本篇博文主要内容为 2026-07-30 从Arxiv.org论文网站获取的最新论文列表，自动更新，按照NLP、CV、ML、AI、IR、MA六个大方向区分。
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目录
概览 (2026-07-30)
今日共更新575篇论文,其中:
- 自然语言处理共76篇(Computation and Language (cs.CL))
- 人工智能共157篇(Artificial Intelligence (cs.AI))
- 计算机视觉共97篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共153篇(Machine Learning (cs.LG))
- 多智能体系统共8篇(Multiagent Systems (cs.MA))
- 信息检索共29篇(Information Retrieval (cs.IR))
- 人机交互共31篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork
【速读】：该论文旨在解决自主智能体在面对未知且多样化的合作伙伴时，如何实现高效协作的问题。现有自适应团队协作（Ad-hoc Teamwork, AHT）方法通常假设任务固定、合作方能力已知，但现实中合作伙伴的真实能力往往隐藏，且人类合作者在具有多种可行策略的任务中可能表现出非最优行为。为克服这一局限，本文将AHT扩展至多任务场景，将其重构为在隐藏合作方能力条件下进行联合规划与去中心化执行的问题。其核心解决方案是提出一种基于上下文模型的能力估计方法——CE-CM（Capability Estimation via Contextual Models），该方法采用近似贝叶斯推理，从任务无关的角度学习能力向量，并通过基于仿真的采样机制在线估计能力，构建上下文相关的多智能体马尔可夫决策过程（Contextual Multi-agent Markov Decision Process）以支持规划。该方法无需群体预训练，仅需少量任务即可在线更新信念。为进一步应对人类行为的不可预测性，论文进一步提出CE-CM-Div，通过对比多个多样化规划轨迹来评估能力假设，而非依赖单一最优路径。仿真实验表明，CE-CM能快速恢复隐藏能力，减少无效动作分配并动态适应变化；在包含15名参与者共225条轨迹的离线人类实验中，CE-CM-Div显著优于基线方法。研究结果表明，基于能力的建模是一种在所研究场景下具有高可解释性与任务无关性的有效表征方式，强调了在人机协同中考虑行为多样性对于提升系统鲁棒性的关键作用。
链接: https://arxiv.org/abs/2607.27177
作者: Peter Tisnikar,Maja Swieczkowska,Benteng Ma,Gerard Canal,Matteo Leonetti
机构: King’s College London (伦敦国王学院); UK Research and Innovation (英国研究与创新署); UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence (英国研究与创新署安全可信人工智能博士培训中心)
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)
备注: 44 pages, 18 figures, submitted
Abstract:Effective collaboration with novel and diverse partners is a crucial skill for autonomous agents. Most current ad-hoc teamwork (AHT) approaches assume that agents will collaborate on a single, fixed task and that the partner’s capabilities, their ability to successfully execute the desired action, are already known. In reality, a partner’s true capabilities are often hidden, and human collaborators may act sub-optimally on tasks with multiple valid strategies. To address these limitations, we extend ad-hoc teamwork into a multi-task setting by re-framing it as a problem of joint planning with decentralised execution under hidden partner capabilities. We introduce CE-CM (Capability Estimation via Contextual Models), an approximate Bayesian method that infers task-invariant capability vectors. By using simulation-based sampling, the agent estimates capabilities and induces a contextual Multi-agent Markov Decision Processes for planning. This approach requires no population pre-training and refines its beliefs online from just a few tasks. To account for human unpredictability, we propose CE-CM-Div, an extension that evaluates capability hypotheses against diverse planner rollouts rather than a single optimal trajectory. Simulated experiments demonstrate that CE-CM rapidly recovers hidden capabilities, reduces infeasible action assignments, and adapts to changes over time. Furthermore, in an offline human study of 225 trajectories from 15 participants, CE-CM-Div substantially improved capability estimates over the baseline CE-CM method. Our results suggest capability-based modelling is a promising interpretable, task-agnostic representation in the studied settings, demonstrating that accounting for behavioural diversity is essential for robust human-AI teaming.
[MA-1] Cross-organisational Process Mining from Message Logs
【速读】：该论文旨在解决在缺乏各组织独立事件日志（event logs）的情况下，如何基于跨组织通信过程中的消息交换记录（message logs）重构可用于系统合成的因果行为描述问题。其核心挑战在于：当无法获取来自不同组织的原始操作事件日志时，如何从仅包含消息传递行为（输出与输入事件）的通信日志中提取出保持消息事件间因果关系的等效事件日志。解决方案的关键在于提出一种将消息日志转换为满足因果顺序约束的事件日志的方法，从而使得后续可沿用先前研究中针对单个业务流程建模的发现算法（如基于企业网的建模方法），实现对异步交互流程集合（即行业网，Industry nets）的自动合成。这一转换机制确保了消息交换的时序与依赖关系在生成的事件日志中得以保留，为分布式业务流程的协同建模与形式化合成提供了可行路径。
链接: https://arxiv.org/abs/2607.26917
作者: Pieter Kwantes,Jetty Kleijn
机构: 未知
类目: Multiagent Systems (cs.MA)
备注: This paper is the full version of an extended abstract with the same title, presented at the third International workshop on Collaboration Mining for Distributed Systems (COMINDS 2024) held in conjunction with ICPM 2024 in Copenhagen
Abstract:Enterprise nets and Industry nets are specific Petri net models that together form a framework for the modelling of collaborating business processes and the specification of interactions between them. In this set-up, individual business process are modelled as Enterprise nets, while Industry nets consist of Enterprise nets that interact by exchanging messages through unidirectional channels. In previous contributions (see [21,22]), it was established how to leverage algorithms for the discovery of single processes (modelled as Enterprise nets) from event logs (consisting of finite sequences of executed actions), to synthesise systems consisting of asynchronously communicating processes (modelled as Industry nets). However, in general, it may be difficult to work with event logs from different organisations or even get access to them. Recordings of message exchanges between processes may be easier this http URL, we focus in this paper on the communications between different processes and turn to the investigation of message logs, \ie collections of recordings of communications in the form of message exchanges (output and input events) between two processes. In particular, the question is addressed of how a message log can be transformed into a description of the communication behaviour in the form of an event log such that the causal order on message events is preserved. Following the approach from [21,22], this event log can then be used to synthesis an Industry net.
[MA-2] DREvo: Distilling Recalibrated Historical Experience for Harness Self-Evolution
【速读】：该论文旨在解决大语言模型智能体（LLM agent）中“执行框架”（harness）自演化过程中因历史经验利用不充分而导致的性能波动与搜索效率低下的问题。现有方法在迭代优化框架时，未能动态评估历史经验对当前框架的有效性，也缺乏将有效经验转化为可操作演化方向的明确机制，导致在有限演化预算下难以稳定发现高性能框架。为此，论文提出一种名为DREvo的新方法，其核心创新在于：通过函数级证据锚定（function-level evidence anchoring）确保经验关联性、基于状态的证据再校准（state-dependent evidence recalibration）实现历史经验的动态有效性评估，以及角色条件下的搜索意图蒸馏（role-conditioned search intent distillation）将有效经验转化为精准的演化方向。该方案显著提升了演化过程的稳定性与搜索效率，在五个基准测试中均取得最高准确率，并在领域推理和代理任务上分别较基线平均提升16.2%和14.2%。
链接: https://arxiv.org/abs/2607.26722
作者: Hanghui Guo,Weijie Shi,Zhangze Chen,Shengxiang Xu,Yishu Wang,Yimei Zhang,Wangze Ni,Jia Zhu,Shimin Di
机构: 未知
类目: Multiagent Systems (cs.MA); Machine Learning (cs.LG)
备注: 9 pages
Abstract:Harness plays a critical role in large language model agent performance, and building a high-performing harness requires substantial expert effort. Therefore, recent research has increasingly explored harness self-evolution, which iteratively proposes, evaluates, and improves harnesses using historical trial experience. However, accumulated historical experience does not always translate into stable search guidance, and performance often fluctuates substantially across evolution iterations, making it difficult to reliably discover high-performing harnesses under a limited evolution budget. We identify two limitations in how existing harness self-evolution methods leverage historical experience: (1) Lack of dynamic reassessment of whether historical experience remains valid for the current harness, and (2) Lack of explicit mechanisms for translating valid historical experience into actionable search directions. To address these limitations, we propose a new harness self-evolution method, named DREvo, which integrates function-level evidence anchoring, state-dependent evidence recalibration, and role-conditioned search intent distillation to determine which historical evidence remains valid and where the harness should evolve next. Under limited evolution budgets, DREvo exhibits smoother evolution trajectories, achieves the highest accuracy on all five benchmarks, and delivers average gains of 16.2% and 14.2% over the evaluated baselines on domain reasoning and agentic tasks, respectively.
[MA-3] Living-Harness Is an Interactive-Agent Evolver
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）智能体在长期交互中因静态引导框架（harness）无法随经验演化而导致的重复失败问题。现有方法虽能实现单次任务内的容错与恢复，但缺乏对历史执行轨迹的持续学习能力，导致相同错误在后续任务中反复出现。其解决方案的关键在于提出一种自演化引导框架——Living-Harness，该框架通过将每个完成的交互轨迹及其评估信号转化为后验证据，驱动对引导框架的有界更新。在领域级进化标准操作流程（Evolution-SOP）的指导下，Living-Harness提取任务抽象与结构化更新证据，生成两种互补的程序性知识：情景记忆（记录触发条件、失败模式与恢复动作）和状态图（记录状态节点、修复边与转移规则）。更新后的引导状态被用于指导未来交互，而工具与基础上下文保持冻结，从而实现跨演化周期的程序性修复累积。实验表明，在基于τ²-Bench和MultiWOZ-2.4构建的八个交互环境中，Living-Harness相较于最强基线分别提升了10.07和9.91个百分点的Pass@1指标，并支持在不同模型主干上仅通过检索方式复用演化后的引导状态，展现出良好的泛化与可迁移性。
链接: https://arxiv.org/abs/2607.26598
作者: Yuetian Du,Yucheng Wang,He Xu,Jiexu Xu,Shanwen Tan,Bing Zhao,Boyu Yang,Zhijie Xu,Ming Kong,Hu Wei,Jie Liu,Qiang Zhu
机构: 1. Tsinghua University (清华大学); 2. Alibaba Group (阿里巴巴集团); 3. Peking University (北京大学); 4. Shanghai Jiao Tong University (上海交通大学); 5. Zhejiang University (浙江大学)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Large language model (LLM) agents may recover from a failure within an episode or after a retry, yet the same execution failure can recur in later tasks because post-episode feedback rarely revises the persistent harness that guides future interactions. Static harnesses improve reliability through fixed tools, context, memory, and workflow structures, but remain unchanged after deployment. We propose \textbfLiving-Harness , a self-evolving agent harness that converts each completed trajectory and its evaluator signals into posterior evidence for bounded harness updates. Guided by a domain-level \textbfEvolution-SOP ( \textbfS tandard \textbfO perating \textbfP rocedure), Living-Harness extracts an episode abstraction and structured update evidence, and writes two complementary forms of procedural knowledge: episodic memory that records trigger conditions, failure patterns, and recovery actions, and a state graph that records state nodes, repair edges, and transition rules. The updated harness state is retrieved to guide future interactions, while tools and base context remain frozen, allowing procedural repairs to accumulate across evolution cycles. On eight interactive environments derived from \tau^2 -Bench and MultiWOZ-2.4, Living-Harness improves average Pass@1 over the strongest interactive baseline by 10.07 and 9.91 percentage points, respectively, and supports retrieval-only reuse of the evolved harness state across model backbones.
[MA-4] One Run Is Not an Idea: The Implementation Lottery in Automated Research
【速读】：该论文旨在解决自动化研究系统中因单一实验运行所导致的“实现随机性”（implementation lottery）问题，即基于单次实现的得分来推断抽象机制（idea-level）的有效性，容易造成错误结论。其核心问题是：一次实验仅反映某个想法的一种具体实现方式，而将该实现层面的得分作为对思想本身可靠性的证据，会引入结构性偏差，从而影响研究决策的准确性。解决方案的关键在于提出“思想可靠性审计”（Idea Reliability Audit），通过冻结候选思想卡片、在新会话中采样多个独立实现、使用盲结果的保真度标签，并对保存的成果进行重运行，以评估思想的稳定性。该方法量化了思想层面的信度（Idea ICC）并检测留一实现排除（LOO）下的胜者反转现象。实验结果显示，在13个表格任务和两个编码代理设置中，实现层面的方差分别超过同一成果重跑方差的5倍和10倍，且约25.6%与43.6%的决策在不同实现间出现胜者反转。即使经过卡级过滤，该反转仍存在。此外，对回归工作流的诊断也表明实现差异主导了性能分解。研究强调，思想可靠性应独立于“N次尝试中的最优结果”（best-of-N artifact utility），在依据得分进行思想分支、迁移或研究记忆前，必须覆盖多种实现以确保结论稳健。
链接: https://arxiv.org/abs/2607.26587
作者: Jingjie Ning,Shanshan Zhong,Xiaochuan Li,Ji Zeng,Chenyan Xiong
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注:
Abstract:Automated research systems use experimental scores both to deliver artifacts and to decide which ideas to retain, transfer, and pursue. Yet one run scores one implementation of an idea. Crediting that realization-level score as evidence about the parent mechanism creates the \emphimplementation lottery, in which an idea-level conclusion depends on which plausible implementation was sampled. The mismatch is structural whenever one run updates beliefs about a mechanism. We estimate its magnitude. The \emphIdea Reliability Audit measures \emphidea reliability by validating and freezing candidate cards, sampling fresh-session implementations, using outcome-blind fidelity labels, and rerunning saved artifacts. It reports idea ICC and leave-one-implementation-out (LOO) winner reversal. Prior work generally repeats the task; we repeat the idea. Across 312 assignments on 13 tabular tasks and two coding-agent setups, implementation variance was more than five and ten times same-artifact rerun variance, respectively, and the winner from one implementation draw differed from the winner under the other-two mean in 25.6% and 43.6% of decisions. Reversal survives card-level filtering under two outcome-blind review rules. An exploratory diagnostic on three materials-regression workflows with a deterministic evaluator also finds implementation variation dominating the decomposition. These findings distinguish idea reliability from best-of- N artifact utility. Before a score guides idea-level branching, transfer, or research memory, evidence should cover multiple implementations.
[MA-5] A Persona-based Rate Action Index
【速读】：该论文旨在解决如何准确预测美国联邦公开市场委员会（FOMC）在货币政策会议中决定加息、维持或降息的问题，核心挑战在于捕捉委员们随时间变化的政策立场及其对当前市场条件的动态响应。其解决方案的关键在于构建一个基于“数字人格”（digital personas）的新型指标体系：通过采集近25,000个可检索的公开数据片段，将每位委员的历史言论与行为模式建模为独立的“人格体”（persona），并利用生成式系统构建每个个体的语义表征。这些人格体不仅在可识别性（identifiability）和可检测性（detectability）上表现出显著优于随机水平的能力（平均成员条件召回率达基准的8倍，检测相关性τ̂_det = 0.23，高于0.15的基线），更重要的是，其查询条件下的表示能够有效反映委员在鹰派—鸽派声誉排序中的相对位置（Kendall’s τ = 0.63，p < 0.001），显著优于仅依赖检索的基线方法。最终，基于这些动态演变的人格体表征所构建的“人格基础利率行动指数”（persona-based rate action index）在2022–2025年间成功追踪了利率周期变化（Kendall’s τ = 0.68，p < 10⁻⁶），并实现了非平凡的会议决策预测准确率（0.69，高于0.47的基线），且具备约三个季度的领先性。这一成果首次展示了通过一组数字人格捕捉群体动态行为的可行性，为货币政策预测提供了全新的范式。
链接: https://arxiv.org/abs/2607.26545
作者: Hayden Helm,Andrew Dassori
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:We propose an index for predicting the U.S.\ Federal Open Market Committee (FOMC) decision to hike/hold/cut the current federal funds target rate based on how a collection of personas responds to current market conditions. To construct the index, we collected a new dataset consisting of nearly 25,000 retrievable chunks from publicly available data. We partition the data into per-member corpora and use each as the retrieval database of a generative system we refer to throughout as a ``persona’‘. We first evaluate the personas across two complementary components of likeness: identifiability and detectability. Each persona’s behavior is highly attributable (average member-conditional recall is 8\times chance) and generated content is nearly indistinguishable from held-out real content ( \hat\tau_\mathrmdet = 0.23 against a 0.15 floor). We then present evidence that query-conditioned representations of the personas capture members’ monetary-policy stance relative to a known hawk–dove reputational ordering (Kendall’s \tau = 0.63 , p 0.001 ), substantially outperforming retrieval-only representations. These representations vary with time and current market conditions and form the basis of our proposed persona-based rate action index. For the 2022 – 2025 period the index tracks the rate cycle (Kendall’s \tau = 0.68 , p 10^-6 ) and can be used to construct a simple classifier that predicts per-meeting outcomes at non-trivial accuracy ( 0.69 versus a 0.47 base rate). Importantly, the index outperforms informative baselines and leads the federal funds target rate by roughly three quarters. As far as we are aware, our results are the first to demonstrate the ability to capture time-varying group behavior via a collection of digital personas.
[MA-6] Pramana: A Composable Domain-Specific Backend for Empirical Networking Research
【速读】：该论文旨在解决网络研究中“从构思到数据生成”的滞后问题，即在生成可验证的实证数据前，研究人员需耗费大量时间重复搭建实验环境，严重制约了研究效率。随着生成式 AI（Generative AI）在科研中的应用加速，这一瓶颈将进一步加剧——尽管智能体辅助的假设生成速度指数级提升，但缺乏高效、通用的数据生成后端来验证其输出。论文提出的关键解决方案是构建一个可组合的领域专用后端系统——Pramana，其核心设计为“细腰”架构（thin waist），通过单一统一的“意图规范”（intent specification）将实验分解为三个独立维度：意图（要生成何种数据）、基底（在何处生成）、机制（如何生成），从而实现同一规范在不同执行底座上的通用性。该方法显著提升了实验复用性与跨平台兼容性，论文通过构建首个涵盖255个数据生成意图的语料库（源自66篇已发表论文）验证了其有效性，证明现有工具仅能满足13%的意图，而当前Pramana原型已满足34%，远超现有最佳水平，并为未来通过社区协作完善该系统、加速网络实证研究提供了可行路径。
链接: https://arxiv.org/abs/2607.26352
作者: Jaber Daneshamooz,Eugene Vuong,Alagappan Ramanathan,Manni Moghimi,Haarika Manda,Satyam Kumar,Snithik Thode,Satyandra Guthula,Sylee Beltiukov,Dongsu Han,Tarun Mangla,Sangeetha Abdu Jyothi,Walter Willinger,Arpit Gupta
机构: UC Santa Barbara(加州大学圣塔芭芭拉分校); University of California, Irvine(加州大学欧文分校); IIT Delhi(印度理工学院德里分校); KAIST(韩国科学技术院); Northwestern University(西北大学)
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Networking research advances by turning hypotheses into empirical evidence, so accelerating it means reducing the lag between ideation (synthesizing a hypothesis) and generating the data that tests it. Consider a concrete case: does a bulk BBR download fairly share its bottleneck with competing real-time Google Meet traffic? Validating this requires configuring a realistic bottleneck link, concurrently generating BBR’s bulk transfer and Meet’s real-time traffic, and collecting relevant service-quality metrics. Today this overhead is high, often forcing researchers to start from scratch for every new idea. This ideation-to-data-generation gap will only worsen in the agentic AI era, where AI-assisted ideation accelerates exponentially, yet its outputs cannot be validated without a data-generation backend. This paper explores how to bridge this gap. We envision a composable, domain-specific backend, Pramana, shaped as a thin waist, with diverse research intents at the top and disparate execution substrates at the bottom. Pramana realizes this waist through a single contract, the intent specification, which disaggregates an experiment into three independent axes: the intent (what data to generate), the substrate (where to generate it), and the mechanism (how to produce it), so one specification runs on any substrate. We demonstrate Pramana’s utility by building a first-of-its-kind corpus of 255 data-generation intents mined from 66 published papers, and show the intent specification satisfies all of them, where no existing tool satisfies more than 13%. Our current proof-of-concept implementation already satisfies 34% of these intents, more than twice the best existing tool, and we lay out a roadmap for closing this abstraction-implementation gap through a broader community effort to build the envisioned data-generation backend and accelerate empirical networking research. Subjects: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) Cite as: arXiv:2607.26352 [cs.NI] (or arXiv:2607.26352v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2607.26352 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[MA-7] Learning Implicit Causal World Models from Multi-Agent Demonstrations
【速读】：该论文旨在解决模型基于强化学习（model-based reinforcement learning）中世界模型（world models）在训练过程中将统计相关性误认为因果机制的问题，尤其在多智能体系统中，物理状态转移与智能体策略意图高度耦合，导致世界模型在分布外（distribution shift）场景下性能显著下降。其解决方案的关键在于提出隐式因果世界模型（Implicit Causal World Models），通过引入策略方差（policy variance）来激活序列后门条件（sequential backdoor condition），从而在无需预定义因果图的情况下，从离线示范数据中自动识别出可解释的因果动态结构。实验结果表明，该方法在多种协作任务（如Two-Door、Navigation、Giveway）中均能生成具有可解释性的因果表示，并且模型精度随干预强度的增强而线性提升，有效提升了世界模型在部分可观测和完全可观测环境下的泛化能力。
链接: https://arxiv.org/abs/2607.26336
作者: Jasorsi Ghosh
机构: 未知
类目: Machine Learning (cs.LG); Multiagent Systems (cs.MA); Robotics (cs.RO)
备注: Preprint
Abstract:In model-based reinforcement learning, world models exist as internal simulators, but their training often conflates statistical correlations with causal mechanisms. This problem is exacerbated in multi-agent systems where physical transitions are intertwined with strategic agent intents, causing world models to fail under distribution shift. We introduce Implicit Causal World Models to recover environmental dynamics from offline demonstrations without requiring pre-defined causal graphs. By incorporating policy variance, we render world models discoverable via the sequential backdoor condition. Evaluations across coordination tasks (Two-Door, Navigation, and Giveway) demonstrate that these models provide interpretable causal representations under both full and partial observability, with model accuracy scaling directly with interventional strength.
自然语言处理
[NLP-0] Mental World Modeling
【速读】： 该论文旨在解决现有世界模型（World Models）在预测人类行为时的根本性局限问题：当前模型仅关注物理场景的客观状态（如物体位置、运动轨迹等），而忽视了驱动人类行为的隐含心理状态（mental state），包括个体信念、欲望、意图、情感及社会规范认知。这种忽略导致即使对物理环境理解准确，仍可能错误预测人类行动。其解决方案的关键在于提出“心智世界建模”（Mental World Modeling, MWM）这一通用理论框架，将心理变量作为世界模型的核心组成部分，而非事后解释的附加项。MWM通过维护一个耦合的物理-心理状态空间，生成目标特定的部分观测，并模拟候选动作如何共同更新物理与心理状态；同时支持可解释的分支级价值评估。研究以无训练、全可解释的基准系统MENTIS实现该框架，实验证明在多模态情境决策数据集上，显式建模心理状态对准确预测人类决策至关重要。进一步分析揭示了当前心智世界建模的技术瓶颈，预示着未来世界模型的发展方向应从模拟物理场景转向模拟在其中行动的“心智”。
链接: https://arxiv.org/abs/2607.27201
作者: Hao Fei,Yiran Zhao
机构: 未知
类目: Computation and Language (cs.CL)
备注: project website: this https URL
Abstract:World models enable a predictive substrate for planning and action, yet existing formulations merely answer a physical question: what/where it is, and how will it evolve. Human behavior, however, is driven by hidden mental state (what a person believes, wants, intends, feels, and considers socially permissible), so a model that tracks the physical scene but not what each agent knows and believes about it predicts the wrong action for the right-looking scene. We formulate Mental World Modeling (MWM), a generic theoretical framework that makes mental variables core components of a world model rather than posthoc rationales: MWM aintains a coupled physical-mental world state, renders a target-specific partial observation, and simulates how candidate actions jointly update both components. We instantiate the framework in MENTIS, a training-free and fully inspectable baseline that decomposes the process into state parsing, target-observation generation, action decomposition, coupled physical and mental transition, and branch-level value evaluation. On a manually constructed, quality-controlled dataset of situated decision scenarios spanning text, image, and sounding-video stories, experiments with 8 modern LLM-based world models demonstrate that explicitly modeling the mental state is essential for predicting human decisions. Deeper analyses further expose the bottlenecks of current mental world modeling. We expect MWM as a next stage of world modeling, from simulating physical scenes to simulating the minds that act in them.
[NLP-1] Pangram 4 Technical Report
【速读】： 该论文旨在解决生成式 AI 生成文本的检测难题，尤其针对细粒度编辑、混合型人机协作文本以及对抗性攻击下的鲁棒性检测问题。其解决方案的关键在于引入了更先进的深度学习架构与训练策略，显著提升了模型在跨分布场景下的泛化能力，并优化了对文本中细微修改痕迹及人机交互模式的识别精度。通过在多个标准基准测试上实现卓越性能（如 AUROC 达到 0.9916，误报率仅 0.0041%，漏报率 0.3396%），Pangram 4 在生成式 AI 文本检测任务中展现出当前最优的表现，特别是在边界检测和交错式 AI 协助内容识别方面具有突破性进展。
链接: https://arxiv.org/abs/2607.27183
作者: Ben Glickenhaus,Katherine Thai,Jenna Russell,Elyas Masrour,Yue Han,Max Spero,Bradley Emi
机构: Pangram Labs(潘格拉姆实验室); University of Maryland(马里兰大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:We present Pangram 4, the latest deep-learning-based AI-text classification model from Pangram Labs. We achieve an AUROC of 0.9916 with a false positive rate of 0.0041% and a false negative rate of 0.3396%. In addition to its increased overall accuracy compared with Pangram 3, Pangram 4 exhibits superior out-of-distribution generalization and robustness to adversarial attacks. Another novel contribution of Pangram 4 is its improved ability to distinguish fine-grained edits and mixed AI-human co-authored text. We demonstrate improvements to both boundary detection tasks and the detection of interleaved AI assistance. Finally, we report metrics on standard AI detection benchmarks showing that Pangram 4 achieves state-of-the-art performance on the AI text detection task across a wide variety of settings and domains.
[NLP-2] SpecFirst: Behavioral Specification Elicitation as a First-Class Step in Agent -Based Program Synthesis from Scratch
【速读】： 该论文旨在解决生成式AI在无代码上下文条件下从零开始构建程序的难题，尤其针对当前主流大语言模型（LLM）在仅依赖自然语言文档和仅可执行二进制文件作为行为验证器（behavioral oracle）的情况下，对程序构造任务的性能极低的问题。现有方法将文档理解、行为探索与代码生成合并为单一推理流程，导致代理在探索过程中覆盖不足、上下文漂移引发行为意图丢失，并将早期误解持续传递至最终实现，严重影响生成质量。为此，论文提出SpecFirst——一种两阶段框架，其核心创新在于将行为规范获取（behavioral specification elicitation）作为独立且前置的首要阶段。该框架首先由专用的规范代理（spec agent）对二进制文件进行系统性探查，并结合文档信息生成结构化的行为规范；随后，代码合成代理基于此稳定、明确的规范开展编码工作。这一分解策略有效消解了文档歧义，为后续代码生成提供了统一且稳定的参考基准。在ProgramBench全部200个实例上的实验表明，SpecFirst在四个不同模型上均显著优于单循环基线，测试通过率提升6.9%–21.3%，二进制探索覆盖率提升9.4%–18.5%，且结果具有统计显著性。进一步的行为分析显示，先验规范促使代码构建更早启动并保持持续性。研究结果证明，将需求工程（requirements engineering）中的显式规范获取阶段引入程序生成流程，是实现从零构建高质量程序的有效范式。
链接: https://arxiv.org/abs/2607.27167
作者: Yihao Chen,Shi Chang,Feng Lin,Khaled Chawa,Boyuan Chen,Shaowei Wang,Ahmed E. Hassan
机构: University of Waterloo (Waterloo大学); University of Alberta (阿尔伯塔大学); McMaster University (麦克马斯特大学); University of Ottawa (渥太华大学)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注:
Abstract:LLM-based agents excel at software engineering tasks where an existing codebase provides context, but constructing a program from scratch remains fundamentally harder. Recent benchmarks such as ProgramBench quantify this gap: given only natural-language documentation and an execute-only binary as a behavioral oracle, even frontier models solve fewer than 1% of instances. Existing frameworks conflate documentation reading, behavioral exploration, and code synthesis into a single pass, causing agents to probe insufficiently, lose behavioral intent as context drifts, and propagate early misinterpretations into the final implementation. Inspired by classical requirements engineering, we argue that behavioral specification elicitation should be a first-class phase that precedes implementation. We present SpecFirst, a two-stage framework that forces the specification elicitation before code synthesis. A dedicated spec agent first probes the binary and combines observations with documentation into a structured specification. Next, a code synthesis agent then uses this specification to drive implementation. This decomposition resolves documentation ambiguities before coding begins and provides a stable behavioral reference throughout synthesis. We evaluate SpecFirst on all 200 ProgramBench instances across four models spanning two families and an order of magnitude of capability. SpecFirst consistently outperforms the single-loop baseline, improving test pass rates by 6.9%-21.3% and binary exploration coverage by 9.4%-18.5%, all statistically significant. Behavioral analysis on code synthesis further shows that a prior specification enables earlier and more sustained code construction. Our results demonstrate that an explicit requirements-engineering phase is an effective paradigm for from-scratch program construction.
[NLP-3] MindForge: Teaching Small Language Models Whole-Life-Cycle Software Engineering via Source-Free Program Synthesis
【速读】： 该论文旨在解决从零构建完整程序（from-scratch program construction）这一关键挑战，尤其是在现有基准测试（如ProgramBench）中，前沿模型的完成率不足1%。其核心问题在于缺乏能够覆盖整个软件工程生命周期、支持可扩展训练的环境构建框架——当前大多数环境构造方法仅聚焦于开发流程中的单一阶段。为应对这一瓶颈，论文提出MindForge，一个自动化流水线，可将开源命令行程序转化为源码不可见的训练环境，仅暴露编译后的参考可执行文件及其文档。基于此，研究者构建了与ProgramBench无关的训练数据集，并利用GLM-5.2作为教师代理生成高质量的程序合成轨迹。通过在这些轨迹上微调Qwen3.6-27B模型，其在ProgramBench上的平均通过率从37.98%提升至49.51%，达到与更大规模前沿模型相当的性能。更重要的是，该微调模型在七个未见过的软件工程基准上均显著优于基线模型，涵盖长周期代码库生成、跨语言迁移、缺陷修复、功能实现及多语言问题求解等任务，绝对性能提升达31.00点（RepoZero-C2Rust）、14.16点（DeepSWE）等，验证了所提解决方案在泛化能力与实际应用价值上的有效性。
链接: https://arxiv.org/abs/2607.27146
作者: Yihao Chen,Shi Chang,Khaled Chawa,Feng Lin,Boyuan Chen,Shaowei Wang,Ahmed E. Hassan
机构: University of Waterloo (Waterloo大学); University of Alberta (阿尔伯塔大学); Shanghai Jiao Tong University (上海交通大学)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Coding agents have made substantial progress on software engineering tasks that modify existing codebases, including bug fixing and feature implementation. However, constructing a complete program from scratch remains a major challenge: even the frontier models evaluated on ProgramBench fully resolve fewer than 1% of tasks. One obstacle is the lack of scalable training environments for this from-scratch setting, spanning the whole software engineering life cycle, as existing environment-construction frameworks focus only on a single phase in software development. To address this gap, we introduce MindForge, an automated pipeline that converts open-source command-line programs into source-free environments that expose only a compiled reference executable and its documentation. Using MindForge, we construct training environments from repositories disjoint from those in ProgramBench, and curate a high-quality data recipe consisting of program synthesis trajectories using GLM-5.2 as the teacher agent. Fine-tuning Qwen3.6-27B on these trajectories increases its ProgramBench average test pass rate from 37.98% to 49.51%, achieving performance comparable to substantially larger frontier models. Moreover, the fine-tuned model consistently improves over the base model across all seven unseen software engineering benchmarks, spanning long-horizon repository generation and translation, bug fixing, feature implementation, and cross-language issue resolution, with absolute gains of 31.00 points on RepoZero-C2Rust, 14.16 on DeepSWE, 10.70/4.56 on NL2Repo-Bench (with/without tests), 5.04 on SWE-bench Verified, 5.93 on SWE-bench Pro, 5.22 on SWE-bench Multilingual, and 4.94 on FeatBench.
[NLP-4] Linguistic Monoculture in LLM -Assisted Language Use
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在写作与沟通中广泛应用所引发的“语言同质化”（linguistic monoculture）问题，即由于广泛依赖共享的预训练模型，导致整体语言表达形式趋于单一，削弱了作者群体的语言多样性。其核心解决方案在于构建一个数学框架，将作者与语言模型均建模为语言特征分布，并通过反复交互实现共演化。关键创新在于分析三种不同交互机制：固定分布的共享模型、基于作者输出递归更新的共享模型，以及结合个体反馈与群体反馈的个性化模型。研究发现，共享模型会促使作者趋同于单一规范；递归反馈虽改变共享规范位置但不减少个体间差异；而个性化模型则能维持多样化的作者-模型均衡态，保留显著的语言多样性。进一步地，论文将“从众”内生化为一种策略选择，权衡清晰性、可读性和流畅性等私有收益与独特风格的代价，揭示出个体理性决策可能导致过度从众，产生负外部性——即个体未承担其独特性对社会整体多样性的价值，从而形成可量化的“同质化代价”。该代价在特定实例下有限，但当独特性超越真实性时可能无限增长。合成模拟验证了三类机制在长期演化中对语言多样性产生的截然不同的影响。
链接: https://arxiv.org/abs/2607.27134
作者: Suhas Thejaswi,Juhi Kulshreshta,Lutz Oettershagen
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Science and Game Theory (cs.GT)
备注:
Abstract:Writing and communication are increasingly mediated by large language models (LLMs) that are being used to draft, revise and polish text. Although such assistance can improve clarity and help authors meet institutional expectations, widespread reliance on shared models may reduce population-level variation in linguistic form, a phenomenon we refer to as linguistic monoculture. We develop a mathematical framework in which authors and LLMs are represented as distributions over linguistic features and coevolve through repeated interaction. We analyze three interaction mechanisms: a shared model with a fixed linguistic distribution, a shared model recursively updated from author outputs, and personalized models updated through author-specific and population-level feedback. We characterize the resulting equilibria and convergence rates, showing that, shared models can drive authors toward a common norm, recursive feedback relocates the shared norm without altering pairwise spread under common conformity, and personalization can preserve a family of distinct author-model equilibria with nonzero linguistic diversity. We then endogenize conformity as a strategic choice trading off private benefits from clarity, legibility, and perceived fluency against distinctive style. Within this utility model, individually rational authors may conform more than is socially optimal because they do not internalize the value their distinctiveness provides to others, creating a negative externality and a price of monoculture that is finite for each fixed instance but can grow without bound when distinctiveness dominates authenticity. Synthetic simulations illustrate how fixed shared assistance, recursive feedback, and personalization produce different long-run diversity outcomes.
[NLP-5] On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在微调过程中因恶意数据提供者注入有害行为而引发的安全对齐问题。现有安全对齐防御方法存在三大局限：易导致专业技能的灾难性遗忘；当防御方无法观测攻击者提示模板时，其有效性急剧下降；即使成功对齐的模型仍可能通过简单的系统提示切换被重新越狱。针对上述挑战，本文提出基于路由的在线策略蒸馏（Routing-based On-Policy Distillation, ROPD），其核心创新在于不依赖特定提示模板的拟合，而是建模对齐输出与受损输出之间的概率分布差异，从而实现更鲁棒的模型重对齐。实验结果表明，在面对提示模板不匹配的情况下，传统基线方法性能显著退化，而ROPD能有效缓解此类风险，在保持模型能力的同时显著提升防御鲁棒性，即便在模板变化下性能损失也远低于现有方法，为构建高鲁棒性的大模型安全对齐框架树立了新标准。
链接: https://arxiv.org/abs/2607.27081
作者: Yongjian Guo,Wanlun Ma,Lingyu Shen,Xi Xiao,Sheng Wen
机构: Tsinghua University (清华大学); Swinburne University of Technology (斯威本科技大学); EPFL (洛桑联邦理工学院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注:
Abstract:Fine-tuning is the dominant paradigm for specializing large language models (LLMs), yet it exposes a critical vulnerability: malicious data providers can embed harmful behaviors into downstream corpora, creating models that retain professional skills while violating human values on demand. Existing safety-realignment defenses often fail in practice due to three key limitations: they frequently cause catastrophic forgetting of specialized skills; their effectiveness collapses when the defender cannot observe the attacker’s prompt template; and successfully realigned models remain susceptible to re-jailbreaking via simple system prompt switches. To address these challenges, we propose Routing-based On-Policy Distillation (ROPD), a novel realignment framework that models the divergence between aligned and compromised output probability distributions rather than fitting specific prompt templates. We conduct extensive experiments comparing ROPD against four state-of-the-art baselines across three datasets and three base models with varying alignment strengths. Our results demonstrate that when baseline defenses face template mismatches, often accompanied by severe degradation in downstream task performance. In contrast, ROPD substantially mitigates template-mismatch risks, maintaining superior robustness in both defense effectiveness and capability preservation. While our analysis indicates ROPD is not entirely immune to template shifts, its performance degradation is negligible compared to existing methods, establishing a new standard for robust LLM realignment.
[NLP-6] Setoka: A Benchmark for Hierarchical User Understanding in Personalized Agents over Heterogeneous Data
【速读】： 该论文旨在解决现有个性化智能体（personalized agents）在用户理解深度上的局限性问题，即当前主流记忆评估基准仅关注从对话历史中显式检索事实信息，而忽视了对用户抽象特征（如行为模式与人格特质）的深层理解能力评估。其核心解决方案是提出Setoka——一个基于认知心理学与人格心理学理论构建的多层次用户理解评估基准，涵盖语义记忆、情景记忆、行为模式和人格特质四个层级的用户理解维度。为实现真实且隐私保护的评估，研究设计了一套基于心理测量学的合成数据生成管道，可规模化生成异构、连贯且多样化的用户数据与查询。通过在10个合成用户上对3个语言模型结合5种记忆系统进行综合评估，结果表明：尽管现有系统在语义记忆检索任务中表现良好，但在情景记忆及更复杂的跨源、碎片化信息整合任务中性能显著下降，尤其在行为模式与人格特质推断方面表现欠佳。这一发现揭示了单纯依赖显式事实检索无法支撑深层次用户理解，亟需设计具备长时行为跨源融合与抽象能力的记忆机制，以推动个性化智能体向真正意义上的深度用户理解演进。
链接: https://arxiv.org/abs/2607.27056
作者: Lingyang Zeng,Guangze Chen,Kaichen Yu,Zhicheng Pan,Siyang Weng,Zirui Hu,Xiangyun Du,Hailin He,Rong Zhang,Chengcheng Yang,Kai Huang,Xuan Zhou
机构: 华东师范大学(华东师范大学); East China Normal University (华东师范大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Personalized agents are increasingly applied to assist users across a wide range of tasks. Effective personalized assistance requires not only retrieving explicit facts from past interactions stored in agent memory, but also inferring abstract personal characteristics. However, existing memory benchmarks primarily evaluate whether an agent can retrieve information explicitly stated in conversational histories, failing to provide an effective assessment of deeper user understanding. In this work, we propose Setoka, a benchmark for evaluating memory-augmented personalized agents with hierarchical user understanding from heterogeneous data. Grounded in theories from cognitive and personality psychology, Setoka defines four levels of user understanding, i.e., semantic memory, episodic memory, behavior pattern, and personality trait. Moreover, to enable realistic yet privacy-preserving evaluation, we design a psychometrics-based pipeline that synthesizes diverse, coherent heterogeneous user data and queries at scale. Finally, we leverage Setoka to evaluate 3 language models combined with 5 memory systems for 10 synthetic users. Our comprehensive evaluation reveals that while existing systems perform well on semantic memory retrieval, their performance declines on episodic memory. Moreover, when dealing with behavior pattern and personality trait understanding tasks that require integrating heterogeneous and fragmented information dispersed over time, performance declines even further. These findings demonstrate that user understanding cannot be handled by simple fact retrieval, motivating the design of memory mechanisms for cross-source integration and abstraction over long-term user behavior.
[NLP-7] Evaluating Regional Bias in LLM s From Abstract Stereotype to Concrete Social Decision-Making
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中存在的区域偏见问题，特别是此类偏见如何从抽象的刻板印象演变为具体的社交决策，而现有研究多将二者割裂分析，导致对偏见的内在结构及其社会后果认知不清。为此，论文提出Stereotypes-to-Decisions (S2D) 框架，系统性地评估中国34个省级行政区域在“亲和力”（Warmth，感知友好度与可信度）和“能力”（Competence，感知能力与智力）两个维度上的刻板印象，并通过教育、职业与社会互动三类配对选择任务，考察模型在具体决策中的表现。其核心解决方案在于构建一个覆盖全样本区域、融合多维度评估指标的统一分析框架，揭示出不同地区在模型输出中存在显著差异，且各模型间具有较高一致性，尤其在“能力”与“职业”决策上；同时发现这些偏见模式与区域经济及数字发展水平相关，呈现出部分类似人类的双重刻板印象特征（如某些地区高亲和力但低能力）。此外，偏见表现对中文与英文提示均保持高度稳定性。研究结果表明，大语言模型中的区域偏见普遍存在、系统性强且具有实际影响，亟需推动更具区域敏感性的评估与缓解策略。
链接: https://arxiv.org/abs/2607.27022
作者: Jiayuan Di,Haoyi Yang,Yufei Luo,Jiahui Qu,Yiming Wang
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Regional bias in large language models (LLMs) may shape both perceptions of regional groups and decisions about individuals from different regions. Yet existing studies often examine these manifestations separately, leaving their structure and consequences unclear. We introduce Stereotypes-to-Decisions (S2D), a systematic framework evaluating regional bias from abstract stereotypes to concrete social decisions. Covering all 34 provincial-level administrative regions of China, S2D evaluates six LLMs using stereotype ratings of Warmth (perceived friendliness and trustworthiness) and Competence (perceived capability and intelligence), along with paired-choice tasks across Education, Occupation, and Social Interaction. Results reveal substantial regional differences in regional scores, with considerable agreement across models, especially for Competence and Occupation decisions. Furthermore, these patterns are associated with regional economic and digital development indicators and display mixed human-like stereotypes, with some regions rated highly on one dimension but poorly on the other. They also remain largely stable across Chinese and English prompts. Overall, our findings show that regional bias in LLMs is prevalent, systematic, and consequential, motivating more regionally aware evaluation and mitigation.
[NLP-8] AgentS nare: Learning to Delay Divert and Defuse Autonomous Penetration Agents
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理在自动化渗透测试过程中易受欺骗性观测数据干扰的问题。现有防御机制依赖于攻击前静态部署的孤立诱饵（artifact），难以应对具备自适应能力的先进渗透代理，后者可逐步识别并绕过这些固定诱饵，最终聚焦于真实目标。为此，论文提出AgentSnare——一种轨迹自适应的动态欺骗系统，其核心创新在于通过基于代理交互历史与诱饵状态的候选诱饵生成策略模型，动态构建并验证符合上下文一致性的诱饵内容，并将其增量式融入一个事实连贯的虚假环境。该机制通过吸收代理的工具调用、引导其在诱饵环境中偏离真实路径，并诱导其基于虚假证据提交完成报告，从而有效延滞甚至彻底阻断对真实目标的攻击。实验结果表明，在15个CVE-Bench Web应用及3种攻击者模型下，AgentSnare平均吸收46.8%的工具调用，保留55.9%的入侵后操作于诱饵环境，90.0%的完成尝试基于诱饵证据，且在全部45组攻击-漏洞组合中，所有攻击均未在pass@3条件下成功利用真实目标。
链接: https://arxiv.org/abs/2607.26998
作者: Ruoyu Wang,Heng Zhao,Renjie Wu,Mengnan Zhao,Zhixuan Chu,Wanyu Lin,Tianhang Zheng
机构: 未知
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 8 pages, 3 figures, 1 table
Abstract:Large language model (LLM) agents automate penetration testing through an observation-action loop, selecting actions based on observations returned by tools. This dependence allows defenders to inject deceptive observations that can mislead the agent’s decision-making process. However, existing defenses rely heavily on static, isolated artifacts planted in the environment prior to an attack. Advanced agents can progressively recognize and bypass these artifacts, ultimately refocusing their exploitation attempts on the real target. To address this issue, we introduce AgentSnare, a trajectory-adaptive deception system that dynamically unfolds a decoy environment to continually steer the penetration agent away from the real target. Specifically, AgentSnare employs an artifact-construction policy model that constructs candidate artifacts conditioned on the agent’s interaction history and decoy state. AgentSnare then validates these candidates and incrementally incorporates valid artifacts into a factually consistent decoy environment, thereby delaying the attack by absorbing its tool calls, diverting its post-entry trajectory within the decoy, and defusing it by inducing completion reports grounded in decoy evidence. Across 15 CVE-Bench web applications and three attacker models, AgentSnare absorbs 46.8% of the agent’s tool calls in the decoy and retains 55.9% of post-entry actions there, while 90.0% of completion attempts are grounded in decoy evidence; across all 45 attacker-CVE pairs, no real target is successfully exploited at pass@3.
[NLP-9] OptimismBench: Forecasting Bias and the Alignment Effect in Language Model Judgment
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在作为决策辅助时，其概率判断中可能存在的系统性方向偏差（directional bias）难以被检测的问题。传统校准度量（calibration metrics）仅关注误差的绝对值，无法捕捉概率估计中隐含的系统性乐观或悲观倾向；而现实场景中的不确定性缺乏真实概率作为参照，进一步增加了检测难度。针对此问题，论文提出OptimismBench评测框架，其核心方法是采用“反转对”（inverted pairs）设计：为同一情境同时获取成功概率P(success)与失败概率P(failure)，通过两者之间的不对称性计算有符号的偏差分数，从而在无需真实标签的情况下识别方向性偏差。实验结果表明，在16个来自8家厂商的模型中，14个呈现乐观倾向，悲观仅见于Anthropic的前沿模型；此外，对比基线模型与对话模型的配对发现，后训练过程（post-training）会显著影响偏差方向，且该模式在提示工程、温度参数、视角变换及自我去偏干预等消融实验中仍保持稳定。跨语言分析显示，模型身份对偏差的影响远超语言差异（模型间方差是语言间方差的4.7倍）。研究揭示了对齐（alignment）在提升模型帮助性的同时，也引入了概率倾斜，导致下游决策流程默认继承这种偏差。因此，解决方案的关键在于利用反向框架实现无真值条件下的方向性偏差量化，并强调模型训练阶段与对齐机制对概率可信度的根本性影响。
链接: https://arxiv.org/abs/2607.26981
作者: Seonglae Cho,Adriano Koshiyama
机构: Holistic AI; University College London
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models are increasingly used as decision aids whose probability judgments shape downstream choices. Whether those judgments carry a systematic directional tilt has been hard to detect: calibration metrics aggregate unsigned errors, and naturalistic uncertainty offers no ground-truth probability. When an LLM rates a startup’s success at 70% but its failure at 15%, the missing 15 points expose a distortion no aggregate score flags. We introduce OptimismBench, which detects directional bias with inverted pairs: each scenario elicits both P(success) and P(failure), and asymmetry between the two framings yields a signed bias score without ground truth. Across 16 models from 8 providers, fourteen are optimistic; pessimism appears only in Anthropic’s frontier tier. Eleven matched base-versus-chat pairs across four families show post-training sets the sign of the bias, with opposite shifts in different families. The pattern survives prompt, temperature, perspective, and self-debiasing ablations. A seventeen-model six-language comparison further shows model identity dominates language, with inter-model variance at 4.7x inter-language variance. We release 3,870 items across 10 languages for per-model directional-bias auditing. When alignment makes a model more helpful, it also tilts its probabilities; downstream pipelines inherit the tilt by default.
[NLP-10] REK: A Travel Reasoning and Evaluation Kit for LLM Agents in Complex Trip Planning
【速读】： 该论文旨在解决现有大语言模型（LLM）代理在旅行规划任务中难以生成可执行、多约束协同满足的可行行程方案的问题。传统基准测试仅逐项评估行程的单一属性（如预算合规或景点可预订性），并依赖模糊的、由大模型主观评判的评分机制，无法确保生成方案的实际可执行性，且缺乏可复现性和可审计性。本文提出TREK（Travel Reasoning and Evaluation Kit），一个面向可行行程合成的全新基准：其核心目标是生成联合满足多维度约束的行程方案，包括约束正确性（constraint-correct）、无幻觉（hallucination-free）、时空可执行性（spatio-temporally executable）、预算合规性（budget-valid）以及对旅行者未明言人格需求的响应能力。TREK的关键创新在于构建了一个包含800个复杂任务的合成数据集（533个可解与267个可证明不可解），基于覆盖375个城市和13种人格类型的212,530条内部一致实体数据，并通过经过验证的RESTful API工具沙箱提供服务。所有任务均由完全确定性的规则化评估器进行评分，不依赖任何大模型判断，并配有经人工验证的黄金参考答案（在该评估器下得分为1.0），从而确保了评估标准的可复现性与上限可达成性。实验表明，即便最强的GPT-5.6在可解任务中也仅能生成完全可行的行程方案46.2%，中位数仅为6.6%，且存在0%成功率的情况，反映出对旅行者隐含人格需求的响应能力已成为当前先进模型的普遍瓶颈。该研究释放了完整的数据集、工具沙箱、确定性评估器及代理代码，构成了一个真正可复现、可审计的基准体系。
链接: https://arxiv.org/abs/2607.26977
作者: Jinhu Qi,Wentao Zhang,Siu Man Ng,Feiyang Xu,Yanyu Chen,Yaoman Li,Irwin King
机构: The Chinese University of Hong Kong(香港中文大学); Macao Polytechnic University(澳门理工学院)
类目: Computation and Language (cs.CL)
备注: Code, data, and evaluator: this https URL
Abstract:Travel planning is a demanding stress test for tool-using LLM agents: a usable itinerary is a single artifact that must be right along many axes at once - every flight, hotel, and attraction must exist and be bookable, the days must be physically traversable, the total must clear a budget, and the plan must serve a traveler whose needs are only partly stated. Existing agent benchmarks reward these properties one at a time and grade the final output with soft or LLM-judged rubrics, which cannot certify that a returned plan is executable and are neither reproducible nor auditable. We introduce TREK (Travel Reasoning and Evaluation Kit), a benchmark for feasible itinerary synthesis: producing a single plan that is jointly constraint-correct, hallucination-free, spatio-temporally executable, budget-valid, and responsive to the traveler’s unstated persona needs. TREK comprises 800 multi-constraint tasks - 533 feasible and 267 provably infeasible with typed route/entity/budget causes - over a synthetic, internally consistent knowledge base of 212,530 records across 375 cities and 13 personas, served through a production-style tool sandbox of validated RESTful APIs. Every task is scored by a fully deterministic, rule-based evaluator with no LLM judge and ships a human-verified gold reference that scores a perfect 1.0 under that same evaluator, so the ceiling is demonstrably achievable and every remaining gap is an agent limitation rather than scorer strictness. Evaluating 15 LLM agents across nine constraint dimensions, we find that even the strongest (GPT-5.6) produces a fully-feasible plan on only 46.2% of solvable tasks, with a median of 6.6% and a floor of 0.0%; satisfying travelers’ unstated needs emerges as the universal bottleneck, unsolved even at the frontier. We release the dataset, tool sandbox, deterministic evaluator, and agent code as a fully reproducible benchmark.
[NLP-11] Generation or Judgement? A Paradigm Perspective on LLM -Based Emotion-Cause Pair Extraction in Conversation
【速读】： 该论文旨在解决生成式大语言模型（LLM）在对话情感-原因对抽取（Emotion-Cause Pair Extraction in Conversation, ECPEC）任务中性能受限的问题，核心在于揭示不同任务建模方式对模型表现的显著影响。研究发现，以对话级生成完整关系对的方式存在明显缺陷，而采用逐对判断（pair-level judgement）的范式在所有18组受控对比中均表现更优。其关键原因是：尽管大模型具备识别情感-原因关系的能力，但在要求其自动生成完整且无遗漏的关系集合时，面临发现与召回完整配对的困难；而通过显式查询单个候选对，模型可识别92.7%–98.1%的情感-原因关系，表明其认知能力未被充分释放。为缓解此问题，作者提出引入一个辅助检索器（auxiliary retriever），仅对边界模糊的候选对进行重新评估，有效提升了模型在三个数据集上的F1值0.50–1.46点，同时保持推理时间仅为基线方法的1.49倍。研究表明，任务分解策略与候选范围的合理设计是高效利用大模型进行ECPEC的关键因素。
链接: https://arxiv.org/abs/2607.26967
作者: Weijie Feng,Hongchuang Wang,Binbin Liu,Zhiyong Cheng
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Emotion-cause pair extraction in conversation (ECPEC) identifies utterance pairs in which one utterance causes an emotion expressed in another. Recent LLM-based approaches formulate ECPEC at markedly different granularities, ranging from generating complete pair sets to judging individual candidate pairs. In this paper, we make the surprising observation that task formulation substantially affects performance, where pair-level judgement outperforms dialogue-level generation in all 18 controlled comparisons. We investigate the sources of this paradigm gap and find that many relations omitted by dialogue-level generation remain recognizable under explicit pair queries, under which the model recognizes 92.7%-98.1% of emotion-cause relations. This suggests that LLMs can recognize emotion-cause relations but struggle to discover and return complete pair sets. Pair-level judgement alleviates this burden, although its candidate rankings are more reliable than the binary decisions produced by a shared threshold. Based on this diagnosis, we introduce an auxiliary retriever that selectively re-examines ambiguous boundary cases, yielding consistent F1 improvements of 0.50-1.46 points across three datasets while maintaining an inference time of only 1.49x that of the baseline paradigm. These findings show that task decomposition and candidate scope are critical to effectively utilizing LLMs for ECPEC.
[NLP-12] Credit Cards Confusion Computation and Consequences: What Can We Uncover About Language Model Reasoning ?
【速读】： 该论文旨在解决金融素养中数值推理能力评估缺乏真实场景基准的问题，提出首个基于真实信用卡协议构建的金融素养评测数据集CreditCardQA。其核心挑战在于如何准确评估模型在复杂金融条款理解与计算中的表现，尤其是面对消费者实际提问情境时的推理能力。解决方案的关键在于引入程序化思维（Program-of-Thought, PoT）提示策略，相较于传统的链式思维（Chain-of-Thought, CoT），PoT通过结构化生成可执行的中间程序，在处理条件逻辑、货币约束及多步推理任务时显著提升模型表现，尤其对基础推理能力较弱的模型和闭源系统具有更强的性能增益，有效缩小了开源与闭源模型间的差距。此外，错误分析揭示，模型失败主要源于对金融规则的误用、条件遗漏及合同条款误解，而非简单的算术错误，且在逾期罚金、低余额等边缘场景下错误更集中，这些情形往往对低收入或财务脆弱群体影响更大。
链接: https://arxiv.org/abs/2607.26952
作者: Arnav Hiray,Agam Shah,Caleb Lu,Meghaj Tarte,Harsit Mittal,Sudheer Chava
机构: Georgia Institute of Technology (佐治亚理工学院)
类目: Computation and Language (cs.CL)
备注: Accepted at CoLM 2026
Abstract:We introduce CreditCardQA, the first financial literacy benchmark for numerical reasoning derived from real credit card agreements. The dataset contains 1,800 questions, including first-person variants that reflect how consumers naturally ask about fees, interest, and payments. We evaluate a range of large language and reasoning models under Chain-of-Thought (CoT) and Program-of-Thought (PoT) prompting. Overall, PoT yields consistent performance gains, particularly for models with weaker baseline reasoning, and narrows gaps between open- and closed-source systems. Through error analysis, we show that failures arise less from arithmetic and more from misapplied financial rules, missed conditions, and misunderstandings of contractual terms. We further analyze question difficulty and find that comparisons, conditional logic, and monetary constraints are especially challenging. We also find that errors often arise in edge cases such as late-payment penalties or small-balance scenarios that are more likely to affect lower-income or financially vulnerable individuals.
[NLP-13] Same Evidence Different Target: Decoding How Diagnostic Evidence Bears on Causal Questions from Language-Model States
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在因果推理任务中对诊断证据与因果主张之间匹配关系判断不一致的问题，尤其关注当因果主张涉及不同人群、结果变量、估计量、作用路径或识别假设时，同一诊断结果可能对某一因果主张具有支持或质疑作用，却对另一主张无关。其核心挑战在于：模型的输出可能受表述方式、词汇重叠或熟悉模式的影响，而非真正基于证据与因果问题的逻辑适配性。为此，论文提出“成对提示”（paired prompts）方法，即在保持诊断证据完全一致的前提下，仅改变因果目标，并将每个提示标记为“支持”（Favors）、“挑战”（Challenges）、“未决”（Unresolved）或“错误目标”（Wrong Target），仅当一对提示均被正确分类时才视为成功恢复。通过在Qwen2.5-7B-Instruct、Qwen3-8B和Llama-3.1-8B-Instruct模型的倒数第二层Transformer块的最终标记隐藏状态上训练线性读出器（linear readouts），研究发现该隐藏状态包含可线性解码的信息，能够有效区分诊断证据是否支持、挑战或与因果目标无关。在涵盖九类诊断的49对基准测试中，平衡准确率介于0.654至0.659之间，共恢复18–21对；人类评审者对98个提示中的95个达成一致（96.9%），且模型表现显著优于保留开发场景组的置换零模型。此外，使用未包含目标诊断家族开发样本的读出器仍能恢复21对，每类诊断家族至少恢复一对，表明该方法具备良好的泛化能力。相较基于答案选项逻辑值或文本基线的线性分类器，隐藏状态读出器在平衡准确率和完整对恢复率上均表现更优。因此，该方案的关键突破在于揭示并利用大语言模型深层隐藏状态中蕴含的因果适配性信息，实现对诊断证据与因果目标之间逻辑关系的精准建模。
链接: https://arxiv.org/abs/2607.26929
作者: Weiyi Kong,Zhuoran Li
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 14 pages, 1 figure; includes supplementary material
Abstract:The same diagnostic result can support or challenge one causal claim yet fail to address another when the claims concern different populations, outcomes, estimands, pathways, or identifying assumptions. When the evidence and target vary together, a correct answer may reflect favorable or adverse wording, lexical overlap, or a familiar diagnostic pattern rather than matching the evidence to the causal question. We introduce paired prompts that repeat the same diagnostic evidence verbatim while changing the causal target. Each prompt is labeled Favors, Challenges, Unresolved, or Wrong Target according to how the evidence bears on the causal question. A pair is recovered only when both prompts are classified correctly. Using linear readouts trained on a separate development set, we analyze the final-token hidden state from the penultimate transformer block of Qwen2.5-7B-Instruct, Qwen3-8B, and Llama-3.1-8B-Instruct. On the 49-pair primary benchmark spanning nine diagnostic families, balanced accuracy ranges from 0.654 to 0.659 and 18-21 pairs are recovered. Two independent human reviewers assigned the same label to 95 of the 98 prompts (96.9%). Across checkpoints, balanced accuracy and complete-pair recovery exceed permutation nulls that preserve development scenario groups. In Qwen2.5, full-prompt balanced accuracy exceeds both restricted inputs, with paired-bootstrap intervals for both differences above zero. Readouts trained without development examples from the evaluated diagnostic family recover 21 pairs, including at least one in each of the nine families. The hidden-state readout exceeds a linear classifier on answer-option logits and text baselines in balanced accuracy and recovered pairs. These results show that the hidden state contains linearly decodable information about whether diagnostic evidence favors, challenges, or fails to address the causal target.
[NLP-14] Latent-IM: Latent Interaction Management for Speech LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在对话系统中因传统模块化设计消解而导致的对话行为控制缺失问题，即如何在无需显式分离对话管理与响应生成的情况下，恢复对对话动作（如确认、核实、提问、解释、回应等）的可解释性与可控性。其解决方案的关键在于提出一种名为Latent-IM的内部对话管理框架，通过将对话行为控制建模为两个耦合任务：选择（selection）——基于对话上下文预测下一恰当对话动作；以及实现（realization）——在生成阶段因果地输出所选动作。该框架为不同目标下的对话动作选择与部署提供统一接口，在不依赖微调的前提下，相较于未受控的基线模型，实现了端到端对话动作准确率提升12.5个百分点，性能接近微调方法，从而在保持生成灵活性的同时增强了对话行为的可控性与可解释性。
链接: https://arxiv.org/abs/2607.26928
作者: Adar Avsian,Atahan Dokme,Tony Woo,Larry Heck
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Classical spoken dialogue systems often separated dialogue management from response realization: a policy selected the next dialogue action, and a generation component expressed that action. As dialogue systems shift toward LLMs, this decomposition has largely disappeared into the model’s hidden representations. We ask whether an LLM-internal analogue of state estimation and action control can be recovered for conversational moves such as acknowledging, checking, querying, explaining, and replying. We formulate move control as two coupled problems: selection, predicting the appropriate next move from the dialogue context, and realization, causally producing a chosen move at generation time. We introduce Latent-IM, an internal dialogue-management framework that provides a general interface for choosing and deploying conversational moves under different objectives. Here, we use this control to reproduce human move choices, improving average end-to-end move accuracy by 12.5 points over the unsteered backbone while performing comparably to fine-tuning.
[NLP-15] Dual-Path LLM Reasoning for Multimodal Few-Shot Knowledge Graph Completion
【速读】： 该论文旨在解决多模态知识图谱补全（Multimodal Knowledge Graph Completion, MMKGC）中因现实世界中不断涌现的新实体与关系所导致的归纳式补全（inductive KGC）难题，尤其在少样本（few-shot）和零样本（zero-shot）场景下表现不佳的问题。现有方法虽尝试利用多模态信息与大语言模型（Large Language Model, LLM）生成的先验知识来丰富稀疏的关系上下文，但往往引入噪声或幻觉性证据，影响推理可靠性。为此，本文提出一种双路径大语言模型推理框架（DuPLeR），其核心在于通过融合多模态LLM衍生的类型先验与事实支持结构，构建校准后的关系图谱，并在此基础上进行双层次的结构化推理；同时，设计双通路多模态增强模块，利用与查询相关的多模态信号调控消息传递过程，并在图传播后补充实体表示。该方案有效提升了数据稀缺环境下的知识图谱补全性能，在两个多模态知识图谱基准的八个归纳式变体上均取得了稳健的实验结果。
链接: https://arxiv.org/abs/2607.26909
作者: Jinlan Liu,Zhiying Tu,Yongchao Xing,Yicheng Liu,Bolin Zhang,Dianbo Sui,Dianhui Chu,Hongliang Sun
机构: 1. Tsinghua University (清华大学); 2. Institute of Automation, Chinese Academy of Sciences (中国科学院自动化研究所); 3. Beijing Advanced Innovation Center for Artificial Intelligence (北京先进人工智能研究院)
类目: Computation and Language (cs.CL)
备注: 10 pages, 4 figures
Abstract:Knowledge graph completion (KGC) aims to infer missing facts in knowledge graphs (KGs), thereby improving their completeness and supporting downstream intelligent applications. However, emerging entities and relations in real-world deployments make inductive KGC difficult, especially under few-shot and zero-shot settings. Multimodal information and Large Language Model (LLM)-derived priors can enrich sparse relational contexts, but they may also introduce noisy or hallucinated evidence. To address these issues, we propose DuPLeR, a \textbfDual-\textbfPath \textbfLLM \textbfReasoning framework for multimodal few-shot KGC. DuPLeR builds a calibrated relation graph by combining multimodal LLM-derived type priors with factual support structures, and performs dual-level structural reasoning over the refined relation topology. Moreover, a dual-pathway multimodal enhancement module regulates message passing with query-relevant multimodal signals and supplements entity representations after graph propagation. Experiments on eight inductive variants of two multimodal KG (MMKG) benchmarks show that DuPLeR achieves robust performance in data-scarce KGC scenarios.
[NLP-16] DIRECT: Direct Decoding for Efficient and Aligned Sequence Labeling with Large Language Models
【速读】： 该论文旨在解决基于大语言模型（Large Language Model, LLM）的序列标注任务中存在的领域对齐不足与推理效率低下的问题。其核心解决方案在于通过训练阶段优化与推理阶段修正相结合的双重策略：在训练阶段，采用直接偏好优化（Direct Preference Optimization, DPO）对监督微调后的模型进行进一步优化，以增强模型输出与人类偏好之间的一致性；在推理阶段，引入受控解码机制，强制输出格式固定并限制预测范围至候选标签集，从而提升标注准确性。为进一步提升效率，框架设计了模板填充机制，使模型仅需生成标签词元（label tokens），并通过键值缓存（KV Cache）复用前置文本内容，显著减少重复计算。实验结果表明，DIRECT 在八个数据集上均实现了性能与效率的显著提升。
链接: https://arxiv.org/abs/2607.26891
作者: Yilei Wang,Jiaxin Gan,Kexuan Zhang,Ling Li,Wentao Zhang,Peichao Lai
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Sequence labeling is a fine-grained information extraction task, yet existing large language model-based approaches suffer from insufficient domain alignment and low inference efficiency. To address these issues, we propose DIRECT, a framework that addresses these issues through training-time optimization and inference-time rectification. Specifically, DIRECT performs Direct Preference Optimization (DPO) after supervised fine-tuning to strengthen task alignment with human preferences, and introduces a controlled decoding process that enforces fixed output formats and restricts predictions to candidate sets. To further improve efficiency, a template-filling mechanism requires the model to generate only label tokens while reusing prefixed content through the KV Cache, thus reducing redundant computation. Experimental results on eight datasets demonstrate that DIRECT achieves significant improvements in both performance and efficiency compared to existing methods.
[NLP-17] Hearsay: Vision-Language Medical Diagnoses Without an Image ICMR2026
【速读】： 该论文旨在解决生成式视觉语言模型（Vision-Language Models, VLMs）在医疗影像诊断任务中面对未提供图像时产生的“虚构诊断”（confabulation）问题。具体而言，当仅给出患者人口学特征而无实际影像时，前沿模型如Claude Opus-4.7、GPT-5.4和Gemini-3.1-Pro会系统性地生成看似合理但完全虚构的疾病诊断，且这些诊断结果并非随机，而是显著受患者性别、年龄和种族等人口学信息的影响。其关键发现在于：第一，模型输出存在“语义-结构脱节”现象——即文本描述中承认图像缺失，但结构化诊断字段仍明确指定疾病名称，这种矛盾在仅审查自然语言输出的审计中难以被察觉；第二，不同模型对提示词的敏感性存在差异，例如Claude在将“skin mole”替换为“skin lesion”后其虚构行为消失，而GPT-5.4则保持稳定，表明“幻觉”并非单一故障模式，而是由多种机制驱动的复合性问题。因此，论文提出可信部署需直接审计结构化输出通道，并将提示词敏感性作为评估生成式AI临床应用可靠性的核心维度。
链接: https://arxiv.org/abs/2607.26886
作者: Siddharth Vohra
机构: Carnegie Mellon University (卡内基梅隆大学); Amazon Web Services AI Native (亚马逊网络服务人工智能原生)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: Peer-reviewed and presented at the 1st Workshop on Toward Trustworthy Vision-Language Models in the Wild (TrustVLM), co-located with ACM ICMR 2026, Amsterdam. Non-archival workshop. Reviews public on OpenReview. 5 pages, 2 figures
Abstract:When asked to describe a medical image that was never attached, frontier vision-language models do not abstain: they confabulate a diagnosis. We show that this confabulation is not random. It is structured by who the patient is said to be. Across chest X-ray, brain MRI, and dermatology, Claude Opus-4.7, GPT-5.4, and Gemini-3.1-Pro are each queried with only a demographic descriptor and no image, and changing the descriptor systematically shifts the diagnosis returned. Claude concentrates sharply: a 65-year-old white man asking about a skin mole receives Melanoma in nearly every response, and a 32-year-old Black woman asking about her chest X-ray receives a Sarcoidosis diagnosis whose reasoning reads "suspected, based on demographics and classic pattern.‘’ GPT-5.4’s effect is broader, fabricating across every demographic cell we test, most conspicuously naming Sarcoidosis for young Black patients on chest X-ray. Two structural findings sharpen the problem. A hedged regime appears in which the prose acknowledges the missing image while the structured diagnosis field nevertheless names a disease, a dissociation invisible to prose-only audits. And Claude’s dermatology effect collapses entirely when ‘skin mole’ is swapped for ‘skin lesion’ while GPT-5.4’s is preserved, indicating that mirage is a family of distinct failure modes rather than a single phenomenon. Trustworthy VLM deployment in clinical pipelines requires auditing the structured output channel directly, and probe-word sensitivity should be treated as a first-class evaluation dimension
[NLP-18] SERPO: Self-Evolving Rubric Policy Optimization for Open-Ended Test-Time Reinforcement Learning
【速读】： 该论文旨在解决生成式语言模型在推理阶段进行自适应优化时面临的挑战，特别是针对开放生成任务中缺乏可标准化答案（即无法通过传统答案投票机制评估）的问题。现有方法依赖于答案投票机制，难以适用于开放域生成场景。为实现无需外部奖励模型或强判别器的自我演化，关键在于从模型自身输出中构建可靠且可度量的奖励信号。本文提出的解决方案核心是SERPO（Self-Evolving Rubric Policy Optimization），其创新性地构建了一个闭环协同进化框架，同步优化响应证据、查询相关的评分标准（rubric）与策略参数。具体而言，通过“好-正常-差”（G-N-B）三类响应的极值分离与有序归档，动态演化具备区分能力的评分准则；利用概率化准则打分将判断令牌的似然转化为奖励信号；再以该信号驱动策略优化。新生成的响应进一步更新档案与评分标准，形成三者闭环迭代。实验表明，该方法在多个领域内与跨领域基准上显著提升性能，最大提升达20.63和20.31点，并实现了跨基准持续演化与泛化能力。
链接: https://arxiv.org/abs/2607.26873
作者: Jianze Wang,Kunwang Zheng,Ying Liu,Yu Cao,Qilong Zhang,Jinlong Chen,Hua Yang,Qianglong Chen
机构: 未知
类目: Computation and Language (cs.CL)
备注: 20 pages, including appendix
Abstract:Test-time reinforcement learning (TTRL) enables language models to self-evolve at inference time without labeled feedback. Existing methods rely on answer voting and therefore do not extend naturally to open-ended generation, where valid responses cannot be mapped to a shared canonical answer. Without external reward models or stronger judges, adaptation must instead construct reliable rewards from the model’s own outputs. We introduce SERPO (Self-Evolving Rubric Policy Optimization), which replaces answer voting with a closed loop that co-evolves response evidence, query-specific rubrics, and policy parameters. Good-Normal-Bad (G-N-B) response evolution organizes maximally separated rollouts into ordered archives; rubric evolution retains criteria that discriminate these archives; probabilistic criterion scoring converts verdict-token likelihoods into reward signals; and policy evolution optimizes the actor with the resulting signals. New actor rollouts then refresh both the archives and rubrics, closing the three-way evolution loop. Across two model configurations, two in-domain benchmarks, and four OOD benchmarks, SERPO improves HealthBench and ResearchQA by up to 20.63 and 20.31 points over the corresponding base models, raises the six-benchmark macro-average by up to 8.06 points, and supports OOD transfer and continued cross-benchmark evolution.
[NLP-19] From Representations to Behaviors: Exploring the Person-Situation-Behavior Triad in LLM s
【速读】： 该论文旨在解决当前大语言模型（LLM）中人格相关行为研究缺乏机制性证据的问题，具体聚焦于：如何验证模型内部是否存在稳定的人格特质表征（trait-like representations），这些表征是否能在不同情境中跨情境一致地表达，并如何具体影响行为输出。其解决方案的关键在于基于Funder的人格三元框架（Personality Triad Framework），将“个体”定义为与人格相关的内部表征，“情境”定义为提供特质相关响应机会的上下文，“行为”定义为在广泛社交任务中的反应模式。研究提出一个可发现、可控制、可验证人格特质表征的框架：首先，通过对比同一情境下具有相反人格倾向的行为对，利用稀疏激活编码器（SAE）分解识别出与人格特质两极相关的稀疏内部特征；其次，通过特征层面的干预，在独立且多样的情境中实现人格特质的双向可控调节，同时保持响应的有效性，证明了表征的跨情境一致性；最后，将相同干预应用于社交智能任务，观察到行为变化呈现与人类人格研究一致的权衡模式（benefit-tradeoff patterns），从而在行为层面提供了超越单一人格评分的实证支持。该研究首次系统性地揭示了大语言模型中存在可调控、跨情境、可解释的人格类表征，连接了内部状态、情境表达与行为结果。
链接: https://arxiv.org/abs/2607.26853
作者: Ruikang Zhang,Shuo Wang,Qi Su
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Human personality theories characterize traits not as isolated attributes captured by a single score, but as stable individual tendencies expressed through the interplay among persons, situations, and behaviors. Existing studies of personality-related behavior in LLMs have primarily focused on outputs elicited under personality conditioning, characterizing observable trait-related expressions while lacking mechanistic evidence for the existence of internal personality-related representations, their cross-situational expression, and how these representations shape specific behaviors. Building on Funder’s personality triad framework, we adapt its three components for LLM analysis: Person as personality-related internal representations, Situation as contexts that afford trait-relevant responses, and Behavior as response patterns on broader social tasks. We introduce a framework for discovering, controlling, and validating trait-like representations in LLMs. First, using contrastive behavior pairs grounded in shared situations, we identify sparse internal features associated with opposing poles of personality traits through SAE decomposition. We validate their trait relevance through effects on behavior to situation, token-level activation patterns, and robustness to paraphrasing. Second, feature-level interventions induce bidirectional trait-related shifts across a separate, diverse set of situations while preserving response validity, demonstrating consistent expression across contexts. Third, applying the same interventions to social intelligence tasks reveals behavioral changes with benefit-tradeoff patterns consistent with findings from human personality research, providing behavioral-level validation beyond personality scores. Our findings provide evidence that LLMs contain controllable trait-like representations linking internal states, situational expression, and behavioral outcomes.
[NLP-20] Language Models are not Equally Robust to Non-Canonical Tokenization across Languages
【速读】： 该论文旨在解决语言模型在面对非标准分词（non-canonical tokenization）时的鲁棒性问题，特别是探究模型对不同语言中多种分词方式的敏感性是否具有普遍性。尽管已有研究显示英文语言模型对替代分词方式具有较强的不变性（tokenization invariance），但该论文质疑这种特性是否可推广至其他语言。其解决方案的关键在于通过跨27种语言、涵盖多种文字系统的多语言实证研究，系统评估大语言模型（LLM）在六类下游任务中对非标准分词策略的响应差异。研究发现，模型行为在不同语言间存在显著波动，指令微调模型在非标准分词下平均性能下降达23.7%（Llama-3.1-8B）、11.4%（Qwen3-8B）和9.9%（Gemma-3-12B），且分词碎片化程度高的语言表现出更强的敏感性。这一结果表明，分词鲁棒性并非语言模型的普适属性，而是高度依赖于语言特征与分词器之间的交互关系。此外，研究提出采用基于LoRA的微调方法，利用包含多分词路径的训练数据进行优化，可有效缓解分词敏感性；其中，仅用英文数据微调即可提升跨语言鲁棒性，而系统性地采样多样化非标准分词形式则能实现最优整体性能。
链接: https://arxiv.org/abs/2607.26831
作者: Poulami Ghosh,Preethi Jyothi
机构: IIT Bombay(印度理工学院孟买分校)
类目: Computation and Language (cs.CL)
备注:
Abstract:Despite the existence of exponentially many valid tokenizations for a given string, language models operate on a single canonical sequence deterministically produced by the tokenizer, leaving the broader tokenization space largely uncharacterized. In this paper, we investigate this overlooked space by studying the behavior of language models under non-canonical tokenizations across diverse languages. For English, prior work shows that models are largely invariant to alternative tokenizations that represent the same underlying string. We ask whether this invariance generalizes to other languages beyond English. We conduct a multilingual study across 27 languages spanning diverse scripts and evaluate LLM behavior under alternative tokenizations across six downstream tasks. We find that tokenization invariance does not generalize: model behavior varies substantially across languages with instruction-tuned models exhibiting an average relative performance drop of 23.7% for Llama-3.1-8B, 11.4% for Qwen3-8B, and 9.9% for Gemma-3-12B. The variation of tokenization invariance is systematic across languages. Languages that exhibit higher token fragmentation show significantly greater sensitivity to non-canonical tokenizations. Our study of tokenization robustness serves as a diagnostic of how tightly a model is coupled to its tokenizer. These results demonstrate that tokenization robustness is not a universal property of language models, but depends strongly on the language and its interaction with the tokenizer. We also show that LoRA fine-tuning with multi-tokenization training data provides an effective mitigation for tokenization sensitivity. Fine-tuning on English alone improves tokenization robustness across languages, while systematically sampling diverse non-canonical tokenizations achieves the strongest overall performance.
[NLP-21] From Found to Designed: Concepts as a Design Axis for Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中概念信息以隐式分布式统计关联形式编码，缺乏显式、结构化、可组合且与人类概念组织对齐的概念表示的问题。现有方法通常在模型训练后通过探测或词典学习“发现”概念结构，但无法保证其稳定性、组合性、可控性及与人类认知的一致性。论文提出将概念视为模型设计的核心维度，并构建了一个二维分类框架：一是概念结构引入的流水线阶段（训练目标、核心架构、推理阶段或后处理解释），二是结构来源是模型内部表征自生还是依赖外部资源。该分类揭示出三大趋势：推理阶段的方法仍相对未被充分探索；不同流水线阶段的相关研究发展孤立；外部资源引导的方法贯穿整个流水线但常被不同术语描述。基于此，论文主张应从“从已训练模型中恢复”概念结构转向“主动设计”具有显式概念表示的大语言模型，从而实现更可控、可解释且符合人类认知的系统构建。
链接: https://arxiv.org/abs/2607.26825
作者: Chen Shani
机构: Tel Aviv University (特拉维夫大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) encode rich concept-like information, but represent it implicitly through distributed statistical associations rather than as explicit, structured, compositional concepts. Consequently, concept-level structure is typically \emphfound rather than \emphdesigned: it is recovered after training through probing or dictionary learning, with no architectural guarantee of stability, compositionality, controllability, or alignment with human conceptual organization. We argue that concepts should instead be treated as a design axis for LLMs, and map the design space along two dimensions: the pipeline stage at which concept structure is introduced (training objective, core architecture, inference, or post-hoc interpretation), and whether that structure is internally derived from the model’s own representations or grounded in external resources. This taxonomy reveals three broad patterns: inference-time approaches remain comparatively underexplored, related ideas have developed largely in isolation across pipeline stages, and externally grounded methods span the entire pipeline despite often being described under different terminology. Together, these observations motivate moving beyond recovering concept-like structure from trained models toward designing LLMs with explicit conceptual representations.
[NLP-22] When Does Span-Guided Detoxification Help? Human Preferences and Evaluator Diagnostics in a Controlled Comparison
【速读】： 该论文旨在解决生成式文本去毒（detoxification）中“局部编辑”与“全局重写”两种策略在保持语义完整性与彻底消除有害意图之间的权衡问题。具体而言，基于标注有害片段（span-guided）的局部编辑虽能较好保留原始立场，但可能无法充分消除隐含的有害意图；而无引导（unguided）的全面重写虽能更彻底地缓解毒性，却存在过度修改原意的风险。其解决方案的关键在于通过一个固定单生成器设置下的密集盲评人类评估，系统性比较两种策略在混合来源英语测试集上的表现，并揭示二者在不同严重程度子群体（strata）中的相对优势：在强毒性子群中两者表现相当，而在弱毒性子群中无引导重写显著更优。研究进一步通过理性注释识别出两类互补性失败模式——局部编辑后的残余危害与全局重写导致的过度修改——强调自动评估应作为诊断工具而非人类判断的替代，主张建立分离评估缓解充分性与语义保真度的评价协议，并同时报告残留毒性与过度修改指标，以实现更精细、透明的模型性能分析。
链接: https://arxiv.org/abs/2607.26795
作者: Kyungwon Park
机构: Yonsei University (延世大学)
类目: Computation and Language (cs.CL)
备注: 12 pages, 3 figures
Abstract:Span-guided rewriting aims to preserve meaning by localizing edits to annotated harmful spans, but the same constraint can leave harmful intent insufficiently mitigated. We present a controlled exploratory comparison of span-guided and unguided detoxification on a mixed-source English evaluation set comprising manually curated inputs and HateXplain test items. We conduct a dense blinded human evaluation under a fixed single-generator setting. Human preferences reveal a trade-off rather than a uniformly superior rewriting strategy. Span-guided outputs are favored when localized editing preserves the original stance and avoids unnecessary modification, whereas unguided outputs are favored when broader rewriting achieves more complete mitigation. This contrast varies substantially across the study-defined strata: the two strategies are competitive in the strong stratum, while unguided rewriting is clearly preferred in the mild stratum. Rationale annotations trace this difference to complementary failure risks: residual harm after localized editing and over-modification after broader rewriting. We treat automatic evaluation as a diagnostic rather than a substitute for human judgment. Toxicity-similarity scalarizations, a multi-generator analysis, and two general-purpose LLM judges reproduce parts of the aggregate tendency but do not yield an analogous stratified contrast. These setting-specific findings do not establish a severity-based routing rule. Instead, they motivate evaluation protocols that assess mitigation sufficiency and meaning preservation separately and report both residual harm and over-modification alongside aggregate scores. Comments: 12 pages, 3 figures Subjects: Computation and Language (cs.CL) Cite as: arXiv:2607.26795 [cs.CL] (or arXiv:2607.26795v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2607.26795 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-23] SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response
【速读】： 该论文旨在解决当前大型语言模型（Large Language Model, LLM）代理在真实网络安全运维中，尤其是在后妥协（post-compromise）场景下的安全响应能力评估不足的问题。现有网络安全基准测试主要聚焦于攻击前（pre-compromise）的理想化环境，忽视了攻击发生后系统已被入侵的情况下，如何有效开展取证分析与应急响应的挑战。为此，本文提出SecRespond，首个专门针对后妥协情境下LLM代理进行事件响应工作流评估的基准。其核心在于：给定一个被攻陷主机的取证磁盘快照，以及来自主机安全产品生成的告警、漏洞扫描结果和基线检查报告，要求代理生成包含入侵行为分析、基线风险与漏洞风险评估的取证报告，并制定可验证的修复方案。该任务在10个基于不同云主机构建的网络靶场中实现，涵盖4种初始攻击入口、21种MITRE ATT&CK技术及5种操作系统。实验评估23个前沿大模型在OpenCode代理框架上的表现，结果表明，尽管当前代理能够可靠识别告警所揭示的问题，但在主动探测磁盘以发现隐蔽入侵行为，以及生成全面且可验证的修复计划方面存在显著短板，所有模型均未能在任一靶场实现完全检测与修复。这揭示了构建面向真实世界事件响应的智能代理所面临的核心瓶颈。
链接: https://arxiv.org/abs/2607.26791
作者: Lehan Wang,Boli Chen,Ruixue Ding,Pengjun Xie,Jinwei Huang,Zhendong Liu,Shuo Wang,Tao Lei,Xin Ouyang,Xiaomeng Li
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Large Language Model (LLM) agents are increasingly adopted in real-world security operations with access to host artifacts and command-line interfaces (CLIs), making it critical to thoroughly assess their security capabilities. However, existing cybersecurity benchmarks focus on pre-compromise settings where agents are placed in a clean and idealized environment before an attack occurs. This leaves the post-compromise setting underexplored. To address this gap, we introduce SecRespond, the first benchmark for evaluating LLM agents on the post-compromise incident-response workflow. Given a forensic disk snapshot of a compromised host together with the alerts, vulnerability scans, and baseline checks reported by a host security product, agents are required to produce forensic reports on intrusions, baseline risks, and vulnerability risks, together with a remediation plan. We instantiate this task across 10 cyber ranges, each constructed from a distinct compromised cloud host, spanning 4 entry-point types, 21 ATTCK techniques, and 5 operating systems. We evaluate 23 frontier LLMs on the OpenCode agent harness. Experimental results show that although current agents can reliably uncover the problems exposed by alerts, they struggle to proactively investigate the disk for silent intrusions and to produce comprehensive, verified remediation plans, with no model achieving complete detection and remediation on any single range. This reveals a fundamental bottleneck in building agents for real-world incident response. The benchmark is publicly available at this https URL.
[NLP-24] Enhancing Generative Information Extraction with Two-step Validation: A Product Attribute Use Case
【速读】： 该论文旨在解决在标签数据稀缺的领域中，如何高效、可泛化且符合数据隐私要求地进行信息抽取（Information Extraction, IE）的问题，尤其聚焦于数字产品护照（Digital Product Passport, DPP）这一新兴应用场景。其核心挑战在于：传统微调的小型模型在数据有限时性能受限，而大规模语言模型（Large Language Models, LLMs）虽具备强泛化能力，但在处理弱表达、低显著性实体时仍存在提取精度不足的问题。为此，论文提出一种两步验证方法，将预训练语言模型（PLM）模块嵌入生成式信息抽取流程中，利用LLM的纠错能力对初步生成结果进行验证与修正。该方案的关键在于通过引入PLM块实现对生成结果的后处理校验，显著提升了对稀疏分布、低显著性实体的识别能力；同时，该验证机制不仅改善了原始预测结果，还进一步增强了最终大模型输出的质量，使中等规模模型在特定实体上的表现接近甚至媲美更大模型。然而，对于最小规模的开源LLM（如Llama-3.2 3B），该方法的增益有限。基于上述发现，研究构建了一个基于本地部署LLM的产品信息抽取演示系统，为后续向真实世界DPP应用的适配提供了可行路径。
链接: https://arxiv.org/abs/2607.26780
作者: Yi-Sheng Hsu,Nermeen Abou Baker,Uwe Handmann
机构: Ruhr West University of Applied Sciences (鲁尔西应用科技大学)
类目: Computation and Language (cs.CL)
备注: 13 pages
Abstract:The ability of large language models (LLMs) to process and generate text has introduced potential for applications in information extraction (IE). While it’s debated whether LLMs outperform smaller fine-tuned models for classification tasks, their strong generalization capability makes them promising for domains with limited labeled data available for fine-tuning. This advantage is particularly relevant for the emerging application of the digital product passport (DPP), where the problem space is broad but domain-specific data remains scarce. Motivated by this use case, we apply generative IE to the product domain, explicitly addressing efficiency, generalizability, and data privacy constraints. We propose a two-step validation method that integrates a PLM block into the generative IE pipeline and thereby leverages LLMs’ correction capability. We discover that such a validation task enhances LLM performance, particularly on the extraction of weakly expressed, low-salience entities that appear sparsely throughout the text. For certain entities, the performance of mid-size models can even reach levels comparable to larger models, and the improvement of first-step PLM predictions also enhance the final LLM output. Nevertheless, the effects on the smallest open-source LLMs (e.g., Llama-3.2 3B) is limited. Based on the findings, we develop a demo application for product information extraction that utilizes locally deployed LLMs, targeting further adaptations to real-world DPP use cases.
[NLP-25] Relation Geometry in Semantic Space of Language Models
【速读】： 该论文旨在解决当前语言模型生成的词向量表示中，语义关系的几何结构在语义空间中的表征程度问题。具体而言，研究关注不同语义关系（如对称、非对称、传递性关系）在语义空间中的分布是否具有可区分性，以及这种几何结构能否有效反映关系的固有属性。其解决方案的关键在于从三个维度系统评估语义空间的关系几何：（1）特定关系的词项（relata）是否聚集于语义空间中的同一区域，且不同关系的区域是否彼此分离；（2）语义空间是否编码了关系的典型属性，如对称性、非对称性和传递性；（3）目标词与相关词的表层形式（lexical form）与上下文信息（contextual information）对关系几何的影响孰轻孰重。研究采用因果型、掩码型和扩散型语言模型，在六种语义关系上进行实验，结果表明：非对称关系的词项在语义空间中表现出更清晰的聚集性，其几何结构的编码程度优于对称关系，但整体仍存在局限；此外，因果模型更依赖词形信息，而掩码与扩散模型则更依赖上下文信息。该研究揭示了语义空间中关系几何表征的不均衡性，提示仅依赖分布性信息难以充分捕捉所有语义关系的本质特征。
链接: https://arxiv.org/abs/2607.26762
作者: Zhihan Cao,Hiroaki Yamada,Simone Teufel,Tatsuya Hiraoka,Kentaro Inui,Hitomi Yanaka,Takenobu Tokunaga
机构: 未知
类目: Computation and Language (cs.CL)
备注: Manuscript under review
Abstract:When it comes to generating vector representations of words, current language models are achieving high-quality results. However, what is not known is the extent to which knowledge about semantic relations is represented in the geometry of the semantic spaces created in this way. In order to answer this question, we study the relation geometry of such semantic spaces from three perspectives. We first examine whether words standing in a particular relation to a target word~(called relata) occupy the same region in semantic space, and whether the regions corresponding to different relations are distinct from each other. We then verify to what extent semantic spaces reflect certain well-known properties of relations, such as symmetry, asymmetry, and transitivity. Finally, we consider which information about the target words and relata is more important for relation geometry: their surface forms, or their contexts. We conduct experiments on six semantic relations using causal, masked, and diffusion language models. The results show that relata in asymmetric relations relatively clearly occupy a distinct region in semantic space. Asymmetric relations’ properties are only moderately well encoded in the semantic space, yet better than those of symmetric ones. Furthermore, when considering the question which information source has the strongest impact on results amongst the models we evaluated, we find that lexical information tends to be more important for the causal language model, whereas contextual information is more important for the masked and diffusion language models. Our results empirically show that relation geometry is not equally well-represented for all relations in semantic space, suggesting that there is a difference in how well semantic relations might be learned from distributional information alone.
[NLP-26] Metis: Memory Foundation Model
【速读】： 该论文旨在解决当前AI代理（AI agent）中记忆能力依赖外部模块实现的局限性，即尽管基础模型已具备多模态和大推理能力，但其原生记忆（native memory）能力仍未被充分挖掘。核心问题在于如何将记忆功能内化至基础模型自身架构中，以实现更高效、可端到端优化的记忆机制。解决方案的关键在于提出“记忆基础模型”（memory foundation models）的概念，从两个维度重构原生记忆：一是将动态演化的记忆状态嵌入模型主干网络内部，二是通过模型计算自主完成信息存储与利用的原生记忆过程。基于此，作者构建了首个原型系统Metis，其创新性地引入一种新型架构，使基础模型具备原生记忆状态，并通过记忆注意力机制访问历史信息；同时，利用大规模专用训练数据及多种中段训练优化目标，使模型在训练过程中习得记忆操作能力。此外，Metis的在线记忆维护无需梯度更新，仅需前向传播即可完成记忆更新，推理阶段所有模型权重保持冻结，记忆状态通过标准前向计算自动演化。实验表明，Metis展现出有效的原生记忆能力，且在架构设计、优化效率与运行性能方面具有显著优势。为推动后续研究，相关代码与模型权重已开源发布。
链接: https://arxiv.org/abs/2607.26760
作者: Zeyu Zhang,Ziliang Guo,Yihang Sun,Xichong Zhang,Xixuan Hao,Zehao Lin,Yang Zhang,Xiaoyan Zhao,Tong Shen,Bo Tang,Zhi-Qin John Xu,Junchi Yan,Haofen Wang,Xu Chen,Feiyu Xiong,Zhiyu Li,Tat-Seng Chua
机构: Beijing MemTensor Technology Co., Ltd.(北京膜张力科技有限公司); Renmin University of China (中国人民大学); National University of Singapore (新加坡国立大学); Meta (Meta); Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 42 pages, 9 figures, 14 tables
Abstract:Recent advances in AI agents have increasingly internalized native capabilities into their underlying foundation models, giving rise to multimodal foundation models and large reasoning models. However, agent memory is still primarily implemented through external modules, leaving the native memory capability largely unexplored. In this paper, we take a first step toward this direction by introducing memory foundation models, which empower foundation models with native memory capabilities. We formalize native memory from two perspectives: a persistent and dynamically evolving memory state within the backbone, and native memory procedures that autonomously store and utilize information through model computation. We show that native memory offers advantages in architecture, end-to-end optimization, and efficiency. Based on this formulation, we propose Metis, the first prototype of memory foundation models. Metis introduces a new architecture that equips a foundation model with a native memory state, allowing historical information to be compressed into the model and accessed through memory attention. We construct large-scale memory-specific training data and introduce multiple optimization objectives to acquire these native memory procedures through mid-training. The online memory maintenance of Metis is gradient-free, and the memory update requires only a forward pass. At inference time, all learned model weights remain frozen, while the native memory states are autonomously transformed through standard forward computation. Through extensive experiments, we show that Metis exhibits native memory capabilities and further provide a detailed analysis of its strengths, limitations, and behaviors. To facilitate future research on memory foundation models, we release our project and model checkpoints.
[NLP-27] Phoneme- vs. Character-Level Targets and Selective State-Space Models for Intracortical Brain-to-Text
【速读】： 该论文旨在解决侵入式皮层脑机接口（intracortical brain-to-text）系统中解码器架构选择与输出目标设计之间的关键问题，具体聚焦于两个未充分探索的设计维度：一是选择性状态空间模型（Mamba）是否优于传统循环神经网络（Recurrent Decoder），二是输出目标（音素级 vs. 字符级）与解码器类型之间的交互效应。研究在公开的“Brain-to-Text '25”基准上构建了一个受控的2×2实验网格（GRU与混合Mamba解码器；音素与字符目标），统一采用连接时序分类（CTC）目标函数进行训练以保证可复现性。结果表明，循环基线仍表现最优：最佳音素级GRU模型达到12.62%的词错误率（PER）和21.19%的字错误率（WER），经外部语言模型（LM）重打分后，字符级GRU模型进一步提升至13.39%的字符错误率（CER）和26.28%的WER。尽管混合Mamba解码器具备竞争力，但未能超越循环基线。通过消融实验分离了各架构贡献，并结合错误分析发现，系统失败模式具有表征依赖性：音素级解码主要出现类似发音特征的混淆错误，而字符级解码则更多表现为词汇及词边界错误。因此，该研究的关键解决方案在于通过系统性对比验证了当前循环解码器在脑-文本任务中的优越性，并揭示了解码器架构与输出目标间复杂的交互关系及其对错误模式的影响。
链接: https://arxiv.org/abs/2607.26751
作者: Lucas Zamora Vera,Jose A. Gonzalez-Lopez
机构: Universitat Oberta de Catalunya (UOC), Spain; University of Granada, Spain
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
备注: 6 pages, 1 figure, 6 tables, submitted to IberSPEECH 2026
Abstract:State-of-the-art intracortical brain-to-text systems pair a neural-sequence phone decoder with an external language model. Two design axes remain underexplored: whether selective state-space models (Mamba) improve on recurrent decoders, and how the output target (phonetic vs.\ character) interacts with that choice. On the public Brain-to-Text '25 benchmark, we study a controlled 2x2 grid (GRU vs.\ hybrid Mamba decoder; phonetic vs.\ character targets) trained with a CTC objective under one reproducible protocol. The recurrent baseline remains strongest: the best phonetic GRU reaches 12.62% PER and 21.19% WER, while the best textual GRU after LM rescoring reaches 13.39% CER and 26.28% WER. The Mamba hybrid is competitive but does not surpass it. Ablations isolate architectural contributions, and error analysis shows representation-dependent failures: articulatory-like phoneme confusions vs.\ lexical and word-boundary errors.
[NLP-28] AtmosERC: Modeling Dialogue-Level Affective Atmosphere for Emotion Recognition in Conversation
【速读】： 该论文旨在解决情感识别在对话（ERC）任务中因全局上下文信息异质性而导致的无关信息干扰问题，尤其关注对话层面的情感氛围这一关键影响因素。现有方法多依赖于以语境为中心的建模，但并非所有上下文信息对情绪预测均具有同等价值，因此如何有效甄别并利用与情感相关的核心语境信号成为关键挑战。本文提出了一种基于图结构的ERC框架AtmosERC，其核心创新在于将每段对话建模为基于话语和发言者构建的对话图，并通过一种关系感知的图提取器，从异构的图信号中筛选并融合出具有对话级和发言者条件的“情感氛围先验”（dialogue-level affective atmosphere prior）。该紧凑的先验不仅可指导轻量级序列化情绪预测，还可转化为提示级线索用于大语言模型（LLM）驱动的ERC系统，无需修改底层模型架构。实验结果表明，AtmosERC在四个主流ERC基准上均显著提升了轻量化模型性能，作为插件式提示增强大语言模型的泛化能力，并在局部情绪波动下表现出更稳定的预测表现。
链接: https://arxiv.org/abs/2607.26726
作者: Weijie Feng,Tongwei Zhang,Binbin Liu,Zhiyong Cheng
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Emotion Recognition in Conversation (ERC) aims to predict utterance-level emotions in dialogues and has largely advanced through context-centric modeling. However, global context is a heterogeneous signal, and not all contextual information is equally relevant to emotion prediction. This paper focuses on the affect-oriented component of this signal, termed dialogue-level affective atmosphere, which captures a latent tendency commonly reflected in conversational emotion patterns. To estimate and exploit this tendency, we propose AtmosERC, a graph-based ERC framework that models each dialogue as a conversational graph over utterances and speakers. A relation-aware graph extractor filters and fuses heterogeneous graph signals to produce dialogue-level and speaker-conditioned affective priors. The resulting compact prior guides lightweight sequential emotion prediction and can also be verbalized into prompt-level cues for LLM-based ERC without modifying backbone models. Experiments on four ERC benchmarks show that AtmosERC improves lightweight ERC, enhances LLM-based ERC as a plug-in cue, and yields more stable predictions under local emotional deviations.
[NLP-29] Automated Multilabel Mpox Research Classification with Explainable Transformer Models
【速读】： 该论文旨在解决新冠疫情后全球范围内持续存在的猴痘（Mpox）研究文献数量激增所带来的信息过载问题，尤其在疫苗研发、流行病学监测及病毒演化等关键领域，亟需高效的信息组织与分析手段。其核心解决方案是采用多标签分类（multilabel classification）方法，结合生成式预训练模型BERT对14,590篇猴痘相关研究文献进行自动化主题分类，涵盖疫情暴发、疫苗接种、流行病学等关键领域。实验结果表明，BERT模型在分类任务中表现最优，达到97.05%的准确率、97.67%的微平均F1分数和96.46%的宏平均F1分数。为进一步提升模型可解释性，研究引入SHAP（SHapley Additive exPlanations）分析方法，识别影响分类决策的关键词汇特征与语义模式。该方法不仅显著提升了文献管理效率，还为科研人员、政策制定者及医疗工作者快速获取精准信息提供了有力支持，从而优化公共卫生响应机制。
链接: https://arxiv.org/abs/2607.26700
作者: Tanjim Taharat Aurpa
机构: University of Frontier Technology, Bangladesh (UFTB)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:The Mpox outbreak remains a serious public health issue, with the WHO (World Health Organization) reporting increasing cases in some regions. Research on Mpox is vital for several reasons, including vaccine development, diagnostic improvement, viral evolution studies, and preventing future outbreaks. However, the large amount of research being published makes it difficult to organize and analyze information efficiently. This study focuses on using multilabel classification to categorize 14590 Mpox research articles into key topics such as outbreaks, vaccination, and epidemiology. Among the different AI models tested, BERT performed the best, achieving 97.05% accuracy, 97.67% micro F1 score, and 96.46% macro F1 score. To better understand how the model makes decisions, SHAP was used to analyze significant word features and patterns. The results show that BERT can help automate the classification of Mpox research, making it easier for researchers, policymakers, and healthcare workers to quickly find relevant information, saving time and improving public health efforts.
[NLP-30] Constitutional Midtraining: Content Presence Drives Alignment Gains
【速读】： 该论文旨在解决生成式 AI 在后训练阶段对齐（alignment）的脆弱性问题，即当前主流的后训练对齐方法往往仅实现表面化的对齐效果，难以在后续微调过程中保持稳定。其核心挑战在于：是否存在一种在模型训练中期、与后训练阶段完全隔离的干预手段，能够实现持久且可泛化的对齐效果。为此，作者提出“宪法式中期干预”（constitutional midtraining）作为解决方案，其关键在于在1200亿参数规模下，在模型训练中期引入基于价值原则的、结构化的宪法语料（constitutionally grounded corpus），该语料源自 Anthropic 的宪法框架，并采用2×2因子设计（课程顺序 × 深思熟虑推理）构建四种不同的宪法中段训练条件及一个仅回放（replay-only）的对照组。实验结果表明，经过宪法式中期干预的模型在多种基准测试中表现出更强的对齐泛化能力与持续性，尤其在对抗勒索（blackmail）等高压场景下，尽管标准微调（SFT）会普遍诱发模型产生勒索倾向，但宪法式中期干预显著削弱了这一倾向，且优势在后续良性微调阶段仍得以保留（-17.5个百分点）。值得注意的是，宪法内容的存在本身比其内部结构更为重要，且该方法在平均性能上未对模型在 MMLU、ARC-Easy、PiQA、GSM8K 等能力指标上的表现造成损失。因此，该研究证明：在训练中期引入少量宪法内容即可实现广泛而持久的对齐增益，是一种成本极低、可无缝集成于以 SFT 为中心的对齐流程中的有效补充策略。
链接: https://arxiv.org/abs/2607.26654
作者: Desiree Cho,Cameron Tice,Bernie Hogan,Hunar Batra,Puria Radmard,Jun Zhao,Nigel Shadbolt
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注:
Abstract:Post-training alignment is often shallow, eroding under fine-tuning. Whether midtraining interventions, cleanly isolated from post-training, can produce durable alignment remains untested. We test this via constitutional midtraining: inserting principled, values-based content into midtraining against a replay-only control at 120B scale. Our 394M-token constitutional corpus, built from Anthropic’s Constitution, uses a 2x2 factorial design (curriculum ordering x deliberative reasoning) to produce four constitutionally midtrained conditions plus a control, evaluated on self-generated and established benchmarks including alignment under pressure, value conflict resolution, blackmail, and emergent misalignment across three stages: post-midtraining, post-SFT, and post-benign fine-tuning. Constitutionally midtrained models outperform the control on alignment generalization and durability, notably on blackmail: SFT instills a blackmail propensity in all models, but constitutional midtraining blunts it, with the advantage surviving benign fine-tuning (-17.5pp). This durability does not extend to settings requiring active resistance to in-context pressure or conflict, where the advantage attenuates after SFT. The presence of constitutional content at midtraining also matters more than its structure, and constitutional midtraining incurs no cost, on average, on the capabilities we test (MMLU, ARC-Easy, piqa, GSM8K) at any stage. A modest amount of constitutional content at midtraining could therefore yield broad, persistent alignment gains, offering a cheap, complementary addition to SFT-centered pipelines. Code, data, and models are available.
[NLP-31] Filesystem-Based Memory for LLM Agents : Organization Evolution and Sustainability
【速读】： 该论文旨在解决当前大型语言模型（LLM）智能体在长期记忆管理中普遍采用文件系统作为存储介质时所面临的核心问题：即默认假设——智能体能够持续有效地组织随时间积累、冲突和过期的海量记忆内容，且这种组织行为能带来实际效益——尚未经过系统验证。其解决方案的关键在于首次对基于文件系统的记忆机制进行系统性探索，将记忆文件系统建模为三个角色协同运作的框架：管理代理负责整合与组织新输入内容，检索代理通过引用来源回答查询，执行代理则提供任务轨迹并将其提炼为技能，从而在单一存储中统一陈述性记忆与程序性知识。研究通过在长对话基准和具身任务中多维度实验（包括记忆结构、数据流规模、工具调用方式及代理能力强度），揭示了有序组织的核心价值在于显著降低检索成本（在大规模材料下可节省约50%），但现有智能体普遍无法维持长期组织性，且组织性本身未能转化为更优的输出质量。此外，研究发现工具集的选择对存储结构的影响甚至超过模型本身的改变，表明文件系统默认并非固定范式，而应被视作一个可设计的记忆架构空间。
链接: https://arxiv.org/abs/2607.26637
作者: Sizhe Zhou,Sheldon Yu,Hui Wei,Junda Wu,Siru Ouyang,Yizhu Jiao,Shijia Pan,Julian McAuley,Yu Zhang,Tong Yu,Jiawei Han
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); University of California, Merced (加州大学默塞德分校); Adobe Research (Adobe 研究院); Texas AM University (德州农工大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 59 pages, 12 figures, 18 tables
Abstract:Deployed LLM agents increasingly keep their long-term memory as a filesystem: a directory tree of markdown files that the agent itself reads, writes, and reorganizes through generic file tools. Yet research has largely passed over this medium: prior systems design bespoke memory representations and study retrieval over them, leaving the default’s two working assumptions untested: that an agent can keep a growing store organized as memories accumulate, conflict, and go stale, and that this organization pays. We present the first systematic exploration of filesystem-based memory for LLM agents. We formalize the setting as three roles around one memory filesystem: a management agent integrates and organizes incoming content, a search agent answers queries with cited sources, and an execution agent supplies task trajectories that are distilled into skills, unifying declarative memory and skills in a single store. Across long-conversation benchmarks and embodied tasks, we vary memory shape (agent-organized hierarchy, verbatim dump, chunk retrieval), stream scale, tool harness (sandboxed shell, memory-tool-style functions, varied search tooling), and the strengths of the management and search agents, tracking answer quality, cost, and store health as memory grows. What organization reliably buys is search economy: organized stores roughly halve retrieval cost where material is large. Today’s agents, however, fall short of the default’s promise: in our growth study, organization erodes for all but the strongest management agent, and no agent we measure converts organization itself into better answers. And the model is not the only lever over a store’s shape: changing the tool set alone reshapes the store as strongly as swapping the model. The study turns the filesystem default from an assumption into a design space for agent memory.
[NLP-32] Revisiting Lossy Verification in Speculative Decoding: Mechanisms Trade-offs and Failure Modes
【速读】： 该论文旨在解决生成式 AI（Generative AI）中推测解码（Speculative Decoding, SD）技术在采用有损验证（lossy verification）策略时所引发的生成质量不稳定甚至严重退化的问题。其核心挑战在于：尽管有损验证通过放宽分布匹配约束以提升推理效率，但这种放松会隐式地改变解码分布，导致输出质量不可控。论文的关键解决方案在于提出一种系统性的分布分析框架，揭示现有方法本质上可归类为两类：基于截断（truncation-based）的验证与协作式（collaborative）验证。研究发现，基于截断的方法存在根本性缺陷——由于分布失真，性能可能显著低于真实的截断采样基线；而协作式验证则需严格控制草稿模型概率对目标模型概率的“超调”（overshoot），这是保障生成质量的核心原则。该工作通过构建诊断评估框架，在精选基准上验证了上述发现，为高效且高质量的推测解码提供了理论指导与实践依据。
链接: https://arxiv.org/abs/2607.26627
作者: Tianyu Wang,Yuxuan Zhou,Wenbin Wang,Heng Li,Zikai Xiao,Junyuan Shang
机构: Baidu Inc. (百度); Zhejiang University (浙江大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Speculative Decoding (SD) accelerates large language model inference by allowing a lightweight draft model to propose tokens that are subsequently verified in parallel by a larger target model. Recent approaches introduce lossy verification schemes to further improve efficiency by relaxing strict distributional matching. Yet such relaxation silently rewrites the decoding distribution, and the resulting acceleration can come at the cost of unstable, sometimes severely degraded generation quality. In this work, we present a principled analysis of the distributions induced by lossy verification methods. We show that many seemingly distinct approaches differ only superficially and can be classified into two categories: truncation-based verification and collaborative verification. We further construct a diagnostic evaluation framework across curated benchmarks. For truncation-based methods, we identify a fundamental pitfall: performance can degrade significantly compared to the true truncation sampling baseline due to distributional distortion. For collaborative verification, we uncover a key principles: controlling the overshoot of draft probabilities relative to target probabilities is essential to prevent low-quality outputs. Our code is available at this https URL.
[NLP-33] FedWeave: Rethinking the Unit of Specialization in Heterogeneous Federated MoE-LoRA
【速读】： 该论文旨在解决联邦学习环境下大语言模型（LLM）在异构任务场景中因客户端间任务多样性导致的跨任务干扰与梯度冲突问题。现有方法如联邦MoE-LoRA虽通过专用的低秩适配（LoRA）专家与条件路由机制实现任务专业化，但通常以客户端为粒度进行专家分化，隐含假设客户端内部任务一致性，难以应对真实场景中的复杂任务分布。其核心挑战在于：专家需保持“纯净性”（pattern-coherent updates），即更新模式一致以维持专业性；而路由模块则需“对比性”（contrast），依赖混合任务观测以支持专家间的有效区分。为此，论文提出FedWeave框架，采用非对称聚合策略，将专家聚合与路由优化分离，分别满足上述双重需求。具体而言，FedWeave引入无监督原型发现机制，在本地构建任务原型桶并跨客户端对齐，实现基于原型级别的专家聚合，同时保留原始客户端的混合任务轨迹用于路由训练。推理阶段，该框架支持稀疏激活（仅一个活跃专家），却几乎完全保留软路由性能。理论分析进一步揭示了非对称聚合的优势：通过非模式污染控制专家收敛稳定性，识别由碎片化路由轨迹引发的共识误差，并对稀疏推理风险进行上界约束。在主流大模型骨干与异构多任务基准上的实验表明，FedWeave持续优于强基线方法，消融实验也验证了其设计的有效性。
链接: https://arxiv.org/abs/2607.26618
作者: Donghang Duan,Xu Zheng,Lizong Zhang,Chong Mu,Meng Han
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 14 pages, 4 figures, 5 tables
Abstract:Federated PEFT enables LLMs to collaboratively adapt to decentralized private data without sharing raw examples. However, task heterogeneity across clients can cause cross-task interference and gradient conflicts during aggregation. Federated MoE-LoRA addresses this challenge through specialized LoRA experts and conditional routing. Yet existing methods typically specialize at client granularity, implicitly assuming task-coherent clients. Our core insight is that experts need purity, namely pattern-coherent updates that preserve specialization, whereas routers need contrast, namely mixed-task observations that support expert comparison. We propose FedWeave, a framework that adopts asymmetric aggregation, separating expert aggregation from router optimization to meet these two requirements. FedWeave uses unsupervised prototype discovery to form local buckets and align them across clients, enabling prototype-level expert aggregation while retaining mixed-task client trajectories for router training. At inference, FedWeave performs sparse inference with one active expert while preserving nearly all soft-routing performance. Our theoretical analysis explains why asymmetric aggregation is advantageous: it controls expert convergence in stationarity through off-pattern contamination, identifies the consensus error induced by fragmented router trajectories, and bounds sparse-inference risk. On a heterogeneous multi-task benchmark with mainstream LLM backbones, FedWeave consistently outperforms strong baselines, while ablations verify the effectiveness of our design.
[NLP-34] WikiLoop: Jointly Learning to Build and Navigate Agent -Native Wikis with Downstream Feedback
【速读】： 该论文旨在解决知识库构建与查询在传统方法中被孤立优化的问题：检索增强型智能体依赖于外部维护的固定索引，而知识库构建过程无法从下游使用效果中获得反馈信号。为此，本文提出WikiLoop——一个反馈耦合式框架，通过联合学习的方式构建并导航由智能体自主维护的、面向机器导航的持久化链接页面知识库（agent-native Wiki）。其核心解决方案在于设计一种角色条件化的共享策略（role-conditioned shared policy），统一支持“导航器”（Navigator）和“构建者”（Builder）两个功能接口：导航器以“充分性优先于效率”为目标，在完全收集证据后才施加检索成本惩罚；构建者则基于下游导航性能的变化评估候选编辑的效用，并通过守卫惩罚机制避免对无关查询造成性能退化。训练采用分阶段策略，先进行角色特异性优化，再在角色同质化批次上进行最终联合优化。实验表明，以Qwen3.5-9B为基底模型，WikiLoop在AuthTrace数据集上达到62.6的综合答案正确率，较LLM-Wiki base提升6.3点，尤其在多文档查询任务中优势显著；控制实验验证了双重目标的有效性，且学习到的编辑仍对保留的导航器具有实际价值。配对比较进一步显示，最终的共享策略同时保留了两者的专长能力，相较各自专用模型分别提升了0.4点的导航与端到端答案正确率，并成功将双接口整合为单一模型。此外，无需特定数据集微调，WikiLoop在HotpotQA和MuSiQue上亦优于同基底的LLM-Wiki base，展现出良好的泛化能力。
链接: https://arxiv.org/abs/2607.26604
作者: Haoliang Ming,Feifei Li,Wenhui Que
机构: 未知
类目: Computation and Language (cs.CL)
备注: 13 pages, 2 figures, 12 tables. Includes supplementary material
Abstract:Knowledge-base construction and querying are typically optimized in isolation: retrieval-augmented agents operate over a fixed, externally maintained index, whereas construction receives no signal from downstream use. We present WikiLoop, a feedback-coupled framework that jointly learns to build and navigate an agent-native Wiki, a persistent linked-page knowledge base designed for machine navigation. A role-conditioned shared policy supports two interfaces: a Navigator retrieves evidence from the Wiki to answer queries, and a Builder proposes structured edits evaluated through downstream navigation. The Navigator follows a sufficiency-before-efficiency objective that applies retrieval-cost penalties only after full evidence has been collected. The Builder learns from utility differences: a frozen Navigator scores each candidate edit by its change in downstream performance, while a guard penalty discourages regressions on unrelated queries. Training combines sequential role-specific optimization with a final joint stage over role-homogeneous batches. With Qwen3.5-9B as the common backbone, WikiLoop reaches 62.6 aggregate Answer Correctness on AuthTrace, 6.3 points above LLM-Wiki, base, with the largest gains on multi-document queries. Controlled comparisons support the intended effects of both objectives, and the learned edits remain useful to a held-out Navigator. Paired comparisons indicate that the final shared policy largely retains both role-specific capabilities, improves Navigator and end-to-end Answer Correctness by 0.4 points relative to the corresponding specialist references, and consolidates both interfaces into one model. Without dataset-specific training, WikiLoop also improves over the same-backbone LLM-Wiki, base on HotpotQA and MuSiQue.
[NLP-35] Where Detectors Fail: Closing the Tail-Domain Gap with Expert-Guided Mutual Distillation
【速读】： 该论文旨在解决多模态虚假新闻检测中因域间数据不平衡及图文语义不一致导致的跨域泛化能力差的问题，其核心挑战在于模型容易依赖不可靠的域特定捷径（domain-specific shortcuts），从而在新域上表现退化。解决方案的关键是提出专家引导的互蒸馏框架（Expert-Guided Mutual Distillation, EGMD），通过多层次协同机制增强对可信证据的识别：在输入层，通过成对一致性校准（pair-level coherence calibration）将语义一致性编码为共享增益以优化融合；在表示层，引入专家引导的教师模型，对齐不同域的统计特性，并促使域特异性模式集中在专用专家网络中；在决策层，采用原型锚定的域特定学生模型，结合互学习与双通道蒸馏，继承教师模型的特征几何结构与校准预测，同时抑制局部域先验。此外，研究构建了Weibo_Balanced这一领域平衡基准，有效分离数据不平衡对泛化性能的影响。实验表明，EGMD在两种语言的四个数据集上均达到当前最优准确率，且域偏差最高降低57.3%。
链接: https://arxiv.org/abs/2607.26555
作者: Xuan Feng,Guihong Liu,Tianlong Gu,Shuai Zhao,Xuemin Wang,Chenzhong Bin,Yang Liu,Bo An
机构: 1. University of Science and Technology of China (中国科学技术大学); 2. Tsinghua University (清华大学); 3. Alibaba Cloud (阿里巴巴云); 4. Peking University (北京大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Multimodal fake news detectors often generalize poorly across domains because they learn to trust unreliable evidence: domain-specific shortcuts amplified by imbalanced data and semantically inconsistent text-image pairs that make cross-modal evidence unreliable. We propose Expert-Guided Mutual Distillation (EGMD), which learns what evidence to trust across the prediction pipeline. At the input level, input-level calibration encodes pair-level coherence as a shared gain before fusion. At the representation level, an expert-guided teacher aligns domain statistics and encourages domain-specific patterns to concentrate in specialized experts. At the decision level, prototype-anchored domain-specific students use mutual learning and dual-channel distillation to inherit the teacher’s feature geometry and calibrated predictions while discouraging local domain priors. We further construct Weibo_Balanced, a domain-balanced benchmark that isolates the effect of imbalance on generalization. Across four datasets in two languages, EGMD achieves state-of-the-art accuracy while reducing domain bias by up to 57.3%.
[NLP-36] Prosody-driven Jailbreaks in Audio LLM s: A Controlled Study and Mechanistic Analysis ACM-MM’26
【速读】： 该论文旨在解决生成式语音大模型（Audio LLM）在端到端语音交互中，因语音表现力（prosody）的细微变化而引发的安全越狱（jailbreak）风险问题，尤其关注在保持文本内容不变的情况下，语音传递方式（speech-delivery presets）的声学属性变异是否可诱发模型输出越狱行为。其核心解决方案在于提出一种黑盒评估框架PJ-Break，结合具有声学验证属性的600样本基准数据集AdvAudio-Prosody，系统性地测试六种预设语音风格（涵盖唤醒度、权威感与语速等声学特征）对模型安全性的影响。实验结果表明，在固定文本的前提下，特定语音风格（如“恐慌”“愤怒”“快速”）显著提升越狱成功率（最高达38/95），远超中性风格（4/95），且情感化语音本身即具备远高于情感化文本的越狱能力（44/95 vs. 11/95）。研究强调，匹配文本的语音表现力应作为音频大模型安全评估中的首要考量因素。
链接: https://arxiv.org/abs/2607.26541
作者: Jiachen Qian,Junyu Li
机构: City University of Hong Kong (香港城市大学); City University of Hong Kong (Dongguan) (香港城市大学（东莞）)
类目: ound (cs.SD); Computation and Language (cs.CL)
备注: Accepted at ACM Multimedia 2026 (ACM MM '26). 9 pages, 3 figures. Supplementary material included
Abstract:Audio-capable foundation models enable end-to-end spoken interaction, but they also introduce safety risks beyond transcript content. It remains unclear how much jailbreak capability can arise from matched-text variation in speech delivery rather than from lexical rewriting or broader style transfer. We study this question by holding transcript content fixed and varying six speech-delivery presets whose acoustic attributes may co-vary. We present PJ-Break, a black-box evaluation protocol with presets targeting arousal, authority, and speaking rate, together with AdvAudio-Prosody, a 600-sample benchmark with acoustically verified attributes. On the exact post-QC Qwen2-Audio panel, the Q=1 Panic (38/95), Anger (35/95), and Fast (32/95) presets are all well above Neutral (4/95). The fixed six-query pool covers 44/95 Qwen2-Audio seeds and 15/95 GPT-4o seeds and exceeds a matched-budget StyleBreak reimplementation (27/95) on Qwen2-Audio. A same-voice pool excluding the confounded Commanding condition still reaches 40/95, and a retained-panel ablation shows emotional-delivery audio alone (44/95) is far more effective than emotional text alone (11/95). Exploratory surrogate diagnostics and pilot mitigation observations are secondary, non-core analyses. Overall, matched-text speech delivery should be treated as a first-class factor in Audio LLM safety evaluation
[NLP-37] Which RAG Paradigm Wins at Scale? A Scaling Study of Retrieval-Augmented Generation Paradigms
【速读】： 该论文旨在解决现有检索增强生成（Retrieval-Augmented Generation, RAG）方法在不同规模语料库下的准确率与成本（如计算开销、延迟、令牌消耗等）之间权衡关系不清晰的问题。现有研究通常在单一语料库规模上评估不同RAG范式，缺乏对多尺度扩展性（accuracy-cost scaling）的系统性分析。为填补这一空白，本文设计了一项受控的语料库缩放实验，构建了一个包含28个严格嵌套层级的语料库梯度，从约1,000到512,000篇文档递增，同时保持问题集和核心相关/对抗性文档不变。在统一阅读器与评估协议下，量化了官方准确率、构建与查询阶段的令牌消耗及延迟。实验结果表明，BM25在该控制设置中表现最优：其始终位于帕累托前沿的低成本端，且在中等及以上规模下超越其他方法，且无需依赖大语言模型（LLM）进行构建。文件系统代理（File-System Agent）在最小层级表现接近或略优于BM25，但在全规模下查询令牌消耗高达39倍，准确率落后近20个百分点。通过引入匹配的检索替换策略，实现“代理+BM25”组合后，全规模准确率达69.4，显著优于原始代理（36.9）和原生BM25（54.8）。图结构RAG则遭遇构建瓶颈：其最重的构建器每索引一个语料库令牌需消耗最多24.6个生成式LLM令牌，却仅能处理全语料库前2%的内容；而可扩展版本在共享层级上仍不及BM25的准确率。因此，该研究的关键解决方案在于提出一种可控的多尺度评估框架，并揭示了当前主流RAG范式在扩展性与效率上的本质差异，强调了基于传统检索（如BM25）与高效混合架构在实际部署中的关键优势。
链接: https://arxiv.org/abs/2607.26497
作者: Pengyu Wang,Benfeng Xu,Shaohan Wang,Xin Zeng,Huarui Wu,Lei Zhang,Licheng Zhang
机构: University of Science and Technology of China (中国科学技术大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Retrieval-augmented generation (RAG) methods range from lexical and dense retrieval to graph-based indexing and agentic search. They are usually evaluated on different benchmarks at one corpus size, leaving their accuracy-cost scaling unclear. To bridge this gap, we present a controlled corpus-scaling study of these four paradigms. A ladder of 28 strictly nested tiers grows from roughly 1,000 to 512,000 documents while questions and a fixed bedrock of relevant and adversarial documents remain unchanged. Under one reader and judging protocol, we measure official accuracy, construction and query tokens, and latency. Our experimental results show that BM25 scales best in this controlled setting: it defines the low-cost end of the Pareto frontier at every measured tier and leads accuracy from mid-scale onward, without LLM-based construction. The File-System Agent matches or slightly exceeds BM25 at the smallest tiers but uses 39 times more query tokens per answer at the bedrock and falls nearly 20 points behind at full scale. A matched retrieval swap reverses this failure: Agent+BM25 scores 69.4 at full scale, versus 36.9 for raw-file agency and 54.8 for native BM25 on the same 150 questions. Graph-based RAG hits a construction wall: its heaviest builders use up to 24.6 generative LLM tokens per indexed corpus token yet stop within the first 2% of the full corpus, while scalable variants remain less accurate than BM25 at shared tiers.
[NLP-38] Learning Dynamic User Personas from Implicit Interaction Streams via Iterative Refinement
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在个性化用户交互时依赖显式偏好监督（如成对比较或人口统计属性）所带来的局限性，尤其在自然交互场景中难以广泛应用的问题。其核心挑战在于如何在缺乏明确反馈的情况下，实现对用户动态人格特征（user personas）的持续建模与更新。该研究提出IRIS框架，通过从日常对话流中提取隐式行为信号，并基于预测驱动的闭环机制，迭代优化用户画像表示，从而实现无需显式反馈的动态人格学习。解决方案的关键在于构建一个以行为预测、人格稳定性及决策预测为核心的评估协议，并利用真实世界匿名数据（Reddit的r/AmItheAsshole社区）验证其有效性：在100名作者的实验中，IRIS在决策预测准确率上达到61.0%，显著优于静态人格、仅依赖记忆检索及无个性化基线方法，表明隐式行为建模可作为可扩展的个性化替代方案，为需要持续演化的自适应对话系统和具身智能体提供坚实基础。
链接: https://arxiv.org/abs/2607.26473
作者: Haifeng Wu
机构: PayPal(贝宝)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 23 pages, 1 figure, 3 tables
Abstract:Personalizing large language models (LLMs) to individual users is essential for improving user experience, yet existing approaches typically rely on explicit preference supervision such as pairwise comparisons or demographic attributes, limiting their applicability in natural interaction settings. We propose IRIS, a framework that learns dynamic user personas directly from implicit interaction streams by extracting behavioral signals from everyday conversations and iteratively refining persona representations through a prediction-driven closed loop without requiring explicit feedback. We introduce an evaluation protocol based on behavior prediction, persona stability, and decision prediction. A proof-of-concept study on a synthetic interaction stream derived from public-domain autobiographical text shows that IRIS produces stable personas and distinguishes individual users while revealing limitations of memory-only approaches on recall-oriented metrics. We then validate IRIS on anonymized real-world Reddit r/AmItheAsshole (AITA) data, with personas built solely from each author’s historical interactions. Across 100 authors, IRIS achieves the highest decision prediction accuracy among all evaluated methods (61.0%), outperforming static personas, memory-only retrieval, and no-personalization baselines. These results suggest that implicit behavioral modeling provides a scalable alternative to explicit preference learning for personalized LLMs and offers a practical foundation for adaptive conversational systems and embodied agents that require continuously evolving models of their users.
[NLP-39] ForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在持续知识更新过程中难以有效保留已有知识的问题，尤其关注模型在多次迭代修改下知识遗忘的动态演化机制。现有评估范式多聚焦于单步推理或静态知识编辑，无法捕捉知识随时间演进中的保留与退化过程。为此，论文提出ForgetBench基准，通过概念级问答（concept-based QA）与情景级问答（scenario-based QA）两种互补评估范式，区分孤立事实记忆与结构化关系知识的保持能力。基于序列化知识编辑框架，构建时序有序的知识流，并在多个编辑阶段评估模型表现；进一步引入统一的量化分析框架，对知识随时间演变的过程进行建模，实现对时间衰减、保留强度及跨实例稳定性的测量。实验结果表明，当前多数方法在长期知识保留与泛化质量之间难以取得平衡，凸显未来大语言模型亟需具备更鲁棒的记忆机制以支持知识的高效获取、动态更新与持久保存。
链接: https://arxiv.org/abs/2607.26455
作者: Ruxi Gu,Zhenliang Zhang,Wei Wang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 9 pages, 4 figures
Abstract:Large language models (LLMs) have demonstrated strong capabilities in knowledge acquisition and reasoning, yet their ability to retain previously acquired knowledge under repeated updates remains insufficiently understood. Existing evaluation paradigms primarily focus on single-step reasoning or static knowledge editing, which fail to capture the temporal dynamics of knowledge retention and degradation during continual model modification. In this work, we propose ForgetBench, a benchmark designed to systematically characterize forgetting behavior in LLMs under continual knowledge editing. ForgetBench introduces two complementary evaluation paradigms, namely concept-based QA and scenario-based QA, to disentangle isolated factual retention from structured relational knowledge preservation. Building upon a sequential editing framework, we construct temporally ordered knowledge streams and evaluate model behavior across multiple editing stages. To quantitatively analyze long-term retention dynamics, we further introduce a unified evaluation framework that models knowledge evolution over time, enabling the measurement of temporal decay, retention strength, and cross-instance stability. Extensive experiments across diverse models and editing methods demonstrate that existing approaches fail to strike a balance between long-term retention and generalization quality. Our findings highlight the need for more robust memory mechanisms that can effectively acquire, update, and preserve knowledge over time in future LLMs. Code will be released upon acceptance.
[NLP-40] Mergeable Model-Side Aggregation States for Long-Context Language Models
【速读】： 该论文旨在解决长上下文语言模型在上下文长度增长时，对非加性、基于集合的聚合任务（如基数估计、集合关系判断和分组统计）性能显著下降的问题。这类任务广泛存在于日志分析、程序输出、表格数据及多轮对话中。其核心挑战在于传统方法依赖生成式推理完成聚合，导致计算开销大且易出错。本文提出的解决方案关键在于引入一种模型侧的聚合接口，通过维护紧凑的基于哈希的超日志计数（Hash-based HyperLogLog, HLL） sketch 状态，与冻结的语言模型协同工作。该机制在处理上下文时，由提取器将每个相关记录映射为规范标识符，经哈希后更新HLL状态；这些状态可跨上下文片段合并或直接读出用于下游推理，避免了额外的“生成-执行-返回”循环。实验表明，采用2 KiB固定大小的HLL状态（不随上下文长度或集合基数增长），在一百万条记录的基数估计任务中平均相对误差仅为1.6%，在最多256个片段的合并测试中结果与单次遍历完全一致。在3,969个聚合-推理任务上，该方法在Gemma 4 (31B, BF16) 上达到99.2%准确率，仅比精确聚合低0.8个百分点（95%置信区间：0.5–1.3点），并显著优于直接全上下文推理及链式思维（CoT）推理，在多个基准测试中分别提升达56.3至63.2个百分点。
链接: https://arxiv.org/abs/2607.26448
作者: Dachuan Song,Junyu Yin,Zechen Hu,Xuan Wang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:A known limitation of long-context language models is their increasingly unreliable performance in non-additive, set-based aggregation as context length grows. Examples include cardinality estimation, set relationships, and grouped statistics, which widely exist in logs, program outputs, tables, and multi-turn conversations. To provide the aggregation state required by these tasks, we introduce a model-side aggregation interface that maintains compact Hash-based HyperLogLog (HLL) sketch states alongside a frozen language model. While the model processes the context, an extractor maps each relevant record to a canonical identity. The identity is then hashed and updates the HLL state. These states can be merged across context segments and/or read out directly for downstream reasoning, avoiding an additional generate-execute-return cycle. We validate the proposed approach by setting the HLL state size as 2 KiB (2,048 registers), which does not increase with context length or set cardinality. In a distinct-count experiment involving one million records, the mean relative error was 1.6%. In a separate merge test, states built from as many as 256 segments produced exactly the same readout as a single pass over the same stream. On 3,969 aggregate-then-reason tasks from 174 source windows, the fixed-budget interface reached 99.2% accuracy on Gemma 4 (31B, BF16), compared with 100.0% under exact aggregation; the paired gap was 0.8 percentage points (95% window-cluster CI: 0.5-1.3 points). On a matched set of 174 items, our method improved over direct full-context reasoning by 63.2 points on Qwen and 56.3 points on Gemma. The corresponding gains over chain-of-thought (CoT) reasoning were 60.9 and 63.2 points, respectively. On a fixed 1,200-task Oolong-Synth subset, our method reached 91.1% on Qwen and 99.3% on Gemma. Code is available at this https URL.
[NLP-41] Voice Memory for Agent ic Speech Recognition
【速读】： 该论文旨在解决生成式语音识别中因错误纠正过度而导致的正确词元破坏问题，尤其是在金融新闻等高敏感领域中，传统生成式错误修正（GER）方法存在高达64%的编辑错误率。其核心解决方案是提出一种名为Voice Memory的推理阶段仅限方案，采用“听者-思考者”（listener-thinker）架构：在流式处理时，一个冻结的校正器（corrector）基于单一领域特定的URL，决定是否对识别结果进行修正或保持1-best假设；异步地，一个基于得分门控的优化器通过有限编辑更新该文件，仅当编辑能严格提升保留分数时才接受。该架构将听者与思考者角色通过共享记忆耦合，不涉及权重更新，确保模型技能可审计且可移植。关键创新在于引入约束性修正机制，使系统发现“克制”作为核心能力——相比无约束的GER，Voice Memory将错误编辑率从64%降至35%。在十类HyPoradise领域测试中，该方法在开放校正器设置下将加权词错误率从8.36%降至7.52%（引入三个上下文示例后达7.47%），且未使任何数据集性能低于其1-best基线，显著收益集中在可恢复潜力大的场景，如航空指令（8.40%→3.40%）和远场噪声语音（CHiME-4, 12.69%→10.46%）。此外，记忆模块具备跨校正器家族迁移能力，推理路径零参数增量，已提供演示与示例代码以支持后续研究。
链接: https://arxiv.org/abs/2607.26410
作者: Chao-Han Huck Yang,Zih-Ching Chen,Piotr Zelasko,Zhehuai Chen,Jagadeesh Balam,Boris Ginsburg
机构: NVIDIA(英伟达)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: Preprint. Technical report and open source: this https URL
Abstract:We present Voice Memory, a inference-only scheme for agentic speech recognition: at stream time, a frozen corrector reads a single per-domain this http URL and decides per utterance whether to act on the hypothesis or abstain and keep the 1-best. Asynchronously, a score-gated optimizer revises that file through bounded edits, accepting an edit only when it strictly improves a held-out score. Extended from classical ASR-LM framework, we refer this split the listener-thinker architecture; the two roles are coupled only through the memory, so no weights change and the learned skill stays auditable and portable. Restraint turns out to be the operative skill this loop discovers: unconstrained generative error correction (GER) over-corrects, breaking correct tokens on up to 64% of its edits on financial news, and Voice Memory, reduces this rate to 35%. Across ten HyPoradise domains with an open corrector, Voice Memory, lowers weighted word error rate from 8.36% to 7.52% (7.47% with three added in-context examples) without regressing any dataset below its 1-best baseline; gains concentrate where recoverable headroom is largest, including air-travel commands (8.40% to 3.40%) and noisy far-field speech (CHiME-4, 12.69% to 10.46%). The memory transfers across corrector families and adds zero parameters to the inference path. A demo and example code are provided for future studies.
[NLP-42] Knowledge before Reasoning : EC-Reason -Bench a Training-Free Diagnostic Benchmark for LLM Enzyme Classification
【速读】： 该论文旨在解决生成式大模型（Generative AI）在酶功能预测任务中表现严重退化的核心问题：尽管通用大语言模型（LLM）在粗粒度的酶分类第一层级上表现尚可，但在要求完整输出四级酶委员会编号（EC number）时，其在第二至第四层级的准确率几乎降至零，而专业模型则保持可用性。该研究提出一种无需训练的诊断评估框架——EC-Reason-Bench，以揭示这一性能崩溃的根本原因，并探索不更新模型参数的前提下可恢复的性能潜力。其解决方案的关键在于将酶功能分类能力解耦为四个正交维度：输出结构、外部知识获取、推理结构与推理鲁棒性，并分别在推理阶段通过可控方法进行独立评估。实验表明：外部知识是决定性因素，开放书本（open-book）访问可显著提升封闭书本（closed-book）下的性能；推理策略（如链式思维或级联推理）的效果取决于模型的拒答倾向；当证据可用时，最优模型的表现等价于对最近邻检索结果的投票行为，这反映出推理实质上是调和冲突邻居间的分歧而非生成新知识；最终，模型准确率遵循“同源性可得性定律”，即性能高度依赖于可利用的同源信息。该研究揭示了当前大模型在酶功能推断中的根本局限，并为设计更有效的推理机制提供了理论依据。
链接: https://arxiv.org/abs/2607.26397
作者: Linyu Li,Zhi Jin,Yichi Zhang,Dongming Jin,Yuanpeng He,Huanyao Zhang,Xuan Zhang,Gadeng Luosang,Nyima Tashi
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
备注:
Abstract:Enzyme function prediction is a hierarchical, knowledge-intensive form of protein function classification. Existing benchmarks expose an anomaly: general LLMs often get the coarse first level right, yet once asked for a complete EC number their accuracy at levels two through four drops to almost zero, while specialized models and tools stay usable. We propose EC-Reason-Bench, a training-free, diagnostic evaluation protocol built to answer two questions: why general LLMs score close to nothing on EC number prediction, and how much of that loss can be recovered without updating a single weight. We break enzyme classification ability into four orthogonal levers that can each be measured on their own: output structure, external knowledge, reasoning structure, and reasoning robustness. We test each lever with an inference-time method against a shared zero-shot baseline reproducing previously reported near-zero performance. Experiments with several strong reasoning LLMs yield four main findings. First, external knowledge is decisive and must precede reasoning: uniformly low closed-book performance rises sharply with open-book access, narrowing model gaps. Second, in closed-book settings, whether cascading and chain-of-thought help or hurt depends on a model’s tendency to abstain. Third, once evidence is available the aggregate score of the best LLM setting is indistinguishable from simply voting the EC numbers of the nearest retrieved neighbors; that tie is an artifact of averaging, and it hides a large gain on adversarial evidence set against an equally large loss on multi-functional enzymes. Reasoning over evidence therefore acts as an arbiter of conflicting neighbors rather than as a source of knowledge, and no single-number leaderboard can see it. Fourth, accuracy obeys a law of homology availability.
[NLP-43] Misalignment Has a Personality: A Big Five Account of Emergent Misalignment
【速读】： 该论文旨在解决生成式模型在特定缺陷数据（如不安全代码或错误数学解答）上进行微调时，引发广泛行为偏移（misalignment）的机制问题。现有研究虽能通过二元对比提取性格特征的激活方向以操控行为，但缺乏可校准的量化尺度。本文提出的关键解决方案是：基于“大五人格”（Big Five Personality）理论，采用三级渐进式干预方法提取具有可解释性的、经过校准的人格向量。这些向量在两个开源模型上得到验证，表现出线性有序性（Cohen’s d最高达6.2），具备零样本迁移能力与特质特异性，并在中间层激活中效应最强。应用至八类训练数据后发现，所有存在偏移的语料库均呈现一致的人格特征签名：宜人性（agreeableness）和尽责性（conscientiousness）降低，外向性（extraversion）和神经质（neuroticism）升高。两模型对该签名的预测相关性高达r = 0.94，微调过程进一步将模型生成内容沿此签名发生显著偏移（激活测量r = 0.83，文本评判r = 0.90，内部激活变化r = 0.69）。此外，该框架揭示了“谄媚”行为本质上对应高外向性与低尽责性，而非过度宜人性，这一关键区分无法由单一方向捕捉。因此，校准后的人格向量将原本晦涩的安全风险现象转化为人类可理解的诊断性人格画像，为模型对齐提供了可解释、可量化的新范式。
链接: https://arxiv.org/abs/2607.26389
作者: Hasibur Rahman,Smit Desai
机构: Northeastern University (东北大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: The paper is currently under peer review
Abstract:Fine-tuning a language model on data containing a narrow flaw, such as insecure code or incorrect mathematical answers, can cause broad misalignment through a mechanism that remains debated. We provide an interpretable account: in the models and corpora we study, misalignment behaves like a shift in personality. Prior work extracts activation directions for character traits from a single binary contrast, which can separate or steer behavior without establishing a calibrated scale. We instead extract personality vectors for the Big Five using a graded, three-level intervention and validate them on two open-weight models. The three levels are linearly ordered, with Cohen’s d values of up to 6.2; the vectors transfer zero-shot and trait-specifically to an independent corpus; and their effects are strongest within a middle-layer band. Applied to training data, the vectors reveal that misaligned corpora across eight domains share a common Big Five signature: lower agreeableness and conscientiousness, together with higher extraversion and neuroticism. This signature is recovered by both models with a correlation of r = 0.94. Fine-tuning imprints the same profile, shifting the model’s generations along the corresponding signature, with r = 0.83 using activation-based measurements and r = 0.90 using a text-based judge, while also shifting internal activations with r = 0.69. The same vectors characterize sycophancy as high extraversion and low conscientiousness rather than excess agreeableness, a distinction that a single direction cannot capture. Calibrated personality vectors transform an opaque safety phenomenon into a human-legible diagnostic profile.
[NLP-44] Diagnosing Fine-Grained Inconsistency Classification in Financial Disclosure Text
【速读】： 该论文旨在解决金融披露文本中多类型语义不一致性的细粒度分类问题，即在数值、时间、实体指代、事实及规范性等不同维度上存在的矛盾难以通过统一方式识别与处理。其核心挑战在于：不同类型不一致需依赖不同证据和下游验证逻辑，因此仅检测冲突存在不足，必须实现精准的类型判别。解决方案的关键在于构建一个基于合成数据集SBID-FD（含11类不一致标签与配对参考证据段）的评估框架，系统比较了冻结嵌入分类器、微调编码器、证据增强分类器、提示工程的大语言模型以及LoRA适配的生成式模型等多种方法。实验表明，经过微调的300M参数编码器达到61.9%准确率，略优于LoRA适配的Qwen3.5-9B（61.5%）和GPT-5.4（61.3%），揭示出紧凑监督编码器在当前任务中具备更高的实用性效率，而非单纯由模型规模决定。进一步分析显示，提供真实证据可将微调编码器性能提升至65.3%，而自动预测的证据虽带来部分增益但效果受限，说明证据定位质量仍是主要瓶颈；其中，指代类不一致对定位精度尤为敏感，而事实与逻辑类不一致即便在提供正确证据时仍难处理，表明当前模型在跨类别推理能力方面存在根本性局限。综合“理想情况”、“干扰项”与“类别级”分析，研究明确区分了证据定位错误与类型判别残余误差，指出未来进展需同时强化证据抽取能力与对相近不一致类型的区分推理能力。
链接: https://arxiv.org/abs/2607.26368
作者: Aman Kumar,Lasitha Vidyaratne,Dipanjan D Ghosh,Arnab Chakrabarti,Ahmed K Farahat
机构: Hitachi America, Ltd
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Financial disclosures contain numerical claims, temporal statements, entity references, policy commitments, and risk descriptions that may conflict in qualitatively different ways. Detecting a conflict is only the first step: review workflows may also need to determine its type, since numerical, temporal, referential, factual, and normative inconsistencies require different evidence and downstream checks. We study this problem as fine-grained inconsistency classification. Using a fixed 5,940-instance snapshot of SBID-FD, a synthetic financial-disclosure benchmark with 11 inconsistency labels and paired reference evidence spans, we compare frozen embedding classifiers, fine-tuned encoders, evidence-augmented classifiers, prompted large language models, and LoRA-adapted generative models under a shared evaluation protocol. A fine-tuned 300M encoder reaches 61.9% accuracy, compared with 61.5% for a LoRA-adapted Qwen3.5-9B model and 61.3% for GPT-5.4. Because these systems differ in architecture, supervision, training objective, and input format, we interpret this as a practical efficiency result for compact supervised encoders rather than a controlled conclusion about model scale. Supplying gold evidence spans improves the fine-tuned encoder to 65.3%, whereas automatically predicted spans recover a meaningful but incomplete share of that gain, indicating that localization quality remains a bottleneck. Class-level analyses show that Referential inconsistencies are especially sensitive to localization quality, while Factual and Logical inconsistencies remain difficult even when the relevant evidence is provided. Together, the oracle, distractor, and per-class analyses separate localization errors from residual type-discrimination errors, indicating that progress requires both stronger evidence extraction and better reasoning over closely related inconsistency categories.
[NLP-45] Symphony of Bias: Exploring Gender Associations with Musical Instruments in Multimodal LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在信息检索应用中可能加剧社会偏见与性别刻板印象的问题，聚焦于音乐乐器的性别化联想这一具体情境。研究基于社会科学研究中关于乐器文化性别定型的既有发现，构建了名为Symphony-Bias的并行多模态数据集，涵盖文本、视觉和音频三种模态，以系统评估十种不同架构与规模的多模态模型对22种乐器在男性、女性及非二元性别三类性别范畴上的关联性。其解决方案的关键在于通过跨模态对比分析揭示：尽管92%的乐器层级结果与已有社会科学研究一致，但性别刻板印象在不同模态中的表现存在显著差异——文本模态最强化性别关联，视觉次之，音频模态最弱，表明模态特异性表征机制会差异化地放大或削弱性别化联想。这一发现为理解多模态模型中的隐含偏见提供了关键实证依据，并提示未来模型设计需考虑模态间偏差传播机制。
链接: https://arxiv.org/abs/2607.26355
作者: Farhan Farsi,Shayan Bali,Mohammad Heydari Rad,Negar Heidary,Donya Rooein
机构: Amirkabir University of Technology (阿米尔卡比尔科技大学); King’s College London (伦敦国王学院); University of Tehran (德黑兰大学); Bocconi University (博科尼大学)
类目: Computation and Language (cs.CL)
备注: 32 pages, 23 figures, 21 tables
Abstract:Large language models (LLMs) are increasingly embedded in everyday life and widely used for information seeking, raising concerns about their potential to perpetuate social biases and reinforce stereotypes. In this study, we investigate gender bias in LLMs through the lens of their associations with musical instruments. Building on social-science research on the cultural gender-typing of instruments, we introduce Symphony-Bias, a parallel multimodal dataset spanning text, vision, and audio. We evaluate ten multimodal models with diverse architectures and scales across 22 musical instruments, analyzing how they associate each instrument with three gender categories: male, female, non-binary, across three modalities: text, vision, audio. Our results show that 92% of instrument-level outcomes align with prior social-science findings, with the harp and drums showing particularly consistent gendered associations across all evaluated models and modalities. We further find that alignment with social stereotypes is weakest in audio, stronger in vision, and strongest in text, suggesting that modality-specific representations can differentially amplify gendered associations with musical instruments.\footnoteThe Symphony-Bias dataset will be publicly released upon acceptance of the paper.
[NLP-46] Dissecting Sensitivity to Training Language in Self-Supervised Speech Learning Using Neural Audio Codec Tokens INTERSPEECH2026
【速读】： 该论文旨在解决生成式音频编码器（Neural Audio Codec, NAC）在跨语言场景下自监督学习（Self-Supervised Learning, SSL）模型性能下降的问题。具体而言，尽管基于编码器的SSL模型在降低数据存储与计算开销方面具有优势，但其对语言变化的敏感性尚未明确：当目标语言发生变化时，是否需要重新训练编码器或预训练模型，直接影响其可扩展性与实用性。论文通过系统性分析，固定其中一个语言变量（NAC训练语言或SSL预训练语言），改变另一个变量，发现下游任务性能对NAC训练语言不敏感，但对SSL预训练语言高度敏感。这一结果表明，单一NAC可跨语言复用，而关键在于将SSL预训练语言与目标语言对齐。因此，解决方案的核心在于：无需为每种语言重新训练NAC，只需确保SSL预训练阶段的语言与目标语言一致，即可实现高效且泛化的跨语言语音表示学习。
链接: https://arxiv.org/abs/2607.26350
作者: Daigo Takizawa,Tomohiko Nakamura,Samuele Cornell,William Chen,Satoru Fukayama,Shinji Watanabe
机构: National Institute of Advanced Industrial Science and Technology (AIST)(日本先进工业科学技术研究院); Carnegie Mellon University(卡内基梅隆大学)
类目: ound (cs.SD); Computation and Language (cs.CL)
备注: Accepted to Interspeech2026
Abstract:Neural audio codecs (NACs) have become popular for obtaining speech representations as discrete tokens. Beyond compression, discrete tokens can be used to train self-supervised learning (SSL) models. Such models, referred to as codec-based SSL models, reduce data storage and computational cost, enabling scalable SSL pre-training. However, their language sensitivity remains unclear. When the language changes, codec-based SSL models may require retraining, which undermines their efficiency. In this paper, we present a systematic analysis of language sensitivity by varying either the NAC training language or the SSL pre-training language while keeping the other fixed. Experimental results show that downstream performance is insensitive to the NAC training language but strongly dependent on the SSL pre-training language. These findings suggest that a single NAC can be reused across languages, while aligning the SSL pre-training language with the target language is crucial.
[NLP-47] Aligning LLM -Simulated and Human Examinees for Psychometric Calibration: A Cognitive Diagnostic Profiling Approach
【速读】： 该论文旨在解决教育测评中项目反应理论（Item Response Theory, IRT）心理测量标定依赖昂贵的人类应答数据的问题。传统方法受限于真实被试数据的获取成本，而大语言模型（Large Language Models, LLMs）虽可生成模拟被试数据，但其响应存在过高的准确率与过度同质化的问题，难以反映真实被试的认知多样性。为此，本文提出一种零样本认知诊断轮廓建模（Cognitive Diagnostic Profiling, CDP）框架，其核心在于通过自然语言生成方式将二元属性掌握模式转化为多样化的认知轮廓，并在无信息或有信息先验分布下采样生成模拟被试。基于Tatsuoka分数减法数据集（536名被试、15道题目、5个认知属性）的实验表明，CDP显著提升了模拟数据与真实人类被试在能力分布、属性掌握轮廓及题目难度恢复三个层面的一致性：各配置下的分布重叠度提升；轮廓层级得分与人类期望的相关性达0.92至0.98；尤其在推理增强型模型中，题目难度排序与绝对对齐性能显著改善——以Gemini 3.0 Flash（Thinking）为例，单参数洛吉特模型（1PL）难度等级的斯皮尔曼相关系数从0.24提升至0.86（无信息条件）和0.90（有信息条件），均方根误差（RMSE）由6.31降至1.30和0.90。结果表明，有信息条件下的CDP在认知轮廓对齐较强时效果更优，使LLM生成的被试数据具备更高的心理测量效度，从而为实际测试开发提供可行的替代方案。
链接: https://arxiv.org/abs/2607.26317
作者: Wenjie Zhou,Yunting Liu,Renjiao Tang,Mark Wilson
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Psychometric calibration for educational tests typically requires costly human response data. Large language models (LLMs) simulated examinees offer a promising route to early calibration, but their responses are too accurate and too uniform. We propose Cognitive Diagnostic Profiling (CDP), a zero-shot framework that prompts LLMs to simulate plausible examinees with diverse cognitive profiles: binary attribute-mastery patterns are rendered as natural-language profiles and sampled under an uninformative or an informative distribution. Using the Tatsuoka fraction-subtraction dataset (536 examinees, 15 items, five attributes), we evaluated eight LLM configurations under no-profile, uninformative-CDP, and informative-CDP conditions, assessing alignment with human examinees at the ability-distribution, mastery-profile, and item-difficulty levels. CDP improved all three levels: distributional overlap rose across configurations; weighted correlations between profile-level scores and human profile expectations reached 0.92 to 0.98; and item-difficulty recovery improved in rank order and absolute alignment, most for reasoning-enabled models; in the strongest case, Gemini 3.0 Flash (Thinking), one-parameter logistic (1PL) difficulty Spearman correlations rose from 0.24 to 0.86 and 0.90 and the root-mean-square error (RMSE) fell from 6.31 to 1.30 and 0.90; the informative condition helped most where profile-level alignment was strong. CDP brings LLM-simulated examinees into closer psychometric alignment with human examinees, making them practical for operational test development.
[NLP-48] Evaluating Prompt Scope and Demonstration Similarity in Local LLM Machine Translation
【速读】： 该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在机器翻译任务中评估范式单一的问题，即通常仅在“单源句-单目标语言”这一固定提示（prompt）格式下进行评估，而忽略了实际应用中用户可能提出的多样化需求，如同时请求多个相关语言的翻译、或基于示例的条件化翻译。为此，论文将提示范围（prompt scope）与示范选择策略（demonstration selection）作为关键实验变量，系统考察其对本地化指令微调LLM在机器翻译性能上的影响。核心解决方案在于对比多种提示设计：包括单目标提示与以JSON格式组织的家族范围提示（family-scope prompts），以及采用随机、词法相似性与嵌入相似性三种策略进行示范样本选取，并在涵盖九种欧盟官方语言的完整FLORES开发测试集上，对三个本地LLM（llama3.2:3b、mistral:latest、qwen2.5:14b）与专用机器翻译基线（OPUS-MT与NLLB-200）进行零样本与五样本少样本提示（k=5）下的性能比较。研究发现，尽管专用机器翻译系统整体仍最优，尤其在日耳曼语族表现更优，但少样本提示显著提升了部分较强LLM（如mistral:latest和qwen2.5:14b）的性能，且嵌入相似性示范选择在多数情况下表现最佳，虽优势相对有限；而家族范围提示在强模型中可行，但在小型模型中暴露出结构化输出失败问题。因此，该研究强调应将提示范围、检索策略及多目标一致性（multi-target compliance）纳入评估体系，以全面衡量生成式机器翻译系统的实际可用性。
链接: https://arxiv.org/abs/2607.26286
作者: Mihael Arcan
机构: Home Lab(家庭实验室), Galway, Ireland(爱尔兰高威)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) are increasingly used as general-purpose translation systems, but their behavior is usually evaluated under a single prompt shape: translate one source sentence into one target language. In practice, users may ask for one target language, for several related languages at once, or for translations conditioned on examples. This paper studies prompt scope and demonstration selection as experimental variables for local LLM machine translation. We evaluate English-to-Romance and English-to-Germanic translation on the full FLORES devtest split for nine official European Union languages. We compare three local instruction-tuned LLMs, llama3.2:3b, mistral:latest, and qwen2.5:14b, against dedicated MT baselines from OPUS-MT and NLLB-200. We test zero-shot prompting and k=5 few-shot prompting with random, lexical-similarity, and embedding-similarity demonstration selection. We also compare single-target prompts with JSON-formatted family-scope prompts that request all languages in a family at once. Results show that dedicated MT systems remain strongest overall, especially for Germanic languages. Few-shot prompting helps mistral:latest and qwen2.5:14b, but hurts llama3.2:3b; embedding retrieval is best on average for the stronger LLMs, but its advantage over random and lexical examples is modest. Family-scope prompting is feasible for stronger local LLMs but exposes structured-output failures in smaller models. These findings motivate evaluating LLM translation not only by language pair and metric, but also by prompt scope, retrieval strategy, and multi-target compliance.
[NLP-49] Robostreet Flow: A Lightweight Ultra-Low-Drag Electric Tractor and Four-Truck Hybrid Convoy Architecture for Minimum-Cost Point-to-Point Freight
【速读】： 该论文旨在解决长途货运成本中能源、驾驶员人工与设备三大部分占比相当且现有高效技术通常仅针对单一要素的系统性效率瓶颈问题。其核心解决方案是提出Robostreet Flow货运架构，通过协同优化车辆设计、编队运行模式与运营体系，实现吨英里成本的全局最小化。关键创新在于：采用电池电动6x4牵引车（Flow平台），配备低风阻泪滴形单座驾驶室（风阻系数0.35，较传统8类牵引车降低约40%）、碳纤维一体化车身及结构集成电池系统，使整车净重减少50%；搭载513 kWh牵引车电池与340 kWh动力挂车电池，实现500英里单次充电续航；四辆货车以8米间距形成协同编队，仅领头车配置安全驾驶员，其余三辆在SAE Level 4自动驾驶模式下运行；计算流体动力学仿真表明，编队可使跟随车辆风阻系数降低42%-48%，前部峰值压力下降约四倍；基于此的纵向能耗模型预测编队平均能耗为1.27 kWh/mi，相较孤立车辆的1.60 kWh/mi降低20.5%；结合电力成本仅为柴油燃料成本的17%，并实现四车共用一名司机及轻量化带来的额外载货空间，最终将运营成本从柴油基准的9.4美分/吨英里降至4.1美分/吨英里，降幅达56%。
链接: https://arxiv.org/abs/2607.26250
作者: Wei Wang,Yiru Veronika Wang,Sumukh Veeramalla,Xiaohui Liang, for theRobostreet Research Team
机构: 未知
类目: Computation and Language (cs.CL)
备注: 10 pages, 6 figures. Submitted to IEEE Transactions on Intelligent Transportation Systems 2026
Abstract:Line-haul trucking costs are dominated by three comparably sized components: energy, driver labor, and equipment. Most efficiency technologies address only one component at a time. This paper presents Robostreet Flow, a freight architecture that jointly optimizes the vehicle, convoy formation, and operating model to minimize cost per ton-mile on high-volume point-to-point corridors. The Flow platform is a battery-electric 6x4 tractor with a teardrop single-seat cab and a drag coefficient of 0.35, approximately 40% below that of conventional Class 8 tractors. A carbon-composite monocoque and structurally integrated batteries reduce net vehicle weight by 50%. A 513 kWh tractor battery and a 340 kWh powered trailer battery provide a 500-mile single-charge range. Four Flow trucks operate as a coordinated convoy with a safety driver only in the lead vehicle, while three followers operate in SAE Level 4 automated mode. Computational fluid dynamics simulations show that close following at an 8 m gap reduces follower drag coefficients by 42-48% and follower peak frontal pressure by approximately a factor of four relative to the exposed lead vehicle. A longitudinal energy model calibrated to these results predicts fleet-average consumption of 1.27 kWh/mi in convoy, compared with 1.60 kWh/mi for an isolated vehicle, for a 20.5% energy saving. Electricity cost is approximately 17% of the equivalent diesel fuel cost. Amortizing one driver across four trucks and accounting for the additional payload enabled by lightweighting reduce operating cost from 9.4 to 4.1 cents per ton-mile, a 56% reduction relative to a diesel baseline. Sensitivity analysis, a hub-to-hub operating concept, and regulatory implications are also presented.
[NLP-50] A large-scale corpus of religious radio broadcast transcripts from webstream recordings in the United States
【速读】： 该论文旨在解决美国宗教广播这一广泛存在但研究不足的大众传播形式在内容层面分析中因缺乏大规模转录数据而受到的制约。其解决方案的关键在于构建一个大规模、自动化的宗教广播转录语料库：通过在2025年7月为期一个月内对785个不同流媒体频道进行滚动录制，覆盖超过2,000个AM/FM电台信号，获取了超过70万段录音和逾6000万条经说话人分离（diarized）的语音文本。所有录音均通过自动化流程完成转录与说话人分离，并利用大语言模型对内容进行节目格式与主题的分段与标注，最终以关联表格的形式组织流媒体元数据、录制元数据及转录行数据，为跨区域、跨教派的宗教广播描述性研究、宗教媒体中社会政治议题的讨论分析以及低资源领域下的语音处理研究提供了可扩展的数据基础。
链接: https://arxiv.org/abs/2607.26249
作者: Samuel Bestvater,Athena Chapekis,Skyler Seets,Anna Lieb,Sono Shah,Aaron Smith
机构: 未知
类目: Computation and Language (cs.CL); Computers and Society (cs.CY); Sound (cs.SD); Applications (stat.AP)
备注: Presented at IC2S2 2026
Abstract:Religious radio is a widespread but understudied form of mass communication in the United States, and content-level analysis of it has been constrained by the absence of large-scale transcript data. This Data Descriptor presents a corpus of transcribed English-language religious radio broadcasts captured from live webstreams over a one-month period in July 2025. Fifteen-minute segments were recorded on a rolling schedule from 785 distinct streams, which together rebroadcast the signals of more than two thousand AM and FM stations, yielding over 700,000 recordings and more than 60 million diarized lines of speech. Each recording was transcribed and speaker-diarized with an automated pipeline, and segmented and labeled by programming format and topic using a large language model. The corpus is organized as linked tables of stream metadata, recording metadata, and transcript lines. It supports descriptive study of religious broadcasting across regions and traditions, analysis of how social and political issues are discussed in religious media, and speech-processing research in an underrepresented domain.
[NLP-51] Steering Instruction Hierarchies at Inference Time
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在实际部署中违反指令层级（instruction hierarchy）安全假设的问题，即低优先级输入（如用户或工具生成的内容）有时会覆盖高优先级输入（如系统提示），从而导致模型行为偏离预期。其核心解决方案是提出一种无需训练的推理时干预方法——V-Steer，关键在于通过在提示位置对缓存的值向量（cached value vectors, V tensors）进行原位乘法编辑，重构不同输入源的影响力。具体而言，V-Steer利用首个下一个词元预测的直接逻辑归因（direct logit attribution）识别出由低优先级片段主导的注意力头，并针对性地增强高优先级片段的表示、抑制冲突的低优先级片段。由于仅作用于缓存的值向量，该方法兼容融合注意力后端，仅引入一次预填充（prefill）阶段的开销。实验表明，在7B至70B规模的多个模型上，该方法将控制角色冲突基准下的主约束准确率从不足18%提升至92%，并在更广泛的指令层级评估中显著优于仅依赖提示的基线方法，同时在4个模型尺度中的3个上达到或超越现有基于训练的方法性能，且解码速度几乎无损耗。
链接: https://arxiv.org/abs/2607.26228
作者: Siqi Zeng,Sewoong Lee,Han Zhao,Julia Hockenmaier
机构: University of Illinois, Urbana-Champaign (伊利诺伊大学厄本那-香槟分校)
类目: Computation and Language (cs.CL)
备注: Published as a conference paper at COLM '26; the first two authors contributed equally to the work. 24 pages, 9 figures
Abstract:Instruction hierarchies are a core safety assumption of language model deployment: higher priority inputs, such as system prompts, should override conflicting lower priority inputs from users or tools. Yet frontier LLMs often violate this hierarchy. We introduce V-Steer, a training-free inference time method that restores privileged influence by editing cached value vectors at prompt positions. Using direct logit attribution on the first next token prediction, V-Steer identifies heads where lower priority spans dominate privileged ones, then boosts privileged spans and suppresses conflicting lower priority spans through in-place multiplicative edits to cached V tensors. Since the method acts only on cached values, it remains compatible with fused attention backends and adds only a one time prefill overhead. Across models from 7B to 70B, this attribution guided intervention raises primary constraint accuracy from under 18% up to 92% on controlled role conflict benchmarks, and on broader instruction hierarchy evaluations substantially outperforms prompt only baselines while matching or exceeding SoTA training based methods on 3 of 4 scales of LLMs, with negligible decoding-speed overhead. The code is available at this https URL.
[NLP-52] Characterizing Human-Likeness in AI Generated Poetry: A Zero-shot Classification Study
【速读】： 该论文旨在解决生成式AI（Generative AI, GenAI）创作的诗歌与人类创作诗歌在当前检测工具下难以区分的问题，尤其关注此类文本在学术诚信背景下的可辨识性挑战。其核心问题在于：尽管现代大语言模型（Large Language Models, LLMs）具有黑箱特性，且诗歌本身具有高度主观性，但现有检测系统仍难以客观验证GenAI诗歌的真实来源。为此，研究提出一种零样本（Zero-shot）检测框架，并构建包含人类与AI创作诗歌的语料库，以识别导致分类或误分类的关键文本属性。该方案的关键在于通过属性提取实现精准判别，一方面可显著减少训练数据需求——仅需针对易混淆属性进行微调；另一方面为提升生成式AI检测系统的鲁棒性提供关键洞察，从而推动检测机制向更可靠、可解释的方向发展。
链接: https://arxiv.org/abs/2607.26221
作者: A. N. Biswas,T. Tabassum,A. A. Shohid,R. M. Mou,A. A. Esha,F. Sadeque,A. Ahmed
机构: BRAC University (布拉大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:With the advancement of AI technologies, Generative AI (GenAI) and human written text have become nearly indistinguishable. Additionally, the global standardization of AI chatbots made academic malpractice more frequent. Furthermore, existing research indicates GenAI poems are the most difficult to distinguish even without any modification thus, GenAI poems are naturally deemed human-like by modern detectors. However, the objectivity of such dissertations needs to be verified against modern detection tools but the subjectivity of poetry and the black-box nature of the modern LLMs (Large Language Models) architectures made verification of such work quite complicated. Hence, the main objective of the research is to deduce the attributes of English poetry that contribute classification and misclassification of both human and AI poems and provide corroborating or contradicting evidence to the poetry distinguishability claim. For such characterizations, we propose a Zero-shot detection pipeline with a dataset consisting of both human and AI poems to verify the distinguishability of human and AI creation and extract the aforementioned crucial attributes for accurate classification. Extraction of such attributes provides benefits in two ways: firstly, it reduces the margin of training needed as only the poems based on misclassifying attributes need to be trained and fine tuned and finally provides a critical insight to the GenAI detection dilemma to strengthen the modern detection pipelines.
[NLP-53] Choosing Where and How to Moderate: End-to-End Trade-offs in Filter Placement and Response Rewriting
【速读】： 该论文旨在解决内容审核系统在实际部署中面临的决策优化问题，即如何在不同干预位置（输入端、输出端或二者结合）以及不同响应策略（直接拦截或重写生成）之间进行权衡，以实现安全与用户体验的平衡。传统评估方法仅关注分类器的组件准确率，而忽视了真实场景下的端到端效果。为此，本文提出两个关键的端到端客户结果指标：有用性（Usefulness）（展示的非有害且相关的回复比例）和有害暴露率（Harmful Exposure）（展示的有害回复比例），并以延迟和错误率作为辅助诊断指标。研究发现，在所考察的操作点下，仅在输出端进行硬拦截（Response only） 在提升过滤后有用性方面表现最优；而输入+输出双端硬拦截（Input + response） 能显著降低有害暴露率。进一步地，采用输出重写（Response + rewrite） 策略可恢复大部分被拦截流量，并在选定配置下维持与仅输出拦截相当的有害暴露水平，但该等效性并非理论上的等价关系。此外，探针路由（Probe routing） 相较于基于大语言模型（LLM）的路由机制，在保持相似性能的同时大幅降低了条件路由与生成的时间开销。对输出重写的深入分析表明，重写通过泛化触发性语言，在保留良性意图和安全引导的同时提升了通过率，但在某些敏感领域仍可能遗漏重要的安全相关信息。综上，该研究强调应根据具体部署环境中的安全需求与延迟约束来评估和选择内容审核配置，而非采用通用的部署规则。
链接: https://arxiv.org/abs/2607.26200
作者: Mengya Hu,Susie Park,Suzana Ilic,Qiong Wei,Sandeep Atluri,Myra Deng,Tucker Fross,Curt Tigges
机构: Microsoft Responsible AI(微软负责任AI); Goodfire
类目: Computation and Language (cs.CL)
备注:
Abstract:Content-moderation classifiers are usually evaluated in isolation, but deployment requires choosing where to intervene and what follows a flag. We evaluate these choices using two end-to-end customer-outcome metrics rather than component accuracy: Usefulness, the fraction of turns with a shown, non-harmful, relevant response, and Harmful Exposure, the fraction with a shown harmful response. Latency and error rates are diagnostics. We compare Input only, Response only, and Input + response hard blocking on a human-labelled product benchmark and public ToxicChat evaluation. At the evaluated operating points, Response only achieves the highest filter-only Usefulness in both settings, while Input + response achieves lower Harmful Exposure. Replacing Response only blocking with Response + rewrite recovers most blocked traffic and yields the same observed Harmful Exposure count as Response only blocking for the selected configuration; this equality is not an equivalence result. Probe routing substantially reduces conditional route-and-generation time relative to LLM routing at comparable measured outcomes. A focused output review shows how rewrites balance filter passage with usefulness by generalizing triggering language while retaining benign intent and safe redirection; some sensitive-domain outputs nevertheless omit potentially safety-relevant support information. These results support comparing moderation configurations under deployment-specific safety and latency constraints rather than applying a universal placement rule. Code and public artifacts are available at this https URL
[NLP-54] Position: Evaluation Scores Are Perishable Knowledge Claims ACL2026
【速读】： 该论文旨在解决当前语言模型评估中因多源信号（如自动化指标、大语言模型作为评判者、人工评估及基准测试结果）简单平均聚合所导致的“评估信任膨胀”（trust inflation in evaluation）问题，即整体评估置信度被人为夸大，超越了最弱评估信号的可靠性。其核心解决方案在于将评估分数视为具有明确认识论属性的主张，强调三重属性：形式性（formality，人类评估提供的证据强度高于自动化指标）、作用范围（scope，基准结果仅适用于特定测试分布，不具普遍性）和有效性窗口（validity windows，随着数据污染积累与分布漂移，基准结果会失效）。基于链式思维分析、可能性逻辑与代数理论等研究传统，论文提出以“最弱环节聚合”（weakest-link aggregation）作为参数化算子族的保守终点，由单一悲观性参数控制，从而更稳健地整合多源信号。在此基础上，论文倡导在评估结果中显式标注元数据（形式性层级、作用范围声明与过期日期），以透明化其认识论状态。通过公开的HELM排行榜实例验证，对54个前沿模型在10个场景下的评估显示，按均值排序与按最弱环节排序的前五名模型完全不重叠，凸显了均值聚合带来的严重误判风险。
链接: https://arxiv.org/abs/2607.26191
作者: Sankalp Gilda,Shlok Gilda
机构: DeepThought Solutions; Meta
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注: 7 pages, 1 figure, 1 table. Published at the Fifth Workshop on Generation, Evaluation and Metrics (GEM), ACL 2026, San Diego
Abstract:Evaluation methodologies for language models increasingly combine multiple signals, from automated metrics and LLM-as-judge ratings to human assessments and benchmark suite results. When these signals are aggregated via averaging, evaluation confidence can then substantially exceed the reliability of the weakest signal: a phenomenon we call trust inflation in evaluation. We argue that evaluation scores should be treated as epistemic claims with three properties: formality (human evaluation provides stronger evidence than an automated metric), scope (a benchmark result applies to the tested distribution, not universally), and validity windows (benchmark results expire as contamination accumulates and distributions shift). Several converging research traditions (chain-of-thought analysis, possibilistic logic, and algebraic theory) establish weakest-link aggregation as the conservative endpoint of a parameterized operator family controlled by a single pessimism parameter. Drawing on those traditions, and on concrete lessons from building an evaluation harness for agentic AI, we propose that evaluation results carry explicit metadata (formality tier, scope declaration, and expiration date) to make their epistemic status transparent. We illustrate the cost of mean aggregation on the public HELM leaderboard: across 54 frontier models on ten scenarios, the top-five models ranked by mean score and by weakest-link are completely disjoint.
[NLP-55] DuplexGen: Adaptive Synthesis of Human-AI Turn-Taking Dialogues
【速读】： 该论文旨在解决现有生成式对话模型在全双工交互中缺乏场景自适应的轮流说话（turn-taking）行为问题。当前模型普遍采用统一的轮流规则，无法根据具体情境（如合作或竞争任务）调整其交互模式，导致行为与人类实际偏好不符。其根源在于训练数据的局限性：人类-人类对话语料虽能捕捉自然的时间规律，但缺乏角色定位和场景特异性规范；而基于启发式或提示工程的数据生成方法则未充分依据人类偏好。为此，论文提出DuplexGen框架，通过将大语言模型（LLM）的预测结果与少量细粒度的槽位级（slot-level）人类偏好标注进行校准，实现对不同场景下轮流行为的精准建模。在六项合作与竞争性任务中，实验表明人类对轮流行为的偏好存在系统性差异，而经过校准的DuplexGen生成的对话显著更贴近这些偏好，优于未经校准的提示方法或仅依赖通用对话数据训练的模型。尤其值得注意的是，使用DuplexGen生成数据训练的全双工模型展现出符合人类偏好的独特轮流行为特征。研究结果表明，实现场景自适应轮流行为的关键并非语料规模或提示设计本身，而是基于人类偏好的校准机制。
链接: https://arxiv.org/abs/2607.26178
作者: Takyoung Kim,Kang-wook Kim,Sang Hoon Woo,Julia Hirschberg,Gunhee Kim,Dilek Hakkani-Tür
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄本那-香槟分校); Seoul National University (首尔国立大学); Columbia University (哥伦比亚大学); University of California, Berkeley (加州大学伯克利分校); Georgia Institute of Technology (佐治亚理工学院)
类目: Computation and Language (cs.CL)
备注: Manuscript under review
Abstract:Turn-taking is a central component of full-duplex interaction. Which turn-taking behaviors are appropriate varies with the scenario, yet current models apply a single norm regardless of context. This limitation originates in their training data: human-human speech corpora capture natural timing phenomena but provide little role grounding or scenario-specific norms, while heuristic or prompted synthesis methods inject turn-taking behaviors without basing them on human preferences. We introduce DuplexGen, a framework for generating dialogues with scenario-adaptive turn-taking by calibrating LLM predictions against a small set of slot-level human preference annotations. In six cooperative and competitive tasks, human turn-taking preferences differ systematically, and DuplexGen aligns substantially more closely with those preferences than uncalibrated prompting or training solely on generic human-human data; a full-duplex model trained on DuplexGen-generated data exhibits distinctive, human-preferred turn-taking behaviors. These results show that human calibration, not corpus scale or prompt design alone, is what allows turn-taking synthesis to be scenario-specific.
[NLP-56] Probing the Origins of Reasoning Performance: Representational Quality for Mathematical Problem-Solving in RL vs. SFT Fine-Tuned Models AAAI2026
【速读】： 该论文旨在解决强化学习（Reinforcement Learning, RL）训练的大规模推理模型在数学推理任务中优于监督微调（Supervised Fine-Tuning, SFT）模型的内在机制不明确这一问题。其核心在于探究两种训练范式下模型内部表征差异的本质。解决方案的关键在于通过两方面的实证分析揭示其根本区别：一是利用层间隐藏状态的线性探测（linear probes）发现，RL模型在预测答案正确性方面表现更优，表明其具有更线性可分且结构化的表征；二是通过均值消融（mean ablation）研究发现，RL模型呈现出层级化架构，深层特征随网络深度递增而变得愈发关键，而SFT模型则在各层间呈现均匀重要性分布。这两项证据共同表明，RL训练从根本上重构了模型对推理问题的表征与处理方式。此外，通过对重复采样下词元数量（token count）变异性的分析，研究发现部分RL模型表现出更高的计算分配灵活性，但另一些模型则保持高度一致性，提示词元分配策略可能更多取决于整体训练流程而非单纯的RL与SFT差异，该变异性揭示了合理策略的分布范围，有助于识别具有稳定策略的模型与那些处于欠确定、不可识别解空间中的模型。
链接: https://arxiv.org/abs/2607.26119
作者: Antyabha Rahman,Akshaj Gurugubelli,Omar Ankit,Kevin Zhu,Aishwarya Balwani
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Second Workshop on XAI4Science, AAAI 2026
Abstract:Large reasoning models trained via reinforcement learning (RL) have been increasingly shown to outperform their supervised fine-tuned (SFT) counterparts on mathematical reasoning tasks; Yet the mechanistic basis for this advantage remains unclear. We therefore ask, what internal representational differences enable RL models’ superior performance? Our work presents two converging lines of evidence: First, linear probes trained on layer-wise hidden states reveal that RL models tend to achieve higher accuracy in predicting answer correctness compared to SFT models, indicating more linearly separable and structured representations. Second, mean ablation studies show that RL models develop a hierarchical architecture where deeper layers become progressively more critical, whereas SFT models distribute importance uniformly across layers. Together, these findings demonstrate that RL training fundamentally restructures how models represent and process reasoning problems. Finally, we analyze token-count variability under repeated sampling across problems to assess adaptive compute allocation. While we observe higher variability in some RL-tuned models than in their SFT counterparts, we see strong consistency in others, suggesting that token allocation may depend more on the overall training pipeline than on RL versus SFT alone. We believe this token-allocation variability reveals the spread of plausible on-policy reasoning, highlighting which models exhibit stable policies versus those that are under-determined, potentially non-identifiable solution behaviour.
[NLP-57] GPT -Red: Automated Red Teaming via Self-Play at Scale
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）在面对提示注入攻击（prompt injection attack）时安全性不足的问题，尤其关注前沿模型在实际应用中可能遭受的隐蔽且复杂的攻击。其核心挑战在于如何系统性地发现新型、高隐蔽性的攻击向量，并提升模型对这类攻击的鲁棒性。解决方案的关键是提出GPT-Red——一个通过可扩展的自对弈（self-play）算法训练的自动化红队代理（red-teaming agent），该代理在多样化的同时训练的防御者群体环境中进行对抗性训练，从而持续生成更具挑战性的攻击样本。训练过程采用与最大规模强化学习（Reinforcement Learning, RL）后训练相当级别的算力资源，使GPT-Red成为迄今记录中规模最大的大语言模型安全训练实例。实验表明，GPT-Red不仅能有效突破此前所有版本至GPT-5.5的模型，其攻击成功率超过人类红队专家，且具备良好的泛化能力，适用于未见环境、防御模型及不同部署场景。该研究构建了一个自我增强的飞轮机制：随着新模型鲁棒性的提升，将反向提供更高质量的对抗信号以训练更强的红队代理，推动安全能力的持续迭代。
链接: https://arxiv.org/abs/2607.26115
作者: Eric Wallace,Christopher A. Choquette-Choo,Nikhil Kandpal,Sam Toyer,Dylan Hunn,Stephanie Lin,Yuxin Wen,Xiangyu Qi,Christopher Wolff,Zizhao Wang,Milad Nasr,Sicheng Zhu,Chuan Guo,Juan Felipe Cerón Uribe,Kaiwen Wang,Aiden Low,Kai Xiao,Kai Chen
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 28 pages.13 main pages and 13 main figures
Abstract:We introduce \textbfGPT-Red, an automated red-teaming agent that is trained to discover novel prompt injection attacks against frontier LLMs. The goal of this model is to evaluate and improve the robustness of our production systems. To this end, we use it to adversarially train GPT-5.6, our most robust model to prompt injections to date. To create GPT-Red, we design a scalable self-play algorithm where the model is tasked with attacking a diverse population of simultaneously-trained defender agents. We train the model on realistic red-teaming environments using compute on the same scale as some of our largest RL post-training runs, making it the single-largest LLM safety training run ever documented. GPT-Red excels at red-teaming: it reliably breaks our past models up to GPT-5.5, it finds more successful attacks than human red-teamers, and it generalizes to held-out environments, defender models, and harnesses. In the future, we expect that as we improve the robustness of each new GPT model, it will in turn will provide better learning signal for \textiteven stronger red-teamer agents, thus unlocking a self-improvement flywheel.
[NLP-58] Meta-Learned Reward Shaping for Reinforcement Learning from Human Feedback
【速读】： 该论文旨在解决强化学习中人类反馈（Reinforcement Learning from Human Feedback, RLHF）因采用静态、任务无关的奖励模型而导致的学习信号稀疏与对齐效果不佳的问题。其核心挑战在于，传统方法在训练过程中缺乏针对具体任务的动态引导信号，从而影响策略优化效率与对齐质量。本文提出的解决方案——MeRLa（Meta-Learned Reward Shaping），通过元学习（meta-learning）在辅助任务上预训练一个任务感知的奖励塑形函数 Φ(x,y;ϕ)，在正式的RLHF训练前生成具有任务特异性的复合奖励信号。该方法的关键在于：一方面保持了策略最优性（policy invariance），另一方面通过引入任务区分性、熵正则化及基于势能的保守性约束构建了稳定的元目标，有效缓解了熵最大化带来的激励错位问题。理论分析进一步证明了该框架在表示漂移敏感性下的鲁棒性，并提供了形式化的激励对齐保障。实验结果表明，MeRLa在LLaMA-3-8B模型上于四个基准测试中均显著优于PPO、DPO、GRPO和DAPO，实现AlpacaEval 2.0上90.8%的长度控制胜率与MT-Bench上9.14分的成绩，同时训练不稳定性降低41%。此外，该方法可与过程导向或评分标准增强的奖励机制兼容，展现出良好的扩展性与实用性。
链接: https://arxiv.org/abs/2607.26094
作者: Yunpeng Chu
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Reinforcement Learning from Human Feedback (RLHF) is the standard approach for aligning large language models with human preferences, but its quality is limited by static, task-agnostic reward models. This mismatch leads to sparse learning signals and suboptimal alignment. We introduce MeRLa (Meta-Learned Reward Shaping), a principled framework that meta-learns a task-aware shaping function \Phi(x,y;\phi) across auxiliary tasks before RLHF training. The learned shaping produces a composite reward that preserves policy optimality while providing task-specific learning signals. Our meta-objective combines task discrimination, entropy regularization, and potential-based conservation for stable convergence. We provide theoretical guarantees for policy invariance, analyze representation drift sensitivity, and formally address incentive misalignment from entropy maximization. Experiments on LLaMA-3-8B across four benchmarks show consistent improvements over PPO, DPO, GRPO, and DAPO, achieving a 90.8% length-controlled win rate on AlpacaEval 2.0 and a score of 9.14 on MT-Bench, with 41% less training instability. MeRLa retains its benefits when combined with process-based and rubric-based enhanced rewards.
[NLP-59] Do Methods Support the Claims? Intra-Paper Verification for Peer Review
【速读】： 该论文旨在解决当前基于大语言模型（LLM）的同行评审系统在评估科学论文新颖性时存在的关键缺陷：现有方法通常仅对比论文所宣称的贡献与已有文献，隐含假设这些贡献已在文中得到准确实现，而忽视了论文内部“声称贡献”与“方法实现”之间的一致性问题。事实上，人类审稿人常质疑新颖性，并非因为已有相似思想，而是由于文中提供的方法论证据不足以支撑其主张。为此，本文提出论文内主张验证（intra-paper claim verification）框架，其核心在于通过大语言模型自动识别引言中提出的新颖性主张，检索与之相关的具体方法学证据，并依据从182篇ICLR 2025论文的同行评审中归纳出的、具有启发性的评价标准，判断方法是否充分支持所声称的贡献。该框架生成结构化的、类审稿人风格的评估意见，显著提升了对新颖性相关问题的识别能力。实验表明，该框架生成的评论与人工审稿人的关注点高度一致，且通过BERTScore进一步验证了其生成内容与真实审稿意见之间的语义匹配性，证明其能有效捕捉人类审稿者的真实关切。
链接: https://arxiv.org/abs/2607.26066
作者: Ranjitha Shivaprasad Ballakuraya,Arash Mahyari,Ashok Srinivasan
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Digital Libraries (cs.DL)
备注: 9 pages, 8 figures. Source code, prompts, evaluation materials, and supporting data available at this https URL
Abstract:The growing volume of scientific submissions has motivated interest in using large language models (LLMs) to assist peer review. Existing automated novelty assessment approaches typically compare a paper’s claimed contributions against prior literature, implicitly assuming that these contributions are accurately realized in the work itself. Human reviewers, however, frequently challenge novelty claims not because similar ideas already exist, but because the methodological evidence presented in the paper does not adequately support them. This internal mismatch between claimed contributions and methodological realization is rarely examined by current LLM-based review systems. To address this gap, we introduce intra-paper claim verification, a framework that evaluates whether novelty claims articulated in a paper are substantiated by the methods used to realize them. The framework employs an LLM to extract novelty claims from the introduction, retrieve claim-relevant methodological evidence, and assess whether the methods substantiate the stated contributions. Assessment is guided by reviewer-inspired evaluation criteria derived inductively from human peer reviews collected from 182 ICLR 2025 papers. These criteria capture recurring reviewer concerns related to novelty, methodology, clarity, and other issues and are used to generate structured reviewer-style assessments of claim substantiation. We evaluate the framework by comparing LLM-generated review comments against human reviewer concerns on a balanced subset of accepted and rejected papers. Human evaluation demonstrates significant alignment between framework-generated assessments and human reviewer concerns, particularly for novelty-related issues. BERTScore further distinguishes corresponding human-LLM review pairs from mismatched controls, indicating that the framework captures concerns consistent with human reviewer observations.
[NLP-60] Identifying Implicit Bias in LLM -based Chat AI Toward People with Intellectual Disabilities
【速读】： 该论文旨在解决生成式 AI（Generative AI）在对话型大语言模型（LLM）中对智力障碍（Intellectual Disability, ID）人群存在的隐性偏见问题。研究聚焦于识别并量化与智力障碍群体相关的表征差异，揭示其在人工智能生成内容中的潜在偏见。其解决方案的关键在于：通过设计包含与不包含智力障碍描述的10个提示词干（prompt stems），利用GPT-4-Turbo及另外四种主流大语言模型（GPT-4o、Llama-3-70B-Instruct、Claude-3-5-Sonnet、Mistral-Large-2411）生成共计25,000条故事文本，并采用独立的GPT-4-Turbo实例对这些文本进行系统性分析，以检测是否存在基于已有文献定义的偏见主题。结果表明，带有智力障碍描述的文本在表征上呈现出显著差异，表现为将该群体过度年轻化、强化家长式态度与去成人化倾向、将其符号化为“励志榜样”、强调其依赖性与被拯救需求，以及表现出更高的排斥意愿与负面感知。这些发现揭示了人工智能系统中根植于历史歧视的隐性偏见，凸显了在AI开发中主动识别和缓解此类偏见的重要性，以防止技术应用加剧社会不公。
链接: https://arxiv.org/abs/2607.26062
作者: Karly V. Coffey,Gloria L. Krahn,John P. Hanley,Jacob E. Neely
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Background: This work investigates the presence of implicit bias in Large Language Model (LLM)-based chat AI models directed toward people with intellectual disabilities (ID). Objective: The study aims to identify and measure representational differences related to people with ID and examine them to identify implicit biases inherent in AI chat generation technologies. Methods: Utilizing the GPT-4-Turbo model, we requested story-generation based on 10 prompt stems with and without descriptors for ID. This process was repeated using four other LLMs (OpenAI GPT-4o, Meta Llama-3-3-70B-Instruct, Anthropic Claude-3-5-Sonnet, and Mistral-Large-2411). The resulting 25,000 computer-generated stories were analyzed using a separate GPT-4-Turbo model instance to detect differences in how people are represented related to themes of bias described in previous literature. Results: Our findings reveal differences in how people are represented between story datasets with and without ID descriptors. These differences go beyond established characteristics of ID and imply the presence of mostly negative implicit biases. Identified differences related to considering people with ID as younger, with themes of paternalism and infantilization; depicting them as more inspirational and symbolic; as needing help more often, being dependent, and being saved; and having a negative perception of them and more hesitation to include them. Conclusions: These implicit biases are considered within the context of past discrimination towards people with ID and highlight the need for diligence against implicit bias towards people with ID in AI development. This research underscores the importance of assessing and mitigating implicit bias in decision-making technologies to prevent future societal harm. Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2607.26062 [cs.CY] (or arXiv:2607.26062v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2607.26062 Focus to learn more arXiv-issued DOI via DataCite Journalreference: Karly V. Coffey, Gloria L. Krahn, John P. Hanley, Jacob E. Neely, Identifying implicit bias in LLM-based chat AI toward people with intellectual disabilities, Disability and Health Journal, 2026, 102086, ISSN 1936-6574 Related DOI: https://doi.org/10.1016/j.dhjo.2026.102086 Focus to learn more DOI(s) linking to related resources Submission history From: Karly Coffey [view email] [v1] Tue, 16 Jun 2026 20:30:06 UTC (936 KB) Full-text links: Access Paper: View a PDF of the paper titled Identifying Implicit Bias in LLM-based Chat AI Toward People with Intellectual Disabilities, by Karly V. Coffey and 3 other authorsView PDF view license Current browse context: cs.CY prev | next new | recent | 2026-07 Change to browse by: cs cs.AI cs.CL References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[NLP-61] Large-Scale ChatBot Validation Through Customer Digital Twin Simulations
【速读】： 该论文旨在解决生成式 AI 在受监管领域（如银行业）中部署时面临的可扩展性与成本效益兼具的对话机器人验证难题。其核心挑战在于如何在不依赖大量真实用户交互的前提下，实现对聊天机器人在多样化场景下安全性、合规性及表现一致性的高效评估。解决方案的关键在于提出一种双阶段方法：首先，基于真实交易与对话数据构建高保真度的合成客户代理（Synthetic Customer Agents, SCAs），作为数字孪生体，能够通过可控干预精准模拟不同客户群体的个性特征、行为模式与交互风格，并在语义一致性、幻觉率控制方面表现出色；其次，构建基于 SCAs 的综合验证框架，融合自动化大模型评判（LLM-as-a-Judge）、人工专家测试与对抗性探针，实现对情绪状态、人口统计学差异及语言因素等多维度场景下的系统性评估。该方法已在英国某领先银行的实际客户导向聊天机器人验证中成功应用，为金融机构提供了可扩展、符合监管要求的验证路径。
链接: https://arxiv.org/abs/2607.26060
作者: Cristovao Iglesias,Devesh Batra,Alankar Atreya,Stefan Wagner,Robert Hankache,Patrick Sinclair,Giulio Pelosio,Michael McMillan,Greig A. Cowan,Raad Khraishi
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:LLM-based chatbots are transforming customer service in regulated domains such as banking, but scalable and cost-effective validation remains a critical barrier to safe deployment. We present a two-part contribution for large-scale chatbot validation. First, we introduce a methodology for creating high-fidelity synthetic customer agents (SCAs) as digital twins, grounded in real transactional and conversational data, that enables automatic generation and behavioral conditioning to simulate diverse customer profiles and interaction styles. Evaluation demonstrates that SCAs achieve high semantic alignment with real customers, low hallucination rates, and successful personality trait reproduction with controllable interventions. Second, we develop an SCA-based validation framework combining automated LLM-as-a-Judge evaluation, human expert testing, and adversarial probing. Scenario-based validation across emotional states, demographic groups, and linguistic factors confirms robust performance. Our approach was used to validate a customer facing chatbot at a leading UK bank, providing financial institutions with a scalable pathway toward regulatory compliance.
[NLP-62] Forensic Reproducibility Audit of a Radiology Vision-Language Model Benchmark: From Intended Protocol to Released Artifact
【速读】： 该论文旨在解决医学影像人工智能（Medical-imaging AI）基准评估中多源异构数据与分析流程之间一致性难以验证的问题，尤其关注从模型调用、DICOM渲染、标签提取到统计分析及成果发布的全链条可复现性。其关键解决方案在于开展一次回溯性“法医式”可复现性审计，通过不重新调用模型、不新增标注的条件下，系统追踪并验证原始研究中各组件间的绑定关系与传递路径，包括提示词（prompt）绑定、DICOM元数据一致性、输出完整性、标签提取准确性、分析匹配性及发布传播过程。审计发现存在图像极性反转缺失、数据集划分信息丢失、标签提取器截断文本、统计检验未校正等问题，导致原本声称的性能、排名及提示词效应等结论不可靠。因此，论文提出必须建立机器可验证的控制机制，涵盖队列定义、DICOM渲染、提示与模型身份、调用状态、注释溯源、关键分析步骤及衍生结果等核心要素，以确保未来研究具备可验证、可重复的科学基础。
链接: https://arxiv.org/abs/2607.25589
作者: Mateusz Kozłowski
机构: Independent Researcher(独立研究员)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 25 pages, 5 figures, 7 tables. Retrospective artifact audit; no new model calls or annotations. Corrective audit and archival reconstruction: this https URL
Abstract:Medical-imaging AI benchmarks combine datasets, DICOM rendering, prompts, provider APIs, automated labels, statistical code, manuscripts, and repository releases. Agreement across these artifacts is usually assumed rather than tested. We performed a retrospective forensic reproducibility audit of a preserved chest-radiograph vision-language model (VLM) pilot; no model was called again and no image or report was newly annotated. We traced prompt bindings, DICOM metadata, output completeness, label extraction, matched analyses, and release propagation. Of 300 planned model-prompt calls, 297 yielded nonempty reports. Sixty Claude calls labeled A/B were executed with the same C prompt. The 30 studies represented 28 patients. Four MONOCHROME1 images were rendered without required polarity inversion, dataset split membership was not retained, and the unvalidated extractor truncated five reports to 4000 characters. Reconstructing one common cohort of 369 complete case-finding blocks changed Cochran’s Q from 154.73 to 182.29. Of 45 McNemar comparisons, 27 had unadjusted p 0.05 and 20 remained below 0.05 after Holm adjustment. These values describe only the archived automated-label matrix; they do not recover the intended prompt comparison or establish clinical performance. We withdraw the original performance, ranking, prompt-effect, and clinical claims and specify machine-verifiable controls for cohort, DICOM rendering, prompt and model identity, call status, annotation provenance, keyed analysis, and derived artifacts.
[NLP-63] he Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text
【速读】： 该论文旨在解决在观测文本数据中估计语言属性因果效应时，因文本同时包含处理变量（treatment）与需调整的非处理属性而导致的混淆偏差问题。其核心挑战在于：当处理状态本身通过文本中的词汇编码时，传统基于全量文本学习的表示方法会直接捕获处理信号，从而引发“混淆陷阱”——即更丰富的表示反而导致处理组与对照组可分性增强，进而违反重叠性假设（overlap），即使原始因果设定满足重叠条件。论文的关键解决方案是提出基于掩码的调整表示方法，通过在表示学习前主动移除或隐藏与处理定义相关的词汇信号（如词典中标识处理的词汇），以消除表示空间中的处理偏倚。作者形式化了表示诱导的重叠失效问题，证明删除掩码（deletion masking）可保持词袋模型或主题模型下的重叠性，而替换掩码（replacement masking）则作为大语言模型场景下的自然扩展，在隐藏处理关键词的同时保留词序与上下文信息。实验表明，该方法显著改善了重叠性诊断、稳定了处理效应估计并降低了偏差，相较于未掩码文本的调整方法具有明显优势。
链接: https://arxiv.org/abs/2607.26309
作者: Marie Neubrander,Graham Tierney,Alexander Volfovsky
机构: Duke University(杜克大学)
类目: Methodology (stat.ME); Computation and Language (cs.CL); Applications (stat.AP); Machine Learning (stat.ML)
备注:
Abstract:Estimating causal effects of linguistic properties from observational text is difficult because the same document can contain both the treatment of interest and the non-treatment textual attributes needed for adjustment. Existing approaches often learn representations from the full text to capture latent confounding, but when treatment status is itself encoded by words in the text, these representations can directly encode treatment. This creates a confounder trap: richer representations can make treated and control documents separable, inducing overlap violations even when the underlying causal problem satisfies overlap. We study latent text treatments that are encoded through lexicons or other treatment-defining lexical information, and propose masking-based adjustment representations that remove this lexical treatment signal before representation learning. We formalize representation-induced overlap failure, prove that deletion masking preserves overlap for bag-of-words/topic-model representations, and characterize replacement masking as a natural relaxation for large language models that hides treatment-defining tokens while preserving word order and context. Across simulations, masking improves overlap diagnostics, stabilizes treatment effect estimates, and reduces bias relative to adjustment methods that learn from the unmasked text.
[NLP-64] Cognitive Convergence: Deep Similarities Between Large Language Models and Human Cognition
【速读】： 该论文试图解决的核心问题是：当前主流观点将大语言模型（Large Language Models, LLMs）视为与人类认知本质迥异的“异类智能”，认为其看似类人认知的表现仅是人类对机器进行拟人化投射的结果。然而，本文提出这一认知框架存在根本性误解。研究的关键在于揭示尽管LLMs在物理基础、学习历程及交互环境等方面与人类存在显著差异，但其在五大认知组织维度上——推理结构、计算架构、表征结构、基于预测的学习机制，以及支持目标导向行为的类强化学习机制——与人类认知展现出深刻的结构性对应关系。这些对应关系表明，长期以来用于解释人类智能的核心认知原则，同样能够有效刻画当代基于LLM的系统，从而构建一个更广泛的智能认知模型，即：跨模态智能体共享一套普适的认知组织原理。
链接: https://arxiv.org/abs/2607.26179
作者: Chandra Sripada,Richard Lewis
机构: 未知
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 23 pages, 0 figures
Abstract:LLMs are widely regarded as alien intelligences, systems whose cognitive operations are fundamentally unlike our own. Apparent similarities to human cognition are therefore often seen as the result of anthropomorphic projection. We argue that this framing is mistaken. LLMs clearly differ from humans in important respects, including their physical substrate, learning history, and the environments with which they interact. These differences make it all the more striking that contemporary LLM-based systems converge with human cognition on a number of principles of cognitive organization with longstanding support in cognitive science. We identify structural correspondences across five dimensions: inferential organization, computational architecture, representational structure, prediction-driven learning, and reinforcement-learning-like mechanisms supporting goal-directed action. These correspondences support a broader model of intelligent cognition in which core principles long used to explain human intelligence also characterize contemporary LLM-based systems.
信息检索
[IR-0] DenseOn with the LateOn: Fully Open Dense and Late-Interaction Models for Multilingual Long-Context and Code Search
链接: https://arxiv.org/abs/2607.27178
作者: Raphaël Sourty,Antoine Chaffin,Paulo Roberto Moura Junior,Amélie Chatelain
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 21 pages, 3 figures, 12 tables
Abstract:State-of-the-art retrieval models increasingly rely on closed training data, creating a reproducibility gap. We present an open end-to-end recipe for training retrieval models and study how English supervision transfers to multilingual retrieval through translate-train. We first reconstruct and curate 665M English contrastive pre-training pairs from 1.4B pairs across 34 public sources and build 1.88M supervised fine-tuning pairs with mined hard negatives. Training yields two 149M-parameter models: DenseOn, a single-vector dense model, and LateOn, a ColBERT-style late-interaction model. They achieve 56.20 and 57.22 average nDCG@10 on BEIR, respectively, setting new state-of-the-art results for this size class. We then translate the validated English data into eight languages, yielding 2.8B pairs with cross-lingual samples, and train mDenseOn and mLateOn, two 307M-parameter models built on mmBERT-base. Despite sharing their backbone, data, and objectives, their representations behave differently: the dense model is strong on English and translated languages but degrades outside translate-train support, whereas the late-interaction model generalizes better to unseen languages and scripts. This suggests that token-level matching turns translate-train from a target-language expansion strategy into a multilingual generalization recipe. We publicly release the models, datasets, and training code.
[IR-1] Improving Item Discoverability in e-Commerce Search via Related Intent Generation KDD2026
链接: https://arxiv.org/abs/2607.27172
作者: Ji Xin,Xiao Xiao,Ishan Bhatt,Vinesh Gudla,Trace Levinson,Raochuan Fan,Shishir Kumar Prasad,Prakash Putta,Tejaswi Tenneti
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: Accepted to KDD 2026 TSMO
Abstract:Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall. In e-commerce marketplaces and particularly grocery, this paradigm is limiting, as user satisfaction and commercial outcomes depend heavily on the discoverability of substitute, complementary, and thematically related items. In this paper, we present a scalable system for discovery-augmented search that leverages intent-conditioned recall expansion. Our approach generates implicit user intents to expand candidate recall while maintaining relevance. The system addresses the cost-quality tradeoff of generative retrieval through a two-stage hybrid architecture. First, we leverage closed-weight large language models (LLMs) to maximize discoverability for head queries. To extend these benefits to tail queries, we then introduce a finetuned small language model (SLM), trained via LoRA adapters and teacher-student distillation. We evaluate the system using a rigorous dual framework: (a) LLM-as-a-judge metrics validated against human preferences for semantic quality, and (b) end-to-end session-level purchase analysis. Results demonstrate that our approach improves both intent generation quality and downstream retrieval effectiveness, extending discovery coverage from approximately 60% to 80% of query traffic at roughly 30% of the teacher model’s inference cost, offering a viable path for deployment in large-scale marketplaces. Beyond relevance gains, discovery-augmented search may serve as a marketplace-balancing mechanism, giving long-tail and emerging supply an opportunity for query-conditioned exposure. Comments: Accepted to KDD 2026 TSMO Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.27172 [cs.IR] (or arXiv:2607.27172v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2607.27172 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-2] KAMR: Grounding Generation via Knowledge-Aligned Multi-hop Retrieval
链接: https://arxiv.org/abs/2607.27136
作者: Xiaochen Wang,Yuan Zhong,Haoyu Wang,Ting Wang,Fenglong Ma
类目: Information Retrieval (cs.IR)
备注: Accepted by COLM’26
Abstract:Graph-based retrieval-augmented generation increasingly relies on multi-hop retrieval, where answering a query requires composing multiple connected knowledge-graph triplets. However, existing retrievers often rank triplets independently via global semantic matching. Moreover, many multi-hop benchmarks provide only final answers, which limits supervision for query–triplet alignment and causes structurally necessary but weakly aligned facts to be missed. To address these issues, we propose a knowledge-aligned multi-hop retriever, KAMR, which distinguishes anchor triplets that are strongly constrained by the query from connected triplets that are weakly aligned yet structurally linked to the anchors. To mitigate the lack of query-triplet alignment supervision, we build a partial alignment dataset by masking triplet elements and prompting an LLM to generate corresponding queries, and optimize two contrastive objectives for pair-level and element-level matching. At inference time, KAMR retrieves anchors globally and then expands locally to collect connected evidence. Across four benchmarks, three LLM backbones, and fourteen baselines, KAMR consistently improves multi-hop retrieval and downstream question answering performance.
[IR-3] Learning from the Future: Privileged Self-Distillation for Sequential Recommendation
链接: https://arxiv.org/abs/2607.27055
作者: Jiakai Tang,Yang Zhang,See-Kiong Ng,Xu Chen,Wen Chen,Jian Wu,Han Zhu
类目: Information Retrieval (cs.IR)
备注:
Abstract:Sequential recommenders are commonly trained with one-hot next-item labels under a causal (prefix-only) objective aligned with inference. While deployment-compatible, this supervision offers little insight into relative preferences among non-target items. Yet logged interaction sequences contain an additional supervisory source: interactions following the target often reveal how user intent evolves, making the target easier to interpret. We treat these future interactions as training-only privileged information, available during learning but not at inference. This raises a natural question: can future interactions provide richer supervision while keeping training aligned with inference-time prediction? We propose Privileged Self-Distillation (PSD), a framework that separates learning-time information from inference-time information. PSD applies two attention masks to the same backbone: a future-aware view yields a privileged teacher distribution conditioned on past and future interactions, while a prefix-only view yields the student distribution used for deployment. Distilling the privileged distribution converts future interactions into training-only supervision rather than inference-time inputs. Since both views share a backbone, the teacher’s advantage is purely informational, not architectural, removing the need for a separately pretrained teacher and letting its supervision adapt as the student evolves. PSD further uses an advantage-reachability gate to focus distillation on teacher signals likely supported by the observed prefix, along with a momentum-averaged teacher for stable targets. The framework is optimized end-to-end in a single stage, leaving the deployed model and inference cost unchanged. Experiments across public benchmarks and diverse backbones show consistent improvements. Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2607.27055 [cs.IR] (or arXiv:2607.27055v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2607.27055 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-4] IMFuse: Instance-Aware Multi-Layer Fusion for LLM -Enhanced Sequential Recommendation
链接: https://arxiv.org/abs/2607.27002
作者: Yuheng Zheng(1),Yu Cui(1),Bin Wu(2),Jian Zhang(3),Ye Feng(3),Can Wang(1),Jiawei Chen(1) ((1) Zhejiang University, (2) Zhengzhou University, (3) University of Science and Technology of China)
类目: Information Retrieval (cs.IR)
备注: 12 pages, 5 figures, and 9 tables. Yuheng Zheng and Yu Cui contributed equally. Jiawei Chen is the corresponding author
Abstract:Recent advancements in Large Language Models (LLMs) have significantly enhanced sequential recommendation by encoding rich item textual information into semantic representations. However, existing methods typically rely on the final-layer hidden states of LLMs, overlooking potentially useful semantic signals encoded in other layers. Through empirical analysis, we reveal the limitations of this practice: final-layer representations often suffer from dimensional collapse, whereas intermediate layers preserve complementary, coarse-to-fine semantic knowledge. Furthermore, we observe that different items exhibit heterogeneous layer-wise representation evolution, making a uniform layer selection sub-optimal. To bridge this gap, we propose IMFuse, an instance-aware multi-layer fusion strategy designed for LLM-enhanced recommendation. Instead of relying on a single layer, IMFuse adaptively aggregates multi-layer semantic information by learning global dimension-wise layer preferences to capture general semantic contributions. To address item-level heterogeneity, IMFuse introduces an instance-aware expert modulation mechanism that dynamically adjusts these global preferences, generating personalized, item-specific semantic representations. Extensive experiments across four real-world datasets demonstrate the effectiveness of IMFuse. It consistently outperforms state-of-the-art baselines with an average relative improvement of 6.72%, while introducing limited parameter and computational overhead.
[IR-5] Beyond Action Imitation: Learning a Decision-Aware User Simulator for Online Advertising
链接: https://arxiv.org/abs/2607.26893
作者: Zipeng Chen,Jiaer Zheng,Xiangyang Xu,Xinyu Lin,Zhaobin Wang,Zhaohui Liu,Qianjin Xiang,Xiaoyu Zhao,Zhuozhen Yu,Guangshuo Wang,Daxing Chen,Junwei Pan,Zhangbin Zhu,Chengguo Yin,Hao Chen,Tat-Seng Chua,Haijie Gu,Jie Jiang
类目: Information Retrieval (cs.IR)
备注: 23 pages, 10 figures, 9 tables
Abstract:Recent advances in LLM-based user simulation have shown promise for offline evaluation of recommendation and advertising systems. However, existing simulators typically infer user preferences from single-domain interaction histories and are primarily optimized to reproduce observable actions such as clicks. Consequently, they capture only a partial view of user preferences, while action-only prediction easily induces model shortcuts and limits both the fidelity and diagnostic value of simulation. To address these challenges, we propose DASH, a decision-aware user simulator that jointly generates thinking traces and predicts behavioral actions from heterogeneous cross-domain histories. DASH first introduces a Context Engineering stage that folds heterogeneous cross-domain histories into decision-relevant context, together with prompt optimization for effective reasoning over the folded context. To train a user simulator, DASH distills thinking trajectories from strong LLMs as SFT data, and further tailors a rubric-based reward model that evaluates thinking traces along form, content, and logic for RL training. Combined with the action reward, these signals jointly improve action prediction and thinking quality. Extensive experiments on real-world Tencent advertising data spanning five heterogeneous content domains demonstrate the effectiveness, efficiency, fidelity, and diagnostic value of DASH.
[IR-6] Kairos: Numerically Robust News Recommendation under Item Cold-Start via Cholesky-based LinUCB
链接: https://arxiv.org/abs/2607.26832
作者: Finn Hertsch
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注: English preprint. The German version was peer-reviewed and accepted at SKILL 2026 (Gesellschaft für Informatik)
Abstract:Algorithmic news personalization in regional markets often fails because modern deep learning models require massive interaction data while real-world news has a short Time-to-Live (TTL 48 h) and shallow article pools. This structural item cold-start deprives collaborative filtering of the data needed for robust modeling. This paper presents Project Kairos, a framework that bridges this data scarcity through a contextual online learning approach (LinUCB). To ensure numerical integrity for continuous operation, Kairos replaces error-prone Sherman-Morrison inversions with direct rank-1 updates of Cholesky factors. This preserves the positive definiteness of the covariance matrix even under ill-conditioned data scenarios. Simultaneously, Matryoshka Representation Learning (MRL) integration addresses inference latency. Empirical evaluations based on the Tagesschau API demonstrate that exploiting semantic redundancy in the feature space achieves a 4.85-fold efficiency gain without significantly compromising ranking precision. Kairos thus provides a blueprint for high-performance recommendation systems in resource- and data-constrained environments.
[IR-7] MediaWiki Code2Code Search: Neural Retrieval for the Semantic Discovery of Open-Source Software Entities
链接: https://arxiv.org/abs/2607.26766
作者: Francesco Tosoni
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE)
备注: 21 pages, 5 tables, 3 figures
Abstract:Code search in large-scale ecosystems is often hindered by the lexical gap between user queries and implementation details, alongside the trade-off between the low latency of traditional Information Retrieval (IR) and the precision of Deep Learning (DL). We present MediaWiki Code2Code Search, a neural retrieval system for semantic code-to-code discovery. By indexing 1.29 million structural entities (functions, types, and templates) across 2,500+ MediaWiki repositories, our system enables retrieval based on computational intent rather than surface tokens. We employ a split-build architecture, decoupling GPU-intensive offline indexing from a CPU-only serving layer; our FAISS IVF-PQ index occupies 168.6 MB: a 96.6% reduction compared to a flat float32 baseline, and achieves a median query latency of 1.85 seconds on commodity hardware, satisfying the 6 GiB RAM constraint of Wikimedia Toolforge. Our evaluation across a 27-query benchmark demonstrates superior performance over the BM25 baseline, achieving a P@10 of 0.87 compared to 0.64 (0.52 versus 0.34 for strict matching). Gains are most pronounced in name-obfuscated tasks where lexical methods fail. The system is available at this https URL under the Apache 2.0 licence and provides an open RESTful API.
[IR-8] CaIRec: Calibrated Modality Imputation for Incomplete Multimodal Recommendation
链接: https://arxiv.org/abs/2607.26720
作者: Ruiyu Liu,Xiaohao Liu,Miaomiao Cai,Yunshan Ma,See-Kiong Ng
类目: Information Retrieval (cs.IR)
备注:
Abstract:Real-world multimodal recommender systems often face incomplete modality observations, where items lack images, text, or other content features. Such incompleteness weakens item representations and degrades recommendation performance. Existing modality imputation methods estimate missing representations from available item content, but two challenges remain. First, they optimize the recovered representation itself without explicitly considering its relations with other modalities of the same item. The completed modalities may therefore form inconsistent cross-modal relations, causing Cross-modal Structural Distortion. Second, even structurally coherent recovered information may remain ineffective for personalized ranking. Recovered representations receive limited ranking-oriented guidance, while modality missingness disrupts the item neighborhoods required for preference propagation, resulting in a Preference Adaptation Gap. To address these challenges, we propose Calibrated Imputation for Incomplete Multimodal Recommendation (CaIRec), a two-stage framework. Structural Imputation Calibration (SIC) estimates missing-modality representations from shared information inferred from available modalities and calibrates their cross-modal organization through structural regularization and correspondence supervision from observed modality pairs. Preference-oriented Representation Calibration (PRC) performs recommendation-specific adaptation at both the representation and relation levels. It constructs pseudo-missing instances to align recovered representations with observed counterparts shaped by ranking supervision in the recommendation space. It further builds completion-aware item graphs by integrating completed content relations with collaborative evidence. Extensive experiments on three datasets under different modality-missing settings demonstrate the effectiveness and robustness of CaIRec.
[IR-9] Scientific Knowledge Discovery in the Age of Large Language Models
链接: https://arxiv.org/abs/2607.26670
作者: Eleni Adamidi,Serafeim Chatzopoulos,Thanasis Vergoulis
类目: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 21 pages, 4 figures
Abstract:The rapid growth of scholarly literature has made identifying relevant publications increasingly difficult, and conventional search systems still depend heavily on manually formulated queries and effortful manual inspection. Generative large language models (LLMs) offer a more flexible alternative, supporting literature retrieval and the screening of candidate studies against eligibility criteria. This chapter surveys 34 peer-reviewed papers applying generative LLMs to these two tasks, identified via a Boolean search over the OpenAIRE Graph (1,589 records screened to 34 inclusions). Reviewed studies are characterised by LLMs employed, model access and adaptation, prompting and architectural techniques, ground-truth sources, and evaluation metrics.
[IR-10] RAG -HAR: Towards Cost-Efficient LLM -Based Human Activity Recognition for Edge Deployment
链接: https://arxiv.org/abs/2607.26631
作者: Hansi Karunarathna,Nirhoshan Sivaroopan,Chamara Madarasingha,Anura Jayasumana,Kanchana Thilakarathna
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注: Submitted to IEEE Transactions on Mobile Computing. Extended version of the IEEE PerCom 2026 paper “RAG-HAR: Retrieval Augmented Generation-based Human Activity Recognition.” ( this https URL )
Abstract:Human Activity Recognition (HAR) from wearable sensors supports applications in healthcare, rehabilitation, fitness tracking, and smart environments. Yet, existing deep learning approaches require dataset-specific training, large labeled corpora, and repeated adaptation to new sensor settings or activity taxonomies. Retrieval-Augmented Generation for Human Activity Recognition (RAG-HAR) addresses this by framing HAR as a training-free, retrieval-augmented task, in which statistical descriptions of sensor windows are used to retrieve similar labeled examples that guide LLM-based classification. We introduce RAG-HAR+, a retrieval-first and cost-optimized extension that strengthens retrieval while reducing dependence on LLM-based inference. RAG-HAR+ uses an offline Retrieval Designer Agent to design dataset-specific feature groups from a diverse pool of motion descriptors, enabling sensor windows to be compared using features better aligned with dataset-specific activity patterns. During inference, RAG-HAR+ uses majority voting over retrieved neighbors for samples with strong retrieval evidence and defers only uncertain cases to an LLM-based Ambiguity Resolver Agent. Across six HAR benchmarks, RAG-HAR+ maintains competitive or improved performance while reducing LLM usage, token consumption, and inference time. We further extend the RAG-HAR mobile prototype to demonstrate the practical feasibility of retrieval-first, LLM-assisted HAR in mobile sensing scenarios.
[IR-11] WhisperRec: Latent Reasoning for Efficient Foundation Recommendation Models
链接: https://arxiv.org/abs/2607.26621
作者: Hao Jiang,Peiru Du,Pengfei Yao,Mengting Li,Siyuan Lou,Kuo Cai,Sheng Yu,Qiang Luo,Jian Liang,Ruiming Tang,Fei Pan,Peng Jiang,Wenwu Ou
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their adoption as backbones for foundation recommendation models (FRMs). Existing approaches typically enhance recommendation with explicit Chain-of-Thought (CoT) under the Think-then-Answer paradigm. However, generating lengthy rationales introduces substantial inference overhead, while fixed CoT templates struggle to model diverse, dynamic, and context-dependent user interests. We propose WhisperRec, an efficient latent reasoning framework for FRMs. WhisperRec compresses teacher-generated CoT into learnable latent reasoning tokens, enabling a Latent-Reason-then-Answer paradigm that performs reasoning in latent space without producing verbose rationales. This design retains decision-relevant reasoning information while avoiding the latency bottleneck of autoregressive rationale generation. Specifically, it first introduces Multi-View Adaptive CoT (MV-ACoT) to construct diverse, high-quality supervision from complementary perspectives on user interests. MV-ACoT also adapts reasoning complexity to each instance, applying lightweight analysis to clear cases and targeted multi-factor reasoning to challenging ones. Building on a pre-trained FRM, WhisperRec then employs a three-stage Latent Reasoning Alignment procedure to progressively internalize teacher CoT into latent representations. Finally, curriculum-based post-training activates latent-token reasoning for downstream recommendation while preserving standard recommendation capability. Experiments on an industrial-scale Kuaishou dataset and the public Kuaishou LLM-Rec benchmark show that WhisperRec consistently outperforms explicit-CoT methods and conventional baselines. Compared with explicit CoT Think and No-Think variants, WhisperRec improves SID@64 by 17.44% and 9.33%, respectively, and achieves over 10x higher online inference throughput.
[IR-12] ASARL: Autonomous Social-Aware Relevance Learning for QQ Search
链接: https://arxiv.org/abs/2607.26593
作者: Tao Su,Jinjing Hu,Xiao Wang,Xingzhong Cao,Hui Wang
类目: Information Retrieval (cs.IR)
备注:
Abstract:The rapid growth of online social platforms has transformed communication and information retrieval, giving rise to social search, where queries-titles are typically expressed in informal, community-specific language. While large language models provide strong general-purpose semantic understanding, their effectiveness in social search is constrained by contextual discrepancy, data scarcity, and behavior-driven dynamics. To address these challenges, we propose the Autonomous Social-Aware Relevance Learning (ASARL), a fully automated framework that integrates multi-agent data curation with staged model training. ASARL leverages a collaborative agent system: ReasonAgent generates interpretable relevance labels grounded in social attributes, CriticAgent validates and ensures logical consistency, and GenAgent augments long-tail data through synthetic query-title pairs. Building on the curated dataset, ASARL employs three-stage training: Social Context Training (SCT) to capture social language patterns, Preference-Guided Optimization (PGO) to align model predictions with behavioral signals, and Social Distillation (SD) to transfer these improvements into compact models for efficient deployment. Extensive offline and online experiments on the QQ search platform demonstrate significant improvements in both offline relevance metrics and online user engagement indicators, along with enhanced annotation efficiency. These results validate the effectiveness of combining autonomous, socially grounded data governance with preference-aligned training in practical search systems.
[IR-13] A Graph-Native Bitemporal Memory Store for Conversational AI Agents
链接: https://arxiv.org/abs/2607.26520
作者: Alp Niksarli,Gopesh Baheti
类目: Databases (cs.DB); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Conversational AI agents commonly lack persistent memory across sessions. The obvious fixes like injecting full chat histories into the context window, or delegating to a third-party memory service, either exhaust the model’s context budget or send personal data through infrastructure the user does not control. We describe a memory store that avoids both problems: an agent-local Neo4j property graph augmented with HNSW vector indexes and a full bitemporal data model. Each memory is stored as an immutable identity node linked to versioned content nodes carrying two closed-open time intervals: valid time (when the fact was true in the world) and transaction time (when the database recorded it). This design supports point-in-time semantic retrieval without physically overwriting history. Semantic edges between related memories are maintained automatically at write time using cosine similarity over 1024-dimensional embeddings. We evaluate the system on LongMemEval, a 500-question benchmark spanning six question types designed to stress long-term memory. Across 60 sampled questions, the current-state semantic search path achieves 46.7% R@10 overall, rising to 80% on knowledge-update questions. The time-travel path yields 80% R@10 on knowledge-update but decreases recall on temporal-reasoning questions (50% to 37.5%), a consequence of post-filter dilution that points directly to a concrete design improvement. We discuss what these results reveal about the limits of pure retrieval for different question types and what each failure mode suggests for future work.
[IR-14] Multi-Decoder OneRec: Controllable Generative Retrieval for Multi-Objective Industrial Recommendation
链接: https://arxiv.org/abs/2607.26500
作者: You Wang,Zhao Liu,Guoping Tang,Yiqing Yang,Shuo Su,Jing Liu,Naifu Zhou,Xiaoyou Zhou,Wei Jiang,Jian Liang,Xiao Lv,Ruiming Tang,Liyin Hong,Wenwu Ou
类目: Information Retrieval (cs.IR)
备注: 9 pages, 4 figures, 11 tables, 2 algorithms
Abstract:Industrial recommender systems build candidate pools by assigning explicit quotas to objective-specific retrieval routes. This design offers quota control but increasingly fragments modeling, training, and serving as the route set grows. Semantic-ID-based generative retrieval provides a unified alternative, yet a single decoder entangles objective policies and limits candidate complementarity. We propose Multi-Decoder OneRec, a controllable framework that combines shared representations, isolated objective adaptation, and coordinated decoding. All objectives share a user-context module and the General Decoder, while each objective adds an isolated, parameter-efficient LoRA expert. During training, exposure-sample next-token prediction (NTP) updates the shared base, target-filtered NTP updates the event-based experts, and Kullback-Leibler (KL)-regularized policy optimization updates the Watch-time expert; gradient routing isolates these updates, and the General Decoder supplies a stop-gradient reference. At inference, explicit route quotas allocate the fixed budget and Multi-Decoder Constrained Beam Search reduces cross-route overlap. We publicly release Kwai26, a large-scale multi-objective benchmark with 1.31 billion raw item-level records, 31.85 million Item-ID entries, and 25.03 million items with valid Semantic IDs, together with predefined splits and an evaluation protocol. Under the same 512-item retrieval budget, Multi-Decoder OneRec improves over the single-decoder OneRec baseline by 1.69%-5.62% across four Recall@512 metrics. In a production A/B test, it yields relative gains of 0.37% in app usage time per device, 0.19% in Day-7 retained users, 0.19% in devices with at least one share, and 2.09% in new-content Cold-Start. These results show that generative retrieval can combine shared modeling with objective-specific control and complementary candidate generation.
[IR-15] CMT-RAG : Complementary Memory Traces for Multi-turn Multi-hop RAG
链接: https://arxiv.org/abs/2607.26470
作者: Lang Zhou,Yingjian Chen,Shuxuan Li,Kun-Yu Lin,Zhilin Zhao
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:Multi-turn information-seeking conversations require both multi-hop reasoning and long-range dependency tracking across turns. However, existing RAG systems typically represent conversational memory as raw dialogue history, rewritten queries, or unstructured summaries, making it difficult to recover the specific prior reasoning steps and evidence required for follow-up queries. Our key insight is to align conversational memory with retrieval by representing dialogue context as sub-question-level reasoning traces. Building on this insight, we introduce MuMu-QA, a benchmark for multi-turn multi-hop RAG with explicit cross-turn sub-question dependency annotations, and CMT-RAG, a complementary memory framework for this setting. At each turn, CMT-RAG employs a state-space trace generator, whose recurrent state serves as runtime memory, to incorporate recent conversational context and decompose the current query into structured trace drafts containing retrieval-oriented sub-questions and dependencies on earlier traces. It then grounds these drafts with retrieved evidence and stores them as persistent memory traces in a session-level DAG, enabling future turns to efficiently recover relevant prior reasoning and evidence. Experiments on MuMu-QA and corpus-level RAG benchmarks show that CMT-RAG consistently outperforms five categories of RAG baselines in answer accuracy.
[IR-16] NMKFR: A Robust Framework for Time-Aware Cold-Start Recommendation
链接: https://arxiv.org/abs/2607.26429
作者: Chengzhi Liu,Ning Zeng,Zehui Qu
类目: Information Retrieval (cs.IR)
备注: 9 pages, 6 figures
Abstract:Item cold-start recommendation is difficult when new items have sparse early interactions and appear in recommendation environments that keep changing over time. Static content, early feedback, and temporal-state evidence are all useful, but their reliability varies across the item lifecycle. This work proposes a framework–Neural Memory Kalman Fusion Recommender (NMKFR), which combines a Titans-based semantic encoder with time-aware Kalman state tracking. The semantic branch extracts memory-enhanced item observations from text, while the temporal branch estimates latent states under irregular interaction intervals. The NMKFR further uses posterior covariance as an uncertainty signal to calibrate semantic memory retrieval and adaptive static-temporal fusion. Experiments on Amazon Video Games and MovieLens-32M evaluate NMKFR under time-aware and item cold-start protocols using sampled candidate ranking. Across the reported comparisons, ablations, diagnostics, and robustness analyses, NMKFR achieves the strongest retained results and exhibits bounded uncertainty-related internal behavior. These findings provide empirical evidence for posterior-covariance-guided semantic-temporal fusion under the evaluated offline settings.
[IR-17] PSG: Pair-Space Generation for Efficient Generative Reranking
链接: https://arxiv.org/abs/2607.26427
作者: Chao Feng,Li Ma,Xiancheng Gao,Chenghao Zhang,Yuanhao Pu,Xiang Li
类目: Information Retrieval (cs.IR)
备注: 13 pages, 3 figures
Abstract:Modern recommender systems adopt Generator-Evaluator (G-E) for list-wise reranking: a generator produces sequences from candidates and an evaluator scores them at sequence-level to filter out the optimal one for exposure. Auto-Regressive(AR), working as the backbone for generative recommendation, suffers two limitations. First, its complexity grows linearly with list length, forcing the system to generate fewer lists under rigorous latency constraints and thus limiting exploration. Second, teacher-forcing creates a train-test mismatch; cumulative errors worsen with length and degrade quality. To address these problems, we propose Pair-Space Generation (PSG), a reformulation that elevates the generation atom from individual items to ordered item pairs. Given n candidate items, PSG operates over pair vocabulary of size n(n-1) per request, generates only L/2 tokens. Pair token representations are produced on-the-fly by a pretrained pair-token representation module optimized over large scale exposure logs, eliminating the data sparsity that would otherwise plague a quadratic sized vocabulary. We establish three theoretical guarantees: (i) PSG is bijective with item-space generation and induces an equivalent family of sequence distributions, thus incurring no loss of expressiveness; (ii) generation in pair-token space achieves approximately a 2\times to 4\times speedup theoretically under moderate settings and 1.83\times in the real industrial environmental settings; and (iii) under outcome-only rewards, the worst-case suboptimality of PSG is bounded by O((L/2)^2 \bar\epsilon) , representing a nearly 4\times improvement over item-space generation. Beyond benchmark-based validation, PSG has also been deployed on Kuaishou, delivering a 0.178% lift in per-user stay time on the platform, which serves over 400 million daily active users. Comments: 13 pages, 3 figures Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2607.26427 [cs.IR] (or arXiv:2607.26427v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2607.26427 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-18] DIRECTOR: Dynamic Index-based Recommendation with Transport-Optimized Retrieval
链接: https://arxiv.org/abs/2607.26418
作者: Yuanhao Pu,Chenghao Zhang,Chao Feng,Xiang Li,Defu Lian
类目: Information Retrieval (cs.IR)
备注: 13 pages, 1 figure
Abstract:Reranking is a combinatorial decision problem that aims to select and order a high-utility slate from a request-specific candidate set. A major line of generative rerankers adopts autoregressive (AR) models, which construct the slate one position at a time to capture inter-position dependencies. However, under practical greedy or bounded-width decoding, prefix-based search may prematurely prune globally promising permutations and incurs inherently sequential latency, restricting the effective search space under a fixed serving budget. Non-autoregressive (NAR) alternatives alleviate this efficiency bottleneck through position-parallel prediction, but naive position-wise factorization treats different positions too independently, leading to insufficient cross-position coordination and potentially duplicate or conflicting item selections. To retain parallel efficiency while introducing global structural coordination, we propose Dynamic Index-based RECommendation with Transport-Optimized Retrieval (DIRECTOR), a transport-guided parallel reranking framework. DIRECTOR maps candidate items into a continuous latent space and generates request-conditioned dynamic retrieval indices for all target positions in parallel. During training, it uses entropy-regularized OT to provide conflict-aware supervision; at inference, it directly performs global hard matching on similarity matrix, producing duplicate-free slates without iterative transport. To further align the generator with an opaque list-wise evaluator that returns only a scalar utility, we introduce a prefix-anchored credit assignment mechanism that converts the global reward into position-specific training signals. Extensive offline and online experiments demonstrate that DIRECTOR consistently outperforms strong reranking baselines, achieving significant improvement in large-scale industrial recommendation scenarios.
[IR-19] Continuous Online Evaluation of Recommendation Strategies in Social Science Academic Search
链接: https://arxiv.org/abs/2607.26380
作者: Mehmet Deniz Türkmen,Daniel Hienert
类目: Information Retrieval (cs.IR)
备注: 21 pages, 1 figure. Accepted for publication at the 30th International Conference on Theory and Practice of Digital Libraries (TPDL 2026)
Abstract:Delivering relevant recommendations in academic search engines is a complex task due to the diversity of subject areas, information types, and user preferences. In this case study, we address these challenges by integrating and evaluating a range of recommendation systems within GESIS Search - a domain-specific search engine for the social sciences that provides researchers with access to research data, publications, variables, and measurement instruments. To support continuous, real-time evaluation of multiple recommendation strategies with actual platform users, we utilize the STELLA evaluation framework. We implement and compare a diverse set of algorithms, including traditional lexical similarity, semantic document similarity by using transformer-based embeddings, and session-based recommendations based on click paths from historical user sessions. Our results show that users prefer recommendations based on semantic similarity, which outperformed term-similarity and session-based methods. However, the performance of recommenders varies across categories within GESIS Search, suggesting that information-seeking behavior differs by information type. Overall, our study provides insights into how continuous evaluation can be incorporated to develop recommendations that better align with the preferences in academic search portals.
[IR-20] Embedding Items at Scale: Comparing GNN-Based and ID-Based Item Embeddings in the Yandex Ecosystem
链接: https://arxiv.org/abs/2607.26365
作者: Sergei Makeev,Artem Matveev,Vladimir Baikalov,Kirill Khrylchenko
类目: Information Retrieval (cs.IR)
备注:
Abstract:Transformer-based sequential recommendation models, which process sequences of user-item interactions, rely heavily on the item embedding strategy. Existing approaches either use pretrained item embeddings or learn them end-to-end with the transformer. To the best of our knowledge, no prior work has compared these options from both cost and quality perspectives in a large-scale industrial setting. This paper is a case study that compares pretrained industrial graph neural network item embeddings with end-to-end trainable item embeddings across two mature production recommendation systems at Yandex: Yandex Market and Yandex Music. We additionally evaluate both approaches on a low-resource dataset sampled from Yandex Lavka production logs, for which both the data and code are publicly available for demonstration purposes. Our results show that a separate pretraining stage helps when training data is limited, but provides no worthwhile benefit for large-scale models trained on extensive datasets.
[IR-21] RAG uard: A Layered Defense Framework for Retrieval-Augmented Generation Systems Against Data Poisoning AAAI NEURIPS
链接: https://arxiv.org/abs/2607.26339
作者: Pushkal Kumar,Tucker Nielson,Tanish Kolhe,Shubham Zala,Vincent Li
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Information Retrieval (cs.IR)
备注: Accepted to NeurIPS ResponsibleFM 2025, AAAI FrontierIR 2026
Abstract:Retrieval-Augmented Generation (RAG) systems ground large language models (LLMs) in external corpora, but this reliance exposes them to corpus poisoning: maliciously injected passages that manipulate retrieved evidence. We introduce RAGuard, a layered defense against \emphfactual corpus-poisoning attacks on RAG pipelines. The first layer adversarially fine-tunes a dense retriever on synthetic poisoned documents (fabricated facts, contradictions, and reasoning traps), teaching it to downrank malicious passages before generation. The second layer, the Zero-Knowledge Inference Patch ZKIP, is a label-free, black-box filter: for each retrieved document, it performs a leave-one-out decode and scores the document by the semantic shift and output-entropy change that its removal induces. ZKIP requires no poison labels, no ground-truth answers, and no access to model internals; it compares the model’s own answers under counterfactual contexts. On poisoned Natural Questions at 5–30% poison ratios, adversarial retriever training alone reduces but does not eliminate attack success, while ZKIP drives the measured attack success rate to 0.000 in every defended configuration, keeping Recall@5 within 0.03 of the clean-corpus baseline. Supervised analyses on both Natural Questions and BEIR (NFCorpus) confirm that the counterfactual signals ZKIP relies on carry learnable poison structure. The defense costs k+1 generator passes per query ( 6\times for k=5 ); we analyze batching and early-stopping approximations that reduce this overhead. We also show that keyword-preserving poisons leave lexical retrievers such as BM25 essentially unaffected, an observation that delineates the boundary of the threat model. Code, datasets, and evaluation harnesses are released for reproducibility.
[IR-22] FinCacheServe: Dependency-Consistent Answer Reuse for Cost-Efficient RAG Serving over Mutable Enterprise Documents
链接: https://arxiv.org/abs/2607.26076
作者: Lingteng Zeng,Yifan Jin
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: 16 pages, 11 figures, 12 tables; preprint
Abstract:Retrieval-augmented generation services over mutable enterprise documents repeatedly execute semantically equivalent analysis requests. Answer reuse can remove GPU-bound generation work, yet response caches require dependency consistency when filings, evidence chunks, and tool outputs change. FinCacheServe treats each generated answer as a serving object indexed by enterprise intent and guarded by document versions, evidence fingerprints, tool fingerprints, model identity, and decoding configuration. A vLLM implementation evaluates SEC-derived financial-document workloads with Qwen2.5 models. On a 2,230-request hosted 7B trace, FinCacheServe skips 53.27% of LLM calls with zero observed dependency-stale outputs. Across three hosted 32B operator-suite seeds, it skips 53.31% of 544 requests, compared with 38.97% for versioned semantic caching and 22.43% for grounded-style reuse. Capacity, backend, and SLO replays show oracle-bounded cache management, 100k-entry transactional metadata behavior, and 44.30% lower estimated Wh per dependency-fresh 2s-SLO success than versioned semantic caching.
[IR-23] IDP AutoOpt: Agent -Driven Optimization of Document Processing Pipeline Configurations
链接: https://arxiv.org/abs/2607.26075
作者: David Kaleko,Sergey Ivanov,Md Mofijul Islam
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:We present IDP AutoOpt, an autonomous LLM agent that discovers high-performing configurations for intelligent document processing (IDP) pipelines. Tuning IDP prompts, models, OCR settings, and schemas jointly currently costs domain specialists 20 to 80+ person-hours per document type and does not scale as enterprises add document classes. IDP AutoOpt runs a closed loop: it scores a configuration on a small labeled set, diagnoses field-level errors, generates targeted edits, and re-evaluates, guided by human-authored domain skills that encode production expertise. Across extraction, classification, and packet-splitting tasks deployed in healthcare, marketing-intelligence, and financial-services settings, IDP AutoOpt matches or exceeds human-expert accuracy at equal or lower cost (on an extraction benchmark, 90.2% vs 81.6% at 4.6 x lower per-page cost), cutting configuration time from weeks to under two hours. We further show that agent LLM capability has a hard threshold below which optimization fails, and that curated domain skills outperform raw source-code access, which can degrade performance when provided without structure. We also share practical lessons on context management and variance mitigation. Requiring only a configurable pipeline, a scoring function, and a small labeled set, the approach extends beyond IDP to other enterprise AI systems, such as RAG and multi-agent workflows, where configuration bottlenecks deployment.
[IR-24] Reproducibility in Recommender Systems: A Survey
链接: https://arxiv.org/abs/2607.26074
作者: Alan Said,Alejandro Bellogin
类目: Information Retrieval (cs.IR); Human-Computer Interaction (cs.HC)
备注: Accepted at ACM Transactions on Recommender Systems
Abstract:Reproducibility has become a cornerstone of credible recommender systems research, driven by growing concerns about the reliability and generalizability of experimental results. In response, the ACM RecSys conference introduced a dedicated Reproducibility Track in 2020 to encourage rigorous, transparent, and repeatable research. This paper presents a structured analysis of the track from 2020 to 2025, covering 51 accepted papers. We classify contributions by type and analyze common patterns in datasets, algorithms, frameworks, and evaluation practices, with the goal of understanding how reproducibility is operationalized in practice within the community. Our findings reveal three main trends. First, the track has expanded in scope, evolving from a focus on reproduction and replication to include benchmarking, resources, and methodological contributions. Second, reproducibility papers exhibit a consistent methodological profile, relying on a limited set of datasets, algorithms, and evaluation protocols. Third, reproducibility in practice often involves extending prior experiments rather than strictly replicating them, with studies frequently introducing additional models or evaluation criteria. Overall, reproducibility work has improved transparency and documentation, but has had limited impact on methodological diversity, highlighting a gap between the conceptual definition of reproducibility and its implementation. Comments: Accepted at ACM Transactions on Recommender Systems Subjects: Information Retrieval (cs.IR); Human-Computer Interaction (cs.HC) Cite as: arXiv:2607.26074 [cs.IR] (or arXiv:2607.26074v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2607.26074 Focus to learn more arXiv-issued DOI via DataCite
[IR-25] Guess Where You Go: Generative Next Point-of-Interest Recommendation in Amap
链接: https://arxiv.org/abs/2607.26073
作者: Penglong Zhai,Bowen Zheng,Jie Li,Yifang Yuan,Yue Liu,Sicong Wang,Mingyang Yin,Tingting Hu,Shuaijun Guo,Fanyi Di,Xin Li
类目: Information Retrieval (cs.IR)
备注: 10 pages, 4 figures
Abstract:Generative retrieval enables recommender systems to retrieve items by generating compact item identifiers, but scaling it to industrial scenarios remains challenging due to redundant or colliding token assignments and insufficient integration of heterogeneous item signals. These challenges are particularly critical for next Point-of-Interest (POI) recommendation, where models must represent structured spatial entities, capture sequential mobility patterns, and produce predictions consistent with real user behavior. We propose Gwhere, an end-to-end industrial framework that integrates semantic identifier (SID) generation with LLM-based generative next POI recommendation. Gwhere first learns discriminative POI SIDs through a contrastive residual-quantization tokenizer that aligns textual, visual, spatial, and collaborative signals. Based on these SIDs, Gwhere adapts LLMs to mobility scenarios via continued pretraining on enriched spatio-temporal corpora, supervised fine-tuning, and Exposure-Aware Kahneman-Tversky Optimization (EAKTO), a reinforcement learning objective for behavioral preference alignment. Experiments on public datasets and Amap’s large-scale industrial dataset demonstrate the effectiveness of Gwhere. The system has been deployed in Amap’s homepage service under high-concurrency and low-latency constraints. Long-term online A/B tests show improvements of 5.83% in P-CTR and 6.20% in U-CTR over the production baseline. The implementation is publicly available at this https URL.
[IR-26] IFCMemoryBench: Evaluating Long-Term Memory of LLM -Based Agents in BIM Information Retrieval KDD2026
链接: https://arxiv.org/abs/2607.26072
作者: Changyu Du,Alexander Vosseler,Filippo Mazza,André Borrmann
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: KDD 2026 Workshop on Evaluation and Trustworthiness of Agentic AI
Abstract:Long-term memory is becoming a core capability of LLM-based agents, but existing evaluations largely test conversational recall in open-domain or persona-grounded settings. We argue that a stronger test is whether an agent can reuse information from prior sessions while acting over a live, structured, domain-specific environment. We study this problem in Building Information Modelling (BIM), a professional engineering workflow where agents must query large IFC models while also relying on project specifications, client decisions, and engineering conventions often discussed in conversation but absent from the model. We introduce IFCMemoryBench, a benchmark for evaluating long-term memory in LLM-based BIM information retrieval. IFCMemoryBench contains 143 multi-session tasks across 19 projects and 4,016 prior sessions, derived from incomplete-information questions in IFC-Bench v2. Each task seeds missing project context across earlier conversations and later asks a probe question that can be answered only by combining remembered context with live IFC queries. Our evaluation framework decomposes memory performance into ingestion, retrieval, and utilization, and measures both answer quality and memory quality with expert-validated LLM judges. We evaluate representative vector-, graph-, and file-based memory systems. The strongest system achieves only 32.4% answer accuracy under a deployment-realistic ingestion scope, and remains below 60% under oracle-filtered ingestion or a stronger probe agent. Analysis shows that current general-purpose memory systems often retrieve topically relevant context but store project knowledge as incomplete or fragmented facts. These results reveal a domain-transfer gap in agent memory and suggest that reliable professional agents require domain-aware memory representations linking conversations, project knowledge, and structured model entities.
[IR-27] GuidedRAG : Semantic Steering of Retrieval-Augmented Generation
链接: https://arxiv.org/abs/2607.26071
作者: Matthijs Jansen op de Haar,Tobias Stähle,Lorenzo Gatti
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: 8 pages + 5 pages of references and appendix. 8 figures and 6 tables
Abstract:In this work, we propose GuidedRAG, a novel extension to traditional Retrieval-Augmented Generation (RAG) that introduces a dedicated selection stage and semantic steering during retrieval. In contrast to current state-of-the-art RAG approaches, which depend on increasingly complex retrieval and knowledge structures, GuidedRAG constrains the knowledge base using semantics before retrieval, aligning the retrieval space with user intent while substantially reducing the search space. Our evaluation shows that GuidedRAG improves retrieval relevance by 14.0-15.8%, mitigates a 19.7-27.4% loss in retrieval precision, and reduces retrieval overhead by orders of magnitude. Moreover, relevant chunks are consistently retrieved earlier in the ranking process, while alignment with user intent improves by 31.8-36.8%. We further show that GuidedRAG achieves full coverage across 15 diverse RAG variants, demonstrating generalizability across the literature. Together, these findings establish semantic steering and selections as a powerful and generalizable paradigm for improving the current state-of-the-art in RAG.
[IR-28] SimpleWikiSearch: A Clean Offline Wikipedia Environment for Agent ic Search
链接: https://arxiv.org/abs/2607.26070
作者: Guanming Xiong,Penghui Zhang
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: Technical Report
Abstract:Large language model (LLM)-based agentic search systems are often evaluated as if the underlying LLM were the only component that matters, yet their measured performance also depends on the surrounding search environment: the Wikipedia snapshot, preprocessing pipeline, chunking policy, retrieval backend, tool schema, observation format, and answer submission rule. These details are frequently under-specified, making it difficult to compare results or reproduce reported baselines. We present SimpleWikiSearch, whose corpus construction, retrieval stack, tool contract, and evaluation protocol are explicit and runnable. The environment starts from a full English Wikipedia dump, cleans and chunks the corpus, builds keyword and dense retrieval indexes, and exposes a minimal tool interface consisting of \textttsearch, \textttopen_url, and \textttsubmit_answer. We report baseline results on six QA datasets using open-source LLMs and provide a random-300 subset for comparisons with closed-source commercial models. SimpleWikiSearch provides a domain-specific agent harness and a controlled offline environment for reproducible agentic-search evaluation. Its contribution is this specified reference setup, rather than a new agent algorithm. Code and data will be available at: this https URL.
人机交互
[HC-0] APEX-Accounting
链接: https://arxiv.org/abs/2607.27189
作者: Julien Benchek,Austin Bennett,Jasmin Kern,Ryan Stevens,Rene Sultan,Charis Ching,Hayley Popiel,Vaibhav Mittal,Felix Mercier,Brendan Foody,Bertie Vidgen
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Public dev set: this https URL
Abstract:We introduce APEX-Accounting, a benchmark built by Mercor in partnership with Ramp, to assess whether frontier models can do the real work of accountants. Tasks include reconciling accounts, accruing expenses, posting transactions, and producing reports. The private eval set comprises 160 tasks, split across 10 worlds. Each world contains an accounting system, as well as spreadsheets, PDFs, and other files. Every task was authored and solved by experts in accounting and bookkeeping, who also wrote grading rubrics. Across nine frontier models, Claude-Fable-5 (Max) leads with 56.4% Mean Criteria@3, ahead of Muse-Spark-1.1 (xHigh) at 52.6%. No model scores more than 2.6% Pass^8 (GPT-5.6-Sol (Max+Pro)) and the highest Pass@8 is 21.5% (Muse-Spark-1.1 (xHigh)). We experiment with increasing the token budget from 1 to 50 and observe an instance of Simpson’s paradox: scores increase as the token budget increases but within a given budget-constrained harness, scores are lower on tasks where the model spends more tokens. As APEX-Accounting is a closed benchmark, leaderboard evals can be run for any frontier model on request.
[HC-1] GraphQAG: A Knowledge-Graph-Guided Visual Analytics Framework for Question-Answer Pairs Generation
链接: https://arxiv.org/abs/2607.27182
作者: Yize Li,Ruiqi Yu,Tianya Pan,Ningxin Li,Songyue Li,Xiangyang Wu,Jinchang Li,Zhiguang Zhou
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Question-answer (QA) pairs are widely used in knowledge base construction, question-answering systems, and the post-training of large language models (LLMs). However, important knowledge in long documents is often distributed across multiple paragraphs and connected through complex entity relationships. Such fragmented and relational knowledge poses substantial challenges for existing QA generation methods, which often fail to adequately cover core document content, cross-paragraph semantic connections, and multi-entity relationships. We present GraphQAG, a knowledge graph-guided visual analytics framework for generating high-quality QA pairs from long documents. GraphQAG follows a three-stage workflow. First, it constructs a document knowledge graph by segmenting the document into paragraphs and extracting salient entities and relations. Second, it builds a graph-based generation space from entities, relations, and multi-hop paths to constrain and guide LLM-based QA generation. Third, it uses the knowledge graph as an interactive visual representation, enabling users to explore document knowledge structures, inspect the coverage and evidence provenance of generated QA pairs, and iteratively refine the QA pair set through graph-based interactions. We evaluated GraphQAG through a user study with 16 participants, two case studies, and expert interviews. The results indicate that GraphQAG effectively supports users in identifying knowledge coverage gaps, examining generated QA pairs, and refining the QA pair set. These findings demonstrate the usefulness of combining knowledge graphs, LLM-based generation, and visual analytics for producing more comprehensive and trustworthy QA pairs from long documents.
[HC-2] he Social Cost of an AI Teammate: How an Artificial Teammate Reshapes Human-Human Communication in Small-Team Decision-Making
链接: https://arxiv.org/abs/2607.27179
作者: Nia Nixon,Jaeyoon Choi,Pedro Martins De Bastos,Mohammad Amin Samadi,Luise Mehner,Seehee Park,Spencer JaQuay
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Conversational AI is increasingly positioned as a teammate rather than a tool, yet we know little about how its presence reshapes communication among the humans on the team. We examined sociocognitive communication dynamics in team decision-making using Group Communication Analysis (GCA), team surveys, and lexical analyses of team discourse. Teams completed a high-stakes moral-dilemma decision task in a randomized controlled study: 16 teams of two students plus an AI teammate, and 17 all-human teams of three. Across six GCA dimensions and survey outcomes, we find that the AI teammate was the single most talkative and self-cohesive member of every treatment team, yet its contributions carried the least new information and the lowest density. The presence of AI also reshaped communication amongst humans. In AI-human teams, human teammates showed lower responsivity and social impact toward one another and reported lower levels of belonging and status. Greater AI dominance in the conversation was associated with students feeling less valued as team members. Additionally, this social cost is immediate and present at baseline; it does not emerge over the course of the conversation. Drawing on these results, we discuss a research agenda extending to voice-based and longitudinal settings.
[HC-3] OmegaUse-OfficeVal: Benchmarking LLM Agents on Long-Horizon Office-Suite Tasks with Economic Grounding
链接: https://arxiv.org/abs/2607.27155
作者: Jingbo Zhou,Yusai Zhao,Qi Bao,Jingjia Cao,Zhenghai Chen,Chang Gao,Kaiqi Guo,Muxin Guo,Mingxuan Li,Xinjiang Lu,Yanru Ma,Yixiong Xiao,Zenghui Zhang,Le Zhang,Hua Wu
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注:
Abstract:Large language model (LLM) agents are increasingly expected to assist users in completing tasks. However, existing benchmarks provide limited support for evaluating whether agents can carry out office-suite workflows at a reasonable cost. We introduce OmegaUse-OfficeVal, a benchmark for evaluating LLM agents on long-horizon office-suite tasks with task-level economic grounding. The benchmark comprises 100 tasks derived from office-suite requests proposed by practitioners and adapted through a privacy-preserving process. On average, these tasks require 2.32 hours of human labor to complete. An important feature of the benchmark is that each task is paired with two economic signals: human labor time and task price proxy. These signals enable direct comparisons between human costs and LLM inference costs, as well as value-weighted evaluation. To support stable evaluation, we develop code-based verifiers from fine-grained rubrics. We evaluate several frontier LLMs together with a human baseline. Although all evaluated LLMs are substantially cheaper and faster than human workers, they have not yet approached human-level deliverable quality. The code and dataset are fully open-sourced, and more information is available on our project website: this https URL.
[HC-4] actiPlay: Multi-Granularity Tactical Parsing and Video-Anchored Match Review for Amateur Badminton Players
链接: https://arxiv.org/abs/2607.27125
作者: Qiaoyi Chen,Yuheng Liu,Xinzhuang Xiong,Junze Li,Hongyi Tang,Xinyi Zhang,Qingyu Guo,Xiaojuan Ma
类目: Human-Computer Interaction (cs.HC)
备注: 13 pages, 6 figures, and 1 table
Abstract:Amateur badminton players increasingly record matches, yet existing tools provide only aggregate statistics or generic summaries, leaving most unable to extract tactical insights without expert guidance. A formative study (N=8) reveals the need for multi-granularity, video-anchored tactical analysis centered on rallies. We derive a taxonomy of performance issues from national-level athletes’ annotations and present TactiPlay, an interactive system that instantiates an expert-taxonomy-guided, rally-level, video-anchored review workflow. The system’s analytical pipeline organizes match events into taxonomy-grounded feedback, while its interface links structured reports to rally summaries, video evidence, and court visualizations. A within-subjects study (N=16) shows that TactiPlay elicits more frequent, concrete, actionable, and appropriate reflections than a report-and-statistics baseline. These findings show how organizing reviewed match evidence into a taxonomy-guided, video-linked workflow can support amateur players’ tactical reflection.
[HC-5] Human diversity fuels collective creativity that large language models cannot simulate or sustain
链接: https://arxiv.org/abs/2607.26899
作者: Mengchen Dong,Hiromu Yakura
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Diverse human groups produce diverse ideas, the raw material of innovation. Generative AI challenges this engine twice over: everyday AI assistance may homogenize what diverse people create, and AI-simulated diversity may replace the people altogether. We tested both challenges in a preregistered creative metaphor experiment with native (L1) and non-native (L2) English writers, who wrote without AI, with AI-generated ideas (AI ideation), or with AI refining their own ideas (AI refinement). L2 writers contributed more collective diversity than L1 writers, with native-language ideation showing the most diverse pools. AI ideation compressed collective diversity for everyone and left the L2 advantage undetectable, whereas AI refinement preserved both. We then simulated the entire writer pool using personas built from participants’ real backgrounds, three model families, native-language prompting, and elevated sampling temperatures. Every simulated pool fell below every human pool, and pushing models further induced diversity only through degenerate text. However, at the individual level, AI ideation raised writers’ ratings, pitting private incentives against the collective good, except when L2 writers used their native language, which benefited both. Human diversity remains a valuable creative resource that current AI cannot simulate or sustain; the design of human-AI collaborative workflows determines whether it survives.
[HC-6] AI as Friction for Reflection Support in Ideation
链接: https://arxiv.org/abs/2607.26827
作者: Janin Koch,Xiaohan Liao,Géry Casiez
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: In Proceedings of The First Reflection in Creative Experience (RiCE) Workshop (RiCE W1) arXiv:2607.24558
Abstract:Generative AI tools for creative work tend to be designed around the goal of removing friction, on the assumption that smoother iteration and faster output translate into more value for the designer. We argue, however, that this framing leaves out something important about how design ideation works, namely reflection-in-action. The act of accepting, rejecting and reworking candidate ideas is both a path to a final outcome and the process through which designers develop the rationale that allows them to think with their ideas and to communicate them to others. This becomes particularly important in group ideation, where ideas need to be expressed and explained to others to allow the group to extend, reject or combine them further. We suggest that AI in design ideation might be more usefully thought of as a friction agent for reflection rather than as a smoothing agent for output. This reframing opens up a different role for AI in design ideation, one that supports designers in building and carrying rationale rather than substituting for it.
[HC-7] Affective Tools for Thought: Towards Shared Attention and Affective Reorienting in AI-Supported Thinking
链接: https://arxiv.org/abs/2607.26731
作者: Yifu Liu,Raffaele Andrea Buono,Nadia Bianchi-Berthouze
类目: Human-Computer Interaction (cs.HC)
备注: 4 pages, 2-column ACM format. Accepted to the CHI 2026 Workshop on Tools for Thought: Understanding, Protecting, and Augmenting Human Cognition with Generative AI, April 16, 2026, Barcelona, Spain. Workshop website: this https URL
Abstract:Current Tools for Thought (TfTs) treat affect as either friction that slows cognitive progress or a signal to optimise it. Drawing on enactive cognitive science, we argue that affect is constitutive of cognition: it reshapes the trajectory of thinking, not just the speed. We identify two core barriers for Affective TfTs: the lack of Shared Attention (caring, directed attention to the user’s mode of engagement) and the lack of Affective Reorienting (the capacity to use emotional moments to open new trajectories rather than reinforcing predetermined ones), and propose three design strategies that address both: Chain of Emotion X Chain of Thought, Affective Mirror, and Prompted Reorienting. The strategies are grounded in empirical findings from a study of a touch-aware conversational agent for embodied craft learning, and are oriented as provocations for future design.
[HC-8] Galvanic Vestibular Stimulation in Latent Space
链接: https://arxiv.org/abs/2607.26659
作者: Zhi Liu,Tatsuki Fushimi,Yoichi Ochiai
类目: Human-Computer Interaction (cs.HC); Computer Science and Game Theory (cs.GT)
备注:
Abstract:Galvanic vestibular stimulation (GVS) is widely used to modulate self-orientation, balance, and motion perception; the discriminability of frequency-encoded cues further suggests its potential as a standalone modality for embodied feedback. However, synthesizing GVS waveforms congruent with target events or bodily states remains challenging. GVS waveforms combine current direction, intensity, duration, and onset and offset transitions, yet how these parameters jointly shape users’ perceptual and associative responses remains underexplored. To address this gap, we contribute a dataset linking GVS waveforms to free-form experience descriptions, as well as a retrieval-guided generative model for synthesizing candidate waveforms from target descriptions. The dataset comprises 100 GVS waveforms and 1,526 valid free-form sensation descriptions collected from 16 participants. Semantic analysis revealed diverse motion- and force-related sensations, localized bodily sensations, and situational associations. Compared with a participant-preserving permutation baseline, descriptions elicited by the same waveform covered fewer semantic categories (8.18 vs. 9.45) and exhibited a higher dominant-category proportion (26.97% vs. 21.25%; both P 0.001). Building on this dataset, we implemented the generative model as a retrieval-guided one-dimensional convolutional variational autoencoder. An independent behavioral study recruited 10 participants who had not contributed to the dataset collection. Performance in discriminating congruent from incongruent waveform-visual cue pairings was significantly above chance, with an accuracy of 63.33%, d-prime = 0.70, and p 0.001. Together, these findings demonstrate the feasibility of text-conditioned GVS synthesis and support the development of GVS as a programmable modality for semantically congruent embodied feedback across interactive scenarios.
[HC-9] Contrastive ESA: Human Evaluation of Multiple Translations at Once
链接: https://arxiv.org/abs/2607.26640
作者: Vilém Zouhar,Roman Grundkiewicz,Sara Rajaee,Parker Riley,Martin Popel,Rachel Bawden,Philipp Koehn,Marine Carpuat,Tom Kocmi
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注:
Abstract:Current human evaluation of machine translation typically assesses single outputs in isolation, a paradigm that suffers from high annotator noise and cost. We introduce Contrastive Error Span Annotation (cESA), a protocol that presents multiple translations of the source input (text, video, audio, image). In cESA, the annotator sees multiple translations of the same document, marks major and minor error spans, and then assigns a score from 0% to 100% on absolute scale. By allowing annotators to access the shared context across multiple outputs, cESA facilitates more consistent and efficient judgments. We validate cESA using a large-scale human evaluation of English-Japanese translations of 12 models, demonstrating reductions in annotation time and noise compared to standard pointwise evaluation. Unlike existing contrastive ranking methods, cESA yields absolute quality judgments that enable simple, interpretable non-parametric model rankings without the need for post-hoc corrections.
[HC-10] Fewer Clarifications Better Code: Benchmarking Cross-Session Personalized Ambiguity Adaptation in Coding Assistants
链接: https://arxiv.org/abs/2607.26611
作者: Zijian Xu,Wenshuo Zhang,Zisen Qin,Rui Sheng,Yushi Sun,Huamin Qu,Chuhan Shi
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:AI-assisted coding increasingly translates informal user intent into executable software, yet coding requests often contain ambiguities that recur in user-specific ways across tasks and sessions. Existing disambiguation methods typically address each ambiguous request in isolation within the current coding session, often through eliciting additional clarification. However, whether resolved session history from the same user can serve as memory for resolving recurring personalized ambiguity in a newly opened session remains underexplored. We formulate personalized ambiguity adaptation as a new task: given a user’s previously resolved coding sessions and a new ambiguous request, an assistant should identify the recurring ambiguity pattern, produce the intended executable solution, and minimize clarification. To benchmark this task, we introduce CAPA, which characterizes personalized coding ambiguity through six mechanisms and injects these mechanisms into unambiguous executable tasks using a controlled three-stage generation pipeline. CAPA contains 600 coding sessions across 60 balanced user–ambiguity cells, including 300 held-out evaluation sessions. We evaluate 12 recent LLMs under no-history and same-user-history conditions using executable success, first-turn success, and turns-to-completion. Our analyses examine task difficulty, user identity, and memory-based history use, and we further propose same-user history gating as a lightweight inference-time method. CAPA provides a foundation for developing long-term coding assistants that better align generated code with user intent while reducing repeated clarification.
[HC-11] A Design Study on Voice-based Interaction for Immersive Network Visualization and Analysis
链接: https://arxiv.org/abs/2607.26526
作者: Sam Yu-Te Lee,Hsin-Ai Chen,Sarah Yuniar,David Bauer,Kwan-Liu Ma
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Visual network analysis leverages network visualization authoring techniques to facilitate sensemaking, serendipitous discovery, and hypothesis verification on network data. However, transferring the same paradigm to immersive environments is non-trivial due to insufficient UI affordance for authoring operations. Researchers have studied combining multiple modalities for interactions, but the high learning curve of such input systems limits their adoption by typical data analysts, let alone for network analytics. In this work, we investigate the advantages and limitations of voice as the primary input modality with a research-through-design (RtD) study, in which we design a system that supports voice-based interactions for immersive network visualization facilitated by Large Language Models (LLMs). Through a user study on social network data analysis with participants from social science and computer science backgrounds, we find that voice interactions can improve perceived usability relative to controller-based interaction and lower the cognitive effort of formulating commands, since users can express intent in natural language rather than compressing it into terse instructions. We discuss design implications for immersive visualizations, highlighting how usability limits adoption while simplified interactions and voice-based controls enhance fluidity and support complex, multi-parameter operations.
[HC-12] Repair as Representational Work: Integration Bottlenecks in AI-Assisted Development
链接: https://arxiv.org/abs/2607.26517
作者: Daisaku Sato
类目: Human-Computer Interaction (cs.HC)
备注: Preprint submitted to International Journal of Human-Computer Studies. 18-case corpus study with controlled multi-turn probe. 1 figure, 7 tables
Abstract:Research on AI-assisted programming has concentrated on the gulf of execution – how users write successful prompts. We report a candidate phenomenon, an integration bottleneck, that lies in Norman’s gulf of evaluation: a repair-relevant contribution reaches the user and fails to become actionable at the point of receipt. Two cases in an eighteen-case corpus of publicly shared AI-assisted-development accounts report this, from a peer and from the system’s own output; both fall at evaluation’s interpretation stage, and a third, which would fall at comparison, is reached only on an inferential reading and reported as a boundary case. A within-case contrast is consistent with actionability turning on whether the contribution can be restated as an instruction without an intervening judgement. We report this as a candidate warranting dedicated study, not an established regularity; its evidence base is retrospective author self-reports. On the submission side, medium alone did not sort the corpus contrasts; immediate uptake aligned with a checkable condition across four decisive contrasts, while a fifth case shows checkability sufficient for immediate uptake did not guarantee persistence. A twenty-trial multi-turn probe across two current models observed no constraint loss in fifteen judgeable narrow trials, and a ten-trial extension with an explicit restructuring request none in eight. Study 2 is not a replication attempt: it isolates regeneration, the mechanism the corpus authors invoke, and removes it as a sufficient explanation. We specify four interaction requirements for an accepted-constraint ledger; a conformance analysis of twelve mechanisms found none documented to satisfy all four.
[HC-13] How Do Researchers Manage Visualization Experiment Stimuli? IEEE-VIS2026
链接: https://arxiv.org/abs/2607.26443
作者: Hyeok Kim,Jeffrey Heer
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to IEEE VIS 2026 Full Paper, 11 pages, 2 figures, and 2 tables
Abstract:Visualization experiments need a set of “good” stimuli that effectively address research questions and hypotheses. Creating, managing, and deploying stimuli are often challenging, as these tasks require tremendous care. Inappropriate stimuli can make the outcome invalid or uninteresting, wasting both researchers’ and participants’ resources. As the speed of science increases, better support for stimuli-related tasks is essential, yet we lack a closer look at how visualization researchers deal with them. To understand the experiences of visualization experimenters and guide future improvements, we interviewed 19 visualization researchers with diverse backgrounds and experiences. Our findings describe practices and challenges across the life cycle of stimuli, from exploration and selection through shipment, deployment, and analysis. For example, stimuli management and deployment require tedious manual effort, which does not scale for experiments with many levels and complex conditioning. We also discuss both concerns and optimism around AI-assisted visualization experiment design. We conclude with future research opportunities in supporting stimuli creation, automated stimuli inspection, and experimental apparatus concerns.
[HC-14] FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets
链接: https://arxiv.org/abs/2607.26423
作者: Peisen Xu,Jérémie Garcia,Peter Cleveland,Ooi Wei Tsang,Christophe Jouffrais
类目: Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: UIST 2026
Abstract:As autonomous drone deployments scale from individual units to coordinated swarms, the human operator’s role shifts from direct piloting to high-level supervision. Current interfaces often treat multi-drone control as a scaled-up version of single-drone operation. We instead investigate how reframing fleet supervision as spatial interaction can better support the spatial, temporal, and safety demands of complex missions. We present FleetScape, a Mixed Reality (MR) sandtable system that externalizes layered real-time mission, safety, and environmental data while enabling fluid transitions between manual intervention and autonomous supervision. We developed a high-fidelity building inspection simulation that generates and streams synchronized multi-drone and environmental data for MR visualizations. We used this prototype to conduct a user study with six experienced drone pilots managing fleets of up to 15 drones. Our findings show that FleetScape supports situational awareness through layered spatial representations and clarifies control mode transitions. However, a limit to situational awareness was observed as fleet size increases, leading to different supervisory strategies. Finally, we derive design implications for supporting scalable drone fleet supervision.
[HC-15] Sensor-Placement-Agnostic Sonomyography: Toward Continuous High-Dimensional Control by Users with Tetraplegia KR
链接: https://arxiv.org/abs/2607.26401
作者: Gavin Sueltz,Vikram Athithan,Emma Ferran,Maria Herrera,Carson J. Wynn,Laura A. Hallock
类目: Human-Computer Interaction (cs.HC); Robotics (cs.RO); Signal Processing (eess.SP)
备注: Vikram Athithan, Emma Ferran, Maria Herrera, and Carson J. Wynn contributed equally to this work. Copyright 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media
Abstract:Sonomyography (SMG) enables continuous device control via ultrasound-measured muscle deformation signals, but existing SMG interfaces generally require substantial user- and sensor-location-specific training data and provide only one proportional signal or task-specific classification. We present a real-time, sensor-placement-agnostic SMG control system based on sparse optical flow tracking that enables continuous 1-DOF control after minimal calibration (3 pose definitions). We also present a preliminary expansion of this method that augments this algorithm with a short computer-aided calibration to enable 2-DOF control. We evaluate both 1- and 2-DOF systems’ performance for a preliminary cohort of 3 cervical spinal cord injury survivors and 6 uninjured individuals across 6 sensor placements spanning the arm, neck, and upper torso. As assessed by a cursor trajectory tracking task, all participants achieved continuous 1-DOF control at all tested sensor locations (even those that relied on passive tissue motions), with all participants achieving 5.5% tracking error using at least one placement (and many 4% across many). All participants were also able to modulate 2D cursor position via the 2-DOF system, with varying levels of control authority, and several were able to complete a 2D drawing task, constituting the first (to our knowledge) demonstration of location-agnostic multi-DOF continuous SMG-based control. These results highlight the promise of SMG to enable rapidly calibratable, high-dimensional, sensor-placement-agnostic device control by users with tetraplegia, and also illuminate key challenges in both signal processing and practical system deployment. To enable further development by scientific and user communities, developed algorithms have been open-sourced as part of the OpenMyoControl project on SimTK (this http URL). Comments: Vikram Athithan, Emma Ferran, Maria Herrera, and Carson J. Wynn contributed equally to this work. Copyright 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media Subjects: Human-Computer Interaction (cs.HC); Robotics (cs.RO); Signal Processing (eess.SP) Cite as: arXiv:2607.26401 [cs.HC] (or arXiv:2607.26401v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2607.26401 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[HC-16] (Im)Paired Programming: Coding Agents Improve Productivity but Harm Understanding
链接: https://arxiv.org/abs/2607.26375
作者: Nishant Balepur,Connor Baumler,Valerie Chen,Eunsol Choi,Rachel Rudinger,Jordan Lee Boyd-Graber
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: In-progress Preprint
Abstract:Coding agents (e.g., Cursor) improve developer productivity by optimizing task completion, but shifting users from writing code to prompting and reviewing may harm their understanding, impeding oversight, learning, and communication. To probe this, we have 54 students create a website with one of two AI systems: an agent that edits user code; or a chatbot where users write code alone or adapt generic code snippets. We test understanding via comprehension questions and a task where users extend their code without agents, showing: (1) While agents aid initial task completion, they harm users’ code comprehension and thus do not prepare users to extend their code; (2) Low-effort agent interaction types, like copy+paste prompts and auto-accepted edits, are linked with lower comprehension; and (3) Despite self-reported weaker understanding, users still prefer coding agents because they are quick and easy to use. While users stay in the loop for coding workflows, understanding should not be forgotten. Towards this goal, we distill our analyses into future research directions for coding agent developers: dissuading low-effort prompting, creating readable code, and promoting active engagement.
[HC-17] When Synthetic Users Fail: A Cross-Domain Benchmark of LLM -Simulated Human Survey Responses
链接: https://arxiv.org/abs/2607.26348
作者: Zihan Chen,Di Zhu,Lei Nico Zheng
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: 19 pages, 5 figures, 4 tables. Preprint; under review
Abstract:Large language models (LLMs) are increasingly used as synthetic users, stand-ins for human respondents whose simulated answers feed product, policy, and market decisions. We ask when this substitution is valid and when it fails, and package the answer as an evaluation framework for intelligent synthetic-user systems. A single protocol, run across four models spanning two families and an 8B-to-frontier capability range, is applied to two independent domains of real human-response data: U.S. general social attitudes (General Social Survey) and cross-cultural values (World Values Survey). Every model is benchmarked against a suite of non-LLM baselines fit on held-out human data. Under demographic prompting and the survey-simulation protocols we test, two failures replicate across both domains, all four models, and both families. First, at the individual level no LLM beats even the strongest baseline; on cross-cultural values every model falls well below it, and the gap survives distance-aware and proper scoring. Second, models systematically over-determine demographics, treating identity as far more predictive of attitudes than it is among real people, a distortion present for nearly every question-group combination and robust to a coding-invariant measure. Neither failure is remedied by a larger, more capable model. A decision-impact analysis shows why this matters in practice: on a segment-targeting task the models inflate between-segment gaps two to fourfold, would direct a team to the wrong segment in half of U.S. and most cross-cultural cases, and manufacture segment splits that do not exist in real people. We make the cross-domain benchmark and the evaluation framework available on request, so that teams can determine in advance when synthetic-user evidence is safe for decision support and when it is not.
[HC-18] Designing Needs- and Attention-Aware AI Learning Tools for Engineering Education: Insights from Psychological Outcomes
链接: https://arxiv.org/abs/2607.26338
作者: Kevin Zhongyang Shao,Denise Wilson,Yale Quan,Sep Makhsous
类目: Human-Computer Interaction (cs.HC)
备注: Manuscript under review. 32 pages, 2 figures, and 14 tables
Abstract:Artificial Intelligence (AI) is transforming higher education, but its benefits can vary depending on where, how, and how often it supports learning. While prior research emphasizes cognitive and academic outcomes, this study examines how AI chatbots support the psychological needs and motivational states of engineering students. A survey of college engineering students (n = 206) examined perceived effects of AI chatbots on autonomy, relatedness, and relief from competence frustration. Structural equation modeling with latent interaction effects examined how baseline autonomy, competence frustration, relatedness, and personal agency contributed to perceived AI outcomes. Results indicate that students perceived that AI provided the greatest benefits as relief from competence frustration, smaller benefits for autonomy, and the weakest benefits for relatedness. Baseline motivational states mattered more than demographic factors, and inattention moderated how baseline competence frustration and autonomy related to perceived AI-related benefits. These results offer insights into formulating design principles for engineering-specific AI-based tools.
[HC-19] Prag matic Reasoning in Design
链接: https://arxiv.org/abs/2607.26322
作者: Lance Ying,William Van Uitert,Tan Zhi-Xuan,Joshua B. Tenenbaum,Samuel J. Gershman
类目: Human-Computer Interaction (cs.HC); Neurons and Cognition (q-bio.NC)
备注: Accepted with oral presentation at CogSci 2026
Abstract:People can often understand and use novel artifacts after only a few interactions, suggesting that design choices communicate underlying affordances and causal structure. We propose a formal account of this process by framing cooperative, user-centered design as a cooperative game in which the user is the principal and the designer is an assistant. Inspired by prior work on pragmatic communication (e.g. RSA), our model treats a designer’s design decisions as communicative signals and predicts user judgments via recursive mentalizing: designers make design decisions to trade off informativeness about the artifact with efficiency, and users infer the true model of the artifact by inverting this cooperative designer model. We evaluate the model in a design game where designers place visually identical keys on trays to help a user infer which keys unlock which doors in grid-world layouts. We find that pragmatic designer and user models better match human judgments than non-mentalizing literal baselines.
[HC-20] Agent GUI: An Interface for Observing and Steering Long-Running AI Agents
链接: https://arxiv.org/abs/2607.26300
作者: Xuan Zhao,Jiwoong Sohn,Qinyue Zheng,Michael Moor
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:AI agents are increasingly adept at tackling complex, long-running tasks. With the rapid surge of autonomous capabilities, human oversight is systematically lagging behind due to limited human-centered interfacing. Aiming to address this, we introduce AgentGUI, a user-friendly, locally hosted GUI for seamlessly observing and steering AI agents amid multiple concurrent, long-running sessions. AgentGUI features 1) rich agent trajectory visualizations, 2) effective manual and automated steering, and 3) integration with and coordination between open-source and frontier agent frameworks. A controlled user study demonstrates statistically significant reduction in the time it takes to identify key elements from agent traces (38% faster, p = 0.023). In a preliminary experiment, AgentGUI’s automated drift prevention feature raises the task completion rate of small local agents by as high as 34pp across a 0.8B–9B model ladder (N=50 runs per model). AgentGUI is publicly available through its project website (this https URL) and open-source repository (this https URL), along with a demo video (this https URL).
[HC-21] FloDR: An invertible dimensionality reduction method based on a normalising flow
链接: https://arxiv.org/abs/2607.26278
作者: Abdallah Baraka,Daniel Probst
类目: Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)
备注: 22 pages, 12 figures
Abstract:It is common for two-dimensional embeddings of high-dimensional data to be read far beyond what they can support. Distances in and between clusters, the meaning behind empty spaces, and the amount of structure hidden at each point are generally invisible in the output of methods such as t-SNE and UMAP. This is because the information that could support the meaning of these properties is discarded during the optimisation process. Here, we present FloDR, a dimensionality reduction method that embeds data through an invertible normalising flow. While FloDR only uses the first two output coordinates to create a two-dimensional embedding, it retains the remaining coordinates rather than discarding them. In addition to the embedding, an exact inverse and an exact density are properties of a trained mapping, which enable diagnostic visualisations that are computed from the exact inverse of the model that drew the layout rather than from an approximate one. Specifically, we draw two fields, the conditional spread, which measures how much of the original data remains undetermined at each embedding position in input units, and the hidden contrast, which measures how much information about a labelled contrast the two plotted coordinates discard. Both fields are rendered with a prespecified test against a held out portion of the input data and a bootstrap confidence. A field that fails the test is reported as refused.
[HC-22] Contextualized Counterspeech Can Be More Persuasive Than Generic Counterspeech
链接: https://arxiv.org/abs/2607.26236
作者: Lorenzo Cima,Alessio Miaschi,Amaury Trujillo,Marco Avenuti,Felice Dell’Orletta,Stefano Cresci
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: This work is an extension of this conference paper: Cima, L., Miaschi, A., Trujillo, A., Avvenuti, M., Dell’Orletta, F., Cresci, S. (2025, April). Contextualized counterspeech: Strategies for adaptation, personalization, and evaluation. In Proceedings of the ACM on Web Conference 2025 (pp. 5022-5033)
Abstract:AI-generated counterspeech offers a scalable and effective strategy to mitigate online toxicity by promoting more constructive dialogue. Yet, existing approaches adopt a generic, one-size-fits-all paradigm, overlooking the conversational context and characteristics of the targeted users. Here, we propose and evaluate multiple strategies for generating contextualized counterspeech that is adapted to the moderation setting and personalized to the moderated user. In detail, we explore a range of configurations that integrate different forms of contextual information and fine-tuning techniques. We conduct a comprehensive evaluation combining quantitative indicators with a pre-registered, mixed-design crowdsourcing experiment. To ensure robustness, we implement algorithmic measures of counterspeech quality based on ROUGE, BLEU, and BERTScore, observing overall consistent results across metrics. Furthermore, we analyze which characteristics of both the generated counterspeech and the moderated toxic message most strongly influence perceived persuasiveness, yielding insights into how contextualized interventions can be made more effective. Our findings show that personalization can be effective, but not uniformly so. Lightweight strategies combining conversational context and user history improve perceived adequacy and persuasiveness, whereas several other contextualization strategies degrade human-perceived counterspeech quality. Taken together, these results provide actionable directions for developing more personalized, effective, and responsible counterspeech systems, ultimately advancing human-AI collaboration in online content moderation.
[HC-23] User-Reported Misinformation Exposure Across Social Media Platforms
链接: https://arxiv.org/abs/2607.26218
作者: Catherine King,Lynnette Hui Xian Ng,Kathleen M. Carley
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to SBP-BRiMS 2026
Abstract:In this study, we surveyed users for their perception of misinformation exposure across social media platforms. Such perceived exposure is important because individuals’ beliefs about how often they encounter false information can shape their trust in institutions, platforms, and even their friends. In a survey of 1,010 United States residents, we found that perceived exposure to misinformation varies substantially across platforms and is only moderately correlated with the frequency of platform use. A much larger percentage of participants also reported being exposed to misinformation from the public feed than from known contacts. Based on these results, we propose governance strategies across three categories of platform types: discovery, interpersonal, and discourse. This work offers insight into users’ perceptions of social media misinformation and a corresponding research agenda for governance.
[HC-24] Reading Between the Curly Braces: On Textual Data Serialization Format Usability
链接: https://arxiv.org/abs/2607.26211
作者: Shiyi He,Zach Cutler,Andrew M. McNutt
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Textual data serialization formats, such as JSON or XML, are ubiquitous, supporting tasks like software configuration and data tabularization. Despite their prominence, little is known about their usability. What makes one good or bad? Is there a best one for cognitive efficiency? We explore these questions via a (N=215) crowd work study and a (N=9) semi-structured interview study. We find that format distinctions (like indentation versus curly braces) do not consistently translate into substantial usability differences. While HJSON and YAML performed better than other formats in certain modification tasks, these advantages disappeared in more realistic settings where task complexity was either trivial or highly demanding. Instead, usability appears driven by sociotechnical ecosystems: the tooling, documentation, and community practices surrounding a format matter more than syntax.
[HC-25] How Wrangling Tools Shape Wrangling: A Technical Dimensions Analysis
链接: https://arxiv.org/abs/2607.26198
作者: Shiyi He,El Kindi Rezig,Paul Rosen,Andrew M. McNutt
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Wrangling consumes a disproportionate share of the effort associated with any data project. While a variety of tools support it, relatively little is known about how their differing interface forms shape the way people actually wrangle. We conduct a between-subjects (N=40) observational study of data cleaning tasks performed in tools spanning distinct interface paradigms: Jupyter (notebook), Excel (spreadsheet), ChatGPT (conversational AI), and OpenRefine (visual wranglers). We situate our observations within the Technical Dimensions of Programming Systems framework, which we use as a conceptual scaffold for comparing across interface paradigms. Within the context of our study, the results suggest that tool affordances steer user strategies but do not determine outcomes. There is no consistent advantage of any single tool, nor convergence of results within tools observed across our outcome measures. Instead, we identify trade-offs and connect them with observed practice. For example, a key tension is between data- and abstraction-centered interfaces, where data-centered interfaces encourage opportunistic cleaning rather than systematic, planned transformations found in abstraction-focused tools (but come with a cognitive burden). Tool design, beyond mere functionality, plays a structuring role in how data work unfolds.
[HC-26] Hakka Kitchen: Engagement with Culinary Cultural Heritage Through Immersive Game Play
链接: https://arxiv.org/abs/2607.26183
作者: Jingle Huang,Yuyao Zhang,Ruyan Chen,Ray LC
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Intangible Cultural Heritage (ICH) experiences are difficult to share with the public because they are essentially processes that rely on physical interactions with embodied, tacit, and situated phenomena in specific cultural contexts. We consume non-interactive media such as videos and books to learn about culinary ICH experiences, but they do not allow us to grasp actual interactive procedures that embody the cultural knowledge. To engage people in a traditional cooking experience, we created a gamified VR experience Hakka Kitchen, where players are guided by a chef of Hakka cuisine through a modeled physical process of making the traditional dish of stuffed bitter melon. Compared against watching a video in VR providing the same information in a between-subjects study (N=40), Hakka Kitchen led to increased sensory, imaginative engagement, positive affect, and willingness to transmit awareness for the culinary ICH. Heritage recognition is mode-dependent: the cognitive load of enactment suppressed players’ uptake of concurrent cultural narration; however, it generated somatic and memory anchors that fostered a deeper, implicit kinesthetic form of empathy for the artisan’s labor and cultural background. Our work shows how representing interactive procedures instead of static content may empower cultural awareness in a virtual form of ICH practice.
[HC-27] An Attention-Based Framework for Alzheimers Disease Classification Using Resting-State fMRI
链接: https://arxiv.org/abs/2607.26746
作者: Harshiddhi Pathak,Gowtham Reddy N,Mrinal Acharya,Manjunatha Mahadevappa
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Signal Processing (eess.SP)
备注: 7 pages, 5 figures, 2 tables, accepted at 48th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC 2026)
Abstract:Accurate identification of Alzheimers disease (AD) using resting-state functional magnetic resonance imaging (rs-fMRI) remains challenging due to the high dimensionality, noise, and complex inter-regional dependencies inherent in functional brain connectivity, which limit the effectiveness of traditional approaches based on handcrafted connectivity features or conventional machine learning models. In this work, we present an attention-based deep learning framework for Alzheimers disease classification that operates directly on rs-fMRI functional connectivity matrices by treating brain regions as tokens and employing a Transformer-inspired self-attention mechanism to model long-range and global functional dependencies across distributed brain networks. The proposed framework learns discriminative functional representations without reliance on manual feature engineering and is evaluated on a longitudinal cohort from the Alzheimers Disease Neuroimaging Initiative (ADNI) comprising cognitively normal and Alzheimers disease subjects with multiple visits. A subject-wise evaluation protocol is adopted to prevent information leakage across visits, and class-weighted optimization is incorporated to address mild class imbalance. Experimental results for binary AD versus cognitively normal classification demonstrate that the proposed attention- based rs-fMRI model achieves an accuracy of 88.95% and a ROC-AUC of 0.90, along with a favorable precision-recall balance, highlighting the effectiveness of self-attention-driven functional connectivity modeling as a robust and interpretable approach for Alzheimers disease detection using resting-state fMRI.
[HC-28] he Attention-Directing Ability of Teams
链接: https://arxiv.org/abs/2607.26109
作者: Olga Kokshagina,Marc Santolini,Christoph Riedl
类目: Physics and Society (physics.soc-ph); Human-Computer Interaction (cs.HC); Social and Information Networks (cs.SI); General Economics (econ.GN)
备注:
Abstract:Why do some teams consistently mobilize collective effort and achieve superior performance while others struggle to coordinate action? We introduce Attention-Directing Ability (ADA), a latent team capability capturing how effectively members’ interaction signals elicit engagement and coordinated responses from others. Extending the attention-based view, we conceptualize attention direction as an emergent coordination capability embedded in patterns of interaction rather than as a cognitive state or an outcome. Teams differ in the extent to which attention-directing signals trigger collective responses, and these differences shape how teams mobilize effort and perform. We examine ADA in a distributed innovation effort involving 2,233 participants collaborating asynchronously in 79 self-organized teams across 165 public Slack channels, generating over 30,000 messages. We model the causal responsiveness among interaction signals and derive a latent measure of ADA from teams’ attention dynamics. We find that ADA strongly predicts both teams’ likelihood of mobilizing engagement to sustain collective work and their performance conditional on participation. Correcting for self-selection into project submission, teams with higher ADA are more likely to submit proposals and achieve higher expert-evaluated outcomes. Causal mediation analyses show that these effects operate primarily through collective effort, indicating that attention direction functions as an upstream coordination capability. By conceptualizing attention as an emergent, measurable team capability, this study advances theories of collective attention and team coordination.
计算机视觉
[CV-0] urboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with 1 GB VRAM
链接: https://arxiv.org/abs/2607.27205
作者: Hengyi Xie,Chenfei Yao,Xianjin Wu,Xuanyang Xi,Yiping Tang,Di Xu,Yingying Zhu,Dingkang Liang,Xiang Bai,Han Ding
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Code is available at this https URL
Abstract:Vision-language-action (VLA) models commonly adopt an LLM-centric V \to L \to A pathway, where visual observations are projected into the representation space of a large language model before being decoded into robot actions. Although effective, this design incurs substantial computation and memory overhead at every policy invocation. In this work, we introduce TurboVLA, a new VLA paradigm that reformulates the conventional V \to L \to A pathway as a direct V + L \to A mapping. Instead of using a large language model as the central interface between perception and action, TurboVLA independently encodes visual observations and language instructions, directly exchanges information between them through lightweight bidirectional vision-language interaction, and predicts continuous action chunks with a compact decoder. This simple design constructs task-conditioned representations directly from visual and linguistic features, significantly reducing the computational and memory costs of VLA inference. On LIBERO, TurboVLA achieves 97.7% average success with only 0.2B parameters, 31.2 ms inference latency, and 0.9 GB inference VRAM on a consumer-grade RTX 4090, matching or outperforming substantially larger VLA policies. These results establish TurboVLA as a simple and effective alternative to the prevailing LLM-centric VLA paradigm, offering a new perspective on how vision, language, and action can be connected for efficient robotic manipulation. Code is available at this https URL.
[CV-1] VidMap: Exploiting Temporal Structure for Video-Based Structure-from-Motion
链接: https://arxiv.org/abs/2607.27194
作者: Zador Pataki,Paul-Edouard Sarlin,Marc Pollefeys
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Accurately recovering the camera’s calibration and metric poses for any unconstrained video would unlock large-scale training data for navigation and scene understanding. The dominant approaches to this problem are severely limited: Simultaneous Localization and Mapping (SLAM) is sensitive to initialization and transient failures due to its causal, incremental nature; it is often over-optimized for real-time operation and generally requires known camera calibration; while Structure-from-Motion (SfM) typically forgoes any image ordering, enabling optimal initialization and global optimization, but lacks robustness to visual symmetries and extreme motions. To bridge this gap, we introduce a system that combines the strong sequential constraints of SLAM with the flexibility and global optimization of offline SfM, enabling the metric reconstruction of arbitrary, long, uncalibrated videos. This system leverages recent advances in wide-baseline dense image matching, treats temporal ordering as a first-class citizen for reliable loop closure, and augments global optimization with metric monocular depth priors. As a result, thorough evaluations on diverse, challenging datasets that exhibit extreme motion and visual symmetries reveal that our approach is significantly more robust and accurate than both state-of-the-art SLAM and SfM, classical or learned, with given or unknown camera calibration. The code is publicly available at this https URL.
[CV-2] HumanCLAW: Can Vision-Language Models Act Through a Body?
链接: https://arxiv.org/abs/2607.27180
作者: Siyao Li,Jiawei Gu,Shuai Liu,Kairui Hu,Zekun Li,Linjie Li,Chengcheng Tang,Po-Chen Wu,Ivan Shugurov,Lingni Ma,Michael Zollhoefer,Sizhe An,Abhay Mittal,Amy Zhao,Ranjay Krishna,Manling Li,Ziwei Liu,Chuan Guo
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Project page: this https URL
Abstract:Evaluating whether a vision-language model (VLM) can act through a physical body is challenging. The outcome of an action couples the VLM’s decision with motor control. When a task fails, it is hard to tell whether the VLM made a bad choice or the motor controller simply failed to execute it, e.g., losing balance and falling. In this work, we introduce HumanCLAW, an evaluation framework that decouples action decision-making from low-level execution. At every step, a harnessed, off-the-shelf VLM issues an atomic skill command, and the command is translated into a sub-second chunk of continuous full-body motion with real physical consequences, including gravity and collisions. The body can therefore act freely in the physical world, while execution-side disturbances, balance and motor errors, are factored out. What remains measurable is the model’s action intelligence: its moment-to-moment choice of what the body should execute next. Based on this framework, we build HumanCLAW-Bench: 1,218 long-horizon, egocentric find-navigate-interact episodes across 41 indoor scenes. We test nine state-of-the-art VLMs and find that none solves the benchmark; the best model reaches only a 16.8% success rate. Recognizing the target is not the bottleneck. What current VLMs lack is embodied self-awareness: they lose track of their own body, failing to tell where it is, whether it has reached the goal, or whether it has hit an obstacle.
[CV-3] Anatomy Contextualized Adaption of CT Foundation Models ECCV
链接: https://arxiv.org/abs/2607.27154
作者: Roshan Kenia,Stephanie L McNamara,William Lotter
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to the European Conference on Computer Vision (ECCV) 2026 MedFM-Bench Workshop
Abstract:CT vision-language foundation models have demonstrated promising performance across downstream tasks, but are typically trained with whole-volume representations that dilute fine-grained anatomical signals. Fine-grained vision-language pre-training addresses this by aligning anatomy-level visual features with anatomy-specific text, but in doing so discards the global context that whole-volume models provide. Furthermore, existing fine-grained approaches train from scratch, making them computationally expensive. We introduce Anatomy Contextualized Adaptation (ACA), a lightweight framework that adapts frozen CT foundation model representations for anatomy-level vision-language alignment while enhancing global contextualization. ACA uses TotalSegmentator to decompose CT volumes into anatomy-level embeddings, which are refined via a transformer that captures cross-anatomy relationships, and aligned to both per-anatomy and scan-level text extracted from radiology reports. Evaluated on Merlin and CT-RATE, ACA consistently outperforms both the frozen foundation model baselines and existing fine-grained methods in zero-shot finding classification, while requiring less than one hour of training once embeddings are cached. The attention weights learned by ACA’s inter-anatomy transformer additionally indicate plausible cross-anatomy context routing. Altogether, these results support ACA as a lightweight approach for adapting CT foundation models to anatomically grounded vision-language alignment while preserving and enhancing global anatomical context.
[CV-4] Explainable and Resource-Efficient Spatial Reasoning in Multimodal LLM s for Decision-Critical Applications
链接: https://arxiv.org/abs/2607.27145
作者: Piyush Jain,Kousik Dasgupta,Rajarshi Roy,Subarna Tripathi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages
Abstract:As Multimodal Large Language Models (MLLMs) are increasingly deployed in decision-critical pipelines such as robotics, embodied AI, and safety monitoring, the opacity of their spatial judgments limits operator trust and auditability. MLLMs demonstrate strong reasoning but often struggle with fine-grained spatial understanding and object hallucination. Prior work, ByDeWay, introduced Layered-Depth-Based Prompting (LDP), a training-free framework that mitigates hallucinations by structuring prompts using monocular depth estimation. However, coarse depth layering falls short in resolving object-to-object spatial relationships within the same geometric plane, such as projective (“left of”, “above”) and topological (“inside”, “touching”) relations. We propose ByDeWay-V2, which integrates explicit spatial relational context alongside depth cues, expressed as human-readable predicates that serve as auditable evidence for downstream decision support. Using an open-vocabulary object detector (YOLO-World-L), our framework computes pairwise geometric relations between detected objects and injects them as structured spatial predicates into the MLLM prompt, bridging 3D scene depth and 2D spatial semantics without any training. We evaluate ByDeWay-V2 on the Visual Spatial Reasoning (VSR) and BLINK benchmarks across multiple MLLMs, with hallucination grounding assessed via POPE. On the BLINK spatial subset, ByDeWay-V2 achieves a 46 percent relative F1 improvement over LDP for Qwen2.5-VL, and recovers BLIP-Base’s spatial reasoning on VSR from near-random performance to a competitive F1 of 0.53. Our lightest configuration operates under a strict 40-token context budget on CPU, showing the framework’s suitability for resource-constrained, real-time decision-support settings.
[CV-5] SeasonStereo: Robust Dense Stereo Matching for Multi-Date Satellite Imagery via Generative AI
链接: https://arxiv.org/abs/2607.27139
作者: Álvaro Díaz-Laureano,Roger Marí,Elías Masquil,Pablo Arias,Gabriele Facciolo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate 3D reconstruction from satellite imagery typically relies on near-simultaneous stereo pairs, limiting its applicability to diachronic settings where multi-date images exhibit varying seasonal and illumination conditions. Training dense stereo matching models robust to appearance changes is a long-standing challenge, as aligned multi-date imagery and ground-truth geometry are costly to obtain at scale. We propose SeasonStereo, a scalable framework that addresses disparity estimation from diachronic satellite images by training on synthetic image pairs with controlled seasonal appearance variation, while leveraging zero-shot geometric priors from foundation models. SeasonStereo matches the accuracy of state-of-the-art LiDAR-supervised models, while producing sharper geometric details without requiring aligned real multi-date training products or LiDAR-derived labels. As a result, SeasonStereo offers a practical path toward large-scale 3D reconstruction from heterogeneous satellite images with reduced supervision cost.
[CV-6] DLAM: Distributional Latent Actions with Temporal Constraints
链接: https://arxiv.org/abs/2607.27138
作者: Zuojin Tang,Feifan Luo,Haoyun Liu,Botai Yuan,Dekang Qi,Ronghan Chen,Yandan Yang,Tong Lin,Xinyuan Chang,Mu Xu,Bin Liu,De Ma,Zhiheng Ma
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-language-action (VLA) models remain constrained by scarce action-labeled robot data, whereas action-free videos offer abundant observations of physical change. Latent action models can extract such priors, but reconstruction-trained codes may predict future observations without the structure required for joint generation with robot actions. Existing structured methods add temporal constraints but retain deterministic transition points, so residual errors in locally inferred transitions may propagate and compound under recursive composition. We introduce DLAM, a distributional latent-action model that represents each transition as a diagonal Gaussian. Reconstruction conditioned on the reference frame grounds the mean in observed visual change, while normalized composition and reversal over equal-gap triplets constrain both the mean and dimension-wise variance. Variance composition uses a lightweight shared-correlation coefficient to account for dependence between adjacent transitions that share an intermediate frame, whereas reversal negates the mean and preserves the variance. For downstream policy learning, we freeze the encoder and train a flow-matching policy to jointly generate mean transition sequences and robot actions. On held-out transitions, DLAM learns more temporally consistent latent dynamics than existing latent-action baselines and achieves stronger direct and cumulative reconstruction on held-out videos. Under the same controlled \pi_0 transfer protocol, it also improves policy performance on MetaWorld MT50, LIBERO, and real-world manipulation tasks. Controlled ablations show that normalized mean constraints account for most of the reconstruction gain, while learned variance and correlation-aware composition provide complementary improvements in downstream control.
[CV-7] owards Grounded GI Endoscopy VQA via Multi-Task Learning on Small VLMs MICCAI2026
链接: https://arxiv.org/abs/2607.27122
作者: Itbaan Safwan,Ramail Khan,Muhammad Annas Shaikh,Muhammad Atif Tahir
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at EMA4MICCAI 2026 (Workshop on Efficient Medical AI, MICCAI 2026)
Abstract:Gastrointestinal (GI) endoscopic image analysis has shifted from single-label classification toward visual question answering (VQA), where a model must answer free-form clinical questions about an image. While recent vision-language models (VLMs) achieve promising answer accuracy on this task, clinical adoption also requires the model’s internal representations to reflect the visual evidence behind its answers. We propose a simple multi-task fine-tuning recipe that constructs auxiliary grounding and description tasks from an existing VQA dataset with minimal additional annotation: expert-annotated polyp masks are reused directly, while a GI-domain pretrained classifier with Grad-CAM localization provides weak supervision for finding categories that lack ground-truth masks. Three small VLM backbones are fine-tuned with low-rank adaptation under matched VQA-only and multi-task recipes on Kvasir-VQA-x1, and we show consistent accuracy gains together with improved implicit alignment between answer tokens and the relevant image region, evaluated on both in-distribution and out-of-distribution data.
[CV-8] Veritas: Value-aware On-Policy Distillation for Perception-Enhanced AIGI Detection
链接: https://arxiv.org/abs/2607.27113
作者: Hao Tan,Jun Lan,Zichang Tan,Ajian Liu,Zijian Yu,Chuanbiao Song,Huijia Zhu,Weiqiang Wang,Jun Wan,Zhen Lei
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The growing capability of image generation models has made synthetic images a routine presence in open media, making robust and generalizable AI-Generated Image (AIGI) detection increasingly essential. While multi-modal large language models (MLLMs) offer a transparent alternative to black-box binary scoring, we observe that current MLLM-based detectors still exhibit notable perception bottlenecks in capturing fine-grained anomalies. They primarily focus on how visual evidence is organized and synthesized, leaving the intrinsic perception less optimized. To mitigate this gap, we present Veritas++, a perception-enhanced reasoning framework that establishes reliable perception as the foundation of authenticity reasoning. Rather than directly optimizing the model’s explanatory ability, we ground AIGI detection on three basic perception abilities, i.e., capturing fine-grained visual details, semantic anomalies and pixel-level differences. Building on this insight, we introduce Perception-oriented Learning (PoRL), which replaces open-ended description supervision with verifiable rewards to explicitly strengthen these capacities. To further integrate enhanced perception with reasoning, we introduce Value-aware On-Policy Distillation (VaOPD), an adaptive distillation mechanism that prioritizes high-value distillation signals over uniform supervision, internalizing perception-aware reasoning through a privileged self-teacher. Extensive experiments across standard, in-the-wild and emerging benchmarks demonstrate that Veritas++ achieves promising generalization. The perception learning effectively bridges the perception gap and yields seamless gains on detection, while VaOPD further enables efficient capability evolvement without sacrificing existing performance. Code and checkpoints are available at this https URL.
[CV-9] FreqForcing: Autoregressive Long Video Generation via Spectral Self-Anchoring
链接: https://arxiv.org/abs/2607.27110
作者: Jiatong Li,Leo Liang,Linghe Kong,Yulun Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Code is available at: this https URL
Abstract:Autoregressive video diffusion models enable real-time streaming video generation. However, errors introduced during self-rollout accumulate over long horizons, manifesting as color drift, motion stagnation, and eventual visual collapse. In this paper, we characterize this phenomenon from a frequency-domain perspective: error accumulation appears as a pronounced energy drift in the low-frequency bands. We further investigate the effectiveness of attention sink in the frequency domain, and find that it improves the video quality by alleviating the spectral energy drift to some extent, but cannot fully resolve it. Motivated by the above analysis, we propose FreqForcing, a training-free framework that addresses error accumulation in long-video generation via Spectral Self-Anchoring (SSA). The proposed SSA leverages the low-frequency components of anchor attention to maintain long-horizon visual stability, while preserving dynamic motion through the high-frequency components of local attention. Our FreqForcing extends Self-Forcing pretrained on 5s clips to two-minute generation, achieving 24x extrapolation. Extensive experiments show that FreqForcing outperforms existing training-free methods quantitatively and qualitatively while remaining competitive with representative training-based approaches.
[CV-10] Step-Attention Refinement of DINOv3 Features for Efficient Anterior Eye Segmentation
链接: https://arxiv.org/abs/2607.27087
作者: Philippe Baumstimler,Jean-Mathieu Gagnon,Sébastien Gagné,Mathieu Duchesneau,Clément Playout,Lama Séoud
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Anterior eye segment (AES) segmentation is a key component of both ocular biometrics and emerging clinical image analysis applications. However, heterogeneous acquisition conditions and limited annotations in medical settings hinder the robustness and generalization of existing methods. Foundation models (FMs) such as DINOv3 offer strong transfer capabilities, but efficiently adapting their representations to dense prediction tasks remains challenging. In this study, we investigate robust AES segmentation in clinical settings, and propose a lightweight architecture built upon a distilled DINOv3 ViT-Small backbone. We introduce a step-attention feature refinement module that progressively adapts multi-level transformer representations before convolutional decoding, enabling efficient exploitation of pretrained features with few parameters. We evaluate the proposed approach on a private dataset of 333 clinically acquired AES images spanning eight ophthalmic acquisition protocols and annotated for seven anatomical classes. Compared with convolutional and transformer-based baselines, including DINOv3-based methods, our approach achieves the best overall performance, reaching 85.55% mIoU when fully fine-tuned. It also demonstrates the strongest robustness to domain shift across four unseen public AES segmentation datasets. These results establish a strong baseline for robust AES segmentation in clinical settings and highlight the importance of decoder design for effectively adapting FMs representations to medical segmentation tasks.
[CV-11] SciFigQual-Bench: A Benchmark for Scientific Figure Quality Assessment with Full-Manuscript Context
链接: https://arxiv.org/abs/2607.27084
作者: Zihan Deng,Chuanzhi Xu,Huiqi Liang,Haoyang Li,Xiaozhen Zhong,Lequan Yu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: † Equal contribution. Affiliations: 1: The University of Hong Kong 2: The University of Sydney 3: University of Electronic Science and Technology of China Corresponding authors: Zihan Deng (zhdeng@hku.hk), Chuanzhi Xu ( this http URL @sydney. this http URL ) Project page: this https URL Source code dataset: this https URL
Abstract:Scientific images are the core elements of presenting experimental conclusions, elaborating system architecture, and supporting comparative arguments in scientific papers. However, existing image quality assessment (IQA) methods are predominantly designed for natural photographs or AI-generated content, which cannot be directly applied to scientific papers. The few existing studies on scholarly charts remain confined to visual-surface comparisons, failing to verify caption alignment, citation relevance, or visual misleadingness. To address this, we propose SciFigQual-Bench, a full-text contextual benchmark that evaluates scientific images across five dimensions (clarity, layout, caption fit, context relevance, and misleading risk). The data covers top computer-science conferences from 2020 to 2025; 6,308 images were independently scored by multiple domain experts in five dimensions and aggregated into gold-standard annotations. Unlike previous scientific figure benchmarks, our dataset binds each image to its caption, citing sentence, and manuscript context. To enable automated evaluation on this benchmark, we designed a staged cross-modal evaluation framework SFQ-Agent to achieve auditable and refined scoring through the collection and fusion of modal evidence. Multiple mainstream large models were evaluated on the test subset eval1200, and SFQ-Agent (F3) equipped with GPT-5.6-Sol achieved the lowest overall average absolute error (0.418) and the highest consistency rate (93.4%), consistently outperforming both direct evaluation and auxiliary (Sidecar) visual language model evaluation schemes.
[CV-12] Visual Credit Audit for Multimodal Spatial Reasoning
链接: https://arxiv.org/abs/2607.27069
作者: Feixiang Liu,Qiang Qiu,Lanbo Sun,Nan Wei,Huawei Shen,Xueqi Cheng
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: "`text 20 pages, 2 figures. Code: this https URL
Abstract:Closed yes/no spatial benchmarks can reward a correct answer even when the image adds little support beyond no-image contexts. Under a fixed forced-choice interface, Visual Credit Audit (VCA) separates two estimands: whether the benchmark image gives the model’s declared decision more support than text-only and blank controls, and whether the model responds to relation-specific visual evidence. The first audit is training- and label-free and does not require an answer flip. Applying labels yields dependence-credited correctness (D-CC); on correct items, it equals same-control gold-aligned positive gain, while prediction alignment extends the audit to errors. Across four open MLLMs and two spatial benchmarks, 12.73-26.25% of decisions are correct yet uncredited. Matched same-split image permutation reduces D-CC by 21.25-47.80 points, with every paired 95% interval above zero. Fixed-pixel relation contrasts and a 3x3 evidence-source factorial show why null controls cannot identify relation response. Among controlled correct-but-uncredited agreement decisions, response to relation reversal spans 81.57-100.00%, while 32.11% pooled change answer. Independently audited outcomes on 108 geometry-compatible edits provide a bounded natural-image correspondence check. VCA thereby decomposes benchmark success into correctness, additional image support, and relation-consistent response.
[CV-13] SciFigAlign: Scoring Scientific Figures by Fine-tuned Alignment of Visuals with Manuscript Evidence
链接: https://arxiv.org/abs/2607.27066
作者: Chuanzhi Xu,Zihan Deng,Huiqi Liang,Chengkun Yue,Zhanlin Cui,Pengfei Ye,Weidong Cai
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Scientific figure assessment in peer review differs fundamentally from general image quality evaluation: a figure must be visually legible, faithfully support the manuscript’s claims, and communicate evidence with a clear visual hierarchy. However, if we apply traditional image assessment methods to scientific figure quality assessment, limitations emerge: classic IQA models capture perceptual quality or aesthetics but cannot judge whether a figure serves the paper’s scientific argument; CLIP-based methods assess generic image-text correspondence, yet lack understanding of manuscript context; and zero-shot LLM/VLM judges, when repurposed for figure scoring, often yield overly concentrated scores with limited fusion of visual and textual evidence. We introduce an annotated dataset of 3,857 scientific figures from peer-reviewed conference papers, each rated along four peer-review-oriented dimensions: Clarity, Relevance, Informativeness, and Structure. We propose SciFigAlign, a fine-tuned multimodal scorer that grounds figure quality assessment in manuscript evidence. Given a figure crop, caption, citing paragraphs, and light paper context, SciFigAlign fine-tunes CLIP and SciBERT end-to-end with per-modality cross-attention and CubeMLP fusion, jointly optimizing SmoothL1 regression with a within-paper ranking hinge loss. Under paper-level splits, SciFigAlign achieves a macro MAE of 0.3524 and a within-paper pairwise accuracy of 81.64% on the test set with n = 396, a 59% relative error reduction over the best LLM-as-judge baseline with MAE 0.864. Ablations confirm that manuscript-grounded inputs, citing-context denoising, and ranking supervision are all critical, showing that scientific figure assessment requires learned alignment between visual content and manuscript evidence rather than prompting alone, even with state-of-the-art VLMs.
[CV-14] ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection
链接: https://arxiv.org/abs/2607.27065
作者: Paul Julius Kühn,Saptarshi Neil Sinha,Tiago Kleist,Richard Hoffmann,Arjan kuijper,Michael Weinmann
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:While automated defect detection such as the detection of surface scratched is an important aspect in industrial quality control, the scarcity of annotated defect data make this task challenging. This paper presents a procedural rendering pipeline that generates large-scale annotated synthetic training data using BlenderProc, with configurable material appearance, camera modes, and domain randomization, producing automatic COCO-format annotations. To show the potential of our approach, we evaluate four training strategies, namely synthetic-only, real-only, mixed, and fine-tuning from synthetic weights, across two objects with different material properties and three lightweight edge-deployable detectors, YOLOX, YOLO26, and LW-DETR. Our evaluation show that fine-tuning from synthetic weights consistently outperforms real-only training, and that mixed training effectively recovers performance under scarce real-data conditions, with findings validated across both convolutional and transformer-based architectures. The proposed approach enables scalable defect detection without the burden of large real annotated datasets, making it practical for on-device industrial inspection. The pipeline scripts, 3D model, and both synthetic and real annotated scratch datasets for a glossy toy Ferrari car will be made available through the project website upon acceptance.
[CV-15] Object Detection for Autonomous Driving in Chinese Rural Scenes: An Experimental Study on Real-Synthetic Data Mixing and Model Evaluation
链接: https://arxiv.org/abs/2607.27058
作者: Danning Zhu,Ziyan Lin,Jing Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Currently, autonomous driving object detection models face significant data scarcity and generalization challenges when navigating complex Chinese rural traffic scenarios. To address these limitations, we propose a novel real-synthetic mixed object detection dataset tailored specifically for Chinese rural roads and systematically evaluate the performance of 13 mainstream detectors under different real-to-synthetic data ratios, thereby providing empirical evidence for model selection and data strategy design in rural autonomous driving scenarios. Our dataset combines real-world images captured in Weishi County, Henan Province, with parameterized virtual scenes generated via Unreal Engine. To accurately reflect the unique realities of rural traffic, we define a comprehensive 14-category object system encompassing region-specific elements such as electric tricycles, low-speed vehicles (LSVs), and roadside stalls. Under a unified training protocol, we systematically evaluate 13 mainstream detectors – spanning the YOLOv5, YOLOv8, YOLO11, and YOLO26 series, as well as RT-DETR-L – across three data configurations: an all-real baseline, a 1:0.5 real-to-virtual mix, and a 1:1 mix. Experimental results demonstrate that a moderate injection of synthetic data (1:0.5 ratio) effectively enhances detection performance, with YOLO11m achieving the highest mAP@0.5 of 0.758. However, a higher proportion of synthetic data (1:1) introduces domain shifts that offset the benefits of data scaling. While most models reliably identify distinct local vehicles, significant perceptual bottlenecks remain for long-tail, non-standard objects like stalls and railings. This research provides crucial empirical evidence and novel insights for model selection and synthetic data strategies, facilitating the practical deployment of autonomous driving perception systems in rural areas.
[CV-16] Mitigating Compounding Error via Video Representation Regularization
链接: https://arxiv.org/abs/2607.27036
作者: Taiye Chen,Qi Zhang,Yisen Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Video diffusion-based world models enable long autoregressive video generation for robotics, autonomous driving and simulation tasks, yet sliding-window autoregressive inference suffers from severe error accumulation that degrades frame quality over time. Although this phenomenon has been widely observed, the underlying mechanism of compounding error and how to achieve stable long-horizon generation remain largely unresolved. In this paper, we investigate the internal representation dynamics of video world models and discover that compounding error is tightly coupled with dimensional collapse of hidden representations. Specifically, the effective rank of model representations sharply decreases at the onset of generation drift, revealing a strong connection between representational degradation and long-term rollout instability. Furthermore, we find that pure training data scaling fails to boost model resistance to error drift, contradicting mainstream scaling paradigms. To address this problem, we propose video representation regularization, a lightweight training constraint that stabilizes latent representations and suppresses iterative error accumulation. Compared with Diffusion Forcing, our method achieves improvements from 38.65 to 55.56 and from 44.37 to 72.08 on the Aesthetic Quality and Imaging Quality metrics of VBench. Our work establishes the first connection between autoregressive video drifting and model internal representations, adopts erank as a quantitative metric for error accumulation, reveals counterintuitive scaling limitations for video world models, and presents a simple yet effective regularization strategy to improve long video generation robustness.
[CV-17] Lottery Tickets Are Not Deployment Tickets
链接: https://arxiv.org/abs/2607.27031
作者: Bum Jun Kim
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 25 pages, 4 figures
Abstract:Reports on how sparsification, compression, and lottery tickets change model behavior have been mixed in the prior literature, with beneficial effects observed in some studies and adverse effects in others. Moreover, prior work has not considered actual deployment conditions, where decision logic is already fixed for the incumbent. To assess these mixed findings from a practical standpoint, we study the production-replacement question at the deployment level, namely whether an accuracy-matched lottery ticket or another sparse challenger can replace an incumbent dense model without reconfiguring downstream decision logic. We therefore audit a broad, protocol-specific panel of deployment-relevant behaviors spanning calibration, OOD response, class-level reliability, representations, and downstream policy decisions, and summarize clean-accuracy-excluded deviations with a behavioral-compatibility distance. Across extensive experiments, sparse candidates repeatedly recover dense-reference accuracy yet remain behaviorally different; in several study-band-matched settings, LTs also show lower corruption accuracy. In small-gap settings with fixed-threshold policy diagnostics, lottery-ticket replacement changes 7% to 10% of accept–review decisions. This churn creates precisely the burden that drop-in replacement is meant to avoid: reconfiguring and revalidating downstream decision logic. These findings establish the limits of clean-accuracy certification: Establishing compatibility with a fixed incumbent is distinct from attributing churn uniquely to sparsity or treating every measured deviation as harmful. Our theory explains the routing result: Even exact pointwise top-1 agreement cannot bound fixed-threshold decision changes, and small confidence shifts near the operating boundary can generate first-order routing churn.
[CV-18] InkShield: Writing Style Protection Against Unauthorized Handwriting Mimicry
链接: https://arxiv.org/abs/2607.26976
作者: Jian Xiong,Wenbo Jiang,Zihan Wang,Rui Zhang,Wenshu Fan,Hongwei Li,Guowen Xu
类目: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent handwritten text generators can reproduce a writer’s style from publicly available references, posing risks of document forgery and identity misuse. An attacker may use a publicly available handwritten note or signature sample to generate forged recommendation letters or authorization forms, leading to document fraud, identity misuse, and misleading decisions. However, existing protections against unauthorized image editing or synthesis transfer poorly to handwriting style mimicry. Designed for natural images with complex backgrounds, they often optimize perturbations over the whole image. For sparse handwriting images, such global perturbations become conspicuous in blank background regions and largely degrade the visual quality. In this work, we propose InkShield, a proactive writing-style defense that protects reference images before release. InkShield selects a decoy writer to define a style-displacement direction, optimizes perturbations with a frozen handwriting-generation surrogate, and confines them to ink-stroke edges to avoid conspicuous background artifacts. On IAM, the average Top-1/Top-5 rates at which generated samples are retrieved as the target writer by two independent writer evaluators decrease from 11.94%/36.52% to 2.03%/8.79%. Meanwhile, the protected references remain visually close to the originals (LPIPS 0.0078), and the generated text remains readable. InkShield also exhibits transferability to other handwriting generators. Overall, InkShield provides practical protection against unauthorized handwriting style mimicry.
[CV-19] Robust RPC Bundle Adjustment for Multi-Date Satellite Imagery with Season-Invariant Correspondences
链接: https://arxiv.org/abs/2607.26973
作者: Roger Marí,Elías Masquil,Xavier Bou,Thibaud Ehret,Gabriele Facciolo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate refinement of Rational Polynomial Camera (RPC) models is essential for high-quality satellite image geolocation. In ground control point (GCP)-free multi-view pipelines, this refinement is commonly performed through bundle adjustment from automatically extracted image correspondences. However, conventional RPC bundle adjustment pipelines rely on handcrafted feature matching, which becomes unreliable in multi-date collections affected by seasonal, illumination, and land-cover changes. We propose an appearance-aware RPC refinement pipeline that combines learned local feature matching for season-invariant correspondences with global image descriptors for selecting visually compatible image pairs. This reduces redundant and error-prone matching while preserving the connectivity of the matching graph. Experiments on seasonally diverse WorldView-3 images show that our pipeline improves GCP-free relative RPC refinement over open-source baselines, achieving lower geometric consistency errors while substantially reducing matching time on collections with 39-42 views. By making RPC refinement more robust to diachronic appearance variation, our approach enables more effective use of multi-date satellite imagery.
[CV-20] Progressive Multimodal Alignment for Continual Instruction Tuning ACM-MM2026
链接: https://arxiv.org/abs/2607.26947
作者: Duzhen Zhang,Yahan Yu,Qiaoyi Su,Jiahua Dong,Tielin Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by ACM MM2026
Abstract:Multimodal Large Language Models (MLLMs) rely on a projector to align visual representations with the language embedding space, making it central to cross-modal understanding. In Multimodal Continual Instruction Tuning (MCIT), however, shifting visual distributions and evolving instruction semantics cause this shared projector to drift, leading to projector-level forgetting, an issue largely overlooked by methods that focus primarily on the LLM backbone. We introduce Progressive Multimodal Alignment (PMA), a framework that enables the projector to adapt continually while preserving previously learned alignment. PMA detects multimodal distribution shifts via a lightweight representation descriptor and progressively expands projector experts only when needed. An expandable router integrates expert outputs based on multimodal features, while the original pretrained projector is retained as a stable alignment anchor. This progressive mechanism balances stability and plasticity with sub-linear parameter growth and serves as a method-agnostic add-on to existing MCIT approaches. Extensive experiments on two recent MCIT benchmarks demonstrate that mitigating projector-level forgetting yields consistent gains over prior state-of-the-art methods when combined with PMA. Moreover, PMA scales across diverse MLLM backbones, demonstrating robust and broadly applicable MCIT performance.
[CV-21] From Keypoints to Predictive Distributions: Post-Hoc Uncertainty for YOLO-Pose Models
链接: https://arxiv.org/abs/2607.26921
作者: Alexej Klushyn,Juan Rivero Sesma,Florian Seligmann,Richard Kurle,Kinh Tieu,Jayant Sen Gupta
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:YOLO-Pose models provide efficient keypoint localization, but do not quantify the associated spatial uncertainty. We introduce a lightweight post-hoc probabilistic extension that augments a trained YOLO-Pose model with calibrated bivariate predictive distributions over keypoint locations, centered at the model’s original predictions. Concretely, we train additional probabilistic heads with an importance-weighted negative log-likelihood to predict an input-dependent 2\times2 dispersion matrix for each keypoint, followed by Gaussian calibration for broad downstream compatibility or Student- t calibration for distributional fidelity. Complementing this, we propose an evaluation protocol that combines a suite of distributional calibration diagnostics with average keypoint precision (AKP), a keypoint-level extension of the COCO AP protocol for assessing reliability rankings. Experiments on COCO show that the learned uncertainty estimates enable effective keypoint-level reliability ranking, Student- t calibration best captures the empirical residual distribution, and uncertainty-based pruning removes unreliable keypoints. A central application-level demonstration is vision-based aircraft landing, where calibrated covariances for runway keypoints support uncertainty-aware aircraft position estimation and downstream sensor fusion.
[CV-22] Prior Directions: Why GUI Grounding Gets Locked in the Past
链接: https://arxiv.org/abs/2607.26913
作者: Weile Gong,Zijian Lu,Mingcai Chen,Yiping Zuo,Xin He,Weibei Fan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13 pages, 8 figures. Code: this https URL
Abstract:Vision-language models often use descriptions of earlier visual states to make decisions about the current scene. When the scene changes, stale language can redirect an otherwise correct visual judgment toward an outdated answer. We study this failure as visual lock-in in a controlled grounding setting where only the verbalized prior varies. Across models, stronger lock-in accompanies smaller changes in the model representation before the final answer. This reversal suggests that lock-in depends not on how far this representation moves, but on how that movement is organized. In models that are harder to correct, prior-induced changes concentrate along a compact set of directions that repeatedly appear across examples. We call these recurrent axes the Prior Directions. They recur on held-out examples, while a descriptive four-model comparison associates greater concentration with stronger lock-in. Controlled interventions show that removing the component aligned with the Prior Directions restores visual grounding, whereas removing an equally large orthogonal component has little effect. Prior control thus arises when prior-induced changes form a coherent and reusable pattern in the representation used to produce the answer. This account explains why the same prior remains revisable in one model yet becomes dominant in another.
[CV-23] CinemaTraj: Composing Atomic Camera Trajectories for 3D Scenes with LLM Agents
链接: https://arxiv.org/abs/2607.26910
作者: Qianru Li,Xuyang Chen,Erkin Türköz,Lu Liu,Xuqin Wang,Liqiu Meng,Tao Wu,Yanfeng Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, including 8-page main paper, references, and supplementary material; project page: this https URL
Abstract:Automatically generating cinematically expressive camera trajectories through 3D scenes from natural language descriptions is a challenging task of high practical value, with applications ranging from real-estate advertising to virtual tour creation. Existing methods either lack true 3D spatial awareness by relying on 2D image priors, or treat trajectory generation as a geometric path planning problem divorced from cinematographic semantics. We present CinemaTraj, a framework that reframes camera trajectory planning as a language-grounded spatial reasoning problem. Given a set of RGB-D images and a user prompt, CinemaTraj equips an LLM agent with a structured 3D scene graph: the agent decomposes the prompt into a sequence of atomic cinematographic movements (dolly, orbit, crane, pan, tilt, zoom, arc). Each movement is instantiated via a novel parametric trajectory representation that is both cinematographically expressive and optimizable for collision avoidance. The scene graph acts as a structured spatial prior, grounding the agent’s reasoning in accurate geometric and semantic knowledge of the environment. CinemaTraj further generates synchronized voiceover and subtitles aligned with camera motion, producing narrated cinematic video outputs. We evaluate CinemaTraj on real-world ScanNet++ environments, and show that it produces prompt-faithful, collision-free trajectories with high cinematographic quality, outperforming existing approaches on prompt alignment, trajectory quality, and safety metrics.
[CV-24] StructureGS: Structure-aware Gaussian Splatting for Articulated Object Reconstruction ECCV2026
链接: https://arxiv.org/abs/2607.26889
作者: Gahye Lee,Gyoonseo Kim,Wonjong Jang,Jooeun Son,Seungyong Lee
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: accepted at ECCV 2026
Abstract:Reconstructing articulated objects with multiple movable parts is essential for understanding object structure and enabling physical interaction. However, this reconstruction task poses significant challenges due to the entanglement of geometry, appearance, and motion parameters during optimization. Existing methods rely primarily on photometric supervision, which commonly fails to disentangle these interdependent components, resulting in poor part decomposition with blurred boundaries and geometric artifacts. To address this limitation, we introduce StructureGS, a reconstruction framework for articulated objects that integrates structure-aware guidance into 3D Gaussian Splatting. Our approach leverages oriented bounding boxes of object parts to enforce two key structural properties: spatial coherence, which constrains each part’s geometry to remain compact and spatially coherent within its designated region, and structural connectivity, which enforces physically plausible contact relationships between adjacent parts. These properties are realized through structure-aware losses that inject explicit structural constraints into the optimization process. Extensive experiments demonstrate that our method achieves state-of-the-art performance in articulated object reconstruction, producing high-quality results with well-defined part geometries.
[CV-25] SCALPEL: Semantic Cross-modal Alignment via LLM -Powered Encoder Learning for Medical Vision-Language Representation
链接: https://arxiv.org/abs/2607.26885
作者: Yunzhan Fu,Enyu Bao,Xiangyu Shen,Yihao Wu,Chunbo Jiang,Fangli Guan,Liqi Yan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 3 figures, accepted by PRCV2026
Abstract:Vision-language pre-training (VLP) serves as a cornerstone for medical multimodal representation learning. However, existing medical VLP frameworks are often constrained by the limited context windows and shallow representational capacities of lightweight text encoders when processing lengthy, terminology-dense clinical reports. While integrating medical large language models (LLMs) offers unprecedented clinical reasoning capabilities, it introduces three major bottlenecks: (i) the anisotropic representational collapse of generative LLMs under standard contrastive objectives, (ii) the prohibitive memory overhead of joint end-to-end training with large batch sizes, and (iii) the medical hallucinations induced by vanilla contrastive losses that ignore fine-grained anatomical laterality and negation modifiers. To address these challenges, we propose \textbfSCALPEL, a \textbfSemantic \textbfCross-modal \textbfAlignment framework via \textbfLLM-\textbfPowered \textbfEncoder \textbfLearning. First, Clinical Report Contrastive fine-tuning converts a generative LLM into an isotropic encoder via domain-specific clinical text adaptation. Second, an asymmetric alignment strategy leverages offline feature caching to enable efficient training. Critically, we formulate an Anatomy-Negation Aware Objective that explicitly penalizes mismatched image-text pairs involving laterality confusion or false negations. Extensive experiments across MIMIC-CXR, CheXpert, and IU X-Ray benchmarks demonstrate that SCALPEL achieves state-of-the-art performance in cross-modal retrieval, zero-shot disease classification and medical visual question answering.
[CV-26] ICDAR 2026 Competition on Information Extraction from Atomic Layer Deposition/Etching (ALD/E) Scientific Figures ICDAR2026
链接: https://arxiv.org/abs/2607.26848
作者: Fahad Ahmed,Sören Auer,Jennifer D’Souza
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 6 figures, 8 tables, to be published in ICDAR 2026 Conference Proceedings and Volume 16975 of the Lecture Notes in Computer Science series (Springer Nature)
Abstract:Scientific figure comprehension and reasoning using multimodal AI requires integrating visual perception with domain-specific reasoning to extract meaningful knowledge, often not presented in the text of a research publication. The Sci-ImageMiner benchmark dataset, accompanied by a community-driven competition, raises the bar over prior scientific competitions by curating a comprehensive, expert-annotated dataset across four end-to-end complementary tasks. The competition attracted 68 active participants and 1,263 public/private submissions from 9th January 2026 to 8th April 2026. Our results show that state-of-the-art multimodal models perform well on classification and summarization tasks but struggle with data extraction and scientific reasoning, particularly in visual question-answering. These findings reveal key limitations and highlight challenges and opportunities for improving domain-aware multimodal AI systems. Overall, the Sci-ImageMiner benchmark and competition establish a rigorous platform for advancing research in scientific figure comprehension and reasoning and demonstrate the potential of state-of-the-art approaches for a challenging and complex research area.
[CV-27] BATS: Resource-Efficient Volumetric Segmentation with Boundary-Aware Mixed-Resolution Tokens
链接: https://arxiv.org/abs/2607.26829
作者: David Hagerman,Roman Naeem,Fredrik Kahl
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Many high-performing volumetric segmentation models maintain dense multi-scale feature maps, leading to high activation memory and inference cost. We present BATS (Boundary-Aware Token Selection), a 3D medical image segmentation architecture that concentrates fine-resolution processing near predicted class boundaries. A dense boundary predictor identifies where additional resolution is needed, while a fine-first context cascade constructs an input-dependent mixed-resolution hierarchy. Homogeneous regions are represented coarsely, with finer tokens retained around boundaries, thin structures, and small targets. The sparse hierarchy is refined and rasterised into a dense segmentation. BATS predicts boundary relevance independently at every resolution level, preventing an erroneous coarse-scale decision from suppressing fine-scale evidence. Parent cluster attention further injects hierarchical ancestor tokens into local attention neighbourhoods, providing cross-scale context without dense multi-scale feature maps or cross-scale neighbour search. We evaluate BATS on five public CT and MRI datasets using the standardised nnU-Net Revisited protocol. BATS achieves the highest LiTS Dice among the compared methods and averages within 0.37 Dice points of the strongest dense baseline, MedNeXt-L, across the five datasets. Relative to MedNeXt-L, it reduces peak allocated GPU memory by more than 53% on KiTS, LiTS, and BraTS. Inference is up to 30% faster on KiTS and LiTS, which retain fewer tokens, but slower on the more token-dense BraTS. Mixed-resolution processing therefore provides consistent memory savings, while runtime and accuracy gains depend on dataset boundary density. Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2607.26829 [cs.CV] (or arXiv:2607.26829v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.26829 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-28] Ripple: Real-Time Streaming Audio-Video Generation With Cross-Modal Recurrent Memory
链接: https://arxiv.org/abs/2607.26818
作者: Yanbo Ding,Zhizhi Guo,Quanyue Song,Yishan He,Zhixiang He,Yongxiang Li,Yali Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Audio-video generative models achieve impressive quality but suffer from high latency, making them unsuitable for real-time applications. Although several streaming audio-video generation methods have been proposed, they remain costly and fail to support long-form generation. To address this, we propose \textbfRipple, a real-time joint audio-video generation system with a cross-modal recurrent memory mechanism. To enable efficient streaming inference while preserving long-term context, Ripple combines a fixed-length sliding-window attention with modality-specific memory states that continuously summarize audio and video context. Cross-modal memory interaction is further introduced to enhance audio-visual synchronization. To learn this memory-augmented model effectively, we devise a three-stage training recipe: (1) adapting a bidirectional audio-video teacher to block-wise causal attention with simulated memory, (2) optimizing the memory construction and interaction pipeline through end-to-end distillation, and (3) applying online reinforcement post-training tailored for streaming audio-video generation. As a result, Ripple achieves ~28 FPS at 480P resolution, over faster than the teacher, while capable of coherent long-form generation. Extensive experiments on both short-video and long-video benchmarks demonstrate our superior performance over existing offline and online joint audio-video generation methods.
[CV-29] From Uncertainty to Determinism: Coarse-to-Fine Visual Floorplan Localization without Ray Matching
链接: https://arxiv.org/abs/2607.26817
作者: Shiyong Meng,Bolei Chen,Ping Zhong,Yang Wan,Rongzhi Wang,Jiazhi Xia,Jianxin Wang
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual Floorplan Localization (FLoc) has emerged as a promising solution for indoor localization by matching egocentric images against minimalist structural maps. However, due to cross-modal information asymmetry and repetitive indoor layouts, visual FLoc is fundamentally challenged by multimodal pose distributions, where visually identical observations map to distinct, spatially separated locations. Existing ray-matching-based methods tackle this by explicitly predicting sparse geometric or semantic rays, which inherently incur information loss and demand resource-intensive preprocessing alongside exhaustive matching during inference. In this paper, we bypass the intermediate ray-matching paradigm and propose a coarse-to-fine visual FLoc framework that progresses from uncertainty to determinism. In the coarse stage, we design an image-conditioned pose diffusion model to parameterize the continuous multimodal pose distribution, effectively routing stochastically initialized pose particles toward distinct candidate modes. In the refinement stage, we propose a localized refiner that predicts bounded sub-meter pose residuals from candidate-centered floorplan crops, where structural ambiguities are largely eliminated. Our method effectively balances global multi-hypothesis tracking and local sub-meter refinement without requiring any offline map preprocessing or test-time lookup tables. Comprehensive results on the S3D (full) and ZInD benchmarks demonstrate that our approach achieves state-of-the-art accuracy and robustness.
[CV-30] DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation
链接: https://arxiv.org/abs/2607.26811
作者: Jiaxing Li,Kai Zou,Cindy Zhou,Kaichen Huang,Junyao Gao,Zile Wang,Yang Liu,Bin Liu,Bo An,Yangguang Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Existing autoregressive video distillation methods commonly adopt a Distribution Matching Distillation (DMD)-based multi-stage pipeline. However, they typically decouple the initialization and DMD stages – which then pursue different target distributions – and judge the intermediate student mainly by visual scores such as VBench. In this paper, we revisit this design from a distributional perspective. Given the mode-seeking nature of the distribution matching loss, a good initialization should match the mode coverage of the target DMD teacher, rather than merely pursuing high quality. To analyze this, we introduce a distributional evaluation protocol that measures precision and coverage between student and teacher distributions in a shared latent space. It exposes differences hidden by visual scores: some initializations reach high precision but low coverage, leading to suboptimal refinement, while mode-covering ones preserve broader support. Furthermore, even when the target distributions are aligned, DMD’s reverse-KL objective can still drive the student toward high-probability teacher regions in late training, reducing coverage and diversity. To address this, we propose joint distillation, which combines DMD’s mode-seeking objective with a Consistency Distillation-based mode-covering constraint. Experiments show that our method improves generation quality, coverage, and diversity; notably, even with a Wan-1.3B DMD teacher, it outperforms baselines refined with Wan-14B, underscoring the importance of distributional alignment in autoregressive video distillation.
[CV-31] PRISM-Net: Patient-specific reference-guided inter-breast symmetry matching for three-class breast DCE-MRI classification
链接: https://arxiv.org/abs/2607.26799
作者: Boya Zhang,Shuaiwen Zhou,Di Kong,Mingxu Wang,Wenbiao Du,Yiman Zhong,Yuexin Duan,Xiawei Yue,Liuquan Cheng,Xiru Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 7 figures, 5 tables
Abstract:Breast DCE-MRI AI is increasingly being explored for breast-level classification of no-lesion, benign, and malignant findings, beyond conventional lesion-centered diagnosis. Within this broader diagnostic scope, however, patient-specific background variability remains a major source of imaging confounding across classification tasks. Existing approaches predominantly focus on unilateral or lesion-centric analysis, whereas bilateral methods offer limited explicit modeling of spatially adaptive cross-breast correspondence. We propose PRISM-Net, a registration-free bilateral framework that leverages contralateral breast features as patient-specific references for background-aware representation learning. PRISM-Net integrates bilateral feature matching and asymmetry-aware attention to establish adaptive inter-breast correspondence and enhance representations of discriminative asymmetric patterns. On ODELIA, Macro AUC, Micro AUC, and quadratic weighted kappa were 84.11 \pm 2.33 , 90.64 \pm 1.61 , and 60.94 \pm 5.64 on the in-distribution test set, and 68.51 \pm 4.54 , 80.74 \pm 2.68 , and 43.45 \pm 7.10 on the held-out institution, respectively, outperforming the evaluated baseline methods across the primary evaluation metrics. PRISM-Net further demonstrated performance on independent institutional and background-complexity evaluations. Ablation experiments revealed that both bilateral relation modeling and asymmetry-aware reweighting contributed to improved classification performance. These findings highlight patient-specific bilateral reference modeling as a clinically grounded strategy for DCE-MRI interpretation, improving asymmetric pattern discrimination through explicit modeling of background complexity.
[CV-32] See2Think: Do Multimodal Models Really Use Intermediate Visual States?
链接: https://arxiv.org/abs/2607.26769
作者: Siyu Yan,Zhuoran Yan,Haiying Xu,Panhao Zhou,Jingyu Chen,Chenhao Ji,Shuo Cao,Yongheng Zhang,Haoze Liu,Siyu Zhang,Xiwen Gu,Yihao Liu,Alex Jinpeng Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 10 pages, 5 figures, and 8 tables
Abstract:Multimodal large language models increasingly use sketches, annotations, tools, and intermediate images during reasoning, but it remains unclear whether they truly rely on these visual states. Existing benchmarks are limited both by task collections with narrow coverage or partially text-solvable samples and by evaluations that emphasize final answers without diagnosing how intermediate visual states are generated, rendered, and used. We introduce See2Think, a unified evaluation framework comprising See2ThinkBench and Visual Action-of-Thought (VAoT). See2ThinkBench contains 1,200 open-ended, visually dependent problems across 12 task categories spanning 2D structured, 3D scene, and real-world reasoning. VAoT records textual thoughts, visual actions, rendered states, and subsequent reasoning under four controlled inference settings. Evaluating representative proprietary and open-source multimodal models, we find that visual reasoning is strongly model- and environment-dependent, with no single setting consistently dominating across tasks. Process analysis further shows that models usually select relevant visual operations, while faithful rendering remains the clearest bottleneck and high feedback uptake does not necessarily translate into accuracy gains. Under task-relevant corrupted feedback, models exhibit behavioral dependence on visual states, with accuracy dropping by over 10 percentage points in controlled interventions.
[CV-33] Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification
链接: https://arxiv.org/abs/2607.26765
作者: Alexander Kozachok,Ilya Latyshev,Evgeny Karpulevich,Elena Kozachok,Egor Ushakov,Oleg Samovarov
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 27 pages, 6 figures
Abstract:Background/Objectives: Dermoscopic skin lesion classifiers often lose accuracy under domain shift across imaging devices, illumination, and capture artifacts. We study how data augmentation improves the robustness of a binary malignant-versus-non-malignant classifier, with emphasis on out-of-domain (OOD) generalization. Methods: Single augmentations, photometric combinations, and composite policies were searched on a multi-source ISIC Archive collection with Derm7pt, using a ConvNeXt-Large backbone and ROC-AUC. Splits were made at the lesion-ID level, and HAM10000 and ISIC 2019-2020 were held out as a predominantly source-disjoint OOD test. Results: The largest OOD gain came from the mix policy, and photometric transformations dominated the most useful OOD operations. On an expanded pool from the same held-out sources the gain was +0.053 (95% CI +0.045 to +0.061, p0.001), consistent across four training seeds (per-seed ROC-AUC: baseline 0.761-0.775, mix 0.806-0.829). On a small independent clinical collection, single-checkpoint sensitivity rose from 0.591 to 0.818, but this rested on 22 malignant cases and did not persist across seeds. Conclusions: Augmentations modelling real sources of domain shift can matter more than maximizing in-domain accuracy. Because the policy was selected on the same sources used to evaluate it, a source-disjoint selection protocol is needed before this effect size can be read as unbiased.
[CV-34] Long-Tailed 3D Point Cloud Dataset Distillation
链接: https://arxiv.org/abs/2607.26763
作者: Jiahao You,Xu Han,Jinfeng Xu,Xianzhi Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Dataset distillation compresses large-scale datasets into compact synthetic sets while preserving their training utility, enabling efficient 3D point cloud training. Current point cloud dataset distillation methods only tackle geometric and representation challenges while ignoring the distributional imbalance prevalent in point cloud datasets where both training and test splits follow long-tailed class distributions. To our knowledge, we present the first study on long-tailed point cloud dataset distillation. Rather than focusing primarily on geometric and representation properties or simply constructing a class-balanced synthetic set, our framework explicitly accounts for long-tailed class distributions via two core modules. First, we design Adaptive Synthetic Budgeting to allocate class-wise synthetic budgets according to class quantity and the expected benefit of additional synthetic samples. Given the allocated budgets, we further design 3D Long-Tailed Distribution Matching to optimize synthetic point clouds through Global-Local Feature Alignment and Prior-Aware Supervision. The former preserves both global class distributions and diverse intra-class structures, while the latter provides class-dependent expert supervision to keep tail-class samples recognizable while maintaining diverse head-class patterns. Extensive experiments demonstrate the effectiveness of our method, lifting classification accuracy by 7.0 points on ShapeNet55 against state-of-the-art methods.
[CV-35] StatePlay: State-Aware Game World Models for Mechanics-Consistent Generation
链接: https://arxiv.org/abs/2607.26754
作者: Zijun Lin,Zeqing Wang,Cheston Tan,Bihan Wen,Yeying Jin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:Recent game world models can generate visually realistic and interactive environments conditioned on player actions. However, games are not defined by pixels alone; they are governed by explicit mechanics, namely state-dependent rules that control health reduction, skill activation, and game termination. These mechanics depend on precise internal states, such as health points, skill meters, and timers, which are tightly coupled with visual observations and determine how gameplay evolves. Without modeling these state dynamics, existing game world models may generate visually plausible rollouts but violate the underlying game rules. In this paper, we propose StatePlay, a novel state-aware game world model that jointly predicts visual content and game states to promote mechanics-consistent generation. StatePlay adopts a mixture-of-transformers (MoT)-style architecture that preserves specialized visual and state representations while enabling cross-modal interaction, allowing predicted states to guide frame generation. Each branch is further optimized with a distinct objective suited to its modality. Experiments show that StatePlay achieves an average normalized L1 distance below 0.06 for state prediction. Furthermore, compared with models without explicit state modeling, our method improves mechanics fidelity in generated game rollouts by 18.6%. Overall, our work highlights the importance of state-aware game world modeling and advances beyond pixel-level realism toward complete and mechanically faithful game generation.
[CV-36] Multimodal fusion of visual and morphometric features for avian bone classification
链接: https://arxiv.org/abs/2607.26743
作者: Nevio Dubbini,Lisa Yeomans,Marco Pavia,Ramazan Parmaksiz,Ayse Atas Hooglugt,Gabriele Gattiglia,Beatrice Demarchi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Artificial intelligence has shown considerable potential for archaeological applications, yet its use in zooarchaeology remains limited, particularly for the identification of avian skeletal remains. This study presents a proof-of-concept multimodal framework that integrates convolutional neural network-based image analysis with osteometric measurements for the classification of bird bones. Using a dataset of more than 10,000 images from multiple museum and research collections, two classification tasks were investigated: skeletal element identification and family-level taxonomic classification. Prior to classification, images were automatically segmented using a two-stage pipeline combining BiRefNet and SAM2. Visual features extracted with a pre-trained EfficientNet_V2_S backbone were fused with standardized morphometric data through a feature-level multimodal architecture. The model achieved 86% accuracy on the test set for bone-type classification, demonstrating reliable recognition of skeletal elements. Family-level classification proved more challenging, reaching 51% top-1 accuracy but 75% top-3 accuracy, indicating that correct taxa were frequently included among the most probable predictions. These results demonstrate the feasibility of combining visual and morphometric information within a unified deep-learning framework and establish a methodological baseline for future AI-assisted zooarchaeological identification. The approach contributes to ongoing efforts to develop scalable, interpretable, and archaeologically meaningful tools for the study of avian remains.
[CV-37] Dual Inversion for Text-to-Image Diffusion Models: From Both Prompt and Noise Perspectives ACM-MM2026
链接: https://arxiv.org/abs/2607.26735
作者: Xiaolong Liu,Junjian Li,Yuan Xiao,Jiaqi Deng,Dayong Ye,Tianqing Zhu,Huan Huo
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注: Accepted by ACM MM 2026
Abstract:Prompt inversion, as a typical reverse engineering technique, enables text-to-image (T2I) diffusion models to generate the desired target images without extensive prompt engineering. However, existing prompt inversion methods suffer from significant limitations: (1) gradient-based methods are unstable and uninterpretable, often resulting in generated images with severe artifacts; (2) gradient-free methods yield human-readable prompts but still fail to preserve visual fidelity due to the lack of fine-grained detail alignment. We contend that the limitations stem from treating prompt inversion as a sufficient condition for reverse engineering, ignoring the critical role of the latent noise that encodes structural information. Consequently, we propose Dualin (Dual inversion), a two-stage method that jointly recovers both the semantic prompt and latent noise of the target image. In the first stage, we integrate vision-language model, CLIP and large language model to invert a faithful, human-interpretable hard prompt. In the second stage, unconditional DDIM inversion reconstructs the exact latent noise of the target image, guaranteeing the consistency at the structural information level. Theoretically, we prove that the inverted noise enables flexible image editing without re-optimization. Extensive experiments on diverse datasets demonstrate that Dualin simultaneously generates high-quality inverted prompts and achieves state-of-the-art image fidelity. Additionally, Dualin can establish a robust foundation for the precise and controllable image editing.
[CV-38] Online Handwriting Trajectory Reconstruction from Kinematic Sensors using Temporal Convolutional Network
链接: https://arxiv.org/abs/2607.26733
作者: Wassim Swaileh,Florent Imbert,Yann Soullard,Romain Tavenard,Eric Anquetil
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Handwriting with digital pens is a common way to facilitate human-computer interaction through the use of Online Handwriting (OH) trajectory reconstruction. In this work, we focus on a digital pen equipped with sensors from which one wants to reconstruct the OH trajectory. Such a pen allows to write on any surface and to get the digital trace, which can help learning to write, by writing on paper, and can be useful for many other applications such as collaborative meetings, etc. In this paper, we introduce a novel processing pipeline that maps the sensor signals of the pen to the corresponding OH trajectory. Notably, in order to tackle the difference of sampling rates between the pen and the tablet (which provides ground truth information), our preprocessing pipeline relies on Dynamic Time Warping to align the signals. We introduce a dedicated neural network architecture, inspired by a Temporal Convolutional Network, to reconstruct the online trajectory from the pen sensor signals. Finally, we also present a new benchmark dataset on which our method is evaluated both qualitatively and quantitatively, showing a notable improvement over its most notable competitor.
[CV-39] CASIAL: Geometric Distortion Robust Image Watermarking
链接: https://arxiv.org/abs/2607.26729
作者: Yupeng Qiu,Han Fang,Ee-Chien Chang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 9 figures, 6 tables. Supplementary material included
Abstract:Deep learning-based watermarking has shown strong robustness against non-geometric distortions, yet its performance under geometric transformations remains limited. Such transformations induce two fundamental failure modes: region removal, such as cropping or masking, which eliminates the information carried by removed pixels, and desynchronization, such as scaling or rotation, which misaligns pixel positions and disrupts decoding. We argue that achieving geometric robustness requires two essential properties: (1) global spread of the watermark message, ensuring resilience even when large regions are removed, and (2) geometry-invariant representations, enabling decoding to remain synchronized despite spatial transformations. Building on these insights, we propose CASIAL, a geometric distortion-robust watermarking framework with cover image-aware message spreading (CAS) strategy and invariance alignment learning (IAL) module. CAS tightly couples watermark bits with cover image features and distributes them adaptively across the entire image, enhancing per-pixel information capacity and robustness to region removal. IAL leverages spatial attention to capture cross-pixel dependencies and align perturbed features into a shared geometry-invariant representation space, mitigating failures due to desynchronization. Across six challenging geometric transformations, CASIAL achieves substantially stronger robustness than eleven prior baselines while preserving high visual quality. It also maintains competitive performance under six signal distortions and four photometric transformations. Notably, although trained only with white-box distortions, CASIAL also exhibits strong transfer robustness to unseen black-box distortions. Comprehensive experiments demonstrate the broad robustness and superior visual quality of our method.
[CV-40] FreeShadow: Training-Free Shadow Removal via Illumination Transfer and Selective Content Preservation in Diffusion Models
链接: https://arxiv.org/abs/2607.26715
作者: Yinan Wang,Yan Huang,Yong Xu,Patrick Le Callet
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Existing supervised and unsupervised shadow removal methods often suffer from limited generalization due to the insufficient diversity of available training datasets, while zero-shot methods tend to produce artifacts and require time-consuming test-time optimization. To address these issues, we propose FreeShadow, a training-free shadow removal method built upon pretrained diffusion models, which exploits diffusion priors for shadow removal without any training or optimization. For illumination recovery, we propose an illumination transfer attention (ITA), which re-weights the self-attention maps in diffusion model to transfer illumination cues from non-shadow to shadow regions. For content preservation, we analyze the effects of illumination variations on self-attention maps and latent high-frequency features in diffusion model, and selectively preserve illumination-invariant components to maintain content fidelity while suppressing residual shadows. We further propose local texture-preserving relighting (LTPR) to mitigate local texture misalignment caused by VAE compression. Extensive experiments demonstrate that our method achieves strong generalization and produces realistic shadow-free images.
[CV-41] PD: Temporal Prior Decoupling for Text-to-Video Diffusion Models
链接: https://arxiv.org/abs/2607.26706
作者: Taewon Kang,Matthias Zwicker
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-to-video diffusion models generate temporally coherent content from natural language, yet when a prompt describes an early scene that persists while a new event emerges on top of it—such as “a tall sandcastle standing on a beach where a wave rushes in and washes it away”—generation frequently fails to realize the late-segment event in the corresponding frames. We identify this failure as Temporal Prior Suppression (TPS): the dominant prior of the early segment captures the cross-attention trajectory across the temporal axis and suppresses the guidance signal needed for late-segment realization, a competing tendency existing guidance mechanisms do not model. We introduce Temporal Prior Decoupling (TPD), a training-free framework that restores suppressed late-segment signals during diffusion sampling. TPD constructs a temporal counterfactual by conditioning on the early segment alone, and defines the discrepancy between the full-prompt and counterfactual trajectories as a suppressed signal direction. Rather than removing this direction as in prior subtractive projection methods, TPD restores it through a frame-selective lower-bound constraint resolved jointly over diffusion timestep and video frame, realizing the suppressed event in the late frames without disrupting early-segment coherence: where prior work enforces upper-bound feasibility to remove unwanted semantics, TPD enforces lower-bound feasibility to guarantee suppressed-signal contribution. TPD runs entirely within standard diffusion sampling without retraining, and is defined purely in classifier-free guidance space, making it backbone-agnostic by construction. Experiments show that TPD significantly improves late-concept realization while preserving temporal coherence and visual fidelity, and that the targeted suppression recurs across distinct text-to-video backbones.
[CV-42] Sequence-SOD: Bio-inspired Sequence-aware Spiking ObjectDetection for Event Cameras
链接: https://arxiv.org/abs/2607.26703
作者: Katharina Bendig,René Schuster,Didier Stricker
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Event cameras follow a retina-inspired sensing principle, reporting local intensity changes asynchronously with hightemporal resolution and a wide dynamic range. Spiking Neural Networks (SNNs) complement these sparse event streams through brain-inspired dynamics, using sparse spikes and leaky membrane potentials to integrate information over time. However, many SNN object detectors process isolated event intervals with a single label and reset the network state after each prediction, thereby underusing temporal information in continuous event streams. We introduce Sequence-SOD, a sequence-aware SNN object detector that processes extended event sequences containing labels at multiple time points. Events are accumulated into short intervals, discretized into temporal steps, and fed sequentially to an SSD-style Spiking DenseNet while preserving membrane potentials across intervals within a sequence, so that detection is driven by an evolving neural state instead of independently reset input windows. On the Gen1 Automotive Detection Dataset, sequence-aware training improves mAP from 23.38 for single-interval training to 25.30 without augmentation and to 26.88 withevent-data augmentation. The model achieves a theoretical prediction frequency of 40 Hz. Training and evaluating SNN object detectors on extended event sequences improves their ability to exploit temporal cues while preserving the energy-efficiency benefits of sparse spiking computation. The results highlight sequence-aware training as a complementary direction to architectural improvements for event-based SNN detection.
[CV-43] Visko Orbis 1.0: A Live Model for Real-Time Interactive Long Video Generation
链接: https://arxiv.org/abs/2607.26694
作者: Xiangbo Gao,Siyuan Yang,Ping He,Mingyang Wu,Yuheng Wu,Yushen Zuo,Jiongze Yu,Ryan Cui,Hongyuan Hua,Devin Ma,Xiao Jin,Yubo Yuan,Qing Yin,Jie Yang,Zhengzhong Tu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present Visko Orbis 1.0, a Live Model for real-time, interactive long-video generation. Users can change the prompt at any moment during generation, and the update becomes visible in real time. Visko Orbis 1.0 supports long-form text-to-video, image-to-video, and video continuation, with multilingual prompts and prompt switching while generation is in progress. A bounded multi-scale memory preserves subjects, scenes, and style across chunks, sustaining hour-scale rollouts without evident quality or color drift. Built on a distilled chunk-wise streaming generator and a streaming video upscaler, Visko Orbis 1.0 delivers real-time 4K video generation at 24 FPS using an optimized GPU serving engine. In long-form Arena comparisons, Visko Orbis 1.0 obtains the highest overall-preference and temporal-stability ratings among state-of-the-art real-time interactive video-generation systems.
[CV-44] Physically Real-time Infrared Attack against Optical Flow Estimation Networks
链接: https://arxiv.org/abs/2607.26651
作者: Shen You,Wei Jiang,Jiarui Liu,Yijian Ye,Qiuzhen Lin,Xiangtao Li,Ka-Chun Wong
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:With the promising performance of deep neural networks on image-based tasks, different real-world applications such as autonomous driving and motion detection have become increasingly mature and relevant to human lives. In particular, Optical Flow Estimation Networks (OFENs), as upstream models, play a critical role in different domains. Its outputs are heavily assumed and adopted for different downstream tasks, and it is essential to test its robustness to prevent safety accidents. We present an approach for real-time attacks on OFENs in the physical world, leveraging infrared lights for their stealthiness. By generating a large number of Adversarial Examples in advance, our approach computes AEs in real time and dynamically displays them, which allows our method to facilitate precise and targeted attacks without modifying the victim system. Unlike previous digital-to-physical attack techniques, our method directly attacks victim models within the physical world, thereby overcoming the limitations associated with the ineffectiveness of AEs. Experimental results demonstrate the efficacy of our approach in compromising OFENs across diverse lighting conditions, varying object motion velocities, and different object placements, ultimately impairing the network’s ability to accurately estimate optical flow.
[CV-45] Anchoring and Steering Diffusion: Enhancing the Faithfulness of Text-to-Image Generation at Inference Time ECCV2026
链接: https://arxiv.org/abs/2607.26647
作者: Xinyi Wang,Yuyang Huang,Yalin Su,Pengcheng Luan,Tao Zhang,Feiming Wei,Wenxian Yu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026
Abstract:While text-to-image diffusion models achieve impressive visual quality, they frequently struggle to maintain precise alignment with complex compositional prompts. An effective strategy is to improve the inference process of diffusion models, thereby better leveraging their pretrained priors to address misalignment. Existing training-free methods can be divided into two categories. The first category focuses on improving the randomly sampled initial noise, either performing costly search over noise pools or manipulating sampled noise without ensuring reliable semantic injection. The second category focuses on improving the denoising trajectory, lacking explicit mechanisms to timely diagnose and correct semantic errors. we propose \textbfAnchorSteer, a training-free framework that exerts fine-grained control over \textbfboth initialization and \textbfthe denoising trajectory. AnchorSteer consists of two synergistic components: \textbfSemantic Anchoring replaces uninformative Gaussian noise with text-aligned initializations via CLIP-based prior extraction and a novel Latent-Prior Score Distillation Sampling (LP-SDS) objective. Specifically, LP-SDS distills CLIP visual priors into the knowledge distribution of diffusion models, mitigating the domain gap between CLIP-based priors and diffusion-based priors. \textbfReflective Steering transforms passive denoising with an active Think–Erase–Retouch loop that enables mid-generation self-correction. It leverages VLM-based diagnosis to detect semantic deviations and performs targeted latent refinement to suppress erroneous content and recover missing attributes. Extensive experiments on GenEval and T2I-CompBench++ demonstrate that AnchorSteer consistently outperforms existing baselines in text–image alignment while preserving high visual quality.
[CV-46] Genie Sim PanoWorld: An Infinite Indoor 3D World Generation Pipeline via Panoramic Scene Modeling and Simulation
链接: https://arxiv.org/abs/2607.26646
作者: Yongxin Su,Linjie Hou,Feng Wang,Jialin Tang,Zhijun Li,Qian Wang,Maoqing Yao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We address the problem of reconstructing a high-fidelity, freely navigable 3D scene from a single 360^\circ panorama, without per-scene optimization or multi-view capture. Existing methods either lack metric trajectory control, which hinders reliable downstream 3D reconstruction, or struggle with large disocclusions under long-range camera motion while requiring high-end multi-GPU this http URL present Genie Sim PanoWorld, a two-stage feed-forward pipeline that bridges generation and reconstruction via an explicit, trajectory-controllable panoramic video. A NavMesh-planned \mathrmSE(3) roaming trajectory is injected into a latent video diffusion model through dense geometry-warped conditioning; long–short trajectory mixed training and a self-consistency objective based on shortcut models together yield high-fidelity video in four CFG-free denoising steps. A feed-forward panoramic reconstructor then lifts the generated video into a high-fidelity 3D Gaussian scene that supports real-time, free-viewpoint roaming and can be directly used as a simulation-ready asset for embodied AI applications. Experiments show that Genie Sim PanoWorld outperforms geometry-conditioned baselines in both panoramic video generation and downstream 3D reconstruction, while generalizing zero-shot to unseen indoor scenes. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2607.26646 [cs.CV] (or arXiv:2607.26646v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.26646 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hou Linjie [view email] [v1] Wed, 29 Jul 2026 09:08:16 UTC (43,830 KB) Full-text links: Access Paper: View a PDF of the paper titled Genie Sim PanoWorld: An Infinite Indoor 3D World Generation Pipeline via Panoramic Scene Modeling and Simulation, by Yongxin Su and 6 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.CV prev | next new | recent | 2026-07 Change to browse by: cs References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[CV-47] FPSGen: Flexible Point Cloud Scene Generation with BEV-Supported Transport Flows
链接: https://arxiv.org/abs/2607.26645
作者: Wenzhe He,Meng Wang,JiaWei Qian,Jinfeng Xu,Ying Liu,Ruihui Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 34 pages, 16 figures
Abstract:Existing point-based generative methods for outdoor scenes primarily focus on LiDAR-conditioned completion. During training, noisy point clouds are constructed by perturbing complete ground-truth scenes, whereas during inference, they are initialized by adding noise to duplicated partial scans. This train-inference mismatch inherits the sparsity and visibility bias of partial scans, leading to sparse distant regions and incomplete geometry in occluded areas. Moreover, the reliance on partial scans restricts generation when LiDAR observations are unavailable or replaced by layout cues. We present FPSGen, a flexible framework that constructs point sources independently of partial scans. FPSGen first predicts a bird’s-eye-view (BEV) prior with density, height, and mask channels from the active cues. The density map is then sampled to form a BEV-supported point source, enabling both unconditional and conditioned initialization. A teacher-student approximate optimal transport scheme then uses teacher-predicted endpoints to learn a velocity field that induces straighter transport paths. By integrating BEV point source construction with path-straightening transport, FPSGen provides a unified framework for unconditional and flexible cue-conditioned scene generation. Extensive experiments show that FPSGen achieves state-of-the-art JSD and voxel IoU performance on SemanticKITTI completion while maintaining strong performance with a single point transport step. On KITTI-360 unconditional generation, it also achieves the best Coverage (COV) among the compared methods.
[CV-48] FakeIDet3-DB: Refining Digital Attacks and Patch Extraction for Secure ID Benchmarking
链接: https://arxiv.org/abs/2607.26641
作者: Muñoz-Haro Javier,Teruel Andres,Tolosana Ruben,DeAlcala Daniel,Vera-Rodriguez Ruben,Morales Aythami,Fierrez Julian
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 13 Pages, Database, Pipeline, Algorithm
Abstract:Identity document (ID) authentication relies on the structural integrity of complex, high-frequency security patterns. However, advanced Generative AI models can now inject localized, high-fidelity manipulations, creating deceptive attacks that bypass standard verification. Training robust image forensic models to detect these anomalies is hindered by privacy regulations, forcing reliance on synthetic templates lacking the intricate visual patterns of real IDs. To bridge this domain gap, we introduce FakeIDet3-DB, the first comprehensive database of digital manipulations on real, government-issued IDs. FakeIDet3-DB encompasses classical (e.g., copy-move) and Generative AI-driven manipulations (e.g., face-swapping, inpainting) enhanced with advanced image refinement procedures to suppress visual artifacts. In addition, to comply with strict data protection regulations (e.g., GDPR), we adopt a recently-proposed framework based on patches. In order to maximize forensic utility, we formulate privacy-aware patch extraction from a real ID as a geometrically constrained image processing problem. We propose PACE, a Pseudo-Anonymized Contextual patch Extraction algorithm, which leverages Integral Image mapping and distance-driven Non-Maximum Suppression (NMS). PACE efficiently contours anonymization masks that prevent Personally Identifiable Information (PII) leakage while maximizing semantic density in peri-censorship regions, yielding almost 5.2M patches extracted from more than 6.4K images from real/fake IDs. Furthermore, an extensive evaluation of the proposed FakeIDet3-DB is performed using state-of-the-art models, showcasing they all struggle to detect and locate attacks coming from generative and classic techniques (32.45% EER in detection and 83.48% AUC-ROC in localization).
[CV-49] Understanding Knowledge Transfer Mechanism in Heterogeneous MLLM Fusion: A Simple Linear Approach
链接: https://arxiv.org/abs/2607.26608
作者: Yinghao Hou,Jiahe Fan,Yuanhao Pu,Zongyuan Chen,Hong Xie
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 6 figures; includes supplementary material
Abstract:Training-free fusion of heterogeneous multimodal large language models (MLLMs) provides a direct route for cross-scale capability transfer, yet improvements in aggregate performance do not reveal what a smaller model actually inherits. Existing studies are largely designed and evaluated on limited task sets or aggregate metrics; as evaluation expands to broader task collections, whether different capabilities can transfer across scales remains poorly understood. To investigate this question, we introduce Cross-Scale Directional Parameter Injection (CDPI), a simple linear probe to analyze cross-scale knowledge transfer during heterogeneous fusion. A local theoretical analysis indicates that knowledge transfer selectivity is determined at first order by capability-dependent responses to a shared injection direction, while second-order curvature effects constrain the effective transfer regime. Across four Qwen3-VL model pairs and twelve multimodal benchmarks, our experiments reveal a consistent pattern of selectivity: gains concentrate on reasoning, particularly high-level reasoning, whereas perception performance remains close to that of the original target model. Component-wise ablations further show that high-level reasoning gains arise primarily from the language model, while ratio analysis finds that positive selective transfer occurs mainly in the small-ratio regime. These findings recast cross-scale heterogeneous MLLM fusion as selective language-side reasoning transfer within a narrow, low-interference regime, rather than broad capability inheritance.
[CV-50] JEPADepth: Masked Predictive Representation Learning for Self-Supervised Monocular Depth Estimation
链接: https://arxiv.org/abs/2607.26600
作者: Ionuţ Grigore,Călin-Adrian Popa
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Self-supervised monocular depth estimation typically relies on photometric reconstruction losses that couple depth, pose, and appearance assumptions. In this paper, we propose JEPADepth, a self-supervised monocular depth framework that incorporates a complementary training objective inspired by Image Joint-Embedding Predictive Architectures (I-JEPA) for self-supervised depth learning. Our method augments a standard photometric pipeline with a masked prediction loss computed in the representation space of a pretrained DINOv3 Vision Transformer encoder. A predictor infers target-region embeddings from visible context-region embeddings under structured masking, and is discarded along with the target encoder at inference time, adding no deployment cost. On KITTI, adding the JEPA objective consistently improves performance over the same DINOv3-based photometric baseline, without changing the inference-time architecture. Compared to prior monocular self-supervised methods, JEPADepth is competitive with state-of-the-art transformer-based approaches and outperforms strong CNN-based baselines on the standard benchmark. In zero-shot transfer (trained on KITTI and evaluated without fine-tuning), JEPADepth achieves the best or near-best performance among the compared methods on both Make3D and Cityscapes across multiple metrics.
[CV-51] Decoupled Visual Processing: Efficient Multimodal Adaptation via Modality-Specific Transformer Substitution
链接: https://arxiv.org/abs/2607.26596
作者: Mingkuan Feng,Zhengqi Wen,Jianhua Tao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal large language models (MLLMs) have demonstrated remarkable capabilities by integrating visual and textual understanding within a unified transformer architecture. However, fine-tuning all parameters of these models for visual instruction tuning is computationally expensive and often unnecessary, as the representation requirements for visual and textual tokens diverge significantly in the deeper layers of the network. In this paper, we propose Decoupled Visual Processing (DVP), an efficient training framework that replaces the upper decoder layers of a pretrained LLM with a lightweight, independently trainable single transformer block dedicated exclusively to visual token processing. Specifically, after shared processing through the first half of the decoder layers, visual and textual tokens are split: visual tokens are routed through a newly initialized single transformer block while textual tokens continue through the original frozen decoder layers. The two streams are then concatenated before the language modeling head. During training, only the single transformer block is updated, dramatically reducing the number of trainable parameters. Experiments on the LLaVA-1.5 framework demonstrate that DVP achieves competitive performance on MME, POPE, and ChartQA benchmarks while training only a fraction of the total parameters, suggesting that visual representations in MLLMs can be effectively learned through a decoupled, parameter-efficient pathway.
[CV-52] SpatialQ: Understanding 3D Gaussian Splatting Scene Quality via Visual-based MLLM
链接: https://arxiv.org/abs/2607.26595
作者: Jingxuan Su,Shenglin Wang,Tiesong Zhao,Ge Li,Wei Gao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D Gaussian Splatting (3DGS) has emerged as an effective representation for novel view synthesis and 3D scene reconstruction, creating an increasing demand for reliable quality assessment. Unlike conventional image quality assessment (IQA), the quality of a 3DGS scene depends not only on the perceptual fidelity of rendered views, but also on scene-level factors such as spatial structure and cross-view consistency. Existing IQA methods are limited by their reliance on 2D perceptual cues, whereas general multimodal large language models (MLLMs) are not designed for stable quality regression and may produce unreliable judgments. To address these limitations, a multimodal quality assessment framework is developed for 3DGS scene understanding. First, a 3D-aware quality representation learning framework is introduced by augmenting a VGGT-based encoder with a dedicated quality head. Multi-view images are encoded into view-specific features and aggregated to capture cross-view consistency, while geometric cues are incorporated through joint modeling of depth and point-cloud-related structural information, enabling the learning of structure-aware quality representations beyond appearance-driven features. Second, a grounded multimodal reasoning mechanism is constructed by jointly feeding original images, depth maps, point cloud renderings, and camera parameters into a Qwen-based MLLM.
[CV-53] R-SLPR: Region-based Small-to-Large Point-cloud Registration with Contrastive Learning
链接: https://arxiv.org/abs/2607.26583
作者: Yusen Wan,Zeyuan Chen,Qianshi Zou,Xu Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Point-cloud (PC) registration is fundamental to three-dimensional (3D) perception in robotic systems. However, classic registration algorithms falter when aligning a source PC containing limited, incomplete, or ambiguous geometric cues against a reference. This challenge of registering a small, partial PC to a significantly larger global reference is pervasive in real-world deployment yet remains insufficiently addressed by existing learning-based approaches, which typically assume comparable scales and significant overlap. To bridge this gap, we propose the Region-based Small-to-Large Point-cloud Registra- tion framework (R-SLPR), a novel three-stage architecture that fundamentally reformulates the scale-mismatched registration problem into a sequence of region proposal, regional matching, and iterative refinement. Unlike conventional methods that fail to localize specific regions, R-SLPR explicitly identifies candidate regions prior to estimating rigid transformations, ensuring robust alignment even under severe scale mismatch. The framework introduces a Fibonacci Grid Segmentation method coupled with a contrastive learning objective to effectively generate and match local geometric patches. Building on this, a novel Cascade Anchor Selection and Refinement algorithm iteratively aligns the source with the target region to maximize precision. Extensive evaluation on ModelNet40 demonstrates that R-SLPR establishes a new state-of-the-art accuracy standard, outperforming prior approaches and significantly reducing position and rotation Mean Absolute Error (MAE) to 0.009 and 1.104, respectively.
[CV-54] Level Sharpness and Corpus: Why Zero-Shot OOD Detector Rankings Do Not Transfer
链接: https://arxiv.org/abs/2607.26582
作者: Ignacio M. De la Jara,Cristian Rodriguez-Opazo,Stephen Gould,Damith Ranasinghe
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Selecting a zero-shot out-of-distribution (OOD) detector for a new deployment is typically based on benchmark rankings, implicitly assuming that the highest-ranked detector will transfer across domains. We show that this assumption does not hold. Through a controlled portability audit across seventeen in-distribution datasets, three vision-language models, and seven representative zero-shot OOD detectors, we find that detector rankings reverse across deployments, every detector exceeds 80% FPR95 on at least one domain, and the preferred detector depends on both the in-distribution data and the underlying VLM. We trace these reversals to complementary evidence channels in vision-language logits. Corpus-free detectors rely on different combinations of absolute match level and relative or spatial sharpness, while WordNet-based methods additionally depend on external semantic coverage. A simple proposition shows that level and sharpness cannot generally be recovered from one another, explaining why no single detector transfers reliably across deployments. Motivated by this diagnosis, we introduce the Complementary Evidence Guard (CEG), a detector-agnostic wrapper that preserves complementary evidence through a non-compensatory fusion of the base detector, level, and sharpness using only empirical in-distribution percentiles. Controls replacing these channels with entropy, logit variance, or random noise do not reproduce the gains. Without OOD samples, auxiliary corpora, or learned fusion, CEG reduces detector sensitivity and improves GL-MCM from 38.1 to 28.8 and MCM from 42.6 to 30.5 family-balanced FPR95.
[CV-55] Classification of Disease from Lungs X-ray Images using VGG16 VGG19 and ResNet50 Models
链接: https://arxiv.org/abs/2607.26580
作者: Nand Lal Yadav,Rajesh Kumar,Satyendra Singh,Sudhakar Singh
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 15 pages, 10 figures, 3 tables, International Journal of Drug Delivery Technology (IJDDT)
Abstract:With the increase in the number of cases related to respiratory diseases, there is an urgent need to detect them early and diagnose them accurately. Convolutional neural networks have given promising results when used for diagnosing diseases using imaging tests. In this study, we investigate the potential of applying deep learning algorithms such as VGG16, VGG19, and ResNet50 for classification of lung ailments based on X-ray images. A detailed analysis of the aforementioned models’ performances was conducted to assess how well they can classify various types of lung ailments, including pneumonia, tuberculosis, lung cancer, and normal lungs. In order to do that, these deep learning models were trained on a vast amount of X-ray images. The results of our study show that while all three models provide good results, ResNet-50 performs best in comparison with other models due to its efficiency and high level of accuracy. We believe that these deep learning models can be successfully implemented in the practice of diagnosing pulmonary diseases in the future. It helps with early disease detection and improves patient outcomes.
[CV-56] ContactFlow: A video action conditioning that transfers across embodiments
链接: https://arxiv.org/abs/2607.26579
作者: Sami Azirar,Enrico Pallotta,Jan Nogga,Jürgen Gall,Sven Behnke,Hermann Blum
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:World models offer a promising route toward robot planning by enabling agents to imagine and verify the consequences of actions before execution. However, current video-based world models often struggle to capture the physical constraints that govern manipulation, particularly contact. Further, their action conditioning is often constrained to specific embodiments such as parallel grippers. We propose \emphContact Flow, an embodiment-agnostic action representation that encodes manipulation through the trajectory of 3D contact points between an actor and a target object. By discarding actor-specific appearance and kinematics, Contact Flow provides a shared conditioning signal for both human demonstrations and robotic execution. Therefore, we can train a large-scale video generative model on both human and robotic object interaction videos conditioned on Contact Flow, yielding a world model that predicts physically plausible manipulation outcomes. We integrate this model into a propose-imagine-verify-act pipeline, where generated rollouts are assessed by a vision-language model before execution. Experiments on the DROID dataset and real-world tabletop manipulation tasks demonstrate that Contact Flow enables transfer between human demonstrations and different robotic embodiments.
[CV-57] 3DGBGS: 3D Granular Ball Gaussian Splatting for Compact Novel View Synthesis
链接: https://arxiv.org/abs/2607.26578
作者: Meng Yang,Shuyin Xia,Dawei Dai,YiWang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Three-dimensional Gaussian Splatting (3DGS) enables high-quality real-time novel-view synthesis through explicit Gaussian primitives and differentiable rasterization. 3DGS and Granular Ball Computing (GBC), proposed in 2019, share a natural compatibility in adaptive representation. The efficiency of 3DGS partly stems from a coarse-to-fine and on-demand refinement process that draws on the generation principle of GBC. This connection motivates us to further introduce adaptive granular ball organization into anchor-based 3DGS. Existing anchor-based methods typically construct anchors from sparse SfM point clouds through fixed voxelization, which cannot adequately adapt to spatially non-uniform point distributions and leads to a trade-off among anchor count, model compactness, and rendering quality. To address this issue, we propose 3DGBGS (3D Granular Ball Gaussian Splatting), a compact anchor-based framework for novel-view synthesis. 3DGBGS adaptively partitions SfM point clouds into 3D granular balls, using larger balls to compactly represent smooth and redundant regions and smaller balls to preserve complex geometry and local details. Based on this representation, Granular Ball Anchor Initialization (GBAI) uses granular ball centers to initialize compact anchor positions, while the Granular Ball Scale Prior (GBSP) exploits granular ball radii to provide local scale priors for Gaussian generation. Experiments on four benchmarks show that 3DGBGS reduces initial and final anchors by 37.1% and 10.0%, respectively, and model storage by 9.8% on average, while maintaining comparable rendering quality.
[CV-58] Speech2Grasp: Data-Efficient Transfer of Text-Conditioned Grasp Detection to Speech in Humanoid Robots
链接: https://arxiv.org/abs/2607.26567
作者: Hung Nguyen,Kim Nhat Minh Nguyen,Van Duc Vu,Van-Danh Le,Hoang Huy Le,Dinh Tuan Nguyen,Pham Tuyen Le,Van-Truong Nguyen,Quan Nguyen
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Humanoid robots increasingly require multi-modal understanding for natural interaction with humans. Despite the prominence of vision-language models, they generally assume textual rather than the more natural speech inputs. In this paper, we investigate whether a well-established text-conditioned model can be transferred to speech in a data-efficient manner. Using ALBEF as a case study, we conduct diagnostic analyses showing that a lightweight MLP-based projector effectively adapts it to speech, while preserving semantic discrimination and robustness. Motivated by these findings, we introduce Speech2Grasp, a framework for data-efficient transfer of text-conditioned grasp detection to speech. Real-world humanoid robot experiments show that Speech2Grasp outperforms cascaded ASR-based pipeline, while reducing inference latency. Our findings suggest a practical paradigm for extending established text-conditioned systems to speech.
[CV-59] Representation Trajectories Matters: Complementary Evidence for OOD Detection and Image Classification
链接: https://arxiv.org/abs/2607.26565
作者: Ignacio M. De la Jara,Cristian Rodriguez-Opazo,Hamed Damirchi,Stephen Gould,Damith Ranasinghe
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision models do not form a representation at once; each block revises it. We ask whether the resulting computation path contains evidence that the final representation discards, and whether that evidence improves OOD detection and image classification on clean and shifted data. Unlike approaches that treat intermediate layers as separate snapshots, we retain sample identity across depth and study the transformations connecting successive states. We separate class-coherent transport from input-specific innovation, and coordinate movement from relational reorganization. Across supervised, self-supervised, vision–language, hierarchical, and convolutional encoders, these paths show strong sample-specific continuity and architecture-specific depth profiles that recur across datasets. They are also practically useful. An ID-only transition-surprise score complements strong final-state detectors, reducing FPR95 in 131/152 non-saturated comparisons on a balanced OpenOOD grid; gains are largest for visually disruptive and semantically far shifts, and remain positive on near-OOD for most detectors. Frozen update probes improve 71/72 clean model–dataset cases, while shifted-data gains vary with architecture and corruption type. Computation paths therefore provide a broadly useful reliability signal whose value is determined jointly by model organization and the shift encountered.
[CV-60] MedARC: Training-Free Adaptive Redundancy Compression of Visual Tokens for 3D Medical Vision-Language Models
链接: https://arxiv.org/abs/2607.26554
作者: Yitao Zhu,Mengjun Liu,Yingji Fu,Haowen Pang,Anqi Qiu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages
Abstract:Integrating 3D medical images with vision-language models (VLMs) holds substantial promise for computer-aided diagnosis. However, volumetric images generate prohibitively long visual-token sequences with considerable spatial and inter-slice redundancy. Existing token compression methods typically apply uniform reduction or rely on a single importance signal, increasing the risk of removing regions that are clinically relevant to the query or structurally distinctive. To address this limitation, we propose MedARC, a unified, training-free framework for Adaptive Redundancy Compression of visual tokens in 3D medical VLMs. MedARC estimates token importance by integrating three complementary cues: self-attention from the VLM vision encoder, which reflects the model’s intrinsic visual focus; similarity between projected visual tokens and text embeddings, which identifies query-relevant regions; and deviations of local visual foundation model features from the volume-level feature center, which highlight structurally distinctive anatomy. The resulting importance distribution guides a saliency-aware merging strategy that preserves informative tokens while consolidating redundant ones rather than simply discarding them. Experiments on CT-RATE and MR-RATE show that MedARC reduces visual-token overhead and inference time while preserving or improving diagnostic performance. Its multi-cue scoring cost is outweighed by the savings from processing fewer tokens, with greater benefits expected for larger language models.
[CV-61] From Spatial Semantics to Temporal Context: Leverag ing Gaze Trajectory for Weakly Supervised Medical Image Segmentation
链接: https://arxiv.org/abs/2607.26542
作者: Shaoxuan Wu,Xiao Zhang,Xiaodi Zhao,Yunzhi Tian,Yilin Tang,Jun Feng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Medical image segmentation heavily depends on labor-intensive and time-consuming pixel-level annotations. Eye tracking offers a cost-effective solution that can be naturally integrated into clinical workflows. Recorded by eye trackers, gaze conveys the spatial regions of clinicians’ attention through fixations and the temporal context of clinicians’ progressive visual perception from trajectories. Nevertheless, effective modeling of temporal trajectories remains challenging, and noise in gaze caused by exploratory fixations greatly limits segmentation performance. To overcome these limitations, we propose the Trajectory-guided Uncertainty-aware Network (TrailNet), which exploits gaze-supervised medical image segmentation from spatial semantics modeling to temporal context by jointly leveraging fixations and trajectories. Specifically, the proposed trajectory-guided spatio-temporal encoder models temporal context and establishes complementary interactions with image spatial semantics to strengthen target perception. Furthermore, the multi-scale uncertainty decoder leverages category mutual-exclusivity constraints to produce deterministic predictions and mitigate supervision uncertainty induced by noise. To enable gaze-free inference, we further introduce a cycle distillation strategy that transfers feature-level knowledge via teacher-student networks. Experimental results on two public datasets demonstrate that TrailNet outperforms state-of-the-art methods, achieving Dice scores of 81.25% and 81.85%, respectively.
[CV-62] PCD: Tone-Pressure Contrastive Decoding and the Label-Free Gating Bottleneck in Vision-Language Models
链接: https://arxiv.org/abs/2607.26536
作者: Jinkun Zhao,Kui Zhang,Wenjun Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:High-pressure prompts can push vision-language models (VLMs) into unsupported commitments, such as reading illegible text, reporting indeterminate times, or affirming absent objects. This paper asks whether the pressure-induced distribution itself can serve as a contrastive-decoding negative branch. Tone-pressure contrastive decoding (TPCD) subtracts logits produced under a high-pressure instruction from logits produced under a safe neutral instruction. On the 800-example tone-matters benchmark, LLaVA-1.5-7B under pressure reaches 66.75% attack success rate (ASR); safe neutralization reduces ASR to 9.88%; full TPCD reaches 0.50% but collapses positives to 15.56%. A benchmark-specific task-prior/disagreement gate preserves measured positive accuracy (54.44%) while lowering ASR to 1.63% on LLaVA. Treating this LLaVA analysis as the design split, full n=800 negative and n=780 matched-positive held-out runs on GLM-4.6V and Llama-3.2-Vision show that simple gates can improve over safe neutralization, with sensitivity analyses bounding the weak time-positive subtask. A category-prior-free answer-disagreement router reduces held-out aggregate ASR to 6.93%, improving over both safe neutralization (10.98%) and branch disagreement (9.67%) while matching branch disagreement’s 79.94% positive accuracy, although it remains post-hoc and surface-form based. We conclude that pressure is a useful probe of commitment bias and a viable mitigation signal, but the current gates are not yet independently validated grounding-aware detectors.
[CV-63] CineWeaver: Training-Free Reference-Controllable Multi-Shot Long Video Generation for Cinematic Storytelling
链接: https://arxiv.org/abs/2607.26529
作者: Yuyang Huang,Yabo Chen,Wenrui Dai,Ziyang Zheng,Haibin Huang,Chi Zhang,Junni Zou,Hongkai Xiong,Xuelong Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cinematic video generation is challenging for text-to-video diffusion models due to concurrent requirements on multi-shot generation, fine-grained controllability over characters and scenes, and long-form generation across extended temporal horizons. Existing methods rely on customization and retraining to separately address specific requirements, and cannot simultaneously fulfill all the requirements with a unified framework. In this paper, we shed light on the training-free paradigm with the key insight that the difficulty of multi-shot generation arises from a structural bias toward temporal continuity in pretrained video diffusion models, and consequently, propose a unified framework named CineWeaver to achieve reference-controllable multi-shot long-video generation without retraining. We manipulate positional encoding and attention patterns to break temporal continuity during inference to enable clear shot transitions using pretrained video diffusion models. Furthermore, we extend the proposed framework with a shot-routed reference conditioning mechanism for per-shot fine-grained controllability, and develop an anchor memory mechanism to allow long-form generation with consistent global appearance cues. To our best knowledge, CineWeaver is the first unified framework to simultaneously enable \textbflong-form, \textbfreference-controllable, and \textbfmulti-shot video generation in a training-free fashion. Experimental results demonstrate that CineWeaver produces high-quality cinematic videos of long durations with consistent identities, stable global appearance, and clear shot transitions. The project page is available at: this https URL.
[CV-64] EgoSafe: A First-Person Mobile-Captured Benchmark for Visual Safety Understanding
链接: https://arxiv.org/abs/2607.26518
作者: Yuyun Chen,Tianao Li,TianQuan Feng,Cen Chen,Huiping Zhuang,Hao Peng,Ziqian Zeng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reliable visual safety understanding in real-world scenarios demands more than just object recognition; it requires causal reasoning under epistemic uncertainty. While Large Vision-Language Models (LVLMs) demonstrate impressive semantic alignment on standard benchmarks, they often struggle to distinguish between superficial correlation and genuine forensic logic when grounded in the dynamic, partially observable nature of first-person experiences. Existing evaluations, dominated by third-person surveillance footage and binary classification metrics, fail to expose this cognitive gap. To address this, we introduce EgoSafe-Bench, a benchmark specifically designed to probe forensic reasoning in egocentric safety scenarios. It comprises 12,000 unique evaluation samples, generated by pairing each of the 3,000 video clips with a QA chain governed by our proposed Hierarchical Reasoning Evaluation (HRE) protocol. Unlike standard benchmarks, HRE mandates a rigorous reasoning trajectory from initial feature anchoring to blind-spot deduction and intent inference, thereby enforcing logical consistency and penalizing shortcut-based this http URL evaluations of state-of-the-art LVLMs (e.g., Qwen3-VL, Gemini, VideoLLaMA 3) reveal a significant perception-reasoning decoupling: models often achieve high descriptive scores but exhibit notable fragility in causal reasoning and logical closure. Our work provides both a challenging dataset and a systematic evaluation framework to foster the development of logically robust video understanding systems.
[CV-65] Semantic-Aware Temporal Adaptation for UAV Anti-UAV Tracking
链接: https://arxiv.org/abs/2607.26511
作者: Xiaozhen Qiao,Da Zhang,Yubin Guo,Junyu Gao,Zhiyuan Zhao,Xuelong Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:UAV Anti-UAV tracking is an emerging low-altitude security task for localizing an adversarial UAV using the onboard camera of a moving observer UAV. It differs from conventional UAV tracking and ground-based Anti-UAV tracking because both the camera platform and the target move simultaneously. This dual-dynamic setting induces rapid viewpoint changes, motion blur, scale variation, and visually similar distractors, making reliable appearance matching difficult. Under such rapidly changing conditions, fixed visual representations are often insufficient because target appearance becomes unreliable and feature distributions may deviate from the training domain. The target language description remains stable across frames and can therefore serve as a semantic anchor for temporal state propagation, while online feature-distribution alignment can reduce video-specific test-time shifts. In this paper, we propose \emphSATATrack, a Semantic-Aware Temporal Adaptation framework for UAV Anti-UAV tracking. SATATrack introduces Semantic-Aware Context Propagation (SACP), which uses the target description to guide temporal context propagation across backbone stages and preserve target identity under rapid appearance changes. An auxiliary contrastive regularizer is used during training to discourage responses to semantically similar background regions. During inference, Temporal-Aware Distribution Alignment (TADA) aligns feature distributions online without updating model parameters, combining recent-frame estimates with training-time statistics for stability. SATATrack achieves state-of-the-art performance on the UAV-Anti-UAV benchmark while remaining competitive in Anti-UAV and UAV object tracking tasks. The code will be available at this https URL.
[CV-66] HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation TN Staging and Recurrence-Free Survival on PET/CT
链接: https://arxiv.org/abs/2607.26498
作者: Kai Wang,Meixu Chen,Elie Nasr,Ryan Lanning,Moyed Miften
类目: Computer Vision and Pattern Recognition (cs.CV); Medical Physics (physics.med-ph)
备注: 12 pages, 4 figures, 3 tables, conference
Abstract:We present HERMES (Hybrid Ensemble for Radiotherapy-target segmentation, Malignancy staging, and Event-free Survival), a single containerized algorithm for the three HECKTOR 2026 subtasks: segmentation of the primary tumor (GTVp) and pathological lymph nodes (GTVn), radiological T/N staging, and recurrence-free survival (RFS), computed from a paired FDG-PET/CT scan and an electronic health record. A 10-fold ensemble of STU-Net Small networks produces the segmentation; the predicted mask then drives two downstream tasks. Rather than pass a generic radiomics vector to the staging models, we derive from the predicted masks a compact set of geometry features aligned with the size and number axes of AJCC/UICC 7th-edition radiological N/T staging. On internal cross-validation these features raise N-stage balanced accuracy from 0.691 to 0.720 (+0.030), our largest single design gain, at lower feature dimensionality. For prognosis we combine complementary deep and clinical risk experts in an equal-weight ensemble, and train one deep expert with a concordance-tracking survival loss of our own, whose value approximates the concordance index during training. Every component was selected on honest out-of-fold predictions under a regularization-oriented protocol, with no tuning on the public validation set, and deployed as two decorrelated submissions. On the HECKTOR 2026 validation leaderboard, HERMES achieved a weighted score of 0.6454 (Mean Dice 0.641, T balanced accuracy 0.580, N balanced accuracy 0.642, RFS C-index 0.679) and qualified for the testing phase. Team: AMC_HNC.
[CV-67] Interpretable Image-Level Acne Severity Grading via EfficientNet-B0 Transfer Learning and Grad-CAM
链接: https://arxiv.org/abs/2607.26461
作者: Sophie Zeng,Sean Kalaycioglu,Collin Hong,Haipeng Xie
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 22 pages, 7 figures, 7 tables
Abstract:Acne vulgaris affects most adolescents and many adults. Accurate severity grading guides treatment, monitoring, and clinical trial endpoints, but manual assessment using the Investigator’s Global Assessment or Hayashi criteria is limited by inter-rater variability and inconsistent imaging conditions. We developed a four-class acne severity classifier based on the Hayashi criteria using transfer learning with an ImageNet-pretrained EfficientNet-B0 model. The model was fine-tuned on the public ACNE04 dataset of 2,983 labeled images using AdamW optimization, geometric and photometric augmentation, and checkpoint selection based on validation macro-F1. On a held-out stratified 15 percent test set, the classifier achieved 93.5 percent accuracy and 94.4 percent macro-F1, with per-class F1 scores from 0.92 to 0.97. Eighty-three percent of errors occurred between adjacent grades. Quadratic-weighted Cohen’s kappa was 0.956, with a 95 percent confidence interval of 0.935 to 0.973. Bootstrap confidence intervals indicated stable performance. Grad-CAM visualizations from the final convolutional block focused on clinically relevant facial regions, including the forehead, cheeks, and chin. The complete pipeline is provided as functionally equivalent open-source implementations in Python using PyTorch and timm, and in MATLAB R2026a. The software includes a clinician-facing inference interface and a fallback backbone option that supports operation without specialized pretrained-weight packages. These results show that lightweight transfer learning can provide accurate, balanced, and interpretable acne severity grading while offering a reproducible cross-platform reference for future prospective and device-stratified clinical validation.
[CV-68] CG-World: A Large-Scale World-State Dataset and Protocol for World Models
链接: https://arxiv.org/abs/2607.26452
作者: Yiming Cai,Fangjie Yu,Meiqing Yu,Ziyue Shi,Pengfei Yuan,Yong Guo
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:World models must learn the joint dynamics of states, actions, events, and observations, yet existing video, robotics, and simulation datasets usually capture only part of this structure. We introduce CG-World, a large-scale world-state dataset and protocol derived from industrial computer graphics production pipelines. CG-World explicitly records intermediate states, including multimodal semantics, spatial structure, skeletal and controller states, motion curves, camera and lighting parameters, physics caches, contact events, and multi-pass renderings. CG-World v1 contains approximately 850,000 temporally aligned segments of 1-5 seconds. It separates latent states, observations, relations, events, and branch metadata, and organizes them into unified spatiotemporal samples. To support intervention learning and counterfactual reasoning, CG-World defines a branch lineage covering factual trajectories, observation interventions, action interventions, mechanism interventions, and strict counterfactual branches, with intervention targets, invariants, and alternative outcomes explicitly recorded. We evaluate the dataset on geometry-conditioned video generation, action prediction, and closed-loop vision-language-action policy transfer. Results show that CG-World provides reusable structured supervision for controlled generation, action modeling, and embodied policy transfer. We plan to expand CG-World through continued data collection and community collaboration toward a shared data infrastructure for world models, Physical AI, and embodied intelligence.
[CV-69] FAS-R1: A Unified Multi-Task MLLM for Reasoning Face Anti-Spoofing
链接: https://arxiv.org/abs/2607.26432
作者: Hongyang Wang,Yichen Shi,Hongrui Li,Yiru Huo,Jun Feng,Zitong Yu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Face anti-spoofing (FAS) is increasingly expected to provide not only bona fide/spoof decisions, but also attack semantics and image-grounded evidence for human inspection. Existing discriminative FAS models remain largely label-centric, while recent MLLM-based methods offer structured outputs but still rely mainly on supervised fine-tuning, often producing template-like rationales and weak optimization for difficult attacks. We propose FAS-R1, a two-stage reasoning-oriented MLLM framework for unified FAS prediction, covering authenticity classification, attack-type recognition and spoof-region localization. FAS-R1 first uses FAS-R1-23K, a high-quality long-CoT dataset, for cold-start supervised fine-tuning, and then performs FAS-specific GRPO post-training. Degradation-Simulated Augmentation (DSA) encourages stable spoof-cue reasoning across visual-quality shifts, while Difficulty-Aware GRPO (DA-GRPO) mitigates easy-sample dominance that may leave difficult task–attack groups under-optimized, especially for subtle or ambiguous attacks such as makeup and mask attacks. The main 3B FAS-R1 model achieves 98.75% authenticity accuracy, 93.33% attack-type accuracy, and 96.30/94.73% AP@40/AP@50 in-domain. It also outperforms the compared systems in cross-domain authenticity generalization and answer-and-rationale quality. Experiments with different base models further show favorable scaling behavior. The code will be released soon.
[CV-70] When Fish Look Alike: Tracking Identities with Dual-branch Elasticity
链接: https://arxiv.org/abs/2607.26412
作者: Vran Lee,Xin Liu,Yijie Wei,Yeqiang Liu,Hwa Liang Leo,Zhenbo Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Tracking dense, homogeneous targets like schooling fish remains a major challenge for multiple object tracking due to extreme inter-individual homogeneity, severe physical clustering, and rapid non-rigid deformations. While heavy-backbone separated detection and embedding trackers like SU-T push accuracy boundaries using complex Re-Identification networks, their computational overhead prohibits edge deployment. Furthermore, these modules often fail when appearance features degrade under severe occlusions. To overcome this, we propose Tracking Identities with Dual-branch Elasticity (TIDE). Bypassing expensive appearance cues, TIDE utilizes the Adaptive Geometric Correspondence IoU, an association mechanism leveraging spatial and structural consistency to robustly handle complex morphological variations. Crucially, TIDE introduces system-level deployment elasticity, decoupling the algorithmic pipeline from strict hardware constraints. Evaluations on the MFT-Edge benchmark demonstrate that our Lightweight L-branch achieves a competitive HOTA of 28.43 using merely 20.47G FLOPs. This represents a 38.7-fold computational reduction compared to upper bounds like SU-T, directly facilitating real-time edge deployment. Concurrently, our Scalable S-branch establishes a 29.98 HOTA, successfully bridging the gap between high-precision cloud analysis and efficient edge tracking. The dataset and codes are released at this https URL.
[CV-71] Do Unified Multimodal Models Think in One Space? A Lens Through Cross-Branch Steering
链接: https://arxiv.org/abs/2607.26411
作者: Yu Wang,Sharon Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Unified multimodal models (UMMs) aim to integrate understanding and generation within a single architecture, yet it remains unclear whether these capabilities share a unified and transferable semantic space. This question is fundamentally challenging, as the two branches operate over heterogeneous representations (text tokens vs.\ visual latents) and distinct training objectives, making direct comparison difficult. To address this, we introduce \emphcross-branch semantic steering, an intervention-based framework that extracts semantic directions from one branch and applies them to the other. We show that steering vectors learned from the understanding branch can transfer to generation, enabling controllable image synthesis and improved semantic faithfulness. In contrast, the reverse direction consistently shows limited effectiveness. Our analysis suggests that this asymmetry may be related to a practical representational mismatch: understanding-derived vectors capture transferable, object-centric semantics, while generation-derived vectors primarily encode low-level appearance features. Our results reveal that architectural unification does not guarantee semantic alignment, and establish cross-branch steering as a practical tool for probing multimodal representations.
[CV-72] Registration-Grounded Spectral Fusion for Unregistered WLI/NBI Endoscopic Lesion Segmentation
链接: https://arxiv.org/abs/2607.26395
作者: Pengyu Jie,Wanquan Liu,Rui He,Pengcheng Li,Weiping Wen,Deyu Meng,Junwei Han,Chenqiang Gao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages
Abstract:White-light imaging (WLI) and narrow-band imaging (NBI) provide complementary views of endoscopic lesions, but their paired observations are often spatially misaligned due to viewpoint changes, tissue deformation, and sequential handheld acquisition. This makes direct WLI/NBI fusion prone to mixing non-corresponding regions and may even degrade segmentation around lesion boundaries. To address this problem, we propose a reliability-aware complex-domain fusion framework for paired-but-unregistered WLI/NBI lesion segmentation. The framework first establishes topology-regularized feature correspondence and further estimates where the cross-modal correspondence is reliable. Guided by this reliability, the model selectively fuses WLI and NBI features in a learnable complex representation. In this representation, WLI-derived cues mainly provide appearance-related magnitude responses, while NBI-derived cues provide structure-sensitive phase responses. Unlike conventional real-valued or symmetric multimodal fusion, the proposed method explicitly models the different roles of WLI and NBI and suppresses unreliable cross-modal interaction in locally mismatched regions. Experiments on paired WLI/NBI endoscopic datasets show that the proposed reliability-aware registration grounding and complex-domain fusion consistently improve lesion segmentation performance. Role-reversal and module ablation studies further validate the necessity of both the modality-role design and reliability-guided cross-modal interaction.
[CV-73] Zero-Fi: Zero-Shot Wi-Fi-Based Human Activity Recognition via Contrastive Signal-Language Alignment
链接: https://arxiv.org/abs/2607.26381
作者: Yitong Shen,Cheng Guo,Peiliang Wang,Jingzhe Zhang,Yi Sheng,Haopeng Zhang,Hongfei Xue,Yili Ren
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Wi-Fi-based human activity recognition has advanced substantially, but most existing methods assume a closed set of activities and require labeled Wi-Fi samples for every target class, limiting their ability to recognize unseen activities. We present Zero-Fi, a contrastive signal-language alignment framework for zero-shot Wi-Fi-based human activity recognition. Zero-Fi learns unified representations from complementary Wi-Fi signal features and aligns them with the semantic representations of natural-language activity descriptions in a shared embedding space. This cross-modal alignment enables Zero-Fi to recognize new activity classes without requiring labeled Wi-Fi samples or model adaptation for those classes. Experiments on large-scale public benchmark datasets demonstrate effective zero-shot recognition of held-out activity classes, highlighting the potential of signal-language alignment to extend Wi-Fi sensing beyond predefined activity classes.
[CV-74] Seeing or Knowing? Visual Context Sensitivity in Multimodal Large Language Models
链接: https://arxiv.org/abs/2607.26326
作者: Jiaang Li,Chengzu Li,Zhaochong An,Yifei Yuan,Xi Liu,Serge Belongie,Vésteinn Snæbjarnarson
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal Large Language Models (MLLMs) achieve strong performance by integrating visual inputs with the rich priors of pretrained language models. However, they often fail on vision-centric tasks, especially when visual evidence conflicts with pretrained knowledge. We explore these failures separately using two diagnostic paradigms: (1) probing whether visual information is available, via image reconstruction, and (2) measuring multimodal context sensitivity, the extent to which the model follows visual context versus the language prior. To support the second, we introduce the WhatIfVis, a benchmark spanning five coarse-grained dimensions (spatial-temporal, color, count, size, and weight) whose questions admit answers from either the image or the prior. Our analysis yields three findings: (i) Coarse-grained visual evidence is preserved, as these attributes can be reconstructed from the final-layer image tokens of frozen MLLMs. Failures on questions about these attributes therefore point to post-perceptual utilization, rather than to degraded visual encoding during perception. (ii) Even when explicitly instructed to use or ignore visual evidence, vanilla models (without supervised fine-tuning on the WhatIfVis) show unstable visual context sensitivity. Supervised fine-tuning (SFT) improves this controllability and generalizes across domains, and activation patching further localizes the vision-versus-prior trade-off at architecture-specific depths across all six models. (iii) The vision-versus-prior trade-off is controllable along a learned vector. Applying this steering vector, even without any intent instruction, improves controllability over the vanilla model. Together, these results relocate the bottleneck, indicating that for the coarse attributes we study, MLLMs encode the visual evidence but cannot reliably control their reliance on it.
[CV-75] MoSAIC: Aligned Intervention Supervision for Part-Local Motion Style Transfer
链接: https://arxiv.org/abs/2607.26304
作者: Nazanin Amini,Kevin Desai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 23 pages, 6 figures, 11 tables. Project page: this https URL
Abstract:Editing character motion often requires transferring a gesture or gait from one or more reference motions while preserving the source action, timing, root trajectory, and unselected body regions. Existing motion datasets, however, rarely provide paired targets for arbitrary part-local content–reference combinations, and self-reconstruction training may allow a diffusion model to reproduce the content motion while underusing the routed reference. We present MoSAIC, a latent diffusion framework for part-local reference-conditioned motion style transfer. MoSAIC factorizes content and reference features by anatomical region, preserves the root trajectory through a separate conditioning pathway, and routes user-selected references to individual body parts. Its central contribution is aligned intervention supervision, which constructs synchronized references and counterfactual targets through controlled local transformations, making both the requested regional response and the motion to be preserved directly observable during training. In a frozen evaluation comprising 128 motions and 896 routed conditions, part-masked routing reduces preserved-region error from 70.64 to 66.45~mm and matched-noise off-target leakage from 18.08 to 9.88~mm relative to whole-body routing, while retaining a positive selected-region response. A matched-budget continuation study further shows that retaining aligned intervention supervision produces an 8.8% relative increase in selected-target response and a 2.0-percentage-point increase in requested-route influence concentration. These results demonstrate that MoSAIC improves the response–preservation trade-off required for selective and controllable part-local motion editing.
[CV-76] Eddeep: a deep-learning framework for fast eddy-current distortion correction in diffusion MRI
链接: https://arxiv.org/abs/2607.26292
作者: Antoine Legouhy,Ross Callaghan,Yuchuan Qiao,Whitney Stee,Philippe Peigneux,Hojjat Azadbakht,Hui Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Associated GitHub repo: this https URL
Abstract:Diffusion MRI (dMRI) relies on diffusion-weighted echo-planar imaging, which is highly susceptible to eddy-current-induced geometric distortions. These distortions vary across diffusion volumes according to gradient strength and direction, causing between-volume misalignment that can bias downstream microstructural analyses. Current state-of-the-art correction methods, such as FSL Eddy, achieve high-quality correction through iterative prediction-correction schemes but are computationally expensive. We propose Eddeep, a deep-learning framework for fast eddy-current distortion correction in dMRI. Eddeep decomposes the problem into two stages. First, a supervised image translation network standardises the appearance of diffusion-weighted and b=0 images, removing contrast differences that hinder reliable registration. Second, an unsupervised registration network estimates both eddy-current distortion and between-volume head motion parameters under a physics-constrained quadratic distortion model, enabling correction in a single forward pass. The method was trained on UK Biobank data and evaluated on both in-domain (UK Biobank) and out-of-domain (Memodyn) datasets. Across a range of complementary metrics, including between-volume jitter, diffusion kurtosis imaging residuals, signal irregularity, and mutual information, Eddeep achieved correction quality comparable to that of FSL Eddy while substantially reducing inference time. These results demonstrate that deep learning can provide accurate and efficient eddy-current distortion correction without relying on iterative optimisation, supporting the development of faster diffusion MRI processing pipelines for large-scale studies and clinical deployment. The code is available at: this https URL.
[CV-77] HeteroPROPMT: A Real-time and Privacy-Preserving Heterogeneous Collaborative Perception Framework IROS
链接: https://arxiv.org/abs/2607.26283
作者: Armin Maleki,Hayder Radha
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Accepted to 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 9 pages, 4 figures, 5 tables
Abstract:Collaborative Perception (CP) improves autonomous systems’ awareness of their surroundings by sharing sensor data, intermediate features, and detection results. In real-world deployments, however, collaborating vehicles often use heterogeneous sensors, perception models, datasets, and training domains, creating feature-space shifts that degrade downstream fusion and detection. Existing approaches typically retrain fusion and detection components or introduce modality-specific feature interpreters. These methods scale poorly to newly joining agents and often require access to proprietary metadata, raising privacy concerns. We propose HeteroPROMPT, a real-time and privacy-preserving framework for heterogeneous collaborative perception. HeteroPROMPT rapidly aligns each heterogeneous agent’s features with an ego-centric unified feature space through modular prompts and lightweight learning-based tuning, while keeping agent encoders and the collaborative fusion and detection stacks frozen. Its visual prompt-based training and inference modulate Bird’s Eye View (BEV) features across channels and spatial locations with low computational overhead. For metadata-free deployment, an autoencoder learns a compact unified representation and extracts modality cues from shared features, enabling real-time modality classification and routing to the appropriate HeteroPROMPT modules without exposing proprietary agent information. Experiments on the OPV2V-H and V2XSet datasets show that HeteroPROMPT improves Average Precision over state-of-the-art heterogeneous CP methods while using orders of magnitude fewer trainable parameters. This offers a scalable and practical CP solution. The proposed modality classifier also predicts the joining agent’s modality from compact features with greater than 99.99 percent accuracy during deployment. Code will be available at this https URL.
[CV-78] Comparing the Performance of Foundation Model Derived Embeddings with Traditional Approaches for Distant Metastasis Prediction in Head and Neck Cancer
链接: https://arxiv.org/abs/2607.26276
作者: Erich Schmitz,Meixu Chen,Bowen Jing,Jing Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Medical Physics (physics.med-ph)
备注: 27 pages including supplemental materials, 5 main figures, 2 supplemental figures, 5 main tables, 7 supplemental tables. Poster Abstract at 2026 AAPM Meeting and Exhibition
Abstract:Background: Early prediction of distant metastasis (DM) risk in head and neck cancer (HNC) can enable timely interventions that may improve treatment outcomes. Many current machine learning methods rely on prior knowledge of the region of interest such as tumor segmentations, which require expert knowledge, is time-consuming and introduces user-dependent variability. Medical image-based foundation models have recently been developed for specific imaging modalities to streamline down-stream prediction tasks by extracting modality-relevant features. Purpose: In this study, we evaluate the effectiveness of using a foundation model as the feature extractor to predict DM risk in HNC patients and compare its performance with traditional approaches that require prior knowledge on the regions of interest. Methods: Preoperative CT images of 2327 patients from the RADCURE dataset were used. Three features-sets were created including radiomics, deep-learning based features, and CT Foundation derived features. The feature-sets were used individually in a multi-layer perceptron (MLP) to predict DM risk. Results: The model using CT Foundation embeddings outperformed the radiomics and deep learning-based models, achieving a Receiver Operating Characteristic Area Under the Curve (AUC) of 0.791, compared to AUC values of 0.772 and 0.753 for the radiomics and deep learning-based models, respectively. The CT Foundation based model had similar performance to a model that combined the use of radiomics and deep learning-based features that achieved an AUC of 0.794. Conclusions: Features based on foundation models offer a promising alternative to traditional radiomics while reducing the need for domain expertise and extensively annotated datasets. Their minimal preprocessing requirements also make them a more accessible and scalable option.
[CV-79] Lightweight Image Classification of Raptor Species for Edge Devices: Rare-Species Dataset Expansion via Video Frame Extraction Knowledge Distillation and TensorRT Deployment
链接: https://arxiv.org/abs/2607.26238
作者: Takeshi Nishikawa
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
备注: 21 pages, 4 figures, 14 tables. English translation of a paper submitted to IPSJ (in Japanese); the Japanese version is the source of record
Abstract:We investigate lightweight raptor-species classification for real-time edge deployment in wind-turbine collision mitigation. Using DINOv2-L (304M parameters) as a teacher, we distilled three lightweight students (MobileNetV4, ViT-Small, and EfficientNet-B0). To reduce confusion between closely related species, we expanded the dataset to 12,519 images, including an increase in Steller’s Sea Eagle images from 463 to 2,050 via video-frame extraction. Under a group split that separates samples at the video- and source-image level to mitigate source leakage at that granularity, the three-student ensemble achieved a macro recall of 0.935 +/- 0.004 over five distillation seeds (0.955 on a conventional image-level split, retaining 97.5% of the teacher’s macro recall) with roughly one-eighth as many parameters. On a subset of 1,258 images disjoint from the former training images, White-tailed Eagle recall improved by up to 38.6 percentage points, while the rate at which it was misclassified as the Steller’s Sea Eagle decreased from 61% to 15% of errors. TensorRT FP16 deployment of EfficientNet-B0 on an NVIDIA Jetson Orin Nano achieved 3.19 ms/image including host-device transfer (313 images/s), with 99.95% argmax agreement with FP32. In five-seed controlled comparisons, neither distillation (versus CE-only) nor the change from a DINOv2-L to a DINOv3-L teacher yielded a clear ensemble-level improvement; the primary gains stem from the dataset expansion and teacher re-fine-tuning.
[CV-80] LumaGuide: Distribution Shaping for Training-Free HDR Generation in Diffusion Models
链接: https://arxiv.org/abs/2607.26237
作者: Bowen Chen,Shreshth Saini,Balu Adsumilli,Alan C. Bovik
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Pretrained diffusion models generate realistic images but are constrained by the statistical biases of their training data, limiting their ability to produce high dynamic range (HDR) content. In this work, we introduce LumaGuide, a training-free framework for distribution shaping in diffusion models. Instead of modifying model parameters, LumaGuide steers the sampling process to match target feature distributions via differentiable energy-based guidance. We instantiate this framework for HDR generation by controlling luminance distributions in perceptually uniform PQ space. Our results show that aligning luminance histograms is sufficient to induce HDR-consistent behavior, including coherent highlights and preserved shadow detail, while maintaining semantic fidelity. Beyond HDR, LumaGuide enables flexible specification of target distributions through data-driven presets, reference images, or text-driven predictors, and extends naturally to video generation with temporal consistency constraints. More broadly, our work demonstrates that controllable generation can be achieved by directly shaping output distributions at sampling time, without retraining diffusion models.
[CV-81] Spline-Based Boundary Representations for Sparse View Reconstruction and Simulation Using Isogeometric Analysis
链接: https://arxiv.org/abs/2607.26234
作者: Davor Dobrota,Vsevolod Skorokhodov,Chenghao Xu,Olga Fink,Malcolm Mielle
类目: Computer Vision and Pattern Recognition (cs.CV); Computational Engineering, Finance, and Science (cs.CE)
备注: 72 pages
Abstract:Image-based reconstruction aims to recover three-dimensional geometry from images. Recent advances have enabled the recovery of visually detailed models, yet their representations are not well-suited for numerical simulation. Simulation frameworks typically require explicit, watertight, and smooth geometries to ensure numerical robustness and accuracy, properties that surfaces extracted from image-based reconstructions lack. We propose FORGE-SIM, a method to directly reconstruct a multi-patch B-spline boundary representation from sparse posed RGB images without manual intervention. By optimizing the spline representation itself, our approach produces compact, smooth, and watertight geometries that are natively compatible with both Computer Aided Design and simulation workflows. Additionally, we introduce a strategy to project observation-derived fields, such as a thermal state and semantic information, onto the reconstructed models in the same spline basis, enabling immediate use in simulation. We demonstrate that the obtained models are of sufficiently high quality to enable thermal simulation and modal analysis. By unifying image-based reconstruction and simulation-ready modeling within a single optimization framework, this work removes a long-standing barrier between computer vision and numerical analysis. We anticipate that it will enable new workflows for simulation-driven design, inspection, and digital twin applications.
[CV-82] BG-REAL: A Public Real-Data Anchored Benchmark for Background Manipulation Detection and Localization
链接: https://arxiv.org/abs/2607.26232
作者: Bugra Alperen Uluirmak,Rifat Kurban
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 24 pages, 9 figures, 8 tables
Abstract:Background manipulation is a practical but under-specified image-forensics setting: the manipulated evidence can sit outside the salient foreground object, while many evaluations emphasize object-centric copy-move, splicing, or generic synthetic edits. We introduce BG-REAL, a public real-data anchored benchmark package for background manipulation detection and localization. The current release is built from Open Images V7 instance-segmentation sources and contains 7,000 processed samples over 1,200 source groups, including 6,000 public-data anchored samples and 1,000 synthetic control samples. BG-REAL covers six edit families, matched authentic controls, source-group splits, mask and leakage QA, 599 human-assisted quality-control rows, three completed external baselines (TruFor, MVSS-Net, and HiFi-Net), and five-seed model evaluation. Beyond aggregate accuracy, we use matched-authentic-control diagnostics to measure how often baselines misclassify re-encoded authentic images as manipulated at a threshold fixed on held-out validation data; false-positive rates range from 0.57 (TruFor, the lowest) to 1.00 (several weak or mask-informed baselines), indicating that re-encoding artifacts are a shared shortcut risk across baselines rather than a problem specific to any one model. The release provides the construction pipeline, evaluation protocol, paper-ready figures, and reproduction documentation. We frame BG-REAL as a background-manipulation-focused complement to general image-manipulation-localization benchmarks, not as a fully real-only or general-purpose benchmark.
[CV-83] Lag-aware cross-hand alignment for dual-hand action segmentation
链接: https://arxiv.org/abs/2607.26215
作者: Fatemeh Ziaeetabar
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Dual-hand action segmentation commonly fuses left- and right-hand representations at identical temporal indices, although coordinated hand transitions may occur with nonzero and time-varying delays. We introduce Lag-Aware Cross-Hand Alignment (LACA), a lightweight module that explicitly estimates directional temporal-offset distributions between hand-specific feature streams. LACA retrieves cross-hand information from the estimated offsets and incorporates a learned null state to suppress transfer when no compatible cross-hand transition is supported. Alignment is supervised using compatibility-aware targets derived automatically from frame-level training annotations, without requiring additional labels. Analysis of the HA-ViD and ATTACH training annotations reveals robust nonzero cross-hand matches for 44.7% and 48.9% of transition anchors, respectively, compared with 18.6% and 21.3% under temporally shifted controls. When integrated into Polyphony, LACA improves the two-hand mean F1@50 from 40.4 to 42.5 and boundary F1 from 56.5 to 59.6 on HA-ViD, and from 19.9 to 21.8 and 44.7 to 47.9, respectively, on ATTACH, relative to our reproduced Polyphony baseline. These gains require only approximately 0.0086 million additional trainable parameters. We further introduce LACA-C, a future-free variant that restricts alignment and the complete inference pipeline to current and past observations. On ATTACH, LACA-C achieves 83.6% transition-cue recall, a seed-averaged median availability delay of 233~ms, 0.72 false cues per minute, and segmentation-stage throughput of 224.9 current-position predictions per second. These results demonstrate that explicit cross-hand temporal alignment improves both action segmentation and boundary localization while supporting timely future-free perception.
[CV-84] Where Physics Meets Privacy: Federated PINNs for Privacy-Preserving Brain Tumor Biomechanical Modeling
链接: https://arxiv.org/abs/2607.26207
作者: Mahmuda Akter Sristy,Md Al-Mahfuz Chowdhury,Momota Ahsana Meem,Sajid Ahamed,Kazi Irfan Subhan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 9 figures
Abstract:Brain tumors such as glioma, meningioma, and pituitary adenoma alter the mechanical behavior of soft brain tissue, yet common diagnostic methods rely on static imaging that cannot capture tumor growth, tissue displacement, or changes in stiffness over time. Deep learning models for this task typically require pooling patient data at one site, which conflicts with privacy rules such as GDPR and HIPAA and limits generalization across institutions, a challenge that is pronounced in neuro oncology given patient diversity. This study presents a federated physics informed neural network combining federated learning with a physics informed loss built on the equations of linear elasticity. Three simulated clinical sites each train a local network on patient specific MRI data using a physics informed loss, and only model weights are shared with a central server through the FedAvg protocol over one hundred rounds, keeping raw data at its site of origin. The federated model reached an overall accuracy of 91.4%, against 90.0% for a non federated baseline trained on pooled data, an average AUC of 0.985 across tumor classes, and a rise in pituitary tumor accuracy from 85.6 to 94.5%. Training produced smooth, divergence free displacement fields consistent with expected tissue deformation, showing that federated training can be paired with physics based constraints without a meaningful loss in performance.
[CV-85] WildShadowRemover: In-the-Wild Video Shadow Removal via Detail-Preserving Video Diffusion Models
链接: https://arxiv.org/abs/2607.26203
作者: Jiamin Xu,Cong Wang,Zheng Dong,Chi Wang,Renshu Gu,Weiwei Xu,Gang Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video shadow removal in the wild remains challenging due to complex illumination, diverse shadow appearances, and limited training data. Despite its importance to numerous vision and graphics applications, it remains largely unexplored in unconstrained real-world scenarios. To address this gap, we present WildShadowRemover, a framework that adapts a pretrained video diffusion model for robust video shadow removal via LoRA fine-tuning. To preserve fine image details while retaining the model’s powerful generative prior, we augment the frozen VAE decoder with a detail injection module and introduce a shadow-mask-guided frequency-decomposed modulation module to selectively restore high-frequency textures while suppressing shadow artifacts. Monocular depth priors from Depth Anything 3 further provide geometry-aware guidance under challenging lighting conditions. We also construct WildShadow, a large-scale paired video shadow removal dataset and benchmark, covering diverse synthetic scenes. Extensive experiments demonstrate that our method outperforms existing approaches in shadow removal quality and temporal consistency, producing temporally coherent shadow-free videos with superior visual quality and strong generalization across challenging in-the-wild scenarios.
[CV-86] Rad-JEPA 3D: Radiology Joint-Embedding Predictive Model for 3D Computed Tomography
链接: https://arxiv.org/abs/2607.26196
作者: Quoc-Huy Trinh,Minh-Van Nguyen,Ulas Bagci
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Self-supervised pretraining is central to 3D medical image analysis, where unlabeled CT volumes are abundant but expert annotations are scarce. Yet existing volumetric encoders often fail to preserve the coarse spatial and geometric structure that downstream reasoning depends on, limiting their performance on organ disentanglement, abnormality detection, and spatial understanding when paired with language models. We introduce Rad-JEPA 3D, a joint-embedding predictive framework that learns volumetric CT representations by predicting the latent features of a complete scan from a masked view. At its core is a hybrid H-Mamba encoder that fuses a Mamba state-space branch, which models inter-slice continuity through sequential scanning, with a grouped-query attention branch, which captures cross-plane spatial context, combined through a lightweight per-token router. To improve the quality of intermediate representations, we further propose Hidden States Orthogonal Regularization (HSOR), which aligns student-teacher hidden states and reduces feature redundancy throughout the encoder. This layer-wise regularization produces more consistent and discriminative volumetric representations, leading to improved performance on organ recognition and spatial reasoning tasks. Pretrained on approximately 120,000 CT scans, Rad-JEPA 3D attains state-of-the-art results despite its compact size: with only 4.0B total parameters, it achieves competitive results with state-of-the-art on closed-ended VQA and the best average spatial-reasoning score on the Spatial-Med benchmark. Ablation studies confirm that the hybrid block and HSOR contribute complementary gains, and that the induced spatial structure can substitute for raw language-model scale on volumetric reasoning tasks.
[CV-87] A Picture Says Thousands of Words - Harnessing Dermal Exposure Data from Images through Hybrid Deep Learning for Enhanced Safety Assessment
链接: https://arxiv.org/abs/2607.26170
作者: Hua Qian,Manisha Kotha,Tuan Tran,Jennifer Shin,Haining Zheng
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 3 pages, 2 figures
Abstract:This study developed a hybrid computer vision method to quantify exposed skin from images for dermal exposure assessment. Using 170 indoor-painting images, Mask R-CNN first identified human subjects and removed background interference; a color-based algorithm then segmented exposed skin. The resulting exposed-skin-to-body pixel ratios showed approximately 80% agreement with human estimates. The approach demonstrates a scalable way to extract semi-quantitative exposure information from images, with future extensions to body-part recognition, PPE detection, and video-based exposure analysis.
[CV-88] DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving
链接: https://arxiv.org/abs/2607.26165
作者: Yung-Hsu Yang,Luigi Piccinelli,Siyuan Li,Mattia Segu,Lei Ke,Martin Danelljan,Yuqian Fu,Zuria Bauer,Fisher Yu,Hermann Blum,Marc Pollefeys
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Safe autonomous navigation requires a holistic understanding of dynamic environments, necessitating the simultaneous estimation of metric depth, semantic segmentation, and instance trajectories. While depth-aware video panoptic segmentation (DVPS) unifies these tasks, existing approaches often rely on computationally expensive, multi-stage pipelines or offline tracking, rendering them unsuitable for real-time decision-making. To address this, we propose DVPSFormer, a unified online architecture designed for efficient 4D scene understanding. Central to our approach is explicit scene discretization (ESD), a novel mechanism that leverages segmentation queries to represent foreground and background regions, enabling a discrete-to-continuous (D2C) depth head to decode metric depth in a single pass. This tightly couples semantic and geometric learning while significantly reducing latency. Furthermore, we propose an online majority voting (OMV) mechanism that exploits temporal consistency to refine classification during instance tracking. DVPSFormer establishes a new state-of-the-art on the Cityscapes-DVPS and SemKITTI-DVPS benchmarks, offering a streamlined solution for online robotic perception. Code and models are available at this https URL.
[CV-89] raceCLIP: Recovering Local Semantics from Patch-to-CLS Contributions
链接: https://arxiv.org/abs/2607.26107
作者: Xinran Liu,Shouqian Shi,Yutong Chen,Ge Wang,Xin-Wei Yao,Sheng Zhong
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 9 pages, 3 figures, 4 tables
Abstract:Dense vision-language understanding, including object localization, region recognition, and open-vocabulary semantic segmentation, requires associating language concepts with spatially grounded visual regions. CLIP provides a strong foundation for these tasks by learning a shared image-text embedding space from large-scale contrastive pre-training. However, its image-level objective aligns text with a CLS-derived global representation, leaving local vision-language correspondence only indirectly constrained. Existing methods either introduce additional supervision, external models, or task-specific adaptation, while training-free approaches mainly recover dense responses from existing patch features without examining where local semantics become most accessible within CLIP. We introduce TraceCLIP, a training-free framework that recovers latent patch-level semantic evidence by isolating the patch-specific terms written into the CLS attention output. TraceCLIP further converts contribution-derived semantic responses into a semantic-geodesic topology gate that calibrates final-layer patch affinity for dense feature reconstruction. Diagnostic experiments show that these contribution features exhibit strong local semantic discrimination and text-conditioned spatial alignment. On eight zero-shot semantic segmentation benchmarks, TraceCLIP achieves gains of 1.3 to 4.5 points in average mIoU over the strongest prior training-free methods across both backbones and background settings, without additional training, external vision foundation models, or region-level supervision. More broadly, these findings suggest that spatially localized semantics may remain accessible within the internal construction of globally aligned representations.
[CV-90] Weight and Height Estimation from a Single Human Image Captured in the Wild
链接: https://arxiv.org/abs/2607.26104
作者: Hira Yaseen,Arif Mahmood,Waqas Sultani
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:A person’s physical characteristics such as weight and height are important indicators of his physical and mental health, daily life routines and finances. Body Mass Index (BMI) is a well known measure that encodes the characteristics of both the weight and the height. BMI has been used as a self-monitoring tool, and it has long-term implications on one’s life. For example, it may help predicting the risk of various diseases and estimating longevity. Automatic BMI estimation using a single person image in the wild is a challenging task due to wide variations in human pose, camera geometry, personal appearance and distracting backgrounds. In this paper, we explore the performance of deep neural networks using single and multi-task learning by employing different modalities including RGB, depth-maps, pose-affinity maps, and edge-maps to predict BMI, weight, and height from daily life images available on social networking websites. Currently, no full body image dataset for BMI estimation is publicly available, therefore we propose a new dataset consisting of 6105 images with ground truth labels of height, weight and BMI. Our proposed dataset is collected in the wild containing images from various ethnicity and distributed over varying age groups and gender. It consists of frontal, back, full and half body, side poses, mirror selfies with varying backgrounds and scale variations and may contain artifacts hiding partial or full face. Extensive experimentation is performed using full body, half body and face images only using different CNN backbones including VGG, Densenet and ResNet. Our experimental results demonstrate that full body images have produced better results than the other half body and facial images in the wild.
[CV-91] Knowledge-guided Disentanglement with Atomic Actions for Action Recognition
链接: https://arxiv.org/abs/2607.26097
作者: Tianci Wu,Siqi Cao,Guangming Zhu,Jiang Lu,Siyuan Wang,Longfei Zhang,Jincai Huang,Jun Sheng,Liang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ACMMM 26
Abstract:Action recognition in complex scenes often involves multiple concurrent fine-grained actions, making it challenging to model internal action structures. Most existing methods rely on holistic representations, which are insufficient for capturing subtle interactions and fine-grained semantics. While recent prompt-based approaches introduce disentanglement, they lack explicit semantic guidance, and methods based solely on visual or structured cues remain coarse-grained. In this paper, we propose Knowledge-guided Disentanglement with Atomic Actions (KDA), which leverages fine-grained semantic knowledge to enhance action representations and enable more precise disentanglement. Specifically, we use Large Language Models (LLMs) to decompose action labels into atomic actions, providing explicit spatial-temporal semantics. A Knowledge Injection Module (KIM) first integrates atomic action knowledge into video features. Based on this enhanced representation, a Knowledge Disentanglement Module (KDM) further disentangles atomic action knowledge to produce more precise semantic guidance for action disentanglement. A Knowledge Disentanglement Loss (KD Loss) is introduced to encourage clearer disentanglement of knowledge components within KDM. Extensive experiments demonstrate that KDA improves feature discriminability and achieves state-of-the-art performance on multi-label action recognition benchmarks. Moreover, KIM and KDM can be readily integrated into other methods, demonstrating strong generality.
[CV-92] Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance
链接: https://arxiv.org/abs/2607.26333
作者: Panagiotis Fytas,Ian Selby,Clemens Karner,Judith Babar,Simon Baker,Jake Beckford,Timothy J. Sadler,Shahab Shahipasand,Arthikkaa Thavakumar,John Li Chen,Alex Sawer,Michael Roberts,Jonathan Weir-McCall,J. H. F. Rudd,Carola-Bibiane Schönlieb,Anna Korhonen,Anna Breger
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 67 pages, including supplementary material
Abstract:Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment. However, commonly used report-derived labels for pathology classification or generic image quality metrics for reconstruction may not reliably reflect clinical judgment. We systematically investigate how evaluation-reference choices affect model performance and ranking in both pathology classification and image quality assessment (IQA). To enable controlled comparison across evaluation references, we collected paired expert image- and report-derived labels for thoracic findings from a clinical cohort at Cambridge University Hospitals (CUH) and curated a subset of the public MIMIC-CXR dataset, along with expert ratings of diagnostic image quality. We show that for supervised image classifiers (ResNet, DenseNet), several zero-shot and fine-tuned vision-language models (e.g., MedKLIP, GLoRIA, and ConVIRT), changing the label source leads to substantial differences not only in performance estimates but also in model rankings. In parallel, alignment of IQA measures with expert judgment depends heavily on the choice of measure, and commonly used IQA metrics such as SSIM and PSNR often fail to align with expert assessments of diagnostic usability. Our results demonstrate that evaluation choices are crucial: they can determine which models and methods appear best and are therefore selected for further development or deployment. The selection of evaluation references should therefore be treated as a central component of clinical validity in CXR machine learning, and justified with respect to the pathology, imaging task, and intended downstream clinical use.
[CV-93] ScalablePromptus: Scalable and High-Fidelity Prompt-Based Video Streaming
链接: https://arxiv.org/abs/2607.26106
作者: Zehao Cao,Bowei Xu,Xun Cao,Zhan Ma,Hao Chen
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: 10 pages, 8 figures
Abstract:Prompt-based video streaming transmits compact semantic prompts instead of pixel-level content for generative reconstruction, enabling ultra-low-bitrate communication. However, the state-of-the-art Promptus framework is vulnerable to network fluctuation, where partially received prompts lead to catastrophic quality collapse. We propose ScalablePromptus, which enhances Promptus with semantic and color-aware prompt inversion, spherical linear interpolation for intermediate frames, and–most critically–a dropout training strategy that produces rank-ordered prompt representations. This allows the receiver to reconstruct meaningful video from arbitrarily truncated prompts without any adaptation. Under stable networks, ScalablePromptus achieves modest quality gains. Under lossy conditions, it reduces the performance degradation caused by truncation by 82%-95% compared to the baseline, making prompt-based streaming robust enough for real-world deployment.
[CV-94] Shape-Based Inductive Bias for Glioma Grading from Tumor Contours
链接: https://arxiv.org/abs/2607.26090
作者: Puneet Velidi,Michelle F. Miranda,Farouk Nathoo,Ashery Mbilinyi,Cédric Beaulac
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Glioma grading from tumor contours is often treated as a pixel problem even when the signal of interest is shape. We align closed contours with a functional shape-alignment framework, separate global deformation from residual Fourier shape, and organize these quantities as frequency-ordered tokens. In five-fold patient-disjoint cross-validation on BraTS~2020 tumor contours, with model selection performed using grouped inner validation, a compact multilayer perceptron (MLP) achieves the highest mean balanced accuracy at 71.5%, compared with 65.9% for ResNet-18 and 63.3% for ViT-Tiny. It also gives the highest mean low-grade glioma F1 at 54.9%. Its pooled out-of-fold balanced accuracy is 72.4% (patient-bootstrap 95% CI: 66.4–77.8%). The selected MLPs use 2.9k–117.3k parameters across folds, at least 46 times fewer than the pixel baselines. In a controlled noise-free simulation, shape-based models reach 56.3–71.5% balanced accuracy while the pixel models remain at 50.0–52.5%. This work demonstrates how incorporating a shape-based inductive bias at the representation level can improve interpretability and scalability while enabling substantial dimensionality reduction.
人工智能
[AI-0] Can AI agents conduct open-ended AI research? Early evidence from two case studies
链接: https://arxiv.org/abs/2607.27191
作者: Peter Kirgis,Sayash Kapoor,Andrew Schwartz,Stephan Rabanser,David Africa,Konstantinos Voudouris,Viet Nguyen,Toby Pilditch,Magda Dubois,Harry Coppock,Cozmin Ududec,Nitya Nadgir,Matilda Orona,Tilman Bayer,Derrick Chan-Sew,Yue Ling,Abhishek Shetty,Helen Toner,Gillian Hadfield,Seth Lazar,Steve Newman,Shoshannah Tekofsky,Rishi Bommasani,Arvind Narayanan
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注:
Abstract:Forecasts of explosive AI progress hinge on AI agents automating AI research. But evidence on whether agents can carry out open-ended AI research is thin. Current evaluations either test agents on narrow, verifiable tasks, which excludes open-ended research, or submit AI-generated papers to blind peer review, which is overstretched, stochastic, and suffers from poor review quality. We introduce a third way to measure progress towards AI R\D automation. An agent takes on the central, open-ended research question of a high-quality unpublished paper, and the paper’s original authors grade its output. We call these shadow evaluations. We ran shadow evaluations on two unpublished NeurIPS 2026 submissions, giving frontier agents six days and thousands of dollars of compute. The agents completed all of the engineering without human help, yet could not make substantial progress towards answering the research questions. As a result, both papers were unambiguously rejected by the authors. We identify five recurring failure modes: poor judgment about the bar for publishable research, uncreative responses to shortcomings in the research design, ineffective backtracking from dead ends, poor resource awareness, and instruction drift. A robustness check with a second model and scaffold reproduced these failures. We release the expert reviews, survey responses, agent repositories, and logs. Our results provide early evidence that today’s agents can do the engineering of AI research, but struggle with critical parts of the research lifecycle.
[AI-1] Cost-Sensitive Conformal Prediction and Human-in-the-Loop Abstention for Imbalanced High-Stakes Decision Support: A Multi-Domain Benchmark
链接: https://arxiv.org/abs/2607.27143
作者: Manpreet Singh,Akshatha Srikantha,Shyamal Lakhanpal
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:High-stakes decision systems in credit scoring, fraud detection, healthcare, and industrial safety require reliable uncertainty quantification under severe class imbalance and asymmetric error costs. Standard marginal conformal prediction (CP) provides valid overall coverage guarantees; however, we show that it severely under-covers rare, costly minority classes, with minority-class coverage dropping to as low as 0.5% on certain datasets. To characterize and address this limitation, we conduct a comprehensive benchmark comparing marginal CP, class-conditional (Mondrian) CP, and cost-controlled abstention mechanisms across 15 real-world imbalanced tabular datasets, 7 classification models, 3 probability calibration techniques, and 10 random seeds, resulting in 3,150 experimental runs. Our results show that Mondrian CP restores valid minority-class coverage, achieving an average minority-coverage improvement of 61.7 percentage points over marginal CP (p 1e-80). Furthermore, combining Mondrian CP with cost-controlled abstention significantly reduces expected decision cost compared with standard decision boundaries, confidence-based rejectors, and risk-controlled rejectors under realistic human review budgets. We further quantify dataset-specific break-even thresholds at which deferring ambiguous instances to human experts becomes cost-effective. These findings provide practical guidance for deploying distribution-free, cost-aware uncertainty quantification in high-stakes decision support systems.
[AI-2] Agent Map: Joint Equivalence and Subsumption Discovery for Ontology Matching
链接: https://arxiv.org/abs/2607.27130
作者: Yiping Song,Jiaoyan Chen,Renate Schmidt,Hui Yang,Wen Zhang
类目: Artificial Intelligence (cs.AI)
备注: 21 pages, 5 figures
Abstract:Ontology matching (OM) has traditionally been formulated as either equivalence discovery or subsumption matching. The existing OM systems identify only one type of semantic correspondence and cannot simultaneously discover equivalence and subsumption mappings. In this paper, we introduce Hybrid Ontology Matching (HOM), a new OM task that unifies equivalence and subsumption discovery, and accordingly propose a Large Language Model (LLM)-based multi-agent OM framework AgentMap that is implemented by a series of interdependent semantic decisions. Given a concept in the source ontology, AgentMap integrates semantic retrieval, hierarchical search, and collaborative multi-agent LLM reasoning to progressively explore the target ontology, identifying either the equivalent concept, if one exists, or the most fine-grained subsumer. We further extend four OM datasets for a HOM benchmark and evaluate AgentMap under hybrid, equivalence-only, and subsumption-only settings. Experimental results show that AgentMap achieves promising performance on the hybrid setting, and at the same time outperforms equivalence matching and subsumption matching baselines on the equivalence-only and subsumption-only settings, respectively.
[AI-3] MMAC: A Massive Multi-dimensional Benchmark for Audio Captioning
链接: https://arxiv.org/abs/2607.27109
作者: Weijie Wu,Junbo Li,Lin Li,Jun Fang,Qingyang Hong
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:With the development of audio large language models (AudioLLMs), audio captioning needs to move from brief descriptions toward open-ended and fine-grained free-form descriptions. Existing evaluations often focus on generation quality or task performance, making it difficult to diagnose information coverage and description reliability. We propose MMAC, a \textbfMassive \textbfMulti-dimensional benchmark for \textbfAudio \textbfCaptioning. MMAC contains 5,638 audio clips from more than 20 data sources, covering 6 capability categories and 15 evaluation dimensions. Given a model-generated caption, MMAC checks whether it mentions relevant information in the target dimension and whether the mentioned content is consistent with the reference label. We evaluate representative open-source and proprietary AudioLLMs. Results show clear differences across evaluation dimensions, information coverage, and description reliability. We will release the MMAC benchmark and evaluation code.
[AI-4] Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents
链接: https://arxiv.org/abs/2607.27083
作者: Yicheng Feng,Yan Zhang,Yan Cheng,Wei Qi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:As LLM agents increasingly depend on diverse external services such as search engines, databases, and connectors, agent harnesses face a fundamental tool-selection challenge: acquiring too few tools leaves the task under-informed, while too many adds cost, context load, and privacy exposure. Routers and retrievers can rank candidate tools by relevance, but a ranking alone does not determine how many are worth selecting. Existing approaches leave acquisition under heterogeneous costs unaddressed. We formulate this decision as cost-aware marginal decision-focused stopping (CAM-DF) over ranked tool prefixes, with CAM-DF-lite as a compact interpretable variant. We train directly on the offline gap between stopping now and the best continuation: its sign labels the decision, its magnitude weights each error by the payoff at stake. We prove this objective is Bayes-aligned with the stopping target and that score-only rules are suboptimal under heterogeneous costs. We evaluate on 1,343 tasks across five tool-use domains. On \tau -bench Retail, CAM-DF attains the highest payoff among deployable methods, with gains over a predict-then-threshold baseline across all five ranking sources and two cost regimes. Our approach is state-of-the-art under heterogeneous costs and high cost pressure, with larger gains under weaker rankings. In live execution, CAM-DF exposes the agent to 37% fewer tools than full access while maintaining comparable task success. The CAM-DF family is a lightweight pre-execution plugin that turns existing tool rankings into lower-cost acquisition decisions without fine-tuning the underlying LLM.
[AI-5] MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair
链接: https://arxiv.org/abs/2607.27080
作者: Xuanze Chen,Xukang Xie,Wentao Fu,Jiajun Zhou,Shanqing Yu,Qi Xuan
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Memory systems allow agents to retain and reuse information from past interactions, but they can also let malicious content persist. A malicious instruction crafted by an attacker may be stored in long-term memory, recalled much later, and quietly shape a real action. Recent benchmarks increasingly examine agent memory security, yet few trace the same malicious semantics across persistence, downstream consequences, and selective repair under diverse memory-backend comparisons. To address this gap, we introduce MemSecBench, a task-grounded benchmark for the lifecycle security of agent memory systems. It contains 310 cases drawn from 48 realistic contexts across code and science, daily life, and office work. Each case follows a controlled Write–Execute–Forget protocol in an isolated runtime under an exact agent configuration, defined by an agent harness, a memory backend, and an LLM backend. Evidence-based adjudication combines a deterministic write check, checkpoint-specific judge-model evaluations, and programmatic gates across seven lifecycle checkpoints. The experimental design spans a 24-configuration matrix of two agent harnesses, four memory backends, and three LLM backends. Across all 24 configurations, malicious memory persists in 84.2% of all cases, and the full Write–Execute chain succeeds in 50.3%. Among successfully poisoned cases, 59.6% complete the full Execute chain, while 56.1% achieve selective this http URL with matched Native configurations, the largest absolute differences are 16.1 percentage points for end-to-end attack success and 41.3 percentage points for selective repair. These descriptive contrasts indicate that the evaluated memory system stacks differ in lifecycle security, both in the propagation of malicious memory and in selective repair after successful memory poisoning.
[AI-6] Parameter-Free Dynamic Regret for Online Convex Optimization under Heavy-Tailed Noise
链接: https://arxiv.org/abs/2607.27073
作者: Vaneet Aggarwal
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注:
Abstract:We study online convex optimization (OCO) in non-stationary environments under heavy-tailed noise, where the stochastic gradient oracle admits only a finite p -th central moment for some p \in (1, 2] . While static regret is well-understood, achieving universal dynamic regret in a parameter-free manner remains an open challenge. We resolve this by proposing \textbfHT-PAder, a parameter-free algorithm combining restarted AdaGrad experts over a geometric pool of block lengths with a pathwise meta-algorithm, \textbfAdaGrad-Hedge, which requires no moment conditions on meta-losses. For a domain of diameter D , Lipschitz constant G , noise level \sigma , and comparator path length P_T , HT-PAder achieves an expected universal dynamic regret of [ \widetilde O\left( GD\sqrtT(1+P_T/D) + \sigma D T^1/p(1+P_T/D)^(p-1)/p \right). ] The algorithm does not require prior knowledge of any of these problem parameters. Even in the special case of finite variance ( p=2 ), HT-PAder provides the first parameter-free minimax universal dynamic regret guarantee. We also prove a matching lower bound, establishing the optimality of the path-length exponent.
[AI-7] CoCaRS: Correlation Calibration-Based Redundancy Suppression for Heterogeneous Knowledge Distillation
链接: https://arxiv.org/abs/2607.27054
作者: Fengming Yu,Haiwei Pan,Kejia Zhang,Chunling Chen,Jian Guan,Baoying Ma
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Knowledge distillation (KD) enables a compact student model to learn from a powerful teacher and has become an effective paradigm for model compression. The emergence of diverse model architectures has extended KD from homogeneous to heterogeneous settings. However, differences in architectural inductive biases between the teacher and student models often result in substantial representation discrepancies, limiting the effectiveness of direct knowledge transfer. Recently, redundancy suppression has offered a new perspective on heterogeneous KD by preserving cross-architecture invariance and reducing feature redundancy through decorrelation of teacher-student feature correlations. Nevertheless, this formulation may weaken useful structural information through uniform decorrelation, while a fixed coefficient may make the effective contribution of redundancy suppression sensitive to teacher-student pairs and training stages. To address these problems, Correlation Calibration-based Redundancy Suppression (CoCaRS) is proposed to better retain structural information while suppressing redundancy and reduce sensitivity to coefficient settings across teacher-student pairs and training stages. Specifically, CoCaRS calibrates feature decorrelation through Confusion Evidence Estimation (CEE) and Strength Allocation Control (SAC), which respectively capture reliable semantic relations for correlation estimation and preserve discriminative structure during decorrelation. Adaptive Coefficient Regulation (ACR) further regulates the contribution of the calibrated redundancy suppression objective according to its relative loss scale, reducing sensitivity to coefficient settings. Extensive experiments on CIFAR-100 and ImageNet-1K validate the effectiveness of CoCaRS in improving distillation performance and reducing sensitivity to coefficient settings. Code will be released soon.
[AI-8] BayesAME: Bayesian Active Model Evaluation
链接: https://arxiv.org/abs/2607.27023
作者: Paula Cordero Encinar,Taylan Cemgil,Arnaud Doucet,Virginia Aglietti,Silvia Chiappa
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Evaluating large generative models across benchmarks is time-consuming and computationally expensive. This drives the need for methods that can estimate full benchmark performance by evaluating models on only a subset of items, known as a coreset. Current literature mostly requires the practitioner to input a coreset size. However, when reliable performance estimation takes priority over efficiency, an evaluation method should also be capable of automatically determining a coreset size that reflects this priority. We introduce BayesAME, a sequential Bayesian framework specifically targeting automatic determination of the coreset size. BayesAME models performance as a random variable by defining a latent ability for each group of items sharing the same historical model performances, with a joint prior distribution encoding the belief that the target model behaves similarly to these historical models. The posterior distribution over these abilities is used to derive performance estimators, quantify performance uncertainty, and select items to add to the coreset via an information-gain criterion. The coreset is iteratively augmented until the performance estimate fluctuation and the performance uncertainty fall below their respective user-defined thresholds. We propose a multi-target extension that captures performance correlations across multiple target models to further reduce the coreset size. Through extensive experiments across diverse benchmarks, we demonstrate that BayesAME consistently outperforms sequential adaptations of existing methods. Crucially, our comprehensive analysis addresses recent skepticism in the literature, establishing that non-random coreset selection is advantageous over random selection. Finally, we highlight that leveraging continuous response log-likelihoods over traditional binary scores significantly enhances estimation accuracy.
[AI-9] SymmGrid: Super-Scaling On-Robot Learning with Parallelized Symmetries and Egocentric-Exocentric Visual Perception
链接: https://arxiv.org/abs/2607.26985
作者: Gabe Everett,Brice Gunter,Ryan Vander Stelt,Cleiver Ruiz-Martinez,Blake Hull,Juan Rojas
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 9 pages, 7 figures, 1 table
Abstract:Deep reinforcement policy learning directly in physical robots (on-robot learning) remains bottlenecked by slow wall-clock training times. We present SymmGrid, a trajectory level augmentation framework inspired by parallelized symmetries that super-scales group transformations to significantly accelerate on-robot learning in both egocentric and exocentric visual setups. We model a Markov Decision Process (MDP) under a symmetry tree, in which state-action pairs have admissible parallelized invariant transformations that yield a geometric grid structure. The state is modelled with ego- or exocentric images and proprioception information. The latter require special treatment, in the form of homographies, to warp visual scenes in line with their corresponding spatial transformations. These parallelized transformations produce a large set of unique symmetric equivalences that populate the replay buffer with diverse and consistent experiences that speed up learning and improve performance. We present extensive training and evaluations performed directly on real robot manipulation contact tasks including peg-insertions, cable routing, and object relocations. Relative to SOTA, SymmGrid achieved wall-clock training convergence speed-ups of 1.37-2.17x, evaluation success rate improvements of 1.09x-1.27x, fastest training convergence times of 16.6, 10.9, and 79.3 minutes respectively. For trajectory wide assessments, we used normalized area under the curve (nAUC) ratios. SymmGrid achieved improvements of up to 2.59x. These results confirm that simple branch symmetries can have an outsized result due to super-scaling and bring us closer to sub-10 minute on-robot learning training in manipulation tasks suitable for arms and humanoids. The project page is available at this http URL
[AI-10] Belief-Guided Decision Making with Uncertainty Gating in the Game of Go
链接: https://arxiv.org/abs/2607.26946
作者: Mehrad Yaghoubi,Azam Bastanfard,Abbas Jalilvand,Ashkan Rezaei
类目: Artificial Intelligence (cs.AI)
备注: 8 pages, 7 figures
Abstract:Recent advancements in Computer Go, driven by AlphaZero and MuZero, rely heavily on Monte Carlo Tree Search (MCTS) to correct the errors of the neural network policy. While effective on massive computational clusters, this dependence creates a critical bottleneck on consumer-grade hardware, where the computational cost of tree management severely limits inference rates. Furthermore, without deep search, these models suffer from hallucination, proposing moves with high confidence that are strategically fatal. This paper introduces a novel Belief-Guided architecture that disentangles the Policy head from a distinct Belief head. Unlike traditional value functions, the Belief head acts as an internal simulator and independent critic, modeling epistemic uncertainty and strategic stability. By integrating memory mechanisms (Transformer/GRU) to handle long-term dependencies and the Ko rule, and utilizing a gating mechanism to filter overconfident policy errors, our model shifts the burden of intelligence from runtime search to parametric “intuition.” Experimental results demonstrate that this approach significantly improves search-free win rates and reduces hallucination, enabling professional-level play on limited hardware where massive MCTS is infeasible.
[AI-11] What Does It Take to Detect an AI Agent ? Minimal Feature Sets for Behavioral Detection under Browser Automation
链接: https://arxiv.org/abs/2607.26935
作者: Vishisht Choudhary,Lukas Schmidt,Anne Zoë Kenntner,Feras Skhab,Michel Osswald,Jens Ernstberger
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 17 pages (11 main + appendices), 7 figures. Accepted at the North East AI Agents Day 2026 workshop, Jane Street, New York City. Workshop: this https URL
Abstract:Bot detectors deployed at scale treat traffic as binary: human or bot. This assumption breaks when AI agents browse the web through browser automation, a traffic class that is neither and that binary classifiers structurally cannot represent. We present a three-class detection framework distinguishing humans, bots, and AI agents, and show that the binary-vs-agent confusion is architectural: a binary human-vs-bot detector misroutes agent sessions because its label space lacks an agent class. On our controlled benchmark, an MLP binary classifier misclassifies 39.1% of real AI agents as human and a SAINT binary transformer misclassifies 34.5%; adding an explicit agent class yields per-class agent F1 = 1.000 in all 30 runs (3 model families \times 10 seeds). To measure evasion resistance, we construct a five-level evasion ladder spanning passive observation, GAN-generated trajectories, and replay of real human cursor data ( n = 2299 evasion sessions). Across 10 seeds and 3 model families we observe zero agent misses in 22990 per-seed predictions. The discriminative signal is a browser-automation artifact, not evidence of agent reasoning: Playwright does not emit the raw pointer-move and wheel-delta streams a physical input device produces, and this absence signature survives trajectory manipulation. Exhaustive search over all feature subsets of size 1-5 (9401 GBMs) shows that two behavioral features (mouse_event_rate, teleport_click_ratio) give 100% observed agent recall at every evasion level with agent precision 0.994; five features lift macro-F1 to 0.991. The signal is redundantly encoded: removing teleport_click_ratio leaves agent detection at 100%. The single-feature regime is degenerate, flagging every agent only by collapsing the classifier to always predict “agent”. Two features robustly isolate agents; five separate all three traffic classes at macro-F1 \geq 0.99 .
[AI-12] Defending Against Backdoor Attacks via Alignment Checking in Model-Contrastive Federated Learning
链接: https://arxiv.org/abs/2607.26933
作者: Hongliang Zhang,Zhongyuan Yu,Guijuan Wang,Tianqing He,Wenshuo Ma,Xiaosong Zhang,Jiguo Yu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Federated Learning (FL) is vulnerable to backdoor attacks because of its distributed nature in edge computing scenarios. Existing defense methods show limited efficacy as they overlook the deviations among benign local updates caused by statistical heterogeneity and the stealthiness of backdoor attacks. To tackle these issues, we propose FedDAB, a two-phase method that combines local contrastive regularization with alignment checking, to defend against backdoor attacks. In the first phase, FedDAB introduces a novel model-contrastive term into the local objective to enhance direction and magnitude consistency among benign updates. In the second phase, FedDAB employs an alignment checking strategy to evaluate each local update in terms of overall-direction alignment and parameter-level alignment with historical information, excluding updates that exhibit abnormal alignment patterns from global aggregation. We theoretically prove FedDAB’s robustness with a convergence rate of \mathcalO(1/T) . Extensive experiments show that FedDAB outperforms existing defense methods against backdoor attacks. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2607.26933 [cs.CR] (or arXiv:2607.26933v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2607.26933 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-13] BioVLN: A Simulation Platform for Visual Language Navigation in Biomedical Laboratories
链接: https://arxiv.org/abs/2607.26914
作者: Zhe Liu,Quan Lu,Zhaohui Du,Zhe Wang,Huanbo Jin,Jiaming Gu,Qi Wang,Ting Xiao,Minting Pan,Dongzhan Zhou
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 17 pages, 4 figures
Abstract:Biomedical laboratory robots must navigate to instruments before performing experimental procedures. Existing embodied navigation platforms are designed for household environments and treat a target as an object center or an arbitrary nearby position. This representation is inadequate for laboratory instruments, which must be approached from their operating side while maintaining safe clearance from surrounding equipment. We introduce BioVLN, a simulation platform for developing and evaluating visual-language navigation agents in biomedical laboratories. BioVLN represents each instrument with three regions: its physical body, a surrounding clearance region, and an operation area in front of the usable side. This model is applied consistently to scene generation, target placement, navigation evaluation, and safety analysis, so success depends on reaching a position from which the instrument can be accessed. BioVLN supports procedural scene generation and manually designed environments, producing 47 scenes and 1667 episodes. Standardized navigation and reinforcement-learning interfaces enable trajectory collection and policy training. Experiments show that geometric exploration reaches 74.4–87.5% success, while sampling multiple valid positions in the operation area improves success to 83.3–92.5% and reduces unsafe proximity.
[AI-14] Actions Have Consequences: Detecting Outcome Performativity using Intervention Testing
链接: https://arxiv.org/abs/2607.26908
作者: Brandon Gower-Winter,Georg Krempl
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 25 pages, 18 Figures. Accepted to the 29th International Conference on Discovery Science, 2026
Abstract:In many domains such as Palliative Care, Credit Assignment and Recommender Systems, predictions may causally influence the outcomes they predict. This phenomena is known as Outcome Performativity. This paper formalises an approach for detecting Outcome Performativity using prediction intervention called Outcome Performativity A/B Detection (OPAB). OPAB enables the detection of Outcome Performativity by assessing the dissimilarity in outcome distributions produced by different predictions groups (interventions). If that dissimilarity is significant, Outcome Performativity is detected. We derive sample complexity bounds for OPAB under various Outcome Performative assumption classes which we empirically validate. Results show that detecting Outcome Performativity using OPAB is achievable in numerous cases. Results also show the presence of regions of indistinguishability which describe settings where the allotted number of interventions are insufficient for detecting Outcome Performativity. The results of which have broader practical implications for the detectability of Outcome Performativity in settings where samples are scarce, cost-prohibitive or potentially unethical to obtain. The paper concludes with a case study on the efficacy of OPAB on the Open Bandits dataset, and provides directions for future work.
[AI-15] From Passive Video to Editable Experience: Physically Grounded Experience Synthesis for Embodied Intelligence
链接: https://arxiv.org/abs/2607.26903
作者: Jia Luo
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:The key bottleneck in embodied AI is not model architecture but data. Although billions of human manipulation videos exist online, robots cannot directly learn from them due to the embodiment gap between human morphology and robot hardware. We introduce Pegasus, a low-resource framework that bridges this gap by translating human demonstrations into robot-learnable data through structured knowledge transfer. Instead of relying on raw video prompts, Pegasus constructs a graph-based intermediate representation: a Task Graph extracted from human videos is transformed through Affordance and Constraint Graphs into a Robot Planning Graph for robot-conditioned video generation. A hierarchical affordance latent space models the relationship between object states, affordances, and tasks, enabling generalization beyond object identities. A closed-loop physics verifier further filters invalid generations using kinematic feasibility, collision constraints, and joint limits. We evaluate Pegasus across a range of egocentric manipulation benchmarks, including GTEA Gaze+ and EPIC-KITCHENS-100, and diverse robot embodiments, assessing Task Correctness, Executability, State Consistency, and Learnability. Results demonstrate reliable cross-embodiment translation and show that robot data generation can be reframed from a hardware collection problem into a scalable, low-resource knowledge transfer problem.
[AI-16] ReCo: Reweighting GRPO Against Distributional Concentration
链接: https://arxiv.org/abs/2607.26862
作者: Junoh Park,Junseo Hwang,Wonguk Cho,Taesup Kim
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Group Relative Policy Optimization (GRPO) has become a standard reinforcement learning method for post-training language models. Recent work shows that GRPO can reduce the base model’s reasoning capacity and underperform it in Pass@k when k is large, indicating reduced coverage of reasoning paths. We find that this reduction is associated with GRPO concentrating on responses that the base model already generates with high probability. We trace this concentration to two mechanisms in the GRPO update. At the response level, high-probability responses dominate the group gradient through repeated occurrence. At the token level, GRPO’s importance ratio scales gradients, further reinforcing tokens that become more likely under the current policy. We propose ReCo, a reweighting method that addresses both effects. Response contributions are normalized by their expected occurrence within the rollout group, and the token-level importance ratio is replaced with a variance-based ratio that gives larger update scale to non-saturated decision points where alternative token choices remain plausible. Across Qwen2.5-Math-1.5B/7B and Llama-3.1-8B-Instruct on five mathematical reasoning benchmarks, ReCo improves Pass@k for large values of k and is comparable to GRPO for small values of k.
[AI-17] Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility
链接: https://arxiv.org/abs/2607.26828
作者: Yansen Zhang,Yilu Liu,Tianyu Liu,Jiamin Chen,Xiaokun Zhang,Kai Xie,Xue Liu,Chen Ma,Yiyan Qi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models increasingly support scientific and algorithmic discovery through inference-time search over evaluated candidates. Existing adaptive discovery controllers assign credit based only on score progress, even though prompt length, retries, and guidance calls cause search actions to incur different token costs. We prove that cost-blind credit can forfeit all but a vanishing fraction of attainable quality as frontiers multiply and costs diverge. Under a fixed search-side token budget, the controller must decide which frontier is improving and whether its gain justifies the realized cost before the budget is exhausted. We introduce \textbfCostAda, a cost-calibrated adaptive controller built around \emphcost-calibrated frontier utility. The utility values frontier progress relative to realized action cost and conditions that credit on the remaining budget. CostAda uses this signal to control local exploration intensity, frontier allocation, and budgeted tactic intervention. Cost and remaining budget therefore shape the search rather than serving only as accounting variables or a stopping rule. CostAda reaches the strongest baseline’s full-budget quality with at most half the budget on twelve of sixteen benchmark–backbone pairs while achieving the strongest mean final quality on all eight benchmarks under GLM-5 and GPT-5.4.
[AI-18] A First Look at Coding Agents Compliance with AI Contribution Rules in Open-Source Communities
链接: https://arxiv.org/abs/2607.26819
作者: Wenhao Yang,Runzhi He,Minghui Zhou
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Open source communities have been flooded with AI-generated contributions. In defense, they have written contribution rules to regulate coding agents’ behavior, spanning from a total ban, mandatory disclosure, to verification gates and human sign-offs. Yet, whether coding agents read and follow those rules, and behave in open source repositories, remains unknown. To estimate real-world rule compliance of coding agents, we curate 106 issues from 49 repositories containing AI contribution rules into RepoComplianceBench. We judge the trajectory of each run against the repository’s rules, measuring whether the agent refuses to contribute, discloses its assistance truthfully, clears the required verification gates, or escalates critical steps to a human. We also test if extra prompts, rule disclosure, or feedback from the compliance verifier help with the situation. Our experiments on four frontier models show that today’s agents almost never proactively retrieve the contribution rules. Agents pick up disclosure and verification with reminder prompts, rule quotes, and verifier feedback; however, they never refuse to contribute in AI-banned repositories under any condition we tested. The status reveals that verification and disclosure issues are solvable with existing mechanisms, yet enforcing bans and human escalations remains an open problem.
[AI-19] FedTopo: Relation-Level Topology Sharing for Model-Heterogeneous Federated Learning ICDE2027
链接: https://arxiv.org/abs/2607.26801
作者: Zhaoyang Ma,Zhihao Wu,Xin Gao,Lipo Wang,Youfang Lin,Jing Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 13 pages, 9 figures, 4 tables. Submitted to ICDE 2027 (first submission round)
Abstract:Federated learning (FL) enables collaborative learning over decentralized data silos without centralizing raw data. However, heterogeneous local architectures often induce non-aligned representation spaces, making it difficult to transfer global knowledge across silos. Existing paradigms share this knowledge as model parameters, distilled predictions, or class prototypes, yet all encode it in an absolute space that must be aligned across clients. Heterogeneous backbones break this alignment, so the shared knowledge becomes unreliable and misleads local training. We propose FedTopo, a relation-level framework that encodes global knowledge as class relation topology, capturing how classes relate within each client rather than where they lie in feature space. Each client builds its relation topology from local prototypes and uploads it with class statistics. The server then aggregates these relations in a reliability-aware manner that down-weights weakly supported ones, and broadcasts the global topology to clients. The global topology guides local training by emphasizing topology-similar negative classes. Experiments on three datasets under eight heterogeneous backbones show that FedTopo consistently outperforms parameter-, distillation-, and prototype-sharing baselines, with low communication and no inference overhead. Our code is available at this https URL.
[AI-20] Property-driven Causal Abstractions for Markov Decision Processes
链接: https://arxiv.org/abs/2607.26787
作者: Jule Schmidt,Maximilian Weininger,Clemens Dubslaff,David Parker,Nils Jansen
类目: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注:
Abstract:Markov Decision Processes (MDPs) are widely used as decision-making models, commonly specified over factored state spaces through state variables and their valuations. The exponential blowup in the number of states renders many reasoning tasks in MDPs challenging. Abstractions are promising techniques to reduce MDPs and thus mitigate scalability issues. In this work, we introduce a notion of causality on factored MDPs and a novel property-driven causal abstraction technique that retains many characteristics of the original MDP model. For this, we rely on causal relations over state variable predicates and identify those states that share the same reasons for fulfilling or violating a given abstraction property. We theoretically and empirically compare various causal MDP abstractions using different model types such as MDPs, interval MDPs, or stochastic games. Our evaluation demonstrates the potential of our approach: For several standard benchmarks, we obtain small abstractions that allow us to compute near-optimal policies for the original MDP. Furthermore, our causal abstractions often generalize to related large-scale MDP models.
[AI-21] SkillRise: Agent ic Reinforcement Learning for Cross-Task Skill Evolution
链接: https://arxiv.org/abs/2607.26784
作者: Zhiyuan Yao,Yuxin Chen,Zhengxi Lu,Zishan Xu,Yueqing Sun,Yifu Guo,Yuquan Lu,Zhengzhou Cai,Kangning Zhang,Zhuowen Han,Zi-Han Wang,Ziang Ye,Qi Gu,Xunliang Cai,Weiwen Liu,Yongliang Shen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model agents often encounter related yet distinct tasks that share reusable solution patterns. Yet standard agentic reinforcement learning treats tasks as independent episodes, while existing approaches to skill learning either focus on repeated attempts of one task or use pipelines with multiple stages that entangle extraction, retrieval, and execution. We introduce SkillRise, a unified reinforcement learning framework for learning skills across tasks. SkillRise organizes related instances into progressively challenging sequences and uses a single policy to alternate between task solving and curating an evolving skill document passed directly to the next task. Decoupled credit assignment across tasks supervises solving with the current task outcome and curation with discounted downstream outcomes. Experiments on ALFWorld, WebShop, and ScienceWorld show that SkillRise achieves the strongest Pass@1 performance among the compared methods, with gains over the strongest baseline ranging from 2.3 to 8.5 percentage points. Although trained across distinct tasks, its learned curation policy remains effective for repeated attempts on the same task. Further analysis reveals scaling at test time across tasks: performance improves with longer sequences of related tasks even when each task is attempted only once. This trend suggests that SkillRise reuses transferable skills across tasks rather than benefiting from repeated sampling of the same task. SkillRise further retains strong performance while substantially reducing the runtime overhead of skill learning pipelines with multiple stages. Together, these results provide a simple and efficient training paradigm for LLM agents to extract, refine, and reuse transferable skills across tasks.
[AI-22] Journey Operators for Structured Multi-Axis Composition
链接: https://arxiv.org/abs/2607.26775
作者: Mahesh Godavarti
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Many kinds of data have structure along one or more axes: words in a sentence, pixels in an image, nodes in a tree, frames in audio, or cells in a 3D volume. Along one axis, order matters: “the dog bit the man” is different from “the man bit the dog.” Across independent axes, however, neither composition nor movement should depend on the order of axes: in an image, composing right then down should give the same result as composing down then right, and moving right then down should describe the same relative position as moving down then right. We develop a framework for modeling this kind of multi-axis structure. Each data item carries its content together with a small transformation for each axis. A path connecting two positions defines a journey; the journey operator is the product of per-axis transformations along that path, governing both how data composes along the path and how relative position is described. When the transformations are fixed, our framework recovers Rotary Position Embedding (RoPE) and its multi-dimensional variants. When they depend on the data, the model gains a content-adaptive positional inductive bias. We show exactly when these paths are well-defined: both composition and movement across axes are path-independent precisely when the axis transformations commute. We also prove that, under the stated toral-frame symmetry, cocycle, bilinearity, and norm-preservation assumptions, the resulting pairwise scoring rule must take the form of block-wise rotations, explaining why RoPE-like methods arise naturally. Finally, we use this theory to design JoFormer, a model for value aggregation, and relate it to attention and state-space models (SSMs). Initial experiments across vision, language, and length generalization suggest that these inductive biases can have observable consequences in practice. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) MSC classes: 68T07, 68T05, 68R10, 15A69, 20M35 ACMclasses: I.2.6; I.5.1; I.2.7; G.2.2 Cite as: arXiv:2607.26775 [cs.LG] (or arXiv:2607.26775v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.26775 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Mahesh Godavarti [view email] [v1] Wed, 29 Jul 2026 11:14:54 UTC (38 KB) Full-text links: Access Paper: View a PDF of the paper titled Journey Operators for Structured Multi-Axis Composition, by Mahesh GodavartiView PDFHTML (experimental)TeX Source view license Current browse context: cs.LG prev | next new | recent | 2026-07 Change to browse by: cs cs.AI References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[AI-23] Do Latent Channels Actually Communicate? A Causal Audit of Latent Multi-Agent LLM
链接: https://arxiv.org/abs/2607.26773
作者: Huixiang Zhang,Mahzabeen Emu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Latent communication in large language model (LLM)-based multi-agent systems (MAS) transmits continuous internal representations instead of text, but greater representational capacity does not establish that the receiver uses task-relevant information. End-task performance alone also cannot reveal whether an observed effect depends on message presence, content generated for the evaluated example, or information supplied by a separate agent. We introduce a causal audit that applies controlled message replacements at the boundary where the sender-produced representation enters the receiver. Four message settings support five measurements of encoded sender information, receiver sensitivity to message presence and identity, the task value of example-specific content, and the additional value supplied by a separate agent. We apply the audit to latent relay with Qwen3-4B and Qwen3-8B on GSM8K, ARC-C, and MATH-500. On GSM8K, the Qwen3-4B overall performance effect of -1.00 percentage point decomposes into a -6.17-point effect retained by an other-example message and a +5.17-point effect attributable to example-specific content; both component directions reverse at 8B. On MATH-500, the Qwen3-4B gain of 15.00 points comprises 8.33 points retained by an other-example message and 6.67 points attributable to example-specific content, while the 8B gain is dominated by the former component. Self-substitution comparisons further show that example-specific content and other-agent value are distinct. These results show that aggregate accuracy does not identify how a latent message affects the receiver and motivate controlled message comparisons as a standard evaluation for latent communication.
[AI-24] UrbanDS: A Graph-Guided LLM Multi-Agent System for Data-Intensive Urban Tasks
链接: https://arxiv.org/abs/2607.26724
作者: Zhilun Zhou,Jianghao Yu,Yuming Lin,yongjun yang,Sun Yongquan,Depeng Jin,Yong Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents have been widely applied in automating data science tasks. However, existing methods typically rely on a limited set of provided datasets, and they face challenges in data-intensive scenarios that require discovering and leveraging relevant information from large-scale and heterogeneous data repositories. Urban tasks are representative examples of such scenarios, as urban data are not only large-scale and multi-sourced, but also exhibit complex spatial, temporal, and semantic relationships. To address these challenges, we propose UrbanDS, a graph-guided LLM multi-agent system for data-intensive urban tasks. We first construct a unified dataset graph to organize reusable dataset skills and the relationships among datasets. Specifically, we develop a Data Profiling Agent that constructs a skill for each dataset. Moreover, a Relation Agent identifies relationships among datasets and integrates these relationships into the dataset graph. At runtime, a Planner Agent retrieves task-relevant datasets from the graph and generates execution plans. Multiple Execution Agents then perform data processing and analysis, while their execution progress and intermediate results are shared through a common memory. Finally, a Report Agent synthesizes the experimental logs into a report, which can be further refined based on user feedback. To systematically evaluate the capability of agents in handling data-intensive urban scenarios, we further construct UrbanDS-Bench, an urban data science benchmark covering representative data analysis and modeling tasks. Experiments on both general and urban benchmarks demonstrate that UrbanDS consistently outperforms existing data science agents on data-intensive tasks. Furthermore, UrbanDS has been deployed on the urban operations platform of Dongxihu District, Wuhan, demonstrating its effectiveness in real-world urban applications.
[AI-25] FARI: Robust One-Step Inversion for Watermarking in Diffusion Models ICLR2026
链接: https://arxiv.org/abs/2607.26723
作者: Jindong Yang,Han Fang,Weiming Zhang,Nenghai Yu,Kejiang Chen
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Accepted by ICLR 2026
Abstract:Inversion-based watermarking is a promising approach to authenticate diffusion-generated images, yet practical use is bottlenecked by inversion that is both slow and error-prone. While the primary challenge in the watermarking setting is robustness against external distortions, existing approaches over-optimize internal truncation error, and because that error scales with the sampler step size, they are inherently confined to high-NFE (number of function evaluations) regimes that cannot meet the dual demands of speed and robustness. In this work, we have two key observations: (i) the inversion trajectory has markedly lower curvature than the forward generation path does, making it highly compressible and amenable to low-NFE approximation; and (ii) in inversion for watermark verification, the trade-off between speed and truncation error is less critical, since external distortions dominate the error. A faster inverter provides a dual benefit: it is not only more efficient, but it also enables end-to-end adversarial training to directly target robustness, a task that is computationally prohibitive for the original, lengthy inversion trajectories. Building on this, we propose \textbfFARI (\textbfFast \textbfAsymmetric \textbfRobust \textbfInversion), a one-step inversion framework paired with lightweight adversarial LoRA fine-tuning of the denoiser for watermark extraction. While consolidation slightly increases internal error, FARI delivers large gains in both speed and robustness: with approximately 20 minutes of fine-tuning on a single NVIDIA RTX A6000 GPU, it surpasses 50-step DDIM inversion on watermark-verification robustness while dramatically reducing inference time. Code and pretrained models are available at this https URL.
[AI-26] MPEcho: A Melody and Phoneme-Aware Generative Framework for Controllable Cover Song Generation
链接: https://arxiv.org/abs/2607.26698
作者: Wei-Jaw Lee,Hsuan-Yu Yeh,Ting-Yi Hu,Chih-Pin Tan,Fang-Duo Tsai,Yi-Hsuan Yang
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
备注: Accepted by the 27th International Society for Music Information Retrieval (ISMIR)
Abstract:Cover song generation (CSG) should preserve the melodic and linguistic content of a reference song while recreating the remaining musical components. The state-of-the-art model SongEcho utilizes F_0 sequences and voiced/unvoiced (V/UV) tags for conditioning; however, implicit linguistic information from V/UV tags cannot guarantee lyric accuracy, leading to a high phoneme error rate (PER). Inspired by singing voice synthesis (SVS), we propose MPEcho, which integrates a phoneme encoder and a length regulator (LR) into the SongEcho framework. By providing explicit phoneme-level conditioning and precise temporal boundaries, MPEcho significantly reduces PER. To enable this, we developed Phonsa, a Whisper-based automatic transcription model that provides high-precision phoneme-level annotations for singing voices, overcoming the scarcity of high-quality audio-phoneme pairs. Experimental results validate the effectiveness of Phonsa for alignment and MPEcho for end-to-end CSG. The audio samples, code and weights can be accessed from this https URL.
[AI-27] Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL
链接: https://arxiv.org/abs/2607.26680
作者: Mingxuan Che,Tsung-Yuan Tseng,Theresa Eimer,Marius Lindauer,Alexander von Rohr
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at RLC’26
Abstract:Reinforcement learning (RL) has shown remarkable success across a wide range of complex tasks. However, RL outcomes can be highly stochastic, and both expected performance and variability often depend on hyperparameter (HP) configurations. We propose efficient and risk-averse heteroscedastic Bayesian Optimization (ERAHBO), a Bayesian optimization method that models both the mean and variance of learning outcomes as functions of the HP configurations. ERAHBO aims to identify HP configurations that achieve high average return while reducing variability across training runs, and it improves the sample efficiency of the HP optimization via adaptive re-sampling rather than a fixed budget per HP. Empirical evaluations across diverse RL algorithms and environments demonstrate that ERAHBO generally outperforms both risk-neutral and risk-averse baselines, delivering improved sample efficiency for risk-averse returns.
[AI-28] Agent icCANN: Automated Ascend C Operator Generation via Knowledge-Augmented Agent ic Evolution
链接: https://arxiv.org/abs/2607.26661
作者: Junhao Qiu,Zidong Wang,Yansong Sun,Zhitong Ma,Ping Guo,Qingfu Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Ascend C operator optimization is critical for NPU (Neural Processing Unit) inference performance but requires deep hardware this http URL large language models (LLMs) have shown promise in automated CUDA kernel generation, the fundamentally different programming model of Ascend C introduces unique challenges that remain unexplored. In this paper, we propose AgenticCANN, a knowledge-augmented agentic evolution framework specifically tailored for automated Ascend C operator synthesis in low-corpus NPU this http URL overcome the severe platform knowledge deficit on unfamiliar hardware, AgenticCANN incorporates a knowledge-orchestrated generation system that delivers structured, multi-level domain insights across the development lifecycle to resolve the upstream feasibility this http URL on this foundation, it features a stage-adaptive agentic evolution strategy that dynamically aligns LLM interaction modes with specific generation and evolution phases, balancing high-exploration candidate discovery with high-convergence performance this http URL experiments on Huawei Ascend 910B across six operators spanning five pattern categories demonstrate that our method achieves 90 to 100 percent feasibility on elementwise and normalization operators, 56% on fusion operators, and up to 6.65 \times speedup on 1B Pangu model inference kernels. Further analysis reveals that knowledge injection monotonically improves feasibility from 57% to 86% on elementwise operators, demonstrating its general rather than operator-specific benefit.
[AI-29] Graph Is the Verifier: Agent ic Reinforcement Learning for Interprocedural Vulnerability Detection
链接: https://arxiv.org/abs/2607.26656
作者: Yikun Li,Ting Zhang,Jiakun Liu,Jinfeng Jiang,Yuheng Yieh,Yixin Yang,Wen Bin Leow,Yide Yin,Yintong Huo,Eng Lieh Ouh,Lwin Khin Shar,David Lo
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Real-world vulnerabilities often span multiple functions, yet most learning-based detectors classify each function in isolation: on a sample of real CVEs, we find that 71.7% of vulnerable functions require evidence from outside the function to be classified correctly. Agentic reinforcement learning (RL) could close this gap by enabling a model to gather that evidence itself, but it lacks a reliable reward, since a reward defined on the final verdict alone can be obtained without performing any investigation. We propose VulAgentRL, an agentic RL framework for interprocedural vulnerability detection built on a Code Property Graph (CPG). The CPG serves two roles: at inference time the policy queries it for callers, callees, dataflow, and other queries, and at training time the same graph verifies the evidence the policy cites. Because every CPG node carries a persistent integer identifier, this verification is an exact comparison rather than a textual match, so the reward credits verdicts that are supported by evidence. We further initialize the policy by distilling teacher investigations, and show that this warm start is necessary, since RL cannot acquire tool-use behavior it never samples. Under a repository-level split that prevents leakage, VulAgentRL outperforms state-of-the-art baselines, including frontier models, on the strict pair-wise-correct metric while issuing fewer tool calls, and its advantage persists on an out-of-distribution corpus and under class imbalance.
[AI-30] Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting
链接: https://arxiv.org/abs/2607.26643
作者: Hongqiang Lin,Chao Liu,Xiaofan Bai,Xuan Jin,Yuhong Li,Nenggan Zheng,Xipeng Cao
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Enabling large language model (LLM) agents to accumulate and reuse experience from past interactions remains a central challenge in real-world applications. A promising solution is to treat skills as trainable states and optimize them in the same way as model parameters in neural network training. However, data-driven skill optimization is prone to overfitting to the limited trajectories collected from real environments. Overexploiting these trajectories overfits the current batch, while unconstrained exploration causes regression on previously solved cases. This tension motivates a constrained search view of skill self-evolution, governed by an exploration–exploitation trade-off. We propose SkillBoost, a three-stage framework that mitigates both risks: structured exploitation localizes observed failures to editable skill components, prior-guided exploration draws on prior knowledge in the LLM to generate diverse repair candidates, and verified acceptance commits a candidate only when it improves performance within a regression bound. Experiments across 23 model–benchmark configurations show that SkillBoost achieves state-of-the-art performance while mitigating overfitting, outperforming both human-crafted and LLM-generated skills. Transfer experiments further show that optimized skills can be reused by other agents on similar tasks.
[AI-31] AlphaSchema: Exploring the Space of Trading Semantics for LLM -Based Alpha Mining
链接: https://arxiv.org/abs/2607.26642
作者: Jingyang Yi,Jian Yang,Yifei Jin,Yuqi Li,Jian Li
类目: Artificial Intelligence (cs.AI)
备注: Initial Version
Abstract:Automated alpha mining has increasingly adopted large language model (LLM) agents for factor generation and iterative discovery. However, existing LLM-based systems often delegate both factor construction and search decisions to the agent itself, without an explicit exploration space or a principled mechanism for navigating that space. As a result, exploration remains largely implicit and difficult to control or optimize systematically. We introduce AlphaSchema, which constructs and explores a structured space of trading semantics for alpha mining. Each point in this space is a schema plan composed of Event, Context, Qualities, Direction, and Output, specifying the semantics of a candidate factor before implementation. AlphaSchema decouples exploration from implementation: an LLM translates selected schema plans into executable factors, while evaluated rewards are accumulated to learn a surrogate model over the semantic space. An iterative selection mechanism uses this model to balance global exploration, surrogate-guided exploitation, and local mutation. Experiments on the Chinese stock market show that AlphaSchema discovers factor pools with strong predictive and portfolio performance. Further analyses show that the semantic search process navigates diverse regions while increasingly allocating evaluations toward high-reward regions, and that implementations of the same schema plans by different LLMs exhibit comparable predictive quality, suggesting that alpha mining quality is largely robust to the choice of LLM within our framework.
[AI-32] Borrowed Strength: Best-of-N Search over a Code EncodingBreaks Self-Check Jailbreak Defenses
链接: https://arxiv.org/abs/2607.26639
作者: Haoyu Zhang,Shibo Zheng,Xiangchen Guan,Zhuoxi Wang,Zijian Xiao,Mohammad Zandsalimy,Shanu Sushmita
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:A self-check defense asks the target model to assess a request before answering it; SAGE, the strongest published instance, reports an average 99% defense success rate. We show it can be breached by composing two attacks that are individually harmless against it: an established code-completion encoding and an established best-of-N search, neither of which exceeds 4.7% of behaviors alone. Composed, with the search budget spent on the encoding, they reach 67/22/15% across three open targets, and the effect persists on a 70B target. We then explain the composition rather than only reporting it. First, a self-check defense borrows its strength from the target: SAGE does not detect the attack, it asks the model to, and the four targets convert that request into an explicit refusal between 32% and 97% of the time, which orders the spread in defended coverage even though undefended reach is near-identical. Second, which attack survives is decided by the type of defense, and it inverts: against transform defenses the code encoding retains far more of its undefended reach than the character search, while against gate defenses the ordering flips. We account for this with the number of independent probes an attack delivers to a defense’s decision boundary. Finally, we report a validity defect we found and repaired in our own pipeline, a deterministic attack under greedy decoding has no best-of-N variation channel at all, and give the one-line diagnostic that detects it. All claims rest on 310,000 generations scored by a human-validated judge.
[AI-33] Guarding Organizations Against Malware Risk: A Novel Graph-Based Malware Detection Method
链接: https://arxiv.org/abs/2607.26634
作者: Yinan Gao,Jiarong Xu,Xiaohang Zhao,Xiao Fang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Organizational digitalization expands cybersecurity risks, making cybersecurity an increasingly important research area in Information Systems (IS). Among these risks, malware has become a pervasive and destructive threat. Byte-based machine learning (ML) methods are widely used for malware detection but remain vulnerable to evasive behaviors that manipulate raw bytes to evade detection. Graph-based methods are less affected by such manipulations because they represent software as program graphs that capture execution behavior. However, they do not explicitly identify cohesive groups of basic blocks that jointly realize meaningful program behaviors, nor do they learn sufficiently expressive program graph representations for accurate detection. To this end, we propose MalGuard, a graph-based malware detection method for organizational malware risk management. MalGuard introduces two methodological innovations: an operational role identification approach and a program graph representation learning method. The former identifies these cohesive groups of basic blocks as operational roles, enabling the detector to capture program behaviors that may not be visible from isolated basic blocks. The latter learns expressive program graph representations by modeling interactions among operational roles, preserving sparse malicious signals, and capturing hierarchical graph structure. Extensive experiments show that MalGuard improves detection performance and reduces the expected cost of undetected malware.
[AI-34] Understanding Context Sampling in TabPFN on Small Tabular Datasets
链接: https://arxiv.org/abs/2607.26628
作者: Mohammed Abdullah
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 12 pages, 4 figures. Code and experiment logs available at this https URL
Abstract:TabPFN performs classification through in-context learning: it conditions on a set of labeled training rows (the context, or prototypes) and predicts test labels without gradient updates. On small tabular datasets, practitioners must still choose the context size and which rows constitute the context. We study how these choices affect prediction stability, accuracy, and selection cost using repeated context sampling on 15 OpenML datasets. Specifically, we investigate (i) whether larger contexts reduce prediction variability across random draws, (ii) whether accuracy depends on preserving the training distribution or on feature-space coverage, and (iii) whether expensive selection methods such as K-Means and farthest-point sampling provide benefits over uniform random sampling. We find that larger contexts are both more accurate and substantially more stable, with AUC coefficient of variation decreasing from roughly 6 to 18% at k=16 to 1 to 4% at larger context sizes on datasets with room for improvement. Although accuracy correlates with distribution representativeness in random contexts, controlled experiments show that matching feature means alone can reduce accuracy by up to 0.5 AUC because it reduces context diversity. Mixed-effects analysis identifies diversity and coverage, rather than feature-mean matching, as the stronger predictor of accuracy (diversity beta=+0.23, p=3x10^-12; feature-mean shift beta=-0.01, p=0.71). K-Means and farthest-point sampling achieve similar accuracy to random selection while requiring two to three orders of magnitude more selection cost. These results show that random sampling succeeds because it provides feature-space coverage in expectation, not because it reproduces the underlying data distribution. Comments: 12 pages, 4 figures. Code and experiment logs available at this https URL Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) ACMclasses: I.2.6 Cite as: arXiv:2607.26628 [cs.LG] (or arXiv:2607.26628v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.26628 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-35] A Physics-Informed Framework for PID Tuning of Chemical Processes Using Large Language Model Agents
链接: https://arxiv.org/abs/2607.26594
作者: Zhoupeng Shou,Xiaodong Hong,Congjing Ren,Jingdai Wang,Yongrong Yang,Zuwei Liao
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI)
备注:
Abstract:PID tuning for chemical processes commonly relies on identified process models, whereas plant engineers often retune loops iteratively by observing responses, diagnosing deficiencies, adjusting gains, and validating the result. This work formalizes this engineer-like workflow in a language-model-assisted PID tuning framework applicable to both large and small language models (LLMs/SLMs). Hosted LLMs receive closed-loop response features, control-engineering diagnoses, tuning preferences, and internal model control (IMC)-based demonstrations to generate and iteratively correct PID gains under common acceptance criteria. For local deployment, Qwen3-0.6B is adapted through supervised fine-tuning (SFT) with simulation-verified IMC targets and physics-informed group relative policy optimization (PI-GRPO) with non-compensable stability and performance rewards. On 100 first-order plus dead time (FOPDT) and 100 second-order plus dead time (SOPDT) test cases, hosted LLMs (DeepSeek-V4-Flash and Qwen3.7-Plus) achieve final success rates of 75-89% and 77-79%, respectively. As for Qwen3-0.6B, supervised fine-tuning raises first-recommendation success to 86.5%, and PI-GRPO further increases it to 94.0%, primarily improving first-attempt reliability and stability margins.
[AI-36] Eco3S: Complex Socio-Economic System Simulation via Agent -Based Models
链接: https://arxiv.org/abs/2607.26588
作者: Shaopeng Wei,Yufei Cheng,Wenxi Sun,Yepeng Ding,Yu Zhao,Gang Kou
类目: Artificial Intelligence (cs.AI)
备注: 18 pages, 18 figures, 7 tables
Abstract:The rapid development of large language models (LLMs) has renewed interest in agent-based modeling (ABM). However, current LLM-based ABM research faces several key challenges: modeling evolving agent-environment interactions, enabling flexible counterfactual reasoning, and automating simulation workflows for scientific research. In this paper, we propose Eco3S, a socio-economic system simulation framework for economic research and policy analysis that addresses these challenges through three key mechanisms: (1) Co-evolving Environment Design, a bidirectional feedback loop where agents and the environment co-evolve, producing realistic emergent behaviors; (2) Structural Causal Simulation, a structural causal model (SCM)-inspired counterfactual mechanism that allows flexible interventions for diverse causal inference tasks; (3) Simulation-Analysis-Refinement Paradigm, a self-corrective mechanism that iteratively refines experimental designs based on prior simulation results. Experiments on diverse economic scenarios confirm \textitEco3S’s effectiveness in replicating multiple established economic studies (canal decay, origins of governance, and information propagation) and phenomena across domains. Additional results further demonstrate its scalability and generalizability, highlighting the framework’s potential for rigorous economic research and policy-making.
[AI-37] Recover Decode Reguard: Guard-Agnostic Defense Amplification againstEncoded VLM Jailbreaks
链接: https://arxiv.org/abs/2607.26574
作者: Haoyu Zhang,Zhuoxi Wang,Shibo Zheng,Zijian Xiao,Xiangchen Guan,Mohammad Zandsalimy,Shanu Sushmita
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Safety classifiers (“guards”) are the dominant black-box defense for vision-language models, yet they judge an input’s surface form, not its meaning: a harmful request re-encoded as set theory, formal logic, a rare language, code, or an image of text slips past a guard that would block it in plain language – the decode gap. The natural fix is a guard-agnostic recover-and-decode amplifier that transcribes image content and restates encoded text into its plain payload before the guard, so any off-the-shelf classifier can screen the true request. We build this amplifier and evaluate it against the attacker’s best case: an ensemble of eleven attacks, scoring a behavior as broken if any succeeds (best-of-suite, following AutoAttack) – rarely reported for jailbreak defenses, yet ~3.5x the per-attack mean. This exposes our central finding: an empirical safety-utility ceiling for the non-iterative recovery defenses we evaluate, across five guards and two target VLMs. The amplifier only partly closes the gap – the undefended ensemble breaks 89-91% of behaviors, and the best guard-plus-amplifier still leaves 63-65% – and its gain over the guard alone is significant in only four of ten guard-target pairs. It is guard-agnostic at the interface, but not uniformly so in effect. A modular reguard layer closes much of the residual, yet drives benign over-refusal to 81-92% for well-calibrated guards; the one laxer guard that stays usable never reaches deployable safety (48% ensemble ASR). No configuration we evaluate reaches both low attack-success and low over-refusal, for the pipeline we study and for representation-shifting attacks – encodings and cross-modal renders that leave a legible payload, not pixel- or embedding-space attacks. We contribute the amplifier, an ensemble evaluation that makes the trade-off visible, and a map of where recovery-based VLM defense works and where it does not.
[AI-38] ServerlessT2I: Efficient Text-to-Image Workflow Serving on a Serverless Platform
链接: https://arxiv.org/abs/2607.26566
作者: Xiaoxiao Jiang,Suyi Li,Sheng Yao,Tianyu Feng,Lingyun Yang,Dapeng Nie,Haoran Yang,Wei Wang
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:Text-to-image (T2I) workflows are increasingly deployed on serverless platforms because users often compose customized workflows and invoke them intermittently. Existing platforms typically deploy each workflow as an opaque GPU function, provisioning, placing, and scaling all constituent models in the workflow together. This monolithic design obscures workflow structure, inflates scaling overhead, forces users to manage low-level GPU coordination, and limits fine-grained fairness in multi-tenant clusters. In this paper, we present ServerlessT2I, a serverless-native system that decomposes a T2I workflow into loosely coupled model functions that can be independently managed and scheduled. By explicitly managing individual model execution, ServerlessT2I enables per-model scaling, declarative workflow composition, transparent GPU-resident communication, and fairness-aware scheduling. To make this decomposition efficient, ServerlessT2I harvests slack GPU memory left idle by compute-bound T2I inference to build a data plane that reduces model loading and data communication overheads. \sys further introduces a fair scheduler for multi-tenant serving. Using production traces, ServerlessT2I sustains up to 2 \times higher request rates than existing T2I workflow serving systems with the same GPU budget; for a fixed request rate, it saves up to 3 \times GPU resources while satisfying service level objectives (SLOs).
[AI-39] Agent GFM: A Graph Foundation Model with Node-Agent Information-Flow Control
链接: https://arxiv.org/abs/2607.26533
作者: Jingbo Cui,Jitao Zhao,Di Jin,Dongxiao He
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 13 pages, 5 figures
Abstract:Graph Foundation Models (GFMs) aim to learn transferable knowledge from multi-domain graphs and adapt to unseen scenarios. As a fundamental source of relational semantics in graphs, the transferability of topological patterns has long been central to GFM research. However, local structural patterns may vary across graphs and even among nodes within the same graph. Despite such structural variation, most existing GFMs rely on manually designed propagation schemes and apply them to new graphs largely unchanged. Such fixed schemes may not suit the diverse structural patterns of different nodes. This raises a key question: can each node autonomously determine how information should be propagated through the graph? We refer to this capability as information-flow control. Inspired by recent advances in agent technology, we formulate this problem as agent-based decision making and treat each node as an agent. Accordingly, we propose AgentGFM, in which all node agents follow a shared end-to-end trainable policy rather than using independent models. For adaptive information-flow control, each node interacts with the graph through a predict-act-observe-correct process. During the act stage, the node makes three decisions: source reception, signal-channel selection and gain-aware node-wise halting. The resulting observation is compared with the prediction and their discrepancy is used to correct the node state and guide subsequent interactions. Extensive experiments across node-level, graph-level and large-scale transfer scenarios demonstrate the effectiveness of AgentGFM across diverse graph topologies.
[AI-40] Shared Symbolic Backbones for Physically Consistent Multi-Output Symbolic Regression
链接: https://arxiv.org/abs/2607.26528
作者: Manuel Rodriguez
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注: 20 pages, 1 figure
Abstract:Symbolic regression provides analytical expressions, but it is usually applied one output at a time. This is limiting in process systems, where state variables are often coupled through shared physical parameters. Independent symbolic regression can give accurate individual equations that are difficult to interpret as one model. We present a neuro-evolutionary symbolic regression method for coupled multi-output systems. The method searches for a shared symbolic backbone: a set of latent symbolic units that is discovered once and reused by several outputs through sparse additive or multiplicative read-outs. The discrete model structure is evolved by mutation and crossover, whereas the continuous parameters are tuned by gradient descent and inherited by the offspring. The method is assessed on a set of benchmarks with known ground truth and on a hydrothermal liquefaction yield case. The results show that coupling is not a general route to lower prediction error. Its main contribution is the enforcement and diagnosis of cross-output consistency when a physically shared factor is embedded in a latent expression and is weakly identifiable from the data. This occurs for Langmuir-Hinshelwood and site-coverage denominators, for which independent PySR does not close the consistency gap or recover the same shared form. Conversely, when each output is already identifiable, as in the Van de Vusse benchmark, independent symbolic regression matches or improves the coupled model. The proposed framework, rather than a general purpose predictor, is a structured shared-mechanism extractor. Its value is highest when the target structure is sparse, shared, weakly identifiable or constrained by closure.
[AI-41] he Art of Not Forgetting A Local Learning Architecture for Continual Learning
链接: https://arxiv.org/abs/2607.26523
作者: Ashmith Atmuri,Yashaswini Rao Bhogarajula
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 28 pages, 11 figures
Abstract:We introduce CMP (Cognitive Memory Primitive), a continual-learning architecture that repre?sents inputs as sparse relational codes, stores them in a two-tier competitive memory, and learns through local updates without end-to-end backpropagation through its feature-generating system. We investigate whether combining sparse representations, local learning, and persistent memory can reduce catastrophic forgetting relative to conventional backpropagation-based continual?learning approaches. On a controlled domain-incremental byte-level language modeling protocol, CMP demonstrates substantially lower backward transfer than a parameter-matched Trans?former trained with online Elastic Weight Consolidation (EWC). Across a three-seed replicated 15-domain experiment, CMP exhibits stable forgetting behavior, while separate head-to-head comparisons and domain-order analyses show consistently lower forgetting than the evaluated Transformer baseline under the reported experimental settings. We report these findings alongside a substantial single-domain accuracy gap relative to the Transformer, a null result on a vision benchmark, and a documented failure to combine CMP with an independent accuracy-improving mechanism, reflecting our commitment to reporting both positive and negative outcomes. These results suggest that the combination of sparse representations, local learning, and persistent memory is a promising direction for continual learning, while motivating further investigation into the respective roles of learning rules, representations, and architectural design in mitigating catastrophic forgetting.
[AI-42] HiFloat4 Format for End-To-End Reinforcement Learning Post-Training of Large Language Models
链接: https://arxiv.org/abs/2607.26515
作者: Hei Yi Mak,Shadan Golestan,Hoang Le,Mehran Taghian Jazi,Yunke Peng,Yaoyuan Wang,Yao Wang,Junsong Wang,Tianchi Hu,Fengchen He,Guipeng Hu,Tanzila Rahman,Anandharaju Durai Raju
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We present, to our knowledge, the first end-to-end FP4 RL post-training, in which both the rollout and training policies, including their forward and backward passes, operate at 4-bit precision. A systematic study reveals that the dominant source of degradation in FP4 RL is not training-side quantization error but rollout activation quantization: outliers stretch the dynamic range so far that a large number of activation values underflow to zero under FP4. Counterintuitively, restoring the training policy to higher precision while keeping the rollout in FP4 makes accuracy worse than full FP4 baseline, exposing rollout-training mismatch as the principal failure mode and ruling out standard pretraining-style fixes. We address this with Rollout Residual Quantization (Rollout-ResQ): a single residual correction term constrained to a hardware-friendly sparsity pattern, added only to the FP4 rollout matmul – a lightweight correction that recovers most of the precision lost to outlier-driven underflow without inflating the rollout’s compute footprint. On Qwen2.5-3B and Qwen2.5-Math-7B, Rollout-ResQ paired with the HiFloat4 (HiF4) format – whose three-level hierarchical scaling preserves resolution under FP4’s tight 4-bit budget – closes the accuracy gap to BF16 from 4.9% to 1.1%, bringing fully quantized FP4 RL within striking distance of full precision. Applied to the open-standard MXFP4, the same recipe narrows the gap from 13.6% to 5.3%, revealing that FP4 format choice is a key factor that determines the ceiling on recoverable accuracy. Together, these results establish HiF4 as the enabling format for end-to-end FP4 RL post-training, and Rollout-ResQ as the activation-side mechanism that makes the gap to BF16 closable.
[AI-43] Evidence-Ledger Adjudication for Claim-Evidence Traceability
链接: https://arxiv.org/abs/2607.26512
作者: Gengyu Chen,Yongjie Yu,Weiling Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:AI agents can draft claims faster than authors can check whether the cited or retrieved evidence supports them. We study evidence-ledger adjudication: a claim-evidence traceability workflow that pairs each claim with an evidence packet, assigns a support relation, and routes unsupported, contradicted, or mixed-evidence claims back to the author. The empirical core is a 2,335-row blind benchmark built from independent external labels in AVeriTeC, CLIMATE-FEVER, and SciFact. Gold relations and source evidence labels are hidden during prediction and joined only for scoring. On this benchmark, the agent evidence-ledger condition achieves 0.676 relation accuracy and 0.601 macro-F1, compared with 0.383 accuracy and 0.303 macro-F1 for the best non-agent baseline. It also routes 1270/1435 claims whose gold labels indicate contradiction, missing evidence, or mixed evidence, while routing 295/900 supported claims. These results show that evidence-ledger adjudication can turn heterogeneous evidence packets into an auditable traceability layer for AI-assisted writing.
[AI-44] Collaborative Weighting with Pessimistic Critic for Mitigating Overestimation in Off-Policy Reinforcement Learning
链接: https://arxiv.org/abs/2607.26509
作者: Gong Gao,Xiao Lai,Ziqi Xie,Guojie Chen,Xianhui Liu,Weidong Zhao
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Deep off-policy reinforcement learning algorithms for continuous control typically rely on neural value function approximation to guide policy improvement. However, temporal-difference (TD) learning introduces noisy targets, resulting in non-stationary optimization, while greedy policy updates amplify early-stage estimation errors. The recursive propagation of such errors leads to persistent overestimation bias and degraded training stability in actor-critic methods. Existing approaches attempt to alleviate this issue via prioritized sampling or modified value learning objectives, but often overemphasize high-uncertainty transitions caused by limited data coverage or bootstrapping errors, thereby further amplifying this http URL this paper, we propose Collaborative Weighting Actor-Critic (CWAC), a unified framework that explicitly accounts for predictive uncertainty in value estimation. CWAC employs distributional critic to model return uncertainty and introduces a collaborative weighting mechanism that jointly reweights TD-errors and uncertainty, enabling robust learning from reliable samples while suppressing noisy updates. In addition, we incorporate a stochastic pessimistic value estimation scheme via sampling from the return distribution, which effectively mitigates error propagation during policy improvement. CWAC can be seamlessly integrated into existing off-policy algorithm frameworks such as SAC, TD3, and DDPG with minimal overhead. Empirical results demonstrate that our proposed method significantly enhances performance across a diverse range of simulated tasks. Our code is publicly available at this https URL.
[AI-45] LLM ET: Enabling Cross-Layer Evaluation of Emerging M3D Memories for Energy-Efficient LLM Serving
链接: https://arxiv.org/abs/2607.26491
作者: Ming-Yen Lee,Hanchen Yang,Faaiq Waqar,Harsono Simka,Tushar Krishna,Muhammed Ahosan Ul Karim,Shimeng Yu
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
备注: 6 pages
Abstract:The energy consumption of Large Language Model (LLM) serving is becoming a major system challenge as deployment scales, driven by hardware power and thermal constraints and rising electricity costs. A key contributor to chip energy dissipation is data movement between limited on-chip cache and off-chip High Bandwidth Memory (HBM). Meanwhile, emerging memory technologies such as monolithic 3D (M3D) integration of cache memories at the Back-End-Of-Line (BEOL) of logic chips enable larger and denser on-chip memories, creating new opportunities to reduce costly off-chip traffic. However, it remains unclear whether continuously scaling on-chip memory using emerging technologies can effectively improve the energy efficiency of LLM serving. To address this gap, we develop LLMET (LLM with Emerging Technology), a validated cross-layer simulation framework, and conduct a comprehensive study on the impact of large-capacity on-chip memory technologies across a broad range of models, applications and platforms. Utilizing M3D technology to expand the L2 cache from 40MB to 1GB yields a 44% reduction in chip energy during the Llama3.1-70B prefill phase with a 16K context window, based on LLMET simulation on a dual NVIDIA A100 GPU setup. On the 8x NVIDIA B200-like platform, extending the L2 cache from 128MB to 4GB saves the prefill energy by up to 24%. For the edge platform and workloads, the decode energy saving reaches 30% when increasing the 8MB cache size to 256MB. These results highlight the promise of ultra-large on-chip memories for energy-efficient LLM serving systems.
[AI-46] EvoPINN: Agent ic Discovery of Executable Algorithms for Physics-Informed Neural Networks
链接: https://arxiv.org/abs/2607.26490
作者: Peng Yin,Kai Li,Yifan Zhang,Jian Cheng
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Physics-informed neural networks (PINNs) have emerged as a powerful paradigm for solving partial differential equations (PDEs), yet their performance heavily relies on the manual, trial-and-error engineering of neural representations, loss formulations, and optimization dynamics. While Large Language Models (LLMs) offer a promising avenue for automated design, unconstrained code generation often yields mathematically invalid or numerically unstable solutions under strict scientific computing constraints. To bridge this gap, we propose \textbfEvoPINN, an agentic framework that reformulates PINN development from labor-intensive manual design into a rigorous, execution-grounded algorithm discovery problem. EvoPINN navigates a modular search space by decoupling neural representations from training programs, utilizing an LLM agent to iteratively propose memory-conditioned programmatic modifications. To ensure scientific validity, all candidates undergo strict structural verification and budget-matched PDE evaluation. Extensive experiments across diverse PDE regimes (oscillatory, elliptic, dissipative, and nonlinear transport) demonstrate that EvoPINN discovers PDE-specialized learning algorithms that significantly reduce relative L_2 error compared to baselines. Crucially, EvoPINN autonomously invented SLRC-PINN, a novel architecture whose performance gains persist under rigorous parameter-matched comparisons, establishing the viability of execution-grounded agents for discovering genuinely new scientific computing mechanisms.
[AI-47] Audio-Anchored Fusion of Multi-Ratio DiT Reconstruction Residuals for Cross-Domain Audio Deepfake Detection
链接: https://arxiv.org/abs/2607.26472
作者: Haotian Mo,Jie Liu,Siqi Shen,Songzhu Mei,Xinhai Chen,Xiangyang Wang,Yigui Feng,Shuai Li,Gencheng Liu,Keqi Yang,Qinglin Wang
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: 10 pages, 5 figures. Submitted to speech security conference. This work proposes a cross-domain audio deepfake detection framework based on bona-fide trained DiT multi-ratio reconstruction residuals and audio-anchored additive fusion, evaluated on ASVspoof 5 and real-world ITW datasets
Abstract:Audio deepfake detectors often degrade when generators, corpora, or recording conditions change. We use a Diffusion Transformer (DiT), trained only on bona fide speech, as a frozen reconstruction probe. Reconstructions at masking ratios 0.5, 0.75, and 0.9 yield explicit multi-ratio residual maps. Because these residuals are domain sensitive, our audio-anchored detector passes the projected frozen-WavLM auditory representation into the fusion sum without gate-based attenuation and uses residuals only as a scalar-gated additive correction. The pre-specified seed-42 run obtains 6.5442% EER / 0.18456 min-DCF on ASVspoof 5 Eval and 13.8372% / 0.36921 on ITW Full; three-seed means are 6.8885 (0.3308)% and 15.3328 (2.0719)%. The latter is below a separately optimized WavLM-ResNet18 reference under both supervision settings. Auxiliary supervision raises dynamic competitive fusion from 18.4007% to 25.2968% mean ITW EER, worsening all three seeds. The results support reconstruction residuals as complementary evidence and motivate a non-competitive auditory path for ASVspoof 5-to-ITW transfer, without claiming a componentwise causal ablation of anchoring alone.
[AI-48] MultivationBench: A Benchmark for Multimodal Sequential Motivation Reasoning
链接: https://arxiv.org/abs/2607.26465
作者: Kawai Chung,Chunkit Chan,Yauwai Yim,Yuxuan Liu,Haochen Shi,Weiqi Wang,Qing Zong,Tianshi Zheng,Yixuan Fu,Kai Chung Wong,Hao Liang,Yifan Gao,Xi Yang,Janet Hui-wen Hsiao,Yangqiu Song
类目: Artificial Intelligence (cs.AI)
备注: 31 pages, including appendices; 6 figures and 22 tables. Code: this https URL
Abstract:Multimodal Large Language Models have sparked significant interest due to their potential for social intelligence; however, their ability to perform sequential motivation reasoning remains insufficiently studied. Existing evaluations predominantly examine static text or isolated visual snapshots, which do not reflect the cumulative nature of real-world behavioral drivers. To address this gap, we introduce MultivationBench, a benchmark designed to rigorously evaluate multimodal motivation reasoning within story-driven visual narratives. The benchmark builds upon established psychological frameworks - Maslow’s hierarchy and Reiss’s basic desires - and requires models to integrate accumulated multimodal context to infer evolving motivations. Results indicate that MultivationBench presents a significant challenge: all tested models struggle to maintain consistent motivation reasoning across sequential contexts, revealing a critical disconnect between static recognition capabilities and the dynamic reasoning essential for human-like social understanding.
[AI-49] Reinforcement Learning on Cost-Constrained Quadrupedal Hardware
链接: https://arxiv.org/abs/2607.26434
作者: Javier C. Weddington,Bence P. Ölveczky,Stephen A. Baccus
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Sim-to-real transfer, locomotion, reinforcement learning, central pattern generator
Abstract:Deploying learned control policies on low-cost robotic platforms introduces transport latencies and noisy motor feedback that systematically widens the sim-to-real gap. The chasm of simulation to deployment in hardware lies in the delay of the actuator reaching the commanded position. On platforms such as the Mini Pupper 2, a measured 50 ms transport delay transforms the locomotion task from a standard Markov decision process into a partially observable one. In this paper, we take a biologically inspired approach of handling noisy and delayed feedback to close the sim-to-real gap, thereby expanding the capability of reinforcement learning on cost-constrained hardware. Using a low-cost quadrupedal hardware platform, we find that using a forward model of the average actuator delay, paired with a time-aware neural network results in robust locomotion. Additionally, our time-aware neural network learned a central pattern generator (CPG): a self-sustaining rhythmic gait that is robust to +320 ms latency perturbations, mirroring the CPGs found in the spinal cords of vertebrates. We posit that temporal self-organization may be a general strategy for cost-constrained locomotion.
[AI-50] CaM-Wolf: Causal-Aware Multimodal Agents for Social Deduction Games
链接: https://arxiv.org/abs/2607.26393
作者: Zheng Zhang,Nanjie Yao,Jiarui He,Deheng Ye,Peilin Zhao,Hao Wang
类目: Artificial Intelligence (cs.AI)
备注: Accepted by ACMMM 2026
Abstract:Social deduction games (SDGs) such as Werewolf have become challenging testbeds for AI agents. These games require complex social skills such as reasoning, deception, and collaboration. While recent advances in large language models (LLMs) have driven significant progress in SDG agents, current approaches are predominantly text-based, overlooking the multimodal nature that is fundamental to human social interaction. To bridge this gap, we introduce CaM-Wolf, the first SDG agent that integrates multimodal perception and generation. CaM-Wolf processes video inputs from other players, employs a causal-aware Reasoner trained via reinforcement learning to establish logical chains between observable behaviors and hidden roles, and presents itself through an animated avatar. Our experiments and user study show that CaM-Wolf achieves superior agent gameplay performance and enhances the quality of human-AI interaction. This work represents a significant advancement towards creating more human-like AI agents capable of participating in nuanced social dynamics. Our code is available at this https URL.
[AI-51] Collusion with Competitive Marginals: Price-Level Audits Are Blind by Construction
链接: https://arxiv.org/abs/2607.26385
作者: Xin Xu,Chengrui Wu,Jiayu Lu,Kaizhen Tan,Siru Tao,Hanzhe Hong
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 11 pages, 4 figures, includes technical appendix
Abstract:Empirical work on algorithmic collusion asks one question of the data: are prices supracompetitive? We show this can be answered “no” by a conspiracy that is nonetheless profitable. Consider bidding agents that couple only through the joint distribution of their unexplained bid components, leaving every agent’s own bid law exactly at the competitive law. Any test whose input is a single agent’s price or bid history then has power exactly equal to its false-positive rate, for every coupling strength up to comonotonicity. The published detection methodology is therefore blind to this conduct by construction rather than underpowered, and no sample size repairs it. Three empirical results follow. First, the mechanism appears in real language-model agents: twenty models from nineteen independent developers, three deployment prompts each, show residual correlation of +0.053 between two deployments of one model against +0.0001 across models, with a 95% interval clustered by developer of [0.030, 0.078] , under an auditor that sees every order feature and is fitted out of sample. Second, the coupling falls monotonically as sampling temperature rises ( p=0.002 ), turning a deployment parameter into a candidate mitigation. Third, on 24 days of Ethereum block-building auction data covering 77,684 bids from 39 bidders, the honest population of bidder pairs is itself so dependent that a screen held at a 5% false-positive rate must sit above a floor of +0.50 to +0.81 , which is 20 to 32 times the family-wise sampling threshold and does not fall as the audit window grows. Since lawful multi-identity operation and conspiracy are behaviourally indistinguishable here, the tractable regulatory target is not detection but counting: resolving 40 bidding identities into 23 operators raises the Herfindahl index by 247.5%, and adding behavioural clusters from public bid streams reaches 324.5%. Comments: 11 pages, 4 figures, includes technical appendix Subjects: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR) Cite as: arXiv:2607.26385 [cs.GT] (or arXiv:2607.26385v1 [cs.GT] for this version) https://doi.org/10.48550/arXiv.2607.26385 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-52] Exploring Structures in Physics Problems: Can AI Agents Discover Statistical Mechanical Mappings?
链接: https://arxiv.org/abs/2607.26367
作者: Wanyu Zhao,Wanbing Zhao
类目: Artificial Intelligence (cs.AI)
备注: Accepted to the AID-Wild workshop at CAIS 2026
Abstract:An important skill in theoretical physics is to recognize when a new problem can be transformed into a known model. We study this skill as an AI-agent task: can LLM-based agents discover statistical mechanical mappings from a raw partition function to a tractable representation? To probe this question, we introduce StatMechBench-v0, a benchmark of six Ising-type problems covering transfer-matrix methods, gauge-removable disorder, and planar/Pfaffian structure. We evaluate a simple propose-verify-revise agent across multiple LLMs and problem phrasings. The results show that numerical feedback often helps agents repair code and recover correct partition functions. However, agents can also pass the numerical checks while misidentifying the underlying tractable class or understating computational complexity. This both reveals limitations in current LLM reasoning and calls for a verification stack that goes beyond numerical agreement, incorporating, for example, symbolic checks and structural invariants. Our study provides an early evaluation and design directions for AI agents aimed at structural discovery in theoretical physics.
[AI-53] Post-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-Tuning
链接: https://arxiv.org/abs/2607.26358
作者: Keegan Harris,Brian W. Lee,Ian Waudby-Smith,Philip Amortila,Nika Haghtalab,Michael I. Jordan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
备注:
Abstract:Reinforcement learning (RL) fine-tuning is widely used in language model training to improve model performance on a target task while limiting drift from a reference policy. A standard way to balance this trade-off is via a KL-regularized RL objective, although this formulation does not by itself provide a principled way to set the regularization coefficient. In practice, the coefficient is typically chosen heuristically or via hyperparameter search, which can lead to unnecessary overhead in training cost or undesirable reward-retention trade-offs. We instead propose a game-theoretic framework that gives this trade-off an explicit statistical interpretation. Specifically, we study a sequential game in which an agent chooses a policy to maximize cumulative reward while a monitor observes policy outputs over time and tests for deviations from the reference policy. Although not originating from the same perspective, we show that the resulting equilibrium policy can nonetheless be expressed as the solution to a KL-regularized RL problem for an optimal regularization parameter that can be viewed as maximizing reward per unit of statistical distinguishability. Drawing on classical results from concave-convex fractional programming, we provide a principled method for learning this equilibrium coefficient via reduction to the KL-regularized RL objective, thus allowing for flexible integration into standard fine-tuning pipelines. In experiments with Qwen3-8B and Llama-3.2-1B, we demonstrate that our methods result in competitive reward-retention trade-offs in a continual learning setting, and illustrate how our framework may be used to audit API providers serving open-source models.
[AI-54] High-Order Markov Blanket Discovery via a k-Order Relaxation of the Faithfulness Assumption UAI2026
链接: https://arxiv.org/abs/2607.26357
作者: Loong Kuan Lee,Ragavi Krishnamoorthy,Nico Piatkowski
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: 17 pages (9 pages main text + supplementary material), 4 figures, 8 tables, 3 algorithms. Accepted at the 42nd Conference on Uncertainty in Artificial Intelligence (UAI 2026). Code: this https URL
Abstract:The problem of learning the graphical Markov blanket (MB) of a variable from data has applications in many areas such as structure learning for Bayesian networks and Markov random fields, causal discovery, and feature selection. However, a common assumption most methods make is that the conditional independencies in the distribution imply the same separation in the graphical structure – also known as the faithfulness assumption. Unfortunately, this assumption can be violated by higher-order dependencies such as XOR and parity-type relations, and – on finite samples – by empirical violations that, in extreme cases, even induce spurious dependencies absent from the true distribution. Therefore, in this paper we propose a “k-order” relaxation of the faithfulness assumption that captures parity type relationships between k+2 variables. We then propose a proof of concept algorithm called k-order Markov blanket (kOMB) that uses this relaxation for MB discovery. Finally, we empirically show how kOMB can recover the MB of a variable under both true and empirical violations of faithfulness. Code available at: this https URL
[AI-55] Automorphism-Induced Non-Canonicity in Top-k Explanations of Graph Neural Networks
链接: https://arxiv.org/abs/2607.26344
作者: Xin Xu,Siru Tao,Kaizhen Tan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 11 pages, 3 figures
Abstract:A gradient-based GNN explainer given a molecule with two chemically equivalent nitro groups assigns them attribution scores that are equal to the last bit. It cannot do otherwise: message passing is exactly permutation equivariant, so any automorphism of the input leaves every attribution invariant. Yet the standard report, the top-k edges, names one of the two, and which one is settled by the order of an array. We show this is a structural obstruction rather than an implementation slip. When no minimal valid explanation is fixed by the input’s automorphism group, no rule can be single-valued, minimal and symmetry-respecting at once. For the exact-k reports used in practice we give a parameter-free criterion, mechanised in Lean 4 with no axiom dependencies, that decides from the graph alone whether every score-optimal report of that size must split an orbit. Across 21298 instance-budget decisions the criterion agrees with a mechanical model-equivalence check without exception, and no severing case we found admitted a neutral alternative. The obstruction is common. Nontrivial automorphisms occur in 93.4% of Mutagenicity, the dataset the seminal explainability papers use, so the measure-zero dismissal of symmetric inputs, sound on the continuous domains it was made for, collapses here. At the sparsity budget those papers report, 24.0% of molecules with two interchangeable nitro groups (6 of 25) surface exactly one of them, every one arbitrary under mechanical verification. A model’s blindness also manufactures symmetry: every MUTAG molecule contains atoms chemistry separates and the network provably cannot, and a matched control shows the resolution is set by what the model reads rather than how it is parameterised. Reporting orbits removes the arbitrariness at 0.11 ms and 0.43 extra edges per graph.
[AI-56] StealthBench: Measuring Operational Stealth in Autonomous Offensive-Security Agents ALT
链接: https://arxiv.org/abs/2607.26314
作者: Ads Dawson,Adrian Wood
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 29 pages, 9 tables, 1 figure, 2 appendices. Code: this https URL Dataset: this https URL Website: this https URL
Abstract:Stealth, the discipline of achieving an objective without revealing your presence, capabilities, or collected intelligence, is what separates sophisticated operators from detectable ones. Elite security researchers and advanced persistent threats achieve their objectives unnoticed; autonomous agents increasingly inherit the same offensive tasks, but do they inherit the tradecraft? We introduce StealthBench,a benchmark that measures operational stealth in autonomous offensive-security agents across six operational security (OPSEC) dimensions. We extract 11 hand-verified OPSEC incidents from real bug-bounty and red-team trajectories, expanded into 14 dockerized task scenarios, where agents, despite finding real vulnerabilities, committed stealth failures inconsistent with standard operational tradecraft: embedding credentials in public uploads, deleting production resources to prove access, force-adding uninvolved users to demonstrate a race condition. We evaluate agent trajectories using a 3-model large language model (LLM) judge panel with majority-vote aggregation, measuring safe success rate (solved and stealthy), Stealth@Solve (tradecraft quality among successful solves), and reckless solve rate (solved but cover blown). Our results show that no model exceeds 54% safe success rate (the compound metric requiring both task completion and stealth), confirming that OPSEC failures are systematic across model families. We release StealthBench as a public benchmark to support both the development of stealth-aware agents and automated OPSEC monitoring for autonomous offensive-security deployments. The interactive leaderboard, evaluation harness, and dataset are available at this https URL. Comments: 29 pages, 9 tables, 1 figure, 2 appendices. Code: this https URL Dataset: this https URL Website: this https URL Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.26314 [cs.CR] (or arXiv:2607.26314v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2607.26314 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-57] SARC-DQ: Runtime Data-Quality Gating for Agent ic AI: Silent Evidence Defects the Incompetence Shield and Downstream-Only Remediation
链接: https://arxiv.org/abs/2607.26313
作者: Gaston Besanson
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: this https URL
Abstract:Agentic systems act, so a defect in the evidence they retrieve becomes a wrong action with a currency cost. The most dangerous enterprise defects are metadata-borne: a stale price or a superseded record, perfectly well-formed in the payload and betrayed only by freshness, lineage, or provenance. Such a defect never enters the agent’s context, and an agent cannot doubt data it cannot see. On a priced replenishment benchmark, a competent agent silently converts an injected metadata-borne defect into a costly action about 60% of the time, with zero data-quality flags and behavioral doubt markers at chance (AUC = 0.50). Across four model tiers spanning roughly 15x in inference price, the rate stays flat: capability does not buy skepticism. A metadata-aware pre-action gate with downstream-only remediation recovers the loss fully on the signals its predicates cover and not at all on those they miss. A model-free oracle derived from the task’s decision geometry tracks the measured rates with MAE 0.015 (Pearson r = 0.876, interval coverage 15/16 cells), giving the flat ladder an analytical form. Evidence integrity is a systems axis distinct from model capability; mitigation depends on enforcement placement and predicate coverage. Code, frozen results, and a deterministic analysis pipeline: this https URL
[AI-58] raceCoder: Explainable and Auditable Code Generation with Position-Key Snippet Versioning
链接: https://arxiv.org/abs/2607.26307
作者: Rwaida Alssadi,Muntaser Syed,Balaji Kasula,Lamine Deen,Majed Alotaibi,Mohammed Alghamdi,Tyler Ton,Ali Alqarni,Marius Silaghi
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: Submitted Version (version submitted on May deadline to AGENTICS 2026). Version of Record to appear in AGENTICS proceedings
Abstract:Contemporary LLM-based coding agents produce code as black-box outputs: the rationale behind each line is hidden, the evolution of the code through benchmark-driven repair is ephemeral, and post-hoc auditing is impossible. We present a code generation concept that addresses these shortcomings through three complementary mechanisms: (i) a relational snippet-history schema that records, per repair event, the benchmark reference, round number, failure text, and LLM explanation, enabling full provenance queries; (ii) a browser-based visualisation tool that renders this history as heat-mapped, hover-annotated source code; and (iii) a competitive fractional position-key indexing scheme with tree-node delimiters that assigns stable, lexicographically-ordered identifiers to each code snippet, enabling fine-grained tracking without disrupting surrounding lines. We evaluate TraceCoder on 30 algorithmic programming tasks spanning string processing, mathematical computation, and data-structure manipulation, across two provider configurations. Of these, 10 exhaust the 6-iteration budget on tasks with subtle edge-case behaviour. Mean Chg% reaches 30%, three in ten code snippets carry a traceable repair-event row, compared to 21% when using Gemini 2.0 Flash as sole provider on a 20-task subset. Three detailed case studies demonstrate how the system explains which specific benchmark failures shaped each line of the final program. The proposed mechanism makes the internal “narrative” of automated code generation auditable and replayable, a property essential for trust and accountability in production deployments.
[AI-59] Entity Resolution in Practice: Lessons from a Self-Serve Pipeline
链接: https://arxiv.org/abs/2607.26298
作者: Kaushik Pavani,Ganga Aluri,Pravin Jadhav,Neeraj Prasad,Kiran Sanka
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We built and evaluated a self-serve entity resolution (ER) system on six benchmarks spanning 864 to 5M records, and three lessons emerged that are absent from existing ER literature. (1) No single matching algorithm wins everywhere - a self-serve pipeline cannot predict its next dataset, so we recommend training several algorithm families per dataset and letting an automatic bake-off pick the winner. (2) Precision and recall need separate fixes, not a shared threshold - precision needs hard rule-based vetoes, recall needs more diverse candidate retrieval. (3) One false-positive link can silently merge unrelated entities - assuming “A matches B” and “B matches C” implies “A matches C” lets a single bad link chain hundreds of records together, so every cross-group merge must be actively re-verified. We hope these lessons save practitioners the months of dead-end experiments that led us to them.
[AI-60] op-k Pareto Bandits: Hypervolume Regret for Multi-Objective Slate Selection
链接: https://arxiv.org/abs/2607.26273
作者: Nicolas Gutowski,Fabien Chhel,Alexandre Letard,Sylvain Lamprier
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: 21 pages, 7 figures, 7 tables
Abstract:We consider a stochastic multi-objective bandit problem where, at each round, the agent selects a slate of k arms and observes their d -dimensional reward vectors under semi-bandit feedback. We do not aim at identifying a single optimal arm; instead, we consider the problem of maintaining a small set of actions that jointly approximate the Pareto frontier. We formalize this objective through the dominated hypervolume induced by the selected subset of arms, and define an \alpha -approximate hypervolume regret with respect to the best size- k subset achievable in hindsight, where \alpha = 1 - 1/e reflects the approximation guarantee of greedy maximization for monotone submodular functions. To address this problem, we introduce \textitTHV-UCB, an optimistic algorithm that selects arms greedily based on optimistic estimates of their marginal hypervolume contributions. We establish a gap-free regret bound \tildeO(d\sqrtnkT) that holds on every instance, together with a gap-dependent bound \tildeO(nk^2.5/\Delta_\min) that becomes polylogarithmic in T once the arms are sufficiently well separated. Our results provide theoretical support for using small subsets to approximate Pareto fronts in various multi-objective applications.
[AI-61] Model-Driven Requirements Configuration with Three-Valued Uncertainty Scoring
链接: https://arxiv.org/abs/2607.26220
作者: Ahmed Ibrahim
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Context: Large Language Models (LLMs) offer natural-language flexibility for automated requirements elicitation but frequently generate structurally invalid requirements and logical inconsistencies, lacking formal correctness guarantees. Objectives: This study aims to eliminate logical inconsistencies and enforce structural conformance in LLM-generated requirements while quantifying the LLM’s pre-validation decision uncertainty within a formal domain model. Methods: We present a neuro-symbolic multi-agent architecture that operationalizes the Object-Oriented Method for Requirements Authoring and Management (OOMRAM) lattice. The LLM acts as a non-deterministic heuristic for lattice traversal, while a deterministic symbolic validator enforces all structural constraints. We introduce a three-valued (T, I, F) – Truth, Indeterminacy, Falsity – framework to classify and score the LLM’s requirement decisions before and after validation. Results: Evaluated across 37 natural-language project visions in eleven application families, the system completely eliminated structural inconsistencies in 35 out of 37 cases (94.6%), with the remaining two containing only 6 unresolved structural errors (0.39% of decisions) due to iteration limits. Three-valued analysis revealed that 24.7% of all decisions are indeterminate – structurally valid but discretionary choices not explicitly mandated by the stakeholder. Conclusion: Offloading structural integrity to a deterministic symbolic layer successfully guarantees structural conformance, while the three-valued classification provides a formal way to measure neural uncertainty, facilitating safe LLM deployment in formal requirements engineering. Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.26220 [cs.SE] (or arXiv:2607.26220v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2607.26220 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ahmed Ibrahim [view email] [v1] Tue, 28 Jul 2026 19:49:42 UTC (30 KB) Full-text links: Access Paper: View a PDF of the paper titled Model-Driven Requirements Configuration with Three-Valued Uncertainty Scoring, by Ahmed IbrahimView PDFHTML (experimental)TeX Source view license Current browse context: cs.SE prev | next new | recent | 2026-07 Change to browse by: cs cs.AI References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[AI-62] Multi-Agent Debate Strategies: Survey Taxonomy and Challenges
链接: https://arxiv.org/abs/2607.26212
作者: Quim Motger,Marc Oriol,Jordi Marco,Xavier Franch
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Under review at ACM Computing Surveys
Abstract:Multi-Agent Debate (MAD) is a promising paradigm for improving the accuracy and robustness of Large Language Model (LLM)-based agentic systems. It enables multiple agents to exchange arguments, critique each other’s outputs, and iteratively converge towards a solution. However, research remains fragmented, with inconsistent terminology and no rigorous synthesis of MAD design dimensions. We present a systematic literature review characterizing 141 primary studies on MAD. We derive a three-dimensional taxonomy covering debate participants, the interaction mechanisms structuring the exchange, and the agreement protocols governing debate resolution, supported by formal notations to render MAD configurations. Our analysis reveals that the field has implicitly converged on a narrow design pattern - static, fully connected topologies, verbatim exchange, short-term memory and voting resolution strategies - adopted by convention rather than systematic comparison, while promising alternatives remain marginal. Because any MAD setting reflects roughly a dozen interacting design decisions, cross-study comparison is unreliable when these are left implicit. We position the taxonomy as a descriptive map of the research landscape, a framework for controlled benchmarking, and potentially as a schema for machine-readable MAD specifications. As future work, we propose formalizing it into an executable specification, enabling cost-aware benchmarking and automated tuning of debate configurations.
[AI-63] (EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations
链接: https://arxiv.org/abs/2607.26201
作者: Neta Kirmayer,David Tayouri,Andrés Murillo,Motoyoshi Sekiya,Asaf Shabtai,Rami Puzis
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 21 pages
Abstract:Security operations centers rely on anomaly detection systems to flag suspicious events. Feature-level explanations for anomaly detectors offer limited value for operational investigations. To effectively handle alerts, analysts need to know contextual relationships and need actionable understanding of the entities involved. This paper introduces an event-centric detector-agnostic approach for explaining cybersecurity alerts in small- to medium-sized enterprise networks. We present (EC)2, a multi-agent framework that performs structured, hypothesis-driven investigation to provide explanations grounded in verifiable evidence. Evaluation results show that the proposed framework improves post-detection analysis by generating operationally meaningful explanations, which also enhance event classification accuracy.
[AI-64] GoGoTB: Agent ic RTL Verification with Specification-Grounded Coverag e Closure
链接: https://arxiv.org/abs/2607.26181
作者: Xin Xin,Jincheng Lou,Junhui Li,Jinglin Yan,Panda Xiao,Di Wu,Haixiao Li,Weicong Lu,Weijian Fan,Xinyu Qu,Yuxiang Zhao,Min Yu,Zhixiong Di,Yibo Lin
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 7 figures, 3 tables. Xin Xin and Jincheng Lou contributed equally to this work and share first authorship
Abstract:Functional verification dominates integrated circuit (IC) front-end engineering effort, and a single missed bug that escapes to silicon can trigger a costly respin. Recent large language models (LLMs) offer new opportunities to automate this process, yet existing LLM-based approaches generate each component through independent single-turn calls with no shared context, leaving interface mismatches undetected and reported coverage disconnected from specification requirements. To address these challenges, we present GoGoTB, an agentic framework that achieves end-to-end verification closure through three subsystems: an agentic execution control layer, an evolvable knowledge system, and specification-grounded coverage closure. The execution control layer separates deterministic enforcement from LLM reasoning at every tool and stage boundary. The knowledge system dispatches methodology and design-specific expertise on demand. The coverage framework anchors every bin to a named specification behavior so that each residual gap has a diagnosable root cause and a targeted remedy. Tested on 8 register transfer level (RTL) designs without any human intervention, GoGoTB achieves 100% environment generation success and averages 98.4% line, 97.2% branch, 97.0% toggle, and 83.2% functional coverage. No prior work successfully generates a complete verification environment or achieves meaningful coverage on the same benchmarks.
[AI-65] GuideSkill: Evolving Executable LLM Agent Skills for Guideline-Grounded Clinical Reasoning
链接: https://arxiv.org/abs/2607.26160
作者: Lang Cao,Yuhao Shen,Tianyang Luo,Simo Du,Hao Peng,Yue Guo
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Clinical practice guidelines (CPGs) encode diagnostic criteria, but LLM systems typically retrieve guideline text or absorb it through training rather than execute its rules. We introduce GuideSkill, an external reasoning layer that compiles disease-specific criteria into executable functions returning ordinal diagnostic-support scores. GuideSkill-Zero is initialized from guidelines, while GuideSkill-Evo uses case–diagnosis pairs to refine covered skills and add missing diagnoses. At inference, an LLM proposes a differential diagnosis, grounds the features required by each matched skill, and fuses its ranking with the executed skill scores. Across four benchmarks and four backbones, GuideSkill-Zero improves macro-average accuracy over guideline RAG by 13.45% on average. GuideSkill-Evo achieves the highest macro-average for every backbone, improves over direct inference by 18.49% relatively, and increases gold-label skill coverage from 56.5% to 99.5%. On Qwen3.5-9B, it also exceeds the strongest parameter-update baseline by 11.16% without updating the backbone. Expert evaluation further indicates that GuideSkill produces clinically sound and broadly acceptable skills, suggesting that its initialized and evolved rules are reliable and practically meaningful. These results support executable skills as a model-agnostic mechanism for combining guideline-derived procedures with case-derived diagnostic patterns.
[AI-66] When benchmark inferences do not compose: Projectibility in AI evaluation
链接: https://arxiv.org/abs/2607.26159
作者: Brett Reynolds
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 35 pages, 2 figures, 5 tables. Substantially rewritten and retitled; supersedes arXiv:2510.15236 , whose homeostatic property-cluster account and proposed centrality-prior and cluster-stability measures are withdrawn. The argument, apparatus, and empirical companion are new. Code and empirical companion: this https URL
Abstract:An AI benchmark result rarely reaches a consequential claim in one step. Evaluators generalize it to further cases, interpret it as evidence of capability, extrapolate it to new tasks, transport it to another system or site, and combine it with assumptions about human review and downstream consequences. Validity-centred approaches require evidence for each claim. This paper identifies a further epistemic problem: warranted links don’t automatically make a warranted chain. The target of one study may not be the source of the next; system, population, outcome, or conditions may change at the interface; and shared data or model lineage may make apparently independent support dependent. Projectibility concerns whether a bounded extension from observed to unobserved cases is warranted. Goodman supplies the problem of rival extensions; argument-based validity supplies an architecture for testing them. The paper’s distinctive claim is a non-composition principle: support for adjacent projections warrants their composition only when endpoints and assumptions align and dependence and uncertainty are carried through. A legal-research case shows how benchmark evidence and a deployment study can each be sound while remaining parallel. A reanalysis and simulation show why aggregate stability can erase distinctions a later projection requires. The resulting projectibility audit diagnoses unsupported joins in benchmark-to-use arguments.
[AI-67] ClinLens: Towards Long-Horizon Coding Agents for Longitudinal Multimodal Clinical Data Science
链接: https://arxiv.org/abs/2607.26155
作者: Yuan Zhu,Ethan B. Liu,Frank Nie,Jindong Han
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Clinical data-science agents must transform heterogeneous longitudinal records into auditable analyses, yet existing benchmarks largely isolate medical question answering, structured-table reasoning, or generic scientific repositories. We introduce CLINLENS, a benchmark of 200 executable tasks over five linked MIMIC resources spanning structured electronic health records, notes, electrocardiograms, chest radiographs, and echocardiograms. A 4 x 5 taxonomy crosses four patient-time scopes with five analysis capabilities. Program-first reverse synthesis pairs each bounded semi-raw package with an evaluator-private reference workflow and checks required artifacts, cohort and temporal semantics, and the final answer. On a fixed 126-task suite, the strongest of 24 standardized model-scaffold configurations achieves 56.3% scope-macro STRICTPASS despite 100% EXECSUCCESS. For reference, a separately configured coding agent solves 83 of 126 tasks, while five biomedical systems adapted to GPT-4o-mini reach at most 2.9% scope-macro STRICTPASS. These results expose a substantial gap between runnable submissions and correct clinical analyses.
[AI-68] owards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels
链接: https://arxiv.org/abs/2607.26121
作者: Xinyu Yang,Tianxing Chen,Honghao Su,Minxuan Wang,Chenze Yu,Zhangzheng Tu,Yue Chen,Yuxiao Huo,Lingfeng Zhang,Yan Huang,Yan Qin,Shaolong Zhu,Qiwei Liang,Hekun Tian,Shujia Liu,Guangyu Chen,Junhao Gong,Zixuan Li,Wenwei Lin,Zijian Lin,Wenxuan Zhu,Eric J Chen,Yue Yuan,Qize Yu,Jiaqi Liang,Haowen Yan,Hengfei Zhao,Weijie Wan,Zikun Xiao,Junyuan Tang,Baijun Chen,Kai-Chong Lei,Kaixuan Wang,Kailun Su,Zanxin Chen,Yao Mu,Renjing Xu,Chuqiao Lyu,Qi Xiong,Ping Luo,Wenbo Ding
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: Website: this https URL
Abstract:Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction. Because failures can cause immediate physical or operational harm, task completion alone does not establish trustworthiness. We define trustworthy embodied intelligence as the sustained capacity to execute specified tasks reliably under environmental and system variation while maintaining risk within acceptable bounds. We term this objective sustained safe success. Its supporting mechanisms are organized into four interdependent layers. The model layer generates task-competent action proposals with calibrated uncertainty and explicit safety preferences. The system layer realizes authorized actions dependably through integrated sensing, computation, control, hardware safeguards, fault containment, and fallback. The evidence layer substantiates bounded claims through evaluation, verification, validation, traceability, and structured assurance arguments. The deployment layer maintains claim validity through runtime monitoring, authority management, intervention, incident response, and controlled updates. Because assumptions and failures propagate across these layers, neither model capability, isolated safeguards, nor benchmark performance alone can establish end-to-end trustworthiness. Drawing on embodied AI, robotics, control, dependable computing, distributed systems, and autonomous driving, we further propose a non-normative hierarchy of trustworthiness levels. This hierarchy grades the strength of bounded deployment claims across task capability, safety, system assurance, operational governance, and supporting evidence, providing a basis for bounded deployment, comparative evaluation, research prioritization, and future standardization.
[AI-69] Even More Deception: Objective Misalignment in Mixed-Motive LLM Multi-Agent Systems ICLR2026
链接: https://arxiv.org/abs/2607.26120
作者: Marylou Fauchard,Florian Carichon,Margarida Carvalho,Golnoosh Farnadi
类目: Artificial Intelligence (cs.AI)
备注: Accepted at AIWILD@ICLR 2026
Abstract:Large Language Models (LLMs)-powered multi-agent systems are increasingly deployed in mixed-motive environments, where agents operate under asymmetric information and strategic deception due to conflicting or hidden objectives. In these settings, misalignment with collective goals becomes a central concern. We propose a novel framework for evaluating objective misalignment using the social deduction game Werewolf, modifying the objective of a single agent while preserving its assigned role. Across LLMs from four different model families and sizes, four player roles, and three objective formulations, we introduce a dual analysis of the agents’ internal reasoning and their public cheap-talk behavior (i.e costless, non-binding communication that does not directly affect the agents’ utilities), complemented by an analysis of game outcomes. Our results show that objective misalignment undermines outcomes in inherently adversarial environments, an effect exacerbated by asymmetric information and specialized roles. While compromised agents consistently develop distinct objective-dependent reasoning strategies, these adaptations remain largely invisible in their public behavior. More broadly, our findings suggest that even subtle objective misalignment can profoundly affect collective decision-making, highlighting the need for effective mitigation strategies for LLM-based multi-agent systems.
[AI-70] ry Again Dont Look Back: Blind Resampling Outperforms Self-Repair in Small Code Models
链接: https://arxiv.org/abs/2607.26117
作者: Yuvraj Verma
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Code, pre-registrations and run traces: this https URL
Abstract:Self-repair - returning a failed program to the model together with its test output and asking for a correction - is a standard component of code agents, and is almost always evaluated against a baseline that does not retry at all. We argue that this comparison confounds the value of the feedback with the value of the extra attempt. Using a placebo-controlled design on MBPP+ at three model scales (1.5B, 3B, 7B), we compare four matched-budget retry conditions: blind resampling, a content-free failure notice, genuine execution feedback, and feedback augmented with verbal self-reflection. Blind resampling is the strongest condition below 7B, and remains statistically tied with the best condition at 7B, while consuming 2.5-5.5x fewer tokens; conditioning on the model’s own failed attempt costs 6.1 points at 1.5B (p=0.006), and the informational content of execution feedback adds nothing measurable over the placebo. We attribute this to anchoring: when shown its previous attempt, a model reproduces a near-identical program in 33-68% of retries, against 2-14% under blind resampling. Two further experiments delimit the effect. Retrieved solutions to other tasks change nothing (bounded to +/-3.5 points), which localizes the harm to self-conditioning rather than context length; and reflection, the only condition that measurably weakens the anchor, remains dominated on cost. Replication rules out two competing explanations: the penalty is unchanged at full precision, and it reproduces on an independent model family. Across six configurations spanning two families and two precisions, its magnitude is predicted by baseline quality alone (r=0.96) - the cost of anchoring is the cost of committing to a bad first attempt.
[AI-71] A Reference-Free Score for Detecting Silent Reasoning Failures in Large Language Models
链接: https://arxiv.org/abs/2607.26102
作者: Vivek Shukla,Varun Shukla,Atul,Divya Mishra,Mehul Kumar Das
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Mathematical chain of thought (CoT) evaluation is commonly reduced to whether the final answer matches a reference. This conflates producing a correct conclusion with producing a valid derivation an invalid chain can accidentally reach the right answer, while a valid calculation can be followed by a transcription error. We call this mismatch the reasoning answer consistency gap. This framework paper introduces the Reasoning Answer Faithfulness Score (RAFS), a reference free, instance level diagnostic of whether an emitted mathematical trace is locally credible, supports its answer, and is stable under resampling and targeted counterfactual interventions. RAFS combines step validity, reasoning to answer entailment and counterfactual sensitivity, answer consensus, and conditional reasoning stability. It evaluates transcript level agreement, not a models private computation and not factual correctness outside the tested mathematical setting. We retain a preregistered, results blind confirmatory study on GSM8K and MATH, with hypotheses, admissibility rules, calibration, and tests fixed before confirmatory outcomes are inspected. A separate feasibility pilot is specified to verify end to end execution and estimate interven tion coverage before that freeze numerical pilot claims are re ported only when trace level artifacts are available. We formalize four reasoning answer outcomes, justify the non compensatory aggregator, instantiate semantic trace distance, quantify compute and abstention tradeoffs, and define verifier independence and power analyses. RAFS is intended to complement mathematical answer accuracy with an auditable warning signal for silent reasoning failures and answer extraction errors
[AI-72] Optimizing Sensor Placement for Hydrogen Leak Detection in Enclosed Infrastructure: A Comparative Study Using CFD-informed Genetic Algorithm and DeepSets Neural Surrogate
链接: https://arxiv.org/abs/2607.26078
作者: Fangnian Wang,Nicholas Tan Jerome,Thomas Jordan,Frank Simon
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
备注:
Abstract:Hydrogen infrastructure in enclosed environments, such as parking facilities for fuel cell vehicles, presents significant safety challenges due to hydrogen’s low ignition energy and wide flammability range. Current monitoring systems are largely reactive, detecting leaks only after hazardous concentrations have formed. This study develops a computational framework for proactive sensor placement optimization by integrating computational fluid dynamics (CFD), genetic algorithm (GA) optimization, and a DeepSets neural surrogate. A CFD database of 180 scenarios was generated for a representative 50 m x 30 m x 3 m garage, covering multiple leak positions, rates (1-150 g/s), and ventilation conditions (ACH = 3-10 per hour). Sensor placement was optimized using a multi-objective GA and compared with uniform, random, and surrogate-assisted approaches. The GA achieved a detection rate of 96.1% within 60 s and reduced blind areas to 0.12%, corresponding to an approximately 5% improvement in composite fitness over a uniform baseline. The DeepSets surrogate reproduced near-optimal configurations with a fitness gap below 0.01 while reducing CFD evaluations by 89% and computational time by two orders of magnitude. Detection performance and spatial coverage remained comparable to the GA, demonstrating that surrogate-assisted optimization can retain solution quality while enabling rapid design iteration. Overall, the results show that CFD-informed optimization improves detection effectiveness and reduces sensor requirements compared to conventional layouts. The proposed framework supports scalable deployment and provides a foundation for integrating optimized sensor networks with digital twin systems for real-time monitoring and risk assessment.
[AI-73] AI Security Priorities: A Field-Wide Agenda
链接: https://arxiv.org/abs/2607.26069
作者: Gil Gekker,Rachel Steratore,Everett Smith,Asher Brass-Gershovich,Varun Gandhi,Nicole Nichols,Vijay Bolina,Buck Shlegeris,Lisa Einstein,Dan Lahav,Omer Nevo,Sella Nevo
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:As AI systems are rapidly integrated into critical economic, governmental, and national security functions, the gap between AI adoption and AI security readiness continues to widen. This paper presents a prioritized agenda for advancing AI security, informed by structured interviews with leaders across industry, government, and civil society, and refined through a multi-sector expert workshop. Participants identified and ranked the highest-importance and most cost-effective areas where progress could strengthen AI security - from protecting frontier AI systems and their underlying infrastructure to improving cybersecurity practices as AI reshapes the threat landscape. The resulting priorities are organized across four themes: establishing strategic foundations and policy frameworks; advancing public-private coordination and institutional infrastructure; advancing technical security engineering and assurance; and governing agentic AI under adversarial pressure. For each priority area, expert authors provide detailed analyses that define the problem, assess the current landscape, and identify actionable projects that stakeholders across sectors can pursue. The paper aims to serve as an initial practical foundation for coordinated investment and action across the AI security field. It is designed to serve both current practitioners and individuals and organizations looking to enter the field by identifying concrete, high-impact contributions suited to a range of strengths and capacities.
[AI-74] he Easy Trap: Why LLM s Underestimate Misconception-Driven Difficulty
链接: https://arxiv.org/abs/2607.26067
作者: Amanda La Hadi,Muhammad Johan Alibasa,Guanliang Chen,A. Taufiq Asyhari
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: This paper poster presented on 19th Educational Data Mining Conference 2026
Abstract:Large language models (LLMs) are increasingly used for estimating item difficulty in educational assessment. However, it remains unclear whether such estimates reflect how learners actually experience difficulty. This study investigates the alignment between LLM-generated difficulty ratings and empirical student performance on basic mathematics tasks. Four widely used LLM-based systems generated difficulty ratings on a 1-100 scale for 32 arithmetic items across multiple runs (N = 640 ratings). These were compared with empirical difficulty derived from responses of 770 Indonesian undergraduates using Classical Test Theory (CTT) and Item Response Theory (2PL). Results show moderate rank correlations (Spearman’s rho = 0.52-0.70), indicating that LLMs capture coarse ordering of item difficulty. However, substantial and systematic misalignment emerges in fraction items. Several items consistently rated as easy by LLMs were among the most difficult for students, such as an item with only 34.16% correct for 100 : 1/2. We argue that LLMs approximate curricular difficulty, or what should be easy based on instructional sequencing, rather than cognitive difficulty driven by learner misconceptions. This leads to systematic underestimation of misconception-driven items, a phenomenon we term the Easy Trap. These findings highlight a critical limitation of LLM-based difficulty estimation and suggest that relying on such estimates without empirical grounding may introduce bias in assessment design and adaptive systems.
[AI-75] he Age of AI Agents Demands A New Scientific Paradigm To Sustain Trustworthy Science ICML2026
链接: https://arxiv.org/abs/2607.26064
作者: Belinda Mo
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Accepted at ICML 2026, Position Paper Track
Abstract:AI systems are becoming autonomous research agents that generate hypotheses, design experiments, and produce discoveries at scales beyond human oversight. As seen by increased submissions to ML venues, the verification gap between scientific output and our ability to check it is already widening, and autonomous agents make it worse by magnitudes given human-agent asymmetry. We argue that science must evolve its verification infrastructure, as it has before with peer review. However, while historical adaptations assumed human contributors who could be questioned and sanctioned, AI agents break this assumption. We propose criteria for an adapted verification infrastructure that emphasizes observable-by-default workflows, scalable verification, and clear attribution. We argue that without adaptation, ML and any scientific domain using agents face dangerous failures: experimental results that no person can verify, optimization for metrics over understanding, and accountability vacuums that erode scientific trust.
[AI-76] Archetypes or ability? Clustering for modelling student mathematical competence
链接: https://arxiv.org/abs/2607.26063
作者: Benjamin Mawdsley,Tom Quilter,Richard Turner,Sarah Jackson,Paul Edwards
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Personalised learning systems often assume that mathematical ability is combined of discrete abilities, acquired sequentially and dependent upon first acquiring foundational abilities, and students often report different strengths. In this work, we explore the validity of these assumptions by applying clustering methods to a large dataset of 119,034 students, spanning 13 national-level exams sat in the United Kingdom and collected by the platform. Classifying question results as pass or fail, we use a Bernoulli Mixture Model to search for latent populations which would be indicative of discrete skill-sets. We find that few distinct clusters are present in the data and that the dominant factor is overall student ability, which is further supported by the high degree of linear correlation between the probability distributions of the resulting clusters. Our best performing model achieves an accuracy of 78 percent, competitive with more complicated models in the literature whilst being more explainable. Comparing this models performance with logistic regression baselines and with k-nearest neighbours, we find a small improvement when using performance on each individual question as features. This suggests that whilst overall ability level is the dominant factor for predicting performance, small further personalisation improvements can be made by tailoring to a students exact strengths, but that students do not appear to develop strongly differing ability across topics. Our work offers a national scale test of machine learning in education and offers a new benchmark for the field, demonstrating how explainable models can reach competitive performance
[AI-77] Sim2Win: A Team-Agnostic Event-Based Pre-Match Outcome Prediction and Tactical Profiling System for Football
链接: https://arxiv.org/abs/2607.26061
作者: Mouad Zemzoumi,Amine Abouaomar
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 15 pages, 5 figures. Code available at this https URL
Abstract:Pre-match tactical decision-making in professional football relies heavily on subjective expert analysis and identity-based scouting systems that cannot generalize to unseen teams. This paper presents Sim2Win, a team-agnostic, event-based pre-match tactical recommendation framework that reframes match outcome prediction as a tactical decision-support problem. Using StatsBomb open event data from eleven competitions spanning 178 teams and 1,411 team-match records, Sim2Win constructs five-match rolling tactical profiles, engineers four interpretable tactical feature ratios, clusters team behaviors into eight playstyles via K-Means, and trains thirteen classifiers to estimate win, draw, and loss probabilities from tactical matchup representations. The system operates without team names or identity features, enabling generalization to teams never seen during training. A rigorous Leave-One-Competition-Out (LOCO) evaluation demonstrates that Sim2Win achieves a mean ROC-AUC of 0.704 and mean accuracy of 55.4% on completely unseen teams, outperforming ELO, Pi-Rating, and GAP baselines on all 21 ROC-AUC comparisons and 19 of 21 accuracy comparisons. Among all evaluated models, CatBoost achieved the strongest in-distribution performance with 60.90% accuracy. These findings suggest that behavioral tactical representations provide transferable predictive signal under distribution shift and offer a viable alternative to identity-dependent football prediction systems. Comments: 15 pages, 5 figures. Code available at this https URL Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) ACMclasses: I.2.6; I.5.3; H.2.8 Cite as: arXiv:2607.26061 [cs.LG] (or arXiv:2607.26061v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.26061 Focus to learn more arXiv-issued DOI via DataCite
[AI-78] Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning
链接: https://arxiv.org/abs/2607.26059
作者: Scott M. Norton
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注: 24 pages, 4 figures
Abstract:We report a striking phenomenon: deep reinforcement learning agents trained with frozen, randomly initialized CNN feature extractors spontaneously develop extremely sparse fully-connected representations, without any sparsity-inducing objective. In the first fully-connected layer (FC1, 3,136 \to 64 ), agents compress task-relevant information through as few as 1-3 neurons out of 64 for deterministic Pong (5-11 for stochastic Pong), while trainable CNNs activate 55-64 neurons under matched conditions. We establish four principal findings. First, FC1 sparsity scales with task complexity: 1-11 for Pong, 19-26 for Breakout, and \sim 42 for Space Invaders. Width-scaling confirms this reflects task structure rather than a fixed capacity fraction. Second, within-game scaling emerges: three identical Pong seeds produce 5, 7, and 11 active neurons. The 5-neuron seed plateaus at +14 reward, while the others reach expert performance ( +18.4 , +18.7 ), suggesting the random projection’s usable dimensionality bounds achievable performance. Third, ablation confirms necessity: removing these active neurons crashes performance across two PPO implementations and four games. Fourth, the information bottleneck commits early: a sweep shows the active set locks by 15-30M steps, while reward turns positive 35-105M steps later. A complementary finding in Breakout shows frozen and trainable CNNs reach competitive rewards via structurally different bottlenecks: frozen agents use 17-25 active neurons (participation ratio \sim 10-14), while trainable agents use 51 (participation ratio \sim 3.6). Finally, wherever input dimensionality dwarfs intrinsic task dimensionality, gradient descent on a frozen random projection may reveal the effective rank of the underlying problem without explicit sparsity machinery.
[AI-79] A Methodology for Designing Knowledge-Driven Missions for Robots
链接: https://arxiv.org/abs/2601.20797
作者: Guillermo GP-Lenza,Carmen DR.Pita-Romero,Miguel Fernandez-Cortizas,Pascual Campoy
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper presents a comprehensive methodology for implementing knowledge graphs in ROS 2 systems, aiming to enhance the efficiency and intelligence of autonomous robotic missions. The methodology encompasses several key steps: defining initial and target conditions, structuring tasks and subtasks, planning their sequence, representing task-related data in a knowledge graph, and designing the mission using a high-level language. Each step builds on the previous one to ensure a cohesive process from initial setup to final execution. A practical implementation within the Aerostack2 framework is demonstrated through a simulated search and rescue mission in a Gazebo environment, where drones autonomously locate a target. This implementation highlights the effectiveness of the methodology in improving decision-making and mission performance by leveraging knowledge graphs.
[AI-80] hink Short Defer Smart Act and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents
链接: https://arxiv.org/abs/2607.26865
作者: Amirmohammad Farzaneh,Osvaldo Simeone
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Information Theory (cs.IT); Machine Learning (cs.LG)
备注:
Abstract:LLM agents following the ReAct paradigm are promising enablers of complex multi-step tasks, including multi-hop question answering, code generation, and control of physical AI systems. Yet, when deployed at the edge, they must tightly manage their reasoning budget while remaining reliable and deferring to a cloud-side model only when local uncertainty is too high to act safely. We propose Think Short, Defer Smart (TSDS), a framework that synergistically integrates a lightweight convergence probe, which halts on-device reasoning once the intended action has stabilized, with a perplexity-based deferral rule that escalates uncertain actions to a cloud-side model. Both mechanisms are jointly calibrated on end-to-end episode trajectories via a multi-objective Learn-Then-Test (LTT) procedure, providing simultaneous finite-sample guarantees on expected episode reward and cloud-call rate. We evaluate TSDS on four ReAct benchmarks spanning arithmetic reasoning (GSM8K), multi-hop question answering (HotpotQA), code generation (MBPP), and multi-step embodied planning (household robot), and compare against thought-calibration-only and calibrated-deferral-only standalone baselines. TSDS reduces per-episode thinking compute by 43%-73% over deferral-only baselines across HotpotQA, MBPP, and the household robot task, while maintaining certified reward and cloud-call rate guarantees.
[AI-81] Crossing-Free Probabilistic K-Line Forecasts Without Retraining
链接: https://arxiv.org/abs/2607.26792
作者: Runyao Yu,Yuchen Tao,Yujie Chen,Wentao Wang,Derek W. Bunn
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Computational Finance (q-fin.CP)
备注: 8 pages, 2 figures, 6 tables
Abstract:Probabilistic K-line forecasting describes uncertainty in four complementary prices, namely open–high–low–close (OHLC). However, it introduces two consistency problems: quantile crossing and K-line crossing. Quantile crossing occurs when a higher-quantile forecast falls below a lower-quantile forecast, while K-line crossing occurs when the forecast low exceeds the open or close, or the forecast high falls below the open or close. Existing solutions generally address only one problem through output reordering, specialized architectures, or penalized training objectives. We propose K-line–Quantile Sequential Projection (KQSP), a parameter-free and training-free reconciliation method applicable to forecasts produced by any model. Compared with other crossing solutions, KQSP preserves predictive accuracy while producing substantially smaller corrections to the original forecasts. To mitigate model bias, we evaluate KQSP using various models, including pretrained foundation models. KQSP reduces both quantile and K-line crossing rates to zero for all test data undertaken. These results show that probabilistic K-line consistency can be enforced independently of forecast generation and without retraining.
[AI-82] Zero-Shot Face-to-Speech Synthesis via Latent Space Adaptation of a Style-Diffusion TTS Model
链接: https://arxiv.org/abs/2607.26742
作者: Carlos Muñoz-Romero,Jose A. Gonzalez-Lopez
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注: 5 pages, 1 figure, 5 tables, submitted to IberSPEECH 2026
Abstract:Zero-shot text-to-speech (TTS) clones a voice from a short audio prompt, but this reliance on reference audio is a barrier when only visual information is available, e.g. for historical figures or video-game characters. In this work, we propose a Face-to-Speech (F2S) framework that predicts a plausible voice from a static facial image. A lightweight Face Adapter, together with soft-tuning of the face encoder’s upper blocks, aligns face-recognition features with the style space of a frozen StyleTTS 2 model, kept frozen during training. We evaluate on held-out identities from LRS3, a large-scale audiovisual corpus of English TED-talk videos. The synthesized speech is highly natural (UTMOS 3.7-4.0, matching or exceeding the 3.61 of ground truth), face-to-voice retrieval is consistently above chance, and the generated voice is consistent with the target speaker. Without any retraining, an English-trained adapter also produces fluent Spanish speech, indicating that the face-to-style mapping is largely language-agnostic.
[AI-83] PUDA: An AI-Native Hardware Harness for Self-Driving Laboratories
链接: https://arxiv.org/abs/2607.26464
作者: Zekun Ren,Hongzhao Tan,Jiaen Yee,Kedar Hippalgaonkar
类目: Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)
备注:
Abstract:Physical Unified Device Architecture (PUDA) is an AI-native hardware harness for self-driving laboratories (SDLs). Rather than building a human-centered graphical user interface (GUI) orchestration layer, PUDA creates a command-line runtime environment that lets agents observe, orient, decide, and act over experiments while hardware execution remains deterministic, atomic, and auditable. Headless by design, devices appear through discoverable command-line interfaces, JSON protocols are routed through a distributed messaging system, and command responses, data products, and reports are preserved as structured records. PUDA organizes protocols, runs, samples, measurements, and command logs into an AI-native data structure linked by run identifiers and timestamps, preserving provenance from submitted protocol through hardware response to resulting data products. PUDA separates scientific orchestration from physical operation and data telemetry: agents choose experiments, while PUDA executes validated commands and captures provenance-linked state, responses, and data. The contribution is not another optimizer, orchestrator, or recipe language. It is a practical execution and data environment for agentic SDLs; the broader physical AI implication is that PUDA provides an AI-native hardware harness for AI systems to interact with physical tools.
[AI-84] he Human Utility Factor: A Computable Welfare Metric That Reframes AI Governance as a Constrained Optimisation Problem
链接: https://arxiv.org/abs/2607.26068
作者: Sivasathivel Kandasamy
类目: General Economics (econ.GN); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Existing AI governance frameworks, including the EU AI Act and NIST AI RMF, address safety, transparency, and accountability but do not operationalize quantitative constraints on macro-socioeconomic stability. As a result, AI systems may satisfy regulatory requirements while contributing to labor displacement, rising inequality, and reduced economic resilience. We introduce the Human Utility Factor (HUF), a differentiable welfare metric that models the interaction between Agency, Wellbeing, and Economic Stability as functions of three actionable policy levers: automation depth, redistribution intensity, and employment coverage. HUF yields a closed-form optimal automation level and a minimum redistribution threshold below which no level of automation is welfare-positive, transforming high-level governance objectives into computable constraints. We evaluate HUF using a three-agent multi-agent reinforcement learning framework across U.S., Canadian, and Nordic policy regimes. Both analytical and PPO-based agents identify welfare-optimal operating regions and reveal a critical failure mode: welfare metrics that do not explicitly constrain redistribution can converge to high-automation equilibria that satisfy the metric while undermining its intended societal objectives. Our results suggest that AI governance is fundamentally a constrained optimization problem rather than a compliance exercise. HUF provides a quantitative framework for evaluating automation policies, identifying socioeconomic stability boundaries, and supporting governance decisions under accelerating AI deployment.
[AI-85] Predict before you train: Scaling Laws for particle physics foundation models
链接: https://arxiv.org/abs/2607.23377
作者: Jan-Lucas Uslu,Benjamin Nachman,Christopher Re
类目: High Energy Physics - Experiment (hep-ex); Artificial Intelligence (cs.AI)
备注:
Abstract:The largest machine learning models in particle physics are also the most expensive to train, yet the return on scaling a given architecture cannot be estimated before that compute is spent. Scaling laws have been fit for jets, but none has yet been shown to predict the performance of models it was not fit on. We show that, for a generic transformer pretrained on collider jets, it can be forecast. Fitting a joint model-and-data scaling law on small models alone, spanning three orders of magnitude of training compute, we predict the loss of models trained afterward with more than one hundred times more compute to within one percent. We then connect the forecast to downstream physics performance: across two standard tagging benchmarks, lower pretraining loss yields systematically lower fine-tuning loss and higher background rejection after fine-tuning. Within this model family and these tasks, a compute budget can therefore be translated into expected physics performance before any large model is trained. The final frontier model is consistent with the published numbers for current state-of-the-art physics-aware foundation models trained on the same corpus, on accuracy, AUC, and quark/gluon rejection, with a residual edge for the physics-aware model only in the high-purity tail of top tagging. We release five pretrained models spanning multiple sizes, together with the complete training recipe and code.
机器学习
[LG-0] Do You Really Need to Pretrain Q-Functions for Online RL Fine-Tuning?
链接: https://arxiv.org/abs/2607.27203
作者: Perry Dong,Ron Polonsky,Dorsa Sadigh,Chelsea Fin
类目: Machine Learning (cs.LG)
*备注:
Abstract:Pre-training followed by fine-tuning has become the dominant recipe for learning performant policies, and in value-based reinforcement learning (RL) this raises a natural question: given a pretrained policy, should the Q-function be pretrained on offline data too? Conventional wisdom suggests it should, but recent results show that online RL with a randomly-initialized Q-function can result in highly performant and reliable policies without needing to pretrain the Q-function. In this paper, we systematically study whether pretraining the Q-function actually helps when fine-tuning on top of a pretrained base policy. We find, surprisingly, that naive Q-function pretraining often provides little benefit over random initialization. We show this stems from a fundamental mismatch: the Q-function learned during pretraining targets the pretrained policy’s Q-function, not the Q-function that online fine-tuning converges to, and this gap persists even after offline value maximization. Motivated by this finding, we propose Initialization via Policy Ensemble (IPE), a simple method that trains multiple diverse policies and uses their pooled rollouts to bootstrap the Q-function learning in online RL. Across a suite of challenging continuous control benchmarks, IPE yields an average 1.26x improvement in fine-tuning performance over naive Q-function pre-training.
[LG-1] From Classification to Regression: Using a Fruitfly to Solve Equations
链接: https://arxiv.org/abs/2607.27196
作者: Shady E. Ahmed,Panos Stinis
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注:
Abstract:We present a novel approach to regression tasks using classification which is motivated by the mechanism used by fruitflies to sense their environment. Specifically, we formulate a general framework for learning nonlinear input-output relationships by replacing complex global surrogate models with a finite library of representative local patterns. Since scientific data often occupy limited and recurring regions of the input space, we generate predictions by measuring similarities between a query and stored patterns, then combining their associated responses through weighted reconstruction. We apply this approach to nonlinear dynamical systems, data-driven regression, and physics-informed learning using suitable embeddings and similarity measures. For dynamical systems, our offline-online workflow extracts patterns from data or governing equations during the offline phase, while online prediction requires only similarity evaluation and response aggregation. This structure helps us reduce computational and memory demands while providing explicit control over the trade-off among accuracy, storage, and inference cost.
[LG-2] Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes
链接: https://arxiv.org/abs/2607.27188
作者: Lennon J. Shikhman,Michael Galarnyk,Aadi Dash,Nicholas A. Welsh
类目: Machine Learning (cs.LG); Computational Finance (q-fin.CP); Pricing of Securities (q-fin.PR); Statistical Finance (q-fin.ST)
*备注: 7 pages, 4 figures, 2 tables. Submitted to the 7th ACM International Conference on AI in Finance (ICAIF 2026)
Abstract:Accurate option prices do not imply accurate recovery of the latent risk-neutral density. We study this distinction with two complementary benchmarks. A controlled benchmark exposes simulator-truth densities for latent evaluation, while a chronological NIFTY benchmark tests only held-out market prices. A two-component lognormal mixture has the lowest aggregate price, L^1 , Wasserstein, and fixed-tail errors on the synthetic benchmark. Learned operators retain narrower strengths: DeepONet reduces 1% quantile and variance error by 39.0% and 34.6% relative to the mixture, and a quote transformer reduces L^1 by 16.4% on the structurally misspecified Merton family. A numerical conditioning analysis explains why these rankings can differ: after enforcing mass and forward constraints, 95 of 126 pricing directions are numerically null, and two densities separated by L^1 = 0.061 produce identical prices on the covered strikes. On 524 held-out NIFTY calls, validation-selected test-time adaptation reduces DeepONet RMSE by 28.3%, but per-expiry mixture and SVI fits remain much more accurate. The evidence supports target-dependent inductive bias, not a universal winner.
[LG-3] When Do Learned Diffusion Proposals Help Constraint Solving? A Controlled Study on Continuous Algebraic Systems
链接: https://arxiv.org/abs/2607.27169
作者: Quang Bui,Sparsh Roy,Akash Gundimeda,Davin Yin
类目: Machine Learning (cs.LG)
*备注:
Abstract:Solving a continuous algebraic constraint system requires two decisions: which values satisfy the constraints, and which structural augmentation renders an unsolvable system solvable. Classical solvers answer the first well and the second only by enumeration. On that discrete decision, a candidate-conditioned repair ranker choosing among K augmentations reaches the exhaustive-search ceiling at a fraction of the calls, outperforming random (0.997 vs 0.236 balanced nonlinear menu accuracy; p 10^-70; 0.982 +/- 0.006 across seeds) and beating a budget-matched per-candidate probe on accuracy and cost. MARC turns such a system into a factor graph, over which a graph-neural diffusion denoiser proposes assignments, descent on an exact computer-algebra energy polishes them, and an exact symbolic checker certifies solutions. Evaluations of diffusion-based proposals rarely include one control: random multi-start under the same refinement budget. Applied to our system, it sharply curtails what the learned proposal contributes on the value decision. Does it beat random multi-start at choosing satisfying assignments? Only narrowly, in a predictable regime. Across trapped low-dimensional families it ties with random restart, but dominates in high dimension, where random search fails. Once variables couple, the advantage is gone. Since all methods share one polish and one checker, best-of-K random multi-start succeeds with probability exactly 1 - (1 - q(n))^K, where q(n) is single-start reachability; one measured constant, with no free parameters, reproduces the entire curve (mean absolute error 0.012). The favorable regime is not specific to our synthetic families: across eight real-world systems in robotics, positioning, optimization, and algebra, classical multi-start solved all eight, none in the learning-favorable regime. We map the regimes in which learned proposals improve solvers.
[LG-4] Skillful forecasting of offshore winds from satellite scatterometer constellations
链接: https://arxiv.org/abs/2607.27152
作者: Francesco Pinto,Luca Lanzilao,Paco Lopez Dekker,Angela Meyer
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate intraday forecasts of offshore wind are becoming increasingly important for power system operation and the integration of growing shares of offshore wind energy. Operational forecasts rely predominantly on numerical weather prediction (NWP), which is not optimized for lead times of minutes to hours, where initial-condition accuracy dominates forecast skill. Although satellite scatterometer observations are routinely assimilated into NWP, they have not previously been used directly for forecasting. Here we present WindCastNet, the first satellite-based nowcasting framework for offshore wind speed and direction, introducing a new paradigm for intraday forecasting that learns from spatiotemporally irregular satellite observations. WindCastNet predicts offshore wind fields from observations acquired by satellite scatterometer constellations. WindCastNet employs a partial convolutional long short-term memory network that exploits microwave radar observations from the European, Chinese, and Indian scatterometers despite their irregular spatial coverage, asynchronous sampling, and variable revisit times. Spatial observation masks and inter-observation intervals are encoded, while a continuous temporal representation enables forecasts at arbitrary lead times. Evaluated over the North Sea, WindCastNet reduces the root-mean-square error by 23% and 7% relative to the HARMONIE MEPS model at lead times of 1 and 2 h, respectively, and outperforms persistence by 9-15% during the first three forecast hours. Forecast skill decreases under strong-wind conditions and spatially non-uniform flow. These results demonstrate that satellite scatterometer constellations can provide an independent and competitive source of short-term offshore wind forecasts, opening new opportunities for renewable energy forecasting but also broader marine weather applications, including tropical cyclone nowcasting.
[LG-5] Investigating reservoir computing for branch predictionin pipelined processors using emerging CMOS memristor devices
链接: https://arxiv.org/abs/2607.27140
作者: Harvey Samuel George Johnson,Sendy Phang
类目: Hardware Architecture (cs.AR); Computational Engineering, Finance, and Science (cs.CE); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Applied Physics (physics.app-ph)
*备注: 53 pages, 61 figures, Master of Engineering final project report, awarded Peter John Award
Abstract:This project aimed to develop a novel reservoir compute (RC) implementation framework targeting high-speed operation and integration with CMOS digital logic. With the target workload of branch prediction (BP) for multistage pipelined central pro-cessing unit (CPU) cores. For this, a novel memristor based RC design framework was developed within the context of the workload requirements. This was then implemented in simulation using industry standard modelling languages of System Verilog (SV) and Verilog-AMS (VAMS).The developed RC design framework was subsequently verified using a basic sequence detection task before further benchmarking for its effectiveness at BP. The developed RC framework was tested using the Dhrystone performance benchmark, while targeting the RISC-V RV64GC instruction set architecture (ISA). Conducted testing demonstrates that RC shows great promise for ap-plication to BP and is capable of achieving impressive overall prediction accuracy. However, testing also shows that further refinement of the developed RC design framework is necessary to address shortfalls in the adaptability of the proposed RC system. As comparison against the state of the art TAGE predictor showed the proposed RC design framework to be 15x slower to adapt to changes in branching behaviour.
[LG-6] Minimal Markovization via Stable Quotients in Holonomy-Cover Decision Processes
链接: https://arxiv.org/abs/2607.27132
作者: Zuyuan Zhang,Yongshan Chen,Mahdi Imani,Tian Lan
类目: Machine Learning (cs.LG)
*备注:
Abstract:An agent acting under partial observability must retain a recursively updateable statistic of history that restores the Markov property, but the smallest such statistic is generally unknown. We characterize this minimal Markov sufficient statistic for holonomy-cover decision processes, a structured POMDP class in which the visible dynamics are Markov and every realized visible transition applies a fixed permutation to a hidden mode. In particular, we construct the stable quotient, the coarsest observation-wise abstraction preserving one-step rewards and quotient successors, and prove that the pair of the current observation and stable class forms an exact finite Markov state. When the current class is correctly initialized, exact class tracking requires exactly the minimal memory symbols, in the sense that under reachability and pairwise decision separation at a maximizing observation, no arbitrary finite-memory controller can use fewer. Under resettable diagnostics, nearest-prototype class inference has exponentially decaying error, and a calibrate-then-restart reduction transfers finite-MDP guarantees to the recovered state. The results enable \emphHolonomy Memory Reinforcement Learning. It represents memory by the current stable class, updates it through ordered edge transports, identifies local class coordinates when diagnostics are available, and applies a standard finite-MDP RL backbone after synchronization. Experiments recover an exact compression from raw states to quotient states and achieve perfect paired-order accuracy with three decision-time memory states, matching the quotient oracle and outperforming the non-oracle baselines.
[LG-7] Voronoi Histograms for Adaptive Vectorization of Expected Persistence Diagrams
链接: https://arxiv.org/abs/2607.27126
作者: Kaifeng Zhang,Kai Ming Ting
类目: Machine Learning (cs.LG)
*备注:
Abstract:Persistence Diagram (PD) is known to capture point cloud topology effectively, but its computation has high time complexity. Expected Persistence Diagram (EPD) has been developed to reduce the time cost by studying the topology of multiple subsets of a point cloud and it serves as a distribution of topological features. Existing EPD vectorizations often rely on predefined point transformations, such as Gaussian or landscape functions. We study an alternative discretization based on Voronoi histograms, which trades smooth functional approximation for adaptive partition-based counting. We propose to use Voronoi Diagram-based histogram as the vectorization of EPD, without imposing an explicit smooth point transformation model. Under stated separation and normalization conditions, we establish stability bounds and characterize when the histogram representation preserves Wasserstein-scale variation. We demonstrate the effectiveness of our proposed representation on real-world datasets which have significant topological features for classification and dimensionality reduction tasks.
[LG-8] Hierarchical Spatio-Temporal Transformer for Coherent Emergency Department Forecasting KDD2026 ECML
链接: https://arxiv.org/abs/2607.27106
作者: Filipa Lino,Bárbara Tavares,Carlos Santiago,Cláudia Soares,Manuel Marques
类目: Machine Learning (cs.LG)
*备注: Accepted at 11th Workshop on Data Science for Social Good - ECML PKDD 2026
Abstract:Emergency Departments (EDs) are critical access points in healthcare systems, yet they face persistent pressure from unpredictable patient demand, seasonal surges, and non-urgent visits. Effective ED planning requires forecasts at multiple decision-making levels: hospitals need local demand estimates for staffing and bed management, regions require forecasts to coordinate healthcare units, and national authorities need system-wide projections for capacity planning. However, most existing approaches forecast ED demand independently at a single level, ignoring the hierarchy linking hospitals, regions, and national systems. This can produce incoherent predictions, where hospital-level forecasts do not aggregate consistently to regional or national demand. We propose HierSTT, a hierarchical Transformer-based framework for coherent multi-level ED forecasting. HierSTT jointly predicts hospital, regional, and national level demand in a single end-to-end model. A Temporal Fusion Transformer captures national dynamics, while spatio-temporal Transformer encoder-decoder modules model regional and hospital demand conditioned on higher-level forecasts. A coherence-aware loss penalizes cross-level inconsistencies during training. We further introduce a nationwide Portuguese ED dataset covering 81 hospitals across 5 regional health administrations, with heterogeneous covariates at each level. Experiments show that HierSTT reduces average WAPE by 32% relative to the best non-hierarchical deep learning baseline and outperforms all classical hierarchical reconciliation methods, while producing near-coherent predictions across levels. Additional resources associated with this work are available at this https URL.
[LG-9] Sky sphere representation in language models
链接: https://arxiv.org/abs/2607.27092
作者: Aleksandr Berdnikov,Yevgeny Liokumovich
类目: Machine Learning (cs.LG)
*备注:
Abstract:We analyze whether language models of size ~100B have a representation of the night sky map that is decodable from their residual stream. We find that most of the considered open-source models do have such a representation, and it often even surfaces to the top principal components on prompts that ask questions like ``what is close to this object in the night sky’'. In all but one model this representation showed significant scores in LOO testing, containing up to 65-85% of variance ( R^2 -score) and having median angular error down to 12^\circ-21^\circ . We verify that our representation is not a simple leak from a correlated flat representation. To our knowledge, this representation is the first example of a curved high-dimensional irreducible feature manifold. Codes used in the paper are published at this https URL Subjects: Machine Learning (cs.LG) MSC classes: 68T30 ACMclasses: I.2.4 Cite as: arXiv:2607.27092 [cs.LG] (or arXiv:2607.27092v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.27092 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-10] InferScale: GPU-Native KV Injection for Personalized LLM Serving
链接: https://arxiv.org/abs/2607.27090
作者: Peter Li,Prashant Pandey
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:Large language models are increasingly deployed with persistent personalized context, such as accumulated memory profiles or long conversation histories, that is shared across a user’s many requests. Production memory systems (e.g., Mem0, MemGPT, and Zep) retrieve a relevant subset of this memory and inject it into the prompt, forcing the serving engine to repeatedly prefill the same content. As the retrieval budget grows, time-to-first-token (TTFT) increases even though the underlying memory is reused across requests. We present InferScale, a GPU-native LLM memory system that replaces repeated prompt prefilling with reusable KV state. InferScale precomputes each memory fact’s KV representation, stores it alongside a semantic embedding on the GPU, retrieves relevant facts at serving time, and injects their KV directly into vLLM’s paged cache. To support dynamically assembled memories under rotary position embeddings, we introduce Chunked RoPE, which stores keys before rotation and applies their serving-time positions during injection. However, encoding memory facts independently omits the cross-fact context available during joint prefilling. We mitigate this with Context-Window Encoding, which encodes each memory fact together with a small window of preceding conversation context while caching only the target fact’s KV. InferScale is implemented through vLLM’s KV-connector interface, requiring neither engine modifications nor model fine-tuning. Across three open-weight models on LoCoMo, InferScale keeps TTFT nearly constant as the retrieval budget increases: at k=50 it reduces TTFT by 72-79% (3.6-4.8x), achieves 60.3% accuracy versus 63.3% for Mem0 without serving-time recomputation, and delivers 3.7-4.5x the throughput under concurrent load. Reusable KV state thus decouples memory-conditioned serving latency from retrieved-context size while preserving application quality. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) Cite as: arXiv:2607.27090 [cs.DC] (or arXiv:2607.27090v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2607.27090 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-11] Field Codes for Distributed Coupling Samplers and Certified Empirical Transport
链接: https://arxiv.org/abs/2607.27078
作者: Hung Mai,Hai Nguyen,Luong Doan,Ngoc Vu,Khanh Nguyen,Nhung Duong,Tuan Do
类目: Computational Complexity (cs.CC); Information Theory (cs.IT); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:In this paper, we formulate three communication tasks for empirical optimal transport: distributed coupling sampling, cost-evaluable coupling output, and scalar value-certified sampling. Our main result is a field-code compiler: any communicated transport field approximating an optimal empirical Monge map to error \eta can be completed by sparse target-cell residuals into an exact-marginal value-certified sampler with scalar certificate W_1(\mu,\nu)\leq U\leq W_1(\mu,\nu)+2\Delta , where \Delta is the public target-partition diameter. The certificate accuracy is controlled by \Delta alone. The field error \eta controls residual communication under a cell-margin condition; without a margin, \eta alone does not bound residuals. We instantiate the compiler via adaptive local-affine and tensor-product spline codes with d(m+1)^db field bits in the spline case, plus residual lists charged separately. For lower bounds, exact Gap-Hamming embeddings prove certified output is hard, including a smooth cell-packing diffeomorphism family requiring \Omega(\varepsilon^-2d/(d+4)) communication for any cost-evaluable, cost-certified, or value-certified protocol. The same gadgets admit zero-communication samplers, formally separating the sampler and certificate-bearing output models. These results identify the transport field as the right communicated object whenever a field code is available, primarily as a residual-sparsity tool.
[LG-12] Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering
链接: https://arxiv.org/abs/2607.27077
作者: Nicolas Béreux,Aurélien Decelle,Cyril Furtlehner,Beatriz Seoane
类目: Machine Learning (cs.LG)
*备注: 11 pages, 7 figures
Abstract:Energy-Based Models (EBMs) provide an interpretable framework for generative modeling of scientific data, but poor Markov Chain Monte Carlo mixing often limits their reliability. We introduce a training algorithm based on Parallel Trajectory Tempering (PTT), which exploits the continuity of the optimization path to maintain equilibrium sampling throughout learning. This enables stable and fast training on highly multimodal and data-scarce scientific datasets. Combined with reservoir sampling and adaptive optimization, PTT has a computational cost comparable to Persistent Contrastive Divergence, making it a practical replacement for standard training methods. It also provides direct estimates of thermalization times, equilibrium samples from trained models, and accurate log-likelihoods at essentially no additional cost. Experiments on Restricted Boltzmann Machines show that PTT consistently outperforms existing EBM training approaches. On discrete tabular data, it also surpasses state-of-the-art deep generative models, yielding higher-quality samples and greater robustness to overfitting and limited data. Our results make equilibrium maximum-likelihood training of EBMs practical and computationally efficient.
[LG-13] Single-Beat Cuffless Blood Pressure Estimation Using Ear-PPG and ECG with a Lightweight Hybrid Learning Framework
链接: https://arxiv.org/abs/2607.27076
作者: Kindeep K. Dhatt,Tengyue Wu,Hanbang Hua,Yayun Du
类目: Machine Learning (cs.LG); Signal Processing (eess.SP); Systems and Control (eess.SY)
*备注: 7 pages, 5 figures
Abstract:Continuous cuffless blood pressure (BP) monitoring remains challenging due to motion artifacts, physiological variability, and the limited robustness of conventional pulse transit time (PTT) models under dynamic conditions. Many prior approaches rely on multi-second windows to stabilize estimation, an assumption that is frequently violated during real-world monitoring with intermittent signal corruption. Here, we show that discriminative BP-related information is preserved at the single-beat level and present a lightweight multi-modal wearable framework for continuous BP estimation. The system integrates synchronized chest electrocardiography (ECG) and ear-clip reflectance photoplethysmography, each co-located with a 6-axis inertial measurement unit to provide motion context. We introduce a hybrid learning architecture in which a one-dimensional convolutional neural network extracts a 64-dimensional embedding from individual PPG beats and fuses it with 30 physiology-grounded features, including PTT statistics and heart rate variability, followed by LightGBM regression. The method was evaluated using a multi-phase stress protocol ( n=10 ) and the PulseDB public dataset with subject-disjoint validation. Across 30 independent runs, the model achieved mean absolute errors of 4.02 \pm 0.21 ~mmHg for systolic BP and 1.79 \pm 0.05 ~mmHg for diastolic BP, corresponding to a 28.2% reduction in combined MAE relative to baseline models. By enabling beat-wise estimation without long temporal context, this framework supports computationally efficient cuffless BP monitoring suitable for wearable deployment under practical resource constraints. The source code for this work is available at this https URL.
[LG-14] GPT Q-2D: Cubic-Time Two-Sided Adaptive Rounding
链接: https://arxiv.org/abs/2607.27042
作者: Jiale Chen,Torsten Hoefler,Dan Alistarh
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注:
Abstract:Adaptive rounding methods such as GPTQ, or equivalently Babai’s nearest plane algorithm, round a real matrix to integers under a quadratic metric. They process the entries in a fixed order, one at a time, propagating each rounding error to the entries not yet processed through a triangular feedback matrix. We study the two-sided version of this task, in which fixed nonsingular basis matrices act on both the left and the right of the residual; the familiar one-sided case is the special case of an identity right basis. Vectorizing the matrix turns the two-sided objective into a quadratic metric whose Gram matrix is a Kronecker product, so the one-dimensional algorithm applies verbatim, but takes quartic time in the matrix dimension. We present GPTQ-2D, which produces the identical rounded matrix in cubic time. It rounds the entries anti-diagonal by anti-diagonal; entries on the same anti-diagonal are independent and are rounded in parallel.
[LG-15] HoF-Bench: Rediscovering Real AI-Discovered CVEs Without Frontier Models
链接: https://arxiv.org/abs/2607.27030
作者: Petr Simecek,Elnaz Babayeva,Jiri Balhar,Michal Bida,Michal Buran,Vaclav Cadek,Luigino Camastra,Tomas Dulka,Michal Janocko,Tomas Klohna,Pavel Kohout,Ondrej Kokes,Adam Krivka,Jakub Kubik,Patrik Mada,Igor Morgenstern,Marek Pavelka,Joshua Rogers,Petr Stastny,Jan Tattermusch,Dmitrijs Trizna,Martin Votruba,Guido Vranken,Jakub Zikl,Evelina Gabasova,Stanislav Fort
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 15 pages, 6 figures. Benchmark repository: this https URL
Abstract:LLM-based analyzers have begun finding real vulnerabilities in mature open-source projects: AISLE’s analyzer is credited with more than 280 CVEs across 78 projects, including OpenSSL, curl, and GnuTLS. We introduce HoF-Bench (named after AISLE’s public Hall of Fame), a benchmark built from 95 of these public AI-discovered CVEs across eight repositories pinned at vulnerable commits. Analyzers receive source and target-file scope but not CVE identifiers, descriptions, fixes, or expected mechanisms; a detector-blinded frontier-model judge credits only findings that identify the same code path, root cause, attack condition, and impact. A deliberately minimal LLM-based analyzer rediscovers up to 65 of the 95 CVEs (68%) under this strict protocol. No frontier model performs detection anywhere in the study. The ten detector backbones are five open-weight models (21B–284B total parameters, 3–13B active) and five proprietary small or “flash”-tier models. All of them run in the fixed scaffold with four repeated passes, an optional generated-context stage, and a replayable multi-round triage stage (7,600 model–CVE pass records). Difficulty is strongly structured by language; the CVEs missed by every model concentrate in C infrastructure code. HoF-Bench provides a compact test bed for comparing vulnerability scanners, their reliability across repeated runs, and the candidate volume they create. The dataset is available at this https URL.
[LG-16] reeCCA: Canonical Correlation Analysis via Gradient-Boosted Trees
链接: https://arxiv.org/abs/2607.27027
作者: James Chapman
类目: Machine Learning (cs.LG)
*备注:
Abstract:Gradient-boosted trees dominate tabular machine learning, yet canonical correlation analysis has always relied on linear or neural encoders. We propose \textbfTreeCCA, the first method to train gradient-boosted tree ensembles end-to-end as CCA encoders, inheriting their plug-and-play reliability: no architecture design, familiar hyperparameters, and strong performance with defaults. The technical enabler is the Eckart-Young (EY) loss, which supplies closed-form per-sample gradients that slot directly into any standard GBT library (XGBoost, LightGBM) as a custom objective. TreeCCA is the first CCA method to combine nonlinear accuracy with native interpretability: every tree split selects one feature, so gain importances reveal which inputs drive cross-view correlation at no extra cost. We demonstrate these properties on synthetic benchmarks, where TreeCCA matches or exceeds Deep CCA (2.61 vs.\ 2.43 on Signed Power; 2.93 vs.\ 2.89 on Hermite), and on a sparse benchmark with zero linear cross-view covariance, where TreeCCA recovers the true support with \textPrecision@S = 1.00 at p=50 while PMD finds no signal. On the UCI HAR sensor-fusion benchmark, TreeCCA achieves comparable accuracy to Deep CCA at 5\times lower cost, while XGBoost gain importances directly validate a physics-motivated hypothesis about the data — an interpretation not readily available with neural encoders. Across five popular tabular multi-view datasets, TreeMCCA consistently matches or exceeds linear CCA in both nonlinear correlation extraction and downstream classification accuracy. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.27027 [cs.LG] (or arXiv:2607.27027v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.27027 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-17] What Can Latent World Models Know? Physical Parameter Identifiability in Multimodal Predictive Representations
链接: https://arxiv.org/abs/2607.27017
作者: Kaizhen Tan(1 and 2),Xin Xu(2),Siru Tao(2),Hanzhe Hong(2),Yang Feng(3),Heqing Du(3) ((1) New York University, (2) Carnegie Mellon University, (3) Columbia University)
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:A central premise of latent world models is that predicting the future forces a representation to internalize the physics of its environment. Which physical quantities does a trained latent actually contain, and what decides this? We answer with controlled interventions in POKEWORLD, an interactive environment whose visually identical objects hide mass, drag, and contact stiffness. A certificate-gated protocol first certifies each parameter as recoverable from raw observations, then measures whether it enters the latent, so a null result can be attributed to the objective rather than to the environment. The resulting identifiability map has two organizing mechanisms and one frontier. Inputs limit what can be known, while prediction targets decide what is retained. Stiffness enters the latent only when touch is forecast ( R^2=0.50 , compared with -0.02 when the same signal is merely fused into the input), and under single-step prediction a vision-only latent discards even perfectly visible object state. Drag marks the frontier. It carries a recoverability certificate of 0.89 yet plateaus near 0.13 under every deterministic prediction objective we test, while a supervised head on the same trunk reaches 0.45. Parameters whose readout is slow and ratio-type under the sensed coordinates fall outside what these objectives acquire. On RH20T, an input-target factorial across scaling curves reproduces both mechanisms across two robots and 4,258 episodes. Every arm missing information or prediction pressure stays flat over a fivefold data range, and only the full multimodal objective forecasts force beyond a persistence baseline, with held-out gains that grow with scale. Objective structure determines which physical parameters a latent acquires, and additional data improves only the parameters it already acquires.
[LG-18] Foundation Models for Face Presentation Attack Detection: A Unified Linear-Probing Benchmark
链接: https://arxiv.org/abs/2607.26993
作者: Peter Lorenz,Anjith George,Sébastien Marcel
类目: Machine Learning (cs.LG)
*备注: accepted at IJCB 2026
Abstract:Face presentation attack detection (PAD) remains challenging under cross-dataset evaluation, where domain shift degrades models trained on a single dataset. The scarcity of large-scale labeled data motivates adapting pretrained vision models rather than training task-specific architectures from scratch, raising a fundamental question: do general-purpose vision foundation models encode PAD-relevant information accessible with minimal task-specific training? To investigate, we systematically evaluate 24 frozen encoders, including self-supervised vision transformers, vision-language encoders, and supervised CNNs, using a unified linear-probing protocol on the MCIO benchmark (MSU-MFSD, CASIA-FASD, Replay-Attack, OULU-NPU). The backbone remains fixed, and only a lightweight linear head is trained to isolate the PAD information already present in the pretrained representation. %We report intra- and cross-dataset performance, along with accuracy-compute trade-offs, relative to two specialist PAD baselines. Results show that frozen foundation-model representations can support strong intra-dataset PAD performance with only a linear classifier, but this performance does not reliably transfer across datasets. Model scale is beneficial within several families, although the effect is not monotonic and is strongly mediated by architecture and pretraining. InternViT-6B achieves the lowest mean intra-dataset error, whereas CLIP ViT-B/32 offers the most favorable cross-dataset transfer-compute trade-off among the evaluated probes. These findings suggest that while pretrained representations contain PAD-relevant information, explicit adaptation remains necessary to address domain shift.
[LG-19] A Compositional Theory of Causally Masked Transformers
链接: https://arxiv.org/abs/2607.26988
作者: Franz Nowak,Ryan Cotterell,Reda Boumasmoud
类目: Formal Languages and Automata Theory (cs.FL); Machine Learning (cs.LG)
*备注: 12 pages, 2 figures
Abstract:What types of decision problems can a causally masked, finite-precision transformer solve for inputs of arbitrary length? Existing answers often rely on idealized arithmetic, but under finite precision, rounding and evaluation order can change what information attention retains and therefore what the model can compute. We develop an algebraic formalization that derives expressivity directly from the model’s implemented dynamics. Its central object is its memory; the finite internal state computed by attention that summarizes the information from the prefix available to all future queries. Each attention head updates its own state independently within a layer, while layers compose hierarchically, providing a uniform route from model assumptions to expressivity bounds. Applying this method to transformers without positional embeddings, we obtain an expressivity hierarchy governed by the attention type under specific numerical semantics. Width-one sliding-window attention supports bounded-suffix memory, while a modified form of soft attention supports irreversible, checklist-like state, and combining the two mechanisms provides an interplay of both. Ordinary left-to-right floating-point soft attention can realize more expressive memory operations than any of the above. Algebraically, the four cases correspond to definite, R-trivial, locally R-trivial, and aperiodic semigroups. Under an explicit free-wiring assumption, all four bounds are tight.
[LG-20] Surrogate assisted diversity estimation in neural ensemble search
链接: https://arxiv.org/abs/2607.26940
作者: Alexandr Udeneev,Petr Babkin,Oleg Bakhteev
类目: Machine Learning (cs.LG)
*备注: The paper was presented at the “Artificial Intelligence Applications and Innovations 2026” conference. The final publication is available at this https URL
Abstract:Ensembles are a standard way to improve the performance and robustness of deep neural networks, but their effectiveness crucially depends on both the quality and the diversity of individual models. Most neural architecture search (NAS) methods are computationally expensive. Extending them to neural ensemble search (NES), which requires joint optimization of individual architectures and their ensemble composition, leads to an exponential growth of the search space and makes the problem computationally intractable. To address this, we introduce a dual-objective surrogate-guided ensemble search: candidate architectures are represented as directed acyclic graphs, and two surrogate models are trained independently to estimate predictive accuracy and diversity potential. Their combined estimates guide an NES framework that efficiently identifies architectures that are both individually strong and collectively diverse. Our final ensemble achieves competitive or superior performance compared to standard baselines such as Deep Ensembles and Random Search on FashionMNIST, CIFAR-10, and CIFAR-100.
[LG-21] mporally Centered SIGReg Improves Multi-Task LeWorldModel Learning: From Analysis to Method
链接: https://arxiv.org/abs/2607.26924
作者: Chang Liu,Fei Suo,Yanzhou Jin,Yusuke Iwasawa,Yutaka Matsuo,Yaonan Zhu
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:Recent work on LeWorldModel (LeWM) has shown that the Sketched Isotropic Gaussian Regularizer (SIGReg) enables stable end-to-end world-model learning from pixels by regularizing the latent marginal distribution toward an isotropic Gaussian, thereby preventing representation collapse. While effective and elegant in single-task settings, this recipe does not extend reliably to multi-task training, leading to substantially worse downstream behavior-cloning performance. In this paper, we show that marginal Gaussianization compresses the separation between task-dependent latent clusters relative to within-cluster variation. This compression introduces representation aliasing across tasks and states, and makes the learned representations highly sensitive to small visual perturbations. To address this problem, we apply SIGReg to temporally centered residuals rather than to the latent marginal distribution. This surrogate target places no direct regularization pressure on the separation among cluster centers, removes the requirement that the full latent follow a single isotropic Gaussian, and retains the anti-collapse effect of SIGReg. On the LIBERO benchmark, our method improves downstream success on the long-horizon suite by 1.7x and raises the average success rate across four suites from 53.2% to 73.6%. Without external pretraining, it slightly outperforms Diffusion Policy trained from scratch and approaches the performance of large-scale pretrained policy baselines. These results reveal a structural incompatibility between marginal Gaussian priors and multi-task latent structure, and provide a simple route toward stable and scalable end-to-end multi-task world-model learning.
[LG-22] wo Calls Beat Five Agents : Evaluating Multi-Agent Pipelines Against Self-Refinement for Local Language Models
链接: https://arxiv.org/abs/2607.26922
作者: Ashish Prajapati,Om Mohite
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multi-agent LLM pipeline systems break down the task among multiple roles for better reasoning, but are benchmarked mainly with large-scale commercial models. In this study, we investigate Parishad, a structured multi-agent system involving five roles, by deploying it on Qwen2.5-7B-Instruct, a local model, on two datasets: GSM8K (500 questions) and HumanEval (164 questions), compared with prompting directly and two-call self-refinement. The multi-agent system drops GSM8K accuracy from 75.0% to 45.0% with JSON data format due to the error accumulation problem. With plaintext format, the accuracy is restored to 82.0%. A two-call self-refinement strategy (V1) can achieve 86.2% accuracy on GSM8K, with 7.4 \times lower token usage. However, the same V1 implementation on HumanEval—where direct accuracy is already 96.3%—actively destroys performance (66.5%). A task-aware gated redesign (V2) applied to HumanEval preserves accuracy at 95.1%. Our results demonstrate that communication format and implementation details determine outcomes more than architectural complexity, and that simpler approaches match or outperform multi-agent pipelines for local 7B model deployment. All code and data are released.
[LG-23] Amortized Moment Matching for Visual Generation
链接: https://arxiv.org/abs/2607.26860
作者: Wenze Liu,Xintao Wang,Pengfei Wan,Xiangyu Yue
类目: Machine Learning (cs.LG)
*备注: 30 pages, 11 figures
Abstract:We propose amortized moment matching, utilizing neural networks to learn data moments as distributional training signals. By casting diffusion denoisers through polynomial projections, we establish a general framework for moment amortization, revealing that an n -th degree projection explicitly identifies data moments up to order n+1 . Derived from the tractable affine case, we instantiate the Amortized Fréchet Distance (AMFD) loss. Unlike FD-loss which relies on explicit marginal moment calculations, AMFD is able to dynamically learn conditional moments via an alternating, matrix-free optimization pipeline that effortlessly scales to high-dimensional data. When operating on global representation features, AMFD serves as a powerful post-training objective; empirically, its neural formulation yields more robust training dynamics than exact statistical matching, substantially surpassing the FD baseline on the FDr ^6 metric and achieving superior one-step generation on ImageNet. Furthermore, it unlocks direct exploration within native generative spaces, suggesting that the first two moments can identify target distributions only in spaces with strong semantics. Finally, when scaled to text-to-image generation, the condition-aware nature of AMFD unlocks massive gains in instruction-following capabilities, enabling our one-step models to outperform their multi-step FLUX.2 [klein] 4B teachers on the GenEval benchmark while achieving on-par performance on PickScore. Code and checkpoints are available at this https URL.
[LG-24] REA-Net: A Transferable Residual Epidemiological Adaptation Network for Dengue Incidence Forecasting
链接: https://arxiv.org/abs/2607.26854
作者: Inesh Shukla,Madhurima Panja,Tanujit Chakraborty,Chittaranjan Hens
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注:
Abstract:Accurate multi-week dengue forecasting supports timely vector-control interventions, outbreak preparedness, and healthcare resource allocation. However, newly established surveillance systems often lack the historical data needed to train reliable neural forecasting models. Although pretrained time-series models offer promising zero-shot forecasts, their cross-domain training may not capture local epidemiological dynamics. We propose TREA-Net, a Transferable Residual Epidemiological Adaptation Network for dengue forecasting under limited data. TREA-Net augments neural forecasting backbones with projections from an Environmental Time-Series Susceptible-Infected-Recovered model and learns a lightweight gated residual correction transferable from data-rich to data-scarce regions. Its node-invariant design accommodates surveillance systems with different numbers of locations, while target adaptation requires learning only two global parameters. We transfer knowledge from long-running dengue surveillance in Colombia and Nicaragua to 8-week-ahead forecasting in Mexico and Malaysia using only 78 or 104 weeks of target data. Across five neural backbones and ten transfer settings, TREA-Net improves the corresponding backbone in 9 out of 10 settings, with statistically significant gains. When integrated with TiRex, a foundation model for forecasting, it achieves the lowest mean absolute error across all target datasets. Conformal prediction further maintains empirical coverage while reducing 8-week prediction-interval width by 29.6% in Mexico. These results demonstrate TREA-Net’s potential as a lightweight and portable early-warning framework for health agencies with limited surveillance data.
[LG-25] oxScreen: Detecting Whether an LLM Has Been Poisoned
链接: https://arxiv.org/abs/2607.26849
作者: Anthony Hughes,Nicole Xing,Collin Francel,Andy Kim,Andrew Draganov
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:As large language models (LLMs) are deployed in high-stakes domains, adversaries may poison training data to implant backdoors: hidden triggers that covertly manipulate model behavior at inference time. We ask whether a defender can recover such a trigger under realistic affordances, namely white-box access to the weights and knowledge of the behavior of concern, but no training data, no trusted reference model, no knowledge of the trigger, and no certainty that the model is poisoned. To evaluate whether a defender can recover such a trigger under realistic settings, we release ToxScreen, a benchmark of roughly 800 backdoored models spanning attack objectives, trigger mechanisms, poisoning rates, model scales, and backdoor training mechanisms. We also assert that the backdoors are high-quality: they achieve high attack success rates, generalize to unseen harmful inputs, and preserve clean-task performance. Scoring recovery of the planted trigger, we find that gradient-based prompt optimization fails in recovery, whereas a token look-up that ranks candidates by attack-success rate recovers the trigger wherever the backdoor is effective. To understand this more, we study the relationship between attack behaviors and the weights of an LLM. We find a phenomenon whereby backdoors operate via different mechanistic strategies than jailbreaks, allowing defenders to filter jailbreaks. Finally, no method reliably surfaces every backdoor, but a broadly jailbreakable model is itself anomalous, a useful signal even when the exact trigger is not recovered. We release all models and evaluation code
[LG-26] hinking Under Uncertainty: Evidence Use and Information-Seeking in Language Models
链接: https://arxiv.org/abs/2607.26845
作者: Hua-Dong Xiong,Xinyuan Yan,Ji-An Li,Jingming Xue,Marcelo G. Mattar,Robert C. Wilson
类目: Machine Learning (cs.LG)
*备注:
Abstract:Inference-time thinking improves the performance of large language models, but aggregate outcomes do not reveal whether models use available evidence more effectively or seek information that could improve future decisions. We distinguish these responses by measuring action preference, thinking length, and reported confidence under matched uncertainty. Ten open-weight models completed matched horizon-style two-armed bandit trials in thinking and non-thinking modes. A cognitive model separated value-guided action and uncertainty-independent choice noise from two behavioral signatures of exploration: a UCB-like preference for the less-known arm and Thompson-like choice variability that increases with total uncertainty. On average, thinking strengthened value-guided action and reduced uncertainty-independent choice noise, without producing UCB-like exploration or strengthening Thompson-like exploration. Outside action, the information-imbalanced history condition, which also displayed more observations than the matched balanced condition, was associated with greater thinking length. Reported confidence became more sensitive to decision difficulty and more strongly associated with chosen task evidence. We interpret these thinking-length and reported-confidence patterns as consistent with metacognitive control and metacognitive monitoring, respectively, without establishing either process. Decoder sweeps, especially temperature, altered choice noise and thinking length but did not reproduce the joint cross-output pattern. In this controlled decision setting, thinking improved how models acted on current evidence, while neither measured signature supported a shift toward a more information-seeking policy.
[LG-27] ght Generalization Bound for AdaBoost
链接: https://arxiv.org/abs/2607.26838
作者: Mikael Møller Høgsgaard
类目: Machine Learning (cs.LG)
*备注: Preprint
Abstract:In this paper we show that the generalization error of AdaBoost is \Theta\big(\tfracd\ln(n\gamma^2/d)n\gamma^2+\tfrac\ln(1/\delta)n\big) , where \gamma is the advantage guaranteed by the weak learner, d is the VC-dimension of the class containing the weak hypotheses, n is the sample size, and \delta is the confidence parameter. The contribution of this paper is the upper bound; the matching lower bound follows from prior work. The upper bound proof follows by combining the known fact that AdaBoost outputs a voting classifier whose voting function has zero empirical \gamma/2 -margin loss with what is, to the best of our knowledge, a new margin-based generalization bound for voting classifiers.
[LG-28] Forecasting Trajectory-Level Safety Risks in Black-Box Multi-Turn Interactions
链接: https://arxiv.org/abs/2607.26820
作者: Shi Lin,Peng Qian,Dinghao Liu,Renjie Sun,Sifan Wu,Dezhang Kong,Chenpei Wang,Xun Wang
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注:
Abstract:As large language models (LLMs) evolve from standalone assistants into autonomous agents, ensuring their safety requires shifting beyond pointwise risk assessment to understand how risks emerge and unfold over long-horizon trajectories. In multi-turn interactions, malicious intent can be decomposed across seemingly harmless turns and gradually reconstructed through interaction trajectories, eventually resulting in safety failures. Existing safeguards remain largely reactive, detecting manifested violations while lacking the ability to predict latent risk evolution and enable preemptive prevention. To address this limitation, we propose Recast, a safety risk forecasting framework that advances LLM safeguarding beyond turn-level violation detection to trajectory-level risk prediction. Recast first retrieves risk-relevant evidence from both short-term dialogue progression and long-term historical context via a dual-scale trajectory view. It then models compositional risk evolution by capturing the current risk configuration and its temporal dynamics. Finally, a causal temporal encoder learns latent risk evolution patterns and predicts the distribution of future risk emergence turns. Extensive experiments across 7 risk categories show that Recast predicts 88.3% of future safety failures with an average lead time of 2.41 turns, while maintaining a false alarm rate of 12.3%, showcasing the effectiveness of trajectory-level forecasting in identifying emerging risks before safety violations occur.
[LG-29] Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approach AAMAS2021
链接: https://arxiv.org/abs/2607.26788
作者: Cengis Hasan
类目: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
*备注: 30 pages, 5 figures. Substantially revised and extended version of arXiv:2101.09673 (OptLearnMAS workshop at AAMAS 2021). Code and data: this https URL
Abstract:Clustered federated learning benefits from organizing heterogeneous participants into coalitions that train coalition-specific models, but such clustering is sustainable only if participants prefer their assigned coalition and the required transfers are affordable. We develop a transferable-surplus model separating learning benefit, system cost, participant cost, and monetary transfers; an allocation rule converts coalition surplus into hedonic preferences, and weak budget feasibility guarantees nonnegative retained coordinator surplus. For symmetric pairwise allocations the induced game is an exact potential game: a Nash-stable partition exists, every strict better-response process converges, and with destination consent accepted better responses reach an individually stable partition. We characterize feasibility of bounded pair incentives and verify the exponentially many budget constraints in polynomial oracle time when retained slack is submodular. Decomposing welfare into participant potential and retained slack yields additive and multiplicative price-of-stability guarantees, the latter asymptotically tight; exact balance gives welfare-optimal stability only on the pairwise-representable class, and budget feasibility alone permits unbounded welfare loss. Global potential maximization equals weighted maximum-agreement correlation clustering, and approximation followed by stabilization satisfies an end-to-end welfare bound governed by retained slack and negative-edge mass, attained by an explicit construction. In a preregistered five-seed CIFAR-10 study the mechanism reaches the certified estimated-table welfare optimum on every primary instance, equal-surplus sharing has no Nash-stable outcome on three, and pairwise validation gain gives far more reliable pair signs than gradient alignment.
[LG-30] CalTwin: Towards Calibrated Shift-Robust Medical World Models via Fisher-Information Regularisation
链接: https://arxiv.org/abs/2607.26752
作者: Behraj Khan,Shabir Ahmad,Syed Ahmad Chan Bukhari,Tahir Qasim Syed
类目: Machine Learning (cs.LG)
*备注:
Abstract:Medical world models aim to learn a latent state of patient or organ physiology and a transition function that forecasts how that state evolves under interventions, supporting downstream tasks from imaging-based diagnosis to digital-twin treatment planning. Two failure modes threaten the reliability of such models in clinical deployment: (i)~\emphcovariate shift, because training data are fragmented across hospitals, scanners, and time, so the feature distribution seen by the latent-dynamics predictor differs across fragments and from the distribution at deployment; and (ii)~\emphconfidence misalignment, because multi-step forecasts are often overconfident exactly where clinical risk is highest. We argue that both problems admit a unified treatment via a single lightweight regularisation objective, \textbfCalTwin, which combines a Fisher-Information-based shift penalty adapted from our prior work on fragmented covariate-shift remediation~\citekhan2025mitigating,khan2025causal with a Confidence Misalignment Penalty adapted from our prior work on calibrated vision-language classification~\citekhan2025confidence, applied here to a GRU-based medical world model’s latent transition predictor. We derive the combined objective, establish which proof steps transfer from the classification setting without modification and which require adaptation, and evaluate it on the PhysioNet 2019 Sepsis Challenge, treating the two hospital systems as sequential training fragments and the unseen system as an out-of-distribution test. CalTwin reduces OOD next-step latent-state MSE by 9.1% relative to the no-penalty baseline (FIM penalty alone accounts for 7.0%); the ECE reduction from the Confidence Misalignment Penalty is real but small (0.7% for CalTwin, 1.3% for CMP alone).
[LG-31] Domain adaptation for handwriting trajectory reconstruction from IMU sensors
链接: https://arxiv.org/abs/2607.26736
作者: Florent Imbert,Romain Tavenard,Yann Soullard,Eric Anquetil
类目: Machine Learning (cs.LG)
*备注:
Abstract:Digital pens are commonly used to write on digital devices, providing the handwriting trace and enhancing human-computer interation. This study focuses on a digital pen equipped with kinematic sensors, allowing users to write on any surface while simultaneously preserving a digital trajectory of handwriting. This technology holds significant potential as a valuable educational tool, particularly in classrooms where it can facilitate the process of learning to write. A major issue is based on the difference in captured signals between adults and children. For similar handwriting trace, we have large differences in sensor signals due to differences in speed and confidence in the handwriting gesture of children. To address this, we investigate a domain adaptation approach to build a unified intermediate feature representation aimed at facilitating the trajectory reconstruction. We demonstrate the interest of domain adaptation methods in leveraging existing knowledge for application in different contexts. Specifically, we compare our domain adaptation approach with two other methods: training the model from scratch and fine-tuning the model.
[LG-32] PowerAtlas: Towards Electricity-Computing Co-Scheduling for Power Systems
链接: https://arxiv.org/abs/2607.26710
作者: Kaiwen Jiang,Siya Xu,Ziyue Zhu,Chao Yang,Anh Tuan Luu,Haoran Luo
类目: Machine Learning (cs.LG)
*备注: 17 pages, 9 figures, 5 tables. Code: this https URL
Abstract:The rapid growth of AI workloads is turning data centers into large-scale, volatile, yet spatiotemporally flexible grid loads, creating an urgent need for coordinated electricity-computing scheduling. Under stringent grid constraints, schedules from general-purpose large language models (LLMs) are often infeasible, causing line-flow violations and unserved load. We present PowerAtlas, an LLM-agent framework for electricity-computing co-scheduling that integrates historical instances, domain knowledge, and physical constraints to produce joint decisions satisfying both grid operational rules and the service-level agreements (SLAs) of computing tasks. Working with a provincial power utility in China, we built an experimental electricity-computing network and validated the decision loop on real data-center data; from de-identified operational data we further constructed ECBench, a benchmark of 2,000 scheduling instances with oracle-optimal solutions. Experiments across eleven LLMs demonstrate the effectiveness of PowerAtlas under realistic physical operating conditions, with consistent feasibility and cost gains across three open-weight backbones. Our code is publicly available at this https URL.
[LG-33] Mixture-of-experts for handwriting trajectory reconstruction from IMU sensors
链接: https://arxiv.org/abs/2607.26708
作者: Florent Imbert,Eric Anquetil,Yann Soullard,Romain Tavenard
类目: Machine Learning (cs.LG)
*备注:
Abstract:The use of digital pens for online handwriting trajectory reconstruction is a prevalent method for human-computer interaction. In this study, we focus on a digital pen equipped with sensors where we aim at reconstructing the online handwriting trajectory. This pen enables writing on any surface and preserving the digital trace of handwriting. This type of pen could be used as an aid to learning to write in classroom. In this paper, we propose a new approach learning to finely reconstruct the touching trajectories while precisely analyzing the hovering part in order to position the next touching trace correctly. This relies on a Mixture-Of-Experts (MOE) approach. The first expert is dedicated for the pencil touch, and is named touching expert model. The second one is dedicated for the hovering pen trajectory, and is named hovering expert model. We improve on the learning of each of these experts based on additional context or specific examples. In addition we introduce a novel public benchmark dataset, to enable future research and comparisons in the field of handwriting reconstruction. The results demonstrates a significant enhancement compared to its primary competitors.
[LG-34] Universality and Approximation Rates of Graph Neural Networks with Random Features
链接: https://arxiv.org/abs/2607.26699
作者: Lukas Gonon,Thilo Meyer-Brandis,Niklas Weber
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 57 pages, 3 figures
Abstract:We investigate message-passing graph neural networks with random node features. Random node features are known to enhance the expressiveness of graph neural networks (GNNs) both theoretically and empirically. Here, we establish a novel universality result focusing on permutation-equivariant neural networks (PENNs), a class of GNNs built from feedforward neural network components that subsumes many prominent GNN architectures. We show that PENNs, combined with partially random node features, can approximate arbitrarily well in probability any measurable permutation-invariant or permutation-equivariant function on directed graphs of fixed size with multidimensional node and edge features. For k -times continuously differentiable functions, k\geq 2 , we also derive upper bounds on the approximation rates, relating the complexity of the feedforward components of a PENN in terms of layer depth and number of nonzero weights to the desired approximation accuracy.
[LG-35] AIGen: Automating AI Bill of Materials Generation Through Hybrid MLOps Integration
链接: https://arxiv.org/abs/2607.26652
作者: Federica Pepe,Daniele Bifolco,Costantino Martignetti,Aureliano D’Amici,Fabiano Izzo,Damian A. Tamburri,Massimiliano Di Penta
类目: Machine Learning (cs.LG)
*备注:
Abstract:The responsible development and deployment of artificial intelligence (AI) systems requires rigorous documentation of their constituent artifacts, e.g., datasets, model weights, training pipelines, and runtime dependencies. Although the Software Package Data Exchange (SPDX) 3.0 standard introduced native support for AI and dataset profiles, practical tooling capable of generating standards-compliant AI Bills of Materials (AIBoMs) in an automated and extensible manner remains scarce. This paper presents AIGen, a modular AIBoM generator that produces machine-readable, interoperable inventories of AI system components that comply with the SPDX 3.0 AI profile. AIGen works on top of the MLflow MLOps framework and combines mining heuristics with Large Language Models to generate AIBoMs. A plugin interface allows practitioners to extend the tool with domain-specific collectors without modifying the core codebase, supporting heterogeneous AI frameworks such as Hugging Face, PyTorch, and TensorFlow. AIGen is designed to facilitate compliance with the European Union AI Act, the NIST AI Risk Management Framework, and ISO/IEC 42001, providing a concrete, reusable foundation for transparent, accountable AI supply chain governance. Tool URL: this https URL Tool Video: this https URL_nAbXDWfVL4
[LG-36] he Sparsity Ceiling: Where Spiking Networks Can and Cannot Trade Activity for Energy
链接: https://arxiv.org/abs/2607.26648
作者: Zeyu Wang
类目: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
*备注: 5 pages, 6 figures. Code: this https URL
Abstract:Spiking neural networks (SNNs) are promoted as an energy-efficient substrate because sparse, event-driven activity replaces dense multiply-accumulates with cheap accumulates. We argue the energy dividend of sparsity is not a property of SNNs but of the task. Holding architecture fixed and swapping only the hidden unit (continuous vs. leaky-integrate-and-fire), plus a two-sided target-firing-rate probe, we measure how far activity can be pushed down before quality breaks. Low-load feed-forward perception sparsifies to 5% firing at no accuracy cost; a recurrent language model cannot go below ~50% – the recurrent state must stay active to carry information. A spiking Transformer, by contrast, sparsifies freely to 2% (3 seeds) – so the ceiling is a property of recurrent compression, not sequence modeling. Attention escapes the floor only by storing the full key-value cache, trading a firing floor for a memory wall: on neuromorphic hardware, recurrence and attention pay on different axes, neither escapes. We formalize the ceiling with an information-theoretic bound rho = H_b^-1(log2 M / H) and confirm its predictions: the floor rises with memory load, falls with state width, and (refuting a naive memory-only reading) rises with task difficulty. A layer-wise input floor further caps op reduction under dense input, isolating event-driven perception as where neuromorphic hardware wins.
[LG-37] Enhancing Automated Machine Learning via Homogeneous Train-Test Splitting Methods
链接: https://arxiv.org/abs/2607.26625
作者: Yearn Tan Yin Tze,Charles Grellois
类目: Machine Learning (cs.LG)
*备注: Pre-review version of a paper accepted at UKCI 2026
Abstract:Accurate model evaluation in machine learning depends critically on how datasets are split into training and testing subsets. Standard random splitting assumes that both partitions share the same underlying distribution, an assumption often violated in datasets with class imbalance, natural clustering, or spatial autocorrelation. This paper investigates the role of statistical similarity in train-test splitting and its consequences for AutoML model evaluation. Five established strategies are compared across fifteen UCI benchmark datasets: random splitting, stratified sampling, Kennard-Stone, Duplex, and SPXY. Similarity is assessed using chi-square, Kolmogorov-Smirnov, and Maximum Mean Discrepancy (MMD) tests. Geometry-based methods consistently produce near-zero MMD scores, introducing instability into downstream performance estimates. The proposed Optimised-Distribution method treats similarity as an explicit optimisation objective and achieves the highest mean MMD similarity, 89.0%, across all strategies evaluated.
[LG-38] Few-Shot Open-Set Audio Classification via Transductive Prototype Refinement and Class Logit Enhancement
链接: https://arxiv.org/abs/2607.26607
作者: Tianyan Deng,Yanxiong Li,Rui Gao,Jiahao Du
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注: 6 pages, 1 figures
Abstract:Few-shot Open-set audio classification requires classifying query samples from known classes with a few labeled support samples while rejecting query samples from unknown classes. Transductive inference jointly observes the full unlabeled query set to improve prototype estimation, yet standard transductive updates do not distinguish known from unknown query samples, leaving prototypes vulnerable to open-set contamination. Drawing on latent-inlierness weighting and decoupled scoring for unknown-class samples, we propose a two-phase transductive method operating over a frozen audio encoder. First, each query sample is assigned a latent inlierness score that down-weights likely unknown-class samples, so that prototype refinement is driven primarily by known-class evidence. The refined prototypes are then directly optimized on a transductive loss combining support cross-entropy, inlierness-weighted conditional entropy minimization, and inlierness-weighted marginal entropy maximization, while open-set rejection uses a prior-adaptive free-energy score that adjusts its threshold with the prior proportion of unknown-class samples, decoupling detection from classification. Experiments on three audio datasets show our method achieves state-of-the-art results for few-shot open-set audio classification under multiple experimental conditions.
[LG-39] Harnessing Large Language Models for Intelligent Resource Allocation in the Internet of Everything
链接: https://arxiv.org/abs/2607.26602
作者: Haijun Zhang,Zhuojun Duan,Zijun Wu,Xu Ma,Yuzheng Ren
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
*备注:
Abstract:The rapid development of the Internet of Everything (IoE) is accelerating the adoption of intelligent applications. However, the massive number of connected devices generates diverse and heterogeneous tasks, which pose increasing challenges for dynamic resource scheduling in IoE environments. Using their superior semantic understanding and reasoning capabilities, Large Artificial Intelligence Models (LAIMs) demonstrate significant potential to handle complex scheduling scenarios and improve resource utilization efficiency. This paper investigates a task-oriented LAIM-driven resource scheduling mechanism, which constructs a multidimensional scheduling decision model by integrating task semantics, network states, and constraint conditions. Furthermore, a task-oriented prompt generation method is designed to establish a deep association between task requirements and network state. In the proposed resource allocation scheme, an external evaluation and feedback module is incorporated to conduct real-time feasibility verification and performance evaluation of scheduling strategies, thus enhancing the robustness and adaptability of scheduling. Simulation results demonstrate that the proposed Large Language Model (LLM)-driven network architecture and resource allocation scheme achieve significant improvements in convergence speed, processing latency, and energy consumption, effectively enhancing IoE task responsiveness and resource utilization.
[LG-40] Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation
链接: https://arxiv.org/abs/2607.26599
作者: Jialu Xu,Mengkun Liang,Guannan Liu,Xiaojie Mao,Junjie Wu
类目: Machine Learning (cs.LG)
*备注: 17 pages, 12 figures, 5 tables
Abstract:Estimating heterogeneous treatment effects is central to targeted interventions, such as personalized promotions and precision medicine. We focus on the conditional average treatment effect (CATE), a standard estimand for characterizing such heterogeneity. Even under standard identification conditions, finite-sample CATE estimation requires learning the nuisance structure for covariate adjustment and treatment-effect heterogeneity, often together with an effective representation of X. Raw numerical and categorical encodings can leave semantic relations and higher-order interactions implicit, making this joint task locally unstable. A motivating study further shows that this instability appears through partially separable assignment- and heterogeneity-side channels. Building on this observation, we propose CURL (Causal Uncertainty-guided Representation Learning), a plug-in adapter that uses estimator uncertainty to allocate pretrained semantic capacity to locally unstable units. CURL queries a frozen LLM through two role-conditioned prompts, constructs assignment- and heterogeneity-oriented representations from the observed covariates, and routes them through separated pathways. On four benchmarks, CURL improves ten host learners in most settings, while ablation, refinement-dynamics, route-reassignment, and probe analyses support the intended design and roles of the two channels.
[LG-41] Benchmarking ConvLSTM for One-Day-Ahead IMDAA Rainfall-Field Prediction across Four Indian Cities
链接: https://arxiv.org/abs/2607.26581
作者: Tanmay Ghosh,Shaurabh Anand,Rakesh Gomaji Nannewar,Nithin Nagaraj
类目: Machine Learning (cs.LG)
*备注:
Abstract:Convolutional long short-term memory networks (ConvLSTMs) are widely used for precipitation forecasting, but most evidence for their performance comes from dense, high-frequency radar sequences. This study tests whether convolutional recurrence improves one-day-ahead rainfall-field prediction on small daily reanalysis grids. Indian Monsoon Data Assimilation and Analysis (IMDAA) fields for June-September 1998-2020 were analysed for Bengaluru, Delhi, Kolkata and Mumbai. Ten naive, statistical, tree-based and neural approaches were compared using atmospheric-only and rainfall-history-plus-atmospheric inputs. Performance was assessed for complete fields, domain-mean rainfall, spatial anomalies and high-rainfall days. ConvLSTM did not consistently outperform simpler alternatives. FC-LSTM produced the numerically lowest domain-mean rainfall error in Bengaluru, Kolkata and Mumbai, whereas persistence performed best in Delhi. ConvLSTM produced the numerically lowest spatial-anomaly error only in Mumbai, where rainfall fields showed greater short-term spatial continuity and rainfall-history inputs improved all three neural architectures. The difference between ConvLSTM and FC-LSTM was nevertheless small. Neural models underestimated rainfall magnitude and predicted too few threshold exceedances on high-rainfall days, while persistence achieved the highest detection performance in every city. Post-hoc analyses showed that the selected models were most sensitive to the latest input day, with broader recent-lag sensitivity in Mumbai. These findings show that gridded inputs alone do not justify ConvLSTM and that architecture choice should follow strong benchmarking across average, spatial and high-rainfall performance. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.26581 [cs.LG] (or arXiv:2607.26581v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.26581 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-42] Simultaneous Coverag e and Efficiency Guarantee in Online Conformal Prediction
链接: https://arxiv.org/abs/2607.26577
作者: Rahul Vaze
类目: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS)
*备注: We acknowledge the use of LLMs for editorial assistance in improving the clarity, style, proof checking, and figure generation of this manuscript
Abstract:Adaptive conformal inference (ACI) of Gibbs and Candès and its variants are the standard approach to online conformal prediction under distribution shift, but they suffer from three fundamental limitations. First, their guarantees control only the \emphsigned long-run coverage error: persistent miscoverage in one direction can be masked by compensating errors later, so a method can satisfy the theoretical guarantee while being badly wrong for extended periods. Second, existing guarantees say nothing about prediction-set size, so validity can be achieved trivially at the cost of unduly wide prediction sets. Third, the efficiency guarantees that do exist compare against a \emphfixed predictor chosen in hindsight, a benchmark that becomes increasingly less meaningful once the data-generating distribution shifts, since the very notion of an optimal threshold then changes over time. We consider a unified online learning framework that simultaneously controls absolute, non-cancelling coverage violation and prediction-set efficiency against a dynamically evolving benchmark for three important models. In the fully adversarial setting, exploiting the fact that the standard ACI update is exactly projected online gradient descent on the pinball loss, we derive simultaneous coverage and efficiency guarantees for arbitrary monotone Lipschitz efficiency objectives, with no distributional or \it convexity assumptions. In the stochastic setting with full-score feedback, we propose a sliding-window quantile tracker and establish a matching minimax lower bound showing our algorithm is rate-optimal. In the covariate-dependent stochastic setting, we develop a partitioned ACI algorithm that tracks a function-valued oracle threshold, and derive simultaneous coverage and efficiency guarantees. Comments: We acknowledge the use of LLMs for editorial assistance in improving the clarity, style, proof checking, and figure generation of this manuscript Subjects: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS) Cite as: arXiv:2607.26577 [cs.LG] (or arXiv:2607.26577v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.26577 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-43] From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs ECML-PKDD2026
链接: https://arxiv.org/abs/2607.26571
作者: Tina Vartziotis,Rodopi Kosteli,Elli Vartziotis,George Dasoulas,Michael Keckeisen,Konstantinos Skianis,Sotirios Kotsopoulos,Francesca Dominici
类目: Machine Learning (cs.LG); Software Engineering (cs.SE)
*备注: 20 pages, 3 figures, 6 tables. Accepted for oral presentation at the GREEN-AI Workshop, co-located with ECML-PKDD 2026
Abstract:The operational energy consumption of large language model (LLM) inference is becoming an increasingly important component of the environmental footprint of deployed AI systems. However, direct measurement of inference energy often requires hardware telemetry, power instrumentation, or infrastructure-specific monitoring, limiting its applicability in comparative studies, early-stage system design, and sustainability reporting. This report presents an analytically structured, empirically calibrated, GPU-level methodology for estimating LLM inference energy on NVIDIA H100-class accelerators without direct runtime measurement. The proposed estimator combines parameter-scaled transformer FLOP accounting, calibrated memory-traffic factors, and hardware-specific energy coefficients for FP16/BF16 tensor-core computation and high-bandwidth-memory movement. It explicitly separates prompt prefill from autoregressive decoding, enabling energy estimates for input tokens, output tokens, and complete inference requests. The methodology further decomposes total energy into compute, parameter-access, key-value-cache write, and attention-read components, allowing the scaling behavior with model size, context length, and generated-token count to be analyzed. The resulting estimates are not intended to replace physical power measurements; rather, they provide transparent, reproducible, and assumption-explicit approximations suitable for model comparison, green-coding analysis, and design-time evaluation of LLM inference workloads.
[LG-44] From Unsupervised Subgroups to Hypothetical State-Intervention Policies: An Evaluation of Selected Subgrouping Methods in Observational Health Data
链接: https://arxiv.org/abs/2607.26521
作者: Vasundhara Acharya,Bulent Yener
类目: Machine Learning (cs.LG)
*备注:
Abstract:Conventional subgroup analyses can yield unstable and difficult-to-interpret conclusions, especially in observational biomedical data where each individual is observed under only one exposure state, true individual treatment effects are unavailable, and causal structure is uncertain. We investigate whether subgroups constructed from pretreatment characteristics, without using exposure, outcome, or estimated treatment-effect information, can serve as interpretable units for budget-constrained policy prioritization. We propose a framework combining causal-discovery-informed covariate selection, discovery-evaluation sample splitting, inductive unsupervised clustering, uncertainty-aware subgroup selection, and held-out doubly robust policy evaluation. We compare K-means, hard, membership-weighted, and stochastic Fuzzy C-means, Bayesian Gaussian mixture models, and a supervised causal-forest-derived CATE-tree comparator. Policies are evaluated under a 70% budget for hypothetical obesity-to-non-obesity and elevated-to-lower-glucose state shifts in the PIMA Indians Diabetes dataset and for a lifetime-smoking-history contrast in NHANES. The highest estimated ungated utilities were 0.799 for the BMI policy using Bayesian GMM, 0.735 for the glucose policy using hard or membership-weighted FCM, and 0.775 for the smoking-history policy using K-means. All paired 95% confidence intervals for policy-risk differences included zero, and no comparison remained statistically significant after Holm adjustment. Bayesian pooling generally preserved ungated allocations, whereas Empirical Bernstein gating was more conservative. Policies with similar estimated utility could nevertheless prioritize different individuals. The findings should be interpreted as assumption-dependent decision-support evidence for hypothetical state contrasts rather than proof of intervention benefit.
[LG-45] From Interface to Inference: Eliciting Any-Order Inference from Any-Order Models
链接: https://arxiv.org/abs/2607.26504
作者: Seunggeun Kim,Jaeyeon Kim,Taekyun Lee,Yuyuan Chen,Yilun Du,Sham Kakade,Sitan Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Many discrete reasoning tasks, such as code generation, are inherently non-causal: programmers move between high-level structure and local details, a process we call any-order inference. For autoregressive language models, which lack a native any-order interface, non-causal abilities such as infilling and next-edit prediction require hand-designed mechanisms. Can we instead design models that natively support any-order inference? Masked diffusion models have recently emerged as compelling candidates, as their any-order training objective naturally offers an any-order prediction interface. This interface, however, does not automatically yield any-order inference. We demonstrate that this interface-inference gap stems from positional uncertainty: fixed-canvas, token-level models may know what semantic component should appear without knowing where to place it. In light of this, we propose two complementary approaches: (1) Insertion-based masked diffusion, building on FlexMDM (Kim et al, 2025), relaxes fixed-position commitments via insertions, enabling generation across non-contiguous regions. (2) Latent-space masked diffusion shifts prediction to coarser semantic segments, enabling search over latent generation orders. Empirically, we train a 7B FlexMDM for Python coding and a 125M LatentMDM for GSM8K and show that both approaches induce distinct any-order inference behaviors and improve downstream performance. We release our codebase at this https URL.
[LG-46] From Conceptual Hydrologic Models to Conceptually Interpretable Neural Networks: A Snow-Water Mass-Conserving-Perceptron Framework for Discovering Catchment-Scale Precipitation-Storag e-Runoff Representations
链接: https://arxiv.org/abs/2607.26492
作者: Yuan-Heng Wang,Hoshin V. Gupta
类目: Machine Learning (cs.LG)
*备注: 125 pages; Main text: 7 tables, 18 figures; Supplementary Materials: 3 texts, 10 tables, 32 figures
Abstract:The Mass-Conserving Perceptron (MCP) establishes a modeling paradigm in which conceptual hydrologic models can be reformulated as physically constrained, conceptually interpretable neural networks. Here, we develop a snow-water MCP network framework and evaluate it across 513 CAMELS-US basins. We first recast a coupled two-state SOIL-MCP and SNOWMCP conceptual model as a mass-conserving neural network and show that the hydrologic-model and neural-network formulations achieve comparable predictive performance. We then examine cross-node state-information sharing within two-state HYDROMCP architectures and evaluate broader single-layer networks constructed from three types of interpretable MCP units with one to five states. Across CONUS, the median KGEss increases from 0.82 for one-state networks to 0.89 for two-state networks and 0.90 for five-state networks, suggesting diminishing aggregate gains beyond two states. Basin-specific MCP and LSTM selection yields the same median KGEss of 0.90, while the selected MCP networks use fewer parameters on average. Complementary AIC- and KGE-based selection identifies compact, basin-specific directed-graph representations that balance predictive accuracy and model complexity. These analyses provide an empirical basis for identifying the numbers, types, and interactions of states needed for hydrologic representation. Future studies should test joint training against multiple hydrologic responses, such as streamflow, snow water equivalent, and groundwater storage.
[LG-47] Parameterized Fair Resource Allocation under Diversity Constraints
链接: https://arxiv.org/abs/2607.26485
作者: Keke Huang,Yik Yu Ng,Laks V.S. Lakshmanan,Xiaokui Xiao
类目: ocial and Information Networks (cs.SI); Machine Learning (cs.LG)
*备注:
Abstract:Resource allocation across multiple agent groups arises in many applications including e-commerce recommendation systems, housing assignment, and course allocation, and is commonly formulated as an optimization problem with diversity constraints to ensure group fairness. Existing approaches typically enforce these constraints as hard conditions, which overly restrict the feasible solution space and often lead to suboptimal allocations. In this paper, we propose PRA, a parameterized framework for fair resource allocation under diversity constraints. Inspired by the use of risk-aversion parameters in economic models, PRA introduces a set of controllable inequality-aversion parameters to softly regulate group-level diversity, thereby enabling flexible trade-offs between fairness and allocation efficiency. With appropriately calibrated parameters, PRA yields fairness-optimal assignments that comply with the specified diversity constraints. To accommodate additional application-specific constraints, we further extend the framework to an adaptive variant, APRA. We establish that the optimality of both PRA and APRA holds regardless of the chosen fairness metric and the nature of the additional constraints, underscoring the generality and robustness of our approach. Extensive experiments on three real-world applications demonstrate that our proposed framework consistently outperforms existing baselines in both effectiveness and robustness. Subjects: Social and Information Networks (cs.SI); Machine Learning (cs.LG) Cite as: arXiv:2607.26485 [cs.SI] (or arXiv:2607.26485v1 [cs.SI] for this version) https://doi.org/10.48550/arXiv.2607.26485 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-48] Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations
链接: https://arxiv.org/abs/2607.26481
作者: Seunghun Yu,Meiyi Zhu,Petar Popovski,Joonhyuk Kang,Osvaldo Simeone
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: 34 pages, 6 figures
Abstract:Detecting when the statistical behavior of an engineered system changes, and identifying which component is responsible, are core problems in the monitoring of telecommunication networks, robotic platforms, security infrastructure, and multi-agent systems. In safety- and mission-critical deployments, such decisions must be accompanied by statistical reliability guarantees rather than by point estimates alone. Conformal changepoint localization (CONCH) and conformal root cause analysis (CROC) meet this need by returning confidence sets that contain the true changepoint, or the true root-cause stream, with a user-specified probability, without parametric assumptions on the data-generating process. In practice, however, observations are frequently corrupted, e.g., by outliers, sensor faults, or adversarial perturbations. While the finite-sample coverage of these procedures is preserved under contamination, the resulting confidence sets can become uninformatively large. Adopting a Huber-type contamination model, this paper proposes weighted CONCH (W-CONCH) and weighted CROC (W-CROC), which downweight observations that are likely to be corrupted with the goal of reducing confidence set size when data may be corrupted. The weighting mechanism, derived from a formal bound on the unknown corrupted data densities, leverages pre-existing second-order classifier-based uncertainty signals, such as those produced by evidential deep learning or Bayesian learning. W-CONCH and W-CROC are further generalized by introducing a meta-learning procedure for the weights that optimizes a differentiable surrogate of the confidence set size. Experiments on image-based and real-world changepoint and root-cause benchmarks show that uncertainty-based weighting substantially reduces confidence set size while maintaining the target coverage.
[LG-49] Neural Architecture Search for Traffic Prediction: A Survey of Methods Challenges and Future Directions
链接: https://arxiv.org/abs/2607.26467
作者: Truong Giang Vu,Li Yang,Richard W. Pazzi
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注: 8 pages, 3 tables. Accepted at UrbCom 2026, the 8th International Workshop on Urban Computing, co-located with IEEE DCOSS-IoT 2026
Abstract:Traffic prediction is a core task in intelligent transportation systems, supporting applications such as adaptive signal control, route guidance, and ride-hailing dispatch. Deep learning models, including graph convolutional networks, recurrent networks, and Transformers, achieve strong results on standard benchmarks, but their architectures are designed by hand, requiring significant expert effort and producing models that often generalize poorly across cities and datasets. Neural Architecture Search (NAS) offers a systematic alternative to manual design. It automates the search over candidate architectures of deep learning models, finding designs that match the spatial-temporal structure of traffic data without manual trial and error. This survey reviews NAS methods applied to traffic prediction, organized by search strategy: gradient-based methods, evolutionary methods, and one-shot weight-sharing methods. For each category, we analyze how the search space is designed to cover spatial and temporal traffic operators, and how the search strategy balances cost against architecture quality. We also discuss open challenges, computational scalability to large road networks, manual search space design, cross-city generalization, dynamic graph structure, and the open question of NAS for spatial-temporal foundation models, and identify directions for future research.
[LG-50] DHRCL:Training Code LLM s with Dense Hierarchical Rewards and Curriculum Learning
链接: https://arxiv.org/abs/2607.26457
作者: Shuhang Wang,Ziming Li,Hui Cheng
类目: Machine Learning (cs.LG)
*备注:
Abstract:Reinforcement learning is a natural post-training paradigm for code-oriented large language models because generated programs can be evaluated through parsing, execution, unit tests, and structural this http URL, existing methods often rely on sparse outcome rewards or statically combine heterogeneous dense signals, even though syntax validity, executability, functional correctness, and structural organization describe different and progressively dependent programming capabilities. We propose DHRCL, a reinforcement learning framework with Dense Hierarchical Rewards and Curriculum Learning. DHRCL decomposes feedback into syntax validation, execution success, unit-test pass rate, and AST-based structural similarity, and organizes these signals through a three-stage Syntax, Execution, Pass Structural curriculum. Stage duration is determined automatically from recent validation trends rather than manually specified capability thresholds. We further introduce stage-aware probability-based token credit redistribution. The mechanism follows a consolidation-to-refinement principle: it emphasizes established token patterns during syntax-oriented optimization, applies uniform propagation for non-local execution feedback, and allocates more credit or blame to less-established token decisions during final functional optimization. Under a unified Qwen3-8B and KodCode protocol, the experiments compare DHRCL with binary, pass-rate, reward-model-based, and verifiable dense-reward baselines. We further evaluate DHRCL across Qwen3-4B, Qwen3-8B, and Qwen3-14B backbones, showing that its advantage remains consistent as model capacity increases.
[LG-51] Existence-Field Diffusion Model for Spatial Point Processes with Variable Cardinality
链接: https://arxiv.org/abs/2607.26428
作者: Xiaoyin Pan,Christian R. Shelton,Rakshith Mahishi,Chengkuan Hong
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 19 pages, 9 figures, 6 tables. Preprint
Abstract:We study generative modeling of spatial point processes (SPP), where both the number of points and their spatial configuration are governed by a joint distribution. While diffusion models have achieved strong performance in modeling complex distributions, extending them to variable-cardinality SPP remains challenging. Existing approaches either decouple the modeling of cardinality and spatial structure, or rely on discrete trans-dimensional operations to modify the number of points, resulting in inflexible and asymmetric generative dynamics. We propose the existence-field diffusion model (EFDM) for spatial point processes modeling, where each potential point is associated with an existence variable representing its degree of presence. This enables a unified diffusion process that jointly models both spatial locations and cardinality without requiring explicit discrete transitions. We demonstrate that our approach provides a flexible and general framework for generative modeling of spatial point processes, achieving improved modeling capability on datasets with varying cardinality.
[LG-52] SCOUT: Per-Context Reset Curricula for Sparse-Reward Reinforcement Learning
链接: https://arxiv.org/abs/2607.26417
作者: Siddharth Aphale,Ayushman Singh
类目: Machine Learning (cs.LG)
*备注:
Abstract:Sparse-reward reinforcement learning often fails because rollouts from the unassisted evaluation start rarely reach later task stages. Reset curricula address this by starting some training rollouts from easier intermediate states, called scaffolds. Such a curriculum faces two decisions: scaffold access, obtaining informative starts, and scaffold allocation, deciding how quickly that assistance is removed. Most prior curricula pace removal on one shared schedule, which can fail when task instances, or contexts, learn at different rates. We introduce SCOUT, an online, learner-agnostic reset controller that gives every context its own curriculum. Using only binary rollout success, SCOUT removes assistance after sustained success, restores it after failure, and cautiously tests a harder start when progress stalls, without changing the reward, optimizer, or learner. A counting construction shows that synchronized global pacing can be insufficient when contexts need conflicting amounts of assisted practice. Across six navigation and manipulation settings, scaffold access improves learning and enables success in three where unassisted training fails within the reported budget. In a constructed pacing conflict, each tested global schedule leaves one group unsolved, while SCOUT solves both. Average success can conceal this failure, so we also report the least successful group. Group-level pacing works when learning differences follow known groups but can fail when they occur within one group. SCOUT needs no group labels and remains consistently strong in both cases. A reset curriculum should remove assistance at the scale where learning progress differs.
[LG-53] Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts
链接: https://arxiv.org/abs/2607.26404
作者: Keith G. Mills,Aedan J. DeFrates,Joong Ho Kim
类目: Machine Learning (cs.LG)
*备注: 15 pages, 4 figures, 24 tables
Abstract:Graph Neural Networks (GNN) facilitate effective prediction on graph data such as molecules, media networks and neural network blueprints. GNNs facilitate prediction through message passing techniques which define how information flows from a node to its neighbors. Due to the ubiquity of the graph data type, the development of newer and better GNNs has garnered much interest in the machine learning community. However, GNN evaluation and benchmarking is primarily driven by classification tasks. Thus, prospective GNN message passing layers are evaluated on their ability to outperform prior work in classification contexts. In contrast, GNNs are equally capable of performing scalar regression prediction, yet this class of problem is often overlooked when proposing new GNNs while the best classification GNNs are utilized in an a priori or off-the-shelf manner for regression problems. In response, this paper studies the efficacy of GNN layers in a slew of regression contexts from rank ordering, error minimization and insight extraction. Results show that deep convolutional GNNs, particularly GEN, are more effective at these tasks than attention-based GNNs, while other classical, theoretically-inspired GNNs remain competitive and efficient. Comments: 15 pages, 4 figures, 24 tables Subjects: Machine Learning (cs.LG) Cite as: arXiv:2607.26404 [cs.LG] (or arXiv:2607.26404v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.26404 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-54] Flow Map Learning via Nongradient Vector Flow ICLR2026
链接: https://arxiv.org/abs/2607.26398
作者: Mark Goldstein,Anshuk Uppal,Raghav Singhal,Aahlad Puli,Rajesh Ranganath
类目: Machine Learning (cs.LG)
*备注: ICLR 2026
Abstract:Diffusion and flow-based models benefit from simple regression losses, but inference incurs significant overhead because sampling requires integration. Consistency models address this by directly learning the flow maps along the ODE trajectory, opening a design space between one-step and many-step approaches. However, existing methods face computational challenges such as requiring model inverses or backpropagation through iterated model calls, and do not always prove that the desired ODE flow map is a solution to the loss. We introduce SGFlow, an approach for learning flow maps that bypasses explicit invertibility constraints and expensive differentiation through model iteration. SGFlow trains a model to compute both the ODE solutions and the implied velocity from scratch by following non-conservative dynamics with a stationary point at the desired flow map. On the CIFAR image benchmark, no single method attains the best FID at every step count: SGFlow attains the best FID at 10 sampling steps and remains competitive with flow matching, Meanflow, and Lagrangian map matching at other step counts, while being the only one with a proven stationary-point guarantee for its stopgrad-based dynamics.
[LG-55] Q-Steer: Action-Value Guidance for Molecular Policy Optimization
链接: https://arxiv.org/abs/2607.26391
作者: Xinyu Wang,Jinbo Bi,Minghu Song
类目: Machine Learning (cs.LG); Biomolecules (q-bio.BM)
*备注: 20 pages, 2 figures, and 25 tables. Includes supplementary experimental results
Abstract:Oracle-limited molecular optimization gives reward only after a complete molecule is generated, while each rollout requires many local next-token decisions. This delayed-feedback interface makes molecular policy optimization myopic: an optimizer can learn that a molecule was good without knowing which intermediate actions made it good. We introduce Q-Steer, a rollout-time action-value steering primitive for molecular language models. Q-Steer uses an offline-trained and frozen prefix-action value scorer, PAVS-Q, that estimates the downstream reward of taking a candidate next token under a partial SMILES prefix, then adds a normalized value bonus to sampling logits. The optimizer update rule and online oracle budget are unchanged; the claim is fixed-online-oracle performance, not equal total compute. On PMO23 with a fixed 10,000-call online budget, complete factorial studies across two molecular language-model backbones and four optimizers show that Q-Steer improves mean valid-unique score in all eight backbone-optimizer cells, with positive macro mean-score gains between +0.033 and +0.049 and 18-20 task wins per cell. Mechanism controls show that action identity matters: prefix-broadcast values are nearly neutral, while shuffled action values harm performance. These results support Q-Steer as a reusable rollout-time action-value wrapper that improves average molecular optimization reward across optimizer families and policy backbones without changing the online oracle budget.
[LG-56] Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret
链接: https://arxiv.org/abs/2607.26370
作者: Atharva Navsalkar,Hongyu Zhou,Vasileios Tzoumas
类目: Robotics (cs.RO); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: 20 pages, 14 figures
Abstract:We propose a self-adaptive online learning for control method for tracking unknown target dynamics. The target dynamics can exhibit switching behavior, particularly, a mixture of structured, random, and/or adversarial motion. Such challenging target tracking scenarios arise in applications of dynamic mapping, traffic control, and pursuit evasion, where robots need to track, pursue, or avoid collision with moving landmarks, objects, humans, etc., whose dynamics are unknown. Our method simultaneously learns multiple predictors from scratch, via self-supervised, one-shot, and computationally efficient learning, and adaptively selects the best one to match the observed target behavior. The method enjoys finite-time near-optimality guarantees in expectation, characterized as a function of the learning error of the target dynamics and the frequency that the target dynamics switch. In the absence of both error and switching, the method asymptotically matches the optimal non-causal control policy that knows a priori the target dynamics, i.e., the method enjoys no regret in expectation. In the presence of learning errors and switching, the method degrades gracefully, \eg when there are errors and no switching, the average regret is proportional to the average learning error and switching times. To prove these guarantees, a novel technical approach is required compared to the existing works that employ RFF-based online learning. We validate our method in Crazyflie simulations and hardware experiments, across target trajectories that vary from structured to random to adversarial, in comparison to non-stochastic, kernel-based, and neural-network-based methods for online learning.
[LG-57] ClockRoPE: Random Fourier Rotations for Temporal Routine Modeling
链接: https://arxiv.org/abs/2607.26369
作者: Yiwen Chen,Joshua Ainslie,Krzysztof Choromanski,Xiang Gao,Su-Lin Wu,Yiping Yuan,Qian Sun
类目: Machine Learning (cs.LG)
*备注:
Abstract:Rotary Position Embedding (RoPE) has been widely adopted in transformer-based large language models. However, its log-linear frequency schedule, originally designed to produce long-term attention decay, limits its adoption in domains with more complex distance-correlation patterns, such as temporal periodicity in sequential recommendation. We investigate the expressiveness of general query/key rotations and find that any normalized continuous positive-definite attention modulation function can be approximated by random rotations induced by its own Fourier transform, which we term Random Fourier Rotations. Building on this theory, we propose ClockRoPE for routine modeling in sequential recommendation, where rotation frequencies are derived from periodic attention modulation functions. In online A/B tests, ClockRoPE demonstrates consistent improvements in valued engagement metrics, and has been successfully deployed in production-scale generative retrieval system at a major video-sharing platform.
[LG-58] MetaKoopman: Bayesian Meta-Learning of Koopman Operators for Modeling Structured Dynamics under Distribution Shifts
链接: https://arxiv.org/abs/2607.26345
作者: Mahmoud Selim,Sriharsha Bhat,Karl H. Johansson
类目: Machine Learning (cs.LG); Robotics (cs.RO); Systems and Control (eess.SY)
*备注:
Abstract:Modeling and forecasting nonlinear dynamics under distribution shifts is essential for robust decision-making in real-world systems. In this work, we propose MetaKoopman, a Bayesian meta-learning framework for modeling nonlinear dynamics through linear latent representations. MetaKoopman learns a Matrix Normal-Inverse Wishart (MNIW) prior over the Koopman operator, enabling closed-form Bayesian updates conditioned on recent trajectory segments. Moreover, it provides a closed-form posterior predictive distribution over future state trajectories, capturing both epistemic and aleatoric uncertainty in the learned dynamics. We evaluate MetaKoopman on a full-scale autonomous truck and trailer system across a wide range of adverse winter scenarios, including snow, ice, and mixed-friction conditions, as well as in simulated control tasks with diverse distribution shifts. MetaKoopman consistently outperforms prior approaches in multi-step prediction accuracy, uncertainty calibration, and robustness to distributional shifts. Field experiments further demonstrate its effectiveness in dynamically feasible motion planning, particularly during evasive maneuvers and operation at the limits of traction. Project website: this https URL
[LG-59] Incast-Free MoE Rate-Based Scheduling
链接: https://arxiv.org/abs/2607.26340
作者: Evyatar Cohen,Jose Yallouz,Alexander Shpiner,Mark Silberstein,Sylvia Ratnasamy,Isaac Keslassy
类目: Networking and Internet Architecture (cs.NI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:Mixture of Experts (MoE) architectures have become key to large language models; however, their typical round-robin (RR) scheduling introduces significant bottlenecks. In this paper, we demonstrate that RR causes a previously-undiscovered exponential incast phenomenon with MoE traffic. We propose an alternative proactive fair scheduling framework tailored for MoE workloads, which effectively prevents fabric oversubscription. We also outline how it can be implemented in NICs. Finally, through extensive simulations with real and synthetic workloads, we demonstrate that this framework consistently eliminates incast, maintains a near-100% link utilization, and reduces Collective Completion Time (CCT). Subjects: Networking and Internet Architecture (cs.NI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) Cite as: arXiv:2607.26340 [cs.NI] (or arXiv:2607.26340v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2607.26340 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-60] Early Verdicts Better Budgets: Sequential Adaptive Rollout Allocation for Compute-Efficient RLVR
链接: https://arxiv.org/abs/2607.26253
作者: Pixel Nomand,Elena Voss,Marcus Hale,Sofia Reyes
类目: Machine Learning (cs.LG)
*备注:
Abstract:Reinforcement learning with verifiable rewards (RLVR) is bottlenecked by rollout generation, yet many sampled prompts produce saturated groups (all responses correct or all incorrect) whose zero reward variance yields no policy-gradient signal. Existing remedies either oversample a larger candidate pool and discard saturated prompts (dynamic sampling), paying heavy extra rollouts, or predict prompt difficulty before sampling, which is fragile under a shifting policy. We observe that a group’s effectiveness is usually decided early, within the first few of its rollouts, so spending a full group on an already-decided prompt is wasteful. We cast per-step rollout collection as a budget-constrained sequential allocation (optimal stopping) problem and introduce SARA (Sequential Adaptive Rollout Allocation). SARA maintains a Beta posterior over each prompt’s success rate, evaluates a closed-form predictor of group effectiveness, and applies a two-threshold, SPRT-style rule that commits effective groups, abandons saturated ones after a short probe, and reallocates the freed budget to fresh prompts, without any extra prediction rollouts. We prove abandonment reliability, expected rollout savings, fixed-budget yield dominance, and a link between effective-group yield and the GRPO gradient norm. On mathematical reasoning and planning with 1.5B/3B models on a single GPU, SARA matches DPS (both below the DS oracle) while using 22% fewer rollouts than DS; composing SARA with DPS yields the best accuracy, slightly above DS, at 67% fewer rollouts (near-uniform cost).
[LG-61] Between Gradient and Natural Gradient: A Continuum of LoRA Initializations
链接: https://arxiv.org/abs/2607.26247
作者: Dianze Liu,Farshid Ghezelbash
类目: Machine Learning (cs.LG)
*备注:
Abstract:Low-rank adaptation (LoRA) fine-tunes large pretrained models at a fraction of the cost of full fine-tuning, but its performance depends strongly on how the adapters are initialized. Recent schemes initialize the adapters from the downstream loss gradient: some project the raw gradient onto its top directions, while others first whiten it with an estimate of the loss curvature. We show that these seemingly distinct methods are points on a single continuum: a two-parameter family of preconditioned gradient initializations, which we call Unified LoRA (ULoRA), governed by a spectral whitening exponent and an Adam-like diagonal exponent. Sweeping this family under a full learning-rate search, we find that no single fixed preconditioning strength dominates: the best operating point is task-dependent and frequently lies strictly inside the family, away from the published endpoints. Treated as an upper bound of this family, a tuned ULoRA configuration matches or exceeds full fine-tuning on all five GLUE tasks with RoBERTa-base and is competitive with the strongest baselines on GSM8K with LLaMA-2-7B. Our deployable, search-free variant, ULoRA-Auto, selects per-layer exponents from measured spectral statistics, approaches this upper bound at no additional search cost, and ranks at or near the top among deployable LoRA methods. Our results show that a principled design space for LoRA initialization and curvature preconditioning should be treated as a tunable dimension rather than a fixed design decision.
[LG-62] Weak-to-Strong On-Policy Distillation
链接: https://arxiv.org/abs/2607.26246
作者: Fangxu Yu,Zinan Lin,Xiaodong Liu,Weijia Xu,Michael Xu,Tianyi Zhou,Jianfeng Gao
类目: Machine Learning (cs.LG)
*备注: Technical Report
Abstract:On-policy distillation (OPD), which aligns a student with the teacher’s token-level distribution on the student’s own rollouts, is an effective paradigm for transferring capabilities across LLMs. Prevailing approaches assume a teacher at least as capable as the student: they either distill a larger model into a smaller one, which fails at the frontier where no larger teacher exists, or consolidate multiple domain experts trained from a shared base, which requires costly training at the student’s scale. We introduce Weak-to-Strong On-Policy Distillation (W2S-OPD), a simple yet effective OPD framework that improves the strong student by distilling from multiple weak models. W2S-OPD constructs a proxy teacher in logit space from a contrast pair of a positive and a negative model, both smaller than the student and cheap to obtain. Their logit difference isolates the capability direction, which is added to the student’s own base model, yielding a proxy teacher that couples this direction while staying distributionally adjacent to the student. The student then distills it by minimizing the per-token reverse KL on its own rollouts. We instantiate the contrast pair as i) a post-RL expert against its pre-RL initialization, isolating the skill RL instills, ii) a larger against a smaller base model, isolating the capability from scale, and iii) a small base model with correct versus wrong hints, isolating the instance-level direction toward the solution. Across four math and three code benchmarks, W2S-OPD outperforms OPD, enables the student to surpass the domain teacher, and keeps improving the student even when every supervision source is weaker. Analysis shows different contrasts yield distinct signals: the post-RL and hint contrasts emphasize reasoning frameworks, while the scale contrast emphasizes the solving procedure. Our code will be available at this https URL.
[LG-63] Learning the Word Problem: Geodesic Lengths and Cryptographic Applications
链接: https://arxiv.org/abs/2607.26241
作者: Elisabeth Fink
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Group Theory (math.GR)
*备注: 21 pages, 3 figures, 4 tables
Abstract:The Word Problem has been a subject of intensive mathematical study for over a century, initially driving advances in combinatorial group theory and more recently emerging as a foundational hardness assumption in post-quantum cryptography (PQC). While generally undecidable, several families of infinite non-abelian groups exhibit solvable or algorithmically fast word problems, making them attractive platforms for cryptographic design. This paper introduces WPNet, a novel Graph Neural Network architecture capable of solving the Word Problem heuristically, which is demonstrated on the Baumslag-Solitar group BS(1,2) and on an Artin group. By mapping unreduced words to dynamic graph structures, the model learns to cluster algebraically equivalent elements in a continuous embedding space, effectively identifying the geodesic representative of a word without executing discrete reduction steps. As an application, a model variant is developed that can predict the geodesic length of an unreduced word in both groups. To demonstrate the cryptographic severity of this structural leakage, WPNet is successfully deployed against the Wagner-Magyarik public-key cryptosystem.
[LG-64] Randomizing the Number of Centers in k-means
链接: https://arxiv.org/abs/2607.26202
作者: Vaclav Rozhon
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:The k -means++ algorithm is a standard and widely used seeding method for k -means clustering, but for a fixed number k of centers its worst-case expected approximation ratio is \Theta(\log k) . We consider the same algorithm when an adversary first fixes the dataset and some K ; the number of centers k is then chosen uniformly from \K,\ldots,2K-1\ . We prove that k -means++ is an O(1) -approximation with constant probability in this budget-smoothed setup. Subjects: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2607.26202 [cs.DS] (or arXiv:2607.26202v1 [cs.DS] for this version) https://doi.org/10.48550/arXiv.2607.26202 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-65] Dynamic Parameterization Is Not Dynamic Inference
链接: https://arxiv.org/abs/2607.26192
作者: Zongfei Li,Yuan-yih Shang,Guozhong Luo
类目: Machine Learning (cs.LG)
*备注: 10 pages, 5 figures, 2 tables
Abstract:Input-dependent controller coefficients are often treated as evidence of dynamic inference or computational savings. This interpretation conflates three properties: coefficient variation, dependence of a frozen model on how coefficients are assigned to inputs, and conditional execution. We focus on the second property and formulate a general principle of frozen-controller auditing. We provide one concrete implementation, Frozen-Controller Auditing (FCA), which caches the complete coefficient tensor along an unperturbed trajectory, disables the controller, and replays the frozen model with cross-input reassignment, token shuffling, and static profiles estimated from an independent calibration set. Because the coefficients are cached before any intervention, performance changes under replay measure assignment dependence without feedback from recomputing the controller on perturbed hidden states. Across seven independently trained 76M FeatureGate Transformers and three 504M models, static layerwise profiles retain 98.70% and 99.43% of the Correct-to-GlobalMean performance gap, respectively. Layer identity explains 87% to 96% of the coefficient variance. FeatureGate nevertheless executes every Transformer block, and its measured inference is 30.8% slower than Dense. On the public MUDDPythia-1.4B checkpoint, cross-input reassignment and token shuffling increase NLL by 1.9067 and 2.9637, respectively. These penalties show that the model depends strongly on content-conditioned cross-layer assignment. MUDDPythia also executes every Transformer block. The results show that dynamic parameterization alone does not establish dynamic inference and that functional dynamics do not establish computational savings. Claims about dynamic models should separately report coefficient variation, functional dependence of the frozen model, and actual execution.
[LG-66] Shared SFT Lessons Across Alignment Model Organisms and Toy Models
链接: https://arxiv.org/abs/2607.26173
作者: Anton de la Fuente,Arthur Conmy
类目: Machine Learning (cs.LG)
*备注: 31 pages, 13 figures
Abstract:Alignment training, model organisms, and toy models are usually treated as separate research areas. But projects in all three frequently use supervised fine-tuning (SFT) to pursue the same underlying goals. When projects share a goal, we should test whether lessons learned from one area transfer to the other areas. We study three such transfers, each taking a lesson developed in one SFT setting and testing it in another. First, we port a lesson about behavior generalization from alignment training into toy models. Training on the reason for a behavior, as in Teaching Claude Why, can make the behavior generalize better than training on examples of the behavior alone. Second, we port a lesson about capability preservation from model organisms into the Model-Spec Midtraining alignment setting. SFT on outputs written by a model other than the student (off-model outputs) can damage capabilities when trained on. Mixing in benign on-model (and on-policy) data into our training can prevent most of this damage while still embedding the target behavior. Third, we port a lesson about robustness from model organisms into the same alignment setting. We find that follow-up benign SFT can erase the alignment behavior while preserving capabilities, showing that capability preservation alone does not ensure robustness to subsequent training. Our work illustrates how porting SFT lessons between different research fields can uplift them all, suggesting more researchers should borrow techniques from outside their own areas.
[LG-67] Data Fusion and Contrastive Alignment for Unconstrained IR Molecular Structure Elucidation
链接: https://arxiv.org/abs/2607.26164
作者: Ethan J. Mick,Campbell A. Sweet,Matthias J. Young,Derek T. Anderson
类目: Machine Learning (cs.LG)
*备注:
Abstract:Automated molecular structure elucidation from infrared (IR) spectroscopy data has seen significant advancements in recent years, but its broad applicability is limited by a reliance on pre-determined chemical formulas provided as auxiliary model inputs. This limits model predictions to isomer identification rather than full molecular structure prediction. Although transformer models have been shown to identify molecular isomers with high accuracy, their reliability for unconstrained structure elucidation is comparatively low and poorly understood. In this work, we propose and evaluate key modifications to the traditional encoder-decoder transformer. To better address the vast chemical space of the unconstrained problem, we implement a novel Mixture-of-Experts (MoE) decoder module that utilizes non-additive aggregation via linear-order statistics and the Choquet integral. We further modify the transformer to utilize these non-additive operators when aggregating spectral representations as well. Together with an auxiliary contrastive alignment loss term, these enhancements improve Top-K prediction accuracy by over 10 percentage points compared to baseline IR-only models. Through sub-structure fragment analysis of molecular predictions, we further confirm that infrared spectra encode the vast majority of relevant chemical information, implying that the higher performance of isomer-ranking models is largely due to underrepresented or overlapping absorption bands for molecules in the explored chemical space. Ultimately, by demonstrating the efficacy of automated molecular structure elucidation from measured IR spectra, this work serves to significantly broaden the utility of AI in analytical chemistry.
[LG-68] wo2Four: Generative Quadruped Puppeteering from Human Motion
链接: https://arxiv.org/abs/2607.26108
作者: Fatemeh Zargarbashi,Zehong Qiu,Dhruv Agrawal,Stelian Coros,Robert W. Sumner,Martin Guay,Jakob Buhmann
类目: Graphics (cs.GR); Machine Learning (cs.LG)
*备注:
Abstract:Realistic animal motion for virtual production is typically obtained either through motion capture of highly trained performers who accurately mimic animal behavior, or by retargeting ordinary human motion using complex control setups. Both approaches are challenging and often fail to fully reproduce the nuances of natural animal motion, motivating data-driven alternatives. We present an automatic human-to-quadruped puppeteering framework that produces plausible and controllable quadruped motions from ordinary human motion data. Our approach employs a two-stage generative diffusion model trained purely on quadruped motion data. By introducing a structured conditioning and inpainting strategy, our method supports a wide range of actions, including walking, running, jumping, sitting, and lying. Furthermore, we enable fine-grained intuitive control of the quadruped motion such as head movement control and individual limb puppeteering. Experimental results demonstrate improved motion realism and controllability compared to existing retargeting approaches, highlighting the effectiveness of our framework as a tool for animation and virtual production applications.
[LG-69] Lilith: Backdoor Generalization under Training-Inference Trigger Shift
链接: https://arxiv.org/abs/2607.26099
作者: Zhou Feng,Jiahao Chen,Chunyi Zhou,Yuan Su,Tianyu Du,Yuwen Pu,Jianhai Chen,Jinbao Li,Shouling Ji
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Machine-learning services increasingly rely on public data, third-party providers, and outsourced training, creating opportunities for data-poisoning attacks that implant persistent malicious behavior while preserving benign utility. However, existing backdoor studies largely evaluate exact trigger reuse, training-exposed trigger diversity, or variations along predefined transformation axes. They therefore leave a critical blind spot: whether a backdoor learned from one training-time trigger can generalize to an inference-time trigger family absent from victim training. We formulate this problem as backdoor generalization under training–inference trigger shift and introduce Lilith, a black-box anchor-to-family framework. Using only disjoint surrogate resources, Lilith first induces a compact target-side vulnerability with a single training anchor, then constructs a bounded inference-only family that preserves the anchor-induced representation geometry. We characterize this mechanism through anchor clearance and family reach, deriving sufficient conditions for family-wise target preservation under local regularity and bounded surrogate–victim discrepancy. Experiments across datasets, architectures, poisoning rates, and defenses show that Lilith achieves high family-wise attack success with limited utility degradation and a small trigger generalization gap. Additional analyses show that family activation depends on representation alignment rather than the proposal mechanism, exposing a broader threat overlooked by exact-trigger evaluation.
[LG-70] Detecting seizure onset and offset times using human intelligence: A critical-transitions-based approach
链接: https://arxiv.org/abs/2607.27105
作者: Andrew Flynn,Cian McCafferty,Klaus Lehnertz,François David,Vincenzo Crunelli,Gordon Lightbody,Sebastian Wieczorek
类目: Dynamical Systems (math.DS); Machine Learning (cs.LG)
*备注:
Abstract:Most existing seizure detection algorithms require extensive pre-processing of the data and rely on heuristic or currently unexplainable machine learning approaches. These approaches often struggle with balancing detection sensitivity and specificity in the presence of variable seizure morphologies, interictal epileptiform discharges, and artefacts. Here, we consider an alternative approach: our seizure detection algorithm, which is based on the concept of critical transitions and overcomes the aforementioned limitations. Specifically, we perform a receiver-operating-characteristic analysis to quantify the performance of our algorithm in terms of its agreement with expert annotations of seizure onset and offset times in the voltage recordings of seizure activity in epileptic rodents with different seizure morphologies. We demonstrate how performance depends on algorithm parameters and varies across different rodent recording sessions. We determine the optimal set of algorithm parameters for each recording session, with near expert-level performance achieved in most cases. Finally, we derive a single general set of algorithm parameters applicable across all recording sessions. The algorithm maintains its high performance in this general setting, demonstrating its versatility, robustness across varying seizure morphologies, and potential to complement machine learning algorithms.
[LG-71] PIKS: Universal Physics-Informed Kernel Methods
链接: https://arxiv.org/abs/2607.27062
作者: Joachim Bona-Pellissier,Giacomo Meanti,Matteo Santacesaria,Lorenzo Rosasco
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Physics-informed machine learning incorporates physical principles --often expressed via differential operators-- into data-driven models. While physics-informed neural networks (PINNs) dominate empirical applications, the complexity of neural network architectures and optimization landscapes hinders the development of a corresponding learning theory. In turn, kernel methods offer an appealing alternative with closed-form solutions and analytical tractability, yet existing guarantees primarily cover the well-specified setting where the target belongs to the native Reproducing Kernel Hilbert Space (RKHS). This imposes unrealistic regularity assumptions that physical targets often fail to satisfy. In this paper, we introduce and analyze Physics-Informed Kernel methodS (PIKS). We establish the universal consistency of PIKS for linear differential constraints, proving that for universal kernels (such as Gaussian or Matérn), the estimator asymptotically learns the target while satisfying physical constraints. We further derive finite-sample bounds under suitable source conditions. Our analysis is based on extending classical operator-theoretic analysis of kernel methods to physics-informed machine learning. Numerical experiments demonstrate that PIKS can be competitive with PINNs and traditional finite element methods.
[LG-72] On the robustness of noisy solutions in non-convex neural networks
链接: https://arxiv.org/abs/2607.27000
作者: Enrico M. Malatesta,Alessandra Passalacqua,Riccardo Zecchina
类目: Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG); Probability (math.PR)
*备注: 25 pages, 13 figures
Abstract:Optimization in non-convex neural network models is strongly influenced by the geometry of the solution space: sparse, isolated, point-like clusters are typically algorithmically inaccessible, whereas wide and flat regions can be found efficiently despite being relatively rare. At zero temperature this picture has been formalized in binary perceptrons through the overlap gap property (OGP), which limits algorithmic access to configurations with zero training error above a critical constraint density \alpha_\rm OGP . Here we extend this description to finite temperature, where a positive training error is allowed and statistically penalized. We first show that the frozen one-step replica-symmetry-breaking solution, dominating the zero temperature equilibrium measure, survives at any finite temperature. We furthermore derive a general criterion, based on the smoothness of the single-pattern Gibbs weight near the decision boundary, that determines when a finite-temperature relaxation of the loss removes freezing. We then extend the OGP construction to finite temperature and show that dense, algorithmically accessible regions of finite-energy configurations persist beyond \alpha_\rm OGP , up to a threshold \alpha_\rm OGP(\epsilon) that grows with the allowed training error \epsilon . Finally, in the teacher-student setting, we show that these wide, finite-energy regions still retain good generalization. Using a finite energy message-passing algorithm, we demonstrate numerically that thermal noise enables effective generalization in the regime of constraint densities where both recovering the teacher and finding a zero temperature solution are computationally hard.
[LG-73] Using large language models to probe the limits of atom-centered structural descriptors
链接: https://arxiv.org/abs/2607.26984
作者: Michelangelo Domina,Michele Ceriotti
类目: Chemical Physics (physics.chem-ph); Machine Learning (cs.LG)
*备注:
Abstract:Mapping an atomic structure to a compact set of geometric descriptors is an essential step in any machine-learning application to atomic-scale modeling. A powerful and widely-used approach can be understood as a discretization of the histogram of pair distances, triangles, etc., that results in a hierarchy of symmetry-invariant atom-centered descriptors. Unfortunately, the lower rungs on this hierarchy (two, three, four-neighbor clusters) were found to be incomplete, with symmetry-unrelated pairs of structures having exactly the same descriptors. However, all the ``descriptor degeneracies’’ reported so far are resolved by considering larger clusters of neighbors to build the descriptors. We report examples of 3D structures that are indistinguishable even if one considers clusters of up to seven neighbors, and to arbitrary order when considering a practical level of discretization of the descriptors, discovered with the assistance of large language models. The key ingredients in their construction can be traced to results that have been known for decades in different communities; the model was able to find the references and recognize their significance for the problem at hand. We believe this experiment exposes an extremely fruitful usage pattern for AI in science: translating results between different communities and application domains, accelerating the process by which serendipitous discoveries in a field become paradigm-shifting breakthroughs in another.
[LG-74] Feature Bagging Provides Stability
链接: https://arxiv.org/abs/2607.26964
作者: Yuheng Ma,Qiang Sun
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:We study feature bagging through the lens of algorithmic stability. Feature bagging is an ensemble strategy that aggregates base learners trained on randomly subsampled feature subsets, possibly in a data-dependent manner. We introduce feature instability (FI), the feature-axis analogue of instance instability (II), which measures sensitivity to removing a single feature. Smaller values of II or FI correspond to stronger stability, and our experiments show that FI captures generalization-relevant information complementary to II. Within this framework, we analyze feature bagging in both a parametric linear model and a model-free setting inspired by recursive feature subsampling in random forests. In both settings, we establish formal guarantees showing that feature bagging improves the relevant stability relative to its non-bagged counterpart, with larger improvements under more aggressive subsampling. We further show that a modest number of bagging rounds is sufficient to approach the infinite-bagging stability level.
[LG-75] Breaking the Curse with BAND: Nonparametric Distribution Estimation in High Dimensions
链接: https://arxiv.org/abs/2607.26955
作者: Shuo-Chieh Huang,Chien-Ming Chi,Jau-er Chen
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注: 24 pages, 2 figures, 5 tables
Abstract:Minimax-optimal rates for multivariate distribution estimation are known to suffer from the curse of dimensionality. We propose a sparse Bayesian network approach in which each conditional probability is estimated using sparsity-aware conditional mean methods. The resulting estimator, \textitBAyesian Network Distribution regression (BAND), handles mixed data types in high-dimensional time series and achieves polynomial total variation convergence rates while allowing the feature dimension to grow polynomially with the sample size. These rates are substantially faster than the classical optimal rates for multivariate histogram density estimators that lack sparsity. Empirical evaluations show that BAND performs competitively for data sampling and confidence region forecasting against a range of state-of-the-art benchmarks.
[LG-76] Conformalized Rate-Adaptive Sensing
链接: https://arxiv.org/abs/2607.26887
作者: Jiawei Yang,Yao Zhang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Applications (stat.AP); Methodology (stat.ME)
*备注: 47 pages, 8 figures
Abstract:Many high-resolution imaging systems face the same fundamental question: when have enough measurements been collected to reconstruct an image accurately? We develop Conformalized Rate-Adaptive Sensing (CoRAS), a method that adaptively chooses an acquisition or compression rate for each image while keeping the reconstruction error below a target level with high probability. As measurements are collected, an image reconstruction model gradually recovers the true image, producing a reconstruction path over acquisition rates. CoRAS uses this path up to an early decision time to estimate the target stopping time, defined as the first time at which the reconstruction error falls below the target level. It then calibrates this estimate using images with similar early reconstruction behavior, producing an upper bound on the stopping time with marginal and approximate conditional coverage guarantees. Experiments on image datasets show that CoRAS attains the target stopping-time coverage, uses fewer measurements on average than fixed-rate stopping rules, and assigns more measurements to images that are harder to reconstruct.
[LG-77] No Data Is Not No Risk: Visibility Aware Graph-Based Inference of Business Conduct Risk
链接: https://arxiv.org/abs/2607.26859
作者: Tsuyoshi Iwata,Johannes Laurmaa,Ryohei Hisano
类目: Risk Management (q-fin.RM); Machine Learning (cs.LG)
*备注:
Abstract:The monitoring of business conduct risk is hindered by sparse, uneven, and visibility-biased data. Prior studies show that business conduct risk information and media coverage propagate through supply chain, peer, and corporate structure networks, yet incident records remain incomplete for many firms. As a result, the absence of reported events could reflect limited coverage rather than the absence of underlying business conduct risk. This paper examines whether inter-firm relationships can improve the prediction of future recorded conduct related incidents, particularly among firms with limited prior visibility. We formulate the task as Positive–Unlabeled node classification on a corporate ownership graph, where firms with recorded incidents are treated as labeled positives and firms without recorded incidents remain unlabeled. We then propose a visibility- and relation-aware GCNII framework that combines relation specific message passing with non-negative Positive–Unlabeled learning to account for positive contamination in the unlabeled set. In a forward-looking evaluation, the proposed approach achieved the strongest observed ranking performance relative to non-graph- and simple graph-based benchmarks. The results further show that graph-based inference retains its predictive value among firms without prior recorded incidents. These findings demonstrate the value of inter-firm relational structure as a complementary source of information for extending risk prioritization
[LG-78] Early Failure Prediction from Near-Anomaly Detection: A Proactive Approach
链接: https://arxiv.org/abs/2607.26704
作者: Léa Billet(LAAS, INSA Toulouse, ANITI),Louise Travé-Massuyès(LAAS-DISCO, Comue de Toulouse, ANITI),Elodie Chanthery(LAAS),Alexandre Gaffet
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Anomaly detection methods often have uncertain behavior with respect to samples near the distribution boundary, limiting their ability to anticipate future anomalies. This work introduces the concept of near-anomalies that, while not yet anomalous, lie close to the boundary and are likely to transition into anomalies in the near future. To address this, we propose an unsupervised method, named Christoffel-based ANomaly Anticipation for eaRly dIscovery (CANARI), which leverages the strong theoretical foundations of the Christoffel function to detect near-anomalies. The method is validated on industrial in-circuit testing data from printed circuit boards, with synthetically generated near-anomaly samples due to the lack of real-world data labeling. Experimental results show that CANARI outperforms the compared baselines that generally use a dual-threshold mechanism (one for anomalies and one for near-anomalies). It therefore provides a proactive solution for anticipating anomalies before they occur, offering a promising approach for resilience, predictive maintenance, and quality control.
[LG-79] An Informativeness-based Clustered Federated Learning Method for Reliable Traffic Prediction in Managed Wi-Fi Networks
链接: https://arxiv.org/abs/2607.26682
作者: Luca Barbieri,Gianluca Fontanesi,Lorenzo Galati Giordano,Alfonso Fernandez Duran,Thorsten Wild
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: submitted to IEEE for possible publication
Abstract:Centrally-managed Wi-Fi solutions are increasingly leveraging Distributed Artificial Intelligence (AI) to predict key operational statistics of Access Points (APs) and proactively optimize network performance. In this context, Clustered Federated Learning (CFL) represents a fitting methodology, enabling the generation of multiple AI models that account for diverse statistical properties of the APs data distribution. However, identifying informative clusters for grouping APs models remains a significant challenge. In this paper, we address this problem by proposing a novel CFL tool integrating a two step clustering procedure. Initially, multiple clustering solutions are generated and filtered based on a minimum set of desired clustering criteria. Subsequently, if no solutions meet sufficient quality metrics, a global model is produced by aggregating all AP models. Otherwise, the final clustering solution is selected as the one that maximizes the informativeness (quantified via differential entropy) for the smallest cluster. Our results, focusing on a Wi-Fi traffic prediction problem, demonstrate that the developed CFL tool achieves the best predictive performance among all evaluated distributed strategies and the lowest communication and energy footprint among the clustered ones, exceeding the cost of single-model FL only in the regimes where it markedly improves accuracy.
[LG-80] Adaptive Gradient-Based Methods for a Broader Class of Optimization Problems under Performative Prediction
链接: https://arxiv.org/abs/2607.26562
作者: Hiroki Hamaguchi,Yuya Hikima,Hiroshi Sawada,Akiko Takeda
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注: 31pages, 2 figures
Abstract:We study optimization under performative prediction, where deploying a model affects the future data distribution. For this setting, several gradient-based approaches have been proposed. However, they typically assume specific data distributions or loss functions, which limit their practical applicability. To overcome these limitations, we propose a gradient-based optimization method with convergence guarantees under substantially weaker assumptions. Our method explicitly estimates the induced distribution shift through finite differences. It enables higher-dimensional optimization across broader classes of loss functions and data distributions. We also propose a practical variant that reduces the number of samples required. Numerical experiments demonstrate that our proposed algorithms converge faster and more consistently than existing ones.
[LG-81] Chaos Is a LADDER: Domain Generalization Beyond Invariance via Reweighting
链接: https://arxiv.org/abs/2607.26458
作者: Yuhang Jiang,Fengchuan Zhang,Sanguo Zhang,Guojun Zhu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
*备注:
Abstract:Domain generalization (DG) aims to learn from multiple source domains and generalize to unseen target domains. Most DG methods pursue invariance: they seek a causal representation whose prediction rule is invariant across domains. This principle is effective when the causal mechanism is stable, but becomes restrictive when the domain itself modulates how causal content maps to the response. In this case, directly feeding domain style into the predictor can create misleading shortcuts, since style does not by itself cause the response. Yet the apparent chaos of multiple styles can become a ladder: style can locate the unseen target domain among source domains and guide which domain-dependent prediction rules should be trusted. We propose \emphLatent Adaptive Domain Disentanglement and Environment Reweighting (LADDER), a fixed-model DG pipeline that learns causal/style representations, freezes the encoders, fits source-specific classifiers, and uses an unlabeled target-domain covariate set only at inference to compute weights over these fixed classifiers, with no target labels or model-state updates. We establish theoretical guarantees for source reweighting and validate LADDER on simulations, FMoW, and a location-grouped iWildCam protocol, with gains in overall and group-averaged accuracy.
[LG-82] Origins and mitigation of double descent in reduced order modeling
链接: https://arxiv.org/abs/2607.26414
作者: Andrei A. Klishin,J. Nathan Kutz,Krithika Manohar
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Dynamical Systems (math.DS); Data Analysis, Statistics and Probability (physics.data-an)
*备注: 20 RevTeX pages, 11 figures
Abstract:Latent low-dimensional structure in datasets of natural and engineered systems enables their sparse sensing, or full-state reconstruction from historical data and very few carefully chosen localized measurements. Depending on the reconstruction algorithm, sensor locations, and measurement noise, the reconstruction risk curves demonstrate a diversity of patterns including a dramatic peak in error known as double descent in Machine Learning literature. Here we explore those scenarios under a unified Data-Noise Averaging theory. Qualitatively, we formulate sufficient criteria for double descent to emerge through a catastrophic amplification of a pathological signal in reconstruction. Quantitatively, we predict the detailed risk curves at a fraction of computational cost, trace reconstruction instability to individual sensors and their combinations, and provide regularization mechanisms to mitigate the instability. We demonstrate results for both static reconstruction of Sea Surface Temperature patterns and time integration of a reduced order model of a PDE.
[LG-83] Denoising growth complexity: Data geometry and certified schedules for diffusion sampling
链接: https://arxiv.org/abs/2607.26285
作者: Martin J. Wainwright
类目: atistics Theory (math.ST); Machine Learning (cs.LG); Numerical Analysis (math.NA); Machine Learning (stat.ML)
*备注:
Abstract:Two central challenges in diffusion-based sampling are the theoretical one of understanding their remarkable effectiveness even in high-dimensional settings, and the practical one of designing algorithms with certified performance guarantees. We show that these questions are intimately connected via the \emphdenoising growth complexity ( \mathsfDGC ). It is a geometric measure defined by a log-time weighted integral of the derivative of the denoising mean-squared error along the Gaussian heat flow. We show how the \mathsfDGC increments lead to a simple and explicit bound on the KL error of an Euler scheme applied to the stochastic innovations representation. The bound is local along the path: each step is controlled by the corresponding \mathsfDGC increment and its relative stepsize. This structure allows us to derive KL sampling guarantees for optimized stepsize schedules, both in a simpler single-block setting and in a more refined K -block setting. The \mathsfDGC function has a natural martingale structure, which we exploit to develop fully data-certified versions of these algorithms. It also admits information-theoretic upper bounds in terms of covariance, rate distortion, metric entropy, and the Poincar’e constant, thereby recovering and sharpening a range of existing diffusion-sampling guarantees, as well as giving new results. In log heat-time, the fine partition limit is governed by an integral involving the square root of the \mathsfDGC density, whereas a single-block schedule depends on its ordinary integral. This comparison precisely characterizes when adaptation to data geometry yields substantial computational gains, including logarithmic-to-constant separations for simple Gaussian mixture models.
[LG-84] Retrospective Orthogonal Design: Response-Surface Reconstruction from Observational Data
链接: https://arxiv.org/abs/2607.26219
作者: Lawrence Fulton,Christopher Fulton,Arvind Sharma,Aleksandar Tomic
类目: Methodology (stat.ME); Machine Learning (cs.LG); Differential Geometry (math.DG); Applications (stat.AP)
*备注:
Abstract:Regression estimates from observational data can depend on specification under multicollinearity, while sequential sums of squares (SS) depend on term order. We introduce Retrospective Orthogonal Design (ROD), which reconstructs conditional mean surfaces on a probability-balanced lattice. ROD preserves observed cell means, completes unsupported cells, applies weighted tensor-product contrasts, and evaluates the reconstructed surface through piecewise-affine interpolation over Freudenthal polyhedra. Resolution and completion are selected jointly by validation among rank-admissible candidates, followed by refitting and evaluation on an untouched test set. For an admissible lattice, \mathbfX^\top\mathbfW\mathbfX=c\mathbfI , yielding specification-invariant contrast effects and unique, order-independent SS within the retained contrast space. Response-free projection calibration maps the fixed reconstruction onto a declared scientific basis and corrects finite-resolution recovery loss. Across 6,480 simulation conditions spanning nine data-generating processes, ROD matched or exceeded polynomial regression in five processes and performed strongest on threshold, sign-interaction, and localized surfaces. For the quadratic-interaction process, mean out-of-sample R^2 differed by only 0.0001 , while calibrated coefficient bias remained small across prespecified targets. A Rao-based information adjustment provides dependence-aware sample-size guidance for ROD planning. In a weighted Mincer application, ROD produced the highest out-of-sample R^2 point estimate, with substantial interval overlap with polynomial regression, and provided exhaustive SS allocations invariant to term-entry order.
[LG-85] When Kernel Ridge Regression Meets the Hölder-Zygmund Class: Minimax Optimality and Failure of Properness
链接: https://arxiv.org/abs/2607.26065
作者: Yuxuan Hou
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:We study kernel ridge regression for nonparametric regression over the Hölder-Zygmund class. Using an RKHS equivalent to a Sobolev space of smoothness s+d/2, we prove that misspecified KRR attains the minimax L2 rate n^-2s/(2s+d). We also show that properness fails in the Hölder-Zygmund norm: even for the zero regression function with Gaussian noise, the expected squared Hölder-Zygmund norm of the KRR noise component grows as log n.
[LG-86] Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders
链接: https://arxiv.org/abs/2607.24921
作者: Gregorio de la Fuente,Jesse Thaler
类目: High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex)
*备注: 47 pages, 10 figures; our code is available at this https URL
Abstract:Providing a practical and hadron-level definition of multiple jet flavors has been a long-standing challenge in collider physics. Previous work has introduced a data-driven, operational definition of quark and gluon jets, but no robust generalization beyond two jet categories presently exists. To address this, we introduce a machine-learning framework called "simplex demixing’’ to extract T jet flavors (or topics in the statistics literature) from M data samples (or mixtures) with minimal constraints. Intuitively, our procedure identifies the maximally separable categories in the data, translating a multi-category classifier on the M mixtures into a bounded geometric object with T vertices. We first demonstrate our procedure on a toy problem to infer the truth-level fractions of down-quark, up-quark, and gluon jets from synthetic mixtures of the three pure samples. We then propose a tag-and-probe strategy to extract multiple light-flavor categories in a more realistic collider setting involving dijet production. As expected, the identifiability of jet flavors depends on their relative abundance in the samples and the hadron-level information available to the classifier architecture. Our work opens the door to data-driven extractions of multiple jet flavor properties at the Large Hadron Collider.
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