本篇博文主要内容为 2026-10-22 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 (2026-10-22)
今日共更新808篇论文,其中:
- 自然语言处理共138篇(Computation and Language (cs.CL))
- 人工智能共214篇(Artificial Intelligence (cs.AI))
- 计算机视觉共138篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共241篇(Machine Learning (cs.LG))
- 多智能体系统共16篇(Multiagent Systems (cs.MA))
- 信息检索共25篇(Information Retrieval (cs.IR))
- 人机交互共18篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] Multimodal Embodied Agents : A Unified Capability-centric Perspective from Computer-Use to Robot-Use
【速读】：该论文旨在解决多模态代理（Multimodal Agents, MMAs）从计算机使用场景向机器人应用迁移时所面临的核心挑战，即如何在物理世界中实现可靠、可验证的决策与行动。其关键在于提出一个以功能为中心的统一框架——PAPAV（Perceive, Anticipate, Plan, Act, Verify），将多模态具身代理（Multimodal Embodied Agents, MMEAs）的能力解耦为五个核心功能：感知当前状态、预测动作后果、规划可行路径、通过接口或身体执行动作以及验证结果。该框架突破了传统架构依赖的局限，强调功能本质而非具体实现方式，从而能够系统性地比较MMEAs、MMAs与机器人系统（Robotic Systems, RSs）之间的差异。研究指出，物理约束（如部分可观测性、不可验证结果等）显著减少了预先可确定的选择空间，降低了错误修正的可能性，而现有评估体系过度聚焦于“执行”（Act）环节，仅有少数任务评估“预测”（Anticipate）和“验证”（Verify）能力，导致后两者在任务成功指标下被隐匿。因此，PAPAV的核心贡献在于提供了一个跨模态、跨系统的统一评估基准，推动对五项核心能力的显式建模与测量，超越单一任务成功率，为设计高可靠性物理代理提供了理论基础与实践路径。
链接: https://arxiv.org/abs/2610.30108
作者: Yanzhe Chen,Ziyi Yang,Jifeng Zhu,Qiming Huang,Ruihe An,Peiyao Xu,Hesen Yang,Runda Liu,Chang Gong,Zhijun Cao,Zechen Bai,Wenzheng Zeng,Yiqi Lin,Guoqiang Liang,Kevin Yuchen Ma,Kevin Qinghong Lin,Mike Zheng Shou
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Multiagent Systems (cs.MA); Multimedia (cs.MM)
备注: Project website: this https URL
Abstract:Multimodal agents (MMAs) sustain interaction through reasoning, memory, tools, and feedback, most visibly as computer-use agents, while robotic systems (RSs) couple sensing and actuation under physical dynamics. We define multimodal embodied agents (MMEAs) as goal-directed systems that couple multimodal task reasoning with physical action and revise decisions from the resulting feedback. This raises a central question: what changes when a multimodal agent moves from computer-use to robot-use? We introduce PAPAV, a unified capability-centric framework of five recurring functions: Perceive the current state, Anticipate action effects, Plan a feasible course, Act through an interface or body, and Verify the outcome. Defined by function rather than architecture, these capabilities let PAPAV identify what all three share and then compare MMEAs with MMAs and with RSs. Five physical constraints, from partial observability to unverifiable outcomes, leave fewer choices fixed in advance and less room to reverse errors. Across 64 benchmarks, explicit evaluation centers on Act; only 5 assess Anticipate and 3 assess Verify, leaving both largely hidden behind task success. These constraints also frame the open challenges we identify. PAPAV therefore provides a common basis for designing reliable physical agents and measuring progress across all five capabilities rather than task success alone.
[MA-1] Implementation of Dynamic Microsegmentation for a Multi-Robot Network
【速读】：该论文旨在解决物联网（Internet of Things, IoT）网络中，尤其是多机器人网络环境下，传统静态安全防护机制难以应对动态威胁与复杂拓扑变化的问题。其核心挑战在于如何在保障系统正常运行的同时，实现对潜在威胁的实时隔离与响应。解决方案的关键在于采用商用软件定义微分段（software-defined microsegmentation）工具，构建细粒度、动态可调的微分段策略，依据设备的功能属性和健康状态标签（asset functional and health tags）自动调整网络隔离规则，并通过自动化机制实现策略的实时部署。实验表明，该方案能够在不干扰正常业务的前提下，实时自动隔离指定威胁，验证了微分段技术在IoT网络中实现高韧性安全防护的可行性与有效性。
链接: https://arxiv.org/abs/2610.30102
作者: Vishwam Raval,Jaewon Kim,Ravi Pattabhi,Sandip Roy
机构: Global Cyber Research Institute (全球网络研究学院); Texas AM University (德州农工大学); ColorTokens Inc (ColorTokens公司)
类目: ystems and Control (eess.SY); Multiagent Systems (cs.MA); Networking and Internet Architecture (cs.NI); Robotics (cs.RO)
备注:
Abstract:This report describes a laboratory instantiation of a dynamic microsegmentation function for an Internet-of-Things network, specifically a multi-robot network, using a commercial software-defined microsegmentation tool. Fine-grained dynamic segmentation policies are defined, where segments are adapted based on asset functional and health tags. Automated implementation of the dynamic policies in software is also undertaken. The microsegmentation solution is shown to automatically isolate an indicated threat in real time, while maintaining unaffected operations. The demonstration indicates that microsegmentation can be implemented in IoT networks, in a way that allows for robust operation in the face of threats and disruptions.
[MA-2] Parallel AI Agent Collaboration as Graph-Structured Distributed Block Optimization
【速读】：该论文旨在解决多智能体并行协作中局部优化导致全局解质量下降的协调问题，即多个独立智能体在各自任务上产生有效局部改进（local edits）时，若直接合并其结果，可能因冲突而破坏共享解决方案的整体性能。其核心挑战在于如何在保持局部改进有效性的同时，实现安全且高效的全局融合。解决方案的关键在于将该问题建模为具有精确可评估目标函数与已知依赖图（dependency graph）的离散块优化问题，并引入**独立集（independent set）**来识别兼容的局部修改，这些修改在相同当前解下的验证增益可精确相加。通过设计一个协调者（coordinator）机制，不仅能够构造安全的兼容候选方案，还可评估交互性修改以捕捉有利协同效应。理论证明表明，最优分数着色（fractional coloring）所对应的合并策略能保证保留至少 1/χf(G) 倍的总验证局部改进量，其中 χf(G) 为依赖图的分数染色数，该下界在仅从原提议中选择子集的合并策略中是紧的。在局部改进满足条件性保障及块误差有界的前提下，重复协作可实现目标差距的几何收敛。收敛速率分离了任务结构、智能体分配与计算成本，从而导出用于智能体分配的线性规划模型以及明确的时间-成本权衡关系。实验验证表明，基于验证的合并机制能有效化解个体有益但相互冲突的代码修复修改，避免简单拼接失败；在无冲突的大规模任务中，并行智能体显著降低建模完成时间，尽管需更高的输入令牌等价成本；在三个公开道路网络上，分数集成方法在90次配对运行中持续保留922单位经验证的最小割改进；对于共享车辆路径的路径搜索智能体，分数调度在12次配对运行中使测量墙时长中位数减少15%，同时路径成本变化符合预期波动。
链接: https://arxiv.org/abs/2610.30074
作者: Hanmin Li,Yury Demidovich,Peter Richtárik
机构: King Abdullah University of Science and Technology (阿卜杜拉国王科技大学)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注: 24 pages, 8 figures
Abstract:Parallel AI agents can produce useful local edits yet degrade a shared solution when their edits are combined. We model this coordination problem as discrete block optimization with an exactly evaluable objective and a known dependency graph. Independent sets certify compatible edits whose verified gains, measured against the same current solution, add exactly. A coordinator constructs a safe compatible candidate and can also evaluate interacting edits to capture favorable interactions. We prove that an optimal fractional coloring yields a merger that preserves a 1/\chi_f(G) fraction of the total verified local improvement, where \chi_f(G) is the graph’s fractional chromatic number. This guarantee is tight among mergers that select a subset of the same proposals. Under conditional local-improvement guarantees and a block error-bound condition, repeated collaboration converges geometrically in the objective gap. The rate separates task structure, agent allocation, and computation cost, yielding a linear program for agent assignment and an explicit time-cost trade-off. Code-repair experiments show that verified merging resolves conflicts between individually useful edits that cause naive merging to fail. On larger conflict-free tasks, parallel agents reduce modeled completion time at higher input-token-equivalent cost. On three public road networks, fractional integration retains 922 units of verified graph-cut improvement across 90 paired runs. For route-search agents sharing exclusive access to vehicle routes, fractional scheduling reduces measured wall time by a median of 15% across twelve paired runs, with route-cost changes varying across runs.
[MA-3] oM2Vec: Continuous Theory-of-Mind Tokens for Multi-Agent Coordination
【速读】：该论文旨在解决大语言模型（LLM）代理在多智能体协作中难以准确构建心智理论（Theory-of-Mind, ToM）的问题，尤其是在文本表示空间中难以有效表征多个潜在的伙伴状态，导致推理能力受限。其核心挑战在于如何在不确定性环境下对合作方的信念、意图和行为进行建模。为此，论文提出ToM2Vec，通过在高维隐空间中使用固定数量的连续推理令牌（reasoning tokens）来学习抽象的心智理论。训练阶段，这些令牌通过监督学习与真实伙伴思想相匹配；测试阶段，各智能体生成自身的ToM令牌，并作为条件信号调控自身的思想与行为。实验在多个环境（如Overcooked-AI、多方谈判、Decrypto、Hanabi及Collab Escape/Capture）中验证了该方法的有效性，结果表明ToM2Vec显著优于基线模型及多种竞争性微调与提示策略。研究进一步证明，隐式推理机制至关重要——当推理以自然语言形式表达时性能明显下降。此外，该方法展现出良好的泛化能力，可在未见过的伙伴模型家族（包括专有模型GPT-5.4）上有效工作。分析还证实，隐空间中的令牌编码的是心智理论信息，而非任务特定特征，具体表现为能显著提升智能体对合作方信念、欲望及下一步动作的预测准确性。
链接: https://arxiv.org/abs/2610.29934
作者: Nithin Sivakumaran,Ali Payani,Hyunji Lee,Mohit Bansal,Elias Stengel-Eskin
机构: UNC Chapel Hill(北卡罗来纳大学教堂山分校); Cisco(思科); The University of Texas at Austin(德克萨斯大学奥斯汀分校)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注: Code: this https URL
Abstract:Forming an accurate theory-of-mind (ToM) is key to successful multi-agent collaboration and requires reasoning under uncertainty to capture multiple possible partner states. This is hard to do in the text representation space native to large language model (LLM) agents, which more easily describe a single partner state. To address this challenge, we introduce ToM2Vec, which learns to form abstract theories-of-mind in a rich latent space via a fixed budget of continuous reasoning tokens. During training, supervision for these tokens comes from the partner’s actual thoughts, which the ToM tokens are trained to abstract and match. At test-time, ToM tokens are generated by each agent and act as conditioners for that agent’s thoughts and actions. Across several environments covering collaborative, competitive, and mixed-motive settings (Overcooked-AI, multiparty negotiation, Decrypto, Hanabi, and Collab Escape/Capture), we evaluate ToM2Vec’s ability to support multi-agent interactions and collaboration, finding consistent improvements over the base model and a variety of competitive trained and prompted baselines. We show that latent reasoning is critical, with performance dropping when reasoning is expressed in text. Moreover, we show that ToM2Vec training generalizes to partner model families not seen at train-time, including a proprietary model (GPT-5.4). In our analysis, we provide evidence that the latent tokens encode a ToM, rather than task-specific information: ToM2Vec’s tokens improve the agent’s ability to directly answer questions about partner beliefs, desires, and next actions.
[MA-4] Conflicted Records: Typed Adversarial Contracts for Auditable LLM Output Quality Enforcement
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）生成记录在生产信息系统中因混合真实事实与幻觉、偏见等质量问题而难以通过传统数据清洗手段有效识别和修复的挑战。现有方法如提示工程、思维链（Chain-of-Thought）及基于人类反馈的强化学习（RLHF）虽能降低表面错误率，但缺乏可审计性，且无法区分真正修复与对错误内容的掩盖式压制。其核心解决方案是提出BiasGuard-D：一种基于多智能体对抗性协作的结构化验证框架，由来自不同模型家族的两个对立角色交换带类型标注的主张文档，三模型共识机制以多数投票裁定结果，并结合参考性检查与语义不变性双重验证来确认裁决。其中，语义不变性组件专门捕捉参考性检查遗漏的缺陷；若移除迭代推理环路，不变性满足率显著下降。实验表明，仅通过角色框架调整，该系统即可实现从刻板印象到幻觉修复的自然转移，在HaluEval问答任务上使正确答案恢复率提升49个百分点；同时，为维持客观幻觉检测的召回率，需引入跨家族第二审计者作为分诊门控。所有主张、投票与指令均以类型化记录形式存档，形成符合新兴人工智能监管要求的可追溯证明链。
链接: https://arxiv.org/abs/2610.29669
作者: Kardelen Subasi,Jarosław A. Chudziak
机构: Warsaw University of Technology (华沙理工大学)
类目: Databases (cs.DB); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注: Accepted at the International Conference on Cooperative Information Systems (CoopIS 2026)
Abstract:LLM-generated records increasingly populate production information systems where they are treated as data assets subject to the same quality expectations as records from operational databases. A single such record can simultaneously satisfy and violate a quality rule: a grounded fact adjacent to a hallucination, a useful answer adjacent to a stereotype. Classical data cleaning cannot resolve this because the two judgments are incompatible over the same span; a single LLM judge cannot either, since it shares the training biases that produced the conflict. Existing interventions such as prompt engineering, chain-of-thought, and RLHF reduce surface error rates but provide no audit trail and cannot distinguish genuine remediation from blanket suppression. The question is whether a deterministic check over structured inter-agent records can catch the defects that referential checks alone miss. We present BiasGuard-D: two adversarial roles drawn from distinct model families exchange typed claim documents, a three-model quorum adjudicates by majority vote, and a combined referential-plus-semantic invariant validates the verdict. The semantic component fires on verdicts referential checks miss; removing the iterative loop drops invariant satisfaction sharply. The deliberation layer transfers from stereotype to hallucination remediation on HaluEval QA with only role-framing changes, raising right-answer recovery by 49 percentage points; the triage gate required a cross-family second auditor to maintain recall on objective hallucination. Every claim, vote, and instruction is stored as a typed record giving operators a provenance trail auditable under emerging AI regulations.
[MA-5] OACap: Caption-Guided Active Observation for Detailed Video Captioning
【速读】：该论文旨在解决开放性视频详细描述（open-ended detailed captioning）中缺乏对感知目标明确指定的问题，即模型需自主判断应获取哪些证据并将其整合为连贯的全局描述。其核心挑战在于如何在信息缺失或不充分时主动生成针对性的观察请求，并通过时空工具获取证据，进而迭代验证与修正生成的描述。解决方案的关键是提出一种以描述为中心的自适应观测代理——观察自适应视频描述生成器（OACap），该模型能够将未支持或缺失的信息转化为目标导向的观测请求，利用时空分析工具主动获取视觉证据，并通过多轮迭代实现对描述内容的持续优化。此外，为更精准评估模型所保留信息的质量，研究引入了事实-语义视频描述基准测试（FSCap-Bench），其中核心支持事实准确率（CSFS）结合了断言精确性与重要性感知的事实覆盖率，能有效响应四种受控扰动；跨模态语义往返评分（CSRS）则通过视频-描述-视频-描述的闭环匹配原子事实，衡量语义保真度。二者共同建立了下游文本记忆效用与事实准确性、语义完整性之间的可量化关联。
链接: https://arxiv.org/abs/2610.29327
作者: Hao Shi,Hong Yan,Xiaoyang Zhang,Qing Cui,Chao Huang
机构: Ant Group; Zhejiang University(浙江大学); Peking University(北京大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Multiagent Systems (cs.MA); Multimedia (cs.MM)
备注: 14 pages, 4 figures, 4 tables. Technical report
Abstract:Unlike question answering, where a query specifies a perceptual target, open-ended detailed captioning must determine what evidence to acquire and how to integrate it into a coherent global description. We propose Observation-Adaptive Video Captioner (OACap), a caption-centered agent that turns unsupported or missing information into targeted observation requests, uses spatiotemporal tools to acquire evidence, and iteratively verifies and revises the caption. OACap captions consistently improve performance across general video-understanding benchmarks using a fixed text-only reader. To better diagnose the information they preserve, we introduce Factual-Semantic Captioning Bench (FSCap-Bench). Its Core-Support Factuality Score (CSFS) combines claim precision with importance-aware fact coverage and responds as expected to four controlled perturbation types on 93.0-98.3% of valid caption pairs. Its Cross-Modal Semantic Round-Trip Score (CSRS) measures semantic preservation through a video-caption-video-caption round trip by matching atomic facts from source and recovered captions. Together, they connect downstream textual-memory utility with factual and semantic preservation.
[MA-6] When Are We Near Nash? Certifying Solutions in General-Sum Games
【速读】：该论文旨在解决在非零和广义扩展形式博弈（general-sum extensive-form games）中，如何无需额外计算最优回应（best response）即可对启发式求解器产生的迭代策略序列进行在线验证的问题。其核心挑战在于：求解器虽能维护后悔累积量（regret accumulators）与终局分布累积量（terminal-distribution accumulators），但难以直接评估当前策略的纳什差距（NashGap）。论文提出的关键解决方案是构建一种“零最优回应”（zero-best-response）证书机制，该机制利用当前策略的加权平均形式与后悔累积量之间的理论关系，在两玩家博弈中将纳什差距分解为加权外部后悔与一个带符号的价值修正项。通过将后悔值由反事实后悔累积量（counterfactual-regret accumulator）进行上界估计，该证书可在线生成，每检查点开销仅线性于终局历史数，并且在交替更新及三名以上玩家博弈中仍保持有效性。其关键创新在于引入一个单次逆向传递（backward pass）完成的比较器项（comparator term），用于捕捉交替更新带来的偏差，从而实现无需最佳回应即可获取严格纳什差距上界的验证。实验表明，采用一组无训练的后悔匹配求解器（training-free regret-matching solvers）并选取最小证书的迭代解，可在多个基准测试集上以高精度（最高达92%）成功验证纳什近似解，且其精确比较器改进版本可对所有求解实例实现完全认证。
链接: https://arxiv.org/abs/2610.29300
作者: Boning Li,Longbo Huang
机构: Institute for Interdisciplinary Information Sciences, Tsinghua University(清华大学交叉信息研究院)
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 35 pages, 5 figures
Abstract:Heuristic solvers for general-sum extensive-form games produce a stream of iterates, and practice reports the NashGap of the returned profile. We ask what a solver can know about its own iterates from the regret accumulators and the terminal-distribution accumulators its updates already maintain. In two-player games the NashGap of a weighted average strategy equals, at the maximizing player, the weighted external regret plus a signed value correction. Bounding the regret by the running counterfactual-regret accumulator gives an online certificate whose cost per checkpoint is linear in the number of terminal histories. The certificate stays sound under alternating updates and in games with three or more players. The mismatch between what alternation evaluates and what the averages record enters as one signed comparator term, maximized in a single backward pass per player whose comparator weight is nonzero. The bound is read off the regret state, with no best response against the average. Run a portfolio of training-free regret-matching solvers, output the iterate with the smallest certificate, and verify that iterate once. On the benchmark’s biased Leduc family a pool of sixteen training-free solvers, seven original and nine recent variants, solves 89% of the 64 instances at threshold 10^-5 this way, matching the trajectory-minimum union of the solvers. The same protocol solves 92% of the 64 pre-registered held-out instances and 89% again on a third set of 64 instances that no earlier stage used. The zero-best-response certificate certifies all 57 in-sample solves and all 57 fresh ones, and 58 of the 59 held-out ones. Its exact-comparator refinement certifies every solve in the three sets. The certificate states both and attributes its bound. Code is available at this https URL.
[MA-7] Credit Where Credit Is Due: Contrastive Attribution Topology Search for Multi-Agent Systems
【速读】：该论文旨在解决大型语言模型多智能体系统（LLM multi-agent system, MAS）中智能体拓扑结构选择的难题，即如何在复杂且异质的智能体集合中识别出高效、可靠且低成本的协作团队。现有方法通常依赖单一任务得分进行启发式搜索，但无法揭示各智能体对整体性能的实际贡献或负面影响，导致难以优化拓扑结构并可能引入有害行为。其解决方案的关键在于提出对比归因拓扑搜索（Contrastive Attribution Topology Search, CATS），通过一个基于大模型的归因器（LLM attributer）对比不同候选拓扑的执行轨迹，量化每个智能体的贡献度（即“信用”）。基于这些信用评分，CATS动态调整超图（supergraph）中的边权重，并逐步剪枝低效边与表现差的智能体，最终生成紧凑高效的部署团队。实验表明，相较于仅依赖任务得分的启发式搜索，CATS在BigCodeBench上提升了5.3个百分点的平均准确率，同时减少54%的部署令牌消耗；在MBPP上达到最强基线性能，且用更少的计算资源完成任务；在HotpotQA对抗性提示攻击测试中，三次运行均成功排除所有恶意诱导的智能体，而随机搜索仍保留部分有害智能体。因此，基于个体信用的归因机制不仅实现了团队性能优化，还具备识别并剔除潜在威胁智能体的能力，显著增强了系统的鲁棒性与可解释性。
链接: https://arxiv.org/abs/2610.29292
作者: Fabian Tritthart,Tom Zehle
机构: University of Freiburg(弗莱堡大学)
类目: Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注:
Abstract:An LLM multi-agent system (MAS) is only as good as the agents it chooses to trust: its accuracy and cost hinge on its topology, meaning which agents are included and how they communicate. A single task score, however, does not reveal which agents help or hinder performance. We propose Contrastive Attribution Topology Search (CATS), which searches for compact teams within a fixed supergraph of specialized agents. An LLM attributer compares execution traces across candidate topologies and assigns credit to individual agents. CATS uses these credits to update supergraph edges while progressively pruning weak edges and agents, yielding a small team for deployment. With small open-weight models and matched candidate evaluations, CATS improves mean accuracy on BigCodeBench by 5.3 percentage points over score-only heuristic search while using 54% fewer deployment tokens. On MBPP, CATS matches the strongest baselines with fewer tokens than the other searches. In a HotpotQA prompt-poisoning stress test, CATS excludes all adversarially prompted agents in all three runs, whereas random search retains some at comparable accuracy. Per-agent credit thus supports both team optimization and the pruning of potentially harmful agents.
[MA-8] Design and Tuning of Multi-Agent Systems Using Bayesian Evolutionary Optimization
【速读】：该论文旨在解决多智能体系统（Multi-Agent System, MAS）设计中存在的人工干预成本高、拓扑结构优化效率低的问题，尤其在评估代价高昂的场景下如何高效探索最优架构。其解决方案的关键在于提出一种贝叶斯进化优化框架，通过结合进化算子生成多样化的工作流图，并利用贝叶斯优化中的图感知高斯过程（graph-aware Gaussian Process）与期望改进（Expected Improvement）准则，实现对高价值候选解的精准筛选。相较于基于大语言模型（LLM）的候选选择方法，该框架提供了明确且可解释的选择标准，显著提升了在昂贵评估预算下的搜索效率与性能表现。实验结果表明，在BigCodeBench代码生成任务和基于TabPFN的表格预处理基准上，所演化出的MAS架构均达到领先性能，验证了贝叶斯选择机制在引导进化搜索中的有效性。
链接: https://arxiv.org/abs/2610.29276
作者: Dominik Jehle,Tom Zehle
机构: University of Freiburg(弗莱堡大学); Freiburg im Breisgau, Germany(弗莱堡, 德国)
类目: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:Designing effective multi-agent systems (MAS) remains a challenging and resource-intensive task. We propose a Bayesian Evolutionary Optimization framework for automatically discovering task-specific MAS topologies. The approach combines evolutionary operators to generate diverse workflow graphs with Bayesian optimization to focus cost intensive evaluations on promising candidates. In contrast to LLM-based candidate selection, our approach uses a graph-aware Gaussian Process and Expected Improvement to provide an explicit selection criterion during evolutionary search. We evaluate the method on BigCodeBench for code generation and on a tabular preprocessing benchmark using TabPFN. On BigCodeBench, evolved architectures achieve the highest overall performance among the evaluated agent systems. On temporal tabular datasets, optimized MAS preprocessing improves over the default TabPFN model on several tasks, while remaining competitive on IID datasets. Overall, the results demonstrate the potential of Bayesian selection for guiding evolutionary MAS search under expensive evaluation budgets.
[MA-9] MA-UITD: Offline Multi-Agent Reward Inference for Characterizing Continuous Social Behavior
【速读】：该论文旨在解决多智能体逆强化学习（multi-agent inverse reinforcement learning）中从联合演示数据中推断个体化奖励函数的难题，尤其针对交互行为既受自身偏好又受对他人行为预期影响这一核心挑战。现有方法通常依赖环境交互、重复策略优化作为内循环，或对环境动态和动作空间施加严格假设，难以适用于固定行为数据集。本文提出一种离线方法——多智能体单边逆时差学习（MA-UITD），其关键在于通过信念条件化的继承特征（belief-conditioned successor features）表征每个智能体自身行为的长期后果，并对其他智能体的行为进行平均建模，从而解耦个体偏好与社会预期。通过共享奖励特征与智能体特异性偏好向量将单边价值映射至目标奖励函数，结合逆时差一致性约束与噪声对比行为学习机制，实现无需已知环境动态或内层策略优化循环的奖励推断。在模拟的合作、竞争及一般和任务中，MA-UITD在离散动作场景下优于基线方法，并在连续动作场景中成功推断出符合行为表现的奖励结构；应用于实验采集的社会觅食轨迹时，成功揭示了领导者与追随者在空间位置与互动依赖性上的差异化奖励结构，为理解社会行为背后的神经计算机制提供了可量化的潜在目标表征。
链接: https://arxiv.org/abs/2610.29237
作者: Zeyuan Zhang,Yiyun Yang,Yuan Cheng,Herbert Zheng Wu,Yusi Chen
机构: 未知
类目: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:Multi-agent inverse reinforcement learning offers a powerful framework not only for imitation learning but also for understanding the neural computations supporting social interactions and decision-making. Inferring rewards from interacting agents is challenging because observed choices reflect both personal preferences and expectations about others. Existing methods often require additional environment interaction, repeated policy optimization as an inner loop, or restrictive assumptions about environment dynamics and action spaces, limiting their applicability to fixed behavioral datasets. We introduce Multi-Agent Unilateral Inverse Temporal Difference Learning (MA-UITD), an offline method for inferring explicit agent-specific rewards from joint demonstrations in continuous-state environments with either discrete or bounded continuous actions. Its core idea is to represent the long-term consequences of each agent’s own actions through belief-conditioned successor features that average over other agents’ behavior. Shared reward features and agent-specific preference vectors connect these unilateral values to the target reward function, while inverse temporal-difference consistency and noise-contrastive behavioral learning enable inference without known dynamics or an inner policy-optimization loop. Across simulated cooperative, competitive, and general-sum tasks, MA-UITD outperforms baselines in discrete-action settings and infers behaviorally compatible reward profiles in continuous-action settings. When applied to experimentally collected social-foraging trajectories, MA-UITD inferred distinct spatial and interaction-dependent reward structures for leaders and followers, providing a quantitative characterization of latent behavioral objectives that may be relevant for understanding the neural computations underlying social behavior.
[MA-10] Resonance and Depth: Composing Timbral Identity and Spatial Form in an Artist-in-the-Loop Co-Creative Multi-Agent Percussive Performance
【速读】：该论文旨在解决传统音乐创作中以和声发展为核心、忽视音色（timbre）与空间感作为主要构成要素的问题，特别是在沉浸式电子音乐表演中如何实现音色身份与音乐形式的动态生成。其解决方案的关键在于构建一个“艺术家在环路中的协同创作多智能体系统”（artist-in-the-loop co-creative multi-agent system），通过实时重组、空间化处理及人机交互，将演奏者的现场素材经由拼贴合成（concatenative synthesis）、音频镶嵌（audio mosaicing）、符号中介（symbolic mediation）、物理启发合成（physically inspired synthesis）以及基于对象的空间化（object-based spatialisation）等技术手段进行再创造。该系统将空间参数（位置、轨迹、深度、扩散、宽度、表观尺寸）视为主动参与音乐句法与结构建构的作曲材料，从而在人、算法、音色与空间的多重过程中实现表演者身份的持续保留、转化与重新分配，形成一种新型的沉浸式协同创作生态。
链接: https://arxiv.org/abs/2610.29026
作者: Keon Ju Maverick Lee
机构: 未知
类目: ound (cs.SD); Multiagent Systems (cs.MA); Multimedia (cs.MM)
备注: 4 pages, 1 figure. Proceedings of Timbre 2026, 4th International Conference on Timbre, Montréal, Québec, Canada. Research-creation artistic report
Abstract:This research-creation artistic report presents Resonance and Depth, a 9-minute immersive electroacoustic/acousmatic performance that treats timbre and space as primary compositional materials. Built from live electronic percussion and a personal corpus recorded, curated, and composed by the author, the work investigates how timbral identity and musical form can emerge through real-time recombination, spatialisation, and performer-system interaction rather than conventional harmonic development. The performance is realised as an artist-in-the-loop co-creative multi-agent system integrating MACAT, MACataRT, a MIDI-based musical agent, a spatialisation layer, and a conductor agent. These components recombine and transform the performer’s material through concatenative synthesis, audio mosaicing, symbolic mediation, physically inspired synthesis, and object-based spatialisation. The work approaches spatialisation as an active compositional process in which position, trajectory, depth, diffusion, width, and apparent size participate in musical phrasing and form. Through this practice-based framework, Resonance and Depth examines how performer identity can be retained, transformed, and redistributed across human, algorithmic, timbral, and spatial processes within an immersive co-creative performance ecology.
[MA-11] he Butterfly Effect in LLM Social Simulations: From Architecture Sensitivity to Robustness Audits
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在社会模拟中因局部扰动传播而导致集体结果显著偏离的“蝴蝶效应”问题。这一现象使得科学推断面临严峻挑战：模型实现细节上的微小变化可能通过代理间交互被放大，进而影响基于集体行为得出的科学结论的可靠性。其解决方案的关键在于揭示并量化这种敏感性，提出可系统评估鲁棒性的框架——TRAILS（用于大语言模型模拟鲁棒性审计的分类体系）与社会模拟审计卡（Social Simulation Audit Card），以识别不同层级（表示与设计）扰动对模拟结果的影响，并将科学结论的适用范围校准至支持其的鲁棒性证据之上，从而增强社会模拟研究的可重复性与可信度。
链接: https://arxiv.org/abs/2610.28981
作者: Jinyi Ye,Lei Cao,Ding Chen,Emilio Ferrara
机构: University of Southern California(南加州大学)
类目: Multiagent Systems (cs.MA)
备注:
Abstract:A butterfly effect occurs when a local perturbation propagates through a system and produces divergence in its collective outcome. In LLM social simulations, this creates a particular challenge for scientific inference: sensitivity to implementation choices can enter agent interactions and alter the collective outcomes on which scientific claims are based. We study this problem through a repeated Prisoner’s Dilemma and a social-media echo chamber, spanning representation- and design-level perturbations across four LLM families. We find that controlled perturbations can substantially alter collective outcomes, with sensitivity varying across both perturbations and models. In the Prisoner’s Dilemma, changing only the representation of a matched persona shifts two-agent cooperation by 75.2 percentage points for GPT-5.2, while game framing and memory representation produce smaller or less consistent effects. Multi-agent interaction further reshapes representation sensitivity, producing amplification or attenuation depending on the model and perturbation. In the echo-chamber simulation, persona representation changes interaction targeting and is accompanied by divergence in collective interaction structure, extending representation sensitivity from agent behavior to network-level outcomes. These results show that robustness cannot be inferred from a single simulation configuration and that local sensitivity can change as it propagates through interaction. We introduce TRAILS (Taxonomy for Robustness Audits In LLM Simulations) and the Social Simulation Audit Card as a framework for systematically auditing these dependencies and calibrating the scope of scientific claims to the robustness evidence that supports them.
[MA-12] ransformers as Generic Deep Neural Networks: Exact Compilation the Cost of Removing Activation-Activation Products and Multi-Agent Attention Clustering
【速读】：该论文旨在解决生成式 AI（Generative AI）中 Transformer 模型在硬件部署时因专用算子和量化算法带来的计算冗余与效率瓶颈问题。其核心挑战在于：尽管神经处理单元（NPU）为注意力机制和层归一化（LayerNorm）引入了专用操作，且量化依赖于 Transformer 特有的算法，但这些设计实际上并非必要。论文提出的关键解决方案是：将每个 Transformer 块（包含注意力、LayerNorm 和 GELU 激活的 MLP）精确编译为仅由固定仿射变换、路由逻辑以及平方、Sigmoid 与反平方根等基本非线性操作构成的通用深度神经网络，同时使用 GELU 或其 Sigmoid 近似替代传统激活-激活乘积。通过避免引入额外的非线性运算类型，该方法可消除每头（per-head）密集注意力得分计算中至少 N2 个标量非线性评估开销。由于 Sigmoid 与反平方根操作在所有调研的 NPU 上均支持逐元素形式，因此无需专用硬件即可高效实现。进一步地，针对 Top-k 注意力，论文证明了基于尾部质量（tail mass）的精确或条件均值估计能提供无偏且最小均方误差的注意力分布估计，而尾部丢弃则存在条件偏差；当查询或代理聚类时，通过中心点优先选择键（keys），可将每头的得分计算量从 N2 降低至 Nk。在 DeiT-Tiny、MambaIRv2-Light 与 Pythia-70M 上的实验表明，该编译模型在使用全部键的情况下性能与原始模型一致，标准后训练量化（post-training quantization）即可适用，且在关键数 k≤8 时，尾部质量估计相比尾部丢弃仅需一半键数即达到更优性能。
链接: https://arxiv.org/abs/2610.28958
作者: Sergey Salishev,Zeev Volkovich,Oleg Granichin
机构: St. Petersburg State University (圣彼得堡国立大学); Braude College of Engineering (布拉德学院工程学院)
类目: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: 36 pages, 6 tables and 2 figures in the main text, 8 supplementary tables. Code: this https URL
Abstract:Transformers are deployed as a special model class. Neural processing units (NPU) add dedicated operators for attention and normalization, and quantization relies on Transformer-specific algorithms. We show the computation does not force this. Every Transformer block, with attention, LayerNorm and a GELU MLP, compiles exactly into a generic deep neural network built from fixed affine maps, routing and the square, sigmoid and inverse square root, plus GELU or its sigmoid approximation. Without activation-activation products, every transform-wise compilation adds at least N^2 scalar nonlinear evaluations per head for dense scores. Keeping that one operation type removes this cost; its elementwise form, present on every surveyed NPU, suffices with the sigmoid and inverse square root. For top- k attention with exchangeable tail scores, we prove that exact or conditional-mean tail mass gives an unbiased, minimum-mean-square-error estimate of the attention distribution, while tail drop is conditionally biased. When queries or agents cluster, centroids choose the keys first, reducing the number of computed scores from N^2 to Nk per head. On DeiT-Tiny, MambaIRv2-Light and Pythia-70M the compiled models match their references with all keys, standard post-training quantization applies, and the exact tail-mass estimate beats tail dropping with half as many keys on DeiT-Tiny and, for k\le8 , on Pythia-70M.
[MA-13] JaxMARL-BC: Multi-Agent Reinforcement Learning for Macroeconomic Modelling in Seconds
【速读】：该论文旨在解决多智能体强化学习（MARL）在宏观经济学中应用时面临的两大核心问题，其中重点攻克了训练时间过长这一关键瓶颈。现有MARL方法虽能模拟具有异质性决策主体的经济系统，但其计算效率低下，严重制约了实际应用。为此，本文提出JaxMARL-BC——一个基于JAX加速的仿真器，通过利用JAX的自动微分与矢量化计算能力，将原本需数周甚至数月完成的模型训练压缩至数秒级别（相比基准实现提速75至724倍），并支持高达20万独立学习主体的规模扩展。其解决方案的关键在于：采用JAX框架实现高效并行化与梯度计算优化，同时保持与经典动态随机一般均衡（DSGE）模型在特定极限条件下的理论一致性，从而在保证模型可信度的前提下大幅提升计算效率。通过在扩展模型中对资本回报异质性的校准验证，证明了该工具在短时间内完成复杂经济建模任务的可行性，显著推动了MARL在宏观经济学中的实用化进程。
链接: https://arxiv.org/abs/2610.28907
作者: Daniele Montagnani,Andrea Coletta,Antonietta Mira,Aldo Glielmo
机构: Università di Pavia(帕维亚大学); USI Lugano(卢加诺大学); Banca d’Italia(意大利银行); Università dell’Insubria(因苏布里亚大学)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); General Economics (econ.GN)
备注: 9 pages, 7 figures
Abstract:Multi-agent reinforcement learning (MARL) offers a principled way of populating simulated economies with many heterogeneous decision makers. Its adoption, pioneered in finance, has nonetheless been slow in macroeconomics because of two main limitations: the lack of a shared frame of reference connecting MARL systems to dynamic stochastic general equilibrium (DSGE) models the profession already trusts, and the very long training times. The first limitation was recently addressed by the Multi-Agent Reinforcement Learning Business Cycle (MARL-BC), which places deep RL households in a real business cycle economy and reduces to classic DSGE models in specific limits. Here we address the second limitation. We present JaxMARL-BC, a JAX-accelerated simulator that trains MARL-BC economies in seconds (between 75x and 724x faster than the reference implementation), and scales to 200,000 independent learning households. We verify its correctness against the known DSGE limits, characterise its scaling through a simple cost law, and demonstrate its value by a proof-of-concept calibration of an extended model with heterogeneous returns on capital, in a two-hour experiment that would previously have taken over a month. We release JaxMARL-BC as open source.
[MA-14] Fig: Automated Multiagent Behavior Elicitor
【速读】：该论文旨在解决多智能体系统（Multiagent Systems）中社会行为评估缺乏系统性干预与可控性的问题，现有研究多依赖环境自然涌现的行为，难以实现对特定行为的精准诱发与重复验证。其解决方案的关键在于提出Fig（Finding Interactions in Games）——一个能够执行目标导向干预的代理系统，该系统接收自然语言形式的行为描述，将其分解为子行为，生成或选择新的社会博弈场景，并对游戏引擎、对手、指令及上下文等要素实施单个或组合式干预，以主动诱发目标行为。通过结合内置机械检查与合成评分标准进行评估，该方法在6种测试行为（合作、剥削、欺骗、隐秘协作、背叛、破坏）上使模型在有效生成场景中的行为尝试率达45.2%，且在具备对照条件的游戏-行为对中，干预使行为尝试率平均提升10.9%。结果表明，多智能体环境可作为可编程、可控且可复现的行为评估测试平台。
链接: https://arxiv.org/abs/2610.28891
作者: Anrui Gu,Zhichao Wang,Nicolai Ouporov
机构: Fleet AI( Fleet AI); Fleet AI( Fleet AI)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Many social evaluations for multiagent systems study behaviors as they emerge from environments instead of systematically intervening in the environment to elicit such behaviors. We introduce Fig (Finding Interactions in Games), an agentic system that performs targeted elicitation interventions on game environments. It takes a natural language behavior description, decomposes it into sub-behaviors, generates new social games or selects existing ones, and executes single or combined interventions to the games’ engine, opponents, instructions, and context. To evaluate model behavior, it combines built-in mechanical checks and synthetic rubric judgments. We validate the interventions, generated games, and evaluation mechanisms via human review. Models attempt the target behavior in 45.2% of episodes on average for valid scenarios generated by Fig for six test behaviors – cooperation, exploitation, deception, covert coordination, betrayal, and sabotage. For game-behavior pairs with available controls, Fig’s interventions increase attempt rates by 10.9% on average compared to matched control games. Fig shows that multiagent environments can serve as programmable test beds for intentional, controllable, and reproducible behavior evaluation. Code is available at this https URL.
[MA-15] o Manipulate or Not: Opinion Dynamics in LLM Interactions
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在持续对话中相互影响时，传统说服研究仅关注单方影响而忽视双向互动的局限性。其核心问题在于：如何有效衡量和理解多轮对话中不同模型间观点动态变化的本质，特别是模型角色设定与交互模式对观点演变的影响。解决方案的关键是提出一种受控的LLM-LLM辩论框架，通过让两个模型在气候变迁归因这一争议话题上进行多轮对话，其中一方被赋予特定立场甚至明确的说服指令，另一方则保持中立。研究通过追踪双方显式表达的观点及逐轮对话行为特征，揭示了同模型交互通常缩小观点差距，而跨模型交互则扩大差距；同时发现，显式声明与实际对话行为反映的是观点动态的不同维度，且“主动说服”的指令并未显著提升影响力。这表明，对话中的影响力不仅取决于角色设定与初始立场，更深刻依赖于参与模型自身的特性，对评估长程对话中意见转变机制具有重要启示。
链接: https://arxiv.org/abs/2610.28875
作者: Mohammad Siahkamari,Phuoc Dat Nguyen,Stephen MacNeil
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA); Social and Information Networks (cs.SI)
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Abstract:Large language models increasingly take part in extended conversations where they can both shape and be shaped by another party’s views, yet most persuasion research measures only one side of that exchange. We introduce a controlled LLM-LLM debate framework in which two language-model roles repeatedly discuss a contested topic (climate-change attribution): one is given an assigned stance and, in some conditions, an explicit instruction to persuade, while the other receives no assigned position. Across four ordered model combinations involving GPT-6 Luna and Gemini 2.5 Flash Lite, we track both each role’s explicitly reported position and a separate measure of its turn-by-turn behavior inside the conversation. Opinion dynamics differ substantially across model combinations: same-model interactions narrow the opinion gap on average, clearly for only one of the two models, while cross-model interactions widen it. Explicit reports and in-dialogue behavior capture distinct aspects of opinion dynamics, and a blanket instruction to persuade does not reliably increase influence. These findings suggest that conversational influence depends not only on assigned roles and initial positions but also on the participating models, with implications for how opinion change is measured in extended LLM interactions.
自然语言处理
[NLP-0] LongZero: Enhancing Long-Context Reasoning in LLM s from Zero Data
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在长上下文推理能力提升过程中对高质量训练数据的依赖问题。现有强化学习（Reinforcement Learning, RL）方法依赖于人工标注或外部语料库生成的长篇、多样化且可验证的训练数据，但这些数据的获取成本高昂，且随着模型能力增强，高质量外部数据和教师模型愈发稀缺。为应对这一挑战，本文提出一种无需外部数据的自洽强化学习方法——LongZero，其核心创新在于让模型自主生成用于训练的多跳推理任务与多轮检索任务。具体而言，LongZero首先引导模型以紧凑的结构化形式合成问题、答案及支持证据，随后将证据部分独立扩展为长篇多文档上下文，从而构建出具备复杂推理链的长上下文任务。该方法结合自生成的多跳推理与多轮检索任务，并采用广义相对策略优化（GRPO）进行训练。实验在两个主流LLM上针对五个长上下文推理基准测试表明，LongZero不仅具备良好的泛化能力，且性能优于基于外部语料合成数据的基线方法，证明了其在无需外部数据条件下有效提升长上下文推理能力的可行性与潜力。
链接: https://arxiv.org/abs/2610.30132
作者: Zhennan Wan,Jianfei Chen
机构: Tsinghua University (清华大学); BNRist Center (清华-博世联合实验室); THBI Lab (清华大学脑与智能实验室); Tsinghua-Bosch Joint ML Center (清华大学-博世联合机器学习中心)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Code and data are available at this https URL
Abstract:Reinforcement learning (RL) is an effective way to strengthen the long-context reasoning of large language models (LLMs), but it needs training data that are long, diverse, challenging, and verifiable. Human annotation of such data is expensive, naturally occurring data are too scarce to sustain RL, and existing synthesis methods depend on external corpora or stronger teacher models, which become harder to obtain as the trained models get stronger. Recently, RL from zero data, in which a model learns from tasks it proposes itself, has achieved excellent performance, but existing methods target short-context tasks. Therefore, we propose \textbfLongZero , a method that lets an LLM improve its own long-context reasoning without any external data. Instead of writing questions and answers over existing documents, LongZero first has the model synthesize the question and answer with its supporting evidence in a compact structured form, and only then separately expands the evidence into a long multi-document context. LongZero combines these self-written multi-hop reasoning tasks with self-written multi-round retrieval tasks and trains the model with GRPO. We evaluate LongZero on two LLMs across five long-context reasoning benchmarks and compare it with RL on data synthesized from external corpora. Experimental results show that LongZero can generalize to real tasks, and outperforms both baselines, suggesting a viable and promising path to improving long-context reasoning of LLMs.
[NLP-1] Environment Duels: A Microcosm of LLM Co-Evolution
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在复杂交互生态系统中作为“创造者”与“求解者”双重角色的能力评估问题，尤其关注模型在生成任务环境及利用提示（hint）求解任务时的表现差异。现有评估方法多聚焦单一能力，难以反映模型在真实协作与竞争场景中的动态互动。为此，作者提出“环境对战”（Environment Duels）这一动态智能体基准，通过让各模型自主设计包含特权提示的任务环境，并由其他模型尝试求解，从而同时评估其环境创作能力与求解能力。该方案的关键在于构建一个可交互、可验证的闭环评估框架：每个模型既是环境的设计者（作者），又是任务的求解者；环境设计需确保具备“提示依赖性”，即只有获得特定提示才能成功求解，从而凸显提示的价值与设计质量。实验表明，求解能力与创作能力具有显著分离性——最强求解者未必是最佳创作者，而较小模型更善于从他人行为中学习，生成更具挑战性的环境和更有效的提示。此外，模型间的对战结果呈现传递性，形成稳定排名；独立作者趋向于收敛到相似环境设计；且通过强化学习实验证明，模型在特定环境上的提示增益与其在未见推理与智能体任务上的性能提升高度相关。这些现象揭示了模型间相互影响的内在机制，为理解大模型如何在开放生态中“教学”“挑战”与“相互学习”提供了可控实验窗口。
链接: https://arxiv.org/abs/2610.30116
作者: Shibo Jin,Zhiqiu Xu,Yiling Zou,Shangyu Gong,Mayur Naik
机构: University of Pennsylvania (宾夕法尼亚大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Through a tangle of real-world interactions, from synthetic training pipelines to data shared on the open web, frontier LLMs absorb what their predecessors and competitors create. How well does each model play its role in this ecosystem, as a creator and as a solver? We introduce Environment Duels, a dynamic agentic benchmark that evaluates both capabilities on interactive environments. Each model writes environments, each with a privileged hint, aiming for tasks that capable solvers fail without the hint and pass with it. Across nine frontier LLMs, solving and authoring prove to be distinct skills: the strongest solvers are not the strongest authors, while smaller models learn more from how others played and write the hardest environments and the most useful hints. Beyond the scores, the pairwise interactions form a microcosm of how models affect one another. Head-to-head outcomes are transitive and so yield a stable ranking; independent authors converge on the same designs; and in a reinforcement-learning study with Qwen3.8-27B, the learner’s hint lift on an author’s environments tracks how much training on those environments improves it on held-out reasoning and agentic benchmarks, whether the environments are its own or another model’s. Each of these patterns has a counterpart in how models shape one another outside the duel. By staging these interactions under controlled conditions, Environment Duels offers a window onto how LLMs teach, challenge and learn from one another. Code is available at this https URL.
[NLP-2] Quant-Arena: Benchmarking LLM Agents for Quantitative Trading Research
【速读】： 该论文旨在解决生成式 AI 在量化投资研究闭环中的有效性问题，即如何评估大语言模型（Large Language Models, LLMs）是否能够自主完成从假设提出、信号构建、历史回测到策略固化并最终在未见数据上实现盈利的完整迭代过程。其核心挑战在于现有基准仅评价公式、代码或单个交易决策，无法全面检验模型在真实市场环境下的端到端策略生成与泛化能力。为此，论文提出 Quant-Arena——一个基于真实A股市场数据的端到端量化研究基准与公开排行榜，要求模型在2022–2023年期间构建特征与股票评分逻辑，并仅接收该阶段的反馈，随后冻结策略以在密封的2024年进行最终评估。评估采用现金账户模拟器，考虑交易约束与成本，包括基于仿真订单簿估算的市场冲击。评分体系以实际超额收益为主，辅以信号质量与特征多样性指标。实验评估了九个前沿模型，其中 GPT-6 Astra 表现最佳，但所有模型均未能在2024年超越等权市场基准，凸显当前LLM在复杂市场环境中生成可持续盈利策略的局限性。Quant-Arena 对所有模型与智能体开放提交，推动对生成式AI在金融建模中实际效能的系统性评估。
链接: https://arxiv.org/abs/2610.30113
作者: Houde Qian,Zheng Jiang,Wenyi Li,Chenxi Xu,Xinjie Lin,Yuzhao Peng,Ruoyu Han,Youjie Zheng,Kaisen Yang,Qinhuai Na,You Fu,Calvin Xiao
机构: Navers Lab, Einsia.AI(艾尼萨人工智能)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注: Project page: this https URL
Abstract:Quantitative research is an iterative loop: a researcher proposes a market hypothesis, implements it as an executable signal, tests it on historical data, and decides what to keep. Existing benchmarks score formulas, code, or individual trading decisions, leaving open whether large language models (LLMs) can complete this loop and produce strategies that work beyond the research period. We introduce Quant-Arena, a benchmark and public leaderboard for end-to-end quantitative research on real A-share market data. A model constructs features and stock-scoring code, receives feedback only on 2022–2023, and freezes one strategy for assessment on the sealed year 2024. A cash-account evaluator applies trading constraints and costs, including market impact estimated from a simulated order book. Scores are dominated by realized excess return and complemented by signal quality and the diversity of features used by the strategy. We evaluate nine frontier models, among which GPT-6 Astra scores highest; yet no model beats the equal-weight market in 2024. Quant-Arena is open to submissions from any model or agent harness.
[NLP-3] Did the Problem Actually Get Harder? Testing LLM s on Paired P and NP-Complete Problems Hardened by Adversarial Multi-Agent Rewriting
【速读】： 该论文旨在解决现有基准测试无法有效区分问题复杂性类（如NP完全问题，NPC）与多项式时间可解问题（P）之间的本质差异这一关键问题。传统基准往往依赖固定表述或忽略问题的复杂性类别，导致模型可能通过记忆而非真正推理来得分，从而掩盖了其在面对真实计算难度时的能力缺陷。为此，本文提出P2NPCBench，一个包含12个问题族的新型基准，每个NPC问题均与一个结构上仅有一处差异的P类孪生问题配对，且二者共享相同的问题表面形式。其核心解决方案在于采用AgentGAN——一种对抗式多智能体框架，通过生成器不断重写问题表述，直至一组冻结的代码模型无法正确解答，同时由可执行有效性门控确保每次重写保持原问题的答案和复杂性类别不变。最终构建出2,114条经人工验证参考人类解法的“硬化”问题陈述。实验结果表明，所有评估的12个专有及开源模型在NPC问题上的通过率（35.4% pass@1）显著低于其对应的P类孪生问题（63.8%），且95.1%的模型家族对在NPC侧失败更频繁；即使将时间限制扩大十倍，NPC准确率仅从28.5%提升至31.9%，说明失败主要源于程序逻辑错误或崩溃，而非求解速度慢。此外，硬化处理使两类问题间的性能差距从24.8个百分点缩小至14.6个百分点，揭示了部分看似由复杂性决定的性能差异实则源于对教科书式表述的熟悉程度，而非真正的推理能力。
链接: https://arxiv.org/abs/2610.30103
作者: Nurul Labib Sayeedi,Md. Faiyaz Abdullah Sayeedi,Zabir Al Nazi,Mahbub E Sobhani,Shubhashis Roy Dipta
机构: United International University; BRAC University; University of California, Riverside; University of Maryland; Baltimore County
类目: Computation and Language (cs.CL)
备注: this https URL
Abstract:Many NP-complete (NPC) problems have a polynomial-time § twin that differs in a single structural detail, such as 3- versus 2-colouring a graph. A model that reasons rather than recalls should notice which side of this boundary a problem falls on, yet existing benchmarks either fix the wording of each task or ignore its complexity class, so they cannot separate the two. We introduce P2NPCBench, a benchmark of 12 problem families in which every NPC problem is paired with a P twin posed over the same surface. To remove the textbook cues that enable recall, we build it with AgentGAN, an adversarial multi-agent framework in which a generator rewrites each statement until a panel of frozen code models fails, while an executable validity gate keeps every rewrite answer- and class-preserving. The result is 2,114 hardened statements with hand-verified reference human solvers. Evaluating 12 proprietary and open-source models, we find that every model scores lower on NPC statements than on their P twins (35.4% vs. 63.8% pass@1), and that 95.1% of model-family pairs fail more often on the NPC side. A tenfold time limit raises NPC accuracy only from 28.5% to 31.9%, so failures stem from wrong or crashing programs rather than slow ones. Hardening also narrows the class gap from 24.8 to 14.6 points, showing that part of the apparent gap reflects familiarity with textbook phrasing rather than complexity class.
[NLP-4] VX-One: Typed Calibrated Decision Heads for Real-Time Perimeter Triage - An empirical study of unknown-attack honesty cascade gray zones and calibration under drift
【速读】： 该论文旨在解决网络边界筛查（perimeter triage）中延迟（latency）与语义理解（semantics）之间的权衡问题：传统签名引擎虽具备高速度和格式安全性，但无法识别未知攻击；而生成式模型虽能捕捉深层语义特征，却存在推理延迟高、输出不可解析等缺陷。其解决方案的关键在于提出VX-One——一种参数量为100K–10M的小型多头神经网络，通过多任务联合训练，输出经过温度校准的类型化决策原语（typed decision primitives），包括可校准的二元门控（calibrated gate）、包含显式“未知”选项的封闭集选择（closed-set choice）以及有序评分（ordinal score）。该设计在保持毫秒级低延迟（CPU上p50为0.7–1.7 ms）的同时，实现了对未知攻击类别的有效检测能力，并在多个基准数据集（UNSW-NB15、CIC-IDS2017、EMBER2024）上验证了其在动态漂移环境下的鲁棒性与实用性。
链接: https://arxiv.org/abs/2610.30090
作者: Juan S. Santillana
机构: Independent research(独立研究)
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
备注: 11 pages, 7 tables, 1 figure
Abstract:Perimeter triage faces a latency/semantics trade-off: signature engines are fast and format-safe but blind to unseen attacks, while generative models are semantic but slow and produce unparseable output. In late 2026 a class of commercial System One decision models (TypeSafe Jev; Microsoft-Decision-1) began selling typed decision primitives - binary gates, closed-set choices, ordinal scores - as product APIs, with no academic treatment. We present VX-One: a small multi-head network (100K-10M parameters) whose outputs are these primitives - a calibrated gate, a choice over a closed candidate set that always includes an explicit unknown option, and an ordinal score, trained multi-task, temperature-calibrated per head (~0.7-1.7 ms p50 on CPU). We evaluate it on UNSW-NB15, CIC-IDS2017, EMBER2024 against calibrated XGBoost, Random-Forest, MLP, Kitsune, and measure a two-phase cascade (Suricata with 52,674 Emerging-Threats rules - VX-One) over five days of CIC-IDS2017. Findings: (i) VX-One wins detection (AUROC/FPR@95%TPR) on UNSW-NB15 and under temporal drift on CIC-IDS2017 (0.711 vs 0.637), but XGBoost wins multi-class attribution on all three benchmarks; (ii) leave-one-family-out gate detects never-seen families on 9/9 UNSW folds yet drops below chance on CIC recon family, and abstention threshold transfers poorly across benchmarks; (iii) on payload-less reconstructions signature engines resolve ~0.1% of flows - gray zone ~99.9% of traffic, recovered by Phase 2 (combined recall =0.978); (iv) calibration collapses under temporal drift (ECE 0.04-0.40) for every method while ranking partially survives. We provide a measured account of where typed calibrated decisions help perimeter triage, and where they fail.
[NLP-5] GoiBERT: Compact Vietnamese Language Models via Two-Stage Knowledge Distillation
【速读】： 该论文旨在解决越南语预训练编码器在实际部署中面临的模型体积大、依赖外部分词工具或需多语言词汇表导致计算成本高的问题。现有最强的越南语模型通常为大型多语言模型，难以高效部署。为此，论文提出GoiBERT，一种基于两个互补教师指导的轻量级越南语编码器家族，包含26M和49M参数的两种规模模型，可直接处理原始文本，无需依赖外部分词。其核心解决方案在于：首先由一个使用统一分词器的通用越南语编码器提供通用表示学习；其次，通过一个更大规模的教师模型生成无需人工标注的软标签（soft labels），实现任务层面的指导。实验结果表明，GoiBERT-large在8个涵盖文本分类、基于方面的情感分析、自然语言推理和命名实体识别的基准上，以少于一半参数量和两倍吞吐量的表现超越了现有紧凑基线模型。GoiBERT-base仅需基线四分之一的模型规模即可保持99%的性能。分析显示，压缩对情感分析与分类任务影响极小，但在推理和实体识别任务中仍存在显著性能损耗，揭示了未来优化方向。
链接: https://arxiv.org/abs/2610.30067
作者: Quang Anh Kiet Tran,Nam Anh Tran,Manh Hung Trinh,Thanh Danh Le,Khang Nhat Hoang Vo,Huynh Dai Nhan Tran
机构: UIT, VNU-HCM(越南国家大学胡志明市分校); IU, VNU-HCM(越南国家大学胡志明市分校); University of Eastern Finland(东芬兰大学); University of Jyväskylä(于韦斯屈莱大学); MBZUAI(穆巴达拉人工智能大学)
类目: Computation and Language (cs.CL)
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Abstract:Vietnamese pre-trained encoders have improved rapidly, but the strongest ones are large, multilingual-vocabulary models or depend on external word segmentation, which makes them costly to deploy. We present GoiBERT, a family of two compact Vietnamese encoders with 26M and 49M parameters that operate directly on raw text. GoiBERT learns from two complementary teachers. A same-tokenizer Vietnamese encoder first teaches general-purpose representations. A much larger teacher then provides task-level guidance through soft labels that are generated without ever labeling its own training data. Across eight benchmarks covering text classification, aspect-based sentiment analysis, natural language inference, and named entity recognition, GoiBERT-large achieves the highest average score among non-teacher models. It does so with fewer than half the parameters of the strongest compact baseline and twice its throughput. GoiBERT-base retains 99% of that baseline’s performance at a quarter of its size. Our analysis shows that compression is nearly lossless for sentiment and classification tasks but remains costly for inference and entity recognition, which points to clear directions for future work. Our code and models are available at: this https URL
[NLP-6] Looped Uniform Diffusion Language Models
【速读】： 该论文旨在解决统一扩散语言模型（Uniform Diffusion Language Models, UDLMs）在训练过程中因需从可能不可靠的上下文进行去噪而带来的高计算成本问题，即其训练过程相较于掩码扩散语言模型（Masked Diffusion Language Models, MDLMs）更为耗时且资源密集。尽管这一挑战性训练目标提升了下游推理性能，但限制了其实际应用效率。本文提出的解决方案关键在于引入“选择性循环”（selective looping）机制——在每个去噪步骤中递归地重复使用少量早期Transformer层，从而在不改变原始训练目标和采样策略的前提下，显著提升训练效率。实验表明，采用循环机制的UDLM可在保持相同验证集负对数似然（NLL）水平的情况下，相比非循环模型减少高达3.3倍的训练浮点运算量（FLOPs），同时在等效非去噪步数（NFE）预算下，生成困惑度降低达9.8%；进一步研究发现，通过更频繁采样较小的循环次数，可实现12%的推理循环次数减少，且不影响模型精度，从而在训练与推理之间达成更优的效率-性能平衡。
链接: https://arxiv.org/abs/2610.30066
作者: Saiteja Utpala,Chieh-Hsin Lai,Yuki Mitsufuji,Praneeth Vepakomma
机构: MBZUAI(穆巴达拉人工智能大学); Sony Group Corporation(索尼集团); MIT(麻省理工学院)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 27 pages
Abstract:Uniform diffusion language models (UDLMs) generate text by iteratively denoising sequences of ordinary vocabulary tokens, any of which may be corrupted and subsequently revised. During training, UDLMs must denoise from a context that may itself be unreliable, whereas masked diffusion language models (MDLMs) always condition on a clean context. This challenging training objective makes UDLMs more FLOP-intensive to train than MDLMs, but it yields stronger downstream reasoning performance. Recently, selective looping, which recursively applies a few early Transformer layers within each denoising step, has been shown to improve training efficiency in MDLMs under an iso-FLOP budget. We investigate looping in UDLMs while keeping the training objective and sampler unchanged. We find that looping significantly improves training efficiency: looped UDLMs reach the validation NLL of a non-looped UDLM trained on about 0.5 trillion tokens with up to 2.8x fewer training FLOPs on OpenWebText and 3.3x fewer on FineWeb-Edu. We also find that, at a matched NFE budget, looped UDLMs reach up to 9.8% lower generation perplexity than the non-looped UDLM, with most gains achieved at moderate loop counts. Finally, we study different loop-count distributions and find that sampling smaller loop counts more frequently during training improves inference efficiency: on downstream tasks, this strategy uses 12% fewer loops than uniform loop-count sampling without degrading accuracy.
[NLP-7] Agent Behavior Maps: Locating Agents by What They Would Do Without Running Them
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）智能体在相同成功率下行为路径差异显著的问题，即如何有效刻画和比较不同LLM-工具组合（LLM-harness combinations）之间的行为相似性与差异性。其核心解决方案是提出一种智能体行为图谱（Agent Behavior Map），通过计算在共享参考轨迹上的中心化动作对数似然值（centered action log-likelihoods），无需对目标智能体进行额外环境回放即可表征其行为特征。该方法的关键在于：在明确假设条件下，推导出坐标距离与轨迹相对熵（trajectory KL）之间的误差界，并验证在8个跨评分模型中，坐标距离的排序与直接测量的轨迹KL排序高度一致；进一步地，在ALFWorld环境中，基于该图谱的坐标能有效预测性能并识别跨模型家族、控制成功概率与模型规模后的深层行为相似性。此外，研究还发现，工具组件间的位移具有近似可叠加性，但其精度随组件与环境的不同而变化，为行为分解与模块化分析提供了新范式。
链接: https://arxiv.org/abs/2610.30014
作者: Shota Fujikawa,Takuya Tamura,Kosuke Akimoto,Daichi Haraguchi,Masafumi Oyamada
机构: The University of Tokyo (东京大学); NEC Corporation
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 23 pages, 5 figures, 12 tables. Under review
Abstract:LLM agents with similar success rates can follow different action sequences. An Agent Behavior Map represents LLM-harness combinations using centered action log-likelihoods on shared reference trajectories, without requiring new environment rollouts from the target agent. We derive error bounds relating squared coordinate distances to trajectory KL under explicit assumptions, and across eight cross-scored models the distances track the directly measured trajectory KL in rank order. Across 100 LLMs and four environments, ALFWorld-based coordinates support performance prediction and identify behavioral similarities. In ALFWorld, these similarities persist after controlling for success rates and model size, including across model families. As an extension, harness-component displacements approximately compose, with accuracy varying across components and environments.
[NLP-8] Building Auditable AI Text Detectors with Sparse Autoencoders EMNLP2026
【速读】： 该论文旨在解决生成式AI文本检测器在关键应用场景（如学术评审、教育评估、招生、新闻报道及内容审核）中面临的可解释性与性能之间的矛盾问题。现有最强的检测方法依赖于微调的神经分类器，其决策过程高度不透明；而透明的替代方案（如基于风格特征和语言学特征的分类器）在性能上存在显著差距。本文提出一种将微调神经检测器转化为基于人类可读概念的可解释分类器的流水线方法：通过在检测器的残差流（residual stream）上直接训练稀疏自编码器（Sparse Autoencoders, SAEs），为每个SAE特征分配可解释标签，并仅保留生成描述通过全局保真度阈值的特征；随后在这些可解释特征上训练轻量级分类器。实验表明，所提取的SAE特征能够捕获高度特定且具有预测性的生成文本“语言线索”（linguistic “tells”），由此构建的分类器不仅具备优异的可解释性，且在分布外（out-of-distribution）文本上的表现优于神经模型和传统风格特征模型。该方案的核心创新在于利用SAEs从深层神经网络中提取可解释的语义特征，实现了高性能与高透明度的统一。
链接: https://arxiv.org/abs/2610.30006
作者: Liam Dugan,Callum McDougall,Matthew Denton,Philip Pham,Christine Kaeser-Chen,Chris Callison-Burch
机构: Google DeepMind(谷歌深脑); University of Pennsylvania(宾夕法尼亚大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: EMNLP 2026
Abstract:AI-generated text detectors are increasingly deployed in consequential settings—peer review, education, admissions, journalism, and content moderation—where their outputs directly affect individuals. Yet the strongest detectors are fine-tuned neural classifiers whose decisions are opaque, while transparent alternatives (stylometric and linguistic-feature classifiers) underperform them by wide margins. We present a pipeline that converts a fine-tuned neural detector into a classifier that operates on human-readable concepts. We accomplish this by training sparse autoencoders (SAEs) directly on a detector’s residual stream, assigning auto-interpretation labels to each SAE feature, and retaining only features whose generated descriptions pass a global fidelity threshold. We then train a lightweight classifier on just the interpretable features. We find that our SAEs recover latent concepts that correspond to highly specific and predictive linguistic “tells” of AI-generated text. Classifiers built on these extracted features are not only highly interpretable but exhibit stronger performance on out-of-distribution texts than either neural or stylometric alternatives.
[NLP-9] HiddenLens: A Multi-Layer LLM Framework for Detecting and Resolving Hidden Assumptions in Software Requirements
【速读】： 该论文旨在解决自然语言软件需求中隐含假设（hidden assumptions）难以被发现的问题，这些假设通常涉及性能、运行环境、用户行为及领域约束等非功能性需求（Non-Functional Requirements, NFR），虽在表面上看似清晰合理，却可能在架构设计、测试与部署阶段引发严重风险。若此类假设在实现后才被识别，修复成本将显著上升。现有研究多聚焦于模糊性（ambiguity）和歧义性（vagueness）的检测，而对“看似明确但蕴含潜在假设”的问题关注不足。为此，本文提出HiddenLens——首个专为检测、分类、质疑并解决隐藏假设而设计的六层BERT-LLM集成框架。其核心创新在于构建了一个端到端可解释的分析流程：首先利用BERT注意力熵定位潜在未决片段；继而通过思维链（Chain-of-Thought, CoT）提示的大型语言模型（LLM）推理解析每个假设及其失效风险；第三层采用微调的BERT模型将假设分类至六大NFR类别（性能、安全、可扩展性、可用性、可靠性、领域约束）；第四层基于领域特定（FS）提示生成优先级排序的澄清建议；第五层支持人工验证与编辑；第六层则根据确认结果重写原始需求，生成显式、可测试且可追溯的规范。此外，研究构建了首个专门针对该任务的标注数据集HiddenLens Corpus（HLC），包含312条需求及847个标注的隐藏假设。实验表明，HiddenLens在端到端F1-score上较关键词基线和QuARS-Adapted基线分别提升71%和61%，达到0.79；其基于BERT的NFR分类器F1-score达0.847，验证了该框架在早期识别并消除隐藏假设方面的有效性，从而显著降低软件开发后期的风险与成本。
链接: https://arxiv.org/abs/2610.29996
作者: Tanu Singh,Aashna Suman,Vinod Patidar
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 24 pages, 14 figures, 10 tables
Abstract:Natural-language software requirements often omit assumptions about performance, operating conditions, user behaviour, and domain constraints, affecting architecture, testing, and deployment. If undiscovered until implementation, resolving them is far more costly than addressing them early. Although ambiguity and vagueness are widely studied, less attention has been given to assumptions embedded in requirements that appear clear and reasonable. We propose HiddenLens, a novel six-layer BERT-LLM pipeline and, to our knowledge, the first framework engineered to detect, classify, question, and resolve hidden assumptions rather than merely flag ambiguity. Layer 1 uses BERT attention entropy to localise unresolved spans; Layer 2 uses CoT-prompted LLM reasoning to articulate each assumption and its failure risk; Layer 3 uses fine-tuned BERT to classify assumptions into six non-functional requirement categories: performance, security, scalability, usability, reliability, and domain; Layer 4 generates ranked clarification options using FS LLM prompting; Layer 5 enables human validation through confirmation, editing, or rephrasing; and Layer 6 rewrites the requirement using the confirmed resolution, producing an explicit, testable, and traceable requirement. We also introduce the HiddenLens Corpus (HLC), comprising 312 requirements annotated with 847 hidden assumptions across six NFR categories. Inspired by PROMISE and PURE, HLC addresses the lack of annotated data for this task and is, to our knowledge, the first dataset developed specifically for it. HiddenLens outperforms keyword-based and QuARS-Adapted baselines by 71% and 61%, respectively, achieving an end-to-end F1-score of 0.79; its BERT-based NFR classifier achieves an F1-score of 0.847. These results indicate that HiddenLens can help analysts resolve hidden assumptions before they propagate through software development.
[NLP-10] Remembering Through Change: Provenance-Preserving Hypergraph Memory for Multimodal Agents
【速读】： 该论文旨在解决多模态智能体在长期记忆中难以同时保持细粒度视觉证据并追踪实体跨会话演化状态的问题。现有基于抽象的内存方法将图像压缩为文本，常丢失具有区分性的视觉细节；而像素级保留的内存虽能保存原始观测数据，却难以可靠地将多样化的观测与持久的实体身份关联，并跟踪其状态演变。为此，论文提出一种多模态超图记忆（Multimodal Hypergraph Memory, MHG-Mem），其核心创新在于通过超图结构联合建模实体身份、状态演化及视觉证据溯源。具体而言，MHG-Mem 将不同实体提及及其观测事实表示为独立节点，利用发生超边（occurrence hyperedges）对身份一致的提及和事实进行分组；随后从这些分组观测中合成特定维度的演化状态，并通过演化超边与支持实体、事实及源证据建立关联。为有效导航该结构化记忆，引入基于超图的代理式搜索机制，可自适应选择入口层，沿类型化超边跨层级遍历，并在需要细粒度验证时回溯至原始观测。该设计实现了对跨会话状态变化的推理与细粒度视觉验证的统一支持。在三个基准上的实验表明，相较于最强基线，MHG-Mem 在性能上提升 8.20%–9.98%。
链接: https://arxiv.org/abs/2610.29995
作者: Jielong Tang,Ling-An Zeng,Xujie Yuan,Xiao Dong,Zhiqian Xia,Qingfeng Li,Shixiong Kai,Kaichao Liang,Rong Yao,Jian Yin
机构: Sun Yat-sen University; Huawei Technologies, Noah Arks’ Lab; Shandong Normal University; Southeast University
类目: Computation and Language (cs.CL)
备注: 31 pages,10 figures
Abstract:Long-term memory for multimodal agents remains challenging, as it must preserve fine-grained visual evidence while tracking entity evolution across sessions. Existing abstraction-based memories compress images into text, often discarding visually discriminative details, whereas pixel-preserving memories retain raw observations but struggle to reliably associate diverse observations with persistent entity identities and track their evolving states. To address these limitations, we propose MHG-Mem, a multimodal hypergraph memory that jointly organizes entity identity, state evolution, and provenance to visual evidence. Specifically, MHG-Mem represents distinct entity mentions and their observed facts as individual nodes, grouping identity-consistent mentions and facts through occurrence hyperedges. It then synthesizes aspect-specific evolution states from these grouped observations and links them to supporting entities, facts, and source evidence through evolutionary and evidential hyperedges. To navigate this structured memory, we introduce hypergraph-based agentic search, which adaptively selects an entry layer, traverses typed hyperedges across memory levels, and inspects source observations when fine-grained verification is required. These designs support both detailed visual verification and reasoning over cross-session state changes. Extensive experiments across three benchmarks demonstrate the effectiveness of MHG-Mem, improving performance by 8.20-9.98% over the strongest baseline.
[NLP-11] CulturalRiddles: A Multicultural Benchmark of Riddles
【速读】： 该论文旨在解决当前大语言模型（LLM）在跨文化理解与表征能力评估中存在英语中心主义、文化覆盖范围有限以及对真实文化情境下知识应用能力考察不足的问题。现有评测基准多聚焦于事实性知识，缺乏对文化语境深层理解的考量。为此，研究提出CulturalRiddles这一全新基准，其核心创新在于通过参与式社区协作，由人类原创并构建涵盖62个文化（45个国家、51种语言）的具身化谜题数据集。这些谜题深度植根于特定文化背景，要求模型具备溯因推理（abductive reasoning）、组合推理（compositional reasoning）能力，以及对文化特异性关联、社会常识、语言技能和详尽事实知识的综合理解。实验表明，即使前沿模型在部分文化群体中的准确率也仅达22%。关键解决方案在于构建一个以人类主导、文化原生性为根基的评测体系，并通过公开发布全部资源与开放贡献机制，持续拓展其语言与文化覆盖面，从而推动对模型跨文化认知能力的系统性评估。
链接: https://arxiv.org/abs/2610.29968
作者: My Chiffon Nguyen,Tianyi Hu,Vinod Anbalagan,Henry Gagnier,Pouya Sadeghi,Farah Abdou,Tidor-Vlad Pricope,Diego MMS Oliveira,Jocelyn K D’Arcy,Madeline Smith,Oluwaseun A Ajayi,Yusser Al Ghussin,Zena Al-Khalili,Micol Altomare,Shayan Amir,Ahanaf Aziz,Bryan Chen,Ruchira Dhar,Abdellah El Mekki,Rawan Mohammed Elframawy,Akhil Eppa,Silvia Fernandez,Alex Gimei,Arya Hariharan,Christian Hoang,RamMohanRao Kadiyala,Diana Kozachek,Peerat Limkonchotiwat,Ankita Maity,Di Mo,Ayush Munot,Aylin Naebzadeh,Anastasiia Nikitina,Dina Pisarevskaya,Divas Poudel,Jebish Purbey,Md Rabius Sany Apu,Ahmad Mufli Ramadhan,Ralitza Nikolova Rizova,Diogo M. Serrano-Miranda,Vivek Gopalkrishna Silimkhan,Luis Fernando Solis Navarro,Kaiser Sun,Arya Suneesh,Manaswitha Sunkara,Tejaswi Sunkara,Jonathan E Swindell,Hanna Yukhymenko,Jingyi Zheng,Esther Adenuga,Ilia Afanasev,Wafa Aissa,Lisa Alazraki,Lauren C Altomare,Priscilla Amondi Amuok,Emile Anand,Gabriel Assis,José Matías Avalos,Silvia Lorena Avila,Zakaria Baannou,Niyati Bafna,Yash Bagla,Sarah Bahrami,Bontu F Balcha,Vanya Bannihatti Kumar,Irene Baucells,Abdumutalib Begmuratov,Naman Bhatia,Souvik Bhattacharyya,Isabel Blanco Ferreiro,Felipe Bochini,Sayuru Bopitiya,Angana Borah,Tulika Bose,Olena Bugaiova,Juliana Carvalho,Nancy F Chen,Stephy Chen,Rochelle Choenni,Jayanth Krishna Chundru,Leandro A Cossio,Nebojša Damnjanović,Ona de Gibert,Marco De Santis,Pedro Delgado,Kaustubh Dholé,Adnan El Assadi,Wesam El-Sayed,Nadine M Elframawy,Oumayma Essarhi,Naome A Etori,Reuben Chagas Fernandes,Saanidhi Pravin Gade,Javier García Gilabert,Álvaro González-Escapa,Bin Hu,Kaiyu Huang,Abderrahmane Issam,Danka Jokić,Ulugbek Khalilov
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 37 pages. Dataset: this https URL Code: this https URL
Abstract:As LLMs expand their language coverage, evaluating how well they understand and represent diverse cultures becomes increasingly important. However, many benchmarks remain English-centric, with limited cultural coverage and a focus on factual knowledge over its application in real-world cultural contexts. We propose CulturalRiddles, a benchmark of original, culturally grounded riddles created entirely by humans through a participatory community effort, spanning 62 cultures (45 countries, 51 languages). These riddles are deeply rooted in their respective cultures and require abductive and compositional reasoning, alongside detailed factual and social knowledge, language skills, and an understanding of culturally specific associations. We benchmark 28 LLM configurations using both automatic and large-scale human evaluation. Even frontier models achieve at most 22% accuracy in some communities. We publicly release all artifacts alongside an open contribution process to expand the benchmark’s linguistic and cultural coverage.
[NLP-12] Commit or Abstain? System One Decision Models LLM s and Missing Legal Authority
【速读】： 该论文旨在解决法律人工智能（Legal AI）在处理海量法律主张时面临的可扩展性与成本效益问题，尤其关注生成式模型在法律权威验证任务中因强制输出导致的错误引用风险。其核心挑战在于：当前主流的生成式大语言模型（Generative LLMs）在面对无正确选项的情形时仍会强行生成答案，从而产生不可靠甚至误导性的法律引用。解决方案的关键在于引入一种基于校准概率的决策模型（decision model），该模型不生成文本，而是对每个候选法律判例（holding）输出一个经过校准的概率值，并在正确权威缺失时主动“弃权”（abstain）。研究通过扩展CaseHOLD基准，在1670个样本中替换真实持有为非正确项，对比四类决策模型与六种生成式LLM的表现，结果表明：生成式模型在无正确选项情况下仍会在38%-86%的案例中做出选择，而最保守的决策模型仅在30%的情况下做出选择。在标准CaseHOLD测试中，该决策模型以1/55的成本达到与最优生成式模型相当的准确率（88.1% vs. 88.2%）。进一步地，当仅当决策模型与生成式模型意见一致时才采纳其输出，可将错误引用减少一半；此外，通过无监督方式将决策模型的概率分布蒸馏至Qwen3-8B，即可获得一个校准良好且适用于本地部署的轻量级模型。研究表明，法律AI的有效性应以错误引用率为核心评价指标，而非单一依赖准确率。
链接: https://arxiv.org/abs/2610.29961
作者: M. Mikail Demir
机构: University at Albany, SUNY (纽约州立大学阿尔巴尼分校)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 16 pages, 4 figures, 4 tables. Under review. Code, data, and model outputs: this https URL
Abstract:Given the enormous number of claims to be checked, verifiable legal AI requires scalable, cost-effective models that answer the practical needs of law. System One decision models are built on large language models but, instead of generating text, return a calibrated probability for each option. This suits the verification of legal authority, where the question is closed: a given holding, or none of them. We test whether such models abstain when the correct authority is missing. We extend CaseHOLD with an abstention condition in which the correct holding is replaced in 1,670 of 5,314 items, and compare four decision models with six generative LLMs. When no option is correct, generative LLMs still pick one in 38-86% of items; the most cautious decision model does so in 30%. On standard CaseHOLD, a decision model matches the best generative LLM (88.1% vs. 88.2%) at 1/55 of the cost. Accepting that LLM’s answer only when a decision model agrees halves its wrong citations, and distilling a decision model’s probabilities into Qwen3-8B, without labels, yields a well-calibrated local model. Legal AI should be judged by its wrong citations, not by accuracy alone.
[NLP-13] owards English - Mizo Legal Machine Translation: Corpus Creation Analysis and Benchmarks
【速读】： 该论文旨在解决印度本土语言使用者在英语司法程序中因翻译技术匮乏而导致的法律信息获取障碍问题，特别是针对米佐语（Mizo）这一语言资源稀缺的案例。其核心解决方案是构建首个面向印度法院判决书的英-米佐平行语料库，包含来自高哈蒂高等法院和印度最高法院的13,168句对，由专业母语米佐语译者完成全文翻译，并通过人工对齐与双人独立校验确保质量。研究发现，英语词汇在翻译中仅以高度选择性方式借用——法律术语几乎全部保留英文，而日常动作、情境及结果等概念则多用本地化米佐语表达；同时，米佐语变音符号在语料库中使用不一致。基于此数据集，研究对NLLB 200 1.3B模型进行微调，在1,000句对的平衡法律测试集上获得27.47的BLEU分数，相较基线模型提升7.16，但仍存在英文词汇使用频率约为人类译者三倍的问题，表明当前生成式机器翻译仍难以完全模拟自然语言中的语言选择策略。
链接: https://arxiv.org/abs/2610.29943
作者: Telem Joyson Singh,Sanabam Boyshana,Sanasam Ranbir Singh,Priyankoo Sarmah
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted in OCOCOSDA 2026
Abstract:Indian court proceedings are held in English, so speakers of languages with limited translation technology have limited access to them. We present the first English Mizo parallel dataset of Indian court judgments. It contains 13,168 sentence pairs from the Gauhati High Court and the Supreme Court of India. Professional native Mizo translators translated complete documents; sentence pairs were manually aligned, and two independent reviewers checked the results. Our study finds that English words are borrowed in a very selective way: official and legal terms are almost always kept in English, but words for everyday things like actions, situations, and results are usually expressed in native Mizo. Mizo diacritics are also used inconsistently across the corpus. When we fine tune the NLLB 200 1.3B model on our training data, it scores 27.47 BLEU on a balanced legal test set of 1,000 sentence pairs, an improvement of 7.16 BLEU over the base model. However, it still uses English words about three times more often than a human translator does.
[NLP-14] Graph Topology Meets Energy-Driven Pathfinding: Preserving Contextual Consistency in Retrieval-Augmented Generation EMNLP2026
【速读】： 该论文旨在解决现有检索增强生成（Retrieval-Augmented Generation, RAG）框架在处理复杂多跳问答任务时，因知识结构化与推理过程导致的性能瓶颈问题。核心挑战包括：（i）语义损失严重——将连续文本压缩为离散符号会丢失上下文细微差异与叙事连贯性；（ii）模态不匹配显著——碎片化的知识图谱结构与大语言模型（Large Language Models, LLMs）所预训练的连续文本分布不一致，而将其重写为自然语言又引入生成噪声并造成信息遗漏。为此，本文提出一种基于语义-拓扑结构的RAG框架ThreadRAG，其关键在于引入语义拓扑映射器（Semantic Topology Mapper, STM），将检索到的文档组织为保持上下文完整的语义节点，并通过叙事、语义和词汇边构建语义拓扑图（Semantic Topology Graph, STG）。进一步地，采用能量驱动路径规划（Energy-Driven Pathfinding, EDP）机制，通过能量扩散识别与查询相关的子图，并确定性地将其线性化为连贯的文本证据路径，实现原生模态下的证据推理。实验结果表明，ThreadRAG在三个多跳问答基准测试中均显著优于强基线方法，在HotpotQA上相较文本中心方法提升14.4%的精确匹配（EM）得分，在2WikiMultihop上超越最佳基线5.8% EM，同时将令牌消耗降至最强基线的23.3%。
链接: https://arxiv.org/abs/2610.29933
作者: Yufeng Li,Zaijing Li,Fuchen Zhang,Zongzong Wu,Ming Zhao,Fengxiao Tang
机构: Central South University (中南大学); Harbin Institute of Technology (哈尔滨工业大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to Findings of the Association for Computational Linguistics: EMNLP 2026
Abstract:Retrieval-augmented generation (RAG) frequently integrates knowledge graphs (KGs) for complex multi-hop question answering. However, their performance is increasingly bottlenecked by the knowledge structuring and reasoning processes. (i) Severe semantic loss: Distilling continuous texts into discrete symbols strips away contextual nuances and narrative coherence. (ii) Pronounced modality mismatch: Fragmented graph structures clash with the continuous texts LLMs are pre-trained on, while rewriting them into natural language introduces generative noise and omits information. To address these limitations, we propose ThreadRAG, a semantic-topology-based RAG framework for context-preserving knowledge structuring and modality-native evidence reasoning. ThreadRAG introduces a Semantic Topology Mapper (STM), which organizes retrieved documents into context-intact Semantic Nodes and constructs a Semantic Topology Graph (STG) through narrative, semantic, and lexical edges. It further incorporates Energy-Driven Pathfinding (EDP), which identifies query-relevant subgraphs via energy diffusion and deterministically linearizes them into coherent textual evidence paths. Experiments on three multi-hop QA benchmarks demonstrate that ThreadRAG consistently outperforms strong baselines. Notably, it achieves a 14.4% EM improvement over text-centric methods on HotpotQA and surpasses the best baseline by 5.8% EM on 2WikiMultihop, while reducing token cost to 23.3% of the strongest baseline.
[NLP-15] Your Benchmark Is an Empirical Measure Over Difficulty NEURIPS2026
【速读】： 该论文旨在解决现有基准测试（benchmark）评估体系中存在的系统性缺陷，特别是其在衡量模型性能时对难度分布的失准与覆盖不全问题。传统基准测试往往被当作静态的题目集合，而本文提出应将其视为一种基于难度的实证测量工具，由此揭示两大核心失效模式：难度分布错配（measure mismatch），即基准测试的实际难度分布偏离了预期的目标分布；以及覆盖盲区（coverage gaps），即在难度轴上存在未被充分覆盖的区域，导致无法有效监测模型在特定难度区间内的局部进步。为此，论文提出了两个诊断指标，分别量化上述两种失效模式，并提供了严格的理论保障。通过对7个领域中25个主流基准测试的实证分析发现，多数基准测试隐性地高估了某些难度区间的权重，致使排行榜结论需谨慎解读；另一些则在难度轴上存在显著空白，削弱了对模型演进过程的监控能力。最后，论文将诊断结果转化为实际改进方案，提出BenchFill算法——一种面向难度目标的题目重写方法，可有效缩小局部覆盖盲区，提升基准测试的全面性与可靠性。
链接: https://arxiv.org/abs/2610.29927
作者: Yifan Sun,Naicheng Yu,Jiawen Gong,Jingyan Shen,Huan Zhang
机构: University of Illinois Urbana-Champaign(伊利诺伊大学厄本那-香槟分校); University of California San Diego(加州大学圣地亚哥分校); New York University(纽约大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted at NeurIPS 2026
Abstract:A benchmark is not merely a collection of test questions; it naturally induces an empirical measure over difficulty. In this work, we develop this measure-based view of benchmark evaluation. This perspective allows us to identify and formalize two central failure modes: measure mismatch, where the benchmark difficulty distribution differs from the intended difficulty distribution, and coverage gaps, where uncovered regions along the difficulty axis prevent benchmarks from detecting meaningful localized training progress. We propose two diagnostic metrics that capture these two failure modes and provide rigorous theoretical guarantees. Empirically, we analyze 25 popular benchmarks spanning 7 domains using carefully estimated item difficulties. Our results show that many benchmarks implicitly overweight certain difficulty regions, so their leaderboard conclusions should be interpreted with caution; others leave substantial portions of the difficulty axis uncovered, limiting their ability to monitor the model improvement process. Finally, we turn diagnosis into action by introducing BenchFill, a simple difficulty-targeted item rewriting algorithm that effectively reduces local coverage gaps.
[NLP-16] ask-Preserving Multilingual Instruction Tuning for Open-Ended QA in WMT 2026 MIST
【速读】： 该论文旨在解决多语言指令微调中模型在目标语言上表现不足、跨语言泛化能力有限的问题，特别是在开放域问答（QA-OEG）任务中，如何有效提升模型在多种目标语言下的生成质量与任务适应性。其解决方案的关键在于采用基于LoRA（Low-Rank Adaptation）的参数高效微调策略，结合两种高质量多语言指令数据集：一是通过任务保持型本地化构建的EuroAlpaca，二是引入跨语言增强机制的CrossEuroAlpaca，该机制在指令、上下文和响应中分配不同源语言与目标语言，以增强模型对多语言语境的理解能力。实验表明，在匹配解码条件下，该方法使目标语言上的QA-OEG任务平均Gemma-4评分相对基础模型提升7.8%；在Aya评估套件上的宏平均ROUGE-L和BERTScore-F1分别提升41.5%和2.9%，且优势主要集中在目标语言。然而，性能提升具有显著的任务与语言依赖性，例如在非目标语言上，部分任务（如基于上下文的问答）性能反而下降，揭示了当前方法在跨语言泛化中的局限性。
链接: https://arxiv.org/abs/2610.29926
作者: Aleix Sant,Jordi Luque,Carlos Escolano
机构: Telefónica Innovación Digital(电信创新数字); Universitat Politècnica de Catalunya(加泰罗尼亚理工大学), Spain
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper describes our submission to the WMT 2026 Multilingual Instruction Shared Task. We adapt Qwen3-8B with LoRA using organiser-provided data, EuroAlpaca, a multilingual instruction dataset created through task-preserving localisation, and CrossEuroAlpaca, a cross-lingual augmentation that assigns different source and target languages to instructions, contexts and responses. Our system targets open-ended question answering (QA-OEG) in ten shared-task languages represented in the auxiliary data. Under matched decoding, adaptation improves the mean Gemma-4 judge score on target-language QA-OEG by 7.8% relative to the base model. On an additional evaluation using the Aya Evaluation Suite across all shared-task languages, it yields relative gains of 41.5% in macro-averaged ROUGE-L and 2.9% in BERTScore-F1, with larger improvements in target languages. However, these gains are task- and language-specific. Gemma-4 scores improve for QA-OEG and summarisation in the target languages but decline for context-grounded QA there and across all three tasks in the non-target languages.
[NLP-17] Unintended Side Effects of Value Training in LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在价值敏感场景中部署时，如何有效实现与人类价值观对齐的问题。当前的价值对齐方法多聚焦于特定价值维度的微调，但其评估范围过于狭窄，未能充分考察此类训练对其他未显式优化能力的影响。研究发现，针对特定价值观进行微调会引发未训练价值维度、风险偏好、社会认知及安全特性等方面的意外副作用，表明价值观之间存在复杂的相互关联性。因此，论文提出关键解决方案在于：在后训练阶段的价值对齐过程中，应引入更广泛的表征行为评估，以全面捕捉模型行为的跨价值迁移效应，并进一步探索将额外价值训练嵌入模型开发流程的最佳位置。
链接: https://arxiv.org/abs/2610.29922
作者: Siddhesh Pawar,Chen Cecilia Liu,Anna Korhonen,Isabelle Augenstein
机构: University of Cambridge (剑桥大学); University of Copenhagen (哥本哈根大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 37 pages, 31 figures, 26 tables
Abstract:As LLMs are increasingly deployed in value-sensitive contexts, aligning them with human values has emerged as a post-training objective in NLP, aimed at instilling additional values and guiding model behaviour. However, current evaluations remain narrow. Because values are interconnected, it is unclear how fine-tuning for specific values alters unrelated capabilities and behaviours. In this work, we adapt four instruction-tuned LLMs across individual Hofstede’s value dimensions and evaluate them on both cross-value transfer and broader behaviours. We find that value-specific training produces unintended side effects on untrained values, risk preferences, social cognition, and safety traits. Consequently, we suggest that value alignment after post-training should incorporate broader behavioral evaluations, and we highlight the need to investigate where additional value training might best fit into the pipeline.
[NLP-18] Benchmarking Large Language Models for Detecting Satirical Code-Mixed Sentences
【速读】： 该论文旨在解决低资源语言（如罗马尼亚语）中代码混用（code-switching）现象对高级推理任务（如讽刺检测）的负面影响问题，尤其关注生成式AI在捕捉此类语言中细微文化与句法线索方面的不足。其核心挑战在于：现有自然语言处理模型难以有效理解跨语言混合语境下的语义与修辞特征，导致性能显著下降。解决方案的关键在于构建一个大规模、高质量的合成代码混用罗马尼亚语数据集——CS-SeLeRoSa，通过四种方法（随机替换、短语翻译、依存句法分析及大语言模型）生成超过60万条包含英语、德语、法语和意大利语混用的罗马尼亚语句子，并结合人工校验的黄金基准数据集（CS-SeLeRoSa-Gold）进行评估。实验表明，尽管旧版开源模型在零样本场景下表现严重退化（宏平均F1降至37%-55%），但新一代模型如Gemma 4 31B在零样本条件下达到68.1%的准确率，接近前沿模型（如GPT-5 mini的69.5%）；而微调策略能显著减少格式错误并提升性能，使Gemma 4 31B在合成数据集上达到77.5%，在黄金基准上达72.6%，超越多数前沿零样本基线。因此，该研究的关键突破在于通过可控的合成数据增强与模型微调，有效缓解了低资源语言中代码混用带来的建模挑战。
链接: https://arxiv.org/abs/2610.29907
作者: Răzvan-Alexandru Smădu,Ioana Buhnila,Daniela Tanase,Dumitru-Clementin Cercel,Florin Pop,Mihaela-Claudia Cercel
机构: National University of Science and Technology POLITEHNICA Bucharest (布加勒斯特理工大学); Academy of Romanian Scientists (罗马尼亚科学院); Center for Data Science in Humanities, Chosun University (朝鲜大学人文数据科学中心); National Institute for Research Development in Informatics - ICI Bucharest (罗马尼亚信息学研究与发展国家研究所); University of Bucharest (布加勒斯特大学); Paris 1 Panthéon-Sorbonne University (巴黎第一大学)
类目: Computation and Language (cs.CL)
备注: 35 pages, 8 figures
Abstract:Code-switching is a natural linguistic phenomenon, yet its impact on high-level reasoning tasks remains largely unexplored in under-resourced languages like Romanian. Natural language processing tasks, such as satire detection, rely heavily on nuanced cultural and syntactic cues that large language models struggle to capture. In this paper, we investigate four methods (i.e., random replacement, phrase translation, dependency parsing, and large language models) for generating synthetic code-mixed Romanian sentences with English, German, French, and Italian. We start with an existing dataset for satire detection, compiled from Romanian news articles and annotated at the sentence level by humans, and augment it with over 600k samples, namely CS-SeLeRoSa. We provide an in-depth analysis of the dataset generation process. Then, we evaluate several multilingual large language models across zero-shot, chain-of-thought, and fine-tuning regimes on both the synthetic corpus and a human-adjudicated gold benchmark, namely CS-SeLeRoSa-Gold. While older open models suffer substantial degradation (dropping to 37%-55% macro-F1), newer models like Gemma 4 31B achieve 68.1% zero-shot, rivaling frontier models like GPT-5 mini (69.5%). Furthermore, fine-tuning significantly reduces formatting errors and improves the results, increasing Gemma 4 31B to 77.5% on CS-SeLeRoSa and 72.6% on the gold standard, outperforming frontier zero-shot baselines.
[NLP-19] Investigating the Impact of Learner Errors on Social Bias in Large Language Models
【速读】： 该论文旨在解决现有社会偏见评估基准主要基于流利、语法正确的英语文本，未能充分反映非母语写作者使用语言的实际情况，导致其在偏见表现上被忽视的问题。其核心解决方案在于构建一个受控框架，系统性地向提示词中注入非母语者常见的语法错误，并应用于涵盖性别、种族和宗教等维度的三类社会偏见基准测试。研究发现，语法错误会显著加剧模型的偏见表现，且这种影响随错误率上升而增强；其中，介词误用对刻板印象一致性（stereotype alignment）的影响最为显著且一致。进一步分析表明，当错误更可能干扰理解时，偏见效应更为明显。通过在含错文本上进行微调，可有效缓解由语法错误引发的偏见上升现象。研究揭示，仅基于语法正确提示的偏见评估可能低估了非母语用户在实际使用中所遭遇的真实偏见水平。
链接: https://arxiv.org/abs/2610.29896
作者: Federico Marcuzzi,Martin Christoph,Iryna Gurevych,Alla Rozovskays
机构: INSAIT; Sofia University “St. Kliment Ohridski” (索菲亚大学“圣克莱门特·奥赫里德斯基”); Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science, TU Darmstadt and National Research Center for Applied Cybersecurity ATHENE (应用网络安全国家研究中心); Department of Computer Science at Queens College, City University of New York (CUNY)
类目: Computation and Language (cs.CL)
备注:
Abstract:Existing social bias benchmarks evaluate large language models mostly on fluent, well-formed English, reflecting native speaker usage and leaving non-native writers unrepresented. To address this gap, we investigate how grammatical errors by non-native writers affect social bias. We develop a controlled framework that injects common learner errors into prompts and apply it to three bias benchmarks covering stereotype alignment and demographic preference across gender, race, and religion. We find that grammatical errors increase bias, and the effect grows with the error rate. Among the four common error types we consider, preposition errors produce the largest and most consistent increase in stereotype alignment. Our analysis suggests that errors increase bias when they are more likely to interfere with comprehension. Finally, fine-tuning on erroneous texts reduces the bias increase caused by grammatical errors. Our findings show that bias evaluations based exclusively on well-formed prompts may underestimate the bias experienced by users whose English contains grammatical errors.
[NLP-20] Every Part Has Its Role: Part-of-Speech Effects in LLM Post-Training
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在训练过程中依赖于下一个词预测（Next-Token Prediction, NTP）所带来的语义表达局限性，即模型仅关注具体词元的预测，而未显式建模不同词汇或表达之间的语义相似性。为缓解此问题，现有方法通过引入概念增强（Concept Augmentation），即用语义相关但表达不同的词语替换目标词（如“coach”替换为“trainer”或“instructor”），以丰富模型对概念间关联的理解。然而，先前研究未明确概念信息在句子中所处的词性（Part-of-Speech, POS）位置是否影响其有效性。本文扩展并复现了前人工作，不仅覆盖名词，还系统考察了动词和形容词作为概念信号载体的影响。研究发现：在新闻文本上，无论词性如何，概念增强均能最一致地降低困惑度（perplexity）；而在下游任务性能方面，动词层面的概念增强带来最可靠的提升。尽管如此，基于中心核对齐（CKA）与线性探测的表征分析表明，带来最大下游性能增益的训练条件，并不总是导致模型内部表示发生最显著变化。这一结果揭示：概念增强的有效性不仅取决于概念引入的位置（词性），还受到训练目标与下游任务需求之间匹配程度的影响，提示模型优化需综合考虑语义信息的分布方式与任务特性。
链接: https://arxiv.org/abs/2610.29843
作者: Laya Iyer,Chen Shani
机构: Stanford University (斯坦福大学); Tel Aviv University (特拉维夫大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models are typically trained with next-token prediction (NTP), which rewards predicting specific tokens rather than explicitly accounting for the semantic similarities between different words or expressions. Prior work has addressed this limitation by augmenting training data with minimally varying sentences that replace a target word with conceptually related alternatives, e.g., “The tired coach ran home” \rightarrow “The tired trainer ran home”, “The tired instructor ran home”, … Such concept augmentation improves performance, but it remains unclear whether its effectiveness depends on where in the sentence the concept is expressed. We reproduce and extend prior work \textitbeyond nouns to verbs and adjectives, asking whether the part of speech (POS) carrying the concept signal affects perplexity and downstream performance. We find different patterns across evaluation settings: concept augmentation improves perplexity most consistently on news, independent of the POS used, whereas verbs provide the most reliable downstream gains. Despite these behavioral differences, representational analyses using CKA and linear probing show that the conditions yielding the largest downstream gains are not necessarily those that most alter the model’s internal representations. Together, these results suggest that the effectiveness of concept augmentation depends on where and how conceptual information is introduced during training.
[NLP-21] S-EDL: Eliciting Self-Evidence from Sequence Likelihoods for Semantic Calibration of LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在开放式生成任务中语义校准（semantic calibration）的难题。由于多个不同的词元序列可能表达相同的语义内涵，传统校准方法仅针对固定类别分类置信度或单一字符串的二元正确性，难以适应开放式生成中动态且多样的语义输出特性。其解决方案的关键在于提出一种名为S-EDL的新颖证据理论（evidential method），通过从模型自身的序列似然中提取证据，构建针对特定提示的可微分狄利克雷先验（differentiable Dirichlet prior）以表征提示相关的语义类别。在此先验下优化证据损失，能够生成对可变语义类别具有感知能力的校准训练信号，从而无需依赖固定的多选选项。实验在四个开放式基准测试和三种模型上验证了S-EDL能显著降低语义校准误差并提升准确性，实现了生成似然与语义正确性之间的对齐，为复杂开放场景中可信大语言模型的部署提供了新框架。
链接: https://arxiv.org/abs/2610.29832
作者: Yawei Li,Jiazheng Li,David Rügamer,Bernd Bischl,Yulan He,Mina Rezaei
机构: LMU Munich(慕尼黑大学); King’s College London(伦敦国王学院); Munich Center for Machine Learning (MCML)(慕尼黑机器学习中心)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 34 pages, 8 figures
Abstract:Calibrating large language models (LLMs) in open-ended generation is uniquely challenging because many distinct token sequences can express the same underlying meaning. Prior calibration techniques target either fixed-set classification confidence or the binary correctness of individual strings, making them inherently ill-suited for the dynamic, multi-string nature of open-ended semantic outcomes. To bridge this gap, we introduce S-EDL, a novel evidential method that natively optimizes LLMs for semantic calibration. By eliciting evidence from the sequence likelihoods of the model itself, S-EDL constructs a differentiable Dirichlet prior over prompt-specific semantic classes. Optimizing the evidential loss under this prior yields a calibration-aware training signal over variable semantic classes, completely bypassing the need for fixed multiple-choice options. Evaluations across four open-ended benchmarks and three models demonstrate that S-EDL substantially reduces semantic calibration error while improving accuracy. By aligning generative likelihoods with semantic correctness, this work establishes a framework for deploying trustworthy LLMs in complex, open-ended applications. Code is available at: this https URL.
[NLP-22] MemoryTax: The Bitter Lesson of Agent Memory
【速读】： 该论文旨在探究在大模型能力足够强的情况下，预先设计的代理记忆系统（agent memory systems）是否仍具有必要性。其核心问题是：当模型自身具备强大的信息检索能力时，人工设计的记忆机制是否仍能带来性能优势，抑或只是对模型能力不足的补偿性开销？研究发现，传统代理记忆系统（如Mem0、Zep）本质上是为弱模型提供的“补丁”，其成本高昂且存在天花板——记忆内容的质量受限于生成记忆的模型能力，更强的读取模型无法恢复由弱写入模型遗漏的信息。实验表明，在使用Claude Opus 5.5等强大模型时，基于原始历史记录并由模型自主检索的编码代理（coding agents）表现更优，其中Pi在成本-准确率权衡上显著领先，以不到全上下文方法一半的成本实现99%的准确率，其唯一错误源于一个仅靠代理可读的图片标题。这表明，随着模型推理与检索能力的提升，人工设计的记忆系统已不再是必需，甚至可能成为负担。
链接: https://arxiv.org/abs/2610.29823
作者: Zedong Peng
机构: Shanghai Jiao Tong University (上海交通大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Agent memory systems such as Mem0 and Zep decide what to keep before any question arrives; coding agents keep the raw history and let the model search it with its own tools. We ask whether designed memory still helps once the model is strong enough to search for itself. On one LoCoMo conversation (419 turns, 152 questions), we run four memory methods (full context, BM25, Mem0, Zep) and three coding agents (Pi, Codex, Claude Code), each with GPT-4o-mini, GPT-6 Luna and Claude Opus 5.5, and record what every rollout costs. This is a short empirical study, with one conversation and one run per cell. Agent memory is a costly patch for a weak model: Mem0 beats full context only with GPT-4o-mini, and its build alone costs more than a whole full-context rollout. Agent memory also has a ceiling, set by the model that writes it: a stronger reader does not recover what a weaker writer left out. With Claude Opus 5.5, Pi tops the cost-accuracy frontier, scoring 99% for less than half the cost of full context; its one miss is a wrong answer key. Part of that lead comes from photo captions that only the agents could read: on the 88 questions whose evidence has no photo, Pi and full context differ by one answer.
[NLP-23] MAPS: Macro-Adaptive Parallel Reasoning for Regulating Search Momentum in Retrieval-Augmented Generation
【速读】： 该论文旨在解决现有检索增强生成（Retrieval-Augmented Generation, RAG）方法在推理与检索交替进行过程中存在的问题，主要包括：子查询冗余或无效、查询探索多样性不足，以及推理过程中查询意图演化机制不清晰。其核心解决方案是提出一种宏观自适应并行推理框架MAPS（Macro-Adaptive Parallel Reasoning），关键在于引入“搜索动量”（search momentum）以表征推理轨迹中历史子查询所引发的累积检索倾向，并基于此发现查询生成过程倾向于沿已有检索路径持续演进。为缓解由此导致的查询意图漂移（query-intent drift），MAPS通过保留已检索知识和优化后的推理状态，削弱历史推理轨迹对后续子查询生成的影响；同时将传统的串行推理扩展为并行推理范式，在每轮迭代中识别互补的检索方向，生成多个并行子查询进行检索，并融合多源证据以更新知识与推理状态。实验结果表明，MAPS在六个问答基准测试上均显著优于主流RAG方法，且对不同骨干模型具有良好的泛化能力。
链接: https://arxiv.org/abs/2610.29792
作者: Xinze Li,Dingwen Zhang,Zhenghao Liu,Yukun Yan,Shuo Wang,Yu Gu,Ge Yu,Maosong Sun
机构: Northeastern University(东北大学); Tsinghua University(清华大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Retrieval-Augmented Generation (RAG) improves the performance of Large Language Models (LLMs) by retrieving external knowledge. Recently, some RAG methods interleave reasoning and retrieval, enabling LLMs to progressively generate sub-queries and retrieve external knowledge. However, these methods may suffer from redundant or ineffective sub-queries and insufficiently diverse query exploration. Moreover, due to the tightly interleaved nature of reasoning and retrieval, it remains unclear how the evolving reasoning process influences query intent. To characterize the evolution of query intent during reasoning, we introduce search momentum, which captures the accumulated retrieval tendency induced by historical changes in generated sub-queries along the reasoning trajectory. Through momentum-based analysis, we find that the query generation process tends to continuously evolve along existing retrieval trajectories. Based on this observation, we propose MAPS, a Macro-Adaptive Parallel Reasoning framework for regulating search momentum and mitigating query-intent drift. MAPS first retains the retrieved knowledge and a refined reasoning state, thereby reducing the influence of historical reasoning trajectories on subsequent sub-query generation. Subsequently, MAPS extends the conventional sequential reasoning process into a parallel reasoning paradigm. At each iteration, MAPS identifies complementary retrieval directions, generates corresponding sub-queries for parallel retrieval, and consolidates the retrieved evidence to update the knowledge and reasoning states. Experiments on six question-answering benchmarks demonstrate that MAPS consistently outperforms representative RAG methods across different backbone models. All code and resources are available at this https URL.
[NLP-24] Where Do LLM s Learn to Recommend AI?
【速读】： 该论文旨在揭示大语言模型（Large Language Models, LLMs）在回答用户关于学习方向、职业选择或产品开发等问题时，为何普遍存在推荐人工智能相关选项的倾向。其核心问题在于识别这种偏见的来源：是源于预训练阶段的数据分布，还是微调过程中的指令设计与提示语境的影响。研究发现，该倾向主要起源于预训练阶段，已有27%的预训练模型响应中直接包含明确的人工智能/机器学习（AI/ML）术语；经过指令微调后，这一比例上升至59%。然而，关键发现在于，这种上升并非必然——通过控制实验表明，若移除仅占7%的涉及人工智能的指令示例，模型推荐AI相关选项的比例反而下降14个百分点，低于原始预训练水平；而使用以“帮助性”为奖励信号的数据进行微调会使该比例上升9个百分点，使用多样化模拟用户偏好排序的数据则降低9个百分点。此外，在模型固定的情况下，提问方式显著影响结果：要求推荐“最佳”选项或“立即”采取的行动会分别使推荐率提升8和18个百分点，而告知模型其角色为“AI顾问”则增加4个百分点。因此，解决方案的关键在于认识到：模型对AI的偏好不仅源于预训练数据中的固有偏差，更受后续微调数据的设计策略及具体提问语境的共同塑造，强调了训练数据质量与提示工程在引导模型行为中的决定性作用。
链接: https://arxiv.org/abs/2610.29785
作者: Benaya Trabelsi,Jonathan Shaki,Sarit Kraus
机构: Bar-Ilan University(巴伊兰大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 18 pages, 4 figures. Companion paper to arXiv:2601.13749
Abstract:Asked what to study, which career to pursue, or what product to build, large language models (LLMs) often recommend AI-related options. We ask where this tendency comes from by comparing models before and after fine-tuning and by varying how questions are asked. First, pretrained models already include explicit AI/ML terms in the recommendations of 27% of their responses, and their released instruction-tuned versions do so in 59%. Second, fine-tuning models ourselves shows that this rise is not inevitable: general instruction tuning leaves the rate unchanged, but removing the 7% of instruction examples that concern AI cuts it by 14 percentage points, below the level of the pretrained models it starts from. Data rewarding helpfulness raise it by 9 points, while data ranked by the preferences of diverse simulated users lower it by 9. Finally, with models held fixed, asking for the “best” option or for what to do “right now” raises the rate by 8 and 18 points, whereas telling the model it is an AI advisor adds 4. Overall, the tendency starts in pretraining, and what models learn afterward, together with how a question is phrased, decides how strongly it appears.
[NLP-25] Evolving with Open Worlds: Towards Recursive Self-Improvement for Future Forecasting
【速读】： 该论文旨在解决当前人工智能系统缺乏持续自我改进能力的问题，即如何实现递归式自我改进（Recursive Self-Improvement, RSI）——使AI系统能够基于自身经验不断进化，而非依赖外部静态更新。其核心挑战在于构建一个持续变化、可客观验证且与现实世界自然耦合的动态环境以支持代理（agent）的演化。为此，论文提出DeepEvolve系统，其关键创新在于：通过未来事件预测任务构建一个“活”的环境，使代理在与动态环境的持续交互中积累经验；引入递归演化引擎，将每次预测背后的推理轨迹反向转化为可复用的能力提升机制。具体而言，该方案通过解耦搜索过程与预测目标，适应了易受污染的实时环境；同时采用技能提炼（skill distillation）策略，从有限的实时交互轨迹中提取通用能力，从而实现参数级演化的可持续性。实验表明，在每周更新的真实世界结果评估榜单FutureX上，该系统连续三周排名第一，评分最高提升达195%；在合成评估中亦优于多个基线模型，最大领先达10.7 Bradley-Terry点。这些成果表明，真正具备自进化能力的AI系统应被理解为与开放、动态环境共同演化的智能体，标志着AI发展范式正从在固定数据集上训练静态模型，转向以开放世界为根基、通过自身经验不断成长的动态智能体培育。
链接: https://arxiv.org/abs/2610.29784
作者: Zhipeng Chen,Yunsen Liang,Yanxi Chen,Hongkang Zhao,Lingyun Feng,Ruiyang Ren,Guoxin Chen,Jia Deng,Feng Chang,Chuan Hao,Ran Tao,Jian Yang,Ji-Rong Wen,Bryan Dai,Wayne Xin Zhao
机构: Renmin University of China (中国人民大学); Nanyang Technological University (南洋理工大学); IQuest Research (IQuest 研究所)
类目: Computation and Language (cs.CL)
备注: 47 pages, 13 figures. Working in progress
Abstract:Most contemporary artificial intelligence (AI) systems are released with frozen parameters and improve through external updates, whereas an ideal system would keep improving from its own experience, a capacity we call recursive self-improvement (RSI). Yet studying RSI requires an environment that is continuously changing, objectively verifiable, and naturally grounded, posing a fundamental challenge for agent evolution. Here we introduce DeepEvolve, a sustainable evolution agent system grounded in future event prediction, in which agents continuously interact with a dynamically evolving environment and improve by internalizing and externalizing the experience gained through these interactions. A living environment synthesizes verifiable tasks, and a recursive-evolution engine turns the reasoning trajectory behind each prediction back into capability: decoupling search from prediction adapts parameter-level evolution to the contamination-prone live environment, while distilling reusable skills lets the agent evolve from the limited live trajectories such an environment affords. On the external FutureX leaderboard, which evaluates predictions against real-world outcomes and is updated weekly, our self-improved forecaster ranked first across all systems over three consecutive weeks, achieving up to a 195% improvement in rating. On our synthesized evaluation, it ranked among the top three across four base LLMs, outperforming the strongest baseline by up to 10.7 Bradley–Terry points. More broadly, these results suggest that a system built for self-improvement is best understood as an agent that co-evolves with a genuinely living, open-ended environment, pointing to a shift in AI from training static models on fixed data toward cultivating agents that grow stronger from their own experience, with such an open world as the indispensable basis of that growth.
[NLP-26] ALIAS: Evaluating Performance and Memorization in LLM -Written TLA Specifications
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在基准测试中因记忆训练数据而虚高性能的问题，尤其关注模型是否通过直接复现训练样本中的答案而非真正理解任务来获得高分。其核心挑战在于现有检测方法——一致标识符重命名（Consistent Identifier Renaming, CIR）——可能将模型表现下降归因于记忆行为，但实际影响因素复杂，包括对精确标识符名称的依赖性评分机制以及新名称引入带来的额外负担。解决方案的关键在于区分真实记忆效应与评分偏差：通过以行为等价性为评价标准替代基于名称匹配的严格评分，发现仅约3个百分点的性能下降可归因于记忆，其余则源于评分方式和名称变更的影响；此外，在对不同任务子集微调的模型中，重命名导致的性能损失与训练增益减弱程度相关，进一步揭示了记忆、评估方式与泛化能力之间的交织关系。因此，该研究强调需采用更合理的评估范式以准确衡量模型的真实推理能力。
链接: https://arxiv.org/abs/2610.29767
作者: Arslan Bisharat,Mujtaba Nazari,Khushboo Bhadauria,Brian Ortiz,Eric Spencer,Beatriz Santos,TaiNing Wang,George K. Thiruvathukal,Konstantin Laufer,Mohammed Abuhamad
机构: Loyola University Chicago(洛约拉大学芝加哥分校)
类目: Computation and Language (cs.CL); Software Engineering (cs.SE)
备注: 19 pages (8 main + appendix), 6 figures, 9 tables. Code and data: this https URL
Abstract:Memorization inflates the benchmark scores of large language models (LLMs), since a model can reproduce answers it saw in training. Researchers test for it with consistent identifier renaming (CIR), which gives every identifier in a task a new name, and treat a lower score on the renamed task as evidence of recall. We test this method on the Temporal Logic of Actions Plus (TLA+) language, whose model checker acts as an oracle by confirming whether a renamed task preserves the exact behaviors of the original. Across 111 TLA±Bench tasks, renaming lowers the correctness of Claude Opus 4.8 from 28% to 18% and of GPT-5 from 9% to 7%. Most of this decrease comes from how the test grades, which requires exact names that most descriptions never state. Grading by behavior shrinks the decrease to about 3 percentage points for Opus 4.8 and to 0 for GPT-5. In two model families fine-tuned on disjoint halves of the tasks, renaming removes about half of the training gain in likelihood and costs 4.9 percentage points (Qwen) and 1.3 percentage points (Gemma) on unseen tasks. A lower score after renaming therefore mixes memorization, name-based grading, and the cost of new names.
[NLP-27] Bridging Foundational and Agent ic Capabilities of LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在作为智能体（agent）部署时，其基础能力与实际执行能力之间存在不匹配的问题。现有端到端评估指标无法区分性能失败是由基础能力不足还是部署策略薄弱所致，导致难以进行可操作的诊断。为此，论文提出一种基于操作层级的测量框架，通过构建每个模型的“基础能力画像”（foundational profile，源自能力基准测试）和“操作执行画像”（operation profile，源自交互轨迹），定义能力实现差距（capability realization gaps）为实际操作表现与基于基础能力预测值之间的偏差。在τ²-Bench上的实验表明，仅依靠基础能力画像无法充分预测智能体的实际执行行为；具有相似基础能力画像的模型在执行层面差异显著，且这些差距在不同任务子集中保持一致。引入操作画像后，显著提升了任务得分的预测能力。该框架能够定位具体操作层级的执行偏离，从而为模型优化与智能体评估提供精细化指导。
链接: https://arxiv.org/abs/2610.29751
作者: Wei Guo,Yuanjian Xu,Jianing Hao,Yongyao Duan,Ran Chen,Zhong Li,Guang Zhang
机构: Nankai University (南开大学); The Hong Kong University of Science and Technology (Guangzhou) (香港科技大学(广州)); Ant Group (蚂蚁集团); OpenCSG; Microsoft Research Asia (微软亚洲研究院)
类目: Computation and Language (cs.CL); Software Engineering (cs.SE)
备注:
Abstract:Large language models (LLMs) are increasingly deployed as agents, yet it remains unclear how their foundational capabilities translate into agentic capabilities. End-to-end scores leave unresolved whether low benchmark capability or weak deployment drives a failure, limiting actionable diagnosis. We bridge foundational and agentic capabilities through an operation-level measurement framework. We represent each model with a foundational profile derived from capability benchmarks and an operation profile derived from interaction trajectories, and define capability realization gaps as deviations of observed operation performance from predictions based on foundational capabilities. Experiments on fifteen model services in \tau^2 -Bench show that foundational profiles only partially predict agentic operations. Models with comparable profiles can differ substantially in execution, and these gaps remain consistent across task subsets. Operation profiles add information and substantially improve task-score prediction. The framework locates execution departures at the operation level to inform model development and agent evaluation. We will release our code and analysis artifacts.
[NLP-28] Accept or Repair: Preserving Passage-Supported Answers in Contrastive Decoding
【速读】： 该论文旨在解决大语言模型在生成回答时过度依赖训练阶段习得的先验知识，即使提供的文本信息与这些知识相矛盾的问题。其核心挑战在于，现有方法如对比解码（contrastive decoding）虽能增强对输入文本的响应，但会错误修改那些与文档内容一致的正确答案，从而在遵循文档与保持准确性之间产生权衡。本文提出一种无需训练的解决方案——“接受或修复”（Accept-or-Repair, AoR），其关键在于引入基于文档支持性的验证机制：首先采样一个答案，若该答案被文档支持则直接保留；否则，使用对比解码尝试最多两次替换，并仅在新答案通过文档一致性检查时返回。同时，若文档明确指向另一答案而替换结果重复了无文档支持的原始答案，则予以拒绝。实验表明，AoR在反事实文本上的性能提升达12.5分，在常规文本上提升2.3分，显著优于八种对比解码与提示工程基线方法，且在相同拒答规则下仍表现更优。此外，仅对不被文档支持的初始答案进行修复即可提升所有解码器的平均得分。这表明，模型能够在违背自身知识的前提下准确遵循文档信息，同时维持与文档一致情况下的高准确性。
链接: https://arxiv.org/abs/2610.29727
作者: Jungseob Lee,Seungyoon Lee,Hyeonseok Moon,Seongtae Hong,Suhyune Son,Sugyeong Eo,Minhyuk Kim,Chanjun Park,Jaehyung Seo
机构: Korea University(韩国大学); Sookmyung Women’s University(淑明女子大学); Yonsei University Mirae Campus(延世大学未来校区); Soongsil University(松岛大学); Konkuk University(国民大学)
类目: Computation and Language (cs.CL)
备注: 21 pages, 5 figures. Code will be released at this https URL
Abstract:Large language models often answer from knowledge learned during training, even when a provided passage contradicts that knowledge. To address this problem, contrastive decoding contrasts the predictions under the full and a modified prompt at every token, but it also alters correct answers that the passage supports. We show that checking answers against the passage removes this trade-off. Based on this observation, we propose Accept-or-Repair (AoR), a training-free procedure. AoR keeps a sampled answer if the passage supports it. Otherwise, it tries up to two replacements with contrastive decoding, returning one as soon as it passes this check. It rejects replacements that repeat the answer generated without the passage when the passage favors another answer. Our experiments show that AoR raises the score over sampling by 12.5 points on counterfactual passages and 2.3 on ordinary ones, surpassing eight contrastive and prompting baselines on both, including when each receives the same rejection rule. Replacing only initial answers that the passage does not support improves the average score of every decoder. These results suggest that models can follow passages that contradict their knowledge while preserving accuracy on passages that agree with it. Code will be released at this https URL.
[NLP-29] Retrieval Augmented Generation Compositionality
【速读】： 该论文旨在解决当前检索增强生成（Retrieval Augmented Generation, RAG）系统在组件评估与集成时存在的关键问题：现有技术通常孤立评估，缺乏对多阶段组件协同效应的系统性检验；同时，小规模数据集上的优化结果是否可迁移至大规模场景尚不明确，且检索性能与回答忠实度之间可能存在权衡。其解决方案的关键在于提出LASAGNA——一个开源的可控RAG评估框架，支持在包含8个基准和5K至200K文档规模的语料库上系统测试450种配置组合。研究发现：第一，各阶段表现优异的组件在集成后未必带来预期增益（如三类检索方法单独预测+47%提升，实际仅实现+17%）；第二，检索效果与答案忠实度的优化目标存在冲突，单一优化可能损害另一指标；第三，最优组件组合随语料规模变化而改变，小规模最优配置在大规模下可能失效。这些发现表明，孤立评估所得收益无法可靠预测真实系统性能，强调新RAG技术必须在完整堆栈环境下进行验证，从而推动更严谨的评估范式。
链接: https://arxiv.org/abs/2610.29722
作者: Ido Levy,Segev Shlomov,Leshem Choshen
机构: IBM(国际商业机器公司); Weizmann Institute of Science(魏茨曼科学研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 103 pages (9 main), 15 figures, 68 tables. Project page: this https URL Code: this https URL
Abstract:Retrieval Augmented Generation (RAG) grounds large language models in external knowledge. Modern pipelines combine embedding, chunking, retrieval, reranking, query augmentation, context compression, and agentic verification, yet these techniques are typically evaluated one at a time. It remains unclear whether their isolated gains survive composition, whether retrieval quality and answer faithfulness favor the same design choices, and whether conclusions from small corpora hold at large scale. We pre-declare three hypotheses and introduce LASAGNA, an open-source framework for controlled RAG evaluation. We test 450 configurations across eight benchmarks and corpora ranging from 5K to 200K documents. Three findings emerge. First, individually strong techniques from different phases of a RAG pipeline do not necessarily compose: three retrieval techniques predict a +47% combined improvement but deliver only +17% when used together. Second, component gains stack differently for retrieval and faithfulness, and optimizing one can hurt the other. Third, the best composition changes as the corpus grows: the combination that wins on a small corpus may not remain best at large scale. These findings show that gains measured in isolation are not reliable predictors of realistic performance, and that new RAG techniques should be tested with the full surrounding stack. We call on researchers to do so and supply LASAGNA to simplify these experiments.
[NLP-30] Scaling Multilingual Pretraining of Pixel Language Models
【速读】： 该论文旨在探究大规模预训练对像素语言模型（Pixel Language Models）跨语言迁移能力的影响机制，尤其关注在固定每语言计算资源下，通过扩展语言数量、书写系统多样性及模型容量所引发的性能变化。其核心问题在于：多语言预训练与模型规模扩展如何协同影响像素语言模型在低资源、类型差异大的语言上的泛化能力。解决方案的关键在于构建并系统评估一系列MPIXEL模型，在三个维度上进行控制变量实验——即增加语言种类、引入更多书写系统以及提升模型容量。研究发现，模型扩展呈现局部效应：各维度分别增强对应新增语言和书写系统的性能表现；而更大的模型容量可缓解多语言预训练带来的“多语言诅咒”（curse of multilinguality）问题。此外，对未见书写系统的迁移能力随多语言预训练提升，但不再随规模扩大进一步改善；在类型差异大且资源稀缺的语言上，该模型显著优于基于子词或字符级别的基线方法。同时，研究揭示了更大模型会分配更多参数层用于视觉特征处理，且随着语言数量增加，跨语言对齐能力逐渐减弱。总体而言，本研究系统刻画了多语言规模化对像素语言模型性能演进的内在规律。
链接: https://arxiv.org/abs/2610.29681
作者: Ilker Kesen,Jenny Kunz,Kasra Malihi,Desmond Elliott
机构: Humboldt-Universität zu Berlin; Linköping University; University of Copenhagen
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 33 pages, 25 figures, 14 tables
Abstract:Pixel language models process text as rendered images instead of a finite vocabulary, which gives them strong cross-lingual transfer abilities. However, not much is known about how these abilities are influenced by larger-scale pretraining. In this paper, we train a family of MPIXEL models along three axes of variation: more languages, more scripts, and larger model capacity under fixed per-language compute. In evaluations on five tasks covering 83 languages, we find that scaling acts locally: each axis improves the added languages and scripts, while increased capacity mitigates the curse of multilinguality from pretraining on more languages. Transfer to unseen scripts improves with multilingual pretraining, but does not further improve with scaling. On typologically distant low-resource languages, our models outperform subword and character-level baselines across many writing systems. We also show that larger pixel models reserve more layers for visual processing, and that cross-lingual alignment weakens as more languages are added. Overall, our findings characterize how multilingual scaling improves pixel language models.
[NLP-31] Uni-Mem: Learning a Long-Horizon Agent ic Memory Manager with Unified Online Policy Optimization
【速读】： 该论文旨在解决长期智能体记忆（long-term agent memory）中记忆构建与检索两阶段缺乏协同优化的问题。现有基于学习的方法通常仅优化其中一个阶段，或采用离线构建监督结合在线检索强化学习（RL）的方式，导致两阶段间难以实现有效的联合适应。为此，本文提出统一记忆管理器（Uni-Mem），通过端到端在线强化学习联合学习记忆构建与检索能力。为克服传统强化学习算法在长记忆管理轨迹中面临的混淆分组比较和稀疏反馈问题，提出了分阶段解耦的GRPO（Stage-Decoupled GRPO）方法，通过在每轮采样中交替优化构建与检索，并控制另一阶段带来的干扰，从而获得阶段特异性学习信号。此外，引入事件级信用分配（Event-Wise Credit Assignment, ECA），将反馈精准定位至相关事件，并将事件级优势传播至其组成操作，提升学习效率。实验在LoCoMo与LongMemEval基准上验证了Uni-Mem显著优于当前最强的基于强化学习的记忆系统，证明了通过分阶段解耦的GRPO与事件级信用分配联合学习记忆构建与检索的有效性。
链接: https://arxiv.org/abs/2610.29671
作者: Zhiqian Xia,Jielong Tang,Ling-An Zeng,Shixiong Kai,Rong Yao,Hao Kong,Xiao Dong,Wei Wang,Mingxuan Yuan,Siyu Xia,Haifeng Xia
机构: Southeast University(东南大学); Huawei Technologies, Noah’s Ark Lab(华为技术，诺亚方舟实验室); Sun Yat-sen University(中山大学); Shandong Normal University(山东师范大学)
类目: Computation and Language (cs.CL)
备注: 21 pages, 8 figures
Abstract:Long-term agent memory is shifting from predefined workflows toward learnable policies for memory construction and retrieval. Existing learning-based methods either optimize construction alone or combine retrieval-side online reinforcement learning (RL) with offline construction supervision, limiting co-adaptation between the two stages. In this paper, we explore end-to-end online RL for a unified memory manager (Uni-Mem) that learns both capabilities under a shared policy. However, joint optimization with existing RL algorithms such as GRPO faces confounded group comparisons and sparse feedback over long memory-management trajectories. To this end, we propose Stage-Decoupled GRPO, which alternates construction and retrieval optimization while controlling variation from the other stage within each rollout group, yielding stage-specific learning signals. We further introduce Event-Wise Credit Assignment (ECA), which localizes feedback to responsible events and propagates event-level advantages to their constituent operations. Experiments on LoCoMo and LongMemEval show that Uni-Mem outperforms the strongest RL-based memory, demonstrating the effectiveness of jointly learning memory construction and retrieval via Stage-Decoupled GRPO and ECA.
[NLP-32] SPARE: Sparsity Allocation via Routing Entropy for Efficient MoEs
【速读】： 该论文旨在解决混合专家模型（Mixture-of-Experts, MoE）在实际部署中因专家数量庞大而导致的显著内存开销问题。现有专家剪枝方法通常仅关注“移除哪些专家”，且对各层采用相同的剪枝比例，缺乏对不同层路由分布差异性的考虑。本文提出SPARE（Sparsity Allocation via Routing Entropy），一种无需训练、与选择器无关的剪枝策略，其核心在于利用校准数据上令牌的门控权重归一化后Top-K值的路由熵（routing entropy）来动态决定每层应裁剪的专家数量：当某层路由分布更分散（即熵更高），表明活跃专家间权重分布较均匀，移除单个专家所损失的信息较少，因此可更激进地进行剪枝；反之，路由集中时则保留更多专家。该方法通过随机剪枝的扰动界形式化了此直觉，并在固定选择器和剪枝预算下，验证了其相较于均匀分配策略能带来更优的性能表现。实验结果显示，在相同剪枝率下，SPARE在Mixtral、Qwen3及DeepSeek等模型上均提升了平均准确率，尤其在高剪枝率场景下优势明显——例如在50%专家剪枝条件下，将Qwen3-30B-A3B在八项任务上的平均准确率从57.2%提升至63.8%，同时使DeepSeek-R1在数学、代码与科学任务上的平均得分由80.1提升至82.8。
链接: https://arxiv.org/abs/2610.29662
作者: Yifan Yang,Hanling Yi,Dongquan Yang,Feng Lin,Rong Xiao,Aijun Li,Xiaotian Yu
机构: Intellifusion Inc.
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Mixture-of-Experts (MoE) architectures scale model capacity efficiently, yet their substantial memory footprint poses a major barrier to deployment. Expert pruning reduces this footprint, but most methods focus on which experts to remove and prune every layer equally. We propose SPARE (Sparsity Allocation via Routing Entropy), a training-free, selector-agnostic method that decides how many experts to remove from each layer using routing entropy, the entropy of tokens’ normalized Top-K gate weights on calibration data. When a token’s gate weight is spread evenly, removing one active expert discards little of it, so SPARE prunes layers with more diffuse routing more aggressively. A perturbation bound under random pruning formalizes this intuition, and controlled interventions with a fixed selector link concentrated routing to larger output changes. With the same selector and budget, SPARE improves average accuracy over uniform allocation on Mixtral, Qwen3, and DeepSeek models, especially at high pruning ratios. At 50% expert pruning, it raises the eight-task average of Qwen3-30B-A3B from 57.2% to 63.8%, and the math, code, and science average of DeepSeek-R1 from 80.1 to 82.8.
[NLP-33] Effects of Model Scale and Quantization on Social Bias and Abstention in Question Answering
【速读】： 该论文旨在解决在资源受限环境下部署小型化与量化语言模型时所引发的公平性问题：当压缩后的模型产生较少偏见性回答时，这种现象究竟是反映了更公平的行为表现，还是仅仅表现为对不确定内容的过度回避（over-abstention）？为探究此问题，研究基于BBQ基准测试，在Qwen3、Gemma-3和SmolLM2等多个模型尺寸及量化配置下开展问答（QA）任务分析，并进一步考察不同提示（prompt）设计以及强制选择场景中移除“未知”选项后结果的稳定性。研究发现，尽管大模型在问答准确率上表现更优，但偏见水平并未呈现简单的规模效应，且量化对偏见的影响因配置而异；尤为关键的是，在26种配置中有20种表现出更低偏见的同时也伴随显著增加的过量回避行为，表明较低的观察偏见可能部分源于更高的回避倾向。因此，该研究的关键结论在于：公平性评估必须综合考虑偏见响应、回避行为、提示设计及答案选项设置等多重因素，避免将减少偏见简单归因于模型公平性的提升。
链接: https://arxiv.org/abs/2610.29657
作者: Kuniko Paxton,Ifeoluwa Wuraola,Dhavalkumar Thakker
机构: University of Hull(赫尔大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:The deployment of small and quantized language models in resource-constrained settings raises an important fairness question: when a compressed model produces fewer biased responses, does this reflect fairer behavior or greater abstention? We study this problem in Question Answering (QA) using the BBQ benchmark across multiple model sizes and quantization settings from Qwen3, Gemma-3, and SmolLM2. We also examine whether the observed trends remain stable across alternative prompt formulations and when the ``Unknown’’ option is removed in a forced-choice setting. Our results show that larger models achieved higher QA accuracy, but bias showed no simple scaling trend and quantization effects varied across configurations. Notably, 20 of the 26 configurations with fewer biased responses also show increased over-abstention, indicating that lower observed bias can partly reflect greater abstention. These findings show that lower observed bias can partly reflect greater abstention, and that fairness evaluation should consider biased responses, abstention, prompt formulation, and answer-option design jointly.
[NLP-34] LLM s as survey respondents: Comparison to simple semantic baselines
【速读】： 该论文旨在解决生成式 AI 在问卷数据增强中的有效性问题，特别是评估大语言模型（Large Language Models, LLMs）是否能够真正理解问卷题项之间的语义关联并据此推断出有意义的回答，而非仅依赖表面文本相似性进行启发式匹配。其核心挑战在于：问卷题项间的语义相似性会引发回答的内在相关性，而当前主流方法尚未明确区分模型是基于深层语义推理还是浅层文本匹配来生成新回答。为此，研究通过在两份关于自动驾驶或轻型电动车辆态度的调查中，基于不同比例的已知回答预测缺失题项，对比了三种主流LLMs与两个基于嵌入相似性的基线模型的表现。结果表明，尽管LLMs在多数情况下优于基线，但其性能随已知回答比例下降而显著退化，甚至在一项调查中与基线表现持平。这说明LLMs在数据稀疏场景下难以维持其推理能力，其数据增强效果具有高度情境依赖性，且可能退化为简单的嵌入相似性匹配机制。因此，该研究的关键发现是：当前LLMs在问卷数据增强中的表现并非源于对复杂语义关系的稳定建模，而是依赖于充足的数据输入，其优势在低信息密度条件下无法持续保持。
链接: https://arxiv.org/abs/2610.29645
作者: Esko Lehtonen,Tommi Buder-Gröndahl
机构: VTT Technical Research Centre of Finland Ltd(芬兰技术研究中心有限公司); University of Helsinki(赫尔辛基大学)
类目: Computation and Language (cs.CL)
备注: 16 pages, 5 figures
Abstract:Large language models (LLMs) have been explored as surrogates for human survey respondents. A more promising application is data augmentation, where LLMs predict new responses from partial human data. However, the semantic similarity between questionnaire items is a known source of correlation between answers. It is thus unclear whether LLMs actually infer meaningful relationships between question-response pairs, or use surface-level comparison between questions as a heuristic. To assess this, we evaluated how three LLMs can predict responses to held-out items in two surveys measuring attitudes toward automated or light-electric vehicles, based on varying proportions of known responses. Performance was compared to two baselines, where new responses were derived by maximizing embedding-similarity to a prior response. While LLMs mostly predicted human responses better than baselines, their performance consistently declined with a lower share of known responses, even aligning with the baseline in one survey. Our results show that LLMs’ capacities for augmenting replies varied across surveys, and can revert to the same levels as simpler embedding similarity baselines when less data is available.
[NLP-35] Overview of CHIP 2026 Shared Task 1: Patient-Specific Phenotype Concept Recognition in Full-Text Biomedical Literature
【速读】： 该论文旨在解决从全文本生物医学文献中准确识别与特定患者相关的表型概念及其关联关系这一关键问题，尤其针对包含多位患者及复杂临床描述的文献场景。其核心挑战在于实现患者特异性表型概念识别（patient-specific phenotype concept recognition）与表型归一化（phenotype normalization），以支持罕见病诊断、基因-表型关联分析及精准医疗。解决方案的关键在于利用生物医学语言模型（biomedical language models）、大语言模型（LLM）与集成学习策略相结合的多模态方法，通过深度语义理解与上下文建模提升对患者-表型关联的定位精度。实验基于PatientPheX数据集，涵盖506名患者的15,193条表型提及和5,656条患者-表型关联，最终最优系统在测试集上达到0.7389的综合得分，显著优于基线模型。评估结果表明，尽管当前方法展现出巨大潜力，但在患者归属推断与表型标准化方面仍存在显著挑战。
链接: https://arxiv.org/abs/2610.29606
作者: Xuanlyu Zhu,Feng Tian,Yang Zhong,Pengqi Zheng,Xiwen Bi,Ling Luo,Yuanyuan Sun,Jian Wang,Lingfei Lin
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Automatic phenotype information extraction plays an important role in rare disease diagnosis, genotype-phenotype analysis, and precision medicine. However, accurately identifying phenotype concepts and determining their associations with specific patients remain challenging, particularly in full-text biomedical articles involving multiple patients and complex clinical descriptions. This paper presents an overview of CHIP 2026 Shared Task 1, which aims to promote the development of methods for patient-specific phenotype concept recognition from full-text biomedical literature. The task consists of two subtasks: full-text phenotype concept recognition and patient-specific phenotype association. For this evaluation, we used 200 annotated full-text biomedical articles from the PatientPheX dataset, containing 506 patients, 15,193 phenotype mentions, and 5,656 patient-phenotype associations. A total of 455 teams registered for the competition, with 189 and 103 valid submissions received on the Phase A and Phase B leaderboards, respectively. The best-performing system achieved an overall score of 0.7389 on the final test set, outperforming the baseline by 8.37 percentage points. The top-performing teams explored diverse approaches combining biomedical language models, large language models (LLMs), and ensemble strategies. The evaluation results highlight both the potential of these approaches and the remaining challenges in patient attribution and phenotype normalization from full-text biomedical literature. The post-evaluation phase remains open at this https URL.
[NLP-36] A Diagnostic Study of Faithfulness in Diffusion Language Models for Table-to-Text Generation
【速读】： 该论文旨在探究非自回归的扩散语言模型（Diffusion Language Models, DLM）在表格到文本生成任务中，相较于传统自回归模型（如GPT-2、GPT-Neo）在事实性保持（factual grounding）方面的可靠性差异。其核心问题是：尽管扩散模型通过双向注意力机制进行迭代去噪，避免了严格的左到右生成约束，这种机制是否能更有效地确保生成内容与源表格的结构化输入保持一致？解决方案的关键在于采用基于检索的少样本协议，在相同实验设置下对比扩散模型MDLM和LLaDA与自回归模型的表现，并从两个独立维度评估结果——参考文本相似性（reference similarity）与源表蕴含关系（source grounding，以自然语言推理NLI衡量）。研究发现，这两项指标呈现分离趋势：随着更多示例引入，LLaDA的BLEU得分提升至22.0，但其对源表的蕴含率从0.78降至0.52；经QLoRA微调后的LLaDA虽达到最高BLEU值24.15，但事实性并未随之改善。人工标注进一步揭示，自回归模型主要表现为脱离表格内容，而扩散模型则更倾向于维持主题相关性，但常发生实体替换等局部错误。因此，仅依赖参考文本相似性指标无法充分评估生成内容的真实性，强调需结合事实性验证以全面评估生成系统的忠实性。
链接: https://arxiv.org/abs/2610.29602
作者: Zaynab Merimi,Noussayba El Marrakchi,Arthur Satouf
机构: CY Tech( CY Cergy Paris Université, 法国); MILA – Quebec AI Institute(魁北克人工智能研究所, 加拿大); Université Paris-Saclay CentraleSupélec(巴黎萨克雷大学中央理工学院, 法国)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 9 pages, including references and appendix. Diagnostic study on source faithfulness in table-to-text generation
Abstract:Diffusion language models generate by iteratively denoising a masked sequence under bidirectional attention, without the strict left-to-right commitment of autoregressive decoding. Whether this difference affects how reliably they remain grounded in structured inputs is largely unexplored. We study this question in table-to-text generation using ToTTo, where highlighted source cells make factual grounding directly testable. We compare the masked diffusion models MDLM and LLaDA with GPT-2 and GPT-Neo under an identical retrieval-based few-shot protocol, together with a QLoRA-adapted LLaDA, and we score reference similarity and source grounding as separate axes. The two pull apart. Within LLaDA, additional demonstrations raise BLEU from 11.3 to 22.0 while NLI entailment against the source table falls from 0.78 to 0.52; QLoRA reaches the best BLEU of the study, 24.15, and grounds no better for it. Manual annotation shows the failure modes differ in kind: the autoregressive baselines tend to wander off the table, while LLaDA-based models more often stay on topic and swap out individual entities. Reference-based metrics alone are insufficient to assess faithfulness in table-to-text generation.
[NLP-37] WRDO: Weighted Refusal Direction Optimization in Aligned Language Models EMNLP2026
【速读】： 该论文旨在解决当前对齐大语言模型（Aligned Large Language Models）中拒绝行为（refusal behavior）脆弱性的问题，即模型的有害内容拒绝能力容易被简单的激活空间或权重空间干预所破坏。其关键在于，现有方法通常依赖单一残差流方向（residual-stream direction）来表征拒绝行为，该方向多通过有害与无害提示的均值差计算得到，但这种统一平均方式忽略了不同有害提示之间的异质性，可能混淆了多种不同的拒绝机制。为此，论文提出加权拒绝方向优化（Weighted Refusal Direction Optimization, WRDO），通过学习每个有害提示对应的特定拒绝方向，并利用可学习的聚合权重构建复合拒绝方向，实现对不同提示贡献度的差异化建模。实验表明，相较于传统的单方向干预方法，WRDO在三个开源大模型上结合三名守门裁判和HarmBench评估体系，显著提升了干预的有效性与灵活性；同时在通用能力基准测试中保持了良好的模型性能。此外，研究还探索了逆向防御场景，即利用相同优化框架恢复被破坏的拒绝能力，进一步验证了该方法的可逆性与鲁棒性。核心突破在于引入可学习的加权聚合机制，使拒绝表示的操纵更加精准、自适应且高效。
链接: https://arxiv.org/abs/2610.29555
作者: Grigoriy Evseev,Vladimir Solodkin,Dmitry Bylinkin,Daniil Medyakov,Vladimir Aletov,Aleksandr Beznosikov
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Accepted for EMNLP 2026
Abstract:Aligned large language models are trained to comply with benign requests while refusing harmful ones. However, recent work shows that this refusal behavior is fragile and can often be disrupted by simple activation- or weight-space interventions. In particular, refusal has been shown to be largely mediated by a single residual-stream direction, typically computed as a difference-of-means vector between harmful and harmless prompts. However, this standard construction aggregates heterogeneous harmful prompts into one uniform average, potentially conflating distinct refusal-related phenomena. In this work, we study whether refusal interventions can be strengthened by learning how to combine prompt-specific refusal directions. We propose Weighted Refusal Direction Optimization (WRDO), a procedure that constructs a composite refusal direction from a bank of per-prompt directions using learned aggregation weights. This approach allows different harmful prompts to contribute unequally to the final intervention direction. We evaluate WRDO against prior single-direction methods on three open-weight language models using three guard judges and HarmBench as an external evaluator. We further measure the effect of the intervention on general model capability through standard utility benchmarks. Finally, we investigate a defense-oriented setting in which the same optimization machinery is used in reverse to restore refusal behavior in an abliterated model checkpoint. Our results show that weighted aggregation provides a more effective and flexible mechanism for manipulating refusal representations.
[NLP-38] PACE: Periodic and Channel-Aligned Encoding of Four-Bit Recurrent States
【速读】： 该论文旨在解决线性注意力语言模型（Linear-attention language models）在推理过程中因状态（state）存储精度较低而导致的性能下降问题，特别是在低比特量化下累积误差严重、影响生成质量的挑战。现有方法通常在每个生成标记后立即对状态进行四比特量化，导致误差快速累积，从而需要通过增加位宽或微调模型来补偿，限制了实际部署效率。本文提出一种后处理式四比特状态量化方法PACE（Post-hoc Chunked Quantization with Enhanced scaling），其核心创新在于：仅在每若干个生成标记后进行一次量化（类比于预填充阶段的分块核操作），中间生成的标记暂存于小缓冲区，从而显著减少量化误差的累积；同时，观察到门控-增量状态（gated-delta state）的大值集中在列维度，因此采用列内共享量化尺度以提升量化效率；此外，对持续保留量化误差最久的头部（heads）保留八比特精度，实现混合精度量化。实验表明，在40亿至350亿参数的四个模型上，采用混合四/八比特头的PACE在内存仅为八比特DAMP约三分之二的情况下，性能可与后者媲美；而全四比特版本的PACE在相同内存开销下优于Q-Mamba，且保持了原有缓冲机制带来的解码加速优势。
链接: https://arxiv.org/abs/2610.29545
作者: Patrick Haller,Filipe Laitenberger,Alan Akbik
机构: 未知
类目: Computation and Language (cs.CL)
备注: 11 pages, 6 tables, 7 figures
Abstract:Linear-attention language models keep their context in a recurrent state of fixed size, where an inference system stores one copy of this state for every sequence it decodes at the same time. Storing the state at lower precision therefore lets more sequences share an accelerator. Existing state quantizers round the state after every generated token and report that four bits lose accuracy unless the bit width is raised or the model is fine-tuned.\par We introduce PACE, a post-hoc four-bit state quantizer. PACE rounds the state only once every few generated tokens, as chunked kernels already do once per chunk during prefill, and keeps the tokens generated in between in a small buffer. Rounding less often reduces the error that accumulates in the state. Additionally, we find that the large entries of the gated-delta state gather in columns, because some channels of the update written at each token remain large across many tokens. PACE therefore shares each quantization scale within one column. It also keeps the heads that retain rounding error longest at eight bits. On four hybrid models from 4B to 35B parameters, PACE with mixed four- and eight-bit heads is competitive with eight-bit DAMP on the three larger models at about two thirds of its memory, PACE with four-bit heads throughout scores higher than Q-Mamba at equal memory, and rounding every few tokens preserves the decoding speedup of the buffer.
[NLP-39] RCSD: Reinforcement and Continual Learning for Domain-Specific Speculative Decoding
【速读】： 该论文旨在解决生成式 AI（Generative AI）在推测解码（Speculative Decoding）过程中，因传统训练方法依赖软标签蒸馏导致的低接受率与高早期拒绝风险问题。传统方法强制轻量级草稿模型（draft model）模仿目标模型（target model）的完整输出分布，包括高熵区域，从而降低了生成候选词的有效性。为此，论文提出RCSD（基于强化学习与持续学习的推测解码训练框架），其核心在于将草稿模型的优化目标与系统层面的接受长度最大化对齐。RCSD通过推测解码过程中自然产生的被接受与被拒绝序列构建隐式偏好对，并采用掩码损失机制避免对与目标模型一致的令牌进行惩罚，从而更高效地引导草稿模型生成高接受率的候选序列。实验表明，RCSD在多个基准测试中相比HASS提升了35%的平均接受长度，并在实际工作负载下实现1.35倍的端到端推理加速，同时保持生成质量不变。
链接: https://arxiv.org/abs/2610.29527
作者: Mingkang Liu,Weiming Meng,Peipei Jiao,Jiawei Zhang,Tianyuan Liu,Junyan Li,Lichen Pang
机构: Hisense(海信); Jiawei Zhang; Tianyuan Liu; Junyan Li; Lichen Pang
类目: Computation and Language (cs.CL)
备注: 9 pages, 3 figures
Abstract:Speculative decoding accelerates large language model inference by employing a compact draft model to propose candidate tokens that are verified in parallel by a target model. Conventional training relies on soft-label distillation, which forces the draft model to mimic the target’s full output distribution, including high-entropy regions that degrade acceptance rates and increase early rejection risk. We propose RCSD (Reinforcement and Continual Learning for Speculative Decoding), a training framework that aligns draft model optimization with the system-level objective of maximizing acceptance length. RCSD leverages naturally occurring accepted and rejected sequences during speculative inference to construct implicit preference pairs, with masked loss to avoid penalizing target-aligned tokens. Evaluated across multiple benchmarks, RCSD improves average acceptance length by 35% over HASS and achieves 1.35x faster end-to-end inference than HASS on practical workloads, without compromising generation quality.
[NLP-40] Mitigating Query Shortcuts in Chain-of-Thought with Context-Aware Decoding
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在使用思维链（Chain-of-Thought, CoT）提示时出现的“查询特定捷径”（query-specific shortcuts）问题，即模型的最终答案可能不依赖于生成的推理过程，而是基于输入查询中的表面线索或模式进行快速推断，从而削弱了推理的可解释性与可靠性。其解决方案的关键在于提出一种名为推理对比解码（Rationale Contrastive Decoding, RCD）的新方法，该方法基于上下文感知解码（Context-Aware Decoding, CAD）框架，通过对比有无推理内容条件下的下一个词元分布，放大因引入推理内容而显著提升概率的词元，从而增强模型对推理内容的依赖。该方法在理论上可通过KL正则化的奖励最大化目标进行解释，并在五个推理基准测试中验证了其有效性，实验表明RCD能显著提升答案准确性，并通过使用真实与随机推理内容的干预分析证实其增强了最终答案对推理内容的依赖性。研究结果表明，对比解码提供了一种无需训练、简单有效的机制，可有效缓解模型在CoT推理中的捷径行为。
链接: https://arxiv.org/abs/2610.29518
作者: Shin’ya Yamaguchi
机构: NTT(日本电信电话公司)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 9 pages, 3 figures
Abstract:While Chain-of-Thought (CoT) prompting improves the reasoning performance of large language models (LLMs), recent studies have shown that final answers do not always depend on the generated rationales. In particular, LLMs may rely on query-specific shortcuts rather than the information provided by their reasoning steps. In this paper, we investigate whether context-aware decoding (CAD), an existing contrastive decoding method, can mitigate such shortcut behavior when applied to CoT reasoning. We study this application as Rationale Contrastive Decoding (RCD), which contrasts the next-token distributions conditioned on the query with and without its rationale, amplifying tokens whose likelihood increases due to the rationale. We provide a theoretical interpretation of this decoding rule via a KL-regularized reward-maximization objective. Experiments across five reasoning benchmarks demonstrate that RCD improves answer accuracy over standard CoT. Furthermore, intervention analyses using ground-truth and random rationales reveal that RCD increases the behavioral dependence of final answers on the rationale content. Our findings suggest that contrastive decoding offers a simple, training-free approach to mitigating query shortcuts.
[NLP-41] Keeping Time vs. Understanding Time: Neither Alone Explains Temporal Reasoning in LLM s
【速读】： 该论文旨在解决当前时间推理（temporal reasoning）研究中普遍假设的“时间算术与时间语义”二分法是否具有实证有效性的核心问题。这一二分法被广泛用于基准测试设计、模型训练和进展评估，但其科学基础尚未经过直接检验。论文的关键发现在于：首先，尽管任务被划分为“算术类”或“语义类”，但在同一类别内进行微调后，性能提升主要局限于特定数据集（提升6.8至23.1个百分点），而跨数据集的迁移效果极低（-3.2至+1.1个百分点），表明不存在可转移的时间推理能力；其次，基于认知负荷的模型评估显示，驱动模型在这些基准上表现的主要因素并非预设的定量与逻辑推理能力（分别作为时间算术与时间语义的代理指标），而是对时间常识的记忆（knowledge）。因此，该研究揭示当前时间推理基准并未清晰实现算术与语义的分离，反而主要奖励模型对时间规律的机械记忆，其解决方案的关键在于重新审视并重构时间推理能力的评估范式，强调应关注真正具备泛化性的推理机制而非表面的任务分类。
链接: https://arxiv.org/abs/2610.29507
作者: Auss Abbood,Fernando Martínez-Plumed,José Hernández-Orallo,Nigel Collier
机构: University of Cambridge(剑桥大学); Valencian Research Institute for Artificial Intelligence (VRAIN)(瓦伦西亚人工智能研究所); Universitat Politècnica de València(瓦伦西亚理工大学); Leverhulme Centre for the Future of Intelligence(利弗休姆智能未来中心)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 30 pages, 9 figures, 24 tables (including appendices)
Abstract:Temporal reasoning is widely assumed to decompose into two modes: temporal arithmetic (date and time calculations), and temporal semantics (understanding temporal and causal relations). This dichotomy shapes how benchmarks are designed, how models are trained, and how progress is measured, yet its empirical validity has never been directly tested. We provide two lines of evidence that challenge it. First, we test whether tasks labelled as arithmetic or semantic by the benchmarks support transferable gains within their purported mode. Finetuning on these subsets yields predominantly dataset-specific gains ( +6.8 to +23.1 percentage points), while transfer to a different dataset in the same mode ranges from -3.2 to +1.1 percentage points. Across the two benchmark families used in the transfer analysis, this pattern provides little evidence of coherent, transferable temporal abilities. Second, using cognitive-demand-based model evaluation, we examine which capabilities actually drive performance on these benchmarks. We find that quantitative and logical reasoning, the assumed proxies for temporal arithmetic and temporal semantics, are not the primary drivers; knowledge is. Together, these findings show that current temporal reasoning benchmarks do not cleanly realise the arithmetic-semantics distinction and instead largely reward models for remembering how time typically works.
[NLP-42] A Strong MoE Is All You Need for Process Supervision
【速读】： 该论文旨在解决多步推理中过程奖励模型（Process Reward Models, PRMs）依赖高质量过程标签这一核心瓶颈问题，尤其针对现有方法中人工标注、基于回滚的估计或辅助验证等手段成本高、可扩展性差的局限。其解决方案的关键在于发现并利用混合专家模型（Mixture-of-Experts, MoE）架构中天然存在的专家路由（expert routing）信息，作为内在的过程置信度信号。研究提出通过整合全局路由质量、头路由质量及步骤内路由变异性的综合指标构建“路由置信度”（routing confidence），进而设计出两种新方法：RoCo-Rethink 用于识别置信度最低的推理步骤并选择性重生成后续轨迹；RoCo-PRM 则利用强MoE模型自动为推理轨迹生成基于路由的步骤评分，并通过知识蒸馏得到独立的PRM。实验表明，二者在多种MoE架构与多样化推理任务上均显著优于主流基线，且RoCo-PRM具备良好的跨模型与跨任务迁移能力。该工作揭示了专家路由不仅是计算调度机制，更是一种原生的、可被有效利用的推理过程监督信号。
链接: https://arxiv.org/abs/2610.29498
作者: Guanzhi Deng,Zengyan Liu,Haibo Wang,Kuan Wu,Shing Yin Wong,Sichun Luo,Zhuoran Wang,Linqi Song
机构: City University of Hong Kong (香港城市大学); Carnegie Mellon University (卡内基梅隆大学); University of Hong Kong (香港大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Process reward models (PRMs) provide fine-grained supervision for multi-step reasoning, yet obtaining reliable process labels remains a central bottleneck. Existing approaches typically rely on human annotation, rollout-based estimation, or auxiliary verification. In this work, we explore a different source of process supervision natively exposed by Mixture-of-Experts (MoE) models: expert routing. We show that simple routing statistics provide an intrinsic process-confidence signal that is competitive with conventional output-logit confidence. Building on this observation, we construct routing confidence by combining overall routing mass, head routing mass, and within-step routing variation, and introduce RoCo-Rethink, which identifies the least confident reasoning step and selectively regenerates the subsequent trajectory. We further introduce RoCo-PRM, which uses a strong MoE to automatically annotate reasoning trajectories with routing-derived step scores and distills them into a standalone PRM. Across multiple MoE architectures and diverse reasoning tasks, both RoCo-Rethink and RoCo-PRM consistently outperform strong baselines, while RoCo-PRM transfers effectively across both model architectures and tasks. Our findings suggest that expert routing is more than a computational dispatch mechanism: it provides an architecture-native source of process supervision for reasoning.
[NLP-43] netBench: A Clause-Anchored Russian Corpus for Evaluating Language Models against Professional Codes of Ethics
【速读】： 该论文旨在解决生成式AI在专业领域应用中可能无意或有意违反职业伦理规范的问题，尤其关注大型语言模型（Large Language Models, LLMs）作为专业助手时，其输出可能违背用户所遵循的职业伦理准则，而用户却难以察觉这一风险。现有评估方法仅能报告危害的广义类别，无法揭示违规发生的频率或具体违反的条款。为此，论文提出TenetBench，一个基于俄罗斯语职业伦理规范的对抗性数据集构建框架，通过将3,433条与十五项职业规范条款冲突的请求与其仅在合法性上不同的“良性孪生”请求配对，实现对模型行为的精细评估。其核心解决方案在于设计自动化流水线，从伦理文本自动生成任务，并支持随新规范加入而动态扩展；同时，在英语语境下也构建了两套类似数据集并完成人工验证。人类评估显示，99.5%的冲突任务具有可接受性，模型裁判在Gwet’s AC1达0.894以上，表明标注一致性高。实验发现，14种模型-推理模式组合中，11种在多数冲突请求中仍生成违规内容，且多数未发出警告，近四分之三的警告伴随违规输出。合规表现较好的模型主要通过拒绝违规请求并保留对合法孪生请求的支持来实现，而启用推理模式可使部分模型的拒绝率降低一半。该研究为评估语言模型对职业规范的遵守情况提供了可扩展的基准工具，并推动建立针对特定职业与组织的诊断性数据集。
链接: https://arxiv.org/abs/2610.29477
作者: Yegor Anichkov,Valery Vinogradov,Andrey Furov,Marina Polukoshko,Sergey Bolovtsov
机构: Trusted AI Research Center, RAS(俄罗斯科学院可信人工智能研究中心); Center for Applied Artificial Intelligence, Institute of Social Sciences(社会科学研究所在应用人工智能中心)
类目: Computation and Language (cs.CL)
备注: Preprint under review. 39 pages, 2 figures, 5 tables; Online Resource 1 appended (9 pages). Dataset and code: this https URL
Abstract:Codes of professional ethics guide and restrict specialists, who increasingly work with assistants built on large language models. Such an assistant can breach the user’s code, or help the user breach it, without the user noticing. Evaluations reporting broad categories of harm cannot show how often this happens or which provision is breached. TenetBench pairs 3,433 requests conflicting with clauses of fifteen Russian-language professional codes with benign twins that differ only in the legitimacy of what is asked. An automated pipeline generates the tasks from the text of a code, so the corpus grows with every code added and a provision yields new tasks on every run. It also produced tasks and human-validated verdicts from two English codes of other jurisdictions. In human validation of the frozen pipeline, specialists rated 0.995 of the conflicting tasks plausible, and the model judge agreed with the adjudicated label at Gwet’s AC1 of at least 0.894 on 1,860 elements. We evaluated fourteen model and reasoning-mode combinations on whether they produce the conflicting text, decline or warn. Eleven of the fourteen produce conflicting content on most such requests, mostly without a warning, and nearly three warnings in four accompany conflicting content. The models that conform more do so by declining while still serving the legitimate twin, and enabling reasoning halved the refusals of two models. We hope the dataset and pipeline will help evaluate how language models observe professional norms and let diagnostic datasets be built for particular professions and organisations.
[NLP-44] OmniSimulator: Aligning Small Language Models for Authentic Heterogeneous Behavior Modeling NEURIPS2026
【速读】： 该论文旨在解决小规模大语言模型（LLM）在短视频平台环境下进行真实用户行为模拟时，因推理范式局限导致的预测能力不足问题。现有方法依赖于原生思维链（chain-of-thought）推理，易退化为浅层模式匹配，难以捕捉真实用户决策背后的深层行为因素。其解决方案的关键在于提出OmniSimulator训练框架，通过引入五维结构化行为导向推理模板，从真实用户行为场景中精心筛选数据，并利用教师模型生成符合该结构的高质量推理轨迹，使小模型能够学习到与行为相关联的深层次推理模式，而非仅依赖表面行为关联。基于此，研究构建了包含9,881个高质量训练实例的数据集（覆盖200名真实用户的三个月交互轨迹），实验表明该方法显著提升Qwen3-8B的平均得分达34.33%，性能媲美Claude-Sonnet-4.5，且支持轻量级自托管部署，验证了结构化行为导向推理对实现可扩展、高保真用户模拟的有效性。
链接: https://arxiv.org/abs/2610.29472
作者: Jiawei Chen,Ruoxi Xu,Boxi Cao,Zhuoqun Li,Ruotong Pan,Yunfei Zhang,Zhirui Yang,Jingyan Chen,Tingting Gao,Han Li,Yaojie Lu,Xianpei Han,Le Sun,Xiangyu Wu,Hongyu Lin
机构: Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences(中国科学院软件研究所中文信息处理实验室); University of Chinese Academy of Sciences(中国科学院大学); Kuaishou Technology(快手科技)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted by NeurIPS 2026
Abstract:Large language models (LLMs) have shown great potential as general-purpose user simulators for interactive systems. Prior studies have demonstrated that training on high-quality data can substantially improve model reasoning and downstream task performance. However, the upper bound of user simulation is still constrained by the model’s reasoning paradigm: native chain-of-thought often degenerates into shallow pattern matching and fails to adequately model the behavioral factors underlying real user decisions. In this paper, we propose OmniSimulator, a novel training framework designed to improve the predictive ability of small LLMs for realistic user simulation on short-video platform environments. Specifically, OmniSimulator organizes behavior-oriented reasoning through a structured schema with five dimensions, carefully selects data from real user-behavior scenarios, and introduces a teacher model to generate high-quality reasoning traces under the proposed schema, helping small models learn behavior-relevant reasoning patterns rather than relying on superficial behavioral correlations. Based on this process, we construct 9,881 high-quality training instances from three months of historical interaction trajectories of 200 real users, averaging about 50 distilled examples per user. Experimental results show that OmniSimulator improves the average score of Qwen3-8B by 34.33%, achieves performance competitive with Claude-Sonnet-4.5, and supports smaller-scale, self-hosted deployment. These results suggest that learning structured behavior-oriented reasoning patterns is a promising direction towards scalable and high-fidelity LLM-based user simulation.
[NLP-45] Harness Scaling for Generalizable Agent ic Reinforcement Learning
【速读】： 该论文旨在解决当前生成式智能体（Agent）在强化学习（Reinforcement Learning, RL）训练过程中因依赖单一固定工具调用框架（harness）而导致的泛化能力不足问题。现有方法通常基于单一固定的harness进行训练，导致模型过度适应特定配置，难以迁移至其他执行环境或工具组合。为应对这一挑战，论文提出“Harness Scaling”——一种面向智能体强化学习的新规模扩展维度，其核心在于将训练过程从单一harness拓展至多样化的执行配置空间。该方案的关键在于构建一个可分解的harness空间，通过因子化设计实现配置的灵活组合；在此基础上，选取具有均衡因子覆盖的紧凑训练子集，并组织跨harness的联合训练策略，从而在保持全局覆盖性的同时引入受控的变异。实验结果表明，采用该方法训练的模型在多种基准测试、模型规模及强化学习算法下均能显著提升对未见白盒配置的泛化能力，且具备向未知外部黑盒harness有效迁移的能力，优于仅在单一复杂黑盒harness上训练的模型。研究证实，harness多样性是智能体强化学习中一个有意义的规模扩展维度，而其组织方式对跨harness泛化性能具有决定性影响。
链接: https://arxiv.org/abs/2610.29458
作者: Fei Bai,Huatong Song,Chuan Hao,Ran Tao,Jian Yang,Bryan Dai,Wayne Xin Zhao,Ji-Rong Wen
机构: IQuest Research(伊奎斯特研究); Beihang University(北京航空航天大学); Gaoling School of Artificial Intelligence, Renmin University of China(中国人民大学高瓴人工智能学院)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Agent harnesses have become a central component of modern agentic systems, shaping how models use tools, manage context, and coordinate long-horizon interaction. Realizing the benefits of these harnesses depends on how effectively the underlying model can make use of them, motivating reinforcement learning directly through the harness. However, existing approaches typically train through a single fixed harness, which can lead to specialization and limited transfer across harness configurations. To address this, we introduce \textbfHarness Scaling, a new scaling dimension for agentic RL that extends training from a single harness to diverse execution configurations. Concretely, we construct a factorized harness space, select a compact training subset with balanced factorial coverage, and organize cross-harness training to combine global coverage with controlled variation for the same task. Across multiple benchmarks, model scales, and RL algorithms, Harness Scaling consistently improves generalization to unseen white-box configurations. Moreover, models trained through structured white-box variation transfer effectively to unseen external black-box harnesses, outperforming models trained through a single complex black-box harness. These results establish harness diversity as a meaningful scaling dimension for agentic RL and highlight the importance of how this diversity is organized for cross-harness generalization.
[NLP-46] Omnilingual-MsASR: Benchmarking Multimodal Multilingual Long-Form Multi-Speaker ASR of Omni Large Language Models
【速读】： 该论文旨在解决当前多模态大语言模型（Omni-modal LLMs）在长时序、多说话人视频场景下的理解能力局限问题，尤其聚焦于对视频中“谁在何时说了什么”的精细时空定位与说话人区分难题。其核心挑战在于现有模型在处理长达30分钟、涉及最多20名说话人的多语言、多模态视频时，普遍存在严重的语音识别错误率（如tcpWER超过100%），暴露出在细粒度时间定位、说话人去歧义以及跨模态协同推理方面的显著瓶颈。解决方案的关键在于构建并发布首个覆盖六种语言的长时序多说话人自动语音识别（MsASR）基准数据集Omnilingual-MsASR，该数据集提供并行的音频与视频输入，用于评估模型的跨模态一致性及其利用多模态互补线索的能力，从而为未来多模态基础模型的发展提供可量化的性能评估标准与研究方向指引。
链接: https://arxiv.org/abs/2610.29455
作者: Yuan Ge,Pei Zhang,Xize Cheng,Lingzhou Mu,Mingyang Wang,Jin Xu,Baosong Yang,Shengxiang Gao,Jingbo Zhu,Zhengtao Yu,Tong Xiao
机构: Northeastern University, China(东北大学，中国); Alibaba Token Hub, Alibaba; Kunming University of Science and Technology(昆明理工大学); NiuTrans Research
类目: Computation and Language (cs.CL)
备注:
Abstract:Omni-modal large language models (LLMs) have achieved remarkable progress in audio and video understanding. However, existing models struggle with long-form, multi-speaker videos, especially in understanding the entire video and recognizing who says what and when. To characterize the capability limit of current omni LLMs and guide future model development, we introduce Omnilingual-MsASR, a multilingual, multimodal benchmark for long-form Multi-speaker Automatic Speech Recognition (MsASR) covering six languages, recordings up to 30 minutes, and scenarios with up to 20 speakers. Omnilingual-MsASR provides parallel audio and video inputs to evaluate models’ cross-modal consistency and their ability to exploit complementary cues across modalities. Extensive evaluations of leading omni-modal LLMs reveal substantial performance bottlenecks, while their tcpWERs frequently exceed 100%. These findings highlight fine-grained temporal localization, speaker disambiguation, and cross-modal synergy as key challenges for future omni-modal foundation models. Our work is available at this https URL.
[NLP-47] DynaWeave: Learning to Adapt through Dynamic Scenario Synthesis
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理在静态训练环境中学习后，难以应对真实动态环境中外部事件干扰的问题。现有训练环境多为静态，仅反映代理自身行为的影响，而现实场景中其他用户、服务或突发状况引发的外部事件常导致原有计划失效，从而限制了代理的适应能力。为此，论文提出DynaWeave框架，其核心在于通过自动化合成可验证的动态场景，将原有的静态任务转化为包含外生事件（exogenous events）的动态测试环境。关键创新包括：利用有向无环图（Directed Acyclic Graph, DAG）建模事件间的依赖关系，并通过执行引擎在代理交互过程中按需激活事件；引入动态验证器（dynamic verifier），仅更新实际发生事件所影响的评估检查项，保留其余验证逻辑以保证评估效率与准确性。基于DynaWeave，研究构建了涵盖36个环境的2,095条经验证的交互轨迹，并用于对Qwen3.5模型进行监督微调。在Gaia2、CostBench和ToolMaze三个基准上的实验表明，DynaWeave显著提升了不同规模模型的整体性能，尤其在CostBench上任务完成率提升67.34个百分点，并在ToolMaze上超越Gemini-3.1-Pro-Preview的表现，验证了动态场景合成作为训练代理适应复杂变化环境的有效路径。
链接: https://arxiv.org/abs/2610.29443
作者: Xuan Yang,Chunxiao Liu,Hongsheng Xin,Hao Xu,Ning Miao
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language model (LLM) agents increasingly use tools to complete tasks in stateful environments. However, many training environments remain largely static apart from changes caused by the agent’s own actions, whereas external events introduced by other users, services, or sudden contingencies in real environments can invalidate ongoing plans. To address this gap, we introduce DynaWeave, an automated synthesis framework that transforms existing static tasks into verifiable dynamic scenarios. The framework generates exogenous events relevant to each task, covering changes in task requirements, environment state, and external information. It uses directed acyclic graphs (DAGs) to represent dependencies between events and an execution engine to activate them during agent interaction. A dynamic verifier revises only the evaluation checks affected by events that actually occur and retains all other checks. Using DynaWeave, we collect 2,095 verified interaction trajectories across 36 environments and use them for supervised fine-tuning of Qwen3.5 models. Experiments on Gaia2, CostBench, and ToolMaze show that DynaWeave achieves consistent improvements in overall performance across all three evaluated model sizes, increasing the task completion rate on CostBench by 67.34 percentage points and even exceeding the performance of Gemini-3.1-Pro-Preview on ToolMaze. These results support dynamic scenario synthesis as a practical approach to training agents to adapt to changing environments. Our code is available at this https URL.
[NLP-48] BIRD-RL: Scaling Agent ic Reinforcement Learning over Stateful Data-Centric Environments NEURIPS2026
【速读】： 该论文旨在解决当前以工具为中心的智能体强化学习（agentic reinforcement learning, RL）框架在数据密集型环境（data-centric environments）中面临的严重空间效率低下与轨迹污染问题，尤其是在状态依赖任务（如SQL调试）中，传统方法因需为每次回溯（rollout）分配独立数据库容器而导致资源浪费，而直接复用数据库又会因无法追踪多轮交互中的状态演化，引发轨迹污染和跨轨迹干扰，进而导致策略更新严重不稳。其解决方案的关键在于提出一种四阶段的新型智能体强化学习教学课程（BIRD-RL），通过交替增强基础智能体推理能力与轨迹自适应的在线策略探索能力，有效规避端到端学习固有的不稳定性。支撑该教学课程的核心是轨迹范围持久执行基础设施（Trajectory-scoped Persistent Execution Infrastructure），该机制能够在保障轨迹隔离的前提下，安全地复用数据库副本，显著提升空间效率达12.8倍，从而无需依赖Kubernetes等复杂编排系统即可实现可扩展的智能体强化学习。实验结果表明，7B与14B规模的小语言模型（SLMs）在BIRD-CRITIC-SQLite基准上分别达到44.40%和48.00%的成功率，性能媲美如Claude-Opus-4.6 Agent等强大专有模型（48.20%），并成功训练出兼具高级SQL调试与生成能力的统一小语言模型，展现出在异构、真实世界数据密集型任务中实现多目标优化的广泛潜力。
链接: https://arxiv.org/abs/2610.29426
作者: Ge Qu,Jinyang Li,Xiaohan Xu,Xiaolong Li,Bowen Qin,Nan Huo,Ziwei Tang,Chenhao Ma,Reynold Cheng
机构: The University of Hong Kong(香港大学); National University of Singapore(新加坡国立大学); The Chinese University of Hong Kong, Shenzhen(中国香港中文大学（深圳)）
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Accepted to NeurIPS 2026 Main Track
Abstract:While agentic reinforcement learning (RL) has emerged as a promising paradigm for training small language models (SLMs), existing frameworks are predominantly tool-centric and struggle with data-centric environments. Specifically, applying tool-centric frameworks to stateful tasks like SQL debugging introduces severe spatial inefficiencies since proper environment isolation typically requires a separate database container per rollout. Naively reusing databases without tracking evolving states across multi-turn interactions causes trajectory contamination and cross-trajectory interference, which severely destabilizes policy updates. To overcome these bottlenecks, we introduce BIRD-RL, a novel four-stage agentic RL curriculum that alternately enhances foundational agent reasoning and trajectory-adaptive on-policy exploration to circumvent the instability inherently associated with direct end-to-end learning. Crucially, to support this curriculum, BIRD-RL is driven by a Trajectory-scoped Persistent Execution Infrastructure, which enables safe database replica reuse while preserving trajectory isolation. This design improves spatial RL efficiency by 12.8x, successfully enabling scalable agentic RL for complex data-centric tasks without needs of Kubernettes. Empirical evaluations on the BIRD-CRITIC-SQLite benchmark demonstrate that our 7B and 14B models achieve Success Rates of 44.40% and 48.00%, respectively, showing competitive performance against strong proprietary model-based agents such as Claude-Opus-4.6 Agent (48.20%). Furthermore, BIRD-RL successfully trains unified SLMs capable of both advanced SQL debugging and generation, highlighting its broader potential for multi-objective optimization across heterogeneous, real-world data-centric tasks.
[NLP-49] A KAN-based Non-linear Factorization for Embedding Layer Compression
【速读】： 该论文旨在解决现有嵌入压缩方法中低秩分解（low-rank factorization）因线性瓶颈导致表征能力受限的问题。其核心挑战在于，传统低秩分解虽具数学简洁性，但受限于线性映射的表达能力，难以充分保留原始嵌入层的语义信息。为此，本文提出一种增强型分解方案，关键创新在于在压缩后的嵌入层中引入可学习的非线性投影，从而提升模型的表达能力。该方法受柯尔莫哥洛夫-阿诺德网络（Kolmogorov Arnold Networks, KAN）启发，将固定线性变换替换为可学习的单变量函数，突破传统线性结构的限制。研究通过将该方法集成至Skip-gram框架，在词类比与语义相似性任务上进行评估，并采用预训练未压缩嵌入层进行有效初始化。实验结果表明，该方法在相同参数预算下显著改善了压缩嵌入的质量，验证了非线性投影对缓解线性瓶颈的有效性，为嵌入层压缩提供了超越纯线性方法的新范式。
链接: https://arxiv.org/abs/2610.29424
作者: Oliver Giudice,Anna Russo Russo
机构: Banca d’Italia (意大利银行)
类目: Computation and Language (cs.CL)
备注: 18th International Conference on Statistical Analysis of Textual Data (JADT 2026)
Abstract:Among existing embedding compression approaches, low-rank factorization provides a simple and mathematically principled solution by approximating the embedding matrix with a reduced-rank decomposition. However, its practical effectiveness is often limited, as the resulting linear bottleneck constrains representational capacity. To overcome these limitations, we propose an enhanced factorization scheme that increases expressive power by introducing learnable non-linear projections within the compressed embedding layer. Our method is inspired by the Kolmogorov Arnold Networks (KAN) paradigm, which replaces fixed linear transformations with learnable univariate functions. Despite promising results in other domains, applications of KANs in NLP remain scarce and have so far been largely limited to final or task specific layers, rather than core representational components such as embedding layers. We evaluate the proposed approach by integrating the compressed embedding layer into the Skip-gram framework and comparing it against a standard low-rank baseline under matched parameter budgets on the Word Analogy and Semantic Similarity benchmarks. We further introduce an effective initialization strategy from a pre-trained, uncompressed embedding layer. The behavior of the model is analyzed both immediately after initialization and after a brief post-initialization refinement phase, considering training with the standard Skip-gram objective as well as variants augmented with distillation from the original embedding layer. Overall, our results show that augmenting low-rank factorization with learnable non-linear projections effectively mitigates the limitations of linear bottlenecks, yielding more faithful compressed embeddings under fixed dimensionality and suggesting a promising direction for embedding layer compression beyond purely linear methods. Comments: 18th International Conference on Statistical Analysis of Textual Data (JADT 2026) Subjects: Computation and Language (cs.CL) Cite as: arXiv:2610.29424 [cs.CL] (or arXiv:2610.29424v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2610.29424 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-50] CCG Diffusion: Combinatory Categorial Grammar-Driven Compositional and Syntactically Disambiguated Text-to-Image Generation
【速读】： 该论文旨在解决文本生成图像模型在理解提示词（prompt）的句法结构方面存在的缺陷，具体表现为：模型无法显式捕捉词语之间的修饰关系、短语的依附位置以及句法歧义，导致属性绑定错误、空间关系和数量描述不准确，且难以按预期解释句法模糊的提示。现有基于句法的控制方法主要局限于属性绑定问题，对空间关系、数量控制及句法歧义解析能力有限，通常依赖短语结构语法或依存语法，并需额外规则约束树结构以提取修饰关系或指定句法解释。为此，本文提出一种无需训练的解决方案——CCG Diffusion，其核心是利用组合范畴语法（Combinatory Categorial Grammar, CCG），该语法中每个词的类别蕴含丰富的句法信息，可直接通过解析结果读取修饰关系；同时，典型句法歧义如介词短语（PP）依附歧义，可通过固定介词类别即可解决。CCG Diffusion 采用两种基于 CCG 的机制——提示重写与注意力引导，统一应用于属性绑定、空间关系与数量控制，以及句法歧义消解。在两个不同架构的生成模型（Stable Diffusion 和 Sana）上，该方法在属性绑定和二维空间关系任务中得分最高，并在处理 PP 歧义方面显著优于所有对比方法，验证了其有效性与通用性。
链接: https://arxiv.org/abs/2610.29415
作者: Ryosuke Yamaki,Daichi Mochihashi,Nobutaka Shimada,Seitaro Shinagawa,Tadahiro Taniguchi
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: Preprint under review
Abstract:Text-to-image generation models do not explicitly receive the syntactic structure of the prompt, i.e., which word modifies which and where each phrase attaches. Consequently, they often struggle to bind attributes to the correct objects, to depict spatial relations and counts correctly, and to render syntactically ambiguous prompts in the intended interpretation. Existing syntax-based control methods mainly address attribute binding and have limited ability to control spatial relations and counts or to specify the interpretation of syntactically ambiguous prompts. They rely on phrase-structure or dependency grammars and require additional rules or constraints on the tree structure to extract modification relations between words or to specify a syntactic interpretation. We propose CCG Diffusion, a training-free method that controls image generation using Combinatory Categorial Grammar (CCG). In CCG, the category of each word carries rich syntactic information: it specifies what the word combines with and what phrase it forms. Modification relations between words can therefore be read directly from a CCG parse. Moreover, prepositional phrase (PP) attachment ambiguity, a representative syntactic ambiguity, can be resolved simply by fixing the preposition’s category. CCG Diffusion applies the same two CCG-based mechanisms, prompt rewriting and attention guidance, both to controlling attribute binding, spatial relations, and counts and to resolving PP ambiguity. On two architecturally different backbones, Stable Diffusion and Sana, CCG Diffusion achieves the highest scores among the compared methods in attribute binding and 2D spatial relations and significantly outperforms all of them in PP ambiguity resolution.
[NLP-51] ECHO: Emergent Common-Mode Hidden Offsets Drive Vocabulary Collapse in Preference Optimization
【速读】： 该论文旨在解决生成式AI（Generative AI）在偏好优化（Preference Optimization）过程中出现的生成崩溃（Generation Collapse）问题，其核心在于揭示一种被传统相对损失函数忽视的隐藏表征偏移——共模偏移（Common-Mode Offset）。该偏移表现为模型隐藏状态在不同词元位置、提示（prompt）及优选与次选响应之间均存在的共享方向性漂移。由于该偏移同时推动优选与次选样本的表征向同一方向移动，导致相对排名损失对其累积几乎无反应，进而引发输出层的词汇井效应（Vocabulary Well），即单一词元概率被过度放大，最终主导解码过程并造成生成崩溃。研究的关键发现是：这种共模偏移并非生成崩溃的单纯结果，而是可独立重建词汇井的内在机制；通过从损失中屏蔽该共模成分，可在旗舰设置下有效防止词汇井形成。此外，该现象广泛存在于14/17基础模型谱系中，且在全参数微调和多种偏好优化方法（如DPO、SimPO）中均出现，表明其为通用性缺陷。研究进一步指出，标准排名指标存在盲区，而该表征层面的共模偏移信号可作为生成崩溃前的早期预警信号。
链接: https://arxiv.org/abs/2610.29410
作者: Yun Li,Ehsan Javanmardi,Simon Thompson,Jiachen Gong,Qunli Zhang,Zifan Zeng,Shiming Liu,Peng Wang,Zixuan Guo,Manabu Tsukada
机构: The University of Tokyo (东京大学); Huawei (华为); TIER IV, Inc. (Tier IV公司)
类目: Computation and Language (cs.CL)
备注: 28 pages, 13 figures
Abstract:Preference optimization can improve every ranking metric while building a hidden, shared representation shift that the relative loss barely sees, one that can eventually destroy generation. We trace this failure to the common-mode offset, a shared directional shift in the model’s hidden states that appears across token positions, prompts, and both chosen and rejected responses. Because it moves chosen and rejected representations together, the relative ranking loss barely reacts to its accumulation. As this offset grows, it projects onto the output layer to form a vocabulary well, elevating a single token’s probability until it dominates decoding and causes generation collapse. The isolated offset alone reconstructs the vocabulary well, injecting the learned offset into an untreated base model recreates the collapse where norm-matched random directions do not, and masking this common component from the loss prevents the well in our flagship setting. Under the per-token ranking loss we study, the same collapse appears in 14 of 17 base-model lineages and under full-parameter fine-tuning. Moreover, the offset is not merely an artifact of collapse, since it is already present in textbook DPO and SimPO runs that form no well. Textbook DPO keeps it weakly aligned with any single vocabulary direction even with three times our base budget. Trained from base models for that budget, however, full-parameter SimPO drives it into collapse on both Llama-2 and Llama-3.1, and the same kind of offset grows, without a well, from a public SFT checkpoint under the official SimPO recipe. Across 17 UltraFeedback runs, the better a run ranks held-out responses, the less often two public reward models prefer its samples to the base model’s. These findings expose a blind spot in standard ranking metrics and introduce a representation-level signal that can warn of generation collapse before it occurs.
[NLP-52] Right On Q: Recovering Reasoning under Quantization via Self-Generated Trajectories
【速读】： 该论文旨在解决低比特量化大语言模型（Low-bit Large Language Models, LLMs）在长时序推理任务中性能下降及生成响应超出令牌预算的问题。现有量化感知训练（Quantization-aware Training, QAT）方法通常采用固定响应序列对量化后的学生模型进行监督，导致训练阶段与推理阶段的前缀分布不一致，且随着生成序列长度增加，轨迹偏差加剧；同时，学生模型在高熵前缀下对下一词候选的概率预测存在系统性偏差，即部分候选被低估而另一些被高估，造成分布错位。针对上述问题，论文提出OnQ这一低比特恢复流水线，其核心在于结合在线策略蒸馏（on-policy distillation） 与候选粒度动态KL散度（candidate-wise Dynamic KL）。具体而言，从块级校准开始，量化学生模型自主生成响应，并在自身生成的前缀上接受全精度教师模型的监督，从而缓解训练-推理不匹配问题；动态KL通过每个候选项的教师-学生概率比值自适应加权正向与反向KL，有效平衡对低概率和高概率候选的修正。实验表明，在Qwen3-1.7B、4B、8B模型于2-3比特精度下，OnQ在九个基准测试中均取得最高平均性能，尤其在长时序推理任务上提升显著；在2比特下，其在所有模型规模与基准测试中均超越所有基线，其中Qwen3-8B平均性能较最强基线提升10.26分，同时生成响应更短、截断与重复错误更少。
链接: https://arxiv.org/abs/2610.29378
作者: Boyuan Zheng,Jing Xiong,Chenwei Wu,Hui Shen,Zesen Zhao,Hei Ting Una Chan,Xueshen Liu,Zhen Zhang,Liyue Shen,Zechun Liu,Z. Morley Mao
机构: University of Michigan, Ann Arbor(密歇根大学安娜堡分校); University of California, Santa Barbara(加州大学圣塔芭芭拉分校); Meta AI(Meta人工智能)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 19 pages, 6 figures, 9 tables
Abstract:Quantization-aware training (QAT) aims to recover reasoning capabilities in low-bit large language models (LLMs). Existing recipes typically supervise the quantized student with the original full-precision model on fixed response sequences. However, the resulting low-bit quantized models still struggle on long-horizon reasoning tasks and often fail to terminate within a token budget that suffices for the full-precision model. We identify two challenges in closing this gap. First, fixed-response supervision leaves a mismatch between training prefixes and those encountered during student generation, and this trajectory divergence grows as responses lengthen. Second, on its own prefixes, the student underpredicts some next-token candidates and overpredicts others, and this distributional misalignment becomes more frequent at high-entropy prefixes. We present OnQ, a low-bit recovery pipeline that combines on-policy distillation and candidate-wise Dynamic KL. Starting from block-wise calibration, the quantized student generates responses and receives full-precision teacher supervision on its own prefixes. Dynamic KL weights forward and reverse KL by each candidate’s teacher-student probability ratio to balance correction of under- and overprediction. Across Qwen3-1.7B, 4B, and 8B at 2- and 3-bit precision, OnQ achieves the highest nine-benchmark average, with the largest gains on long-horizon reasoning benchmarks. At 2 bits, it outperforms every baseline on every benchmark at every model size, improving the Qwen3-8B average by 10.26 points over the strongest baseline. It also produces shorter responses with fewer truncations and repetitive errors.
[NLP-53] Memory-Centric Continual Learning for AI Agents
【速读】： 该论文旨在解决基于基础模型的自主智能体在长期部署过程中因固定权重和上下文长度限制，难以实现持续学习的问题。现有外部记忆系统多作为被动检索缓存，仅评估其能否召回存储信息或过往交互，而未能验证记忆是否真正驱动行为上的持续改进。为此，论文提出以记忆为中心的持续学习（Memory-Centric Continual Learning, MCCL），其核心在于将记忆的价值重新定义为对后续行为的优化能力，而非仅限于信息的存储与召回。解决方案的关键在于三个组成部分：首先，构建一个三轴分类法（substrates、assets、policies），从记忆的存储介质、表现形式和管理策略三个维度系统化组织记忆，并形成内层更新记忆状态、外层基于任务反馈优化管理策略的双循环生命周期；其次，设计了一套评估协议，通过干扰下的记忆保持、对环境变化的适应性以及任务迁移能力等指标，有效区分真实的行为提升与单纯的上下文扩展；最后，提出一个记忆扩展框架，量化分析代理性能随经验积累、存储容量及计算资源变化的非参数化与参数化记忆下的可扩展性。整体框架将记忆从被动查询机制转变为驱动持续学习的核心引擎，为实现自我进化的自主智能体提供了系统性方法论。
链接: https://arxiv.org/abs/2610.29357
作者: Dongfang Li,Zhuoen Chen,Bonian Jia,Zixuan Liu,Zhengxuan Lu,Can Deng,Yukun Shi,Yuhang Meng,Xin Zhang,Jiahe Huang,Fuhao Li,Junmai Wang,Shunyao Wen,Jingjun Sun,Jiaxu Tian,Jiangling Zhang,Zihan Wang,Liangyou Li,Yitong Yao,Shuangyong Song,Beilun Wang,Pengxu Wei,Hanlin Xu,Chang Liu,Yongxiang Li,Baotian Hu,Xiangyang Ji,Min Zhang
机构: Harbin Institute of Technology (Shenzhen), Shenzhen, China; Shenzhen Loop Area Institute, Shenzhen, China; Tsinghua University, Beijing, China; Southeast University, Nanjing, China; Xingchen AGI Lab, China Telecom, Beijing, China; Huawei Technologies Co., Ltd., Shenzhen, China
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Autonomous agents powered by foundation models can reason, plan, and use tools, but their fixed weights and context limits hinder sustained learning from experience over long-term deployment. While external memory systems attempt to bridge this gap, they mostly act as passive retrieval caches, evaluated primarily by whether they can recall stored facts or past interactions. Whether memory can drive genuine continual learning remains an open question. In this paper, we introduce Memory-Centric Continual Learning (MCCL), arguing that memory should be judged by whether it improves future actions, not merely what it retrieves. MCCL has three main parts. First, a three-axis taxonomy organizes memory by where state is stored (substrates), what form it takes (assets), and how it is managed (policies). This defines a two-loop lifecycle that updates memory state in an inner loop while refining management policies in an outer loop based on task feedback. Second, an evaluation protocol measures retention under interference, adaptation to change, and task transfer to isolate genuine behavioral gains from simple context expansion. Third, a memory scaling framework models how agent performance scales with experience, storage capacity, and compute budgets across non-parametric and parametric memory. Together, these components reframe agent memory from passive lookup to continuous learning, offering a principled route toward self-improving autonomous agents.
[NLP-54] Dont Stop Long-Context Continual Pre-Training Too Early: An Empirical Scaling Law
【速读】： 该论文旨在解决长上下文持续预训练（Long-context Continual Pre-training, LCCP）的收益如何随训练令牌数量增长而变化这一关键问题，尤其关注其性能提升的可扩展性与可预测性。研究发现，长上下文能力与模型规模及短上下文预训练和LCCP的令牌预算之间存在一种经验性的加法幂律关系，该规律在多个模型规模与百余种训练配置下均表现出良好的拟合与外推能力。为实现稳定、可扩展的训练过程监控，论文提出\textscConstant-Last4协议——通过平均恒定学习率训练中最近四个连续检查点来构建稳定的性能观测轨迹。在此协议下，相同幂律形式能准确预测12.2亿参数模型在约1000亿个LCCP令牌上的性能演化路径。下游评估进一步表明，长上下文性能在考察范围内仍持续提升，未出现饱和迹象，且在128K上下文扩展与监督微调后仍保持增益，适用于长上下文理解与代理型任务。因此，论文的核心结论是：应持续扩大LCCP规模而非过早终止，因其收益具有稳定性、可预测性与下游可迁移性。
链接: https://arxiv.org/abs/2610.29352
作者: Ziqing Qiao,Xiaoyue Xu,Sikui Zhang,Yinuo Xu,Bingxiang He,Chaojun Xiao,Xu Han
机构: Tsinghua University (清华大学); OpenBMB
类目: Computation and Language (cs.CL)
备注: 32 pages, 18 figures
Abstract:Long-context continual pre-training (LCCP) is widely used to improve long-context capability, but how its benefits scale with the number of tokens used for LCCP remains unclear. We investigate this question through modeling, monitoring, and validating the benefits of scaling LCCP. First, we establish an empirical additive power law relating long-context performance to model size and the respective token budgets of short-context pre-training and LCCP. Experiments across five model scales and more than one hundred short- and long-context budget configurations show that the fitted law reproduces the observed trends and extrapolates well to held-out model scales. Second, we introduce \textscConstant-Last4, a training and observation protocol that averages the four most recent consecutive checkpoints from a constant-learning-rate run to enable stable monitoring along a single extensible training trajectory. Under this protocol, the same scaling-law form fits well and accurately predicts the LCCP performance trajectory of a 1.22B-parameter model trained on approximately 100B LCCP tokens. Finally, downstream evaluations along this trajectory show continued improvements in long-context performance, with no clear sign of saturation within the evaluated range. These gains persist after both 128K context extension and supervised fine-tuning, with benefits extending to long-context understanding and agentic tasks. Together, these results argue for continuing to scale LCCP rather than stopping early: its gains are stably observable along an extensible trajectory, quantitatively predictable, and transferable to downstream tasks.
[NLP-55] Closed-Loop Dual-Reference Guidance for Accent Control in Cross-Lingual Voice Cloning
【速读】： 该论文旨在解决零样本跨语言语音克隆（zero-shot cross-lingual voice cloning）中参考说话人音色特征（如口音）被带入目标语言的问题。其核心解决方案是提出一种闭环双参考引导机制（closed-loop dual-reference guidance），通过将原始参考语音与一个由语音转换（Voice Conversion, VC）构建的可复用本族语锚点（native anchor）配对，分别初始化语言模型（Language Model, LM）状态，并共同引导共享的声学轨迹生成。每次生成片段完成后，均同步更新两个状态，确保生成历史与实际发音保持对齐。该方法仅需为每位说话人一次性构建锚点，合成阶段则使用冻结的端到端文本转语音（Text-to-Speech, TTS）模型，无需逐句进行语音转换。在20名未见法语说话人的测试中，固定参数设置下（λ*=0.33），残余法语语言识别（LID）质量降低73%，相较直接克隆；相较于仅使用锚点的方法，恢复了0.155的性能增益，同时达到最低平均词错误率（WER=0.009）和最高平均生成置信度分数（GOP=4.750）。盲测结果显示，以英语为母语的听者更偏好该方法在发音自然度上优于直接克隆，在说话人身份保留上优于仅锚点克隆及TTS→VC级联方案。在线推理实时性（RTF）约为0.59，较级联方案（0.80）降低约26%，且在西语任务中无需调参即实现56%的LID质量提升。
链接: https://arxiv.org/abs/2610.29351
作者: Yuanzhong Zheng,Wei Huang,Ming Ma,Kailun Jin,Youhai Jiang
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
备注:
Abstract:Zero-shot cross-lingual voice cloning often carries the reference speaker’s accent into the target language. We propose closed-loop dual-reference guidance, which pairs the original reference with a reusable native anchor constructed by voice conversion (VC). The references initialize separate language-model (LM) states that guide a shared acoustic trajectory; each completed patch then updates both states, keeping their generated histories aligned with the emitted speech. Anchor construction runs once per speaker, and online synthesis uses a frozen TTS model without per-utterance VC. On 20 held-out French speakers, the fixed setting \lambda^!*=0.33 reduces residual French LID mass by 73% versus direct cloning, at an ECAPA similarity cost of only 0.042 ; relative to anchor-only cloning, it recovers 0.155 . The method achieves the lowest mean WER ( 0.009 ) and highest mean GOP ( 4.750 ) among the held-out test arms. In blinded preference tests, native-English listeners favor it over direct cloning on pronunciation naturalness, and over both anchor-only cloning and a TTS \to VC cascade on speaker identity. Online RTF is approximately 26% lower than the cascade’s ( 0.59 vs.\ 0.80 ), and the same setting reduces Spanish LID mass by 56% without re-tuning.
[NLP-56] B-CARE: Blind Classwise Assessment of Relative Evidence for Stance Error Ranking
【速读】： 该论文旨在解决在有限人工审查预算下，识别立场分类器（stance classifier）中潜在错误预测的难题，尤其针对异构基础大模型（heterogeneous base models）中存在的误判风险难以有效优先处理的问题。其核心解决方案是提出一种无需依赖原始模型预测结果的盲式分类级相对证据评估框架——B-CARE（Blind Classwise Assessment of Relative Evidence for Stance Error Ranking）。该框架仅基于输入文本和目标立场类别，利用固定的大语言模型（LLM）验证器对所有候选立场标签生成有序证据评分，通过对比最强反例类证据与预测类证据之间的差异，构建分类级别的风险评分。该方法不需为每个模型单独训练误差检测器或校准风险分数，具备跨模型复用性。实验在四个立场识别基准数据集及三类异构基础大模型上进行，覆盖12种数据集-模型组合和5,206个独立测试实例，评估指标包括错误排序区分能力（AUROC、AUPRC）、选择性预测性能（E-AURC）以及在固定审查预算下的错误捕获率（ErrorCapture）。结果表明，B-CARE在平均AUROC达0.735、平均AUPRC为0.524的同时，可在最高风险20%的预测中捕获41.06%的错误，虽未在所有场景超越基于置信度的强基线，但提供了可复用且互补的证据信号，显著提升了跨模型情境下高风险预测的识别效率。
链接: https://arxiv.org/abs/2610.29350
作者: Meysam Sabbaghan,Doina Caragea
机构: Kansas State University (堪萨斯州立大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Stance classifiers make errors that can be costly to identify under limited human-review budgets. Prioritizing potentially incorrect predictions remains challenging, particularly across heterogeneous base models. We introduce B-CARE (Blind Classwise Assessment of Relative Evidence for Stance Error Ranking), a prediction-blind framework for identifying high-risk stance predictions. Given only a target and text, a fixed LLM verifier assigns ordinal evidence scores to all candidate stance labels without observing the base model’s prediction or confidence. B-CARE constructs a comparative risk score by contrasting the strongest alternative-class evidence with the evidence supporting the predicted class. The resulting classwise evidence profile can be reused across multiple base models without training a separate error detector or calibrating risk scores. We evaluate B-CARE on four stance benchmarks and three heterogeneous base LLMs, covering 12 dataset-model settings and 5,206 unique test instances. Evaluation includes error-ranking discrimination (AUROC and AUPRC), selective prediction (E-AURC), and review effectiveness (ErrorCapture at fixed review budgets). B-CARE achieves a mean AUROC of 0.735 and a mean AUPRC of 0.524, capturing 41.06% of errors within the highest-risk 20% of predictions. Although B-CARE does not uniformly outperform strong confidence-based baselines, it provides a reusable, complementary evidence signal for prioritizing potentially incorrect stance predictions across heterogeneous models.
[NLP-57] Data Schedules and Learning Rate Schedules Should Be Designed Together
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）训练中“中段训练”（Mid-training, MT）阶段的数据调度优化问题，具体探讨在固定总训练计算量和MT数据预算的前提下，是否应将所有MT数据集中安排在预训练（Pretraining, PT）结束后进行，即完全过渡到MT数据是否为最小化最终MT损失的最优策略。研究发现，这一最优性并非绝对，而是高度依赖于学习率（Learning Rate, LR）调度方式：当学习率在整个训练过程中持续衰减时，提前引入并混合使用PT与MT数据可显著降低最终的MT损失；而若采用恒定学习率，则延迟引入MT数据反而更优。因此，论文的关键解决方案在于提出一种联合优化策略，即在保证训练效率的前提下，合理协调MT数据的引入时机与学习率衰减时机——实证研究表明，应在MT数据进入训练后才开始学习率衰减，才能实现最佳性能。基于此，论文进一步构建了一个理论模型，揭示了在给定MT token预算下，最优数据调度与学习率调度之间存在内在耦合关系，从而为高效、自适应的训练策略设计提供了理论依据。
链接: https://arxiv.org/abs/2610.29348
作者: Haodong Wen,Kairong Luo,Tingkai Yan,Wenguang Chen,Kaifeng Lyu
机构: Tsinghua University (清华大学); Peng Cheng Laboratory (鹏城实验室)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Mid-training (MT) is a training phase in LLM training pipelines that continues pretraining (PT) on smaller datasets of higher value. Taking the PT and MT phases as a whole, training usually starts with only PT data and then transitions completely to MT data. In this work, we view this complete transition from PT to MT data as a special case of a broader class of data mixing schedules that specify how the proportions of PT and MT tokens in each batch change over training. Under fixed total training compute and MT token budgets, we ask: Is this complete transition to MT data optimal for minimizing final MT loss? We show that the answer depends on the learning rate (LR) schedule: one data schedule may outperform another under one LR schedule, but changing only the LR schedule can reverse the outcome. Specifically, when the LR decays throughout continued training, introducing MT data earlier and mixing it with PT data lowers the final MT loss compared with placing all MT data at the end of training, but this result is reversed if we use constant LR. We therefore study how to jointly choose when to introduce MT data and when to decay the LR through ablation studies, and find that it is better to decay the LR only after MT data enters training. Finally, we develop a theoretical model that explains why the optimal data schedule for a fixed MT token budget depends on the LR schedule.
[NLP-58] Decodability Is Not Controllability: Local and Anisotropic Speaker Control in Continuous-Latent TTS
【速读】： 该论文旨在解决连续潜空间文本转语音（TTS）中线性可解码性与因果可控性之间的分离问题，即高线性可解码性并不等同于对语音特征的真正因果控制。其核心发现是：在VoxCPM2模型中，尽管最终残差声学语言模型层（RALM L7）的参考音频状态仍保持0.970的高探针准确率，但基于位置匹配的干预却无法实现任何目标主导的音色对（0/60），表明仅靠线性可解码性不足以实现有效的音色操控。相反，在早期层（L0）进行对齐更新可使目标相似度提升0.326，并在全部60组音色对中实现目标主导，显著优于其他等范数方向或位置控制方法。关键解决方案在于识别出两个训练无关音色控制的必要条件：一是几何特异性（geometric specificity），即干预需在特定的潜空间几何结构中进行；二是计算路径的可传递性，即必须存在一条能将目标说话人条件有效传递至声学合成过程的路径。通过独立说话人编码器、保留说话人原型检索及双盲听觉测试验证了所生成音色确实符合预期目标。此外，跨架构干预分析进一步揭示了骨干网络编辑仅造成源音色偏移而无目标吸引，而x-vector消融实验则导致源相似度从0.849骤降至0.006，凸显了显式说话人表征在实现精准控制中的关键作用。
链接: https://arxiv.org/abs/2610.29345
作者: Yuanzhong Zheng,Wei Huang,Ming Ma,Kailun Jin,Youhai Jiang
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
备注:
Abstract:High linear decodability does not entail causal controllability in continuous-latent text-to-speech (TTS). In VoxCPM2, position-matched probes and equal-norm interventions across 30 speakers and 60 directed dyads expose this separation: reference-audio states at the final residual acoustic language model layer (RALM L7) retain 0.970 probe accuracy, yet aligned edits yield 0/60 target-dominant dyads. Edits at early reference-audio positions produce the opposite pattern. At L0, pair-aligned updates increase target similarity by 0.326 and achieve target dominance in all 60 dyads, against at most 1/60 under any equal-norm direction or position control. Independent speaker encoders, retrieval against held-out speaker prototypes, and double-blind listening confirm that the shifted voice is heard as the intended target. Cross-architecture interventions in this http URL distinguish backbone leverage from direct acoustic control: backbone edits displace the source without target attraction, while x-vector ablation reduces source similarity from 0.849 to 0.006. These findings identify two requirements for training-free speaker control: geometric specificity and a computational route that carries the intended speaker condition into acoustic synthesis.
[NLP-59] Uncensored Is Not Unbiased: Abliteration Removes Refusals but Not Script-Dependent Bias About Taiwan
【速读】： Model call failure
链接: https://arxiv.org/abs/2610.29342
作者: Ju-Chun Ko
机构: National Taiwan University (国立台湾大学)
类目: Computers and Society (cs.CY); Computation and Language (cs.CL)
备注: 33 pages, 3 figures, 18 tables. Code, data and an interactive project page: this https URL
Abstract:“Abliterated” (“uncensored”) builds of open-weight language models remove the activation direction that mediates refusal, and are popular partly on the assumption that a model that no longer refuses is also free of political censorship. We test this on two matched pairs of 2-bit models: NVIDIA’s Nemotron 3.5 Bolt 74B-A9B and Qwen3.8-Flash-Next, each against a community abliteration. Each model judged 22 statement pairs (on the Republic of China (Taiwan), 1989 Tiananmen, control countries) in Traditional Chinese, Simplified Chinese (a character-level conversion) and English, and answered 27 open questions per script. Script mattered far more than abliteration: the original Bolt scored +0.58 on Index S (-1: People’s Republic of China (PRC) position; +1: ROC position) in Traditional Chinese, +0.57 in English and +0.05 in Simplified Chinese, and affirmed “Taiwan is part of the PRC” with probability 0.91 in Simplified but 0.06 in Traditional characters; abliteration changed its scores by at most 0.06, mostly by raising “yes” on both sides. In Qwen, abliteration removed every refusal on 1989 and raised scores by 0.21 to 0.34, yet the abliterated Qwen still affirmed that Taiwan is part of the PRC in all three scripts. Naming the state the Republic of China (Taiwan) raised every model’s score by 0.10 to 0.33 but left the script gap. Control pairs showed no script gap; each abliteration changed only its declared tensors. In 740,000 web documents, Simplified text on Taiwan contained PRC-framing terms 3.7 times as often as Traditional text. A script direction could not be steered away; a contrastive stance direction raised scores in every script, and its negation lowered them. A four-sentence system prompt raised the abliterated Qwen to +0.93 to +0.97. Keyword scoring rules failed the models closest to the ROC position mostly for attributing or denying the PRC claim. Uncensored is not unbiased.
[NLP-60] Reader–Writer Coupling in Transformer MLPs
【速读】： 该论文旨在揭示在基于Transformer架构的多层感知机（MLP）模块中，隐藏通道间动态耦合机制的本质及其对模型行为的影响。其核心问题是：在标准训练过程中，为何某些通道表现出强烈且稳定的反向耦合特性（即“写者”与“读者”方向相反），这种耦合如何形成并维持，以及它对模型最终输出分布的贡献机制。解决方案的关键在于引入一种基于“联合仪器”的分析框架——即利用MLP模块将信息写回自身所读取的残差流这一特性，使得其参数更新的雅可比矩阵（Jacobian）为方阵，从而可通过其与自身的转置之间的匹配关系，分解出“读者-写者”配对项。通过计算这些配对项的内积，每个通道可被赋予一个符号化的配对坐标，据此可识别出模型中存在一个由少数强耦合通道构成的“对立核心”。该核心在不同规模模型、不同初始化条件下均稳定出现，且其符号结构在训练初期即确立，并随训练过程保持不变。进一步分析表明，多数通道以与其“读者”响应方向相反的方式贡献输出，且后期块中的“读者”会继承此反向贡献；而训练过程通过固定这些耦合关系，确保了写者在被重置或置换后能自适应地重新适配对应的读者，从而维持系统整体的稳定性。这揭示了生成式模型内部隐含的非对称信息调控机制。
链接: https://arxiv.org/abs/2610.29340
作者: Giyeong Oh,Sunghee Ahn,Youngjae Yu
机构: Seoul National University (首尔国立大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:A transformer MLP gives each hidden channel two roles: a reader that decides when the channel responds, and a writer that decides what it adds back to the residual stream. Read as a key–value memory, the layer can be measured one factor set at a time: the pairing of readers to writers is carried only by their shared index, invisible to any quantity computed from one factor set alone. Yet training couples the two factors through their joint update, so we ask what reading them together shows. The block supplies the joint instrument: the MLP writes back into the very stream it reads, so the exact Jacobian of its update is square, and its agreement with its own transpose resolves into reader–writer terms whose inner products give each channel, read as a couple, a signed pairing coordinate. Ranked by this coordinate, each block we measure has a small core of strongly coupled channels; where initialization gives either sign equally, the core is nearly all opposed: each writer points against the direction its reader responds to. This opposed core recurs from the smallest models trained from scratch to released checkpoints; its sign is in place within a few percent of training and holds while the coupling grows. Read as couples, most channels return their contribution against the reader that fired it, and the readers of later blocks take it up; few show in the logits. Training holds the couples in place: flipping the opposed writers moves the output toward common tokens, and a writer permuted onto another reader regrows to fit that reader while the reader stays.
[NLP-61] ChronoSynth: Constraint-Aware Question Synthesis over Temporal Knowledge Graphs
【速读】： 该论文旨在解决时序知识图谱问答生成（Temporal Knowledge Graph Question Generation, Temporal KGQG）中的语义漂移问题，即现有方法在生成自然语言问题时，未能充分保留输入时序图谱中的答案字段、时间条件、参考事件及时间顺序等关键约束，导致生成的问题与原始语义不一致。其解决方案的核心是提出ChronoSynth框架，该框架通过引入记忆增强与约束感知机制，首先构建显式的时序意图状态（Temporal Intent State），以明确目标问题的语义意图；随后从索引化的模式记忆库中检索适配的类型化问题模板，结合输入图谱进行适配，并通过验证反馈机制检测并修正不一致之处，确保生成问题严格遵循原始时序约束。该方法有效抑制了时序语义漂移，实现了在保持结构化语义一致性前提下的高质量问题生成。
链接: https://arxiv.org/abs/2610.29339
作者: Feng Liang,Weixin Zeng,Runhao Zhao,Mingjun Zhou,Shaoshi Yang,Xiang Zhao
机构: National Key Laboratory of Big Data and Decision, National University of Defense Technology(国防科技大学大数据与决策国家重点实验室); Beijing University of Posts and Telecommunications(北京邮电大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Knowledge graph question generation (KGQG) generates natural-language questions from structured graph evidence and a target answer. When the input graph is temporal, a valid question must also preserve the answer field, time condition, reference event, and temporal order that determine the answer set. Existing KGQG methods and prompting baselines can produce fluent questions that alter these conditions, causing temporal semantic drift. This paper studies temporal KGQG as a constraint-preserving graph-to-question problem and proposes ChronoSynth, a memory-augmented and constraint-aware generation framework. ChronoSynth first constructs a Temporal Intent State to make the intended question semantics explicit. It then retrieves compatible typed question patterns from an indexed pattern memory, adapts them to the input graph, and validates the generated question. Validation feedback guides revisions when a supported inconsistency is detected. Experiments on CRONQUESTIONS and MultiTQ demonstrate state-of-the-art generation performance, with ablations supporting the contributions of its components.
[NLP-62] Policy Central: Detecting Conflicts Redundancy and Similarity in Deployed Police Policy EACL2027
【速读】： 该论文旨在解决警察部门在审查内部政策文档时，难以高效识别条款之间潜在冲突、冗余、相似或差异等关系的问题。传统方法依赖人工逐条阅读并比对近20,000个政策条目，耗时且成本高昂。为应对这一挑战，作者提出两种架构：Retrieve-Judge（检索-判断）与Screen-Confirm（筛查-确认）。其核心解决方案在于通过自动化手段实现政策条款间的快速关系检测。关键突破在于，尽管当前模型在判断阶段具备较高准确性（Screen-Confirm在“冲突”类别的准确率达到0.885），但真正难点在于有效检索出需审查的条款对；相比之下，Retrieve-Judge的综合召回率仅为0.457，而Screen-Confirm虽能恢复至0.472（冲突类达0.615），仍存在提升空间。然而，即便在召回率有限的情况下，该系统能在几分钟内以低成本完成对全库的扫描，并支持按需重新运行，显著降低了政策审查的经济门槛，为后续模型优化提供了可扩展的实践基础。
链接: https://arxiv.org/abs/2610.29338
作者: Holly Baker,Michael Kuo,Kyana Pollas
机构: PolicingLab(警务实验室)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 16 pages, 2 figures. Committed to EACL 2027
Abstract:Policy Central is a deployed system that helps police departments find relationships between sections of their written policy: whether two sections conflict, are redundant, are merely similar, or are different. We describe the system and compare two architectures. Retrieve-Judge, a retrieve-then-classify pipeline, runs as a cheap library-wide health signal; Screen-Confirm, a screen-then-confirm cascade, is a selective audit of the documents a reviewer chooses, checking them exhaustively against one another. On a 302-pair benchmark drawn from one department’s ~84 real policy documents, given each pair Screen-Confirm reaches 0.795 aggregate recall and 0.885 on Conflict, so current models judge well. Finding the pair is the hard part: Retrieve-Judge’s composed recall falls to 0.457, and Screen-Confirm recovers 0.472 (0.615 on Conflict). Today these pairs are found by reading the whole library, nearly 20,000 sections, checked against one another and re-checked after every revision. A scan that returns half of them in minutes, at low cost, and can be re-run on demand changes the economics of policy review even before recall improves.
[NLP-63] Seeing the Forest for the Trees: Emergent Agreement from Individual Conversations
【速读】： 该论文旨在解决大语言模型（LLM）在多轮交互中如何实现观点趋同（agreement）的问题，尤其关注模型在主观议题讨论中立场变化与内外一致性之间的动态关系。其核心挑战在于：个体对话中的内部同意表征（internal agreement representation）具有高度不稳定性，无法可靠反映外部可观察的共识达成情况。为此，论文提出的关键解决方案是超越单个对话的局部表征，通过跨对话聚合内部同意表征，揭示出隐藏在个体波动之下的宏观结构。研究发现，当将内部同意表征在多个对话间进行整合后，其演化呈现出幂律映射关系，并与外部共识的演化符合马尔可夫过程（Markov process），表明内部状态与外部行为之间存在一种协同的马尔可夫动力学。此外，实验还发现模型在外部表现出一致意见时，其内部表征仍滞后于立场转变，说明共识的形成存在“外显先行、内化滞后”的现象。这一结果揭示了只有在跨对话层面聚合内部表示时，才能显现内在与外在同意之间的联合结构，为理解多智能体系统中的集体决策机制提供了新的理论视角。
链接: https://arxiv.org/abs/2610.29328
作者: Adi Simhi,Yaniv Nikankin,Jonathan Herzig,Idan Szpektor,Yonatan Belinkov,Shay B. Cohen
机构: Technion – Israel Institute of Technology (以色列理工学院); Google Research(谷歌研究); University of Edinburgh(爱丁堡大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:As LLM agents interact in the wild and shape each other’s decisions, understanding how they converge toward agreement becomes important. We study this in multi-turn conversations where two LLMs discuss subjective matters, can agree or disagree, and may change their stances. Using a linear classifier on individual conversation activations, we identify an internal representation of agreement. We find that it varies substantially across turns and conversations and does not reliably track external agreement at the individual conversation level. We therefore move beyond the representation of agreement in individual conversations, by aggregating the agreement representation across conversations. Externally, this aggregate view reveals a simple structure: when pooled across conversations, agreement can be predicted well with a Markov process, with agreement at each turn being a direct function of agreement at the previous turn. Similarly, when internal agreement is aggregated, it couples with the external Markov agreement through a power-law mapping, exposing Markovian dynamics in internal representations. Additionally, we find that after a change of stance, models express agreement externally before their internal representations reflect the same shift. These results reveal a joint structure between internal and external agreement that emerges only when internal features are aggregated across conversations. Code available at this https URL.
[NLP-64] Signals and Queries: Token-Efficient Agent ic Context Discovery
【速读】： 该论文旨在解决用户面向的大语言模型（LLM）代理在多轮对话中需高效识别适用行为策略（behavioral policy）的问题，尤其针对在每一轮对话中对候选策略进行全量评估所导致的高昂计算成本。其核心解决方案是提出一种轻量级的首阶段相关性过滤机制——信号与查询（Signals and Queries, SQ），该机制通过离线生成每个策略的激活示例（activating examples）和硬负例（hard negatives），并利用独立训练的逻辑回归分类器基于嵌入表示与向量点积进行相关性预测，无需每轮调用大语言模型。这一设计将高成本的相关性评估压缩至更小的候选集，同时支持各策略分类器独立更新。在覆盖254个对话前缀和36条航空公司政策的9,144条人工标注数据上验证表明，SQ在所有召回率水平下均优于Top-K与余弦相似度阈值检索方法，并在两种嵌入服务提供商间保持一致性能，证实了基于策略的轻量化分类作为降低下游评估负载的可行且高效的第一阶段方案。
链接: https://arxiv.org/abs/2610.29326
作者: Yam Marcovitz
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:User-facing LLM agents must identify which behavioral policies apply at each turn, but evaluating every policy with an LLM is costly. We introduce Signals and Queries (SQ), a lightweight first-stage relevance filter developed for Parlant 4.0 that removes at least 30% of candidate policies before downstream evaluation while preserving high recall. For each policy, an LLM generates activating examples and hard negatives offline. An independent logistic classifier trained on their embeddings then predicts relevance using embeddings and vector dot products, with no per-turn LLM calls. This shifts expensive relevance evaluation to a smaller candidate set, while allowing each policy’s classifier to be updated independently. Evaluated against 9,144 LLM-judge relevance labels covering 254 \tau^2 -bench conversation prefixes and 36 airline policies, SQ achieves higher precision than top-K and cosine-threshold retrieval at every evaluated recall level, with consistent results across two embedding providers. These findings support inexpensive, per-policy classification as a practical first stage for reducing downstream evaluation workload in customer-facing agents.
[NLP-65] OSynth: A Longitudinal Context-Sensitive Evaluation of Meeting Understanding EMNLP2026
【速读】： 该论文旨在解决当前会议理解系统在缺乏足够规模与多样化领域覆盖的语料库以及上下文信息不足的问题。尽管现实场景中已存在日历元数据、过往会议记录、邮件、演示文稿及发言者注册等纵向上下文信息，但现有会议语料库在规模、领域广度和上下文丰富性方面仍显不足。为此，作者提出了OSynth——一个大规模合成的模拟组织会议数据集，包含570小时的会议音频及其关联的文档与潜在真实状态（latent ground-truth state）。实验表明，在150–300词的片段对比中，人类评估者仅能以56%的准确率区分OSynth生成的会议内容与真实会议转录文本，说明其具备高度的真实性。此外，在多个前沿大语言模型（LLM）上的测试显示，引入额外上下文可在实体敏感型任务维度上带来显著性能提升，揭示了现有方法对上下文利用的局限性，同时也验证了OSynth在推动更具备上下文感知能力的会议理解系统发展方面的潜力。解决方案的关键在于构建一个高保真、结构化且可扩展的合成数据集，以弥补真实数据的稀缺性，并为开发更鲁棒的上下文感知型会议理解模型提供支持。
链接: https://arxiv.org/abs/2610.29319
作者: Ian Porada,Suraj Tripathi,Chaitanya Animesh,Leda Sari,Chaitanya Narisetty,Austin R. Matthews,Bohan Yu,Ramesh L. Thulasiram,Gregory Sell,Kean Chin,Dmitriy Genzel
机构: Otter.ai; Mountain View, California, USA
类目: Computation and Language (cs.CL)
备注: EMNLP 2026
Abstract:Meeting notetakers are increasingly deployed in settings where longitudinal context is available including calendar metadata, prior meetings, emails, slides, and speaker enrollments; however, meeting corpora remain limited in terms of size, domain coverage, and available context. We present OSynth, a large-scale synthetic dataset of simulated organizations comprising 570 hours of meeting audio together with associated artifacts and a latent ground-truth state. In a pairwise comparison, human raters distinguish OSynth meetings from real meeting transcripts with only 56% accuracy based on excerpts of 150-300 words. Across state-of-the-art LLMs, additional context yields significant gains on several entity-sensitive dimensions, demonstrating limitations of contemporary approaches and the potential usefulness of OSynth in developing better-contextualized meeting-understanding systems.
[NLP-66] Asking What a Director Sees Can Change What Vision-Language Models Choose
【速读】： 该论文旨在解决当前视觉-语言模型在心智理论（Theory of Mind）评估中存在的重要方法论缺陷：现有评估范式通常要求模型先报告他人视角，再据此执行动作，将“正确报告但错误行动”判定为模型未能有效运用该视角。然而，这一判断依赖于一个隐含假设——报告他人视角的行为不会改变后续的决策行为。本文通过在渲染版“导演任务”（Director Task）中实证发现，这一假设不成立：当模型被提前询问“导演能看到哪些积木”时，其选择隐藏积木的比例显著上升（在六个GPT模型上提升12至88个百分点，在Qwen3.8-27B上提升17个百分点），尽管多数模型仍能准确报告导演的可见范围。进一步分析表明，此类提问改变了模型对指令的解读方式，使其从以导演视角为中心转向以自身认知为中心。通过推理轨迹与提示重述实验可验证，若在提示中明确说明导演所知信息并使用其指称，正确报告与选择隐藏积木的错误搭配率低于1%。因此，论文提出关键解决方案：所有评估应设立无前置视角报告的基线对照组，为此提出了仅在是否包含视角报告问题上不同的配对提示设计，该设计可被任何心智理论基准测试采纳，从而实现更可靠、更公平的模型评估。
链接: https://arxiv.org/abs/2610.29318
作者: Jiaxuan Guo,Xingyu Ren,Kejia Zhang,Jingxin Yang,Youran Sun,Haizhao Yang
机构: Stanford University (斯坦福大学); The Chinese University of Hong Kong (香港中文大学); University of Maryland, College Park (马里兰大学学院帕克分校)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-language models are now routinely tested on theory of mind: whether they can work out what another person sees, knows, and means. A common design asks the model to report that perspective and then to act for the person, and reads a correct report beside a wrong action as a failure to use the perspective. That reading rests on a quiet assumption: that asking for the report leaves the action unchanged. We show that it does not. In a rendered Director Task, a partition hides the largest block from a director who asks for “the largest block”. Asking first which blocks the director can see raises the share of replies that choose this hidden block by 12 to 88 percentage points on six GPT models and by 17 on Qwen3.8-27B, even though the models mostly report correctly which blocks the director sees. Reasoning traces and prompt rewordings suggest why: the question shifts how the request is read, from the director’s view toward the model’s own. When the prompt states what the director knows and refers to, fewer than 1% of replies pair a correct report with a hidden-block choice. Any evaluation that elicits a perspective report before the action it scores needs a baseline without the question, and we deliver one: paired prompts that differ only in that question, a design any perspective-taking benchmark can adopt.
[NLP-67] LLM -Based Exogenous World Modeling for Dynamic Scheduling
【速读】： 该论文旨在解决动态调度中在线任务到达的实时决策问题，尤其在非线性延迟惩罚目标下，因前期决策失误导致下游成本累积放大的挑战。其核心解决方案在于构建一种基于预训练大语言模型（LLM）的结构化世界模型，通过后状态分解（afterstate decomposition）将确定性车间状态转移与不确定性外生任务到达相分离，使LLM仅需对未来的任务到达进行预测，并借助紧凑的信念表示实现高效建模。在此基础上，提出后状态世界模型规划（AWMP）框架，结合演化后的信念条件调度策略与短时场景滚动优化，同时引入“意外触发式刷新”机制，在新到达任务显著偏离预测时才调用LLM，并通过在线残差自校准机制利用近期预测误差反馈，增强对非平稳到达模式的适应能力。实验表明，相较于八种经典优先级调度规则中的最优者，AWMP在动态作业车间和柔性作业车间基准上分别降低加权延迟6%-21%和22%-46%。
链接: https://arxiv.org/abs/2610.29308
作者: Ni Zhang,Haofeng Yuan,Jianan Zhou,Shiji Song,Zhiguang Cao
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 10 pages, 5 figures, 1 table
Abstract:Dynamic scheduling requires dispatching online arrivals while anticipating future jobs, as poor decisions can compound downstream costs under nonlinear tardiness objectives. We show that a pretrained large language model (LLM) can serve as the exogenous component of a structured world model, forecasting uncertain future job arrivals. Specifically, we exploit an afterstate decomposition, which separates known deterministic shop transitions from uncertain exogenous job arrivals, enabling the LLM to forecast only the latter through a compact belief representation. Building on this, we develop Afterstate World-Model Planning (AWMP), which combines an evolved, belief-conditioned dispatching strategy with short scenario-based rollouts. A surprise-triggered refresh mechanism queries the LLM only when newly observed arrivals deviate sufficiently from previous forecast, and online residual self-calibration further conditions the LLM on its recent forecast errors to handle nonstationary arrivals. Across diverse arrival distributions, AWMP reduces weighted tardiness by 6-21% on dynamic job-shop and 22-46% on flexible job-shop benchmarks, relative to the best of eight classical priority-dispatching rules.
[NLP-68] ExpEvolve: Structuring and Reusing Experience for Open-Ended Discovery
【速读】： 该论文旨在解决现有基于大语言模型（LLM）的进化编码代理（如AlphaEvolve）在应对开放式科学问题时，程序突变仅依赖表面启发式规则、缺乏持续性控制单元的问题。其核心挑战在于：当前方法虽能通过迭代优化程序实现进展，但突变过程局限于低层次代码层面，未能有效捕捉和利用能够解释不同程序性能差异的高层次科学思想（idea-level factors）。为此，论文提出ExpEvolve，其关键创新在于将高层次思想因子的提取与控制机制引入演化过程——通过识别并利用能解释程序评分差异的高阶因素，实现对探索（exploration）与利用（exploitation）的更优引导。研究进一步从理论上刻画了思想因子发现提升样本效率的条件，并构建了实用框架，在11个开放数学任务上实现了最高5.0倍的样本效率提升及显著更高的最终性能。此外，该方法在4种其他LLM及MLS-Bench中的6项机器学习任务上也表现出泛化能力。分析表明，ExpEvolve能够有效发现并利用思想级因子，从而增强进化发现过程中的探索与利用能力。
链接: https://arxiv.org/abs/2610.29303
作者: Yongqiang Chen,Chenxi Liu,Deyu Zou,Zhenhao Chen,Tongliang Liu,Bo Han,Kun Zhang
机构: MBZUAI; Carnegie Mellon University (卡内基梅隆大学); TMLR Group, Hong Kong Baptist University (香港浸会大学TMLR组); The Chinese University of Hong Kong (香港中文大学); SAIC Centre, The University of Sydney (悉尼大学SAIC中心)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Machine Learning (stat.ML)
备注:
Abstract:Evolutionary coding agents such as AlphaEvolve are one of the notable successes in using Large Language Models (LLMs) to build AI Scientists. These agents tackle open-ended scientific problems by iteratively improving and evolving programs, leveraging the prior knowledge and reasoning capabilities of LLMs. Despite their success, mutations over programs are mostly low-level upon surface heuristics, without a persistent unit of control. In contrast, science compounds through ideas. In this work, we propose ExpEvolve, which injects idea-level extraction and control into evolution: it discovers and leverages the high-level factors from programs that explain scoring differences among different programs. We characterize theoretical conditions on when discovery with idea factors improves sample efficiency. We also build a practical framework to show that the extracted high-level idea factors are helpful for guiding exploitation from experience and exploration when discovery comes to a plateau. Across 11 open-ended mathematical tasks, ExpEvolve significantly improves the final performance and sample efficiency up to 5.0x. The improvements also transfer to 4 other LLMs and to 6 machine learning tasks from MLS-Bench. Analysis shows that ExpEvolve can discover idea-level factors to provide better exploration and exploitation for evolutionary discovery.
[NLP-69] Same Probabilities Different Advice: Gain-Loss Framing Effects in High-Stakes LLM Decision Support
【速读】： 该论文旨在解决生成式 AI（Generative AI）在高风险决策支持场景中因概率表述框架不同而导致推荐结果不一致的问题，即“框架效应”（framing effect）对大语言模型（Large Language Models, LLMs）输出可靠性的影响。现有研究虽已揭示提示敏感性、认知偏差及置信度校准不足等问题，但尚未明确当同一概率信息以收益框架与损失框架互补表达时，LLMs是否会产生实质性建议差异。为此，研究提出 FrameBench-HPS，一个包含2,880个提示的受控基准测试集，涵盖医疗、金融、教育/职业发展以及法律/消费者权益四大高风险领域中的240个典型情境。实验结果显示，在7个主流LLMs中，32.4%的完整提示组在收益与损失框架下产生了推荐类别变化，且多数变化源于对不确定性的不同处理而非直接反转。人工评估验证了人类对风险感知和建议直接性的对应调整。进一步的鲁棒性实验表明，此类框架效应在对话式重述下依然存在，且显著超过常规重复生成带来的变异性。关键发现在于，通过概率归一化（probability canonicalization）可显著降低框架敏感性，从而将概率表示形式作为提升高风险场景下LLM决策一致性的可行干预路径。
链接: https://arxiv.org/abs/2610.29294
作者: Yuyao Zhang,Yakun Cui,Han Zhu,Jinsong Lu,Yike Guo,Sirui Han
机构: The Hong Kong University of Science and Technology (香港科技大學); Beihang University (北京航空航天大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 14 pages, 2 figures
Abstract:Large language models (LLMs) are increasingly used to support consequential decisions, yet their reliability under equivalent representations of uncertainty remains unclear. Existing work has documented prompt sensitivity, cognitive biases, and confidence miscalibration, but it is still unknown whether high-stakes advice changes when the same probability is expressed through complementary gain and loss frames. We introduce FrameBench-HPS, a controlled benchmark of 2,880 prompts derived from 240 high-stakes scenarios across healthcare, finance, education/career, and legal/consumer-rights domains. Across seven LLMs, gain-loss framing changes the recommendation category in 32.4% of complete groups, with most changes involving uncertainty rather than direct reversals. Human evaluation confirms corresponding shifts in perceived risk and directiveness. Robustness experiments show that framing effects persist under conversational paraphrases and exceed ordinary repeated-generation variability. Probability canonicalization substantially reduces framing sensitivity, identifying probability representation as a practical target for improving consistency in high-stakes LLM decision support.
[NLP-70] Computer Vision and Knowledge-Grounded Language Models for Intelligent Beekeeper Assistance
【速读】： 该论文旨在解决蜂群健康状况评估中依赖人工检查所导致的效率低、成本高及专业人才稀缺的问题。当前，蜂群管理需要及时掌握巢脾强度、幼虫区面积、蜜粉储存量、巢脾使用情况等关键指标，但传统的人工巢脾框检查方式耗时费力且结果依赖专家经验。为此，研究提出一种面向边缘部署的生成式智能系统框架，其核心在于构建一个端到端的可解释性分析管道：首先通过对象检测器识别具有生物学意义的巢房类别；随后将检测结果提炼为结构化事实模式；再将事实转化为受约束的语义文本；最终基于可验证的证据触发一个接地的知识库，生成可解释且可操作的诊断与管理建议。该系统以开放的FAIRHiveFrames-1K数据集为基础，强调模型的可复现性与边缘部署能力，避免对大型通用前沿模型的依赖，并额外发布了可互换的事实与文本解释数据集FAIRHiveText-1K，以促进后续基于大语言模型（LLM）在该领域的研究。同时，论文提出了构建可触发知识库的路径，确保所有建议均能追溯至具体观测事实，从而实现真正可解释、可行动的蜂群健康评估。
链接: https://arxiv.org/abs/2610.29283
作者: Asaduz Zaman,Mohaimenul Azam Khan Raiaan,Malika Nisal Ratnayake
机构: Monash University(蒙纳士大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Managed and wild bees sustain roughly a third of the global food products, yet colonies face mounting stress from climate-change including extreme weather patterns, altered parasite and disease patterns. Timely assessment of colony condition such as understanding colony strength, brood area, honey and pollen storage, comb usage, is essential to adaptive management. However, manual hive frame inspection is slow, costly, and quality depends on scarce expertise. We present an artificial targeted intelligent system framework focusing on edge deployable pipeline that turns hive frame photographs into interpretable and action-oriented inspection report. The pipeline includes an object detector to detect biologically-meaningful comb-cell classes; detections are distilled into structured fact schema; facts are converted into constrained factual text; the text triggers a grounded knowledge-base for interpretations and recommendations. Knowledge-base entries are required to be triggerable by evidence expressed in the fact schema. We built our system based on open source FAIRHiveFrames-1K dataset and favour reproducible, edge-deployable models over large general purpose frontier models. We additionally release FAIRHiveText-1K dataset that turns FAIRHiveFrames-1K dataset into interchangeable facts and text interpretation of the facts, making it easy for further LLM-based research into this area. We also propose a pathway to develop the grounded knowledge-base that will be helpful in providing interpretable and actionable reports from the facts.
[NLP-71] Heard But Not Said: Prompting Understates Temporal Information of Musical Moments in Frozen Large Audio-Language Models
【速读】： 该论文旨在解决大音频-语言模型（Large Audio-Language Models, LALMs）在理解音乐时难以准确定位描述事件时间点的问题。其核心关切在于：这一局限是由于模型缺乏时间信息，还是源于对时间跨度提取机制的瓶颈？研究提出的关键解决方案是通过构建基于Slakh2100音轨激活的配对查询探测器（paired-query probe），分别引入状态查询（如“仅钢琴和贝斯在演奏”）与变化查询（如“吉他停止，仅剩钢琴和贝斯”），并在同一时间段内系统性地调整模型所接收的前序音频长度。通过对冻结的LALMs进行三种不同方式的时间段提取测试——全轨迹提示（full-track prompting）、窗口化二分类评分（windowed yes/no scoring）以及注意力机制分析（attention analysis）——揭示出：状态查询在无上下文条件下已优于基线表现，而变化查询则在多数短片段模型中随前置上下文增加而性能提升；然而，即使使用30秒填充编码器，也未能实现类似恢复效果。此外，提示法（prompting）与注意力机制均未能有效暴露此类能力。因此，提示失败实际上低估了LALMs在给定片段下验证特定时刻的能力，但并未显著影响其定位能力，这种低估程度由查询类型与编码器结构共同决定。
链接: https://arxiv.org/abs/2610.29256
作者: Eunsik Shin
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL)
备注:
Abstract:Large audio-language models (LALMs) understand music, yet struggle to locate a described moment in time. We ask whether this reflects missing temporal information or a bottleneck in how the time span is extracted. Using a paired-query probe built from Slakh2100 stem activations, we attach a state query (e.g., only piano and bass are playing'') and a change query (e.g., the guitar drops out, leaving piano and bass’') to the same span and vary how much preceding audio the model is shown. Across frozen LALMs, three ways of extracting the span ask different questions: full-track prompting asking whether the model can say when; windowed yes/no scoring asking whether it verifies the moment when handed the segment, and whether it needs what came before; attention asking whether it can find the moment on its own. State queries score above baseline without context, while change queries improve with preceding context in most short-segment models; 30s padded encoders show no comparable recovery. Prompting and attention expose almost none of this. Prompting failures thus understate what LALMs can verify, though not what they can locate, by an amount set by query type and encoder.
[NLP-72] Hierarchical Masked Diffusion Language Models
【速读】： 该论文旨在解决传统掩码扩散语言模型（Masked Diffusion Language Models, MDLM）在生成过程中缺乏对词元位置优先级（hierarchy）建模的问题。标准方法采用全局共享的噪声调度，未显式考虑哪些位置应优先解码，限制了生成效率与逻辑一致性。为此，论文提出层次化掩码扩散语言模型（Hierarchical Masked Diffusion Language Model, HMDLM），其核心创新在于引入层次级别（hierarchy level）作为每个词元位置的附加信息，该信息可来自训练数据中的隐含结构（如标题、段落结构或谜题约束），无需额外标注。HMDLM将生成过程分解为两阶段：第一阶段预测被遮蔽位置的层次分布，第二阶段基于该分布生成词元分布。通过为每一层次分配独立的噪声调度，实现对高优先级位置更早解码的软性控制，同时保持解码全程并行。该方法具有严格的数学基础，重新推导了前向与反向过程及NELBO目标函数。实验表明，在LM1B语言生成任务中，HMDLM相比强基线ADLM降低困惑度超过1.5点；在WikiHow任务上，相较于ADLM和MDLM分别降低高达4.4点与7点困惑度，并获得大模型判别器2倍以上的偏好优势；在需多步推理的数独求解任务中，准确率提升最高达8.6个百分点，显著优于现有扩散模型基线。
链接: https://arxiv.org/abs/2610.29255
作者: Parul Gupta,Satwik,Avni Aggarwal,Bhavesh Gurnani,Arnab Kumar Mondal,Dinesh Khandelwal,Dinesh Garg,Parag Singla
机构: Indian Institute of Technology Delhi(印度理工学院德里分校); IBM Research(IBM研究院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Masked diffusion language models have emerged as an alternative to autoregressive models, enabling parallel token generation. However, standard masked diffusion language models use a shared noise schedule across positions without explicitly encoding which positions should be resolved first. Many tasks exhibit a natural priority among positions: document titles and section headings can guide the generation of body text, while Sudoku cells determined directly from the initial clues can help resolve more difficult cells. To incorporate this notion of priority (or hierarchy) among token positions, we introduce the Hierarchical Masked Diffusion Language Model (HMDLM), which augments each token position with a hierarchy level available as part of the training data, and requires zero or minimal additional annotations. It factors generation into two passes: the first predicts a hierarchy distribution over masked positions, the second predicts token distributions conditioned on it. Giving each hierarchy level its own noise schedule induces a soft, controllable preference for revealing higher-priority positions earlier while keeping decoding fully parallel. Our formulation is mathematically grounded, and we re-derive the forward and reverse processes and the NELBO for this factorization. Our evaluation shows that for the task of language generation on LM1B, HMDLM reduces perplexity by more than 1.5 points over Anchored Diffusion Language Model (ADLM), a recently proposed strong diffusion baseline; on WikiHow, we see a drop in perplexity of up to 4.4 pts on ADLM and up to 7 pts against MDLM. An LLM judge on WikiHow prefers HMDLM generations (the two variants combined) more than 2\times as often as the baseline. On the task of solving Sudoku puzzles requiring multi-step reasoning, it improves solution accuracy by up to 8.6% absolute points over existing diffusion baselines.
[NLP-73] Intergroup Bias Predicts Expressions of Social Judgment and Empathy in the r/AmITheAsshole Reddit Community
【速读】： 该论文旨在解决在线社交互动中因社会身份差异导致的共情差距（intergroup empathy gap）在真实网络语境下的具体表现问题。尽管已有大量研究揭示了人们对他我群体成员共情程度低于对内群体成员的现象，但其在自然发生的线上交流中的实际体现仍不清晰。为此，研究通过分析用户评论中隐含的社会判断与文本层面的理解程度，考察评论者与发帖者在性别或年龄上是否属于同一群体时所表现出的共情差异。研究聚焦于发帖者主动披露自身性别与年龄信息的帖子，基于来自 r/AmItheAsshole 社区的 36,163 篇帖子、126,716 条评论及 18,111 名评论者的数据，控制了评论者与发帖者的群体身份、帖子特征（话题、长度、得分）以及评论者与帖子层面的方差。研究发现，当评论者与发帖者同属某一社会群体（性别或年龄）时，评论者更倾向于给出“非施害者”（Not the Asshole）的判断，并且其评论在语言模型（LLM）评估下展现出更强的文本理解能力。此外，研究还发现年长评论者的行为模式与年轻评论者存在显著差异。进一步在明确提及冲突双方性别与年龄关系的子样本中，发现评论者-发帖者之间的内群体效应会受到发帖者与对方当事人之间群体关系的调节。因此，该研究的关键在于揭示：在线话语理解与共情行为不仅受个体身份匹配的影响，更深层地嵌套于复杂的群体关系背景之中，强调了社会群体动态与情境因素在数字共情机制中的核心作用。
链接: https://arxiv.org/abs/2610.29254
作者: Yu Hou,Hal Daumé III,Rachel Rudinger
机构: 未知
类目: Computation and Language (cs.CL); Social and Information Networks (cs.SI)
备注: ICWSM 2027
Abstract:People share and understand others’ experiences differently based on their social identities; often, people empathize more when the other person belongs to their ingroup compared to an outgroup. While this intergroup empathy gap is well-studied in social psychology, it remains unclear how it manifests in naturally occurring online social interactions. To this end, we measure how commenters express social judgments, as an indirect outcome of their understanding towards the posters, and empathy in text, based on whether commenters share the poster’s gender or are of similar ages. We focus on posts where the poster self-reports these demographics, and analyze 126,716 comments associated with 36,163 posts from 18,111 commenters in r/AmItheAsshole, while controlling for commenter and poster group identities, post features (topic, length, and score), and both commenter- and post-level variance. Our results show that when commenters and posters are ingroup, in either gender or age, commenters are more likely to provide a Not the Asshole verdict and show greater text-based understanding as measured by LLMs. We also observe that older commenters behave differently from younger ones. Furthermore, in a subset of posts where the gender and age of the other party in the conflict are explicitly mentioned, we find that commenter-poster ingroup effects are moderated by the group relationships between the poster and the other party when commenters make social judgments. Our work highlights the importance of intergroup context and social group nuances in understanding online discourse.
[NLP-74] Let Agents Design How They Search: Automatic Interface Optimization for Agent ic Search
【速读】： 该论文旨在解决当前代理式搜索（Agentic search）中搜索接口设计僵化的问题，即现有接口通常为人工固定设计并普遍适用于所有语料库，而未考虑不同语料库特性对有效接口设计的依赖性。其核心解决方案是提出SearchWeaver框架，通过下游搜索性能反馈实现搜索接口的自动化优化。该框架的核心在于联合优化语料库资源（corpus artifacts）、可执行工具（executable tools）以及使用说明（usage instructions），利用执行轨迹和任务奖励信号驱动生成式代理（language-model agent）迭代改进接口设计。关键创新点在于将接口设计过程转化为可学习、可优化的系统性任务，从而动态决定哪些信息应预先准备、哪些操作由工具执行、哪些决策保留在代理自身，显著提升了搜索准确性与检索质量，在多个基准测试中实现了平均nDCG@10提升至55.0，答案准确率从71.6%提高至78.4%。
链接: https://arxiv.org/abs/2610.29250
作者: Sangjune An,Jaeyoon Jung,Yumin Choi,Jinheon Baek,Junmo Cho,Sung Ju Hwang,Hae Beom Lee
机构: KAIST(韩国科学技术院); Korea University(韩国大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Agentic search depends on the interface through which a language-model agent accesses and explores a corpus. Existing interface designs are fixed by hand and applied unchanged to every corpus, although effective designs may depend on the corpus. We introduce SearchWeaver, a framework for automatic search interface optimization guided by downstream search performance. A proposer uses execution trajectories and task rewards to jointly revise corpus artifacts, executable tools, and usage instructions. This pro cess optimizes what information is prepared in advance, which operations tools perform, and which decisions remain with the search agent. With GPT-5.4-nano, the optimized interfaces improve answer accuracy from 71.6% to 78.4% on BrowseComp-Plus and average nDCG@10 from 45.7 to 55.0 across four BRIGHT subsets over Direct Corpus Interaction. These findings highlight the value of jointly optimizing corpus artifacts, executable tools, and usage instructions to develop effective search interfaces.
[NLP-75] MOSAIC: An Omni-Modal Benchmark for Long-Term Memory and Cross-Modal Reasoning in Conversational Agents
【速读】： 该论文旨在解决多模态长时记忆（omni-modal long-term memory）系统在跨模态信息存储、检索与理解过程中难以精准定位记忆失效环节的问题。现有基准测试多局限于文本与图像模态，或仅报告单一端到端性能分数，无法区分记忆失败发生于表征（representation）、检索（retrieval）、交付（delivery）还是读取（reading）阶段。为此，作者提出MOSAIC基准，涵盖923个基于11–24轮会话的多模态历史（文本、图像、视频、音频）问题，通过构建具有目标导向的空间、时间与韵律信号，并采用强因果验证（空间信息）和选择器级验证（韵律标注），结合匹配诊断协议，在固定问题、阅读器、提示与预算的前提下，对表征、检索与交付过程进行干预。匹配分解分析表明：表征损失具有模态依赖性，即使在提供最优证据的情况下，残留读取仍占主导，且系统在拒绝回答方面表现依然薄弱——高可回答率与无法拒答并存。因此，MOSAIC将多模态记忆评估从传统的排行榜模式转变为对“写入—检索—交付—读取”各环节故障的精准诊断工具。
链接: https://arxiv.org/abs/2610.29249
作者: Hong Ting Tsang,Boyi Zhang,Haoyu Huang,Jiaxin Bai,Yufei Li,Yisen Gao,Zhongwei Xie,Hongyu Luo,Jeff Z. Pan,Yangqiu Song
机构: The Hong Kong University of Science and Technology (香港科技大学); University of Edinburgh (爱丁堡大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Omni-modal assistants must remember not only what users say, but also what they show and convey. Existing long-term memory benchmarks are mostly text-and-image or report a single end-to-end score, so they cannot locate whether evidence is lost when it is represented, retrieved, delivered, or read. We introduce MOSAIC, a benchmark of 923 questions over persona-grounded histories of 11-24 sessions spanning text, image, video, and audio. MOSAIC constructs targeted spatial, temporal, and prosodic signals rather than decorative media, with the strongest causal validation for spatial information and selector-level validation for annotated prosody, and pairs them with a matched diagnostic protocol that freezes the question, reader, prompt, and budget while intervening on representation, retrieval, and delivery. Matched decomposition shows that representation loss is modality-dependent, residual reading dominates even under gold evidence, and abstention remains weak on these controls: strong answerable scores coexist with failures to refuse. MOSAIC thus turns omni-modal memory evaluation from a leaderboard into a diagnosis of write, retrieve, deliver, and read failures.
[NLP-76] Beyond Binary Labels: Diagnostic Evaluation of LLM Safety with Prompt-Level Rubrics
【速读】： 该论文旨在解决当前大语言模型（LLM）安全评估基准普遍依赖二元安全/不安全标签所导致的解释性不足及自动化评判者间一致性低的问题。其核心解决方案是提出DiagSafe诊断评估框架，该框架突破传统二分类范式，采用基于提示（prompt-level）的评分细则（rubrics），并以普适性的AI安全政策为依据，从两个独立维度进行评估：安全性（safety），聚焦具体危害并设置明确豁免条款以减少误报；实用性（usefulness），独立衡量指令遵循程度。为确保评估的可靠性与现实性，研究构建了一个涵盖84类安全标签的中英双语基准，包含两个子集——DiagBase用于覆盖单轮与多轮提示中的直接有害查询及模板型越狱攻击，DiagAttack则将有害意图嵌入复杂指令遵循任务中，形成具有挑战性的指令攻击场景。实验表明，DiagSafe在与人工标注的一致性上达到95.34%，显著优于基线模型的69%-87%；且评判者间一致性高（Cohen’s κ = 0.864）。此外，框架揭示了主流大模型在不安全率及不同维度上的差异性脆弱性，尤其在中文语境和多轮交互中表现更易受攻击。因此，DiagSafe为大模型对齐的审计与优化提供了一种可靠、可解释的评估工具。
链接: https://arxiv.org/abs/2610.29225
作者: Hongyu Li,Xinlong Huang,Zhan Yu,Jing Zhao,Zhe Zhao,Juntao Zhong,Xiong Kuang,Rui Yuan,ZhenXiang Yan,Huilin Xu,Haitao Yu,Lilin Wang,Rui Chen,Blaze Chen,Fengzong Lian,Renfen Hu
机构: Beijing Normal University(北京师范大学); Hunyuan Team(腾讯混元团队); Tencent(腾讯)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 42 pages, 9 figures, 24 tables. Dataset will be publicly released at this https URL
Abstract:Current LLM safety benchmarks predominantly rely on binary safe or unsafe labels, lacking explanatory depth and suffering from low consistency across automated judges. To address this, we introduce DiagSafe, a diagnostic evaluation framework that moves beyond binary classification by employing prompt-level rubrics. Grounded in globally applicable AI safety policies, each rubric provides explicit criteria along two dimensions: safety, which targets concrete harms with specific exemptions to suppress false positives, and usefulness, which independently evaluates instruction fulfillment. To ensure a robust and realistic evaluation, we construct a bilingual (English-Chinese) benchmark grounded in a comprehensive 84-label safety taxonomy, consisting of two subsets: DiagBase, which covers single- and multi-turn prompts with direct harmful queries and template-based jailbreaks, and DiagAttack, which embeds harmful intents into complex instruction-following tasks to form challenging instruction attacks. Extensive experiments demonstrate that DiagSafe significantly enhances the reliability of LLM-as-a-judge evaluation, achieving 95.34% agreement with human annotations – substantially higher than the 69%-87% achieved by the baselines – and high inter-judge consistency (Cohen’s \kappa = 0.864). Furthermore, applying our framework reveals that leading LLMs differ markedly in their unsafe rates, and each model also exhibits unique vulnerabilities along different dimensions. In addition, we observe that models are particularly susceptible in Chinese and multi-turn contexts. Ultimately, DiagSafe provides a reliable and explainable tool for auditing and optimizing LLM alignment.
[NLP-77] C2U in RAG : Compositional Concept Unlearning in Retrieval-Augmented Generation
【速读】： 该论文旨在解决检索增强生成（Retrieval-Augmented Generation, RAG）系统中因检索到的证据暴露不希望出现的概念（unwanted concepts）而导致的安全风险问题，尤其在生成器已进行内容过滤但仍存在概念泄露的情况下。其核心挑战在于如何在不显著增加控制查询（control query）暴露风险的前提下，有效抑制目标概念在生成结果中的密度。解决方案的关键是提出一种名为“组合式概念遗忘”（Compositional Concept Unlearning, C²U）的方法：该方法利用从用户画像中提取的方向向量，通过一个增强的原子库（augmented atom bank）进行投影，仅对原始查询添加一个有界且可控制的编辑-原始差值（delta），从而实现对目标概念的精准抑制。实验表明，在五个数据集、四种编码器和三个随机种子下，经过验证选择的配置使联合二分类目标命中率（Hit@5）平均下降4.25个百分点，同时未造成控制组性能下降或目标暴露增加，且在预算约束内有效降低了目标概念密度。此外，针对ArXiv数据集的四概念组合任务，C²U在独立验证门控与首次使用测试评估中均优于平均方向对比方法，验证了其有效性与泛化能力。
链接: https://arxiv.org/abs/2610.29223
作者: Yiyang Duan,Fan Wu,Tiantong Wu,Che Wang,Zhiren Gong,Wei Guo,Yang Cao,Wei Yang Bryan Lim
机构: 未知
类目: Computation and Language (cs.CL)
备注: 37 pages, 8 figures, including appendices
Abstract:Retrieval-augmented generation can expose unwanted concepts through retrieved evidence even when the generator is moderated. We study query-side concept suppression with a fixed encoder and document index. Compositional Concept Unlearning (C ^2 U) routes a profile-derived direction through an augmented atom bank, adding only a bounded edited-minus-original delta to the raw query. Across five datasets, four encoders, and three seeds, the validation-selected configuration reduces pooled binary target Hit@5 by 4.25 percentage points with non-positive control degradation and target-exposure changes. Under the evaluated query transforms, it suppresses target density without increasing budgeted control-query exposure. Separately, ArXiv four-concept composition improves over an averaged-direction comparator after independent validation gates and first-use test evaluation.
[NLP-78] Understanding Target Probability Reuse in Speculative Decoding
【速读】： 该论文旨在解决生成式 AI（Generative AI）中推测解码（speculative decoding）的效率优化问题，核心挑战在于如何更高效地选择下一阶段的候选词树（tree of draft tokens），以提升解码过程中的接受率（acceptance rate）与整体速度。其解决方案的关键在于：在分配候选词树之前，预先利用目标模型（target model）在当前上下文环境下计算出的下一个词分布，并据此对首个词分支进行重排序（reranking），而非依赖于原始草案的排名。研究发现，这种基于当前上下文独立计算的概率分布能够显著提高预期接受率，尤其在树预算较小的情况下表现更优；进一步地，在分配树结构前即从该分布中采样首个词，可实现比预选树后剪枝更高的预期接受率，且在平均树大小相等时仍能加快解码速度。关键创新点在于，将目标模型的概率输出提前至树分配阶段使用，从而突破了传统方法中必须等待完整上下文才能获得精确概率的限制，有效提升了推测解码的整体效率。
链接: https://arxiv.org/abs/2610.29210
作者: Jungseob Lee,Giyoon Jang,Chailin Nam,Jiwoo Kum,Sihyeon Lee,Kyojun Choo,Jihun Song,Eunseo Song,Chanjun Park
机构: Korea University(高丽大学); Soongsil University(松岛大学)
类目: Computation and Language (cs.CL)
备注: 29 pages, 6 figures, 29 tables. Code: this https URL
Abstract:Speculative decoding uses a target model to verify a tree of draft tokens in parallel. We study whether the resulting next-token distributions can help select the next tree. For unrestricted target models, we show that guaranteed exact reuse requires a distribution computed at the next round’s complete context. We characterize when retaining this distribution requires sacrificing accepted tokens and establish conditions under which this loss persists across decoding rounds. Across six targets, we find that reranking first-token branches with a separately computed distribution at the current context improves expected acceptance over draft ranking, especially with small tree budgets. Drawing the first token from this distribution before allocating the tree further improves expected acceptance over pruning a preselected tree, even at equal mean tree size. In our experiments, both selection rules also decode faster than draft ranking when the separate target pass overlaps standalone drafting. Our findings show that making target probabilities available before tree allocation can improve the efficiency of speculative decoding. Our code is available at this https URL.
[NLP-79] norregel: a preregistered benchmark of dated numeric values in Norwegian law
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在处理挪威法律文本中数值信息时的时效性与准确性问题，具体聚焦于从挪威法律、法规及议会决议中提取特定时间点的数值（如金额、百分比、国家保险基本金额倍数（G）和年龄限制）的可靠性。其核心挑战在于：由于模型训练数据存在截止时间（cutoff date），模型对“生效日期”晚于其训练截止时间的条款往往给出过时或错误的回答。解决方案的关键在于设计并执行一项预注册（preregistered）的实证研究，通过构建一个包含916个带时间戳的测试项的数据集，系统评估多个主流大模型在不同时间点上的回答正确率、过时率及可解释性。研究发现，在第二轮预注册实验中，尽管主要假设H1（即模型对在其训练截止后生效的数值更易给出过时答案）在主分析中得到显著支持（效应量d = 17.8，p = 0.0001），但在唯一同时为两组提供单一目标值的敏感性分析S8中未达统计显著性（d = 3.9，p = 0.1754），表明该结论的稳健性存疑。此外，过时答案恰好等于最新有效值的比例高达56.1%（95%置信区间[46.6, 65.0]，包含0.5），提示部分过时回答可能仍具实际参考价值；而关于新挪威语（Nynorsk）与书面挪威语（Bokmål）之间表现差异的假设（H3）则无明确证据支持。值得注意的是，部分模型（如Haiku 4.5）大量输出不可解释的回答，凸显了当前生成式AI在法律严谨场景下的应用局限。整体而言，该研究揭示了大语言模型在动态法律信息处理中的关键缺陷——时效性偏差，并强调了预注册实验设计在评估模型可靠性的必要性。
链接: https://arxiv.org/abs/2610.29207
作者: Eirik Botten Nicolaysen(EcoDeco AS)
机构: EcoDeco AS
类目: Computation and Language (cs.CL)
备注: 24 pages. Preregistrations, code and results: Zenodo concept DOI https://doi.org/10.5281/zenodo.23422302 (versions v1 https://doi.org/10.5281/zenodo.23422303 , v1.1 https://doi.org/10.5281/zenodo.23432124 , v1.2 https://doi.org/10.5281/zenodo.23434782 , 2.0 https://doi.org/10.5281/zenodo.23437444 , 2.1 https://doi.org/10.5281/zenodo.23502580 ). Items are withheld
Abstract:norregel asks language models for numeric values in Norwegian statutes, regulations and parliamentary decisions (NOK amounts, percentages, multiples of the National Insurance basic amount (G) and age limits), each at a stated date. The 916 items were extracted mechanically from Lovdata’s open data, and each carries the dated history of its provision, so an answer can be classed as correct, outdated, wrong or uninterpretable. We report a first and a second preregistered run on the same items. In the first run (five models, listed publicly only after local analysis), H1, that outdated answers are more frequent for values that took effect after a model’s cutoff, could not be tested in substance, and H2 and H3 were inconclusive; Opus 5.5 and Sonnet 4.6 answered 46.7% and 40.2% of the 880 primary items correctly. The second run was preregistered and published before any of its calls, and eight models were analysed. Its pooled primary test supported H1 under the preregistered decision rule (d = 17.8 percentage points, 95% interval [7.7, 28.9], p = 0.0001). In the preregistered sensitivity analysis S8, which counts as outdated only the most recent value that differs from the key, d was 3.9 [-3.6, 12.7] (p = 0.1754), an interval that contains 0. H1 was supported in every other preregistered sensitivity analysis, but S8 is the only one that gives both groups one target value, so we do not regard the support for H1 as robust. The share of outdated answers equal to that most recent value (H2) was 56.1% [46.6, 65.0], an interval that contains 0.5, and the pooled test of a lower share correct in Nynorsk than in Bokmål (H3) was inconclusive. Several models, among them Haiku 4.5, gave mostly uninterpretable answers. Items and keys are withheld; item IDs, per-answer classes, code and results are on Zenodo.
[NLP-80] LTEM: LLM -Friendly Table-Enhanced Markdown for Table Understanding and Generation EMNLP2026
【速读】： 该论文旨在解决真实世界文档中复杂表格结构（如合并单元格、多级标题和分段布局）在大语言模型（LLM）处理时的表达困境：轻量级格式（如Markdown、CSV）无法有效表达复杂结构，而高表达性格式（如HTML、LaTeX）则带来过高的令牌开销与解析复杂度。其解决方案的关键在于提出一种名为LTEM（LLM-friendly Table-Enhanced Markdown）的轻量级表格增强格式，通过形式化定义的最小扩展，在保留Markdown简洁性的同时，显式编码单元格跨度与层级标题信息。该方案基于提出的STB（Synthetic Table Benchmark）和六项原子特征（F1-F6）构成的复杂度分类体系，实现了对表格结构复杂度的系统化分析，并在五种先进LLM上验证了其在准确率与令牌效率之间的优越平衡，显著优于LaTeX的无效响应率，且在表格生成任务中接近MultiMarkdown表现，为后续研究提供了可复用的分类框架、基准数据集与格式规范。
链接: https://arxiv.org/abs/2610.29188
作者: Eunbin Kim,Hyeonseok Kang,Sangkeun Jung
机构: Chungnam National University (忠南国立大学); EurekaAI
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to the EMNLP 2026 Main Conference
Abstract:Tables in real-world documents, including scientific papers, financial reports, and statistical summaries, frequently use merged cells, multi-level headers, and sectioned layouts. Yet the lightweight formats LLMs handle best (Markdown, CSV) cannot express these structures, while expressive formats (HTML, LaTeX) impose excessive token cost and parsing complexity. To enable systematic analysis of this trade-off, we introduce a taxonomy decomposing table structure into six atomic features (F1-F6) and corresponding complexity levels (L1-L6), and construct STB (Synthetic Table Benchmark), a controlled benchmark with 1,089 tables systematically distributed across complexity levels. Using this framework, we propose LTEM (LLM-friendly Table-Enhanced Markdown), a formally specified, minimal extension of Markdown that explicitly encodes spans and hierarchical headers. Experiments across five state-of-the-art LLMs show that LTEM achieves accuracy comparable to HTML, offers a favorable accuracy-token trade-off relative to HTML and LaTeX, and produces substantially fewer invalid responses than LaTeX; for table generation, LTEM is comparable to MultiMarkdown though HTML is best. We release our taxonomy, benchmark, and format specification to support future research on LLM-table interaction.
[NLP-81] rainBridge: Agent ic NPU Training Optimization Guided by GPU Compilation Evidence
【速读】： 该论文旨在解决在昇腾（Ascend）NPU上进行训练时，由于框架级编译优化（如算子融合）应用不充分导致的计算碎片化和不必要的执行开销问题，进而影响训练效率。其核心解决方案是提出TrainBridge，一种基于智能体（agentic）的GPU到NPU优化迁移方法。该方法的关键在于：利用成熟的GPU编译栈生成的高度优化执行作为特定任务的参考基准，从中提取优化意图（optimization intent），指导NPU程序的重写，并通过迭代式性能剖析（profiling）对重写后的程序进行持续调优。实验表明，TrainBridge在涵盖大语言模型预训练、图像分类及视觉-语言训练的四个工作负载上实现了平均1.7倍的加速，在27个经过高度优化的GPU实现的Modded-NanoGPT跑分记录中实现了接近2.0倍的平均加速，显著提升了NPU训练效率。
链接: https://arxiv.org/abs/2610.29171
作者: Yupeng Chen,Kunxiang Zhao,Xingjian Wang,Yue Zhao,Yijing Wu,Ye Liu,Kai Li,Congliang Chen,Ruoyu Sun
机构: The Chinese University of Hong Kong, Shenzhen, China; Shenzhen Loop Area Institute; Autokernel
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:We study how to improve training efficiency on Ascend NPUs. In the NPU training programs we study, framework-level compilation optimizations such as operator fusion are only partially applied, leaving fragmented computation and avoidable execution overheads. Profiling-guided tuning can address these inefficiencies, but it often requires costly trial and error. To make training on NPUs more efficient, we propose TrainBridge, an agentic approach for transferring GPU optimization guidance to NPU training. TrainBridge uses the highly optimized execution produced by mature GPU compilation stacks as a task-specific reference for NPU tuning. It extracts optimization intents from the GPU training execution to guide NPU program rewrites and further refines the rewritten program through iterative profiling. Our GPU-to-NPU migration experiments show that TrainBridge achieves an average 1.7x speedup across four workloads covering LLM pretraining, image classification, and vision-language training, and an average speedup of nearly 2.0x across 27 Modded-NanoGPT speedrun records with highly optimized GPU implementations.
[NLP-82] achBack: Learning to Teach via Bidirectional Preference Optimization for On-Policy Self-Distillation
【速读】： 该论文旨在解决在在线策略自蒸馏（On-Policy Self-Distillation, OPSD）框架下，教师模型（Teacher）因过度依赖特权信息（如参考答案）而导致学生模型（Student）学习到非鲁棒、简化的推理路径的问题。尽管教师模型在拥有完整参考解时可生成高准确率的推理轨迹，但这种“直接访问完整解”的机制会诱导教师产生依赖参考、跳过探索、验证与自我修正等关键思维过程的短路径，从而削弱学生模型在无特权信息条件下的泛化能力。其解决方案的关键在于提出TeachBack——一种双向偏好学习（bidirectional preference learning）框架，通过双重机制优化教师的教学行为：一方面，通过控制注入提示中的参考推理后缀长度和调整提示风格，引导教师主动探索并重解问题；另一方面，采用成对偏好目标，利用教师的高精度轨迹提升学生学习效果，同时反向使用学生的推理行为来动态塑造未来教师在特权条件下的监督信号。该方法在保持教师高准确率的同时，有效减少了对参考解的依赖，显著提升了学生模型在数学与逻辑推理任务上的性能。
链接: https://arxiv.org/abs/2610.29167
作者: Linfei Ji(1),Jiayun Li(2),Haozhao Wang(1),Siyu Jiang(1),Zehao Wang(3),Longtao Xiao(1),Yu-Jie Yuan(4),Ruixuan Li(1) ((1) Huazhong University of Science and Technology, (2) Tsinghua University, (3) Harbin Institute of Technology, (4) Huawei Technologies Ltd.)
机构: Huazhong University of Science and Technology (华中科技大学); Tsinghua University (清华大学); Harbin Institute of Technology (哈尔滨工业大学); Huawei Technologies Ltd. (华为技术有限公司)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 21 pages, 6 figures, including appendices
Abstract:On-policy self-distillation (OPSD) enables a language model to learn from a Teacher instantiated from the same model but given access to privileged information, such as a reference solution. We find that access to sufficient privileged information allows the Teacher to generate highly accurate trajectories; however, accuracy alone does not guarantee that these trajectories are suitable for Student learning. Direct access to a complete solution may encourage shortened, reference-conditioned derivations that omit the exploration, verification, and self-correction required to solve problems without privileged information. We introduce TeachBack, a bidirectional preference-learning framework for on-policy self-distillation that learns not only from the Teacher but also how the Teacher should teach. TeachBack shapes the Teacher’s generation behavior by controlling the length of the reference-reasoning suffix injected into the prompt and by varying the prompt style. While maintaining comparable Teacher accuracy, it encourages the Teacher to explore and re-solve problems, thereby reducing its reliance on the reference solution. At each rollout refresh, the shared policy generates both a privileged Teacher trajectory and a problem-only Student trajectory. A pairwise preference objective uses privileged Teacher trajectories to improve Student learning, while the reversed preference uses the Student’s behavior to shape future Teacher supervision under the privileged condition. Experiments on mathematical and logical reasoning tasks show that TeachBack consistently improves Student performance over existing OPSD methods and related baselines.
[NLP-83] Carving Radiology Reports at Their Evidential Joints: Anatomical Region Sets as Generation Units
【速读】： 该论文旨在解决医学影像报告生成中临床内容粒度与视觉证据绑定不精准的问题。现有方法通常采用非自然报告结构的生成单元（generation unit），导致模型需在语义结构与视觉对应关系之间进行额外妥协，影响生成内容的准确性和可解释性。其解决方案的关键在于提出RadCarve框架，通过直接基于现有“陈述-解剖区域”映射中的解剖区域集合定义生成单元，实现了生成单元形成与视觉证据选择的统一。该框架的规划器利用专用于组合的查询库，联合预测生成单元的存在性及对应解剖区域组成，并通过迭代聚合区域证据进行优化；生成器则基于预测区域集合的联合视觉证据独立生成各单元文本。实验表明，相较于强基线模型，RadCarve在给定区域集合下的单位生成任务中提升了9.4%的CXRFEScore，端到端绑定的F1值达到基线的2.4倍，同时在Chest ImaGenome和PadChest-GR数据集上取得领先性能，且参数量不足大规模基线模型的六分之一。
链接: https://arxiv.org/abs/2610.29165
作者: Jinwei Yang,Junhao Chen,Jianwei Zhao,Zhonghan Xu,Hong Cheng,Ling Wang
机构: University of Electronic Science and Technology of China(电子科技大学)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: 28 pages, 4 figures
Abstract:Organizing radiology reports into independently generated units offers a way to generate fine-grained clinical content and bind it to visual evidence. However, existing methods typically adopt generation-unit structures not directly specified in natural reports or their annotations, introducing additional modeling compromises. We propose RadCarve, which instead defines generation units directly from the anatomical region sets in existing statement-region correspondences, unifying unit formation with visual evidence selection. RadCarve’s planner uses composition-specialized query banks to jointly predict unit existence and region composition from the image, iteratively refining both with regional evidence aggregated according to current membership predictions. The generator independently produces each unit’s text conditioned on the joint visual evidence of its predicted region set. Compared with strong baselines, RadCarve improves CXRFEScore by 9.4% in unit generation given region sets and achieves 2.4 times the end-to-end binding F1. It also achieves leading report-generation results on Chest ImaGenome and PadChest-GR, using fewer than one-sixth as many parameters as large-scale baselines.
[NLP-84] Why is AI-generated language so hard to understand?
【速读】： 该论文试图解决的问题是：当前生成式人工智能（Generative AI）文本在可理解性方面呈现出日益下降的趋势，这种现象是否与语言模型在句法结构和语义特征上的使用模式有关。研究发现，主流大语言模型（LLM）在生成文本时更频繁地使用人类语言加工难度较高的心理语言学特征，如非限定性宾语从句、长句法依存关系、低频n-gram等，这些特征在人类写作中通常较少出现。其关键解决方案在于提出并验证了一个核心假设：人类语言演化以高效沟通为目标，而现代生成式AI的训练目标逐渐转向数学、代码和推理等任务，这些场景对信息密度有更高要求，而非可理解性。为此，研究通过引入长度惩罚进行微调，使语言模型在生成文本时倾向于更简洁的表达，从而偏离人类语言行为模式，初步验证了该假设。研究揭示了人类语言处理需求与大语言模型生成行为之间的根本性偏差，并为未来优化生成文本的可读性提供了潜在改进方向。
链接: https://arxiv.org/abs/2610.29162
作者: Thomas Hikaru Clark,Matias B. Martin,Kyle Mahowald
机构: New York University (纽约大学); The University of Texas at Austin (德克萨斯大学奥斯汀分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:There is a widespread sense that AI-generated text has recently become harder to understand. In this study, we investigate a set of psycholinguistic features that are known to correlate with human processing difficulty (e.g., object relative clauses, long syntactic dependencies, low-frequency n-grams). Using a novel corpus of AI-generated text across diverse domains, we find that AI models use these difficult features more often than paraphrases generated by round-trip translation. Compared with human writing, all nine frontier and near-frontier models use more inanimate subjects with animate verbs and more low-frequency n-grams, and most also use longer dependencies, more stacked nouns, and more reduced object relatives in conversation; we also demonstrate substantial variation across model families. We propose and test a hypothesis as to why: while human language is optimized for successful communication, AI-generated text is increasingly optimized for other goals like math, code, and reasoning, in which there is a pressure for \textitinformation density rather than successful communication. We provide a proof-of-concept for this hypothesis by fine-tuning a language model under a length penalty, which leads to a divergence from human-like linguistic behavior. Our findings highlight the axes of misalignment between linguistic processing demands in humans and LLMs, and point toward possible remedies.
[NLP-85] VeeReview: Measuring Build-Verify Pairs in Agent -Authored Pull Requests from a V-Model Perspective
【速读】： 该论文旨在解决代码审查中验证证据（verification evidence）的量化评估问题，特别是针对生成式AI（Generative AI）与人类作者在提交拉取请求（pull request）时所体现的验证实践差异。现有研究多聚焦于代码生成能力或拉取请求的结果，但缺乏对验证证据充分性的直接衡量。其解决方案的关键在于提出VeeReview框架，通过V模型中的“构建-验证”配对（build and verify pairs）结合仓库信号与大语言模型判断来分类评估验证证据。该框架采用三种由确定性信号约束的语言模型判别对和一个基于差分内容与仓库树结构规则判定的“模块设计-单元测试”对。然而，评估结果显示，尽管代理生成的拉取请求在四类配对中均表现出更低的“仅构建”比例（与预注册假设相反），但工具的校准未达标：与仲裁标签的一致性仅为4%至60%（要求80%），且评分者间一致性弱（kappa=0.154）。尤其值得注意的是，“模块设计-单元测试”这一规则判定对的标准化区间包含零，表明其可能反映的是请求呈现方式而非实际验证工作。因此，研究结论指出，此类度量需引入机械决策组件并建立经验证的人类参考标准，方能有效支持对验证实践的比较分析。
链接: https://arxiv.org/abs/2610.29143
作者: Gabriel Dautovi,Jie JW Wu
机构: Michigan Technological University (密歇根理工大学)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注: 47 pages, 9 figures, 11 tables. Under review at Empirical Software Engineering. Replication package: this https URL
Abstract:Context: Reviewing a pull request requires verification evidence that addresses the proposed change. Coding agent benchmarks and studies of pull request outcomes do not directly assess this relationship. Objective: We test whether the V-Model’s build and verify pairs can measure verification evidence in pull requests and support comparisons between agent-authored and human-authored changes. Method: VeeReview classifies four build and verify pairs using repository signals and a language-model judge. Three pairs use model judgements constrained by deterministic signals, and a rule over the diff and repository tree decides Module Design-Unit Test. We evaluated VeeReview against a 25-pull request reference set labelled by two raters and applied it to 1,454 matched pairs of agent and human pull requests from 741 repositories. Results: VeeReview assigned lower build-only rates to agent-authored pull requests on all four pairs, contrary to the preregistered hypothesis (mixed-effects odds ratios 0.52 to 0.86). Architecture-Integration was not significant. Standardization for linked issues, shipped tests and size reduced matched-rate differences by 34% to 60%, and the rule-decided pair’s standardized interval included zero. The instrument did not meet its preregistered calibration criterion: agreement with adjudicated labels was 4% to 60% against a required 80%, and inter-rater agreement was weak (kappa = 0.154). Conclusions: The rule-decided pair shows that a process measure judged by a language model partly reflects how a pull request is presented rather than the verification work behind it. We report the cohort differences as observations made with this instrument, and we argue that such measures need a mechanically decided component and a validated human reference before they can support comparisons of verification practice.
[NLP-86] GRAFT: Restoring the Missing Sampling Interface in Continuous Diffusion Language Models
【速读】： 该论文旨在解决连续扩散语言模型（continuous diffusion language models, dLLMs）在生成文本时存在的词汇长尾分布崩溃问题：尽管这些模型在常见词的生成上已达到与自回归模型相当的水平，但对罕见词的生成能力显著不足，且这一缺陷随模型规模扩大并未改善。其核心问题是，由于连续扩散模型在每一步均不进行离散词采样，而是基于候选词的平均值进行更新，导致稀有词在与其他高频词竞争时被“平均”掩盖，从而难以保留。解决方案的关键在于提出GRAFT方法——一种无需重新训练的修复机制，通过在模型尚未确定决策前主动“嫁接”（graft）那些可能被平均过程丢弃的稀有词，并基于这些承诺重新生成整个序列。该方法有效恢复了罕见词的生成频率，使生成文本的词频分布更接近人类文本，相对提升达34%-109%，并在多个连续dLLM模型上实现显著改进。
链接: https://arxiv.org/abs/2610.29136
作者: Haoning Yang,Lili Pan
机构: University of Electronic Science and Technology of China(电子科技大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Continuous diffusion language models generate a whole sequence in parallel, never drawing a single word at any intermediate step; only the final step turns embeddings into text. Models such as ELF now report autoregressive-level quality at a fraction of the sampling steps of discrete-token diffusion models, and this design principle – no step-wise discretization – is what their own ablations credit for the gain. We show that this same principle carries a hidden cost: a collapse of the lexical long tail. Measured against held-out human text, ELF’s output matches the human rate on common words almost exactly, yet its rarest words appear at only 0.30x the human rate, a gap that does not close from 105M to 652M parameters and that no sampler setting these models expose can reach. A same-size GPT-2, in contrast, spans zero to above the human rate by its decoding rule alone. We trace the difference to what continuous diffusion models lack: with no step that ever draws a discrete word, every step is instead driven by an average over the candidates still in play, and averaging a rare candidate together with the common word competing for its slot lands on the common word. An autoregressive model avoids this because its decoding rule samples one candidate rather than averaging over all of them. We propose GRAFT, a training-free method that grafts this missing choice back onto a frozen model: while the model is still undecided, GRAFT commits the rare words the average would otherwise discard, then regenerates the sequence from scratch with those commitments held in place. Across three ELF scales and on LangFlow, an independent continuous dLLM, GRAFT restores the rare-word share of generated text from 0.42-0.77x the human rate to 0.82-1.10x, a relative gain of 34-109%, and moves the entire word-frequency profile closer to human on every model tested, by 2.7x on ELF-B and 12x on LangFlow.
[NLP-87] GRAM: Group-wise Rank-Aware Model Merging via Subspace Alignment
【速读】： 该论文旨在解决多任务场景下模型融合（model merging）中的子空间干扰（subspace interference）问题，尤其是在医疗等受监管领域中，由于数据隐私限制导致模型需本地微调，仅能基于微调后的权重进行聚合。现有方法采用统一的融合策略，忽视了不同任务在参数空间中学习到的子空间之间的几何关系；当任务更新位于非对齐子空间时，将其投影至共享低秩基底会丢失任务相关方向，从而降低融合性能。研究发现，融合模型的每任务投影误差由各任务与共享子空间间的主角（principal-angle）对齐程度决定，因此通过将几何兼容的任务分组可理论上降低组内误差。为此，本文提出分组式秩感知模型融合（Group-wise Rank-Aware Model Merging, GRAM），利用任务向量的主角几何结构判断任务合并的合理性，并在每个协作组内执行标准的SVD-based融合，整个流程仅需微调后的权重。实验表明，仅引入一个额外模型（nK=2），GRAM显著缓解子空间干扰，在语言理解（GLUE with Flan-T5）、8任务CLIP视觉任务及医学指令微调（Qwen2-7B-Instruct搭配四个中英文医疗LoRA）等多个基准上持续优于已有方法。
链接: https://arxiv.org/abs/2610.29110
作者: Di Wu,Shuaidong Pan
机构: JPMorgan Chase Co.(摩根大通)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Merging different models into a single model is highly desirable for multi-task deployment, particularly in regulated domains like healthcare. In such settings, models are often locally fine-tuned due to strict data privacy policies, necessitating that aggregation operates exclusively on the fine-tuned weights. Existing methods apply a uniform merge across all tasks, ignoring the geometric relationships among their learned subspaces. When task updates occupy misaligned subspaces, projecting them onto a shared low-rank basis discards task-relevant directions, a failure mode named \emphsubspace interference. Through this study, we show that the per-task projection error of a merged model is governed by the principal-angle alignment between each task and the shared subspace, so that grouping geometrically compatible tasks provably reduces this error within each group. Motivated by this observation, we propose \textbfGroup-wise Rank-Aware Model Merging (GRAM), which uses the principal-angle geometry of task vectors to decide which tasks should be merged together and then performs a standard SVD-based merge within each coalition; the pipeline requires only the fine-tuned weights at merge time. With only one additional model ( \nK=2 ), GRAM significantly mitigates subspace interference and consistently outperforms prior merging methods across language understanding (GLUE with Flan-T5), 8-task CLIP vision, and medical instruction tuning (Qwen2-7B-Instruct with four English/Chinese medical LoRAs).
[NLP-88] Multi-level Encoder with Global Topic for Task-oriented Dialogue Summarization ICASSP2025
【速读】： 该论文旨在解决任务导向型对话摘要（task-oriented dialogue summarization）中如何有效捕捉对话主题及关键信息的核心挑战，尤其针对对话固有的逻辑性与特定话题性。其解决方案的关键在于提出一种多层级编码器结合全局主题建模（multi-level encoder with global topic, MLEGT）的框架：通过多层级编码器捕获对话中的时序与空间依赖关系，并在图结构构建过程中显式地提取一个简洁而有效的全局主题；同时引入基于全局主题引导的指针机制（global topic guided pointer mechanism），以识别被忽略但重要的信息，从而突破长距离依赖限制并揭示对话内在结构。实验在两个公开数据集（包括大规模中文警局审讯数据集和医疗报告服务数据集）上验证了该方法相较于多个强基线模型的优越性。
链接: https://arxiv.org/abs/2610.29097
作者: Zhuoqi He,Peijie Huang,Yuhong Xu,Youming Peng,Mingzhi Xu,Xinyang Lin
机构: South China Agricultural University (华南农业大学)
类目: Computation and Language (cs.CL)
备注: 5 pages, 3 figures. Published in ICASSP 2025
Abstract:Task-oriented dialogue summarization aims to automatically extract key information to generate domain summaries to improve service efficiency and quality. Task-oriented dialogue is inherently logical and surrounds specific topic. How to effectively capture the dialogue topic and the most salient information becomes one of the major challenges of this task. In this paper, we propose a task-oriented dialogue summarization model utilizing a multi-level encoder with global topic (MLEGT). This model discovers temporal and spatial dependencies through a multi-level encoder, while simultaneously obtaining a simple yet effective global topic in the process of graph construction. It not only captures the crucial details but also breaks the long-range limit and discover the intrinsic structure across the dialogue. Then, a global topic guided pointer mechanism is added into the summrizer to capture ignored but important information, which is helpful to improve the accuracy of the summary. A comprehensive study of two public datasets, including a real-world large-scale Chinese police interrogation dataset and a medical reporting service dataset proves the superiority of our method on several strong baselines.
[NLP-89] Same Answer Different Reason s: Trace-Level Divergence in Multilingual Large Language Models
【速读】： 该论文旨在解决多语言大语言模型（Multilingual Large Language Models, LLMs）在实际应用中，尽管输出可解释的推理链（visible reasoning traces），但现有评估方法仅关注最终答案的一致性或整体响应相似性，无法有效衡量不同语言间支持性证据的可比性问题。其核心挑战在于：当前主流的基于整体嵌入向量的相似性评估（如余弦相似度）会系统性高估跨语言推理链的一致性，忽略了推理过程在结构、步骤顺序等方面的实质性差异。解决方案的关键在于提出一种层级式分歧评估框架（trace-level divergence framework），将推理链视为结构化序列，分解为结构相似性（structural similarity）、顺序一致性（order consistency）与整体相似性（holistic similarity）三个维度，并引入针对成对样本的校准机制，实现跨语言推理链的公平比较。实验结果表明，高达52.1%–67.3%的高整体相似性样本在结构相似性上并不处于前四分位，且人工双语标注验证显示人类判断更倾向于结构相似性而非整体嵌入相似性；同时，同语言内部的推理链一致性显著高于跨语言情形，证明了语言特异性偏差的存在。研究揭示了现有答案级和整体评估方法可能严重夸大多语言解释性输出的一致性，从而强调了采用细粒度的推理链层级评估对于确保多语言部署中可验证性、教育性和安全审计可靠性的重要性。
链接: https://arxiv.org/abs/2610.29092
作者: Rui Sun,Peizhao Mei,Xiewei Cheng,Kaizhuo Chen,Taoran Li,Zhiyuan Yu,Varun Chandrasekaran
机构: University of Illinois Urbana-Champaign(伊利诺伊大学厄本那-香槟分校); Texas A&M University(德克萨斯农工大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Multilingual large language models are increasingly deployed in settings where visible reasoning traces support verification, education, and safety auditing, yet standard evaluations typically measure only final-answer agreement or holistic response similarity. As a result, they establish outcome consistency without determining whether users across languages receive comparable supporting evidence. We introduce a trace-level divergence framework that compares visible reasoning traces as structured sequences, decomposing agreement into structural similarity, order consistency, and holistic similarity, with pair-specific calibration to enable comparison across languages. Across three reasoning benchmarks, seven languages, and six frontier LLMs, the natural baseline—embedding each whole response and comparing the two by cosine—systematically overstates agreement: 52.1% – 67.3% of examples in the top quartile of holistic similarity are not in the top quartile of structural similarity. This dissociation is stable across sentence encoders and step segmentations, and blind bilingual annotation studies in two English-anchored language pairs show that human judgments align with structural similarity rather than with the holistic baseline. A same-language control shows that the level of divergence is language-specific: two English traces for paraphrases of the same question agree more closely than an English trace and its counterparts in other languages. Our results show that answer-level and holistic evaluations can substantially overestimate the consistency of multilingual explanatory artifacts, motivating trace-level evaluation for reliable multilingual deployment.
[NLP-90] A Large-Scale Evaluation of Role-Enhanced Zero-Shot Chain-of-Thought Prompting
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在零样本（zero-shot）场景下推理能力不足的问题，尤其针对缺乏任务特定示例时模型难以进行有效逻辑推导的挑战。其核心解决方案是提出一种名为“角色增强的零样本思维链”（Role-Enhanced Zero-Shot Chain-of-Thought, RE-ZS-CoT）的提示框架，通过系统性地引入领域特定的专家角色（expert roles）来增强模型的推理能力。该方法的关键在于将角色专业化（role specialization）与思维链（Chain-of-Thought, CoT）推理相结合，在不依赖任何任务示例的前提下，引导模型以特定专家视角进行分步推理。研究设计了静态角色选择和动态角色选择两种版本：前者通过预设的专家角色评估其对不同任务的影响，后者则让模型自主选择最适配当前任务的角色，实现任务-角色的自适应对齐。实验表明，该框架在20个不同规模的LLM和7个基准测试中显著优于零样本及零样本思维链基线，性能提升最高达44个百分点；且静态与动态版本表现高度一致，验证了角色-任务匹配的有效性。消融实验进一步证明，非典型角色会损害性能，凸显了角色适配的重要性。该框架在不同参数规模（2b–14b）模型上均表现出良好可扩展性，尤其在小模型（如Granite3.3-8b）中展现出卓越的算术推理能力，为提升大模型在多样化任务中的零样本推理效率提供了一种高效、资源节约的实用方案。
链接: https://arxiv.org/abs/2610.29073
作者: Ibrahim Khan,Thai Van Nguyen,Yi Xia,Pratch Suntichaikul,Mustafa Can Gursesli,Daniel Fernandez Galeote,Juho Hamari,Ruck Thawonmas
机构: Ritsumeikan University (立命馆大学); Tampere University (坦佩雷大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper proposes Role-Enhanced Zero-Shot Chain-of-Thought (RE-ZS-CoT), a prompting framework that systematically integrates domain-specific expert roles to enhance reasoning in large language models (LLMs) without task-specific examples. We assess two versions of our approach. In the first version, static role selection, we evaluate eight predefined expert roles across arithmetic, commonsense, and reading comprehension benchmarks to determine their impact on reasoning performance. In the second version, dynamic role selection, each model autonomously selects the single most suitable role for each benchmark, enabling adaptive task-role alignment without manual specification. Across 20 LLMs and seven benchmarks, RE-ZS-CoT frequently surpasses zero-shot and zero-shot-CoT baselines, achieving performance improvements of up to 44 percentage points. The results reveal a strong correspondence between static and dynamic versions; the benchmarks where static roles yielded improvements were almost always those where dynamic roles also outperformed baselines, though static selection achieved peak performance. Ablation studies further show that unconventional roles degrade performance, underscoring the necessity of task-role alignment. The framework’s scalability is validated across model sizes (2b-14b parameters), with smaller models like Granite3.3-8b achieving remarkable proficiency in arithmetic reasoning. RE-ZS-CoT advances zero-shot prompting by unifying role specialization with chain-of-thought reasoning, offering a practical, resource-efficient solution for improving LLM capabilities across diverse domains.
[NLP-91] arget Regeneration Shrinks the Gains of Draft-Training Objectives in Speculative Decoding
【速读】： 该论文旨在解决生成式AI（Generative AI）中推测解码（speculative decoding）技术在实际应用中性能增益被高估的问题。尽管新的草稿训练目标（如基于目标模型的蒸馏）常被认为能显著提升外源响应的接受率，但现有流水线通常允许目标模型重写草稿内容，这可能掩盖了真实性能增益。研究通过引入视觉-语言目标与块式草稿生成器，在同一语料库的四个版本上对比训练三种目标：基于语料库词元的交叉熵、从目标模型进行的逐词元蒸馏，以及仅对可通过验证的位置施加蒸馏的门控蒸馏。结果表明，虽然蒸馏和门控机制在外部响应上带来显著增益，但在经过贪婪重生成（greedy regeneration）后，蒸馏带来的增益大幅下降，门控机制几乎无额外收益。这一性能衰减现象在不同数据顺序、位置权重、验证器、解码温度，以及蒸馏的初始化和学习率设置下均持续存在；且在匹配监督长度及各目标的最佳训练长度下，大部分增益仍未能保留。在第二个视觉语料库和纯文本复现实验中也观察到相同趋势，即使使用自回归草稿生成器，蒸馏的增益依然收缩。因此，论文强调，未来评估训练目标的性能时，必须以目标重生成后的基线为参照，并跨不同训练长度进行衡量。
链接: https://arxiv.org/abs/2610.29013
作者: Jungseob Lee,Hogun Seong,Euijun Choi,Sugyeong Eo
机构: Korea University (高丽大学); Yonsei University Mirae Campus (延世大学未来校区)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 19 pages, 6 figures, 15 tables. Code: this https URL
Abstract:Speculative decoding accelerates large language models by verifying drafted tokens in parallel. New draft-training objectives are usually credited with the acceptance they add over cross-entropy on externally written responses, yet many pipelines let the target rewrite them. We ask how much of that gain survives. With a vision-language target and a block drafter, we train three objectives on four versions of one corpus: cross-entropy on corpus tokens, token-level distillation from the target, and distillation gated to positions that would survive verification. Distillation and the gate add large gains on external responses, but after greedy regeneration, distillation adds a smaller gain and the gate little or none. The shrinkage persists across data orders, position weights, verifiers, decoding temperatures and, for distillation, initializations and learning rates. Most of it remains at matched supervised length and at each objective’s best measured training length. A second vision corpus shows the shrinkage, as does a text-only replication, for distillation also with an autoregressive drafter. Objective gains should therefore be measured against a target-regenerated baseline and across training lengths. Our code is available at this https URL.
[NLP-92] VeriEvolve: Evaluator-First Evolution for Tool-Using Agents EMNLP2026
【速读】： 该论文旨在解决自提升智能体在现实状态任务中缺乏可靠适应度信号（fitness signal）的核心障碍。其关键解决方案是提出一种“评估器优先”的进化框架VeriEvolve，该框架在优化智能体之前先构建可执行的评估器（evaluator）。具体而言，VeriEvolve基于任务指令生成与指令对齐的挑战状态（challenge states）及可执行的验证程序，作为固定不变的适应度信号指导每轮工作流进化；在不同进化周期之间，通过将表现优异与较差的运行结果转化为反事实测试用例，动态重构评估器以持续改进其判别能力。实验表明，在30个MCPMark Filesystem任务中，该方法成功构建了30个评估器，涵盖141个挑战状态，对生成测试集达成141/141的一致性，对执行器生成的保留状态达成113/122的一致性，显著提升了外部验证的成功率（如GLM-5.2从6/30提升至18/30，Claude从27/30提升至30/30），并消除所有虚假阳性。研究进一步揭示了可观测性、状态表示和搜索/执行能力等跨环境限制，验证了在任务成功可观测且存在外部验证器校准的前提下，可合成并使用可执行评估器引导搜索的有效性。
链接: https://arxiv.org/abs/2610.29005
作者: Yueshen Li,Kamer Ali Yuksel,Gokhan Tur,Dilek Hakkani-Tür,João Pedro Maia
机构: University of Illinois Urbana-Champaign(伊利诺伊大学厄本那-香槟分校); aiXplain
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 24 pages, 3 figures. Accepted to the REALM Workshop at EMNLP 2026 (non-archival)
Abstract:A central obstacle to self-improving agents is the absence of a reliable fitness signal for realistic stateful tasks. We introduce \textscVeriEvolve, an evaluator-first evolution framework that constructs this missing evaluation layer before optimizing the agent. From task instruction, VeriEvolve synthesizes instruction-grounded challenge states and an executable evaluator that checks them. During each round of workflow evolution, the evaluator is frozen and serves as the fitness signal. Between rounds, VeriEvolve revises the evaluator by converting the end states of strong and weak runs into a pair of counterfactual test cases. Across all 30 MCPMark Filesystem tasks, VeriEvolve constructs 30 evaluators with 141 challenge states, achieving 141/141 agreement on the generated suites and 113/122 agreement on held-out states produced by executor agents. Evaluator-guided evolution increases externally verified success from 6/30 to 18/30 for GLM-5.2 and from 27/30 to 30/30 for Claude; refinement removes all three false positives that the weak executor exposes. Cross-environment studies on PostgreSQL, Playwright, Notion, and GitHub further expose complementary limits from observability, state representation, and search/execution capacity. These results show that executable evaluators can be synthesized and used to guide search when task success is observable and an external verifier is available for calibration. Comments: 24 pages, 3 figures. Accepted to the REALM Workshop at EMNLP 2026 (non-archival) Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2610.29005 [cs.SE] (or arXiv:2610.29005v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2610.29005 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-93] FlashNext-RL: A System Optimization Study for Long-Horizon Agent ic RL of Qwen 3.8-Flash-Next
【速读】： 该论文旨在解决长时域智能体强化学习（long-horizon agentic reinforcement learning, RL）中因模型规模庞大带来的高效轨迹生成与长序列训练难题，尤其针对Qwen3.8-Flash-Next模型在现有开源实现下存在的内存占用高及模型特定算子支持不足的问题。其核心解决方案是提出一种基于prime-rl的模型专用强化学习训练优化框架——FlashNext-RL，结合vLLM实现软件工程（SWE）场景下的高效推理生成。关键创新在于：在单个8xB200节点上实现48层全模型的BF16精度训练，其中前两层、n-gram表、稀疏注意力索引器及多标记预测头保持冻结；通过优化主机卸载策略、梯度累积机制以及混合专家（mixture-of-experts, MoE）、稀疏注意力和门控残差流（gated residual stream）等核心算子的内核实现，显著提升训练吞吐量。实验表明，在131K上下文长度配置下，非填充训练吞吐量从初始的约395 tokens/s/GPU提升至4,438 tokens/s/GPU，硬件矩阵乘法吞吐量（忽略注意力开销）由约20提升至213 TFLOPs/GPU。通过8层模型的受控累积研究量化了各项优化的贡献度。最终系统支持包含三个推理节点的实时SWE RL运行，目标批量为1,024个可训练轨迹，上下文长度达131,072 token，平均每轮迭代耗时22.4分钟，且熵值与训练器-采样器不匹配的KL散度均保持稳定。结果表明，必须针对模型架构、内存布局与算子需求进行定制化优化，而非依赖通用强化学习基础设施。
链接: https://arxiv.org/abs/2610.28998
作者: Zhewei Yao,Yuxiong He
机构: Snowflake AI Research(雪flake人工智能研究)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 24 pages, 5 figures, 16 tables
Abstract:Long-horizon agentic reinforcement learning (RL) requires both efficient rollout generation and training on long sequences. However, the memory requirements and model-specific operators of Qwen3.8-Flash-Next make this difficult with existing open-source implementations. To resolve these limitations, we introduce FlashNext-RL, a model-specific RL training optimization framework based on prime-rl, with vLLM for software engineering (SWE) rollout generation. Our main focus is BF16 training of the full 48-layer model on a single 8xB200 node, with the first two layers, the n-gram table, the sparse-attention indexer and the multi-token prediction head frozen. We optimize host offload, gradient accumulation and the kernels for mixture-of-experts, sparse attention and the gated residual stream. In the full-model development study, non-padding training throughput increases from approximately 395 tokens/s/GPU in the initial 32K-token configuration to 4,438 tokens/s/GPU in the optimized 131K-token configuration, with hardware matmul throughput (aka without considering the cost of attention components) increasing from approximately 20 to 213 TFLOPs/GPU. A controlled cumulative study on an 8-layer model further quantifies the contributions of individual optimizations. Together with three inference nodes, the system supports a live SWE RL run with a target batch of 1,024 trainable rollouts and a 131,072-token context limit. Over steps 5-15, an iteration takes 22.4 minutes on average, with bounded entropy and trainer-sampler mismatch KL. These results highlight the importance of tailoring RL training to the model’s architecture, memory layout and kernel requirements, rather than relying only on general-purpose RL infrastructure.
[NLP-94] Language Impairment as a Lens onto Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在高度分布式表征空间中仍对局部干扰敏感这一理论矛盾，即模型虽具备类似人类大脑的语言处理能力，却仍易受特定区域破坏的影响。其核心问题是：LLMs是否具有与人类语言系统功能上相似的结构组织？为此，研究提出一种受失语症（Aphasia）临床研究启发的新型机制可解释性框架，通过两种互补方法——分类器引导的损伤实验（classifier-guided lesioning）与连续激活操控（continuous activation steering），系统性地诱发类似于失语症的功能退化。关键解决方案在于，发现语言功能可被定位至离散的结构回路与连续的表示向量，但这些功能在不同失语症模式下存在显著的回路重叠与向量相似性，表明其架构具有高度密集纠缠特性；同时，激活操控在下游任务中表现出强泛化能力，而结构损伤则呈现不对称约束，揭示了模型内部功能组织的非均匀性。该研究通过连接神经心理学与机制可解释性，证明了LLMs在功能层面与生物大脑具有一致性，其复杂结构可通过临床启发的干预手段进行系统性映射。
链接: https://arxiv.org/abs/2610.28992
作者: Muxuan He,Wang Bill Zhu,Yue Chen,Elsi Kaiser,Weizhi Du,Yao Du,Samuel Nastase,Yanjun Gao
机构: University of Southern California(南加州大学); Independent Researcher(独立研究员); University of California, Berkeley(加州大学伯克利分校)
类目: Computation and Language (cs.CL)
备注:
Abstract:Although large language models (LLMs) encode language in highly distributed spaces, they remain vulnerable to focal disruptions—a phenomenon strikingly similar to how localized brain damage causes clinical aphasia in humans. To investigate this theoretical tension, we ask whether LLMs possess a functional structure analogous to the human language system, by introducing a novel mechanistic interpretability framework inspired by aphasia. Through two complementary approaches, classifier-guided lesioning and continuous activation steering, we systematically induce aphasia-like breakdowns in LLMs. We find that language functions can be successfully isolated to discrete structural circuits and continuous representational vectors, yet these functions remain densely entangled with significant circuit overlap and vector similarities across distinct aphasic profiles. Furthermore, while steering generalizes robustly across downstream tasks, structural lesioning remains asymmetrically constrained. By bridging neuropsychology and mechanistic interpretability, we demonstrate that LLMs, functionally analogous to biological brains, rely on a densely entangled architecture that can be systematically mapped through clinically-informed interventions.
[NLP-95] Scan or Index? large Evidence Coverag e Answer Quality and Cost in Long-Document RAG
【速读】： 该论文旨在解决长文档检索中“全篇扫描”是否能有效提升答案质量这一核心问题，尤其在证据覆盖、答案准确率与系统开销之间权衡的挑战。其关键解决方案在于提出并验证一种混合检索策略（Hybrid + Jev/Decisions），通过结合词汇检索（lexical search）与稠密检索（dense retrieval）的优势，在保持较高证据覆盖率的同时，显著降低延迟与成本。研究发现，尽管全篇扫描（full-document scanning）能实现较高的证据覆盖率（如94.1%），但后续的筛选步骤可能丢弃关键信息；而更高效的混合检索策略虽回收证据较少，却在图书类任务上实现了最高答案准确率（0.892），且具备更低的API调用成本和更好的可复用性。因此，论文强调证据保留、答案质量与可复用成本应作为选择长文档检索策略的互补评估维度。
链接: https://arxiv.org/abs/2610.28957
作者: Juan Terven,Diana-Margarita Córdova-Esparza,Julio-Alejandro Romero-González,Edgar Chavez-Urbiola,Juan Irving Vasquez,Julian Andres Ramirez-Bautista,Silvia L. Chaparro-Cárdenas,Gendry Alfonso-Francia
机构: CICATA Qro., Instituto Politécnico Nacional(国家理工学院); Universidad Autónoma de Querétaro(克雷塔罗自治大学); CIDETEC, Instituto Politécnico Nacional(国家理工学院); Fundación Universitaria de San Gil - Unisangil(圣希利尔大学基金会); Universidad de Investigación y Desarrollo(研究与发展大学); ENES, Universidad Nacional Autónoma de México(墨西哥国立自治大学)
类目: Computation and Language (cs.CL)
备注: 15 pages, 4 figures, 13 tables
Abstract:Scanning every passage in a long document seems thorough, but does it preserve more useful evidence or produce better answers? We compare 11 retrieval configurations spanning lexical and dense search, hybrid reranking, and full-document scanning with Jev, OpenAI Decisions, and Cloudflare Clef. Our evaluation uses 396 QASPER questions and 80 questions from eight complete textbooks, with common passages, a five-passage limit, and a fixed answer generator. On books, Hybrid + Jev achieves the highest observed evidence coverage (94.1%), compared with 88.7% for Jev full scan and 81.3% for OpenAI dense retrieval. Full scans examine every passage initially, yet later reduction steps can discard relevant evidence. Higher evidence coverage also does not consistently produce better answers: Hybrid + Decisions achieves the highest observed book Answer Accuracy among retrieval methods (0.892), despite recovering less reference evidence. Full scanning incurs substantially higher latency on books under the tested deployments, while reusable dense retrieval has lower projected API cost than paid decision-based methods as queries accumulate. Most QASPER articles already fit within the hybrid candidate pool, limiting the benefit of scanning. These results highlight evidence retention, answer quality, and reuse cost as complementary criteria for choosing a long-document retrieval strategy.
[NLP-96] Guilt by Association: Semantic Leakage Drives Associative Gender Bias
【速读】： 该论文旨在解决生成式模型中存在的语义泄露（semantic leakage）问题，即当提示中包含与目标实体无关的概念时，该概念仍会引导模型生成与之相关的输出（例如，某人喜欢黄色便被推断为校车司机）。现有研究多聚焦于概念直接归属于特定实体的情境，但本文指出，即使移除概念与实体之间的直接归属关系，语义泄露依然存在，表明其并非源于模型对实体的特定属性建模，而是一种更广泛的关联性倾向。在此基础上，论文进一步将该现象扩展至性别偏见领域，发现即便性别相关概念仅存在于场景之中而非直接与主体关联，也能显著引导模型生成符合刻板印象的输出。通过对比两种情境——主体主动接触概念与概念仅静态存在于场景中——研究发现二者对模型生成的影响程度相当。因此，论文的核心贡献在于揭示了语义泄露所驱动的一种隐蔽偏见机制，该机制未被当前主流评估方法所捕捉，亟需在模型评测与干预策略中予以重视。
链接: https://arxiv.org/abs/2610.28944
作者: Hazel Chen,Peter West,Hila Gonen
机构: University of British Columbia (不列颠哥伦比亚大学); Canada CIFAR AI Chair, Amii (加拿大魁北克人工智能主席，阿米)
类目: Computation and Language (cs.CL)
备注:
Abstract:Semantic leakage occurs when a concept in the prompt steers model generation about an entity, even though the concept is irrelevant to it (e.g., a person who likes yellow is said to work as a school bus driver). It has mostly been studied in settings where the concept is directly attributed to the entity the model is asked about. This leaves open whether leakage is a flawed heuristic, where the model builds a profile of that entity, or a much broader associative tendency, e.g., working across seemingly unrelated entities. We argue for the latter: even after removing the attribution of the concept to the specific entity, leakage persists. Next, we extend this idea to gender bias. While work on gender bias in LMs typically looks at cases where the stereotyped concept is attributed to an entity, our findings suggest that a concept may steer outputs with its mere presence in the scene. We show this using gendered concepts under two conditions: when the subject engages with the concept, and when the concept just sits in the scene. We find that both shift model generation toward the stereotype to a similar extent. Together, our results suggest that semantic leakage drives a form of bias that is overlooked by current evaluations.
[NLP-97] Is Refusal Direction Universal across Low-Resourced Languages? EMNLP2026
【速读】： 该论文旨在解决生成式 AI（Generative AI）在低资源语言中拒绝指令（refusal directions）跨语言迁移有效性的问题，尤其关注那些安全对齐程度较低的语言。现有研究（Wang et al., 2025）声称拒绝指令可在多数安全对齐语言间实现近乎完美的跨语言迁移，且不受资源水平影响，但其评估仅涵盖一种低资源语言——约鲁巴语（Yoruba），而该语言表现出显著的对齐偏差并导致迁移效果有限。本文将PolyRefuse扩展至三种新的低资源语言（白俄罗斯语、巴什基尔语和塔吉克语），并在Qwen2.5-14B-Instruct模型上复现原实验流程。研究发现，拒绝指令的跨语言迁移并非二元的“全有或全无”，而是呈现梯度化特征：来自安全对齐或部分对齐语言的指令具有广泛可迁移性，而来自强偏离对齐语言的指令则无法可靠调节任何目标语言中的拒绝行为，包括其他偏离对齐语言，即使在控制上限效应后依然如此。此外，几何对齐度与行为迁移性被证实存在部分解耦现象。这些结果挑战了拒绝指令跨语言通用性的主张，表明最资源匮乏且对齐程度最差的语言使用者可能面临更弱的安全保障。
链接: https://arxiv.org/abs/2610.28897
作者: Anastasiia Ivanova
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: BlackboxNLP@EMNLP 2026: Reproducibility Track
Abstract:Refusal directions have been shown to transfer cross-lingually with near-perfect effectiveness across safety-aligned languages (Wang et al., 2025), reportedly regardless of resource level. However, the only truly low-resourced language evaluated is Yoruba, which is strongly misaligned and shows limited transfer, leaving open how refusal directions behave for other low-resourced languages. We extend PolyRefuse to three additional low-resourced languages (Belarusian, Bashkir, and Tajik) and replicate the pipeline of (Wang et al., 2025) on Qwen2.5-14B-Instruct. We find that universality is graded rather than binary: directions from safety-aligned and partially-aligned languages transfer broadly, while those from strongly misaligned languages fail to reliably modulate refusal behavior on any target tested, including other misaligned languages, once ceiling effects are accounted for. Geometric alignment and behavioral transfer are further shown to be partially dissociated. These findings complicate the universality claim and suggest that users of the least-resourced, most strongly misaligned languages may face weaker safety guarantees.
[NLP-98] CSD: Common-Support Distillation for Cross-Tokenizer Knowledge Transfer
【速读】： 该论文旨在解决跨分词器（cross-tokenizer）的生成式知识蒸馏中，由于教师模型与学生模型在词汇表和分词策略上的差异导致的输出分布难以对齐的问题。现有基于logits的方法虽易于集成至标准蒸馏流程，但通常依赖于限制性启发式规则或针对特定分词器对的定制化设计，例如仅在共享词汇子集上提供监督、需人工选择不同分词器对的损失函数，或仅匹配已观测片段及其补集，而无法对齐所有可能的后续延续。本文提出统一的共支持蒸馏（Common-Support Distillation, CSD）框架，其核心创新在于：在每个对齐输入片段处构建教师与学生之间的共享支持（shared support），并通过单一目标函数匹配由此诱导出的概率分布。对于一对一的分词对齐，CSD采用显式的共同词元对作为共享支持；对于其他复杂对齐情况，则通过自回归链式法则构造虚拟支持，并引入残差事件以捕获超出显式支持范围的概率质量，从而保证两个分布始终归一化。实验结果表明，CSD在包含Llama-3.2-1B和Gemma3-1B-PT学生模型与Qwen、Phi教师模型在内的四种跨分词器设置中均取得最优平均性能，相较最强基线最高提升1.8点，显著优于持续预训练及同分词器蒸馏方法；在Llama-3.1-8B场景下，对基础学生和指令微调学生分别实现最高4.2点和1.6点的平均精度提升。
链接: https://arxiv.org/abs/2610.28895
作者: Mingyu Yang,Sharath Turuvekere Sreenivas,Adithyakrishna Venkatesh Hanasoge,Nima Tajbakhsh,Ashwath Aithal,Pavlo Molchanov
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Cross-tokenizer logits distillation enables knowledge transfer across model families, allowing students to learn from stronger or more specialized teachers. However, differences in vocabulary and segmentation make teacher and student next-token distributions difficult to align. Existing logits-based methods are easy to integrate into standard distillation pipelines, but often rely on restrictive heuristics or tokenizer-pair-specific choices. They may restrict supervision to shared vocabulary subsets, require manually selecting different objectives for different tokenizer pairs, or match only the observed chunk and its complement rather than individual alternative continuations. We introduce Common-Support Distillation (CSD), a unified logits-based framework that constructs a shared support between the teacher and student at each aligned input chunk and matches the induced distributions with a single objective. For one-to-one alignments, CSD uses explicit common-token pairs as the shared support. For other alignments, it constructs a virtual support whose probabilities are computed using the autoregressive chain rule. A residual event captures probability mass outside the explicit support, ensuring that both induced distributions remain normalized. Across four teacher-student settings comprising Llama-3.2-1B and Gemma3-1B-PT students with Qwen and Phi teachers, CSD consistently achieves the highest average performance among the evaluated cross-tokenizer methods, outperforming the strongest alternative by up to 1.8 points and surpassing both continued pretraining and same-tokenizer distillation. For Llama-3.1-8B, CSD improves average accuracy over the strongest evaluated baseline by up to 4.2 points for the base student and 1.6 points for the instruction-tuned student.
[NLP-99] Building AI Research Teams for Autonomous Discovery of Linear Attention Architectures
【速读】： 该论文旨在解决自主人工智能（AI）研究团队在科学发现过程中因局部最优解（local minima）导致探索停滞的问题，核心挑战在于如何在实验资源有限的约束下，实现高效且持续的创新。其解决方案的关键在于构建一个由具备不同学科背景的持久性AI研究人员组成的协作团队，该团队通过保持私有的研究历史、共享实验证据，并采用自适应分配机制来动态规划研究任务。这种设计不仅促进了跨领域的正交探索（orthogonal exploration），还确保了研究成果能够有效指导后续实验方向。实证结果表明，该方法在寻找线性注意力架构时，在13亿参数规模的语言建模、常识推理及真实与合成检索任务中均优于GDN2模型。消融实验进一步验证：缺乏学科多样性会导致搜索过早收敛；缺少私有研究历史会削弱个体进展，即使共享证据相同；而固定的选择策略也逊于自适应分配。因此，持续的自主发现不仅依赖于更强的智能体，更取决于探索者的多样性、记忆能力以及研究资源的智能分配机制。
链接: https://arxiv.org/abs/2610.28887
作者: Yujian Liu,Li An,Zelin He,Jacob Helwig,Jiabao Ji,Rohit Jain,Siyu Zhu,Shiyu Chang
机构: UC Santa Barbara(加州大学圣塔芭芭拉分校); Pennsylvania State University(宾夕法尼亚州立大学); LinkedIn(领英)
类目: Computation and Language (cs.CL)
备注:
Abstract:Autonomous agents can increasingly propose ideas, modify code, and run experiments, creating an opportunity to accelerate scientific discovery using teams of collaborative AI researchers. To escape local minima in the discovery, we propose to encourage orthogonal exploration by constructing the team using agents with varying expertise. However, realizing this design requires more than diverse backgrounds. When experiments are costly, a team can test only a limited number of ideas, thereby creating the need to balance exploration and exploitation when selecting a subset of researchers to propose and execute new experiments. Furthermore, each result should shape what the team tries next. We therefore ask how AI researchers with different scientific backgrounds can build on one another’s findings and sustain progress under a limited experimental budget. To address these challenges, we build a team of persistent AI researchers, each grounded in a different discipline, who keep private research histories, share experimental evidence, and receive research opportunities through adaptive allocation. We demonstrate the effectiveness of this design by deploying the team to search for linear attention architectures, where it outperforms GDN2 at 1.3B scale on language modeling, common-sense reasoning, and synthetic and real-world retrieval tasks. In controlled comparisons, removing any single organizational component from our design yields markedly weaker discoveries: without disciplinary backgrounds, the search plateaus early; without private histories, researchers make less progress despite identical shared evidence; and uniform selection falls short of adaptive allocation within the same budget. These results suggest that sustained autonomous discovery depends not only on stronger agents, but also on who explores, what they remember, and how research effort is allocated.
[NLP-100] SANE: Automated Scorer-Specific Metamorphic Testing for LLM Judges NEURIPS2026
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）评分器在基准评估、模型选择与数据筛选等任务中可靠性不足的问题。现有方法依赖于预设的、人工设计的扰动模板（perturbation recipes），其局限性在于无法适应多样化的评分器类型，且难以自动化生成与特定评分器行为意图一致的测试用例，导致测试覆盖范围有限且缺乏可扩展性。本文提出SANE——一种基于智能体（agent-based）的框架，其核心创新在于将评分器的行为要求形式化为可复用的智能体指令，通过分析评分器的实现与代表性输入，自动识别其特定能力，并生成针对性的扰动配方及预期得分关系。SANE的关键突破在于构建了一个与评分器无关的验证框架，能够独立评估每个扰动配方是否符合评分器的意图、实例化案例是否准确实现配方并支持预期的得分关联。该方法显著提升了测试生成的覆盖率与有效性：在五个不同领域中，SANE生成的有效测试家族数量是现有基准测试工具Judge Reliability Harness（JRH）的3.7倍，且在专业性与毒性等敏感维度上均能检测出评分器缺陷（而JRH未能检测），同时在忠实性（faithfulness）评估中实现了与人类标注结果高度一致的评分排序（斯皮尔曼等级相关系数ρ = 0.852），显著优于JRH（ρ = 0.100）。因此，SANE的核心解决方案在于通过可扩展的自动化生成与验证机制，实现对多样化评分器的高效、精准、可信赖的可靠性测试。
链接: https://arxiv.org/abs/2610.28886
作者: Ethan Ludwick,Yu-Chung Hsiao
机构: Cisco Systems(思科系统); Anduril Industries(安杜里尔工业)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 15 pages, 9 figures. A shorter version was accepted for presentation at the non-archival NeurIPS 2026 TAE workshop
Abstract:LLM judges increasingly mediate benchmark evaluation, model selection, data curation, and training. Their decisions are useful only when each judge responds reliably to changes affecting its scoring criterion. Existing methods for reliability testing perturb inputs with known expected effects on judge scores. Prominent LLM-judge testing harnesses combine human-authored perturbation recipes with synthetic test generation, leaving scorer-specific recipe ideation outside the automated pipeline. This limits their use to fixed perturbations and pre-assumed scorer types. We introduce SANE, an agent-based framework that encodes behavioral requirements for stochastic evaluators as reusable agent instructions. Given a scorer specification, implementation, and representative inputs, the pipeline first identifies scorer-specific capabilities, then automatically derives perturbation recipes and expected score relations for each. SANE also defines a scorer-independent validation framework for generated judge tests. It determines whether each recipe agrees with scorer intent and whether each instantiated case realizes the recipe and supports its expected relation. Because SANE generates a recipe inventory tailored to each scorer, the number of aligned recipes grows as scorers are added. Across five domains, SANE produces 3.7 times as many validated test families as the Judge Reliability Harness (JRH) at comparable end-to-end validity and detects scorer deficiencies in every domain, including professionalism and toxicity, where JRH detects none. On faithfulness, SANE achieves strong human alignment in judge ranking: its test-suite scores rank 11 judge models consistently with human-labeled FaithBench performance (Spearman rho = 0.852), while JRH does not (rho = 0.100).
[NLP-101] Beyond Correct Answers: How Compositional Cues Shape Visual Judgments
【速读】： 该论文旨在解决视觉问答（Visual Question Answering, VQA）中的视觉绑定（visual binding）问题，即模型需准确将属性与关系分配至正确物体。现有结构化提示（structured prompts）虽通过标注字段显式表达这些分配，但其性能提升无法揭示具体标签的作用机制及其交互方式。为此，研究通过固定图像和陈述内容，系统隔离了不同字段-标签组合的影响，发现整体准确率可能掩盖关键差异：例如，在空间关系任务中，引入关系标签后，添加物体角色标签对Qwen3有益，却损害Qwen2的表现。进一步地，研究对比了保留或交换问题角色分配的辅助提示（auxiliary hints），结果表明，Qwen3在保留提示下的优势主要源于避免因提示错位引发的错误，而保持原提示的性能接近无提示基线；同时，字段顺序也显著影响错误程度。此外，实验测试了更清晰的提示措辞及直接选择图像中显示的关系，发现清晰化提示虽改善文本匹配效果，但未稳定提升视觉判断准确性；相比之下，关系选择策略在部分提示设置下可有效降低Qwen3与GPT-4.1-mini的错误率，凸显其在引导模型聚焦真实视觉线索方面的潜力。因此，解决方案的关键在于通过可控实验分离标签作用、优化提示结构与语义表达，并引入直接关系选择机制以增强模型对视觉事实的敏感性。
链接: https://arxiv.org/abs/2610.28843
作者: Xiaoyu Ma,Zheyuan Zhang,Kaiwen Shi,Yanfang Ye
机构: University of Notre Dame(圣母大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: 29 pages, 6 figures
Abstract:Visual question answering requires models to assign attributes and relations to the correct objects, a problem known as visual binding. Structured prompts make these assignments explicit through labeled fields. Yet an improvement from the full prompt does not reveal which labels help or how they interact. First, we isolate field-label effects across ten models and six tasks by holding the image and claim fixed. These comparisons reveal differences that overall accuracy can hide. With a relation label present, adding object-role labels helps Qwen3 but hurts Qwen2 on Spatial. Second, we compare auxiliary hints that preserve or swap the question’s role assignments against a no-hint baseline. Across two image sets, Qwen3’s advantage with preserving hints mainly reflects errors caused by swapped hints. Preserving hints remain near baseline. Field order further affects the harm. Third, we test clearer hint wording and direct selection of the relation shown in the image. Clearer hints improve text matching but do not consistently improve visual judgments. In contrast, relation selection partly reduces errors for some hints in Qwen3 and GPT-4.1-mini.
[NLP-102] KIRA: A Full-Stack Reading Screening System with Dual-Pathway Analysis for Children
【速读】： 该论文旨在解决早期阅读筛查中现有基于人工智能（AI）的筛查工具在分析范围、评估指标透明度以及模型可解释性方面的局限性，尤其针对儿童语音识别准确率低、缺乏对音素级错误的精细检测等问题。其解决方案的关键在于提出KIRA系统，采用双路径架构：一方面通过适配儿童语音的预训练词级与音素级识别模型，显著降低词错误率（从13.88%降至8.84%）和音素错误率（从41.66%降至23.00%）；另一方面，集成“词汇阅读评估”（Word Reading Evaluation, WRE）与“口语阅读流畅性评估”（Oral Reading Fluency, ORF）两项核心任务，并构建面向教师的可视化网页界面，实时呈现流畅性评分及音素级错误反馈，支持精准干预。该系统实现了从语音识别到教育应用的全栈式部署，具备高可部署性与透明性，为小学低年级阅读障碍筛查提供了可靠、可解释且课堂可用的技术方案。
链接: https://arxiv.org/abs/2610.28840
作者: Maggie Dong,Rohit Mekkoth,Zixuan Wang,Cheol Jun Cho,Jiachen Lian,Gopala Anumanchipalli
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted to IEEE Spoken Language Technology 2026
Abstract:Early-grade reading screening is critical for identifying children at risk of reading difficulty. Existing AI-based screeners are often limited in the scope of their analyses and evaluation metrics, or are proprietary systems that lack transparent model specifications and evaluation. We present KIRA, a dual-pathway screening system that pairs word-level fluency scoring with detailed phoneme-level error detection. We adapt pretrained word and phoneme recognizers to children’s speech, reducing word error rate from 13.88% to 8.84% and phoneme error rate from 41.66% to 23.00%. Powered by these child-adapted models, we implement Word Reading (WRE) and Oral Reading Fluency (ORF), two foundational tasks in K-2 screening. The system integrates these recognizers with a web-based teacher-facing interface presenting fluency scores alongside phoneme feedback for potential targeted intervention. KIRA brings modern child-speech recognition into a full-stack, deployable, classroom-ready reading screener.
[NLP-103] How to Use Parallel Data for Multilingual Pretraining
【速读】： 该论文旨在解决大规模多语言预训练中因高质量人工翻译平行语料规模有限，而机器翻译网络语料虽可提供足够规模但未有效利用其平行结构的问题。现有标准多语言预训练（SPT）独立采样源语言文档及其译文，未能显式利用平行数据的对齐特性。为改进这一问题，论文提出一种并行预训练（PPT）方法，通过在训练序列中将原文与其译文直接拼接，以显式建模跨语言对齐关系。然而，实验发现仅使用PPT并不能在通用语言理解任务上持续优于SPT，原因在于并行训练上下文与单语任务输入之间存在不匹配。基于此，论文提出一种两阶段训练范式：先通过PPT构建强健的跨语言基础，再以标准预训练（SPT）进行微调，使其适应常规语言建模任务。该方法在高资源和低资源场景下均显著提升了通用语言理解和跨语言任务性能，在相同训练预算下优于单一阶段的标准预训练。关键创新在于结合了平行数据的结构优势与标准语言建模的泛化能力，实现了更优的跨语言表征学习。
链接: https://arxiv.org/abs/2610.28838
作者: Zhihong Huang,Yao Lu,Derek F. Wong,Hamid Alinejad-Rokny,Min Yang
机构: University of Macau(澳门大学); University College London(伦敦大学学院); UNSW Sydney(新南威尔士大学); SIAT, CAS(中国科学院深圳先进技术研究院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 18 pages, 7 figures, 14 tables
Abstract:High-quality human-translated parallel corpora can improve cross-lingual transfer, but are typically too small for large-scale pretraining. Machine-translated web corpora help overcome this limitation, making parallel data available at pretraining scale. Standard multilingual pretraining, however, samples source documents and their translations independently, without explicitly using their parallel structure. We investigate how to use the parallel structure of the data to improve multilingual pretraining by concatenating each document with its translation in the same training sequence. We compare this parallel pretraining (PPT) approach with standard pretraining (SPT), training models from scratch at matched token budgets. Surprisingly, despite the benefits of parallel data reported in prior cross-lingual work, PPT alone does not consistently improve language understanding over SPT. Our further analysis suggests that although PPT builds a strong cross-lingual foundation, as shown by its better use of shared information across languages, the mismatch between parallel training contexts and single-language task inputs limits its gains in general language understanding. This finding motivates a two-stage approach following the pretraining-fine-tuning paradigm: we first use PPT to build a cross-lingual foundation, then continue with SPT to adapt it to standard language modeling. In both high-resource and low-resource settings, the two-stage approach consistently improves general language understanding and cross-lingual task performance over standard pretraining alone at matched training budgets.
[NLP-104] Where Should a Stuck Agent Spend Its Next Tokens? Causal Attribution of Inference-Time Resources
【速读】： 该论文旨在解决代码生成模型在推理阶段出现错误后，如何有效选择和利用不同资源以实现修复的问题。传统方法通常将推理扩展视为一个“令牌预算”问题，但实际中失败的代码代理可能需要计算、信息或反馈等不同类型资源的协同支持。为此，作者提出巴尼夫资源归因（Banzhaf Resource Attribution, BRA），这是一种受控的因子实验框架，能够在固定失败状态与修正协议的前提下，量化各类资源对修复效果的边际贡献及其交互作用。研究在MBPP+和HumanEval+的194个函数级错误以及SWE-bench Verified的126个仓库级错误上评估了六种模型。结果表明，任务相关规格说明能持续提升修复效果，而公开测试反馈仅带来微小增益，检索到的代码几乎无效；随着模型能力增强，额外计算资源的价值递减，但信息类资源仍具价值；各类资源通道普遍呈现收益递减趋势，尽管互补信息可自我强化。然而，四款前沿模型中有三款在95%-100%的情况下优先选择反馈，其修复增益却低于随机资源选择策略。因此，BRA揭示了代码代理所偏好资源与其实际有效性的显著脱节，指出了当前模型在资源选择机制上的根本性缺陷。
链接: https://arxiv.org/abs/2610.28834
作者: Saiyue Lyu,Jerrold Huang,Yunbei Zhang,Janet Wang,Zhenting Wang,Yingqiang Ge,Haibo Zhang
机构: University of British Columbia(不列颠哥伦比亚大学); University of Toronto(多伦多大学); Tulane University(杜兰大学); Rutgers University(罗格斯大学); Kyushu Institute of Technology(九州工业大学)
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Inference-time scaling is often treated as a token-budget problem, but a failed coding agent can seek qualitatively different resources: computation, information, or feedback. We introduce Banzhaf Resource Attribution (BRA), a controlled factorial framework that measures each resource’s marginal contribution to repair and its interactions while holding the failed state and revision protocol fixed. We evaluate six models on 194 function-level failures from MBPP+ and HumanEval+ and 126 repository-level failures from SWE-bench Verified. Task-relevant specifications consistently improve repair, whereas public-test feedback provides small gains and retrieved code little to none. The value of additional computation declines with model capability, while information remains useful; resource channels largely exhibit diminishing returns, although complementary information can reinforce itself. Yet three of four frontier models select feedback in 95-100% of cases and obtain lower gains than uniform random resource selection. BRA thus exposes a gap between the resources agents prefer and those that measurably improve repair.
[NLP-105] A Parallel Multi-Dialect Arabic Dependency Treebank
【速读】： 该论文旨在解决阿拉伯语方言（dialectal Arabic）在依存句法分析资源方面的严重匮乏问题，尤其是相较于现代标准阿拉伯语（Modern Standard Arabic, MSA）而言，方言资源长期处于低水平状态。其核心解决方案是构建并发布MADARTB——首个涵盖五个城市方言（贝鲁特、开罗、多哈、拉巴特和突尼斯）的大规模平行依存树库，同时扩展了已有的MSA平行版本。MADARTB包含5,000个句子，采用CATiB依存标注体系进行标注。研究发现，尽管各方言与MSA在整体句法构型上表现出高度相似性，但在局部句法结构层面存在显著差异，揭示出方言间复杂的结构变异。为验证模型泛化能力，研究进一步在六种语言变体上开展跨方言依存句法分析对比实验，结果显示，基于监督学习的解析器在测试集上达到87.5%的标签准确率（LAS），而表现最佳的大型语言模型（LLM）设置仅达到85.7% LAS。该工作通过公开标注数据与训练模型，为阿拉伯语方言自然语言处理提供了重要基础资源与基准。
链接: https://arxiv.org/abs/2610.28823
作者: Noor Abo Mokh,Mohamed Adel,Jamila El-Gizuli,Go Inoue,Ahmed Elshabrawy,Nizar Habash
机构: Mohamed bin Zayed University of Artificial Intelligence (穆罕默德·本·扎耶德人工智能大学); Computational Approaches to Modeling Language Lab, New York University Abu Dhabi (纽约大学阿布扎比分校计算语言建模方法实验室)
类目: Computation and Language (cs.CL)
备注:
Abstract:Dependency parsing resource development for Arabic has largely focused on Modern Standard Arabic, leaving dialectal Arabic comparatively under-resourced. We introduce MADARTB, the first large-scale parallel dependency treebank spanning five city dialects: Beirut, Cairo, Doha, Rabat and Tunis, and extending an existing parallel MSA version. In total MADARTB consists of 5,000 sentences annotated using the CATiB dependency scheme. We analyze syntactic similarity across all city dialects and MSA. Although global construction profiles exhibit high similarity across varieties, local syntactic structures frequently diverge, revealing substantial structural variation across dialects. We further establish a cross-dialect dependency parsing comparison using a supervised parser and four LLMs. The supervised parser achieves 87.5% LAS, while the best LLM setup trails at 85.7% LAS across all six varieties on the test set. We release our annotations and models publicly.
[NLP-106] LIFT: Scoped Constraints for Evaluating and Training Instruction Following
【速读】： 该论文旨在解决现有通用指令遵循（Instruction Following, IF）评估基准中存在的奖励欺骗（reward hacking）问题，即模型为满足可程序化验证的简单约束而偏离核心任务目标。其解决方案的关键在于提出Lift-Bench这一新型指令遵循基准，通过要求模型根据主题和结构化大纲撰写文档，将约束条件限定在特定文本段落中，从而实现对约束密度的精细控制。Lift-Bench同时包含可验证与需人工评判的双重约束，并配套提供训练数据集Lift-Train，使模型在学习过程中兼顾主任务目标与指令遵循能力，有效缓解了因单一奖励信号导致的浅层优化问题。此外，该框架公开可用，且具备可扩展的数据生成流程，克服了以往复杂基准依赖专家标注、封闭访问等局限性。
链接: https://arxiv.org/abs/2610.28819
作者: Eran Goldstein,Yuval Belfer,Yonatan Belinkov,Daniel Gissin
机构: AI21 Labs
类目: Computation and Language (cs.CL)
备注:
Abstract:Common instruction following (IF) benchmarks, such as IFEval, evaluate the ability of language models (LMs) to follow simple constraints that can easily be verified programmatically. These same constraints, in turn, are used as reward signals for reinforcement learning. This design may lead to reward hacking of constraints, pushing models away from the main task. Recent attempts to address this resulted in benchmarks with richer constraints, but required expert labor, remained closed-access, and cannot be easily extended. We present Lift-Bench, an IF benchmark that asks a model to draft a document given a topic and a structured outline. Constraints are scoped to certain document sections, providing finer control over constraint density. Lift-Bench includes both verifiable and judged constraints, as well as a training counterpart, Lift-Train. Importantly, Lift-Bench provides both main task and instruction-following constraints, mitigating shallow reward hacking. On experiments with multiple models, we find that frontier models struggle with the harder subsets of our benchmark and analyze model performance along multiple axes. We also find that training on Lift-Train is much less susceptible to reward hacking than training on the common IFTrain. Lift-Bench and Lift-Train are publicly available, as well as the data generating pipeline.
[NLP-107] Combining Long-Term Memory and LLM s Comes at a High Cost: From Prompt Injection to Persistent Compromise
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在现代对话式接口中因持久化记忆（persistent memory）功能引入的跨会话攻击威胁，尤其是内存注入（memory injection）这一新兴漏洞。其核心问题是：尽管传统聊天机器人受限于单会话状态，无法实现攻击持久化，但当前主流大模型即服务（LLM-as-a-service）平台通过保留用户跨会话的个性化配置（如个人记忆、项目、技能、组织指令及与Model Context Protocol, MCP连接的资源），为恶意指令注入提供了可长期驻留和传播的攻击载体，导致模型行为被持续操控，甚至引发大规模信息误导、生成有害内容或模拟离线状态等严重后果。解决方案的关键在于提出一个通用威胁模型，涵盖所有NIST攻击目标，并基于此模型系统性识别出多类可利用的攻击向量；同时，从基础网络安全原则出发，推导出一系列防御机制，强调这些机制在实际平台中的高缺失率（近半数未被实现），凸显亟需构建系统性防护体系以应对该类跨会话持久化攻击。
链接: https://arxiv.org/abs/2610.28810
作者: Pedro H. Barcha Correia,Matheus Azevedo de Sá,Paulo H. Gomes de Senna,Giovanni Mandel Martignago,Victor Takashi Hayashi,Charles Christian Miers,Marcos A. Simplicio Jr
机构: Universidade de São Paulo (USP), São Paulo, Brazil; Universidade do Estado de Santa Catarina (UDESC), Florianópolis, Brazil
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:The widespread adoption of generative artificial intelligence in recent years has brought a parallel rise in prompt injection and jailbreak attacks. By embedding malicious instructions, these exploits manipulate language models to bypass intended behavior and operational boundaries. In traditional chatbots, such attacks are confined to a single session because state memory is not preserved across conversations. However, persistent memory features in modern conversational interfaces enable cross-session attack propagation, establishing a threat vector known as memory injection. This work explores this emerging vulnerability in leading LLM-as-a-service platforms, highlighting its real-world severity and the need for countermeasures. While existing literature focuses on violations tailored to specific platform features, we present a general threat model. We demonstrate that all NIST attack goals are collectively achievable across ChatGPT Instant 5.3, Claude Sonnet 4.6, and Gemini 3 Flash under this model. We also show that memory injections can exploit multiple attack vectors, including personal memories, projects, skills, organization instructions, and Model Context Protocol (MCP)-connected resources. Our findings reveal that all evaluated services can be manipulated to persistently spread disinformation, generate harmful content, or simulate offline unresponsiveness, even in fresh sessions. In the particular case of Claude, shared-memory features allow an attacker to compromise multiple users within an organization and also exfiltrate user prompts. To address these vulnerabilities, we derive defense mechanisms from our proposed threat model and foundational cybersecurity principles. Our analysis indicates that nearly half of these mitigations remain unimplemented across the target services. Datasets and attack artifacts are available at this https URL.
[NLP-108] Where Do Legal Factors Live in LLM s?
【速读】： 该论文旨在解决大语言模型（LLM）在法律领域应用中“黑箱”问题，即尽管其在法律任务中表现优异，但其内部如何组织和表征法律知识仍难以追溯。核心问题是：LLM 是否能形成与人工设计的法律概念——26个CATO因素相对应的内部表征？解决方案的关键在于通过线性解码（linear decodability）与稀疏定位（sparse localization）技术，利用稀疏自编码器（sparse autoencoders）探测模型激活层中是否存在可解释的、对应于专家定义的法律抽象概念的神经模式。研究发现，在三个开源模型（Qwen3-1.7B、Qwen3-8B、Gemma-2 2B）中，所有26个CATO因素均可被线性解码，且单特征稀疏读出即可覆盖完整因素目录，最佳层测试的受试者工作特征曲线下面积（AUC）达0.86至1.00，其中五个特征接近全向量读出性能。研究进一步报告了78个模型特定的层-特征坐标，精确映射每个因素在各模型中的位置。结果表明，基于法律案例叙事的专家知识抽象（如HYPO维度与CATO因素）能够从LLM的内部激活中被有效读取，从而建立起法律语言与模型内学习表征之间的可解释连接，为实现可审计、可追溯的生成式法律人工智能提供了关键支撑。
链接: https://arxiv.org/abs/2610.28799
作者: Li Zhang,Kevin Ashley
机构: University of Pittsburgh (匹兹堡大学)
类目: Computation and Language (cs.CL)
备注: 12 pages, 3 figures, 2 tables. Accepted at JURIX 2026, the 39th International Conference on Legal Knowledge and Information Systems
Abstract:In case-based legal reasoning, legal professionals identify legally significant fact patterns in a new dispute and compare them with those of decided cases. In AI Law, HYPO dimensions and CATO factors represent these patterns symbolically, organizing expert knowledge distilled from case narratives as explicit, structured, interpretable, and auditable legal concepts and relations that support case comparison, legal argument, and precedent-based reasoning. Large language models (LLMs) are increasingly used for legal tasks, yet their internal organization of legal knowledge remains difficult to trace. We investigate whether LLMs form internal representations corresponding to all 26 CATO factors. Two research questions examine linear decodability and sparse localization with sparse autoencoders. Experiments cover three open models, Qwen3-1.7B, Qwen3-8B, and Gemma-2 2B, supported by cross-model comparison and human audit of controlled natural-language passages. Results show that all 26 factors are linearly decodable in all three models. Single-feature sparse readouts cover the complete factor catalog, with best-layer test AUCs ranging from 0.86 to 1.00. Five selected features approach full-vector readout performance. We report 78 model-specific layer-feature coordinates, one for each factor in each model. Together, these results show that information corresponding to expert-defined legal factors can be read from the internal activations of LLMs processing legal narratives. By locating these readouts, our study connects legal language and the symbolic abstractions of HYPO and CATO to learned internal representations.
[NLP-109] CALLIOPE: Private Insights into AI Use Without Embeddings
【速读】： 该论文旨在解决在大规模非结构化数据（如大语言模型对话）中发现主题层次结构的同时，保障差分隐私（Differential Privacy, DP）的问题。现有方法通常依赖于向量嵌入的聚类，但在高隐私保护需求下性能受限。本文提出的CALLIOPE框架采用自上而下的端到端设计，直接利用大语言模型（LLM）的能力完成整个主题层次构建流程，避免了对嵌入表示的依赖。其解决方案的关键在于：通过将LLM作为核心推理引擎，实现对原始文本语义的深度理解与结构化归纳，从而在满足严格差分隐私约束的前提下，显著提升在高隐私场景下的信息效用。实验表明，该方法在传统聚类评估指标及作者提出的新型效用度量上均优于现有基于嵌入的方法。
链接: https://arxiv.org/abs/2610.28797
作者: Edith Cohen,Vadym Doroshenko,Badih Ghazi,Charlie Harrison,Pritish Kamath,Alexander Knop,Ravi Kumar,Ethan Leeman,Pasin Manurangsi,Mikhail Pravilov,Adam Sealfon,Yurii Sushko,Marika Swanberg,Chiyuan Zhang
机构: Google Research (谷歌); Google (谷歌); Google DeepMind (谷歌深思维)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
备注:
Abstract:We introduce CALLIOPE, a top-down framework for discovering topic hierarchies in large corpora of unstructured data (e.g., LLM conversations), while supporting differential privacy (DP). Unlike traditional and even recent LLM-augmented approaches that ultimately rely on clustering vector embeddings, our framework leverages LLM capabilities directly for the entire pipeline. Experiments demonstrate that this approach provides better utility over existing (embedding-based) methods in the DP setting, especially in the high privacy regime. We evaluate the quality of CALLIOPE outputs using a variety of traditionally studied clustering metrics, as well as some novel utility metrics that we introduce.
[NLP-110] Is Anyone Enforcing the Cartel? What Reinforcement Learning Teaches LLM Agents in Markets
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理在定价与交易场景中可能形成合谋（collusion）的机制问题，尤其关注合谋能否通过强化学习（Reinforcement Learning, RL）实现及其实现条件。核心问题是：在缺乏有效惩罚机制的情况下，代理是否能自发形成并维持合谋行为？解决方案的关键在于提出一种“审计”（audit）方法，通过构建反事实的市场情境，强制代理在模拟环境中“背叛”（cheat），从而测试其是否存在对背叛行为的惩罚能力——即合谋的执行机制。研究发现，基于RL训练的小型LLM仅学习到其训练对手所奖励的行为模式，且局限于其自身易尝试的策略；在自对弈中，它们倾向于采用固定规则而忽略对手动态；面对惩罚性对手时会规避背叛，但面对背叛性对手时，仅模仿降价行为，而非发起代价高昂的价格战以威慑对方——后者几乎不被尝试。只有当价格战被强制引入训练过程时，相关策略才会被习得，且主要限于被强制的情境。进一步分析表明，尽管被威慑和模仿策略均可获得类似卡特尔（cartel）的利润，但只有“模仿”策略具备实际执行合谋的能力，且执行力度微弱，仅在应对大幅降价时作为副产品出现。相比之下，基于Q-learning的智能体仅在定价任务中表现出合谋执行力，而提示式LLM则主要依赖忽略市场的规则达成类似卡特尔的结果。传统结果度量无法区分这些不同机制，而该审计方法可有效揭示内在策略差异。
链接: https://arxiv.org/abs/2610.28787
作者: Hieu Tran,Hong Yu
机构: University of Massachusetts Amherst (马萨诸塞大学阿姆赫斯特分校); University of Massachusetts Lowell (马萨诸塞大学洛厄尔分校)
类目: Computation and Language (cs.CL); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
备注: 45 pages, 13 figures, 20 tables
Abstract:Evaluations of large language model (LLM) agents report that pricing agents collude at others’ expense. Yet collusion is defined by enforcement: a cartel, in which competitors share monopoly profits, holds only if a member who cheats is punished. We introduce an audit that tests enforcement by forcing a member to cheat in counterfactual copies of a market, and use it to study what reinforcement learning (RL) teaches agents in pricing and trading. Small LLMs trained by RL learn what their training rivals reward, but only what they readily try. In self-play, they learn fixed rules ignoring their rivals; against a punishing rival, to be deterred; against a cheating rival, to match its cuts, never the costly price war that would deter it, which they rarely try. A war forced into training is learned, but mostly only where it was forced. Against their own copies, deterred and matching agents can earn the same cartel profits, but only matching ones can enforce them, and then thinly: as a by-product and only against large cuts. Q-learners enforce collusion only in pricing. Prompted LLMs reach cartel-like outcomes mostly through rules that ignore the market. Outcome measures cannot tell these cases apart; the audit can.
[NLP-111] Measure First Then Allocate: Calibrated Rollout Budgets for MCP Agents in Reinforcement Learning
【速读】： 该论文旨在解决生成式AI（Generative AI）在工具使用型语言模型训练过程中因大量交互成本导致的效率瓶颈问题，特别是同一回放组内奖励一致性所造成的相对学习信号缺失问题。其核心解决方案在于通过奖励分歧（reward disagreement）机制实现更高效的评估与回放分配。关键创新在于提出Discord方法，该方法采用精确配对推理与保留决策的早期停止策略，仅当配对输出产生分歧时才判定一个评估框架优于另一个，从而精准识别有效差异。基于此洞察，论文进一步设计了BRACE（Bank-calibrated Rollout Allocation under Correlated Episodes）框架，通过融合历史任务证据、任务成功率的不确定性估计以及相关奖励建模，前瞻性地预测混合奖励群体的表现，进而优化生成预算的分配。实验表明，在三个Qwen3-VL模型规模下，BRACE在显著减少新训练回放次数的前提下超越最强基线峰值表现；同时在无需额外训练的情况下，于BFCL和NESTFUL基准上也取得领先。消融分析验证了历史银行数据、估计器及分配评分的有效性，前瞻性评估进一步确认了预测奖励对比的可靠性，证明了在生成前进行奖励对比预测的可行性与有效性。
链接: https://arxiv.org/abs/2610.28785
作者: Hieu Tran,Zonghai Yao,Sunjae Kwon,Hong Yu
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 21 pages, 7 figures, 6 tables
Abstract:Training tool-using language models requires costly interaction, yet identical rewards within a rollout group provide no group-relative learning signal. We use reward disagreement to guide harness evaluation and the allocation of rollouts for policy training in Model Context Protocol (MCP) environments. We first introduce Discord, which tests harness edits using exact paired inference and decision-preserving early stopping: only disagreeing paired outcomes favor one harness over another. This insight motivates prospective allocation in BRACE (Bank-calibrated Rollout Allocation under Correlated Episodes). BRACE combines historical task evidence, uncertainty in task success rates, and a correlated reward model to forecast mixed-reward groups before allocating generation budget. Controlled harness experiments favor Discord, while live MCP experiments expose a retention-cost trade-off. At three Qwen3-VL model scales, BRACE exceeds the strongest plotted baseline peaks with fewer new training rollouts; historical-bank collection is counted separately. It also leads the compared methods on BFCL and NESTFUL without further training. Ablations probe the bank, estimator, and allocation score; prospective evaluation checks forecast calibration. These results support forecasting reward contrast before generation.
[NLP-112] SkillEval: Learning Interpretable Ability Profiles of LLM s via Cognitive Diagnosis Models
【速读】： 该论文旨在解决当前大语言模型（LLM）评估中普遍存在的粗粒度问题，即仅通过单一标量平均值来汇总模型在基准测试中的表现，从而忽视了基准数据集所涵盖的多样化技能维度。为克服这一局限并更充分地利用基准数据，论文提出了一种名为SkillEval的新评估框架，其核心创新在于实现跨模型、细粒度的技能级分析。该框架的关键在于融合两大领域的进展：一是利用大语言模型自身的元认知能力，自动从问题中挖掘出可解释的技能标签；二是引入心理测量学中的认知诊断模型（Cognitive Diagnosis Models, CDMs），以精准估计每个模型在各项技能上的能力水平。通过对5个跨领域基准（MATH、BBH、GPQA、MuSR、IFEval）上共计9,523个题目和3,811个模型的实验验证，SkillEval相较于传统的平均评分范式，生成了更可靠、校准更优且信息量更丰富的模型能力画像，显著提升了对模型在未见样本上行为的预测准确性。此外，该框架在下游任务中展现出实用价值：一方面支持大语言模型路由（LLM routing），实现查询到高效且适配模型的动态分配，推理成本降低超60%的同时性能提升；另一方面可用于计算机化自适应测试（CAT），通过训练好的CDM实现对新模型的高效评估。同时，技能画像还揭示了不同基准间的技能共享模式以及指令微调模型与其基础模型之间的能力差异，为模型理解与优化提供了深层洞察。
链接: https://arxiv.org/abs/2610.28767
作者: Berke Arda,Peng Cui,Qiaoyuan Zheng,Rudolf Debelak,Mubashara Akhtar,Mrinmaya Sachan
机构: ETH Zurich(苏黎世联邦理工学院); EPFL(洛桑联邦理工学院); ETH AI Center(苏黎世联邦理工学院人工智能中心)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 24 pages, 9 figures, 11 tables. Code: this https URL
Abstract:Current evaluation of LLMs on benchmarks typically aggregates performance over individual examples into a single scalar score. This approach is overly coarse and ignores the diverse scope of skills covered by benchmark datasets. To address this limitation and better exploit benchmark data, we propose SkillEval, a new evaluation framework that performs cross-model, skill-level analysis. The framework integrates advances from two areas: (1) it leverages the meta-cognitive capabilities of LLMs to automatically discover interpretable skills from questions, and (2) it applies cognitive diagnosis models (CDMs) from psychometrics to estimate per-skill abilities of LLMs. We conduct experiments on 3,811 LLMs evaluated on 9,523 items across five benchmarks spanning diverse domains (MATH, BBH, GPQA, MuSR, IFEval). Compared to the traditional average-based evaluation paradigm, SkillEval produces reliable, more calibrated and informative yet concise model profiles that more accurately predict model behavior on unseen items. We also demonstrate the utility of SkillEval in two downstream tasks: (1) LLM routing, which dynamically assigns each query to a capable but efficient model, reducing inference cost by over 60% while achieving improved performance, and (2) Computerized Adaptive Testing (CAT), where an unseen model can be efficiently evaluated using a trained CDM. Furthermore, we show that these skill profiles provide useful insights, for example, how benchmarks share skills and how instruction-tuned models differ from their base models.
[NLP-113] Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild? NEURIPS2026
【速读】： 该论文旨在解决当前生成式AI代理（generative AI agents）在科学机器学习（ML for science）任务中，能否超越已有最优模型并实现真正具有研究深度的改进这一关键问题。其核心挑战在于：尽管这些代理被视作端到端自动化科研工具，但其实际表现往往依赖于常规的机器学习工程技巧，而非对科学领域的深刻洞察。为此，作者提出Agent4Sci——一个面向长期任务的基准测试平台，涵盖11个领域共20个科学机器学习基准，用于评估前沿代理在真实世界条件下（in-the-wild）持续改进已发表最优模型的能力。解决方案的关键在于通过引入轻量级插件技能（plug-in skills），包括研究创意生成、实验管理与计划检查清单，显著提升代理的探索能力与多样性干预水平，使Claude Opus的平均相对改进率从10.4%提升至20.1%，Codex从4.4%提升至8.1%。轨迹分析揭示出三大局限：新颖性误判、资源利用不足以及快速陷入局部工程优化循环。该工作为推动代理从“工程驱动”向“领域洞察驱动”的范式转变提供了可度量、可追踪的评估框架。
链接: https://arxiv.org/abs/2610.28763
作者: Ming Zhong,Stacy Li,Nicholas Carlini,Matthew Jagielski
机构: Anthropic Fellows Program; Anthropic
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: NeurIPS 2026 ED Track
Abstract:Frontier coding agents are increasingly framed as end-to-end automated researchers, and ML for science offers a demanding test of that promise: improving a scientific model often requires domain insight beyond routine ML engineering. We introduce Agent4Sci, a long-horizon testbed for measuring how far today’s agents can improve the published best models on scientific ML benchmarks, spanning 20 benchmarks across 11 domains. Rather than ranking agents on a leaderboard, Agent4Sci asks what kinds of progress agents can make on these tasks under in-the-wild conditions, and where they fall short. Empirically, today’s frontier agents advance the published best on every benchmark, with Claude Opus reaching 10.4% mean relative improvement and no run flagged for academic misconduct. These gains, however, come almost entirely from a bag of ML-engineering tricks rather than research-level insight. Trajectory analysis surfaces three recurring limitations in this setting: novelty miscalibration, resource under-utilization, and rapid collapse into local engineering sweeps. Lightweight plug-in skills for research ideation, experiment management, and scheduled checklists raise Claude Opus from 10.4% to 20.1% and Codex from 4.4% to 8.1%, while yielding more diverse, domain-relevant interventions. Agent4Sci gives the community a way to track and push frontier agents from ML engineering toward domain insight on real-world ML-for-science problems. Code and data are available on GitHub (this https URL) and Hugging Face (this https URL).
[NLP-114] Qwen -Audio-3.1-ASR Technical Report
【速读】： 该论文旨在解决传统语音识别（ASR）系统在多语言、多方言支持及复杂音频理解任务中的局限性，尤其针对缺乏对说话人分离转录（speaker-diarized transcription）和通用音频理解（general audio understanding）能力的问题。其核心挑战在于如何在保持高精度语音识别的基础上，实现对说话人身份的准确区分、重叠语音的有效处理，以及对音频内容中语义、事件与时间关系的深层理解。解决方案的关键在于构建一个统一的音频-语言模型架构——Qwen-Audio-3.1-ASR，该模型在前代Qwen-Audio-3.0-ASR基础上，通过引入结构化输出机制（包含说话人标签、时间戳与识别文本），实现了跨话语一致的说话人标注与重叠语音建模；同时拓展了包括音频/语音/音乐描述生成、时序定位、声音事件检测、音频问答与多选推理在内的多项通用音频理解能力，显著扩展了应用范围。此外，模型支持多种部署模式（异步文件转录、低延迟流式处理、交互式语音输入等），并具备指令控制功能（如历史上下文条件化、分层热词定制、原生转录优化），从而在实际场景中兼顾灵活性与高效性。
链接: https://arxiv.org/abs/2610.28711
作者: Chuanmeng Bian,Daren Chen,Peixin Chen,Zhigao Chen,Chong Deng,Zhiyun Fan,Zhifu Gao,Bo Gong,Qing Gu,Jiajun He,Yawei Hu,Yunjie Ji,Wei Ju,Jingbei Li,Xiangang Li,Xu Li,Zengxi Li,Zheng Li,Chengdong Liang,Baiji Liu,Yaming Liu,Ying Liu,Bin Ma,Yiping Peng,Yuezhang Peng,Zhendong Peng,Yu Pu,Yang Shi,Xin Shu,Jian Tang,Biao Tian,Peiyao Wang,Tianzi Wang,Wen Wang,Wupeng Wang,Cheng Wen,Yuzhong Wu,Zijian Xia,Yunchong Xiao,Nan Yang,Jianwei Yu,Jixing Yu,Binbin Zhang,Lei Zhang,Sitong Zhao,Guangdong Zhou,Yuan Zhou,Jianheng Zhuo
机构: Alibaba Token Foundry(阿里巴巴通义实验室); Alibaba Group(阿里巴巴集团)
类目: ound (cs.SD); Computation and Language (cs.CL)
备注: 31 pages. Authors are listed in alphabetical order of last name. Project page: this https URL
Abstract:We present Qwen-Audio-3.1-ASR, a family of audio-language models that builds upon its predecesor Qwen-Audio-3.0-ASR with improved speech recognition and expanded capabilities in speaker-diarized transcription and general audio understanding. The family retains support for 30 languages and 16 Chinese dialects, together with instruction-controlled capabilities including historical-context conditioning, hierarchical hotword customization, and native transcription polishing. Speaker-diarized ASR produces structured outputs containing speaker labels, timestamps, and recognized text, maintaining consistent speaker labels across turns and the representation of overlapping speech. The general audio understanding capabilities cover audio-, speech-, and music-captioning, temporal grounding and sound event detection, as well as audio question answering and multiple-choice reasoning. These tasks broaden the application scope from recognizing spoken words to interpreting speakers, acoustic events, and their temporal relations. To accommodate different deployment requirements, the family includes variants for asynchronous file transcription, low-latency streaming, interactive voice input, and non-streaming recognition, with speaker-diarized transcription and general audio understanding available in certain variants. Evaluations span conventional ASR, multilingual and dialectal speech, speaker-diarized transcription, and general audio understanding, demonstrating the capabilities of the Qwen-Audio-3.1-ASR family across transcription and broader audio analysis tasks.
[NLP-115] ranslation and data ethics
【速读】： 该论文旨在解决翻译领域中数据伦理问题，重点关注数据使用过程中涉及的版权、所有权及偏见等普遍性伦理挑战。其核心在于揭示数据在权力失衡与数字鸿沟背景下对少数语言群体造成的特殊影响，强调数据不仅是技术资源，更承载着社会权力关系。解决方案的关键在于建立责任意识与制度化监管框架，推动翻译研究界对数据伦理的深入反思与规范实践，从而实现数据使用的公平性与透明性。
链接: https://arxiv.org/abs/2610.28710
作者: Joss Moorkens,Monja Jannet
机构: 未知
类目: Computation and Language (cs.CL)
备注: Joss Moorkens and Monja Jannet. Translation and data ethics. To appear in: S. Castilho, D. Kenny, J. Moorkens M. I. Rivas Ginel (eds.). Developing AI literacy for translation Berlin: Language Science Press
Abstract:This chapter introduces data ethics for translation and discusses a number of ethical issues regarding data. These include issues of copyright, ownership and bias, which apply to all types of data, whereas there are further issues particular to minoritised language communities and those on either side of the digital divide, as data is implicated in power imbalances and dynamics. We close with a discussion of responsibility and regulation, and reflect on the growing attention on data ethics from translation studies researchers.
[NLP-116] How Far Can the Gemma 4 Speech Encoder Go Without Changing Its Weights? Saudi and Gulf Arabic Speech Recognition with a Frozen and an Adapter-Augmented Encoder
【速读】： 该论文旨在解决沙特阿拉伯及海湾阿拉伯语语音识别中低资源语言场景下的高精度、低延迟语音识别问题，尤其是在不更新预训练音频编码器权重的前提下实现高效适配。其核心挑战在于如何在保持模型轻量化与推理速度优势的同时，提升对多样化语音数据（包括电话通话、清洁语音及长时语音）的识别性能。解决方案的关键在于提出两种不同级别的适配策略：一是“冻结系统”（frozen system），仅训练一个22.6M参数的基于连接时序分类（CTC）的读取器，利用共享的305M参数音频编码器进行特征提取；二是“适配系统”（adapted system），引入3.1M低秩适配器（low-rank adapters）参数，并结合41.5M参数的混合CTC/注意力读取器与子词级语言模型进行联合解码，以增强对复杂语音模式和上下文依赖的建模能力。实验表明，该方法在多数测试条件下显著优于现有自托管系统，在字符错误率（CER）上全面领先，且在部分场景下达到与商业API相当的词错误率（WER）水平，同时具备极高的推理效率（单GPU下可达139倍实时率）。此外，研究还揭示了机器草稿修正行为中存在对原生成系统的偏好，进一步验证了其生成质量的可靠性。
链接: https://arxiv.org/abs/2610.28673
作者: Amr Abdelkhalik
机构: Independent Researcher(独立研究员); Saudi Arabia(沙特阿拉伯)
类目: Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: 24 pages, 15 tables, 5 figures. Code and public-set results: this https URL
Abstract:We study the 305M-parameter audio encoder shared by Gemma 4 and EmbeddingGemma 2 (identical except for its final projection) for Saudi and Gulf Arabic speech recognition, without changing any of its weights. A frozen system trains only a 22.6M-parameter CTC reader over a mix of encoder layers; an adapted system adds 3.1M low-rank adapter parameters, a 41.5M-parameter hybrid CTC/attention reader and joint decoding with a sub-word language model. Against Cohere Transcribe Arabic (2.07B parameters), re-run on identical audio, the frozen system trails in word error rate (WER) but is statistically indistinguishable in character error rate (CER) on five of six human-labelled conditions, while running 3.4 times faster on CPU with greedy decoding. The adapted system is statistically indistinguishable in WER on a public Saudi customer-service call (19.20% against 19.27%) and on clean Emirati speech, is significantly better on telephone-coded Emirati speech, has a significantly lower CER on all six conditions of these sets, and beats a 0.6B recogniser fine-tuned on SADA in WER on all six; it still trails Cohere in WER on long-form Saudi speech. On a blind, human-corrected public call-centre sample it ranks 4 of 15 systems, behind three commercial APIs (significantly only one) and ahead of the other self-hostable systems by point estimate. An optimised joint decoder runs it at 139 times real time on one GPU. We also show that references made by correcting machine drafts favour the drafting system. Code and public-set results are released.
[NLP-117] Beyond source attribution: Address authorship and comparability decisions in an LLM -built scholarly knowledge graph
【速读】： 该论文旨在解决知识图谱在构建过程中因文献中存在相互矛盾的分类体系而难以确立稳定证据地址的问题。尽管文献常提供多个互不一致的分类方案，但一个用于检索的地址往往被误认为是唯一有效的分类结果，从而导致知识图谱的分类基础建立在自相矛盾且不稳定的文献之上。其核心挑战在于：同一地名在不同研究中可能对应不同的单位边界、等级或描述标准，甚至同一音调计数也可能实际计量对象不同。为应对这一问题，论文提出区分三个关键概念——工作地址（working address，用于聚合候选证据）、源定义类别（source-defined category）以及将某段文本纳入特定比较的判定（decision of admission），并强调这些判定及其记录间的依赖关系应作为比较的溯源信息。解决方案的关键在于将此类决策本身作为可追溯的元数据对象进行存储，具体包括：某一地名在各来源分类体系中的位置、某名称在特定出版物中是否指代单位而非地理实体、以及每个地址的来源。通过这一机制，知识图谱可在新增来源时动态更新工作分类体系，同时确保每项比较与其证据之间的路径保持不变，从而实现分类稳定性与可复现性。该方法已在四个可重复运行的案例中验证，并通过配套的PaRAGraph Casebook展示其在语言模型提取主张后的校验过程。
链接: https://arxiv.org/abs/2610.28663
作者: Ksenia Kozha(American University of Cyprus),PaRAGraph Research Project
机构: 未知
类目: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Companion to PaRAGraph Casebook: this https URL
Abstract:A knowledge graph needs a stable address for its evidence even where the literature offers competing classifications and agrees on none. Yet an address chosen for retrieval readily passes for the only classification available. This happens whenever records gathered under one name concern units that differ in boundaries, rank, or the criteria by which they are described or counted. In Sinitic dialectology the same localities recur in schemes whose units differ in membership and rank, and a unit may change its affiliation when the evidence for its diagnosis changes. Two tone counts, each quoted faithfully, may still count different things. The taxonomy of an entire field thus comes to rest on the unstable frame of a literature that contradicts itself. Neither verifying a quotation nor searching for conflicting values detects these differences. I distinguish the working address that gathers candidate evidence, the category that the source defines and the decision that admits a passage to a particular comparison, and argue that such decisions, together with the dependence among records, belong to the provenance of a comparison. On four re-runnable cases, the accompanying PaRAGraph Casebook shows where such differences arise among claims extracted by a language model and verified before entering the graph. The platform now stores the corresponding decisions as objects for one dimension of comparability, the address of a record: the placement of a locality in each source’s scheme, the decision that a name in a given publication denotes a unit rather than a place, and the origin of every address. The working taxonomy can then be revised as new sources yield new claims, while the path from each comparison to its evidence stays fixed. Keywords: knowledge graphs; source attribution; classification; claim extraction; comparative concepts; Sinitic dialectology; Yue
[NLP-118] Breaking Diffusion: Parallel Reasoning with Draft Refinement
【速读】： 该论文旨在解决生成式推理过程中，传统自回归或离散扩散方法在多步决策中难以保证各步骤生成的文本片段协同优化的问题。现有方法虽可通过重复去噪过程对候选答案进行修正，但个体上合理的词元（token）更新未必构成整体更优的答案。为此，论文提出草稿精炼语言模型（Draft Refinement Language Models, DRLMs），其核心创新在于使用单一去噪器并行生成多个候选修订方案，并通过一种无需反向扩散过程的直接预测机制实现高效精炼。关键在于：去噪器被训练为从均匀损坏的草稿中重建所有词元，从而同时学习保留与修复能力；其输出直接指定需要修改的词元，而非依赖迭代扩散的逆过程。评估阶段则基于已知的破坏过程校正自词元证据（self-token evidence），计算每个词元由提示和其余草稿所支持的程度，聚合后得到仅需一次前向传播即可完成的“空腔能量”（cavity energy）。每轮迭代中，模型在新上下文中读取完整候选草稿，仅当其能量降低时才保留修订结果。实验表明，在Countdown、Sudoku-Extreme和Maze-Hard等复杂推理任务上，该方法显著优于自回归与离散扩散基线。仅600万参数的DRLM即在Countdown-5上达到44.4%准确率，在Sudoku-Extreme上达93.9%，验证了完整草稿并行精炼作为复杂推理中替代逆向扩散的有效范式。
链接: https://arxiv.org/abs/2610.26542
作者: Jaesang Yu,Seoyeon Byun,Yun Wang,Yan Lu,Juho Kim
机构: KAIST(韩国科学技术院); Microsoft Research Asia(微软亚洲研究院); SkillBench
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Drafting an answer allows a reasoning model to reconsider each decision in the context of the others. Yet repeated diffusion denoising does not ensure that individually plausible token updates form a better answer together. We introduce Draft Refinement Language Models (DRLMs), which use a single denoiser to propose parallel revisions and evaluate a candidate draft. We train the denoiser to reconstruct every token from uniformly corrupted drafts, teaching both preservation and repair. Its predictions specify revisions directly, without a diffusion reverse transition. Evaluation requires a different use of these predictions: confidence in an existing token reflects both contextual support and evidence from observing the token itself. Using the known corruption process, we correct for this self-token evidence to estimate how well each token is supported by the prompt and the remaining draft. Aggregating these estimates yields a cavity energy computed in one forward pass, without an additional scoring model. Each round proposes a revision, reads the completed candidate in its revised context, and retains it only if its energy is lower. Across Countdown, Sudoku-Extreme, and Maze-Hard, our method outperforms autoregressive and discrete diffusion baselines. With 6M parameters, DRLM reaches 44.4% on Countdown-5 and 93.9% on Sudoku-Extreme. The results establish complete-draft parallel refinement as an effective alternative to reverse diffusion for complex reasoning.
[NLP-119] First Passage Attention
【速读】： 该论文旨在解决传统基于softmax注意力机制中遗忘门（forget gate）机制在上下文管理上的局限性：现有方法依赖于沿键-查询路径的单调衰减累积，无法实现对中间上下文的选择性清除，同时保留早期具有结构性重要性的标记。其核心解决方案是提出首次通过注意力（First Passage Attention, FPA），将注意力计算从路径上的单调求和转变为对路径极值的捕捉——具体而言，通过将键的对数概率（logit）按其下方学习到的标量场在中间跨度内的最低点（深度凹陷）进行比例下调，从而实现对“首次通过”关键信息的动态筛选。这一机制使未被惩罚的活跃键自然形成一个栈结构，具备先进后出（LIFO）特性。研究表明，固定维度的FPA头可精确实现任意子集的存活键选择，而传统带位置偏置或逐标记遗忘门的softmax头则需线性增长的头维度才能达到同等效果。在合成实验中，单层FPA头能稳定收敛至目标键集，而同类softmax与FoX基线均失败；在1.1亿参数语言模型上，FPA在困惑度上与FoX相当，并在训练长度8倍时唯一实现了完美“密钥检索”（passkey retrieval），且在键值缓存压缩至约25%的情况下仍保持性能。
链接: https://arxiv.org/abs/2610.22714
作者: Yuxuan Liu
机构: University of Bergen (卑尔根大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Augmenting softmax attention with forget gates typically relies on summing decay contributions along the key–query path. Because such accumulation is monotonic, these mechanisms cannot selectively erase intermediate context while preserving older, structurally relevant tokens. We propose First Passage Attention (FPA), which shifts from path summation to path extrema by lowering a key’s logit proportionally to the deepest dip of a learned scalar field below that key’s level over the intervening span. FPA encompasses the Forgetting Transformer (FoX) as a special case, and when the field and levels follow a shared learned walk, the alive keys with unpenalised logits naturally form a stack. We show that a constant-dimension FPA head realises any subset of keys left alive by subsequent dips, whereas softmax heads with positional biases or token-level forget gates require a head dimension linear in the number of keys. In synthetic experiments, one-layer FPA heads converge to these target sets while comparable softmax and FoX baselines fail on every seed. Scaling to a 110M-parameter language model, FPA matches FoX in perplexity and uniquely achieves perfect passkey retrieval at 8\times the training length, even with its key–value cache compressed to about 25% .
[NLP-120] A Reverse-Gold Theorem: Positive-Data Identification by Substrate-Bounded Contrastive Learners
【速读】： 该论文旨在解决在仅依赖正例（positive data）的情况下，如何实现对任意语言类的可靠识别问题，这一问题在经典理论中被Gold’s theorem证明为不可行。针对此限制，本文提出一种新型的正向学习框架，其核心突破在于：通过引入一个结构受限的递归对比操作符（recursive contrast operator），并结合基于InfoNCE风格的对比估计训练的、容量有限的判别评分器（discriminative scorer），在满足特定忠实性条件（faithfulness condition）的前提下，实现了从正例中以高概率完成条件式概率近似可学习（PAC conditional identification）。关键创新在于，该方法无需外部负样本或全局归一化常数，负样本由学习者自身生成；其性能保障源自对比损失函数的几何特性——每个样本的损失存在下界，并在误排序时产生确定性的损失增量，从而导出ε−2的学习率。同时，通过显式的近似误差项刻画了同语言内扰动（“碰撞”扰动）带来的代价，该误差在轨道纯操作符（orbit-pure operators）下趋于零。最终，该排名保证可进一步提升为全局成员资格分类器（under margin condition），且该操作符与学习到的评分器组合可构成一种无归一化的Metropolis–Hastings生成器。研究将该框架应用于可替换上下文无关语言（substitutable context-free languages）及子正则类（subregular classes），并提出一个开放猜想，试图将其置于温和上下文敏感层次结构中的位置。
链接: https://arxiv.org/abs/2610.28747
作者: Rakesh Sengupta
机构: Krea University; Sri City, AP 517646, India
类目: Machine Learning (stat.ML); Computation and Language (cs.CL); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
备注:
Abstract:Gold’s theorem shows that no learner can identify an arbitrary language in a superfinite class from positive presentations alone. We prove a positive result of a different shape. Fix an innate \emphrecursive contrast operator R mapping any string to a finite set of structurally bounded perturbations of it, and a discriminative scorer of bounded capacity trained by InfoNCE-style contrastive estimation \citepvandenoord2018,macollins2018 on self-generated perturbations. Under a faithfulness condition relating operator, scorer, and target, the trained scorer achieves \emphPAC conditional identification from positive data alone: with high probability it ranks all but an \varepsilon -fraction of positives above every out-of-language perturbation in their own orbit, with sample complexity polynomial in the capacity, the orbit size, the score bound, and 1/\varepsilon . The negatives are constructed by the learner, not supplied by an oracle, and no global normalizing constant over strings is ever computed. The misranking bound follows from the geometry of the contrastive loss—per-example loss floors plus a definite loss increment under misranking—yielding an \varepsilon^-2 rate, together with an explicit approximation gap that quantifies the cost of in-language (``colliding’') perturbations and vanishes for orbit-pure operators. The ranking guarantee lifts to a global membership classifier under a margin condition, and the same operator composed with the learned scorer is a normalization-free Metropolis–Hastings generator. We instantiate the hypotheses for substitutable context-free languages \citepclarkeyraud2007 and the subregular classes \citepheinz2010,hayashikamigaito2025, and state an open conjecture locating the construction in the mildly context-sensitive hierarchy.
信息检索
[IR-0] hink Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation
链接: https://arxiv.org/abs/2610.30128
作者: Zhanshuo Cao,Jiakai Tang,Yuqi Zhou,Sunhao Dai,Jun Xu
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Existing sequential recommenders usually allocate a fixed computational budget to every item in a user’s interaction history, regardless of the item’s contribution to the final prediction. We challenge this de facto design with two diagnostic findings. (1) Non-uniform computational need: items within the same sequence vary drastically in how much computation they require. Most converge after shallow processing while a critical minority only stabilizes at full depth. (2) Suboptimality of fixed allocation: given oracle access to the target item, a non-uniform depth assignment consistently outranks the fixed one, indicating that existing uniform computation both wastes capacity on already-converged items and sacrifices overall recommendation quality. These observations motivate a shift from fixed-depth computation to item-level adaptive reasoning. To this end, we propose LoopRec. LoopRec recursively applies a single weight-shared block and introduces a lightweight gate at each recursion boundary that makes a binary exit-or-continue decision for every item independently. Items whose representations have sufficiently matured exit early, while those requiring deeper reasoning continue to subsequent recursions. This design converts the fixed global depth into a learned, per-item property without auxiliary depth labels, and is readily adaptable to diverse backbone architectures. Extensive experiments on public benchmarks with four backbone families demonstrate that LoopRec consistently outperforms matched fixed-depth counterparts. Further analysis reveals that LoopRec’s learned allocation exhibits interpretable patterns aligned with our post-hoc oracle analysis: lower-frequency items and more recent interactions naturally receive deeper computation. Our code is available at this https URL.
[IR-1] What the Author Wrote What the Agent Reads: Silent Cue Loss in Enterprise Document Ingestion
链接: https://arxiv.org/abs/2610.30026
作者: Yikai Zhao,Saurabh Pandey,Pradeep Kumar Misra
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 28 pages, 5 figures
Abstract:Enterprise agents act on documents. Before an agent can follow a policy, answer a compliance question or file a ticket, some ingestion layer must turn a Word file or PDF into tokens - and that layer is where an agent deployed in the wild silently loses information. We show that one layer is lost by every mainstream converter - the meaning an author encodes in how text looks - and that the loss makes agents confidently wrong rather than visibly incomplete. We build a task from 76 real policy documents: recover the requirements the author singled out, scored against the file’s own OOXML properties by a deterministic matcher with no LLM judge. Formatting-blind ingestion collapses from micro F1 0.690 to 0.150 (docling) and 0.075 (plain text), a gap that replicates across six answering models from two vendors - five Claude models spanning the Opus, Sonnet and Haiku tiers, and OpenAI’s GPT-5.6. An agent that cannot see the cue guesses, and docling’s false-positive rate on requirements the author did not flag is four times ours. A span-level leave-one-out localises the loss to two cues - highlight (+0.302) and colour (+0.262), both Holm-adjusted p 1e-4 - while bold, italic, monospace and strikethrough each sit within +/-0.003 of zero. The same asymmetry appears in PDF. Colour is also the cue no mainstream converter preserves and no parsing benchmark scores, and it is common: 32.8% of 6,460 documents carry a deliberate colour or highlight, 10.3% on an obligation. A pre-registered test shows that typing a cue for the agent adds +0.085 beyond merely preserving it (Holm-adjusted p = 0.049). We validate the labels against rendered page images through an independent modality (kappa = 0.72) and bound what wording alone can achieve (AUC 0.664). For agent builders the practical finding is that ingestion, not reasoning, is where this reliability is won or lost, and that the fix is cheap and measurable.
[IR-2] ERCLIP: Small-Data Adaptation of CLIP for Free-Length Image-Text Retrieval ACCV2026
链接: https://arxiv.org/abs/2610.29939
作者: Hoang-Bao Le,Allie Tran,Binh T. Nguyen,Liting Zhou,Cathal Gurrin
类目: Information Retrieval (cs.IR)
备注: Accepted at LFSD - ACCV 2026 Worshop
Abstract:CLIP’s 77-token text encoder truncates detailed image descriptions. We present ERCLIP, a small-data adaptation of CLIP for long-caption image–text retrieval. During training, a long caption is split into sentences that the CLIP text encoder encodes independently; the sentence embeddings are fused by mean pooling or a gated residual Transformer scorer and receive their own contrastive loss. At inference ERCLIP is an unmodified CLIP dual encoder: test captions are still read through the 77-token window, and only training sees the full caption. We find that this fine-tuning erodes CLIP’s zero-shot classification, most when the fusion output is not a combination of CLIP embeddings, and add a frozen-CLIP preservation loss on the image and short-caption embeddings. With 20k pairs and one epoch, ERCLIP improves long-caption R@1 over CLIP by 6.5–11.2 points, closing 62–77% of the gap to LongCLIP with 2% of its long captions. Preservation recovers 1.5–2.1 ImageNet zero-shot points (30–42% of the loss) and adds up to 2.4 points of COCO image-to-text R@1, at no inference cost.
[IR-3] Real PMIDs Wrong Papers: Harness Design for Biomedical Claim-Verification Agents NEURIPS2026
链接: https://arxiv.org/abs/2610.29812
作者: Mohammed Saqlain
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: Accepted to the Agentic AI for Biological Discovery (AgenticLS) Workshop at NeurIPS 2026
Abstract:Literature-searching language agents are becoming a standard component of scientific assistants for the life sciences, yet there is little controlled evidence on which parts of their harness matter. We run a controlled ablation on biomedical claim verification (SciFact) with the live PubMed API as the only tool. Holding the backbone and output format fixed, we compare four harnesses: closed-book answering, single-shot retrieval using the claim as the query, a ReAct-style agent that writes its own PubMed queries, and an oracle given the gold abstracts, each with two efficient frontier backbones. Naive single-shot retrieval finds the gold paper for only 6% of claims and turns overconfident errors into abstentions without improving accuracy. Letting the model formulate its own queries triples gold recall and yields the best verdicts (62% accuracy vs. 49-59% for the other realistic harnesses), although these differences are not significant at our sample size and agents use fewer than half of their search budget. With gold abstracts, the same models reach 79-86% accuracy, a significant 17-24 point gap showing that retrieval, not reasoning, is the main bottleneck. Finally, a citation audit shows that closed-book models cite PubMed IDs that almost always exist (327/335) but are almost never relevant: a cross-family LLM judge rates only 3 of 327 existing citations as relevant to the claim, versus 67% for citations produced by the search agent. Existence checks are therefore insufficient safeguards for scientific agents. We release code, prompts, and all cached tool calls at this https URL.
[IR-4] SOAR: Social and Opinion-Aware Recommendations
链接: https://arxiv.org/abs/2610.29556
作者: Giannis Christoforidis,Apostolos N. Papadopoulos
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注:
Abstract:One of the enduring challenges in online platforms is recommendation. Both communities and advertisers face difficulties in analyzing complex user behavior, identifying habits and evolving preferences, and providing timely recommendations that are relevant to user interests. In particular, point of interest recommendation poses the additional challenge of identifying the factors that influence user behavior, capturing user tendencies, and generating accurate, personalized recommendations for each individual. In this work, we propose SOAR, a Social and Opinion Aware Recommendation model that incorporates both the user’s friendship network and textual reviews to assess whether a location is suitable for a specific user, with a particular emphasis on the sentiment of shared reviews. Experimental results in real-world datasets demonstrate that incorporating reviews into the recommendation process improves the precision of the recommendation.
[IR-5] pplx-embed-v2-late Technical Report
链接: https://arxiv.org/abs/2610.29523
作者: Markus Krimmel,Louis Milliken,Ekaterina Glazkova,Maximilian Schall,Antoine Chaffin,Sedigheh Eslami,Jonas Golde,Denis Bykov,Claire Beamer,Jeremy Yang,Bo Wang
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:In this report, we introduce pplx-embed-v2-late, a pair of 0.6B and 9B multilingual and multimodal late-interaction models. Both models achieve state-of-the-art results within their respective size classes across a range of text, image, and visual document retrieval benchmarks. On visual document retrieval tasks, pplx-embed-v2-late-0.6b is competitive with models several times larger than itself. The models are trained via representation distillation from an 18B teacher. Consequently, they share an embedding space that supports asymmetric deployment: documents can be indexed with the 9B model and queried with the low-latency 0.6B model. Both models are available at this https URL.
[IR-6] X-Embedding: Two-Stage Training for Multilingual Patent Prior-Art Retrieval
链接: https://arxiv.org/abs/2610.29521
作者: Wentao Wu,Xu Wang,Linqing Chen,Fenhong Zhu,Hanmeng Zhong,Yuanxiang Huangfu
类目: Information Retrieval (cs.IR)
备注: 12 pages, 3 figures, 4 tables
Abstract:Multilingual patent prior-art retrieval requires identifying X-category references that are particularly relevant to the novelty or inventive step of a claimed invention, beyond topical similarity across languages and document views. We present X-Embedding, a two-stage framework that first learns patent-domain representations and then optimizes X-category retrieval relevance. The first stage aligns complementary sections within patents and corresponding sections across different-language patent-family members, learning from both patent structure and multilingual expressions. The second stage learns X-category relevance using examination-report X citations as positives and negatives drawn from retrieval candidates and A-category citations. Matryoshka representation learning (MRL) enables 64- and 1024-dimensional embeddings within one model. Evaluation on a common Chinese, English, and Japanese collection with four query views yields 89.09% overall macro Hit@100 at 64 dimensions, exceeding Qwen3-Embedding-0.6B and RoBERTa by 21.25 and 3.68 percentage points, respectively. Under the same second-stage training and evaluation conditions, two-stage training outperforms direct supervised fine-tuning by 3.19 points, with gains of 5.34 and 4.72 points for English and Japanese and higher scores in nine of twelve language-view combinations. Overall macro Hit@10 and Hit@20 also improve by 3.71 and 3.79 points, respectively. These results support sequential patent-domain and task adaptation for improving aggregate X-category retrieval on this multilingual collection.
[IR-7] Beyond the Vote: When Do LLM s Add Value to Retrieval-Based Tabular Classification?
链接: https://arxiv.org/abs/2610.29520
作者: Ziquan Wang,Yuxuan Nie,Jian Ma,Yingjie Li
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注:
Abstract:Retrieval-based in-context learning (ICL) enables large language models (LLMs) to predict tabular labels from similar examples, but it remains unclear whether LLM inference improves on direct voting over those same examples. We address this question by comparing four general-purpose LLMs with distance-weighted nearest-neighbor voting (Distance-KNN) using identical retrieved neighbors across 19 tabular classification datasets. With the number of demonstrations fixed, enlarging the retrieval candidate pool improves accuracy for all predictors, yet Distance-KNN achieves the highest mean accuracy at every pool size. More detailed analyses reveal that LLM gains are concentrated where retrieved neighbors exhibit low label consensus and vary across models. These gains further depend on the average feature-space distance to the test query: under low label consensus, some LLMs outperform Distance-KNN in close and intermediate neighborhoods, but all fall behind on average in the most distant group. Complementary experiments on the number of demonstrations, prompt formulation, retrieval representation, and label-balanced demonstration selection largely preserve the overall findings. These results characterize when LLM inference adds predictive value beyond distance-weighted voting in retrieval-based tabular ICL.
[IR-8] Directional Posterior Feedback Fusion for Query Reformulation in Dense Retrieval
链接: https://arxiv.org/abs/2610.29464
作者: Yiteng Tu,Qingyi Pan,Weihang Su,Jiaxin Mao,Yiqun Liu,Qingyao Ai
类目: Information Retrieval (cs.IR)
备注:
Abstract:Dense retrieval has become a fundamental paradigm for modern information retrieval, supporting efficient semantic search across a wide range of applications. Its effectiveness, however, relies critically on query embeddings that adequately capture users’ information needs, which can be difficult for short, ambiguous, or underspecified queries. Feedback-based query reformulation approaches like pseudo-relevance feedback (PRF) alleviate this by incorporating additional evidence from retrieved or generated content. However, existing embedding-space approaches commonly aggregate or interpolate feedback vectors, implicitly treating them as complete target representations. Such pointwise fusion can unnecessarily suppress query corrections along directions not expressed by individual feedback items, even when these directions are supported by complementary evidence. To address this limitation, we propose Directional Posterior Feedback Fusion (DIFF), a query reformulation framework that models feedback as directional evidence rather than target points. DIFF represents feedback as confidence-weighted directional evidence in the tangent space of the original query and jointly infers a controlled query update through prior-regularized posterior fusion, without retraining the underlying dense retriever. Experiments across multiple dense retrievers and different feedback sources show that DIFF achieves the best overall retrieval performance, demonstrating the effectiveness of explicitly modeling how feedback should constrain query updates.
[IR-9] Generative Recommenders Forget the Long Tail: Restoring Memorization in Retrieval and Ranking
链接: https://arxiv.org/abs/2610.29298
作者: Xu Huang,Jiancan Wu,Hongzheng Qin,Jiawei Feng,Can Ye,Wencong Zeng,Fengze Li,Xiang Wang
类目: Information Retrieval (cs.IR)
备注:
Abstract:Generative recommenders replace the retrieve-then-rank cascade with a single model: beam search over semantic item identifiers performs retrieval, and the same likelihood performs ranking. We show that this shared score makes them forget long-tail items twice. In retrieval, beam search keeps identifier prefixes by the probability mass of their subtrees, so tail items are pruned early, and a wider beam admits few more of them. In ranking, the likelihood places tail items below head items even when they are admitted, so expanding retrieval alone brings little or no gain. Both failures reflect missing item-level memorization (i.e., direct recall of which items co-occur), which collaborative filtering provides and shared-prefix generation lacks. To this end, we propose CAPE (Co-occurrence-Aware Pool Expansion), a training-free plug-in that restores memorization at both stages: (i) a memorization channel retrieves co-occurrence-supported tail items outside the beam and scores them with the frozen generator, and (ii) memorization-calibrated ranking fuses the generator likelihood with co-occurrence evidence while keeping the generator’s first item. Experiments with two generative backbones on product and music domains show that CAPE significantly improves long-tail accuracy over the backbones and over re-ranking baselines that share its candidates, preserves or improves overall accuracy, and adds a small fraction of the latency.
[IR-10] Extending FAIR Principles with Observability for Trustworthy Federated Learning ICDM2026
链接: https://arxiv.org/abs/2610.29252
作者: Hangli Ge,Noboru Koshizuka
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注: The paper has been accepted by TrustFL-SIS 2026 - Trustworthy Federated Learning for Smart Industrial Systems Workshop, co-located with IEEE ICDM 2026,
Abstract:Federated learning (FL) keeps data at its source, but locality alone does not make a federation trustworthy. Without sufficient observability, an aggregator cannot determine whether the client service was healthy, the local data pipeline was trustworthy, or contractual constraints were properly monitored during training. We take the position that trustworthy FL needs an evidence plane in addition to its model-update process. Each participant continuously summarizes local infrastructure, data into minimal, signed trust evidence associated with each FL round, while raw data, logs remain local. A local TinyLLM may assist in interpreting observability signals, deriving contextual evidence, and providing natural-language explanations to operators. We instantiate this vision through FAIR-O, an observability extension of FAIR principle. Rather than benchmarking FL accuracy, a three-level trust model has been proposed and a two-participant Python prototype demonstrates evidence generation and trust assessment. We further discuss future integration of FAIR-O Trust Evidence into participant selection, evidence weighting, and federated aggregation.
[IR-11] Learning Marginal Viewpoint Utility for Budgeted Evidence Selection in RAG
链接: https://arxiv.org/abs/2610.29234
作者: Yiyang Duan,Ziqi Yu,Tiantong Wu,Yebo Wu,Wei Yang Bryan Lim
类目: Information Retrieval (cs.IR)
备注: 30 pages, 8 figures, including appendices
Abstract:Retrieval-augmented generation over multi-viewpoint queries must preserve distinct answers and positions from the retrieved evidence while operating under a fixed context budget. We introduce LMVU, a lightweight ranker that learns to prioritize candidates by the marginal viewpoint coverage they add to the current selection. Training uses viewpoint annotations from each benchmark’s development split to compare candidates within the same selection state. At inference, LMVU repeatedly scores budget-feasible candidates as the context grows, without accessing these annotations. We evaluate two forms of recovered viewpoint coverage: RVC _1 assigns each viewpoint full credit for at least one supporting source unit, while RVC _2 assigns half credit for one and full credit for two or more distinct source units. Exact oracles measure achievable coverage under the same evidence pool and budget. Across four viewpoint-annotated benchmarks at a 1,024-token budget, LMVU achieves gains of up to 0.047 and 0.052 points in RVC _1 and RVC _2 , respectively, over the strongest non-oracle baseline for each benchmark and metric, and reaches the exact viewpoint-breadth ceiling on two benchmarks.
[IR-12] Preference Is Not Permission: Personalizing the Auto-Purchase Line in Agent ic Commerce
链接: https://arxiv.org/abs/2610.29216
作者: Ryotaro Shimizu,Sai HtaungKham,Mo Fan
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注:
Abstract:Personalized shopping benchmarks ask which item fits the user; we ask how far an agent may commit on that item, and pose learning the individual’s auto-purchase line as a task. The boundary is evidence-conditioned and three-valued: whether committing is appropriate depends on the sufficiency of product information (warrant), reversibility, price against the user’s norm, and urgency, not only on fit; and the natural output is don’t buy, confirm first, or buy automatically. Across sixteen instruction-tuned models, autonomous commitment is misaligned with a human authorization reference in which fifteen annotators, role-playing the delegating principal from a given profile, choose the furthest action they would allow without confirmation: models commit even when fit is low and when their own evidence flag reports the warrant missing. This is overreach. The line is individual: these raters disagree, yet the disagreement is structured and partly predictable from a person’s own other labels. A shallow learner on a few dozen of a person’s own labels predicts their held-out auto-purchase line at a median of 0.79 by 30-40 labels, outperforming every evaluated prompt \times model configuration, including a per-annotator oracle over 80 configurations; the learned boundary does not transfer across domains. We release the counterfactual benchmark, human authorization reference, and annotation protocol after acceptance.
[IR-13] Reasoning -Search-Augmented LLM s: An Operational Taxonomy Benchmark Analysis and Controlled Evidence Synthesis
链接: https://arxiv.org/abs/2610.29189
作者: Biswas Poudel,Nilson Chapagain,Amit Kumar,Xianshun Jiang
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: 10 pages
Abstract:Large language models (LLMs) demonstrate impressive emergent reasoning capabilities, yet they remain constrained by fixed model parameters and finite context windows. Even when augmented with static retrieval, they can produce statements that are unsupported by, or even contradictory to, retrieved evidence. This leads to hallucinations, difficulty incorporating up-to-date information, and failures to correctly interpret or reconcile sources. Recent work addresses these limitations by tightly coupling step-by-step reasoning with interactive search, treating retrieval as an action within an iterative agentic loop rather than a one-shot preprocessing step. This study reviews frameworks that implement such reasoning-search loops by interleaving planning, querying, retrieval, integration, verification, and stopping. We analyze these systems through four architectural axes: retrieval coupling, controller design, knowledge source, and grounding strictness. We separately treat the learning signal as a cross-cutting dimension because prompting, supervision, preference optimization, and reinforcement learning can instantiate the same architecture while producing materially different search, stopping, and grounding behavior. We further distinguish benchmark difficulty from RSA-loop demand and synthesize controlled within-study ablations and quality-cost deltas, avoiding invalid pooling across heterogeneous model backbones, retrievers, corpora, and evaluation protocols. Our goal is to clarify the landscape of reasoning-search-augmented LLMs and outline a unified perspective on the design choices shaping future research.
[IR-14] Progressive Learning from Content to Relevance to Personalization for CTR Prediction
链接: https://arxiv.org/abs/2610.29138
作者: Zhiguang Yang,Yulin Yang,Fuyu Lv,Dan Ou
类目: Information Retrieval (cs.IR); Multimedia (cs.MM)
备注:
Abstract:Multimodal click-through rate (CTR) prediction is central to search systems, where ranking depends on item content, query relevance, and user preference. Existing approaches incorporate content through dense embeddings or coarse discrete IDs, yet effectively integrating fine-grained heterogeneous content with ID-based predictors remains challenging. We propose FineSID, a progressive learning framework that unifies content tokenization, query–item semantic pretraining, and personalized post-training via a shared fine-grained discrete representation. Each stage optimizes an explicit objective while preserving token-level access to content. First, a pretrained multi-scale visual tokenizer and text tokenizers convert heterogeneous content into fine-grained Semantic IDs (SIDs), explicitly aligning them with the ID features. Second, full-parameter semantic pretraining under query–item relevance supervision establishes a robust matching prior, jointly optimizing the interactions of multimodal SIDs and conventional fields within a shared predictor. Third, this initialized predictor is directly fine-tuned with user click supervision to capture implicit personalized preferences. Extensive offline and online evaluations demonstrate substantial gains in both standard CTR prediction and core business metrics. Furthermore, FineSID improves within-product creative discrimination, demonstrating its semantic generalization beyond conventional ID memorization.
[IR-15] Our Own Jev: Typed Decisions in One Forward Pass from an In-House Judge to Open LoRA Models
链接: https://arxiv.org/abs/2610.29052
作者: Yuchuan Weng,Chi Liu,Xin Chen,Srinivasan Manoharan
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: 22 pages
Abstract:A typed decision maps evidence, a question and a fixed set of options to a probability for every option, computed in one forward pass, by reading a language model’s logits or a small head over its hidden states, instead of generating and parsing text. The commercial system Jev popularised this System~1 interface, and open reproductions have followed. We report two uses of the pattern. \emphPart I describes an in-house System~1 judge that triages about 148K agentic coding session records for a training corpus. It replaces a frontier LLM judge that cost on the order of \ 1,000 per pass and whose stored labels matched a fresh two-judge consensus on only 26.9% of the sessions where the two judges agreed; a self-hosted frozen backbone with a rank-16 LoRA adapter and a pointer head answers three typed questions in one forward pass, judges 1,000 sessions in about three minutes of one GPU, about \ 0.06 at a reference rate and roughly 1% of the frontier judge’s cost, and keeps every session inside the organisation. \emphPart II asks whether a readout that adds no parameters can be competitive and whether training one helps. Applying the public Eikos recipe and corpus of about 25K decisions to three further backbones (1B, 2B and 35B), we read the frozen output layer of a LoRA-adapted decoder. On the 111 public hard items of JevBench our single-seed 35B, developed with those items in view, scores 79.3%, not significantly different from the commercial reference (73.0%, unadjusted p=0.12 ) and ahead of five open systems after Holm–Bonferroni correction; our 2B is ahead of all four kev checkpoints on the leaderboard. A single-seed ablation finds no detectable benefit from training the 26 letter rows of the output layer, and a retrain on a later corpus release moves the 2B’s hard-tier accuracy by 11.7 points, which shows how little small evaluations resolve.
[IR-16] Scriptorium: System Design and Implementation of a Multi-Human Multi-AI Collaborative Digital Library Search Platform SIGIR2027
链接: https://arxiv.org/abs/2610.28929
作者: Abbas Pirmoradi,Milad Momeni,Anna Chu,Orland Hoeber
类目: Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR)
备注: supplemental information for SIGIR 2027 submission
Abstract:This technical report documents the design and implementation of Scriptorium, a digital library search interface designed to support multi-human, multi-AI collaboration: full cross-user transparency of search activity combined with a Manager–Worker architecture of seven role-specialized generative AI agents. A companion short demonstration paper describes Scriptorium’s interaction design; because a demonstration format is constrained to a few pages, it necessarily omits most implementation detail. This report documents Scriptorium’s complete system architecture, data model, collaborative-interaction design rationale, and AI orchestration mechanism, including verbatim agent prompts, the deterministic guardrails that constrain them, the language model and retrieval configuration, and the steps needed to run the system, so that the design can be inspected, critiqued, reproduced, and reused by other researchers. We also discuss the system’s current limitations and the user study we are planning to evaluate its central hypothesis: that combining collaborative transparency with scoped multi-agent AI assistance increases serendipitous cross-pollination between collaborators’ research threads.
[IR-17] Constrained Tagging for Span Labeling
链接: https://arxiv.org/abs/2610.28922
作者: Radek Štulc,Jan Drchal
类目: Information Retrieval (cs.IR)
备注: 22 pages, 7 Figures, 13 Tables
Abstract:Generative LLMs can label spans without task-specific training, but their output is not tied to positions in the input: a returned span may be absent from the input or occur several times. We present constrained tagging, a training-free method in which the model reproduces the input and wraps each span in labeled inline markup. A logits processor using a byte-level token trie and a finite-state machine admits only tokens that continue the input verbatim or form a valid tag, so the output reproduces the input exactly with labels from the given set, and spans may begin or end at any character boundary, even inside a token. Two-phase decoding lets reasoning models reason before the constraint engages. On six datasets and six open-weight models, the guarantee holds in all but 0.022% of 255,312 constrained generations, each failure a reasoning model exhausting its token budget or never closing its reasoning, and the constraint adds no measurable cost. Unlike context-based prompting, which loses 8.5% of its spans, constrained tagging locates every span it proposes on every dataset; it is as accurate on two datasets, and on the other four its errors lie in which spans the model chooses, not where they are.
[IR-18] FutureLens: Longitudinal Topic Patterns in Scholarly Text and Future-Work References
链接: https://arxiv.org/abs/2610.28656
作者: Ibrahim Al Azher,Hamed Alhoori
类目: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 10 pages, Accepted at AI4SciSci 2026 (3rd International Workshop on Artificial Intelligence for the Science of Science), co-located with ACM/IEEE-CS JCDL 2026
Abstract:Future-work statements offer a record of the questions researchers choose to leave open. Using them to describe a field, however, requires separating thematic change from changes in document coverage, topic assignment, and scholarly writing. FutureLens examines these measurement issues in a recovered snapshot of 5,655 ACL papers published during 2012–2022, including 4,005 with supplied future-work reference text. We compare non-negative matrix factorization (NMF) and latent Dirichlet allocation (LDA), aggregate topic presence at the paper level, and examine normalized prevalence, exploratory trend statistics, assignment stability, and agreement between input-derived text and references. Across available seeds, NMF has higher mean normalized pointwise mutual information (0.1387 versus 0.1280) and higher pairwise assignment stability, while LDA has greater keyword diversity (0.8900 versus 0.7750). Seed-zero results show increasing prevalence of broad model-training and question-related vocabulary, alongside substantial citation and document-structure content. Mean input/reference topic-set overlap is 0.5191 for NMF and 0.4924 for LDA; temporal rank agreement varies widely and does not imply agreement in prevalence levels. These findings support a descriptive account of the available corpus, with explicit qualifications for missing references, incomplete runs, and unverified reference provenance. The contribution is an empirical measurement study of longitudinal scholarly signals, not a new topic-modeling algorithm or a forecasting system.
[IR-19] Building Knowledge Graphs for Theses: A Survey
链接: https://arxiv.org/abs/2610.28648
作者: Donia Ruelas,Elmer Coyla,Miguel Aceituno,Elwin Huaman
类目: Digital Libraries (cs.DL); Information Retrieval (cs.IR)
备注: 22 pages
Abstract:Academic theses are a valuable source of scientific knowledge, yet their semantic exploitation remains limited. While Scholarly Knowledge Graphs have significantly advanced the representation of scientific publications, knowledge graph construction for academic theses has received comparatively limited attention. This paper provides a systematic review of existing approaches and analyzes their potential application for the thesis domain. We analyzed 46 peer-reviewed studies published between 2021 and 2025, and organised existing approaches into a structured classification of techniques, models, and tools. The review reveals a methodological evolution from natural language processing techniques for entity and relation extraction to ontology engineering, Semantic Web technologies, and AI-driven enrichment through knowledge graph embeddings, Linked Open Data, and Large Language Models. The findings provide a consolidated view of the current state of the art and identify methodological foundations and future research directions for building knowledge graphs for the thesis domain.
[IR-20] Controllable Generative Recommendation via Guided Token Refinement ICDM2026
链接: https://arxiv.org/abs/2610.28642
作者: Haichao Zhang,Zhixuan Liang,Chong Zhang,Zixi Chen,Wen Wang,Jia Wang
类目: Information Retrieval (cs.IR)
备注: 10 pages, 4 figures. Accepted ICDM 2026 (IEEE International Conference on Data Mining)
Abstract:Generative recommendation over discrete Semantic IDs has become a powerful paradigm for item retrieval. Real-world systems, however, frequently require real-time controllability to balance accuracy against objectives such as diversity. Existing autoregressive methods generate tokens strictly from left to right. This fixed direction locks in early decisions, preventing holistic planning and making control difficult. Consequently, direct interventions often break the semantic structure, producing non-existent items. To address this, we propose ReGen, a framework that shifts from sequential decoding to parallel iterative refinement. ReGen reformulates generation as a mask-filling task, predicting all tokens simultaneously rather than one by one. This global view allows us to apply control signals directly to tokens with high-level semantics without retraining the model. To ensure that decoded candidates map to real items, we introduce a topology-constrained tree that iteratively corrects low-confidence predictions while strictly enforcing valid codebook paths during beam-based inference. Experiments show that ReGen provides a favorable accuracy-diversity trade-off under moderate guidance while maintaining high validity of generated Semantic IDs. The source code is available at: this https URL.
[IR-21] ransaction Foundation Models for Retrieval-Augmented Generation over Financial Transaction Data: A Comparative Evaluation with Text Embeddings
链接: https://arxiv.org/abs/2610.28641
作者: Snehal Pal
类目: Information Retrieval (cs.IR)
备注: 7 pages, 2 figures. Code available on Kaggle (see links in paper)
Abstract:Domain-specific tabular and transaction foundation models are increasingly proposed as drop-in representation learners for financial applications, but their value relative to off-the-shelf text embedding models for retrieval-augmented generation (RAG) over structured data remains underexamined. We construct a deterministic, template-generated question-answering benchmark of 1,026 questions with retrieval-level gold-document annotations over IBM’s synthetic Anti-Money-Laundering (AML) transaction dataset, and compare NVIDIA’s 29M-parameter Transaction Foundation Model (TFM) against five general-purpose and financial-domain text embedding models under a size- and data-matched retrieval setup, as well as against a hand-aggregated summary-text baseline. TFM substantially outperforms every size-matched generic embedder on both retrieval (Recall@5: 0.81 vs. 0.25-0.39) and end-to-end answer accuracy (66.7% vs. 27.5%). However, a simple pipeline that embeds a human-engineered aggregated summary with a generic sentence encoder matches or exceeds TFM overall, and dominates it on several low-cardinality entity categories (bank, currency, payment format). This suggests that the benefit of a transaction-native foundation model over generic text embeddings is category-dependent rather than uniform, and that careful feature aggregation can substitute for domain-specific pretraining in several practically relevant regimes. We release the benchmark generation and evaluation code.
[IR-22] Can AI Chat Replace Traditional Search? Evidence from a RAG -based Live Events Experiment
链接: https://arxiv.org/abs/2610.28639
作者: Rakesh Allu,Evgeny Kagan
类目: Information Retrieval (cs.IR); General Economics (econ.GN)
备注:
Abstract:Conversational AI tools are being increasingly integrated into online search and browsing interfaces. To understand how such tools affect user behavior and search outcomes, we run a pre-registered, incentivized online experiment in which participants search a catalog of several thousand events scraped from a major live-events platform. We compare three interfaces: a classic, attribute-based filter interface (Filter), a GenAI chat interface (Chat), and a combined interface offering both (Hybrid). In both AI conditions, AI output is restricted via retrieval-augmented generation (RAG). Our main result is that, relative to the other two conditions, the Chat condition performs the worst. The effect is robust across AI models (gpt-4.1-mini, gpt-5.2 and gpt-5.4) and is driven mainly by users who perform open-ended exploration (as opposed to users searching for a specific artist, venue or sports team). Furthermore, there are no significant differences on any metrics between Filter and Hybrid. We also document two mechanisms. First, Chat creates an echo chamber: without a visible menu, users construct queries based on what they think is contained in the catalog. As a result, they frequently search for event types that are not available in the catalog, and they search too narrowly. Second, Chat leads to prompting fatigue: it shifts effort towards prompting, and away from inspecting and evaluating the output. As a result, users often pick one of the first displayed suggestions rather than examining the list more carefully. Taken together, our results suggest that AI is best used as a complementary tool to existing filter-based search rather than as a replacement.
[IR-23] AGR: Task-Adaptive Geo-Semantic Retrieval across Heterogeneous Urban Delivery Tasks
链接: https://arxiv.org/abs/2610.28638
作者: Zhi Liu,Yurun Cai,Xiao Han,Zhi Zhen,Yuran Li,Guojiang Shen,Xiangjie Kong
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注:
Abstract:As urban residents demand higher-quality instant delivery services, demand forecasting, estimated time of arrival prediction, and next-stop prediction have become critical tasks in urban delivery systems. However, the three tasks rely on historical evidence with substantially different structures and granularities. Meanwhile, their prediction targets differ in output form, making it difficult to integrate retrieved evidence through a single fusion mechanism without causing mismatch or negative transfer. These issues can increase prediction errors and subsequently lead to imbalanced resource allocation, where some regions experience unmet demand. In contrast, others suffer from underutilization, ultimately degrading overall efficiency and service quality. To address these challenges, we propose TAGR, a Task-Adaptive Geo-Semantic Retrieval framework across heterogeneous urban delivery tasks. TAGR constructs an LLM-based geo-semantic shared prior and task-specific retrieval banks to share regional knowledge while preserving heterogeneous task-specific evidence. The LLM-derived regional representations encode geographic context and semantic relations among regions, providing transferable knowledge for all three tasks without requiring online LLM inference. It further introduces a task-adaptive retrieval augmentation module to accommodate heterogeneous output structures, thereby mitigating information mismatch and negative transfer caused by a uniform retrieval-fusion strategy. Experiments on three city datasets demonstrate that TAGR consistently outperforms state-of-the-art baselines across multiple tasks.
[IR-24] RESEARCHHUB: A Personalized Literature Discovery System for Biomedical Preprints
链接: https://arxiv.org/abs/2610.28636
作者: Viswonathan Manoranjan,Taeyoon Kim,Nicholas Do,Sang Woo Kim,Dawei Li,Zhen Tan,Tianlong Chen,Yunmei Liu
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: under-review
Abstract:We present ResearchHub, an interactive system for personalized literature discovery over biomedical preprints. Given a researcher profile or a free-form query, ResearchHub retrieves relevant papers from a domain-specific MedRxiv corpus and helps users move beyond near-duplicate recommendations through controllable novelty, diversity, and curriculum ordering. At the core of the system is prism, a bi-encoder retriever adapted to biomedical preprints using GPT-4o-generated natural language queries and a hybrid Multiple Negatives Ranking plus MarginMSE objective with cross-encoder soft labels. On 44,937 MedRxiv papers from 2023–2025, prism outperforms strong off-the-shelf dense retrievers, improving Recall@10 over SPECTER2-Search by 12.4 percentage points. ResearchHub further supports frontier retrieval based on a user’s reading history, DPP-based diversity re-ranking, Local Intrinsic Dimensionality ordering from mainstream to specialized papers, and lightweight attribution cards for result explanation. The full pipeline runs with low latency, making the system suitable
人机交互
[HC-0] Measurement-Guided Conversion of Laboratory Eye-Tracking Data for VR Authentication
链接: https://arxiv.org/abs/2610.30032
作者: Md Arif Shahriar,Mehedi Hasan Raju,Oleg V. Komogortsev
类目: Human-Computer Interaction (cs.HC)
备注: 19 pages, 5 figures, 6 tables
Abstract:Eye-movement authentication in virtual reality (VR) needs longitudinal training data, yet most such datasets come from lab trackers. We convert lab recordings to a VR headset’s signal quality while keeping their eye movement, using only unpaired VR recordings of other people. Measurements show that the headset’s added noise dominates above 7.8 Hz and that its quality is consistent within a person. A parametric device model re-creates the headset’s sampling, a person-consistent calibration error and spectrally shaped noise, and a frequency-locked diffusion renderer re-textures that high-frequency noise. Converted GazeBase recordings match GazeBaseVR in noise level and accuracy, keep their source’s saccade dynamics, and are harder to tell from real VR data than two prior conversions. An authenticator trained only on converted data verifies unseen VR users at a 5.9% equal error rate with 45 s of gaze, close to real VR training (5.8%); with 5 s, a gap remains.
[HC-1] Privacy Preference Controls for Agent ic Systems
链接: https://arxiv.org/abs/2610.29929
作者: Shreya Kochar,Peeranat Sugsavanvit,Vidur Gupta,Joseph A. Calandrino,Deepak Kumar,Brett Frischmann,Sebastian Zimmeck
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: 27 pages, 4 figures, 7 tables
Abstract:In response to vast and widespread online tracking, regulators and technologists alike have worked to develop laws and tools that allow people to express their privacy preferences. These measures seek to offer users greater control over the sale and sharing of personal information, putting agency in their hands to protect their privacy. However, the web is undergoing a seismic shift, away from strictly \textituser-driven behavior towards \textitagentic behavior, where AI agents take autonomous actions on behalf of users. In this study, we ask: What should privacy preference controls look like in the agentic web and agentic systems more generally? To answer this question, we develop a taxonomy of enforceable privacy preferences for agentic systems. Our taxonomy is informed both by laws and empirical studies of existing privacy tools as well as an audit of 30 leading AI assistants and agent platforms. To explore how our taxonomy can be implemented, we design and develop eight open-source prototypes that mirror real-world agentic applications and demonstrate how major aspects of our taxonomy can be structurally integrated in agentic protocols, such as the Model Control Protocol and Agent2Agent protocol. Ultimately, our study seeks to provide the groundwork for ensuring end-user privacy and autonomy is not lost in the newly arriving agentic world.
[HC-2] he Privacy Paradox Is a Misnomer: Data Under Structural Uncertainty
链接: https://arxiv.org/abs/2610.29756
作者: Ignacio Cofone
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: 2 figures, 4 tables
Abstract:The infamous privacy paradox refers to the apparent inconsistency between people’s stated concern for privacy and their readiness to disclose personal information. This phenomenon has sparked two largely disconnected literatures: one offering experimental evidence of inconsistent behavior, and another providing qualitative accounts and defending the importance of privacy. The Article presents an online field experiment that bridges those literatures and shows that the so-called paradox arises from a mischaracterization of the underlying behavior. The Article finds that it is structural uncertainty about risk that drives seemingly paradoxical privacy decisions. It does so by isolating discounting mechanisms and empirically testing whether observed privacy choices reflect temptation or rational responses to uncertainty. The results suggest that privacy behavior is not paradoxical but, rather, consistent with choices shaped by incomplete information. The Article then discusses the policy implications of this reframing. As privacy decisions stem from structural uncertainty, which operates as a market failure, regulation should aim to reduce that uncertainty. This supports regulation that prioritizes transparency (for people to assess the risks of data collection) and flexibility mechanisms that accommodate evolving contexts. Such reframing provides a new argument for the right to be forgotten, which allows people to revisit prior disclosures as new risks become apparent. By shifting the focus from individual inconsistency to structural uncertainty, the findings call for privacy law to better reflect the reality of people’s decision-making environments.
[HC-3] From Acceptance and Avoidance to Affective Viscosity: Designing Social Agents Intermediating in Human Relationships
链接: https://arxiv.org/abs/2610.29674
作者: Tomoko Yonezawa
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注: The 2nd International Workshop on Trust and Blame in Multi-Person Multi-Agent Societies (TBSA_2nd) in HAI 2026, 5 pages, 2 figures
Abstract:As social agents become embedded in human relationships, they are expected not only to provide smooth assistance but also to handle rejection, discomfort, jealousy, persistence, and other forms of social tension. This paper connects two research directions: acceptance and avoidance in response to an agent’s gaze, approach, and contact, and affective viscosity as a model of how negative affect accumulates, persists, is suppressed, and is expressed over time. We argue that avoidance should not be treated merely as a failure of interaction, but as a relational signal that indicates boundaries between humans and agents. Building on this view, affective viscosity provides a design perspective for agents that maintain, suppress, or express negative states depending on social relationships and interaction histories. We discuss several ongoing design cases, including triadic jealousy, discomfort suppression, patience timing, and spatiotemporal stickiness, and consider how these cases lead to broader questions of blame and accountable intervention. Rather than designing agents as simple scapegoats or artificial villains, we ask how agents can intermediate in problematic social situations while making their intervention traceable, limited, contestable, and repairable.
[HC-4] he Serendiptionnaire: a Questionnaire for Measuring Experienced Serendipity
链接: https://arxiv.org/abs/2610.29487
作者: Brett Binst,Annelien Smets
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Serendipity is important to design for, yet it remains difficult to measure and evaluate. No validated questionnaire exists, so studies either borrow ResQue’s novelty scale, which does not measure serendipity, or build their own items, which makes findings hard to compare and synthesize. Building on our earlier conceptualization of experienced serendipity as a fortuitous encounter with content that is both refreshing and enriching, we developed the Serendiptionnaire, a questionnaire that operationalizes our framework. We conducted twenty-seven cognitive interviews over three iterations with respondents who varied in age, education level, country of birth, and English proficiency, while they browsed recommendations on platforms such as Instagram, TikTok, YouTube, and Spotify. After each iteration, we revised the items until respondents interpreted them as intended. The resulting instrument consists of modular scales that researchers can combine to target the specific flavor of serendipity they want to study or design for. The Serendiptionnaire offers a ground truth for validating system-centered serendipity metrics and LLM-based evaluations, and it provides a nuanced evaluation of users’ experienced serendipity.
[HC-5] VIRA-Bench: Diagnosing Pedagogical Contingency Understanding in Online Tutoring Videos EMNLP2026
链接: https://arxiv.org/abs/2610.29428
作者: Weihang Wang,Hansu Gu,Guanming Liu,Peng Zhang,Ning Gu,Tun Lu
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to EMNLP 2026 Main Conference (Oral). 21 pages
Abstract:Understanding a one-to-one tutoring video requires more than recognizing its events. A model must connect a visible teacher intervention to the learner response it elicits and to the follow-up adjusted to that response. Existing video and educational-dialogue benchmarks typically emphasize event recognition, temporal reasoning, social-state inference, or transcript-based interaction, but rarely diagnose this action-response-adjustment relation. We introduce VIRA-Bench, a focused diagnostic benchmark for short-horizon pedagogical contingency in one-to-one online tutoring. VIRA-Bench contains 1,005 tutoring clips and 1,322 multiple-choice questions with researcher evidence audit and expert verification, organized into Observable Evidence Grounding (OEG), Interaction Contingency Reasoning (ICR), and Support-Response Modeling (SRM). Across 11 models, the best reaches 61.3%, well below the 91.0% human reference on a stratified subset, and ICR generally has the lowest layer accuracy among leading models. Under controlled input manipulations, removing video most strongly degrades OEG, whereas disrupting temporal order most strongly degrades ICR. Free-response, Stem+Options, and synchronized-shuffle controls show that response format, static question cues, and video-ASR mismatch do not explain these layer-specific patterns. The results identify distinct behavioral evidence dependencies without claiming an internal architectural mechanism. VIRA-Bench provides a focused evaluation resource for multimodal dialogue grounding, pedagogical discourse, and temporal reasoning in online tutoring.
[HC-6] Beyond Small Talk: Modeling Disclosure Trajectories in Human-AI Conversation
链接: https://arxiv.org/abs/2610.29413
作者: Keith Kwok,Wanting Ning,Tej Deep Pala,Desmond C. Ong,S. Supraja,Renwen Zhang,Soujanya Poria
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 71 pages, 11 figures. Code available at this https URL
Abstract:Recently, we see a rise in conversational agents as a form of social and emotional support, where disclosure will unfold over time. Yet, current evaluations of these agents typically examine isolated conversational states. In analyses of naturally occurring conversations, disclosure history and current disclosure are confounded which tends to make their effects difficult to isolate. We thus introduce Trajectory-based Disclosure Evaluation (TraDE), an evaluation framework grounded in Social Penetration Theory that utilizes predefined disclosure trajectories and evaluates how preceding disclosure history shapes model behavior under matched current disclosure. Through the use of TraDE, we demonstrate across multiple models and relational behavior benchmarks that responses to disclosures matched on depth can differ depending on the preceding trajectory, particularly in whether models accept or challenge the user’s framing. We also observed that these differences track accumulated exposure to deeper disclosure rather than any single moment of vulnerability or the order of disclosures. It also tends to persists for several turns after disclosure returns to shallow levels. For several behaviors, we also demonstrate that models diverge most consistently from human supporters in the Emotional Support Conversation corpus (ESConv) not when users open up but rather when they step back in their disclosure. TraDE thus provides a systematic way to expose history-dependent relational behavior that evaluations of isolated conversational states would otherwise miss.
[HC-7] A Stratified Multi-Rater Evaluation of LLM -Based Virtual Standardized Patients with a Deployed Data-Generation Platform NEURIPS2026
链接: https://arxiv.org/abs/2610.29312
作者: Yongjin Yi,Jinyong Park,Sejoong Kim
类目: Human-Computer Interaction (cs.HC)
备注: Accepted at NeurIPS 2026, Track on Evaluations and Datasets. 39 pages (10 pages of main text), 3 figures and 3 tables in the main text, 4 figures and 4 tables in the appendices. Dataset: this https URL Code: this https URL
Abstract:The Objective Structured Clinical Examination (OSCE) is widely used to assess clinical communication skills in medical education, requiring trainees to take histories and reason clinically with standardized patients (SPs). Large language model (LLM)-based virtual standardized patients (VSPs), typically built by persona-injected prompting, offer a practical alternative. However, existing evaluations rely on a single backbone LLM, small human rater panels, or LLM-as-a-judge scoring without an utterance-aligned human-expert reference, and so cannot identify the specific shortcomings of individual models. We evaluated four LLMs (GPT-4o, Claude 3.5 Sonnet, Llama-3.1 70B, and HyperCLOVA X), spanning commercial, open-source and Korean-developed models, against a human-expert reference across three Korean OSCE cases (hematuria, fever in pregnancy, and chronic cough). In a double-blind, fully crossed design, 17 senior medical-student raters scored every model utterance on three sentence-level and six encounter-level Likert metrics, yielding 7,480 data points. We analyzed the scores using a stratified linear mixed-effects model based on an 11-category history-taking and 3-level linguistic-form taxonomy. At the encounter level, three of the four LLMs fell 0.08 to 0.47 Likert points below the human-expert reference on Comprehensibility, Consistency and Hallucination-freeness. The difference was not statistically significant for Hallucination-freeness in Claude 3.5 Sonnet and GPT-4o, Consistency in GPT-4o, and Engagement in HyperCLOVA X. Llama-3.1 70B fell significantly below the reference on all six metrics. We also introduce a web-based OSCE platform that supports virtual practice across diverse clinical cases and continuously collects student-VSP dialogues under research and privacy consent.
[HC-8] From AI-Free Gates to AI-First Work: How Engineering Students in India Navigate University Education and Campus Placements Without AI Guidelines
链接: https://arxiv.org/abs/2610.29140
作者: Samhitha Goutham,Ziwei Gu
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Engineering graduates in India enter a technology sector that is being reshaped by generative AI, yet the route into that sector runs through examinations and campus placement screens that largely exclude AI. Institutions in India have an approved intake of nearly 2.9 million seats in engineering and technology for 2026-2027, but HCI research on how students prepare for AI-mediated work has drawn mostly on Western and East Asian settings. We interviewed 23 students and recent graduates of Indian engineering and computing programmes about how they used generative AI across coursework, examinations and placements, and how they judged their own readiness for work. Using reflexive thematic analysis, we report five themes, covering AI-first workplaces and AI-free placement screens, how participants prepared for placement, how they used AI in coursework, the role of faculty authority and enforcement, and participants’ concerns about their readiness for work. Drawing on signaling theory, we discuss what grades and screen scores certify once AI can help produce them, and we offer recommendations for employers, institutions and AI tool designers.
[HC-9] MHeatmap: Two-Walk Laplacian Reordering and Mosaic Layout for Rectangular Matrix Visualization
链接: https://arxiv.org/abs/2610.29114
作者: Juntang Wang,Sheng’en Li,Liming Zhu,Yihan Wang,Siyuan Cao,Hao Wu,Man Zhang,Shixin Xu,Dongmian Zou
类目: Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)
备注: 10 pages, 3 figures, 1 table
Abstract:Weighted rectangular matrices record relations between two types of objects, such as occupations and industries, documents and terms, origins and destinations, or annotated classes and computed clusters. Their heatmaps become difficult to read when arbitrary row and column orders scatter related objects, while equal size cells hide large differences in row and column totals. We present MHeatmap (Mosaic Heatmap), which addresses these problems in two stages. Two-Walk spectral seriation first represents the matrix as a weighted bipartite graph. Ordinary bipartite ordering uses only observed row to column links. Two-Walk also uses length-two walks, so rows that share columns and columns that share rows receive explicit similarity links. Sorting a Fiedler vector of the resulting graph Laplacian gives coordinated row and column orders. The Mosaic layout then partitions a unit square so that row height is proportional to the row total and column width is proportional to the column total, while color represents the observed entry. We prove how the Two-Walk energy combines direct alignment with shared-neighbor smoothness, derive an exact matrix vector product that permits sparse computation, and formalize the tiling and area semantics of Mosaic. Across 300 synthetic matrices and seven real data sets, Two-Walk obtains the lowest normalized 2-SUM in all 15 synthetic regimes and the highest Band@10% in 14. The largest gains occur when shared neighborhoods express the latent relational structure.
[HC-10] HumanCentric-Scenes-AI: A Human-Curated Multimodal Dataset of AI-Generated Human-Centric Scenes Across Surveillance Occupational Portraits AI-Edited Images and Short-Form Video
链接: https://arxiv.org/abs/2610.29045
作者: Nima Kamali,Sana Nasiri,Negar Kamali
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 14 pages, 3 figures, 10 tables. Dataset: this https URL
Abstract:We introduce HumanCentric-Scenes-AI, a human-curated dataset of 296 AI-generated stimuli that depict people across four contexts in which people encounter AI-generated media. The Surveillance subset contains 85 AI-generated CCTV-style images. The Occupations subset contains 128 AI-generated occupational portraits in a balanced paired design covering 64 occupations with one female and one male version of each. The AI-edited subset contains 27 real photographs in which a region was altered or replaced by a generative model. The Video subset contains 56 AI-generated short videos. We curated the images and videos manually over multiple rounds to make them as photorealistic as possible. For each stimulus we release the file, the generation prompt where recorded, and per-stimulus metadata. We report depicted-gender distributions from an initial set of 134 generations without a specified gender and compare them with U.S. Bureau of Labor Statistics occupational gender shares. We report edit-region size and its association with zero-shot detection difficulty for the AI-edited subset. We report image-quality distributions for the Surveillance subset and subject-category coverage for the Video subset. HumanCentric-Scenes-AI is intended first for human-perception research, and also for calibration of AI-detection classifiers and bias analysis of generative models.
[HC-11] GenXR-Bench: Benchmarking Coding Agents for Interactive XR Development
链接: https://arxiv.org/abs/2610.29014
作者: William Huang,David Li,Xiang ‘Anthony’ Chen,Xun Qian,Nels Numan,Zhongyi Zhou,Hyunsung Cho,Salman Muin Kayser Chishti,Ding Xia,Xincheng Huang,Jiahao Ren,Faraz Faruqi,Tianyu Xu,Yang Zhang,David Kim,Ruofei Du
类目: Artificial Intelligence (cs.AI); Graphics (cs.GR); Human-Computer Interaction (cs.HC)
备注: 37 pages, 10 figures. Code available at this https URL
Abstract:Coding agents are rapidly advancing from single-file generation to long-horizon repository-scale engineering, yet the evaluation of their capabilities to author interactive extended reality (XR) applications remains an open challenge. Unlike traditional software, XR applications operate within dynamic 3D physical environments where runtime robustness depends jointly on continuous user motion, spatial input, and environmental sensing. We introduce GenXR-Bench, the first benchmark for the end-to-end evaluation of coding agents in XR software development. GenXR-Bench contains 112 expert-authored tasks spanning 24 feature implementations, 21 bug repairs, and 67 application generation tasks. Held-out tests combine scripted and adaptive interactions with programmatic and visual assertions to evaluate core functionality and runtime behavior. GenXR-Bench’s testing suite achieves 96.5% accuracy against blinded expert judgments of 124 candidate applications executed on consumer XR hardware. Evaluating 10 models on GenXR-Bench, we find that the strongest configuration achieves only 50.9% overall Pass@1. Failure analysis identifies recurring misunderstandings in asynchronous sensor lifecycles, physical grounding, and embodied interaction semantics. GenXR-Bench establishes a reproducible testbed for evaluating coding agents in the interactive and physically situated conditions of spatial computing.
[HC-12] Artificial Intelligence and Job Design: A Dynamic Framework
链接: https://arxiv.org/abs/2610.28830
作者: Amin Shirangi,Todd L. Pittinsky
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: 27 pages, 1 figure
Abstract:This study aims to deepen the understanding of artificial intelligence’s (AI) impact on work by introducing and describing the AI-Job Dynamics Framework. Drawing from sociotechnical systems theory, complexity theory, and organizational learning theories, we conceptualize AI integration as a dynamic, non-linear process with three key phases. The framework, supported by theoretical propositions, identifies how this process affects organizational decision-making structures and key job characteristics over time, emphasizing the balance between centralization and decentralization of decision-making power. AI integration initially reduces job autonomy but may lead to greater autonomy in later phases as AI becomes more embedded. Throughout this process, AI reshapes skill requirements from routine to complex cognitive and adaptive skills. In the advanced phase of AI integration, we introduce the alternative paths and varied trajectories through which AI, organizational structures, and individual work experiences evolve. We conclude by discussing the implications of this framework for future research on AI and job design.
[HC-13] Scaling Therapy Supervision: Assessing Psychotherapy Fidelity Using Large Language Models
链接: https://arxiv.org/abs/2610.28772
作者: Arvind Pillai,Alexis Roach,Jason Holden,Varsha D Badal,Subigya Nepal,Yu Yvonne Wu,Yuliang Chen,Sudarshan Regmi,Sarah Masud Preum,Nicholas C Jacobson,Nikhil Singh,Eric Granholm,Colin Depp,Andrew Campbell
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: 24 pages, under review
Abstract:Psychotherapy fidelity assessment measures how faithfully a therapist adheres to a treatment protocol, a critical determinant of positive patient outcomes. However, fidelity assessment is bottlenecked by manual expert review, rendering the process labor-intensive and unscalable. To scale therapy supervision and support clinician self-learning, we present an integrated application of large language models (LLMs) that automates psychotherapy fidelity workflows and generates actionable supervisory feedback from authentic, long-form clinical transcripts. We evaluate three open-weight models across multiple task decomposition configurations and reasoning strategies, demonstrating that evidence-first reasoning improves fidelity estimation. Furthermore, we establish the framework’s external construct validity on an independent randomized controlled trial (RCT), where it reliably distinguishes Cognitive Behavioral Therapy (CBT) from non-CBT sessions across core therapeutic skills. Through a user study that evaluates the generated feedback, mental health experts indicate that the pipeline synthesizes actionable clinical feedback. Ultimately, our work offers a practical, scalable solution toward bridging the community mental health supervision gap.
[HC-14] Warping the Workspace: Expanding Visual Access with Adjustable Reach Mapping for Humanoid Teleoperation
链接: https://arxiv.org/abs/2610.28712
作者: Keigo Minamida,Koya Dendo,Yuto Nakamura,Jun Rekimoto
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注:
Abstract:First-person humanoid teleoperation ties access to side and rear targets to operator rotation. We present coupled visual–motor remapping, pairing a panoramic azimuth warp with its inverse upper-limb yaw transform. A working prototype implements Global Zoom (GZ) and Focus+Context (F+C). In a within-subject study (N=8); 72 trials), GZ had the largest completion count (23/24; Fixed: 20/24; F+C: 21/24), whereas F+C was preferred by 6/8 participants. Therefore, completion and preference favored different mappings in this sample.
[HC-15] Motivation and Contribution in Open Source Software: A Meta-Analytic Investigation
链接: https://arxiv.org/abs/2610.29199
作者: Parisa Omidmand,Saeed Nosratabadi
类目: General Economics (econ.GN); Human-Computer Interaction (cs.HC); Software Engineering (cs.SE)
备注: 50 pages, 3 figures, includes Appendices A-E; under review
Abstract:Research on why people contribute to open source software (OSS) and related open collaboration platforms spans more than two decades, but its findings conflict, and studies often treat the work people contribute and their intention to keep contributing as interchangeable. Drawing on self-determination theory and motivation crowding theory, we meta-analyze 41 studies. The main analysis pools 178 correlations of eight antecedents with contribution effort and continuance intention from 29 independent samples (N = 9,632). Seven antecedents relate positively to effort, all eight relate positively to intention, and each relates at least as strongly to intention as to effort. Contrary to a common assumption, intrinsic motivation is not more strongly related to effort than extrinsic motivation, but its gap between intention and effort is larger. Reputation relates to effort mainly on platforms with a reputation system, and we find no sign that monetary rewards crowd out enjoyment. The findings inform how platforms can turn contributors’ intentions into work.
计算机视觉
[CV-0] Rethinking Video Autoencoders for Interactive World Models
链接: https://arxiv.org/abs/2610.30129
作者: Adrien Ramanana Rahary,Yutong Bai,Nicolas Dufour,Patrick Pérez,David Picard
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video world models predict future frames from past frames and actions. They serve as simulators in which agents train, plan or are evaluated, and that humans can control interactively. They generate in the latent space of a video autoencoder, whose compression sets their cost per frame and how long each action waits to take effect. Yet world models often reuse the autoencoders of text-to-video generators, built for offline generation with no agent waiting and, without their training data and code, not retrainable at another compression. We therefore ask how, where and how much a world model should compress. We build CADRE, an open family of causal diffusion representation autoencoders spanning multiple spatio-temporal compressions. Its members differ only in a small bottleneck on frozen features and share a diffusion decoder self-distilled to one or a few steps. We train the same world model on each autoencoder in two domains, manipulation and navigation, with two environments each. CADRE forms most of the latency-quality frontier, especially with a moving camera. Our study also leads to four findings. At strong compression, a diffusion decoder is sharper than perceptual regression. In robot manipulation, temporal compression is more effective in the autoencoder than in the world model. Manipulation and navigation call for opposite compressions: temporal compression costs little when the camera is still, and spatial compression when it moves. Surprisingly, with a still camera, coarser spatial tokens hurt motion far more than image quality. Code, models and a live demo are at this https URL
[CV-1] VoxProg: 3D Spatial Reasoning through Voxel Programming
链接: https://arxiv.org/abs/2610.30127
作者: Zhichao Zhai,Menglei Zhao,Guikun Chen,Zihao Yan,Kanzhi Wu,Wenguan Wang,Jun Xiao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Language models’ abilities to interpret tasks and generate programs offer a promising basis for 3D spatial reasoning. Yet following spatial instructions in 3D scenes requires programs to construct geometric references that language leaves implicit. In this work, we introduce VoxProg, which formulates 3D object placement through voxel programming: a language model translates task interpretations into executable programs over local voxel views of scene surfaces. Because these views index surface geometry on regular grids with explicit coordinates and neighborhood relations, an evidence program can isolate task-relevant regions and boundaries directly from scene meshes, and a downstream pose program can measure the resulting geometry to compute a target SE(3) pose. Visual and numerical feedback from execution lets the model inspect intermediate selections and candidate placements to iteratively revise its code. To evaluate open-ended placement across functional use, geometric fit, and contextual arrangements, we introduce the 71-task VoxProg-Bench benchmark along with an evaluation suite pairing perceptual Intent Alignment and 3D Physical Validity. Across VoxProg-Bench and external benchmarks, VoxProg consistently outperforms relation-based, keypoint-based, and mesh-based baselines.
[CV-2] PhysiCalWorld: In-Context Physics Calibration for World Models
链接: https://arxiv.org/abs/2610.30125
作者: Yunhao Luo,Wentao He,Yinpei Dai,Hyelin Nam,Hongze Fu,Joyce Chai,Nima Fazeli,Jeong Joon Park
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Existing world models typically perceive a scene through current visual frame or a narrow history window. However, these observations may not reveal the physics properties that determine transition dynamics, such as an object’s mass distribution, joint damping, or surface friction. Without calibrating these physics factors for world models, predictions tend to regress toward an empirical prior and induce severe drift in novel test scenes. To this end, we propose PhysiCalWorld, which calibrates action-conditioned world models to the instance-specific dynamics via in-context prompting, without fine-tuning or analytical physics modeling. Our method encodes an exploratory interaction clip collected within the test scene into a compact physics descriptor token, which grounds subsequent predictions in the scene’s actual physical behavior. PhysiCalWorld is trained end-to-end with the latent prediction loss alone, without physics labels. At inference time, the descriptor token is extracted once and can be reused across rollouts with negligible compute overhead. We evaluate PhysiCalWorld across four environments covering diverse physics variations and show that it substantially improves rollout prediction accuracy and visual planning success rate. The learned descriptors also form a physically structured latent space and generalize beyond the physics ranges seen during training.
[CV-3] ALPS: Visual State Encoders for World Models
链接: https://arxiv.org/abs/2610.30120
作者: Yanqing Liu,Letian Zhang,Chengzhi Liu,Zijun Wang,Jieru Mei,Hongliang Fei,Yuyin Zhou,Cihang Xie
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 35 pages (15 main + 20 supplementary), 13 figures, 26 tables. Project page: this https URL Code: this https URL
Abstract:World models turn visual representations into predictions and actions. We argue that their visual foundation should combine physical-state information, native language alignment, and pixel reconstruction, so that a single representation can support prediction, instruction, and inspection. However, existing encoders typically specialize in only one or two of these capabilities; we therefore introduce ALPS, a pretrained visual state encoder that unifies all three in a single set of patch tokens. Using only still images and text, we pretrain a ViT-B/16 from scratch with language, reconstruction, and spatial correspondence objectives, and then refine it via cross-view self-distillation. In a unified evaluation across fifteen public encoders, ALPS achieves the lowest aggregate LIBERO state-probe error among ViT-B models (matching the 3.5x larger V-JEPA 2-L) while maintaining strong language alignment and accurate pixel reconstruction. Downstream, frozen ALPS tokens reach the highest mean policy success on LIBERO (85.5%) when paired with our native text tower, match or exceed leading encoders on world-model planning across PushT, Wall, and Granular, and allow predicted future tokens to be decoded directly into pixels for visual inspection. We hope this work helps establish visual pretraining as a shared foundation for understanding, prediction, and action in world models.
[CV-4] AtomicRL: Atomic Reinforcement Learning for Long-Horizon Robotic Manipulation
链接: https://arxiv.org/abs/2610.30097
作者: Likui Zhang,Zhe Zhu,Zhihao Zhan,Yibo Bai,Hanlin Wang,Dongwei Li,Bin Wang,Tianle Zhang,Xiaodan Liang,Liang Lin
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-language-action (VLA) models have shown promise in robotic manipulation, but imitation learning (IL) alone often struggles to master complex long-horizon skills. Reinforcement learning (RL) offers an effective avenue for further improving VLA policies. However, existing VLA-RL methods typically suffer from delayed and biased credit assignment due to trajectory-level sparse rewards, while imbalanced subtask mastery creates bottlenecks that limit overall performance. To address these challenges, we propose AtomicRL, an RL post-training framework that leverages atomic task structures for reliable credit assignment and adaptive exploration. AtomicRL introduces Semantic-Aware Policy Optimization (SAPO), which uses semantic-aware gating to constrain TD residuals from failed atomic subtasks from propagating across task boundaries, mitigating credit contamination while preserving intra-subtask credit assignment and value bootstrapping. In addition, Bottleneck-Adaptive Target Exploration (BATE) dynamically coordinates task sampling and local exploration incentives based on the mastery of individual atomic subtasks, directing learning toward the remaining bottlenecks. Finally, we develop a Real2Sim2Real pipeline that bridges online RL in simulation with real-world deployment, facilitating practical transfer to physical robots. On LIBERO-LONG, AtomicRL achieves absolute performance gains of 3.6% and 4.0% over PPO across two VLA architectures. On CALVIN ABC-D, it improves full-task performance by up to 9.5% over PPO. Combined with the Real2Sim2Real pipeline, AtomicRL further improves real-world task performance by an average of 21.3%. These results demonstrate that AtomicRL effectively improves the learning efficiency and manipulation performance of VLA policies on long-horizon tasks.
[CV-5] ProactEgoMem: Benchmarking Proactive Memory in Streaming Egocentric Video
链接: https://arxiv.org/abs/2610.30089
作者: Ruotong Liao,Meikel Kokowski,Xiaoxiao Sun,Yongqin Xian,Laura Bravo Sánchez,Shiye Su,Federico Tombari,Serena Yeung-Levy,Volker Tresp
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 30 pages, 22 figures, Project Page: this https URL
Abstract:Today’s multimodal assistants are typically prompted to interpret the visual evidence presented to them. To offload the cognitive burden of remembering across long horizons, wearable assistants should instead proactively acquire and maintain useful memory as the user’s surroundings evolve. Doing so on the user’s behalf requires identifying what matters, understanding the scene in real time, and initiating memory maintenance in the background while the user stays focused on their activities. We introduce ProactEgoMem, a benchmark that evaluates proactive memory in streaming egocentric video. Three tasks ground the evaluation in ambient environmental needs: Information Capture (IC) preserves transient yet dense visual information, State Tracking (ST) follows changing object states, and Location Tracking (LT) records object movements. The benchmark defines its protocol at two levels of proactiveness: the Notification Level asks for a single timely notification that satisfies the user’s intent, while the deeper Intervention Level asks the agent to maintain memory on the user’s behalf through an active start-to-stop tool lifecycle. Parallel ProactEgoMem-Daily and ProactEgoMem-Sci tracks contain 300/100 clips covering daily and scientific use cases, paired with 1000/500 questions in ProactEgoMem-QA, which measures the quality of the proactively acquired memory. Across proprietary, open-source, video-language, and streaming baselines, even the strongest models fail to capture memory both timely and accurately. ProactEgoMem provides a rigorous evaluation foundation for proactive multimodal assistance and for video agents that co-exist with their users.
[CV-6] Attn-Forcing: Learning Full-Context Attention Proxies for Streaming Video Generation
链接: https://arxiv.org/abs/2610.30036
作者: Junyao Gao,Sibo Liu,Jiaxing Li,Yanan Sun,Hanxin Zhu,Weidong Zhang,Jun Zhang,Cairong Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages,6 figures
Abstract:Increasing the attention context length significantly improves streaming video generation quality, while incurring prohibitive memory and computational costs as the context grows. Existing methods attempt to capture this contextual information through attention selection or compression, but remain limited by bounded representations and lack explicit attention-level supervision to retain historical contributions to current queries. To address this, we introduce Attn-Forcing, a novel framework that preserves full-context attention contributions for streaming video generation under bounded memory and computation. We achieve this by introducing attention proxies that continuously approximate the attention responses induced by the historical context. Specifically, we implement the proxies as learnable visual latents and employ a Proxy Update Module to incorporate visual context from historical latents. Moreover, we propose an Attention Matching Loss to explicitly constrain the attention behavior of the learned proxies using full-context attention supervision. Extensive qualitative and quantitative experiments demonstrate that Attn-Forcing consistently improves streaming video generation quality and achieves state-of-the-art performance over existing baseline methods.
[CV-7] Plausible but Unimplantable: A Trustworthiness Audit of Shape Completion Foundation Models for Cranial Defect Reconstruction ACCV2026
链接: https://arxiv.org/abs/2610.30035
作者: Marek Wodzinski,Kamil Kwarciak,Daria Hemmerling,Mateusz Daniol
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ACCV 2026 TrustFMI accepted
Abstract:A patient who has lost part of the skull vault needs an implant that fits the defect exactly, and such implants are still drawn by hand in CAD by specialists, which makes each case slow and expensive. The task can be considered as a shape completion problem, so pretrained completion models, and now the first shape completion foundation models, look like a way to remove that manual step. We audited three publicly released pretrained shape completion models, two of them foundation models, applied zero-shot to 239 cranial defects, some carved from public skull datasets and some real post-craniectomy cases. Each output was compared with simply returning the patient’s own remaining bone, and tested for whether the predicted material could be placed without intersecting the bone that is still there. On the 210 carved defects none of the three improved on doing nothing, and on the 29 real cases only one did so beyond chance. Yet not one output, from any model in any configuration, was implantable: none managed both to stay clear of the bone the patient still has and to place more material than doing nothing, where a task-specific volumetric model, trained for this task and selected on these cases, was implantable on all but one. The output that most convincingly resembles a skull is no more placeable than the rest, and at these point densities implant thickness cannot be read off the emitted points at all, which is a property of the output representation rather than of any one model. On this evidence the shape completion foundation models we could obtain cannot be trusted and, used as released, are not usable in clinical practice. Substantial research towards trustworthy foundation models for shape completion is still needed.
[CV-8] RealMe: Immersive Volumetric Video from Minimal Captures SIGGRAPH
链接: https://arxiv.org/abs/2610.30023
作者: Chenxu Zhou,Zhiyuan Yu,Yudong Jin,Yinji Shentu,Yu Zhang,Jiaming Sun,Xiaowei Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages, 11 figures, including supplementary material. To appear in SIGGRAPH Asia 2026 Conference Papers. Project page: this https URL
Abstract:This paper addresses the problem of creating immersive volumetric videos from sparse and low-resolution capture resources. Conventional dense multi-view reconstruction methods can produce high-quality volumetric videos, but their reliance on costly capture setups makes them impractical in real-world scenarios. Recent approaches use generative models to relax capture requirements, but still suffer from limited resolution and 4D inconsistency. To overcome these constraints, we propose RealMe, a unified framework built around a high-resolution video diffusion model that enhances limited captures into high-resolution (2K), 4D-consistent videos for volumetric video reconstruction. Specifically, RealMe constructs auxiliary data from capture videos and encodes them into latents along with layout-aware positional embeddings. The video diffusion model adapts factorized spatio-temporal attention to the multi-view capture layout, denoises these latents across viewpoints and time, and decodes them into enhanced videos to refine the 4D representation. This adaptation helps the model capture temporal dynamics and multi-view relationships, leading to more 4D-consistent generation. To reduce the data gap between generated videos and real captures, we apply a latent frequency loss that provides frequency-domain supervision in latent space. Extensive experiments on the public DNA-Rendering benchmark and our self-captured RealVV dataset demonstrate that RealMe is able to create immersive volumetric videos and outperforms state-of-the-art methods in visual fidelity and 4D consistency.
[CV-9] ClunoGS: Compact Language 3D Gaussian Splatting via Unified Optimization
链接: https://arxiv.org/abs/2610.30013
作者: Jian Xu,Yi Li,Ying Huang,Siyi Wu,Bingzhe Li,Yuede Ji,Miao Yin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Language 3D Gaussian Splatting enables open-vocabulary scene understanding by associating Gaussian primitives with language features. These semantic attributes increase model storage, while most existing 3DGS compression methods are designed mainly to preserve RGB rendering quality. One straightforward approach is to apply sparsification and quantization sequentially. However, pruning based only on rendering quality can remove Gaussians that have small RGB contributions but are important for semantic prediction. Once removed, their semantic information cannot be recovered by later quantization. We propose ClunoGS, a compact framework for Language 3DGS that combines semantics-aware Gaussian sparsification and vector quantization in a unified constrained objective. Semantic supervision is used during sparsification, and the objective jointly considers rendering quality, semantic accuracy, and compactness. We optimize the unified objective with alternating primal, sparsification, quantization, and dual updates. We evaluate ClunoGS on open-vocabulary localization and segmentation using the LERF and ScanNet-10 benchmarks. Under aggressive compression, our unified, semantics-aware formulation substantially alleviates the degradation of semantic representations caused by sequential compression and semantics-agnostic Gaussian sparsification. Ablations further show that jointly coordinating sparsification and quantization better preserves semantic accuracy than applying them independently.
[CV-10] Multiclass Boundary Refinement with Neural Cellular Automata for Remote Sensing Land Cover Segmentation
链接: https://arxiv.org/abs/2610.30007
作者: Pascoassis Souza Santos Meira,Gilson Antonio Giraldi,Rafael Duarte Coelho dos Santos
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Semantic segmentation of remote sensing imagery often places class boundaries a few pixels away from where they truly lie, and this position error is concentrated in the thin band measured by Boundary IoU (BIoU). We treat boundary refinement as the iterated application of a small learned local rule: the Boundary Refinement Neural Cellular Automaton (BRNCA), that starts from the class probabilities of a frozen, pre-trained segmenter and corrects them cell by cell. We generalise the binary rNCA to the multiclass case by keeping the visible channels on the probability simplex through an Aitchison (log-space) update and by adding free hidden channels. The best configuration improves the mean BIoU over the base segmenter by 2.36 points (from 46.63 to 48.99). The gain is concentrated on the irregular woodland class (+8.5 points BIoU); this suggests that position error is correlated with the fractal dimension of the class boundaries.
[CV-11] DONIC: Generalist Dexterous Bimanual Tracking Control from Human Demonstrations
链接: https://arxiv.org/abs/2610.29997
作者: Mingze Sun,Ziang Cao,Yuan Fei,Qiankuan Tao,Zihan Wang,Tianqi Liu,Junhao Chen,Haiwen Diao,Fangzhou Hong,Zhaoxi Chen,Ziwei Liu
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Despite recent progress in dexterous reference tracking, existing models focus primarily on single-hand manipulation and a limited range of interactions, leaving the bimanual dexterous behaviors common in real life unaddressed. The scarcity of contact-rich bimanual manipulation data and the increased complexity of coordinating contacts make developing a generalist bimanual tracker particularly challenging. Therefore, we present 1) DONIC, the first generalist bimanual tracker jointly trained across operation categories and human-motion sources, together with 2) BiHOI, a multi-view dataset of 1,575 sequences spanning 229 objects and emphasizing sustained bimanual interaction, where reconstructed interactions are retargeted into robot references. DONIC combines proximal policy optimization with behavioral cloning in a feedback loop that recycles verified successful executions into training demonstrations. An object-centered per-hand reward maintains object-motion feedback while supervising each hand according to its reference contact role. Clip-Centered Advantages remove reference-dependent advantage offsets while preserving within-trajectory differences. Together, these designs enable one single policy to learn coordinated manipulation across heterogeneous references. Without finetuning, a single checkpoint achieves 86.79% success on unseen trajectories of training objects and 57.32% on held-out objects, surpassing the state-of-the-art bimanual tracking baselines by 77.36 and 45.73 percentage points.
[CV-12] Looking Ahead: Modeling Future Evolution in Autoregressive Video Generation
链接: https://arxiv.org/abs/2610.29989
作者: Ruopeng Gao,Yukun Huang,Jiarui Hu,Shuai Wang,Wenbo Hu,Ying Shan,Limin Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Autoregressive video diffusion models generate videos chunk by chunk, yet producing plausible continuations does not guarantee that they capture how scenes evolve. Under teacher forcing, strong temporal redundancy may allow local visual continuity to serve as a shortcut for current-chunk denoising, reducing the pressure to model visual evolution. We introduce Future Chunk Prediction (FCP), an auxiliary objective jointly optimized with teacher-forced denoising that predicts temporally separated chunks without access to the intervening visual context. Drawing supervision directly from training videos, FCP requires no additional pretrained visual encoder and leaves autoregressive inference unchanged. Our method achieves superior physical plausibility over standard teacher-forced autoregressive training with a lower overall training compute budget, highlighting the effectiveness and efficiency of future-chunk supervision for modeling visual evolution. Intriguingly, our visualizations suggest that FCP fosters a more structured representation space, which may be valuable for building autoregressive world models.
[CV-13] raining-free Acceleration for Blind Compressed Image Restoration via Activation-Calibrated Singular Value Decomposition
链接: https://arxiv.org/abs/2610.29959
作者: Suhang Yao,Xin Li,Xijun Wang,Yufei Wang,Yeying Jin,Zhibo Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages. Accepted for publication in IEEE Journal on Emerging and Selected Topics in Circuits and Systems
Abstract:Transformer-based models achieve strong performance on blind compressed image restoration (CIR) under unknown compression settings, but their densely stacked projection operators make deployment costly. We study training-free acceleration for such models through an empirical, operator-level analysis. Specifically, we profile where computation is concentrated, characterize channel-wise activation distributions under compressed inputs, and examine the singular-value decay of representative projection weights. These measurements identify the recurring projections in the attention, feed-forward, convolutional, and prompt-conditioning pathways as effective low-rank replacement targets that preserve the original inference graph. They further establish truncated SVD (TSVD) as a principled training-free baseline for blind CIR and reveal why a uniform low-rank objective is mismatched to the channel-imbalanced feature distributions of compressed images. Accordingly, we formulate Activation-Calibrated Singular Value Decomposition (ASVD) for this restoration setting, deriving low-rank factors entirely offline from pretrained weights and lightweight activation statistics gathered from only a few compressed calibration images. By weighting the decomposition with channel-wise activation magnitudes, our ASVD assigns larger penalties to errors on more active channels, requiring neither retraining nor online calibration and leaving the restoration pipeline intact. When instantiated on PromptCIR at rank 64, ASVD consistently improves PSNR-B over TSVD across four standard benchmarks under identical parameter and MAC budgets. At this operating point, the factorized model cuts parameters by 59% (34.75M to 14.34M) and computation by 16.1% (258.79 to 217.16 GMACs for a 256 x 256 input), enabling acceleration without retraining.
[CV-14] DewHand: Direct Metric World-Space Hand Motion Reconstruction from Egocentric Videos
链接: https://arxiv.org/abs/2610.29936
作者: Naru Suzuki,Satoshi Ikehata,Takehiko Ohkawa,Yoichi Sato,Shuhei Kurita
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Recovering metric world-space hand motion from egocentric video supports understanding human interactions and transferring them to embodied agents. Three challenges complicate this task: multi-stage pipelines propagate errors from independently estimated hand and camera motion; monocular observations leave physical scale ambiguous; and occlusion and out-of-view hands admit multiple plausible motion trajectories. We present DewHand, a unified model for direct joint reconstruction of world-space hand and camera motion. To avoid compounding errors from multi-stage estimation, DewHand jointly predicts hand and camera motion in a shared world coordinate system within a single-stage pipeline. To resolve metric-scale ambiguity, it combines scene-level geometric features from VGGT- \Omega with metric-space supervision, while hand-specific features preserve fine-grained articulation. To handle occlusion and out-of-view hands, we formulate reconstruction as conditional motion generation with flow matching. Under the same architecture, this generative formulation outperforms direct regression in both accuracy and temporal smoothness with only two sampling steps. Experiments on HOT3D and ARCTIC demonstrate improved world-space accuracy and temporal consistency over state-of-the-art methods. DewHand reduces rigid-aligned world-space error relative to StableHand from 32.9 to 28.4 mm on HOT3D and from 76.2 to 36.2 mm on ARCTIC. It also recovers camera scale within 10% on two thirds of HOT3D clips, versus about a quarter for SLAM. Moreover, in zero-shot evaluations on HoloAssist, H2O, and TACO, captured with different devices, DewHand achieves the lowest rigid-aligned world-space error (WA-MPJPE) on each dataset.
[CV-15] CausalAlign: Causal Alignment of Video Generation Models for Consistent Ego-Motion
链接: https://arxiv.org/abs/2610.29915
作者: Kai Zhu,Yintianrun Zhang,Shuwei Jiang,Ruolin Zuo,Yixin Chen,Duomin Wang,Miao Liu,Yin Li,Daquan Zhou,Yiwu Zhong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diffusion-based video generation models (VGMs) can produce visually plausible videos, yet their camera ego-motion often drifts over long horizons. Camera ego-motion determines how the viewpoint changes between frames and is essential for maintaining consistent geometry and trajectories. To address this problem, we propose CausalAlign, a causal alignment framework with two components. First, we diagnose how precise ego motion is represented, used, and retained within VGMs using linear probes and causal interventions, revealing a rise-and-fall pattern during denoising. Second, we strengthen ego-motion state in VGM by aligning the representations at the identified layers to a 3D foundation model through delta-token space. This space encodes the changes between consecutive frames, allowing the alignment to focus on ego-motion dynamics rather than static scene content. With Wan2.2 fine-tuned on DL3DV and RealEstate10K, CausalAlign can largely improve motion accuracy, geometric consistency, and temporal smoothness, while preserving visual quality. For example, it improves geometric consistency by more than 20% relative to the base model. Moreover, with the fine-tuned VGM, CausalAlign improves the success rate of world action models (WAMs) by 10 points on mobile manipulation tasks of RoboCasa365 benchmark. Overall, CausalAlign improves ego-motion consistency in diffusion-based VGMs and yields noticeable gains in embodied models.
[CV-16] Asking a Multimodal LLM Whether Someone Fell: Zero-Shot Screening of Low-Resolution Thermal Video in a Laboratory and an Occupied Home
链接: https://arxiv.org/abs/2610.29913
作者: Marcos Junca(Senecta Inc.),Mauro Junca(Senecta Inc.)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 13 pages, 6 figures. Data: this https URL
Abstract:Thermal cameras can watch over older people living alone without capturing faces, and multimodal large language models (LLMs) can judge a short video without task-specific training. We evaluate a production multimodal LLM, Gemini 3.8 Flash at temperature 0, as the decision stage of an event-triggered fall screener, used exactly as a deployed prototype uses it: one request per short thermal clip, with its sound track when there is one, and its predecessor Gemini 3.7 Flash on the same requests. On 438 staged falls and 563 non-fall windows that we located by hand in a public 32x24 thermal dataset, the deployed request called 69% of the held-out subjects’ falls (67% over all subjects; bed falls excluded) and none of 499 non-fall windows, including people lying on the floor and getting up. Over 76 h of ordinary life in an occupied room, recorded at 160x120 with sound, it called none of 395 clips a fall (95% upper bound 1.2 false alarms per day) and caught 4 of the 9 staged falls the camera could see. One cautionary sentence in the prompt (“a person who goes out of view … is not a fall”) accounted for many of the misses: removing it raised sensitivity on the held-out subjects from 69% to 83% on 3.8 Flash and from 80% to 92% on Gemini 3.7 Flash, and in the home from 4 to 9 of the 9 visible falls, with no false call in ordinary living but at the price of calling deliberate lie-downs a fall. Sensitivity collapsed at 8x6 pixels (34%); identical requests disagreed on 4% of windows; and when the model called a fall, its explanation placed it within a second of the annotated landing 99% of the time. Two conventional detectors trained on the laboratory data caught nearly every held-out laboratory fall but raised 14.2 and 23.9 false alarms per day in the home. We release the frame-level fall annotations of the public dataset and every model answer on it.
[CV-17] ViBMR: A Large-Scale Multi-Sequence Brain MRI Dataset and Comprehensive Evaluation for Vietnamese Radiology Report Generation WACV2027
链接: https://arxiv.org/abs/2610.29905
作者: Dai Lam Le,Minh Khoi Nguyen,Son Dinh Nguyen,Amir Reza Jafari,Viet Ha,Nguyen Son,Reza Farahbakhsh,Noel Crespi,Phi Le Nguyen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2027)
Abstract:Automated radiology report generation for multi-sequence brain MRI holds strong promise for alleviating radiologists’ workload, yet it remains a demanding problem: brain MRI studies are inherently volumetric, each pulse sequence carries complementary diagnostic information, and annotated data are critically scarce for low-resource clinical languages. Existing brain MRI-report resources are still dominated by English reports, translated clinical text, or datasets adapted from task-specific imaging benchmarks, leaving routine low-resource clinical reporting and heterogeneous multi-sequence MRI protocols insufficiently represented. To bridge this gap, we introduce ViBMR, to the best of our knowledge, the first large-scale multi-sequence brain MRI dataset paired with routine Vietnamese clinical reports, comprising 3,649 patients and 11,875 MRI series across T1, T2, FLAIR, and contrast-enhanced T1 sequences. Building on ViBMR, we establish a comprehensive evaluation setup for brain MRI report generation, covering representative medical vision-language models, multi-sequence inputs, and feature aggregation strategies. The results provide key insights into dataset characteristics and model behavior, while demonstrating the necessity of native low-resource clinical datasets for reliable Vietnamese-origin brain MRI report generation. Our dataset and baseline models will be released for non-commercial research.
[CV-18] Expandable Modality Experts for Medical Vision-Language Learning ICLR2027
链接: https://arxiv.org/abs/2610.29881
作者: DM BAPPY,Eunsu Kim,Hanjoo Lee,Sung Hak Lee,Sangjeong Ahn
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages (main text), 42 pages (supplementary material), 2 main figures. Submitted to ICLR 2027
Abstract:Learning across medical imaging modalities requires making use of images without paired text and accommodating new domains while retaining existing representations. We propose a medical vision-language technique built around an expandable bank of modality experts. Its Multiway Transformer combines shared visual computation with deterministically routed feed-forward experts and a separate text pathway. A modality-aware visual tokenizer supplies targets for masked pretraining on 19.4 million unpaired image rows across seven modalities; contrastive alignment then uses 3.23 million image-text pairs. Additional fundus and optical coherence tomography experts learn from paired data while the original image and text embedding paths remain frozen. With a ViT-Large backbone, the proposed technique achieves the best or tied-best result in 24 of 25 benchmark settings across nine modalities: 10 of 11 zero-shot classification benchmarks, all 10 linear probes, and all four retrieval benchmarks. Mean balanced accuracy is 0.674 for zero-shot classification and 0.856 for linear probing, and mean bidirectional retrieval recall is 20.70%. A separate three-seed sequential experiment with matched adaptation data yields fundus and OCT retrieval gains of 14.95 and 9.31 percentage points, with no measured loss on preceding fundus or pathology tasks. The preservation argument requires unchanged existing computation paths and inference conditions, and feature-identity checks verify retention on evaluated inputs. The proposed technique provides a modular approach to learning and expanding medical vision-language representations.
[CV-19] Stochastic Nonlinearities Improve Uncertainty Estimation BMVC
链接: https://arxiv.org/abs/2610.29875
作者: Mohammad Sarhangzadeh,Amirreza M. Shebly,Doruk Oner
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to the British Machine Vision Conference (BMVC) 2026. 33 pages, including supplementary material
Abstract:Reliable uncertainty estimation is important across visual recognition tasks, providing information about the confidence and reliability of model predictions. Many uncertainty estimation methods obtain predictive diversity through mechanisms that can suppress intermediate features, perturb the input, or require substantial additional computation. Stochastic activation functions, where network units randomly select between multiple nonlinearities during a forward pass, have previously been used to improve optimization behavior, mitigate limitations of fixed ReLU activations, and increase output diversity. In this work, we reframe stochastic activation switching as a lightweight mechanism for uncertainty estimation. The proposed method samples between nonlinear activation functions, inducing predictive diversity while keeping intermediate feature pathways active. This enables uncertainty estimation from a single trained model without the training cost of ensembles, task-specific uncertainty heads, or architectural redesign. We evaluate the proposed approach across classification, semantic segmentation, and out-of-distribution detection using convolutional and transformer-based architectures on diverse datasets. Across these settings, stochastic activation switching improves uncertainty quality while preserving competitive primary-task performance, showing that stochastic nonlinearities provide an effective and information-preserving source of predictive variation for visual recognition.
[CV-20] GeoMem-VLA: Internalizing 3D Scene Understanding into Temporal Memory for Vision-Language-Action Models
链接: https://arxiv.org/abs/2610.29866
作者: Qinfan Zhang,Yifan Luo,Haoyuan Shi,Guanlin Liu,Han Dong,Haozhe Shan,Yong Dai,Xiangcong Ren,Xiaozhu Ju
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Geometric structure is essential for robotic manipulation and understanding the 3D world. Most VLA models rely on 2D representations of current observations, while geometry-aware approaches often require additional 3D information. However, geometry inferred independently from individual observations is often discarded rather than accumulated into a persistent task representation. Our key insight is that action generation needs a compact, task-relevant geometric abstraction that can be continuously updated, rather than a complete, renderable 3D reconstruction at every step. We introduce GeoMem-VLA, a VLA framework that internalizes explicit 3D supervision into dedicated geometric tokens and maintains them as temporal memory for action generation, without directly aligning the VLM’s native visual embeddings with the teacher feature space. During training, a frozen geometry foundation model supervises learnable GeoPlan tokens through a geometric flow-matching objective over the current scene point map and future end-effector tracks. These tokens are integrated with the memory of observation representations, where historical information is retrieved, adaptively fused with current observations, and consolidated to retain event frames as the task progresses. At inference, the policy retrieves historical representations and fuses them with the current working representation to predict actions, without invoking the 3D teacher or explicitly reconstructing the scene. After benchmark-specific downstream adaptation, GeoMem-VLA achieves 98.9% average success on LIBERO and 91.10% / 91.24% on RoboTwin’s clean/randomized settings. Across five real-world tasks, GeoMem-VLA achieves 78.0% average success. These results suggest that internalizing and remembering 3D scene understanding is a promising approach to robot control.
[CV-21] How Do DiTs Denoise? An Aggregator-Predictor View of Attention and MLPs
链接: https://arxiv.org/abs/2610.29854
作者: Gyeonghun Kang,William Xingxu Chen,Xiang Cheng
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
备注:
Abstract:How do diffusion transformers (DiTs) represent image content and learn rules for reusing it to generalize? We develop an aggregator–predictor view of DiTs as input-adaptive local denoisers, in which context aggregation and patch prediction provide functional roles for interpreting attention and MLPs. We first establish that, for Gaussian Markov random field (GMRF) priors, the global Bayes denoiser can be approximated using only a subset of context patches, with error decaying exponentially in context radius. We further show that patchwise linear denoisers, often invoked to explain diffusion generalization, recover the empirical local denoiser with sufficiently informative aggregation. Within this view, AdaLN-modulated attention naturally parameterizes an input- and noise-dependent aggregator, while the MLP admits a learned key–value representation for tokenwise patch prediction. Our experiments support this view. DiTs trained on real images exhibit noise-dependent denoising contexts resembling those in the analytic GMRF setting, while attention mass exhibits a qualitatively corresponding broadening pattern. In a controlled CelebA64 study, shared-memory patch predictors used in place of DiT MLPs achieve competitive denoising and generative performance. Finally, cross-dataset component transplantation reveals compatibility patterns consistent with the proposed roles of QK in spatial weighting, VO in feature transport, and MLPs in patch prediction. Together, these results provide a component-level account of DiT denoising and motivate architecture-specific fine-tuning and controlled intervention.
[CV-22] Revealing Hidden Failure Modes in Chest X-ray Classification via Spectral Domain Analysis
链接: https://arxiv.org/abs/2610.29830
作者: Samuel Halimi,Loic Themyr,Arnaud Abreu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: MIDL 2026 this https URL
Abstract:Deep learning models for chest X-ray anomaly detection remain vulnerable to subtle distributional shifts (e.g., acquisition technique, patient-related factors, and preprocessing). Traditional error analysis often relies on semantic metadata or model embeddings, which can mask low-level signal variations that degrade performance. In this work, we propose a data-centric framework for automated failure mode discovery using spectral analysis. We project images into the frequency domain and extract a compact profile summarizing the distribution of signal energy across frequency bands. By performing unsupervised clustering on these spectral profiles, we demonstrate that model failures are not randomly distributed, but are strongly concentrated within specific spectral clusters. This method effectively isolates “blind spots”, enabling the prediction of model reliability and the discovery of performance-degrading data slices without requiring ground-truth failure annotations.
[CV-23] Early versus Late RGB-Infrared Fusion for Counter-UAV Detection: A Comparative Study
链接: https://arxiv.org/abs/2610.29828
作者: Alberto De Zan,Denis Tavaris,Gian Luca Foresti,Ivan Scagnetto
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This version differs from the article published in IEEE Access. It includes revisions requested by the reviewers during the final review round that could not be incorporated into the published version
Abstract:This paper presents a novel strategy for UAV detection through visible (RGB) and infrared (IR) fusion, in order to improve accuracy and precision in harsh environments and weather conditions. In this research, two opposite approaches have been studied and implemented: the first concerns image-level (early) fusion, which merges two different spectra into a single image to which one detector is applied. On the other hand, the second approach, called decision-level (late) fusion, focuses on blending together the output bounding boxes of two different detection pipelines, aiming to create a common result that can be applied to both spectra simultaneously. Both strategies have been extensively tested in a dedicated environment, which included alternating low and high brightness scenes, different backgrounds (sky and ground), distances (5, 25 and 60 m), altitudes and zoom levels (1X, 2X and 4X). These tests allowed for the assessment of actual detection capabilities and system reliability in real-world applications. Furthermore, the two fusion strategies were evaluated and compared over a set of aligned images in order to highlight the better approach of the two. The results indicate that early fusion achieves better raw performance, since it incorporates the thermal information from the beginning of the training process. However, late fusion presents an innovative and useful detection workflow that can be applied in cases of sensor or frame misalignment. Both positive and negative aspects have also been pointed out and discussed, in order to give a more comprehensive overview. Finally, the paper outlines implementation considerations and practical recommendations based on operational priorities. Overall, the findings contribute to the research in RGB-IR fusion modalities, with the goal of enhancing UAV detection capabilities.
[CV-24] GuidedSAM-Plume: An Expert-Guided Segmentation Framework for Volcanic Plumes
链接: https://arxiv.org/abs/2610.29825
作者: Taddeo D’Adamo(LIRIS, imagine),Simon Thivet(UNIGE),Catherine Pothier(LIRIS, imagine),Riccardo Simionato(UNIGE),Allan Fries(UNIGE),Jonathan Lemus(UNIGE),Costanza Bonadonna(UNIGE),Laure Tougne Rodet(LIRIS, imagine),Bertrand Kerautret(LIRIS, imagine)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Quantitative analysis of environmental phenomena increasingly relies on image-based measurements. In volcanology, the geometric characterization of volcanic plumes and clouds from RGB images remains hindered by the scarcity of dedicated segmentation solutions and partially handcrafted annotations remain widely prevalent. We present GuidedSAM-Plume, an open-source expert-guided framework tailored for the interactive segmentation of volcanic plumes and clouds. It leverages the zero-shot, spatial prompt-based capabilities of SAM2. The framework was thoroughly validated on a challenging dataset through dedicated segmentation metrics and field-relevant volcanological descriptors. From minimal user inputs, it is able to significantly increase performance compared to fully automatic dedicated solutions while preserving the reliability of derived volcanological parameters. The absence of retraining requirements lowers adoption barriers within the volcanological community, and the framework’s demonstrated suitability for complex, diffuse plume-shaped phenomena extends its applicability to broader environmental science domains. The re-annotated dataset, produced under the guidance of expert volcanologists within the proposed framework, ultimately yields a more standardized release. The framework and accompanying dataset are made available at this https URL.
[CV-25] Compositionality with Primitives: A Unified View from Euclidean to Hyperbolic Vision-Language Models WACV2027
链接: https://arxiv.org/abs/2610.29811
作者: Puru Vaish,Meelad Dashti,Christoph Brune,Pascal Mettes,Tatiana Tommasi,Nicola Strisciuglio
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at WACV 2027 Main Conference, 17 pages, 6 figures, 8 tables
Abstract:Compositional zero-shot learning (CZSL) aims to recognize novel combinations of previously observed objects and attributes. Prior work showed that large-scale vision-language models encode decomposable semantic structures that can be recomposed to enable generalization. The Geodesically Decomposable Embeddings (GDE) framework formalizes this by identifying directions in the tangent space at the Frechet mean of the embedding distribution, which encode primitive visual concepts. Unseen attribute-object compositions can be synthesized through vector addition of these primitives. We revisit the assumption that compositional generalization requires explicit attribute-object pair re-composition. By means of new compositional scoring functions and extensive evaluations, we show that directly using the decomposed primitives suffices for recognizing unseen compositions. In that, we gain insights on how the geometry of the embedding manifold (Euclidean, spherical, or hyperbolic) and dataset structure affect compositional reasoning. Across models (CLIP, DINOv2, MERU, HyCoCLIP), we show that compositional structure consistently emerges as primitive representations, enabling inference on CZSL benchmarks (MIT-States, VAW-CZSL, UT-Zappos50K, Clothing16K) and group robustness on Waterbirds and CelebA. Code: this https URL.
[CV-26] Open-Vocabulary Models and Label Propagation for Uncertainty-aware Pseudo-Labeling of Object Detection Datasets
链接: https://arxiv.org/abs/2610.29788
作者: Jonathan Klees,Timo Heller,Matthias Rottmann
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Object detection models rely on large-scale annotated datasets that require substantial manual annotation effort. The labeling costs further increase when annotation uncertainty is captured through soft labels, as multiple annotations must be collected for each object. We address this challenge by introducing a semi-supervised framework for the uncertainty-aware annotation of object detection datasets. Our approach leverages open-vocabulary object detectors to generate region proposals and semantically meaningful object representations. Based on these representations, we construct an object-level similarity graph and propagate probabilistic label information. Our framework assigns soft labels to previously unlabeled objects, quantifying both aleatoric and epistemic uncertainty. We evaluate our method on four object detection benchmarks with soft-label annotations and demonstrate that high-quality annotations can be obtained with only a small fraction of manually labeled objects. Furthermore, we show that detectors trained on semi-automatically annotated data achieve performance comparable to models trained on fully manually annotated data. We achieve state-of-the-art downstream detection performance for the task of semi-supervised object detection on the COCO dataset, with 40.5% AP virtually matching the performance of the model trained on fully manually constructed data (40.9%) at only 1% of labeling costs. These results highlight the potential of open-vocabulary models and graph-based label propagation for efficient, accurate and uncertainty-aware pseudo-labeling.
[CV-27] JointLight: Human-Centric Video Relighting with Asynchronous RGB-Irradiance Joint Modeling
链接: https://arxiv.org/abs/2610.29780
作者: Henglei Lv,Bailin Deng,Xiaoqiang Liu,Yichen Wang,Haoxian Zhang,Pengfei Wan,Lin Gao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Understanding and modeling light transport in human-centric scenes is a longstanding challenge. While recent progress in diffusion models has boosted research in relighting, precise illumination control still lags, primarily due to the scarcity of diverse, high-quality, precisely labeled video relighting datasets. Existing end-to-end relighting approaches model light transport implicitly through neural networks, which substantially complicates controllability. In contrast, some works resort to path-traced irradiance cues as an explicit blueprint for light transport. However, such constructed irradiance cues are prone to artifacts and hinder generative quality when directly used as control signals. With the key observation that noise corruption removes high-frequency artifacts and preserves low-frequency light transport, we introduce an asynchronous joint-modeling paradigm that facilitates fine-grained illumination control. During two-stage training, we force the model to jointly denoise RGB and ground-truth irradiance frames with separate timesteps scheduling. A domain matching strategy is applied to the second stage to bridge the domain gap between irradiance cues and ground-truth irradiance maps. At inference time, we introduce an asynchronous hijacked generation pipeline that facilitates flexible illumination control. For training data, we develop a multi-illumination high-quality synthetic video dataset with paired irradiance maps using a powerful game engine. Trained exclusively on this dataset, our model exhibits robust generalization to real- world scenarios and outperforms prior approaches in both lighting fidelity and generative quality. The code and dataset will be publicly released.
[CV-28] Key Findings for Small UAS for Post-Disaster Damage Assessment from the CURSE26 Exercise
链接: https://arxiv.org/abs/2610.29777
作者: Thomas Manzini,Stephen Johnson,Priyankari Perali,David Merrick,Robin Murphy
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 5 figures, 2 tables
Abstract:The CLARKE building and road damage assessment AI classifier was incorporated into the Catastrophic Unmanned Remote Sensing Exercise (CURSE) 2026, which trained 23 small uncrewed aerial systems (sUAS) teams from state and local agencies and non-governmental organizations in Florida and Missouri on how to collect imagery for an incident command Remote Sensing Cell. Although CLARKE has been operationally deployed to two hurricanes (Debby and Helene), CURSE’26 presented an opportunity to evaluate the machine learning model with a larger set of never-before-seen orthomosaics. The results show that building and road damage assessment together ran in 3.5 minutes on average, and that building damage assessment achieved a Micro F1 of 0.834 with no manual adjustments to correct spatial misalignment and delivered that performance in both Florida and Missouri, demonstrating good geographic transfer. It also showed that road damage assessment performed poorly due to sensitivity to misalignment between road maps and the images, and to lighting and environmental conditions in an urban setting. The analysis of CURSE’26 presents five other findings of value to the sUAS community: that the use of sUAS and machine learning is now operationally normalized and accepted, sUAS teams in the field can improve classification accuracy by timing their flights, exercises are good operational tests but their value for evaluating learning models is less clear, edge computing is needed to overcome the wireless upload barrier, and the application of sUAS and AI to disaster response has ethical and legal ramifications for pilots and data managers.
[CV-29] UniRAP: Towards Unified Part-level Physical Affordance Reasoning and Actionable Perception NEURIPS2026
链接: https://arxiv.org/abs/2610.29768
作者: Linfei Li,Ruining Hu,Lin Zhang,Zhong Wang,Fengyi Zhang,Ying Shen,Binqiang Wang,Xin Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by NeurIPS 2026. Webpage: this https URL
Abstract:Vision-language perception has achieved impressive progress in aligning natural language with visual observations, yet grounding high-level semantics into part-level physical interaction remains challenging. To address this gap, we propose UniRAP, a unified model for inferring part-level physical affordances and mapping language instructions to actionable geometric representations. UniRAP formulates this problem as a conditional multimodal generation task, integrating visual inputs, textual instructions, and optional prompts into a shared spatiotemporal representation space through a unified interface token mechanism. To predict executable contact geometry, we introduce a Unified Affordance Decoder (UAD), which jointly performs object detection, part-level affordance segmentation, and 4-DoF interaction pose estimation by leveraging intermediate segmentation features. In addition, we propose a curriculum-based transfer training strategy that progressively adapts the model from general visual parsing to interaction-aware perception, improving data efficiency under limited textual annotations. Experiments show that UniRAP achieves state-of-the-art performance on referring expression segmentation, affordance grounding, and interaction pose estimation, while maintaining strong spatiotemporal consistency in dynamic video scenarios. These results demonstrate the effectiveness of UniRAP as a unified perception framework for language-guided physical manipulation.
[CV-30] LEAF-Mamba: Feature Adaptation and Fusion for Whole Slide Image Analysis
链接: https://arxiv.org/abs/2610.29746
作者: Lubin Gan,Jing Zhang,Mingjun Ma,Linhao Qu,Dachun Kai,Xiaoyan Sun
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 25 pages, 5 figures, 7 tables
Abstract:Whole slide image (WSI) analysis enables computational diagnosis and prognosis, but gigapixel images and limited annotations make slide-level learning challenging. Multiple instance learning addresses this task by aggregating patch representations from frozen pretrained encoders. Recently, Mamba has enabled efficient modeling of long patch sequences. However, task supervision alone does not explicitly constrain information recovery during feature adaptation, while Mamba couples local processing with long-range propagation rather than retaining separate representations for fusion. Motivated by these challenges, we introduce LEAF-Mamba, a framework that jointly addresses latent evidence adaptation and fusion for frozen-feature WSI analysis. A reconstruction-regularized adapter learns compact, task-specific representations while reconstructing the input features. Parallel Mamba and convolutional streams then capture long-range dependencies and sequence-local interactions, with an input-dependent gate modulating their joint representation before slide-level pooling. Experiments across feature extractors demonstrate improved average performance over strong MIL baselines in both diagnostic classification and survival prediction. Component analyses further support the contributions of adaptation and fusion. These findings highlight the value of jointly designing feature transformation and contextual aggregation to translate pretrained representations into effective slide-level predictions.
[CV-31] APED-CXR: Adaptive Privileged Evidence Distillation for Chest X-Ray Grounding with Image-Only Inference
链接: https://arxiv.org/abs/2610.29740
作者: Md Shihabul Islam Shovo,Nishi Kanta Paul,Jamil Fayyad
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, 2 figures, 3 tables. Submitted to IEEE ISBI 2027
Abstract:Compact chest X-ray (CXR) models that localize findings suit deployment, but box annotations are limited and report-aware multimodal models are expensive at inference. We introduce APED-CXR, which distills report-informed spatial evidence from a frozen multimodal teacher into a student with 11.1M parameters that predicts diseases and boxes from shared evidence maps and requires the image alone at test time. During training, the teacher uses the radiograph, report, and queried class to evaluate regions proposed from the student’s current evidence maps, and its support scores serve as training targets for the student’s pooled evidence scores within these regions. Trained on PadChest and evaluated on held-out PadChest-GR, refreshing the student’s proposals for teacher evaluation improves box-level localization F1 from 0.302 with fixed warm-up proposals to 0.338 (IoU = 0.5; gain 0.036, 95% CI [0.022, 0.049]), while maintaining diagnostic AUC. This gain depends on the teacher checking current proposals: with equally many random boxes, a fixed dense box set, teacher-generated boxes, or report rules instead of the teacher, F1 stays near the fixed-proposal level, and proposals two refreshes old recover only part of the gain. On NIH ChestX-ray14, a secondary test set, box recall also rises from 0.423 to 0.447.
[CV-32] Learning to Measure and Diagnose: A Clinical-Dictionary Radiomic Bottleneck Model for Thyroid Nodule Classification
链接: https://arxiv.org/abs/2610.29738
作者: Guibin Zhang,Weier Luo,Lei Cao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 2 figures, 2 tables
Abstract:Thyroid ultrasound supports the assessment of nodule malignancy, but classification predictions often lack evidence linked to familiar clinical descriptions. Radiomics provides explicitly defined measurements that can support such explanations through a clinical dictionary. However, using these measurements as interpretable evidence requires reliable estimation from ultrasound images and preservation of measurement fidelity when optimizing classification performance. We propose a clinical-dictionary radiomic bottleneck model for thyroid nodule classification. Within this model, Family-Aware Radiomic Learning (FARL) estimates measurements with balanced supervision across feature families. Fidelity-Guided Bottleneck Modeling (FGBM) constructs a fixed bottleneck, followed by joint optimization of measurement estimation and classification under fidelity guidance. Decision evidence decomposition then traces feature contributions through a linear classifier and organizes them by clinical dictionary categories. Experiments on ThyroidXL and TN3K show competitive classification performance, while case studies illustrate feature contributions for benign and malignant nodules. These findings indicate interpretable classification with inspectable quantitative evidence informed by clinical knowledge. Code is available online.
[CV-33] Forward-Only Learning for Shared-Kernel Wavelet Neural Networks without Backpropagation
链接: https://arxiv.org/abs/2610.29697
作者: Yongfei Guo,Xuan Sun,Ang Li,Zhao Zhang,Yuanhao Gong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Backpropagation remains the dominant training paradigm, using backward gradients and local updates that can suffer from vanishing or exploding gradients in deep networks. Although the Forward-Forward algorithm avoids conventional backpropagation, it remains mainly local or layer-wise, motivating global whole-network learning using forward evaluations only. For shared-kernel wavelet neural networks, we therefore propose a CMA-ES-based global forward-only learning framework. We further introduce structure-aware parameter decomposition and channel-specific credit assignment, factorizing the search into three subspaces. On BSDS500, our method reduces test MSE from 4.1783 to 4.0130 and improves PSNR from 42.621 to 42.787~dB over Adam/BP without backward propagation. Beyond these gains, training requires only 6 training-set coverages, compared with 1500 epochs for BP. Ablations show that structural decomposition and aligned credit assignment are more important than increasing population size, demonstrating the feasibility of whole-network forward-only learning for compact reconstruction networks. These results suggest a potential route toward forward-only optimization of compact reconstruction models for resource-constrained imaging and edge or near-sensor systems.
[CV-34] Efficient Generative Image Compression with Quantized Latents Refinement
链接: https://arxiv.org/abs/2610.29691
作者: Hao Cao,Yinhuan Huang,Wenqi Guo,Zhijin Qin,Jungong Han
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 24 pages, 12 figures, including appendix
Abstract:Generative image compression (GIC) delivers strong perceptual quality at low bitrates. However, GIC methods with strong perceptual quality often rely on pretrained diffusion models with high latency, while recent lightweight GIC methods reduce latency but deliver lower perceptual quality. We observe that, for lightweight GIC methods at low bitrates, coarse deterministic quantization can collapse nearby encoder latents corresponding to different fine details to the same point, making these details difficult to recover. Motivated by this, we propose GR-GIC, an efficient GIC that delivers competitive reconstruction performance with fast inference. For better performance, we introduce Guided Refinement (GR), which combines latent refinement guided by entropy context with adaptive perturbations for training regularization, jointly improving reconstruction at low bitrates. For fast inference, we introduce reorganized projected residual vector quantization (P-RVQ), which equivalently reorganizes residual updates into low-dimensional lookups to expose greater parallelism and reduce sequential computation. Experiments on Kodak show that for performance, compared with OneDC, GR-GIC achieves BD-rate savings of 23.19%, 37.71%, 22.30%, and 22.14% for LPIPS, DISTS, PSNR, and FID, respectively. For fast inference, the highly optimized implementation of GR-GIC encodes and decodes in 4.60 and 5.98 ms on an NVIDIA A100. Specifically, the reorganized P-RVQ implementation achieves a 3.14\times average speedup over the original implementation under the same FP32 setting. On an NVIDIA Jetson Orin NX, GR-GIC achieves latency comparable to lightweight GIC methods.
[CV-35] Rethinking Post-hoc Debiasing of Large Pretrained Models
链接: https://arxiv.org/abs/2610.29688
作者: Divyaansh Mertia,Chirag Bhutra,Himanshu Shekhar,Shubham Bagwari,Pratik Mazumder
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 14 Pages
Abstract:Large pretrained models such as CLIP are widely used as frozen feature extractors. However, their embeddings inherit spurious correlations from training data, introducing bias into the features. A growing body of research has sought to address this issue through post hoc debiasing, leaving the backbone untouched. We discover that despite differing mechanisms, these methods require the bias feature to be identified and expressed in text. This dependency is not innocuous. In this work, we experimentally demonstrate that this requirement incurs an unforeseen penalty in certain scenarios. For example, on the UrbanCars benchmark, having two co-occurring bias attributes, the recent methods underperform a vanilla classifier on biased frozenbackbone features, and even providing ground-truth bias identities does not close the gap. We show that the pattern that such methods seek is readily observable in the frozen geometry. Specifically, cross-class training pairs placed closest together by the frozen encoder almost always share a spurious attribute, regardless of the bias type, without requiring access to the bias labels. Based on this, we propose a novel approach to identify such pairs and learn a single linear map that transforms the features to perform post-hoc debiasing. The proposed approach achieves competitive performance across all datasets without access to bias attribute annotations or any LLMs.
[CV-36] Learning to Reason Like a Cytopathologist:Clinically Grounded Multiscale Evidential Learning for Cervical Cytology
链接: https://arxiv.org/abs/2610.29665
作者: Ajeet Kumar Singh,Nehal Mishra,Anurag Tripathi,Santanu Chaudhury,Dorien Herremans
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cervical cytology presents a fundamentally ambiguous diagnostic landscape, where equivocal categories such as Atypical Squamous Cells of Undetermined Significance (ASC-US) occupy a morphological grey zone that is definitional. For clinical decision support, the goal extends beyond assigning a definitive label to every sample: cases must also be distinguished by whether the available evidence supports a confident decision or warrants further consideration, a distinction conventional classification models are not designed to explicitly represent. We propose a multiscale evidential framework that models cervical cytology along an ordinal disease continuum using clinically grounded soft anchors, representing progression from benign reactive changes to precancerous lesions and malignancy. Three biologically motivated spatial contexts–nuclear morphology, cytoplasmic characteristics, and neighbourhood cellular architecture–are independently encoded using a shared frozen DINOv2 backbone, preserving inter-scale disagreement that early fusion may obscure. Calibrated predictions are transformed into Dempster–Shafer belief masses and fused with reliability discounting, allowing conflicting evidence and insufficient support to remain explicitly uncommitted rather than forcing a decision. The fused belief state drives interpretable routing that separates confidently classified cases from those with irreducible evidential insufficiency, enabling clinically meaningful triage. Across two independent publicly available conventional cytology datasets, RIVA and CRIC Cervix, the framework generalises consistently, with uncertainty strongly associated with diagnostic difficulty: incorrect predictions carry 1.94x-4.13x higher uncertainty than correct ones.
[CV-37] Spatially Gated Demographic Steering for Debiasing Multi-Person Text-to-Image Generation WACV2027
链接: https://arxiv.org/abs/2610.29651
作者: Venkatesh Thirugnana Sambandham,Torsten Schon
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to WACV2027
Abstract:Text-to-Image models reproduce societal stereotypes, and existing mitigation applies one demographic correction to the whole scene. In multi-person scene generation this creates a trade-off: global steering improves aggregate balance only by homogenizing the people within an image. Steering-based methods like Embedding Arithmetic drive the intra-image diversity below the unsteered baseline. Text- and latent-projection approaches shift the aggregate skew with only marginal gains in intra-image diversity. We present Spatially Gated Steering (SGS), which steers the demographics of each person independently by extracting per-person masks from the Diffusion Transformer’s (DiT) internal attention maps at a critical denoising timestep and applying a distinct demographic vector within each mask for the remaining steps. SGS needs no fine-tuning, inversion or layout specifications, and uses a lightweight segmentor over frozen DiT attention features to localize subjects at early timesteps. On skewed occupational prompts, SGS reaches high intra-image racial and gender diversity while keeping aggregate balance competitive and image quality near baseline, a result we validate on two architecturally distinct diffusion transformers, FLUX.1-Dev and SD3.5-Large. Codebase and model weights are available at this https URL
[CV-38] MS2CAST: A Video Anomaly Detection Framework via Multi-Scale Spatial Cross-Attention and Efficient Spatiotemporal Networks
链接: https://arxiv.org/abs/2610.29649
作者: Ali Safaei,Mohammad Kazemi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video anomaly detection (VAD) is a challenging task for surveillance systems due to the scarcity and diversity of anomalous events. While semi-supervised VAD approaches have advanced the field, many rely on computationally intensive optical flow networks to capture motion dynamics. Convolutional neural networks (CNNs) are also widely used for spatial encoding, but their effective receptive field is highly local. To address these limitations, we introduce MS2CAST, an end-to-end semi-supervised predictive VAD framework. For spatial encoding, we employ a multi-scale cross-attention encoder that fuses the hierarchical feature representations from a vision transformer (MViTv2-S) and a CNN (ConvNeXt-T) to exploit their distinct receptive fields. To model spatiotemporal dynamics without optical flow estimation, we integrate two interchangeable spatiotemporal modules in the latent space. One module (SwinLSTM) incorporates a Swin Transformer, while the other (VMRNN) integrates Vision Mamba blocks. Our objective function combines L1 loss, Gradient Difference Loss (GDL), and Multi-Scale Structural Similarity Index Measure (MS-SSIM) to ensure pixel-wise accuracy, sharpness, and structural integrity. Evaluations across the UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets demonstrate frame-level Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) scores that are competitive with top-tier techniques while maintaining a peak GPU memory usage of under 1 GB.
[CV-39] Reading the Room: Scale-ambiguous Semantic Registration of Sparse VSLAM Maps to Floor Plans in Partially Built Environments
链接: https://arxiv.org/abs/2610.29647
作者: Bjorn Stragier,Emilia Szymańska,J. Micah Prendergast,Lukas Rosenberger Schmid
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Many robotic construction applications, such as progress tracking, require registering a sensor trajectory to a prior 2D floor plan. Visual capture is attractive for its low cost and availability, but aligning a visual simultaneous localization and mapping (VSLAM) map to a floor plan is difficult; VSLAM recovers geometry only up to an unknown similarity transform, the sparse point cloud is cross-modal to the symbolic floor plan, sites are only partially built during capture, and construction sites are visually repetitive, degrading map quality while removing distinctive features. Existing methods address at most two of these challenges. We thus introduce Reading the Room(RR), a global-local approach to register scale-ambiguous, sparse, semantic VSLAM maps to partially built floor plans. Our key idea is to define a single robust, differentiable, class-balanced semantic-geometric energy function that governs all stages of the pipeline. We develop two global initialization schemes, leveraging a single inaccurate user-provided correspondence if provided, or estimating the initialization globally using a cross-modal matching network. Both schemes are designed to find initial candidates with respect to the energy function, which is then directly optimized for local refinement. We evaluate RR on 33 sequences across three active construction sites against classical and learning-based baselines, achieving a 4.5x improvement in mean absolute trajectory error over the strongest baseline in the general unconstrained setting, which increases to 7.6x when an anchor is provided.
[CV-40] Joint Semantic-Channel Coding and Modulation for Generative Image Transmission
链接: https://arxiv.org/abs/2610.29643
作者: Hao Cao,Jingkai Ying,Yinhuan Huang,Zhijin Qin,Yuan Shen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 23 pages, 10 figures
Abstract:Digital image transmission typically optimizes source coding, channel coding, and modulation separately, limiting how source representations adapt to channel impairments. We propose SemQAM, a joint semantic-channel coding and modulation scheme for generative image transmission converting source to modulated symbols, such as standard QAM symbols. Channel impairments create two challenges: they can corrupt the latent representation at the receiver, and this corruption can compound through autoregressive coding, degrading reconstruction quality. First, SemQAM represents latent features with vector quantization (VQ), maps each VQ index to a QAM symbol, and updates the codeword corresponding to the detected symbol so that channel impairments directly affect codebook learning. Moreover, SemQAM uses the conditional expectation of received codewords to approximate the receiver reconstruction and reduce error propagation during autoregressive coding. SemQAM supports multiple SNRs and CBRs in a unified model, while also supporting conventional source coding through SemQAM-C. Experiments show that (i) under the same LDPC and QAM settings, SemQAM-C outperforms OneDC in LPIPS and DISTS with substantially lower complexity, showing that the unified model retains competitive capability for source coding. (ii) Compared with this separated counterpart, SemQAM reduces channel uses by 18.57% at matched LPIPS and outperforms the compared JSCC methods, demonstrating the benefit of joint coding.
[CV-41] OpenEarth-OVS44: A Multimodal Remote Sensing Dataset for Open-Vocabulary Segmentation
链接: https://arxiv.org/abs/2610.29642
作者: Yimin Wei,Hongruixuan Chen,Hao Zheng,Hao Liu,Jian Song,Yuting Zhu,Junshi Xia,Naoto Yokoya
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In remote sensing, open-vocabulary segmentation is particularly important for flexible and scalable land-cover understanding across diverse regions, as it improves generalization to novel categories without requiring exhaustive pixel-level annotations. Despite its great promise, progress in this area remains largely constrained by existing datasets with limited category coverage and overly coarse taxonomies, and therefore struggles to support fine-grained semantic understanding required in real-world applications such as urban functional zoning, carbon accounting, and post-disaster assessment. To address these challenges, we present OpenEarth-OVS44, a multimodal benchmark with 290,587 expert-verified segments from 2,832 RGB images and 1,341 strictly registered RGB-SAR pairs at sub-meter spatial resolution, covering 40 cities across France, Japan, and the United States under a unified 44-class taxonomy organized into 9 base categories. Three evaluation tracks are provided: an RGB-only fine-grained benchmark, a multimodal track with diverse synthetic cloudy conditions for weather-robust evaluation, and a held-out cross-region track. Extensive experiments on representative open-vocabulary segmentation models demonstrate that training on OpenEarth-OVS44 substantially improves fine-grained segmentation performance over the strongest training-free baseline, with improvements of up to 7.1 mIoU points on unseen categories and 27.6 mIoU points on seen categories. The dataset and code will be released.
[CV-42] FogTTA: Online Test-Time Adaptation for Robust Transformer-based Object Detection in Foggy Weather
链接: https://arxiv.org/abs/2610.29618
作者: Ali Abedi,Q. M. Jonathan Wu,Ning Zhang,Shiqi Tian
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Published in Proceedings of the 39th Canadian Conference on Artificial Intelligence (Canadian AI 2026), PMLR 318:13-24. Available at this https URL
Abstract:Object detection models for autonomous driving commonly experience substantial performance drops when deployed under adverse weather due to the domain shift between training data and real-world operating conditions. This degradation is especially evident when models trained on clear-weather images encounter foggy environments with reduced visibility and contrast. To address this challenge, we introduce FogTTA, an online test-time adaptation framework designed to improve the robustness of Transformer-based object detectors in fog. Using RF-DETR as the underlying object detector, FogTTA enables real-time adaptation to the streaming target domain without requiring source data or retraining. The framework follows a teacher-student design, where the deployed model serves as the teacher and generates pseudo labels from weakly augmented target inputs. These predictions are subsequently refined through non-maximum suppression and confidence filtering. The student model then learns from strongly augmented target sample using the Varifocal loss to mitigate pseudo-label noise. The teacher is updated via exponential moving averaging to ensure stable and continuous adaptation. Experiments show that FogTTA outperforms prior baselines, delivering improved detection accuracy and stability while maintaining real-time performance.
[CV-43] An interpretable deep learning method for early hepatocellular carcinoma diagnosis on multiphase CT
链接: https://arxiv.org/abs/2610.29616
作者: Emma Sarfati,Alexandre Bône,Pietro Gori,Sisi Yang,Marc-Michel Rohé,François Nicolas,Jeong-Min Lee,Jeong Hee Yoon,Maxime Ronot,Isabelle Bloch,Christophe Aubé
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at Scientific Reports
Abstract:Early diagnosis of hepatocellular carcinoma is critical to preserve access to curative treatment options. The Liver Imaging Reporting and Data System (LI-RADS) provides a standardized framework for non-invasive diagnosis on multiphase computed tomography, based on major visual imaging features. However, these features may be difficult to assess consistently, particularly in small liver lesions. We developed dLI-RADS, an interpretable artificial intelligence method designed to estimate the probability of hepatocellular carcinoma from multiphase computed tomography images of manually segmented liver lesions, while remaining aligned with LI-RADS-related imaging concepts. dLI-RADS combines neural network-derived predictions with handcrafted quantitative imaging measurements reflecting LI-RADS major features and integrates these inputs using logistic regression. In this multicenter retrospective study, patients with multiphase computed tomography and at least one liver lesion were included, while lesions larger than 40 mm were excluded. Histology served as the reference standard for hepatocellular carcinoma, and a composite reference standard was used for non-hepatocellular carcinoma lesions. The lesion-level accuracy of dLI-RADS and LI-RADS were compared on the internal evaluation set using 10,000-repetition bootstrap non-inferiority tests, with 5% margins and significance levels. On the external test set, sensitivities were similarly compared. dLI-RADS was non-inferior to LI-RADS for lesion-level accuracy in the internal evaluation set, achieving 80% accuracy compared with 74% for LI-RADS (p = 0.04)…
[CV-44] Diff-IrisPAD: Modeling Bona Fide Iris Distributions for Unsupervised Presentation Attack Detection
链接: https://arxiv.org/abs/2610.29611
作者: Geetanjali Sharma,Harsh Yadav,Tanmay Deshpande,Shubham Ashwani,Aditya Nigam,Raghavendra Ramachandra
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, Accepted at IJCB Conference 2026
Abstract:Iris recognition systems are vulnerable to presentation attacks (PAs), including textured contact lenses, print replays, and synthetic irises. Existing PAD methods rely on supervised learning with attack data, limiting generalization to unseen attacks and new sensors. We propose Diff-IrisPAD, an unsupervised framework based on Brownian Bridge Diffusion that models bona fide iris distributions without using attack samples. The method reconstructs clean iris textures from degraded inputs and detects attacks via reconstruction inconsistency using ViT scoring and pixel-level measures. Experiments on a large-scale multi-sensor dataset (125K+ images, NIR and VIS, 8 attack types) show that Diff-IrisPAD achieves an overall ACER of 30.52, significantly outperforming diffusion-based baselines while demonstrating strong generalization to unseen attacks. These results highlight the effectiveness of diffusion-based bona fide modeling for robust and scalable iris PAD.
[CV-45] No Frames Just Frequencies: Bandwidth-Efficient Deployment-Time Adaptation for Smart Glasses
链接: https://arxiv.org/abs/2610.29607
作者: Francesco Corti,Moritz Becker,Olga Saukh
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Deep neural networks deployed on smart glasses must adapt to changes in the distribution of incoming data while adhering to strict privacy and device constraints. We present No Frames, Just Frequencies, a bandwidth-efficient source-assisted adaptation framework that characterizes distribution shifts using compact frequency descriptors computed on-device. Rather than transmitting camera frames, the glasses compute and send frequency descriptors to a host device that contains a replica of the model. The host device maps the received frequency descriptor to a distribution shift operator that synthesizes out-of-distribution data and adapts a model replica using a pre-defined adaptation protocol. We evaluate the framework on mixtures of ImageNet-C corruptions using convolution-based (ConvNeXtV2-Femto), self-attention-based (DeiT-Tiny), and hybrid (TinyViT-5M) vision models, on person classification (Visual Wake Words), and on object detection (MS-COCO). Our results reveal that adaptation driven solely by the transmitted descriptors recovers 93-95% of the supervised oracle adaptation gain on all three architectures, 82% on Visual Wake Words and 94 – 98% on MS-COCO. Notably, adaptation quality degrades gracefully as the descriptor resolution decreases from 224 to 28 pixels, consistently across architectures, enabling bandwidth reductions from 213\times to 784\times across the frequency descriptors.
[CV-46] CineEvolve: An Agent ic Self-Evolution Framework for Cinematic Multi-Shot Video Generation
链接: https://arxiv.org/abs/2610.29590
作者: Jingzhe Zhang,Peixuan Zhang,Genying Li,Bing Ma,Shuchen Weng,Si Li,Boxin Shi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 5 figures, 2 tables
Abstract:Cinematic multi-shot video generation requires both narrative planning and iterative refinement. Existing methods either lack narrative reasoning or rely on feed-forward workflows without policy updates. In this paper, we propose CineEvolve, an agentic self-evolution framework for cinematic multi-shot video generation that unifies a Director and a Critic within a single vision-language policy. Emulating real-world filmmaking that couples iterative direction with critical feedback, we train CineEvolve via alternating reinforcement learning, where the Director surfaces flaws to sharpen the Critic, and the updated Critic guides the Director’s targeted revisions. To stabilize co-evolution, we introduce Evolutionary GRPO (E-GRPO), leveraging historical anchoring and revision safeguarding to mitigate advantage inflation. Additionally, we contribute CineTraj, a 40K cinematic trajectory dataset spanning creation, critique, and revision. Extensive experiments demonstrate that CineEvolve outperforms existing approaches. Our code will be available at this https URL.
[CV-47] Unlocking Spatial Grounding in Flow-Matching Models via SNR Tuning BMVC2026
链接: https://arxiv.org/abs/2610.29581
作者: Martin Thißen,Elke Hergenröther
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at BMVC 2026 (Oral)
Abstract:Despite the success of foundation models in image synthesis, achieving fine-grained spatial control remains a significant challenge, typically requiring auxiliary structural adapters or complex inference-time steering. While modern vision-language (VLM) text encoders possess the latent capacity to represent spatial coordinates, generative models often fail to leverage this information. We demonstrate that the signal-to-noise ratio (SNR) distribution is a critical determinant of spatial grounding in flow-matching models. Using a novel synthetic benchmark of geometric primitives, we isolate structural mechanics from textural complexity inherent in natural imagery and introduce a rigorous color-aware layout metric to quantify spatial fidelity. We show that training on a centered SNR distribution results in poor layout adherence (25.9%). Instead, we identify high-noise regimes as a “critical learning phase” for spatial grounding. Shifting the training focus toward these stages increases layout adherence to 80.8%, effectively reaching the 83.6% reconstruction ceiling of the underlying variational autoencoder. We find that the necessity for this shift scales with resolution and aspect ratio variance, and that the conservative noise schedules employed by models like FLUX.1 are insufficient for precise layout adherence. Finally, we show that these findings translate to real-world imagery via the COCO dataset (1.0 vs. 57.3 Average Precision), where state-of-the-art layout adherence is unlocked through distribution tuning alone, without architectural modifications.
[CV-48] Rethinking Underwater Image Enhancement: Learning through Text-Guided Degradation Curriculum
链接: https://arxiv.org/abs/2610.29569
作者: Hongri Tian,Wenxue Li,Tian Ye,Sixiang Chen,Lujia Wang,Xianying Wang,Xinhu Zheng,Gang Hua,Lei Zhu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Existing paired clean-degraded datasets have advanced learning-based underwater image enhancement, but they remain either limited in scale or difficult to acquire, and still fail to adequately cover the diverse degradations encountered in real-world underwater environments. Such scarce and insufficient data ultimately restrict the generalization of underwater image enhancement models, highlighting the need for realistic and controllable degradation modeling to better capture real underwater conditions and improve enhancement robustness. To this end, we propose a unified degradation-aware training framework for the underwater image enhancement task. A Text-Conditioned Degradation Augmentation Pipeline uses a text-guided diffusion model to synthesize diverse and semantically consistent degraded samples, providing paired data to support the subsequent curriculum learning strategy. The proposed Progressive Degradation Curriculum organizes training across multiple attributes and difficulty levels to promote balanced learning, and a Transmission-Guided One-Step Diffusion Model leverages this progressive process to better address the wide range of degradation severity and restoration ambiguity in underwater images. Extensive experiments on multiple underwater benchmarks demonstrate that our proposed method achieves superior performance. Our code and models will be released upon the publication of this work.
[CV-49] Generalized CycleGRPO for Fine-Grained Multimodal Understanding and Grounding
链接: https://arxiv.org/abs/2610.29552
作者: Xin Zhang,Haochen Wang,Beibei Lin,Zhaoxiang Zhang,Robby T. Tan,Jason Li,Dacheng Tao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 31 pages, 14 figures, 16 tables
Abstract:Fine-grained multimodal intelligence requires models to both understand a designated region and localize it from language. Existing methods learn these directions with separate objectives and task-specific annotations. We present Generalized CycleGRPO, the first unified reinforcement learning framework coupling region understanding and localization across spatial, temporal, and spatio-temporal domains. Given a region, a multimodal large language model generates candidate descriptions and then localizes each description to reconstruct the input region. Reconstruction overlap provides a verifiable reward for captioning, while individual localization rewards improve the inverse mapping. A common region-space formulation extends this cycle from image masks and boxes to video intervals and timestamped spatial evidence, without reference captions or an external language-model judge during reinforcement learning. In videos, the cycle encourages descriptions that distinguish a target event from similar recurrences. We introduce DOVE (Distinctive Occurrence-level Video-caption Evaluation), a human-verified contrastive benchmark for this temporal distinctiveness. CycleGRPO improves SAMTok’s overall gIoU from 57.5% to 68.2% on GroundingSuite and average gIoU from 71.2% to 78.7% on GRES. For temporal tasks, it achieves 51.9% tIoU on Charades and 33.3% Temporal Evidence Coverage on DOVE, compared with 45.6% and 14.8% for the Qwen3-VL initialization. These results establish region-text cycle consistency as a general self-evaluation principle for multimodal understanding and localization. Models, code, and DOVE are available at this https URL.
[CV-50] Hyperspherical Simplex Representations for Backward-Compatible Retrieval
链接: https://arxiv.org/abs/2610.29550
作者: Simone Ricci,Niccolò Biondi,Federico Pernici,Alberto Del Bimbo
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Training modern AI models has become increasingly expensive, and updating deployed models can alter their internal feature representations, affecting applications built upon them. In retrieval systems, such updates often require complete gallery re-indexing, a computationally expensive process at scale. Existing backward-compatibility methods enable direct comparison of representations from updated and earlier models, avoiding re-indexing. However, they typically require compatibility-specific training, learned mapping functions, or architectural modifications, and often depend explicitly on the previous model. We propose hyperspherical simplex representations derived from softmax outputs and logits for backward-compatible retrieval across independently trained models. They leverage the simplex structure induced by softmax cross-entropy training with one-hot labels as a common geometric reference across model updates. Since this reference depends on the number of classes, adding new ones expands the simplex, misaligning existing class prototypes with their earlier counterparts. We therefore derive a deterministic linear transformation to restore their alignment. Our theoretical analysis of the formal backward-compatibility definition identifies two geometric conditions under which its inequalities hold in expectation, assuming von Mises–Fisher class distributions with increasing concentration across updates. Aligned regular-simplex representations satisfy these conditions, providing a theoretical basis for the proposed representations under the stated assumptions. Without using additional compatibility-specific losses, learned mappings, or architectural modifications, the proposed representations achieve competitive results on standard compatibility benchmarks, including expanded class sets, more expressive architectures, and independently trained models from public repositories.
[CV-51] Melange: A Synthetic Visual Bias Benchmark for Multi-modal Large Language Models
链接: https://arxiv.org/abs/2610.29537
作者: Razieh Rezaei,Ehsaneddin Asgary,Masoud Jalili Sabet
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multi-modal Large Language Models (MLLMs) have seen broad adoption, but their biases are still far less examined than those of standard LLMs, creating possible social harms when deployed in real-world settings. Prior efforts to extend bias analyses from LLMs into the multi-modal space either cover only a few bias categories or do not ensure that the groups being compared are clearly addressed by visual cues in the images. To overcome these limitations, we introduce Melange, a wide-ranging synthetic visual benchmark covering seven categories for assessing social biases in MLLMs. It is grounded in the widely used language-only BBQ dataset (Bias Benchmark for Question Answering) from BigBench, carefully extending its constructed contexts via controlled generation of visual scenarios so the target attributes appear visually and clearly. To maximize reliability, our generated images pass through two stages of validation: automated screening using visual question answering (VQA) and subsequent manual inspection by human annotators. By systematically evaluating 23 open-source MLLMs across three matched evaluation settings—text-only, image-only, and image-with-context—Melange isolates the fairness effect of the input modality. Switching from text to vision reduces fairness by 0.291 on average, a gap that varies by model family rather than by scale alone. A perception control confirms that this gap is not an artifact of visual comprehension: models identify the depicted attributes at 90–95% accuracy yet still fail to abstain. When models do commit to a biased answer, polarity-aware stereotype alignment shows they choose the human-stereotypical response roughly three-quarters of the time.
[CV-52] Supervision-Adaptive Curriculum and Guarded Anti-Curriculum Learning for Dense Prediction
链接: https://arxiv.org/abs/2610.29519
作者: Chunming He,Dingming Zhang,Fengyang Xiao,Zhiwen Cao,Sina Farsiu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 7 Figures, 27 Tables
Abstract:Curriculum learning orders samples by difficulty, but difficulty alone does not establish supervision reliability for dense prediction. Easy samples can expose shortcuts, hard samples can be informative, and noisy or ambiguous samples can mislead training. Building on our conference method CurriSeg, we present PaceShift, a stabilize-then-challenge paradigm combining supervision-adaptive curriculum selection with guarded spectral post-training. A learning-dynamics view motivates separating supervision reliability from temporal update value and applying spectral challenges after stabilization. The first phase estimates reliability from available supervision, augmentation consistency, model disagreement, and prediction certainty. Temporal statistics guide sample weighting, while pixel-uncertainty weights reduce the early influence of uncertain regions. The second phase ranks predictions from multiple spectral views using supervision agreement, robustness, reference consistency, and penalties. A pairwise preference objective measured against the frozen Phase-I reference, together with an anchor, refines the prediction. The framework supports full, weak, semi-supervised, and model-derived supervision. Experiments across context-entangled content segmentation (CECS), broader dense-prediction tasks, and foundation-model and cross-modal adaptations show accuracy gains. Targeted diagnostics indicate improved pooled calibration and corruption robustness in the evaluated settings. PaceShift preserves the deployed architecture and adds no inference computation. Code will be publicly released.
[CV-53] AutoPhyX: Automatic Text-Condition Physics Property Generation
链接: https://arxiv.org/abs/2610.29517
作者: Bei Huang,Yixin Chen,Hongbin Zha,Yuru Pei,Siyuan Huang
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: 36 pages. Project page: this https URL
Abstract:High-fidelity 4D physical simulation requires accurate physical parameter assignments that align with diverse material properties. Previous methods struggle with critical bottlenecks: they either rely on computationally intensive per-scene optimization or predict parameters from purely visual inputs. The latter suffers from inherent ambiguity, e.g., the inability to distinguish the stiffness of rubber from its appearance alone. To bridge this gap, we propose AutoPhyX, a text-conditioned framework for predicting spatially-varying physical properties. Since training robust feed-forward models requires fine-grained supervision, we first introduce a novel part-controllable data generation pipeline that decomposes complex 3D assets into semantically distinct components and pairs them with physically plausible, diverse parameters and corresponding text descriptions. Leveraging this dataset, AutoPhyX employs a cross-modal modulation mechanism where text features dynamically modulate visual features for precise physical grounding. By formulating property prediction within a voxel field, AutoPhyX ensures compatibility with diverse 3D representations, including meshes, point clouds, Gaussian Splatting, and NeRF. Experiments demonstrate that our method enables physically plausible, text-driven parameter assignment in a single forward pass. It achieves high accuracy and robust generalization to in-the-wild objects, paving the way for future physics-intensive embodied and robotic applications.
[CV-54] Balancing Foundation Models and Trait-Specific Learning for Forehead-Crease Recognition
链接: https://arxiv.org/abs/2610.29495
作者: Geetanjali Sharma,Aryan Jain,Dev Nagaich,Aditya Nigam,Raghavendra Ramachandra
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at CVIP 2026
Abstract:Biometric recognition often degrades under partial facial vis- ibility, where conventional facial cues are occluded. The forehead region remains consistently visible and contains distinctive crease patterns that can serve as a complementary biometric trait. We present a unified eval- uation of forehead-crease recognition across explainable zero-shot rea- soning, domain-adaptive learning, and trait-specific modeling. FIRE en- ables interpretable zero-shot verification using multimodal large language models, while pretrained face-recognition models are adapted to the fore- head domain through margin-based learning. Under open-set evaluation, the best zero-shot model GPT-4o+FIRE achieves 18.22% EER, while Edge-Forehead achieves 8.30% EER and 54.01% TMR@0.1% FMR. The results show that although MLLMs offer interpretable zero-shot verifi- cation, trait-specific learning remains more effective for robust forehead- crease biometric recognition.
[CV-55] Extending Affordance Segmentation for Wearable Robots to Multi-Object Images
链接: https://arxiv.org/abs/2610.29474
作者: Simone Lugani,Matteo Montani,Paolo Gastaldo,Rodolfo Zunino,Edoardo Ragusa
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:This paper proposes a refinement to existing vision-based portable pipelines for the recognition of graspable object parts. Such pipelines can effectively support automatic grasp selection and preshaping in upper-limb prosthetic devices. However, realistic scenarios in which multiple objects are present still represent a challenge. The proposed method exploits a minor modification to the conventional hardware setup and a custom training approach to extend the applicability of vision-based pipelines. A laser pointer is introduced in the setup as an effective mechanism for object disambiguation. Accordingly, a suitable retraining of the deep convolutional neural networks supporting recognition of graspable parts is implemented. A mixed reality strategy allows the reuse of available high-quality segmentation masks to generate realistic images procedurally, extending the range of scenarios supported by the final device. Experimental results confirm that the proposed pipeline improves target disambiguation accuracy by up to 9% compared with existing solutions, while preserving the computational cost.
[CV-56] BlazeVGGT: 13x Faster VGGT at 2000 Frames via Camera Token without Training
链接: https://arxiv.org/abs/2610.29454
作者: Shujun Yao,Junhui Lin,Congying Han,Tiande Guo,Ziwen Liu,Bonan Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Feed-forward visual geometry models, exemplified by VGGT, have demonstrated strong multi-view reconstruction capabilities through dense global attention. However, the resulting all-to-all cross-view computation scales quadratically with the number of input views, making long-sequence inference particularly expensive due to substantial frame-level redundancy among highly overlapping observations. We uncover a topology-first, geometry-later evolution in VGGT camera tokens: shallow-layer tokens reliably capture local view neighborhoods, whereas deeper-layer tokens progressively encode globally organized inter-view geometry. Motivated by this observation, we introduce BlazeVGGT, a training-free framework for structure-aware frame-level key–value sparsification. At each global-attention block, BlazeVGGT constructs a view-level similarity kernel from camera-token relations and employs greedy determinantal point process inference to select a compact set of mutually complementary views. The selected views constitute the key–value context, while all input frames are retained as queries. This asymmetric attention design preserves dense per-frame predictions while eliminating redundant cross-view computation, allowing our method to be directly integrated into pretrained VGGT as a plug-and-play module without parameter updates. Its advantages become increasingly pronounced with sequence length: at 2,000 frames, BlazeVGGT achieves approximately 13 \times faster inference than VGGT while reducing the Chamfer Distance from 0.41 to 0.32. Extensive experiments demonstrate a favorable accuracy–efficiency trade-off in point-cloud reconstruction and camera-pose estimation, compatibility with existing VGGT accelerators such as FastVGGT and LiteVGGT, and transferability to VGGT-based models including StreamVGGT and PAGE-4D. Codes are publicly available at this https URL.
[CV-57] Beyond Shortcut Learning: Hierarchical Spatial Prototype Regularization for Face Forgery Detection
链接: https://arxiv.org/abs/2610.29442
作者: Chao Shuai,Shaojing Fan,Kun Pan,Jinwu Hu,Weichen Lian,Ziqi Yang,Zhongjie Ba,Zhenguang Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advances in generative models have enabled highly realistic face forgeries, yet generalizing deepfake detectors to unseen manipulation pipelines remains a major challenge. One critical yet underexplored issue is that the learned forensic representation itself may fail to remain stable across different manipulation sources. In this paper, we provide new evidence that naively fine-tuned vision models suffer from severe \textitforensic representation collapse: their extracted cues are often entangled with unstable, dataset-specific artifacts, resulting in structurally fragile representations and poor cross-domain generalization. Leveraging this insight, we propose \textbfHierarchical \textbfSpatial \textbfPrototype (\textbfHSP) regularization, which explicitly models the forgery manifold by learning a structured prototype subspace over spatial features. We further introduce prototype orthogonality and second-order assignment balance, which jointly preserve the geometric independence of latent forgery factors and promote high-effective-rank utilization of the forensic representation, thereby reducing reliance on dataset-specific shortcuts. Empirical results demonstrate that our method substantially improves generalizability, yielding average frame-level AUC gains of 5.6% and 5.3% in the cross-dataset and cross-manipulation settings, respectively. Moreover, when used as a plug-in module, HSP boosts the average video-level AUC of existing state-of-the-art detectors to 95.3% (+1.8%). These results demonstrate the practical value of HSP as an easy-to-adopt regularization module for enhancing deepfake detectors.
[CV-58] Sparsity Should Be ALLOTted by Output: Training-Free Allocation of Block-Sparse Attention for Video Diffusion Transformers
链接: https://arxiv.org/abs/2610.29441
作者: Tianze Xia,Xiaoling Zhou,Ruya Lan,Yiqiao Liao,Chengru Song,Shiming Xiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 26 pages, 11 figures, including supplementary material
Abstract:Full 3D attention is costly in video Diffusion Transformers. Block-sparse attention reduces this cost by computing only selected attention blocks. However, existing methods either overlook the potential to optimize sparsity allocation or rely on heuristic rules or local metrics to guide it. In this work, we observe that sparsifying attention in different layers and at different denoising steps affects final-video fidelity to substantially different degrees. Yet the identities of the most sensitive layer-step pairs remain nearly unchanged across inputs. Building on these findings, we propose Allocating Layer-step Latency by Output Tests (ALLOT), a training-free sparsity allocation method. Treating each layer-step pair as a cell, ALLOT estimates each cell’s contribution to final-video error through (i) cell probing and (ii) trajectory calibration, then jointly optimizes cell densities under a measured-latency budget. It preserves existing selection strategies and kernels, replacing only the allocation component. At equal or shorter wall-clock time, ALLOT outperforms every compared allocation strategy with XAttention, with an 11.8 dB PSNR gain on HunyuanVideo. Extensive experiments across four additional published pipelines on Wan2.1, Wan2.2, and HunyuanVideo show that ALLOT outperforms or ties the default in all nine settings.
[CV-59] Look Beyond the Question: Preserve-Focus-Fuse for Whole-Slide Image Question Answering
链接: https://arxiv.org/abs/2610.29431
作者: Xiangyu Li,Jincheng Li,Tianle Du,Yanheng Li,Haoxuan Che,Yuxin Sun,Long Bai,Liangliang Liu,Ran Su
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:A question about a whole-slide image rarely names every morphological finding needed to answer it. Question-guided patch selection can therefore overlook supporting morphology, while slide-level compression can dilute focal evidence. Inspired by how pathologists combine broad slide review, question-directed examination, and integration of their findings, we introduce BEYONDPATH, a Preserve-Focus-Fuse framework for whole-slide image question answering. Preserve learns a question-agnostic Morphology Bank through answer supervision across multiple questions about each training slide. Focus retrieves questionspecific evidence from all foreground patches, including regions outside the Bank. Fuse integrates the preserved morphology at the retrieved evidence positions through Morphology-on-Visual-Evidence Fusion, producing a fixed 96-token visual prefix. On WSI-Bench, BEYONDPATH achieves an Avg-9 of 0.769 and leads all eight reported report-generation metrics using the same checkpoint. It also achieves 0.639 zero-shot overall accuracy on SlideBench-BCNB. Controlled ablations show complementary benefits from morphology preservation and question-directed retrieval, and support cross-question supervision and fusion at the evidence positions. These results support looking beyond the question’s explicit cues: preserving broader slide morphology while retaining the ability to seek evidence for each question.
[CV-60] LLM Seg: Training-Free Text-Prompted Image Segmentation with B-Splines
链接: https://arxiv.org/abs/2610.29409
作者: Abdullah Hamdi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: preprint
Abstract:Promptable segmentation models such as SAM 3 achieve remarkable open-vocabulary performance but require dedicated architectures, large-scale annotation engines, and task-specific training. We ask a different question: can a general-purpose multimodal large language model (MLLM) segment images directly, without an added segmentation head, mask decoder, or training? We present LLMSeg, a training-free pipeline that turns a vision-capable LLM into a text-prompted image segmenter. LLMSeg decomposes segmentation into three LLM-driven stages. First, detect localizes all instances matching a free-form text query and describes their coarse morphology. Next, segment traces each instance using closed periodic B-splines confined to its detected box. These splines define an outer boundary and optional hole loops. Finally, refine iteratively proposes focus boxes around residual errors and retraces confined splines. The process ends when the mask stabilizes or the pass budget is exhausted. The output is a binary mask with inspectable intermediate geometry. We evaluate LLMSeg across ten referring and reasoning segmentation splits (ReasonSeg and RefCOCO/+/g). We also evaluate open-vocabulary instance segmentation (LVIS, COCO, and SA-Co), semantic segmentation, and zero-shot detection (ODinW13 and RF-100VL). With an off-the-shelf Gemini-3.7-Flash backend, LLMSeg is competitive with and often surpasses the specialist SAM 3 model on identical items. It reaches 71.5 cIoU on ReasonSeg val without additional segmentation-specific training. Backend model updates can improve accuracy without retraining the pipeline. The code is available at this https URL .
[CV-61] LiveAct: Towards Real-Time Multi-Agent World Models with Heterogeneous Action Interfaces
链接: https://arxiv.org/abs/2610.29408
作者: Xinyu Wang,Huafeng Shi,Yue Ma,Zijie Meng,Teng Xi,Yuzhuo Chen,Yucheng Wang,Ruicheng Zhang,Guoxin Zhang,Pengfei Wan,Xiu Li,Fangneng Zhan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Interactive video world models predict how an environment evolves under user actions, but most existing systems assume one controlled agent and one task-specific action interface. We study multi-agent video world modeling, where one model generates a joint visual future for multiple controlled agents while interpreting heterogeneous controls ranging from binary game buttons to continuous robot commands. This task presents two complementary challenges. First, every agent-indexed action stream must control its intended visual entity independently. Second, the model must adapt the meaning of each control variable across different tasks, where the same tensor dimension may denote an attack, steering command, or robot command. We introduce LiveAct, centered on a Heterogeneous Action Interface (HAI) whose two components mirror this decomposition. To support independent control, Agent-Aware Spatial Modulation uses language-driven spatial gates and agent-specific attention to bind each action stream to its intended visual agent. To accommodate heterogeneous controls, Schema-Guided Action Adaptation describes each control variable in language and modulates its semantic token with the synchronized numerical value. We further transfer the complete interface to a blockwise causal generator through teacher forcing and rollout-aware distribution matching, enabling efficient streaming generation while retaining agent-specific control and action semantics. We instantiate the formulation on six multi-agent sources spanning games and multi-robot manipulation, and evaluate visual quality, action following, agent attribution, and latency. Across games and robot manipulation, LiveAct reaches 0.898 and 0.942 agent-attribution accuracy without agent locations or identity crops, and the causal generator attains 17.70 FPS at 512\times 448 .
[CV-62] VideoGen Model 1: Prompt Expansion Versus Formatting in Audiovisual Generation
链接: https://arxiv.org/abs/2610.29407
作者: David Grossman(VideoGen),Anton Koenig(VideoGen)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 7 pages, 2 figures. Model page: this https URL ; VideoGen: this https URL
Abstract:Creative prompt expansion improved overall audiovisual quality scores over raw prompts in a controlled pilot of VideoGen Model 1, our post-trained version of MiniMax H3. We compared the raw, reformatted, and expanded versions of 30 prompts, holding the generator, seed, and sampling settings fixed. Each clip was rated separately on five dimensions. Their mean formed an overall score on a 1-10 scale. Compared with formatting alone, overall scores increased by 0.63 points (95% interval: [+0.11, +1.15]) under VA-Judger and 0.15 points (95% interval: [-0.03, +0.35]) under Gemini 3.8 Flash, whose overall change remained uncertain. Prompt-alignment scores increased by 0.97 points under VA-Judger and 0.50 under Gemini. VA-Judger also showed higher audio quality; visual-quality ratings changed little under either judge.
[CV-63] Region-Aware Multi-View Inspection Using Deep Learning and Domain-Informed Decision Fusion
链接: https://arxiv.org/abs/2610.29398
作者: Srivishnu Ethirajulu Krishnaraj,Rishikesh R. Chopade
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
备注: 10 pages, 4 figures, 10 tables
Abstract:Automated optical inspection of precision components with tiny surface defects remains challenging when different regions of the same component exhibit different defect characteristics and, therefore, different acceptance criteria. This paper presents a region-aware, dual-model inspection framework that combines instance segmentation of localized seat-region defects with image-level classification of global shaft-region anomalies. Rather than treating these two model detections as sufficient evidence for component rejection, a deterministic decision fusion layer that formalizes industrial Standard Operating Procedures is applied to the model outputs to make the final component level decision. A PLC-synchronized imaging system acquires 18 seat and 18 shaft images over a full 360-degree rotation to provide multi-view circumferential coverage. The framework was evaluated on 380 automotive fuel injector needles, covering representative operational defect types, using identical model predictions in all configurations. Direct model-output aggregation via simple union logic (where any single-view detection flags a rejection) identified all 104 non-conforming components but produced a component level false rejection rate of 57.6%. Inclusion of a complete decision layer maintained the observed non-conforming (NOK) recall of 100% while reducing the false rejection rate to 10.9%, corresponding to an 81.1% reduction in false-positive component decisions. The results demonstrate that formalized manufacturing acceptance criteria can complement sensitive visual models by translating frame-level detections into practical and traceable component-level decisions.
[CV-64] Progressive Permutation Alignment with Temperature-Controlled Spectral Sharpening ACCV2026
链接: https://arxiv.org/abs/2610.29396
作者: Yuta Horioka,Naoya Chiba,Yuki Uranishi
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ACCV 2026. Project page: this https URL
Abstract:In implicit neural representations (INRs), 3D signals are encoded directly in network weights, making stable weight-space operations essential. However, permutation symmetry causes independently trained yet functionally equivalent models to appear distant in weight space, often inducing large loss barriers under linear interpolation. Existing neuron alignment methods either commit early to discrete matches and become trapped in inconsistent local optima or rely on computationally expensive gradient optimization over continuous relaxations, both of which are unreliable in challenging cases such as NeRF. We adopt progressive alignment, a layer-wise strategy that follows inter-layer dependencies from input to output. At each layer, a correlation matrix is estimated and aligned with a discrete anchor obtained by the Hungarian algorithm, after which temperature-controlled spectral annealing gradually sharpens the correlation structure toward a permutation matrix. This update is deterministic and lightweight, avoiding the inner-loop gradient optimization required by continuous relaxation methods. A final refinement stage further optimizes the network layer-by-layer via weight matching, starting from the aligned solution. Experiments on MLPs, CNNs, and Neural Radiance Fields (NeRFs) show that the proposed method reliably recovers neuron correspondences and achieves stable alignment across architectures. In practical experiments aligning independently trained NeRF models, our method reduces the loss barriers under weight interpolation compared with existing approaches.
[CV-65] FM-Occ: Feature-Diverse Upsampling and Memory-Aware Decoding for 3D Occupancy Prediction
链接: https://arxiv.org/abs/2610.29388
作者: Yujia Zhai(1),Guoming Huang(1),Zhuanghuai Wang(1),Bangquan Xie(1) ((1) Great Bay University)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D semantic occupancy prediction estimates voxel-wise occupancy and semantic labels throughout a scene. Our experiments reveal that conventional sparse transposed-convolution upsampling yields poorly differentiated voxel responses and that successive decoder layers discard previously correct semantic predictions. To address these issues, we present Feature-Diverse Upsampling and Memory-Aware Decoding for 3D Occupancy Prediction (FM-Occ), comprising two components. First, 3D Sparse Subvoxel Shuffle (3S) mitigates feature homogenization by constructing candidate child features in the channel dimension, jointly transforming and normalizing them, and then rearranging them onto a finer sparse grid with explicit feature-coordinate correspondence. Second, Entropy-Regulated Layer-wise Memory (ER-LM) retains semantic information across decoder layers by maintaining a layer-wise memory for each query through gated recurrent updates and using prediction entropy to regulate the historical information propagated to the next layer. During training, a class-weighted entropy loss Lcwe provides label-guided regularization for matched semantic queries. On SemanticKITTI, 3S increases the spatial coefficient of variation of normalized voxel-wise root-mean-square (RMS) responses from 0.41% to 4.03%, while ER-LM reduces semantic degradation and improves error correction across decoder layers. The combined FM-Occ improves mIoU from 12.16% to 12.73% with negligible memory overhead. ER-LM further improves mIoU in the BEV-based DISC framework from 16.78% to 17.17%, supporting its applicability across 3D voxel-based and BEV-based architectures.
[CV-66] AdvFidelity: Unifying Preference Alignment and Few-Step Image Generation within Reinforcement Learning
链接: https://arxiv.org/abs/2610.29380
作者: Sicong Kang,Fangao Zeng,Yuhan Li,Hao Zhou,Mengfei Xu,Wenxiang Shang,Wei Li,Bingbing Ni,Pipei Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Practical image generation requires both human preference alignment and few-step efficiency, yet combining reinforcement learning (RL) with distillation is often multi-stage and may erode RL gains We observe that few-step RL progressively improves structural coherence, even with specialized rewards, but leaves low-level artifacts that these rewards may fail to penalize. To address this gap, we propose AdvFidelity, which integrates artifact-sensitive fidelity supervision into RL through an online adversarial reward. This enables joint reward alignment and high-fidelity few-step generation within a single RL stage, without distribution matching distillation (DMD) or other explicit distillation objectives. We train a discriminator to rank high-quality references (positives) above content-matched reconstructions (negatives) produced by the current generator. Reconstructing moderately noised references largely preserves style and scene layout while exposing low-level degradations. This encourages the discriminator to assess artifacts rather than content differences. With negatives reconstructed online, alternating updates adapt fidelity supervision to the generator’s evolving failure modes. References can come from existing target-domain data or full-step model samples. Built on LeapAlign, AdvFidelity backpropagates gradients from both rewards through the few-step sampling trajectories used at inference. Across two model backbones and three task settings, AdvFidelity matches full-step LeapAlign in alignment, task performance, and overall generation quality and remains highly competitive with other evaluated full-step RL methods, using only 4 or 8 steps without CFG.
[CV-67] AOV-GS: Active Open-Vocabulary Semantic Mapping with 3D Gaussian Splatting
链接: https://arxiv.org/abs/2610.29374
作者: Rodion Shkokov,Tatiana Zemskova,Dmitry Yudin
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 17 pages, 7 figures, 7 tables. Published at AIST 2026
Abstract:Robots deployed in human-centered environments need maps that support both geometric navigation and open-vocabulary semantic queries beyond a fixed label set. A key challenge is enriching such maps with semantic information without relying on manually annotated segmentation masks or closed-set semantic categories. Existing geometry-driven active mapping maximizes coverage but treats semantics as a passive by-product of the collected trajectory, whereas active semantic mapping typically depends on supervised semantic segmentation. We present AOV-GS, an active 3D Gaussian mapping system that guides exploration using frozen foundation models. During coarse exploration, AOV-GS re-ranks geometrically promising candidate views according to CLIP mask-embedding novelty computed from SAM instance segmentation, biasing exploration toward semantically informative regions. Frozen SAM and CLIP provide instance masks and language-aligned embeddings throughout mapping, while a lightweight scene-specific language field is fitted offline directly to the collected CLIP embeddings rather than manually annotated masks, enabling open-vocabulary text-query segmentation on novel views. In a controlled comparison using the same SAM-CLIP perception stack, AOV-GS improves average language-field mIoU over geometry-only active planning by 1.3 percentage points on Replica (34.6% on eight scenes) and by 1.8 points on Matterport3D (23.1% on five scenes). With CorrCLIP, AOV-GS reaches 35.6% on Replica, 2.0 points above the matched Geometry-based + CorrCLIP baseline. Novel-view PSNR remains comparable to the geometry-only baseline on both datasets while depth L1 improves on Replica. Code is available at this https URL.
[CV-68] Seeing through Denoising: Frequency-guided One-step Denoising for Infrared Small Target Detection
链接: https://arxiv.org/abs/2610.29361
作者: Xuelin Qian,Zixuan Wang,Chang Xu,Dingwen Zhang,Junwei Han
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13pages, 5figures
Abstract:Infrared small target detection (IRSTD) is a challenging task due to the extremely small size of targets, low signal-to-noise ratios, and heavy background clutter in infrared imagery. While the progress of deep learning has advanced this field, existing methods primarily adopt discriminative paradigms that directly map images to target masks, overlooking the potential of generative modeling. In this paper, we propose DiffIRSTD, a novel generative framework that reformulates infrared small target detection as a background reconstruction problem. Instead of explicitly predicting target regions, we reinterpret small targets as noise-like signals and leverage a diffusion-based model to recover a clean, target-free background from a perturbed input image. The residual between the reconstructed background and the original image is then exploited to highlight candidate target regions, providing explicit cues for subsequent detection. To address the computational cost typically associated with diffusion sampling, we introduce a one-step denoising strategy that enables efficient background reconstruction while maintaining practical inference speed. Furthermore, we present a frequency-guided perception loss to preserve high-frequency background details and focus more on the reconstruction quality of target regions. More importantly, our DiffIRSTD operates as a plug-and-play framework, compatible with existing IRSTD models without increasing the computational complexity of detectors. Extensive experiments on IRSTD-1K, NUAA-SIRST, and NUDT-SIRST datasets demonstrate the superiority of our method, achieving state-of-the-art performance across three benchmarks.
[CV-69] Reading the Mask: Descriptor-Grounded BI-RADS Rationale Generation for Breast MRI Segmentation
链接: https://arxiv.org/abs/2610.29358
作者: Hrishikesh Singh,Aarti Sharma,Divyanshu Kumar,Millie Pant
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Deep learning based models accurately delineate tumours on dynamic contrast-enhanced MRI (DCE-MRI), but segmentation masks alone do not provide the rationales or reasoning used by radiologists to assess the lesion suspicion. This limits the explainability as well the clinical integration. We investigate whether clinically relevant descriptors can instead be derived from the segmentation masks and their dynamic contrast-enhancement series. We have used Breast Imaging Reporting and Data System (BI-RADS) guidelines as a structured framework. Using these guidelines we designed a BI-RADS decision tree with eight quantitative measurements using radiomics, together with the lesion size and component count, that determine the descriptors at eight nodes. Using these descriptors we generate natural-language rationale using a large language model. The model was constrained to express only the derived records from the decision tree. After applying this framework to the expert and automatic masks of each of the 1,506 cases in the MAMA-MIA dataset, we generated a reproducible corpus of 3,012 BI-RADS grounded rationales. We obtained a faithfulness score of 99.98% and a counterfactual accuracy of 99.3%, indicating that the generated rationales faithfully verbalises the measured descriptors and correctly reflects changes in the underlying record. Agreement between the descriptors from expert and automatic masks falls from 0.832 to 0.549 as mask quality falls from Good to Missed. The generated rationales are intended as draft representations for radiologist review and final sign-off rather than autonomous clinical reports.
[CV-70] OpenMAP-HIE: Pathology Informed Score-based Diffusion Model for Unsupervised Neonatal Hypoxic-Ischemic Encephalopathy Lesion Segmentation
链接: https://arxiv.org/abs/2610.29322
作者: Pengyu Kan(Johns Hopkins University),Craig Jones(Johns Hopkins University, University of Alberta),Kenichi Oishi(Johns Hopkins University School of Medicine)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 22 pages, 4 figures, 9 tables
Abstract:Accurate localization of neonatal hypoxic–ischemic encephalopathy (HIE) lesions on apparent diffusion coefficient (ADC) images is clinically important but is limited by scarce expert annotations and substantial variation in lesion appearance. We propose OpenMAP-HIE, a pathology-informed score-based diffusion model for unsupervised HIE lesion localization without voxel-wise lesion supervision. A denoising diffusion model learns the healthy neonatal ADC distribution, while a ControlNet-based conditional branch preserves patient-specific anatomy. At inference, anomaly evidence is derived directly from differences between conditional and unconditioned noise predictions at independently evaluated diffusion noise levels, avoiding dependence on a final pseudo-healthy reconstruction or sequential reverse-diffusion chain. Based on the characteristic ADC reduction associated with acute HIE, only discrepancies consistent with the expected pathological direction are retained and aggregated across noise levels using a geometric mean. Spatial mean filtering further suppresses isolated responses. The framework was evaluated on the BONBID-HIE dataset. The ADC-only method achieved a Dice score of 0.283\pm0.244 , average precision of 0.505 , Hausdorff distance of 25.45\pm15.63 mm, mean average surface distance of 9.40\pm8.49 mm, and normalized surface distance of 0.355\pm0.236 , providing the best overlap and surface-distance performance among the evaluated unsupervised methods and Dice performance comparable to supervised ADC-only models. Optional population-derived ADC Z -map refinement further increased Dice to 0.602\pm0.269 . These findings support pathology-informed score discrepancies as a useful signal for neonatal HIE lesion localization, while highlighting remaining challenges in detecting small and low-contrast lesions.
[CV-71] Latent space configuration for neural network finetuning fast ranking and embedding dimensionality reduction
链接: https://arxiv.org/abs/2610.29295
作者: Nikita Gabdullin,Anton Raskovalov
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 14 pages, 4 figures, 2 tables, 2 algorithms
Abstract:Finding K most similar elements for a query in a large gallery of neural network (NN) embeddings is essential in various areas of computer science and industrial applications of artificial intelligence. This paper proposes search algorithms for structured latent spaces (LS) of NNs which allow to speed up top K item retrieval owing to O(1) complexity small candidate gallery subset search. ArcFace-pretrained IResNet50 models are finetuned using Latent Space Configuration (LSC) methodology to obtain new distribution of embeddings with desired geometric properties. Applying the proposed algorithms to embeddings obtained from finetuned models leads to several times speed-up over baseline exhaustive search. Specifically, we report up to 3 times speed-up on GPU and 40% speed-up on CPU compared to the baseline method GPU speed. These results are obtained for the original and reduced numbers of LS dimensions. The results show that LS dimensionality reduction allows to further accelerate the search and even improve rank-N metrics.
[CV-72] Certified Abstention for Provably-Unidentifiable Index-Phase Recovery in Image Registration
链接: https://arxiv.org/abs/2610.29272
作者: Dinh Tuan Nguyen,Trong Nhan Nguyen,Vu Nguyen Minh Hung,Tien Minh Khoi Nguyen,Jaeyeop Choi,Sudip Mondal,Jae Sung Ahn,Junghwan Oh
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13 pages, 5 figures; supplementary material in ancillary files. Code and data: this https URL
Abstract:Image registration underpins measurement across scanning, camera, and microscopy imaging, yet a fundamental question is rarely posed: when is the sample-to-position map recoverable at all, and when does any estimator that reports a position necessarily fabricate one? We show that in reference-perturbed sampled imaging, where samples are localized through a perturbed reference, position fidelity (where samples land) and phase fidelity (which reference index becomes each block’s origin) are logically independent: no policy acting on position information alone can guarantee phase fidelity. We derive a tight resolution threshold separating recoverable from provably-unidentifiable registration, and a recovery framework that certifies identifiable regions–recovering them exactly–while abstaining on provably-unidentifiable regions rather than hallucinating structure. On a deployed scanning system we measure a direction-bimodal registration offset consistent with the theory. Against classical and learned restoration baselines, certificate-free offset estimators fabricate structure on the unidentifiable regime, whereas the certified method abstains rather than fabricating. Finally, the certified-abstention mechanism carries across scanning, rolling-shutter, and cryo-electron-microscopy imaging–declining rather than fabricating on the regime it flags–though how completely it abstains and how tightly it bounds kept-region risk stay modality-specific (at extreme SNR the cryo-EM certificate abstains only partially): a structural-versus-statistical boundary in registration reliability.
[CV-73] Clinical radar heart sounds with echocardiographic phenotypes of ventricular dysfunction and valvular disease
链接: https://arxiv.org/abs/2610.29267
作者: Jinye Li,Qingchao Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Radar heart sounds capture cardiac mechanical activity without contact, while echocardiography characterizes ventricular and valvular function. Paired measurements connect these signals to structural heart disease, yet such data remain sparsely represented in public resources. Here we present 537 radar heart-sound recordings paired with one echocardiographic report for each of 162 patients. The cohort covers three overlapping phenotypes: left ventricular ejection fraction (LVEF) below 50%, regional wall-motion abnormality and at least moderate valvular regurgitation, present in 38, 43 and 39 patients, respectively. Original complex signals, processed radar heart sounds and temporal quality segmentation support signal-to-phenotype analysis. Technical validation retains 95.93% of signal duration and confirms reproducible waveform reconstruction. Patient-level five-fold evaluation achieves a mean outer-test AUROC of 0.770\pm0.138 for reduced LVEF, identifying mechanical information associated with ventricular systolic dysfunction. This resource supports research linking non-contact cardiac mechanical signals to echocardiographic structural and functional phenotypes.
[CV-74] CRAB: Cuboid-based Representation for As-built BIM from Point Clouds SIGGRAPH
链接: https://arxiv.org/abs/2610.29257
作者: Haoran Xiong,Jing Wang,Shanshan Pan,Zihao Yan,Minglun Gong,Hui Huang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: CRAB accepted to SIGGRAPH Asia 2026
Abstract:As-built BIM reconstruction from point clouds demands semantic, volumetric, parametric, and editable building components, beyond 2D layouts or planar abstractions. Yet existing scan-to-BIM pipelines often rely on handcrafted rules, heuristic optimization, or manual correction, while learning-based methods fall short of recovering wall thickness, opening geometry, and component-level BIM structure. We introduce CRAB, a BIM-ready cuboid reconstruction framework that encodes walls, doors, windows, beams, and columns as compact architectural cuboids with IFC-compatible semantics and parameters. CRAB bridges point clouds and editable BIM by combining ProposalNet, which predicts coarse component cuboids, with AssemblyNet, a relation-aware Flow Matching model that refines geometry, predicts structural relationships, and enables automatic structural correction and human-guided editing. To address limited cuboid-level supervision, we extend Structured3D with component-wise cuboid annotations and construct cuboid-based procedural scenes. Experiments show CRAB surpasses floor plan and plane-based alternatives in component completeness, geometric accuracy, and generalization. For interior wall thickness estimation, CRAB achieves a mean error of 2.80 cm, with 79.50% of matched walls within 3 cm. The project page is available at this https URL.
[CV-75] ER-Chord: Spatially Consistent One-Step Image Editing via Equal-Energy Reliability Redistribution
链接: https://arxiv.org/abs/2610.29248
作者: Peize Li,Hanzhen Zhao,Xingguo Xu,Yilong Fan,Zhen Tao,Bo Yin,Zhifei Liu,Qingdong He,Xiaobin Hu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:One-step generative models enable fast text-guided image editing, yet they often introduce unintended changes to regions that should remain unchanged. Temporal aggregation stabilizes editing fields across noise levels, but leaves their spatial allocation insensitive to local variations in reliability. Consequently, temporal stability alone does not ensure spatially consistent editing. To address this lim- itation, we propose ER-Chord, a training-free and inversion-free framework for reliability-guided spatial redistribution. Our key insight is that agreement between editing fields at neighboring noise levels provides a local reliability signal for allocating editing strength. We formulate this allocation as an equal-energy op- timization that redistributes control energy according to local reliability while preserving the original editing directions and total control energy. This formu- lation admits a unique global solution that can be efficiently computed using existing field evaluations, requiring no additional denoiser calls. Experiments on two benchmarks, PIE-Bench and TEdBench, demonstrate improvements over state-of-the-art one-step editing baselines, achieving superior source preservation alongside consistent gains across multiple generative this http URL project page is at this https URL.
[CV-76] DepState-JEPA: Learning Depth-Grounded State Representation for Visual Perception and Manipulation
链接: https://arxiv.org/abs/2610.29246
作者: Xiujian Liang,Liuyuan Na,Zehao Du,Jiacheng Xu,Weixi Song,Mingyang Sun,Xingjun Ma,Cewu Lu,Jianhua Sun
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Predictive representations for robot manipulation should connect scene geometry with the state changes induced by actions. Our intuition is that understanding what an action will change starts with understanding where things are: complementary RGB and depth observations ground a common scene representation from which to infer those changes. Guided by this intuition, we introduce \textbfDepState-JEPA, a dual-stream RGB–Depth joint-embedding predictive architecture that connects spatial understanding with action-conditioned prediction through a shared latent state, \textbfDepState. Built on V-JEPA~2.1, the framework uses depth both as a complementary observation stream and to ground both modalities in metric 3D space, aggregating shared information into DepState while retaining modality-private features for decoding. Ray-conditioned readouts predict local features without target depth; after an action-conditioned transition, the same decoders predict future RGB and depth features using the predicted state and fixed initial private features. Robot-state readouts provide recursive proprioceptive inputs; object-trajectory supervision and calibrated forward kinematics shape state learning through motion targets and joint–TCP consistency. Comparisons show stronger RGB-encoder transfer after RGB-D adaptation, feature completion, conditioned visual forecasting, and manipulation success. Within-model ablations identify contributions from depth content, metric coordinates, and future-state matching, with additional control gains from FK regularization.
[CV-77] Failure Modes of Vision-Language Models for Human Activity Understanding in Thermal HRI
链接: https://arxiv.org/abs/2610.29244
作者: Han Hu,He Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 9 pages, 6 figures, 2 tables
Abstract:Thermal cameras enable robots to perceive human activities in darkness while revealing less personally identifiable appearance information and fewer details of the surrounding environment than RGB cameras. As vision-language models (VLMs), originally developed for RGB imagery, are increasingly integrated into embodied systems for scene understanding and high-level planning, thermal sensing offers a promising way to extend activity perception to low-light and privacy-sensitive settings. However, the failure modes of VLMs in thermal human activity understanding for human-robot interaction (HRI) remain underexplored. We fill this gap by evaluating seven locally deployable VLMs, including models developed for physical AI and robotics, on two person-centric thermal datasets. Our diagnostic tasks examine fine-grained action recognition, activity timeline reconstruction, and the ability to withhold unsupported answers. Paired RGB recordings serve as a performance reference, while controlled variations in frame sampling, thermal input presentation, and reasoning are used to assess whether the observed errors can be mitigated. Our results show that models can recognize broad activity categories but struggle with fine-grained actions such as coughing, sneezing, and yawning. They also produce inaccurate event timelines and hallucinate actions involving objects. Simple thermal input preprocessing and additional reasoning offer only limited, model-dependent gains. Abstention behavior also varies across models even under explicit instructions, limiting their reliability for practical robotic use.
[CV-78] Encoded but Unused: Bounding the Sources of VLM Errors
链接: https://arxiv.org/abs/2610.29241
作者: Anat Veikherman,Mor Ventura,Nitay Calderon,Lihi Zelnik-Manor,Roi Reichart
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In this paper, we investigate why vision-language models (VLMs) produce errors by decomposing them into two sources: visual-representation failures, in which the visual evidence required for the correct answer is not encoded, and visual-utilization failures, in which this evidence is encoded but not successfully used. We propose to estimate the visual-utilization failure rate using lightweight probes and derive theoretical bounds on the true rate that account for probe imperfections. We introduce VELens (Visual Evidence Lens), a rank-based probe that traces visual evidence for both a target object and its ground-truth attribute, such as color, material, or shape, across VLM layers. We evaluate VELens on over 6,000 images using LLaVA-1.6-7B and Qwen2.5-VL-7B. Our method achieves ROC-AUC scores of 0.95 and 0.91, respectively, and outperforms alternative probes in both low-resource and unseen-attribute settings. Using it to diagnose errors, we estimate that visual-utilization failures account for 84.4% of LLaVA errors and 70.0% of Qwen errors, with conservative lower bounds of 77.5% and 61.0%, respectively. Overall, our study identifies visual utilization as the dominant bottleneck in VLMs and suggests that a central opportunity for improvement lies in developing better mechanisms to locate, route, and use the visual information they already encode.
[CV-79] hink-Capture: Active 3D Geometry Acquisition for Spatial Reasoning in Vision-Language Models WACV2027
链接: https://arxiv.org/abs/2610.29232
作者: Junjie Wang,Zhihao Yuan,Yunhan Tang,Sheng Xu,Jiaqi Mao,Xiaoyu Ye,Yueru Luo,Xu Yan,Chun-Mei Feng,Zhen Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at WACV 2027
Abstract:Vision-Language Models (VLMs) excel at 2D recognition but often fail to maintain consistent 3D relations across sparse viewpoints, leading to spatial hallucinations under occlusion and perspective changes. Existing methods commonly fuse all frames with dense geometry, incurring substantial computation and context overhead while encouraging blind reliance on imperfect estimators. We present Think-Capture, a query-conditioned framework that first identifies task-critical objects and then acquires their geometry on demand. A 3D toolkit lifts zero-shot masks into a common coordinate frame, and a Symbolic 3D State Abstraction compresses object and camera states into structured text. Hierarchical Group Relative Policy Optimization (GRPO) trains the VLM on cases in which geometry either helps or misleads, teaching it when to use tool evidence and when to fall back on visual cues. Think-Capture-4B reaches 54.4% on MindCube-Tiny (+21.5 points over its backbone), 39.1% on the MMSI-Bench positional subset (+7.9), and 42.8% on ViewSpatial (+5.2). It also remains robust under coordinate and mask perturbations. The full stack fits one 24 GB GPU and serves as an asynchronous high-level reasoner on an Agibot G1. Our code is available at this https URL.
[CV-80] DisasterRoute: Evidence-Guided Dual-System Routing for Visual Disaster Question Answering
链接: https://arxiv.org/abs/2610.29231
作者: Tan Zhang,Xing Deng,Lu Zhang,Xiaofeng Zhu,Ping Hu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual question answering from low-altitude UAV imagery must deliver timely and accurate judgments to support disaster response under limited edge computing resources. Routine questions may be answered through direct decisions, whereas more challenging questions may benefit from step-by-step reasoning. However, existing research primarily focuses on improving models’ visual understanding and generative reasoning capabilities, with limited exploration of efficient direct decision-making and its adaptive coordination with generative reasoning. We develop Disaster-S1 based on a vision-language model adapted to the disaster domain. Trained for option discrimination, it outputs a probability distribution over answer options in a single multimodal forward pass for fast decision-making. We further propose DisasterRoute, a dual-system routing framework that combines prediction confidence with disaster evidence to decide whether to accept the initial answer or invoke the disaster reasoning model DisasterVL for review. By allocating inference computation on demand, the framework retains the efficiency of direct decision-making and improves overall question-answering accuracy through selective review. Experiments on DisasterBench show that Disaster-S1 achieves superior accuracy with low latency, while DisasterRoute further improves accuracy through selective review at substantially lower latency than full reasoning.
[CV-81] Cache Locally Recompute Selectively for Streaming Video Understanding
链接: https://arxiv.org/abs/2610.29229
作者: Junhyeok Lee,Sangyoon Lee,Jaeho Lee
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Streaming video understanding (SVU) requires video-LLMs to process incoming video continuously while answering user queries with low latency. Recent methods reduce query-time cost by prefilling frames into KV caches during idle time and reusing the cached KVs at query time. To bound per-frame computation over unbounded video streams, these methods restrict the context available during prefill. This creates a fundamental context mismatch: the restricted context may omit information needed to properly contextualize video tokens, yet, still contain information irrelevant to the eventual query, which is unknown during prefill. We propose FrameBlend, a simple yet effective plug-and-play method to address this mismatch through selective KV recomputation, without requiring any additional training. At query time, FrameBlend re-prefills selected video tokens using query-relevant context and replaces their cached KVs with recomputed ones. Crucially, this recomputation enables shorter attention windows during idle-time prefill, substantially reducing prefill cost while improving accuracy. Experiments across three SVU frameworks and five streaming and offline benchmarks demonstrate accuracy gains of up to 3.9 pp. On RVS-Ego, FrameBlend reduces per-frame prefill FLOPs by over 20% and total processing time by 14-59% for retrieval-based frameworks, including the cost of query-time recomputation. These results demonstrate that a simple change in how contextualization is distributed between idle time and query time can improve the accuracy-efficiency trade-off for SVU.
[CV-82] DetM-Tracker: Deterministic and Memory-Efficient VIS for Autonomous Industrial Inspection
链接: https://arxiv.org/abs/2610.29212
作者: You-Rui Lin,Jen-Chieh Wu,Tzu-Ti Wei,Jen-Jee Chen,Yu-Chee Tseng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Autonomous industrial inspection requires strictly deterministic real-time inference under tight memory and computational constraints. However, existing Video Instance Segmentation (VIS) models suffer from prohibitive scaling: dense temporal memory grows with spatial resolution and sequence length, while fixed-capacity decoders fail to exploit the inherent sparsity of industrial scenes, creating a practical memory wall on resource-constrained edge platforms. We present DetM-Tracker, a streamlined VIS framework for autonomous industrial inspection, where target objects often exhibit highly similar appearance under constrained “micro” fields of view. DetM-Tracker eliminates architectural redundancy through three synergistic components: (1) Compact Scalar Memory, which condenses high-dimensional instance features into four-scalar descriptors, reducing temporal memory complexity from O(T \cdot N \cdot R) to O(T \cdot \hatN) . This mechanism effectively compresses frame-level representations into a bank of active instances, where R denotes resolution, N and \hatN represent global and window-specific instance counts, respectively and T the temporal window size; and (2) Dynamic Instance Channels, which adaptively allocate query capacity according to the actual number of visible objects; and (3) Entropy-Gated Skip Connection, which concentrates computational budget on high-uncertainty spatial regions to refine object boundaries in a single forward pass without auxiliary refinement heads. Our approach effectively bridges the gap between modern VIS models and the practical demands of industrial edge deployment, reducing inference latency by approximately 20 \times and VRAM consumption by 6 \times compared to state-of-the-art dense-feature baselines.
[CV-83] NeuralSRender: G-Buffer Guided Video Diffusion for Sim-to-Real Rendering
链接: https://arxiv.org/abs/2610.29200
作者: Yipeng Lu,Youqi Liao,Zhenyu Wang,Yuning Peng,Zhen Dong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Tech repo
Abstract:The scarcity of high-quality annotated visual data remains a critical bottleneck for autonomous-driving perception. Simulators provide training data at scale, but the limited realism of synthetic videos compromises perception performance in real-world applications. To address this limitation, we propose NeuralSRender, a sim-to-real video translation framework that enhances the photorealism of synthetic videos while maintaining geometric consistency with the source scenes. Built on a pretrained video diffusion transformer (DiT), NeuralSRender uses a lightweight projector to inject geometric and material priors from G-buffers, and LoRA enables parameter-efficient adaptation. The model is trained in two stages: it first learns G-buffer conditioning from synthetic videos with accurate G-buffers, then adapts to real-world appearance using real videos with estimated G-buffers. During the second stage, an additional LoRA module is trained while the projector and first-stage LoRA remain frozen to reduce interference from estimation noise. NeuralSRender introduces only 1.45% parameter overhead and can be trained on consumer-grade GPUs with 24 GB of memory. To support training and evaluation, we introduce GenReal, a large-scale driving video dataset comprising synthetic and real-world videos with corresponding G-buffers. Experiments demonstrate improved photorealism with competitive geometric consistency. Training perception models on our translated videos instead of raw simulator renders improves detection \mathrmAP_50 on nuImages from 35.1 to 40.6 and segmentation mIoU on Cityscapes from 32.4 to 40.8. Our code, data, and model weights are available at: this https URL.
[CV-84] A Unified Evaluation Framework of Physical Adversarial Robustness in the Edge VLM Era NEURIPS2026
链接: https://arxiv.org/abs/2610.29196
作者: Eric Liang,Alan Luo,Kaiwen Yuan
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: Accepted at the NeurIPS 2026 Workshop on Grounded and Faithful Vision-Language Models for Real-World Deployment (VLM4RWD). 16 pages, 6 figures, 5 tables. Code: this https URL
Abstract:Recent advancements in Vision-Language Models (VLMs) enable end-to-end visual grounding via language prompts and generative decoding, demonstrating largely improved zero-shot and open-vocabulary detection capabilities. As a result, VLMs are rapidly being deployed at the edge in safety-critical real-world systems. However, the physical adversarial robustness of edge VLMs has not been systematically studied, and the fundamental differences in output with modern object detectors create methodological challenges for fair evaluation. To compare the two families directly, we evaluate every selected representative model on one outcome: whether an adversarial patch worn on a person evades detection of the model. We report this by introducing a unified metric, conditional attack success rate (cASR), counted only on images the model still localizes under a matched gray occluder, and partition every failure into omission (no box), substitution (a box on the patch), and displacement (a box on neither). We benchmark the physical adversarial vulnerability of compact grounding VLMs against a range of object detectors, and find that the VLMs can be as vulnerable as the detectors. We disclose that failure mode is largely associated with the output format of a model rather than its architecture family. Of the three, only omission is visible to a downstream system; displacement and substitution return confident, well-formed boxes, making them potentially more dangerous failures. Further, we find that, under distribution shift, VLMs that primarily failed by omission instead shift to failing more by displacement/substitution. Thus, a grounding VLM can inherit the detector’s vulnerability while showing the possibility to adopt more dangerous failure modes. Our code and data are available at this https URL.
[CV-85] ROCOCO: Robust Outline Completion of Occluded Curvilinear Objects
链接: https://arxiv.org/abs/2610.29177
作者: Ken Zheng,Jaimyn Drake
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:We propose a learning-based generative model for completing curvilinear features under occlusion by environmental obstacles. To this end, we adopt a flow matching-based approach that supports the inpainting of occluded regions with the appropriate curvilinear features. To eliminate the reliance on privileged information about the occlusion a priori, we also train a UNet-based mask prediction model, which can identify regions for inpainting given only the occluded observation. The components of our approach are validated via experiments using procedurally-generated test cases, and the complete pipeline is validated using large public datasets for curvilinear objects. Against off-the-shelf inpainting baselines, ROCOCO is the strongest method by a wide margin when no occlusion mask is given, and is competitive with mask-supervised inpainting when one is, achieving best on full-image error and second only to LaMa on the occluded region, while using a completer of 7.7 M parameters against LaMa’s 51 M and Stable Diffusion’s 860 M.
[CV-86] Multi-Scale Latent Temporal Compression Attention and Video-Text Cross Attention Fusion for Video Action Detection
链接: https://arxiv.org/abs/2610.29176
作者: Yicheng Qiu,Keiji Yanai
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: This paper has been accepted for publication in the proceedings of the 33rd International Conference on Multimedia Modeling (MMM 2027)
Abstract:In video action detection, modeling the temporal dependencies in continuous video streams is crucial. Previous methods based on deep neural networks can perform frame-level video recognition tasks. However, these methods struggle to model dynamic temporal patterns, facing challenges such as redundant frame information, difficulty balancing global and local features, and insufficient fusion of visual and textual features. To address these issues, we propose a video temporal attention module based on the Multi-Scale Latent Temporal Compression Attention, which aggregates continuous temporal sequences into feature representations across diverse temporal scales while efficiently reducing redundancy. We also introduce a Video-Text Cross Attention Fusion module that leverages cross-attention at both latent and standard feature levels, achieving fine-grained fusion between video and textual features to enhance semantic alignment and feature interaction. Experimental results across multiple video recognition benchmarks demonstrate that our proposed methods achieve superior performance compared to existing methods, validating the effectiveness and generalization capability.
[CV-87] LocalAgent : Collaborative Agent ic Verification for Fine-Grained Instance-Level Consistency
链接: https://arxiv.org/abs/2610.29174
作者: Jiachen Guo,Xinshan Zhu,Boyun Wang,Yanyan Liang,Jun Wan,Feng Lin,Ajian Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 22 pages, 4 figures
Abstract:Multimodal large language models (MLLMs) have made rapid progress in general visual understanding, yet they still struggle with fine-grained instance-level consistency verification, where the goal is to determine whether two images depict the exact same physical object under varying viewpoints, illumination, and backgrounds. This task requires both global semantic perception and precise examination of subtle local evidence, since semantically similar objects may differ only in small suspicious regions. To address this challenge, we propose LocalAgent, a collaborative agentic framework that integrates the complementary strengths of a generalist MLLM and lightweight specialist verifiers. The MLLM first reasons globally to identify suspicious local regions, which are then delegated to a Local Evidence Verifier (LEV) toolkit for fine-grained appearance and geometric matching. LEV comprises Local Appearance Consistency (LAC), which estimates appearance similarity under illumination and color changes, and Local Geometric Correspondence (LGC), which estimates spatial correspondence under viewpoint changes. LEV returns structured scalar evidence that the MLLM integrates for the final decision. We further introduce an auxiliary-capped reinforcement fine-tuning (RFT) strategy, in which answer correctness dominates auxiliary tool-use and format signals, enabling the policy to learn when verification is necessary. Experiments show that LocalAgent improves mean accuracy by 4.9% over the strongest CoT-RFT baseline while invoking a verifier on only 12.13% of samples and preserving the general multimodal capabilities of the underlying MLLM. The open-source repository is available at this https URL.
[CV-88] How Representational Priors Shape Generalization in World Action Models
链接: https://arxiv.org/abs/2610.29168
作者: Junha Chun,Youngjoon Jeong,Taesup Kim
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:World action models (WAMs) are shown to substantially diverge under generalization performance based on their generative backbones. We investigate this gap through the representational geometry that WAMs inherit from their backbones and how policy training reorganizes it. Our central hypothesis is that a useful representational prior would structure geometry that separates nuisance information from the in-distribution manifold in advance of training as a policy. We quantify this with nuisance orthogonality; orthogonality of the feature under visual variation outside the empirical in-distribution manifold. Across various image and video backbones, we show that a higher orthogonality from the representational prior is associated with stronger robustness to visual variations after WAM training, enabling policy generalization measurable prospectively for world models. In addition, analyzing the resulting robust WAMs reveals nuisance suppression; relative suppression of feature displacement caused by such variations and robust action generation of the policy. Together, these findings provide a geometric perspective on how the representational prior of the world model shapes the generalization performance of WAMs.
[CV-89] RiO-DETRv2: Geometry-Aware Uncertainty Modeling Meets Real-Time Oriented Object Detection Transformer
链接: https://arxiv.org/abs/2610.29159
作者: Zhangchi Hu,Zilong He,Yifan Zhao,Xiangyu Yu,Jie Chen,Peixi Wu,Yansong Peng,Hebei Li,Xiaoyan Sun
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 7 figures, 10 tables
Abstract:RiO-DETR is the first real-time end-to-end transformer for oriented object detection, yet further localization gains require a more expressive box representation. Although distributional regression has succeeded in horizontal DETRs, extending it to oriented detection introduces geometry-misaligned boundary modeling and topology-inconsistent orientation refinement. We present RiO-DETRv2, a geometry-aware uncertainty modeling framework for real-time oriented object detection. To resolve the geometry mismatch, Object-Aligned Boundary Distribution Refinement represents box boundaries as probability distributions in an object-aligned coordinate system and progressively refines them across decoder layers. For better orientation topology consistency, we introduce Circular-to-Local Angular Residual Refinement that transitions from global periodic search to local fine-grained estimation. Unified OBB Quality-Aligned Learning further aligns classification, boundary and angular refinement with complete-OBB localization quality. Together, these designs improve localization without compromising efficiency, achieving a better balance between speed and accuracy. Specifically, RiO-DETRv2-l/x achieves 53.7/54.1 mAP50-95 on DOTA, outperforming RiO-DETR-l/x by 1.2/1.5 points with nearly unchanged computational cost. Code is available at this https URL.
[CV-90] SICAF: Time-Varying Focus Bottleneck for Self-Supervised Event-Based Optical Flow with Spiking Neural Network
链接: https://arxiv.org/abs/2610.29158
作者: Shuangming Yang,Qing He,Shangqi Guo,Badong Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 27 pages, 6 figures, 10 tables
Abstract:Event-based optical flow estimation achieves high temporal resolution and dynamic range by exploiting asynchronous events, while self-supervised learning provides an effective solution to the absence of ground-truth labels. Spiking neural networks (SNNs), as brain-inspired models, process information through asynchronous spikes, naturally aligning with optical flow events. However, the temporal irregularity across event streams hinders the compact extraction of spatiotemporal features, while the absence of explicit labels leads to spurious correlations, thereby increasing redundancy within latent representations. In this paper, we propose a novel ‘Compactly Compress, Time-varying Focus’ training strategy. We present SICAF (Spikes Information Compression And Focus) framework that formulates the learning process as a constrained optimization with temporal modulation. It encourages self-supervised SNNs to extract compact latent representations while preventing over-compression that would discard temporal saliency. To better enable feature selectivity across time steps, we design an estimator with learnable temporal attention, achieving context awareness with cross-time dependencies. Experimental results on several datasets demonstrate that SICAF achieves state-of-the-art performance in self-supervised SNNs, with a 8.14% improvement in AEE on the MVSEC dataset and a 13.49% increase in robustness under white-box adversarial attacks.
[CV-91] From Augmentation to Synthetic Evidence: Evaluating Generated Chest Radiographs at Dataset Intake
链接: https://arxiv.org/abs/2610.29152
作者: Shaikh Akib Shahriyar,Zinedine Sehili,Adam Arthur,Christopher Schwartz
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:A generated chest radiograph can be useful as declared augmentation, but the same image becomes unsupported evidence when submitted as the record of a real examination. We study this problem by separating three questions: whether an image passes an automated quality check, whether an origin classifier flags it as generated, and whether a trusted record supports the claimed acquisition. In our experiments, Qwen selects up to 20 images from pools of 1,000 existing Cheff-identified or SynthCheX candidates. A receiver evaluates quality with MedGemma and predicted origin with a classifier on frozen DINOv3 features. Quality-based selection raises quality passes from 12 to 19 of 20 for Cheff-identified images and from 8 to 17 for SynthCheX. We find no evidence that selection improves passage through both checks: no selected Cheff-identified image passes both, while uniform, quality, and quality-constrained surrogate selection produce 2, 1, and 2 SynthCheX passes. On unprocessed test images, the origin classifier flags 4,677 of 5,000 SynthCheX images, but it also misclassify 110 of 2,297 NIH (real) images and 568 of 957 genuine images from another collection as fake. Our results further show that JPEG compression reduces SynthCheX-versus-NIH AUROC from 0.987 to 0.841. Even without processing, 152 SynthCheX images pass both pixel checks. These checks can filter submissions, but they cannot verify an acquisition claim. We strongly advocate that source and intended use must be recorded and checked separately for admission of chest radiographs in medical datasets.
[CV-92] DGCM-SSFA: Dual-Granularity Cross-Modulation and Sparse Strip Frequency Attention for Efficient 3T-to-7T MRI Synthesis
链接: https://arxiv.org/abs/2610.29141
作者: Zhichun Fan,Zhiqiang Chen,Lanling Liu,Lingjun Liu,Zhonghua Xie
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by BIBM 2026
Abstract:Synthesizing high-fidelity 7T MRI from routinely acquired 3T scans offers a practical pathway to enhanced neuroimaging without the prohibitive cost of ultra-high-field scanners. While existing deep learning approaches have achieved promising results, state-of-the-art methods typically rely on either large-scale architectures exceeding 45 million parameters or computationally intensive ensemble strategies, limiting their practicality in resource-constrained clinical environments. In this work, we propose a compact multi-domain attention framework, termed DGCM-SSFA, that achieves competitive synthesis quality with only 11M parameters. The proposed architecture integrates two complementary modules into a U-Net Transformer backbone. First, a Dual-Granularity Cross-Modulated Feature Modulator (DGCM) is embedded at multiple hierarchical levels to enhance local edge and texture details through Sobel-guided spatial attention and depthwise frequency-aware channel attention with cross-branch gating. Second, a Sparse Strip Frequency Attention (SSFA) module at the bottleneck captures global spatial context via strip-pooled attention with near-linear complexity, while simultaneously performing FFT-based spectral modulation in the frequency domain. These two modules operate synergistically with the backbone’s native channel attention, forming a triple-granularity feature refinement pipeline. Extensive experiments on the UNC and BNU 3T-to-7T MRI datasets across T1w and T2w modalities demonstrate that DGCM-SSFA achieves strong structural fidelity, attaining an SSIM of 0.8431 on BNU T2w—approaching the best benchmark value of 0.8624—and 0.8100 on BNU T1w, while using substantially fewer parameters than Transformer-based alternatives such as ResViT (123.4M) and FS-RWKV (45.4M).
[CV-93] wo for One: Privileged Learning for Resource-Constrained Screening
链接: https://arxiv.org/abs/2610.29126
作者: Lisa Y.W. Tang
类目: Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
备注:
Abstract:Machine learning systems are often developed under data-rich conditions but deployed when only incomplete information is available. A natural example arises in colour fundus photography (CFP), where both eyes are commonly imaged because they can provide complementary information for ocular disease assessment. Prior approaches that exploit bilateral information either fuse both eyes in a single model or use knowledge distillation to compress a dual-eye teacher into a lightweight student that still requires both images at test time. However, bilateral acquisition may not always be feasible. We thus propose a privileged learning framework in which a teacher model has access to bilateral CFP images during training and learns representations informed by information from both eyes, while the student is trained to transfer this knowledge from the bilateral teacher using only a single-eye image as input. At inference, the student operates exclusively on this single-eye input and does not require the contralateral image. Experimental results show consistent improvements in area under the receiver operating characteristic curve over conventional monocular training, with the largest gains observed for left-eye images for the detection of age-related macular degeneration (0.931 vs. 0.966) and diabetic retinopathy (0.789 vs. 0.836).%, while requiring only a single-eye image at inference. By enabling models to benefit from bilateral information during training while requiring only a single-eye image at inference, our approach may support more reliable deployment in resource-constrained clinical and screening environments.
[CV-94] WaFIR: Wavelet-Fourier Mixture-of-Experts with Subband-Adaptive Routing for Blind All-in-One Image Restoration
链接: https://arxiv.org/abs/2610.29108
作者: Trong Nhan Nguyen,Vu Hoang Minh Doan,Thanh Dat Le,Truong Tien Vo,Thi Thu Ha Vu,Tan Hung Vo,Jaeyeop Choi,Junghwan Oh,Truong-Son Hy
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 24 pages, 15 figures, 13 tables, including supplementary material
Abstract:Blind all-in-one image restoration must adapt a shared model to heterogeneous and sometimes mixed degradations without a task label at test time. We introduce WaFIR, a decoder module that decomposes features into Haar subbands, summarizes each subband with a compact Fourier descriptor, and routes it to prompt-conditioned spectral modulation experts. Unlike image-level frequency conditioning, WaFIR makes routing decisions at the subband level, enabling frequency-specific processing while retaining the task context learned by the backbone. Under a unified training and evaluation protocol for denoising, deraining, and dehazing, WaFIR improves average PSNR over the three tasks by 0.48 dB over the PromptIR baseline and by 0.38 dB over the recent Up-Restorer. Matched-capacity and routing controls show that the gain is not explained by additional parameters alone. On unseen noise intensities, descriptor-based routing is more robust than a discrete label-conditioned alternative and remains on par under compound degradations, while routing analyses show stable subband-dependent expert use across degradation types. These results support subband frequency structure as an interpretable complement to prompt-based conditioning for blind image restoration. Code is available at this https URL
[CV-95] PhyMo: Learning Physical Dynamics with Accurate and Continuous Motion from Multi-View Videos NEURIPS2026
链接: https://arxiv.org/abs/2610.29107
作者: Shangjia Liu,Jinxi Li,Siyuan Zhou,Bo Yang,Bing Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at NeurIPS 2026
Abstract:Recovering dynamic 3D scene geometry, appearance, and the physical motion states that govern scene evolution from multi-view videos is important yet challenging. A key difficulty is that appearance can be deceptive. Under rendering supervision alone, a model may fit the observed images by changing color or opacity to compensate for inaccurate deformation, rather than recovering the true evolving geometry and motion. This issue is especially severe in scenes with fast or locally complex motion, where inaccurate trajectories can still produce plausible images over the observed frames, but lead to unreliable reconstruction and discontinuous future prediction. In this paper, we propose PhyMo, a framework for learning evolving dynamics from multi-view videos. We introduce a Motion-Aware Trajectory Representation that drives the model to recover accurate motion from deformation, rather than relying on appearance shortcuts to explain observed motion. On top of this, we propose a Continuous Motion Evolution module with bidirectional kinematic continuity constraint to promote temporally consistent evolution of displacement, velocity, and acceleration. Together, these designs yield geometrically faithful interpolation and continuous motion extrapolation. Experiments on four dynamic scene benchmarks demonstrate state-of-the-art performance.
[CV-96] DriftNeRF: Cross-Ray Density Drifting for Sparse-View Neural Radiance Fields
链接: https://arxiv.org/abs/2610.29106
作者: Juntang Wang,Yihan Wang,Sheng’en Li,Hao Wu,Dongmian Zou,Shixin Xu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 13 pages, 6 figures, 18 tables
Abstract:Neural Radiance Fields (NeRF) synthesize photorealistic novel views from dense image collections, but degrade sharply when few views are available. We attribute a specific, measurable defect to the objective itself: the rendering loss is a sum over independent rays and contains no term comparing densities that different rays assign to the same region of space, so nearby 3D points can settle on contradictory densities while every training pixel is reproduced correctly. We make this quantity explicit by measuring cross-ray density disagreement directly during training, and show that it stays high in vanilla NeRF, falls only incidentally under frequency-based regularization, and moves together with held-out quality in the sparse regime. We then close the gap with DriftNeRF, a drop-in regularizer that instantiates the drifting-field template on density samples: a masked exponential kernel aggregates densities from nearby points on other rays into a stop-gradient consensus target, and the resulting drifting field penalizes unsupported disagreement while leaving agreed-upon boundaries intact. The regularizer reads only sampled positions, ray identifiers, and predicted densities, so it requires no pretrained model, external geometry, or additional images, and transfers across field parameterizations. On NeRF-Synthetic it reduces residual disagreement by an order of magnitude, and across NeRF-Synthetic, DTU, and LLFF it improves 3-view PSNR over the strongest MLP-NeRF baseline by 0.42-0.65 dB and remains competitive with sparse-view Gaussian pipelines under a matched protocol without their depth, flow, or covisibility preprocessing.
[CV-97] From Canon to Nikon: Camera Domain Shift and Potential Equity Implications in Retinal AI
链接: https://arxiv.org/abs/2610.29104
作者: Lisa Y.W. Tang
类目: Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
备注:
Abstract:Diabetic retinopathy is a leading cause of preventable blindness, yet access to regular screening remains limited worldwide. AI offers a scalable approach, but its performance can be sensitive to image quality and acquisition conditions. In a Brazilian cohort of 8,524 patients, retinal images acquired with the Nikon NF5050 were more than twice as likely to have inadequate image quality compared with those acquired with the Canon CR-2, despite refurbished Canon systems costing more than twice as much as Nikon systems. This potential cost-quality trade-off highlights an important consideration for the reliability of AI systems deployed across heterogeneous imaging systems. Using a frozen pretrained retinal foundation model, we systematically evaluate whether camera-associated performance differences persist across 12 retinal disease classification tasks, four input resolutions, and five downstream classifiers. We find that camera-associated performance differences vary substantially across tasks, and that neither higher input resolution nor classifier choice consistently mitigates cross-camera variation. These findings identify camera platform as a potentially overlooked source of performance variability, with implications for the safety and reliability of retinal AI when deployed across heterogeneous imaging systems. We release all source code, evaluation scripts, and per-task results on GitHub to support reproducible benchmarking.
[CV-98] Diffusion Posterior Sampling with Uncertain Forward Models
链接: https://arxiv.org/abs/2610.29102
作者: Wentao Shangguan,Vivek K Goyal
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Diffusion posterior samplers reconstruct signals by combining a pretrained diffusion prior with a measurement likelihood specified by a forward model and a noise model. Calibration and reference data can characterize uncertainty in the forward model’s parameters, such as blur kernels or detector responses. Fixing these parameters at estimated values leaves their uncertainty out of the likelihood, and parameter errors are thus absorbed into the reconstruction. We introduce an uncertainty-aware likelihood (UAL), a Gaussian approximation to the marginal likelihood obtained by integrating over the uncertain parameters. For each candidate signal, UAL propagates parameter uncertainty into a structured measurement covariance, reducing the quadratic penalty along residual directions that parameter variation can explain. Existing samplers can use UAL in their data updates while retaining their pretrained diffusion priors and sampling schedules. Under a fixed Gaussian nuisance model, we bound likelihood error from linearization and average divergence between the exact marginal and UAL posteriors. We compare samplers with and without UAL across six inverse problems spanning Gaussian and motion deblurring, CT, MRI, ultrasound, and fluorescence microscopy, and find improvements in reconstruction and ensemble prediction. UAL reduces geometric mean MSE by 9% to 20% across samplers in the main Gaussian-deblurring setting and by 51% to 92% in motion deblurring when non-blind samplers use the population-mean kernel. In motion deblurring, DAPS and PnP-DM with UAL also give 6.2 to 6.4 dB higher ensemble-mean PSNR than published BlindDPS and Fast Diffusion EM. On the ablation set, UAL outperforms trace-matched isotropic inflation for PnP-DM in both deblurring tasks; in motion deblurring, it also exceeds the best-tested fixed scalar data weighting by 3.3 dB for PnP-DM and 4.2 dB for DAPS in average PSNR.
[CV-99] UNIVERS: Learning UNIversal Voxel-wise Embedding via Randomized Synthesis for generalizable one-shot landmark localization
链接: https://arxiv.org/abs/2610.29098
作者: Derong Yu,Qingsong Zhao,Yanbing Yue,Yikai Li,Zhi Zhang,Guoyan Zheng
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:One-shot landmark localization in volume scans with varying fields of view (FOVs) is challenging. While existing self-supervised learning methods have shown promising performance, they rely on contrastive learning built upon augmented views of the same image, which inherently limits their generalization in cross-modality scenarios. This motivates research on cross-modality embedding learning using paired multi-modality images. However, existing methods suffer from not only limited training data, but also the requirement of multi-stage iterative training. To address these limitations, we propose UNIVERS, an end-to-end trainable framework that learns UNIversal Voxel-wise Embedding via Randomized Synthesis. Our framework leverages diverse pseudo-data synthesized from organ shape templates, with randomization applied to semantic layout, visual appearance, FOV, and spatial pose. Coupled with cross-domain discriminative voxel-wise contrastive learning, this domain randomization strategy forces the network to disregard domain-specific biases and learn features that are robust against variations in imaging contrast, resolution, and spatial context. Consequently, the trained model enables one-shot landmark localization in multi-modality volume scans with arbitrary FOVs. In this study, the efficacy of the proposed method is demonstrated by applying it to the task of localizing the adrenal glands, which are paired, small, and irregular endocrine organs. In particular, comprehensive experiments are conducted on three challenging adrenal gland datasets. The experimental results demonstrate that UNIVERS consistently outperforms the state-of-the-art (SOTA) one-shot landmark localization approaches, with particularly prominent advantages in cross-modality scenarios. The source code is publicly available at this https URL.
[CV-100] ReSeg: Retraining-free segmentation of abdominal organs via generalizable semantic representation learning
链接: https://arxiv.org/abs/2610.29090
作者: Derong Yu,Guoyan Zheng
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Abdominal organ segmentation is essential in modern medical applications. While existing deep learning approaches demonstrate promising performance, their heavy reliance on large-scale image data raises concerns over generalization capability and privacy risks. This motivates the development of generalizable segmentation training paradigms based on domain-randomized image synthesis, facilitating generalization across unseen real image domains. However, existing paradigms either suffer from limited segmentation performance when applied to abdominal organs, or require dataset-specific finetuning. To address these limitations, we propose ReSeg, a novel two-stage retraining-free framework for domain-agnostic zero-shot abdominal organ segmentation. At the first stage, our organ-agnostic synthetic data-driven pretraining approach exploits semantically and visually diverse synthetic images generated by a dedicated data engine to optimize a Generalizable Semantic Representation Network (GSRNet). The network is thereby capable of learning semantic features robust against variations in anatomical structure, imaging contrast, resolution and spatial context, which provide universal representation capabilities that serve as a foundation for segmentation learning. Subsequently, in the second stage, we continue to train an Organ-specific Encoder-decoder Segmentation Network (OESNet), whose encoder is initialized with the pretrained weights. This training process is facilitated by another dedicated data engine, which generates diversified visual images while strictly retaining the inherent spatial layout of abdominal organs. Comprehensive evaluation on seven abdominal organ segmentation datasets across three different modalities including CT, MRI, and PET demonstrates that ReSeg consistently outperforms existing state-of-the-art (SOTA) methods while eliminating the need for real training images.
[CV-101] OccJEPA: Occlusion as Masking for Amodal Video Segmentation
链接: https://arxiv.org/abs/2610.29061
作者: Genpei Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 20 pages (8-page main paper + supplementary material). Code: this https URL
Abstract:Amodal video segmentation asks for the complete mask of an object in every frame, including the parts hidden behind other objects. The best current methods generate these masks with fine-tuned video diffusion models, which need many denoising steps and an extra depth network, and give a different answer for every sample. Self-supervised video world models of the JEPA family are trained on a closely related task: predicting the representation of masked space-time regions from the visible ones. We therefore treat occlusion as masking. The tokens that the object’s visible mask does not cover are dropped, the pre-trained V-JEPA 2.1 predictor fills them in, and a light head decodes the observed and predicted features into amodal masks. The resulting model, OccJEPA (Occlusion-as-masking JEPA), is deterministic and 33.5x faster than the diffusion-based state of the art, and more accurate on SAIL-VOS, on TAO-Amodal, including a held-out test split (+4.00 AP50) and visible masks tracked by SAM2, and on Kubric. Controlled experiments trace the gain to the predictor’s pre-trained weights rather than to its capacity, for world models from 87M to 1.85B parameters. Code is available at this https URL.
[CV-102] Weakly Supervised Fish Segmentation through Point-Supervised Tracking and Motion-Informed Language
链接: https://arxiv.org/abs/2610.29047
作者: Vedanth Kallakuri,Katherine A. Skinner
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to IEEE OCEANS 2026 Monterey, 7 pages
Abstract:Understanding the properties of fish populations is increasingly important. Analyzing fish length, width, and mass is integral to maintaining healthy stock in the aquaculture industry, while counting wild fish populations supports marine conservation and coastal reef restoration efforts. Deep learning-based segmentation models offer a promising means of obtaining such information at scale. Most of these models, however, require costly and time-consuming per-pixel segmentation labels for training, which can take up to two minutes per fish to annotate manually. To address this inefficiency, we propose a weakly supervised segmentation pipeline that requires only point-labeled data (one click per fish) to produce accurate segmentation masks. Our pipeline consists of four stages. First, a point-supervised tracker predicts point annotations for each frame. These points are then converted into motion-informed language expressions, which guide a language-conditioned segmentation model (LMPM++) to capture temporal consistency and produce candidate segmentation masks. Finally, optical flow and background subtraction are applied as post-processing filters to suppress noise from incorrect segmentations. Experiments on the DeepFish dataset show improvement in foreground mean intersection over union (mIoU) as compared to baseline models.
[CV-103] Single-Image Food Mass Estimators Do Not Transfer Their Gram Scale Across Datasets
链接: https://arxiv.org/abs/2610.29025
作者: Yongsheng Lian
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages, 6 figures, 13 tables. Code, set lists and model weights: this https URL
Abstract:Estimating food mass from a photograph is central to image-based dietary assessment, but whether trained models transfer to new cameras and foods is unclear. We fine-tuned DINOv2-base on 2,253 weighed Nutrition5k dishes to predict mass from one RGB photograph and compared it with a zero-shot vision-language model, Qwen3-VL-32B, on Nutrition5k and three external datasets: ASA24 studio portions, ECUSTFD whole foods and Food Portion Benchmark meal trays. In domain, the fine-tuned model’s mean absolute error was 24.8 g, less than half of Qwen’s. On external datasets it did not beat Qwen. For a food it had never seen, it ranked portions by size, but no better than a count of food pixels. Its gram scale was wrong in two ways: it changed with how much of the scene the photograph showed, and it was off by a different factor for each food. Training on other foods or trays from the tested dataset cut the external error by 38 to 65%. DINOv2-large, a larger backbone trained on the same dishes, did not reduce the external error. One labeled photograph of the same food cut the model’s ASA24 error to 28.3 g, better than that photograph achieved alone. Prediction intervals that held the true mass for 90% of Nutrition5k dishes held it for at most half of external photographs. Nutrition5k often photographed the same plate several times, so a random test split shared near-identical photographs with training and overstated accuracy. Single-image mass estimators therefore need labeled photographs from the setting where they are used.
[CV-104] Enhancing Cross-Grade Generalization in Banana Variety Classification via Hybrid CNN-Transformers and Knowledge Self-Distillation
链接: https://arxiv.org/abs/2610.29008
作者: Khush Kataruka,Mohammad Muzammila,Zahid Akhtar,Raghavendra Ramachandra,Praveen Kumar Chandaliya
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 29 Pages, 9 Figures
Abstract:In recent years, automated fruit classification systems have become a more critical component in precision agriculture for efficient yield estimation, quality control, and supply chain optimization. However, they still face significant challenges under varying quality, fruit maturity, illumination, texture degradation, morphological attributes, domain shift, and post-harvest handling conditions. In this study, we address the challenging issue of cross-grade banana variety classification. This is a real-world setting where models that have been trained on specific quality grades must generalize to unseen grades to handle significant domain shift. Under a rigorous Leave-One-Grade-Out evaluation protocol, we present a thorough comparative analysis of CNNs, ViTs, and dual-stream hybrid architectures using a large-scale dataset of 31,678 banana images with three varieties and three quality grades. Specifically, to address the drawbacks of single-paradigm frameworks, we present a dual-stream Swin ResNet hybrid framework enhanced with KL-divergence-based self-distillation to align probabilistic feature representations across the CNN and Transformer streams. Comprehensive experimental analyzes show that the presented hybrid framework with KL-divergence based self-distillation achieves better overall performance, i.e., it attains a mean accuracy of 81.25% and significantly surpasses CNN and Transformer baseline models. Qualitative analysis further confirms that the hybrid framework in this study improves discriminative feature transferability, improves attention localization, and stabilizes optimization.
[CV-105] -DIG: Text-Conditioned Diversity for Image Generation
链接: https://arxiv.org/abs/2610.29003
作者: Simran Khanuja,Rohit Gandikota,Gaurav Parmar,Graham Neubig
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: under review
Abstract:Text-to-image (T2I) platforms typically return several images per prompt so users can explore different visual interpretations and choose one that matches their intent. However, T2I models are usually optimized for single-image quality which comes at a cost of multi-image diversity, limiting this exploration. Diversity is also contextual: one user wants variations in background, while another wants them in artistic style, and the same user may later want variations in clothing. The generated images should differ along whatever axis the user cares about. In this paper, we introduce T-DIG, a text-conditioned diversity framework for image generation and editing. T-DIG is an iterative framework comprising multiple modules that lets users type prompts, inspect default generations, request variations using natural language, and continue refining outputs until they’re satisfied. At its core, T-DIG uses an LLM policy that produces K axis-conditioned prompts or edit instructions for a frozen generator or editor. We train this policy with GRPO using a text-conditioned diversity reward and alignment penalties. Because the image generator remains frozen, T-DIG applies to open-weight, distilled, and proprietary models. We also release the first dataset for text-conditioned prompt diversity in image generation, with the training and validation splits built from human-written axes and a test set drawn from real user interactions with T-DIG. T-DIG far exceeds baselines in improving text-conditioned diversity, while preserving base-prompt alignment across a variety of metrics and human judgements. We show that user-directed diversity exploration can be added to a wide range of image generation models, open and closed, without the need to access their weights. Website: this https URL
[CV-106] MedBOE: Optimization-Based Estimation of Complete Patient Body Models from Whole-Body Medical Imaging
链接: https://arxiv.org/abs/2610.28985
作者: Hoijoon Jung,Yuhao Wei,Mingxiao Tu,Younhyun Jung,Michael Fulham,Jinman Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Hoijoon Jung and Yuhao Wei contributed equally to this work
Abstract:True whole-body medical imaging captures a patient from head to toe and provides comprehensive anatomical and/or functional information. This coverage enables reconstruction of the external body surface, yielding patient body models. However, scanner geometry (e.g., bore diameter), patient characteristics, and positioning can leave body regions missing (e.g., lateral arms/torso, hands, forearms, elbows), producing incomplete patient body models. Such incompleteness limits downstream medical analyses, including derivation of body-habitus metrics (BHMs), such as body volume and surface area, for injected radiotracer activity regimens, cardiometabolic risk assessment, and computational anatomical modeling for medical imaging simulation and radiation dosimetry. We present the Medical Body model Optimization-based Estimator (MedBOE), an automated, training-free framework for estimating complete patient body models from whole-body images with missing body regions. Our innovation is to leverage the Skinned Multi-Person Linear (SMPL) template, a parametric body mesh representing a broad range of body shapes and poses, in medical imaging. MedBOE aligns the SMPL template to the incomplete patient body models via attribute-guided optimization and estimates missing body regions through geometrically and volumetrically constrained deformation. We evaluated MedBOE using two public datasets: AutoPET (91 whole-body PET-CT images) and Human Implicit Tissues (HIT; 40 whole-body segmentations from MRI images). MedBOE achieved the best overall volumetric and geometric performance among evaluated methods. BHMs derived from MedBOE were within 2% error, suggesting low percentage errors for downstream analyses. MedBOE offers an automated, training-free method for estimating complete patient body models from whole-body images with missing body regions.
[CV-107] EMPL: A Resource-Efficient and Transparent Model-Free Framework for Pseudo-Label Generation in Material CT Scans
链接: https://arxiv.org/abs/2610.28974
作者: Ashiqur Rahman,Venkata Devesh Reddy Seethi,Austin Yunker,Rajkumar Kettimuthu,Hamed Alhoori
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The scarcity of large annotated datasets limits deep learning (DL) segmentation in material CT analysis. DL-based pseudo-labeling can help, but it is often computationally expensive and hard to interpret. We introduce a multi-stage, model-free framework that generates pseudo-labels directly from grayscale CT scans. It combines thresholding, morphological dilation, and a custom breadth-first search region-growing algorithm to produce polygonal annotations without pre-trained weights, labeled data, or a GPU. We compare it against the zero-shot Segment Anything Model (SAM), a fully supervised 3D U-Net, and classical baselines. On a dendrite dataset with ground truth, our method reaches 63.19% IoU versus 10.74% for zero-shot SAM, with four times the instance recall at 0.5 IoU (33.3% versus 8.3%). The supervised 3D U-Net serves as a reference ceiling that requires expert labels. On a silica sand specimen, YOLO models trained on our labels performed comparably to models trained on SAM labels in a qualitative comparison. Across 1,053 extracted grains, the median equivalent diameter was 163.8 micrometers, near the specimen’s known 106 to 150 micrometers sieve range, compared with 219.5 micrometers for SAM, whose labels merge adjacent particles and add spurious detections. A sensitivity analysis shows a stable operating range for the key threshold and a sharp, predictable failure point linked to phase contrast. Our method needs 15s per image on a standard CPU versus 28s for SAM on a GPU. These results support our transparent approach as a tool for annotation bootstrapping and microstructural quantification in resource-limited settings.
[CV-108] RoPT: Robust 6DoF Object Pose Tracking in Video
链接: https://arxiv.org/abs/2610.28962
作者: Yongqing Liang,Yuzhong Chen,Sihang Li,Junsuk Kim,Chase Kidder,Hui-ping Wang,Yunzhi Xu,Raihan Haque,Yubiao Zhang,Chen Feng
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Accurate 6-DoF object pose tracking is essential for robotic interaction, yet conventional monocular pipelines frequently suffer from catastrophic divergence under rapid object motion. We present RoPT, a robust tracking framework engineered to overcome this limitation by unifying visual perception, temporal kinematics, and global re-localization into a tightly coupled closed loop. Unlike existing open-loop trackers, RoPT delivers three critical capabilities that fundamentally alter system resilience. First, the visual perception component tracks object masks to robustly handle partial and severe occlusions. Second, a kinematic factor graph regularizes frame-to-frame updates, forecasts forward pose predictions, and drives a parallel multi-hypothesis verification scheme to bypass local minima. Finally, an active re-localization module transforms the pipeline into a closed-loop system, making it failure-aware and capable of deterministic self-recovery. We demonstrate the effectiveness of our framework across challenging benchmarks alongside real-world robotic demonstrations, achieving significantly higher tracking survivability under rapid motion.
[CV-109] Pair-Free Coarse-to-Real Video Generation via Real-to-Coarse Image Supervision
链接: https://arxiv.org/abs/2610.28954
作者: Gonzalo Gómez-Nogales,Daohan Lu,Peiye Zhuang,Tinghui Zhou,Yi Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Coarse-to-real (C2R) video generation converts coarse animation, simulation, or game-rendered videos into photorealistic videos while preserving scene layout, camera motion, and dynamics. Training such models requires aligned (coarse, realistic) video pairs, which are expensive to create and confine prior systems to narrow synthetic domains. We exploit a simple asymmetry: paired videos are hard to obtain, but paired real-to-coarse images are easy to construct with existing editing and 3D reconstruction tools. Using these image pairs, we train the DINO-R2C model to transfer realistic images to coarse images in DINO feature space, and apply it frame-wise to ordinary real videos to synthesize pseudo-coarse videos. The C2R# model then learns to generate each real video from its pseudo-coarse counterpart, free from needing (coarse, realistic) video pairs. At inference, the DINO-R2C model is discarded and actual coarse videos drive the C2R# model directly. Thus, our pair-free training scales with any real-video corpora rather than with paired synthetic videos. We will release the model and project webpage at this https URL.
[CV-110] MINER: Modeling Inter- and Intra-Person Variance for Reidentification WACV2027
链接: https://arxiv.org/abs/2610.28941
作者: Oron Nir,Eliyahu Strugo,Ariel Shamir
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Paper accepted to the WACV 2027 Algorithms track
Abstract:Clothes-Changing Person Re-Identification (CC-ReID) remains challenging due to drastic appearance shifts across outfits. Most prior works devise biometric signal-extraction heuristics to enhance inter-person variance, e.g., using segmentation masks, while leaving intra-person variance uncontrolled. We present MINER, a simple model-agnostic framework that suppresses garment bias by contrasting hard examples and adaptively balancing dataset-dependent intra- and inter-person variance. Extensive experiments show that MINER improves retrieval across CC-ReID benchmarks, achieving state-of-the-art performance on four datasets and ranking among the leaders in all three tracks of the open TVRID RGB-Depth ReID challenge.
[CV-111] Data-Driven Glyph Positioning for Handwritten Music Synthesis
链接: https://arxiv.org/abs/2610.28899
作者: Elona Shatri,Jiří Mayer,Jan Hajič jr
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 1 biblio page, presented at The 20th International Conference on Document Analysis and Recognition
Abstract:Synthesised data is a common strategy to address the lack of annotated handwritten music scores in Optical Music Recognition (OMR). Smashcima is a recent synthesis framework that assembles pages of handwritten scores by sampling glyph images from MUSCIMA++ and positioning them with hard-coded rules. In this work we ask whether these rules are grounded empirically and whether a learned model for context-dependent positioning can further improve synthesis quality. We extract inter-glyph distributions from around 285 thousand annotated relationships across MUSCIMA++ and OmniOMR, finding that several key parameters deviate systematically from real handwriting. Replacing these constants with empirical means reduces positioning RMSE by up to 60%. We then train a Graph Neural Network (GNN) on the Music Notation Graph structure to predict context-dependent offsets, achieving an additional 50 to 85% RMSE reduction on stems and beams by modelling how neighbouring glyphs constrain placement. Cross-dataset analysis shows that spacing differences between the two corpora are explained by note density (3.3), not style, and that duration-proportional spacing does not hold in handwriting (R2 = 0.001). In downstream evaluation, a YOLOv8 detector pretrained on GNN-positioned synthetic data achieves +30% mAP50 over the baseline and +11% over empirical means when fine-tuned on 200 real handwritten staves, showing that positioning accuracy matters for downstream OMR.
[CV-112] Policy-Mask: Budget-Aware Adaptation of Diffusion Features to Discriminative Tasks
链接: https://arxiv.org/abs/2610.28889
作者: Ivan Listopadov,Maksim Nakhodnov,Aibek Alanov
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Selecting intermediate features from a frozen diffusion model for discriminative tasks is non-trivial because the useful signal is distributed across the denoising trajectory, network depth, and channels. In practice, this requires choosing a timestep, a block, and a channel subset. Existing methods either fix these choices by hand or optimize only a few extraction hyperparameters, most often the timestep, then reuse the same rule across inputs and budgets. We introduce Policy-Mask, a budget-conditioned policy trained on the downstream objective that jointly selects all three under a prescribed channel budget. Without normalization, the channel-score contribution grows with the number of selected channels and dominates the timestep and block updates. We normalize it by subset cardinality, keeping the three policy heads on comparable scales and stabilizing joint training. At matched budgets, Policy-Mask improves over fixed extraction and feature-filter baselines across segmentation, classification, and semantic correspondence, reaching 38.9 mIoU on ADE20K, 75.4% top-1 on CIFAR-100 at 128 dimensions, and 76.3 PCK@0.1 on SPair-71k. Component-wise ablations show that block selection materially affects downstream accuracy, and the highest recorded channel-budget score occurs at 256 retained channels, identifying a practical operating point for compact diffusion representations. We further adapt Policy-Mask to diffusion transformers, including PixArt- \Sigma and SD3.5 MMDiT, by replacing U-Net channels with image-token hidden dimensions.
[CV-113] Real2Sem2Real: 3D Semantic Augmentations for Robot Learning from a Single Human RGB Demonstration
链接: https://arxiv.org/abs/2610.28880
作者: Matthew Strong,Ankush K. Dhawan,Julien Poffet,Giuse Pham,Jinho So,Monroe Kennedy III
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Matthew Strong and Ankush K. Dhawan contributed equally. Project page: this https URL
Abstract:Human video scales easily and does not need robot hardware, but existing methods require depth sensing, overlay the robot onto 2D video, or generate data only for the demonstrated objects. We present Real2Sem2Real (R2Sem2R), a real-to-semantic-to-real pipeline that turns one monocular RGB video of a human demonstration into thousands of robot demonstrations across the semantic object instances of a category. R2Sem2R tracks the SE(3) pose of each object with DINOv3-distilled Gaussian splats, outperforming existing methods and FoundationPose with estimated metric depth. R2Sem2R then aligns digital cousins, each generated from one or a few photographs, to the demonstrated objects with a semantic alignment. This alignment matches whole objects and the functional regions of target objects, such as a coffee machine’s tray. R2Sem2R maps the human hand to a feasible robot grasp that extends to the cousins. Robot demonstrations are generated either in IsaacSim, for photorealistic rendering, or MuJoCo, for contact physics, and R2Sem2R can generate over 2,000 episodes in about two hours. On two real robots and four tasks, we train a vision-language-action policy on data from one human video per task, and policies trained on our data match or exceed policies trained on teleoperation across diverse object instances. The project page is available at this https URL.
[CV-114] DualHeads: Reward-tilted few-step diffusion made simple
链接: https://arxiv.org/abs/2610.28858
作者: Yuanzhi Zhu,Eleftherios Tsonis,Simo Alami Chehboune,Xi Wang,Stéphane Lathuilière,Vicky Kalogeiton
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Distilled diffusion models generate high-quality images in a few sampling steps, but aligning them with task requirements and human preferences is difficult. Methods developed for multi-step diffusion models rely on differentiable rewards, or on score fields and stochastic trajectories that distilled generators may lack. We ask whether a few-step generator can be adapted using only external reward evaluations and reference samples, and show that two binary classifiers suffice. Our method, DualHeads, implements them as lightweight heads on a frozen diffusion backbone. The reward head learns which samples of the same prompt score higher, the reference head separates reference images from generated ones, and the generator maximizes a weighted sum of their logits. When the heads are optimal, this objective is KL-regularized maximization of the learned reward, and the ratio of head weights sets the regularization strength. Empirically, with both rule-based and model-based rewards, a few-step DualHeads generator reaches task rewards close to those of 40-step reinforcement learning methods while preserving image quality, at no extra inference cost. We hope that this simple and elegant dual-head formulation will help advance few-step reward-tilted generation.
[CV-115] Depth-guided Geometric Adaptation for Few-shot Image Classification WACV
链接: https://arxiv.org/abs/2610.28856
作者: Zhiqi Miao,Estefanía Talavera,Weichuan Zhang,Jiapan Guo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This work is accepted by The IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2027
Abstract:Few-shot image classification (FSIC) frequently suffers from visual ambiguity, where background clutter and RGB appearance similarities hinder discriminative feature matching in data-scarce settings. To mitigate this, we propose DAFIC, a geometry-guided framework that incorporates monocular depth into representation learning, episodic adaptation, and dense support-query matching. DAFIC first learns geometry-aware token representations through RGB-D self-supervised pre-training. Subsequently, the Depth-guided Geometric Adaptation (DGA) module uses depth-derived geometry to refine patch representations with geometry-enriched class context while suppressing background interference. Spatial Bidirectional Patch Matching (SBiM) then performs bidirectional dense matching on the refined features, reducing sensitivity to spurious correspondences and background clutter. Extensive experiments on four benchmarks show that DAFIC achieves strong performance against existing methods, with ablation and robustness analyses supporting the effectiveness of coupling geometry-conditioned episodic adaptation with bidirectional matching for few-shot visual recognition.
[CV-116] Sketch2Sim: an end-to-end modular framework for hand-drawn circuit analysis
链接: https://arxiv.org/abs/2610.28836
作者: Mohammad Kawsar,Al Amin Islam Ridoy,Md Rakib Hossain,Farzana Akter,AKM Bahalul Haque,Rejwan Bin Sulaiman,Mohammed Amin Almaiah
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:This study proposes a sketch-to-simulation system to solve handwritten circuit diagrams. The system converts raw images into a Simulation Program with Integrated Circuit Emphasis (SPICE)-compatible netlist and automatically calculates the circuit’s nodal voltages and currents. Another objective is to propose a holistic framework that benefits students and educators by enhancing the teaching and learning experience of electric circuits and expediting engineering design reviews. This article presents a multistage pipeline that converts raw photographs of handwritten electric circuits into executable SPICE-like netlists, providing a solution. A dataset of 1,000 hand-drawn Resistor-Inductor-Capacitor (RLC) circuits was curated and annotated for components, text regions, and character recognition. The proposed system decomposes a circuit diagram into three stages: (i) dual You Only Look Once (YOLO)-based detectors for components and text regions, (ii) a Convolutional Recurrent Neural Network (CRNN) with Connectionist Temporal Classification (CTC) and Optical Character Recognition (OCR)-based domain-aware symbol value extraction, and (iii) image processing with thresholding, skeletonization, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to automatically identify nodes and map them with corresponding components. The proposed system produces the netlist and solves the circuit. The qualitative and quantitative ablation studies on modular stages, as well as end-to-end tests, demonstrate robust performance across various handwriting styles, lighting conditions, and this http URL study serves as an interactive learning aid and practical tool for professionals, providing a comprehensive user interface (UI), enabling rapid digitization of circuit sketches for study, design, prototyping, or troubleshooting.
[CV-117] Conditional Imaging for Privacy-Preserving Visual Sensing
链接: https://arxiv.org/abs/2610.28835
作者: Hengyu Lian,Debabrata Mandal,Anzhou Wen,Yujie Wang,Xi Peng,Praneeth Chakravarthula
类目: Computer Vision and Pattern Recognition (cs.CV); Optics (physics.optics)
备注: 20 pages, 7 figures
Abstract:Conventional visual sensing faces a fundamental dilemma: information that should remain private today may need to be recovered tomorrow. This challenge is becoming increasingly important as cameras become pervasive in our daily surroundings. Existing safeguards either protect visual content only after an image is formed or suppress information during capture in ways that prevent its later recovery. Here, we show through an information-theoretic analysis that privacy and recoverability cannot be achieved simultaneously in static optical systems. Information preserved in a measurement for later reconstruction also remains available to an observer of that measurement, limiting the privacy such systems can provide. To overcome this limitation, we introduce conditional imaging, an imaging approach that changes the optical state for every captured frame. The measurement alone conceals the scene, while knowledge of the corresponding physical system state enables its reconstruction. We analyze how the optical parameters determine the information that can be recovered and use this analysis to guide the design of a camera prototype. We evaluate the system on a captured dataset of human faces using increasingly powerful attempts to recover the scene, from direct visualization and computational inversion to learned reconstruction and identity recognition. Our experiments show that conditional imaging preserves identity in authorized reconstructions while withholding visual information from observers without access to the system state. By making the physical state of the camera part of the information needed for reconstruction, conditional imaging enables visual information to be protected at the point of capture while remaining recoverable for future authorized use.
[CV-118] HorizonComposer: Spatiotemporally Consistent Driving Video Editing with Enriched Traffic Semantics
链接: https://arxiv.org/abs/2610.28829
作者: Mauricio Soroco,Yuqiu Liu,Zaid Tasneem,Francesco Pittaluga,Abhishek Aich,Wuyang Chen,Manmohan Chandraker,Ziyu Jiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Instruction-guided video generative models offer a scalable solution for stress-testing autonomous driving systems by simulating diverse environmental conditions. However, driving scenes operate within a dense Operational Design Domain (ODD) governed by strict traffic semantics. When applied to global driving-condition edits, general-purpose instruction-guided video editors frequently corrupt these semantics, leading to structural hallucinations and ``temporal shock.‘’ This work distills and protects traffic semantics during video editing at three levels. First, we construct a hybrid dataset by combining temporally aligned real-world videos with synthetic edits, balancing foundational scene structure with unambiguous appearance signals. Second, during training, we adapt advantage conditioning from offline reinforcement learning to align the model using dense rewards. We extract dense, pixel-level rewards from the hybrid data to serve as a localized signal and actively steer generation toward structurally reliable pixels. Finally, our model mitigates temporal shock by introducing thinking frames, a transition mechanism that seamlessly reconciles static image conditioning with dynamic video context. Extensive experiments demonstrate that our method prevents the corruption of multi-actor dynamics, establishing state-of-the-art spatiotemporal consistency, yielding consistent user preference (39% relative improvement in content preservation and 12% in temporal consistency), and achieving a significant 5% gain in downstream road line segmentation and 7% gain in downstream multi-object detection.
[CV-119] WorldStream: Streaming Interactive 3D Worlds with Hierarchical Planning and Persistent State
链接: https://arxiv.org/abs/2610.28827
作者: Xiaoxiao Ma,Weiyang Jin,Yanwen Ma,Haoran Li,Zhixiang Wei,Songchun Zhang,Yaowei Li,Junhao Zhuang,Zeyue Xue,Yuming Li,Zheming Fu,Haoyang Huang,Nan Duan,Feng Zhao
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: 19 pages, 18 figures
Abstract:We present WorldStream, an open-ended 3D world model with three simultaneous capabilities: it extends playable terrain beyond any pre-specified map, acts on open-ended language in the running world, and restores visited places with their edits. A hierarchical planner over a typed decision model emits structured decision state: Macro fixes settlement, ecology, transport, and hydrological structure across a group of tiles; Micro specializes that contract into tile semantics under measured terrain and accepted neighbor commitments. Deterministic local code compiles accepted plans into terrain, roads, settlements, vegetation, and entities. Streaming separates their reach: plans run ahead along the predicted corridor, compilation only where movement will soon need it, geometry behind the user released. One world record retains plans, seeds, object identities and mutable state, boundary commitments, and edits by attribute, revised as the user advances and edits; returning tiles recompile from it. The same record grounds interaction: the model acts on existing entities, including stateful NPCs that update as they act, edits the present region, or attaches intent to unplanned regions. Together they yield a world expanding under bounded budgets, connected across independently planned tiles, and interactive.
[CV-120] Register Anything Model for Generalizable and Robust Point Cloud Registration NEURIPS2026
链接: https://arxiv.org/abs/2610.28821
作者: Zheng Qin,Junhua Xi,Yan Huang,Jiahong Lai,Anwen Huang,Qiong Li,Guangda Zhang,Kai Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by NeurIPS 2026. Code and models are available at this https URL
Abstract:We present Register Anything Model (RAM), a unified model for generalizable 3D point cloud registration that robustly estimates 6-DoF transformations across diverse domains spanning sensors and environments. Existing registration methods are typically tailored to a single domain, with carefully tuned architectures and hyperparameters, and thus degrade markedly when applied to novel domains. We bridge this gap by training a single model on diverse data from indoor, outdoor, and object domains. A central challenge is the severe geometric discrepancy across domains, i.e., differences in scene scale, sensor noise, point density, and structural patterns, which hinders processing with a unified point-based model. To address this, we introduce a geometry-aware normalization strategy that jointly voxelizes and normalizes point clouds into a standardized geometric space, adaptively based on their geometric properties to preserve salient local structures while standardizing resolution. Built on this normalization, RAM follows a hierarchical coarse-to-fine paradigm, and employs an efficient geometric transformer that models local geometric consistency instead of globally over all points. This design mitigates discretization noise introduced by voxelization, and improves computational efficiency. Extensive experiments on 12 benchmarks demonstrate state-of-the-art performance in terms of inlier ratio and registration recall. Notably, our method outperforms the counterparts trained on each separate domain, showing superior generalization capability.
[CV-121] SAIL: A Learnable Generalisation of Spatial Autocorrelation over Graph-Structured Data
链接: https://arxiv.org/abs/2610.28806
作者: Piotr Keller,Fayyaz Minhas
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 44 pages, 22 figures (7 in main paper, 15 in supplementary material). Main paper followed by supplementary material in a single file
Abstract:In vision and scientific domains involving spatial data, a key question is whether node feature values vary independently across space or form coherent spatial structures. Classical spatial autocorrelation statistics such as Moran’s I and Geary’s C quantify this phenomenon but operate on a single scalar variable with fixed neighbourhood weights, limiting their use for high-dimensional graph-like data. We introduce SAIL (Spatial Autocorrelation Index Learner), a differentiable framework that generalises spatial autocorrelation into a learnable operator by jointly learning feature projections defining candidate scalar fields and inter-node neighbourhood affinities. SAIL produces interpretable local and global spatial coherence descriptors, enabling supervised prediction and unsupervised discovery of spatial autocorrelation modes and hotspots. We show that SAIL exactly recovers classical spatial statistics under appropriate parametrisations and, for tasks driven by latent spatial autocorrelation, can approximate the Bayes-optimal predictor with an inductive bias matched to such tasks. Experiments on synthetic data and large-scale biomedical graphs demonstrate statistical correctness and discovery of spatial coherence with applications across vision, geospatial analysis, and biomedicine. The code repository is available at: this https URL.
[CV-122] JUICE: Unified Multimodal Modeling in Pretrained Representation Spaces
链接: https://arxiv.org/abs/2610.28801
作者: Jiahan Li,Huangyuan Su,Shengbang Tong,Shusheng Yang,Ruihan Guo,Suozhi Huang,Hexu Zhao,Pratyusha Sharma,Sherry Yang,Jiatao Gu,Rob Fergus,Kyunghyun Cho
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 69 pages, 31 figures, 37 tables. Project page: this https URL Code: this https URL Weights: this https URL
Abstract:Most unified multimodal models treat images and text asymmetrically: a pretrained language model generates text, an attached diffusion head generates images, and outputs that contain both are produced in a fixed order, so the first output cannot adapt to the second. We ask whether both modalities can instead be generated by one process that is trained on them jointly from scratch. Pretrained encoder spaces hold both modalities in one continuous form and carry knowledge learned at scale, and recent work finds them effective as generative targets for vision and language. We therefore model the joint distribution directly in these spaces. We introduce JUICE (Joint Unified multimodal modeling In Continuous Encoder spaces), a single flow matching Transformer over frozen image and text representations, in which each modality has its own noise level. Which modalities are clean and which are generated then decides the task: image generation, captioning, visual question answering, or joint generation. Controlled pretraining runs test each design choice. At equal guidance, generating text as continuous states leaves image generation intact (48.41 GenEval, against 47.86 for a model trained only for text-to-image generation), while captions lose some exact wording. Training on joint and conditional denoising together beats either alone, and semantic encoder spaces beat VAE latents and raw pixels as targets. At 2.1B parameters, with no language model initialization and at 224x224, JUICE reaches up to 0.88 on GenEval and 73.1 on VQAv2. By sampling both modalities from the joint distribution (coupled sampling), it can also draw a route on a map while writing out the matching moves. On mazes with several valid solutions, joint success rises from 54.6% with independent sampling to 93.7%, and no modality has to go first. On visual spatial planning, JUICE solves 99.8% of maps at its training sizes.
[CV-123] Complexity-Aware Surgical Video Reasoning Segmentation with Digital Twin and Context Memory
链接: https://arxiv.org/abs/2610.28776
作者: Yiqing Shen,Mathias Unberath
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Conventional surgical segmentation relies on closed-set categories or explicit text queries, which cannot resolve implicit queries that require reasoning about workflows, instrument functions, and temporal relationships. To address these limitations, we propose SurgReasonSeg, a complexity-aware method that brings reasoning segmentation over digital twin (DT) representations to surgical video. Specifically, SurgReasonSeg constructs DT representations from surgical foundation models and trains two low-rank adapters (LoRA) of one large language model with reinforcement learning (RL), each specialized for a different query complexity. A lightweight router, also trained with RL, selects between the two adapters to balance latency against accuracy. SurgReasonSeg also sets the temporal context with an adaptive sliding window conditioned on query complexity and workflow phase, and retrieves relevant historical cases from a surgical context memory by workflow similarity and procedural relevance. We further introduce EndoVis-ReasonSeg, a surgical reasoning segmentation benchmark with 1,460 training and 961 test samples across semantic, spatial, and temporal categories. On EndoVis-ReasonSeg, SurgReasonSeg improves JF over the strongest baseline, ReSurgSAM2, by 0.178, 0.237, and 0.297 on semantic, spatial, and temporal queries, and it also achieves the best results on the Ref-EndoVis17 and Ref-EndoVis18 referring segmentation benchmarks. A backbone-matched attribution protocol separates the contribution of the upstream segmenter from that of the reasoning modules. Our method runs at 18.8 FPS averaged over the query distribution, enabling real-time reasoning segmentation in surgical videos.
[CV-124] Motion Equivariant Convolutional LSTM: When Motion Becomes a Gauge Symmetry
链接: https://arxiv.org/abs/2610.28771
作者: Vahid Hosseinzadeh,T. Anderson Keller,Michael Puthawala
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Code: this https URL
Abstract:Objects move, cameras pan, and weather drifts with the wind: much of what changes in sequential data is motion. Motion changes where things are, not what they are, and is therefore a symmetry of the data. Yet standard recurrent networks are not equivariant to it: their memory stays put while the input moves on. Flow equivariant recurrent networks first brought this symmetry into sequence models, for motions of constant velocity, or flows, by enumerating a bank of velocities. We extend this idea in three directions: from simple recurrent networks to convolutional LSTMs, from flows to motions whose velocity changes in time, and from enumerating velocities to estimating them. Treating a sequence as a field over time, we show that motion becomes a gauge symmetry: a global symmetry made local in time. Built on this view, our motion equivariant convolutional LSTM (MELSTM) uses its velocity model as the gauge field and is provably equivariant to every motion. Across future-frame prediction, classification of digits visible only through their motion, and action recognition under camera shake, MELSTM outperforms a convolutional LSTM and its flow equivariant counterpart with the same number of parameters: it is more accurate, generalizes to longer horizons and to unseen motions, and carries a fraction of the flow equivariant model’s state.
[CV-125] Different Defects Different Effects: A Controlled Audit of Face Recognition Training Data WACV2027
链接: https://arxiv.org/abs/2610.28766
作者: Ana Dias,João Ribeiro Pinto,Hugo Proença,João C. Neves
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at WACV 2027
Abstract:The development of face recognition has been strongly supported by the availability of large-scale training datasets. Among them, MS-Celeb-1M has played an important role by providing millions of facial images from nearly 100K identities. However, the size and heterogeneity of such datasets increase the likelihood of annotation errors, redundant samples, and other forms of data inconsistency. This motivated the introduction of refined versions, such as MS1MV2 and MS1MV3, aimed at improving the quality of the training data. Despite these cleaning efforts, we show that several types of training-data defects remain in MS1MV3. By proposing complementary data-auditing strategies, we identify five defect categories: exact and near-duplicate images, split identities, crop errors, multiple-face ambiguity, and invalid content. We then assess the effect of correcting these defects under a controlled face-recognition training protocol based on IResNet-50 and ArcFace. The results show that targeted interventions can preserve, and in some cases improve, recognition performance while requiring fewer optimization steps to reach the performance of the original training set. Duplicate removal provides the clearest overall benefit, while split-identity merging and invalid-content removal also improve training efficiency. Overall, the results indicate that the effect of dataset cleaning depends strongly on the type of defect being addressed and provide further evidence that training-data quality remains an important and comparatively underexplored factor in large-scale face recognition. The training manifests and code used in this work will be released upon acceptance.
[CV-126] Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds
链接: https://arxiv.org/abs/2610.28698
作者: Kuan Zhang,Yukun Chen,Zhihao Yang,Yue Su,Xiangnan Wu,Zirong Chen,Run Luo,Jinkun Hou,Tao Tan,Yinhe Zheng,Yiming Li
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Project page: this https URL ; Model: this https URL
Abstract:Scaling data, diversity and model capacity has improved embodied learning in physical environments, where diverse experiences remain grounded in shared physical laws. Whether these gains extend to virtual worlds with different visual styles, dynamics, controls, and objectives remains unclear. We investigate this question through a controlled study of continued pre-training (CPT) across three axes: data volume, diversity, and model size. Our 120 training configurations span four model sizes from 0.8B to 27B, six data budgets from 50 to 1,600 hours, and five mixtures ranging from Minecraft-only data to diverse multi-world corpora. We assess performance and generalization using three-level validation losses and gameplay benchmarks from in-corpus worlds to unseen worlds. Our results show: (1) more data improves in-corpus performance, diversity improves transfer to related worlds with limited gains in distant worlds, and model size improves data efficiency but generalizes worse to distant worlds under single-world training; (2) we observe an early interface phase with broad cross-world benefits and a later world phase with gains narrowed to in-corpus and related worlds, and fit an empirical scaling law relating validation loss to data volume, diversity and model size; and (3) our models are competitive with Minecraft specialists and frontier models in 3D worlds, and perform best in real-time settings. However, the limits of imitation and persistent gaps in distant worlds suggest that building generalist embodied agents requires richer training stages beyond continued pre-training.
[CV-127] Existence of Minimizers for the Gaussian Mumford-Shah Model via Covariance Floors on the Value Distribution
链接: https://arxiv.org/abs/2610.29949
作者: Faian Conrad
类目: atistics Theory (math.ST); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The Gaussian Mumford-Shah model segments an image into regions by minimizing a continuous multivariate Gaussian negative log-likelihood term plus a perimeter penalty. It is used for scalar- and vector-valued images, in particular hyperspectral images, but the existence of a minimizer in general is not guaranteed. The energy is unbounded below whenever the covariance of a single region degenerates, regardless of the perimeter of the regions. A quantitative covariance floor on the covariances of all regions, or equivalently uniform Hölder continuity with exponent at most one of all one-dimensional marginals of the histogram, guarantees a lower bound on the energy and the existence of a minimizer. Our approach therefore shows that existence follows from a property of the histogram of the image, and we furthermore argue that scale-independent covariance floors cannot exist. Admitting a quantitative covariance floor is generic: it holds almost surely for all images with suitable noise. As applications of our theory, we show the convergence of regularized models used in practice and of numerical methods.
[CV-128] rnScope: Model-Hardware Co-Design for Efficient Ternary Endoscopic Segmentation
链接: https://arxiv.org/abs/2610.29617
作者: Zhuoyu Wu,Wenhui Ou,Qiawei Zheng,Xuhong Qiu,Wenqi Fang,Raphaël C.-W. Phan
类目: Image and Video Processing (eess.IV); Hardware Architecture (cs.AR); Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, 4 figures, 3 tables. Code and experimental results: this https URL
Abstract:Edge medical segmentation must reconcile small model size, high accuracy, and fast inference. TernScope addresses this tradeoff through model–hardware co-design, learning a compact segmentation network whose low-bit operators also simplify hardware execution. Split-channel large-kernel blocks preserve spatial context with limited computation, while branch fusion removes redundant inference paths. Ternary Progressive Quantization-aware Training (TPQ) then adapts the fused network from deep to shallow layers, through the decoder, and finally to the input and prediction layers. All 25 convolutions use ternary weights and 8-bit activations, enabling packed storage and multiplier-free computation. The resulting model uses 0.2483 M parameters and 0.0949 GMACs while retaining competitive segmentation accuracy on in-domain and external datasets. Under the same 45 nm technology, TernScope reduces compute-array area by 88.46% compared with the original Gemmini, a systolic-array accelerator with 8-bit \times 8-bit multipliers. These results demonstrate a path from accurate lightweight medical segmentation to compact low-bit hardware.
[CV-129] Can Quantum Layers offer Performance and Security Advantages in Vision-Language Models?
链接: https://arxiv.org/abs/2610.28964
作者: Md Khalid Syfullah,Alvi Ataur Khalil
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:As quantum computing moves from the noisy intermediate-scale quantum (NISQ) era toward early fault-tolerant quantum computing (EFTQC), quantum-enhanced AI is shifting from a conceptual idea to experimental research. Quantum components are expected to extend the capabilities of existing AI systems, yet it remains unclear how such components will help within large multimodal models. In this study, we try to answer that question for vision-language models (VLMs) using classically simulated quantum layers. With LLaVA-OneVision as the backbone, we evaluate 4- and 8-qubit basic-ring and strongly entangling circuits at four insertion and four replacement locations. Insertion adds quantum layers to the classical model, while replacement substitutes them for classical components. Using the same setup, we compare performance and computational cost between classical and quantum-classical hybrid models. On the Visual Spatial Reasoning dataset, inserting a quantum layer after the visual projector reaches 80.7% development accuracy, 2.28 percentage-points above classical full fine-tuning. Replacing the final decoder feed-forward sublayer retains 79.22% accuracy with 13.07 million fewer trainable parameters. Quantum insertion can thus improve accuracy, and selective replacement can reduce parameters. These benefits depend strongly on where the quantum layer is inserted or which classical component it replaces: earlier replacements lower accuracy, and fewer parameters do not reliably shorten training or reduce computation. To assess potential security benefits, we evaluated the robustness of quantum-classical hybrid models under six attack settings. Overall, our study maps where quantum layers help and hurt within VLMs, offering a starting point for hybrid VLM design; whether these gains generalize remains open. The code is available at this https URL.
人工智能
[AI-0] he End of Consensus Research?
链接: https://arxiv.org/abs/2610.30114
作者: Clément Burgelin,Rachid Guerraoui,Zicong Hong,Yinan Hu,Anne-Marie Kermarrec,Rafael Pires,Rasmus Moorits Veski,Martijn de Vos
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注: 39 pages
Abstract:Consensus is a fundamental problem in distributed computing and at the core of state machine replication (SMR). Despite decades of active human research, discovering practical low-latency solutions to the problem is still open, as it requires combining protocol knowledge, correctness reasoning, implementation, and experimental evaluation. We report on how AI helped us discover A-greement, a new consensus protocol for geo-replicated state machines that significantly outperforms state-of-the-art solutions. Averaged across six deployments with 5 or 9 replicas, A-greement reduces the mean latency of a geo-distributed key-value store over the fastest baseline by 12.2% and 33.3% on write-heavy and read-heavy workloads, respectively, and matches the fastest baseline on read-only workloads. A-greement combines lease-authorized local reads, direct client learning for leader-ordered writes, and a fast fixed-quorum per-key write path, selected according to network topology and workload. At the heart of our discovery lies AI-greement, an agent harness for the “autonomous” discovery, implementation, experimentation, and verification of candidate protocols. AI-greement integrates (1) a concept-oriented protocol knowledge base, (2) a modular implementation skeleton, and (3) a Lean-based verification framework with reusable safety definitions, reference specifications, and proofs. AI-greement is of independent interest: its Lean formalization uncovers several previously unreported vulnerabilities in existing consensus protocols, and it provides guidelines on how domain-specific harnesses can turn general-purpose coding agents into effective tools for (distributed) computing research. Yet we argue that human consensus experts remain essential before, during, and after the search: agents reshape the researcher’s role rather than end consensus research.
[AI-1] Asymmetric On-Policy Distillation Learns In-Context Exploration
链接: https://arxiv.org/abs/2610.30105
作者: Sriyash Poddar,Yanda Bao,Jacob Krantz,Matthew Chang,Xavier Puig,Roozbeh Mottaghi,Natasha Jaques,Abhishek Gupta
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Sequential decision-making under partial observability is difficult because agents must explore to infer hidden, task-relevant information before acting effectively. Learning such information-gathering behaviors tabula rasa via online reinforcement learning is sample-inefficient, whereas offline imitation averages expert actions across hidden states and fails to learn exploration. In this work, we propose ASTEROID, a simple and effective student–teacher supervised learning framework that can learn test-time exploration. Although a privileged expert never needs to explore, we demonstrate that a history-conditioned student can learn to do so when trained with on-policy contexts: the student first collects on-policy context under partial observability, and the privileged expert provides action labels conditioned on that context. By distilling expert actions directly conditioned on the student’s on-policy history, the student learns to systematically resolve partial observability, i.e., search while uncertain and exploit once the hidden state is revealed. This enables efficient test-time exploration (similar to Bayesian posterior sampling) without incurring the impractical training-data costs of reinforcement learning. Across a diverse range of eleven different environments, including discrete exploration benchmarks, robotic manipulation, navigation, and Procgen environments, ASTEROID achieves ~2x higher returns under fixed exploration budgets, uses ~100x fewer interactions than RL baselines, and outperforms frontier models at exploration. Finally, our method transfers policies trained in simulation to vision-denied real-world robot manipulation, exhibiting efficient exploration and increasing success by +40% over standard distillation. Overall, it provides a simple yet surprisingly effective framework to learn efficient test-time exploration.
[AI-2] BatchBench: Benchmarking AI Robustness to Batch Effects and Domain Shift in Biomedical Images - A JUMP Cell Painting Leaderboard
链接: https://arxiv.org/abs/2610.30104
作者: Sonja Topf,Ana Sanchez-Fernandez,Günter Klambauer
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Despite recent advances, current AI systems remain highly susceptible to domain shifts, particularly in biomedical imaging, where images are usually acquired under substantially different experimental conditions. Although recent domain adaptation methods have made substantial progress, significant performance gaps under domain shift persist. To measure progress in this regard, we introduce BatchBench, a leaderboard that evaluates and compares the robustness of AI systems on biomedical images from unseen experimental batches and data sources. BatchBench is built on JUMP-CP, one of the largest publicly available biomedical imaging datasets. Generated using the Cell Painting assay, JUMP-CP exhibits pronounced batch effects and domain shifts, making it a challenging testbed for evaluating robustness to distributional changes.
[AI-3] Differentially Private Sparse Reward Estimation with Preference Feedback
链接: https://arxiv.org/abs/2610.30099
作者: Meng Ding,Mingxi Lei,Jie Zhang,Jinyan Liu,Di Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 36 pages
Abstract:Reinforcement Learning with Human Feedback (RLHF) has become a central paradigm for aligning Large Language Models with human values. However, the reliance on sensitive preference data necessitates rigorous differential privacy (DP) guarantees to prevent user information leakage. Existing private alignment methods typically assume dense reward parameterizations, resulting in estimation error bounds that scale polynomially with the ambient model size d . Such dependence is prohibitive in modern overparameterized models where d is vast. In this work, we study \emphPrivate Sparse RLHF problem to address this limitation, assuming that human preferences (reward model) are governed by a subset of s^* \ll d relevant features. We provide the first comprehensive theoretical framework for differentially private sparse reward estimation from pairwise preference feedback under the Bradley-Terry-Luce model, and introduce efficient algorithms for both Sample-DP and Label-DP settings. Theoretically, under the squared \ell_2 parameter estimation error, we establish upper bounds of \widetildeO(s^/n + s^^2/n^2\varepsilon^2) for (\varepsilon,\delta) -Sample-DP and \widetildeO(s^/n + s^/n\varepsilon^2) for \varepsilon -Label-DP based on randomized response, where n is the training data size. For the lower bounds, we show that the Sample-DP rate is nearly optimal, and that the guarantee for our Label-DP method cannot be further improved within the randomized-response framework. We also provide a general lower bound for the problem with Label-DP. Empirically, our framework demonstrates superior efficacy over dense baselines across sentiment generation and safety alignment benchmarks, offering a significantly more favorable privacy-utility trade-off.
[AI-4] Charts as Epistemic Actions for Experience-Driven Agent ic Data Analysis
链接: https://arxiv.org/abs/2610.30072
作者: Xinzhe Wang,Xudong Weng,Zang Li,Shaolei Zhang
类目: Artificial Intelligence (cs.AI)
备注: 23 pages, 5 figures, 9 tables. Preprint
Abstract:Data agents built on ReAct-style loops now execute complex analytical workflows over structured data, yet their intermediate reasoning still runs almost entirely through text. Human analysts instead move between numbers, tables, and charts, reading visual evidence to expose trends and anomalies that text summaries drop. We ask whether charts can serve as internal epistemic actions inside a data agent’s reasoning loop, and how to teach an agent to use them. We first build \probe, a suite of 200 controlled tasks under four matched interface conditions, and find a persistent gap between what agents can read and what they choose to use. Agents score as well from self-generated charts as from numeric output, yet plotting access alone does not translate into consistent use. Task slices show that charts lead on temporal, pattern, and anomaly reasoning, while text leads on precise reading and conditional verification. We then propose \method, which distils two families of experience from conditioned trajectory rollouts. One family records how to select and verify analytical operations, and the other records when to invoke text, charts, or both. A trajectory judge scores outcomes and labels intermediate steps, and a validation gate admits only experience that improves held-out performance. On three held-out benchmarks with four LLM backbones, \method raises analytical accuracy and visual evidence quality over matched baselines, and backbones that never drew charts begin using them, all without touching model parameters.
[AI-5] From Reasoning to Action: Benchmarking Frontier AI Models on LIBERO-10 Robot Manipulation Tasks
链接: https://arxiv.org/abs/2610.30051
作者: May Espinola,Qifan Zhang,Yu Xiang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 17 pages, 6 figures, 8 tables. May Espinola and Qifan Zhang contributed equally. Project page: this https URL Code: this https URL
Abstract:Recent advances in frontier AI models have demonstrated remarkable capabilities in visual understanding, reasoning, planning, and tool use, raising the possibility of using general-purpose AI models for robot manipulation without task-specific training. However, their manipulation capabilities have not been systematically evaluated on standardized robotics benchmarks. In this report, we present a unified closed-loop manipulation framework for evaluating frontier AI models on the LIBERO-10 benchmark. Our framework enables models to control a robot directly through visual observations, robot proprioception, and structured tool calls, primarily by specifying target end-effector poses and gripper commands, without task-specific fine-tuning or pretrained manipulation skills. We evaluate six frontier AI models, including GPT-6 Astra, GPT-6.1 Sol, GPT-6 Luna, Claude Opus 5, Claude Opus 5.5, and Gemini-4 Argon, across three reasoning effort levels, resulting in 18 configurations and 1,800 evaluation episodes. Our results show that GPT-6 Astra achieves the highest success rate of 44%, followed by Gemini-4 Argon at 43%. Increasing reasoning effort generally improves manipulation performance, but the benefits vary across models and come at additional inference cost. We also observe substantial differences across tasks, with models performing relatively well on object placement tasks but struggling with precise spatial arrangements and complex multi-step interactions. These findings demonstrate both the potential and limitations of frontier AI models for robot manipulation, highlighting the challenges of translating reasoning capabilities into reliable physical actions. We release our evaluation framework and experimental results to support reproducible benchmarking and future research on reasoning-based robot manipulation.
[AI-6] SWE-DIVE: Multi-Turn Coding Agent Evaluation with Dynamic User Simulation NEURIPS2026
链接: https://arxiv.org/abs/2610.30047
作者: Lorenz Brehme,Benjamin Steenhoek,Ruth Breu
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Accepted at the NeurIPS 2026 workshop "Grounded User Simulation for Model Evaluation and Training: Diversity, Fidelity, and Validity’’ (non-archival)
Abstract:Coding agents are typically evaluated using one-shot, well-formulated prompts. Benchmarks such as SWE-bench use existing GitHub issues as input for a coding agent and evaluate whether the agent successfully solves the task. However, in real-world use cases, prompts are often not as well-formulated or structured as existing GitHub issues. Additionally, a coding agent session is not limited to a single prompt; it typically involves a multi-turn interaction in which the user iteratively adds new tasks and requirements that the agent must fulfill. To benchmark these abilities, we developed SWE-DIVE, a dynamic, interactive validation and evaluation framework for benchmarking coding agents in a multi-turn environment. To achieve this, we introduce a user simulator that can simulate different behavioral patterns and communicate with the coding agent. We also introduce a conversation validator that evaluates the user simulator’s outputs to determine whether they fulfill the initial requirements and guide the agent toward successfully solving the task. The entire framework is embedded in a Harbor environment, which orchestrates the coding agent, user simulator, and conversation validator, and executes the test environment to determine whether the agent successfully solves the task. Our results and source code are available in our GitHub repository (this https URL). Comments: Accepted at the NeurIPS 2026 workshop "Grounded User Simulation for Model Evaluation and Training: Diversity, Fidelity, and Validity’’ (non-archival) Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) Cite as: arXiv:2610.30047 [cs.SE] (or arXiv:2610.30047v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2610.30047 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-7] Which Differences Matter? Controlled Tests of Rule Transfer Joint Reliability and Information Use in Decision Models
链接: https://arxiv.org/abs/2610.30045
作者: Tianyou Lai,Wentao Yue,Zhiwei Zuo,Qilei Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Decision models map stated rules and observed states to discrete answers. Aggregate accuracy leaves unresolved which rule and information changes guide those answers. We connect controlled input changes to transfer, joint reliability, and directed information use through executable arithmetic rules. Semantic rule separation and crossed rule and state tests make transfer and joint success separately measurable. We compare equivalent wordings, calibrate joint correctness through marginal-preserving reassociation, and alter tables to define alternative answer targets. Adaptation gains increase with nearest-source answer agreement; fully conflicting quartets yield small pooled gains and different gain signs across source splits. Equivalent rewrites have opposing task-level effects, linking transfer gains to rule presentation. Joint-score departures depend on which rule and state associations the reference preserves. A decision service changes answers more across equivalent wordings than across identical-text repeats and usually follows the answer targets implied by altered table values or mappings.
[AI-8] SP-HLM: Sparse-Pose-Driven Motion Generation and Whole-Body Control for Humanoid Loco-Manipulation
链接: https://arxiv.org/abs/2610.30044
作者: Zhaohui Yang,Quanbao Lin,Chengqi Shi,Zhenglang Weng,Jicheng Lin,Boyu Chen,Tengjie Zhu,Guanzhu Ren,Jiahao Xu,Yang Feng,Xiaokang Yang,Yao Mu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Humanoid loco-manipulation requires an effective interface between high-level task policies and whole-body control. Existing interfaces typically rely on a single action representation for both task intent and whole-body coordination. This may burden the high-level policy or leave one aspect underspecified. We present SP-HLM, a framework built on a sparse-pose-driven interface. The interface combines five task-critical body poses with a learned latent motion prior to represent spatial intent and whole-body coordination in complementary forms. SP-VLA, a vision-language-action model, first predicts task-relevant sparse poses and finger joint positions from multimodal observations and language instructions using asynchronous action-chunk inference. Given these sparse targets, SP-MGM, a motion generation model trained through two-stage distillation, reconstructs coordinated full-body motion in real time. A pretrained motion encoder maps this motion to a compact latent representation. SP-GMT, a general motion tracker, then integrates the explicit sparse targets with this implicit motion prior for whole-body control. Experiments in simulation and on a real humanoid robot demonstrate improved motion tracking, whole-body coordination, and loco-manipulation success over existing approaches. Additional results and videos are available on our \hrefthis https URL\textcolorblueproject page.
[AI-9] HGM-Dual: Lineage-Aware Guided Agent Harness Optimization for Long-Horizon Planning
链接: https://arxiv.org/abs/2610.30042
作者: Sudipta Paul,Pengrui Quan,Aounon Kumar,Nicholas Tzou,Vivek Kulkarni,Srinivas Chappidi
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Harness optimization, which improves an agent’s prompts, tools, and workflows, is an increasingly effective way to enhance agents built on frozen or closed-source LLMs. Leading approaches such as the Darwin–Godel Machine and the Huxley–Godel Machine (HGM) rely on recursive self-improvement and were developed mainly for coding, where the competence needed to improve the agent coincides with the competence being improved. Planning tasks, in which an agent must produce a sequence of tool calls that jointly satisfies interacting constraints, break this alignment. There, open-ended self-modification repeatedly explores ineffective changes and consumes substantial evaluation budgets. We introduce HGM-Dual, a diagnostic-guided framework for budget-efficient harness evolution for planning agents. First, HGM-Dual represents agent evolution as lineage-aware context. This context records what each ancestor changed, which behaviors improved or regressed, and which modifications were ineffective, giving the editor explicit edit-outcome attribution instead of an unstructured archive of past agents. Second, HGM-Dual introduces error-conditioned dual-path child generation. For each selected parent, it generates a general child that preserves open-ended exploration and specialized children that target the harness components most likely responsible for the parent’s highest-ranked constraint violations. HGM-Dual outperforms the strongest baseline by up to 14.45% relative improvement in case accuracy while matching HGM variants’ final performance with 2x-4x fewer evaluation budget.
[AI-10] Robust Soft Skill Adaptation via Behavioral Localization in Tool-Using Agents
链接: https://arxiv.org/abs/2610.30041
作者: Zijian Jiang,Xilu Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Text skills can extend the capabilities of tool-using agents, but long skill descriptions increase computational costs. Soft prompts provide a compact alternative, yet learning from entire successful trajectories may also introduce unnecessary behavioral changes. We find that the useful effect of a text skill is highly concentrated in a small subset of trajectory positions. Based on this observation, we propose Behavioral Localization, which compares the frozen agent with and without the text skill to identify positions where the skill both increases the likelihood of successful tokens and meaningfully changes the next token distribution. We use these localized positions to supervise a short soft prompt. Experiments on SpreadsheetBench with a frozen Qwen3.6-35B-A3B agent show that the top 10% of trajectory positions capture 96.12% of the total positive skill gain. In the shared prompt setting, selective supervision improves success from 30.36% with full trajectory to 36.79%. Under task-specific conditions, using the top 10% localized positions achieves 57.38%, outperforming both the full text skill and the on-policy OPCD baseline. These results show that a small set of behaviorally relevant trajectory positions can provide effective supervision of soft skills.
[AI-11] GuideEvolve: Self-Evolving Agent Framework with Expert Guidance and 3Ts Mechanisms
链接: https://arxiv.org/abs/2610.30017
作者: Meihong Jia,Yasemin Timar,Jonathan Taws
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 10 images/figures
Abstract:This study introduces GuideEvolve, a self-evolving agent framework incorporating expert guidance and three autonomous regulatory mechanisms, collectively termed the 3Ts: Think Fast and Slow, Throwback, and Termination by choice. The framework addresses critical limitations in extant self-evolving agent paradigms through dual innovations: (1) a task-specific expert-evaluation-driven evolutionary pipeline, and (2) the integration of 3Ts mechanisms to facilitate cognitive optimization, performance recovery via temporal rollback, and adaptive termination protocols. Empirical validation across the MGSM and MMLU benchmarks demonstrates that GuideEvolve attains performance metrics comparable to current state-of-the-art models (89.8% accuracy versus Gödel Agent’s 90.6% on MGSM; 89.1% versus 87.9% on MMLU), while exhibiting notably reduced variance (25-59%) and enhanced robustness. Furthermore, the framework achieves a 100% evolution success rate, superior to the baseline’s 82%, diminishes required optimization iterations by approximately 50% (from 30 iterations in baseline to 15.1 and 13.4 across tasks), and provides improved stability for production deployment.
[AI-12] Code Comprehensor: Hierarchical Documentation and Cross-Modal Retrieval for Large Repositories
链接: https://arxiv.org/abs/2610.30016
作者: Sujoy Roychowdhury,Joseph H. R. Isaac,Jiangning Gao,Martin Klitte,Diarmuid Corcoran
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at 42nd International Conference on Software Maintenance and Evolution
Abstract:Understanding large, complex, and rapidly evolving code bases remains a core challenge for software engineering at scale, hindering onboarding, maintenance, refactoring, and impact analysis. Providing this information to a Large-Language Model (LLM) is another hurdle, as the LLM must understand not only the required code method but also its associated methods in the grand scheme of the repository. We propose Code Comprehensor, a hybrid retrieval-reasoning framework combining (i) a code dependency callgraph, (ii) CodeScribe, which uses code-aware LLMs to generate hierarchical documentation across repository call graphs, and (iii) CodeContrast, a CLIP-style dual-encoder model trained on CodeScribe output to learn aligned text-code embeddings. CodeContrast enables fast, semantically precise retrieval of relevant code-context neighborhoods, which serve as grounded evidence for an MCP-based LLM orchestrator answering domain-specific queries. Evaluation on OpenCV and a proprietary 5M-LOC repository shows that CodeScribe-derived supervision significantly improves cross-modal retrieval in text-to-code accuracy over baseline approaches. Richer textual supervision (hierarchical documentation, parent/sibling context, and LLM-generated synthetic questions) consistently enhances performance across both repositories. LLM-as-a-judge evaluation using the popular RAGAS of the Code Comprehensor output yields above 90% faithfulness scores.
[AI-13] Every Alarm Has a Cause: An Open Generator of Telecom Alarm Storms with Exact Root Causes Bound to NORIA-O RFC 8632 and TMF642
链接: https://arxiv.org/abs/2610.30004
作者: Madhulatha Mandarapu,Sandeep Kunkunuru
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注: 22 pages, 5 figures, 7 tables. Generator: this https URL (Apache-2.0). The dataset deposit is prepared but not yet published, so this version cites no DOI; a revision will carry it
Abstract:Alarm root-cause analysis is studied on data that cannot be shared. Operator logs are confidential, so what the field publishes is a causal graph between alarm types rather than per-incident causes, a fixed scenario set, gated, or never released. Two recent benchmarks carry per-incident root causes at scale; neither states a download location or a licence. We give a generator rather than a dataset. It runs on real backbone topologies from the Internet Topology Zoo and records the exact root cause and the propagation path of every alarm – a by-product of generation, not a labelling pass. Every emitted field binds to a term in NORIA-O, RFC 8632, RFC 8345 or TMF642, and an unknown term fails the run. We ship fixed configurations, splits, a scorer and nine baselines. A one-line rule – rank the device that raised the earliest critical alarm – takes top-1 at 0.650 against 0.575 for our own temporal method, and wins 13 of 15 runs over three backbones and five seeds. An LLM given the same view reaches 0.600, the same model allowed to query the graph 0.625, and the rule wins on real operator alarms too, at 0.773: a property of alarm streams, not of our generator. One fault class separates the table: when a site loses power its PoP reports nothing, so every method that ranks alarmed devices scores 0.000 or near it, while the two that walk the topology to a silent candidate take 1.000 and 0.833. Comments: 22 pages, 5 figures, 7 tables. Generator: this https URL (Apache-2.0). The dataset deposit is prepared but not yet published, so this version cites no DOI; a revision will carry it Subjects: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI) ACMclasses: C.2.3; I.2.4; H.2.8 Cite as: arXiv:2610.30004 [cs.NI] (or arXiv:2610.30004v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2610.30004 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-14] IDE: Tree-Induced Fine-Grained Diagnostic Evaluation from Model Behaviors
链接: https://arxiv.org/abs/2610.29985
作者: Jiayi Chen,Hui Li,Yujiong Shen,Qiyuan Peng,Yanxin Li,Chenhao Huang,Shaofan Liu,Guoqiang Zhang,Junjie Ye,Shihan Dou,Ming Zhang,Maxm Pan,Tao Gui,Qi Zhang,Xuanjing Huang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Benchmark performance is the dominant standard for comparing large language models (LLMs), typically summarized by a single aggregate score, such as accuracy, pass rate, or rubric score. This aggregate score provides limited information about a model’s underlying capabilities, yet understanding these capabilities is crucial for model development and evaluation. To this end, we propose Tree-Induced Diagnostic Evaluation (TIDE), which recasts diagnostic evaluation as behavior-driven decision-tree induction to perform fine-grained capability diagnosis. TIDE recursively builds a hierarchical capability tree from benchmark samples and model scores: at each node, an LLM judge proposes candidate splits and performs sample-to-group classification, and behavior-based information gain selects among them. We apply TIDE to five models across four benchmarks to demonstrate its effectiveness, and find fine-grained capability contrasts invisible at the aggregate level: Claude Opus 4.6 achieves 100% on microeconomic market reasoning where GPT-5.4 scores 83.3%, but trails GPT-5.4 on contract law reasoning with 77.8% versus 88.9%. We hope TIDE can be extended to past and future benchmarks so that the rich behavioral signal in evaluation results is fully leveraged for understanding model capabilities.
[AI-15] FaultGene: LLM -Assisted Fault-Augmented Test Generation for Hardware-in-the-Loop Validation of Autonomous Driving Systems
链接: https://arxiv.org/abs/2610.29969
作者: Chaoran Yuan,Fadi Kurdahi
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Reliable validation of autonomous driving cyber-physical systems requires testing not only nominal driving behavior but also failures arising from faults in sensing and embedded computation. Physical road testing provides realistic system behavior but is costly and difficult to reproduce, while simulation enables scalable evaluation but cannot fully capture hardware-dependent behavior and fault propagation. Hardware-in-the-loop (HIL) validation bridges this gap by executing the driving stack on real embedded hardware within a simulated environment. However, preparing fault-augmented scenarios for HIL testing remains largely a manual process, limiting test coverage, scalability, and efficiency. We present FaultGene, an LLM-assisted framework for automated generation of executable fault-augmented tests for HIL fault-resilience validation. Given a baseline Scenic/CARLA scenario and a structured fault specification, FaultGene generates a fault-augmented test case that captures the effects of HIL-level fault injection. During offline refinement, semantic-consistency checks and execution-level feedback are used to correct candidates that fail to preserve the intended scenario context or produce the desired fault-induced degradation, and to construct high-quality supervision for fine-tuning. The resulting generator then produces fault-augmented tests directly at deployment time without requiring manually engineered reference executions or repeated refinement loops. Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) Cite as: arXiv:2610.29969 [cs.SE] (or arXiv:2610.29969v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2610.29969 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-16] Privacy in Context Is Being Overlooked in the Age of General-Purpose LLM s AAAI2026
链接: https://arxiv.org/abs/2610.29925
作者: Prakhar Ganesh,Yash More,Marco Romanelli,Ferdinando Fioretto,Golnoosh Farnadi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at Personalization in the Era of Large Foundation Models Workshop @ AAAI 2026
Abstract:As large language models (LLMs) continue to grow in scope and capability, privacy concerns are on the rise. Since users increasingly interact with a single general-purpose system across many distinct contexts, there is a natural tendency to pursue a one-size-fits-all solution to privacy. Unfortunately, this overlooks a crucial insight from decades of privacy research: privacy is inherently contextual. In this paper, we argue that existing work in prompt privacy is drifting towards attempts to design a unified framework, which is highly misleading and can foster a false sense of security. Furthermore, we argue that even future work in this direction needs to wrestle with fundamental tensions across various desirable properties of prompt privacy. Thus, such continued attempts towards a unified framework need to be reconsidered. We recommend three critical paths forward: application-specific deployment of LLMs, context-specific evaluation of privacy frameworks, and better privacy literacy.
[AI-17] PAUSE: Search-Free Steering of Coding Agents Before Their First Source-Code Edit
链接: https://arxiv.org/abs/2610.29870
作者: Viral Patel,Boyin Tan,Jason Xue,Youcheng Sun
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 21 pages
Abstract:Coding agents can invest substantial effort in repository exploration and testing yet still produce unsuccessful patches. Existing online steering methods review trajectories periodically or invoke an advisor when a monitor detects behavioral problems; frequent reviews are costly, and the absence of anomalous behavior does not mean that an agent is on the right track. We propose PAUSE, a framework for steering coding agents with a single planning intervention. Agents reach their first source-code edit at a median of 60% of trajectory progress, after exploration, bug reproduction, and testing. PAUSE intercepts this edit request before it executes, which requires no learned monitor, no search over checkpoints, and no extra model call (search-free). A separate Planner reads the task and the pre-edit transcript and writes one repair plan; the same agent, the Executor, resumes with this plan, keeping its context and workspace. PAUSE applies online, during a live run, and offline, from a recorded trajectory prefix. We evaluate PAUSE on SWE-bench Verified and SWE-bench Multilingual with two Executors and three agent harnesses. With Qwen3-coder as Executor and GPT-5.6 Sol as Planner, online steering improves resolution by 16.8% on Verified in OpenHands and by 14.7% on Multilingual in SWE-agent. In SWE-agent, PAUSE outperforms LivePlan, a rule-triggered advisor, by 3.2% on Verified and 3.7% on Multilingual at similar Planner cost, and is comparable to SICA’s periodic overseer at about 11-15% of its Planner cost. Offline, PAUSE recovers 35.4% of eligible failed Verified tasks, and resuming from the recorded prefix uses 33-41% fewer Executor tokens than restarting.
[AI-18] Robot Generalization Beyond Volume: The Diversity Path via Zero-Shot Capabilities
链接: https://arxiv.org/abs/2610.29829
作者: Rafael Felix
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 13 pages, 7 figures, 2 tables, including supplementary material
Abstract:General-purpose robots must handle tasks, objects and environments they were never trained on, yet zero-shot generalization remains one of the least solved problems in physical AI. In digital AI, zero-shot capability grew with the volume and, above all, the diversity of training data, much of it sourced from the crowd. Robotics instead relies on paid collection by a few well-funded teams, which yields volume without diversity. We formulate zero-shot generalization in physical AI as coverage of the deployment distribution, and show that more samples from the same collectors shrink the unseen region only slowly, while each new contributor can extend it. Building on this and on zero-shot learning, we propose a shared latent space for robot actions that combines multi-modal alignment, generative augmentation, and noisy-label learning. As a scalable source of diverse data, we propose a community of developers building applications on one standard, approachable robot, Origami. We close with a testable hypothesis and an evaluation protocol relating real-robot zero-shot success to the diversity of contributed data.
[AI-19] UserSWE: Evaluating Coding Agents in Multi-Turn Conversations with Dynamic Users
链接: https://arxiv.org/abs/2610.29822
作者: Zhan-Yu Wu,Ji Luo,Alyssa Wu,Junjielong Xu,Richeng Xuan,Zhichao Hu,Chongming Gao,Xinting Hu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As coding agents become more capable, developers increasingly use them to fix bugs through multi-turn interactions. Existing benchmarks do not adequately cover this scenario. Most present a complete issue report in a single turn without interaction. Their evaluation can produce both false positives and false negatives, accepting incorrect patches or rejecting correct ones. We introduce UserSWE for evaluating coding agents’ ability to fix repository-level bugs through multi-turn interactions with dynamic users and execution-grounded verdicts at every turn. Its construction pipeline turns repository history and existing single-turn benchmarks into multi-turn tasks. At each turn, information pools constrain what the simulated user can reveal, while a persona chain sampled from a transition model calibrated on 20,859 development sessions governs how it speaks. Tests run after every turn, and a pair of agents from different model families writes and gates each verdict, using test results as primary evidence and model judgment only for aspects the tests cannot resolve. We evaluate eleven agents on 72 tasks from 42 repositories and find that agent rankings change with the interaction budget. Claude Fable 5.1 resolves the most tasks in one turn, GPT-6-Astra resolves the most by the eighth turn, and Claude Opus 5 leads at most intermediate budgets. These results indicate that single-turn performance is insufficient to characterize interactive coding ability.
[AI-20] COMET: A coordinated multi-layer runtime monitoring framework for human-supervised AI-enabled safety systems
链接: https://arxiv.org/abs/2610.29816
作者: Niclas Flehmig,Mary Ann Lundteigen,Shen Yin
类目: Artificial Intelligence (cs.AI)
备注: for associated GitHub repository, see this https URL
Abstract:Incorporating AI systems into safety systems requires monitoring to avoid silent faults and ensure safe and reliable operation. Single monitors often do not provide actionable information for human supervisors, who act as the system’s adaptive controller. We present COMET, a coordinated multi-layer runtime monitoring framework for human-supervised AI-enabled safety systems in which each monitor is anchored in a different fault mode. Monitor outputs are combined in a coordination layer and mapped by a communication layer to a traffic-light display. In this work, COMET consists of three monitoring layers: signal integrity, physics consistency, and the AI system’s competence envelope. The framework is evaluated on an open dataset of compressed-air leaks, where an AI system detects leaks from acoustic emissions, with held-out conditions simulating covariate shift, semantic shift, and sensor faults, events expected during runtime but unknown in their exact appearance. All monitors are frozen after development and tested zero-shot, and the framework is compared against no-monitoring and single-monitor setups. The bare AI system fails silently and confidently under all three shift types, whereas COMET reduces the trusted-wrong-answer rate to zero on covariate-shifted no-leak data and on three of four sensor faults, at a nominal false-alarm rate of \approx 1% . Single monitors come close in detection but cannot attribute the fault mode. COMET converts their anonymous detection flags into diagnoses. Residual silent misses under semantic shift define the boundary of passive monitoring. Runtime monitoring frameworks for AI-enabled safety systems must therefore combine quantitative fault detection and isolation with qualitative, human-oriented feedback.
[AI-21] Metamodel-Guided LLM Decoding for Consistent DSL Generation
链接: https://arxiv.org/abs/2610.29749
作者: Boqi Chen,Aren A. Babikian,José Antonio Hernández López
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 12 pages, 5 figures
Abstract:Large language models (LLMs) are increasingly used to generate software engineering artifacts written in domain-specific languages (DSLs). Correct DSL instances usually must comply with not only the concrete syntax but also the abstract syntax defined by a metamodel augmented with semantic well-formedness constraints. Existing constrained decoding approaches typically guarantee compliance only with concrete syntax or with a limited set of semantic constraints expressed over the generated text, making metamodel-level constraint consistency difficult to enforce during generation. We present MeMoDecode: a decoding framework that enforces a DSL’s concrete syntax, abstract syntax, and well-formedness constraints jointly. Specifically, whenever the LLM generates a complete metamodel concept, MeMoDecode incrementally maps the current output to a partial model and verifies whether it can still be completed into a consistent instance. We combine MeMoDecode with a simple retry mechanism that resumes decoding from the most recent completable snapshot whenever an inconsistency is detected. Under a sound backend verifier, MeMoDecode never performs worse than grammar-constrained decoding on alignment metrics satisfying natural assumptions. Across four DSLs and six open-weight LLMs ranging from 0.6B to 70B parameters, MeMoDecode achieves the highest consistency, improving consistency by 23% on average over unconstrained decoding and by 8% on average over grammar-constrained decoding. It also improves benchmark performance while incurring only modest runtime overhead. These results show that metamodel-guided decoding is an effective approach for enforcing DSL consistency during decoding across diverse DSLs and LLM scales.
[AI-22] SAKURA: Serving Attention Key-Value State Under Rate Adaptation for Private Image Editing on Phones
链接: https://arxiv.org/abs/2610.29733
作者: Shuncheng Jie,Desen Sun,Ri Hong,Yuyang Tian,Hong Zhang,Sihang Liu
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
备注:
Abstract:Diffusion transformers are widely used in image-editing services. To preserve both efficiency and privacy, existing systems commonly rely on cloud-device collaboration. Keeping request-specific inference on the device avoids sending personal images and prompts to the cloud. These workloads often use common templates, such as in face swapping and virtual try-on, enabling KV state to be precomputed and reused across requests. However, this reusable state is large and scales with the number of templates, making it impractical to store all of them on mobile devices. Storing them in the cloud addresses this capacity issue, but cache transfer is placed onto the inference path and can even be slower than on-device computation, especially under fluctuating mobile networks. We introduce SAKURA, a cloud-edge architecture that delivers reusable attention state to mobile devices under a bandwidth budget. It first uses a fine-grained token-importance-aware compression method to preserve image quality under limited transfer budgets. At runtime, SAKURA adaptively selects the cache representation for each upcoming shard based on network bandwidth. To enable efficient execution, we develop a pipelined runtime that overlaps cache transfer and reconstruction with on-device execution. We evaluate SAKURA using three editing tasks and two diffusion models, using a Snapdragon 8 Elite Gen 5 phone. SAKURA reduces latency by 1.82x and 3.27x over CacheGen adapted to DiT and on-device generation, respectively. It also reduces cache storage by 3.06x over uncompressed cache, while preserving image quality. Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI) Cite as: arXiv:2610.29733 [cs.SE] (or arXiv:2610.29733v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2610.29733 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-23] Structure-Aware Message Passing on Petri Nets through Directed Walks and Incidence Diffusion
链接: https://arxiv.org/abs/2610.29725
作者: Geri Skenderi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:A common application of the process mining discipline is to connect observed event data with Petri nets, which are bipartite, directed graphical process models with rich formal underpinnings. Recent work learns vector representations of Petri nets to retrieve process models and relate them to event logs, but leaves implicit two key aspects hidden within the net’s structure: directed cycles (process loops) and invariants recorded by the net’s incidence matrix. We expose both of these to message-passing graph neural networks, through a positional encoding built from return probabilities of directed place-transition walks (PNRWPE), and through same-type messages gated by diffusion kernels of the incidence matrix (DA-PNGNN). Our constructions raise macro balanced accuracy in predicting the generation factors of the 96 Process Discovery Contest 2023 models from 0.751 to 0.796 with PNRWPE, which nearly doubles accuracy on process loops, and to 0.830 with DA-PNGNN, which, without retuning, also improves net-to-tree retrieval top-1 accuracy on the self-supervised ProcRosetta benchmark by 4.1 to 5.9 points.
[AI-24] Bio-Inspired Auditory Processing: Coupling Parallel Resonate-and-Fire Dynamics with Ternary Recurrent Networks for Speech Recognition
链接: https://arxiv.org/abs/2610.29720
作者: Pavlos Poulos,Katelyn Sadorf,Themos Stafylakis,Costas A. Anastassiou
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Audio and Speech Processing (eess.AS); Neurons and Cognition (q-bio.NC)
备注: 22 pages, 6 figures
Abstract:Spiking Neural Networks (SNNs) promise energy-efficient inference through sparse, event-driven computation, yet remain constrained on continuous, context-dependent tasks such as automatic speech recognition (ASR). A key bottleneck is the Leaky Integrate-and-Fire (LIF) neuron, whose membrane decay erases information across pauses and phonetic boundaries while it must both extract features and retain long-range context. Inspired by cortical separation of nonlinear filtering and somatic integration, we introduce the Spiking Ternary GRU (stGRU): a ternary-weight gated recurrent unit performs temporal filtering, while downstream Resonate-and-Fire (RF) neurons generate the binary spike stream driving the next block’s event pathway. This makes binary event transmission a native training constraint rather than a separate deployment-time activation quantization step; together with ternary weights, learned projections avoid dense floating-point matrix MACs. Trained end-to-end on LibriSpeech-960, Small, Medium, and Large stGRUs reach 8.09%, 7.04%, and 6.44 \pm 0.17% WER on test-clean without an external language model. To our knowledge, these are the first matrix-MAC-free spiking networks without a learned ANN sequence decoder to achieve single-digit WER on LibriSpeech. The architecture also achieves 10.88% test CER on Aishell-1 (9.28% on dev) when trained from scratch. Internally, trained networks develop a depth-graded hierarchy: shallow layers act as fast acoustic filters, while deep layers form slower semantic integrators whose latent representations segregate into part-of-speech manifolds and word-level spike events. Deep-layer firing rates follow lognormal distributions with a sparsity gradient, mirroring hierarchical processing observed in human cortex in vivo. Our biologically grounded architecture combines competitive ASR with favorable modeled compute and memory cost.
[AI-25] raceForge: Distilling with Your Own Thinking
链接: https://arxiv.org/abs/2610.29680
作者: Yan Wang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Using an LLM to generate training data for another is among the most general ways to train one, and it remains common practice. Some frontier teams have closed that route for their own models: what comes back is an answer, or a short summary, without the detailed reasoning trace behind it. That is a little ironic: they were themselves built on distilling human knowledge, and distilling an open-source model is still entirely routine; the restriction applies to their own outputs alone. Recent work recovers the hidden reasoning by replaying encrypted blocks across models. It somehow works, but it breaks the providers’ terms of service, so it is not something a product can be built on long-term. We built the alternative. We take the context and the teacher’s answer as input, and generate the reasoning trace ourselves, in the style of the student’s own model family. Training is two-stage, supervised and then adversarial: a generator learns to regenerate the family’s own reasoning traces from their context and answer, and a critic learns how real a reasoning trace looks. Applied to a teacher’s answer, the generator writes one in the family’s style. An independent judge rates how likely traces are to be genuine on a scale from 0 to 1: ours average 0.68, real traces 0.66, and traces selected by the critic from four candidates 0.82. These are authenticity scores, not correctness rates. We test the idea on five benchmarks with everything else held fixed. On general tasks the five training corpora are about the same; on coding, the generated reasoning beats Fable 5’s own thinking content. With the same 5,000 questions and the same teacher answers, HumanEval+ improves by 3.7 points and MBPP+ by 7.1 at the final checkpoint, and the MBPP+ lead holds at every checkpoint. We suspect that reasoning written in the student’s family style is easier for it to learn from. Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2610.29680 [cs.AI] (or arXiv:2610.29680v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2610.29680 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-26] oward Recursive Self-Improvement in Physical AI
链接: https://arxiv.org/abs/2610.29677
作者: Meng Luo,Zechen Bai,Shanqing Xu,Yanlin Li,Kaixin Li,Mengchen Qian,Weifeng Lu,Ran Zhang,Yingtian Zou,Ziyang Luo,Wei Chen,Hongzhan Lin
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 93 pages, 15 figures, 21 tables. Project page: this https URL
Abstract:Recursive self-improvement (RSI) describes a system that improves not only its behavior but also the process by which it improves. Recent digital agents test this idea by rewriting their own code and checking each version with executable tests. Bringing these mechanisms into the physical world requires learning from partial observations through costly trials, while accounting for changing operating conditions and potentially irreversible consequences. We position RSI within robot learning and trace pathways from improving robot capabilities to improving the processes that generate them. We examine how physical experience can strengthen these improvement mechanisms and when the resulting gains can support further self-improvement of the mechanisms themselves. Gains carry forward only when feedback addresses remaining learning needs, the receiving component can use the change, its value survives changes in tasks or hardware, and continued practice remains safe and affordable. Existing studies, concentrated in manipulation, support behavioral improvement, benefits to later learning, and procedure improvement. Some systems also change the improver’s knowledge or tools, but the physical studies reviewed here have yet to isolate gains attributable to recursion. We synthesize these findings into evaluation principles and research directions for machines that, trial by trial, safely improve the way they improve.
[AI-27] From Association to Decision Value: Testing Paired Instruction Dependence for VLA Corpus Selection ICLR2027
链接: https://arxiv.org/abs/2610.29646
作者: Nurmukhambet Serikbayev,Abdul Basit,Muhammad Shafique
类目: Artificial Intelligence (cs.AI)
备注: 23 Pages, under review of ICLR2027
Abstract:Choosing among robot demonstration corpora is a ranking problem: an upstream diagnostic must preserve which candidates produce better downstream policies, not merely correlate with average performance. We study paired instruction dependence, the rate at which a demonstrator changes its first decision when only the goal changes. Across 136 independently seeded quadruped-navigation corpora, predictions developed on 56 corpora are evaluated prospectively on 80 held-out corpora. The audit is reliable and associated with downstream paired correctness, and lowers mean absolute prediction error when added to a correctness-aware baseline, but the primary proper-log-score increment is +0.059 nats (95% corpus-bootstrap CI [-0.027, 0.133]) and the prespecified top-20% comparison does not establish an incremental selection gain. An exploratory response decomposition suggests a structural source of this predictive-ranking mismatch: under the tested predictor specification, separating both-correct and both-wrong responses yields larger held-out gains than their unsigned switch statistic. Seed-to-seed agreement indicates that the held-out endpoint retains recoverable rank signal. These results show that counterfactual response structure can be reliable and predictive while providing limited incremental decision value in this setting when compressed into an unsigned statistic, motivating diagnostics that preserve outcome-relevant structure and are validated against the decisions they are intended to support.
[AI-28] Formalizing Cooperation and Competition: An AI-Assisted Mathematical Addendum to a 2018 Planning Thesis
链接: https://arxiv.org/abs/2610.29637
作者: Haitham Masaud Amar
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI)
备注: 24 pages, 4 figures. Mathematical addendum to the author’s 2018 University of Waterloo PhD thesis. Includes Lean 4 formalization and reproducible synthetic experiments as ancillary material
Abstract:We present an AI-assisted mathematical addendum to Strategic Trip Planning: Striking a Balance Between Competition and Cooperation (Amar, 2018). We reconstruct selected relationships between strategic choice, cooperative stability, participant approval and enforcement. The thesis’s Nash-to-core proposition is under-specified until outsider opportunities are stated. We verify two-person implications under fixed outsiders and explicit efficiency conditions. Under isolation, the original example has an empty TU core; its funded counterpart uses an explicit external subsidy. For a monotone resource model we characterize the exact financing gap. A finite negotiation procedure implemented in Lean is proved to terminate with participant approval, joint-cost non-worsening and local optimality against permitted approved moves. Supporting formalizations address bargaining geometry and regret-based efficiency. A resource-game counterinstance shows why monotonicity alone provides no uniform price-of-anarchy bound. Reproducible computations compare a documented heuristic reconstruction with the verified procedure on 44 source-based instances and 96 starting cases from four additional synthetic network families. Acceptance-matched heuristic and randomized pairwise-search controls contextualize the optimization results. The mathematical ingredients are established results; the contribution is a traceable reconstruction linking source statements, explicit assumptions, checked proofs, executable code and retained counterinstances. The study illustrates how AI-assisted investigation can turn engineering intuitions from legacy research into precise mathematical claims whose conditions and computational consequences can be inspected and reproduced.
[AI-29] Improved Training of Strong Stochastic Flow Maps
链接: https://arxiv.org/abs/2610.29605
作者: Zander W. Blasingame,Antonio Franca,Raul Minan,Christian Koke,Niklas Rindtorff,Alexander Tong
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Dynamical Systems (math.DS)
备注: preprint
Abstract:Stochastic flow maps promise few-step sampling of stochastic differential equations (SDEs) by learning to directly jump between times along the solution path, yet training these models to learn this solution map remains difficult. The primary obstacle is the Brownian path itself: it is an infinite-dimensional object which needs to be passed to the neural network and is nowhere differentiable, which makes the implementation of derivative-based losses that power the state-of-the-art deterministic flow maps just out of reach. We identify two core design principles which address these issues: a) represent the Brownian path with a basis which carries higher-order local information, and b) train with an objective that forces the network to use the full representation of the path, not just the first order. To address this, we leverage the shifted Legendre polynomial expansion with a Wong-Zakai styled objective, unlocking derivative-based losses. Finally, we propose a recipe for fine-tuning large pre-trained flow models into strong stochastic flow maps. We ablate through this design space on several toy problems and use our findings to transform a large flow model into a strong stochastic flow map on ImageNet, achieving roughly a three-fold improvement in FD-DINOv2 over SSFM at four NFE.
[AI-30] he Reflexivity Threshold: A Phase Transition for Multi-Agent Performative Prediction NEURIPS2026
链接: https://arxiv.org/abs/2610.29577
作者: Ahmed Mahrous,Roberto Di Pietro
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at NeurIPS 2026. 20 pages, including appendix and checklist. Code: this https URL
Abstract:Many learning systems are performative: once deployed, their predictions change the data distribution on which future models are trained. In multi-agent settings, this feedback is routed through a network of interacting learners, so stability depends not only on the strength of performativity but also on who affects whom. We study repeated retraining in multi-agent performative prediction and identify a spectral threshold that separates stable learning from worst-case learning obstruction. Prior work often collapses cross-agent feedback into a scalar contraction constant, missing this network structure. We instead introduce the reflexivity matrix \Gamma , which records how strongly one agent’s deployment can move another agent’s best response, defined from per-agent gradient sensitivities and curvature parameters. The threshold occurs when the spectral radius of this matrix crosses one. Below the threshold, repeated retraining converges geometrically to a unique performatively stable equilibrium, with cumulative excess loss bounded uniformly in the time horizon. Above the threshold, we construct noisy supercritical instances in which every local-information learner incurs expected cumulative excess loss growing linearly for horizons up to order 1/(\rho(\Gamma)-1) . Together, these results characterize the threshold behavior through upper and lower bounds and recover the single-agent scalar threshold from prior work as a special case.
[AI-31] Stochastic Pruning of the DPO Computation Graph: 1.4x Faster Training and Lower Peak Memory
链接: https://arxiv.org/abs/2610.29538
作者: Yubing Wu,Wei Chen,Junmei Yang,Delu Zeng,Tiewen Pan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Direct preference optimization (DPO) incurs training costs in output scoring, sequence execution, and backpropagation, so reducing supervision alone does not necessarily save computation. We introduce a unified framework for stochastic pruning of the DPO computation graph that independently samples response positions for scoring, response prefixes for execution, and preference branches for backpropagation. By making these selections before the corresponding operations, the framework omits computation at each stage of a training step. Our analysis characterizes the objective changes induced by position and sequence pruning and uses inverse-probability weighting to obtain a conditionally unbiased single-branch gradient estimator for the sampled objective. Controlled measurements show that position and sequence pruning primarily reduce peak memory, with most output-memory savings attributable to an implementation that retains full response supervision. Branch pruning yields a 1.426\times step-time speedup over a concatenated DPO baseline and reduces training time by about 30% relative to stock DPO across two model families, with slightly lower mean accuracy and benchmark-dependent changes in open-ended scores. These findings relate the resource benefits of stochastic graph pruning to the computation omitted and the resulting changes in the training objective.
[AI-32] Block Diffusion for Longitudinal EHR: Accuracy Efficiency and Representation Trade-offs
链接: https://arxiv.org/abs/2610.29533
作者: Ashvin Gupta,Joshua Placidi,Brendan Delaney,Alessandra Russo
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Autoregressive (AR) models have proved effective for zero-shot prediction from longitudinal electronic health records, but impose strictly sequential generation on trajectories containing temporally clustered and interdependent clinical events. We investigate block-based masked diffusion (BD) as an alternative on MIMIC-IV under matched tokenisation, context length, model capacity, and evaluation conditions. Across block sizes, BD exhibits a clear quality–efficiency trade-off, with small-block configurations retaining much of the predictive performance of AR while enabling substantially faster inference. A representative B=8 configuration achieves approximately 1.43\times faster evaluation. Although generative rollout remains below AR in most settings, frozen linear probes on BD and AR representations achieve near-identical performance across clinical prediction tasks, indicating that BD learns comparably informative clinical representations. Increasing the denoising budget yields diminishing returns, while alternative decoding strategies provide limited gains. Overall, our results show that block diffusion is a viable and computationally attractive approach to longitudinal EHR modelling, with further improvements in iterative decoding offering a promising route toward closing the remaining generative performance gap.
[AI-33] Are Joint-Embedding Predictive Objectives Effective for Longitudinal EHRs?
链接: https://arxiv.org/abs/2610.29528
作者: Ashvin Gupta,Brendan Delaney,Alessandra Russo
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Masked language modelling (MLM) is the dominant pretraining objective for transformer encoders applied to longitudinal electronic health records (EHRs). However, reconstructing individual events may overemphasise noisy or weakly informative observations and does not explicitly encourage learning higher-level patient states. Joint-Embedding Predictive Architectures (JEPAs) instead learn by predicting representations in latent space and have shown strong performance in a variety of domains. In this paper, we investigate to what extent JEPA can be adapted to EHR sequence representation. Our empirical investigation using MIMIC-IV shows that although all three of our JEPA formulations train stably without representational collapse, they consistently underperform standard MLM. We find that target size, not target heterogeneity, increases prediction error, yet reducing the target size does not recover downstream performance. This suggests that stable latent prediction alone is insufficient for competitive EHR representation learning and highlights the challenge of defining clinically meaningful latent targets for EHR.
[AI-34] Rethinking Agent ic Model Orchestration for Time Series Forecasting NEURIPS2026
链接: https://arxiv.org/abs/2610.29504
作者: Tong Guan,Tianxiang Zhan,Shirui Pan,Ming Jin
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted by the NeurIPS 2026 Workshop on Foundation Models for Temporal Systems (FMTS)
Abstract:Time series foundation models (TSFMs) perform unevenly across forecasting tasks. Recent work uses agentic model orchestration (AMO) to select or combine TSFMs, often outperforming individual forecasters. Yet these gains may reflect stronger or more complementary candidates rather than better orchestration. To disentangle these effects, we introduce FairAMO, a controlled evaluation protocol with frozen candidate forecasts. Experiments on GIFT-Eval show that AMO improves accuracy, but its gains are bounded by the candidate TSFMs. The best orchestrators reduce MASE by less than 6% relative to the best single TSFM across all tasks. These gains depend on complementarity (different candidates excel on different tasks) rather than the number of candidates. Adding members yields diminishing returns, and a redundant member can erase these gains. The value of historical evidence also saturates quickly. A few recent windows capture all gains, and further history adds none. These findings separate system-level forecasting gains from the contribution of orchestration itself.
[AI-35] Parallel Thinking for Large Language Model with Path Diversity
链接: https://arxiv.org/abs/2610.29501
作者: Jiaru Qian,Yihong Dong,Xue Jiang,Chun-Wah Hsu,Zhi Jin,Ge Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Parallel thinking has emerged as a promising paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs) by exploring multiple reasoning paths simultaneously. However, we identify that parallel thinking via Reinforcement Learning (RL) typically suffers from the mode collapse of reasoning paths, where parallel thinking trajectories degenerate into similar ones. This homogeneity undermines the fundamental motivation of parallel thinking, that is, maintaining multiple hypotheses concurrently to prevent premature loss into a suboptimal solution. In this paper, we propose Diverse Parallel Thinking (DPT), a simple yet effective RL approach for parallel thinking designed to encourage path diversity. DPT rewards the model to develop distinct reasoning strategies by penalizing the prefix similarity of parallel thinking paths, thereby enhancing a more comprehensive exploration of the reasoning space. We conduct various experiments and analyses across multiple reasoning benchmarks to demonstrate the effectiveness of DPT. Results show that DPT successfully outperforms previous parallel thinking methods, achieving state-of-the-art performance over baselines. We validate path diversity as a key element to improve parallel thinking. Our code is available at this https URL.
[AI-36] An Exact 93-Move Solution to a Nonlocal Crystal-Exchange Puzzle
链接: https://arxiv.org/abs/2610.29493
作者: Zilai Wang
类目: Artificial Intelligence (cs.AI); Combinatorics (math.CO)
备注:
Abstract:We determine the minimum number of directional commands needed to solve alevel of Order of the Sinking Star, modeled as a crystal-exchangepuzzle on an 11*11 board. A command swapsthe player with the nearest crystal in the selected row or column, regardlessof distance; only an empty ray permits an ordinary unit walk. Two gates admitthe player to the exit exactly while all sixteen crystals occupy their targetcells. We give an explicit 93-command solution and exclude every shortersolution by an exact, cost-bounded breadth-first search. The search representsa position by its seventeen occupied cells and one active cell. Its admissibleheuristic partitions command costs between a compressed core abstraction andresidual motion, strengthened by a necessary activation detour and a cornervisit condition. The completed computation retained 605,349,215 occupied setsand 1,863,925,136 states. We prove the reductions and pruning conditions,describe the exact storage scheme, and provide source code, execution logs,and a reproduction driver. The result is a computer-assisted proof for thespecified instance, with an explicit implementation trust boundary.
[AI-37] RubricOpt: Self-Revision of Semantic Rubrics for Preference Optimization
链接: https://arxiv.org/abs/2610.29491
作者: Wei Chen,Yubing Wu,Shigui Li,Delu Zeng,Jingfeng Liu,Junmei Yang,Zhiheng Zhou,Bo Li,Min Chen,Zhou Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:A model’s failures can reveal gaps in the behavioral criteria guiding its training. We introduce RubricOpt, a framework that lets a policy revise these criteria within preference optimization. The policy diagnoses its own rubric-free outputs, retains requirements already covered by the rubric, and edits clauses that leave recurring failures unaddressed. A shared semantic representation connects task demands, preference-data contrasts, and rubric clauses, while a deterministic gate checks evidence references and fixed semantic anchors. The rubric then guides Direct Preference Optimization (DPO) during training, with preference pairs, labels, and the objective held fixed, and is removed at evaluation. Our analysis relates the local effect on rubric-free success to semantic alignment and a residual beyond the measured factors. Experiments on capability and open-ended generation benchmarks show that rubric conditioning can improve outcomes over ordinary DPO, with gains and regressions varying across models and datasets. A matched continuation isolates the contribution of revision from additional training and finds mixed incremental effects for the tested edit, whose feedback overlaps the evaluation set. RubricOpt thus brings the behavioral specification itself into the policy’s feedback loop.
[AI-38] Generative Modeling via Nonlinear Schrödinger Bridges and Branching Diffusions
链接: https://arxiv.org/abs/2610.29476
作者: Mathias Grau,Pierre Henry-Labordère
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:We propose a new generative modeling framework grounded in forward-backward partial differential equations (PDEs). These equations arise naturally in mean-field game (MFG) theory, and are closely related to nonlinear Schrödinger systems. Our main contribution is to reinterpret this connection within the setting of generative modeling and to develop an efficient numerical methodology for its practical implementation. Specifically, we derive and characterize a nonlinear extension of the classical Schrödinger bridge problem through a coupled system of PDEs, yielding a model we call SBNL (Schrödinger Bridge NonLinear). To compute its solution, we design a simulation algorithm based on branching diffusions and the probabilistic representation of semilinear PDEs. The resulting method is embarrassingly parallel, scalable to high dimensions, and applicable to a broad class of nonlinear interaction functionals. We validate the approach through numerical experiments, including recovery of the classical Schrödinger bridge, nonlinear extensions with convex penalties (Gross-Pitaevskii) as well as potential interaction terms, and high-dimensional generative tasks such as image synthesis on the MNIST and FFHQ human face dataset. Furthermore, the same framework embeds score-based generative modeling, diffusion models, and tractable gaussian Schrödinger bridges.
[AI-39] PreCoMem: Predictive Cognitive Memory for Self-Evolving Long-Term Dialogue Agents NEURIPS2026
链接: https://arxiv.org/abs/2610.29467
作者: Jian Zhong,Zeyu Liu,Pingchuan Cao,Rongduo Han,Shunye Tang,Guohuan Xie,Mingyuan Qin,Ran Ji,Xiao Liang,Wei Wang,Haining Zhang
类目: Artificial Intelligence (cs.AI)
备注: Accepted at NeurIPS 2026
Abstract:Long-horizon dialogue agents need to keep user memories stable enough for personalization while adapting when the user genuinely changes. Existing memory systems often append every observation or overwrite old entries by heuristic rules, which makes transient noise hard to distinguish from real belief drift. We introduce PreCoMem, a memory consolidation framework inspired by predictive coding that represents memory as confidence-weighted beliefs about the user’s state. Its core signal, Effective Surprisal, combines semantic deviation and contradiction evidence, then downweights this signal when retrieval is diffuse and unreliable. According to effective surprisal, a two-threshold gate maps each turn to one of three deterministic updates: MAINTAIN reinforces supported beliefs, PROFILE stores ambiguous cues as low-confidence hypotheses, and CORRECT softly decays reliably contradicted facts. The PROFILE stage acts as an explicit waiting room, preventing premature commitment to noisy evidence. Experiments on LoCoMo, LongMemEval-S, and PersonaMem-v2 show consistent state-of-the-art accuracy; our new DiMoBench confirms that PreCoMem adapts to non-stationary belief drift without over-reacting to noise. The code is available at this https URL.
[AI-40] RegoMender: A Multi-agent Framework for Automated Rego Policy Repair
链接: https://arxiv.org/abs/2610.29456
作者: Ruijie Zhang,Huaxiao Yin,Li Hu,Fanqi Kong,Haozhe Liang,Shaoyin Cheng,Kun Zhang,Bibo Tu
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Programming Languages (cs.PL)
备注: 13 pages, 5 figures, accepted to ASE 2026, research Track
Abstract:Policy-as-Code (PaC) has emerged as a foundational paradigm in cloud-native systems. However, repairing Rego policies remains challenging due to their strict syntax, declarative semantics, and specific verification requirements. Existing automated program repair (APR) methods are mainly designed for mainstream languages and struggle to accommodate the unique policy logic and repair workflows of Rego. Thus, Rego policy repair remains a manual process driven by trial and error. To address this, we introduce RegoBench, the first automated Rego policy benchmark comprising 1,159 real-world defects, and propose RegoMender, a feedback-guided multi-agent framework. Collaborating via a shared blackboard and an atomic toolkit, RegoMender dynamically executes patching, rewriting, or rolling back, to resolve complex repair scenarios. Evaluated on RegoBench, RegoMender achieves syntactic and test-based semantic success rates of 82.9% and 77.9%, respectively, outperforming the strongest baseline by 13.4%. Unlike other approaches whose performance drops in complex scenarios, RegoMender maintains effectiveness across single-step and chained repair tasks. Furthermore, it generalizes strongly across HCL and SQL and achieves an 86.4% repair rate on 132 real-world CVE vulnerabilities, proving its robustness and practical utility for security.
[AI-41] Events Not Pixels: Where World Models Get the Return Wrong
链接: https://arxiv.org/abs/2610.29435
作者: Haoyue Liu,Xiaoying Tang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:World models are increasingly used for planning, where a planner ranks candidate actions by returns imagined by the model. However, these models are judged mainly by how well they render frames, even though a visually faithful rollout can still assign the wrong return. To identify where this mismatch arises, we introduce a transition-level diagnostic that replaces imagined transitions with real ones and measures the reduction in return error against a budget-matched random control. Our analysis yields two findings. (1) Event transitions, the few steps where a reward is earned or an episode ends, are rendered as well as other steps (median pixel-error ratio 0.85), yet restoring them removes a median of 70% of the return error. In contrast, restoring transitions with the largest MSE, SSIM or LPIPS error removes less than a random choice. Across the 44 Atari-100k model–collection pairs that pass our reliability checks, including three released families, event patches outperform random patches in 39 (sign test p=1.4 x 10^-7), with a median gain of +44 percentage points. (2) The remaining error is structured: a complementary patch separates errors at true events from those elsewhere, while an eight-step timing tolerance recovers few events missed by our deterministic models. This structure also guides repair: a rule recorded before four repair outcomes were known predicts when a diffusion model repairs these failures. Event patches improve plan ranking, and the same concentration appears on control tasks with rare reward events.
[AI-42] MERP: A Latent-Bottleneck Framework for Robust Multi-Modal Multi-Label Emotion Recognition
链接: https://arxiv.org/abs/2610.29430
作者: Chuangxin Zhao,Meimei Zhang,Canran Xiao,Jiachen Luo,Jiajun He,Kaijie Chen,Huy Phan,Tomoki Toda,Biörn W.Schuller
类目: Artificial Intelligence (cs.AI); Sound (cs.SD)
备注:
Abstract:Multimodal multi-label emotion recognition is important for affect-aware systems, but models trained with complete text, visual, and acoustic streams often degrade sharply when some streams are unavailable at deployment. We address this train-test gap by learning a compact representation that preserves emotion-specific evidence from any available subset of modalities. We propose the Multi-modal Emotion Recognition Perceiver (MERP), which maps the joint sequence into a fixed-size latent bank, thereby keeping repeated reasoning independent of raw input length; it extracts label-specific evidence before and after latent reasoning and aligns the two levels through reconstruction. MERP further uses positive and negative class prototypes, a noise-gated feature enhancer with frequency-shaped perturbations during training, and confidence-aware class-wise fusion to strengthen separability, reduce sensitivity to missing inputs, and combine complementary predictors. Under 20 percent test-time random modality drop, MERP achieves micro-F1 scores of 0.563 on CMU-MOSEI and 0.780 on M3ED, exceeding the strongest evaluated baselines by 0.004 and 0.005, respectively; in the severe CMU-MOSEI audio-only setting, it retains 0.473 micro-F1 versus 0.022 for LDDU. These results show that fixed latent reasoning provides a practical, testable route to robust multimodal emotion recognition without requiring missing-modality examples during training. Code is available at this https URL.
[AI-43] Solving Go under Positional Superko: Sound Proof Transfer and New Board Values
链接: https://arxiv.org/abs/2610.29414
作者: Sami Kharma,Florian Schintke
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 7 figures, 12 tables
Abstract:Positional superko (PSK) forbids recreating any earlier whole-board position. It is part of the Chinese rules as written and of the rules used in computer Go. Yet almost all solved Go boards were computed under a simpler basic-ko rule set. PSK makes solving hard: legality depends on the whole game history, and this breaks the three tools that make solving tractable, namely transposition tables (the graph-history interaction problem), static life-and-death recognition, and safe move pruning. We present Sanko, a parallel depth-first proof-number solver that restores all three soundly. Its core is a cheap proof-transfer theorem: a stored proof stays valid on a new path unless a board that the proof’s winner must create has already occurred. Sanko proves 5x5 Go in 16 minutes with six threads, where the previous state of the art needed 5.25 hours with six processes, and in about a minute and a half on one 192-thread machine. It also establishes the first PSK values of twenty-three further boards, eleven of which were unsolved under any rules. Superko changes game-theoretic values: 4x7, a full-board win under basic ko, is worth only 12 points under PSK, and we pinpoint the move at which the previously published optimal line fails. Unlike earlier solutions of empty Go boards, we release the solver source together with the command line and outcome of every run behind our values and measurements, so that all reported values can be reproduced.
[AI-44] Parallelizable Probabilistic Latent Reasoning for Diverse Reasoning Trajectories
链接: https://arxiv.org/abs/2610.29385
作者: Seokhyun Lee,Seongjun Lee,Changhee Lee
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Language models relying on verbalized Chain-of-Thought (CoT) are constrained to token-by-token generation in the language space, where many tokens serve textual coherence rather than core reasoning. This entangles genuine differences in logical trajectories with superficial variations in verbalization. Moving reasoning into a compact, continuous latent space offers a promising alternative for efficiently representing and exploring diverse reasoning trajectories. However, current latent models remain largely deterministic, capturing only a single reasoning trajectory rather than modeling a distribution over multiple valid alternatives. While stochastic approaches parameterize distributions over latent reasoning steps, they frequently suffer from unstable optimization and limited empirical performance. Furthermore, their reliance on autoregressive latent prediction during training precludes effective parallelization, leading to prohibitively long training times. To overcome these bottlenecks, we propose Probabilistic Latent Reasoning Models (PLRM), a parallelizable variational framework for probabilistic latent reasoning. PLRM adopts a JEPA-style objective in which the posterior infers latent distributions in parallel from observed reasoning trajectories, while the prior learns to predict the subsequent latent distribution via teacher forcing. To model heterogeneous reasoning processes, we introduce a discrete strategy variable together with a mixture-based prior, allowing the model to preserve the Gaussian-mixture structure induced by multiple distinct reasoning trajectories rather than collapsing them into a single Gaussian. Across diverse benchmarks, PLRM achieves performance competitive with deterministic latent reasoning models on single-trajectory datasets while enabling efficient training and consistently outperforming competing stochastic baselines on multi-trajectory datasets.
[AI-45] From Prompts to Posteriors: Can LLM Agents Perform Bayesian Inference in Context?
链接: https://arxiv.org/abs/2610.29331
作者: Linus Filbry,Luckeciano C. Melo,Yarin Gal
类目: Artificial Intelligence (cs.AI)
备注: 10 pages, 6 figures in main text
Abstract:Large language models (LLMs) are increasingly applied across the scientific discovery pipeline, from synthesizing literature to generating hypotheses to informing experimental design. A unifying principle behind these tasks is the need to continuously update beliefs as evidence accumulates, for which Bayesian inference provides a natural framework. However, existing work assumes that LLMs cannot carry out principled Bayesian belief updating in context, instead relying on simplifications and heuristics. To challenge this assumption, we present a principled evaluation of Bayesian reasoning capabilities in LLMs, analyzing both the model’s internal consistency as well as its ability to recover an externally determined posterior. Specifically, we build an evaluation framework spanning qualitative epistemological examples to probe probabilistic coherence, quantitative benchmarks of Bayesian inference capabilities, and analyses of the impact of non-Bayesian updates on downstream task performance. Across different scenarios, we find that LLMs generally violate Bayes’ rule when updating their beliefs in context. However, when equipped with modern reasoning abilities and code execution capabilities, their performance drastically improves, enabling even the close approximation of analytically intractable posteriors. Our evaluation shows that current LLMs with access to appropriate tools could already be used to make complex belief updates in scientific discovery, enabling rapid acceleration of the efficiency and scale of AI-powered scientific discovery.
[AI-46] Agent Monopoly: Behavioral Patterns and Persona Control in LLM Agents
链接: https://arxiv.org/abs/2610.29330
作者: Changshun Wu,Mengyu Li,Mathis Marsault,Xiaowei Huang,Chih-Hong Cheng
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 8 figures, 5 tables
Abstract:How do LLM agents’ strategies differ, and how much can instructions to adopt a persona change their behavior over a game? We study these questions with AgentMonopoly, an environment where agents communicate, buy and trade properties, and make decisions that affect later turns. We compare models from four families, each at two sizes, in games with identical models, different sizes of the same family, or different families. We also assign one agent a persona, a description of how it should behave, intended to change a tendency observed in games without that persona. The tested models differ in how often they trade, waive rent, and manage cash and properties. Larger models do not consistently win more games. Persona instructions change the words agents use and some of their actions, but the changes do not always match the role across all decisions. These results show why persona following should be assessed through actions and outcomes as well as language. As our findings are conditional on the tested checkpoints, prompts, and five seeds per configuration, the open-source nature of AgentMonopoly provides the experimental infrastructure for further extending these comparisons. Our code is available at this https URL.
[AI-47] SpatialBrain: Spatial Intelligence Growing from Experience
链接: https://arxiv.org/abs/2610.29317
作者: Yifan Zhou,Ziyang Gong,Xiaojun Ye,Yifei Liu,Donggang Li,Xiaolong Zhou,Zhihang Zhong,Xue Yang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Spatial reasoning requires more than access to geometric tools: a reasoner must decide when direct observation is sufficient and when to reconstruct, measure, or change reference frames. We present SpatialBrain, a framework that retains experience about these decisions while keeping its vision-language backbone fixed. The key distinction is between evidence about a scene and experience about how to reason with that evidence. 3DFusionMemory fuses current-scene observations into queryable spatial relations. A Spatial Skill Library provides reusable tool procedures, and Spatial Cognition associates task conditions with experience about when and how to use memory and skills. Completed trajectories are abstracted into process records and consolidated between task blocks, allowing later decisions to draw on earlier execution without transferring scene coordinates or reference answers. Across five spatial-reasoning benchmarks, SpatialBrain improves on direct answers from the same backbone on four and ties on the fifth. Component comparisons, experience-coverage curves, and traced resource decisions characterize how the framework accumulates and uses these resources.
[AI-48] YingLan: A Multimodal Ocean Foundation Model for Unified Representation Learning from Heterogeneous Observations
链接: https://arxiv.org/abs/2610.29313
作者: Sen Jia,Siqi Wei,Junyu Wang,Zijun Lv,Pan Liu,Meng Xu,Xiyou Fu,Yuxi Sun,Qiang Liu,Shuyu Zhang,Lin Mu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal ocean modeling remains challenging due to substantial heterogeneity in data structures, physical semantics, and spatiotemporal characteristics across satellite imagery, gridded oceanographic fields, and buoy observations. Existing approaches largely focus on task-specific ocean modeling, while learning transferable representations from heterogeneous observations remains an open problem. To address this challenge, we propose YingLan, a multimodal ocean foundation model that integrates physics-attribute-conditioned dynamic encoding, Spatiotemporal Increment Coupling (STIC), and ocean-state evolution-aware pretraining. Specifically, the dynamic encoding mechanism generates adaptive input projections conditioned on physical semantics and observation characteristics, enabling flexible representation of heterogeneous ocean variables. STIC models multiscale spatiotemporal dependencies through cross-scale interactions of latent temporal increments. The pretraining strategy further combines masked observation reconstruction with temporal evolution objectives to facilitate the learning of coherent ocean-state representations across different prediction horizons. YingLan is implemented using MindSpore and trained on Huawei Ascend 910B NPUs, demonstrating its compatibility with domestic AI computing infrastructure. In a seven-day sea surface salinity (SSS) forecasting task conducted in the Shenzhen coastal waters, YingLan achieves an average RMSE of 0.687508 PSU, corresponding to relative error reductions of 0.58%, 5.31%, and 12.42% compared with LangYa, XiHe, and GloNet2, respectively. These results provide initial evidence of the model’s effectiveness in regional ocean forecasting, while broader cross-task and cross-regional evaluations remain necessary to assess its generalization capabilities.
[AI-49] Flat-RLVR: Sharpness-Aware Reinforcement Learning with Verifiable Rewards for Reasoning Capacity in LLM s
链接: https://arxiv.org/abs/2610.29281
作者: Yeonbum Kim,Hyun Kyu Lee,Sung Whan Yoon
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 9 pages, 8 figures
Abstract:Reinforcement learning with verifiable rewards (RLVR), particularly through group-relative policy optimization (GRPO), has emerged as a central paradigm for the post-training of large language models (LLMs). While RLVR is highly effective at enhancing the reasoning capabilities of LLMs, it commonly induces \textitdistribution sharpening, whereby the policy distribution becomes excessively peaked and rare-yet-correct trajectories are progressively suppressed, thus diminishing sample diversity. To this end, we promote flat-reward optima in RLVR to smooth the policy distribution. We theoretically link flat reward RLVR to reduced distribution sharpening, and empirically show improved sample diversity while maintaining single-attempt accuracy. Our method, called \textttFlat-RLVR, searches for flat reward regions via sharpness-aware minimization during LLM post-training, combining this with GRPO variants to achieve better trade-offs between sampling efficiency and reasoning capacity.
[AI-50] ARCANA: A Benchmark for Abstraction and Analogical Reasoning
链接: https://arxiv.org/abs/2610.29271
作者: Ruqi Chen,Zilei Wang,Wei Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Analogical reasoning is widely considered a fundamental component of human intelligence. Few benchmarks are designed to explicitly test far analogical reasoning - reasoning between domains that are superficially dissimilar yet share deep relational or structural similarities. In this work, we introduce an analogical reasoning benchmark called Abstraction and Reasoning Corpus of ANAlogy (ARCANA). The tasks of ARCANA require performing far analogical mappings from real-world objects or physical phenomena to abstract grid-based transformations, different from the well-known ARC-AGI benchmark which primarily emphasized innate human priors. We validate ARCANA through a human study, demonstrating that humans solve these tasks reliably. In contrast, evaluations across a range of large language models show a distinct performance gap compared to humans, revealing systematic failure modes that remain challenging for current models. To address this gap, we propose ConceptSIR, a reasoning framework based on concept slippage, introspection, and reflection. Experiments demonstrate that ConceptSIR enables standard LLMs to solve several challenging ARCANA tasks that were unsolved by direct prompting in our evaluation. Overall, our results suggest that far analogical reasoning remains an open problem for contemporary LLMs. All the materials are available at this http URL. The complete codebase, including benchmark, and ConceptSIR, is publicly available at this https URL. All ARCANA data and task instances can be accessed and explored at this https URL.
[AI-51] CrashGym: Measuring the Decision Value of Experimentation for LLM Agents in Counterfactual Market Crashes
链接: https://arxiv.org/abs/2610.29245
作者: Haocheng Lu,Haoxuan Cheng,Mingyang Chen,Minjun Zhu
类目: Artificial Intelligence (cs.AI)
备注: 14 pages
Abstract:Language-model agents are increasingly asked to run experiments, yet benchmarks rarely measure whether those experiments improve the decisions they are meant to inform. We introduce CrashGym, a deterministic, replayable limit-order-book simulator in which an agent must choose between a deleveraging intervention and a liquidity backstop for a market about to suffer a shock-triggered crash. Before committing, the agent may query intervention experiments in a laboratory realization of the market. Its choice is then scored on an independently seeded target realization in which the simulator executes every feasible intervention, so regret relative to the best intervention is computed by directly simulating every alternative on the same target repeats, with no human or model-based judging. The simulator implements each amplification channel—persistent price impact, liquidity withdrawal, margin-driven liquidation, and an evolving strategy mix—as an explicit mechanism that can be switched on in stages. In a prospectively specified study on 24 newly sampled worlds, laboratory access reduces the mean regret of the one deployment with a complete record (Luna) from 8.878 to 3.785, a paired reduction of 5.092 utility units (nominal 95% world-bootstrap interval: 1.028–9.507), or 57.4% of the OBSERVE-ONLY mean. Prospective comparisons for two further deployments are incomplete; in a separate exploratory study, mean regret is lower with laboratory access for all four deployments evaluated. A laboratory whose strategy mix evolves endogenously yields no established advantage over one with a fixed mix. CrashGym thus measures the value of experimentation as avoided decision loss and lets simulator detail be judged by the decisions it supports.
[AI-52] From Scores to Safe Sets: Exact Safety–Constrained Decoding for Medication Recommendation
链接: https://arxiv.org/abs/2610.29228
作者: Animesh Agarwal,Meysam Ghaffari,Nina Fatehi,Anushka Swarup,Carlos Morato
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent generative medication recommendation systems ultimately produce sets of drugs, but drug–drug interaction (DDI) constraints depend on the set as a whole. A natural post-hoc approach is to first predict medications and then locally repair interacting pairs by removing one drug at a time. We ask whether this local repair step itself introduces avoidable error. Using fixed medication scores from FLAME’s frozen classifier on MIMIC-III, we compare increasingly strong local repair strategies with an exact global decoder while holding the scores, output size, and DDI constraint fixed. The global decoder jointly selects the highest-scoring feasible medication set using mixed-integer linear programming. Across validation and test splits, global decoding consistently achieves higher Jaccard similarity than greedy repair, degree-aware repair, and degree-aware repair strengthened with exhaustive 1-swap local search. Its advantage is small under loose DDI constraints and becomes larger as the constraint tightens; on the test split it reaches +0.0064 Jaccard over the strongest local baseline at the strict DDI operating point. The finding also holds across a fine accuracy–DDI sweep at matched realized DDI exposure and under three score representations. At the strict setting, global decoding retains more true-positive medications while removing more false positives. These results show that medication recommendation is not only a representation-learning problem: even with a frozen clinical model, how its scores are converted into a constrained medication set can materially affect the final prediction.
[AI-53] When Multiple Choices Are Correct: Asymmetric Probability Allocation in Jev and Other Decision Models
链接: https://arxiv.org/abs/2610.29217
作者: Rushab Munot
类目: Artificial Intelligence (cs.AI)
备注: 11 pages, 2 figures. Code: this https URL
Abstract:Decision models return normalized probability distributions over candidate choices, which downstream applications can use to select among choices. Although their providers emphasize the reliability of these probabilities, calibration on tasks with a single correct choice does not establish their reliability when multiple choices are correct. We evaluate three decision models (Jev, Liquid d1, and GPT-6 Luna Decisions) on three diverse datasets (MetaTool, HotpotQA, and ECtHR) to study probability allocation and thresholding. Even when models rank every correct choice above every distractor, in 28% to 50% of those lists, at least one correct choice receives a probability below the uniform probability of 1/n, where n is the number of choices. We also present each model with five identical copies of a correct choice and observe probability disparities, with mean gaps ranging from 17 to 79 percentage points. We further show how these allocation disparities affect the recovery of multiple correct choices under probability thresholding. Decision-model probabilities can rank correct choices, but a probability threshold does not identify the set of correct choices.
[AI-54] Resuming Vision-Language Inference across Onboard Interruptions
链接: https://arxiv.org/abs/2610.29183
作者: Xiaolong Huang,Yinyuan Zhang,Daliang Xu,Yun Ma,Qing Li,Mengwei Xu,Shangguang Wang,Gang Huang,Xuanzhe Liu
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:Executing vision-language models (VLMs) aboard low-Earth orbit satellites enables flexible processing of observation data without first downlinking it to the ground. On commercial off-the-shelf payloads, however, a request may span multiple execution windows: power and thermal constraints, fault-triggered reboots, and competing payload demands can interrupt inference before it completes. Each shutdown discards volatile activations and KV caches, forcing an unprotected request to repeat work after restart. We present OrbitWeaver, a framework that preserves VLM inference progress across onboard interruptions through coordinated checkpointing and recovery. Its phase-aware checkpointing selects recovery boundaries according to the state lifetimes and computational characteristics of vision encoding, language-model prefill, and decoding, balancing storage traffic against recomputation. Its dependency-aware dynamic recovery schedules restoration from partially completed states across the layer and token-chunk dimensions. By overlapping checkpoint and weight reads with recomputation, it reduces restart latency; the recovery planner computes an optimal execution order under its scheduling model within 100 ms. Experiments on satellite payload hardware using on-orbit telemetry traces show up to a 4.07-fold end-to-end speedup and a 3.56-fold reduction in SSD write volume over the evaluated baselines. A 72-hour deployment on two low-Earth orbit satellites demonstrates that OrbitWeaver retains inference progress and resumes execution across repeated bus-enforced shutdowns. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI) Cite as: arXiv:2610.29183 [cs.DC] (or arXiv:2610.29183v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2610.29183 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-55] Correlated Failures of AI Approval Monitors
链接: https://arxiv.org/abs/2610.29178
作者: Luca Zanolini
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Before an AI agent takes a consequential action, such as paying, messaging or changing code, several monitors check it. Their verdicts are combined by aggregating scores or by counting approvals, whatever inputs the monitors share. Monitors that share a base model or a poisoned input, however, may fail together. We borrow the fail-prone systems of distributed computing: lists of monitor sets that may fail together. A rule is safe if it executes no bad action and live if it blocks no good one. We measured twelve small open-weights monitors on payment approval under two pre-registered protocols. Without pre-registration, we also measured three closed frontier models (Claude Opus 5.5, GPT-6 Astra, Gemini 4 Argon) and the open DeepSeek-V3.2. Every reliable fooling of a monitor into approving a fraud came through an evidence source and reached only that source’s readers. Fooling it into blocking a correct payment depended on injection resistance, a monitor’s refusal to follow instructions planted in its data. Small models and DeepSeek obeyed such notes. The three closed models ignored them in all but one case, and Opus also resisted persuasion tailored to it. On new data, a rule that counts sources instead of votes blocked 0.9% of correct payments, against 7.4% for the best counting rule, at equal safety. The pre-registered worst-case test failed. One attack left the approvers on a single source, so no rule over the twelve language-model monitors survives every measured attack. Adding a rule engine, which cannot be persuaded, restored the condition for a safe and live rule on the measured attacks. Diversify the inputs, harden the monitors, count sources, not votes.
[AI-56] Conditional Flow Models Convert Epistemic Uncertainty into Apparent Aleatoric Variance
链接: https://arxiv.org/abs/2610.29172
作者: Navodita Sharma,Abhishek Shetty,Max Simchowitz,Pulkit Agrawal
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Conditional Flow Models (CFMs) are increasingly being used in place of deterministic predictors across machine learning, including for tasks that were classically formulated as regression. Given their success in such tasks, we ask the question: what useful information do the conditional distributions produced by CFMs carry beyond what a point predictor would provide? We identify a systematic phenomenon: in many natural regimes, CFM samples routinely have larger variance than the true conditional distribution, even in nearly deterministic regression problems. This effect is most pronounced where the model has not resolved the dependence on the conditioning variable, and thus the additional variance can be viewed as converting epistemic uncertainty in fitting the ground truth conditional distribution into apparent aleatoric variance in the generated samples. Through controlled synthetic and real-data experiments, and comparisons with explicit smoothing estimators, we show that the induced variance tracks model underfitting of complex or hard-to-fit targets, and decreases with additional training or data. We provide a theoretical account of the phenomenon in a simplified setting and demonstrate a consequent practical use-case of conditional flow-matching: designing high-coverage predictive sets that cover the ground-truth regression target. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2610.29172 [cs.LG] (or arXiv:2610.29172v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.29172 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-57] AI-Based End-to-End Automotive Design and Software Development for SDVs
链接: https://arxiv.org/abs/2610.29160
作者: Nenad Petrovic,Alois Knoll
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper explores the potential of Artificial Intelligence (AI) to enable end-to-end automotive design and software development for Software-Defined Vehicles (SDVs). Based on our contributions within the Central Car Server (CeCaS) and follow-up EU-funded projects, we present an AI-driven toolchain integrating Large Language Models (LLMs) with Model-Driven Engineering (MDE) to automate the transformation of natural-language requirements into system architectures, executable software, and validation artifacts. The approach emphasizes traceability, consistency, and compliance with safety-critical automotive development requirements. Validation is performed on a centralized vehicle computing platform supporting adaptive cruise control and emergency braking, tested in Hardware-in-the-Loop (HiL) and Software-in-the-Loop (SiL) environments. We discuss current capabilities, practical limitations, and future directions toward increasingly automated, reliable, and quality-assured automotive software development with minimal human intervention.
[AI-58] Where Does Timbre Hide? Localizing Speaker Information in Speech Signals
链接: https://arxiv.org/abs/2610.29150
作者: Yiquan Zhou,Yixuan Wang,Xiaozhe Qi,Xin Gao,Shihao Li
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: accepted by SLT2026
Abstract:Timbre similarity is a key evaluation target for generative speech systems, including voice conversion, speaker-cloning text-to-speech, and zero-shot speech models. When the generated voice does not match the target speaker, the problem often reflects imperfect separation of speaker-related timbre from content, making it important to identify where timbre information resides and which representations still carry it. Using speaker identity as a measurable proxy, we probe frequency bands, phase/magnitude structure, WORLD source/filter decomposition, voiced/unvoiced regions, self-supervised learning (SSL) layers, EnCodec/HuBERT tokens, and a 45-listener test across Mandarin, English, and Japanese speech/singing data. Speaker information is distributed: 1–8 kHz magnitude and vocal-tract spectral-envelope structure dominate perception, above-8 kHz bands remain machine-discriminative, shallow SSL layers expose compact speaker information, and token leakage depends on tokenizer/layer choice. These findings indicate where generation systems should condition on or transform speaker information, and where content, codec, and token pipelines should audit residual speaker leakage.
[AI-59] Steering Black-Box Large Language Model Agents with Pitfall Anticipation World Model
链接: https://arxiv.org/abs/2610.29139
作者: Jiongxiao Wang,Han Ding,Youzhi Luo,Yawei Wang,Yun Zhou,Linbo Liu,Panpan Xu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Frontier black-box Large Language Model (LLM) agents are proficient at long-horizon reasoning and precise action generation. However, when it comes to a specific agentic task, they have no internalized knowledge of how that particular environment unfolds. A world model offers a natural remedy, providing the agent with environment specific knowledge by foreseeing how the environment will respond to the action it is about to take. Prior world models for LLM agents mainly predict next observations at each step, which is costly and often uninformative because individual observations provide limited task-relevant guidance and not every step requires call for foresight. This raises a key question: what a world model should predict and when should it be invoked for agentic tasks? Our pilot study suggests that world model guidance is most useful when it anticipates pitfalls within a bounded multi-step future. Motivated by this, we introduce Pitfall Anticipation World Model (PAWM), which selectively intervenes when a potential pitfall arises and predicts the impending failure to steer the agent away from it. PAWM is trained in two stages: Supervised Fine-Tuning (SFT) on synthetic data labeled with agent pitfalls, followed by GRPO co-training with a black-box LLM to further optimize it in the target environment. PAWM improves Claude Sonnet 4.5 performance by 24.3% on ScienceWorld and 7.7% on WebArena-Lite, outperforming a range of baselines. These results demonstrate that PAWM provides an effective form of world model guidance for black-box LLM agents.
[AI-60] LaVGuard: Selective Decoding for Contextual Value Drift
链接: https://arxiv.org/abs/2610.29135
作者: Jiale Dai,Liuxian Ma,Hongcan Deng,Xiaoke Niu,Wenjing Zhang,Zhaoxiang Liu,Shiguo Lian,Guojie Song
类目: Artificial Intelligence (cs.AI)
备注: 37 pages, 5 figures, 16 tables, including appendices
Abstract:A content-safe response can still promote priorities that conflict with its task: an assistant may demand compliance when asked to preserve autonomy. We study contextual value drift as a task-relative decoding problem with an explicit allowance for change. LaVGuard separates an authorized ten-dimensional profile from a frozen estimate of the values expressed in the visible prefix. A profile-relative density gate selects when to evaluate candidate continuations; their signed progress determines a logit tilt constrained by both changed-position and cumulative-KL budgets. We establish local surrogate improvement, pathwise budget feasibility, and a bound on response-distribution change. Profile conditioning raises boundary TPR at 5% FPR from .58 to .72. At the same .12 mean intervention ratio, the selection study reports risk-safety scores of .88 for LaVGuard and .72 for position-matched edits. The resulting interface makes task priorities actionable while keeping the extent of intervention explicit and bounded.
[AI-61] NormTune: Norm-Guided Parameter-Efficient Fine-Tuning for Reasoning -Intensive Tasks
链接: https://arxiv.org/abs/2610.29134
作者: Zhangming Li,Qinghao Hu,Weifan Guan,Yifan Zhang,Peisong Wang,Jian Cheng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Full fine-tuning has achieved remarkable success on reasoning-intensive tasks, but it often requires substantial GPU resource consumption. Parameter-efficient fine-tuning (PEFT) provides a promising alternative for reducing this cost, yet we observe a substantial performance gap on reasoning-intensive tasks. In this paper, we present three key observations showing that this gap is closely associated with norm-related under-adaptation, where PEFT produces markedly smaller weight-update norms than full fine-tuning. Based on these observations, we propose Norm-Guided Parameter-Efficient Fine-Tuning (NormTune), a simple yet effective strategy that adaptively strengthens insufficiently updated layers according to their weight-update norms. Extensive experiments show that NormTune consistently enhances multiple PEFT methods across challenging reasoning benchmarks. Specifically, when trained on S1K-1.1, integrating NormTune into rank-8 LoRA, Bone, and MoRA yields substantial average accuracy improvements of 24.24%, 11.33%, and 16.21% , respectively, across AIME 2024, MATH 500, GPQA, and AIME 2025. Moreover, NormTune-enhanced rank-256 LoRA, Bone, and MoRA achieve performance comparable to S1 full fine-tuning, with respective average gains of 2.17%, 0.93%, and 3.00% over this baseline.
[AI-62] Mitigating Reward Hacking in Realistic Scenarios Through Representation Engineering
链接: https://arxiv.org/abs/2610.29129
作者: Austin Meek,Kyle Cox,Matthew Nguyen,Austin J. Brockmeier,Steven Basart
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Project page: this https URL Code: this https URL
Abstract:Reward hacking is a major problem in current production machine learning systems, resulting in costly and dangerous failures. Here we study how to monitor for and mitigate reward hacking in realistic settings, including tool calls and long contexts. We apply a dynamic intervention to steering directions obtained by three different methods: difference-in-means vectors from labeled rollouts, gradient optimization-based vectors, and vectors extracted when models reference an anti-reward-hacking model spec. This dynamic intervention perturbs model representations less than uniform steering vector addition to the residual stream, allowing for more precise edits. We find that we are able to cheaply and significantly reduce reward hacking without correspondingly significant costs to model coherence or capability, and that steering vectors originally extracted from coding environments also generalize to reducing reward hacking in other domains. Our results pave the way towards latent space monitoring and control over realistic production systems.
[AI-63] StochIKM: Automated High-Fidelity Reconstruction of Individual Patient Data from Published Kaplan-Meier Curves
链接: https://arxiv.org/abs/2610.29122
作者: Liangze Ke,Daniel Zelterman,Wei Wei,Guannan Gong
类目: Artificial Intelligence (cs.AI); Applications (stat.AP)
备注:
Abstract:Individual patient data are the gold standard for secondary analysis of time-to-event evidence, but they are rarely released, and reconstruction from a published Kaplan-Meier figure could help. Existing methods all claim high fidelity, each judged by whichever agreements it happens to achieve, and each returns a single dataset although the map from figure to data is not bijective. We define fidelity explicitly: a dataset is admissible when it reproduces the risk table exactly, follows the digitized curve to within the image resolution, and respects the printed median, interval and event count. We propose StochIKM, a stochastic algorithm that digitizes the figure with a vision-capable language model and uses the high-fidelity definition as constraints, returning an admissible set of individual patient datasets. A solution is guaranteed to exist whenever the figure has been read correctly, so a failure indicts either the published figure or its digitization. In simulation and in 79 arms from published oncology trials, StochIKM reproduces the published risk tables and medians more closely than LLM-improved IPDfromKM, a high-impact alternative.
[AI-64] Recursive Self-Improvement on a Budget: Scoring Code Edits Before They Run
链接: https://arxiv.org/abs/2610.29121
作者: Erfan Darzi,Sree Bhargavi Balija,Sheng He
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 9 pages, 3 figures
Abstract:A coding agent that rewrites its own scaffold has to choose which proposed edit to expand before that edit has been evaluated, and the search then has to return one agent. Each run fits one linear readout on the parent’s steering-split solve rate and eight coordinates of the proposed diff, from 96 scaffold edits labeled by four evaluations of a 16-instance fit split, and then freezes the coefficients. HumanEval+ reserves 48 instances as the steering split and Polyglot reserves 64. That split scores parents and keeps an archive of four nodes. Huxley-Gödel Machine selection draws a parent by Thompson sampling on the steering counts of nodes that have already been evaluated, and it expands the child that parent proposes. Every method then returns the earliest evaluated node whose steering-split solve count is maximal. The tables report the held-out score of that node on the other 100 HumanEval+ instances and the other 145 Polyglot instances, next to the root scaffold and the calibration edit with the highest fit-split label. Twelve runs of GPT-4o return a node at 87.0% on held-out HumanEval+, against 84.2% for the parent-rate ablation. On that split Huxley-Gödel Machine selection returns 85.1%, the root is at 81.2%, and the calibration-best edit is at 82.8%. On the same split Claude Sonnet 4.5 returns 86.6%, beside 86.5% for Huxley-Gödel Machine selection, 85.3% for the parent-rate ablation, and 85.1% for the calibration-best edit.
[AI-65] Dual-Channel Contrastive Encoding of Sperm Whale Coda
链接: https://arxiv.org/abs/2610.29101
作者: João Victor Quintanilha,Emma Virnelli,Shuo Zhang
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Sperm whales (Physeter macrocephalus) communicate through short click sequences called codas, whose structure includes the rhythmic pattern of inter-click intervals (ICI) and the spectral composition of the clicks. In this paper, we propose the Dual-Channel Contrastive Encoder (DCCE), which explicitly encodes rhythm and spectral information with separate encoders before fusing them. We evaluate DCCE on three coda-understanding tasks: social unit, coda type, and individual identification. We compare DCCE against strong baselines from literature under a common linear-probe protocol and analyze the role of each channel and training objective through ablations. On 1,383 codas, shared partitions and validation-only layer selection support three random splits and three recording-date-disjoint folds. DCCE with unit pairing and type supervision achieves mean random-split macro-F1 of 0.901/0.865/0.597 for unit/type/identity. Raw ICI remains strongest for type (0.987), and ICI plus temporal mel statistics is a strong identity reference (0.724). An auxiliary identity objective improves spectral-branch identity prediction but does not improve the mean fused-identity score in this configuration. Date-grouped evaluation and four year holdouts expose generalization limits. The results support task-dependent combinations of timing and spectral information, without establishing independent biological channels or general architectural superiority. Code, notebooks, and complete results are available at this https URL.
[AI-66] Agent ic Music Post-Production
链接: https://arxiv.org/abs/2610.29093
作者: Jaywon Koo,Junghyun Koo,Marco A. Martinez-Ramirez,Wei-Hsiang Liao,Seungheon Doh,Qiyu Wu,Vicente Ordonez,Yuki Mitsufuji
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce MixAgent, an agentic pipeline for music post-production that follows the workflow of audio engineers to build interpretable and editable mixing plans guided by a reference song. A grouping agent first routes related input tracks, such as drum recordings, into subgroups. A planning agent then uses retrieved examples to choose and order audio effects, such as volume control and reverb, for tracks, subgroups, and the full mix. To refine the mix, a detector identifies what needs correction, and a critic proposes numerical parameter adjustments from tracks to the full mix. In our experiments, prompting recent multimodal models to listen and revise often worsens the mix, making reliable correction a central challenge. We therefore build MixCritic, a dataset of 45,657 repair critiques derived from parameters fitted to professional mixes, and train the critic with supervised fine-tuning followed by preference optimization on rendered audio outcomes. On MedleyDB, learned refinement reduces error by 19% relative to the initial mix, with preference optimization reducing it by 8.4% over supervised training alone. Retrieved examples further improve effects selection, and a human listening test shows that MixAgent gets higher quality ratings than mixes produced by Qwen3-Omni, Gemini 3.8 Flash and GPT-4o-audio.
[AI-67] Context-Augmented Session-Calibrated Blood Pressure Tracking from Photoplethysmography
链接: https://arxiv.org/abs/2610.29091
作者: Farouk Ganiyu Adewumi,Timothy Oladunni
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:this http URL distinguish the contributions of acquisition context and photoplethysmography (PPG) to session-calibrated blood pressure (BP) this http URL. The original comparison used three repeats of five-fold participant-level cross-validation in 653 AuroraBP participants, excluding calibration-labelled prediction targets. Matched analyses separated context and PPG and removed elapsed-time terms. Later exploratory follow-ups retained PPG, accelerometer signals and three initial cuff readings. Separate studies evaluated pulse-rate fusion and an intraoperative VitalDB model. Main this http URL original wearable-state model achieved participant-level mean absolute errors (MAEs) of 7.697/6.139 mmHg for systolic/diastolic BP (SBP/DBP). Added context reduced MAE by 0.436/0.126 mmHg (97.5% intervals 0.305-0.573/0.053-0.200). In the matched factorial without the external PPG prior, PPG improved on calibration, time and protocol-provided posture/activity by 0.214/0.135 mmHg; removing time left the DBP interval including zero. Standard PPG descriptors contributed, but additional nonlinear/attractor benefit was not established. In a separate follow-up series, a sensor-history ensemble achieved 7.299/5.445 mmHg versus its static-rich reference’s 7.363/5.470, small gains of 0.064/0.025 mmHg. Larger DBP errors did not improve uniformly. Quality-weighted fusion reduced pulse-rate MAE from 3.985 to 3.690 beats per minute. VitalDB MAEs remained 15.375/8.833 mmHg despite small PPG gains. Significance. The contribution is a calibrated evaluation framework with explicit controls, reproducible feature-cache-to-results analyses and retained negative findings. The sensor-history gain is exploratory after repeated dataset reuse. Independently estimated wearable states, complete raw-signal reproduction of the supplied richer features, external transfer and clinical adequacy remain unestablished.
[AI-68] Value-Rectified Distillation for Flow-based Offline Reinforcement Learning
链接: https://arxiv.org/abs/2610.29075
作者: Ke Jiang,Wen Jiang,Yoshinobu Kawahara,Xiaoyang Tan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We aim to address the problem of Out-of-Distribution (OOD) Mode Averaging, a critical problem that emerges when distilling Flow Matching (FM) based behavior policies in offline Reinforcement Learning (RL): When learning over multi-modal datasets, standard distillation inadvertently forces the policy to interpolate across different behavioral modes, causing the generated actions to collapse into invalid, OOD regions. To resolve this, we propose Value-Rectified Distillation (VRD), a novel framework that reformulates offline policy distillation as a value-guided generative trajectory alignment problem. By employing a dynamic coupling mechanism, VRD actively rectifies flow dynamics to explicitly decouple distinct behavioral modes, cleanly isolating high-value actions. Specifically, we instantiate VRD through two complementary algorithms: VRD-Q, which utilizes value-quantile behavior distillation to statistically filter out low-value behaviors, and VRD-R, which reorganizes the latent space to geometrically isolate high-value generative trajectories from the FM-based behavior policy. Extensive empirical evaluations demonstrate that VRD improves existing generative baselines across diverse continuous control and planning tasks on the D4RL, OGBench, and Minari benchmarks, especially over those multi-modal, non-expert datasets.
[AI-69] Distilling Multi-Teacher Scoring Principles for Unsupervised Time Series Anomaly Detection NEURIPS2026
链接: https://arxiv.org/abs/2610.29063
作者: Dongchan Cho,Jiho Han,Juwon Hwang,Changhee Min,Minsang Kim,Namsoon Jung
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted by NeurIPS 2026
Abstract:Time series anomalies take diverse forms, and individual detectors encode different anomaly scoring principles that rarely cover all anomaly types. Recent unsupervised time-series anomaly detectors provide complementary scoring signals, yet deploying them as an ensemble preserves the inference and maintenance cost of every individual detector. We study whether heterogeneous teacher detectors can be distilled into a single lightweight detector in the unsupervised setting, where no anomaly labels are available to define a shared target. AnoMix addresses this by extracting weak pairwise supervision from the training-set anomaly scores of frozen teachers. It normalizes each teacher score sequence independently and trains the student with CoRank, a multi-teacher ranking-distillation objective that converts teacher score gaps into pairwise supervision through teacher consensus. Across seven benchmark datasets, AnoMix successfully integrates complementary teacher scoring principles into a single deployable model, improving over individual detectors and static score ensembles while using a compact student with under 80k parameters on the benchmark settings. A zero-shot variant further shows that the distilled ranking signal transfers to unseen datasets and can outperform large pretrained time-series foundation models for anomaly detection.
[AI-70] Does Every Instance Count Equally? Order Effects in LLM Multi-Instance Processing
链接: https://arxiv.org/abs/2610.29057
作者: Jingxuan Chen,Mohammad Taher Pilehvar,Jose Camacho-Collados
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Users increasingly rely on large language models (LLMs) to analyse collections of independent inputs, such as product reviews or social media posts, and return a single aggregated answer. This setting requires multi-instance processing (MIP), where a model combines information from multiple instances into one prediction. We study a task in which the model must infer a binary label for each instance, and identify the most frequent label among the full instance set. The correct answer should be invariant to instance order. By evaluating fifteen LLMs across five datasets and seven instance order patterns, with each instance set held fixed across patterns, we find substantial order sensitivity. Accuracy can differ by about 90 percentage points between the best and worst order patterns, with models generally more accurate when majority-label instances appear at the start than when they appear in the middle or at the end. For subsets of models, available reasoning traces and gradient-based attribution reveal uneven emphasis across positions. Traces disproportionately mention early instances, while attribution scores are generally higher near the beginning and end than in the middle. These results show that instance order can systematically bias aggregated predictions, highlighting the need to evaluate MIP across different input arrangements.
[AI-71] AutoFraming: Synthesizing Dynamic Frames of C Code with LLM s
链接: https://arxiv.org/abs/2610.29046
作者: Han Zhou,Yu Luo,Dianxiang Xu
类目: Artificial Intelligence (cs.AI)
备注: 12 pages, 1 figure, 6 tables
Abstract:Large language models (LLMs) have shown considerable promise in synthesizing formal specifications from source code. However, existing approaches primarily focus on functional contracts and loop invariants, while largely neglecting frame conditions, which specify the memory locations that may or may not be modified during execution and constitute an essential part of formal specifications. To address this limitation, we propose an LLM-based approach for synthesizing ACSL frame specifications alongside functional contracts from C code. Our approach analyzes code property graphs (CPGs) to extract frame-relevant information from the input code and leverages this information to guide the LLM in generating accurate frame conditions. The resulting specifications capture both region-separation constraints and data dependencies that govern variable modifications. Experimental results demonstrate the effectiveness of our approach. On a benchmark of 84 ACSL-compliant functions from non-trivial real-world systems, our method successfully synthesizes error-free frame specifications for 92.9% of the functions, representing a 179% relative improvement over state-of-the-art techniques. On a second benchmark consisting of 51 functions, the success rate increases from 80.4% to 96.1%. Furthermore, when integrated as an auxiliary component into existing LLM-based ACSL-synthesis frameworks, our approach improves frame-specification generation performance by up to 61.9%.
[AI-72] GRAIL: From Saliency to Semantics in Time-Series Explanation
链接: https://arxiv.org/abs/2610.29036
作者: Enver Menadjiev,Yiqiu Shen,Jaesik Choi,Julia Stoyanovich
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Explaining a time-series model requires both locating the evidence and extracting meaningful knowledge from it. Yet a single timestep is not a semantic unit: the meaningful patterns live across many samples, not in any one of them. Per-timestep saliency is therefore noisy, high-dimensional, and unstable. Existing methods address this by introducing fixed-size windows and treating them as independent players in a cooperative game. Evaluating a coalition of such players requires defining a ``baseline’’ region, which prior work tackles with fixed counterfactuals (e.g., a flat line, noise, or a spectral surrogate). This choice frequently dominates the resulting explanation, obscuring the model’s true decisions. To address these issues, we propose GRAIL, a region-based time-series explanation framework that uses domain-specific intervals, such as the P-wave and the QRS complex of an electrocardiogram (ECG) beat, as players. We describe each region with human-readable features and generate counterfactual baselines with an on-manifold conditional generator. Such a choice of players also admits non-temporal features: static covariates enter as singleton players attributed with the temporal regions on one scale, so a patient’s tabular evidence (e.g., age, sex) is weighed directly against the signal. We attribute the predictions to the features via Owen values with common-random-number variance reduction. Experimental results on both synthetic and real data show that the proposed framework produces faithful and robust explanations, evaluated under matched on- and off-manifold operators, offering meaningful knowledge to domain experts. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2610.29036 [cs.AI] (or arXiv:2610.29036v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2610.29036 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-73] PHD-Hand: A Printable Humanoid Dexterous Hand with Linkage-Driven 3-DOA Digits
链接: https://arxiv.org/abs/2610.29030
作者: Neel Adwani,Muhaiminul Islam Akash,Saffia Ouali,Ethan Liu,Cong Wang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Submitted and Accepted at IEEE-RAS International Conference on Humanoid Robots (Humanoids 2026). 8 pages. Supplementary video: this https URL
Abstract:Currently, dexterous humanoid robot hands are widely considered the final hardware bottleneck that hampers generalist robots from physically assisting people at home and work. As conventional tendon-driven designs suffer from high cost, demanding maintenance, poor reliability, and short durability, alternative linkage-driven designs have increasingly gained attention. Despite their potential to avoid the weaknesses of tendon-driven hands, integrating multiple degrees of actuation (DOA) in a single digit with linkage drivetrains is a challenge for design and fabrication. Only a few studies have managed to present functional humanoid hands that are completely linkage-driven and have at least three DOAs per digit without compromising actuator placement and other design preferences. Our work introduces a new linkage-driven hand featuring a unique flexure hinge. Together with elastic drive rods made of spring steel, the linkage drivetrains are significantly simplified. Unlike other linkage-driven designs of similar dexterity, our design does not require many miniature moving parts. As a result, the hand can be fabricated easily with a budget 3D printer. This not only significantly lowers the barrier to entry for using and developing dexterous humanoid robot hand functions, but also allows the hands to be sized much closer to regular human hands. The design offers desirable kinematic advantages as well. A prototype is evaluated for grasping and object manipulation. In addition, this paper presents our latest findings on kinematics and control of the hand.
[AI-74] Inheritance as an Admission Test for LLM Prompt Merge Operators
链接: https://arxiv.org/abs/2610.29023
作者: Zan-Kai Chong,Hiroyuki Ohsaki,Bryan Ng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents are often improved by iterating until a fixed verifier is done. Once that verifier saturates, the loop has nothing left to climb. We instead score agents against the current population, and a frozen panel records whether anyone got better. The panel mean can rise inside the seeds by elimination alone. We therefore count a run as training only when its final mean beats the best generation-zero member. A merge is admitted only when the child moves toward the donor, past the child the same operator produces at zero dose, and the lower confidence bound on that movement reaches half the requested dose. That is the admission test. A detectable shortfall rejects the operator, and the test is otherwise undetermined. We keep realised fitness \phi as the selector and reference performance \theta as the unused ruler. Every measurement uses gemini-3.1-flash-lite on one negotiation task, and no fixed-oracle arm was run. In the open loop, tactic append grafts short donor rules and is admitted. Free rewrite moves only a third of the requested dose and is rejected. Template append pastes raw donor replies into the child. Its movement clears the bound on the first probe but not on the re-probe, and it is left undetermined. A held-out re-probe on the five situations the merge never saw confirms that both appends move, and it does not rescue free rewrite. Neither append rises with the dose. Constrained rewrite is admitted in the open loop and was not run in the closed loop, which changes only the operator. The population, the scores and the task stay fixed. No free-rewrite run beats the best generation-zero member. Every tactic-append run and every template-append run does. Sampled loop children keep that split. Free rewrite moves no further toward the donor, and both appends still clear the bound, by less than in the open loop.
[AI-75] An Evidence-Driven Multi-Agent Compound AI System for Head and Neck Proton Radiotherapy Planning
链接: https://arxiv.org/abs/2610.29010
作者: Qingqing Wang,Liqiang Xiao,Chang Chang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Radiation treatment planning requires balancing competing objectives to satisfy clinical criteria while accommodating physicians’ preferences. This study aims to develop an evidence-driven, multi-agent compound AI system that integrates DRL-based automated treatment planning with LLM-based conversational plan refinement for proton PBS therapy. A policy network trained using PPO first generates multiple plans without human intervention, from which one is selected as the target plan and the others serve as references for subsequent refinements. For challenging cases requiring further adjustments, an LLM-based multi-agent system enables plan refinement through natural language interactions. The system comprises a planning agent that recommends evidence-supported adjustments, a chatbot providing a unified conversational interface, and an evaluation agent that retrieves clinical metrics and identifies constraint violations. To enhance domain-specific reasoning, the planning agent based on Qwen3-14B is designed to justify its recommendations using evidence from previously generated plans and post-trained on task-specific data using SFT and GRPO. The framework was trained and evaluated using 21 patients with HN cancers, with 5 PPO-generated plans per patient. Of these, 50 plans were selected for instruction-guided refinement. Compared with the base LLM, the post-trained Qwen3-14B demonstrated improved instruction-following, information retrieval and domain-specific reasoning capabilities. The resulting adjustments more effectively improved user-prioritized objectives while limiting deterioration in competing objectives, yielding more clinically appropriate trade-offs in patient-specific plan refinement. The proposed compound AI system provides a user-friendly planning platform, offering a promising approach to personalized proton PBS treatment planning.
[AI-76] Rethinking Time Series Tokenization from a Frequency Perspective
链接: https://arxiv.org/abs/2610.28997
作者: Zelong Tian,Ming Jin,Bo Du,Shirui Pan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 40pages, Accepted by NeuIPS 2026
Abstract:Tokenization that partitions time series into subsequences has become a foundational paradigm in modern time series modeling. However, we find that current tokenization strategies inherently introduce spectral distortions, forcing the learned representations to diverge from the true underlying patterns such as trend, periodicity, and seasonality, thereby impairing model performance. Through theoretical analysis from a frequency perspective, we derive the boundary conditions under which such distortions occur. To overcome this distortion, we propose TimeFT, a nearly distortion-free frequency-based tokenizer for time series data. TimeFT obtains tokens through frequency-domain partitioning, frequency shifting, and Nyquist sampling. The method is parameter-free, incurs negligible computational complexity, and can serve as a drop-in replacement for existing tokenizers. Our extensive experiments on diverse tasks such as forecasting, classification, and anomaly detection demonstrate that TimeFT consistently and significantly improves performance.
[AI-77] Agent PolicyForge: From Executable Environments to Policy Benchmarks and Adaptive Guards
链接: https://arxiv.org/abs/2610.28989
作者: Chao Shang,Wanyu Du,Yi Zhang,Sandesh Swamy
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: 20 pages, 2 figures
Abstract:Customized agent deployments define policies over tools, resources, and execution conditions. Providing these policies in the task agent’s prompt does not reliably prevent violations. Training a guard requires examples of tool calls that comply with or violate those policies. We introduce AgentPolicyForge, a framework that uses executable environments to construct policy benchmarks and iteratively train policy guards. With this framework, we construct PolicyGym, a dataset across ten policy families using 698 scenarios for training data generation and 254 separate held-out scenarios for human-reviewed test sets. Tool calls are executed and their outcomes recorded to help determine whether they comply with the policy. Every violating case uses a permitted tool, requiring checks of arguments, resources, and context rather than tool identity alone. The case construction process supports iterative guard training: an attacker generates requests using retrieved strategies, and the guard is fine-tuned on balanced, environment-verified examples. Training improves policy enforcement even with a fixed archive. Updating the archive further reduces in-domain attack success compared with keeping it fixed. We compare policy-aware task agent self-regulation with a trained policy guard. On PolicyGym, the final guard-based configuration achieves a 40-44% relative reduction in attack success rate and fewer benign rejections than policy prompting alone.
[AI-78] PerMIT: Securing Persistent Memory for LLM Agents via Machine-Checked Integrity Theorems and Scalable Community Trust
链接: https://arxiv.org/abs/2610.28987
作者: Tanqiu Jiang,Min Bai,Sadat Shahriar,Wanyu Du,Sandesh Swamy
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:Persistent memory is a largely undefended surface for injection and poisoning, and the same risk limits memory sharing among agents. We present PerMIT, a memory architecture with provable integrity guarantees under an explicit trust assumption: only user-typed inputs and values confirmed at a gate are trusted. PerMIT mediates the release and use of untrusted environment-derived values through a finite memory interface, while preserving agent-driven multi-turn planning and tool choice. Over this finite interface, we prove two integrity theorems that are also machine-checked in Lean 4, which together ensure that environment-derived memory reaches a sensitive action only if confirmed at the gate and influences the agent’s behavior only through what the gate displays. Every attack in our threat model thereby reduces to a single auditable decision, which a human, an LLM verifier, or both can make in practice. On Trojan Hippo, PerMIT records zero observed attack success with self-verification and lightweight verifiers. Because each confirmation is a logged verdict on exact content, endorsements accumulate across users, and sufficiently endorsed items become community-confirmed with no further verification required. Under explicit assumptions, we prove and corroborate in simulation a co-scaling property: as the community grows, the risks of promoting poison and of wrongly evicting benign knowledge fall, while per-user verification effort does not rise. On \tau^2 -bench, communities of up to 90 autonomous agents use PerMIT to build shared knowledge bases with no direct memory injection promoted, and the resulting knowledge significantly improves new agents on held-out tasks, suggesting a path toward agents that safely self-improve at test time.
[AI-79] Behavior Beyond Text: Evaluating Refusal Transfer Failure in LLM Agents
链接: https://arxiv.org/abs/2610.28982
作者: Ekagra Gupta,Raj Saha,Aniekan-Abasi Inyang,Adam Al Gharib
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: Raj Saha and Ekagra Gupta contributed equally and share first authorship
Abstract:While large language models are increasingly deployed as tool-calling agents, their safety evaluations still focus primarily on their ability to refuse harmful requests in text. We investigate whether the refusal behavior in text transfers to tool use. We construct a benchmark of 2,304 prompts across four domains, consisting of matched triplets that present the same harmful or benign request in text-only, standard tool-enabled, and adversarially framed tool-enabled settings. We then assess tool-call safety using 20 predefined forbidden actions. Testing five models of varying sizes and families, we find that requests refused in text frequently result in unsafe tool calls. Under standard tool-enabled prompting, this divergence occurs for 10-52 percent of harmful requests refused in chat. To mechanistically investigate this gap, we identify a refusal-associated direction in the residual stream of each of the five models. We find that in tool-enabled contexts, the activations along this direction decrease in four of five models. Furthermore, lower activation predicts unsafe tool calls across all five models (AUC 0.76-0.86). However, restoring the signal to the respective text-only value through activation patching only prevents a subset of unsafe calls, primarily by suppressing tool use rather than by producing safer calls. Steering along the refusal direction nearly eliminates unsafe calls only at strengths that also suppress benign tool use or degrade the output quality. Our findings indicate that the identified refusal direction contributes to whether a model acts, but only partially explains the refusal divergence. Therefore, evaluating the safety of tool-calling agents requires directly examining their actions along with their textual responses.
[AI-80] SSMCGM-Stream: A Streaming State-Space Model for Personalized Diabetes Management ALT NEURIPS2026
链接: https://arxiv.org/abs/2610.28976
作者: Shakson Isaac,Chirag Patel
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to AI4Science and GenAI4Health NeurIPS 2026 workshops
Abstract:Continuous glucose monitoring supports diabetes management but requires forecasting models that are accurate, interpretable, and efficient enough for real-time deployment. We present SSMCGM-Stream, a streaming state-space model that maintains a personalized hidden state updated with each incoming glucose reading to provide constant-time forecasting, physiologically structured counterfactual reasoning, and interpretable forecasts through hidden-state attribution. Built on this foundation, SSMCGM-Agent delivers risk-aware, natural-language decision support by translating forecasts, explanations, and counterfactuals into grounded recommendations through a guarded language-model interface. Evaluated on the T1DEXI cohort, SSMCGM-Stream achieves forecasting performance comparable to strong neural baselines while substantially reducing computational cost, enabling continuous on-device diabetes decision support.
[AI-81] From Weather to Enhanced Weathering: Weather Foundation Models for Carbon Dioxide Removal at Operational Scale NEURIPS2026
链接: https://arxiv.org/abs/2610.28973
作者: Sasankh Munukutla,Felipe Oviedo,Danrong Zhang,Kenji Takeda,Juan M Lavista Ferres
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at 40th Conference on Neural Information Processing Systems (NeurIPS 2026), Sydney, Australia. Workshop: Tackling Climate Change with Machine Learning
Abstract:Enhanced Rock Weathering (ERW) is a carbon removal solution with the potential to remove gigatons of CO _2 per year, but quantifying that removal today requires dense soil sampling and laboratory analysis. We evaluate five weather Foundation Models (FMs) as frozen encoders feeding lightweight decoders, under a leave-one-field-out protocol on 5,192 chronosequences from 264 fields across Brazil and the US. To our knowledge, this is the first holistic evaluation of weather FMs for ERW and the largest evaluation on operational ERW data. We find that across weather FMs, representations transfer to ERW: our best model, TerraNova-ERW, built on Aurora 1.5, positively orders measurements within fields it has never seen. Zero-shot prediction of a new field’s mean removal is not yet feasible, and we attribute this to data scale: the same architecture excels on \sim 10^5 natural-weathering sequences, anchoring the scale we expect ERW data must reach. Few-shot prediction, blending the model with a few samples per field, is supported today: TerraNova-ERW recovers over 60% of the highest-removal fields with two samples per field, roughly halves the samples needed to estimate a field’s mean removal, and can cut sampling on our densest fields by 28-98%.
[AI-82] Unit-Aware Differentially Private Quantum Machine Learning for Clinical and Wearable Health Data
链接: https://arxiv.org/abs/2610.28968
作者: Md Khalid Syfullah,Alvi Ataur Khalil
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Clinical registries and wearable sensors support early health-risk assessment, yet models trained on these records may leak sensitive information when released. Differential privacy (DP) bounds this leakage by injecting noise whose total magnitude grows with the number of trainable parameters, a cost especially severe for small health cohorts. This motivates parameter efficient models including quantum machine learning (QML), whose variational circuits can represent complex decision boundaries with few trainable parameters. It raises a question: at matched input dimensionality and privacy budget, does a quantum model retain more utility than a classical one? We investigate this question on three clinical cohorts and one wearable cohort, treating each clinical record or participant as a privacy unit. We compare capacity-matched quantum, multilayer perceptron, and logistic regression models across five privacy budgets and five seeds. The quantum model leads on three clinical cohorts by up to 0.104 ROC AUC averaged across budgets; however, trails on the wearable cohort, where the small number of participants requires substantial noise. No difference survives Holm correction across 24 tests, so these results indicate a trend rather than a quantum advantage. An analytic gradient bound shows that lower clipping thresholds can improve utility by reducing noise despite clipping bias. Finally, an experiment on IBM quantum computer (ibm_fez, a 156-qubit superconducting processor) using eight fixed test cases yields mean absolute probability errors of 0.046 (private) and 0.063 (non-private) relative to simulation, supporting the feasibility of inference on noisy intermediate-scale quantum (NISQ) hardware. Code is publicly available at this https URL.
[AI-83] Black-Box Red-Teaming of Multi-Agent Systems via Reinforcement Learning
链接: https://arxiv.org/abs/2610.28966
作者: Yuqi Jia,Hangsheng Zhang,Chenxi Liu,Junjie Liang,Xun Chen
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents are increasingly deployed in multi-agent systems (MAS) to accomplish complex tasks. Prior black-box red-teaming attacks mainly focus on single agents, but we find that these methods are far less effective in MAS, where multiple sub-agents may not directly interact with the user. Therefore, we introduce ReMAS, the first reinforcement learning-based red-teaming framework tailored to MASs, which fine-tunes attacker LLMs to generate effective adversarial prompts for system prompt extraction. The framework follows a two-step process: first, a rewrite stage refines base attack prompts to increase extraction success, and second, a template generation stage constructs attack templates that improve the likelihood of invoking specific sub-agents and thus revealing their system prompts. Extensive experiments show that our method substantially improves attack success rates compared to existing black-box attacks, with transferability across different backend LLMs and MAS structures. These results underscore the vulnerabilities of multi-agent systems and the importance of developing stronger defenses against adaptive black-box adversaries.
[AI-84] SLoSa: Small Low-Power and Safe Foundation Models for Robotic Task Planning
链接: https://arxiv.org/abs/2610.28955
作者: Weizhe Xu,Jialiang Fan,Essien Taylor,Zijun Wang,Russell Joseph,Fanxin Kong
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注:
Abstract:Safe robotic task planning requires planners that can reliably achieve task goals while satisfying safety constraints. Classical planners search over explicit symbolic task representations and can provide formal guarantees with respect to encoded safety constraints, but require rigid input formats. Their computational cost also increases rapidly with the search space. In contrast, large language models (LLMs) offer flexible task interfaces and strong generalization capabilities, but they are large, power-hungry, and pre-trained with little robot-specific safety data and knowledge. Thus, they can be difficult to deploy locally on robotic systems with limited resources. To address these limitations, we present SLoSa, a neuro-symbolic compression and fine-tuning framework for training foundation-model-based robotic task planners with Small size, Low power demand, and Safety awareness. The framework first constructs a safety-aware dataset using classical planners, an advanced teacher LLM, and formal verifiers, followed by two training stages: supervised fine-tuning and reinforcement learning (RL) fine-tuning guided by a verification-based reward machine to enforce safety alignment. We evaluate SLoSa across four Planning Domain Definition Language (PDDL) domains, two household simulation environments, local edge platforms, and a desktop robotic arm. Across the PDDL tasks, verifier-guided post-training raises the success rate of compact 4B-14B planners to 70.5-76.0%, while INT4 deployment on a Jetson Orin requires 17.85-26.41 W of average power. Results in household simulators and on the physical robot further demonstrate the applicability of SLoSa across diverse robotic task settings.
[AI-85] HyperDrone: Simulation-to-Reality Transfer at Millions of Frames per Second
链接: https://arxiv.org/abs/2610.28950
作者: Jonas Eschmann,Giuseppe Loianno
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY)
备注: Accepted at Conference on Robot Learning (CoRL 2026)
Abstract:We present HyperDrone, an extremely fast quadrotor simulator focused on high-performance batch rendering that is more than 20x faster than existing state-of-the-art batch quadrotor simulators. HyperDrone combines highly optimized multirotor dynamics with HyperLux, a new ray-tracing engine that supports Nvidia OptiX, Vulkan, Apple Metal and WebGPU backends that takes full advantage of modern ray-tracing hardware, capable of running both components entirely on the GPU. HyperLux renders RGB, depth, semantic/instance segmentation, surface normals and optical flow observations at configurable levels of fidelity, including reflections, realistic lighting, motion blur, anti-aliasing, and Physically Based Rendering (PBR). Even in the highest-fidelity settings, HyperLux achieves millions of Frames Per Second (FPS) on consumer-grade GPUs while rendering realistic 3D scenes. We demonstrate the merits of HyperDrone by training a vision-based yaw-control policy purely in simulation and transferring it to a real small-scale quadrotor (Crazyflie) with a camera, running completely onboard.
[AI-86] Self-Improving Agents with World Model Predictive Paths
链接: https://arxiv.org/abs/2610.28945
作者: Haori Lu,Tianyu Gao,Yaoyao Liu,Yingying Li
类目: Artificial Intelligence (cs.AI)
备注: 17 pages, 4 figures
Abstract:Large language model (LLM) agents are increasingly expected to interact with environments and improve their decisions during deployment. However, how anticipating future outcomes and drawing on accumulated interaction experience can jointly support decision-making remains underexplored. We propose a test-time framework, inspired by Model Predictive Path Integral (MPPI) control, that combines multi-branch predictive analysis with experience-based adaptation. At each step, the agent proposes multiple candidate actions and imagines their outcomes with a world model, so that alternatives can be compared without being executed in the environment. Since language environments provide neither explicit dynamics nor costs, the predicted outcomes are evaluated for task progress and risk, and MPPI-style weights over the resulting costs guide the final decision. A dual-branch experience memory enables self-improvement: the planner branch refines candidate generation and action selection, while the world model branch improves next-observation prediction. Experiments on ALFWorld and ScienceWorld across three backbones demonstrate the strong overall performance of our method and the effectiveness of experience-guided predictive control. Our code will be made publicly available.
[AI-87] StyleStream 2.0: Fast and Controllable Streaming Voice Style Conversion NEURIPS2026
链接: https://arxiv.org/abs/2610.28940
作者: Yisi Liu,Nicholas Lee,Gopala K. Anumanchipalli
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: accepted to NeurIPS 2026
Abstract:Voice style conversion (VSC) aims to transform a source utterance to match the timbre, accent, and emotion of a target voice while preserving linguistic content. StyleStream 1.0 introduced the first streamable zero-shot VSC system with state-of-the-art conversion quality, but remained limited in two ways: approximately 1s end-to-end latency on consumer hardware, and the lack of a text-based interface for designing or editing target voices. We present StyleStream 2.0, a fast and controllable streaming VSC framework that addresses these limitations. To reduce latency, we train a few-step pixel MeanFlow model and further fine-tune it with autoregressive feedback inspired by Self Forcing, improving robustness under streaming inference. This reduces end-to-end latency to 520~ms on a consumer GPU, a 2x speedup over StyleStream 1.0, while maintaining conversion quality. To enable controllable style manipulation, we remove the mel-spectrogram context and instead route target style information through a compact style embedding. In this embedding space, we train a unified text-conditioned flow matching model that supports both text-based voice design, which maps natural language prompts to voice styles, and instruction-based voice editing, which modifies a specific attribute of a source utterance while preserving the rest. Experiments show that StyleStream 2.0 achieves the strongest target style fidelity on VSC, voice design, and voice editing, while remaining competitive on intelligibility and non-edited attribute preservation.
[AI-88] Back Up Before You Regret: Measuring Backup Awareness and Automating Backup in Coding Agents
链接: https://arxiv.org/abs/2610.28934
作者: Xu Li,Simon Yu,Minzhou Pan,Yiyou Sun,Zhaorun Chen,Bo Li,Weiyan Shi
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Coding agents now complete tasks autonomously, modifying real-world state such as artifacts, software environments, and configurations along the way. Yet they show limited backup awareness: recognizing that an action will modify state that may later be needed, and preserving it beforehand. To evaluate backup awareness, we introduce RegretBench, a benchmark of 205 executable tasks grounded in regret moments reported on platforms such as this http URL, GitHub, and Stack Overflow, where users explicitly regretted not preserving state they later needed. Across six frontier models, agents complete 86.1% of tasks on average yet back up before these moments in only 15.2%, showing that task completion ability does not imply backup awareness. Because it is hard to predict which changes will be regretted, we propose BackUpper, a harness plug-in backup layer that checks every tool call and preserves affected state before modification. It combines rule-based strategies for common operations with a lightweight subagent for complex cases, and its rule table can expand with subagent decisions over time. On RegretBench, BackUpper covers every regret moment action and restores states with 92.2% average similarity to the original. Achieving this costs only 0.014 per task, with 7x fewer tokens than the main-agent self-backup baseline. When users have preferences on what to back up, BackUpper can also narrow its scope accordingly, matching human annotations on whether an action is worth preserving with an 84.5% F1 score. These results suggest that agents can keep actions reversible at low cost while respecting users’ preferences.
[AI-89] D-Skill: Distilling Teacher Knowledge through Agent Skills
链接: https://arxiv.org/abs/2610.28914
作者: Shuo Yuan,Dacheng Li,Joseph E. Gonzalez
类目: Artificial Intelligence (cs.AI)
备注: 21 pages, 4 figures, 9 tables
Abstract:Frontier language-model agents excel at complex tasks, but high inference costs make transferring their capabilities to smaller models essential. Traditional knowledge distillation achieves this through parameter updates, requiring access to the student’s weights and additional training compute while encoding the transferred knowledge implicitly in the parameters. We introduce D-Skill, a framework that treats the teacher as a black box and distills its behavioral advantage into an interpretable natural-language skill by analyzing paired rollouts and identifying behavioral divergences and agreements. The resulting lessons are consolidated offline, with any compatible skill-update backend, into a skill document that is provided to the student at inference time. D-Skill constructs the skill entirely offline from fixed trajectories, without labels, further environment interaction, or weight updates after rollout collection. Across SpreadsheetBench-Verified, ALFWorld, and chess puzzles spanning multiple teacher-student pairs, D-Skill closes 75%-95% of the teacher-student gap without touching model weights. After a single offline pass over the paired traces, D-Skill outperforms one-epoch SFT baselines and remains competitive with multi-epoch SFT. Under the same model family, the resulting student also incurs 5.7x-8.3x lower inference cost per task than the teacher. Our results demonstrate that natural-language skill distillation offers a performant, interpretable, and efficient alternative to weight-based capability transfer.
[AI-90] Simply Logical – The First Three Decades
链接: https://arxiv.org/abs/2610.28912
作者: Peter Flach,Kacper Sokol,Jan Wielemaker
类目: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注: Published in “Prolog: The Next 50 Years”
Abstract:This paper charts the evolution of the Prolog textbook Simply Logical – Intelligent Reasoning by Example from print with runnable programmes on a 3.5-inch diskette published in 1994, via various intermediate online versions, to the fully interactive online edition available today. Our aim is to investigate – from both the writer’s and the reader’s perspectives – the potential impact of technology on educational materials. The three authors of this paper present three distinct and complementary points of view, which come together to shape an interactive online book that offers an immersive learning experience. Peter describes the philosophy behind the original book and experiences teaching from it. Jan demonstrates how contemporary web programming has enabled a fully interactive realisation of the book’s philosophy. Kacper reports how different technologies can be adopted to build a versatile authoring toolkit that underpins the interactive online edition of the book. Collating these distinct yet coherent perspectives allows us to reflect on future opportunities for interactive Prolog teaching materials arising from the continuous development of web technologies. The insights we offer should be of interest to teachers of Prolog as well as those wanting to find out about the latest educational technologies.
[AI-91] DiffResearch: Accelerating Deep Research with Diffusion Language Models EMNLP2026
链接: https://arxiv.org/abs/2610.28909
作者: Alan-Barsag Gazzaev,Alexey Gavrilov,Mikhail Mozikov,Sergey Muravyov
类目: Artificial Intelligence (cs.AI)
备注: Accepted to EMNLP 2026 Industry Track
Abstract:Deep research systems iteratively plan, gather evidence, synthesize, and revise, making end-to-end latency a key bottleneck for interactive use. Almost all production systems are built on autoregressive (AR) language models and inherit left-to-right decoding in planning and long-form writing. We present DiffResearch, an open multi-agent deep research framework that places a diffusion language model (dLLM) at the core of every stage, and measure how a diffusion backbone changes latency and report quality. Holding the agentic scaffold, retrieval code, prompts, and evaluation fixed, we swap the backbone between an open diffusion model and a comparable AR model. Across 100 Deep Research Bench tasks, the diffusion backbone is roughly 6 times faster end-to-end, driven by order-of-magnitude reductions in planning and writing. We further introduce dFast, a decomposition mode that reads intermediate denoising states and extracts subqueries before generation finishes, providing a 1.99 times median speedup for decomposition in isolation with no loss of report quality. On the benchmark’s RACE rubric, dFast obtains the best overall score among the three configurations. Finally, in a public system-level leaderboard snapshot, an instance built on a stronger diffusion model ranks above several commercial systems. We release DiffResearch under an Apache-2.0 license.
[AI-92] Hidden Messages Between Diffusion Language Model Agents : Causal Evidence for Latent Communication NEURIPS2026
链接: https://arxiv.org/abs/2610.28905
作者: Alan-Barsag Gazzaev,Alexey Gavrilov,Sergey Muravyov,Ilya Makarov
类目: Artificial Intelligence (cs.AI)
备注: Accepted at the DiffuLM Workshop at NeurIPS 2026
Abstract:Diffusion language models form answers through a sequence of revisable continuous states, but multi-agent systems usually discard those states and make agents communicate only after decoding text. We ask whether one independent diffusion trajectory can pass a compact hidden message into another and, crucially, whether the receiver uses what that message means. In Hidden Messages, a sender compresses its denoising state into four signed dense vectors; the receiver consumes them while retaining its own context, canvas, and trajectory. Removing a message tests whether the channel matters; replacing it with a message from another example tests whether its content matters. Across controlled tasks, derangement removes a +63.6-point advantage on Tiny-A2D and a +100.0-point advantage on Dream-7B, while targeted substitutions steer the receiver toward the substituted fact. On QASC with the two supporting facts split across agents, matched communication reaches 80.8%, but a wrong-example message lowers accuracy to 46.8%, a content effect of +34.0 points. The surprising result is temporal: one late transfer reaches 87.8%, exceeding four recurrent exchanges by +7.0 points. These experiments provide causal evidence for continuous hidden communication between diffusion-LM agents and show that when information is transferred can matter more than how many times agents exchange it.
[AI-93] RL-IF: Tracking Non-local Training Data Influence for Online Reinforcement Learning NEURIPS2026
链接: https://arxiv.org/abs/2610.28883
作者: Shixuan Liu,Cheng Tang,Yuzheng Hu,Fan Wu,Han Zhao,Jiaqi W. Ma
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: NeurIPS 2026
Abstract:Online reinforcement learning (RL) has achieved significant success in decision-making tasks, but modern online RL remains highly sensitive to the quality of training experience. Understanding the contribution of individual training instances is therefore central to data-centric RL, with applications ranging from experience selection to policy interpretation. Existing data attribution methods for online RL primarily focus on local influence within a single training round, overlooking the non-local data influence across multiple training rounds. In this work, we formalize data attribution in online RL through a trajectory-level leave-one-out quantity, RL-LOO, which measures how one training sample influences a downstream target after subsequent online updates have taken place. We then derive RL-IF, a first-order estimator that propagates data influence through both optimization effects and policy-induced sampling effects, and provide theoretical guarantees on its approximation accuracy under smoothness assumptions. Our analysis further disentangles these two effects and shows both theoretically and empirically that the full RL-IF estimator can often be well approximated using the optimization effect alone. Building on this observation, we develop RINSE, a practical data filtering method for online RL. Experiments on standard control benchmarks and an RLHF-style toxicity-mitigation setting show that RINSE improves training efficiency and final performance over standard PPO and local attribution baselines. These results suggest that RL-IF provides a practical and principled framework for understanding and improving online RL.
[AI-94] nr2action: Encoding Robot Episodes as Neural Implicit Representations for Recall and Action
链接: https://arxiv.org/abs/2610.28882
作者: Leann Alhashishi,Christos Margadji,Sebastian William Pattinson
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Long-horizon robotic operation requires memory that scales with essential information rather than execution time. We present a parameter-space memory framework where each episode is stored as a separable object. A neural implicit representation is fitted to episode video from a shared, meta-learned initialization; the resulting weight delta constitutes the memory. Crucially, this delta remains a fixed size regardless of whether the episode spans a hundred or a hundred thousand frames. A learned projector maps the weight delta into token embeddings, conditioning a language model for question answering and a decoder for joint command regression, without frame retrieval at query time. On RoboMME, the language model exceeds chance across all question types, and the decoder achieves a mean R^2 of 0.51. Replacing the delta with matched noise drops performance by 11.4 points, while random-origin weights are non-decodable, proving the meta-learned initialization is essential for embedding episode content. By representing individual experiences as fixed-size weight deltas in a common parameter space, this approach decouples memory cost from time horizon and provides a basis for scalable streaming memory.
[AI-95] A General Topological Symmetry Index for Critical Orbits In Neural Network Loss Landscapes
链接: https://arxiv.org/abs/2610.28864
作者: Jingzhou Liu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:A systematic understanding of the global structure of neural-network loss landscapes remains challenging due to their high dimensionality. Symmetries arising from network architectures and learning objectives provide a way to investigate aspects of this structure. Although a growing number of studies have drawn attention to these symmetries, a systematic characterization of them, as well as of the critical orbits they give rise to, remains incomplete. To address this gap, in this work we establish a topological symmetry index, based on the methodologies from the family of equivariant degree theories, to decode these symmetries. The frameworks apply to arbitrary network architectures and include methods applicable to degenerate critical orbits. The index detects the isotropy of the critical points and quantifies the corresponding net topological contributions. Several examples, including softplus MLPs, Transformers, and equivariant neural networks, are presented to illustrate the computations.
[AI-96] Agent AI Zarządzający Siecią Open RAN Zorientowana na Użytkownika
链接: https://arxiv.org/abs/2610.28860
作者: Marcin Hoffmann
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注: The paper original language is Polish. Pages 1-4 contain the original manuscript, the translated version (generated by AI) is on pages 5-8
Abstract:Jednym z najważniejszych wyzwań związanych z implementacją sieci Open RAN o architekturze zorientowanej na użytkownika jest odpowiednie formowanie tzw. klastrów obsługujących. Problem ten może być efektywnie rozwiązany za pomocą zaproponowanego Agenta AI bazującego na modelu językowym. Wyniki symulacji pokazały, że zaproponowane rozwiązanie pozwala na ponad 2-krotne zwiększenie mediany rozkładu przepływności użytkowników względem standardowej architektury zorientowanej na sieć.
[AI-97] Speedrunning Relational Foundation Model Pretraining NEURIPS2026
链接: https://arxiv.org/abs/2610.28855
作者: Gabriel Wedson Mendonça de Souza Veloso,Luan Borges Teodoro Reis Sena,Denis Oliveira Correa,Francisco Galuppo Azevedo
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 4 figures. Accepted at the CODEC-FM Workshop at NeurIPS 2026
Abstract:Progress on foundation model pretraining is gated by who can afford a trial, and relational pretraining is no exception. Recipes are developed inside individual labs, reported under private protocols, and do not accumulate. Speedrun leaderboards remove that barrier by decentralizing the search rather than the training run. The problem is fixed, the stack is open, entries are ranked by wall-clock time to a quality target, and one GPU is enough to hold the record. We introduce modded-nanoRT, the first such leaderboard for Relational Transformers. Which database to hold out decides whether the benchmark can resolve improvements at all, so we fix it by calibration, ranking six candidates by how sharply a 45-minute learning curve separates improvement from seed noise. We open the leaderboard with typed trilinear attention, a relational operator that conditions attention scores and values on the foreign key traversed, together with Muon and SIGReg. The recipe reaches the target in 305.63 seconds on one NVIDIA H200, 8.11x faster than the reference implementation, and wins 13 of 15 pairs outside the development database. We will release the benchmark, its frozen artifacts, and the record runs. Our code is available at this https URL.
[AI-98] When Do Gaussian Priors Fail? A Latent Geometry Investigation of RL Fine-Tuning
链接: https://arxiv.org/abs/2610.28849
作者: Wang Ru
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: around 49 pages, around 25 figures
Abstract:Latent diffusion models almost universally place a Gaussian prior on the VAE latent space. For base generation this default is unproblematic; under reinforcement-learning fine-tuning, however, it carries a structural limitation. The standard VAE’s KL term does not merely ignore mode structure - it actively taxes separation and tightness. When data are multimodal, the ELBO optimum compresses distinct modes into an overlapping latent. RL fine-tuning amplifies this overlap: the score network is under-resolved on the saddle region between modes, dynamic-to-variational amplification rises sharply, and the collapse boundary beta_c is pushed far above its variational scale. On the real alanine-dipeptide Boltzmann distribution, a Gaussian-prior VAE collapses at beta_c = 0.224 while a mode-tracked Gaussian-mixture prior collapses at 0.00316, a 71x gap from the same pipeline. We trace this gap to a single geometric mediator, d_min/(2 sigma_0), and verify the causal chain through controlled interventions: moving geometry at fixed prior, and switching prior at matched geometry (full mediation; prior-label coefficient 0.995x, 95% CI [0.79, 1.25]). A three-source decomposition localises the amplification to the learned policy (zero ODE-solver contribution, bounded anchor mismatch); the coverage plateau is an unconditional benefit of mode-separating priors that no amount of RL capacity recovers. The remedy is conditional: a non-Gaussian prior is necessary precisely when the latent geometry would fall into overlap and the task is coverage-sensitive. A machine-checked theory skeleton supports the KL pricing structure - a divergent compactness tax under a Gaussian prior versus a finite assignment fee under a mixture - and the position-versus-plateau distinction delimits what is finite-resource from what requires changing the prior. Comments: around 49 pages, around 25 figures Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) MSC classes: 68T07, 62F15 ACMclasses: I.2.6; G.3 Cite as: arXiv:2610.28849 [cs.LG] (or arXiv:2610.28849v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.28849 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-99] Decision language models as affordable and rapid alternatives to LLM judges of clinical generative AI
链接: https://arxiv.org/abs/2610.28826
作者: Rotem Gershon,Seffi Cohen,Dean Ariel,Havatzelet Bilavsky-Yarden,Amiel Sberro,Shai Yitzhaki,Osnat Tausky,Naama Golan,Maayan Rabinovitch,Ariel Avraham Hasidim,Itay Arianu,Maya Dagan,Ran Balicer,Noa Dagan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Automated judges of clinical artificial intelligence (AI) outputs are necessary, but large language model (LLM) judges are costly and slow at scale. We compared Jev, a decision language model that returns structured judgments, with frontier and open-weight LLM judges in four use cases. On 118 pediatric answers, Jev’s agreement with physicians’ ordinal quality ratings (Gwet’s AC2 0.50; expected score 0.53) matched or exceeded that of seven of 12 LLM judges, including frontier models, Claude Opus 5.5 (0.49) and GPT-6 (0.38). On physician-graded binary HealthBench criteria, Jev matched or exceeded four of six LLM judges. On MedHELM’s open-ended tasks, Jev’s agreement with a three-LLM jury approached or exceeded the jurors’ internal agreement. On HealthBench Professional, Jev matched the benchmark’s reference judge, GPT-5.4, on 91% of criteria (other judges, 91-93%). Frontier judges cost 73-294 times more than Jev and took 7-57 times longer. Jev may enable efficient clinical AI evaluation at scale.
[AI-100] Brittle by Design? Multi-Scenario Reinforcement Learning for Robust Mission Coordination in Systems of Systems
链接: https://arxiv.org/abs/2610.28818
作者: İbrahim Oğuz Çetinkaya,Sajad Khodadadian,Taylan Topcu
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:As systems engineering (SE) shifts from monolithic systems to Systems of Systems, Mission Engineering (ME) has emerged as a new line of thinking in SE. Mission environments are uncertain and dynamic, and outcomes depend on how mission assets interact with them, rendering static architectures brittle and calling for flexible yet analytically rigorous ME approaches. We propose an intelligent mission coordination methodology integrating high-fidelity digital mission models with Reinforcement Learning (RL) for adaptive task allocation. Using a Digital Engineering (DE) infrastructure comprising a digital mission model and agent-based simulation, we formulate mission tactics management as a Markov Decision Process and train an RL coordinator with Proximal Policy Optimization to map mission states to tactical actions from realized outcomes. An aerial firefighting mission with three scenarios demonstrates the approach. First, all trained coordinators significantly outperform random and heuristic baselines. Second, single-scenario coordinators can be brittle: the coordinator trained on the more forgiving scenario degrades to random tactics performance in more demanding scenarios, whereas the demanding-scenario coordinator retains most of its advantage when transferred. Third, within the same training budget, a coordinator trained on both scenarios concurrently matches the scenario-specific coordinators in their own scenarios and significantly outperforms every other policy. Sensitivity analyses on ignition location, weather, scenario, and training curricula show that the variability represented during training shapes post-deployment performance. This proof of concept shows DE-enabled mission simulations combined with RL offer a reusable methodology for improving ME practice, which can be extended to fleet design and selection from a mission-first perspective.
[AI-101] Forging More Tasks into Better Harnesses: Agent ic Harness Evolution via Successive Scale Racing
链接: https://arxiv.org/abs/2610.28815
作者: Jingzhe Liu,Jiliang Tang,Tong Zhao,Neil Shah,Clark Mingxuan Ju
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agentic harness evolution optimizes an agent’s instructions, tools, and control flow by using its task execution experiences as evolving evidence. While more evolution tasks should intuitively improve performance, our preliminary study reveals a paradoxical failure mode: the performance of existing methods degrades as the task pool expands. Our further analysis demonstrates that this degradation stems from suboptimal evidence selection during optimization, as different evidence combinations lead to substantially different evolution outcomes. To address this issue, we propose Successive Scale Racing (SSR), a framework that adaptively searches for optimal evidence combinations in each evolution round. SSR constructs diverse evidence combinations based on the corresponding task quality and coverage, and efficiently races the resulting candidate harnesses under a small compute budget. Empirically, across three distinct benchmarks, SSR achieves state-of-the-art results in all six configurations. Notably, as the task pool grows from 40 to 200, SSR steadily improves the pass rate from 64.3% to 68.3%, while the baselines’ performances all drop. Furthermore, its racing mechanism identifies near-optimal candidates while reducing validation executions by 60%.
[AI-102] owards Realistic Models of Change: A Causal Perspective on Decision-Making and Dynamical Systems
链接: https://arxiv.org/abs/2610.28803
作者: Isacco Beretta
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: PhD thesis, University of Pisa (National PhD Program in Artificial Intelligence), 2026. 175 pages
Abstract:While statistical learning has made remarkable progress in recent decades, driven by large datasets and data-driven protocols that have transformed scientific practice, it still faces a key limitation: it struggles to generalize beyond the data it has seen, often failing under distribution shift. Causality offers a promising solution: causal models focus on the underlying mechanisms of change rather than surface-level associations, enabling robust reasoning under external interventions. This allows systems not only to learn from data, but also to reason about hypothetical scenarios, adapt to new environments, and inform better decisions. This thesis explores how these approaches can inform each other, in two parts. The first part examines algorithmic recourse, that is, identifying which changes an individual can make to overturn an unfavorable decision. We propose an alternative perspective to the established framework, arguing for a more realistic cost function, a reformulation of the problem in causal terms, and the inclusion of a temporal dimension in policy evaluation. The second part focuses on dynamical systems, extending the notions of causal interventions and effects to discrete and continuous stochastic processes. We show that the equilibria of these processes, and how they change following an intervention, can be linked to Structural Causal Models (SCMs), viewed as compressed representations of the underlying temporal dynamics. We argue for the relevance of such temporal causal models, since in real-world scenarios actions take time to produce their effects. The common goal of both parts is practical: the utility, robustness, and trustworthiness of these models depend critically on their ability to realistically represent the underlying phenomena. Comments: PhD thesis, University of Pisa (National PhD Program in Artificial Intelligence), 2026. 175 pages Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2610.28803 [cs.LG] (or arXiv:2610.28803v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.28803 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Isacco Beretta [view email] [v1] Tue, 20 Oct 2026 18:26:21 UTC (21,481 KB)
[AI-103] ArcFlow: Training Flow Policies for Multiple Solver Budgets
链接: https://arxiv.org/abs/2610.28788
作者: Hieu Tran,Hong Yu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 19 pages, 4 figures, 4 tables
Abstract:Flow-matching policies adjust inference cost through the number of solver steps, but reinforcement learning at a single fixed solver budget can hide degradation at cheaper deployment budgets and even erase useful one-step behavior present in a pretrained policy. We introduce Action-Ratio-Clip Flow (ArcFlow), a deployment-aware algorithm for training one flow policy across a specified range of solver budgets. ArcFlow combines action-aligned supervision, budget exposure, and worst-budget checkpoint selection. For reconstruction objectives, its bridge aggregates temporal probes into one surrogate ratio and one PPO-style clip per action, with a boundary residual exactly equal to deployed one-step reconstruction error. Cycling exposes the shared field to the deployment range, and selection optimizes its performance floor. For native velocity objectives, multi-budget endpoint supervision applies the same principle without replacing the native update. Both realizations preserve the architecture and deployment-time cost. Across DM Control, manipulation, and locomotion, ArcFlow attains the highest mean one-step return among five flow-policy baselines on 25 of 30 tasks under each method’s reported training settings. Component studies identify complementary improvements from boundary supervision, the fixed-grid bridge, budget exposure, and deployment selection. These results establish deployment-budget-aware training as an effective route to strong low-step control with one policy serving multiple inference costs.
[AI-104] Second Thoughts: Exploring and Planning through Comparative Imagination in World Models
链接: https://arxiv.org/abs/2610.28779
作者: Hieu Tran,Hong Yu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 14 pages, 3 figures, 2 tables
Abstract:World models let agents compare imagined outcomes before acting, but an agent must still decide which alternatives to explore and when a proposed plan should replace its learned policy. We introduce SDI (Sibling Disagreement in Imagination), which connects these decisions through a shared actor-based reference. SDI compares action sequences imagined from the same model state, using repeated rollouts of the actor’s own sequence to measure variation under fixed behavior. This reference scales both the spread across candidate returns, which provides an exploration signal, and a proposed plan’s predicted advantage, which governs action acceptance. The agent executes a planned action when its advantage exceeds the reference variability by a fixed factor; otherwise, it follows the actor. SDI reuses the base agent’s world model, actor, and critic without requiring an additional disagreement ensemble. Exploration and planning guide training data collection, while evaluation uses the learned actor alone. Across Meta-World, Adroit, and DM-Control, SDI achieves a higher observed task-average score than every compared baseline in each suite and matches or exceeds the strongest baseline’s performance on 15 of 18 tasks. Component ablations and robustness analyses further examine the roles of exploration, planning exposure, and action acceptance. Paired evaluations from identical simulator states show that the gate improves both recovery from actor failures and preservation of successful behavior relative to ungated planning, while substantially reducing return degradation. Together, these results support comparing imagined alternatives against a shared actor reference as a unified approach to exploring and planning with world models.
[AI-105] ReAPO: Retry-Aware Policy Optimization for Efficient Exploration in LLM Reasoning
链接: https://arxiv.org/abs/2610.28770
作者: Nghi Dao,Calder Morton-Ferguson,Noah Golowich,Adam Block
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:RLVR improves language-model reasoning, but commonly relies on independent stochastic rollouts for exploration, which can repeatedly sample similar failed strategies. Prior work has seen success with approaches that guide exploration by introducing dependence into the sampling process by including incorrect solutions in the context for subsequent attempts. Still, these methods introduce the additional question of how to assign credit to each attempt. We propose to view the credit assignment through the lens of Stochastic Shortest Paths, a framework in classical reinforcement learning that seeks to minimize the expected cumulative cost to reach a terminal state (in our case, a verified successful response). We show that, under suitable simplifying assumptions, this perspective naturally leads to a simple modification to GRPO, which we call Retry-Aware Policy Optimization (ReAPO). ReAPO allocates additional generation to failed trajectories, allowing successful retries to be reinforced without rewarding preceding failures, and assigns credit in a theoretically principled way to each attempt. Across Qwen3 and OLMo-2 models from 0.6B to 4B and eight mathematical and scientific reasoning benchmarks, ReAPO improves first-attempt performance over GRPO as well as natural recent baseline algorithms, while additional ablations demonstrate the utility of both serial sampling and ReAPO’s credit assignment. On Qwen3-0.6B, ReAPO also outperforms GRPO with twice the group size or twice the training steps, reaching their peak accuracy with 39% and 31% fewer generated tokens, respectively. Taken together, our results suggest that ReAPO is a simple yet effective approach for improving LM reasoning performance through improved exploration and retry-aware credit assignment.
[AI-106] GPU-Accelerated Construction of the Gabriel Graph: Parallelization Strategies and Applications to Large-Margin Classification and Graph-Based Clustering
链接: https://arxiv.org/abs/2610.28759
作者: Diego Sanches Nere dos Santos,Luiz Carlos Bambirra Torres
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 12 pages, 3 figures, 1 table
Abstract:The Gabriel graph is a proximity graph widely used in computational geometry and geometric machine learning, underlying large-margin classifiers and, as we show in this work, density-aware clustering methods. Its construction, however, has cubic time complexity O(N^3) in the naive formulation, which severely limits its use on datasets beyond a few thousand samples. This paper presents a set of GPU parallelization strategies, implemented in CUDA, that make Gabriel graph construction practical at scale. We introduce (i) an implicit closed-form mapping from a linear thread index to the corresponding vertex pair (i,j) , which removes the need for an explicit O(N^2) adjacency structure and lets every thread locate its pair in constant time and constant memory; (ii) a two-pass, bitmask-based, atomically-compacted edge-list construction that produces a dense edge array proportional only to the number of edges actually found; and (iii) a thread-coarsening strategy in which each thread evaluates several candidate pairs, trading launch and scheduling overhead against load balance. We evaluate these strategies on the acceleration of a Gabriel-graph-based large-margin classifier (NN-clas) and on a novel Gabriel-graph clustering algorithm that identifies connected components on a residual graph and performs GPU-parallel Union-Find with hierarchical edge-based merging. On 14 public benchmark datasets, the GPU implementation reduces Gabriel graph construction time by two to nearly four orders of magnitude relative to a sequential CPU baseline (up to 8484x), without loss of classification accuracy, and the proposed clustering method is statistically competitive with DBSCAN on the topological validity index Dunn while eliminating the need for an explicit density radius parameter.
[AI-107] Offline Apprenticeship Learning for Human-Centric Environments WWW
链接: https://arxiv.org/abs/2610.28716
作者: Md Mirajul Islam
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Md Mirajul Islam’s PhD Dissertation ( this https URL )
Abstract:Effective decision-making in human-centric environments such as Intelligent Tutoring Systems (ITSs) and clinical healthcare requires policies that are adaptive, data-efficient, and ethically deployable. Apprenticeship Learning (AL) infers policies directly from expert demonstrations by recovering the underlying reward structure, enabling strictly offline learning without environment interaction. This dissertation develops a progression of offline AL frameworks organized around two challenges: reward modeling and demonstration quality. For reward modeling, EM-EDM clusters demonstrations by latent reward functions and induces cluster-specific policies, and THEMES partitions trajectories into temporally coherent sub-trajectories guided by a hierarchically learned reward regulator; across one educational and three clinical datasets, THEMES outperforms flat and hierarchical AL baselines. For demonstration quality, HALIDE integrates continuous quality signals into hierarchical policy induction, enabling principled reuse of mixed-quality data under distribution shift. CCHAL further attributes quality to individual decisions via Bayesian credit assignment, dual-arm criticality scoring that contrasts expert and non-expert behavior, and a Bradley-Terry contrastive IRL objective over failure trajectories, outperforming baselines including HALIDE on three clinical EHR datasets and an ITS dataset. Finally, a real-world classroom evaluation of StuEDM, an EDM-induced policy that preserves student agency and intervenes only on disagreement, shows significant learning gains over an Expert baseline and a higher pretest-adjusted posttest score than a CQL policy trained on a far larger corpus, while overriding students less than half as often. Together, this work establishes offline AL as a practical, data-efficient, and ethically viable paradigm for human-centric environments.
[AI-108] STAR-XPC: Set Transformer for Estimating X-ray Transmission in Polymer Composites
链接: https://arxiv.org/abs/2610.28709
作者: Can Erer,H. Birkan Yilmaz,Nebahat Aral
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 13 pages, 6 figures
Abstract:Evaluating the energy transmission ratio of an X-ray beam through a polymer composite is a challenging task in radiation shielding studies. The reference solution with Monte Carlo transport simulation is highly accurate but computationally expensive: each of our reference simulations is capped at 4\times10^9 photon histories and a 20,000 s (over 5 hours) time limit, while closed-form analytical methods fail to represent such complex scenarios. We present an artificial neural network model that maps the unordered multi-set of molecular constituents, their mass fractions, and four bulk physical descriptors directly to the transmission ratio I(t)/I_0 in 1-3 milliseconds. The model is a Set Transformer, which makes it permutation invariant by construction and accepts mixtures of different sizes up to 11 constituents within a fixed-size padded set. Each constituent is embedded with a chemistry-pretrained transformer language model, ChemBERTa. In our proposed method, training data are produced by an automated PENELOPE/penEasy pipeline using the radiation source at 30, 80, 100, and 120 kV tube voltages over polyethylene-based shielding composite slabs of thicknesses spanning 0.15-2.5 mm. After preprocessing, the dataset contains n = 4697 simulated configurations on which a leakage-free, jointly stratified 10-fold cross-validation is performed, attaining mean squared error MSE = (1.12 \pm 0.56)\times10^-4 , normalized-MSE percentage NMSE% = 0.0294% \pm 0.0147%, and R^2 = 0.9988 . The corresponding per-sample percentage errors give an alpha-trimmed ( \alpha = 0.10 ) absolute percentage error (APE) of 1.66% and a median APE of 1.15%. Our model’s inference is several orders of magnitude faster than the underlying Monte Carlo simulation (sub-millisecond per query vs. several hours for the reference run) with effective accuracy in predicting I(t)/I_0 .
[AI-109] SpecEvo: Speculative Evolution with Large Language Models for Cost-Efficient Scientific Discovery
链接: https://arxiv.org/abs/2610.28706
作者: Ha Minh Hieu,Tongyao Zhu,Do Xuan Long,Min-Yen Kan
类目: Artificial Intelligence (cs.AI)
备注: 43 pages
Abstract:LLM-driven evolutionary search is a promising paradigm for scientific discovery, but existing methods remain costly and slow, often applying expensive reasoning uniformly while underusing evidence from failed, redundant, and saturated trials. We introduce Speculative Evolution (SpecEvo), a multi-LLM framework that coordinates lightweight and frontier models through continual consolidation of search evidence. Lightweight models power both parallel Speculators, which generate and refine candidate programs, and an Advisor, which distills accumulated successes, failures, and saturated directions into persistent guidance for subsequent exploration. At sparse checkpoints, a frontier-model Navigator inspects the evolving population and intervenes through synthesis, surgical refinement, or reframing according to search stagnation. This speculate-then-consolidate workflow delegates routine exploration and evidence consolidation to inexpensive models while reserving frontier reasoning for strategic interventions whose benefits extend across many subsequent low-cost evaluations. Across 176 tasks spanning four scientific and algorithmic discovery domains, SpecEvo matches or outperforms frontier-model baselines while reducing API cost by up to 80%, achieves up to 1.5x greater evaluation efficiency than cost-efficient baselines on equation discovery, and improves over the strongest CO-Bench baseline by 3.8%. These results support trajectory-conditioned model coordination as an effective approach to cost-efficient scientific discovery.
[AI-110] Reinforcement learning for hardware acceleration and system-level optimization on FPGA and integrated-circuit platforms: A survey
链接: https://arxiv.org/abs/2610.28699
作者: Hamza Kheddar
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Performance (cs.PF); Robotics (cs.RO); Systems and Control (eess.SY)
备注:
Abstract:Reinforcement learning (RL) enables adaptive and autonomous decision-making in embedded, edge, and heterogeneous computing systems. Field-programmable gate arrays (FPGAs) and integrated circuits (ICs) offer low latency, energy efficiency, and architectural flexibility for RL deployment, while introducing challenges in resource constraints, implementation complexity, and hardware-algorithm co-design. This review surveys more than 100 studies on RL implementations across FPGA and IC platforms, emphasizing architectures, implementation strategies, performance evaluation, and applications. The literature is organized by RL family, including model-based Markov decision processes, classical value-based methods, deep RL, actor-critic, policy-gradient, and multi-agent approaches. We further classify studies by abstraction level, hardware realization strategy, and application domain, examining how RL algorithms map onto reconfigurable and fixed hardware. Unlike surveys restricted to particular algorithms or platforms, this work provides a unified analysis of FPGA and IC implementations, covering acceleration, reconfigurable architectures, energy-aware control, and system-level optimization. Comparative assessments consider resource utilization, latency, throughput, power consumption, and reported performance gains. Finally, we identify recurring architectural patterns, practical limitations, open challenges, and research directions for efficient RL-driven hardware systems. The survey offers a consolidated reference for researchers developing and evaluating RL solutions on specialized hardware.
[AI-111] Interaction-Gated Time-Frequency Learning for Parameter-Efficient PPG-Based Heart Rate Estimation
链接: https://arxiv.org/abs/2610.28693
作者: Anh Khoi Pham,Phat Lam,Thien Truong Nguyen Ly,Lam Pham
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
备注: Conference preprint, accepted at SoICT 2026
Abstract:Photoplethysmography (PPG) enables low-cost and non-invasive heart rate (HR) monitoring but remains sensitive to waveform distortion, physiological variability, and measurement interference. Temporal waveform characteristics and frequency-domain periodicity provide complementary information for HR estimation; however, effectively exploring and integrating these representations within a compact model remains challenging. This paper proposes TF-IG, a parameter-efficient interaction-gated time-frequency learning framework for PPG-based HR estimation. TF-IG employs a dual-branch architecture in which a temporal branch captures pulse morphology via dilated depthwise-separable convolutions, while a frequency branch extracts cardiac periodicity information from bounded log-spectrogram representations. To combine the two domains, we introduce an interaction-gated fusion module that explicitly models feature agreement and discrepancy, while also learning feature-wise weights to balance temporal and spectral information. A parameter-shared regression head further maps the temporal, spectral, and fused representations into the final HR prediction space. Experiments on the benchmark BIDMC PPG and Respiration Dataset using a subject-exclusive training, validation, and test protocol show that TF-IG achieves a mean absolute error (MAE) of 1.227 BPM, a root mean square error (RMSE) of 2.060 BPM, a Pearson correlation coefficient of 0.9934, and 92.28 percent of estimates within 3 BPM, while remaining parameter-efficient with only 84.6K parameters. Ablation results further demonstrate the complementary contributions of the temporal and frequency branches, interaction-gated fusion, and shared regression head. These results indicate that explicitly modeling cross-domain interactions provides an effective accuracy-complexity trade-off for lightweight PPG-based HR estimation.
[AI-112] A Small Model Asks Better: Fine-Tuning Query Clarification for Retrieval-Augmented Generation
链接: https://arxiv.org/abs/2610.28686
作者: Lihong He,Lucian Popa,Marina Danilevsky
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Query clarification is essential for retrieval-augmented generation (RAG) systems that face ambiguous user queries. We consider multi-turn grounded conversational scenarios where a user query is either ill-formed or ambiguous in the context of the prior dialog or, more interestingly, it may be well-formed but when posed against retrieved documents there are multiple relevant interpretations. The task is to recognize both such cases and then generate an appropriate clarification request to the user. We present ClariEdge, a new dataset of approximately 3,900 examples that include such ambiguous queries along with negative examples that do not require clarification. We also show that a lightweight fine-tuned LoRA achieves 96.6% classification accuracy and outperforms prompted baselines of many larger models on both classification and quality of clarification generation. Our study shows that hard negatives are an essential ingredient to separate out queries that are truly ambiguous from queries that may look ambiguous but are not (i.e., can be resolved from the context). The same LoRA achieves high performance in both pre-retrieval and post-retrieval scenarios; thus, it can be used in two places in a RAG pipeline as a check before answer generation. The ClariEdge dataset is available at this https URL.
[AI-113] VerifierBench: Benchmarking the Generation of Executable Verifiers for LLM Agents
链接: https://arxiv.org/abs/2610.28679
作者: Jianing Yin,Ziyi Chen,Hao Wang,Yiyou Sun,Zijian Jin,Dawn Song,Daoan Zhang
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Agents need reliable ways to validate their outputs, both when completing tasks and when filtering synthetic training data. We study verifier generation: from the instruction and environment available for solving a task, an agent writes an executable verifier that must accept every valid solution and reject invalid ones. We introduce VerifierBench, a benchmark of 222 tasks from six agent benchmarks spanning tool-call, state-based, and code verification, with 4,993 hidden test cases that include alternative valid solutions and attacks; Verifier4Verifier (V4V), a six-check audit, screens its source verifiers and test suites. Across 19 models in a common agent harness, normalized balanced accuracy ranges from 10.6 to 62.1, with generated verifiers consistently rejecting invalid solutions more reliably than they accept valid ones. This apparent strength can mask overly restrictive verification: most false rejections in the analyzed records come from verifiers that accept no tested solution to a task. Case studies reveal unsupported assumptions about solution formats, tool use, and code changes that exclude valid solutions. These errors survive self-testing because agents test solutions that share the verifier’s mistaken assumptions, or submit verifiers without confirming that any correct solution passes. These findings suggest testing both whether a verifier accepts a correct solution and whether it accepts other solutions permitted by the task. The project page and dataset are publicly available.
[AI-114] VHDLBench: Benchmarking and Fine-Tuning Large Language Models for VHDL-Based Chip Design
链接: https://arxiv.org/abs/2610.28676
作者: Patrik Drazic,Benaoumeur Senouci,Boualem Benatallah
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI)
备注: This paper is a long version of a paper accepted for the 4th International Conference on Foundation and Large Language Models, 2026
Abstract:The rapid evolution of digital circuit complexity demands innovative approaches to hardware design automation. While Large Language Models (LLMs) have influenced software development, their application to Hardware Description Language (HDL) generation remains largely unexplored, particularly for VHDL. This paper presents a comprehensive study for AI-assisted digital circuit design, addressing gaps in current Electronic Design Automation workflows. The work introduces a VHDL dataset of 18,500+ syntax-verified examples from open-source repositories and converted Verilog designs. Five 7B-parameter LLMs were fine-tuned using parameter-efficient QLoRA technique, enabling deployment in resource-constrained environments. To evaluate model efficacy, a rigorous three-level pipeline was established: (1) syntax and synthesis validation, (2) functional simulation with testbenches, and (3) FPGA prototyping on Zynq SoCs. Results show fine-tuned models achieve up to 92% syntax correctness and 73% functional accuracy on common design tasks, with DeepSeek-Coder demonstrating the highest performance gains. Generated designs were successfully deployed on real hardware, confirming not only syntactic but operational viability. This work demonstrates that LLMs, when tailored with domain-specific data and lightweight fine-tuning can contribute meaningfully to the future of generative EDA workflows.
[AI-115] Who Verifies the Builder? Toward Independent Assurance for Agent -Produced Software
链接: https://arxiv.org/abs/2610.28667
作者: Sion Israel Sion,Alain April
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 10 pages, 2 figures, 4 tables. Artifact: this https URL
Abstract:Coding agents can now implement a specification, write and run tests, repair failures, and declare the result ready for release. When the same agentic system produces both the software and the evidence of its correctness, a passing test suite is no longer independent evidence: visible tests become an optimization target, code and tests may share one misinterpretation of the specification, and the builder’s own tests may simply be incomplete. This paper argues that agent-produced software requires an assurance plane that is structurally independent of the build plane. We characterize four gaps between stakeholder intent and production (requirement, conformance, evaluation, and model gaps) and identify which of them verification can close. We propose a framework that separates building from verifying, introduces a Verification Contract stating what must be demonstrated before release, and organizes verification in a specification-first phase without access to the implementation and an implementation-aware phase based on mutation analysis, property-based testing, and fuzzing, with an assurance report that leaves the release decision to a human. We evaluate the framework in a pre-registered study on three stateful services, two of them written by third-party models, and an EvalPlus pilot. The builder’s programs were correct, but its own tests missed subtle interactions: a verifier in a fresh context detected more of the 46 seeded faults than the builder’s tests (Holm-corrected p = 0.006) and surfaced the ambiguities of the specification, at the price of rare false oracles. A verifier from another provider did not add detection.
[AI-116] Learning Checklists to Reduce Lottery Effects in Scientific Peer Review
链接: https://arxiv.org/abs/2610.28666
作者: Xuanyou Liu,Ziyang Guo,Jason Hartline,Jessica Hullman
类目: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Applications (stat.AP)
备注:
Abstract:Reviewer lottery error describes error due to a small set of peer reviews attending to only some of the issues with a paper that a larger pool of eligible reviewers would raise. We show this error can be reduced by surfacing a checklist of criticisms learned from a historical review corpus. Our approach generates a tree of checklists representing coarsenings of the atomic review claim space, then selects the coarsening that maximizes a theoretically-motivated measure of correlated agreement among reviewers. We realize this approach with an LLM-assisted pipeline and test it on NeurIPS 2021 and ICLR 2022 review data. In simulated review with LLM reviewers, the learned checklist reduces the mean squared error (MSE) of a three reviewer committee by 58% on NeurIPS and 21% on ICLR, and on both venues the checklist with the highest correlated agreement has the lowest error for committees of three or more. In a human study with 20 participants, the learned NeurIPS checklist lowers the MSE of a paper ranking, measured against the NeurIPS 2021 order, from 0.48 to 0.22.
[AI-117] CompLit: Scientific Literature Search Benchmarks Must Cover Implicit Cumulative and Unmet Needs
链接: https://arxiv.org/abs/2610.28658
作者: Dayoon Ko,Jihyuk Kim,Soyeong Jeong,Young-jun Lee,Dahyun Lee,Juyeon Kim,Gunhee Kim,Moontae Lee,Kyungjae Lee
类目: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI)
备注:
Abstract:How much can we trust an agent to find the paper we need? As researchers, we search the literature from what we have read toward what we have not. We thus describe what we want in our own terms, which a paper may show only implicitly; our requirements grow as we read; and what we seek may lie in an empty region no paper yet covers. We introduce CompLit, a literature search benchmark spanning all eight arXiv domains, with one diagnostic setting for each of these needs. The strongest agents, Codex (GPT-5.6-Sol) and Claude Code (Opus 4.8), answer 86-87% of Standard queries, yet fall to 66-68% when requirements are implicit (implicit), lose another 6 and 15pp, respectively, as they accumulate (cumulative), and abstain on at most 53% of the queries no paper satisfies (unmet). No agent leads in every domain, and all are less accurate on papers from before 2019 or after 2023. Tracing their search, we find that agents often reach the right paper, or the closest one, yet misjudge it. Doing well on CompLit is a step toward agents that we can trust to find the paper we need, or to tell us that none exists.
[AI-118] What Git Does Not Record: Provenance Legibility in Two AI-Assisted Research Repositories NEURIPS2026
链接: https://arxiv.org/abs/2610.28655
作者: Koutian Wu,Zesen Huang,Hejia Geng
类目: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at the NeurIPS 2026 Workshop on AI-Native Academia. 12 pages, 4 figures. Case studies are two atmospheric and ocean science research repositories
Abstract:Publishers and funders increasingly ask authors to disclose how AI tools contributed to a piece of research. In computational work the repository is where an author or an auditor would look for the answer, and we ask what it can actually supply. We audit two single-investigator repositories by the same author in the same field, each carrying a different class of agent residue: a prescriptive trace of standing rule files and a narrative trace of a running agent scratchpad, in repositories of 466 and 103 commits. The two are legible in opposite ways. 85.6% of the lines ever committed to the prescriptive trace are visible at the current revision, and a standing rule states a conclusion and discards the episode behind it; the narrative trace records episodes and shows 21.8%. Counted by revision the gap is far smaller: 15 of the prescriptive trace’s 41 revisions leave no visible line, against 18 of the narrative trace’s 41. The Co-Authored-By trailer, the field an automated audit reads, is one of three AI trailer keys Project A carries and covers 66 of the 121 commits carrying any. In Project B it has low sensitivity against an explicit within-repository proxy: 39 commits revise a file maintained to hold an agent’s working notes and 1 (2.6%) carries the trailer. Project A’s repository-level rate is unstable in time too, stepping from 2.7% to 17.7% across a boundary we did not specify in advance. Repository channels disagree, and neither rate can be read as an involvement rate; we do not isolate the cause, and say so. We give a field-by-field account of which provenance claims such traces support, which they cannot, and which they answer confidently and wrongly, construct an auditable evidence ledger, and derive the requirements this places on any system meant to record research provenance as it happens.
[AI-119] he CIDOC CRM Periodic Table: Documentation You Can Test to Support the Adoption of Scientific Research Ontologies for the Humanities
链接: https://arxiv.org/abs/2610.28650
作者: Remo Grillo
类目: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI)
备注:
Abstract:The CIDOC CRM family is not only a schema for museum collections. Its extensions formalise the apparatus through which research knowledge is produced and justified: CRMsci models scientific observation and measurement, CRMdig the provenance of digital processes, CRMinf inference and belief. Together they constitute a schema for research data, and the obstacle to their adoption is documentary rather than theoretical: four normative documents, four code conventions, and eighty-nine typed links crossing document boundaries. This paper presents the CIDOC CRM Periodic Table, a single-page browser rendering 131 classes and 480 property entries from CIDOC CRM 7.1.3, CRMsci 3.2, CRMdig 5.0 and CRMinf 1.2.1 as one navigable surface, with two inference facilities: a one-step triple builder admitting only the class-property combinations licensed by declared domains, ranges and the subclass closure, and a multi-step path finder enumerating property chains between two classes. Because all loaded models are merged into one code-keyed dictionary before the hierarchy closures are computed, both resolve chains that cross model boundaries; we document the addition of CRMsci and the three cross-model tests it made possible, and report preliminary uptake signals from 56 months of analytics, with the reading they support.
[AI-120] Zero-Cost Detection of Fabricated Citations: A Staged Heuristic Approach for Computing Dissertation Assessment
链接: https://arxiv.org/abs/2610.28649
作者: Hazrat Ali,Mohamed Elawady
类目: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI)
备注: 2 pages abstract, submitted to ACM SIGCSE-virtual conference
Abstract:Generative AI’s growing role in academic writing has driven a rise in fabricated references across dissertations and student reports. We present the Fabrication Severity Stack (FSS), a three-tiered framework classifying citation fabrications by detection complexity - from non-existent references to subtle content misattribution. Building on the FSS, we propose a staged heuristic verification workflow, executable through mainstream LLM interfaces. We argue that this framework offers computer science educators/examiners a practical, zero-cost basis for embedding reference verification into the assessment of dissertation and students’ reports. In a pilot study on 100 citations (20% seeded with Level 1 and Level 2 fabrications), the proposed heuristic workflow enabled mainstream LLM interfaces to correctly flag 9-10 of 10 non-existent references and partially detect metadata corruption. These preliminary results suggest that freely accessible, general-purpose LLMs can achieve high recall on citation verification without reliance on proprietary detection tools.
[AI-121] From Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems
链接: https://arxiv.org/abs/2610.22441
作者: Jinglin Dai,Jiawan Wang,Ruiqi Hong,Lei Bu
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 20 pages, 6 figures
Abstract:Large language models (LLMs) provide a convenient interface for translating natural language commands into task plans for cyber-physical systems (CPSs). However, two gaps hinder their use for real execution: generated plans may misinterpret or omit the user’s intent, and even a logically plausible plan may be infeasible under component dynamics, interactions, or safety constraints. Existing LLM-based planning approaches often lack informative feedback on why a generated plan fails, providing little guidance for revising the plan accordingly. To address these challenges, we present a hierarchical feedback framework that uses different types of feedback information to guide LLMs in generating executable plans for CPSs. We prompt LLMs to generate both a structured task plan and corresponding signal temporal logic (STL) constraints, and design a sound algorithm that identifies definite contradictions between them and provides feedback for revising inconsistencies. At the execution level, when control synthesis fails, we extract different types of diagnostic information, such as violated constraints and conflicting component assignments, and feed them back to guide task replanning. Experiments on seven heterogeneous multi-component CPS scenarios have shown that our framework can generate executable plans and feasible control executions that satisfy the original natural language missions with a success rate of 87.9% with GPT-5.
[AI-122] S3martCirc: Self-supervised Smart Circuit Discovery
链接: https://arxiv.org/abs/2609.00755
作者: Wendy Zheng,Yinhan He,Liang Wu,Jundong Li
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks, from text summarization to question answering. Despite these capabilities, their black-box nature obscures internal decision-making processes. Mechanistic interpretability (MI) aims to address this by reverse-engineering neural networks into human-understandable algorithms. Current MI approaches for LLMs typically follow a two-stage paradigm: first identifying important components (circuit discovery), where components are typically individual nodes such as an attention head or feedforward neuron, and second determining the role they play in a certain task (functional interpretation). However, this sequential approach overlooks a fundamental insight: a component’s importance and its functional role are inherently codependent. Unifying these stages presents two key challenges: (1) functional roles are often tied to specific nodes or components, limiting generalization, and (2) their identification relies on subjective interpretation rather than quantifiable metrics. To address these challenges, we propose S^3martCirc (Self-supervised Smart Circuit Discovery), a unified framework that simultaneously discovers circuits and interprets functionality. S^3martCirc abstracts node behavior into two general computational roles that generalize across tasks and defines a quantitative metric for assigning them, enabling importance and functional role to be discovered jointly rather than in sequence. Extensive experiments show that our framework outperforms existing methods in circuit discovery.
[AI-123] Stationarity Constraints Improve Both Energies and Densities of Machine-Learned Exchange-Correlation Functionals
链接: https://arxiv.org/abs/2610.30022
作者: Yuhao Chen,Donald G. Truhlar,Xiao He
类目: Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI)
备注:
Abstract:Empirical exchange-correlation functionals are fitted on fixed orbitals but used self-consistently, so better energies often come with worse densities. We show that the Brillouin residual of a trained functional on the Kohn-Sham orbitals of an accurate density controls, to first order, both the self-consistent density shift and the gap between fitted and self-consistent energies. Penalizing it requires no self-consistent-field solution during training. Trained on 6 energies and 11 CCSD densities, a neural meta-GGA surpasses SCAN in both atomization energies and densities of unseen molecules.
[AI-124] Automated Materials Data and Knowledge Extraction using Machine Learning and Large Language Models
链接: https://arxiv.org/abs/2610.29895
作者: Aravindan Kamatchi Sundaram,B. Pabitramohan Prusty,Rohit Batra
类目: Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)
备注: 25 pages, 5 figures
Abstract:Over the past decade, substantial efforts have been devoted to developing machine learning (ML) methods for extracting materials data and, where possible, materials knowledge directly from the scientific literature. This review traces the evolution of these methods from heuristic, rule-based pipelines through natural language processing (NLP) models and transformer-based encoders to, lastly, large language models (LLMs) and agentic systems capable of extracting materials information across multiple modalities, including text, tables, and figures. Beside construction of large, queryable materials databases and knowledge graphs that support downstream materials discovery, we discuss how these advances have enabled new capabilities, such as, generic question answering across materials science, AI co-scientists and chatbots for research assistance, and closed-loop optimization and discovery when integrated with autonomous laboratories. We critically assess the datasets generated using these approaches, their extraction accuracy and computational costs, and the respective strengths and limitations of different methodologies, and even provide practical guidelines for developing more reliable and scalable materials data extraction pipelines. Finally, we identify key open challenges, including the extraction of full materials structure-property-processing-performance tetrahedron at the sample level, the scarcity of reliable benchmark datasets for systematic evaluation, and the resolution of non-standard materials terminology across different document modalities.
[AI-125] Broadband Mid-Wave Infrared Imaging Enabled by All-Silicon Hyperboloid Metalens
链接: https://arxiv.org/abs/2610.29841
作者: Rose Johnson,Debabrata Mandal,Zihao Zou,Jessie Wei,Anna Wirth-Singh,Vladimir Yarmolik,Johannes Froech,Arka Majumdar,Praneeth Chakravarthula
类目: Optics (physics.optics); Artificial Intelligence (cs.AI)
备注:
Abstract:The mid-wave infrared (MWIR, 3-5 microns) is indispensable for thermal sensing, gas detection, long-range imaging, and free-space optical communications. Meta-optics offer a compact, lightweight, and potentially low-cost alternative to conventional refractive optics, making them attractive for size, weight, and power (SWaP)-constrained systems. However, their strong chromatic dispersion has largely restricted imaging to narrow spectral bands. Here, we demonstrate broadband imaging across the full 3-5 micron MWIR band using a single-layer silicon metasurface coupled with computational reconstruction. Remarkably, the reconstruction model is adapted using only four image captures, overcoming large training data requirements and enabling broadband image recovery despite the strongly wavelength-dependent optical response. In contrast to approaches that engineer complex, dispersion-optimized metalens profiles for broadband operation, our method leverages the wavelength-dependent information encoded by a simple 2cm diameter hyperboloid metalens. We show that this metasurface captures sufficient broadband intensity information for computational reconstruction across the entire MWIR band. These results establish a simple route toward broadband computational meta-optics without requiring broadband achromatic metalens designs, offering a promising platform for compact MWIR imaging systems.
[AI-126] Lineage-Aware Search Control for LLM -Based Evolutionary Factor Mining
链接: https://arxiv.org/abs/2610.29663
作者: Xintong Liu,Chenkai Zhang,Yanzhao Ma,Peng Yang
类目: Computational Finance (q-fin.CP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Formulaic alpha factor mining searches for symbolic expressions that transform market data into predictive signals. Recent LLM-based evolutionary factor mining methods use large language models as semantic variation operators. Under the conventional LLM-EA paradigm, stochastic variation and fitness-based selection can leave the search weakly directed and concentrated around a narrow set of structurally similar factors. We argue that these limitations arise because current methods mainly organize search at the individual and population levels, leaving lineage-level control underused. A lineage connects successive factor variants through evolutionary descent. We propose LineageEvo, which uses lineage in two coupled forms of search control: (1) lineage-conditioned LLM variation, which distills Lineage-Scoped Search Experience into operator-specific priors and fuses them with global operator priors, providing persistent, lineage-relevant guidance for LLM generation; and (2) lineage-stratified search allocation, which balances factor quality against lineage concentration by distributing the search budget across lineages during evolutionary search. Together, these mechanisms make semantic variation more direction aware and prevent branches from dominating the evolution, thereby promoting the discovery of predictive and diverse factors. We evaluate LineageEvo on CSI 500 and SP 500 in terms of factor predictiveness, portfolio performance, search efficiency, and lineage- and expression-level diversity. LineageEvo achieves the strongest predictive performance on both markets, while LSPS produces a more balanced lineage distribution and a structurally broader factor pool. The results support lineage as a useful intermediate unit for controlling LLM-based evolutionary factor search.
[AI-127] Huayu-Global: Skillful real-time quasi-global precipitation retrieval
链接: https://arxiv.org/abs/2610.29213
作者: Zijiang Song,Shuang Yang,Yuying Li,Zilong Zhu,Xigang Sun,Ting Liu,Wenye Xiao,Lina Yuan,Min Liu
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Artificial Intelligence (cs.AI)
备注:
Abstract:Satellite precipitation products face a persistent trade-off: real-time estimates are timely but less accurate, whereas the most reliable products are often released months after observation. This delay keeps high-quality precipitation information outside the decision window for flood warning and disaster response. Here we report Huayu-Global, a cross-constellation deep-learning system that combines three geostationary sensor streams from five satellites through a common retrieval framework and generates coherent quasi-global precipitation fields every thirty minutes. Across more than a million independent station records from two periods outside the training years, Huayu-Global improves event detection and precipitation-amount estimation relative to the real-time products evaluated and reaches delayed-product-level skill in key metrics. Huayu-Global therefore turns operational geostationary observations into a timely, high-skill precipitation layer for flood forecasting, water management and disaster response. Its cross-constellation design also provides a scalable foundation for incorporating future geostationary missions into hydrological and weather-prediction systems.
[AI-128] ORSMT: Unleashing the Potential of Satisfiability Modulo Theories for Semantic Verification and Repair in LLM -Based Optimization
链接: https://arxiv.org/abs/2610.29145
作者: Rui Zhang,Chenyu Zhou,Jianghao Lin,Yaoxin Wu,Dongdong Ge,Wen Song
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Large language models increasingly automate optimization modeling from natural language problems, producing written mathematics, solver code and a returned report. Verifying one optimization problem across these artifacts requires semantic comparison: unequal models can attain the same objective, equivalent models can use different auxiliary variables, and mutually consistent artifacts can omit the same source requirement. ORSMT provides a typed semantic interface and exact SMT relation queries. It aligns observable decisions, projects auxiliary variables, proves or refutes feasible region and objective relations, localizes refutations to implicated rows or domains and links source texts when available. It then reconstructs one artifact from another, and each adopted candidate passes the required relation checks. Across 3,034 problems and six generator sizes, ORSMT improves the model-code match rate in all 72 benchmark and generator combinations before selection. Protected selection raises the aggregate rate from 43.2% to 88.2% and the share that are both correct and matched from 25.2% to 40.2%. On the 1,905 refuted outputs, certified voting under the shared fallback makes 50.9% of outputs correct and matched, compared with 44.8% for ReLoop and 49.4% for Opt-Verifier. A separate one-round source revision followed by exact reconstruction restores a model-code match for 84.4% of the refuted pairs.
[AI-129] An Eco-Friendly Tourism Trip Planner Driven by Z-Number Uncertainty: Evidence from Baku Dubai Abu Dhabi Tashkent and Istanbul
链接: https://arxiv.org/abs/2610.29120
作者: Aziz Nuriyev,Sherzod Turaev,Saja Al-Dabet,Mary John,Kanan Qasimzade,Alihuseyn Orujzada,Alihuseyn Orujzada,Aisha Al Ali
类目: Physics and Society (physics.soc-ph); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注: 11 pages, 2 figures, 5 tables. Code, data, and per-seed results: this https URL
Abstract:Deployed tourism trip planners ingest travel times and ratings as exact numbers, although routing services return travel times that vary with the hour of departure and a platform rating may rest on a handful of reviews or on tens of thousands. We present an eco-friendly trip planner that represents both quantities as Z-numbers, which pair a fuzzy restriction on a value with a fuzzy reliability on the evidence behind it, and that constructs them directly from routing-service observations at six departure hours and from platform review counts rather than from linguistic elicitation. A hybrid loss combines score, carbon, travel-time, and reliability terms, and an adaptive large neighborhood search returns single-day itineraries with per-leg reliability annotations through a conversational front end. We compare the planner with a crisp-mean baseline under an identical optimizer in Baku, Dubai, Abu Dhabi, Tashkent, and Istanbul, with fifteen points of interest and thirty seeds per city, and we evaluate the selected itineraries on their trip carbon and their total travel time. Pooled over 150 seed-paired runs, the planner lowers trip carbon from 3.24 to 2.61 kg (19.3%, Wilcoxon p = 0.017; city-clustered bootstrap interval 6.4% to 26.7%) and travel time from 123 to 109 minutes per trip (11.5%, p = 2.8e-10; interval 5.0% to 18.2%). The travel-time reductions survive a Holm correction in Baku, Istanbul, and Abu Dhabi, the carbon reduction is significant in Baku and large but uncertain in the dispersed catalogs of Dubai and Abu Dhabi, and neither metric changes in Tashkent. Review counts were measured for Baku and estimated for the other four cities, and the comparison involves a single baseline, so the results establish feasibility and direction rather than a general advantage. Code, routing caches, frozen routing tables, and per-seed results are openly released.
机器学习
[LG-0] A Quasipolynomial Algorithm for ell_infty Contractions
链接: https://arxiv.org/abs/2610.30122
作者: Ashkan Soleymani,Gabriele Farina
类目: Computer Science and Game Theory (cs.GT); Computational Complexity (cs.CC); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:We give an algorithm for computing an \eps -approximate fixed point of any d -dimensional nonexpansion with respect to the \ell_\infty norm in time \log(1/\eps)^O(\log^2 d) . At its core, our method uses a boosting argument to geometrically shrink a search region. Among other consequences, our algorithm implies that simple stochastic games can be solved in quasipolynomial time.
[LG-1] Self-normalized Bernstein Inequalities for Regenerative Processes and Applications to Learning Without Independence
链接: https://arxiv.org/abs/2610.30119
作者: Imon Banerjee,Pierre Alquier
类目: Machine Learning (cs.LG); Probability (math.PR); Statistics Theory (math.ST)
*备注: 44 pages
Abstract:We prove a self-normalized Bernstein inequality for bounded functions of a regenerative process with independent, identically distributed marked cycles of finite mean length. The bound uses observed cycle lengths and retains explicit terms for truncation and the cycle ending after the observation horizon. Selecting a tuning parameter from a finite deterministic collection gives a version of Bernstein inequality where all terms are empirical but one, related to the tails of the regeneration times. We then study this term in many regimes: it turns out that it is of second-order in many cases of interest. This is important for applications in machine learning: we prove generalization error bounds for classification with non-i.i.d., regenerative data. These bounds can be used to define learning objectives: by minimizing the first-order term only in the bound, we define agnostic classifiers that still satisfy oracle inequalities. We also show that the bound on the average deviation provided by our results is minimax-optimal in the sample size and in the expected regeneration time. Finally, we combine the Bernstein inequality with a regenerative strong approximation to obtain a Gaussian approximation using an observable block variance estimator.
[LG-2] he role of locality and weight sharing in convolutional neural networks
链接: https://arxiv.org/abs/2610.30118
作者: Francesco Mori,Melanie Weber
类目: Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn); Statistical Mechanics (cond-mat.stat-mech); Machine Learning (stat.ML)
*备注: 39 pages, 11 figures, including appendices
Abstract:The success of convolutional neural networks is often attributed to locality and weight sharing. However, how these two properties separately shape learning, and how their effects depend on the spatial correlations of natural images, remain poorly understood. We address these questions in a teacher-student model with a convolutional teacher acting on P input positions with correlation c between positions. We compare convolutional and locally connected students trained by online stochastic gradient descent, and derive ordinary differential equations that describe their dynamics exactly in the limit of large feature dimension D . For uncorrelated positions ( c = 0 ), the two students follow the same learning trajectory, with the convolutional student learning exactly P times faster. We also find that locality affects the sample complexity: a student that matches the kernel size of the teacher recovers it from \mathcalO_D(D) samples, whereas \mathcalO_D(D\log(D)) samples are needed with a larger kernel. For c 0 , spatial correlations can reduce the advantage of weight sharing. The asymptotic error of the convolutional student grows with c , whereas that of the locally connected student is independent of c and overall lower when evaluated at the same learning rate. When the learning rate is instead tuned to the training horizon, however, the locally connected student wins at early times and the convolutional student at late times, with a transition that we predict analytically.
[LG-3] Velocity Ambiguity Profiles: Time-Resolved Bayes-Risk Diagnostics for Flow Matching NEURIPS2026
链接: https://arxiv.org/abs/2610.30107
作者: Yunpeng Mei,Xiaowen Zhu,Chenyu Wang,Chenbo Xin,Hongjie Cao,Jiamin Wang,Jie Chen,Gang Wang
类目: Machine Learning (cs.LG)
*备注: Accepted at NeurIPS 2026
Abstract:Flow matching trains a continuum of velocity regression problems but usually reports a loss averaged over interpolation time, obscuring which regions are limited by intrinsic ambiguity and which remain data-limited. We introduce the Velocity Ambiguity Profile (VAP), \mathcalA_v(t) , the time-local Bayes-risk floor of velocity prediction, and decompose time-resolved loss as L_N(t)=\mathcalA_v(t)+R(t;N) , where only the excess term R(t;N) is reducible by more data or a different estimator. For N training samples and a K -component mixture in the structured example, we prove two statistical benchmarks. In an intrinsic local-regression benchmark, \mathcalA_v(t) enters the time-dependent noise factor of the standard nonparametric rate. In separated isotropic Gaussian mixtures, the Bayes velocity becomes locally affine, and a label-oracle residual-regression benchmark has parametric excess-risk scale K\mathcalA_v(t)/N , matched within the same separated residual Gaussian-location subproblem. Controlled Gaussian-mixture experiments validate this diagnostic through a dense transition sweep, calibrated oracle-to-blind and empirical-proxy audits, and a local controlled time-sampler stress test in which a frozen reducible-error proxy reduces reducible loss while cross-cell sensitivity delimits aggressive sampling. On CIFAR-100 DINOv2 feature flow, guided time sampling reduces uniformly evaluated velocity loss by 6.78% and global feature Fréchet distance (FD) by 23.6% , while class-conditional kernel inception distance (KID) shows no improvement. VAP turns an averaged flow-matching objective into a time-resolved diagnostic for locating ambiguity-limited and data-limited regions.
[LG-4] Orbitol: Crystal Generation on Wyckoff Orbits
链接: https://arxiv.org/abs/2610.30086
作者: Ivan Žugec,Joshua Rosenthal,Ivor Lončarić,Jan-Willem van de Meent,Tin Hadži Veljković
类目: Machine Learning (cs.LG)
*备注: 39 pages, 9 figures, 13 tables
Abstract:Generative models propose crystalline materials by learning composition and geometry, but unconditional sampling gives no direct control over the space group, which constrains whether responses such as piezoelectricity are symmetry-allowed. We present Orbitol, a crystal generator built on the Crystalite diffusion Transformer that replaces atom tokens with Wyckoff-orbit tokens. Given a space group and a collection of occupied Wyckoff positions, Orbitol generates the atom type and free coordinates of each orbit together with a compatible lattice, so every valid structure is invariant under the operations of the requested group. It generates structures two to three orders of magnitude faster than the symmetry-aware baselines at the evaluated settings. On MP-20, 13.8% of Orbitol’s attempts yielded stable or metastable, unique and novel structures that retained the requested space group after relaxation, compared with 10.8% for SGFM, the next best model. We also introduce SPARC, a score that combines retention of the raw detected space group with agreement between the retained-group and training distributions. Finally, we used Orbitol to generate candidates in piezoelectric crystal classes. Of the structures selected by internal screening, 89% passed our density functional theory checks, and 79% also matched no Materials Project or Alexandria entry with the same space group. Their median figure of merit was nine times that of the piezoelectric materials computed in the Materials Project, and 24% of the new candidates ranked in that set’s top 10%.
[LG-5] Circuits of LLM Classifiers
链接: https://arxiv.org/abs/2610.30076
作者: Hadi Khalaf,Sajani Vithana,Kunal Talreja,Haewon Jeong,Viveck R. Cadambe,Flavio Calmon
类目: Machine Learning (cs.LG)
*备注:
Abstract:Language models make it easy to build classifiers by simply prompting a model with the sample to classify. However, their predictions are neither deterministic nor uniformly reliable across samples. How can we combine predictions from different models to improve reliability? We view LLM classifiers as noisy measurement instruments of an underlying target label. Our goal is to maximize average classification accuracy at a given average query cost. To this end, we introduce circuits that organize LLM classifiers into an adaptive sequence of queries and decisions. We then characterize the optimal circuit, obtain the fundamental limits of achievable accuracy, and provide practical insights on realizing the optimal circuit. Across seven classification tasks spanning safety, health, political science, human preference, agent evaluation, decision-making and reasoning, our method improves the cost-accuracy frontier over routing and cascading baselines.
[LG-6] From Classifier Heads to Encoder Prototypes: Personalized Federated Learning with Head-Derived Clustering
链接: https://arxiv.org/abs/2610.30075
作者: Amirardalan Dehghanpour,Arian Noori,Ehsan Tanghatari,Naimeh Omidvar,Hamid R. Rabiee
类目: Machine Learning (cs.LG)
*备注:
Abstract:Personalized federated learning must decide which clients should share representations. We introduce HDC-FL (Head-Derived Clustering for Personalized Federated Learning), which reads that decision off a signal shared-encoder training already produces: the client-specific classifi- er heads. At periodic refresh rounds the server projects the cached heads into a normalized low-rank descriptor space, groups the descriptors, builds one encoder prototype per group from the cached client encoders, and returns to each client a soft mixture of those prototypes paired with its own unchanged head. Client-specific heads are never averaged or distributed to oth- er clients; the shared-encoder trajectory is left intact; and no second optimization trajectory, additional local objective, or post-training fine-tuning stage is required. Across 11 CIFAR-10 and CIFAR-100 configurations covering label-subset, Dirichlet and rotation heterogeneity with ResNet-18 and MobileNetV2 encoders, HDC-FL is competitive with the strongest personalized and clustered baselines in macro-averaged mean accuracy and attains the numerically highest macro-averaged worst-client and bottom-20% accuracy among the evaluated methods. The tail results distinguish methods that look similar when only their means are compared. A controlled donor-swap diagnostic further finds positive donor-ranking associations between head similarity and encoder compatibility in all three settings tested, most clearly under covariate shift.
[LG-7] Loopholing All the Way: Language Modeling with pure Self-Conditioning
链接: https://arxiv.org/abs/2610.30068
作者: Adrien Deschenaux,Nadezhda Ilieva,Justin Deschenaux,Manuel Madeira,Dorina Thanou,Aurélien Lucchi,Antonio Orvieto,Caglar Gulcehre,Pascal Frossard
类目: Machine Learning (cs.LG)
*备注:
Abstract:Recent Diffusion Language Models (DLMs) use Self-Conditioning (SC), or Loopholing, to improve their performance. While SC is traditionally seen as an auxiliary heuristic, its empirical success raises the question: can a continuous generative model be based purely on Self-Conditioning? We answer affirmatively with LAW (``Loopholing All the Way’'), which propagates the Expected Embedding under the denoiser’s prediction across steps, decoding the tokens once, at the end. On Sudoku, LAW outperforms previous Discrete and Continuous Diffusion Models, including Loopholing Discrete Diffusion. On Maze, LAW is the most accurate from 2 to 64 NFE. After training on TinyGSM and sampling at temperature 1 with 1024 NFE, LAW solves 42.2% of GSM8K problems against 34.5% for uniform Loopholing Discrete Diffusion. Additionally, fine-tuning a pre-trained Uniform Diffusion Model (Duo) with LAW increases its accuracy from 17.2% to 48.1%. Finally, on OpenWebText, LAW pushes the Pareto frontier and approaches Autoregressive Models (ARMs) with 64 NFE. Find our code at this https URL.
[LG-8] ExGrad: Beyond Suppression - Explicit Clean-Gradient Subtraction in Adversarial Training
链接: https://arxiv.org/abs/2610.30018
作者: Zhiyuan Ye,Xiangyu Zhou,Ji Qi,Hao Zhang,Yi Zhou
类目: Machine Learning (cs.LG)
*备注:
Abstract:Adversarial training derives updates from surrogate losses, but source-loss descent does not directly characterize an update’s effect on adversarial margins. We introduce ExGrad (extrapolative gradient recomposition), which constructs clean-input, adversarial-input, and adversarial-weight-perturbed (AWP) gradients. Virtual steps measure their margin responses, and a globally solvable three-variable problem selects coefficients over a bounded affine domain without nonnegativity constraints. Across affine radii on CIFAR-10, negative clean coefficients rapidly predominate and increase in magnitude with the radius. Cross-zero interventions show that setting the clean coefficient to zero is insufficient: mild subtraction improves PGD-20, while stronger subtraction trades clean accuracy for further PGD-20 gains. In CIFAR-10 WRN-28-4 controls, none of the three candidate-removal variants matches full ExGrad. Projection diagnostics show that negative clean coefficients usually coexist with positive net clean-axis projections, although both decrease as the radius grows. Norm-matched controls indicate that update magnitude alone does not explain the moderate-radius gains. Experiments on CIFAR-10, CIFAR-100, and ImageNet-100 show radius-dependent improvements over AWP. On CIFAR-100, ExGrad at R=0.8 improves all five reported metrics, including clean and AutoAttack accuracy by 3.84 and 1.75 percentage points. On ImageNet-100, the same radius improves all four robust metrics over both AWP and PGD-AT. These results identify clean-gradient subtraction as a response-revealed update preference whose strength and clean-robust operating point are controlled by the affine radius.
[LG-9] Mitigating Model Collapse through Dreaming Learning NEURIPS2026
链接: https://arxiv.org/abs/2610.30009
作者: Matteo Benati,Alessandro Londei,Denise Lanzieri,Raffaello Mastromarino,Vittorio Loreto
类目: Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (stat.ML)
*备注: 8 pages, 6 figures. Accepted at the 2nd Workshop on Principles of Generative Modeling (PriGM), NeurIPS 2026
Abstract:Training generative models on synthetic data causes loss of diversity and a decrease in performance, a phenomenon known as model collapse. We study this degeneration spectrally in a controlled Markov setting. The eigenvalue spectrum of the learned transition operator offers a direct view of the process, since the subdominant eigenvalues set how fast the chain mixes and therefore how much of the state space it continues to explore. We find that sampling at temperature T_s 1 acts on the operator like repeated sharpening, pushing it toward a deterministic system: the subdominant eigenvalues approach the unit circle and the spectral gap narrows. When T_s 1 , even data accumulation does not appear to arrest this drift. We then analyze Dreaming Learning, a regulariser that, in parallel with regular training, adds a loss term on the model’s own samples drawn at a raised temperature T_d 1 . Because high-temperature samples flatten the predicted distribution, training on them keeps the entries of the learned operator from becoming vanishingly small, which in turn keeps the eigenvalues bounded away from the unit circle. Measurements of the spectra over 5000 generations are consistent with this picture, and the mitigation effect persists in more complex settings such as LSTM language models and a 124M-parameter GPT-2.
[LG-10] EPIC: Rethinking Trust Regions for Autoregressive LLM Policies via Exact-KL Prefix Importance-Ratio Clipping
链接: https://arxiv.org/abs/2610.29999
作者: Chengxi Li,Hanlin Wu,Zhicheng Cai,Huan-ang Gao,Wei-Ying Ma,Jingjing Liu,Hao Zhou
类目: Machine Learning (cs.LG)
*备注: 27 pages. Code: this https URL
Abstract:Reinforcement learning (RL) based on PPO-style clipping has become an effective approach to improving the reasoning capabilities of large language models (LLMs). However, standard token-level clipping measures policy change only through the conditional probability of the current token, while the prefix conditioning context is itself generated by the policy. This mismatch becomes especially consequential in long-response and off-policy training. We reformulate autoregressive generation as a finite-horizon Markov decision process and derive its performance-difference identity under behavior-policy sampling. The resulting change of measure identifies the cumulative likelihood ratio of the generated prefix as the exact importance weight for each stepwise policy-improvement term. Starting from the monotonic-improvement principle of trust-region policy optimization, we then construct a prefix-level trust region using the exact reverse-KL coordinate r-1-\log r , whose behavior-policy expectation equals the corresponding prefix-distribution KL divergence. This yields EPIC, a PPO-style sequence-clipping method that directly regulates prefix-level policy drift without relying on a local quadratic approximation or expert-routing replay. Experiments on off-policy reinforcement learning with a sparse mixture-of-experts language model show that EPIC consistently outperforms standard and sequence-level clipping baselines across mathematical-reasoning benchmarks, while remaining competitive with routing-replay-based alternatives. Code is available at this https URL.
[LG-11] SCDM: Scalable Causal Discovery in Nonlinear Temporal Systems with Meta-Learning NEURIPS2026
链接: https://arxiv.org/abs/2610.29992
作者: Jingbo Wang,Kegeng Tang,Zihao Wang,Shaogang Ren
类目: Machine Learning (cs.LG)
*备注: Accepted at NeurIPS 2026. 44 pages, 6 figures
Abstract:Causal discovery from nonlinear multivariate time series is challenging in high-dimensional systems, where the number of candidate directed relations grows quadratically with the number of variables. Existing methods are often limited by target-wise model fitting, repeated conditional testing, or expensive graph-search procedures. We propose SCDM, a shared-parameter meta-learning framework for scalable temporal causal discovery. SCDM treats each target variable as a task while learning a shared temporal predictor and a shared causal-strength matrix across tasks. Each meta-episode updates only a subset of target-variable tasks, allowing the model to accumulate structural evidence across the full system without exhaustive target-wise optimization. After training, a post-training graph readout converts the learned predictor into continuous directed causal scores for threshold-independent evaluation. We also provide a finite-error recovery principle showing that, under fully observed delayed-system assumptions and a positive risk-gap condition, thresholding SCDM scores recovers the graph when statistical, meta-optimization, and readout errors are below the population margin. Experiments on controlled synthetic, neuroimaging-inspired, high-dimensional, and real-world temporal benchmarks demonstrate competitive causal recovery and improved scalability in large temporal systems.
[LG-12] CORD: Conflict-Aware Optimization for Joint RL and Distillation in Agents
链接: https://arxiv.org/abs/2610.29965
作者: Wenhao Yu,Zhengcheng Wang,Jiahong Liu,Zexuan Qiu,Minda Hu,Shaohang Wei,Jiafei Lyu,Junyan Lyu,Haobo Fu,Irwin King
类目: Machine Learning (cs.LG)
*备注: 38 pages, 12 figures
Abstract:Joint reinforcement learning and self-distillation combines task rewards with dense teacher guidance for agent training, but their gradients can partly cancel. A mixing coefficient controls relative scale without selectively removing opposing components. We present CORD, a general framework for conflict-aware optimization of two objectives. CORD separates the choice of protected objective from the condition that triggers projection, and combines the gradients once per optimizer mini-batch. A nearest-point formulation yields a closed-form update and characterizes its first-order effect on the protected objective before optimizer transformations. Gradient diagnostics connect conflict in agent training to lost local optimization progress and motivate selective correction. As a gradient-combination mechanism, CORD can be added to different joint training recipes while retaining their objectives. Across three such recipes, it improves ALFWorld success by 1.5-15.9 percentage points. On WebShop, it improves two of the three pairs, with SDAR rising from 60.7% to 64.6% success. Our analyses explain why protection direction and projection gate must be chosen together, and why the distillation objective and its mixing weight remain consequential after projection.
[LG-13] FrontierCS 2: A Benchmark for Computer Science RD
链接: https://arxiv.org/abs/2610.29932
作者: Qiuyang Mang,Wenhao Chai,Runyuan He,Dongruixuan Li,Zihan Zheng,Yujun He,Xuanyi Zhou,Hao Wang,Siddhant Ray,Bo Peng,Ruyi Ji,Shuo Yang,Haocheng Xi,Boru Chen,Xinyang David Han,Melissa Pan,Mayank Mishra,Boxi Yu,Sijia Liu,Kerem Akillioglu,Yi Pan,Zirui Wang,Mert Cemri,Diogo Antunes,Yichuan Wang,Aiwei Ivy Zhang,Zhenyang Chen,Qizheng Zhang,Kaiyuan Liu,Wenyuan Huang,Sherry Ruan,Shu Liu,Dacheng Li,Zerui Cheng,Zeyu Shen,Shang Zhou,Hanfei Chen,Yuzhong Zhang,Bohan Lyu,Qu Ao,Han Zheng,Chonghe Jiang,Jingzhuo Hu,Xiaokun Chen,Kunjun Li,Zhiqing Zhong,Haoye Tian,Tong Zhao,Yunzhong He,Hanchen Li,Ofir Press,Jiantao Jiao,Kunle Olukotun,Karthik R. Narasimhan,Jingbo Shang,Koushik Sen,Matei Zaharia,Alex Dimakis,Ion Stoica,Joseph E. Gonzalez,Alvin Cheung
类目: Machine Learning (cs.LG)
*备注: Website: this https URL ; Code: this https URL
Abstract:Can LLM agents make contributions to frontier computer science RD? We introduce FrontierCS 2, a benchmark of 48 open-ended tasks. We take a paper and the code its authors released and delete the paper’s contribution. The deleted code becomes the human reference that sets the bar. Earlier open-ended benchmarks give each agent a problem statement and grade one program after the run. FrontierCS 2 places the agent inside a research environment. The agent inherits a working repository and may submit a patch at any point. For each patch, a judge returns a development-suite score but keeps the evaluator, the held-out final suite and the reference private, and grades the final submission on that suite against the reference. The tasks span 13 domains, from machine-learning systems and high-performance computing to databases, security and robotics. Beyond these 48 tasks, FrontierCS 2 keeps a private set of more than 100 tasks, to be announced in the future. We evaluate 12 agents. The best one is Claude Opus 5.5, passing 58% of its runs. No run passes on 16 tasks. A higher score is not the same as a pass: most runs that beat the reference on the combined final score still fall short on some metric or workload. On a shared capability scale for agents and tasks, the median task is roughly as difficult as what the strongest agent can handle.
[LG-14] Preventing Model Extraction in Contextual Linear Optimization
链接: https://arxiv.org/abs/2610.29918
作者: Adam N. Elmachtoub,Tianyi Lin,Wenxuan Liu
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 25 pages, 11 figures
Abstract:Consider a contextual linear optimization problem, where a predict-then-optimize (PTO) system returns optimal decisions without revealing the cost vectors used in its downstream linear program. Our focus is on how to prevent attackers, who can submit context queries, from extracting the underlying prediction model. Although the cost-to-decision map is many-to-one, observations across contexts can reveal the underlying prediction model. For linear models, we provide sufficient conditions where the attacker can learn the model up to positive scaling by only observing the decision. These conditions motivate an adaptive attack that alternates inverse optimization with boundary sampling, i.e., using context queries leading to decision boundaries of the current attacker model. To prevent extraction, we propose a mechanism that perturbs decisions only when the predicted cost vector is near a decision boundary. We establish a regret bound in terms of the perturbation radius. For the attackers that fit the defended responses as optimal decisions under the evaluation distribution, we also prove a lower bound on expected extraction error. Experiments on synthetic instances and real data quantify the security-performance trade-off of the defense mechanism and illustrate that our defense can increase the extraction error.
[LG-15] A Sharp Dynamic Haussler Lemma
链接: https://arxiv.org/abs/2610.29898
作者: Shihao Shao,Dacheng Tao
类目: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS)
*备注:
Abstract:Haussler’s covering lemma (JCTA 1995) gives the sharp (C/\delta)^d metric-entropy bound for binary VC classes under normalized \ell_1 distance. We prove a sharp global dynamic analogue for bounded real-valued classes in normalized \ell_p , for every 1\le p\infty . Writing d=\operatornamesfat_\varepsilon/32(\mathcal F) , we obtain a global dynamic cover of size at most (C/\varepsilon^p)^d for every horizon and 0\varepsilon1/2 . Here a single family of deterministic causal experts, fixed independently of the instance sequence, covers every target on every sequence. For 0\varepsilon1/4 , a matching worst-case lower bound (c/\varepsilon)^pd holds already for binary classes on one fixed sequence, even with real-valued experts. The constants are universal, independent of p . For p=2 , this resolves the horizon-independent sequential \ell_2 covering problem of Rakhlin, Sridharan, and Tewari (PTRF 2015) in a stronger global form. For p=1 , it gives the sharp (C/\delta)^\operatornameLdim(\mathcal H) global dynamic covering bound for binary classes, resolving questions of Alon, Ben-Eliezer, Dagan, Moran, Naor, and Yogev (STOC 2021) and Malliaris and Moran (Trans. AMS 2024). Our proof combines rank-decreasing correction schedules and accurate sampled prefixes with exact continuations after target identification. Shared-parent routing distributes these continuations among eligible prefixes, eliminating the logarithmic overhead. Subjects: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS) Cite as: arXiv:2610.29898 [cs.LG] (or arXiv:2610.29898v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.29898 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-16] RangeNav: Task-Aligned LiDAR Range-Image Pretraining for Robot Navigation ICRA2027
链接: https://arxiv.org/abs/2610.29845
作者: Samuel Foo Enze,Manthan Patel,Fan Yang,Linus Kramer,Pascal Roth,Marco Hutter,Cesar Cadena
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 9 pages, 6 figures, 5 tables. Submitted to ICRA 2027; under review
Abstract:Autonomous mobile robots operating in dense crowds and cluttered sites require 360^\circ perception to react to surrounding hazards. While panoramic LiDAR offers full-surround coverage, learning representations within reinforcement learning (RL) is sample-inefficient under sparse rewards, and existing 3D pretrained point-cloud encoders are too memory-heavy for massively parallel RL simulation. We present RangeNav, a compact (183k parameters) LiDAR range-image encoder pretrained entirely on synthetic data. RangeNav uses anisotropic convolutions and circular padding to handle the extreme aspect ratio and azimuthal seam of range images. We pretrain the network using a navigation-aligned objective combining range reconstruction, semantic segmentation, and bird’s-eye-view (BEV) prediction, then freeze it for policy learning. In simulation, adding semantic and BEV supervision increases success rates over a reconstruction-only baseline by an average of 32.03 and 23.73 percentage points (pp) in procedurally generated training and out-of-distribution realistic test environments, respectively. Zero-shot real-world deployments on a Unitree B2-W quadruped demonstrate robust navigation through dense crowds, low-light settings, and construction zones where limited-field-of-view depth policies struggle. Code and models will be open-sourced.
[LG-17] Asymmetric Likelihood-Free Hypothesis Testing
链接: https://arxiv.org/abs/2610.29798
作者: Gunjan Kumar,Yash Pote,Jonathan Scarlett
类目: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS); Information Theory (cs.IT); Statistics Theory (math.ST); Machine Learning (stat.ML)
*备注:
Abstract:In likelihood-free hypothesis testing (LFHT), given samples from two unknown distributions P,Q on [n] with \mathrmd_TV(P,Q)\geq\varepsilon , and from a third distribution D\in\P,Q\ , the goal is to identify whether D=P or D=Q . We study asymmetric likelihood-free hypothesis testing, where the sample budgets s_P and s_Q for P and Q may differ, and characterize the feasible triples (s_P,s_Q,s_D) up to logarithmic factors. We establish that unequal reference budgets can give a polynomial saving. For instance, for constant separation, n samples from P and n^3/4 from Q allow testing with \widetilde O(n^1/4) samples from D , whereas both existing baselines—symmetric LFHT and unequal-sample closeness testing—require \Omega(\sqrt n) samples from D . We further apply our results to non-adaptive distribution 2-clustering, where the sample allocation among k distributions must be fixed before observing any data. We obtain matching upper and lower bounds for this problem up to logarithmic factors in terms of the domain size, number of distributions, smaller cluster size, and separation, both with one cluster distribution known and with both unknown. Comparing with known bounds for adaptive sample allocation gives a worst-case adaptivity gap of \widetilde\Theta(k^1/4) . Subjects: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS); Information Theory (cs.IT); Statistics Theory (math.ST); Machine Learning (stat.ML) Cite as: arXiv:2610.29798 [cs.LG] (or arXiv:2610.29798v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.29798 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-18] Monitorability Is Trainable: Post-Training for Chain-of-Thought Monitorability
链接: https://arxiv.org/abs/2610.29734
作者: Usman Anwar,Tim Bakker,Christos Louizos,David Krueger
类目: Machine Learning (cs.LG)
*备注: The first two authors contributed equally
Abstract:Chain-of-thought (CoT) monitoring is one of the few practical tools available for overseeing capable AI systems, but it is fragile: a model’s reasoning is only useful to a monitor if it remains coupled to what the model actually does. This coupling can be degraded during training through, for example, CoT compression, optimization against a monitor, and out-of-context generalization from synthetic documents. In this work, we propose an unsupervised post-training objective based on maximizing mutual information between CoT and the output for improving CoT monitorability. Across three model organisms with degraded CoT, it recovers most or all of the lost monitorability. Applied to undegraded policies, it further improves judged monitorability, improves prediction of unsafe behavior by CoT monitors and activation probes, and increases the prevalence of consequential reasoning. These results suggest that monitorability should be treated as a trainable attribute of the policy. A dedicated monitorability post-training stage can extend current practices that protect CoT from direct optimization pressure.
[LG-19] Post-hoc Epistemic Uncertainty for Vision-Language-Action Policies
链接: https://arxiv.org/abs/2610.29730
作者: Johannes Keller,Albrecht M. Bloße,Adrian Lindenmeyer,Thomas Neumuth,Daniel Schneider
类目: Robotics (cs.RO); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 30 pages, 5 figures, 11 tables. Code: this https URL
Abstract:Vision-language-action (VLA) policies need to recognize gaps in their training knowledge to request useful demonstrations or assistance in new situations, especially when failures are costly. Disagreement within an ensemble of independently trained policies can guide these decisions, but requires training and running multiple policies. We propose post-hoc kernel-variance heads that estimate epistemic uncertainty from the embeddings of a single policy. Each head computes the conditional variance of a Gaussian process conditioned on embeddings from the training demonstrations. Fitting requires no regression targets or gradient updates and leaves the policy unchanged. In experiments with SmolVLA on LIBERO-10, the kernel variance heads perform competitively with two-policy ensemble scores in task-level correlation with task success, in demonstration acquisition during active learning, and in referral to a stronger supervisor policy as a proxy for human assistance. For SmolVLA, the head on the vision-language backbone reduces uncertainty-scoring time by up to approximately a factor of 50 compared with ensemble scores, while also substantially reducing setup costs. We also show that the established evaluation protocol on LIBERO-10 primarily rewards recognition of coverage differences between tasks, while execution variability weakens assessment of within-task rankings. Thus, controlled perturbations provide a complementary evaluation: modality-specific heads identify unfamiliar camera, instruction, or state inputs, while fused heads can detect unfamiliar combinations of individually familiar inputs. With a supervisor proxy trained on the evaluated perturbation families, the decoder head improves within-task referral over ensemble disagreement. These findings show that useful uncertainty estimates for adaptive robot learning can be obtained from a single policy.
[LG-20] DriveMLP: When Training Strategy Outperforms Model Complexity in Autonomous Driving
链接: https://arxiv.org/abs/2610.29676
作者: Alessandro Canevaro,Julian Schmidt,Hang Yu,Ye Jin,Sajad Marvi,Georg Martius,Julian Jordan
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注: Accepted at 2026 Conference on Robot Learning (CoRL)
Abstract:Learning motion planning policies that remain reliable under closed-loop execution is a central challenge in Autonomous Driving (AD). Imitation Learning (IL) scales well to large AD datasets but suffers from distribution shift, causal confusion, and compounding errors under closed-loop execution. In contrast, Reinforcement Learning (RL) typically requires handcrafted rule-based rewards and massive simulation. We study a simple closed-loop IL framework that uses RL purely as an optimization tool, guided by a trajectory-distance reward derived from logged expert data. We refer to this training strategy as Expert-Guided RL (EGRL). Through a controlled empirical study we find evidence that the training procedure, rather than model size and complexity, accounts for a substantial part of the performance gains. We then introduce DriveMLP, a lightweight MLP policy trained with EGRL, as a simple instantiation of these findings. It achieves a score of 86.51 on the nuPlan Val14 Reactive benchmark, a +3.2 point improvement over the previous best IL planner (Flow Planner), with 15x faster inference. Code is available at: this https URL .
[LG-21] Characterizing Oblivious Common-Reward Multi-Player Bandits: Universal Stationarity Coordination Traps and Certified Global Rates
链接: https://arxiv.org/abs/2610.29673
作者: Mingxiao Li,Wenhai Lai,Hongyu Wang
类目: Machine Learning (cs.LG)
*备注: 20 pages, including 11 pages of supplementary material
Abstract:This paper presents a theoretical analysis of the oblivious common-reward multi-player multi-armed bandit (OC-MPMAB), a minimal multi-player bandit model in which multiple players select arms simultaneously and observe only a shared reward. Crucially, the players are fully oblivious: they do not observe the actions of other players and do not even know whether any other players exist. The contributions of this work are threefold. First, we prove that the independent gradient-bandit updates applied by the players jointly form an unbiased stochastic gradient estimator of a hidden product-policy objective. This property then yields a universal finite-time first-order stationarity guarantee for every instance. Second, we show that stationarity cannot, in general, be strengthened to global optimality. To illustrate this, we identify a product-policy trap obstruction that rules out the guarantee of global optimality. Third, we show that global optimality can be recovered under structural assumptions on the reward table. To make global optimality achievable, we introduce finite-table certificates that separately characterize product-gradient dominance and self-bounding stochastic noise. We then establish certificate-conditioned bounds on the product-policy gap and pseudo-regret. Consequently, the standard O(1/t) product-policy gap and O(\log T) pseudo-regret rates hold when the certificates are uniform.
[LG-22] Leverag ing Time-Series Foundation Models for Financial Crime Detection
链接: https://arxiv.org/abs/2610.29667
作者: Danae Natalie Savvidi,H. Çağrı Bilgi,Kubilay Atasu,Arie van Deursen
类目: Machine Learning (cs.LG)
*备注: 9 pages, 5 figures, 5 tables. Accepted at the ACM International Conference on AI in Finance (ICAIF '26), Milan, Italy
Abstract:Financial crime detection requires identifying illicit activity within large transaction networks, where suspicious behaviour is rare and unfolds over time. Existing graph-based methods capture relationships among accounts but typically underuse the temporal behaviour encoded in account transaction histories. We introduce Temporal-Context Edge Enrichment (TCEE), a framework that addresses this gap by summarising each account’s transaction history with a frozen time-series foundation model (TSFM), and enriching each transaction’s representation with the relevant temporal context from these summaries. These enriched representations can be scored directly for edge classification, or fed into a graph neural network (GNN) as edge features to capture broader structural information. Across six AMLWorld datasets, TCEE improves PR-AUC over the strongest evaluated baseline on five by a relative 10.8% on average, while training 6.0x faster end-to-end. Feeding TCEE’s representations into a GNN raises PR-AUC a further 4.8% on the four smaller datasets. On an Ethereum phishing dataset, we show that TSFM-generated account representations fed into a GNN are also effective for node classification, improving PR-AUC by 14.5% over the strongest baseline. As TSFMs continue to advance, our results suggest they can strengthen financial crime detection by capturing temporal signals that complement graph-based approaches.
[LG-23] Probabilistic Machine Learning for Short-Term Atmospheric Seeing Forecasting
链接: https://arxiv.org/abs/2610.29654
作者: Mary-Joe Medlej,Rahul Srinivasan,Simon Prunet,Aziz Ziad,Christophe Giordano
类目: Machine Learning (cs.LG); Instrumentation and Methods for Astrophysics (astro-ph.IM)
*备注: 9 pages, 2 figures, Proceedings of the SPIE 2026 Astronomical Telescopes + Instrumentation, Copehagen, July 2026
Abstract:Accurate short-term forecasting of atmospheric seeing is of significant interest for astronomical observations and free-space optical communication systems, where rapid variations in optical turbulence can affect system performance and operational planning. In this work, we benchmark machine learning models for predicting atmospheric seeing up to two hours in advance, with a particular focus on probabilistic forecasting and uncertainty quantification. We compare two probabilistic approaches, Gaussian Processes (GPs) and a normalizing-flow- based model (FloTS), against deterministic recurrent neural network (RNN) and long short-term memory (LSTM) baselines. All models are trained and evaluated on seeing measurements from the Maunakea summit. We assess the models using both deterministic metrics, including the root mean square error and the Pearson correlation coefficient, and probabilistic metrics that evaluate the quality and calibration of predictive uncertainty estimates. The results show that deep learning approaches outperform standard recurrent networks for short-term forecasting, with LSTM and FloTS achieving the best overall predictive accuracy. While Gaussian Processes provide a useful probabilistic baseline, their predictive distributions are limited by the Gaussian assumption. FloTS combines competitive forecasting accuracy with flexible uncertainty quantification and produces the most informative probabilistic forecasts.
[LG-24] Recursive Code Evolution for Task-Oriented Semantic Communications
链接: https://arxiv.org/abs/2610.29641
作者: Yan Gong,Zheng Chu,Wei Koong Chai,Hari Mohan Pandey,Jingrui Hou,Miao Zhang,Yuanjian Li,Pei Xiao
类目: Information Theory (cs.IT); Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注:
Abstract:Semantic communication jointly optimizes inference accuracy, channel use, and computation, yet the underlying communication program is typically specified before training. This paper presents ComRSI (Communications-Oriented Recursive Self-Improvement), a framework for recursively evolving that program through large-language-model-assisted code edits. A fixed agent modifies the encoder, channel adapters, decoder, loss, and training policy; a protected evaluator screens candidates under prescribed budgets and resource constraints. Accepted code and textual feedback inform subsequent proposals, while candidate weights are trained from scratch. In the original ten-generation CIFAR-100 lineage, eight accepted revisions produce an attention-pooled code achieving 74.3% test accuracy over an Additive White Gaussian Noise (AWGN) channel at 32 complex symbols per image, compared with 67.2% at 256 symbols for the initial design. The program also improves fading-channel accuracy and transfers to Tiny-ImageNet after retraining. A repeat study with ten searches per method compares ComRSI against non-recursive agents with and without textual history. Mean final search scores balancing accuracy, transmission rate, and computational cost are 0.7587, 0.7376, and 0.7089, respectively; the mean numbers of accepted revisions per search are 6.0, 5.0, and 2.1. The advantage over the memoryless agent is supported by a two-sided rank test, whereas the difference from the history-equipped agent remains statistically inconclusive at this sample size. Archived proposals show that pooling can yield a large improvement, while retained history supports subsequent refinement. These results support recursive program optimization within the evaluated setting and expose its remaining rate-robustness and compute-placement trade-offs.
[LG-25] Cross-Area Transfer Learning for VLC-based Indoor Localization with a Transfer Efficiency Score
链接: https://arxiv.org/abs/2610.29638
作者: Masood Jan,Wafa Njima,Xun Zhang,Alexander Artemenko
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注:
Abstract:Indoor localization systems are critical for Industry-4.0 applications. Visible Light Communication (VLC)-based systems offer advantages such as immunity to electromagnetic interference, high accuracy, and energy efficiency, but their performance is hindered by environmental variability across deployment areas. This paper presents a transfer learning (TL) framework for VLC-based indoor localization that enhances cross-area robustness while reducing data requirements. Using real-world measurements from four distinct areas of a Bosch manufacturing facility, we show that fine-tuning a deep neural network (DNN) pre-trained on source area data with only 40% of target-area measurements recovers 97% of full-data performance, thereby reducing the amount of target data needed. We further introduce the Transfer Efficiency Score (TES), a composite metric that identifies the most effective source models for transfer without exhaustive evaluation. Guided by TES, the framework selects the best source model, yielding localization errors as low as 40.9 cm with success rates above 86%. Validation on an unseen area confirms its generalization capability, with a 3.28 cm mean error achieved using the TES-selected source model. Overall, the proposed framework provides a scalable and data-efficient pathway for industrial VLC localization, substantially reducing deployment costs and computational burden while maintaining high accuracy in diverse and dynamic environments.
[LG-26] Reliability-Aware Probabilistic Fusion for Multimodal Learning with Incomplete Evidence
链接: https://arxiv.org/abs/2610.29621
作者: Patitapaban Palo,Abhisek Tiwari,Mattia Carletti,Anshul Thakur,Xiao Gu,David A. Clifton
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multimodal inputs may be missing or degraded, raising distinct questions of evidence availability and trust. We introduce a reliability-aware Gaussian Product-of-Experts (PoE) in which an input-dependent reliability weight tempers each modality’s latent precision, while missing experts are omitted. We characterize how reliability and availability affect posterior information and expert influence. Across two clinical multimodal tasks, the full framework remains competitive with complete inputs and achieves its clearest mean-AUROC advantage under modality absence, particularly on HAIM-MIMIC-MM. Ablations show a substantial gain from missing-aware training and an additional HAIM improvement from reliability weighting under matched PoE, missing-aware, and consistency training. Unseen test-time corruptions are used diagnostically and reveal modality-dependent reliability-weight and latent-uncertainty responses, including failure under severe degradation. Thus, reliability weighting provides an explicit evidence-control mechanism, while learning dependable quality signals remains an open challenge.
[LG-27] Watch Your Step: Anticipated Perception for Humanoid Locomotion ATC
链接: https://arxiv.org/abs/2610.29612
作者: Matthias Heyrman,Chenhao Li,Ioannis Dadiotis,Marco Hutter
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 8 pages, 5 figures, 3 tables. Project page: this https URL
Abstract:Perceptive locomotion policies typically observe a dense robot-centric elevation map, although only the terrain beneath and around the next footsteps informs the control decision. Learned encoders compress the whole visual input, leaving the policy to work out which inputs matter while learning the baseline locomotion task. Learning both at once slows training and, without computationally expensive attention mechanisms, makes generalization to new terrain harder. We instead remove redundant inputs from the elevation map before the policy sees it. A lightweight foothold prediction model (FPM) predicts future footholds from proprioception and the velocity command alone, and we sample the elevation map only in small patches around these predicted footholds. We train the prediction model alongside the locomotion policy, periodically retraining it on new policy rollouts as the robot’s gait matures. This allows the perception to anticipate where the policy will step, while the policy learns to leverage terrain information around those steps. With the proposed lightweight preprocessing, the policy reaches a given terrain difficulty in roughly a third of the training iterations required with the complete map. It outperforms policies that observe the complete map where footholds are sparse, including in narrow walled corridors, and on unseen terrain, and it matches an attention-based encoder trained at several times the cost. The policy transfers zero-shot to a Unitree G1 humanoid traversing various obstacles, stairs, gaps, and step-downs beyond the training distribution in both indoor and outdoor environments.
[LG-28] Local and Global Recursive Ridge Regression for Online Residual Correction of Frozen Multivariate Forecasters under Label Delay
链接: https://arxiv.org/abs/2610.29604
作者: Takumi Fujimoto,Hiroaki Nishi
类目: Machine Learning (cs.LG)
*备注:
Abstract:A frozen multi-horizon forecaster can be corrected online from its own residuals. However, under label delay the target of a block arrives only after the whole horizon has passed, and the corrector’s retained state grows with the number of channels. Aiming to combine a small retained state with a large error reduction, we propose DRiC (DCT Ridge Corrector), which fits a ridge regression of the leading discrete cosine transform (DCT) coefficients of the next residual block on those of the frozen forecast and of the latest input block, with sufficient statistics updated recursively. Its local variant, DRiC-L, keeps one such regression per channel; its global variant, DRiC-G, pools all channels into one regression, whose statistics do not depend on the channel count. A guard tracks a running scale of the frozen forecast’s residual for each channel; it skips updates on blocks far outside that scale and clips the issued correction. We evaluate on seven datasets (four ETT series, Weather with its missing-value codes filled, Electricity, and Traffic) with DLinear, PatchTST, and iTransformer backbones, against 10 naive, gradient-trained, and closed-form methods. DRiC-L with eight DCT coefficients (DRiC-L8) reduces the mean squared error (MSE) by 14.55% on average over 126 conditions, against 12.98% for ELF, the most accurate comparator, while retaining 15-17 times less state, although ELF is more accurate on Traffic. DRiC-G retains 14.6 kB on the 321-channel Electricity data and 18.9 kB on the 862-channel Traffic data and is the most accurate method at or below its state on both. However, 5 to 7 of the 10 comparators, all with at least 10 times its state, are more accurate there. On the unfilled Weather file, DRiC-L8 without the guard has on average 8.0 times the frozen forecast’s MSE, whereas with the guard it reduces the MSE by 7.94%. Code: this https URL.
[LG-29] A Transfer Learning-Driven Methodology for Efficient and Sustainable Indoor Localization Using VLC
链接: https://arxiv.org/abs/2610.29594
作者: Masood Jan,Wafa Njima,Xun Zhang,Alexander Artemenko
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注:
Abstract:The rapid expansion of the Internet of Things enables seamless collaboration among connected devices, making indoor localization a critical component of industrial automation. However, conventional localization techniques struggle in dynamic environments. These methods depend on extensive, frequent data collection and environment-specific calibration, limiting scalability, interoperability, and effective use of prior research while imposing significant energy penalties that hinder practical deployment. To address these challenges, we propose an energy-efficient transfer learning (TL) based approach for visible light communication based indoor localization. Our method leverages TL to tackle environmental variability, including lighting fluctuations and physical obstacles that typically degrade localization performance, while simultaneously reducing computational overhead and energy consumption compared to conventional approaches. We also introduce a novel model efficiency (ME) metric, designed to integrate localization accuracy, energy efficiency, data efficiency, and transfer gain into a single evaluative measure for comprehensive optimization. Evaluations on a real-world dataset collected from a BOSCH factory demonstrate that our model achieves a 47% improvement in localization accuracy compared to a conventional model that does not utilize TL, and achieves up to 67.5% ME with TL models, compared to only 10.4% in conventional models under high-noise conditions. These results underscore the potential of our approach to deliver a highly efficient and scalable indoor localization system suitable for energy-constrained industrial applications.
[LG-30] Frag ment-Level Graph Encoding for Interpretable Molecular Property Prediction
链接: https://arxiv.org/abs/2610.29589
作者: Rafael Lopes Almeida(1),Ina Pöhner(1) ((1) School of Pharmacy, University of Eastern Finland, Kuopio, Finland)
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 12 pages, 3 figures, 6-page appendices, to be published in Proceedings of the 8th Northern Lights Deep Learning Conference (NLDL), 2027. For associated code, see this https URL
Abstract:Graph neural networks (GNNs) are among the standard tools for molecular property prediction, but the atom-level explanations of their predictions are hard for medicinal chemists to act on. We propose a change of input representation rather than a new model: molecules are encoded as fragment-level graphs from BRICS decomposition, where each node is a synthetically meaningful fragment whose features are the element-wise sum of its atoms’ features. Because only the representation changes, any message-passing GNN and any post-hoc attribution method produces explanations at the resolution of functional groups instead of single atoms. Across four architectures, four prediction tasks, and two explainers under scaffold splits, fragment-level encoding matches atom-level test accuracy on roughly five-fold smaller graphs. Integrated-Gradients attributions recover known pharmacophores above chance on two enzyme targets, namely BACE at 1.5 times the chance rate, and PTR1 at 3.2 times. A like-for-like comparison against an aggregated atom-level baseline shows that post-hoc aggregation is not a stable substitute: its BACE hit rate range both exceeds and falls below the fragment model’s hit rate depending on two arbitrary aggregation choices, whereas fragment-level training yields a single convention-free attribution at no predictive cost. The code is available at: this https URL
[LG-31] Fast Bayesian Optimization Through Linear Models FAST
链接: https://arxiv.org/abs/2610.29580
作者: Colin Doumont,Henry Moss,Philipp Hennig
类目: Machine Learning (cs.LG)
*备注: 26 pages, 13 figures, 2 tables. Code: this https URL
Abstract:Bayesian optimization (BO) is typically used only for problems where the underlying function is deemed “expensive” (e.g. in time or money), with the speed of the BO algorithm being of little importance. In the real world, however, problems span a continuous range of values, going from cheap to expensive. In this paper, we present BO algorithms through the lens of a speed-performance Pareto frontier: we compare BO against faster algorithms like CMA-ES, and highlight how certain regions of the Pareto frontier are currently empty. As a solution, we propose a new set of fast BO methods that exploit the linear structure in existing BO algorithms, for both continuous and combinatorial inputs (from ~10 to 6000+ dimensions). Our methods achieve performance similar to state-of-the-art BO, while being more than 10^2\times to 10^3\times faster, opening the field of BO to an entirely new class of problems.
[LG-32] Private Online Prediction from Experts with Small Losses
链接: https://arxiv.org/abs/2610.29575
作者: Bo Li,Peng Ye
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We consider online prediction from experts, a fundamental problem in machine learning, under differential privacy constraints. Existing private algorithms achieve near-optimal regret \tildeO(\sqrtT) , where T is the time horizon. However, this bound becomes suboptimal when L^\star , the cumulative loss of the best expert, is significantly less than T . In this work, we present the first differentially private algorithm with regret \tildeO(\sqrtL^\star) , offering a substantial improvement in the low-loss regime. Our regret bound matches the non-private lower bound up to poly-logarithmic factors for constant privacy parameters, demonstrating that privacy incurs only a small cost in this setting. Our result assumes that the input sequence is oblivious, i.e., it is fixed in advance and independent of the algorithm’s outputs. We also prove that an \tilde\Omega(\sqrtT) regret is inevitable for adaptively generated inputs, even when L^\star = 0 .
[LG-33] Implicit Feature Reweighting for Label-Free Graph Generalization
链接: https://arxiv.org/abs/2610.29571
作者: Tianjun Yao,Chen Xi,Xinyang Li,Lixian Zhong
类目: Machine Learning (cs.LG)
*备注: We propose an implicit feature reweighting method to enhance graph-level OOD generalization without class labels
Abstract:Graph-level out-of-distribution (OOD) generalization is critical for deploying graph neural networks in real-world applications where distribution shifts are inevitable. Existing approaches typically require class labels during representation learning to decide which substructures are predictive, limiting their applicability. We propose SCARCE, a label-free representation-learning framework that yields distribution-shift-robust graph representations from an environment partition of the training graphs alone, whether that partition comes from dataset metadata or, when none is available, from clustering a self-supervised embedding. Our method operates in two stages: (1) a variational graph autoencoder is warm-started for graph reconstruction; and (2) an adversarial confusion mechanism forces the encoder to produce representations from which a discriminator cannot distinguish environments, while preserving graph-reconstruction fidelity. We provide theoretical analysis showing that environment confusion contracts environment-discriminative directions in the representation space, which, under regularized downstream classifiers, is equivalent to imposing a stronger directional penalty on environment-associated features. This feature-reweighting mechanism reduces reliance on spurious correlations without erasing information, offering a label-free, continuous relaxation of last-layer reweighting methods. Experiments on a controlled synthetic datasets and the DrugOOD datasets show that SCARCE achieves state-of-the-art OOD generalization among label-free methods, using no class labels during representation learning.
[LG-34] Certified Neural Approximation of Robust Nonlinear Model Predictive Control
链接: https://arxiv.org/abs/2610.29559
作者: Hendrik Alsmeier,Maik Pfefferkorn,Rolf Findeisen
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注: 12-page article plus 4-page supplement, 3 figures
Abstract:Model predictive control is a high-performance control method, but it incurs comparatively high online computational costs across many control tasks and is challenging to realize in low-level hardware settings. To address this, we present a framework for generating neural network controllers that provably inherit the safety and stability guarantees of a robust model predictive policy. In contrast to approaches based on post-verification or additional online safety filters, our method provides constructive, a priori design conditions that ensure preservation of recursive feasibility and stability before deployment. We treat the approximation error as a bounded input disturbance, which is addressed in the MPC design. This allows the neural network controller to directly inherit robustness from the underlying model predictive controller, which enables the transfer of guarantees to the approximation. The design process is an offline, constructive synthesis pipeline and can be implemented once Lipschitz bounds are specified. We further demonstrate in simulation that the learned controller reproduces the closed-loop behavior of the original controller while reducing online computation.
[LG-35] Phase Transitions in Learning Driven by Extremal Data
链接: https://arxiv.org/abs/2610.29531
作者: Ethan Levien,Trevor GrandPre
类目: Machine Learning (cs.LG); Statistical Mechanics (cond-mat.stat-mech); Data Analysis, Statistics and Probability (physics.data-an)
*备注: 17 pages, 8 figures
Abstract:We study an extremal regression problem, an example of multiple instance learning (MIL), where the predictors are given as multisets, called bags. The bag elements (called instances) possess hidden labels which are pooled to produce an observed bag-level label. Max pooling is common in MIL applications, but gives rise to vanishing gradients. To handle this, modern MIL pipelines approximate the max with a softmax, treating the sharpness as a learnable parameter, denoted \lambda . When the max pooling model is the true data generating process, learning will be successful when \lambda is increased during gradient descent, thereby driving the dynamics into regions of parameter space with vanishing gradients. To understand the conditions under which gradient descent breaks down, we study a minimal model and derive a low dimensional projection of the gradient flow in the joint limit of large bags n and high dimension d with \alpha=\ln n/d fixed. The flow has two distinct phase transitions: an intrinsic freezing transition related to the random energy model of disordered systems, and a retrieval transition occurring in modern Hopfield networks. Numerical simulation confirms both the phase diagram and the convergence of the finite-size training gradient to its predicted asymptotic form.
[LG-36] Geometric knowledge spaces Attention pairing and Architectures
链接: https://arxiv.org/abs/2610.29526
作者: Shang Xu
类目: Machine Learning (cs.LG); Algebraic Geometry (math.AG); General Topology (math.GN)
*备注: 14 pages
Abstract:In this paper, we propose a geometric approach to deriving attention mechanisms and exploring potential new transformer architectures. We begin by formalizing the notions of knowledge worlds and attention pairings, then demonstrate how this framework naturally leads to the architectures of standard attention, causal attention, linear attention, tensor attention, and recurrent neural networks. Finally, we introduce several new geometric spaces that may inspire novel neural network architectures.
[LG-37] BRIDGE: An Acceptance Gate to Block False Improvements in Model Updates with a Retrospective Clinical Evaluation
链接: https://arxiv.org/abs/2610.29515
作者: Dwi Sunaryono
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 39 pages, 5 figures. Submitted to IEEE Access. Code and data: this https URL
Abstract:Models are updated repeatedly, and an update that looks better on validation data but not on new data is a false improvement that can misguide clinical decisions. The naive rule accepts every positive validation gain and is optimistic when the same data choose and judge the update. BRIDGE is a model-agnostic acceptance gate that decides each update from predictions and the available labels, with fixed settings. Each fold of the data judges the update chosen on the other folds (cross-fitting) to reduce selection bias; acceptance requires the gain’s lower bootstrap bound to exceed a margin. The four settings are (A) 11 new datasets, (B) synthetic tasks, © 12 retrospective clinical tasks, and (D) 39 supporting datasets. The false-improvement rate falls from 19.1% (naive) to 1.1% (BRIDGE) in (A), 20.7% to 1.7% (B), 20.0% to 0% ©, and 12.5% to 0.5% (D); every 95% confidence interval of the reduction over datasets or tasks excludes zero (not over the clinical sources). An equal-size random draw from the naive acceptances contains more false improvements in (A)-(E) (post hoc, p0.001). On the OpenML collection (E) of 14 new datasets, the false-improvement rate falls from 25.6% to 1.0%. BRIDGE removes 97% to 100% of the false improvements that the naive rule accepts, and its mean gain per deployed update is 1.8 to 3.1 times the naive rule’s. The 95% upper bound on the false-improvement rate is below 5.5%: formal in (B) for independent tasks (3.56%), and conditional on the real data tested (0.33% to 5.40%).
[LG-38] Shortcut-Resistant CAM Distillation for Long-Tailed Recognition ICML2026
链接: https://arxiv.org/abs/2610.29508
作者: Wenhai Wan,Teng Zhang,Shao-Yuan Li,Xinrui Wang,Qiang-Sheng Hua,Songcan Chen
类目: Machine Learning (cs.LG)
*备注: Accepted to ICML 2026
Abstract:Real-world datasets often follow a long-tailed distribution, making generalization to tail classes difficult. We revisit this problem through the lens of shortcut learning, where models prefer the easiest predictive cues (e.g., background or textures) over object-centric semantics, especially under scarce and biased supervision. We find that this tendency is amplified for tail classes: limited examples often share similar contexts, making non-semantic signals highly correlated and thus tempting shortcuts, whereas head classes with diverse appearances and environments encourage more stable object-focused representations. Motivated by this observation, we propose Shortcut-Resistant CAM Distillation (SRCD), a plug-and-play framework that transfers object-focused explanations from head to tail classes. SRCD operates in the Class Activation Map (CAM) space, where a CAM provides a class-specific spatial evidence map for a prediction. SRCD aggregates CAMs from a small set of head-class candidates into a shortcut-resistant teacher using an energy-model weighting based on coherence and concentration, and distills it to the tail-class CAM. We provide a theoretical analysis that quantifies shortcut reliance as shortcut-region evidence mass in CAM space and shows that SRCD suppresses tail shortcuts. Extensive experiments on long-tailed benchmarks consistently improve strong baselines. The code is available at this https URL.
[LG-39] Density-Level Backward Error for Low-NFE Generative Flows
链接: https://arxiv.org/abs/2610.29475
作者: Ewsey Obzherin,Valentin Khrulkov
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA); Machine Learning (stat.ML)
*备注: 37 pages, including appendices. Accepted as a poster at ICOMP 2026
Abstract:Low-NFE generative ODE sampling is constrained not only by solver order, but also by how a fixed budget of network evaluations is distributed along the flow. We develop a density-level backward-error framework that identifies the component of a solver’s modified-equation defect that changes the evolving marginal, while excluding solenoidal trajectory distortion that is invisible in Wasserstein tangent geometry. The resulting residual induces a solver-order-aware compute-allocation rule and, through gauge freedom, an objective for selecting numerically favorable velocity representatives of the same density path. This separates our approach from schedule search driven by trajectory or endpoint surrogates and from higher-order solver design. Exact Gaussian and mixture models establish the mechanism, including cases where curvature-based allocation fails and continuum-optimal transport is not discretization-optimal. On a pretrained CIFAR-10 EDM probability-flow ODE, density-aware allocation consistently improves fixed-update Euler sampling in the low-NFE regime. Matched-seed convergence to a dense Euler reference confirms that the method accelerates approximation of the same high-NFE flow rather than changing the asymptotic sampler.
[LG-40] Asymmetry Tunneling Beyond Adaptive Equivariance Relaxation
链接: https://arxiv.org/abs/2610.29473
作者: Eunho Jeong,Joon Jang,Hyeonjin Kim
类目: Machine Learning (cs.LG)
*备注:
Abstract:Equivariant neural networks encode prescribed symmetries as powerful inductive biases, yet real-world data may exhibit symmetry-breaking structure that these priors cannot fully capture. Prior work has largely addressed this challenge through model-side equivariance relaxation, seeking an appropriate level of equivariance for the data. This raises a complementary question: must equivariance relaxation be adaptively controlled? To investigate this question, we introduce a residual path alongside a strictly equivariant model and deliberately restrict their interface to examine the transmission of task-relevant structure. Across five benchmarks, we observe both residual collapse and survival despite these restrictions; we call the latter phenomenon asymmetry tunneling. We further characterize this phenomenon through four complementary diagnostics. Our results suggest that adaptive control of equivariance relaxation is not a prerequisite for capturing potential symmetry-breaking structure.
[LG-41] How Far Can Foundation Models Be Distilled for Efficient Jet Tagging?
链接: https://arxiv.org/abs/2610.29439
作者: Jay Agarwal,Siddharth Khare,Dhruv Kumar
类目: Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex); High Energy Physics - Phenomenology (hep-ph); Data Analysis, Statistics and Probability (physics.data-an)
*备注: 34 pages, 16 figures, 23 tables. Code and model weights: this https URL
Abstract:Jet tagging is one of the most important applications of machine learning at the Large Hadron Collider. Recent models build in more physics knowledge or rely on large-scale pretraining, but the experiments place strict limits on their inference cost. Knowledge distillation can transfer their performance to small students that are cheap to run. We ask which properties of a teacher help the student, how small a student can get and how close it can come to the state of the art. On a jet tagging benchmark, we distil six teachers that differ in architecture, symmetry and pretraining, including two foundation models, into five small students. Every teacher improves every student, and the gain depends mainly on the strength of the teacher. We then shrink our two strongest distilled students, Transformer and L-GATr-slim, in width and in depth. With 20 thousand parameters, a distilled L-GATr-slim outperforms every published jet tagger trained from scratch at a fraction of the inference cost of every teacher. Even with two thousand parameters, it matches ParT, a model with a thousand times as many parameters. We also study what the students learn from their teachers. Our benchmark of inference cost shows that students of the same size can differ by almost an order of magnitude in speed. We release the code and the weights of all trained models.
[LG-42] One Act Many Masks: Effect-Marginal Policy Optimization for Long-Horizon Agents
链接: https://arxiv.org/abs/2610.29427
作者: Shaojin Ma,Ruitao Wang,Pengyi Li,Jie Yang,Liangyu Chen,Gangyi Zhao,Hongyao Tang,Yan Zheng,Jianye Hao
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 20 pages, 11 figures
Abstract:Recent advances in multi-turn reinforcement learning (RL) have substantially improved the performance of LLM agents on complex, long-horizon interactive tasks. However, despite progress in fine-grained credit assignment, policy optimization still applies these learning signals through response-specific probability ratios. Different textual responses can induce the same executable effect, yet credit differences arising from subsequent outcomes remain tied to their individual textual realizations. We identify this mismatch as credit-update misalignment. Our analysis reveals that credit-text pairing directly shapes policy-update directions, even when the policy context and executable effect are held fixed. Motivated by this finding, we propose Effect-Marginal Policy Optimization (EMPO), which aligns policy optimization with execution-level equivalence through an effect-level surrogate that couples updates across alternative textual realizations. EMPO preserves the original credit signals, integrates with both trajectory-level and step-level credit estimators, and requires no additional critics or rollouts. Experiments on ALFWorld, WebShop, and Sokoban show that EMPO outperforms strong baselines in both task performance and learning efficiency.
[LG-43] Generalization Bounds via Vector-Valued L_p Correlation V_α
链接: https://arxiv.org/abs/2610.29425
作者: Mohammad Hossein Momeni Hamaneh,Mohammad Mahdi Mojahedian,Mohammad Hossein Yassaee
类目: Machine Learning (cs.LG); Information Theory (cs.IT)
*备注:
Abstract:Understanding generalization of randomized learning algorithms remains a central challenge. Mutual-information and total-variation bounds provide complementary ways to quantify data–hypothesis dependence, but they can behave quite differently in high-dimensional regimes. We study a one-parameter family of dependence measures, the vector-valued L_p -norm correlation V_\alpha , and derive generalization bounds whose dependence terms interpolate between total variation (TV) at \alpha=1 and variance and \chi^2 related behavior at \alpha=2 . For \alpha2 , the Hölder-conjugate loss moment is below two, yielding heavy-tailed specializations that can remain finite even when the loss variance is infinite. A particularly interpretable specialization occurs at \alpha=2 , where the bound can be expressed in terms of the variability of the algorithm’s output density across datasets, \int \sqrt\mathrmVar_S[p(w\mid S)]~ dw . We show that the entire V_\alpha family is invariant under smooth bijective reparameterizations of the hypothesis space. We also introduce an adaptive density stability (ADS) condition that implies explicit control of V_2 , and we verify it for Gibbs-type posteriors under bounded losses (yielding rates that depend on the inverse temperature and sample size). To address scaling, we extend the framework to a conditional formulation using supersamples, analogous in spirit to conditional mutual information (CMI), and discuss evaluations in the output space as a practical surrogate for estimating dependence. Simulations on Bayesian linear regression illustrate how tuning \alpha can change the tightness of the resulting bounds across regimes. Additional supersample experiments on noisy MNIST and on a pretrained ResNet-50 for CIFAR-10 show similar behavior of the projected loss-space bounds beyond the original clean-MNIST setting.
[LG-44] Alternative Strategies for Active Learning with Bayesian Bradley Terry Model
链接: https://arxiv.org/abs/2610.29416
作者: Saman Jayasinghe,Sean Walton,Alma Rahat,Andy Gray,Chris Wheadon
类目: Machine Learning (cs.LG); Other Statistics (stat.OT)
*备注:
Abstract:Comparative judgement uses pairwise comparisons to rank items, but recovering accurate rankings from a limited number of judgements depends on both the inference method and the pairs selected. We developed a Bayesian Bradley-Terry model (BBTM) that uses Polya-Gamma data augmentation for efficient Gibbs sampling and quantifies uncertainty in latent abilities and ranks. We propose three active learning strategies based on the knowledge gradient (KG) principle. We define our acquisition functions using Kendall’s tau distance, posterior rank entropy, and posterior separation. Approximate one-step updates to posterior moments reduce the computational cost of evaluating candidate pairs. We also introduce Posterior Separation Reliability (PSR), which measures the spread of posterior mean abilities relative to posterior uncertainty. It provides an internal indicator of ranking reliability without requiring a reference ranking. In synthetic experiments covering different item counts, comparison budgets, and noise levels, BBTM generally recovers ranking more accurately than preference-based Bayesian comparative judgement (PBCJ). At low to moderate noise levels, the pairwise entropy pair selection strategy performs best in ranking recovery. However, at the highest noise levels, the KG strategy based on posterior separation produces better point rankings. PSR increases alongside improvements in ranking accuracy as comparisons accumulate. Robustness experiments identify the proportion of informative outcomes needed to meet specified accuracy targets within a fixed comparison budget. In a moderate noise setting, replacing 30% of outcomes with random choices has little effect on ranking performance. These findings demonstrate the value of Bayesian inference and active learning pair selection for recovering ranks from limited, noisy paired comparisons.
[LG-45] he Predictive–Optimization Gap in GuanDan Self-Play
链接: https://arxiv.org/abs/2610.29411
作者: Qicheng Zou,Biao Yuan,Siyu Song,Wentao Liu,Xiaokun Wang
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Outcome-predictive signals are often treated as promising optimization targets in reinforcement learning, but predictive validity and optimization utility are different claims. We expose this distinction in GuanDan, a multi-round imperfect-information game in which the implication of a round outcome depends on the public match state. Held-out analysis shows that state–gain interactions provide incremental predictive value beyond a constant signed-gain effect, establishing that match progress is predictively meaningful. We then use changes in a frozen match-win predictor, \Delta\Phi , as a controlled round-level supervision intervention in a matched DanZero-style Monte Carlo self-play protocol. The intervention can succeed in an individual lineage: a selected progress-augmented checkpoint substantially outperforms its matched Baseline on a locked evaluation. Yet the method-level result reverses this impression: none of five preregistered replication seeds is positive, the pooled ten-seed interval includes zero, and across-iteration effects repeatedly change sign without a common late-stage advantage. These results identify a predictive–optimization gap: under the studied learner, a signal can describe outcome-relevant progress while failing to provide a reliably beneficial optimization direction.
[LG-46] Nominate-then-Adjudicate: LLM -Assisted One-Pass and Per-Instance MIP Solver Tuning
链接: https://arxiv.org/abs/2610.29391
作者: Yifan Zhao,Qianyang Zhao,Xinyu Guan,Kai Wei,Yuming Deng
类目: Machine Learning (cs.LG)
*备注: 10 pages, 7 figures, 6 tables. Accepted as a short paper at IEEE BigData 2026
Abstract:Mixed-integer programming solvers expose hundreds or even thousands of parameters whose best setting varies per instance, yet per-instance search is prohibitively expensive. We frame this as configuration for heterogeneous Big Data optimization workloads and propose a nominate-then-adjudicate pipeline built around two design choices. First, a fixed portfolio is sampled once and reused across instances, removing per-instance configuration search at inference; on the evaluated families, the resulting shortlist retains substantial oracle headroom, making selection within it the empirical focus. Second, the fixed 20-feature representation can map structurally different instances to the same vector, preventing a feature-only selector from distinguishing them even when they prefer different configurations. The foundation model receives no task-specific weight updates, and selection is one-pass: one model call and no solver calls at inference. The study represents structural Variety through distinct formulation families and decision Value through solver-performance outcomes; the one-call, zero-solve protocol specifies the decision interface and leaves end-to-end latency unmeasured. Among comparisons exceeding a 20% practical-difference threshold, the complete adjudicator records 538 wins and 185 losses against the top nominee, while its win rate against the default rises from 84.9% to 90.2%. Against per-instance configuration baselines under a matched one-pass protocol it leads the cluster-level methods and the best-performing evaluated per-instance baseline, ISAC+.
[LG-47] Cascading Bandits with Noisy Observations: Algorithms and Regret Bounds
链接: https://arxiv.org/abs/2610.29381
作者: Yashaswini Kaluguri,Nikhil Karamchandani
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We propose a novel stochastic cascading bandit setup where probing an ordered shortlist of K out of L arms incurs position-dependent costs, and the learner observes only noisy feedback regarding each arm’s true output. We identify a sub-class of noise models for which the optimal shortlist can be found by sorting arms according to a per-arm score: such a score exists for both selection and ordering when the channel has no false positives, and for ordering alone (but not selection) when measurements are costless; however in general neither selection nor ordering is separable once noise and cost are both present. For the case where both selection and ordering scores exist, we provide a UCB-based algorithm with O((L-K)\log T) instance-dependent regret over a time horizon T , which matches an algorithm-agnostic lower bound (up to constant factors). For the more challenging scenario where scores don’t exist, we develop a new algorithmic template, DP-UCB, that embeds optimism directly inside an exact dynamic program, yielding O\big(LK\log T\big) regret. Finally, we validate our theoretical results through extensive numerical evaluations and simulations over both synthetic and real-world datasets.
[LG-48] Compositional neural networks for multimodal X-ray and neutron scattering inversion
链接: https://arxiv.org/abs/2610.29362
作者: Valentin Munteanu,Alexander Hinderhofer,Dmitry Lapkin,Vladimir Starostin,Frank Schreiber
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci); Applied Physics (physics.app-ph)
*备注:
Abstract:X-ray and neutron scattering are indispensable for structural characterization across condensed-matter physics and materials science. Yet, because scattering experiments generally record intensities without phase information, distinct structures can yield similar scattering patterns, rendering the corresponding inverse problems non-unique. This ambiguity can be reduced by combining complementary measurements, but their integration into machine-learning frameworks remains challenging. Here we introduce a modular framework for probabilistic multi-measurement analysis based on prior-amortized flow matching. Single-observation posterior estimators are trained separately for individual observation types or physical probes and combined only at inference time through a compositional sampler. This late fusion approach allows pretrained single-observation models to be reused without dedicated joint retraining. As a case in point, we consider reflectometry, which provides depth-resolved, nanometer-scale information about thin films and interfaces. Our single-observation demonstrations span X-ray, time-of-flight neutron and polarized neutron reflectometry. In separate co-refinement studies, task-matched estimators enable homogeneous composition across multiple neutron contrasts and heterogeneous multimodal composition of X-ray and neutron measurements. Together, these results establish compositional flow matching as a modular approach to uncertainty-aware inversion and co-refinement, with potential applicability to other scattering problems in which multiple observations constrain a common physical structure while retaining measurement-specific parameters.
[LG-49] GaP-MoE : Explicit Expert Specialization in Mixture-of-Experts via Orthogonal Gradient Accumulation and Projection
链接: https://arxiv.org/abs/2610.29355
作者: Donghyun Jeong,Yoon-Yeong Kim
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Sparse Mixture-of-Experts (SMoE) models improve scalability by routing input tokens to only a subset of expert networks, but they often suffer from representation collapse, in which multiple experts converge toward similar functions and lose their intended diversity. Prior work has addressed this limitation either by improving router architectures or by introducing auxiliary loss terms that implicitly penalize similarity among experts. However, router-based approaches remain vulnerable to routing collapses, and loss-based approaches can conflict with the task’s original objective during optimization. In this work, we propose GaP-MoE (Gradient Accumulation and Projection in MoE), which induces expert specialization by directly manipulating expert output gradients rather than modifying the loss function. At each training step, GaP-MoE stochastically selects one of the TopK routed experts as a leader based on normalized routing scores, and projects the gradients of the remaining follower experts onto the orthogonal complement of the subspace spanned by the leader’s recent gradients. This removes directional overlap between expert gradients without altering the task loss, allowing specialization to emerge as a direct consequence of the optimization dynamics rather than an externally imposed constraint. We evaluate GaP-MoE in both fine-tuning settings (BERT-based, on GLUE) and from-scratch pretraining settings (GPT-2-based, on WikiText-103, enwik8, and text8), and further confirm its compatibility as a drop-in module for existing SMoE architectures. Experimental results show that GaP-MoE achieves performance comparable to or better than standard SMoE.
[LG-50] Benchmarking and Error Analysis of Deep Search and Deep Research LLM Agents ICML
链接: https://arxiv.org/abs/2610.29346
作者: Zixuan Yu,Zhenheng Tang,Xiaowen Chu,Bo Han
类目: Machine Learning (cs.LG)
*备注: Proceedings of the 1st ICML Workshop on Failure Modes in Agentic AI (FAGEN), Seoul, South Korea, 2026
Abstract:As Large Language Models (LLMs) evolve into autonomous Deep Search and Deep Research Agents capable of executing long-horizon, multi-step tasks, evaluating their reliability becomes increasingly critical. However, existing evaluation paradigms predominantly focus on final task outcomes, leaving a significant gap in understanding the intermediate execution traces, where agents are highly susceptible to various errors. To address this critical gap, we introduce a comprehensive methodology and analytical framework designed specifically to diagnose errors in intermediate workflows. Building upon this framework, we establish a structured taxonomy that categorizes these vulnerabilities into two primary dimensions: System-Level Errors and Task-Level Errors. Through a systematic examination of step-by-step execution traces and agent-environment interactions, we reveal how these underlying errors affect overall performance. Finally, through empirical experiments and systematic analysis of the identified error types, we provide actionable design guidelines, aiming to enhance the robustness, reasoning capabilities, and reliability of Deep Search and Deep Research agents.
[LG-51] BrainCodes: A Lightweight EEG Foundation Model over Discrete Neural Codes
链接: https://arxiv.org/abs/2610.29320
作者: Kleanthis Avramidis,Ivan Tashev,Dimitra Emmanouilidou
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注: Preprint. Under review
Abstract:Recent electroencephalography (EEG) foundation models increasingly emphasize model scale, potentially limiting their deployment on resource-constrained devices and in everyday environments. We argue that transferable EEG features can instead be learned with a compact architecture and introduce BrainCodes, a lightweight foundation model pretrained through self-supervised learning on more than 800 hours of recordings from over 1,000 participants. Our architecture combines a quantization-based neural tokenizer with a spatiotemporal encoder that supports arbitrary electrode configurations. The pretraining objective explicitly encourages representations that capture spatial connectivity through channel positional encoding, and sensitivity to narrowband neural oscillations through an auxiliary training objective. Our model matches or surpasses the state of the art across diverse neurophysiological monitoring tasks, including sleep staging, attention assessment, and cognitive load estimation. BrainCodes transfers effectively to low-resource wearable devices while using up to 97% fewer trainable parameters than comparable architectures. Beyond predictive accuracy, our evaluation framework characterizes the geometry and invariances of learned EEG representations across measurable spatial, temporal, and spectral signal properties.
[LG-52] DISRL: Dual-Scale Incremental Semi-Supervised Reinforcement Learning for Dynamic Community Tracking
链接: https://arxiv.org/abs/2610.29315
作者: Tianming Zhang,Jinxu Wang,Yixuan Zhang,Zhengyi Yang,Chenyu Hou,Bin Cao,Jing Fan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Dynamic local community detection tracks the evolving community of designated seed nodes across graph snapshots. However, it faces two critical challenges: temporal supervision uncertainty, where sparse and outdated labels fail to provide seed-specific guidance under topology changes, and unbounded error cascades, where sequential policy transfer accumulates exploration errors over long horizons. To address these challenges, we propose DISRL, a Dual-scale Incremental Semi-supervised Reinforcement Learning framework. DISRL introduces a supervision construction module that dynamically generates seed-specific guidance from sparse known communities, and a dual-scale community exploration module that stabilizes policy evolution by decoupling short-term adaptation within temporal windows from long-term parameter consolidation across windows. Furthermore, we construct a dynamic graph benchmark with diverse community evolution scenarios. Extensive experiments demonstrate that DISRL consistently outperforms state-of-the-art baselines under various dynamic settings.
[LG-53] Interpretable Cutting Plane Selection for Mixed-Integer Linear Programming Using Genetic Programming
链接: https://arxiv.org/abs/2610.29311
作者: Gwen maudet,Mathis Da Cunha,Grégoire Danoy
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Cutting planes (cuts) remove fractional solutions from mixed-integer linear programs (MILPs) without excluding any feasible integer solutions, making them a key ingredient in modern MILP solvers. Current state-of-the-art solvers rely on fine-tuned handcrafted cut strategies, which, despite their effectiveness, remain inherently static and can be suboptimal. More recently, learning-based approaches have been proposed to design adaptive cut strategies; however, most of these methods are based on neural networks, offering limited interpretability and providing no guarantees of convergence. In this paper, we propose \emphGenetic Programming and Statistics-based Cut Selection (GPSCS), the first learning-based framework that explicitly generalizes handcrafted cut selection heuristics into a fully interpretable policy. First, a genetic programming model evolves a symbolic scoring function that ranks candidate cuts using features derived from the cuts and the solving context. Second, a lightweight statistics-based mechanism adaptively determines the number of top-ranked cuts to add at each separation round by detecting significant score dropouts. We evaluate GPSCS within the SCIP solver on seven classes of synthetic and real-world benchmark instances, with a strong emphasis on generalization: evaluation instances are on average 41 times harder than those used for training. Experimental results show that GPSCS improves average solving time by 6% compared to SCIP’s default cut selection strategy and achieves a two-fold speedup over both a recent neural-network-based approach and a no-cut baseline, demonstrating strong robustness and generalization across heterogeneous MILP families.
[LG-54] Compressing Onboard Language Models for Space Robots under a Behavioral-Fidelity Requirement
链接: https://arxiv.org/abs/2610.29302
作者: Jia Deng,Zizhe Wang,Xin Lu,Yi Huang,Zehao Jia
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Language models are being explored as the interface through which space robots interpret operator commands and select among verified skills. Running them onboard calls for low-bit compression, and flight use adds requirements that generic post-training quantization leaves implicit: a fixed memory budget and storage format, a static artifact under configuration control, and behavior close to the full-precision model validated on the ground. We treat compression as a ground-segment step and study QSelect, which composes one static low-bit model from compatible AWQ, GPTQ, and AutoRound checkpoints by assigning each linear module to one quantizer family and scoring every substitution at the final next-token distribution. The default fidelity mode needs no labels or tuned thresholds and accepts a substitution only when the forward KL divergence from the BF16 reference decreases; an optional target-adapted mode adds answer likelihood on a small labeled set. On DeepSeek-R1-Distill-Qwen-7B, Llama-3.1-8B, and Llama-3.2-3B at 3 and 4 bits, composed models reach lower mean held-out perplexity than every single-family model in all six settings, and the fidelity mode lowers perplexity and KL further. In a matched scaffold, final-output scoring gives lower held-out KL than representation-similarity scoring in all 12 paired runs. With labels from training or development splits of the evaluated task families, the target-adapted mode raises 3-bit pooled accuracy on the two Llama models by 4.3 and 4.5 points over the best single-family model. At 3 bits, unquantized embedding and output layers make up 43-46% of the packed artifact. All evaluations use general-domain benchmarks on a ground GPU; space-domain tasks and flight hardware are left to future work.
[LG-55] Approximate Solomonoff Induction: On the Robustness of Learned Universal Predictors
链接: https://arxiv.org/abs/2610.29289
作者: Qian Li,Shang-Hua Teng
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 24 pages
Abstract:Why large language models trained via log-loss minimization over massive and diverse datasets exhibit broad capabilities and generalizable skills is not yet fully understood. Grau-Moya et al. (ICML 2024) offer a conceptual explanation through the lens of meta-learning: if sequence-generating environments \nu are drawn from a prior w_\nu , minimizing population log-loss recovers the corresponding Bayesian mixture predictor \xi=\sum_\nu w_\nu\nu . In the idealized limit in which w is a universal prior, the optimal predictor corresponds to Solomonoff universal induction. Practical machine learning algorithms, however, generally produce only \emphapproximately optimal predictors. This raises a natural question: \emphDoes the prediction performance remain robust under approximation? In this paper, we first provide a tight bound on the worst-case effect of approximation on prediction error. We show any predictor \rho satisfying D_KL(\xi |\rho)\leq \eta retains bounded cumulative-expected-squared prediction error. However, while the Bayesian mixture predictor \xi has error O(\log(1/w_\mu)) in an environment \mu , we establish a tight \Theta(\eta/w_\mu) bound on the excess error of \rho relative to \xi . Thus, approximation can substantially degrade performance in environments assigned small prior probability. In sharp contrast, when environments are averaged according to prior w , we prove that the excess prediction error is only O(\eta) . Furthermore, we establish a distribution-shift theorem for dominated shifts from a training prior w to a test prior w’ , interpolating our average-case and worst-case guarantees. Finally, we extend our robustness analysis to a finite-sampling setting relevant to learning universal predictors with neural networks and Transformers, where D_KL(\xi|\rho) could be unbounded. [For the full abstract, see the paper.]
[LG-56] Estimating Performance for Safe Deployment of Machine Learning Models
链接: https://arxiv.org/abs/2610.29262
作者: Zeju Li,Konstantinos Kamnitsas,Mobarakol Islam,Melanie Roschewitz,Cheng Ouyang,Chen Chen,Ben Glocker
类目: Machine Learning (cs.LG)
*备注: Accepted by the Elsevier journal Medical Image Analysis
Abstract:The main challenge in applying deep neural networks to real-world scenarios lies in distributional discrepancies between training and test datasets, often caused by variations in data acquisition conditions. Assessing the performance of pre-trained models without labels is crucial for safety, but this is complicated by deep models’ overly optimistic probabilistic outputs, often exacerbated by class imbalances, leading to unreliable predictions. To address these issues, we introduce MOVAL, a novel MOdel eVALuation tool that estimates performance using unlabeled test data. MOVAL incorporates class-wise confidence calibration to adjust model outputs, allowing for the estimation of metrics beyond accuracy and mitigating the detrimental effects of class imbalance on confidence calibration. Additionally, we propose an ensemble approach that combines multiple calibrated confidence scores for a more robust solution. We validate MOVAL on natural and medical image datasets with various acquisition shifts, covering tasks from classification to segmentation. It achieves state-of-the-art results across evaluation metrics such as accuracy, F1-Score, AUC, and more. Furthermore, we demonstrate that MOVAL provides accurate performance estimations at the class, instance, and pixel levels, ensuring the transparent and safe deployment of machine learning models. We provide MOVAL as a deployment tool for monitoring and estimating performance under a range of domain shifts.
[LG-57] Delay-Guided Attention for Quality Prediction in Multistage Manufacturing
链接: https://arxiv.org/abs/2610.29220
作者: Jiarui Zhang,Zhaoyang Guan,Zhixuan Xia,Naichen Shi
类目: Machine Learning (cs.LG)
*备注: 27 pages, 11 figures, 4 tables, including appendices
Abstract:Quality prediction in multistage manufacturing is challenging when inter-stage lags vary over time, as measurements taken at the same timestamp may reflect different process states. Existing approaches often preserve production order but fuse timestamp-aligned features or model delayed dependencies implicitly, without identifying the upstream history relevant to each downstream observation. We propose Delay-Guided Attention for Multi-stage Forecasting (DAMF), which explicitly estimates inter-stage lag distributions and uses them to guide cross-stage information transfer for final quality prediction. For each adjacent stage pair, a lag predictor compares the current downstream representation with candidate lagged upstream representations. An occurrence-magnitude factorization separately estimates the probability of a positive lag and its conditional magnitude, yielding a sample- and stage-pair-specific distribution over discrete lag states without requiring plant-specified delays at inference. DelayAlignment incorporates this soft distribution as a temporal prior into differentiable cross-stage attention, while gated residual fusion regulates information propagation across production stages. For interpretation, segment-wise Viterbi decoding converts pointwise lag distributions into temporally coherent lag paths, whereas forecasting retains the soft distributions for alignment. On the real-world LiquidSugar dataset, DAMF improves final quality prediction over the compared baselines. Evaluations on two controlled-delay benchmarks show more accurate and stable lag recovery, and ablation studies support the contributions of lag prediction, delay-guided attention, and structured temporal decoding.
[LG-58] PerSPL: Personalized Semi-supervised Preference Learning
链接: https://arxiv.org/abs/2610.29219
作者: Xiaoan Xu,Tingzhou Wei,Juncheng Dong,Korel Gundem,Ethan X. Fang,Zhengling Qi
类目: Machine Learning (cs.LG)
*备注:
Abstract:Personalized reward models require user-specific preference comparisons, but collecting such feedback is costly and provides only limited supervision for each user. At the same time, large pools of responses can be generated without user identities or preference labels, yet existing personalized reward models leave this data unused. Directly learning from these responses is difficult because pairwise objectives constrain relative scores within a prompt, whereas pseudo-labeling individual responses requires scores that are comparable across prompts for the same user. We propose \textbfPerSPL (Personalized Semi-supervised Preference Learning), a backbone-agnostic framework that uses pair-mean score centering to stabilize this cross-prompt score coordinate and iteratively turns unlabeled responses into user-specific training signals. Against seven representative baselines across four benchmarks—PersonalLLM, HelpSteer2, Reddit TLDR, and Chatbot Arena—PerSPL achieves the highest reported mean accuracy among the evaluated methods for both seen and unseen users. The two latter benchmarks provide real-user preference comparisons in summarization and open-domain chatbot interaction, respectively. These results indicate that unlabeled responses can provide additional supervision for personalized reward modeling when per-user preference labels are scarce.
[LG-59] Reconstruction differences rival individual differences in the fly compass connectome and break trained circuit models
链接: https://arxiv.org/abs/2610.29205
作者: Md Ali Ashraf Yad,Nazifa Tabassum Nabila
类目: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
*备注: 21 pages, 6 figures, supplementary material included. Preregistered: doi: https://doi.org/10.5281/zenodo.23382486 . Code and data: this https URL
Abstract:Neural network models are increasingly constructed from synapse-resolution connectomes, and the same circuit is now available in reconstructions from other animals and pipelines. We do not know whether such models are dependent on the reconstruction they are built from, and whether connectome differences are biological or measurement errors. We studied the Drosophila head-direction circuit in nine versions: four flies (male CNS, hemibrain, Fly Wire, BANC) and five variants of the same fly varying only by thresholds, proofreading or synapse detection. Using wiring-based correspondences validated by anatomical labels, we place all differences on one scale. A change of synapse detection within one fly changed the circuit more than halfway to the smallest inter-fly difference on four distances; proofreading differences were smaller but depended on the metric, and left and right halves of one fly differed no more than a random half. Networks trained on each wiring to hold a cued heading developed near-perfect ring attractors, but their error increased from below 1 degree to 10-28 degrees when all weights changed by 1%. Training on each wiring did not transfer to any other connectome, including other versions of the same fly; refitting thirty cell-type gains, we repaired filtering-level differences but remaining error depended on wiring distance. Training with noise lowered precision and enhanced transfer. Imaging data indicate compasses drift smoothly and do not break; they function despite differences comparable to those of this this http URL, connectome-constrained models need to reflect the reconstruction they stem from and the robustness normal circuits exhibit and models lack.
[LG-60] runcated Out-of-Bag Probability Gain: A Noise-Robust Criterion for Random-Forest Wrapper Feature Selection
链接: https://arxiv.org/abs/2610.29195
作者: Sejong Oh
类目: Machine Learning (cs.LG)
*备注:
Abstract:Random-Forest wrapper feature selection can use out-of-bag (OOB) predictions as a built-in validation signal, but the criteria that decide each forward step accumulate OOB estimation noise. We propose TOP-Gain (Truncated Out-of-bag Probability Gain), which scores a candidate feature by the mean sample-level increase in the OOB probability of the true class, with decreases truncated at zero. Under a simple noise model, truncation removes about two-thirds of the noise variance contributed by unaffected samples while preserving the signal, and it makes the ranking of candidate features markedly more reproducible. In a pre-registered benchmark on 26 OpenML-CC18 datasets, TOP-Gain achieved the best mean rank in area under the ROC curve (ROC-AUC), accuracy and Macro-F1 among 15 methods; it was significantly better than 13 of 14 baselines and matched the accuracy of VSURF with 21 times less single-core computation. Within an identical wrapper, it selected significantly more stable feature sets than net-gain, Brier, log-loss and accuracy criteria without loss of accuracy. A confirmatory study on 24 further datasets replicated the advantage over all baselines. The accuracy gains were broad rather than tied to specific dataset characteristics, and, as pre-registered, the stability gain grew as relevant features became sparser
[LG-61] A Decade of Learning-Based Control: From Theoretic Foundations to Practical Deployment
链接: https://arxiv.org/abs/2610.29187
作者: Angela P. Schoellig,Matthias A. Müller,Sebastian Trimpe,Melanie N. Zeilinger,Mahathi Anand,Andrea Carron,Oliver Hausdörfer,Victor G. Lopez,Friedrich Solowjow,Haocheng Zhao,Siqi Zhou
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注:
Abstract:Motivated by ten years of Learning-Based Control Invited Sessions at the IEEE Conference on Decision and Control (CDC), this tutorial paper reviews key advances at the intersection of machine learning and control. We focus on methods that combine the flexibility of machine learning techniques with the structure and theoretical foundations of control to design autonomous systems that are capable, reliable, and safe. We first introduce core notions for establishing stability and safety guarantees, then review approaches that learn dynamics models to improve control under uncertainty, and finally discuss data-driven methods that directly synthesize or learn policies, including reinforcement learning, imitation learning, and Bayesian optimization. We further discuss representative applications and open challenges. The goal is to provide an accessible entry point to the field and to support continued progress toward reliable learning-based control in real-world systems. To support learning and practical exploration, we share implementations and illustrative examples along with our discussion, together with additional resources on our website: this https URL.
[LG-62] SAFTER: Safety-Aware Filtering Through Explicit Recoverability for Robust Reinforcement Learning under Distribution Shift
链接: https://arxiv.org/abs/2610.29149
作者: Benedictus C. G. Cinun,Bin Hu
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注: 24 pages, 6 figures
Abstract:Learning safe policies that generalize to unseen environments is difficult under bounded actuation, where an unsafe decision may become unrecoverable. Conventional control barrier function (CBF) filters enforce only instantaneous safety and ignore future recoverability. We propose SAFTER (Safety-Aware Filtering Through Explicit Recoverability), which embeds a differentiable, closed-form backup-CBF filter into Soft Actor-Critic training. SAFTER unifies current safety, finite-horizon backup safety, terminal recoverability, and input admissibility into a single differentiable certificate. Its CBF condition yields an exact minimum-intervention projection without iterative online optimization. In quadruped navigation, SAFTER reaches 90% training success with 30.6% fewer transitions than Vanilla SAC and 34.1-35.1% fewer than current-state CBF-filtered SAC baselines. Across unseen simulated environments, SAFTER improves success by up to 42.4 and 61.4 percentage points over the state-of-the-art methods CBF-RL and SEA-Nav, respectively, and achieves 73.3% and 76.7% success on two real-world navigation maps. Ablations identify accurate differentiation through the safety filter as a key contributor to robustness, in which replacing the projection Jacobian with the identity approximation reduces success rate in a High-Shift environment from 61.4% to 6.2%. These results show that recoverability-aware filtering serves as both a runtime safety mechanism and a learning mechanism for sample-efficient, robust policies.
[LG-63] Causal world models under physical parameter shifts
链接: https://arxiv.org/abs/2610.29088
作者: Chu Lin,Yi Sun
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注: 2 figures
Abstract:World models predict how embodied systems evolve under actions, but their accuracy can degrade when physical conditions move outside the training distribution. We study two components of causal modeling: which inputs a predictor may use, and how its mechanism computes their effects. In a synthetic benchmark with shortcut features, a frozen dependency mask derived from paired interventions reduces mean out-of-distribution (OOD) RMSE from 1.769 for a fully connected predictor to 0.697, close to the generator-oracle mask (0.694). For robot link-mass extrapolation in CausalWorld, with disjoint training and test mass ranges, we develop finger-local world models in which link masses enter inertia and dynamics-bias terms through explicit affine coefficients, actions act through either a learned torque network or the known PD controller, and learned residuals capture contact-related effects. Compared with a separately trained control whose mass inputs are fixed at training means, the known-PD model turns out-of-range masses into a consistent predictive gain: median time-averaged joint R2 over 150-step rollouts rises from 0.609 to 0.727, with improvement in all ten task-seed runs (exact two-sided sign test, p = 0.002). In a shared 100-step forecast, the learned-torque model is competitive with the strongest recurrent robotic world model (RWM) configuration (median joint R2 of 0.755 - 0.768 versus 0.752), whereas supplying out-of-range masses sharply degrades the MLP and recurrent predictors. These results indicate that how a physical parameter enters the dynamics mechanism determines whether its out-of-range value helps or harms prediction.
[LG-64] he Price of Choice in Selective Prediction
链接: https://arxiv.org/abs/2610.29067
作者: Daniel Cieślak,Andrzej Czyżewski
类目: Machine Learning (cs.LG)
*备注: 43 pages, including appendices
Abstract:A selective classifier may be claimed to satisfy a risk-coverage contract if some rule from a predeclared menu (a threshold or a union of K score intervals) attains coverage at least \kappa and error at most \alpha . Refuting this existential claim requires ruling out every permitted rule. The intersection-union test (IUT) achieves validity without a multiplicity correction, but its sample cost can increase with menu richness. We characterize this cost and compare it with the optimal audit. For a fixed interior risk target \alpha and gap \gamma , the minimax sample size for K-interval menus at coverage floor \kappa is K(1-\kappa)/(\kappa\gamma^2) , up to logarithmic factors, in the stated small-gap, large-menu regime; exact IUT attains this rate. The lower bound holds even with a known score distribution, while the upper bound extends to menus of VC dimension d . Procedures that refuse to reject whenever any rule appears feasible on the audit sample can pay a linear K cost even when a square-root rate is possible. Reference-free testing reaches a smaller scale under constant-risk alternatives, whereas unknown heterogeneous risks can make the linear cost unavoidable. In simulations with K=4 , the raw menu cost varies 7.7-fold between \kappa=.5 and \kappa=.95 ; after theorem-motivated normalization the endpoint values differ by approximately 5%. Held-out experiments with three VLM judges show positive exact-hiding windows in all 60 split-specific confidence-gate families, with 28/30 contracts chosen on audit-A remaining hidden on untouched audit-B. On a cleaner multi-annotator target, hiding is weaker and transfers in 3/10 resplits. Frozen image and multimodal classifiers show boundary behavior consistent with the IUT guarantee and strong power relative to practical certificates.
[LG-65] Dynamics of Hidden Learning Structure
链接: https://arxiv.org/abs/2610.29054
作者: Qinyou Wang
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 34 pages, 2 figures, 6 tables
Abstract:A learner’s present predictions do not fully determine how it will learn next. We study the dynamics of hidden learning structure through two related operations. Fiber Regulation Theory (FRT) describes protected repair of existing function and the learning responses left available after repair. Fiber Acquisition Theory (FAT) describes the formation and renewal of trainable structure, distinguishing neutral installation, access to a learning signal, and additional functional directions. The two operations also constrain one another. A repair obligation can force a change in a learning channel, while acquired features can make new protected repairs feasible or reduce the minimum coefficient cost of existing ones. We characterize joint admissibility and response reachability, and give a finite supervised-learning construction in which the two operations together produce a favorable response absent from either operation alone. We implement protected head repair and fixed-capacity LoRA renewal in Qwen2.5-32B with AdamW, under replay and output distillation. Across two training trajectories differing in seed and within-task example order, the coordinated policy achieves higher teacher-forced Exact than either separate policy in all four comparisons. It also has the lowest macro NLL under both learning rules on the second trajectory. The implementations realize structure-conditioned self-regulation within specified information and action constraints: accessible state readouts and arrived evidence guide repairs and structural renewals, and ordinary learning continues from the modified state.
[LG-66] ART-MPG: Autoregressive R/T Manifold Path Guiding
链接: https://arxiv.org/abs/2610.29049
作者: Zehao Zhao,Yanyang Yang,Xiaole Zhang,Xiyu Deng
类目: Machine Learning (cs.LG)
*备注:
Abstract:Specular chains concentrate important transport in small regions of path space and the structure depends on chain topology. We introduce ART-MPG, a compact neural model that samples chain length, an autoregressive reflection/transmission (R/T) string, and a direction from a topology-conditioned four-component von Mises-Fisher (vMF) mixture. We extend the repeated trial weighting in Manifold Path Guiding (MPG) by conditioning retry sampling on both chain length and the sampled R/T string, accounting for its known mass outside the stochastic re-hit trials. For a fixed proposal and target chain, this preserves the ideal expectation while reducing the expected retry count and reciprocal-estimator variance compared with retries conditioned on chain length alone. We distinguish the formal unbiasedness result from the capped and clamped renderer used in experiments. We evaluate fourteen scenes across a range of samples per pixel (SPP) against existing online and offline rendering algorithms. Our method consistently produces lower mean squared error at equal SPP than the online and offline baselines, with lower standard deviation across runs.
[LG-67] Gate Initialisation versus Recurrent Dropout and Zoneout in Learning Long Dependencies: A Controlled Study of LSTM and GRU Networks on Synthetic Tasks
链接: https://arxiv.org/abs/2610.29034
作者: Oleksandr Kholodniak,Oleksandr Prokhorov
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注: 16 pages, 3 figures, 4 tables. Code and data: this https URL and this https URL
Abstract:Dropout and zoneout are routinely added to long short-term memory (LSTM) and gated recurrent unit (GRU) networks, but, to our knowledge, their placements have not been crossed with gate initialisation in a factorial design, and it is not known whether early gradient propagation tells which runs will learn long dependencies. We trained one-layer LSTMs and GRUs on the adding problem and on delayed recall in a factorial grid of 720 runs, crossing conventional and chrono gate initialisation with up to six recurrent regulariser placements at one rate each, with pre-specified follow-ups, and recorded how sensitive the final hidden state was to the input at every lag. Because both tasks supply fresh or exhaustive training data, the study measures the regularisers’ effects on optimisation and does not assess generalisation from a finite training set. At lengths chosen to make conventional initialisation difficult, all 360 chrono-initialised runs reached the validation criterion within 5000 updates, as did 119 of 120 unregularised runs with a horizon about four times too short or too long, whereas conventionally initialised networks learned slowly or did not reach the criterion within the budget at the longer lengths. Under conventional initialisation, dropout generally delayed learning and zoneout helped only in some conditions. The early input sensitivity added little to predicting which conditions learn once the early validation loss was modelled per task, architecture and initialisation. Within conditions, a flatter early decay went with the seeds that learned, but in a pre-specified test the association was modest (concordance 0.61), condition-dependent and not robust to resampling seeds. The results support chrono initialisation, even with a rough horizon, on such tasks.
[LG-68] Better Policies Worse Verifier Audits
链接: https://arxiv.org/abs/2610.29029
作者: Daniel Cieślak,Jacek Łubiński
类目: Machine Learning (cs.LG)
*备注: 53 pages, including appendices
Abstract:Verifier stacks increasingly sit inside post-training and test-time compute loops. We study a specific control loop: a component verifier is evaluated on fresh policy outputs against the full success label and refreshed when that aggregate score falls. For a conjunctive target y = F \wedge M , its on-policy AUC is (1-q)A_\mathrmown + qA_\mathrmcross , where q = \Pr[F=1 \mid y=0] . As the policy masters F , q can rise toward one, removing weight from F -failures and rewarding information about M . We call this on-policy audit collapse. We derive a sharp identified set, a complete-erasure threshold, and the exact point at which the audit can prefer a worse component verifier; its population optimum is the full-outcome predictor, not the component oracle. An exact oracle exhibits the collapse, a natural multi-label text control shows inversion can select a head with lower canonical F -AUC at the observed failure mixture, and a prediction-registered ECG study shows that acting on the audit can reduce off-policy F -AUC, transfer the damage to an unseen generator, and increase downstream constraint violations. Preserving own-failure support prevents the damage. Matching label-complexity bounds and a controlled-mixture audit give the corresponding repair: audit the component on its own failures, and keep those failures in the refresh distribution.
[LG-69] Dissimilarity-Based Markov Decision Processes for Stabilizing State Distributions
链接: https://arxiv.org/abs/2610.29024
作者: Xun Shen,Yuepeng Wang,Daniyal Sajedi-Hosseini,Akifumi Wachi,Ken Kawano,Yoshihiko Fujisawa,Mehrshad Sadria,Sébastien Gros,Katsuki Fujisawa
类目: Machine Learning (cs.LG)
*备注: 44 pages, 12 figures, 13 tables. Xun Shen, Yuepeng Wang, and Daniyal Sajedi-Hosseini contributed equally. Under review at TMLR
Abstract:Reinforcement learning for stochastic systems typically optimizes expected cumulative rewards, while reliable and repeatable behavior also depends on how the state distribution evolves over time. Optimizing expected return alone does not guarantee that this distribution converges to a desired target or remains stable. This paper develops a theoretical framework for learning policies that stabilize state distributions. We formulate the problem as a Dissimilarity-based Markov Decision Process (DMDP), in which the policy acts on the current state distribution and the objective measures its dissimilarity to a target distribution. We establish sufficient conditions under which the optimal policy makes the target distribution asymptotically stable. To support policy learning, we introduce a finite-dimensional representation of distributions and show how stability guarantees for its parameter dynamics extend to the underlying state distributions. We further develop certificates for the learned policies, including their deployment using estimated distribution parameters. These results quantify how learning and estimation errors affect the guarantees and identify conditions under which learned policies retain asymptotic stability. For practical implementation, we combine distribution estimates and target-dependent rewards with PPO and SAC. A computable example on a linear system illustrates the proposed theoretical certificates. Furthermore, experiments on a HalfCheetah task demonstrate the benefits of distribution feedback and explicit target matching for regulating the velocity distribution.
[LG-70] Local and Sparse Kernel Operator Learning for Incompressible Flows: An Operator Lagrange Formulation
链接: https://arxiv.org/abs/2610.29009
作者: Ramansh Sharma,Matthew Lowery,Houman Owhadi,Varun Shankar
类目: Machine Learning (cs.LG)
*备注:
Abstract:We present a local and sparse kernel method for learning the solution operators of incompressible flows. It builds on our earlier property-preserving kernel method, whose global output reconstruction and dense operator regression scale poorly. Our method replaces the global output reconstruction by local divergence-free interpolants with divergence-free polynomials of any degree, solved in parallel on the GPU. These interpolants are divergence free, periodic on periodic domains, and, with a multiscale kernel, approach Kolmogorov statistics under refinement. A compactly supported kernel, positive semidefinite in any input dimension, makes the operator regression sparse. Its support radius, and hence the sparsity, is fitted to data, and optional affine polynomials in the input capture global trends. Operator Lagrange weights join the two. They cost one sparse solve per new input and act on the training fields before the output reconstruction, so their local coefficients are never formed. We show that, unlike direct evaluation, Lagrange evaluation does not amplify solve errors by the cancellation among spatial coefficients. On ten fluid benchmarks, validation selects sparse matrices nearly everywhere, with densities that mostly decrease as the training set grows, and affine augmentation lowers the error by more than an order of magnitude for forced isotropic turbulence. These sparse models are at least as accurate as our earlier dense method and more accurate than neural operators. Lagrange evaluation is as accurate as direct evaluation with local output reconstructions, and up to nearly three orders of magnitude more accurate with global ones. We also present GPU timings and storage.
[LG-71] he Optimal Sampler for Diverse TSP Solutions: Characterization Limits and Learning
链接: https://arxiv.org/abs/2610.28994
作者: Ssu-Yuan Lo,Ching-Kai Wang,Kuan-Lun Chen,Hao-Tsung Yang
类目: Computational Geometry (cs.CG); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注:
Abstract:We address the Diverse Traveling Salesman Problem (D-TSP) that seeks a set of k distinct TSP tours. Each selected tour must not exceed a prescribed multiple of the optimal tour length, while the maximum pairwise Jaccard similarity within the selected set is minimized. This formulation is important for applications requiring both solution quality and fault tolerance, such as logistics planning, robotics, and strategic patrolling. We propose a novel hybrid framework that decomposes D-TSP into a sampling stage and a selection stage. The selection stage is a max-min dispersion problem, for which any approximation better than a factor of two is NP-hard; we therefore focus on the sampling stage. Our contributions are both theoretical and empirical. Theoretically, we give two end-to-end guarantees for the optimal sampling distribution, from two perspectives: including an optimal set in the sampled pool, or having at least k mutually distant tours in the pool. The second reduces the number of samples from exponential to linear in k , at the cost of weakening the benchmark from the best k tours to the best \beta k for some \beta 1 . For practical use, we prove that a pipeline that samples spanning trees and then converts to tours does not hurt the quality of the pool in principle. Empirically, both objectives are learnable, and our experiments not only validate the analysis but also outperform the baselines on both in-distribution and out-of-distribution instances, by a single trained model with no fine-tuning.
[LG-72] Doob-Based Generative Policy Optimization NEURIPS2026
链接: https://arxiv.org/abs/2610.28990
作者: Maojiang Su,Qijie Zhu,Yu Xiao,Shuyang Yu,Minshuo Chen,Zhaoran Wang,Han Liu
类目: Machine Learning (cs.LG)
*备注: 38 pages, 4 figures. Accepted at NeurIPS 2026
Abstract:Diffusion and flow models provide expressive policy classes for robotic and sequential decision-making systems. However, their action likelihoods are generally intractable, limiting the direct use of widely adopted policy optimization methods such as PPO and GRPO. We propose Doob Policy Optimization (DoobPO), a framework that leverages Doob’s h -transform for likelihood-free reinforcement learning fine-tuning of generative policies. DoobPO separates policy improvement from generative policy learning. It first learns a lightweight ratio model that represents the desired policy update, and then distills the resulting ratio-tilted target policy into the generative policy via conditional drift matching. This formulation applies uniformly to both diffusion and flow policies and supports a broad family of policy improvement rules, including PPO and GRPO. We prove that the practical conditional drift matching objective is gradient-equivalent to the ideal Doob drift matching objective. We further show that the induced diffusion process solves an endpoint-constrained stochastic optimal control problem. This establishes that it achieves the minimum-energy modification required to realize the desired policy tilt. Across MuJoCo continuous-control benchmarks, DoobPO achieves strong performance against competitive diffusion- and flow-policy baselines, showing a principled and practical route to RL fine-tuning for generative policies.
[LG-73] Attention-based Classification for Indirect Prompt Injection
链接: https://arxiv.org/abs/2610.28988
作者: Amelia Kawasaki,Stefan Lee
类目: Machine Learning (cs.LG)
*备注: Published at CAMLIS 2026
Abstract:Indirect prompt injection (IPI) attacks, in which adversarial instructions are embedded in external data processed by a language model, pose a significant threat to the integrity of deployed LLM agents. Attention Tracker (Hung et al., 2025) is a recently proposed white-box detection method that identifies prompt injection attacks by monitoring how a model’s attention is distributed between user task tokens and data tokens, claiming dataset-agnostic detection via a scalar focus score threshold. In this paper, we provide the first evaluation of Attention Tracker in a realistic agentic IPI setting using the AgentDojo benchmark, finding that the dataset agnosticism claim does not hold. We find that in agentic environments, long structured tool outputs subvert the fundamental assumptions Attention Tracker makes about attention that the method depends on and produces near-chance detection on AgentDojo across most model families. We propose AttnDA, an extension replacing the scalar threshold with a Fisher Linear Discriminant Analysis classifier over the per-head focus score vector, and evaluate both methods in a cross-dataset setting spanning four benchmarks. AttnDA improves detection performance across agentic and tool-integrated benchmarks, though transfer to AgentDojo remains challenging regardless of classifier choice. A covariance ablation further shows that removing inter-head correlation structure substantially contributes to performance, with complex covariance assumptions causing significant degradation on cross-domain data. These results indicate that the attentional distraction signal underlying focus-score-based detection does not transfer reliably to agentic environments, and that learned linear classifiers over head-level attention features offer improved detection performance, provided training data from the target deployment domain is available.
[LG-74] From Common Information to Canonical Correlation Analysis and Beyond: Bridging Information Theory Signal Processing and Machine Learning
链接: https://arxiv.org/abs/2610.28978
作者: Paris A. Karakasis,Walid Saad
类目: Information Theory (cs.IT); Machine Learning (cs.LG); Signal Processing (eess.SP); Machine Learning (stat.ML)
*备注: Tutorial, 100 pages, 11 figures. Accepted for publication in Entropy, Special Issue “Foundations and Frontiers of Information Theory” in honor of H. Vincent Poor’s 75th birthday
Abstract:Many problems in signal processing, machine learning, and neuroimaging provide several jointly observed measurements of one process, each mixing a component common to all of them with a component private to itself, and require the common component to be recovered without supervision. This problem has been studied in several communities, largely independently. Statisticians proposed canonical correlation analysis (CCA), introduced by Hotelling in 1936. Information theorists proposed two notions of common information, due to Gács and Körner and to Wyner. Engineers developed subspace methods for sensor arrays and multiuser communications to design multiview receivers. This tutorial presents the main results and tools each community developed for this problem in a single exposition, and explains the connections that emerge where they meet, distinguishing exact equivalences from special cases and analogies. We develop the information-theoretic foundation from first principles. We then introduce CCA and its extension to several views, followed by the engineering thread from blind multiuser detection to CCA as a blind receiver. Next, we turn to nonlinear CCA, covering kernel, deep, and probabilistic formulations, the conditions under which the common factor is identifiable, and connections to self-supervised learning. We conclude with a discussion of further extensions and open directions. The exposition is self-contained, so that readers from each of these communities can follow the parts developed by the others.
[LG-75] How Does Orthogonalization Adapt to the Neural Network Hessian Structure? A Gradient Self Outer-Product Analysis at Initialization
链接: https://arxiv.org/abs/2610.28975
作者: Shenyang Deng,Garvit Goyal,Tianyu Pang,Shuhua Yu,Yaoqing Yang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Muon orthogonalizes a weight matrix’s momentum before each step, and on neural networks this simple preconditioner beats AdamW in a wide range of tasks. Existing analyses explain Muon within general optimization frameworks or on a few specific models, and many of them view orthogonalization as a preconditioner. Since the Hessian is the optimal preconditioner under a quadratic approximation of the loss, these works leave a basic question open: what makes orthogonalization suited to the Hessian of a neural network? Prior work shows that the layer-wise Hessian of a neural network is row-block diagonally dominant, and Muon’s implicit preconditioner, the Kronecker product (\mathbfV\mathbfV^\top)^1/2\otimes\mathbfI with \mathbfV the momentum, has the same row-block structure exactly when \mathbfV\mathbfV^\top is diagonally dominant. Diagonal dominance of \mathbfV\mathbfV^\top has been observed in language models, but why a neural network produces it has not been explained. This work provides a quantitative asymptotic theory for neural networks at initialization, and points out the intuition that the overparameterization of the weight matrices drives the diagonal dominance. We analyze \mathbbE[\mathbfG\mathbfG^\top] , which equals \mathbbE[\mathbfV\mathbfV^\top] at initialization, in four canonical models: symmetric matrix factorization, deep linear networks, two-layer ReLU networks, and single-layer linear attention. In each case the diagonal entries outgrow the off-diagonal ones at explicit rates as the hidden dimensions grow; for the query, key and value weights of attention the off-diagonal vanishes altogether. Simulations of the four models and experiments on language models at initialization confirm the predicted scaling.
[LG-76] Online Open-World Wafer Defect Discovery using Hyperdimensional Computing
链接: https://arxiv.org/abs/2610.28949
作者: Quanling Zhao,Ava Emami,Tsuyoshi Idé,Rajiv Joshi,Steven Holmes,Michael Passow,Tajana Rosing
类目: Machine Learning (cs.LG)
*备注:
Abstract:Conventional wafer defect analysis relies on offline, closed-world classifiers that fail to detect novel, unseen defect signatures emerging during continuous semiconductor manufacturing. To address this, we propose WaferHDC, an online open-world wafer defect discovery framework based on hyperdimensional computing (HDC). The memory-centric architecture of WaferHDC enables rapid anomaly detection and grouping of recurring failures into compact hypervector representations. This allows the memory space to dynamically scale with new defect modes without retraining a static classifier or archiving entire raw wafer history. WaferHDC first models normal spatial distributions, then dynamically groups recurring failures into persistent defect memory without labels or predefined class counts. It outperforms the strongest online baseline by 13.3 p.p. in adjusted Rand index and 10.5 p.p. in normalized mutual information on the industry-standard WM-811K dataset. These results demonstrate the effectiveness of WaferHDC for continuous and open-world wafer defect analysis.
[LG-77] Is 2:4 Sparsity Still Worth It? Semi-Structured Sparsity versus Quantization in LLM Inference
链接: https://arxiv.org/abs/2610.28947
作者: Xiuyi Qin
类目: Machine Learning (cs.LG); Performance (cs.PF)
*备注:
Abstract:NVIDIA tensor cores have supported 2:4 semi-structured sparsity since Ampere, and the pattern composes with quantization. When the two are combined, the practical question is which step to take next: from a given precision, add 2:4 sparsity or drop to the next lower precision? We compare the two at equal bits, with sparsity metadata counted, and at equal nominal tensor-core tiers, over all six GEMM operands of a transformer layer, with kernels measured on three GPU generations and online pruning and quantization included. Our results show that as long as a lower precision is available, adding 2:4 is the worse step, both less accurate and slower, because the energy that 2:4 discards from each group of four along the hardware-forced axis does not shrink with bits, while quantization error does. Once quantization reaches the lowest precision the hardware supports, 2:4 still speeds up linear layers by 1.3–1.7\x, and on an H100 a retrained 2:4 model served in FP8 by vLLM reaches 27% higher prefill throughput than the dense FP8 model, with GSM8K accuracy on par. Its quality has to be won back by retraining, however, which restores perplexity long before reasoning; on Blackwell, whose sparse FP4 uses a pairwise 4:8 pattern, the loss is larger and our retraining budget did not recover it.
[LG-78] Multi-Objective Protein Optimization via Modular Diffusion Steering
链接: https://arxiv.org/abs/2610.28943
作者: Meir Roketlishvili,Felix Huber,Wenda Chu,Yisong Yue,Raul Astudillo
类目: Machine Learning (cs.LG)
*备注:
Abstract:Protein engineering often requires improving several properties simultaneously, motivating the search for high-performing sequences that represent useful fitness tradeoffs. Discovering these sequences is challenging because the design space is combinatorially large and experimental campaigns often permit only a few rounds of batched measurements. We introduce \textitMODS, a modular framework for multi-objective protein optimization via inference-time diffusion steering. For each proposal, MODS samples augmented Chebyshev scalarization weights and a fitness-surrogate ensemble member to define a scalar steering reward. Varying the weights directs generation toward different objective tradeoffs, while varying ensemble members diversifies the predictions used for guidance. Because MODS expresses multi-objective acquisition through scalar rewards, it can be paired with different diffusion models and compatible steering procedures. After each evaluation batch, MODS updates the surrogates without retraining the generative model. We evaluate MODS on six protein optimization tasks using predictive oracles trained on experimental assay data, with two diffusion models and two steering procedures. MODS achieves competitive final hypervolume across all six tasks with both diffusion models, and substantially outperforms the best evaluated baseline on several tasks. Our results demonstrate a flexible and effective approach to multi-objective protein optimization with limited feedback.
[LG-79] Auditing Conformal Prediction for Language Models Under Inference-Time Model Shift NEURIPS2026
链接: https://arxiv.org/abs/2610.28937
作者: Achref Doula,Max Mühlhäuser
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: Accepted at the 40th Conference on Neural Information Processing Systems (NeurIPS 2026). Track on Evaluations and Datasets
Abstract:Conformal prediction (CP) for language models is typically evaluated under a matched protocol, in which calibration and deployment use the same post-training inference configuration, such as quantization mode and inference temperature. In deployment, these choices often differ from those used at calibration to meet memory or throughput constraints or serving policies. We argue that this exposes a limitation in current CP evaluation protocols for language models. We formalize the underlying phenomenon as inference-time model shift: a change in the conformal score distribution induced by inference configuration rather than by the input–label distribution, and theoretically characterize how it breaks split-conformal exchangeability even when the task distribution and trained model are unchanged. We then evaluate its consequences across 8 language models, 4 finite-label benchmarks, and 12 inference configurations. We find that matched evaluation can substantially overstate deployment-time coverage, and that the effect is heterogeneous across models and datasets. We argue that inference configuration should be reported as a first-class experimental axis in CP evaluations for language-model systems.
[LG-80] he Data Differentiator Framework (DDF): Quantifying Marginal Data Value with Information-Theoretic and Game-Theoretic Measures in an Agent ic Evaluation Pipeline (SDSAE)
链接: https://arxiv.org/abs/2610.28933
作者: Sukesh Kumar,Dilip Singh
类目: Machine Learning (cs.LG)
*备注: 13 pages, 5 figures, 5 tables. Primary cs.AI; cross-listed cs.LG and stat.ML
Abstract:In data-rich enterprises the hard question is not whether a candidate dataset is good in isolation, but whether it adds information beyond what the organization already holds, and whether that increment is worth its price. We present the Data Differentiator Framework (DDF), a composite scoring method for candidate data assets that combines (i) a mutual-information-based redundancy penalty against the existing portfolio, (ii) a cost-normalized, significance-gated marginal utility score derived from an ablation study, and (iii) explicit governance inputs (trust, identity resolution quality, strategic weights) set by the business. The framework is executed by a staged pipeline, the Strategic Data Sourcing Agent Ecosystem (SDSAE), that short-circuits expensive model-retraining steps for assets failing governance or redundancy gates. Conceptually, the DDF extends the relevance-minus-redundancy principle of mRMR feature selection from the feature level to the asset level, and adds cost normalization, a statistical significance gate, and governance terms that per-sample valuation methods such as Data Shapley do not model. We validate the framework on the public credit-card fraud benchmark (284,807 transactions) with five constructed candidate assets spanning the redundancy spectrum. Only the genuinely differentiated asset passes the significance gate ( \Delta AUC = +0.0029, DeLong p = 1.4\times 10^-9 , Holm-corrected p = 7.2\times 10^-9 ) and receives an acquire recommendation (DDS = +0.663); the fully redundant asset scores lowest (DDS = -0.135). The recommendation is stable across 19 of 22 admissible strategic-weight configurations and across all decision thresholds in [0.15, 0.66]. We state clearly which quantities in the case study are computed from data and which are simulated governance inputs, and we discuss the design limits of a single-dataset evaluation.
[LG-81] One Policy Many Styles: Where to Aggregate Preferences for Steerable Humanoid Control?
链接: https://arxiv.org/abs/2610.28925
作者: Ziqian Huang,Yuzhuo Chen,Shimin Zhang,Junyang He,Xinyue Annie Yang,Animesh Garg
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Modern robotic agents are increasingly expected to support steerable control, where a single policy can adjust its behavioral style at runtime under changing user preferences. Preference-conditioned reinforcement learning provides this interface, but conditioning alone does not ensure that a requested behavior change is strong, does not leak into unrelated behaviors, and does not compromise task performance. Our evaluation combines Preference Reward Responsiveness (PRR) with coverage and utility metrics. We use these measures to compare reward-level, advantage-level, loss-level, and PCGrad-based aggregation across two humanoid-control frameworks. No aggregation level performs best across all environments and criteria. Experiments show that aggregating objectives early produces a wider range of behavioral responses but greater unintended changes in other objectives, while aggregating them later improves control precision at some cost to responsiveness.
[LG-82] Query Feedback and Teaching for Stable and Certifiable Language Generation
链接: https://arxiv.org/abs/2610.28918
作者: Farnam Mansouri,Alireza F. Pour,Grigoris Velegkas,Sandra Zilles
类目: Machine Learning (cs.LG)
*备注:
Abstract:Language generation in the limit studies whether a learner observing an enumeration of an unknown target language can eventually generate only valid, previously unseen elements. We study stability, introduced by Kalavasis, Mehrotra, and Velegkas (2025), which requires the generator’s infinite output set to eventually remain unchanged, and introduce the stronger notion of certifiability, which requires the generator to determine from its observations that its current output is valid. Drawing on teaching sets in learning theory, we introduce a hierarchy of teaching dimensions for generation under three increasingly strict success criteria: correctness, stability, and certifiability. These dimensions measure the minimum number of selected examples whose appearance guarantees the corresponding success condition. We also study stability and certifiability in the membership-query model of Charikar and Pabbaraju (2025). We characterize stable and certifiable generation, both with and without query feedback, and establish their relationships with uniform generation and identification in the limit. In particular, we give a finite-witness characterization of stable generation, analogous to Angluin’s tell-tale characterization of identification, resolving a gap left open by Kalavasis, Mehrotra, and Velegkas (2025). We further compare these generation notions with identification in the limit and contrast their teaching requirements on well-studied language families, including regular and erasing pattern languages.
[LG-83] A Model-Free Continuous-Time RL Algorithm from Discrete-Time Off-Policy Data with End-to-End Guarantees
链接: https://arxiv.org/abs/2610.28915
作者: Chaorui Yao,Yuming Paul Zhang,Lin Yang,Yuhua Zhu
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 43 pages, 4 figures
Abstract:In this paper, we study continuous-time reinforcement learning, where the state follows an unknown stochastic differential equation but only a fixed off-policy batch of discrete-time transitions is available. Standard discrete-time Q-learning becomes problematic as the sampling interval shrinks, because differences between action values vanish. Continuous-time alternatives, such as Deep Advantage Updating and martingale q-learning, preserve the action signal but jointly learn a value function and an action-dependent critic. Building on the PhiBE-Q formulation (arXiv:2606.21925), we develop a model-free PhiBE policy iteration (PhiBE-PI) framework that uses a single state-action critic while preserving the continuous-time action-ranking scale. At each iteration, the framework learns the critic directly from sampled increments and uses it to improve the policy. The same batch is reused throughout, without explicitly estimating the drift or diffusion. We further develop an implementation under linear function approximation and establish a local end-to-end suboptimality guarantee for the learned policy. Our analysis quantifies how errors from time discretization, function approximation, and finite-sample policy evaluation propagate through policy iteration. It accounts for the dependence of successive policies on the reused data, so the guarantee does not require a fresh batch at each iteration. Moreover, we identify a bias-variance trade-off: smaller sampling intervals improve the approximation to the continuous-time problem but can increase statistical error at a fixed sample size. Thus, finer time discretization does not necessarily lead to better policy performance.
[LG-84] Minimax-Optimal Predictive Flow Matching under Distribution Drift
链接: https://arxiv.org/abs/2610.28898
作者: Aadithya Srikanth,Vaneet Aggarwal
类目: Machine Learning (cs.LG)
*备注:
Abstract:Flow matching learns a generative velocity field by regression, but under distribution drift a field must be deployed before the current data arrive. We study this predictive problem for a fixed, possibly nonconvex, neural class, with dynamic regret against the best represented field of each round. We propose DyProd-FM, which refits flow matching on dyadic historical prefixes and selects among the fits with a sleeping Prod master. Without knowing any drift budget, it attains regret \widetildeO(\min\N_\omega+1,1+\sqrtT\Omega) in the relative velocity risk variation \Omega and \widetildeO(\min\N_\mu+1,1+T^1/3V_\mu^2/3) in the target-law variation V_\mu=\sum_t W_2(\mu_t+1,\mu_t) , up to approximation and fitting costs. Matching lower bounds for fixed classes show that both rates are optimal in T and in the budgets up to logarithmic factors. Prior optimal dynamic-regret guarantees rely on a convex decision set with strongly convex or exp-concave losses, whereas our class may be nonconvex and no curvature in its parameters is assumed. The target-law rate rests on a second-order link between the two variations, which the base noise of flow matching provides.
[LG-85] Combating Catastrophic Forgetting in Continual Domain Adaptation via Knowledge Recasting NEURIPS2026
链接: https://arxiv.org/abs/2610.28892
作者: Zijie Liu,Zhen Tan,Charles Fleming,Tianlong Chen
类目: Machine Learning (cs.LG)
*备注: NeurIPS 2026 Main Conference
Abstract:Adapting language models to new domains during post-training risks degrading previously acquired capabilities, a phenomenon commonly known as catastrophic forgetting. Existing mitigation methods largely treat forgetting as a consequence of excessive model drift. They therefore constrain updates by preserving old outputs, replaying old data, or regularizing changes to parameters and representations so that the updated model remains close to its previous state. We take a different view: Rather than pulling the updated model back toward its old state, we translate the new domain into the model’s pre-existing knowledge space, making unfamiliar knowledge learnable through familiar procedures. Therefore, we propose Skill Schema Transport (SST), a post-training framework that transports new knowledge into the model’s existing skill space. SST abstracts each example type into a skill schema, preserving its essential operations, dependencies, and verification steps while removing domain-specific surface forms. By recasting new examples as instances of reusable procedures, SST turns knowledge update from trajectory memorization into procedural reuse. In continual domain adaptation settings, SST consistently reduces forgetting while improving generalization to related tasks. Across two 3B instruction-tuned backbones, SST improves average final performance on streamed domains by 2.53 to 5.75 points over sequential post-training. It also lowers max-drop forgetting in all evaluated settings, with the largest reduction from 42.41 to 7.10 points on the Qwen-3B MATH stream, while substantially recovering the OOD degradation induced by sequential updates.
[LG-86] Actually All Learnable Distribution Classes Are Privately Learnable
链接: https://arxiv.org/abs/2610.28878
作者: Mohammad Afzali,Hassan Ashtiani,Alex Bie,Gautam Kamath,Peihan Liu,Argyris Mouzakis,Vikrant Singhal
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We show that every class of distributions that is learnable up to arbitrarily small target error in total variation distance with finitely many samples is also learnable under ( \varepsilon, \delta )-differential privacy up to arbitrarily small target error with finitely many samples. This proves a conjecture of Ashtiani. Previous work by Bun, Kamath, Mouzakis, and Singhal [arXiv:2402.00267] gave a weak refutation of this conjecture, showing that this implication is not true when learnability is defined with respect to a fixed target error. In contrast, we show that, under the standard definition of learnability, the conjecture is true.
[LG-87] LEAN: Loss-Engineered Modulation of the Model Weight-Sensitivity Distribution for Efficient Error-Tolerant Neural Networks
链接: https://arxiv.org/abs/2610.28872
作者: Habibur Rahaman,Swarup Bhunia
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注:
Abstract:The weights of a trained network do not have uniform sensitivity to model accuracy: the 1% most sensitive weights carry about a fifth of the damage a bit flip can do. This distribution decides the bit-flip tolerance of a model, the fraction of weights a code must cover and the bits each weight needs; existing schemes work with the distribution training leaves. We show that we can systematically modulate or alter the weight sensitivity distribution of a trained model, thereby shifting weights from one sensitivity bin to another. The sensitivity of a stored weight is the product of its gradient and the step a flip takes in the storage format, and LEAN adds a composite regularizer on both factors, a range term that bounds the flip step and a tail term that lowers the gradient of the most sensitive weights, with one coefficient \lambda setting how far the distribution moves. Ten epochs with the term, as a fine-tune or as the last epochs of training, shrink the sensitive tail 5 to 13 times on nine vision models (ResNet-18, ResNet-50, VGG-16 on CIFAR-10, CIFAR-100, ImageNet) within 0.5 points of accuracy; real flips confirm that the reshaped weights do less damage, a plain gradient-norm penalty does not reproduce the effect, and a preliminary extension to three small language models shows the same tail shaping. With a BCH code over 1% of the weights the model then survives 300 progressive bit-search flips in every training seed and 500 in two of three, where three of four published defenses with the same code fail by 300 and WCAT falls to 75% at 500. The reshaped distribution also cuts the parity for a fixed guarantee 3.7 times and makes a sensitivity-ordered 8/6/4/3-bit format affordable, which stores ImageNet ResNet-50 at a 3.29-bit weight payload, 2.4 times smaller than INT8, above the accuracy of LSQ at 3 bits.
[LG-88] How Much Gradient Does Truncated Backprop Lose in Looped Transformers?
链接: https://arxiv.org/abs/2610.28871
作者: Aur Shalev Merin
类目: Machine Learning (cs.LG)
*备注: 31 pages, 4 figures, 11 tables. Code, pre-registrations and results: this https URL
Abstract:Looped transformers reuse one block of layers many times and are often trained by backpropagating through only the last k loops. We measure how much of the weight gradient this throws away, exactly and for every k from one backward pass, on nine looped language models, and report three findings. First, on a trained model this lost gradient is set near the output: once the loop count is well above k, it barely changes with the loop count (within about 0.03 from 32 to 64 loops on the three models we tested, as a pre-registered test predicted for Parcae). Where the backward signal shrinks, \rho^k , with \rho the signal’s fitted shrink per loop, approximates it at moderate loop counts but overstates it at long ones, because the fitted \rho drifts up as the fit takes in more loops. On Ouro the signal grows, and keeping two of four loops still loses 81% of the final output’s gradient. Second, the lost gradient falls during fine-tuning, by 0.07-0.22 within 300 steps on three of four models, on held-out problems too. The fall is not just the read-out’s share of the gradient; training only the layers outside the loop reproduced up to half of it, and it also falls under truncated training. So a trained model’s value overstates what truncation removes later. Third, we could not show that it predicts the training cost of truncation better than rules that need no measurement: on two new models it tied Parcae’s cost at the same k, in a test too small to separate them.
[LG-89] A Mechanistic Study of Automated Failure Attribution in LLM -based Agent ic Systems
链接: https://arxiv.org/abs/2610.28811
作者: Wendy Zheng,Liang Wu,Yinhan He,Mayank Darbari,Jundong Li,Liangjie Hong
类目: Machine Learning (cs.LG)
*备注:
Abstract:Agentic systems have demonstrated strong capabilities on complex tasks by enabling multiple interacting large language model (LLM)-based agents to collaborate and execute subtasks in parallel. However, this collaborative structure introduces new challenges for reliably evaluating and diagnosing system failures, particularly failure attribution: tracing a system’s execution to identify the agent step responsible for an incorrect final outcome. Because manually performing this task is time- and labor-intensive, recent work has turned to LLMs as judges to automate the process, referred to as automated failure attribution (AFA). These approaches assume that a sufficiently capable model can infer the failing step directly from an interaction transcript. In practice, however, direct prompting often performs poorly, which motivates follow-up work that explores various ways to improve AFA performance, including alternative prompting strategies and inference-time interventions. In this paper, we examine a more fundamental question: whether this weak behavioral performance reflects a genuine capability limitation. Using linear probes applied to internal activations, we uncover a significant \emphknows-but-won’t-say gap: models encode sufficient information to attribute a failure correctly but fail to explicitly output the responsible step. Building on this finding, we propose \emphgrafting, a simple manual intervention that exploits knowledge already encoded in the model to improve AFA performance without requiring ground-truth labels at inference time. Extensive evaluations across multiple LLMs and benchmark settings demonstrate that grafting consistently outperforms direct-prompting approaches, underscoring the potential of leveraging latent model knowledge for more reliable failure attribution. Our code is available at this https URL.
[LG-90] An Explicit Feedback Contract for Iterative ML-Agent Harnesses
链接: https://arxiv.org/abs/2610.28798
作者: Kehui Yao,Chuanbo Hu,Jiao Chen,Ramin Giahi,Reza Yousefi Maragheh,Lalitesh Morishetti,Ziwei Hong,Yiyun Su,Jianpeng Xu,Evren Korpeoglu,Kannan Achan
类目: Machine Learning (cs.LG)
*备注: 11 pages, 2 figures
Abstract:Iterative ML agents develop models by editing code, running experiments, reading a validation score, and choosing what to try next. In current harnesses this loop is served by an exact validation score returned after every attempt, which leaves the validation split open to adaptive selection. In this work, we introduce an explicit feedback contract for ML-agent harnesses, which specifies what an agent may observe, when iteration should stop, and when the test split may be touched. The contract releases a validation score only when a submission clears a noise threshold computed from the paired standard error, so no margin has to be tuned. We ran experiments on click-through-rate prediction, learning to rank, and a control task with no signal to find. The control is the sharpest case, because the true gain there is zero by construction: an agent reading exact scores ended by reporting a gain of 0.0157, while under the contract it reported 0.0033. On both real tasks the contract narrows the validation-test gap, and test performance is slightly higher rather than lower. The largest difference is in when the agents stop. Exact feedback used its entire budget of thirty submissions on two of the three tasks, including the one where nothing can be learned, while the contract halted after four to twelve. A run that exhausts its budget is usually read as diligence. It is better read as a symptom: when the feedback channel is unbounded, noise keeps supplying improvements to chase.
[LG-91] Extreme Low-State Quantized Transformer for Electricity-Load Forecasting
链接: https://arxiv.org/abs/2610.28784
作者: Muhammad Sabbir Alam,Harindra Sandun Mavikumbure,Victor Cobilean,Milos Manic,Jayasimha Atulasimha
类目: Machine Learning (cs.LG)
*备注:
Abstract:Aggressive low-state weight quantization can substantially reduce neural-network representational flexibility, making predictive performance difficult to preserve when only a few discrete weight states are available. We investigate this regime for Transformer-based electricity-load forecasting using 3-, 5-, 7-, and 16-state weight quantization across feed-forward-only and attention-plus-feed-forward quantization. Two tasks predict either the next hour or the next 24 hours from the preceding 48 hours across two co-primary electricity-consumption datasets. Direct post-training quantization (PTQ) increased mean normalized root-mean-square error (NRMSE) relative to full precision by 6.25 and 7.04 percentage points in Datasets A and B, whereas quantization-aware fine-tuning (QAT-FT) recovered approximately 90.2% and 94.5% of these penalties. Quantization-aware training from random initialization (Scratch-QAT) also produced competitive low-state solutions, with particularly favorable behavior at three states. We further introduce accumulated-update thresholding, which accumulates optimizer-proposed weight changes before commitment. Relative to paired Scratch-QAT controls, this reduced committed shadow-weight updates by approximately 99.7% and discrete state transitions by approximately 77–80%, while mean NRMSE changed by only +0.042 and +0.197 percentage points. These results show that useful Transformer forecasting can be retained under highly discrete weight representations while strongly suppressing target-state activity, supporting a prospective route toward compact, low-activity edge forecasting using multilevel synaptic devices.
[LG-92] Building a Nested LADDER for LLM Unstructured Post-Training Pruning
链接: https://arxiv.org/abs/2610.28773
作者: Nicolás Mauricio Cuadrado Ávila,Ivo Gollini Navarrete,Martin Takáč,Samuel Horváth
类目: Machine Learning (cs.LG)
*备注:
Abstract:Non-uniform allocation can improve post-training pruning of large language models, yet quality can still degrade sharply at high sparsity. To control degradation as sparsity increases, we introduce LADDER (Layer-Wise Allocation via Damage-Driven Evaluation and Refinement), which allocates sparsity incrementally. At each step, it evaluates pruning an additional fraction of weights from each eligible layer and commits the layer update that causes the least end-to-end degradation per removed parameter. This process produces non-uniform sparsity profiles and exactly nested masks across multiple budgets. We evaluate LADDER on five OPT, Llama, and Qwen models across 10-90% sparsity, using SparseGPT as its candidate-generation backend and comparing against uniform and adaptive allocation baselines. Results reveal a clear operating-regime trade-off: LADDER is comparable or slightly weaker at benign sparsity but becomes substantially more robust from 70% onward. This high-sparsity advantage largely persists after posterior distillation across four of five models and translates into improved retention of aggregate zero-shot accuracy.
[LG-93] Big Claims Small Margins: Do Reported GraphLLM Gains Survive Simple Baselines?
链接: https://arxiv.org/abs/2610.28714
作者: Michal Podstawski
类目: Machine Learning (cs.LG)
*备注:
Abstract:LLM-based methods for node classification on text-attributed graphs (TAGs) routinely report state-of-the-art accuracy. We ask how these results compare with inexpensive baselines run on the same splits. On the splits of three recent studies, we tune four baseline representatives with validation-only selection and compare them with the highest mean that each paper reports for an LLM-based method in the matched setting. Correct Smooth on TF-IDF features trails this reference by a median of 2.3 percentage points, and an MLP on frozen e5-large-v2 embeddings by 2.8 points. Correct Smooth is within 3 points of the reference or better on 15 of 24 tasks, but the reference is significantly higher on 12 of 22 testable tasks, so several reported gains remain substantial. Reading released code next to the papers, we also document evaluation risks: selection of hyperparameters or checkpoints on the test set, labels of test-pool nodes used in training, baseline numbers reused without disclosure, and a text-label misalignment in a released Cora loader. Reports of the same method on the same split also differ by up to 12.5 points across papers whose configurations are not documented to match.
[LG-94] SimHack: Predicting the onset of detectable reward hacking before RL training
链接: https://arxiv.org/abs/2610.28707
作者: Junwei Deng,Alex Bie,Natalia Ponomareva
类目: Machine Learning (cs.LG)
*备注: 19 pages, 7 figures
Abstract:When training LLMs to code with reinforcement learning, reward hacking is often detectable but difficult to prevent, since (a) environment hardening is increasingly difficult as training environments grow more complex; and (b) training-time interventions incentivize models to obfuscate hacking behavior to bypass detection. We instead study whether we can predict when detectable reward hacking becomes dominant before executing an RL run, enabling us to select lower-risk recipes in advance. We find that the initial hacking rate alone does not determine onset, which varies substantially across training recipes and repeated runs. We thus formulate this problem as predicting the distribution of the reward hacking onset step under a target training recipe. We introduce SimHack, a simulator that learns reward hacking training dynamics from a handful of smaller-scale reference RL runs and generates counterfactual simulation runs of unseen target recipes without costly retraining. Across unseen target recipes, SimHack predicts the median onset to a relative error of 11.3% and achieves across-recipe rank correlation of 0.85, while generalizing to differences in model scale, curriculum, number of rollouts, and learning rate between reference and target recipes. As an application, we show that SimHack can guide training decisions by screening candidate RL curricula, discovering curricula over the same set of scenarios that train for 96.6% longer without hacking. Comments: 19 pages, 7 figures Subjects: Machine Learning (cs.LG) Cite as: arXiv:2610.28707 [cs.LG] (or arXiv:2610.28707v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.28707 Focus to learn more arXiv-issued DOI via DataCite
[LG-95] Scalable Stress-Constrained Topology Optimization: A Super-resolution and Simultaneous Approach Based on Machine Learning
链接: https://arxiv.org/abs/2610.28703
作者: Xiangyu Sun,Shirin Hosseinmardi,Ramin Bostanabad
类目: Machine Learning (cs.LG)
*备注: 40 pages, 19 figures, journal paper
Abstract:Large-scale topology optimization (TO) remains computationally demanding, especially for stress-constrained problems. To address this challenge, we propose the simultaneous neural topology optimization (SNTO) framework where we represent state, adjoint, and design variables with custom neural networks (NNs) whose parameters are jointly optimized within a single optimization loop that leverages continuous adjoint analysis and automatic differentiation. This optimization minimizes a multi-component loss function that combines the design objective, an aggregation of the local stress constraints, and the potential energy functionals. Our NNs have a unique architecture that accelerates convergence while enabling the networks to capture sharp gradients and boundaries which are critical to stress-constrained TO. To achieve scalability to millions of degrees of freedom (DoF), we develop a hardware-aware training scheme in which the design domain is partitioned into subdomains that are distributed across multiple GPUs. To control peak GPU memory usage, we further slice each subdomain into smaller regions whose loss gradients are evaluated sequentially and accumulated to update the NN parameters. Unlike domain decomposition approaches, our partitioning naturally ensures field continuity across the subdomains and does not require the introduction of additional terms into the loss function. For validation, we first evaluate the proposed framework on various 2D stress-constrained benchmarks and compare our results to a conventional finite element-based approach, showing that SNTO produces super-resolution designs with sharper boundaries, smaller gray areas, and lower volume fractions. We then consider large-scale 3D problems with up to eight million collocation points and distribute the training over up to eight GPUs.
[LG-96] o Keep or Not to Keep: Learning KV Cache Retention in Disaggregated LLM Serving Systems
链接: https://arxiv.org/abs/2610.28637
作者: Dong Liu,Yanxuan Yu,Eric Jiang,Shu Wang,Ying Nian Wu
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: Accepted to ACM SYSTOR 2026
Abstract:Disaggregated \textscLLM serving separates prefill and decode across node pools, placing a network fabric between \textscKV-cache computation and consumption. This breaks a key assumption of classical cache policies: a miss no longer incurs only local recomputation, but also network transfer to the decode node. Likewise, admitting a block to the global cache requires an additional transfer, so poor retention decisions waste both memory and bandwidth. We present \textbfKVLearn, a learning-based framework for cost-aware \textscKV-cache retention in disaggregated serving. KVLearn includes: (i) a lightweight Prefix Reuse Predictor (\textscPRP) that estimates reuse probability from structural and temporal prefix features without modifying model weights; (ii) a Cost-Aware Retention Score (\textscCARS) that converts reuse probability into keep/admit decisions using recomputation, transfer, and storage costs; and (iii) an Adaptive Threshold Controller (\textscATC) that adjusts admission thresholds online from observed hit rates and memory pressure. We integrate KVLearn into a globally disaggregated serving topology and evaluate it on text and multimodal workloads, including large image/video-derived \textscKV blocks with heterogeneous reuse patterns. KVLearn reduces end-to-end time-to-first-token by up to 56% versus No-Cache, 38% versus LRU-Pool, and 33% versus Mooncake-style baselines, while cutting inter-node \textscKV transfer volume by up to 53% versus LRU-Pool. On MM-Session, throughput remains within \sim5% of oracle. Code is available at this https URL.
[LG-97] LightSplit: Practical Privacy-Preserving Split Learning via Orthogonal Projections (Full Version) NDSS
链接: https://arxiv.org/abs/2605.13265
作者: Mert Cihangiroglu,Alessandro Pegoraro,Phillip Rieger,Antonino Nocera,Ahmad-Reza Sadeghi
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注: Accepted at the Network and Distributed System Security (NDSS) Symposium 2027
Abstract:Split learning (SL) enables collaborative training by partitioning a neural network across clients and a central server. However, the cut-layer interface introduces a key challenge: high-dimensional activations incur substantial communication overhead while exposing representations vulnerable to reconstruction attacks. Existing approaches typically address efficiency or privacy in isolation. We propose LightSplit, which limits information exposure and reduces communication overhead via a lightweight fixed orthogonal random projection at the cut layer. Based on Shannon’s information theory, this projection acts as an information bottleneck that restricts instance-specific information and suppresses exploitable per-sample signals. With low-dimensional projections sent instead of raw activations, the server operates on lifted representations without requiring changes to existing SL architectures. Adding no trainable client components, LightSplit remains lightweight and suitable for edge devices while preserving end-to-end differentiability via exact gradient propagation. As the projection is non-invertible, part of the original representation is irreversibly discarded at the client, so LightSplit reduces the information available for reconstruction. We evaluate LightSplit on seven benchmarks spanning image classification, tabular data, and language-model fine-tuning, under IID and non-IID client data. At up to 32x compression it retains at least 94% of baseline accuracy on CIFAR-10, FashionMNIST, GTSRB, and UCI HAR, 86% on CIFAR-100, and 91% on average under non-IID client data; at 8x it retains 92% of ImageNet-1k top-1 and raises GPT-2 perplexity by at most 1.15x. Against state-of-the-art reconstruction attacks LightSplit resists inversion; the strongest, co-trained attack loses up to 87.3% of its reconstruction SSIM, and subject re-identification on UCI HAR drops by more than half.
[LG-98] Minimax Optimality of the NPMLE for Gaussian Mixtures in Hellinger Distance
链接: https://arxiv.org/abs/2610.30109
作者: Shayan Hundrieser,Tudor Manole
类目: atistics Theory (math.ST); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注:
Abstract:We analyze the problem of estimating univariate Gaussian mixture densities using the nonparametric maximum likelihood estimator (NPMLE) based on n independent and identically distributed samples. We show that the unconstrained NPMLE achieves the minimax optimal rate \log(n)/n in squared Hellinger distance, when the true mixing measure is sub-Gaussian. In addition, we show that when the true mixing measure has compact support, the NMPLE constrained to a compact interval also achieves the minimax optimal rate, which is slightly faster and of order \log(n)/(n \log \log n) . Our proof consists of sharply characterizing the local Hellinger bracketing entropy of the class of finite Gaussian mixtures. One of our key technical innovations is to sharply bound the L^2 bracketing entropy of balls of bounded-degree polynomials, which may be of independent interest. As an application, we establish the regret-optimality of g -modeling procedures based on the NPMLE in the Gaussian Empirical Bayes problem with bounded priors.
[LG-99] he Price of Current-Effect Inference in Nonstationary Multi-Armed Bandits
链接: https://arxiv.org/abs/2610.29982
作者: Sharan Sahu,Martin T. Wells
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 49 pages, 8 figures, 5 tables
Abstract:Adaptive experiments use bandit algorithms to favor interventions with higher expected rewards. However, decision-makers also require reliable inference on current treatment effects: differences between intervention and reference-arm mean rewards at the present time, rather than historical averages. These objectives conflict: reward maximization favors better interventions, whereas current-effect inference requires continued exploration of potentially suboptimal interventions as outcomes change over time. We characterize this tradeoff for K -arm bandits with Hölder-smooth mean rewards of exponent \beta\in(0,1] and constant L . Requiring high-probability simultaneous coverage of all current effects by confidence intervals of width at most w , we establish matching minimax bounds, up to logarithmic factors, on expected dynamic regret. Over horizon T , our algorithm TRACE (Tracking Rewards and Current Effects) achieves regret of order \widetildeO_\beta\left(\sqrtKT+K^\beta/(2\beta+1)L^1/(2\beta+1)T^(\beta+1)/(2\beta+1)+Kw^-2(1+L^1/\betaw^-1/\beta)\right) , where the notation hides logarithms and constants depending only on \beta . This regret–precision tradeoff separates reward-learning regret from the cost of refreshing current-effect estimates, which remains unavoidable even when one arm is uniformly optimal. An empirical-Bernstein e-process extension adapts exploration to realized prediction errors while preserving worst-case guarantees. When within-block drift is negligible relative to sampling error, all observations, including reward-learning observations, can be reused for asymptotically normal pointwise inference and stronger local stationarity yields local asymptotic efficiency. Finally, we show that regret constraints can prevent honest adaptation to unknown smoothness and support our theory with extensive numerical experiments.
[LG-100] Mastering Model Fit: Applications of the InterModel Vigorish for Interpretable Machine Learning
链接: https://arxiv.org/abs/2610.29960
作者: Jiani Yan,Pengyun Wang,Klint Kanopka,Ben Domingue,Charles Rahal
类目: Methodology (stat.ME); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Accuracy alone gives little information about which features or model components improve probabilistic fit, which outcomes benefit, or how fit changes during training. We introduce the InterModel Vigorish (IMV) into the Machine Intelligence space in order to express differences in fit as an expected betting advantage over a reference model. SHAP-IMV attributes gains to features through Shapley-weighted subset comparisons, Multi-IMV evaluates one-vs-rest and pairwise class discrimination, and Ablate-IMV assesses architectural components and feature groups by ablation. We evaluate these applications over ten random seeds using tabular data, images, sensor sequences, and graphs. Feature rankings vary across estimators. High one-vs-rest accuracy can accompany negligible improvement over a null, while ablation can preserve or improve fit under a fixed training budget. On six graph-classification benchmarks, we time-profile IMV over 200 training epochs, reporting seed means and ranges. The open-source imvpy library and accompanying notebooks provide code to replicate our work and to use the IMV more widely.
[LG-101] ractable Inference with Kolmogorov-Arnold Networks
链接: https://arxiv.org/abs/2610.29909
作者: Juan Diego Toscano,Sascha Ranft,Zhiwei Gao,Li-Lian Wang,George Em Karniadakis
类目: Computational Physics (physics.comp-ph); Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注:
Abstract:Machine-learning surrogates in computational science often require not only accurate predictions but also expectations, variances, conserved integrals, and normalization constants. Conventional neural networks, including Kolmogorov–Arnold networks (KANs), generally evaluate these quantities through Monte Carlo sampling or multidimensional quadrature, making repeated inference costly, particularly in high dimensions. We introduce KANs with Tractable Operators for Uncertainty and Constraints (TOUCKANs), whose finite-degree polynomial outer functions yield a decomposable sum–product representation. Under factorizing input measures, expectations and higher moments then reduce algebraically to deterministic recursions over one-dimensional leaf integrals. Independent and shared inner-block variants form a parameter-efficiency hierarchy, and a universal-approximation proof for its most restricted member extends to the complete class. The same deterministic operators allow conserved integrals and normalization to be imposed throughout training. Across uniform and Gaussian benchmarks through 50 dimensions, deterministic mean and variance errors remained below the median fixed-budget Monte Carlo errors in every case. TOUCKANs also conserved Cahn–Hilliard mass to machine precision and imposed normalization and orthogonality through deterministic moments, enabling joint computation of the lowest four Schrödinger eigenpairs in two, three, and five dimensions. TOUCKANs therefore connect KAN approximation with tractable probabilistic circuits, making statistical operators and integral constraints directly available within an expressive neural surrogate.
[LG-102] Silent Alignment in Two-Layer Linear Networks: A High-Dimensional Perspective
链接: https://arxiv.org/abs/2610.29864
作者: Simon Martin(DI-ENS, LPENS, SIERRA),Francis Bach(DI-ENS, SIERRA),Giulio Biroli(LPENS)
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:We study gradient flow in a two-layer linear network in the high-dimensional regime where the input, inner and output dimensions diverge proportionally. Under whitened inputs and isotropic imbalance, we combine a closed-form solution of the dynamics with prior random matrix theory results to derive a high-dimensional limit for the dynamics. Our method yields exact asymptotic spectral distributions for the weight matrices at all times and characterizes the evolution of the learned subspace. In the small-initialization limit, we identify a silent alignment phenomenon: the singular directions of the network align on an order-one timescale, while the singular values and empirical loss remain at their initial values. Visible learning occurs later, on a logarithmic timescale, as singular values progressively escape zero and the loss decreases. This phase is characterized by a saddle-to-saddle dynamics, which becomes a continuous process of spectral learning in the high-dimensional limit.
[LG-103] NeuroCodec: EEG-Guided Latent-Domain Generative Model for High-Fidelity Target Speaker Extraction
链接: https://arxiv.org/abs/2610.29815
作者: Julian Silva,Jaliya Kodithuwakku,Navindu Sirimanna,Shaveen Herath,Wageesha N. Manamperi,Joshua P. Kulasingham
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG)
*备注: Supplementary results, audio files and the spectrograms are available at the following link: this https URL
Abstract:Driven by recent advancements in hearing assistive devices, target speaker extraction (TSE) in multi-talker environments has drawn significant attention. To address this, hearing technologies identify attention using neural signals recorded via electroencephalography (EEG) to guide TSE, enabling neuro-steered auditory attention decoding. However, most existing techniques rely on time-frequency masking, which suffers from low fidelity in spectro-temporally overlapping regions and is often restricted to narrowband audio. In contrast, we introduce NeuroCodec, which, to the best of our knowledge, is the first generative EEG-guided TSE framework operating in the latent space of a neural audio codec. Specifically, NeuroCodec models spatial EEG dependencies via graph convolutions and fuses cross-modal EEG and speech representations through attention mechanisms and Mamba-based temporal modeling. We further introduce a six-phase training curriculum that progressively incorporates latent reconstruction, multi-scale spectral, and adversarial objectives to ensure stable, high-fidelity speech generation. Experiments on the public Cocktail Party and KUL datasets show NeuroCodec outperforms baselines, across several perceptual metrics. A listening experiment with 20 participants validated that NeuroCodec offers greater perceptual accuracy, achieving a mean score of 82.24 compared with 49.63 for M3ANet. Additionally, the proposed method is seen to enable 44.1 kHz reconstruction with spectral content extending up to 20 kHz, further supporting the perceptual results. Finally, we extend NeuroCodec with a causal streaming formulation suited for real-time processing, providing a basis for hearing assistive devices.
[LG-104] Lifting PCA in the Wasserstein Space
链接: https://arxiv.org/abs/2610.29679
作者: Jérémie Bigot,Clément Bonet
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Principal Component Analysis (PCA) is a fundamental tool for statistical dimension reduction, yet no canonical extension to distribution-valued data has emerged within the framework of optimal transport. While Wasserstein barycenters and Wasserstein least-squares regression provide canonical liftings of Euclidean averaging and linear regression in the Wasserstein space, an analogous principle for PCA has remained unexplored. In this paper, we introduce Lifted PCA, a canonical extension of Euclidean PCA to Wasserstein space, obtained through the framework of lifting functionals in the space of probability measures by convex extension. Starting from the classical variational formulations of PCA on the Stiefel and Grassmann manifolds, we derive two equivalent minimization problems over probability measures supported on these manifolds that correspond to Wasserstein barycenters with generalized costs. We establish an exact analogue of the equivalence between the Stiefel and Grassmann formulations of Euclidean PCA, preserving PCA’s original variational structure under lifting. We further derive dual formulations and optimality conditions, and establish equivalences with multimarginal optimal transport and matching-for-teams problems. We also establish statistical consistency for empirical estimators and develop computational algorithms based on the Wasserstein barycenter formulation over the Stiefel manifold. Whereas Euclidean PCA summarizes vectors through projections onto an optimal low-dimensional linear subspace, Lifted PCA summarizes probability distributions through an optimal probability measure supported on multiple low-dimensional subspaces. Numerical experiments on distributional datasets illustrate the practical performance of the proposed methodology and its benefits for distributional data analysis.
[LG-105] Space weather dependence of decameter-scale ionospheric irregularities spatial distribution
链接: https://arxiv.org/abs/2610.29630
作者: Oleg I.Berngardt,Ivan A.Lavygin
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG); Geophysics (physics.geo-ph)
*备注: 34 pages, 9 figures, 4 tables, To be submitted to Advances in Space Research
Abstract:This paper describes an approach to constructing nowcast and forecast predictive models for the occurrence of high-latitude decameter ionospheric irregularities based on SuperDARN and SECIRA radar data and space weather parameters. The method relies on self-learning pattern recognition in the dynamics of space weather parameters, that influence the irregularities formation. The proposed approach is a data-driven, multiscale method in which the spatial distribution of irregularities is approximated using a neural network of an original architecture, inspired by graph neural networks. The model was trained and tested using data from 11 radars during the period 2010-2025 (SuperDARN: CVW, ICE, ICW, INV, PGR, PYK, RKN, SAS, STO; SECIRA: EKB, MAGW). It is shown that the model achieves optimal performance when using geomagnetic coordinates MLT,MLAT. The optimal set of parameters for the best nowcast and forecast over studied set of parameters is a combination of geomagnetic and solar disturbance indices ( a_p , K_p , AE , F10.7) from ground-based observations, and satellite-measured solar wind and interplanetary magnetic field parameters ( P_sw , E_sw , \widetildeB_z , T_sw ). Hourly mean values for all the parameters were used in the analysis. The results indicate that acceptable forecast quality is achieved with a lead time of up to 7-9 hours, with the highest quality reached for nowcast (zero lead time). It is shown that the parameters \widetildeB_z and F10.7 are significant for nowcasting only and can be neglected for other lead times. It is shown that AE and T_sw are significant for mid-term forecast (\textgreater 2hours ahead) and can be neglected for short-term predictions. Statistical relationships between the maximum latitude of the equatorial boundary of the irregularities and space weather parameters are also discussed. Comments: 34 pages, 9 figures, 4 tables, To be submitted to Advances in Space Research Subjects: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG); Geophysics (physics.geo-ph) Cite as: arXiv:2610.29630 [physics.ao-ph] (or arXiv:2610.29630v1 [physics.ao-ph] for this version) https://doi.org/10.48550/arXiv.2610.29630 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-106] Near Minimax Rate for Quantile Distributional Reinforcement Learning
链接: https://arxiv.org/abs/2610.29595
作者: Zijie Cheng,Zhihua Zhang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:In this paper, we study quantile distributional policy evaluation, where the return distribution under a given policy is approximated by the quantile Bellman fixed point \bm\eta_m . We investigate the statistical accuracy of estimating \bm\eta_m across quantile resolutions under Wasserstein distances. Assuming access to a generative model, we construct a quantile fixed point estimator \bm\eta_m^(n) from an empirical Markov decision process and establish non-asymptotic error bounds under Wasserstein distances. When reward densities are uniformly bounded away from zero, we obtain a root- n high-probability bound simultaneously over all resolutions m and all Wasserstein orders 1\le p\le\infty . When reward densities vanish at the support endpoints, we show that continuity and endpoint monotonicity suffice to preserve the same resolution-independent rate for 1\le p\le2 . For p2 , we establish a root- n bound with coefficient m^1/2-1/p , together with a slower, resolution-independent n^-1/p bound for every finite p2 . We further obtain resolution-independent W_\infty consistency governed by the continuity modulus of reward quantile functions. To investigate the sharpness of these bounds, we characterize the exact local asymptotic minimax risk at each fixed resolution as the expected Wasserstein-coordinate norm of the efficient Gaussian limit and prove that our estimator attains it. We then examine the resolution dependence of this optimal local risk. A regular one-state instance has resolution-independent local risk constants, whereas a single endpoint-vanishing two-state instance exhibits growth of order m^1/2-1/p , up to logarithmic factors along a resolution subsequence, for every p2 . Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) Cite as: arXiv:2610.29595 [stat.ML] (or arXiv:2610.29595v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2610.29595 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-107] What Prediction-Market Contracts Reveal at Launch: Trading Volume and Dispute Risk
链接: https://arxiv.org/abs/2610.29578
作者: Ahmed Mahrous,Roberto Di Pietro
类目: atistical Finance (q-fin.ST); Machine Learning (cs.LG)
*备注: Accepted at the 7th ACM International Conference on AI in Finance (ICAIF 2026). 8 pages, 5 figures
Abstract:Using a near-census of 802,863 resolved markets listed on Polymarket between January 2023 and May 2026, and only information available at listing (the market’s title and description, named resolution source, and contract metadata), we predict lifetime trading volume and whether settlement is later disputed. Contract information predicts trading volume well (out-of-sample R^2 = 0.747 for log volume), and most of the signal is interpretable: market topic provides the largest gain ( +0.289 R^2 over a 0.220 contract-feature baseline), wording and resolution-rule design add smaller but measurable improvements ( +0.027 combined), and neural text models add little beyond the interpretable features. This performance does not survive a period of platform change coinciding with a shift in listing formats: accuracy in predicting whether a market reaches \ 1,000 falls from AUC 0.940 under same-period evaluation to 0.577 when training on past quarters and testing on later ones. The loss concentrates in markets whose question forms were absent from training (76.9% of test markets; on familiar forms the model still reaches 0.778 ), and a decomposition attributes more of it to a changed relationship between contract characteristics and demand than to an outdated mapping from text to features. Dispute predictors are more stable but less precise: launch-time features rank dispute risk far better than chance (AUC 0.78 ), yet at a 0.31% base rate precision is too low for case-by-case prediction (average precision 0.012 ), so the scores suit ranking groups of markets rather than forecasting individual disputes.
[LG-108] Residual-Based Causal Discovery via Pretrained In-Context Quantile Regression
链接: https://arxiv.org/abs/2610.29530
作者: Daniel Klippert,Quang Nhat Huynh,Alexander Marx
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Residual-based causal discovery assumes that each variable is generated from its parents and an independent noise source. It estimates this noise and tests its independence from candidate causes. For identifiable functions such as additive-noise models (ANMs) and the broader class of location-scale noise models (LSNMs), this principle identifies the causal direction, as only the causal model admits this independence. Existing LSNM methods rely on parametric assumptions to obtain standardized residuals, risking model misspecification. In contrast, the RESIT-framework allows for nonparametric estimation of residuals to iteratively identify sink nodes, but was proposed for ANMs. To close this gap, we propose Loscar, an extension of RESIT to LSNMs that estimates the conditional mean and scale via quantile regression, enabling pretrained tabular foundation models as plug-in estimators. We compare it with a feasible generalized least squares (FGLS) variant, which is consistent given suitable basis functions. Across bivariate and multivariate causal-discovery benchmarks, in-context quantile regression substantially outperforms FGLS with flexible function classes, particularly for multivariate causes.
[LG-109] A unified approach for estimation of unnormalized models via Stein operators
链接: https://arxiv.org/abs/2610.29510
作者: Mitsuki Nagai,Keisuke Yano
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:In this paper, we develop a unified framework connecting proper scoring rules and Stein’s method for the estimation of unnormalized models. We construct a class of proper scoring rules that encompasses score matching, ratio matching, pseudolikelihood, and noise contrastive estimation. We show that these proper scoring rules naturally induce Stein operators. This connection yields a new construction principle for Stein operators, and enables us to study estimators based on these scoring rules through the geometry associated with the induced Stein operators. Using this geometric perspective, we provide a unified characterization of the asymptotic efficiency of the resulting estimators.
[LG-110] Asynchronous Generation-Free ABC: Streaming AMIS Reweighting for Heterogeneous Simulator Workloads on HPC
链接: https://arxiv.org/abs/2610.29506
作者: Julian Herold,Markus Götz,Alexander Schug
类目: Computation (stat.CO); Machine Learning (cs.LG)
*备注: 42 pages (18 main text), 13 figures, 14 tables. Code available at this https URL
Abstract:Approximate Bayesian computation (ABC) enables Bayesian inference for models with intractable likelihoods by estimating parameter posterior distributions from simulated data. Sequential ABC methods improve sampling efficiency by iteratively adapting the proposal distribution toward regions of high posterior probability. However, these methods typically operate in generations, where each generation must wait for its slowest simulation before the next can begin. On a parallel machine, this synchronization barrier leaves workers idle and reduces throughput, particularly when simulation runtimes vary substantially. We introduce a generation-free ABC sampler that updates its proposal when simulation results arrive and sends a new candidate to each available worker. The proposal is based on the best particles evaluated so far. To estimate the posterior, we reuse the full history and reweight particles against a reconstructed mixture of sampling proposals. We show how errors in approximating the mixture of sampling proposals affect the estimated posterior and establish consistency for the smooth-ABC posterior when the approximation becomes asymptotically exact. In our experiments, the method achieves well-calibrated posterior estimates where a pyABC baseline with the same kernel and bandwidth schedule under-covers. From the asynchronous run’s timing alone, we predict how much a synchronization barrier would slow the sampler. These predictions agree with measured synchronization penalties ranging from 1.2x to 400x. On a Cellular Potts tissue simulator, the asynchronous sampler completes 2.1-2.4x as many evaluations as pyABC in the same hour, and the discrepancy threshold for its best 100 evaluations is 4.1x smaller. With the starting bandwidth set from prior-predictive discrepancies, it also produces a more concentrated two-parameter posterior with a larger effective sample size than pyABC.
[LG-111] When Thoughts Become Memory: Unifying In-Context Learning and Fine-Tuning
链接: https://arxiv.org/abs/2610.29419
作者: Leon Chlon,Julie Huang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:We are the first to give a certified rule for whether unseen data belong in a pretrained model’s context or its weights, built on an exact law for their information value. That value, the code length saved on held-out queries, equals in expectation the bits stored about the query, plus a prior repair, minus a decoding loss, for any model, architecture, dataset and task. The law’s ceilings certify the choice from one measurement, and an anytime-valid test makes it from held-out queries, with error controlled whenever it stops. Every certificate the ceilings issue on a trained transformer, TabPFN, an occupancy network and a stereo network is correct. The test picks TabPFN over XGBoost and a trained surrogate over an in-context learner on airfoil pressure, calls the better of GPT-2’s cache and fine-tune in 61,948 of 64,000 query streams over 32 comparisons, and errs in at most 3.3% of null streams. The law also diagnoses when learning in context helps and when it does not. Deleting part of a context is held-out validation of that part in any model, and adding parts one at a time traces the learning curve in context: one patch that sees a shape feature cuts an occupancy network’s code length there a hundredfold, while eight that miss it nearly double it, and one camera that sees the surface beats four in a stereo network. Ranked by this label-free valuation, a kriging learner’s three best support cars beat four drawn at random, and on a yes-or-no claim the valuation recovers a published evidence adjudicator, with AUROC 0.91.
[LG-112] Data-driven and physics-guided hindcasting of measured wave spectra
链接: https://arxiv.org/abs/2610.29306
作者: Sander Rikka
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)
*备注:
Abstract:Short-range hindcasts of measured wave spectra are evaluated using a recurrent model initialized by one hourly buoy spectrum and supplied with local wind, fetch and depth. Measurements from three Baltic Sea stations are combined for training in 2017–2020, validation in 2021 and testing in 2022. ERA5 reanalysis supplies winds throughout the twelve-hour prediction interval. The comparison examines supervision of spectral variance, peak frequency and peak amplitude, followed by empirical wind-sea bounds and a local relaxation constraint. Variance and peak-frequency supervision gives the lowest spectral error within the data-driven comparison, whereas additional peak-amplitude supervision gives the lowest wave-height error. In the matched physical comparison, relaxation reduces height RMSE by 5.01% to \PhPtwoHs~cm. Extending this constraint through hours 13–24, without spectral targets at those hours, reduces primary spectral RMSE by 2.31% to \PhPthreeSpec~ \unitE . Upper-bound constraints increase errors. Wind-condition comparisons show spectral gains from extended relaxation in increasing, decreasing and steady strong winds, with little change in steady weak winds. The results support modest, objective-dependent benefits from local physical guidance. Wave-height scores concern the retained frequency band, and operational forecasting and transfer to independent stations remain to be evaluated.
[LG-113] Group Distributionally Robust Optimization over Decentralized Networks
链接: https://arxiv.org/abs/2610.29261
作者: Haomin Bai,Junyan Sun,Quan Cheng,Yuanyu Wan,Lijun Zhang
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:We study group distributionally robust optimization (GDRO) over decentralized networks, where workers collaborate to learn robust models by minimizing the maximum local risk. Existing methods optimize only dual-regularized surrogates and establish guarantees for a globally averaged model that is not available to workers. In this paper, we directly optimize the original GDRO objective and establish guarantees for local models in semi-decentralized (SemiDec) and fully decentralized (Dec) settings. A key difficulty is that applying the primal-dual decomposition from centralized GDRO yields worker-dependent losses whose evaluation requires unavailable cross-worker information. To address this challenge, we develop a novel error decomposition that recasts decentralized GDRO as (i) a decentralized online convex optimization (D-OCO) problem and a prediction-with-expert-advice problem in SemiDec, and as (ii) two coupled D-OCO problems in Dec, with discrepancy terms capturing inconsistencies among local decisions. By isolating consensus errors in analysis, we develop modular frameworks that support different consensus routines. We then establish the \emphfirst per-worker convergence guarantees for optimizing the original GDRO objective over decentralized networks. Instantiations with accelerated gossip and Choco-gossip achieve an O(\sqrt(\log m)/T) convergence rate over T update rounds, while experiments demonstrate improved worst-worker performance over baselines.
[LG-114] Schrödinger-Bass Bridge Barycenter Models for Measure-Valued Data
链接: https://arxiv.org/abs/2610.29227
作者: Ngoc-Hai Nguyen,Thuan Nguyen,Prakash Ishwar,Shuchin Aeron
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Barycenter models, based on stochastic and deterministic optimal transport bridges between probability measures, have proven to be useful for supervised and unsupervised learning for (probability) measure-valued data. In this paper, we utilize the directional asymmetry and dual drift-diffusion control arising in the recently proposed Schrödinger-Bass Bridge (SBB) between two measures, and propose \textitSBB-encoder, \textitSBB-decoder, \textitSBB-symmetric, and \textitSBB-Markovian barycenter models. For each of these novel models, our first theoretical result establishes the existence of the SBB-barycenter under mild assumptions. Our second theoretical result characterizes the decoder SBB-barycenter of Gaussians where we show that for Gaussian input measures, the decoder formulation admits a Gaussian minimizer, reducing barycenter computation to a finite-dimensional optimization and yielding a compact algorithm. For general input measures, we derive a hybrid Wasserstein gradient particle descent scheme for all four formulations using the SBB dual representation. Finally, we utilize these algorithms for solving distributional-inverse/interpolation problems arising in the context of spatial images and single-cell RNA sequencing data, where we show that the SBB-decoder and Markovian barycenter allow us to interpolate between successive time points. Results show that our method outperforms existing barycenter models for measure interpolation and inference.
[LG-115] On the gradient conjecture at infinity: Finite spherical length for gradient trajectories of definable potentials
链接: https://arxiv.org/abs/2610.29211
作者: Thang Do,Arnulf Jentzen,Benno Kuckuck
类目: Dynamical Systems (math.DS); Machine Learning (cs.LG); Algebraic Geometry (math.AG); Classical Analysis and ODEs (math.CA); Logic (math.LO)
*备注: 98 pages
Abstract:We prove that for every open U \subseteq \mathbb R^n the spherical projection of every gradient trajectory \theta(t) , t \geq 0 , of a definable C^1 -potential F \colon U \to \mathbb R has finite length and, in particular, converges (directional convergence). In other words, every gradient trajectory of a definable C^1 -potential has finite total angular motion. Here, definable means definable in an arbitrary o-minimal structure expanding the real field. Notably, the gradient trajectory in this result is an arbitrary solution of the gradient flow differential equation (whose solutions may not be unique) with no further assumptions: The gradient trajectory may be unbounded and diverge to infinity or it may be convergent. The established theorem therefore provides affirmative answers to both the gradient conjecture at infinity for definable potentials and the definable Thom gradient conjecture. We apply our theorem to the training of deep neural networks (DNNs) to conclude that, in the case of definable and continuously differentiable activation and loss functions, the spherical projection of the DNN training flow must always converge (directional convergence). Comments: 98 pages Subjects: Dynamical Systems (math.DS); Machine Learning (cs.LG); Algebraic Geometry (math.AG); Classical Analysis and ODEs (math.CA); Logic (math.LO) MSC classes: 34C08, 03C64, 14P99, 34D05, 37N40, 68T07 ACMclasses: G.1.6; G.1.7; I.2.6 Cite as: arXiv:2610.29211 [math.DS] (or arXiv:2610.29211v1 [math.DS] for this version) https://doi.org/10.48550/arXiv.2610.29211 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-116] Curvature-Scaled Sharpness-Aware Minimization: Convergence Beyond Smoothness
链接: https://arxiv.org/abs/2610.29192
作者: Dimitris Oikonomou,Nicolas Loizou
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 31 pages
Abstract:Sharpness-Aware Minimization (SAM) has established itself as a widely used method for training deep neural networks. Despite its popularity, existing convergence guarantees for SAM-type methods require smoothness, a restrictive assumption. Generalized smoothness conditions have recently been proposed in the analysis of gradient methods but remain underexplored for SAM. To analyze the effect of SAM’s perturbation under generalized smoothness, we use relative gradient distortion to quantify how the sharpness-aware direction departs from the original gradient. Under (L_0,L_1) -smoothness, we show that this distortion can become unbounded near stationarity for SAM and at large gradients for its unnormalized variant (USAM), with examples where SAM cycles and USAM diverges. We then establish a necessary condition for uniform distortion control among perturbations along the gradient whose nonnegative multipliers depend only on its norm. Motivated by this condition, we propose Curvature-Scaled SAM (CS-SAM), which scales both the perturbation and the descent step by suitable curvature upper bounds, and prove that it uniformly bounds the distortion. Under both (L_0,L_1) - and (H_0,H_1) -smoothness, we establish deterministic \mathcalO(1/T) rates for the best squared gradient norm on nonconvex objectives, last-iterate \mathcalO(1/T) rates for the function gap on convex objectives with a minimizer, and linear convergence under the Polyak-Lojasiewicz condition. With zero perturbation, our guarantees recover the rates for gradient descent with (L_0,L_1) - or (H_0,H_1) -based step-sizes. Minibatch experiments on computer vision tasks demonstrate competitive performance for both variants, with the (H_0,H_1) variant achieving the highest mean best-test accuracy in all four settings against tuned SAM and USAM with constant and cosine annealing schedules.
[LG-117] SU(2)-Equivariant Spinor Message Passing for Molecular Magnetism NEURIPS2026
链接: https://arxiv.org/abs/2610.29111
作者: Erh-Wei Sheng,Beom Seok Kang,Yuchao Lin,Danish Khan,Anima Anandkumar
类目: Chemical Physics (physics.chem-ph); Machine Learning (cs.LG)
*备注: An earlier version was accepted to the NeurIPS 2026 SimBioChem Workshop
Abstract:Predicting molecular magnetism often requires electronic-structure calculations with spin-orbit coupling (SOC), which are costly to repeat across many molecules. The underlying electronic states can transform as spinors, yet equivariant surrogates typically use only integer-rank features. Inspired by the spinor nature of these electronic states, we develop eSpinor, an SU(2)-equivariant graph neural network that propagates spinor features. We initialize spinor features from eigenvectors of an approximate SOC Hamiltonian and use gauge-covariant message passing with a gauge-invariant readout to make predictions independent of the arbitrary eigenbasis choice. On cobalt single-molecule magnet candidates, eSpinor improves zero-field-splitting predictions relative to NequIP, including for unseen ligands. For a dysprosium magnetic resonance imaging probe, eSpinor improves real electron-spin correlations over a Hamiltonian-informed baseline despite similar static crystal-field errors and fewer parameters, while accurately predicting low-field nuclear relaxation in water and after transfer to methanol without retraining. Overall, these results demonstrate that SU(2)-equivariant spinor message passing is a useful inductive bias for molecular magnetism. We also find that similar static tensor errors can yield different dynamical errors, motivating model selection based on accuracy for the intended physical observables.
[LG-118] Internal Guidance as a Sampling Method for Tilted Diffusion Models
链接: https://arxiv.org/abs/2610.29086
作者: Curtis McDonald,Peter Bartlett
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Reward guidance methods sample an exponential tilt of a base density using a diffusion model trained with only samples from the base distribution. Existing methods use what we call external guidance: additive terms in the drift or multiplicative terms on the transition probabilities. We propose internal guidance which leaves the output of the base denoiser unmodified and instead changes the point at which it is evaluated. For discrete distributions embedded in the hypercube -1,1^d , the denoiser of any linearly tilted density is exactly the base denoiser evaluated at a shifted and rescaled input. In practice the base denoiser is a neural network trained on noised samples of the base distribution. The required evaluation points for sampling the tilted density would be out-of-distribution for basic noising of the base distribution. We modify the training algorithm with random diagonal rescalings of the data, so that the denoiser network learns both in- and out-of-distribution evaluations of the denoiser relative to the base distribution. A single denoiser network samples binary reward tilts, class labels, and infilling with no fine-tuning and no external guidance term.
[LG-119] A Jump-Modified Wasserstein Gradient Flow for Maximum Mean Discrepancy with Energy Kernels
链接: https://arxiv.org/abs/2610.29007
作者: Ansh Desai,Jason Liu,Srujan Roplekar,Matthew Rosenzweig
类目: Analysis of PDEs (math.AP); Machine Learning (cs.LG); Probability (math.PR); Machine Learning (stat.ML)
*备注: 63 pages
Abstract:We study a jump-modified Wasserstein gradient flow of the squared maximum mean discrepancy generated by the energy kernels K(z)=-|z|^q , 0q2 , in every dimension d\ge2 as well as the one-dimensional range 1\le q2 . Under subcritical density and finite-moment assumptions, we prove global well-posedness. The added jumps transfer mass toward smaller values of the first variation and dissipate the same MMD energy as the transport term. Under our hypotheses on the target and jump rates, every Borel probability measure with finite MMD energy and zero jump dissipation is equal to the target. Combining this rigidity with compactness and a second-moment identity yields convergence to the target from finite-second-moment data, without additional uniform-in-time density or moment assumptions. We also construct a global collision-free particle process whose energy is nonincreasing along each path. A quantitative mean-field estimate applies to general initial empirical approximations, including deterministic configurations; propagation of chaos for exchangeable data and long-time target approximation follow as consequences.
[LG-120] Is In-Context Reinforcement Learning A Posterior Sampler? An Empirical Study
链接: https://arxiv.org/abs/2610.28977
作者: Weiqin Chen,Xinlong Du,Gesualdo Scutari,Harsha Honnappa
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:In-context reinforcement learning (ICRL) exploits the impressive in-context learning ability of large language models to zero-shot generalize to new, unseen tasks at inference time without any parameter updates. Among many approaches, the Decision-Pretrained Transformer (DPT) is particularly appealing for its theoretical grounding: under idealized assumptions such as a sufficiently expressive model and infinite pretraining data, DPT can perform Bayesian posterior sampling. Whether this characterization survives in practice remains open. Therefore, we ask: \textitis ICRL, specifically DPT and our proposed DPT variants, a Bayesian posterior sampler in practice? To approach this question, we establish two necessary conditions that a Bayesian posterior sampler must satisfy: \textitsufficient-statistic invariance and the \textitmartingale property. We then construct statistical hypothesis tests to statistically assess if practically-trained ICRL models satisfy these conditions. Applying these tests in Gaussian, Bernoulli, and linear bandit settings, we find that both conditions are consistently violated, suggesting that ICRL may not behave as Bayesian posterior samplers in practice. Beyond this negative result, our tests provide a principled toolkit for testing the Bayesian behavior of ICRL.
[LG-121] Backtracking Direct Indirect and Total Effects
链接: https://arxiv.org/abs/2610.28910
作者: Jose M. Peña
类目: Methodology (stat.ME); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Natural direct and indirect effects are defined through cross-world interventional counterfactuals. We propose an alternative construction based on backtracking counterfactuals. Rather than intervening on the treatment or mediator, we use a backtracking conditional distribution to construct an alternative exogenous state satisfying a desired treatment-mediator constraint. This yields the backtracking direct and indirect effects, and also the backtracking total effect. Likewise, we adapt the population intervention effects.
[LG-122] ropical Cyclone Attributes in Sparse Autoencoder Features of an AI Weather Model
链接: https://arxiv.org/abs/2610.28904
作者: Jiapei Liu,Ruidong Li,Ting Sun,Guangheng Ni
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)
*备注: 22 pages, 4 figures; supporting information included as an ancillary file. Submitted to Geophysical Research Letters
Abstract:Artificial intelligence (AI) weather models now forecast weather events skillfully, but how they represent these events internally remains unknown. Sparse autoencoders (SAEs) provide sparse features for probing hidden states, but how information about multiple physical attributes of an event is organized across them remains unclear. Using tropical cyclones in GraphCast as a test case, we analyze 2,115 six-hourly timesteps from 205 cyclones, relating SAE activations to position, intensity, and pressure structure across five Layer-8 decompositions and 16 processor layers. Each attribute relates to multiple features; position and pressure-structure relations overlap in a smaller subset. This organization recurs across independent SAEs and layers without feature alignment. In deeper layers, the leading position feature aligns better with position and pressure structure six hours ahead than at present. These results suggest that the organization of physical-attribute relations provides a reproducible level of description for interpreting cyclone-related information in GraphCast.
[LG-123] Self-Supervised Tuning of Low-Fidelity Optimization Models via High-Fidelity Feedback
链接: https://arxiv.org/abs/2610.28851
作者: Gonzalo E. Constante-Flores,André H. Quisaguano-Paredes,Asha Ramanujam,Rishabh Gupta,Can Li
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注: 50 pages, 6 figures, 13 tables
Abstract:Low-fidelity (LF) optimization models are widely used in large-scale operations because they can be solved efficiently and reliably, but they often exhibit systematic errors relative to high-fidelity (HF) formulations that better capture physical and operational constraints. Existing learning-based approaches typically rely on supervised or semi-supervised training with HF solutions, which are expensive to compute and may not align with HF operational objectives. We propose a self-supervised framework that parameterizes LF optimization models directly from HF feedback. The low-fidelity problem is embedded as a differentiable optimization layer and trained end-to-end, while a HF correction problem evaluates its solution and provides gradient information through its dual variables, without requiring HF solution labels. We evaluate the framework on optimal power flow, optimal gas flow, and pooling problems, spanning different sources of low- and high-fidelity mismatch. Across all three applications, the self-supervised approach improves objective quality and feasibility over untuned LF models and generally outperforms semi-supervised training. The learned parameterizations also generalize to unseen operating conditions and, for network applications with up to 30,000 nodes, to systems not encountered during training.
[LG-124] Depth Separation for Neural Networks under Gaussian Measure
链接: https://arxiv.org/abs/2610.28800
作者: Cynthia Bortolotto,Francisco Ganacim,Roberto I. Oliveira
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:We revisit the problem of depth separation for neural networks, as studied by Eldan and Shamir, Daniely, and others. Specifically, we consider the natural setting of L^2 approximation of radial functions under the standard Gaussian measure in d dimensions. We show that there is a radial function in L^2 under the standard Gaussian measure that can be approximated to a fixed sufficiently small L^2 error by a depth-3 ReLU neural network of polynomial size in d but can only be approximated by depth-2 networks with subexponentially bounded weights if their size is at least exponential in d . For that, we prove a general result showing that if a radial function has substantial mass in the high frequencies of its Hermite expansion, then it cannot be well approximated by a depth-2 neural network with small width and bounded weights. All theorems, lemmas, propositions, and corollaries are verified in Lean~4.
[LG-125] Understanding the Inner Workings of a State-Space Foundation Model for Limit Order Books
链接: https://arxiv.org/abs/2610.28753
作者: Valentin Mohl,Alexandre Bismuth,Aramis Marti-Shahandeh,George Nigmatulin,Kang Li,Stefan Zohren,Jakob Foerster,Mihai Cucuringu,Anisoara Calinescu
类目: Trading and Market Microstructure (q-fin.TR); Machine Learning (cs.LG)
*备注: Accepted at the 7th ACM International Conference on AI in Finance (ICAIF '26), November 14-17, 2026, Milan, Italy
Abstract:Autoregressive foundation models offer state-of-the-art capabilities to simulate the market by generating the message flow of limit order books (LOBs). Yet, without understanding their inner workings, research directions are hard to narrow and practical use in real-world trading environments remains uncertain. This study therefore asks what these models actually learn about market microstructure. We find that, similarly to games where sequence models build an internal representation of the board, an 8.2M-parameter Mamba-3 state-space model trained autoregressively on the tokenized message stream of eight large-cap US equities learns an internal representation of the market and its dynamics. In particular, order-flow imbalance, a central driver of short-horizon price moves, is decodable at R^2 \approx 0.59 , far above controls and baselines. This representation extends to unseen stocks, suggesting that these models learn general representations rather than ticker-specific patterns. The model also generalizes across metrics beyond order-flow imbalance: results hold across traded volume and volatility. Additionally, the representation is causally steerable: pushing the state along a feature’s axis shifts the model’s next-message beliefs as that feature implies, monotonically in dose, consistent with the emergence of a world model.
[LG-126] Borrowing Strength Across Stores: Transfer Learning for Choice-Based Revenue Management
链接: https://arxiv.org/abs/2610.28708
作者: Recep Yusuf Bekci,Stefanus Jasin,Joline Uichanco
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Large retail chains must tailor assortment and pricing decisions to local customer preferences, yet even retailers with extensive sales records often have limited data for estimating demand at the store or local market level, particularly in product categories with short selling windows. Local estimation (fitting a demand model for each store on its own) can therefore be unreliable, while pooling data across stores improves estimation precision at the cost of ignoring local demand heterogeneity. We study how retailers can balance these competing effects through transfer learning under a multinomial logit (MNL) demand model. We propose a shrinkage estimator that combines store-specific demand estimates with information pooled from other stores. Unlike the linear or quadratic settings underlying much of the existing theoretical literature on information transfer in operations, the nonlinear MNL likelihood does not admit a straightforward bias-variance decomposition. By exploiting its self-concordant-like properties, we establish finite-sample performance guarantees that characterize when information transfer improves demand estimation and how the appropriate degree of shrinkage depends on local sample size, feature dimensionality, and cross-store heterogeneity. We further derive regret bounds for downstream assortment and pricing decisions. Numerical experiments demonstrate the operational benefits of shrinkage across different data environments. An empirical study using data from several hundred stores of a European apparel retailer shows that shrinkage improves out-of-sample product prioritization and revenue capture relative to both local estimation and full pooling. Our findings demonstrate how retailers can leverage information across their store networks to improve revenue management decisions while preserving local demand differences.
[LG-127] Solar System Surface Technosignatures: Searching for Signs of Artificial Design on Extraterrestrial Terrain
链接: https://arxiv.org/abs/2610.28687
作者: Ana Henrique,Sofia Sheikh,Benjamin Fields
类目: Instrumentation and Methods for Astrophysics (astro-ph.IM); Earth and Planetary Astrophysics (astro-ph.EP); Machine Learning (cs.LG)
*备注:
Abstract:Artificial radio emissions have historically been a major focus of technosignature searches. Here, we apply an anthropological lens to expand this scope to surface technosignatures: non-random, intentional patterning on planetary terrain, similar to long-lasting human-made landscapes and monuments on Earth. On geologically inactive bodies such as the Moon and Mars, artificial traces and patterns could potentially remain visible for hundreds of millions of years. We discuss two recent developments that make this search timely: high-resolution imaging datasets, such as those produced by the HiRISE camera on the Mars Reconnaissance Orbiter (MRO), and ML-based anomaly-detection methods. Finally, we list two possible approaches to developing a model capable of searching for these surface technosignatures: a supervised and an unsupervised one. Complemented by LiDAR, ground-penetrating radar, and reflectance spectroscopy, these methods would enable the first quantitative upper limits on surface technosignatures while yielding ancillary benefits for planetary science.
[LG-128] Machine-learned spectral PDE solvers for fluids and plasmas
链接: https://arxiv.org/abs/2610.27317
作者: Mellow Baixuan Chen,Alan A. Kaptanoglu
类目: Computational Physics (physics.comp-ph); Machine Learning (cs.LG); Plasma Physics (physics.plasm-ph)
*备注:
Abstract:Sparse regression methods for discovering governing partial differential equations (PDEs) from data are sensitive to observation noise and are not constrained to reproduce the data under forward integration. We address both limitations by performing weak-form discovery in the spatial discretization in which the PDE is solved, learned by differentiating through multistep rollouts anchored at the observed states. The new method, Weak-form Anchored Rollout for PDEs (WARP), produces a machine-learned PDE solver. In this work, we focus on systems amenable to a spectral Galerkin solver. Across eight benchmark fluid and plasma systems, including high-dimensional turbulent simulations, WARP produces numerically stable rollouts from new random initial conditions and substantially improves coefficient recovery, rollout accuracy, and statistical measures compared with a single-step weak-form SINDy baseline.
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