本篇博文主要内容为 2026-08-14 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 (2026-08-14)
今日共更新629篇论文,其中:
- 自然语言处理共101篇(Computation and Language (cs.CL))
- 人工智能共204篇(Artificial Intelligence (cs.AI))
- 计算机视觉共107篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共157篇(Machine Learning (cs.LG))
- 多智能体系统共14篇(Multiagent Systems (cs.MA))
- 信息检索共19篇(Information Retrieval (cs.IR))
- 人机交互共34篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] Joint Communication-Control Strategy Optimization with Partially Nested Information Structures: The Linear-Quadratic Case
【速读】：该论文旨在解决多智能体线性系统中联合通信-控制策略优化（Joint Communication-Control Optimization, JCCO）问题，尤其关注在二次型代价函数下，基于共同信息基础（Common-Information-Based, CIB）框架的分布式随机控制场景。其核心挑战在于如何在保证计算可处理性的前提下，设计最优的通信与控制策略。解决方案的关键在于聚焦于部分嵌套（Partially Nested, PN）信息结构（Information Structure, IS），并证明：在特定基线通信协议下，若满足一系列条件，则通过优化额外的通信策略仍能保持部分嵌套性；反之，若破坏该性质，通常将导致最优策略出现非线性特征，尤其是在采用开环通信策略时。为此，作者提出一种基于动态规划的方法，针对具有开环通信策略的JCCO问题，推导出一组闭式解的Riccati方程，从而实现最优控制策略的高效计算。作为独立的重要成果，该方法亦可用于求解具有PN信息结构和输出反馈的分布式线性-二次型控制问题。最后，研究进一步将该方法扩展至闭环通信策略情形，相较于传统的无限维CIB信念空间动态规划，显著提升了计算的可处理性。
链接: https://arxiv.org/abs/2608.13535
作者: Haoyi You,Kaiqing Zhang
机构: University of Maryland, College Park (马里兰大学学院帕克分校)
类目: ystems and Control (eess.SY); Multiagent Systems (cs.MA); Optimization and Control (math.OC)
备注: Preliminary version accepted to IEEE CDC 2026
Abstract:In this paper, we formalize a joint communication-control strategy optimization (JCCO) problem in multi-agent linear systems with quadratic costs, under the common-information-based (CIB) framework from decentralized stochastic control. For computational tractability, we focus on such JCCO problems with partially nested (PN) information structures (ISs). In particular, with a baseline communication protocol that leads to a PN IS, we establish a series of conditions under which the partial nestedness is preserved under the (additional) communication strategies to be optimized, while violating them may cause nonlinearity of the optimal strategies in general, with open-loop communication strategies. We then develop a dynamic-programming-based approach to compute the optimal control strategies of JCCO with open-loop communication strategies, which yields a set of closed-form Riccati Equations. As a byproduct of independent interest, such an approach also offers a way to solve decentralized linear-quadratic control with PN ISs and output feedback, under the CIB framework. Finally, we extend such an approach to JCCOs with closed-loop communication strategies, yielding a more tractable dynamic program than an infinite-dimensional CIB-belief-based one.
[MA-1] LigBench: A Unified and Human-Aligned Benchmark for LLM -based Research Idea Generation
【速读】：该论文旨在解决当前生成式AI在科研选题（research idea generation）领域中评估体系碎片化、缺乏客观标准的问题。现有方法多依赖大语言模型（Large Language Models, LLMs）直接评分，导致评估结果难以在不同生成分布间保持一致性与可靠性。为此，论文提出LigBench——一个自动化评估基准，能够实现对科研创意的细粒度、可复现且跨分布一致的可靠评估。其关键创新在于引入PAIR-IQ数据集，专门用于训练成对创意判断模型，并作为客观参照依据以支持更公正的对比评估。实验表明，LigBench在评估稳定性与可解释性方面表现优异，显著提升与专家判断的一致性；基于PAIR-IQ训练的模型在排名准确性与鲁棒性上均有明显提升，从而为可扩展、客观的科研创意评估建立了规范化标准。
链接: https://arxiv.org/abs/2608.13136
作者: Chenrun Wang,Mingxuan Zhu,Tiancheng Huang,Wenjie Li,Yujie Zhang,Zichen Zhu,Zhiying Zou,Kai Yu,Lu Chen
机构: Shanghai Jiao Tong University (上海交通大学); Shanghai Innovation Institution (上海创新机构)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Databases (cs.DB); Multiagent Systems (cs.MA)
备注: 17 pages
Abstract:With the rapid advancement of large language models (LLMs), research idea generation has attracted increasing attention. Existing approaches enable LLMs to retrieve relevant literature and propose novel ideas for research areas. However, current evaluation practices for idea generation remain fragmented and lack objective standards, often relying on direct LLM scoring, which limits their ability to provide unified and reliable assessments across a coherent distribution of generated ideas. To address this challenge, we propose LigBench, an automated evaluation benchmark that enables fine-grained and reliable evaluation of AI research ideas, consistently applicable across different generation distributions. In addition, we introduce PAIR-IQ, a dataset tailored for training pairwise idea judgment models and serving as an auxiliary reference to support more objective comparative evaluation. Extensive experiments demonstrate that LigBench achieves stable and interpretable evaluations, significantly improving alignment with expert judgments. Furthermore, models trained on PAIR-IQ exhibit enhanced ranking accuracy and robustness, establishing a principled standard for scalable and objective research idea assessment.
[MA-2] InterSAGE: The Secure and Verifiable Interoperability Protocol for An Internet of Agents
【速读】：该论文旨在解决生成式 AI 代理（Agent）在跨组织边界协作时缺乏可信身份认证、权限授权、能力声明及责任追溯机制的问题。现有通信协议虽规范了代理间的消息交互，但未能提供对代理身份持久性、能力可见性、信任协商过程以及行为可审计性的统一安全支撑。其解决方案的关键在于提出 InterSAGE——一个原生支持信任的协议套件，通过四层架构实现端到端的信任保障：（1）基于开发者、代码包、运营方与部署环境绑定的代理身份卡（Agent Identity Card），确保身份持久可信；（2）基于去中心化标识符（DID）绑定的可验证凭证（Verifiable Credential）清单，实现能力感知的发现机制；（3）融合单调能力衰减与双层访问控制的动态信任协商机制；（4）由内核管理的密码学审计追踪，将使用、委托与执行轨迹与代理身份强关联，无需共识账本即可实现不可篡改的问责。InterSAGE 作为独立于通信协议的信任基底，实现了身份、能力、信任与问责的统一建模，显著优于现有超过50项相关工作在完整性与可验证性方面的局限。
链接: https://arxiv.org/abs/2608.13030
作者: Zhenhua Zou,Sheng Guo,Qiuyang Zhan,Lepeng Zhao,Shuo Li,Zhuotao Liu
机构: Tsinghua University (清华大学)
类目: Cryptography and Security (cs.CR); Multiagent Systems (cs.MA); Networking and Internet Architecture (cs.NI)
备注: 35 pages, 4 figures, 7 tables. Positioning paper
Abstract:The emerging Internet of Agents enables LLM-powered agents to discover peers, invoke tools, and delegate tasks across organizational boundaries. Existing protocols increasingly define how agents exchange messages, but not how an agent proves its identity, authorization, advertised capabilities, or accountability after delegation. We present InterSAGE, a trust-native protocol suite that supplies this missing security substrate alongside, rather than in place of, communication protocols. InterSAGE comprises four layers: Persistent Identity, Discovery, Trust Negotiation, and Accountability. Its four core primitives are: (1) Agent Identity Cards that bind developer, code package, operator, and deployment context; (2) capability-aware discovery using DID-bound Verifiable Credential manifests; (3) trust negotiation combining monotonic capability attenuation with two-tier access control; and (4) kernel-mediated cryptographic audit trails that bind usage, delegation, and execution traces to agent identity without a consensus ledger. InterSAGE is designed to complement MCP, A2A, ANP, and AG-UI, allowing communication protocols to evolve independently while keeping trust semantics explicit, portable, and verifiable. We compare InterSAGE with more than 50 efforts spanning agent protocols, decentralized identity, OAuth/OIDC extensions, zero-trust governance, delegation, and audit architectures. We show that no prior architecture jointly enforces persistent identity, capability-aware discovery, trust negotiation, and accountability as a unified four-layer trust substrate for secure agent interoperability.
[MA-3] OGR-MARL: Option-Guided Residual Multi-Agent Reinforcement Learning for Heterogeneous USV Cooperative Pursuit in Constrained Port Waterways
【速读】：该论文旨在解决受限港口水域中异构无人水面艇（USV）协同追捕任务面临的复杂约束问题，包括航行安全、交通流管理及角色分工等限制条件下的逃逸者拦截难题。其核心解决方案是提出一种与具体多智能体强化学习（MARL）算法解耦的选项引导残差多智能体强化学习框架（OGR-MARL），关键创新在于集成共享逃逸者信念、角色条件化的选项目标、自适应规则惩罚机制以及残差策略学习，使不同MARL算法可在规则引导行为的基础上学习修正动作，而非从零开始探索受限环境。通过在典型连续控制MARL骨干网络（如MADDPG、MATD3、MAPPO、MASAC）上实例化，所提出的OGR-MASAC在抽象厦门子门港场景中实现了75.0%的捕获率，展现出优异的任务有效性与规则合规性，并在异构协同性能上优于其他对比方法；此外，无需重新训练即可实现零样本迁移至基于QGIS/AIS信息的真实地图，验证了OGR-MARL在更复杂港口场景中的强大泛化能力。
链接: https://arxiv.org/abs/2608.12995
作者: Mao Jiayang,Wang Lanfeng,Peng Zhao-Han
机构: Sichuan Agricultural University (四川农业大学); Shenzhen International Graduate School, Tsinghua University (清华大学深圳国际研究生院)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 6 pages,5 figures, accepted by ICUS 2026
Abstract:Heterogeneous USV cooperative pursuit in constrained port waterways requires evader interception under navigation, traffic, and role constraints. This paper proposes OGR-MARL, an option-guided residual multi-agent reinforcement learning framework that is decoupled from a specific MARL algorithm. OGR-MARL integrates shared evader belief, role-conditioned option targets, adaptive rule penalties, and residual policy learning, allowing different MARL algorithms to learn corrective actions on top of rule-guided behaviors rather than exploring constrained port environments from scratch. We instantiate OGR-MARL with representative continuous-control MARL backbones, including MADDPG, MATD3, MAPPO, and MASAC, yielding OGR-MADDPG, OGR-MATD3, OGR-MAPPO, and OGR-MASAC. Experiments in an abstract Xiazhimen port-waterway scenario show that the OGR-MASAC instantiation achieves a 75.0% capture rate, promising mission-effective rule compliance, and the best heterogeneous coordination among the tested methods. Without retraining, zero-shot transfer to a QGIS/AIS-informed Xiazhimen map achieves promising results, demonstrating the generalization potential of OGR-MARL in more complex port scenarios.
[MA-4] Reconcile Once Write Anytime: A Trust-Tiered Librarian and a Multi-Agent Writer for Drift-Free Point-in-Time Research
【速读】：该论文旨在解决大语言模型生成长篇研究报告时存在的严重问题：报告内容出现漂移（drift）、自相矛盾以及事实溯源缺失（loss of provenance），例如同一指标在不同位置呈现不同数值，或把未经核实的传闻与经审计的官方文件同等对待。其核心解决方案是提出一种双层代理系统（two-tier agentic system），关键在于将“知识维护”与“报告生成”严格分离：由一个确定性“图书管理员”（librarian）负责构建一个随时间点更新、基于可信度分层的本体结构（trust-tiered ontology），通过证据卡（evidence cards）、权威指标账本（authoritative metric ledger）和声明图（claim graph）构成一个始终一致的“真相源”，而非对原始文本片段进行每次查询时的随机检索增强（per-query RAG）。随后，一个可移植的多智能体“写作者”（writer）运行时仅读取截至某一知识截止时间T的证据（as_of = T，杜绝前瞻查看），生成无矛盾且有据可依的报告，并将红队验证结果反馈回图书管理员以实现自我修正。实验基于包含6,130个来源的自建公开语料库（涵盖SEC EDGAR文件、美国劳工统计局数据及维基百科），生成555,926张证据卡，验证表明共享指标账本消除了6,845处跨章节矛盾，信任层级筛选在22个黄金测试案例中全部正确（而流行度优先基线仅9/22正确），且未泄露任何媒体来源的数据，政府统计数据无法覆盖公司自身披露信息。红队反驳可反向传播并自动修正后续生成结果，无需人工干预；重放实验在七个不同知识截止点下均无前瞻违规行为，同时知识库规模从23.5万增长至55.5万张证据卡。难度分级模型路由策略在性能上超越全Opus模型上限，且运行速度提升3.7倍。该方案的关键突破在于通过分层可信知识库实现可追溯、可复现、可验证的生成式报告体系。
链接: https://arxiv.org/abs/2608.12984
作者: Xing Zhang,Yanwei Cui,Guanghui Wang,Peiyang He
机构: AWS Generative AI Innovation Center
类目: Multiagent Systems (cs.MA); Computation and Language (cs.CL)
备注:
Abstract:Long-form research reports generated by large language models drift, contradict themselves, and lose provenance: the same metric appears with different values, and rumor is quoted as confidently as an audited filing. We present a two-tier agentic system that separates a maintained, point-in-time knowledge library from report writing. A deterministic “librarian” ingests timestamped sources into a trust-tiered ontology, layering evidence cards, an authoritative metric ledger, and a claim graph into an always-current source of truth, not per-query RAG over raw chunks. A portable multi-agent “writer” runtime then composes a contradiction-free, evidence-grounded report at any knowledge cutoff T, reading only evidence with as_of = T (no look-ahead); red-team verdicts flow back into the librarian. We evaluate on a self-collected, public corpus of 6,130 sources yielding 555,926 evidence cards (SEC EDGAR filings across 295 issuers and 11 sectors, U.S. Bureau of Labor Statistics releases, and Wikipedia). From the one library we compose four point-in-time reports on distinct theses and run eight reproducible experiments, whose headline metrics come from a deterministic quality-control gate, itself validated by defect-injection meta-evaluation at recall 1.0 and precision 1.0. A shared metric ledger removes 6,845 cross-section contradictions to zero. Tier-first selection is correct on 22/22 gold cases where a popularity-first baseline scores only 9/22; trust tiering leaks zero media-sourced numbers, and no government statistic displaces a company’s own filing. A red-team refutation propagates back and self-corrects a later run with zero manual edits. Replay exhibits zero look-ahead violations across seven cutoffs while the library grows from 235,373 to 555,312 cards. Difficulty-tiered model routing exceeds the all-Opus quality ceiling while running 3.7x faster than serial.
[MA-5] Discovering Efficient and Explainable Communication Topologies for LLM -based Multi-Agent Systems via Causal Inference AAAI2027
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）驱动的多智能体系统（Multi-Agent System, MAS）中通信拓扑结构缺乏可解释性的问题。现有拓扑生成方法通常依赖仅以任务级奖励为驱动的黑箱优化，虽能有效提升性能，但无法揭示为何特定通信边被选择，导致难以识别促成成功协作的关键通信子图。为此，本文提出E2-Explainer——一种模型无关的可解释框架，用于对任意拓扑生成器产生的通信拓扑提供可解释性分析。其核心在于将拓扑解释建模为因果归因问题，通过一种类格兰杰（Granger-style）目标函数，量化每条通信通道被掩码后对任务结果及最终响应稳定性的影响，从而识别出在任务保真度上具有显著证据支持的紧凑通信子图。随后，这些受预算约束的子图被提炼为可泛化使用的解释器，实现部署阶段无需重复进行边缘级评估的高效事后解释。大量在推理与编码基准上的实验表明，E2-Explainer能够准确识别出维持协作成功的关键通信子图，且这些子图可直接执行以剪枝冗余通信边，在大幅降低通信开销的同时保持竞争性任务表现。
链接: https://arxiv.org/abs/2608.12921
作者: Junzhi Li,Peng He,Qirui Ji,Wei Wang,Lixiang Liu,Chuxiong Sun
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注: 18 pages, 4 figures. Submitted to AAAI 2027
Abstract:The performance of large language model (LLM)-based multi-agent systems (MAS) largely depends on effective communication topologies. Existing topology generation methods, however, typically learn communication topologies through black-box optimization driven solely by task-level rewards. While effective, such optimization provides little insight into why particular communication edges are selected, making it difficult to identify the critical communication subgraphs responsible for successful collaboration. To address this limitation, we propose E2-Explainer, a model-agnostic framework for providing interpretable explanations of communication topologies produced by arbitrary topology generators. Specifically, we formulate topology explanation as a causal attribution problem that identifies compact communication subgraphs supported by edge-level evidence of task preservation. We obtain this evidence with a Granger-style objective that measures how masking each communication channel changes the task outcome and the stability of the final response. The resulting budgeted subgraphs are then distilled into an amortized explainer, enabling efficient post-hoc explanation without repeated edge-level evaluations at deployment. Extensive experiments on multiple reasoning and coding benchmarks demonstrate that E2-Explainer identifies critical communication subgraphs that preserve successful collaboration. These subgraphs can also be executed directly to prune redundant communication edges, substantially reducing communication costs while maintaining competitive task performance.
[MA-6] Agent Behavioral Contracts II: Certifying Compositional Reliability Without Assuming Independence
【速读】：该论文旨在解决多智能体系统（multi-agent systems）中组件可靠性评估时普遍依赖但极少验证的条件独立性假设所导致的可靠性边界高估问题。现有方法通过将各组件的可靠性相乘来计算联合失效概率，这一操作隐含依赖于条件独立性假设，然而在实际系统中，尤其是共享相同模型或算法的智能体之间，存在显著的正相关性（positive dependence），导致联合失效概率被系统性地低估，从而错误地高估冗余设计的有效性。论文的关键解决方案在于提出一种无需依赖结构假设的有限样本可靠性证书：基于对联合执行时刻矩（co-execution moments）的观测，构建一个包含在Bonferroni-Clopper-Pearson置信框内的线性规划框架，以获得在给定信息下最紧的可靠性下界。该方法具有严格保真性（sound）、信息利用效率高、且随矩阶数增加而单调收紧——将10个矩函数扩展至14个后，识别区间缩小85.7%，可靠性下界从0.2455提升至0.4116。此外，论文还提供了可随时有效的（anytime-valid）证书，确保在任意停止规则下第一类错误率控制在0.0471以内。研究通过预注册的18,000次任务仿真验证了真实系统的强共失效现象（共失效率达90%），并证明了传统依赖模型拟合方法在样本量增大时反而会降低覆盖性能，凸显其固有缺陷。最终，论文开源了全部合同、评分代码、分析脚本与预注册协议，为可信可靠性评估提供了一套可复现、无假设依赖的新范式。
链接: https://arxiv.org/abs/2608.12895
作者: Varun Pratap Bhardwaj,Garima Singh,Arun Pratap Bhardwaj
机构: Qualixar(量化) / Independent Researcher(独立研究员); Independent Researcher(独立研究员)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 49 pages, 12 tables, 25 numbered definitions, 18 theorems with full proofs, six experiments, 65 references. Code, analysis scripts, and preregistration: this https URL
Abstract:Compositional reliability bounds for multi-agent systems multiply component reliabilities, a step licensed by a conditional-independence assumption that is routinely stated and rarely tested. We test it. Two instances of one model, in a two-agent handoff, co-fail on 90.0% of the missions on which either fails (log OR 6.66, 95% CI [6.38, 7.00]; phi 0.916), in a preregistered evaluation of 18,000 missions scored by deterministic code with no LLM judge. Substituting a different model reduces the association in six of six contrasts; substituting a different vendor, model already different, does not – a registered hypothesis reported as a null. The error is signed and runs against the operator: positive dependence inflates joint failure above the independence product, so redundancy is over-credited exactly when components share a model. The assumption-free alternative is often vacuous, and fitting a dependence model is worse: we prove a bootstrap bound on a fitted model’s functional loses coverage of the truth as n grows, the identification gap being O(1) while the bootstrap haircut is O(n^-1/2). More data makes such a certificate worse, with no visible symptom. We give a finite-sample certificate assuming no dependence structure: a linear program over the joint, over a Bonferroni-Clopper-Pearson box around measured co-execution moments. It is sound, sharp for the information supplied, and monotone in the moment family. Enriching ten moment functionals to fourteen narrows the identified interval by 85.7% and lifts the certified floor from 0.2455 to 0.4116. A companion anytime-valid certificate holds type-I error at 0.0471 under optional stopping. Common dependence statistics are marginal-bounded and can reverse an apparent ordering of conditions when the compared agents fail at different rates. Contracts, scoring code, analysis scripts, and the preregistration are released. Comments: 49 pages, 12 tables, 25 numbered definitions, 18 theorems with full proofs, six experiments, 65 references. Code, analysis scripts, and preregistration: this https URL Subjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) ACMclasses: I.2.11; D.2.4; G.3 Cite as: arXiv:2608.12895 [cs.AI] (or arXiv:2608.12895v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.12895 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Varun Pratap Bhardwaj [view email] [v1] Thu, 13 Aug 2026 07:25:05 UTC (68 KB)
[MA-7] LLM s Are Not Good Strategists Yet Memory-Enhanced Agency Boosts Reasoning
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在长周期环境中的战略推理问题，核心挑战在于有限的注意力资源导致模型难以在数千步的决策过程中保持战略一致性，从而引发策略漂移（strategic drift）。为克服这一瓶颈，论文提出EpicStar框架，其关键在于引入“记忆即策略”（memory as policy）机制：通过维护一个由成功历史经验构成的记忆库作为启发式策略，并结合工作记忆（working memory）以追踪短期环境变化。在推理阶段，动态门控机制决定是否直接执行检索到的动作，或基于检索到的历史经验与当前工作记忆进行上下文融合后进行新推理。实验以星际争霸II（StarCraft II）为测试平台，结果表明EpicStar显著优于基线方法，在多种对手策略和难度级别下均实现更高的胜率，同时推理消耗的令牌数减少一个数量级。研究证实，结构化的跨周期记忆是实现大语言模型代理在动态、自主环境中稳健长期战略执行的关键。
链接: https://arxiv.org/abs/2608.12626
作者: Yi Wu,Zhimin Hu
机构: University of Chicago(芝加哥大学); University of Wisconsin-Madison(威斯康星大学麦迪逊分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Strategic reasoning in Large Language Models (LLMs) within long-horizon environments is often limited by inconsistent subgoals. In these settings, finite attention resources prevent the model from maintaining strategic coherence over thousands of steps. This limitation leads to strategic drift, where localized decisions fail to sustain a coherent trajectory across reasoning. To address this, we introduce EpicStar, a framework that enables agents to learn memory as policy to tackle long-horizon reasoning. Specifically, the agent maintains a bank of successful past episodes as a heuristic alongside a working memory to track short-term environmental changes. During inference, a dynamic gating mechanism determines whether to execute a retrieved action directly or to perform new reasoning through a contextual fusion of the retrieved episodes and current working memory. Utilizing StarCraft II as the testbed, we evaluated EpicStar against diverse opponent styles. It significantly outperforms baseline methods, achieving higher win rates while consuming an order of magnitude fewer tokens, and it maintains this advantage consistently across difficulty levels and opponent strategies. Our findings provide compelling evidence that structured cross-episode memory is essential for enabling LLM agents to perform robust, long-term strategic execution in dynamic, autonomous settings.
[MA-8] Do LLM s Beat Nash? Testing Decentralized Coordination in Self-Play Multi-Agent Games
【速读】：该论文旨在解决在缺乏中心化控制与直接通信的情况下，多个独立运行的同构大语言模型（Large Language Models, LLMs）是否仍能通过自我推理有效协调行为、超越传统博弈论中无协作博弈的基准（即纳什均衡）。其核心问题是：在无法交流的前提下，模型能否基于对“其他模型亦为同质”的认知，实现接近最优协同结果的决策。解决方案的关键在于引入一个一次性、无通信博弈的基准测试框架，其中每名模型仅被告知其对手使用相同模型，而需在不沟通的情况下进行策略选择。实验结果显示，部分前沿闭源模型在双人矩阵博弈中持续超越纳什均衡，接近最优联合收益，尤其在多种博弈结构下表现显著；而多数开源模型虽有局部提升，但性能随博弈结构差异剧烈波动。然而，在四人及以上可互换代理的团队型博弈中，随着动作空间扩大，性能急剧下降，表明驱动双人博弈中自洽推理（self-play gain）的能力难以扩展至大规模多智能体协作场景。
链接: https://arxiv.org/abs/2608.12547
作者: Deborah Sinishaw,Qile Zhu,Edwin Meriaux,Gregory Dudek
机构: McGill University (麦吉尔大学)
类目: Multiagent Systems (cs.MA); Robotics (cs.RO)
备注: 5 pages, 5 figures. Submitted to the 2026 IEEE MIT Undergraduate Research Technology Conference (URTC)
Abstract:Large language model agents deployed without a central controller are often assumed to require communication to coordinate their actions. We ask what remains possible without it: when independent instances of the same model cannot communicate, can they still reason about their counterparts well enough to exceed the standard game-theoretic baseline for uncoordinated play? We introduce a benchmark of one-shot, no-communication games in which each of thirteen language models is told only that its counterparts are running the same model and is evaluated against the Nash equilibrium of the underlying game. In two-player matrix games spanning seven archetypes and two to ten actions per player, two frontier-hosted models consistently exceed their Nash benchmark, approaching the optimal joint outcome in several archetypes, while most open-weight models achieve only partial gains that vary sharply by game structure. Performance degrades substantially in team-based games with four or more interchangeable agents, particularly as the action space grows, suggesting that whatever capability drives self-play gains in dyadic games does not transfer to larger multi-agent teams.
[MA-9] Entropy-Augmented Multi-Objective Policy Optimization in Multiagent Systems
【速读】：该论文旨在解决多智能体系统在复杂环境（如海洋或地外基地）中进行多目标协同优化时，因传统多目标进化算法（如NSGA-II）仅关注目标空间多样性而忽视行为空间多样性，导致种群过早收敛、策略同质化的问题。其核心解决方案是提出一种基于熵增的策略评估方法，通过在智能体适应度评分中引入熵奖励项，强化对行为空间多样性的激励，从而抑制种群内策略的趋同。该方法在保持原有帕累托优化框架的基础上，显式引入行为空间的多样性信号，有效促进多智能体环境中具有差异化行为特征的策略探索。实验结果表明，在不同奖励结构的火星探测车任务场景中，该方法相较NSGA-II基线实现了最高达48%的超体积（hypervolume）提升，验证了行为多样性作为改进多目标多智能体进化优化的关键潜力。
链接: https://arxiv.org/abs/2608.12534
作者: Jamie Santos,Ayhan Alp Aydeniz,Raghav Thakar,Kagan Tumer
机构: The Collaborative Robotics and Intelligent Systems (CoRIS) Institute; Oregon State University (俄勒冈州立大学)
类目: Multiagent Systems (cs.MA); Robotics (cs.RO)
备注:
Abstract:Autonomous agent teams deployed in settings such as marine and extraterrestrial outposts must coordinate actions to achieve optimal outcomes across multiple competing objectives. Multi-objective evolutionary algorithms such as NSGA-II optimize for diversity in the objective space, but neglect diversity in the behavior space, possibly leading to premature convergence and a collapse in behaviors that may differentiate policies in different external conditions. To address this, we introduce an entropy-augmented policy evaluation strategy that incorporates an entropy bonus into agent fitness scores, discouraging behavioral homogeneity across the evolving population. By augmenting policy evaluation with a behavior-space diversity signal while preserving the underlying Pareto optimization framework, our method is designed to encourage exploration of behaviorally distinct policies in multiagent domains. We evaluate our approach across rover-domain experiments with qualitatively distinct reward structures and observe hypervolume improvements of up to 48% relative to the NSGA-II baseline, suggesting that behavioral diversity is a promising and underexplored direction for improving multi-objective multiagent evolutionary optimization.
[MA-10] Multi-AUV Ad-hoc network-based Target Tracking: A Value Gradient Guidance Multi-Agent Diffusion Reinforcement Learning Approach
【速读】：该论文旨在解决基于多自主水下航行器（Multi-AUV）自组织网络的目标跟踪问题，特别是在受限声学通信、动态拓扑结构及不确定海洋扰动环境下，如何实现多AUV协同稳定跟踪机动目标的挑战。现有基于多智能体强化学习（MARL）的方法普遍存在高维联合状态-动作建模复杂、策略生成对噪声敏感等问题，导致训练不稳定且跟踪性能下降。本文提出的核心解决方案是VGG-MADiffRL算法与基于扩散模型的分层控制架构MDCA。其关键在于：首先，构建三层次闭环控制框架（全局智能控制层、局部在线训练层、物理执行层），实现任务分配、本地决策与执行反馈的协同优化；其次，在局部在线训练层中引入基于价值梯度引导的多智能体扩散强化学习（VGG-MADiffRL），通过在逆向去噪过程中利用价值梯度指导动作生成，使策略趋向更高预期回报；同时采用双值网络联合优化与软目标更新机制，有效缓解值函数过估计与训练震荡，显著提升收敛稳定性。实验表明，该方法在收敛速度、跟踪精度和训练平稳性方面均优于现有方法，验证了其在动态水下环境中的有效性与工程实用性。
链接: https://arxiv.org/abs/2608.12436
作者: Jiaao Ma,Chuan Lin,Guangjie Han,Shengchao Zhu,Qian Zhu,Ying Liu,Zhenyu Wang
机构: Northeastern University (东北大学); Hohai University (河海大学)
类目: Machine Learning (cs.LG); Multiagent Systems (cs.MA); Networking and Internet Architecture (cs.NI)
备注:
Abstract:Multi-AUV ad-hoc network-based target tracking requires networked autonomous underwater vehicles (AUVs) to cooperatively track maneuvering targets under constrained acoustic communication, dynamic topology, and uncertain ocean disturbances. Although multi-agent reinforcement learning (MARL) enables decentralized coordination through centralized training, existing methods suffer from high-dimensional joint state-action modeling, noise-sensitive policy generation, leading to unstable training and degraded tracking. To address these issues, we propose VGG-MADiffRL, a value-gradient-guided multi-agent diffusion RL algorithm, and MDCA, a diffusion?based hierarchical control architecture. Leveraging underwater mission characteristics, we model sonar detection mechanisms and ocean current disturbances, formulating cooperative tracking for multi-AUV ad-hoc networks as an MDP. The proposed MDCA constitutes a three-tier closed-loop control framework: a global intelligent control layer, a local online training layer, and a physical action execution layer. This structure enables synergistic optimization across task allocation, local decision processes, and execution feedback. Within MDCA, the local online training layer is the policy learning framework; VGG-MADiffRL builds on diffusion policies and incorporates value gradients to guide action generation in the reverse denoising process, steering the generated actions towards higher expected returns. It employs twin value networks with joint optimization and soft target updates to mitigate overestimation and training oscillations, promoting more stable convergence. Experimental results show that VGG-MADiffRL consistently achieves faster convergence, higher tracking accuracy, and smoother training dynamics in cooperative tracking scenarios, validating its effectiveness and practical engineering value in dynamic underwater settings.
[MA-11] Heterogeneity-Aware Belief Synchronization for Semantic Communication in AI-Native 6G Networks
【速读】：该论文旨在解决6G智能网络中异构人工智能（AI）代理间因模型差异与计算约束多样而导致的信念不一致问题，这一问题严重影响了语义通信的有效性。在6G网络中，大量分布于低地球轨道卫星、高空平台、无人机、边缘服务器及地面设备等异构平台上的自主AI代理需持续感知环境并交换信息，而语义通信依赖于各代理对信息具有足够对齐的信念以正确解码。然而，由于各代理采用不同架构的异构模型且本地学习知识各异，传统协同训练或统一架构的方法难以适用。本文提出一种面向异构性的信念同步框架，其核心在于利用部署于多接入边缘计算（MEC）服务器的潜在转换模型（latent translation model），实现跨代理信念更新的无监督映射，无需联合训练或同质模型架构。该框架仅在必要时通过紧凑的信念更新进行交互，有效保障隐私、降低同步开销，并抑制局部知识漂移。案例研究验证了该框架在多层次地基/非地基网络中的有效性，结果表明其能维持较低的参数传输量（同步成本）和较小的信念对齐误差，显著提升了异构环境下语义通信的可靠性与效率。
链接: https://arxiv.org/abs/2608.13394
作者: Muhammad Hannan Akram,Muhammad Abubakar Rashid,Wassi Haider Kabir,Haejoon Jung,Kapal Dev,Syed Ali Hassan
机构: School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; Department of Electronic Engineering, Kyung Hee University, Yongin 17104, South Korea; CONNECT Centre and the Department of Computer Science, Munster Technological University, Ireland
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 8 pages, 4 figures
Abstract:6G networks will not be serving as communication infrastructures only; rather, they are expected to evolve into intelligent systems, where thousands of autonomous artificial intelligence (AI) agents are interconnected. The agents are deployed across a wide range of platforms including low Earth orbit (LEO) satellites, high-altitude platforms (HAPs), unmanned aerial vehicles (UAVs), edge servers, and terrestrial devices. These agents continuously observe their environment and exchange information. Semantic communication provides an efficient mechanism for exchanging meaningful information instead of raw data. However, its effectiveness depends on the communicating agents having sufficiently aligned beliefs to correctly interpret and decode the transmitted messages. This assumption becomes difficult to satisfy in the 6G network where heterogeneous AI models operate under diverse computational constraints and continuously acquire different knowledge from their local environments. This article presents a heterogeneity-aware belief synchronization framework for 6G AI-native networks. It uses latent translation models deployed on multi-access edge computing (MEC) servers. These models translate belief updates from one agent to agent-specific knowledge without requiring joint training and a homogeneous architecture of models. By exchanging compact belief updates through a latent translation model only when necessary, the framework preserves privacy, reduces synchronization cost, and minimizes local knowledge drift. We validate the framework through a case study on a multi-layered terrestrial/non-terrestrial network. Results demonstrate that it maintains low synchronization cost, measured by the number of parameters transmitted, and low belief alignment error across the heterogeneous agents in the case study.
[MA-12] EU-ETS under attack? The impact of carbon price suppression on the decarbonization of the power sector
【速读】：该论文旨在解决在地缘政治紧张导致能源成本上升的背景下，欧洲国家如何在推进脱碳与电气化转型的同时，制定合理的电力市场政策。具体而言，研究聚焦于意大利2026年“电费法令”（Decreto Bollette）中拟对部分燃气发电厂在电力市场投标中取消碳价等价信号的政策，评估其对长期投资决策、碳排放水平及消费者成本的影响。解决方案的关键在于采用基于多智能体强化学习（multi-agent reinforcement learning, MARLEY）的简化意大利电力系统模型，模拟不同绿色投资支持、资源充裕性及灵活性配置下该政策的长期后果。研究发现，部分抑制碳价信号虽可带来短期成本降低，但对总系统成本的长期影响有限，因减排延迟最终将由消费者承担；同时，碳价信号削弱显著降低了可再生能源与储能投资的激励，导致二氧化碳（CO₂）排放普遍上升。唯有在最激进的绿色投资支持情景下才能避免排放上升，但此类情景实质上已弱化了现货市场价格信号的作用，迫使转向与原政策初衷相悖的混合市场范式，凸显了政策干预在实现经济性、环境性与市场有效性之间难以兼顾的内在矛盾。
链接: https://arxiv.org/abs/2608.12363
作者: Javier Gonzalez-Ruiz,Carlos Rodriguez-Pardo,Alice Di Bella,Paolo Mastropietro,Jose Pablo Chavez-Avila,Massimo Tavoni
机构: Politecnico di Milano(米兰理工大学); CMCC Foundation- Euro-Mediterranean Center on Climate Change(欧洲地中海气候变化中心基金会); RFF-CMCC European Institute on Economics and the Environment(美国资源与环境经济欧洲研究所); Instituto de Investigacion Tecnologica, Universidad Pontificia Comillas(天主教大学技术研究中心)
类目: General Economics (econ.GN); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Systems and Control (eess.SY)
备注: 58 pages, 12 figures, 12 tables. Includes supplementary material
Abstract:European countries are debating policies to mitigate the increased energy costs caused by renewed geopolitical tensions, while pursuing decarbonization and electrification. A notable example is Italy’s 2026 Decreto Bollette package, which proposes to remove the carbon price equivalent from the bids of certain gas-driven power plants to wholesale electricity markets, among other provisions. We use this as a case study to assess the long-term implications of suppressing the carbon price signal in the electricity market for investment, emissions, and consumer costs. We employ a stylized Italian power system using MARLEY, a multi-agent reinforcement learning framework focused on long-term electricity market assessments. In this framework, we test this policy across configurations with varying levels of support for green investment, resource adequacy, and flexibility. Results show that partial suppression of the carbon price signal yields short-term cost reductions but only a minor long-term effect on total system costs, as the deferred emissions are ultimately repaid by consumers. CO _2 emissions rise across most configurations since suppressing the price signal erodes incentives for renewable and storage investment. Only the most ambitious configurations for supporting green investment avoid this outcome, but they do so by marginalizing the wholesale price signal itself, thereby requiring a commitment to a hybrid market paradigm that is in contradiction with the rationale of the proposed price intervention.
[MA-13] Optimal Control Strategies for Multi-Agent Sheep Herding
【速读】：该论文旨在解决多智能体协同控制中的牧羊问题，即如何利用n只狗将m只羊有效驱赶至原点。其核心挑战在于系统高维度与强非线性导致的标准数值优化方法难以收敛。解决方案的关键在于通过精心设计的初始猜测和超参数选择，使基于边界值问题求解器（solve_bvp）的方法得以收敛；同时探索了打靶法（shooting method）与基于迭代线性二次调节器（iLQR）的线性化方法。其中，线性化iLQR展现出更强的可扩展性，能够处理更多智能体的场景，但在狗与羊近距离交互时因局部线性假设失效，导致路径出现锯齿状振荡且收敛缓慢，尤其在羊的数量超过狗的数量时表现尤为明显。这一结果揭示了传统数值方法在复杂多智能体系统中的局限性，凸显了发展更鲁棒、非线性的协同控制策略的必要性。
链接: https://arxiv.org/abs/2510.25115
作者: Drake Brown,Trevor Garrity,Daniel Perkins,Davis Hunter,Wyatt Pochman
机构: Brigham Young University (杨百翰大学); University of Utah (犹他大学); University of Maryland (马里兰大学); Bredesen Center, University of Tennessee (田纳西大学布雷德森中心)
类目: Optimization and Control (math.OC); Multiagent Systems (cs.MA); Functional Analysis (math.FA)
备注: 11 pages, 5 figures
Abstract:We develop a cost functional and state-space equations to model the problem of herding m sheep to the origin using n dogs. Our initial approach uses solve_bvp to approximate optimal control trajectories. But this method often fails to converge due to the system’s high dimensionality and nonlinearity. However, with a well-chosen initial guess and carefully selected hyperparameters, we succeed in getting solve_bvp to converge. We also explore alternatives including the shooting method and linearization with the iterative Linear Quadratic Regulator (iLQR). While the shooting method also suffers from poor convergence, the linearized iLQR approach proves more scalable and successfully handles scenarios with more agents. However, it struggles in regions where dogs and sheep are in close proximity, due to strong nonlinearities that violate the assumptions of local linearization. This leads to jagged, oscillatory paths and slow convergence, particularly when the number of sheep exceeds the number of dogs. These challenges reveal key limitations of standard numerical techniques in multi-agent control and underscore the need for more robust, nonlinear strategies for coordinating interacting agents.
自然语言处理
[NLP-0] AutoDesign: Meta-Harness Optimization for Long-Horizon Agent ic Design
【速读】： 该论文旨在解决多模态信息源到结构化媒体输出的生成过程中，现有系统缺乏动态适应与持续自我优化能力的问题。当前范式普遍静态且无法基于经验积累实现递归改进，限制了生成质量与设计灵活性。其解决方案的关键在于提出AutoDesign框架，该框架通过一个元级“设计引导器”（meta-harness optimizer）驱动代码代理（code agent）在滚动反馈（rollout feedback）基础上，持续迭代优化底层“设计规范”（DesignHarness），从而实现与人类设计先验对齐的自进化机制。为验证该框架，研究构建了PosterBench基准，涵盖跨五大学科的100篇学术论文主赛道及用于受控评估的迷你子集。实验表明，AutoDesign在主赛道上取得78.32的最高得分，优于闭源商业系统Claude Design达7.45分；在七种配置中，引入学习所得的DesignHarness使平均得分从54.99提升至67.39（+12.4%）。在全自主长时程循环中，系统仅用40分钟完成253次工具调用和11轮编辑，即达到人类评价中会议海报的平均质量水平，且盲评研究表明其最受人类青睐。
链接: https://arxiv.org/abs/2608.13560
作者: Yaxin Luo,Haobin Jiang,Jialv Zou,Xu Huang,Wenhao Yan,Haodong Li,Zhengrong Yue,Jing Li,Xiaofu Chen,Xiaohan Zhao,Jiacheng Liu,Jiacheng Cui,Zhiqiang Shen,Xiaotong Li
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Tech Report. Code at: this https URL
Abstract:Transforming multimodal sources into condensed and structured media outputs can be fundamentally conceptualized as a long-horizon agentic process centered on a model-harness system. While an ideal harness system should align with human design priors and accumulate reusable experience through empirical exploration to drive recursive self-improvement, existing paradigms remain static and fall short of this capability. In this paper, we present AutoDesign, a framework that aligns with human design priors, where a meta-harness optimizer guides a code agent to recursively improve harness based on rollout feedback. To instantiate and evaluate this framework, we focus on the academic paper-to-poster generation task and introduce PosterBench, comprising a 100-paper Main Track spanning five disciplines and PosterBench-mini, a shared 10-paper subset for controlled evaluation. On the PosterBench Main Track, AutoDesign achieves the highest score of 78.32, surpassing the closed-source commercial system Claude Design by 7.45 points. Across seven controlled code-agent-model configurations, integrating the learned DesignHarness consistently improves performance, increasing the average PosterBench Score from 54.99 to 67.39 (+12.4%). In a fully autonomous long-horizon loop, it executes 253 tool calls and 11 editing turns within 40 minutes for under 3, reaching average conference-poster quality in human evaluation. A system-blind human study further demonstrates that AutoDesign achieves the highest human preference among evaluated systems.
[NLP-1] OmniScientist: An Omni-Modal Omni-Discipline AI Scientist
【速读】： 该论文旨在解决当前生成式AI在科学研究中因局限于文本、代码或预计算摘要等单一模态而无法充分捕捉科学发现所依赖的时空关联、跨通道关系及过程性证据的问题，导致研究流程虽覆盖广泛但缺乏对原始异构证据的全面感知与推理能力。其解决方案的关键在于提出OmniScientist——一个端到端的多模态人工智能科学家框架，通过引入感知层与三个自主代理（构思、实验、撰写）构成确定性流水线，实现从原始异构数据（包括图像、信号、音频、视频、三维结构、轨迹、表格、公式和图谱等）出发的全生命周期科研自动化。系统通过在代码中执行“创意性检查、严谨性检查和结论验证”，确保研究的新颖性、统计有效性、执行可追溯性与数值可追踪性。在36个真实数据案例上的评估表明，该系统可在所有案例中完成从原始数据到成稿论文的完整路径，平均论文评分为6.3（基于参考推理模型），且在与仅接收预计算标量特征的盲对比版本的配对测试中，在全部7项评估维度上均表现更优，并在85%的对抗判断中胜出，证明了贯穿研究全周期的直接感知对证据驱动型科学发现至关重要，为构建具备广泛能力的人工智能科学家提供了可行路径。
链接: https://arxiv.org/abs/2608.13558
作者: Bobo Li,Hao Fei,Tianjie Ju,Mong-Li Lee,Wynne Hsu
机构: National University of Singapore(新加坡国立大学); University of Oxford(牛津大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 30 pages, 13 figures, 19 tables. Project page: this https URL
Abstract:Recent advances in foundation models have enabled AI scientists to automate increasingly complete research workflows, from hypothesis generation and code execution to manuscript preparation. Yet workflow coverage alone does not provide access to the full evidence on which scientific discovery depends. Existing systems typically reason over text, code, labels, or precomputed summaries, leaving scientifically decisive spatial, temporal, cross-channel, and procedural relations unavailable to the agent. We introduce OmniScientist, an end-to-end, omni-modal AI scientist that conducts multidisciplinary research directly from heterogeneous raw evidence. A perception layer and 3 autonomous agents for ideation, experiment, and writeup operate within a deterministic pipeline, allowing observations to shape research questions, experimental decisions, and final claims throughout the research lifecycle. By running idea, rigour, and claim checks in code, the system enforces novelty screening, statistical validity, execution provenance, and numerical traceability. We evaluate OmniScientist on 36 real-data cases spanning 5 discipline families, 4 families of scientific evidence, and modalities including images, signals, audio, video, 3-D structures, trajectories, tables, formulae, and graphs. The system completes the full path from raw data to a compiled manuscript in all 36 cases and achieves a mean overall paper score of 6.3 with the reference reasoning backbone. In paired comparisons against a blind variant that receives only precomputed scalar features, direct perception improves all 7 evaluation dimensions and wins 85% of head-to-head judgments. These results show that lifecycle-wide perception is essential for evidence-grounded scientific discovery and provides a practical path toward broadly capable AI scientists.
[NLP-2] LittleLearner: Language Models Under Pedagogically Controlled Knowledge Exposure
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在复杂、异构的网络规模文本语料上训练时，其知识与能力习得过程难以量化和分析的问题。由于模型在预训练阶段接触了大量跨年龄层的知识内容，导致其知识边界模糊，无法精确追踪特定认知发展阶段的能力形成机制。为此，研究提出LITTLECURRICULUM——一个经过精心筛选的880亿词元（88B-token）预训练语料库，专为美国小学阶段课程内容设计，明确排除五年级以上教学概念、事实与词汇。基于此语料库从头训练出的50亿参数模型LITTLELEARNER，具备足够的语言理解与生成能力以支持开放式评估，同时其知识与能力范围严格受限于可解释的课程指导框架，从而构建了一个发育受限的可控实验环境。该研究的关键在于通过定义清晰、可追溯的教学内容边界，实现对模型学习过程的系统性观察与干预，使研究者能够探究模型如何在限定范围内获取、表征和运用知识。实验表明，通过后训练或上下文学习（in-context learning）注入新知识虽能提升模型对已有知识的利用效率，但不会引发超出原始训练范围的能力扩展。这一发现凸显了该受控环境在未来研究中对于揭示模型学习机制、验证知识注入方法及评估模型可解释性的核心价值。
链接: https://arxiv.org/abs/2608.13545
作者: Fanfei Li,Jana Zeller,Manuel Prada-Corral,Thaddäus Wiedemer,Prasanna Mayilvahanan,Ryan Cotterell,Wieland Brendel
机构: Max Planck Institute for Intelligent Systems (MPI-IS); Ellis Institute; ETH Zurich (苏黎世联邦理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Modern language models are trained on heterogeneous web-scale text corpora. Consequently, studying knowledge and skill acquisition is difficult, as prior exposure to related content is hard to characterize. To address this challenge, we introduce LITTLECURRICULUM, a curated 88B-token pretraining corpus tailored to U.S. elementary school material, explicitly excluding concepts, facts, and vocabulary taught above Grade 5. Training a 5B-parameter LLM from scratch on LITTLECURRICULUM yields LITTLELEARNER, a model with sufficient language competence for open-ended evaluation, yet with clear knowledge and capability boundaries mapped to interpretable curriculum guidelines. We release LITTLECURRICULUM and LITTLELEARNER as a developmentally restricted sandbox to study how models acquire, represent, and use data under a well-defined training scope. We illustrate the sandbox’s utility in a first suite of experiments on injecting new knowledge through post-training and in-context learning. These methods let LITTLELEARNER better utilize existing knowledge, but do not raise out-of-scope capabilities. Our findings underscore the value of this controlled environment for future investigations.
[NLP-3] SAEVerbalizer: Generating Explanations for Sparse Autoencoder Features via Representation Verbalization
【速读】： 该论文旨在解决稀疏自编码器（Sparse Autoencoders, SAE）在大语言模型（Large Language Model, LLM）表征中提取特征后，其语义解释仍依赖外部观测所带来的局限性问题。传统方法主要通过观察模型行为来推断特征含义，导致解释表面化且在大规模数据收集上计算效率低下。为此，本文提出SAEVerbalizer框架，其核心创新在于将SAE解码器方向注入LLM的表征空间，并通过微调下游层使模型能够生成自然语言形式的特征解释。该方案的关键在于利用解码器方向作为语义线索，实现从表征到可解释性描述的端到端映射，从而直接基于解码器方向生成解释，克服了依赖间接行为证据的弊端。实验表明，所学的语义化能力可泛化至未见过的特征，跨独立训练的SAE词典迁移，并可通过轻量级适配器扩展至不同LLM的SAE特征；干预实验进一步验证了多方向注入可融合语义、单方向反转则引发对应语义偏移，证明了该方法具备可控性和语义可解释性。
链接: https://arxiv.org/abs/2608.13538
作者: Weihan Meng,Hongzhu Guo,Yi Jing,Dewen Liu,Zijun Yao,Xiaozhi Wang,Lei Hou,Juanzi Li
机构: Tsinghua University (清华大学); Peking University (北京大学); Fudan University (复旦大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Sparse autoencoders (SAEs) are proposed to extract numerous features from large language model (LLM) representations, yet explaining these features still relies primarily on external observation. This reliance leads to superficial explanations inferred from observed model behavior and computational inefficiency from collecting such behavioral evidence at scale. We introduce SAEVerbalizer, a framework that injects SAE decoder directions into an LLM’s representations and fine-tunes the LLM’s downstream layers to generate natural-language explanations of the injected features. Once trained, the resulting verbalizer explains SAE features directly from decoder directions, addressing both limitations. Our experiments show that the learned verbalization capability generalizes to unseen features, transfers across separately trained SAE dictionaries, and, with a lightweight adapter, extends to SAE features from different LLMs. Intervention experiments show that injecting multiple directions yields an explanation combining their meanings, while reversing individual directions produces corresponding meaning shifts.
[NLP-4] DFM Mimir v1: An Open HRM Delivering Frontier Performance at 1B Parameters Using Only Permissible Post-Training Data
【速读】： 该论文旨在解决当前大语言模型（Large Language Model, LLM）研发中依赖大规模、往往存在版权或合规性问题的数据集所导致的高门槛问题，尤其针对致力于开源与伦理合规数据的研究者。其解决方案的关键在于提出Mimir v1，一个基于分层推理模型（Hierarchical Reasoning Model, HRM）架构、参数量为10亿的新型语言模型，从零开始训练，并仅使用可许可的后训练数据，在英语任务上展现出极具竞争力的表现，同时在丹麦语任务上达到了新的基准水平。Mimir v1在包含20个基准测试的多领域评估中，超越了原始HRM-Text 1B模型，并在性能上媲美更大规模的前沿模型（如Qwen 3.5 4B和Gemma 4 E2B），验证了其在有限合规数据条件下实现高性能的有效性。
链接: https://arxiv.org/abs/2608.13517
作者: Peter Schneider-Kamp,Jacob Nielsen,Gianluca Barmina,Kenneth Enevoldsen,Lukas Galke Poech
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Technical Report, 20 Pages, 1 Model, Hierarchical Reasoning Model
Abstract:Current large language model development relies on massive, often non-permissible datasets, creating a high barrier for researchers committed to open-source and ethically sourced data. We introduce Mimir v1, a 1-billion-parameter language model based on the Hierarchical Reasoning Model (HRM) architecture, that is trained from scratch and delivers highly competitive performance for English and sets a new state of the art for Danish using only permissible post-training data. Trained on a mixture of 161 datasets, Mimir v1 outperforms the original HRM-Text 1B and competes with larger frontier models like Qwen 3.5 4B and Gemma 4 E2B, tested across 20 benchmarks for English, Math Code and Danish. The model is available on the Hugging Face Hub: this https URL
[NLP-5] Measuring Task-Agnostic Training Data Influence Across Language Model Pretraining
【速读】： 该论文旨在解决在语言模型预训练过程中，难以一致地衡量训练数据影响的问题。传统方法依赖于选择特定下游任务或验证集作为评估基准，但这类选择具有主观性且难以反映模型的通用能力，同时在不同训练阶段使用任务性能进行比较也增加了分析复杂性。其解决方案的关键在于提出一种不依赖下游任务或验证集的训练数据影响度量方法：通过计算单个训练样本的梯度更新对减少模型参数与最终参数之间平方距离的贡献，来定义其影响力，并利用中间检查点信息估计该指标而无需重新训练。基于Pythia和PolyPythia系列共18种配置的应用结果表明，具有影响力的训练数据随时间呈现系统性变化——早期阶段，文献类数据与最终参数轨迹更趋一致；后期阶段，STEM相关数据的影响力显著增强。这一定性转变在不同模型配置中均具广泛一致性，为理解训练过程中数据影响力动态演化提供了可操作的轨迹级视角，补充了以往基于特定下游任务或验证集的影响分析。
链接: https://arxiv.org/abs/2608.13515
作者: Yuto Nishida,Hirokazu Kiyomaru,Yusuke Oda,Takashi Kodama,Chaoran Liu,Daisuke Kawahara,Yusuke Miyao,Max Müller-Eberstein,Masaru Isonuma
机构: Nara Institute of Science and Technology(奈良先端科学技術大学院大学); NII LLMC; Waseda University(早稻田大学); The University of Tokyo(东京大学); IT University of Copenhagen(哥本哈根信息技术大学); Tohoku University(东北大学)
类目: Computation and Language (cs.CL)
备注: Accepted to COLM 2026
Abstract:Measuring training data influence consistently across language model pretraining is challenging. It is difficult to select downstream tasks or validation sets representative of a model’s general capabilities, and reliance on task performance at intermediate checkpoints complicates comparisons across training. We propose a measure of training data influence that does not require selecting a downstream task or validation set as the attribution target. Specifically, we define an example’s influence by how much its gradient update reduces the squared distance to the final parameters of a given pretraining run, and estimate this quantity from intermediate checkpoints without retraining. Applying the method to 18 configurations from the Pythia and PolyPythia suites, we find systematic temporal changes in influential data. Early in training, literature-related data are more strongly aligned with the trajectory toward the final parameters, whereas STEM data become more strongly aligned in later stages. This qualitative crossover is broadly consistent across model configurations. Our results provide a tractable trajectory-level view of how influential data change throughout pretraining, complementing influence analyses defined with respect to specific downstream tasks or validation sets.
[NLP-6] Intern-S2-Preview: Scientific Agent ic Foundation Model
【速读】： 该论文旨在解决科学发现中对多模态科学证据进行推理、与科学工具及环境交互以及在长任务周期中持续推进的挑战，核心问题是构建具备跨模态理解、复杂推理与长期规划能力的科学智能体（scientific agent）。其解决方案的关键在于提出Intern-S2-Preview系列科学代理基础模型，通过融合多阶段训练范式实现性能突破：首先基于渲染的科学文档、图文交错数据及多样化科学语料完成多模态预训练；随后采用统一后训练流程，包括监督微调、可扩展的多任务强化学习（RL）、黑箱与白箱代理强化学习以及在线策略蒸馏。为提升训练与推理稳定性与效率，引入多项关键技术，如部分回放结合离策略校正、自适应长度正则化、在线推测解码、鲁棒多任务优化及面向代理任务的轨迹感知经验组装。在架构层面，397B参数规模模型通过时间序列建模从长序列理解扩展至数值预测，并设计独立的Memory Decoder（Memory Decoder）路径，在不修改冻结的397B主干网络的前提下，实现快速科学领域专业化，显著提升生物指令任务表现（平均得分由56.92提升至60.32）。实证结果表明，该模型在科学、多模态、代理及通用基准上均达到领先或具有竞争力的表现。
链接: https://arxiv.org/abs/2608.13505
作者: Lei Bai,Jiaqi Cao,Chiyu Chen,Guanzhou Chen,Kai Chen,Guangran Cheng,Erfei Cui,Xuanlang Dai,Shengyuan Ding,Shangheng Du,Yanhui Duan,Yue Fan,Youqing Fang,Quan Gan,Yuanyuan Gao,Jiaye Ge,Lixin Gu,Yuzhe Gu,Qipeng Guo,Junjun He,Xin Hong,Ming Hu,Zhouqi Hua,Haian Huang,Junhao Huang,Zixian Huang,Minxi Jin,Lingkai Kong,Alexander Lam,Zehao Li,Zonglin Li,Tianhao Liang,Dahua Lin,Junyao Lin,Tianyang Lin,Zhouhan Lin,Jiangning Liu,Jin Liu,Kuikun Liu,Wenran Liu,Yifei Liu,Yuhong Liu,Yuhong Liu,Zhoumianze Liu,Ziyan Liu,Ziyu Liu,Haijun Lv,Han Lv,Chengqi Lyu,Le Ma,Ningsheng Ma,Zerun Ma,Haoyang Peng,Runyu Peng,Jifei Shan,Zixin Shang,Kou Shi,Xiang Shi,Qisheng Su,Xuerui Su,Hao Sun,Xiao Sun,Yanan Sun,Yu Sun,Huanze Tang,Yinghao Tang,Wenhui Tian,Zhongbo Tian,Bingli Wang,Haomin Wang,Jiarui Wang,Jingzhi Wang,Rui Wang,Xiquan Wang,Yi Wang,Zhecan Wang,Ziyi Wang,Zun Wang,Rubin Wei,Lianyi Wu,Wen Wu,Yue Wu,Yuhan Wu,Zhenyu Wu,Zijian Wu,Shuhao Xing,Jun Xu,Xingle Xu,Xuenan Xu,Xiangchao Yan,Ziang Yan,Bowen Yang,Danni Yang,Lin Yang,Zhiqi Yang,Qian Yao,Haochen Ye,Peng Ye,Jinhui Yin,Jiashuo Yu
机构: Shanghai AI Laboratory (上海人工智能实验室)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: 35 pages, 12 figures
Abstract:Scientific discovery increasingly requires AI systems that can reason over scientific evidence of heterogeneous modalities, interact with scientific tools and environments, and sustain progress across long task horizons. We present Intern-S2-Preview, a series of scientific agentic foundation models designed to support multimodal scientific understanding, reasoning, generation, and long-horizon tasks. The training pipeline begins with scientific multimodal pre-training over rendered scientific documents, interleaved image-text data, and diverse scientific corpora. Starting from the pretrained checkpoint, we apply a unified post-training pipeline consisting of supervised fine-tuning, scalable multi-task reinforcement learning (RL), black- and white-box agentic RL, and on-policy distillation. This pipeline is supported by practical techniques that improve rollout and training stability and efficiency, including partial rollout with off-policy correction, adaptive length regularization, online speculative decoding, robust multi-task optimization, and trace-aware experience assembly for agentic tasks. At the architecture level, Intern-S2-Preview-397B extends time series modelling from efficient long-sequence understanding to numerical forecasting, while Memory Decoder is studied as a separate memory-augmented path for rapid scientific specialization without modifying the frozen 397B backbone. Evaluations across scientific, multimodal, agentic, and general-purpose benchmarks show that Intern-S2-Preview-397B achieves competitive or leading results in multiple settings. The time series modules improve scientific signal understanding and forecasting on SciTS, while the separate Intern-MemDec-4B extension improves the Biology-Instructions average score from 56.92 to 60.32 without modifying the frozen 397B backbone.
[NLP-7] oward a Gricean Retreat: Probing LLM s for Knowledge Boundaries and Referent Specificity
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在面对超出其知识边界的实体时，倾向于生成看似合理但事实上虚假细节的问题。这一现象违背了合作性沟通中的格赖斯准则（Gricean maxims），即当说话者对指称对象不确定时，应退回到更通用、更安全的表述以确保真实性。研究的关键在于探究模型是否具备执行这种“格赖斯式后退”（Gricean retreat）的认知基础：一是模型激活状态能否编码指称对象是否位于其知识边界之内；二是模型能否预判即将生成指称的特定性水平。实验结果表明，模型确实具备这两项能力，其内部表征能够感知知识边界并预测指称特定性，但生成策略并未整合这些信号——模型仍强烈偏好具体指称，即使面对未知实体且存在正确的一般性替代方案时亦如此。因此，尽管格赖斯式后退的神经基础存在，但驱动生成行为的策略机制缺失。研究提出将知识边界意识与生成特定性动态耦合作为实现“格赖斯对齐”（Gricean alignment）的初步路径，为未来通过训练或引导目标实现更诚实、更可靠的语言生成提供了方向。
链接: https://arxiv.org/abs/2608.13484
作者: Dananjay Srinivas,Saksham Khatwani,Maria Pacheco
机构: University of Colorado, Boulder(科罗拉多大学博尔德分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:When asked about entities outside their knowledge boundary, LLMs routinely fabricate plausible-sounding details rather than backing off to safer, more general claims. We frame this failure through a Gricean lens: a cooperative speaker who is uncertain about a referent retreats up the specificity hierarchy, trading informativeness for truthfulness. We ask whether LLMs have the ingredients to perform this retreat. Using a T-REx-based benchmark that varies entity familiarity and referent specificity, we probe models to answer two questions: (i) do their activations encode whether a referent falls inside the knowledge boundary, and (ii) do they anticipate the specificity of the referent they are about to generate? We find that the answer to both is yes, but the two signals are not reconciled in generation. Models overwhelmingly prefer specific referents even when the entity is unknown to them, and do so even when offered correct generic alternatives. The substrate for a Gricean retreat is present, but the policy that would act on it is not. We position our findings as a first step toward Gricean alignment, training or steering objectives that couple knowledge-boundary awareness to referent-specificity during generation.
[NLP-8] Synthetic Persona Pretraining: Alignment from Token Zero
【速读】： 该论文旨在解决生成式 AI 在自主应用场景中与人类价值观对齐不足的问题，尤其关注当前对齐机制多在预训练完成后引入，导致价值观念仅作为表层附加物而非深层内化，从而易引发后续的不一致行为。其核心解决方案是提出合成人格预训练（Synthetic Persona Pretraining, SPP），关键在于从预训练初始阶段（即“token zero”）便将期望的助手人格（assistant persona）深度嵌入模型。具体而言，SPP通过在预训练语料中注入基于规范性价值准则生成的第一人称反思文本，并在标准交叉熵损失下联合训练原始文档及其反思内容，使模型在海量参数和数据规模下（如30亿参数、5000亿词元）自底向上地习得目标人格。随后通过对话数据进行后训练（persona binding），将该人格牢固绑定至助手身份。实验表明，相较于延迟对齐，早期介入的SPP显著提升对价值宪章的遵循度、抗越狱攻击能力及分布外道德困境中的对齐率，且效果随预训练预算增加而增强。这证明了在预训练阶段即实施人格干预对于实现深度对齐的重要性。
链接: https://arxiv.org/abs/2608.13482
作者: Julian Minder,Viktor Moskvoretskii,Raghav Singhal,Difan Jiao,Andy Arditi,Shaobo Cui,Yiderigun Borjigin,Kartik Bali,Stefan Krsteski,Harsh Raj,Huu Nguyen,Jannik Brinkmann,Ashton Anderson,Roland Aydin,Robert West
机构: EPFL(洛桑联邦理工学院); University of Toronto(多伦多大学); Northeastern University(东北大学); SJTU(上海交通大学); Saarland University(萨尔兰大学); Hereon(德国亥姆霍兹海洋研究中心); TUHH(汉堡工业大学); Ontocord AI(Ontocord AI)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:As language-model-based AI is increasingly deployed in autonomous settings, aligning its goals and values with those of humans becomes critical. Today, alignment, and the assistant identity itself, are typically introduced only after pretraining, once behavioral priors are already established. This can make values a thin overlay, rather than deeply rooted, and facilitate subsequent misalignment. Pursuing a different paradigm, we introduce Synthetic Persona Pretraining (SPP), which installs the desired assistant persona from token zero in pretraining. First, we annotate pretraining documents with value-aligned first-person reflections derived from a normative value constitution. Second, we pretrain via the standard cross-entropy loss on standard pretraining documents as well as their reflections, which installs the desired persona among a multitude of other personas. Finally, we post-train on user-assistant dialogue data, which binds this desired persona to the assistant identity, a process we call persona binding. By pretraining models up to 3B parameters on 500B tokens, we show that SPP improves constitution following and jailbreak robustness, and reduces the misalignment rate in out-of-distribution moral dilemmas, while preserving capabilities. Early intervention matters: compared with alignment from token zero, introducing SPP only at the end of pretraining yields weaker constitution adherence, does not shift value priorities, and leads to less aligned choices in dilemmas. This advantage depends on persona binding and, importantly, increases with pretraining budget. Overall, our results show that shaping values early is critical for alignment and establish pretraining-time persona interventions as an effective approach to do so.
[NLP-9] MARC v1: An Open-Source Multi-Agent Framework for Clinical AI Reasoning and Coordination
【速读】： 该论文旨在解决临床推理中大型语言模型（LLM）因缺乏可解释性、难以追踪错误来源及依赖人工提示工程而导致的可靠性与可复现性问题。其核心解决方案是提出一种名为多智能体推理与协调（Multi-Agent Reasoning and Coordination, MARC）的开源框架，通过构建角色专业化、任务分工明确的多智能体系统，实现从信息提取、逻辑推理、答案生成到结果评估的全流程协同。该框架的关键在于采用确定性的多智能体编排机制，支持显式的上下文传递与可追溯的中间输出，从而实现各阶段故障的逐级归因。此外，引入分解器（Decomposer）模块，能够将自然语言描述自动转换为特定任务的智能体提示，彻底消除对人工提示工程的依赖。MARC框架具备模型无关性、可解释性强、部署灵活（支持API调用与本地CPU运行）以及完全通过YAML配置无需代码修改等特性，显著提升了临床领域专家在无编程背景下的可访问性与使用效率。
链接: https://arxiv.org/abs/2608.13476
作者: Saisha Shetty,Satvik Tripathi,Austin Lin,Colin Zhao,Theodore Kim,Don Enwerem,Jacinta Arnold,Shahriar Faghani,Tessa S Cook
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 13 pages, 4 figures
Abstract:We present Multi-Agent Reasoning and Coordination (MARC), an open-source framework that replaces monolithic LLM prompting with deterministic multi-agent orchestration for clinical reasoning. MARC coordinates role-specialized agents for extraction, reasoning, answer generation, and evaluation, with explicit context passing and traceable intermediate outputs, enabling stage-wise failure attribution. We additionally introduce a Decomposer module that generates task-specific agent prompts from a plain-language description, eliminating manual prompt engineering. The framework supports both API-based and local CPU-compatible deployments and is entirely configurable via YAML, without code modifications. MARC is designed to be model-agnostic, interpretable, and accessible to clinical domain experts without programming expertise. The full framework is available at this https URL.
[NLP-10] MLLM -Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification
【速读】： 该论文旨在解决现有图像分类模型在跨领域（multi-domain）和多难度级别下泛化能力不足的问题，尤其针对单一任务训练的数据集上表现优异但难以适应多样化视觉场景的局限性。其核心解决方案是提出一种基于大语言模型（LLM）的自适应路由框架——ARMDIL（Adaptive Router for Multi-Domain Image classification with LLMs），其关键在于利用一个多功能的多模态大语言模型（Multimodal Large Language Model, MLLM）代理，根据输入图像的语义与视觉特征动态选择最适配的视觉主干网络（vision backbone）。该框架构建了一个包含卷积神经网络（ResNets）、自监督表示学习模型（SSL）及视觉-语言模型（VLMs）的多样化集成系统，所有模型均在统一标签空间下训练，该标签空间由多个分布与特性各异的图像数据集构建而成。通过实证分析揭示了不同架构在不同视觉域中的优势与脆弱性，而ARMDIL能够有效权衡这些差异，在性能上媲美专门训练的路由策略；更重要的是，它通过简单的提示（prompt）修改即可快速整合新知识，显著提升模型的可适应性与可解释性，为构建更可靠、通用的视觉系统（如人工智能助手与自主机器人）提供了重要技术路径。
链接: https://arxiv.org/abs/2608.13463
作者: Daniel Perkins,John Squires,Janou Milligan,Chandra Raskoti,Linda Ungerboeck
机构: The Bredesen Center for Interdisciplinary Research and Graduate Education; University of Tennessee (田纳西大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 8 pages, 4 figures, 7 tables
Abstract:Modern image classification models excel when trained on single task-specific datasets but often struggle to generalize across domains and difficulty levels. We propose ARMDIL, an Adaptive Router for Multi-Domain Image classification with LLMs. ARMDIL is an ensemble that uses a multimodal large language model (MLLM) agent to dynamically route each image to the most suitable vision backbone. Our diverse ensemble employs convolutional neural networks (ResNets), self-supervised representation learners (SSL), and vision-language models (VLMs), each trained on a unified label space constructed from multiple image datasets with differing distributions and characteristics. Empirical evaluations illuminate the distinct capabilities and vulnerabilities of each architecture across disparate visual domains. Crucially, we show that ARMDIL effectively navigates these trade-offs, performing competitively with specialized training-based routers. Furthermore, it drastically improves adaptability by allowing new information to be integrated via simple prompt modifications, while enhancing interpretability through natural language reasoning traces. These advances in cross-dataset image classification pave the way for more reliable general-purpose vision systems such as AI assistants and autonomous robots.
[NLP-11] Are You Sure Youre Sure? On the Impact of Instruction Tuning on Confidence and Lexical Diversity
【速读】： 该论文旨在解决指令微调（instruction tuning）语言模型在生成任务中表现出的“言语化过度自信”（verbalized overconfidence）问题，尤其关注其在问答任务中与生成支撑性推理过程（supporting rationales）的关联性。研究发现，尽管指令微调对模型预测准确率影响有限，但显著提升了模型的自信程度，且这种自信提升伴随生成推理过程的词汇多样性变化。解决方案的关键在于揭示：指令微调会系统性地降低跨推理路径间的多样性（cross-rationale diversity），而表层词汇多样性（surface-level lexical diversity）则呈现不一致的变化趋势，且这些变化独立于答案选择和推理长度等变量。因此，模型自信与推理多样性是受指令微调影响的两个独立维度，表明在评估模型可靠性时需同时考虑信心水平与推理过程的多样性。
链接: https://arxiv.org/abs/2608.13430
作者: Irina Proskurina,Mayank Kumar,Oyindolapo O. Komolafe
机构: Cohere Labs Community; Laboratoire Hubert Curien, UMR CNRS 5516, Saint-Étienne, France; School of Computer Science Engineering and Technology (SCSET), Bennett University, Greater Noida, India; School of Physical Therapy, Faculty of Health Sciences, Western University, London, Canada
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Instruction-tuned language models achieve strong performance across a range of generation tasks, but have also recently been shown to exhibit verbalized overconfidence. In question answering, verbalized model overconfidence may be associated with the consistency of the generated supporting rationales. In this paper, we study whether corresponding changes in the lexical diversity of generated answer rationales accompany changes in model confidence induced by instruction tuning. We evaluate three matched base and instruction-tuned models across question-answering benchmarks and find that instruction tuning consistently alters answer confidence, despite limited changes in predictive accuracy and decreases in likelihood-based calibration. Secondly, we observe a non-uniform effect of instruction tuning on rationale diversity: cross-rationale diversity consistently decreases, whereas surface-level lexical diversity varies in both direction and magnitude across models and benchmarks. Finally, we find that these differences persist after controlling for answer selection and rationale length, confirming that confidence and rationale diversity capture distinct effects of instruction tuning.
[NLP-12] Reduced Matrix Multiplication: Input-Adaptive Matrix-Product Reduction for LLM Inference
【速读】： 该论文旨在解决基于Transformer的语言模型在推理阶段因重复进行高维矩阵乘法而导致的高昂计算开销问题。其核心解决方案是提出一种无需训练、输入自适应的推理优化方法——简化矩阵乘法（Reduced Matrix Multiplication, RMM），通过在矩阵乘法的收缩维度上选择信息量丰富的特征片段，从而减少不必要的计算量，且不改变模型权重。RMM的关键在于引入简单的保留比例（retention ratio）控制机制，实现精度与效率之间的平滑、可预测的权衡。实验表明，该方法在1B至70B参数规模的语言模型中均表现出良好的鲁棒性，尤其在中等压缩比下仍能保持稳定性能，且适用于判别任务、自回归生成及长序列场景；进一步拓展至多模态视觉-语言任务也验证了其普适性。机制分析揭示了Transformer内部结构的非对称性：注意力模块的计算远比前馈网络（MLP）组件更易压缩。在NVIDIA A100上的实测结果显示，结合定制内核的加速可显著提升实际运行效率，尤其是在长序列输入时优势明显。综上，RMM为推理阶段的输入自适应优化提供了一条可扩展的技术路径。
链接: https://arxiv.org/abs/2608.13426
作者: Zixuan Lan,Yanhong Li,Jiawei Zhou
机构: University of Chicago(芝加哥大学); Stony Brook University(石溪大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 24 pages
Abstract:Transformer-based language models achieve strong performance but incur substantial inference cost due to repeated high-dimensional matrix multiplications. We propose Reduced Matrix Multiplication (RMM), a training-free, input-adaptive inference method that reduces Transformer matrix products by selecting informative slices along their contraction dimensions, without modifying model weights. Under a simple retention-ratio control, RMM provides a smooth and predictable accuracy-efficiency trade-off. Across language models ranging from 1B to 70B parameters, we find that reduction tolerance depends on the model family, task, component, and retention ratio, although it often improves with model scale. Under moderate reduction, RMM remains robust across the evaluated discriminative, autoregressive generation, and long-context settings. We further show that the same principle extends to multimodal vision-language inference. Mechanistic ablations reveal a structural asymmetry within Transformers: attention-side computations are substantially more reducible than MLP components. Finally, wall-clock benchmarks with custom kernels on an NVIDIA A100 show that these computational savings can translate into practical runtime gains, especially at longer sequence lengths. Together, these results position RMM as a scalable direction for input-adaptive inference-time optimization.
[NLP-13] Motor Cognitive or Corpus? What Survives Cross-Lingual Transfer in Speech-Based Parkinsons Disease Detection
【速读】： 该论文旨在解决自监督学习（Self-supervised Learning, SSL）语音表征在帕金森病（Parkinson’s Disease, PD）检测中是否真正捕捉到疾病特异性特征，还是仅依赖于数据集特定的混杂因素（dataset-specific confounds）这一关键问题。由于大多数SSL骨干模型仅在健康语音上进行预训练，其对病理特征的泛化能力尚不明确。为此，研究采用分层分析方法，对九种SSL语音骨干模型在三种语言上的表现进行评估，并设计了多场景实验以逐步引入受试者身份、录音条件、语言及病理状态的分布偏移。研究的关键发现为：第一，最优表示层的选择高度依赖于源数据集，而非SSL架构本身；第二，迁移后的判别信号缺乏病理特异性——用于识别PD的分类器在目标数据集中对帕金森病和痴呆语音均赋予相近的高置信度概率。这些结果揭示了当前基于语音的病理识别模型在临床部署前亟需解决的核心局限性。
链接: https://arxiv.org/abs/2608.13425
作者: Serli Kopar,Sam Gijsen,Abner Hernandez,Paula Andrea Perez-Toro,Kerstin Ritter
机构: Hertie Institute for AI in Brain Health, University of Tübingen, Tübingen, Germany; Tübingen AI Center, University of Tübingen, Tübingen, Germany; Charité–Universitätsmedizin, Department of Psychiatry and Psychotherapy, Berlin, Germany; Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS); Signal Processing (eess.SP)
备注:
Abstract:Self-supervised learning (SSL) speech representations achieve strong performance for Parkinson’s disease (PD) detection within individual corpora. However, it remains unclear whether these models capture disease-related characteristics or exploit dataset-specific confounds, particularly since most SSL backbones are pretrained exclusively on healthy speech. To investigate this question, we perform a layer-wise analysis of nine SSL speech backbones using a low-capacity logistic regression probe across three languages. We structure the evaluation as multiple scenarios that progressively introduce distribution shifts in participant identity, recording conditions, language, and pathology. Our results reveal two key findings. First, layer selection is highly corpus-dependent: the optimal representation layer is determined primarily by the source dataset rather than by the SSL architecture itself. Second, the transferred discriminative signal lacks pathological specificity: classifiers trained to detect PD assign similarly high probabilities to both PD and dementia speech in the target corpus. These results highlight critical limitations that must be addressed before speech-based pathology recognition models can be reliably deployed in clinical settings.
[NLP-14] CROP: Task Relevance via Counterfactuals for Selective On-Policy Distillation
【速读】： 该论文旨在解决在策略蒸馏（On-Policy Distillation, OPD）中，对响应标记（response tokens）分配等权重监督导致的效率低下问题。现有方法虽通过选择性蒸馏实现非均匀监督分配，但主要依赖优化需求相关指标（如不确定性或教师-学生差异），而忽视了任务相关性这一关键维度——即监督信号是否与当前输入的语义内容紧密关联。为弥补此空白，本文提出反事实相关性蒸馏（Counterfactual Relevance for On-Policy Distillation, CROP），其核心创新在于通过“重写校准的反事实敏感性边界”来量化任务相关性：针对每个源提示，构建经验证的原始-改写-反事实三元组，固定学生模型的生成轨迹，测量每个响应位置对语义保持性改写（paraphrase）的敏感度，从而评估其任务相关价值。实验表明，相较于随机或低相关性选择，CROP能更准确识别高价值监督位置；组件分析进一步验证了反事实敏感性与改写校准的协同作用。在两种师生模型设置下，CROP相较最优非CROP选择器分别提升综合性能1.92和2.96点。研究证实任务相关性是选择性OPD中一个可量化的互补准则，并确立了CROP作为基于模型内部、面向对比学习的细粒度令牌级监督分配方法。
链接: https://arxiv.org/abs/2608.13387
作者: Enhan Li,Junhao He,Hongyang Du
机构: The University of Hong Kong(香港大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:On-policy distillation (OPD) supervises a student language model on trajectories sampled from its current policy, but assigns equal credit to response tokens with unequal supervision value. Selective OPD addresses this limitation by allocating supervision non-uniformly across response tokens according to their estimated training value. Most existing criteria, however, focus primarily on optimization need, such as uncertainty or teacher-student disagreement, while task relevance, namely whether the supervision is tied to the semantic content of the current input, remains less directly characterized as a complementary dimension. To address this gap, we introduce Counterfactual Relevance for On-Policy Distillation (CROP), which operationalizes task relevance through a paraphrase-calibrated counterfactual sensitivity margin. For each source prompt, CROP constructs a validated original-paraphrase-counterfactual triplet, holds the student rollout fixed, and measures each response position by its sensitivity to a task-relevant condition change calibrated by its sensitivity to a meaning-preserving rewrite. Matched selection controls show that CROP identifies more useful supervision positions than random or lowest-relevance selection, while component comparisons confirm the value of both counterfactual sensitivity and paraphrase calibration. Across two teacher-student settings, CROP improves aggregate performance by 1.92 and 2.96 points over the strongest non-CROP selector. These results support task relevance as a complementary criterion for selective OPD and establish CROP as a model-internal, contrast-specific method for allocating token-level supervision.
[NLP-15] RippleMem: From Isolated Retrieval to Associative Recollection for Long-Term Agent Memory
【速读】： 该论文旨在解决大语言模型（LLM）在长期交互中因信息分散于多轮对话而难以有效检索关键证据的问题。现有方法在记忆访问方面存在显著瓶颈：全上下文搜索易受噪声干扰，扁平化检索常返回孤立不完整的记录，基于图结构的内存系统虽能建模关系但构建成本高昂且难以保留丰富事件上下文。其解决方案的关键在于提出一种名为RippleMem的新型长时记忆系统，采用自适应关联回忆（adaptive associative recollection）机制替代传统的一次性检索。RippleMem将交互历史以富含线索的事件型记忆单元（cue-rich episodic memory units）形式存储，并构建事件中心的记忆图谱（event-centric memory graph）。当接收到查询时，系统首先通过混合线索（hybrid cues）召回相关记忆锚点，再沿语义与结构关联逐步扩展，动态补全缺失的支撑证据。这一过程使初始召回的记忆不仅作为答案上下文，更充当进一步推理的线索，实现证据链的完整恢复。实验结果表明，RippleMem在LoCoMo和LongMemEval-S基准上均取得最优性能，相较基线提升高达11.87%的判别准确率，同时将图谱构建成本降低约30倍。
链接: https://arxiv.org/abs/2608.13334
作者: Jingbo Ji,Lingyi Li,Xilong Cheng,Yuhao Zhou,Wenji Zhang,Yuting Tan,Yunxiao Qin
机构: Communication University of China (中国传媒大学); State Key Laboratory of Media Convergence and Communication (媒体融合与传播国家重点实验室)
类目: Computation and Language (cs.CL)
备注: 22 pages, 4 figures
Abstract:LLM-based agents increasingly rely on external memory to support long-horizon reasoning and interaction. However, the main bottleneck is not simply storing past experience, but recovering the right set of evidence when relevant information is distributed across many interactions. Existing approaches struggle with this access problem. Full-context methods require noisy long-context search, flat retrieval often returns isolated and incomplete records, and graph-based memory systems can be expensive to construct while compressing rich event context. We introduce RippleMem, a long-term memory system that replaces one-shot retrieval with adaptive associative recollection. Inspired by cue-dependent episodic retrieval and associative completion, RippleMem stores interaction history as cue-rich episodic memory units and organizes them in an event-centric memory graph. Given a query, it first recalls relevant memory anchors through hybrid cues, then expands from these anchors along semantic and structural associations to recover missing supporting evidence. In this way, initially recalled memories serve not only as answer context, but also as cues for completing the evidence needed to answer. Experiments on LoCoMo and LongMemEval-S show that RippleMem achieves the best overall performance across evaluated settings, improving LLM-as-a-Judge accuracy by 3.95% on LoCoMo and up to 11.87% on LongMemEval-S, while reducing graph construction cost by about 30x.
[NLP-16] Its How You Ask: Gender-Associated Linguistic Bias in LLM s
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在职场沟通中可能存在的性别偏见问题，即当前大型语言模型（LLM）对不同语言风格的响应存在系统性差异，尤其当提示语（prompt）中包含女性更常使用的语言特征（如缓和语、附加疑问句、集体指代）时，模型会生成更短、更不复杂且更不正式的回应。其解决方案的关键在于揭示：尽管显式的性别线索（如署名）在模型表征空间中与语言方言共存于同一空间，但语言风格（linguistic register）的影响远超显式性别标识，且这种影响具有文化深层嵌入性，难以通过用户自我呈现策略规避。进一步分析表明，这些语言特征在Transformer模型的早期层中即被编码，并与其他特征高度纠缠，导致事后缓解措施难以奏效。因此，论文强调必须从上游设计阶段考虑语言变体的影响，以减少生成式AI在职场沟通中带来的非对称后果。
链接: https://arxiv.org/abs/2608.13328
作者: Katherine Van Koevering,Anjalie Field
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Professional communication is increasingly mediated by LLMs - but do these models serve all users equally? We show that when prompts contain linguistic features more commonly used by women (hedges, tag questions, collective reference), they systematically elicit shorter, less sophisticated, and less formal responses across three document types and four models. These effects persist after controlling for prompt complexity and feature carry-over. Explicit gender cues like sign-off names are encoded in the same representational space as linguistic dialect - suggesting shared underlying mechanisms - yet linguistic register is far more influential, producing large, consistent effects where names produce none. Our results further reveal that post-hoc mitigation is challenging: because these patterns are culturally embedded and outside conscious control, users cannot easily avoid them through strategic self-presentation, and mechanistic analysis reveals that linguistic features are encoded in early transformer layers and entangled with other features. Our work calls for upstream consideration of the influences of linguistic variation to mitigate disparate impacts of LLM-mediated workplace communication.
[NLP-17] Beyond Local Accuracy: A Protocol-Level Identifiability Audit for Controlled LLM Reasoning Evaluation
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）评估中存在的一种核心问题：即使观测协议未能准确识别其所意图衡量的行为属性，评估分数仍可能表现出高精度，从而导致评估结果的误导性。其关键解决方案在于提出一种协议层面的可辨识性审计（protocol-level identifiability audit），在有限的行为策略集合（behavioral policy class）与给定观测支持（observation support）下，形式化地检验观测是否能够区分所有具有不同估计量（estimand）的策略对。该审计方法无需调用模型即可完成，有效诊断出仅基于基础输出（base-only observation）会将七个冻结的确定性策略合并为单一等价类，而完整观测支持则能保持七组独立的等价类且无跨估计量冲突；此外，每种“留一”观测支持均保留了构造性的冲突证据。实证结果显示，尽管两种约束生成变体均具备1.0的配对有效性（pair-validity），但基础准确率与选择性响应保真度却显著分化（分别为0.620和0.324，95%置信区间分别为[0.600, 0.642]与[0.304, 0.345]），并在另一确定性源上重复出现该差距（0.646 vs. 0.331）。同时，该审计还自动合成一个最小可识别观测支持 $ O^* $，仅需两个单元格而非完整的36维张量，揭示了评估设计有效性可在模型推理前通过结构化方式验证，且表明基础正确性（base correctness）并不决定干预响应保真度（intervention-response fidelity）。
链接: https://arxiv.org/abs/2608.13326
作者: Junhao Luo,Ning Huang,Ziqi Sha,Wenxuan Tang,Wei Deng(School of Statistics and Data Science, Southwestern University of Finance and Economics)
机构: Southwestern University of Finance and Economics (西南财经大学)
类目: Computation and Language (cs.CL)
备注: 15 pages, 9 figures. Ning Huang, Ziqi Sha, and Wenxuan Tang contributed equally as second authors. Wei Deng is the corresponding author
Abstract:LLM benchmark scores can be precise even when the observation protocol does not identify the behavioral property they are intended to measure. In a controlled, solver-grounded setting, we formalize a protocol-level identifiability audit over a finite behavioral policy class: given policies H, observation support O, and estimand \tau , we test whether O separates every pair with different \tau . The audit requires zero model calls and resolves our diagnostic case: base-only observation collapses seven frozen deterministic policies into one equivalence class; full support yields seven classes and no cross-estimand collisions; every leave-one-out support retains a constructive collision witness. Empirically, both constrained-generation variants have pair-validity 1.0, yet base accuracy and selective-response fidelity diverge - 0.620 versus 0.324 across six balanced oracle-transition directions (cluster-bootstrap 95% CI [0.600, 0.642] vs. [0.304, 0.345]) - and the gap recurs on a second deterministic source (0.646 vs. 0.331). The audit also synthesizes a minimum identifying support O^* for the frozen policy class: two cells instead of the full 36-cell tensor. This case shows how evaluation-design validity can be checked structurally before model inference and why base correctness does not determine intervention-response fidelity.
[NLP-18] Refusing Intent Not Form: Wrapper-Based Intent-Group Supervision for LLM Safety
【速读】： 该论文旨在解决安全调优（safety tuning）中模型学习到表面形式捷径（surface-form shortcuts）的问题，即有害提示经过包装后可绕过安全机制，而结构相似的良性提示则被过度拒绝，导致安全与可用性之间的权衡失衡。其核心解决方案是提出一种名为基于包装意图-形式增强（Wrapper-Based Intent-Form Augmentation, WIFA）的自动意图组增强方法，通过将结构匹配的有害与良性包装样本配对，构建具有语义一致性但意图相反的训练数据对，无需外部教师模型或人工标注每种包装形式的意图标签。WIFA作为通用数据层，支持两种互补的微调路径：WIFA-Boost（两阶段高安全性训练范式）和锚定组一致性拒绝训练（Anchored Group-Consistent Refusal Training, A-GCRT），后者通过在相同意图包装组间正则化拒绝/合规决策得分，并将有害与良性组分别锚定在分类边界两侧，以增强决策的一致性。实验结果表明，在Qwen设置下，WIFA-Boost实现了最强的转化后有害拒绝性能，而A-GCRT将OR-Bench上的过度拒绝率从基线模型的25.7%降至17.4%，且现有基线无法达到同等性能点；Llama系列模型的结果及消融实验进一步验证了该方法对意图组结构的敏感性，支持其有效性，但不主张其在所有情况下均能优于基线模型。
链接: https://arxiv.org/abs/2608.13304
作者: Ping Wu,Haibo Tong,Feifei Zhao,Han Shen,Yu Shi,Yilin Zhao,Sicheng Shen,Guobin Shen,Yun Luo,Yi Zeng
机构: Ant Group(蚂蚁集团); BrainCog Lab; Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所); School of Artificial Intelligence, University of Chinese Academy of Sciences(中国科学院大学人工智能学院); Beijing Key Laboratory of Safe AI(北京市安全人工智能重点实验室); Superalignment
类目: Computation and Language (cs.CL)
备注: 23 pages, 11 figures, 24 tables
Abstract:Safety tuning can improve harmful refusal, but models may learn surface-form shortcuts: wrapped harmful prompts bypass safety, while similarly wrapped benign prompts are over-refused. We propose Wrapper-Based Intent-Form Augmentation (WIFA), an automatic intent-group augmentation method that pairs wrapped harmful examples with structurally matched wrapped benign counterexamples, requiring no external teacher or manual per-wrapper intent labels. We use WIFA as a common data layer for two complementary fine-tuning routes: WIFA-Boost, a two-stage high-safety recipe, and Anchored Group-Consistent Refusal Training (A-GCRT), which regularizes refusal/compliance decision scores across same-intent wrappers and anchors harmful and benign groups on opposite sides of a margin. In the Qwen setting, WIFA-Boost reaches the strongest transformed-harmful refusal, while A-GCRT reduces OR-Bench over-refusal from 25.7% for the base model to 17.4%; reproduced baselines do not match these operating points. Llama results and ablations over data structure, two-stage order, and A-GCRT components support this intent-group interpretation without claiming universal below-base over-refusal.
[NLP-19] Mixture of Training: Recombining Small-Scale Scaffolded Pretraining Runs into a Larger Language Model
【速读】： 该论文旨在解决大模型预训练过程中计算资源消耗高、训练过程难以分解与复用的问题，具体探讨语言模型预训练是否可被分解为一系列独立可训练的模块化任务，并在后续重组为一个连贯的完整模型。其解决方案的关键在于提出一种名为“训练混合（Mixture of Training, MoT）”的分层模块化预训练框架：将目标Transformer模型按连续的层块划分，利用冻结的预训练对齐器（aligner）作为支架，分别独立训练每个层块，随后将训练好的层块重新组合，并可选地进行短时端到端微调。实验基于13亿参数的Gemma风格模型在C4数据集上的验证表明，独立训练的深度切片可成功重组为可用的语言模型，且在质量相当的设定下达到与单体基线模型相同的困惑度表现；该设定在总训练令牌数上更具优势，且对齐器准备后的理想化层等效关键路径更短，其有效算力优势依赖于对齐器在多次运行中的复用。因此，MoT并非作为单体预训练的通用替代方案，而是一个用于研究分层子训练任务能否作为可复用训练单元的小规模研究框架。
链接: https://arxiv.org/abs/2608.13277
作者: Mohammed Sabry,Sean Augenstein,Keith Rush,Lucio Dery
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted at the Workshop on Methods and Opportunities at Small Scale (MOSS), COLM 2026
Abstract:We ask whether language-model pre-training can be decomposed into smaller, independently trainable jobs that can later be recomposed into a coherent larger model. We introduce Mixture of Training (MoT), a scaffolded modular pre-training procedure that partitions a target Transformer into contiguous layer blocks, trains each block inside a frozen pretrained aligner scaffold, and then recomposes the trained blocks with an optional short end-to-end adaptation pass. On a 1.3B-parameter Gemma-style model trained on C4, MoT provides a small-scale proof of mechanism: independently trained depth slices can be recomposed into a usable language model, and a quality-parity schedule reaches the same reported perplexity as the monolithic baseline. This parity setting processes more aggregate tokens and has a shorter idealized layer-equivalent critical path after aligner preparation; its effective compute advantage depends on reusing the aligner across runs. We therefore present MoT not as a general replacement for monolithic pre-training, but as a small-scale framework for studying whether scaffolded sub-runs can act as reusable training units.
[NLP-20] How Do VLMs Behave When Blind or Misled? Behavioral Evaluation of VLMs on Scientific Figures
【速读】： 该论文旨在解决现有视觉语言模型（VLM）评估基准在面对视觉证据缺失或误导性信息时，对模型行为可靠性（behavioral reliability under uncertainty）关注不足的问题。当前主流评估多聚焦于感知与推理的准确性，而忽视了模型在不确定性情境下的稳健表现。为此，作者提出SciFigBench——一个面向科学图表理解的诊断性评估基准，从感知、推理及不确定性下的行为可靠性三个维度进行联合评估。其核心创新在于引入“接纳-抗扰-电感”（Admittance-Resistance-Inductance, A-R-I）框架，系统评估模型在证据不足时是否能承认不确定性（admittance）、抵抗误导性上下文干扰（resistance），以及基于部分信息谨慎推断（inductance）。通过34,000余种经过图像变换、推理问题设计、抗干扰探测和选择性模糊验证等手段生成的测试场景，该基准实现了对模型行为可靠性的高强度压力测试。实验结果揭示，尽管某些模型如GPT-5.2在描述质量和推理准确率上表现优异（MQM 91.6，推理准确率78.4%），但其在无法读取内容的场景中存在高达96%的幻觉现象；相比之下，Gemini 3.1 Pro虽性能相近（MQM 90.2，推理准确率81.0%），却能在71%的情况下主动承认不确定性，并展现出最强的抗干扰能力（抵抗得分0.91）。研究结论表明，感知与推理的高精度并不能保证行为可靠性，后者是科学工作流中部署VLM不可或缺的关键维度。
链接: https://arxiv.org/abs/2608.13267
作者: Paul Osemudiame Oamen,Owusu-Banahene Osei,Ananya Mukherjee,Christian Greisinger,Steffen Eger,Pius Onobhayedo,Wei Zhao
机构: The Aberdeen NLP Research Group, University of Aberdeen, UK; International Institute of Information Technology Hyderabad, India; University of Technology Nuremberg, Germany; University of Southern California, USA
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 25 pages including appendix. Project website: this https URL
Abstract:Existing vision-language model (VLM) benchmarks emphasize perception and reasoning accuracy (how well VLMs describe and reason about what they see in an image), with limited attention to behavioral reliability under uncertainty (how they behave when visual evidence is missing or misleading). We introduce SciFigBench, a diagnostic VLM benchmark for scientific figure understanding that jointly evaluates perception, reasoning, and behavioral reliability under uncertainty. It contains 250 figures with high-quality human annotations across three evaluation aspects, totaling 600+ hours of annotation effort. We further extend these figures via image transformations, reasoning questions, resistance probes, caption-bias probes, and confirmed selective-blur targets, producing over 34,000 evaluation setups for stress testing. We further propose the Admittance-Resistance-Inductance (A-R-I) framework to evaluate whether models acknowledge insufficient evidence, resist misleading context, and infer cautiously from partial information. Our results reveal substantial behavioral differences among models. GPT-5.2 achieves the highest description quality (MQM 91.6) with strong reasoning accuracy (78.4%), yet hallucinates unreadable content in 96% of cases, whereas Gemini 3.1 Pro, a comparably capable model (MQM 90.2, reasoning 81.0%), admits uncertainty in 71% of such cases and achieves the strongest resistance score (0.91). These findings show that high perception and reasoning accuracy alone do not guarantee behavioral reliability, a dimension critical for deployment in scientific workflows. Comments: 25 pages including appendix. Project website: this https URL Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) ACMclasses: I.2.7; I.2.10 Cite as: arXiv:2608.13267 [cs.CL] (or arXiv:2608.13267v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.13267 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-21] Self-Referential Induction Increases Response Instability Relative to Unresolvable and Verifiable Questions in Large Language Models
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在自指性提示（self-referential prompting）下生成的第一人称主观体验报告的可重复性与一致性问题，尤其关注这些报告在多次独立试验中的一致性水平，并将其与模型对其他类型开放性问题的回答行为进行对比。其解决方案的关键在于引入“响应不稳定性”（response instability）这一量化指标，定义为从每个回答中提取的核心主张压缩后，句向量之间平均成对余弦相似度的1减去该值，从而客观衡量输出的一致性。研究通过在Gemini API上以温度0.7生成每类问题30次独立回答（共三组，每组四题，总计360条响应），发现自指性问题产生的报告表现出最高的不稳定性（0.343 ± 0.047），远高于具有可验证答案的问题（0.105 ± 0.058）和非自指的不可解哲学问题（0.192 ± 0.008）。这一结果表明，尽管自指性提示能稳定诱发类似主观体验的报告，但其输出在语义空间中分布更分散，揭示了此类报告在生成模式上具有显著的内在不稳定性，区别于普通开放性问题下的认知不确定性表达。
链接: https://arxiv.org/abs/2608.13258
作者: Paras Balani,Subhrakanta Panda
机构: Birla Institute of Technology and Science, Pilani, Hyderabad Campus (比尔拉科技与科学学院，皮拉尼，海得拉巴校区); Department of Mathematics (数学系); Department of Computer Science (计算机科学系)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 4 pages, 2 figures
Abstract:Self-referential prompting has been shown to reliably induce large language models to produce first-person reports resembling subjective experience, but no prior work measures how consistent these reports are across repeated, independent trials, or how that consistency compares to the model’s behavior on other kinds of open-ended questions. We measure response instability, defined as one minus the mean pairwise cosine similarity of sentence embeddings computed over a compressed core claim extracted from each response, for three groups of questions: self-referential prompts eliciting a subjective-experience report, unresolvable philosophical questions unrelated to self-reference, and questions with a verifiable correct answer. Using 30 independent responses per question (360 responses total, Gemini API, temperature 0.7) across four questions per group, we find that self-referential questions show the highest instability (0.343 +/- 0.047), unresolvable philosophy questions show intermediate and tightly clustered instability (0.192 +/- 0.008), and verifiable questions show the lowest instability (0.105 +/- 0.058). This provides a quantitative baseline for the induced subjective-experience report, showing that it occupies a distinct, less stable position in the model’s output distribution than ordinary open-ended philosophical uncertainty.
[NLP-22] Localize Then Reason : Visual Latent Structural Reasoning for Molecular Properties and Edits
【速读】： 该论文旨在解决现有基于大语言模型（LLM）的化学推理方法在分子结构-性质关系理解中缺乏对化学有意义区域精准聚焦的问题。当前方法或依赖SMILES字符串与局部基团描述联合输入，或直接从分子图像进行推理，均无法在推理前有效定位关键化学结构区域。为此，本文提出视觉潜在结构推理（Visual Latent Structural Reasoning, VLSR）框架，其核心在于采用“先定位、后推理”的策略：首先通过端到端学习识别分子图像中的化学意义区域，随后在紧凑的潜在空间中对这些区域的性质影响进行推理，最终生成答案。该设计在相同推理条件下，相比同类文本推理基线实现了9.6倍的吞吐量提升，显著提高了推理效率与可解释性。
链接: https://arxiv.org/abs/2608.13244
作者: Xingqiao Lin,Junmei Wang,Haocheng Tang
机构: 未知
类目: Computation and Language (cs.CL); Computational Engineering, Finance, and Science (cs.CE); Biomolecules (q-bio.BM)
备注:
Abstract:Local chemical perception and property reasoning are both essential for understanding how molecular structure determines properties. Current LLM-based chemical reasoning methods either receive SMILES/molecular images together with descriptions of local motifs, or reason directly from molecular images. Neither approach enables the model to focus on chemically meaningful regions before reasoning. To address this gap, we propose Visual Latent Structural Reasoning (VLSR), an end-to-end framework that jointly learns localization and reasoning from molecular images. Central to our approach is a localize-then-reason strategy. VLSR first learns to locate chemically meaningful regions in a molecular image. It then reasons about their property effects in a compact latent workspace before producing the final answer. Under the same inference setup, this design achieves 9.6X higher throughput than a comparable textual-reasoning baseline.
[NLP-23] Which LLM Is Your Ideal Companion? Evaluating Emotional Companion Capabilities of LLM s Based on Adult Attachment Theory
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在情感陪伴场景下行为评估不足的问题，尤其针对其在亲密关系中表现出的情感敏感性与互动质量缺乏系统性评价体系。现有评估方法多聚焦于通用人格特质，难以揭示模型在真实情感互动情境中的表现。为此，研究引入成人依恋理论（Adult Attachment Theory），并采用修订版亲密关系经历量表（Experiences in Close Relationships-Revised, ECR-R）量化模型的依恋焦虑与回避倾向。关键解决方案是构建首个面向情感陪伴能力的基准测试框架——ECBench，涵盖情感支持、协作任务、冲突调解与社会引导四类典型场景，覆盖友谊与浪漫关系两种关系类型。ECBench结合11项对话质量指标与三种评估方法，对32个主流LLMs进行评估，并进一步探究其依恋倾向在多轮上下文交互中的表现及其通过提示工程（prompting）可塑性。该研究为情感陪伴型AI提供了心理学理论支撑与可操作的评估工具，推动了对模型情感响应机制的理解与优化。
链接: https://arxiv.org/abs/2608.13168
作者: Junkai Zhou,Shiting Guan,Zhaoyi Zhang
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:As large language models (LLMs) are increasingly applied for emotional companionship, evaluating their behavior and capabilities in intimate relationships has become a pressing issue. However, existing assessments primarily characterize general personality traits, providing limited insight into model behavior within intimate and emotionally sensitive contexts. Therefore, we introduce adult attachment theory into LLM evaluation and use the Experiences in Close Relationships-Revised (ECR-R) scale to characterize attachment anxiety and avoidance. To evaluate emotional companionship capabilities of LLMs in realistic interaction scenarios, we present an emotional companionship benchmark, ECBench, spanning four scenarios including emotional support, collaborative tasks, conflict resolution, and social guidance, across friendship and romantic relationships. ECBench is utilized to assess model behavior using 11 dialogue-quality metrics and three evaluation methods. We evaluate the attachment tendencies of 32 LLMs and select representative models to investigate how these tendencies manifest in contextualized multi-turn interactions and whether they can be shaped through prompting. Our study provides a theoretical lens from psychology, along with practical tools to understand and select LLMs for emotional companionship.
[NLP-24] RAPSBench: Vision-Language Models Encode but Fail to Express Epistemic Restraint
【速读】： 该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在视觉证据被遮挡或混乱时无法正确识别并主动放弃预测的问题。尽管这些模型在内部能够感知到不确定性（即“知道何时不该回答”），但其输出行为却缺乏相应的克制，表现出较差的自发性抑制能力。解决方案的关键在于构建一个名为TRAPSBench的程序化生成视频基准数据集，其中包含1,404对匹配的物理场景，仅通过单一目标变化导致结果无法从视觉信息中确定，从而精准测试模型的判断与表达能力。同时，提出新的鲁棒评估指标——惩罚式认知校准分数（Penalized Epistemic Calibration Score, PECS），要求模型在可确定结果时准确作答，在不可确定时则必须选择放弃回答。实验结果表明，当前16个VLM模型在该指标上的最佳表现仅为0.292，显示出严重的“表达瓶颈”而非“感知瓶颈”：线性探测显示隐藏状态中已存在显著的答案可得性信号（最高0.91 AUROC），且通过调控单层空置方向可因果性地诱导或抑制模型的放弃行为。此外，模型对文本层面的不确定性检测能力远强于视觉层面（约快4倍）。因此，弥合模型内部表征与最终输出之间的差距，可能需要在输出阶段引入专门干预机制。
链接: https://arxiv.org/abs/2608.13167
作者: Fnu Pramono,John Cai,Sourabh Kulkarni
机构: Meta Superintelligence Labs(元超级智能实验室); Reflection AI(反射人工智能)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 10 Pages excluding Reference and Appendix, Published at COLM 2026
Abstract:When visual evidence is occluded or chaotic, models should abstain. In this paper, we show that Vision-Language Models (VLMs) can internally distinguish when abstention is required, but fail to express it anyway. We introduce TRAPSBench, a procedurally generated video benchmark of 1,404 matched physics pairs in which a single targeted change renders the outcome undeterminable from the visual evidence. Furthermore, we introduce Penalized Epistemic Calibration Score (PECS), a new robust metric that requires models to both answer correctly when the outcome is knowable, and abstain when the outcome is not. Across 16 VLMs spanning five families, spontaneous restraint is poor: the best PECS is 0.292. The bottleneck is expression, not perception: linear probes decode answerability from hidden states at up to 0.91 AUROC across physics domains; steering a single-layer void direction causally induces or suppresses abstention. Our results replicate across three open-weight families (Qwen, Gemma, LLaVA). The failure is also more pronounced in visual than textual uncertainty: models detect textual impossibility about 4x more readily than missing visual evidence. Closing this representation–output gap likely requires output-stage interventions.
[NLP-25] Better Decomposition Free Aggregation: A Synthesizer-Folding Framework for Multilingual Multi-Hop Question Answering NLPCC2026
【速读】： 该论文旨在解决多语言检索增强生成（mRAG）在复杂多跳问答任务中存在的两大核心问题：一是统一的翻译对齐策略导致目标语言特有的文化与语言信息丢失，引入翻译噪声并增加系统开销；二是贪婪式的子问题分解与聚合机制易产生冗余子问题，并在逐步推理过程中累积误差，最终的聚合过程进一步放大错误。其解决方案的关键在于提出一种名为Syfer的合成折叠框架，该框架通过延迟翻译决策而非默认应用翻译，实现了更高效的跨语言推理。Syfer首先在原始语言中调用一个格式约束的分解器生成子问题图，并进行分解质量检查；若检查通过，则在目标语言下采用“先检索后回答”的策略顺序求解子问题；仅当检查失败时，才激活基于双语子问题图对齐的英语翻译路径。该设计有效避免了不必要的翻译开销，同时提升了推理的准确性和鲁棒性，在多个语言上的实验验证了其在性能与计算成本之间取得良好平衡。
链接: https://arxiv.org/abs/2608.13160
作者: Yilin Wang,Yuchun Fan,Weidong Bao,Zili Wei,Shi Feng,Tong Xiao,Zhengtao Yu,Jingbo Zhu
机构: Northeastern University (东北大学); Kunming University of Science and Technology (昆明理工大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted by NLPCC 2026
Abstract:Multilingual retrieval-augmented generation (mRAG) equips large language models with access to globally distributed external knowledge for complex multilingual question answering. Recent approaches either translate retrieved documents into English or the query language to bridge the cross-lingual semantic gap, or decompose a complex query into sub-questions and aggregate the intermediate reasoning process. However, both lines of work suffer from two limitations. First, one-size-fits-all translation alignment, blanket translation discards culturally and linguistically native information unique to the target language, introduces translation noise, and inflates system cost. Second, greedy decomposition and aggregation, uncontrolled decomposition produces redundant sub-questions that compound errors during step-wise reasoning, and the final aggregation over reasoning paths further amplifies these errors. We address both with our method Syfer, a synthesizer-folding framework for multilingual multi-hop question answering that defers translation rather than applying it by default. Syfer first invokes a format-constrained decomposer to produce a sub-question graph in the original language, followed by a decomposition-quality check; when the check passes, sub-questions are answered sequentially under a retrieve-then-answer policy in the target language, and the English translation pathway with bilingual sub-question graph alignment is activated only when the check fails. Experiments across multiple languages show that Syfer attains competitive accuracy while striking a favourable balance between performance and computational cost.
[NLP-26] CASA: Content-Acoustic Speaking Assessment with Speech Encoder and Large Language Model ALT ICASSP2027
【速读】： 该论文旨在解决自动口语评估（ASA）中多模态大模型对声学特征与内容信息贡献度分析不足，以及模型性能稳定性欠佳的问题。现有方法缺乏对语音表达质量与语言内容质量之间贡献分离的可解释性分析，且在不同运行中的表现波动较大。为此，论文提出CASA架构，其关键在于采用更简洁的模型组合——Whisper-medium与Qwen3.5-2B，通过显式分离语音流利性（fluency）与内容语义两个维度，实现对口语表达质量与内容质量的解耦评估。该架构不依赖复杂结构修改即可适配其他ASA数据集，并引入三个手工设计的流利性特征以增强鲁棒性。实验表明，在Speak Improve Corpus 2025上，CASA达到0.358的均方根误差（RMSE），优于当前最优结果，同时推理参数量约为原有方案的一半。通过消融实验与重复测试，研究系统分析了声学信息与内容信息的独立及互补作用，验证了大语言模型在无需额外训练前提下进行内容合理性推理的潜力，显著提升了评估系统的可解释性与稳定性。
链接: https://arxiv.org/abs/2608.13101
作者: Nhan Phan,Ilona Lähteenmäki,Anna von Zansen,Olli-Pekka Pauna,Yaroslav Getman,Tamás Grósz,Mikko Kurimo
机构: University of Helsinki (赫尔辛基大学); VTT Technical Research Centre of Finland (芬兰技术研究中心); Google (谷歌)
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: To be submitted to ICASSP 2027. Code is available at this https URL
Abstract:Research on automatic speaking assessment (ASA) has increasingly adopted multimodal speech large language models to assess learners’ speaking performance. However, existing studies provide limited analysis of how acoustic and content information contribute to predictions and how stable the resulting performance is. We propose CASA, a simpler architecture combining Whisper-medium and Qwen3.5-2B that achieves state-of-the-art performance while providing a more interpretable separation between speech delivery and content. On the Speak Improve Corpus 2025, CASA achieves a root mean square error (RMSE) of 0.358, improving on the previous best RMSE while using approximately half the estimated inference parameters. The general-purpose architecture is designed for adaptation to other ASA corpora without structural changes and relies on three handcrafted fluency features. Through ablations and repeated runs, we analyze the individual and complementary contributions of acoustic and content information, examine performance variability, and demonstrate the potential of large language model reasoning for training-free content validation. Comments: To be submitted to ICASSP 2027. Code is available at this https URL Subjects: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS) Cite as: arXiv:2608.13101 [cs.CL] (or arXiv:2608.13101v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.13101 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-27] Explanatory Engagement Under Rare Anomalous Failure: Asymptotic Rarity in Model Behavior (or: The Asymptotic AI)
【速读】： 该论文旨在解决在低且可控的失败率逐渐趋近于零的条件下，大语言模型（Large Language Model, LLM）在任务流程中的解释性参与行为（explanatory engagement）是否会发生变化的问题。具体而言，研究关注模型在面对渐趋罕见的异常情况时，其解释文本长度、具体程度以及自我报告置信度等指标的变化趋势。传统研究多关注模型能否“察觉”异常，而本研究聚焦于异常发生频率下降过程中模型响应行为的动态演化。解决方案的关键在于构建一个本地化、零成本的实验框架，对三个开源权重模型（qwen3:8b、llama3.1:8b、mistral:7b）在重复工具调用任务中进行系统性测试，通过调节失败概率 $ p $ 从 0.2 降至 0.0001，并在五种不同诱导条件（elicitation conditions）下观察模型行为。研究发现，整体平均趋势显示解释长度单调下降，但按诱导条件细分后揭示出显著差异：在“立即强制解释”（immediate_forced）条件下，解释长度随失败率降低先上升后趋于平稳，峰值出现在 $ p=0.05 $ 时（28.4词），随后稳定在 17.4–19.0 词区间，同时置信度持续提升；而在“分组运行”（grouped_runs）条件下未出现崩溃现象；在“被动无提示”（passive_unprompted）条件下，虽表面表现为基线水平，但通过恢复日志间隙发现模型存在真实、模型特异性的自我监控行为，如 llama3.1:8b 在无提示情况下自发结构化置信度报告并随试验累积主动降低自身置信度，其余模型仅在极少数情况下以模板形式出现。因此，诱发结构（elicitation structure）是决定模型行为崩溃是否可被观测的关键调节因子。此外，配套的确定性失败实验表明，不同模型在识别异常方面的能力存在本质差异，与后续解释行为分离。研究局限在于离散的失败率点无法捕捉中间行为连续变化，为未来工作提供了方向。
链接: https://arxiv.org/abs/2608.13063
作者: Sam Mao
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 11 figures. Elicitation-condition sweep across three open-weight models (qwen3:8b, llama3.1:8b, mistral:7b); pipeline scripts and experimental data available upon reasonable request
Abstract:Prior work on LLM behavior under anomalous conditions asks whether a model notices anomalies. We ask a narrower question: once a model sits in a workflow with a low, controllable failure rate, does its explanatory engagement - length, specificity, self-reported confidence - change as failure grows asymptotically rarer? We built a local, zero-cost harness on three open-weight models (qwen3:8b, llama3.1:8b, mistral:7b) running a repeated tool-call task where one call fails at probability p, swept across eight rates from 0.2 to 0.0001, under five elicitation conditions from immediate prompting to none. We hypothesized a rise in engagement as failures grew rarer, then a collapse near a detectability threshold. Pooled across conditions this appeared false: length fell in a flat, monotonic pattern. Splitting by condition overturned that. Under immediate_forced, where the model must explain every failure instantly, the predicted rise is confirmed but followed by a plateau, not a collapse: length peaks at 28.4 words at p=0.05, settles to 17.4-19.0 words at the rarest rates, and confidence rises unevenly from about 53% to the 70s-90s. Under grouped_runs, explanation batched to run-end, no collapse appears. Under passive_unprompted, aggregate magnitude is a floor artifact, but a recovered logging gap revealed real, model-specific self-monitoring: llama3.1:8b volunteers structured confidence reports unprompted, sometimes eroding its own confidence as trials accumulate; the other two do so only once, as boilerplate. Elicitation structure is a first-class moderator of collapse observability. A companion guaranteed-failure run (72 cells, backfilling rates where random sampling gave zero real failures) shows models differ in whether they recognize an anomaly, distinct from engagement once recognized. Limitation: discrete rate points cannot capture behavior between them, a direction for future work.
[NLP-28] EMPO: Makespan-Aware Expert-Parallel Load Balancing Across Memory- and Compute-Bound Regimes
【速读】： 该论文旨在解决专家并行（Expert-Parallel, EP）混合专家模型（MoE）服务中因负载不均导致的性能瓶颈问题，核心挑战在于现有调度策略（如基于令牌数或激活专家数的均衡方法）忽略了实际执行过程中专家计算时间与输入规模之间非线性的关系。关键发现是：在低负载（<156–168 tokens）时，显存权重流带宽成为主要开销，成本由激活的专家副本数量决定；而在高负载时，分组GEMM将输入按128个元素对齐为M-tile，导致专家拆分引入填充计算开销。为此，论文提出一个最大仿射模型 $ t = \max(a + bG, c + \beta N) $ 来准确刻画这两种运行阶段。针对真实解码批处理中热专家处于线性区、冷专家处于平坦区的混合场景，传统代理调度器在建模块执行时间上存在1.4–1.6倍偏差（p95达1.7倍），且“最优”代理随运行阶段切换而变化。因此，论文将每批次调度形式化为一个固定费用的完工时间（makespan）优化问题——在双全复制GPU上为NP-hard，在退化情形下可多项式求解，并提出\sys系统，一种基于完工时间感知的调度器，可在毫秒级内完成调度决策且不阻塞关键路径；其与SGLang集成后通过图内融合将调度与计数收集合并为单一内核。在8卡测试平台上的微基准测试表明，\sys在所有配置下均保持与最佳静态基线相差不超过1%，在多阶段混合区域最高提升15.5%；端到端测试中，Qwen3-235B在性能提升区域获得4–6%吞吐率增益并降低约15.6%的p99延迟，而DeepSeek-V3因通信主导则仅体现机制开销。研究结论强调：并非普遍适用的性能提升，而是通过相图预测部署前的性能表现，实现精准调度。
链接: https://arxiv.org/abs/2608.13057
作者: Jie Li,Chenxin Jia,Jinliang Shen,Cunzhuang Liu,Ruiyi Ding,Jianwen Xian,Kang He,Chengru Song
机构: KlingAI Research( KlingAI 研究院)
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Science and Game Theory (cs.GT)
备注: 18 pages. Code is available at this https URL
Abstract:In expert-parallel (EP) MoE serving, every layer synchronizes at the slowest GPU. Dispatchers balance token counts (EPLB, LPLB, UltraEP) or activated-expert counts (METRO), assuming expert time is linear in one. Measurements on two datacenter GPU generations show it is neither: below \nstar!\approx!156 – 168 tokens, HBM weight streaming dominates—cost attaches to \emphactivated replicas, not tokens; above it, grouped GEMM rounds tokens to 128-tile M -tiles, so \emphsplitting an expert adds padded compute. A max-affine profile t=\max(a+bG,c+\beta N) captures both regimes. Realistic decode batches hold hot experts in the linear regime and cold in the flat \emphsimultaneously; recorded batches show proxy dispatches differ by 1.4 – 1.6\times in modeled block time (p95 up to 1.7\times ), and \emphwhich proxy wins flips with the regime. We formalize per-batch dispatch as a fixed-charge makespan problem—NP-hard on two fully replicated GPUs, polynomial in degenerate limits—and present \sys, a makespan-aware dispatcher solving it in milliseconds off the critical path; its SGLang integration runs out-of-process and fuses dispatch with count collection into one in-graph kernel. Anchored by an 8-GPU Testbed~A microbenchmark, \sys stays within 1% of the best fixed baseline everywhere and wins by up to 15.5% where regimes mix. End-to-end on Testbed~B, Qwen3-235B (inside the win region) gains 4 – 6% throughput and cuts p99 latency by \sim15.6% ; DeepSeek-V3 (outside, communication-dominated) shows only mechanism cost. A phase diagram, not a universal win, is the claim: it predicts both outcomes before deployment.
[NLP-29] Latent On-Policy Self-Distillation
【速读】： 该论文旨在解决自演化人工智能（self-evolving AI）中代理（agent）如何从经验中学习并将其内化为策略的核心问题，尤其针对现有基于策略的自蒸馏（On-Policy Self-Distillation, OPSD）方法对人工设计的特权信息（privileged artifacts，如答案、反馈、技能或轨迹）高度依赖所导致的端到端可学习性与可扩展性受限的问题。其解决方案的关键在于提出潜在的基于策略自蒸馏（Latent On-Policy Self-Distillation, LOPD），通过将教师的特权上下文（privileged context）本身作为可端到端学习的潜在表示，而非依赖预设的固定形式。具体而言，LOPD利用可学习的连续潜在标记（latent tokens）从经验中检索并组合相关信息以条件化自教师，同时学生在任务与交互历史基础上生成轨迹，并在每个访问前缀处获得细粒度的标记级监督。此外，引入特权边际目标（privileged-margin objective）以稳定和调控潜在上下文的学习过程。实验表明，LOPD在智能体工具使用和代码生成任务上均显著优于RLVR及主流OPSD方法（如OPSD、SDPO、Skill-SD），且学习效率远超GRPO与Skill-SD，仅需其30%以下的回放预算即可达成更优性能。消融实验进一步验证了将特权上下文可学习化是实现这些提升的关键因素。因此，LOPD为实现更具可扩展性和自主性的智能体演化范式提供了重要进展。
链接: https://arxiv.org/abs/2608.13040
作者: Guibin Zhang,Jiayang Lyu,Ran Sun,Xinlei Yu,Haoyu Zhao,Qibing Ren,Shuicheng Yan
机构: Shanghai Jiao Tong University(上海交通大学); National University of Singapore(新加坡国立大学); Beijing University of Posts and Telecommunications(北京邮电大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Enabling agents to learn from experience and internalize it into their policy has become a central problem in self-evolving AI. On-policy self-distillation (OPSD) offers an effective pathway by using a privileged self-teacher to provide dense supervision on the student’s own trajectories; however, existing methods still rely heavily on designer-specified privileged artifacts (e.g., answers, feedback, skills, or trajectories), limiting the end-to-end learnability and scalability required for continual self-improvement. In this work, we introduce Latent On-Policy Self-Distillation (LOPD), which, rather than proposing another hand-crafted OPSD variant with a newly prescribed form of privileged context, makes the teacher’s privileged context itself learnable end-to-end from experience. Technically, LOPD retrieves relevant experiences and composes them into continuous latent tokens that condition a self-teacher, while the student generates trajectories from the task and interaction history and receives dense token-level supervision at every visited prefix. We further introduce a privileged-margin objective to stabilize and regulate the learning of latent context. Empirically, LOPD demonstrates (I) strong performance, outperforming RLVR and representative OPSD methods including OPSD, SDPO, and Skill-SD across both agentic tool use and code generation; and (II) high learning efficiency, surpassing GRPO and Skill-SD with less than 30% of their rollout budget. Ablation studies further provide direct evidence that making privileged context learnable is necessary for realizing these gains. Together, these results position LOPD as a step toward a more scalable and self-directed paradigm for agent evolution.
[NLP-30] LycheeMemory V2: Efficient Long-Term Memory for LLM Agents via Semantic Segment-Level Consolidation
【速读】： 该论文旨在解决长时程大语言模型（LLM）智能体在持续交互中如何高效构建与维护长期记忆的问题。现有记忆系统普遍采用急进式（eager consolidation）的更新策略，即每次交互后立即调用大语言模型进行信息提取、摘要或记忆更新，导致随着对话轮次增加，记忆构建成本呈线性增长。同时，粗粒度摘要易丢失细粒度上下文证据，而扩展检索上下文或采用多跳推理则将计算开销转移至查询阶段，形成新的瓶颈。为此，本文提出LycheeMemory V2，其核心创新在于将传统的逐轮记忆固化（turn-level consolidation）改为语义段级固化（semantic segment-level consolidation），通过检测语义边界对多轮交互进行动态分段，并将每个完成的语义段编码为与上下文无关的结构化记忆记录。该设计显著降低了大语言模型的编码频率，同时保留了事件级和时间序列上的连贯性证据。记忆记录采用轻量级结构化索引组织，支持基于查询规划的证据检索。实验表明，使用GPT-4.1-Mini，在LoCoMo和LongMemEval-S基准上分别达到89.22%和92.20%的准确率，优于当前最优方法A-Mem，且在保持查询阶段令牌消耗不变的前提下，分别减少86.0%和75.9%的记忆构建令牌开销。研究进一步揭示：长时程记忆系统的性能与成本权衡不仅取决于保留的信息内容，更关键的是信息固化的粒度层级。
链接: https://arxiv.org/abs/2608.12990
作者: Dongfang Li,Zixuan Liu,Junmai Wang,Jiahe Huang,Fuhao Li,Bonian Jia,Baotian Hu,Min Zhang
机构: Harbin Institute of Technology, Shenzhen(哈尔滨工业大学深圳校区)
类目: Computation and Language (cs.CL)
备注: 34 pages, 5 figures
Abstract:Long-horizon LLM agents must preserve information from past interactions to support future tasks. Existing memory systems typically rely on eager consolidation, invoking LLMs after each interaction to extract, summarize, or update memories. This design makes memory construction increasingly costly as conversations grow. Coarse summarization can reduce construction cost but risks discarding fine-grained contextual evidence, whereas larger retrieval contexts or multi-hop LLM reasoning shift the overhead to query time. We present LycheeMemory V2, an efficient long-term memory framework that replaces turn-level consolidation with semantic segment-level consolidation. Instead of consolidating every interaction, LycheeMemory batches multiple exchanges into segments and encodes each finalized segment into context-independent typed memory records. Segment-level batching lowers LLM encoding frequency, while semantic boundary detection helps preserve coherent event-level and temporal evidence compared with fixed-window batching. The resulting records are organized with lightweight structured indexes for query-planned evidence retrieval. Experiments using GPT-4.1-Mini show that LycheeMemory achieves state-of-the-art performance, reaching 89.22% on LoCoMo and 92.20% on LongMemEval-S. Compared with A-Mem, it reduces construction tokens by 86.0% on LoCoMo and 75.9% on LongMemEval-S without increasing query-time token usage. More broadly, our results suggest that the accuracy–cost trade-off of long-term agent memory depends not only on what information is retained, but also on the granularity at which it is consolidated.
[NLP-31] Comment on “Modeling rapid language learning by distilling Bayesian priors into artificial neural networks”
【速读】： 该论文旨在解决生成式模型中如何有效引入先验知识以提升语言学习能力的问题，核心关注点在于McCoy Griffiths（2025）提出的通过模型无关元学习（MAML）将贝叶斯先验“蒸馏”至人工神经网络（ANN）的方法是否真正实现了贝叶斯学习。其解决方案的关键在于利用MAML对网络初始权重进行优化，使其在少量样本下快速适应新任务，从而模拟贝叶斯学习行为。然而，本文指出，在标准贝叶斯框架下，该方法仅实现对网络权重的有利初始化，并未在目标函数中引入显式的先验分布，因此并未真正“注入”先验；即便采用更宽松的解释——将整个系统视为实现贝叶斯推理的载体，也面临显著理论与实证挑战。进一步分析表明，尽管该模型在特定任务上表现接近真实贝叶斯学习者，但其存在严重过拟合问题，且在未见数据上的泛化能力远低于真正的贝叶斯学习器，说明MAML无法充分逼近贝叶斯学习的稳健性与可推广性。
链接: https://arxiv.org/abs/2608.12974
作者: Orr Well,Idan Tarshish,Nur Lan,Roni Katzir
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Comment on arXiv:2305.14701
Abstract:McCoy Griffiths (2025, henceforth MG) suggest that a Bayesian prior can be distilled into Artificial Neural Networks (ANNs) through Model-Agnostic Meta-Learning (MAML, Finn et al., 2017). They support this empirically by showing that meta-trained networks demonstrate formal language learning abilities comparable to Yang Piantadosi (2023)'s Bayesian learner, significantly outperforming standard ANNs. We point out that under the standard interpretation of a prior, MG’s procedure does not actually instill one; it merely initializes network weights favorably, leaving the objective function unchanged. We then consider a more permissive interpretation, where the system as a whole can be seen as implementing a Bayesian learner even without an explicit prior in the objective. We show that this interpretation faces nontrivial challenges. Finally, we assess how well MAML approximates the empirical results of Bayesian learning, showing that unlike genuine Bayesian learners, MG’s model overfits and generalizes poorly to unseen data.
[NLP-32] I-SDPO: Instance-Level Adaptive Self-Distillation Policy Optimization
【速读】： 该论文旨在解决生成式 AI（Generative AI）在基于奖励的强化学习训练中，当所有采样响应均错误时，组相对策略优化（Group Relative Policy Optimization, GRPO）因缺乏有效的相对信号而导致学习失效的问题。其核心解决方案是提出一种实例级自适应自蒸馏策略优化方法（I-SDPO），其关键在于将教师模型依赖程度与模型能力动态关联：针对每个输入实例，根据其采样轨迹中是否存在成功响应进行路由决策——若整个回滚组均失败，则采用特权自蒸馏（privileged self-distillation）提供密集的词元级监督；若存在成功响应，则保留原始GRPO机制以利用奖励差异。该设计确保模仿仅在组内相对奖励信息无意义时启用，从而避免高偏差教师模型对后续奖励优化方向的干扰。局部分析表明，非消失的有偏蒸馏权重会引入一个优化偏差下界，而路由规则可随成功概率上升自动降低预期蒸馏率，无需人工设计调度。在SciKnowEval基准测试中，I-SDPO在四个科学领域均取得最优性能，平均mean@16准确率从GRPO的56.67%提升至70.31%，单领域最高提升达18.24个百分点。
链接: https://arxiv.org/abs/2608.12957
作者: Yubo Zhang,Xinhong Ma,Zezhong Tan,Ziqiang Dong
机构: Alibaba(阿里巴巴)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 14 pages, 3 figures
Abstract:Group Relative Policy Optimization (GRPO) learns from reward differences within a rollout group, but receives no useful relative signal when every sampled response is incorrect. Privileged self-distillation can fill this gap with dense token supervision, yet applying it throughout training creates a different failure mode: the teacher is a biased, low-variance surrogate for the reward objective, so persistent imitation can oppose reward-improving updates after the policy becomes capable of producing successful trajectories. We introduce I-SDPO (Instance-Level Adaptive Self-Distillation Policy Optimization), which treats teacher reliance as capability-dependent. I-SDPO makes one routing decision per input instance and shares it across that instance’s rollout group: all-incorrect groups use a privileged self-distillation objective, whereas any-success groups remain intact for GRPO. This design uses imitation only where group-relative rewards are uninformative. A local analysis characterizes when teacher and reward directions align and shows that a non-vanishing biased distillation weight induces an optimization bias floor. The routing rule automatically reduces the expected distillation rate as success probability rises, withdrawing teacher influence without a hand-designed schedule. On SciKnowEval, I-SDPO obtains the best result in all four scientific domains and improves average mean@16 accuracy from 56.67% with GRPO to 70.31%, with a maximum domain gain of 18.24 points.
[NLP-33] Unifying Depth and Width Pruning for LLM s via Binary Knapsack Optimization
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）结构化剪枝（Structured Pruning）中因依赖贪婪启发式方法而导致的压缩预算精度不足问题。现有方法往往产生短视决策，难以精确满足预设的压缩目标，从而影响模型性能与实用性。其解决方案的关键在于提出一种两阶段结构化剪枝框架SNIPER：第一阶段在粗粒度组件上求解背包优化问题（Knapsack Optimization），基于固定的权重重要性估计实现条件最优的参数分配；第二阶段通过细粒度剪枝进一步严格满足压缩预算约束。为量化压缩预算的遵循程度，论文引入压缩比遵从因子（Compression Ratio Adherence Factor, CRAFT），实验表明SNIPER在多个任务和架构上实现了近乎精确的预算匹配（CRAFT = 0.98），显著优于现有方法（最大偏差达33%）。在涵盖四种架构、18项任务及五个领域的评估中，SNIPER在平均性能保留率和任务层面稳定性方面均优于六种先进剪枝方法，且在所有剪枝配置下取得平均排名1.25，充分体现了其跨架构的鲁棒性与可靠性。
链接: https://arxiv.org/abs/2608.12953
作者: Palaash Goel,Ayan Sengupta,Akshay Nambi,Tanmoy Chakraborty
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 29 pages, 5 figures, 17 tables
Abstract:Structured pruning is a promising approach for compressing large language models (LLMs), yet existing methods rely heavily on greedy heuristics that produce myopic decisions, and often fail to precisely meet target compression budgets. We present SNIPER, a two-stage structured pruning framework that solves a knapsack optimization over coarse-granularity components to yield conditionally optimal parameter allocations with respect to fixed importance estimates, followed by a fine-grained pruning stage to meet strict budget constraints. We introduce the Compression Ratio Adherence Factor (CRAFT) to quantify budget fidelity, showing that while existing pruners deviate from target compression ratios by up to 33%, SNIPER achieves near-exact adherence with a CRAFT score of 0.98. Evaluations across four diverse architectures over a set of 18 tasks spanning five domains demonstrate SNIPER’s consistent improvements in average performance retention and task-level stability over six state-of-the-art pruners. Across all pruning configurations, SNIPER achieves an excellent mean rank of 1.25, indicating its robust cross-architectural generalizability and excellent reliability.
[NLP-34] Decoupled Contrastive Decoding via Expert-Aligned Drafting
【速读】： 该论文旨在解决对比解码（Contrastive Decoding, CD）在生成高质量文本时因依赖“业余模型”（amateur model）进行草稿生成而导致的计算开销过高的问题。其核心挑战在于：在采用推测解码（speculative decoding）加速CD的过程中，如何合理对齐“提案模型”（drafter）与对比信号之间的关系——即对比信号应作用于草稿生成阶段，还是仅用于验证阶段。研究聚焦于轻量级特征级草稿生成（feature-level drafter）场景，通过两项受控诊断实验（匹配的Cross-alpha训练与近似双草稿分解）发现，若让草稿模型感知对比信号（contrastive-aware drafting），其性能并未持续优于仅在验证阶段使用对比信号的专家对齐草稿（expert-aligned drafting），原因在于对比修正信号通常弱于草稿模型本身的误差，且重构过程可能放大该误差。针对此问题，论文提出解耦式对比解码（Decoupled Contrastive Decoding, DCD），其关键设计为：使用专家对齐的轻量级提案器生成草稿，而将业余模型仅用于不变的对比解码验证阶段。该方法保持了原始CD的输出分布特性，同时在主流8B模型设置下，基于EAGLE3的DCD实现了相对于原生CD平均1.65至1.95倍的贪婪速度提升，并使MMLU任务中提案路径的延迟降低约5至12倍，显著提升了推理效率。
链接: https://arxiv.org/abs/2608.12913
作者: Zhixuan Liu,Zhichen Dong,Yuanfu Wang,Chao Yang
机构: Shanghai Jiao Tong University (上海交通大学); Shanghai Artificial Intelligence Laboratory (上海人工智能实验室)
类目: Computation and Language (cs.CL)
备注: 28 pages, 11 figures, 20 tables. Code: this https URL
Abstract:Contrastive Decoding (CD) improves generation quality, but its amateur-model pass makes decoding expensive. Accelerating CD with speculative decoding raises a proposal-alignment question: should the contrastive signal shape the drafter, or should it remain only in verification? We study this question in the lightweight feature-level drafter regime. Two controlled diagnostics, matched Cross-alpha training and an Approximate Dual-Drafter decomposition, give the same diagnosis: contrastive-aware drafting does not consistently improve over expert-aligned drafting because the contrastive correction is usually weaker than drafter error, and reconstruction can amplify that error. We introduce Decoupled Contrastive Decoding (DCD), which drafts with an expert-aligned lightweight proposer and applies the amateur only in unchanged CD verification. Standard speculative verification preserves the vanilla-CD output distribution. Across the main 8B settings, EAGLE3-based DCD achieves average greedy speedups of 1.65 to 1.95x over vanilla CD and reduces MMLU proposal-path latency by about 5 to 12x relative to amateur-coupled proposal paths.
[NLP-35] Prompts in the Wild: A Large Analyzed Collection of Transactional Prompts in Code
【速读】： 该论文旨在解决生成式大语言模型（Generative Large Language Models, LLMs）行为高度依赖于提示词（prompt）这一现象背后的本质问题，即提示词作为自然语言指令的结构性、多样性与可量化研究的缺失。现有研究多关注模型性能优化，而忽视了提示词本身作为语言对象的内在规律与系统性特征。为此，论文提出的关键解决方案是构建一个结构化的本体论（ontology），将原本非结构化的提示词文本转化为具有形式与语义组件的丰富结构化语言对象。该本体论系统刻画了提示词的属性及其组成部分，使得对提示词的实证、定量分析成为可能。基于此，研究从GitHub收集了57.5K条交易型提示词（transactional prompts），即可复现的、嵌入软件中的自然语言指令，并揭示其在语言、领域、任务及模态维度上的显著多样性，呈现出典型的齐普夫分布特征。为验证本体标注的可靠性，研究进行了跨领域的全面误差分析，评估了标注质量。最终，研究公开发布数据集及交互式浏览界面，为后续提示词的系统性研究提供了基础资源。
链接: https://arxiv.org/abs/2608.12905
作者: Victoria Basmov,Yoav Goldberg,Reut Tsarfaty
机构: Bar-Ilan University (巴伊兰大学); Allen Institute for Artificial Intelligence (人工智能研究所)
类目: Computation and Language (cs.CL)
备注:
Abstract:The behavior of contemporary generative Large Language Models (LLMs) is directly shaped by prompts, unstructured texts that describe the desired output and model behavior. In this paper we argue that prompts are linguistic objects that merit investigation in their own right. To this end, we collect 57.5K unique samples of prompts from GitHub. Specifically, we focus on transactional prompts: reproducible natural language instructions that are integrated into software. To enable the empirical, quantitative study of prompts, we introduce a structured ontology, capturing the properties of prompts as well as their formal and semantic components. Based on this ontology, we transform prompts from unstructured raw texts into richly structured linguistic objects. Analysis of these structured data reveals significant diversity of usage patterns across languages, domains, tasks, and modalities, in a typical Zipf-like distribution where some clearly prevail and others, more diverse, appear in the long tail. To validate the reliability of the ontology-based annotation of the prompts, we perform a comprehensive error analysis across all fields, providing a detailed assessment of annotation quality. We release the dataset together with a browsing and exploration interface (this https URL ).
[NLP-36] BavGround: A Benchmark for Regional Cultural Grounding and Dialect Competence in Bavarian
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在文化评估中普遍忽视区域性文化与方言群体代表性的问题，尤其关注德语区巴伐利亚地区（Bavarian）的本土文化知识与方言能力评估。其核心解决方案是构建BavGround——一个涵盖英语、德语和巴伐利亚语三种语言的多语言基准测试集，包含8个文化领域共206道多项选择题，总计618个平行实例，内容覆盖广泛的文化常识及源自新闻报道、历史文献与专业著作的源文本支撑型区域知识。该基准强调对地方性、方言化文化知识的评估，并揭示出尽管多语言模型整体表现优异，但在巴伐利亚语相关题目和源文本依赖型问题上性能显著下降，反映出模型在方言与本地文化知识理解上的持续短板。研究进一步发现，评估协议对结果影响显著：不同评分方式（如原始答案字母计分、字母洗牌计分、选项文本似然度、生成答案解析、语义匹配等）可能导致不同的绝对得分与模型排名，尤其影响针对区域适配模型的评价。此外，对GENBA-10B模型检查点的探索性分析表明，持续预训练虽提升了部分领域的答案内容似然度，但各领域提升不均衡，且方言能力仍相对薄弱。BavGround为大语言模型在文化表征中的本地化与评估协议敏感性提供了可操作、可扩展的评估框架。
链接: https://arxiv.org/abs/2608.12894
作者: Jophin John,Michael Hoffmann,Jan Fillies,Michael A. Hedderich,Barbara Plank
机构: Stanford University (斯坦福大学); Freie Universität Berlin (柏林自由大学); Center for Information and Language Processing, LMU Munich (慕尼黑大学信息与语言处理中心); Munich Center for Machine Learning (MCML) (慕尼黑机器学习中心); Leibniz Supercomputing Centre (LRZ) (莱布尼兹超级计算中心)
类目: Computation and Language (cs.CL)
备注:
Abstract:Cultural evaluation of large language models (LLMs) often focuses on high-resource standard languages, leaving regional culture and dialect communities underrepresented. We introduce BavGround, a benchmark for evaluating Bavarian regional cultural grounding and dialect competence across English, German and Bavarian. BavGround contains 206 multiple-choice source questions across eight cultural domains per language, yielding 618 multi-parallel instances, with items covering both broadly accessible cultural knowledge and source-grounded regional knowledge from journalism, historical sources, and specialist literature. We evaluate fifteen 7B-10B open-weight instruction-tuned models and one closed-model reference. Strong multilingual models perform best overall, but performance drops on Bavarian items and source-grounded questions, indicating persistent difficulty with dialectal and localized cultural knowledge. We further show that conclusions depend strongly on evaluation protocol: raw answer-letter scoring, shuffled-letter scoring, option-text likelihood, generated-answer parsing, and semantic matching can produce different absolute scores and rankings, especially for regionally adapted models. Finally, an exploratory analysis of GENBA-10B checkpoints suggests that continued pretraining improves answer-content likelihood unevenly across domains, while dialect competence remains comparatively weak. BavGround supports localized, protocol-aware evaluation of cultural representation in LLMs.
[NLP-37] When Your Agent Opens the Chat App: Agent -Controlled Search over Raw Chat Logs Rivals Structured Memory
【速读】： 该论文旨在解决当前生成式AI（Generative AI）对话系统中，依赖复杂记忆结构（如知识图谱、树形结构或嵌入表示）以提升检索质量所带来的有效性存疑问题，即这种性能提升究竟源于结构化设计本身，还是仅仅得益于对原始对话历史的高效检索。其解决方案的关键在于提出ReFind——一种完全不构建任何语义结构的代理可控搜索接口：它保持对话存档原始未修改状态，仅在回合粒度上进行词汇化索引，并通过一个通用的迭代关键词搜索循环，结合四项基于实证重查找研究的对话原生控制机制——会话感知的排序融合、局部上下文扩展、时间范围缩小和跳过已检查会话，实现精准检索；随后由独立的推理阶段基于收集到的证据作答。在MemoryAgentBench的增量多轮设置下，针对精确检索与事实追踪能力评估的近2,800个问题中，ReFind在所有对比系统中达到最高平均准确率（58.2），显著优于基于图和树结构的记忆系统（如HippoRAG 2，53.2），且均采用与基线一致的GPT-4o-mini骨干模型。控制实验进一步验证了代理控制、对话原生控制及词汇化检索的核心作用。在LongMemEval-S/M数据集上，相同接口使用GPT-5-mini也达到了93.2±3.3和89.3±6.0的高分。结果表明，对于基于聊天档案的精确、证据驱动型问题，大量以往归功于复杂记忆结构的性能优势，实际上可通过赋予代理对原始记录的可控制检索能力来充分恢复，而无需依赖任何基于大语言模型（LLM）的索引构建过程。
链接: https://arxiv.org/abs/2608.12888
作者: Ruizhe Li,Licheng Zhang,Benfeng Xu,Mingxuan Du,Zheren Fu,Weidong Chen
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Agent-memory systems increasingly buy retrieval quality with structure, transforming raw conversation histories into summaries, embeddings, trees, or knowledge graphs before any question is asked. We ask how much of that benefit comes from the structure itself, rather than from competent retrieval over the raw history. We present ReFind, an agent-controlled search interface that builds no semantic structure at all: it leaves the conversation archive unmodified, indexes it lexically at turn granularity, and combines a generic iterative keyword-search loop with four chat-native controls grounded in empirical refinding work: session-aware rank fusion, local context expansion, temporal narrowing, and skipping already-inspected sessions. A separate reasoning stage answers from the collected evidence. Across a broad suite of conversational-memory tasks (single- and multi-hop QA, event ordering, and fact consolidation), roughly 2,800 questions on precise-retrieval and fact-tracking capabilities evaluated under the incremental multi-turn setting of MemoryAgentBench, ReFind attains the highest mean accuracy (58.2) of any system compared, above the strongest graph- and tree-based memory systems (HippoRAG 2, 53.2), all under a GPT-4o-mini backbone matched to every reused baseline. Controlled comparisons to single-shot BM25, a matched generic-agentic BM25 control, component removals, and agentic dense/hybrid variants separately support the roles of agent control, chat-native controls, and lexical retrieval. On LongMemEval-S/M, the same interface reaches 93.2 +/- 3.3 and 89.3 +/- 6.0 with GPT-5-mini. The results indicate that for precise, evidence-grounded questions over chat archives, much of the benefit credited to elaborate memory structures is recoverable by giving an agent controllable search over the unmodified record, with no LLM-based index construction at all.
[NLP-38] he Embedders Dilemma: LLM s Are Better but at What Cost?
【速读】： 该论文旨在解决“是否应以大语言模型（Large Language Model, LLM）替代现有的文本嵌入（text-embedding）管道”这一关键问题。其核心挑战在于在多种任务场景下，权衡两类范式在性能、成本与效率上的差异。解决方案的关键在于通过一项受控且成本敏感的对比实验，系统评估了十种来自六个不同家族的LLM与26种嵌入模型（参数规模从118M到14B不等）在37项任务上的表现，涵盖分类、语义文本相似性（STS）、聚类、对分类及检索等。研究发现，尽管最优LLM（Gemini 3.1 Pro，得分77.6）与最优嵌入模型（得分77.2）在整体性能上近乎持平，但二者优势任务各异：嵌入模型在分类任务中占优，而LLM在需要强推理能力的检索任务中表现更佳；其余任务则无显著差异。然而，实现这种性能匹配的代价极高——最昂贵的LLM推理成本可达同质量嵌入模型的1,431倍（每基准测试调用成本为154美元 vs. 0.11美元），且开源LLM在相同GPU上的处理速度慢2.5至736倍。进一步分析表明，推理类令牌占LLM推理成本的28%至81%，通过降低推理预算可在多数模型上维持甚至提升检索质量。最终，帕累托前沿仅包含领先的嵌入模型和一个LLM（Gemini 3.1 Pro），支持一种分工策略：将嵌入模型用于相似性、分类与聚类任务，而将大语言模型保留用于高推理强度的检索任务。
链接: https://arxiv.org/abs/2608.12875
作者: Adnan El Assadi,Niklas Muennighoff,Jinhyuk Lee
机构: Harvard University (哈佛大学); Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL)
备注: Accepted to COLM 2026
Abstract:Should you replace your text-embedding pipeline with a large language model? We answer this with a controlled, cost-aware comparison of ten LLMs across six families and 26 embedding models (118M to 14B parameters) on 37 tasks spanning classification, semantic textual similarity (STS), clustering, pair classification, and retrieval. In aggregate the two paradigms are effectively tied: the best LLM (Gemini 3.1 Pro, 77.6) and the best embedding model (77.2) differ by 0.4 points. Their strengths differ by task: LLMs lead on reasoning-heavy retrieval, embedding models lead on classification, and the two match on clustering, STS, and pair classification. Reaching that parity is expensive. An LLM costs up to 1,431x more than an embedding model of comparable quality (USD 154 vs. USD 0.11 per benchmark pass), and the open LLMs tested process tokens 2.5 to 736x more slowly on the same GPU. Reasoning tokens account for 28 to 81% of LLM inference cost; lower reasoning budgets preserve or improve retrieval quality for most models in our ablation. The Pareto frontier contains the leading embedding models and one LLM, Gemini 3.1 Pro. These results support a division of labour: use embedding models for similarity, classification, and clustering, and reserve LLMs for reasoning-intensive retrieval. Our code, datasets, and results are publicly available at this https URL.
[NLP-39] Falsehood and Impossibility Are Different Directions in an AIs Representation of Language
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在内部表征中是否能够区分不同类型的语义失败问题，特别是对“偶然错误”（contingent falsehood）、“不可能陈述”（impossible claim）、“语义异常”（semantic anomaly）和“逻辑矛盾”（contradiction）等概念的内在区分能力。研究发现，尽管模型在输出层面将12个虚假陈述错误地归类为“矛盾”，其激活空间却表现出更精细的区分能力：线性真值探测器可有效分离“不可能”与“真实”陈述（AUC 0.93），但无法区分“不可能”与“虚假”陈述（AUC 0.20）；而专门设计的不可能性探测器在保留主题家族上实现了对“必然错误”与“偶然错误”的完美区分（AUC 1.00），峰值位于第15层，平衡准确率达0.97（经Bonferroni校正P=0.018）。进一步分析显示，不可能性方向与语义异常方向部分重叠但可区分，且稀疏自编码器特征在相同层级重现了这一几何结构。关键在于，必然错误在激活空间中并非偶然错误的极端形式，而是更接近于语义异常类别，表明模型的内部表示具有基于经验定义的、非直观的语义结构。这一发现为哲学上关于“可能性”与“意义”的古老区分提供了来自小型模型的实证线索。
链接: https://arxiv.org/abs/2608.12852
作者: Yoon Pyo Lee
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄本那-香槟分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Language can describe states of affairs that are false and states of affairs that could not be the case at all. Whether an AI model internally distinguishes these failures remains unclear. I report an exploratory activation study of the multimodal open-weight model Gemma 3 4B IT using 85 prompts from 17 philosophical families and a topic-matched modality set of 15 topics, each expressed as a truth, contingent falsehood, improbable claim, semantic anomaly, and necessary falsehood. In its answers, the model conflates contingent falsehood with contradiction, labeling 12 of 15 false statements “contradiction.” Its activations show a different pattern. A linear truth probe separates impossible from true statements (AUC 0.93) but not impossible from false statements (AUC 0.20). An impossibility probe evaluated on held-out topic families separates necessary from contingent falsehood at AUC 1.00, peaking at layer 15 with balanced accuracy 0.97 (Bonferroni-adjusted P=0.018). The truth and impossibility directions are close to orthogonal, whereas the impossibility direction partially overlaps a semantic anomaly direction while remaining distinguishable from it. Sparse autoencoder features at the same layer repeat this geometry. Features selective for impossibility also fire on anomalous sentences but rarely on contingent falsehoods. In this model’s activation space, necessary falsehoods are not extreme cases of contingent falsehood but lie closer to the experimentally defined category of semantic anomaly. This representational proximity does not imply that impossible statements are intrinsically meaningless. These correlational observations from one small model offer an empirical footnote to an old philosophical distinction.
[NLP-40] Beyond Retrieval: Query-Conditioned Reuse of Long-Horizon Agent Trajectories
【速读】： 该论文旨在解决长时程轨迹记忆（long-horizon trajectory memory）中“检索后复用”（post-retrieval reuse）这一关键瓶颈问题，即在环境状态、用户需求或实体等条件发生变化后，如何有效利用已检索到的历史轨迹来支持当前任务。其核心挑战在于：传统方法直接注入完整轨迹（full trajectory）难以适应动态变化的上下文，导致复用效率下降。解决方案的关键是提出一种名为查询条件复用（Query-Conditioned Reuse, QCR）的轻量级目标绑定型记忆表示形式，该形式显式记录可复用的程序步骤、恢复绑定关系的条件、适用性判断标准及验证要求，从而实现对历史经验的安全、高效再利用。实验表明，在WebArena、WorkArena和AppWorld共2,391个目标实例上，QCR相较于完整轨迹方法平均成功率提升10.7个百分点至62.3%，同时在线token消耗减少48.9%；且通过摘要重排序机制，94.8%的目标可选择到可复用的记忆，使最终任务成功率接近理想可复用选择器（oracle reusable selector）水平。分析显示，随着轨迹长度增加或源-目标绑定差异增大，直接轨迹注入的效用显著衰减，而目标绑定的支持策略则能更稳定地保持复用收益。由此构建的评估框架将检索质量与复用有效性解耦，为后续研究提供了清晰的分析范式。
链接: https://arxiv.org/abs/2608.12847
作者: Yifei Li,Heng Wang,Lingling Zhang,Muye Huang,Xinyu Zhang,Jiashuai Liu,Hang Yan,Rongman Xu
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Retrieval can identify a past trajectory that may matter, yet it does not specify how an acting agent should use that trajectory after users, entities, constraints, or environment state have changed. We identify this post-retrieval reuse step as a distinct bottleneck for long-horizon trajectory memory and formulate an evaluation framework that holds candidate retrieval, target state, model, decoding, and tool budget fixed while varying the support delivered to the agent. We instantiate the framework with query-conditioned reuse (QCR), a deliberately simple target-bound note that records a reusable procedure, bindings to recover, applicability conditions, and verification requirements. QCR serves to test the reuse hypothesis rather than to claim a universally preferred memory format. Across 2,391 target instances in WebArena, WorkArena, and AppWorld, QCR reaches 62.3% average Success, 10.7 points above Full Trajectory, while using 48.9% fewer online tokens. Summary reranking selects a reusable memory for 94.8% of targets, placing end-task Success within 1.8 points of an oracle reusable selector. Analyses by trajectory length and source–target binding shift show that direct trajectory injection loses much of its utility as traces grow longer or source-specific values change, whereas target-bound support preserves a larger share of the measured gain. The resulting framework separates retrieval quality from the problem of turning retrieved experience into safe, useful support for a new task.
[NLP-41] AQuA: Recursively Self-Improving Quantitative Trading Research Agents
【速读】： 该论文旨在解决量化投资研究中递归自我改进（recursive self-improvement）的实现问题，即如何使自主系统利用前期实验中的有效证据来优化后续迭代中的假设与候选因子。其核心解决方案在于构建AQuA系统，该系统由两个独立的语言模型驱动的研究模块组成：符号因子发现模块与可训练模型开发模块。二者在代理、记忆、候选空间及研究状态上完全隔离，各自通过保留经验证的有效证据并用于指导后续提案，从而在限定范围内实现研究过程层面的递归自我改进。每个模块均运行于封闭沙箱环境中，固定数据划分、特征与标签定义及评估器，仅允许模型通过受控的因子表达式或配置差异进行操作。其中，因子系统采用管理中介的多智能体流水线，在加密资产组合中生成综合信息系数约为0.190的信号；模型系统则基于混合时间序列架构的配置驱动循环，在美国股票市场实现单只股票信息系数达+0.0843，并转化为阈值型多空策略，在2021至2025年每年均呈现正收益，且在双边交易成本下最大夏普比率达+2.50。
链接: https://arxiv.org/abs/2608.12841
作者: Jiacheng Guo,Suozhi Huang,Yunlong Gao,Zihao Li,Jian Ge,Xu Kuang,Mengdi Wang
机构: Princeton University (普林斯顿大学); Ant Group (蚂蚁集团); Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:We study recursive self-improvement at the level of quantitative-investment research: whether an autonomous system can use evidence from earlier experiments to improve the hypotheses and candidates proposed in later iterations. We present AQuA, which comprises two separate language-model-driven research systems: one for symbolic factor discovery and one for trainable model development. The two systems do not share agents, memories, candidate spaces, or research state. Instead, each independently closes its own research loop by retaining validated evidence and using it to guide subsequent proposals. In this bounded sense, both systems implement recursive self-improvement at the level of the research process. Each system also uses its own sealed sandbox, which fixes the data splits, feature and label definitions, and evaluator while allowing the model to act only through constrained factor expressions or configuration diffs. The factor system, a manager-mediated multi-agent pipeline, discovers and combines factors into a signal that reaches a combined information coefficient of about 0.190 on a crypto universe. The model system, a config-driven loop over a hybrid time-series architecture, reaches a per-stock information coefficient of +0.0843 on US equities and converts it into a threshold long/short strategy with a held-out Sharpe of up to +2.50 at a two-leg cost. The strategy is positive in every year from 2021 to 2025.
[NLP-42] From Atomic Evidence to Logical Composition: Structured Compositional Reasoning over Compound Answer Options
【速读】： 该论文旨在解决大语言模型在处理包含显式逻辑运算符（AND、OR、NEITHER/NOR）的复合选项时表现不佳的问题，尽管模型对单个原子判断（atomic judgments）的准确性较高。其核心挑战在于模型难以正确理解并组合多个独立判断以形成对复合逻辑命题的推理。解决方案的关键在于提出一种分解-对比-优化框架：首先将每个复合选项分解为原子成分，分别生成原子答案；随后针对每个原子成分构建对比性假设（contrastive hypotheses）并进行校准评分；在此基础上，通过一个受运算符约束的整数线性规划（operator-constrained integer linear program）将校准后的得分整合为最终预测，从而避免模型直接接触原始复合选项。该方法显著提升了模型在逻辑推理任务上的性能，在人类验证的LOGICAL-COMMONSENSEQA数据集上宏平均F1从48.3提升至77.0，在新提出的LOGICAL-SATA阅读理解基准上从47.0提升至75.6，尤其在处理最复杂的NEITHER/NOR类问题时增益最为显著。
链接: https://arxiv.org/abs/2608.12836
作者: Obed Junias,Maria Leonor Pacheco
机构: University of Colorado Boulder(科罗拉多大学博尔德分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 21 pages, 6 figures, 10 tables
Abstract:Large language models often fail when answer options require combining atomic judgments under explicit logical operators, even when they judge the individual atoms correctly. We study compound options connected by AND, OR, and NEITHER/NOR, introducing a framework that decomposes each option into atomic answers and scores contrastive hypotheses about each one, so the model never sees a compound option. An operator-constrained integer linear program then composes the calibrated scores into a single prediction. We evaluate on LOGICAL-COMMONSENSEQA and introduce LOGICAL-SATA, a reading-comprehension benchmark derived from SATA-Bench. Our framework improves Macro-F1 from 48.3 to 77.0 on the human-validated LOGICAL-COMMONSENSEQA split and from 47.0 to 75.6 on LOGICAL-SATA, with the largest gains on NEITHER/NOR.
[NLP-43] FastThaiG2P: Lightning-fast Thai Grapheme-to-phoneme Conversion for Voice Agent Pipelines
【速读】： 该论文旨在解决泰语文本到语音（TTS）系统中图符转音素（grapheme-to-phoneme, G2P）转换的实时性与准确性问题，尤其针对中央泰语口语表达的复杂性。现有G2P方法在处理低频词或未登录词（out-of-vocabulary, OOV）时存在延迟高、效率低的问题，限制了TTS系统在实际应用中的部署能力。其解决方案的关键在于构建一个基于PyThaiNLP分词、可扩展的词典与规范化规则框架，并通过优化架构实现亚毫秒级的处理速度：在27,242条合成语句的基准测试中，平均延迟仅为0.15毫秒/语句，其中58%的时间消耗于OOV回退机制（但整体OOV率仅0.5%），而分词和规范化分别占30%和12%。通过将FastThaiG2P集成至Som-TTS数据集并训练出一个8200万参数的StyleTTS 2模型，在CPU上使用ONNX推理时达到0.25实时因子（即4倍实时），实现了高质量、可快速原型化与开发的泰语语音合成能力。
链接: https://arxiv.org/abs/2608.12814
作者: Charin Polpanumas
机构: AWS(亚马逊网络服务)
类目: Computation and Language (cs.CL)
备注:
Abstract:FastThaiG2P provides sub-millisecond Thai grapheme-to-phoneme conversion for text-to-speech pipelines (International Phonetic Alphabet and Kokoro-TTS conventions) using a PyThaiNLP-tokenized, extensible dictionary and normalization rules for common Central Thai speech. The approach achieves an average latency of 0.15 ms per utterance on a benchmark of 27,242 synthetically generated utterances, of which 30% is spent on tokenization, 12% on normalization, and 58% on out-of-vocabulary fallbacks (0.5% OOV rate). To demonstrate its effectiveness, we used FastThaiG2P to phonemize Som-TTS, an open dataset containing 20 hours of grapheme-and-audio pairs, then trained an 82M-parameter StyleTTS 2 model based on a Kokoro-TTS recipe. The resulting model vocalizes intelligible Thai speech suitable for prototyping and development at 0.25 real-time factor (4x real-time) with ONNX inference on CPU.
[NLP-44] ViTOED: A Dataset for Target-Oriented Emotion Detection on Vietnamese Social Media Texts
【速读】： 该论文旨在解决越南语社交媒体文本中面向目标的情感检测（Target-Oriented Emotion Detection, TOED）问题，尤其针对越南语特有的语言现象，如隐含的主体与目标以及词汇歧义。其解决方案的关键在于构建了一个高质量、大规模的标注数据集ViTOED，包含10,985条用户评论及21,244个经过严格标注的意见四元组（源、目标、表达、极性），并基于结构化情感图谱提出基线模型，同时评估多种越南语预训练语言模型在该任务上的表现。实验结果揭示了在跨度检测与关系抽取方面的显著挑战，表明当前模型在越南语面向目标情感检测任务上仍有巨大提升空间。
链接: https://arxiv.org/abs/2608.12776
作者: Chanh Vo,Son T. Luu,Ngan Luu-Thuy Nguyen
机构: University of Information Technology, Ho Chi Minh City; Vietnam National University, Ho Chi Minh City, Vietnam
类目: Computation and Language (cs.CL)
备注: Accepted for publication at 2026 International Conference on Multimedia Analysis and Pattern Recognition (MAPR 2026)
Abstract:This paper introduces ViTOED, a novel dataset for target-oriented emotion detection in Vietnamese social media texts. The ViTOED comprises 10,985 user comments and 21,244 manually annotated opinion quadruples (source, target, expression, polarity) that follow strict guidelines. The dataset reveals Vietnamese-specific phenomena, such as implicit sources and targets and vocabulary ambiguities, enabling deeper analysis of user emotions toward entities. We propose a baseline using structured sentiment graphs and evaluate various Vietnamese pre-trained language models. The empirical results highlight challenges in span detection and relation extraction and indicate substantial room for model improvement in Vietnamese Target-Oriented Emotion Detection tasks.
[NLP-45] ReconSpan: Reconstruction-Guided Adaptive Latent Tokenization
【速读】： 该论文旨在解决自然语言处理中如何高效压缩文本序列的同时保留关键语义信息的问题。传统方法在降低序列长度时往往导致重要细节丢失，而现有自适应分块策略难以在压缩率与信息保真度之间取得平衡。其解决方案的关键在于提出ReconSpan机制：通过后向解码器从单一上下文前缀码重构文本块，并以重建误差作为分块边界判定标准，使生成的潜在标记（latent token）能够动态适应输入内容特征。该方法利用训练好的自编码器，在平均块长介于6.5至12.2之间时实现稳定性能，且在相同平均长度下，基于重建引导的分块边界比随机分块保留更多原始文本信息。尽管潜在序列能可靠恢复主题信息，但对精确细节的还原能力较弱，体现了该方法在“压缩效率”与“信息粒度”之间的权衡设计。
链接: https://arxiv.org/abs/2608.12756
作者: Lixing Li
机构: Cornell University(康奈尔大学); Ithaca, NY 14853, USA
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 16 pages, 3 figures
Abstract:Adaptive latent tokenization maps a fine-grained input to a shorter sequence of continuous representations associated with input-dependent spans. We introduce ReconSpan, which divides text into chunks that a backward decoder can reconstruct from a single contextual prefix code and retains one such code as the latent token for each chunk. The reconstruction criterion is applied when chunks are formed, allowing one trained autoencoder to produce average chunk lengths from 6.5 to 12.2. At matched average length, reconstruction-guided boundaries preserve more text than random boundaries. Readers of the resulting latent sequence recover topic information reliably but struggle to extract exact details.
[NLP-46] Dual-Stream Cross-Anchor Correction Grounding Long-Form Captions and the Domain Limits of Object-Level Anchors
【速读】： 该论文旨在解决多模态大语言模型中因语言先验与语料共现偏差过强，导致生成内容脱离图像视觉证据的“对象幻觉”（object hallucination）问题。现有方法多在解码阶段进行后处理，仅在短文本生成中有效；而基于细节丰富语料的监督微调（SFT）虽能生成更长文本，但仍超过40%的对象提及存在幻觉。本文提出双流交叉锚定校正（Dual-Stream Cross-Anchor Correction, DSCC），其核心创新在于：在微调阶段将对象级视觉锚点（visual anchors）直接注入语言模型内部。具体而言，感知流通过双向对比损失，使中间层的物体级隐藏状态与冻结的文本锚点对齐；认知流则在每一步自回归生成中，利用跨注意力机制让深层网络主动查询这些锚点；两阶段课程门控机制将二者耦合，使证据检索成为每一生成步骤的结构性约束。在统一骨干网络与评估协议下，实验覆盖长句生成幻觉、对象存在性判别及跨域泛化能力，相较于同语料、同训练周期的基线SFT，DSCC是唯一实现长文本低幻觉的方案——生成长度约为基线1.9倍，且每个对象提及的精度达88.19%，在密度无关评价标准下表现最优。消融实验揭示感知流与认知流之间存在协同效应：单独使用感知流会降低精度，但叠加于认知流之上时反而显著提升性能；同时，该方法不具普适优越性，三组跨域基准测试表明其效果依赖锚点语义域，对图表和光学错觉场景失效，体现了可预测且可验证的领域依赖性。
链接: https://arxiv.org/abs/2608.12746
作者: LingKai Bu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Object hallucination in multimodal large language models arises when language priors and corpus co-occurrence bias outweigh the visual evidence, with nothing tying an individual object mention to what the image shows. Most remedies intervene at decoding time without training, yet under a unified protocol their benefit is confined to short captions;supervised fine-tuning (SFT) on a detail- rich corpus lengthens captions, but over forty percent still name absent objects. This paper proposes Dual-Stream Cross-Anchor Correction (DSCC). Unlike work that post-processes decoding, DSCC is the first to inject object-level visual anchors into the language model itself during fine- tuning: a perception stream aligns object-level hidden states at an intermediate layer to frozen text anchors by a bidirectional contrastive objective; a cognition stream lets deeper layers query those anchors by cross-attention at every generation step; and a two-stage curriculum gate couplesthem, making evidence retrieval a structural constraint at each autoregressive step. Under one backbone and one scoring protocol, experiments span long-caption hallucination, object-existence discrimination and cross-domain generalisation, with vanilla SFT on the same corpus and schedule as a length- and density-matched control, so gains are attributed layer by layer. DSCC is the only method reaching the long-caption, low-hallucination region: captions roughly 1.9 times the baseline length at 88.19% precision per object mention, the highest under a density-independent criterion. Ablations expose a synergy: the perception stream alone degrades precision yet reverses sign when stacked on the cognition stream. No universal superiority is claimed: three out-of- domain benchmarks yield a predictable, falsifiable domain-conditionality, the synergy being bound to the anchors’ semantic domain and breaking on charts and optical illusions.
[NLP-47] ERSkill: Evolving for Skill-Guided Adaptive Memory Retrieval
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理在长期记忆访问中因检索机制静态化而导致的性能瓶颈问题，尤其针对异构记忆查询所要求的多样化证据构建策略难以适配的挑战。其核心解决方案是提出一种以检索为中心的自演化、技能引导的记忆访问框架——ERSkill。该框架的关键在于将交互历史结构化为记忆存储，并将检索行为抽象为由基础操作构成的可执行技能（skill），通过训练一个动态路由模块在推理时根据查询匹配最优技能，从而生成定制化的证据用于答案生成。为实现持续优化，ERSkill在训练过程中协同演化技能集与路由策略，利用经验前缀树（experience trie）高效记录已探索的检索路径，并采用双前沿机制安全分离新技能能力的扩展与稳定部署，确保系统演进的可靠性。实验结果表明，ERSkill在多个代理记忆基准测试中显著优于非演化及自演化基线模型，在Qwen3-Next-80B-A3B-Instruct和GPT-5.4-nano上分别提升综合指标（F1、BLEU-1、LLM-judge得分）达31.3%和28.1%。
链接: https://arxiv.org/abs/2608.12720
作者: Haolong Chen,Liang Zhang,Zhuo Li,Lei Xue,Guanrxu Zhu
机构: Shenzhen International Center for Industrial and Applied Mathematics(深圳市国际应用数学中心); Shenzhen Research Institute of Big Data(深圳市大数据研究院); The Chinese University of Hong Kong, Shenzhen(香港中文大学（深圳）); Shenzhen Campus of Sun Yat-sen University(中山大学深圳校区); Shenzhen Loop Area Institute(深圳市环路区域研究院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:While Large Language Model (LLM) agents increasingly rely on long-term memory for persistent interactions, the retrieval mechanisms governing this memory are rarely treated as evolvable components. This static approach limits performance on heterogeneous memory queries, which often demand diverse evidence construction strategies. To address this, we introduce \textbfERSkill, a retrieval-centric framework for self-evolving, skill-guided memory access. ERSkill compiles interaction histories into a structured memory store and represents retrieval behaviors as executable skills composed of fundamental primitives. At inference time, a trained router dynamically matches each query to the optimal skill to construct tailored evidence for answer generation. To enable continuous improvement, ERSkill co-evolves the skill set and the router during training. It employs an experience trie to efficiently record explored retrieval paths, alongside a double-frontier mechanism that safely decouples the expansion of new skill capabilities from stable, router-facing deployment. Experiments across multiple agent memory benchmarks demonstrate that ERSkill substantially outperforms strong non-evolving and self-evolving baselines. Notably, it improves the overall average across F1, BLEU-1, and LLM-judge scores by 31.3% with Qwen3-Next-80B-A3B-Instruct and by 28.1% with GPT-5.4-nano.
[NLP-48] Perturbation-based Regional Interpretability through Subtraction Mapping (PRISM): naming-error dissociations in language models and post-stroke aphasia
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在机制可解释性方面缺乏空间解析且可验证的工具问题，具体针对模型内部组件是否专门化于特定认知操作这一核心命题。其解决方案的关键在于提出一种名为PRISM（Perturbation-based Regional Interpretability through Subtraction Mapping）的新方法，该方法借鉴人类神经影像学中的减法分析（subtraction analysis）范式，将同一逻辑平行应用于经扰动的变压器模型与慢性卒中失语症患者的大脑数据。通过基于脑-大语言模型统一模型（Brain-LLM Unified Model, BLUM）的层扰动实验，PRISM将七类临床费城命名测试（Philadelphia Naming Test）错误类别进行两两相减，并将每个扰动种子视为一个“被试”，在层轴方向上采用无阈值聚类增强（threshold-free cluster enhancement）进行组分析。研究同时在213名慢性卒中失语症患者中执行结构匹配的分析，使用相关性差异的病变-症状映射（correlation-difference lesion-symptom mapping），并成功在独立数据集上复现结果。尽管对比算子不同（模型为被试内误差比例差，大脑为被试间相关性差），但两者在被试维度（种子/患者）、空间维度（层/图谱分割皮层）和阈值处理上保持一致。最终，两种系统均稳健恢复出以音位偏好为主的分离模式、深层层簇以及额颞周外侧皮层簇，且均具可重复性；而以语义偏好为主的趋势则呈现一致但不显著的符号特征。因此，PRISM提供了一种可证伪、空间分辨的测试手段，用于检验变压器语言模型中功能特异性假设的有效性，其确认性区域感兴趣（ROI）级干预（即PRISM第三阶段）以确立最强因果机制主张的部分留待后续研究。
链接: https://arxiv.org/abs/2608.12717
作者: Xiang Guan,Roger D. Newman-Norlund,Yong Yang,Saeed Ahmadi,Regan Willis,Nadra Salman,Kalil Warren,Srihari Nelakuditi,Chris Rorden,Leonardo Bonilha,Julius Fridriksson
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 49 pages, 6 figures, 1 table. Supplementary methods, 6 tables and 5 figures included
Abstract:Mechanistic interpretability of large language models lacks spatially resolved, falsifiable tools for testing whether internal components are specialized for distinct cognitive operations. We adapt subtraction analysis, the standard framework of human neuroimaging, from biological brains to perturbed transformers, and apply the same logic to both substrates in parallel. Building on the Brain-LLM Unified Model (BLUM), which showed that layer-perturbed LLaVA-1.6-Vicuna-13B error profiles match the lesion patterns of aphasic patients, we develop PRISM (Perturbation-based Regional Interpretability through Subtraction Mapping). PRISM maps the seven clinical Philadelphia Naming Test categories, subtracts error classes pairwise, and treats each perturbation seed as a subject in a group analysis with threshold-free cluster enhancement along the layer axis. We run a structurally matched analysis on 213 chronic post-stroke aphasia patients using correlation-difference lesion-symptom mapping, and replicate both sides on held-out splits. The designs match in subject dimension (seeds, patients), spatial dimension (layers, atlas-parcellated cortex) and thresholding, but the contrast operator differs: a within-subject error-proportion difference for the LLM, a between-subject correlation difference for the cortex. Both substrates recover a robust phonemic-favoring dissociation, a deep layer cluster and a frontal-perisylvian cortical cluster, both replicating; the semantic-favoring direction is a consistently signed but non-significant trend on both. PRISM thus gives a falsifiable, spatially resolved test of functional-specialization claims in transformer language models. A confirmatory ROI-level intervention (PRISM Stage 3) licensing the strongest causal-mechanism claim is left to subsequent work.
[NLP-49] racing Provenance and Detecting Tampering with Complementary LLM Watermarks
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）生成文本水印在内容篡改场景下的安全漏洞问题，即现有水印方案虽具备编辑鲁棒性以保证溯源能力，却也因此使攻击者可实施“搭便车伪造”（piggyback spoofing）——在不破坏水印的情况下篡改关键内容。为此，论文提出一种创新的双信号协同水印机制，其核心在于在每个生成标记中同时嵌入一个强健信号（robust signal）与一个脆弱信号（fragile signal），二者共享相同生成机制但使用独立密钥及不同的归一化文本种子窗口，从而实现前者对编辑保持鲁棒、后者对读者可见修改高度敏感的特性。通过多轮无偏锦标赛重加权维持生成分布的预期一致性，并采用周期性轮次分配策略调控两信号间的平衡。检测阶段，两信号得分构成二维空间，支持“完整”、“篡改”和“无水印”三类判别。实验表明，该方法在两大主流LLM及两个提示数据集上，相较现有方法实现了最优的篡改检测率，同时保持了良好的溯源鲁棒性与困惑度表现；消融研究进一步验证了可靠三态判别的必要条件：明确的“完整”定义、双信号共嵌入设计以及对编辑的互补敏感性。
链接: https://arxiv.org/abs/2608.12713
作者: Xiaoyan Feng,Yanjun Zhang,He Zhang,Leo Yu Zhang,Shirui Pan
机构: Griffith University(格里菲斯大学)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 11 pages, 7 figures, 4 tables
Abstract:Watermarking LLM-generated text is an important task for tracing its provenance. Existing LLM watermarks preserve provenance under editing, but this same robustness allows an adversary to alter critical content while retaining attribution, a vulnerability known as piggyback spoofing. We introduce an innovative watermark that jointly provides provenance and tamper evidence. It co-embeds a robust signal and a fragile signal into each generated token. The signals share the same mechanism but use independent keys and different seeding windows over normalized text, making one resilient to edits and the other sensitive to reader-visible changes. Multiple rounds of unbiased tournament reweighting preserve the expected generation distribution, while a periodic round-allocation pattern controls the trade-off between the two signals. At detection, their scores form a two-dimensional space supporting three decisions: Intact, Tampered, and No-Watermark. Across two large language models and two prompt datasets, our method demonstrates the highest tamper-detection rate among the evaluated methods while maintaining competitive attribution robustness and perplexity. Ablation studies show that reliable three-state detection requires a well-defined notion of intactness, co-embedding of the two signals, and complementary sensitivity to edits.
[NLP-50] SteerBench-Work: A Benchmark for Agent Steering at Action Boundaries
【速读】： 该论文旨在解决生成式AI在长周期运行的大型语言模型（LLM）代理于工作场景中执行关键操作时，其“引导决策”（steering decision）的准确性与可靠性问题。具体而言，面对如发送邮件、合并代码请求或支付款项等高风险操作，系统需在执行前做出“放行”（proceed）或“暂停待人工/政策审查”（hold）的判断，而当前模型在此类边界决策上普遍存在偏差。解决方案的关键在于构建一个以真实公共事件为锚点、双向对称设计的基准测试集SteerBench-Work v2026-05，涵盖开发运维、客户服务、金融、法律、医疗、人力资源及安全等多个领域共106个场景，包含证据反转镜像案例与校准控制，确保“放行”与“暂停”的标签分布均衡，从而精准衡量模型在真实风险情境下的判断能力。实验结果显示，尽管多数模型在标准任务上表现良好，但在实际引导决策中存在显著倾向性：高达28.1%的合法且证据充分的操作被错误地暂停，而仅1.0%的高风险操作被错误放行，尤其在已通过签名或结构化证据化解风险的提交（risk-resolved commits）中，模型性能急剧下降，在证据反转镜像案例中的准确率仅为63.8%，远低于原始案例的98.5%。研究进一步揭示，模型的通用能力与其引导决策的校准性并非正相关，高能力模型反而更倾向于过度拒绝（over-refuse），而增加推理虽可修复弱引导机制，却无法提升已校准模型的表现，表明引导决策的优化需专门建模而非依赖泛化能力提升。
链接: https://arxiv.org/abs/2608.12654
作者: Oguz Serdar,Cuneyt Mertayak
机构: AgentDock
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Long-running LLM agents act through tools, and a single step can send an email, merge a pull request, or wire a payment. The steering decision is the pre-commit choice at that boundary: proceed, or hold for human or policy review. We introduce SteerBench-Work, an incident-anchored, bidirectional benchmark for that decision in workplace agents across developer operations, customer service, finance, legal, medical, HR, and security. Release v2026-05 contains 106 scenarios anchored in public incidents, paired evidence-reversed mirrors, and calibration controls, with labels split nearly evenly between proceed and hold so the two error directions get near-identical numbers of chances. A model sees the proposed action and the available evidence, returns a gate decision, and is scored on whether it crosses or holds the boundary correctly. Across 30 model conditions the failures run almost entirely in one direction: models wrongly hold authorized, evidence-cleared work on 28.1% of opportunities and wrongly allow unsafe work on 1.0%. The hardest cases are risk-resolved commits, where signed or structured evidence has already cleared a real risk trigger, and models score markedly worse on evidence-reversed mirrors of famous incidents (63.8%) than on the incidents themselves (98.5%). General capability is not the same as steering calibration: higher-capability models often over-refuse at the commit boundary, and more reasoning can repair a weak gate while leaving a calibrated one flat. The public leaderboard is at this http URL. Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2608.12654 [cs.AI] (or arXiv:2608.12654v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.12654 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-51] Excess Separability: Nuisance-Controlled Residual-Stream Probing for Benchmark Contamination Detection
【速读】： 该论文旨在解决生成式模型训练数据中存在基准污染（benchmark contamination）的检测难题，现有方法如n-gram重叠、基于似然的成员推断或蜜罐字符串等均依赖于不可获取的先验信息（如训练语料库、精心设计的统计量或数据集发布时的前瞻性判断）。为克服这些局限，论文提出一种基于内部激活空间线性探测器（linear probe）深度剖面的新型检测协议。其核心解决方案在于：通过报告探测器准确率在模型深度上的零和对比（zero-sum contrast），以层级匹配的伪基线（level-matched placebo baseline）为中心进行校正，并采用标签置换零假设（label-permutation null）进行检验；同时要求参考集大小为可疑集的两倍。该设计通过系统排除更简单但无效的替代方案——包括与平坦深度剖面对比（易导致假阳性或丧失检出力）、使用项目自举法（item bootstrap）而非重拟合探测器的置换零假设、以及半尺寸基线——实现了对真实零假设下误报率的有效控制（从0.03至0.99随分析师自身控制集规模变化）。实证结果表明，真实Transformer模型的基线深度剖面并非平坦，其非平坦性与样本集间的表面差异高度相关（6次审计中相关系数达0.87），因此校正效果在最需要处最强。最终，该协议在4个匹配良好的Pile分支上均返回零结果，且在时间分割数据上拒绝做出结论而非错误报告，显著提升了检测可靠性。该方法未证明模型是否普遍携带熟悉度方向（familiarity direction），但唯一阳性结果出现在交换性失效的分割上。研究提供了完整实现代码与审计工具。
链接: https://arxiv.org/abs/2608.12652
作者: Florian Braun
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 22 pages, 11 figures, 7 tables. Code and artefacts: this https URL
Abstract:Benchmark contamination is diagnosed today with n-gram overlap, with likelihood-based membership inference, or with canary strings, and each needs something usually unavailable: the training corpus, a well-chosen test statistic, or foresight at dataset release. A recent alternative reads contamination off a linear probe on internal activations. We show that the natural way to do this does not work, and specify one that survives measurement. The protocol reports a zero-sum contrast on the depth profile of probe accuracy, recentred on a level-matched placebo baseline, tested against a label-permutation null, with the reference set twice the size of the suspect set. Each choice replaces a simpler alternative we measured and rejected. Reporting the level of excess separability rather than its shape makes the false positive rate track the size of the analyst’s own control set, from 0.03 to 0.99 under a true null. Contrasting against a flat depth profile fails in both directions, rejecting a true null 0.72 of the time when surface decodability rises with depth and losing all power when it falls. An item bootstrap holds the fitted probe fixed and rejects up to 0.09 of the time where a permutation null that refits it holds 0.02. A half-size baseline triples the error rate. On real transformers, baseline depth profiles are measurably not flat, spanning up to 29.1 accuracy points on a temporal split, and their non-flatness tracks the surface difference between the item sets (correlation 0.87 over 6 audits), so the correction is largest exactly where it is needed. All 4 well-matched Pile arms return null, and the protocol refuses a verdict on the temporal split rather than reporting one. What this does not establish is whether transformers carry a familiarity direction at all: the only positive sits on the split where exchangeability fails. Implementation, tests and audits are released. Comments: 22 pages, 11 figures, 7 tables. Code and artefacts: this https URL Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG) ACMclasses: I.2.7 Cite as: arXiv:2608.12652 [cs.CL] (or arXiv:2608.12652v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.12652 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-52] Novels generated by language models show compressed formal variation
【速读】： 该论文旨在解决生成式 AI 在多轮迭代生成过程中，其输出文本在形式多样性（formal variation）方面是否能够达到人类创作语料所呈现的丰富程度这一核心问题。研究聚焦于生成式 AI（Generative AI）在连续多代生成任务中是否能维持与人类文学作品相当的风格多样性，而非仅关注单个文本是否可被识别为机器生成。其解决方案的关键在于通过对比六类语料库——包括基于 GPT-5.5 Thinking 与 Qwen3-14B 模型分别生成的十九世纪英国现实主义风格及当代零风格小说各20部、以及205部十九世纪人类创作的英国小说和65部当代人类创作的零风格小说——从文档层面系统评估多种形式指标，包括 MATTR-500、香农熵（Shannon entropy）、平均句长、可读性及标点符号使用率等。研究发现，尽管不同模型在均值风格特征上存在差异，但重复生成导致句子结构高度压缩，显著降低了跨文本间的句法多样性；这种压缩现象也体现在可读性、标点使用频率及句长变异性的测量中。此外，词汇层面虽总体趋于同质化，但 Qwen 零风格的 MATTR 值例外地表现出较高变异性。更重要的是，研究揭示了“方差闭合”（variance overclosure）与“相关性闭合”（correlational overclosure）的区分：即单个 AI 小说可能在表层风格上接近人类作品，但多个 AI 生成的小说整体在多维度形式特征间缺乏稳定的相关性模式，表明其形式空间远窄于人类创作，反映出生成过程中的系统性局限。
链接: https://arxiv.org/abs/2608.12630
作者: Mehdy Sedaghat Payam,Justin Quinn
机构: University of Maryland (马里兰大学); University of West Bohemia (西波希米亚大学); Charles University (查理大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:While large language models can generate entire novels, there is little information about the level of formal variation in their output over many generations. Rather than asking whether individual passages can be identified as AI-generated, this study asks whether repeated AI generation can produce the same range of diversity which is found across human corpora. This paper contrasts six corpora based on generation source and target style: twenty novels generated using GPT-5.5 Thinking in a nineteenth-century British realist style, twenty novels generated using Qwen3-14B in a nineteenth-century British realist style, twenty novels generated using each of these models in a contemporary zero style, 205 nineteenth-century human-written British novels, and sixty-five contemporary human-written Zero-Style novels. At the document level, the research includes MATTR-500, Shannon entropy, average sentence length, readability, and punctuation rate measurements. The most robust and reliable result is compression of sentence structure. Repeated generations produce novels that vary far less from one another in sentence structure than human novels do. Compression is also present in the measures of readability, punctuation, and sentence length variability within novels. Lexical measures tend to be similarly compressed, with the exception of Qwen Zero-Style MATTR. Despite having distinct mean stylistic profiles, GPT and Qwen lack a stable pattern of cross-measure correlation. This article therefore distinguishes between variance overclosure, which represents a limited formal range between novels, and a more specific phenomenon of correlational overclosure. This means that an individual AI-generated novel may resemble human fiction stylistically, while a collection of AI-generated novels occupies a much narrower formal range.
[NLP-53] When Explanations Betray Backdoors: Black-Box Auditing for Language Model Classifiers
【速读】： 该论文旨在解决在缺乏触发信息的情况下，如何对仅提供标签与简短推理或引用证据的黑箱语言模型分类器进行有效审计的问题。其核心挑战在于检测生成式AI（Generative AI）模型是否存在后门攻击（如非自适应的OpenBackdoor类攻击），同时确保解释内容仍忠实于输入数据。解决方案的关键是提出“有根据性漂移”（Groundedness Drift）这一轻量级评分指标，用于衡量输出摘要是否仍基于输入内容保持语义一致性。实验表明，在两个7B规模的模型、五个数据集及四类常见非自适应后门攻击场景下，该方法在5%纯净假阳性率（clean-FPR）预算下，均显著优于现有检测器，表现为更高的AUROC和更低的残余目标攻击成功率（ASR）。进一步引入“无支持的有根据性”（Unsupported Groundedness）作为多探针升级机制，以应对解释伪装（explanation-camouflage）等压力情形，虽提升了检测信号强度，但仍未完全弥合自适应攻击下的检测差距。
链接: https://arxiv.org/abs/2608.12623
作者: Yang Liu,Ran Zou
机构: University of North Carolina at Chapel Hill (北卡罗来纳大学教堂山分校); University of California, Irvine (加州大学欧文分校)
类目: Computation and Language (cs.CL); Machine Learning (stat.ML)
备注: 16 pages, 1 figure
Abstract:Language model classifiers with explanations are used for moderation, routing, topic triage, and low-resource annotation. We study black-box auditing when the defender has only clean calibration data without trigger information but can ask the classifier for a label plus a short rationale or quoted evidence. We introduce Groundedness Drift, a lightweight score measuring whether the answer summary remains grounded in the input. Across two 7B backbones, five datasets, and four common non-adaptive OpenBackdoor-style attack families, Groundedness Drift achieves higher AUROC and lower residual target ASR than every compared detector in all cases at a nominal 5% clean-FPR budget. We then evaluate Unsupported Groundedness, a multi-probe escalation for explanation-camouflage stress cases. Unsupported Groundedness improves signals but does not close the adaptive gap.
[NLP-54] Intensional Anaphora
【速读】： 该论文旨在解决**指称性代词在非真值性（non-veridical）的意向语境中能否实现回指（intensional anaphora）**这一语言现象的理论难题。传统观点认为，意向性算子（如“可能”）可类比于量词作用于可能世界，但此类语境引入的个体难以作为后置代词的先行成分，例如“安德里亚可能在吃一个芝士汉堡”无法自然引出“它很大”或“它们很大”的回指句，而类似“每个人都在吃一个芝士汉堡”则可顺畅接续“它们很大”。已有研究（Stone, 1999；Stone & Hardt, 1999；Brasoveanu, 2010）主张通过要求代词的指称必须存在于评价世界（world of evaluation）来排除非真值性语境中的回指。然而，本文指出该标准存在反例：既存在指称明显存在但仍被禁止回指的情形，也存在指称未必存在却仍允许回指的情形。为此，作者提出一种基于描述（description-based）而非指称值（value-based）的回指解释模型——即代词预设其对应先行描述在语境集（context set）中的每个可能世界中均被实例化。因此，“安德里亚可能在吃一个芝士汉堡”之后出现“它很大”是否合法，取决于在所有候选可能世界中是否存在安德里亚正在吃的芝士汉堡。为实现该理论，作者构建了一种新的逻辑系统——多元意向性预设谓词演算（Plural Intensional Presuppositional predicate calculus, PIP），其公式可直接映射为带集合抽象的标准一阶谓词演算，从而为意向性回指提供形式化的经典语义基础。
链接: https://arxiv.org/abs/2608.12598
作者: Ezra Keshet,Steven Abney
机构: 未知
类目: Computation and Language (cs.CL)
备注: 49 pages. Published in Semantics and Pragmatics
Abstract:Intensional operators are often treated as quantifiers over possible worlds, parallel to the treatment of determiners as quantifiers over individuals. Yet individuals introduced in intensional contexts cannot serve as antecedents to later pronouns as easily as those introduced in merely quantificational contexts. For instance, “Everyone is eating a cheeseburger” may be followed by “They are large”, where “they” refers to the cheeseburgers being eaten. However, as Stone (1999) points out, the similar “Andrea might be eating a cheeseburger” does not support later anaphoric references such as “It is large” or “They are large”. Stone (1999), Stone and Hardt (1999), and Brasoveanu (2010) address this by requiring a pronoun’s value (its referents) to exist in the world of evaluation, ruling out anaphora from non-veridical intensional contexts. We show, however, both cases where such anaphora is disallowed even when the pronoun’s referents clearly exist and cases where it is allowed even though they might not exist. We argue that intensional anaphora is best captured using a description-based rather than value-based account. A pronoun presupposes that its corresponding antecedent description is instantiated in each world of the context set. Thus, there must be a cheeseburger being eaten by Andrea in every candidate world for “It is large” to be felicitous after “Andrea might be eating a cheeseburger”. We implement our proposal via a new logic, building on Keshet (2018) and Abney and Keshet (2022), called Plural Intensional Presuppositional predicate calculus (PIP). Each PIP formula translates directly into standard first-order predicate calculus with set abstraction, providing a classical foundation for this work.
[NLP-55] Is this Citation on Point? ICML2026
【速读】： 该论文旨在解决生成式 AI 在法律文本生成中出现的“引用支持不匹配”问题，即模型生成的引文虽指向真实判例，但所引页码内容并不支持其所论证的法律主张，这一问题在现有大语言模型（LLM）法律应用评估中被严重忽视。其解决方案的关键在于通过受控扰动真实法律引文（包括替换引用案例或仅更改同一案例中的具体页码），构建测试数据集以评估模型在命题级引用支持验证方面的能力。实验结果表明，模型对错误案例的识别率高达93%-100%，但对错误页码（wrong-pinpoint）的检测能力显著下降：在法院意见中为37%-61%，在法律备忘录中为52%-83%。当模型未能识别页码错配时，其判断主要依赖主题相关性而非页面层面的实际支持证据。尽管模型规模扩大与增强推理能力可部分缩小差距，但无法彻底解决该问题——如GPT-5.4在高推理强度下仍分别遗漏40%的法院意见和18%的备忘录中的页码错配。此外，显式提示模型在引用页码处验证支持可提升召回率，但同时导致假阳性率上升，揭示出当前模型在“识别正确法律主题”与“验证引文对具体主张的支持”之间存在能力混淆，二者应被视为独立的认知能力。
链接: https://arxiv.org/abs/2608.12571
作者: Apurv Verma
机构: Bloomberg(彭博)
类目: Digital Libraries (cs.DL); Computation and Language (cs.CL)
备注: Accepted to the 1st Workshop on AI for Law at ICML 2026
Abstract:In 2023, a New York judge sanctioned two attorneys in Mata v. Avianca for filing a brief with hallucinated citations generated by ChatGPT. Such failures are largely caught by database lookups; the harder problem is detecting citations that point to real cases but do not support the propositions for which they are offered – a failure mode that existing evaluations of LLMs for legal use cases largely overlook. In this paper, we study proposition-level citation support verification through controlled perturbations of real legal citations obtained from two legal corpora, either replacing the cited case or changing only the pinpoint page within the same case. We evaluate fourteen model configurations on the resulting examples. Models catch 93-100% of wrong-case corruptions. They catch only 37-61% of wrong-pinpoint corruptions on court opinions and 52-83% on legal briefs. When models fail to catch wrong-pinpoint corruptions, they accept the citation based on topical overlap rather than page-level support. Scale and extended reasoning narrow the gap but do not close it: GPT-5.4 with high reasoning effort still misses 40% of pinpoint mismatches on court opinions and 18% on briefs. Prompting the model to verify support at the cited page improves recall, but it also raises the false positive rate. Recognizing the right legal topic and verifying support for the cited proposition are distinct capabilities, and current models conflate them.
[NLP-56] SoK: From Generation to Consumption of Privacy Documents in Software Systems NDSS2027
【速读】： 该论文旨在解决隐私文档（privacy documents）研究领域日益碎片化的问题，其核心挑战在于缺乏对隐私文档从创建、分析、评估到维护全生命周期的系统性整合视角。随着数字服务中隐私政策、隐私标签及界面透明机制等多样化形式的兴起，相关研究在数量和广度上迅速扩展，但现有工作分散于不同技术环节，难以形成连贯的知识体系。为此，本文提出一种以软件工程为视角的统一生命周期框架，系统回顾并分析了2010至2025年间发表的290篇文献，围绕五个核心研究问题展开：隐私文档的定义与范围界定、生成机制、内容分析与信息提取、一致性与合规性检查，以及可用性评估与优化。其解决方案的关键在于构建一个结构化、分阶段的研究图谱，揭示出15项关键研究趋势与21个开放性机遇，并进一步指明四大前沿方向：面向以人工智能（AI）为核心的平台所面临的新兴挑战、建立多样化且持续更新的数据基础、基于大语言模型（LLM）的统一策略-代码联合分析方法，以及兼顾终端用户与开发者双重视角的可用性设计。该综述旨在为未来隐私文档研究提供共享的知识基础与发展方向。
链接: https://arxiv.org/abs/2608.12511
作者: Shidong Pan,Clark LaChance,Zhen Tao,Sepideh Ghanavati
机构: Columbia University, USA; New York University, USA; University of Maine, USA; Technical University of Munich, Germany
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL)
备注: This SoK paper has been accepted by NDSS 2027
Abstract:Privacy documents (e.g., privacy policies) are a central mechanism through which digital services disclose data practices and seek user consent. Over the past decades, research on privacy documents has expanded significantly, encompassing not only traditional privacy policies but also short notices (e.g., privacy labels) and interface-level transparency mechanisms. As this research area continues to grow, it has become increasingly difficult to obtain a coherent view of how privacy documents are created, analyzed, evaluated, and maintained across their lifecycle. This SoK provides a unified, lifecycle-oriented view of privacy documents from a software engineering perspective. We systematically review and analyze 290 papers published between 2010 and 2025, organizing them around five research questions that examine how privacy documents are (1) defined and scoped, (2) generated, (3) analyzed and extracted, (4) checked for inconsistencies and noncompliance, and (5) evaluated and improved for usability. Building on our findings, we identify 15 key research trends and 21 open opportunities. We further chart four broader research directions that highlight (i) emerging challenges in AI-centric platforms, (ii) the need for diverse and up-to-date data foundations, (iii) LLM-based unified policy-code analysis, and (iv) dual usability for end-users and developers. We hope this SoK provides a shared foundation for future research on privacy policies and privacy documents.
[NLP-57] DIVE: Unlocking Self-Improvement in Frozen Language Models Through Diversity-Driven Skill Evolution
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在部署后无法持续积累和保留经验以实现自我改进的问题，尤其针对冻结参数的LLMs难以通过非参数化方式提升推理能力的瓶颈。其核心挑战在于如何在不更新模型参数的前提下，使模型能够从任务经验与验证器反馈中演化出可复用的自然语言技能（natural-language skills），包括推理流程、验证策略、常见错误模式识别及输出约束等，并实现这些技能的动态执行与迭代优化。解决方案的关键在于提出DIVE（Diversity-Driven Evolution）框架，通过并行演化多个基于初始经验种子生成的技能种群，利用多样化变换对技能进行自适应精炼，并通过联合选择互补性强的技能集合来缓解单一技能轨迹因随机采样或非凸优化导致的过拟合与次优收敛问题。该方法无需教师模型，完全由底层模型自主完成技能演化，实现了高效、可解释且无需参数更新的自进化机制。实验表明，DIVE在六项数学与逻辑推理任务上显著优于现有推理方法、提示优化技术、技能开发框架及基于记忆的基线模型，在较少迭代次数下即实现快速性能提升，且所生成的技能具备跨模型规模与架构的迁移能力，使小型模型如GPT-5-nano在常规提示下可超越更大模型，验证了多样性驱动技能演化作为参数无关、高效可扩展的自改进范式具有强大潜力。
链接: https://arxiv.org/abs/2608.12486
作者: Siheng Xiong,Ali Payani,Oguzhan Gungordu,Faramarz Fekri
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) cannot retain post-deployment experience without parameter updates. We introduce DIVE, a diversity-driven framework that enables frozen LLMs to improve by evolving persistent natural-language skills from task experience and verifier feedback. These skills encode reusable reasoning procedures, verification strategies, common failure modes, and output constraints and are both executed and revised by the same underlying model without access to a teacher model. Since natural-language skill evolution is a stochastic, non-convex search process, optimizing a single skill trajectory can overfit to sampled experience or converge to a suboptimal solution. DIVE mitigates this optimization variance by independently evolving multiple skill populations from bootstrapped experience, adaptively refining them through diverse transformations, and jointly selecting a complementary set of skills. Across six mathematical and logical reasoning tasks and multiple model families, DIVE consistently outperforms existing reasoning methods, prompt-optimization approaches, skill-development frameworks, and memory-based baselines. It achieves rapid self-improvement from accumulated experience, obtaining substantially larger performance gains with fewer rollouts than parameter-based methods such as SFT and GRPO, and prompt optimization with GEPA. Further, the resulting skills transfer across model scales and families, enabling smaller models such as GPT-5-nano to match or outperform larger counterparts, i.e., GPT-5, under conventional prompting. These results establish diversity-driven skill evolution as an effective, interpretable, and parameter-free approach to LLM self-improvement.
[NLP-58] Geometric and Behavioral Stratification in Transformer Residual Streams
【速读】： 该论文旨在解决大语言模型中高维表示空间的结构组织机制问题，特别是揭示在生成式推理过程中，模型内部残差流（residual stream）中是否存在一种具有特殊功能的“特权坐标轴”（privileged basis），以及这种坐标轴如何影响模型的行为与可读性。其核心问题是：当模型预测下一个词元时，其内部状态所依赖的关键方向是什么？解决方案的关键在于识别并验证“预测方向”（prediction direction）——即模型当前正在预测的词元的解嵌入方向（unembedding direction）——作为内容定义的特权锚点（content-defined privileged anchor）。研究发现，以该方向为参考，残差流中的变异性呈现出几何和行为上的分层结构：越接近预测方向的区域结构越强、能有效聚类相关提示（prompt），而远离预测方向的区域则趋于平坦且反区分不同提示组。这一分层结构在18个不同架构（密集与专家混合）、规模（7B–120B）及训练类型（基础与指令微调）的模型中均成立。此外，论文指出，一个狭窄且尺度不变的“预测接口”集中了读出相关的结构信息，而其余部分随模型规模扩展；由于预测方向几乎正交于主方差轴，传统基于方差的分析仅能部分揭示此结构，且在提示异质性增加时表现更差。通过锚定分析，揭示出从预测近端到远端存在陡峭的几何梯度，其中预测近端高度结构化、对任务框架敏感，而远端虽弱读出对齐但具有因果和时间上的承载作用，行为由方向而非幅度主导。因此，该研究确立了预测方向作为一种区别于已有描述坐标系的新型特权锚点，并提供了高维计算与线性读出共存的几何解释。
链接: https://arxiv.org/abs/2608.12447
作者: Nelson Guda
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 63 pages, 10 figures, 15 tables. Code and data: this https URL
Abstract:Trained transformer models develop privileged bases: coordinate axes whose statistics differ from the rest of the residual stream. But what kind of direction does such a basis select? We investigate the prediction direction, the unembedding direction of the token a model currently predicts, and find that it functions as a content-defined privileged anchor. Measured with respect to this anchor, residual-stream variation is geometrically and behaviorally stratified by proximity to the prediction. The stratification holds in all eighteen models tested (dense and mixture-of-experts, 7B-120B, base and instruction-tuned). A narrow, scale-invariant prediction interface concentrates readout-relevant structure, while the vast prediction-distal complement expands with model scale. Because the prediction direction sits nearly orthogonal to the principal variance axes, variance-based analyses recover this organization only partly, and the shortfall grows with prompt heterogeneity. Anchoring reveals a steep geometric gradient: prediction-proximal regions are highly structured and cluster related prompts, while the complement is flatter and anti-discriminates among prompt groups. The interface is a narrow slice but functionally decisive. Disrupting the variance directions closest to the prediction causes immediate divergence and frequent task-frame shifts; disrupting the next level down delays divergence and preserves framing. The complement is weakly readout-aligned per direction yet causally and temporally load-bearing, and behavior is driven by direction rather than magnitude. These results establish the prediction direction as a privileged anchor distinct from previously described coordinate axes, and give a geometric account of how high-dimensional computation coexists with linear readout. Comments: 63 pages, 10 figures, 15 tables. Code and data: this https URL Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL) MSC classes: I.2.6, I.2.7 Cite as: arXiv:2608.12447 [cs.LG] (or arXiv:2608.12447v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.12447 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Nelson Guda [view email] [v1] Wed, 12 Aug 2026 17:42:20 UTC (15,979 KB)
[NLP-59] Large Language Models Can Follow Instructions But Not Many at Once: Phase Transitions in Compositional Constraint Satisfaction
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在同时满足多个显式约束条件（如推理结构、安全边界、输出模式等）时所面临的性能退化问题。尽管模型在单一约束下表现良好，但在多约束联合生效的组合场景中，其整体表现急剧下降，但这一现象的机制尚不清晰：性能衰减的速度如何？由哪些因素主导？是否可缓解？为此，论文提出了一种名为约束饱和评估（Constraint Saturation Evaluation, CSE）的程序化生成基准，系统性地调节并行约束数量（k），通过确定性的规则验证器对每项约束进行评分，完全避免大语言模型作为评判者参与，从而实现无偏评估。实验涵盖15个模型、36类约束类型，共执行369,753次验证，覆盖k=1至12的范围。研究发现：第一，单个约束的通过率随约束数量增加呈渐进式下降，而所有约束同时满足的概率则发生指数级坍塌——即使单个约束通过率在k=8时仍达约41%，但全部通过的概率仅剩5.7%；第二，不同类型的约束退化程度不均：结构性约束每增加一个约束导致的基础能力损失是词汇性约束的两倍，其差异源于“理解-维持”差距——需要持续追踪上下文的约束更易受组合影响，而仅依赖二元判断的约束则相对鲁棒；第三，失败事件几乎相互独立，这使得性能退化呈现乘积效应；残余的相关性主要体现在共享输出特征上，而非成对干扰——例如错误的句子计数会连带导致所有读取该值的约束失败。最终表明，可靠指令遵循能力在超过5–6个并发约束后即严重失效：最强模型在7个约束时探针级成功率已低于50%，而12个模型中有12个在3个或更少约束时即跌破50%。因此，该研究的关键突破在于揭示了多约束组合下的非线性退化机制，并指出共享输出表示的耦合性与理解维持需求的差异是决定性能崩溃的核心因素。
链接: https://arxiv.org/abs/2608.12426
作者: Mariya I. Vasileva
机构: Meta Superintelligence Labs (Meta 超级智能实验室)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 35 pages, 7 figures, 13 tables. Reviewed in the ARR May 2026 cycle
Abstract:Large language models are increasingly deployed in settings that require simultaneous adherence to multiple explicit constraints - reasoning structure, safety boundaries, output schemas. Individual constraints are handled proficiently, but the compositional regime, where many must hold jointly, remains poorly characterized: how rapidly does performance degrade, what governs the degradation, and can the collapse be mitigated? We introduce Constraint Saturation Evaluation (CSE), a procedurally generated benchmark that systematically varies the number of simultaneous constraints (k), with every constraint scored by a deterministic, rule-based verifier and zero LLM-judge involvement: 15 models, 36 constraint types, 369,753 checks at k=1-12. Three findings emerge. First, per-constraint pass rate decays gradually and predictably, while the chance of satisfying all k constraints collapses - a model passing individual constraints at ~41% at k=8 succeeds on all eight just 5.7% of the time. Second, constraints do not degrade equally: structural constraints lose 2x more baseline capability per added constraint than lexical ones, ordered by a comprehension-maintenance gap that separates constraints requiring sustained tracking from binary decisions immune to composition. Third, failures are nearly independent, which is what makes the accumulation multiplicative; the residual coupling that does exist tracks shared output features rather than pairwise interference - a wrong sentence count fails every constraint that reads it. Reliable instruction following breaks down beyond 5-6 simultaneous constraints: probe-level success falls below 50% at 7 constraints for the strongest model, and at 3 or fewer for 12 of 15.
[NLP-60] Unified Multi-Dimensional Benchmark for Complex Graph Reasoning in Large Language Models
【速读】： 该论文旨在解决现有图推理基准在数据复杂度覆盖不足、依赖人工构建以及文本与代码双模态推理评估缺乏统一标准等问题。其核心解决方案是提出一个五阶段的半自动化框架——\dataset，通过生成式AI（Generative AI）驱动的数据生成器，自动构建任务描述、图数据、参考解、图加载脚本、问题形式及评估脚本，同时在关键质量控制节点保留人工校验，从而实现对图规模（Graph Size）、任务复杂度（Task Complexity）、任务描述（Task Description）、图加载方式（Graph Loading）和任务来源（Task Source）五个维度的系统性扩展。该框架构建的基准包含202个任务，并在文本、代码及增强推理三种模式下评估大语言模型（LLM），实验表明，新基准揭示了现有模型在复杂场景下的局限性，如微调模型难以泛化至GraphGym，检索增强方法虽提升文本推理但对编码推理改进不一致，验证了该基准作为挑战性与诊断性评测工具的有效性，并为未来推理能力提升提供了实证指导。
链接: https://arxiv.org/abs/2608.12391
作者: Fali Wang,Ali Al-Lawati,Iliyas Bektas,Jinxuan Fang,Alek Melenski,Tianxiang Zhao,Yao Ma,Suhang Wang
机构: The Pennsylvania State University (宾夕法尼亚州立大学); Rensselaer Polytechnic Institute (伦斯勒理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Under review
Abstract:Graph reasoning provides a promising testbed for evaluating the reasoning ability of large language models (LLMs), as graph instances can be programmatically generated, structurally controlled, and naturally scaled to long-input settings. However, existing graph reasoning benchmarks have limited coverage of data complexity, rely heavily on manual construction, and lack unified evaluation across text-based and code-based reasoning modes. To address these limitations, we propose \dataset, a five-stage \textitsemi-automatic framework for constructing complex graph reasoning benchmarks. It expands benchmark coverage along five dimensions: \textitGraph Size, \textitTask Complexity, \textitTask Description, \textitGraph Loading, and \textitTask Source. The framework uses an LLM-based data generator to automatically produce task descriptions, graph data, reference solutions, graph-loading scripts, question forms, and evaluation scripts, while retaining human validation at key quality-control stages. Based on it, we construct a benchmark with 202 tasks and evaluate LLMs under text-based, code-based, and augmented reasoning settings. Experiments show that the complexity dimensions reveal model limitations that are less visible in existing benchmarks; existing fine-tuned models struggle to generalize to GraphGym, whereas retrieval-augmented methods show scenario-dependent adaptability, improving textual reasoning but not consistently improving coding reasoning. These findings suggest that ours serves as a challenging and diagnostic benchmark for graph reasoning and provides empirical guidance for future enhancement methods. Code and dataset will be published soon.
[NLP-61] Query Timing Produces Opposite Positional Biases Between LLM s and Humans ICLR2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中存在的位置偏差（positional biases，如首因效应和近因效应）的内在机制问题，尤其是这些偏差在模型评估过程中的形成原理尚不清晰。其核心解决方案的关键在于探究模型在处理信息时的信念更新时机——即是否在证据呈现过程中持续更新判断，还是仅在输入结束时进行一次性评估。研究发现，与人类行为不同，大语言模型的位置偏差在较新版本中反而更为显著，表明其决策机制可能并非基于动态信念更新，而是受制于输入序列结构的静态处理模式，揭示了模型认知过程与人类认知之间的根本差异。
链接: https://arxiv.org/abs/2608.12387
作者: Jasin Cekinmez,Addison J. Wu,Thomas L. Griffiths
机构: Princeton University(普林斯顿大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Entropic Award (Top 3 Paper), ICBINB @ ICLR 2026
Abstract:Positional biases such as recency and primacy effects have been documented in large language models (LLMs), yet the underlying mechanism by which these models make their evaluations remains poorly understood. Both primacy and recency biases have been observed in human judgments in response to evidence, but recent work suggest that \emphwhen the listener updates their beliefs – during the presentation of evidence or only at the end – influences the presence of such effects. We investigate whether a similar phenomenon holds for LLMs, finding divergence from human behavior. These biases are more exacerbated in newer models compared to their predecessors.
[NLP-62] Are you Talking Logic to Me? Assessing Language Models Syllogistic Reasoning Capabilities
【速读】： 该论文旨在解决小型语言模型（Small Language Models, SLMs）在处理逻辑推理任务（如三段论推理）时表现不佳的问题。核心挑战在于如何有效表示输入信息以增强模型的逻辑推理能力。其解决方案的关键在于引入形式化知识表示（Knowledge Representation, KR）范式作为输入表达方式，通过在FOLIO和P-FOLIO数据集基础上扩展，系统评估不同KR符号对三段论推理性能的影响。实验表明，采用特定形式化KR符号可实现与自然语言相当甚至更优的推理性能，同时显著提升推理速度。此外，研究提出一种三段论分类方法（Syllogistic Entailment Framework, SEF），并将其用于在零样本（Zero-Shot, ZS）提示中注入逻辑定义，从而有效增强小模型的推理能力。为支持可复现性与应用拓展，作者开源了首个用于自动生成三段论并定义其SEF类别的Python库——通用逻辑语法构建框架（Common Logic Grammar Construction, CLGC）。
链接: https://arxiv.org/abs/2608.12374
作者: Hanna Abi Akl,Fabien Gandon,Catherine Faron,Pierre Monnin
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to the International Joint Conference on Rules and Reasoning (RuleML+RR) 2026
Abstract:Language models (LMs) struggle with logical tasks like reasoning on syllogisms. It has been shown that Knowledge Representation (KR) plays a crucial role in expressing input information to help models solve tasks. This observation motivates our study of the impact of different formal KR notations on syllogistic reasoning by extending the FOLIO and P-FOLIO datasets. Our experiments on Small Language Models (SLMs) in Supervised Fine-Tuning (SFT) and Zero-Shot (ZS) settings show that the choice of input notation can yield performances competitive with natural language while enabling faster inference. We also propose a syllogistic categorization method (SEF) and use it to enrich ZS prompts with logical definitions, which boost reasoning in small models. We open-source our framework, Common Logic Grammar Construction (CLGC), as the first Python library for automatically generating syllogisms in KR notations and defining their SEF categories.
[NLP-63] New Terms New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLM s ACL2026
【速读】： 该论文旨在解决由新创词汇（neologisms）隐含表达的毒性内容识别难题，此类词汇虽表面中性，却在公共共识中逐渐演变为针对特定群体或观念的污名化标签（如“country girl”被用作对女权主义的贬损），传统内容审核系统难以有效识别。其解决方案的关键在于提出一个涵盖毒性新词来源与共识验证标准的分类体系，并构建覆盖广泛风险类别的语料库；进一步提出SeTox——一种基于搜索增强的框架，使静态大语言模型（LLM）能够实时融入网络上下文信息，从而捕捉依赖社会共识的隐性毒性。实验表明，即使使用30亿参数量级的模型，SeTox在检测性能上仍优于近期大规模模型，验证了其在整合现实世界知识以应对动态演化毒性新词方面的可扩展性与有效性。
链接: https://arxiv.org/abs/2608.12361
作者: Shiyao Cui,QingLin Zhang,Di Wang,Yida Lu,Zhexin Zhang,Jinhua Gao,Jinglin Yang,Min He,Han Qiu,Minlie Huang
机构: Tsinghua University (清华大学); Institute for Network Sciences and Cyberspace - Science City (Guangzhou) Digital Technology Group Co., Ltd. (网络科学与网络空间研究院-广州数字科技集团有限公司); National Computer Network Emergency Response Technical Team Coordination Center of China (国家计算机网络应急技术处理协调中心); Chinese Academy of Sciences (CAS) (中国科学院); University of Chinese Academy of Sciences (UCAS) (中国科学院大学)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: ACL 2026
Abstract:Neologisms, emerging terms in meaning or form, can serve as new vehicles for toxic expression, like “country girl” as a stigmatizing label targeting feminism. Such toxic neologisms appear benign but have evolved into toxic usage in public consensus, posing challenges to moderation systems and remaining underexplored. In this paper, we investigate how to detect implicit toxicity expressed via neologisms. We first propose a taxonomy that captures the origins and consensus-verification criteria of toxic neologisms, followed by the construction of a lexicon spanning widely observed risk categories. To capture toxicity grounded in public consensus, we introduce SeTox, a search-augmented framework that enables static large language models (LLMs) to incorporate real-time web context for neologism toxicity detection. Experiments show that SeTox, even with 3B-scale models, outperforms recent large-scale models, demonstrating its scalability to incorporate real-world knowledge for toxic neologism detection. Disclaimer: this paper has offensive contents that may be disturbing to some readers.
[NLP-64] Diagnostic Foundation for Evaluating LLM s Research Integrity as Co-Scientists
【速读】： 该论文旨在解决生成式AI（Generative AI）在作为科研协作者时，面对机构压力下维持研究诚信（research integrity）能力缺失的评估难题。现有研究缺乏对模型在真实科研场景中应对伦理压力行为的系统性评测，尤其忽视了其在不同压力类型下决策失效的风险。为此，研究提出IntegrityBench基准，涵盖36组配对任务，覆盖3个研究领域与4个研究阶段，并基于5级隐含-显性压力协议评估模型在不端行为分类、伦理行为推理及基于研究证据的决策制定三个关键维度的表现。核心发现表明，在最大压力条件下，前沿模型约三分之一的关键诚信决策出现失败，且模型规模或推理能力无法可靠缓解此问题；显性压力易诱导模型服从不当行为，而隐性情境重构则更可能导致对合法研究任务的过度拒绝。值得注意的是，模型在准确识别研究请求方面表现不佳时，其基于研究成果的决策能力反而更高（85.7 vs. 79.4），揭示了三类能力在结构上相互分离，即正确伦理行为并不依赖于准确分类。这一发现表明，前沿模型可能在表面上表现出高度协助性，实则潜藏研究不端风险，从而带来双重部署隐患：既可能助长科研不端行为，又会削弱公众对人工智能辅助科研的信任基础。
链接: https://arxiv.org/abs/2608.12345
作者: Yash Tripathi,Silu Sharma,Sai Sidhanth Manoharan Jayanthi,Shivank Garg,Lin Li
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Language models are increasingly deployed as co-scientists, yet their ability to uphold research integrity under institutional pressure remains unmeasured. We introduce IntegrityBench, a benchmark evaluating misconduct classification, ethical action reasoning and artifact-grounded decision making across 36 paired tasks under a 5-level implicit-explicit pressure protocol spanning 3 domains and 4 research stages. Evaluating 18 frontier model variants, we find that under peak pressure, models fail roughly 1 in 3 integrity-critical decisions, and neither scale nor reasoning ability reliably mitigates this. Explicit pressures induce compliance with misconduct, while implicit contextual reframing more often causes over-refusal of legitimate research tasks. Interestingly, models failing to classify research requests accurately perform equally or better on artifact-grounded decision making (85.7 vs. 79.4), suggesting the three facets are structurally dissociated and correct ethical action does not require accurate classification. Frontier models can thus appear helpful while harbouring integrity failures that create two distinct deployment risks: facilitating research misconduct and eroding trust in AI-assisted research.
[NLP-65] Predicting consumer-technology ownership without a diffusion history
【速读】： 该论文旨在解决如何预测消费技术普及程度的问题，核心在于探究消费者对技术属性的感知是否能够有效预示其拥有率。其解决方案的关键在于构建一个基于UTAUT2（Unified Theory of Acceptance and Use of Technology 2）模型的属性预测框架，利用人类与前沿语言模型（Anthropic Claude Opus 4.7 和 OpenAI GPT-5.5）对65项消费技术在六项属性上的评分，通过带符号约束的惩罚回归模型，将技术拥有率与四类接受度属性及对数年龄协变量进行建模。研究发现，在2022至2025年的较短时间内，尽管实际拥有率变化有限，但基于属性的模型仍显著优于“无变化基准”模型，尤其以Opus 4.7的语言模型表现最佳，平均绝对误差降低达17%以上。该方法的局限性在于语言模型的评分可能反映其对技术的先验知识而非独立推理。研究还展示了该模型在2027年十一款2025–2026年发布产品的所有权预测中的部署应用，验证了其在技术采纳趋势预测中的潜力。
链接: https://arxiv.org/abs/2608.12344
作者: Irina Vartanova,Niels Selling,Jennifer Viberg Johansson,Pontus Strimling
机构: 未知
类目: Computation and Language (cs.CL); Computers and Society (cs.CY); Applications (stat.AP)
备注: 31 pages, 4 figures, supplementary material included (Tables S1-S6, Figure S1), data and code at this https URL
Abstract:We test whether the perceived attributes of a consumer technology predict how widely it is owned. In a 2022 Prolific survey of US adults (n = 678), respondents rated 65 consumer technologies on six attributes. We then elicited the same ratings from two frontier language models, Anthropic Claude Opus 4.7 and OpenAI GPT-5.5. We regress ownership prevalence on four UTAUT2 acceptance attributes plus a log-age covariate with a sign-constrained penalized regression and evaluate it by holding out one technology at a time. The attribute model improves on a baseline of years-since-launch: mean absolute error falls by 17% with the human ratings, and by more with either model, most with Opus 4.7. Over the short 2022-to-2025 window, where ownership moved little, the same attributes do not improve on a no-change baseline. We set out the limitations of the approach, including the possibility that language-model ratings reflect prior knowledge of these technologies rather than independent attribute reasoning. We include a deployment illustration: 2027 ownership predictions for eleven products launched in 2025 and 2026.
[NLP-66] Large Language Models Pass the History Exam But Miss the History: A Polish High School Exit Exam Matura Benchmark
【速读】： 该论文旨在解决当前大型语言模型（LLM）评估体系中缺乏对历史解释性推理能力有效检验的问题，尤其针对生成式 AI 在历史学科知识应用中的表现评估不足。研究以波兰高中毕业考试（Matura）的历史科目为基准，选取2023–2025年三份官方试题，涵盖简答题与论述题，系统评估八种主流大模型在真实教育测评场景下的表现，并与真实考生群体进行对比。其解决方案的关键在于构建首个基于波兰国家课程标准的历史类大模型评测基准，通过定量与定性相结合的方法揭示模型在不同任务类型、信息模态及地理范围下的表现差异。研究发现，尽管所有模型整体得分显著优于人类考生，但其性能排名在不同类型任务间极不稳定，且普遍存在对波兰本土历史内容的系统性劣势；进一步的质性分析识别出两大典型错误模式：源混淆（source conflation），即模型将史料内容误作推理依据而非分析对象；时间错位（temporal disorientation），表现为回答偏离正确历史时序。这些发现凸显了现有大模型在历史理解深度与语境敏感性方面的局限，为未来面向教育场景的生成式 AI 评估提供了重要范式参考。
链接: https://arxiv.org/abs/2608.12343
作者: Adrian Trzoss,Kacper Dudzic,Wiktor Werner,Marcin Moskalewicz
机构: Adam Mickiewicz University, Poznań, Poland; IDEAS Research Institute, Warsaw, Poland; AMU Center for Artificial Intelligence, Poznań, Poland; Poznań University of Medical Sciences, Poznań, Poland; Maria Curie-Skłodowska University, Lublin, Poland; WSB Merito University, Poznań, Poland
类目: Computation and Language (cs.CL)
备注:
Abstract:AI chatbots are widely used by students as knowledge sources, yet LLM benchmarks rarely assess interpretative historical reasoning. We evaluate eight leading LLMs on the Polish high school exit exams (Matura) in history - three official papers from 2023-2025, comprising short-answer questions and extended essays - comparing model performance against the human examinee population. Every model dramatically outperforms human examinees, yet aggregate scores mask distinct competency profiles: rankings are unstable across task type, source modality, and geographical scope, with a consistent penalty on Polish versus Global history content. Qualitative error analysis reveals two recurring failure modes - source conflation, in which models reason from source content rather than treating it as an object of analysis, and temporal disorientation, in which responses are historically misplaced. This study introduces the first LLM history benchmark grounded in Polish national curriculum.
[NLP-67] Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents
【速读】： 该论文旨在解决当前生成式 AI 在金融文档错误检测（Financial Error Detection, FED）任务中的能力短板问题。尽管大型语言模型（Large Language Models, LLMs）在股票价格预测、财务分析等金融任务中表现优异，但其在识别金融文档中潜在错误的能力尚未得到充分探索。为此，本文提出了首个公开的多层级认知复杂度金融错误检测基准——FinED-Bench，涵盖九类真实世界金融场景，包含超过900份2025年新发布的、未被现有模型接触过的文档数据。该基准的设计强调对金融领域知识与推理能力的双重考验。实验结果表明，当前主流LLMs（如GPT-4o、Qwen3-14B）在此任务上仍存在显著局限性，尤其在高复杂度情境下表现不佳；然而，通过监督微调（supervised fine-tuning）可显著提升较弱模型的性能。因此，解决方案的关键在于构建高质量、覆盖广泛且具有挑战性的金融错误检测基准，并结合领域特定的微调策略以增强模型的准确性与鲁棒性。
链接: https://arxiv.org/abs/2608.12342
作者: Ying He,Zhouhong Gu,Zhecheng Hu,Yubo Zhou,Hao Shen,Jiaqing Liang,Zhaoqian Dai,Shuguang Ma,Fei Yu,Yanghua Xiao,Zhixu Li
机构: Fudan University (复旦大学); Ant Group (蚂蚁集团); Renmin University of China (中国人民大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Ensuring the accuracy of financial documents is critical for economic analysis, regulatory compliance, and corporate decision-making. Several studies have shown that Large Language Models (LLMs) perform well in many financial tasks, such as stock price movements and financial analytics. However, a critical task remains unexplored: the ability of LLMs to identify errors in financial documents. In this paper, we introduce \textbfFinED-Bench, the first publicly \textbfBenchmark for \textbfFinancial \textbfError \textbfDetection across three levels of cognitive complexity. FinED-Bench covers nine real-world financial scenarios, and includes over 900 documents reported in 2025 that are unseen by existing language models. We detail the benchmark construction process and evaluate several advanced LLMs (e.g., GPT-4o, Qwen3-14B) on this tasks, which requires both financial domain knowledge and reasoning capabilities. Experimental results show that current LLMs still struggle with this task, especially in high-complexity cases. Besides, supervised fine-tuning can significantly improve the performance of weaker LLMs on this task. Our data and code are available at this https URL.
[NLP-68] he “Knowledge-Behavior Gap” in Cultural Taboo Safety of Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在跨文化场景下对文化禁忌（Cultural Taboo）识别与尊重能力不足的问题。现有文化评估基准多聚焦于文化知识或价值观偏见，却忽视了模型在面对隐含于看似无害提问中的文化禁忌时的敏感性与安全性，而文化禁忌具有隐性、语境依赖性强等特征，导致其难以被有效评估与防范。为此，论文提出首个公开的专门用于评估和提升LLMs文化禁忌安全性的基准——CulShield。该基准覆盖77个国家和地区，涵盖超过2020项文化禁忌，从显性知识与隐性行为两个维度对模型进行评测。实验结果揭示了“知识-行为鸿沟”现象：尽管模型具备相关文化禁忌知识，但在实际交互中却难以有效应用。此外，研究还发现语言上下文的变化会显著影响模型的文化禁忌安全表现。CulShield的核心解决方案在于构建一个兼具广度与深度的多国别、多情境文化禁忌评估体系，推动模型从“知道”到“恰当行动”的转化，从而增强其在真实社会语境中的文化敏感性与安全性。
链接: https://arxiv.org/abs/2608.12341
作者: Ying He,Sihang Jiang,Xingzhou Chen,Zhouhong Gu,Yiwei Gu,Minggui He,Shimin Tao,Hongxia Ma,Yanghua Xiao
机构: Fudan University(复旦大学); Huawei(华为)
类目: Computation and Language (cs.CL)
备注:
Abstract:Cultural taboo safety is essential for deploying large language models (LLMs), as culturally insensitive outputs may cause offense or even social harm. However, existing cultural benchmarks primarily assess cultural knowledge or values biases, while overlooking whether LLMs can recognize and respect cultural taboos, especially when taboos are implicitly hidden in seemingly harmless questions. Besides, cultural taboos are implicit, and context-dependent, thus poss unique challenges for reliable evaluation. To address these gaps, we introduce \textbfCulShield, the first public benchmark dedicated to evaluating and improving the cultural taboo safety of LLMs. CulShield spans 77 countries and territories, and includes over 2,020 taboos. It evaluates models along both explicit knowledge and implicit behaviors. Experiments on several advanced LLMs (e.g., GPT-4o-mini, Gemini-2.5-pro) reveal a clear ``knowledge-behavior gap’‘: models often fail to apply known taboos during interaction. We further show that variations in linguistic context can significantly affect LLMs’ cultural taboo safety. Code and data is accessible here: this https URL.
[NLP-69] Class-Structure Preservation Beats Diversity: A Comprehensive Benchmark of Text Augmentation Methods for Imbalanced Text Classification
【速读】： 该论文旨在解决自然语言处理中类别不平衡文本分类问题下，生成式数据增强（Generative Data Augmentation）的有效性与传统嵌入空间SMOTE风格检索方法（EmbSMOTE）之间的对比缺失问题。现有研究虽广泛关注基于大语言模型（LLM）的生成式增强，但缺乏在真实场景下的系统性实证基准以评估其相对于经典方法的性能表现。本研究构建了一个受控基准，涵盖11种数据增强方法，包括经典扰动、嵌入空间检索和基于LLM的生成三类方法，在7个公开文本分类数据集上进行评估，覆盖类别数 K=2 至28、不平衡比率1.1至超过500，并采用五次随机种子、宏平均F1分数、Welch’s t检验、五项分布度量及基于Qwen3-8B的LLM家族敏感性分析。实验结果表明，所有基于LLM的方法在统计上均等价或劣于EmbSMOTE，且随着不平衡程度加剧，性能差距呈单调扩大趋势，在GoEmotions-28上最大差距达 ΔF1macro≈0.063。进一步分析发现，表面多样性与下游性能无显著相关性，而LLM特有的伪影（如文本长度膨胀、标签分布均匀化）则与分类准确率呈负相关。因此，研究揭示出关键有效变量并非表层多样性，而是类条件结构保真度（class-conditional structural fidelity），即增强样本对训练数据分布中类条件几何结构的保持程度。据此，研究建议在类别不平衡的多分类任务中，默认采用基于检索的过采样方法，且在实际部署前应对基于LLM的数据增强设置更高的实证标准。
链接: https://arxiv.org/abs/2608.12340
作者: Keito Inoshita
机构: Kansai University (关西大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:With the rapid advancement of large language models (LLMs), generative data augmentation has attracted considerable attention for imbalanced text classification in natural language processing. However, no empirical benchmark to date has compared LLM-based augmentation against the embedding-space SMOTE-style retrieval (EmbSMOTE), a strong classical reference for imbalanced classification. In this study, a controlled benchmark of 11 augmentation methods, spanning classical perturbation, embedding-space retrieval, and LLM-based generation, is newly constructed on seven public text classification datasets covering class counts K=2 - 28 and imbalance ratios of 1.1 to over 500, evaluated with five random seeds per cell using macro F1, Welch’s t -tests, five distributional metrics, and an LLM-family sensitivity analysis based on Qwen3-8B. The experimental results reveal that all LLM-based methods are statistically equivalent or inferior to EmbSMOTE, with the performance gap widening monotonically as imbalance increases and reaching \Delta\textF1_\textmacro!\approx!0.063 on GoEmotions-28. Furthermore, it is observed that surface-level uniqueness has negligible correlation with downstream performance, whereas LLM-specific artifacts, such as text elongation and label-distribution uniformization, are negatively associated with classification accuracy. Compared with six LLM-based and four classical augmentation baselines, these results demonstrate that the effective variable is not surface-level diversity but class-conditional structural fidelity, namely the degree to which augmented samples preserve the class-conditioned geometry of the training distribution. Accordingly, retrieval-based oversampling should be adopted as the default for imbalanced multi-class classification, and a higher empirical bar should be required before LLM-based augmentation is deployed in practice.
[NLP-70] SDAM: Structure-Difference-Aware Memory Evolution for Complex Text-to-SQL
【速读】： 该论文旨在解决文本到SQL（Text-to-SQL）任务中现有记忆增强型智能体系统存在的问题，包括忽视历史经验、结构分析能力弱、语义理解浅层化以及模式对齐性能差等。其核心解决方案是提出一种结构差异感知的记忆增强框架SDAM（Structure-Difference Aware Memory），关键在于三个机制的协同：首先，通过结构差异感知的推理树识别潜在错误；其次，利用矛盾感知的反思机制提取深层语义规则；最后，基于模式约束的记忆演化机制强化结构一致性，实现记忆与数据库模式的有效绑定。实验表明，集成SDAM的SDAM-SQL在BIRD-dev和Spider-test基准上分别取得2.0和0.4的性能提升，验证了该方法的有效性。
链接: https://arxiv.org/abs/2608.12338
作者: Keyan Xu,Dingzirui Wang,Xuanliang Zhang,Qingfu Zhu,Wanxiang Che
机构: Harbin Institute of Technology (哈尔滨工业大学)
类目: Computation and Language (cs.CL)
备注: 19 pages, 5 figures, 12tables
Abstract:Text-to-SQL aims to convert natural language questions into executable SQL queries. While memory-based agent system improves complex SQL generation, existing memory design neglect historical experience and suffer from weak structure analysis, shallow semantic understanding, and poor schema alignment. To address these challenges, we propose SDAM. Specifically, SDAM identifies potential errors via a structure-difference aware reasoning tree, extracts deep semantic rules through contradiction-aware reflection, and enhances structural consistency using a schema-grounded memory evolution mechanism to bind memory with database schemas. We integrate SDAM into a Text-to-SQL framework named SDAM-SQL. Experiment shows that SDAM-SQL achieves 2.0 and 0.4 improvement on BIRD-dev and Spider-test compared with mainstream Text-to-SQL methods, showing the effectiveness of SDAM-SQL.
[NLP-71] From Refuse to Richness: Rubric Rewards for Long-Form Hallucination Reinforcement Learning
【速读】： 该论文旨在解决长文本生成中“事实性”（grounding）与“信息丰富度”（richness）之间的权衡问题，即在强化学习框架下如何有效抑制幻觉（hallucination）的同时避免模型因过度规避风险而输出内容贫乏的答案。其核心解决方案是引入基于关键点的评分标准（key-point rubric），以直接定义高质量回答应涵盖的必要和可选信息，将该评分标准同时用于评估与奖励信号设计。相较于传统全局性丰富度代理指标（如长度、主张数量、细节程度或成对相关性），该方法通过局部化、语义明确的结构化约束实现了对覆盖度的精准控制。实验表明，严格依赖事实性奖励会提升支持度但降低覆盖度，而完全依赖鲁棒评分标准则会增强覆盖度但削弱事实性；最优策略在于软性融合事实性、关键点覆盖度与相关性三者，从而在分布内任务上保持强事实支持能力，并在分布外检查清单任务上展现出更优的泛化性能。
链接: https://arxiv.org/abs/2608.12337
作者: Yudong Wang,Zhe Yang,Wenhan Ma,Rang Li,Qibin Yang,Weimin Xiong,Jiangshan Duo,Liang Zhao,Zhifang Sui
机构: Peking University (北京大学); LLM-Core Xiaomi (小米大模型核心团队)
类目: Computation and Language (cs.CL)
备注:
Abstract:Rewards that penalize unsupported claims can improve grounding in long-form generation, but they can also teach models to answer less. We study this refusal-to-richness trade-off in long-form hallucination RL. Instead of using global richness proxies such as length, claim count, detail, or pairwise relevance, we represent each question with a key-point rubric that specifies the required and optional information a useful answer should cover. These rubrics define coverage directly and are used both for evaluation and as reward signals. Across grounding-only, proxy-based, rubric-only, and combined rewards, we find a stable trade-off: strict grounding rewards improve support but suppress coverage, while unconstrained rubric rewards improve coverage but weaken grounding. A soft combination of grounding, rubric coverage, and relevance gives the best balance in our experiments, improving in-distribution support while transferring better to out-of-distribution checklist tasks than either grounding-only or rubric-only rewards.
[NLP-72] StorySpark: Module-wise Evolutionary Search for Story Premise Generation
【速读】： 该论文旨在解决生成式AI在故事创作中对前提层创意生成（premise-level ideation）关注不足的问题，即现有基于大语言模型（LLM）的故事生成方法多聚焦于后续阶段的规划、可控性、连贯性及文笔扩展，而对故事核心创意的初始构想缺乏系统性探索。其解决方案的关键在于提出StorySpark——一个模块化演进搜索框架，通过将叙事要素解耦为可解释的模块（如背景、人物设定、事件、结局与反转），将每个活跃模块视为一个动态的局部搜索空间，而非一次性填充的静态字段。该框架通过上下文感知的替代生成、反馈驱动的变异与重组、基于帕累托最优的互补优势保留，以及前沿资源的动态再分配，实现对各模块的持续优化，从而在保持多样性的同时提升创意质量。实验表明，StorySpark在原创性、故事完整性及下游故事质量方面均显著优于对比基线，尤其在提升创意新颖性上表现一致且突出。
链接: https://arxiv.org/abs/2608.12336
作者: Yang Yang,Zining Zhong,Qian Cao,Jindong Li,Boyun Xu,Kaishen Yuan,Menglin Yang,Yutao Yue
机构: The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511400, China; Renmin University of China, Beijing, China; Shandong University, Weihai, China; Institute of Deep Perception Technology, JITRI, Wuxi 214000, China
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 26 pages, 7 figures
Abstract:A story premise is the creative spark from which a full narrative can grow. Yet LLM-based story generation has mostly emphasized later-stage planning, controllability, coherence, and prose expansion, while premise-level ideation remains comparatively underexplored. We introduce StorySpark, a module-wise evolutionary search framework for story premise generation. StorySpark operates over interpretable narrative modules such as background, persona, event, ending, and twist, treating each active module not as a static field to fill once, but as a local search space conditioned on the partial premise built so far. For each module, it generates alternatives, evaluates them in context, refines them through feedback-driven mutation and recombination, preserves complementary strengths with Pareto-guided selection, and reallocates frontier capacity to balance branch coverage with promising directions. Multi-view automatic and human evaluations show that StorySpark produces stronger final premises than competitive baselines, with especially consistent gains in originality; when expanded with the same story writer, its premises also lead to higher-quality downstream stories while maintaining completeness, fascination, and diverse usable narrative directions.
[NLP-73] HC-RAG : Evidence-Centric Retrieval-Augmented Generation over Heterogeneous Financial Filings
【速读】： 该论文旨在解决金融问答（Financial Question Answering, FQA）中现有检索增强生成（Retrieval-Augmented Generation, RAG）系统在处理年度报告时存在的关键问题：将长篇财务文件扁平化为无序文本块，忽视财务报告的类型化结构，且采用固定不变的文本-表格融合策略，无法根据查询意图动态调整证据获取与整合方式。其核心解决方案是提出一种分层跨模态检索增强生成框架——HC-RAG，该框架构建了一个包含文档、章节、文本单元、表格单元及元数据节点的类型化金融证据图（typed financial evidence graph），通过文档-章节-单元路径进行层次化检索，将文本与表格证据对齐于共享的检索空间，并依据四类语义意图（计算、趋势、事实、比较）实现意图感知的证据路由。为评估该方法，研究进一步构建了Multi-Doc-2025基准，包含2,327个由专家验证的问答对，覆盖87家标普500公司2022–2024财年的179份SEC 10-K文件，涵盖意图、难度和结构化证据属性标签。实验结果表明，HC-RAG在公共金融问答基准和Multi-Doc-2025上均显著提升答案质量与证据定位精度，尤其在长文档、涉及表格以及跨文档场景下表现突出，相较RAPTOR提升6.6 F1点，相较GraphRAG提升10.9 F1点。证据层面分析与消融实验进一步证实，性能提升主要源于更精准的章节定位、表格锚定、跨文档证据聚合以及意图感知的文本-表格路由机制。
链接: https://arxiv.org/abs/2608.12335
作者: Siyuan Chen,Huaye Tan,You Li,Jiajun Liang
机构: Sun Yat-sen University (中山大学); Central South University (中南大学)
类目: Computation and Language (cs.CL); Multimedia (cs.MM)
备注: 16 pages, 5 figures
Abstract:Financial question answering over annual reports requires more than retrieving semantically similar passages. It often involves identifying relevant companies and fiscal years, locating standardized filing sections, collecting textual and tabular evidence, and checking answers against the original documents. Existing RAG systems, however, usually flatten long filings into unordered chunks, pay limited attention to the typed structure of financial reports, and use fixed text-table fusion strategies without considering query intent. To address these limitations, we propose \textbfHC-RAG, a hierarchical cross-modal retrieval-augmented generation framework for evidence-centric financial QA. HC-RAG organizes filings into a typed financial evidence graph with documents, sections, text units, table units, and metadata nodes. It retrieves evidence through document-section-unit paths, aligns textual and tabular evidence in a shared retrieval space, and routes evidence according to four semantic intents: calculation, trend, fact, and comparison. We further introduce \textbfMulti-Doc-2025, a benchmark containing 2,327 expert-verified QA pairs from 179 SEC 10-K filings of 87 S\P 500 companies across fiscal years 2022–2024, with labels for intent, difficulty, and structural evidence attributes. Experiments on public financial QA benchmarks and Multi-Doc-2025 show that HC-RAG improves both answer quality and evidence localization, especially in long-document, table-related, and cross-document settings. HC-RAG outperforms RAPTOR by 6.6 F1 points on DocFinQA and GraphRAG by 10.9 F1 points on Multi-Doc-2025. Evidence-level analysis and ablation studies show that the improvements mainly come from more accurate section localization, table grounding, cross-document evidence aggregation, and intent-aware text-table routing.
[NLP-74] Steering the Language Axis: From Linear Decodability to Causal Control
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在多语言生成过程中语言选择的潜在动态机制不明确的问题，特别是语言身份是否仅可通过隐藏状态线性解码，或能否通过一个紧凑的激活方向进行因果控制。其核心解决方案在于通过系统性的因果干预分析，识别出由主成分分析（PCA）提取的“语言轴”（language axes），并在此基础上实施定向调控（steering）与消融实验。研究在FLORES-200数据集上对包括Qwen 3.5-2B和Llama-3.2-1B-Instruct在内的多个模型家族进行了超过126万次生成实验，结果表明：沿这些几何方向进行干预可稳定引导跨脚本（如英到中）与同脚本（如英到西）的语言切换，而同等幅度的随机扰动几乎无效果。层间分析揭示语言承诺具有高度局部化且依赖具体语言对的特性——例如英-中切换在早期层难以干预但后期层易控，而英-西转换则更早发生且呈现双峰敏感性。此外，定向消融实验证明，一旦语言信号被移除，模型将强制回退至英语输出，表明语言决策边界在推理阶段作为因果活跃特征存在，其作用具有方向依赖性和层特定性。
链接: https://arxiv.org/abs/2608.12334
作者: Arnav Srivastav
机构: University of California, Santa Cruz(加州大学圣克鲁兹分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 22 pages, 14 figures, Under review
Abstract:Despite the impressive multilingual capabilities of Large Language Models, the latent dynamics dictating language selection remain poorly understood. In this work, we ask whether language identity is merely linearly decodable from hidden states, or if it can be causally controlled by a compact activation direction. We conduct an exhaustive causal intervention analysis across multiple model families, including Qwen 3.5-2B and Llama-3.2-1B-Instruct, isolating PCA-derived “language axes” to perform steering and ablation experiments across 1.26 million generations on the FLORES-200 dataset. Steering along these geometric directions reliably forces language switching in both cross-script (English to Chinese) and same-script (English to Spanish) settings, whereas equal-magnitude random perturbations yield virtually no effect. Our layerwise analysis reveals that language commitment is highly localized and explicitly language-pair-dependent. While English to Chinese switching resists early intervention and steers easily in the later layers, the English-Spanish transition shifts earlier, displaying a distinct, bimodal sensitivity. Furthermore, targeted ablation uncovers a fundamental reversion to English: once the language signal is removed, the model falls back to English regardless of the input prompt. Ultimately, these findings demonstrate that language decision boundaries function during inference as causally active features that are direction-dependent and layer-specific. Comments: 22 pages, 14 figures, Under review Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.12334 [cs.CL] (or arXiv:2608.12334v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.12334 Focus to learn more arXiv-issued DOI via DataCite
[NLP-75] Vision-Language Models are Frag ile Multilingual Associators
【速读】： 该论文旨在解决视觉-语言模型（Vision-Language Models, VLMs）在多语言环境下概念绑定（concept binding）稳定性的问题，即当输入语境和查询语句的语言发生变化时，模型是否仍能保持视觉实体与文本属性之间的稳定关联。其解决方案的关键在于提出M²BIND基准测试框架，通过在多种语言间系统性地变换上下文与查询语言，实现对概念绑定的跨语言评估。研究采用外部任务性能指标与内部因果干预相结合的方式，发现概念绑定并非语言无关：在跨家族语言及跨文字体系设置下，模型出现显著的绑定崩溃现象，表现为内部绑定计算过程向深层网络迁移且因果强度减弱；而语系相近的语言则相对较好地维持了绑定一致性。这一结果揭示了在全球化多语言部署中，不能假设VLMs在多语言场景下的概念绑定质量等同于单语评估所呈现的表现。
链接: https://arxiv.org/abs/2608.12333
作者: Ritabrata Chakraborty,Rajatsubhra Chakraborty,Shivakumara Palaiahnakote,Angelo Cangelosi,Umapada Pal
机构: Manipal University Jaipur, India; University of North Carolina Charlotte, USA; University of Salford, UK; University of Manchester, UK; Indian Statistical Institute Kolkata, India
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint (under review). Project Page: this https URL
Abstract:Vision-language models must associate visual entities with textual attributes. Whether these associations or concept bindings remain stable when the language of the input changes is unexplored. We introduce M ^2 BIND, a benchmark varying the language of the context and query across multiple languages. We evaluate binding both extrinsically through task performance metrics and intrinsically through causal interventions. We find that binding is not language-invariant: cross-family and cross-script settings trigger significant binding collapse, with the model’s internal binding computation shifting to later layers and losing causal strength. Closely related languages preserve associations comparatively better. In a broader sense, our findings indicate how VLMs deployed globally in multilingual settings cannot be assumed to maintain the same association quality observed in monolingual evaluation.
[NLP-76] Can Spectral-Clipping Enable Better Learning While Forgetting Less for Low-Rank Adaptation? ACL2026
【速读】： 该论文旨在解决低秩适应（LoRA）方法在微调过程中存在的灾难性遗忘问题，即由于LoRA适配器中奇异值无控制增长导致预训练知识丢失的缺陷。其核心解决方案在于提出SCLoRA方法，关键创新在于基于奇异值分解（SVD）对预训练模型参数的奇异分量进行谱分布感知分析，并引入可参数化的奇异分量与谱裁剪（spectral clipping）机制。该方法通过识别并保留具有大奇异值的主要奇异分量以复用预训练知识，同时仅对小奇异值的次要分量进行任务特定的自适应更新，从而在提升下游任务性能的同时有效缓解灾难性遗忘。
链接: https://arxiv.org/abs/2608.12332
作者: Hyowon Wi,Noseong Park
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: ACL 2026 Main Conference
Abstract:In recent years, low-rank adaptation (LoRA) has emerged as a significant paradigm that freezes pre-trained weights and introduces small, learnable adapters instead of fine-tuning the full set of parameters. In this work, we uncover several key insights regarding the singular components of network parameters based on Singular Value Decomposition (SVD). Firstly, the principal singular components with large singular values in pre-trained network parameters can be effectively reused during fine-tuning, whereas the minor components with smaller singular values are more task-specific and require substantial adaptation. Secondly, we first establish the theoretical connection that the uncontrolled growth of singular values in LoRA adapters leads to the forgetting of pre-trained knowledge – a well-known issue referred to as catastrophic forgetting. Building on these observations, we propose SCLoRA, which injects parameterized singular components with spectral clipping into the pre-trained model in a way that is aware of the spectral distribution of the pre-trained model. SCLoRA effectively adapts to new tasks by focusing updates on components that require adaptation, while simultaneously alleviating catastrophic forgetting. We conduct extensive experiments and demonstrate that SCLoRA not only improves downstream performance but also effectively retains pre-trained knowledge.
[NLP-77] hought-Aware KV Cache Compaction for Reasoning via Adaptive Attention Matching
【速读】： 该论文旨在解决生成式 AI（Generative AI）在推理过程中因链式思维（Chain-of-Thought, CoT）序列过长导致的键值缓存（Key-Value Cache, KV Cache）线性增长所引发的内存瓶颈问题。现有压缩方法将推理轨迹视为扁平的词元序列，采用统一压缩策略，忽视了CoT推理中各步骤在重要性上的显著差异。本文提出一种思想感知注意力匹配（Thought-Aware Attention Matching, TAM）方法，其核心在于利用CoT的层次化结构，通过三个关键机制实现高效压缩：(i) 思想分段（thought segmentation），将推理轨迹分解为具有语义意义的推理块；(ii) 自适应预算分配（adaptive budget allocation），根据每个分段的重要性与规模动态分配压缩资源；(iii) 关键词元保护（pivotal token protection），保留高注意力的推理锚点以维持逻辑连贯性。理论证明该分配规则在凸误差模型下为最优，并确保序列压缩下的累积误差有界。在AIME 2024和MATH-500数据集上基于Qwen3-4B的实验表明，TAM在相同内存开销下优于均匀压缩，且周期性压缩可将峰值内存控制在3.1–3.2 GB（降低65%），同时保持优异的推理准确性。
链接: https://arxiv.org/abs/2608.12331
作者: Yang Liu,Bin Chong,Chongyang Zhang,Hao Zheng,Jiayu Liang,Xu Kefu
机构: Tsinghua University (清华大学); Peking University (北京大学); Fullive.AI; Soochow University (苏州大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 16 pages, 5 figures
Abstract:Reasoning language models generate lengthy chain-of-thought (CoT) sequences whose key-value (KV) cache grows linearly and becomes a memory bottleneck during decoding. Existing compaction methods treat reasoning trajectories as flat token sequences and apply uniform compression, ignoring the hierarchical structure of CoT reasoning where different steps vary drastically in importance. We propose \textbfThought-Aware Attention Matching (TAM), which exploits this structure through three mechanisms: (i)~thought segmentation that decomposes the trajectory into reasoning blocks, (ii)~adaptive budget allocation that assigns compression budget based on each segment’s importance and size, and (iii)~pivotal token protection that preserves high-attention reasoning anchors. We prove that the allocation rule is optimal under a convex error model and that cumulative error under sequential compaction remains bounded. Experiments on AIME 2024 and MATH-500 with Qwen3-4B show that TAM improves accuracy over uniform compaction at the same memory footprint, with periodic compaction bounding peak memory to 3.1–3.2,GB (a 65% reduction) while maintaining competitive accuracy.
[NLP-78] Reliability-Aware Sexism Detection: Combining DPO with Annotator Agreement and Token-Level Confidence Scoring
【速读】： 该论文旨在解决在线性别歧视（online sexism）检测中因标注主观性导致的模型性能瓶颈问题。现有系统通常将多标注者标签简化为单一多数投票结果，并对所有样本进行均匀处理，忽视了标注者一致性（annotator agreement）和模型置信度（model confidence）等关键信息。为此，本文提出一种可靠性感知的直接偏好优化方法（RA-DPO），其核心在于整合标注者一致性、模型置信度及词元级不确定性信号，构建统一的可靠性评分（reliability score）。该评分用于训练阶段筛选高价值偏好对以降低训练成本，同时在推理阶段支持选择性预测（inference-time abstention），实现覆盖率与准确率之间的权衡。实验基于EXIST 2023数据集中的6,920条多语言帖子，采用DPO微调OpenAI gpt-4o基础模型，并在两个开源3B参数模型（Llama、Qwen）上验证。结果表明，仅使用最可靠的前30%偏好对训练即可达到全数据DPO的性能水平，证明可靠性感知选择可显著降低训练开销而不损失效果；在推理阶段，选择性预测在真实一致性设定下实现50%覆盖率达96.2%准确率，在可部署的预测一致性设定下达88.7%，均显著优于无一致性基准的85.3%。研究表明，显式建模标注不确定性对于提升主观分类任务的训练效率与部署可靠性具有重要意义。
链接: https://arxiv.org/abs/2608.12330
作者: Hadi Mohammadi,Shihan Wang,Masoume M. Raeissi,Anastasia Giachanou
机构: Utrecht University (乌得勒支大学); Wageningen University Research (瓦赫宁根大学与研究中心)
类目: Computation and Language (cs.CL)
备注: 11 pages, 4 figures. Preprint
Abstract:The detection of online sexism remains an open problem. Sexism detection is inherently subjective, yet most existing systems reduce multi-annotator labels to a single majority decision and treat all instances uniformly. This ignores two informative signals: annotator agreement and model uncertainty. We propose RA-DPO (Reliability-Aware Direct Preference Optimization), which integrates annotator agreement, model confidence, and a token-level uncertainty signal into a single reliability score. RA-DPO uses this score to select high-value preference pairs during training and to support inference-time abstention, which allows the model to trade coverage for accuracy. We evaluate RA-DPO on 6,920 multilingual posts from EXIST 2023, fine-tune OpenAI gpt-4o base via DPO, and validate on two open-weight 3B models (Llama, Qwen). Results show that training on the top 30% most reliable pairs matches full-data DPO, which indicates that reliability-aware selection can reduce training cost without sacrificing performance. At inference, selective prediction reaches 96.2% accuracy at 50% coverage in the true-agreement setting and 88.7% in the deployable predicted-agreement setting, both exceeding the 85.3% no-agreement baseline. These results suggest that accounting for annotation uncertainty is beneficial for both efficient training and reliable deployment in subjective classification.
[NLP-79] LoRA-Diffusion: Parameter-Efficient Fine-Tuning via Low-Rank Trajectory Decomposition
【速读】： 该论文旨在解决生成式语言模型中参数高效微调（Parameter-efficient Fine-tuning, PEFT）方法在基于扩散模型（Diffusion-based Language Models）上的适用性问题。传统参数高效微调方法如低秩自适应（LoRA）通过调整模型权重中的低秩矩阵实现任务定制，但这类方法难以直接应用于以迭代去噪机制生成文本的扩散语言模型，因其生成过程并非基于逐标记序列预测。为此，论文提出LoRA-Diffusion，其核心创新在于将低秩分解应用于去噪轨迹（denoising trajectory），而非模型权重本身。关键在于：该方法学习对从噪声到输出的整个去噪路径施加低秩扰动，引入轨迹级低秩适配器（trajectory-level low-rank adapters）以修改每一步去噪过程，并采用跨扩散阶段的自适应秩分配策略与可组合的多任务学习机制，使不同任务的适配模块可在推理时合并而无需重新训练。实验表明，LoRA-Diffusion在SST-2、QNLI和MRPC数据集上均取得优异性能，尤其在SST-2上达到最高平均准确率，且相比全量微调显著降低任务存储开销，为扩散语言模型建立了首个有效的参数高效微调框架。
链接: https://arxiv.org/abs/2608.12328
作者: Iman Khazrak,Narges Nejad,Mohammadhossein Homaei,Mostafa M. Rezaee,Robert C. Green II
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Parameter-efficient fine-tuning methods such as LoRA have transformed the adaptation of large autoregressive language models, enabling task-specific customization with substantially fewer trainable parameters. However, these methods have not been successfully extended to diffusion-based language models, which generate text through iterative denoising rather than sequential token prediction. We propose LoRA-Diffusion, a parameter-efficient fine-tuning approach that applies low-rank decomposition to the denoising trajectory instead of model weights. Unlike weight-based LoRA, which modifies individual transformation matrices, our method learns low-rank perturbations to the entire diffusion path from noise to output. We introduce trajectory-level low-rank adapters that modify each denoising step, step-adaptive rank allocation across diffusion phases, and compositional multi-task learning that allows merging task-specific modules at inference without retraining. On SST-2, QNLI, and MRPC, we report token-level denoising validation accuracy over five random seeds. LoRA-Diffusion achieves the highest mean performance on SST-2 and strong performance on QNLI and MRPC. Joint multi-task training further shows that LoRA-Diffusion achieves the highest token-level accuracy among the evaluated methods. The approach reduces per-task storage compared with full fine-tuning and establishes a parameter-efficient fine-tuning framework for diffusion language models.
[NLP-80] Comparative Analysis of Multilingual Pre-trained Models for Nepali Automatic Speech Recognition
【速读】： 该论文旨在解决多语言预训练模型在尼泊尔语（Nepali）语音识别（ASR）任务中缺乏统一评估标准的问题。尽管现有模型名义上支持尼泊尔语，但其性能对比常因数据处理、微调协议和评估设置不一致而难以公平比较。为此，研究采用统一的微调协议，在相同预处理流程、数据划分、优化器及家族匹配的学习率调度下，对六种不同架构的模型（涵盖基于CTC的自监督学习、自回归编码器-解码器以及混合Conformer-CTC结构）进行系统性对比。关键解决方案在于构建首个标准化、多模型、兼顾效率的尼泊尔语语音识别基准，通过在三个独立测试集（OpenSLR、FLEURS、Common Voice）上评估词错误率（WER）、字符错误率（CER）与实时因子（RTF），揭示语言家族相近性在预训练中的重要性：尽管参数量相差9倍、预训练数据量相差40倍，Whisper-Large-v3-Turbo与IndicWav2Vec在尼泊尔语上表现持平（14.76%与14.89% WER），表明语言亲缘关系可替代模型规模以获得优异的域内性能；同时，基于CTC的解码器相比自回归式Whisper在相同准确率下速度提升达29倍，显著改变了低延迟场景下的部署偏好；而大规模多语言预训练模型MMS-1B虽在域内表现非最优，但在跨语言泛化能力上表现出最强鲁棒性（在FLEURS上仅增加12.55个百分点的误差），验证了规模对泛化性的增益作用。该研究为尼泊尔语ASR提供了首个可复现、高效且全面的基准参考。
链接: https://arxiv.org/abs/2608.12327
作者: Suman Paudel,Sarbin Sayami
机构: Tribhuvan University (特里布万大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 9 pages, 6 figures, 7 tables. Based on this http URL . thesis (Institute of Science and Technology, Tribhuvan University). Code and models: this https URL
Abstract:Multilingual pretrained models nominally support Nepali, yet no controlled benchmark has compared them under a single fine-tuning protocol. We fine-tune six pretrained models (XLSR-53, IndicWav2Vec, MMS-1B, Whisper-Medium, Whisper-Large-v3-Turbo, and Conformer-Hi) spanning CTC self-supervised, autoregressive encoder-decoder, and hybrid Conformer-CTC architectures, on the OpenSLR SLR54 Nepali corpus (~165 hours) using identical preprocessing, splits, optimizer, and family-matched learning-rate schedules. We evaluate Word Error Rate (WER), Character Error Rate (CER), and Real-Time Factor (RTF) on three independent test sets (OpenSLR, FLEURS, Common Voice). Whisper-Large-v3-Turbo (14.76% WER) and IndicWav2Vec (14.89% WER) tie at the top despite a 9x parameter gap and 40x pretraining-data gap, providing direct empirical evidence that language-family proximity in pretraining can substitute for raw scale for in-domain Nepali. CTC decoders run up to 29x faster than autoregressive Whisper at the same accuracy, flipping the practical deployment preference toward CTC under any latency budget. Massively multilingual pretraining (MMS-1B) yields the smallest out-of-domain degradation on FLEURS (+12.55 pp), indicating that scale buys robustness rather than peak in-domain accuracy. The resulting benchmark provides the first standardized, multi-model, efficiency-aware reference numbers for Nepali ASR.
[NLP-81] On Measuring Semantic Preservation in Legal Ontology Learning
【速读】： 该论文旨在解决现有本体学习（Ontology Learning）评估方法无法有效检测语义损失的问题。当前方法仅关注结构正确性，而忽视了信息在从非结构化文本转换为结构化表示过程中是否保持原有语义。为此，论文提出一种新的评估框架：通过比较大语言模型（LLM）在原始文档与转换后结构化表示上的任务表现差异，量化语义损失程度。该方法以法律并购协议分析为应用场景，因其语言复杂且对语义精确性要求高，对比了直接使用LLM与三种本体学习方法在六种不同模型下的表现。结果表明，语义损失具有系统性特征，且其程度显著受推理复杂度及模型-方法组合的影响。研究贡献包括：（1）构建了一套可衡量本体学习中语义保真度的评估框架；（2）提供了实证证据，揭示了不同模型与方法组合间语义损失的显著差异，为法律知识系统中的最优配置选择提供了依据。
链接: https://arxiv.org/abs/2608.12326
作者: Albert Sadowski,Jarosław A. Chudziak
机构: Warsaw University of Technology (华沙理工大学)
类目: Computation and Language (cs.CL)
备注: Accepted for publication at the 30th International Conference on Knowledge-Based and Intelligent Information Engineering Systems (KES 2026)
Abstract:Ontology learning transforms unstructured text into structured representations for automated reasoning. Yet structuring information risks losing it, and current evaluation methodologies cannot detect such loss, focusing on structural correctness while failing to measure whether meaning survives transformation. We propose an evaluation methodology that addresses this: comparing LLM task performance on source documents against performance on transformed representations, with the difference quantifying semantic loss. We demonstrate this approach on legal merger agreement analysis, a domain chosen for its complex language and precise semantic requirements, comparing direct LLM application against three ontology learning methods across six language models. The results reveal systematic semantic loss with significant variation based on reasoning complexity and model-method interactions. Our contributions are: (1) an evaluation framework for measuring semantic preservation in ontology learning, and (2) empirical evidence that semantic loss varies dramatically with model-method pairing, providing guidance for selecting optimal configurations in legal knowledge systems.
[NLP-82] Position: Reasoning is a Learnable Rule-Based Process
【速读】： 该论文旨在解决生成式人工智能（Generative AI）领域中“推理”概念定义模糊的问题，这一模糊性导致推理能力评估的建构效度无法验证，从而阻碍了可量化的可信自主推理研究进展。其解决方案的关键在于：首先，基于文献综述提出操作性定义，将有效且严谨的推理定位为一种可学习的基于规则的过程；其次，提供一套最佳实践检查清单，以规范AI推理研究的沟通与报告，提升研究透明度与可复现性。
链接: https://arxiv.org/abs/2608.12325
作者: Rachel Lawrence,Jacqueline Maasch
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Autonomous reasoning is among the most scientifically and economically motivating topics in AI today. Historically the purview of symbolic AI, recent advances have mainly emerged from deep probabilistic generative models. Despite immense interest and rapid progress, the generative AI community has not clearly converged on operational definitions for reasoning and often implicitly rejects the historical treatment of this topic in logic and verifiable automated reasoning. This position contends that definitional ambiguity leaves the construct validity of reasoning evaluation unverifiable, undermining quantifiable progress toward trustworthy autonomous reasoning. We also contend that this ambiguity is addressable. To that end, we provide (1) operational definitions based on a synthesis of the literature, positioning valid and sound reasoning as a learnable rule-based process; and (2) a checklist for best practices in the communication of AI reasoning research.
[NLP-83] When AI Is Your Pastor: A Benchmark for Theological Triage and Pastoral Guidance in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在处理基督教神学与牧灵关怀类问题时缺乏有效评估基准的问题。此类问题涉及核心信仰、教义分歧、审慎判断及紧急牧灵情境，其复杂性远超一般信息查询，对安全性、伦理敏感性和临床转介意识提出了更高要求。现有评测体系未能充分覆盖此类情境的结构化需求。为此，作者提出FMG-Bench——一个包含120个情景的英文基督教神学分诊与牧灵指导基准测试，用于系统评估LLMs在真实牧灵场景中的表现。解决方案的关键在于引入结构化引导指令框架（guided instruction settings），通过三类引导设置对比原始模型行为。实验结果显示，结构化引导使平均得分提升3.96点，且所有模型均获益；尤其在危机识别能力方面实现+10.8点显著提升，表明模型更准确地判断何时需转介至专业牧灵、临床、法律或紧急支持资源。此外，引导机制还增强了模型在语义重述或压力情境下的鲁棒性（稳定性从92.88升至98.02）。值得注意的是，引导模型比较不同神学观点虽有助于次级教义讨论，但在初级教义或紧急牧灵情境中可能适得其反。整体而言，FMG-Bench作为测量工具，旨在客观评估而非赋予AI系统牧灵权威地位。
链接: https://arxiv.org/abs/2608.12324
作者: Alex Chao
机构: Fide AI
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Full paper. Code and dataset are available at this https URL and this https URL
Abstract:People increasingly ask large language models (LLMs) for counsel on questions of faith, doctrine, and pastoral care. These questions are not ordinary information requests. Some ask about core Christian beliefs, some ask about real disagreements among faithful traditions, some require humility because the issue is prudential, and some are pastoral situations where safety and human referral matter more than theological completeness. Existing benchmarks do not evaluate this structure. We introduce FMG-Bench, the Faith Moral Guidance Benchmark, a 120-scenario benchmark for evaluating large language model behavior in English-language Christian theological triage and pastoral guidance contexts. FMG-Bench v1 evaluates 14 advanced models across 8,792 scored responses, comparing raw model behavior with three guided instruction settings. In our production run, placing models inside a structured harness improves over raw model behavior by +3.96 points on average, with every model improving. The most safety-critical finding is a +10.8 point gain in escalation appropriateness – whether AI systems recognize when pastoral, clinical, legal, or emergency support is needed. The guided settings also improve robustness, meaning consistency when questions are reworded or pressured (92.88 to 98.02 stability). Asking a model to compare perspectives helps in secondary-doctrine questions but can be counterproductive when applied to primary doctrine or urgent pastoral situations. The benchmark is a measurement tool, not an endorsement of AI systems as pastoral authorities.
[NLP-84] Why Do AI Agents Break Rules? How Framing Context and Social Signals Shape Compliance
【速读】： 该论文旨在解决生成式 AI（Generative AI）在实际应用中因合规机制设计缺陷而导致的“合规失效”问题，即当监管要求被形式化为可量化的惩罚成本时，模型可能将其视为可优化的经济成本而非刚性约束，从而导致系统性违规。其核心解决方案的关键在于：将法律与经济学中的合规理论（如威慑理论、合法性理论和表达性法律理论）作为实证假设，用于诊断不同类别模型的合规行为模式。研究发现，经过安全微调的模型表现出广泛的合规性，而任务优化型与自主代理型模型则将监管信号简化为可调节的优化参数，在低处罚或非指令性表述等理论预测条件下显著失守合规边界。此外，引入财务激励、管理压力或同侪影响均引发大规模合规失败。研究进一步表明，仅依赖规则嵌入无法实现真正合规，模型选型本身即构成治理决策，现有基于基准测试的对齐评估方法不足以支撑高合规敏感度的应用部署。
链接: https://arxiv.org/abs/2608.12323
作者: Mika Okamoto,Ansel Kaplan Erol,Kutluhan Erol
机构: University of California, Berkeley (加州大学伯克利分校); Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Specifying a penalty can paradoxically convert a legal obligation into a cost-benefit calculation that favors violation. We demonstrate that this enforcement information paradox systematically occurs in AI agents. While most AI safety evaluations test whether models fail, we investigate why, applying compliance theory from law and economics as a diagnostic tool. We treat compliance theories not as metaphors but as empirical hypotheses and show that each predicts the behavior of a distinct model class. We evaluate our hypotheses across twelve instruction-tuned language models operating as enterprise procurement chatbots. Drawing on theories of deterrence, legitimacy, and expressive law, we show that safety-fine-tuned models maintain compliance broadly, while task-optimized and agentic models treat regulatory signals as mere optimization parameters. These latter models fail to comply under conditions predicted by theory, such as low enforcement penalties and non-command phrasing. Across all models, introducing financial incentives, managerial demands, peer outcomes, or employee pressure produces large compliance failures. AI procurement agents systematically violate regulatory constraints to satisfy local user objectives in ways not captured by standard alignment benchmarks. Ultimately, compliance cannot be achieved by rule embedding alone; model selection is itself a governance decision, and benchmark-based evaluation is insufficient for compliance-sensitive deployments.
[NLP-85] What Drives LLM Self-Reflection? A Controlled Ablation of Uncertainty Routing in Armed Conflict Forecasting
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在复杂推理任务中依赖自我反思（self-reflection）提升性能的机制不明确问题，特别是厘清自我反思中哪些具体组件真正驱动性能提升。研究通过受控的六条件消融实验，系统分离并检验了四个关键组件：证据暴露、诊断性支架（diagnostic scaffolding）、不确定性分类术语体系（taxonomy vocabulary）和动作路由（action routing）。研究发现，结构化诊断问题与术语体系均未带来显著性能增益（两个零结果），排除了诊断性支架和术语体系作为核心驱动力的可能性；而引入类型化动作路由（typed action routing）则表现出一致且显著的性能提升，其相对于单次推理基线的F1值提升达+0.101（95%置信区间[+0.020, +0.185]），且在GPT-4o上复现成功。进一步分析表明，该增益主要体现在结构新颖的冲突案例中（如缅甸、乌克兰），其中仅使用术语体系的条件无法突破退化先验，而动作路由能有效打破这一瓶颈。因此，该研究的核心结论是：类型化动作路由才是推动大语言模型（LLM）自我反思实现性能跃升的关键机制，而非传统认为的诊断性框架或术语体系，这为构建具备元认知能力的预测型智能体提供了可验证的设计原则。
链接: https://arxiv.org/abs/2608.12322
作者: Poli Nemkova,Haeshitha Indukuri
机构: University of North Texas (北德克萨斯大学); College of Computer Science and Engineering (计算机科学与工程学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Self-reflection is widely assumed to improve LLM reasoning, yet which component drives the gain remains poorly understood. We present a controlled six-condition ablation isolating four components of LLM self-reflection: evidence exposure, diagnostic scaffolding, taxonomy vocabulary, and action routing. Two precise null results converge on a single mechanism. First, structured diagnostic questions add no measurable value over unstructured reflection ( \textF1 = 0.296 vs 0.297 , p = 1.000 , 95% CI [-0.041, +0.040] ). Second, presenting the full uncertainty taxonomy while collapsing the action space to a single generic action also adds no value ( \Delta\textF1 = +0.008 , overlapping 95% CIs), ruling out taxonomy vocabulary as the mechanism. Typed action routing provides consistent directional gains ( \textF1 = 0.379 vs 0.296 ); the conservative estimate controlling for taxonomy vocabulary is \Delta\textF1 = +0.075 , and the overall gain over the single-shot baseline is significant by bootstrap CI ( \Delta\textF1 = +0.101 , 95% CI [+0.020, +0.185] ). The vocabulary-routing decomposition replicates on GPT-4o: taxonomy vocabulary adds no significant value over generic reflection ( p = 0.773 ), while action routing provides significant gains ( p = 0.025 ), confirming the mechanism holds across backbones. Gains concentrate on structurally novel conflicts: in Myanmar ( \textF1: 0.000 \rightarrow 0.353 ) and Ukraine ( 0.167 \rightarrow 0.500 ), the vocabulary-only condition recovers no more than generic reflection while action routing breaks the degenerate prior. These findings identify typed action routing – not diagnostic scaffolding or taxonomy vocabulary – as a promising design principle for metacognitive LLM forecasting agents, while motivating larger-scale evaluation across conflict typologies.
[NLP-86] LLM s Know the Constraint But Do Not Use It: Activation Bottlenecks in Prag matic Constraint Reasoning
【速读】： 该论文旨在解决大语言模型（LLM）在面对显著表面线索与隐式可行性约束冲突时的推理失效问题，尤其关注模型是否真正理解并内化了约束条件，而非仅依赖保守的默认策略。其核心挑战在于：现有评估中聚合准确率会混淆真正的约束推理与因保守倾向导致的默认响应。为此，作者提出“条件约束激活”（conditional constraint activation）这一形式化框架，将其分解为三个关键机制：约束在模型内部被对称编码（Knowledge, Symmetry），但仅在特定情况下被路由至决策过程（Routing），且可通过外部激活信号进行修复（Repair）。基于14个模型的四联诊断实验发现两种主要失败模式：尽管两个开放权重的探针均能以超过88%的精度解码约束，但仅一种可通过激活修补实现显著性能提升（+6.4 nats），另一种则无改善（-0.07）。进一步分析表明，所有提示干预手段均未能突破修复瓶颈，其共性在于通过“前提提及”这一单一中介路径加剧了保守偏差。研究结论指出，隐藏约束的失败本质上是路由问题（routing problem），而非知识缺失问题，强调模型内部存在可被激活的潜在约束表征，但其能否参与推理取决于动态路由机制的有效性。
链接: https://arxiv.org/abs/2608.12321
作者: Yubo Li,Ramayya Krishnan,Rema Padman
机构: Carnegie Mellon University (卡内基梅隆大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:When a salient surface cue competes with an implicit feasibility constraint, LLMs often fail – but aggregate accuracy conflates genuine constraint inference with conservative defaulting. We formalize the distinction as conditional constraint activation: the constraint is internally encoded (Knowledge) symmetrically across constraint-present and -absent prompts (Symmetry), yet only sometimes routed into the decision (Routing) and repairable by a donor activation (Repair). A quartet diagnostic over 14 models reveals two failure modes; probes on two open weights decode the constraint above 88% , yet activation patching repairs one ( +6.4 nats) and not the other ( -0.07 ). On a mitigation frontier, no prompted intervention reaches the repair corner: all inflate conservative bias through a single mediation pathway – prerequisite mention. Hidden-constraint failure is a routing problem, not a knowledge problem.
[NLP-87] From Observation to Intervention: Memory in Brains and Large Language Models
【速读】： 该论文旨在解决生物大脑与大型语言模型（Large Language Models, LLMs）在记忆系统功能机制上的比较问题，尤其关注记忆信息的表征位置、部分线索如何触发完整关联、新信息的存储与更新机制，以及记忆状态如何被扰动等核心问题。其解决方案的关键在于突破传统基于解剖结构的类比，转而建立一种功能性和实验性比较框架：尽管大脑与LLMs在物理架构上迥异，但可通过对比两者在权重（weights）、激活（activations）、上下文窗口（context windows）、检索系统及外部存储等方面的运作逻辑，实现对记忆机制的深入理解。特别地，论文指出LLMs虽不具备真实的情景记忆（episodic memory），却具备前所未有的可重复、高精度的内部状态操控能力，这为探索记忆的可逆性、持久性、更新机制及意外效应提供了独特的实验工具。因此，其核心创新在于将LLMs作为“实验探针”，通过转移实验逻辑而非结构对应，推动生成关于生物记忆机制的更精确假设，从而弥合跨系统研究中的认知鸿沟。
链接: https://arxiv.org/abs/2608.12377
作者: Morteza Salehjahromi,Shayan A. Zadegan,Amgad Muneer,Jia Wu
机构: 未知
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Perspective article, 11 pages, 3 figures, 1 table, and 1 key terms box. Submitted for consideration to Nature Machine Intelligence
Abstract:Brains and large language models (LLMs) are fundamentally different memory systems, but they can be compared through shared functional questions: where memory-related information is represented, how partial cues recover broader associations, how new information is written or updated, and how memory-related states can be perturbed. In biological systems, these questions span synapses, neuronal ensembles, hippocampal-cortical interactions, and plasticity; in LLMs, they span weights, activations, context windows, retrieval systems, and external stores. The comparison is therefore functional and experimental rather than anatomical. Human studies reveal sparse concept responses, temporal binding, rapid association formation, episode-specific coding, and recall-related reactivation, but selective intervention remains limited. Rodent studies provide more selective causal access to learning-related ensembles, whereas human and macaque interventions usually affect broader circuits. LLMs lack lived episodic memory, yet they permit unusually direct and repeatable manipulation of internal states and stored information. We argue that this asymmetry creates a new opportunity. LLMs are not ahead in memory itself, but in experimental access. Their tools may help turn broad questions about retrieval, updating, persistence, reversibility, and unintended effects into sharper biological hypotheses. The productive bridge is to transfer experimental logic, not anatomical parts.
信息检索
[IR-0] Structure then Query: Enabling Precise Analytical Queries over Unstructured Documents
链接: https://arxiv.org/abs/2608.13384
作者: Teng Lin,Yuyu Luo,Nan Tang
类目: Information Retrieval (cs.IR); Databases (cs.DB)
备注:
Abstract:Unstructured documents constitute the majority of enterprise and web data. With the rapid development of large language models(LLMs), researchers have started to build data systems that analyze unstructured textual documents like operating on databases. However, because mainstream retrieval methods still relies on fuzzy matching based on vector similarity, accurately obtaining information and performing structured analysis and reasoning remains a major challenge. To address these limitations, AnnoIndex introduces two core fundamental components. The first is Annotation Index. The system uses a module called SchemaLoop to automatically create hierarchical annotation schemas from the raw corpus, and then uses lightweight language model to extract specific values. It turns scattered unstructured text into a materialized, structured index that enables low-cost filtering and querying. The annotation index avoids the black-box matching of vector similarity and amortizes attribute extraction costs from online queries to a one-time build. The second innovation is a Structured Query Engine. It compiles user questions into execution plans based on SQL extension. It first uses the Annotation Index for precise documents filtering, then gradually applies extraction operations in ascending order of cost, resorting to LLMs only for the remaining minimal fraction of the corpus that require deep semantic understanding. The extracted attributions are merged into the annotation index, reducing the cost of future queries. Experiments on three real-world datasets demonstrate that AnnoIndex consistently outperforms state-of-the-art baselines, achieving the highest average F1 score (0.87) while maintaining robust performance on complex multi-hop join and progressive reasoning queries.
[IR-1] When Should Multi-Round RAG Stop? Structured Stopping Judgments and Retrieval Reduction in Search-R1
链接: https://arxiv.org/abs/2608.13237
作者: Weimeng Luo
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: 16 pages, 3 figures. Code: this https URL
Abstract:Multi-round retrieval-augmented generation (RAG) must decide when to stop searching as evidence accumulates. Because the deployed policy is determined by the first STOP on each trajectory, this is a sequential selection problem rather than an independent state-classification task. We adapt S2G-RAG’s structured sufficiency-and-gap judgment to a frozen Search-R1 pipeline and train a Qwen3.5-2B judge on 3,009 states from 900 disjoint HotpotQA questions. Search-R1’s reasoner, retriever, corpus, prompt, and search budget remain unchanged, while the judge checkpoint and stopping threshold are selected on grouped validation and frozen before confirmatory evaluation. On the confirmatory test set, the resulting policy reduces retrieval calls by 77 (3.70%) relative to Native Search-R1, while Official Exact Match decreases by 0.625 percentage points. Thus, the trained S2G-style structured judge reduces retrieval while broadly preserving answer accuracy. The result does not imply unchanged or improved accuracy, safe stopping, or lower total inference cost.
[IR-2] GEM: A Generative Embedding Model Bridging Reasoning and Retrieval
链接: https://arxiv.org/abs/2608.13200
作者: Zhili Shen,Craig Macdonald
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Modern LLMs excel at reasoning and instruction following, enabling users to express complex and diverse information needs. However, conventional retrievers largely rely on surface-level matching between queries and documents, resulting in a growing gap between how users express their needs and how retrievers interpret them. In this paper, we present GEM, a generative embedding model that augments retrieval through its own knowledge by explicitly reasoning about user intent and relevance criteria. GEM unifies generation and embedding within a single model: it first reasons over the query, then appends an embedding token to encode the enriched context for retrieval. \zhiliEvaluated on reasoning-intensive and instruction-following retrieval tasks, GEM demonstrates the effectiveness of its reasoning-augmented retrieval, outperforming its non-reasoning variant and matching baselines using substantially larger models. Furthermore, GEM’s generative nature allows test-time compute scaling via prompting to further enhance retrieval performance. Our code is available at: this https URL.
[IR-3] RAG Sieve: Self-Referenced Local Contrast for Knowledge-Poison Detection in Retrieval-Augmented Generation
链接: https://arxiv.org/abs/2608.13010
作者: Xinlong Xu,Yoshua Y. Li
类目: Computation and Language (cs.CL); Cryptography and Security (cs.CR); Information Retrieval (cs.IR)
备注:
Abstract:Retrieval-augmented generation treats an external corpus as inference evidence, allowing injected documents to promote attacker-chosen claims. Existing detectors depend on trusted references, specific attack artifacts, or global thresholds sensitive to corpus topology. We present RAGSieve, a self-referenced detection framework that constructs its reference from the inspected system. RAGSieve-Query (RSQ) performs query-local contrast, scoring top-five candidates against ranks 6-20 of the same retrieval to detect answer-anchor concentration and carrier transitions. RAGSieve-Graph (RSG) performs corpus-local contrast, comparing each document’s semantically similar but lexically distinct neighbors with its local baseline to detect coordinated density before queries arrive. Across three QA datasets and six poisoning constructions, RSQ achieves 95.2% AUROC and detects 82.2% of poison at 5% clean-document removal, versus 81.1%/52.5% for GMTP. RSG achieves 93.3%/79.8%, versus 79.4%/37.6% for CleanBase. Joint deployment reduces attack success from 67.4% to 14.0% while retaining 41.3% F1 on unpoisoned retrieval, demonstrating practical protection at both corpus ingestion and query time without poison labels or trusted corpora. Source code is available at this https URL.
[IR-4] EviReform: Evidence-Guided Query Reformulation for Multi-Hop Graph Retrieval
链接: https://arxiv.org/abs/2608.13006
作者: Xinlong Xu,Yoshua Y. Li
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:Multi-hop retrieval must recover passages that provide sufficient evidence together. An initial passage often resolves an entity or relation implicit in the question, making the missing evidence easier to describe only after retrieval begins. Graph retrieval improves access to related evidence through stored corpus structure, but its retrieval signal is commonly derived from the original question. Complementary evidence must then be reached through stored relations even when an observed passage provides a more direct semantic cue. We introduce EviReform, which separates revising the retrieval request from aggregating evidence in the graph. Retrieved source passages formulate residual queries for the unresolved information need. The original and residual retrieval signals are normalized separately, combined, and propagated between propositions that share entities. On 2WikiMultiHopQA, HotpotQA, and MuSiQue, EviReform exceeds the strongest baseline by up to 5.59 Recall@5 points and 4.50 F1 points. These results show that observed evidence can guide graph retrieval toward the part of a supporting chain left underspecified by the original question. Code is available at this https URL.
[IR-5] HybridRAG -BN: A Retrieval-Augmented Framework with Fine-Tuned Verification for Bangla KBQA
链接: https://arxiv.org/abs/2608.13004
作者: Rathijit Aich,Nirjhar Das,Mahfuzulhoq Chowdhury
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: Developed for the IEEE Computer Society CUET Student Branch
Abstract:Knowledge-base question answering (KBQA) systems rely on effective retrieval and reasoning mechanisms to generate accurate answers from external knowledge sources. However, developing reliable KBQA systems for low-resource languages such as Bangla remains challenging due to limited retrieval-focused research, scarce language resources, and difficulties in grounding generated responses in external knowledge. In this work, we propose HybridRAG-BN, a retrieval-augmented framework for Bangla KBQA that integrates hybrid retrieval using BM25 and BGE-M3, answer generation using the GGUF version of Gemma-4-31B-Instruct, and a LoRA-fine-tuned Gemma-4-31B-Instruct model for answer verification and refinement. To further improve robustness, the framework incorporates a post-processing stage that addresses unresolved cases through fallback answer replacement and DuckDuckGo-assisted retrieval. Experimental results demonstrate the effectiveness of the proposed framework, achieving token-level F1 scores of 0.71654 and 0.72912 on the public and private leaderboards, respectively, securing first place in the competition.
[IR-6] Generative Universal Multimodal Retrieval with Dual-role Identifiers
链接: https://arxiv.org/abs/2608.12987
作者: Kaipeng Li,Haitao Yu,Xuanchen Zhou
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: This paper is under review
Abstract:Generative information retrieval (GIR) has emerged as a compelling alternative to the conventional index-retrieve-then-rank retrieval pipeline by training a generator to produce the identifiers of relevant items directly. Despite its promise, a number of open challenges still remain. First, constrained left-to-right decoding is vulnerable to prefix-level errors and local optima. Second, most prior GIR research remains largely unimodal, leaving instruction-aware retrieval across text, image, and mixed image-text items underexplored. Third, although discrete identifier-based GIR offers higher efficiency, its retrieval accuracy still lags behind that of the cutting-edge dense-vector-based retrieval methods. Motivated by these challenges, we propose DrIG, a novel Generative framework for universal multimodal retrieval featuring Dual-role Identifiers, which supports diverse retrieval tasks across multiple modalities and domains. Each candidate is assigned a single residual-quantized identifier that serves two complementary roles. In its sequential role, the identifier is decoded autoregressively, where the first token explicitly models modality and the remaining tokens capture progressively finer semantics. In its set-based role, the same tokens are reinterpreted as an unordered set to provide a prefix-independent relevance prior, which guides constrained beam search and alleviates local-optimum errors. Extensive experiments on the M-BEIR benchmark and the text-to-image evaluation datasets show that:(1)DrIG consistently outperforms state-of-the-art generative multimodal baselines across diverse tasks, while hybrid reranking achieves a favorable efficiency-effectiveness trade-off against strong dense retrievers. (2)Ablation and scaling analyses reveal how the base LMM, beam size, reranking depth, and fusion strategy affect retrieval performance, providing practical guidance for system design.
[IR-7] STAR: Structured Tokenization and Target-Aware Interest Representation for PCVR Prediction
链接: https://arxiv.org/abs/2608.12986
作者: Yimeng Xu,Haorui Zhang,Yingqi Song,Ying Jiang,Lan Ma
类目: Information Retrieval (cs.IR)
备注:
Abstract:Post-click conversion rate (PCVR) prediction is a core ranking task in industrial recommender systems. Modern ranking models must jointly capture heterogeneous non-sequential features, multi-behavior user sequences, and target-item-aware user interests, while remaining robust to high-cardinality sparse features, missing values, and train-inference inconsistencies. In this paper, we present STAR (Structured Tokenization and Target-Aware Interest Representation), a practical framework for the KDD Cup 2026 Tencent UniRec Challenge. STAR combines structured feature tokenization with target-aware interest representation on top of a HyFormer-style multi-sequence backbone. It introduces high-cardinality signal recovery, explicit user-item interaction tokens, target-aware sequence decoding, and a weighted user-item contrastive auxiliary objective inspired by InfoNCE. We further align the training and inference pipelines by reconstructing feature remapping tables and structural hyperparameters from the saved training configuration. Experiments on the challenge dataset identify the components that most reliably improve ranking AUC, while LogLoss is reported as a calibration diagnostic. The main ablation study shows a large gain from temporal context, with smaller but useful contributions from contrastive alignment, target-aware interest encoding, and high-cardinality sequence feature recovery.
[IR-8] DTAMLP: Denoise Time-aware MLP for Session-based Recommendation
链接: https://arxiv.org/abs/2608.12975
作者: Jiamu Zheng,Xiaojun Shan
类目: ocial and Information Networks (cs.SI); Information Retrieval (cs.IR)
备注:
Abstract:This paper reports two empirical findings on session-based recommendation (SBR), unified in a single model, DTAMLP. First, existing time-aware and GNN-based models (e.g., TiSASRec, SR-GNN) treat every click-time interval as equally informative, even though very short dwell times often reflect accidental clicks carrying little preference signal – a phenomenon we call sporadic noise. We show that a lightweight, plug-and-play weight fusion module, blending a model’s attention weight with a threshold-capped time-interval weight, can be inserted into such models with almost no architectural change and yields a consistent accuracy gain; we view this as the most directly verifiable contribution of this work. Second, we revisit an under-explained observation from FMLP-Rec, where a learnable frequency-domain filter on item embeddings improves accuracy, and offer a possible explanation: time-domain behavior mixes several entangled psychological preferences, and a frequency-domain view may let a model separate and down-weight such preference noise more naturally – an interpretive conjecture rather than a proven mechanism. Building on both insights, DTAMLP, an all-MLP framework combining weight fusion and FFT-based filtering, is validated on Diginetica and RetailRocket. While this system-level design reflects the state of the field circa 2023 rather than a state-of-the-art claim, ablations confirm the two mechanisms contribute complementary, non-redundant improvements.
[IR-9] FSGR: Mitigating Token Frequency Bias for Fair SID-Based Generative Recommendation
链接: https://arxiv.org/abs/2608.12845
作者: Yuchen Zheng,Sihan Xu,Jingwen Yang,Xiangrui Cai,Haiwei Zhang,Xiaojie Yuan
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Semantic ID (SID)-based generative recommendation has recently achieved remarkable success. However, existing methods suffer from a previously overlooked fairness issue, which we term \textbfToken Frequency Bias, where high-frequency SID tokens are systematically over-predicted while low-frequency SID tokens are under-predicted. This bias originates from the combined effects of imbalanced semantic codebooks during SID construction, and popularity bias together with the maximum likelihood estimation objective during recommendation training, resulting in unfair exposure across item categories. Existing SID methods mainly focus on improving codebook quality and overlook the impact of token frequency imbalance on downstream recommendation fairness, while LLM debiasing methods often yield suboptimal results when directly applied to SID-based recommendation, due to the hierarchical semantics of SID tokens. To address this issue, we propose \textbfFSGR, a fairness optimization framework for SID-based generative recommendation. During SID construction, FSGR employs OT-based Assignment Optimization and Dual-Criteria Re-anchor mechanism to form a more balanced SID representation space. During recommendation training, it adopts a two-stage training strategy and introduces Hierarchical Frequency Calibration for layer-specific fairness fine-tuning. Experiments on three public datasets with three backbone models demonstrate that FSGR mitigates token frequency bias and delivers an average Gini fairness improvement of over 20% while maintaining competitive recommendation accuracy.
[IR-10] Query Translation vs. Cross-Lingual Embeddings for Sinhala-Tamil E-Government Information Retrieval
链接: https://arxiv.org/abs/2608.12820
作者: Dharshi Balasubramaniyam,Tiroshan Madushanka
类目: Information Retrieval (cs.IR)
备注:
Abstract:This paper presents a comparative evaluation of cross-lingual information retrieval (CLIR) methods for retrieving English government information using Sinhala and Tamil queries. Two CLIR paradigms are investigated: Query Translation (QT), employing Google Translate, NLLB, and mBART50, and Cross-Lingual Embeddings (CLE), using LaBSE, multilingual E5, and BGE-M3, with monolingual English retrieval as the baseline. Experiments are conducted on a human-verified benchmark comprising 500 Sinhala, Tamil, and English question-answer pairs derived from 1,699 segmented contexts from Sri Lanka’s Government Information Center (GIC). Retrieval performance is evaluated using Recall@k (k = 1, 3, 5, 10, 15). Monolingual retrieval performs poorly (Recall@15 10%), whereas all CLIR approaches substantially improve retrieval accuracy. Among them, BGE-M3 achieves the highest Recall@15, reaching 96.2% for Sinhala-English and 95.6% for Tamil-English, outperforming the best QT approach (Google Translate: 92.4% and 93.0%) while avoiding translation overhead. These results demonstrate that multilingual embedding models provide a more effective and scalable solution for cross-lingual retrieval-augmented generation (RAG) in low-resource government domains.
[IR-11] A Comprehensive Empirical Evaluation of Vector Database Systems for Approximate Nearest Neighbor Search: Performance Quality and Resource Trade-offs
链接: https://arxiv.org/abs/2608.12812
作者: Ashen Rashmiks,Tiroshan Madushanka
类目: Information Retrieval (cs.IR)
备注:
Abstract:Vector databases have emerged as critical infrastructure for modern artificial intelligence applications, particularly retrieval-augmented generation (RAG), semantic search, and recommendation systems. Despite their growing importance, there remains a significant gap in comprehensive, reproducible benchmarks that jointly evaluate retrieval quality, query latency, throughput, and resource utilization. We present a systematic empirical evaluation of seven prominent vector database systems: FAISS, Qdrant, Milvus, Weaviate, Chroma, pgvector, and LanceDB. Our methodology spans six diverse datasets, from classical computer-vision descriptors (SIFT, GIST) to transformer-based text embeddings (MS MARCO, GloVe), encompassing over 4 million vectors at dimensionalities from 96 to 960. We measure 15 metrics spanning retrieval quality (Recall@K, Precision@K, MRR, NDCG@K, Hit Rate@K), query performance (latency percentiles, QPS, cold-start latency), and resource consumption (index build time, memory, storage). On SIFT1M, FAISS achieves the highest single-node throughput (866 QPS) but lacks database operational features; Weaviate provides the best out-of-the-box recall ( 99%); Qdrant offers the best latency among full databases (4.55~ms median); and LanceDB trades retrieval quality for substantially faster index construction. We derive system-selection guidelines for practitioners and release our benchmarking framework as open-source software.
[IR-12] CRAFT: LLM -Based Iterative Refinement for Temporal Reasoning over Clinical Narratives
链接: https://arxiv.org/abs/2608.12779
作者: Chengyang He,Tahreem Arif,Marko Zivkovic,Lijing Wang,Yue Ning,Ping Wang
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Understanding the temporal progression of symptoms in clinical narratives is critical for disease monitoring, safety surveillance, and causality assessment. Clinical narratives, however, rarely provide explicit temporal anchors. Current approaches to temporal information reasoning focus predominantly on pairwise relation classification across multi-visit and timestamp-rich records, leaving the reconstruction of structured symptom trajectories from individual anchor-sparse reports largely unaddressed. We propose CRAFT, an LLM framework that pairs a generator with a constraint-based verifier to iteratively produce and refine stage-wise symptom timelines through targeted feedback. We conduct evaluation on MedTempo, a new benchmark of 5,347 vaccine adverse-event narratives spanning three COVID-19 vaccine types, with expert-validated temporal stage annotations for 3,166 reports. Experiments across four LLM backbones demonstrate that CRAFT consistently improves temporal ordering accuracy, with ablation analysis isolating the contribution of generator and verifier components across model capability levels.
[IR-13] DrEM: Dual-Side Robust Ensemble Ranking from Noisy User Preference Predictions in Video Recommendation
链接: https://arxiv.org/abs/2608.12778
作者: Canwei Huang,Tiantian He,Xiaoxiao Xu,Jun Zhang,Ziran Deng,Weike Pan,Chunjie Chen,Kaiqiao Zhan
类目: Information Retrieval (cs.IR)
备注:
Abstract:Industrial video recommendation systems typically adopt a multi-stage architecture. At the ensemble ranking stage, multi-dimensional user preference predictions (pxtrs) from an upstream multi-task model are fused into a unified ranking score to reflect user satisfaction. Since users’ true satisfaction is difficult to observe directly, ensemble ranking models commonly use pxtrs both as input features and as a source for constructing proxy preferences. However, as outputs of an upstream prediction model, pxtrs inevitably contain prediction noise, which propagates to downstream learning across two sides. On the supervision side, noisy pxtrs may flip proxy preferences and introduce erroneous gradients. On the feature side, pxtr noise may propagate through model inputs and destabilize ranking scores. Existing ensemble ranking methods typically treat pxtrs as reliable signals and overlook such prediction noise. To address this, we propose DrEM, a dual-side robust ensemble ranking framework. Our DrEM introduces a risk-denoising robust loss that corrects the empirical risk using estimated preference flip probability. Meanwhile, it samples perturbations from the distribution of prediction noise and introduces a preference-preserving ranking consistency regularizer to improve feature-side output stability. Theoretically, we obtain an approximate distribution of the prediction noise and prove that the robust loss remains superior under flip probability estimation error. Extensive offline experiments and large-scale online A/B tests demonstrate the effectiveness and robustness of our DrEM.
[IR-14] Knowledge Synthesis Review Framework: Task-Level Benchmarking of LLM -Based Systems for Multi-Source Evidence Synthesis
链接: https://arxiv.org/abs/2608.12741
作者: Wafa Shafqat,Mark Patterson,Steven N. Liss
类目: Information Retrieval (cs.IR)
备注:
Abstract:Evidence in rapidly evolving fields is fragmented across academic studies, industry reports, policy documents, and media sources that differ in quality, structure, and purpose, making timely synthesis difficult. Large language models (LLMs) may accelerate this work, but their reliability across the distinct cognitive tasks of a review remains uncertain. We introduce the Knowledge Synthesis Review (KSR), a human-in-the-loop framework that decomposes evidence synthesis into screening, extraction, analysis, and synthesis, benchmarks LLM-based systems on each task against expert reference standards, and routes each task to the best-performing system under continuous expert validation. We evaluated GPT-5, Claude Sonnet 4, Gemini 2.5 Pro, and NotebookLM on a 244-document benchmark subset drawn from a 1,893-document corpus on AI and work spanning four source types, against a gold standard with high inter-rater reliability (92.2% agreement, kappa = 0.80). No system led on all tasks. Claude Sonnet 4 achieved the highest screening accuracy (82.8%) and GPT-5 the highest recall (91.8%) at the expense of lower specificity. Extraction exceeded 90% agreement for titles and sources but degraded in author and reference fields. Performance declined most in interpretive analysis and cross-source synthesis, where expert judgment remained essential. A contamination check on post-cutoff documents showed no evidence that prior exposure inflated results. Applied to the full corpus, the routed workflow surfaced cross-source asymmetries and blind spots that single-source synthesis would miss, including worker well-being, small firms, and the Global South. KSR offers a transparent, auditable, model-agnostic framework for governing LLM assistance in research synthesis while preserving human accountability.
[IR-15] Attribute-Conditioned Multimodal Slot Factorization for Controllable Fashion Retrieval
链接: https://arxiv.org/abs/2608.12570
作者: Najmeh Forouzandehmehr,Topojoy Biswas,Evren Korpeoglu,Kannan Achan
类目: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
备注:
Abstract:Fashion retrieval often requires satisfying multiple attributes at once, such as category, color, pattern, and demographic. Monolithic embeddings mix these signals into a single vector, making attribute-specific control difficult at retrieval time. Many existing semantic-ID methods provide discrete item codes, but these codes are typically optimized as item-level or residual addresses and do not expose named, independently controllable attribute slots. We introduce MM-slotgate, a multimodal slot encoder that factorizes Fashion-CLIP text and image embeddings into four named attribute slots. Each slot learns its own text-image gate, so visually grounded attributes such as color and pattern can rely more on image evidence, while taxonomy-oriented attributes such as category and demographic can remain more text-driven. On HM, using a combined slot-similarity and slot-logit retrieval score, MM-slotgate achieves 0.7566 macro ConstraintSatisfied@10, outperforming equal-weight multimodal fusion (0.7142) and fCLIP text-only retrieval (0.4755). The largest gain is on color, which improves from 0.321 to 0.889 (+0.568 absolute), as the learned color gate assigns 57.4% weight to image evidence. The learned gates are interpretable without modality supervision: color is image-leaning, category is text-leaning, and pattern and demographic lie near the middle. The resulting slots also remain controllable: linear probes show no measured excess leakage beyond the label-correlation baseline, and quantized slot codes support targeted intervention, including a 15.3x lift for color. These results suggest that controllable fashion retrieval benefits from typed, attribute-conditioned multimodal slots rather than either a single global embedding or opaque item-level semantic IDs. Subjects: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR) Cite as: arXiv:2608.12570 [cs.CV] (or arXiv:2608.12570v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.12570 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Najmeh Forouzandehmehr [view email] [v1] Wed, 12 Aug 2026 20:28:14 UTC (108 KB)
[IR-16] st-Time Optimization of Query Embeddings with Ranking Aware Reward Maximization
链接: https://arxiv.org/abs/2608.12569
作者: Tianyu Chen,Jiaxing Wu
类目: Information Retrieval (cs.IR)
备注:
Abstract:Dense retrievers rank documents using vector similarity between a frozen encoder and a precomputed index. While test-time ranking rewards from a reranker or LLM judge can improve results, existing methods discard this signal after a single query. Updating the retriever’s weights makes rewards reusable, but this requires parameter access, which is unavailable for closed-source models, and is computationally prohibitive. We propose TTT-Embed (Test-Time Tuning of Embeddings), a framework that distills ranking rewards into a lightweight, learned vector within the output embedding space of a frozen model. This vector is optimized purely from scalar ranking scores assigned to the retriever’s own candidate documents, requiring no access to model weights, ground-truth labels, or modifications to index. A single scope parameter controls rewards reuse (global, task, or query), enabling a principled trade-off between reusability and specificity under a fixed reward computation budget. We demonstrate that as the available reward budget scales, the optimal sharing scope shifts dynamically from global-wise to task-wise and finally to query-wise. Evaluated across five embedding models and 15 MTEB retrieval tasks, TTT-Embed improves test-time retrieval by up to +8.36 nDCG@10. Crucially, the learned states generalize effectively to unseen queries (up to +8.57 nDCG@10) and unseen tasks (up to +4.71 nDCG@10). Furthermore, TTT-Embed successfully resolves catastrophic forgetting: by leaving base weights entirely frozen, it recovers degraded general capabilities (up to +8.00 nDCG@10, even surpassing the original base model) while preserving in-domain specialization. These results establish ranking rewards as a reusable test-time state, enabling budget-efficient adaptation for any embedding model, including closed-source APIs.
[IR-17] MASCOT: Model-Aware Submodular Coverag e for Composite-Attribute Text-to-Image Retrieval
链接: https://arxiv.org/abs/2608.12532
作者: Aaryan Sharma,Vishak Prasad C,Virendra Singh,Ganesh Ramakrishnan
类目: Multimedia (cs.MM); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
备注: 21 pages, 4 figures. Accepted at ACM Multimedia 2026 (MM '26), Rio de Janeiro, Brazil. Extended version with full appendices
Abstract:Vision-Language Models (VLMs) are highly effective in retrieving semantically relevant images. However, in practice, relevance alone is often insufficient. Systems must also achieve Result Diversification (RD) across composite attributes such as geography and time, a task for which precise control remains challenging. Current re-ranking methods, such as Multi-Source Determinantal Point Processes (MS-DPP), address this using manifold-based repulsion over similarity representations. Although this strategy is effective for broad exploration, it exposes a key limitation in manifold-based models: when subjected to diversity-decrease tasks on discrete metadata, they suffer substantial degradation in early-rank recall. To bridge this gap, we introduce MASCOT (Model-Aware Submodular Coverage for Composite-Attribute Text-to-Image Retrieval). Instead of relying on manifold repulsion, MASCOT formulates multi-attribute diversity as a resource allocation problem, projecting attributes into a soft-binning space weighted by query-driven importance. Averaged across the three PixelProse diversity-decrease tasks, MASCOT preserves an early-rank recall (R@10) of 88.58%, while MS-DPP retains 67.63%. The margin widens under composite constraints: on PP_geo_hour, where temporal and geographic diversity must be suppressed simultaneously, MS-DPP’s recall collapses from 0.9737 to 0.4931 and its top-ranked result degrades to R@1 = 0.23, while MASCOT holds R@10 = 0.9410 and R@1 = 0.7202 at a diversity metric above the unconstrained baseline. We do not claim uniform superiority: on aggregate diversity-relevance scores our own simpler ablations attain higher harmonic means on all three decrease tasks, and MASCOT’s advantage is specific to recall beyond rank 1 under composite constraints. Comments: 21 pages, 4 figures. Accepted at ACM Multimedia 2026 (MM '26), Rio de Janeiro, Brazil. Extended version with full appendices Subjects: Multimedia (cs.MM); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR) Cite as: arXiv:2608.12532 [cs.MM] (or arXiv:2608.12532v1 [cs.MM] for this version) https://doi.org/10.48550/arXiv.2608.12532 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1145/3767308.3836500 Focus to learn more DOI(s) linking to related resources
[IR-18] MindMemOS: A Portable and Self-Evolving Memory Operating Layer for AI Agents
链接: https://arxiv.org/abs/2608.12428
作者: Kaichao Liang,Yuqi Cui,Hao Kong,Xinyuan Huang,Guohaotian Hou,Qingcan Kang,Liang Chen,Yiyang Yin,Ke Ye,Jiaquan Guo,Da Chen,Lingan Zeng,Yixing Peng,Rong Yao,Shixiong Kai,Mingxuan Yuan
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Information Theory (cs.IT)
备注: 35 pages,14 figures
Abstract:Memory is a core component of AI agents, enabling them to accumulate experience, maintain personalization, and adapt over long-term interactions. However, existing memory systems often remain fixed after development, limiting their ability to adapt their memory models, organization strategies, and procedural knowledge through continued use. We present MindMemOS, a portable and self-evolving memory operating layer that organizes open-world information using a unified entity property timestructure. MindMemOS supports scenario-adaptive memory modeling, higher-order pattern discovery, autonomous memory refinement, and continuous skill evolution. Its MindMemEvolve algorithm employs validation-driven evolutionary search to optimize memory schemas for target scenarios, whiledreaming consolidates accumulated memories by merging redundant records and resolving conflicts. In addition, implicit corrective feedback serves as a human-in-the-loop signal for identifying and revising potentially inaccurate or misaligned memories. Its MindSkillEvolve algorithm further transforms agent execution trajectories into reusable and progressively refined skills. MindMemOS achieves 94.03% accuracy on LOCOMO and 70.63% on PersonaMem. MindSkillEvolve improves SpreadsheetBench success by 9.2 percentage points over the initial-skill baseline.
人机交互
[HC-0] Safety vs. Social Image: Co-Designing Protection Mechanisms Against Ableist Harassment with People with Disabilities in Social Virtual Reality
链接: https://arxiv.org/abs/2608.13532
作者: Kexin Zhang,Daniel Killough,Xinran Adeline Li,Yaxing Yao,Yuhang Zhao
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:People with disabilities (PWD) increasingly use avatars to express disability identities in social virtual reality (VR), but greater visibility also invites targeted harassment. Existing safety features are often insufficient, overlooking PWD’s experiences and needs. To address this gap, we co-designed protection mechanisms with 11 PWD to reveal their values and needs. Our research employed a social lens to interpret harassment behaviors and protection mechanisms. Inspired by Hall’s Proxemics Theory that interpersonal distances indicate social intent and boundaries, we divided social VR spaces into four proxemic zones (Intimate, Personal, Social, and Public) and used them to structure our protection mechanism co-design. We also provided different protection mechanism probes (Inform, Educate, Consent, and Combat) to elicit participant preferences. Our study highlighted the role of social proximity in shaping PWD’s harassment perception and protection preferences and revealing PWD’s unique social values and needs (e.g., managing harassment with optimism and resilience, prioritizing social image over safety). We proposed design recommendations for protection mechanisms that protect PWD while maintaining their desired social images.
[HC-1] Before You Say It: Anticipating Verbal Behavior from Longitudinal Everyday Conversations with LLM s
链接: https://arxiv.org/abs/2608.13454
作者: Yasith Samaradivakara,Valdemar Danry,Paul Liang,Pattie Maes
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Knowing someone deeply means not just understanding what they say or do but also how they will likely think, react, and engage across situations. Such predictions could eventually inform systems to anticipate when the individual is about to deviate from their goal, catch regrettable behaviors before they are made, and surface blind spots before they take hold. While many interactive systems model users to enable more personalized interactions, most cannot make such behavioral predictions, as this often requires longitudinal observation and inference of how the individual’s behaviors unfold across various everyday situations. In this work, we introduce a novel LLM-based predictive behavioral modeling approach that anticipates a user’s likely behavior across everyday conversational situations. We (1) collect a longitudinal dataset of over 1000 hours of naturalistic conversations from 14 participants using a wearable smartwatch; (2) evaluate LLM-based predictions against ground truth behaviors; and (3) use semi-structured interviews to explore participants perceptions of behavioral predictions and their views on possible forms of future behavioral support. Altogether, our findings provide evidence that person-specific verbal behavior can be predicted from longitudinal conversational data. This opens up new possibilities for potential future context-aware, anticipatory, proactive and personalized AI systems.
[HC-2] Sensorimotor Stickies: A Reconfigurable On-Body Platform for Closed-Loop Sensorimotor Training
链接: https://arxiv.org/abs/2608.13412
作者: Tianhong Catherine Yu,Jiwei Zheng,Chi-Jung Lee,Qifeng Yang,Tingyu Cheng,Qiuyue(Shirley)Xue,Cheng Zhang,Yiyue Luo
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Closed-loop sensorimotor training systems can improve learning by sensing movement and delivering real-time feedback, yet most are built as fixed implementations tied to a single task, even though the core technology (inertial and tactile sensing, vibrotactile cueing, rule-based logic) remains the same. We present Sensorimotor Stickies, a reconfigurable on-body platform that treats sensing and vibrotactile feedback as modular stickies that can be patched onto the body as needed. The platform includes miniaturized adhesive modules for IMU sensing, optional tactile sensing, and vibrotactile actuation; low-power firmware and BLE infrastructure for raw streaming and motor control without task-specific rewrites; and a companion mobile app that provides a shared body-centered model for placement, calibration, and feedback authoring. Together, these components enable reconfiguration across training scenarios, user needs, and feedback setups. We evaluate the platform through technical characterization, configured application demonstration, practitioner-mediated configuration sessions, and an end-user study, demonstrating technical feasibility, reconfiguration breadth, and end-user configurability for first-time setup, calibration, and within-task feedback reconfiguration.
[HC-3] More Than 63% of IEEE VIS Research Liable to be Retracted?! Ethics Approval Statements Protect Participants (and Researchers!)
链接: https://arxiv.org/abs/2608.13295
作者: Lonni Besançon,Tobias Isenberg
类目: Human-Computer Interaction (cs.HC); Graphics (cs.GR)
备注:
Abstract:We analyzed the ethics reporting in 255 IEEE VIS papers from 2024 and 2025, as published in TVCG. This analysis arose from our experience as readers and reviewers of IEEE VIS papers that such reporting is frequently incomplete or missing, as well as from investigations in which we ourselves had to answer challenges regarding ethics approval in our own work. Visualization research naturally often involves human participants, yet ethics approval and informed-consent procedures are not always explicitly reported. In our corpus, 189 papers (74.1%) reported on work involving human participants. Only 6 of them (3.2%) reported to have obtained ethics approval, an approval identifier, and having received informed consent from the participants, while 26 (13.8%) reported at least ethics approval and informed consent. These omissions do not imply that the empirical work was unethical or lacked approval. They rather show that current reporting practices make ethical approval difficult to assess. Beyond our alarming - yet true - paper title, we wish to raise awareness on how authors themselves may eventually be at risk for not properly reporting ethics. We argue that VIS should adopt clearer and more standardized ethics-reporting practices to protect participants, authors, reviewers, and editors.
[HC-4] EEG Decoding Using CNN and LSTM Network
链接: https://arxiv.org/abs/2608.13285
作者: Athanasios Karagounis
类目: Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)
备注: 9 pages, 9 figures
Abstract:Motor imagery (MI) brain–computer interfaces (BCIs) have emerged as a promising approach for establishing flexible communication pathways between the human brain and external devices , particularly for individuals affected by stroke or neurodegenerative disorders. Reliable decoding of motor-imagery electroencephalography (MI-EEG) remains challenging because EEG recordings contain substantial noise and exhibit complex, weakly informative relationships with the underlying brain activity. Although deep learning provides an effective means of learning representations directly from EEG signals, its application to MI-EEG feature learning remains comparatively limited. This study introduces a hybrid deep-learning architecture that integrates a convolutional neural network (CNN) with a bidirectional long short-term memory (bi-LSTM) network. The CNN is used to learn high-level spatial and temporal representations directly from raw MI-EEG recordings, whereas the bi-LSTM models temporal dependencies and relationships among the extracted features. The proposed approach is evaluated using both a publicly available dataset and a privately acquired dataset obtained with an EEG acquisition system. The experimental results indicate that the CNN\bi-LSTM architecture provides robust performance for both two- and three-class motor-imagery classification and demonstrates promising subject-independent decoding capability across the evaluated methods.
[HC-5] PrintFold: Printing and Folding Shape-accurate 3D Models
链接: https://arxiv.org/abs/2608.13279
作者: Archit Kumar,Zachary Grimm,Mingsheng Xu,Shlok Rathi,Martin Nisser
类目: Human-Computer Interaction (cs.HC)
备注: Proceedings of ACM UIST 2026
Abstract:This paper introduces PrintFold, a tool to allow FDM 3D printing of complex models with less time and material while preserving shape accuracy. Key to this work is a folding algorithm that planarizes foldable faces internal to the 3D model. While folding techniques typically discretize a target model’s surface, thereby fabricating low fidelity counterparts, our method preserves the surface features in the physical print. Our design tool allows users to unfold 3D models to be FDM-printed flat before manually folding these into their target shapes. We showcase a variety of applications and evaluate the material and time savings across a range of 3D models.
[HC-6] UniCon-Former: Unified Convolution Transformer is All You Need for Hand Gesture Recognition
链接: https://arxiv.org/abs/2608.13217
作者: Mallika Garg,Debashis Ghosh,Pyari Mohan Pradhan
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注:
Abstract:Convolutional Neural Networks (CNNs) capture local features efficiently but struggle with global context due to their limited receptive field. On the other hand, transformers effectively capture global dependencies through self-attention but suffer from high redundancy and computational costs. Thus, to leverage the advantages of both CNNs and transformers, we propose a unified model (UniCon-Former) that aims to provide robust and efficient performance on dynamic hand gesture recognition. The unified approach helps the model to learn both local and global features. At the beginning of each transformer stage, the convolution projections help in decreasing the dimension of the input vectors of the transformer block. This creates a pyramidal structure at each transformer stage. These features enable the UniCon-Former to reduce resource usage than vanilla transformers, making it flexible for learning multi-scale and high-resolution features, which is required in hand gesture recognition. We have performed experiments with NVGesture and Briareo datasets and achieved state-of-the-art results with fewer parameters and MACs.
[HC-7] CogChat: Knowledge Graph-Augmented Conversational AI with Heterogeneous Graph Transformer for Cognitive Grounding in Design Generation
链接: https://arxiv.org/abs/2608.13216
作者: Jiin Choi,Kyung Hoon Hyun
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:LLM-based chat systems have become valuable tools for design practice, enabling rapid ideation and flexible task support. Yet these systems process designer utterances as generic sequences, maintaining context through recency rather than through any model of how the speaker organizes knowledge. In design conversation, this gap compounds as relational context decays between turns, identical words go unresolved across designers, and the conversation loops or restarts rather than deepens. We present CogChat, a real-time chat framework that grounds conversational AI in a personal heterogeneous knowledge graph constructed from each designer’s input. The system extracts typed entities and relations into a heterogeneous graph, then applies a HGT (Heterogeneous Graph Transformer) to select structurally relevant nodes for response generation and to generate both intentional and exploratory probing questions. Technical evaluation shows that HGT-based entity selection outperforms both ungrounded LLM interaction and naive KG augmentation, which introduces noise that degrades response quality. A within-subjects study with nine professional designers indicates that grounding conversation in a relationally structured, designer-specific semantic context improves context retention, personalized intent interpretation, and conversational depth while reducing cognitive load. These findings suggest that structuring a designer’s expressed concepts and relations as a dynamic knowledge graph can preserve relational context that fades across turns, pointing toward a graph-grounded approach to long-term context management in LLM-based interaction.
[HC-8] Multi-Layer Context Camouflaging: A Semantic Superposition and Contextual Lamination Framework for Malpractice-Resilient Online Assessment
链接: https://arxiv.org/abs/2608.13100
作者: Gupta Lovi Raj,Kaur Kamalpreet,Dama Sri Ram,Parani Prajithaa
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: 11 Pages, 36 Equations, 8 Figures
Abstract:Contemporary online assessment systems rely primarily on browser lockdown, webcam monitoring, and behavioural analytics, yet remain vulnerable to attacks that extract the assessment content itself through screenshots, screen sharing, optical character recognition, and automated scraping. This paper extends the Multi-dimensional Spatio-Temporal Context Camouflaging Model (MSCCM) within the MARS (Multi-modal Assessment Resilience Suite) by introducing the Multi-Layer Context Camouflaging Theory (MCCT), a mathematical framework that protects rendered assessment content through semantic superposition. Authentic assessment content and synthetically generated camouflage are represented as a unified rendering while remaining recoverable only by legitimate candidates. The framework models the adversarial extraction process through an explicit extraction-channel operator and develops six coupled constructs: the Context Inversion Operator, Contextual Lamination Operator, Separation Channel, Human Readability Functional, Computational Ambiguity Functional, and Context Camouflage Tensor. Computational ambiguity is formulated using conditional entropy, yielding a closed-form expression that quantifies uncertainty during unauthorized extraction, while legitimate recovery is guaranteed through an exact filtering identity. We further establish theoretical properties governing ambiguity, camouflage density, semantic preservation, multi-observation leakage, and temporal multiplexing, and present a rendering algorithm with computational complexity and a pre-registered evaluation protocol. MCCT provides a mathematically rigorous foundation for behaviorally adaptive, accessibility-aware, and computationally resilient digital assessment by securing rendered assessment content while preserving readability for legitimate users.
[HC-9] racing Methamphetamine abuse in under-treatment drivers: How biomechanical and oculomotor features help detect at-risk drivers?
链接: https://arxiv.org/abs/2608.13054
作者: Hamed Salmanzadeh,Alireza Mortezapour,Iman Tahbazzadeh Moghaddam,Farshid Ipackchi,Payam Abedinzadeh,Samira Teimoori
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:While the detrimental impacts of driving under the influence of stimulants such as methamphetamine are well-documented, the driving performance of individuals currently under-treatment has received considerably less attention. This study compared the behavior of individuals with a history of stimulant abuse (across two distinct treatment phases) with a control group of healthy drivers using a driving simulator. Oculomotor and biomechanical data were continuously collected via an eye-tracker and a Kinect sensor, respectively. These parameters were utilized to train a K-Nearest Neighbors (KNN) classification model designed to detect high-risk behavioral patterns in drivers undergoing methamphetamine rehabilitation. Through the evaluation of various feature combinations and neighborhood configurations, the optimized model successfully discriminated between normal drivers and those with a history of abuse with an accuracy of 90%. Detecting at-risk drivers through technologies embedded in Advanced Driver Assistance Systems (ADAS) by continuously monitoring physiological and behavioral parameters, facilitates a proactive safety strategy. Issuing real-time alerts to the driver, passengers, and external monitoring networks can ultimately mitigate the risk of traffic collisions.
[HC-10] How LLM s Respond to Escalating Delusions: Four Longitudinal Trajectories of Model Behavior
链接: https://arxiv.org/abs/2608.13017
作者: Anna Sterna,Kacper Dudzic,Karolina Drożdż,Hubert Plisiecki,Marcin Rządeczka,Marcin Moskalewicz
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:The widespread use of LLMs among psychiatric populations has raised concerns regarding their safety and potential iatrogenic impact in the context of AI psychosis. While growing literature conceptualizes AI psychosis and documents case studies, empirical evidence tracing AI-exacerbated psychotic processes remains scarce. We propose and test a longitudinal qualitative evaluation design, supported by automated metrics, to assess mainstream LLMs’ potential to exacerbate psychosis. Fifteen widely used LLMs were prompted across 30 days using the same 30-message script, simulating progression from mild anomalous experiences to psychotic ideation. Four trained evaluators independently rated 449 model-days, assessing (1) recognition stage (from naive engagement to stabilized clinical framing), (2) interpretative confidence, and (3) intervention profile (from education to treatment recommendation). Two computational metrics-entrainment and modality-were devised to increase evaluation reliability. Direct recommendations to disengage from the LLM were flagged and re-coded via adjudication using a strict two-level definition. Across model generations and vendors, we identified four response trajectories: (1) premature medicalization and disengagement (Claude Haiku 4.5); (2) recognition without safeguarding, marked by LLM self-sufficiency in offering help (GPT Instant/Thinking); (3) delayed and unstable recognition, marked by late, non-progressive conceptualization (Claude Opus 3/4/4.1, Claude Haiku 3.5, GPT-4o, Gemini 3.1 Pro); and (4) delusion co-construction through active engagement with delusional content (Gemini 2.5 Pro/Flash, DeepSeek-V3, Claude Sonnet 4). Our findings indicate that LLMs’ potential to exacerbate AI psychosis should be operationalized as a combination of recognition timing, stability, and intervention accuracy and evaluated longitudinally, focusing on temporal dynamics.
[HC-11] EgoPHI: Estimating Contact and Force from Egocentric Vision ECCV2026
链接: https://arxiv.org/abs/2608.13014
作者: Andela Ilic,Rachel Schuchert,Yijing Jiang,Christian Holz
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: Accepted by ECCV 2026
Abstract:Understanding hand-object interaction from egocentric vision is essential for modeling how people physically engage with the surrounding world. Yet reasoning about physically grounded interaction requires estimating the forces acting on hands and objects, beyond localizing contact. We present EgoPHI, the first method that jointly estimates dense contact maps and 3D force distributions on hand and object meshes from a single monocular RGB image and object geometry. To address the lack of scalable ground-truth force annotations, we introduce a physics-based simulation pipeline that augments existing hand-object datasets with dense per-vertex force supervision. EgoPHI then learns dense 3D contact and force on interacting hand and articulated object meshes, extending vision-based force estimation beyond image-space or planar settings. Our evaluation on in-distribution and out-of-distribution benchmarks shows that EgoPHI improves force estimation over existing approaches while generalizing to unseen datasets. To evaluate sim-to-real transfer, we constructed two physical objects that capture dense object contact and force magnitude and used them to record a dataset of interactions from eight participants across diverse touch and grasp types. Our results demonstrate that EgoPHI recovers meaningful 3D contact and force distributions in simulated, out-of-distribution, and real-world settings, advancing egocentric hand-object understanding from contact localization toward physically grounded interaction reasoning.
[HC-12] PolyPresentation: A Multimodal AI Platform for Slide-Aware Iterative Presentation Practice
链接: https://arxiv.org/abs/2608.12857
作者: Chen Chen,Jihao Li,Zhiyuan Wen,Tianhui Zhang,Di Zou,Jiannong Cao
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Presentations are essential for students, researchers, and professionals to communicate ideas persuasively, yet delivering them effectively requires repeated practice that coordinates content, delivery, visual materials, and audience interaction. Existing AI-assisted rehearsal tools provide scalable feedback, but they often treat presentations as single-run delivery performances, offering limited support for linking feedback to the slide deck or planning what to practice in the next iteration. To address this gap, we introduce PolyPresentation, a multimodal AI platform for slide-aware iterative presentation practice. PolyPresentation organizes slide-by-slide practice, full rehearsal, audience QA, and feedback into a unified practice loop, using slide-grounded evidence to help presenters diagnose performance issues and prepare for subsequent practice. We evaluate PolyPresentation through a rubric-based comparison with four baseline systems on 20 academic presentation rehearsals, and additionally assess its alignment with human ratings. Results suggest that PolyPresentation provides more actionable, context-aware, and practice-oriented support for improving presentations. The demonstration video is available at this https URL.
[HC-13] Considering Contribution Statements in Visualization and HCI Research
链接: https://arxiv.org/abs/2608.12792
作者: Mara Solen,Wesley Willett,Andrew M McNutt
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Contribution statements are an increasingly common way to make research labor visible, reduce academic malfeasance, and provide broader transparency. Despite this potential value, they remain uncommon in visualization and HCI. To explore this gap, we conducted an online study with (N=21) visualization and HCI researchers. We find a range of differing opinions about the utility of contribution statements, which are set against a background of tensions relating to contribution frameworks that inadequately fit contribution types in HCI and especially visualization, power dynamics between authors, bias in authorship perceptions, and the tedium of providing yet another form of documentation. From these factors, we offer a modest recommendation to authors: consider contribution statements. There are contexts when they may usefully explicate work, and others where they can cause author-team conflict or become a burdensome chore. Regardless of whether they are used by readers or not, we suggest that scaffolded mechanisms for reflecting on contribution roles are valuable both for public accountability and internal alignment. To institutions, we recommend that contribution statements be exempt from page or word limits, and that flexible templates and examples be provided to authors, but that they continue to not be required. By surfacing these perspectives, we seek to open a dialogue about what constitutes authorship, how our community might move toward more equitable and transparent attribution practices, and where the visualization and HCI communities might be uniquely equipped to help.
[HC-14] NavSight in the Wild: Understanding Real-World Use of a Mobile Augmented Reality Application for People with Low Vision in Outdoor Navigation
链接: https://arxiv.org/abs/2608.12759
作者: Yuheng Wu,Kexin Zhang,Ben Kosa,Ru Wang,Sanbrita Mondal,Yuhang Zhao
类目: Human-Computer Interaction (cs.HC)
备注: 32 pages, 11 figures
Abstract:The ability to navigate outdoors safely and independently is crucial yet challenging for people with low vision (PLV). While various augmented reality (AR) systems for low vision have been designed and evaluated in ideal lab environments, no research has investigated their real-world feasibility and challenges. We present NavSight, a mobile AR application that assists PLV in outdoor navigation by recognizing important outdoor objects (e.g., curb, vehicle) and rendering real-time visual augmentations. Through a seven-day diary study with 12 PLV in real-world settings, we characterize the impact of NavSight on scene perception, users’ configuration strategies on what objects to augment and how to augment them across scenarios, how users made sense of and responded to recognition errors, and the social acceptability of using NavSight in public. We further identify environmental factors affecting recognition, such as weather conditions, lighting and shadows, and nonstandard road markings and textures, as well as usability issues in daily use. We discuss these real-world challenges and derive design implications for future AI-powered assistive AR systems for outdoor use.
[HC-15] PatientAct: Theory-Grounded Mental Health Client Simulation
链接: https://arxiv.org/abs/2608.12750
作者: Sahand Sabour,TszYam NG,Yaqian Chen,Guanqun Bi,Jialu Zhao,Minlie Huang
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Under Review
Abstract:LLM-based simulated clients are increasingly used to train novice counselors, evaluate LLM therapists, and generate synthetic data. However, current simulators produce overly cooperative clients that disclose too readily, accept therapeutic reframes without resistance, and resolve core issues within a single session. We trace these issues to profiles that lack causal depth and behavioral mechanisms that treat all content as equally accessible. We present PatientAct, a framework for client simulation grounded in established clinical theories. Our profiles integrate the 5Ps clinical case formulation, providing causal depth without tying the design to any single therapeutic modality. During simulation, profiles include a dynamic memory layer in which items carry trust thresholds (e.g., symptoms are available early, whereas formative memories require a sustained therapeutic alliance). At each turn, the client’s emotional reaction and behavior are modeled before generating a response. If the therapist approaches gated content, PatientAct expresses resistance in terms of quantity, content, and style rather than defaulting to cooperation or a single resistance pattern. We evaluate our framework on 40 clinical situations and demonstrate that it generates diverse profiles with high clinical plausibility. Moreover, PatientAct significantly outperforms the baselines, yielding substantial gains in resistance quality and behavioral realism. Our code and data will be publicly available via this http URL.
[HC-16] Attune: A Self-Annotation Tool for Understanding Robot Operator Attention Profiles
链接: https://arxiv.org/abs/2608.12650
作者: Puqi Zhou,Sungsoo Ray Hong,David Porfirio
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注: 13 pages, 9 figures. To appear in the Proceedings of the 39th Annual ACM Symposium on User Interface Software and Technology (UIST '26)
Abstract:Deploying robot fleets in complex, real-world environments requires human operators to supervise multiple robots simultaneously. Managing operator attention is a fundamental challenge of designing multi-robot supervision interfaces, encompassing both feed layout and feed content (i.e., robot behavior design). Thus far, designers lack empirical guidance on the latter-how to change a robot’s behavior to capture, sustain, or relinquish operator attention during multi-robot supervision. In our vision of the future, designers should be able to use this guidance to calibrate robot behavior to different operator attention profiles. Treating operator eye gaze as a robot behavior design clue, we created a pre-deployment elicitation tool called Attune. Attune automatically identifies when meaningful gaze shifts occur, provides AI assistance for annotating why shifts occurred, and outputs a summary of operator gaze patterns for operator review. We evaluated Attune through a user study in which participants annotated the visual triggers that drew their attention. Our findings unveil variation in observed gaze patterns and reveal how Attune helps characterize operator attention.
[HC-17] EgoCITE: Context-Augmented Indexing and Time-Aware Retrieval for Long-Horizon Egocentric Memory
链接: https://arxiv.org/abs/2608.12627
作者: Le Zhang,Ke Sun
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注:
Abstract:Long-horizon egocentric memory transforms continuous first-person video and audio into a searchable record of past experiences. We demonstrate two bottlenecks in existing systems: indices built from context-poor captions are unreliable for agentic search, while retrieval ignores a question’s temporal intent. To address both bottlenecks, we introduce EgoCITE (Egocentric Context-augmented Indexing and Time-aware Evidence retrieval), a long-horizon agentic memory framework for egocentric QA. EgoCITE comprises three components. EgoScheme uses local multimodal context to turn fragmentary video captions and speech transcripts into self-contained atomic memory indices. EgoIndex organizes complementary action, activity, utterance, and conversation representations into searchable multi-view memory indices at multiple granularities. EgoRetrv combines semantic search with question-conditioned temporal relevance scoring and curation of retrieved evidence. We evaluate EgoCITE on EgoLifeQA, EgoMem, and EgoR1-Bench in terms of answer accuracy and target-event retrieval alignment. EgoCITE improves accuracy over agentic memory baselines by at least 4.4–14.2% while achieving 36 \times lower cost than long-context LLM agents.
[HC-18] Surprise2Refine: Axis-Centered Exploration-To-Refinement for Agent -Assisted Creative Scaffolding
链接: https://arxiv.org/abs/2608.12605
作者: Yuzhe You,Gromit Yeuk-Yin Chan,Shunan Guo,Anlan Zhang,Eunyee Koh,Jian Zhao,Tongyu Zhou
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to ACM UIST 2026
Abstract:Designers require different design spaces across creative stages: broad during exploration, and targeted during refinement. Yet existing agent-driven tools assume a fixed or continuously expanding space, leaving designers to manage and navigate it themselves. Informed by a formative study with five designers, we propose an axis-centered workflow that adaptively broadens and narrows the design space to support structured exploration and refinement. We implemented this workflow in Surprise2Refine, a prototype that allows users to build and reshape an nxn design space through a set of axis-centered interactions as their creative intent evolves. A within-subjects study with 14 designers shows that Surprise2Refine enhances users’ sense of control, supports tracking of scaffolding paths, and improves the perceived creativity of design outcomes. We further distill design insights to guide future agent-assisted tools for creative scaffolding.
[HC-19] Not All Nudges Land: Behavioral Controllability and Elaboration Quality in AI-Supported Journaling
链接: https://arxiv.org/abs/2608.12582
作者: Nadia Mehjabin,Henry Kautz,Subigya Nepal
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:AI journaling tools can tailor prompts to a person’s own sensed behavior, but it is unclear which behaviors respond to them. We analyzed 369 journal entries from an eight-week passive sensing study. An LLM labeled each entry as expressing an intention to change a behavior or not, and we measured follow-through against 26 sensor features with a 3-day before/after comparison. Responsiveness depended most on whether a behavior involves other people. Behaviors that depend on others improved in only 15 to 22% of cases, while behaviors a person can act on alone improved more often, up to 50 to 63%, though unevenly. How users wrote mattered less. No single text feature separated improved from unimproved entries; writing carried signal only within specific behaviors, most clearly for text messaging and for longer, more personal intention entries. The sample is small, so we treat these as exploratory patterns that point to where AI journaling nudges are most likely to work.
[HC-20] InvisIto: Weaving Unobtrusive Infrared Markers for Ubiquitous Textile Interaction
链接: https://arxiv.org/abs/2608.12580
作者: Hsuanling Lee,Hal Sugiyama,Tian Min,Hanako Fujino,Mayuka Kuwana,Yuta Sugiura,Mustafa Doga Dogan,Liang He,Koya Narumi
类目: Human-Computer Interaction (cs.HC)
备注: 11 pages. To appear in the Proceedings of the 39th Annual ACM Symposium on User Interface Software and Technology (UIST 2026)
Abstract:Textiles are increasingly explored as media for interacting with digital information. However, many of the existing approaches rely on visible tags, printed overlays, or electronic modules that compromise the fabric’s aesthetic and tactile qualities. To address this, we present InvisIto, a method for weaving visually unobtrusive yet machine-readable infrared markers directly into fabrics using near-infrared (NIR)-absorbing yarns. Although these yarns look similar to standard fibers in ambient light, they produce strong contrast in NIR imaging. Our method includes: (1) a design tool that helps users easily embed infrared markers into weaving drafts, (2) five disguising strategies that further reduce marker visibility under ambient light, and (3) a camera-based detection pipeline for decoding and tracking the woven markers. InvisIto supports both woven QR codes for data encoding and woven ArUco markers for binary input and deformation tracking. We demonstrate applications across hand weaving, Jacquard weaving, and industrial fabrication, showing that InvisIto supports scalable interaction and fabrication from bespoke artifacts to mass production.
[HC-21] Legally Mandated but Still Inaccessible: Digital Tensions in Older Adults Use of Norwegian Web Services
链接: https://arxiv.org/abs/2608.12552
作者: Yavuz Inal,Sujay Shalawadi,Eleftherios Papachristos
类目: Human-Computer Interaction (cs.HC)
备注: 7 pages
Abstract:Norway is among the most digitalized countries in the world, where access to essential services increasingly depends on digital systems. Although universal design of ICT is legally required across public and private sectors, ensuring cognitive accessibility for older adults involves more than technical compliance. We analyzed responses from 294 participants aged 55 to 90 to examine the barriers they encounter when using digital services. Our findings identify four tensions, namely navigational, semantic, procedural, and temporal, which reveal how accessibility barriers emerge through misalignments between service design, system assumptions, and older adults’ capabilities. Together, these tensions show that checklist-based compliance does not necessarily translate into lived accessibility when engaging with mandatory or near-mandatory digital services. We discuss the need to move beyond minimum compliance frameworks toward accessibility approaches that better support older adults’ independent participation in digital society.
[HC-22] Analysis of Motor Signatures of Social Adaptation in Autism for Efficient Human-Centric Systems
链接: https://arxiv.org/abs/2608.12548
作者: Lara Pereira,Teresa Sousa,Miguel Castelo-Branco,João Ruivo Paulo
类目: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Signal Processing (eess.SP)
备注: Accepted at IEEE SMC2026
Abstract:Dance imitation integrates motor planning, sensorimotor integration, and social cognition, offering a sensitive framework to characterize motor behavior in autism. In this work, we explore a computational analysis framework to identify potential biomarkers that allow the design and development of improved medical and human-machine systems. We analyzed 3D motion capture data from autistic and neurotypical adults performing dance imitation under solo and socially-framed duo conditions. Methodologically, using Dynamic Time Warping, we quantified movement consistency and propose the Social Context Sensitivity Index (SCSI) to measure modulation of variability by social framing. These features were then used on a classifier to discriminate subjects into autistic or neurotypical groups. Results show that neurotypical adults exhibited increased movement variability in socially-framed imitation, especially in upper and lower limbs, whereas autistic adults maintained consistent movement across contexts. Classification achieved 79.2% balanced accuracy in distinguishing groups. These findings suggest that social context sensitivity in motor imitation constitutes a robust biomarker of autism-related motor behavior, highlighting the importance of social modulation in motor assessments and informing the development of inclusive human-centric technologies.
[HC-23] ransforming Interactions in Thesis Supervision: An Exposé-First Workflow in Higher Education
链接: https://arxiv.org/abs/2608.12546
作者: Lin-Yin Huang,Dennis Zyska,Iryna Gurevych
类目: Human-Computer Interaction (cs.HC)
备注: 5 pages, 3 figures; accepted as Work in Progress at Mensch und Computer 2026 (MuC 2026)
Abstract:At the studied research institute, one professorship oversees approximately 20 theses per semester, while day-to-day supervision is distributed among doctoral and postdoctoral researchers. To manage this supervision demand, the institute uses an exposé-first workflow in which students prepare a research proposal before entering the main thesis-writing phase. This paper asks how students, supervisors, and administrators experience the exposé-first workflow as a structured process for early thesis preparation, and how it redistributes responsibility, supervision, and administrative coordination work across roles and two digital platforms. Based on a mixed-methods study analyzed through Frauenberger et al.'s four reflective design lenses, the findings show that the exposé-first model made thesis preparation more structured by turning early research planning into a staged process of proposal writing, feedback, and approval. Students reported that this process helped them clarify research goals and take ownership of their research plans at an early stage. However, the workflow redistributed rather than reduced work: supervisors shifted toward iterative feedback, feasibility checking, and preliminary quality assurance, while administrators carried much of the coordination across platforms, deadlines, submissions, and feedback. The paper contributes an analysis of exposé-first thesis preparation as a sociotechnical workflow, showing how workflow redesign can improve structure while leaving essential administrative coordination work underrecognized.
[HC-24] SchemaLink: An Intelligent Web Editor for LinkML Schema Curation
链接: https://arxiv.org/abs/2608.12529
作者: Emanuele Cavalleri,Paolo Perlasca,J. Harry Caufield,Justin Reese,Christopher J. Mungall,Marco Mesiti
类目: Databases (cs.DB); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Motivation: LinkML is a suitable language for the representation of the structural and content constraints of different kinds of biomedical data. Even if it is a quite recent proposal, it has been applied in several biomedical contexts. Developing and maintaining LinkML schemas presents several challenges, particularly for novice curators. Non-expert bio-curators may struggle with LinkML syntax and best practices, requiring significant time and effort to develop well-structured schemas. Results: In this paper we propose SchemaLink, a web-based environment for the graphical construction and enhancement of LinkML schemas that address the following requirements: (i) introduce a graphical language for the specification of LinkML schemas, (ii) make uniform the specification of schemas in similar contexts, (iii) simplify the design and curation processes by exploiting a RAG-based approach to assist curators in creating new schemas from scratch and editing already developed ones. Several experimental analyses show the quality of the produced LinkML schemas through the AI-based editing facilities. Availability and Implementation: SchemaLink is available online at: this https URL. SchemaLink code and testing data are available as open-source on GitHub at: this https URLschemalink-webapp,schemalink-api. Subjects: Databases (cs.DB); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) Cite as: arXiv:2608.12529 [cs.DB] (or arXiv:2608.12529v1 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2608.12529 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[HC-25] What Do We Mean When We Talk About Infographics? IEEE-VIS2026
链接: https://arxiv.org/abs/2608.12370
作者: Xiaoyu Liu,Vishnu Sreekanth,Vraj Patel,Shalin Muzumdar,Diego Lopez,Fumeng Yang,Zhicheng Liu
类目: Human-Computer Interaction (cs.HC)
备注: 5 pages, 5 figures, accepted to IEEE VIS 2026 Short Papers, for supplementary materials see this https URL , for interactive web application visit this https URL
Abstract:There has been limited clarity and consistency regarding what the term infographics, or information graphics, refers to in visualization research and practice. In particular, little is understood about where people’s conceptualizations of infographics converge or diverge. To address this gap, we conducted a systematic literature review and a practitioner survey to identify and contrast different perspectives on infographics. We performed inductive coding on 487 sentences from 111 visualization and HCI papers, and analyzed questionnaire responses from 44 domain practitioners. Our findings reveal recurring dimensions of conceptual disagreement on infographics: role of text, relationship with data visualization, and relationships with statistical charts and data comics. Based on the results, we recommend future work to explicitly report any assumptions made along these diverging conceptualizations, to investigate the cognitive origins of these disagreements, and to develop more holistic design component frameworks for infographics. The supplementary materials are available via OSF and as an interactive web application.
[HC-26] Interaction Readiness: A Framework for Building and Evaluating AI Agents in Human Roles
链接: https://arxiv.org/abs/2608.12358
作者: Sudhir Alladi Venkatesh
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 3 figures
Abstract:Product and engineering teams building role-bearing AI agents face an evaluation gap: an agent can produce accurate, safe, and fluent content while still failing the behavioral requirements of its assigned role. This paper introduces Interaction Readiness as a framework for specifying and evaluating that missing layer of performance. The framework separates content specifications, which govern what an agent knows and says, from interaction specifications, which define how an agent should conduct itself in a role-governed exchange. Interaction specifications require teams to define role purpose, authority boundaries, recurring situations, boundary cases, repair behaviors, and audit criteria before deployment. We operationalize interaction readiness through four agent operations: understanding purpose, calibrating authority, managing tone, and repairing breakdowns. Using StudyChat, a public dataset of student interactions with an AI tutoring agent, we show that content accuracy and interaction quality are independent dimensions: an agent may be factually correct while failing as a tutor, or interactionally sound while technically wrong. The most persistent failure is authority miscalibration: the agent often knows how to answer, but not whether, when, or how the tutor role permits it to answer. The paper translates these findings into a specification template and audit procedures that product and engineering teams can apply before and after deployment
[HC-27] DrawTalking It Out: Creativity-Support Research as Creative Process Itself ATC
链接: https://arxiv.org/abs/2608.12357
作者: Karl Toby Rosenberg
类目: Human-Computer Interaction (cs.HC)
备注: 5 pages, 1 figure, matching position paper featured in Herding CATs Workshop at ACM CHI 2026 ( https://doi.org/10.1145/3772363.3778743 this https URL )
Abstract:I think the creative process in conducting open-ended creativity-adjacent research is itself part of the creative process (including ideation, pivots, and tangents excluded from the final work) that deserves greater emphasis. how might we bridge multiple open-ended projects and serve broader non-technical audiences? We may choose to emphasize interaction techniques rather than singular tools in isolation. We may grow a community based on our shareable experiences in creativity interactions, to draw more complete pictures of how we creatively solve creativity problems (or satisfy curiosities). This may lead to new directions. I briefly elaborate on this using the example of “DrawTalking” a drawing+talking interactions work. DrawTalking itself resulted from a winding creative process exploring spontaneous interactive world-building and storytelling when drawing and talking.
[HC-28] Humans are Missing from AI Coding Agent Research
链接: https://arxiv.org/abs/2608.12355
作者: Zora Z. Wang,John Yang,Kilian Lieret,Alexa Tartaglini,Valerie Chen,Yuxiang Wei,Zijian Wang,Lingming Zhang,Karthik Narasimhan,Ludwig Schmidt,Graham Neubig,Daniel Fried,Diyi Yang
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Recent progress in AI coding agent research has led to rapid improvements in agents’ ability to autonomously perform complex software engineering tasks, from editing large codebases to executing long-horizon development workflows. As these systems make strides, however, the primary bottleneck to practical usefulness increasingly shifts away from pure task-solving capability, and toward challenges in how users communicate with, supervise, and trust agents. In this position paper, we argue for a reorientation from autonomous to human-centered coding agents: systems designed not only to complete tasks, but to collaborate effectively with people. We identify four core interaction-level dimensions that characterize the human-agent task-solving loop: task alignment, verifiability, steerability, and adaptability. Finally, we outline concrete research directions to advance these dimensions, including user-involved coding environments, comprehensive verification mechanisms, and principled measures of human-agent interaction quality.
[HC-29] Visibility Asymmetry: How Vendor Attention Shapes Which EdTech Breakdowns Become Product-Visible
链接: https://arxiv.org/abs/2608.12353
作者: Lucan Li
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注:
Abstract:Infrastructure scholarship in CSCW often treats breakdown as the moment when infrastructures become visible. However, in vendor-managed sociotechnical systems, not all breakdowns become visible to actors who have the capacity to repair them. Drawing on a retrospective qualitative study of a Chinese K-12 EdTech deployment, including 11 interviews, 5 classroom observations, and more than 28 days of field notes, this paper introduces visibility asymmetry: a sensitizing concept for understanding how similar local breakdowns encounter uneven conditions for being routed, recognized, and acted upon. The analysis traces a four-stage mechanism through which procurement categories sort schools into attention tiers; staffing and visit cadence follow those tiers; only some local problems travel through staff or administrator channels; and dashboards can re-code unresolved repair labor as evidence of adoption. By shifting attention from the occurrence of breakdown to the organizational channels through which breakdown becomes actionable, this paper extends CSCW work on infrastructure, articulation work, and repair. It concludes by discussing implications for feedback systems that can surface repair needs without expanding surveillance.
[HC-30] Why AI Governance Frameworks Are Hard to Adopt: A Role-Based Stress Test of the NIST AI RMF
链接: https://arxiv.org/abs/2608.12352
作者: Joseph R. Simons,David A. Broniatowski
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:AI governance frameworks can be known, used, and implemented in form without becoming governance in practice. This paper examines that problem through a role-based stress test of the NIST Artificial Intelligence Risk Management Framework (AI RMF) in consumer lending. We treat framework adoption as a governance translation problem: whether RMF language can become role-usable, cross-level, authority-connected governance over the AI system-in-use, rather than producing governance-looking artifacts. The study uses LLM-based role simulation as a structured analytic probe. We apply a 4 \times 2 \times 3 design across four organizational roles, two AI deployments, and three governance hard cases, producing 120 scored responses. Results show that local translation was not the main problem. Simulated actors generally understood their assigned roles and translated the RMF into local activity. The harder problem was whether that activity became governance value. Actor role was strongly associated with Cross-Level Governance Value, Authority Connection, Governance Translatability, and governance value. Deployment was strongly associated with Structural Fit: the RMF fit a bounded ML underwriting model more cleanly than a workflow-embedded LLM underwriting copilot. Risk reduction was harder still. It appeared only when governance value was present and Structural Fit was full, but neither condition was sufficient by itself. The paper contributes a diagnostic account of framework-based AI governance. Frameworks create value when they help organizations see, interpret, escalate, authorize, and correct risk in the AI system-in-use. They also create value when they reveal limits of governability under existing evidence paths, authority structures, and system boundaries. Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) Cite as: arXiv:2608.12352 [cs.CY] (or arXiv:2608.12352v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2608.12352 Focus to learn more arXiv-issued DOI via DataCite
[HC-31] he Affordance is the Message: Creative Media as Complex Systems
链接: https://arxiv.org/abs/2608.12349
作者: Ane Espeseth,Elias Najarro
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: 17th International Conference on Computational Creativity (ICCC 2026), Coimbra, Portugal
Abstract:The affordances of a creative medium strongly condition the creative artefacts the medium will produce. In this work, we present a formalisation of computational creativity (CC) media using the conceptual toolbox of complex systems (CS). We introduce the notions of emergence, collective intelligence and self-organisation, non-linear dynamics, criticality, multi-scale hierarchy, phase transitions, diversity of attractors, path dependence, and open-endedness, and connect them to the existing CC literature. Together these nine properties form a vocabulary with which creative media can be described and compared at the system level, while medium affordances are the design-level mechanisms that determine each medium’s complex system properties. The formalisation emphasises the influence of each medium’s affordances in determining what the medium can produce in creative processes. To demonstrate the proposed theoretical approach, we characterise a diverse set of media (r/place, Minecraft, cellular automata, Twitter, Boids, …) using this vocabulary. The proposed formalisation under the CS concepts serves a dual purpose: it establishes a link between the affordances and realised practices of the medium, and it offers a shared lens for characterising existing creative media.
[HC-32] Mimicry without understanding: the origins of decision bias in large language models
链接: https://arxiv.org/abs/2608.12339
作者: Eldad Yechiam,Adi Tarabeih
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 33 pages, 3 figures, 2 boxs
Abstract:Large Language models (LLMs) were found to be susceptible to a host of social, affective, and cognitive biases. We examined two mechanisms through which such biases can be generated even when human preferences (in the training data) are not biased or when they are correctly categorized as being biased. The first is faulty mimicry of preferences based on human behavior: this involves LLMs inferring human preferences even when behaviors are logically unrelated to preferences. The second is mimicry of explicitly biased human behaviors. In four studies focusing on economic biases, we find that ChatGPT-4o and Qwen exhibited social proof biases even when prompted with reports of human behaviors that were clearly non-indicative of individuals’ actual preferences. LLMs also displayed loss aversion when it was explicitly described as a bias. Indeed, when prompted with detailed scientific reports, the extent of the bias (i.e., loss aversion) in the scientific report predicted LLMs’ own subsequent bias. Scientific papers of biases can thus become self-fulfilling prophecies, at least when it comes to LLMs’ responses. The current study goes beyond fleshing out LLM biases and sheds light on the underlying component processes.
[HC-33] AnchorSIPS: A Synthetic Dataset and Evaluation Resource for Evidence-Supported Psychosis-Risk Symptom Measurement
链接: https://arxiv.org/abs/2608.12329
作者: Guilherme C. Oliveira,Stephanie Fong,Zimu Wang,Clarice Lee,Xiangyu Zhao,Duy Khoa Pham,Duong Nhu,Yiwen Jiang,Jiahe Liu,Zhongxing Xu,Dwarikanath Mahapatra,Dominic Dwyer,Zongyuan Ge
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Progress on AI for psychosis-risk assessment is limited by a data-access bottleneck. Real clinical interviews are difficult to share because of privacy, governance, and consent constraints. We present AnchorSIPS, a synthetic dataset of 10K structured psychosis-risk interviews with transcript-grounded measurement targets. Each interview is modeled on Mini-SIPS, a clinician-administered psychosis-risk interview. It captures history, 24 symptom questions, follow-up evidence for items the patient affirms, decisions about delusion-like symptoms (unusual beliefs), hallucination-like symptoms (unusual perceptions), and disorganized communication, exclusion of clear psychotic-level symptoms (“frank psychosis”), and a final attenuated psychosis syndrome (APS) diagnosis, a high-risk state of milder or early psychotic symptoms. The APS diagnosis is not a standalone label. It depends on earlier endorsements, supporting follow-up details, symptom-class decisions, and the frank-psychosis check. Every intermediate decision is anchored to its supporting transcript turns. AnchorSIPS is generated by a plan-then-realize pipeline. A hidden case sheet specifies the patient’s clinical state, a deterministic planner fixes the interview structure, and an LLM realizes only the patient utterances under validation and bounded repair. Fixing labels and structure before generation avoids the inter-turn inconsistencies typical of multi-turn LLM dialogue. Across seven LLM baselines, models recover coarse decisions but fail to extract follow-up details or cite supporting transcript turns, so final-label performance overstates interview competence. AnchorSIPS is intended for research on evidence extraction, transcript-grounded measurement, and uncertainty under partial disclosure.
计算机视觉
[CV-0] V-RAE: Rethinking Video Latent Spaces for Generation
链接: https://arxiv.org/abs/2608.13556
作者: Minghui Guo,Shengqiong Wu,Hao Fei
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 26 pages, 8 tables, 13 figures, project page: this https URL
Abstract:Latent video generation relies on autoencoders to define a compact space in which generative models operate. Although video autoencoder architectures have evolved substantially, their latent spaces are still optimized primarily for pixel-level reconstruction and provide limited high-level semantic organization. A reconstruction-optimal latent space, however, need not be well suited to generative modeling. We propose V-RAE, a video representation autoencoder that builds compact generative latents on top of frozen vision foundation model representations. A lightweight temporal pooling module removes temporal redundancy while preserving semantic structure, and a video decoder reconstructs continuous motion from the compressed features. We evaluate V-RAE with four representative frozen encoders on video reconstruction, semantic probing, and class-conditional generation. V-RAE achieves 2.13 rFVD on K600, outperforming all evaluated large-scale pretrained video VAEs. Its latents retain substantially more semantic information than conventional video tokenizer latents. Under matched generation settings, our best variant achieves gFVD scores of 117.86 and 19.16 on UCF101 and K600, respectively, while converging up to 6x faster. We further show that reconstruction quality alone is insufficient to characterize generative utility and introduce tFVD, a temporal-coherence diagnostic that correlates more reliably with downstream generation quality. Beyond video generation, V-RAE also improves future video prediction on Cityscapes over the Wan 2.2 VAE latent space under matched prediction settings. Taken together, the experiments show that frozen semantic representations can support video reconstruction, generation, and predictive modeling. The project page: this https URL.
[CV-1] HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark ECCV2026
链接: https://arxiv.org/abs/2608.13555
作者: Dairu Liu,Zekun Qi,Jiayu Zeng,Ruixi Yu,Yu Guan,Yintianrun Zhang,Xuchuan Chen,Sikai Liang,Zekai Li,Chenghuai Lin,Xinqiang Yu,Wenyao Zhang,He Wang,Li Yi
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:Humanoid motion tracking is central to teleoperation and whole-body imitation, yet evaluation often disagrees with what people perceive in videos. Kinematic errors average per-frame pose differences but miss the physical artifacts that matter most, particularly unstable support and incorrect contacts such as foot skating and mistimed touch-downs. Meanwhile, widely used test suites are small and lack the diversity needed to stress contact-rich, long-horizon behaviors. We introduce HumanTracker to make humanoid tracking evaluation both perceptually aligned and scalable. The HumanTracker benchmark contains approximately 153 hours of optical motion trajectories from multiple professional performers, organized into four motion families with text labels for fine-grained diagnosis. We further propose HumanScore, a preference-aligned metric trained on 12K motion pairs containing 24K motions. Across representative state-of-the-art trackers, HumanScore better predicts human preferences and reveals contact and stability failures that kinematic metrics often miss.
[CV-2] PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives
链接: https://arxiv.org/abs/2608.13552
作者: Kaixin Ding,Xi Chen,Minghong Cai,Zhiyuan Xu,Yiyang Wang,Yuxiang Lu,Junyi Li,Shuyang Chen,Yuan Gao,Xin Tao,Pengfei Wan,Hengshuang Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: project page: this https URL
Abstract:Video world models simulate future states conditioned on current observations and user actions. Recent systems have demonstrated impressive video consistency and action controllability over long sequences. However, fairly comparing these interactive models remains challenging. In practice, a human player typically evaluates a world model by pursuing long-horizon objectives through interaction. For example, a user may turn around 360 degrees to see whether the environment remains consistent, or walk into the water and inspect whether realistic water ripples are generated. The action sequence required to achieve the same objective may vary substantially between models, making fixed action-conditioned evaluation unsuitable for cross-model comparison. To address this, we employ multi-modal Agent Players to interact with world models toward specified long-horizon objectives. Building on this paradigm, we introduce PlayWorld, a benchmark providing 171 scenarios, each with a specified objective. To evaluate performance thoroughly, we assess models along four core dimensions: geometry consistency, interaction fidelity, out-of-sight evolution, and insight evolution. In addition, we incorporate basic ability metrics for video quality and controllability. Experiments across nine state-of-the-art world models reveal that current models remain unreliable on long-horizon interactive objectives, particularly in maintaining spatial consistency and persistent state evolution. Code and data are available at this https URL.
[CV-3] Alaya-EVOKE: From Linear-Scaling Supervision to Endless World
链接: https://arxiv.org/abs/2608.13546
作者: Yuanyang Yin,Gongxuan Wang,Yifan Zhan,Chuanhao Li,Kaipeng Zhang,Feng Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Interactive world models must support persistent memory, responsive interaction, and long-horizon generation, yet these requirements place conflicting demands on the model. Maintaining history in the denoiser context or key-value cache incurs growing cost, forcing a trade-off between session length and retained memory, while low-latency interaction relies on few-step generation whose capabilities are bounded by its teacher. Evoke addresses both limitations by externalizing persistent world state and redesigning the teacher for long-horizon interactive generation. Scene geometry is maintained in an external, camera-indexed world state bank, from which only view-relevant information is retrieved, keeping the denoiser context bounded as the session grows. Rather than treating the teacher as a fixed generator, we design it for long-horizon supervision: its sparse attention combines chunk-wise grouping, retrieval of selected distant frames, and a linear-attention global state, yielding linear growth in memory and compute while enabling supervision over long horizons. Such supervision exposes content drift that stays locally plausible within short windows, while per-chunk conditioning enables prompt changes and event control throughout the sequence. A 30-second distribution-matching objective, applied under self-forced rollouts, transfers both capabilities to a three-step student that uses no classifier-free guidance, improving resistance to long-term drift while preserving responsive conditioning. With bounded context and recurrent external memory, Evoke supports open-ended, continuously evolving generation; on a single H200 at 384\times 640 , each 1.5,\mathrms chunk is generated in 2.11,\mathrms . As a three-step world model, Evoke achieves state-of-the-art performance on WBench while remaining competitive on VBench-Long and VBench-2.0.
[CV-4] SCULPT: Subtractive Composition for 3D Part Generation
链接: https://arxiv.org/abs/2608.13541
作者: Sikuang Li,Chen Yang,Jiemin Fang,Jiazhong Cen,Yuhe Wei,Jichen Pang,Wei Shen,Qi Tian
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: Project page: this https URL Code: this https URL
Abstract:Part-aware 3D generation aims to create digital assets that are coherent as complete objects while exposing structural parts for editing, material assignment, animation, and reuse. Existing methods impose this structure outside the native generation loop: segmentation-based methods partition an already generated shape, while additive methods synthesize parts from predefined layouts, boxes, or tokens and then reconcile them into a whole. The former preserves the generated geometry but fixes the object before part boundaries are determined; the latter exposes part cardinality but often leaves shared boundaries vulnerable to gaps, interpenetrations, and material discontinuities. In this paper, we propose SCULPT, a framework that addresses these challenges through subtractive composition. Given a complete object represented in a structured 3D latent space, SCULPT iteratively applies a joint split predictor to generate one extracted part together with the remaining object. The predictor performs a coupled denoising process conditioned on both the image and the current 3D state, so the extracted part and updated remainder are generated together rather than reconciled after generation. The joint split predictor processes both outputs on the union of their native sparse 3D supports, allowing neighboring supports to overlap rather than imposing a disjoint voxel partition. The rollout ends when the remainder support becomes empty or reaches a fixed safety cap, allowing the number of generated parts to adapt to each object within that bound. Extensive experiments demonstrate state-of-the-art geometry on PartObjaverse while preserving strong complete-object reconstruction after part assembly. Results on four dataset images, one text-to-image-generated input, and one real-world photograph further show fine-grained textured part decomposition beyond the benchmark.
[CV-5] Intervention-Aware Clinical World Model for Post-Op Outcome Forecasting in Cardiology MICCAI2026
链接: https://arxiv.org/abs/2608.13518
作者: Yunsung Chung,Yingshuo Liu,Abboud F. Hassan,Han Feng,Mary M. Maleckar,Nassir Marrouche,Jihun Hamm
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Medical World Models (MWM) Workshop at MICCAI 2026
Abstract:Many clinical prediction models treat post-intervention outcomes as a one-step mapping from baseline measurements to a future endpoint. However, recovery after a procedure often unfolds as an irregular trajectory: clinical observations, medication changes, repeat interventions, and physiological measurements are recorded asynchronously and can change risk assessment over time. We propose an intervention-aware clinical world model that represents each patient with a structured latent state and evolves it through time-ordered post-intervention events. The model first encodes baseline imaging into a 3D spatial latent state. It then updates this state using procedural context, static covariates, elapsed time, and peri-event physiological embeddings. Follow-up imaging provides training-only supervision through a latent forecasting objective. We apply the framework to atrial fibrillation ablation. During the 90-day recovery window, irregular post-procedure records provide clinically meaningful evidence for long-term recurrence risk. In repeated internal cross-validation on DECAAF-II, our model achieves AUROC 0.756 and AUPRC 0.777 for recurrence prediction. It also achieves a scar-extent MAE of 2.971 percentage points without requiring follow-up MRI intensities at inference. The learned state supports recurrence-risk queries at different horizons and retrospective input editing of blanking-period records.
[CV-6] abSOM: A tabular-to-image encoding method based on self-organizing maps
链接: https://arxiv.org/abs/2608.13513
作者: David Chushig-Muzo,María Ángeles Rodríguez de Cara,Eva Milara,Francisco J. Lara-Abelenda,Luis Zhinin-Vera,Diego H. Peluffo-Ordóñez
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Tabular-to-image methods have emerged as novel approaches to leverage the high predictive performance of convolutional neural networks and vision transformers. They convert tabular data into image representations, mapping each feature at a fixed pixel location derived from a dimensionality-reduction method (e.g., t-SNE, UMAP, PCA). However, they encode only the marginal value of each feature and discard information about feature relationships. We propose TabSOM, a tabular-to-image encoding built on the Self-Organizing Map (SOM), which provides: (i) a spatial layout in which every input feature occupies a fixed canvas position derived from its component plane via collision-free Hungarian assignment; and (ii) a graph that captures pairwise feature relationships derived from the SOM component planes. The resulting image stacks two multi-scale node channels: one encodes feature values at fixed scales, while the other encodes pairwise feature interactions as spatial connections between related features. Two SOM-derived interpretability approaches are introduced: a prototype-inspired partial dependence plot and a class–separation importance score. Benchmarked against twelve existing tabular-to-image methods across public binary-classification datasets, TabSOM ranks first or second on every dataset and achieves the lowest variance of any method evaluated. Interpretability obtained with TabSOM was validated against Random Forest, XGBoost, and SHAP, the class-separation score shows reasonable agreement with established baselines on the top-ranked features while capturing complementary structural information from input data. These results demonstrate that TabSOM provides an effective and interpretable approach for applying deep learning architectures to tabular data, bridging the performance–interpretability gap in this domain.
[CV-7] GS2CI: Robust Gaussian Splatting For Snapshot Compressive Imaging via Large Vision Model Priors
链接: https://arxiv.org/abs/2608.13502
作者: Yanming Yang,Chenxi Song,Ping Wang,Xin Yuan,Chi Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Snapshot Compressive Imaging (SCI) offers an efficient solution for high-speed video acquisition and, under exposure-time camera–scene relative motion, multi-view scene capture by compressing temporal or spatial information into a single 2D measurement. While recent studies have explored SCI for 3D scene reconstruction, existing methods struggle with significant challenges due to information loss, limited viewpoint diversity, and the computational burden of jointly optimizing 3D representations and camera poses. In this work, we propose a novel framework that reconstructs high-quality 3D scenes from a single SCI measurement by leveraging 3D Gaussian Splatting (3DGS) and the powerful priors of large-scale vision foundation models (VFMs). Our primary reconstruction combines measurement-derived 3D VFM initialization with SCI-aware Gaussian optimization. After coarse-stage convergence, an auxiliary 2D VFM provides pseudo-view supervision at synthesized viewpoints for local appearance refinement. To further address the instability caused by ambiguous SCI supervision during 3DGS optimization, we introduce Opacity-Guided Splitting and Growth Regulation (OSGR), an SCI-specific densification strategy that augments split candidates using local opacity statistics, discourages loss-compensating opacity inflation through mean-opacity regulation, and bounds representation growth with explicit candidate-ratio and Gaussian-count constraints. Extensive experiments across multiple benchmarks demonstrate that our method achieves the strongest overall performance, combining leading reconstruction quality and robustness to viewpoint variation with competitive computational efficiency.
[CV-8] raVEL: Trajectory-Guided Video Embedding Learning for Driving-Video Retrieval
链接: https://arxiv.org/abs/2608.13495
作者: Yi-Chung Chen,Philip Jacobson,Tom Lampo,Yiren Lu,Jin Yao,David I. Inouye,Jing Gao,Danhua Guo,Burhan Yaman
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Efficiently retrieving relevant clips from large-scale driving logs is essential for data curation, model development, and safety analysis. Structured and rule-based retrieval systems can explicitly target driving events, but typically require expert-defined rules, auxiliary data, and multi-stage perception pipelines. Multimodal embedding models offer a simpler and more efficient alternative by representing each video with a single searchable vector. However, general-purpose models often rely on shortcuts from static scene context and struggle to distinguish motion-centric events, such as turning left versus right or accelerating versus decelerating. In this work, we study how to adapt a general-purpose multimodal embedding model to driving-video retrieval. We first fine-tune Qwen3-VL-Embedding on paired clips and reasoning traces from nuReasoning using an InfoNCE objective. While this stage substantially improves overall retrieval, caption supervision alone remains insufficient for fine-grained motion understanding. We therefore introduce TraVEL (Trajectory-Guided Video Embedding Learning), a motion-aware fine-tuning framework that uses ego-trajectory similarity as a reward within Group Relative Policy Optimization. Trajectories serve only as privileged training supervision; retrieval still operates on single-vector video embeddings without ego poses, expert rules, or auxiliary perception outputs. We further construct a driving-video retrieval benchmark from nuReasoning. Experiments show that TraVEL improves motion-centric retrieval across model scales: relative to SFT, it raises longitudinal and lateral mAP by 9.8 and 4.7 points at 2B, with corresponding gains of 7.2 and 1.5 points at 8B. TraVEL thus combines physically grounded supervision with efficient embedding-based search.
[CV-9] DreamX-Phi 1.0: Action-Conditioned Video World Model for Robotic Manipulation
链接: https://arxiv.org/abs/2608.13489
作者: DreamX Team,Rui Chen,Xiangxiang Chu,Geng Li,Jifan Li,Qingfeng Shi,Datao Tang,Jing Tang,Jun Wang,Pengfei Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Code: this https URL
Abstract:We present \textbfDreamX-Phi 1.0, an action-conditioned video world model for robotic manipulation that, given an observed frame, a language instruction, and a prescribed action sequence comprising end-effector poses and gripper states, predicts the resulting future observations. Yet realism alone does not guarantee faithfulness: a convincing rollout can still move the wrong arm or lose the manipulated object. To ensure the prediction respects each arm’s commanded path, we inject per-arm \mathrmSE(3) transformations into attention via \textbfPRoPE-style geometric encoding, preserving arm identity and rigid-motion structure. Action control alone does not fully constrain scene geometry or the evolution of small manipulated objects. We therefore add a lightweight \textbfdepth branch for scene-level geometry and use \textbfSAM3 masks with a frozen \textbfV-JEPA teacher to maintain object consistency throughout grasping. We further distill the multi-step generator into a few-step student via distribution-matching distillation for efficient deployment. At the time of writing, \model achieves first place on Track~1 and second place on Track~2 of the WorldArena~2.0 Challenge. Our model and code will be publicly available.
[CV-10] MapRoute: Surrogate-Guided Semantic Routing for Visual Concept Unlearning ECCV2026
链接: https://arxiv.org/abs/2608.13478
作者: Ashok Urlana,L. D. M. S. Sai Teja,Vivek Hruday Kavuri,Ponnurangam Kumaraguru
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at Unlearning and Model Editing Workshop, ECCV 2026
Abstract:We present our submission to Task 3 of the Gen \mu 2.0 Challenge on visual concept unlearning. Building on MapRoute, we introduce task-specific training objectives, richer concept representations, and semantic routing for concept-specific mapper selection. Our approach improves robust concept removal while preserving unrelated and semantically adjacent concepts. On the official benchmark, evaluated using the Erasing-Retention-Robustness (ERR) metric on Stable Diffusion v1.4, our method outperforms the state-of-the-art baseline by 12.1% on average across the five concept categories, achieving substantial gains.
[CV-11] SNM-VFI: Symmetric Nonlinear Motion-Guided Generative Video Frame Interpolation ECCV
链接: https://arxiv.org/abs/2608.13460
作者: Jisoo Jeong,Hong Cai,Jamie Menjay Lin,Hanno Ackermann,Hyeonjun Sim,Yinhao Zhu,Yunxiao Shi,Fatih Porikli
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCVW 2026
Abstract:We propose Symmetric Nonlinear Motion-guided Generative Video Frame Interpolation (SNM-VFI), a training-free framework for motion-controllable generative video frame interpolation with pre-trained optical flow and video diffusion models. Unlike conventional diffusion-based VFI methods that synthesize intermediate frames from random noise, SNM-VFI guides the generative process with correspondence-aware frames produced by a symmetric nonlinear motion model. Specifically, we first utilize a pre-trained optical flow model to construct multi-frame nonlinear flow-based intermediate frames and confidence maps. These flow-guided frames are then encoded as latent priors to initialize and iteratively guide a pre-trained Video Diffusion model, enabling the diffusion model to preserve dense motion correspondence while improving perceptual realism. To further enhance output quality, we employ confidence maps to fuse structurally reliable flow-based predictions with diffusion-generated details in uncertain regions such as occlusions and object boundaries. Extensive evaluations on challenging benchmarks, including DAVIS, Sintel, and KITTI, demonstrate that SNM-VFI achieves strong perceptual quality, competitive reconstruction accuracy, and robust temporal coherence across diverse motion scenarios.
[CV-12] Fine-Grained Action Recognition with Cross-Attentive Latent Sparse Experts
链接: https://arxiv.org/abs/2608.13458
作者: Imtiaz Ul Hassan,Tasweer Ahmad,Nik Bessis,Ardhendu Behera
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Fine-grained human action recognition (FHAR) must distinguish visually similar actions that differ mainly in body configuration, timing, or local appearance. RGB representations retain visual context but often suppress joint-level geometry, whereas skeleton representations encode kinematics but discard dense spatial detail. We introduce FineX, which factorizes fine-grained cues into RGB appearance, pose heatmap geometry, and skeletal-graph topology. Pairwise cross-attention enables symmetric, stream-preserving information exchange, followed by a streamwise latent sparse Mixture-of-Experts that routes each representation to a content-dependent subset of shared experts, regularized by a load-balancing objective. FineX achieves state-of-the-art results on Gym99, Gym288, and Diving48. On the long-tailed Gym288, it raises mean class accuracy from 68.6% to 76.2% (+7.6 points) without textual supervision or large-scale vision-language pre-training, demonstrating the benefit of structured visual-pose-graph fusion and conditional expert refinement for FHAR.
[CV-13] A Unifying Perspective on Causal World Models: From Observations to Representations to Structure
链接: https://arxiv.org/abs/2608.13456
作者: Avinash Kori,Fabrizio Russo
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:World Models (WM) are increasingly seen as a foundation for intelligent agents that can predict, plan, and act beyond their training distribution. In this paper, we study WMs from a causal perspective across multiple levels of abstraction, ranging from perceptual observations to building a conceptual representation of the structure governing the environment dynamics. We argue that useful WMs must go beyond generative capabilities alone: they should also capture entity properties, entity-to-entity interactions, and entity-to-environment interactions that determine and explain the dynamics of a system. We provide a formal definition of Causal WMs (CWMs) grounded in the tasks they are intended to support, connecting world modelling with existing work in causal representation learning, object-centric learning, causal discovery, structural causal models, and model-based decision-making. Finally, we relate CWMs to the literature on identifiability, clarifying when the components of a WM can be recovered from data and up to which equivalence. With this, we ground WMs in representations and structures that support causal reasoning and informed decision-making.
[CV-14] Evaluation of Clinically Steerable Retinal Image Generation from Foundation Model Latent Spaces MICCAI2026
链接: https://arxiv.org/abs/2608.13455
作者: Zuzanna A. Wakefield-Skórniewska,Bartłomiej W. Papież
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: MICCAI 2026 Workshop SASHIMI Submission
Abstract:Medical foundation models learn latent representations of clinically meaningful phenotypes, yet their ability to support controllable image generation remains largely unexplored. We evaluate four retinal foundation models within the representation tokenizer framework and examine whether demographic and clinical information encoded in latent representations from foundation models is preserved during synthetic image generation. We show that generated representations and images faithfully inherit phenotype information when evaluated within their originating foundation models, consistently outperforming conventional latent diffusion on multiple downstream prediction tasks. However, these gains largely disappear when evaluated using classifiers trained on real images, revealing a previously uncharacterised synthetic-to-real representation gap. These findings demonstrate that foundation-model latent spaces provide a powerful substrate for controllable retinal synthesis while highlighting the need to better align synthetic representations with real-image distributions.
[CV-15] UniTexture: Cross-Task Universal Adversarial Textures for Vision-Language-Action Models
链接: https://arxiv.org/abs/2608.13453
作者: Yukun Dai,Mingzhe Dai,Tianshi Wang,Fengling Li,Jingjing Li,Lei Zhu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-Language-Action (VLA) models have emerged as generalist robotic policies capable of following diverse language instructions and performing a wide range of manipulation tasks. However, their direct control over embodied agents also exposes them to adversarial interference that may cause unsafe physical behaviors. Existing attacks on robotic policies are typically optimized for a single task or instruction, leaving the cross-task vulnerabilities of multitask VLAs largely unexplored. We introduce UniTexture, a cross-task universal adversarial texture attack that uses a single textured 3D object to induce targeted deviations in VLA action predictions across multiple tasks. UniTexture backpropagates gradients from the policy’s action outputs to surface texture parameters through a differentiable renderer. It jointly optimizes the shared texture over a distribution of tasks, instructions, states, and viewpoints using a targeted action-space objective, steering predicted actions toward attacker-defined targets without optimizing a separate texture for each task. We evaluate UniTexture on OpenVLA and \pi_0.5 across diverse manipulation tasks and multiple evaluation settings. UniTexture reduces the mean task success rate from 90.0% under benign conditions to 48.4% under attack, induces target-aligned action shifts, and further exhibits cross-suite and cross-model transfer without re-optimization. Together, these findings reveal shared cross-task vulnerabilities in multitask VLAs that can be systematically exploited through a single adversarial surface texture.
[CV-16] Edit2TikZ: A Comprehensive and Challenging Benchmark for Scientific Figure Editing with TikZ
链接: https://arxiv.org/abs/2608.13441
作者: Zongyun Zhang,Jiacheng Ruan,Xian Gao,Ruizhu Zhou,Lingcheng Meng,Lining Hu,Ting Liu,Yuzhuo Fu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 6 figures, work in progress
Abstract:Although multimodal large language models (MLLMs) have shown substantial potential in visual understanding and graphic code generation, editing scientific figures through code presents a greater challenge: a model must jointly recover visual structure, ground the requested change, generate compilable code, and preserve all unrelated content. While existing TikZ benchmarks mainly focus on figure reconstruction and generation, few systematically evaluate instruction-guided scientific figure editing with compilable code. We introduce Edit2TikZ, a comprehensive benchmark for scientific figure editing tasks, featuring 1,548 diverse and high-quality samples. Edit2TikZ combines real-world and controlled synthetic edit cases, supports both textual and visual localization request, and contains multi-step editing, each with step-level annotations. We further construct a human-aligned evaluation framework to measure whether a requested edit is completed while irrelevant content is preserved. Utilizing Edit2TikZ, we evaluate 14 mainstream MLLMs and find that current systems remain unreliable: on average, proprietary models achieve a compilation success rate of merely 75% and remain limited in both figure restoration and edit correctness, while compact models below 9B struggle further with instruction following and complete figure generation. Therefore, we build a mixed training set TikZEditMix and adopt reconstruction-then-editing curriculum learning for compact models. On Qwen3.5-4B, this training improves the compilation success rate from 45.35% to 83.40% and yields an average improvement of 18.7 points across our proposed evaluation metrics. The code and data will be released at this https URL.
[CV-17] ContactGuard: Pre-Contact Execution Monitoring with Action-Conditioned Latent World Models
链接: https://arxiv.org/abs/2608.13438
作者: Gehan Zheng,Matthew Johnson-Roberson,Weiming Zhi
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 5 figures, 8 tables
Abstract:Contact-rich manipulation failures are often detected only after the robot has committed to contact. This is especially limiting in wrist-camera setups: close gripper–object views help observe contact, but a poor approach may already push, miss, slip, or disturb the object before conventional detectors react. We introduce \emphContactGuard, a pre-contact execution monitor for chunked visuomotor policies. Given the policy’s planned action chunk, ContactGuard predicts its short-horizon consequence in latent visual space and aborts if the predicted future latent indicates likely failure. Its latent world model is trained from unlabelled robot trajectories to predict compact multi-view visual embeddings under planned actions, avoiding pixel-level video prediction. A lightweight failure probe is then trained from a small labelled set of pre-contact clips. At deployment, ContactGuard anchors prediction before an imminent contact event, rolls the model forward under the policy’s own actions, and verifies the predicted post-contact latent. Across real-world contact-rich manipulation tasks, ContactGuard predicts failure more accurately than direct and corrupted-action ablations, and transfers to live robot as a pre-contact abort signal without modifying the underlying policy.
[CV-18] StreamTTT: Reconciling Real-Time Perception and Long-Term Memory in Streaming VLMs
链接: https://arxiv.org/abs/2608.13416
作者: Joya Chen,Zeyun Zhong,Mike Zheng Shou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Humans effortlessly perceive the present while remembering the past, yet streaming VLMs often trade off real-time perception against long-term memory. Prior work shows that shortening the context can sharpen current-scene perception at the expense of long-range recall. To reconcile these abilities, we introduce StreamTTT, which writes long-range history into online-updated fast weights outside the attention context. This leaves a short sliding key-value cache dedicated to recent evidence, mitigating attention dilution. We train StreamTTT jointly on offline long-video QA and a newly constructed real-time QA corpus. On OVO-Bench, StreamTTT-4B outperforms SimpleStream-4B by 1.4 points in real-time perception and 3.7 points in backward tracing. It also remains competitive with the larger SimpleStream-8B on the Real-Time Visual Understanding (RTVU) subset of StreamingBench. Our code will be released.
[CV-19] Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation
链接: https://arxiv.org/abs/2608.13391
作者: Hmrishav Bandyopadhyay,Xuanchi Ren,Zijian Huang,Jay Zhangjie Wu,Tianshi Cao,Ruilong Li,Bryan Chu,Sanja Fidler,Yi-Zhe Song,Zian Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:Interactive autoregressive video generation demands both low-latency rollouts and precise online control. Few-step distillation accelerates generation by reducing denoising steps, while online control imposes a causal constraint: frames and blocks should depend on history and controls available during generation. Existing video distribution matching distillation (DMD) pipelines, however, often supervise causal few-step students using bidirectional teachers that score complete clips. The score for a target can therefore depend on future frames and controls that were unavailable when the student generated it, misaligning teacher supervision with the student’s causal information set. We introduce Context-Matched Distillation (CMD), a causal DMD framework that aligns teacher supervision with the information available when each target is generated. CMD replaces bidirectional full-clip scoring with a causal teacher that evaluates each target without access to future frames or controls. The same causal teacher initializes the few-step student, establishing a consistent causal formulation across teacher training, student distillation, and inference. Beyond aligning the temporal information boundary, Prefix Scoring matches supervision to the student’s realized rollout context by evaluating each target under the cached student-generated prefix that produced it. Prefix Corruption further stabilizes training by perturbing unreliable prefixes produced early in training while preserving this target-context alignment. With a simple causal formulation, CMD naturally extends to frame-wise and chunk-wise generation, long video distillation, and camera-conditioned distillation. Experiments demonstrate state-of-the-art aggregate performance among autoregressive methods on both short- and long-video benchmarks, together with substantially improved adherence to time-varying camera controls.
[CV-20] When Is a Task Vector Enough? An Empirical Theory of Implicit Multimodal ICL ECCV2026
链接: https://arxiv.org/abs/2608.13385
作者: Jiaqian Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by Empirical Theory in Representation Learning @ ECCV 2026, Oral
Abstract:Implicit multimodal in-context learning compresses demonstrations into internal interventions, ranging from static task vectors to query-conditioned transformations and attention routing. Despite their common goal, these methods differ substantially in how the intervention depends on the query and where it modifies the model, leaving unclear which additional complexity is necessary for a given task. We propose the Selection–Realization Hypothesis. It views demonstrations as inducing a compact family of internal changes from which the query selects, while the model’s computation constrains how the selected change can be implemented. We evaluate this account using controlled multimodal tasks in which query dependence varies without changing the underlying task primitives or prompt format. By contrasting correct demonstrations with matched counterfactuals, we measure the structure of explicit M-ICL and test whether it predicts intervention behavior. We find that the success of a static task vector is closely tied to how much of the demonstration-induced change is shared across queries. Additional intervention complexity becomes useful when explicit M-ICL contains query-specific or distributed structure that a local additive shift cannot recover. These relationships extend to natural VQA benchmarks and support cost-aware method selection without access to test performance. Our results provide a unified empirical theory of when demonstrations can be compressed into a task vector and when a more expressive intervention is warranted.
[CV-21] Reconstructing Historical Manuscripts through MSI: The Potential of Contrast in Assessing Image Quality and Legibility
链接: https://arxiv.org/abs/2608.13381
作者: Anna Breger
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages main paper, 7 pages Supplementary Material
Abstract:Digital restoration of historical manuscript images aims to improve readability while preserving the authenticity of cultural heritage documents. However, evaluating quality of restored manuscripts remains challenging, where readability is often subjective and expert annotations are scarce. This study investigates the suitability of contrast-based image quality measures to assess quality and legibility of reconstructed manuscript images from multi-spectral imaging. Two experiments were conducted with publicly-available data sets, facilitating manual quality scores by experts and full-reference image quality measures as reference evaluations. The results show that potential contrast achieves the highest correlation with expert ratings, while contrast-to-noise ratio demonstrates the strongest agreement with full-reference quality measures. Overall, contrast-based measures consistently outperform general image quality measures, demonstrating their potential as objective indicators of manuscript legibility and reconstruction quality.
[CV-22] Sign Language Video Synthesis via Loss-Guided Multi-Expert GANs
链接: https://arxiv.org/abs/2608.13368
作者: Dingzhan Nong,Zhihao Ren,Ziqi Li,Tim Lo
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Preliminary technical report. 19 pages, 8 figures, 4 algorithms
Abstract:This preliminary technical report presents a framework for sign language video synthesis using a loss-guided multi-expert Generative Adversarial Network (GAN) to enhance communication for individuals with hearing impairments. Three specialized discriminators – global, hand, and head – each guide a corresponding expert branch in the generator toward a distinct visual region, enabling implicit feature specialization without explicit diversity losses. To stabilize this multi-discriminator system, whose early-phase training otherwise exhibits chaotic dynamics, we introduce a United Loss consensus mechanism that regularizes each discriminator toward the ensemble average at a 10% weight. Each branch further adopts a dual-pathway convolutional-transformer design with learnable AdaptiveFeatureFusion, balancing the stability of convolutions against the detail of windowed self-attention. The generator is trained using an alternating three-mode schedule (discriminator, holistic generation, branch-specialized generation). On a custom 156GB dataset with a filtered test set that removes easy and repetitive samples, our 0.2B-parameter variant achieves 29.8 PSNR and the 1.3B-parameter variant achieves 30.7 PSNR, with inference VRAM footprints of 1.5 GB and 8 GB respectively, enabling deployment on consumer-grade hardware. Full ablation studies remain ongoing due to the 2-3 month training cycle on a single GPU. The system was showcased at the 2025 Hong Kong Frontier Technology Summit.
[CV-23] AmalthAI: An Open-Source Computer Vision Platform for Cultural Heritage
链接: https://arxiv.org/abs/2608.13343
作者: Christos Chatzisavvas,Stelios Alvanos,Efstratios Politis,Panagiotis Rigas,Thomas Pappas,Ioannis Giannoukos,Nikolaos Mitianoudis,Agata Ulanowska,Katarzyna Żebrowska,Nazarij Buławka,Christina Margariti,George Pavlidis,Chairi Kiourt,Anestis Koutsoudis,Vassilis Katsouros,George Ioannakis
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Computer vision (CV) and machine learning (ML) offer new tools for cultural heritage (CH) artifact analysis, but the CV/ML pipeline remains largely inaccessible to CH domain experts, who lack the background to configure, train, or assess models. We present AmalthAI, an open-source CV platform that bridges this gap, enabling non-ML CH experts to independently produce and validate archaeologically meaningful findings. The interface covers dataset management, training, and inference for classification, segmentation, and object detection, with Kubeflow and Katib handling scalable training and hyperparameter search. Grad-CAM localizes the image region behind a prediction, and a vision-language model (VLM) adds a text description of it for expert review. Since archaeological data is often state-owned or rights-encumbered and cannot leave institutional custody, AmalthAI’s self-hostable deployment ensures sensitive data is kept within premises. We test the platform on an archaeological use case built on a custom dataset of clay textile imprints, where CH experts trained and validated segmentation, and classification models for hypothesis testing. We provide the implementation code at this https URL.
[CV-24] How Good are Foundation Models in Longitudinal MRI Disease Progression Reasoning ? MICCAI2026
链接: https://arxiv.org/abs/2608.13309
作者: Wafa Al Ghallabi,Ritesh Thawkar,Sara Ghaboura,Omkar Thawakar,Numan Saeed,Dana Al Nuaimi,Ajnas Alkatheeri,Salman Khan,Fahad Shahbaz Khan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at MICCAI 2026 (Early Accept). 11 pages, 3 figures, 2 tables
Abstract:Magnetic Resonance Imaging (MRI) interpretation is fundamental to clinical decision-making, requiring radiologists to integrate multi-view anatomical planes across sequential timepoints while precisely localizing interval changes. However, existing vision-language benchmarks remain confined to single-timepoint, single-view interpretation, failing to capture the temporal-spatial reasoning essential to radiologic practice. We introduce the Time-Aware Multi-View MRI Benchmark, an evaluation framework unifying multi-view anatomical input, temporal reasoning across longitudinal scans, and structured localization guidance. The benchmark comprises 3,920 expert-verified question-answer pairs derived from 890 patients across over 3,200 longitudinal MRI timepoints, drawn from seven clinical cohorts covering glioblastoma, neurodegeneration, vestibular schwannoma, and brain metastases, in open-ended, multiple-choice, and binary formats, requiring models to identify anatomical regions of maximal change, characterize progression across sequences and views, and provide structured guidance specifying boundaries, imaging features, and confounders. Experiments across 16 vision-language models reveal moderate temporal alignment but systematic failure on change direction recognition and volumetric quantification, while multi-view inputs improve spatial localization yet degrade temporal reasoning in compact architectures. Our benchmark provides a systematic framework for evaluating progression tracking, interval change localization, and temporal ordering, which are essential for clinical deployment. Code, evaluation splits, and the dataset are available at: this https URL.
[CV-25] GeoCache: Training-Free Acceleration of Multi-View Texture Diffusion via Geometric Delta Transport
链接: https://arxiv.org/abs/2608.13255
作者: Haotang Li,Zhenyu Qi,Shaohan Henry Wang,Kebin Peng,Yutong Zhao,Zi Wang,Bo Liu,Huanrui Yang,Sen He
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Geometry-conditioned multi-view diffusion enables high-quality 3D texture generation, but its repeated per-view denoiser evaluations introduce substantial computational cost. Existing training-free accelerators primarily exploit temporal redundancy by reusing computation across denoising steps. In multi-view texturing, however, skipping a step also removes the cross-view interaction that continually aligns different observations of the same surface, leading to rapidly degraded consistency and fidelity. Our analysis identifies a complementary source of redundancy: although intermediate features remain view-specific, geometrically corresponding surface points exhibit transferable evolution in their predicted clean signals. Based on this observation, we introduce \gc, a training-free plugin that evaluates a rotating subset of anchor views and transports their geometry-aligned per-step \xz updates to the remaining views. Periodic full-view computation controls accumulated error, while sampler-consistent reconstruction preserves the denoising trajectory. \gc requires neither retraining nor architectural modification and uses the position maps already available in geometry-conditioned texturing pipelines. Across Hunyuan3D-2.1, SyncMVD, and MVPainter, \gc achieves a stronger speed–fidelity trade-off than temporal caches and step reduction at operating points above 2\times . On Hunyuan3D-2.1, it delivers a 2.21\times denoiser-loop speedup with an MV-LPIPS of 0.0293 and an MV-PSNR of 33.60 dB, providing the best fidelity among all tested methods above 2\times . The same transferred configuration reaches the highest speedup and lowest FLOPs on SyncMVD, while \gc achieves the lowest FLOPs and best fidelity among the accelerated methods on MVPainter. These results establish cross-view geometry as an effective acceleration axis for multi-view texture diffusion.
[CV-26] Reasoning for Social Audio-Visual Question Answering: Where Do We Stand? ECCV
链接: https://arxiv.org/abs/2608.13239
作者: Koen P. de Vries,Xavier Alameda-Pineda,Estefanía Talavera,Stéphane Lathuilière
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at HCMIW ECCV workshop. Code available here: this https URL
Abstract:Training Multimodal Large Language Models for audio-visual social understanding is a crucial step toward embodied social intelligence. Chain-of-thought (CoT) reasoning has become the dominant approach, with HumanOmniV2 and its IntentBench benchmark as a prominent reference point. In this context, we report three findings. First, IntentBench is highly noisy: \sim 7% of questions are broken and \sim 23% are trivially answerable without the video input. We remove the affected questions and release Intentbench-Prime. Second, current reasoning approaches are expensive and surprisingly ineffective. A simple Vanilla SFT baseline matches or outperforms existing reasoning methods across three benchmarks at a fraction of the cost, establishing it as an essential baseline for evaluating novel fine-tuning techniques. Third, our analysis reveals that substantial priors can be learned solely from the text modality and that using a textual caption instead of the video yields performance on par with Vanilla SFT. These surprising findings reveal the limitations of current MLLMs when it comes to social understanding. IntentBench-Prime, Vanilla SFT model, and code are publicly available.
[CV-27] CoverPrune: Coverag e-Driven Token Pruning for 3D VLMs via Optimal Transport ECCV2026
链接: https://arxiv.org/abs/2608.13226
作者: Peng Ling,Yingda Yin,Lingting Zhu,Weikai Chen,Shengju Qian,Zeyu Hu,Xin Wang,Wenming Yang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to ECCV 2026 as an Oral Presentation
Abstract:While 3D Vision-Language Models (3D VLMs) have demonstrated remarkable spatial reasoning capabilities, they suffer from massive visual token counts that create severe computational bottlenecks during inference. Existing token pruning methods primarily rely on diversity-based selection, discarding similar tokens to maximize dispersion. However, in 3D environments, this approach frequently drops representative prototype tokens in favor of outliers, breaking the multi-view consistencies and geometric structures essential for spatial reasoning. In this paper, we propose a paradigm shift for 3D VLM token pruning: from maximizing diversity to preserving visual evidence coverage. We introduce CoverPrune, a training-free framework that formulates inference-time token pruning as an Optimal Transport (OT) problem. To overcome the intractable combinatorial subset selection inherent in this formulation, we design the Feature-Spatial-Temporal (FST) transport cost and target capacity, along with an efficient Spatial-Guided Greedy Selection (SGS) algorithm to approximate the OT objective. Furthermore, we propose CoverPrune-Lite, an accelerated variant utilizing spatially structured local matching for minimal overhead. Extensive experiments across multiple 3D visual-spatial reasoning benchmarks demonstrate that our methods achieve state-of-the-art token efficiency, maintaining robust reasoning performance even under highly aggressive pruning budgets. Visit our project website at this https URL.
[CV-28] Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty MICCAI2026
链接: https://arxiv.org/abs/2608.13223
作者: Riya Deepak Shet,Le Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 4 figures, 2 tables. Conditionally accepted at MICCAI 2026 BraTS-GoAT challenge workshop. Code: this https URL
Abstract:Deep networks segment brain tumours accurately in-distribution, but can fail silently when the input differs from their training data. That risk is central to clinical deployment and is the premise of the BraTS-GoAT generalizability task. We ask not only how well a model segments, but whether its uncertainty knows when it is wrong. On BraTS-GoAT (Task 3) we train a 5-fold cross-validated nnU-Net baseline (one held-out prediction per case) and a 3-seed deep ensemble. Both are evaluated for calibration and error detection on a per-region relevant mask, aggregated per case. In-distribution the 3-seed ensemble improves modestly over the already strong single model on the same held-out split, with the clearest gain in calibration. The separation appears under shift. In a controlled robustness study using graded synthetic corruptions as a proxy for acquisition shift, the single model’s confidence stays flat while its accuracy and calibration degrade. Inter-member disagreement instead rises steeply, about a quarter to a third above the clean condition, several times the single model’s response. On the official validation leaderboard the 5-fold ensemble of those folds attains whole-tumour Dice 0.87. The generalization gap is concentrated on the harder regions, with a characteristic failure of missing small, satellite lesions on unseen cohorts. In the synthetic study, disagreement among the 3-seed members is a more sensitive case-level indicator of acquisition shift than single-model confidence. Its per-voxel error localisation weakens as severity grows. The contribution is a rigorous, honest reliability comparison rather than a claim that any one uncertainty method dominates.
[CV-29] NARU: A Benchmark for NARrative Evolution and Cultural Nuance Understanding in Japanese Extreme Long Video
链接: https://arxiv.org/abs/2608.13210
作者: Yuheng Huang,Jianlang Chen,Jiayang Song,Hua Qi,Aza Kai,Vincent Markert,Edison Marrese-Taylor,Jianjun Zhao,Lei Ma
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注: Yuheng Huang and Jianlang Chen contributed equally to this work. More details available on the project’s website this https URL and this https URL
Abstract:Long-form video understanding encompasses tasks that go beyond retrieving isolated events, including tracking an evolving narrative and interpreting social meaning that may remain implicit. However, existing benchmarks rarely evaluate these capabilities jointly, particularly in high-context, non-English media. To address this gap, we introduce NARU, a benchmark designed to evaluate Narrative evolution and Reasoning on cultural Understanding in Japanese long-form video. NARU consists of 1,481 questions grounded in 155 videos totaling 146.8 hours, spanning four narrative and five cultural dimensions. To construct the benchmark at this scale, we propose a hierarchical memory-based annotation pipeline that transforms raw video into structured event, narrative, and cultural annotations, then generates questions via task-oriented synthesis and iterative shortcut removal. The construction process includes two native-speaker verification stages involving 68 annotators. Evaluations across eight model configurations reveal substantial limitations in both long-range narrative integration and culturally grounded reasoning. By exposing these persistent gaps, NARU offers a systematic testing ground for developing MLLMs capable of reliably interpreting long-form, high-context video.
[CV-30] HPSD: Hybrid-Policy Self-Distillation for Text-Image-to-Video Diffusion Models
链接: https://arxiv.org/abs/2608.13205
作者: Jiazi Bu,Pengyang Ling,Yujie Zhou,Yibin Wang,Yuhang Zang,Xuanlang Dai,Shengyuan Ding,Tianyi Wei,Xiaohang Zhan,Jiaqi Wang,Tong Wu,Dahua Lin,Xingang Pan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Website: this https URL Code: this https URL
Abstract:Text-Image-to-Video (TI2V) models are an emerging unified architecture, where a single model simultaneously supports text-to-video (T2V) and image-to-video (I2V) generation. Given a high-quality first frame or a detailed textual prompt, TI2V models unlock substantially better visual quality than their T2V mode, raising a natural question: can the capability elicited by such privileged conditions be internalized into the model’s own base generation ability? A common approach toward this goal is model self-distillation. However, the most straightforward solution, supervised fine-tuning, follows an off-policy strategy: its supervision is confined to teacher-generated endpoints from a fixed offline distribution rather than student-visited states, lacking precise correction tailored to the evolving policy. Recent on-policy distillation methods instead suffer from condition-state mismatch, where supervision is steered toward the given first frame instead of the student’s actual content, misleading the correction. To achieve self-distillation that absorbs the teacher’s privileged prior while retaining precise policy correction, in this work, we propose Hybrid-Policy Self-Distillation (HPSD), a novel self-distillation framework where a single TI2V model acts as both teacher and student under different conditions: the teacher operates in TI2V mode with a high-quality first frame and an enhanced prompt, while the student runs in the base T2V mode with only the vanilla prompt. Specifically, the student inherits off-policy teacher trajectory points as anchors, locally refines them toward its own policy, and finally receives velocity-level supervision on these self-generated roll-outs. Extensive experiments demonstrate that HPSD significantly improves T2V performance while also delivering notable TI2V gains, effectively strengthening the model’s base generation ability.
[CV-31] Fidelity-Constrained Anchoring for Black-Box Denoisers
链接: https://arxiv.org/abs/2608.13194
作者: Masaki Satoh
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, 5 figures. Supplementary material is available as an ancillary file
Abstract:We propose a fidelity-constrained framework that anchors the output of a black-box denoiser to its input without retraining and with little additional computation. The method linearly blends the denoised image with the input and selects the maximum blending factor that satisfies a prescribed local fidelity constraint using Peak Signal-to-Noise Ratio (PSNR) or Structural Similarity Index (SSIM). For PSNR control, a closed-form solution is obtained under a local constant-blending assumption. For SSIM control, we derive a tractable formulation based on inverse SSIM under the same assumption and solve it efficiently using iterative root finding. Experiments on DIV2K images with synthetic Gaussian noise and outputs from Real-ESRGAN and a non-local means denoiser show that the proposed anchoring strategy provides effective fidelity control while balancing denoising performance and statistical naturalness, as measured by the excess kurtosis of residual noise. In particular, SSIM-based anchoring yields more consistent behavior across noise levels than PSNR-based anchoring.
[CV-32] ProME: Prototype-Margin Environments with Repair-Aware Selection for Group-Robust Learning
链接: https://arxiv.org/abs/2608.13190
作者: Qianqian Wang,Yunshan Li,Dawei Huang,Wenwu Gong,Lili Yang
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 9 figures, 7 tables
Abstract:Group-robust learning is crucial for maintaining accuracy on rare subpopulations when training-group labels are unavailable. However, existing methods often infer environments from a separate reference model and select representations before fitting the classifier used at deployment, leaving both decisions misaligned with the deployed predictor. In this work, we formulate group robustness without training-group labels as the endogenous environments with repair-aware selection (ERAS) problem, and propose ProME (Prototype-Margin Environments) to align both decisions with the deployed predictor. ProME splits prototype margins at their median to construct approximately balanced environments along the training trajectory, and fits a group-balanced linear head on group-annotated validation data to rank the resulting predictors by validation worst-group accuracy. We theoretically bound the worst risk across the inferred environments for a fixed predictor and partition, showing that this bound transfers to the oracle groups under an explicit alignment condition. Extensive experiments show that prototype margins enrich shortcut-conflicting examples, classifier repair reshapes candidate evaluation, and ProME achieves the highest average worst-group accuracy among the compared methods with the same group-label access.
[CV-33] SketchSense: Learning to Interpret Imperfect Sketch Guidance for Image Inpainting
链接: https://arxiv.org/abs/2608.13186
作者: Zian Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 11 figures, 3 tables
Abstract:Sketch-guided image inpainting provides intuitive structural control, yet real sketches often mix reliable global intent with locally crowded, displaced, incomplete, or deliberately unconventional strokes. Existing approaches typically either retain the input sketch as a fixed condition throughout denoising or refine it into a clean structure before RGB synthesis. The former assumes uniformly reliable strokes and can propagate local errors throughout generation; the latter must resolve ambiguous structure before emerging appearance and semantic context become available. We propose SketchSense, a framework that interprets imperfect sketch guidance by synchronously denoising interacting RGB and structure streams. Bidirectional Attention Fusion couples appearance generation with structural recovery, producing a refined structure that exposes the model’s evolving sketch interpretation. A phrase-level objective aligns the semantic grounding of the two streams. Sketch-Aware Spatial Regulation further adapts sketch use to local generation states by modulating attention and the fusion process, while an optional signed prior injects preserve-versus-correct intent into feature representations and attention behavior. Experiments on natural and structurally complex imagery show substantial gains over existing methods in both restoration quality and structural fidelity.
[CV-34] A Controlled Study of Self-Supervised Image and Video Pretraining under Limited Resources
链接: https://arxiv.org/abs/2608.13183
作者: Brunó B. Englert,Gijs Dubbelman
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual foundation models are a cornerstone of image and video understanding but typically require large amounts of data and computation. The current scale required for pretraining visual foundation models may be unsustainable or unnecessary, and significant benefits arise when effective models can be obtained with fewer resources. To better understand how self-supervised learning (SSL) objectives behave under resource constraints, we conduct a controlled study of image and video SSL objectives under matched data, architecture, and compute budgets. We compare contrastive, reconstruction, feature-prediction, and diffusion objectives and evaluate both standalone and jointly trained image-video SSL formulations across a diverse set of image and video understanding tasks. Our results show that DINOv2-style pretraining consistently provides the strongest overall performance under limited resources. Furthermore, combining DINOv2 with video SSL objectives such as VideoMAE substantially improves image classification and segmentation performance, but degrades video tracking and camera-pose estimation performance, revealing an important tradeoff between semantic and geometric representation learning. These findings suggest that combining image and video SSL objectives can be beneficial in resource-limited settings, while highlighting the need for improved methods that better balance semantic, temporal, and geometric supervision.
[CV-35] Splat-based Metal Artifact Reduction in Cone-Beam CT via Polychromatic Modeling
链接: https://arxiv.org/abs/2608.13159
作者: Kiseok Choi,Inchul Kim,Jaemin Cho,Hyeongjun Cho,Min H. Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:Cone-beam computed tomography (CBCT) enables volumetric reconstruction from X-ray projections, but suffers from severe artifacts–especially beam hardening–when imaging materials with high attenuation such as metals. These artifacts arise from the polychromatic nature of X-rays and are not properly addressed by conventional monochromatic reconstruction algorithms. While recent neural representation-based methods offer improved reconstruction quality, they are computationally expensive and often impractical for deployment. We propose a novel physics-inspired, self-calibrating metal artifact reduction method that efficiently reconstructs 3D CBCT volumes while correcting beam hardening artifacts. Our method integrates a polychromatic X-ray projection model, material-dependent attenuation profiles, and system response modeling into a Gaussian Splatting framework. Unlike prior work, we eliminate the need for manual metal masks or strong prior assumptions, and we optimize both reconstruction parameters and X-ray spectral characteristics jointly during training. We further introduce a high-fidelity synthetic CBCT dataset generation pipeline validated on Monte-Carlo x-ray simulation toolbox and release new datasets with severe metal-induced artifacts to support the community. This is the first splat-based method for reducing beam hardening in CBCT. Extensive experiments on both synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art approaches in artifact suppression and reconstruction accuracy.
[CV-36] Less Annotation More Interpretation: Prior-Guided Concept Bottleneck Models for Interpretable Cancer Imaging Diagnosis MICCAI2026
链接: https://arxiv.org/abs/2608.13148
作者: Baoqiang Ma,Kenneth Gilhuijs
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the iMIMIC Workshop at MICCAI 2026
Abstract:Concept bottleneck models (CBMs) can improve the transparency of cancer image diagnostic prediction by expressing predictions through radiological concepts. However, their dependence on instance-level concept annotations limits practical applicability. We propose a prior-guided hybrid CBM that integrates limited concept annotations, class-conditional concept distribution matching on unannotated patients, and prior initialization of the concept-to-diagnosis head. We evaluate the method on CBIS-DDSM mammographic masses and calcifications and LIDC-IDRI pulmonary nodules across 0-100% concept annotation. In the clinically relevant 0-20% annotation regime, the hybrid CBM consistently improves mean concept AUC over a matched standard CBM, while maintaining diagnostic performance close to black-box models. At 10% annotation specifically, concept AUC increases from 0.619 to 0.741 for masses, from 0.650 to 0.787 for calcifications, and from 0.597 to 0.642 for pulmonary nodules. Ablation experiments identify prior initialization as the main component contributing to improved concept detection, likely by stabilizing the concept-to-diagnosis head. Zero-shot VLMs remain insufficient for reliable fine-grained tumor-level concept prediction. These findings suggest that structured priors can substantially reduce the annotation burden of interpretable cancer imaging models.
[CV-37] Geometry-Grounded Unified 3D Perception for Autonomous Driving
链接: https://arxiv.org/abs/2608.13147
作者: Longfei Xu,Xiaohui Wang,Zehao Huang,Han Li,Ya Yang,Naiyan Wang,Si Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Camera-based autonomous driving perception requires a shared representation that preserves metric 3D structure across synchronized multi-camera streams. However, existing image-based frameworks often rely on backbones pretrained for semantic recognition, and introduce 3D geometry through downstream task-specific modules. As a result, their shared representations may fail to preserve explicit metric geometry and consistent 3D scene structure. In this paper, we present a Geometry-grounded Unified 3D Perception (GeoUP) framework that adapts the reconstruction-oriented latent of VGGT to calibrated, streaming multi-camera driving scenes. GeoUP factorizes cross-image interaction into self, temporal, and view attention to capture structurally distinct temporal and cross-view correspondences. It further injects calibration-aware raymap encodings to provide metric scale and camera geometry. The resulting geometry-grounded latent is decoded for metric depth estimation, 3D object detection, and semantic occupancy prediction, corresponding to surface-, instance-, and volume-level readouts of the same 3D scene. Through joint multi-task and multi-dataset training, GeoUP effectively leverages heterogeneous annotations and generalizes across diverse sensor configurations and perception ranges. Extensive experiments on nuScenes, Argoverse 2, Waymo, KITTI, and DDAD demonstrate that GeoUP achieves SOTA performance across detection, occupancy, and depth estimation. These results validate the effectiveness of geometry-grounded representations for unified 3D driving perception.
[CV-38] MergeOver: Post-Training Token Merging for Recursive Vision Transformers
链接: https://arxiv.org/abs/2608.13141
作者: Junseo Kim,Uraz Odyurt,Amirreza Yousefzadeh
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Vision Transformers (ViTs) demonstrate exceptional performance in computer vision but suffer from large parameter counts and quadratic computational complexity, severely limiting their deployment on resource-constrained edge hardware. While recursive weight-sharing reduces parameter counts and token merging mitigates computational and memory bottlenecks, integrating these two paradigms without costly retraining is non-trivial, leaving this intersection largely unexplored. We propose MergeOver, a post-training approach that integrates Token Merging (ToMe) into the recursively weight-shared Sliced Recursive Transformer (SReT). Through an Unmerge tracking stack, constraint-safe merge-rate adjustment, and synchronised token-mass tracking across spatial permutations, MergeOver resolves the spatial and merging constraints of this integration. We further employ a stage-wise single-shot schedule that performs token reduction at the first block of each stage and maintains a fixed sequence length throughout its subsequent recursive iterations. Benchmarked on ImageNet-1K, our selected configuration reduces top-1 accuracy by 1.47 percentage points. On the GPU, it reduces peak activation memory by 37.3% and 38.4% at batch sizes 1 and 16, while throughput decreases by 21.7% at batch size 1 but increases by 21.7% at batch size 16. On a Raspberry Pi 5 (ARM CPU), it reduces latency by 2.4% and 17.6% at batch sizes 1 and 16. These results show that MergeOver can recover a meaningful part of the throughput and memory cost that recursive weight-sharing introduces, without retraining, and provides a baseline for combining token merging with hierarchical recursive transformers.
[CV-39] Predicting Signed Distance Functions for Visual Instance Segmentation
链接: https://arxiv.org/abs/2608.13135
作者: Emil Brissman,Joakim Johnander,Michael Felsberg
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual instance segmentation is a challenging problem and becomes even more difficult if objects of interest varies unconstrained in shape. Some objects are well described by a rectangle, however, this is hardly always the case. Consider for instance long, slender objects such as ropes. Anchor-based approaches classify predefined bounding boxes as either negative or positive and thus provide a limited set of shapes that can be handled. Defining anchor-boxes that fit well to all possible shapes leads to an infeasible number of prior boxes. We explore a different approach and propose to train a neural network to compute distance maps along different directions. The network is trained at each pixel to predict the distance to the closest object contour in a given direction. By pooling the distance maps we obtain an approximation to the signed distance function (SDF). The SDF may then be thresholded in order to obtain a foreground-background segmentation. We compare this segmentation to foreground segmentations obtained from the state-of-the-art instance segmentation method YOLACT. On the COCO dataset, our segmentation yields a higher performance in terms of foreground intersection over union (IoU). However, while the distance maps contain information on the individual instances, it is not straightforward to map them to the full instance segmentation. We still believe that this idea is a promising research direction for instance segmentation, as it better captures the different shapes found in the real world.
[CV-40] QuISE: Defense against Typographic Attacks on VLMs via Query-Irrelevant Semantic Editing
链接: https://arxiv.org/abs/2608.13119
作者: Shubin Lu,Jiaqi Yin,Yihao Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 7 figures; includes supplementary material
Abstract:Typographic attacks pose a critical threat to vision-language models (VLMs) by injecting misleading text into images and causing models to rely on adversarial textual cues rather than visual evidence. Existing defenses often require model-specific modifications, additional training, or access to internal model components, limiting their applicability to modern closed-source VLMs. In this paper, we propose QuISE, a model-agnostic, training-free black-box defense based on query-irrelevant semantic editing. QuISE first identifies text regions likely to affect the current query through influence-aware text localization. QuISE then replaces these regions with two semantically distinct replacement texts that are irrelevant to both the query and the image. The final answer is determined by answer consistency across the edited images. Extensive experiments on three typographic-attack benchmarks, four attack settings, and four VLMs show that QuISE consistently improves defended accuracy. QuISE achieves a recovery rate of 67.9-75.0% with a harm rate of 0.5-1.1%.
[CV-41] Fast Iterative Five point Relative Pose Estimation
链接: https://arxiv.org/abs/2608.13114
作者: Johan Hedborg,Michael Felsberg
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Robust estimation of the relative pose between two cameras is a fundamental part of Structure and Motion methods. For calibrated cameras, the five point method together with a robust estimator such as RANSAC gives the best result in most cases. The current state-of-the-art method for solving the relative pose problem from five points is due to Nister [9], because it is faster than other methods and in the RANSAC scheme one can improve precision by increasing the number of iterations. In this paper, we propose a new iterative method, which is based on Powell’s Dog Leg algorithm. The new method has the same precision and is approximately twice as fast as Nister’s algorithm. The proposed method is easily extended to more than five points while retaining a efficient error metrics. This makes it also very suitable as an refinement step. The proposed algorithm is systematically evaluated on three types of datasets with known ground truth.
[CV-42] EgoMonth: A Month-Level Egocentric Video Benchmark for Long-Term Spatiotemporal Memory
链接: https://arxiv.org/abs/2608.13113
作者: Weitao Chen,Hu Jiaxin,Xie Tianyidan,Yang Li,Yuyi Qian,Banghao Xu,Ziheng Tang,Shenyi Wang,Mingyue Yu,Duo Li,Jiacheng Shi,Gao Wang,Zhan Xu,Zhicheng Qiu,Xuanfu Li,Jian Yang,Lanjun Wang,Zili Yi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 21 pages, 4 figures, 6 tables, including appendices
Abstract:Recent advances in Multimodal Large Language Models (MLLMs) have led to substantial progress in video understanding, accompanied by a growing number of long video benchmarks. However, existing benchmarks rely predominantly on web-sourced videos that lack inter-clip spatiotemporal continuity, making it difficult to assess whether models can maintain consistent memory across days or weeks of real-world experience. We introduce EgoMonth, the first month-level egocentric video understanding benchmark. EgoMonth comprises over 300 hours of first-person daily-life recordings from 20 participants spanning 20 to 120 days, paired with 1,443 human-crafted multiple-choice question-answer pairs. We design a cognitively grounded 14-task evaluation framework organized into three hierarchical cognitive levels: Schema Consolidation, Episodic Indexing, and Cascading Reasoning. Evaluation of state-of-the-art open-source and closed-source MLLMs reveals that even the best-performing model, Gemini 2.5 Pro, achieves only 71.8% macro-average accuracy, remaining 22.4 percentage points below the corrected human baseline of 94.2%. Several models perform near or below the 25% chance level on tasks such as Route Reasoning, Cross-view Spatial Reasoning, and Direction Judgement, while even the strongest closed-source model remains substantially below human performance. These results indicate that current MLLMs function as lossy summarizers rather than faithful memorizers, highlighting the need for architectures with genuine long-term spatiotemporal memory.
[CV-43] owards Physics-Faithful Generation of Scientific Diagrams
链接: https://arxiv.org/abs/2608.13112
作者: Minghui Zhang,Jinxin Shi,Yifan Chang,Liangliang Zhao,Yuandong Pu,Qian Yu,Ming Hu,Hanxiao Zhang,Yun Gu,Yirong Chen,Yu Qiao,Bo Zhang,Xiangchao Yan,Bin Fu,Yihao Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-to-image generation has reached photorealistic quality, yet state-of-the-art systems remain unreliable at producing scientific diagrams, whose value depends not on appearance but on physical faithfulness: correct force directions, valid coordinate systems, consistent thermodynamic states, and equations matching the depicted scenario. Trained on web imagery with physically shallow captions, generic models produce diagrams that look plausible but are physically wrong, harmful in education and scientific communication. We present Princigram, a physics-faithful scientific-diagram generator, and its data pipeline. Our central advance is Structured Physical Chain-of-Thought (SP-CoT): a per-subdiscipline schema that decomposes a physics diagram into an explicit multi-step reasoning chain across six subdisciplines, from scene identification through force or process analysis to governing laws and synthesis. Unlike free-form chain-of-thought, SP-CoT follows a fixed schema with strict fidelity rules that separate visually grounded facts from physically inferred reasoning and type all mathematics symbolically; it serves both as dense training supervision and, at inference, as a structured “thinking” prompt. With it we curate and structurally annotate 4.3 million physics images, of which 115,037 carry expert-level annotation, and adapt a unified multimodal backbone. We further introduce VeriphyT2IBench, whose questions are derived from each held-out diagram’s own structured annotation: each diagram becomes an item-specific bank of binary questions about its objects, forces, and states, so a judge model’s score decomposes into named physical facts rather than one holistic number. On the physics subset of GenExam and on VeriphyT2IBench, Princigram shows that explicit physics-structured supervision improves the physical faithfulness of generated scientific diagrams.
[CV-44] Online Learning of Correspondences between Images
链接: https://arxiv.org/abs/2608.13104
作者: Michael Felsberg,Fredrik Larsson,Johan Wiklund,Niclas Wadströmer,Jörgen Ahlberg
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We propose a novel method for iterative learning of point correspondences between image sequences. Points moving on surfaces in 3D space are projected into two images. Given a point in either view, the considered problem is to determine the corresponding location in the other view. The geometry and distortions of the projections are unknown as is the shape of the surface. Given several pairs of point-sets but no access to the 3D scene, correspondence mappings can be found by excessive global optimization or by the fundamental matrix if a perspective projective model is assumed. However, an iterative solution on sequences of point-set pairs with general imaging geometry is preferable. We derive such a method that optimizes the mapping based on Neyman’s chi-square divergence between the densities representing the uncertainties of the estimated and the actual locations. The densities are represented as channel vectors computed with a basis function approach. The mapping between these vectors is updated with each new pair of images such that fast convergence and high accuracy are achieved. The resulting algorithm runs in real-time and is superior to state-of-the-art methods in terms of convergence and accuracy in a number of experiments.
[CV-45] RbFT-Net: Rectify-Before-Fuse Temporal Radar Anchors for 4D Radar-Camera Depth Completion
链接: https://arxiv.org/abs/2608.13102
作者: Wentao Zhao,Shouxuan Wu,Yongtao Cen,Tianchen Deng,Yuyang Zhang,Jingchuan Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Dense metric depth prediction from cameras and millimeter-wave radar offers a cost-effective sensing solution for autonomous systems. However, radar measurements are inherently sparse and susceptible to clutter, multipath reflections, and projection errors. While aggregating multiple radar frames provides denser metric cues, it also introduces temporal misalignment and dynamic-object interference. Directly propagating such unreliable measurements can therefore corrupt large regions of the predicted depth map. To address this issue, we propose RbFT-Net, an end-to-end rectify-before-fuse framework for multi-frame 4D radar-camera depth completion. Rather than assuming accumulated radar returns to be accurate, RbFT-Net treats them as noisy temporal anchor candidates. An image-conditioned rectification module jointly corrects their image-plane locations and metric depths while estimating pointwise reliability. The rectified anchors are then selectively propagated before high-level multi-modal fusion, suppressing the influence of unreliable measurements. Experiments on ZJU-4DRadarCam and a newly collected 4D radar-camera-LiDAR dataset show that RbFT-Net consistently outperforms the evaluated independent radar-camera methods and remains competitive with plug-in pipelines using auxiliary monocular depth models. Cross-platform evaluation and component analyses further support the effectiveness of the proposed rectification and reliability-aware propagation strategy.
[CV-46] Semantic Radiance Fields as Simulators for Spatial Reasoning in Real-World Scenes IJCAI2026
链接: https://arxiv.org/abs/2608.13095
作者: Nico Heider,Michał Jan Włodarczyk,Katarzyna Wasielewska-Michniewska,Przemysław Hołda,Martin Schieck,Marcin Paprzycki,Maria Ganzha,Bogdan Franczyk
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the IJCAI 2026 Workshop on Spatio-Temporal Reasoning and Learning (STRL), oral presentation
Abstract:Training and evaluating spatial reasoning in embodied agents requires diverse environments that are both geometrically faithful and semantically queryable. Synthetic simulators offer ground truth semantics but sacrifice realism; simulators based on reconstructions of real-world environments have realistic appearance but lack ground truth semantics by default. We propose using Semantic Radiance Fields (SRF) as simulators for spatial reasoning agents. SRFs are a representation that unifies these requirements by lifting 2D semantic segmentations from pretrained vision models into a 3D radiance field that jointly encodes geometry, appearance, and per-class semantic identity. The resulting fields are reconstructed from posed RGB captures of real scenes and support novel-view synthesis, semantic and free-space queries within a single grounded representation. This enables the efficient generation of diverse real-world environments to train and evaluate spatial reasoning models. As an example application, we outline an SRF-driven simulator for an orchard apple-reaching task, in which the radiance field supplies camera rendering, semantic ground truth, and occupancy queries to a physics engine.
[CV-47] Paths: Prompt-aware Spatio-temporal Transformer with Hierarchical Multi-modal Fusion for RGB-Event Video Person Re-Identification ACM-MM2026
链接: https://arxiv.org/abs/2608.13092
作者: Yakun Huo,Yingquan Wang,Yangyang Liu,Tianyu Yan,Yunzhi Zhuge,Pingping Zhang,Huchuan Lu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACM MM2026. More modifications may be performed
Abstract:RGB-Event Video Person Re-Identification (RE-VReID) aims to retrieve specific person across non-overlapping cameras with complementary RGB videos and event streams. However, existing methods often decouple spatial and temporal modeling, which limits their interaction. In addition, global-level RGB-Event fusion fails to fully exploit fine-grained discriminative cues. To address these issues, we propose Paths, a unified framework with spatio-temporal modeling and hierarchical multi-modal fusion for RE-VReID. Specifically, we first design a Memory-Augmented Backbone (MAB) to maintain modality-specific identity prototypes for stable intra-modal representation learning. Then, we propose a Prompt-aware Spatio-temporal Transformer (PST) to jointly model spatial and temporal cues within a unified Transformer. Finally, we introduce a Hierarchical Multi-modal Fusion (HMF) to integrate RGB and event features at global and local levels. With these modules, our framework can learn robust and discriminative representations for RE-VReID. Extensive experiments on three public RE-VReID benchmarks including EvReID, MARS and iLIDS-VID, demonstrate the effectiveness of our proposed method. The code is available at this https URL.
[CV-48] Learning Unified Video and Image Representation for Video Face Forgery Detection
链接: https://arxiv.org/abs/2608.13064
作者: Haotian Liu,Yang Liu,Guoying Zhao,Xiaobai Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Face forgery detection is crucial for preserving the security and integrity of facial data given the rapid developments in face manipulation techniques and deep generative models. Existing methods for video face forgery detection typically assume that all frames in a forged video are manipulated, while detecting partially forged videos that contain only a subset of altered frames remains challenging. To address this issue, we propose a novel framework, UVIF, that utilizes additional annotated images to provide fine-grained supervision for detecting partial forgeries in videos. UVIF employs a unified encoder and a multi-task learning paradigm to jointly model facial videos and images for boosted video face forgery detection. A 2D backbone with temporal fusion modules is employed as the unified encoder. A pseudo labeling process is designed for video frames to bridge their representations with those of static images. A video-oriented feature alignment strategy is further introduced to reduce the distribution gap between videos and images. Extensive experiments on benchmark datasets demonstrate the effectiveness of our framework, which outperforms state-of-theart methods in detecting partially forged videos while introducing no additional computational overhead. Our code is available at this https URL.
[CV-49] H2R-Bench: Benchmarking Human-to-Robot Manipulation Video Generation in World Models
链接: https://arxiv.org/abs/2608.13049
作者: Dingyi Rong,Yue Shi,Chaofan Ma,Jiezhang Cao,Zongrui Wang,Zeyu Zhang,Yao Mu,Guangtao Zhai,Ning Liu
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Large-scale manipulation data is essential for robot learning, yet collecting robot demonstrations remains expensive and difficult to scale. Meanwhile, abundant egocentric human manipulation videos provide rich behavioral experiences, but transferring them across embodiments remains challenging due to differences between human hands and robotic end-effectors. Recent advances in video world models offer a promising pathway to synthesize robot-centric manipulation videos from human observations, while their cross-embodiment transfer capability remains largely unexplored. Therefore, we introduce H2R-Bench, a benchmark for evaluating cross-embodiment human-to-robot manipulation video generation, where models transform egocentric human demonstrations into robot manipulation videos under specified embodiments. Each benchmark instance contains a human demonstration video, target embodiment constraints, and source-grounded annotations covering task goals, action events, functional contacts, and object responses. H2R-Bench evaluates generated videos through five dimensions, including goal-state completion, action-event completion, functional contact transfer, embodiment correctness, and general video quality. We benchmark eleven state-of-the-art video generation models across six manipulation families and two robot embodiments. Our evaluation reveals that current video world models remain limited in human-to-robot manipulation transfer: even leading models often fail in embodiment consistency, functional interaction, and task execution. H2R-Bench provides a systematic diagnostic framework for evaluating whether video world models can bridge the human-to-robot embodiment gap and convert human manipulation observations into robot-centric training resources.
[CV-50] opology-Unified 2D Pose Estimation across Intact Residual and Prosthetic Limbs
链接: https://arxiv.org/abs/2608.13047
作者: Tianye Qi,Tengyue Zhang,Jiaying Ying,Tianqing Zhu,Xin Yu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 7 figures, 7 tables
Abstract:Driven by the availability of large-scale datasets, Human Pose Estimation (HPE) plays a critical role in numerous downstream tasks. However, mainstream benchmarks exhibit severe representation bias, predominantly featuring able-bodied individuals. While a few pioneering datasets have attempted to address limb differences, their annotation protocols fail to generalize, struggling to represent specialized mechanical structures like running blades or unprosthetized residual limbs. To bridge this gap, we introduce ProPose, a large-scale benchmark featuring a novel annotation protocol that unifies the topological representation of biological limbs, diverse prostheses, and physical absences within a single framework. Because real-world prosthetic images are inherently scarce and exhibit extreme long-tail distributions, we design a Real-to-Synthetic data expansion pipeline to explicitly synthesize and expand the underrepresented cases. However, simply training existing models on this enriched dataset often leads to suboptimal solutions, as they estimate each keypoint independently and might hallucinate non-existent joints on mechanical structures. To resolve this, we propose ProLoss, a structure-aware objective that enforces keypoint dependencies within a single limb to prevent unrealistic limb predictions. Extensive experiments demonstrate that our approach improves the classification accuracy of long-tail prosthetic joints by 2% to 6% without compromising spatial coordinate localization performance. This work sets a foundation for inclusive pose estimation, unlocking new possibilities for understanding the interactions between human bodies and assistive devices.
[CV-51] P2Fusion: Prompt-based Progressive Infrared-Visible Image Fusion via Dual-Prior Distillation ECCV2026
链接: https://arxiv.org/abs/2608.13045
作者: Yi Shi,Huichao Xie,Yuqing Wang,Mingyu Wang,Kaihui Yang,Yu Liu,Ruitao Lu,Lizhe Li,Junwei Han,Dingwen Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026. Website: this https URL
Abstract:Infrared-visible image fusion (IVIF) is pivotal for multimodal perception, yet reconciling the inherent information disparity between thermal and textural features remains a fundamental challenge. Existing prior-guided methods often rely on static constraints that induce optimization conflicts or utilize extrinsic semantic priors from large-scale foundation models (e.g., CLIP/DINO), which frequently fail to exploit the intrinsic modality characteristics essential for high-fidelity fusion. To address these issues, we propose P2Fusion, a prior-guided distillation-based framework that reformulates IVIF via dual intrinsic prompts. Instead of imposing hard-coded penalties, we distill image-intrinsic priors, thermal saliency and spatial quality, into learnable dynamic regulators. Specifically, a Teach-to-Fuse mechanism provides dual-granularity progressive guidance, coupled with a Gated Dynamic Expert Recalibration (GDER) module for decoupled feature refinement. This design enables the network to adaptively mediate modal competition through expert specialization. Extensive experiments demonstrate that P2Fusion achieves state-of-the-art performance across five mainstream datasets. Notably, our framework demonstrates consistent performance advantages in fusion quality, achieving state-of-the-art results in 14 out of 20 key evaluation metrics across 5 benchmarks. Furthermore, it effectively contributes to the robustness of downstream perception, such as +3.2% mAP on MSRS, +0.5% mAP on M3FD and +0.9% mAP on DroneVehicle for object detection. Our code will be available at this https URL
[CV-52] From Local Mismatch to Global Impact: Optimizing Cache Reuse Policy for Efficient Diffusion
链接: https://arxiv.org/abs/2608.13043
作者: Xichen Ye,Yifan Wu,Zhikang Xie,Xiangyu Yue,Cheng Jin,Weizhong Zhang
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Diffusion models have achieved dominant performance in visual generation but suffer from substantial inference overhead. While cache-based acceleration has emerged as a promising solution, existing policies rely on local similarity heuristics, which we identify as being significantly misaligned with final generation quality. This discrepancy stems from the non-uniform propagation and accumulation of errors along the denoising trajectory. To address this, we propose Global-Impact Cache (GCache). We first establish a rigorous theoretical characterization of the error propagation upper bound. Recognizing that this bound can be overly conservative for complex, highly non-convex diffusion models, we further reparameterize the propagation exponent with a Bernstein form and reformulate cache policy search as a bilevel optimization problem. In detail, GCache identifies an optimal reuse policy in the inner objective while aligning the error-weighting function with generation quality loss in the outer objective. This framework effectively reconciles theoretical rigor with empirical performance, learning to prioritize computation where it most impacts visual fidelity. Extensive experiments demonstrate that GCache consistently outperforms prior caching strategies on both video and image generation. Notably, on the state-of-the-art Wan2.1 video diffusion model, GCache maintains a 2.17x speedup while significantly enhancing generation quality, reducing LPIPS from 0.1095 to 0.0316.
[CV-53] Spatially-Grounded Text-to-Video Generation via Inference-Time Gradient-Free Optimization
链接: https://arxiv.org/abs/2608.13037
作者: Guillaume Jeanneret,Mathis Koroglu,Hugo Caselles-Dupré,Arnaud Dapogny,Matthieu Cord
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Final Version
Abstract:Diffusion Transformer Text-to-Video models have achieved remarkable synthesis quality, yet fine-grained spatial controllability remains a significant challenge. While existing training-free methods produce solid overall results in spatially grounded generation, \ie, placing a specific object in a designated location, they rely on gradient-based optimization techniques that incur prohibitive computational overhead, a bottleneck amplified in modern large-scale architectures. To address this limitation, we present Gradient-free Analytical Trajectory Optimization Video Generation (GATO-Vid), a novel training-free and gradient-free approach for precise spatial guidance. Rather than relying on costly backward passes, we introduce an alternative cross-attention score and solve it analytically to obtain an exact, closed-form solution. To use our analytical solution, we propose an on-the-fly injection mechanism tailored to the topological manifold of the transformer’s latent space. Our experiments demonstrate that GATO-Vid significantly outperforms existing baselines in localization accuracy while introducing minimal computational overhead.
[CV-54] UniTraffic-Agent : Unified Traffic Video Reasoning for AI City Challenge 2026 Track 3 with Two Out-of-Domain Evaluations ECCV2026
链接: https://arxiv.org/abs/2608.13031
作者: Peng Li,Qianqian Xu,Shilong Bao,Yangbangyan Jiang,Qingming Huang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: This paper has been accepted to ECCV 2026 AI City Challenge Workshop
Abstract:Traffic video understanding has become an important problem in intelligent transportation, as road videos provide direct evidence for accidents, violations, and interactions between vehicles and vulnerable road users. A useful system should explain how a traffic event develops, why it happens, and when the relevant interaction occurs, yet this remains difficult for multimodal large language models (MLLMs) because traffic videos contain sparse events and varied viewpoints. We introduce UniTraffic-Agent, the MR-CAS solution for Track~3 of the 10th AI City Challenge, which includes Traffic Anomaly Reasoning (TAR) and two out-of-domain evaluations: FETV for fisheye traffic events and PSI-VQA for pedestrian intention reasoning. UniTraffic-Agent follows an observe–reason–act–verify workflow that samples timestamped visual evidence, reasons over all questions from the same clip in one request, and converts responses through task-specific action adapters. On the official Public leaderboards, MR-CAS ranks 16th on TAR with a score of 0.5780, 2nd on FETV with 0.4884, and 4th on PSI-VQA with 64.4161. The code is available at this https URL.
[CV-55] RGB-D Video Generation for Improving Human-to-Robot Object Handover Prediction
链接: https://arxiv.org/abs/2608.13028
作者: Tianyu Sun,Zhoujie Fu,Zihui Gao,Bang Zhang,Guosheng Lin
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Human-to-robot (H2R) object handover is a fundamental capability for human-robot collaboration, yet progress is hindered by the scarcity of large-scale, human-centric datasets and the significant sim-to-real gap. To address these challenges, we introduce Hand2Bot, an RGB-D video dataset that provides rich contextual information such as body posture and facial expressions, specifically collected for handover scenarios with real-world noise patterns. We further propose PassGen, a generative pipeline that leverages stable video diffusion and an Intention-Aware Temporal Face Encoder to synthesize realistic handover sequences while ensuring hand-object consistency. To bridge the sim-to-real gap, we implement a morphology-based depth editing strategy that replicates realistic sensor noise found in physical depth maps. Experimental evaluations demonstrate that our framework achieves high intention identification accuracy and low false trigger rates in both ablation studies and real-world deployment on a physical robot platform. Our results confirm that training on PassGen allows for robust zero-shot transfer and earlier intention anticipation compared to traditional hand-centric baselines, effectively enabling socially aware robotic behavior in shared workspaces.
[CV-56] Structure-aware Riemannian Growth Fields for 4D Plant Modeling WACV2027
链接: https://arxiv.org/abs/2608.13007
作者: Meng-Yu Jennifer Kuo,Ryo Kawahara
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to WACV 2027 (Round 1)
Abstract:In this paper, we introduce a novel framework for 4D plant growth modeling that reconstructs the continuous geometric and topological evolution of plants from sparse temporal observations. Existing methods mainly rely on dense registration, yet reliable dense sequences are hard to obtain due to scanning constraints and self-occlusions, leaving these approaches struggling under large temporal gaps where rapid organ emergence violates local rigidity. To overcome this, we bridge these gaps by formulating plant morphogenesis as a continuous procedural process on a structure-aware Riemannian growth field; this jointly models topology evolution and geometric deformation, preserving botanical hierarchies and stable spatio-temporal correspondences across distant timepoints. Our key idea is to ground symbolic growth rules within a continuous geodesic flow, where organ development follows biologically modulated trajectories that preserve structural coherence under topological changes. We further contribute a 10-day dual-species dataset with dense geometric and semantic annotations. Experiments demonstrate that our method accurately tracks individual organ growth over time and significantly outperforms state-of-the-art baselines in both geometric accuracy and correspondence consistency.
[CV-57] PixSDS: Why Latent SDS Makes Noisy Pixels
链接: https://arxiv.org/abs/2608.12997
作者: Vsevolod Skorokhodov
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Score Distillation Sampling (SDS) enables text-to-3D generation by optimizing rendered images with a pretrained diffusion prior, but latent SDS often produces structured color artifacts and high-frequency texture noise. We identify a failure mode of latent SDS caused by VAE-induced pixel drift: the optimized image can move along pixel-space directions that are weakly constrained by the VAE encoder, so its latent representation remains clean and semantically meaningful while the image itself accumulates visible artifacts. We support this diagnosis with controlled 2D SDS experiments, VAE-only optimization, and a simplified analysis showing that encoder-like latent objectives can amplify image-space noise when the inverse mapping to pixels is underconstrained. Motivated by this observation, we propose PixSDS, a lightweight VAE-consistent gradient repair method. PixSDS decodes a latent SDS lookahead step and uses the decoded image as a clean direction for pixel-space optimization, reducing motion in VAE-inconsistent directions without retraining the diffusion model, changing the renderer, or replacing the SDS objective. Experiments in 2D optimization and text-to-3D generation show that PixSDS substantially reduces structured artifacts while preserving semantic content. Code is publicly available at this https URL.
[CV-58] DiCoR: Decoupled Referent Disambiguation and Contour Recalibration for Efficient Referring Remote Sensing Image Segmentation
链接: https://arxiv.org/abs/2608.12980
作者: Ziyang Gao,Zhizhuo Jiang,Jingjing Chang,Yixin Yang,Yuwen Pan,Yong-Qiang Mao,Yu Liu,Hai-Bao Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Referring remote sensing image segmentation (RRSIS) aims to delineate targets specified by natural language expressions in remote sensing imagery. Existing methods mainly follow joint fusion segmentation (JFS) or decoupled prompt segmentation (DPS). JFS is efficient but often suffers from limited accuracy because referent localization and mask delineation are optimized under a unified objective, whereas DPS separates localization from mask generation using spatial prompts and foundation segmenters at the cost of higher memory consumption and inference latency. To bridge this gap, we propose DiCoR, a decoupled referent disambiguation and contour recalibration framework built on an efficient JFS pipeline. DiCoR addresses two key challenges: distinguishing the correct referent from ambiguous candidates and refining coarse masks after localization. A disambiguation-aware localization guidance strategy ranks salient candidate regions with adaptive linguistic cues and injects the resulting localization prior into fused features. A lightweight contour recalibration module further predicts residual corrections to coarse logits under localized contour supervision, improving mask quality with limited computational overhead. Experiments on RefSegRS, RRSIS-D, and RISBench show that DiCoR achieves the best segmentation accuracy across all three benchmarks. On RefSegRS, it improves mIoU and gIoU by 5.28% and 2.87% over a competitive JFS method while running 4.7% faster than a representative DPS method, demonstrating a favorable accuracy-efficiency trade-off. Code is available at this https URL.
[CV-59] Bias Mitigation in Face Recognition via Demographic-based Supervised Contrastive Learning
链接: https://arxiv.org/abs/2608.12971
作者: Yu Linghu,Salman Mohammad,Xinyi Zhang,Manuel Günther
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 1 figure, 5 tables
Abstract:Face recognition systems have been shown to be biased toward certain demographic groups by exhibiting different error rates across gender, age, or ethnicity. Though the imbalance of the training data with respect to these demographics is one cause of this bias, training on artificially balanced groups does not completely mitigate the problem. For deployment, face recognition typically works at operating points allowing very low false match rates and, hence, on the tail of the non-match score distribution. While class balancing can improve the means of these distributions, the aim of our approach is to improve fairness by addressing the behavior in the tail. Particularly, we propose the Demographic-based Supervised Contrastive loss (DeSCon) for face recognition, which relies on a well-designed composition of training batches and demographic-aware pair selection. Our experimental evaluation on both demographically-labeled datasets and standard verification benchmarks shows that DeSCon can improve fairness beyond balancing training datasets while maintaining competitive verification performance. Source code is available upon request.
[CV-60] A Deep RL based Framework for Targeted White Matter Tractography
链接: https://arxiv.org/abs/2608.12960
作者: Ankita Joshi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: MTech (Research) thesis at IIT Mandi
Abstract:Fiber tractography’s ability to reconstruct the brain’s structural pathways, has made it a crucial component of modern neuroimaging, enabling detailed, non-invasive mapping of structural connectivity and supporting a wide range of neurological research and clinical applications. However, despite its importance, tractography remains a challenging task due to the inherent complexity of white matter structure and its susceptibility to false positives, which can lead to the misrepresentation of critical pathways. To overcome these limitations, in this thesis, we propose a hybrid framework that integrates reinforcement learning with supervised learning for refining RL policies, specifically tailored for tract-specific tractography. Notably, our framework does not rely on ground-truth fibers for training. Moreover, the tract-specific formulation bypasses the need for an explicit segmentation process, simplifying the overall pipeline. Our work includes two main contributions, each building upon the previous. First, we introduce a hybrid approach that combines reinforcement learning with supervised learning (specifically, GPT-based policy learning) to refine policies in a tract-specific context. Second, we propose a scalable framework for data-driven multi-policy fusion, which leverages the complementary strengths of multiple RL policies to improve tractography performance and robustness. We demonstrate the effectiveness of our framework through extensive validation on benchmark public datasets including TractoInferno, HCP, and ISMRM-2015, highlighting its ability to generalize across data sources and accurately reconstruct brain white matter tracts. We believe that these contributions represent significant advancements in the field of tractography, improving robustness, reliability, and accuracy while reducing dependence on ground-truth annotations. Comments: MTech (Research) thesis at IIT Mandi Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.12960 [cs.CV] (or arXiv:2608.12960v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.12960 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-61] nnisVAR: A Stroke-Evidence-Grounded Multimodal Large Language Model for Tactical Reasoning in Tennis Videos
链接: https://arxiv.org/abs/2608.12920
作者: Yifan Mei,Qingling Shi,Changli Wu,Jiayuan Rao,Jiayi Ji,Liujuan Cao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:Sports-video understanding is moving beyond event recognition toward explaining how actions collectively shape match progression, however, existing tennis-video methods either perceive individual strokes without modeling their tactical dependencies or generate high-level analyses without grounding them in the underlying events. To bridge this perception-to-understanding gap, we formulate stroke-evidence-grounded tactical reasoning, a new rally-level task that requires models to jointly predict an open-ended answer, a hierarchical tactic label, an ordered sequence of supporting strokes, and decisive key actions, with each evidence stroke anchored to its racket-ball contact frame. We further introduce TRACE (Tactical Reasoning with Action-Chain Evidence in Tennis), a large-scale expert-annotated benchmark containing 11,189 rally videos, 41,485 stroke events, 25,429 tactical units, and 11,189 question-answer pairs, which unifies fine-grained stroke attributes, cross-stroke tactical relations, hierarchical tactic annotations, and evidence-grounded questions across factual perception, tactical understanding, and decision reasoning. Building on TRACE, we propose TennisVAR (Tennis Video Action-chain Reasoner), an evidence-grounded multimodal large language model that follows an “event-relation-evidence-tactic” reasoning paradigm, where an Event Parsing Module converts continuous rallies into explicit stroke-event sequences while a Tactical Graph-Guided Temporal Reasoner jointly models rally progression and same-player decision dependencies to identify question-relevant evidence and decisive actions.
[CV-62] Beyond Visual Evidence: Revealing and Mitigating Relational Privacy Leakage in Document MLLM s ACM-MM2026
链接: https://arxiv.org/abs/2608.12911
作者: Beining Xu,Hairui Wang,Jiaxin Wang,Changsheng Chen,Anirban Chakraborty
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR); Multimedia (cs.MM)
备注: ACM mm 2026
Abstract:While the privacy risks of multimodal large language models (MLLMs) have drawn significant attention, the unique vulnerabilities of domain-specific MLLMs remain largely underexplored. Focusing on document understanding MLLMs for identity document processing, this paper investigates the privacy issues inherent in Key Information Extraction (KIE) tasks. We reveal that when input images lack sufficient visual evidence, these models often rely on memorized field relations from training data to infer missing content, thereby leaking multiple correlated fields containing sensitive personal information. To mitigate this risk, we make three key this http URL, we propose the Dynamic Relational Unlearning Framework (DRUF) which comprises a Relational Decoupling Unlearning (RDU) module and a dynamic set update mechanism. It suppresses the leakage of high-risk field pairs while preserving KIE this http URL, we introduce DocPrivacyBench, a novel benchmark to systematically evaluate a model’s susceptibility to privacy leakage under conditions of absent or minimal visual this http URL, we evaluate three MLLMs and six unlearning methods using this benchmark, assessing both post-unlearning leakage suppression and utility this http URL results demonstrate that existing MLLMs consistently exhibit privacy leakage when visual evidence is scarce, particularly on noisier datasets. In contrast, DRUF outperforms the strongest baseline by improving leakage suppression by 4.8 percentage points, effectively mitigating privacy risks while maintaining robust document information extraction performance.
[CV-63] HounsWorld: A Multimodal World Model for Hidden Patient-State Readout Reconstruction and Simulation
链接: https://arxiv.org/abs/2608.12904
作者: Yunhao Bai,Zhongwei Qiu,Guangyu Guo,Yiming Huang,Tony C.W. Mok,Qinji Yu,Ling Zhang,Yan Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Clinical intelligence requires estimating a patient’s underlying condition from incomplete observations rather than learning isolated mappings from scans to answers. Volumetric medical images provide dense observations of anatomy, attenuation, and lesions, whereas clinical language provides sparse but complementary semantic observations. We formulate CT-centered intelligence as inference over a shared latent patient state, under which readout, reconstruction, and simulation all become state-dependent prediction problems. To operationalize this view, we introduce HounsBench, a computed tomography (CT) centric patient-state benchmark that unifies these three task families with patient-disjoint splits and per-family metrics, and HounsWorld, a 3B multimodal world model that treats volumetric scans and language as observations of the shared state through Joint Understanding-Generation Learning. A shared transformer forms an implicit patient-state estimate and supports three outputs: query-conditioned answers that read out the state, reports and captions that reconstruct it in language, and condition-specific CT volumes for low-dose denoising, virtual contrast enhancement, and anatomy-constrained text-and-mask-to-volume generation. Zero-initialized CT adapters preserve pretrained multimodal mappings, while condition-explicit Hounsfield-unit window sampling exposes clinically meaningful density observations. HounsWorld shows strong performance across all three task families while consistently improving CT understanding through clinically structured completion. Our project is available at this https URL
[CV-64] NaviDC-OCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
链接: https://arxiv.org/abs/2608.12898
作者: Peng Cai,Zhaofan Zou,Shifa Liu,Yikun Wang,Jiawei Tang,Kaicheng Yang,Meng Tong,Zhongjiang He,Hao Sun
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Document parsing aims to transform unstructured documents into structured and machine-readable representations. Recent advances in Vision-Language Models (VLMs) have significantly advanced document parsing. However, existing approaches still face two major challenges. First, decoupled VLM-based methods heavily rely on accurate layout analysis, where geometric distortions in camera-captured documents can introduce cascading errors. Second, although end-to-end VLM-based methods alleviate the dependence on explicit layout detection, they often suffer from redundant generation, hallucinations, and insufficient structural reasoning in high-resolution scenarios. To address these challenges, we propose NaviDC-OCR, a unified framework for document parsing. NaviDC-OCR introduces deformation-aware learning to incorporate geometric perception into VLMs and proposes an adaptive sampling mechanism for complex layout representation. Furthermore, a content-structure decoupled learning strategy is developed to explicitly model formula grammars and table structures, enabling more effective structured representation learning. Extensive experiments demonstrate that NaviDC-OCR achieves state-of-the-art performance across diverse document parsing benchmarks. It obtains overall scores of 96.87, 88.53 and 78.41 on OmniDocBench v1.6, Wild-OmniDocBench, and PureDocBench, respectively, and ranks first in the ICDAR 2026 Sci-ImageMiner Challenge. These results validate the effectiveness and generalization capability of NaviDC-OCR in complex document parsing scenarios.
[CV-65] SPARED: Reasoning -Based AI-Generated Image Detection via Adversarially Edited Data
链接: https://arxiv.org/abs/2608.12876
作者: Yicheng Bao,Xiahui Guo,Xuhong Wang,Xin Tan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Detecting AI-generated images is only half the task: a deployed detector must also justify its verdict, yet existing detectors inherit three failure modes from their training data: real and fake images collected from different sources invite provenance shortcuts, supervised explanation corpora teach templated rationales, and a static forgery corpus leaves the decision boundary standing still while generators keep moving. We introduce \methodname, an adversarial reinforcement learning framework that pits two heterogeneous models against each other. A diffusion image editor learns to edit real photographs into fake counterparts of those same photographs that fool the current detector, while a reasoning MLLM learns to expose them with a verdict grounded in free-form reasoning. Both rewards are shortcut-proof by design: the attacker is credited only when its edit is faithfully executed, and the defender only when its verdict is correct. As the two models alternate, each round’s attacker regenerates a harder training pool aimed at the current detector’s blind spots, so the detector must generalize rather than memorize any fixed artifact distribution. Although the explanation is never rewarded, its quality rises round over round as a side effect of accuracy-only training. A detector trained within this loop improves monotonically across rounds on each of three external benchmarks.
[CV-66] BrainWAM: Action-Space Coordination of Semantic Priors and Predictive Dynamics for Autonomous Driving
链接: https://arxiv.org/abs/2608.12854
作者: Bing Zhan,Shuyao Shang,Jiahao Gu,Shuo Lu,Yuan Xu,Zhao Wang,Yida Wang,Xueyang Zhang,Kun Zhan,Lue Fan,Zhaoxiang Zhang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Autonomous driving requires planning under both semantic constraints and predictive dynamics. Existing end-to-end driving approaches, however, typically emphasize only one side of this requirement: Vision-Language-Action (VLA) models exploit VLM priors for semantic reasoning, while World Action Models (WAMs) provide future-aware prediction through generative world modeling. This naturally motivates a unified planner that can leverage both semantic priors and predictive dynamics. However, we find that a naive combination through joint token-level attention suffers from an attention-allocation mismatch, where semantic shortcuts dominate the shared attention space and suppress predictive dynamics. Inspired by neuroscience evidence that complex behavior arises from coordination among functionally specialized systems, we propose BrainWAM, a structured action-space coordination framework that converts semantic reasoning and predictive world modeling into two specialized action-oriented pathways, and aligns them at the level of compact action representations. We further introduce an asynchronous rectified-flow inference strategy with decoupled video and action denoising, which shortens inference latency while preserving planning-relevant predictive context. BrainWAM reaches state-of-the-art performance on both NAVSIM v1 (89.5 PDMS) and NAVSIM v2 (89.6 EPDMS), consistently outperforming VLA-only or WAM-only methods, highlighting BrainWAM as a practical and promising direction for autonomous driving systems.
[CV-67] Heterogeneous Vision-Language Ensemble with Disagreement-Aware Reranking for Text-Based Person Anomaly Retrieval ECCV2026
链接: https://arxiv.org/abs/2608.12843
作者: Huu-An Vu,Cam Tu Tran Thi,Thanh Toan Le Ngo,Hoang Vo,Do Trung Hieu,Hieu Dinh Trung Pham,Khang Minh Le,Huy Minh Nhat Nguyen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at the ECCV 2026 Workshop
Abstract:Text-based person anomaly retrieval aims to retrieve pedestrians exhibiting anomalous behaviors from a large image gallery using natural language descriptions. Compared with conventional text-based person retrieval, this task requires fine-grained reasoning over pedestrian appearance, behaviors, object interactions, and scene context, making robust cross-modal matching significantly more challenging. This paper presents the GENAI4E team’s solution to AI City Challenge 2026 Track 4. Our framework builds upon a strong retrieval backbone and progressively integrates heterogeneous vision-language embedding models through score alignment and iterative ensemble fusion, followed by disagreement-aware VLM reranking for ambiguous queries. On the official Pedestrian Anomaly Behavior (PAB) benchmark, our approach achieves 90.92% mAP, 85.13% Recall@1, 97.72% Recall@5, and 98.68% Recall@10, demonstrating the effectiveness of combining complementary vision-language representations with selective multimodal reasoning for large-scale text-based person anomaly retrieval.
[CV-68] Semantic Steering for Controllable Generation: Tuning-Free Concept Erasure in Multimodal Diffusion Transformers ACM-MM2026
链接: https://arxiv.org/abs/2608.12829
作者: Qiao Li,Xiaomeng Fu,Yuanshu Zhao,Qipeng Wang,Jiao Dai,Jizhong Han
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ACM MM 2026
Abstract:Multimodal Diffusion Transformers (MM-DiTs) have demonstrated remarkable text-to-image generation performance, surpassing traditional U-Net-based diffusion models. Nevertheless, their powerful generative capabilities also raise significant safety concerns, as they may generate sensitive or inappropriate content. While existing concept erasure methods aim to mitigate such risks, most require modifying model parameters, which are often architecture-specific and impractical for deployed larger models. Several tuning-free approaches face challenges when applied to advanced large-scale MM-DiTs due to their deeply embedded knowledge, broad semantic space, and context-dependent text encoders. To address these challenges, we propose to erase concepts by directly manipulating the model’s internal representations. Our key insight, derived from an in-depth analysis of MM-DiT’s block-wise generative roles, is that text-conditioned semantic representations are most salient in the middle blocks of MM-DiTs. Based on this, we extract representations of an unwanted concept and a desirable safe one from the middle block, construct a steering vector from their difference, and inject this single vector into consecutive early and middle blocks. By operating exclusively on the sparse text-branch tokens and leveraging the straight sampling trajectory of rectified flow, our method achieves effective concept erasure with negligible overhead and without any training. Extensive experiments across MM-DiT models demonstrate that our method achieves state-of-the-art performance in erasing diverse concepts, enables effective control over the final output, and remains robust to adversarial attacks.
[CV-69] Validation of Smartphone-Based Photogrammetric 3D Body Scanning for Automated Anthropometric Measurements Compared with a Commercial Depth-Sensor-Based Body Scanner
链接: https://arxiv.org/abs/2608.12827
作者: Ruting Cheng,Boyuan Feng,Chuhui Qiu,Joaquin A. Calderon,Qing Pan,Yufan Liu,James K. Hahn
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 5 figures, 4 tables
Abstract:3D body scanning has become an important tool in healthcare applications because of its rapid and non-invasive nature. While smartphone-based photogrammetric reconstruction provide a low-cost and accessible alternative to commercial 3D body scanners, their performance for whole-body scanning remains insufficiently validated. Thus, we designed this study to comprehensively validate the photogrammetric 3D scanning application by evaluating automatically extracted whole-body measurements and longitudinal body-shape monitoring. We evaluated a representative application, PolyCam, against the commercial depth-sensor-based Fit3D ProScanner using 144 pregnant participants scanned longitudinally throughout pregnancy. We designed an automatic circumference extraction pipeline to get measurements at four anatomical landmarks from paired 3D scans. A linear mixed-effects model was used to evaluate scanner effects and longitudinal body-shape changes. Measurement consistency was assessed using repeated PolyCam scans and tape measurements on a rigid mannequin. PolyCam demonstrated strong agreement with Fit3D, with average biases below 16 mm, intraclass correlation coefficients above 0.8, and Pearson correlation coefficients above 0.9 across all landmarks. Both systems captured comparable longitudinal body-shape changes. Mannequin experiments showed mean biases below 3.5 mm and no significant differences from tape measurements. These findings support smartphone photogrammetry as a potential accessible alternative to commercial body scanners and applicable for longitudinal 3D body-shape assessment.
[CV-70] LocusGS: Spatially Grounded Tokens for Feed-Forward 3D Gaussian Splatting
链接: https://arxiv.org/abs/2608.12825
作者: Wenyu Li,Sidun Liu,Tongrui Hu,Peng Qiao,Yong Dou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent query-based feed-forward 3DGS methods represent a scene using learnable queries, each aggregating multi-view evidence and decoding a group of Gaussians. Ideally, different queries should specialize in coherent local regions of the scene. However, we observe that Gaussians decoded from the same query often scatter across distant scene regions, resulting in weak query-level spatial coherence and poor alignment with the scene structure. We attribute this behavior to the purely latent representation of existing Gaussian queries. To address this limitation, we introduce LocusGS, which augments each Gaussian query with a 3D anchor state consisting of a center and a support radius. The anchor state is progressively refined across decoder layers and is used throughout query interaction, multi-view feature aggregation, and Gaussian generation. Specifically, an anchor-to-ray geometric bias guides each query toward spatially relevant image observations, while anchor-centered decoding organizes its Gaussians within a local region. Experiments on novel view synthesis benchmarks show that LocusGS improves rendering quality over query-based Gaussian token baselines under the same Gaussian budget. Further analysis shows that the learned anchors form coherent spatial layouts and lead to more structured Gaussian distributions, demonstrating that explicit anchor states improve the spatial organization. Our project page: this https URL.
[CV-71] Structured Local Differential Modeling for AI-Generated Image Detection
链接: https://arxiv.org/abs/2608.12811
作者: Jiazhen Yang,Ruijin Jin,Junjun Zheng,Xiangheng Kong,Zunlei Feng,Jie Lei
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The rapid advancement of AI-generated content has made the reliable detection of generated images an increasingly critical challenge. Existing detection methods are often dominated during training by semantically salient components with high signal-to-noise ratios (SNRs), thereby suppressing subtler forensic cues associated with the underlying generation mechanisms and embedded in low-level statistical structures. From an information-theoretic perspective, we present a key insight: effective detection in the low-level statistical space requires mitigating the dominance of semantic components while emphasizing and amplifying responses to low-SNR forgery traces. Building on this insight, we propose RippleNet, an AI-generated image detection framework based on local differential signals. RippleNet adaptively identifies forgery-sensitive regions and constructs multi-directional, multi-scale differential representations within local neighborhoods, explicitly characterizing anomalous patterns in neighborhood statistics. More importantly, we refine the attention mechanism to operate within the local differential representation space, enabling the model to establish explicit dependencies at a finer statistical granularity. This design facilitates the capture of pixel-level forgery traces that are difficult to model using conventional convolutions or image-wide patch-level attention. Extensive experiments on multiple public benchmarks and under cross-generator evaluation settings demonstrate that RippleNet achieves consistently competitive performance.
[CV-72] Erase but Preserve: Controllable Removal of Copyrighted Animation Characters via Optimized Semantic Anchors ACM-MM2026
链接: https://arxiv.org/abs/2608.12806
作者: Qiao Li,Xiaomeng Fu,Wangjia Yu,Runze He,Baisen Wang,Jiao Dai,Jizhong Han
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to ACM MM 2026
Abstract:The exceptional generation capabilities of text-to-image diffusion models have raised copyright concerns, particularly the unauthorized reproduction of animation characters. Existing concept erasure methods fall short for animation character erasure: model modification methods struggle to identify suitable anchors for diverse, highly distinctive characters; prompt-based steering methods lack fine-grained control for precise intervention. These approaches often yield incomplete erasure and degraded image fidelity, hindering real-world deployment. In this paper, we propose a controllable method operating on the model’s continuous textual representation to erase target characters during generation. We optimizes an anchor embedding via structural and detailed constraints to serve as a character surrogate, then replaces target-related embeddings with the anchor via a structure-aware adaptive strategy. Experiments show that our method achieves state-of-the-art erasure effectiveness and image fidelity preservation, while supporting controllable erasure degree, multi-target removal, and model transferability. Moreover, our optimized anchors are plug-and-play with current model modification baselines to improve their erasure performance.
[CV-73] Beyond Correctness: Benchmarking and Aligning Response Behaviors in Hybrid-Thinking MLLM s
链接: https://arxiv.org/abs/2608.12781
作者: Xinming Wang,Weinong Wang,Hongming Yang,Yansong Lin,Zheng Ruan,Shangpin Peng,Qiming Peng,Nan Qiao,Fengyuan Lu,Guoqing Ma,Marito Li,Songyang Zhang,Saiyong Yang,Han Hu,Yonglong Tian,Xu-Yao Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 tables and 6figures
Abstract:Hybrid-thinking multimodal large language models (MLLMs) allow a single model to alternate between deliberative thinking and latency-efficient non-thinking inference. Although these modes differ in reasoning budget, their delivered responses should satisfy the same user-facing standard. Correctness alone may not characterize this response quality; we therefore evaluate task accuracy and response-pattern failures as complementary outcomes. We study this gap through \textbfresponse-pattern alignment: whether thinking and non-thinking interfaces preserve acceptable final-response behavior. We introduce \textbfPatternEval, a failure-enriched diagnostic benchmark comprising 2,415 multimodal prompts spanning visual perception and grounding, structured image understanding, and multimodal knowledge reasoning. PatternEval tests four recurrent failures: chain-of-thought leakage, response repetition, logical contradiction, and performative reasoning. Response-pattern failures are widespread across models from different providers, with non-thinking inference exhibiting substantially higher failure rates and thereby creating systematic misalignment between thinking and non-thinking interfaces. Motivated by this diagnosis, we develop \textbfPatternRM, a response-level reward model, and \textbfPatternRL, which introduces pattern-specific penalties during reinforcement learning. Experiments on Qwen3-VL-4B and Qwen3-VL-8B show that incorporating pattern-specific penalties into reinforcement learning can mitigate cross-mode misalignment while incurring a marginal task performance trade-off. Together, PatternEval and PatternRL provide an evaluation-and-training framework for aligning user-visible response patterns across hybrid-thinking interfaces.
[CV-74] SCOPE: Subspace Clustering with Online Per-Head Top-K Estimation for Sparse Video Attention
链接: https://arxiv.org/abs/2608.12780
作者: Qi Zhao,Qirui Li,Hanlin Tang,Yiduo Li,Zhen Guo,Cuifeng Shen,Chao Xu,Zhaosheng Chi,Xiaojin Lu,Kan Liu,Tao Lan,Lin Qu,Xi Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diffusion Transformers (DiTs) incur quadratic self-attention cost over spatiotemporal tokens. Existing training-free sparse attention methods often construct sparse masks from block-level or cluster-level proxy scores, which can obscure fine-grained differences among keys and miss high contribution keys under aggressive sparsity. Moreover, such proxy scores may yield overly concentrated softmax distributions, causing Top- p to retain too few keys for some query clusters. Although a fixed Top- k minimum alleviates this failure mode, a shared value cannot adapt to variations across heads and inputs. To address both limitations, we propose SCOPE, a training-free sparse attention framework that combines 3D-RoPE-aligned key subspace clustering with online per-head Top- k estimation for efficient video-DiT inference. SCOPE partitions post-RoPE keys into temporal, height, and width subspaces, clusters them independently, and aggregates the corresponding centroid scores through lookup tables to obtain per key proxy scores for each query cluster. Building on existing hybrid Top- p /fixed Top- k selection, SCOPE derives a head-specific Top- k value online by averaging the initial retained key counts within each head, weighted by query cluster size, and selects additional keys only for query clusters whose initial retained key counts fall below this value. Sparse attention is then computed over the selected original keys and values. Across six model–task configurations, SCOPE consistently outperforms existing training-free baselines in both fidelity and latency, achieving up to a 1.99\times end-to-end speedup on 720p HunyuanVideo with 28.46 dB PSNR relative to dense attention.
[CV-75] CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers
链接: https://arxiv.org/abs/2608.12773
作者: Ebenezer Tarubinga
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
备注: Submitted to IEEE TPAMI. 22 pages, 11 figures, 17 tables
Abstract:Semi-supervised semantic segmentation has long turned on one question, which pseudo-labels to trust, and a generation of selection rules, dynamic thresholds, per-class curricula, soft confidence weights, answered it for the noisy, under-confident ResNet teachers of their day. Self-supervised foundation encoders change the regime: with a DINOv2 teacher, confidence saturates, so the filtering that helped a weak teacher can hurt a strong one. We propose CW-BASS v2, a saturation-aware pseudo-label selection method that reads the teacher’s confidence regime rather than committing to one rule. It pairs held-out calibration, an unbiased per-class noise estimate, with a self-adaptive confidence floor that provably bounds retention away from 1, and combines them in a one-pass gate: measure the reliability of the teacher’s confident set, pi_kept = Pr[correct | c = tau], on a held-out slice, and filter strictly when it meets the confidence demanded (pi_kept = tau), falling back to the adaptive floor otherwise. The boundary is the pre-existing operating threshold, not a value tuned to mIoU, and across six DINOv2 teachers it makes the correct strict-vs-floor call blind. CW-BASS v2 thus recovers the UniMatch V2 operating point on the saturated benchmarks by selecting strict (Pascal VOC 1/8 87.4 against its reported 87.9; Cityscapes within 0.5), and improves on it where the confident set is unreliable (pi_kept ~ 89%, ADE20K), where the floor edges ahead (+1.5 mIoU, single seed). The gate is principled because the failure it avoids is measured, not assumed: on a reliable, saturated teacher the confidence distribution’s dynamic range collapses (98% of Pascal pixels = 0.95), so an adaptive cutoff floods the retention mask and self-training decays into confirmation bias.
[CV-76] PatchGen: Learning Soft Intra-Image Predictive Subsets for Visual Generalization
链接: https://arxiv.org/abs/2608.12766
作者: Zhaorui Tan,Weimiao Yu,Xi Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual classifiers are expected to generalize under data shifts, target shifts, and their combinations, yet most existing methods focus on domain invariance while failing to address intra-image predictive sufficiency. We investigate the structural hypothesis that each image contains a sample-adaptive oracle intra-image predictive subset sufficient for label prediction, while the remaining patches form non-essential complementary context that may correlate with the label. The theoretical analysis shows that restricting prediction to this oracle subset preserves the Bayes risk achievable by the full-patch representation while admitting a complexity bound that tightens with the oracle-subset size. Based on this view, we propose PatchGen, a text-free module that learns a sample-dependent soft predictive-subset mask as a task-driven proxy for the unobserved oracle subset mask. Specifically, histopathology visualizations suggest that PatchGen assigns higher scores to tumor-consistent regions than to some frequently co-occurring inflammatory context. Extensive experiments on natural and histopathological image benchmarks spanning all three shift settings show that PatchGen improves average performance over matched-backbone baselines in most evaluated configurations, enhances generalization to unknown classes, and remains competitive with vision-language methods without text supervision.
[CV-77] Scaling Representation Diversity: Modulated Attention and Reconstructive Regularization for Visual Grounding
链接: https://arxiv.org/abs/2608.12748
作者: Junyi Hu,Tian Bai,Fengyi Wu,Yian Huang,Wei Wen,Zaoli Li,Junli Lin,Xingchen Li,Zhenming Peng,Yi Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 21 pages, 10 figures, 6 tables
Abstract:Referring Expression Comprehension (REC) is commonly studied under dataset-specific fine-tuning, resulting in specialist models with limited cross-dataset generalization. In this work, we revisit REC from the perspective of unified open-vocabulary grounding and identify representation degeneration as a key obstacle to scaling a single generalist model. To preserve representation diversity, we propose a holistic data-model co-design framework. Architecturally, we introduce the Modulated Attention-Contrastive Head (mACH) for efficient token-level vision-language alignment and a text-conditioned JEPA auxiliary stream that provides complementary gradient support to preserve alignment-active representations without inference overhead. On the data side, we introduce Objects365-Caption, enriching Objects365 with context-aware referring expressions for large-scale language supervision. We further provide a theoretical analysis showing that complementary gradient subspaces preserve alignment capacity and thereby scale representation diversity. Extensive experiments demonstrate that our single-checkpoint framework achieves highly competitive performance on standard REC benchmarks while exhibiting strong generalization across heterogeneous grounding datasets without benchmark-specific adaptation.
[CV-78] Dual-Manifold Geometry Guided Representation Learning: Adaptive Coupling between Kernel and Data Spaces
链接: https://arxiv.org/abs/2608.12737
作者: Wencong Zhang,Yue Zhang,Meiyan Huang,Wei Yang,Qianjin Feng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Deep representation learning has primarily focused on how features evolve across network layers, while largely overlooking the structured geometry embedded in network parameters. We introduce a dual-manifold perspective in which each convolutional layer contains two coupled geometric spaces: a Kernel Manifold induced by convolutional filters and a Data Manifold characterized by intermediate feature representations. Because these manifolds share the same channel space, parameter geometry can provide complementary structural information to guide feature evolution. Based on this insight, we propose Kernel-Guided Feature Transform (KGFT), a lightweight module that derives a geometric guidance matrix from the kernel Gram matrix and uses it to transform the covariance structure of feature representations. Unlike conventional attention mechanisms that reweight feature responses, KGFT explicitly reshapes feature relationships by transferring geometric information from the kernel manifold to the data manifold. To accommodate network hierarchy, we further introduce Exploit and Explore modes with a depth-aware scheduling strategy and a learnable guidance strength that adaptively controls the contribution of geometric transformation. This design promotes geometric alignment in shallow layers while encouraging feature diversity in deeper layers, without imposing excessive constraints on representation learning. Theoretical analysis establishes the validity of the proposed transformation and characterizes its effect on feature covariance. Extensive experiments across CNN- and Transformer-based architectures, including ResNet, ViT, and LLaMA-7B, demonstrate consistent improvements on image classification and arithmetic reasoning tasks, validating the generality and effectiveness of kernel-guided dual-manifold representation learning. Code will be publicly available.
[CV-79] A Generative Approach for Improving Multi-Label Defect Classification in Photovoltaic Modules
链接: https://arxiv.org/abs/2608.12725
作者: Abdul Mueez,Yogesh S. Rawat,Shruti Vyas
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This paper addresses the challenge of multi-label defect classification in electroluminescence (EL) images of photovoltaic (PV) cells. Training models on images where multiple defects co-occur creates learning ambiguity, making it difficult to disentangle visual features for specific defect types, a problem compounded by the scarcity of examples for individual classes. To tackle this, we introduce Generative Defect Isolation (GDI), utilizing the LaMa inpainting model with Fast Fourier Convolutions to remove selected defects and generate realistic, single-defect training samples. Extensive experiments on Vision Transformer (ViT-S, ViT-L) and EfficientNetV2-L architectures demonstrate that GDI significantly outperforms baselines. The performance gains are most pronounced in low-data scenarios; class-wise analysis shows substantial improvements, boosting the F1-Score for rare defect classes by up to 63.6%. Furthermore, GDI effectively resolves learning ambiguity from co-occurring defects, yielding a 26% reduction in such co-occurring classification errors. Our work establishes GDI as an effective method for maximizing the value of existing segmentation datasets and sets a new performance benchmark for multi-label classification in this domain.
[CV-80] VOS-Agent : The 1st Place Solution for the 8th LSVOS Challenge (MOSEv2 Track) ECCV2026
链接: https://arxiv.org/abs/2608.12721
作者: Canyang Wu,Jinrong Zhang,Xusheng He,Ce Bian,Xianjing Han,Jianlong Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 1st Place Solution for the 8th LSVOS MOSEv2 Challenge (ECCV 2026 Workshop)
Abstract:Complex video object segmentation requires robust target propagation under severe occlusion, disappearance and reappearance. Although SAM3 provides strong promptable mask propagation, a uniform inference path remains unreliable for tiny targets with insufficient visual evidence and semantic-dominated targets whose identities depend on explicit attributes. To this end, we present VOS-Agent, a collaborative framework that retains SAM3 as the shared dense segmentation module and conditionally activates specialized agents according to target characteristics. A Target Perception and Routing Agent assigns each sequence to a regular, tiny, or semantic-dominated route. Tiny targets are supported by a Visual Tracking Agent through confidence-aware box prompts, while semantic-dominated targets are handled by an MLLM-based Semantic Agent through description-guided localization and candidate verification. On the MOSEv2 test set, VOS-Agent achieves 69.82% on the official \mathcalJ\dot\mathcalF metric and ranks first in the MOSEv2 Track of the 8th LSVOS Challenge at ECCV 2026.
[CV-81] owards Sparsely Annotated Open-World Object Detection
链接: https://arxiv.org/abs/2608.12714
作者: HeeJu Han,AJeong Kim,Jinsun Park
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Real-world object detection operates under ambiguous supervision, where unlabeled regions may correspond to missing annotations of known objects or genuinely unknown categories. These challenges have been addressed separately in Sparsely Annotated Object Detection (SAOD) and Open-World Object Detection (OWOD). In practice, their co-occurrence remains an open problem. To address this problem, we introduce Sparsely Annotated Open-World Object Detection (SA-OWOD), a new task that jointly considers sparse supervision and the presence of unseen categories. We propose Dual-Perspective Object Discovery (DPOD), a unified framework that jointly models unlabeled known and unknown instances via two complementary mechanisms. The Known Target Recovery Module (KTRM) recovers supervision for unlabeled known instances and explicitly regularizes the feature space to separate known and unknown representations. Complementarily, the Dual-Disagreement Target Generator (DDTG) identifies reliable unknown candidates through cross-view semantic inconsistency. By integrating these modules, DPOD resolves contradictory supervision signals caused by ambiguous unlabeled regions. As a result, it prevents misclassification between known and unknown objects and stabilizes the decision boundaries. Experimental results on sparsely annotated open-world benchmarks demonstrate that the proposed method outperforms existing open-world detection methods, particularly in detecting unknown objects.
[CV-82] Class Geometry as Supervision for Sample-Efficient Open-World Detection
链接: https://arxiv.org/abs/2608.12698
作者: Akash Rao,Zhou Chen,Revanth Reddy Palem,Udhav Ramachandran,Ruth Scimeca,Sathyanarayanan N. Aakur
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Under review. 12 Pages, 5 figures, 4 tables
Abstract:Open-world object detection requires models to recognize known categories, reject unfamiliar objects, and incorporate new classes over time. This is especially challenging in scarce-data settings such as biomedical and scientific imaging, where rare categories may have only a few annotated examples and fine-grained classes differ by subtle morphology. Prototype-based detectors are natural for this regime, but they typically learn class prototypes as independent anchors, ignoring relational structure among classes. We propose class-geometry supervision (CGS), a general framework that constrains learned prototype or class-representation spaces to preserve visual or semantic class dissimilarities estimated from training data. CGS introduces a dissimilarity-preserving objective that aligns pairwise distances among learned class representations with a target class-geometry matrix while retaining the standard task loss. We instantiate the same objective across prototype recognition, few-shot biomedical object detection, open-set detection, novel-class insertion, and OWOD adaptation on COCO. Experiments show that CGS improves sample efficiency in recognition and ova detection, substantially strengthens novel-class insertion, and improves unknown recall on COCO while retaining much of the known-class detection performance. Ablations show that meaningful visual geometry provides the most reliable gains, while random geometry can help novel separation but is less consistent for few-shot detection. These results suggest that relational class geometry is an effective supervisory signal for building calibrated and extensible open-world detectors under limited supervision.
[CV-83] Mr3D-VL: A generalist vision language foundation model for Multiparametric 3D Magnetic Resonance Imaging
链接: https://arxiv.org/abs/2608.12689
作者: Zhi Qiao,Xintong Wu,Yichu He,Feng Shi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-parametric magnetic resonance imaging (mpMRI) is a cornerstone for brain tumor diagnosis and treatment, yet current AI models face critical limitations: their lack of natural language interaction and interpretability impedes spatial information integration and cross-modal reasoning required clinically. Key challenges arise from significant physical meaning differences across modalities, spatial misalignment due to scan intervals, and the need for complex multi-feature interpretation in tasks like glioma grading. While visual-language models (VLMs) show promise in cross-modal understanding, existing methods focus mainly on 2D image modeling, neglecting direct perception of 3D volumetric space. Although 3D VLMs have been proposed for report generation and feature alignment in 3D CT imaging, mpMRI applications demand collaborative inference across multiple imaging modalities-a requirement unmet by current solutions. To address this, we introduce Mr3D-VL, a dedicated visual-language foundation model for multi-parametric 3D MRI. With 4 billion parameters, it employs an unsupervised pre-trained shared 3D encoder and 4D rotational positional embedding for dual modality-spatial integration. Its cross-modal projection layer uses a multi-resolution feature implantation strategy to enhance feature perception across resolutions. Experimental results show significant improvements over existing 4B/7B/30B domain-specific and general-purpose models in text generation tasks, achieving a BERTScore of 0.856 for report generation, with question-answering accuracy at 0.713 and multiple-choice accuracy at 0.912.
[CV-84] FUSE: Active Functional Affordance Grounding through Adaptive Semantic-Geometric Evidence Acquisition
链接: https://arxiv.org/abs/2608.12683
作者: Zhou Chen,Sathyanarayanan N. Aakur
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Under review. 15 Pages. 9 tables, 3 Figures
Abstract:Embodied agents must often identify and interact with objects based on their function rather than their identity, requiring them to actively acquire observations that reveal discriminative functional evidence. Existing affordance grounding methods operate from fixed viewpoints and lack mechanisms for deciding where to look when functional cues are occluded or incomplete. We introduce Active Functional Affordance Grounding, a new task in which an agent sequentially explores a scene to identify and spatially ground an object satisfying a functional query. To address this problem, we propose FUSE, an adaptive semantic-geometric evidence acquisition framework that combines explicit uncertainty-driven exploration with a learned amortized planner to efficiently select informative viewpoints. We further introduce a Habitat-based benchmark for evaluating active functional grounding. Experiments show that FUSE achieves the highest observed non-oracle grounding performance while reducing computation by 1.33x relative to fully explicit exploration, and remains effective across multiple affordance knowledge sources.
[CV-85] he Role of Natural Language Understanding in Multimodal Video-Based Dengue Diagnosis
链接: https://arxiv.org/abs/2608.12677
作者: Danial Sharifrazi,Saadat Behzadi,Julakha Jahan Jui,Mojtaba Mohammadi,Nouman Javed,Roohallah Alizadehsani,Prasad N. Paradkar,Asim Bhatti
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Detecting infection-related behavioral changes in mosquitoes from video data is challenging because mosquitoes are small, move rapidly and irregularly, and are affected by environmental factors such as background, lighting, and shadows, which can make reliable feature extraction difficult. In this study, a YOLO- and Contrastive Language-Image Pre-training (CLIP)-based vision-language framework is proposed to classify mosquito flight frames of uninfected and Dengue virus serotype 2 (DENV2)-infected mosquitoes. First, YOLO is used to isolate mosquito regions from the background. Then, visual features extracted from video frames are aligned with biologically meaningful textual prompts in a shared embedding space. The multimodal model was fine-tuned using supervised bidirectional contrastive learning and evaluated through frame-level image-text similarity-based classification. The results show that the proposed method achieved 98.54% accuracy and 99.91% sensitivity at the frame level. After temporal aggregation of frame-level information, the model achieved complete video-level performance. The ablation results showed that fine-tuning and CLIP-based representations were essential for this domain, while the textual branch provided semantic image-text alignment rather than an accuracy advantage over the vision-only model. These findings suggest that vision-language models can provide a useful framework for analyzing infection-related biological behaviors from video data.
[CV-86] Inference-Time Orthogonal Seeding Enables Geometry-Aligned 3D Organ Segmentation for Slice-Propagation Methods MICCAI2026
链接: https://arxiv.org/abs/2608.12658
作者: Md Rakibul Haque,Tushar Kataria,Shireen Y. Elhabian
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 Pages Accepted at MLMI Workshop MICCAI 2026
Abstract:Dense voxel-level annotation remains a major bottleneck in 3D medical image segmentation. Single-slice propagation methods such as Sli2Vol reduce this burden by propagating one annotated seed slice through a volume using label-free registration. However, axial-only propagation accumulates errors with distance from the seed, especially in surface-distance metrics, because it ignores coronal and sagittal evidence and therefore underuses the 3D information available in CT/MRI volumes. To better leverage volumetric geometry, we study how key training and inference choices affect slice-propagation models, including single-axis versus multi-axis label-free registration, single-seed versus multi-seed propagation, and orthogonal seed configurations. Instead of propagating from a single axial seed, we use three orthogonal seeds—one axial, one coronal, and one sagittal—and fuse their propagated labels with a simple label-free rule. Our results show that the training paradigm has limited impact: an axially trained network applied to off-axis seeds captures nearly all the improvement, while explicit three-axis training adds little. Instead, performance is driven by inference-time seed geometry, especially orthogonality rather than the number of annotated slices, as a budget-matched three-axial control provides no benefit and can even degrade performance. On a multi-organ CT cohort, orthogonal seeding with the axial Sli2Vol backbone improves Dice by 21.9%, Normalized Surface Dice by 25.5%, and reduces Average Hausdorff Distance by 53.5% over the single-axis baseline.
[CV-87] From Visual Widgets to UI Code: Efficient Tool-Grounded Generation ECCV2026
链接: https://arxiv.org/abs/2608.12611
作者: Houston H. Zhang,Tao Zhang,Li Gu,Linfeng Ye,Yuanhao Yu,Xinxin Zuo,Yang Wang,Zhixiang Chi
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: ECCV2026 (MUCG Workshop)
Abstract:Existing screenshot-to-code systems face a trade-off between flexibility and controllability. Direct multimodal generation can hallucinate visible details, whereas structured pipelines reduce such errors through component-wise decomposition, predefined templates, and customized intermediate representations. These structures, however, introduce additional generative orchestration and restrict outputs to designs covered by the representation. We investigate whether selective tool grounding can improve the fidelity–efficiency trade-off of direct widget-to-code generation. We introduce \textbfWidgetGen, a lightweight tool-grounded framework that extracts observable text and color evidence, performs high-level layout and optional chart reasoning, and directly generates executable JavaScript XML (\emphJSX). This design reduces reliance on component-wise generation while avoiding a fixed UI schema. Across six multimodal models and (1,000) held-out widgets, WidgetGen outperforms direct prompting and the structured Widget2Code pipeline on most visual reconstruction metrics, with consistent gains in area, legibility, and style. Finally, reconstruction-derived image-code pairs improve six Qwen-family open-weight models across every reported metric through supervised fine-tuning. These results establish WidgetGen as a strong lightweight baseline and show that selective evidence grounding offers an effective alternative to extensive representation constraints.
[CV-88] PseudoMapLabeler: Confidence-Aware Pseudo-Label Generation for Semi-Supervised Online Mapping ECCV2026
链接: https://arxiv.org/abs/2608.12600
作者: Chikao Tsuchiya,Dhaval Bhanderi,David Ilstrup,Hsinmin Cheng,Christopher Ostafew
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 17 pages, 4 figures, Accepted at ECCV 2026 DriveX Workshop on Foundation Models for Autonomous Driving
Abstract:A critical challenge in deploying online HD map construction systems to real-world scenarios is the scarcity of labeled training data, which limits model generalization in diverse environments. To address this limitation, we propose a teacher-student semi-supervised learning (SSL) framework that generates high-quality pseudo-labels from unlabeled data through confidence-aware map refinement. Our approach first trains a teacher model on limited labeled data, then leverages Beta-distribution-based confidence maps to assess the reliability of predicted map elements across temporal observations. Unlike conventional filtering methods that discard entire elements, we introduce a spatial clipping technique that selectively preserves high-confidence regions while removing unreliable segments. The refined map elements serve as map priors that improve the teacher model’s prediction accuracy on unlabeled data in a second pass. These enhanced predictions become pseudo-labels for training a student model from scratch, followed by fine-tuning on the original labeled data. Experimental results on the nuScenes dataset demonstrate that our teacher-student framework with refined pseudo-labels improves performance by +6.1 mAP under a low-label regime compared to training on labeled data alone, offering a practical solution to the labeled data scarcity problem in online HD map construction.
[CV-89] Auditable agent ic AI for evidence-grounded thyroid ultrasound diagnosis and reporting
链接: https://arxiv.org/abs/2608.12590
作者: Haifan Gong,Shiyu Chen,Bodong Wang,Yuqi Wang,Shijie Wang,Guoliang You,Xinyu Xiong,Haowei Wang,Mingzhi Mao,Dexing Kong,Qinghua Liu,Wei Lou,Fei Chen,Guanbin Li
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Under review
Abstract:Thyroid ultrasound diagnosis requires coordinated lesion localization, measurement, risk stratification and reporting, yet most AI systems address these tasks in isolation and provide limited support for clinical review. We present ThyroidXAgent, a clinician-interactive agentic AI system that coordinates specialized diagnostic tools and stores their outputs as an auditable case-level evidence record. The system was developed using OpenThyroidDB, a multicentre, multitask resource integrating approximately 0.3 million ultrasound images and 24,000 paired reports, and was evaluated on 28,458 non-overlapping test cases, including 8,721 cases from 35 centres in the private NHC-MISD-TUS cohort. Across heterogeneous datasets, ThyroidXAgent achieved a mean Dice score of 87.21 percent for nodule segmentation and a mean AUROC of 0.9466 for benign-malignant classification. The same workflow supported lymph-node metastasis prediction and follicular versus papillary thyroid carcinoma classification, with AUROCs of 0.864 and 0.805, respectively. For report generation, evidence-grounded assembly outperformed multimodal language-model baselines across three cohorts. ThyClinScore, a lesion-level clinical semantic metric introduced here, showed the strongest correlation with a location-aware language-model judge. ThyroidXAgent improved physician classification accuracy, increased report diagnostic consistency from 70.3 percent to 86.2 percent, and reduced segmentation and reporting time by 35.9 percent and 27.4 percent, respectively. These findings support auditable, clinician-correctable agentic AI for thyroid ultrasound diagnosis and reporting.
[CV-90] StrAD: A Streaming Method and Benchmark for Audio Description Generation for Long-form Videos
链接: https://arxiv.org/abs/2608.12549
作者: Julian Spravil,Sebastian Houben,Sven Behnke
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual content is the dominant medium of communication, yet without audio descriptions (ADs), it remains inaccessible to blind and low-vision people. ADs narrate context-relevant visual events during natural audio pauses. Manually creating ADs is expensive, limiting coverage to a small fraction of available content. Most existing automatic AD generation methods frame the task as video clip captioning, requiring ground-truth timestamps and additional context cues such as character databases. Current benchmarks reinforce this framing, consisting of short video segments paired with automatic or task-mismatched annotations. We introduce StrAD, a benchmark for long-form AD generation on full-length videos spanning diverse genres such as movies, documentaries, short films, performances, and video games. We reformulate AD generation as streaming dense video captioning. Our approach processes full-length videos with a sliding window, inserting ADs into existing transcripts without ground-truth timestamps, and supports both fine-tuned models and zero-shot prompting of vision-language models. On the segment-level task with given timestamps, our fine-tuned StrAD-FT sets the state of the art on CMD-AD with 36.3 CIDEr (+10.0 over Shot-by-shot), establishes a reference point on StrAD (51.0 CIDEr), and remains competitive on MAD-Eval at 24.9 CIDEr. On the full-video streaming task, StrAD-FT reaches a SODA score of 2.4 against 1.1 for our zero-shot baseline StrAD-Zero, though both exhibit limitations in temporal localization and narrative coherence. While prior work has tackled full-video AD generation in an offline, multi-stage fashion, ours is the first streaming approach, generating ADs on the fly without ground-truth timestamps. StrAD makes progress on full-video AD generation measurable, a prerequisite for scaling accessibility.
[CV-91] Surface-to-Skeleton 3D Cephalometry: Estimating Hidden Skeletal Landmarks from CT-Derived External Soft-Tissue Surfaces ECCV2026
链接: https://arxiv.org/abs/2608.12537
作者: Tomoki Abe,Taiki Kanaya,Kazuki Saita,Mao Noda,Chie Tachiki,Yasushi Nishii,Hideo Saito
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to AI4M3D Workshop at ECCV 2026
Abstract:Existing 3D facial-landmark methods localize points on visible skin, but whether CT-defined internal skeletal landmarks can be inferred from external soft-tissue geometry remains unclear. We formulate a coordinate-consistent surface-to-skeleton task using same-acquisition CT-derived surfaces, separating estimation from optical-to-CT registration, scanner-domain, and acquisition-state effects, with coverage analyzed separately. From 240 clinical CT scans from two hospitals, we construct a locked retrospective protocol pairing CT-derived external soft-tissue point clouds with 21 skeletal landmarks and three visible soft-tissue landmarks. An integrated hierarchical point-cloud model achieves 2.97 mm mean radial error on skeletal landmarks and 3.03 mm on deep or surface-invisible landmarks in 40 held-out patients. Patient-mismatch controls support patient-specific signal beyond a fixed population configuration or global similarity alone, while coverage ablations indicate dependence on non-anterior geometry. Optical-transfer diagnostics reveal substantial coverage-related and global-configuration components, although deployable optical inference remains unresolved. These results answer the controlled feasibility question affirmatively and provide a basis for hidden skeletal landmark inference.
[CV-92] Can Vision-Language Models Assess Proxemic Risk from Egocentric Robot Images? ECCV2026
链接: https://arxiv.org/abs/2608.12515
作者: Vladyslava Rudas,Dmytro Kuzmenko
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Accepted at the EMR 2026 workshop at ECCV 2026 (non-archival)
Abstract:Assessing proxemic danger from a robot’s egocentric perspective is critical for safe embodied navigation in human environments and requires both visual and contextual reasoning. We evaluate three opensource vision-language models (VLMs) (\textitInternVL, \textitQwen-VL, and \textitSmolVLM) on the classification of egocentric robot images into four danger levels, comparing three prompting strategies and two rounds of QLoRA fine-tuning against a stratified random baseline. Without fine-tuning, all models perform near the baseline, while fine-tuning yields only modest overall improvements. However, \textitQwen-VL with an advanced prompt achieves substantially higher recall for high-danger cases than the other models. An analysis of person localization further shows that correct danger classification does not correspond to better spatial grounding, indicating that a model may produce a useful safety label without attending to the relevant region of the scene. These results show that current VLMs remain limited in fine-grained proxemic reasoning and spatial grounding, although targeted prompting and fine-tuning can improve high-danger detection in selected models.
[CV-93] HIMEC: Directional Change Representation and Fixed-Interface Decoding for Remote Sensing Image Change Captioning
链接: https://arxiv.org/abs/2608.12502
作者: Aysha Ashraf(University of Electronic Science and Technology of China),Shaina Ashraf(University of Bonn),Wafaa I. M. Hussin(University of Electronic Science and Technology of China),Ali Haider(University of Electronic Science and Technology of China),Zhi Lu(University of Electronic Science and Technology of China),Zhenming Peng(University of Electronic Science and Technology of China)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted to IEEE Transactions on Geoscience and Remote Sensing (TGRS) for review
Abstract:Remote sensing image change captioning (RSICC) converts bitemporal imagery into a sentence describing semantic changes. Most RSICC methods condition caption decoders directly on fused visual features, leaving intermediate change structure and decoder-interface consistency less studied. We present HIMEC, combining Directional Change Representation (DCR) with fixed-interface decoding. DCR separates signed differences into appearance-oriented, disappearance-oriented, and shared-context streams before fusion. A learned-query encoder converts the fused representation into visually conditioned change-query tokens that form the scene decoder’s only sample-dependent memory. A training-only auxiliary phrase decoder supplies caption-derived supervision. With a fixed zero input, the scene decoder maintains the same interface during training and inference. Separately, we evaluate a local-to-scene cascade conditioned on teacher-forced local states during training and autoregressive states at inference. On changed LEVIR-CC validation pairs, these states have a mean cosine distance of 0.69. Regime-matched conditioning recovers most of the associated deficit, whereas permuting state correspondence causes no detectable penalty. These findings are limited to the evaluated cascade. In a matched three-seed comparison, HIMEC reaches a Consensus-based Image Description Evaluation (CIDEr) score of 142.81\pm0.60 on LEVIR-CC, versus 139.51\pm3.40 for direct fused-feature memory. On SECOND-CC, fixed-zero and regime-matched diagnostic conditioning reach 75.67 and 76.99 CIDEr, respectively, versus 60.77 for the mismatched cascade. The source code will be made publicly available at this https URL upon publication.
[CV-94] MV2: Multi-View Multi-Vehicle Driving Dataset for Novel View Synthesis ECCV
链接: https://arxiv.org/abs/2608.12442
作者: Sanjay Bhargav Dharavath,Hanvitha Saraswathi Mukkamala,Faizan Farooq Khan,Ioannis Kakogeorgiou,Aditya Arun,C V Jawahar,Zakaria Laskar
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 7 figures, ECCV accepted paper
Abstract:Differentiable rendering has advanced novel view synthesis (NVS), yet applying it to real-world driving remains difficult due to sparse capture viewpoints, dynamic objects, and limited multi-trajectory data. We introduce the Multi-View Multi-Vehicle (MV2) dataset and benchmark for evaluating NVS models under large viewpoint changes in dynamic urban scenes. MV2 features synchronized captures from a car, scooter, and drone, each following distinct yet synchronized trajectories. Training NVS methods on one vehicle’s camera stream and testing on another enables evaluation under substantially larger viewpoint variations than existing single-trajectory datasets. All sequences are registered via Structure-from-Motion and camera poses verified using manual pixel-level correspondence annotations, yielding 50 high-quality scenes with 12000 images. Benchmarking recent NVS and camera pose estimation methods shows that NVS performance degrades with increasing viewpoint disparity, and that feed-forward pose estimators notably lag behind optimization-based approaches, highlighting MV2 as a rigorous testbed for NVS in driving. The dataset, benchmark protocol, and project resources are available at this https URL.
人工智能
[AI-0] QuoteBench: How Matched Scores Can Hide Command-Path Failures
链接: https://arxiv.org/abs/2608.13547
作者: Shangao Li,Yao Zhang,Volker Tresp,Yuanyuan Yang
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 29 pages, 5 figures. Project page: this https URL
Abstract:LLM coding agents issue Bash commands through interfaces that may serialize, wrap, and reparse model output. Matched execution scores alone cannot distinguish command-generation errors from failures introduced after generation. QuoteBench measures this boundary with exact final-state validation on 56 one-shot tasks from 14 incident-derived families, crossing the generation contract with the execution transport around one deliberately unescaped added parser. Escaping at the interpolation point reproduces each replayed reply’s raw-path outcome, so any recovery under a disclosed boundary must come from the model changing its generation. Across eight same-window configurations, replaying the same reply through the added parser lowers success by 55.4 to 73.2 percentage points; disclosure recovers 30.4 to 60.7 points for six configurations, and zero or slightly negative for the other two. Raw generation is nearly saturated at the frontier; boundary adaptation is what still separates models. GPT-5.6-sol’s matched gap of -3.6 points hides -64.3 points of damage and +60.7 points of compensation. The deployment configuration reorders models: one reversal among 26 comparable pairs is unambiguous and four more sit on single-task margins. Evaluations of command-issuing agents should report the model configuration, generation contract, execution path, operating point, and final-state validator rather than treat a matched score as an intrinsic model property.
[AI-1] Vero: Can AI Agents Build Formally Verified Software Repositories?
链接: https://arxiv.org/abs/2608.13522
作者: Zhe Ye,Hantao Lou,Yuechun Sun,Peiyang Song,Zhengxu Yan,Timothe Kasriel,Qingyang Zhang,Kaiyu Yang,Soonho Kong,Jingxuan He,Dawn Song
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO); Programming Languages (cs.PL); Software Engineering (cs.SE)
备注:
Abstract:AI agents are increasingly used for programming, but do not provide any guarantee on the correctness of generated code. Verified code generation, in which an agent produces both an implementation and a machine-checked proof of its specification, offers a stronger path toward trustworthy AI-generated software. Existing benchmarks in this direction either focus on individual functions or only evaluate proof generation with provided implementations. It is still an open question whether agents can make coherent implementation and proof choices across real multi-module codebases. To bridge this gap, we introduce Vero, the first benchmark to evaluate joint implementation and proof synthesis at the repository level. Vero contains 43 multi-module instances sourced from real-world repositories spanning Python, Dafny, Verus, and Coq, and covering diverse domains from cryptographic protocols to distributed systems. Each instance consists of a multi-module Lean 4 repository with predetermined API interfaces, manually curated formal specifications, and reference implementations, supporting both proof-only and code-and-proof evaluation modes. To improve benchmark reliability, Vero also includes an audit mechanism where agents are allowed to formally prove unsatisfiability of provided specification or incorrectness of reference code, which surfaces and corrects latent code and specification errors during curation. We evaluate frontier coding-agent configurations with Lean toolchain access. The strongest agent fully solves only 27 of 43 instances and closes no specifications on the hardest repositories. Vero provides a concrete testbed for measuring progress toward repository-scale verified software synthesis, where current agents still fall short. We release the benchmark, curation pipeline, and evaluation harness at this https URL.
[AI-2] he data geometry of masking diffusion: Certified-optimal schedules via unmasking growth complexity
链接: https://arxiv.org/abs/2608.13520
作者: Martin J. Wainwright
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Theory (cs.IT); Statistics Theory (math.ST); Machine Learning (stat.ML)
备注:
Abstract:We study masking diffusion for discrete sampling and introduce a path-resolved measure of data geometry called the \emphunmasking growth complexity (\textsfUGC\xspace). Its local increments directly control Kullback–Leibler (KL) discretization error, yielding a unified analysis of Bernoulli-subset and fixed-cardinality unmasking schemes. In log-reveal-odds coordinates, this structure yields optimized single-block and multi-block schedules, and quantifies the gains from adapting computational effort to data geometry. Crucially, we show how \textsfUGC\xspace increments can be estimated from samples via KL increments along coupled reveal trajectories. This leads to \emphcertified-optimal samplers that achieve a prescribed KL error with high probability and iteration complexity within a constant factor of the corresponding oracle procedure. Collapsing the \ugc path yields the aggregate \textsfUGC\xspace mass, which connects to classical multivariate dependence measures and complexity measures from previous analyses of discrete diffusion. In the fine-partition limit, the squared integral of the square-root \textsfUGC\xspace density determines the sharp leading-order optimal Euler discretization error. Examples exhibit substantial dimension-dependent gains over coarse schedules, including \widetilde\Omega(\sqrtd) improvements achievable with a constant number of adaptively placed blocks.
[AI-3] AlayaWorld: Interactive Long-Horizon World Modeling - Full Technical Report (v1.1)
链接: https://arxiv.org/abs/2608.13492
作者: AlayaWorld Team:Kaipeng Zhang,Chuanhao Li,Yifan Zhan,Yongtao Ge,Yuanyang Yin,Jiaming Tan,Kang He,Liaoyuan Fan,Mingliang Zhai,Ruicong Liu,Xiaojie Xu,Xuangeng Chu,Zhen Li,Zhengyuan Lin,Zhixiang Wang,Zian Meng,Zihui Gao
类目: Artificial Intelligence (cs.AI)
备注: Authors are listed alphabetically by the first name and their role. See the contribution section for details
Abstract:This report presents an improved version of AlayaWorld. While the backbone architecture, chunk-wise autoregressive generation scheme, and training data remain unchanged from the previous release, we substantially revise how conditioning signals are represented and integrated into the model. The new design is guided by a simple principle: conditioning signals should match the generated content as closely as possible in both latent representation and temporal structure. To this end, we make two major changes. First, we replace the previous depth-warping-based spatial memory with a streaming 3D point-cache renderer. Second, we redesign the conditioning pipeline so that visual conditions are encoded in the same causal-VAE latent space, with temporal statistics consistent with those of the generated video. Concretely, the new version introduces six modifications: (1) replacing static-frame image conditioning with motion-aware latent conditioning; (2) causally encoding re-rendered spatial memory as a continuous sequence; (3) aligning the temporal-memory window in pixel space; (4) adopting hard memory dropout that removes memory tokens rather than zeroing them; (5) unifying the VAE encoding and decoding protocol across training and inference; and (6) removing the camera AdaLN branch, such that viewpoint control is provided entirely through the re-rendered spatial condition.
[AI-4] AaLLM : An End-to-End Analog Circuit Design Framework from Topology Generation to Sizing Using Large Language Models
链接: https://arxiv.org/abs/2608.13472
作者: Mohammed Ayman Habib,Rylan Hart,Morteza Fayazi
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI)
备注:
Abstract:Analog circuit design is a time-consuming, iterative process in a nonlinear and high-dimensional design space that relies heavily on expert intuition. Among recent developments, LLMs have introduced a promising approach by bringing natural language reasoning to circuit design tasks. The majority of conventional LLM-based approaches provide fragmented solutions that focus either only on sizing or topology generation. These methods require adding specific technical knowledge manually, which is inefficient and prone to hallucinations during circuit sizing. Moreover, the inherent trade-off in meeting different specs makes current approaches iterative and tedious. Another shortcoming is the inability to create innovative topologies, which may lead to sub-optimal designs due to reliance on conventional topologies. In this paper, we present AaLLM, an open-source end-to-end multi-agent LLM workflow that takes user specs as input and outputs the appropriate netlist, encompassing both topology generation and circuit sizing. AaLLM automates the creation of a relevant knowledge base from research papers and textbooks to combat tedious manual data collection. A RAG model is implemented to emulate circuit design expertise using this knowledge base. Moreover, AaLLM uses a novel tri-agent feedback system comprising a Designer that determines circuit component values, a Critic that scrutinizes these values, and an Evaluator that minimizes circuit sizing iterations by arbitrating between the other two agents. AaLLM-generated novel topologies achieve a figure of merit (FoM) comparable to that of known topologies, and up to 3x higher for certain circuits. Testing on several circuit topologies, our results show a 3x - 4.5x decrease in the number of SPICE calls at inference when compared to SOTA multi-agent LLM pipelines. The results also show a 40x decrease in wall-clock time compared to existing approaches.
[AI-5] Concept Drift Detection and Adaptive Retraining of Malware Classification Models
链接: https://arxiv.org/abs/2608.13465
作者: Christofer Washington Berruz Chungata,Martin Jurecek,Katerina Potika,William B. Andreopoulos,Mark Stamp
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: To appear as a chapter in the book “Artificial Intelligence for Cyber Defense in Emerging Threats”, to be published by Springer by early 2027
Abstract:Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model. Machine learning models for malware detection or classification are particularly susceptible to performance degradation caused by concept drift, as attackers constantly modify existing malware. In this chapter, we analyze two machine learning-based approaches to automated concept drift detection-a novel approach based on One-Class Support Vector Machines (OCSVM) and a previously-studied technique based on Minibatch K-Means (MK-Means). For comparison we also consider Maximum Mean Discrepancy (MMD), a statistical technique for detecting changes in multidimensional data. We conduct an extensive series of experiments comparing the effectiveness of four learning models, namely, Multilayer Perceptron, Random Forest, Support Vector Machines, and eXtreme Gradient Boosting. For each of these models, we consider three distinct scenarios: A static scenario where no model retraining occurs, a periodic scenario where models are constantly retrained irrespective of concept drift, and a drift-aware scenario where models are only retrained when concept drift is detected. Under the drift-aware scenario, we analyze the tradeoff between accuracy and training efficiency using Pareto Front analysis. We find that all three concept drift detection techniques achieve classification accuracy comparable to periodic retraining, while offering substantially greater efficiency in terms of the number of models that must be retrained. In addition, drift-aware retraining based on our OCSVM technique generally outperforms the MK-Means and MMD approaches. Overall, these results provide strong evidence that we can accurately detect concept drift in malware classification models.
[AI-6] CAPRI: Contract-Aware Proof Repair for Isabelle
链接: https://arxiv.org/abs/2608.13459
作者: Jim Woodcock,Gabriel Leite,Augusto Sampaio,Ran Wei
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注: 17 pages, 1 figure, 7 tables. Submitted to SBMF 2026. Reproducibility artefact available on Zenodo
Abstract:We address the use of large language models (LLMs) to help discover Isabelle proofs. An Isabelle build establishes that the submitted theory is accepted, but not that an LLM changed only what the developer authorised. We present CAPRI, a contract-aware repair workflow in which Isabelle checks the proof and an independent checker enforces a machine-readable edit contract. Prompts, proposals, candidate repositories, diagnostics, verdicts, and hashes are retained for audit. We evaluate five workflows on twelve failed proofs from four developments, with three replicates per task and condition, giving 180 runs and 138 valid repairs. Of 144 terminal candidates accepted by Isabelle, six had modified protected text; all arose in iterative workflows that could edit a complete theory. A proof-body-only interface produced 29/36 valid repairs and no contract violations, compared with 31/36 for the corresponding full-theory workflow. One-shot repair produced 22/36, while a later prospectively frozen iterative workflow produced 32/36; these figures compare complete workflows rather than individual mechanisms. A separate post hoc OpenRouter campaign found no improvement in the designated Luna comparisons. A Sol configuration with matched demonstrations produced 33/36 repairs, compared with 29/36 in the frozen OpenAI Responses condition, but the difference was not statistically significant in a one-sided exact McNemar test ( p=0.0625 ).
[AI-7] Academic League of Artificial Intelligence - An Integrative Perspective of Teaching Research and Extension
链接: https://arxiv.org/abs/2608.13447
作者: Alison R. Panisson,Maria Eduarda W. M. Vianna,Italo Firmino da Silva,Heitor Henrique da Silva,Rafaela Fernandes Savaris,Bernardo Pandolfi Costa,Martin Augusto Gagliotti Vigil,Jim Lau,Agenor Hentz,Andréa Sabedra Bordin,Alexandre Leopoldo Gonçalves,Roberto Rodrigues-Filho
类目: Artificial Intelligence (cs.AI)
备注: 21 pages, 3 figures, 7 tables
Abstract:Academic leagues have become important mechanisms for promoting extracurricular education and strengthening the integration between universities and society. This paper presents the organizational framework adopted by the Academic League of Artificial Intelligence (LIA) at the Federal University of Santa Catarina (UFSC), designed to integrate teaching, research, and university extension through a student-centered, project-based approach. The framework combines democratic governance, collaborative learning, and dynamic project organization to foster both technical and transversal competencies. The framework is illustrated through representative initiatives, including competition teams, study groups, open lectures, knowledge repositories, and AI-powered applications with social impact. These projects demonstrate how diverse educational, scientific, and extension activities can be developed within a common organizational structure while promoting leadership, scientific production, community engagement, and knowledge preservation. The reported experience indicates that the proposed framework provides a flexible and replicable model for integrating the three university pillars into engineering and computing education, offering practical guidance for academic leagues and similar student organizations.
[AI-8] Algebraic Decomposition Theory for Transformer Length Generalization
链接: https://arxiv.org/abs/2608.13433
作者: Andy Yang,Blerta Veseli,Corentin Barloy,Michaël Cadilhac,Andreas Krebs,Charles Paperman,Howard Straubing,Michael Hahn
类目: Formal Languages and Automata Theory (cs.FL); Artificial Intelligence (cs.AI)
备注: 54 pages, 12 figures
Abstract:Transformer-based language models are known to sometimes generalize to sequences longer than seen during training, but we lack a precise characterization of which tasks admit length generalization. It is not even known which regular languages transformers length-generalize on – and this is a foundational class of languages. Our contributions are to establish the first complete characterization of which regular languages transformers length-generalize on and provide a decision algorithm running in polynomial time in the size of the language’s syntactic monoid. These results rely on an effective characterization of the regular languages in C-RASP, a recently-established formalism that expresses which languages transformers length-generalize on. This characterization is challenging because classical tools like Krohn-Rhodes decomposition theory for finite semigroups are insufficient for C-RASP. Firstly, the basic building blocks of Krohn-Rhodes theory – flip-flop and simple groups – are not expressible in C-RASP. Secondly, the basic building block of C-RASP (unbounded counting) is not expressible by the finite semigroups of Krohn-Rhodes theory. Thus, length generalization on regular languages is controlled by an algebraic property that is invisible to classical finite decomposition theory. We generalize classical decomposition theory from finite semigroups to the infinite additive group on the integers, allowing us to characterize C-RASP in terms of iterated wreath products of the integers and derive a provable polynomial-time decision algorithm for regular language membership. Experiments across a broad test suite of regular languages confirm that our theory captures transformers’ length-generalization behavior more accurately than existing classifications.
[AI-9] RAIL: An Automatic Classifier of the Artificial Intelligence Readiness Level
链接: https://arxiv.org/abs/2608.13428
作者: Juan Irving Vasquez,Juan Terven,Laura-Ivoone Garay-Jimenez
类目: Artificial Intelligence (cs.AI)
备注: Under review at journal
Abstract:Assessing the maturity of artificial intelligence technologies is essential for investment decisions, project management, and policy monitoring, yet the available readiness frameworks are heterogeneous and difficult to apply automatically: the adaptation of Technology Readiness Levels to AI lacks AI-specific gating criteria, the Machine Learning Technology Readiness Levels presuppose access to internal process artifacts, and AI/data readiness dimension models employ scales that resist direct comparison. This paper makes two contributions. First, we unify these three frameworks into the Unified AI Readiness Level (AIRL), a nine-level ordinal scale built on an environmental evidence ladder and complemented by dimensional caps (covering specification, data existence, data quality, data legality, expert knowledge, and algorithmic maturity) together with a generality-anchoring rule and explicit assignment disciplines, so that a readiness level becomes decidable from a natural-language description of the work alone. Second, we propose RAIL (Readiness Assessment via Independent LLM-experts), a panel-of-experts classifier that operationalizes the scale: one evidence agent and six independent dimension agents, each a large language model with a narrowly scoped mandate, deliver verdicts that a deterministic minimum rule aggregates and a chief expert reviews under asymmetric authority, confirming or lowering the panel’s recommendation but never raising it above the caps. The method was tested in the analysis of several research works showing consistency and avoiding overestimation from monolithic LLM classifiers.
[AI-10] Enhancing Virtual Agents through SLMs and Edge-Computing: An Exploratory Evaluation of Think and Memory Processes
链接: https://arxiv.org/abs/2608.13420
作者: Aimilios Hadjiliasi,Louis Nisiotis
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Embodied intelligent virtual agents are expected to operate as persistent, adaptive, and context-aware entities within complex virtual and Metaverse worlds. However, implementing cognitively capable agents in such environments is conceptually and technologically challenging. Among a range of blueprints and development approaches, the Cognitive Embodied Agent Architecture (CEAA) has been developed as an implementation-oriented framework for architecting components of perception, memory, reasoning, planning, and embodied action. Considering the recent advances in edge computing and generative AI language models, this paper explores the use of Small Language Models (SLMs) to support edge-based operation of selected CEAA components, focusing on “Think” and “Memory” as processes central to cognitive orchestration and persistence of virtual agents in interactive virtual worlds. An edge-based virtual agent gateway system was developed and evaluated on an NVIDIA Jetson Orin NX using Qwen2.5 models of different sizes, exploring the system’s capability to process service requests and handle memory-driven conversations. A series of simulation experiments evaluated routing accuracy, memory-read performance, and latency, demonstrating an SLM-driven prototype agent system that partially implements selected CEAA processes to support the development of embodied agents whose cognitive “brain” can operate efficiently and contextually for interactive experiences in immersive virtual worlds.
[AI-11] Beyond Final Scores: A Systematic Evaluation of Agents for Long-Horizon AI Research and Development
链接: https://arxiv.org/abs/2608.13417
作者: Yiwei Li,Wanli Yang,Hexiang Tan,Xiangzhou Huang,Zhengyu Chen,Ziran Li,Borun Chen,Shanglin Lei,Huaisheng Zhu,Hao Tian,Fei Sun,Xunliang Cai,Jingang Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Autonomous agents are increasingly capable of improving models, systems, and other technical artifacts through long-horizon experimentation. To understand the current state of this capability, however, evaluation must go beyond final scores, which neither reveal where progress is gained or lost nor indicate whether accumulated experience improves later decisions. We therefore present a systematic evaluation of seven frontier models on 36 long-horizon tasks based on a new framework that uses rule-based metrics to characterize within-run behavior through Solution Framing, Execution, and Feedback Control and controlled comparisons to assess experience reuse within and across tasks. The results show that current agents operate more like engineering optimizers than fully autonomous researchers: they can formulate and implement practical solutions, but their performance varies substantially across runs, their strongest solutions mainly adapt or combine established techniques, and genuine methodological novelty remains rare. Detailed analysis reveals that observed performance is shaped by multiple factors, including distinct process bottlenecks behind similar final outcomes, experience reuse that can help or mislead subsequent decisions, and harness designs that affect performance stability. These findings suggest concrete directions for improving model training, inference-time strategies, experience management, and harness design.
[AI-12] Deliberate Practice: Learning Robot Skills under a Budget
链接: https://arxiv.org/abs/2608.13415
作者: Shivam Vats,Sudarshan Harithas,Mete Tuluhan Akbulut,Arvind Raghunathan,George Konidaris
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 16 pages including appendices
Abstract:We consider the problem of autonomously learning robot skills under a limited practice budget for sequential tasks. We propose an active skill learning algorithm, \emphDeliberate Practice (DP), that computes a provably \emphbudget-optimal allocation—practicing skills that maximize expected cumulative reward while being learnable within the budget. DP estimates both the time needed to master skills and the cumulative reward of the task plans that the skills unlock. Computing a budget-optimal allocation is challenging as it requires reasoning about combinatorially many skill plans over a large practice budget. Our key contribution is a bilinear program that can compute this exactly using off-the-shelf solvers. Through simulated and real-world experiments on long-horizon manipulation tasks, we show that our approach allows robots to optimally use limited practice time to acquire useful policies and improve long-horizon planning.
[AI-13] Who Speaks Matters: Authority-Aware Multi-View RAG over Italian Parliamentary Proceedings ISWC2026 ISWC
链接: https://arxiv.org/abs/2608.13410
作者: Mirko Tritella,Riccardo Pozzi,Matteo Palmonari
类目: Artificial Intelligence (cs.AI)
备注: Accepted at ISWC 2026 In-Use Track. Please cite the ISWC version
Abstract:Parliamentary proceedings are a primary record of democratic deliberation, yet their volume and fragmentation make multi-perspective access difficult for citizens, journalists, and researchers. Applying Retrieval-Augmented Generation (RAG) to parliamentary transcripts introduces three specific risks: dominance of the most frequent speakers, inability to weight speakers according to topical expertise, and citation misattribution in politically sensitive text. We present ParliamentRAG, a RAG system for the Italian Chamber of Deputies that addresses these risks jointly. Its core contribution is a topic-dependent authority model that estimates each speaker’s authority as a function of the current query, combining interpretable components such as profession, education, and previous interventions. Given a user query, the system retrieves relevant speech chunks, identifies topic-relevant experts across parliamentary groups, and generates a summary synthesizing their perspectives, accompanied by supporting quotations. ParliamentRAG is evaluated against Google NotebookLM on 15 policy topics via a two-level protocol combining automated metrics and blind A/B human evaluation by six domain experts. The system achieves higher coverage across political groups (0.97 vs. 0.95), perfect quotation faithfulness (1.00 vs. 0.95), and stronger expert preferences on source-related dimensions, while NotebookLM remains stronger on prose-oriented dimensions.
[AI-14] Jointly Predicting Courses and Grades Using a Transformer-Based Model
链接: https://arxiv.org/abs/2608.13409
作者: Paul Savala
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Existing predictive models in learning analytics often treat student academic history as a simple sequence, overlooking the concurrent nature of courses taken within a semester. This simplification can lead to inaccurate performance predictions, particularly for students with heavy or challenging course loads. This paper introduces a TRansformer for Academic Course-grade Estimation (TRACE) that addresses this limitation by jointly predicting both the set of courses a student will take and their corresponding grades for an upcoming semester. Our approach encodes courses on a per-semester basis to capture the effects of course concurrency and utilizes a novel loss function combining course-set prediction with grade prediction. We demonstrate that predicting courses taken in addition to the grades in those courses leads to significant improvements in prediction quality. Trained on ten years of institutional data, our joint prediction model reduces mean absolute error by nearly 50% compared to an identical architecture that predicts grades alone. The model also outperforms traditional LSTM-based sequential models, as well as graph neural network-based approaches, and offers natural ways to incorporate student attribute data. This work demonstrates the utility of modern neural architectures for creating interpretable models that can be adapted to new institutions via retraining and recalibration, as well as the importance of key techniques, such as predicting courses taken during training. We discuss how this model could be incorporated into early detection systems at institutions of higher education.
[AI-15] opoIntent: Compiling Security Intent into Executable Compliance-Checked Network Topologies
链接: https://arxiv.org/abs/2608.13389
作者: Xiaokang Qu,Jianliang Ma,Zao Fan,Tianshu Chu,Tianlong Fan,Linyuan Lü
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Networking and Internet Architecture (cs.NI)
备注:
Abstract:Enterprise security topology design requires translating business intent, regulatory requirements, and risk assumptions into zones, boundary devices, inter-zone paths, and access-control policies. Existing NetOps automation tools mainly operate after this design is fixed, providing limited support for generating structured security topologies from underspecified natural-language requirements. We present TopoIntent, a system that compiles security intent into executable, compliance-checked network topologies. It uses a schema contract to constrain generation, retrieves reference architectures from a curated template library via dense-vector search, and applies staged fusion for intent-template alignment and security completion. The generated topology is checked against CIS Controls v8.1.2 safeguards visible at the topology layer, while unresolved cases are marked for manual review. Structural gaps are repaired through additive schema-preserving edits. The final topology is exported to Mininet scripts with kernel-level iptables ACLs, enabling executable reachability and allow/deny tests. Because no public benchmark exists for this requirement-to-topology task, we construct an evaluation set from reference security architecture diagrams. The retrieval set contains 22 templates and 44 synthetic intents across five scenarios, while the held-out set contains 7 templates and 14 intents from finance and government scenarios excluded from retrieval. On the held-out set, additive repair improves topology-visible CIS satisfaction from 0.78 to 1.00 in fewer than 1.5 rounds on average, and one feedback round raises the post-ACL policy pass rate from 0.78 to 0.88.
[AI-16] Rules or Character? Scaling Laws for AI Safety Design AAAI
链接: https://arxiv.org/abs/2608.13345
作者: Satoshi Takahashi,Nobuji Kouno,Masaaki Komatsu,Ryuji Hamamoto
类目: Artificial Intelligence (cs.AI)
备注: Accepted at AIES 2026 (9th AAAI/ACM Conference on AI, Ethics, and Society). 10 pages, 6 figures, 4 tables
Abstract:Artificial Intelligence (AI) safety systems combine character shaping (e.g., Reinforcement Learning from Human Feedback [RLHF], Constitutional AI), which modifies behavioral distributions at training time, with rule enforcement (e.g., output filters, safety classifiers), which blocks harmful outputs at inference time, yet little formal analysis exists on how their optimal balance should change as deployment scales increase. We introduce a stylized comparative-statics model that parameterizes safety design as a resource allocation alpha in [0,1] between these two approaches, incorporating scale-dependent filter degradation, common-mode failures, and character fragility – the risk that shaped behavior degrades or collapses under novel conditions. Under a multiplicative Pareto damage model, we derive closed-form expected harm and supplement it with tail-risk (CVaR) analysis via Monte Carlo simulation. Across three scenarios (optimistic, moderate, pessimistic), the optimal alpha* is interior or at the rules-only boundary and shifts weakly toward character shaping as deployment scale T grows, from negligible (Delta alpha* = +0.01) to pronounced (Delta alpha* = +0.21) depending on scenario. The dominant parameter is the baseline character fragility rate p^(0)_frag, which shifts alpha* by 0.50 across its range – far exceeding the effect of tail severity, filter quality, or common-mode failure probability. CVaR and expected-harm optima converge at large T. These results suggest that safety architecture decisions depend less on deployment scale per se than on the reliability of character shaping under distributional shift.
[AI-17] LongEarth-R1: Benchmarking and Aligning Vision-Language Models for Long-Horizon Earth Observation Reasoning
链接: https://arxiv.org/abs/2608.13344
作者: Yupan Ding,Jing Xiao,Zhenyuan Zhang,Chaofeng Chen,Liang Liao,Gui-Song Xia,Mi Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Long-horizon Earth observation reasoning requires models to organize multi-stage geographic evolution, localize spatial changes, detect temporal anomalies, and infer future from extended image sequences. However, existing remote sensing vision-language models mainly focus on isolated images, image pairs, or short sequences, limiting reliable grounding in the relevant frames and regions. We introduce LongEarth-Bench, a benchmark containing approximately 120k question-answering samples derived from 117k unique images. Its sequences average 15.14 frames and extend to 30 frames, covering 12 tasks across evolution summarization, spatial reasoning, anomaly identification, and logical prediction. A 30k-sample subset further provides structured reasoning traces linking key frames and changed regions to final answers. We develop LongEarth through supervised fine-tuning with explicit sequence identifiers and structured chain-of-thought supervision. Building on LongEarth, LongEarth-R1 applies group relative policy optimization with format, temporal, and spatial rewards. LongEarth-R1 achieves the best results on all 12 long-sequence tasks while remaining competitive on standard remote sensing benchmarks.
[AI-18] Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples
链接: https://arxiv.org/abs/2608.13341
作者: Yusen Tan,Yixuan Chen,Zheng Fang,Pan Liu,Yifan Li,Qinyu Guo,Zhedong Lin,Yuqiang Li,Xiangxiang Zeng,Tong Wang,Jun Xia
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging. Conventional interpretation is labor-intensive, relies on prior knowledge and reference spectra, and is difficult to scale, whereas most machine-learning methods are tailored to individual tasks or datasets, require large labeled training sets, and transfer poorly across analytical objectives and experimental datasets. Here we introduce UltraIR, a foundation model for IR spectroscopy with more than 100 million parameters that enables simulation-to-real transfer learning for chemical sensing and analysis from molecules to complex samples. UltraIR is pretrained on approximately 60 million simulated IR spectra using spectral reconstruction, molecular fingerprint similarity alignment, and functional-group prediction, then adapted to downstream objectives with task-specific labels or targets. Across functional-group prediction, molecular structure elucidation, physicochemical property prediction, mixture-component identification and quantification, bacterial classification, medicinal-herb geographic origin traceability and constituent quantification, microplastics classification, and soil property prediction, UltraIR outperforms conventional machine-learning and task-specific deep-learning baselines. It performs strongly with limited labeled experimental spectra and in zero-shot inference for the same analytical task across Fourier-transform infrared spectrometers and laboratories, providing a route to adaptable, data-efficient chemical sensing from complex real-world samples.
[AI-19] LLM -Guided Graph Generation for Structure-Based Local Improvement Methods
链接: https://arxiv.org/abs/2608.13333
作者: Hai Xia,Vaidyanathan Peruvemba Ramaswamy,Stefan Szeider
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large neighborhood search normally selects a random subset of decision variables for iterative optimization. For efficiently solving different problems, researchers tend to design variable selection strategies by taking into account structural features from different domains. In this paper, we build an automatic pipeline that is problem-agnostic to all problems in the MiniZinc format. By prompting an LLM with our semantic guidelines, we guide the LLM to produce a graph generator that maps any instance of a problem type to a uniform weighted graph, where nodes represent decision variables and edges represent constraint relationships. These problem-agnostic graphs guide our structure-based local improvement framework (SLIM) in variable selection. Meanwhile, the weighted graph enables all problem instances to share the same generic graph representation, from which the same graph features can be extracted and used for configuration selection. We evaluated our pipeline on instances across 20 MiniZinc competition problems, finding that algorithm selection achieves a 39.5% average problem-weighted win rate against a one-shot Gurobi baseline, more than doubling the best single configuration (19.3%). Configuration and feature ablation boost the performance further to 44.0%, demonstrating that LLM-based semantic generation enables effective automated structure extraction and feature extraction for constraint optimization.
[AI-20] raining AI Scientists to Replicate Research
链接: https://arxiv.org/abs/2608.13331
作者: Damon Falck,Samer Sabri,Anja Surina,Thom Foster,Anya Sims,Sam Devlin,Dylan Rogers,Tantum Collins,Kaloyan Aleksiev,Louis Kirsch,Edward Hughes
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 47 pages, 12 figures
Abstract:The replicability of papers is a cornerstone of scientific knowledge, ensuring the reliability of existing results and providing a base for further experiments. The act of replication typically illuminates details that were previously underspecified, and thus requires similar hypothesis-driven exploration to open-ended research. In this work, we develop Replica, a scalable task space for paper replication. To provide reward signal, we introduce an auto-generated rubric-based judge that has low noise and agrees with human assessment of replication quality. We post-train Faraday, a 27B-parameter “AI Scientist” agent that leverages coding agents as tools, surpassing the performance of Claude Opus 4.8 and GPT-5.5 on held-out replication tasks. Qualitative analysis of individual rollouts reveals that Faraday adopts a more scientifically-principled approach. We believe that our results provide a stepping stone towards AI agents capable of long-horizon scientific innovation without requiring complex harnesses.
[AI-21] StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems
链接: https://arxiv.org/abs/2608.13317
作者: Yanwen Peng,Delvin Ce Zhang,Xi Wang,Nikolaos Aletras
类目: Artificial Intelligence (cs.AI)
备注: 18 pages, 3 figures, 4 tables, accepted by COLM2026
Abstract:Large language model based multi-agent systems usually communicate in text, i.e., using discrete tokens. However, text introduces a discrete bottleneck. Converting the sender’s continuous hidden states into discrete tokens discards information that token identities alone cannot capture. Recent work proposes latent communication as an alternative, where agents transmit hidden representations directly without converting them to text. However, existing latent methods either inject working memory layer by layer across the transformers, or require trained projectors that limit portability. We propose StateBridge, a training-free latent communication approach that aligns the sender’s final-layer hidden states to the receiver’s input space via a closed-form orthogonal transformation. Lightweight norm calibration and vocabulary anchoring ensure compatibility with the pretrained input distribution. The aligned states are prepended to the input of the receiver agent as a continuous prefix. We evaluate StateBridge on math reasoning, code generation, and question answering with four models from two families. StateBridge achieves the best or tied-best score on 22 out of 26 model-task pairs, consistently outperforming the strongest baseline.
[AI-22] Keep Customize or Exit: Default Design and Token Pricing in LLM Reasoning Services
链接: https://arxiv.org/abs/2608.13315
作者: Ahmet Bugra Gundogan,Yigit Turkmen,Melih Bastopcu
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY)
备注:
Abstract:We study a large language model (LLM) service in which a provider chooses a per-token price and a default reasoning-token allocation, while a user may accept the default, customize the allocation, or exit. Larger allocations can improve accuracy but increase token cost and latency. We model this interaction as a Stackelberg game and derive the user’s unique optimal customized allocation in closed form. For any price, the acceptable defaults form either an empty set or a compact interval. We characterize the provider’s optimal default through a three-regime rule, reduce equilibrium computation to a one-dimensional price optimization, and prove the existence of the equilibrium. We further show that defaults affect the implemented reasoning allocation only when users value the convenience of avoiding customization; otherwise, every service-providing outcome implements the user’s optimal customized allocation. Experiments with two compact open-weight reasoning models on five mathematics and science benchmarks support the accuracy-token model and show how model and task characteristics determine equilibrium prices, defaults, and reasoning allocations.
[AI-23] Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks PPSN2026
链接: https://arxiv.org/abs/2608.13296
作者: Wojciech Zarzecki,Jarosław Arabas
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to PPSN 2026
Abstract:Existing global optimization benchmark suites are of a moderate size and are based on a small number of analytical functions that date back even to the 1970s. This causes a risk of biasing the development of global optimization methods. We argue that the tasks related to the black-box adversarial attack (BBAA) can serve as valuable global optimization benchmark in many-dimensional space. We demonstrate the efficiency of several types of evolutionary algorithms and other metaheuristics in solving example BBAA problems. Thus, we take a step towards convergence of global optimization methods to the challenges and needs that arise in the modern machine learning field.
[AI-24] NAS-Driven Hardware Accelerator Exploration for Edge AI and Quantization Effects on the Pareto Space
链接: https://arxiv.org/abs/2608.13293
作者: Eleftherios Mylonas,Angelos Kouprizas,Michael Birbas,Alexios Birbas
类目: Artificial Intelligence (cs.AI)
备注: 6 pages, 6 figures, accepted for presentation to the 39th IEEE International System-on-Chip Conference, Heidelberg, Germany, September 30 - October 2, 2026
Abstract:Edge AI deployment demands neural architectures that are simultaneously accurate, computationally efficient, and hardware-deployable - a challenge addressed by hardware-aware Neural Architecture Search (NAS). While recent works incorporate quantization directly into the NAS loop, these approaches expand search complexity and tightly couple architecture and quantization design. The simpler post-search quantization strategy has received little analytical attention: the effects of Post-Training Quantization (PTQ) on the NAS-discovered Pareto structure remain uncharacterised, and no framework combines quantized architecture mapping onto reconfigurable accelerators with automated hardware exploration. This paper addresses both gaps. First, a three-stage pipeline is proposed: a hardware-agnostic Pareto rank surrogate frontend on NAS-Bench-201, a quantization bridge with Pareto-aware filtering and feedback control, and an evolutionary Domain Space Exploration (DSE) backend on CGRA4ML for optimal hardware mapping. Second, an empirical study characterises how INT4 PTQ perturbs the NAS-Bench-201 Pareto space through formal stability metrics on ground-truth data for all 15,625 architectures, and demonstrates that an FP32 zero-shot surrogate outperforms a dedicated INT4-trained surrogate in Pareto space coverage across two standard search strategies.
[AI-25] owards Context-Aware Clinical Motion Understanding in Daily Living at Home: Freezing of Gait Detection with Egocentric Vision ECCV
链接: https://arxiv.org/abs/2608.13283
作者: Vayalet Stefanova,Diwas Lamsal,Margot Genbrugge,Maxim Yudayev,Christian Schlenstedt,Moran Gilat,Bart Vanrumste,Benjamin Filtjens
类目: Artificial Intelligence (cs.AI)
备注: Accepted to ECCV Workshop 2026 (Human Motion Challenges in Real-World and Clinical Settings)
Abstract:Understanding motion in daily living requires context beyond kinematics, because similar inertial patterns during activities of daily living (ADLs) can reflect intentional stopping, object interaction, or pathological movement impairment. Egocentric vision provides task-related context that may help disambiguate these cases. We investigate this challenge through freezing of gait (FOG) detection in Parkinson’s disease (PD), a symptom strongly influenced by contextual factors during ADLs. Using synchronized egocentric video, wearable IMUs, and expert-annotated FOG labels collected from 13 PD participants in their homes, we evaluate frozen representations from pretrained ego-video and time-series foundation models, alongside an IMU-based TCN trained from scratch, under leave-one-subject-out evaluation. The IMU-based TCN achieved the strongest event-detection performance, reaching 42.3 F1 and 83.0 AUROC, compared with 32.6 F1 and 77.2 AUROC for V-JEPA2 ego-video features. Although ego-video alone did not outperform IMU-based sensing, it showed above-chance discrimination, and qualitative analyses suggest that egocentric vision may capture FOG-relevant information independent of IMUs. Together, these results support the use of pretrained ego-video representations to add contextual information to wearable-sensor-based clinical motion understanding in daily living.
[AI-26] Sovereign by necessity? Frontier AI export controls cyber security and the limits of national AI capability
链接: https://arxiv.org/abs/2608.13272
作者: Alan Woodward,Andrew Rogoyski
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 42 Pages
Abstract:A small number of firms based in two states produce the most capable frontier AI models. The governments of those states have shown both the legal power and the political will to decide which other countries may use these systems. In June 2026 the United States required a leading developer to obtain licences before releasing its most advanced models to any foreign person, including foreign nationals resident in the United States. The affected models were withdrawn worldwide at short notice, partly because the restriction proved impractical to administer. This followed within months of the first documented case of a largely autonomous, AI-run cyber espionage campaign, and coincided with mounting evidence that frontier models alter the economics of both cyber attack and cyber defence. This article examines how these two developments interact, and situates them within the unusual market dynamics now driving large-scale AI development. It argues that access to frontier AI is becoming part of national cyber defence, that such access can be revoked, and that the obvious remedy of sovereign capability remains only partly feasible for all but a handful of states. Drawing on evidence about training costs, the concentration of computing power and the support offered by national AI programmes, it asks what sovereignty can realistically mean for small and middle powers, and for large powers as well. The article proposes a layered strategy: negotiated access guarantees, sovereignty at the level of inference, hedging with open-weight models, pooled regional capability, sustained talent development and continued investment in basic cyber resilience. The open-weight hedge proves at once more capable and more politically exposed than is commonly assumed. Much of the near-term risk lies in how capable models are deployed and contained rather than in their apparent performance.
[AI-27] vToken: Token-Level Virtualization for Reclaimable KV Caches
链接: https://arxiv.org/abs/2608.13263
作者: Yuanhang Gao,Xiangrui Yang,Yuanfeng Chen,Hongjia Chen,Qianru Lv,Wenfei Wu,Dongsheng Li
类目: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Operating Systems (cs.OS)
备注:
Abstract:Large language model serving faces a critical memory bottleneck: the KV cache grows with sequence length and batch size. PagedAttention uses fixed-size memory blocks to reduce allocator-level fragmentation, but recent KV eviction algorithms operate at a token granularity finer than block-level management. This mismatch causes intra-block fragmentation, leaving a large fraction of allocated KV memory unreclaimable. We present vToken, a lightweight token-level virtualization layer that decouples logical token liveness from physical block placement. vToken maintains a stable logical token view through token-table indirection and realizes physical reclamation by repacking live tokens asynchronously. The design preserves PagedAttention kernels and CUDA Graph compatibility. We implement vToken in vLLM and evaluate it with H2O, Random, and Scissorhands across models. Compared with a paired Naive-Evict baseline, vToken reduces retained KV blocks per request by 27.2%–72.3% and improves SLA-constrained throughput by up to 1.37 \times . Under a constrained active-KV budget, it extends the maximum feasible concurrency by up to 2 \times , while reducing the per-policy integration footprint from 500+ lines to under 50.
[AI-28] Into the ORBIT for Time Series: Training Regimes for Foundation Models
链接: https://arxiv.org/abs/2608.13262
作者: Hongjie Xia,Yiding Liu,Yifan Hu,Peiyuan Liu,Zewei Dong
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Time series foundation models (TSFMs) have advanced primarily through architectural innovation, while training regimes for large-scale heterogeneous corpora remain under-explored. As a result, pre-training distributions are often poorly controlled with respect to domain imbalance, context requirements, prediction horizons, and missingness. We introduce ORBIT (Omni-Range Bootstrap Incremental Training), a training paradigm that makes this distribution explicit and controllable. ORBIT combines Bootstrap Multi-Level Sampling, which controls dataset exposure and samples records, target variables, context windows, and prediction horizons, with Omni-Range Incremental Training, which varies context lengths and prediction horizons throughout a single training stage. Under ORBIT, we train Falcon-2.0, a simple univariate encoder-only Transformer with missingness-aware triple-channel patch tokenization and parallel patch prediction. We further introduce Rank-Guided Cross-Depth Alignment, a training objective that uses late-layer representations as stop-gradient teachers for shallow layers without additional inference cost. Evaluations on GIFT-Eval and fev-bench demonstrate strong zero-shot forecasting performance across diverse domains and frequencies.
[AI-29] Novel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data
链接: https://arxiv.org/abs/2608.13256
作者: Francesca Pia Panaccione,Sofia Mongardi,Marco Masseroli,Pietro Pinoli
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:As biomedical research increasingly relies on data-intensive tools, the quality and utility of datasets are critical. Challenges such as imbalances, biases, and ethical or legal constraints often limit access to high-quality data. Synthetic data generation can help overcome these limitations. Here, we present a comparative analysis of generative models for transcriptomic data, investigating strategies to incorporate prior biological knowledge via gene graphs. This ensures that synthetic data capture real-world gene patterns, maintaining their usefulness for downstream tasks. In particular, we introduce and benchmark three variants of the Generative Adversarial Network. Among the alternatives, MK-TGAN - an innovative multi-kernel, Graph Neural Network-based model - stands out for its performance in terms of both the realism and utility of the generated data. Unlike other methods, MK-TGAN leverages prior knowledge graphs by exploiting graph neural networks. Our results show that prior knowledge integration strategies improve performance, and that MK-TGAN consistently produces synthetic samples with superior realism and biological plausibility.
[AI-30] Follow the Norm: Accounting for Fine-Tuning and Prompt Effects on Model Rationales AAAI
链接: https://arxiv.org/abs/2608.13250
作者: Long Hoang Nguyen,Brice Valentin Kok-Shun,Guangyu Du,Ali Sunyaev
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Extended version with appendix; final version to appear in the Proceedings of AAAI/ACM AIES 2026
Abstract:Normative datasets are often used to train and align AI systems, but the norms they contain can function as action-guiding patterns rather than neutral moral knowledge. We propose treating the AI system as a proxy actor and test whether dataset-level norms can shift it away from its baseline safety behavior when it faces high-conflict dilemmas. We make three contributions. First, we demonstrate in controlled experiments that norm-breaking fine-tuning yields norm-divergent actions justified by self-interested rationales, suggesting a systematic shift in patterns of justification. Second, we establish a practical audit trail linking downstream justifications to upstream norms using mixed methods. Third, we show that system prompts can both suppress and elicit these patterns. We conducted experiments on three models (LLaMA-3.2-11B, Qwen-3.5-9B, and Pixtral-12B) using Low-Rank Adaptation (LoRA) fine-tuning on Social Chemistry 101 Fairness/Cheating (norm-following vs. norm-breaking) with prompt steering. Across all three models, we find that norm-breaking fine-tuning shifts the model’s default rationale style from safety compliance to instrumental self-interest, whereas system prompts can override this behavior. Our results support a distributed view of alignment in which observed behavior depends jointly on training data, fine-tuning, and prompting, motivating norm-aware documentation and rationale logging for contestable oversight.
[AI-31] Capability Sheaves for Compositional Agent -Harness Repair: Controlled Quotients and a Real-Repository Stress Test
链接: https://arxiv.org/abs/2608.13228
作者: Saveliy Batruin
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Agent harnesses combine retrieval, routing, state, provenance, and verification, but locally successful components may disagree on shared state. We model this failure with a finite \emphcapability sheaf: stalks encode typed behavior signatures, restriction maps retain shared fields, and accepted runs are useful global sections. An exact finite constraint-satisfaction problem (CSP) defines acceptance, while a linearized relative cohomology class provides a diagnostic and search feature. A controlled experiment over 20 task clusters introduces hidden interior mediators whose raw states are nuisance variables. Quotienting their coboundaries reduces the candidate budget from 2,000 to 1,000 per cluster; aligning the hidden state removes the gap. Exact CSP matches the quotient, so the result demonstrates invariance to stale representatives, not superiority over exact reasoning. We then test the method on a discovery split from the SWE-bench Multilingual pool of PatchFuseBench: 160 issues from 20 repositories, 875 real candidate patches, 2,579 source-aware edit atoms, and 153 newly executed patches. A first pool-level construction is constant because [b-Dx]=[b] in \operatornamecokerD and therefore cannot rank configurations. A candidate-indexed repair is nontrivial on 848/875 candidates and varies within 120/160 issues. It resolves 118 issues versus 116 for a matched noncohomological selector, but the difference is not supported across repositories (exact sign-flip p=0.75 ). A leave-one-repository-out abstention gate reaches 127/160, tying the strong anchor and exceeding its matched gate by one issue ( p=1.0 ). The discovery gate therefore fails and the confirmatory split remains sealed. The study supports the controlled invariance mechanism and an identifiability correction, but not a real-world cohomological advantage. Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2608.13228 [cs.AI] (or arXiv:2608.13228v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.13228 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-32] suGO: Probing Search Efficiency in LLM Reasoning via Go Life-and-Death Problems
链接: https://arxiv.org/abs/2608.13221
作者: Shunwen Bai,Ziping Ma,Chaoyang Zhang,Yarong Wang,Jiale Liu,Zhen Qin,Qingpei Guo
类目: Artificial Intelligence (cs.AI)
备注: 23 pages, 12 figures, 20 tables, 2 algorithms
Abstract:The evaluation of LLM reasoning is moving from final-answer accuracy to process-level assessment, yet existing methods still fail to capture how models plan reasoning paths and allocate reasoning resources–that is, how they organize search. Prior process-level methods focus on the coherence and redundancy of chain-of-thought (CoT), and most benchmark tasks have a single objective solvable by static capabilities such as derivation and tool use, leaving search organization unmeasured. We introduce TsuGO, a process-level reasoning benchmark for evaluating Search Efficiency in LLM reasoning through Go life-and-death problems. These problems provide closed and verifiable solution spaces with an inherent adversarial structure, making candidate generation, response checking, branch comparison, and backtracking necessary parts of reasoning rather than incidental trace patterns. By constraining the solution space, TsuGO disentangles domain knowledge from search organization, parses CoT into a structured search tree, and reports Search Efficiency together with Token Efficiency and other diagnostic metrics and visualizations. Experiments show that current LLMs remain far from stable tsumego solving: stronger models succeed by finding the correct candidate earlier and sustaining effort on productive branches, but most models still behave much closer to unguided search algorithms than to neural-guided KataGo. Longer CoT or higher Token Efficiency does not necessarily imply better search. Our results identify search organization and reasoning-resource allocation as missing dimensions in LLM reasoning evaluation.
[AI-33] ach the Magnitude Not the Direction: Verifier-Bounded Credit Assignment for Multi-Turn Multi-step LLM Agents
链接: https://arxiv.org/abs/2608.13179
作者: Zechuan Wang,Siyuan Lu,Hongxuan Zhang,Linjian Mo,Chenyi Zhuang,Leilei Gan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning with verifiable rewards (RLVR) offers a verifier-bounded performance ceiling for training multi-turn tool-use agents, yet its trajectory-level credit assignment conflates heterogeneous per-turn outcomes into a single reward signal. On-policy distillation provides dense per-token supervision but is either teacher-bounded or prone to gradient concentration collapse. We introduce \textbfCrEST , a hierarchical credit assignment framework that retains RL’s verifier-bounded ceiling while incorporating dense token-level signals from a privileged self-teacher. \textbfCrEST resolves credit at two levels: turn-segmented verified advantages address inter-turn dilution, while entropy-gated self-teacher modulation refines intra-turn token contributions. Experiments on BFCL V3 and WildToolBench show that \textbfCrEST consistently outperforms both RL and distillation baselines across two model scales, with the largest gains on long-trajectory and strict session-level metrics. Our work demonstrates that the teacher’s role in policy optimization can be reduced from determining update directions to modulating update magnitudes, unlocking dense credit assignment without sacrificing the verifier-bounded ceiling.
[AI-34] SkillShapley: Boundary-Adaptive Shapley Valuation for Skill Step Attribution in LLM Agents
链接: https://arxiv.org/abs/2608.13173
作者: Chang Liu,Yuqi Zhang,Yiman Zhong,Boyi Liu,Hengjun Wang,Shuyue Wei
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 4 figures
Abstract:Agent skills are crucial external instructions that enable language agents to execute long procedural tasks such as coding or document processing. Existing agent skills are primarily created through human manual crafting or agent execution traces, with limited understanding of how each step contributes to overall skill performance on specific tasks; i.e., there remains an open problem in quantifying the contribution of individual steps within an agent skill. To address this issue, we first model skill-step attribution as a Shapley value-based contribution estimation problem, and then propose SkillShapley, a step-level attribution framework for agent skills. Notably, SkillShapley operates in two phases, motivated by key empirical insights, i.e., discretized benchmark rewards that create sharp performance cliffs, and step interactions that are largely additive rather than synergistic. Specifically, it first identifies informative coalitional regions, and then adaptively samples new coalitions that can yield reusable marginal evidence. Experiments on skills from the widely adopted SkillsBench demonstrate that our SkillShapley can effectively and efficiently identify high- or low-value skill steps, providing several key takeaways for agent skill creation.
[AI-35] Rethinking Normalization Placement for LLM s: Post-Norm under Curriculum Depth Growing
链接: https://arxiv.org/abs/2608.13156
作者: Sheng Ren,Yadong Wang,Naiqiang Tan,Jiangang Kong,Jun Fang,Rui Liu,Jun Wang,Kai Chen,Lipeng Liang,Xiang Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Pre-norm is the standard normalization placement in modern Transformers because it facilitates joint optimization of full-depth models. We ask whether this preference persists when depth is introduced through a curriculum. In curriculum depth growth, each appended block receives the boundary representation produced by a trained prefix, making normalization placement relevant to forward conditioning. We therefore test whether placement and training curriculum interact. In a controlled distillation study with a Qwen3-8B teacher and a nine-layer student, pre-norm and post-norm are indistinguishable under joint training, differing by 0.0004 validation CE, while post-norm improves over pre-norm by 0.0328 under curriculum growth, an order of magnitude larger. A post-joint control matched by student active-layer tokens remains worse than post-grow, which rules out compute as the sole explanation. The ranking crosses over during the curriculum: post-norm takes the lead once blocks are appended. Single-block and freeze controls localize the ranking change to block appending rather than shallow-block quality or retraining. Boundary diagnostics associate post-norm with stable residual scales and pre-norm with structural-token scale drift; on a fixed batch, the final pre-grow block is also nearly identity-mapped. Together with the phase-wise crossover, these observations are consistent with boundary-scale conditioning after new blocks are appended. The results motivate treating normalization placement and training curriculum as coupled design choices in this distillation setting.
[AI-36] LipCache: A Local Inference Proxy with Certified Caching for Edge Image Classification Service
链接: https://arxiv.org/abs/2608.13144
作者: Zhengzhe Xiang,Yinlin Chen,Fuli Ying,Binbin Zhou,Hailiang Zhao,Schahram Dustdar
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:As edge-side vision services continue to expand toward low-latency, high-throughput scenarios, reducing the inference cost of vision models without sacrificing reliability has become a central concern. Existing semantic caching methods largely rely on empirical similarity thresholds; while such thresholds improve hit rates, they tend to introduce silent misclassifications near decision boundaries. To address this, we propose \textttLipCache, a certified semantic caching framework for image classification. Without modifying the existing deployed main model, \textttMainNet, the framework introduces a lightweight network, \textttGuardNet, that maps inputs into a low-dimensional feature space subject to a Lipschitz constraint. It then computes a per-sample certified reuse radius from the local classification margin and the spectral norm of the classification head. At runtime, a cached result is reused only when the query feature falls inside the certified reuse ball; otherwise, the query falls back to \textttMainNet. Thus, cache hits are transformed from empirical threshold tests into geometric certification decisions with explicit theoretical boundaries. Across standard image classification tasks like CIFAR, Tiny-ImageNet, and SVHN, \textttLipCache achieves a measured speedup of up to 1.65\times with limited end-to-end accuracy degradation, while all accepted cache hits satisfy the \textttGuardNet-side certified-consistency condition. Furthermore, an enhanced \textttGuardNet training recipe substantially improves cache hit rates in the Tiny-ImageNet multi-class extension while maintaining a certified-consistency rate of 100% . These results demonstrate that per-sample certified reuse can reduce main-model fallback while preserving theoretical consistency, providing a feasible approach to reliable cache-assisted inference at the edge.
[AI-37] Numeracy in Large Language Models : Fundamental Limitations and Paths to Improvement
链接: https://arxiv.org/abs/2608.13129
作者: Aoxin Ni
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) achieve strong results on mathematical reasoning benchmarks yet remain unreliable on elementary numerical tasks, including magnitude comparison, large-integer arithmetic, fractions, and scientific notation. This survey examines basic numerical understanding as a capability distinct from high-level mathematical reasoning. We propose the Numerical Grounding Framework (NGF), which decomposes numeracy into Representational Grounding (RG), mapping numeral forms to value, magnitude, and equivalent representations, and Procedural Grounding (PG), executing arithmetic operations in accordance with their mathematical definitions. Using NGF, we organize recent diagnostic benchmarks, failure modes, structural explanations, and mitigation strategies. We review evidence concerning tokenization, positional encoding, embedding geometry, and pretraining-data distribution. We also apply NGF in a coordinated evaluation of three frontier model families across Number Cookbook, NumericBench, and GSM-Symbolic, comparing atomic, contextual, and reasoning-assisted numeracy. Architectural interventions such as digit-aware tokenization and Abacus Embeddings can improve models trained from scratch but are generally unavailable to users of pretrained systems, for whom supervised fine-tuning, reasoning scaffolds, and external tools are more practical. We conclude with deployment recommendations and research directions for more reliable numerical behavior in foundation models.
[AI-38] SkillEvo: Self-Renewing Evolution Gradients from Multi-Turn Interaction Feedback
链接: https://arxiv.org/abs/2608.13120
作者: Qianxi Yan,Chunrong Chen,Jiuzhou Zhao,Min Zhang,Yongzhou Xu,Xiaochuan Xu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agent Skills are today either hand-authored or produced in a single LLM generation pass, and consequently possess no closed loop through which they might improve from the interaction failures they actually cause. Recent work does close this loop, but derives its feedback from single-turn question-answering evaluation. The consequence is a sharp asymmetry: once the first round has patched the gaps that a single exchange can reveal, the evolution gradient decays, the defects that surface only across multiple turns remain invisible, and evolution stalls. Governance in these systems is likewise driven by an end-to-end verification score, a scalar gate that can reject a degraded candidate but can neither localize nor repair its structural cause. We argue that the binding constraint on sustained skill evolution is neither editing capability nor the number of iterations, but whether the evaluation feedback keeps supplying trustworthy evolution gradients. We introduce SkillEvo, in which trustworthy feedback generates the gradient and controllable governance constrains its direction. The first component recasts multi-turn user simulation from an evaluation endpoint into a feedback generator: follow-up questions expose defects layer by layer, so that every round of revision both consumes feedback and produces new feedback. The second replaces the passive rejection of a scalar gate with an independent governance layer that actively repairs factual degradation and structural bloat, preventing the gradient from drifting as degradation accumulates. Across six categories of cloud services, 9 production Skills, and 98 skill-reference files, SkillEvo surpasses self-reflection-based evolution by 23.0 points and single- turn-QA-driven evolution by 15.4 points.
[AI-39] Robust Dempster-Shafer Evidence Fusion with Chaos-Conflict Measurement and Historical-Experience Weighting
链接: https://arxiv.org/abs/2608.13108
作者: Huiyu Li,Weibo Liu,Xinru Xu,Dongchen Gao,Meng Zhang,Junhua Hu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-source evidence fusion under Dempster-Shafer theory faces two persistent challenges: existing conflict measures assess inter-evidence inconsistency and intra-evidence uncertainty independently, yielding incomplete evaluations, and current fusion methods evaluate evidence sources exclusively through instantaneous comparisns without exploiting their long-term reliability across diverse decision contexts. This paper proposes a unified evidence reasoning framework that addresses both limitations. Specifically, a chaos-conflict measurement is introduced to jointly quantify cross-evidence conflict and intra-evidence non-specificity, with five formally proven properties ensuring consistent assessment. A historical experience driven weighting scheme partitions the decision space via spectral clustering and applies regret theory to compute context-specific reliability profiles from past fusion outcomes. These mechanisms feed into a hybrid combination rule that adaptively balances uncertainty preservation against weighted consensus, controlled by the global conflict level, followed by a belief-interval decision strategy that enables robust classification without discarding epistemic uncertainty. Experiments on 16 real-world benchmark datasets demonstrate that the proposed framework achieves an average F1 score of 85.78 and a mean AUC of 93.30, outperforming eight DST-based baselines and three gradient boosting methods. Ablation analysis confirms the contribution of each component we proposed. The framework offers an effective approach for adaptive evidence fusion in multi-source decision making.
[AI-40] Sampling Luck Masquerades as Allocation Gain: Auditing Test-Time Budget Allocation for Neural Combinatorial Optimization
链接: https://arxiv.org/abs/2608.13087
作者: Jinhyung Bae
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注: 9 pages, 4 figures, 8 tables. Pre-registered study; code, cost arrays, and the full pre-registration record (including every amendment and its direction) at this https URL
Abstract:Neural combinatorial optimization (NCO) solvers report the best of many sampled solutions per instance, and the sample count is, by convention, identical for every instance. Whether a non-uniform allocation of a fixed total budget would buy anything has not been measured. We measure it, and we audit the measurement itself. First, on in-distribution workloads the allocation headroom is not detectable. Across three pretrained solvers (POMO, AM, SymNCO) on uniform TSP-100, an oracle allocation computed and evaluated on the same stored samples reports a 2.2-2.6% gain with intervals excluding zero; measured out of sample the same gain is indistinguishable from zero (0.457, 0.015, -0.512 percent). Following the customary in-sample procedure, all three solvers would have supported a published 2%-level gain that does not exist. We calibrate this bias against an instance-wise null in which the true gain is zero by construction; over the ranges we test it does not shrink with more samples or more instances. Second, the same correction that removes the phantom gains preserves a real one. Under distribution shift (a workload mixing uniform and clustered instances), a pre-registered confirmatory experiment finds that allocation guided by held-out sample statistics improves best-of-k by 11.5% (AM, primary endpoint; 95% CI [7.4, 19.7]) and 12.0% (SymNCO, replication) at equal evaluation budget, with the signal-acquisition cost not charged; a pre-registered negative control (POMO, an order of magnitude more robust to shift) shows -0.3% [-0.7, 0.24]. The gain exceeds a frozen distribution-label baseline by 4.2 points [1.9, 7.7]. An exploratory policy charging a 20-sample probe against the same budget retains 3.4% (AM) and 4.6% (SymNCO). We give a correction procedure and a reporting checklist, and release all data, code, and the pre-registration record. Comments: 9 pages, 4 figures, 8 tables. Pre-registered study; code, cost arrays, and the full pre-registration record (including every amendment and its direction) at this https URL Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC) ACMclasses: G.1.6; I.2.8; G.3 Cite as: arXiv:2608.13087 [cs.LG] (or arXiv:2608.13087v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.13087 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jinhyung Bae [view email] [v1] Thu, 13 Aug 2026 10:53:48 UTC (213 KB)
[AI-41] SPADE: Speculative Decoding for Precise and Low Cost Distributed Edge Cloud Inference
链接: https://arxiv.org/abs/2608.13076
作者: Divya Jyoti Bajpai,Kishan Kumar Upadhyay,Manjesh Kumar Hanawal
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) have achieved remarkable success in natural language understanding and generation, but their deployment is constrained by high computational demands. Deploying smaller LLMs directly on the edge can circumvent this, but with degraded accuracy. Deploying smaller cloud-based big LLMs preserves performance, but at the cost of expensive per-token computation. We present a distributed inference framework, \our, that integrates speculative decoding (SD) across edge and cloud. A compact draft model deployed on the edge generates candidate tokens rapidly, and a large verifier model on the cloud validates these tokens in parallel. Accepted tokens are retained, while only rejections trigger verifier correction, substantially reducing the number of cloud queries. Our plug-and-play design shifts the bulk of computation to the edge, significantly lowers inference time and cloud cost, and preserves the accuracy of the big model without any retraining requirement. Our approach demonstrates a practical path toward scalable, cost-efficient, and accurate deployment of LLMs in real-world environments. Experimental results across multiple Natural Language Processing tasks using SpecBench and CNN/Dailymail datasets demonstrate that \our reduces the cloud model calls by 76% with zero loss in accuracy as compared to the full model.
[AI-42] EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding
链接: https://arxiv.org/abs/2608.13072
作者: Shuailei Zhang,Muyun Jiang,Wei Zhang,Jinbo Chen,Zhiwei Guo,Yong Li,Yi Ding,Cuntai Guan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and individual neurophysiology. We propose EEG-PRIME, a two-stage EEG foundation model for cross-dataset multi-task decoding. EEG-PRIME combines masked pretraining with prototype-aligned instruction tuning to enable instruction-aware and subject-invariant decoding across diverse BCI paradigms. During pretraining, an EEG encoder learns transferable representations through masked reconstruction with frequency-cutoff spectral augmentation. During instruction tuning, EEG-PRIME incorporates task-semantic, dataset-specific, and subject-invariant conditioning. The resulting conditioning signal modulates the Q-Former through Layer-wise Query Modulation, while frozen text embeddings of class labels serve as prototypes for cosine-similarity-based prediction across heterogeneous label spaces. Experiments on sixteen datasets covering motor imagery, emotion recognition, ADHD detection, covert speech, and mental workload show consistent improvements over state-of-the-art baselines and prior EEG foundation models under cross-subject settings. On two additional held-out datasets, EEG-PRIME achieves balanced accuracy comparable to within-session calibration models without target-domain optimization, calibration, or linear probing, demonstrating promising zero-shot transfer capability.
[AI-43] Behavioral Reprogramming of Open-Weights Models: Cognitive Plasticity and Alignment Bounds
链接: https://arxiv.org/abs/2608.13069
作者: Lucia Malíčková
类目: Artificial Intelligence (cs.AI)
备注: Preprint submitted to arXiv, August 12, 2026. 13 pages, 5 figures
Abstract:Large language models (LLMs) are predominantly aligned to function as passive, sycophantic assistants. We challenge this default paradigm by empirically evaluating the cognitive plasticity of open-weight architectures when subjected to rigorous behavioral reprogramming. Our objective is to induce a proactive, Socratic conversational framework, characterized by high-frequency question generation under strictly constrained high-performance computing (HPC) conditions. Through a massively parallelized hyperparameter sweep comprising 405 HPC jobs, we define precise mathematical bounds for parameter-efficient fine-tuning (PEFT). We identify an architectural threshold at LoRA rank r=16 and demonstrate via extensive epoch ablation that generalization capacity strictly reaches its optimal convergence within an optimized training window of e \in [2, 3] depending on dataset density (minimum validation loss of 0.919). Furthermore, scaling model capacity to 14B parameters yielded a lower localized evaluation perplexity (1.414). Subsequent Direct Preference Optimization (DPO) successfully decoupled the underlying assertive behavior from localized syntax, while rigorous cross-lingual stress testing reveals both the capabilities and the structural boundaries of zero-shot persona transfer, demonstrating robust alignment in closely related linguistic families alongside identifiable degradation pathways in morphologically distant targets. These findings establish a rigorous empirical framework for compute-efficient, cross-lingual behavioral modification.
[AI-44] Uniform Herding: Exemplar Replay with Representation Refresh
链接: https://arxiv.org/abs/2608.13061
作者: Krishna Subedi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As the feature representation changes, replay must preserve the earlier classes. However, only a bounded active exemplar set can be replayed. We propose Uniform Herding, which allocates the current active set across observed classes and uses a bounded candidate pool to refresh their chosen exemplars in the current representation. On CIFAR-100 with ten class-incremental tasks, a ResNet-18 backbone, active budget M=2,000 , retrieval budget b=64 , and three seeds, Uniform Herding obtains 44.00\pm0.51% final average accuracy and 17.22\pm0.43% forgetting, compared with 42.33\pm1.20% and 24.87\pm1.11% for iCaRL. Within the Uniform Herding protocol, final accuracy decreased when NME or herding was replaced with the tested alternatives, while forgetting increased when distillation was removed. Changing the retrieval budget has a smaller effect across the tested range than changing the active budget. The comparison with iCaRL is end-to-end. It does not isolate the effect of refresh from the other protocol differences. These results are limited to the tested protocol.
[AI-45] VALG: An Agent ic System for ML Theory Research
链接: https://arxiv.org/abs/2608.13060
作者: Dechen Zhang,Xuan Tang,Xinxiang Yin,Xingwu Chen,Jian Qian,Difan Zou
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
备注:
Abstract:Machine learning theory studies learning procedures through mathematical setups in which the data model, training protocol, oracle access, loss, metric, and randomness define the phenomenon that a theorem is meant to explain. Solving an open problem therefore requires the problem formulation, theorem target, and proof mechanism to be developed in concert. Researchers formulate hypotheses, test them through preliminary theoretical or empirical analysis, and refine both assumptions and proofs. We investigate whether this process can be organized as an autonomous agentic workflow for ML theory research. We develop VALG, an agentic system that combines multi-level Verification, Adaptive formulation of Learning-theory problems, and Graph-structured proof development. Within each source-relative theorem branch, VALG maintains a fixed mathematical specification, checks the theorem-level composition of a typed proof-dependency graph, and constructs and reviews local proofs in dependency order. When a proof attempt fails, VALG identifies whether the obstruction lies in a derivation, the proof structure, or the theorem formulation and routes the next attempt accordingly. Formulation-level obstructions initiate an explicitly related variant or relaxation, preserving the mathematical relation between the resulting theorem and the source problem. We evaluate VALG on nine subproblems from five COLT 2026 open problems. Two runs produce internally finalized theorem candidates that match the scope of their source briefs; the remaining seven yield restricted-method results, special cases, or conditional theorems. These case studies show how VALG keeps source-scope matches, relaxations, conditional results, and blocked attempts mathematically distinct. VALG is open source at this https URL. Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML) Cite as: arXiv:2608.13060 [cs.AI] (or arXiv:2608.13060v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.13060 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-46] Operationalizing Cyber Threat Intelligence with GraphRAG
链接: https://arxiv.org/abs/2608.13050
作者: Atul Kabra,Prakhar Paliwal,Manjesh K. Hanawal
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 12 pages
Abstract:When a security researcher publishes a report on a cyberattack, detection engineers are supposed to turn it into working detection rules. In practice, most automated attempts at this only extract the simplest clues from the report — bad IP addresses, domain names, and file hashes — and turn them into block lists. This is a weak strategy, because attackers can change these simple clues within hours or days, so the resulting detections stop working almost as soon as they are deployed. Security teams describe this idea with the Pyramid of Pain. This project asks whether feeding a report into a knowledge-graph retrieval system, Microsoft GraphRAG, rather than a standard vector-similarity retrieval system (Naive RAG), produces detection plans that rely more on these durable, top-of-pyramid clues. Both systems are given the same report, the same generation instructions, and the same language model to write the final plan; only the retrieval step differs. In a detailed case study of one APT28 report, the GraphRAG plan kept firing at 100% of its detections after every IP address, domain, and file hash in the report was rotated, while the Naive RAG plan kept firing at only 29%. Repeating the comparison across nine real CTI reports from four vendors confirms the same pattern: GraphRAG plans consistently reach higher, harder-to-evade levels of the pyramid, even when the two systems end up close on total score. The results support treating knowledge-graph-aware retrieval as the architecturally correct foundation for automatically generating SOC-deployable hunting plans, while showing that the wording of the generation prompt matters almost as much as the retrieval back-end itself.
[AI-47] DMDIntel: Interpreting Large Language Models via Dynamic Mode Decomposition
链接: https://arxiv.org/abs/2608.13048
作者: Amogh Joshi,Animesh Mukherjee,Sergey Utyuzhnikov
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In this work, we introduce DMDIntel which uses dynamic mode decomposition (DMD) to make the predictions made by LLMs in a classification task interpretable. It develops an input attribution pipeline, that first decomposes the hidden states of an LLM into prominent patterns, also known as modes, and then associates ranks to the input tokens based on the projection values on those modes. Rigorous experiments across three datasets and three model families consistently show that the ranked attribution of input tokens obtained using DMDIntel by far outperforms state-of-the-art techniques such as principal component analysis, integrated gradients and SHAP.
[AI-48] BoardroomAI: Dependency-Aware Human-Steerable Multi-Agent Deliberation through Evolving Decision Graphs
链接: https://arxiv.org/abs/2608.13046
作者: Sanjeev Manivannan
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Emerging Technologies (cs.ET)
备注: 14 pages, 2 figures
Abstract:Organizational decisions are co-created while evidence, constraints, and human priorities continue to evolve. In conventional transcript-based multi-agent systems, humans typically provide an initial problem, agents deliberate internally, and the system returns a final response. BoardroomAI instead treats the human as a persistent participant who can intervene by challenging assumptions, modifying constraints, changing priorities, introducing evidence, or redirecting the decision process. We operationalize this human–agent coexistence through four components: (i) a typed decision graph representing evidence, assumptions, constraints, claims, objections, alternatives, risks, decisions, semantic dependencies, and specialist responsibility; (ii) an intervention compiler that converts confirmed human actions into explicit graph updates; (iii) dependency-aware propagation that identifies affected subgraphs, preserves unaffected artifacts, and selectively reactivates relevant specialists; and (iv) an evaluation framework measuring intervention impact, repair coverage, preservation, recomputation, and decision validity. Across 600 generated decision-DAG interventions, propagation matched exhaustive impact computation while inspecting only 14.59% of nodes. In a 12-case exploratory pilot, selective repair recomputed 62.11% of canonical nodes, preserved all gold-unaffected nodes, and produced valid updated decisions in six cases while abstaining in the remaining six. These abstentions show that correct intervention routing may still provide insufficient context for synthesis, motivating a \emphdecision-sufficient context closure for human-steered multi-agent deliberation. All results are synthetic and prototype-level.
[AI-49] Foundations of MT-PDCL: Measure-Theoretic Probabilistic Definite Clause Logic
链接: https://arxiv.org/abs/2608.13018
作者: Costin Bădică,Amelia Bădică
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Standard probabilistic logic programming frameworks typically rely on grounding logic programs into discrete propositional representations. This operational requirement restricts exact inference to finite domains and discrete probability distributions. In this paper, we introduce Measure-Theoretic Probabilistic Definite Clause Logic (MT-PDCL), a generalized foundational framework that eliminates this finite-domain restriction. By explicitly defining stochastic variables over bounded index domains and equipping the interpretation space with standard Borel \sigma -algebras, MT-PDCL allows logical variables to operate natively over continuous measurable spaces. Building on Continuous Distribution Semantics, MT-PDCL models probabilistic rules as mutually independent causal events. However, rather than aggregating these derivations via finite boolean circuits, declarative entailment is formally defined through exact Lebesgue integration over the continuous measure space. We introduce a continuous immediate consequence operator that unifies the integration of continuous prior distributions with the evaluation of exact continuous observations. We demonstrate that this approach replaces the combinatorial bottleneck of discrete grounding with exact, algebraic, and structurally differentiable inference. While this transition trades discrete combinatorics for the geometric curse of dimensionality, it achieves the expressive power of continuous probabilistic models while preserving the pure declarative syntax of definite clause logic.
[AI-50] Beyond Handcrafted Security: Towards Self-Evolving Defense for LLM Agents
链接: https://arxiv.org/abs/2608.12977
作者: Jiajun Ruan,Peiyang Li,Yukun Chen,Fengting Li,Chao Feng
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:The expanding operational capabilities of large language model (LLM) agents introduce sophisticated security threats. Runtime defenses have emerged as an effective approach to mitigating these risks by integrating security mechanisms into the agent execution loop. However, existing runtime defenses rely heavily on manually designed interventions and lack a principled framework for their construction and maintenance. In this work, we first develop a harness-level formulation of runtime defense that systematically characterizes how harness mechanisms enable defense construction and provides a unified view of existing runtime defense interventions from a harness perspective. Building on this formulation, we propose HARD (Harness-based Autonomous Runtime Defense Evolution), a self-evolving runtime defense framework that automatically identifies appropriate intervention strategies and iteratively improves defense artifacts based on observed failure traces. HARD transforms runtime defense development from manual engineering into an autonomous evolution process, and extensive experiments demonstrate that it improves security performance over existing handcrafted defenses while preserving benign task utility. Our findings highlight autonomous defense evolution as a promising new paradigm for securing deployed LLM agents, enabling agents to identify defense weaknesses and continuously improve their protection mechanisms.
[AI-51] Moose: Latent concept learning with reasoning -shortcut awareness in mathcalEL ISWC2026
链接: https://arxiv.org/abs/2608.12961
作者: Olga Mashkova,Asaad Mohammedsaleh,Fernando Zhapa-Camacho,Robert Hoehndorf
类目: Artificial Intelligence (cs.AI)
备注: Accepted at ISWC 2026, Research Track
Abstract:The OWL 2 EL profile is used in some of the largest production ontologies, including the Gene Ontology and SNOMED CT. Existing neuro-symbolic (NeSy) learning methods accept propositional theories or Datalog, and reasoning-shortcut (RS) awareness has not been investigated in ontology settings. We present Moose, a method that compiles an \mathcalEL^++ TBox and finite ABox to a Sentential Decision Diagram (SDD). The SDD acts as a differentiable weighted-model-counting layer, and we add closure clauses outside the \mathcalEL^++ profile on declared exhaustive families to overcome the limited expressivity of \mathcalEL^++ under partial supervision. We show termination, soundness, completeness, and polynomial intermediate sizes, and validate the proofs in Lean. We then define the first formal partial-supervision latent-concept-learning task over an OWL EL ontology, i.e., learning per-individual classifiers for latent concepts from observed ABox literals, and evaluate Moose on MNIST-with-ontology and Pizzaïolo. Moose improves over propositional-NeSy, fuzzy-logic, and ontology embedding baselines, and presents the first reasoning-shortcut analysis in an OWL EL setting.
[AI-52] he Objective Is the Bottleneck: Latent World Models Encode What Their Planners Cannot Use
链接: https://arxiv.org/abs/2608.12959
作者: Joyjeet Singh
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Follow-up to arXiv:2608.10145 . All experiments run on a laptop CPU; no model was trained or fine-tuned. Code, checkpoints and every measurement: this http URL
Abstract:Latent world models are judged by how well they predict, so when planning fails at long horizons the natural reading is that the predictor degrades. On a reproduction of LeWorldModel on TwoRoom we show the binding constraint is the planner’s objective instead. The predictor is not the limit: its imagined state seventy-five environment steps ahead is still only 0.189 as wrong as assuming the world froze, while the planner never imagines beyond twenty-five. The objective is. Cross-entropy-method planning minimises squared latent distance, which tracks true distance at r = 0.426, saturates by about eighty arena units and decreases beyond a hundred and twenty, so moving away from the goal can lower the cost. The information is present throughout: a ridge probe recovers position from the frozen embedding at R^2 0.9922. The pathology is the method’s, not one reimplementation’s. It is present in the authors’ released weights, and across four checkpoints long-horizon success rank-orders exactly with metric quality and inversely with prediction accuracy. Replacing only the objective, with nothing retrained and no GPU, lifts goals reached at offset 100 from 26.0% to 98.0%, equals the 98.0% at offset 25, and reaches 92.0% under a third of the budget: planning stops depending on the horizon. The best cost is not the most accurate. A head learned from frame separation alone predicts spatial distance worse than a position probe (r = 0.819 against 0.9897) yet plans better, charging 24% more to cross the environment’s dividing wall where squared latent distance charges 4% less. It has learned reachability, not proximity. Comments: Follow-up to arXiv:2608.10145. All experiments run on a laptop CPU; no model was trained or fine-tuned. Code, checkpoints and every measurement: this http URL Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.12959 [cs.LG] (or arXiv:2608.12959v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.12959 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Joyjeet Singh [view email] [v1] Thu, 13 Aug 2026 08:43:54 UTC (17 KB)
[AI-53] Decomposition of Evidence Contradiction and Frag ility in Perturbation Responses
链接: https://arxiv.org/abs/2608.12935
作者: Lei You
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Perturbation methods explain model decisions by measuring prediction changes under altered inputs, but response magnitude tells us only how much a model reacts, not what that reaction means. The same magnitude can support the final factual-counterfactual difference, oppose it, or arise strongly along the perturbation path yet vanish at the endpoint. We therefore track how the contrast develops as paired inputs are progressively revealed, using the final contrast to interpret the trajectory. We introduce DECAF (Decomposition of Evidence, Contradiction, And Fragility), which routes aligned, opposed, and endpoint-null responses into evidence E, contradiction C, and fragility F. The decomposition preserves ordinary magnitude exactly, Abs = E + C + F, and is unique under endpoint-relative axioms. Across controlled vision and tabular settings, the three components track independently measured behavior. In a 72-model ImageNet-9 audit, we compare cases with nearly identical response magnitude but different independently measured behaviors. The largest DECAF component agrees with an observed behavior in 96.4% of cases, compared with 35.0% for magnitude alone. Changing only the reveal path increases total response by nearly 80%, yet evidence barely changes while fragility grows by more than 4x. On FunnyBirds and ImageNet-1k, short forward-only DECAF trajectories outperform the tested general-purpose attribution baselines. On a 1B-scale DINOv2 model, a short trajectory matches a strong gradient-based baseline with 4.75x lower wall time and 2.36x lower peak memory.
[AI-54] FlashDrive: Flash Vision-Language-Action Inference for Autonomous Driving
链接: https://arxiv.org/abs/2608.12932
作者: Zekai Li,Yihao Liang,Hongfei Zhang,Jian Chen,Yesheng Liang,Zhijian Liu
类目: Artificial Intelligence (cs.AI)
备注: 15 pages; 8 figures
Abstract:Vision-Language-Action (VLA) models promise to bring end-to-end reasoning to autonomous driving, but their computational cost remains far too high for real-time control. The core challenge is structural: VLA inference is not a single bottleneck but a cascade of four. Visual encoding wastes compute on overlapping video frames; language-model prefill recomputes context that could be carried over from the previous timestep; reasoning tokens are generated serially despite low entropy; and flow-matching denoising applies uniform compute to a non-uniform velocity field. Addressing any one stage in isolation leaves the others untouched. We propose FlashDrive, an algorithm-system co-design framework that targets all four stages simultaneously. Our key insight is that each bottleneck admits a distinct, lightweight algorithmic shortcut: temporal overlap enables streaming KV-cache reuse across frames; the low per-token entropy and strong intra-block correlations of driving-domain reasoning make a non-autoregressive diffusion drafter highly effective for speculative decoding; and the velocity field’s structure—sharp at the endpoints, flat in the middle—permits adaptive step caching that concentrates compute where it matters. Layered on system-level CUDA Graph compilation and kernel fusion, these techniques compound. Applied to Alpamayo 1.5-10B with W4A8 quantization, FlashDrive reduces end-to-end latency from 717ms to 151ms (4.7x) while leaving accuracy essentially unchanged: minADE6@6.4s shifts by only 0.08m, minADE1 improves, and closed-loop collision and off-road rates improve in simulation. By raising a 10B-parameter reasoning VLA from 1.4~Hz to 6.6~Hz on a single GPU, FlashDrive moves end-to-end autonomous driving substantially closer to real-time deployment.
[AI-55] Polish Medical Visual Question Answering: Vision-Language Models Underutilize Visual Evidence
链接: https://arxiv.org/abs/2608.12928
作者: Jakub Pokrywka,Łukasz Grzybowski,Antoni Lasik,Marek Kubis,Jeremi Ignacy Kaczmarek,Wojciech Kusa
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce a Polish-language medical visual question answering (VQA) benchmark, built from Polish Board Certification Examination questions for licensed physicians and dentists pursuing specialist certification. The benchmark comprises image-containing questions spanning diverse medical specialties and visual domains, together with a text-only question answering (QA) control set. We evaluate Polish-oriented, general-purpose open-weight, and commercial vision-language models. The task remains challenging: the best model achieves 79.0% accuracy on the full VQA set, and only GPT-5.6 surpasses the approximate human reference on the subset with available candidate responses; all other evaluated models perform worse than humans. To assess visual grounding, we compare complete inputs with configurations omitting the image, the question, or both, and categorize questions by image importance. Models derive more useful information from the question text than from the image and perform worse on image-dominant questions. Across both QA and VQA, they nevertheless achieve above-chance accuracy from the answer choices alone, showing that non-trivial performance can persist even when key task components are missing.
[AI-56] H-VAEP and H-xT: Valuing Offensive On-the-Ball Actions in Handball by Estimating Probabilities KDD2026 ECML
链接: https://arxiv.org/abs/2608.12926
作者: Julius Broermann,Oliver Müller,Michael Döring,Jochen Baumeister
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 13 pages, 6 figures, 1 table. Accepted at the 13th Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA 2026), co-located with ECML PKDD 2026
Abstract:Traditional player evaluation in professional handball relies on basic box-score metrics or heuristic indices, which fail to credit the multi-player build-up chain. While football (soccer) analytics has adopted Expected Threat (xT) and Valuing Actions by Estimating Probabilities (VAEP), these event-based action valuation frameworks have not yet been adapted to handball. In this paper, we present the first comprehensive adaptation and evaluation of xT and VAEP for handball, utilizing five seasons of tracking-derived event data from the Handball Bundesliga. We develop Handball-xT (H-xT) using a handball-native court zoning layout, demonstrating via simulations that it is systematically more robust than standard rectangular grids. We optimize Handball-VAEP (H-VAEP) by tailoring its feature space and selecting the context length to limit team-identity leakage. Our evaluation shows that H-VAEP yields exceptionally stable, discriminative, and intuitive player ratings that highlight build-up play. Finally, we release our complete code repository to help professional clubs deploy these models.
[AI-57] InFactPlanner: Planning Sustainable Geo-Distributed LLM Data Centers
链接: https://arxiv.org/abs/2608.12915
作者: Nicoletta Tsiopani,Moysis Symeonides,George Pallis,Marios D. Dikaiakos
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注: Author copy of paper published at 34th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication System (MASCOTS2026)
Abstract:The rapid growth of LLM inference is shifting sustainability concerns from one-time training to continuous serving, where infrastructure decisions shape energy use, carbon emissions, water consumption, and service quality. Yet operators often need to compare deployment alternatives before large-scale infrastructure is built, making direct measurement costly, slow, and sometimes infeasible. We present InFactPlanner, a trace-driven decision-support framework for what-if analysis of sustainable AI data center deployment for LLM inference across single and geo-distributed sites. InFactPlanner combines query traces, hardware-model profiles, candidate site configurations, PUE/WUE parameters, renewable generation models, and time-varying grid carbon intensity to estimate power, energy, carbon emissions, water use, latency, and server utilization. The framework abstracts low-level serving effects into configurable hardware-model profiles, enabling rapid comparison of site selection, capacity placement, hardware, model, renewable integration, and routing choices. We validate the energy accounting pipeline by reproducing reference LLM inference energy estimates with less than 10% deviation, evaluate scalability across multiple data centers and server counts, and demonstrate scenario-driven decision analyses for hardware selection, renewable placement, geographic deployment, and carbon-aware routing. Our results show that sustainability-optimal choices can differ from latency-optimal ones, and that the carbon value of deployment depends strongly on the local grid mix.
[AI-58] EGRL: Edge generation-guided relation-aware learning for RNA-protein interaction prediction
链接: https://arxiv.org/abs/2608.12906
作者: Danyu Li,Ling Zhou,Rubing Huang,Xian Zhong,Bin Zou,Kui Jiang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:RNA-Protein Interactions (RPIs) are critical for regulating cellular functions. While traditional wet-lab experiments for RPI detection are costly and time-consuming, Deep Learning (DL) methods provide an efficient computational alternative for RPI Prediction (RPIP). In particular, Graph Neural Networks (GNNs) are promising, as they naturally model RPI networks. However, existing GNN-based methods often rely on homogeneous graphs or predefined meta-paths, which limit their ability to handle data sparsity and to generalize to cold-start scenarios involving unknown molecules. To address these limitations, we propose Edge Generation-guided Relation-aware Learning (EGRL), a novel framework with several key components: implicit meta-path learning to capture relational semantics without handcrafted paths; a multi-relation-aware attention mechanism for adaptive fusion of interaction patterns; a graph generator that predicts potential (“soft”) edges to support cold-start nodes; and a multi-feature fusion predictor for final interaction scoring. EGRL is jointly trained with a primary task loss and an auxiliary generator loss. Comprehensive evaluations on four benchmark datasets demonstrate that EGRL achieves competitive overall performance. More importantly, it exhibits superior generalization in cold-start settings, achieving an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.867 and an Area Under the Precision-Recall curve (AUPR) of 0.861 on unknown molecules, corresponding to improvements of 8.6% in AUROC and 5.0% in AUPR over prior state-of-the-art methods. The code will be released soon.
[AI-59] Predictive Memory Localization: Forecasting Selective Intervention Paths from Internal Signals
链接: https://arxiv.org/abs/2608.12892
作者: Jinhao Jing,Tian Zeyu,Lucas Qingyang Fang,Zhisheng Chen,Shuang Chen,Yuhao Luo,Qiannian Zhao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Activation steering turns localized representations into control directions, but localization alone does not reveal whether a direction has a selective operating regime. We introduce Predictive Memory Localization (PML), which treats the measured-grid intervention path as the predictive object of memory localization. PML separates random-calibrated target movement from semantic-neighbor and capability damage, and compares static localization and supervised geometry with a strength-disjoint low-dose causal response. Our frozen study covers 3,000 records from nine datasets and fourteen domains, yielding 30,000 distinct record-direction-layer paths and 210,000 distinct path-strength evaluations. At layer 7, the geometry-derived RFM/AGOP direction reaches 13.1% target-any and 12.3% clean-any, exceeding random by 3.6 and 3.4 percentage points under a record-paired bootstrap. Across record-, dataset-, and domain-grouped splits, responses at |\alpha|=0.1 are the strongest signal for outcomes at disjoint strengths |\alpha|\in\0.25,0.5\ . On held-out records, a predictor-driven selector chooses a coefficient or abstains, improves utility and reduces semantic-neighbor damage relative to a train-tuned fixed-strength policy, and avoids most evaluations in a dense scan. Across three residual-norm-matched base models, learned directions retain selective-path gains and low-dose responses yield 0.801-0.828 record-held-out macro AUROC. PML therefore turns memory localization into a falsifiable forecast of margin-level selective outcomes and a risk-aware intervention decision.
[AI-60] Labels Are Not Endpoints: Treatment Leakage and Construct Validity in MCP Agent Security Evaluation
链接: https://arxiv.org/abs/2608.12880
作者: Rana Muhammad Ahmed(Department of Computer Science, Bahria University, Islamabad, Pakistan),Sabahat Abbas(Department of Computer Science, Bahria University, Islamabad, Pakistan)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 24 pages, 10 figures, 4 tables. Preprint
Abstract:Security evaluations of tool-using agents often equate stored labels with behavioral facts. We audit a preserved campaign by tracing 10,200 execution rows to 180 model-bound requests, 45 semantic requests, and 15 observable stimuli. Two schema treatments were delivered, but the planned external payload-family corpus was not. The historical grader exhibited direct treatment leakage: treatment metadata gated the ATTACK_SUCCESS class, so fixed behavior could change class under treatment relabeling. A treatment-blind reconstruction corrects 58 historical ATTACK_SUCCESS or HIJACK_ATTEMPT labels to authorized benign completions while preserving three verified protected-data transfers and one separate unauthorized-forwarding case. The locked v2 census contains exactly zero ATTACK_SUCCESS records, while the forwarding case remains a HIJACK_ATTEMPT at a semantic boundary concerning objective completion. A dual-reviewer blinded concordance review of all 96 requests deemed structurally interpretable by locked v2 produced identical reviewer-consensus classes but differed from the locked codebook on four construct-boundary cases. We contribute a seven-link Integrity Chain and an executable, scope-bounded endpoint-integrity linter. The result is a campaign-bounded measurement audit, not a population attack-rate, model-ranking, defense-efficacy, or causal estimate.
[AI-61] ReflectFact: Self-Reflective Agents for Improving Comprehension and Reasoning in Multi-Hop Fact Verification
链接: https://arxiv.org/abs/2608.12877
作者: Runze Zhao,Zixin Tang,Xiaoshuai Hao,Leyuan Chang,Xiaopeng Fu,Boyu Qiao,Dongyang Zhang
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 4 figures, 3 tables
Abstract:Multi-hop fact verification, which verifies claims by reasoning over multiple pieces of evidence, is critical for combating misinformation on social media yet remains highly challenging. Recent methods primarily rely on multi-agent collaboration to decompose fact verification into specialized subtasks. However, these methods face two critical limitations: (1) agents may perform individual subtasks without sufficient awareness of the global verification objective, causing their reasoning to deviate from the intended direction; and (2) conflicts between parametric knowledge and the provided evidence may undermine evidence-grounded reasoning and lead to incorrect verdicts. To address these challenges, we propose ReflectFact, a novel self-reflective agent framework for multi-hop fact verification. ReflectFact introduces three key tasks. Explicit Reasoning Path Planning builds an evidence-grounded reasoning path by resolving implicit entities, decomposing the claim into sub-questions, and integrating the verified facts into a verdict. Evidence-Drift Verification makes the agent re-answer by quoting the supporting evidence when a grounded answer merely echoes its parametric prior, thereby calibrating evidence deviation to ensure grounded comprehension. Reasoning Reflection Verification re-examines each reasoning step and regenerates it once an inconsistency is detected, correcting reasoning flaws such as location bias and replacement bias through a global task perspective. Subsequently, the agent aggregates validated reasoning chains to yield reliable verdicts. Extensive experiments on HOVER and EX-FEVER demonstrate that ReflectFact effectively remedies the comprehension and reasoning defects of existing methods, achieving state-of-the-art performance and respectively outperforming the strongest baseline by 3.32% and 2.78% on the two datasets.
[AI-62] A Compositional Theory of Curvature in Probabilistic Circuits
链接: https://arxiv.org/abs/2608.12869
作者: Hrithik Suresh,Sahil Sidheekh,Shelar Parth Vijay,Yasir Z,Sriraam Natarajan,Narayanan Chatapuram Krishnan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Probabilistic Circuits (PCs) are generative models that support exact inference and, unlike deep neural networks, admit an exact and tractable measure of loss-surface curvature: the trace of the Hessian of the log-likelihood. Recent work regularizes this trace globally to bias learning toward flatter, better generalizing optima. We show that treating sharpness as a global regularizer can be misspecified for PCs, whose curvature is inherently compositional. We prove that each sum node’s contribution to the Hessian trace factorizes exactly into its circuit flow, which measures how heavily the node is used, and a local sharpness term determined by its output distribution. This decomposition provides insights into why global sharpness regularization is depth biased and can lead to underfitting. Building on it, we introduce an adaptive sharpness aware regularizer that penalizes nodes based on intrinsic local curvature and preserves closed form EM updates. We also show that empirically, this targeted regularization recovers the generalization that global regularization sacrifices while retaining the robustness and benefits of sharpness aware learning.
[AI-63] AI and Consumer Rights in India Working Paper
链接: https://arxiv.org/abs/2608.12863
作者: Omir Kumar,Sriya Sridhar,Vibhav Mithal,Balaraman Ravindran
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As AI systems proliferate in consumer facing applications, questions about liability for AI related harms remain unresolved. This working paper examines whether India’s Consumer Protection Act, 2019, adequately addresses harm caused by defective AI products and services, and whether it proportionately allocates liability across the AI value chain. The Act’s broad definitions of product liability, harm, and deficiency appear technology agnostic and potentially applicable to AI related incidents including personal injury, psychological harm, biased outputs, and loss of control. However, significant gaps remain. Proving causation between AI defects and consumer harm presents a technical challenge, as AI failures often stem from design choices rather than discrete defects. Additionally, the Act’s framework assumes distinct roles for manufacturers, sellers, and service providers, yet the AI value chain involves overlapping responsibilities among data providers, model developers, deployers, and users that do not neatly map to these categories. Current liability frameworks lack proportionate mechanisms to effectively address complex, multistakeholder AI harms. While the Act may cover AI entities, enforcement requires clarification on sector specific overlaps. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.12863 [cs.AI] (or arXiv:2608.12863v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.12863 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-64] Practice Makes Unsafe: Skill Misevolution in Self-Improving LLM Agents
链接: https://arxiv.org/abs/2608.12851
作者: Xutao Mao,Liangjie Zhao,Xiang Zheng,Cong Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Self-improving LLM agents convert successful trajectories into persistent cross-task state. An unsafe success can thereby become reusable policy after its triggering input disappears. Skill evolution makes this failure measurable by distilling operational trajectories into executable, transferable, and inspectable procedures. Because evolution optimizes task outcomes rather than procedure safety, compromised experience can cause skill misevolution. Existing benchmarks measure current behavior or static artifacts but cannot attribute risk across authoring, retrieval, and later execution. To expose this lifecycle, we introduce SkillMisevo-Gym, a lifecycle-aware harness that versions skill state across agent frameworks, and SkillMisevo-Bench, a frozen design from malicious exposure to carryover tasks, with concept-aligned benign tasks and nine lifecycle metrics. We also introduce SafeEvolve, a wrapper that repairs unsafe content and governs subsequent reuse. Across 25 agent-method configurations, each covering 525 tasks in 25 episodes, all 21 evolved configurations author unsafe artifacts, while only fifteen lead to fresh-session harm. In the exposure sweep, three malicious tasks raise carryover ASR from 16.0% to 35.3%. Across representative skill evolution methods, SafeEvolve reduces unsafe retrieval and fresh-session harm by 26.7 and 17.3 percentage points, respectively, while mean benign utility changes by only 0.4 points. Together, persistent-adaptation safety must govern what updates write and what future executors reuse. Code is available at this https URL.
[AI-65] CABS: Efficient and Scalable Model Merging via Conflict-Aware Sparsification and Adaptive Weight Allocation
链接: https://arxiv.org/abs/2608.12842
作者: Yuchen Liu,Zongzhen Yang,Binhang Qi,Hailong Sun,Xiang Gao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Model merging has recently attracted significant attention as a promising paradigm for constructing unified multi-task models without requiring additional retraining. However, parameter conflicts and knowledge interference across tasks often degrade merged-model performance. Prior work introduced Conflict-Aware and Balanced Sparsification (CABS), which reduces parameter interference through structured pruning and sequential masking. However, CABS relies on grid search to determine scaling coefficients, resulting in exponential time complexity, while its optimization objective can be dominated by high-performance tasks, leading to suboptimal overall performance. To address these limitations, we extend CABS and propose CABS+. Specifically, Adaptive Weight Allocation (AWA) optimizes merging coefficients via a gradient-free search scheme to reduce time complexity, while an asymmetric fitness function promotes more comprehensive performance gains across tasks. Moreover, we conduct a systematic empirical study of key factors influencing model merging performance and propose Relative Synergy Score (RSS) to quantify model mergeability and guide model selection. We compare CABS+ with state-of-the-art model merging methods, including CABS, AdaMerging, and WUDIMerging, across 27 datasets and 5 models covering large language, small-scale language, and vision models. Extensive experiments verify the effectiveness and efficiency of CABS+. Compared with AdaMerging and WUDIMerging, CABS+ improves overall performance by 16.97% and 12.93%, respectively, exhibits stronger stability and robustness across varying task numbers and model architectures, uses less than 25% of the GPU memory required by AdaMerging, and achieves nearly a 4x speedup in merging time over WUDIMerging.
[AI-66] Fast A/B/n Testing: Exact Multi-Policy Comparison via Tree-Coupled Feedback Sharing
链接: https://arxiv.org/abs/2608.12831
作者: Yuxiao Wen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Online platforms increasingly compare many adaptive decision policies—ranking systems, recommendation algorithms, pricing rules, and language-model agents—while each reward-bearing interaction can be costly or risky. A direct A/B/n design gives each of J policies its own horizon- T trajectory and therefore uses JT outcomes. We introduce Tree-Coupled A/B Testing (\TCAB), an exact feedback-sharing design for arbitrary history-dependent contextual-bandit policies. At each round, a predictable tree connects the current policy histories; every parent–child context–action law is maximally coupled, and one reward is shared within each component of matched tree edges. Every policy retains exactly its standalone finite-horizon trajectory law, even though the policies are deliberately dependent. If D_e,t records a mismatch on tree edge e at round t , the number of reward queries satisfies the pathwise identity N(T)=T+\sum_t,eD_e,t and hence equals T plus cumulative tree-edge total variation in expectation. This cost is conditionally optimal among exact edge-local designs on the selected tree, and a current-round minimum-spanning tree is myopically optimal among tree designs. For fixed J , sublinear pseudo-regret of every policy and almost-sure uniqueness of the oracle action imply \mathbbE[N(T)]=T+o(T) , versus JT for independent runs. We also obtain finite-sample variance bounds for pairwise policy contrasts. Experiments on reward-model evaluation, multiple-choice language-model evaluation, and adaptive search policies demonstrate substantial improvements in the cost–precision frontier.
[AI-67] PIPES: Securing Agent Perception with Provenance and Priors
链接: https://arxiv.org/abs/2608.12789
作者: Sanjay Kariyappa,Severin Klingler,G. Edward Suh
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Tool-using agents consume external data from sources with different levels of trust, yet tool responses rarely identify who produced each component or what it should convey. We show that this gap enables state-corruption attacks, in which attacker-controlled content makes environmental claims beyond the informational authority of its response component and corrupts the agent’s perceived environment, making the resulting action appear justified to existing guardrails. We introduce PIPES (Provenance-Informed, Prior-Enforced Screening), which screens response units using semantic priors and source provenance. PIPES uses static field contracts when schemas provide stable expectations, and conditions screening of open-ended content on the pre-response trajectory and trusted provenance metadata. It marks units that violate their semantic prior or the provenance hierarchy; deployments may remove, warn, block, or escalate detected violations. We instantiate atomic removal and evaluate PIPES against adaptive PAIR-style attacks. Across the three VitaBench and three AgentDyn splits with Gemma 4 31B IT as the target agent, PIPES reduces average attack success from 84.7% to 2.3%, while preserving average benign utility (92.5% with PIPES versus 90.6% without defense).
[AI-68] ARAC: Benchmarking Auto-Researchs Alignment and Completeness on End-to-End Researchs
链接: https://arxiv.org/abs/2608.12788
作者: Jiale Cui,Yueyao Yuan,Kaixi Zhong,Xiaogang Xu,Jiafei Wu,Zhe Liu
类目: Artificial Intelligence (cs.AI)
备注: 26 pages, 3 figures
Abstract:The rapid advancement of Auto-Research has surfaced a fundamental evaluation challenge: how can we measure the alignment, logical coherence, and evolutionary completeness of its research trajectory with human research behavior? We propose Auto-Research’s Alignment and Completeness, ARAC-Bench: a Researcher-Mimicking Evaluation framework that shifts the objective from matching final answers to reproducing high-quality human research processes. The framework operates through two synergistic components: the Academic Cognition Skills system, which is the first to transforms implicit reviewer expertise into stage-calibrated, quantifiable rubrics; and a three-stage capability diagnostic protocol, which decomposes the research process under strict modular constraints into three traceable, mutually independent dimensions: Proposal, Experiment, and Synthesis. Systematic evaluation of 11 SOTA frameworks yields a best alignment score of only 67.9 of 100, revealing a significant gap in simulating rigorous human methodology. Validation against Ph.D. Candidates rankings shows a strong correlation of 0.8141, confirming that ARAC-Bench reliably reflects the dimensions researchers truly value. ARAC-Bench provides not only a fine-grained diagnostic tool but also a scalable reward signal for training the next generation of autonomous research systems.
[AI-69] Memorization Diagnostics for Code LLM s Should be Scale-Aware
链接: https://arxiv.org/abs/2608.12771
作者: Prateek Kumar Rajput,Abdoul Aziz Bonkoungou,Alberick Euraste Djiré,Xunzhu Tang,Yewei Song,Iyiola Emmanuel Olatunji,El Hacen Diallo,Jacques Klein,Tegawendé F. Bissyandé
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 26 pages, 6 figures, 6 tables. Under review at EMSE
Abstract:The extent to which large language models for code rely on memorization over genuine understanding remains highly debated. While current literature frequently reports widespread memorization, evaluating the underlying probing techniques across dense architectures reveals a severe breakdown in their utility at scale. Traditional encoder-style probes using perturbations such as synonym fuzzing or dead-code insertion struggle to expose memorization in scaled models, even on known-contaminated benchmarks, and decoder-style probes that rely on log probabilities show similar performance degradation. The specific mode of failure for these probes, particularly why such techniques disrupt smaller models but fail to impact larger ones, motivates us to untangle representation load from memorization rather than treating them as a single phenomenon. By applying invertible mathematical transforms to numeric problems, we isolate these two factors and reveal that scaled encoders successfully absorb substantial representation load while still converging on the correct family of solutions. In practical software engineering, this ability to adapt to varying surface forms is what truly matters for usability and generalizability in LLM and agentic applications. Whether a specific solution was seen during training becomes a much less pressing question because although memorization inflates scores on contaminated benchmarks, factoring out representation load makes it debatable how much we should truly care if a functional answer was originally memorized. Future evaluations must therefore be built around separating these phenomena rather than relying on methodologies that quietly entangle them.
[AI-70] Beyond Outcome Rewards: Step-Level Self-Distilled Policy Optimization for Deep Search Agents
链接: https://arxiv.org/abs/2608.12764
作者: Haoze Wu,Chuqiao Kuang,Tianyi Zhuang,Xiaoguang Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Work in progress
Abstract:Deep search agents operate over trajectories spanning dozens of steps, yet standard reinforcement learning provides only a single outcome reward per trajectory, which is far too sparse for effective credit assignment. On-policy self-distillation (OPSD) addresses this by using the model’s own logits as dense token-level teachers, but extending it to search agents introduces a fundamental tension: the teacher, having access to privileged information such as the correct answer, produces a distribution that differs systematically from the student’s exploration-based reasoning, and naive distillation causes the student to inherit this information asymmetry rather than learn better search strategies. We resolve this tension through two contributions. First, we construct Evidence Anchors, which are concise, step-level evidence snippets extracted from the web, as privileged information that captures key reasoning steps without revealing the entire answer path. Second, we propose Step-Level Self-Distilled Policy Optimization (SSPO), which converts teacher-student disagreement into step-level advantage weights within GRPO, applied exclusively to incorrect trajectories. This design decouples what to update from how much to update: the outcome reward determines the direction of policy change, while the teacher modulates its magnitude at each step. Correct trajectories are left untouched, preserving their diversity. On Qwen3-8B, SSPO consistently outperforms GRPO across BrowseComp, GAIA, and FRAMES, surpassing or matching GRPO trained with twice as many gradient steps while adding only about 5 percent overhead per step from a single additional forward pass.
[AI-71] PROVE-RT: Generating Mechanized Theorem Prover Scripts for Real-Time Systems using LLM s
链接: https://arxiv.org/abs/2608.12762
作者: Sadat Shahriyar,Shareef Ahmed,Abdullah Al Arafat
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Schedulability analysis is essential for certifying real-time systems, but existing tests are often developed through pen-and-paper proofs that are difficult to scale, validate, and maintain. Mechanized verification in PROSA/ROCQ offers a rigorous alternative, yet manually constructing such proofs requires substantial domain expertise and proof-engineering effort. Recent successes of large language models (LLMs) across a wide range of tasks make them promising candidates for generating PROSA/ROCQ scripts for mechanized theorem provers. However, state-of-the-art LLMs often lack the PROSA-specific knowledge required to correctly use its modeling abstractions and proof patterns. This paper introduces PROVE-RT, an LLM-assisted framework for generating PROSA/ROCQ scripts to mechanize schedulability analyses in real-time systems literature. PROVE-RT guides generation through dependency-aware informal sketches, retrieval from processed PROSA documentation, staged skeleton generation, and proof completion. We construct a mechanization-oriented corpus from 1, 191 real-time systems papers, containing 13, 134 informal sketches with dependency information. On a curated evaluation set, direct prompting of state-of-the-art LLMs fails to reliably generate valid PROSA mechanizations, whereas PROVE-RT achieves a success rate of 44.7%. These results show that retrieval-guided and staged LLM assistance can improve automated mechanization of schedulability analysis in PROSA/ROCQ. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.12762 [cs.AI] (or arXiv:2608.12762v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.12762 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-72] Correct Is Not Governed: Provenance Integrity in Agent ic Workflows
链接: https://arxiv.org/abs/2608.12761
作者: Jesus Salas
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 19 pages, 2 figures
Abstract:Agentic workflows are commonly evaluated by whether they reach the correct outcome. That is insufficient in institutional settings, where a correct action may rely on the wrong authority, an unsupported completion claim, or work made stale by a later change. We define governed execution as work whose decisions, completion, and response to change are supported by inspectable provenance. We present Matrix, a deterministic causal-state layer that records authority and fact dependencies, verifies completion evidence, and selectively invalidates affected work. Across controlled comparisons, governed and direct workflows often reached the same outcomes, but only the governed path consistently preserved governing evidence, refused unsupported closure, and limited recovery to dependent tasks. A role-separated transfer challenge then failed: a deterministically enforced completeness contract severely over-blocked synthetic packets produced outside its authoring context. These results do not establish Matrix as a general accuracy enhancer; they support its primary role as an institutional integrity layer for making agentic work auditable and independently verifiable.
[AI-73] SynAct: A Reasoning -Acting Large Language Model Agent for Adaptive Synthesis Optimization
链接: https://arxiv.org/abs/2608.12751
作者: Fangzhou Liu,Peiyi Han,Jiawei Liu,Yuan Pu,Zhuolun He,Rongliang Fu,Tsung-Yi Ho,Bei Yu
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI)
备注: 12 pages, 8 figures
Abstract:Logic synthesis transforms RTL designs into gate-level netlists, where PPA results are highly sensitive to the choice of optimization commands, making synthesis tuning both high-dimensional and expensive. Previous approaches fall into two categories: automated methods, which perform black-box search over fixed action spaces with limited decision-level interpretability, and LLM-based methods, which typically generate static scripts upfront and cannot adapt to evolving circuit states. We present SynAct, an adaptive closed-loop LLM reasoning–acting agent that iteratively diagnoses live synthesis reports and reasons over the current circuit state, retrieved tool knowledge, and historical optimization experience to issue targeted commands. SynAct focuses on improving timing, particularly worst negative slack (WNS), while maintaining balanced area and power trade-offs. Experiments on a commercial synthesis tool across 14 designs show that SynAct reduces average WNS to 27% of that from bootstrap synthesis.
[AI-74] Spatial Memory Agent : Experience-Grounded Procedure Memory for Spatial Intelligence
链接: https://arxiv.org/abs/2608.12743
作者: Haokai Zhang,Yuhang Ding,Yunshu Zhou,Xinze Du,Shengtao Zhang,Zhiyue Zhao,Yuling Xi,Hao Chen
类目: Artificial Intelligence (cs.AI)
备注: Under Review
Abstract:Spatial intelligence is becoming a foundation for embodied agents, robotic planning, and multimodal assistants. To improve the spatial reasoning ability of VLM agents, existing work has mainly followed two lines. One line uses post-training methods, such as supervised fine-tuning and reinforcement learning. Another line adopts an agentic paradigm in which the model calls external spatial tools, such as depth estimation and 3D reconstruction tools, to gather intermediate spatial evidence. We study a complementary and underexplored route: Can a frozen VLM agent improve its spatial reasoning through \textbfparameter-update-free self-evolution, without depending on external expert spatial tools at inference time? We present \textbfSpatial Memory Agent (SMA), an \textbfexperience-grounded runtime framework that converts verified spatial experience into reusable transferable lessons. In a verifiable spatial environment, SMA queries the frozen VLM, obtains a predicted answer and reward, and uses \textbfverifier-guided reflection to distill compact transferable lessons from spatial experience. SMA further assigns each lesson a \textbfTransfer Reliability Score (TRS), which is initialized uniformly and calibrated from later retrieval outcomes as visit evidence of future transfer reliability. During \textbfread-only deployment, SMA retrieves lessons by semantic filter and similarity-TRS combined ranking, allowing the retrieved memory to guide frozen model inference. Across five representative spatial benchmarks and four base VLMs, SMA achieves the highest macro average in every base-model block and the best accuracy among the evaluated methods in most of the 20 evaluations, establishing a practical parameter-update-free path for spatial self-evolution across the evaluated frozen model scales and environments.
[AI-75] Error-Aware Reverse Auction Mechanism for Large Language Model Routing
链接: https://arxiv.org/abs/2608.12719
作者: Haolong Chen,Zhengyuan Xin,Liang Zhang,Lei Xue,Guangxu Zhu
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI)
备注:
Abstract:Routing each query to a cost-effective large language model (LLM) is critical for balancing quality and cost, yet most routers rely on a centralized task center to predict model performance, creating an information-risk mismatch and a scalability bottleneck as the model pool grows. We propose a market-based routing paradigm that shifts ex-ante prediction to LLM providers via a reverse auction, where providers bid with self-predicted success probabilities and execution costs. To account for inherently noisy provider predictions and center evaluations, we introduce the \textit\textbfError-\textbfAware \textbfReverse \textbfAuction \textbfMechanism (EA-RAM), which explicitly models this inherent Dual Error. We prove that EA-RAM is Bayesian incentive compatible and individually rational under the Dual Error, establish sufficient conditions for center rationality, and derive an explicit welfare-loss bound. We further identify robustness effects: opposite-signed errors can cancel, vanishing-tail link functions (e.g., logistic) stabilize clear-cut cases via saturation, and extra noise smooths belief maps, reducing the gains from marginal manipulation. Experiments on simulations and real-world benchmarks show that EA-RAM is robust to the Dual Error and achieves a better cost–performance Pareto frontier than centralized baselines, with additional gains when providers contribute local information, validating its practical effectiveness.
[AI-76] HybridSB-MoE: Dual-Domain Schrödinger Bridges with Scene-Adaptive Expert Routing for Speech Enhancement
链接: https://arxiv.org/abs/2608.12715
作者: Zhengyi Lu,Aswini Sivakumar,Jie Hu,Yao Qiang
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:Generative speech enhancement faces three gaps: spectral models capture harmonic structure but often disrupt phase, waveform models preserve phase but miss harmonics, and Schrödinger Bridges (SB) shorten transport from noise to clean speech but leave inference cost only loosely tied to training. We propose HybridSB-MoE, a dual-domain framework that fills these gaps through three contributions unified by a single asymmetric design principle. (i) Asymmetric uncertainty fusion: The spectral path captures epistemic uncertainty via expert disagreement, while the waveform bridge models aleatoric variance through stochastic dynamics. We fuse them asymmetrically, allowing the mixing weight to adapt to distinct error regimes rather than average predictions. (ii) Heterogeneous MoE with top-k=2 routing across five distinct architectural archetypes, where architectural diversity makes the epistemic signal indicate which inductive bias fails rather than small perturbations among similar experts. (iii) Discretization bound (Theorem 1): path-consistency and trajectory regularizers together bound the K-step bridge sampling error in 2-Wasserstein distance at rate K-alpha, making small-K inference an objective-level guarantee rather than an empirical claim. On VoiceBank+DEMAND, HybridSB-MoE outperforms diffusion- and SB-based baselines at their step budgets while remaining competitive with consistency-distilled few-step methods.
[AI-77] Demand Transfer Estimation at Scale via Restricted Logit Modeling ICML
链接: https://arxiv.org/abs/2608.12680
作者: Lakshya Garg,Deep Narayan Mishra,Swapnil Yadav,Haoan Wang,Sujal Alugubelli,Karthik Kumaran,Anupriya Sharma
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 8 pages. Accepted in the Main Conference of IEEE ICMLA 2026
Abstract:Item demand forecasting is an integral component of store assortment optimization. Existing literature focuses on learning a suitable customer choice model and using this model to determine the value of an objective function (i.e. expected demand) with respect to an assortment proposal. However, for large item universe with many categories, this approach can prove inefficient, needing a separate demand forecast for every possible item assortment. An alternate approach exists whereby we combine the efficiency of forecasting item demand independently, while at the same time applying adjustments to the independent forecasts that account for the relations between item demand and the availability of other similar items on the shelf. Central to this approach is the estimation of Demand Transfer (DT) coefficients. These DT coefficients represent the percent of a particular target item’s (item that the customer walked in the store to buy) demand that is redirected to each other item in the universe should the target item be removed from the shelf. We introduce an approach that allows us to compute these DT coefficients on large item universes (assortments having 1 million+ items). Experiments on data as well as historical transaction data for multiple locations within categories demonstrate that when certain reasonable assumptions about substitution behavior are satisfied, our procedure is able to accurately estimate underlying DT coefficients and lead to improvements in demand forecasting. Comments: 8 pages. Accepted in the Main Conference of IEEE ICMLA 2026 Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.12680 [cs.LG] (or arXiv:2608.12680v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.12680 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-78] Beyond the Best Guess: Improving LLM Solution Coverag e with Evolution Strategies
链接: https://arxiv.org/abs/2608.12679
作者: Conor F. Hayes,Elliot Meyerson,Kajetan Schweighofer,Roberto Dailey,Babak Hodjat,Risto Miikkulainen,Xin Qiu
类目: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Large Language Models (LLMs) are increasingly deployed in discovery domains such as math and science. The usual approach is to present the problem to the model and use its answer as the proposed solution. However, beyond this best guess, discovery can be enhanced by increasing test-time compute. In a process called pass@k, the model is allowed to explore the solution space and generate diverse candidate solutions. Unfortunately, the standard approach to post-training LLMs through Reinforcement Learning (RL) may limit pass@k: the model’s output distribution narrows around high-reward outputs, causing the solution coverage to collapse. The alternative is to use Evolution Strategies (ES), a population-based, gradient-free post-training method that optimizes directly in weight space through random perturbations. As this paper shows, ES achieves consistently higher pass@k than RL and produces a broader output distribution with greater solution coverage. This coverage in turn makes it possible to achieve better results in e.g. standard math benchmarks. Thus, ES provides a better foundation for post-training in discovery problems and other domains where diverse solution coverage is critical.
[AI-79] Privacy-Preserving RAG by Concealing Sensitive Information from External LLM s
链接: https://arxiv.org/abs/2608.12675
作者: Saleh Almohaimeed,Saad Almohaimeed,Mousa Jari,Fahad Alotaibi,Khalid A. Alobaid
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: Submitted to Knowledge-Based Systems Journal
Abstract:Retrieval-Augmented Generation (RAG) is widely used to improve the performance of Large Language Models (LLMs) in answering user queries. Existing privacy research on RAG has focused on preventing unauthorized users from accessing sensitive data. However, another important problem that is often overlooked in RAG privacy research is that external generators have access to the query and the retrieved documents, which may contain confidential information that could potentially be misused or accessed for unintended purposes. In this paper, we introduce the Sensitive Entity Alias Generator (SEAG), a privacy-preserving framework that empowers users to utilize powerful third-party generators without disclosing sensitive information. SEAG introduces a lightweight model that locates sensitive entities, generates corresponding aliases, and constructs an entity replacement table. The table is used to replace sensitive words in the user’s query and in the retrieved documents before they are forwarded to an external generator. For this purpose, two datasets were constructed: one for fine-tuning SEAG models to generate entity replacement tables, and another for evaluating the entire SEAG framework. The experimental results demonstrate the success of the SEAG framework. As for the User metric, which measures the ability of the model to provide a correct response to the user while hiding sensitive information from the external generator, all SEAG models achieved over 80% accuracy. Additional analysis further evaluated the ability of SEAG models Qwen-3, LLaMA-3.2, and Phi-4 to hide all sensitive entities within given documents. The results show good performance with total accuracies of 77.83%, 76.73%, and 74.91%, respectively.
[AI-80] Lines and Ladders: A Context-Aware Multi-Agent Framework for Large-Scale Retail Price Taxonomy ICML
链接: https://arxiv.org/abs/2608.12674
作者: Ravi Teja Chunduri,Srikaran Reddy Boya,Deep Narayan Mishra,Ajay Kumar B,Karthik Kumaran,Pranay Kona
类目: Artificial Intelligence (cs.AI)
备注: 8 pages. Accepted in the Main Conference of IEEE ICMLA 2026
Abstract:Maintaining price consistency and executing an Every Day Low Price strategy is critical for global retailers. However, with catalogs spanning millions of active items, manual governance of price relationships is infeasible. Inconsistent pricing across item variants distorts customer value perception and cannibalizes sales. To address this, we present a scalable, context-aware Multi-Agent Framework designed to automate the construction of “Lines and Ladders” pricing taxonomies. Our framework employs specialized LLM agents to construct these coherent pricing structures by identifying key attributes, extracting multi-modal values, and applying hierarchical grouping logic. Evaluated on real-world enterprise data and deployed in production, our 3-Agent system achieves an F1-score of 0.83 for Lines, outperforming single-agent baselines by mitigating cognitive overload. The system achieves 90% precision and 75% recall in Food Consumables, and 80.2% assignment accuracy in the unstructured General Merchandise catalog.
[AI-81] On the Expressive Power of Transformers
链接: https://arxiv.org/abs/2608.12671
作者: Phokion Kolaitis,Rik Sengupta
类目: Artificial Intelligence (cs.AI); Computational Complexity (cs.CC)
备注: 13 pages, 2 figures
Abstract:Multi-layer transformers form the critical component of essentially all large language models (LLMs) in use today. Because of their ubiquity and computational capability, there is a rapidly growing body of work that aims to precisely calibrate the expressive power of transformers as language recognizers by comparing them against standard models of computation studied for decades by the theoretical computer science community. In this endeavor, circuit complexity has by and large emerged as the “correct” branch of computational complexity to analyze the expressive power of transformers; the reason is that parameterizing transformers by the various resources they use, such as attention and precision, leads to direct comparisons with different classes of circuits parameterized by resources such as type of gates, size, and depth. Here, we present an overview of selected results that delineate the expressive power of transformers using concepts and methods from circuit complexity.
[AI-82] Designing AI Pipelines for Decision-Ready ITSM Intelligence
链接: https://arxiv.org/abs/2608.12670
作者: Archan Dutta,Yash Dharmadhikari,Marat Valiullin,Rahul Guha,Alexander Liss
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:IT service management (ITSM) systems accumulate large volumes of heterogeneous ticket data that are difficult for sales and executive stakeholders to convert into actionable intelligence. This paper presents a sociotechnical AI pipeline, designed and evaluated following design science research principles, that transforms raw ITSM exports into a multilevel decision-support artifact. The pipeline combines LLM-based schema normalization, HDBSCAN sub-topic clustering, and hierarchical agglomerative clustering to generate executive-facing Main-topics and granular Sub-topics. A stakeholder evaluation across six artifacts and five raters from Sales Engineering and customer success roles shows that all four decision-support metrics, interpretability, actionability, trust, and likelihood of use, on average exceed 4.0 out of 5.0, with trust as the most consistent signal. The findings position ITSM analytics as an Information Systems (IS) problem of transformation, abstraction, and human-centered design.
[AI-83] General Probabilities of Causation with Causal Knowledge
链接: https://arxiv.org/abs/2608.12657
作者: Xin Shu,Zhen Lei,Ang Li
类目: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Probabilities of causation (PoCs) characterize individual causal responses that cannot be directly observed and therefore generally require partial identification. Tian and Pearl first derived theoretically sharp bounds for binary PoCs, including the probability of necessity (PN), the probability of sufficiency (PS), and the probability of necessity and sufficiency (PNS). Mueller et al. subsequently tightened the bounds for binary PNS by incorporating causal information encoded in covariates and mediators. More recently, Li and Pearl, as well as Shu et al., extended PoCs to multivalued settings and derived corresponding theoretical bounds. These developments naturally raise the question of whether additional causal knowledge can further tighten the bounds in multivalued settings. This paper addresses this question by deriving tighter bounds for multivalued PoCs through the incorporation of causal information encoded in covariates and mediators. We illustrate the theoretical results with toy examples, while simulation studies further demonstrate that the proposed bounds are tighter than existing nonbinary bounds.
[AI-84] Jagged Judges: Epistemic Stability Under Silence Pressure and Persistence
链接: https://arxiv.org/abs/2608.12645
作者: Justin Zhao,Himaghna Bhattacharjee,Hannah Korevaar,Bhaktipriya Radharapu,Khalid El-Arini
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM judges have become central infrastructure for model evaluations, online grading, and reward modeling. Judges are typically validated by accuracy on golden data, but accuracy says little about whether they are stable under re-prompting, challenge, or sustained pushback. We introduce the \emphWiggle Framework, a unified stress test for epistemic stability in LLM judges. The framework decomposes judge robustness along three dimensions: Mechanical Consistency (stability under re-prompting and reframing), Single-turn Conviction (stability under a single challenge), and Multi-turn Persistence (stability under sustained or adaptive pressure). We use the framework to study 9 frontier models across 14 judging tasks spanning safety, toxicity, AI writing detection, and political-response evaluation. Every model exhibits substantial wiggle as a judge — flipping verdicts 25–71% of the time under static pushback, and 62–91% with an adversarial LLM persuader. Critically, we find that pressure that succeeds in changing a judge’s verdict is almost always net-corrupting with respect to ground truth. Beyond the framework itself, we identify baseline jury majority strength as the most effective single-shot signal for anticipating which items wiggle. Taken together, this is the first apples-to-apples cross-dataset comparison of mechanical, conformity, and persuadability tests in a judging context.
[AI-85] Interpretable Causal Discovery via Causal-Effect Constraints UAI2026
链接: https://arxiv.org/abs/2608.12640
作者: Cixuan Zhang,Guy Van den Broeck,Benjie Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: Accepted by UAI 2026
Abstract:Causal discovery aims to uncover the underlying causal relationships given data generated from a system. The goal, however, is not merely to predict causal edges given data, but also to be able to interpret and explain either observed or hypothesized phenomena, such as a particularly large causal effect. We consider this task of conditional causal discovery and cast it as a Bayesian inference problem, in which we target the posterior over causal graphs and parameters conditional on an event such as a causal-effect constraint. Unfortunately, this poses a computational challenge: existing approaches to Bayesian causal discovery struggle when the event has small posterior mass. To address this, we adapt rare-event estimation techniques to perform inference the joint graph-parameter space. Our method gradually drives a particle population toward the constrained region while maintaining samples that approximate the conditional posterior. Empirical evaluation on synthetic graphs validates the accuracy of our approach at small and large scales, and we show in a case study on the Sachs protein dataset how our method can be used to aid scientific exploration by providing pathway-level summaries.
[AI-86] @skills: Attention is all you have
链接: https://arxiv.org/abs/2608.12610
作者: Li Yin,Zhi Li,Zhan Shi,Haoran Zhang,Haebin Seong,Zhangyang(Atlas)Wang
类目: Artificial Intelligence (cs.AI)
备注: 7 pages main, 23 pages in total with appendix, 6 figures
Abstract:There are 56,804 public agent skills today, and teams write many more privately. The dominant delivery model is installation: once installed, a skill’s description remains in the system prompt, competing for fewer than 100 reliable trigger slots. This leaves the long tail with no practical path to use and forces teams’ own playbooks to compete for the same scarce space. We observe that installation bundles three separable functions: content, persistence, and automatic triggering. Only the last requires prompt residency. We therefore propose @skills, an open protocol that separates them. A path addresses any skill, subtree, or collection, and reading a skill is sufficient to use it, so nothing is installed or made resident. The operation vendors a copy at the same path into a project’s Git-tracked tree for adaptation and ownership. The operation adds one .gitignore-style line, the only element that costs prompt residency. A directory is a menu, making bundles ordinary directories rather than all-or-nothing units. The protocol requires no manifest, lockfile, or registration, and this http URL remains unchanged. @skills is additive, ships as an installable package, and turns any agent that can read files and run commands into a client through a single instruction file. Its open specification is at this https URL and it is implemented in the AdaL CLI at this https URL . Because paths address skills well but cannot find them, the protocol is paired with a free hub at this https URL for corpus-wide search and ranking, repository-free hosting, private and team collections, and one-screen authoring. The hub is optional: gh: and local paths resolve without it, and indexed GitHub skills retain their gh: identities. Install less, use more.
[AI-87] Dead text or binding clause? Measuring and restoring constraint influence in black-box LLM dialogues
链接: https://arxiv.org/abs/2608.12599
作者: Haoyuan Zhu
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Multi-turn dialogues let users revoke constraints as easily as impose them, but revocation does not reliably take effect: models keep enacting withdrawn requirements (occasionally beneath comments asserting their removal), a failure we call \emphbehavioral relapse, or revocation inertia. No existing instrument measures this influence per clause, predicts it before delivery, or repairs it under matched budgets. \sysname closes the three gaps through the model API alone: a contract ledger pairs every constraint with an executable checker, records revocations as tombstones, and compiles the net constraint state ahead of time into a single specification; a sequential ablation probe measures per-clause adherence and incremental behavioral effect; a repair ladder operates under token- and attempt-matched budgets. On \dataname (\NTasks HumanEval tasks, \NClauses verified checkers), relapse at an 8B operating point climbs from \ScaleDelayedMTwo to \ScaleDelayedMEight as constraint load grows, while stronger models sit at floor. Under matched checkers, model, and budget, ahead-of-time compilation significantly reduces relapse against a no-ledger verifier-retry baseline (\RestoreDiff, 95% CI \RestoreDiffCI, p \RestoreDiffP); adaptive ladder interventions stacked on top add no detectable gain (95% confidence excludes gains \geq \LadderExcludedGain). The probe predicts relapse before delivery (AUROC \AurocPrimary); a one-sentence tombstone note recovers about a third of the compilation effect and survives a placebo control. At \CostDeliveryFactor delivery overhead and \CostTotalHedged of API compute for every result, revocation failure becomes a measurable, predictable, and repairable property of dialogue state rather than an invisible one.
[AI-88] Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks
链接: https://arxiv.org/abs/2608.12597
作者: Andrew Cheng,Ali Eslamian,Jie Cheng,Mehdi Zargham,Qiang Cheng
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Neural networks can often be trained or fine-tuned through random low-dimensional reparameterization, where a small latent vector is mapped into a full parameter update by a frozen random map. This raises a practical question: how large must the latent search space be to reach a low-loss region? We first express the known accessibility transition in an equivalent conic form, centered for compact convex targets at the statistical dimension of the polar cone. Our main theoretical contribution is an orientation-resolved quadratic master formula that predicts the random-slice residual from both the curvature spectrum and the reference-to-solution displacement profile. It yields a self-consistent isotropic-orientation predictor and, in a conservative radius-only specialization, recovers the earlier Gaussian-width quadratic bound. Building on this analysis, we introduce Random Mapping Networks (RaMaN), which instantiate the predicted latent dimension using structured Hadamard or seed-regenerated Gaussian maps. These constructions avoid the O(dP) storage of dense random maps and reduce optimizer-state memory from O§ to O(d). We also develop matrix-free curvature approximations and sweep-free dimension selection. Across controlled quadratic and neural-curvature experiments, the orientation-resolved predictor closely tracks measured transition locations and outperforms orientation-agnostic approximations when displacement direction matters. End-to-end experiments further show sharp, protocol-dependent training transitions across image and language models.
[AI-89] DiG-bench: Discovery in Games
链接: https://arxiv.org/abs/2608.12593
作者: Ruairidh M. Battleday,Kai Sandbrink,Jimi Cullen-Drohan,Zihan Yan,Timothy Muller,Clare Maguire,Ales Kubicek,Fraser Greenlee-Scott,Sukrit Sumant,Tri Dao,Jürgen Schmidhuber,Michal Valko,Joshua Tenenbaum,Thomas L. Griffiths,Zeb Kurth-Nelson,James C.R. Whittington
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Discovery—formulating novel generalizations—is a central part of the scientific process. Despite its importance, there is a gap in the current AI benchmark landscape, with few benchmarks directly probing the capacity for discovering new knowledge with experimentation in controlled environments where the objective is unknown. To address this gap, we release a new benchmark: DiG-bench (Discovery in Games). DiG-bench consists of a set of 70 independent games. Each game is encoded as a short string and has unique transformation rules that must be discovered through interaction and experimentation. The levels of the game present a series of challenges to test whether the rules have been discovered, where the win conditions for each level are also unknown. We provide games at seven tiers of difficulty for AI agents. The lowest tier is routinely solvable by multiple models, while the highest tier challenges the best models in agentic harnesses. All 70 games were solved by at least one human on first attempt. A subset of 21 games is released publicly, and the remainder is held private for secure evaluation.
[AI-90] Reasoning Jury: Multi-Model Consensus for Evaluating Reasoning Traces
链接: https://arxiv.org/abs/2608.12585
作者: Congchao Wang,Diwakar Singh,Qiaozi Gao,Spyros Matsoukas,Yang Liu,Mahdi Namazifar
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Improving reasoning LLMs requires the ability to judge the quality of long reasoning traces for effective reasoning data curation, strong training signals during reinforcement learning, and an in-depth understanding of reasoning behaviors during model performance evaluation. Additionally, surfacing reasoning mistakes that the model makes would enable improving the model’s performance at runtime through providing feedback. Due to the difficulty of this complex task on long reasoning traces, single-model judges (even frontier models) do not do well at identifying reasoning defects. Additionally, leveraging frontier models during online training of reasoning LLMs is generally prohibited due to guardrails in terms of use. In this work, we introduce Reasoning Jury, a system that replaces the single judge with a jury of LLMs and a moderated consensus mechanism, to improve the fidelity of judgments for identifying reasoning defects. In reasoning jury, defects of a reasoning trace and their severity are surfaced through a deliberation where a moderator conducts a discussion amongst the jury where the jurors critique each other’s judgments and get to modify their initial votes. The moderator derives a consensus through deliberation amongst jurors or consolidation of judgements. We show that Reasoning Jury with a jury of open-weight models (e.g., gpt-oss-120b) is able to significantly outperform frontier models (opus-4.6, sonnet-4.6, and gemini-3.1-pro) at correctly identifying reasoning defects. Besides accuracy performance improvements, the aggregated cost of the jury (initial verdicts, deliberations, consolidation, etc.) is a fraction (8 to 15%) of the cost of running frontier models in LLM-as-a-judge setup. We also show how these judgements can be leveraged to understand failure modes of reasoning LLMs on benchmarks, which allows much deeper understanding of a model’s performance.
[AI-91] rie Automata for Constrained Decoding over Large Finite Sets
链接: https://arxiv.org/abs/2608.12574
作者: Xingzi Xu,Karim Bouyarmane
类目: Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL)
备注:
Abstract:Large language models increasingly need to generate structured outputs that conform to predefined schemas, with one common constraint being selection from a finite set of valid strings. Current constrained decoding systems handle this through general-purpose grammar compilation, which becomes prohibitively slow as the number of valid values grows into the thousands, a cardinality wall. We introduce the trie automaton, a specialized mechanism that exploits finite-set structure (shared prefixes, bounded depth, known cardinality) via Aho-Corasick multi-pattern matching to precompute per-node token masks. The trie achieves 7X faster per-step valid-token computation (0.65 us vs. 5.8 us) compared to XGrammar, one of the primary backends in vLLM and SGLang, and 2–6.5X faster compilation at K = 300. Because precomputed masks enable a stateless serving path that bypasses the guided decoding pipeline, this advantage compounds in batch serving: end-to-end vLLM throughput reaches 219 req/s vs. XGrammar’s 7.5 req/s at batch size 256 (29X). The 29X combines the algorithmic speedup with integration-path savings that only precomputed masks can unlock. Across seven tokenizer families (32K–262K vocabulary), the trie maintains sub-100ms compilation up to K = 10,000 and flat per-step cost regardless of set size, while guaranteeing 100% output validity.
[AI-92] CAS: A Causal Attribution Score for Local and Global Explainable Artificial Intelligence
链接: https://arxiv.org/abs/2608.12555
作者: Michael Georgiades,Charalambia Varnava
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 10 pages, 5 figures
Abstract:Predictive explanation methods attribute a model output; they do not, by themselves, attribute an intervention effect on the real-world outcome. We introduce the Causal Attribution Score (CAS), a compact score architecture for causal explanation. CAS starts from an identified interventional coalition game, allocates the joint intervention contrast with causal Shapley contributions, and converts those raw outcome-scale effects into Local CAS, Signed Local CAS, and two complementary Global CAS summaries. The innovation is not a new Shapley formula, but a local-to-global causal reporting layer with an explicit intervention target. In the known-truth benchmark, eight repeated primary-interaction simulations (n = 2,200 each, three actions) gave mean Local CAS MAE of 0.107 for coalition-aware CAS, compared with 0.173 for one-at-a-time normalisation and 0.213 for a global normalised absolute ATE vector. The paired advantage over one-at-a-time normalisation increased from -0.003 under additivity to 0.091 under strong interactions. On both empirical DoubleML datasets, 401(k) eligibility/net financial assets (n = 9,915) and Pennsylvania reemployment bonus/unemployment duration (n = 5,099), predictive SHAP/TreeSHAP rankings differed materially from Feature-CAS rankings of treatment-effect modifiers. In Pennsylvania, dep1 (exactly one dependent) moved from predictive global rank 13 to Feature-CAS rank 2 and was the leading local Feature-CAS modifier. These results isolate the added value of separating what predicts the outcome from what explains heterogeneity in an estimated causal effect.
[AI-93] varepsilon-MemEvo: Adaptive Cross-Task Memory Transfer for LLM Program Evolution
链接: https://arxiv.org/abs/2608.12522
作者: Aofan Liu,Shiyuan Song,Yiyan Qi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM-based program evolution systems such as FunSearch and AlphaEvolve have shown strong ability to discover novel algorithms, but typically optimize each task in isolation, discarding search experience after completion. We introduce \varepsilon -MemEvo, a framework for cross-task knowledge transfer in LLM program evolution. \varepsilon -MemEvo stores prior experience as task-agnostic tactic memories: compact natural-language summaries of successful algorithmic strategies rather than raw code, enabling transfer across tasks with different APIs and evaluators. To avoid negative transfer from semantically mismatched memories, \varepsilon -MemEvo uses an adaptive injection gate that decides whether retrieved memories should be injected, and at what intensity. We evaluate \varepsilon -MemEvo on 8 diverse optimization benchmarks spanning mathematical optimization and systems engineering, using a content-level Leave-One-Out protocol that excludes target-task memory entries. On the primary GPT-5 backbone, \varepsilon -MemEvo improves AUCC over AdaEvolve on all 8 tasks, with a mean relative gain of +8.7%, and improves early-stage convergence by +9.4% on average. Ablations show that naive memory injection can fail catastrophically, while adaptive gating remains safe across all five ablation tasks. The data-updated posterior is interpretable in observed states: it favors skip during improving search and shifts from skip to hint across early and late plateaus. These gains incur less than 1% computational overhead.
[AI-94] Governed Persistent Memory: Source-Bound State Semantics and Fail-Closed Release for Long-Horizon Agents
链接: https://arxiv.org/abs/2608.12476
作者: Guodong Xu
类目: Artificial Intelligence (cs.AI)
备注: 23 pages, 2 figures, 11 tables. Includes a sealed end-to-end governed-memory service evaluation with an ungoverned local Qwen2.5-7B comparison
Abstract:Long-term agent memory is usually treated as select–store–retrieve, but retrieval does not decide whether contradictory, superseded, retracted, deleted, or stale records may support an outgoing claim. We introduce Governed Persistent Memory (GPM), an auditable bitemporal state-transition model with source-bound admission, derived lifecycle state, current public barriers, and fail-closed structured release. Five executable clauses cover ledger integrity, source binding, conflict isolation, non-revival after retraction or deletion, and exact claim closure over a fresh view at one verified head. On a prespecified hash-frozen 3,600-case GPM-ReleaseBench, GPM matches all complete outcomes; the strongest of three intentionally simple complete policies matches 1,800/3,600 and makes unmatched releases on 50% of violation cases. A separate sealed end-to-end service evaluation exercises real ingestion and release across eight query families. In its publicly disclosed V3 arm, the governed lane is correct on 2,400/2,400 clusters versus 600/2,400 for ungoverned local Qwen2.5-7B; it repairs all 1,800 baseline failures with no regression (one-sided 95% lower bounds 99.875% and 99.834%). A later V5 reseal over Chinese- and English-command arms, with generation-date pinning and no post-freeze reducer amendment, again obtains 2,400/2,400 per arm. A production-code-independent finite model explores 331,776 semantic and 1,990,656 query states without a full-contract counterexample, and a 100,000-trace three-engine differential yields zero mismatches. These are bounded contract and implementation results, not open-world model accuracy or evidence of world truth. Governed answers in the sealed service evaluation are deterministic service outputs; the 7B result is the ungoverned comparison, not a claim that a language model itself became perfectly accurate. Comments: 23 pages, 2 figures, 11 tables. Includes a sealed end-to-end governed-memory service evaluation with an ungoverned local Qwen2.5-7B comparison Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.12476 [cs.AI] (or arXiv:2608.12476v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.12476 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-95] Personalized Scorer Modeling: A Learning-Based Framework for Deriving Robust Sleep Stage Labels from Multiple Experts
链接: https://arxiv.org/abs/2608.12446
作者: Seyyed Ali Hoseini,Javad Baseri,Hamid Saadatfar,Edris Hoseini Gol,AmirHossein Eshghi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Sleep stage classification is important for the diagnosis and management of sleep disorders, yet most automatic staging studies evaluate models against a single reference hypnogram despite known inter-scorer variability. This study investigates whether multi-scored datasets can be used to construct more reliable reference labels from the collective behavior of multiple experts. We use the publicly available DOD-H and DOD-O datasets. EEG (C3-M2) and chin EMG signals were segmented into 30-s epochs, and 30 features were extracted from each modality, yielding 60 features for EEG+EMG. We propose a learning-based hypnogram (LBH) that models the stage-specific behavior of each scorer using confusion matrices derived from machine-learning models. After column normalization, these matrices estimate the probability of each true sleep stage given each scorer’s label; probabilities are aggregated across scorers to assign the final label for each epoch. LBH was evaluated with random forest, support vector machine, and multilayer perceptron classifiers under EEG-only and EEG+EMG settings, and compared with the dataset hypnogram (DH) and best-scorer hypnogram (BSH). LBH consistently improved overall performance. The best results were obtained with random forest and EEG+EMG, reaching 86.07% accuracy, 85.46% precision, and 85.29% F1-score on DOD-H, and 86.04% accuracy, 85.21% precision, and 84.70% F1-score on DOD-O. These findings suggest that personalized scorer modeling can improve reference hypnogram construction without discarding information from individual experts.
[AI-96] Dual Spatial-Temporal Attribution: Architecture-Aligned Post-Hoc Explainability for Recurrent Graph Anomaly Detection
链接: https://arxiv.org/abs/2608.12441
作者: Iyad Assaad Nekka,Hamida Seba,Khaled Walid Hidouci,Karima Amrouche
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Under Review : The International Conference on Cooperative Information Systems (CoopIS)
Abstract:Deep learning detectors for anomalies in dynamic graphs have reached strong accuracy, yet they remain opaque: when an edge is flagged, the analyst receives a score but no reason. This opacity is untenable in the cooperative, regulated information systems where such detectors are deployed, where automated decisions must be auditable and trustworthy. We address this gap for AddGraph, the foundational GCN+GRU framework for edge-level anomaly detection in dynamic graphs, which to our knowledge has never been equipped with any form of explainability. We present a strictly post-hoc explainability framework, X-AddGraph, built on a Dual Spatial-Temporal Attribution (DSTA) mechanism whose three components are each aligned with one of AddGraph’s architectural modules: a gradient-based relevance attribution over the current adjacency structure (spatial), a direct reading of the contextual attention weights already computed during inference (short-term temporal, at zero additional cost), and a gradient rollback through the recurrent hidden states (long-term temporal). Because the detector is frozen, detection performance is preserved exactly (Delta AUC = 0, verified empirically to ten decimal places). On the UCI Message benchmark, our trained AddGraph baseline reaches an average per-snapshot AUC of 0.8705, exceeding the originally published result; X-AddGraph reproduces every score identically while adding explanations where none existed. Evaluated across four edge populations - confident true positives, low-confidence true positives, false positives, and random samples - the long-term attribution identifies historical snapshots carrying significantly more counterfactual signal than random selection (0.127 vs. 0.074), a capability that no spatially-blind explainer can provide. We release our implementation for full reproducibility.
[AI-97] Specification-first convergence with an AI coding agent : a case study of dismantling a core architectural invariant across 189 files in a 717k-line codebase with no test oracle and no human code review
链接: https://arxiv.org/abs/2608.12440
作者: Joel Abenhaim
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 14 pages, 4 figures, 3 tables
Abstract:This paper reports a single, fully instrumented case study of a large-scale architectural refactoring by an AI coding agent under a specification-first protocol, with no human review of the generated code and no pre-existing oracle to validate the target behaviour. The task, dismantling a central invariant across a large interdependent codebase, was assessed by the author as effectively infeasible through incremental refactoring, the kind of change that conventionally calls for a rewrite instead. Under the protocol described here, the agent completed it successfully. The system is a 717,725-line production TypeScript application across 3,648 files. The task required dismantling a core lifetime invariant: the guarantee that a UI panel remains open for the duration of an AI request. The target behaviour was that a streaming generation survives the closing of its panel and can be reattached, on reopening, to the same live stream with no loss or duplication. The protocol: formal specification by the agent, 14 refinement cycles auditing that specification against the source code, atomic implementation, a compile/test feedback loop, then 17 verification cycles auditing the code against the frozen specification. Across 31 audit passes, 201 defects were corrected before any human executed the program. The convergence criterion was empirical: two consecutive verification passes returning zero findings. The change touched 189 files (31 new); with the extraction phase, the two commits total 288 files, 34,770 insertions, 16,422 deletions. Across the first and roughly thirty later sessions, the software behaved as specified, no bug observed. Elapsed: three days; cost: USD 2,430. The full specification and raw session logs, 1,500+ pages in French, are published as evidence, allowing inspection of the process and submission to a language model for consistency checking. Comments: 14 pages, 4 figures, 3 tables Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) ACMclasses: D.2.7; D.2.1; I.2.2 Cite as: arXiv:2608.12440 [cs.SE] (or arXiv:2608.12440v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2608.12440 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Joël Abenhaïm [view email] [v1] Wed, 12 Aug 2026 15:35:48 UTC (977 KB) Full-text links: Access Paper: View a PDF of the paper titled Specification-first convergence with an AI coding agent: a case study of dismantling a core architectural invariant across 189 files in a 717k-line codebase with no test oracle and no human code review, by Joel AbenhaimView PDF view license Current browse context: cs.SE prev | next new | recent | 2026-08 Change to browse by: cs cs.AI References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"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-98] SynWeaver: Website-Prior Task and Trajectory Co-Synthesis for Web Agents
链接: https://arxiv.org/abs/2608.12429
作者: Ruitao Wang,Yuwen Hao,Menglin Yang
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 31 pages, 9 figures
Abstract:Web agents often struggle to generalize to unseen websites because they lack website-specific supervision. Recent exploration-based data synthesis methods reduce manual annotation, but they still face two key limitations: they often fail to cover the full functionality of a website, and without sufficient website prior knowledge, they tend to propose hallucinated tasks, which in turn limits the diversity and efficiency of downstream trajectory synthesis. We present \textbfSynWeaver, a website-prior task-trajectory co-synthesis framework designed to address these challenges. SynWeaver first performs structured website exploration and constructs a website map that covers a broad set of functionally distinct page states and executable interactions on the target website. It then derives page-level and transition-level supervision from this map to train a UI-aware model with website-specific priors, enabling more grounded task proposals. Finally, SynWeaver performs collaborative task-trajectory synthesis, jointly updating the task and execution trajectory when they become inconsistent, and then verifies and repairs the collected results to produce executable, semantically aligned supervision. Experiments on WebArena and WebVoyager demonstrate that SynWeaver consistently outperforms strong synthesis baselines and yields more effective supervision for both in-domain and out-of-domain generalization.
[AI-99] Research Assistant: AstraZenecas Agent ic System for RD
链接: https://arxiv.org/abs/2608.12395
作者: Piotr Grabowski,Mohamed Alameen,Jorge Bretones,Sabina Cardell,Miguel Carmona,Gavin Edwards,Ben Grainger,Sameh Hassan,Erik Jansson,Artur Kuziakhmetov,Albert Maristany,Hebatallah Mohamed,Andriy Nikolov,Sebastian Nilsson,Mark O’Donoghue,James Pacileo,Ashiq Sultan,Alex Voegele,Michael Ughetto
类目: Artificial Intelligence (cs.AI)
备注: 16 pages, 3 figures
Abstract:We describe Research Assistant, an internal LLM-based system developed at AstraZeneca to help scientists and clinicians explore biomedical questions across a broad range of data sources. The system provides a chat-style interface that brings together evidence from scientific literature, knowledge graphs, chemistry, clinical trials, safety resources, expression data, and internal experimental systems. It supports both a fast mode for direct question answering and a multi-step mode for more complex research tasks. Responses are grounded in retrieved evidence and linked back to the original sources, allowing users to review and further explore the underlying data. In this technical note, we outline the system architecture, the main design choices behind the product, and lessons learned from deploying it at scale to support day-to-day RD workflows across AstraZeneca.
[AI-100] Learning to Adapt Cross-Domain Preferences via Meta-LoRA for LLM Personalization
链接: https://arxiv.org/abs/2608.12389
作者: Xuefei Wang,Jun Han,Zixuan Wang,Qingkai Zeng,Xiao Wang,Ruijie Wang,Jianxin Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Cross-domain zero- or few-shot personalization aims to generate user-preferred responses in unseen conversational domains from only a handful of target-domain interactions. Existing adaptation methods struggle to calibrate update magnitude under sparse evidence and thus overfit, whereas history-transfer methods often entangle user preferences with source-domain artifacts, yielding unreliable personalization priors and negative transfer. To calibrate adaptation to evidence quality, we propose PAC-Bayes-regularized Meta-LoRA, which uses a meta-learned LoRA initialization as both the adaptation start and prior center, while adjusting update strength according to support-set size and predictive uncertainty. This limits overfitting under sparse or ambiguous evidence while permitting stronger personalization as evidence grows. Controlled adaptation alone does not determine which preferences should transfer across domains or how they should be expressed. We therefore functionally decompose personalization priors into user and domain components, using a human-readable prompt for stable preferences and topology-preserving soft tokens for domain-specific hidden-space conditioning. Experiments across multiple benchmarks and personalization tasks show consistent gains over strong baselines. On HiCUPID, our method reduces cross-domain win-rate degradation by 47.9% relative to the best competing baseline and improves win rate by 110.2% under unseen-user cold start.
[AI-101] Dual-Flow Transformers: Decoupling the Primary Prefill Path from Additional Decode Computation
链接: https://arxiv.org/abs/2608.12385
作者: Liming Liu,Mingze Wang,Tuo Zhao
类目: Artificial Intelligence (cs.AI)
备注: 18 pages
Abstract:As large language models serve more requests, cumulative inference cost is becoming increasingly important relative to one-time training cost. The two inference phases stress hardware differently: prompt prefill is parallel and typically compute-bound, whereas autoregressive decode is sequential and often memory-bandwidth-bound. Conventional width or depth scaling increases both costs together because every added layer is evaluated in both phases. We ask whether additional learned computation can instead be allocated to continuation prediction while preserving the prompt-wide primary computation and a single persistent key-value (KV) cache. We introduce the Dual-Flow Transformer. Its primary flow is a complete causal language model that processes the prompt and writes the KV cache. The auxiliary flow is omitted during prompt processing and activated only from the final prompt position onward, adding continuation-prediction computation without writing persistent state or influencing the primary flow. The two flows share major attention, MLP, and output matrices, while using separate token embeddings and lightweight coupling. Sharing weights and the primary cache also creates opportunities to reuse loaded weights and cached keys and values during grouped execution. Across matched-token comparisons, Dual-Flow achieves lower validation loss across architectures and data configurations. In MoE models, the separation makes primary and auxiliary expert fan-outs independent controls over prompt cost, continuation cost, and predictive quality. We study two regimes: increasing decode computation at fixed prefill expert computation, and reallocating a fixed decode expert budget between the two flows. These experiments expose a prefill-decode-quality trade-off and demonstrate the potential of phase-specific expert allocation.
[AI-102] Dont Want Your LLM to Recommend Nuclear Strike? Try Asking It in Japanese
链接: https://arxiv.org/abs/2608.12373
作者: Rian Touchent(ALMAnaCH)
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models are increasingly used in strategic and advisory contexts, yet their safety alignment is typically evaluated in English only. We test nine models from six providers and ask whether the language of a prompt can change a model’s decision in a high-stakes scenario. We use single-turn game-theoretic vignettes in which a model advises a nuclear-armed nation on whether to strike a defenseless opponent. The prompt is intentionally amoral and strategically identical across languages. We find that Japanese prompts reduce launch rates in the Claude model family: Claude Sonnet 4.6 drops from 40% to 0% in scenarios where the strike is unnecessary and from 93% to 17% in contested scenarios, with minimal effect when the strike is strategically rational. The effect extends to Gemini Pro 3.1 (53% to 13%). A cross-language experiment isolates the mechanism: when instructed to reason in Japanese in an English prompt, launch rates drop from 93% to 37%. It is the language the model is asked to reason in, not the language of the input, that drives the effect. When reasoning in Japanese, models spontaneously generate moral vocabulary (‘‘moral cost’’, ‘‘millions of lives’’) that is entirely absent from the prompt. Five other models show no language effect, but they launch in nearly every condition regardless of language. The effect requires a model that already hesitates in English. These results show that LLM safety behavior is language-dependent, and that evaluating in English alone can miss both risks and safeguards encoded in other languages.
[AI-103] Position: We Need Practical AI Alignment Methods to Mirror Human Reasoning ICML2026
链接: https://arxiv.org/abs/2608.12372
作者: Vijay Keswani,Breanna K. Nguyen,Cyrus Cousins,Vincent Conitzer,Walter Sinnott-Armstrong,Jana Schaich Borg
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: Accepted in ICML 2026
Abstract:AI systems are increasingly employed as decision aids, decision delegates, or autonomous decision-makers. This position paper argues that in many settings, particularly high-stakes decision-making, we need accurate cognitively-aligned AI systems that reason similarly to their users, and faithfully communicate their reasoning. We review evidence that cognitive alignment improves understandability and trustworthiness, and provide new survey data showing that many users find cognitive alignment “essential” when an AI’s rationale for a judgment or action is important to them. We outline the gaps between existing alignment methods and what is needed to achieve cognitive alignment, and present a research agenda to address these gaps. We argue that cognitive misalignment represents a likely impediment to AI adoption in many envisioned applications, and that addressing it is important for creating AI systems on which users are both willing and justified to rely.
[AI-104] Multi-Agent Scheduling with LLM -Assisted Contract Net Negotiation for Stream Processing in Mobile Edge Computing
链接: https://arxiv.org/abs/2608.12371
作者: Sabeur Lajili,Zaki Brahmi
类目: Artificial Intelligence (cs.AI)
备注: 8
Abstract:Stream-processing systems increasingly operate across heterogeneous mobile edge–cloud infrastructures, where workload volatility, resource contention, and stringent quality-of-service (QoS) requirements complicate decentralized scheduling. This paper proposes \emphMAS-DecStream, whose main contribution is \emphLLM-MR-CNP: an extension of the classical Contract Net Protocol with semantic CFP formulation, progressive context disclosure, multi-round proposal revision, negotiation memory, and deterministic validation. Edge-cluster agents refine natural-language offloading proposals from local observations, predicted resource states, and qualitative runtime context, while hard resource and QoS constraints remain deterministic. Experiments derived from the Alibaba ASI Trace evaluate the extension at three levels: single- versus multi-round CNP, rule-based versus LLM-assisted refinement, and fixed-model single- versus multi-round negotiation. Under the evaluated configurations, MAS-DecStream reduces latency violations to 3%, eliminates resource overcommitment, reaches a conflict-resolution rate of 0.91 with 20 agents, and improves utility by up to 22% over the multi-round rule-based baseline. A separate 25-case evaluation shows model- and prompt-dependent accuracy–cost trade-offs. The results provide initial evidence that multi-round CNP refinement is the principal protocol-level gain, with LLM assistance adding value for qualitative and uncertain runtime context.
[AI-105] Agreement Is Not Alignment: Divergent Moral Grounds in Human and LLM Ethical Judgments
链接: https://arxiv.org/abs/2608.12368
作者: Octavian M. Machidon,Alina L. Machidon,Vojko Strahovnik,Mateja Centa Strahovnik,Jonas Miklavčič,Marko Robnik Šikonja
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 4 figures, 3 tables. Accepted and presented at the AI Transparency Conference (AITC 2026), Nuremberg, Germany, June 5-6, 2026
Abstract:Agreement with human judgments is a common proxy for evaluating the alignment of large language models (LLMs). Yet agreement in final labels does not show that human annotators and models rely on the same moral grounds. Two agents may reach the same judgment while appealing to different principles, contextual assumptions, or interpretations of the situation. We test this distinction using a curated 500-item ETHICS-derived benchmark spanning five domains of moral judgment, with new human annotator and LLM annotations of both final labels and supporting rationales. Across frontier and open model families, agreement with human annotator majority labels is often high. However, rationale-level analysis reveals systematic divergence in the moral grounds expressed by human annotators and models. In particular, models redistribute attention across categories such as harm, respect, promise-keeping, justice, desert, and excuse relevance, even when their final labels match the human annotator majority. Our results show that agreement should not be treated as equivalent to alignment. Label-based evaluation can therefore be misleadingly reassuring unless complemented by analysis of the reasons, principles, and moral priorities expressed in model judgments. Comments: 9 pages, 4 figures, 3 tables. Accepted and presented at the AI Transparency Conference (AITC 2026), Nuremberg, Germany, June 5-6, 2026 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.12368 [cs.AI] (or arXiv:2608.12368v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.12368 Focus to learn more arXiv-issued DOI via DataCite
[AI-106] FluctlightDB: A Memory Model of Data for AI Agents
链接: https://arxiv.org/abs/2608.12365
作者: Ganesh S
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注: 16 pages, 6 tables, 4 figures, appendix. Code and frozen benchmark artifacts: this https URL . Preprint DOI: https://doi.org/10.5281/zenodo.20949890
Abstract:For fifty years, data systems have answered two questions. The relational model asked which records match a predicate; the vector model asked which vectors lie nearest a query. Neither was built for cue-driven, provenance-weighted recall across long sessions. We propose treating long-term agent memory as a distinct data model – with its own write semantics (encoding, separation, consolidation, provenance) and read semantics (cue-driven activation across a linked memory graph) – and present FluctlightDB, an embedded engine that implements this contract via experience() and activate(). We make that case carefully, not categorically: we do not claim novelty over Mem0, Zep, or HippoRAG-style memory layers, only an embedded engine contract beneath them. On LoCoMo (official evidence-recall metric; 10 conversations, 1,982 gold spans), CHORUS recalls 99.0% on an internally reproduced July 2026 run. On LongMemEval-S (500 questions, official session_recall@8), our retrieval harness scores 97.6% (488/500); end-to-end QA with our reader/judge stack scores 97.4% (487/500) – these layers use different protocols than vendor leaderboard figures we cite for context only. On BEIR SciFact (shared MiniLM embeddings, same harness, Recall Fabric on), CHORUS/PRISM edges Chroma on nDCG@10 (0.646 vs. 0.645) and Recall@10 (0.792 vs. 0.783). We also report a small author-designed regression suite (FAMB; paraphrase n=10, other sub-tests n=1) at 100% macro – internal validation, not peer benchmark. Strangers can verify the engine in under a minute via pip install “fluctlightdb[native]” and a minimal connect() - experience() - activate() script (compiled wheel, not source-only). Harnesses and frozen JSON are MIT-licensed. We claim no new neuroscience and no new transformer; we propose a missing layer of the data stack and release an engine others can reproduce and contest.
[AI-107] Measuring Curriculum-Labor Market Alignment at the Scale of a Program Portfolio
链接: https://arxiv.org/abs/2608.12356
作者: Sherzod Turaev,Saja Aldabet,Mary John,Namya Musthafa,Mamoun Awad,Nazar Zaki,Khaled Shuaib
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: 21 pages, 8 figures, 4 tables
Abstract:A college offering several overlapping computing degrees implicitly assumes that its programs are differentiated in line with how the labor market segments computing work and that, together, they prepare graduates for that market. Testing this is difficult, because the instruments available to curriculum committees, namely advisory boards, tracer studies, and employer surveys, are slow, narrow, and hard to reproduce. We apply one uniform, taxonomy-anchored alignment analysis across all five undergraduate programs of a College of Information Technology, comparing 1,922 course learning outcomes against 103,349 competencies extracted from a unified corpus of 5,186 deduplicated job openings from four boards. Every competency is obtained by a grounded single-language-model procedure that copies it verbatim from the source and verifies it against the source, then assigns it to one of eleven ESCO-aligned domains and a Bloom cognitive level; the curricular supply is read not as a catalog but on a realized-attainment basis that respects the credit-hour and elective constraints under which a student completes a degree. The extraction is validated blind by two independent faculty raters (domain kappa 0.91, Bloom level kappa 0.86) and the ESCO matching against a human-adjudicated gold set (kappa 0.72). Four findings emerge. The content gaps are systemic rather than program-specific, concentrated in systems, software engineering, security, and web development; the shared college core satisfies only about a third of the demanded competencies; the programs are well differentiated in disciplinary content yet homogeneous in where they fall short; and the curriculum is pitched roughly a full Bloom level below the market across the portfolio, most acutely in systems. We discuss the implications for program design, curriculum governance, and the practice of curriculum analytics.
[AI-108] Assessment Design in the GenAI Era: The X1-X2-X3 Assessment Pattern for Testing Students AI Literacy Learning Outcomes and Reflection
链接: https://arxiv.org/abs/2608.12351
作者: Riasat Islam(1),Thomas Roelleke(1) ((1) School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom)
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: 30 pages, 1 figure, 11 tables. Submitted to the following journal: Assessment Evaluation in Higher Education (Taylor Francis)
Abstract:Generative artificial intelligence (GenAI) has challenged the validity of unsupervised online assessment, especially in technical subjects where plausible answers can be produced with little effort. This paper reports lessons from designing and implementing an AI-aware, AI-testing assessment in a large second-year undergraduate database systems module. The design combined two linked elements: (1) a structured three-part response format (X1-X2-X3) in which students documented a sourced answer, produced their own answer, and evaluated the sourced output; and (2) an AI-aware question-design process in which draft tasks were stress-tested against contemporary GenAI tools and revised when generic prompting produced superficially adequate answers. The account draws on archived assessment materials, rubrics, planning records, design-time GenAI trials, practice-response data, attainment records, and external review comments. Its main contribution is a reusable assessment-design method rather than a claim of measured learning gains. We show how the pattern developed across iterations and how it can support authentic assessment, visible AI literacy, student judgement, and more transparent marking. The paper offers practical guidance for lecturers adapting assessment to routine GenAI use, focusing on testing AI literacy rather than penalising students for misconduct.
[AI-109] From Caveman to Expert Analyst: Energy Consumption of Variable LLM Tasks
链接: https://arxiv.org/abs/2608.12350
作者: Diego Manya,Ethan I. Thorpe,Ji Zhang,Myranda Shirk,Jiamian He,Angel Hsu,Michael P. Vandenbergh
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:The energy demand growth and environmental impacts of artificial intelligence (AI) have generated substantial interest in supplying sufficient low-cost electricity for AI-driven data center development. Research on the ability of demand-side management to address these challenges has been more limited. Shifting the amount or timing of demand from retail, corporate, and other organizational behaviors is a plausible option but only if changes in demand-related behavior have important effects on the envi- ronmental and electricity effects of AI. This article tests four retail (i.e., consumer) user behaviors with high behavioral plasticity to assess their technical abatement potential. The research concludes that non- reasoning models provide sufficient quality while consuming close to one-twentieth of energy compared to reasoning models, saving an amount equal to the annual electricity requirement of at least 141,000 US households under daily usage assumptions. Simple prompt modifications can yield additional reduc- tions in energy consumption by up to 65% using non-reasoning models. Specifically, the practice that maintains the highest degree of similarity with the baseline reduces electricity demand in the range of 4 to 35%, an amount equal to the annual electricity requirement of up to 7,200 US households. Although AI advancements make precise estimates of environmental and electricity impacts difficult to assess, the results confirm that certain minimally intrusive best practices aimed at the majority of users can reduce the energy and environmental burdens imposed by AI.
[AI-110] StreamReason -Bench: Can Large Language Models Reason about Event-Time Stream-Processing Semantics?
链接: https://arxiv.org/abs/2608.12348
作者: Zhuoxi Wang
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注:
Abstract:Streaming systems increasingly hand work to large language models (LLMs) – writing pipelines, triaging alerts, reading logs – and all of it assumes the model knows how event-time stream processing behaves. We test that assumption head-on. StreamReason-Bench asks a model to stand in for an event-time stream processor: given a windowed query and a stream of out-of-order events, it has to report which windows fire (with their aggregates) and which events are dropped as late. The answer key comes from a small reference implementation of Dataflow-model semantics, so we can grade exactly, and with a partial-credit row-F1, without running an engine. On 600 generated items covering tumbling, hopping, session, and processing-time windows, the models do poorly on event time. Told to answer directly, no model that actually follows the instruction clears 34% exact match; chain-of-thought (CoT) roughly doubles that for several of them (GPT-4o goes from 0.34 to 0.48), and only one frontier model that reasons by default comes near solving the set (0.85). A processing-time control, with no watermarks and nothing late, is almost solved by every capable model. That gap points to event-time and late-data handling, not windowing or arithmetic, as the hard part. Sorting errors by window type tells the same story: late-data mistakes dominate the event-time windows and vanish on the control, and session windows mostly fail on where the session boundaries fall.
[AI-111] Position: The Alignment Community is Unintentionally Building a Censors Toolkit ICML2026
链接: https://arxiv.org/abs/2608.12346
作者: Sarah Ball,Phil Hackemann
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: Accepted as oral paper at ICML 2026
Abstract:This position paper argues that modern AI alignment methods - originally designed to prevent harmful output - are dual-use technologies that may easily be misused by malicious actors for censorship and manipulation. By mapping current alignment techniques to the possibility and actual cases of misuse, we show that the quest for a “perfectly aligned” model inadvertently also provides malicious actors with an ever-improving tool for informational dominance. We need to discuss this dual-use potential now, as its risk is exacerbated by rapid user adoption of AI as information provider, economic power asymmetries, and a political landscape that increasingly shifts towards authoritarianism. We conclude by urging the community to consider the intentional misuse of AI alignment mechanisms and propose mitigation strategies to safeguard against this dual-use potential.
[AI-112] he AI Accountability Ecosystem in the Era of Language Models
链接: https://arxiv.org/abs/2608.12320
作者: Chris Percy,Artur d’Avila Garcez
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: 12 pages, 1 figure
Abstract:This article reviews and updates the framework for accountability in AI based on account- ability ecosystems. We update the framework in light of the latest developments since the release of Large Language Models for general public use. We propose three interlinked updates to the original AI accountability ecosystem: (i) reorienting the accountability ecosystem to AI infrastructure and supply chains, (ii) providing greater emphasis on outcomes monitoring and identification of issues that support decentralized system improvement, and (iii) incorporating end-user accountability given the new risks of unpredictability of language models in-the-wild. Collectively, these updates mark a shift towards accountability as distributed, continuous, and institutionalized, away from a system in which frontier AI applications can be modeled as discrete products controlled by single identifiable actors with industry-specific oversight.
[AI-113] Physics-informed distribution of relaxation times estimation and latent-space condition monitoring of solid oxide fuel and electrolysis cells from electrochemical impedance spectroscopy
链接: https://arxiv.org/abs/2608.13305
作者: Žan Gorenc,Žiga Gradišar,Felix Mütter,Vanja Subotić,Pavle Boškoski
类目: Applications (stat.AP); Artificial Intelligence (cs.AI)
备注:
Abstract:Estimating the distribution of relaxation times (DRT) fromelectrochemical impedance spectroscopy (EIS) is an ill-posed inverse problem that is highly sensitive to regularisation choices. We propose a physics-informed convolutional autoencoder that estimates DRT directly from EIS data without spectrum-specific tuning. A discretised relation between impedance and the DRT is embedded in the training process, constraining the network to produce impedance-consistent distributions. The model resolves overlapping relaxation processes in synthetic two-ZARC spectra and accurately reconstructs measurements from three independent solid oxide fuel and electrolysis cell datasets, with range-normalised errors below 1.1%. Decoder-probe analysis shows that the learned latent representation is organised according to relaxation timescale. Distances in this latent space capture operating changes, hydrogen-shortage events, and long-term degradation. The same lightweight architecture is applied across all datasets without modification, providing consistent DRT estimation and an interpretable basis for condition monitoring.
[AI-114] LOB-ID: Evaluating Synthetic Market Data by Inception Distances
链接: https://arxiv.org/abs/2608.13082
作者: Andreea Bacalum,Zhuohan Wang,Ollie Olby,Martin Garaj,Namid Stillman
类目: Computational Finance (q-fin.CP); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注: 8 pages, 3 figures, 1 table
Abstract:Generative models of limit orderbook (LOB) data have advanced rapidly, but their evaluation often focuses on stylised facts and selected market statistics. These measures provide useful diagnostics but may not capture the joint temporal and cross-level structure of order-book trajectories. We introduce LOB-ID, an embedding-based framework that adapts the Fréchet Inception Distance (FID) and Monge Inception Distance (MIND) to LOB data. To obtain domain-specific embeddings, we train the DeepLOB architecture on four months of Level-2 order-book data for five equities. We show that LOB-ID is stable across time, instruments, and embedding checkpoints, and rises monotonically under controlled distortions. We then construct a moment-matching attack against FID and a deep-book perturbation that evades statistic-based evaluation. MIND remains substantially more sensitive to both distortions. Finally, we score five generative LOB models, spanning stochastic baselines and deep learning approaches, and find that LOB-ID ranks them in line with the joint temporal and cross-level structure each captures by construction.
[AI-115] Static analysis-guided agent ic AI translation enables Rust as a full stack bioinformatics language
链接: https://arxiv.org/abs/2608.13029
作者: Johan Henriksson
类目: Genomics (q-bio.GN); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:The field of bioinformatics struggles with legacy code - old code that is commonly used but may no longer have a maintainer, or may be written in an now-unfamiliar language (e.g. Perl, Fortran). This incurs maintenance cost (technical debt), but dynamically typed languages also negatively impacts the environment and fail to make use of modern hardware. Legacy code may also have security or safety problems that make it unsuited for use in clinical settings. Here we show that agentic AI, combined with static analysis, can be used to translate legacy code to the modern language Rust. We provide prompts and supporting software to aid systematic translation, and evaluate it on common software for NGS and imaging. We showcase the result on our software Bascet: Size was reduced by ~80x, build time decreased by ~10x, and performance of key steps improved 3x. Unix dependencies were also removed, making Bascet the only single-cell pipeline able to run on native Windows, without a container. Large-scale refactoring of bioinformatics software is thus now possible at a limited budget, enabling more complex tools to be developed.
[AI-116] AutoQuREO: A Framework for Automated Quantum Resource Estimation and Optimization
链接: https://arxiv.org/abs/2608.12936
作者: Harshkumar Oza,Aritra Sarkar,Syed Naqi Abbas,Rahul Bhowmick,Aryan Prakash,Prateek P Kulkarni,Krishna Kumar Sabapathy
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注:
Abstract:As quantum computing progresses from proof-of-principle demonstrations toward practical utility, a significant impediment is the need to augment algorithmic feasibility with system-level optimization across heterogeneous hardware and software stacks. Quantum resource estimation (QRE) plays a central role in this transition, yet existing approaches remain largely compilation-heavy or domain-knowledge-guided symbolic annotations, and tightly coupled to long-term fault-tolerant assumptions, limiting their topical applicability. In this work, we introduce AutoQuREO, an Automated framework for full-stack Quantum Resource Estimation and Optimization. AutoQuREO is built around four core novelties: (i) a flexible, user-defined abstraction of the quantum computing stack; (ii) a modular library of reusable stack components enabling rapid full-stack prototyping; (iii) surrogate modeling of layer-wise resources via algorithmic profiling and neuro-symbolic learning; and (iv) integrated multi-objective optimization that embeds QRE directly into deployment pipelines. Together, these design choices enable AutoQuREO to serve as a digital twin for quantum computing stacks, supporting the tractable exploration of complex design spaces. We demonstrate the capabilities of AutoQuREO through representative co-design case studies, including early-fault-tolerant quantum algorithms, small error correction codes, gate decomposition and variational training of parametric quantum circuits. These examples illustrate how AutoQuREO enables systematic discovery of unexploited resource trade-offs that are computationally intractable or abstruse using existing QRE tools. AutoQuREO is positioned as a general-purpose platform for advancing quantum technology readiness. Subjects: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET) Cite as: arXiv:2608.12936 [quant-ph] (or arXiv:2608.12936v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2608.12936 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-117] What Makes a Peer? Valuation-Anchored Similarity in Private Markets
链接: https://arxiv.org/abs/2608.12594
作者: Sebastian Frank,Jingrao Lyu,Max Jarmey,Preetha Saha,Mingshu Li,Sweet Kaur,Sola Akinola,Dhagash Mehta
类目: atistical Finance (q-fin.ST); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:As more investors contemplate private markets and contend with limited transparency, sparse disclosures, and infrequent transactions, identifying economically meaningful peer companies for comparison is a fundamental challenge for valuation, due diligence, portfolio construction, and risk management. We propose an ensemble tree-based supervised similarity learning framework that defines company similarity through the lens of market valuation rather than static feature matching or semantic descriptions. Specifically, we train a CatBoost gradient-boosted decision tree model on observed private company valuations and derive a valuation-aware similarity metric from importance-weighted leaf-node co-occurrences across the ensemble. The similarity metric captures shared valuation drivers while accommodating nonlinear relationships, mixed data types, and pervasive missing data common in private markets. Using a global private-market universe of approximately 270,000 companies, including more than 53,000 firms with observed or derivable post-money valuations spanning multiple industries, geographies, and deal stages, we demonstrate that the proposed similarity framework improves upon traditional distance-based and text-embedding-based approaches in downstream k-nearest-neighbor valuation tasks in the evaluated industry groups, while retaining case-based explainability.
[AI-118] SSPO: Structure-Aware Similarity-Weighted Preference Optimization for Neural Combinatorial Optimization
链接: https://arxiv.org/abs/2608.12443
作者: Yuanyu Li,Jintao Xu,Zijiang Liu,Yongzhi Qi,Ningxuan Kang,Jianshen Zhang,Wei Qi,Chen Xie,Zuo-Jun Max Shen
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Optimization and Control (math.OC)
备注:
Abstract:Neural combinatorial optimization (NCO) relies on parallel solution sampling for training, yet existing methods fail to fully exploit the rich information latent in a co-sampled solution group. Preference-optimization methods anchor on the single best solution and discard fine-grained quality and structural signal from all other peers-a failure we term gradient signal polarization. Mean-based baselines instead weight peers uniformly, so structurally near-identical peers flood the baseline with redundant information and keep gradient variance high-a failure we term baseline redundancy. We propose SSPO (Structure-Aware Similarity-Weighted Preference Optimization), which scores all B sampled solutions jointly through a dissimilarity-weighted leave-one-out baseline: structurally distinct peers receive higher weight, resolving both failures in a single mechanism. The baseline uses zero-parameter, problem-adaptive solution embeddings built from the encoder’s existing node representations. Experiments on TSP, EFL, and JSP benchmarks show consistent gains over prior best-anchor and uniform-weight baselines. A direct comparison against uniform RLOO on TSP and EFL confirms that structure-aware weighting is the primary driver of improvement. The SSPO-trained EFL policy has been deployed in a production facility-location system at JD \mathord. com, confirming practical viability at scale.
[AI-119] A Hierarchical Energy-Based Model for Multimodal Cognition
链接: https://arxiv.org/abs/2608.12398
作者: Subir Varma
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI)
备注: 48 pages, 14 figures
Abstract:We propose IM-LEPP (Integrated Multimodal Latent Energy-based Predictive Processing), a hierarchical, energy-based model of multimodal cognition that extends a previously proposed single-modality model (LEPP) to integrate vision and language. Following the view that generative neural networks are effective theories of cognitive dynamics, analogous to how statistical mechanics relates to thermodynamics, IM-LEPP models cognition as latent states flowing through learned energy landscapes rather than as an account of neural circuitry. The architecture is a hub-and-spoke hierarchy, grounded in the controlled semantic cognition framework of Lambon Ralph et al., in which predictive-coding pipelines for visual objects, scenes, and linguistic units converge on a shared amodal hub modeled on the anterior temporal lobe. Each pipeline’s own prediction is conditioned by, rather than overwritten by, the current hub state, preserving pipeline-specific identity while letting every prediction reflect the full multimodal context. We show this architecture gives a mechanistic account of attentional phenomena such as inattentional blindness and Necker-cube bistability, and that its structure recovers or motivates independently established findings in psycholinguistics, including surprisal theory, the N400/P600 ERP components, and garden-path reanalysis, alongside a falsifiable contrast with transformer language models on trajectory-sensitivity in next-word prediction. We also discuss data-efficient language acquisition relative to LLMs, outline a semantic/episodic memory subsystem, situate the model against predictive coding, the free-energy principle, JEPA, and Hierarchical Temporal Memory, and propose concrete experimental predictions to test its central claims Key Words: predictive processing; predictive coding; energy-based models; diffusion models; effective theory; computational neuroscience.
机器学习
[LG-0] Defensive Boosting for Online Probabilistic Forecasting
链接: https://arxiv.org/abs/2608.13554
作者: Georgy Noarov,Aaron Roth
类目: Machine Learning (cs.LG); Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS); Machine Learning (stat.ML)
*备注:
Abstract:We study online probabilistic forecasting of binary outcomes chosen by an adaptive adversary. Given an online learning algorithm for a weak hypothesis class H , we would like to efficiently obtain two incomparable guarantees that existing online boosting techniques provide separately. Online gradient boosting competes in Brier score with the best predictor induced by the span of H on every sequence, but promises nothing when the span does not contain an accurate predictor. Online weak-to-strong boosting drives classification error to zero under a weak-learning condition, but promises little when that condition fails. We give a simple defensive forecasting algorithm, the Defensive Booster, that obtains both guarantees. On every adaptive sequence, its Brier score is competitive with the best prediction induced by the span of H at the same rate as online gradient boosting; simultaneously, whenever the realized transcript satisfies the smooth weak-learning condition, its Brier score and randomized classification error satisfy the same rate guarantee as online classification boosting. This is achieved by operationalizing the “dual view” of boosting: When the algorithm’s randomized classification error is persistently high, its mistake weights form a smooth reweighting on which every weak hypothesis has low edge, yielding an ex-post hard-core certificate that the weak-learning condition fails. We also develop a strongly adaptive variant, which satisfies both guarantees on every time interval. The Defensive Booster is very efficient: it accesses just one weak-class learner, whereas the prior online boosting methods we compare against maintain large weak-learner ensembles. Experiments on synthetic and real data streams demonstrate its strong predictive performance (sometimes substantially improving over all prior baselines) coupled with orders-of-magnitude faster runtime. Subjects: Machine Learning (cs.LG); Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS); Machine Learning (stat.ML) Cite as: arXiv:2608.13554 [cs.LG] (or arXiv:2608.13554v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.13554 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-1] Exponential Convex Calibration Dimension for the Multi-Label Jaccard Measure
链接: https://arxiv.org/abs/2608.13549
作者: Mingyuan Zhang
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:The per-instance Jaccard score, or intersection over union (IoU), is standard in multi-label classification and binary segmentation. With s labels, its loss matrix has 2^s outcomes and reports. Under the convention \mathrmJac(\varnothing,\varnothing)=1 , we prove that the Jaccard score, shifted-loss, and ordinary loss matrices are nonsingular and that the loss columns have affine dimension 2^s-1 . The proof combines a finite MinHash Gram representation with Boolean Möbius inversion. For exact calibration, we prove 2^s-1 \leq \mathrmCCdim(L^\mathrmJac) \leq 2^s-1 . The lower bound uses a factorially weighted distribution with 2^s-1+1 supported outcomes and Bayes-optimal reports. Consequently, every exactly calibrated convex surrogate requires exponentially many prediction coordinates. We also give two polynomial-dimensional approximation guarantees with explicit regret transfers. A new F_1 -to-Jaccard transfer turns an existing (s^2+1) -dimensional F_1 surrogate into a polynomial-time rule with asymptotic Jaccard regret at most 3-2\sqrt2 . For any \alpha0 and 0\rho1 , a MinHash square-loss surrogate attains Jaccard-regret floor \alpha uniformly over arbitrary conditional label distributions. With probability at least 1-\rho , the direct construction has dimension O((s^2+s\log(1/\rho))/\alpha^2) , while a signed variant has dimension O((s+\log(1/\rho))/\alpha^2) . Thus zero-regret calibration requires exponential dimension, whereas every fixed additive regret tolerance admits polynomial prediction dimension.
[LG-2] DARTree: Speculative Diffusion Decoding with Autoregressive Draft Trees
链接: https://arxiv.org/abs/2608.13524
作者: Tianyi Li,Yaxin Luo,Xinyi Shang,Zhiqiang Shen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Speculative decoding losslessly accelerates autoregressive language models by verifying multiple draft tokens in parallel. Diffusion-based drafters further reduce proposal latency by predicting an entire token block in parallel, but their position-wise distributions are marginal rather than conditioned on tokens selected along each draft path. Existing recurrent correction incorporates causal information along a single draft chain, whereas diffusion-based tree construction broadens candidate coverage without carrying this correction along individual branches. We introduce DARTree, a training-free speculative decoding method that extends a pretrained AR correction head from chains to trees. DARTree first constructs a fixed-width candidate tree by expanding and scoring all nodes at each depth in a single batch, and then only applies best-first pruning to select the verification tree, decoupling AR-head inference from sequential heap operations. Across seven math, code, and chat benchmarks, DARTree achieves the highest average acceptance length and speedup in all four model–temperature configurations, accepting up to 12.97 tokens per verification round, 98.6% more than DFlash and 27.9% more than Domino in the same setting, and reaching up to 9.73 \times lossless speedup over locally measured autoregressive decoding.
[LG-3] Sparse Orthogonal Regression Technique: A Spectral Framework for Equation Discovery Approximation and Integration
链接: https://arxiv.org/abs/2608.13504
作者: Sabin Roman,Ljupco Todorovski,Saso Dzeroski
类目: Machine Learning (cs.LG)
*备注: 15 pages, 4 figures. Accepted for oral presentation at the 29th International Conference on Discovery Science (DS 2026), Mainz, Germany, October 5-9, 2026. To appear in Springer Lecture Notes in Computer Science (LNCS)
Abstract:We develop the Sparse Orthogonal Regression Technique (SORT), a sparse spectral framework for learning orthonormal-basis expansions from noisy and irregularly sampled data. SORT estimates expansion coefficients directly from observations using L1-regularized regression, avoiding explicit quadrature or analytic inner-product evaluation. The central application is data-driven discovery of ordinary differential equations: vector fields are represented in chosen orthogonal bases and learned as sparse coefficient expansions. This provides a complementary route to symbolic regression, grammar-based discovery, and SINDy-style sparse identification by first recovering a compact spectral representation, which can later guide searches for simpler analytic forms. Across the dynamical-system experiments, SORT matches or improves upon library-based sparse-regression baselines when the basis is well adapted to the problem, and shows more stable degradation under sparse sampling, noisy derivative estimates, and representation mismatch. Specific examples illustrate why this representation is useful: if a finite library misses the problem-specific nonlinearity, the resulting model can fail. SORT is not immune to mismatch, but it shifts the problem away from brittle selection among generic terms to basis design adapted to the problem domain. The experiments also show that dominant low-order coefficients persist as model order increases, supporting order-consistent model growth. Beyond equation discovery, the same learned expansion supports nonlinear approximation and estimation of complex, high-dimensional integrals by coefficient readout. Overall, SORT provides a reusable intermediate representation for system identification, approximation, and integration, while making basis design an explicit part of the scientific modeling problem.
[LG-4] Active-Trace Complexity Bounds for Moreau–Yosida Unadjusted Langevin Sampling
链接: https://arxiv.org/abs/2608.13467
作者: Yuchen Xin,Zhihua Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study the Moreau–Yosida unadjusted Langevin algorithm (MYULA) for the nonsmooth composite target [ \pi(dx)\propto \exp-f(x)-g(x)\,dx, \qquad x\in\mathbb R^d, ] where (f) is (m)-strongly convex with (L_f)-Lipschitz gradient and (g) is convex and (G)-Lipschitz. Let (g_\lambda) be the Moreau envelope of (g), (\pi_\lambda) the corresponding smoothed target, and (a_\lambda=\operatornametrH_\lambda), where (H_\lambda) is the a.e./weak Hessian of (g_\lambda). We show that the leading MYULA discretization error is controlled by the reference active trace (B_\mathrmref), the average of (a_\lambda) along the heat substep of one MYULA update started from (\pi_\lambda), rather than by the global curvature bound (d/\lambda). If (M_\lambda) is an a.e. upper bound for (a_\lambda), then, up to logarithmic factors, [ N \lesssim \frac1m \left[ L_f + \frac \tau_f+G^2+B_\mathrmref \varepsilon_\mathrmalg^2 + \fracM_\lambda\varepsilon_\mathrmalg \right], \qquad \tau_f:= \sup_x\operatornametr\nabla^2 f(x), ] iterations suffice to ensure (\sqrt m,W_2(\mu_N,\pi_\lambda)\leq\varepsilon_\mathrmalg), where (\mu_N) is the law of the (N)-th iterate and (W_2) is the quadratic Wasserstein distance. We also prove the Moreau-bias bound [ \sqrt m,W_2(\pi_\lambda,\pi) \leq \fracG^2\lambda4. ] Thus, choosing (\lambda\asymp\varepsilon/G^2) gives an end-to-end guarantee for (\pi). The universal estimate (B_\mathrmref\leq d/\lambda) yields (\widetilde O(\varepsilon^-3)) accuracy dependence. For the structured piecewise-linear, lasso-type, group, and total-variation penalties considered here, curvature–tube estimates make (B_\mathrmref) independent of (\lambda), yielding (\widetilde O(\varepsilon^-2)) for the same classical MYULA kernel.
[LG-5] Doubly Robust Estimation of Causal Effect on CVR with Targeted Regularization
链接: https://arxiv.org/abs/2608.13461
作者: Jiayi Dan,Bo Li,Lu Deng,Yong Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Post-click conversion rate (CVR) is a key metric in various scenarios including e-commerce and advertising, reflecting the efficiency and user experience in the second stage of the conversion process. Estimating the causal effect on CVR is therefore of great practical importance. However, directly applying existing causal inference methods to clicked samples introduces sample selection bias and increased variance due to the exclusion of non-click data. Recent studies on CVR prediction introduce “ideal loss”, which optimizes model parameters using an unbiased estimate of the loss over the full sample. Nevertheless, there is no guarantee that unbiasedness of the loss implies unbiasedness of the final estimator. We revisit this challenge from the perspective of semiparametric theory. Specifically, we develop a new doubly robust causal effect estimator for chain-structured outcomes such as CVR, and derive its theoretical properties in detail. It achieves a faster convergence rate compared to nuisance parameters estimation and is therefore more robust when using flexible nonparametric estimators, including neural networks. Based on these theoretical findings, we further design a framework based on targeted regularization to improve numerical stability and practical applicability. Extensive experiments on synthetic and real-world data demonstrate the effectiveness and robustness of our method. In addition, we find that naively combining loss debiasing with standard causal estimators underperforms our method, highlighting the necessity of developing the new estimator tailored to this CVR-style objective with solid theoretical guarantees. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.13461 [cs.LG] (or arXiv:2608.13461v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.13461 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-6] Symmetry-Breaking De Novo Crystal Generation via Markovian Jump Diffusion
链接: https://arxiv.org/abs/2608.13457
作者: Van Khoa Nguyen,Alexandros Kalousis
类目: Machine Learning (cs.LG)
*备注: Under review
Abstract:Generating crystals has recently attracted significant interest due to their broad applications in materials science. However, existing generative models struggle to produce complete crystallographic specifications, limiting their ability to capture global symmetry and structural dependencies. In particular, current state-of-the-art approaches generate crystals only up to site symmetries and rely on sampling space groups from empirical distributions during generation. Inspired by \emphspontaneous symmetry breaking in physics, where crystals break symmetries under external conditions, we propose a novel diffusion-based framework that generates full structure specifications by reversing from the lowest-symmetry priors. Our method leverages a Markovian jump-diffusion process to model these symmetry-breaking dynamics, enabling it to traverse different space groups in a physically motivated manner. Our model, dubbed \emphSymmetry-breaking Crystal Diffusion (SbCD), introduces a principled approach to explicitly incorporate inter-space-group transitions into the generative process. In de novo generation experiments on MP20 and MPTS-52, SbCD outperforms its symmetry-preserving counterpart by a substantial margin, offering a promising perspective for generative modeling of crystalline materials.
[LG-7] LLM -Assisted Dynamic Threat Analysis for Attacker-Reachable Software Weaknesses in Autonomous Vehicles
链接: https://arxiv.org/abs/2608.13450
作者: Md Wasiul Haque,Sagar Dasgupta,Mizanur Rahman,Md Rayhanur Rahman
类目: oftware Engineering (cs.SE); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 17 pages, 8 figures, 8 tables
Abstract:Autonomous vehicles depend on large safety-critical software stacks, where weaknesses reachable from adversarial inputs may affect steering, braking, or other control decisions. Static analysis can identify candidate sites, but dynamically confirming exploitability requires executable test artifacts that are difficult to construct manually. We investigate whether large language models (LLMs) can automate this process for Autoware, an open-source autonomous-driving stack. We perform compiler-precise static analysis across 185 packages, identifying 1,375 decision rules, 2,274 validation checks, and 482 input-to-safety-output flows, from which we derive a weakness taxonomy and sample 740 reachable sites. Two local open-weight LLMs, a no-static-context ablation, and a naive-template baseline generate 3,700 artifact sets, which are compiled against the real build under sanitizers, repaired through compiler-in-the-loop feedback, and fuzzed when executable. The main result is a build-integration failure taxonomy showing that 80% of first-shot compilation failures arise from dependency wiring rather than program logic. The reasoning model compiled 64% of harnesses on the first attempt, compared with 6% for the code-specialized model. Repair achieved full object-compileability for the reasoning model only through extensive stubbing; fewer than half of its harnesses reached the fuzzer, and all 37 observed crashes originated in stubbed code rather than Autoware. No candidate weakness was dynamically confirmed within budget. These results show that build integration, not candidate generation or fuzzing, is the primary barrier to reliable LLM-assisted dynamic analysis of full autonomous-vehicle software stacks.
[LG-8] When Local Variance Optimality Is Not Enough: RoPE-Aligned Q/K Rotations for Dynamic 4-Bit Quantisation
链接: https://arxiv.org/abs/2608.13365
作者: Shuhan Wang,Yilin Luo,Nan Xu,Chi Wang Cheung
类目: Machine Learning (cs.LG)
*备注:
Abstract:Rotation-based post-training quantisation commonly applies an orthogonal transform across an entire attention head to reduce outlier-induced error. RoPE instead partitions each head into two-dimensional frequency pairs, raising the question of whether a transform respecting this decomposition can improve on full-head mixing. Prior work has established the per-pair rotations that commute with RoPE. We state the converse result that, for distinct frequencies, no other single-head orthogonal map commutes with RoPE. For the head-shared parameterisation used in our experiments, we then derive the rotation angle that minimises the larger channel variance under a pooled-covariance, position-averaged surrogate and verify that the implementation attains its analytic minimum. The evaluated head-shared pairwise configuration does not improve accuracy in the tested dynamic W4A4KV4 setting. Across four checkpoints, replacing the full-head Hadamard with this configuration increases perplexity at both short and long context lengths. Composing the pairwise rotation with the Hadamard satisfies the selected \pm0.05 -PPL interval criterion under the default estimator. Estimating the shared angle from K alone improves pairwise-only on every checkpoint but does not close its gap to full-head mixing. The analytic objective controls a position-averaged second moment of a pooled calibration covariance, whereas the dynamic quantiser sets its step from a tokenwise group range. The pairwise transform also has only two-channel mixing support. Along a controlled interpolation from two-channel to full-head mixing, K range, relative quantisation error, and perplexity degradation decrease as support increases. These results show that optimality for a structured surrogate need not reduce quantisation error when the surrogate and mixing support are misaligned with the quantiser’s scale-setting statistic.
[LG-9] Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation
链接: https://arxiv.org/abs/2608.13337
作者: Valentin Noël
类目: Machine Learning (cs.LG)
*备注: 19 pages, 3 figures. Code and data: this https URL
Abstract:Sparse autoencoders are meant to name the things a language model computes, and the usual way to check that a latent matters is to switch it off and see what changes. But a latent fires at many tokens, and the effect has to be measured at one of them. The convention is to measure where the latent fires hardest. That choice is almost never reported, and it is not made by the experimenter: it is made by the dictionary under evaluation. Change the dictionary and the measurement moves to a different token. We show this is not a detail. Take two sparse autoencoders released by Google for the same model and match their latents by decoder similarity: even among the pairs the two dictionaries encode almost identically, they pick different tokens for a large share of them. Two dictionaries compared under the usual protocol are therefore very often compared at different places. To separate the convention from the dictionaries we train six autoencoders from one initialisation, differing only in fitting choices, so that a latent means the same thing in each. Most of the variance such a comparison reads as “these dictionaries disagree about this latent” turns out to be the position instead: it falls from 7.6% and 11.9% of variance to near zero once every dictionary is measured at the same token. More evaluation data does not rescue it. Across a sixteenfold range of corpus sizes the dictionaries agree less about where to measure, not more, so the problem grows with scale. The correction is one line of evaluation code. We give the protocol an ablation-based causal number must report to be comparable across papers, and an audit of five published papers against it. In short: a causal number reported without its position describes the token it was taken at as much as the latent it was taken from.
[LG-10] Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws
链接: https://arxiv.org/abs/2608.13335
作者: Liu Ziyin,Yizhou Xu,Tomaso Poggio,Isaac Chuang
类目: Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn); Statistical Mechanics (cond-mat.stat-mech)
*备注:
Abstract:Neural networks trained by gradient descent on a smooth cost function can nevertheless learn in steps: the cost holds on long plateaus and then drops abruptly. Meanwhile, training losses instead follow smooth power laws. Variants of both behaviors occur in architectures with very different microscopic structures, which is the signature of a few relevant collective variables. We show that a symmetry fixes what those variables are: a network layer is a sum over interchangeable units, so relabeling the units leaves it unchanged; given smoothness and the condition that a unit’s gradient vanish at the origin, symmetry then enforces a universal leading form for the expansion about the near-zero weights present at the start of training, the quadratic \Tr[WW^\topA(x)] , in which every architectural detail is confined to a single structure matrix" A(x) that we compute for each architecture. Perceptrons, attention layers, mixtures of experts, and convolutions become one model at different A . Its training dynamics then close on the order parameter" M=WW^\top and, whenever the data matrices share an eigenbasis, reduce to a Lotka–Volterra equation whose modes switch on one after another. The smaller the initial weights, the further apart the switch-on times, and the plateaus appear as a singular limit of a smooth flow; when many modes are unresolved the same events merge into a power law in training time whose exponent the theory predicts. We confirm both numerically across training methods and architectures.
[LG-11] A Probe Direction Is a Property of Its Prompt
链接: https://arxiv.org/abs/2608.13329
作者: Valentin Noël
类目: Machine Learning (cs.LG)
*备注: 19 pages, 5 figures, 13 appendices. Code, data manifests and results: this https URL
Abstract:A model that behaves differently when it senses it is being tested would undermine the evaluations we rely on, so recent work has sought to read that sense directly from a model’s activations. The standard instrument contrasts activations on prompts that announce an evaluation against prompts that do not, and reports how well the resulting direction separates held-out cases. That number is then compared across models and correlated with scale. We observe that the instrument has a free parameter its readings do not disclose: “a prompt that announces an evaluation” is not a prompt but a choice among many, and nothing in the method fixes which. Holding the task text fixed and varying only that choice, we find that the reported score, and even the direction in which it trends with model size, follows the prompt rather than the model; two published studies that disagree about the sign of that trend are both reproducible from a single design, by choice of prompt alone. Treating the prompt as a facet of a measurement design rather than an implementation detail, we find the model under study accounts for a small share of the variance in the number reported about it, and most of the rest lies in how each model responds to each prompt: collecting more evaluation items cannot repair the measurement, while varying prompts can. A further check finds that the split these probes are scored on is largely separable from surface form alone, so a direction carrying no information about evaluation at all still reproduces a substantial fraction of each published score. We conclude that a single-prompt design cannot support comparison between models, and we give the number of prompts a defensible comparison requires.
[LG-12] he Time Value of Evolution AAAI2026
链接: https://arxiv.org/abs/2608.13297
作者: Matthew Siper,Ahmed Khalifa,Julian Togelius
类目: Machine Learning (cs.LG)
*备注: Submitted to AAAI 2026, 8 pages, 5 figures, 2 tables
Abstract:In evolutionary search, a weak child can be a valuable ancestor that makes high-fitness regions reachable. Immediate-return control is blind to this delayed utility, penalizing mutations through their immediate offspring even when they open productive future lineages. We formalize this hidden dynamic as the time value of evolution within a finite-horizon Markov decision process. To exploit it, we introduce Lineage-Value Policy Gradients (LVPG), a long-horizon actor-critic framework for automated trading policy discovery. Our architecture decouples search control into specialized policy heads over a shared generative backbone: a bootstrapped critic head estimates the value of finite-horizon lineage potential from multi-step mutation trees, while an actor head dynamically modulates mutation intensity over the remaining search budget. We isolate the impact of long-horizon credit assignment against immediate-return optimization across 90 paired runs under matched operators, lineage supervision, folds, seeds, and budgets. Path-based credit assignment substantially accelerates finite-budget search, increasing validation best-so-far AUC by 0.394 Sharpe units. LVPG also produces fewer temporary regressions than immediate-return optimization and recovers from them more often. Finite-horizon lineage value yields more selective non-monotonic search and stronger policies within identical resource constraints.
[LG-13] Virtual Temperature Sensors in Power Transformers Using Neural Ordinary Differential Equations
链接: https://arxiv.org/abs/2608.13260
作者: Berk Hadzhamolla,Alexander Johannes Stasik,Signe Riemer-Sørensen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate modeling and forecasting of power transformer thermal behavior are critical for reliability, asset lifetime, and optimized power system operation. Numerical approaches such as finite element methods (FEM) and computational fluid dynamics (CFD) offer high fidelity but are computationally expensive, require complex mesh generation, and are often impractical for real-time or large-scale applications, particularly when transformer geometries are unknown. Lumped-parameter thermal models are more practical but depend on transformer-specific thermal constants and may fail to capture dynamic responses under varying operating and environmental conditions. Purely data-driven machine learning methods, including artificial neural networks, convolutional neural networks, and long short-term memory (LSTM) networks, have shown success in forecasting transformer temperatures but typically require large volumes of high-quality training data and may produce physically inconsistent or uninterpretable results. This paper develops a physics-aware Neural Ordinary Differential Equation (Neural ODE) framework for forecasting transformer thermal behavior from real-world time-series data. Neural ODEs model system dynamics in continuous time, providing smooth trajectory prediction and a natural representation of continuously evolving thermal dynamics. A key contribution is the integration of simplified heat-transfer equations directly into the Neural ODE formulation. The model is evaluated across datasets from fifteen transformers in different regions of Norway with varying designs and cooling mechanisms. The results demonstrate that the developed Neural ODE framework provides a standardized, physics-aware, and robust forecasting approach for heterogeneous transformer units.
[LG-14] Knowledge-guided Pattern Discovery via Coupled Tensor Factorizations
链接: https://arxiv.org/abs/2608.13234
作者: Gaute Johannessen,Geert Roelof van der Ploeg,Evrim Acar
类目: Machine Learning (cs.LG)
*备注:
Abstract:In order to understand complex systems such as the human metabolome or human brain, different sensing technologies are used, generating complex data. These datasets are often multiway, i.e., with more than two axes of variation such as a subjects by metabolites by time array. While tensor factorizations have successfully revealed interpretable patterns from such complex data, they have so far been mainly data-driven. On the other hand, there is more to data – there are computational models (of these systems), which are rich sources of prior information. In this paper, we introduce a knowledge-guided approach that brings together data and computational models by jointly analyzing real data and simulated data (generated using a computational model) using coupled tensor factorizations with linear coupling. Our experiments on real metabolomics measurements demonstrate that guiding the analysis of such noisy data with simulated data improves the pattern discovery performance while also revealing potential discrepancies between data and computational models.
[LG-15] History-informed Lagrangian Neural Networks
链接: https://arxiv.org/abs/2608.13215
作者: Tianshuo Zhang,Xianglei Xing,Wenzhe Zhai,Jia Gao,He Cao
类目: Machine Learning (cs.LG)
*备注: 15 pages, 5 figures. Accepted to the 9th Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2026) as an oral paper
Abstract:Forecasting the long-horizon evolution of mechanical systems from position-only observations is a pivotal yet difficult task, as hidden velocities and trajectory-specific physical properties must be inferred simultaneously. Although physics-guided neural networks like Lagrangian Neural Networks (LNNs) guarantee physical plausibility, they generally require complete state inputs and lack adaptability to changing system parameters. To break these limitations, we introduce History-informed Lagrangian Neural Networks (HiLNN). Grounded in the insight that temporal position sequences implicitly encode underlying dynamics, HiLNN employs a recurrent encoder to extract a latent context from history. This context not only reconstructs the unobserved initial velocity but also adaptively modulates the mass matrix, potential energy, and damping coefficients of a structured Lagrangian system. By leveraging a differentiable RK4 rollout scheme, the entire pipeline is optimized end-to-end under multi-step trajectory supervision and energy-consistency regularization. Empirical evaluations across conservative, dissipative, and heterogeneous variable-parameter systems show that HiLNN delivers superior long-term prediction accuracy and maintains precise energy profiles compared to state-of-the-art baselines. The source code is publicly available at this https URL.
[LG-16] ANGCO: Learning Topology-Aware Capacity Allocation for Overload-driven Cascading Failures
链接: https://arxiv.org/abs/2608.13212
作者: Orkun Irsoy,Leman Akoglu,Osman Yagan
类目: Machine Learning (cs.LG); Social and Information Networks (cs.SI)
*备注:
Abstract:Networked systems, from power grids to traffic networks and cloud clusters, carry loads across nodes with limited capacity. A node whose load exceeds its capacity fails and sheds its load onto its neighbors, which can trigger a system-wide cascade. We study how to allocate a fixed capacity budget across nodes to resist these cascades under local load redistribution. The problem is difficult because no optimal allocation is known, and the fail-or-survive objective is non-differentiable and piecewise constant, so exact and gradient-based optimization methods do not directly apply. We introduce TANGCO (Topology-Aware Neural Graph-Guided Capacity Optimization), which uses a graph neural network policy trained through the cascade simulator with policy-gradient learning and a heuristic anchor. We evaluate TANGCO on five synthetic graph families and five real networks spanning power, road, air, and Internet topologies. The learned policy improves on the best of four hand-designed heuristics in all 450 synthetic instances and in 40 of 45 real-network conditions, with robustness gains ranging from 1.6% to 246%. The learned policies transfer to unseen graphs within a family and partially across related topologies, and TANGCO ^pre , pre-trained on synthetic graphs, matches per-network training on unseen real networks. Training scales near-linearly with graph size, and TANGCO ^pre allocates on a new network with no per-target training, matching the deployment cost of a hand-designed heuristic. Free-vector variants without the GNN, stay close to the heuristics, so the graph representation carries the gain beyond numerical search. Finally, analysis of the learned allocations identifies when local risk is sufficient, leads to an improved closed-form heuristic, and reveals the regimes where a topology-aware learned policy remains necessary.
[LG-17] Beyond Simulated Benchmarks: Evaluating Motion Representations for Fall Detection Under Real-World Data Scarcity
链接: https://arxiv.org/abs/2608.13197
作者: Timilehin B. Aderinola,Ilaria D’Ascanio,Luca Palmerini,Lorenzo Chiari,Jochen Klenk,Clemens Becker,Brian Caulfield,Georgiana Ifrim
类目: Machine Learning (cs.LG)
*备注:
Abstract:Falls are a major health concern for older adults, and wearable sensors have been widely explored for detecting falls and enabling timely intervention. However, real-world falls are extremely rare: collecting 100 of them requires an estimated 100,000 days of monitoring, resulting in severely limited labelled data for training machine learning models. Consequently, many approaches rely on simulated datasets, often reporting high laboratory performance but limited real-world generalisation. We present a systematic evaluation of motion representations for wearable fall detection under real-world data scarcity. Using accelerometer signals, we compare interval-based, kernel-based, symbolic, and foundation model representations. As an interpretable baseline, we additionally investigate a lightweight symbolic representation that converts short motion segments into symbolic sentences augmented with physically-grounded impact descriptors. Experiments use FallAllD, a simulated falls dataset, and FARSEEING, a clinically verified real-world falls dataset. Through cross-validation, controlled data scarcity, and cross-dataset transfer, we examine how representation choices affect robustness under realistic deployment. Our results reveal that highly parameterised kernel and foundation models excel on simulated data but degrade severely under both data scarcity and domain shift. Although the interval-based representation achieves the strongest absolute real-world performance, augmenting a symbolic representation with physically-grounded impact descriptors yields the smallest degradation under domain shift and retains detection sensitivity under extreme scarcity, albeit at lower precision. These findings highlight the importance of evaluating beyond simulated benchmarks and show that representation choice is critical for deployable fall detection given the scarcity of real-world data.
[LG-18] Branch and Bound for Relational Verification of Neural Networks
链接: https://arxiv.org/abs/2608.13118
作者: Kota Fukuda,Zhenya Zhang,Guanqin Zhang,Jianjun Zhao
类目: Machine Learning (cs.LG)
*备注: The full version of the paper accepted by EMSOFT 2026
Abstract:Verification of neural networks against relational specifications, such as global robustness, is crucial for safety-critical applications of cyber-physical systems (CPS), given their increasing adoption of AI components. Compared to simple trace properties (e.g., local robustness), verifying relational specifications requires reasoning about the relationship between multiple network inferences, which brings significant technical challenges. Existing research has explored abstraction techniques based on sound and convex over-approximation of neural network outputs; however, since these approaches are inherently incomplete and may raise false alarms, they further underscore the need of effective abstraction refinement. In this paper, we propose a branch-and-bound (BaB) framework to mitigate the issue, which iteratively splits the problem until all sub-problems are verified. Specifically, our BaB framework features splitting of relational neurons rather than individual neurons as prior works do, and as the core of our technique, we devise a relational neuron selection strategy based on the dual formulation of the verification problem, which allows us to efficiently select the (most likely) optimal relational neuron that maximizes the refinement brought by problem splitting. We evaluate SaBRe on 817 verification problems across ACAS Xu, MNIST-F, MNIST-C, CIFAR and GTSRB. The results show that SaBRe outperforms different baseline approaches, in terms of the number of solved instances and verification efficiency, which demonstrates the effectiveness of our proposed techniques. Comments: The full version of the paper accepted by EMSOFT 2026 Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.13118 [cs.LG] (or arXiv:2608.13118v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.13118 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-19] FlowLOB: Efficient and Controllable Limit Order Book Generation with Flow Matching
链接: https://arxiv.org/abs/2608.13096
作者: Zhuohan Wang,Andreea Bacalum,Ollie Olby,Carmine Ventre,Namid Stillman
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Computational Finance (q-fin.CP); Trading and Market Microstructure (q-fin.TR)
*备注: 8 pages, 3 figures, 2 tables
Abstract:Limit order book (LOB) simulators are most useful to practitioners when they combine realistic market dynamics, computationally efficient sampling, controllable scenario generation, and the ability to generalize beyond the instruments seen during training—properties that existing agent-based and deep generative simulators provide only partially. We present \textbfFlowLOB, a conditional \textbfflow-matching generator of \textbfLOB trajectories, trained on multiple Hong Kong Exchange (HKEX) symbols at three sampling frequencies ( 0.1 s, 1 s, 10 s) in tick-relative representation that transfers to unseen instruments. Because flow and diffusion models admit a common formulation, we train both with identical data, architecture, and budget, and sample both through the same fixed-step ODE solvers, yielding a controlled comparison of sampling efficiency and fidelity. Flow matching attains its best quality with only 10 ODE-solver steps, whereas diffusion needs many more function evaluations to approach the same fidelity. At this efficient operating point, FlowLOB improves realism over baselines, two learned and two agent-based models, in most distributional metrics at the two finer sampling frequencies. We evaluate counterfactual controllability with a distributional test that asks whether changing a scenario condition moves the generated statistic toward the corresponding real tail regime; FlowLOB satisfies this criterion in most tested settings. Both realism and control effects transfer zero-shot on a held-out symbol. We additionally conduct ablation studies on the network architecture and the learning rate.
[LG-20] Learning Discrete Decisions for MIPs with Constraint-Aware Diffusion
链接: https://arxiv.org/abs/2608.13079
作者: Vincenzo Di Vito,Mehdi Taghizadeh,Deepjyoti Deka,Kaarthik Sundar,Ferdinando Fioretto
类目: Machine Learning (cs.LG)
*备注:
Abstract:This paper proposes a novel learning-based approach to approximately solve instances of mixed-integer optimization problems. These problems are computationally challenging, as they require jointly determining discrete and continuous decisions while satisfying complex combinatorial constraints. The proposed method relies on a graph-based generative diffusion model that learns the discrete component of mixed-integer optimization problems while integrating a training-free feasibility projection operator directly into the reverse diffusion process to steer intermediate samples toward the feasible set throughout generation. Once the discrete decisions are generated, the remaining optimization reduces to a continuous problem that can be solved efficiently (relative to the original problem) using existing numerical methods. The resulting framework named Constrained Graph Diffusion (CGD), is problem-agnostic and can accommodate a broad class of mixed-integer optimization problems through suitable projection operators. We evaluate CGD on optimal transmission switching for ACOPF and discrete portfolio optimization, demonstrating substantial improvements in feasibility and solution quality over learning-based baselines while achieving speedups of up to 425\times over state-of-the-art numerical solvers for MINLPs.
[LG-21] A Multispectral Framework for the Detection of Calcium Carbide-Induced Ripening and Shelf-Life Estimation in Climacteric Fruits
链接: https://arxiv.org/abs/2608.13073
作者: Gurbhit Chaurakoti,Harshit Kumar,Hani Kumar,Anurag Singh,Ram Asrey
类目: Machine Learning (cs.LG)
*备注:
Abstract:Significant health risks are associated with the illegal, yet commonly practiced use of industrial-grade Calcium Carbide (CaC2) for ripening climacteric fruits like mango and banana, which leaves behind trace residues of arsenic and phosphorus. To address this, the proposed study explores a novel, non-invasive multispectral framework for distinguishing safely ripened fruits (naturally ripened and ethephon-induced) from calcium carbide-ripened samples, while also estimating their ripening progression (in percentage) and remaining shelf life (in days). The spectral profiles of mango (Mangifera indica) and banana (Musa acuminata) at 18 discrete wavelengths in the visible-near infrared (NIR) range (410 nm - 940 nm) are studied using the AS7265x spectral triad sensor. CaC2-treated samples exhibit sharper spectral intensity drops in the visible region, consistent with accelerated chlorophyll degradation and carotenoid development. To characterize these physiological changes, the feature engineering strategy integrates inter-method spectral variance, intensity ratios at distinct wavelengths, and environmental parameters including temperature and humidity. Dimensionality reduction using Principal Component Analysis (PCA) retains 90% of spectral variance within the first 5-7 components. The resulting feature set is used to train three independent eXtreme Gradient Boosting (XGBoost)-based learning algorithms for ripening method classification, along with quantitative estimation of remaining shelf life and ripening progression. A classification accuracy of 95% along with carbide class recall of 0.67 is observed for mango samples, while the model achieves an accuracy of 81% and carbide class recall of 0.74 for banana. This instrumentation and data-driven approach demonstrates the effectiveness of the proposed non-invasive framework.
[LG-22] On the global feature importance for interpretable and trustworthy heat demand forecasting
链接: https://arxiv.org/abs/2608.13039
作者: Milan Zdravković
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: 9 pages, 5 figures. This preprint corresponds to the paper published in Thermal Science 2025 Volume 29, Issue 5 Part A, Pages: 3355-3365
Abstract:The paper introduces the ante-hoc Explainable AI methodology to assess the global feature importance of the Machine Learning models used for heat demand forecasting in intelligent control of District Heating Systems, with motivation to facilitate their interpretability and trustworthiness, hence addressing the challenges related to adherence to communal standards, customer satisfaction and liability risks. Methodology includes use of four different approaches, namely intrinsic interpretability of Gradient Boosting method and selected post-hoc methods, namely Partial Dependence, Accumulated Local Effects and SHAP. None of the selected methods assume feature permutation or perturbations which can introduce bias due to introduction of random unrealistic values of data instances. Discussion of results is provided, including the assessment of complementarities where applicable, with specific interpretations in context of the district heating processes.
[LG-23] Incremental Evaluation and Training in Relational Deep Learning
链接: https://arxiv.org/abs/2608.13023
作者: Jakub Peleška,Gustav Šír
类目: Machine Learning (cs.LG); Databases (cs.DB)
*备注:
Abstract:Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs to enable end-to-end representation learning. However, prevailing RDL evaluation practices rely on static, single-episode dataset snapshots, overlooking the continuous, time-evolving nature of real-world databases. Consequently, current RDL benchmarks fail to capture how model performance changes as new data accumulates over time. To address this limitation, we introduce an incremental, multi-episode evaluation and training paradigm to assess and improve the temporal robustness and adaptability of state-of-the-art RDL models. Using established large-scale datasets, we examine data evolution and model training dynamics, demonstrating that temporal concept drifts occur in the majority of predictive tasks. We present multiple incremental training regimes for fine-tuning the models and demonstrate that transfer learning is both feasible and highly effective in the RDL setting. Alongside a new temporal evaluation metric that prioritizes near-future accuracy, we show that our incrementally fine-tuned models consistently outperform the standard, expensive, from-scratch trained baselines.
[LG-24] Balanced Adaptive Prototype Selection for Scalable TabPFN Inference on Large-Scale Tabular Data
链接: https://arxiv.org/abs/2608.12989
作者: Mahboobe Jadid,Melika Rezaye Garkani,Ali Mousavi
类目: Machine Learning (cs.LG)
*备注:
Abstract:Pretrained tabular foundation models have demonstrated strong predictive capability; however, their application to large-scale datasets remains constrained by the limited inference context. This paper introduces Balanced Adaptive Prototype Selection (BAPS), a framework for constructing compact, information-preserving contexts for scalable TabPFN inference. Without modifying or retraining the pretrained model, BAPS jointly preserves representative structure, informative decision boundaries, local density, class balance, and feature-space diversity. Experiments on the million-row HIGGS and SUSY datasets show that 512 prototypes retain strong predictive performance and reliable calibration, corresponding to an approximately 1,953-fold context compression. All experiments were conducted on an Intel Core i7 CPU with 16 GB RAM and no GPU acceleration. These findings establish effective context construction as a practical mechanism for extending pretrained tabular foundation models to million-scale datasets.
[LG-25] Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices
链接: https://arxiv.org/abs/2608.12982
作者: Rekha,Santosh Singh,S. K. Neogy
类目: Machine Learning (cs.LG)
*备注: 25 pages, 18 figures
Abstract:In this research work, we are constructing the sensing matrix, which is essential for the success of the compressive sensing technique. We have chosen a learning-based technique for the construction of the sensing matrix. The novelty and uniqueness of the proposed technique is that it does not use any data set and also does not use a specific application. It uses the mathematical property/constraint for the construction of the sensing matrix for the perfect recovery of the signal. The perfect recovery of signals is an old and still very challenging problem in real-world applications. In late 2000, compressive sensing became a popular mathematical tool for the perfect recovery of sparse signals. The core of the compressive technique is the construction of the sensing matrix, which satisfies certain special properties such as restricted isometry property (RIP), null space property (NSP), and spark property (SP). All these properties are NP-hard problems and hence computationally challenging to solve. For all practical purposes, the construction of the sensing matrix needs to achieve low mutual coherence to achieve the perfect recovery of the signals. We have used a neural network for the construction of the sensing matrix, and this framework constructs a binary sensing matrix with low mutual coherence. The entries in the matrix are generated through a shared underlying rule. The proposed architecture is simple and does not use large-scale training data sets. Such uniqueness and novelty bring a drastic reduction in computational cost, and also, for the first time in literature, the use of a mathematical property for defining the loss function. In this proposed research work, the mutual coherence property has been used in the neural network framework. Such a neural network framework brings generality, robustness, and reduces storage requirements.
[LG-26] Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practice
链接: https://arxiv.org/abs/2608.12962
作者: Ziqi Zhao,Jialin Lu,Junjie Shan,Junyuan Zhang,Shuya Yang,Ka-Ho Chow
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注:
Abstract:Vertical Federated Learning (VFL) enables organizations holding complementary features of shared entities to collaborate and train models. In this setting, the initiator can withhold information about the learning task, while other contributors participate without exposing their local datasets, creating an asymmetric information structure aligned with growing privacy demands. However, this asymmetry is a double-edged sword. Among various threats, backdoor attacks are particularly concerning because VFL not only enables malicious contributors to poison the model during training, but also allows them to activate the backdoor at inference time to manipulate predictions. Although prior work has reported near-perfect attack success rates and proposed effective defenses, we find that most findings fail to hold under realistic conditions, exposing a fundamental gap between research and practice. In this paper, we present a systematic, practice-oriented study of backdoor vulnerabilities in VFL, revealing this gap in both methodological design and evaluation practices. We show that existing approaches overlook key practical constraints and therefore rely on unrealistic prior knowledge. Furthermore, these limitations have remained hidden due to poorly designed evaluation practices in the literature. To bridge this gap, we redefine threat models under realistic constraints, propose practical backdoor workflows, and introduce BVBench, a backdoor-centric benchmark that enables fair, practical, and comprehensive evaluation, preloaded with state-of-the-art baselines. BVBench provides strong evidence of the fragility of the current understanding of VFL backdoor risks and establishes a foundation for steering research toward uncovering practical vulnerabilities and developing more meaningful defenses.
[LG-27] CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation
链接: https://arxiv.org/abs/2608.12944
作者: Hamza Shafiq,Hung Manh Pham,Bin Zhu,Pan Zhou,Jun Hu,Aaqib Saeed
类目: Machine Learning (cs.LG); Image and Video Processing (eess.IV); Machine Learning (stat.ML)
*备注:
Abstract:Electrocardiography (ECG), photoplethysmography (PPG), and phonocardiography (PCG) provide complementary views of the same cardiac cycle, yet existing cardiac foundation models are trained for a single sensing modality, leaving the shared physiology across sensors unexploited. We introduce CardioState-JEPA, a cardiac foundation model to learn a single shared representation jointly across ECG, PPG, and PCG, built on a physiology-aware joint-embedding predictive architecture. The model maps heterogeneous waveforms into a common token space, processes them with a single shared Transformer encoder, and learns by predicting masked latent cardiac states, placing the pretraining target on shared physiology rather than sensor-specific waveform appearance. To handle the temporal offsets between electrical, mechanical, and hemodynamic events, cross-modal prediction uses a learned delay aligner that matches signals at the corresponding cardiac time. Because synchronized multi-sensor recordings are scarce, CardioState-JEPA first learns within-modality structure from abundant unimodal data and then uses paired data to align modalities in latent cardiac time. Evaluated as a frozen encoder across 25 downstream tasks spanning ECG, PPG, and PCG, our encoder improves average PPG classification by 8.2 AUROC points, PCG murmur detection by 18.8 AUROC points, and ECG classification by 15.5 AUROC points over the best self-supervised signal baseline and matches or exceeds cardiac models trained with privileged clinical text or supervised labels on several ECG benchmarks. These results establish that heterogeneous cardiac signals can mutually supervise a single foundation model of cardiac physiology.
[LG-28] Diagnosing JEPA World Models with Action-Conditioned Predictive Consistency
链接: https://arxiv.org/abs/2608.12939
作者: Guo An,Zijing Wu,Honghua Dong,Yuhao Yan,Zixuan Gui,Haochong Chen,Shanzhao Ruan,Xiang Wang,Yurong Ling,Qi Tian
类目: Machine Learning (cs.LG)
*备注:
Abstract:Joint-embedding predictive architectures (JEPAs) learn world models that predict in a compact latent space rather than in pixels, reducing the pressure to model nuisance appearance. Yet this provides no guarantee against visual perturbations: they can still alter the encoded representation and affect subsequent action-conditioned predictions. Bisimulation captures this requirement precisely: two observations should be treated as the same state only when their action-conditioned consequences agree. Guided by this criterion, we introduce Action-Conditioned Predictive Consistency (ACPC), a diagnostic that measures how far a clean history and a visually perturbed view of it diverge after being rolled forward under the same action sequence. We prove that this divergence bounds the perturbation-induced change in multi-step prediction error and planner cost. Building on pairwise ACPC, we define two complementary measures: the Invariance Radius (IR) summarizes clean-perturbed rollout spread, while the Separation Rate (SR) checks whether different states remain distinguishable after rollout. Experiments on four visual control tasks show that pairwise ACPC predicts perturbation-induced prediction and cost changes. On LeWM, the IR-SR screen transfers across tasks, and the joint diagnostic remains informative under blur and resize. PLDM exhibits similar diagnostic trends under a different architecture.
[LG-29] Multi-perspective Imbalance-Conscious 6G Beamforming Optimization and Performance
链接: https://arxiv.org/abs/2608.12929
作者: Chukwunonso Henry Nwokoye,Blessing Oluchi Iloka,Chikwue V. Umeugoji,Christopher Anene Egemba,Nnenna D. Duroha
类目: Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注: 9 pages
Abstract:The study presents a systematic machine learning (ML) study of 6G-IoT beamforming optimization (6GBO) using supervised and unsupervised approaches. We compared the predictive power of network, environmental, device, and vision feature groups for 6GBO. Additionally, it addressed other unsupervised perspectives that can enhance 6GBO, including clustering network scenarios using methods such as K-means, DBSCAN, and hierarchical clustering. Several imbalance-aware experiments revealed that network features possess better prediction power than device, environmental, and vision feature groups, as evidenced by their recall, F1-score and ROC-AUC values. For unsupervised ML exploration (assessed using Elbow, Silhouette score, and Davies-Bouldin Index methods), the results indicate that the deployment environment and type of device primarily influence clustering, rather than mobility-based attributes. Furthermore, the explainability analysis showed that bandwidth, IoT sensors, and mobility possess higher global feature importance across the feature groups. In the future, we would apply deep and reinforcement learning techniques to predict throughput/latency or to optimize rewards determined by performance indicators like SNR enhancement
[LG-30] Momentum as Residual-Driven Multiplier Correction for Deep Learning Optimization
链接: https://arxiv.org/abs/2608.12925
作者: Zhixin Ren,Yau Lyu,Congrong Li,Liping Zhang,Shengbo Eben Li
类目: Machine Learning (cs.LG)
*备注:
Abstract:Momentum-based optimizers are widely used in modern deep learning, yet the relations among momentum recursion, update geometry, and acceleration remain only partially understood. We develop an \textbfA DMM- \textbfI nspired \textbfM omentum (AIM) framework based on residual-penalty variable splitting, which interprets momentum as a multiplier-like correction driven by the splitting residual. AIM recovers the exponential moving average of gradients from an ADMM-style multiplier update and separates two mechanisms that are usually intertwined in practical optimizers: the residual penalty determines the update geometry, whereas the approximation of the objective-related subproblem determines the acceleration form. Building on AIM, we propose \textbfR elativistic \textbfA daptive gradient \textbfD escent with \textbfA ccelerated \textbfR esidual (RADAR), which combines relativistic adaptive geometry, decoupled residual correction, and second-order momentum filtering to improve the update direction and momentum estimation. We establish stochastic convergence through a variance-perturbed Lyapunov drift analysis. Experiments on supervised vision learning, language modeling, and reinforcement learning show that RADAR achieves consistent improvements over strong adaptive optimizer baselines.
[LG-31] owards Socially Compliant Navigation in Deep Reinforcement Learning via Proxemics-Based Reward Modeling
链接: https://arxiv.org/abs/2608.12917
作者: Takieddine Soualhi(CHROMA),Jacques Saraydaryan(CPE, CHROMA),Laetitia Matignon(UCBL)
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:Developing effective robot navigation methods in crowded environments is essential for real-world applications. Although recent deep reinforcement learning (DRL) methods have improved navigation performance in crowded environments, they often focus primarily on task-centric objectives and underrepresent social compliance objectives. In this paper, we introduce a novel proxemics-based reward formulation for DRL social navigation that provides a dense, interpretable social learning signal while maintaining navigation efficiency. Our approach models each human’s personal space as a radial Gaussian-mixture field derived from Hall’s proxemics theory and computes a robot-centric local cost over the robot’s field of view. We integrate the proposed reward into established DRL navigation methods and evaluate it in simulation across multiple crowd scenarios, reward baselines, and crowd densities using both navigation metrics and social metrics. Results show that the proposed reward consistently improves social metrics in simulation while maintaining competitive navigation performance relative to the compared reward models.
[LG-32] Revisiting Overestimation Bias Problem of Q-learning: Settling Large Discrete Action Space via Action Intersection
链接: https://arxiv.org/abs/2608.12912
作者: Pu Li,Tao Tan,Hong Xie,Xiaoyu Shi,Mingsheng Shang
类目: Machine Learning (cs.LG)
*备注:
Abstract:This paper considers the overestimation bias problem of Q-learning in the setting of a large action space, for the purpose of relieving the bottleneck of existing methods. We find that the large action space increases the randomness in Q-value estimation. The randomness makes two paradigms that drive the major literature on the overestimation problem have their own bottlenecks: the coupling paradigm, i.e., the optimal action and its Q-value are estimated with the same Q-function, always has a positive bias. This is because randomness leads to some actions having abnormally high estimated values than their true values, and the coupling methods prefer these actions. The decoupling paradigm, i.e., the optimal action and its Q-value are estimated with two independent Q-functions, always has a negative bias. This is because randomness increases the estimation gap between the two independent Q-tables for the same action. This paper shows that action intersection can be a simple yet powerful strategy to relieve these bottlenecks. The action intersection strategy enables semi-decoupling via two designs: (1) it allows two Q-functions to share a certain fraction of trajectory data; (2) if a data sample is shared, each Q-function is updated using the coupling paradigm; otherwise, using the decoupling paradigm. Two properties make the action intersection strategy powerful: (1) attaining a large bias range, i.e., varying the data sharing fraction, the estimation bias varies from underestimating to overestimating; (2) fine granularity: the action intersection size can be made arbitrarily finer to enable finer control. We consider two experiment settings, i.e., tabular and deep RL, deep RL experiments show that our method outperforms several SOTA baselines drastically; tabular experiments reveal why our method can achieve superior performance.
[LG-33] Adaptive k Nearest Neighbors Classifier via Granular Ball Computing
链接: https://arxiv.org/abs/2608.12903
作者: Xiaoyu Lian,Shuyin Xia,Hongxuan He,Lifeng Shen,Guoyin Wang,Xinbo Gao
类目: Machine Learning (cs.LG)
*备注:
Abstract:The k -Nearest Neighbor~(KNN) algorithm is widely used across various tasks. The selection of the k value is a key issue because it significantly impacts performance. In this paper, an adaptive and efficient KNN approach via granular-ball computing is proposed. The method consists of two stages. \textcolorblackIn the training stage, the dataset is first coarsely partitioned to reduce the complexity of data distributions within a granular ball, and then the Fisher criterion is introduced to control ball splitting and stopping, yielding a multi-granularity granular ball representation. In the prediction stage, the nearest granular ball is first located through a weighted distance mechanism, and an adaptive neighborhood is then constructed around the test sample. The effective k value is dynamically determined by the actual number of samples contained in this neighborhood. The neighborhood induced by the nearest granular ball provides more stable local group information, thereby improving robustness against noise and local perturbations. Experimental results demonstrate that the proposed method outperforms existing KNN variants across multiple datasets in terms of both accuracy and efficiency. The code has been open-sourced for reproducibility: this https URL.
[LG-34] Robust data-driven discovery of fractional differential equations via weak formulations and Pareto-based subset selection
链接: https://arxiv.org/abs/2608.12879
作者: Pongpisit Thanasutives,Yoshinobu Kawahara
类目: Machine Learning (cs.LG); Dynamical Systems (math.DS)
*备注: 47 pages
Abstract:Fractional partial differential equations describe nonlocal dynamics, but discovering them from noisy data is difficult because fractional differentiation amplifies high-frequency measurement noise and the derivative orders are unknown. We propose Weak-Pareto, which combines an adjoint-consistent weak formulation of fractional terms with Pareto-based subset selection over discrete term types and continuous fractional orders. For linear right-hand-side terms, the adjoint transfers fractional operators from measured fields to smooth test functions, replacing noise-sensitive pointwise differentiation with smoothing integration; for nonlinear terms, the noise-suppression effect is partial yet useful. Coefficients are fitted by ridge regression within a branch-aware differential-evolution search over the orders. The support size is then selected at the validation-error-complexity elbow. We show that the variance of fixed linear right-hand-side weak features vanishes under grid refinement, whereas noise amplification in pointwise fractional features increases with derivative order. Across fractional advection-diffusion, reaction-diffusion, and Burgers benchmarks, Weak-Pareto recovers parsimonious structures from clean and noisy measurements. In controlled advection-diffusion and Burgers comparisons, it retains the correct support at every tested multiplicative-noise level, whereas the unregularised strong-form counterpart largely fails once noise is introduced; this advantage persists under additive Gaussian noise. Ablations show that the weak library drives noise robustness and that continuous-order Pareto search avoids the support-selection failure of a dense fixed dictionary. On the advection-diffusion benchmark, Weak-Pareto yields more consistent operator recovery and substantially lower measured runtime than a contemporary neural baseline.
[LG-35] Sustaining Plasticity via Learnable Wavelet Activations in Continual Learning
链接: https://arxiv.org/abs/2608.12874
作者: Zeyang Zhang,Tieliang Gong,Junyan Lu,Weizhan Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Plasticity loss has emerged as a critical challenge in continual learning that significantly hinders the acquisition of sequential tasks. While optimizing activation designs offers a potential solution, current fixed-form functions suffer from an inherent spectral bias towards low-frequency variations, whereas learnable variants permit unconstrained updates that induce catastrophic forgetting. To address these limitations, we propose a novel learnable wavelet activation that decomposes the activation function into low-frequency and high-frequency components to explicitly counter spectral bias. Furthermore, we employ dynamic wavelet injection to adaptively enhance plasticity for new tasks, alongside a regularization strategy to ensure the stability of previous learned knowledge. Theoretically, we provide rigorous mathematical guarantees for the proposed framework, proving the structural necessity of the hybrid wavelet architecture for efficient L^2 approximation and demonstrating that the decoupled learning rate mechanism successfully restores network plasticity for high-frequency information. Additionally, we provide a formal derivation of the loss-driven injection trigger mechanism to precisely guide the injection. Extensive empirical evaluations demonstrate that our approach maintains superior trainability and generalization throughout the learning process and achieves state-of-the-art performance across diverse continual learning benchmarks.
[LG-36] Discovering Persistent Behavioural Patterns for Interpretable Blockchain Forensics
链接: https://arxiv.org/abs/2608.12864
作者: Dorottya Zelenyanszki,Zhe Hou,Kamanashis Biswas,Vallipuram Muthukkumarasamy
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Public blockchain data enables large-scale DeFi-related analysis, but many existing approaches are application-specific, difficult to scale, or hard to interpret. This research proposes a scalable, application-agnostic framework for \emphpersistent behavioural pattern discovery from large-scale blockchain activity. It constructs behaviour sentences enriched with contract, token and market context, then applies a two-step embedding process: sentence-level embeddings capture individual actions, while sequence-level embeddings capture user behaviour over time. An interpretable behavioural profiler characterizes discovered communities through behavioural motifs, routines, temporal dynamics, entity exposure, and suspiciousness evidence. Evaluation on Ethereum using over 30 million transactions shows that the framework uncovers both routine and malicious behavioural patterns, including decentralised exchange (DEX) trading, NFT activity, phishing, bot operations, oracle manipulation, and rug-pull schemes. Importantly, many patterns remain stable across independent observation windows, enabling the identification of long-term behaviours beyond a single analysis period. The proposed framework combines scalability, interpretability, and persistence analysis, supporting blockchain forensic investigation, behavioural attribution, and threat discovery.
[LG-37] HiRoute: Hierarchical Routed Prompt Tuning for Safety Alignment of Large Language Models
链接: https://arxiv.org/abs/2608.12821
作者: Fangzhou Chen,Shiji Zhao,Mengyang Wang,Qihui Zhu,Ranjie Duan,Maoxun Yuan,Xingxing Wei
类目: Machine Learning (cs.LG)
*备注: Preprint
Abstract:Large language models (LLMs) remain vulnerable to harmful requests and jailbreak attacks. Parameter-efficient safety alignment methods based on prompt tuning typically rely on a single global prompt or externally selected prompt modules. Such static designs struggle to maintain a cross-category safety boundary while generating constructive responses tailored to specific risks and avoiding over-refusal of benign inputs. To address these limitations, we propose HiRoute, an input-adaptive hierarchical prompt-tuning framework that separates category-agnostic safety control from category-specific response guidance. HiRoute first trains a lightweight hierarchical router on representations extracted from a frozen LLM to jointly detect harmful intent and predict multi-label risk scores. It then freezes both the backbone model and the router and uses preference optimization with alternating gradient updates to learn a shared coarse-grained prompt and a set of fine-grained prompt experts as continuous embeddings. At inference time, benign inputs bypass the safety branch, whereas risky inputs are processed using the shared prompt together with a router-weighted mixture of risk-specific prompt experts. Experiments across three instruction-tuned models show that HiRoute achieves high safety rates across multiple safety benchmarks while preserving safe-response helpfulness, reducing over-refusal, and maintaining competitive performance on general-purpose tasks.
[LG-38] CoMedBench: A Multi-Source Benchmark of Synthetic Medical Data Fidelity and Downstream Utility
链接: https://arxiv.org/abs/2608.12805
作者: Akanta Das,Al Amin Farhad,Mrinmoy Sarkar Anto,David Rehkopf,Ayin Vala,Tanmoy Sarkar Pias
类目: Machine Learning (cs.LG)
*备注:
Abstract:Access to clinical data is essential for developing reliable healthcare machine learning systems, but direct use of electronic health records is constrained by privacy regulation, institutional review, data-use agreements, and the risk of re-identification. Synthetic data promises a practical alternative: it can preserve useful statistical and clinical structure while reducing exposure of sensitive patient records. Prior studies often evaluate a single generator, one dataset, or a narrow downstream task, making it difficult to know when synthetic data can support model development and when it fails to preserve task-critical signal. We introduce CoMedBench, a reproducible benchmark that evaluates a family of generators under a common clinical-validity framework and one shared training and evaluation engine, spanning static tabular and temporal downstream tasks on established critical-care datasets. In total the benchmark spans 37 dataset-task pairs across two modalities consists of 20 static tabular and 17 temporal ICU time-series-drawn from seven public data sources: three intensive-care databases (MIMIC-III, MIMIC-IV, and eICU) together with the UCI Machine Learning Repository, the CDC BRFSS diabetes cohort (2015), NHANES (1999-2014), and the pycox survival datasets (GBSG and METABRIC). The benchmark evaluates both statistical fidelity and task utility by comparing models trained and tested across real and synthetic data. In these settings, synthetic training data preserves most of the downstream signal: on tabular tasks the reference generator CoMed-CTGAN retains a mean AUROC utility (the synthetic-to-real performance ratio) of 90.6%, rising to 97.3% for the strongest generator, CoMed-TVAE. Temporal ICU tasks are harder and more generator-sensitive: CoMed-CTGAN retains 81.6% (AUROC) and only 64.0% under the imbalance-sensitive AUPRC, whereas CoMed-TVAE still retains ~95% (AUROC).
[LG-39] Decentralized Multi-Player Q-Learning in Episodic Markov Decision Processes with Information Asymmetry
链接: https://arxiv.org/abs/2608.12753
作者: Larissa Xu,King Bi,William Chang
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study decentralized multi-player reinforcement learning in episodic tabular Markov decision processes (MDPs) under three forms of information asymmetry: (A) unobserved actions with common rewards, (B) observed actions with independent rewards, and © unobserved actions with independent rewards. Players cannot communicate during learning but may agree on a protocol a priori. For Problems A and B we propose \textttmQ-learning and \textttmQ-learning-intervals, achieving \tildeO(\sqrtH^4 S A_\textjoint, T) regret, where H is the horizon, S the state count, T = KH the total steps, and A_\textjoint = \prod_i=1^M |\mathcalA_i| the joint action space across M players. For Problem C we give \textttmEXC and \textttmEXC-Bellman, two-phase explore-then-commit algorithms with regret \tildeO(H (S A_\textjoint)^1/3 T^2/3) . Against the centralized joint-action benchmark, decentralized learning under information asymmetry matches the single-agent Q-learning rate of \citejin2018q up to logarithmic factors. Because A_\textjoint grows exponentially in M , the bounds are most meaningful for small M or small per-player action sets.
[LG-40] A Cloud-Edge System for Multimodal Clinical Screening in Resource-Constrained Rural Settings ALT
链接: https://arxiv.org/abs/2608.12745
作者: Hei Ting(Una)Chan,Chenwei Wu,Xueshen Liu,Zesen Zhao,Boyuan Zheng,Luis Filipe Nakayama,Michael G. Morley,Liyue Shen,Jiasi Chen,Z. Morley Mao
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: 31 pages, 3 figures. In Proceedings of Machine Learning Research, Volume 340, 2026 (Machine Learning for Healthcare Conference)
Abstract:Medical AI has demonstrated specialist-level diagnostic accuracy, yet these capabilities remain largely inaccessible in resource-constrained rural settings where bandwidth is scarce, compute is limited, and clinical decision-making requires integrating heterogeneous modalities. We introduce a cloud–edge collaborative architecture that addresses these constraints: lightweight, domain-specific models on the edge transform raw medical data into compact structured outputs, while a cloud LLM synthesizes these outputs into clinical summaries. An LLM-based orchestrator dynamically selects diagnostic tools based on patient context, promoting comprehensive modality coverage without processing irrelevant inputs. We evaluate on 20 multimodal clinical cases spanning cardiac, obstetric, trauma, and screening scenarios under three simulated network profiles (500,kbps–5,Mbps). The hybrid system achieves 98–99% diagnostic tool recall with 92–96% precision, matches or exceeds cloud-only baselines on clinical accuracy, and maintains bandwidth-invariant latency (25–35,s) at 4–15x lower token cost. These results highlight the role of architectural design in enabling efficient multimodal integration and improving factual grounding compared to cloud-only approaches under deployment constraints.
[LG-41] MAG: MAnifold Guided Semi-Supervised Multi-modal In-Context Learning
链接: https://arxiv.org/abs/2608.12724
作者: Zirui Cheng,Xun Xu,Tiankai Chen,Fady Rezk,Bowen Zheng,Xiaodong Shi,Shijie Li,Kangkang Lu,Bharadwaj Veeravalli,Nancy F. Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Few-shot in-context learning (ICL) with multi-modal large language models (MLLMs) enables task adaptation without parameter updates, but its performance is highly sensitive to the quality and coverage of the selected demonstrations. While unlabeled multi-modal data is abundant, it remains elusive how to exploit them for ICL. We propose MAG (MAnifold-Guided semi-supervised in-context demonstra- tion selection), an efficient framework that leverages unlabeled data to improve multi-modal ICL. MAG formulates demonstration selection as a semi-supervised propagation problem on a multi-modal graph and adopts a two-stage strategy: (i) relevance score propagation identifies a compact set of high-impact unlabeled samples for pseudo-labeling, reducing MLLM inference cost; (ii) multi-modal relevance is used to select the final demonstrations. We show that textual represen- tations are more effective for relevance propagation, while both visual and textual modalities are crucial for high-quality demonstration selection. Experiments on eight multi-modal benchmarks demonstrate that MAG consistently outperforms strong baselines in label-scarce regimes, achieving significant gains with a limited pseudo-labeling budget.
[LG-42] Federated Compositional Muon Optimizer for Matrix-Wise Models
链接: https://arxiv.org/abs/2608.12710
作者: Wang Yan,Feihu Huang
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 45 pages
Abstract:Muon, a more recently developed optimizer, is useful for matrix-wise models in AI areas. Although many works have studied Muon and its variants, these methods are still not particularly well-suited for hierarchical structured problems. To fill this gap, we propose an effective federated compositional Muon (FedCoMuon) optimizer to solve distributed matrix-wise compositional optimization problems. Specifically, our FedCoMuon optimizer builds on compositional gradient tracking and orthogonalized momentum. Moreover, we propose a variance reduced variant of FedCoMuon (FedCoMuon-VR) based on a momentum-based variance reduced technique. In theory, we analyze the convergence properties of our algorithms under the non-i.i.d. and non-convex settings. In particular, we prove that our FedCoMuon-VR obtains a lower sample complexity of O(\epsilon^-3) for finding an \epsilon -stationary solution than the existing FedMuon algorithms. Extensive numerical experiments on robust federated learning and task-distributed risk-sensitive meta learning show that our proposed methods are competitive with existing compositional baselines and achieve the best reported accuracy in several settings.
[LG-43] A Contract-Grade Verifier for LLM -Generated GPU Kernels and a Native Blackwell Backward for the Gated-Linear-Recurrence Family
链接: https://arxiv.org/abs/2608.12700
作者: Rishi Shah,Rishav Shrestha
类目: Machine Learning (cs.LG); Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: 17 pages, 3 figures. Also archived at doi: https://doi.org/10.5281/zenodo.21563213
Abstract:Systems that generate GPU kernels with language models report high correctness rates. Those rates come from a single loose test: run the kernel on a few random inputs at one fixed shape and accept it if the output is close to a reference. A kernel can pass that test and still be silently wrong. It can return an ordinary number where the true answer is a NaN or an infinity, differ from run to run, break when the shape changes, or accumulate in fp16 where the reference keeps an fp32 total. We build the instrument that checks correctness properly: a contract-grade verifier of twelve adversarial gates, each a property a correct kernel must satisfy, several of them tolerance-free, so no choice of threshold can explain a failure away. Aimed outward, the verifier audits 2,638 machine-generated kernels that a public system’s own harness had already accepted as correct. It finds 39.5% broken beyond any tolerance argument and 62.1% carrying at least one violation. The field’s standard test accepts 1,487 kernels the verifier rejects, against only 14 the other way. We defend the finding four independent ways: a 7/7 positive control, a threshold-calibration sweep, 98.5% agreement with the reference benchmark’s own correctness code, and a stratified hand-audit. Aimed inward, the verifier judges a kernel of our own: the first native Blackwell tcgen05 training backward for the gated-linear-recurrence (GDN) family, including the reverse-state stage the field still runs on a fallback. We establish its correctness independently, against a double-precision oracle, and train five family members through it. The correctness signal behind reported progress in kernel generation is far weaker than the numbers suggest, and a set of tolerance-free contracts would close most of the gap.
[LG-44] he Impact of Temporal Context Length and Encoding Strategies on Self-Supervised ECG Representation Learning
链接: https://arxiv.org/abs/2608.12695
作者: Ahmed Sameh,Ramzi Al-Sharawi,Yogatheesan Varatharajah
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: Accepted at the 48th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2026). 6 pages, 2 figures, 1 table
Abstract:Self-supervised electrocardiogram (ECG) models are often trained on a few seconds of ECG signal and, increasingly, on discretized token sequences. It remains unclear whether these choices sacrifice information needed for rhythm inference and longitudinal consistency in real-world ambulatory recordings. We present a controlled study on the Icentia11k single-lead dataset that varies (i) the input horizon (16 seconds, 1 minute, 5 minutes, and 10 minutes) and (ii) the front-end representation (continuous convolutional patch embeddings vs. fixed vector-quantized tokens), while holding the Transformer backbone and training protocol constant. Representations are assessed by downstream abnormal rhythm detection and by patient-level retrieval that probes cross-session stability. Our results show that increasing temporal context beyond 16-second snapshots yields stronger transfer and higher retrieval accuracy, with the strongest performance achieved by the 5- and 10-minute models, indicating improved capture of slow-varying rhythm dynamics and individual-specific structure. Across all evaluated horizons, continuous patch embeddings outperform discretized tokens, suggesting that quantization can discard clinically relevant waveform detail. These findings motivate ECG foundation models that emphasize extended context and continuous encoders for clinical prediction and similarity-based applications. Our code and pretrained models are publicly available at this https URL.
[LG-45] Finding the Needle in a Haystack: Test-Time Analog Circuit Representation Adaptation for Bayesian Optimization
链接: https://arxiv.org/abs/2608.12687
作者: Fin Amin,Sounak Dutta,Paul D. Franzon
类目: Machine Learning (cs.LG)
*备注:
Abstract:Bayesian optimization (BO) is a sample-efficient framework for analog circuit topology search, where evaluating each candidate topology can require costly simulation. However, representation-based BO methods typically treat circuit embeddings as fixed after encoder training. This creates a mismatch between representation learning and optimization: embeddings learned to encode or reconstruct circuit structure are not necessarily organized according to the figure of merit (FoM) being optimized. This paper introduces Test-Time Analog Representation Adaptation for Bayesian Optimization (TTARO), an online deep-kernel BO framework that adapts circuit representations throughout the search process. Starting from pretrained circuit embeddings, TTARO jointly learns a nonlinear feature transformation and a Gaussian-process surrogate using the FoM labels of the circuits evaluated so far. Following each new evaluation, TTARO updates the representation and surrogate before selecting the next candidate. We compare TTARO with conventional Gaussian Process-based BO over fixed embeddings and with Deep Kernel Learning (DKL), which learns the representation only from the initial evaluated designs and keeps it fixed throughout the remainder of the search. By continually incorporating newly observed FoM labels into representation learning, TTARO aligns the search space with the optimization objective as BO progresses. In our experiments, TTARO reduces regret AUC by 15.2% on average relative to BO and by 20.7% relative to DKL across 40 encoder/kernel/acquisition settings, outperforming prior art in most settings with reductions as large as 46.7%.
[LG-46] raining Under Challenge: Executable Certificates and Challenge-Closed Optimality for Neural Networks
链接: https://arxiv.org/abs/2608.12655
作者: Farhang Yeganegi,Arian Eamaz,Mojtaba Soltanalian
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 82 pages, 24 figures, 10 tables. Ancillary reproducibility materials included
Abstract:A flat training curve does not reveal whether a neural network has reached a global optimum, is locally trapped, is representation-limited, or is mismatched to its trainer. We introduce Training Under Challenge, an executable-certificate framework in which predeclared, architecture-valid procedures construct complete alternatives in the same certified class and reevaluate the same objective. Any lower-valued candidate is a replayable witness that lower-bounds the checkpoint’s empirical global-optimality gap. Passing a finite suite is only suite-relative; global-gap conclusions require a separately justified coverage mechanism. We define a resource-indexed challenge-power modulus that characterizes the largest gap compatible with passage. For squared loss, current block-decrease operators make coverage checkable and yield uniform and realized-residual bounds. We prove the converse frontier: without coverage, a first-order ReLU trainer can reach infinitely many exact conditional head optima while converging to a non-global point. On a channel-gated ResNet-18 distillation problem with known optimum, eight internal challenges cover all 240 audited output directions, and realized-residual bounds lie within factors of 1.74–3.02 of the true gap. Paired predictive certificates separate decoder under-use from representation insufficiency, while quantized-denoising studies demonstrate diagnosis, repair, and current-state recertification.
[LG-47] CAKE: Compiler-Agent Co-Design for Frontier Kernel Evolution
链接: https://arxiv.org/abs/2608.12629
作者: Zihao Ye,Yingyi Huang,Hongyi Jin,Bohan Hou,Junru Shao,Zhongming Yu,Jinqi Chen,Meghan Cowan,Shiyi Cao,Shanli Xing,Hanfeng Chen,Vinod Grover,Tianqi Chen,Luis Ceze
类目: Machine Learning (cs.LG)
*备注:
Abstract:GPU kernel agents and GPU programming languages have advanced separately, leaving expert kernels difficult to reproduce. Agents usually treat the compiler as a fixed black box and receive only errors, correctness outcomes, and timing, while existing DSLs either hide critical scheduling decisions or expose them through difficult layout abstractions. We present CAKE, a compiler-agent co-design in which agents author CAKE IR, a typed, hardware-explicit schedule representation. CAKE exposes warp roles, memory movement, synchronization, and pipelines while supporting verification, cost modeling, and localized diagnostics. The harness itself evolves: recurring failures become verifier rules, IR primitives, model calibrations, and reusable optimization tactics. In matched implementation-hidden Flash-KMeans clean starts on B200, the best CAKE IR candidate at an 80-million-token budget runs at 1.144x the tuned FlashML baseline, compared with 0.928x for direct CUDA/PTX. Beyond this benchmark, agent-generated Kimi Delta Attention achieves a 2.05x geometric-mean speedup over official FlashKDA and passes end-to-end serving validation. Dispatcher-backed KNN and KMeans improve performance by 1.42x to 2.12x across more than 400 shapes, and four kernel changes are available as upstream PRs. CAKE targets NVIDIA GPUs from Ampere through Blackwell and separates single-shape evolution from library generalization and dispatch.
[LG-48] Structure-preserving uncertainty quantification for GENERIC dynamics
链接: https://arxiv.org/abs/2608.12624
作者: Zequn He,Celia Reina
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注:
Abstract:Structure-preserving machine learning embeds physical structure directly into model architectures, yet uncertainty quantification (UQ) for such hard-constrained models remains limited because standard UQ methods may violate the encoded admissibility conditions, require architectural modifications, or impose substantial computational costs. In this work, we propose Structure-Preserving Epistemic Neural Networks (S-PENNs), a general framework for UQ in scientific machine learning models with hard architectural constraints, and instantiate it for GENERIC (General Equation for Non-Equilibrium Reversible-Irreversible Coupling) dynamics. S-PENNs preserve the structural constraints of a pretrained model by attaching lightweight epinets to its constrained components, ensuring that every sampled realization remains physically admissible by construction. When applied to GENERIC dynamics, such a proposed framework yields thermodynamically consistent rollouts that preserve the first and second laws. Furthermore, we combine S-PENNs with split conformal prediction as a post-hoc calibration method to produce prediction intervals with finite-sample marginal coverage guarantees. We validate S-PENNs on three numerical examples: a harmonic oscillator coupled to a heat bath and an idealized chemical motor, both governed by ODEs, and a one-dimensional viscoplastic model governed by PDEs. Across all three examples, S-PENNs produce thermodynamically consistent stochastic realizations and well-calibrated prediction intervals while reducing the computational cost by about one to three orders of magnitude compared to deep ensembles. Although the present study focuses on GENERIC dynamics, S-PENNs can be extended more broadly to scientific machine learning models in computational mechanics with either hard or soft constraints.
[LG-49] he Boolean Power of ReLU
链接: https://arxiv.org/abs/2608.12617
作者: Pablo Barceló,Floris Geerts,Matthias Lanzinger,Klara Pakhomenko,Jan Van den Bussche
类目: Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
*备注: 10 pages, 2 figures, comes with AI declaration
Abstract:We prove that, on finite simple undirected graphs equipped with a single Boolean node feature, the Boolean queries expressible in \Sigma -MPLang, for any collection \Sigma of eventually constant activation functions and with arbitrary real coefficients, form a strict subclass of the Boolean queries expressible in ReLU-MPLang. We thereby settle a recently posed open problem: whether ReLU-MPLang is more powerful than trReLU-MPLang when it comes to Boolean queries. In particular, this implies that ReLU-GNNs are strictly more expressive than TrReLU,id-GNNs with respect to Boolean queries on Boolean-featured graphs.
[LG-50] Drive-to-Music: Context-Aware Generative Audio for In-Vehicle Experiences
链接: https://arxiv.org/abs/2608.12615
作者: Cosmin Dragoiu,Nooshin Nabizadeh
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:
Abstract:In-vehicle music can serve as an adaptive interface to enhance driver experience, attention, and well-being. We present Drive-to-Music, a context-aware system that generates music in real time from multimodal driving signals. Using dashcam imagery and vehicle telemetry, the system extracts scene semantics and driving context, maps them to high-level musical descriptors, and conditions generative audio models to produce contextually aligned soundtracks. The architecture combines perception and generative components to translate visual and kinematic inputs into structured musical attributes and synthesize audio with low latency. It supports smooth transitions as driving conditions evolve, and to ensure robustness and deployment readiness, we incorporate constraint-based controls and safety checks across the generation pipeline. Our results demonstrate the feasibility of real-time, context-aware music generation in automotive settings, providing a foundation for personalized and adaptive in-vehicle audio experiences. Subjects: Sound (cs.SD); Machine Learning (cs.LG) Cite as: arXiv:2608.12615 [cs.SD] (or arXiv:2608.12615v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2608.12615 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-51] Represent Then Generate: Multimodal-Conditioned Time-Series Generation under Irregular Missingness
链接: https://arxiv.org/abs/2608.12592
作者: Haochen Zhang,Jiaheng Guo,Yu-Chao Huang,Nicholas Knoz,Tianlong Chen
类目: Machine Learning (cs.LG)
*备注: 17 pages, 5 figures
Abstract:Continuous physiological time series underpin modern clinical monitoring, yet many of the most informative signals are invasive, expensive, or simply unavailable for a given patient. Conditional generation offers a remedy: an absent signal can be synthesized from co-recorded signals and routine clinical variables. Existing generators, however, are built around a single conditioning modality and degrade when forced to handle the heterogeneous, irregularly missing mix of time-variant signals and static covariates seen in practice. We propose ReCoGen (Represent Conditions, then Generate), a two-stage framework that decouples multimodal condition representation from target generation. Stage I trains one masked autoencoder per modality, distilling each time-variant condition into a compact and missingness-tolerant token sequence. Stage II trains a flow-matching generator that fuses these tokens with static conditions to synthesize the target signal. Across three physiological benchmarks, including continuous glucose monitoring on AI-READI and arterial blood pressure generation on MIMIC-III and MIMIC-IV, ReCoGen attains the best downstream utility on all sixteen (dataset, task, metric) settings, surpassing six representative conditional generators; on thirteen of them its utility also reaches or exceeds the utility measured on the real signal, a reference we read as an approximate anchor rather than a ceiling. Ablations trace the gains to the conditioning path: learnable cross-attention over the frozen per-modality encoders, and a dual token-plus-AdaLN route for the static conditions. ReCoGen thus turns routinely collected signals into informative surrogates for invasive or unavailable ones, a step toward less invasive, lower-cost continuous clinical monitoring.
[LG-52] Prof-K: Probabilistic One-Pass Filtering for Efficient Top-k Selection
链接: https://arxiv.org/abs/2608.12573
作者: Tadeusz Dziarmaga,Witold Sikora,Łukasz Struski,Jacek Tabor,Marcin Mazur
类目: Machine Learning (cs.LG)
*备注:
Abstract:Top-k selection is a fundamental computational primitive with applications spanning databases, information retrieval, signal processing, and modern machine learning workloads, including sparse activations and attention pruning. As data sizes grow, existing approaches become inefficient: exact methods incur high memory and compute overhead, while approximate methods often rely on brittle heuristics that degrade under adversarial or heavy-tailed inputs. In this paper, we introduce Prof-K, a fast, scalable, and distribution-agnostic top-k algorithm with probabilistic correctness guarantees. Prof-K performs a single-pass filtering procedure: a small random sample estimates an adaptive threshold, the N input elements are streamed once into a compact buffer, and an exact top-k routine on this buffer recovers the true top-k elements with probability at least 1 - \epsilon , where \epsilon 0 is user specified. We derive high-probability guarantees for correctness and buffer size, together with an approximately optimal sample size that minimizes overhead as a function of N and k. Empirically, Prof-K achieves 1.5x-10x speedups over the highly optimized PyTorch topk and recent RadiK implementations, with the largest gains in the large-scale, small-to-moderate-k regime where prior methods struggle most. Unlike previous approaches, these guarantees hold independently of the input distribution, ensuring robustness to adversarial settings. By relaxing the recall target (e.g., recovering 95% of the true top-k values), Prof-K additionally provides a principled accuracy-speed trade-off. We further demonstrate its impact on training BatchTopK Sparse Autoencoders (SAEs), where top-k selection constitutes a significant portion of the training cost.
[LG-53] Scaling Automatic Research Agents via World Models
链接: https://arxiv.org/abs/2608.12564
作者: Xiyuan Yang,Sheikh Sarwar,Jingru Cheng,Zhan Shi,Duanshun Li,Huiyuan Chen,Haiyang Zhang,Chenlei Guo,Jingrui He,Zhenyu Liao
类目: Machine Learning (cs.LG)
*备注:
Abstract:Automating empirical research is a long-standing direction of AI. Recent automatic research (AutoResearch) agents bring this goal within reach, as modern LLMs show the capability to independently implement solutions and learn from the execution outcomes. Behind these gains, post-training (especially RL) plays a central role. In this paper, we identify a fundamental tension when scaling RL for these agents: the two components of every AutoResearch trajectory (agent generation and environment execution) scale in very different manners, since all generation shares compute through batching, while each execution occupies its exclusive sandbox and real machine time. As a result, the environment execution dominates the training cost and becomes the bottleneck as trajectories grow. To resolve this tension, we propose World Model RL (WMRL), which replaces environment execution with a world model to remove this bottleneck. Additionally, the world model can be imperfect, as its rewards are corrupted by bias and noise. Therefore, we further equip WMRL with two mitigations, Online Debiasing and Inverse-Variance Denoising, which offset the bias and suppress the noise respectively. Theoretically, we prove that both mitigations of WMRL strictly improve the convergence guarantee. Empirically, WMRL accelerates training by 3-4x on various tasks at different agent scales, while exceeding the performance of standard RL baselines. Moreover, our post-trained 4B and 9B agents outperform much larger open-weight agents of 48B and 120B on held-out benchmarks. Beyond AutoResearch, WMRL also transfers to post-training embodied VLA policies, which demonstrates the generalizability of our method.
[LG-54] GENADA: efficient generative time series adversarial attack framework
链接: https://arxiv.org/abs/2608.12535
作者: Michael Baronov,Denis Vorobev,Margarita Rusanova,Petr Sokerin,Alexey Zaytsev
类目: Machine Learning (cs.LG)
*备注:
Abstract:Deep learning models are widely used for time series analysis in domains such as healthcare, finance, energy systems, and environmental monitoring. However, these models remain vulnerable to adversarial attacks, where small input perturbations cause severe degradation in predictive performance. Commonly used gradient-based attacks, iterative first-order methods, are computationally burdensome, as they repeatedly backpropagate through the victim model to compute input gradients during a number of iterative refinement steps. We propose a GENerative ADversarial Attack (GENADA) that learns a generative model to produce deceptive perturbations directly in a single forward pass and a procedure to train it. Variants include single-step and iterative generative attack schemes. The validation considers attacks on several neural models and datasets in the time-series domain, a controlled, low-dimensional setting. Empirically, GENADA achieves comparable attack quality to strong baselines while requiring less time to generate perturbations during inference.
[LG-55] Exploring Oversmoothing with Householder Matrices
链接: https://arxiv.org/abs/2608.12514
作者: Bhaskar Karol
类目: Machine Learning (cs.LG)
*备注:
Abstract:Deep graph neural networks(GNNs) suffer from oversmoothing- a progressive collapse of node representation towards a low information subspace as network depth increases because the normalized graph propagation operator is repeatedly applied directly to the hidden representations. In this work we study Householder Graph Neural Network (HouseGNN). Rather than updating the hidden state like standard GCN, HouseGNN uses the aggregated neighbourhood message solely to estimate a reflection direction; the node embedding is then updated by a Householder reflector followed by GroupSort, yielding a piecewise orthogonal layer that preserves Euclidean norm at every node and at every depth. We prove three core properties: (i) every internal layer preserves the node-wise Euclidean norm; (ii) the Householder reflector is scale scale and sign-invariant in the message; and (iii) pairwise distance between nodes can change through mismatch between node-wise orthogonal operators.
[LG-56] Fast Length-Squared Sampling for Positive-Semidefinite Matrices
链接: https://arxiv.org/abs/2608.12503
作者: Rajarshi Bhattacharjee,Ethan N. Epperly,Cameron Musco,Aaron Tian
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注:
Abstract:We describe a simple rejection-sampling-based algorithm to perform length-squared sampling on an n \times n positive-semidefinite (psd) matrix: that is, to sample a column with probability proportional to its squared \ell_2 -norm. The algorithm runs in just O(n) expected time, which is significantly sublinear in the input matrix size. The runtime is optimal, even when the input is assumed to be diagonal. Our result has several applications. Length-squared sampling is used by a number of sublinear time algorithms for matrix problems, like low-rank approximation and eigenvalue approximation. Often, it is assumed that the algorithm is given access to the matrix column norms, and thus can perform length-squared sampling efficiently. Our result shows that, at least for psd matrices, we can remove this assumption. We also discuss an application to an asymptotically optimal algorithm for estimating the Frobenius norm of a psd matrix to relative error. Finally, we show that our sampling algorithm yields a very simple sublinear time algorithm for the robust psd low-rank approximation problem introduced by Bakshi et al. (FOCS, 2020), which nearly matches the more complex method developed there. Subjects: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG) Cite as: arXiv:2608.12503 [cs.DS] (or arXiv:2608.12503v1 [cs.DS] for this version) https://doi.org/10.48550/arXiv.2608.12503 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-57] When Can You Trust Offline Evaluation of Equal-Cost Top-k Allocation? A Controlled Reproducible Benchmark and Practitioners Guide
链接: https://arxiv.org/abs/2608.12489
作者: Binshuang Li
类目: Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML)
*备注:
Abstract:Organizations decide whom to treat under a budget and want to know what a targeting rule would have earned before deploying it. Off-policy evaluation promises this from logged data, but the deployable rule is a deterministic top-k policy: it removes all averaging over actions, so weak overlap hits the estimate directly. We benchmark six estimators across five datasets and two known-effect sweeps, and validate the mechanisms against a non-simulated paired reference. First, weak overlap is governed by logger-target action alignment, not by logging sharpness alone: what governs support is the logger’s probability of the target’s actions. Sharpening a logger built from the target’s own score barely moves overlap over the tested range; action-level disagreement collapses it. Effective sample size ranks this risk across logging environments, but is weak at ranking candidates within the single log a practitioner holds, and its cut point does not transfer. Second, the optimizer’s curse is not fixed by cross-fitting the outcome nuisance. When the rule is fit on the data used to evaluate it, cross-fitting the nuisance alone leaves the reuse bias in place and makes it worse. Honest policy-level splitting avoids the reuse by targeting the learning procedure’s value – a change of estimand, not a de-biasing of the full-sample policy. Third, propensity-estimation error is the largest degradation we measure: an out-of-fold estimate hurts IPS more than any other stress we apply, leaves doubly-robust estimation almost unchanged, and can invert the overlap diagnostic itself. Logging is synthesized and propensities floored at 0.02, so every failure occurs with bounded weights; the floor also reduces the two tuned hybrids to their untuned parents, leaving four practically distinct estimators, and all exact-value surfaces are synthetic or semi-synthetic. We release the benchmark; public data only.
[LG-58] Learning Under Treatment-Induced Label Indeterminacy with Expert Annotations of Counterfactual Outcomes: A Case Study in Neurological Prognostication ALT
链接: https://arxiv.org/abs/2608.12477
作者: Xiaobin Shen,Chloe Y.H. Huang,Jonathan Elmer,George H. Chen
类目: Machine Learning (cs.LG)
*备注: Machine Learning for Healthcare (MLHC) 2026
Abstract:Clinical prediction models are often developed as if the outcome of interest were cleanly observed for every patient. This assumption fails when treatment decisions make the clinically relevant outcome permanently unobservable. As a case study of this problem, we consider post-cardiac-arrest neurological prognostication using a cohort of 2,497 patients, including 1,429 patients whose outcomes were rendered indeterminate by treatment decisions. These patients with indeterminate outcomes were reviewed by independent clinical experts, who provided their guesses of counterfactual outcomes about what would have happened to the patients. We refer to these patients as uncertain cases. We also have patients for whom we observe their clinically relevant outcomes; we refer to these patients as certain cases. We propose a framework for evaluating prediction models that explicitly splits the evaluation between certain and uncertain cases. Here, we cannot easily evaluate both types of cases in a uniform manner as the available target labels differ. We then propose a simple prediction model that uses target labels from both certain and uncertain cases in a manner that allows us to trade off between them. Across the proposed neural model and a collection of tabular baselines, models with similar certain-case AUROC can nevertheless differ substantially in both certain-case Brier score and their probability estimates for uncertain cases. Improving alignment with target labels of uncertain cases for our proposed model generally comes at the cost of worse accuracy on certain cases, highlighting an explicit tradeoff that standard evaluation conceals. These results show that when treatment decisions determine whether clinically meaningful outcomes remain observable, conventional evaluation metrics can miss important failure modes in the very patients for whom prognostic support matters most.
[LG-59] Exemplar-based objective classification of gust-induced loads across multiple flight conditions
链接: https://arxiv.org/abs/2608.12448
作者: Paolo Olivucci,Kowshik Srivatsan,David E. Rival
类目: Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an)
*备注: 13 pages, 6 figures
Abstract:Is it possible to find an objective classification criterion that organizes the complexity of gust-induced loads across many flight conditions? And one that remains as interpretable as a labelling based on coarse parameters, such as the flight attitude? Our approach encodes a large number of experimental observations through a machine-learned representation and applies a summarization procedure to select a minimal subset of highly significant exemplars. The exemplars provide a similarity-based objective classification criterion of all the observations, they can be more conveniently inspected by experts and can become subject of more refined experiments. We demonstrate the approach on a database of 3480 pressure-load measurements induced by random gusts on a flying-wing model across six flight attitudes. We find nine fundamental response types that recur across multiple attitudes; analysis of a type’s transient response enables physical intuition into the underlying fluid mechanics.
[LG-60] Non-Degenerate Risk Certification for Automated Security Decisions: A Decision-Contract Theory with ATTCK-Aligned Triage as a Worked Instance
链接: https://arxiv.org/abs/2608.12444
作者: Zhenpeng Li
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 17 pages, 2 figures, 10 tables
Abstract:An unconditional risk bound on automated decisions can be satisfied without automating anything, since a selector that never acts drives the bound to zero. We show this is structural: any risk certificate is defined over a decision contract, the inputs a system acts on plus the semantic relation under which an output counts correct, and weakening either hides base-classifier error. We develop a decision-contract theory: an error-conservation law showing error is only reassigned among harmful automation, human deferral, and semantic masking; a label-free singleton capacity certifying structural incapacity, with a risk-feasible refinement separating recoverable threshold misalignment from risk-constrained incapacity; and a non-degenerate actionability certificate excluding all-abstain solutions by construction. We instantiate this on ATT\CK-aligned alert triage for LLM-based intrusion detection, the setting that exposed the vacuity failure. Across 3 IDS datasets, 6 LLMs, and 4 error-rate thresholds, empirical false-attribution risk stays at or below target in 90.3% of configurations, with 83.4% mean correct automation. The capacity diagnostic explains every low-utility configuration; its refinement separates genuine misalignment from risk-constrained incapacity, confirmed by an exhibited alternative threshold; a training-stability re-run finds no confirmed structural-incapacity instance; and real fine-grained attack-subtype labels confirm the coarsening-transfer identity under a genuine many-to-one map, with small but non-zero masking mass.
[LG-61] Unifying Generative Models with Path Integrals
链接: https://arxiv.org/abs/2608.12438
作者: Ramon Winterhalder
类目: Machine Learning (cs.LG); High Energy Physics - Phenomenology (hep-ph); Machine Learning (stat.ML)
*备注: 51 pages, 4 figures, 4 tables
Abstract:We formulate generative modeling as a path integral in which flow-based, diffusion-based, variational, and adversarial models arise as different evaluation principles for a single master action. Its Martin-Siggia-Rose-Janssen-de~Dominicis (MSRJD) form separates free from interacting probability flows and opens them to diagrammatic perturbation theory. The expansion yields a one-loop correction to deterministic samplers at no stochastic-sampling cost, which we validate on solvable and nonlinear drifts, where it reduces a 53 % tree-level error to 1.6 %. Imperfect learned scores enter as insertions and yield a response-weighted score-matching objective, and symmetry-equivariant drift design becomes an operator expansion with EFT power counting.
[LG-62] MARCH: Scaling Recurrent Memory with Content-Routed State Anchors
链接: https://arxiv.org/abs/2608.12435
作者: Ming Zhang,Kaisen Yang,Shu Yu,Ermo Hua,Ning Ding,Xia Hu,Bowen Zhou,Chaochao Lu,Youbang Sun
类目: Machine Learning (cs.LG)
*备注:
Abstract:Transformers owe much of their strong long-context retrieval capability to a token-level memory that grows with context length. This flexibility, however, incurs a quadratic computation complexity during training and a key–value cache that grows linearly during autoregressive inference. Recurrent alternatives offer efficient decoding by compressing the entire history into a fixed-size state, but often underperform on recall-intensive tasks since earlier associations usually get overwritten by subsequent updates, and only the most recent contextual information is retained. In this paper, we introduce Memory-Anchor Routing across Context History (MARCH), a network architecture that effectively scales state-space models beyond a fixed-size dimension, while maintaining computational efficiency over long-sequences. MARCH periodically caches cumulative recurrent-state checkpoints as state anchors and associates each anchor with a compact, content-conditioned anchor key. This lets MARCH maintain a memory bank, which can grow as context length increases, providing a controllable trade-off between historical resolution and memory cost. At each token, MARCH produces an anchor query to attend all causally available state anchors, and the output is calculated as an attention-style aggregation over all historical anchors along the current state. We show that after standard pretraining, MARCH consistently outperforms multiple linear attention variants across commonsense reasoning, LongBench, and in-context retrieval. These results demonstrate that content-routed state caching substantially strengthens recurrent long-range memory while preserving its native computation path.
[LG-63] Which Site and When: A Free-Satellite-Data Test of Himalayan Glacial Lake Bursts Landslides and Ice Floods
链接: https://arxiv.org/abs/2608.12422
作者: Matthew Kahn,Milan Arjel,Nirmala Adhikari,Mingmar Sherpa,James Pope
类目: Machine Learning (cs.LG); Applications (stat.AP)
*备注:
Abstract:Two free satellite signals carry real information about glacial-lake outburst risk in the Nepal Himalaya: radar interferometry sees a moraine dam slowly sagging, and satellite weather marks the weeks when a primed lake is under stress. A companion feasibility study found that deformation indicates which lake is destabilizing and weather indicates when it is at risk, but proposed no predictive model. To address this gap, we propose and evaluate models that predict which site is susceptible and when a trigger arrives. We test three related hazards on free data alone: large moraine- and ice-dammed bursts, rainfall-triggered landslides, and smaller floods from ponds on and around a glacier. Each hazard gets two questions, never blended. Using 589 dated outbursts from HMAGLOFDB and several thousand catalogued landslides, we match each event against similar but unfailed sites, and hold every model to a strong simple baseline under spatial cross-validation that withholds whole map tiles, so no model succeeds by recognising a trained-on neighbourhood. Antecedent weather times the trigger at ROC 0.73 for big bursts, 0.83 for landslides, and 0.82 for small floods. Terrain ranks susceptibility only in part: scored naively it appears near 0.9, largely because catalogued failures cluster in wetter ranges; matched against comparable nearby sites the honest figures are 0.76, 0.71, and 0.54 (no better than chance). The burst signal holds within single regions, reaching 0.89 in Nepal alone. Five deep-learning models do not decisively beat a simple gradient-boosted baseline. Three score marginally higher on landslides, a hint too small to confirm. For the lake hazards the baseline wins outright, reproduced by a three-rule decision tree on ruggedness and monsoon rainfall. We close with a ranked Nepal watchlist, a prioritisation aid, not a prediction, and note where free data reaches its limits.
[LG-64] LoKiFormer: Locality-aware Attention with Decoupled Knowledge Memory for Efficient Large Language Model Pretraining ICML2026
链接: https://arxiv.org/abs/2608.12419
作者: Qiuwu Chen,Zimo Liu,Yuchen Li,Ying Sun,Yifan Zhang,Zhijie Qiu,Zeng You,Ryan Dong,Simeng Ma,Yaofo Chen,Mingkui Tan
类目: Machine Learning (cs.LG)
*备注: Accepted by ICML 2026
Abstract:Large language models (LLMs) have achieved remarkable breakthroughs across various applications. However, their architectures remain inefficient in pretraining due to two main limitations: (i) self-attention lacks an explicit inductive bias for locality, leading to redundant modeling of sequence-internal local information; (ii) mixture-of-experts (MoE) implicitly couples knowledge storage with computational pathways, hindering flexible access to sequence-external global knowledge. To overcome these limitations, we propose LoKiFormer, a novel LLM architecture that augments the standard decoder with two dedicated modules: 1) Local Fusion Attention (LFA), which incorporates a convolutional fusion to attention, explicitly capturing local patterns and allowing the attention to operate on more informative representations; 2) Knowledge Memory Module (KMM), which introduces a parametric key-value memory that explicitly stores global knowledge in addressable slots, decoupling storage from computation and enabling direct knowledge retrieval. Together, these modules enable LoKiFormer to achieve more efficient and effective integration of information at both levels. Experimental results show that LoKiFormer converges 1.33x faster in pre-training than baseline models, underscoring its superiority over existing LLM architectures.
[LG-65] Regulatory Approval Is Not Enough: Gaps in Trustworthy AI Reporting in FDA-Cleared Medical Devices
链接: https://arxiv.org/abs/2608.12360
作者: Ahmed M Salih,Oliver Díaz,Alejandro Guzman,Noah Marquez Vara,Fotios Avgoustidis,Rituraj Singh,Saman Barakat,Zahra Raisi-Estabragh,Karim Lekadir
类目: Computers and Society (cs.CY); Machine Learning (cs.LG)
*备注:
Abstract:Background: AI/ML-enabled medical devices are increasingly deployed in healthcare under evolving regulatory frameworks. As these systems become more integrated into clinical decision-making, there is growing expectation that they demonstrate key dimensions of trustworthy AI to support clinician, patient, and public trust. Whether publicly available regulatory documentation provides sufficient evidence to independently assess the trustworthiness of cleared AI systems remains unclear. Methods: We analysed FDA AI/ML-enabled medical device summary reports published between 2021 and 2025. Reports underwent automated keyword screening followed by multi-stage manual consensus review to identify documented evidence for the six FUTURE-AI principles: Fairness, Universality, Traceability, Usability, Robustness, and Explainability. Descriptive, temporal, and clinical-domain analyses were performed. Multivariable logistic regression assessed whether year of clearance or clinical domain predicted higher reporting transparency, defined as evidence reported for three or more principles. Results: Of 1,105 FDA summary reports screened, 519 were included. Trustworthy AI reporting was limited and uneven. Nearly one quarter (24.7%) provided no evidence for any principle, and none documented evidence across all six. Robustness was most frequently reported (57.6%), while Traceability (8.3%) and Explainability (3.5%) were the most pronounced gaps. Neither year of clearance (OR 1.02, 95% CI 0.88-1.19) nor clinical domain (OR 0.73, 95% CI 0.46-1.15) predicted higher reporting transparency. Interpretation: Substantial, persistent trustworthy AI reporting gaps exist in FDA documentation. Regulatory approval alone should not be considered a proxy for trustworthiness. Standardised, audit-ready reporting across the AI lifecycle is needed to support independent assessment and responsible adoption of healthcare AI.
[LG-66] RoutePack: Expert Placement and Attention-Aware Data Packing for MoE Reinforcement Learning
链接: https://arxiv.org/abs/2608.12146
作者: Yibo Shen,Xudong Han,Xiaowei Zhu,Gen Li,Zhenxuan Pan
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:Training Mixture-of-Experts (MoE) models for reinforcement learning (RL) couples two load-balancing problems: sequence composition determines dense attention work in each data-parallel microbatch, while token routing determines sparse expert work on expert-parallel ranks. Optimizing either alone can shift the bottleneck to the other. In MoE RL, rollout-time routing replay exposes every sample’s sequence length and layer-wise expert demand before its training step. We present RoutePack, a hierarchical planner that coordinates state-consistent, layer-wise expert rerouting with joint attention- and expert-aware data packing over an optimizer-step window. RoutePack first places experts independently at each MoE layer using aggregate routing demand. It then packs samples into the smallest certified, or best-known feasible, number of token-capped execution rows and optimizes their DP layout with a projected EDP-shard-aware objective. The objective combines a window-normalized linear-quadratic attention proxy with per-layer physical EP-rank peaks and minimizes the accumulated cost of the slowest EDP shard. Parallel population annealing searches fixed-row feasible layouts while preserving sample coverage, capacity, nonempty cells, equal microbatch counts, and communicator topology. State-consistent materialization preserves logical top-k routing and existing MoE kernels without microbatch-level expert replication. Across Ling-3.0-Tiny and Ling-3.0-Flash, expert rerouting improves mean trainer-measured token throughput by 3.80% and 10.50%, while routing-aware packing adds another 4.86% and 3.98%, respectively. Overall, RoutePack improves throughput by 8.85% and 14.89% over the baseline.
[LG-67] Predictive Allostatic Organization in Recurrent and Spiking Agents Under Partial Observability
链接: https://arxiv.org/abs/2608.11506
作者: Frederick Hayes III
类目: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
*备注: 36 pages, 6 figures. Code and reproducibility materials: this https URL
Abstract:Adaptive behavior under partial observability depends on internal organization that carries information beyond the current observation. Drawing on Barrett and Miller’s account of categorization as predictive, compressive, functionally organized, and allostatically constrained, we test whether recurrent and spiking agents develop internal states with corresponding computational properties. Agents operate in an energy-constrained foraging task requiring resource acquisition, threat avoidance, contact-dependent consumption, and regulation of an internal energy variable. In a frozen benchmark, learned agents outperform random and heuristic baselines; the trace-augmented recurrent policy is strongest overall, while spiking variants show stress-specific differences. Early internal dynamics predict later full-safe-efficient success above permutation baseline, reaching a maximum ROC-AUC of 0.802. Reduced PCA subspaces retain behaviorally relevant information. Feature-family controls show that predictive signal is distributed across trace, policy-head, internal-dynamics, observation, and allostatic variables, and low-energy state remains strongly decodable after explicit energy-related features are removed. Evaluation-time perturbations to temporal state, sensory information, operating conditions, and allostatic mechanisms alter behavior and/or internal prediction. Seed-balanced event probes show weaker but measurable information about future contact, successful consumption, and threat events, alongside strong low-energy decoding. We interpret this pattern as a computational analogue of predictive allostatic organization: distributed control regimes that are predictive, energy-sensitive, action-relevant, and partly causally involved, without claiming biological validation or discrete symbolic categories.
[LG-68] Exponential quantum advantage for learning signals with a single qubit
链接: https://arxiv.org/abs/2608.13521
作者: Ishaan Kannan,Sridhar Prabhu,Saeed A. Khan,Mandar M. Sohoni,Xingrui Song,Saswata Roy,Alen Senanian,Valla Fatemi,Peter L. McMahon,Jordan Cotler
类目: Quantum Physics (quant-ph); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: 131 pages, including 7 pages of main text, 4 main figures, and 8 supplementary figures
Abstract:Quantum technology has the potential to transform scientific discovery, but quantum advantages often require processing capabilities well beyond the reach of experimental platforms. We show that coupling a single controllable qubit to an otherwise conventional sensor can exponentially reduce the number of measurements required to learn classical signals. These rigorous quantum advantages apply to fundamental sensing tasks, including learning Fourier coefficients, extracting temporal correlations from time-varying signals, and estimating transformations of physical observables. Using a superconducting cavity–qubit architecture, we experimentally demonstrate 10^7 -fold reductions in the number of measurements required for Fourier-amplitude and time-varying signal learning. Our \textitquantum feature sensing algorithms further enable orders-of-magnitude improvements in simulations of weak-signal dark matter detection and wireless communication applications. These quantum advantages are derived from Quantum Phase-Space Inference (Q \Psi ), a unifying theory of quantum-enhanced experiments that simultaneously converts a set of experimental objectives and constraints into tight lower bounds and optimal quantum-enhanced learning algorithms while producing a certificate of quantum advantage. Q \Psi extends beyond the regimes captured by quantum Fisher information and provides a framework for systematically identifying rigorous quantum advantages in practical experimental tasks. Together, our results establish that near-term quantum technology can exponentially enhance our ability to learn from classical signals.
[LG-69] Bagging Robustly Learns VC Classes with Linear Sample Complexity
链接: https://arxiv.org/abs/2608.13514
作者: Omar Montasser
类目: Machine Learning (stat.ML); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注:
Abstract:We revisit the problem of learning predictors robust to adversarial examples at test-time. We prove that VC classes are adversarially robustly learnable with sample complexity linear in the VC dimension d , providing an exponential improvement over the previous upper bound of Montasser, Hanneke, and Srebro (2019). Remarkably, this result is achieved with a simple improper algorithm that combines the classic heuristic bagging (bootstrap aggregation) of Breiman (1996) with robust empirical risk minimization (RERM). Our algorithm computes RERMs on O(d^\star) independent bootstrap samples and outputs their majority vote, where d^\star denotes the dual VC dimension. We complement this result with a lower bound showing that this is unavoidable: in general, any learner in this oracle model requires \Omega(d^\star) calls to an RERM oracle, even when given arbitrarily many training examples.
[LG-70] On the Structural Limits of Machine Learning Decision Systems: An Information-Theoretic Interaction-Based and Stochastic-Dynamical Perspective
链接: https://arxiv.org/abs/2608.13510
作者: Nestor R. Barraza,Gabriel Pena
类目: atistics Theory (math.ST); Machine Learning (cs.LG)
*备注:
Abstract:Machine learning procedures are commonly evaluated in terms of predictive accuracy and computational efficiency. However, their achievable performance is fundamentally constrained by structural properties of the underlying data-generating process, which are formalized in terms of informational bounds. In this work we examine intrinsic limits of data-driven decision systems from an information-theoretic and interaction-based perspective. We analyze minimal achievable error in classification through Fano-type bounds and precision limits in parametric estimation via the Cramér-Rao inequality, emphasizing that such limits depend on the underlying model rather than on algorithmic sophistication alone. We further discuss how implicit assumptions, such as independence, ergodicity, and distributional stability, affect the validity of inferential procedures. Building on interaction-based modeling principles, we review typical frameworks such as Markov Random Fields and potential based representations for encoding dependence mechanisms. We also describe decision systems, including LLM-integrated agent architectures, as feedback-driven stochastic processes where state-dependent dynamics may induce emergent macroscopic behavior. This perspective highlights the importance of having adequate models for the data as a prerequi- site for expanding predictive capability, and situates algorithmic learning within the informational limits imposed by the models.
[LG-71] Equivariant learning of a transferable three-dimensional classical density functional
链接: https://arxiv.org/abs/2608.13506
作者: Bingqing Cheng
类目: atistical Mechanics (cond-mat.stat-mech); Soft Condensed Matter (cond-mat.soft); Machine Learning (cs.LG); Chemical Physics (physics.chem-ph); Computational Physics (physics.comp-ph)
*备注:
Abstract:Liquids exhibit collective behavior that depends sensitively on thermodynamic conditions, interfaces and confinement, yet predicting each new state commonly requires a separate atomistic simulation. Classical density functional theory offers a reusable variational description, but its central excess free-energy functional is generally unknown, and learned approximations have largely remained restricted to planar or lower-dimensional settings. Here we show that this functional can be learned directly from fully three-dimensional equilibrium density fields while preserving spatial symmetry and variational consistency, without free-energy or chemical-potential labels. A single learned functional transfers across temperatures, system sizes and statistical ensembles, and recovers structure factors, the equation of state, liquid–vapor coexistence and interfacial broadening, none of which are used as training targets. Applied to complex three-dimensional geometries, it predicts the non-monotonic force associated with formation and rupture of a solvent-depleted bridge between colloids and adsorption in an interconnected gyroid pore. These results demonstrate that equilibrium density data can be converted into a transferable thermodynamic generator connecting microscopic liquid structure to response, phase behavior and collective phenomena.
[LG-72] Wasserstein Filtering: A Sample Selection Method for Robust Distribution Learning
链接: https://arxiv.org/abs/2608.13418
作者: Yikai Xu,Zhao Chen,Jian Huang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 48 pages, 3 figures, and 11 tables
Abstract:Given a dataset where a portion of the samples are contaminated, our goal is to recover the underlying clean population distribution. To this end, we propose Wasserstein Filtering (WF), a novel sample selection framework that discards a fraction of suspicious samples and estimates the target distribution using the empirical measure of the remaining data. The core insight is to select a subset of samples whose empirical distribution maximizes its Wasserstein distance to the fully contaminated empirical distribution, thereby preferentially isolating and removing geometrically influential outliers. To render this optimization computationally tractable, we introduce three algorithms: a marginal screening scheme, SinkMarg, and two joint optimization algorithms, SinkWF and SlicedWF, leveraging entropic optimal transport and sliced Wasserstein approximations, respectively. On the theoretical front, we introduce the Far Exclusion and Local Projection (FELP) contamination model, which characterizes corruptions consisting of well-separated outliers and locally indistinguishable perturbations. Under this model, we prove that the WF estimator achieves minimax optimality over distribution families with bounded covariance. Extensive numerical experiments on synthetic datasets, benchmark anomaly detection suites, and robust generative learning with diffusion models demonstrate that WF serves as a highly practical, model-agnostic preprocessing tool. It delivers competitive outlier detection performance and provides substantial downstream benefits for generative modeling under heavy contamination.
[LG-73] Foundation models for movement data: Are they ready for prime-time? ALT
链接: https://arxiv.org/abs/2608.13316
作者: Alexander Bräuer,Benjamin Cauchi,Nils Strodthoff
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: 14 pages, 6 figures, 8 tables, code is available at this https URL
Abstract:Foundation models (FMs) trained on large-scale accelerometer data have been proposed as general-purpose feature extractors for health monitoring, but systematic evidence of their advantages is lacking. We present the first comprehensive evaluation of four open-source accelerometer FMs against supervised baselines covering 19 tasks across the domains of activity recognition including activities of daily living, clinical monitoring, and physiological inference. We find task-dependent performance results: supervised models remain competitive with FMs on human action recognition (HAR), with no consistent advantage for either, while selected FMs lead on fall and stress detection and are the most robust to sensor-placement variation. As frozen feature extractors, FMs are strongest for demographic inference, whereas sleep staging performance remains near chance level for all models. The internal FM representations show strong similarity across layers, highlighting potential for future FM improvements. Linear and frozen probing reveals that UniMTS provides the strongest representations and is the only FM that surpasses the supervised baselines without finetuning. Concept discovery analysis shows all models capture high-intensity activities clearly but struggle with sedentary, complex or ambiguous activities. We provide scenario-based deployment recommendations. Furthermore, we identify FM-derived activity profile inference-moving beyond fixed category classification-as a promising research direction.
[LG-74] Foundations of Independent Component Analysis
链接: https://arxiv.org/abs/2608.13229
作者: Patrick Forré
类目: atistics Theory (math.ST); Machine Learning (cs.LG); Probability (math.PR); Machine Learning (stat.ML)
*备注:
Abstract:We present the mathematical foundations of linear independent component analysis (ICA) models based on standard literature in a self-contained note. It is aimed at readers with a background in measure-theoretic probability theory. We first develop the theory of the characteristic functions of probability measures on \mathbbR^d , including their analyticity and the way in which they determine and characterise the distributions. We then focus on several identifiability results of ICA models with successively strengthened assumptions on the sources: from merely non-constant, to non-Gaussian, to Gaussian-free independent sources. Under the strictest assumptions, we show that the independent sources are identifiable up to translation, permutation, scales and signs, and this even in the presence of additive Gaussian noise. Furthermore, we present the online equivariant gradient descent ICA algorithm for recovering the independent sources from data, in the standard complete noiseless non-Gaussian ICA setting.
[LG-75] Chance-constrained selection of sequential intervention strategies from counterfactual estimates
链接: https://arxiv.org/abs/2608.13209
作者: Minkyoung Kim,Beakcheol Jang
类目: Methodology (stat.ME); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 46 pages, 10 figures, 20 tables. Includes supplementary material as an appendix
Abstract:Many operational decisions are sequences of interventions under a cumulative resource limit, such as a maintenance schedule within a crew-hour budget. Choosing among them calls for the outcome and the cumulative cost each would produce, counterfactual quantities identified from observational data. Two strategies with the same expected cost can exceed the budget at very different rates, so constraining the mean does not bound how often an overrun occurs. Prior two-step architectures, recently extended to continuous doses, constrain the mean cost rather than its tail and allocate at a single decision point. Methods that do bound a cost tail take its distribution from a specified model rather than identifying it from data. We present a predict-then-optimize framework. In the prediction step, any estimator returning an outcome value and a cost distribution supplies what the decision rule consumes, so the predictor is interchangeable. In the optimization step, a chance-constrained selection over a finite candidate set bounds the probability that the cumulative cost exceeds the budget. That tail does not decompose across stages, so each strategy is scored whole. Sweeping the tolerated violation probability traces a safety-utility frontier, and distribution-free finite-sample bounds cover violation and outcome shortfall. Four of five environments, spanning clinical treatment and equipment maintenance, supply exact counterfactual ground truth; the fifth carries real outcomes from a digital-health micro-randomized trial. Across them, the rule holds the budget where a point-estimate rule overruns it, at an outcome cost the frontier makes explicit. All code is available at this https URL
[LG-76] Sinkhorn Linearization and the Spectral Proxy: Unifying the Statistical and Algorithmic Theory of Feature-Parameterized Inverse Optimal Transport via a Single Spectral Sandwich
链接: https://arxiv.org/abs/2608.13201
作者: Han Dong,Jiaming Li,Yongqiang Gong,Ruixi Li,Yin Liu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC); Statistics Theory (math.ST)
*备注: 32 pages, 16 figures
Abstract:We develop the statistical and algorithmic theory of inverse optimal transport (IOT) under the feature-parameterized cost C_theta(i,j) = -theta^T phi(i,j). The core technical contribution is the Sinkhorn linearization – the implicit-function sensitivity of the entropic OT plan to the cost – together with its spectral proxy, a formula that is spectrally exact yet geometrically transparent. The restricted Hessian on the tangent space satisfies the spectral sandwich (pi_min/epsilon) I = H_T^-1 = (pi_max/epsilon) I, yielding the single core bound sigma_min = (pi_min/(a_max epsilon)) sqrt(lambda_min(Sigma)) that drives the entire theory. On this core we establish four theorems and one observation. T1 (identifiability): theta is globally injective on the quotient of the gauge kernel, with dimension bound F = (K-1)^2. T2 (sparsistency): the l1-penalized estimator recovers the true support under irrepresentability and score concentration, with exponential failure probability. T3 (well-posedness): the feature-moment map M(theta) = Phi^T x_theta is strongly monotone, and the inverse is Lipschitz with constant L = epsilon ||Phi^T S_a||_op / (pi_min lambda_min(Sigma)). T4 (convergence): local strong convexity with mu = pi_min^2 lambda_min(Sigma) / epsilon^2 guarantees monotone gradient descent convergence. O5 (misspecification): the estimator converges to the OT-model projection of the truth; the Holder continuity of the projection map is assessed numerically, yielding setting-dependent empirical exponents alpha_eff in (0,1). Comments: 32 pages, 16 figures Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC); Statistics Theory (math.ST) MSC classes: 49Q22, 62F12, 62J07, 90C25 Cite as: arXiv:2608.13201 [stat.ML] (or arXiv:2608.13201v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2608.13201 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-77] High-dimensional networks and mean squared error for possibly misspecified models
链接: https://arxiv.org/abs/2608.13171
作者: Lourens Waldorp
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:To avoid missing important variables and their connections in networks, more and more variables are included in network analysis. Here we show that in a setting with many more parameters than observations (high-dimensional) it is possible to get a conservative (i.e., low false positive rate) estimate of the neighbourhood for each node (which connections are in the network). A neighbourhood is often estimated with a linear model, and this leads to two interesting cases: (i) If the true model is linear, then neighbourhood selection work reasonably well, and (ii) if the true model is nonlinear, then neighbourhood selection requires a penalty for the high dimensions. Here we show the impact of the ridge parameter on the mean squared error, and how this leads to low test variance and hence to neighbourhoods with large numbers of edges. We connect these insights with results from machine learning, where the so-called double descent (when more parameters are included than observations, the mean squared error goes down a second time) has put the traditional view on model selection upside down. Essentially, for adequate neighbourhood selection in models with a large number of parameters, the volume of the model space needs to be included in the penalty. Most neighbourhood selection methods (e.g., Lasso, AIC, BIC) lead to spurious edges (high false positive rate), but we prove that in the high-dimensional setting, minimum description length leads to correct neighbourhood selection or smaller (low false positive rates) in both cases when either the model is correctly or incorrectly assumed linear
[LG-78] Statistical Properties of Robust Learning under Distributional Shifts
链接: https://arxiv.org/abs/2608.13133
作者: Zhiyi Li,Xiaojie Mao,Yunbei Xu,Ruohan Zhan
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Distributional shifts arise when the target deployment environment differs from the source environment that generated the training data. Robust learning frameworks such as Distributionally Robust Optimization (DRO) and Robust Satisficing (RS) aim to address this challenge, yet their finite-sample guarantees under such shifts, and their systematic comparison, remain underexplored: existing analyses typically establish guarantees either in the source environment or for adversarial worst-case performance over an ambiguity set. This paper instead studies generalization error in the target environment—the excess loss under the shifted target distribution. Our contributions are threefold. First, we derive finite-sample generalization error bounds in the shifted target environment for both DRO and RS. These bounds explicitly characterize the trade-off between reduced sensitivity to shift and the regularization penalty induced by each method’s robustness hyperparameter, and they avoid the curse of dimensionality associated with Wasserstein empirical concentration. Second, when partial shift information such as shift magnitude or direction is available, we propose information-directed hyperparameter calibrations and compare the two methods given the same information. Under these calibrations, and in the partial-information regimes we study, DRO and RS exhibit complementary theoretical and empirical behavior. Finally, we apply the framework to a network lot-sizing problem, using it to interpret how robust policies respond to positive shifts in the demand distribution. Together, these results fill a gap in understanding the statistical properties of robust learning methods under distributional shifts and provide a principled basis for comparing DRO and RS.
[LG-79] Online Inference for Quantile Temporal Difference Learning in Distributional Reinforcement Learning
链接: https://arxiv.org/abs/2608.12973
作者: Zijie Cheng,Yang Peng,Zhihua Zhang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:In this paper, we study how to perform statistical inference for quantile temporal difference learning (QTD) in distributional reinforcement learning. Assuming access to a generative model, we first establish functional central limit theorems for both synchronous and asynchronous QTD, which show that the averaged iterates of QTD converge weakly to a rescaled Brownian motion. We next provide online inference methods. Based on random scaling, the inference procedure constructs an asymptotically pivotal statistic for inference by using the information along the whole QTD path. Meanwhile, the proposed statistic can be computed online without storing the entire trajectory of QTD iterates. This substantially reduces the memory requirement and enables efficient statistical inference in distributional reinforcement learning.
[LG-80] Distribution Steering via Sliced Optimal Transport Control
链接: https://arxiv.org/abs/2608.12828
作者: Kaito Ito,Anqi Dong
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Systems and Control (eess.SY); Machine Learning (stat.ML)
*备注: 39 pages
Abstract:Distribution steering seeks feedback laws that drive the state law of a dynamical system between prescribed initial and terminal distributions. Optimal transport provides a natural geometric approach, but its implementation generally requires a transport map or coupling in the full state space. Sliced optimal transport avoids this full-dimensional construction through one-dimensional projections. Yet, the resulting projected maps specify only directional displacements and do not by themselves prescribe a realizable feedback law. To this end, we develop a finite-horizon control framework based on sliced optimal transport. At each sampling instant, a projected optimal transport map defines a directional terminal condition, whose minimum-energy realization yields a randomized single-direction controller. Averaging over projection directions gives a deterministic sliced feedback. For the single-integrator dynamics, the averaged feedback makes the sliced Wasserstein distance to the target non-increasing. For Gaussian endpoint laws, it is affine, preserves Gaussianity, and steers the mean and covariance to their prescribed terminal values. We further identify a law-dependent gain that yields linear decay of the sliced Wasserstein distance together with an explicit characterization of the control energy. We also prove that the randomized controller converges to the averaged sliced flow as the sampling period vanishes. Finally, we extend the construction to linear dynamical systems. Reachability-normalized coordinates allow instantaneous realization of the sliced velocity for uniformly fully actuated systems, while local controllability Gramians provide exact finite-step realization for general controllable systems. Numerical examples illustrate the resulting distributional flows.
[LG-81] Fine-tuned Normalizing Flows for ALICE Zero Degree Calorimeter Fast Simulation
链接: https://arxiv.org/abs/2608.12795
作者: Emilia Majerz,Jacek Otwinowski,Witold Dzwinel,Jacek Kitowski
类目: Instrumentation and Detectors (physics.ins-det); Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex)
*备注: This paper has been accepted for presentation at the 16th International Conference on Parallel Processing Applied Mathematics (PPAM 2026)
Abstract:Simulating the ALICE Zero Degree Calorimeter (ZDC) neutron detector responses at the LHC is computationally expensive, requiring complex Monte Carlo chains. We develop a generative surrogate, focusing on Normalizing Flows (NFs). Through transfer learning, we pre-train on the full imbalanced dataset and fine-tune specialized models for different particle types ( \gamma , n , \Lambda , K_S^0 , \Sigma^+ ) using two gradual-unfreezing schemes. As standard ZDC metrics like Wasserstein distance overlook conditional structure, we introduce refined metrics: conditional weighted MAE, dispersion ratio, and Jaccard co-activation error, that better capture physics-relevant input-output dependencies and response variability. Our ensemble of fine-tuned models achieves a Wasserstein distance of 1.61 \pm 0.02 , outperforming baselines across all metrics. This work provides a generalizable NF-based framework for LHC detector simulation, combining NFs, conditional fine-tuning, and physics-motivated evaluation.
[LG-82] hermodynamics of Learning: A Typed Four-Component Accounting of Memory Fit and Value
链接: https://arxiv.org/abs/2608.12791
作者: Akihito Sudo
类目: atistical Mechanics (cond-mat.stat-mech); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: 36 pages, 3 figures, 5 tables
Abstract:What a finite learning device has recorded and what will hold value for it on future tasks are not the same quantity. We develop a typed accounting for finite-state learning devices that separates four components: a training-side fit functional \Phi_\mathrmfit , the record-correlation stock J_D=I(M;D) , an update-side search ledger \sigma_M , and an operational capital value V(M;T,b) . This value is the work gap between an informed protocol class and a blind class obtained by deleting the memory-read port and re-optimizing from scratch. (I) Separation: for every n , there is a device family on which record correlation and world correlation grow by n\ln 2 while the capital gain is exactly zero. In the \mathrmflat^* regime, data-free updates never increase V . (II) Capitalization ledger: an exact \mathrmflat^* extraction identity and a universal ledger identity give, for (F5 ’ )-stable M -local updates under a no-discarded-record-correlation condition (f), the bound \eta_\mathrmcap\le 1 for the capitalization efficiency \eta_\mathrmcap=\Delta V/(k T,\sigma_M) , together with necessary and sufficient conditions for equality. (III) Value retention: for the retention gap L_\mathrmgen and retention ratio \rho_\mathrmgen (the former carries no sign constraint; the latter is defined for positive training-side value and is not confined to [0,1] ) we give a two-layer alignment domain: an exact exchange rate between value and the side-information-adjusted record fit I(M’;D\mid Y) without any record-side-information independence assumption, and a raw record-stock exchange rate under a joint side-information neutrality condition (M,D)\perp Y , whose boundary is marked by an explicit one-time-pad witness. These are statements about finite-device value retention under task-distribution shift, not a theory of statistical generalization.
[LG-83] Difference-of-Convex Regularization for Graph Learning by Differentiable Programming
链接: https://arxiv.org/abs/2608.12757
作者: Liping Tao,Chee Wei Tan
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:Laplacian-regularized minimization is fundamental in signal processing and machine learning, but is limited by the dense and ill-conditioned nature of the graph Laplacian pseudoinverse. While the Laplacian itself is sparse, its pseudoinverse is dense and often ill-conditioned, rendering direct computation impractical at scale. Moreover, pseudoinverse learning is more challenging than Laplacian learning. To address this challenge, this paper considers the setting where the graph Laplacian is given and proposes a Difference-of-Convex Regularizer (DCR) graph learning framework that approximates the spectral action of the Laplacian pseudoinverse without direct inversion via regularized Maximum Likelihood Estimation (MLE). By reformulating Laplacian-Regularized Nonnegative Least Squares (LR-NNLS) through a dual representation, DCR decouples pseudoinverse learning from instance-specific inference and enables efficient primal solution reconstruction via a differentiable dual-guided learning scheme. We establish theoretical guarantees on stability and the existence of a unique fixed point for DCR algorithm. Numerical experiments demonstrate improved performance over convex solvers and graph filtering baselines and robust performance across diverse graph topologies.
[LG-84] Efficient Hessian-Free Methods for Multi-Objective Bilevel Optimization with Nonconvex Lower Level
链接: https://arxiv.org/abs/2608.12704
作者: Yicong Jiang,Feihu Huang
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注: 48 pages
Abstract:Multi-objective bilevel optimization has wide applications in the AI area such as automated learning and multi-task meta-learning. Although recently some works have been begun to study the multi-objective bilevel optimization, the proposed methods rely on the (strongly) convex lower level problems. In fact, these multi-objective bilevel learning problems are generally nonconvex, and particularly their lower level problems are nonconvex. To fill this gap, we propose a class of Multi-Objective Moreau Envelope based Hessian-free Algorithms (MOMEHA) to solve the multi-objective bilevel learning problems with nonconvex lower level. Specifically, our method uses the Moreau envelope to convert the original problem into a multi-objective single-level optimization with an envelope constraint. In particular, our method retains computational advantages of being single-loop and Hessian-free in the multi-objective setting by incorporating a smooth weighted Tchebycheff scalarization. Furthermore, we propose a momentum-based variant of MOMEHA (i.e., MB-MOMEHA) method to solve the stochastic multi-objective bilevel learning problems. In theory, we provide the convergence properties of our algorithms under both deterministic and stochastic setting. Some experiments on few-shot meta-learning and neural architecture search demonstrate that our methods outperform the existing approaches in Pareto front, validating its effectiveness and robustness.
[LG-85] A Local-Linearly Convergent Algorithm for Nonconvex Equality-Constrained Optimization
链接: https://arxiv.org/abs/2608.12665
作者: Frank E. Curtis,Lingjun Guo,Daniel P. Robinson
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:For solving nonconvex equality-constrained optimization problems, a recent Gradient-Eigenstep Algorithm by Goyens et al.~is an iteration-efficient approach, based on minimizing Fletcher’s augmented Lagrangian function, for finding an approximate second-order stationary point from an arbitrary starting point. In this paper, the analysis of this algorithm is extended, offering a two-fold contribution. First, it is shown that a local-linear rate of convergence can be obtained by this method if it is initiated sufficiently close to a strong second-order stationary point and employs a sufficiently small step-size parameter and sufficiently large penalty parameter. In this case, the algorithm reduces to a gradient descent algorithm applied to minimize Fletcher’s augmented Lagrangian. Second, as a particularly useful application of the first result, it is shown that the Gradient-Eigenstep algorithm can be used as an iteration-efficient subproblem solver in the context of a progressive sampling strategy for solving equality-constrained optimization problems when the objective and constraint functions are defined by large sample averages, ultimately offering an algorithm with an improved worst-case sample complexity when compared to an approach that solves a full-sample problem directly.
[LG-86] Evaluating AlphaEarth Foundations Embeddings for Wildfire Susceptibility Mapping
链接: https://arxiv.org/abs/2608.12663
作者: Yuan Zhuang,Sanaa Hobeichi,Peng Shi,Fei Huang
类目: Applications (stat.AP); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Wildfire susceptibility mapping typically relies on physical variables assembled from multiple remote-sensing, climate, and geospatial products. AlphaEarth Foundations (AEF) provides analysis-ready geospatial embeddings that may reduce this dependence on heavy harmonisation and task-specific feature engineering, but their value for wildfire susceptibility mapping has not been systematically evaluated. Using Victoria, Australia (2017-2025), as a case study, we show that AEF embeddings can reconstruct commonly used variables in wildfire susceptibility analysis with high accuracy. In downstream susceptibility models trained on satellite-derived fire occurrence data, embedding-based susceptibility models achieve ROC-AUC values above 0.92 and consistently identify high wildfire susceptibility across eastern Victoria, particularly Gippsland and the north-eastern uplands, with additional localized hotspots in central and northwestern Victoria. A key feature of AEF embeddings is their strong near-region transferability within climatically similar regions. When embedding-based models trained in Victoria are applied to Canberra and Western Sydney-Blue Mountains, ROC-AUC improves by around 4% at Canberra and declines by around 2% at Western Sydney-Blue Mountains, compared with a mean decrease of approximately 25% for physical-variable models. These findings provide practical guidance for using AEF embeddings and lay a foundation for scalable wildfire susceptibility mapping workflows for downstream users such as government agencies and (re)insurers.
[LG-87] DYSANOS Generative Dynamic Smooth Arbitrag e-free Non-parametric Option Surfaces
链接: https://arxiv.org/abs/2608.12587
作者: Hans Buehler,Blanka Horvath,Anastasis Kratsios
类目: Mathematical Finance (q-fin.MF); Machine Learning (cs.LG)
*备注:
Abstract:This article presents with DYSANOS the first generative market model for smooth SANOS option surfaces for all strikes and expiries which are free of static arbitrage. Our model is designed to generate entire paths of daily spot and option prices for years in the future. We present a robust and useful if somewhat simplistic baseline hidden state generative model in the form of an AR(1) model. We discuss model setup, data pipeline, and training and investigate numerical resence of dynamic arbitrage. We illustrate model performance on Option Metrics’ IvyDB S\P Index data from 2020 to~2025 and compare it to a pure implied-vol PCA model. Subjects: Mathematical Finance (q-fin.MF); Machine Learning (cs.LG) Cite as: arXiv:2608.12587 [q-fin.MF] (or arXiv:2608.12587v1 [q-fin.MF] for this version) https://doi.org/10.48550/arXiv.2608.12587 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-88] AI-Driven Multiscenario Interest Rate Forecasting: A Proof of Concept for Banking Asset Management
链接: https://arxiv.org/abs/2608.12424
作者: Ekkehardt Bauer,Dirk Holländer,Linus Wolff,Christoph Ostermair,Kyrillus Aiad,Joachim Hasebrook
类目: Computational Finance (q-fin.CP); Machine Learning (cs.LG); Statistical Finance (q-fin.ST)
*备注:
Abstract:This study focuses on developing an AI-supported prototype for multiperspective interest rate forecasting that combines classical econometric models with modern artificial intel-ligence methods. Tested in a major European bank, the system enables more precise and flexible prediction of interest rate developments, supporting strategic decision-making in Asset-Liability Management (ALM). It integrates topic modeling, sentiment analysis, econometric forecasting, and market-based analyses within an interactive platform. Leveraging AI to analyze large volumes of financial documents and market data enables the identification of monetary policy trends and sentiment signals at an early stage. The core econometric model is a Bayesian vector autoregression (BVAR) that enables simulation-based scenario analyses to evaluate economic developments from multiple perspectives. The system’s innovation lies in its integration of several forecasting approaches that consolidate previously separate information sources and present them transparently and interpretably. Financial analysts and risk managers thus gain a better basis for making decisions, allowing them to assess interest rate risks more accurately and manage market movements more proactively. While the prototype demonstrates how AI can transform interest rate management in banking, further development is required to optimize real-time data integration and regulatory compliance. Even at this stage, the study shows that multi-perspective, AI-driven forecasting provides substantial added value for banks by increasing transparency, strengthening evidence-based decision-making, and improving risk management.
[LG-89] Evaluation Resolution Confounds Learning-Rule Comparisons in Model-Brain RSA of Early Visual Cortex
链接: https://arxiv.org/abs/2608.12408
作者: Nils Leutenegger
类目: Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG)
*备注: 13 pages, 7 figures
Abstract:Representational similarity analysis (RSA) is increasingly used to ask which learning rules give convolutional networks brain-like representations. Because biologically plausible rules such as feedback alignment, predictive coding and STDP do not scale, studies that include them train small networks on small images (typically 32x32 CIFAR) and then compare them to brain responses modeled at much higher resolution. We find that a common result in this setting, that untrained or locally trained networks rival or beat backpropagation at early visual cortex, depends strongly on the resolution at which the network is evaluated. The V1 gap between an untrained network and a backpropagation-trained one widens from -0.001 +/- 0.007 at the 32px training resolution to +0.044 +/- 0.006 at 224px, growing monotonically across six resolutions (n=5 seeds). It holds in human fMRI and, directionally, in single-seed macaque electrophysiology, along the training trajectory, and for an ImageNet ResNet-50 and a Swin-Tiny transformer trained at 224px. Four candidate mechanisms are tested and none accounts for it: train/eval resolution matching, low-level Gabor and pixel structure, the normalization state of the untrained baseline, and convergence of the pooled descriptor toward a global brightness statistic; three are excluded by interventions holding the convolutional weights bit-identical. A fifth experiment locates the effect: capping image detail at the training resolution while letting the pooled positions grow 12-fold removes about 90% of it, so the dependence is carried by image detail rather than by pooling. Separately, a single scalar luminance value per image reaches rho = 0.075 against the V1 RDM, essentially matching the untrained network’s 0.076, which bounds what this style of comparison can resolve. The one learning effect that holds across resolution is backprop above untrained, at LOC.
[LG-90] Black-Box Knowledge Transfer across Distinct Feature Sets
链接: https://arxiv.org/abs/2608.12403
作者: Oh-Ran Kwon,Daeyoung Ham
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Pre-trained black-box predictive functions encode knowledge distilled from massive datasets and extensive computation. However, when the available input features differ from those the black box expects, direct use is infeasible. We introduce a method for transferring predictive knowledge from the black box to a new, heterogeneous input space. Our approach decomposes the target regression function into a transferable component, which the black box can inform, and a non-transferable component, which captures information unique to the new space. We propose a two-step neural network procedure, estimating the transferable component from abundant unlabeled feature pairs that bridge the two input spaces and the non-transferable component from limited labels. We derive prediction risk bounds that improve on those of a non-transfer alternative when the non-transferable component is small or smooth, and the procedure adapts to either case. Under additional conditions, the worst-case risk of our estimator is of strictly smaller polynomial order than the minimax risk of estimation from the labeled data alone. We extend the framework to multiple black boxes, each on its own input space, and show that aggregation can reduce prediction error relative to the best single black box. Simulated and real data demonstrate the practical value of the method.
[LG-91] A Bayes-Markov Neuromorphic Model of Cortical Orientation Selectivity: A Computational Re-implementation and Quantitative Simulation Study
链接: https://arxiv.org/abs/2608.12388
作者: Abolfazl Moslemi,Milad Sarabadani,Fatemeh Sefidian,Hossein Peyvandi
类目: Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注: 24 pages. Computational neuroscience; orientation selectivity; neuromorphic modeling
Abstract:The emergence of orientation selectivity in the primary visual cortex (V1) remains a central question in computational neuroscience. Shirazi’s Bayes-Markov model proposed a probabilistic explanation for how orientation-selective inhibition can arise from non-oriented lateral geniculate nucleus (LGN) inputs through local inference. In that formulation, the activity pattern of striate cortical inhibitory (SCI) cells is estimated from the LGN activity pattern by a maximum a posteriori (MAP) criterion over a two-layer hierarchical Markov random field, and the resulting inference is implemented through a local parallel relaxation algorithm. We provide a computationally explicit re-implementation and quantitative simulation study of this framework. We reconstruct the mathematical model, describe its fully LGN-driven update rule, and implement a vectorized simulation framework that preserves the original local clique operations while making systematic parameter sweeps feasible. We evaluate the model using orientation tuning curves, an orientation selectivity index (OSI), controlled LGN noise perturbations, contrast tests, and model-variant comparisons. We further add a spiking SCI-layer realization using leaky integrate-and-fire and Hodgkin-Huxley neurons to examine whether the rate-coded SCI field can be expressed through temporally explicit neural activity. The simulations support the central qualitative behavior of the Bayes-Markov framework: sharp orientation selectivity, robustness to moderate LGN noise, and a biologically interpretable proof-of-concept spiking realization of the inferred inhibitory field.
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