffic state reconstruction. This article presents a Python implementation of ASM, featuring end-to-end calibration using real-world ground truth data. The calibration is formulated as a parameterized kernel optimization problem. The model is calibrated using data from a full-state observation testbed, with input from a sparse radar sensor network. The implementation is developed in PyTorch, enabling integration with various deep learning methods. We evaluate the results in terms of speed distribution, spatio-temporal error distribution, and spatial error to provide benchmark metrics for the traffic reconstruction problem. We further demonstrate the usability of the calibrated method across multiple freeways. Finally, we discuss the challenges of reproducibility in general traffic model calibration and the limitations of ASM. This article is reproducible and can serve as a benchmark for various freeway operation tasks.
[LG-70] Learning Random Geometric Graphs Drawn in Probabilistic Metric Spaces
链接: https://arxiv.org/abs/2608.19082
作者: Dalia Chakrabarty,Kangrui Wang,Chuqiao Zhang,Ye Liu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Applications (stat.AP)
*备注:
Abstract:We present a new data-driven learning of a Random Geometric Graph (RGG) of a multivariate dataset, where the graph is drawn in a probabilistic metric space. This graph learning works for generic datasets, irrespective of the type of the observables; their probability distributions; or size of the data. We identify a metric of the space that the graph is drawn in, as a probability distribution of a random variable that we introduce, namely, a variable that represents the disparity between the connectedness of two vertices of the graph, and the correlation between the two random variables that are attached to the respective vertex. It is the closed-form \itcdf of this disparity variable that we advance as the distance function of the host space of the learnt RGG, such that the edge exists between any two nodes, if this inter-nodal distance falls short of a chosen cutoff probability. Drawing the RGG in this probabilistic space leads to the graph being an Soft RGG, such that any edge - if it exists - exists with an identified probability. We forward a simple Rejection Sampling-based technique for learning the probability of any edge. The expected degree distribution of a vertex of this RGG is identified as local, and dependent on the inter-observable correlation matrix. If said correlation matrix is not known, it can be learnt given the data, using its closed-form posterior probability density function, that we forward. We illustrate our graph learning method by learning multiple RGGs of highly multivariate real datasets.
[LG-71] Diffusion Models for High-Dimensional Clustered Data: Intrinsic-Dimension Adaptivity via Bayesian Classification
链接: https://arxiv.org/abs/2608.19067
作者: Yuga Iguchi,Paul Fearnhead
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:The empirical success of diffusion models in generative modelling has motivated theoretical work, including quantitative error bounds and qualitative analyses that characterise the different phases of denoising. We bring these two areas together by studying the adaptivity of diffusion models to the structured geometry of multimodal high-dimensional data that consists of multiple clusters in \mathbbR^D , each with its own low-dimensional structure, and inter-cluster separation depending on D . We employ K -mixture Gaussian distributions as a canonical framework to capture this geometry and establish two theoretical results. First, we interpret denoising as a dynamical Bayesian classifier: the mixture score is a posterior-weighted average of cluster-wise scores, and we show that, with high probability, the posterior class probabilities concentrate on a single cluster once the signal-to-noise ratio reaches the scale \Theta (\log (KD)/D) . Second, by separately analysing the denoising process in its mixing and cluster-commitment phases, we prove that the KL error bound depends linearly on the maximum intrinsic dimension of a cluster, up to a logarithmic factor, even when K grows polynomially with D . This improves on ambient-dimensional bounds and extends existing low-dimensional adaptivity analyses to multimodal distributions with heterogeneous, approximately low-rank covariances.
[LG-72] Quantum Tensor Network Learning with DMRG
链接: https://arxiv.org/abs/2608.18901
作者: Gustav J L Jäger,Martin B Plenio,Hans-Martin Rieser
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 6 pages, 2 figures, 1 table, ESANN conference
Abstract:Tensor Networks are a relatively new machine learning approach. The architectures proposed initially are inspired by approaches from quantum many-body physics simulations. One common layout is the matrix product state (MPS) also known as a tensor train optimized with gradient descent techniques. We introduce a global normalization condition, so that the MPS represents a quantum state. We investigate two optimization methods that find the locally optimal tensors and compare them regarding their effectiveness. One is based on gradient descent and the other on an adaptation of DMRG.
[LG-73] Sharper Regret Bounds for Time-Varying Gaussian Process Bandits with Constant Exploration
链接: https://arxiv.org/abs/2608.18863
作者: Matthias Mandl,Hanne Kekkonen
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 23 pages, 1 figure. Code included as ancillary files
Abstract:We study Bayesian optimization in a time-varying environment where the unknown reward function evolves according to a Gaussian process drift model. Existing GP-UCB analyses in this setting typically require the exploration parameter to grow with the horizon to maintain uniform confidence bounds. Using per-round local confidence events, we show that GP-UCB can instead be run with a constant exploration parameter and obtain an expected-regret bound whose coefficient depends on the drift rate. We also derive a sharper time-varying maximum-information-gain bound. For the squared exponential kernel, it yields \tilde\gamma_T/T=\widetilde\mathcal O(\epsilon^1/2) and expected average regret \widetilde\mathcal O(\epsilon^1/4) in the persistent-drift regime. The same constant-exploration analysis also yields realized-regret guarantees. Simulations support the predicted logarithmic dependence of the bound-suggested exploration parameter on 1/\epsilon .
[LG-74] Regularised Iterative Generalised Least Squares with Optimal Selection of the Hyper-Parameter for Identifying Nonlinear Phenomenological Models
链接: https://arxiv.org/abs/2608.18742
作者: Mark Cary,Charles Bokor
类目: Methodology (stat.ME); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: 18 pages, 5 figures, 2 tables
Abstract:In some fields currently dominated by empirical approaches, such as state of health (SoH) prediction for lithium-ion batteries, phenomenological models motivated by quasi-physical thinking contain parameters to be estimated from experimental data. Often the structure of such models yields fully or partially confounded parameters, which are difficult or even impossible to estimate reliably. To preserve the desired model formulation and simultaneously improve the numerical conditioning for the problem we introduce a ridge regression scheme. An automated method is provided, based on information theoretic measures of model performance, which optimises the ridge regression hyper-parameter at each iteration. The formulae presented require fixed point iteration to solve for the hyper-parameter. Given a suitable starting value, analysis demonstrates convergence is very rapid. The optimal hyper-parameter selection mechanism is incorporated within an efficient regularised iterative generalised least squares mechanism, capable of fitting both heteroscedastic and serially correlated data as required. Simulation confirms the efficacy of the overall method.
[LG-75] A single design choice determines whether machine learning models of materials make physically impossible predictions
链接: https://arxiv.org/abs/2608.18714
作者: Can Polat,Mustafa Kurban,Erchin Serpedin,Hasan Kurban
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG); Computational Physics (physics.comp-ph); Quantum Physics (quant-ph)
*备注:
Abstract:Machine-learned models are replacing first-principles calculations across materials discovery, and physical symmetry is the central guarantee built into them. The debate over how much symmetry to hard-wire rather than learn has run on rotations, where a symmetry error is an approximation error. Some constraints are exact: symmetry forces certain property tensors to exactly zero, so a nonzero prediction is physically impossible rather than inaccurate. Here we show that whether a model can make such predictions is decided before training by one rarely reported design bit, whether its features carry parity labels, and derive a criterion, the parity gap, that computes from group theory alone which properties and crystals are exposed. Across matched architecture pairs differing only in that bit, evaluated on two thousand centrosymmetric crystals whose piezoelectric tensor must vanish, parity-labelled arms sit at the floating-point floor while rotation-only arms predict forbidden responses on 90-96% of crystals, six orders of magnitude apart, at no accuracy cost. Training on explicit zeros does not recover exactness, and a head on a frozen universal potential inherits its backbone’s symmetry group. One reflection at random initialization verifies the label in seconds.
[LG-76] Quantum-Logic Tsetlin Machines: Interpretable Quantum Machine Learning with Commuting Projector Clauses
链接: https://arxiv.org/abs/2608.18659
作者: Krishna Bhatia(QuantumAI Lab, Fractal Analytics)
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
*备注: 8 pages, 5 figures. Accepted at the Fifth International Symposium on the Tsetlin Machine (ISTM 2026), Indian Institute of Science, Bangalore, India, 12-14 October 2026
Abstract:Tsetlin Machines (TMs) learn interpretable Boolean clauses using finite-state automata. We introduce the Quantum-Logic Tsetlin Machine (QL-TM), which replaces Boolean literals with quantum propositions represented by projectors while retaining classical include/exclude automata. Clauses are restricted to commuting measurement contexts and activate through the Born probability of their joint projector. We prove an exact reduction to ordinary Boolean TM clauses in diagonal computational-basis contexts and connect Pauli-projector clauses to stabilizer and syndrome semantics. Controlled experiments on Bell states, phase-flip syndromes, randomized 16-class stabilizer tasks, mixed literal pools, context-budget ablations, and finite-shot noise show that correct non-diagonal contexts recover physically meaningful clauses, while diagonal or wrong contexts lose the relevant phase/syndrome information. The context-budget results closely follow the predicted separability ladder 2^(b-k) as true stabilizer generators are removed. The contribution is a controlled bridge between Tsetlin clause learning and quantum logic, not a claim of quantum advantage.
[LG-77] Learning Topological Features of widehat Z-invariants
链接: https://arxiv.org/abs/2608.18570
作者: Brandon Robinson,Shimal Harichurn,Fabian Ruehle,Sergei Gukov,Rak-Kyeong Seong,Miranda C. N. Cheng
类目: High Energy Physics - Theory (hep-th); Machine Learning (cs.LG); Geometric Topology (math.GT)
*备注: 77 pages, 25 figures
Abstract:Machine learning and data analysis techniques have recently emerged as powerful tools for identifying patterns and formulating conjectures in mathematical research, most notably in the field of low-dimensional topology. In this paper, we initiate a systematic approach to handling mathematical data structured as (truncated) infinite q -series, or equivalently, infinite series of integers. To apply this data analysis pipeline, we construct a comprehensive dataset of \widehatZ -invariants (homological blocks) for plumbed 3-manifolds. We demonstrate that neural networks can reliably extract essential topological information, such as homology class and underlying graph structure, directly from the q -series coefficients. A central feature of our methodology is a focus on interpretability; by contrasting local gradient sensitivity with global feature relevance, we reveal that the networks learn to bypass complex topological rules in favor of specific spectral and geometric proxies. Finally, we apply this pipeline to probe homology cobordism, discovering a high-accuracy predictive relationship between the \widehatZ -invariant exponents and the Heegaard Floer d -invariant (correction term). These results suggest that \widehatZ -invariants capture subtle geometric information regarding cobordism equivalences, warranting a new direction for the study of quantum invariants.
[LG-78] Algorithms for adaptive and heteroskedastic linear regression at the computational threshold
链接: https://arxiv.org/abs/2608.18402
作者: Spencer Compton,Tselil Schramm
类目: atistics Theory (math.ST); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注: shortened arxiv abstract
Abstract:We study finite-sample linear regression in the presence of varied and unknown label noise, focusing on the heteroskedastic and adaptive linear regression models. Heteroskedastic linear regression models settings where the labels are of varying quality. We receive n pairs (X_i,Y_i) with labels Y_i=X_i^\top\beta+\varepsilon_i , where \varepsilon_i\sim N(0,\sigma_i^2) and the variances are unknown to the estimator. One natural measurement of the difficulty of this problem is the number of samples m for which \sigma_i^2\le1 (larger m is easier). We obtain a polynomial-time estimator with rate \tildeO((nd^3/m^4)^1/6) when m\gg d^3/4n^1/4 , as well as nearly-matching lower bounds. For d=O(1) , our estimator achieves error o(1) when m\gg n^1/4 , whereas L_1 regression and other traditional approaches require m\gg n^1/2 . In adaptive linear regression, the errors are drawn i.i.d. from an unknown distribution p , and our goal is to design a generic estimator that performs nearly as well as the best custom estimator that knows p . We introduce a (computationally inefficient) adaptive estimator that, so long as p is a mixture of k symmetric log-concave densities, achieves error comparable with the optimal estimator that knows p and has \tilde\Theta(n/k) samples. For k=1 , we show that L_q regression (with data-dependent q ) gives a polynomial-time estimator. Finally, to study the computational limits of both problems, we introduce the planted linear regression problem, where X_i\sim N(0,I_d) , m unknown samples are noiseless, and the rest have error \varepsilon_i\sim N(0,1) . We conjecture that recovering \beta up to error \ll\sqrtd/n (or exactly) may have an information-computation gap between m=d+1 and m\sim d^3/4n^1/4 , as is suggested by our near-matching polynomial-time estimator and statistical query (SQ) lower bound. Comments: shortened arxiv abstract Subjects: Statistics Theory (math.ST); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG) Cite as: arXiv:2608.18402 [math.ST] (or arXiv:2608.18402v1 [math.ST] for this version) https://doi.org/10.48550/arXiv.2608.18402 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Spencer Compton [view email] [v1] Wed, 19 Aug 2026 00:17:22 UTC (232 KB)
[LG-79] Inference and Uncertainty Quantification for Streaming r-PCA
链接: https://arxiv.org/abs/2608.18374
作者: Haoshu Xu,Hongzhe Li
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:We address two open questions in streaming PCA via Oja’s algorithm: sharp operator-norm convergence for general rank under sub-Gaussian data, and distributional inference for the resulting subspace estimator. Existing convergence analyses, even in the rank-one case, either assume bounded data or leave non-vanishing remainder terms that prevent adaptation to a polynomially vanishing tail spectrum, while existing distributional results are confined to the rank-one case. Our convergence theory removes these remainder terms and yields a sharp rate. In the dense-tail spiked covariance regime, this rate matches the minimax rate up to logarithmic factors. More generally, we prove a matching lower bound, up to logarithmic factors, across both dense-tail and sparse-tail regimes under a mild nondegeneracy condition. The analysis yields a linearization of Oja’s iterates, which in turn enables a high-dimensional Gaussian approximation for the general-rank subspace estimation error with an explicit limiting covariance. We also establish a row-wise Gaussian approximation over convex sets for the aligned difference, recovering prior rank-one results as special cases. For practical inference, we develop an online multiplier bootstrap algorithm and prove its consistency. Beyond streaming PCA, our techniques contribute to Gaussian approximation and bootstrap inference for nonconvex stochastic approximation.
[LG-80] Self-supervised In-context Operator Learning for Stochastic Mean-Field Control
链接: https://arxiv.org/abs/2608.18282
作者: Suyi Gao,Mo Zhou,Rongjie Lai
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:Stochastic mean-field control (MFC) provides a fundamental framework for coordinating large populations of interacting agents under uncertainty, with a wide range of applications. Existing numerical and deep-learning methods solve one MFC problem instance at a time and must be re-optimized whenever the task changes. In this work, we formulate stochastic MFC as an operator-learning problem and develop, to the best of our knowledge, the first mesh-free, self-supervised neural operator for stochastic MFC. The main challenge is that the diffusion term in the controlled Fokker–Planck equation precludes deterministic transport-map representations. We address this challenge by combining the probability-flow ODE with an invertible normalizing-flow-based transformer, which recasts the dynamics as a deterministic continuity equation and enables closed-form score evaluation through the exact inverse and analytical log-determinant of the normalizing flow, with \mathcalO(d) cost per particle for networks of fixed size. Through transformer-based in-context learning, task prompts, represented by compact distribution parameters or raw particle clouds, condition the transport map, enabling a single pretrained operator to solve unseen tasks in one forward pass. The resulting \emphNormalizing Flow Invertible Solution Transformer (NFIST) is trained end-to-end by minimizing the stochastic control objective directly, requiring no precomputed numerical solutions for training. We further prove the consistency of the proposed operator-learning formulation with task-by-task optimization. Numerical experiments on stochastic optimal control, Schrödinger bridge, systemic-risk control, and obstacle-avoiding path planning demonstrate effective zero-shot generalization while substantially reducing the computational cost of solving large families of stochastic MFC problems.
[LG-81] A Comprehensive Review of Large Language Models for Nanophotonics: From Surrogate Modeling to Autonomous Design
链接: https://arxiv.org/abs/2608.18279
作者: Huanshu Zhang,Kegeng Tang,Lei Kang,Sawyer D. Campbell,Zihao Wang,Douglas H. Werner
类目: Optics (physics.optics); Machine Learning (cs.LG)
*备注: Accepted for publication in Advanced Photonics
Abstract:Metasurfaces have revolutionized the development of photonic devices by enabling unprecedented precision in light manipulation. However, their design processes are often constrained by computationally expensive simulations and complex high-dimensional design spaces. Although deep learning has accelerated the design process by serving as a surrogate model, it remains constrained by task-specific architectures and lacks universal reasoning capabilities. This review surveys how Large Language Models (LLMs) are adding semantic interfaces, code generation, and tool orchestration to established numerical nanophotonic workflows. We first outline the development from classical neural networks to transformer-based models and their applications in nanophotonic design. We then review the emergence of LLM-related methods in nanophotonics and organize them into two operational modes: surrogate models that treat structure-spectrum mapping as a language task, and agentic systems that have been demonstrated to generate code, orchestrate selected simulation steps, and support closed-loop optimization. Furthermore, to identify future cross-disciplinary opportunities, we briefly explore applications of LLMs in research fields such as materials science and wireless communications. This review concludes by looking ahead to the next generation of multimodal foundation models with physical perception capabilities. In this vision, artificial intelligence is evolving from passive tools into active collaborators, participating in autonomous scientific discovery.
[LG-82] Sobolev Regularized Score Difference Estimation in Diffusion Models ICML2026
链接: https://arxiv.org/abs/2608.18237
作者: Chenghan Xie,Jose Blanchet,Renyuan Xu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: Accpeted by ICML 2026
Abstract:Estimating the difference of two Stein’s score functions is a fundamental problem in generative modeling. In particular, score differences arise naturally in transfer learning, where the score difference provides the mechanism for adapting a pre-trained model to a new target distribution, and in diffusion model-based post-training methods such as discriminator guidance. Existing estimators for score differences in these settings either lack of statistical consistency or are difficult to scale up in high-dimensions. We propose a statistically consistent and scalable estimator for score differences based on Sobolev regularization, which plays a crucial role in ensuring consistency and stablizing the training in the small-sample regime. Mathematically, we establish a convergence rate of O(n^-\fracs-1d+2s-2) where d is the dimension and s denotes the smoothness of the underlying densities, and provide a minimax lower bound of \tilde\Omega(n^-\frac2(s-1)d+2s) (in mean-squared error). Empirically, our estimator exhibits significantly improved stability in small-sample regimes compared to existing methods. We demonstrate its effectiveness on real-world tasks, including transfer learning for ECG signal generation, where it substantially outperforms non-regularized score difference estimators in downstream classification performance.
[LG-83] Fair Multi-View Determinantal Coresets via Adaptive NEPv
链接: https://arxiv.org/abs/2608.18181
作者: Richard Yi Da Xu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Selecting a small, diverse subset from a large candidate pool often means balancing several incompatible notions of diversity. In trademark curation, for instance, a subset should cover both the language used to describe marks and the visual space of their logos. A single determinantal point process (\DPP) kernel can hide failure in one view, and averaging kernels replaces the multi-view relaxation by an ordinary single-kernel spectral problem. We formulate \emphfair multi-view determinant selection: maximize the weakest per-view log determinant of a size- k subset. We smooth this nonsmooth objective and relax it to the Stiefel manifold. The relaxation embeds every discrete subset exactly, but unlike its single-view counterpart it has no closed-form spectral solution in general. Its stationarity condition is a gauge-invariant nonlinear eigenvalue problem with eigenvector-dependent, view-adaptive weights. We derive an adaptive self-consistent-field (\SCF) solver with damping and level shifting, and round the resulting subspace by leverage-score screening followed by fair local refinement. The solver needs only feature-map products for each view. We report conflicting-view synthetic experiments and specify a multimodal USPTO protocol; the real-data multimodal results require aligned logo embeddings and are not claimed in this version.
[LG-84] Seasonal false alarms in customer churn and decline early-warning systems: adjacent-window labels confound seasonality with decline and a year-over-year correction
链接: https://arxiv.org/abs/2608.18174
作者: Md Rezwanul Islam,Wael Mohammed
类目: Applications (stat.AP); Machine Learning (cs.LG); Methodology (stat.ME)
*备注: 34 pages, 4 figures. Replication package included as ancillary files
Abstract:Customer decline early-warning systems feed account-manager action lists, and every flagged account consumes intervention capacity. In a deployed business-to-business marketplace system, one action-list slot in three went to flags that dissolve under a seasonally aligned label. The standard target in non-contractual churn prediction compares an entity’s next k months of activity with its trailing k months. The two windows cover different calendar months, so for seasonal entities the threshold-ratio construction confounds seasonality with decline, and the event rate depends on the label’s anchor calendar month. We formalize the mechanism and measure it on three public panels and the production panel (the public arms pre-specified). Of the adjacent-window decay events, 37–69% on the public panels and 28–50% in production have no counterpart under a seasonally aligned definition. Pooling anchors, the standard remedy, balances the training mixture but corrects no individual label. Measured alternatives repair the curve only partially or change the detection horizon. Aligning the baseline to the same k calendar months one year prior – advice practitioners already state, here formalized, measured, and costed – flattens the curve at the source. With the classifier held fixed, production holdout ROC-AUC rises from 0.767 to 0.864 for decline (different targets; the gap closes on synthetic ground truth and a production hindsight referee). The served action list shrinks by a third, 119 to 79 accounts. The measured price is extra history, a blind spot to decline-then-stabilization, and a stricter cut under sustained growth.
[LG-85] Flux-form spatiotemporal neural operators for coarse-grained dynamics of multiscale PDEs
链接: https://arxiv.org/abs/2608.18148
作者: Junfeng Chen
类目: Chaotic Dynamics (nlin.CD); Machine Learning (cs.LG); Analysis of PDEs (math.AP); Computational Physics (physics.comp-ph)
*备注:
Abstract:We study data-driven prediction of coarse-grained dynamics in multiscale PDE systems. Adopting a closure-free operator-learning viewpoint, we apply a linear coarse-graining map and learn a surrogate evolution operator for the resolved field directly from filtered high-fidelity trajectories. Motivated by the Mori-Zwanzig formalism, we propose a spatiotemporal neural operator mapping a resolved history slab on \Omega\times[-T_\mathrmin,0] to a resolved future slab on \Omega\times[0,T_\mathrmout] . Spatial mixing uses Fourier convolution, while temporal mixing uses a causal kernel operator with position-attention weights on time lags. This causal temporal operator encodes finite-memory effects in the resolved dynamics while preserving the directionality of the history-to-future map. To improve rollout robustness and suppress nonconservative artifacts, we embed a flux-form inductive bias by parameterizing the windowed update in explicit divergence form. We also provide a data-driven guideline for selecting the memory length T_\mathrmin via the decorrelation time of a closure-injection diagnostic computed from filtered trajectories. We validate on the coarse-grained viscous Burgers’ equation, the Kuramoto-Sivashinsky equation, and two-dimensional turbulent flows, obtaining stable autoregressive rollouts with improved long-horizon accuracy and statistical fidelity.
[LG-86] Online Bipartite Matching with Reusable Capacity under Non-Stationary Rewards
链接: https://arxiv.org/abs/2608.18130
作者: Xi Chen,Shixin Wang,Bingkun Zhou,Yuan Zhou
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:We study online bipartite matching with reusable server capacity and non-stationary rewards. Jobs arrive sequentially, reveal compatible servers, reward rates, and processing durations, and must be accepted or rejected irrevocably. An accepted job occupies one unit of server capacity only during its processing interval, so an assignment may displace an unknown sequence of future jobs. Existing guarantees are typically calibrated by a global reward range, which can become arbitrarily large when rewards drift over a long horizon. We instead impose a locally bounded reward condition: reward rates of jobs that can compete for the same server within a relevant time window differ by at most a factor \delta . Under this condition, we develop two BALANCE-type algorithms with time-aware opportunity-cost losses. TS-BAL maximizes cumulative blocking losses over feasible reuse schedules and achieves a competitive ratio of 2\ln(\delta D)+\mathcal O(\ln\ln(\delta\vee D)) . GR-BAL uses a greedy relaxation of this loss and achieves \ln(\delta D)+\mathcal O(\ln\ln(\delta\vee D)) , matching a lower bound of \ln(\delta D) in the leading term. Numerical experiments demonstrate robust performance under substantial global reward drift and favorable finite-capacity performance.
[LG-87] Optimizing Energy Efficiency and Grid Stability via Public EV Charging Flexibility
链接: https://arxiv.org/abs/2608.18126
作者: Marek Miltner,Artem Bryksa,Ondřej Štogl,Daniel Vašata,Magda Friedjungová,Ram Rajagopal,Oldřich Starý
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: Submitted to Young Energy Researchers Conference at World Sustainable Energy Days 2025
Abstract:This study evaluates the potential of electric vehicle (EV) charging flexibility to enhance both energy efficiency and power grid stability. Using real-world data from public charging stations in Prague, we analyze individual and aggregated charging sessions to explore how optimizing charging times can reduce energy waste, minimize grid imbalances, and support the integration of renewable energy. By aligning EV charging with periods of lower grid demand and higher renewable generation, we demonstrate a significant improvement in energy efficiency, reduc ing the need for costly system support and ancillary services. Our findings suggest that cooperation between power distributors and transmission system operators can unlock new opportunities for maintaining grid stability while promoting sustainable energy use in an increasingly uncertain energy landscape.
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